From 9306b8c6c8c412b9d0d5cffb6bd7a87784f0f6bf Mon Sep 17 00:00:00 2001
From: Liquan Pei <liquanpei@gmail.com>
Date: Mon, 18 Aug 2014 01:15:45 -0700
Subject: [PATCH 001/489] [MLlib] Remove transform(dataset: RDD[String]) from
 Word2Vec public API

mengxr
Remove  transform(dataset: RDD[String]) from public API.

Author: Liquan Pei <liquanpei@gmail.com>

Closes #2010 from Ishiihara/Word2Vec-api and squashes the following commits:

17b1031 [Liquan Pei] remove transform(dataset: RDD[String]) from public API
---
 .../scala/org/apache/spark/mllib/feature/Word2Vec.scala  | 9 ---------
 1 file changed, 9 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala b/mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala
index d2ae62b482aff..1dcaa2cd2e630 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala
@@ -434,15 +434,6 @@ class Word2VecModel private[mllib] (
     }
   }
   
-  /**
-   * Transforms an RDD to its vector representation
-   * @param dataset a an RDD of words 
-   * @return RDD of vector representation 
-   */
-  def transform(dataset: RDD[String]): RDD[Vector] = {
-    dataset.map(word => transform(word))
-  }
-  
   /**
    * Find synonyms of a word
    * @param word a word

From c0cbbdeaf4f2033be03d32e3ea0288812b4edbf6 Mon Sep 17 00:00:00 2001
From: CrazyJvm <crazyjvm@gmail.com>
Date: Mon, 18 Aug 2014 09:34:36 -0700
Subject: [PATCH 002/489] SPARK-3093 : masterLock in Worker is no longer need

there's no need to use masterLock in Worker now since all communications are within Akka actor

Author: CrazyJvm <crazyjvm@gmail.com>

Closes #2008 from CrazyJvm/no-need-master-lock and squashes the following commits:

dd39e20 [CrazyJvm] fix format
58e7fa5 [CrazyJvm] there's no need to use masterLock now since all communications are within Akka actor
---
 .../apache/spark/deploy/worker/Worker.scala   | 41 +++++++------------
 1 file changed, 14 insertions(+), 27 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala b/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala
index 80fde7e4b2624..81400af22c0bf 100755
--- a/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala
@@ -72,7 +72,6 @@ private[spark] class Worker(
   val APP_DATA_RETENTION_SECS = conf.getLong("spark.worker.cleanup.appDataTtl", 7 * 24 * 3600)
 
   val testing: Boolean = sys.props.contains("spark.testing")
-  val masterLock: Object = new Object()
   var master: ActorSelection = null
   var masterAddress: Address = null
   var activeMasterUrl: String = ""
@@ -145,18 +144,16 @@ private[spark] class Worker(
   }
 
   def changeMaster(url: String, uiUrl: String) {
-    masterLock.synchronized {
-      activeMasterUrl = url
-      activeMasterWebUiUrl = uiUrl
-      master = context.actorSelection(Master.toAkkaUrl(activeMasterUrl))
-      masterAddress = activeMasterUrl match {
-        case Master.sparkUrlRegex(_host, _port) =>
-          Address("akka.tcp", Master.systemName, _host, _port.toInt)
-        case x =>
-          throw new SparkException("Invalid spark URL: " + x)
-      }
-      connected = true
+    activeMasterUrl = url
+    activeMasterWebUiUrl = uiUrl
+    master = context.actorSelection(Master.toAkkaUrl(activeMasterUrl))
+    masterAddress = activeMasterUrl match {
+      case Master.sparkUrlRegex(_host, _port) =>
+        Address("akka.tcp", Master.systemName, _host, _port.toInt)
+      case x =>
+        throw new SparkException("Invalid spark URL: " + x)
     }
+    connected = true
   }
 
   def tryRegisterAllMasters() {
@@ -199,9 +196,7 @@ private[spark] class Worker(
       }
 
     case SendHeartbeat =>
-      masterLock.synchronized {
-        if (connected) { master ! Heartbeat(workerId) }
-      }
+      if (connected) { master ! Heartbeat(workerId) }
 
     case WorkDirCleanup =>
       // Spin up a separate thread (in a future) to do the dir cleanup; don't tie up worker actor
@@ -244,9 +239,7 @@ private[spark] class Worker(
           manager.start()
           coresUsed += cores_
           memoryUsed += memory_
-          masterLock.synchronized {
-            master ! ExecutorStateChanged(appId, execId, manager.state, None, None)
-          }
+          master ! ExecutorStateChanged(appId, execId, manager.state, None, None)
         } catch {
           case e: Exception => {
             logError("Failed to launch executor %s/%d for %s".format(appId, execId, appDesc.name))
@@ -254,17 +247,13 @@ private[spark] class Worker(
               executors(appId + "/" + execId).kill()
               executors -= appId + "/" + execId
             }
-            masterLock.synchronized {
-              master ! ExecutorStateChanged(appId, execId, ExecutorState.FAILED, None, None)
-            }
+            master ! ExecutorStateChanged(appId, execId, ExecutorState.FAILED, None, None)
           }
         }
       }
 
     case ExecutorStateChanged(appId, execId, state, message, exitStatus) =>
-      masterLock.synchronized {
-        master ! ExecutorStateChanged(appId, execId, state, message, exitStatus)
-      }
+      master ! ExecutorStateChanged(appId, execId, state, message, exitStatus)
       val fullId = appId + "/" + execId
       if (ExecutorState.isFinished(state)) {
         executors.get(fullId) match {
@@ -330,9 +319,7 @@ private[spark] class Worker(
         case _ =>
           logDebug(s"Driver $driverId changed state to $state")
       }
-      masterLock.synchronized {
-        master ! DriverStateChanged(driverId, state, exception)
-      }
+      master ! DriverStateChanged(driverId, state, exception)
       val driver = drivers.remove(driverId).get
       finishedDrivers(driverId) = driver
       memoryUsed -= driver.driverDesc.mem

From f45efbb8aaa65bc46d65e77e93076fbc29f4455d Mon Sep 17 00:00:00 2001
From: Chandan Kumar <chandan.kumar@imaginea.com>
Date: Mon, 18 Aug 2014 09:52:25 -0700
Subject: [PATCH 003/489] [SPARK-2862] histogram method fails on some choices
 of bucketCount

Author: Chandan Kumar <chandan.kumar@imaginea.com>

Closes #1787 from nrchandan/spark-2862 and squashes the following commits:

a76bbf6 [Chandan Kumar] [SPARK-2862] Fix for a broken test case and add new test cases
4211eea [Chandan Kumar] [SPARK-2862] Add Scala bug id
13854f1 [Chandan Kumar] [SPARK-2862] Use shorthand range notation to avoid Scala bug
---
 .../apache/spark/rdd/DoubleRDDFunctions.scala | 15 ++++++++----
 .../org/apache/spark/rdd/DoubleRDDSuite.scala | 23 +++++++++++++++++++
 2 files changed, 34 insertions(+), 4 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/rdd/DoubleRDDFunctions.scala b/core/src/main/scala/org/apache/spark/rdd/DoubleRDDFunctions.scala
index f233544d128f5..e0494ee39657c 100644
--- a/core/src/main/scala/org/apache/spark/rdd/DoubleRDDFunctions.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/DoubleRDDFunctions.scala
@@ -95,7 +95,12 @@ class DoubleRDDFunctions(self: RDD[Double]) extends Logging with Serializable {
    * If the elements in RDD do not vary (max == min) always returns a single bucket.
    */
   def histogram(bucketCount: Int): Pair[Array[Double], Array[Long]] = {
-    // Compute the minimum and the maxium
+    // Scala's built-in range has issues. See #SI-8782
+    def customRange(min: Double, max: Double, steps: Int): IndexedSeq[Double] = {
+      val span = max - min
+      Range.Int(0, steps, 1).map(s => min + (s * span) / steps) :+ max
+    }
+    // Compute the minimum and the maximum
     val (max: Double, min: Double) = self.mapPartitions { items =>
       Iterator(items.foldRight(Double.NegativeInfinity,
         Double.PositiveInfinity)((e: Double, x: Pair[Double, Double]) =>
@@ -107,9 +112,11 @@ class DoubleRDDFunctions(self: RDD[Double]) extends Logging with Serializable {
       throw new UnsupportedOperationException(
         "Histogram on either an empty RDD or RDD containing +/-infinity or NaN")
     }
-    val increment = (max-min)/bucketCount.toDouble
-    val range = if (increment != 0) {
-      Range.Double.inclusive(min, max, increment)
+    val range = if (min != max) {
+      // Range.Double.inclusive(min, max, increment)
+      // The above code doesn't always work. See Scala bug #SI-8782.
+      // https://issues.scala-lang.org/browse/SI-8782
+      customRange(min, max, bucketCount)
     } else {
       List(min, min)
     }
diff --git a/core/src/test/scala/org/apache/spark/rdd/DoubleRDDSuite.scala b/core/src/test/scala/org/apache/spark/rdd/DoubleRDDSuite.scala
index a822bd18bfdbd..f89bdb6e07dea 100644
--- a/core/src/test/scala/org/apache/spark/rdd/DoubleRDDSuite.scala
+++ b/core/src/test/scala/org/apache/spark/rdd/DoubleRDDSuite.scala
@@ -245,6 +245,29 @@ class DoubleRDDSuite extends FunSuite with SharedSparkContext {
     assert(histogramBuckets === expectedHistogramBuckets)
   }
 
+  test("WorksWithoutBucketsForLargerDatasets") {
+    // Verify the case of slighly larger datasets
+    val rdd = sc.parallelize(6 to 99)
+    val (histogramBuckets, histogramResults) = rdd.histogram(8)
+    val expectedHistogramResults =
+      Array(12, 12, 11, 12, 12, 11, 12, 12)
+    val expectedHistogramBuckets =
+      Array(6.0, 17.625, 29.25, 40.875, 52.5, 64.125, 75.75, 87.375, 99.0)
+    assert(histogramResults === expectedHistogramResults)
+    assert(histogramBuckets === expectedHistogramBuckets)
+  }
+
+  test("WorksWithoutBucketsWithIrrationalBucketEdges") {
+    // Verify the case of buckets with irrational edges. See #SPARK-2862.
+    val rdd = sc.parallelize(6 to 99)
+    val (histogramBuckets, histogramResults) = rdd.histogram(9)
+    val expectedHistogramResults =
+      Array(11, 10, 11, 10, 10, 11, 10, 10, 11)
+    assert(histogramResults === expectedHistogramResults)
+    assert(histogramBuckets(0) === 6.0)
+    assert(histogramBuckets(9) === 99.0)
+  }
+
   // Test the failure mode with an invalid RDD
   test("ThrowsExceptionOnInvalidRDDs") {
     // infinity

From 7ae28d1247e4756219016206c51fec1656e3917b Mon Sep 17 00:00:00 2001
From: Patrick Wendell <pwendell@gmail.com>
Date: Mon, 18 Aug 2014 10:00:46 -0700
Subject: [PATCH 004/489] SPARK-3096: Include parquet hive serde by default in
 build

A small change - we should just add this dependency. It doesn't have any recursive deps and it's needed for reading have parquet tables.

Author: Patrick Wendell <pwendell@gmail.com>

Closes #2009 from pwendell/parquet and squashes the following commits:

e411f9f [Patrick Wendell] SPARk-309: Include parquet hive serde by default in build
---
 sql/hive/pom.xml | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/sql/hive/pom.xml b/sql/hive/pom.xml
index 93d00f7c37c9b..30ff277e67c88 100644
--- a/sql/hive/pom.xml
+++ b/sql/hive/pom.xml
@@ -36,6 +36,11 @@
   </properties>
 
   <dependencies>
+    <dependency>
+      <groupId>com.twitter</groupId>
+      <artifactId>parquet-hive-bundle</artifactId>
+      <version>1.5.0</version>
+    </dependency>
     <dependency>
       <groupId>org.apache.spark</groupId>
       <artifactId>spark-core_${scala.binary.version}</artifactId>

From 6a13dca12fac06f3af892ffcc8922cc84f91b786 Mon Sep 17 00:00:00 2001
From: Matei Zaharia <matei@databricks.com>
Date: Mon, 18 Aug 2014 10:05:52 -0700
Subject: [PATCH 005/489] [SPARK-3084] [SQL] Collect broadcasted tables in
 parallel in joins

BroadcastHashJoin has a broadcastFuture variable that tries to collect
the broadcasted table in a separate thread, but this doesn't help
because it's a lazy val that only gets initialized when you attempt to
build the RDD. Thus queries that broadcast multiple tables would collect
and broadcast them sequentially. I changed this to a val to let it start
collecting right when the operator is created.

Author: Matei Zaharia <matei@databricks.com>

Closes #1990 from mateiz/spark-3084 and squashes the following commits:

f468766 [Matei Zaharia] [SPARK-3084] Collect broadcasted tables in parallel in joins
---
 .../src/main/scala/org/apache/spark/sql/execution/joins.scala   | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins.scala
index c86811e838bd8..481bb8c05e71b 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins.scala
@@ -424,7 +424,7 @@ case class BroadcastHashJoin(
     UnspecifiedDistribution :: UnspecifiedDistribution :: Nil
 
   @transient
-  lazy val broadcastFuture = future {
+  val broadcastFuture = future {
     sparkContext.broadcast(buildPlan.executeCollect())
   }
 

From 4bf3de71074053af94f077c99e9c65a1962739e1 Mon Sep 17 00:00:00 2001
From: Matei Zaharia <matei@databricks.com>
Date: Mon, 18 Aug 2014 10:45:24 -0700
Subject: [PATCH 006/489] [SPARK-3085] [SQL] Use compact data structures in SQL
 joins

This reuses the CompactBuffer from Spark Core to save memory and pointer
dereferences. I also tried AppendOnlyMap instead of java.util.HashMap
but unfortunately that slows things down because it seems to do more
equals() calls and the equals on GenericRow, and especially JoinedRow,
is pretty expensive.

Author: Matei Zaharia <matei@databricks.com>

Closes #1993 from mateiz/spark-3085 and squashes the following commits:

188221e [Matei Zaharia] Remove unneeded import
5f903ee [Matei Zaharia] [SPARK-3085] [SQL] Use compact data structures in SQL joins
---
 .../apache/spark/sql/execution/joins.scala    | 67 +++++++++----------
 1 file changed, 33 insertions(+), 34 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins.scala
index 481bb8c05e71b..b08f9aacc1fcb 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins.scala
@@ -19,16 +19,15 @@ package org.apache.spark.sql.execution
 
 import java.util.{HashMap => JavaHashMap}
 
-import scala.collection.mutable.{ArrayBuffer, BitSet}
 import scala.concurrent.ExecutionContext.Implicits.global
 import scala.concurrent._
 import scala.concurrent.duration._
 
 import org.apache.spark.annotation.DeveloperApi
-import org.apache.spark.sql.SQLContext
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans._
 import org.apache.spark.sql.catalyst.plans.physical._
+import org.apache.spark.util.collection.CompactBuffer
 
 @DeveloperApi
 sealed abstract class BuildSide
@@ -67,7 +66,7 @@ trait HashJoin {
   def joinIterators(buildIter: Iterator[Row], streamIter: Iterator[Row]): Iterator[Row] = {
     // TODO: Use Spark's HashMap implementation.
 
-    val hashTable = new java.util.HashMap[Row, ArrayBuffer[Row]]()
+    val hashTable = new java.util.HashMap[Row, CompactBuffer[Row]]()
     var currentRow: Row = null
 
     // Create a mapping of buildKeys -> rows
@@ -77,7 +76,7 @@ trait HashJoin {
       if (!rowKey.anyNull) {
         val existingMatchList = hashTable.get(rowKey)
         val matchList = if (existingMatchList == null) {
-          val newMatchList = new ArrayBuffer[Row]()
+          val newMatchList = new CompactBuffer[Row]()
           hashTable.put(rowKey, newMatchList)
           newMatchList
         } else {
@@ -89,7 +88,7 @@ trait HashJoin {
 
     new Iterator[Row] {
       private[this] var currentStreamedRow: Row = _
-      private[this] var currentHashMatches: ArrayBuffer[Row] = _
+      private[this] var currentHashMatches: CompactBuffer[Row] = _
       private[this] var currentMatchPosition: Int = -1
 
       // Mutable per row objects.
@@ -140,7 +139,7 @@ trait HashJoin {
 
 /**
  * :: DeveloperApi ::
- * Performs a hash based outer join for two child relations by shuffling the data using 
+ * Performs a hash based outer join for two child relations by shuffling the data using
  * the join keys. This operator requires loading the associated partition in both side into memory.
  */
 @DeveloperApi
@@ -179,26 +178,26 @@ case class HashOuterJoin(
   @transient private[this] lazy val EMPTY_LIST = Seq.empty[Row]
 
   // TODO we need to rewrite all of the iterators with our own implementation instead of the Scala
-  // iterator for performance purpose. 
+  // iterator for performance purpose.
 
   private[this] def leftOuterIterator(
       key: Row, leftIter: Iterable[Row], rightIter: Iterable[Row]): Iterator[Row] = {
     val joinedRow = new JoinedRow()
     val rightNullRow = new GenericRow(right.output.length)
-    val boundCondition = 
+    val boundCondition =
       condition.map(newPredicate(_, left.output ++ right.output)).getOrElse((row: Row) => true)
 
-    leftIter.iterator.flatMap { l => 
+    leftIter.iterator.flatMap { l =>
       joinedRow.withLeft(l)
       var matched = false
-      (if (!key.anyNull) rightIter.collect { case r if (boundCondition(joinedRow.withRight(r))) => 
+      (if (!key.anyNull) rightIter.collect { case r if (boundCondition(joinedRow.withRight(r))) =>
         matched = true
         joinedRow.copy
       } else {
         Nil
       }) ++ DUMMY_LIST.filter(_ => !matched).map( _ => {
         // DUMMY_LIST.filter(_ => !matched) is a tricky way to add additional row,
-        // as we don't know whether we need to append it until finish iterating all of the 
+        // as we don't know whether we need to append it until finish iterating all of the
         // records in right side.
         // If we didn't get any proper row, then append a single row with empty right
         joinedRow.withRight(rightNullRow).copy
@@ -210,20 +209,20 @@ case class HashOuterJoin(
       key: Row, leftIter: Iterable[Row], rightIter: Iterable[Row]): Iterator[Row] = {
     val joinedRow = new JoinedRow()
     val leftNullRow = new GenericRow(left.output.length)
-    val boundCondition = 
+    val boundCondition =
       condition.map(newPredicate(_, left.output ++ right.output)).getOrElse((row: Row) => true)
 
-    rightIter.iterator.flatMap { r => 
+    rightIter.iterator.flatMap { r =>
       joinedRow.withRight(r)
       var matched = false
-      (if (!key.anyNull) leftIter.collect { case l if (boundCondition(joinedRow.withLeft(l))) => 
+      (if (!key.anyNull) leftIter.collect { case l if (boundCondition(joinedRow.withLeft(l))) =>
         matched = true
         joinedRow.copy
       } else {
         Nil
       }) ++ DUMMY_LIST.filter(_ => !matched).map( _ => {
         // DUMMY_LIST.filter(_ => !matched) is a tricky way to add additional row,
-        // as we don't know whether we need to append it until finish iterating all of the 
+        // as we don't know whether we need to append it until finish iterating all of the
         // records in left side.
         // If we didn't get any proper row, then append a single row with empty left.
         joinedRow.withLeft(leftNullRow).copy
@@ -236,7 +235,7 @@ case class HashOuterJoin(
     val joinedRow = new JoinedRow()
     val leftNullRow = new GenericRow(left.output.length)
     val rightNullRow = new GenericRow(right.output.length)
-    val boundCondition = 
+    val boundCondition =
       condition.map(newPredicate(_, left.output ++ right.output)).getOrElse((row: Row) => true)
 
     if (!key.anyNull) {
@@ -246,8 +245,8 @@ case class HashOuterJoin(
       leftIter.iterator.flatMap[Row] { l =>
         joinedRow.withLeft(l)
         var matched = false
-        rightIter.zipWithIndex.collect { 
-          // 1. For those matched (satisfy the join condition) records with both sides filled, 
+        rightIter.zipWithIndex.collect {
+          // 1. For those matched (satisfy the join condition) records with both sides filled,
           //    append them directly
 
           case (r, idx) if (boundCondition(joinedRow.withRight(r)))=> {
@@ -260,7 +259,7 @@ case class HashOuterJoin(
           // 2. For those unmatched records in left, append additional records with empty right.
 
           // DUMMY_LIST.filter(_ => !matched) is a tricky way to add additional row,
-          // as we don't know whether we need to append it until finish iterating all 
+          // as we don't know whether we need to append it until finish iterating all
           // of the records in right side.
           // If we didn't get any proper row, then append a single row with empty right.
           joinedRow.withRight(rightNullRow).copy
@@ -268,8 +267,8 @@ case class HashOuterJoin(
       } ++ rightIter.zipWithIndex.collect {
         // 3. For those unmatched records in right, append additional records with empty left.
 
-        // Re-visiting the records in right, and append additional row with empty left, if its not 
-        // in the matched set. 
+        // Re-visiting the records in right, and append additional row with empty left, if its not
+        // in the matched set.
         case (r, idx) if (!rightMatchedSet.contains(idx)) => {
           joinedRow(leftNullRow, r).copy
         }
@@ -284,15 +283,15 @@ case class HashOuterJoin(
   }
 
   private[this] def buildHashTable(
-      iter: Iterator[Row], keyGenerator: Projection): JavaHashMap[Row, ArrayBuffer[Row]] = {
-    val hashTable = new JavaHashMap[Row, ArrayBuffer[Row]]()
+      iter: Iterator[Row], keyGenerator: Projection): JavaHashMap[Row, CompactBuffer[Row]] = {
+    val hashTable = new JavaHashMap[Row, CompactBuffer[Row]]()
     while (iter.hasNext) {
       val currentRow = iter.next()
       val rowKey = keyGenerator(currentRow)
 
       var existingMatchList = hashTable.get(rowKey)
       if (existingMatchList == null) {
-        existingMatchList = new ArrayBuffer[Row]()
+        existingMatchList = new CompactBuffer[Row]()
         hashTable.put(rowKey, existingMatchList)
       }
 
@@ -311,20 +310,20 @@ case class HashOuterJoin(
       val rightHashTable = buildHashTable(rightIter, newProjection(rightKeys, right.output))
 
       import scala.collection.JavaConversions._
-      val boundCondition = 
+      val boundCondition =
         condition.map(newPredicate(_, left.output ++ right.output)).getOrElse((row: Row) => true)
       joinType match {
         case LeftOuter => leftHashTable.keysIterator.flatMap { key =>
-          leftOuterIterator(key, leftHashTable.getOrElse(key, EMPTY_LIST), 
+          leftOuterIterator(key, leftHashTable.getOrElse(key, EMPTY_LIST),
             rightHashTable.getOrElse(key, EMPTY_LIST))
         }
         case RightOuter => rightHashTable.keysIterator.flatMap { key =>
-          rightOuterIterator(key, leftHashTable.getOrElse(key, EMPTY_LIST), 
+          rightOuterIterator(key, leftHashTable.getOrElse(key, EMPTY_LIST),
             rightHashTable.getOrElse(key, EMPTY_LIST))
         }
         case FullOuter => (leftHashTable.keySet ++ rightHashTable.keySet).iterator.flatMap { key =>
-          fullOuterIterator(key, 
-            leftHashTable.getOrElse(key, EMPTY_LIST), 
+          fullOuterIterator(key,
+            leftHashTable.getOrElse(key, EMPTY_LIST),
             rightHashTable.getOrElse(key, EMPTY_LIST))
         }
         case x => throw new Exception(s"HashOuterJoin should not take $x as the JoinType")
@@ -550,7 +549,7 @@ case class BroadcastNestedLoopJoin(
 
     /** All rows that either match both-way, or rows from streamed joined with nulls. */
     val matchesOrStreamedRowsWithNulls = streamed.execute().mapPartitions { streamedIter =>
-      val matchedRows = new ArrayBuffer[Row]
+      val matchedRows = new CompactBuffer[Row]
       // TODO: Use Spark's BitSet.
       val includedBroadcastTuples =
         new scala.collection.mutable.BitSet(broadcastedRelation.value.size)
@@ -602,20 +601,20 @@ case class BroadcastNestedLoopJoin(
     val rightNulls = new GenericMutableRow(right.output.size)
     /** Rows from broadcasted joined with nulls. */
     val broadcastRowsWithNulls: Seq[Row] = {
-      val arrBuf: collection.mutable.ArrayBuffer[Row] = collection.mutable.ArrayBuffer()
+      val buf: CompactBuffer[Row] = new CompactBuffer()
       var i = 0
       val rel = broadcastedRelation.value
       while (i < rel.length) {
         if (!allIncludedBroadcastTuples.contains(i)) {
           (joinType, buildSide) match {
-            case (RightOuter | FullOuter, BuildRight) => arrBuf += new JoinedRow(leftNulls, rel(i))
-            case (LeftOuter | FullOuter, BuildLeft) => arrBuf += new JoinedRow(rel(i), rightNulls)
+            case (RightOuter | FullOuter, BuildRight) => buf += new JoinedRow(leftNulls, rel(i))
+            case (LeftOuter | FullOuter, BuildLeft) => buf += new JoinedRow(rel(i), rightNulls)
             case _ =>
           }
         }
         i += 1
       }
-      arrBuf.toSeq
+      buf.toSeq
     }
 
     // TODO: Breaks lineage.

From 6bca8898a1aa4ca7161492229bac1748b3da2ad7 Mon Sep 17 00:00:00 2001
From: Patrick Wendell <pwendell@gmail.com>
Date: Mon, 18 Aug 2014 10:52:20 -0700
Subject: [PATCH 007/489] SPARK-3025 [SQL]: Allow JDBC clients to set a fair
 scheduler pool

This definitely needs review as I am not familiar with this part of Spark.
I tested this locally and it did seem to work.

Author: Patrick Wendell <pwendell@gmail.com>

Closes #1937 from pwendell/scheduler and squashes the following commits:

b858e33 [Patrick Wendell] SPARK-3025: Allow JDBC clients to set a fair scheduler pool
---
 docs/sql-programming-guide.md                 |  5 ++++
 .../scala/org/apache/spark/sql/SQLConf.scala  |  3 +++
 .../server/SparkSQLOperationManager.scala     | 27 ++++++++++++++-----
 3 files changed, 28 insertions(+), 7 deletions(-)

diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md
index cd6543945c385..34accade36ea9 100644
--- a/docs/sql-programming-guide.md
+++ b/docs/sql-programming-guide.md
@@ -605,6 +605,11 @@ Configuration of Hive is done by placing your `hive-site.xml` file in `conf/`.
 
 You may also use the beeline script comes with Hive.
 
+To set a [Fair Scheduler](job-scheduling.html#fair-scheduler-pools) pool for a JDBC client session,
+users can set the `spark.sql.thriftserver.scheduler.pool` variable:
+
+    SET spark.sql.thriftserver.scheduler.pool=accounting;
+
 ### Migration Guide for Shark Users
 
 #### Reducer number
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
index 90de11182e605..56face2992bcf 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
@@ -33,6 +33,9 @@ private[spark] object SQLConf {
   val DIALECT = "spark.sql.dialect"
   val PARQUET_BINARY_AS_STRING = "spark.sql.parquet.binaryAsString"
 
+  // This is only used for the thriftserver
+  val THRIFTSERVER_POOL = "spark.sql.thriftserver.scheduler.pool"
+
   object Deprecated {
     val MAPRED_REDUCE_TASKS = "mapred.reduce.tasks"
   }
diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/server/SparkSQLOperationManager.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/server/SparkSQLOperationManager.scala
index 9338e8121b0fe..699a1103f3248 100644
--- a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/server/SparkSQLOperationManager.scala
+++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/server/SparkSQLOperationManager.scala
@@ -17,24 +17,24 @@
 
 package org.apache.spark.sql.hive.thriftserver.server
 
-import scala.collection.JavaConversions._
-import scala.collection.mutable.ArrayBuffer
-import scala.math.{random, round}
-
 import java.sql.Timestamp
 import java.util.{Map => JMap}
 
+import scala.collection.JavaConversions._
+import scala.collection.mutable.{ArrayBuffer, Map}
+import scala.math.{random, round}
+
 import org.apache.hadoop.hive.common.`type`.HiveDecimal
 import org.apache.hadoop.hive.metastore.api.FieldSchema
 import org.apache.hive.service.cli._
 import org.apache.hive.service.cli.operation.{ExecuteStatementOperation, Operation, OperationManager}
 import org.apache.hive.service.cli.session.HiveSession
-
 import org.apache.spark.Logging
+import org.apache.spark.sql.{Row => SparkRow, SQLConf, SchemaRDD}
+import org.apache.spark.sql.catalyst.plans.logical.SetCommand
 import org.apache.spark.sql.catalyst.types._
-import org.apache.spark.sql.hive.thriftserver.ReflectionUtils
 import org.apache.spark.sql.hive.{HiveContext, HiveMetastoreTypes}
-import org.apache.spark.sql.{SchemaRDD, Row => SparkRow}
+import org.apache.spark.sql.hive.thriftserver.ReflectionUtils
 
 /**
  * Executes queries using Spark SQL, and maintains a list of handles to active queries.
@@ -43,6 +43,9 @@ class SparkSQLOperationManager(hiveContext: HiveContext) extends OperationManage
   val handleToOperation = ReflectionUtils
     .getSuperField[JMap[OperationHandle, Operation]](this, "handleToOperation")
 
+  // TODO: Currenlty this will grow infinitely, even as sessions expire
+  val sessionToActivePool = Map[HiveSession, String]()
+
   override def newExecuteStatementOperation(
       parentSession: HiveSession,
       statement: String,
@@ -165,8 +168,18 @@ class SparkSQLOperationManager(hiveContext: HiveContext) extends OperationManage
         try {
           result = hiveContext.sql(statement)
           logDebug(result.queryExecution.toString())
+          result.queryExecution.logical match {
+            case SetCommand(Some(key), Some(value)) if (key == SQLConf.THRIFTSERVER_POOL) =>
+              sessionToActivePool(parentSession) = value
+              logInfo(s"Setting spark.scheduler.pool=$value for future statements in this session.")
+            case _ =>
+          }
+
           val groupId = round(random * 1000000).toString
           hiveContext.sparkContext.setJobGroup(groupId, statement)
+          sessionToActivePool.get(parentSession).foreach { pool =>
+            hiveContext.sparkContext.setLocalProperty("spark.scheduler.pool", pool)
+          }
           iter = {
             val resultRdd = result.queryExecution.toRdd
             val useIncrementalCollect =

From 9eb74c7d2cbe127dd4c32bf1a8318497b2fb55b6 Mon Sep 17 00:00:00 2001
From: Matei Zaharia <matei@databricks.com>
Date: Mon, 18 Aug 2014 11:00:10 -0700
Subject: [PATCH 008/489] [SPARK-3091] [SQL] Add support for caching metadata
 on Parquet files

For larger Parquet files, reading the file footers (which is done in parallel on up to 5 threads) and HDFS block locations (which is serial) can take multiple seconds. We can add an option to cache this data within FilteringParquetInputFormat. Unfortunately ParquetInputFormat only caches footers within each instance of ParquetInputFormat, not across them.

Note: this PR leaves this turned off by default for 1.1, but I believe it's safe to turn it on after. The keys in the hash maps are FileStatus objects that include a modification time, so this will work fine if files are modified. The location cache could become invalid if files have moved within HDFS, but that's rare so I just made it invalidate entries every 15 minutes.

Author: Matei Zaharia <matei@databricks.com>

Closes #2005 from mateiz/parquet-cache and squashes the following commits:

dae8efe [Matei Zaharia] Bug fix
c71e9ed [Matei Zaharia] Handle empty statuses directly
22072b0 [Matei Zaharia] Use Guava caches and add a config option for caching metadata
8fb56ce [Matei Zaharia] Cache file block locations too
453bd21 [Matei Zaharia] Bug fix
4094df6 [Matei Zaharia] First attempt at caching Parquet footers
---
 .../scala/org/apache/spark/sql/SQLConf.scala  |  1 +
 .../sql/parquet/ParquetTableOperations.scala  | 84 ++++++++++++++++---
 2 files changed, 72 insertions(+), 13 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
index 56face2992bcf..4f2adb006fbc7 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
@@ -32,6 +32,7 @@ private[spark] object SQLConf {
   val CODEGEN_ENABLED = "spark.sql.codegen"
   val DIALECT = "spark.sql.dialect"
   val PARQUET_BINARY_AS_STRING = "spark.sql.parquet.binaryAsString"
+  val PARQUET_CACHE_METADATA = "spark.sql.parquet.cacheMetadata"
 
   // This is only used for the thriftserver
   val THRIFTSERVER_POOL = "spark.sql.thriftserver.scheduler.pool"
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala
index 759a2a586b926..c6dca10f6ad7c 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala
@@ -17,22 +17,23 @@
 
 package org.apache.spark.sql.parquet
 
-import scala.collection.JavaConversions._
-import scala.collection.mutable
-import scala.util.Try
-
 import java.io.IOException
 import java.lang.{Long => JLong}
 import java.text.SimpleDateFormat
-import java.util.{Date, List => JList}
+import java.util.concurrent.{Callable, TimeUnit}
+import java.util.{ArrayList, Collections, Date, List => JList}
 
+import scala.collection.JavaConversions._
+import scala.collection.mutable
+import scala.util.Try
+
+import com.google.common.cache.CacheBuilder
 import org.apache.hadoop.conf.Configuration
-import org.apache.hadoop.fs.{FileStatus, Path}
+import org.apache.hadoop.fs.{BlockLocation, FileStatus, Path}
 import org.apache.hadoop.mapreduce._
 import org.apache.hadoop.mapreduce.lib.input.{FileInputFormat => NewFileInputFormat}
 import org.apache.hadoop.mapreduce.lib.output.{FileOutputFormat => NewFileOutputFormat}
 import org.apache.hadoop.mapreduce.lib.output.FileOutputCommitter
-
 import parquet.hadoop._
 import parquet.hadoop.api.{InitContext, ReadSupport}
 import parquet.hadoop.metadata.GlobalMetaData
@@ -41,7 +42,7 @@ import parquet.io.ParquetDecodingException
 import parquet.schema.MessageType
 
 import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.SQLContext
+import org.apache.spark.sql.SQLConf
 import org.apache.spark.sql.catalyst.expressions.{Attribute, Expression, Row}
 import org.apache.spark.sql.execution.{LeafNode, SparkPlan, UnaryNode}
 import org.apache.spark.{Logging, SerializableWritable, TaskContext}
@@ -96,6 +97,11 @@ case class ParquetTableScan(
       ParquetFilters.serializeFilterExpressions(columnPruningPred, conf)
     }
 
+    // Tell FilteringParquetRowInputFormat whether it's okay to cache Parquet and FS metadata
+    conf.set(
+      SQLConf.PARQUET_CACHE_METADATA,
+      sqlContext.getConf(SQLConf.PARQUET_CACHE_METADATA, "false"))
+
     sc.newAPIHadoopRDD(
       conf,
       classOf[FilteringParquetRowInputFormat],
@@ -323,10 +329,40 @@ private[parquet] class FilteringParquetRowInputFormat
   }
 
   override def getFooters(jobContext: JobContext): JList[Footer] = {
+    import FilteringParquetRowInputFormat.footerCache
+
     if (footers eq null) {
+      val conf = ContextUtil.getConfiguration(jobContext)
+      val cacheMetadata = conf.getBoolean(SQLConf.PARQUET_CACHE_METADATA, false)
       val statuses = listStatus(jobContext)
       fileStatuses = statuses.map(file => file.getPath -> file).toMap
-      footers = getFooters(ContextUtil.getConfiguration(jobContext), statuses)
+      if (statuses.isEmpty) {
+        footers = Collections.emptyList[Footer]
+      } else if (!cacheMetadata) {
+        // Read the footers from HDFS
+        footers = getFooters(conf, statuses)
+      } else {
+        // Read only the footers that are not in the footerCache
+        val foundFooters = footerCache.getAllPresent(statuses)
+        val toFetch = new ArrayList[FileStatus]
+        for (s <- statuses) {
+          if (!foundFooters.containsKey(s)) {
+            toFetch.add(s)
+          }
+        }
+        val newFooters = new mutable.HashMap[FileStatus, Footer]
+        if (toFetch.size > 0) {
+          val fetched = getFooters(conf, toFetch)
+          for ((status, i) <- toFetch.zipWithIndex) {
+            newFooters(status) = fetched.get(i)
+          }
+          footerCache.putAll(newFooters)
+        }
+        footers = new ArrayList[Footer](statuses.size)
+        for (status <- statuses) {
+          footers.add(newFooters.getOrElse(status, foundFooters.get(status)))
+        }
+      }
     }
 
     footers
@@ -339,6 +375,10 @@ private[parquet] class FilteringParquetRowInputFormat
       configuration: Configuration,
       footers: JList[Footer]): JList[ParquetInputSplit] = {
 
+    import FilteringParquetRowInputFormat.blockLocationCache
+
+    val cacheMetadata = configuration.getBoolean(SQLConf.PARQUET_CACHE_METADATA, false)
+
     val maxSplitSize: JLong = configuration.getLong("mapred.max.split.size", Long.MaxValue)
     val minSplitSize: JLong =
       Math.max(getFormatMinSplitSize(), configuration.getLong("mapred.min.split.size", 0L))
@@ -366,16 +406,23 @@ private[parquet] class FilteringParquetRowInputFormat
     for (footer <- footers) {
       val fs = footer.getFile.getFileSystem(configuration)
       val file = footer.getFile
-      val fileStatus = fileStatuses.getOrElse(file, fs.getFileStatus(file))
+      val status = fileStatuses.getOrElse(file, fs.getFileStatus(file))
       val parquetMetaData = footer.getParquetMetadata
       val blocks = parquetMetaData.getBlocks
-      val fileBlockLocations = fs.getFileBlockLocations(fileStatus, 0, fileStatus.getLen)
+      var blockLocations: Array[BlockLocation] = null
+      if (!cacheMetadata) {
+        blockLocations = fs.getFileBlockLocations(status, 0, status.getLen)
+      } else {
+        blockLocations = blockLocationCache.get(status, new Callable[Array[BlockLocation]] {
+          def call(): Array[BlockLocation] = fs.getFileBlockLocations(status, 0, status.getLen)
+        })
+      }
       splits.addAll(
         generateSplits.invoke(
           null,
           blocks,
-          fileBlockLocations,
-          fileStatus,
+          blockLocations,
+          status,
           parquetMetaData.getFileMetaData,
           readContext.getRequestedSchema.toString,
           readContext.getReadSupportMetadata,
@@ -387,6 +434,17 @@ private[parquet] class FilteringParquetRowInputFormat
   }
 }
 
+private[parquet] object FilteringParquetRowInputFormat {
+  private val footerCache = CacheBuilder.newBuilder()
+    .maximumSize(20000)
+    .build[FileStatus, Footer]()
+
+  private val blockLocationCache = CacheBuilder.newBuilder()
+    .maximumSize(20000)
+    .expireAfterWrite(15, TimeUnit.MINUTES)  // Expire locations since HDFS files might move
+    .build[FileStatus, Array[BlockLocation]]()
+}
+
 private[parquet] object FileSystemHelper {
   def listFiles(pathStr: String, conf: Configuration): Seq[Path] = {
     val origPath = new Path(pathStr)

From 3abd0c1cda09bb575adc99847a619bc84af37fd0 Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Mon, 18 Aug 2014 13:17:10 -0700
Subject: [PATCH 009/489] [SPARK-2406][SQL] Initial support for using
 ParquetTableScan to read HiveMetaStore tables.

This PR adds an experimental flag `spark.sql.hive.convertMetastoreParquet` that when true causes the planner to detects tables that use Hive's Parquet SerDe and instead plans them using Spark SQL's native `ParquetTableScan`.

Author: Michael Armbrust <michael@databricks.com>
Author: Yin Huai <huai@cse.ohio-state.edu>

Closes #1819 from marmbrus/parquetMetastore and squashes the following commits:

1620079 [Michael Armbrust] Revert "remove hive parquet bundle"
cc30430 [Michael Armbrust] Merge remote-tracking branch 'origin/master' into parquetMetastore
4f3d54f [Michael Armbrust] fix style
41ebc5f [Michael Armbrust] remove hive parquet bundle
a43e0da [Michael Armbrust] Merge remote-tracking branch 'origin/master' into parquetMetastore
4c4dc19 [Michael Armbrust] Fix bug with tree splicing.
ebb267e [Michael Armbrust] include parquet hive to tests pass (Remove this later).
c0d9b72 [Michael Armbrust] Avoid creating a HadoopRDD per partition.  Add dirty hacks to retrieve partition values from the InputSplit.
8cdc93c [Michael Armbrust] Merge pull request #8 from yhuai/parquetMetastore
a0baec7 [Yin Huai] Partitioning columns can be resolved.
1161338 [Michael Armbrust] Add a test to make sure conversion is actually happening
212d5cd [Michael Armbrust] Initial support for using ParquetTableScan to read HiveMetaStore tables.
---
 project/SparkBuild.scala                      |   1 -
 .../spark/sql/execution/basicOperators.scala  |  12 ++
 .../spark/sql/parquet/ParquetRelation.scala   |   8 +-
 .../sql/parquet/ParquetTableOperations.scala  |  74 ++++++--
 .../apache/spark/sql/hive/HiveContext.scala   |   9 +
 .../spark/sql/hive/HiveStrategies.scala       | 119 +++++++++++-
 .../sql/hive/parquet/FakeParquetSerDe.scala   |  56 ++++++
 .../sql/parquet/ParquetMetastoreSuite.scala   | 171 ++++++++++++++++++
 8 files changed, 427 insertions(+), 23 deletions(-)
 create mode 100644 sql/hive/src/main/scala/org/apache/spark/sql/hive/parquet/FakeParquetSerDe.scala
 create mode 100644 sql/hive/src/test/scala/org/apache/spark/sql/parquet/ParquetMetastoreSuite.scala

diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala
index 63a285b81a60c..49d52aefca17a 100644
--- a/project/SparkBuild.scala
+++ b/project/SparkBuild.scala
@@ -228,7 +228,6 @@ object SQL {
 object Hive {
 
   lazy val settings = Seq(
-
     javaOptions += "-XX:MaxPermSize=1g",
     // Multiple queries rely on the TestHive singleton. See comments there for more details.
     parallelExecution in Test := false,
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
index 0027f3cf1fc79..f9dfa3c92f1eb 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
@@ -303,3 +303,15 @@ case class Intersect(left: SparkPlan, right: SparkPlan) extends BinaryNode {
     left.execute().map(_.copy()).intersection(right.execute().map(_.copy()))
   }
 }
+
+/**
+ * :: DeveloperApi ::
+ * A plan node that does nothing but lie about the output of its child.  Used to spice a
+ * (hopefully structurally equivalent) tree from a different optimization sequence into an already
+ * resolved tree.
+ */
+@DeveloperApi
+case class OutputFaker(output: Seq[Attribute], child: SparkPlan) extends SparkPlan {
+  def children = child :: Nil
+  def execute() = child.execute()
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala
index 053b2a154389c..1713ae6fb5d93 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala
@@ -47,7 +47,8 @@ import org.apache.spark.sql.catalyst.plans.logical.{LogicalPlan, LeafNode}
 private[sql] case class ParquetRelation(
     path: String,
     @transient conf: Option[Configuration],
-    @transient sqlContext: SQLContext)
+    @transient sqlContext: SQLContext,
+    partitioningAttributes: Seq[Attribute] = Nil)
   extends LeafNode with MultiInstanceRelation {
 
   self: Product =>
@@ -61,12 +62,13 @@ private[sql] case class ParquetRelation(
 
   /** Attributes */
   override val output =
+    partitioningAttributes ++
     ParquetTypesConverter.readSchemaFromFile(
-      new Path(path),
+      new Path(path.split(",").head),
       conf,
       sqlContext.isParquetBinaryAsString)
 
-  override def newInstance = ParquetRelation(path, conf, sqlContext).asInstanceOf[this.type]
+  override def newInstance() = ParquetRelation(path, conf, sqlContext).asInstanceOf[this.type]
 
   // Equals must also take into account the output attributes so that we can distinguish between
   // different instances of the same relation,
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala
index c6dca10f6ad7c..f6cfab736d98a 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala
@@ -34,6 +34,7 @@ import org.apache.hadoop.mapreduce._
 import org.apache.hadoop.mapreduce.lib.input.{FileInputFormat => NewFileInputFormat}
 import org.apache.hadoop.mapreduce.lib.output.{FileOutputFormat => NewFileOutputFormat}
 import org.apache.hadoop.mapreduce.lib.output.FileOutputCommitter
+
 import parquet.hadoop._
 import parquet.hadoop.api.{InitContext, ReadSupport}
 import parquet.hadoop.metadata.GlobalMetaData
@@ -42,6 +43,7 @@ import parquet.io.ParquetDecodingException
 import parquet.schema.MessageType
 
 import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.SQLConf
 import org.apache.spark.sql.catalyst.expressions.{Attribute, Expression, Row}
 import org.apache.spark.sql.execution.{LeafNode, SparkPlan, UnaryNode}
@@ -60,11 +62,18 @@ case class ParquetTableScan(
   // The resolution of Parquet attributes is case sensitive, so we resolve the original attributes
   // by exprId. note: output cannot be transient, see
   // https://issues.apache.org/jira/browse/SPARK-1367
-  val output = attributes.map { a =>
-    relation.output
-      .find(o => o.exprId == a.exprId)
-      .getOrElse(sys.error(s"Invalid parquet attribute $a in ${relation.output.mkString(",")}"))
-  }
+  val normalOutput =
+    attributes
+      .filterNot(a => relation.partitioningAttributes.map(_.exprId).contains(a.exprId))
+      .flatMap(a => relation.output.find(o => o.exprId == a.exprId))
+
+  val partOutput =
+    attributes.flatMap(a => relation.partitioningAttributes.find(o => o.exprId == a.exprId))
+
+  def output = partOutput ++ normalOutput
+
+  assert(normalOutput.size + partOutput.size == attributes.size,
+    s"$normalOutput + $partOutput != $attributes, ${relation.output}")
 
   override def execute(): RDD[Row] = {
     val sc = sqlContext.sparkContext
@@ -72,16 +81,19 @@ case class ParquetTableScan(
     ParquetInputFormat.setReadSupportClass(job, classOf[RowReadSupport])
 
     val conf: Configuration = ContextUtil.getConfiguration(job)
-    val qualifiedPath = {
-      val path = new Path(relation.path)
-      path.getFileSystem(conf).makeQualified(path)
+
+    relation.path.split(",").foreach { curPath =>
+      val qualifiedPath = {
+        val path = new Path(curPath)
+        path.getFileSystem(conf).makeQualified(path)
+      }
+      NewFileInputFormat.addInputPath(job, qualifiedPath)
     }
-    NewFileInputFormat.addInputPath(job, qualifiedPath)
 
     // Store both requested and original schema in `Configuration`
     conf.set(
       RowReadSupport.SPARK_ROW_REQUESTED_SCHEMA,
-      ParquetTypesConverter.convertToString(output))
+      ParquetTypesConverter.convertToString(normalOutput))
     conf.set(
       RowWriteSupport.SPARK_ROW_SCHEMA,
       ParquetTypesConverter.convertToString(relation.output))
@@ -102,13 +114,41 @@ case class ParquetTableScan(
       SQLConf.PARQUET_CACHE_METADATA,
       sqlContext.getConf(SQLConf.PARQUET_CACHE_METADATA, "false"))
 
-    sc.newAPIHadoopRDD(
-      conf,
-      classOf[FilteringParquetRowInputFormat],
-      classOf[Void],
-      classOf[Row])
-      .map(_._2)
-      .filter(_ != null) // Parquet's record filters may produce null values
+    val baseRDD =
+      new org.apache.spark.rdd.NewHadoopRDD(
+        sc,
+        classOf[FilteringParquetRowInputFormat],
+        classOf[Void],
+        classOf[Row],
+        conf)
+
+    if (partOutput.nonEmpty) {
+      baseRDD.mapPartitionsWithInputSplit { case (split, iter) =>
+        val partValue = "([^=]+)=([^=]+)".r
+        val partValues =
+          split.asInstanceOf[parquet.hadoop.ParquetInputSplit]
+            .getPath
+            .toString
+            .split("/")
+            .flatMap {
+              case partValue(key, value) => Some(key -> value)
+              case _ => None
+            }.toMap
+
+        val partitionRowValues =
+          partOutput.map(a => Cast(Literal(partValues(a.name)), a.dataType).eval(EmptyRow))
+
+        new Iterator[Row] {
+          private[this] val joinedRow = new JoinedRow(Row(partitionRowValues:_*), null)
+
+          def hasNext = iter.hasNext
+
+          def next() = joinedRow.withRight(iter.next()._2)
+        }
+      }
+    } else {
+      baseRDD.map(_._2)
+    }.filter(_ != null) // Parquet's record filters may produce null values
   }
 
   /**
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
index a8da676ffa0e0..ff32c7c90a0d2 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
@@ -79,6 +79,14 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) {
   // Change the default SQL dialect to HiveQL
   override private[spark] def dialect: String = getConf(SQLConf.DIALECT, "hiveql")
 
+  /**
+   * When true, enables an experimental feature where metastore tables that use the parquet SerDe
+   * are automatically converted to use the Spark SQL parquet table scan, instead of the Hive
+   * SerDe.
+   */
+  private[spark] def convertMetastoreParquet: Boolean =
+    getConf("spark.sql.hive.convertMetastoreParquet", "false") == "true"
+
   override protected[sql] def executePlan(plan: LogicalPlan): this.QueryExecution =
     new this.QueryExecution { val logical = plan }
 
@@ -326,6 +334,7 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) {
       TakeOrdered,
       ParquetOperations,
       InMemoryScans,
+      ParquetConversion, // Must be before HiveTableScans
       HiveTableScans,
       DataSinks,
       Scripts,
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala
index 5fcc1bd4b9adf..389ace726d205 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala
@@ -17,14 +17,20 @@
 
 package org.apache.spark.sql.hive
 
-import org.apache.spark.sql.SQLContext
+import org.apache.spark.annotation.Experimental
+import org.apache.spark.sql._
+import org.apache.spark.sql.catalyst.analysis.UnresolvedAttribute
 import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.expressions.codegen.GeneratePredicate
 import org.apache.spark.sql.catalyst.planning._
 import org.apache.spark.sql.catalyst.plans._
-import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
+import org.apache.spark.sql.catalyst.plans.logical.{LogicalPlan, LowerCaseSchema}
 import org.apache.spark.sql.execution._
 import org.apache.spark.sql.hive.execution._
 import org.apache.spark.sql.columnar.InMemoryRelation
+import org.apache.spark.sql.parquet.{ParquetRelation, ParquetTableScan}
+
+import scala.collection.JavaConversions._
 
 private[hive] trait HiveStrategies {
   // Possibly being too clever with types here... or not clever enough.
@@ -32,6 +38,115 @@ private[hive] trait HiveStrategies {
 
   val hiveContext: HiveContext
 
+  /**
+   * :: Experimental ::
+   * Finds table scans that would use the Hive SerDe and replaces them with our own native parquet
+   * table scan operator.
+   *
+   * TODO: Much of this logic is duplicated in HiveTableScan.  Ideally we would do some refactoring
+   * but since this is after the code freeze for 1.1 all logic is here to minimize disruption.
+   *
+   * Other issues:
+   *  - Much of this logic assumes case insensitive resolution.
+   */
+  @Experimental
+  object ParquetConversion extends Strategy {
+    implicit class LogicalPlanHacks(s: SchemaRDD) {
+      def lowerCase =
+        new SchemaRDD(s.sqlContext, LowerCaseSchema(s.logicalPlan))
+
+      def addPartitioningAttributes(attrs: Seq[Attribute]) =
+        new SchemaRDD(
+          s.sqlContext,
+          s.logicalPlan transform {
+            case p: ParquetRelation => p.copy(partitioningAttributes = attrs)
+          })
+    }
+
+    implicit class PhysicalPlanHacks(originalPlan: SparkPlan) {
+      def fakeOutput(newOutput: Seq[Attribute]) =
+        OutputFaker(
+          originalPlan.output.map(a =>
+            newOutput.find(a.name.toLowerCase == _.name.toLowerCase)
+              .getOrElse(
+                sys.error(s"Can't find attribute $a to fake in set ${newOutput.mkString(",")}"))),
+          originalPlan)
+    }
+
+    def apply(plan: LogicalPlan): Seq[SparkPlan] = plan match {
+      case PhysicalOperation(projectList, predicates, relation: MetastoreRelation)
+          if relation.tableDesc.getSerdeClassName.contains("Parquet") &&
+             hiveContext.convertMetastoreParquet =>
+
+        // Filter out all predicates that only deal with partition keys
+        val partitionKeyIds = relation.partitionKeys.map(_.exprId).toSet
+        val (pruningPredicates, otherPredicates) = predicates.partition {
+          _.references.map(_.exprId).subsetOf(partitionKeyIds)
+        }
+
+        // We are going to throw the predicates and projection back at the whole optimization
+        // sequence so lets unresolve all the attributes, allowing them to be rebound to the
+        // matching parquet attributes.
+        val unresolvedOtherPredicates = otherPredicates.map(_ transform {
+          case a: AttributeReference => UnresolvedAttribute(a.name)
+        }).reduceOption(And).getOrElse(Literal(true))
+
+        val unresolvedProjection = projectList.map(_ transform {
+          case a: AttributeReference => UnresolvedAttribute(a.name)
+        })
+
+        if (relation.hiveQlTable.isPartitioned) {
+          val rawPredicate = pruningPredicates.reduceOption(And).getOrElse(Literal(true))
+          // Translate the predicate so that it automatically casts the input values to the correct
+          // data types during evaluation
+          val castedPredicate = rawPredicate transform {
+            case a: AttributeReference =>
+              val idx = relation.partitionKeys.indexWhere(a.exprId == _.exprId)
+              val key = relation.partitionKeys(idx)
+              Cast(BoundReference(idx, StringType, nullable = true), key.dataType)
+          }
+
+          val inputData = new GenericMutableRow(relation.partitionKeys.size)
+          val pruningCondition =
+            if(codegenEnabled) {
+              GeneratePredicate(castedPredicate)
+            } else {
+              InterpretedPredicate(castedPredicate)
+            }
+
+          val partitions = relation.hiveQlPartitions.filter { part =>
+            val partitionValues = part.getValues
+            var i = 0
+            while (i < partitionValues.size()) {
+              inputData(i) = partitionValues(i)
+              i += 1
+            }
+            pruningCondition(inputData)
+          }
+
+          hiveContext
+            .parquetFile(partitions.map(_.getLocation).mkString(","))
+            .addPartitioningAttributes(relation.partitionKeys)
+            .lowerCase
+            .where(unresolvedOtherPredicates)
+            .select(unresolvedProjection:_*)
+            .queryExecution
+            .executedPlan
+            .fakeOutput(projectList.map(_.toAttribute)):: Nil
+        } else {
+          hiveContext
+            .parquetFile(relation.hiveQlTable.getDataLocation.getPath)
+            .lowerCase
+            .where(unresolvedOtherPredicates)
+            .select(unresolvedProjection:_*)
+            .queryExecution
+            .executedPlan
+            .fakeOutput(projectList.map(_.toAttribute)) :: Nil
+        }
+      case _ => Nil
+    }
+  }
+
   object Scripts extends Strategy {
     def apply(plan: LogicalPlan): Seq[SparkPlan] = plan match {
       case logical.ScriptTransformation(input, script, output, child) =>
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/parquet/FakeParquetSerDe.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/parquet/FakeParquetSerDe.scala
new file mode 100644
index 0000000000000..544abfc32423c
--- /dev/null
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/parquet/FakeParquetSerDe.scala
@@ -0,0 +1,56 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.hive.parquet
+
+import java.util.Properties
+
+import org.apache.hadoop.conf.Configuration
+import org.apache.hadoop.hive.serde2.objectinspector.ObjectInspector.Category
+import org.apache.hadoop.hive.serde2.{SerDeStats, SerDe}
+import org.apache.hadoop.hive.serde2.objectinspector.ObjectInspector
+import org.apache.hadoop.io.Writable
+
+/**
+ * A placeholder that allows SparkSQL users to create metastore tables that are stored as
+ * parquet files.  It is only intended to pass the checks that the serde is valid and exists
+ * when a CREATE TABLE is run.  The actual work of decoding will be done by ParquetTableScan
+ * when "spark.sql.hive.convertMetastoreParquet" is set to true.
+ */
+@deprecated("No code should depend on FakeParquetHiveSerDe as it is only intended as a " +
+            "placeholder in the Hive MetaStore")
+class FakeParquetSerDe extends SerDe {
+  override def getObjectInspector: ObjectInspector = new ObjectInspector {
+    override def getCategory: Category = Category.PRIMITIVE
+
+    override def getTypeName: String = "string"
+  }
+
+  override def deserialize(p1: Writable): AnyRef = throwError
+
+  override def initialize(p1: Configuration, p2: Properties): Unit = {}
+
+  override def getSerializedClass: Class[_ <: Writable] = throwError
+
+  override def getSerDeStats: SerDeStats = throwError
+
+  override def serialize(p1: scala.Any, p2: ObjectInspector): Writable = throwError
+
+  private def throwError =
+    sys.error(
+      "spark.sql.hive.convertMetastoreParquet must be set to true to use FakeParquetSerDe")
+}
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/parquet/ParquetMetastoreSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/parquet/ParquetMetastoreSuite.scala
new file mode 100644
index 0000000000000..0723be7298e15
--- /dev/null
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/parquet/ParquetMetastoreSuite.scala
@@ -0,0 +1,171 @@
+
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.parquet
+
+import java.io.File
+
+import org.apache.spark.sql.hive.execution.HiveTableScan
+import org.scalatest.BeforeAndAfterAll
+
+import scala.reflect.ClassTag
+
+import org.apache.spark.sql.{SQLConf, QueryTest}
+import org.apache.spark.sql.execution.{BroadcastHashJoin, ShuffledHashJoin}
+import org.apache.spark.sql.hive.test.TestHive
+import org.apache.spark.sql.hive.test.TestHive._
+
+case class ParquetData(intField: Int, stringField: String)
+
+/**
+ * Tests for our SerDe -> Native parquet scan conversion.
+ */
+class ParquetMetastoreSuite extends QueryTest with BeforeAndAfterAll {
+
+  override def beforeAll(): Unit = {
+    setConf("spark.sql.hive.convertMetastoreParquet", "true")
+  }
+
+  override def afterAll(): Unit = {
+    setConf("spark.sql.hive.convertMetastoreParquet", "false")
+  }
+
+  val partitionedTableDir = File.createTempFile("parquettests", "sparksql")
+  partitionedTableDir.delete()
+  partitionedTableDir.mkdir()
+
+  (1 to 10).foreach { p =>
+    val partDir = new File(partitionedTableDir, s"p=$p")
+    sparkContext.makeRDD(1 to 10)
+      .map(i => ParquetData(i, s"part-$p"))
+      .saveAsParquetFile(partDir.getCanonicalPath)
+  }
+
+  sql(s"""
+    create external table partitioned_parquet
+    (
+      intField INT,
+      stringField STRING
+    )
+    PARTITIONED BY (p int)
+    ROW FORMAT SERDE 'org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe'
+     STORED AS
+     INPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat'
+     OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat'
+    location '${partitionedTableDir.getCanonicalPath}'
+    """)
+
+  sql(s"""
+    create external table normal_parquet
+    (
+      intField INT,
+      stringField STRING
+    )
+    ROW FORMAT SERDE 'org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe'
+     STORED AS
+     INPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat'
+     OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat'
+    location '${new File(partitionedTableDir, "p=1").getCanonicalPath}'
+    """)
+
+  (1 to 10).foreach { p =>
+    sql(s"ALTER TABLE partitioned_parquet ADD PARTITION (p=$p)")
+  }
+
+  test("project the partitioning column") {
+    checkAnswer(
+      sql("SELECT p, count(*) FROM partitioned_parquet group by p"),
+      (1, 10) ::
+      (2, 10) ::
+      (3, 10) ::
+      (4, 10) ::
+      (5, 10) ::
+      (6, 10) ::
+      (7, 10) ::
+      (8, 10) ::
+      (9, 10) ::
+      (10, 10) :: Nil
+    )
+  }
+
+  test("project partitioning and non-partitioning columns") {
+    checkAnswer(
+      sql("SELECT stringField, p, count(intField) " +
+        "FROM partitioned_parquet GROUP BY p, stringField"),
+      ("part-1", 1, 10) ::
+      ("part-2", 2, 10) ::
+      ("part-3", 3, 10) ::
+      ("part-4", 4, 10) ::
+      ("part-5", 5, 10) ::
+      ("part-6", 6, 10) ::
+      ("part-7", 7, 10) ::
+      ("part-8", 8, 10) ::
+      ("part-9", 9, 10) ::
+      ("part-10", 10, 10) :: Nil
+    )
+  }
+
+  test("simple count") {
+    checkAnswer(
+      sql("SELECT COUNT(*) FROM partitioned_parquet"),
+      100)
+  }
+
+  test("pruned count") {
+    checkAnswer(
+      sql("SELECT COUNT(*) FROM partitioned_parquet WHERE p = 1"),
+      10)
+  }
+
+  test("multi-partition pruned count") {
+    checkAnswer(
+      sql("SELECT COUNT(*) FROM partitioned_parquet WHERE p IN (1,2,3)"),
+      30)
+  }
+
+  test("non-partition predicates") {
+    checkAnswer(
+      sql("SELECT COUNT(*) FROM partitioned_parquet WHERE intField IN (1,2,3)"),
+      30)
+  }
+
+  test("sum") {
+    checkAnswer(
+      sql("SELECT SUM(intField) FROM partitioned_parquet WHERE intField IN (1,2,3) AND p = 1"),
+      1 + 2 + 3
+    )
+  }
+
+  test("non-part select(*)") {
+    checkAnswer(
+      sql("SELECT COUNT(*) FROM normal_parquet"),
+      10
+    )
+  }
+
+  test("conversion is working") {
+    assert(
+      sql("SELECT * FROM normal_parquet").queryExecution.executedPlan.collect {
+        case _: HiveTableScan => true
+      }.isEmpty)
+    assert(
+      sql("SELECT * FROM normal_parquet").queryExecution.executedPlan.collect {
+        case _: ParquetTableScan => true
+      }.nonEmpty)
+  }
+}

From 66ade00f91a9343ac9277c5a7c09314087a4831e Mon Sep 17 00:00:00 2001
From: Marcelo Vanzin <vanzin@cloudera.com>
Date: Mon, 18 Aug 2014 13:25:30 -0700
Subject: [PATCH 010/489] [SPARK-2169] Don't copy appName / basePath
 everywhere.

Instead of keeping copies in all pages, just reference the values
kept in the base SparkUI instance (by making them available via
getters).

Author: Marcelo Vanzin <vanzin@cloudera.com>

Closes #1252 from vanzin/SPARK-2169 and squashes the following commits:

4412fc6 [Marcelo Vanzin] Simplify UIUtils.headerSparkPage signature.
4e5d35a [Marcelo Vanzin] [SPARK-2169] Don't copy appName / basePath everywhere.
---
 .../apache/spark/deploy/master/Master.scala   |  2 +-
 .../scala/org/apache/spark/ui/SparkUI.scala   |  9 +++++++++
 .../scala/org/apache/spark/ui/UIUtils.scala   | 12 +++++-------
 .../scala/org/apache/spark/ui/WebUI.scala     |  3 +++
 .../apache/spark/ui/env/EnvironmentPage.scala |  4 +---
 .../apache/spark/ui/env/EnvironmentTab.scala  |  4 +---
 .../apache/spark/ui/exec/ExecutorsPage.scala  |  5 +----
 .../apache/spark/ui/exec/ExecutorsTab.scala   |  6 ++----
 .../spark/ui/jobs/JobProgressPage.scala       |  4 +---
 .../apache/spark/ui/jobs/JobProgressTab.scala |  7 +++----
 .../org/apache/spark/ui/jobs/PoolPage.scala   |  5 +----
 .../org/apache/spark/ui/jobs/PoolTable.scala  |  7 +++----
 .../org/apache/spark/ui/jobs/StagePage.scala  |  8 ++------
 .../org/apache/spark/ui/jobs/StageTable.scala | 19 ++++++++++---------
 .../org/apache/spark/ui/storage/RDDPage.scala |  8 ++------
 .../apache/spark/ui/storage/StoragePage.scala |  6 ++----
 .../apache/spark/ui/storage/StorageTab.scala  |  4 +---
 .../spark/streaming/ui/StreamingPage.scala    |  3 +--
 .../spark/streaming/ui/StreamingTab.scala     |  6 ++----
 19 files changed, 51 insertions(+), 71 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/deploy/master/Master.scala b/core/src/main/scala/org/apache/spark/deploy/master/Master.scala
index cfa2c028a807b..5017273e87c07 100644
--- a/core/src/main/scala/org/apache/spark/deploy/master/Master.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/master/Master.scala
@@ -697,7 +697,7 @@ private[spark] class Master(
       appIdToUI(app.id) = ui
       webUi.attachSparkUI(ui)
       // Application UI is successfully rebuilt, so link the Master UI to it
-      app.desc.appUiUrl = ui.basePath
+      app.desc.appUiUrl = ui.getBasePath
       true
     } catch {
       case e: Exception =>
diff --git a/core/src/main/scala/org/apache/spark/ui/SparkUI.scala b/core/src/main/scala/org/apache/spark/ui/SparkUI.scala
index 6c788a37dc70b..cccd59d122a92 100644
--- a/core/src/main/scala/org/apache/spark/ui/SparkUI.scala
+++ b/core/src/main/scala/org/apache/spark/ui/SparkUI.scala
@@ -76,6 +76,8 @@ private[spark] class SparkUI(
     }
   }
 
+  def getAppName = appName
+
   /** Set the app name for this UI. */
   def setAppName(name: String) {
     appName = name
@@ -100,6 +102,13 @@ private[spark] class SparkUI(
   private[spark] def appUIAddress = s"http://$appUIHostPort"
 }
 
+private[spark] abstract class SparkUITab(parent: SparkUI, prefix: String)
+  extends WebUITab(parent, prefix) {
+
+  def appName: String = parent.getAppName
+
+}
+
 private[spark] object SparkUI {
   val DEFAULT_PORT = 4040
   val STATIC_RESOURCE_DIR = "org/apache/spark/ui/static"
diff --git a/core/src/main/scala/org/apache/spark/ui/UIUtils.scala b/core/src/main/scala/org/apache/spark/ui/UIUtils.scala
index 715cc2f4df8dd..bee6dad3387e5 100644
--- a/core/src/main/scala/org/apache/spark/ui/UIUtils.scala
+++ b/core/src/main/scala/org/apache/spark/ui/UIUtils.scala
@@ -163,17 +163,15 @@ private[spark] object UIUtils extends Logging {
 
   /** Returns a spark page with correctly formatted headers */
   def headerSparkPage(
-      content: => Seq[Node],
-      basePath: String,
-      appName: String,
       title: String,
-      tabs: Seq[WebUITab],
-      activeTab: WebUITab,
+      content: => Seq[Node],
+      activeTab: SparkUITab,
       refreshInterval: Option[Int] = None): Seq[Node] = {
 
-    val header = tabs.map { tab =>
+    val appName = activeTab.appName
+    val header = activeTab.headerTabs.map { tab =>
       <li class={if (tab == activeTab) "active" else ""}>
-        <a href={prependBaseUri(basePath, "/" + tab.prefix)}>{tab.name}</a>
+        <a href={prependBaseUri(activeTab.basePath, "/" + tab.prefix)}>{tab.name}</a>
       </li>
     }
 
diff --git a/core/src/main/scala/org/apache/spark/ui/WebUI.scala b/core/src/main/scala/org/apache/spark/ui/WebUI.scala
index 5f52f95088007..5d88ca403a674 100644
--- a/core/src/main/scala/org/apache/spark/ui/WebUI.scala
+++ b/core/src/main/scala/org/apache/spark/ui/WebUI.scala
@@ -50,6 +50,7 @@ private[spark] abstract class WebUI(
   protected val publicHostName = Option(System.getenv("SPARK_PUBLIC_DNS")).getOrElse(localHostName)
   private val className = Utils.getFormattedClassName(this)
 
+  def getBasePath: String = basePath
   def getTabs: Seq[WebUITab] = tabs.toSeq
   def getHandlers: Seq[ServletContextHandler] = handlers.toSeq
   def getSecurityManager: SecurityManager = securityManager
@@ -135,6 +136,8 @@ private[spark] abstract class WebUITab(parent: WebUI, val prefix: String) {
 
   /** Get a list of header tabs from the parent UI. */
   def headerTabs: Seq[WebUITab] = parent.getTabs
+
+  def basePath: String = parent.getBasePath
 }
 
 
diff --git a/core/src/main/scala/org/apache/spark/ui/env/EnvironmentPage.scala b/core/src/main/scala/org/apache/spark/ui/env/EnvironmentPage.scala
index b347eb1b83c1f..f0a1174a71d34 100644
--- a/core/src/main/scala/org/apache/spark/ui/env/EnvironmentPage.scala
+++ b/core/src/main/scala/org/apache/spark/ui/env/EnvironmentPage.scala
@@ -24,8 +24,6 @@ import scala.xml.Node
 import org.apache.spark.ui.{UIUtils, WebUIPage}
 
 private[ui] class EnvironmentPage(parent: EnvironmentTab) extends WebUIPage("") {
-  private val appName = parent.appName
-  private val basePath = parent.basePath
   private val listener = parent.listener
 
   def render(request: HttpServletRequest): Seq[Node] = {
@@ -45,7 +43,7 @@ private[ui] class EnvironmentPage(parent: EnvironmentTab) extends WebUIPage("")
         <h4>Classpath Entries</h4> {classpathEntriesTable}
       </span>
 
-    UIUtils.headerSparkPage(content, basePath, appName, "Environment", parent.headerTabs, parent)
+    UIUtils.headerSparkPage("Environment", content, parent)
   }
 
   private def propertyHeader = Seq("Name", "Value")
diff --git a/core/src/main/scala/org/apache/spark/ui/env/EnvironmentTab.scala b/core/src/main/scala/org/apache/spark/ui/env/EnvironmentTab.scala
index bbbe55ecf44a1..0d158fbe638d3 100644
--- a/core/src/main/scala/org/apache/spark/ui/env/EnvironmentTab.scala
+++ b/core/src/main/scala/org/apache/spark/ui/env/EnvironmentTab.scala
@@ -21,9 +21,7 @@ import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.scheduler._
 import org.apache.spark.ui._
 
-private[ui] class EnvironmentTab(parent: SparkUI) extends WebUITab(parent, "environment") {
-  val appName = parent.appName
-  val basePath = parent.basePath
+private[ui] class EnvironmentTab(parent: SparkUI) extends SparkUITab(parent, "environment") {
   val listener = new EnvironmentListener
 
   attachPage(new EnvironmentPage(this))
diff --git a/core/src/main/scala/org/apache/spark/ui/exec/ExecutorsPage.scala b/core/src/main/scala/org/apache/spark/ui/exec/ExecutorsPage.scala
index b814b0e6b8509..02df4e8fe61af 100644
--- a/core/src/main/scala/org/apache/spark/ui/exec/ExecutorsPage.scala
+++ b/core/src/main/scala/org/apache/spark/ui/exec/ExecutorsPage.scala
@@ -43,8 +43,6 @@ private case class ExecutorSummaryInfo(
     maxMemory: Long)
 
 private[ui] class ExecutorsPage(parent: ExecutorsTab) extends WebUIPage("") {
-  private val appName = parent.appName
-  private val basePath = parent.basePath
   private val listener = parent.listener
 
   def render(request: HttpServletRequest): Seq[Node] = {
@@ -101,8 +99,7 @@ private[ui] class ExecutorsPage(parent: ExecutorsTab) extends WebUIPage("") {
         </div>
       </div>;
 
-    UIUtils.headerSparkPage(content, basePath, appName, "Executors (" + execInfo.size + ")",
-      parent.headerTabs, parent)
+    UIUtils.headerSparkPage("Executors (" + execInfo.size + ")", content, parent)
   }
 
   /** Render an HTML row representing an executor */
diff --git a/core/src/main/scala/org/apache/spark/ui/exec/ExecutorsTab.scala b/core/src/main/scala/org/apache/spark/ui/exec/ExecutorsTab.scala
index 5c2d1d1fe75d3..61eb111cd9100 100644
--- a/core/src/main/scala/org/apache/spark/ui/exec/ExecutorsTab.scala
+++ b/core/src/main/scala/org/apache/spark/ui/exec/ExecutorsTab.scala
@@ -23,11 +23,9 @@ import org.apache.spark.ExceptionFailure
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.scheduler._
 import org.apache.spark.storage.StorageStatusListener
-import org.apache.spark.ui.{SparkUI, WebUITab}
+import org.apache.spark.ui.{SparkUI, SparkUITab}
 
-private[ui] class ExecutorsTab(parent: SparkUI) extends WebUITab(parent, "executors") {
-  val appName = parent.appName
-  val basePath = parent.basePath
+private[ui] class ExecutorsTab(parent: SparkUI) extends SparkUITab(parent, "executors") {
   val listener = new ExecutorsListener(parent.storageStatusListener)
 
   attachPage(new ExecutorsPage(this))
diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/JobProgressPage.scala b/core/src/main/scala/org/apache/spark/ui/jobs/JobProgressPage.scala
index 0da62892118d4..a82f71ed08475 100644
--- a/core/src/main/scala/org/apache/spark/ui/jobs/JobProgressPage.scala
+++ b/core/src/main/scala/org/apache/spark/ui/jobs/JobProgressPage.scala
@@ -26,8 +26,6 @@ import org.apache.spark.ui.{WebUIPage, UIUtils}
 
 /** Page showing list of all ongoing and recently finished stages and pools */
 private[ui] class JobProgressPage(parent: JobProgressTab) extends WebUIPage("") {
-  private val appName = parent.appName
-  private val basePath = parent.basePath
   private val live = parent.live
   private val sc = parent.sc
   private val listener = parent.listener
@@ -94,7 +92,7 @@ private[ui] class JobProgressPage(parent: JobProgressTab) extends WebUIPage("")
         <h4 id ="failed">Failed Stages ({failedStages.size})</h4> ++
         failedStagesTable.toNodeSeq
 
-      UIUtils.headerSparkPage(content, basePath, appName, "Spark Stages", parent.headerTabs, parent)
+      UIUtils.headerSparkPage("Spark Stages", content, parent)
     }
   }
 }
diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/JobProgressTab.scala b/core/src/main/scala/org/apache/spark/ui/jobs/JobProgressTab.scala
index 8a01ec80c9dd6..c16542c9db30f 100644
--- a/core/src/main/scala/org/apache/spark/ui/jobs/JobProgressTab.scala
+++ b/core/src/main/scala/org/apache/spark/ui/jobs/JobProgressTab.scala
@@ -21,12 +21,10 @@ import javax.servlet.http.HttpServletRequest
 
 import org.apache.spark.SparkConf
 import org.apache.spark.scheduler.SchedulingMode
-import org.apache.spark.ui.{SparkUI, WebUITab}
+import org.apache.spark.ui.{SparkUI, SparkUITab}
 
 /** Web UI showing progress status of all jobs in the given SparkContext. */
-private[ui] class JobProgressTab(parent: SparkUI) extends WebUITab(parent, "stages") {
-  val appName = parent.appName
-  val basePath = parent.basePath
+private[ui] class JobProgressTab(parent: SparkUI) extends SparkUITab(parent, "stages") {
   val live = parent.live
   val sc = parent.sc
   val conf = if (live) sc.conf else new SparkConf
@@ -53,4 +51,5 @@ private[ui] class JobProgressTab(parent: SparkUI) extends WebUITab(parent, "stag
       Thread.sleep(100)
     }
   }
+
 }
diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/PoolPage.scala b/core/src/main/scala/org/apache/spark/ui/jobs/PoolPage.scala
index 0a2bf31833d2b..7a6c7d1a497ed 100644
--- a/core/src/main/scala/org/apache/spark/ui/jobs/PoolPage.scala
+++ b/core/src/main/scala/org/apache/spark/ui/jobs/PoolPage.scala
@@ -26,8 +26,6 @@ import org.apache.spark.ui.{WebUIPage, UIUtils}
 
 /** Page showing specific pool details */
 private[ui] class PoolPage(parent: JobProgressTab) extends WebUIPage("pool") {
-  private val appName = parent.appName
-  private val basePath = parent.basePath
   private val live = parent.live
   private val sc = parent.sc
   private val listener = parent.listener
@@ -51,8 +49,7 @@ private[ui] class PoolPage(parent: JobProgressTab) extends WebUIPage("pool") {
         <h4>Summary </h4> ++ poolTable.toNodeSeq ++
         <h4>{activeStages.size} Active Stages</h4> ++ activeStagesTable.toNodeSeq
 
-      UIUtils.headerSparkPage(content, basePath, appName, "Fair Scheduler Pool: " + poolName,
-        parent.headerTabs, parent)
+      UIUtils.headerSparkPage("Fair Scheduler Pool: " + poolName, content, parent)
     }
   }
 }
diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/PoolTable.scala b/core/src/main/scala/org/apache/spark/ui/jobs/PoolTable.scala
index f4b68f241966d..64178e1e33d41 100644
--- a/core/src/main/scala/org/apache/spark/ui/jobs/PoolTable.scala
+++ b/core/src/main/scala/org/apache/spark/ui/jobs/PoolTable.scala
@@ -25,7 +25,6 @@ import org.apache.spark.ui.UIUtils
 
 /** Table showing list of pools */
 private[ui] class PoolTable(pools: Seq[Schedulable], parent: JobProgressTab) {
-  private val basePath = parent.basePath
   private val listener = parent.listener
 
   def toNodeSeq: Seq[Node] = {
@@ -59,11 +58,11 @@ private[ui] class PoolTable(pools: Seq[Schedulable], parent: JobProgressTab) {
       case Some(stages) => stages.size
       case None => 0
     }
+    val href = "%s/stages/pool?poolname=%s"
+      .format(UIUtils.prependBaseUri(parent.basePath), p.name)
     <tr>
       <td>
-        <a href={"%s/stages/pool?poolname=%s".format(UIUtils.prependBaseUri(basePath), p.name)}>
-          {p.name}
-        </a>
+        <a href={href}>{p.name}</a>
       </td>
       <td>{p.minShare}</td>
       <td>{p.weight}</td>
diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala b/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala
index 8bc1ba758cf77..d4eb02722ad12 100644
--- a/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala
+++ b/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala
@@ -29,8 +29,6 @@ import org.apache.spark.scheduler.AccumulableInfo
 
 /** Page showing statistics and task list for a given stage */
 private[ui] class StagePage(parent: JobProgressTab) extends WebUIPage("stage") {
-  private val appName = parent.appName
-  private val basePath = parent.basePath
   private val listener = parent.listener
 
   def render(request: HttpServletRequest): Seq[Node] = {
@@ -44,8 +42,7 @@ private[ui] class StagePage(parent: JobProgressTab) extends WebUIPage("stage") {
             <h4>Summary Metrics</h4> No tasks have started yet
             <h4>Tasks</h4> No tasks have started yet
           </div>
-        return UIUtils.headerSparkPage(content, basePath, appName,
-          "Details for Stage %s".format(stageId), parent.headerTabs, parent)
+        return UIUtils.headerSparkPage("Details for Stage %s".format(stageId), content, parent)
       }
 
       val stageData = stageDataOption.get
@@ -227,8 +224,7 @@ private[ui] class StagePage(parent: JobProgressTab) extends WebUIPage("stage") {
         maybeAccumulableTable ++
         <h4>Tasks</h4> ++ taskTable
 
-      UIUtils.headerSparkPage(content, basePath, appName, "Details for Stage %d".format(stageId),
-        parent.headerTabs, parent)
+      UIUtils.headerSparkPage("Details for Stage %d".format(stageId), content, parent)
     }
   }
 
diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/StageTable.scala b/core/src/main/scala/org/apache/spark/ui/jobs/StageTable.scala
index 15998404ed612..16ad0df45aa0d 100644
--- a/core/src/main/scala/org/apache/spark/ui/jobs/StageTable.scala
+++ b/core/src/main/scala/org/apache/spark/ui/jobs/StageTable.scala
@@ -32,7 +32,6 @@ private[ui] class StageTableBase(
     parent: JobProgressTab,
     killEnabled: Boolean = false) {
 
-  private val basePath = parent.basePath
   private val listener = parent.listener
   protected def isFairScheduler = parent.isFairScheduler
 
@@ -88,17 +87,19 @@ private[ui] class StageTableBase(
   private def makeDescription(s: StageInfo): Seq[Node] = {
     // scalastyle:off
     val killLink = if (killEnabled) {
+      val killLinkUri = "%s/stages/stage/kill?id=%s&terminate=true"
+        .format(UIUtils.prependBaseUri(parent.basePath), s.stageId)
+      val confirm = "return window.confirm('Are you sure you want to kill stage %s ?');"
+        .format(s.stageId)
       <span class="kill-link">
-        (<a href={"%s/stages/stage/kill?id=%s&terminate=true".format(UIUtils.prependBaseUri(basePath), s.stageId)}
-            onclick={"return window.confirm('Are you sure you want to kill stage %s ?');".format(s.stageId)}>kill</a>)
+        (<a href={killLinkUri} onclick={confirm}>kill</a>)
       </span>
     }
     // scalastyle:on
 
-    val nameLink =
-      <a href={"%s/stages/stage?id=%s".format(UIUtils.prependBaseUri(basePath), s.stageId)}>
-        {s.name}
-      </a>
+    val nameLinkUri ="%s/stages/stage?id=%s"
+      .format(UIUtils.prependBaseUri(parent.basePath), s.stageId)
+    val nameLink = <a href={nameLinkUri}>{s.name}</a>
 
     val cachedRddInfos = s.rddInfos.filter(_.numCachedPartitions > 0)
     val details = if (s.details.nonEmpty) {
@@ -111,7 +112,7 @@ private[ui] class StageTableBase(
           Text("RDD: ") ++
           // scalastyle:off
           cachedRddInfos.map { i =>
-            <a href={"%s/storage/rdd?id=%d".format(UIUtils.prependBaseUri(basePath), i.id)}>{i.name}</a>
+            <a href={"%s/storage/rdd?id=%d".format(UIUtils.prependBaseUri(parent.basePath), i.id)}>{i.name}</a>
           }
           // scalastyle:on
         }}
@@ -157,7 +158,7 @@ private[ui] class StageTableBase(
     {if (isFairScheduler) {
       <td>
         <a href={"%s/stages/pool?poolname=%s"
-          .format(UIUtils.prependBaseUri(basePath), stageData.schedulingPool)}>
+          .format(UIUtils.prependBaseUri(parent.basePath), stageData.schedulingPool)}>
           {stageData.schedulingPool}
         </a>
       </td>
diff --git a/core/src/main/scala/org/apache/spark/ui/storage/RDDPage.scala b/core/src/main/scala/org/apache/spark/ui/storage/RDDPage.scala
index 84ac53da47552..8a0075ae8daf7 100644
--- a/core/src/main/scala/org/apache/spark/ui/storage/RDDPage.scala
+++ b/core/src/main/scala/org/apache/spark/ui/storage/RDDPage.scala
@@ -27,8 +27,6 @@ import org.apache.spark.util.Utils
 
 /** Page showing storage details for a given RDD */
 private[ui] class RDDPage(parent: StorageTab) extends WebUIPage("rdd") {
-  private val appName = parent.appName
-  private val basePath = parent.basePath
   private val listener = parent.listener
 
   def render(request: HttpServletRequest): Seq[Node] = {
@@ -36,8 +34,7 @@ private[ui] class RDDPage(parent: StorageTab) extends WebUIPage("rdd") {
     val storageStatusList = listener.storageStatusList
     val rddInfo = listener.rddInfoList.find(_.id == rddId).getOrElse {
       // Rather than crashing, render an "RDD Not Found" page
-      return UIUtils.headerSparkPage(Seq[Node](), basePath, appName, "RDD Not Found",
-        parent.headerTabs, parent)
+      return UIUtils.headerSparkPage("RDD Not Found", Seq[Node](), parent)
     }
 
     // Worker table
@@ -96,8 +93,7 @@ private[ui] class RDDPage(parent: StorageTab) extends WebUIPage("rdd") {
         </div>
       </div>;
 
-    UIUtils.headerSparkPage(content, basePath, appName, "RDD Storage Info for " + rddInfo.name,
-      parent.headerTabs, parent)
+    UIUtils.headerSparkPage("RDD Storage Info for " + rddInfo.name, content, parent)
   }
 
   /** Header fields for the worker table */
diff --git a/core/src/main/scala/org/apache/spark/ui/storage/StoragePage.scala b/core/src/main/scala/org/apache/spark/ui/storage/StoragePage.scala
index 9813d9330ac7f..716591c9ed449 100644
--- a/core/src/main/scala/org/apache/spark/ui/storage/StoragePage.scala
+++ b/core/src/main/scala/org/apache/spark/ui/storage/StoragePage.scala
@@ -27,14 +27,12 @@ import org.apache.spark.util.Utils
 
 /** Page showing list of RDD's currently stored in the cluster */
 private[ui] class StoragePage(parent: StorageTab) extends WebUIPage("") {
-  private val appName = parent.appName
-  private val basePath = parent.basePath
   private val listener = parent.listener
 
   def render(request: HttpServletRequest): Seq[Node] = {
     val rdds = listener.rddInfoList
     val content = UIUtils.listingTable(rddHeader, rddRow, rdds)
-    UIUtils.headerSparkPage(content, basePath, appName, "Storage ", parent.headerTabs, parent)
+    UIUtils.headerSparkPage("Storage", content, parent)
   }
 
   /** Header fields for the RDD table */
@@ -52,7 +50,7 @@ private[ui] class StoragePage(parent: StorageTab) extends WebUIPage("") {
     // scalastyle:off
     <tr>
       <td>
-        <a href={"%s/storage/rdd?id=%s".format(UIUtils.prependBaseUri(basePath), rdd.id)}>
+        <a href={"%s/storage/rdd?id=%s".format(UIUtils.prependBaseUri(parent.basePath), rdd.id)}>
           {rdd.name}
         </a>
       </td>
diff --git a/core/src/main/scala/org/apache/spark/ui/storage/StorageTab.scala b/core/src/main/scala/org/apache/spark/ui/storage/StorageTab.scala
index 5f6740d495521..67f72a94f0269 100644
--- a/core/src/main/scala/org/apache/spark/ui/storage/StorageTab.scala
+++ b/core/src/main/scala/org/apache/spark/ui/storage/StorageTab.scala
@@ -25,9 +25,7 @@ import org.apache.spark.scheduler._
 import org.apache.spark.storage._
 
 /** Web UI showing storage status of all RDD's in the given SparkContext. */
-private[ui] class StorageTab(parent: SparkUI) extends WebUITab(parent, "storage") {
-  val appName = parent.appName
-  val basePath = parent.basePath
+private[ui] class StorageTab(parent: SparkUI) extends SparkUITab(parent, "storage") {
   val listener = new StorageListener(parent.storageStatusListener)
 
   attachPage(new StoragePage(this))
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingPage.scala b/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingPage.scala
index 451b23e01c995..1353e487c72cf 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingPage.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingPage.scala
@@ -42,8 +42,7 @@ private[ui] class StreamingPage(parent: StreamingTab)
       <h4>Statistics over last {listener.retainedCompletedBatches.size} processed batches</h4> ++
       generateReceiverStats() ++
       generateBatchStatsTable()
-    UIUtils.headerSparkPage(
-      content, parent.basePath, parent.appName, "Streaming", parent.headerTabs, parent, Some(5000))
+    UIUtils.headerSparkPage("Streaming", content, parent, Some(5000))
   }
 
   /** Generate basic stats of the streaming program */
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingTab.scala b/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingTab.scala
index 51448d15c6516..34ac254f337eb 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingTab.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingTab.scala
@@ -19,15 +19,13 @@ package org.apache.spark.streaming.ui
 
 import org.apache.spark.Logging
 import org.apache.spark.streaming.StreamingContext
-import org.apache.spark.ui.WebUITab
+import org.apache.spark.ui.SparkUITab
 
 /** Spark Web UI tab that shows statistics of a streaming job */
 private[spark] class StreamingTab(ssc: StreamingContext)
-  extends WebUITab(ssc.sc.ui, "streaming") with Logging {
+  extends SparkUITab(ssc.sc.ui, "streaming") with Logging {
 
   val parent = ssc.sc.ui
-  val appName = parent.appName
-  val basePath = parent.basePath
   val listener = new StreamingJobProgressListener(ssc)
 
   ssc.addStreamingListener(listener)

From 3a5962f0f5acea5cbfd3cf1e3ed16e03b3bec37a Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@apache.org>
Date: Mon, 18 Aug 2014 13:38:03 -0700
Subject: [PATCH 011/489] Removed .travis.yml file since we are not using
 Travis.

---
 .travis.yml | 32 --------------------------------
 1 file changed, 32 deletions(-)
 delete mode 100644 .travis.yml

diff --git a/.travis.yml b/.travis.yml
deleted file mode 100644
index 8ebd0d68429fc..0000000000000
--- a/.travis.yml
+++ /dev/null
@@ -1,32 +0,0 @@
-# Licensed to the Apache Software Foundation (ASF) under one or more
-# contributor license agreements.  See the NOTICE file distributed with
-# this work for additional information regarding copyright ownership.
-# The ASF licenses this file to You under the Apache License, Version 2.0
-# (the "License"); you may not use this file except in compliance with
-# the License.  You may obtain a copy of the License at
-#
-#    http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
- 
- language: scala
- scala:
-   - "2.10.3"
- jdk:
-   - oraclejdk7
- env:
-  matrix:
-   - TEST="scalastyle assembly/assembly"
-   - TEST="catalyst/test sql/test streaming/test mllib/test graphx/test bagel/test"
-   - TEST=hive/test
- cache:
-   directories:
-     - $HOME/.m2
-     - $HOME/.ivy2
-     - $HOME/.sbt
- script:
-   - "sbt ++$TRAVIS_SCALA_VERSION $TEST"

From d1d0ee41c27f1d07fed0c5d56ba26c723cc3dc26 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies.liu@gmail.com>
Date: Mon, 18 Aug 2014 13:58:35 -0700
Subject: [PATCH 012/489] [SPARK-3103] [PySpark] fix saveAsTextFile() with
 utf-8

bugfix: It will raise an exception when it try to encode non-ASCII strings into unicode. It should only encode unicode as "utf-8".

Author: Davies Liu <davies.liu@gmail.com>

Closes #2018 from davies/fix_utf8 and squashes the following commits:

4db7967 [Davies Liu] fix saveAsTextFile() with utf-8
---
 python/pyspark/rdd.py   | 4 +++-
 python/pyspark/tests.py | 9 +++++++++
 2 files changed, 12 insertions(+), 1 deletion(-)

diff --git a/python/pyspark/rdd.py b/python/pyspark/rdd.py
index 240381e5bae12..c708b69cc1e31 100644
--- a/python/pyspark/rdd.py
+++ b/python/pyspark/rdd.py
@@ -1191,7 +1191,9 @@ def func(split, iterator):
             for x in iterator:
                 if not isinstance(x, basestring):
                     x = unicode(x)
-                yield x.encode("utf-8")
+                if isinstance(x, unicode):
+                    x = x.encode("utf-8")
+                yield x
         keyed = self.mapPartitionsWithIndex(func)
         keyed._bypass_serializer = True
         keyed._jrdd.map(self.ctx._jvm.BytesToString()).saveAsTextFile(path)
diff --git a/python/pyspark/tests.py b/python/pyspark/tests.py
index f1fece998cd54..69d543d9d045d 100644
--- a/python/pyspark/tests.py
+++ b/python/pyspark/tests.py
@@ -256,6 +256,15 @@ def test_save_as_textfile_with_unicode(self):
         raw_contents = ''.join(input(glob(tempFile.name + "/part-0000*")))
         self.assertEqual(x, unicode(raw_contents.strip(), "utf-8"))
 
+    def test_save_as_textfile_with_utf8(self):
+        x = u"\u00A1Hola, mundo!"
+        data = self.sc.parallelize([x.encode("utf-8")])
+        tempFile = tempfile.NamedTemporaryFile(delete=True)
+        tempFile.close()
+        data.saveAsTextFile(tempFile.name)
+        raw_contents = ''.join(input(glob(tempFile.name + "/part-0000*")))
+        self.assertEqual(x, unicode(raw_contents.strip(), "utf-8"))
+
     def test_transforming_cartesian_result(self):
         # Regression test for SPARK-1034
         rdd1 = self.sc.parallelize([1, 2])

From 6201b27643023569e19b68aa9d5c4e4e59ce0d79 Mon Sep 17 00:00:00 2001
From: Marcelo Vanzin <vanzin@cloudera.com>
Date: Mon, 18 Aug 2014 14:10:10 -0700
Subject: [PATCH 013/489] [SPARK-2718] [yarn] Handle quotes and other
 characters in user args.

Due to the way Yarn runs things through bash, normal quoting doesn't
work as expected. This change applies the necessary voodoo to the user
args to avoid issues with bash and special characters.

The change also uncovered an issue with the event logger app name
sanitizing code; it wasn't cleaning up all "bad" characters, so
sometimes it would fail to create the log dirs. I just added some
more bad character replacements.

Author: Marcelo Vanzin <vanzin@cloudera.com>

Closes #1724 from vanzin/SPARK-2718 and squashes the following commits:

cc84b89 [Marcelo Vanzin] Review feedback.
c1a257a [Marcelo Vanzin] Add test for backslashes.
55571d4 [Marcelo Vanzin] Unbreak yarn-client.
515613d [Marcelo Vanzin] [SPARK-2718] [yarn] Handle quotes and other characters in user args.
---
 .../scheduler/EventLoggingListener.scala      |  3 +-
 .../yarn/ApplicationMasterArguments.scala     |  6 +-
 .../apache/spark/deploy/yarn/ClientBase.scala |  9 +--
 .../deploy/yarn/ExecutorRunnableUtil.scala    |  4 +-
 .../deploy/yarn/YarnSparkHadoopUtil.scala     | 25 ++++++++
 .../yarn/YarnSparkHadoopUtilSuite.scala       | 64 +++++++++++++++++++
 6 files changed, 101 insertions(+), 10 deletions(-)
 create mode 100644 yarn/common/src/test/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtilSuite.scala

diff --git a/core/src/main/scala/org/apache/spark/scheduler/EventLoggingListener.scala b/core/src/main/scala/org/apache/spark/scheduler/EventLoggingListener.scala
index 7378ce923f0ae..370fcd85aa680 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/EventLoggingListener.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/EventLoggingListener.scala
@@ -54,7 +54,8 @@ private[spark] class EventLoggingListener(
   private val testing = sparkConf.getBoolean("spark.eventLog.testing", false)
   private val outputBufferSize = sparkConf.getInt("spark.eventLog.buffer.kb", 100) * 1024
   private val logBaseDir = sparkConf.get("spark.eventLog.dir", DEFAULT_LOG_DIR).stripSuffix("/")
-  private val name = appName.replaceAll("[ :/]", "-").toLowerCase + "-" + System.currentTimeMillis
+  private val name = appName.replaceAll("[ :/]", "-").replaceAll("[${}'\"]", "_")
+    .toLowerCase + "-" + System.currentTimeMillis
   val logDir = Utils.resolveURI(logBaseDir) + "/" + name.stripSuffix("/")
 
   protected val logger = new FileLogger(logDir, sparkConf, hadoopConf, outputBufferSize,
diff --git a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMasterArguments.scala b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMasterArguments.scala
index 4c383ab574abe..424b0fb0936f2 100644
--- a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMasterArguments.scala
+++ b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMasterArguments.scala
@@ -29,7 +29,7 @@ class ApplicationMasterArguments(val args: Array[String]) {
   var numExecutors = ApplicationMasterArguments.DEFAULT_NUMBER_EXECUTORS
 
   parseArgs(args.toList)
-  
+
   private def parseArgs(inputArgs: List[String]): Unit = {
     val userArgsBuffer = new ArrayBuffer[String]()
 
@@ -47,7 +47,7 @@ class ApplicationMasterArguments(val args: Array[String]) {
           userClass = value
           args = tail
 
-        case ("--args") :: value :: tail =>
+        case ("--args" | "--arg") :: value :: tail =>
           userArgsBuffer += value
           args = tail
 
@@ -75,7 +75,7 @@ class ApplicationMasterArguments(val args: Array[String]) {
 
     userArgs = userArgsBuffer.readOnly
   }
-  
+
   def printUsageAndExit(exitCode: Int, unknownParam: Any = null) {
     if (unknownParam != null) {
       System.err.println("Unknown/unsupported param " + unknownParam)
diff --git a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala
index 1da0a1b675554..3897b3a373a8c 100644
--- a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala
+++ b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala
@@ -300,11 +300,11 @@ trait ClientBase extends Logging {
   }
 
   def userArgsToString(clientArgs: ClientArguments): String = {
-    val prefix = " --args "
+    val prefix = " --arg "
     val args = clientArgs.userArgs
     val retval = new StringBuilder()
     for (arg <- args) {
-      retval.append(prefix).append(" '").append(arg).append("' ")
+      retval.append(prefix).append(" ").append(YarnSparkHadoopUtil.escapeForShell(arg))
     }
     retval.toString
   }
@@ -386,7 +386,7 @@ trait ClientBase extends Logging {
     // TODO: it might be nicer to pass these as an internal environment variable rather than
     // as Java options, due to complications with string parsing of nested quotes.
     for ((k, v) <- sparkConf.getAll) {
-      javaOpts += "-D" + k + "=" + "\\\"" + v + "\\\""
+      javaOpts += YarnSparkHadoopUtil.escapeForShell(s"-D$k=$v")
     }
 
     if (args.amClass == classOf[ApplicationMaster].getName) {
@@ -400,7 +400,8 @@ trait ClientBase extends Logging {
     // Command for the ApplicationMaster
     val commands = Seq(Environment.JAVA_HOME.$() + "/bin/java", "-server") ++
       javaOpts ++
-      Seq(args.amClass, "--class", args.userClass, "--jar ", args.userJar,
+      Seq(args.amClass, "--class", YarnSparkHadoopUtil.escapeForShell(args.userClass),
+        "--jar ", YarnSparkHadoopUtil.escapeForShell(args.userJar),
         userArgsToString(args),
         "--executor-memory", args.executorMemory.toString,
         "--executor-cores", args.executorCores.toString,
diff --git a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnableUtil.scala b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnableUtil.scala
index 71a9e42846b2b..312d82a649792 100644
--- a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnableUtil.scala
+++ b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnableUtil.scala
@@ -68,10 +68,10 @@ trait ExecutorRunnableUtil extends Logging {
     // authentication settings.
     sparkConf.getAll.
       filter { case (k, v) => k.startsWith("spark.auth") || k.startsWith("spark.akka") }.
-      foreach { case (k, v) => javaOpts += "-D" + k + "=" + "\\\"" + v + "\\\"" }
+      foreach { case (k, v) => javaOpts += YarnSparkHadoopUtil.escapeForShell(s"-D$k=$v") }
 
     sparkConf.getAkkaConf.
-      foreach { case (k, v) => javaOpts += "-D" + k + "=" + "\\\"" + v + "\\\"" }
+      foreach { case (k, v) => javaOpts += YarnSparkHadoopUtil.escapeForShell(s"-D$k=$v") }
 
     // Commenting it out for now - so that people can refer to the properties if required. Remove
     // it once cpuset version is pushed out.
diff --git a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtil.scala b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtil.scala
index e98308cdbd74e..10aef5eb2486f 100644
--- a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtil.scala
+++ b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtil.scala
@@ -148,4 +148,29 @@ object YarnSparkHadoopUtil {
     }
   }
 
+  /**
+   * Escapes a string for inclusion in a command line executed by Yarn. Yarn executes commands
+   * using `bash -c "command arg1 arg2"` and that means plain quoting doesn't really work. The
+   * argument is enclosed in single quotes and some key characters are escaped.
+   *
+   * @param arg A single argument.
+   * @return Argument quoted for execution via Yarn's generated shell script.
+   */
+  def escapeForShell(arg: String): String = {
+    if (arg != null) {
+      val escaped = new StringBuilder("'")
+      for (i <- 0 to arg.length() - 1) {
+        arg.charAt(i) match {
+          case '$' => escaped.append("\\$")
+          case '"' => escaped.append("\\\"")
+          case '\'' => escaped.append("'\\''")
+          case c => escaped.append(c)
+        }
+      }
+      escaped.append("'").toString()
+    } else {
+      arg
+    }
+  }
+
 }
diff --git a/yarn/common/src/test/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtilSuite.scala b/yarn/common/src/test/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtilSuite.scala
new file mode 100644
index 0000000000000..7650bd4396c12
--- /dev/null
+++ b/yarn/common/src/test/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtilSuite.scala
@@ -0,0 +1,64 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.deploy.yarn
+
+import java.io.{File, IOException}
+
+import com.google.common.io.{ByteStreams, Files}
+import org.scalatest.{FunSuite, Matchers}
+
+import org.apache.spark.Logging
+
+class YarnSparkHadoopUtilSuite extends FunSuite with Matchers with Logging {
+
+  val hasBash =
+    try {
+      val exitCode = Runtime.getRuntime().exec(Array("bash", "--version")).waitFor()
+      exitCode == 0
+    } catch {
+      case e: IOException =>
+        false
+    }
+
+  if (!hasBash) {
+    logWarning("Cannot execute bash, skipping bash tests.")
+  }
+
+  def bashTest(name: String)(fn: => Unit) =
+    if (hasBash) test(name)(fn) else ignore(name)(fn)
+
+  bashTest("shell script escaping") {
+    val scriptFile = File.createTempFile("script.", ".sh")
+    val args = Array("arg1", "${arg.2}", "\"arg3\"", "'arg4'", "$arg5", "\\arg6")
+    try {
+      val argLine = args.map(a => YarnSparkHadoopUtil.escapeForShell(a)).mkString(" ")
+      Files.write(("bash -c \"echo " + argLine + "\"").getBytes(), scriptFile)
+      scriptFile.setExecutable(true)
+
+      val proc = Runtime.getRuntime().exec(Array(scriptFile.getAbsolutePath()))
+      val out = new String(ByteStreams.toByteArray(proc.getInputStream())).trim()
+      val err = new String(ByteStreams.toByteArray(proc.getErrorStream()))
+      val exitCode = proc.waitFor()
+      exitCode should be (0)
+      out should be (args.mkString(" "))
+    } finally {
+      scriptFile.delete()
+    }
+  }
+
+}

From 115eeb30dd9c9dd10685a71f2c23ca23794d3142 Mon Sep 17 00:00:00 2001
From: "Joseph K. Bradley" <joseph.kurata.bradley@gmail.com>
Date: Mon, 18 Aug 2014 14:40:05 -0700
Subject: [PATCH 014/489] [mllib] DecisionTree: treeAggregate + Python example
 bug fix

Small DecisionTree updates:
* Changed main DecisionTree aggregate to treeAggregate.
* Fixed bug in python example decision_tree_runner.py with missing argument (since categoricalFeaturesInfo is no longer an optional argument for trainClassifier).
* Fixed same bug in python doc tests, and added tree.py to doc tests.

CC: mengxr

Author: Joseph K. Bradley <joseph.kurata.bradley@gmail.com>

Closes #2015 from jkbradley/dt-opt2 and squashes the following commits:

b5114fa [Joseph K. Bradley] Fixed python tree.py doc test (extra newline)
8e4665d [Joseph K. Bradley] Added tree.py to python doc tests.  Fixed bug from missing categoricalFeaturesInfo argument.
b7b2922 [Joseph K. Bradley] Fixed bug in python example decision_tree_runner.py with missing argument.  Changed main DecisionTree aggregate to treeAggregate.
85bbc1f [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-opt2
66d076f [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-opt2
a0ed0da [Joseph K. Bradley] Renamed DTMetadata to DecisionTreeMetadata.  Small doc updates.
3726d20 [Joseph K. Bradley] Small code improvements based on code review.
ac0b9f8 [Joseph K. Bradley] Small updates based on code review. Main change: Now using << instead of math.pow.
db0d773 [Joseph K. Bradley] scala style fix
6a38f48 [Joseph K. Bradley] Added DTMetadata class for cleaner code
931a3a7 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-opt2
797f68a [Joseph K. Bradley] Fixed DecisionTreeSuite bug for training second level.  Needed to update treePointToNodeIndex with groupShift.
f40381c [Joseph K. Bradley] Merge branch 'dt-opt1' into dt-opt2
5f2dec2 [Joseph K. Bradley] Fixed scalastyle issue in TreePoint
6b5651e [Joseph K. Bradley] Updates based on code review.  1 major change: persisting to memory + disk, not just memory.
2d2aaaf [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-opt1
26d10dd [Joseph K. Bradley] Removed tree/model/Filter.scala since no longer used.  Removed debugging println calls in DecisionTree.scala.
356daba [Joseph K. Bradley] Merge branch 'dt-opt1' into dt-opt2
430d782 [Joseph K. Bradley] Added more debug info on binning error.  Added some docs.
d036089 [Joseph K. Bradley] Print timing info to logDebug.
e66f1b1 [Joseph K. Bradley] TreePoint * Updated doc * Made some methods private
8464a6e [Joseph K. Bradley] Moved TimeTracker to tree/impl/ in its own file, and cleaned it up.  Removed debugging println calls from DecisionTree.  Made TreePoint extend Serialiable
a87e08f [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-opt1
c1565a5 [Joseph K. Bradley] Small DecisionTree updates: * Simplification: Updated calculateGainForSplit to take aggregates for a single (feature, split) pair. * Internal doc: findAggForOrderedFeatureClassification
b914f3b [Joseph K. Bradley] DecisionTree optimization: eliminated filters + small changes
b2ed1f3 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-opt
0f676e2 [Joseph K. Bradley] Optimizations + Bug fix for DecisionTree
3211f02 [Joseph K. Bradley] Optimizing DecisionTree * Added TreePoint representation to avoid calling findBin multiple times. * (not working yet, but debugging)
f61e9d2 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-timing
bcf874a [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-timing
511ec85 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-timing
a95bc22 [Joseph K. Bradley] timing for DecisionTree internals
---
 .../src/main/python/mllib/decision_tree_runner.py  |  4 +++-
 .../org/apache/spark/mllib/tree/DecisionTree.scala |  3 ++-
 python/pyspark/mllib/tree.py                       | 14 ++++++++------
 python/run-tests                                   |  1 +
 4 files changed, 14 insertions(+), 8 deletions(-)

diff --git a/examples/src/main/python/mllib/decision_tree_runner.py b/examples/src/main/python/mllib/decision_tree_runner.py
index 8efadb5223f56..db96a7cb3730f 100755
--- a/examples/src/main/python/mllib/decision_tree_runner.py
+++ b/examples/src/main/python/mllib/decision_tree_runner.py
@@ -124,7 +124,9 @@ def usage():
     (reindexedData, origToNewLabels) = reindexClassLabels(points)
 
     # Train a classifier.
-    model = DecisionTree.trainClassifier(reindexedData, numClasses=2)
+    categoricalFeaturesInfo={} # no categorical features
+    model = DecisionTree.trainClassifier(reindexedData, numClasses=2,
+                                         categoricalFeaturesInfo=categoricalFeaturesInfo)
     # Print learned tree and stats.
     print "Trained DecisionTree for classification:"
     print "  Model numNodes: %d\n" % model.numNodes()
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala
index 6b9a8f72c244e..5cdd258f6c20b 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala
@@ -22,6 +22,7 @@ import scala.collection.JavaConverters._
 import org.apache.spark.annotation.Experimental
 import org.apache.spark.api.java.JavaRDD
 import org.apache.spark.Logging
+import org.apache.spark.mllib.rdd.RDDFunctions._
 import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.tree.configuration.Strategy
 import org.apache.spark.mllib.tree.configuration.Algo._
@@ -826,7 +827,7 @@ object DecisionTree extends Serializable with Logging {
     // Calculate bin aggregates.
     timer.start("aggregation")
     val binAggregates = {
-      input.aggregate(Array.fill[Double](binAggregateLength)(0))(binSeqOp, binCombOp)
+      input.treeAggregate(Array.fill[Double](binAggregateLength)(0))(binSeqOp, binCombOp)
     }
     timer.stop("aggregation")
     logDebug("binAggregates.length = " + binAggregates.length)
diff --git a/python/pyspark/mllib/tree.py b/python/pyspark/mllib/tree.py
index e1a4671709b7d..e9d778df5a24b 100644
--- a/python/pyspark/mllib/tree.py
+++ b/python/pyspark/mllib/tree.py
@@ -88,7 +88,8 @@ class DecisionTree(object):
                   It will probably be modified for Spark v1.2.
 
     Example usage:
-    >>> from numpy import array, ndarray
+    >>> from numpy import array
+    >>> import sys
     >>> from pyspark.mllib.regression import LabeledPoint
     >>> from pyspark.mllib.tree import DecisionTree
     >>> from pyspark.mllib.linalg import SparseVector
@@ -99,15 +100,15 @@ class DecisionTree(object):
     ...     LabeledPoint(1.0, [2.0]),
     ...     LabeledPoint(1.0, [3.0])
     ... ]
-    >>>
-    >>> model = DecisionTree.trainClassifier(sc.parallelize(data), numClasses=2)
-    >>> print(model)
+    >>> categoricalFeaturesInfo = {} # no categorical features
+    >>> model = DecisionTree.trainClassifier(sc.parallelize(data), numClasses=2,
+    ...                                      categoricalFeaturesInfo=categoricalFeaturesInfo)
+    >>> sys.stdout.write(model)
     DecisionTreeModel classifier
       If (feature 0 <= 0.5)
        Predict: 0.0
       Else (feature 0 > 0.5)
        Predict: 1.0
-
     >>> model.predict(array([1.0])) > 0
     True
     >>> model.predict(array([0.0])) == 0
@@ -119,7 +120,8 @@ class DecisionTree(object):
     ...     LabeledPoint(1.0, SparseVector(2, {1: 2.0}))
     ... ]
     >>>
-    >>> model = DecisionTree.trainRegressor(sc.parallelize(sparse_data))
+    >>> model = DecisionTree.trainRegressor(sc.parallelize(sparse_data),
+    ...                                     categoricalFeaturesInfo=categoricalFeaturesInfo)
     >>> model.predict(array([0.0, 1.0])) == 1
     True
     >>> model.predict(array([0.0, 0.0])) == 0
diff --git a/python/run-tests b/python/run-tests
index 1218edcbd7e08..a6271e0cf5fa9 100755
--- a/python/run-tests
+++ b/python/run-tests
@@ -79,6 +79,7 @@ run_test "pyspark/mllib/random.py"
 run_test "pyspark/mllib/recommendation.py"
 run_test "pyspark/mllib/regression.py"
 run_test "pyspark/mllib/tests.py"
+run_test "pyspark/mllib/tree.py"
 run_test "pyspark/mllib/util.py"
 
 if [[ $FAILED == 0 ]]; then

From c8b16ca0d86cc60fb960eebf0cb383f159a88b03 Mon Sep 17 00:00:00 2001
From: "Joseph K. Bradley" <joseph.kurata.bradley@gmail.com>
Date: Mon, 18 Aug 2014 18:01:39 -0700
Subject: [PATCH 015/489] [SPARK-2850] [SPARK-2626] [mllib] MLlib stats
 examples + small fixes

Added examples for statistical summarization:
* Scala: StatisticalSummary.scala
** Tests: correlation, MultivariateOnlineSummarizer
* python: statistical_summary.py
** Tests: correlation (since MultivariateOnlineSummarizer has no Python API)

Added examples for random and sampled RDDs:
* Scala: RandomAndSampledRDDs.scala
* python: random_and_sampled_rdds.py
* Both test:
** RandomRDDGenerators.normalRDD, normalVectorRDD
** RDD.sample, takeSample, sampleByKey

Added sc.stop() to all examples.

CorrelationSuite.scala
* Added 1 test for RDDs with only 1 value

RowMatrix.scala
* numCols(): Added check for numRows = 0, with error message.
* computeCovariance(): Added check for numRows <= 1, with error message.

Python SparseVector (pyspark/mllib/linalg.py)
* Added toDense() function

python/run-tests script
* Added stat.py (doc test)

CC: mengxr dorx  Main changes were examples to show usage across APIs.

Author: Joseph K. Bradley <joseph.kurata.bradley@gmail.com>

Closes #1878 from jkbradley/mllib-stats-api-check and squashes the following commits:

ea5c047 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into mllib-stats-api-check
dafebe2 [Joseph K. Bradley] Bug fixes for examples SampledRDDs.scala and sampled_rdds.py: Check for division by 0 and for missing key in maps.
8d1e555 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into mllib-stats-api-check
60c72d9 [Joseph K. Bradley] Fixed stat.py doc test to work for Python versions printing nan or NaN.
b20d90a [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into mllib-stats-api-check
4e5d15e [Joseph K. Bradley] Changed pyspark/mllib/stat.py doc tests to use NaN instead of nan.
32173b7 [Joseph K. Bradley] Stats examples update.
c8c20dc [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into mllib-stats-api-check
cf70b07 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into mllib-stats-api-check
0b7cec3 [Joseph K. Bradley] Small updates based on code review.  Renamed statistical_summary.py to correlations.py
ab48f6e [Joseph K. Bradley] RowMatrix.scala * numCols(): Added check for numRows = 0, with error message. * computeCovariance(): Added check for numRows <= 1, with error message.
65e4ebc [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into mllib-stats-api-check
8195c78 [Joseph K. Bradley] Added examples for random and sampled RDDs: * Scala: RandomAndSampledRDDs.scala * python: random_and_sampled_rdds.py * Both test: ** RandomRDDGenerators.normalRDD, normalVectorRDD ** RDD.sample, takeSample, sampleByKey
064985b [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into mllib-stats-api-check
ee918e9 [Joseph K. Bradley] Added examples for statistical summarization: * Scala: StatisticalSummary.scala ** Tests: correlation, MultivariateOnlineSummarizer * python: statistical_summary.py ** Tests: correlation (since MultivariateOnlineSummarizer has no Python API)
---
 examples/src/main/python/als.py               |   2 +
 .../src/main/python/cassandra_inputformat.py  |   2 +
 .../src/main/python/cassandra_outputformat.py |   2 +
 examples/src/main/python/hbase_inputformat.py |   2 +
 .../src/main/python/hbase_outputformat.py     |   2 +
 examples/src/main/python/kmeans.py            |   2 +
 .../src/main/python/logistic_regression.py    |   2 +
 .../src/main/python/mllib/correlations.py     |  60 +++++++++
 .../main/python/mllib/decision_tree_runner.py |   5 +
 examples/src/main/python/mllib/kmeans.py      |   1 +
 .../main/python/mllib/logistic_regression.py  |   1 +
 .../python/mllib/random_rdd_generation.py     |  55 ++++++++
 .../src/main/python/mllib/sampled_rdds.py     |  86 ++++++++++++
 examples/src/main/python/pagerank.py          |   2 +
 examples/src/main/python/pi.py                |   2 +
 examples/src/main/python/sort.py              |   2 +
 .../src/main/python/transitive_closure.py     |   2 +
 examples/src/main/python/wordcount.py         |   2 +
 .../spark/examples/mllib/Correlations.scala   |  92 +++++++++++++
 .../mllib/MultivariateSummarizer.scala        |  98 ++++++++++++++
 .../examples/mllib/RandomRDDGeneration.scala  |  60 +++++++++
 .../spark/examples/mllib/SampledRDDs.scala    | 126 ++++++++++++++++++
 .../mllib/linalg/distributed/RowMatrix.scala  |  14 +-
 .../stat/MultivariateOnlineSummarizer.scala   |   8 +-
 .../spark/mllib/stat/CorrelationSuite.scala   |  15 ++-
 .../MultivariateOnlineSummarizerSuite.scala   |   6 +-
 python/pyspark/mllib/linalg.py                |  10 ++
 python/pyspark/mllib/stat.py                  |  22 +--
 python/run-tests                              |   1 +
 29 files changed, 664 insertions(+), 20 deletions(-)
 create mode 100755 examples/src/main/python/mllib/correlations.py
 create mode 100755 examples/src/main/python/mllib/random_rdd_generation.py
 create mode 100755 examples/src/main/python/mllib/sampled_rdds.py
 create mode 100644 examples/src/main/scala/org/apache/spark/examples/mllib/Correlations.scala
 create mode 100644 examples/src/main/scala/org/apache/spark/examples/mllib/MultivariateSummarizer.scala
 create mode 100644 examples/src/main/scala/org/apache/spark/examples/mllib/RandomRDDGeneration.scala
 create mode 100644 examples/src/main/scala/org/apache/spark/examples/mllib/SampledRDDs.scala

diff --git a/examples/src/main/python/als.py b/examples/src/main/python/als.py
index c862650b0aa1d..5b1fa4d997eeb 100755
--- a/examples/src/main/python/als.py
+++ b/examples/src/main/python/als.py
@@ -97,3 +97,5 @@ def update(i, vec, mat, ratings):
         error = rmse(R, ms, us)
         print "Iteration %d:" % i
         print "\nRMSE: %5.4f\n" % error
+
+    sc.stop()
diff --git a/examples/src/main/python/cassandra_inputformat.py b/examples/src/main/python/cassandra_inputformat.py
index 39fa6b0d22ef5..e4a897f61e39d 100644
--- a/examples/src/main/python/cassandra_inputformat.py
+++ b/examples/src/main/python/cassandra_inputformat.py
@@ -77,3 +77,5 @@
     output = cass_rdd.collect()
     for (k, v) in output:
         print (k, v)
+
+    sc.stop()
diff --git a/examples/src/main/python/cassandra_outputformat.py b/examples/src/main/python/cassandra_outputformat.py
index 1dfbf98604425..836c35b5c6794 100644
--- a/examples/src/main/python/cassandra_outputformat.py
+++ b/examples/src/main/python/cassandra_outputformat.py
@@ -81,3 +81,5 @@
         conf=conf,
         keyConverter="org.apache.spark.examples.pythonconverters.ToCassandraCQLKeyConverter",
         valueConverter="org.apache.spark.examples.pythonconverters.ToCassandraCQLValueConverter")
+
+    sc.stop()
diff --git a/examples/src/main/python/hbase_inputformat.py b/examples/src/main/python/hbase_inputformat.py
index c9fa8e171c2a1..befacee0dea56 100644
--- a/examples/src/main/python/hbase_inputformat.py
+++ b/examples/src/main/python/hbase_inputformat.py
@@ -71,3 +71,5 @@
     output = hbase_rdd.collect()
     for (k, v) in output:
         print (k, v)
+
+    sc.stop()
diff --git a/examples/src/main/python/hbase_outputformat.py b/examples/src/main/python/hbase_outputformat.py
index 5e11548fd13f7..49bbc5aebdb0b 100644
--- a/examples/src/main/python/hbase_outputformat.py
+++ b/examples/src/main/python/hbase_outputformat.py
@@ -63,3 +63,5 @@
         conf=conf,
         keyConverter="org.apache.spark.examples.pythonconverters.StringToImmutableBytesWritableConverter",
         valueConverter="org.apache.spark.examples.pythonconverters.StringListToPutConverter")
+
+    sc.stop()
diff --git a/examples/src/main/python/kmeans.py b/examples/src/main/python/kmeans.py
index 036bdf4c4f999..86ef6f32c84e8 100755
--- a/examples/src/main/python/kmeans.py
+++ b/examples/src/main/python/kmeans.py
@@ -77,3 +77,5 @@ def closestPoint(p, centers):
             kPoints[x] = y
 
     print "Final centers: " + str(kPoints)
+
+    sc.stop()
diff --git a/examples/src/main/python/logistic_regression.py b/examples/src/main/python/logistic_regression.py
index 8456b272f9c05..3aa56b0528168 100755
--- a/examples/src/main/python/logistic_regression.py
+++ b/examples/src/main/python/logistic_regression.py
@@ -80,3 +80,5 @@ def add(x, y):
         w -= points.map(lambda m: gradient(m, w)).reduce(add)
 
     print "Final w: " + str(w)
+
+    sc.stop()
diff --git a/examples/src/main/python/mllib/correlations.py b/examples/src/main/python/mllib/correlations.py
new file mode 100755
index 0000000000000..6b16a56e44af7
--- /dev/null
+++ b/examples/src/main/python/mllib/correlations.py
@@ -0,0 +1,60 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""
+Correlations using MLlib.
+"""
+
+import sys
+
+from pyspark import SparkContext
+from pyspark.mllib.regression import LabeledPoint
+from pyspark.mllib.stat import Statistics
+from pyspark.mllib.util import MLUtils
+
+
+if __name__ == "__main__":
+    if len(sys.argv) not in [1,2]:
+        print >> sys.stderr, "Usage: correlations (<file>)"
+        exit(-1)
+    sc = SparkContext(appName="PythonCorrelations")
+    if len(sys.argv) == 2:
+        filepath = sys.argv[1]
+    else:
+        filepath = 'data/mllib/sample_linear_regression_data.txt'
+    corrType = 'pearson'
+
+    points = MLUtils.loadLibSVMFile(sc, filepath)\
+        .map(lambda lp: LabeledPoint(lp.label, lp.features.toArray()))
+
+    print
+    print 'Summary of data file: ' + filepath
+    print '%d data points' % points.count()
+
+    # Statistics (correlations)
+    print
+    print 'Correlation (%s) between label and each feature' % corrType
+    print 'Feature\tCorrelation'
+    numFeatures = points.take(1)[0].features.size
+    labelRDD = points.map(lambda lp: lp.label)
+    for i in range(numFeatures):
+        featureRDD = points.map(lambda lp: lp.features[i])
+        corr = Statistics.corr(labelRDD, featureRDD, corrType)
+        print '%d\t%g' % (i, corr)
+    print
+
+    sc.stop()
diff --git a/examples/src/main/python/mllib/decision_tree_runner.py b/examples/src/main/python/mllib/decision_tree_runner.py
index db96a7cb3730f..6e4a4a0cb6be0 100755
--- a/examples/src/main/python/mllib/decision_tree_runner.py
+++ b/examples/src/main/python/mllib/decision_tree_runner.py
@@ -17,6 +17,8 @@
 
 """
 Decision tree classification and regression using MLlib.
+
+This example requires NumPy (http://www.numpy.org/).
 """
 
 import numpy, os, sys
@@ -117,6 +119,7 @@ def usage():
     if len(sys.argv) == 2:
         dataPath = sys.argv[1]
     if not os.path.isfile(dataPath):
+        sc.stop()
         usage()
     points = MLUtils.loadLibSVMFile(sc, dataPath)
 
@@ -133,3 +136,5 @@ def usage():
     print "  Model depth: %d\n" % model.depth()
     print "  Training accuracy: %g\n" % getAccuracy(model, reindexedData)
     print model
+
+    sc.stop()
diff --git a/examples/src/main/python/mllib/kmeans.py b/examples/src/main/python/mllib/kmeans.py
index b308132c9aeeb..2eeb1abeeb12b 100755
--- a/examples/src/main/python/mllib/kmeans.py
+++ b/examples/src/main/python/mllib/kmeans.py
@@ -42,3 +42,4 @@ def parseVector(line):
     k = int(sys.argv[2])
     model = KMeans.train(data, k)
     print "Final centers: " + str(model.clusterCenters)
+    sc.stop()
diff --git a/examples/src/main/python/mllib/logistic_regression.py b/examples/src/main/python/mllib/logistic_regression.py
index 9d547ff77c984..8cae27fc4a52d 100755
--- a/examples/src/main/python/mllib/logistic_regression.py
+++ b/examples/src/main/python/mllib/logistic_regression.py
@@ -50,3 +50,4 @@ def parsePoint(line):
     model = LogisticRegressionWithSGD.train(points, iterations)
     print "Final weights: " + str(model.weights)
     print "Final intercept: " + str(model.intercept)
+    sc.stop()
diff --git a/examples/src/main/python/mllib/random_rdd_generation.py b/examples/src/main/python/mllib/random_rdd_generation.py
new file mode 100755
index 0000000000000..b388d8d83fb86
--- /dev/null
+++ b/examples/src/main/python/mllib/random_rdd_generation.py
@@ -0,0 +1,55 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""
+Randomly generated RDDs.
+"""
+
+import sys
+
+from pyspark import SparkContext
+from pyspark.mllib.random import RandomRDDs
+
+
+if __name__ == "__main__":
+    if len(sys.argv) not in [1, 2]:
+        print >> sys.stderr, "Usage: random_rdd_generation"
+        exit(-1)
+
+    sc = SparkContext(appName="PythonRandomRDDGeneration")
+
+    numExamples = 10000 # number of examples to generate
+    fraction = 0.1 # fraction of data to sample
+
+    # Example: RandomRDDs.normalRDD
+    normalRDD = RandomRDDs.normalRDD(sc, numExamples)
+    print 'Generated RDD of %d examples sampled from the standard normal distribution'\
+        % normalRDD.count()
+    print '  First 5 samples:'
+    for sample in normalRDD.take(5):
+        print '    ' + str(sample)
+    print
+
+    # Example: RandomRDDs.normalVectorRDD
+    normalVectorRDD = RandomRDDs.normalVectorRDD(sc, numRows = numExamples, numCols = 2)
+    print 'Generated RDD of %d examples of length-2 vectors.' % normalVectorRDD.count()
+    print '  First 5 samples:'
+    for sample in normalVectorRDD.take(5):
+        print '    ' + str(sample)
+    print
+
+    sc.stop()
diff --git a/examples/src/main/python/mllib/sampled_rdds.py b/examples/src/main/python/mllib/sampled_rdds.py
new file mode 100755
index 0000000000000..ec64a5978c672
--- /dev/null
+++ b/examples/src/main/python/mllib/sampled_rdds.py
@@ -0,0 +1,86 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""
+Randomly sampled RDDs.
+"""
+
+import sys
+
+from pyspark import SparkContext
+from pyspark.mllib.util import MLUtils
+
+
+if __name__ == "__main__":
+    if len(sys.argv) not in [1, 2]:
+        print >> sys.stderr, "Usage: sampled_rdds <libsvm data file>"
+        exit(-1)
+    if len(sys.argv) == 2:
+        datapath = sys.argv[1]
+    else:
+        datapath = 'data/mllib/sample_binary_classification_data.txt'
+
+    sc = SparkContext(appName="PythonSampledRDDs")
+
+    fraction = 0.1 # fraction of data to sample
+
+    examples = MLUtils.loadLibSVMFile(sc, datapath)
+    numExamples = examples.count()
+    if numExamples == 0:
+        print >> sys.stderr, "Error: Data file had no samples to load."
+        exit(1)
+    print 'Loaded data with %d examples from file: %s' % (numExamples, datapath)
+
+    # Example: RDD.sample() and RDD.takeSample()
+    expectedSampleSize = int(numExamples * fraction)
+    print 'Sampling RDD using fraction %g.  Expected sample size = %d.' \
+        % (fraction, expectedSampleSize)
+    sampledRDD = examples.sample(withReplacement = True, fraction = fraction)
+    print '  RDD.sample(): sample has %d examples' % sampledRDD.count()
+    sampledArray = examples.takeSample(withReplacement = True, num = expectedSampleSize)
+    print '  RDD.takeSample(): sample has %d examples' % len(sampledArray)
+
+    print
+
+    # Example: RDD.sampleByKey()
+    keyedRDD = examples.map(lambda lp: (int(lp.label), lp.features))
+    print '  Keyed data using label (Int) as key ==> Orig'
+    #  Count examples per label in original data.
+    keyCountsA = keyedRDD.countByKey()
+
+    #  Subsample, and count examples per label in sampled data.
+    fractions = {}
+    for k in keyCountsA.keys():
+        fractions[k] = fraction
+    sampledByKeyRDD = keyedRDD.sampleByKey(withReplacement = True, fractions = fractions)
+    keyCountsB = sampledByKeyRDD.countByKey()
+    sizeB = sum(keyCountsB.values())
+    print '  Sampled %d examples using approximate stratified sampling (by label). ==> Sample' \
+        % sizeB
+
+    #  Compare samples
+    print '   \tFractions of examples with key'
+    print 'Key\tOrig\tSample'
+    for k in sorted(keyCountsA.keys()):
+        fracA = keyCountsA[k] / float(numExamples)
+        if sizeB != 0:
+            fracB = keyCountsB.get(k, 0) / float(sizeB)
+        else:
+            fracB = 0
+        print '%d\t%g\t%g' % (k, fracA, fracB)
+
+    sc.stop()
diff --git a/examples/src/main/python/pagerank.py b/examples/src/main/python/pagerank.py
index 0b96343158d44..b539c4128cdcc 100755
--- a/examples/src/main/python/pagerank.py
+++ b/examples/src/main/python/pagerank.py
@@ -68,3 +68,5 @@ def parseNeighbors(urls):
     # Collects all URL ranks and dump them to console.
     for (link, rank) in ranks.collect():
         print "%s has rank: %s." % (link, rank)
+
+    sc.stop()
diff --git a/examples/src/main/python/pi.py b/examples/src/main/python/pi.py
index 21d94a2cd4b64..fc37459dc74aa 100755
--- a/examples/src/main/python/pi.py
+++ b/examples/src/main/python/pi.py
@@ -37,3 +37,5 @@ def f(_):
 
     count = sc.parallelize(xrange(1, n+1), slices).map(f).reduce(add)
     print "Pi is roughly %f" % (4.0 * count / n)
+
+    sc.stop()
diff --git a/examples/src/main/python/sort.py b/examples/src/main/python/sort.py
index 41d00c1b79133..bb686f17518a0 100755
--- a/examples/src/main/python/sort.py
+++ b/examples/src/main/python/sort.py
@@ -34,3 +34,5 @@
     output = sortedCount.collect()
     for (num, unitcount) in output:
         print num
+
+    sc.stop()
diff --git a/examples/src/main/python/transitive_closure.py b/examples/src/main/python/transitive_closure.py
index 8698369b13d84..bf331b542c438 100755
--- a/examples/src/main/python/transitive_closure.py
+++ b/examples/src/main/python/transitive_closure.py
@@ -64,3 +64,5 @@ def generateGraph():
             break
 
     print "TC has %i edges" % tc.count()
+
+    sc.stop()
diff --git a/examples/src/main/python/wordcount.py b/examples/src/main/python/wordcount.py
index dcc095fdd0ed9..ae6cd13b83d92 100755
--- a/examples/src/main/python/wordcount.py
+++ b/examples/src/main/python/wordcount.py
@@ -33,3 +33,5 @@
     output = counts.collect()
     for (word, count) in output:
         print "%s: %i" % (word, count)
+
+    sc.stop()
diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/Correlations.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/Correlations.scala
new file mode 100644
index 0000000000000..d6b2fe430e5a4
--- /dev/null
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/Correlations.scala
@@ -0,0 +1,92 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.examples.mllib
+
+import scopt.OptionParser
+
+import org.apache.spark.mllib.stat.Statistics
+import org.apache.spark.mllib.util.MLUtils
+import org.apache.spark.{SparkConf, SparkContext}
+
+
+/**
+ * An example app for summarizing multivariate data from a file. Run with
+ * {{{
+ * bin/run-example org.apache.spark.examples.mllib.Correlations
+ * }}}
+ * By default, this loads a synthetic dataset from `data/mllib/sample_linear_regression_data.txt`.
+ * If you use it as a template to create your own app, please use `spark-submit` to submit your app.
+ */
+object Correlations {
+
+  case class Params(input: String = "data/mllib/sample_linear_regression_data.txt")
+
+  def main(args: Array[String]) {
+
+    val defaultParams = Params()
+
+    val parser = new OptionParser[Params]("Correlations") {
+      head("Correlations: an example app for computing correlations")
+      opt[String]("input")
+        .text(s"Input path to labeled examples in LIBSVM format, default: ${defaultParams.input}")
+        .action((x, c) => c.copy(input = x))
+      note(
+        """
+        |For example, the following command runs this app on a synthetic dataset:
+        |
+        | bin/spark-submit --class org.apache.spark.examples.mllib.Correlations \
+        |  examples/target/scala-*/spark-examples-*.jar \
+        |  --input data/mllib/sample_linear_regression_data.txt
+        """.stripMargin)
+    }
+
+    parser.parse(args, defaultParams).map { params =>
+      run(params)
+    } getOrElse {
+        sys.exit(1)
+    }
+  }
+
+  def run(params: Params) {
+    val conf = new SparkConf().setAppName(s"Correlations with $params")
+    val sc = new SparkContext(conf)
+
+    val examples = MLUtils.loadLibSVMFile(sc, params.input).cache()
+
+    println(s"Summary of data file: ${params.input}")
+    println(s"${examples.count()} data points")
+
+    // Calculate label -- feature correlations
+    val labelRDD = examples.map(_.label)
+    val numFeatures = examples.take(1)(0).features.size
+    val corrType = "pearson"
+    println()
+    println(s"Correlation ($corrType) between label and each feature")
+    println(s"Feature\tCorrelation")
+    var feature = 0
+    while (feature < numFeatures) {
+      val featureRDD = examples.map(_.features(feature))
+      val corr = Statistics.corr(labelRDD, featureRDD)
+      println(s"$feature\t$corr")
+      feature += 1
+    }
+    println()
+
+    sc.stop()
+  }
+}
diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/MultivariateSummarizer.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/MultivariateSummarizer.scala
new file mode 100644
index 0000000000000..4532512c01f84
--- /dev/null
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/MultivariateSummarizer.scala
@@ -0,0 +1,98 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.examples.mllib
+
+import scopt.OptionParser
+
+import org.apache.spark.mllib.linalg.Vectors
+import org.apache.spark.mllib.stat.MultivariateOnlineSummarizer
+import org.apache.spark.mllib.util.MLUtils
+import org.apache.spark.{SparkConf, SparkContext}
+
+
+/**
+ * An example app for summarizing multivariate data from a file. Run with
+ * {{{
+ * bin/run-example org.apache.spark.examples.mllib.MultivariateSummarizer
+ * }}}
+ * By default, this loads a synthetic dataset from `data/mllib/sample_linear_regression_data.txt`.
+ * If you use it as a template to create your own app, please use `spark-submit` to submit your app.
+ */
+object MultivariateSummarizer {
+
+  case class Params(input: String = "data/mllib/sample_linear_regression_data.txt")
+
+  def main(args: Array[String]) {
+
+    val defaultParams = Params()
+
+    val parser = new OptionParser[Params]("MultivariateSummarizer") {
+      head("MultivariateSummarizer: an example app for MultivariateOnlineSummarizer")
+      opt[String]("input")
+        .text(s"Input path to labeled examples in LIBSVM format, default: ${defaultParams.input}")
+        .action((x, c) => c.copy(input = x))
+      note(
+        """
+        |For example, the following command runs this app on a synthetic dataset:
+        |
+        | bin/spark-submit --class org.apache.spark.examples.mllib.MultivariateSummarizer \
+        |  examples/target/scala-*/spark-examples-*.jar \
+        |  --input data/mllib/sample_linear_regression_data.txt
+        """.stripMargin)
+    }
+
+    parser.parse(args, defaultParams).map { params =>
+      run(params)
+    } getOrElse {
+        sys.exit(1)
+    }
+  }
+
+  def run(params: Params) {
+    val conf = new SparkConf().setAppName(s"MultivariateSummarizer with $params")
+    val sc = new SparkContext(conf)
+
+    val examples = MLUtils.loadLibSVMFile(sc, params.input).cache()
+
+    println(s"Summary of data file: ${params.input}")
+    println(s"${examples.count()} data points")
+
+    // Summarize labels
+    val labelSummary = examples.aggregate(new MultivariateOnlineSummarizer())(
+      (summary, lp) => summary.add(Vectors.dense(lp.label)),
+      (sum1, sum2) => sum1.merge(sum2))
+
+    // Summarize features
+    val featureSummary = examples.aggregate(new MultivariateOnlineSummarizer())(
+      (summary, lp) => summary.add(lp.features),
+      (sum1, sum2) => sum1.merge(sum2))
+
+    println()
+    println(s"Summary statistics")
+    println(s"\tLabel\tFeatures")
+    println(s"mean\t${labelSummary.mean(0)}\t${featureSummary.mean.toArray.mkString("\t")}")
+    println(s"var\t${labelSummary.variance(0)}\t${featureSummary.variance.toArray.mkString("\t")}")
+    println(
+      s"nnz\t${labelSummary.numNonzeros(0)}\t${featureSummary.numNonzeros.toArray.mkString("\t")}")
+    println(s"max\t${labelSummary.max(0)}\t${featureSummary.max.toArray.mkString("\t")}")
+    println(s"min\t${labelSummary.min(0)}\t${featureSummary.min.toArray.mkString("\t")}")
+    println()
+
+    sc.stop()
+  }
+}
diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/RandomRDDGeneration.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/RandomRDDGeneration.scala
new file mode 100644
index 0000000000000..924b586e3af99
--- /dev/null
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/RandomRDDGeneration.scala
@@ -0,0 +1,60 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.examples.mllib
+
+import org.apache.spark.mllib.random.RandomRDDs
+import org.apache.spark.rdd.RDD
+
+import org.apache.spark.{SparkConf, SparkContext}
+
+/**
+ * An example app for randomly generated RDDs. Run with
+ * {{{
+ * bin/run-example org.apache.spark.examples.mllib.RandomRDDGeneration
+ * }}}
+ * If you use it as a template to create your own app, please use `spark-submit` to submit your app.
+ */
+object RandomRDDGeneration {
+
+  def main(args: Array[String]) {
+
+    val conf = new SparkConf().setAppName(s"RandomRDDGeneration")
+    val sc = new SparkContext(conf)
+
+    val numExamples = 10000 // number of examples to generate
+    val fraction = 0.1 // fraction of data to sample
+
+    // Example: RandomRDDs.normalRDD
+    val normalRDD: RDD[Double] = RandomRDDs.normalRDD(sc, numExamples)
+    println(s"Generated RDD of ${normalRDD.count()}" +
+      " examples sampled from the standard normal distribution")
+    println("  First 5 samples:")
+    normalRDD.take(5).foreach( x => println(s"    $x") )
+
+    // Example: RandomRDDs.normalVectorRDD
+    val normalVectorRDD = RandomRDDs.normalVectorRDD(sc, numRows = numExamples, numCols = 2)
+    println(s"Generated RDD of ${normalVectorRDD.count()} examples of length-2 vectors.")
+    println("  First 5 samples:")
+    normalVectorRDD.take(5).foreach( x => println(s"    $x") )
+
+    println()
+
+    sc.stop()
+  }
+
+}
diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/SampledRDDs.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/SampledRDDs.scala
new file mode 100644
index 0000000000000..f01b8266e3fe3
--- /dev/null
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/SampledRDDs.scala
@@ -0,0 +1,126 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.examples.mllib
+
+import org.apache.spark.mllib.util.MLUtils
+import scopt.OptionParser
+
+import org.apache.spark.{SparkConf, SparkContext}
+import org.apache.spark.SparkContext._
+
+/**
+ * An example app for randomly generated and sampled RDDs. Run with
+ * {{{
+ * bin/run-example org.apache.spark.examples.mllib.SampledRDDs
+ * }}}
+ * If you use it as a template to create your own app, please use `spark-submit` to submit your app.
+ */
+object SampledRDDs {
+
+  case class Params(input: String = "data/mllib/sample_binary_classification_data.txt")
+
+  def main(args: Array[String]) {
+    val defaultParams = Params()
+
+    val parser = new OptionParser[Params]("SampledRDDs") {
+      head("SampledRDDs: an example app for randomly generated and sampled RDDs.")
+      opt[String]("input")
+        .text(s"Input path to labeled examples in LIBSVM format, default: ${defaultParams.input}")
+        .action((x, c) => c.copy(input = x))
+      note(
+        """
+        |For example, the following command runs this app:
+        |
+        | bin/spark-submit --class org.apache.spark.examples.mllib.SampledRDDs \
+        |  examples/target/scala-*/spark-examples-*.jar
+        """.stripMargin)
+    }
+
+    parser.parse(args, defaultParams).map { params =>
+      run(params)
+    } getOrElse {
+      sys.exit(1)
+    }
+  }
+
+  def run(params: Params) {
+    val conf = new SparkConf().setAppName(s"SampledRDDs with $params")
+    val sc = new SparkContext(conf)
+
+    val fraction = 0.1 // fraction of data to sample
+
+    val examples = MLUtils.loadLibSVMFile(sc, params.input)
+    val numExamples = examples.count()
+    if (numExamples == 0) {
+      throw new RuntimeException("Error: Data file had no samples to load.")
+    }
+    println(s"Loaded data with $numExamples examples from file: ${params.input}")
+
+    // Example: RDD.sample() and RDD.takeSample()
+    val expectedSampleSize = (numExamples * fraction).toInt
+    println(s"Sampling RDD using fraction $fraction.  Expected sample size = $expectedSampleSize.")
+    val sampledRDD = examples.sample(withReplacement = true, fraction = fraction)
+    println(s"  RDD.sample(): sample has ${sampledRDD.count()} examples")
+    val sampledArray = examples.takeSample(withReplacement = true, num = expectedSampleSize)
+    println(s"  RDD.takeSample(): sample has ${sampledArray.size} examples")
+
+    println()
+
+    // Example: RDD.sampleByKey() and RDD.sampleByKeyExact()
+    val keyedRDD = examples.map { lp => (lp.label.toInt, lp.features) }
+    println(s"  Keyed data using label (Int) as key ==> Orig")
+    //  Count examples per label in original data.
+    val keyCounts = keyedRDD.countByKey()
+
+    //  Subsample, and count examples per label in sampled data. (approximate)
+    val fractions = keyCounts.keys.map((_, fraction)).toMap
+    val sampledByKeyRDD = keyedRDD.sampleByKey(withReplacement = true, fractions = fractions)
+    val keyCountsB = sampledByKeyRDD.countByKey()
+    val sizeB = keyCountsB.values.sum
+    println(s"  Sampled $sizeB examples using approximate stratified sampling (by label)." +
+      " ==> Approx Sample")
+
+    //  Subsample, and count examples per label in sampled data. (approximate)
+    val sampledByKeyRDDExact =
+      keyedRDD.sampleByKeyExact(withReplacement = true, fractions = fractions)
+    val keyCountsBExact = sampledByKeyRDDExact.countByKey()
+    val sizeBExact = keyCountsBExact.values.sum
+    println(s"  Sampled $sizeBExact examples using exact stratified sampling (by label)." +
+      " ==> Exact Sample")
+
+    //  Compare samples
+    println(s"   \tFractions of examples with key")
+    println(s"Key\tOrig\tApprox Sample\tExact Sample")
+    keyCounts.keys.toSeq.sorted.foreach { key =>
+      val origFrac = keyCounts(key) / numExamples.toDouble
+      val approxFrac = if (sizeB != 0) {
+        keyCountsB.getOrElse(key, 0L) / sizeB.toDouble
+      } else {
+        0
+      }
+      val exactFrac = if (sizeBExact != 0) {
+        keyCountsBExact.getOrElse(key, 0L) / sizeBExact.toDouble
+      } else {
+        0
+      }
+      println(s"$key\t$origFrac\t$approxFrac\t$exactFrac")
+    }
+
+    sc.stop()
+  }
+}
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
index e76bc9fefff01..2e414a73be8e0 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
@@ -53,8 +53,14 @@ class RowMatrix(
   /** Gets or computes the number of columns. */
   override def numCols(): Long = {
     if (nCols <= 0) {
-      // Calling `first` will throw an exception if `rows` is empty.
-      nCols = rows.first().size
+      try {
+        // Calling `first` will throw an exception if `rows` is empty.
+        nCols = rows.first().size
+      } catch {
+        case err: UnsupportedOperationException =>
+          sys.error("Cannot determine the number of cols because it is not specified in the " +
+            "constructor and the rows RDD is empty.")
+      }
     }
     nCols
   }
@@ -293,6 +299,10 @@ class RowMatrix(
         (s1._1 + s2._1, s1._2 += s2._2)
     )
 
+    if (m <= 1) {
+      sys.error(s"RowMatrix.computeCovariance called on matrix with only $m rows." +
+        "  Cannot compute the covariance of a RowMatrix with <= 1 row.")
+    }
     updateNumRows(m)
 
     mean :/= m.toDouble
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/stat/MultivariateOnlineSummarizer.scala b/mllib/src/main/scala/org/apache/spark/mllib/stat/MultivariateOnlineSummarizer.scala
index 5105b5c37aaaa..7d845c44365dd 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/stat/MultivariateOnlineSummarizer.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/stat/MultivariateOnlineSummarizer.scala
@@ -55,8 +55,8 @@ class MultivariateOnlineSummarizer extends MultivariateStatisticalSummary with S
    */
   def add(sample: Vector): this.type = {
     if (n == 0) {
-      require(sample.toBreeze.length > 0, s"Vector should have dimension larger than zero.")
-      n = sample.toBreeze.length
+      require(sample.size > 0, s"Vector should have dimension larger than zero.")
+      n = sample.size
 
       currMean = BDV.zeros[Double](n)
       currM2n = BDV.zeros[Double](n)
@@ -65,8 +65,8 @@ class MultivariateOnlineSummarizer extends MultivariateStatisticalSummary with S
       currMin = BDV.fill(n)(Double.MaxValue)
     }
 
-    require(n == sample.toBreeze.length, s"Dimensions mismatch when adding new sample." +
-      s" Expecting $n but got ${sample.toBreeze.length}.")
+    require(n == sample.size, s"Dimensions mismatch when adding new sample." +
+      s" Expecting $n but got ${sample.size}.")
 
     sample.toBreeze.activeIterator.foreach {
       case (_, 0.0) => // Skip explicit zero elements.
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/stat/CorrelationSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/stat/CorrelationSuite.scala
index a3f76f77a5dcc..34548c86ebc14 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/stat/CorrelationSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/stat/CorrelationSuite.scala
@@ -39,6 +39,17 @@ class CorrelationSuite extends FunSuite with LocalSparkContext {
     Vectors.dense(9.0, 0.0, 0.0, 1.0)
   )
 
+  test("corr(x, y) pearson, 1 value in data") {
+    val x = sc.parallelize(Array(1.0))
+    val y = sc.parallelize(Array(4.0))
+    intercept[RuntimeException] {
+      Statistics.corr(x, y, "pearson")
+    }
+    intercept[RuntimeException] {
+      Statistics.corr(x, y, "spearman")
+    }
+  }
+
   test("corr(x, y) default, pearson") {
     val x = sc.parallelize(xData)
     val y = sc.parallelize(yData)
@@ -58,7 +69,7 @@ class CorrelationSuite extends FunSuite with LocalSparkContext {
 
     // RDD of zero variance
     val z = sc.parallelize(zeros)
-    assert(Statistics.corr(x, z).isNaN())
+    assert(Statistics.corr(x, z).isNaN)
   }
 
   test("corr(x, y) spearman") {
@@ -78,7 +89,7 @@ class CorrelationSuite extends FunSuite with LocalSparkContext {
 
     // RDD of zero variance => zero variance in ranks
     val z = sc.parallelize(zeros)
-    assert(Statistics.corr(x, z, "spearman").isNaN())
+    assert(Statistics.corr(x, z, "spearman").isNaN)
   }
 
   test("corr(X) default, pearson") {
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/stat/MultivariateOnlineSummarizerSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/stat/MultivariateOnlineSummarizerSuite.scala
index db13f142df517..1e9415249104b 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/stat/MultivariateOnlineSummarizerSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/stat/MultivariateOnlineSummarizerSuite.scala
@@ -139,7 +139,8 @@ class MultivariateOnlineSummarizerSuite extends FunSuite {
     assert(summarizer.numNonzeros ~== Vectors.dense(3, 5, 2) absTol 1E-5, "numNonzeros mismatch")
 
     assert(summarizer.variance ~==
-      Vectors.dense(3.857666666666, 7.0456666666666, 2.48166666666666) absTol 1E-5, "variance mismatch")
+      Vectors.dense(3.857666666666, 7.0456666666666, 2.48166666666666) absTol 1E-5,
+      "variance mismatch")
 
     assert(summarizer.count === 6)
   }
@@ -167,7 +168,8 @@ class MultivariateOnlineSummarizerSuite extends FunSuite {
     assert(summarizer.numNonzeros ~== Vectors.dense(3, 5, 2) absTol 1E-5, "numNonzeros mismatch")
 
     assert(summarizer.variance ~==
-      Vectors.dense(3.857666666666, 7.0456666666666, 2.48166666666666) absTol 1E-5, "variance mismatch")
+      Vectors.dense(3.857666666666, 7.0456666666666, 2.48166666666666) absTol 1E-5,
+      "variance mismatch")
 
     assert(summarizer.count === 6)
   }
diff --git a/python/pyspark/mllib/linalg.py b/python/pyspark/mllib/linalg.py
index 9a239abfbbeb1..f485a69db1fa2 100644
--- a/python/pyspark/mllib/linalg.py
+++ b/python/pyspark/mllib/linalg.py
@@ -23,6 +23,7 @@
 SciPy is available in their environment.
 """
 
+import numpy
 from numpy import array, array_equal, ndarray, float64, int32
 
 
@@ -160,6 +161,15 @@ def squared_distance(self, other):
                 j += 1
             return result
 
+    def toArray(self):
+        """
+        Returns a copy of this SparseVector as a 1-dimensional NumPy array.
+        """
+        arr = numpy.zeros(self.size)
+        for i in xrange(self.indices.size):
+            arr[self.indices[i]] = self.values[i]
+        return arr
+
     def __str__(self):
         inds = "[" + ",".join([str(i) for i in self.indices]) + "]"
         vals = "[" + ",".join([str(v) for v in self.values]) + "]"
diff --git a/python/pyspark/mllib/stat.py b/python/pyspark/mllib/stat.py
index a73abc5ff90df..feef0d16cd644 100644
--- a/python/pyspark/mllib/stat.py
+++ b/python/pyspark/mllib/stat.py
@@ -118,16 +118,18 @@ def corr(x, y=None, method=None):
         >>> from linalg import Vectors
         >>> rdd = sc.parallelize([Vectors.dense([1, 0, 0, -2]), Vectors.dense([4, 5, 0, 3]),
         ...                       Vectors.dense([6, 7, 0,  8]), Vectors.dense([9, 0, 0, 1])])
-        >>> Statistics.corr(rdd)
-        array([[ 1.        ,  0.05564149,         nan,  0.40047142],
-               [ 0.05564149,  1.        ,         nan,  0.91359586],
-               [        nan,         nan,  1.        ,         nan],
-               [ 0.40047142,  0.91359586,         nan,  1.        ]])
-        >>> Statistics.corr(rdd, method="spearman")
-        array([[ 1.        ,  0.10540926,         nan,  0.4       ],
-               [ 0.10540926,  1.        ,         nan,  0.9486833 ],
-               [        nan,         nan,  1.        ,         nan],
-               [ 0.4       ,  0.9486833 ,         nan,  1.        ]])
+        >>> pearsonCorr = Statistics.corr(rdd)
+        >>> print str(pearsonCorr).replace('nan', 'NaN')
+        [[ 1.          0.05564149         NaN  0.40047142]
+         [ 0.05564149  1.                 NaN  0.91359586]
+         [        NaN         NaN  1.                 NaN]
+         [ 0.40047142  0.91359586         NaN  1.        ]]
+        >>> spearmanCorr = Statistics.corr(rdd, method="spearman")
+        >>> print str(spearmanCorr).replace('nan', 'NaN')
+        [[ 1.          0.10540926         NaN  0.4       ]
+         [ 0.10540926  1.                 NaN  0.9486833 ]
+         [        NaN         NaN  1.                 NaN]
+         [ 0.4         0.9486833          NaN  1.        ]]
         >>> try:
         ...     Statistics.corr(rdd, "spearman")
         ...     print "Method name as second argument without 'method=' shouldn't be allowed."
diff --git a/python/run-tests b/python/run-tests
index a6271e0cf5fa9..b506559a5e810 100755
--- a/python/run-tests
+++ b/python/run-tests
@@ -78,6 +78,7 @@ run_test "pyspark/mllib/linalg.py"
 run_test "pyspark/mllib/random.py"
 run_test "pyspark/mllib/recommendation.py"
 run_test "pyspark/mllib/regression.py"
+run_test "pyspark/mllib/stat.py"
 run_test "pyspark/mllib/tests.py"
 run_test "pyspark/mllib/tree.py"
 run_test "pyspark/mllib/util.py"

From 217b5e915e2f21f047dfc4be680cd20d58baf9f8 Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Mon, 18 Aug 2014 18:20:54 -0700
Subject: [PATCH 016/489] [SPARK-3108][MLLIB] add predictOnValues to
 StreamingLR and fix predictOn

It is useful in streaming to allow users to carry extra data with the prediction, for monitoring the prediction error for example. freeman-lab

Author: Xiangrui Meng <meng@databricks.com>

Closes #2023 from mengxr/predict-on-values and squashes the following commits:

cac47b8 [Xiangrui Meng] add classtag
2821b3b [Xiangrui Meng] use mapValues
0925efa [Xiangrui Meng] add predictOnValues to StreamingLR and fix predictOn
---
 .../mllib/StreamingLinearRegression.scala     |  4 +--
 .../regression/StreamingLinearAlgorithm.scala | 31 +++++++++++++++----
 2 files changed, 27 insertions(+), 8 deletions(-)

diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/StreamingLinearRegression.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/StreamingLinearRegression.scala
index 0e992fa9967bb..c5bd5b0b178d9 100644
--- a/examples/src/main/scala/org/apache/spark/examples/mllib/StreamingLinearRegression.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/StreamingLinearRegression.scala
@@ -59,10 +59,10 @@ object StreamingLinearRegression {
     val testData = ssc.textFileStream(args(1)).map(LabeledPoint.parse)
 
     val model = new StreamingLinearRegressionWithSGD()
-      .setInitialWeights(Vectors.dense(Array.fill[Double](args(3).toInt)(0)))
+      .setInitialWeights(Vectors.zeros(args(3).toInt))
 
     model.trainOn(trainingData)
-    model.predictOn(testData).print()
+    model.predictOnValues(testData.map(lp => (lp.label, lp.features))).print()
 
     ssc.start()
     ssc.awaitTermination()
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearAlgorithm.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearAlgorithm.scala
index b8b0b42611775..8db0442a7a569 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearAlgorithm.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearAlgorithm.scala
@@ -17,8 +17,12 @@
 
 package org.apache.spark.mllib.regression
 
-import org.apache.spark.annotation.DeveloperApi
+import scala.reflect.ClassTag
+
 import org.apache.spark.Logging
+import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.mllib.linalg.Vector
+import org.apache.spark.streaming.StreamingContext._
 import org.apache.spark.streaming.dstream.DStream
 
 /**
@@ -92,15 +96,30 @@ abstract class StreamingLinearAlgorithm[
   /**
    * Use the model to make predictions on batches of data from a DStream
    *
-   * @param data DStream containing labeled data
+   * @param data DStream containing feature vectors
    * @return DStream containing predictions
    */
-  def predictOn(data: DStream[LabeledPoint]): DStream[Double] = {
+  def predictOn(data: DStream[Vector]): DStream[Double] = {
     if (Option(model.weights) == None) {
-      logError("Initial weights must be set before starting prediction")
-      throw new IllegalArgumentException
+      val msg = "Initial weights must be set before starting prediction"
+      logError(msg)
+      throw new IllegalArgumentException(msg)
     }
-    data.map(x => model.predict(x.features))
+    data.map(model.predict)
   }
 
+  /**
+   * Use the model to make predictions on the values of a DStream and carry over its keys.
+   * @param data DStream containing feature vectors
+   * @tparam K key type
+   * @return DStream containing the input keys and the predictions as values
+   */
+  def predictOnValues[K: ClassTag](data: DStream[(K, Vector)]): DStream[(K, Double)] = {
+    if (Option(model.weights) == None) {
+      val msg = "Initial weights must be set before starting prediction"
+      logError(msg)
+      throw new IllegalArgumentException(msg)
+    }
+    data.mapValues(model.predict)
+  }
 }

From 1f1819b20f887b487557c31e54b8bcd95b582dc6 Mon Sep 17 00:00:00 2001
From: Josh Rosen <joshrosen@apache.org>
Date: Mon, 18 Aug 2014 20:42:19 -0700
Subject: [PATCH 017/489] [SPARK-3114] [PySpark] Fix Python UDFs in Spark SQL.

This fixes SPARK-3114, an issue where we inadvertently broke Python UDFs in Spark SQL.

This PR modifiers the test runner script to always run the PySpark SQL tests, irrespective of whether SparkSQL itself has been modified.  It also includes Davies' fix for the bug.

Closes #2026.

Author: Josh Rosen <joshrosen@apache.org>
Author: Davies Liu <davies.liu@gmail.com>

Closes #2027 from JoshRosen/pyspark-sql-fix and squashes the following commits:

9af2708 [Davies Liu] bugfix: disable compression of command
0d8d3a4 [Josh Rosen] Always run Python Spark SQL tests.
---
 dev/run-tests            | 17 +++++++++++++----
 python/pyspark/rdd.py    |  2 +-
 python/pyspark/worker.py |  2 +-
 python/run-tests         |  4 +---
 4 files changed, 16 insertions(+), 9 deletions(-)

diff --git a/dev/run-tests b/dev/run-tests
index 0e24515d1376c..132f696d6447a 100755
--- a/dev/run-tests
+++ b/dev/run-tests
@@ -58,7 +58,7 @@ if [ -n "$AMPLAB_JENKINS" ]; then
   diffs=`git diff --name-only master | grep "^sql/"`
   if [ -n "$diffs" ]; then
     echo "Detected changes in SQL. Will run Hive test suite."
-    export _RUN_SQL_TESTS=true # exported for PySpark tests
+    _RUN_SQL_TESTS=true
   fi
 fi
 
@@ -89,13 +89,22 @@ echo "========================================================================="
 echo "Running Spark unit tests"
 echo "========================================================================="
 
+# Build Spark; we always build with Hive because the PySpark SparkSQL tests need it.
+# echo "q" is needed because sbt on encountering a build file with failure
+# (either resolution or compilation) prompts the user for input either q, r,
+# etc to quit or retry. This echo is there to make it not block.
+BUILD_MVN_PROFILE_ARGS="$SBT_MAVEN_PROFILES_ARGS -Phive -Phive-thriftserver "
+echo -e "q\n" | sbt/sbt $BUILD_MVN_PROFILE_ARGS clean package assembly/assembly | \
+  grep -v -e "info.*Resolving" -e "warn.*Merging" -e "info.*Including"
+
+# If the Spark SQL tests are enabled, run the tests with the Hive profiles enabled:
 if [ -n "$_RUN_SQL_TESTS" ]; then
   SBT_MAVEN_PROFILES_ARGS="$SBT_MAVEN_PROFILES_ARGS -Phive -Phive-thriftserver"
 fi
-# echo "q" is needed because sbt on encountering a build file with failure 
-# (either resolution or compilation) prompts the user for input either q, r, 
+# echo "q" is needed because sbt on encountering a build file with failure
+# (either resolution or compilation) prompts the user for input either q, r,
 # etc to quit or retry. This echo is there to make it not block.
-echo -e "q\n" | sbt/sbt $SBT_MAVEN_PROFILES_ARGS clean package assembly/assembly test | \
+echo -e "q\n" | sbt/sbt $SBT_MAVEN_PROFILES_ARGS test | \
   grep -v -e "info.*Resolving" -e "warn.*Merging" -e "info.*Including"
 
 echo ""
diff --git a/python/pyspark/rdd.py b/python/pyspark/rdd.py
index c708b69cc1e31..86cd89b245aea 100644
--- a/python/pyspark/rdd.py
+++ b/python/pyspark/rdd.py
@@ -1812,7 +1812,7 @@ def _jrdd(self):
             self._jrdd_deserializer = NoOpSerializer()
         command = (self.func, self._prev_jrdd_deserializer,
                    self._jrdd_deserializer)
-        ser = CompressedSerializer(CloudPickleSerializer())
+        ser = CloudPickleSerializer()
         pickled_command = ser.dumps(command)
         broadcast_vars = ListConverter().convert(
             [x._jbroadcast for x in self.ctx._pickled_broadcast_vars],
diff --git a/python/pyspark/worker.py b/python/pyspark/worker.py
index 77a9c4a0e0677..6805063e06798 100644
--- a/python/pyspark/worker.py
+++ b/python/pyspark/worker.py
@@ -72,7 +72,7 @@ def main(infile, outfile):
             value = ser._read_with_length(infile)
             _broadcastRegistry[bid] = Broadcast(bid, value)
 
-        command = ser._read_with_length(infile)
+        command = pickleSer._read_with_length(infile)
         (func, deserializer, serializer) = command
         init_time = time.time()
         iterator = deserializer.load_stream(infile)
diff --git a/python/run-tests b/python/run-tests
index b506559a5e810..7b1ee3e1cddba 100755
--- a/python/run-tests
+++ b/python/run-tests
@@ -59,9 +59,7 @@ $PYSPARK_PYTHON --version
 run_test "pyspark/rdd.py"
 run_test "pyspark/context.py"
 run_test "pyspark/conf.py"
-if [ -n "$_RUN_SQL_TESTS" ]; then
-  run_test "pyspark/sql.py"
-fi
+run_test "pyspark/sql.py"
 # These tests are included in the module-level docs, and so must
 # be handled on a higher level rather than within the python file.
 export PYSPARK_DOC_TEST=1

From 82577339dd58b5811eab5d10667775e61e37ff51 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@apache.org>
Date: Mon, 18 Aug 2014 20:51:41 -0700
Subject: [PATCH 018/489] [SPARK-3116] Remove the excessive lockings in
 TorrentBroadcast

Author: Reynold Xin <rxin@apache.org>

Closes #2028 from rxin/torrentBroadcast and squashes the following commits:

92c62a5 [Reynold Xin] Revert the MEMORY_AND_DISK_SER changes.
03a5221 [Reynold Xin] [SPARK-3116] Remove the excessive lockings in TorrentBroadcast
---
 .../spark/broadcast/TorrentBroadcast.scala    | 66 ++++++++-----------
 1 file changed, 27 insertions(+), 39 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/broadcast/TorrentBroadcast.scala b/core/src/main/scala/org/apache/spark/broadcast/TorrentBroadcast.scala
index fe73456ef8fad..d8be649f96e5f 100644
--- a/core/src/main/scala/org/apache/spark/broadcast/TorrentBroadcast.scala
+++ b/core/src/main/scala/org/apache/spark/broadcast/TorrentBroadcast.scala
@@ -17,8 +17,7 @@
 
 package org.apache.spark.broadcast
 
-import java.io.{ByteArrayOutputStream, ByteArrayInputStream, InputStream,
-  ObjectInputStream, ObjectOutputStream, OutputStream}
+import java.io._
 
 import scala.reflect.ClassTag
 import scala.util.Random
@@ -53,10 +52,8 @@ private[spark] class TorrentBroadcast[T: ClassTag](
 
   private val broadcastId = BroadcastBlockId(id)
 
-  TorrentBroadcast.synchronized {
-    SparkEnv.get.blockManager.putSingle(
-      broadcastId, value_, StorageLevel.MEMORY_AND_DISK, tellMaster = false)
-  }
+  SparkEnv.get.blockManager.putSingle(
+    broadcastId, value_, StorageLevel.MEMORY_AND_DISK, tellMaster = false)
 
   @transient private var arrayOfBlocks: Array[TorrentBlock] = null
   @transient private var totalBlocks = -1
@@ -91,18 +88,14 @@ private[spark] class TorrentBroadcast[T: ClassTag](
     // Store meta-info
     val metaId = BroadcastBlockId(id, "meta")
     val metaInfo = TorrentInfo(null, totalBlocks, totalBytes)
-    TorrentBroadcast.synchronized {
-      SparkEnv.get.blockManager.putSingle(
-        metaId, metaInfo, StorageLevel.MEMORY_AND_DISK, tellMaster = true)
-    }
+    SparkEnv.get.blockManager.putSingle(
+      metaId, metaInfo, StorageLevel.MEMORY_AND_DISK, tellMaster = true)
 
     // Store individual pieces
     for (i <- 0 until totalBlocks) {
       val pieceId = BroadcastBlockId(id, "piece" + i)
-      TorrentBroadcast.synchronized {
-        SparkEnv.get.blockManager.putSingle(
-          pieceId, tInfo.arrayOfBlocks(i), StorageLevel.MEMORY_AND_DISK, tellMaster = true)
-      }
+      SparkEnv.get.blockManager.putSingle(
+        pieceId, tInfo.arrayOfBlocks(i), StorageLevel.MEMORY_AND_DISK, tellMaster = true)
     }
   }
 
@@ -165,21 +158,20 @@ private[spark] class TorrentBroadcast[T: ClassTag](
     val metaId = BroadcastBlockId(id, "meta")
     var attemptId = 10
     while (attemptId > 0 && totalBlocks == -1) {
-      TorrentBroadcast.synchronized {
-        SparkEnv.get.blockManager.getSingle(metaId) match {
-          case Some(x) =>
-            val tInfo = x.asInstanceOf[TorrentInfo]
-            totalBlocks = tInfo.totalBlocks
-            totalBytes = tInfo.totalBytes
-            arrayOfBlocks = new Array[TorrentBlock](totalBlocks)
-            hasBlocks = 0
-
-          case None =>
-            Thread.sleep(500)
-        }
+      SparkEnv.get.blockManager.getSingle(metaId) match {
+        case Some(x) =>
+          val tInfo = x.asInstanceOf[TorrentInfo]
+          totalBlocks = tInfo.totalBlocks
+          totalBytes = tInfo.totalBytes
+          arrayOfBlocks = new Array[TorrentBlock](totalBlocks)
+          hasBlocks = 0
+
+        case None =>
+          Thread.sleep(500)
       }
       attemptId -= 1
     }
+
     if (totalBlocks == -1) {
       return false
     }
@@ -192,17 +184,15 @@ private[spark] class TorrentBroadcast[T: ClassTag](
     val recvOrder = new Random().shuffle(Array.iterate(0, totalBlocks)(_ + 1).toList)
     for (pid <- recvOrder) {
       val pieceId = BroadcastBlockId(id, "piece" + pid)
-      TorrentBroadcast.synchronized {
-        SparkEnv.get.blockManager.getSingle(pieceId) match {
-          case Some(x) =>
-            arrayOfBlocks(pid) = x.asInstanceOf[TorrentBlock]
-            hasBlocks += 1
-            SparkEnv.get.blockManager.putSingle(
-              pieceId, arrayOfBlocks(pid), StorageLevel.MEMORY_AND_DISK, tellMaster = true)
+      SparkEnv.get.blockManager.getSingle(pieceId) match {
+        case Some(x) =>
+          arrayOfBlocks(pid) = x.asInstanceOf[TorrentBlock]
+          hasBlocks += 1
+          SparkEnv.get.blockManager.putSingle(
+            pieceId, arrayOfBlocks(pid), StorageLevel.MEMORY_AND_DISK, tellMaster = true)
 
-          case None =>
-            throw new SparkException("Failed to get " + pieceId + " of " + broadcastId)
-        }
+        case None =>
+          throw new SparkException("Failed to get " + pieceId + " of " + broadcastId)
       }
     }
 
@@ -291,9 +281,7 @@ private[broadcast] object TorrentBroadcast extends Logging {
    * If removeFromDriver is true, also remove these persisted blocks on the driver.
    */
   def unpersist(id: Long, removeFromDriver: Boolean, blocking: Boolean) = {
-    synchronized {
-      SparkEnv.get.blockManager.master.removeBroadcast(id, removeFromDriver, blocking)
-    }
+    SparkEnv.get.blockManager.master.removeBroadcast(id, removeFromDriver, blocking)
   }
 }
 

From cd0720ca77894d481fb73a8b5bb517013843cb1e Mon Sep 17 00:00:00 2001
From: Matt Forbes <matt@tellapart.com>
Date: Mon, 18 Aug 2014 21:43:32 -0700
Subject: [PATCH 019/489] Fix typo in decision tree docs

Candidate splits were inconsistent with the example.

Author: Matt Forbes <matt@tellapart.com>

Closes #1837 from emef/tree-doc and squashes the following commits:

3be14a1 [Matt Forbes] Fix typo in decision tree docs
---
 docs/mllib-decision-tree.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/mllib-decision-tree.md b/docs/mllib-decision-tree.md
index 9cbd880897578..c01a92a9a1b26 100644
--- a/docs/mllib-decision-tree.md
+++ b/docs/mllib-decision-tree.md
@@ -84,8 +84,8 @@ Section 9.2.4 in
 [Elements of Statistical Machine Learning](http://statweb.stanford.edu/~tibs/ElemStatLearn/) for
 details). For example, for a binary classification problem with one categorical feature with three
 categories A, B and C with corresponding proportion of label 1 as 0.2, 0.6 and 0.4, the categorical
-features are ordered as A followed by C followed B or A, B, C. The two split candidates are A \| C, B
-and A , B \| C where \| denotes the split. A similar heuristic is used for multiclass classification
+features are ordered as A followed by C followed B or A, C, B. The two split candidates are A \| C, B
+and A , C \| B where \| denotes the split. A similar heuristic is used for multiclass classification
 when `$2^(M-1)-1$` is greater than the number of bins -- the impurity for each categorical feature value
 is used for ordering.
 

From 7eb9cbc273d758522e787fcb2ef68ef65911475f Mon Sep 17 00:00:00 2001
From: Thomas Graves <tgraves@apache.org>
Date: Tue, 19 Aug 2014 09:40:31 -0500
Subject: [PATCH 020/489] [SPARK-3072] YARN - Exit when reach max number failed
 executors

In some cases on hadoop 2.x the spark application master doesn't properly exit and hangs around for 10 minutes after its really done.  We should make sure it exits properly and stops the driver.

Author: Thomas Graves <tgraves@apache.org>

Closes #2022 from tgravescs/SPARK-3072 and squashes the following commits:

665701d [Thomas Graves] Exit when reach max number failed executors
---
 .../spark/deploy/yarn/ApplicationMaster.scala | 33 ++++++++++++-------
 .../spark/deploy/yarn/ExecutorLauncher.scala  |  5 +--
 .../spark/deploy/yarn/ApplicationMaster.scala | 16 ++++++---
 .../spark/deploy/yarn/ExecutorLauncher.scala  |  5 +--
 4 files changed, 40 insertions(+), 19 deletions(-)

diff --git a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala b/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
index 62b5c3bc5f0f3..46a01f5a9a2cc 100644
--- a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
+++ b/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
@@ -267,12 +267,10 @@ class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration,
       // TODO: This is a bit ugly. Can we make it nicer?
       // TODO: Handle container failure
 
-      // Exists the loop if the user thread exits.
-      while (yarnAllocator.getNumExecutorsRunning < args.numExecutors && userThread.isAlive) {
-        if (yarnAllocator.getNumExecutorsFailed >= maxNumExecutorFailures) {
-          finishApplicationMaster(FinalApplicationStatus.FAILED,
-            "max number of executor failures reached")
-        }
+      // Exits the loop if the user thread exits.
+      while (yarnAllocator.getNumExecutorsRunning < args.numExecutors && userThread.isAlive
+          && !isFinished) {
+        checkNumExecutorsFailed()
         yarnAllocator.allocateContainers(
           math.max(args.numExecutors - yarnAllocator.getNumExecutorsRunning, 0))
         Thread.sleep(ApplicationMaster.ALLOCATE_HEARTBEAT_INTERVAL)
@@ -303,11 +301,8 @@ class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration,
 
     val t = new Thread {
       override def run() {
-        while (userThread.isAlive) {
-          if (yarnAllocator.getNumExecutorsFailed >= maxNumExecutorFailures) {
-            finishApplicationMaster(FinalApplicationStatus.FAILED,
-              "max number of executor failures reached")
-          }
+        while (userThread.isAlive && !isFinished) {
+          checkNumExecutorsFailed()
           val missingExecutorCount = args.numExecutors - yarnAllocator.getNumExecutorsRunning
           if (missingExecutorCount > 0) {
             logInfo("Allocating %d containers to make up for (potentially) lost containers".
@@ -327,6 +322,22 @@ class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration,
     t
   }
 
+  private def checkNumExecutorsFailed() {
+    if (yarnAllocator.getNumExecutorsFailed >= maxNumExecutorFailures) {
+      logInfo("max number of executor failures reached")
+      finishApplicationMaster(FinalApplicationStatus.FAILED,
+        "max number of executor failures reached")
+      // make sure to stop the user thread
+      val sparkContext = ApplicationMaster.sparkContextRef.get()
+      if (sparkContext != null) {
+        logInfo("Invoking sc stop from checkNumExecutorsFailed")
+        sparkContext.stop()
+      } else {
+        logError("sparkContext is null when should shutdown")
+      }
+    }
+  }
+
   private def sendProgress() {
     logDebug("Sending progress")
     // Simulated with an allocate request with no nodes requested ...
diff --git a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala b/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala
index 184e2ad6c82cd..72c7143edcd71 100644
--- a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala
+++ b/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala
@@ -249,7 +249,8 @@ class ExecutorLauncher(args: ApplicationMasterArguments, conf: Configuration, sp
     // Wait until all containers have finished
     // TODO: This is a bit ugly. Can we make it nicer?
     // TODO: Handle container failure
-    while ((yarnAllocator.getNumExecutorsRunning < args.numExecutors) && (!driverClosed)) {
+    while ((yarnAllocator.getNumExecutorsRunning < args.numExecutors) && (!driverClosed) &&
+        !isFinished) {
       yarnAllocator.allocateContainers(
         math.max(args.numExecutors - yarnAllocator.getNumExecutorsRunning, 0))
       checkNumExecutorsFailed()
@@ -271,7 +272,7 @@ class ExecutorLauncher(args: ApplicationMasterArguments, conf: Configuration, sp
 
     val t = new Thread {
       override def run() {
-        while (!driverClosed) {
+        while (!driverClosed && !isFinished) {
           checkNumExecutorsFailed()
           val missingExecutorCount = args.numExecutors - yarnAllocator.getNumExecutorsRunning
           if (missingExecutorCount > 0) {
diff --git a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
index 035356d390c80..9c2bcf17a8508 100644
--- a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
+++ b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
@@ -247,13 +247,12 @@ class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration,
       yarnAllocator.allocateResources()
       // Exits the loop if the user thread exits.
 
-      var iters = 0
-      while (yarnAllocator.getNumExecutorsRunning < args.numExecutors && userThread.isAlive) {
+      while (yarnAllocator.getNumExecutorsRunning < args.numExecutors && userThread.isAlive
+          && !isFinished) {
         checkNumExecutorsFailed()
         allocateMissingExecutor()
         yarnAllocator.allocateResources()
         Thread.sleep(ApplicationMaster.ALLOCATE_HEARTBEAT_INTERVAL)
-        iters += 1
       }
     }
     logInfo("All executors have launched.")
@@ -271,8 +270,17 @@ class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration,
 
   private def checkNumExecutorsFailed() {
     if (yarnAllocator.getNumExecutorsFailed >= maxNumExecutorFailures) {
+      logInfo("max number of executor failures reached")
       finishApplicationMaster(FinalApplicationStatus.FAILED,
         "max number of executor failures reached")
+      // make sure to stop the user thread
+      val sparkContext = ApplicationMaster.sparkContextRef.get()
+      if (sparkContext != null) {
+        logInfo("Invoking sc stop from checkNumExecutorsFailed")
+        sparkContext.stop()
+      } else {
+        logError("sparkContext is null when should shutdown")
+      }
     }
   }
 
@@ -289,7 +297,7 @@ class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration,
 
     val t = new Thread {
       override def run() {
-        while (userThread.isAlive) {
+        while (userThread.isAlive && !isFinished) {
           checkNumExecutorsFailed()
           allocateMissingExecutor()
           logDebug("Sending progress")
diff --git a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala
index fc7b8320d734d..a7585748b7f88 100644
--- a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala
+++ b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala
@@ -217,7 +217,8 @@ class ExecutorLauncher(args: ApplicationMasterArguments, conf: Configuration, sp
     // Wait until all containers have launched
     yarnAllocator.addResourceRequests(args.numExecutors)
     yarnAllocator.allocateResources()
-    while ((yarnAllocator.getNumExecutorsRunning < args.numExecutors) && (!driverClosed)) {
+    while ((yarnAllocator.getNumExecutorsRunning < args.numExecutors) && (!driverClosed) &&
+        !isFinished) {
       checkNumExecutorsFailed()
       allocateMissingExecutor()
       yarnAllocator.allocateResources()
@@ -249,7 +250,7 @@ class ExecutorLauncher(args: ApplicationMasterArguments, conf: Configuration, sp
 
     val t = new Thread {
       override def run() {
-        while (!driverClosed) {
+        while (!driverClosed && !isFinished) {
           checkNumExecutorsFailed()
           allocateMissingExecutor()
           logDebug("Sending progress")

From cbfc26ba45f49559e64276c72e3054c6fe30ddd5 Mon Sep 17 00:00:00 2001
From: Kousuke Saruta <sarutak@oss.nttdata.co.jp>
Date: Tue, 19 Aug 2014 10:15:11 -0700
Subject: [PATCH 021/489] [SPARK-3089] Fix meaningless error message in
 ConnectionManager

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

Closes #2000 from sarutak/SPARK-3089 and squashes the following commits:

02dfdea [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into SPARK-3089
e759ce7 [Kousuke Saruta] Improved error message when closing SendingConnection
---
 .../main/scala/org/apache/spark/network/ConnectionManager.scala | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/network/ConnectionManager.scala b/core/src/main/scala/org/apache/spark/network/ConnectionManager.scala
index e77d762bdf221..b3e951ded6e77 100644
--- a/core/src/main/scala/org/apache/spark/network/ConnectionManager.scala
+++ b/core/src/main/scala/org/apache/spark/network/ConnectionManager.scala
@@ -467,7 +467,7 @@ private[spark] class ConnectionManager(
 
           val sendingConnectionOpt = connectionsById.get(remoteConnectionManagerId)
           if (!sendingConnectionOpt.isDefined) {
-            logError("Corresponding SendingConnectionManagerId not found")
+            logError(s"Corresponding SendingConnection to ${remoteConnectionManagerId} not found")
             return
           }
 

From 31f0b071efd0b63eb9d6a6a131e5c4fa28237583 Mon Sep 17 00:00:00 2001
From: freeman <the.freeman.lab@gmail.com>
Date: Tue, 19 Aug 2014 13:28:57 -0700
Subject: [PATCH 022/489] [SPARK-3128][MLLIB] Use streaming test suite for
 StreamingLR

Refactored tests for streaming linear regression to use existing  streaming test utilities. Summary of changes:
- Made ``mllib`` depend on tests from ``streaming``
- Rewrote accuracy and convergence tests to use ``setupStreams`` and ``runStreams``
- Added new test for the accuracy of predictions generated by ``predictOnValue``

These tests should run faster, be easier to extend/maintain, and provide a reference for new tests.

mengxr tdas

Author: freeman <the.freeman.lab@gmail.com>

Closes #2037 from freeman-lab/streamingLR-predict-tests and squashes the following commits:

e851ca7 [freeman] Fixed long lines
50eb0bf [freeman] Refactored tests to use streaming test tools
32c43c2 [freeman] Added test for prediction
---
 mllib/pom.xml                                 |   7 +
 .../StreamingLinearRegressionSuite.scala      | 121 ++++++++++--------
 .../spark/streaming/TestSuiteBase.scala       |   4 +-
 3 files changed, 77 insertions(+), 55 deletions(-)

diff --git a/mllib/pom.xml b/mllib/pom.xml
index fc1ecfbea708f..c7a1e2ae75c84 100644
--- a/mllib/pom.xml
+++ b/mllib/pom.xml
@@ -91,6 +91,13 @@
       <artifactId>junit-interface</artifactId>
       <scope>test</scope>
     </dependency>
+    <dependency>
+      <groupId>org.apache.spark</groupId>
+      <artifactId>spark-streaming_${scala.binary.version}</artifactId>
+      <version>${project.version}</version>
+      <type>test-jar</type>
+      <scope>test</scope>
+    </dependency>
   </dependencies>
   <profiles>
     <profile>
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionSuite.scala
index 45e25eecf508e..28489410f8225 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionSuite.scala
@@ -17,20 +17,19 @@
 
 package org.apache.spark.mllib.regression
 
-import java.io.File
-import java.nio.charset.Charset
-
 import scala.collection.mutable.ArrayBuffer
 
-import com.google.common.io.Files
 import org.scalatest.FunSuite
 
 import org.apache.spark.mllib.linalg.Vectors
-import org.apache.spark.mllib.util.{LinearDataGenerator, LocalSparkContext}
-import org.apache.spark.streaming.{Milliseconds, StreamingContext}
-import org.apache.spark.util.Utils
+import org.apache.spark.mllib.util.LinearDataGenerator
+import org.apache.spark.streaming.dstream.DStream
+import org.apache.spark.streaming.TestSuiteBase
+
+class StreamingLinearRegressionSuite extends FunSuite with TestSuiteBase {
 
-class StreamingLinearRegressionSuite extends FunSuite with LocalSparkContext {
+  // use longer wait time to ensure job completion
+  override def maxWaitTimeMillis = 20000
 
   // Assert that two values are equal within tolerance epsilon
   def assertEqual(v1: Double, v2: Double, epsilon: Double) {
@@ -49,35 +48,26 @@ class StreamingLinearRegressionSuite extends FunSuite with LocalSparkContext {
   }
 
   // Test if we can accurately learn Y = 10*X1 + 10*X2 on streaming data
-  test("streaming linear regression parameter accuracy") {
+  test("parameter accuracy") {
 
-    val testDir = Files.createTempDir()
-    val numBatches = 10
-    val batchDuration = Milliseconds(1000)
-    val ssc = new StreamingContext(sc, batchDuration)
-    val data = ssc.textFileStream(testDir.toString).map(LabeledPoint.parse)
+    // create model
     val model = new StreamingLinearRegressionWithSGD()
       .setInitialWeights(Vectors.dense(0.0, 0.0))
       .setStepSize(0.1)
-      .setNumIterations(50)
+      .setNumIterations(25)
 
-    model.trainOn(data)
-
-    ssc.start()
-
-    // write data to a file stream
-    for (i <- 0 until numBatches) {
-      val samples = LinearDataGenerator.generateLinearInput(
-        0.0, Array(10.0, 10.0), 100, 42 * (i + 1))
-      val file = new File(testDir, i.toString)
-      Files.write(samples.map(x => x.toString).mkString("\n"), file, Charset.forName("UTF-8"))
-      Thread.sleep(batchDuration.milliseconds)
+    // generate sequence of simulated data
+    val numBatches = 10
+    val input = (0 until numBatches).map { i =>
+      LinearDataGenerator.generateLinearInput(0.0, Array(10.0, 10.0), 100, 42 * (i + 1))
     }
 
-    ssc.stop(stopSparkContext=false)
-
-    System.clearProperty("spark.driver.port")
-    Utils.deleteRecursively(testDir)
+    // apply model training to input stream
+    val ssc = setupStreams(input, (inputDStream: DStream[LabeledPoint]) => {
+      model.trainOn(inputDStream)
+      inputDStream.count()
+    })
+    runStreams(ssc, numBatches, numBatches)
 
     // check accuracy of final parameter estimates
     assertEqual(model.latestModel().intercept, 0.0, 0.1)
@@ -91,39 +81,33 @@ class StreamingLinearRegressionSuite extends FunSuite with LocalSparkContext {
   }
 
   // Test that parameter estimates improve when learning Y = 10*X1 on streaming data
-  test("streaming linear regression parameter convergence") {
+  test("parameter convergence") {
 
-    val testDir = Files.createTempDir()
-    val batchDuration = Milliseconds(2000)
-    val ssc = new StreamingContext(sc, batchDuration)
-    val numBatches = 5
-    val data = ssc.textFileStream(testDir.toString()).map(LabeledPoint.parse)
+    // create model
     val model = new StreamingLinearRegressionWithSGD()
       .setInitialWeights(Vectors.dense(0.0))
       .setStepSize(0.1)
-      .setNumIterations(50)
-
-    model.trainOn(data)
-
-    ssc.start()
+      .setNumIterations(25)
 
-    // write data to a file stream
-    val history = new ArrayBuffer[Double](numBatches)
-    for (i <- 0 until numBatches) {
-      val samples = LinearDataGenerator.generateLinearInput(0.0, Array(10.0), 100, 42 * (i + 1))
-      val file = new File(testDir, i.toString)
-      Files.write(samples.map(x => x.toString).mkString("\n"), file, Charset.forName("UTF-8"))
-      Thread.sleep(batchDuration.milliseconds)
-      // wait an extra few seconds to make sure the update finishes before new data arrive
-      Thread.sleep(4000)
-      history.append(math.abs(model.latestModel().weights(0) - 10.0))
+    // generate sequence of simulated data
+    val numBatches = 10
+    val input = (0 until numBatches).map { i =>
+      LinearDataGenerator.generateLinearInput(0.0, Array(10.0), 100, 42 * (i + 1))
     }
 
-    ssc.stop(stopSparkContext=false)
+    // create buffer to store intermediate fits
+    val history = new ArrayBuffer[Double](numBatches)
 
-    System.clearProperty("spark.driver.port")
-    Utils.deleteRecursively(testDir)
+    // apply model training to input stream, storing the intermediate results
+    // (we add a count to ensure the result is a DStream)
+    val ssc = setupStreams(input, (inputDStream: DStream[LabeledPoint]) => {
+      model.trainOn(inputDStream)
+      inputDStream.foreachRDD(x => history.append(math.abs(model.latestModel().weights(0) - 10.0)))
+      inputDStream.count()
+    })
+    runStreams(ssc, numBatches, numBatches)
 
+    // compute change in error
     val deltas = history.drop(1).zip(history.dropRight(1))
     // check error stability (it always either shrinks, or increases with small tol)
     assert(deltas.forall(x => (x._1 - x._2) <= 0.1))
@@ -132,4 +116,33 @@ class StreamingLinearRegressionSuite extends FunSuite with LocalSparkContext {
 
   }
 
+  // Test predictions on a stream
+  test("predictions") {
+
+    // create model initialized with true weights
+    val model = new StreamingLinearRegressionWithSGD()
+      .setInitialWeights(Vectors.dense(10.0, 10.0))
+      .setStepSize(0.1)
+      .setNumIterations(25)
+
+    // generate sequence of simulated data for testing
+    val numBatches = 10
+    val nPoints = 100
+    val testInput = (0 until numBatches).map { i =>
+      LinearDataGenerator.generateLinearInput(0.0, Array(10.0, 10.0), nPoints, 42 * (i + 1))
+    }
+
+    // apply model predictions to test stream
+    val ssc = setupStreams(testInput, (inputDStream: DStream[LabeledPoint]) => {
+      model.predictOnValues(inputDStream.map(x => (x.label, x.features)))
+    })
+    // collect the output as (true, estimated) tuples
+    val output: Seq[Seq[(Double, Double)]] = runStreams(ssc, numBatches, numBatches)
+
+    // compute the mean absolute error and check that it's always less than 0.1
+    val errors = output.map(batch => batch.map(p => math.abs(p._1 - p._2)).sum / nPoints)
+    assert(errors.forall(x => x <= 0.1))
+
+  }
+
 }
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/TestSuiteBase.scala b/streaming/src/test/scala/org/apache/spark/streaming/TestSuiteBase.scala
index cc178fba12c9d..f095da9cb55d3 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/TestSuiteBase.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/TestSuiteBase.scala
@@ -242,7 +242,9 @@ trait TestSuiteBase extends FunSuite with BeforeAndAfter with Logging {
     logInfo("numBatches = " + numBatches + ", numExpectedOutput = " + numExpectedOutput)
 
     // Get the output buffer
-    val outputStream = ssc.graph.getOutputStreams.head.asInstanceOf[TestOutputStreamWithPartitions[V]]
+    val outputStream = ssc.graph.getOutputStreams.
+      filter(_.isInstanceOf[TestOutputStreamWithPartitions[_]]).
+      head.asInstanceOf[TestOutputStreamWithPartitions[V]]
     val output = outputStream.output
 
     try {

From 94053a7b766788bb62e2dbbf352ccbcc75f71fc0 Mon Sep 17 00:00:00 2001
From: Vida Ha <vida@databricks.com>
Date: Tue, 19 Aug 2014 13:35:05 -0700
Subject: [PATCH 023/489] SPARK-2333 - spark_ec2 script should allow option for
 existing security group

    - Uses the name tag to identify machines in a cluster.
    - Allows overriding the security group name so it doesn't need to coincide with the cluster name.
    - Outputs the request id's of up to 10 pending spot instance requests.

Author: Vida Ha <vida@databricks.com>

Closes #1899 from vidaha/vida/ec2-reuse-security-group and squashes the following commits:

c80d5c3 [Vida Ha] wrap retries in a try catch block
b2989d5 [Vida Ha] SPARK-2333: spark_ec2 script should allow option for existing security group
---
 docs/ec2-scripts.md | 14 +++++----
 ec2/spark_ec2.py    | 71 +++++++++++++++++++++++++++++++--------------
 2 files changed, 57 insertions(+), 28 deletions(-)

diff --git a/docs/ec2-scripts.md b/docs/ec2-scripts.md
index 156a727026790..f5ac6d894e1eb 100644
--- a/docs/ec2-scripts.md
+++ b/docs/ec2-scripts.md
@@ -12,14 +12,16 @@ on the [Amazon Web Services site](http://aws.amazon.com/).
 
 `spark-ec2` is designed to manage multiple named clusters. You can
 launch a new cluster (telling the script its size and giving it a name),
-shutdown an existing cluster, or log into a cluster. Each cluster is
-identified by placing its machines into EC2 security groups whose names
-are derived from the name of the cluster. For example, a cluster named
+shutdown an existing cluster, or log into a cluster. Each cluster
+launches a set of instances, which are tagged with the cluster name,
+and placed into EC2 security groups.  If you don't specify a security
+group, the `spark-ec2` script will create security groups based on the
+cluster name you request. For example, a cluster named
 `test` will contain a master node in a security group called
 `test-master`, and a number of slave nodes in a security group called
-`test-slaves`. The `spark-ec2` script will create these security groups
-for you based on the cluster name you request. You can also use them to
-identify machines belonging to each cluster in the Amazon EC2 Console.
+`test-slaves`.  You can also specify a security group prefix to be used
+in place of the cluster name.  Machines in a cluster can be identified
+by looking for the "Name" tag of the instance in the Amazon EC2 Console.
 
 
 # Before You Start
diff --git a/ec2/spark_ec2.py b/ec2/spark_ec2.py
index 0c2f85a3868f4..3a8c816cfffa1 100755
--- a/ec2/spark_ec2.py
+++ b/ec2/spark_ec2.py
@@ -124,7 +124,7 @@ def parse_args():
         help="The SSH user you want to connect as (default: root)")
     parser.add_option(
         "--delete-groups", action="store_true", default=False,
-        help="When destroying a cluster, delete the security groups that were created")
+        help="When destroying a cluster, delete the security groups that were created.")
     parser.add_option(
         "--use-existing-master", action="store_true", default=False,
         help="Launch fresh slaves, but use an existing stopped master if possible")
@@ -138,7 +138,9 @@ def parse_args():
     parser.add_option(
         "--user-data", type="string", default="",
         help="Path to a user-data file (most AMI's interpret this as an initialization script)")
-
+    parser.add_option(
+        "--security-group-prefix", type="string", default=None,
+        help="Use this prefix for the security group rather than the cluster name.")
 
     (opts, args) = parser.parse_args()
     if len(args) != 2:
@@ -285,8 +287,12 @@ def launch_cluster(conn, opts, cluster_name):
             user_data_content = user_data_file.read()
 
     print "Setting up security groups..."
-    master_group = get_or_make_group(conn, cluster_name + "-master")
-    slave_group = get_or_make_group(conn, cluster_name + "-slaves")
+    if opts.security_group_prefix is None:
+        master_group = get_or_make_group(conn, cluster_name + "-master")
+        slave_group = get_or_make_group(conn, cluster_name + "-slaves")
+    else:
+        master_group = get_or_make_group(conn, opts.security_group_prefix + "-master")
+        slave_group = get_or_make_group(conn, opts.security_group_prefix + "-slaves")
     if master_group.rules == []:  # Group was just now created
         master_group.authorize(src_group=master_group)
         master_group.authorize(src_group=slave_group)
@@ -310,12 +316,11 @@ def launch_cluster(conn, opts, cluster_name):
         slave_group.authorize('tcp', 60060, 60060, '0.0.0.0/0')
         slave_group.authorize('tcp', 60075, 60075, '0.0.0.0/0')
 
-    # Check if instances are already running in our groups
+    # Check if instances are already running with the cluster name
     existing_masters, existing_slaves = get_existing_cluster(conn, opts, cluster_name,
                                                              die_on_error=False)
     if existing_slaves or (existing_masters and not opts.use_existing_master):
-        print >> stderr, ("ERROR: There are already instances running in " +
-                          "group %s or %s" % (master_group.name, slave_group.name))
+        print >> stderr, ("ERROR: There are already instances for name: %s " % cluster_name)
         sys.exit(1)
 
     # Figure out Spark AMI
@@ -371,9 +376,13 @@ def launch_cluster(conn, opts, cluster_name):
                 for r in reqs:
                     id_to_req[r.id] = r
                 active_instance_ids = []
+                outstanding_request_ids = []
                 for i in my_req_ids:
-                    if i in id_to_req and id_to_req[i].state == "active":
-                        active_instance_ids.append(id_to_req[i].instance_id)
+                    if i in id_to_req:
+                        if id_to_req[i].state == "active":
+                            active_instance_ids.append(id_to_req[i].instance_id)
+                        else:
+                            outstanding_request_ids.append(i)
                 if len(active_instance_ids) == opts.slaves:
                     print "All %d slaves granted" % opts.slaves
                     reservations = conn.get_all_instances(active_instance_ids)
@@ -382,8 +391,8 @@ def launch_cluster(conn, opts, cluster_name):
                         slave_nodes += r.instances
                     break
                 else:
-                    print "%d of %d slaves granted, waiting longer" % (
-                        len(active_instance_ids), opts.slaves)
+                    print "%d of %d slaves granted, waiting longer for request ids including %s" % (
+                        len(active_instance_ids), opts.slaves, outstanding_request_ids[0:10])
         except:
             print "Canceling spot instance requests"
             conn.cancel_spot_instance_requests(my_req_ids)
@@ -440,14 +449,29 @@ def launch_cluster(conn, opts, cluster_name):
         print "Launched master in %s, regid = %s" % (zone, master_res.id)
 
     # Give the instances descriptive names
+    # TODO: Add retry logic for tagging with name since it's used to identify a cluster.
     for master in master_nodes:
-        master.add_tag(
-            key='Name',
-            value='{cn}-master-{iid}'.format(cn=cluster_name, iid=master.id))
+        name = '{cn}-master-{iid}'.format(cn=cluster_name, iid=master.id)
+        for i in range(0, 5):
+            try:
+                master.add_tag(key='Name', value=name)
+            except:
+                print "Failed attempt %i of 5 to tag %s" % ((i + 1), name)
+                if (i == 5):
+                    raise "Error - failed max attempts to add name tag"
+                time.sleep(5)
+
+
     for slave in slave_nodes:
-        slave.add_tag(
-            key='Name',
-            value='{cn}-slave-{iid}'.format(cn=cluster_name, iid=slave.id))
+        name = '{cn}-slave-{iid}'.format(cn=cluster_name, iid=slave.id)
+        for i in range(0, 5):
+            try:
+                slave.add_tag(key='Name', value=name)
+            except:
+                print "Failed attempt %i of 5 to tag %s" % ((i + 1), name)
+                if (i == 5):
+                    raise "Error - failed max attempts to add name tag"
+                time.sleep(5)
 
     # Return all the instances
     return (master_nodes, slave_nodes)
@@ -463,10 +487,10 @@ def get_existing_cluster(conn, opts, cluster_name, die_on_error=True):
     for res in reservations:
         active = [i for i in res.instances if is_active(i)]
         for inst in active:
-            group_names = [g.name for g in inst.groups]
-            if group_names == [cluster_name + "-master"]:
+            name = inst.tags.get(u'Name', "")
+            if name.startswith(cluster_name + "-master"):
                 master_nodes.append(inst)
-            elif group_names == [cluster_name + "-slaves"]:
+            elif name.startswith(cluster_name + "-slave"):
                 slave_nodes.append(inst)
     if any((master_nodes, slave_nodes)):
         print ("Found %d master(s), %d slaves" % (len(master_nodes), len(slave_nodes)))
@@ -474,7 +498,7 @@ def get_existing_cluster(conn, opts, cluster_name, die_on_error=True):
         return (master_nodes, slave_nodes)
     else:
         if master_nodes == [] and slave_nodes != []:
-            print >> sys.stderr, "ERROR: Could not find master in group " + cluster_name + "-master"
+            print >> sys.stderr, "ERROR: Could not find master in with name " + cluster_name + "-master"
         else:
             print >> sys.stderr, "ERROR: Could not find any existing cluster"
         sys.exit(1)
@@ -816,7 +840,10 @@ def real_main():
             # Delete security groups as well
             if opts.delete_groups:
                 print "Deleting security groups (this will take some time)..."
-                group_names = [cluster_name + "-master", cluster_name + "-slaves"]
+                if opts.security_group_prefix is None:
+                    group_names = [cluster_name + "-master", cluster_name + "-slaves"]
+                else:
+                    group_names = [opts.security_group_prefix + "-master", opts.security_group_prefix + "-slaves"]
 
                 attempt = 1
                 while attempt <= 3:

From 76eaeb4523ee01cabbea2d867daac48a277885a1 Mon Sep 17 00:00:00 2001
From: hzw19900416 <carlmartinmax@gmail.com>
Date: Tue, 19 Aug 2014 14:04:49 -0700
Subject: [PATCH 024/489] Move a bracket in validateSettings of SparkConf

Move a bracket in validateSettings of SparkConf

Author: hzw19900416 <carlmartinmax@gmail.com>

Closes #2012 from hzw19900416/codereading and squashes the following commits:

e717fb6 [hzw19900416] Move a bracket in validateSettings of SparkConf
---
 core/src/main/scala/org/apache/spark/SparkConf.scala | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/SparkConf.scala b/core/src/main/scala/org/apache/spark/SparkConf.scala
index 13f0bff7ee507..b4f321ec99e78 100644
--- a/core/src/main/scala/org/apache/spark/SparkConf.scala
+++ b/core/src/main/scala/org/apache/spark/SparkConf.scala
@@ -227,7 +227,7 @@ class SparkConf(loadDefaults: Boolean) extends Cloneable with Logging {
     // Validate spark.executor.extraJavaOptions
     settings.get(executorOptsKey).map { javaOpts =>
       if (javaOpts.contains("-Dspark")) {
-        val msg = s"$executorOptsKey is not allowed to set Spark options (was '$javaOpts)'. " +
+        val msg = s"$executorOptsKey is not allowed to set Spark options (was '$javaOpts'). " +
           "Set them directly on a SparkConf or in a properties file when using ./bin/spark-submit."
         throw new Exception(msg)
       }

From d7e80c2597d4a9cae2e0cb35a86f7889323f4cbb Mon Sep 17 00:00:00 2001
From: Davies Liu <davies.liu@gmail.com>
Date: Tue, 19 Aug 2014 14:46:32 -0700
Subject: [PATCH 025/489] [SPARK-2790] [PySpark] fix zip with serializers which
 have different batch sizes.

If two RDDs have different batch size in serializers, then it will try to re-serialize the one with smaller batch size, then call RDD.zip() in Spark.

Author: Davies Liu <davies.liu@gmail.com>

Closes #1894 from davies/zip and squashes the following commits:

c4652ea [Davies Liu] add more test cases
6d05fc8 [Davies Liu] Merge branch 'master' into zip
813b1e4 [Davies Liu] add more tests for failed cases
a4aafda [Davies Liu] fix zip with serializers which have different batch sizes.
---
 python/pyspark/rdd.py         | 25 +++++++++++++++++++++++++
 python/pyspark/serializers.py |  3 +++
 python/pyspark/tests.py       | 27 ++++++++++++++++++++++++++-
 3 files changed, 54 insertions(+), 1 deletion(-)

diff --git a/python/pyspark/rdd.py b/python/pyspark/rdd.py
index 86cd89b245aea..140cbe05a43b0 100644
--- a/python/pyspark/rdd.py
+++ b/python/pyspark/rdd.py
@@ -1687,6 +1687,31 @@ def zip(self, other):
         >>> x.zip(y).collect()
         [(0, 1000), (1, 1001), (2, 1002), (3, 1003), (4, 1004)]
         """
+        if self.getNumPartitions() != other.getNumPartitions():
+            raise ValueError("Can only zip with RDD which has the same number of partitions")
+
+        def get_batch_size(ser):
+            if isinstance(ser, BatchedSerializer):
+                return ser.batchSize
+            return 0
+
+        def batch_as(rdd, batchSize):
+            ser = rdd._jrdd_deserializer
+            if isinstance(ser, BatchedSerializer):
+                ser = ser.serializer
+            return rdd._reserialize(BatchedSerializer(ser, batchSize))
+
+        my_batch = get_batch_size(self._jrdd_deserializer)
+        other_batch = get_batch_size(other._jrdd_deserializer)
+        if my_batch != other_batch:
+            # use the greatest batchSize to batch the other one.
+            if my_batch > other_batch:
+                other = batch_as(other, my_batch)
+            else:
+                self = batch_as(self, other_batch)
+
+        # There will be an Exception in JVM if there are different number
+        # of items in each partitions.
         pairRDD = self._jrdd.zip(other._jrdd)
         deserializer = PairDeserializer(self._jrdd_deserializer,
                                         other._jrdd_deserializer)
diff --git a/python/pyspark/serializers.py b/python/pyspark/serializers.py
index 74870c0edcf99..fc49aa42dbaf9 100644
--- a/python/pyspark/serializers.py
+++ b/python/pyspark/serializers.py
@@ -255,6 +255,9 @@ def __init__(self, key_ser, val_ser):
 
     def load_stream(self, stream):
         for (keys, vals) in self.prepare_keys_values(stream):
+            if len(keys) != len(vals):
+                raise ValueError("Can not deserialize RDD with different number of items"
+                                 " in pair: (%d, %d)" % (len(keys), len(vals)))
             for pair in izip(keys, vals):
                 yield pair
 
diff --git a/python/pyspark/tests.py b/python/pyspark/tests.py
index 69d543d9d045d..51bfbb47e53c2 100644
--- a/python/pyspark/tests.py
+++ b/python/pyspark/tests.py
@@ -39,7 +39,7 @@
 
 from pyspark.context import SparkContext
 from pyspark.files import SparkFiles
-from pyspark.serializers import read_int
+from pyspark.serializers import read_int, BatchedSerializer, MarshalSerializer, PickleSerializer
 from pyspark.shuffle import Aggregator, InMemoryMerger, ExternalMerger
 
 _have_scipy = False
@@ -339,6 +339,31 @@ def test_large_broadcast(self):
         m = self.sc.parallelize(range(1), 1).map(lambda x: len(bdata.value)).sum()
         self.assertEquals(N, m)
 
+    def test_zip_with_different_serializers(self):
+        a = self.sc.parallelize(range(5))
+        b = self.sc.parallelize(range(100, 105))
+        self.assertEqual(a.zip(b).collect(), [(0, 100), (1, 101), (2, 102), (3, 103), (4, 104)])
+        a = a._reserialize(BatchedSerializer(PickleSerializer(), 2))
+        b = b._reserialize(MarshalSerializer())
+        self.assertEqual(a.zip(b).collect(), [(0, 100), (1, 101), (2, 102), (3, 103), (4, 104)])
+
+    def test_zip_with_different_number_of_items(self):
+        a = self.sc.parallelize(range(5), 2)
+        # different number of partitions
+        b = self.sc.parallelize(range(100, 106), 3)
+        self.assertRaises(ValueError, lambda: a.zip(b))
+        # different number of batched items in JVM
+        b = self.sc.parallelize(range(100, 104), 2)
+        self.assertRaises(Exception, lambda: a.zip(b).count())
+        # different number of items in one pair
+        b = self.sc.parallelize(range(100, 106), 2)
+        self.assertRaises(Exception, lambda: a.zip(b).count())
+        # same total number of items, but different distributions
+        a = self.sc.parallelize([2, 3], 2).flatMap(range)
+        b = self.sc.parallelize([3, 2], 2).flatMap(range)
+        self.assertEquals(a.count(), b.count())
+        self.assertRaises(Exception, lambda: a.zip(b).count())
+
 
 class TestIO(PySparkTestCase):
 

From 825d4fe47b9c4d48de88622dd48dcf83beb8b80a Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Tue, 19 Aug 2014 16:06:48 -0700
Subject: [PATCH 026/489] [SPARK-3136][MLLIB] Create Java-friendly methods in
 RandomRDDs

Though we don't use default argument for methods in RandomRDDs, it is still not easy for Java users to use because the output type is either `RDD[Double]` or `RDD[Vector]`. Java users should expect `JavaDoubleRDD` and `JavaRDD[Vector]`, respectively. We should create dedicated methods for Java users, and allow default arguments in Scala methods in RandomRDDs, to make life easier for both Java and Scala users. This PR also contains documentation for random data generation. brkyvz

Author: Xiangrui Meng <meng@databricks.com>

Closes #2041 from mengxr/stat-doc and squashes the following commits:

fc5eedf [Xiangrui Meng] add missing comma
ffde810 [Xiangrui Meng] address comments
aef6d07 [Xiangrui Meng] add doc for random data generation
b99d94b [Xiangrui Meng] add java-friendly methods to RandomRDDs
---
 docs/mllib-guide.md                           |   2 +-
 docs/mllib-stats.md                           |  74 ++-
 .../mllib/random/RandomDataGenerator.scala    |  18 +-
 .../spark/mllib/random/RandomRDDs.scala       | 476 +++++++-----------
 .../mllib/random/JavaRandomRDDsSuite.java     | 134 +++++
 python/pyspark/mllib/random.py                |  20 +-
 6 files changed, 418 insertions(+), 306 deletions(-)
 create mode 100644 mllib/src/test/java/org/apache/spark/mllib/random/JavaRandomRDDsSuite.java

diff --git a/docs/mllib-guide.md b/docs/mllib-guide.md
index 23d5a0c4607af..ca0a84a8c53fd 100644
--- a/docs/mllib-guide.md
+++ b/docs/mllib-guide.md
@@ -9,7 +9,7 @@ filtering, dimensionality reduction, as well as underlying optimization primitiv
 
 * [Data types](mllib-basics.html)
 * [Basic statistics](mllib-stats.html)
-  * data generators  
+  * random data generation  
   * stratified sampling
   * summary statistics
   * hypothesis testing
diff --git a/docs/mllib-stats.md b/docs/mllib-stats.md
index ca9ef46c15186..f25dca746ba3a 100644
--- a/docs/mllib-stats.md
+++ b/docs/mllib-stats.md
@@ -25,7 +25,79 @@ displayTitle: <a href="mllib-guide.html">MLlib</a> - Statistics Functionality
 \newcommand{\zero}{\mathbf{0}}
 \]`
 
-## Data Generators 
+## Random data generation
+
+Random data generation is useful for randomized algorithms, prototyping, and performance testing.
+MLlib supports generating random RDDs with i.i.d. values drawn from a given distribution:
+uniform, standard normal, or Poisson.
+
+<div class="codetabs">
+<div data-lang="scala" markdown="1">
+[`RandomRDDs`](api/scala/index.html#org.apache.spark.mllib.random.RandomRDDs) provides factory
+methods to generate random double RDDs or vector RDDs.
+The following example generates a random double RDD, whose values follows the standard normal
+distribution `N(0, 1)`, and then map it to `N(1, 4)`.
+
+{% highlight scala %}
+import org.apache.spark.SparkContext
+import org.apache.spark.mllib.random.RandomRDDs._
+
+val sc: SparkContext = ...
+
+// Generate a random double RDD that contains 1 million i.i.d. values drawn from the
+// standard normal distribution `N(0, 1)`, evenly distributed in 10 partitions.
+val u = normalRDD(sc, 1000000L, 10)
+// Apply a transform to get a random double RDD following `N(1, 4)`.
+val v = u.map(x => 1.0 + 2.0 * x)
+{% endhighlight %}
+</div>
+
+<div data-lang="java" markdown="1">
+[`RandomRDDs`](api/java/index.html#org.apache.spark.mllib.random.RandomRDDs) provides factory
+methods to generate random double RDDs or vector RDDs.
+The following example generates a random double RDD, whose values follows the standard normal
+distribution `N(0, 1)`, and then map it to `N(1, 4)`.
+
+{% highlight java %}
+import org.apache.spark.SparkContext;
+import org.apache.spark.api.JavaDoubleRDD;
+import static org.apache.spark.mllib.random.RandomRDDs.*;
+
+JavaSparkContext jsc = ...
+
+// Generate a random double RDD that contains 1 million i.i.d. values drawn from the
+// standard normal distribution `N(0, 1)`, evenly distributed in 10 partitions.
+JavaDoubleRDD u = normalJavaRDD(jsc, 1000000L, 10);
+// Apply a transform to get a random double RDD following `N(1, 4)`.
+JavaDoubleRDD v = u.map(
+  new Function<Double, Double>() {
+    public Double call(Double x) {
+      return 1.0 + 2.0 * x;
+    }
+  });
+{% endhighlight %}
+</div>
+
+<div data-lang="python" markdown="1">
+[`RandomRDDs`](api/python/pyspark.mllib.random.RandomRDDs-class.html) provides factory
+methods to generate random double RDDs or vector RDDs.
+The following example generates a random double RDD, whose values follows the standard normal
+distribution `N(0, 1)`, and then map it to `N(1, 4)`.
+
+{% highlight python %}
+from pyspark.mllib.random import RandomRDDs
+
+sc = ... # SparkContext
+
+# Generate a random double RDD that contains 1 million i.i.d. values drawn from the
+# standard normal distribution `N(0, 1)`, evenly distributed in 10 partitions.
+u = RandomRDDs.uniformRDD(sc, 1000000L, 10)
+# Apply a transform to get a random double RDD following `N(1, 4)`.
+v = u.map(lambda x: 1.0 + 2.0 * x)
+{% endhighlight %}
+</div>
+
+</div>
 
 ## Stratified Sampling 
 
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/random/RandomDataGenerator.scala b/mllib/src/main/scala/org/apache/spark/mllib/random/RandomDataGenerator.scala
index 9cab49f6ed1f0..28179fbc450c0 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/random/RandomDataGenerator.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/random/RandomDataGenerator.scala
@@ -20,14 +20,14 @@ package org.apache.spark.mllib.random
 import cern.jet.random.Poisson
 import cern.jet.random.engine.DRand
 
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.util.random.{XORShiftRandom, Pseudorandom}
 
 /**
- * :: Experimental ::
+ * :: DeveloperApi ::
  * Trait for random data generators that generate i.i.d. data.
  */
-@Experimental
+@DeveloperApi
 trait RandomDataGenerator[T] extends Pseudorandom with Serializable {
 
   /**
@@ -43,10 +43,10 @@ trait RandomDataGenerator[T] extends Pseudorandom with Serializable {
 }
 
 /**
- * :: Experimental ::
+ * :: DeveloperApi ::
  * Generates i.i.d. samples from U[0.0, 1.0]
  */
-@Experimental
+@DeveloperApi
 class UniformGenerator extends RandomDataGenerator[Double] {
 
   // XORShiftRandom for better performance. Thread safety isn't necessary here.
@@ -62,10 +62,10 @@ class UniformGenerator extends RandomDataGenerator[Double] {
 }
 
 /**
- * :: Experimental ::
+ * :: DeveloperApi ::
  * Generates i.i.d. samples from the standard normal distribution.
  */
-@Experimental
+@DeveloperApi
 class StandardNormalGenerator extends RandomDataGenerator[Double] {
 
   // XORShiftRandom for better performance. Thread safety isn't necessary here.
@@ -81,12 +81,12 @@ class StandardNormalGenerator extends RandomDataGenerator[Double] {
 }
 
 /**
- * :: Experimental ::
+ * :: DeveloperApi ::
  * Generates i.i.d. samples from the Poisson distribution with the given mean.
  *
  * @param mean mean for the Poisson distribution.
  */
-@Experimental
+@DeveloperApi
 class PoissonGenerator(val mean: Double) extends RandomDataGenerator[Double] {
 
   private var rng = new Poisson(mean, new DRand)
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/random/RandomRDDs.scala b/mllib/src/main/scala/org/apache/spark/mllib/random/RandomRDDs.scala
index 36270369526cd..c5f4b084321f7 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/random/RandomRDDs.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/random/RandomRDDs.scala
@@ -20,9 +20,10 @@ package org.apache.spark.mllib.random
 import scala.reflect.ClassTag
 
 import org.apache.spark.SparkContext
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{DeveloperApi, Experimental}
+import org.apache.spark.api.java.{JavaDoubleRDD, JavaRDD, JavaSparkContext}
 import org.apache.spark.mllib.linalg.Vector
-import org.apache.spark.mllib.rdd.{RandomVectorRDD, RandomRDD}
+import org.apache.spark.mllib.rdd.{RandomRDD, RandomVectorRDD}
 import org.apache.spark.rdd.RDD
 import org.apache.spark.util.Utils
 
@@ -34,335 +35,279 @@ import org.apache.spark.util.Utils
 object RandomRDDs {
 
   /**
-   * :: Experimental ::
-   * Generates an RDD comprised of i.i.d. samples from the uniform distribution on [0.0, 1.0].
+   * Generates an RDD comprised of i.i.d. samples from the uniform distribution `U(0.0, 1.0)`.
    *
-   * To transform the distribution in the generated RDD from U[0.0, 1.0] to U[a, b], use
-   * `RandomRDDGenerators.uniformRDD(sc, n, p, seed).map(v => a + (b - a) * v)`.
+   * To transform the distribution in the generated RDD from `U(0.0, 1.0)` to `U(a, b)`, use
+   * `RandomRDDs.uniformRDD(sc, n, p, seed).map(v => a + (b - a) * v)`.
    *
    * @param sc SparkContext used to create the RDD.
    * @param size Size of the RDD.
-   * @param numPartitions Number of partitions in the RDD.
-   * @param seed Seed for the RNG that generates the seed for the generator in each partition.
-   * @return RDD[Double] comprised of i.i.d. samples ~ U[0.0, 1.0].
+   * @param numPartitions Number of partitions in the RDD (default: `sc.defaultParallelism`).
+   * @param seed Random seed (default: a random long integer).
+   * @return RDD[Double] comprised of i.i.d. samples ~ `U(0.0, 1.0)`.
    */
-  @Experimental
-  def uniformRDD(sc: SparkContext, size: Long, numPartitions: Int, seed: Long): RDD[Double] = {
+  def uniformRDD(
+      sc: SparkContext,
+      size: Long,
+      numPartitions: Int = 0,
+      seed: Long = Utils.random.nextLong()): RDD[Double] = {
     val uniform = new UniformGenerator()
-    randomRDD(sc, uniform,  size, numPartitions, seed)
+    randomRDD(sc, uniform,  size, numPartitionsOrDefault(sc, numPartitions), seed)
   }
 
   /**
-   * :: Experimental ::
-   * Generates an RDD comprised of i.i.d. samples from the uniform distribution on [0.0, 1.0].
-   *
-   * To transform the distribution in the generated RDD from U[0.0, 1.0] to U[a, b], use
-   * `RandomRDDGenerators.uniformRDD(sc, n, p).map(v => a + (b - a) * v)`.
-   *
-   * @param sc SparkContext used to create the RDD.
-   * @param size Size of the RDD.
-   * @param numPartitions Number of partitions in the RDD.
-   * @return RDD[Double] comprised of i.i.d. samples ~ U[0.0, 1.0].
+   * Java-friendly version of [[RandomRDDs#uniformRDD]].
    */
-  @Experimental
-  def uniformRDD(sc: SparkContext, size: Long, numPartitions: Int): RDD[Double] = {
-    uniformRDD(sc, size, numPartitions, Utils.random.nextLong)
+  def uniformJavaRDD(
+      jsc: JavaSparkContext,
+      size: Long,
+      numPartitions: Int,
+      seed: Long): JavaDoubleRDD = {
+    JavaDoubleRDD.fromRDD(uniformRDD(jsc.sc, size, numPartitions, seed))
   }
 
   /**
-   * :: Experimental ::
-   * Generates an RDD comprised of i.i.d. samples from the uniform distribution on [0.0, 1.0].
-   * sc.defaultParallelism used for the number of partitions in the RDD.
-   *
-   * To transform the distribution in the generated RDD from U[0.0, 1.0] to U[a, b], use
-   * `RandomRDDGenerators.uniformRDD(sc, n).map(v => a + (b - a) * v)`.
-   *
-   * @param sc SparkContext used to create the RDD.
-   * @param size Size of the RDD.
-   * @return RDD[Double] comprised of i.i.d. samples ~ U[0.0, 1.0].
+   * [[RandomRDDs#uniformJavaRDD]] with the default seed.
    */
-  @Experimental
-  def uniformRDD(sc: SparkContext, size: Long): RDD[Double] = {
-    uniformRDD(sc, size, sc.defaultParallelism, Utils.random.nextLong)
+  def uniformJavaRDD(jsc: JavaSparkContext, size: Long, numPartitions: Int): JavaDoubleRDD = {
+    JavaDoubleRDD.fromRDD(uniformRDD(jsc.sc, size, numPartitions))
   }
 
   /**
-   * :: Experimental ::
-   * Generates an RDD comprised of i.i.d. samples from the standard normal distribution.
-   *
-   * To transform the distribution in the generated RDD from standard normal to some other normal
-   * N(mean, sigma), use `RandomRDDGenerators.normalRDD(sc, n, p, seed).map(v => mean + sigma * v)`.
-   *
-   * @param sc SparkContext used to create the RDD.
-   * @param size Size of the RDD.
-   * @param numPartitions Number of partitions in the RDD.
-   * @param seed Seed for the RNG that generates the seed for the generator in each partition.
-   * @return RDD[Double] comprised of i.i.d. samples ~ N(0.0, 1.0).
+   * [[RandomRDDs#uniformJavaRDD]] with the default number of partitions and the default seed.
    */
-  @Experimental
-  def normalRDD(sc: SparkContext, size: Long, numPartitions: Int, seed: Long): RDD[Double] = {
-    val normal = new StandardNormalGenerator()
-    randomRDD(sc, normal, size, numPartitions, seed)
+  def uniformJavaRDD(jsc: JavaSparkContext, size: Long): JavaDoubleRDD = {
+    JavaDoubleRDD.fromRDD(uniformRDD(jsc.sc, size))
   }
 
   /**
-   * :: Experimental ::
    * Generates an RDD comprised of i.i.d. samples from the standard normal distribution.
    *
    * To transform the distribution in the generated RDD from standard normal to some other normal
-   * N(mean, sigma), use `RandomRDDGenerators.normalRDD(sc, n, p).map(v => mean + sigma * v)`.
+   * `N(mean, sigma^2^)`, use `RandomRDDs.normalRDD(sc, n, p, seed).map(v => mean + sigma * v)`.
    *
    * @param sc SparkContext used to create the RDD.
    * @param size Size of the RDD.
-   * @param numPartitions Number of partitions in the RDD.
+   * @param numPartitions Number of partitions in the RDD (default: `sc.defaultParallelism`).
+   * @param seed Random seed (default: a random long integer).
    * @return RDD[Double] comprised of i.i.d. samples ~ N(0.0, 1.0).
    */
-  @Experimental
-  def normalRDD(sc: SparkContext, size: Long, numPartitions: Int): RDD[Double] = {
-    normalRDD(sc, size, numPartitions, Utils.random.nextLong)
+  def normalRDD(
+      sc: SparkContext,
+      size: Long,
+      numPartitions: Int = 0,
+      seed: Long = Utils.random.nextLong()): RDD[Double] = {
+    val normal = new StandardNormalGenerator()
+    randomRDD(sc, normal, size, numPartitionsOrDefault(sc, numPartitions), seed)
   }
 
   /**
-   * :: Experimental ::
-   * Generates an RDD comprised of i.i.d. samples from the standard normal distribution.
-   * sc.defaultParallelism used for the number of partitions in the RDD.
-   *
-   * To transform the distribution in the generated RDD from standard normal to some other normal
-   * N(mean, sigma), use `RandomRDDGenerators.normalRDD(sc, n).map(v => mean + sigma * v)`.
-   *
-   * @param sc SparkContext used to create the RDD.
-   * @param size Size of the RDD.
-   * @return RDD[Double] comprised of i.i.d. samples ~ N(0.0, 1.0).
+   * Java-friendly version of [[RandomRDDs#normalRDD]].
    */
-  @Experimental
-  def normalRDD(sc: SparkContext, size: Long): RDD[Double] = {
-    normalRDD(sc, size, sc.defaultParallelism, Utils.random.nextLong)
+  def normalJavaRDD(
+      jsc: JavaSparkContext,
+      size: Long,
+      numPartitions: Int,
+      seed: Long): JavaDoubleRDD = {
+    JavaDoubleRDD.fromRDD(normalRDD(jsc.sc, size, numPartitions, seed))
   }
 
   /**
-   * :: Experimental ::
-   * Generates an RDD comprised of i.i.d. samples from the Poisson distribution with the input mean.
-   *
-   * @param sc SparkContext used to create the RDD.
-   * @param mean Mean, or lambda, for the Poisson distribution.
-   * @param size Size of the RDD.
-   * @param numPartitions Number of partitions in the RDD.
-   * @param seed Seed for the RNG that generates the seed for the generator in each partition.
-   * @return RDD[Double] comprised of i.i.d. samples ~ Pois(mean).
+   * [[RandomRDDs#normalJavaRDD]] with the default seed.
    */
-  @Experimental
-  def poissonRDD(sc: SparkContext,
-      mean: Double,
-      size: Long,
-      numPartitions: Int,
-      seed: Long): RDD[Double] = {
-    val poisson = new PoissonGenerator(mean)
-    randomRDD(sc, poisson, size, numPartitions, seed)
+  def normalJavaRDD(jsc: JavaSparkContext, size: Long, numPartitions: Int): JavaDoubleRDD = {
+    JavaDoubleRDD.fromRDD(normalRDD(jsc.sc, size, numPartitions))
   }
 
   /**
-   * :: Experimental ::
-   * Generates an RDD comprised of i.i.d. samples from the Poisson distribution with the input mean.
-   *
-   * @param sc SparkContext used to create the RDD.
-   * @param mean Mean, or lambda, for the Poisson distribution.
-   * @param size Size of the RDD.
-   * @param numPartitions Number of partitions in the RDD.
-   * @return RDD[Double] comprised of i.i.d. samples ~ Pois(mean).
+   * [[RandomRDDs#normalJavaRDD]] with the default number of partitions and the default seed.
    */
-  @Experimental
-  def poissonRDD(sc: SparkContext, mean: Double, size: Long, numPartitions: Int): RDD[Double] = {
-    poissonRDD(sc, mean, size, numPartitions, Utils.random.nextLong)
+  def normalJavaRDD(jsc: JavaSparkContext, size: Long): JavaDoubleRDD = {
+    JavaDoubleRDD.fromRDD(normalRDD(jsc.sc, size))
   }
 
   /**
-   * :: Experimental ::
    * Generates an RDD comprised of i.i.d. samples from the Poisson distribution with the input mean.
-   * sc.defaultParallelism used for the number of partitions in the RDD.
    *
    * @param sc SparkContext used to create the RDD.
    * @param mean Mean, or lambda, for the Poisson distribution.
    * @param size Size of the RDD.
+   * @param numPartitions Number of partitions in the RDD (default: `sc.defaultParallelism`).
+   * @param seed Random seed (default: a random long integer).
    * @return RDD[Double] comprised of i.i.d. samples ~ Pois(mean).
    */
-  @Experimental
-  def poissonRDD(sc: SparkContext, mean: Double, size: Long): RDD[Double] = {
-    poissonRDD(sc, mean, size, sc.defaultParallelism, Utils.random.nextLong)
+  def poissonRDD(
+      sc: SparkContext,
+      mean: Double,
+      size: Long,
+      numPartitions: Int = 0,
+      seed: Long = Utils.random.nextLong()): RDD[Double] = {
+    val poisson = new PoissonGenerator(mean)
+    randomRDD(sc, poisson, size, numPartitionsOrDefault(sc, numPartitions), seed)
   }
 
   /**
-   * :: Experimental ::
-   * Generates an RDD comprised of i.i.d. samples produced by the input DistributionGenerator.
-   *
-   * @param sc SparkContext used to create the RDD.
-   * @param generator DistributionGenerator used to populate the RDD.
-   * @param size Size of the RDD.
-   * @param numPartitions Number of partitions in the RDD.
-   * @param seed Seed for the RNG that generates the seed for the generator in each partition.
-   * @return RDD[Double] comprised of i.i.d. samples produced by generator.
+   * Java-friendly version of [[RandomRDDs#poissonRDD]].
    */
-  @Experimental
-  def randomRDD[T: ClassTag](sc: SparkContext,
-      generator: RandomDataGenerator[T],
+  def poissonJavaRDD(
+      jsc: JavaSparkContext,
+      mean: Double,
       size: Long,
       numPartitions: Int,
-      seed: Long): RDD[T] = {
-    new RandomRDD[T](sc, size, numPartitions, generator, seed)
+      seed: Long): JavaDoubleRDD = {
+    JavaDoubleRDD.fromRDD(poissonRDD(jsc.sc, mean, size, numPartitions, seed))
   }
 
   /**
-   * :: Experimental ::
-   * Generates an RDD comprised of i.i.d. samples produced by the input DistributionGenerator.
-   *
-   * @param sc SparkContext used to create the RDD.
-   * @param generator DistributionGenerator used to populate the RDD.
-   * @param size Size of the RDD.
-   * @param numPartitions Number of partitions in the RDD.
-   * @return RDD[Double] comprised of i.i.d. samples produced by generator.
+   * [[RandomRDDs#poissonJavaRDD]] with the default seed.
    */
-  @Experimental
-  def randomRDD[T: ClassTag](sc: SparkContext,
-      generator: RandomDataGenerator[T],
+  def poissonJavaRDD(
+      jsc: JavaSparkContext,
+      mean: Double,
       size: Long,
-      numPartitions: Int): RDD[T] = {
-    randomRDD[T](sc, generator, size, numPartitions, Utils.random.nextLong)
+      numPartitions: Int): JavaDoubleRDD = {
+    JavaDoubleRDD.fromRDD(poissonRDD(jsc.sc, mean, size, numPartitions))
   }
 
   /**
-   * :: Experimental ::
-   * Generates an RDD comprised of i.i.d. samples produced by the input DistributionGenerator.
-   * sc.defaultParallelism used for the number of partitions in the RDD.
+   * [[RandomRDDs#poissonJavaRDD]] with the default number of partitions and the default seed.
+   */
+  def poissonJavaRDD(jsc: JavaSparkContext, mean: Double, size: Long): JavaDoubleRDD = {
+    JavaDoubleRDD.fromRDD(poissonRDD(jsc.sc, mean, size))
+  }
+
+  /**
+   * :: DeveloperApi ::
+   * Generates an RDD comprised of i.i.d. samples produced by the input RandomDataGenerator.
    *
    * @param sc SparkContext used to create the RDD.
-   * @param generator DistributionGenerator used to populate the RDD.
+   * @param generator RandomDataGenerator used to populate the RDD.
    * @param size Size of the RDD.
+   * @param numPartitions Number of partitions in the RDD (default: `sc.defaultParallelism`).
+   * @param seed Random seed (default: a random long integer).
    * @return RDD[Double] comprised of i.i.d. samples produced by generator.
    */
-  @Experimental
-  def randomRDD[T: ClassTag](sc: SparkContext,
+  @DeveloperApi
+  def randomRDD[T: ClassTag](
+      sc: SparkContext,
       generator: RandomDataGenerator[T],
-      size: Long): RDD[T] = {
-    randomRDD[T](sc, generator, size, sc.defaultParallelism, Utils.random.nextLong)
+      size: Long,
+      numPartitions: Int = 0,
+      seed: Long = Utils.random.nextLong()): RDD[T] = {
+    new RandomRDD[T](sc, size, numPartitionsOrDefault(sc, numPartitions), generator, seed)
   }
 
   // TODO Generate RDD[Vector] from multivariate distributions.
 
   /**
-   * :: Experimental ::
    * Generates an RDD[Vector] with vectors containing i.i.d. samples drawn from the
-   * uniform distribution on [0.0 1.0].
+   * uniform distribution on `U(0.0, 1.0)`.
    *
    * @param sc SparkContext used to create the RDD.
    * @param numRows Number of Vectors in the RDD.
    * @param numCols Number of elements in each Vector.
    * @param numPartitions Number of partitions in the RDD.
    * @param seed Seed for the RNG that generates the seed for the generator in each partition.
-   * @return RDD[Vector] with vectors containing i.i.d samples ~ U[0.0, 1.0].
+   * @return RDD[Vector] with vectors containing i.i.d samples ~ `U(0.0, 1.0)`.
    */
-  @Experimental
-  def uniformVectorRDD(sc: SparkContext,
+  def uniformVectorRDD(
+      sc: SparkContext,
       numRows: Long,
       numCols: Int,
-      numPartitions: Int,
-      seed: Long): RDD[Vector] = {
+      numPartitions: Int = 0,
+      seed: Long = Utils.random.nextLong()): RDD[Vector] = {
     val uniform = new UniformGenerator()
-    randomVectorRDD(sc, uniform, numRows, numCols, numPartitions, seed)
+    randomVectorRDD(sc, uniform, numRows, numCols, numPartitionsOrDefault(sc, numPartitions), seed)
   }
 
   /**
-   * :: Experimental ::
-   * Generates an RDD[Vector] with vectors containing i.i.d. samples drawn from the
-   * uniform distribution on [0.0 1.0].
-   *
-   * @param sc SparkContext used to create the RDD.
-   * @param numRows Number of Vectors in the RDD.
-   * @param numCols Number of elements in each Vector.
-   * @param numPartitions Number of partitions in the RDD.
-   * @return RDD[Vector] with vectors containing i.i.d. samples ~ U[0.0, 1.0].
+   * Java-friendly version of [[RandomRDDs#uniformVectorRDD]].
    */
-  @Experimental
-  def uniformVectorRDD(sc: SparkContext,
+  def uniformJavaVectorRDD(
+      jsc: JavaSparkContext,
       numRows: Long,
       numCols: Int,
-      numPartitions: Int): RDD[Vector] = {
-    uniformVectorRDD(sc, numRows, numCols, numPartitions, Utils.random.nextLong)
+      numPartitions: Int,
+      seed: Long): JavaRDD[Vector] = {
+    uniformVectorRDD(jsc.sc, numRows, numCols, numPartitions, seed).toJavaRDD()
   }
 
   /**
-   * :: Experimental ::
-   * Generates an RDD[Vector] with vectors containing i.i.d. samples drawn from the
-   * uniform distribution on [0.0 1.0].
-   * sc.defaultParallelism used for the number of partitions in the RDD.
-   *
-   * @param sc SparkContext used to create the RDD.
-   * @param numRows Number of Vectors in the RDD.
-   * @param numCols Number of elements in each Vector.
-   * @return RDD[Vector] with vectors containing i.i.d. samples ~ U[0.0, 1.0].
+   * [[RandomRDDs#uniformJavaVectorRDD]] with the default seed.
    */
-  @Experimental
-  def uniformVectorRDD(sc: SparkContext, numRows: Long, numCols: Int): RDD[Vector] = {
-    uniformVectorRDD(sc, numRows, numCols, sc.defaultParallelism, Utils.random.nextLong)
+  def uniformJavaVectorRDD(
+      jsc: JavaSparkContext,
+      numRows: Long,
+      numCols: Int,
+      numPartitions: Int): JavaRDD[Vector] = {
+    uniformVectorRDD(jsc.sc, numRows, numCols, numPartitions).toJavaRDD()
+  }
+
+  /**
+   * [[RandomRDDs#uniformJavaVectorRDD]] with the default number of partitions and the default seed.
+   */
+  def uniformJavaVectorRDD(
+      jsc: JavaSparkContext,
+      numRows: Long,
+      numCols: Int): JavaRDD[Vector] = {
+    uniformVectorRDD(jsc.sc, numRows, numCols).toJavaRDD()
   }
 
   /**
-   * :: Experimental ::
    * Generates an RDD[Vector] with vectors containing i.i.d. samples drawn from the
    * standard normal distribution.
    *
    * @param sc SparkContext used to create the RDD.
    * @param numRows Number of Vectors in the RDD.
    * @param numCols Number of elements in each Vector.
-   * @param numPartitions Number of partitions in the RDD.
-   * @param seed Seed for the RNG that generates the seed for the generator in each partition.
-   * @return RDD[Vector] with vectors containing i.i.d. samples ~ N(0.0, 1.0).
+   * @param numPartitions Number of partitions in the RDD (default: `sc.defaultParallelism`).
+   * @param seed Random seed (default: a random long integer).
+   * @return RDD[Vector] with vectors containing i.i.d. samples ~ `N(0.0, 1.0)`.
+   */
+  def normalVectorRDD(
+      sc: SparkContext,
+      numRows: Long,
+      numCols: Int,
+      numPartitions: Int = 0,
+      seed: Long = Utils.random.nextLong()): RDD[Vector] = {
+    val normal = new StandardNormalGenerator()
+    randomVectorRDD(sc, normal, numRows, numCols, numPartitionsOrDefault(sc, numPartitions), seed)
+  }
+
+  /**
+   * Java-friendly version of [[RandomRDDs#normalVectorRDD]].
    */
-  @Experimental
-  def normalVectorRDD(sc: SparkContext,
+  def normalJavaVectorRDD(
+      jsc: JavaSparkContext,
       numRows: Long,
       numCols: Int,
       numPartitions: Int,
-      seed: Long): RDD[Vector] = {
-    val uniform = new StandardNormalGenerator()
-    randomVectorRDD(sc, uniform, numRows, numCols, numPartitions, seed)
+      seed: Long): JavaRDD[Vector] = {
+    normalVectorRDD(jsc.sc, numRows, numCols, numPartitions, seed).toJavaRDD()
   }
 
   /**
-   * :: Experimental ::
-   * Generates an RDD[Vector] with vectors containing i.i.d. samples drawn from the
-   * standard normal distribution.
-   *
-   * @param sc SparkContext used to create the RDD.
-   * @param numRows Number of Vectors in the RDD.
-   * @param numCols Number of elements in each Vector.
-   * @param numPartitions Number of partitions in the RDD.
-   * @return RDD[Vector] with vectors containing i.i.d. samples ~ N(0.0, 1.0).
+   * [[RandomRDDs#normalJavaVectorRDD]] with the default seed.
    */
-  @Experimental
-  def normalVectorRDD(sc: SparkContext,
+  def normalJavaVectorRDD(
+      jsc: JavaSparkContext,
       numRows: Long,
       numCols: Int,
-      numPartitions: Int): RDD[Vector] = {
-    normalVectorRDD(sc, numRows, numCols, numPartitions, Utils.random.nextLong)
+      numPartitions: Int): JavaRDD[Vector] = {
+    normalVectorRDD(jsc.sc, numRows, numCols, numPartitions).toJavaRDD()
   }
 
   /**
-   * :: Experimental ::
-   * Generates an RDD[Vector] with vectors containing i.i.d. samples drawn from the
-   * standard normal distribution.
-   * sc.defaultParallelism used for the number of partitions in the RDD.
-   *
-   * @param sc SparkContext used to create the RDD.
-   * @param numRows Number of Vectors in the RDD.
-   * @param numCols Number of elements in each Vector.
-   * @return RDD[Vector] with vectors containing i.i.d. samples ~ N(0.0, 1.0).
+   * [[RandomRDDs#normalJavaVectorRDD]] with the default number of partitions and the default seed.
    */
-  @Experimental
-  def normalVectorRDD(sc: SparkContext, numRows: Long, numCols: Int): RDD[Vector] = {
-    normalVectorRDD(sc, numRows, numCols, sc.defaultParallelism, Utils.random.nextLong)
+  def normalJavaVectorRDD(
+      jsc: JavaSparkContext,
+      numRows: Long,
+      numCols: Int): JavaRDD[Vector] = {
+    normalVectorRDD(jsc.sc, numRows, numCols).toJavaRDD()
   }
 
   /**
-   * :: Experimental ::
    * Generates an RDD[Vector] with vectors containing i.i.d. samples drawn from the
    * Poisson distribution with the input mean.
    *
@@ -370,124 +315,85 @@ object RandomRDDs {
    * @param mean Mean, or lambda, for the Poisson distribution.
    * @param numRows Number of Vectors in the RDD.
    * @param numCols Number of elements in each Vector.
-   * @param numPartitions Number of partitions in the RDD.
-   * @param seed Seed for the RNG that generates the seed for the generator in each partition.
+   * @param numPartitions Number of partitions in the RDD (default: `sc.defaultParallelism`)
+   * @param seed Random seed (default: a random long integer).
    * @return RDD[Vector] with vectors containing i.i.d. samples ~ Pois(mean).
    */
-  @Experimental
-  def poissonVectorRDD(sc: SparkContext,
+  def poissonVectorRDD(
+      sc: SparkContext,
       mean: Double,
       numRows: Long,
       numCols: Int,
-      numPartitions: Int,
-      seed: Long): RDD[Vector] = {
+      numPartitions: Int = 0,
+      seed: Long = Utils.random.nextLong()): RDD[Vector] = {
     val poisson = new PoissonGenerator(mean)
-    randomVectorRDD(sc, poisson, numRows, numCols, numPartitions, seed)
+    randomVectorRDD(sc, poisson, numRows, numCols, numPartitionsOrDefault(sc, numPartitions), seed)
   }
 
   /**
-   * :: Experimental ::
-   * Generates an RDD[Vector] with vectors containing i.i.d. samples drawn from the
-   * Poisson distribution with the input mean.
-   *
-   * @param sc SparkContext used to create the RDD.
-   * @param mean Mean, or lambda, for the Poisson distribution.
-   * @param numRows Number of Vectors in the RDD.
-   * @param numCols Number of elements in each Vector.
-   * @param numPartitions Number of partitions in the RDD.
-   * @return RDD[Vector] with vectors containing i.i.d. samples ~ Pois(mean).
+   * Java-friendly version of [[RandomRDDs#poissonVectorRDD]].
    */
-  @Experimental
-  def poissonVectorRDD(sc: SparkContext,
+  def poissonJavaVectorRDD(
+      jsc: JavaSparkContext,
       mean: Double,
       numRows: Long,
       numCols: Int,
-      numPartitions: Int): RDD[Vector] = {
-    poissonVectorRDD(sc, mean, numRows, numCols, numPartitions, Utils.random.nextLong)
+      numPartitions: Int,
+      seed: Long): JavaRDD[Vector] = {
+    poissonVectorRDD(jsc.sc, mean, numRows, numCols, numPartitions, seed).toJavaRDD()
   }
 
   /**
-   * :: Experimental ::
-   * Generates an RDD[Vector] with vectors containing i.i.d. samples drawn from the
-   * Poisson distribution with the input mean.
-   * sc.defaultParallelism used for the number of partitions in the RDD.
-   *
-   * @param sc SparkContext used to create the RDD.
-   * @param mean Mean, or lambda, for the Poisson distribution.
-   * @param numRows Number of Vectors in the RDD.
-   * @param numCols Number of elements in each Vector.
-   * @return RDD[Vector] with vectors containing i.i.d. samples ~ Pois(mean).
+   * [[RandomRDDs#poissonJavaVectorRDD]] with the default seed.
    */
-  @Experimental
-  def poissonVectorRDD(sc: SparkContext,
+  def poissonJavaVectorRDD(
+      jsc: JavaSparkContext,
       mean: Double,
       numRows: Long,
-      numCols: Int): RDD[Vector] = {
-    poissonVectorRDD(sc, mean, numRows, numCols, sc.defaultParallelism, Utils.random.nextLong)
+      numCols: Int,
+      numPartitions: Int): JavaRDD[Vector] = {
+    poissonVectorRDD(jsc.sc, mean, numRows, numCols, numPartitions).toJavaRDD()
   }
 
   /**
-   * :: Experimental ::
-   * Generates an RDD[Vector] with vectors containing i.i.d. samples produced by the
-   * input DistributionGenerator.
-   *
-   * @param sc SparkContext used to create the RDD.
-   * @param generator DistributionGenerator used to populate the RDD.
-   * @param numRows Number of Vectors in the RDD.
-   * @param numCols Number of elements in each Vector.
-   * @param numPartitions Number of partitions in the RDD.
-   * @param seed Seed for the RNG that generates the seed for the generator in each partition.
-   * @return RDD[Vector] with vectors containing i.i.d. samples produced by generator.
+   * [[RandomRDDs#poissonJavaVectorRDD]] with the default number of partitions and the default seed.
    */
-  @Experimental
-  def randomVectorRDD(sc: SparkContext,
-      generator: RandomDataGenerator[Double],
+  def poissonJavaVectorRDD(
+      jsc: JavaSparkContext,
+      mean: Double,
       numRows: Long,
-      numCols: Int,
-      numPartitions: Int,
-      seed: Long): RDD[Vector] = {
-    new RandomVectorRDD(sc, numRows, numCols, numPartitions, generator, seed)
+      numCols: Int): JavaRDD[Vector] = {
+    poissonVectorRDD(jsc.sc, mean, numRows, numCols).toJavaRDD()
   }
 
   /**
-   * :: Experimental ::
+   * :: DeveloperApi ::
    * Generates an RDD[Vector] with vectors containing i.i.d. samples produced by the
-   * input DistributionGenerator.
+   * input RandomDataGenerator.
    *
    * @param sc SparkContext used to create the RDD.
-   * @param generator DistributionGenerator used to populate the RDD.
+   * @param generator RandomDataGenerator used to populate the RDD.
    * @param numRows Number of Vectors in the RDD.
    * @param numCols Number of elements in each Vector.
-   * @param numPartitions Number of partitions in the RDD.
+   * @param numPartitions Number of partitions in the RDD (default: `sc.defaultParallelism`).
+   * @param seed Random seed (default: a random long integer).
    * @return RDD[Vector] with vectors containing i.i.d. samples produced by generator.
    */
-  @Experimental
+  @DeveloperApi
   def randomVectorRDD(sc: SparkContext,
       generator: RandomDataGenerator[Double],
       numRows: Long,
       numCols: Int,
-      numPartitions: Int): RDD[Vector] = {
-    randomVectorRDD(sc, generator, numRows, numCols, numPartitions, Utils.random.nextLong)
+      numPartitions: Int = 0,
+      seed: Long = Utils.random.nextLong()): RDD[Vector] = {
+    new RandomVectorRDD(
+      sc, numRows, numCols, numPartitionsOrDefault(sc, numPartitions), generator, seed)
   }
 
   /**
-   * :: Experimental ::
-   * Generates an RDD[Vector] with vectors containing i.i.d. samples produced by the
-   * input DistributionGenerator.
-   * sc.defaultParallelism used for the number of partitions in the RDD.
-   *
-   * @param sc SparkContext used to create the RDD.
-   * @param generator DistributionGenerator used to populate the RDD.
-   * @param numRows Number of Vectors in the RDD.
-   * @param numCols Number of elements in each Vector.
-   * @return RDD[Vector] with vectors containing i.i.d. samples produced by generator.
+   * Returns `numPartitions` if it is positive, or `sc.defaultParallelism` otherwise.
    */
-  @Experimental
-  def randomVectorRDD(sc: SparkContext,
-      generator: RandomDataGenerator[Double],
-      numRows: Long,
-      numCols: Int): RDD[Vector] = {
-    randomVectorRDD(sc, generator, numRows, numCols,
-      sc.defaultParallelism, Utils.random.nextLong)
+  private def numPartitionsOrDefault(sc: SparkContext, numPartitions: Int): Int = {
+    if (numPartitions > 0) numPartitions else sc.defaultMinPartitions
   }
 }
diff --git a/mllib/src/test/java/org/apache/spark/mllib/random/JavaRandomRDDsSuite.java b/mllib/src/test/java/org/apache/spark/mllib/random/JavaRandomRDDsSuite.java
new file mode 100644
index 0000000000000..a725736ca1a58
--- /dev/null
+++ b/mllib/src/test/java/org/apache/spark/mllib/random/JavaRandomRDDsSuite.java
@@ -0,0 +1,134 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.mllib.random;
+
+import com.google.common.collect.Lists;
+import org.apache.spark.api.java.JavaRDD;
+import org.junit.Assert;
+import org.junit.After;
+import org.junit.Before;
+import org.junit.Test;
+
+import org.apache.spark.api.java.JavaDoubleRDD;
+import org.apache.spark.api.java.JavaSparkContext;
+import org.apache.spark.mllib.linalg.Vector;
+import static org.apache.spark.mllib.random.RandomRDDs.*;
+
+public class JavaRandomRDDsSuite {
+  private transient JavaSparkContext sc;
+
+  @Before
+  public void setUp() {
+    sc = new JavaSparkContext("local", "JavaRandomRDDsSuite");
+  }
+
+  @After
+  public void tearDown() {
+    sc.stop();
+    sc = null;
+  }
+
+  @Test
+  public void testUniformRDD() {
+    long m = 1000L;
+    int p = 2;
+    long seed = 1L;
+    JavaDoubleRDD rdd1 = uniformJavaRDD(sc, m);
+    JavaDoubleRDD rdd2 = uniformJavaRDD(sc, m, p);
+    JavaDoubleRDD rdd3 = uniformJavaRDD(sc, m, p, seed);
+    for (JavaDoubleRDD rdd: Lists.newArrayList(rdd1, rdd2, rdd3)) {
+      Assert.assertEquals(m, rdd.count());
+    }
+  }
+
+  @Test
+  public void testNormalRDD() {
+    long m = 1000L;
+    int p = 2;
+    long seed = 1L;
+    JavaDoubleRDD rdd1 = normalJavaRDD(sc, m);
+    JavaDoubleRDD rdd2 = normalJavaRDD(sc, m, p);
+    JavaDoubleRDD rdd3 = normalJavaRDD(sc, m, p, seed);
+    for (JavaDoubleRDD rdd: Lists.newArrayList(rdd1, rdd2, rdd3)) {
+      Assert.assertEquals(m, rdd.count());
+    }
+  }
+
+  @Test
+  public void testPoissonRDD() {
+    double mean = 2.0;
+    long m = 1000L;
+    int p = 2;
+    long seed = 1L;
+    JavaDoubleRDD rdd1 = poissonJavaRDD(sc, mean, m);
+    JavaDoubleRDD rdd2 = poissonJavaRDD(sc, mean, m, p);
+    JavaDoubleRDD rdd3 = poissonJavaRDD(sc, mean, m, p, seed);
+    for (JavaDoubleRDD rdd: Lists.newArrayList(rdd1, rdd2, rdd3)) {
+      Assert.assertEquals(m, rdd.count());
+    }
+  }
+
+  @Test
+  @SuppressWarnings("unchecked")
+  public void testUniformVectorRDD() {
+    long m = 100L;
+    int n = 10;
+    int p = 2;
+    long seed = 1L;
+    JavaRDD<Vector> rdd1 = uniformJavaVectorRDD(sc, m, n);
+    JavaRDD<Vector> rdd2 = uniformJavaVectorRDD(sc, m, n, p);
+    JavaRDD<Vector> rdd3 = uniformJavaVectorRDD(sc, m, n, p, seed);
+    for (JavaRDD<Vector> rdd: Lists.newArrayList(rdd1, rdd2, rdd3)) {
+      Assert.assertEquals(m, rdd.count());
+      Assert.assertEquals(n, rdd.first().size());
+    }
+  }
+
+  @Test
+  @SuppressWarnings("unchecked")
+  public void testNormalVectorRDD() {
+    long m = 100L;
+    int n = 10;
+    int p = 2;
+    long seed = 1L;
+    JavaRDD<Vector> rdd1 = normalJavaVectorRDD(sc, m, n);
+    JavaRDD<Vector> rdd2 = normalJavaVectorRDD(sc, m, n, p);
+    JavaRDD<Vector> rdd3 = normalJavaVectorRDD(sc, m, n, p, seed);
+    for (JavaRDD<Vector> rdd: Lists.newArrayList(rdd1, rdd2, rdd3)) {
+      Assert.assertEquals(m, rdd.count());
+      Assert.assertEquals(n, rdd.first().size());
+    }
+  }
+
+  @Test
+  @SuppressWarnings("unchecked")
+  public void testPoissonVectorRDD() {
+    double mean = 2.0;
+    long m = 100L;
+    int n = 10;
+    int p = 2;
+    long seed = 1L;
+    JavaRDD<Vector> rdd1 = poissonJavaVectorRDD(sc, mean, m, n);
+    JavaRDD<Vector> rdd2 = poissonJavaVectorRDD(sc, mean, m, n, p);
+    JavaRDD<Vector> rdd3 = poissonJavaVectorRDD(sc, mean, m, n, p, seed);
+    for (JavaRDD<Vector> rdd: Lists.newArrayList(rdd1, rdd2, rdd3)) {
+      Assert.assertEquals(m, rdd.count());
+      Assert.assertEquals(n, rdd.first().size());
+    }
+  }
+}
diff --git a/python/pyspark/mllib/random.py b/python/pyspark/mllib/random.py
index 3f3b19053d32e..4dc1a4a912421 100644
--- a/python/pyspark/mllib/random.py
+++ b/python/pyspark/mllib/random.py
@@ -35,10 +35,10 @@ class RandomRDDs:
     def uniformRDD(sc, size, numPartitions=None, seed=None):
         """
         Generates an RDD comprised of i.i.d. samples from the
-        uniform distribution on [0.0, 1.0].
+        uniform distribution U(0.0, 1.0).
 
-        To transform the distribution in the generated RDD from U[0.0, 1.0]
-        to U[a, b], use
+        To transform the distribution in the generated RDD from U(0.0, 1.0)
+        to U(a, b), use
         C{RandomRDDs.uniformRDD(sc, n, p, seed)\
           .map(lambda v: a + (b - a) * v)}
 
@@ -60,11 +60,11 @@ def uniformRDD(sc, size, numPartitions=None, seed=None):
     @staticmethod
     def normalRDD(sc, size, numPartitions=None, seed=None):
         """
-        Generates an RDD comprised of i.i.d samples from the standard normal
+        Generates an RDD comprised of i.i.d. samples from the standard normal
         distribution.
 
         To transform the distribution in the generated RDD from standard normal
-        to some other normal N(mean, sigma), use
+        to some other normal N(mean, sigma^2), use
         C{RandomRDDs.normal(sc, n, p, seed)\
           .map(lambda v: mean + sigma * v)}
 
@@ -84,7 +84,7 @@ def normalRDD(sc, size, numPartitions=None, seed=None):
     @staticmethod
     def poissonRDD(sc, mean, size, numPartitions=None, seed=None):
         """
-        Generates an RDD comprised of i.i.d samples from the Poisson
+        Generates an RDD comprised of i.i.d. samples from the Poisson
         distribution with the input mean.
 
         >>> mean = 100.0
@@ -105,8 +105,8 @@ def poissonRDD(sc, mean, size, numPartitions=None, seed=None):
     @staticmethod
     def uniformVectorRDD(sc, numRows, numCols, numPartitions=None, seed=None):
         """
-        Generates an RDD comprised of vectors containing i.i.d samples drawn
-        from the uniform distribution on [0.0 1.0].
+        Generates an RDD comprised of vectors containing i.i.d. samples drawn
+        from the uniform distribution U(0.0, 1.0).
 
         >>> import numpy as np
         >>> mat = np.matrix(RandomRDDs.uniformVectorRDD(sc, 10, 10).collect())
@@ -125,7 +125,7 @@ def uniformVectorRDD(sc, numRows, numCols, numPartitions=None, seed=None):
     @staticmethod
     def normalVectorRDD(sc, numRows, numCols, numPartitions=None, seed=None):
         """
-        Generates an RDD comprised of vectors containing i.i.d samples drawn
+        Generates an RDD comprised of vectors containing i.i.d. samples drawn
         from the standard normal distribution.
 
         >>> import numpy as np
@@ -145,7 +145,7 @@ def normalVectorRDD(sc, numRows, numCols, numPartitions=None, seed=None):
     @staticmethod
     def poissonVectorRDD(sc, mean, numRows, numCols, numPartitions=None, seed=None):
         """
-        Generates an RDD comprised of vectors containing i.i.d samples drawn
+        Generates an RDD comprised of vectors containing i.i.d. samples drawn
         from the Poisson distribution with the input mean.
 
         >>> import numpy as np

From 8b9dc991018842e01f4b93870a2bc2c2cb9ea4ba Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@apache.org>
Date: Tue, 19 Aug 2014 17:40:35 -0700
Subject: [PATCH 027/489] [SPARK-2468] Netty based block server / client module

Previous pull request (#1907) was reverted. This brings it back. Still looking into the hang.

Author: Reynold Xin <rxin@apache.org>

Closes #1971 from rxin/netty1 and squashes the following commits:

b0be96f [Reynold Xin] Added test to make sure outstandingRequests are cleaned after firing the events.
4c6d0ee [Reynold Xin] Pass callbacks cleanly.
603dce7 [Reynold Xin] Upgrade Netty to 4.0.23 to fix the DefaultFileRegion bug.
88be1d4 [Reynold Xin] Downgrade to 4.0.21 to work around a bug in writing DefaultFileRegion.
002626a [Reynold Xin] Remove netty-test-file.txt.
db6e6e0 [Reynold Xin] Revert "Revert "[SPARK-2468] Netty based block server / client module""
---
 .../spark/network/netty/FileClient.scala      |  85 ---------
 .../network/netty/FileClientHandler.scala     |  50 ------
 .../spark/network/netty/FileHeader.scala      |  71 --------
 .../spark/network/netty/FileServer.scala      |  91 ----------
 .../network/netty/FileServerHandler.scala     |  68 --------
 .../spark/network/netty/NettyConfig.scala     |  59 +++++++
 .../spark/network/netty/ShuffleCopier.scala   | 118 -------------
 .../spark/network/netty/ShuffleSender.scala   |  71 --------
 .../BlockClientListener.scala}                |  16 +-
 .../netty/client/BlockFetchingClient.scala    | 132 ++++++++++++++
 .../client/BlockFetchingClientFactory.scala   |  99 +++++++++++
 .../client/BlockFetchingClientHandler.scala   | 103 +++++++++++
 .../netty/client/LazyInitIterator.scala       |  44 +++++
 .../netty/client/ReferenceCountedBuffer.scala |  47 +++++
 .../network/netty/server/BlockHeader.scala    |  32 ++++
 .../netty/server/BlockHeaderEncoder.scala     |  47 +++++
 .../network/netty/server/BlockServer.scala    | 162 ++++++++++++++++++
 .../BlockServerChannelInitializer.scala}      |  22 ++-
 .../netty/server/BlockServerHandler.scala     | 140 +++++++++++++++
 .../spark/storage/BlockDataProvider.scala     |  32 ++++
 .../spark/storage/BlockFetcherIterator.scala  | 137 +++++++--------
 .../apache/spark/storage/BlockManager.scala   |  49 +++++-
 .../storage/BlockNotFoundException.scala      |  21 +++
 .../spark/storage/DiskBlockManager.scala      |  13 +-
 .../netty/ServerClientIntegrationSuite.scala  | 161 +++++++++++++++++
 .../BlockFetchingClientHandlerSuite.scala     | 105 ++++++++++++
 .../server/BlockHeaderEncoderSuite.scala      |  64 +++++++
 .../server/BlockServerHandlerSuite.scala      | 107 ++++++++++++
 pom.xml                                       |   2 +-
 29 files changed, 1484 insertions(+), 664 deletions(-)
 delete mode 100644 core/src/main/scala/org/apache/spark/network/netty/FileClient.scala
 delete mode 100644 core/src/main/scala/org/apache/spark/network/netty/FileClientHandler.scala
 delete mode 100644 core/src/main/scala/org/apache/spark/network/netty/FileHeader.scala
 delete mode 100644 core/src/main/scala/org/apache/spark/network/netty/FileServer.scala
 delete mode 100644 core/src/main/scala/org/apache/spark/network/netty/FileServerHandler.scala
 create mode 100644 core/src/main/scala/org/apache/spark/network/netty/NettyConfig.scala
 delete mode 100644 core/src/main/scala/org/apache/spark/network/netty/ShuffleCopier.scala
 delete mode 100644 core/src/main/scala/org/apache/spark/network/netty/ShuffleSender.scala
 rename core/src/main/scala/org/apache/spark/network/netty/{FileClientChannelInitializer.scala => client/BlockClientListener.scala} (65%)
 create mode 100644 core/src/main/scala/org/apache/spark/network/netty/client/BlockFetchingClient.scala
 create mode 100644 core/src/main/scala/org/apache/spark/network/netty/client/BlockFetchingClientFactory.scala
 create mode 100644 core/src/main/scala/org/apache/spark/network/netty/client/BlockFetchingClientHandler.scala
 create mode 100644 core/src/main/scala/org/apache/spark/network/netty/client/LazyInitIterator.scala
 create mode 100644 core/src/main/scala/org/apache/spark/network/netty/client/ReferenceCountedBuffer.scala
 create mode 100644 core/src/main/scala/org/apache/spark/network/netty/server/BlockHeader.scala
 create mode 100644 core/src/main/scala/org/apache/spark/network/netty/server/BlockHeaderEncoder.scala
 create mode 100644 core/src/main/scala/org/apache/spark/network/netty/server/BlockServer.scala
 rename core/src/main/scala/org/apache/spark/network/netty/{FileServerChannelInitializer.scala => server/BlockServerChannelInitializer.scala} (58%)
 create mode 100644 core/src/main/scala/org/apache/spark/network/netty/server/BlockServerHandler.scala
 create mode 100644 core/src/main/scala/org/apache/spark/storage/BlockDataProvider.scala
 create mode 100644 core/src/main/scala/org/apache/spark/storage/BlockNotFoundException.scala
 create mode 100644 core/src/test/scala/org/apache/spark/network/netty/ServerClientIntegrationSuite.scala
 create mode 100644 core/src/test/scala/org/apache/spark/network/netty/client/BlockFetchingClientHandlerSuite.scala
 create mode 100644 core/src/test/scala/org/apache/spark/network/netty/server/BlockHeaderEncoderSuite.scala
 create mode 100644 core/src/test/scala/org/apache/spark/network/netty/server/BlockServerHandlerSuite.scala

diff --git a/core/src/main/scala/org/apache/spark/network/netty/FileClient.scala b/core/src/main/scala/org/apache/spark/network/netty/FileClient.scala
deleted file mode 100644
index c6d35f73db545..0000000000000
--- a/core/src/main/scala/org/apache/spark/network/netty/FileClient.scala
+++ /dev/null
@@ -1,85 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.network.netty
-
-import java.util.concurrent.TimeUnit
-
-import io.netty.bootstrap.Bootstrap
-import io.netty.channel.{Channel, ChannelOption, EventLoopGroup}
-import io.netty.channel.oio.OioEventLoopGroup
-import io.netty.channel.socket.oio.OioSocketChannel
-
-import org.apache.spark.Logging
-
-class FileClient(handler: FileClientHandler, connectTimeout: Int) extends Logging {
-
-  private var channel: Channel = _
-  private var bootstrap: Bootstrap = _
-  private var group: EventLoopGroup = _
-  private val sendTimeout = 60
-
-  def init(): Unit = {
-    group = new OioEventLoopGroup
-    bootstrap = new Bootstrap
-    bootstrap.group(group)
-      .channel(classOf[OioSocketChannel])
-      .option(ChannelOption.SO_KEEPALIVE, java.lang.Boolean.TRUE)
-      .option(ChannelOption.TCP_NODELAY, java.lang.Boolean.TRUE)
-      .option(ChannelOption.CONNECT_TIMEOUT_MILLIS, Integer.valueOf(connectTimeout))
-      .handler(new FileClientChannelInitializer(handler))
-  }
-
-  def connect(host: String, port: Int) {
-    try {
-      channel = bootstrap.connect(host, port).sync().channel()
-    } catch {
-      case e: InterruptedException =>
-        logWarning("FileClient interrupted while trying to connect", e)
-        close()
-    }
-  }
-
-  def waitForClose(): Unit = {
-    try {
-      channel.closeFuture.sync()
-    } catch {
-      case e: InterruptedException =>
-        logWarning("FileClient interrupted", e)
-    }
-  }
-
-  def sendRequest(file: String): Unit = {
-    try {
-      val bSent = channel.writeAndFlush(file + "\r\n").await(sendTimeout, TimeUnit.SECONDS)
-      if (!bSent) {
-        throw new RuntimeException("Failed to send")
-      }
-    } catch {
-      case e: InterruptedException =>
-        logError("Error", e)
-    }
-  }
-
-  def close(): Unit = {
-    if (group != null) {
-      group.shutdownGracefully()
-      group = null
-      bootstrap = null
-    }
-  }
-}
diff --git a/core/src/main/scala/org/apache/spark/network/netty/FileClientHandler.scala b/core/src/main/scala/org/apache/spark/network/netty/FileClientHandler.scala
deleted file mode 100644
index 017302ec7d33d..0000000000000
--- a/core/src/main/scala/org/apache/spark/network/netty/FileClientHandler.scala
+++ /dev/null
@@ -1,50 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.network.netty
-
-import io.netty.buffer.ByteBuf
-import io.netty.channel.{ChannelHandlerContext, SimpleChannelInboundHandler}
-
-import org.apache.spark.storage.BlockId
-
-
-abstract class FileClientHandler extends SimpleChannelInboundHandler[ByteBuf] {
-
-  private var currentHeader: FileHeader = null
-
-  @volatile
-  private var handlerCalled: Boolean = false
-
-  def isComplete: Boolean = handlerCalled
-
-  def handle(ctx: ChannelHandlerContext, in: ByteBuf, header: FileHeader)
-
-  def handleError(blockId: BlockId)
-
-  override def channelRead0(ctx: ChannelHandlerContext, in: ByteBuf) {
-    if (currentHeader == null && in.readableBytes >= FileHeader.HEADER_SIZE) {
-      currentHeader = FileHeader.create(in.readBytes(FileHeader.HEADER_SIZE))
-    }
-    if (in.readableBytes >= currentHeader.fileLen) {
-      handle(ctx, in, currentHeader)
-      handlerCalled = true
-      currentHeader = null
-      ctx.close()
-    }
-  }
-}
diff --git a/core/src/main/scala/org/apache/spark/network/netty/FileHeader.scala b/core/src/main/scala/org/apache/spark/network/netty/FileHeader.scala
deleted file mode 100644
index 607e560ff277f..0000000000000
--- a/core/src/main/scala/org/apache/spark/network/netty/FileHeader.scala
+++ /dev/null
@@ -1,71 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.network.netty
-
-import io.netty.buffer._
-
-import org.apache.spark.Logging
-import org.apache.spark.storage.{BlockId, TestBlockId}
-
-private[spark] class FileHeader (
-  val fileLen: Int,
-  val blockId: BlockId) extends Logging {
-
-  lazy val buffer: ByteBuf = {
-    val buf = Unpooled.buffer()
-    buf.capacity(FileHeader.HEADER_SIZE)
-    buf.writeInt(fileLen)
-    buf.writeInt(blockId.name.length)
-    blockId.name.foreach((x: Char) => buf.writeByte(x))
-    // padding the rest of header
-    if (FileHeader.HEADER_SIZE - buf.readableBytes > 0 ) {
-      buf.writeZero(FileHeader.HEADER_SIZE - buf.readableBytes)
-    } else {
-      throw new Exception("too long header " + buf.readableBytes)
-      logInfo("too long header")
-    }
-    buf
-  }
-
-}
-
-private[spark] object FileHeader {
-
-  val HEADER_SIZE = 40
-
-  def getFileLenOffset = 0
-  def getFileLenSize = Integer.SIZE/8
-
-  def create(buf: ByteBuf): FileHeader = {
-    val length = buf.readInt
-    val idLength = buf.readInt
-    val idBuilder = new StringBuilder(idLength)
-    for (i <- 1 to idLength) {
-      idBuilder += buf.readByte().asInstanceOf[Char]
-    }
-    val blockId = BlockId(idBuilder.toString())
-    new FileHeader(length, blockId)
-  }
-
-  def main(args:Array[String]) {
-    val header = new FileHeader(25, TestBlockId("my_block"))
-    val buf = header.buffer
-    val newHeader = FileHeader.create(buf)
-    System.out.println("id=" + newHeader.blockId + ",size=" + newHeader.fileLen)
-  }
-}
diff --git a/core/src/main/scala/org/apache/spark/network/netty/FileServer.scala b/core/src/main/scala/org/apache/spark/network/netty/FileServer.scala
deleted file mode 100644
index dff77950659af..0000000000000
--- a/core/src/main/scala/org/apache/spark/network/netty/FileServer.scala
+++ /dev/null
@@ -1,91 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.network.netty
-
-import java.net.InetSocketAddress
-
-import io.netty.bootstrap.ServerBootstrap
-import io.netty.channel.{ChannelFuture, ChannelOption, EventLoopGroup}
-import io.netty.channel.oio.OioEventLoopGroup
-import io.netty.channel.socket.oio.OioServerSocketChannel
-
-import org.apache.spark.Logging
-
-/**
- * Server that accept the path of a file an echo back its content.
- */
-class FileServer(pResolver: PathResolver, private var port: Int) extends Logging {
-
-  private val addr: InetSocketAddress = new InetSocketAddress(port)
-  private var bossGroup: EventLoopGroup = new OioEventLoopGroup
-  private var workerGroup: EventLoopGroup = new OioEventLoopGroup
-
-  private var channelFuture: ChannelFuture = {
-    val bootstrap = new ServerBootstrap
-    bootstrap.group(bossGroup, workerGroup)
-      .channel(classOf[OioServerSocketChannel])
-      .option(ChannelOption.SO_BACKLOG, java.lang.Integer.valueOf(100))
-      .option(ChannelOption.SO_RCVBUF, java.lang.Integer.valueOf(1500))
-      .childHandler(new FileServerChannelInitializer(pResolver))
-    bootstrap.bind(addr)
-  }
-
-  try {
-    val boundAddress = channelFuture.sync.channel.localAddress.asInstanceOf[InetSocketAddress]
-    port = boundAddress.getPort
-  } catch {
-    case ie: InterruptedException =>
-      port = 0
-  }
-
-  /** Start the file server asynchronously in a new thread. */
-  def start(): Unit = {
-    val blockingThread: Thread = new Thread {
-      override def run(): Unit = {
-        try {
-          channelFuture.channel.closeFuture.sync
-          logInfo("FileServer exiting")
-        } catch {
-          case e: InterruptedException =>
-            logError("File server start got interrupted", e)
-        }
-        // NOTE: bootstrap is shutdown in stop()
-      }
-    }
-    blockingThread.setDaemon(true)
-    blockingThread.start()
-  }
-
-  def getPort: Int = port
-
-  def stop(): Unit = {
-    if (channelFuture != null) {
-      channelFuture.channel().close().awaitUninterruptibly()
-      channelFuture = null
-    }
-    if (bossGroup != null) {
-      bossGroup.shutdownGracefully()
-      bossGroup = null
-    }
-    if (workerGroup != null) {
-      workerGroup.shutdownGracefully()
-      workerGroup = null
-    }
-  }
-}
-
diff --git a/core/src/main/scala/org/apache/spark/network/netty/FileServerHandler.scala b/core/src/main/scala/org/apache/spark/network/netty/FileServerHandler.scala
deleted file mode 100644
index 96f60b2883ad9..0000000000000
--- a/core/src/main/scala/org/apache/spark/network/netty/FileServerHandler.scala
+++ /dev/null
@@ -1,68 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.network.netty
-
-import java.io.FileInputStream
-
-import io.netty.channel.{DefaultFileRegion, ChannelHandlerContext, SimpleChannelInboundHandler}
-
-import org.apache.spark.Logging
-import org.apache.spark.storage.{BlockId, FileSegment}
-
-
-class FileServerHandler(pResolver: PathResolver)
-  extends SimpleChannelInboundHandler[String] with Logging {
-
-  override def channelRead0(ctx: ChannelHandlerContext, blockIdString: String): Unit = {
-    val blockId: BlockId = BlockId(blockIdString)
-    val fileSegment: FileSegment = pResolver.getBlockLocation(blockId)
-    if (fileSegment == null) {
-      return
-    }
-    val file = fileSegment.file
-    if (file.exists) {
-      if (!file.isFile) {
-        ctx.write(new FileHeader(0, blockId).buffer)
-        ctx.flush()
-        return
-      }
-      val length: Long = fileSegment.length
-      if (length > Integer.MAX_VALUE || length <= 0) {
-        ctx.write(new FileHeader(0, blockId).buffer)
-        ctx.flush()
-        return
-      }
-      ctx.write(new FileHeader(length.toInt, blockId).buffer)
-      try {
-        val channel = new FileInputStream(file).getChannel
-        ctx.write(new DefaultFileRegion(channel, fileSegment.offset, fileSegment.length))
-      } catch {
-        case e: Exception =>
-          logError("Exception: ", e)
-      }
-    } else {
-      ctx.write(new FileHeader(0, blockId).buffer)
-    }
-    ctx.flush()
-  }
-
-  override def exceptionCaught(ctx: ChannelHandlerContext, cause: Throwable): Unit = {
-    logError("Exception: ", cause)
-    ctx.close()
-  }
-}
diff --git a/core/src/main/scala/org/apache/spark/network/netty/NettyConfig.scala b/core/src/main/scala/org/apache/spark/network/netty/NettyConfig.scala
new file mode 100644
index 0000000000000..b5870152c5a64
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/network/netty/NettyConfig.scala
@@ -0,0 +1,59 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.network.netty
+
+import org.apache.spark.SparkConf
+
+/**
+ * A central location that tracks all the settings we exposed to users.
+ */
+private[spark]
+class NettyConfig(conf: SparkConf) {
+
+  /** Port the server listens on. Default to a random port. */
+  private[netty] val serverPort = conf.getInt("spark.shuffle.io.port", 0)
+
+  /** IO mode: nio, oio, epoll, or auto (try epoll first and then nio). */
+  private[netty] val ioMode = conf.get("spark.shuffle.io.mode", "nio").toLowerCase
+
+  /** Connect timeout in secs. Default 60 secs. */
+  private[netty] val connectTimeoutMs = conf.getInt("spark.shuffle.io.connectionTimeout", 60) * 1000
+
+  /**
+   * Percentage of the desired amount of time spent for I/O in the child event loops.
+   * Only applicable in nio and epoll.
+   */
+  private[netty] val ioRatio = conf.getInt("spark.shuffle.io.netty.ioRatio", 80)
+
+  /** Requested maximum length of the queue of incoming connections. */
+  private[netty] val backLog: Option[Int] = conf.getOption("spark.shuffle.io.backLog").map(_.toInt)
+
+  /**
+   * Receive buffer size (SO_RCVBUF).
+   * Note: the optimal size for receive buffer and send buffer should be
+   *  latency * network_bandwidth.
+   * Assuming latency = 1ms, network_bandwidth = 10Gbps
+   *  buffer size should be ~ 1.25MB
+   */
+  private[netty] val receiveBuf: Option[Int] =
+    conf.getOption("spark.shuffle.io.sendBuffer").map(_.toInt)
+
+  /** Send buffer size (SO_SNDBUF). */
+  private[netty] val sendBuf: Option[Int] =
+    conf.getOption("spark.shuffle.io.sendBuffer").map(_.toInt)
+}
diff --git a/core/src/main/scala/org/apache/spark/network/netty/ShuffleCopier.scala b/core/src/main/scala/org/apache/spark/network/netty/ShuffleCopier.scala
deleted file mode 100644
index e7b2855e1ec91..0000000000000
--- a/core/src/main/scala/org/apache/spark/network/netty/ShuffleCopier.scala
+++ /dev/null
@@ -1,118 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.network.netty
-
-import java.util.concurrent.Executors
-
-import scala.collection.JavaConverters._
-
-import io.netty.buffer.ByteBuf
-import io.netty.channel.ChannelHandlerContext
-import io.netty.util.CharsetUtil
-
-import org.apache.spark.{Logging, SparkConf}
-import org.apache.spark.network.ConnectionManagerId
-import org.apache.spark.storage.BlockId
-
-private[spark] class ShuffleCopier(conf: SparkConf) extends Logging {
-
-  def getBlock(host: String, port: Int, blockId: BlockId,
-      resultCollectCallback: (BlockId, Long, ByteBuf) => Unit) {
-
-    val handler = new ShuffleCopier.ShuffleClientHandler(resultCollectCallback)
-    val connectTimeout = conf.getInt("spark.shuffle.netty.connect.timeout", 60000)
-    val fc = new FileClient(handler, connectTimeout)
-
-    try {
-      fc.init()
-      fc.connect(host, port)
-      fc.sendRequest(blockId.name)
-      fc.waitForClose()
-      fc.close()
-    } catch {
-      // Handle any socket-related exceptions in FileClient
-      case e: Exception => {
-        logError("Shuffle copy of block " + blockId + " from " + host + ":" + port + " failed", e)
-        handler.handleError(blockId)
-      }
-    }
-  }
-
-  def getBlock(cmId: ConnectionManagerId, blockId: BlockId,
-      resultCollectCallback: (BlockId, Long, ByteBuf) => Unit) {
-    getBlock(cmId.host, cmId.port, blockId, resultCollectCallback)
-  }
-
-  def getBlocks(cmId: ConnectionManagerId,
-    blocks: Seq[(BlockId, Long)],
-    resultCollectCallback: (BlockId, Long, ByteBuf) => Unit) {
-
-    for ((blockId, size) <- blocks) {
-      getBlock(cmId, blockId, resultCollectCallback)
-    }
-  }
-}
-
-
-private[spark] object ShuffleCopier extends Logging {
-
-  private class ShuffleClientHandler(resultCollectCallBack: (BlockId, Long, ByteBuf) => Unit)
-    extends FileClientHandler with Logging {
-
-    override def handle(ctx: ChannelHandlerContext, in: ByteBuf, header: FileHeader) {
-      logDebug("Received Block: " + header.blockId + " (" + header.fileLen + "B)")
-      resultCollectCallBack(header.blockId, header.fileLen.toLong, in.readBytes(header.fileLen))
-    }
-
-    override def handleError(blockId: BlockId) {
-      if (!isComplete) {
-        resultCollectCallBack(blockId, -1, null)
-      }
-    }
-  }
-
-  def echoResultCollectCallBack(blockId: BlockId, size: Long, content: ByteBuf) {
-    if (size != -1) {
-      logInfo("File: " + blockId + " content is : \" " + content.toString(CharsetUtil.UTF_8) + "\"")
-    }
-  }
-
-  def main(args: Array[String]) {
-    if (args.length < 3) {
-      System.err.println("Usage: ShuffleCopier <host> <port> <shuffle_block_id> <threads>")
-      System.exit(1)
-    }
-    val host = args(0)
-    val port = args(1).toInt
-    val blockId = BlockId(args(2))
-    val threads = if (args.length > 3) args(3).toInt else 10
-
-    val copiers = Executors.newFixedThreadPool(80)
-    val tasks = (for (i <- Range(0, threads)) yield {
-      Executors.callable(new Runnable() {
-        def run() {
-          val copier = new ShuffleCopier(new SparkConf)
-          copier.getBlock(host, port, blockId, echoResultCollectCallBack)
-        }
-      })
-    }).asJava
-    copiers.invokeAll(tasks)
-    copiers.shutdown()
-    System.exit(0)
-  }
-}
diff --git a/core/src/main/scala/org/apache/spark/network/netty/ShuffleSender.scala b/core/src/main/scala/org/apache/spark/network/netty/ShuffleSender.scala
deleted file mode 100644
index 95958e30f7eeb..0000000000000
--- a/core/src/main/scala/org/apache/spark/network/netty/ShuffleSender.scala
+++ /dev/null
@@ -1,71 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.network.netty
-
-import java.io.File
-
-import org.apache.spark.Logging
-import org.apache.spark.util.Utils
-import org.apache.spark.storage.{BlockId, FileSegment}
-
-private[spark] class ShuffleSender(portIn: Int, val pResolver: PathResolver) extends Logging {
-
-  val server = new FileServer(pResolver, portIn)
-  server.start()
-
-  def stop() {
-    server.stop()
-  }
-
-  def port: Int = server.getPort
-}
-
-
-/**
- * An application for testing the shuffle sender as a standalone program.
- */
-private[spark] object ShuffleSender {
-
-  def main(args: Array[String]) {
-    if (args.length < 3) {
-      System.err.println(
-        "Usage: ShuffleSender <port> <subDirsPerLocalDir> <list of shuffle_block_directories>")
-      System.exit(1)
-    }
-
-    val port = args(0).toInt
-    val subDirsPerLocalDir = args(1).toInt
-    val localDirs = args.drop(2).map(new File(_))
-
-    val pResovler = new PathResolver {
-      override def getBlockLocation(blockId: BlockId): FileSegment = {
-        if (!blockId.isShuffle) {
-          throw new Exception("Block " + blockId + " is not a shuffle block")
-        }
-        // Figure out which local directory it hashes to, and which subdirectory in that
-        val hash = Utils.nonNegativeHash(blockId)
-        val dirId = hash % localDirs.length
-        val subDirId = (hash / localDirs.length) % subDirsPerLocalDir
-        val subDir = new File(localDirs(dirId), "%02x".format(subDirId))
-        val file = new File(subDir, blockId.name)
-        new FileSegment(file, 0, file.length())
-      }
-    }
-    val sender = new ShuffleSender(port, pResovler)
-  }
-}
diff --git a/core/src/main/scala/org/apache/spark/network/netty/FileClientChannelInitializer.scala b/core/src/main/scala/org/apache/spark/network/netty/client/BlockClientListener.scala
similarity index 65%
rename from core/src/main/scala/org/apache/spark/network/netty/FileClientChannelInitializer.scala
rename to core/src/main/scala/org/apache/spark/network/netty/client/BlockClientListener.scala
index f4261c13f70a8..e28219dd7745b 100644
--- a/core/src/main/scala/org/apache/spark/network/netty/FileClientChannelInitializer.scala
+++ b/core/src/main/scala/org/apache/spark/network/netty/client/BlockClientListener.scala
@@ -15,17 +15,15 @@
  * limitations under the License.
  */
 
-package org.apache.spark.network.netty
+package org.apache.spark.network.netty.client
 
-import io.netty.channel.ChannelInitializer
-import io.netty.channel.socket.SocketChannel
-import io.netty.handler.codec.string.StringEncoder
+import java.util.EventListener
 
 
-class FileClientChannelInitializer(handler: FileClientHandler)
-  extends ChannelInitializer[SocketChannel] {
+trait BlockClientListener extends EventListener {
+
+  def onFetchSuccess(blockId: String, data: ReferenceCountedBuffer): Unit
+
+  def onFetchFailure(blockId: String, errorMsg: String): Unit
 
-  def initChannel(channel: SocketChannel) {
-    channel.pipeline.addLast("encoder", new StringEncoder).addLast("handler", handler)
-  }
 }
diff --git a/core/src/main/scala/org/apache/spark/network/netty/client/BlockFetchingClient.scala b/core/src/main/scala/org/apache/spark/network/netty/client/BlockFetchingClient.scala
new file mode 100644
index 0000000000000..5aea7ba2f3673
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/network/netty/client/BlockFetchingClient.scala
@@ -0,0 +1,132 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.network.netty.client
+
+import java.util.concurrent.TimeoutException
+
+import io.netty.bootstrap.Bootstrap
+import io.netty.buffer.PooledByteBufAllocator
+import io.netty.channel.socket.SocketChannel
+import io.netty.channel.{ChannelFutureListener, ChannelFuture, ChannelInitializer, ChannelOption}
+import io.netty.handler.codec.LengthFieldBasedFrameDecoder
+import io.netty.handler.codec.string.StringEncoder
+import io.netty.util.CharsetUtil
+
+import org.apache.spark.Logging
+
+/**
+ * Client for fetching data blocks from [[org.apache.spark.network.netty.server.BlockServer]].
+ * Use [[BlockFetchingClientFactory]] to instantiate this client.
+ *
+ * The constructor blocks until a connection is successfully established.
+ *
+ * See [[org.apache.spark.network.netty.server.BlockServer]] for client/server protocol.
+ *
+ * Concurrency: thread safe and can be called from multiple threads.
+ */
+@throws[TimeoutException]
+private[spark]
+class BlockFetchingClient(factory: BlockFetchingClientFactory, hostname: String, port: Int)
+  extends Logging {
+
+  private val handler = new BlockFetchingClientHandler
+
+  /** Netty Bootstrap for creating the TCP connection. */
+  private val bootstrap: Bootstrap = {
+    val b = new Bootstrap
+    b.group(factory.workerGroup)
+      .channel(factory.socketChannelClass)
+      // Use pooled buffers to reduce temporary buffer allocation
+      .option(ChannelOption.ALLOCATOR, PooledByteBufAllocator.DEFAULT)
+      // Disable Nagle's Algorithm since we don't want packets to wait
+      .option(ChannelOption.TCP_NODELAY, java.lang.Boolean.TRUE)
+      .option(ChannelOption.SO_KEEPALIVE, java.lang.Boolean.TRUE)
+      .option[Integer](ChannelOption.CONNECT_TIMEOUT_MILLIS, factory.conf.connectTimeoutMs)
+
+    b.handler(new ChannelInitializer[SocketChannel] {
+      override def initChannel(ch: SocketChannel): Unit = {
+        ch.pipeline
+          .addLast("encoder", new StringEncoder(CharsetUtil.UTF_8))
+          // maxFrameLength = 2G, lengthFieldOffset = 0, lengthFieldLength = 4
+          .addLast("framedLengthDecoder", new LengthFieldBasedFrameDecoder(Int.MaxValue, 0, 4))
+          .addLast("handler", handler)
+      }
+    })
+    b
+  }
+
+  /** Netty ChannelFuture for the connection. */
+  private val cf: ChannelFuture = bootstrap.connect(hostname, port)
+  if (!cf.awaitUninterruptibly(factory.conf.connectTimeoutMs)) {
+    throw new TimeoutException(
+      s"Connecting to $hostname:$port timed out (${factory.conf.connectTimeoutMs} ms)")
+  }
+
+  /**
+   * Ask the remote server for a sequence of blocks, and execute the callback.
+   *
+   * Note that this is asynchronous and returns immediately. Upstream caller should throttle the
+   * rate of fetching; otherwise we could run out of memory.
+   *
+   * @param blockIds sequence of block ids to fetch.
+   * @param listener callback to fire on fetch success / failure.
+   */
+  def fetchBlocks(blockIds: Seq[String], listener: BlockClientListener): Unit = {
+    // It's best to limit the number of "write" calls since it needs to traverse the whole pipeline.
+    // It's also best to limit the number of "flush" calls since it requires system calls.
+    // Let's concatenate the string and then call writeAndFlush once.
+    // This is also why this implementation might be more efficient than multiple, separate
+    // fetch block calls.
+    var startTime: Long = 0
+    logTrace {
+      startTime = System.nanoTime
+      s"Sending request $blockIds to $hostname:$port"
+    }
+
+    blockIds.foreach { blockId =>
+      handler.addRequest(blockId, listener)
+    }
+
+    val writeFuture = cf.channel().writeAndFlush(blockIds.mkString("\n") + "\n")
+    writeFuture.addListener(new ChannelFutureListener {
+      override def operationComplete(future: ChannelFuture): Unit = {
+        if (future.isSuccess) {
+          logTrace {
+            val timeTaken = (System.nanoTime - startTime).toDouble / 1000000
+            s"Sending request $blockIds to $hostname:$port took $timeTaken ms"
+          }
+        } else {
+          // Fail all blocks.
+          val errorMsg =
+            s"Failed to send request $blockIds to $hostname:$port: ${future.cause.getMessage}"
+          logError(errorMsg, future.cause)
+          blockIds.foreach { blockId =>
+            listener.onFetchFailure(blockId, errorMsg)
+            handler.removeRequest(blockId)
+          }
+        }
+      }
+    })
+  }
+
+  def waitForClose(): Unit = {
+    cf.channel().closeFuture().sync()
+  }
+
+  def close(): Unit = cf.channel().close()
+}
diff --git a/core/src/main/scala/org/apache/spark/network/netty/client/BlockFetchingClientFactory.scala b/core/src/main/scala/org/apache/spark/network/netty/client/BlockFetchingClientFactory.scala
new file mode 100644
index 0000000000000..2b28402c52b49
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/network/netty/client/BlockFetchingClientFactory.scala
@@ -0,0 +1,99 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.network.netty.client
+
+import io.netty.channel.epoll.{EpollEventLoopGroup, EpollSocketChannel}
+import io.netty.channel.nio.NioEventLoopGroup
+import io.netty.channel.oio.OioEventLoopGroup
+import io.netty.channel.socket.nio.NioSocketChannel
+import io.netty.channel.socket.oio.OioSocketChannel
+import io.netty.channel.{EventLoopGroup, Channel}
+
+import org.apache.spark.SparkConf
+import org.apache.spark.network.netty.NettyConfig
+import org.apache.spark.util.Utils
+
+/**
+ * Factory for creating [[BlockFetchingClient]] by using createClient. This factory reuses
+ * the worker thread pool for Netty.
+ *
+ * Concurrency: createClient is safe to be called from multiple threads concurrently.
+ */
+private[spark]
+class BlockFetchingClientFactory(val conf: NettyConfig) {
+
+  def this(sparkConf: SparkConf) = this(new NettyConfig(sparkConf))
+
+  /** A thread factory so the threads are named (for debugging). */
+  val threadFactory = Utils.namedThreadFactory("spark-shuffle-client")
+
+  /** The following two are instantiated by the [[init]] method, depending ioMode. */
+  var socketChannelClass: Class[_ <: Channel] = _
+  var workerGroup: EventLoopGroup = _
+
+  init()
+
+  /** Initialize [[socketChannelClass]] and [[workerGroup]] based on ioMode. */
+  private def init(): Unit = {
+    def initOio(): Unit = {
+      socketChannelClass = classOf[OioSocketChannel]
+      workerGroup = new OioEventLoopGroup(0, threadFactory)
+    }
+    def initNio(): Unit = {
+      socketChannelClass = classOf[NioSocketChannel]
+      workerGroup = new NioEventLoopGroup(0, threadFactory)
+    }
+    def initEpoll(): Unit = {
+      socketChannelClass = classOf[EpollSocketChannel]
+      workerGroup = new EpollEventLoopGroup(0, threadFactory)
+    }
+
+    conf.ioMode match {
+      case "nio" => initNio()
+      case "oio" => initOio()
+      case "epoll" => initEpoll()
+      case "auto" =>
+        // For auto mode, first try epoll (only available on Linux), then nio.
+        try {
+          initEpoll()
+        } catch {
+          // TODO: Should we log the throwable? But that always happen on non-Linux systems.
+          // Perhaps the right thing to do is to check whether the system is Linux, and then only
+          // call initEpoll on Linux.
+          case e: Throwable => initNio()
+        }
+    }
+  }
+
+  /**
+   * Create a new BlockFetchingClient connecting to the given remote host / port.
+   *
+   * This blocks until a connection is successfully established.
+   *
+   * Concurrency: This method is safe to call from multiple threads.
+   */
+  def createClient(remoteHost: String, remotePort: Int): BlockFetchingClient = {
+    new BlockFetchingClient(this, remoteHost, remotePort)
+  }
+
+  def stop(): Unit = {
+    if (workerGroup != null) {
+      workerGroup.shutdownGracefully()
+    }
+  }
+}
diff --git a/core/src/main/scala/org/apache/spark/network/netty/client/BlockFetchingClientHandler.scala b/core/src/main/scala/org/apache/spark/network/netty/client/BlockFetchingClientHandler.scala
new file mode 100644
index 0000000000000..83265b164299d
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/network/netty/client/BlockFetchingClientHandler.scala
@@ -0,0 +1,103 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.network.netty.client
+
+import io.netty.buffer.ByteBuf
+import io.netty.channel.{ChannelHandlerContext, SimpleChannelInboundHandler}
+
+import org.apache.spark.Logging
+
+
+/**
+ * Handler that processes server responses. It uses the protocol documented in
+ * [[org.apache.spark.network.netty.server.BlockServer]].
+ *
+ * Concurrency: thread safe and can be called from multiple threads.
+ */
+private[client]
+class BlockFetchingClientHandler extends SimpleChannelInboundHandler[ByteBuf] with Logging {
+
+  /** Tracks the list of outstanding requests and their listeners on success/failure. */
+  private val outstandingRequests = java.util.Collections.synchronizedMap {
+    new java.util.HashMap[String, BlockClientListener]
+  }
+
+  def addRequest(blockId: String, listener: BlockClientListener): Unit = {
+    outstandingRequests.put(blockId, listener)
+  }
+
+  def removeRequest(blockId: String): Unit = {
+    outstandingRequests.remove(blockId)
+  }
+
+  override def exceptionCaught(ctx: ChannelHandlerContext, cause: Throwable): Unit = {
+    val errorMsg = s"Exception in connection from ${ctx.channel.remoteAddress}: ${cause.getMessage}"
+    logError(errorMsg, cause)
+
+    // Fire the failure callback for all outstanding blocks
+    outstandingRequests.synchronized {
+      val iter = outstandingRequests.entrySet().iterator()
+      while (iter.hasNext) {
+        val entry = iter.next()
+        entry.getValue.onFetchFailure(entry.getKey, errorMsg)
+      }
+      outstandingRequests.clear()
+    }
+
+    ctx.close()
+  }
+
+  override def channelRead0(ctx: ChannelHandlerContext, in: ByteBuf) {
+    val totalLen = in.readInt()
+    val blockIdLen = in.readInt()
+    val blockIdBytes = new Array[Byte](math.abs(blockIdLen))
+    in.readBytes(blockIdBytes)
+    val blockId = new String(blockIdBytes)
+    val blockSize = totalLen - math.abs(blockIdLen) - 4
+
+    def server = ctx.channel.remoteAddress.toString
+
+    // blockIdLen is negative when it is an error message.
+    if (blockIdLen < 0) {
+      val errorMessageBytes = new Array[Byte](blockSize)
+      in.readBytes(errorMessageBytes)
+      val errorMsg = new String(errorMessageBytes)
+      logTrace(s"Received block $blockId ($blockSize B) with error $errorMsg from $server")
+
+      val listener = outstandingRequests.get(blockId)
+      if (listener == null) {
+        // Ignore callback
+        logWarning(s"Got a response for block $blockId but it is not in our outstanding requests")
+      } else {
+        outstandingRequests.remove(blockId)
+        listener.onFetchFailure(blockId, errorMsg)
+      }
+    } else {
+      logTrace(s"Received block $blockId ($blockSize B) from $server")
+
+      val listener = outstandingRequests.get(blockId)
+      if (listener == null) {
+        // Ignore callback
+        logWarning(s"Got a response for block $blockId but it is not in our outstanding requests")
+      } else {
+        outstandingRequests.remove(blockId)
+        listener.onFetchSuccess(blockId, new ReferenceCountedBuffer(in))
+      }
+    }
+  }
+}
diff --git a/core/src/main/scala/org/apache/spark/network/netty/client/LazyInitIterator.scala b/core/src/main/scala/org/apache/spark/network/netty/client/LazyInitIterator.scala
new file mode 100644
index 0000000000000..9740ee64d1f2d
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/network/netty/client/LazyInitIterator.scala
@@ -0,0 +1,44 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.network.netty.client
+
+/**
+ * A simple iterator that lazily initializes the underlying iterator.
+ *
+ * The use case is that sometimes we might have many iterators open at the same time, and each of
+ * the iterator might initialize its own buffer (e.g. decompression buffer, deserialization buffer).
+ * This could lead to too many buffers open. If this iterator is used, we lazily initialize those
+ * buffers.
+ */
+private[spark]
+class LazyInitIterator(createIterator: => Iterator[Any]) extends Iterator[Any] {
+
+  lazy val proxy = createIterator
+
+  override def hasNext: Boolean = {
+    val gotNext = proxy.hasNext
+    if (!gotNext) {
+      close()
+    }
+    gotNext
+  }
+
+  override def next(): Any = proxy.next()
+
+  def close(): Unit = Unit
+}
diff --git a/core/src/main/scala/org/apache/spark/network/netty/client/ReferenceCountedBuffer.scala b/core/src/main/scala/org/apache/spark/network/netty/client/ReferenceCountedBuffer.scala
new file mode 100644
index 0000000000000..ea1abf5eccc26
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/network/netty/client/ReferenceCountedBuffer.scala
@@ -0,0 +1,47 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.network.netty.client
+
+import java.io.InputStream
+import java.nio.ByteBuffer
+
+import io.netty.buffer.{ByteBuf, ByteBufInputStream}
+
+
+/**
+ * A buffer abstraction based on Netty's ByteBuf so we don't expose Netty.
+ * This is a Scala value class.
+ *
+ * The buffer's life cycle is NOT managed by the JVM, and thus requiring explicit declaration of
+ * reference by the retain method and release method.
+ */
+private[spark]
+class ReferenceCountedBuffer(val underlying: ByteBuf) extends AnyVal {
+
+  /** Return the nio ByteBuffer view of the underlying buffer. */
+  def byteBuffer(): ByteBuffer = underlying.nioBuffer
+
+  /** Creates a new input stream that starts from the current position of the buffer. */
+  def inputStream(): InputStream = new ByteBufInputStream(underlying)
+
+  /** Increment the reference counter by one. */
+  def retain(): Unit = underlying.retain()
+
+  /** Decrement the reference counter by one and release the buffer if the ref count is 0. */
+  def release(): Unit = underlying.release()
+}
diff --git a/core/src/main/scala/org/apache/spark/network/netty/server/BlockHeader.scala b/core/src/main/scala/org/apache/spark/network/netty/server/BlockHeader.scala
new file mode 100644
index 0000000000000..162e9cc6828d4
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/network/netty/server/BlockHeader.scala
@@ -0,0 +1,32 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.network.netty.server
+
+/**
+ * Header describing a block. This is used only in the server pipeline.
+ *
+ * [[BlockServerHandler]] creates this, and [[BlockHeaderEncoder]] encodes it.
+ *
+ * @param blockSize length of the block content, excluding the length itself.
+ *                 If positive, this is the header for a block (not part of the header).
+ *                 If negative, this is the header and content for an error message.
+ * @param blockId block id
+ * @param error some error message from reading the block
+ */
+private[server]
+class BlockHeader(val blockSize: Int, val blockId: String, val error: Option[String] = None)
diff --git a/core/src/main/scala/org/apache/spark/network/netty/server/BlockHeaderEncoder.scala b/core/src/main/scala/org/apache/spark/network/netty/server/BlockHeaderEncoder.scala
new file mode 100644
index 0000000000000..8e4dda4ef8595
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/network/netty/server/BlockHeaderEncoder.scala
@@ -0,0 +1,47 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.network.netty.server
+
+import io.netty.buffer.ByteBuf
+import io.netty.channel.ChannelHandlerContext
+import io.netty.handler.codec.MessageToByteEncoder
+
+/**
+ * A simple encoder for BlockHeader. See [[BlockServer]] for the server to client protocol.
+ */
+private[server]
+class BlockHeaderEncoder extends MessageToByteEncoder[BlockHeader] {
+  override def encode(ctx: ChannelHandlerContext, msg: BlockHeader, out: ByteBuf): Unit = {
+    // message = message length (4 bytes) + block id length (4 bytes) + block id + block data
+    // message length = block id length (4 bytes) + size of block id + size of block data
+    val blockIdBytes = msg.blockId.getBytes
+    msg.error match {
+      case Some(errorMsg) =>
+        val errorBytes = errorMsg.getBytes
+        out.writeInt(4 + blockIdBytes.length + errorBytes.size)
+        out.writeInt(-blockIdBytes.length)  // use negative block id length to represent errors
+        out.writeBytes(blockIdBytes)  // next is blockId itself
+        out.writeBytes(errorBytes)  // error message
+      case None =>
+        out.writeInt(4 + blockIdBytes.length + msg.blockSize)
+        out.writeInt(blockIdBytes.length)  // First 4 bytes is blockId length
+        out.writeBytes(blockIdBytes)  // next is blockId itself
+        // msg of size blockSize will be written by ServerHandler
+    }
+  }
+}
diff --git a/core/src/main/scala/org/apache/spark/network/netty/server/BlockServer.scala b/core/src/main/scala/org/apache/spark/network/netty/server/BlockServer.scala
new file mode 100644
index 0000000000000..7b2f9a8d4dfd0
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/network/netty/server/BlockServer.scala
@@ -0,0 +1,162 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.network.netty.server
+
+import java.net.InetSocketAddress
+
+import io.netty.bootstrap.ServerBootstrap
+import io.netty.buffer.PooledByteBufAllocator
+import io.netty.channel.{ChannelFuture, ChannelInitializer, ChannelOption}
+import io.netty.channel.epoll.{EpollEventLoopGroup, EpollServerSocketChannel}
+import io.netty.channel.nio.NioEventLoopGroup
+import io.netty.channel.oio.OioEventLoopGroup
+import io.netty.channel.socket.SocketChannel
+import io.netty.channel.socket.nio.NioServerSocketChannel
+import io.netty.channel.socket.oio.OioServerSocketChannel
+import io.netty.handler.codec.LineBasedFrameDecoder
+import io.netty.handler.codec.string.StringDecoder
+import io.netty.util.CharsetUtil
+
+import org.apache.spark.{Logging, SparkConf}
+import org.apache.spark.network.netty.NettyConfig
+import org.apache.spark.storage.BlockDataProvider
+import org.apache.spark.util.Utils
+
+
+/**
+ * Server for serving Spark data blocks.
+ * This should be used together with [[org.apache.spark.network.netty.client.BlockFetchingClient]].
+ *
+ * Protocol for requesting blocks (client to server):
+ *   One block id per line, e.g. to request 3 blocks: "block1\nblock2\nblock3\n"
+ *
+ * Protocol for sending blocks (server to client):
+ *   frame-length (4 bytes), block-id-length (4 bytes), block-id, block-data.
+ *
+ *   frame-length should not include the length of itself.
+ *   If block-id-length is negative, then this is an error message rather than block-data. The real
+ *   length is the absolute value of the frame-length.
+ *
+ */
+private[spark]
+class BlockServer(conf: NettyConfig, dataProvider: BlockDataProvider) extends Logging {
+
+  def this(sparkConf: SparkConf, dataProvider: BlockDataProvider) = {
+    this(new NettyConfig(sparkConf), dataProvider)
+  }
+
+  def port: Int = _port
+
+  def hostName: String = _hostName
+
+  private var _port: Int = conf.serverPort
+  private var _hostName: String = ""
+  private var bootstrap: ServerBootstrap = _
+  private var channelFuture: ChannelFuture = _
+
+  init()
+
+  /** Initialize the server. */
+  private def init(): Unit = {
+    bootstrap = new ServerBootstrap
+    val bossThreadFactory = Utils.namedThreadFactory("spark-shuffle-server-boss")
+    val workerThreadFactory = Utils.namedThreadFactory("spark-shuffle-server-worker")
+
+    // Use only one thread to accept connections, and 2 * num_cores for worker.
+    def initNio(): Unit = {
+      val bossGroup = new NioEventLoopGroup(1, bossThreadFactory)
+      val workerGroup = new NioEventLoopGroup(0, workerThreadFactory)
+      workerGroup.setIoRatio(conf.ioRatio)
+      bootstrap.group(bossGroup, workerGroup).channel(classOf[NioServerSocketChannel])
+    }
+    def initOio(): Unit = {
+      val bossGroup = new OioEventLoopGroup(1, bossThreadFactory)
+      val workerGroup = new OioEventLoopGroup(0, workerThreadFactory)
+      bootstrap.group(bossGroup, workerGroup).channel(classOf[OioServerSocketChannel])
+    }
+    def initEpoll(): Unit = {
+      val bossGroup = new EpollEventLoopGroup(1, bossThreadFactory)
+      val workerGroup = new EpollEventLoopGroup(0, workerThreadFactory)
+      workerGroup.setIoRatio(conf.ioRatio)
+      bootstrap.group(bossGroup, workerGroup).channel(classOf[EpollServerSocketChannel])
+    }
+
+    conf.ioMode match {
+      case "nio" => initNio()
+      case "oio" => initOio()
+      case "epoll" => initEpoll()
+      case "auto" =>
+        // For auto mode, first try epoll (only available on Linux), then nio.
+        try {
+          initEpoll()
+        } catch {
+          // TODO: Should we log the throwable? But that always happen on non-Linux systems.
+          // Perhaps the right thing to do is to check whether the system is Linux, and then only
+          // call initEpoll on Linux.
+          case e: Throwable => initNio()
+        }
+    }
+
+    // Use pooled buffers to reduce temporary buffer allocation
+    bootstrap.option(ChannelOption.ALLOCATOR, PooledByteBufAllocator.DEFAULT)
+    bootstrap.childOption(ChannelOption.ALLOCATOR, PooledByteBufAllocator.DEFAULT)
+
+    // Various (advanced) user-configured settings.
+    conf.backLog.foreach { backLog =>
+      bootstrap.option[java.lang.Integer](ChannelOption.SO_BACKLOG, backLog)
+    }
+    conf.receiveBuf.foreach { receiveBuf =>
+      bootstrap.option[java.lang.Integer](ChannelOption.SO_RCVBUF, receiveBuf)
+    }
+    conf.sendBuf.foreach { sendBuf =>
+      bootstrap.option[java.lang.Integer](ChannelOption.SO_SNDBUF, sendBuf)
+    }
+
+    bootstrap.childHandler(new ChannelInitializer[SocketChannel] {
+      override def initChannel(ch: SocketChannel): Unit = {
+        ch.pipeline
+          .addLast("frameDecoder", new LineBasedFrameDecoder(1024))  // max block id length 1024
+          .addLast("stringDecoder", new StringDecoder(CharsetUtil.UTF_8))
+          .addLast("blockHeaderEncoder", new BlockHeaderEncoder)
+          .addLast("handler", new BlockServerHandler(dataProvider))
+      }
+    })
+
+    channelFuture = bootstrap.bind(new InetSocketAddress(_port))
+    channelFuture.sync()
+
+    val addr = channelFuture.channel.localAddress.asInstanceOf[InetSocketAddress]
+    _port = addr.getPort
+    _hostName = addr.getHostName
+  }
+
+  /** Shutdown the server. */
+  def stop(): Unit = {
+    if (channelFuture != null) {
+      channelFuture.channel().close().awaitUninterruptibly()
+      channelFuture = null
+    }
+    if (bootstrap != null && bootstrap.group() != null) {
+      bootstrap.group().shutdownGracefully()
+    }
+    if (bootstrap != null && bootstrap.childGroup() != null) {
+      bootstrap.childGroup().shutdownGracefully()
+    }
+    bootstrap = null
+  }
+}
diff --git a/core/src/main/scala/org/apache/spark/network/netty/FileServerChannelInitializer.scala b/core/src/main/scala/org/apache/spark/network/netty/server/BlockServerChannelInitializer.scala
similarity index 58%
rename from core/src/main/scala/org/apache/spark/network/netty/FileServerChannelInitializer.scala
rename to core/src/main/scala/org/apache/spark/network/netty/server/BlockServerChannelInitializer.scala
index aaa2f913d0269..cc70bd0c5c477 100644
--- a/core/src/main/scala/org/apache/spark/network/netty/FileServerChannelInitializer.scala
+++ b/core/src/main/scala/org/apache/spark/network/netty/server/BlockServerChannelInitializer.scala
@@ -15,20 +15,26 @@
  * limitations under the License.
  */
 
-package org.apache.spark.network.netty
+package org.apache.spark.network.netty.server
 
 import io.netty.channel.ChannelInitializer
 import io.netty.channel.socket.SocketChannel
-import io.netty.handler.codec.{DelimiterBasedFrameDecoder, Delimiters}
+import io.netty.handler.codec.LineBasedFrameDecoder
 import io.netty.handler.codec.string.StringDecoder
+import io.netty.util.CharsetUtil
+import org.apache.spark.storage.BlockDataProvider
 
-class FileServerChannelInitializer(pResolver: PathResolver)
+
+/** Channel initializer that sets up the pipeline for the BlockServer. */
+private[netty]
+class BlockServerChannelInitializer(dataProvider: BlockDataProvider)
   extends ChannelInitializer[SocketChannel] {
 
-  override def initChannel(channel: SocketChannel): Unit = {
-    channel.pipeline
-      .addLast("framer", new DelimiterBasedFrameDecoder(8192, Delimiters.lineDelimiter : _*))
-      .addLast("stringDecoder", new StringDecoder)
-      .addLast("handler", new FileServerHandler(pResolver))
+  override def initChannel(ch: SocketChannel): Unit = {
+    ch.pipeline
+      .addLast("frameDecoder", new LineBasedFrameDecoder(1024))  // max block id length 1024
+      .addLast("stringDecoder", new StringDecoder(CharsetUtil.UTF_8))
+      .addLast("blockHeaderEncoder", new BlockHeaderEncoder)
+      .addLast("handler", new BlockServerHandler(dataProvider))
   }
 }
diff --git a/core/src/main/scala/org/apache/spark/network/netty/server/BlockServerHandler.scala b/core/src/main/scala/org/apache/spark/network/netty/server/BlockServerHandler.scala
new file mode 100644
index 0000000000000..40dd5e5d1a2ac
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/network/netty/server/BlockServerHandler.scala
@@ -0,0 +1,140 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.network.netty.server
+
+import java.io.FileInputStream
+import java.nio.ByteBuffer
+import java.nio.channels.FileChannel
+
+import io.netty.buffer.Unpooled
+import io.netty.channel._
+
+import org.apache.spark.Logging
+import org.apache.spark.storage.{FileSegment, BlockDataProvider}
+
+
+/**
+ * A handler that processes requests from clients and writes block data back.
+ *
+ * The messages should have been processed by a LineBasedFrameDecoder and a StringDecoder first
+ * so channelRead0 is called once per line (i.e. per block id).
+ */
+private[server]
+class BlockServerHandler(dataProvider: BlockDataProvider)
+  extends SimpleChannelInboundHandler[String] with Logging {
+
+  override def exceptionCaught(ctx: ChannelHandlerContext, cause: Throwable): Unit = {
+    logError(s"Exception in connection from ${ctx.channel.remoteAddress}", cause)
+    ctx.close()
+  }
+
+  override def channelRead0(ctx: ChannelHandlerContext, blockId: String): Unit = {
+    def client = ctx.channel.remoteAddress.toString
+
+    // A helper function to send error message back to the client.
+    def respondWithError(error: String): Unit = {
+      ctx.writeAndFlush(new BlockHeader(-1, blockId, Some(error))).addListener(
+        new ChannelFutureListener {
+          override def operationComplete(future: ChannelFuture) {
+            if (!future.isSuccess) {
+              // TODO: Maybe log the success case as well.
+              logError(s"Error sending error back to $client", future.cause)
+              ctx.close()
+            }
+          }
+        }
+      )
+    }
+
+    def writeFileSegment(segment: FileSegment): Unit = {
+      // Send error message back if the block is too large. Even though we are capable of sending
+      // large (2G+) blocks, the receiving end cannot handle it so let's fail fast.
+      // Once we fixed the receiving end to be able to process large blocks, this should be removed.
+      // Also make sure we update BlockHeaderEncoder to support length > 2G.
+
+      // See [[BlockHeaderEncoder]] for the way length is encoded.
+      if (segment.length + blockId.length + 4 > Int.MaxValue) {
+        respondWithError(s"Block $blockId size ($segment.length) greater than 2G")
+        return
+      }
+
+      var fileChannel: FileChannel = null
+      try {
+        fileChannel = new FileInputStream(segment.file).getChannel
+      } catch {
+        case e: Exception =>
+          logError(
+            s"Error opening channel for $blockId in ${segment.file} for request from $client", e)
+          respondWithError(e.getMessage)
+      }
+
+      // Found the block. Send it back.
+      if (fileChannel != null) {
+        // Write the header and block data. In the case of failures, the listener on the block data
+        // write should close the connection.
+        ctx.write(new BlockHeader(segment.length.toInt, blockId))
+
+        val region = new DefaultFileRegion(fileChannel, segment.offset, segment.length)
+        ctx.writeAndFlush(region).addListener(new ChannelFutureListener {
+          override def operationComplete(future: ChannelFuture) {
+            if (future.isSuccess) {
+              logTrace(s"Sent block $blockId (${segment.length} B) back to $client")
+            } else {
+              logError(s"Error sending block $blockId to $client; closing connection", future.cause)
+              ctx.close()
+            }
+          }
+        })
+      }
+    }
+
+    def writeByteBuffer(buf: ByteBuffer): Unit = {
+      ctx.write(new BlockHeader(buf.remaining, blockId))
+      ctx.writeAndFlush(Unpooled.wrappedBuffer(buf)).addListener(new ChannelFutureListener {
+        override def operationComplete(future: ChannelFuture) {
+          if (future.isSuccess) {
+            logTrace(s"Sent block $blockId (${buf.remaining} B) back to $client")
+          } else {
+            logError(s"Error sending block $blockId to $client; closing connection", future.cause)
+            ctx.close()
+          }
+        }
+      })
+    }
+
+    logTrace(s"Received request from $client to fetch block $blockId")
+
+    var blockData: Either[FileSegment, ByteBuffer] = null
+
+    // First make sure we can find the block. If not, send error back to the user.
+    try {
+      blockData = dataProvider.getBlockData(blockId)
+    } catch {
+      case e: Exception =>
+        logError(s"Error opening block $blockId for request from $client", e)
+        respondWithError(e.getMessage)
+        return
+    }
+
+    blockData match {
+      case Left(segment) => writeFileSegment(segment)
+      case Right(buf) => writeByteBuffer(buf)
+    }
+
+  }  // end of channelRead0
+}
diff --git a/core/src/main/scala/org/apache/spark/storage/BlockDataProvider.scala b/core/src/main/scala/org/apache/spark/storage/BlockDataProvider.scala
new file mode 100644
index 0000000000000..5b6d086630834
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/storage/BlockDataProvider.scala
@@ -0,0 +1,32 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.storage
+
+import java.nio.ByteBuffer
+
+
+/**
+ * An interface for providing data for blocks.
+ *
+ * getBlockData returns either a FileSegment (for zero-copy send), or a ByteBuffer.
+ *
+ * Aside from unit tests, [[BlockManager]] is the main class that implements this.
+ */
+private[spark] trait BlockDataProvider {
+  def getBlockData(blockId: String): Either[FileSegment, ByteBuffer]
+}
diff --git a/core/src/main/scala/org/apache/spark/storage/BlockFetcherIterator.scala b/core/src/main/scala/org/apache/spark/storage/BlockFetcherIterator.scala
index 5f44f5f3197fd..ca60ec78b62ee 100644
--- a/core/src/main/scala/org/apache/spark/storage/BlockFetcherIterator.scala
+++ b/core/src/main/scala/org/apache/spark/storage/BlockFetcherIterator.scala
@@ -18,19 +18,17 @@
 package org.apache.spark.storage
 
 import java.util.concurrent.LinkedBlockingQueue
+import org.apache.spark.network.netty.client.{BlockClientListener, LazyInitIterator, ReferenceCountedBuffer}
 
 import scala.collection.mutable.ArrayBuffer
 import scala.collection.mutable.HashSet
 import scala.collection.mutable.Queue
 import scala.util.{Failure, Success}
 
-import io.netty.buffer.ByteBuf
-
 import org.apache.spark.{Logging, SparkException}
 import org.apache.spark.executor.ShuffleReadMetrics
 import org.apache.spark.network.BufferMessage
 import org.apache.spark.network.ConnectionManagerId
-import org.apache.spark.network.netty.ShuffleCopier
 import org.apache.spark.serializer.Serializer
 import org.apache.spark.util.Utils
 
@@ -54,18 +52,28 @@ trait BlockFetcherIterator extends Iterator[(BlockId, Option[Iterator[Any]])] wi
 private[storage]
 object BlockFetcherIterator {
 
-  // A request to fetch one or more blocks, complete with their sizes
+  /**
+   * A request to fetch blocks from a remote BlockManager.
+   * @param address remote BlockManager to fetch from.
+   * @param blocks Sequence of tuple, where the first element is the block id,
+   *               and the second element is the estimated size, used to calculate bytesInFlight.
+   */
   class FetchRequest(val address: BlockManagerId, val blocks: Seq[(BlockId, Long)]) {
     val size = blocks.map(_._2).sum
   }
 
-  // A result of a fetch. Includes the block ID, size in bytes, and a function to deserialize
-  // the block (since we want all deserializaton to happen in the calling thread); can also
-  // represent a fetch failure if size == -1.
+  /**
+   * Result of a fetch from a remote block. A failure is represented as size == -1.
+   * @param blockId block id
+   * @param size estimated size of the block, used to calculate bytesInFlight.
+   *             Note that this is NOT the exact bytes.
+   * @param deserialize closure to return the result in the form of an Iterator.
+   */
   class FetchResult(val blockId: BlockId, val size: Long, val deserialize: () => Iterator[Any]) {
     def failed: Boolean = size == -1
   }
 
+  // TODO: Refactor this whole thing to make code more reusable.
   class BasicBlockFetcherIterator(
       private val blockManager: BlockManager,
       val blocksByAddress: Seq[(BlockManagerId, Seq[(BlockId, Long)])],
@@ -95,10 +103,10 @@ object BlockFetcherIterator {
 
     // Queue of fetch requests to issue; we'll pull requests off this gradually to make sure that
     // the number of bytes in flight is limited to maxBytesInFlight
-    private val fetchRequests = new Queue[FetchRequest]
+    protected val fetchRequests = new Queue[FetchRequest]
 
     // Current bytes in flight from our requests
-    private var bytesInFlight = 0L
+    protected var bytesInFlight = 0L
 
     protected def sendRequest(req: FetchRequest) {
       logDebug("Sending request for %d blocks (%s) from %s".format(
@@ -262,77 +270,58 @@ object BlockFetcherIterator {
       readMetrics: ShuffleReadMetrics)
     extends BasicBlockFetcherIterator(blockManager, blocksByAddress, serializer, readMetrics) {
 
-    import blockManager._
+    override protected def sendRequest(req: FetchRequest) {
+      logDebug("Sending request for %d blocks (%s) from %s".format(
+        req.blocks.size, Utils.bytesToString(req.size), req.address.hostPort))
+      val cmId = new ConnectionManagerId(req.address.host, req.address.port)
 
-    val fetchRequestsSync = new LinkedBlockingQueue[FetchRequest]
+      bytesInFlight += req.size
+      val sizeMap = req.blocks.toMap // so we can look up the size of each blockID
+
+      // This could throw a TimeoutException. In that case we will just retry the task.
+      val client = blockManager.nettyBlockClientFactory.createClient(
+        cmId.host, req.address.nettyPort)
+      val blocks = req.blocks.map(_._1.toString)
+
+      client.fetchBlocks(
+        blocks,
+        new BlockClientListener {
+          override def onFetchFailure(blockId: String, errorMsg: String): Unit = {
+            logError(s"Could not get block(s) from $cmId with error: $errorMsg")
+            for ((blockId, size) <- req.blocks) {
+              results.put(new FetchResult(blockId, -1, null))
+            }
+          }
 
-    private def startCopiers(numCopiers: Int): List[_ <: Thread] = {
-      (for ( i <- Range(0,numCopiers) ) yield {
-        val copier = new Thread {
-          override def run(){
-            try {
-              while(!isInterrupted && !fetchRequestsSync.isEmpty) {
-                sendRequest(fetchRequestsSync.take())
+          override def onFetchSuccess(blockId: String, data: ReferenceCountedBuffer): Unit = {
+            // Increment the reference count so the buffer won't be recycled.
+            // TODO: This could result in memory leaks when the task is stopped due to exception
+            // before the iterator is exhausted.
+            data.retain()
+            val buf = data.byteBuffer()
+            val blockSize = buf.remaining()
+            val bid = BlockId(blockId)
+
+            // TODO: remove code duplication between here and BlockManager.dataDeserialization.
+            results.put(new FetchResult(bid, sizeMap(bid), () => {
+              def createIterator: Iterator[Any] = {
+                val stream = blockManager.wrapForCompression(bid, data.inputStream())
+                serializer.newInstance().deserializeStream(stream).asIterator
               }
-            } catch {
-              case x: InterruptedException => logInfo("Copier Interrupted")
-              // case _ => throw new SparkException("Exception Throw in Shuffle Copier")
+              new LazyInitIterator(createIterator) {
+                // Release the buffer when we are done traversing it.
+                override def close(): Unit = data.release()
+              }
+            }))
+
+            readMetrics.synchronized {
+              readMetrics.remoteBytesRead += blockSize
+              readMetrics.remoteBlocksFetched += 1
             }
+            logDebug("Got remote block " + blockId + " after " + Utils.getUsedTimeMs(startTime))
           }
         }
-        copier.start
-        copier
-      }).toList
-    }
-
-    // keep this to interrupt the threads when necessary
-    private def stopCopiers() {
-      for (copier <- copiers) {
-        copier.interrupt()
-      }
-    }
-
-    override protected def sendRequest(req: FetchRequest) {
-
-      def putResult(blockId: BlockId, blockSize: Long, blockData: ByteBuf) {
-        val fetchResult = new FetchResult(blockId, blockSize,
-          () => dataDeserialize(blockId, blockData.nioBuffer, serializer))
-        results.put(fetchResult)
-      }
-
-      logDebug("Sending request for %d blocks (%s) from %s".format(
-        req.blocks.size, Utils.bytesToString(req.size), req.address.host))
-      val cmId = new ConnectionManagerId(req.address.host, req.address.nettyPort)
-      val cpier = new ShuffleCopier(blockManager.conf)
-      cpier.getBlocks(cmId, req.blocks, putResult)
-      logDebug("Sent request for remote blocks " + req.blocks + " from " + req.address.host )
-    }
-
-    private var copiers: List[_ <: Thread] = null
-
-    override def initialize() {
-      // Split Local Remote Blocks and set numBlocksToFetch
-      val remoteRequests = splitLocalRemoteBlocks()
-      // Add the remote requests into our queue in a random order
-      for (request <- Utils.randomize(remoteRequests)) {
-        fetchRequestsSync.put(request)
-      }
-
-      copiers = startCopiers(conf.getInt("spark.shuffle.copier.threads", 6))
-      logInfo("Started " + fetchRequestsSync.size + " remote fetches in " +
-        Utils.getUsedTimeMs(startTime))
-
-      // Get Local Blocks
-      startTime = System.currentTimeMillis
-      getLocalBlocks()
-      logDebug("Got local blocks in " + Utils.getUsedTimeMs(startTime) + " ms")
-    }
-
-    override def next(): (BlockId, Option[Iterator[Any]]) = {
-      resultsGotten += 1
-      val result = results.take()
-      // If all the results has been retrieved, copiers will exit automatically
-      (result.blockId, if (result.failed) None else Some(result.deserialize()))
+      )
     }
   }
   // End of NettyBlockFetcherIterator
diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManager.scala b/core/src/main/scala/org/apache/spark/storage/BlockManager.scala
index e4c3d58905e7f..c0491fb55e3a4 100644
--- a/core/src/main/scala/org/apache/spark/storage/BlockManager.scala
+++ b/core/src/main/scala/org/apache/spark/storage/BlockManager.scala
@@ -25,17 +25,20 @@ import scala.concurrent.{Await, Future}
 import scala.concurrent.duration._
 import scala.util.Random
 
-import akka.actor.{ActorSystem, Cancellable, Props}
+import akka.actor.{ActorSystem, Props}
 import sun.nio.ch.DirectBuffer
 
 import org.apache.spark._
 import org.apache.spark.executor._
 import org.apache.spark.io.CompressionCodec
 import org.apache.spark.network._
+import org.apache.spark.network.netty.client.BlockFetchingClientFactory
+import org.apache.spark.network.netty.server.BlockServer
 import org.apache.spark.serializer.Serializer
 import org.apache.spark.shuffle.ShuffleManager
 import org.apache.spark.util._
 
+
 private[spark] sealed trait BlockValues
 private[spark] case class ByteBufferValues(buffer: ByteBuffer) extends BlockValues
 private[spark] case class IteratorValues(iterator: Iterator[Any]) extends BlockValues
@@ -60,7 +63,7 @@ private[spark] class BlockManager(
     securityManager: SecurityManager,
     mapOutputTracker: MapOutputTracker,
     shuffleManager: ShuffleManager)
-  extends Logging {
+  extends BlockDataProvider with Logging {
 
   private val port = conf.getInt("spark.blockManager.port", 0)
   val shuffleBlockManager = new ShuffleBlockManager(this, shuffleManager)
@@ -88,13 +91,25 @@ private[spark] class BlockManager(
     new TachyonStore(this, tachyonBlockManager)
   }
 
+  private val useNetty = conf.getBoolean("spark.shuffle.use.netty", false)
+
   // If we use Netty for shuffle, start a new Netty-based shuffle sender service.
-  private val nettyPort: Int = {
-    val useNetty = conf.getBoolean("spark.shuffle.use.netty", false)
-    val nettyPortConfig = conf.getInt("spark.shuffle.sender.port", 0)
-    if (useNetty) diskBlockManager.startShuffleBlockSender(nettyPortConfig) else 0
+  private[storage] val nettyBlockClientFactory: BlockFetchingClientFactory = {
+    if (useNetty) new BlockFetchingClientFactory(conf) else null
   }
 
+  private val nettyBlockServer: BlockServer = {
+    if (useNetty) {
+      val server = new BlockServer(conf, this)
+      logInfo(s"Created NettyBlockServer binding to port: ${server.port}")
+      server
+    } else {
+      null
+    }
+  }
+
+  private val nettyPort: Int = if (useNetty) nettyBlockServer.port else 0
+
   val blockManagerId = BlockManagerId(
     executorId, connectionManager.id.host, connectionManager.id.port, nettyPort)
 
@@ -219,6 +234,20 @@ private[spark] class BlockManager(
     }
   }
 
+  override def getBlockData(blockId: String): Either[FileSegment, ByteBuffer] = {
+    val bid = BlockId(blockId)
+    if (bid.isShuffle) {
+      Left(diskBlockManager.getBlockLocation(bid))
+    } else {
+      val blockBytesOpt = doGetLocal(bid, asBlockResult = false).asInstanceOf[Option[ByteBuffer]]
+      if (blockBytesOpt.isDefined) {
+        Right(blockBytesOpt.get)
+      } else {
+        throw new BlockNotFoundException(blockId)
+      }
+    }
+  }
+
   /**
    * Get the BlockStatus for the block identified by the given ID, if it exists.
    * NOTE: This is mainly for testing, and it doesn't fetch information from Tachyon.
@@ -1064,6 +1093,14 @@ private[spark] class BlockManager(
     connectionManager.stop()
     shuffleBlockManager.stop()
     diskBlockManager.stop()
+
+    if (nettyBlockClientFactory != null) {
+      nettyBlockClientFactory.stop()
+    }
+    if (nettyBlockServer != null) {
+      nettyBlockServer.stop()
+    }
+
     actorSystem.stop(slaveActor)
     blockInfo.clear()
     memoryStore.clear()
diff --git a/core/src/main/scala/org/apache/spark/storage/BlockNotFoundException.scala b/core/src/main/scala/org/apache/spark/storage/BlockNotFoundException.scala
new file mode 100644
index 0000000000000..9ef453605f4f1
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/storage/BlockNotFoundException.scala
@@ -0,0 +1,21 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.storage
+
+
+class BlockNotFoundException(blockId: String) extends Exception(s"Block $blockId not found")
diff --git a/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala b/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala
index 4d66ccea211fa..f3da816389581 100644
--- a/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala
+++ b/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala
@@ -23,7 +23,7 @@ import java.util.{Date, Random, UUID}
 
 import org.apache.spark.{SparkEnv, Logging}
 import org.apache.spark.executor.ExecutorExitCode
-import org.apache.spark.network.netty.{PathResolver, ShuffleSender}
+import org.apache.spark.network.netty.PathResolver
 import org.apache.spark.util.Utils
 import org.apache.spark.shuffle.sort.SortShuffleManager
 
@@ -52,7 +52,6 @@ private[spark] class DiskBlockManager(shuffleBlockManager: ShuffleBlockManager,
     System.exit(ExecutorExitCode.DISK_STORE_FAILED_TO_CREATE_DIR)
   }
   private val subDirs = Array.fill(localDirs.length)(new Array[File](subDirsPerLocalDir))
-  private var shuffleSender : ShuffleSender = null
 
   addShutdownHook()
 
@@ -186,15 +185,5 @@ private[spark] class DiskBlockManager(shuffleBlockManager: ShuffleBlockManager,
         }
       }
     }
-
-    if (shuffleSender != null) {
-      shuffleSender.stop()
-    }
-  }
-
-  private[storage] def startShuffleBlockSender(port: Int): Int = {
-    shuffleSender = new ShuffleSender(port, this)
-    logInfo(s"Created ShuffleSender binding to port: ${shuffleSender.port}")
-    shuffleSender.port
   }
 }
diff --git a/core/src/test/scala/org/apache/spark/network/netty/ServerClientIntegrationSuite.scala b/core/src/test/scala/org/apache/spark/network/netty/ServerClientIntegrationSuite.scala
new file mode 100644
index 0000000000000..02d0ffc86f58f
--- /dev/null
+++ b/core/src/test/scala/org/apache/spark/network/netty/ServerClientIntegrationSuite.scala
@@ -0,0 +1,161 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.network.netty
+
+import java.io.{RandomAccessFile, File}
+import java.nio.ByteBuffer
+import java.util.{Collections, HashSet}
+import java.util.concurrent.{TimeUnit, Semaphore}
+
+import scala.collection.JavaConversions._
+
+import io.netty.buffer.{ByteBufUtil, Unpooled}
+
+import org.scalatest.{BeforeAndAfterAll, FunSuite}
+
+import org.apache.spark.SparkConf
+import org.apache.spark.network.netty.client.{BlockClientListener, ReferenceCountedBuffer, BlockFetchingClientFactory}
+import org.apache.spark.network.netty.server.BlockServer
+import org.apache.spark.storage.{FileSegment, BlockDataProvider}
+
+
+/**
+ * Test suite that makes sure the server and the client implementations share the same protocol.
+ */
+class ServerClientIntegrationSuite extends FunSuite with BeforeAndAfterAll {
+
+  val bufSize = 100000
+  var buf: ByteBuffer = _
+  var testFile: File = _
+  var server: BlockServer = _
+  var clientFactory: BlockFetchingClientFactory = _
+
+  val bufferBlockId = "buffer_block"
+  val fileBlockId = "file_block"
+
+  val fileContent = new Array[Byte](1024)
+  scala.util.Random.nextBytes(fileContent)
+
+  override def beforeAll() = {
+    buf = ByteBuffer.allocate(bufSize)
+    for (i <- 1 to bufSize) {
+      buf.put(i.toByte)
+    }
+    buf.flip()
+
+    testFile = File.createTempFile("netty-test-file", "txt")
+    val fp = new RandomAccessFile(testFile, "rw")
+    fp.write(fileContent)
+    fp.close()
+
+    server = new BlockServer(new SparkConf, new BlockDataProvider {
+      override def getBlockData(blockId: String): Either[FileSegment, ByteBuffer] = {
+        if (blockId == bufferBlockId) {
+          Right(buf)
+        } else if (blockId == fileBlockId) {
+          Left(new FileSegment(testFile, 10, testFile.length - 25))
+        } else {
+          throw new Exception("Unknown block id " + blockId)
+        }
+      }
+    })
+
+    clientFactory = new BlockFetchingClientFactory(new SparkConf)
+  }
+
+  override def afterAll() = {
+    server.stop()
+    clientFactory.stop()
+  }
+
+  /** A ByteBuf for buffer_block */
+  lazy val byteBufferBlockReference = Unpooled.wrappedBuffer(buf)
+
+  /** A ByteBuf for file_block */
+  lazy val fileBlockReference = Unpooled.wrappedBuffer(fileContent, 10, fileContent.length - 25)
+
+  def fetchBlocks(blockIds: Seq[String]): (Set[String], Set[ReferenceCountedBuffer], Set[String]) =
+  {
+    val client = clientFactory.createClient(server.hostName, server.port)
+    val sem = new Semaphore(0)
+    val receivedBlockIds = Collections.synchronizedSet(new HashSet[String])
+    val errorBlockIds = Collections.synchronizedSet(new HashSet[String])
+    val receivedBuffers = Collections.synchronizedSet(new HashSet[ReferenceCountedBuffer])
+
+    client.fetchBlocks(
+      blockIds,
+      new BlockClientListener {
+        override def onFetchFailure(blockId: String, errorMsg: String): Unit = {
+          errorBlockIds.add(blockId)
+          sem.release()
+        }
+
+        override def onFetchSuccess(blockId: String, data: ReferenceCountedBuffer): Unit = {
+          receivedBlockIds.add(blockId)
+          data.retain()
+          receivedBuffers.add(data)
+          sem.release()
+        }
+      }
+    )
+    if (!sem.tryAcquire(blockIds.size, 30, TimeUnit.SECONDS)) {
+      fail("Timeout getting response from the server")
+    }
+    client.close()
+    (receivedBlockIds.toSet, receivedBuffers.toSet, errorBlockIds.toSet)
+  }
+
+  test("fetch a ByteBuffer block") {
+    val (blockIds, buffers, failBlockIds) = fetchBlocks(Seq(bufferBlockId))
+    assert(blockIds === Set(bufferBlockId))
+    assert(buffers.map(_.underlying) === Set(byteBufferBlockReference))
+    assert(failBlockIds.isEmpty)
+    buffers.foreach(_.release())
+  }
+
+  test("fetch a FileSegment block via zero-copy send") {
+    val (blockIds, buffers, failBlockIds) = fetchBlocks(Seq(fileBlockId))
+    assert(blockIds === Set(fileBlockId))
+    assert(buffers.map(_.underlying) === Set(fileBlockReference))
+    assert(failBlockIds.isEmpty)
+    buffers.foreach(_.release())
+  }
+
+  test("fetch a non-existent block") {
+    val (blockIds, buffers, failBlockIds) = fetchBlocks(Seq("random-block"))
+    assert(blockIds.isEmpty)
+    assert(buffers.isEmpty)
+    assert(failBlockIds === Set("random-block"))
+  }
+
+  test("fetch both ByteBuffer block and FileSegment block") {
+    val (blockIds, buffers, failBlockIds) = fetchBlocks(Seq(bufferBlockId, fileBlockId))
+    assert(blockIds === Set(bufferBlockId, fileBlockId))
+    assert(buffers.map(_.underlying) === Set(byteBufferBlockReference, fileBlockReference))
+    assert(failBlockIds.isEmpty)
+    buffers.foreach(_.release())
+  }
+
+  test("fetch both ByteBuffer block and a non-existent block") {
+    val (blockIds, buffers, failBlockIds) = fetchBlocks(Seq(bufferBlockId, "random-block"))
+    assert(blockIds === Set(bufferBlockId))
+    assert(buffers.map(_.underlying) === Set(byteBufferBlockReference))
+    assert(failBlockIds === Set("random-block"))
+    buffers.foreach(_.release())
+  }
+}
diff --git a/core/src/test/scala/org/apache/spark/network/netty/client/BlockFetchingClientHandlerSuite.scala b/core/src/test/scala/org/apache/spark/network/netty/client/BlockFetchingClientHandlerSuite.scala
new file mode 100644
index 0000000000000..903ab09ae4322
--- /dev/null
+++ b/core/src/test/scala/org/apache/spark/network/netty/client/BlockFetchingClientHandlerSuite.scala
@@ -0,0 +1,105 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.network.netty.client
+
+import java.nio.ByteBuffer
+
+import io.netty.buffer.Unpooled
+import io.netty.channel.embedded.EmbeddedChannel
+
+import org.scalatest.{PrivateMethodTester, FunSuite}
+
+
+class BlockFetchingClientHandlerSuite extends FunSuite with PrivateMethodTester {
+
+  test("handling block data (successful fetch)") {
+    val blockId = "test_block"
+    val blockData = "blahblahblahblahblah"
+    val totalLength = 4 + blockId.length + blockData.length
+
+    var parsedBlockId: String = ""
+    var parsedBlockData: String = ""
+    val handler = new BlockFetchingClientHandler
+    handler.addRequest(blockId,
+      new BlockClientListener {
+        override def onFetchFailure(blockId: String, errorMsg: String): Unit = ???
+        override def onFetchSuccess(bid: String, refCntBuf: ReferenceCountedBuffer): Unit = {
+          parsedBlockId = bid
+          val bytes = new Array[Byte](refCntBuf.byteBuffer().remaining)
+          refCntBuf.byteBuffer().get(bytes)
+          parsedBlockData = new String(bytes)
+        }
+      }
+    )
+
+    val outstandingRequests = PrivateMethod[java.util.Map[_, _]]('outstandingRequests)
+    assert(handler.invokePrivate(outstandingRequests()).size === 1)
+
+    val channel = new EmbeddedChannel(handler)
+    val buf = ByteBuffer.allocate(totalLength + 4)  // 4 bytes for the length field itself
+    buf.putInt(totalLength)
+    buf.putInt(blockId.length)
+    buf.put(blockId.getBytes)
+    buf.put(blockData.getBytes)
+    buf.flip()
+
+    channel.writeInbound(Unpooled.wrappedBuffer(buf))
+    assert(parsedBlockId === blockId)
+    assert(parsedBlockData === blockData)
+
+    assert(handler.invokePrivate(outstandingRequests()).size === 0)
+
+    channel.close()
+  }
+
+  test("handling error message (failed fetch)") {
+    val blockId = "test_block"
+    val errorMsg = "error erro5r error err4or error3 error6 error erro1r"
+    val totalLength = 4 + blockId.length + errorMsg.length
+
+    var parsedBlockId: String = ""
+    var parsedErrorMsg: String = ""
+    val handler = new BlockFetchingClientHandler
+    handler.addRequest(blockId, new BlockClientListener {
+      override def onFetchFailure(bid: String, msg: String) ={
+        parsedBlockId = bid
+        parsedErrorMsg = msg
+      }
+      override def onFetchSuccess(bid: String, refCntBuf: ReferenceCountedBuffer) = ???
+    })
+
+    val outstandingRequests = PrivateMethod[java.util.Map[_, _]]('outstandingRequests)
+    assert(handler.invokePrivate(outstandingRequests()).size === 1)
+
+    val channel = new EmbeddedChannel(handler)
+    val buf = ByteBuffer.allocate(totalLength + 4)  // 4 bytes for the length field itself
+    buf.putInt(totalLength)
+    buf.putInt(-blockId.length)
+    buf.put(blockId.getBytes)
+    buf.put(errorMsg.getBytes)
+    buf.flip()
+
+    channel.writeInbound(Unpooled.wrappedBuffer(buf))
+    assert(parsedBlockId === blockId)
+    assert(parsedErrorMsg === errorMsg)
+
+    assert(handler.invokePrivate(outstandingRequests()).size === 0)
+
+    channel.close()
+  }
+}
diff --git a/core/src/test/scala/org/apache/spark/network/netty/server/BlockHeaderEncoderSuite.scala b/core/src/test/scala/org/apache/spark/network/netty/server/BlockHeaderEncoderSuite.scala
new file mode 100644
index 0000000000000..3ee281cb1350b
--- /dev/null
+++ b/core/src/test/scala/org/apache/spark/network/netty/server/BlockHeaderEncoderSuite.scala
@@ -0,0 +1,64 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.network.netty.server
+
+import io.netty.buffer.ByteBuf
+import io.netty.channel.embedded.EmbeddedChannel
+
+import org.scalatest.FunSuite
+
+
+class BlockHeaderEncoderSuite extends FunSuite {
+
+  test("encode normal block data") {
+    val blockId = "test_block"
+    val channel = new EmbeddedChannel(new BlockHeaderEncoder)
+    channel.writeOutbound(new BlockHeader(17, blockId, None))
+    val out = channel.readOutbound().asInstanceOf[ByteBuf]
+    assert(out.readInt() === 4 + blockId.length + 17)
+    assert(out.readInt() === blockId.length)
+
+    val blockIdBytes = new Array[Byte](blockId.length)
+    out.readBytes(blockIdBytes)
+    assert(new String(blockIdBytes) === blockId)
+    assert(out.readableBytes() === 0)
+
+    channel.close()
+  }
+
+  test("encode error message") {
+    val blockId = "error_block"
+    val errorMsg = "error encountered"
+    val channel = new EmbeddedChannel(new BlockHeaderEncoder)
+    channel.writeOutbound(new BlockHeader(17, blockId, Some(errorMsg)))
+    val out = channel.readOutbound().asInstanceOf[ByteBuf]
+    assert(out.readInt() === 4 + blockId.length + errorMsg.length)
+    assert(out.readInt() === -blockId.length)
+
+    val blockIdBytes = new Array[Byte](blockId.length)
+    out.readBytes(blockIdBytes)
+    assert(new String(blockIdBytes) === blockId)
+
+    val errorMsgBytes = new Array[Byte](errorMsg.length)
+    out.readBytes(errorMsgBytes)
+    assert(new String(errorMsgBytes) === errorMsg)
+    assert(out.readableBytes() === 0)
+
+    channel.close()
+  }
+}
diff --git a/core/src/test/scala/org/apache/spark/network/netty/server/BlockServerHandlerSuite.scala b/core/src/test/scala/org/apache/spark/network/netty/server/BlockServerHandlerSuite.scala
new file mode 100644
index 0000000000000..3239c710f1639
--- /dev/null
+++ b/core/src/test/scala/org/apache/spark/network/netty/server/BlockServerHandlerSuite.scala
@@ -0,0 +1,107 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.network.netty.server
+
+import java.io.{RandomAccessFile, File}
+import java.nio.ByteBuffer
+
+import io.netty.buffer.{Unpooled, ByteBuf}
+import io.netty.channel.{ChannelHandlerContext, SimpleChannelInboundHandler, DefaultFileRegion}
+import io.netty.channel.embedded.EmbeddedChannel
+
+import org.scalatest.FunSuite
+
+import org.apache.spark.storage.{BlockDataProvider, FileSegment}
+
+
+class BlockServerHandlerSuite extends FunSuite {
+
+  test("ByteBuffer block") {
+    val expectedBlockId = "test_bytebuffer_block"
+    val buf = ByteBuffer.allocate(10000)
+    for (i <- 1 to 10000) {
+      buf.put(i.toByte)
+    }
+    buf.flip()
+
+    val channel = new EmbeddedChannel(new BlockServerHandler(new BlockDataProvider {
+      override def getBlockData(blockId: String): Either[FileSegment, ByteBuffer] = Right(buf)
+    }))
+
+    channel.writeInbound(expectedBlockId)
+    assert(channel.outboundMessages().size === 2)
+
+    val out1 = channel.readOutbound().asInstanceOf[BlockHeader]
+    val out2 = channel.readOutbound().asInstanceOf[ByteBuf]
+
+    assert(out1.blockId === expectedBlockId)
+    assert(out1.blockSize === buf.remaining)
+    assert(out1.error === None)
+
+    assert(out2.equals(Unpooled.wrappedBuffer(buf)))
+
+    channel.close()
+  }
+
+  test("FileSegment block via zero-copy") {
+    val expectedBlockId = "test_file_block"
+
+    // Create random file data
+    val fileContent = new Array[Byte](1024)
+    scala.util.Random.nextBytes(fileContent)
+    val testFile = File.createTempFile("netty-test-file", "txt")
+    val fp = new RandomAccessFile(testFile, "rw")
+    fp.write(fileContent)
+    fp.close()
+
+    val channel = new EmbeddedChannel(new BlockServerHandler(new BlockDataProvider {
+      override def getBlockData(blockId: String): Either[FileSegment, ByteBuffer] = {
+        Left(new FileSegment(testFile, 15, testFile.length - 25))
+      }
+    }))
+
+    channel.writeInbound(expectedBlockId)
+    assert(channel.outboundMessages().size === 2)
+
+    val out1 = channel.readOutbound().asInstanceOf[BlockHeader]
+    val out2 = channel.readOutbound().asInstanceOf[DefaultFileRegion]
+
+    assert(out1.blockId === expectedBlockId)
+    assert(out1.blockSize === testFile.length - 25)
+    assert(out1.error === None)
+
+    assert(out2.count === testFile.length - 25)
+    assert(out2.position === 15)
+  }
+
+  test("pipeline exception propagation") {
+    val blockServerHandler = new BlockServerHandler(new BlockDataProvider {
+      override def getBlockData(blockId: String): Either[FileSegment, ByteBuffer] = ???
+    })
+    val exceptionHandler = new SimpleChannelInboundHandler[String]() {
+      override def channelRead0(ctx: ChannelHandlerContext, msg: String): Unit = {
+        throw new Exception("this is an error")
+      }
+    }
+
+    val channel = new EmbeddedChannel(exceptionHandler, blockServerHandler)
+    assert(channel.isOpen)
+    channel.writeInbound("a message to trigger the error")
+    assert(!channel.isOpen)
+  }
+}
diff --git a/pom.xml b/pom.xml
index ef12c8f1a5c49..0d44cf4ea5f92 100644
--- a/pom.xml
+++ b/pom.xml
@@ -420,7 +420,7 @@
       <dependency>
         <groupId>io.netty</groupId>
         <artifactId>netty-all</artifactId>
-        <version>4.0.17.Final</version>
+        <version>4.0.23.Final</version>
       </dependency>
       <dependency>
         <groupId>org.apache.derby</groupId>

From 1870dbaa5591883e61b2173d064c1a67e871b0f5 Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Tue, 19 Aug 2014 17:41:37 -0700
Subject: [PATCH 028/489] [MLLIB] minor update to word2vec

very minor update Ishiihara

Author: Xiangrui Meng <meng@databricks.com>

Closes #2043 from mengxr/minor-w2v and squashes the following commits:

be649fd [Xiangrui Meng] remove map because we only need append
eccefcc [Xiangrui Meng] minor updates to word2vec
---
 .../apache/spark/mllib/feature/Word2Vec.scala  | 18 ++++++++----------
 1 file changed, 8 insertions(+), 10 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala b/mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala
index 1dcaa2cd2e630..c3375ed44fd99 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala
@@ -30,11 +30,9 @@ import org.apache.spark.SparkContext._
 import org.apache.spark.annotation.Experimental
 import org.apache.spark.api.java.JavaRDD
 import org.apache.spark.mllib.linalg.{Vector, Vectors}
-import org.apache.spark.mllib.rdd.RDDFunctions._
 import org.apache.spark.rdd._
 import org.apache.spark.util.Utils
 import org.apache.spark.util.random.XORShiftRandom
-import org.apache.spark.util.collection.PrimitiveKeyOpenHashMap
 
 /**
  *  Entry in vocabulary 
@@ -285,9 +283,9 @@ class Word2Vec extends Serializable with Logging {
     
     val newSentences = sentences.repartition(numPartitions).cache()
     val initRandom = new XORShiftRandom(seed)
-    var syn0Global =
+    val syn0Global =
       Array.fill[Float](vocabSize * vectorSize)((initRandom.nextFloat() - 0.5f) / vectorSize)
-    var syn1Global = new Array[Float](vocabSize * vectorSize)
+    val syn1Global = new Array[Float](vocabSize * vectorSize)
     var alpha = startingAlpha
     for (k <- 1 to numIterations) {
       val partial = newSentences.mapPartitionsWithIndex { case (idx, iter) =>
@@ -349,21 +347,21 @@ class Word2Vec extends Serializable with Logging {
         }
         val syn0Local = model._1
         val syn1Local = model._2
-        val synOut = new PrimitiveKeyOpenHashMap[Int, Array[Float]](vocabSize * 2)
+        val synOut = mutable.ListBuffer.empty[(Int, Array[Float])]
         var index = 0
         while(index < vocabSize) {
           if (syn0Modify(index) != 0) {
-            synOut.update(index, syn0Local.slice(index * vectorSize, (index + 1) * vectorSize))
+            synOut += ((index, syn0Local.slice(index * vectorSize, (index + 1) * vectorSize)))
           }
           if (syn1Modify(index) != 0) {
-            synOut.update(index + vocabSize,
-              syn1Local.slice(index * vectorSize, (index + 1) * vectorSize))
+            synOut += ((index + vocabSize,
+              syn1Local.slice(index * vectorSize, (index + 1) * vectorSize)))
           }
           index += 1
         }
-        Iterator(synOut)
+        synOut.toIterator
       }
-      val synAgg = partial.flatMap(x => x).reduceByKey { case (v1, v2) =>
+      val synAgg = partial.reduceByKey { case (v1, v2) =>
           blas.saxpy(vectorSize, 1.0f, v2, 1, v1, 1)
           v1
       }.collect()

From c7252b0097cfacd36f17357d195b12a59e503b35 Mon Sep 17 00:00:00 2001
From: freeman <the.freeman.lab@gmail.com>
Date: Tue, 19 Aug 2014 18:07:42 -0700
Subject: [PATCH 029/489] [SPARK-3112][MLLIB] Add documentation and example for
 StreamingLR

Added a documentation section on StreamingLR to the ``MLlib - Linear Methods``, including a worked example.

mengxr tdas

Author: freeman <the.freeman.lab@gmail.com>

Closes #2047 from freeman-lab/streaming-lr-docs and squashes the following commits:

568d250 [freeman] Tweaks to wording / formatting
05a1139 [freeman] Added documentation and example for StreamingLR
---
 docs/mllib-linear-methods.md | 75 ++++++++++++++++++++++++++++++++++++
 1 file changed, 75 insertions(+)

diff --git a/docs/mllib-linear-methods.md b/docs/mllib-linear-methods.md
index e504cd7f0f578..9137f9dc1b692 100644
--- a/docs/mllib-linear-methods.md
+++ b/docs/mllib-linear-methods.md
@@ -518,6 +518,81 @@ print("Mean Squared Error = " + str(MSE))
 </div>
 </div>
 
+## Streaming linear regression
+
+When data arrive in a streaming fashion, it is useful to fit regression models online, 
+updating the parameters of the model as new data arrives. MLlib currently supports 
+streaming linear regression using ordinary least squares. The fitting is similar
+to that performed offline, except fitting occurs on each batch of data, so that
+the model continually updates to reflect the data from the stream.
+
+### Examples
+
+The following example demonstrates how to load training and testing data from two different
+input streams of text files, parse the streams as labeled points, fit a linear regression model
+online to the first stream, and make predictions on the second stream.
+
+<div class="codetabs">
+
+<div data-lang="scala" markdown="1">
+
+First, we import the necessary classes for parsing our input data and creating the model. 
+
+{% highlight scala %}
+
+import org.apache.spark.mllib.linalg.Vectors
+import org.apache.spark.mllib.regression.LabeledPoint
+import org.apache.spark.mllib.regression.StreamingLinearRegressionWithSGD
+
+{% endhighlight %}
+
+Then we make input streams for training and testing data. We assume a StreamingContext `ssc`
+has already been created, see [Spark Streaming Programming Guide](streaming-programming-guide.html#initializing)
+for more info. For this example, we use labeled points in training and testing streams, 
+but in practice you will likely want to use unlabeled vectors for test data.
+
+{% highlight scala %}
+
+val trainingData = ssc.textFileStream('/training/data/dir').map(LabeledPoint.parse)
+val testData = ssc.textFileStream('/testing/data/dir').map(LabeledPoint.parse)
+
+{% endhighlight %}
+
+We create our model by initializing the weights to 0
+
+{% highlight scala %}
+
+val numFeatures = 3
+val model = new StreamingLinearRegressionWithSGD()
+    .setInitialWeights(Vectors.zeros(numFeatures))
+
+{% endhighlight %}
+
+Now we register the streams for training and testing and start the job. 
+Printing predictions alongside true labels lets us easily see the result.
+
+{% highlight scala %}
+
+model.trainOn(trainingData)
+model.predictOnValues(testData.map(lp => (lp.label, lp.features))).print()
+
+ssc.start()
+ssc.awaitTermination()
+ 
+{% endhighlight %}
+
+We can now save text files with data to the training or testing folders.
+Each line should be a data point formatted as `(y,[x1,x2,x3])` where `y` is the label 
+and `x1,x2,x3` are the features. Anytime a text file is placed in `/training/data/dir` 
+the model will update. Anytime a text file is placed in `/testing/data/dir` you will see predictions. 
+As you feed more data to the training directory, the predictions 
+will get better!
+
+</div>
+
+</div>
+
+
 ## Implementation (developer)
 
 Behind the scene, MLlib implements a simple distributed version of stochastic gradient descent

From 0e3ab94d413fd70fff748fded42ab5e2ebd66fcc Mon Sep 17 00:00:00 2001
From: wangfei <wangfei_hello@126.com>
Date: Tue, 19 Aug 2014 19:37:02 -0700
Subject: [PATCH 030/489] [SQL] add note of use synchronizedMap in SQLConf

Refer to:
http://stackoverflow.com/questions/510632/whats-the-difference-between-concurrenthashmap-and-collections-synchronizedmap
Collections.synchronizedMap(map) creates a blocking Map which will degrade performance, albeit ensure consistency. So use ConcurrentHashMap(a more effective thread-safe hashmap) instead.

also update HiveQuerySuite to fix test error when changed to ConcurrentHashMap.

Author: wangfei <wangfei_hello@126.com>
Author: scwf <wangfei1@huawei.com>

Closes #1996 from scwf/sqlconf and squashes the following commits:

93bc0c5 [wangfei] revert change of HiveQuerySuite
0cc05dd [wangfei] add note for use synchronizedMap
3c224d31 [scwf] fix formate
a7bcb98 [scwf] use ConcurrentHashMap in sql conf, intead synchronizedMap
---
 sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala | 1 +
 1 file changed, 1 insertion(+)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
index 4f2adb006fbc7..5cc41a83cc792 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
@@ -54,6 +54,7 @@ private[spark] object SQLConf {
 trait SQLConf {
   import SQLConf._
 
+  /** Only low degree of contention is expected for conf, thus NOT using ConcurrentHashMap. */
   @transient protected[spark] val settings = java.util.Collections.synchronizedMap(
     new java.util.HashMap[String, String]())
 

From 068b6fe6a10eb1c6b2102d88832203267f030e85 Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Tue, 19 Aug 2014 21:01:23 -0700
Subject: [PATCH 031/489] [SPARK-3130][MLLIB] detect negative values in naive
 Bayes

because NB treats feature values as term frequencies. jkbradley

Author: Xiangrui Meng <meng@databricks.com>

Closes #2038 from mengxr/nb-neg and squashes the following commits:

52c37c3 [Xiangrui Meng] address comments
65f892d [Xiangrui Meng] detect negative values in nb
---
 docs/mllib-naive-bayes.md                     |  3 +-
 .../mllib/classification/NaiveBayes.scala     | 28 +++++++++++++++----
 .../classification/NaiveBayesSuite.scala      | 28 +++++++++++++++++++
 3 files changed, 53 insertions(+), 6 deletions(-)

diff --git a/docs/mllib-naive-bayes.md b/docs/mllib-naive-bayes.md
index 86d94aebd9442..7f9d4c6563944 100644
--- a/docs/mllib-naive-bayes.md
+++ b/docs/mllib-naive-bayes.md
@@ -17,7 +17,8 @@ Bayes](http://en.wikipedia.org/wiki/Naive_Bayes_classifier#Multinomial_naive_Bay
 which is typically used for [document
 classification](http://nlp.stanford.edu/IR-book/html/htmledition/naive-bayes-text-classification-1.html).
 Within that context, each observation is a document and each
-feature represents a term whose value is the frequency of the term. 
+feature represents a term whose value is the frequency of the term.
+Feature values must be nonnegative to represent term frequencies.
 [Additive smoothing](http://en.wikipedia.org/wiki/Lidstone_smoothing) can be used by
 setting the parameter $\lambda$ (default to $1.0$). For document classification, the input feature
 vectors are usually sparse, and sparse vectors should be supplied as input to take advantage of
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala b/mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala
index 6c7be0a4f1dcb..8c8e4a161aa5b 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala
@@ -19,9 +19,9 @@ package org.apache.spark.mllib.classification
 
 import breeze.linalg.{DenseMatrix => BDM, DenseVector => BDV, argmax => brzArgmax, sum => brzSum}
 
-import org.apache.spark.Logging
+import org.apache.spark.{SparkException, Logging}
 import org.apache.spark.SparkContext._
-import org.apache.spark.mllib.linalg.Vector
+import org.apache.spark.mllib.linalg.{DenseVector, SparseVector, Vector}
 import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.rdd.RDD
 
@@ -73,7 +73,7 @@ class NaiveBayesModel private[mllib] (
  * This is the Multinomial NB ([[http://tinyurl.com/lsdw6p]]) which can handle all kinds of
  * discrete data.  For example, by converting documents into TF-IDF vectors, it can be used for
  * document classification.  By making every vector a 0-1 vector, it can also be used as
- * Bernoulli NB ([[http://tinyurl.com/p7c96j6]]).
+ * Bernoulli NB ([[http://tinyurl.com/p7c96j6]]). The input feature values must be nonnegative.
  */
 class NaiveBayes private (private var lambda: Double) extends Serializable with Logging {
 
@@ -91,12 +91,30 @@ class NaiveBayes private (private var lambda: Double) extends Serializable with
    * @param data RDD of [[org.apache.spark.mllib.regression.LabeledPoint]].
    */
   def run(data: RDD[LabeledPoint]) = {
+    val requireNonnegativeValues: Vector => Unit = (v: Vector) => {
+      val values = v match {
+        case sv: SparseVector =>
+          sv.values
+        case dv: DenseVector =>
+          dv.values
+      }
+      if (!values.forall(_ >= 0.0)) {
+        throw new SparkException(s"Naive Bayes requires nonnegative feature values but found $v.")
+      }
+    }
+
     // Aggregates term frequencies per label.
     // TODO: Calling combineByKey and collect creates two stages, we can implement something
     // TODO: similar to reduceByKeyLocally to save one stage.
     val aggregated = data.map(p => (p.label, p.features)).combineByKey[(Long, BDV[Double])](
-      createCombiner = (v: Vector) => (1L, v.toBreeze.toDenseVector),
-      mergeValue = (c: (Long, BDV[Double]), v: Vector) => (c._1 + 1L, c._2 += v.toBreeze),
+      createCombiner = (v: Vector) => {
+        requireNonnegativeValues(v)
+        (1L, v.toBreeze.toDenseVector)
+      },
+      mergeValue = (c: (Long, BDV[Double]), v: Vector) => {
+        requireNonnegativeValues(v)
+        (c._1 + 1L, c._2 += v.toBreeze)
+      },
       mergeCombiners = (c1: (Long, BDV[Double]), c2: (Long, BDV[Double])) =>
         (c1._1 + c2._1, c1._2 += c2._2)
     ).collect()
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/classification/NaiveBayesSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/classification/NaiveBayesSuite.scala
index 06cdd04f5fdae..80989bc074e84 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/classification/NaiveBayesSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/classification/NaiveBayesSuite.scala
@@ -21,6 +21,7 @@ import scala.util.Random
 
 import org.scalatest.FunSuite
 
+import org.apache.spark.SparkException
 import org.apache.spark.mllib.linalg.Vectors
 import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.util.{LocalClusterSparkContext, LocalSparkContext}
@@ -95,6 +96,33 @@ class NaiveBayesSuite extends FunSuite with LocalSparkContext {
     // Test prediction on Array.
     validatePrediction(validationData.map(row => model.predict(row.features)), validationData)
   }
+
+  test("detect negative values") {
+    val dense = Seq(
+      LabeledPoint(1.0, Vectors.dense(1.0)),
+      LabeledPoint(0.0, Vectors.dense(-1.0)),
+      LabeledPoint(1.0, Vectors.dense(1.0)),
+      LabeledPoint(1.0, Vectors.dense(0.0)))
+    intercept[SparkException] {
+      NaiveBayes.train(sc.makeRDD(dense, 2))
+    }
+    val sparse = Seq(
+      LabeledPoint(1.0, Vectors.sparse(1, Array(0), Array(1.0))),
+      LabeledPoint(0.0, Vectors.sparse(1, Array(0), Array(-1.0))),
+      LabeledPoint(1.0, Vectors.sparse(1, Array(0), Array(1.0))),
+      LabeledPoint(1.0, Vectors.sparse(1, Array.empty, Array.empty)))
+    intercept[SparkException] {
+      NaiveBayes.train(sc.makeRDD(sparse, 2))
+    }
+    val nan = Seq(
+      LabeledPoint(1.0, Vectors.sparse(1, Array(0), Array(1.0))),
+      LabeledPoint(0.0, Vectors.sparse(1, Array(0), Array(Double.NaN))),
+      LabeledPoint(1.0, Vectors.sparse(1, Array(0), Array(1.0))),
+      LabeledPoint(1.0, Vectors.sparse(1, Array.empty, Array.empty)))
+    intercept[SparkException] {
+      NaiveBayes.train(sc.makeRDD(nan, 2))
+    }
+  }
 }
 
 class NaiveBayesClusterSuite extends FunSuite with LocalClusterSparkContext {

From fce5c0fb6384f3a142a4155525a5d62640725150 Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Tue, 19 Aug 2014 22:05:29 -0700
Subject: [PATCH 032/489] [HOTFIX][Streaming][MLlib] use temp folder for
 checkpoint

or Jenkins will complain about no Apache header in checkpoint files. tdas rxin

Author: Xiangrui Meng <meng@databricks.com>

Closes #2046 from mengxr/tmp-checkpoint and squashes the following commits:

0d3ec73 [Xiangrui Meng] remove ssc.stop
9797843 [Xiangrui Meng] change checkpointDir to lazy val
89964ab [Xiangrui Meng] use temp folder for checkpoint
---
 .../StreamingLinearRegressionSuite.scala        |  6 ------
 .../apache/spark/streaming/TestSuiteBase.scala  | 17 +++++++++++------
 2 files changed, 11 insertions(+), 12 deletions(-)

diff --git a/mllib/src/test/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionSuite.scala
index 28489410f8225..03b71301e9ab1 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionSuite.scala
@@ -49,7 +49,6 @@ class StreamingLinearRegressionSuite extends FunSuite with TestSuiteBase {
 
   // Test if we can accurately learn Y = 10*X1 + 10*X2 on streaming data
   test("parameter accuracy") {
-
     // create model
     val model = new StreamingLinearRegressionWithSGD()
       .setInitialWeights(Vectors.dense(0.0, 0.0))
@@ -82,7 +81,6 @@ class StreamingLinearRegressionSuite extends FunSuite with TestSuiteBase {
 
   // Test that parameter estimates improve when learning Y = 10*X1 on streaming data
   test("parameter convergence") {
-
     // create model
     val model = new StreamingLinearRegressionWithSGD()
       .setInitialWeights(Vectors.dense(0.0))
@@ -113,12 +111,10 @@ class StreamingLinearRegressionSuite extends FunSuite with TestSuiteBase {
     assert(deltas.forall(x => (x._1 - x._2) <= 0.1))
     // check that error shrunk on at least 2 batches
     assert(deltas.map(x => if ((x._1 - x._2) < 0) 1 else 0).sum > 1)
-
   }
 
   // Test predictions on a stream
   test("predictions") {
-
     // create model initialized with true weights
     val model = new StreamingLinearRegressionWithSGD()
       .setInitialWeights(Vectors.dense(10.0, 10.0))
@@ -142,7 +138,5 @@ class StreamingLinearRegressionSuite extends FunSuite with TestSuiteBase {
     // compute the mean absolute error and check that it's always less than 0.1
     val errors = output.map(batch => batch.map(p => math.abs(p._1 - p._2)).sum / nPoints)
     assert(errors.forall(x => x <= 0.1))
-
   }
-
 }
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/TestSuiteBase.scala b/streaming/src/test/scala/org/apache/spark/streaming/TestSuiteBase.scala
index f095da9cb55d3..759baacaa4308 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/TestSuiteBase.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/TestSuiteBase.scala
@@ -17,18 +17,18 @@
 
 package org.apache.spark.streaming
 
-import org.apache.spark.streaming.dstream.{DStream, InputDStream, ForEachDStream}
-import org.apache.spark.streaming.util.ManualClock
+import java.io.{ObjectInputStream, IOException}
 
 import scala.collection.mutable.ArrayBuffer
 import scala.collection.mutable.SynchronizedBuffer
 import scala.reflect.ClassTag
 
-import java.io.{ObjectInputStream, IOException}
-
 import org.scalatest.{BeforeAndAfter, FunSuite}
+import com.google.common.io.Files
 
-import org.apache.spark.{SparkContext, SparkConf, Logging}
+import org.apache.spark.streaming.dstream.{DStream, InputDStream, ForEachDStream}
+import org.apache.spark.streaming.util.ManualClock
+import org.apache.spark.{SparkConf, Logging}
 import org.apache.spark.rdd.RDD
 
 /**
@@ -119,7 +119,12 @@ trait TestSuiteBase extends FunSuite with BeforeAndAfter with Logging {
   def batchDuration = Seconds(1)
 
   // Directory where the checkpoint data will be saved
-  def checkpointDir = "checkpoint"
+  lazy val checkpointDir = {
+    val dir = Files.createTempDir()
+    logDebug(s"checkpointDir: $dir")
+    dir.deleteOnExit()
+    dir.toString
+  }
 
   // Number of partitions of the input parallel collections created for testing
   def numInputPartitions = 2

From 8adfbc2b6b5b647e450d30f89c141f935b6aa94b Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@apache.org>
Date: Tue, 19 Aug 2014 22:11:13 -0700
Subject: [PATCH 033/489] [SPARK-3119] Re-implementation of TorrentBroadcast.

This is a re-implementation of TorrentBroadcast, with the following changes:

1. Removes most of the mutable, transient state from TorrentBroadcast (e.g. totalBytes, num of blocks fetched).
2. Removes TorrentInfo and TorrentBlock
3. Replaces the BlockManager.getSingle call in readObject with a getLocal, resuling in one less RPC call to the BlockManagerMasterActor to find the location of the block.
4. Removes the metadata block, resulting in one less block to fetch.
5. Removes an extra memory copy for deserialization (by using Java's SequenceInputStream).

Basically for a regular broadcasted object with only one block, the number of RPC calls goes from 5+1 to 2+1).

Old TorrentBroadcast for object of a single block:
1 RPC to ask for location of the broadcast variable
1 RPC to ask for location of the metadata block
1 RPC to fetch the metadata block
1 RPC to ask for location of the first data block
1 RPC to fetch the first data block
1 RPC to tell the driver we put the first data block in
i.e. 5 + 1

New TorrentBroadcast for object of a single block:
1 RPC to ask for location of the first data block
1 RPC to get the first data block
1 RPC to tell the driver we put the first data block in
i.e. 2 + 1

Author: Reynold Xin <rxin@apache.org>

Closes #2030 from rxin/torrentBroadcast and squashes the following commits:

5bacb9d [Reynold Xin] Always add the object to driver's block manager.
0d8ed5b [Reynold Xin] Added getBytes to BlockManager and uses that in TorrentBroadcast.
2d6a5fb [Reynold Xin] Use putBytes/getRemoteBytes throughout.
3670f00 [Reynold Xin] Code review feedback.
c1185cd [Reynold Xin] [SPARK-3119] Re-implementation of TorrentBroadcast.
---
 .../spark/broadcast/BroadcastFactory.scala    |  11 +
 .../spark/broadcast/TorrentBroadcast.scala    | 282 +++++++-----------
 .../spark/broadcast/BroadcastSuite.scala      | 128 ++++----
 3 files changed, 181 insertions(+), 240 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/broadcast/BroadcastFactory.scala b/core/src/main/scala/org/apache/spark/broadcast/BroadcastFactory.scala
index a8c827030a1ef..6a187b40628a2 100644
--- a/core/src/main/scala/org/apache/spark/broadcast/BroadcastFactory.scala
+++ b/core/src/main/scala/org/apache/spark/broadcast/BroadcastFactory.scala
@@ -32,8 +32,19 @@ import org.apache.spark.annotation.DeveloperApi
  */
 @DeveloperApi
 trait BroadcastFactory {
+
   def initialize(isDriver: Boolean, conf: SparkConf, securityMgr: SecurityManager): Unit
+
+  /**
+   * Creates a new broadcast variable.
+   *
+   * @param value value to broadcast
+   * @param isLocal whether we are in local mode (single JVM process)
+   * @param id unique id representing this broadcast variable
+   */
   def newBroadcast[T: ClassTag](value: T, isLocal: Boolean, id: Long): Broadcast[T]
+
   def unbroadcast(id: Long, removeFromDriver: Boolean, blocking: Boolean): Unit
+
   def stop(): Unit
 }
diff --git a/core/src/main/scala/org/apache/spark/broadcast/TorrentBroadcast.scala b/core/src/main/scala/org/apache/spark/broadcast/TorrentBroadcast.scala
index d8be649f96e5f..6173fd3a69fc7 100644
--- a/core/src/main/scala/org/apache/spark/broadcast/TorrentBroadcast.scala
+++ b/core/src/main/scala/org/apache/spark/broadcast/TorrentBroadcast.scala
@@ -18,50 +18,116 @@
 package org.apache.spark.broadcast
 
 import java.io._
+import java.nio.ByteBuffer
 
+import scala.collection.JavaConversions.asJavaEnumeration
 import scala.reflect.ClassTag
 import scala.util.Random
 
 import org.apache.spark.{Logging, SparkConf, SparkEnv, SparkException}
 import org.apache.spark.io.CompressionCodec
 import org.apache.spark.storage.{BroadcastBlockId, StorageLevel}
+import org.apache.spark.util.ByteBufferInputStream
 
 /**
- *  A [[org.apache.spark.broadcast.Broadcast]] implementation that uses a BitTorrent-like
- *  protocol to do a distributed transfer of the broadcasted data to the executors.
- *  The mechanism is as follows. The driver divides the serializes the broadcasted data,
- *  divides it into smaller chunks, and stores them in the BlockManager of the driver.
- *  These chunks are reported to the BlockManagerMaster so that all the executors can
- *  learn the location of those chunks. The first time the broadcast variable (sent as
- *  part of task) is deserialized at a executor, all the chunks are fetched using
- *  the BlockManager. When all the chunks are fetched (initially from the driver's
- *  BlockManager), they are combined and deserialized to recreate the broadcasted data.
- *  However, the chunks are also stored in the BlockManager and reported to the
- *  BlockManagerMaster. As more executors fetch the chunks, BlockManagerMaster learns
- *  multiple locations for each chunk. Hence, subsequent fetches of each chunk will be
- *  made to other executors who already have those chunks, resulting in a distributed
- *  fetching. This prevents the driver from being the bottleneck in sending out multiple
- *  copies of the broadcast data (one per executor) as done by the
- *  [[org.apache.spark.broadcast.HttpBroadcast]].
+ * A BitTorrent-like implementation of [[org.apache.spark.broadcast.Broadcast]].
+ *
+ * The mechanism is as follows:
+ *
+ * The driver divides the serialized object into small chunks and
+ * stores those chunks in the BlockManager of the driver.
+ *
+ * On each executor, the executor first attempts to fetch the object from its BlockManager. If
+ * it does not exist, it then uses remote fetches to fetch the small chunks from the driver and/or
+ * other executors if available. Once it gets the chunks, it puts the chunks in its own
+ * BlockManager, ready for other executors to fetch from.
+ *
+ * This prevents the driver from being the bottleneck in sending out multiple copies of the
+ * broadcast data (one per executor) as done by the [[org.apache.spark.broadcast.HttpBroadcast]].
+ *
+ * @param obj object to broadcast
+ * @param isLocal whether Spark is running in local mode (single JVM process).
+ * @param id A unique identifier for the broadcast variable.
  */
 private[spark] class TorrentBroadcast[T: ClassTag](
-    @transient var value_ : T, isLocal: Boolean, id: Long)
+    obj : T,
+    @transient private val isLocal: Boolean,
+    id: Long)
   extends Broadcast[T](id) with Logging with Serializable {
 
-  override protected def getValue() = value_
+  /**
+   * Value of the broadcast object. On driver, this is set directly by the constructor.
+   * On executors, this is reconstructed by [[readObject]], which builds this value by reading
+   * blocks from the driver and/or other executors.
+   */
+  @transient private var _value: T = obj
 
   private val broadcastId = BroadcastBlockId(id)
 
-  SparkEnv.get.blockManager.putSingle(
-    broadcastId, value_, StorageLevel.MEMORY_AND_DISK, tellMaster = false)
+  /** Total number of blocks this broadcast variable contains. */
+  private val numBlocks: Int = writeBlocks()
+
+  override protected def getValue() = _value
+
+  /**
+   * Divide the object into multiple blocks and put those blocks in the block manager.
+   *
+   * @return number of blocks this broadcast variable is divided into
+   */
+  private def writeBlocks(): Int = {
+    // For local mode, just put the object in the BlockManager so we can find it later.
+    SparkEnv.get.blockManager.putSingle(
+      broadcastId, _value, StorageLevel.MEMORY_AND_DISK, tellMaster = false)
+
+    if (!isLocal) {
+      val blocks = TorrentBroadcast.blockifyObject(_value)
+      blocks.zipWithIndex.foreach { case (block, i) =>
+        SparkEnv.get.blockManager.putBytes(
+          BroadcastBlockId(id, "piece" + i),
+          block,
+          StorageLevel.MEMORY_AND_DISK_SER,
+          tellMaster = true)
+      }
+      blocks.length
+    } else {
+      0
+    }
+  }
+
+  /** Fetch torrent blocks from the driver and/or other executors. */
+  private def readBlocks(): Array[ByteBuffer] = {
+    // Fetch chunks of data. Note that all these chunks are stored in the BlockManager and reported
+    // to the driver, so other executors can pull these chunks from this executor as well.
+    val blocks = new Array[ByteBuffer](numBlocks)
+    val bm = SparkEnv.get.blockManager
 
-  @transient private var arrayOfBlocks: Array[TorrentBlock] = null
-  @transient private var totalBlocks = -1
-  @transient private var totalBytes = -1
-  @transient private var hasBlocks = 0
+    for (pid <- Random.shuffle(Seq.range(0, numBlocks))) {
+      val pieceId = BroadcastBlockId(id, "piece" + pid)
 
-  if (!isLocal) {
-    sendBroadcast()
+      // First try getLocalBytes because  there is a chance that previous attempts to fetch the
+      // broadcast blocks have already fetched some of the blocks. In that case, some blocks
+      // would be available locally (on this executor).
+      var blockOpt = bm.getLocalBytes(pieceId)
+      if (!blockOpt.isDefined) {
+        blockOpt = bm.getRemoteBytes(pieceId)
+        blockOpt match {
+          case Some(block) =>
+            // If we found the block from remote executors/driver's BlockManager, put the block
+            // in this executor's BlockManager.
+            SparkEnv.get.blockManager.putBytes(
+              pieceId,
+              block,
+              StorageLevel.MEMORY_AND_DISK_SER,
+              tellMaster = true)
+
+          case None =>
+            throw new SparkException("Failed to get " + pieceId + " of " + broadcastId)
+        }
+      }
+      // If we get here, the option is defined.
+      blocks(pid) = blockOpt.get
+    }
+    blocks
   }
 
   /**
@@ -79,26 +145,6 @@ private[spark] class TorrentBroadcast[T: ClassTag](
     TorrentBroadcast.unpersist(id, removeFromDriver = true, blocking)
   }
 
-  private def sendBroadcast() {
-    val tInfo = TorrentBroadcast.blockifyObject(value_)
-    totalBlocks = tInfo.totalBlocks
-    totalBytes = tInfo.totalBytes
-    hasBlocks = tInfo.totalBlocks
-
-    // Store meta-info
-    val metaId = BroadcastBlockId(id, "meta")
-    val metaInfo = TorrentInfo(null, totalBlocks, totalBytes)
-    SparkEnv.get.blockManager.putSingle(
-      metaId, metaInfo, StorageLevel.MEMORY_AND_DISK, tellMaster = true)
-
-    // Store individual pieces
-    for (i <- 0 until totalBlocks) {
-      val pieceId = BroadcastBlockId(id, "piece" + i)
-      SparkEnv.get.blockManager.putSingle(
-        pieceId, tInfo.arrayOfBlocks(i), StorageLevel.MEMORY_AND_DISK, tellMaster = true)
-    }
-  }
-
   /** Used by the JVM when serializing this object. */
   private def writeObject(out: ObjectOutputStream) {
     assertValid()
@@ -109,99 +155,30 @@ private[spark] class TorrentBroadcast[T: ClassTag](
   private def readObject(in: ObjectInputStream) {
     in.defaultReadObject()
     TorrentBroadcast.synchronized {
-      SparkEnv.get.blockManager.getSingle(broadcastId) match {
+      SparkEnv.get.blockManager.getLocal(broadcastId).map(_.data.next()) match {
         case Some(x) =>
-          value_ = x.asInstanceOf[T]
+          _value = x.asInstanceOf[T]
 
         case None =>
-          val start = System.nanoTime
           logInfo("Started reading broadcast variable " + id)
-
-          // Initialize @transient variables that will receive garbage values from the master.
-          resetWorkerVariables()
-
-          if (receiveBroadcast()) {
-            value_ = TorrentBroadcast.unBlockifyObject[T](arrayOfBlocks, totalBytes, totalBlocks)
-
-            /* Store the merged copy in cache so that the next worker doesn't need to rebuild it.
-             * This creates a trade-off between memory usage and latency. Storing copy doubles
-             * the memory footprint; not storing doubles deserialization cost. Also,
-             * this does not need to be reported to BlockManagerMaster since other executors
-             * does not need to access this block (they only need to fetch the chunks,
-             * which are reported).
-             */
-            SparkEnv.get.blockManager.putSingle(
-              broadcastId, value_, StorageLevel.MEMORY_AND_DISK, tellMaster = false)
-
-            // Remove arrayOfBlocks from memory once value_ is on local cache
-            resetWorkerVariables()
-          } else {
-            logError("Reading broadcast variable " + id + " failed")
-          }
-
-          val time = (System.nanoTime - start) / 1e9
+          val start = System.nanoTime()
+          val blocks = readBlocks()
+          val time = (System.nanoTime() - start) / 1e9
           logInfo("Reading broadcast variable " + id + " took " + time + " s")
-      }
-    }
-  }
-
-  private def resetWorkerVariables() {
-    arrayOfBlocks = null
-    totalBytes = -1
-    totalBlocks = -1
-    hasBlocks = 0
-  }
-
-  private def receiveBroadcast(): Boolean = {
-    // Receive meta-info about the size of broadcast data,
-    // the number of chunks it is divided into, etc.
-    val metaId = BroadcastBlockId(id, "meta")
-    var attemptId = 10
-    while (attemptId > 0 && totalBlocks == -1) {
-      SparkEnv.get.blockManager.getSingle(metaId) match {
-        case Some(x) =>
-          val tInfo = x.asInstanceOf[TorrentInfo]
-          totalBlocks = tInfo.totalBlocks
-          totalBytes = tInfo.totalBytes
-          arrayOfBlocks = new Array[TorrentBlock](totalBlocks)
-          hasBlocks = 0
 
-        case None =>
-          Thread.sleep(500)
-      }
-      attemptId -= 1
-    }
-
-    if (totalBlocks == -1) {
-      return false
-    }
-
-    /*
-     * Fetch actual chunks of data. Note that all these chunks are stored in
-     * the BlockManager and reported to the master, so that other executors
-     * can find out and pull the chunks from this executor.
-     */
-    val recvOrder = new Random().shuffle(Array.iterate(0, totalBlocks)(_ + 1).toList)
-    for (pid <- recvOrder) {
-      val pieceId = BroadcastBlockId(id, "piece" + pid)
-      SparkEnv.get.blockManager.getSingle(pieceId) match {
-        case Some(x) =>
-          arrayOfBlocks(pid) = x.asInstanceOf[TorrentBlock]
-          hasBlocks += 1
+          _value = TorrentBroadcast.unBlockifyObject[T](blocks)
+          // Store the merged copy in BlockManager so other tasks on this executor don't
+          // need to re-fetch it.
           SparkEnv.get.blockManager.putSingle(
-            pieceId, arrayOfBlocks(pid), StorageLevel.MEMORY_AND_DISK, tellMaster = true)
-
-        case None =>
-          throw new SparkException("Failed to get " + pieceId + " of " + broadcastId)
+            broadcastId, _value, StorageLevel.MEMORY_AND_DISK, tellMaster = false)
       }
     }
-
-    hasBlocks == totalBlocks
   }
-
 }
 
-private[broadcast] object TorrentBroadcast extends Logging {
+
+private object TorrentBroadcast extends Logging {
+  /** Size of each block. Default value is 4MB. */
   private lazy val BLOCK_SIZE = conf.getInt("spark.broadcast.blockSize", 4096) * 1024
   private var initialized = false
   private var conf: SparkConf = null
@@ -223,7 +200,9 @@ private[broadcast] object TorrentBroadcast extends Logging {
     initialized = false
   }
 
-  def blockifyObject[T: ClassTag](obj: T): TorrentInfo = {
+  def blockifyObject[T: ClassTag](obj: T): Array[ByteBuffer] = {
+    // TODO: Create a special ByteArrayOutputStream that splits the output directly into chunks
+    // so we don't need to do the extra memory copy.
     val bos = new ByteArrayOutputStream()
     val out: OutputStream = if (compress) compressionCodec.compressedOutputStream(bos) else bos
     val ser = SparkEnv.get.serializer.newInstance()
@@ -231,44 +210,27 @@ private[broadcast] object TorrentBroadcast extends Logging {
     serOut.writeObject[T](obj).close()
     val byteArray = bos.toByteArray
     val bais = new ByteArrayInputStream(byteArray)
+    val numBlocks = math.ceil(byteArray.length.toDouble / BLOCK_SIZE).toInt
+    val blocks = new Array[ByteBuffer](numBlocks)
 
-    var blockNum = byteArray.length / BLOCK_SIZE
-    if (byteArray.length % BLOCK_SIZE != 0) {
-      blockNum += 1
-    }
-
-    val blocks = new Array[TorrentBlock](blockNum)
     var blockId = 0
-
     for (i <- 0 until (byteArray.length, BLOCK_SIZE)) {
       val thisBlockSize = math.min(BLOCK_SIZE, byteArray.length - i)
       val tempByteArray = new Array[Byte](thisBlockSize)
       bais.read(tempByteArray, 0, thisBlockSize)
 
-      blocks(blockId) = new TorrentBlock(blockId, tempByteArray)
+      blocks(blockId) = ByteBuffer.wrap(tempByteArray)
       blockId += 1
     }
     bais.close()
-
-    val info = TorrentInfo(blocks, blockNum, byteArray.length)
-    info.hasBlocks = blockNum
-    info
+    blocks
   }
 
-  def unBlockifyObject[T: ClassTag](
-      arrayOfBlocks: Array[TorrentBlock],
-      totalBytes: Int,
-      totalBlocks: Int): T = {
-    val retByteArray = new Array[Byte](totalBytes)
-    for (i <- 0 until totalBlocks) {
-      System.arraycopy(arrayOfBlocks(i).byteArray, 0, retByteArray,
-        i * BLOCK_SIZE, arrayOfBlocks(i).byteArray.length)
-    }
+  def unBlockifyObject[T: ClassTag](blocks: Array[ByteBuffer]): T = {
+    val is = new SequenceInputStream(
+      asJavaEnumeration(blocks.iterator.map(block => new ByteBufferInputStream(block))))
+    val in: InputStream = if (compress) compressionCodec.compressedInputStream(is) else is
 
-    val in: InputStream = {
-      val arrIn = new ByteArrayInputStream(retByteArray)
-      if (compress) compressionCodec.compressedInputStream(arrIn) else arrIn
-    }
     val ser = SparkEnv.get.serializer.newInstance()
     val serIn = ser.deserializeStream(in)
     val obj = serIn.readObject[T]()
@@ -284,17 +246,3 @@ private[broadcast] object TorrentBroadcast extends Logging {
     SparkEnv.get.blockManager.master.removeBroadcast(id, removeFromDriver, blocking)
   }
 }
-
-private[broadcast] case class TorrentBlock(
-    blockID: Int,
-    byteArray: Array[Byte])
-  extends Serializable
-
-private[broadcast] case class TorrentInfo(
-    @transient arrayOfBlocks: Array[TorrentBlock],
-    totalBlocks: Int,
-    totalBytes: Int)
-  extends Serializable {
-
-  @transient var hasBlocks = 0
-}
diff --git a/core/src/test/scala/org/apache/spark/broadcast/BroadcastSuite.scala b/core/src/test/scala/org/apache/spark/broadcast/BroadcastSuite.scala
index 17c64455b2429..978a6ded80829 100644
--- a/core/src/test/scala/org/apache/spark/broadcast/BroadcastSuite.scala
+++ b/core/src/test/scala/org/apache/spark/broadcast/BroadcastSuite.scala
@@ -17,10 +17,12 @@
 
 package org.apache.spark.broadcast
 
-import org.apache.spark.storage.{BroadcastBlockId, _}
-import org.apache.spark.{LocalSparkContext, SparkConf, SparkContext, SparkException}
 import org.scalatest.FunSuite
 
+import org.apache.spark.{LocalSparkContext, SparkConf, SparkContext, SparkException}
+import org.apache.spark.storage._
+
+
 class BroadcastSuite extends FunSuite with LocalSparkContext {
 
   private val httpConf = broadcastConf("HttpBroadcastFactory")
@@ -124,12 +126,10 @@ class BroadcastSuite extends FunSuite with LocalSparkContext {
   private def testUnpersistHttpBroadcast(distributed: Boolean, removeFromDriver: Boolean) {
     val numSlaves = if (distributed) 2 else 0
 
-    def getBlockIds(id: Long) = Seq[BroadcastBlockId](BroadcastBlockId(id))
-
     // Verify that the broadcast file is created, and blocks are persisted only on the driver
-    def afterCreation(blockIds: Seq[BroadcastBlockId], bmm: BlockManagerMaster) {
-      assert(blockIds.size === 1)
-      val statuses = bmm.getBlockStatus(blockIds.head, askSlaves = true)
+    def afterCreation(broadcastId: Long, bmm: BlockManagerMaster) {
+      val blockId = BroadcastBlockId(broadcastId)
+      val statuses = bmm.getBlockStatus(blockId, askSlaves = true)
       assert(statuses.size === 1)
       statuses.head match { case (bm, status) =>
         assert(bm.executorId === "<driver>", "Block should only be on the driver")
@@ -139,14 +139,14 @@ class BroadcastSuite extends FunSuite with LocalSparkContext {
       }
       if (distributed) {
         // this file is only generated in distributed mode
-        assert(HttpBroadcast.getFile(blockIds.head.broadcastId).exists, "Broadcast file not found!")
+        assert(HttpBroadcast.getFile(blockId.broadcastId).exists, "Broadcast file not found!")
       }
     }
 
     // Verify that blocks are persisted in both the executors and the driver
-    def afterUsingBroadcast(blockIds: Seq[BroadcastBlockId], bmm: BlockManagerMaster) {
-      assert(blockIds.size === 1)
-      val statuses = bmm.getBlockStatus(blockIds.head, askSlaves = true)
+    def afterUsingBroadcast(broadcastId: Long, bmm: BlockManagerMaster) {
+      val blockId = BroadcastBlockId(broadcastId)
+      val statuses = bmm.getBlockStatus(blockId, askSlaves = true)
       assert(statuses.size === numSlaves + 1)
       statuses.foreach { case (_, status) =>
         assert(status.storageLevel === StorageLevel.MEMORY_AND_DISK)
@@ -157,21 +157,21 @@ class BroadcastSuite extends FunSuite with LocalSparkContext {
 
     // Verify that blocks are unpersisted on all executors, and on all nodes if removeFromDriver
     // is true. In the latter case, also verify that the broadcast file is deleted on the driver.
-    def afterUnpersist(blockIds: Seq[BroadcastBlockId], bmm: BlockManagerMaster) {
-      assert(blockIds.size === 1)
-      val statuses = bmm.getBlockStatus(blockIds.head, askSlaves = true)
+    def afterUnpersist(broadcastId: Long, bmm: BlockManagerMaster) {
+      val blockId = BroadcastBlockId(broadcastId)
+      val statuses = bmm.getBlockStatus(blockId, askSlaves = true)
       val expectedNumBlocks = if (removeFromDriver) 0 else 1
       val possiblyNot = if (removeFromDriver) "" else " not"
       assert(statuses.size === expectedNumBlocks,
         "Block should%s be unpersisted on the driver".format(possiblyNot))
       if (distributed && removeFromDriver) {
         // this file is only generated in distributed mode
-        assert(!HttpBroadcast.getFile(blockIds.head.broadcastId).exists,
+        assert(!HttpBroadcast.getFile(blockId.broadcastId).exists,
           "Broadcast file should%s be deleted".format(possiblyNot))
       }
     }
 
-    testUnpersistBroadcast(distributed, numSlaves, httpConf, getBlockIds, afterCreation,
+    testUnpersistBroadcast(distributed, numSlaves, httpConf, afterCreation,
       afterUsingBroadcast, afterUnpersist, removeFromDriver)
   }
 
@@ -185,67 +185,51 @@ class BroadcastSuite extends FunSuite with LocalSparkContext {
   private def testUnpersistTorrentBroadcast(distributed: Boolean, removeFromDriver: Boolean) {
     val numSlaves = if (distributed) 2 else 0
 
-    def getBlockIds(id: Long) = {
-      val broadcastBlockId = BroadcastBlockId(id)
-      val metaBlockId = BroadcastBlockId(id, "meta")
-      // Assume broadcast value is small enough to fit into 1 piece
-      val pieceBlockId = BroadcastBlockId(id, "piece0")
-      if (distributed) {
-        // the metadata and piece blocks are generated only in distributed mode
-        Seq[BroadcastBlockId](broadcastBlockId, metaBlockId, pieceBlockId)
-      } else {
-        Seq[BroadcastBlockId](broadcastBlockId)
-      }
+    // Verify that blocks are persisted only on the driver
+    def afterCreation(broadcastId: Long, bmm: BlockManagerMaster) {
+      var blockId = BroadcastBlockId(broadcastId)
+      var statuses = bmm.getBlockStatus(blockId, askSlaves = true)
+      assert(statuses.size === 1)
+
+      blockId = BroadcastBlockId(broadcastId, "piece0")
+      statuses = bmm.getBlockStatus(blockId, askSlaves = true)
+      assert(statuses.size === (if (distributed) 1 else 0))
     }
 
-    // Verify that blocks are persisted only on the driver
-    def afterCreation(blockIds: Seq[BroadcastBlockId], bmm: BlockManagerMaster) {
-      blockIds.foreach { blockId =>
-        val statuses = bmm.getBlockStatus(blockIds.head, askSlaves = true)
+    // Verify that blocks are persisted in both the executors and the driver
+    def afterUsingBroadcast(broadcastId: Long, bmm: BlockManagerMaster) {
+      var blockId = BroadcastBlockId(broadcastId)
+      var statuses = bmm.getBlockStatus(blockId, askSlaves = true)
+      if (distributed) {
+        assert(statuses.size === numSlaves + 1)
+      } else {
         assert(statuses.size === 1)
-        statuses.head match { case (bm, status) =>
-          assert(bm.executorId === "<driver>", "Block should only be on the driver")
-          assert(status.storageLevel === StorageLevel.MEMORY_AND_DISK)
-          assert(status.memSize > 0, "Block should be in memory store on the driver")
-          assert(status.diskSize === 0, "Block should not be in disk store on the driver")
-        }
       }
-    }
 
-    // Verify that blocks are persisted in both the executors and the driver
-    def afterUsingBroadcast(blockIds: Seq[BroadcastBlockId], bmm: BlockManagerMaster) {
-      blockIds.foreach { blockId =>
-        val statuses = bmm.getBlockStatus(blockId, askSlaves = true)
-        if (blockId.field == "meta") {
-          // Meta data is only on the driver
-          assert(statuses.size === 1)
-          statuses.head match { case (bm, _) => assert(bm.executorId === "<driver>") }
-        } else {
-          // Other blocks are on both the executors and the driver
-          assert(statuses.size === numSlaves + 1,
-            blockId + " has " + statuses.size + " statuses: " + statuses.mkString(","))
-          statuses.foreach { case (_, status) =>
-            assert(status.storageLevel === StorageLevel.MEMORY_AND_DISK)
-            assert(status.memSize > 0, "Block should be in memory store")
-            assert(status.diskSize === 0, "Block should not be in disk store")
-          }
-        }
+      blockId = BroadcastBlockId(broadcastId, "piece0")
+      statuses = bmm.getBlockStatus(blockId, askSlaves = true)
+      if (distributed) {
+        assert(statuses.size === numSlaves + 1)
+      } else {
+        assert(statuses.size === 0)
       }
     }
 
     // Verify that blocks are unpersisted on all executors, and on all nodes if removeFromDriver
     // is true.
-    def afterUnpersist(blockIds: Seq[BroadcastBlockId], bmm: BlockManagerMaster) {
-      val expectedNumBlocks = if (removeFromDriver) 0 else 1
-      val possiblyNot = if (removeFromDriver) "" else " not"
-      blockIds.foreach { blockId =>
-        val statuses = bmm.getBlockStatus(blockId, askSlaves = true)
-        assert(statuses.size === expectedNumBlocks,
-          "Block should%s be unpersisted on the driver".format(possiblyNot))
-      }
+    def afterUnpersist(broadcastId: Long, bmm: BlockManagerMaster) {
+      var blockId = BroadcastBlockId(broadcastId)
+      var expectedNumBlocks = if (removeFromDriver) 0 else 1
+      var statuses = bmm.getBlockStatus(blockId, askSlaves = true)
+      assert(statuses.size === expectedNumBlocks)
+
+      blockId = BroadcastBlockId(broadcastId, "piece0")
+      expectedNumBlocks = if (removeFromDriver || !distributed) 0 else 1
+      statuses = bmm.getBlockStatus(blockId, askSlaves = true)
+      assert(statuses.size === expectedNumBlocks)
     }
 
-    testUnpersistBroadcast(distributed, numSlaves,  torrentConf, getBlockIds, afterCreation,
+    testUnpersistBroadcast(distributed, numSlaves,  torrentConf, afterCreation,
       afterUsingBroadcast, afterUnpersist, removeFromDriver)
   }
 
@@ -262,10 +246,9 @@ class BroadcastSuite extends FunSuite with LocalSparkContext {
       distributed: Boolean,
       numSlaves: Int,  // used only when distributed = true
       broadcastConf: SparkConf,
-      getBlockIds: Long => Seq[BroadcastBlockId],
-      afterCreation: (Seq[BroadcastBlockId], BlockManagerMaster) => Unit,
-      afterUsingBroadcast: (Seq[BroadcastBlockId], BlockManagerMaster) => Unit,
-      afterUnpersist: (Seq[BroadcastBlockId], BlockManagerMaster) => Unit,
+      afterCreation: (Long, BlockManagerMaster) => Unit,
+      afterUsingBroadcast: (Long, BlockManagerMaster) => Unit,
+      afterUnpersist: (Long, BlockManagerMaster) => Unit,
       removeFromDriver: Boolean) {
 
     sc = if (distributed) {
@@ -278,15 +261,14 @@ class BroadcastSuite extends FunSuite with LocalSparkContext {
 
     // Create broadcast variable
     val broadcast = sc.broadcast(list)
-    val blocks = getBlockIds(broadcast.id)
-    afterCreation(blocks, blockManagerMaster)
+    afterCreation(broadcast.id, blockManagerMaster)
 
     // Use broadcast variable on all executors
     val partitions = 10
     assert(partitions > numSlaves)
     val results = sc.parallelize(1 to partitions, partitions).map(x => (x, broadcast.value.sum))
     assert(results.collect().toSet === (1 to partitions).map(x => (x, list.sum)).toSet)
-    afterUsingBroadcast(blocks, blockManagerMaster)
+    afterUsingBroadcast(broadcast.id, blockManagerMaster)
 
     // Unpersist broadcast
     if (removeFromDriver) {
@@ -294,7 +276,7 @@ class BroadcastSuite extends FunSuite with LocalSparkContext {
     } else {
       broadcast.unpersist(blocking = true)
     }
-    afterUnpersist(blocks, blockManagerMaster)
+    afterUnpersist(broadcast.id, blockManagerMaster)
 
     // If the broadcast is removed from driver, all subsequent uses of the broadcast variable
     // should throw SparkExceptions. Otherwise, the result should be the same as before.

From 0a984aa155fb7f532fe87620dcf1a2814c5b8b49 Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Tue, 19 Aug 2014 22:16:22 -0700
Subject: [PATCH 034/489] [SPARK-3142][MLLIB] output shuffle data directly in
 Word2Vec

Sorry I didn't realize this in #2043. Ishiihara

Author: Xiangrui Meng <meng@databricks.com>

Closes #2049 from mengxr/more-w2v and squashes the following commits:

050b1c5 [Xiangrui Meng] output shuffle data directly
---
 .../apache/spark/mllib/feature/Word2Vec.scala | 23 ++++++++++---------
 1 file changed, 12 insertions(+), 11 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala b/mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala
index c3375ed44fd99..fc1444705364a 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala
@@ -347,19 +347,20 @@ class Word2Vec extends Serializable with Logging {
         }
         val syn0Local = model._1
         val syn1Local = model._2
-        val synOut = mutable.ListBuffer.empty[(Int, Array[Float])]
-        var index = 0
-        while(index < vocabSize) {
-          if (syn0Modify(index) != 0) {
-            synOut += ((index, syn0Local.slice(index * vectorSize, (index + 1) * vectorSize)))
+        // Only output modified vectors.
+        Iterator.tabulate(vocabSize) { index =>
+          if (syn0Modify(index) > 0) {
+            Some((index, syn0Local.slice(index * vectorSize, (index + 1) * vectorSize)))
+          } else {
+            None
           }
-          if (syn1Modify(index) != 0) {
-            synOut += ((index + vocabSize,
-              syn1Local.slice(index * vectorSize, (index + 1) * vectorSize)))
+        }.flatten ++ Iterator.tabulate(vocabSize) { index =>
+          if (syn1Modify(index) > 0) {
+            Some((index + vocabSize, syn1Local.slice(index * vectorSize, (index + 1) * vectorSize)))
+          } else {
+            None
           }
-          index += 1
-        }
-        synOut.toIterator
+        }.flatten
       }
       val synAgg = partial.reduceByKey { case (v1, v2) =>
           blas.saxpy(vectorSize, 1.0f, v2, 1, v1, 1)

From ebcb94f701273b56851dade677e047388a8bca09 Mon Sep 17 00:00:00 2001
From: Josh Rosen <joshrosen@apache.org>
Date: Tue, 19 Aug 2014 22:42:50 -0700
Subject: [PATCH 035/489] [SPARK-2974] [SPARK-2975] Fix two bugs related to
 spark.local.dirs

This PR fixes two bugs related to `spark.local.dirs` and `SPARK_LOCAL_DIRS`, one where `Utils.getLocalDir()` might return an invalid directory (SPARK-2974) and another where the `SPARK_LOCAL_DIRS` override didn't affect the driver, which could cause problems when running tasks in local mode (SPARK-2975).

This patch fixes both issues: the new `Utils.getOrCreateLocalRootDirs(conf: SparkConf)` utility method manages the creation of local directories and handles the precedence among the different configuration options, so we should see the same behavior whether we're running in local mode or on a worker.

It's kind of a pain to mock out environment variables in tests (no easy way to mock System.getenv), so I added a `private[spark]` method to SparkConf for accessing environment variables (by default, it just delegates to System.getenv).  By subclassing SparkConf and overriding this method, we can mock out SPARK_LOCAL_DIRS in tests.

I also fixed a typo in PySpark where we used `SPARK_LOCAL_DIR` instead of `SPARK_LOCAL_DIRS` (I think this was technically innocuous, but it seemed worth fixing).

Author: Josh Rosen <joshrosen@apache.org>

Closes #2002 from JoshRosen/local-dirs and squashes the following commits:

efad8c6 [Josh Rosen] Address review comments:
1dec709 [Josh Rosen] Minor updates to Javadocs.
7f36999 [Josh Rosen] Use env vars to detect if running in YARN container.
399ac25 [Josh Rosen] Update getLocalDir() documentation.
bb3ad89 [Josh Rosen] Remove duplicated YARN getLocalDirs() code.
3e92d44 [Josh Rosen] Move local dirs override logic into Utils; fix bugs:
b2c4736 [Josh Rosen] Add failing tests for SPARK-2974 and SPARK-2975.
007298b [Josh Rosen] Allow environment variables to be mocked in tests.
6d9259b [Josh Rosen] Fix typo in PySpark: SPARK_LOCAL_DIR should be SPARK_LOCAL_DIRS
---
 .../scala/org/apache/spark/SparkConf.scala    |  8 ++-
 .../apache/spark/api/python/PythonRDD.scala   |  2 +-
 .../org/apache/spark/executor/Executor.scala  | 25 -------
 .../apache/spark/storage/BlockManager.scala   |  3 +-
 .../spark/storage/DiskBlockManager.scala      | 14 ++--
 .../scala/org/apache/spark/util/Utils.scala   | 67 +++++++++++++++++--
 .../spark/storage/BlockManagerSuite.scala     |  3 +-
 .../spark/storage/DiskBlockManagerSuite.scala |  4 +-
 .../apache/spark/storage/LocalDirsSuite.scala | 61 +++++++++++++++++
 python/pyspark/shuffle.py                     |  2 +-
 .../spark/deploy/yarn/ApplicationMaster.scala | 18 -----
 .../spark/deploy/yarn/ExecutorLauncher.scala  | 19 ------
 .../spark/deploy/yarn/ApplicationMaster.scala | 18 -----
 .../spark/deploy/yarn/ExecutorLauncher.scala  | 19 ------
 14 files changed, 145 insertions(+), 118 deletions(-)
 create mode 100644 core/src/test/scala/org/apache/spark/storage/LocalDirsSuite.scala

diff --git a/core/src/main/scala/org/apache/spark/SparkConf.scala b/core/src/main/scala/org/apache/spark/SparkConf.scala
index b4f321ec99e78..605df0e929faa 100644
--- a/core/src/main/scala/org/apache/spark/SparkConf.scala
+++ b/core/src/main/scala/org/apache/spark/SparkConf.scala
@@ -45,7 +45,7 @@ class SparkConf(loadDefaults: Boolean) extends Cloneable with Logging {
   /** Create a SparkConf that loads defaults from system properties and the classpath */
   def this() = this(true)
 
-  private val settings = new HashMap[String, String]()
+  private[spark] val settings = new HashMap[String, String]()
 
   if (loadDefaults) {
     // Load any spark.* system properties
@@ -210,6 +210,12 @@ class SparkConf(loadDefaults: Boolean) extends Cloneable with Logging {
     new SparkConf(false).setAll(settings)
   }
 
+  /**
+   * By using this instead of System.getenv(), environment variables can be mocked
+   * in unit tests.
+   */
+  private[spark] def getenv(name: String): String = System.getenv(name)
+
   /** Checks for illegal or deprecated config settings. Throws an exception for the former. Not
     * idempotent - may mutate this conf object to convert deprecated settings to supported ones. */
   private[spark] def validateSettings() {
diff --git a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
index 10210a2927dcc..747023812f754 100644
--- a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
+++ b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
@@ -62,7 +62,7 @@ private[spark] class PythonRDD(
     val env = SparkEnv.get
     val localdir = env.blockManager.diskBlockManager.localDirs.map(
       f => f.getPath()).mkString(",")
-    envVars += ("SPARK_LOCAL_DIR" -> localdir) // it's also used in monitor thread
+    envVars += ("SPARK_LOCAL_DIRS" -> localdir) // it's also used in monitor thread
     val worker: Socket = env.createPythonWorker(pythonExec, envVars.toMap)
 
     // Start a thread to feed the process input from our parent's iterator
diff --git a/core/src/main/scala/org/apache/spark/executor/Executor.scala b/core/src/main/scala/org/apache/spark/executor/Executor.scala
index fb3f7bd54bbfa..2f76e532aeb76 100644
--- a/core/src/main/scala/org/apache/spark/executor/Executor.scala
+++ b/core/src/main/scala/org/apache/spark/executor/Executor.scala
@@ -62,16 +62,6 @@ private[spark] class Executor(
   val conf = new SparkConf(true)
   conf.setAll(properties)
 
-  // If we are in yarn mode, systems can have different disk layouts so we must set it
-  // to what Yarn on this system said was available. This will be used later when SparkEnv
-  // created.
-  if (java.lang.Boolean.valueOf(
-      System.getProperty("SPARK_YARN_MODE", System.getenv("SPARK_YARN_MODE")))) {
-    conf.set("spark.local.dir", getYarnLocalDirs())
-  } else if (sys.env.contains("SPARK_LOCAL_DIRS")) {
-    conf.set("spark.local.dir", sys.env("SPARK_LOCAL_DIRS"))
-  }
-
   if (!isLocal) {
     // Setup an uncaught exception handler for non-local mode.
     // Make any thread terminations due to uncaught exceptions kill the entire
@@ -134,21 +124,6 @@ private[spark] class Executor(
     threadPool.shutdown()
   }
 
-  /** Get the Yarn approved local directories. */
-  private def getYarnLocalDirs(): String = {
-    // Hadoop 0.23 and 2.x have different Environment variable names for the
-    // local dirs, so lets check both. We assume one of the 2 is set.
-    // LOCAL_DIRS => 2.X, YARN_LOCAL_DIRS => 0.23.X
-    val localDirs = Option(System.getenv("YARN_LOCAL_DIRS"))
-      .getOrElse(Option(System.getenv("LOCAL_DIRS"))
-      .getOrElse(""))
-
-    if (localDirs.isEmpty) {
-      throw new Exception("Yarn Local dirs can't be empty")
-    }
-    localDirs
-  }
-
   class TaskRunner(
       execBackend: ExecutorBackend, val taskId: Long, taskName: String, serializedTask: ByteBuffer)
     extends Runnable {
diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManager.scala b/core/src/main/scala/org/apache/spark/storage/BlockManager.scala
index c0491fb55e3a4..12a92d44f4c36 100644
--- a/core/src/main/scala/org/apache/spark/storage/BlockManager.scala
+++ b/core/src/main/scala/org/apache/spark/storage/BlockManager.scala
@@ -67,8 +67,7 @@ private[spark] class BlockManager(
 
   private val port = conf.getInt("spark.blockManager.port", 0)
   val shuffleBlockManager = new ShuffleBlockManager(this, shuffleManager)
-  val diskBlockManager = new DiskBlockManager(shuffleBlockManager,
-    conf.get("spark.local.dir", System.getProperty("java.io.tmpdir")))
+  val diskBlockManager = new DiskBlockManager(shuffleBlockManager, conf)
   val connectionManager =
     new ConnectionManager(port, conf, securityManager, "Connection manager for block manager")
 
diff --git a/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala b/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala
index f3da816389581..ec022ce9c048a 100644
--- a/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala
+++ b/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala
@@ -21,7 +21,7 @@ import java.io.File
 import java.text.SimpleDateFormat
 import java.util.{Date, Random, UUID}
 
-import org.apache.spark.{SparkEnv, Logging}
+import org.apache.spark.{SparkConf, SparkEnv, Logging}
 import org.apache.spark.executor.ExecutorExitCode
 import org.apache.spark.network.netty.PathResolver
 import org.apache.spark.util.Utils
@@ -33,9 +33,10 @@ import org.apache.spark.shuffle.sort.SortShuffleManager
  * However, it is also possible to have a block map to only a segment of a file, by calling
  * mapBlockToFileSegment().
  *
- * @param rootDirs The directories to use for storing block files. Data will be hashed among these.
+ * Block files are hashed among the directories listed in spark.local.dir (or in
+ * SPARK_LOCAL_DIRS, if it's set).
  */
-private[spark] class DiskBlockManager(shuffleBlockManager: ShuffleBlockManager, rootDirs: String)
+private[spark] class DiskBlockManager(shuffleBlockManager: ShuffleBlockManager, conf: SparkConf)
   extends PathResolver with Logging {
 
   private val MAX_DIR_CREATION_ATTEMPTS: Int = 10
@@ -46,7 +47,7 @@ private[spark] class DiskBlockManager(shuffleBlockManager: ShuffleBlockManager,
   /* Create one local directory for each path mentioned in spark.local.dir; then, inside this
    * directory, create multiple subdirectories that we will hash files into, in order to avoid
    * having really large inodes at the top level. */
-  val localDirs: Array[File] = createLocalDirs()
+  val localDirs: Array[File] = createLocalDirs(conf)
   if (localDirs.isEmpty) {
     logError("Failed to create any local dir.")
     System.exit(ExecutorExitCode.DISK_STORE_FAILED_TO_CREATE_DIR)
@@ -130,10 +131,9 @@ private[spark] class DiskBlockManager(shuffleBlockManager: ShuffleBlockManager,
     (blockId, getFile(blockId))
   }
 
-  private def createLocalDirs(): Array[File] = {
-    logDebug(s"Creating local directories at root dirs '$rootDirs'")
+  private def createLocalDirs(conf: SparkConf): Array[File] = {
     val dateFormat = new SimpleDateFormat("yyyyMMddHHmmss")
-    rootDirs.split(",").flatMap { rootDir =>
+    Utils.getOrCreateLocalRootDirs(conf).flatMap { rootDir =>
       var foundLocalDir = false
       var localDir: File = null
       var localDirId: String = null
diff --git a/core/src/main/scala/org/apache/spark/util/Utils.scala b/core/src/main/scala/org/apache/spark/util/Utils.scala
index 019f68b160894..d6d74ce269219 100644
--- a/core/src/main/scala/org/apache/spark/util/Utils.scala
+++ b/core/src/main/scala/org/apache/spark/util/Utils.scala
@@ -449,12 +449,71 @@ private[spark] object Utils extends Logging {
   }
 
   /**
-   * Get a temporary directory using Spark's spark.local.dir property, if set. This will always
-   * return a single directory, even though the spark.local.dir property might be a list of
-   * multiple paths.
+   * Get the path of a temporary directory.  Spark's local directories can be configured through
+   * multiple settings, which are used with the following precedence:
+   *
+   *   - If called from inside of a YARN container, this will return a directory chosen by YARN.
+   *   - If the SPARK_LOCAL_DIRS environment variable is set, this will return a directory from it.
+   *   - Otherwise, if the spark.local.dir is set, this will return a directory from it.
+   *   - Otherwise, this will return java.io.tmpdir.
+   *
+   * Some of these configuration options might be lists of multiple paths, but this method will
+   * always return a single directory.
    */
   def getLocalDir(conf: SparkConf): String = {
-    conf.get("spark.local.dir",  System.getProperty("java.io.tmpdir")).split(',')(0)
+    getOrCreateLocalRootDirs(conf)(0)
+  }
+
+  private[spark] def isRunningInYarnContainer(conf: SparkConf): Boolean = {
+    // These environment variables are set by YARN.
+    // For Hadoop 0.23.X, we check for YARN_LOCAL_DIRS (we use this below in getYarnLocalDirs())
+    // For Hadoop 2.X, we check for CONTAINER_ID.
+    conf.getenv("CONTAINER_ID") != null || conf.getenv("YARN_LOCAL_DIRS") != null
+  }
+
+  /**
+   * Gets or creates the directories listed in spark.local.dir or SPARK_LOCAL_DIRS,
+   * and returns only the directories that exist / could be created.
+   *
+   * If no directories could be created, this will return an empty list.
+   */
+  private[spark] def getOrCreateLocalRootDirs(conf: SparkConf): Array[String] = {
+    val confValue = if (isRunningInYarnContainer(conf)) {
+      // If we are in yarn mode, systems can have different disk layouts so we must set it
+      // to what Yarn on this system said was available.
+      getYarnLocalDirs(conf)
+    } else {
+      Option(conf.getenv("SPARK_LOCAL_DIRS")).getOrElse(
+        conf.get("spark.local.dir", System.getProperty("java.io.tmpdir")))
+    }
+    val rootDirs = confValue.split(',')
+    logDebug(s"Getting/creating local root dirs at '$confValue'")
+
+    rootDirs.flatMap { rootDir =>
+      val localDir: File = new File(rootDir)
+      val foundLocalDir = localDir.exists || localDir.mkdirs()
+      if (!foundLocalDir) {
+        logError(s"Failed to create local root dir in $rootDir.  Ignoring this directory.")
+        None
+      } else {
+        Some(rootDir)
+      }
+    }
+  }
+
+  /** Get the Yarn approved local directories. */
+  private def getYarnLocalDirs(conf: SparkConf): String = {
+    // Hadoop 0.23 and 2.x have different Environment variable names for the
+    // local dirs, so lets check both. We assume one of the 2 is set.
+    // LOCAL_DIRS => 2.X, YARN_LOCAL_DIRS => 0.23.X
+    val localDirs = Option(conf.getenv("YARN_LOCAL_DIRS"))
+      .getOrElse(Option(conf.getenv("LOCAL_DIRS"))
+      .getOrElse(""))
+
+    if (localDirs.isEmpty) {
+      throw new Exception("Yarn Local dirs can't be empty")
+    }
+    localDirs
   }
 
   /**
diff --git a/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala b/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala
index 20bac66105a69..f32ce6f9fcc7f 100644
--- a/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala
@@ -825,8 +825,7 @@ class BlockManagerSuite extends FunSuite with Matchers with BeforeAndAfter
     val blockManager = mock(classOf[BlockManager])
     val shuffleBlockManager = mock(classOf[ShuffleBlockManager])
     when(shuffleBlockManager.conf).thenReturn(conf)
-    val diskBlockManager = new DiskBlockManager(shuffleBlockManager,
-      System.getProperty("java.io.tmpdir"))
+    val diskBlockManager = new DiskBlockManager(shuffleBlockManager, conf)
 
     when(blockManager.conf).thenReturn(conf.clone.set(confKey, 0.toString))
     val diskStoreMapped = new DiskStore(blockManager, diskBlockManager)
diff --git a/core/src/test/scala/org/apache/spark/storage/DiskBlockManagerSuite.scala b/core/src/test/scala/org/apache/spark/storage/DiskBlockManagerSuite.scala
index 777579bc570db..aabaeadd7a071 100644
--- a/core/src/test/scala/org/apache/spark/storage/DiskBlockManagerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/storage/DiskBlockManagerSuite.scala
@@ -71,7 +71,9 @@ class DiskBlockManagerSuite extends FunSuite with BeforeAndAfterEach with Before
   }
 
   override def beforeEach() {
-    diskBlockManager = new DiskBlockManager(shuffleBlockManager, rootDirs)
+    val conf = testConf.clone
+    conf.set("spark.local.dir", rootDirs)
+    diskBlockManager = new DiskBlockManager(shuffleBlockManager, conf)
     shuffleBlockManager.idToSegmentMap.clear()
   }
 
diff --git a/core/src/test/scala/org/apache/spark/storage/LocalDirsSuite.scala b/core/src/test/scala/org/apache/spark/storage/LocalDirsSuite.scala
new file mode 100644
index 0000000000000..dae7bf0e336de
--- /dev/null
+++ b/core/src/test/scala/org/apache/spark/storage/LocalDirsSuite.scala
@@ -0,0 +1,61 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.storage
+
+import java.io.File
+
+import org.apache.spark.util.Utils
+import org.scalatest.FunSuite
+
+import org.apache.spark.SparkConf
+
+
+/**
+ * Tests for the spark.local.dir and SPARK_LOCAL_DIRS configuration options.
+ */
+class LocalDirsSuite extends FunSuite {
+
+  test("Utils.getLocalDir() returns a valid directory, even if some local dirs are missing") {
+    // Regression test for SPARK-2974
+    assert(!new File("/NONEXISTENT_DIR").exists())
+    val conf = new SparkConf(false)
+      .set("spark.local.dir", s"/NONEXISTENT_PATH,${System.getProperty("java.io.tmpdir")}")
+    assert(new File(Utils.getLocalDir(conf)).exists())
+  }
+
+  test("SPARK_LOCAL_DIRS override also affects driver") {
+    // Regression test for SPARK-2975
+    assert(!new File("/NONEXISTENT_DIR").exists())
+    // SPARK_LOCAL_DIRS is a valid directory:
+    class MySparkConf extends SparkConf(false) {
+      override def getenv(name: String) = {
+        if (name == "SPARK_LOCAL_DIRS") System.getProperty("java.io.tmpdir")
+        else super.getenv(name)
+      }
+
+      override def clone: SparkConf = {
+        new MySparkConf().setAll(settings)
+      }
+    }
+    // spark.local.dir only contains invalid directories, but that's not a problem since
+    // SPARK_LOCAL_DIRS will override it on both the driver and workers:
+    val conf = new MySparkConf().set("spark.local.dir", "/NONEXISTENT_PATH")
+    assert(new File(Utils.getLocalDir(conf)).exists())
+  }
+
+}
diff --git a/python/pyspark/shuffle.py b/python/pyspark/shuffle.py
index 2c68cd4921deb..1ebe7df418327 100644
--- a/python/pyspark/shuffle.py
+++ b/python/pyspark/shuffle.py
@@ -214,7 +214,7 @@ def __init__(self, aggregator, memory_limit=512, serializer=None,
 
     def _get_dirs(self):
         """ Get all the directories """
-        path = os.environ.get("SPARK_LOCAL_DIR", "/tmp")
+        path = os.environ.get("SPARK_LOCAL_DIRS", "/tmp")
         dirs = path.split(",")
         return [os.path.join(d, "python", str(os.getpid()), str(id(self)))
                 for d in dirs]
diff --git a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala b/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
index 46a01f5a9a2cc..4d4848b1bd8f8 100644
--- a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
+++ b/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
@@ -72,10 +72,6 @@ class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration,
   private var registered = false
 
   def run() {
-    // Setup the directories so things go to yarn approved directories rather
-    // then user specified and /tmp.
-    System.setProperty("spark.local.dir", getLocalDirs())
-
     // set the web ui port to be ephemeral for yarn so we don't conflict with
     // other spark processes running on the same box
     System.setProperty("spark.ui.port", "0")
@@ -138,20 +134,6 @@ class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration,
       params)
   }
 
-  /** Get the Yarn approved local directories. */
-  private def getLocalDirs(): String = {
-    // Hadoop 0.23 and 2.x have different Environment variable names for the
-    // local dirs, so lets check both. We assume one of the 2 is set.
-    // LOCAL_DIRS => 2.X, YARN_LOCAL_DIRS => 0.23.X
-    val localDirs = Option(System.getenv("YARN_LOCAL_DIRS"))
-      .orElse(Option(System.getenv("LOCAL_DIRS")))
-
-    localDirs match {
-      case None => throw new Exception("Yarn Local dirs can't be empty")
-      case Some(l) => l
-    }
-  }
-
   private def getApplicationAttemptId(): ApplicationAttemptId = {
     val envs = System.getenv()
     val containerIdString = envs.get(ApplicationConstants.AM_CONTAINER_ID_ENV)
diff --git a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala b/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala
index 72c7143edcd71..c3310fbc24a98 100644
--- a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala
+++ b/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala
@@ -95,11 +95,6 @@ class ExecutorLauncher(args: ApplicationMasterArguments, conf: Configuration, sp
   }
 
   def run() {
-
-    // Setup the directories so things go to yarn approved directories rather
-    // then user specified and /tmp.
-    System.setProperty("spark.local.dir", getLocalDirs())
-
     appAttemptId = getApplicationAttemptId()
     resourceManager = registerWithResourceManager()
 
@@ -152,20 +147,6 @@ class ExecutorLauncher(args: ApplicationMasterArguments, conf: Configuration, sp
     System.exit(0)
   }
 
-  /** Get the Yarn approved local directories. */
-  private def getLocalDirs(): String = {
-    // Hadoop 0.23 and 2.x have different Environment variable names for the
-    // local dirs, so lets check both. We assume one of the 2 is set.
-    // LOCAL_DIRS => 2.X, YARN_LOCAL_DIRS => 0.23.X
-    val localDirs = Option(System.getenv("YARN_LOCAL_DIRS"))
-      .orElse(Option(System.getenv("LOCAL_DIRS")))
-
-    localDirs match {
-      case None => throw new Exception("Yarn Local dirs can't be empty")
-      case Some(l) => l
-    }
-  }
-
   private def getApplicationAttemptId(): ApplicationAttemptId = {
     val envs = System.getenv()
     val containerIdString = envs.get(ApplicationConstants.AM_CONTAINER_ID_ENV)
diff --git a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
index 9c2bcf17a8508..1c4005fd8e78e 100644
--- a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
+++ b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
@@ -72,10 +72,6 @@ class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration,
   private var registered = false
 
   def run() {
-    // Setup the directories so things go to YARN approved directories rather
-    // than user specified and /tmp.
-    System.setProperty("spark.local.dir", getLocalDirs())
-
     // Set the web ui port to be ephemeral for yarn so we don't conflict with
     // other spark processes running on the same box
     System.setProperty("spark.ui.port", "0")
@@ -144,20 +140,6 @@ class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration,
       "spark.org.apache.hadoop.yarn.server.webproxy.amfilter.AmIpFilter.params", params)
   }
 
-  // Get the Yarn approved local directories.
-  private def getLocalDirs(): String = {
-    // Hadoop 0.23 and 2.x have different Environment variable names for the
-    // local dirs, so lets check both. We assume one of the 2 is set.
-    // LOCAL_DIRS => 2.X, YARN_LOCAL_DIRS => 0.23.X
-    val localDirs = Option(System.getenv("YARN_LOCAL_DIRS"))
-      .orElse(Option(System.getenv("LOCAL_DIRS")))
- 
-    localDirs match {
-      case None => throw new Exception("Yarn local dirs can't be empty")
-      case Some(l) => l
-    }
-  }
-
   private def registerApplicationMaster(): RegisterApplicationMasterResponse = {
     logInfo("Registering the ApplicationMaster")
     amClient.registerApplicationMaster(Utils.localHostName(), 0, uiAddress)
diff --git a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala
index a7585748b7f88..45925f1fea005 100644
--- a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala
+++ b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala
@@ -94,11 +94,6 @@ class ExecutorLauncher(args: ApplicationMasterArguments, conf: Configuration, sp
   }
 
   def run() {
-
-    // Setup the directories so things go to yarn approved directories rather
-    // then user specified and /tmp.
-    System.setProperty("spark.local.dir", getLocalDirs())
-
     amClient = AMRMClient.createAMRMClient()
     amClient.init(yarnConf)
     amClient.start()
@@ -141,20 +136,6 @@ class ExecutorLauncher(args: ApplicationMasterArguments, conf: Configuration, sp
     System.exit(0)
   }
 
-  /** Get the Yarn approved local directories. */
-  private def getLocalDirs(): String = {
-    // Hadoop 0.23 and 2.x have different Environment variable names for the
-    // local dirs, so lets check both. We assume one of the 2 is set.
-    // LOCAL_DIRS => 2.X, YARN_LOCAL_DIRS => 0.23.X
-    val localDirs = Option(System.getenv("YARN_LOCAL_DIRS"))
-      .orElse(Option(System.getenv("LOCAL_DIRS")))
-
-    localDirs match {
-      case None => throw new Exception("Yarn Local dirs can't be empty")
-      case Some(l) => l
-    }
-  }
-
   private def registerApplicationMaster(): RegisterApplicationMasterResponse = {
     val appUIAddress = sparkConf.get("spark.driver.appUIAddress", "")
     logInfo(s"Registering the ApplicationMaster with appUIAddress: $appUIAddress")

From 8a74e4b2a8c7dab154b406539487cf29d578d208 Mon Sep 17 00:00:00 2001
From: Ken Takagiwa <ugw.gi.world@gmail.com>
Date: Tue, 19 Aug 2014 22:43:22 -0700
Subject: [PATCH 036/489]  [DOCS] Fixed wrong links

Author: Ken Takagiwa <ugw.gi.world@gmail.com>

Closes #2042 from giwa/patch-1 and squashes the following commits:

216fe0e [Ken Takagiwa] Fixed wrong links
---
 docs/streaming-custom-receivers.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/streaming-custom-receivers.md b/docs/streaming-custom-receivers.md
index 1e045a3dd0ca9..27cd085782f66 100644
--- a/docs/streaming-custom-receivers.md
+++ b/docs/streaming-custom-receivers.md
@@ -186,7 +186,7 @@ JavaDStream<String> words = lines.flatMap(new FlatMapFunction<String, String>()
 ...
 {% endhighlight %}
 
-The full source code is in the example [JavaCustomReceiver.java](https://github.com/apache/spark/blob/master/examples/src/main/java/org/apache/spark/streaming/examples/JavaCustomReceiver.java).
+The full source code is in the example [JavaCustomReceiver.java](https://github.com/apache/spark/blob/master/examples/src/main/java/org/apache/spark/examples/streaming/JavaCustomReceiver.java).
 
 </div>
 </div>
@@ -215,7 +215,7 @@ And a new input stream can be created with this custom actor as
 val lines = ssc.actorStream[String](Props(new CustomActor()), "CustomReceiver")
 {% endhighlight %}
 
-See [ActorWordCount.scala](https://github.com/apache/spark/blob/master/examples/src/main/scala/org/apache/spark/streaming/examples/ActorWordCount.scala)
+See [ActorWordCount.scala](https://github.com/apache/spark/blob/master/examples/src/main/scala/org/apache/spark/examples/streaming/ActorWordCount.scala)
 for an end-to-end example.
 
 

From 0a7ef6339f18e68d703599aff7db2dd9c2003866 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies.liu@gmail.com>
Date: Tue, 19 Aug 2014 22:43:49 -0700
Subject: [PATCH 037/489] [SPARK-3141] [PySpark] fix sortByKey() with take()

Fix sortByKey() with take()

The function `f` used in mapPartitions should always return an iterator.

Author: Davies Liu <davies.liu@gmail.com>

Closes #2045 from davies/fix_sortbykey and squashes the following commits:

1160f59 [Davies Liu] fix sortByKey() with take()
---
 python/pyspark/rdd.py | 18 ++++++++----------
 1 file changed, 8 insertions(+), 10 deletions(-)

diff --git a/python/pyspark/rdd.py b/python/pyspark/rdd.py
index 140cbe05a43b0..3eefc878d274e 100644
--- a/python/pyspark/rdd.py
+++ b/python/pyspark/rdd.py
@@ -575,6 +575,8 @@ def sortByKey(self, ascending=True, numPartitions=None, keyfunc=lambda x: x):
         # noqa
 
         >>> tmp = [('a', 1), ('b', 2), ('1', 3), ('d', 4), ('2', 5)]
+        >>> sc.parallelize(tmp).sortByKey().first()
+        ('1', 3)
         >>> sc.parallelize(tmp).sortByKey(True, 1).collect()
         [('1', 3), ('2', 5), ('a', 1), ('b', 2), ('d', 4)]
         >>> sc.parallelize(tmp).sortByKey(True, 2).collect()
@@ -587,14 +589,13 @@ def sortByKey(self, ascending=True, numPartitions=None, keyfunc=lambda x: x):
         if numPartitions is None:
             numPartitions = self._defaultReducePartitions()
 
+        def sortPartition(iterator):
+            return iter(sorted(iterator, key=lambda (k, v): keyfunc(k), reverse=not ascending))
+
         if numPartitions == 1:
             if self.getNumPartitions() > 1:
                 self = self.coalesce(1)
-
-            def sort(iterator):
-                return sorted(iterator, reverse=(not ascending), key=lambda (k, v): keyfunc(k))
-
-            return self.mapPartitions(sort)
+            return self.mapPartitions(sortPartition)
 
         # first compute the boundary of each part via sampling: we want to partition
         # the key-space into bins such that the bins have roughly the same
@@ -610,17 +611,14 @@ def sort(iterator):
         bounds = [samples[len(samples) * (i + 1) / numPartitions]
                   for i in range(0, numPartitions - 1)]
 
-        def rangePartitionFunc(k):
+        def rangePartitioner(k):
             p = bisect.bisect_left(bounds, keyfunc(k))
             if ascending:
                 return p
             else:
                 return numPartitions - 1 - p
 
-        def mapFunc(iterator):
-            return sorted(iterator, reverse=(not ascending), key=lambda (k, v): keyfunc(k))
-
-        return self.partitionBy(numPartitions, rangePartitionFunc).mapPartitions(mapFunc, True)
+        return self.partitionBy(numPartitions, rangePartitioner).mapPartitions(sortPartition, True)
 
     def sortBy(self, keyfunc, ascending=True, numPartitions=None):
         """

From 8c5a2226932c572898c76eb6fab9283f02ad4103 Mon Sep 17 00:00:00 2001
From: Hari Shreedharan <hshreedharan@apache.org>
Date: Wed, 20 Aug 2014 04:09:54 -0700
Subject: [PATCH 038/489] [SPARK-3054][STREAMING] Add unit tests for Spark
 Sink.

This patch adds unit tests for Spark Sink.

It also removes the private[flume] for Spark Sink,
since the sink is instantiated from Flume configuration (looks like this is ignored by reflection which is used by
Flume, but we should still remove it anyway).

Author: Hari Shreedharan <hshreedharan@apache.org>
Author: Hari Shreedharan <hshreedharan@cloudera.com>

Closes #1958 from harishreedharan/spark-sink-test and squashes the following commits:

e3110b9 [Hari Shreedharan] Add a sleep to allow sink to commit the transactions
120b81e [Hari Shreedharan] Fix complexity in threading model in test
4df5be6 [Hari Shreedharan] Merge remote-tracking branch 'asf/master' into spark-sink-test
c9190d1 [Hari Shreedharan] Indentation and spaces changes
7fedc5a [Hari Shreedharan] Merge remote-tracking branch 'asf/master' into spark-sink-test
abc20cb [Hari Shreedharan] Minor test changes
7b9b649 [Hari Shreedharan] Merge branch 'master' into spark-sink-test
f2c56c9 [Hari Shreedharan] Update SparkSinkSuite.scala
a24aac8 [Hari Shreedharan] Remove unused var
c86d615 [Hari Shreedharan] [SPARK-3054][STREAMING] Add unit tests for Spark Sink.
---
 external/flume-sink/pom.xml                   |   7 +
 .../streaming/flume/sink/SparkSink.scala      |   1 -
 .../streaming/flume/sink/SparkSinkSuite.scala | 204 ++++++++++++++++++
 .../flume/FlumePollingStreamSuite.scala       |   2 +-
 4 files changed, 212 insertions(+), 2 deletions(-)
 create mode 100644 external/flume-sink/src/test/scala/org/apache/spark/streaming/flume/sink/SparkSinkSuite.scala

diff --git a/external/flume-sink/pom.xml b/external/flume-sink/pom.xml
index d0bf1cf1ea796..0c68defa5e101 100644
--- a/external/flume-sink/pom.xml
+++ b/external/flume-sink/pom.xml
@@ -72,6 +72,13 @@
       <groupId>org.scalatest</groupId>
       <artifactId>scalatest_${scala.binary.version}</artifactId>
     </dependency>
+    <dependency>
+      <groupId>org.apache.spark</groupId>
+      <artifactId>spark-streaming_${scala.binary.version}</artifactId>
+      <version>${project.version}</version>
+      <type>test-jar</type>
+      <scope>test</scope> <!-- Need it only for tests, don't package it -->
+    </dependency>
   </dependencies>
   <build>
     <outputDirectory>target/scala-${scala.binary.version}/classes</outputDirectory>
diff --git a/external/flume-sink/src/main/scala/org/apache/spark/streaming/flume/sink/SparkSink.scala b/external/flume-sink/src/main/scala/org/apache/spark/streaming/flume/sink/SparkSink.scala
index 948af5947f5e1..98ae7d783aec8 100644
--- a/external/flume-sink/src/main/scala/org/apache/spark/streaming/flume/sink/SparkSink.scala
+++ b/external/flume-sink/src/main/scala/org/apache/spark/streaming/flume/sink/SparkSink.scala
@@ -53,7 +53,6 @@ import org.apache.flume.sink.AbstractSink
  *
  */
 
-private[flume]
 class SparkSink extends AbstractSink with Logging with Configurable {
 
   // Size of the pool to use for holding transaction processors.
diff --git a/external/flume-sink/src/test/scala/org/apache/spark/streaming/flume/sink/SparkSinkSuite.scala b/external/flume-sink/src/test/scala/org/apache/spark/streaming/flume/sink/SparkSinkSuite.scala
new file mode 100644
index 0000000000000..44b27edf85ce8
--- /dev/null
+++ b/external/flume-sink/src/test/scala/org/apache/spark/streaming/flume/sink/SparkSinkSuite.scala
@@ -0,0 +1,204 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.spark.streaming.flume.sink
+
+import java.net.InetSocketAddress
+import java.util.concurrent.atomic.AtomicInteger
+import java.util.concurrent.{TimeUnit, CountDownLatch, Executors}
+
+import scala.collection.JavaConversions._
+import scala.concurrent.{ExecutionContext, Future}
+import scala.util.{Failure, Success}
+
+import com.google.common.util.concurrent.ThreadFactoryBuilder
+import org.apache.avro.ipc.NettyTransceiver
+import org.apache.avro.ipc.specific.SpecificRequestor
+import org.apache.flume.Context
+import org.apache.flume.channel.MemoryChannel
+import org.apache.flume.event.EventBuilder
+import org.apache.spark.streaming.TestSuiteBase
+import org.jboss.netty.channel.socket.nio.NioClientSocketChannelFactory
+
+class SparkSinkSuite extends TestSuiteBase {
+  val eventsPerBatch = 1000
+  val channelCapacity = 5000
+
+  test("Success") {
+    val (channel, sink) = initializeChannelAndSink()
+    channel.start()
+    sink.start()
+
+    putEvents(channel, eventsPerBatch)
+
+    val port = sink.getPort
+    val address = new InetSocketAddress("0.0.0.0", port)
+
+    val (transceiver, client) = getTransceiverAndClient(address, 1)(0)
+    val events = client.getEventBatch(1000)
+    client.ack(events.getSequenceNumber)
+    assert(events.getEvents.size() === 1000)
+    assertChannelIsEmpty(channel)
+    sink.stop()
+    channel.stop()
+    transceiver.close()
+  }
+
+  test("Nack") {
+    val (channel, sink) = initializeChannelAndSink()
+    channel.start()
+    sink.start()
+    putEvents(channel, eventsPerBatch)
+
+    val port = sink.getPort
+    val address = new InetSocketAddress("0.0.0.0", port)
+
+    val (transceiver, client) = getTransceiverAndClient(address, 1)(0)
+    val events = client.getEventBatch(1000)
+    assert(events.getEvents.size() === 1000)
+    client.nack(events.getSequenceNumber)
+    assert(availableChannelSlots(channel) === 4000)
+    sink.stop()
+    channel.stop()
+    transceiver.close()
+  }
+
+  test("Timeout") {
+    val (channel, sink) = initializeChannelAndSink(Map(SparkSinkConfig
+      .CONF_TRANSACTION_TIMEOUT -> 1.toString))
+    channel.start()
+    sink.start()
+    putEvents(channel, eventsPerBatch)
+    val port = sink.getPort
+    val address = new InetSocketAddress("0.0.0.0", port)
+
+    val (transceiver, client) = getTransceiverAndClient(address, 1)(0)
+    val events = client.getEventBatch(1000)
+    assert(events.getEvents.size() === 1000)
+    Thread.sleep(1000)
+    assert(availableChannelSlots(channel) === 4000)
+    sink.stop()
+    channel.stop()
+    transceiver.close()
+  }
+
+  test("Multiple consumers") {
+    testMultipleConsumers(failSome = false)
+  }
+
+  test("Multiple consumers with some failures") {
+    testMultipleConsumers(failSome = true)
+  }
+
+  def testMultipleConsumers(failSome: Boolean): Unit = {
+    implicit val executorContext = ExecutionContext
+      .fromExecutorService(Executors.newFixedThreadPool(5))
+    val (channel, sink) = initializeChannelAndSink()
+    channel.start()
+    sink.start()
+    (1 to 5).foreach(_ => putEvents(channel, eventsPerBatch))
+    val port = sink.getPort
+    val address = new InetSocketAddress("0.0.0.0", port)
+    val transceiversAndClients = getTransceiverAndClient(address, 5)
+    val batchCounter = new CountDownLatch(5)
+    val counter = new AtomicInteger(0)
+    transceiversAndClients.foreach(x => {
+      Future {
+        val client = x._2
+        val events = client.getEventBatch(1000)
+        if (!failSome || counter.getAndIncrement() % 2 == 0) {
+          client.ack(events.getSequenceNumber)
+        } else {
+          client.nack(events.getSequenceNumber)
+          throw new RuntimeException("Sending NACK for failure!")
+        }
+        events
+      }.onComplete {
+        case Success(events) =>
+          assert(events.getEvents.size() === 1000)
+          batchCounter.countDown()
+        case Failure(t) =>
+          // Don't re-throw the exception, causes a nasty unnecessary stack trace on stdout
+          batchCounter.countDown()
+      }
+    })
+    batchCounter.await()
+    TimeUnit.SECONDS.sleep(1) // Allow the sink to commit the transactions.
+    executorContext.shutdown()
+    if(failSome) {
+      assert(availableChannelSlots(channel) === 3000)
+    } else {
+      assertChannelIsEmpty(channel)
+    }
+    sink.stop()
+    channel.stop()
+    transceiversAndClients.foreach(x => x._1.close())
+  }
+
+  private def initializeChannelAndSink(overrides: Map[String, String] = Map.empty): (MemoryChannel,
+    SparkSink) = {
+    val channel = new MemoryChannel()
+    val channelContext = new Context()
+
+    channelContext.put("capacity", channelCapacity.toString)
+    channelContext.put("transactionCapacity", 1000.toString)
+    channelContext.put("keep-alive", 0.toString)
+    channelContext.putAll(overrides)
+    channel.configure(channelContext)
+
+    val sink = new SparkSink()
+    val sinkContext = new Context()
+    sinkContext.put(SparkSinkConfig.CONF_HOSTNAME, "0.0.0.0")
+    sinkContext.put(SparkSinkConfig.CONF_PORT, 0.toString)
+    sink.configure(sinkContext)
+    sink.setChannel(channel)
+    (channel, sink)
+  }
+
+  private def putEvents(ch: MemoryChannel, count: Int): Unit = {
+    val tx = ch.getTransaction
+    tx.begin()
+    (1 to count).foreach(x => ch.put(EventBuilder.withBody(x.toString.getBytes)))
+    tx.commit()
+    tx.close()
+  }
+
+  private def getTransceiverAndClient(address: InetSocketAddress,
+    count: Int): Seq[(NettyTransceiver, SparkFlumeProtocol.Callback)] = {
+
+    (1 to count).map(_ => {
+      lazy val channelFactoryExecutor =
+        Executors.newCachedThreadPool(new ThreadFactoryBuilder().setDaemon(true).
+          setNameFormat("Flume Receiver Channel Thread - %d").build())
+      lazy val channelFactory =
+        new NioClientSocketChannelFactory(channelFactoryExecutor, channelFactoryExecutor)
+      val transceiver = new NettyTransceiver(address, channelFactory)
+      val client = SpecificRequestor.getClient(classOf[SparkFlumeProtocol.Callback], transceiver)
+      (transceiver, client)
+    })
+  }
+
+  private def assertChannelIsEmpty(channel: MemoryChannel): Unit = {
+    assert(availableChannelSlots(channel) === channelCapacity)
+  }
+
+  private def availableChannelSlots(channel: MemoryChannel): Int = {
+    val queueRemaining = channel.getClass.getDeclaredField("queueRemaining")
+    queueRemaining.setAccessible(true)
+    val m = queueRemaining.get(channel).getClass.getDeclaredMethod("availablePermits")
+    m.invoke(queueRemaining.get(channel)).asInstanceOf[Int]
+  }
+}
diff --git a/external/flume/src/test/scala/org/apache/spark/streaming/flume/FlumePollingStreamSuite.scala b/external/flume/src/test/scala/org/apache/spark/streaming/flume/FlumePollingStreamSuite.scala
index 8a85b0f987e42..32a19787a28e1 100644
--- a/external/flume/src/test/scala/org/apache/spark/streaming/flume/FlumePollingStreamSuite.scala
+++ b/external/flume/src/test/scala/org/apache/spark/streaming/flume/FlumePollingStreamSuite.scala
@@ -198,7 +198,7 @@ class FlumePollingStreamSuite extends TestSuiteBase {
   }
 
   def assertChannelIsEmpty(channel: MemoryChannel) = {
-    val queueRemaining = channel.getClass.getDeclaredField("queueRemaining");
+    val queueRemaining = channel.getClass.getDeclaredField("queueRemaining")
     queueRemaining.setAccessible(true)
     val m = queueRemaining.get(channel).getClass.getDeclaredMethod("availablePermits")
     assert(m.invoke(queueRemaining.get(channel)).asInstanceOf[Int] === 5000)

From f2f26c2a1dc6d60078c3be9c3d11a21866d9a24f Mon Sep 17 00:00:00 2001
From: Patrick Wendell <pwendell@gmail.com>
Date: Wed, 20 Aug 2014 12:13:31 -0700
Subject: [PATCH 039/489] SPARK-3092 [SQL]: Always include the thriftserver
 when -Phive is enabled.

Currently we have a separate profile called hive-thriftserver. I originally suggested this in case users did not want to bundle the thriftserver, but it's ultimately lead to a lot of confusion. Since the thriftserver is only a few classes, I don't see a really good reason to isolate it from the rest of Hive. So let's go ahead and just include it in the same profile to simplify things.

This has been suggested in the past by liancheng.

Author: Patrick Wendell <pwendell@gmail.com>

Closes #2006 from pwendell/hiveserver and squashes the following commits:

742ea40 [Patrick Wendell] Merge remote-tracking branch 'apache/master' into hiveserver
034ad47 [Patrick Wendell] SPARK-3092: Always include the thriftserver when -Phive is enabled.
---
 README.md                            |  6 +-----
 assembly/pom.xml                     |  5 -----
 dev/create-release/create-release.sh | 10 +++++-----
 dev/run-tests                        |  2 +-
 dev/scalastyle                       |  2 +-
 docs/building-with-maven.md          |  8 ++------
 docs/sql-programming-guide.md        |  4 +---
 pom.xml                              |  2 +-
 8 files changed, 12 insertions(+), 27 deletions(-)

diff --git a/README.md b/README.md
index a1a48f5bd0819..8906e4c1416b1 100644
--- a/README.md
+++ b/README.md
@@ -118,11 +118,7 @@ If your project is built with Maven, add this to your POM file's `<dependencies>
 ## A Note About Thrift JDBC server and CLI for Spark SQL
 
 Spark SQL supports Thrift JDBC server and CLI.
-See sql-programming-guide.md for more information about those features.
-You can use those features by setting `-Phive-thriftserver` when building Spark as follows.
-
-    $ sbt/sbt -Phive-thriftserver assembly
-
+See sql-programming-guide.md for more information about using the JDBC server.
 
 ## Configuration
 
diff --git a/assembly/pom.xml b/assembly/pom.xml
index 703f15925bc44..9fbb037115db3 100644
--- a/assembly/pom.xml
+++ b/assembly/pom.xml
@@ -163,11 +163,6 @@
           <artifactId>spark-hive_${scala.binary.version}</artifactId>
           <version>${project.version}</version>
         </dependency>
-      </dependencies>
-    </profile>
-    <profile>
-      <id>hive-thriftserver</id>
-      <dependencies>
         <dependency>
           <groupId>org.apache.spark</groupId>
           <artifactId>spark-hive-thriftserver_${scala.binary.version}</artifactId>
diff --git a/dev/create-release/create-release.sh b/dev/create-release/create-release.sh
index 28f26d2368254..905dec0ced383 100755
--- a/dev/create-release/create-release.sh
+++ b/dev/create-release/create-release.sh
@@ -60,14 +60,14 @@ if [[ ! "$@" =~ --package-only ]]; then
     -Dmaven.javadoc.skip=true \
     -Dhadoop.version=2.2.0 -Dyarn.version=2.2.0 \
     -Dtag=$GIT_TAG -DautoVersionSubmodules=true \
-    -Pyarn -Phive -Phive-thriftserver -Phadoop-2.2 -Pspark-ganglia-lgpl -Pkinesis-asl \
+    -Pyarn -Phive -Phadoop-2.2 -Pspark-ganglia-lgpl -Pkinesis-asl \
     --batch-mode release:prepare
 
   mvn -DskipTests \
     -Darguments="-DskipTests=true -Dmaven.javadoc.skip=true -Dhadoop.version=2.2.0 -Dyarn.version=2.2.0 -Dgpg.passphrase=${GPG_PASSPHRASE}" \
     -Dhadoop.version=2.2.0 -Dyarn.version=2.2.0 \
     -Dmaven.javadoc.skip=true \
-    -Pyarn -Phive -Phive-thriftserver -Phadoop-2.2 -Pspark-ganglia-lgpl -Pkinesis-asl \
+    -Pyarn -Phive -Phadoop-2.2 -Pspark-ganglia-lgpl -Pkinesis-asl \
     release:perform
 
   cd ..
@@ -117,10 +117,10 @@ make_binary_release() {
     spark-$RELEASE_VERSION-bin-$NAME.tgz.sha
 }
 
-make_binary_release "hadoop1" "-Phive -Phive-thriftserver -Dhadoop.version=1.0.4" &
-make_binary_release "cdh4" "-Phive -Phive-thriftserver -Dhadoop.version=2.0.0-mr1-cdh4.2.0" &
+make_binary_release "hadoop1" "-Phive -Dhadoop.version=1.0.4" &
+make_binary_release "cdh4" "-Phive -Dhadoop.version=2.0.0-mr1-cdh4.2.0" &
 make_binary_release "hadoop2" \
-  "-Phive -Phive-thriftserver -Pyarn -Phadoop-2.2 -Dhadoop.version=2.2.0 -Pyarn.version=2.2.0" &
+  "-Phive -Pyarn -Phadoop-2.2 -Dhadoop.version=2.2.0 -Pyarn.version=2.2.0" &
 make_binary_release "hadoop2-without-hive" \
   "-Pyarn -Phadoop-2.2 -Dhadoop.version=2.2.0 -Pyarn.version=2.2.0" &
 wait
diff --git a/dev/run-tests b/dev/run-tests
index 132f696d6447a..20a67cfb361b9 100755
--- a/dev/run-tests
+++ b/dev/run-tests
@@ -99,7 +99,7 @@ echo -e "q\n" | sbt/sbt $BUILD_MVN_PROFILE_ARGS clean package assembly/assembly
 
 # If the Spark SQL tests are enabled, run the tests with the Hive profiles enabled:
 if [ -n "$_RUN_SQL_TESTS" ]; then
-  SBT_MAVEN_PROFILES_ARGS="$SBT_MAVEN_PROFILES_ARGS -Phive -Phive-thriftserver"
+  SBT_MAVEN_PROFILES_ARGS="$SBT_MAVEN_PROFILES_ARGS -Phive"
 fi
 # echo "q" is needed because sbt on encountering a build file with failure
 # (either resolution or compilation) prompts the user for input either q, r,
diff --git a/dev/scalastyle b/dev/scalastyle
index b53053a04ff42..eb9b467965636 100755
--- a/dev/scalastyle
+++ b/dev/scalastyle
@@ -17,7 +17,7 @@
 # limitations under the License.
 #
 
-echo -e "q\n" | sbt/sbt -Phive -Phive-thriftserver scalastyle > scalastyle.txt
+echo -e "q\n" | sbt/sbt -Phive scalastyle > scalastyle.txt
 # Check style with YARN alpha built too
 echo -e "q\n" | sbt/sbt -Pyarn -Phadoop-0.23 -Dhadoop.version=0.23.9 yarn-alpha/scalastyle \
   >> scalastyle.txt
diff --git a/docs/building-with-maven.md b/docs/building-with-maven.md
index 4d87ab92cec5b..a7d7bd3ccb1f2 100644
--- a/docs/building-with-maven.md
+++ b/docs/building-with-maven.md
@@ -98,12 +98,8 @@ mvn -Pyarn-alpha -Phadoop-2.3 -Dhadoop.version=2.3.0 -Dyarn.version=0.23.7 -Dski
 
 # Building Thrift JDBC server and CLI for Spark SQL
 
-Spark SQL supports Thrift JDBC server and CLI.
-See sql-programming-guide.md for more information about those features.
-You can use those features by setting `-Phive-thriftserver` when building Spark as follows.
-{% highlight bash %}
-mvn -Phive-thriftserver assembly
-{% endhighlight %}
+Spark SQL supports Thrift JDBC server and CLI. See sql-programming-guide.md for 
+more information about the JDBC server.
 
 # Spark Tests in Maven
 
diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md
index 34accade36ea9..c41f2804a6021 100644
--- a/docs/sql-programming-guide.md
+++ b/docs/sql-programming-guide.md
@@ -578,9 +578,7 @@ evaluated by the SQL execution engine.  A full list of the functions supported c
 
 The Thrift JDBC server implemented here corresponds to the [`HiveServer2`]
 (https://cwiki.apache.org/confluence/display/Hive/Setting+Up+HiveServer2) in Hive 0.12. You can test
-the JDBC server with the beeline script comes with either Spark or Hive 0.12.  In order to use Hive
-you must first run '`sbt/sbt -Phive-thriftserver assembly/assembly`' (or use `-Phive-thriftserver`
-for maven).
+the JDBC server with the beeline script comes with either Spark or Hive 0.12.
 
 To start the JDBC server, run the following in the Spark directory:
 
diff --git a/pom.xml b/pom.xml
index 0d44cf4ea5f92..dd4c4ee80a0df 100644
--- a/pom.xml
+++ b/pom.xml
@@ -1179,7 +1179,7 @@
     </profile>
 
     <profile>
-      <id>hive-thriftserver</id>
+      <id>hive</id>
       <activation>
         <activeByDefault>false</activeByDefault>
       </activation>

From ceb19830b88486faa87ff41e18d03ede713a73cc Mon Sep 17 00:00:00 2001
From: Patrick Wendell <pwendell@gmail.com>
Date: Wed, 20 Aug 2014 12:18:41 -0700
Subject: [PATCH 040/489] BUILD: Bump Hadoop versions in the release build.

Also, minor modifications to the MapR profile.
---
 dev/create-release/create-release.sh | 10 +++----
 pom.xml                              | 39 +++++++++++++++++++++++++---
 2 files changed, 40 insertions(+), 9 deletions(-)

diff --git a/dev/create-release/create-release.sh b/dev/create-release/create-release.sh
index 905dec0ced383..eab6313733dfd 100755
--- a/dev/create-release/create-release.sh
+++ b/dev/create-release/create-release.sh
@@ -118,11 +118,11 @@ make_binary_release() {
 }
 
 make_binary_release "hadoop1" "-Phive -Dhadoop.version=1.0.4" &
-make_binary_release "cdh4" "-Phive -Dhadoop.version=2.0.0-mr1-cdh4.2.0" &
-make_binary_release "hadoop2" \
-  "-Phive -Pyarn -Phadoop-2.2 -Dhadoop.version=2.2.0 -Pyarn.version=2.2.0" &
-make_binary_release "hadoop2-without-hive" \
-  "-Pyarn -Phadoop-2.2 -Dhadoop.version=2.2.0 -Pyarn.version=2.2.0" &
+make_binary_release "hadoop2.3" "-Phadoop-2.3 -Phive -Pyarn" &
+make_binary_release "hadoop2.4" "-Phadoop-2.4 -Phive -Pyarn" &
+make_binary_release "hadoop2.4-without-hive" "-Phadoop-2.4 -Pyarn" &
+make_binary_release "mapr3" "-Pmapr3 -Pyarn -Phive" &
+make_binary_release "mapr4" "-Pmapr4 -Pyarn -Phive" &
 wait
 
 # Copy data
diff --git a/pom.xml b/pom.xml
index dd4c4ee80a0df..7ed07ad7df88d 100644
--- a/pom.xml
+++ b/pom.xml
@@ -1115,18 +1115,49 @@
     </profile>
 
     <profile>
-      <id>mapr</id>
+      <id>mapr3</id>
       <activation>
         <activeByDefault>false</activeByDefault>
       </activation>
       <properties>
         <hadoop.version>1.0.3-mapr-3.0.3</hadoop.version>
-        <yarn.version>2.3.0-mapr-4.0.0-beta</yarn.version>
-        <hbase.version>0.94.17-mapr-1403</hbase.version>
-        <zookeeper.version>3.4.5-mapr-1401</zookeeper.version>
+        <yarn.version>2.3.0-mapr-4.0.0-FCS</yarn.version>
+        <hbase.version>0.94.17-mapr-1405</hbase.version>
+        <zookeeper.version>3.4.5-mapr-1406</zookeeper.version>
       </properties>
     </profile>
 
+    <profile>
+      <id>mapr4</id>
+      <activation>
+        <activeByDefault>false</activeByDefault>
+      </activation>
+      <properties>
+        <hadoop.version>2.3.0-mapr-4.0.0-FCS</hadoop.version>
+        <yarn.version>2.3.0-mapr-4.0.0-FCS</yarn.version>
+        <hbase.version>0.94.17-mapr-1405-4.0.0-FCS</hbase.version>
+        <zookeeper.version>3.4.5-mapr-1406</zookeeper.version>
+      </properties>
+      <dependencies>
+        <dependency>
+          <groupId>org.apache.curator</groupId>
+          <artifactId>curator-recipes</artifactId>
+          <version>2.4.0</version>
+          <exclusions>
+            <exclusion>
+              <groupId>org.apache.zookeeper</groupId>
+              <artifactId>zookeeper</artifactId>
+            </exclusion>
+          </exclusions>
+        </dependency>
+        <dependency>
+          <groupId>org.apache.zookeeper</groupId>
+          <artifactId>zookeeper</artifactId>
+          <version>3.4.5-mapr-1406</version> 
+        </dependency>
+      </dependencies>
+    </profile>
+
     <!-- Build without Hadoop dependencies that are included in some runtime environments. -->
     <profile>
       <id>hadoop-provided</id>

From cf46e725814f575ebb417e80d2571bccc6dac4a7 Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian.cs.zju@gmail.com>
Date: Wed, 20 Aug 2014 12:57:39 -0700
Subject: [PATCH 041/489] [SPARK-3126][SPARK-3127][SQL] Fixed
 HiveThriftServer2Suite

This PR fixes two issues:

1. Fixes wrongly quoted command line option in `HiveThriftServer2Suite` that makes test cases hang until timeout.
1. Asks `dev/run-test` to run Spark SQL tests when `bin/spark-sql` and/or `sbin/start-thriftserver.sh` are modified.

Author: Cheng Lian <lian.cs.zju@gmail.com>

Closes #2036 from liancheng/fix-thriftserver-test and squashes the following commits:

f38c4eb [Cheng Lian] Fixed the same quotation issue in CliSuite
26b82a0 [Cheng Lian] Run SQL tests when dff contains bin/spark-sql and/or sbin/start-thriftserver.sh
a87f83d [Cheng Lian] Extended timeout
e5aa31a [Cheng Lian] Fixed metastore JDBC URI quotation
---
 dev/run-tests                                  |  2 +-
 .../spark/sql/hive/thriftserver/CliSuite.scala |  2 +-
 .../thriftserver/HiveThriftServer2Suite.scala  | 18 ++++--------------
 3 files changed, 6 insertions(+), 16 deletions(-)

diff --git a/dev/run-tests b/dev/run-tests
index 20a67cfb361b9..d751961605dfd 100755
--- a/dev/run-tests
+++ b/dev/run-tests
@@ -55,7 +55,7 @@ JAVA_VERSION=$($java_cmd -version 2>&1 | sed 's/java version "\(.*\)\.\(.*\)\..*
 # Partial solution for SPARK-1455. Only run Hive tests if there are sql changes.
 if [ -n "$AMPLAB_JENKINS" ]; then
   git fetch origin master:master
-  diffs=`git diff --name-only master | grep "^sql/"`
+  diffs=`git diff --name-only master | grep "^\(sql/\)\|\(bin/spark-sql\)\|\(sbin/start-thriftserver.sh\)"`
   if [ -n "$diffs" ]; then
     echo "Detected changes in SQL. Will run Hive test suite."
     _RUN_SQL_TESTS=true
diff --git a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/CliSuite.scala b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/CliSuite.scala
index 2bf8cfdcacd22..70bea1ed80fda 100644
--- a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/CliSuite.scala
+++ b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/CliSuite.scala
@@ -32,7 +32,7 @@ class CliSuite extends FunSuite with BeforeAndAfterAll with TestUtils {
     val commands =
       s"""../../bin/spark-sql
          |  --master local
-         |  --hiveconf ${ConfVars.METASTORECONNECTURLKEY}="$jdbcUrl"
+         |  --hiveconf ${ConfVars.METASTORECONNECTURLKEY}=$jdbcUrl
          |  --hiveconf ${ConfVars.METASTOREWAREHOUSE}=$WAREHOUSE_PATH
        """.stripMargin.split("\\s+")
 
diff --git a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suite.scala b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suite.scala
index aedef6ce1f5f2..326b0a7275b34 100644
--- a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suite.scala
+++ b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suite.scala
@@ -51,9 +51,6 @@ class HiveThriftServer2Suite extends FunSuite with BeforeAndAfterAll with TestUt
     port
   }
 
-  // If verbose is true, the test program will print all outputs coming from the Hive Thrift server.
-  val VERBOSE = Option(System.getenv("SPARK_SQL_TEST_VERBOSE")).getOrElse("false").toBoolean
-
   Class.forName(DRIVER_NAME)
 
   override def beforeAll() { launchServer() }
@@ -68,8 +65,7 @@ class HiveThriftServer2Suite extends FunSuite with BeforeAndAfterAll with TestUt
     val command =
       s"""../../sbin/start-thriftserver.sh
          |  --master local
-         |  --hiveconf hive.root.logger=INFO,console
-         |  --hiveconf ${ConfVars.METASTORECONNECTURLKEY}="$jdbcUrl"
+         |  --hiveconf ${ConfVars.METASTORECONNECTURLKEY}=$jdbcUrl
          |  --hiveconf ${ConfVars.METASTOREWAREHOUSE}=$METASTORE_PATH
          |  --hiveconf ${ConfVars.HIVE_SERVER2_THRIFT_BIND_HOST}=$HOST
          |  --hiveconf ${ConfVars.HIVE_SERVER2_THRIFT_PORT}=$PORT
@@ -77,12 +73,10 @@ class HiveThriftServer2Suite extends FunSuite with BeforeAndAfterAll with TestUt
 
     val pb = new ProcessBuilder(command ++ args: _*)
     val environment = pb.environment()
-    environment.put("HIVE_SERVER2_THRIFT_PORT", PORT.toString)
-    environment.put("HIVE_SERVER2_THRIFT_BIND_HOST", HOST)
     process = pb.start()
     inputReader = new BufferedReader(new InputStreamReader(process.getInputStream))
     errorReader = new BufferedReader(new InputStreamReader(process.getErrorStream))
-    waitForOutput(inputReader, "ThriftBinaryCLIService listening on")
+    waitForOutput(inputReader, "ThriftBinaryCLIService listening on", 300000)
 
     // Spawn a thread to read the output from the forked process.
     // Note that this is necessary since in some configurations, log4j could be blocked
@@ -91,12 +85,8 @@ class HiveThriftServer2Suite extends FunSuite with BeforeAndAfterAll with TestUt
       while (true) {
         val stdout = readFrom(inputReader)
         val stderr = readFrom(errorReader)
-        if (VERBOSE && stdout.length > 0) {
-          println(stdout)
-        }
-        if (VERBOSE && stderr.length > 0) {
-          println(stderr)
-        }
+        print(stdout)
+        print(stderr)
         Thread.sleep(50)
       }
     }

From 0ea46ac80089e9091d247704b17afbc423c0060d Mon Sep 17 00:00:00 2001
From: Kousuke Saruta <sarutak@oss.nttdata.co.jp>
Date: Wed, 20 Aug 2014 13:26:11 -0700
Subject: [PATCH 042/489] [SPARK-3062] [SPARK-2970] [SQL] spark-sql script ends
 with IOException when EventLogging is enabled

#1891 was to avoid IOException when EventLogging is enabled.
The solution used ShutdownHookManager but it was defined only Hadoop 2.x. Hadoop 1.x don't have ShutdownHookManager so #1891 doesn't compile on Hadoop 1.x

Now, I had a compromised solution for both Hadoop 1.x and 2.x.
Only for FileLogger, an unique FileSystem object is created.

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

Closes #1970 from sarutak/SPARK-2970 and squashes the following commits:

240c91e [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into SPARK-2970
0e7b45d [Kousuke Saruta] Revert "[SPARK-2970] [SQL] spark-sql script ends with IOException when EventLogging is enabled"
e1262ec [Kousuke Saruta] Modified Filelogger to use unique FileSystem instance
---
 .../scala/org/apache/spark/util/FileLogger.scala  | 15 ++++++++++++++-
 1 file changed, 14 insertions(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/util/FileLogger.scala b/core/src/main/scala/org/apache/spark/util/FileLogger.scala
index 2e8fbf5a91ee7..ad8b79af877d8 100644
--- a/core/src/main/scala/org/apache/spark/util/FileLogger.scala
+++ b/core/src/main/scala/org/apache/spark/util/FileLogger.scala
@@ -52,7 +52,20 @@ private[spark] class FileLogger(
     override def initialValue(): SimpleDateFormat = new SimpleDateFormat("yyyy/MM/dd HH:mm:ss")
   }
 
-  private val fileSystem = Utils.getHadoopFileSystem(logDir)
+  /**
+   * To avoid effects of FileSystem#close or FileSystem.closeAll called from other modules,
+   * create unique FileSystem instance only for FileLogger
+   */
+  private val fileSystem = {
+    val conf = SparkHadoopUtil.get.newConfiguration()
+    val logUri = new URI(logDir)
+    val scheme = logUri.getScheme
+    if (scheme == "hdfs") {
+      conf.setBoolean("fs.hdfs.impl.disable.cache", true)
+    }
+    FileSystem.get(logUri, conf)
+  }
+
   var fileIndex = 0
 
   // Only used if compression is enabled

From c1ba4cd6b4db22a9325eee50dc40a78593a10de1 Mon Sep 17 00:00:00 2001
From: Kousuke Saruta <sarutak@oss.nttdata.co.jp>
Date: Wed, 20 Aug 2014 14:04:39 -0700
Subject: [PATCH 043/489] [SPARK-3149] Connection establishment information is
 not enough.

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

Closes #2060 from sarutak/SPARK-3149 and squashes the following commits:

1cc89af [Kousuke Saruta] Modified log message of accepting connection
---
 .../main/scala/org/apache/spark/network/ConnectionManager.scala | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/network/ConnectionManager.scala b/core/src/main/scala/org/apache/spark/network/ConnectionManager.scala
index b3e951ded6e77..e5e1e72cd912b 100644
--- a/core/src/main/scala/org/apache/spark/network/ConnectionManager.scala
+++ b/core/src/main/scala/org/apache/spark/network/ConnectionManager.scala
@@ -418,7 +418,7 @@ private[spark] class ConnectionManager(
         newConnection.onReceive(receiveMessage)
         addListeners(newConnection)
         addConnection(newConnection)
-        logInfo("Accepted connection from [" + newConnection.remoteAddress.getAddress + "]")
+        logInfo("Accepted connection from [" + newConnection.remoteAddress + "]")
       } catch {
         // might happen in case of issues with registering with selector
         case e: Exception => logError("Error in accept loop", e)

From b3ec51bfd795772ff96d18228e979a52ebc82ec4 Mon Sep 17 00:00:00 2001
From: Andrew Or <andrewor14@gmail.com>
Date: Wed, 20 Aug 2014 15:01:47 -0700
Subject: [PATCH 044/489] [SPARK-2849] Handle driver configs separately in
 client mode

In client deploy mode, the driver is launched from within `SparkSubmit`'s JVM. This means by the time we parse Spark configs from `spark-defaults.conf`, it is already too late to control certain properties of the driver's JVM. We currently ignore these configs in client mode altogether.
```
spark.driver.memory
spark.driver.extraJavaOptions
spark.driver.extraClassPath
spark.driver.extraLibraryPath
```
This PR handles these properties before launching the driver JVM. It achieves this by spawning a separate JVM that runs a new class called `SparkSubmitDriverBootstrapper`, which spawns `SparkSubmit` as a sub-process with the appropriate classpath, library paths, java opts and memory.

Author: Andrew Or <andrewor14@gmail.com>

Closes #1845 from andrewor14/handle-configs-bash and squashes the following commits:

bed4bdf [Andrew Or] Change a few comments / messages (minor)
24dba60 [Andrew Or] Merge branch 'master' of github.com:apache/spark into handle-configs-bash
08fd788 [Andrew Or] Warn against external usages of SparkSubmitDriverBootstrapper
ff34728 [Andrew Or] Minor comments
51aeb01 [Andrew Or] Filter out JVM memory in Scala rather than Bash (minor)
9a778f6 [Andrew Or] Fix PySpark: actually kill driver on termination
d0f20db [Andrew Or] Don't pass empty library paths, classpath, java opts etc.
a78cb26 [Andrew Or] Revert a few changes in utils.sh (minor)
9ba37e2 [Andrew Or] Don't barf when the properties file does not exist
8867a09 [Andrew Or] A few more naming things (minor)
19464ad [Andrew Or] SPARK_SUBMIT_JAVA_OPTS -> SPARK_SUBMIT_OPTS
d6488f9 [Andrew Or] Merge branch 'master' of github.com:apache/spark into handle-configs-bash
1ea6bbe [Andrew Or] SparkClassLauncher -> SparkSubmitDriverBootstrapper
a91ea19 [Andrew Or] Fix precedence of library paths, classpath, java opts and memory
158f813 [Andrew Or] Remove "client mode" boolean argument
c84f5c8 [Andrew Or] Remove debug print statement (minor)
b71f52b [Andrew Or] Revert a few more changes (minor)
7d94a8d [Andrew Or] Merge branch 'master' of github.com:apache/spark into handle-configs-bash
3a8235d [Andrew Or] Only parse the properties file if special configs exist
c37e08d [Andrew Or] Revert a few more changes
a396eda [Andrew Or] Nullify my own hard work to simplify bash
0effa1e [Andrew Or] Add code in Scala that handles special configs
c886568 [Andrew Or] Fix lines too long + a few comments / style (minor)
7a4190a [Andrew Or] Merge branch 'master' of github.com:apache/spark into handle-configs-bash
7396be2 [Andrew Or] Explicitly comment that multi-line properties are not supported
fa11ef8 [Andrew Or] Parse the properties file only if the special configs exist
371cac4 [Andrew Or] Add function prefix (minor)
be99eb3 [Andrew Or] Fix tests to not include multi-line configs
bd0d468 [Andrew Or] Simplify parsing config file by ignoring multi-line arguments
56ac247 [Andrew Or] Use eval and set to simplify splitting
8d4614c [Andrew Or] Merge branch 'master' of github.com:apache/spark into handle-configs-bash
aeb79c7 [Andrew Or] Merge branch 'master' of github.com:apache/spark into handle-configs-bash
2732ac0 [Andrew Or] Integrate BASH tests into dev/run-tests + log error properly
8d26a5c [Andrew Or] Add tests for bash/utils.sh
4ae24c3 [Andrew Or] Fix bug: escape properly in quote_java_property
b3c4cd5 [Andrew Or] Fix bug: count the number of quotes instead of detecting presence
c2273fc [Andrew Or] Fix typo (minor)
e793e5f [Andrew Or] Handle multi-line arguments
5d8f8c4 [Andrew Or] Merge branch 'master' of github.com:apache/spark into submit-driver-extra
c7b9926 [Andrew Or] Minor changes to spark-defaults.conf.template
a992ae2 [Andrew Or] Escape spark.*.extraJavaOptions correctly
aabfc7e [Andrew Or] escape -> split (minor)
45a1eb9 [Andrew Or] Fix bug: escape escaped backslashes and quotes properly...
1cdc6b1 [Andrew Or] Fix bug: escape escaped double quotes properly
c854859 [Andrew Or] Add small comment
c13a2cb [Andrew Or] Merge branch 'master' of github.com:apache/spark into submit-driver-extra
8e552b7 [Andrew Or] Include an example of spark.*.extraJavaOptions
de765c9 [Andrew Or] Print spark-class command properly
a4df3c4 [Andrew Or] Move parsing and escaping logic to utils.sh
dec2343 [Andrew Or] Only export variables if they exist
fa2136e [Andrew Or] Escape Java options + parse java properties files properly
ef12f74 [Andrew Or] Minor formatting
4ec22a1 [Andrew Or] Merge branch 'master' of github.com:apache/spark into submit-driver-extra
e5cfb46 [Andrew Or] Collapse duplicate code + fix potential whitespace issues
4edcaa8 [Andrew Or] Redirect stdout to stderr for python
130f295 [Andrew Or] Handle spark.driver.memory too
98dd8e3 [Andrew Or] Add warning if properties file does not exist
8843562 [Andrew Or] Fix compilation issues...
75ee6b4 [Andrew Or] Remove accidentally added file
63ed2e9 [Andrew Or] Merge branch 'master' of github.com:apache/spark into submit-driver-extra
0025474 [Andrew Or] Revert SparkSubmit handling of --driver-* options for only cluster mode
a2ab1b0 [Andrew Or] Parse spark.driver.extra* in bash
250cb95 [Andrew Or] Do not ignore spark.driver.extra* for client mode
---
 bin/spark-class                               |  49 ++++--
 bin/spark-submit                              |  28 +++-
 bin/utils.sh                                  |   0
 conf/spark-defaults.conf.template             |  10 +-
 .../apache/spark/api/python/PythonUtils.scala |  25 ---
 .../api/python/PythonWorkerFactory.scala      |   3 +-
 .../apache/spark/deploy/PythonRunner.scala    |   4 +-
 .../org/apache/spark/deploy/SparkSubmit.scala |  17 +-
 .../SparkSubmitDriverBootstrapper.scala       | 149 ++++++++++++++++++
 .../scala/org/apache/spark/util/Utils.scala   |  21 +++
 10 files changed, 250 insertions(+), 56 deletions(-)
 mode change 100644 => 100755 bin/utils.sh
 create mode 100644 core/src/main/scala/org/apache/spark/deploy/SparkSubmitDriverBootstrapper.scala

diff --git a/bin/spark-class b/bin/spark-class
index 3f6beca5becf0..22acf92288b3b 100755
--- a/bin/spark-class
+++ b/bin/spark-class
@@ -17,6 +17,8 @@
 # limitations under the License.
 #
 
+# NOTE: Any changes to this file must be reflected in SparkSubmitDriverBootstrapper.scala!
+
 cygwin=false
 case "`uname`" in
     CYGWIN*) cygwin=true;;
@@ -39,7 +41,7 @@ fi
 
 if [ -n "$SPARK_MEM" ]; then
   echo -e "Warning: SPARK_MEM is deprecated, please use a more specific config option" 1>&2
-  echo -e "(e.g., spark.executor.memory or SPARK_DRIVER_MEMORY)." 1>&2
+  echo -e "(e.g., spark.executor.memory or spark.driver.memory)." 1>&2
 fi
 
 # Use SPARK_MEM or 512m as the default memory, to be overridden by specific options
@@ -73,11 +75,17 @@ case "$1" in
     OUR_JAVA_MEM=${SPARK_EXECUTOR_MEMORY:-$DEFAULT_MEM}
     ;;
 
-  # Spark submit uses SPARK_SUBMIT_OPTS and SPARK_JAVA_OPTS
-    'org.apache.spark.deploy.SparkSubmit')
-    OUR_JAVA_OPTS="$SPARK_JAVA_OPTS $SPARK_SUBMIT_OPTS \
-      -Djava.library.path=$SPARK_SUBMIT_LIBRARY_PATH"
+  # Spark submit uses SPARK_JAVA_OPTS + SPARK_SUBMIT_OPTS +
+  # SPARK_DRIVER_MEMORY + SPARK_SUBMIT_DRIVER_MEMORY.
+  'org.apache.spark.deploy.SparkSubmit')
+    OUR_JAVA_OPTS="$SPARK_JAVA_OPTS $SPARK_SUBMIT_OPTS"
     OUR_JAVA_MEM=${SPARK_DRIVER_MEMORY:-$DEFAULT_MEM}
+    if [ -n "$SPARK_SUBMIT_LIBRARY_PATH" ]; then
+      OUR_JAVA_OPTS="$OUR_JAVA_OPTS -Djava.library.path=$SPARK_SUBMIT_LIBRARY_PATH"
+    fi
+    if [ -n "$SPARK_SUBMIT_DRIVER_MEMORY" ]; then
+      OUR_JAVA_MEM="$SPARK_SUBMIT_DRIVER_MEMORY"
+    fi
     ;;
 
   *)
@@ -101,11 +109,12 @@ fi
 # Set JAVA_OPTS to be able to load native libraries and to set heap size
 JAVA_OPTS="-XX:MaxPermSize=128m $OUR_JAVA_OPTS"
 JAVA_OPTS="$JAVA_OPTS -Xms$OUR_JAVA_MEM -Xmx$OUR_JAVA_MEM"
+
 # Load extra JAVA_OPTS from conf/java-opts, if it exists
 if [ -e "$FWDIR/conf/java-opts" ] ; then
   JAVA_OPTS="$JAVA_OPTS `cat $FWDIR/conf/java-opts`"
 fi
-export JAVA_OPTS
+
 # Attention: when changing the way the JAVA_OPTS are assembled, the change must be reflected in CommandUtils.scala!
 
 TOOLS_DIR="$FWDIR"/tools
@@ -146,10 +155,28 @@ if $cygwin; then
 fi
 export CLASSPATH
 
-if [ "$SPARK_PRINT_LAUNCH_COMMAND" == "1" ]; then
-  echo -n "Spark Command: " 1>&2
-  echo "$RUNNER" -cp "$CLASSPATH" $JAVA_OPTS "$@" 1>&2
-  echo -e "========================================\n" 1>&2
+# In Spark submit client mode, the driver is launched in the same JVM as Spark submit itself.
+# Here we must parse the properties file for relevant "spark.driver.*" configs before launching
+# the driver JVM itself. Instead of handling this complexity in Bash, we launch a separate JVM
+# to prepare the launch environment of this driver JVM.
+
+if [ -n "$SPARK_SUBMIT_BOOTSTRAP_DRIVER" ]; then
+  # This is used only if the properties file actually contains these special configs
+  # Export the environment variables needed by SparkSubmitDriverBootstrapper
+  export RUNNER
+  export CLASSPATH
+  export JAVA_OPTS
+  export OUR_JAVA_MEM
+  export SPARK_CLASS=1
+  shift # Ignore main class (org.apache.spark.deploy.SparkSubmit) and use our own
+  exec "$RUNNER" org.apache.spark.deploy.SparkSubmitDriverBootstrapper "$@"
+else
+  # Note: The format of this command is closely echoed in SparkSubmitDriverBootstrapper.scala
+  if [ -n "$SPARK_PRINT_LAUNCH_COMMAND" ]; then
+    echo -n "Spark Command: " 1>&2
+    echo "$RUNNER" -cp "$CLASSPATH" $JAVA_OPTS "$@" 1>&2
+    echo -e "========================================\n" 1>&2
+  fi
+  exec "$RUNNER" -cp "$CLASSPATH" $JAVA_OPTS "$@"
 fi
 
-exec "$RUNNER" -cp "$CLASSPATH" $JAVA_OPTS "$@"
diff --git a/bin/spark-submit b/bin/spark-submit
index 9e7cecedd0325..32c911cd0438b 100755
--- a/bin/spark-submit
+++ b/bin/spark-submit
@@ -17,14 +17,18 @@
 # limitations under the License.
 #
 
+# NOTE: Any changes in this file must be reflected in SparkClassLauncher.scala!
+
 export SPARK_HOME="$(cd `dirname $0`/..; pwd)"
 ORIG_ARGS=("$@")
 
 while (($#)); do
   if [ "$1" = "--deploy-mode" ]; then
-    DEPLOY_MODE=$2
+    SPARK_SUBMIT_DEPLOY_MODE=$2
+  elif [ "$1" = "--properties-file" ]; then
+    SPARK_SUBMIT_PROPERTIES_FILE=$2
   elif [ "$1" = "--driver-memory" ]; then
-    DRIVER_MEMORY=$2
+    export SPARK_SUBMIT_DRIVER_MEMORY=$2
   elif [ "$1" = "--driver-library-path" ]; then
     export SPARK_SUBMIT_LIBRARY_PATH=$2
   elif [ "$1" = "--driver-class-path" ]; then
@@ -35,10 +39,24 @@ while (($#)); do
   shift
 done
 
-DEPLOY_MODE=${DEPLOY_MODE:-"client"}
+DEFAULT_PROPERTIES_FILE="$SPARK_HOME/conf/spark-defaults.conf"
+export SPARK_SUBMIT_DEPLOY_MODE=${SPARK_SUBMIT_DEPLOY_MODE:-"client"}
+export SPARK_SUBMIT_PROPERTIES_FILE=${SPARK_SUBMIT_PROPERTIES_FILE:-"$DEFAULT_PROPERTIES_FILE"}
+
+# For client mode, the driver will be launched in the same JVM that launches
+# SparkSubmit, so we may need to read the properties file for any extra class
+# paths, library paths, java options and memory early on. Otherwise, it will
+# be too late by the time the driver JVM has started.
 
-if [ -n "$DRIVER_MEMORY" ] && [ $DEPLOY_MODE == "client" ]; then
-  export SPARK_DRIVER_MEMORY=$DRIVER_MEMORY
+if [[ "$SPARK_SUBMIT_DEPLOY_MODE" == "client" && -f "$SPARK_SUBMIT_PROPERTIES_FILE" ]]; then
+  # Parse the properties file only if the special configs exist
+  contains_special_configs=$(
+    grep -e "spark.driver.extra*\|spark.driver.memory" "$SPARK_SUBMIT_PROPERTIES_FILE" | \
+    grep -v "^[[:space:]]*#"
+  )
+  if [ -n "$contains_special_configs" ]; then
+    export SPARK_SUBMIT_BOOTSTRAP_DRIVER=1
+  fi
 fi
 
 exec $SPARK_HOME/bin/spark-class org.apache.spark.deploy.SparkSubmit "${ORIG_ARGS[@]}"
diff --git a/bin/utils.sh b/bin/utils.sh
old mode 100644
new mode 100755
diff --git a/conf/spark-defaults.conf.template b/conf/spark-defaults.conf.template
index 2779342769c14..94427029b94d7 100644
--- a/conf/spark-defaults.conf.template
+++ b/conf/spark-defaults.conf.template
@@ -2,7 +2,9 @@
 # This is useful for setting default environmental settings.
 
 # Example:
-# spark.master            spark://master:7077
-# spark.eventLog.enabled  true
-# spark.eventLog.dir      hdfs://namenode:8021/directory
-# spark.serializer        org.apache.spark.serializer.KryoSerializer
+# spark.master                     spark://master:7077
+# spark.eventLog.enabled           true
+# spark.eventLog.dir               hdfs://namenode:8021/directory
+# spark.serializer                 org.apache.spark.serializer.KryoSerializer
+# spark.driver.memory              5g
+# spark.executor.extraJavaOptions  -XX:+PrintGCDetail -Dkey=value -Dnumbers="one two three"
diff --git a/core/src/main/scala/org/apache/spark/api/python/PythonUtils.scala b/core/src/main/scala/org/apache/spark/api/python/PythonUtils.scala
index 52c70712eea3d..be5ebfa9219d3 100644
--- a/core/src/main/scala/org/apache/spark/api/python/PythonUtils.scala
+++ b/core/src/main/scala/org/apache/spark/api/python/PythonUtils.scala
@@ -40,28 +40,3 @@ private[spark] object PythonUtils {
     paths.filter(_ != "").mkString(File.pathSeparator)
   }
 }
-
-
-/**
- * A utility class to redirect the child process's stdout or stderr.
- */
-private[spark] class RedirectThread(
-    in: InputStream,
-    out: OutputStream,
-    name: String)
-  extends Thread(name) {
-
-  setDaemon(true)
-  override def run() {
-    scala.util.control.Exception.ignoring(classOf[IOException]) {
-      // FIXME: We copy the stream on the level of bytes to avoid encoding problems.
-      val buf = new Array[Byte](1024)
-      var len = in.read(buf)
-      while (len != -1) {
-        out.write(buf, 0, len)
-        out.flush()
-        len = in.read(buf)
-      }
-    }
-  }
-}
diff --git a/core/src/main/scala/org/apache/spark/api/python/PythonWorkerFactory.scala b/core/src/main/scala/org/apache/spark/api/python/PythonWorkerFactory.scala
index bf716a8ab025b..4c4796f6c59ba 100644
--- a/core/src/main/scala/org/apache/spark/api/python/PythonWorkerFactory.scala
+++ b/core/src/main/scala/org/apache/spark/api/python/PythonWorkerFactory.scala
@@ -17,7 +17,6 @@
 
 package org.apache.spark.api.python
 
-import java.lang.Runtime
 import java.io.{DataOutputStream, DataInputStream, InputStream, OutputStreamWriter}
 import java.net.{InetAddress, ServerSocket, Socket, SocketException}
 
@@ -25,7 +24,7 @@ import scala.collection.mutable
 import scala.collection.JavaConversions._
 
 import org.apache.spark._
-import org.apache.spark.util.Utils
+import org.apache.spark.util.{RedirectThread, Utils}
 
 private[spark] class PythonWorkerFactory(pythonExec: String, envVars: Map[String, String])
   extends Logging {
diff --git a/core/src/main/scala/org/apache/spark/deploy/PythonRunner.scala b/core/src/main/scala/org/apache/spark/deploy/PythonRunner.scala
index 0d6751f3fa6d2..b66c3ba4d5fb0 100644
--- a/core/src/main/scala/org/apache/spark/deploy/PythonRunner.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/PythonRunner.scala
@@ -22,8 +22,8 @@ import java.net.URI
 import scala.collection.mutable.ArrayBuffer
 import scala.collection.JavaConversions._
 
-import org.apache.spark.api.python.{PythonUtils, RedirectThread}
-import org.apache.spark.util.Utils
+import org.apache.spark.api.python.PythonUtils
+import org.apache.spark.util.{RedirectThread, Utils}
 
 /**
  * A main class used by spark-submit to launch Python applications. It executes python as a
diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
index 318509a67a36f..f8cdbc3c392b5 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
@@ -195,18 +195,21 @@ object SparkSubmit {
       OptionAssigner(args.jars, YARN, CLUSTER, clOption = "--addJars"),
 
       // Other options
-      OptionAssigner(args.driverExtraClassPath, STANDALONE | YARN, CLUSTER,
-        sysProp = "spark.driver.extraClassPath"),
-      OptionAssigner(args.driverExtraJavaOptions, STANDALONE | YARN, CLUSTER,
-        sysProp = "spark.driver.extraJavaOptions"),
-      OptionAssigner(args.driverExtraLibraryPath, STANDALONE | YARN, CLUSTER,
-        sysProp = "spark.driver.extraLibraryPath"),
       OptionAssigner(args.executorMemory, STANDALONE | MESOS | YARN, ALL_DEPLOY_MODES,
         sysProp = "spark.executor.memory"),
       OptionAssigner(args.totalExecutorCores, STANDALONE | MESOS, ALL_DEPLOY_MODES,
         sysProp = "spark.cores.max"),
       OptionAssigner(args.files, LOCAL | STANDALONE | MESOS, ALL_DEPLOY_MODES,
-        sysProp = "spark.files")
+        sysProp = "spark.files"),
+
+      // Only process driver specific options for cluster mode here,
+      // because they have already been processed in bash for client mode
+      OptionAssigner(args.driverExtraClassPath, STANDALONE | YARN, CLUSTER,
+        sysProp = "spark.driver.extraClassPath"),
+      OptionAssigner(args.driverExtraJavaOptions, STANDALONE | YARN, CLUSTER,
+        sysProp = "spark.driver.extraJavaOptions"),
+      OptionAssigner(args.driverExtraLibraryPath, STANDALONE | YARN, CLUSTER,
+        sysProp = "spark.driver.extraLibraryPath")
     )
 
     // In client mode, launch the application main class directly
diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitDriverBootstrapper.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitDriverBootstrapper.scala
new file mode 100644
index 0000000000000..af607e6a4a065
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitDriverBootstrapper.scala
@@ -0,0 +1,149 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.deploy
+
+import java.io.File
+
+import scala.collection.JavaConversions._
+
+import org.apache.spark.util.{RedirectThread, Utils}
+
+/**
+ * Launch an application through Spark submit in client mode with the appropriate classpath,
+ * library paths, java options and memory. These properties of the JVM must be set before the
+ * driver JVM is launched. The sole purpose of this class is to avoid handling the complexity
+ * of parsing the properties file for such relevant configs in Bash.
+ *
+ * Usage: org.apache.spark.deploy.SparkSubmitDriverBootstrapper <submit args>
+ */
+private[spark] object SparkSubmitDriverBootstrapper {
+
+  // Note: This class depends on the behavior of `bin/spark-class` and `bin/spark-submit`.
+  // Any changes made there must be reflected in this file.
+
+  def main(args: Array[String]): Unit = {
+
+    // This should be called only from `bin/spark-class`
+    if (!sys.env.contains("SPARK_CLASS")) {
+      System.err.println("SparkSubmitDriverBootstrapper must be called from `bin/spark-class`!")
+      System.exit(1)
+    }
+
+    val submitArgs = args
+    val runner = sys.env("RUNNER")
+    val classpath = sys.env("CLASSPATH")
+    val javaOpts = sys.env("JAVA_OPTS")
+    val defaultDriverMemory = sys.env("OUR_JAVA_MEM")
+
+    // Spark submit specific environment variables
+    val deployMode = sys.env("SPARK_SUBMIT_DEPLOY_MODE")
+    val propertiesFile = sys.env("SPARK_SUBMIT_PROPERTIES_FILE")
+    val bootstrapDriver = sys.env("SPARK_SUBMIT_BOOTSTRAP_DRIVER")
+    val submitDriverMemory = sys.env.get("SPARK_SUBMIT_DRIVER_MEMORY")
+    val submitLibraryPath = sys.env.get("SPARK_SUBMIT_LIBRARY_PATH")
+    val submitClasspath = sys.env.get("SPARK_SUBMIT_CLASSPATH")
+    val submitJavaOpts = sys.env.get("SPARK_SUBMIT_OPTS")
+
+    assume(runner != null, "RUNNER must be set")
+    assume(classpath != null, "CLASSPATH must be set")
+    assume(javaOpts != null, "JAVA_OPTS must be set")
+    assume(defaultDriverMemory != null, "OUR_JAVA_MEM must be set")
+    assume(deployMode == "client", "SPARK_SUBMIT_DEPLOY_MODE must be \"client\"!")
+    assume(propertiesFile != null, "SPARK_SUBMIT_PROPERTIES_FILE must be set")
+    assume(bootstrapDriver != null, "SPARK_SUBMIT_BOOTSTRAP_DRIVER must be set")
+
+    // Parse the properties file for the equivalent spark.driver.* configs
+    val properties = SparkSubmitArguments.getPropertiesFromFile(new File(propertiesFile)).toMap
+    val confDriverMemory = properties.get("spark.driver.memory")
+    val confLibraryPath = properties.get("spark.driver.extraLibraryPath")
+    val confClasspath = properties.get("spark.driver.extraClassPath")
+    val confJavaOpts = properties.get("spark.driver.extraJavaOptions")
+
+    // Favor Spark submit arguments over the equivalent configs in the properties file.
+    // Note that we do not actually use the Spark submit values for library path, classpath,
+    // and Java opts here, because we have already captured them in Bash.
+
+    val newDriverMemory = submitDriverMemory
+      .orElse(confDriverMemory)
+      .getOrElse(defaultDriverMemory)
+
+    val newLibraryPath =
+      if (submitLibraryPath.isDefined) {
+        // SPARK_SUBMIT_LIBRARY_PATH is already captured in JAVA_OPTS
+        ""
+      } else {
+        confLibraryPath.map("-Djava.library.path=" + _).getOrElse("")
+      }
+
+    val newClasspath =
+      if (submitClasspath.isDefined) {
+        // SPARK_SUBMIT_CLASSPATH is already captured in CLASSPATH
+        classpath
+      } else {
+        classpath + confClasspath.map(sys.props("path.separator") + _).getOrElse("")
+      }
+
+    val newJavaOpts =
+      if (submitJavaOpts.isDefined) {
+        // SPARK_SUBMIT_OPTS is already captured in JAVA_OPTS
+        javaOpts
+      } else {
+        javaOpts + confJavaOpts.map(" " + _).getOrElse("")
+      }
+
+    val filteredJavaOpts = Utils.splitCommandString(newJavaOpts)
+      .filterNot(_.startsWith("-Xms"))
+      .filterNot(_.startsWith("-Xmx"))
+
+    // Build up command
+    val command: Seq[String] =
+      Seq(runner) ++
+      Seq("-cp", newClasspath) ++
+      Seq(newLibraryPath) ++
+      filteredJavaOpts ++
+      Seq(s"-Xms$newDriverMemory", s"-Xmx$newDriverMemory") ++
+      Seq("org.apache.spark.deploy.SparkSubmit") ++
+      submitArgs
+
+    // Print the launch command. This follows closely the format used in `bin/spark-class`.
+    if (sys.env.contains("SPARK_PRINT_LAUNCH_COMMAND")) {
+      System.err.print("Spark Command: ")
+      System.err.println(command.mkString(" "))
+      System.err.println("========================================\n")
+    }
+
+    // Start the driver JVM
+    val filteredCommand = command.filter(_.nonEmpty)
+    val builder = new ProcessBuilder(filteredCommand)
+    val process = builder.start()
+
+    // Redirect stdin, stdout, and stderr to/from the child JVM
+    val stdinThread = new RedirectThread(System.in, process.getOutputStream, "redirect stdin")
+    val stdoutThread = new RedirectThread(process.getInputStream, System.out, "redirect stdout")
+    val stderrThread = new RedirectThread(process.getErrorStream, System.err, "redirect stderr")
+    stdinThread.start()
+    stdoutThread.start()
+    stderrThread.start()
+
+    // Terminate on broken pipe, which signals that the parent process has exited. This is
+    // important for the PySpark shell, where Spark submit itself is a python subprocess.
+    stdinThread.join()
+    process.destroy()
+  }
+
+}
diff --git a/core/src/main/scala/org/apache/spark/util/Utils.scala b/core/src/main/scala/org/apache/spark/util/Utils.scala
index d6d74ce269219..69a84a3604a52 100644
--- a/core/src/main/scala/org/apache/spark/util/Utils.scala
+++ b/core/src/main/scala/org/apache/spark/util/Utils.scala
@@ -1480,3 +1480,24 @@ private[spark] object Utils extends Logging {
   }
 
 }
+
+/**
+ * A utility class to redirect the child process's stdout or stderr.
+ */
+private[spark] class RedirectThread(in: InputStream, out: OutputStream, name: String)
+  extends Thread(name) {
+
+  setDaemon(true)
+  override def run() {
+    scala.util.control.Exception.ignoring(classOf[IOException]) {
+      // FIXME: We copy the stream on the level of bytes to avoid encoding problems.
+      val buf = new Array[Byte](1024)
+      var len = in.read(buf)
+      while (len != -1) {
+        out.write(buf, 0, len)
+        out.flush()
+        len = in.read(buf)
+      }
+    }
+  }
+}

From fb60bec34e0b20ae95b4b865a79744916e0a5737 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@apache.org>
Date: Wed, 20 Aug 2014 15:37:27 -0700
Subject: [PATCH 045/489] [SPARK-2298] Encode stage attempt in SparkListener &
 UI.

Simple way to reproduce this in the UI:

```scala
val f = new java.io.File("/tmp/test")
f.delete()
sc.parallelize(1 to 2, 2).map(x => (x,x )).repartition(3).mapPartitionsWithContext { case (context, iter) =>
  if (context.partitionId == 0) {
    val f = new java.io.File("/tmp/test")
    if (!f.exists) {
      f.mkdir()
      System.exit(0);
    }
  }
  iter
}.count()
```

Author: Reynold Xin <rxin@apache.org>

Closes #1545 from rxin/stage-attempt and squashes the following commits:

3ee1d2a [Reynold Xin] - Rename attempt to retry in UI. - Properly report stage failure in FetchFailed.
40a6bd5 [Reynold Xin] Updated test suites.
c414c36 [Reynold Xin] Fixed the hanging in JobCancellationSuite.
b3e2eed [Reynold Xin] Oops previous code didn't compile.
0f36075 [Reynold Xin] Mark unknown stage attempt with id -1 and drop that in JobProgressListener.
6c08b07 [Reynold Xin] Addressed code review feedback.
4e5faa2 [Reynold Xin] [SPARK-2298] Encode stage attempt in SparkListener & UI.
---
 .../apache/spark/scheduler/DAGScheduler.scala |  77 +--
 .../spark/scheduler/SparkListener.scala       |  11 +-
 .../org/apache/spark/scheduler/Stage.scala    |   8 +-
 .../apache/spark/scheduler/StageInfo.scala    |  11 +-
 .../spark/scheduler/TaskSchedulerImpl.scala   |   8 +-
 .../org/apache/spark/scheduler/TaskSet.scala  |   4 -
 .../apache/spark/ui/jobs/ExecutorTable.scala  |   6 +-
 .../spark/ui/jobs/JobProgressListener.scala   |  40 +-
 .../org/apache/spark/ui/jobs/StagePage.scala  |  11 +-
 .../org/apache/spark/ui/jobs/StageTable.scala |  14 +-
 .../org/apache/spark/util/JsonProtocol.scala  |  12 +-
 .../storage/StorageStatusListenerSuite.scala  |  17 +-
 .../ui/jobs/JobProgressListenerSuite.scala    |  68 +--
 .../spark/ui/storage/StorageTabSuite.scala    |  16 +-
 .../apache/spark/util/JsonProtocolSuite.scala | 476 ++++++++++++++----
 15 files changed, 555 insertions(+), 224 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
index b86cfbfa48fbe..34131984570e4 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
@@ -164,7 +164,7 @@ class DAGScheduler(
    */
   def executorHeartbeatReceived(
       execId: String,
-      taskMetrics: Array[(Long, Int, TaskMetrics)], // (taskId, stageId, metrics)
+      taskMetrics: Array[(Long, Int, Int, TaskMetrics)], // (taskId, stageId, stateAttempt, metrics)
       blockManagerId: BlockManagerId): Boolean = {
     listenerBus.post(SparkListenerExecutorMetricsUpdate(execId, taskMetrics))
     implicit val timeout = Timeout(600 seconds)
@@ -677,7 +677,10 @@ class DAGScheduler(
   }
 
   private[scheduler] def handleBeginEvent(task: Task[_], taskInfo: TaskInfo) {
-    listenerBus.post(SparkListenerTaskStart(task.stageId, taskInfo))
+    // Note that there is a chance that this task is launched after the stage is cancelled.
+    // In that case, we wouldn't have the stage anymore in stageIdToStage.
+    val stageAttemptId = stageIdToStage.get(task.stageId).map(_.latestInfo.attemptId).getOrElse(-1)
+    listenerBus.post(SparkListenerTaskStart(task.stageId, stageAttemptId, taskInfo))
     submitWaitingStages()
   }
 
@@ -695,8 +698,8 @@ class DAGScheduler(
       // is in the process of getting stopped.
       val stageFailedMessage = "Stage cancelled because SparkContext was shut down"
       runningStages.foreach { stage =>
-        stage.info.stageFailed(stageFailedMessage)
-        listenerBus.post(SparkListenerStageCompleted(stage.info))
+        stage.latestInfo.stageFailed(stageFailedMessage)
+        listenerBus.post(SparkListenerStageCompleted(stage.latestInfo))
       }
       listenerBus.post(SparkListenerJobEnd(job.jobId, JobFailed(error)))
     }
@@ -781,7 +784,16 @@ class DAGScheduler(
     logDebug("submitMissingTasks(" + stage + ")")
     // Get our pending tasks and remember them in our pendingTasks entry
     stage.pendingTasks.clear()
-    var tasks = ArrayBuffer[Task[_]]()
+
+    // First figure out the indexes of partition ids to compute.
+    val partitionsToCompute: Seq[Int] = {
+      if (stage.isShuffleMap) {
+        (0 until stage.numPartitions).filter(id => stage.outputLocs(id) == Nil)
+      } else {
+        val job = stage.resultOfJob.get
+        (0 until job.numPartitions).filter(id => !job.finished(id))
+      }
+    }
 
     val properties = if (jobIdToActiveJob.contains(jobId)) {
       jobIdToActiveJob(stage.jobId).properties
@@ -795,7 +807,8 @@ class DAGScheduler(
     // serializable. If tasks are not serializable, a SparkListenerStageCompleted event
     // will be posted, which should always come after a corresponding SparkListenerStageSubmitted
     // event.
-    listenerBus.post(SparkListenerStageSubmitted(stage.info, properties))
+    stage.latestInfo = StageInfo.fromStage(stage, Some(partitionsToCompute.size))
+    listenerBus.post(SparkListenerStageSubmitted(stage.latestInfo, properties))
 
     // TODO: Maybe we can keep the taskBinary in Stage to avoid serializing it multiple times.
     // Broadcasted binary for the task, used to dispatch tasks to executors. Note that we broadcast
@@ -826,20 +839,19 @@ class DAGScheduler(
         return
     }
 
-    if (stage.isShuffleMap) {
-      for (p <- 0 until stage.numPartitions if stage.outputLocs(p) == Nil) {
-        val locs = getPreferredLocs(stage.rdd, p)
-        val part = stage.rdd.partitions(p)
-        tasks += new ShuffleMapTask(stage.id, taskBinary, part, locs)
+    val tasks: Seq[Task[_]] = if (stage.isShuffleMap) {
+      partitionsToCompute.map { id =>
+        val locs = getPreferredLocs(stage.rdd, id)
+        val part = stage.rdd.partitions(id)
+        new ShuffleMapTask(stage.id, taskBinary, part, locs)
       }
     } else {
-      // This is a final stage; figure out its job's missing partitions
       val job = stage.resultOfJob.get
-      for (id <- 0 until job.numPartitions if !job.finished(id)) {
+      partitionsToCompute.map { id =>
         val p: Int = job.partitions(id)
         val part = stage.rdd.partitions(p)
         val locs = getPreferredLocs(stage.rdd, p)
-        tasks += new ResultTask(stage.id, taskBinary, part, locs, id)
+        new ResultTask(stage.id, taskBinary, part, locs, id)
       }
     }
 
@@ -869,11 +881,11 @@ class DAGScheduler(
       logDebug("New pending tasks: " + stage.pendingTasks)
       taskScheduler.submitTasks(
         new TaskSet(tasks.toArray, stage.id, stage.newAttemptId(), stage.jobId, properties))
-      stage.info.submissionTime = Some(clock.getTime())
+      stage.latestInfo.submissionTime = Some(clock.getTime())
     } else {
       // Because we posted SparkListenerStageSubmitted earlier, we should post
       // SparkListenerStageCompleted here in case there are no tasks to run.
-      listenerBus.post(SparkListenerStageCompleted(stage.info))
+      listenerBus.post(SparkListenerStageCompleted(stage.latestInfo))
       logDebug("Stage " + stage + " is actually done; %b %d %d".format(
         stage.isAvailable, stage.numAvailableOutputs, stage.numPartitions))
       runningStages -= stage
@@ -892,8 +904,9 @@ class DAGScheduler(
     // The success case is dealt with separately below, since we need to compute accumulator
     // updates before posting.
     if (event.reason != Success) {
-      listenerBus.post(SparkListenerTaskEnd(stageId, taskType, event.reason, event.taskInfo,
-        event.taskMetrics))
+      val attemptId = stageIdToStage.get(task.stageId).map(_.latestInfo.attemptId).getOrElse(-1)
+      listenerBus.post(SparkListenerTaskEnd(stageId, attemptId, taskType, event.reason,
+        event.taskInfo, event.taskMetrics))
     }
 
     if (!stageIdToStage.contains(task.stageId)) {
@@ -902,14 +915,19 @@ class DAGScheduler(
     }
     val stage = stageIdToStage(task.stageId)
 
-    def markStageAsFinished(stage: Stage) = {
-      val serviceTime = stage.info.submissionTime match {
+    def markStageAsFinished(stage: Stage, errorMessage: Option[String] = None) = {
+      val serviceTime = stage.latestInfo.submissionTime match {
         case Some(t) => "%.03f".format((clock.getTime() - t) / 1000.0)
         case _ => "Unknown"
       }
-      logInfo("%s (%s) finished in %s s".format(stage, stage.name, serviceTime))
-      stage.info.completionTime = Some(clock.getTime())
-      listenerBus.post(SparkListenerStageCompleted(stage.info))
+      if (errorMessage.isEmpty) {
+        logInfo("%s (%s) finished in %s s".format(stage, stage.name, serviceTime))
+        stage.latestInfo.completionTime = Some(clock.getTime())
+      } else {
+        stage.latestInfo.stageFailed(errorMessage.get)
+        logInfo("%s (%s) failed in %s s".format(stage, stage.name, serviceTime))
+      }
+      listenerBus.post(SparkListenerStageCompleted(stage.latestInfo))
       runningStages -= stage
     }
     event.reason match {
@@ -924,7 +942,7 @@ class DAGScheduler(
                 val name = acc.name.get
                 val stringPartialValue = Accumulators.stringifyPartialValue(partialValue)
                 val stringValue = Accumulators.stringifyValue(acc.value)
-                stage.info.accumulables(id) = AccumulableInfo(id, name, stringValue)
+                stage.latestInfo.accumulables(id) = AccumulableInfo(id, name, stringValue)
                 event.taskInfo.accumulables +=
                   AccumulableInfo(id, name, Some(stringPartialValue), stringValue)
               }
@@ -935,8 +953,8 @@ class DAGScheduler(
               logError(s"Failed to update accumulators for $task", e)
           }
         }
-        listenerBus.post(SparkListenerTaskEnd(stageId, taskType, event.reason, event.taskInfo,
-          event.taskMetrics))
+        listenerBus.post(SparkListenerTaskEnd(stageId, stage.latestInfo.attemptId, taskType,
+          event.reason, event.taskInfo, event.taskMetrics))
         stage.pendingTasks -= task
         task match {
           case rt: ResultTask[_, _] =>
@@ -1029,6 +1047,7 @@ class DAGScheduler(
       case FetchFailed(bmAddress, shuffleId, mapId, reduceId) =>
         // Mark the stage that the reducer was in as unrunnable
         val failedStage = stageIdToStage(task.stageId)
+        markStageAsFinished(failedStage, Some("Fetch failure"))
         runningStages -= failedStage
         // TODO: Cancel running tasks in the stage
         logInfo("Marking " + failedStage + " (" + failedStage.name +
@@ -1142,7 +1161,7 @@ class DAGScheduler(
     }
     val dependentJobs: Seq[ActiveJob] =
       activeJobs.filter(job => stageDependsOn(job.finalStage, failedStage)).toSeq
-    failedStage.info.completionTime = Some(clock.getTime())
+    failedStage.latestInfo.completionTime = Some(clock.getTime())
     for (job <- dependentJobs) {
       failJobAndIndependentStages(job, s"Job aborted due to stage failure: $reason")
     }
@@ -1182,8 +1201,8 @@ class DAGScheduler(
           if (runningStages.contains(stage)) {
             try { // cancelTasks will fail if a SchedulerBackend does not implement killTask
               taskScheduler.cancelTasks(stageId, shouldInterruptThread)
-              stage.info.stageFailed(failureReason)
-              listenerBus.post(SparkListenerStageCompleted(stage.info))
+              stage.latestInfo.stageFailed(failureReason)
+              listenerBus.post(SparkListenerStageCompleted(stage.latestInfo))
             } catch {
               case e: UnsupportedOperationException =>
                 logInfo(s"Could not cancel tasks for stage $stageId", e)
diff --git a/core/src/main/scala/org/apache/spark/scheduler/SparkListener.scala b/core/src/main/scala/org/apache/spark/scheduler/SparkListener.scala
index d01d318633877..86ca8445a1124 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/SparkListener.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/SparkListener.scala
@@ -39,7 +39,8 @@ case class SparkListenerStageSubmitted(stageInfo: StageInfo, properties: Propert
 case class SparkListenerStageCompleted(stageInfo: StageInfo) extends SparkListenerEvent
 
 @DeveloperApi
-case class SparkListenerTaskStart(stageId: Int, taskInfo: TaskInfo) extends SparkListenerEvent
+case class SparkListenerTaskStart(stageId: Int, stageAttemptId: Int, taskInfo: TaskInfo)
+  extends SparkListenerEvent
 
 @DeveloperApi
 case class SparkListenerTaskGettingResult(taskInfo: TaskInfo) extends SparkListenerEvent
@@ -47,6 +48,7 @@ case class SparkListenerTaskGettingResult(taskInfo: TaskInfo) extends SparkListe
 @DeveloperApi
 case class SparkListenerTaskEnd(
     stageId: Int,
+    stageAttemptId: Int,
     taskType: String,
     reason: TaskEndReason,
     taskInfo: TaskInfo,
@@ -75,10 +77,15 @@ case class SparkListenerBlockManagerRemoved(blockManagerId: BlockManagerId)
 @DeveloperApi
 case class SparkListenerUnpersistRDD(rddId: Int) extends SparkListenerEvent
 
+/**
+ * Periodic updates from executors.
+ * @param execId executor id
+ * @param taskMetrics sequence of (task id, stage id, stage attempt, metrics)
+ */
 @DeveloperApi
 case class SparkListenerExecutorMetricsUpdate(
     execId: String,
-    taskMetrics: Seq[(Long, Int, TaskMetrics)])
+    taskMetrics: Seq[(Long, Int, Int, TaskMetrics)])
   extends SparkListenerEvent
 
 @DeveloperApi
diff --git a/core/src/main/scala/org/apache/spark/scheduler/Stage.scala b/core/src/main/scala/org/apache/spark/scheduler/Stage.scala
index 800905413d145..071568cdfb429 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/Stage.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/Stage.scala
@@ -43,6 +43,9 @@ import org.apache.spark.util.CallSite
  * stage, the callSite gives the user code that created the RDD being shuffled. For a result
  * stage, the callSite gives the user code that executes the associated action (e.g. count()).
  *
+ * A single stage can consist of multiple attempts. In that case, the latestInfo field will
+ * be updated for each attempt.
+ *
  */
 private[spark] class Stage(
     val id: Int,
@@ -71,8 +74,8 @@ private[spark] class Stage(
   val name = callSite.shortForm
   val details = callSite.longForm
 
-  /** Pointer to the [StageInfo] object, set by DAGScheduler. */
-  var info: StageInfo = StageInfo.fromStage(this)
+  /** Pointer to the latest [StageInfo] object, set by DAGScheduler. */
+  var latestInfo: StageInfo = StageInfo.fromStage(this)
 
   def isAvailable: Boolean = {
     if (!isShuffleMap) {
@@ -116,6 +119,7 @@ private[spark] class Stage(
     }
   }
 
+  /** Return a new attempt id, starting with 0. */
   def newAttemptId(): Int = {
     val id = nextAttemptId
     nextAttemptId += 1
diff --git a/core/src/main/scala/org/apache/spark/scheduler/StageInfo.scala b/core/src/main/scala/org/apache/spark/scheduler/StageInfo.scala
index 2a407e47a05bd..c6dc3369ba5cc 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/StageInfo.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/StageInfo.scala
@@ -29,6 +29,7 @@ import org.apache.spark.storage.RDDInfo
 @DeveloperApi
 class StageInfo(
     val stageId: Int,
+    val attemptId: Int,
     val name: String,
     val numTasks: Int,
     val rddInfos: Seq[RDDInfo],
@@ -56,9 +57,15 @@ private[spark] object StageInfo {
    * shuffle dependencies. Therefore, all ancestor RDDs related to this Stage's RDD through a
    * sequence of narrow dependencies should also be associated with this Stage.
    */
-  def fromStage(stage: Stage): StageInfo = {
+  def fromStage(stage: Stage, numTasks: Option[Int] = None): StageInfo = {
     val ancestorRddInfos = stage.rdd.getNarrowAncestors.map(RDDInfo.fromRdd)
     val rddInfos = Seq(RDDInfo.fromRdd(stage.rdd)) ++ ancestorRddInfos
-    new StageInfo(stage.id, stage.name, stage.numTasks, rddInfos, stage.details)
+    new StageInfo(
+      stage.id,
+      stage.attemptId,
+      stage.name,
+      numTasks.getOrElse(stage.numTasks),
+      rddInfos,
+      stage.details)
   }
 }
diff --git a/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala b/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala
index 6c0d1b2752a81..ad051e59af86d 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala
@@ -333,12 +333,12 @@ private[spark] class TaskSchedulerImpl(
       execId: String,
       taskMetrics: Array[(Long, TaskMetrics)], // taskId -> TaskMetrics
       blockManagerId: BlockManagerId): Boolean = {
-    val metricsWithStageIds = taskMetrics.flatMap {
-      case (id, metrics) => {
+
+    val metricsWithStageIds: Array[(Long, Int, Int, TaskMetrics)] = synchronized {
+      taskMetrics.flatMap { case (id, metrics) =>
         taskIdToTaskSetId.get(id)
           .flatMap(activeTaskSets.get)
-          .map(_.stageId)
-          .map(x => (id, x, metrics))
+          .map(taskSetMgr => (id, taskSetMgr.stageId, taskSetMgr.taskSet.attempt, metrics))
       }
     }
     dagScheduler.executorHeartbeatReceived(execId, metricsWithStageIds, blockManagerId)
diff --git a/core/src/main/scala/org/apache/spark/scheduler/TaskSet.scala b/core/src/main/scala/org/apache/spark/scheduler/TaskSet.scala
index 613fa7850bb25..c3ad325156f53 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/TaskSet.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/TaskSet.scala
@@ -31,9 +31,5 @@ private[spark] class TaskSet(
     val properties: Properties) {
     val id: String = stageId + "." + attempt
 
-  def kill(interruptThread: Boolean) {
-    tasks.foreach(_.kill(interruptThread))
-  }
-
   override def toString: String = "TaskSet " + id
 }
diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/ExecutorTable.scala b/core/src/main/scala/org/apache/spark/ui/jobs/ExecutorTable.scala
index 0cc51c873727d..2987dc04494a5 100644
--- a/core/src/main/scala/org/apache/spark/ui/jobs/ExecutorTable.scala
+++ b/core/src/main/scala/org/apache/spark/ui/jobs/ExecutorTable.scala
@@ -24,8 +24,8 @@ import org.apache.spark.ui.{ToolTips, UIUtils}
 import org.apache.spark.ui.jobs.UIData.StageUIData
 import org.apache.spark.util.Utils
 
-/** Page showing executor summary */
-private[ui] class ExecutorTable(stageId: Int, parent: JobProgressTab) {
+/** Stage summary grouped by executors. */
+private[ui] class ExecutorTable(stageId: Int, stageAttemptId: Int, parent: JobProgressTab) {
   private val listener = parent.listener
 
   def toNodeSeq: Seq[Node] = {
@@ -65,7 +65,7 @@ private[ui] class ExecutorTable(stageId: Int, parent: JobProgressTab) {
       executorIdToAddress.put(executorId, address)
     }
 
-    listener.stageIdToData.get(stageId) match {
+    listener.stageIdToData.get((stageId, stageAttemptId)) match {
       case Some(stageData: StageUIData) =>
         stageData.executorSummary.toSeq.sortBy(_._1).map { case (k, v) =>
           <tr>
diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/JobProgressListener.scala b/core/src/main/scala/org/apache/spark/ui/jobs/JobProgressListener.scala
index 74cd637d88155..f7f918fd521a9 100644
--- a/core/src/main/scala/org/apache/spark/ui/jobs/JobProgressListener.scala
+++ b/core/src/main/scala/org/apache/spark/ui/jobs/JobProgressListener.scala
@@ -43,12 +43,16 @@ class JobProgressListener(conf: SparkConf) extends SparkListener with Logging {
   // How many stages to remember
   val retainedStages = conf.getInt("spark.ui.retainedStages", DEFAULT_RETAINED_STAGES)
 
-  val activeStages = HashMap[Int, StageInfo]()
+  // Map from stageId to StageInfo
+  val activeStages = new HashMap[Int, StageInfo]
+
+  // Map from (stageId, attemptId) to StageUIData
+  val stageIdToData = new HashMap[(Int, Int), StageUIData]
+
   val completedStages = ListBuffer[StageInfo]()
   val failedStages = ListBuffer[StageInfo]()
 
-  val stageIdToData = new HashMap[Int, StageUIData]
-
+  // Map from pool name to a hash map (map from stage id to StageInfo).
   val poolToActiveStages = HashMap[String, HashMap[Int, StageInfo]]()
 
   val executorIdToBlockManagerId = HashMap[String, BlockManagerId]()
@@ -59,9 +63,8 @@ class JobProgressListener(conf: SparkConf) extends SparkListener with Logging {
 
   override def onStageCompleted(stageCompleted: SparkListenerStageCompleted) = synchronized {
     val stage = stageCompleted.stageInfo
-    val stageId = stage.stageId
-    val stageData = stageIdToData.getOrElseUpdate(stageId, {
-      logWarning("Stage completed for unknown stage " + stageId)
+    val stageData = stageIdToData.getOrElseUpdate((stage.stageId, stage.attemptId), {
+      logWarning("Stage completed for unknown stage " + stage.stageId)
       new StageUIData
     })
 
@@ -69,8 +72,10 @@ class JobProgressListener(conf: SparkConf) extends SparkListener with Logging {
       stageData.accumulables(id) = info
     }
 
-    poolToActiveStages.get(stageData.schedulingPool).foreach(_.remove(stageId))
-    activeStages.remove(stageId)
+    poolToActiveStages.get(stageData.schedulingPool).foreach { hashMap =>
+      hashMap.remove(stage.stageId)
+    }
+    activeStages.remove(stage.stageId)
     if (stage.failureReason.isEmpty) {
       completedStages += stage
       trimIfNecessary(completedStages)
@@ -84,7 +89,7 @@ class JobProgressListener(conf: SparkConf) extends SparkListener with Logging {
   private def trimIfNecessary(stages: ListBuffer[StageInfo]) = synchronized {
     if (stages.size > retainedStages) {
       val toRemove = math.max(retainedStages / 10, 1)
-      stages.take(toRemove).foreach { s => stageIdToData.remove(s.stageId) }
+      stages.take(toRemove).foreach { s => stageIdToData.remove((s.stageId, s.attemptId)) }
       stages.trimStart(toRemove)
     }
   }
@@ -98,21 +103,21 @@ class JobProgressListener(conf: SparkConf) extends SparkListener with Logging {
       p => p.getProperty("spark.scheduler.pool", DEFAULT_POOL_NAME)
     }.getOrElse(DEFAULT_POOL_NAME)
 
-    val stageData = stageIdToData.getOrElseUpdate(stage.stageId, new StageUIData)
+    val stageData = stageIdToData.getOrElseUpdate((stage.stageId, stage.attemptId), new StageUIData)
     stageData.schedulingPool = poolName
 
     stageData.description = Option(stageSubmitted.properties).flatMap {
       p => Option(p.getProperty(SparkContext.SPARK_JOB_DESCRIPTION))
     }
 
-    val stages = poolToActiveStages.getOrElseUpdate(poolName, new HashMap[Int, StageInfo]())
+    val stages = poolToActiveStages.getOrElseUpdate(poolName, new HashMap[Int, StageInfo])
     stages(stage.stageId) = stage
   }
 
   override def onTaskStart(taskStart: SparkListenerTaskStart) = synchronized {
     val taskInfo = taskStart.taskInfo
     if (taskInfo != null) {
-      val stageData = stageIdToData.getOrElseUpdate(taskStart.stageId, {
+      val stageData = stageIdToData.getOrElseUpdate((taskStart.stageId, taskStart.stageAttemptId), {
         logWarning("Task start for unknown stage " + taskStart.stageId)
         new StageUIData
       })
@@ -128,8 +133,11 @@ class JobProgressListener(conf: SparkConf) extends SparkListener with Logging {
 
   override def onTaskEnd(taskEnd: SparkListenerTaskEnd) = synchronized {
     val info = taskEnd.taskInfo
-    if (info != null) {
-      val stageData = stageIdToData.getOrElseUpdate(taskEnd.stageId, {
+    // If stage attempt id is -1, it means the DAGScheduler had no idea which attempt this task
+    // compeletion event is for. Let's just drop it here. This means we might have some speculation
+    // tasks on the web ui that's never marked as complete.
+    if (info != null && taskEnd.stageAttemptId != -1) {
+      val stageData = stageIdToData.getOrElseUpdate((taskEnd.stageId, taskEnd.stageAttemptId), {
         logWarning("Task end for unknown stage " + taskEnd.stageId)
         new StageUIData
       })
@@ -222,8 +230,8 @@ class JobProgressListener(conf: SparkConf) extends SparkListener with Logging {
   }
 
   override def onExecutorMetricsUpdate(executorMetricsUpdate: SparkListenerExecutorMetricsUpdate) {
-    for ((taskId, sid, taskMetrics) <- executorMetricsUpdate.taskMetrics) {
-      val stageData = stageIdToData.getOrElseUpdate(sid, {
+    for ((taskId, sid, sAttempt, taskMetrics) <- executorMetricsUpdate.taskMetrics) {
+      val stageData = stageIdToData.getOrElseUpdate((sid, sAttempt), {
         logWarning("Metrics update for task in unknown stage " + sid)
         new StageUIData
       })
diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala b/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala
index d4eb02722ad12..db01be596e073 100644
--- a/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala
+++ b/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala
@@ -34,7 +34,8 @@ private[ui] class StagePage(parent: JobProgressTab) extends WebUIPage("stage") {
   def render(request: HttpServletRequest): Seq[Node] = {
     listener.synchronized {
       val stageId = request.getParameter("id").toInt
-      val stageDataOption = listener.stageIdToData.get(stageId)
+      val stageAttemptId = request.getParameter("attempt").toInt
+      val stageDataOption = listener.stageIdToData.get((stageId, stageAttemptId))
 
       if (stageDataOption.isEmpty || stageDataOption.get.taskData.isEmpty) {
         val content =
@@ -42,14 +43,15 @@ private[ui] class StagePage(parent: JobProgressTab) extends WebUIPage("stage") {
             <h4>Summary Metrics</h4> No tasks have started yet
             <h4>Tasks</h4> No tasks have started yet
           </div>
-        return UIUtils.headerSparkPage("Details for Stage %s".format(stageId), content, parent)
+        return UIUtils.headerSparkPage(
+          s"Details for Stage $stageId (Attempt $stageAttemptId)", content, parent)
       }
 
       val stageData = stageDataOption.get
       val tasks = stageData.taskData.values.toSeq.sortBy(_.taskInfo.launchTime)
 
       val numCompleted = tasks.count(_.taskInfo.finished)
-      val accumulables = listener.stageIdToData(stageId).accumulables
+      val accumulables = listener.stageIdToData((stageId, stageAttemptId)).accumulables
       val hasInput = stageData.inputBytes > 0
       val hasShuffleRead = stageData.shuffleReadBytes > 0
       val hasShuffleWrite = stageData.shuffleWriteBytes > 0
@@ -211,7 +213,8 @@ private[ui] class StagePage(parent: JobProgressTab) extends WebUIPage("stage") {
           def quantileRow(data: Seq[Node]): Seq[Node] = <tr>{data}</tr>
           Some(UIUtils.listingTable(quantileHeaders, quantileRow, listings, fixedWidth = true))
         }
-      val executorTable = new ExecutorTable(stageId, parent)
+
+      val executorTable = new ExecutorTable(stageId, stageAttemptId, parent)
 
       val maybeAccumulableTable: Seq[Node] =
         if (accumulables.size > 0) { <h4>Accumulators</h4> ++ accumulableTable } else Seq()
diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/StageTable.scala b/core/src/main/scala/org/apache/spark/ui/jobs/StageTable.scala
index 16ad0df45aa0d..2e67310594784 100644
--- a/core/src/main/scala/org/apache/spark/ui/jobs/StageTable.scala
+++ b/core/src/main/scala/org/apache/spark/ui/jobs/StageTable.scala
@@ -97,8 +97,8 @@ private[ui] class StageTableBase(
     }
     // scalastyle:on
 
-    val nameLinkUri ="%s/stages/stage?id=%s"
-      .format(UIUtils.prependBaseUri(parent.basePath), s.stageId)
+    val nameLinkUri ="%s/stages/stage?id=%s&attempt=%s"
+      .format(UIUtils.prependBaseUri(parent.basePath), s.stageId, s.attemptId)
     val nameLink = <a href={nameLinkUri}>{s.name}</a>
 
     val cachedRddInfos = s.rddInfos.filter(_.numCachedPartitions > 0)
@@ -121,7 +121,7 @@ private[ui] class StageTableBase(
     }
 
     val stageDesc = for {
-      stageData <- listener.stageIdToData.get(s.stageId)
+      stageData <- listener.stageIdToData.get((s.stageId, s.attemptId))
       desc <- stageData.description
     } yield {
       <div><em>{desc}</em></div>
@@ -131,7 +131,7 @@ private[ui] class StageTableBase(
   }
 
   protected def stageRow(s: StageInfo): Seq[Node] = {
-    val stageDataOption = listener.stageIdToData.get(s.stageId)
+    val stageDataOption = listener.stageIdToData.get((s.stageId, s.attemptId))
     if (stageDataOption.isEmpty) {
       return <td>{s.stageId}</td><td>No data available for this stage</td>
     }
@@ -154,7 +154,11 @@ private[ui] class StageTableBase(
     val shuffleWrite = stageData.shuffleWriteBytes
     val shuffleWriteWithUnit = if (shuffleWrite > 0) Utils.bytesToString(shuffleWrite) else ""
 
-    <td>{s.stageId}</td> ++
+    {if (s.attemptId > 0) {
+      <td>{s.stageId} (retry {s.attemptId})</td>
+    } else {
+      <td>{s.stageId}</td>
+    }} ++
     {if (isFairScheduler) {
       <td>
         <a href={"%s/stages/pool?poolname=%s"
diff --git a/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala b/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala
index 1e18ec688c40d..db7384705fc1b 100644
--- a/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala
+++ b/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala
@@ -96,6 +96,7 @@ private[spark] object JsonProtocol {
     val taskInfo = taskStart.taskInfo
     ("Event" -> Utils.getFormattedClassName(taskStart)) ~
     ("Stage ID" -> taskStart.stageId) ~
+    ("Stage Attempt ID" -> taskStart.stageAttemptId) ~
     ("Task Info" -> taskInfoToJson(taskInfo))
   }
 
@@ -112,6 +113,7 @@ private[spark] object JsonProtocol {
     val taskMetricsJson = if (taskMetrics != null) taskMetricsToJson(taskMetrics) else JNothing
     ("Event" -> Utils.getFormattedClassName(taskEnd)) ~
     ("Stage ID" -> taskEnd.stageId) ~
+    ("Stage Attempt ID" -> taskEnd.stageAttemptId) ~
     ("Task Type" -> taskEnd.taskType) ~
     ("Task End Reason" -> taskEndReason) ~
     ("Task Info" -> taskInfoToJson(taskInfo)) ~
@@ -187,6 +189,7 @@ private[spark] object JsonProtocol {
     val completionTime = stageInfo.completionTime.map(JInt(_)).getOrElse(JNothing)
     val failureReason = stageInfo.failureReason.map(JString(_)).getOrElse(JNothing)
     ("Stage ID" -> stageInfo.stageId) ~
+    ("Stage Attempt ID" -> stageInfo.attemptId) ~
     ("Stage Name" -> stageInfo.name) ~
     ("Number of Tasks" -> stageInfo.numTasks) ~
     ("RDD Info" -> rddInfo) ~
@@ -419,8 +422,9 @@ private[spark] object JsonProtocol {
 
   def taskStartFromJson(json: JValue): SparkListenerTaskStart = {
     val stageId = (json \ "Stage ID").extract[Int]
+    val stageAttemptId = (json \ "Stage Attempt ID").extractOpt[Int].getOrElse(0)
     val taskInfo = taskInfoFromJson(json \ "Task Info")
-    SparkListenerTaskStart(stageId, taskInfo)
+    SparkListenerTaskStart(stageId, stageAttemptId, taskInfo)
   }
 
   def taskGettingResultFromJson(json: JValue): SparkListenerTaskGettingResult = {
@@ -430,11 +434,12 @@ private[spark] object JsonProtocol {
 
   def taskEndFromJson(json: JValue): SparkListenerTaskEnd = {
     val stageId = (json \ "Stage ID").extract[Int]
+    val stageAttemptId = (json \ "Stage Attempt ID").extractOpt[Int].getOrElse(0)
     val taskType = (json \ "Task Type").extract[String]
     val taskEndReason = taskEndReasonFromJson(json \ "Task End Reason")
     val taskInfo = taskInfoFromJson(json \ "Task Info")
     val taskMetrics = taskMetricsFromJson(json \ "Task Metrics")
-    SparkListenerTaskEnd(stageId, taskType, taskEndReason, taskInfo, taskMetrics)
+    SparkListenerTaskEnd(stageId, stageAttemptId, taskType, taskEndReason, taskInfo, taskMetrics)
   }
 
   def jobStartFromJson(json: JValue): SparkListenerJobStart = {
@@ -492,6 +497,7 @@ private[spark] object JsonProtocol {
 
   def stageInfoFromJson(json: JValue): StageInfo = {
     val stageId = (json \ "Stage ID").extract[Int]
+    val attemptId = (json \ "Attempt ID").extractOpt[Int].getOrElse(0)
     val stageName = (json \ "Stage Name").extract[String]
     val numTasks = (json \ "Number of Tasks").extract[Int]
     val rddInfos = (json \ "RDD Info").extract[List[JValue]].map(rddInfoFromJson(_))
@@ -504,7 +510,7 @@ private[spark] object JsonProtocol {
       case None => Seq[AccumulableInfo]()
     }
 
-    val stageInfo = new StageInfo(stageId, stageName, numTasks, rddInfos, details)
+    val stageInfo = new StageInfo(stageId, attemptId, stageName, numTasks, rddInfos, details)
     stageInfo.submissionTime = submissionTime
     stageInfo.completionTime = completionTime
     stageInfo.failureReason = failureReason
diff --git a/core/src/test/scala/org/apache/spark/storage/StorageStatusListenerSuite.scala b/core/src/test/scala/org/apache/spark/storage/StorageStatusListenerSuite.scala
index 51fb646a3cb61..7671cb969a26b 100644
--- a/core/src/test/scala/org/apache/spark/storage/StorageStatusListenerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/storage/StorageStatusListenerSuite.scala
@@ -69,10 +69,10 @@ class StorageStatusListenerSuite extends FunSuite {
     // Task end with no updated blocks
     assert(listener.executorIdToStorageStatus("big").numBlocks === 0)
     assert(listener.executorIdToStorageStatus("fat").numBlocks === 0)
-    listener.onTaskEnd(SparkListenerTaskEnd(1, "obliteration", Success, taskInfo1, taskMetrics))
+    listener.onTaskEnd(SparkListenerTaskEnd(1, 0, "obliteration", Success, taskInfo1, taskMetrics))
     assert(listener.executorIdToStorageStatus("big").numBlocks === 0)
     assert(listener.executorIdToStorageStatus("fat").numBlocks === 0)
-    listener.onTaskEnd(SparkListenerTaskEnd(1, "obliteration", Success, taskInfo2, taskMetrics))
+    listener.onTaskEnd(SparkListenerTaskEnd(1, 0, "obliteration", Success, taskInfo2, taskMetrics))
     assert(listener.executorIdToStorageStatus("big").numBlocks === 0)
     assert(listener.executorIdToStorageStatus("fat").numBlocks === 0)
   }
@@ -92,13 +92,13 @@ class StorageStatusListenerSuite extends FunSuite {
     // Task end with new blocks
     assert(listener.executorIdToStorageStatus("big").numBlocks === 0)
     assert(listener.executorIdToStorageStatus("fat").numBlocks === 0)
-    listener.onTaskEnd(SparkListenerTaskEnd(1, "obliteration", Success, taskInfo1, taskMetrics1))
+    listener.onTaskEnd(SparkListenerTaskEnd(1, 0, "obliteration", Success, taskInfo1, taskMetrics1))
     assert(listener.executorIdToStorageStatus("big").numBlocks === 2)
     assert(listener.executorIdToStorageStatus("fat").numBlocks === 0)
     assert(listener.executorIdToStorageStatus("big").containsBlock(RDDBlockId(1, 1)))
     assert(listener.executorIdToStorageStatus("big").containsBlock(RDDBlockId(1, 2)))
     assert(listener.executorIdToStorageStatus("fat").numBlocks === 0)
-    listener.onTaskEnd(SparkListenerTaskEnd(1, "obliteration", Success, taskInfo2, taskMetrics2))
+    listener.onTaskEnd(SparkListenerTaskEnd(1, 0, "obliteration", Success, taskInfo2, taskMetrics2))
     assert(listener.executorIdToStorageStatus("big").numBlocks === 2)
     assert(listener.executorIdToStorageStatus("fat").numBlocks === 1)
     assert(listener.executorIdToStorageStatus("big").containsBlock(RDDBlockId(1, 1)))
@@ -111,13 +111,14 @@ class StorageStatusListenerSuite extends FunSuite {
     val droppedBlock3 = (RDDBlockId(4, 0), BlockStatus(StorageLevel.NONE, 0L, 0L, 0L))
     taskMetrics1.updatedBlocks = Some(Seq(droppedBlock1, droppedBlock3))
     taskMetrics2.updatedBlocks = Some(Seq(droppedBlock2, droppedBlock3))
-    listener.onTaskEnd(SparkListenerTaskEnd(1, "obliteration", Success, taskInfo1, taskMetrics1))
+
+    listener.onTaskEnd(SparkListenerTaskEnd(1, 0, "obliteration", Success, taskInfo1, taskMetrics1))
     assert(listener.executorIdToStorageStatus("big").numBlocks === 1)
     assert(listener.executorIdToStorageStatus("fat").numBlocks === 1)
     assert(!listener.executorIdToStorageStatus("big").containsBlock(RDDBlockId(1, 1)))
     assert(listener.executorIdToStorageStatus("big").containsBlock(RDDBlockId(1, 2)))
     assert(listener.executorIdToStorageStatus("fat").containsBlock(RDDBlockId(4, 0)))
-    listener.onTaskEnd(SparkListenerTaskEnd(1, "obliteration", Success, taskInfo2, taskMetrics2))
+    listener.onTaskEnd(SparkListenerTaskEnd(1, 0, "obliteration", Success, taskInfo2, taskMetrics2))
     assert(listener.executorIdToStorageStatus("big").numBlocks === 1)
     assert(listener.executorIdToStorageStatus("fat").numBlocks === 0)
     assert(!listener.executorIdToStorageStatus("big").containsBlock(RDDBlockId(1, 1)))
@@ -135,8 +136,8 @@ class StorageStatusListenerSuite extends FunSuite {
     val block3 = (RDDBlockId(4, 0), BlockStatus(StorageLevel.DISK_ONLY, 0L, 300L, 0L))
     taskMetrics1.updatedBlocks = Some(Seq(block1, block2))
     taskMetrics2.updatedBlocks = Some(Seq(block3))
-    listener.onTaskEnd(SparkListenerTaskEnd(1, "obliteration", Success, taskInfo1, taskMetrics1))
-    listener.onTaskEnd(SparkListenerTaskEnd(1, "obliteration", Success, taskInfo1, taskMetrics2))
+    listener.onTaskEnd(SparkListenerTaskEnd(1, 0, "obliteration", Success, taskInfo1, taskMetrics1))
+    listener.onTaskEnd(SparkListenerTaskEnd(1, 0, "obliteration", Success, taskInfo1, taskMetrics2))
     assert(listener.executorIdToStorageStatus("big").numBlocks === 3)
 
     // Unpersist RDD
diff --git a/core/src/test/scala/org/apache/spark/ui/jobs/JobProgressListenerSuite.scala b/core/src/test/scala/org/apache/spark/ui/jobs/JobProgressListenerSuite.scala
index 147ec0bc52e39..3370dd4156c3f 100644
--- a/core/src/test/scala/org/apache/spark/ui/jobs/JobProgressListenerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/ui/jobs/JobProgressListenerSuite.scala
@@ -34,12 +34,12 @@ class JobProgressListenerSuite extends FunSuite with LocalSparkContext with Matc
     val listener = new JobProgressListener(conf)
 
     def createStageStartEvent(stageId: Int) = {
-      val stageInfo = new StageInfo(stageId, stageId.toString, 0, null, "")
+      val stageInfo = new StageInfo(stageId, 0, stageId.toString, 0, null, "")
       SparkListenerStageSubmitted(stageInfo)
     }
 
     def createStageEndEvent(stageId: Int) = {
-      val stageInfo = new StageInfo(stageId, stageId.toString, 0, null, "")
+      val stageInfo = new StageInfo(stageId, 0, stageId.toString, 0, null, "")
       SparkListenerStageCompleted(stageInfo)
     }
 
@@ -70,33 +70,37 @@ class JobProgressListenerSuite extends FunSuite with LocalSparkContext with Matc
     taskInfo.finishTime = 1
     var task = new ShuffleMapTask(0)
     val taskType = Utils.getFormattedClassName(task)
-    listener.onTaskEnd(SparkListenerTaskEnd(task.stageId, taskType, Success, taskInfo, taskMetrics))
-    assert(listener.stageIdToData.getOrElse(0, fail()).executorSummary.getOrElse("exe-1", fail())
-      .shuffleRead === 1000)
+    listener.onTaskEnd(
+      SparkListenerTaskEnd(task.stageId, 0, taskType, Success, taskInfo, taskMetrics))
+    assert(listener.stageIdToData.getOrElse((0, 0), fail())
+      .executorSummary.getOrElse("exe-1", fail()).shuffleRead === 1000)
 
     // finish a task with unknown executor-id, nothing should happen
     taskInfo =
       new TaskInfo(1234L, 0, 1, 1000L, "exe-unknown", "host1", TaskLocality.NODE_LOCAL, true)
     taskInfo.finishTime = 1
     task = new ShuffleMapTask(0)
-    listener.onTaskEnd(SparkListenerTaskEnd(task.stageId, taskType, Success, taskInfo, taskMetrics))
+    listener.onTaskEnd(
+      SparkListenerTaskEnd(task.stageId, 0, taskType, Success, taskInfo, taskMetrics))
     assert(listener.stageIdToData.size === 1)
 
     // finish this task, should get updated duration
     taskInfo = new TaskInfo(1235L, 0, 1, 0L, "exe-1", "host1", TaskLocality.NODE_LOCAL, false)
     taskInfo.finishTime = 1
     task = new ShuffleMapTask(0)
-    listener.onTaskEnd(SparkListenerTaskEnd(task.stageId, taskType, Success, taskInfo, taskMetrics))
-    assert(listener.stageIdToData.getOrElse(0, fail()).executorSummary.getOrElse("exe-1", fail())
-      .shuffleRead === 2000)
+    listener.onTaskEnd(
+      SparkListenerTaskEnd(task.stageId, 0, taskType, Success, taskInfo, taskMetrics))
+    assert(listener.stageIdToData.getOrElse((0, 0), fail())
+      .executorSummary.getOrElse("exe-1", fail()).shuffleRead === 2000)
 
     // finish this task, should get updated duration
     taskInfo = new TaskInfo(1236L, 0, 2, 0L, "exe-2", "host1", TaskLocality.NODE_LOCAL, false)
     taskInfo.finishTime = 1
     task = new ShuffleMapTask(0)
-    listener.onTaskEnd(SparkListenerTaskEnd(task.stageId, taskType, Success, taskInfo, taskMetrics))
-    assert(listener.stageIdToData.getOrElse(0, fail()).executorSummary.getOrElse("exe-2", fail())
-      .shuffleRead === 1000)
+    listener.onTaskEnd(
+      SparkListenerTaskEnd(task.stageId, 0, taskType, Success, taskInfo, taskMetrics))
+    assert(listener.stageIdToData.getOrElse((0, 0), fail())
+      .executorSummary.getOrElse("exe-2", fail()).shuffleRead === 1000)
   }
 
   test("test task success vs failure counting for different task end reasons") {
@@ -119,16 +123,18 @@ class JobProgressListenerSuite extends FunSuite with LocalSparkContext with Matc
       UnknownReason)
     var failCount = 0
     for (reason <- taskFailedReasons) {
-      listener.onTaskEnd(SparkListenerTaskEnd(task.stageId, taskType, reason, taskInfo, metrics))
+      listener.onTaskEnd(
+        SparkListenerTaskEnd(task.stageId, 0, taskType, reason, taskInfo, metrics))
       failCount += 1
-      assert(listener.stageIdToData(task.stageId).numCompleteTasks === 0)
-      assert(listener.stageIdToData(task.stageId).numFailedTasks === failCount)
+      assert(listener.stageIdToData((task.stageId, 0)).numCompleteTasks === 0)
+      assert(listener.stageIdToData((task.stageId, 0)).numFailedTasks === failCount)
     }
 
     // Make sure we count success as success.
-    listener.onTaskEnd(SparkListenerTaskEnd(task.stageId, taskType, Success, taskInfo, metrics))
-    assert(listener.stageIdToData(task.stageId).numCompleteTasks === 1)
-    assert(listener.stageIdToData(task.stageId).numFailedTasks === failCount)
+    listener.onTaskEnd(
+      SparkListenerTaskEnd(task.stageId, 1, taskType, Success, taskInfo, metrics))
+    assert(listener.stageIdToData((task.stageId, 1)).numCompleteTasks === 1)
+    assert(listener.stageIdToData((task.stageId, 0)).numFailedTasks === failCount)
   }
 
   test("test update metrics") {
@@ -163,18 +169,18 @@ class JobProgressListenerSuite extends FunSuite with LocalSparkContext with Matc
       taskInfo
     }
 
-    listener.onTaskStart(SparkListenerTaskStart(0, makeTaskInfo(1234L)))
-    listener.onTaskStart(SparkListenerTaskStart(0, makeTaskInfo(1235L)))
-    listener.onTaskStart(SparkListenerTaskStart(1, makeTaskInfo(1236L)))
-    listener.onTaskStart(SparkListenerTaskStart(1, makeTaskInfo(1237L)))
+    listener.onTaskStart(SparkListenerTaskStart(0, 0, makeTaskInfo(1234L)))
+    listener.onTaskStart(SparkListenerTaskStart(0, 0, makeTaskInfo(1235L)))
+    listener.onTaskStart(SparkListenerTaskStart(1, 0, makeTaskInfo(1236L)))
+    listener.onTaskStart(SparkListenerTaskStart(1, 0, makeTaskInfo(1237L)))
 
     listener.onExecutorMetricsUpdate(SparkListenerExecutorMetricsUpdate(execId, Array(
-      (1234L, 0, makeTaskMetrics(0)),
-      (1235L, 0, makeTaskMetrics(100)),
-      (1236L, 1, makeTaskMetrics(200)))))
+      (1234L, 0, 0, makeTaskMetrics(0)),
+      (1235L, 0, 0, makeTaskMetrics(100)),
+      (1236L, 1, 0, makeTaskMetrics(200)))))
 
-    var stage0Data = listener.stageIdToData.get(0).get
-    var stage1Data = listener.stageIdToData.get(1).get
+    var stage0Data = listener.stageIdToData.get((0, 0)).get
+    var stage1Data = listener.stageIdToData.get((1, 0)).get
     assert(stage0Data.shuffleReadBytes == 102)
     assert(stage1Data.shuffleReadBytes == 201)
     assert(stage0Data.shuffleWriteBytes == 106)
@@ -195,14 +201,14 @@ class JobProgressListenerSuite extends FunSuite with LocalSparkContext with Matc
       .totalBlocksFetched == 202)
 
     // task that was included in a heartbeat
-    listener.onTaskEnd(SparkListenerTaskEnd(0, taskType, Success, makeTaskInfo(1234L, 1),
+    listener.onTaskEnd(SparkListenerTaskEnd(0, 0, taskType, Success, makeTaskInfo(1234L, 1),
       makeTaskMetrics(300)))
     // task that wasn't included in a heartbeat
-    listener.onTaskEnd(SparkListenerTaskEnd(1, taskType, Success, makeTaskInfo(1237L, 1),
+    listener.onTaskEnd(SparkListenerTaskEnd(1, 0, taskType, Success, makeTaskInfo(1237L, 1),
       makeTaskMetrics(400)))
 
-    stage0Data = listener.stageIdToData.get(0).get
-    stage1Data = listener.stageIdToData.get(1).get
+    stage0Data = listener.stageIdToData.get((0, 0)).get
+    stage1Data = listener.stageIdToData.get((1, 0)).get
     assert(stage0Data.shuffleReadBytes == 402)
     assert(stage1Data.shuffleReadBytes == 602)
     assert(stage0Data.shuffleWriteBytes == 406)
diff --git a/core/src/test/scala/org/apache/spark/ui/storage/StorageTabSuite.scala b/core/src/test/scala/org/apache/spark/ui/storage/StorageTabSuite.scala
index 6e68dcb3425aa..b860177705d84 100644
--- a/core/src/test/scala/org/apache/spark/ui/storage/StorageTabSuite.scala
+++ b/core/src/test/scala/org/apache/spark/ui/storage/StorageTabSuite.scala
@@ -53,7 +53,7 @@ class StorageTabSuite extends FunSuite with BeforeAndAfter {
     assert(storageListener.rddInfoList.isEmpty)
 
     // 2 RDDs are known, but none are cached
-    val stageInfo0 = new StageInfo(0, "0", 100, Seq(rddInfo0, rddInfo1), "details")
+    val stageInfo0 = new StageInfo(0, 0, "0", 100, Seq(rddInfo0, rddInfo1), "details")
     bus.postToAll(SparkListenerStageSubmitted(stageInfo0))
     assert(storageListener._rddInfoMap.size === 2)
     assert(storageListener.rddInfoList.isEmpty)
@@ -63,7 +63,7 @@ class StorageTabSuite extends FunSuite with BeforeAndAfter {
     val rddInfo3Cached = rddInfo3
     rddInfo2Cached.numCachedPartitions = 1
     rddInfo3Cached.numCachedPartitions = 1
-    val stageInfo1 = new StageInfo(1, "0", 100, Seq(rddInfo2Cached, rddInfo3Cached), "details")
+    val stageInfo1 = new StageInfo(1, 0, "0", 100, Seq(rddInfo2Cached, rddInfo3Cached), "details")
     bus.postToAll(SparkListenerStageSubmitted(stageInfo1))
     assert(storageListener._rddInfoMap.size === 4)
     assert(storageListener.rddInfoList.size === 2)
@@ -71,7 +71,7 @@ class StorageTabSuite extends FunSuite with BeforeAndAfter {
     // Submitting RDDInfos with duplicate IDs does nothing
     val rddInfo0Cached = new RDDInfo(0, "freedom", 100, StorageLevel.MEMORY_ONLY)
     rddInfo0Cached.numCachedPartitions = 1
-    val stageInfo0Cached = new StageInfo(0, "0", 100, Seq(rddInfo0), "details")
+    val stageInfo0Cached = new StageInfo(0, 0, "0", 100, Seq(rddInfo0), "details")
     bus.postToAll(SparkListenerStageSubmitted(stageInfo0Cached))
     assert(storageListener._rddInfoMap.size === 4)
     assert(storageListener.rddInfoList.size === 2)
@@ -87,7 +87,7 @@ class StorageTabSuite extends FunSuite with BeforeAndAfter {
     val rddInfo1Cached = rddInfo1
     rddInfo0Cached.numCachedPartitions = 1
     rddInfo1Cached.numCachedPartitions = 1
-    val stageInfo0 = new StageInfo(0, "0", 100, Seq(rddInfo0Cached, rddInfo1Cached), "details")
+    val stageInfo0 = new StageInfo(0, 0, "0", 100, Seq(rddInfo0Cached, rddInfo1Cached), "details")
     bus.postToAll(SparkListenerStageSubmitted(stageInfo0))
     assert(storageListener._rddInfoMap.size === 2)
     assert(storageListener.rddInfoList.size === 2)
@@ -106,7 +106,7 @@ class StorageTabSuite extends FunSuite with BeforeAndAfter {
     val myRddInfo0 = rddInfo0
     val myRddInfo1 = rddInfo1
     val myRddInfo2 = rddInfo2
-    val stageInfo0 = new StageInfo(0, "0", 100, Seq(myRddInfo0, myRddInfo1, myRddInfo2), "details")
+    val stageInfo0 = new StageInfo(0, 0, "0", 100, Seq(myRddInfo0, myRddInfo1, myRddInfo2), "details")
     bus.postToAll(SparkListenerBlockManagerAdded(bm1, 1000L))
     bus.postToAll(SparkListenerStageSubmitted(stageInfo0))
     assert(storageListener._rddInfoMap.size === 3)
@@ -116,7 +116,7 @@ class StorageTabSuite extends FunSuite with BeforeAndAfter {
     assert(!storageListener._rddInfoMap(2).isCached)
 
     // Task end with no updated blocks. This should not change anything.
-    bus.postToAll(SparkListenerTaskEnd(0, "obliteration", Success, taskInfo, new TaskMetrics))
+    bus.postToAll(SparkListenerTaskEnd(0, 0, "obliteration", Success, taskInfo, new TaskMetrics))
     assert(storageListener._rddInfoMap.size === 3)
     assert(storageListener.rddInfoList.size === 0)
 
@@ -128,7 +128,7 @@ class StorageTabSuite extends FunSuite with BeforeAndAfter {
       (RDDBlockId(0, 102), BlockStatus(memAndDisk, 400L, 0L, 200L)),
       (RDDBlockId(1, 20), BlockStatus(memAndDisk, 0L, 240L, 0L))
     ))
-    bus.postToAll(SparkListenerTaskEnd(1, "obliteration", Success, taskInfo, metrics1))
+    bus.postToAll(SparkListenerTaskEnd(1, 0, "obliteration", Success, taskInfo, metrics1))
     assert(storageListener._rddInfoMap(0).memSize === 800L)
     assert(storageListener._rddInfoMap(0).diskSize === 400L)
     assert(storageListener._rddInfoMap(0).tachyonSize === 200L)
@@ -150,7 +150,7 @@ class StorageTabSuite extends FunSuite with BeforeAndAfter {
       (RDDBlockId(2, 40), BlockStatus(none, 0L, 0L, 0L)), // doesn't actually exist
       (RDDBlockId(4, 80), BlockStatus(none, 0L, 0L, 0L)) // doesn't actually exist
     ))
-    bus.postToAll(SparkListenerTaskEnd(2, "obliteration", Success, taskInfo, metrics2))
+    bus.postToAll(SparkListenerTaskEnd(2, 0, "obliteration", Success, taskInfo, metrics2))
     assert(storageListener._rddInfoMap(0).memSize === 400L)
     assert(storageListener._rddInfoMap(0).diskSize === 400L)
     assert(storageListener._rddInfoMap(0).tachyonSize === 200L)
diff --git a/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala b/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala
index 97ffb07662482..2fd3b9cfd221a 100644
--- a/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala
+++ b/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala
@@ -35,13 +35,13 @@ class JsonProtocolSuite extends FunSuite {
     val stageSubmitted =
       SparkListenerStageSubmitted(makeStageInfo(100, 200, 300, 400L, 500L), properties)
     val stageCompleted = SparkListenerStageCompleted(makeStageInfo(101, 201, 301, 401L, 501L))
-    val taskStart = SparkListenerTaskStart(111, makeTaskInfo(222L, 333, 1, 444L, false))
+    val taskStart = SparkListenerTaskStart(111, 0, makeTaskInfo(222L, 333, 1, 444L, false))
     val taskGettingResult =
       SparkListenerTaskGettingResult(makeTaskInfo(1000L, 2000, 5, 3000L, true))
-    val taskEnd = SparkListenerTaskEnd(1, "ShuffleMapTask", Success,
+    val taskEnd = SparkListenerTaskEnd(1, 0, "ShuffleMapTask", Success,
       makeTaskInfo(123L, 234, 67, 345L, false),
       makeTaskMetrics(300L, 400L, 500L, 600L, 700, 800, hasHadoopInput = false))
-    val taskEndWithHadoopInput = SparkListenerTaskEnd(1, "ShuffleMapTask", Success,
+    val taskEndWithHadoopInput = SparkListenerTaskEnd(1, 0, "ShuffleMapTask", Success,
       makeTaskInfo(123L, 234, 67, 345L, false),
       makeTaskMetrics(300L, 400L, 500L, 600L, 700, 800, hasHadoopInput = true))
     val jobStart = SparkListenerJobStart(10, Seq[Int](1, 2, 3, 4), properties)
@@ -397,7 +397,8 @@ class JsonProtocolSuite extends FunSuite {
 
   private def assertJsonStringEquals(json1: String, json2: String) {
     val formatJsonString = (json: String) => json.replaceAll("[\\s|]", "")
-    assert(formatJsonString(json1) === formatJsonString(json2))
+    assert(formatJsonString(json1) === formatJsonString(json2),
+      s"input ${formatJsonString(json1)} got ${formatJsonString(json2)}")
   }
 
   private def assertSeqEquals[T](seq1: Seq[T], seq2: Seq[T], assertEquals: (T, T) => Unit) {
@@ -485,7 +486,7 @@ class JsonProtocolSuite extends FunSuite {
 
   private def makeStageInfo(a: Int, b: Int, c: Int, d: Long, e: Long) = {
     val rddInfos = (0 until a % 5).map { i => makeRddInfo(a + i, b + i, c + i, d + i, e + i) }
-    val stageInfo = new StageInfo(a, "greetings", b, rddInfos, "details")
+    val stageInfo = new StageInfo(a, 0, "greetings", b, rddInfos, "details")
     val (acc1, acc2) = (makeAccumulableInfo(1), makeAccumulableInfo(2))
     stageInfo.accumulables(acc1.id) = acc1
     stageInfo.accumulables(acc2.id) = acc2
@@ -558,84 +559,246 @@ class JsonProtocolSuite extends FunSuite {
 
   private val stageSubmittedJsonString =
     """
-      {"Event":"SparkListenerStageSubmitted","Stage Info":{"Stage ID":100,"Stage Name":
-      "greetings","Number of Tasks":200,"RDD Info":[],"Details":"details",
-      "Accumulables":[{"ID":2,"Name":"Accumulable2","Update":"delta2","Value":"val2"},
-      {"ID":1,"Name":"Accumulable1","Update":"delta1","Value":"val1"}]},"Properties":
-      {"France":"Paris","Germany":"Berlin","Russia":"Moscow","Ukraine":"Kiev"}}
+      |{
+      |  "Event": "SparkListenerStageSubmitted",
+      |  "Stage Info": {
+      |    "Stage ID": 100,
+      |    "Stage Attempt ID": 0,
+      |    "Stage Name": "greetings",
+      |    "Number of Tasks": 200,
+      |    "RDD Info": [],
+      |    "Details": "details",
+      |    "Accumulables": [
+      |      {
+      |        "ID": 2,
+      |        "Name": "Accumulable2",
+      |        "Update": "delta2",
+      |        "Value": "val2"
+      |      },
+      |      {
+      |        "ID": 1,
+      |        "Name": "Accumulable1",
+      |        "Update": "delta1",
+      |        "Value": "val1"
+      |      }
+      |    ]
+      |  },
+      |  "Properties": {
+      |    "France": "Paris",
+      |    "Germany": "Berlin",
+      |    "Russia": "Moscow",
+      |    "Ukraine": "Kiev"
+      |  }
+      |}
     """
 
   private val stageCompletedJsonString =
     """
-      {"Event":"SparkListenerStageCompleted","Stage Info":{"Stage ID":101,"Stage Name":
-      "greetings","Number of Tasks":201,"RDD Info":[{"RDD ID":101,"Name":"mayor","Storage
-      Level":{"Use Disk":true,"Use Memory":true,"Use Tachyon":false,"Deserialized":true,
-      "Replication":1},"Number of Partitions":201,"Number of Cached Partitions":301,
-      "Memory Size":401,"Tachyon Size":0,"Disk Size":501}],"Details":"details",
-      "Accumulables":[{"ID":2,"Name":"Accumulable2","Update":"delta2","Value":"val2"},
-      {"ID":1,"Name":"Accumulable1","Update":"delta1","Value":"val1"}]}}
+      |{
+      |  "Event": "SparkListenerStageCompleted",
+      |  "Stage Info": {
+      |    "Stage ID": 101,
+      |    "Stage Attempt ID": 0,
+      |    "Stage Name": "greetings",
+      |    "Number of Tasks": 201,
+      |    "RDD Info": [
+      |      {
+      |        "RDD ID": 101,
+      |        "Name": "mayor",
+      |        "Storage Level": {
+      |          "Use Disk": true,
+      |          "Use Memory": true,
+      |          "Use Tachyon": false,
+      |          "Deserialized": true,
+      |          "Replication": 1
+      |        },
+      |        "Number of Partitions": 201,
+      |        "Number of Cached Partitions": 301,
+      |        "Memory Size": 401,
+      |        "Tachyon Size": 0,
+      |        "Disk Size": 501
+      |      }
+      |    ],
+      |    "Details": "details",
+      |    "Accumulables": [
+      |      {
+      |        "ID": 2,
+      |        "Name": "Accumulable2",
+      |        "Update": "delta2",
+      |        "Value": "val2"
+      |      },
+      |      {
+      |        "ID": 1,
+      |        "Name": "Accumulable1",
+      |        "Update": "delta1",
+      |        "Value": "val1"
+      |      }
+      |    ]
+      |  }
+      |}
     """
 
   private val taskStartJsonString =
     """
-      |{"Event":"SparkListenerTaskStart","Stage ID":111,"Task Info":{"Task ID":222,
-      |"Index":333,"Attempt":1,"Launch Time":444,"Executor ID":"executor","Host":"your kind sir",
-      |"Locality":"NODE_LOCAL","Speculative":false,"Getting Result Time":0,"Finish Time":0,
-      |"Failed":false,"Accumulables":[{"ID":1,"Name":"Accumulable1","Update":"delta1",
-      |"Value":"val1"},{"ID":2,"Name":"Accumulable2","Update":"delta2","Value":"val2"},
-      |{"ID":3,"Name":"Accumulable3","Update":"delta3","Value":"val3"}]}}
+      |{
+      |  "Event": "SparkListenerTaskStart",
+      |  "Stage ID": 111,
+      |  "Stage Attempt ID": 0,
+      |  "Task Info": {
+      |    "Task ID": 222,
+      |    "Index": 333,
+      |    "Attempt": 1,
+      |    "Launch Time": 444,
+      |    "Executor ID": "executor",
+      |    "Host": "your kind sir",
+      |    "Locality": "NODE_LOCAL",
+      |    "Speculative": false,
+      |    "Getting Result Time": 0,
+      |    "Finish Time": 0,
+      |    "Failed": false,
+      |    "Accumulables": [
+      |      {
+      |        "ID": 1,
+      |        "Name": "Accumulable1",
+      |        "Update": "delta1",
+      |        "Value": "val1"
+      |      },
+      |      {
+      |        "ID": 2,
+      |        "Name": "Accumulable2",
+      |        "Update": "delta2",
+      |        "Value": "val2"
+      |      },
+      |      {
+      |        "ID": 3,
+      |        "Name": "Accumulable3",
+      |        "Update": "delta3",
+      |        "Value": "val3"
+      |      }
+      |    ]
+      |  }
+      |}
     """.stripMargin
 
   private val taskGettingResultJsonString =
     """
-      |{"Event":"SparkListenerTaskGettingResult","Task Info":
-      |  {"Task ID":1000,"Index":2000,"Attempt":5,"Launch Time":3000,"Executor ID":"executor",
-      |   "Host":"your kind sir","Locality":"NODE_LOCAL","Speculative":true,"Getting Result Time":0,
-      |   "Finish Time":0,"Failed":false,
-      |   "Accumulables":[{"ID":1,"Name":"Accumulable1","Update":"delta1",
-      |   "Value":"val1"},{"ID":2,"Name":"Accumulable2","Update":"delta2","Value":"val2"},
-      |   {"ID":3,"Name":"Accumulable3","Update":"delta3","Value":"val3"}]
+      |{
+      |  "Event": "SparkListenerTaskGettingResult",
+      |  "Task Info": {
+      |    "Task ID": 1000,
+      |    "Index": 2000,
+      |    "Attempt": 5,
+      |    "Launch Time": 3000,
+      |    "Executor ID": "executor",
+      |    "Host": "your kind sir",
+      |    "Locality": "NODE_LOCAL",
+      |    "Speculative": true,
+      |    "Getting Result Time": 0,
+      |    "Finish Time": 0,
+      |    "Failed": false,
+      |    "Accumulables": [
+      |      {
+      |        "ID": 1,
+      |        "Name": "Accumulable1",
+      |        "Update": "delta1",
+      |        "Value": "val1"
+      |      },
+      |      {
+      |        "ID": 2,
+      |        "Name": "Accumulable2",
+      |        "Update": "delta2",
+      |        "Value": "val2"
+      |      },
+      |      {
+      |        "ID": 3,
+      |        "Name": "Accumulable3",
+      |        "Update": "delta3",
+      |        "Value": "val3"
+      |      }
+      |    ]
       |  }
       |}
     """.stripMargin
 
   private val taskEndJsonString =
     """
-      |{"Event":"SparkListenerTaskEnd","Stage ID":1,"Task Type":"ShuffleMapTask",
-      |"Task End Reason":{"Reason":"Success"},
-      |"Task Info":{
-      |  "Task ID":123,"Index":234,"Attempt":67,"Launch Time":345,"Executor ID":"executor",
-      |  "Host":"your kind sir","Locality":"NODE_LOCAL","Speculative":false,
-      |  "Getting Result Time":0,"Finish Time":0,"Failed":false,
-      |  "Accumulables":[{"ID":1,"Name":"Accumulable1","Update":"delta1",
-      |  "Value":"val1"},{"ID":2,"Name":"Accumulable2","Update":"delta2","Value":"val2"},
-      |  {"ID":3,"Name":"Accumulable3","Update":"delta3","Value":"val3"}]
-      |},
-      |"Task Metrics":{
-      |  "Host Name":"localhost","Executor Deserialize Time":300,"Executor Run Time":400,
-      |  "Result Size":500,"JVM GC Time":600,"Result Serialization Time":700,
-      |  "Memory Bytes Spilled":800,"Disk Bytes Spilled":0,
-      |  "Shuffle Read Metrics":{
-      |    "Shuffle Finish Time":900,
-      |    "Remote Blocks Fetched":800,
-      |    "Local Blocks Fetched":700,
-      |    "Fetch Wait Time":900,
-      |    "Remote Bytes Read":1000
+      |{
+      |  "Event": "SparkListenerTaskEnd",
+      |  "Stage ID": 1,
+      |  "Stage Attempt ID": 0,
+      |  "Task Type": "ShuffleMapTask",
+      |  "Task End Reason": {
+      |    "Reason": "Success"
       |  },
-      |  "Shuffle Write Metrics":{
-      |    "Shuffle Bytes Written":1200,
-      |    "Shuffle Write Time":1500
+      |  "Task Info": {
+      |    "Task ID": 123,
+      |    "Index": 234,
+      |    "Attempt": 67,
+      |    "Launch Time": 345,
+      |    "Executor ID": "executor",
+      |    "Host": "your kind sir",
+      |    "Locality": "NODE_LOCAL",
+      |    "Speculative": false,
+      |    "Getting Result Time": 0,
+      |    "Finish Time": 0,
+      |    "Failed": false,
+      |    "Accumulables": [
+      |      {
+      |        "ID": 1,
+      |        "Name": "Accumulable1",
+      |        "Update": "delta1",
+      |        "Value": "val1"
+      |      },
+      |      {
+      |        "ID": 2,
+      |        "Name": "Accumulable2",
+      |        "Update": "delta2",
+      |        "Value": "val2"
+      |      },
+      |      {
+      |        "ID": 3,
+      |        "Name": "Accumulable3",
+      |        "Update": "delta3",
+      |        "Value": "val3"
+      |      }
+      |    ]
       |  },
-      |  "Updated Blocks":[
-      |    {"Block ID":"rdd_0_0",
-      |      "Status":{
-      |        "Storage Level":{
-      |          "Use Disk":true,"Use Memory":true,"Use Tachyon":false,"Deserialized":false,
-      |          "Replication":2
-      |        },
-      |        "Memory Size":0,"Tachyon Size":0,"Disk Size":0
+      |  "Task Metrics": {
+      |    "Host Name": "localhost",
+      |    "Executor Deserialize Time": 300,
+      |    "Executor Run Time": 400,
+      |    "Result Size": 500,
+      |    "JVM GC Time": 600,
+      |    "Result Serialization Time": 700,
+      |    "Memory Bytes Spilled": 800,
+      |    "Disk Bytes Spilled": 0,
+      |    "Shuffle Read Metrics": {
+      |      "Shuffle Finish Time": 900,
+      |      "Remote Blocks Fetched": 800,
+      |      "Local Blocks Fetched": 700,
+      |      "Fetch Wait Time": 900,
+      |      "Remote Bytes Read": 1000
+      |    },
+      |    "Shuffle Write Metrics": {
+      |      "Shuffle Bytes Written": 1200,
+      |      "Shuffle Write Time": 1500
+      |    },
+      |    "Updated Blocks": [
+      |      {
+      |        "Block ID": "rdd_0_0",
+      |        "Status": {
+      |          "Storage Level": {
+      |            "Use Disk": true,
+      |            "Use Memory": true,
+      |            "Use Tachyon": false,
+      |            "Deserialized": false,
+      |            "Replication": 2
+      |          },
+      |          "Memory Size": 0,
+      |          "Tachyon Size": 0,
+      |          "Disk Size": 0
+      |        }
       |      }
-      |    }
       |    ]
       |  }
       |}
@@ -643,80 +806,187 @@ class JsonProtocolSuite extends FunSuite {
 
   private val taskEndWithHadoopInputJsonString =
     """
-      |{"Event":"SparkListenerTaskEnd","Stage ID":1,"Task Type":"ShuffleMapTask",
-      |"Task End Reason":{"Reason":"Success"},
-      |"Task Info":{
-      |  "Task ID":123,"Index":234,"Attempt":67,"Launch Time":345,"Executor ID":"executor",
-      |  "Host":"your kind sir","Locality":"NODE_LOCAL","Speculative":false,
-      |  "Getting Result Time":0,"Finish Time":0,"Failed":false,
-      |  "Accumulables":[{"ID":1,"Name":"Accumulable1","Update":"delta1",
-      |  "Value":"val1"},{"ID":2,"Name":"Accumulable2","Update":"delta2","Value":"val2"},
-      |  {"ID":3,"Name":"Accumulable3","Update":"delta3","Value":"val3"}]
-      |},
-      |"Task Metrics":{
-      |  "Host Name":"localhost","Executor Deserialize Time":300,"Executor Run Time":400,
-      |  "Result Size":500,"JVM GC Time":600,"Result Serialization Time":700,
-      |  "Memory Bytes Spilled":800,"Disk Bytes Spilled":0,
-      |  "Shuffle Write Metrics":{"Shuffle Bytes Written":1200,"Shuffle Write Time":1500},
-      |  "Input Metrics":{"Data Read Method":"Hadoop","Bytes Read":2100},
-      |  "Updated Blocks":[
-      |    {"Block ID":"rdd_0_0",
-      |      "Status":{
-      |        "Storage Level":{
-      |          "Use Disk":true,"Use Memory":true,"Use Tachyon":false,"Deserialized":false,
-      |          "Replication":2
-      |        },
-      |        "Memory Size":0,"Tachyon Size":0,"Disk Size":0
+      |{
+      |  "Event": "SparkListenerTaskEnd",
+      |  "Stage ID": 1,
+      |  "Stage Attempt ID": 0,
+      |  "Task Type": "ShuffleMapTask",
+      |  "Task End Reason": {
+      |    "Reason": "Success"
+      |  },
+      |  "Task Info": {
+      |    "Task ID": 123,
+      |    "Index": 234,
+      |    "Attempt": 67,
+      |    "Launch Time": 345,
+      |    "Executor ID": "executor",
+      |    "Host": "your kind sir",
+      |    "Locality": "NODE_LOCAL",
+      |    "Speculative": false,
+      |    "Getting Result Time": 0,
+      |    "Finish Time": 0,
+      |    "Failed": false,
+      |    "Accumulables": [
+      |      {
+      |        "ID": 1,
+      |        "Name": "Accumulable1",
+      |        "Update": "delta1",
+      |        "Value": "val1"
+      |      },
+      |      {
+      |        "ID": 2,
+      |        "Name": "Accumulable2",
+      |        "Update": "delta2",
+      |        "Value": "val2"
+      |      },
+      |      {
+      |        "ID": 3,
+      |        "Name": "Accumulable3",
+      |        "Update": "delta3",
+      |        "Value": "val3"
       |      }
-      |    }
-      |  ]}
+      |    ]
+      |  },
+      |  "Task Metrics": {
+      |    "Host Name": "localhost",
+      |    "Executor Deserialize Time": 300,
+      |    "Executor Run Time": 400,
+      |    "Result Size": 500,
+      |    "JVM GC Time": 600,
+      |    "Result Serialization Time": 700,
+      |    "Memory Bytes Spilled": 800,
+      |    "Disk Bytes Spilled": 0,
+      |    "Shuffle Write Metrics": {
+      |      "Shuffle Bytes Written": 1200,
+      |      "Shuffle Write Time": 1500
+      |    },
+      |    "Input Metrics": {
+      |      "Data Read Method": "Hadoop",
+      |      "Bytes Read": 2100
+      |    },
+      |    "Updated Blocks": [
+      |      {
+      |        "Block ID": "rdd_0_0",
+      |        "Status": {
+      |          "Storage Level": {
+      |            "Use Disk": true,
+      |            "Use Memory": true,
+      |            "Use Tachyon": false,
+      |            "Deserialized": false,
+      |            "Replication": 2
+      |          },
+      |          "Memory Size": 0,
+      |          "Tachyon Size": 0,
+      |          "Disk Size": 0
+      |        }
+      |      }
+      |    ]
+      |  }
       |}
     """
 
   private val jobStartJsonString =
     """
-      {"Event":"SparkListenerJobStart","Job ID":10,"Stage IDs":[1,2,3,4],"Properties":
-      {"France":"Paris","Germany":"Berlin","Russia":"Moscow","Ukraine":"Kiev"}}
+      |{
+      |  "Event": "SparkListenerJobStart",
+      |  "Job ID": 10,
+      |  "Stage IDs": [
+      |    1,
+      |    2,
+      |    3,
+      |    4
+      |  ],
+      |  "Properties": {
+      |    "France": "Paris",
+      |    "Germany": "Berlin",
+      |    "Russia": "Moscow",
+      |    "Ukraine": "Kiev"
+      |  }
+      |}
     """
 
   private val jobEndJsonString =
     """
-      {"Event":"SparkListenerJobEnd","Job ID":20,"Job Result":{"Result":"JobSucceeded"}}
+      |{
+      |  "Event": "SparkListenerJobEnd",
+      |  "Job ID": 20,
+      |  "Job Result": {
+      |    "Result": "JobSucceeded"
+      |  }
+      |}
     """
 
   private val environmentUpdateJsonString =
     """
-      {"Event":"SparkListenerEnvironmentUpdate","JVM Information":{"GC speed":"9999 objects/s",
-      "Java home":"Land of coffee"},"Spark Properties":{"Job throughput":"80000 jobs/s,
-      regardless of job type"},"System Properties":{"Username":"guest","Password":"guest"},
-      "Classpath Entries":{"Super library":"/tmp/super_library"}}
+      |{
+      |  "Event": "SparkListenerEnvironmentUpdate",
+      |  "JVM Information": {
+      |    "GC speed": "9999 objects/s",
+      |    "Java home": "Land of coffee"
+      |  },
+      |  "Spark Properties": {
+      |    "Job throughput": "80000 jobs/s, regardless of job type"
+      |  },
+      |  "System Properties": {
+      |    "Username": "guest",
+      |    "Password": "guest"
+      |  },
+      |  "Classpath Entries": {
+      |    "Super library": "/tmp/super_library"
+      |  }
+      |}
     """
 
   private val blockManagerAddedJsonString =
     """
-      {"Event":"SparkListenerBlockManagerAdded","Block Manager ID":{"Executor ID":"Stars",
-      "Host":"In your multitude...","Port":300,"Netty Port":400},"Maximum Memory":500}
+      |{
+      |  "Event": "SparkListenerBlockManagerAdded",
+      |  "Block Manager ID": {
+      |    "Executor ID": "Stars",
+      |    "Host": "In your multitude...",
+      |    "Port": 300,
+      |    "Netty Port": 400
+      |  },
+      |  "Maximum Memory": 500
+      |}
     """
 
   private val blockManagerRemovedJsonString =
     """
-      {"Event":"SparkListenerBlockManagerRemoved","Block Manager ID":{"Executor ID":"Scarce",
-      "Host":"to be counted...","Port":100,"Netty Port":200}}
+      |{
+      |  "Event": "SparkListenerBlockManagerRemoved",
+      |  "Block Manager ID": {
+      |    "Executor ID": "Scarce",
+      |    "Host": "to be counted...",
+      |    "Port": 100,
+      |    "Netty Port": 200
+      |  }
+      |}
     """
 
   private val unpersistRDDJsonString =
     """
-      {"Event":"SparkListenerUnpersistRDD","RDD ID":12345}
+      |{
+      |  "Event": "SparkListenerUnpersistRDD",
+      |  "RDD ID": 12345
+      |}
     """
 
   private val applicationStartJsonString =
     """
-      {"Event":"SparkListenerApplicationStart","App Name":"The winner of all","Timestamp":42,
-      "User":"Garfield"}
+      |{
+      |  "Event": "SparkListenerApplicationStart",
+      |  "App Name": "The winner of all",
+      |  "Timestamp": 42,
+      |  "User": "Garfield"
+      |}
     """
 
   private val applicationEndJsonString =
     """
-      {"Event":"SparkListenerApplicationEnd","Timestamp":42}
+      |{
+      |  "Event": "SparkListenerApplicationEnd",
+      |  "Timestamp": 42
+      |}
     """
 }

From a2e658dcdab614058eefcf50ae2d419ece9b1fe7 Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Wed, 20 Aug 2014 15:51:14 -0700
Subject: [PATCH 046/489] [SPARK-2967][SQL] Fix sort based shuffle for spark
 sql.

Add explicit row copies when sort based shuffle is on.

Author: Michael Armbrust <michael@databricks.com>

Closes #2066 from marmbrus/sortShuffle and squashes the following commits:

fcd7bb2 [Michael Armbrust] Fix sort based shuffle for spark sql.
---
 .../apache/spark/sql/execution/Exchange.scala | 30 ++++++++++++++-----
 1 file changed, 23 insertions(+), 7 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala
index 77dc2ad733215..09c34b7059fc3 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala
@@ -18,7 +18,8 @@
 package org.apache.spark.sql.execution
 
 import org.apache.spark.annotation.DeveloperApi
-import org.apache.spark.{HashPartitioner, RangePartitioner, SparkConf}
+import org.apache.spark.shuffle.sort.SortShuffleManager
+import org.apache.spark.{SparkEnv, HashPartitioner, RangePartitioner, SparkConf}
 import org.apache.spark.rdd.ShuffledRDD
 import org.apache.spark.sql.{SQLContext, Row}
 import org.apache.spark.sql.catalyst.errors.attachTree
@@ -37,6 +38,9 @@ case class Exchange(newPartitioning: Partitioning, child: SparkPlan) extends Una
 
   def output = child.output
 
+  /** We must copy rows when sort based shuffle is on */
+  protected def sortBasedShuffleOn = SparkEnv.get.shuffleManager.isInstanceOf[SortShuffleManager]
+
   def execute() = attachTree(this , "execute") {
     newPartitioning match {
       case HashPartitioning(expressions, numPartitions) =>
@@ -45,8 +49,12 @@ case class Exchange(newPartitioning: Partitioning, child: SparkPlan) extends Una
           @transient val hashExpressions =
             newMutableProjection(expressions, child.output)()
 
-          val mutablePair = new MutablePair[Row, Row]()
-          iter.map(r => mutablePair.update(hashExpressions(r), r))
+          if (sortBasedShuffleOn) {
+            iter.map(r => (hashExpressions(r), r.copy()))
+          } else {
+            val mutablePair = new MutablePair[Row, Row]()
+            iter.map(r => mutablePair.update(hashExpressions(r), r))
+          }
         }
         val part = new HashPartitioner(numPartitions)
         val shuffled = new ShuffledRDD[Row, Row, Row](rdd, part)
@@ -58,8 +66,12 @@ case class Exchange(newPartitioning: Partitioning, child: SparkPlan) extends Una
         implicit val ordering = new RowOrdering(sortingExpressions, child.output)
 
         val rdd = child.execute().mapPartitions { iter =>
-          val mutablePair = new MutablePair[Row, Null](null, null)
-          iter.map(row => mutablePair.update(row, null))
+          if (sortBasedShuffleOn) {
+            iter.map(row => (row.copy(), null))
+          } else {
+            val mutablePair = new MutablePair[Row, Null](null, null)
+            iter.map(row => mutablePair.update(row, null))
+          }
         }
         val part = new RangePartitioner(numPartitions, rdd, ascending = true)
         val shuffled = new ShuffledRDD[Row, Null, Null](rdd, part)
@@ -69,8 +81,12 @@ case class Exchange(newPartitioning: Partitioning, child: SparkPlan) extends Una
 
       case SinglePartition =>
         val rdd = child.execute().mapPartitions { iter =>
-          val mutablePair = new MutablePair[Null, Row]()
-          iter.map(r => mutablePair.update(null, r))
+          if (sortBasedShuffleOn) {
+            iter.map(r => (null, r.copy()))
+          } else {
+            val mutablePair = new MutablePair[Null, Row]()
+            iter.map(r => mutablePair.update(null, r))
+          }
         }
         val partitioner = new HashPartitioner(1)
         val shuffled = new ShuffledRDD[Null, Row, Row](rdd, partitioner)

From a1e8b1bc973bc0517681c09e5a5a475c0f395d31 Mon Sep 17 00:00:00 2001
From: wangfei <wangfei_hello@126.com>
Date: Wed, 20 Aug 2014 16:00:46 -0700
Subject: [PATCH 047/489] SPARK_LOGFILE and SPARK_ROOT_LOGGER no longer need in
 spark-daemon.sh

Author: wangfei <wangfei_hello@126.com>

Closes #2057 from scwf/patch-7 and squashes the following commits:

1b7b9a5 [wangfei] SPARK_LOGFILE and SPARK_ROOT_LOGGER no longer need in spark-daemon.sh
---
 sbin/spark-daemon.sh | 2 --
 1 file changed, 2 deletions(-)

diff --git a/sbin/spark-daemon.sh b/sbin/spark-daemon.sh
index 323f675b17848..9032f23ea8eff 100755
--- a/sbin/spark-daemon.sh
+++ b/sbin/spark-daemon.sh
@@ -113,8 +113,6 @@ if [ "$SPARK_PID_DIR" = "" ]; then
 fi
 
 # some variables
-export SPARK_LOGFILE=spark-$SPARK_IDENT_STRING-$command-$instance-$HOSTNAME.log
-export SPARK_ROOT_LOGGER="INFO,DRFA"
 log=$SPARK_LOG_DIR/spark-$SPARK_IDENT_STRING-$command-$instance-$HOSTNAME.out
 pid=$SPARK_PID_DIR/spark-$SPARK_IDENT_STRING-$command-$instance.pid
 

From d9e94146a6e65be110a62e3bd0351148912a41d1 Mon Sep 17 00:00:00 2001
From: Alex Liu <alex_liu68@yahoo.com>
Date: Wed, 20 Aug 2014 16:14:06 -0700
Subject: [PATCH 048/489] [SPARK-2846][SQL] Add
 configureInputJobPropertiesForStorageHandler to initialization of job conf

...al job conf

Author: Alex Liu <alex_liu68@yahoo.com>

Closes #1927 from alexliu68/SPARK-SQL-2846 and squashes the following commits:

e4bdc4c [Alex Liu] SPARK-SQL-2846 add configureInputJobPropertiesForStorageHandler to initial job conf
---
 .../src/main/scala/org/apache/spark/sql/hive/TableReader.scala | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/TableReader.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/TableReader.scala
index 82c88280d7754..329f80cad471e 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/TableReader.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/TableReader.scala
@@ -22,7 +22,7 @@ import org.apache.hadoop.fs.{Path, PathFilter}
 import org.apache.hadoop.hive.metastore.api.hive_metastoreConstants._
 import org.apache.hadoop.hive.ql.exec.Utilities
 import org.apache.hadoop.hive.ql.metadata.{Partition => HivePartition, Table => HiveTable}
-import org.apache.hadoop.hive.ql.plan.TableDesc
+import org.apache.hadoop.hive.ql.plan.{PlanUtils, TableDesc}
 import org.apache.hadoop.hive.serde2.Deserializer
 import org.apache.hadoop.hive.serde2.objectinspector.StructObjectInspector
 
@@ -249,6 +249,7 @@ private[hive] object HadoopTableReader extends HiveInspectors {
   def initializeLocalJobConfFunc(path: String, tableDesc: TableDesc)(jobConf: JobConf) {
     FileInputFormat.setInputPaths(jobConf, path)
     if (tableDesc != null) {
+      PlanUtils.configureInputJobPropertiesForStorageHandler(tableDesc)
       Utilities.copyTableJobPropertiesToConf(tableDesc, jobConf)
     }
     val bufferSize = System.getProperty("spark.buffer.size", "65536")

From c9f743957fa963bc1dbed7a44a346ffce1a45cf2 Mon Sep 17 00:00:00 2001
From: Marcelo Vanzin <vanzin@cloudera.com>
Date: Wed, 20 Aug 2014 16:23:10 -0700
Subject: [PATCH 049/489] [SPARK-2848] Shade Guava in uber-jars.

For further discussion, please check the JIRA entry.

This change moves Guava classes to a different package so that they don't conflict with the user-provided Guava (or the Hadoop-provided one). Since one class (Optional) was exposed through Spark's public API, that class was forked from Guava at the current dependency version (14.0.1) so that it can be kept going forward (until the API is cleaned).

Note this change has a few implications:
- *all* classes in the final jars will reference the relocated classes. If Hadoop classes are included (i.e. "-Phadoop-provided" is not activated), those will also reference the Guava 14 classes (instead of the Guava 11 classes from the Hadoop classpath).
- if the Guava version in Spark is ever changed, the new Guava will still reference the forked Optional class; this may or may not be a problem, but in the long term it's better to think about removing Optional from the public API.

For the end user, there are two visible implications:

- Guava is not provided as a transitive dependency anymore (since it's "provided" in Spark)
- At runtime, unless they provide their own, they'll either have no Guava or Hadoop's version of Guava (11), depending on how they set up their classpath.

Note that this patch does not change the sbt deliverables; those will still contain guava in its original package, and provide guava as a compile-time dependency. This assumes that maven is the canonical build, and sbt-built artifacts are not (officially) published.

Author: Marcelo Vanzin <vanzin@cloudera.com>

Closes #1813 from vanzin/SPARK-2848 and squashes the following commits:

9bdffb0 [Marcelo Vanzin] Undo sbt build changes.
819b445 [Marcelo Vanzin] Review feedback.
05e0a3d [Marcelo Vanzin] Merge branch 'master' into SPARK-2848
fef4370 [Marcelo Vanzin] Unfork Optional.java.
d3ea8e1 [Marcelo Vanzin] Exclude asm classes from final jar.
637189b [Marcelo Vanzin] Add hacky filter to prefer Spark's copy of Optional.
2fec990 [Marcelo Vanzin] Shade Guava in the sbt build.
616998e [Marcelo Vanzin] Shade Guava in the maven build, fork Guava's Optional.java.
---
 assembly/pom.xml         | 18 ++++++++++++++++++
 core/pom.xml             | 35 +++++++++++++++++++++++++++++++++++
 examples/pom.xml         | 26 +++++++++++++++++++++++++-
 pom.xml                  | 16 ++++++++++++++++
 project/SparkBuild.scala |  4 ++--
 project/plugins.sbt      |  4 ++++
 6 files changed, 100 insertions(+), 3 deletions(-)

diff --git a/assembly/pom.xml b/assembly/pom.xml
index 9fbb037115db3..de7b75258e3c5 100644
--- a/assembly/pom.xml
+++ b/assembly/pom.xml
@@ -43,6 +43,12 @@
   </properties>
 
   <dependencies>
+    <!-- Promote Guava to compile scope in this module so it's included while shading. -->
+    <dependency>
+      <groupId>com.google.guava</groupId>
+      <artifactId>guava</artifactId>
+      <scope>compile</scope>
+    </dependency>
     <dependency>
       <groupId>org.apache.spark</groupId>
       <artifactId>spark-core_${scala.binary.version}</artifactId>
@@ -113,6 +119,18 @@
               <goal>shade</goal>
             </goals>
             <configuration>
+              <relocations>
+                <relocation>
+                  <pattern>com.google</pattern>
+                  <shadedPattern>org.spark-project.guava</shadedPattern>
+                  <includes>
+                    <include>com.google.common.**</include>
+                  </includes>
+                  <excludes>
+                    <exclude>com.google.common.base.Optional**</exclude>
+                  </excludes>
+                </relocation>
+              </relocations>
               <transformers>
                 <transformer implementation="org.apache.maven.plugins.shade.resource.ServicesResourceTransformer" />
                 <transformer implementation="org.apache.maven.plugins.shade.resource.AppendingTransformer">
diff --git a/core/pom.xml b/core/pom.xml
index 6d8be37037729..83c708dfc9619 100644
--- a/core/pom.xml
+++ b/core/pom.xml
@@ -68,9 +68,15 @@
       <groupId>org.eclipse.jetty</groupId>
       <artifactId>jetty-server</artifactId>
     </dependency>
+    <!--
+      Promote Guava to "compile" so that maven-shade-plugin picks it up (for packaging the Optional
+      class exposed in the Java API). The plugin will then remove this dependency from the published
+      pom, so that Guava does not pollute the client's compilation classpath.
+    -->
     <dependency>
       <groupId>com.google.guava</groupId>
       <artifactId>guava</artifactId>
+      <scope>compile</scope>
     </dependency>
     <dependency>
       <groupId>org.apache.commons</groupId>
@@ -322,6 +328,35 @@
           </arguments>
         </configuration>
       </plugin>
+      <plugin>
+        <groupId>org.apache.maven.plugins</groupId>
+        <artifactId>maven-shade-plugin</artifactId>
+        <executions>
+          <execution>
+            <phase>package</phase>
+            <goals>
+              <goal>shade</goal>
+            </goals>
+            <configuration>
+              <shadedArtifactAttached>false</shadedArtifactAttached>
+              <artifactSet>
+                <includes>
+                  <include>com.google.guava:guava</include>
+                </includes>
+              </artifactSet>
+              <filters>
+                <!-- See comment in the guava dependency declaration above. -->
+                <filter>
+                  <artifact>com.google.guava:guava</artifact>
+                  <includes>
+                    <include>com/google/common/base/Optional*</include>
+                  </includes>
+                </filter>
+              </filters>
+            </configuration>
+          </execution>
+        </executions>
+      </plugin>
     </plugins>
 
     <resources>
diff --git a/examples/pom.xml b/examples/pom.xml
index 8c4c128bb484d..9b12cb0c29c9f 100644
--- a/examples/pom.xml
+++ b/examples/pom.xml
@@ -46,8 +46,14 @@
       </dependencies>
     </profile>
   </profiles>
-  
+
   <dependencies>
+    <!-- Promote Guava to compile scope in this module so it's included while shading. -->
+    <dependency>
+      <groupId>com.google.guava</groupId>
+      <artifactId>guava</artifactId>
+      <scope>compile</scope>
+    </dependency>
     <dependency>
       <groupId>org.apache.spark</groupId>
       <artifactId>spark-core_${scala.binary.version}</artifactId>
@@ -209,6 +215,12 @@
             </includes>
           </artifactSet>
           <filters>
+            <filter>
+              <artifact>com.google.guava:guava</artifact>
+              <excludes>
+                <exclude>com/google/common/base/Optional*</exclude>
+              </excludes>
+            </filter>
             <filter>
               <artifact>*:*</artifact>
               <excludes>
@@ -226,6 +238,18 @@
               <goal>shade</goal>
             </goals>
             <configuration>
+              <relocations>
+                <relocation>
+                  <pattern>com.google</pattern>
+                  <shadedPattern>org.spark-project.guava</shadedPattern>
+                  <includes>
+                    <include>com.google.common.**</include>
+                  </includes>
+                  <excludes>
+                    <exclude>com.google.common.base.Optional**</exclude>
+                  </excludes>
+                </relocation>
+              </relocations>
               <transformers>
                 <transformer implementation="org.apache.maven.plugins.shade.resource.ServicesResourceTransformer" />
                 <transformer implementation="org.apache.maven.plugins.shade.resource.AppendingTransformer">
diff --git a/pom.xml b/pom.xml
index 7ed07ad7df88d..9cbf3ea5995c3 100644
--- a/pom.xml
+++ b/pom.xml
@@ -260,6 +260,7 @@
         <groupId>com.google.guava</groupId>
         <artifactId>guava</artifactId>
         <version>14.0.1</version>
+        <scope>provided</scope>
       </dependency>
       <dependency>
         <groupId>org.apache.commons</groupId>
@@ -1017,6 +1018,21 @@
 
   <profiles>
 
+    <!--
+      This profile is enabled automatically by the sbt built. It changes the scope for the guava
+      dependency, since we don't shade it in the artifacts generated by the sbt build.
+    -->
+    <profile>
+      <id>sbt</id>
+      <dependencies>
+        <dependency>
+          <groupId>com.google.guava</groupId>
+          <artifactId>guava</artifactId>
+          <scope>compile</scope>
+        </dependency>
+      </dependencies>
+    </profile>
+
     <!-- Ganglia integration is not included by default due to LGPL-licensed code -->
     <profile>
       <id>spark-ganglia-lgpl</id>
diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala
index 49d52aefca17a..4c696d3d385fb 100644
--- a/project/SparkBuild.scala
+++ b/project/SparkBuild.scala
@@ -61,7 +61,7 @@ object SparkBuild extends PomBuild {
   def backwardCompatibility = {
     import scala.collection.mutable
     var isAlphaYarn = false
-    var profiles: mutable.Seq[String] = mutable.Seq.empty
+    var profiles: mutable.Seq[String] = mutable.Seq("sbt")
     if (Properties.envOrNone("SPARK_GANGLIA_LGPL").isDefined) {
       println("NOTE: SPARK_GANGLIA_LGPL is deprecated, please use -Pspark-ganglia-lgpl flag.")
       profiles ++= Seq("spark-ganglia-lgpl")
@@ -116,7 +116,7 @@ object SparkBuild extends PomBuild {
     retrieveManaged := true,
     retrievePattern := "[type]s/[artifact](-[revision])(-[classifier]).[ext]",
     publishMavenStyle := true,
-  
+
     resolvers += Resolver.mavenLocal,
     otherResolvers <<= SbtPomKeys.mvnLocalRepository(dotM2 => Seq(Resolver.file("dotM2", dotM2))),
     publishLocalConfiguration in MavenCompile <<= (packagedArtifacts, deliverLocal, ivyLoggingLevel) map {
diff --git a/project/plugins.sbt b/project/plugins.sbt
index 2a61f56c2ea60..8096c61414660 100644
--- a/project/plugins.sbt
+++ b/project/plugins.sbt
@@ -26,3 +26,7 @@ addSbtPlugin("com.alpinenow" % "junit_xml_listener" % "0.5.1")
 addSbtPlugin("com.eed3si9n" % "sbt-unidoc" % "0.3.1")
 
 addSbtPlugin("com.cavorite" % "sbt-avro" % "0.3.2")
+
+libraryDependencies += "org.ow2.asm"  % "asm" % "5.0.3"
+
+libraryDependencies += "org.ow2.asm"  % "asm-commons" % "5.0.3"

From ba3c730e35bcdb662396955c3cc6f7de628034c8 Mon Sep 17 00:00:00 2001
From: Andrew Or <andrewor14@gmail.com>
Date: Wed, 20 Aug 2014 17:07:39 -0700
Subject: [PATCH 050/489] [SPARK-3140] Clarify confusing PySpark exception
 message

We read the py4j port from the stdout of the `bin/spark-submit` subprocess. If there is interference in stdout (e.g. a random echo in `spark-submit`), we throw an exception with a warning message. We do not, however, distinguish between this case from the case where no stdout is produced at all.

I wasted a non-trivial amount of time being baffled by this exception in search of places where I print random whitespace (in vain, of course). A clearer exception message that distinguishes between these cases will prevent similar headaches that I have gone through.

Author: Andrew Or <andrewor14@gmail.com>

Closes #2067 from andrewor14/python-exception and squashes the following commits:

742f823 [Andrew Or] Further clarify warning messages
e96a7a0 [Andrew Or] Distinguish between unexpected output and no output at all
---
 python/pyspark/java_gateway.py | 13 ++++++++++---
 1 file changed, 10 insertions(+), 3 deletions(-)

diff --git a/python/pyspark/java_gateway.py b/python/pyspark/java_gateway.py
index c7f7c1fe591b0..6f4f62f23bc4d 100644
--- a/python/pyspark/java_gateway.py
+++ b/python/pyspark/java_gateway.py
@@ -54,12 +54,19 @@ def preexec_func():
             gateway_port = proc.stdout.readline()
             gateway_port = int(gateway_port)
         except ValueError:
+            # Grab the remaining lines of stdout
             (stdout, _) = proc.communicate()
             exit_code = proc.poll()
             error_msg = "Launching GatewayServer failed"
-            error_msg += " with exit code %d! " % exit_code if exit_code else "! "
-            error_msg += "(Warning: unexpected output detected.)\n\n"
-            error_msg += gateway_port + stdout
+            error_msg += " with exit code %d!\n" % exit_code if exit_code else "!\n"
+            error_msg += "Warning: Expected GatewayServer to output a port, but found "
+            if gateway_port == "" and stdout == "":
+                error_msg += "no output.\n"
+            else:
+                error_msg += "the following:\n\n"
+                error_msg += "--------------------------------------------------------------\n"
+                error_msg += gateway_port + stdout
+                error_msg += "--------------------------------------------------------------\n"
             raise Exception(error_msg)
 
         # Create a thread to echo output from the GatewayServer, which is required

From e1571874f26c1df2dfd5ac2959612372716cd2d8 Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Wed, 20 Aug 2014 17:41:36 -0700
Subject: [PATCH 051/489] [SPARK-3143][MLLIB] add tf-idf user guide

Moved TF-IDF before Word2Vec because the former is more basic. I also added a link for Word2Vec. atalwalkar

Author: Xiangrui Meng <meng@databricks.com>

Closes #2061 from mengxr/tfidf-doc and squashes the following commits:

ca04c70 [Xiangrui Meng] address comments
a5ea4b4 [Xiangrui Meng] add tf-idf user guide
---
 docs/mllib-feature-extraction.md | 83 ++++++++++++++++++++++++++++++--
 1 file changed, 80 insertions(+), 3 deletions(-)

diff --git a/docs/mllib-feature-extraction.md b/docs/mllib-feature-extraction.md
index 4b3cb715c58c7..2031b96235ee9 100644
--- a/docs/mllib-feature-extraction.md
+++ b/docs/mllib-feature-extraction.md
@@ -7,9 +7,88 @@ displayTitle: <a href="mllib-guide.html">MLlib</a> - Feature Extraction
 * Table of contents
 {:toc}
 
+
+## TF-IDF
+
+[Term frequency-inverse document frequency (TF-IDF)](http://en.wikipedia.org/wiki/Tf%E2%80%93idf) is a feature 
+vectorization method widely used in text mining to reflect the importance of a term to a document in the corpus.
+Denote a term by `$t$`, a document by `$d$`, and the corpus by `$D$`.
+Term frequency `$TF(t, d)$` is the number of times that term `$t$` appears in document `$d$`,
+while document frequency `$DF(t, D)$` is the number of documents that contains term `$t$`.
+If we only use term frequency to measure the importance, it is very easy to over-emphasize terms that
+appear very often but carry little information about the document, e.g., "a", "the", and "of".
+If a term appears very often across the corpus, it means it doesn't carry special information about
+a particular document.
+Inverse document frequency is a numerical measure of how much information a term provides:
+`\[
+IDF(t, D) = \log \frac{|D| + 1}{DF(t, D) + 1},
+\]`
+where `$|D|$` is the total number of documents in the corpus.
+Since logarithm is used, if a term appears in all documents, its IDF value becomes 0.
+Note that a smoothing term is applied to avoid dividing by zero for terms outside the corpus.
+The TF-IDF measure is simply the product of TF and IDF:
+`\[
+TFIDF(t, d, D) = TF(t, d) \cdot IDF(t, D).
+\]`
+There are several variants on the definition of term frequency and document frequency.
+In MLlib, we separate TF and IDF to make them flexible.
+
+Our implementation of term frequency utilizes the
+[hashing trick](http://en.wikipedia.org/wiki/Feature_hashing).
+A raw feature is mapped into an index (term) by applying a hash function.
+Then term frequencies are calculated based on the mapped indices.
+This approach avoids the need to compute a global term-to-index map,
+which can be expensive for a large corpus, but it suffers from potential hash collisions,
+where different raw features may become the same term after hashing.
+To reduce the chance of collision, we can increase the target feature dimension, i.e., 
+the number of buckets of the hash table.
+The default feature dimension is `$2^{20} = 1,048,576$`.
+
+**Note:** MLlib doesn't provide tools for text segmentation.
+We refer users to the [Stanford NLP Group](http://nlp.stanford.edu/) and 
+[scalanlp/chalk](https://github.com/scalanlp/chalk).
+
+<div class="codetabs">
+<div data-lang="scala" markdown="1">
+
+TF and IDF are implemented in [HashingTF](api/scala/index.html#org.apache.spark.mllib.feature.HashingTF)
+and [IDF](api/scala/index.html#org.apache.spark.mllib.feature.IDF).
+`HashingTF` takes an `RDD[Iterable[_]]` as the input.
+Each record could be an iterable of strings or other types.
+
+{% highlight scala %}
+import org.apache.spark.rdd.RDD
+import org.apache.spark.SparkContext
+import org.apache.spark.mllib.feature.HashingTF
+import org.apache.spark.mllib.linalg.Vector
+
+val sc: SparkContext = ...
+
+// Load documents (one per line).
+val documents: RDD[Seq[String]] = sc.textFile("...").map(_.split(" ").toSeq)
+
+val hashingTF = new HashingTF()
+val tf: RDD[Vector] = hasingTF.transform(documents)
+{% endhighlight %}
+
+While applying `HashingTF` only needs a single pass to the data, applying `IDF` needs two passes: 
+first to compute the IDF vector and second to scale the term frequencies by IDF.
+
+{% highlight scala %}
+import org.apache.spark.mllib.feature.IDF
+
+// ... continue from the previous example
+tf.cache()
+val idf = new IDF().fit(tf)
+val tfidf: RDD[Vector] = idf.transform(tf)
+{% endhighlight %}
+</div>
+</div>
+
 ## Word2Vec 
 
-Word2Vec computes distributed vector representation of words. The main advantage of the distributed
+[Word2Vec](https://code.google.com/p/word2vec/) computes distributed vector representation of words.
+The main advantage of the distributed
 representations is that similar words are close in the vector space, which makes generalization to 
 novel patterns easier and model estimation more robust. Distributed vector representation is 
 showed to be useful in many natural language processing applications such as named entity 
@@ -69,5 +148,3 @@ for((synonym, cosineSimilarity) <- synonyms) {
 {% endhighlight %}
 </div>
 </div>
-
-## TFIDF
\ No newline at end of file

From e0f946265b9ea5bc48849cf7794c2c03d5e29fba Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Wed, 20 Aug 2014 17:47:39 -0700
Subject: [PATCH 052/489] [SPARK-2843][MLLIB] add a section about
 regularization parameter in ALS

atalwalkar srowen

Author: Xiangrui Meng <meng@databricks.com>

Closes #2064 from mengxr/als-doc and squashes the following commits:

b2e20ab [Xiangrui Meng] introduced -> discussed
98abdd7 [Xiangrui Meng] add reference
339bd08 [Xiangrui Meng] add a section about regularization parameter in ALS
---
 docs/mllib-collaborative-filtering.md | 11 +++++++++++
 1 file changed, 11 insertions(+)

diff --git a/docs/mllib-collaborative-filtering.md b/docs/mllib-collaborative-filtering.md
index ab10b2f01f87b..d5c539db791be 100644
--- a/docs/mllib-collaborative-filtering.md
+++ b/docs/mllib-collaborative-filtering.md
@@ -43,6 +43,17 @@ level of confidence in observed user preferences, rather than explicit ratings g
 model then tries to find latent factors that can be used to predict the expected preference of a
 user for an item.
 
+### Scaling of the regularization parameter
+
+Since v1.1, we scale the regularization parameter `lambda` in solving each least squares problem by
+the number of ratings the user generated in updating user factors,
+or the number of ratings the product received in updating product factors.
+This approach is named "ALS-WR" and discussed in the paper
+"[Large-Scale Parallel Collaborative Filtering for the Netflix Prize](http://dx.doi.org/10.1007/978-3-540-68880-8_32)".
+It makes `lambda` less dependent on the scale of the dataset.
+So we can apply the best parameter learned from a sampled subset to the full dataset
+and expect similar performance.
+
 ## Examples
 
 <div class="codetabs">

From 050f8d01e47b9b67b02ce50d83fb7b4e528b7204 Mon Sep 17 00:00:00 2001
From: "Joseph K. Bradley" <joseph.kurata.bradley@gmail.com>
Date: Thu, 21 Aug 2014 00:17:29 -0700
Subject: [PATCH 053/489] [SPARK-2840] [mllib] DecisionTree doc update (Java,
 Python examples)

Updated DecisionTree documentation, with examples for Java, Python.
Added same Java example to code as well.
CC: @mengxr  @manishamde @atalwalkar

Author: Joseph K. Bradley <joseph.kurata.bradley@gmail.com>

Closes #2063 from jkbradley/dt-docs and squashes the following commits:

2dd2c19 [Joseph K. Bradley] Last updates based on github review.
9dd1b6b [Joseph K. Bradley] Updated decision tree doc.
d802369 [Joseph K. Bradley] Updates based on comments: cache data, corrected doc text.
b9bee04 [Joseph K. Bradley] Updated DT examples
57eee9f [Joseph K. Bradley] Created JavaDecisionTree example from example in docs, and corrected doc example as needed.
d939a92 [Joseph K. Bradley] Updated DecisionTree documentation.  Added Java, Python examples.
---
 docs/mllib-decision-tree.md                   | 352 ++++++++++++++----
 .../examples/mllib/JavaDecisionTree.java      | 116 ++++++
 2 files changed, 399 insertions(+), 69 deletions(-)
 create mode 100644 examples/src/main/java/org/apache/spark/examples/mllib/JavaDecisionTree.java

diff --git a/docs/mllib-decision-tree.md b/docs/mllib-decision-tree.md
index c01a92a9a1b26..1166d9cd150c4 100644
--- a/docs/mllib-decision-tree.md
+++ b/docs/mllib-decision-tree.md
@@ -7,20 +7,26 @@ displayTitle: <a href="mllib-guide.html">MLlib</a> - Decision Tree
 * Table of contents
 {:toc}
 
-Decision trees and their ensembles are popular methods for the machine learning tasks of
+[Decision trees](http://en.wikipedia.org/wiki/Decision_tree_learning)
+and their ensembles are popular methods for the machine learning tasks of
 classification and regression. Decision trees are widely used since they are easy to interpret,
-handle categorical variables, extend to the multiclass classification setting, do not require
+handle categorical features, extend to the multiclass classification setting, do not require
 feature scaling and are able to capture nonlinearities and feature interactions. Tree ensemble
-algorithms such as decision forest and boosting are among the top performers for classification and
+algorithms such as random forests and boosting are among the top performers for classification and
 regression tasks.
 
+MLlib supports decision trees for binary and multiclass classification and for regression,
+using both continuous and categorical features. The implementation partitions data by rows,
+allowing distributed training with millions of instances.
+
 ## Basic algorithm
 
 The decision tree is a greedy algorithm that performs a recursive binary partitioning of the feature
-space by choosing a single element from the *best split set* where each element of the set maximizes
-the information gain at a tree node. In other words, the split chosen at each tree node is chosen
-from the set `$\underset{s}{\operatorname{argmax}} IG(D,s)$` where `$IG(D,s)$` is the information
-gain when a split `$s$` is applied to a dataset `$D$`.
+space.  The tree predicts the same label for each bottommost (leaf) partition.
+Each partition is chosen greedily by selecting the *best split* from a set of possible splits,
+in order to maximize the information gain at a tree node. In other words, the split chosen at each
+tree node is chosen from the set `$\underset{s}{\operatorname{argmax}} IG(D,s)$` where `$IG(D,s)$`
+is the information gain when a split `$s$` is applied to a dataset `$D$`.
 
 ### Node impurity and information gain
 
@@ -52,9 +58,10 @@ impurity measure for regression (variance).
   </tbody>
 </table>
 
-The *information gain* is the difference in the parent node impurity and the weighted sum of the two
-child node impurities. Assuming that a split $s$ partitions the dataset `$D$` of size `$N$` into two
-datasets `$D_{left}$` and `$D_{right}$` of sizes `$N_{left}$` and `$N_{right}$`, respectively:
+The *information gain* is the difference between the parent node impurity and the weighted sum of
+the two child node impurities. Assuming that a split $s$ partitions the dataset `$D$` of size `$N$`
+into two datasets `$D_{left}$` and `$D_{right}$` of sizes `$N_{left}$` and `$N_{right}$`,
+respectively, the information gain is:
 
 `$IG(D,s) = Impurity(D) - \frac{N_{left}}{N} Impurity(D_{left}) - \frac{N_{right}}{N} Impurity(D_{right})$`
 
@@ -62,14 +69,15 @@ datasets `$D_{left}$` and `$D_{right}$` of sizes `$N_{left}$` and `$N_{right}$`,
 
 **Continuous features**
 
-For small datasets in single machine implementations, the split candidates for each continuous
+For small datasets in single-machine implementations, the split candidates for each continuous
 feature are typically the unique values for the feature. Some implementations sort the feature
 values and then use the ordered unique values as split candidates for faster tree calculations.
 
-Finding ordered unique feature values is computationally intensive for large distributed
-datasets. One can get an approximate set of split candidates by performing a quantile calculation
-over a sampled fraction of the data. The ordered splits create "bins" and the maximum number of such
-bins can be specified using the `maxBins` parameters.
+Sorting feature values is expensive for large distributed datasets.
+This implementation computes an approximate set of split candidates by performing a quantile
+calculation over a sampled fraction of the data.
+The ordered splits create "bins" and the maximum number of such
+bins can be specified using the `maxBins` parameter.
 
 Note that the number of bins cannot be greater than the number of instances `$N$` (a rare scenario
 since the default `maxBins` value is 100). The tree algorithm automatically reduces the number of
@@ -77,109 +85,315 @@ bins if the condition is not satisfied.
 
 **Categorical features**
 
-For `$M$` categorical feature values, one could come up with `$2^(M-1)-1$` split candidates. For
-binary classification, we can reduce the number of split candidates to `$M-1$` by ordering the
-categorical feature values by the proportion of labels falling in one of the two classes (see
-Section 9.2.4 in
+For a categorical feature with `$M$` possible values (categories), one could come up with
+`$2^{M-1}-1$` split candidates. For binary (0/1) classification and regression,
+we can reduce the number of split candidates to `$M-1$` by ordering the
+categorical feature values by the average label. (See Section 9.2.4 in
 [Elements of Statistical Machine Learning](http://statweb.stanford.edu/~tibs/ElemStatLearn/) for
-details). For example, for a binary classification problem with one categorical feature with three
-categories A, B and C with corresponding proportion of label 1 as 0.2, 0.6 and 0.4, the categorical
-features are ordered as A followed by C followed B or A, C, B. The two split candidates are A \| C, B
-and A , C \| B where \| denotes the split. A similar heuristic is used for multiclass classification
-when `$2^(M-1)-1$` is greater than the number of bins -- the impurity for each categorical feature value
-is used for ordering.
+details.) For example, for a binary classification problem with one categorical feature with three
+categories A, B and C whose corresponding proportions of label 1 are 0.2, 0.6 and 0.4, the categorical
+features are ordered as A, C, B. The two split candidates are A \| C, B
+and A , C \| B where \| denotes the split.
+
+In multiclass classification, all `$2^{M-1}-1$` possible splits are used whenever possible.
+When `$2^{M-1}-1$` is greater than the `maxBins` parameter, we use a (heuristic) method
+similar to the method used for binary classification and regression.
+The `$M$` categorical feature values are ordered by impurity,
+and the resulting `$M-1$` split candidates are considered.
 
 ### Stopping rule
 
 The recursive tree construction is stopped at a node when one of the two conditions is met:
 
-1. The node depth is equal to the `maxDepth` training parameter
+1. The node depth is equal to the `maxDepth` training parameter.
 2. No split candidate leads to an information gain at the node.
 
+## Implementation details
+
 ### Max memory requirements
 
-For faster processing, the decision tree algorithm performs simultaneous histogram computations for all nodes at each level of the tree. This could lead to high memory requirements at deeper levels of the tree leading to memory overflow errors. To alleviate this problem, a 'maxMemoryInMB' training parameter is provided which specifies the maximum amount of memory at the workers (twice as much at the master) to be allocated to the histogram computation. The default value is conservatively chosen to be 128 MB to allow the decision algorithm to work in most scenarios. Once the memory requirements for a level-wise computation crosses the `maxMemoryInMB` threshold, the node training tasks at each subsequent level is split into smaller tasks.
+For faster processing, the decision tree algorithm performs simultaneous histogram computations for
+all nodes at each level of the tree. This could lead to high memory requirements at deeper levels
+of the tree, potentially leading to memory overflow errors. To alleviate this problem, a `maxMemoryInMB`
+training parameter specifies the maximum amount of memory at the workers (twice as much at the
+master) to be allocated to the histogram computation. The default value is conservatively chosen to
+be 128 MB to allow the decision algorithm to work in most scenarios. Once the memory requirements
+for a level-wise computation cross the `maxMemoryInMB` threshold, the node training tasks at each
+subsequent level are split into smaller tasks.
+
+Note that, if you have a large amount of memory, increasing `maxMemoryInMB` can lead to faster
+training by requiring fewer passes over the data.
+
+### Binning feature values
+
+Increasing `maxBins` allows the algorithm to consider more split candidates and make fine-grained
+split decisions.  However, it also increases computation and communication.
+
+Note that the `maxBins` parameter must be at least the maximum number of categories `$M$` for
+any categorical feature.
+
+### Scaling
 
-### Practical limitations
+Computation scales approximately linearly in the number of training instances,
+in the number of features, and in the `maxBins` parameter.
+Communication scales approximately linearly in the number of features and in `maxBins`.
 
-1. The implemented algorithm reads both sparse and dense data. However, it is not optimized for sparse input.
-2. Python is not supported in this release.
+The implemented algorithm reads both sparse and dense data. However, it is not optimized for sparse input.
 
 ## Examples
 
 ### Classification
 
-The example below demonstrates how to load a CSV file, parse it as an RDD of `LabeledPoint` and then
-perform classification using a decision tree using Gini impurity as an impurity measure and a
+The example below demonstrates how to load a
+[LIBSVM data file](http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/),
+parse it as an RDD of `LabeledPoint` and then
+perform classification using a decision tree with Gini impurity as an impurity measure and a
 maximum tree depth of 5. The training error is calculated to measure the algorithm accuracy.
 
 <div class="codetabs">
+
 <div data-lang="scala">
 {% highlight scala %}
-import org.apache.spark.SparkContext
 import org.apache.spark.mllib.tree.DecisionTree
-import org.apache.spark.mllib.regression.LabeledPoint
-import org.apache.spark.mllib.linalg.Vectors
-import org.apache.spark.mllib.tree.configuration.Algo._
-import org.apache.spark.mllib.tree.impurity.Gini
-
-// Load and parse the data file
-val data = sc.textFile("data/mllib/sample_tree_data.csv")
-val parsedData = data.map { line =>
-  val parts = line.split(',').map(_.toDouble)
-  LabeledPoint(parts(0), Vectors.dense(parts.tail))
-}
+import org.apache.spark.mllib.util.MLUtils
 
-// Run training algorithm to build the model
+// Load and parse the data file.
+// Cache the data since we will use it again to compute training error.
+val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").cache()
+
+// Train a DecisionTree model.
+//  Empty categoricalFeaturesInfo indicates all features are continuous.
+val numClasses = 2
+val categoricalFeaturesInfo = Map[Int, Int]()
+val impurity = "gini"
 val maxDepth = 5
-val model = DecisionTree.train(parsedData, Classification, Gini, maxDepth)
+val maxBins = 100
+
+val model = DecisionTree.trainClassifier(data, numClasses, categoricalFeaturesInfo, impurity,
+  maxDepth, maxBins)
 
-// Evaluate model on training examples and compute training error
-val labelAndPreds = parsedData.map { point =>
+// Evaluate model on training instances and compute training error
+val labelAndPreds = data.map { point =>
   val prediction = model.predict(point.features)
   (point.label, prediction)
 }
-val trainErr = labelAndPreds.filter(r => r._1 != r._2).count.toDouble / parsedData.count
+val trainErr = labelAndPreds.filter(r => r._1 != r._2).count.toDouble / data.count
 println("Training Error = " + trainErr)
+println("Learned classification tree model:\n" + model)
+{% endhighlight %}
+</div>
+
+<div data-lang="java">
+{% highlight java %}
+import java.util.HashMap;
+import scala.Tuple2;
+import org.apache.spark.api.java.function.Function2;
+import org.apache.spark.api.java.JavaPairRDD;
+import org.apache.spark.api.java.JavaRDD;
+import org.apache.spark.api.java.JavaSparkContext;
+import org.apache.spark.api.java.function.Function;
+import org.apache.spark.api.java.function.PairFunction;
+import org.apache.spark.mllib.regression.LabeledPoint;
+import org.apache.spark.mllib.tree.DecisionTree;
+import org.apache.spark.mllib.tree.model.DecisionTreeModel;
+import org.apache.spark.mllib.util.MLUtils;
+import org.apache.spark.SparkConf;
+
+SparkConf sparkConf = new SparkConf().setAppName("JavaDecisionTree");
+JavaSparkContext sc = new JavaSparkContext(sparkConf);
+
+// Load and parse the data file.
+// Cache the data since we will use it again to compute training error.
+String datapath = "data/mllib/sample_libsvm_data.txt";
+JavaRDD<LabeledPoint> data = MLUtils.loadLibSVMFile(sc.sc(), datapath).toJavaRDD().cache();
+
+// Set parameters.
+//  Empty categoricalFeaturesInfo indicates all features are continuous.
+Integer numClasses = 2;
+HashMap<Integer, Integer> categoricalFeaturesInfo = new HashMap<Integer, Integer>();
+String impurity = "gini";
+Integer maxDepth = 5;
+Integer maxBins = 100;
+
+// Train a DecisionTree model for classification.
+final DecisionTreeModel model = DecisionTree.trainClassifier(data, numClasses,
+  categoricalFeaturesInfo, impurity, maxDepth, maxBins);
+
+// Evaluate model on training instances and compute training error
+JavaPairRDD<Double, Double> predictionAndLabel =
+  data.mapToPair(new PairFunction<LabeledPoint, Double, Double>() {
+    @Override public Tuple2<Double, Double> call(LabeledPoint p) {
+      return new Tuple2<Double, Double>(model.predict(p.features()), p.label());
+    }
+  });
+Double trainErr =
+  1.0 * predictionAndLabel.filter(new Function<Tuple2<Double, Double>, Boolean>() {
+    @Override public Boolean call(Tuple2<Double, Double> pl) {
+      return !pl._1().equals(pl._2());
+    }
+  }).count() / data.count();
+System.out.println("Training error: " + trainErr);
+System.out.println("Learned classification tree model:\n" + model);
+{% endhighlight %}
+</div>
+
+<div data-lang="python">
+{% highlight python %}
+from pyspark.mllib.regression import LabeledPoint
+from pyspark.mllib.tree import DecisionTree
+from pyspark.mllib.util import MLUtils
+
+# Load and parse the data file into an RDD of LabeledPoint.
+# Cache the data since we will use it again to compute training error.
+data = MLUtils.loadLibSVMFile(sc, 'data/mllib/sample_libsvm_data.txt').cache()
+
+# Train a DecisionTree model.
+#  Empty categoricalFeaturesInfo indicates all features are continuous.
+model = DecisionTree.trainClassifier(data, numClasses=2, categoricalFeaturesInfo={},
+                                     impurity='gini', maxDepth=5, maxBins=100)
+
+# Evaluate model on training instances and compute training error
+predictions = model.predict(data.map(lambda x: x.features))
+labelsAndPredictions = data.map(lambda lp: lp.label).zip(predictions)
+trainErr = labelsAndPredictions.filter(lambda (v, p): v != p).count() / float(data.count())
+print('Training Error = ' + str(trainErr))
+print('Learned classification tree model:')
+print(model)
 {% endhighlight %}
+
+Note: When making predictions for a dataset, it is more efficient to do batch prediction rather
+than separately calling `predict` on each data point.  This is because the Python code makes calls
+to an underlying `DecisionTree` model in Scala.
 </div>
+
 </div>
 
 ### Regression
 
-The example below demonstrates how to load a CSV file, parse it as an RDD of `LabeledPoint` and then
-perform regression using a decision tree using variance as an impurity measure and a maximum tree
+The example below demonstrates how to load a
+[LIBSVM data file](http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/),
+parse it as an RDD of `LabeledPoint` and then
+perform regression using a decision tree with variance as an impurity measure and a maximum tree
 depth of 5. The Mean Squared Error (MSE) is computed at the end to evaluate
 [goodness of fit](http://en.wikipedia.org/wiki/Goodness_of_fit).
 
 <div class="codetabs">
+
 <div data-lang="scala">
 {% highlight scala %}
-import org.apache.spark.SparkContext
 import org.apache.spark.mllib.tree.DecisionTree
-import org.apache.spark.mllib.regression.LabeledPoint
-import org.apache.spark.mllib.linalg.Vectors
-import org.apache.spark.mllib.tree.configuration.Algo._
-import org.apache.spark.mllib.tree.impurity.Variance
-
-// Load and parse the data file
-val data = sc.textFile("data/mllib/sample_tree_data.csv")
-val parsedData = data.map { line =>
-  val parts = line.split(',').map(_.toDouble)
-  LabeledPoint(parts(0), Vectors.dense(parts.tail))
-}
+import org.apache.spark.mllib.util.MLUtils
 
-// Run training algorithm to build the model
+// Load and parse the data file.
+// Cache the data since we will use it again to compute training error.
+val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").cache()
+
+// Train a DecisionTree model.
+//  Empty categoricalFeaturesInfo indicates all features are continuous.
+val categoricalFeaturesInfo = Map[Int, Int]()
+val impurity = "variance"
 val maxDepth = 5
-val model = DecisionTree.train(parsedData, Regression, Variance, maxDepth)
+val maxBins = 100
+
+val model = DecisionTree.trainRegressor(data, categoricalFeaturesInfo, impurity,
+  maxDepth, maxBins)
 
-// Evaluate model on training examples and compute training error
-val valuesAndPreds = parsedData.map { point =>
+// Evaluate model on training instances and compute training error
+val labelsAndPredictions = data.map { point =>
   val prediction = model.predict(point.features)
   (point.label, prediction)
 }
-val MSE = valuesAndPreds.map{ case(v, p) => math.pow((v - p), 2)}.mean()
-println("training Mean Squared Error = " + MSE)
+val trainMSE = labelsAndPredictions.map{ case(v, p) => math.pow((v - p), 2)}.mean()
+println("Training Mean Squared Error = " + trainMSE)
+println("Learned regression tree model:\n" + model)
 {% endhighlight %}
 </div>
+
+<div data-lang="java">
+{% highlight java %}
+import java.util.HashMap;
+import scala.Tuple2;
+import org.apache.spark.api.java.function.Function2;
+import org.apache.spark.api.java.JavaPairRDD;
+import org.apache.spark.api.java.JavaRDD;
+import org.apache.spark.api.java.JavaSparkContext;
+import org.apache.spark.api.java.function.Function;
+import org.apache.spark.api.java.function.PairFunction;
+import org.apache.spark.mllib.regression.LabeledPoint;
+import org.apache.spark.mllib.tree.DecisionTree;
+import org.apache.spark.mllib.tree.model.DecisionTreeModel;
+import org.apache.spark.mllib.util.MLUtils;
+import org.apache.spark.SparkConf;
+
+// Load and parse the data file.
+// Cache the data since we will use it again to compute training error.
+String datapath = "data/mllib/sample_libsvm_data.txt";
+JavaRDD<LabeledPoint> data = MLUtils.loadLibSVMFile(sc.sc(), datapath).toJavaRDD().cache();
+
+SparkConf sparkConf = new SparkConf().setAppName("JavaDecisionTree");
+JavaSparkContext sc = new JavaSparkContext(sparkConf);
+
+// Set parameters.
+//  Empty categoricalFeaturesInfo indicates all features are continuous.
+HashMap<Integer, Integer> categoricalFeaturesInfo = new HashMap<Integer, Integer>();
+String impurity = "variance";
+Integer maxDepth = 5;
+Integer maxBins = 100;
+
+// Train a DecisionTree model.
+final DecisionTreeModel model = DecisionTree.trainRegressor(data,
+  categoricalFeaturesInfo, impurity, maxDepth, maxBins);
+
+// Evaluate model on training instances and compute training error
+JavaPairRDD<Double, Double> predictionAndLabel =
+  data.mapToPair(new PairFunction<LabeledPoint, Double, Double>() {
+    @Override public Tuple2<Double, Double> call(LabeledPoint p) {
+      return new Tuple2<Double, Double>(model.predict(p.features()), p.label());
+    }
+  });
+Double trainMSE =
+  predictionAndLabel.map(new Function<Tuple2<Double, Double>, Double>() {
+    @Override public Double call(Tuple2<Double, Double> pl) {
+      Double diff = pl._1() - pl._2();
+      return diff * diff;
+    }
+  }).reduce(new Function2<Double, Double, Double>() {
+    @Override public Double call(Double a, Double b) {
+      return a + b;
+    }
+  }) / data.count();
+System.out.println("Training Mean Squared Error: " + trainMSE);
+System.out.println("Learned regression tree model:\n" + model);
+{% endhighlight %}
+</div>
+
+<div data-lang="python">
+{% highlight python %}
+from pyspark.mllib.regression import LabeledPoint
+from pyspark.mllib.tree import DecisionTree
+from pyspark.mllib.util import MLUtils
+
+# Load and parse the data file into an RDD of LabeledPoint.
+# Cache the data since we will use it again to compute training error.
+data = MLUtils.loadLibSVMFile(sc, 'data/mllib/sample_libsvm_data.txt').cache()
+
+# Train a DecisionTree model.
+#  Empty categoricalFeaturesInfo indicates all features are continuous.
+model = DecisionTree.trainRegressor(data, categoricalFeaturesInfo={},
+                                    impurity='variance', maxDepth=5, maxBins=100)
+
+# Evaluate model on training instances and compute training error
+predictions = model.predict(data.map(lambda x: x.features))
+labelsAndPredictions = data.map(lambda lp: lp.label).zip(predictions)
+trainMSE = labelsAndPredictions.map(lambda (v, p): (v - p) * (v - p)).sum() / float(data.count())
+print('Training Mean Squared Error = ' + str(trainMSE))
+print('Learned regression tree model:')
+print(model)
+{% endhighlight %}
+
+Note: When making predictions for a dataset, it is more efficient to do batch prediction rather
+than separately calling `predict` on each data point.  This is because the Python code makes calls
+to an underlying `DecisionTree` model in Scala.
+</div>
+
 </div>
diff --git a/examples/src/main/java/org/apache/spark/examples/mllib/JavaDecisionTree.java b/examples/src/main/java/org/apache/spark/examples/mllib/JavaDecisionTree.java
new file mode 100644
index 0000000000000..e4468e8bf1744
--- /dev/null
+++ b/examples/src/main/java/org/apache/spark/examples/mllib/JavaDecisionTree.java
@@ -0,0 +1,116 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.examples.mllib;
+
+import java.util.HashMap;
+
+import scala.Tuple2;
+
+import org.apache.spark.api.java.function.Function2;
+import org.apache.spark.api.java.JavaPairRDD;
+import org.apache.spark.api.java.JavaRDD;
+import org.apache.spark.api.java.JavaSparkContext;
+import org.apache.spark.api.java.function.Function;
+import org.apache.spark.api.java.function.PairFunction;
+import org.apache.spark.mllib.regression.LabeledPoint;
+import org.apache.spark.mllib.tree.DecisionTree;
+import org.apache.spark.mllib.tree.model.DecisionTreeModel;
+import org.apache.spark.mllib.util.MLUtils;
+import org.apache.spark.SparkConf;
+
+/**
+ * Classification and regression using decision trees.
+ */
+public final class JavaDecisionTree {
+
+  public static void main(String[] args) {
+    String datapath = "data/mllib/sample_libsvm_data.txt";
+    if (args.length == 1) {
+      datapath = args[0];
+    } else if (args.length > 1) {
+      System.err.println("Usage: JavaDecisionTree <libsvm format data file>");
+      System.exit(1);
+    }
+    SparkConf sparkConf = new SparkConf().setAppName("JavaDecisionTree");
+    JavaSparkContext sc = new JavaSparkContext(sparkConf);
+
+    JavaRDD<LabeledPoint> data = MLUtils.loadLibSVMFile(sc.sc(), datapath).toJavaRDD().cache();
+
+    // Compute the number of classes from the data.
+    Integer numClasses = data.map(new Function<LabeledPoint, Double>() {
+      @Override public Double call(LabeledPoint p) {
+        return p.label();
+      }
+    }).countByValue().size();
+
+    // Set parameters.
+    //  Empty categoricalFeaturesInfo indicates all features are continuous.
+    HashMap<Integer, Integer> categoricalFeaturesInfo = new HashMap<Integer, Integer>();
+    String impurity = "gini";
+    Integer maxDepth = 5;
+    Integer maxBins = 100;
+
+    // Train a DecisionTree model for classification.
+    final DecisionTreeModel model = DecisionTree.trainClassifier(data, numClasses,
+      categoricalFeaturesInfo, impurity, maxDepth, maxBins);
+
+    // Evaluate model on training instances and compute training error
+    JavaPairRDD<Double, Double> predictionAndLabel =
+      data.mapToPair(new PairFunction<LabeledPoint, Double, Double>() {
+        @Override public Tuple2<Double, Double> call(LabeledPoint p) {
+          return new Tuple2<Double, Double>(model.predict(p.features()), p.label());
+        }
+      });
+    Double trainErr =
+      1.0 * predictionAndLabel.filter(new Function<Tuple2<Double, Double>, Boolean>() {
+        @Override public Boolean call(Tuple2<Double, Double> pl) {
+          return !pl._1().equals(pl._2());
+        }
+      }).count() / data.count();
+    System.out.println("Training error: " + trainErr);
+    System.out.println("Learned classification tree model:\n" + model);
+
+    // Train a DecisionTree model for regression.
+    impurity = "variance";
+    final DecisionTreeModel regressionModel = DecisionTree.trainRegressor(data,
+        categoricalFeaturesInfo, impurity, maxDepth, maxBins);
+
+    // Evaluate model on training instances and compute training error
+    JavaPairRDD<Double, Double> regressorPredictionAndLabel =
+      data.mapToPair(new PairFunction<LabeledPoint, Double, Double>() {
+        @Override public Tuple2<Double, Double> call(LabeledPoint p) {
+          return new Tuple2<Double, Double>(regressionModel.predict(p.features()), p.label());
+        }
+      });
+    Double trainMSE =
+      regressorPredictionAndLabel.map(new Function<Tuple2<Double, Double>, Double>() {
+        @Override public Double call(Tuple2<Double, Double> pl) {
+          Double diff = pl._1() - pl._2();
+          return diff * diff;
+        }
+      }).reduce(new Function2<Double, Double, Double>() {
+        @Override public Double call(Double a, Double b) {
+          return a + b;
+        }
+      }) / data.count();
+    System.out.println("Training Mean Squared Error: " + trainMSE);
+    System.out.println("Learned regression tree model:\n" + regressionModel);
+
+    sc.stop();
+  }
+}

From 220c2d7699a522c50d26cd6fdd94f4635823e408 Mon Sep 17 00:00:00 2001
From: XuTingjun <1039320815@qq.com>
Date: Fri, 22 Aug 2014 10:45:00 -0500
Subject: [PATCH 054/489] [SPARK-2742][yarn] delete useless variables

Author: XuTingjun <1039320815@qq.com>

Closes #1614 from XuTingjun/yarn-bug and squashes the following commits:

f07096e [XuTingjun] Update ClientArguments.scala
---
 .../scala/org/apache/spark/deploy/yarn/ClientArguments.scala | 5 +----
 1 file changed, 1 insertion(+), 4 deletions(-)

diff --git a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala
index 62f9b3cf5ab88..afa4fd4c6959e 100644
--- a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala
+++ b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala
@@ -39,7 +39,6 @@ class ClientArguments(val args: Array[String], val sparkConf: SparkConf) {
   var amMemory: Int = 512 // MB
   var amClass: String = "org.apache.spark.deploy.yarn.ApplicationMaster"
   var appName: String = "Spark"
-  var inputFormatInfo: List[InputFormatInfo] = null
   var priority = 0
 
   parseArgs(args.toList)
@@ -58,8 +57,7 @@ class ClientArguments(val args: Array[String], val sparkConf: SparkConf) {
 
   private def parseArgs(inputArgs: List[String]): Unit = {
     val userArgsBuffer: ArrayBuffer[String] = new ArrayBuffer[String]()
-    val inputFormatMap: HashMap[String, InputFormatInfo] = new HashMap[String, InputFormatInfo]()
-
+  
     var args = inputArgs
 
     while (!args.isEmpty) {
@@ -145,7 +143,6 @@ class ClientArguments(val args: Array[String], val sparkConf: SparkConf) {
     }
 
     userArgs = userArgsBuffer.readOnly
-    inputFormatInfo = inputFormatMap.values.toList
   }
 
 

From a5219db1ece20a1c45f927d4d0e085474f0bf34e Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@apache.org>
Date: Fri, 22 Aug 2014 17:05:35 -0700
Subject: [PATCH 055/489] Link to Contributing to Spark wiki page on README.md.

---
 README.md | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/README.md b/README.md
index 8906e4c1416b1..c25a4fc6a6c67 100644
--- a/README.md
+++ b/README.md
@@ -136,3 +136,5 @@ submitting any copyrighted material via pull request, email, or other means
 you agree to license the material under the project's open source license and
 warrant that you have the legal authority to do so.
 
+Please see [Contributing to Spark wiki page](https://cwiki.apache.org/SPARK/Contributing+to+Spark)
+for more information.

From 3004074152b7261c2a968bb8e94ec7c41a7b43c1 Mon Sep 17 00:00:00 2001
From: Tathagata Das <tathagata.das1565@gmail.com>
Date: Fri, 22 Aug 2014 21:34:48 -0700
Subject: [PATCH 056/489] [SPARK-3169] Removed dependency on spark streaming
 test from spark flume sink

Due to maven bug https://jira.codehaus.org/browse/MNG-1378, maven could not resolve spark streaming classes required by the spark-streaming test-jar dependency of external/flume-sink. There is no particular reason that the external/flume-sink has to depend on Spark Streaming at all, so I am eliminating this dependency. Also I have removed the exclusions present in the Flume dependencies, as there is no reason to exclude them (they were excluded in the external/flume module to prevent dependency collisions with Spark).

Since Jenkins will test the sbt build and the unit test, I only tested maven compilation locally.

Author: Tathagata Das <tathagata.das1565@gmail.com>

Closes #2101 from tdas/spark-sink-pom-fix and squashes the following commits:

8f42621 [Tathagata Das] Added Flume sink exclusions back, and added netty to test dependencies
93b559f [Tathagata Das] Removed dependency on spark streaming test from spark flume sink
---
 external/flume-sink/pom.xml                    | 18 ++++++++++++------
 .../streaming/flume/sink/SparkSinkSuite.scala  | 10 +++++-----
 2 files changed, 17 insertions(+), 11 deletions(-)

diff --git a/external/flume-sink/pom.xml b/external/flume-sink/pom.xml
index 0c68defa5e101..19192e40a7dc3 100644
--- a/external/flume-sink/pom.xml
+++ b/external/flume-sink/pom.xml
@@ -62,7 +62,7 @@
           <groupId>org.apache.thrift</groupId>
           <artifactId>libthrift</artifactId>
         </exclusion>
-      </exclusions>
+    </exclusions>
     </dependency>
     <dependency>
       <groupId>org.scala-lang</groupId>
@@ -71,13 +71,19 @@
     <dependency>
       <groupId>org.scalatest</groupId>
       <artifactId>scalatest_${scala.binary.version}</artifactId>
+      <scope>test</scope>
     </dependency>
     <dependency>
-      <groupId>org.apache.spark</groupId>
-      <artifactId>spark-streaming_${scala.binary.version}</artifactId>
-      <version>${project.version}</version>
-      <type>test-jar</type>
-      <scope>test</scope> <!-- Need it only for tests, don't package it -->
+      <!--
+        Netty explicitly added in test as it has been excluded from
+        Flume dependency (to avoid runtime problems when running with
+        Spark) but unit tests need it. Version of Netty on which
+        Flume 1.4.0 depends on is "3.4.0.Final" .
+      -->
+      <groupId>io.netty</groupId>
+      <artifactId>netty</artifactId>
+      <version>3.4.0.Final</version>
+      <scope>test</scope>
     </dependency>
   </dependencies>
   <build>
diff --git a/external/flume-sink/src/test/scala/org/apache/spark/streaming/flume/sink/SparkSinkSuite.scala b/external/flume-sink/src/test/scala/org/apache/spark/streaming/flume/sink/SparkSinkSuite.scala
index 44b27edf85ce8..75a6668c6210b 100644
--- a/external/flume-sink/src/test/scala/org/apache/spark/streaming/flume/sink/SparkSinkSuite.scala
+++ b/external/flume-sink/src/test/scala/org/apache/spark/streaming/flume/sink/SparkSinkSuite.scala
@@ -30,14 +30,14 @@ import org.apache.avro.ipc.specific.SpecificRequestor
 import org.apache.flume.Context
 import org.apache.flume.channel.MemoryChannel
 import org.apache.flume.event.EventBuilder
-import org.apache.spark.streaming.TestSuiteBase
 import org.jboss.netty.channel.socket.nio.NioClientSocketChannelFactory
+import org.scalatest.FunSuite
 
-class SparkSinkSuite extends TestSuiteBase {
+class SparkSinkSuite extends FunSuite {
   val eventsPerBatch = 1000
   val channelCapacity = 5000
 
-  test("Success") {
+  test("Success with ack") {
     val (channel, sink) = initializeChannelAndSink()
     channel.start()
     sink.start()
@@ -57,7 +57,7 @@ class SparkSinkSuite extends TestSuiteBase {
     transceiver.close()
   }
 
-  test("Nack") {
+  test("Failure with nack") {
     val (channel, sink) = initializeChannelAndSink()
     channel.start()
     sink.start()
@@ -76,7 +76,7 @@ class SparkSinkSuite extends TestSuiteBase {
     transceiver.close()
   }
 
-  test("Timeout") {
+  test("Failure with timeout") {
     val (channel, sink) = initializeChannelAndSink(Map(SparkSinkConfig
       .CONF_TRANSACTION_TIMEOUT -> 1.toString))
     channel.start()

From 323cd92b9b762dc3ae456cc97564a71dd6e58244 Mon Sep 17 00:00:00 2001
From: Kousuke Saruta <sarutak@oss.nttdata.co.jp>
Date: Fri, 22 Aug 2014 22:28:05 -0700
Subject: [PATCH 057/489] [SPARK-2963] REGRESSION - The description about how
 to build for using CLI and Thrift JDBC server is absent in proper document  -

The most important things I mentioned in #1885 is as follows.

* People who build Spark is not always programmer.
* If a person who build Spark is not a programmer, he/she won't read programmer's guide before building.

So, how to build for using CLI and JDBC server is not only in programmer's guide.

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

Closes #2080 from sarutak/SPARK-2963 and squashes the following commits:

ee07c76 [Kousuke Saruta] Modified regression of the description about building for using Thrift JDBC server and CLI
ed53329 [Kousuke Saruta] Modified description and notaton of proper noun
07c59fc [Kousuke Saruta] Added a description about how to build to use HiveServer and CLI for SparkSQL to building-with-maven.md
6e6645a [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into SPARK-2963
c88fa93 [Kousuke Saruta] Added a description about building to use HiveServer and CLI for SparkSQL
---
 README.md                   |  5 ++++-
 docs/building-with-maven.md | 11 +++++++----
 2 files changed, 11 insertions(+), 5 deletions(-)

diff --git a/README.md b/README.md
index c25a4fc6a6c67..f75226ce11335 100644
--- a/README.md
+++ b/README.md
@@ -118,7 +118,10 @@ If your project is built with Maven, add this to your POM file's `<dependencies>
 ## A Note About Thrift JDBC server and CLI for Spark SQL
 
 Spark SQL supports Thrift JDBC server and CLI.
-See sql-programming-guide.md for more information about using the JDBC server.
+See sql-programming-guide.md for more information about using the JDBC server and CLI.
+You can use those features by setting `-Phive` when building Spark as follows.
+
+    $ sbt/sbt -Phive  assembly
 
 ## Configuration
 
diff --git a/docs/building-with-maven.md b/docs/building-with-maven.md
index a7d7bd3ccb1f2..bce7412c7d4c9 100644
--- a/docs/building-with-maven.md
+++ b/docs/building-with-maven.md
@@ -96,10 +96,13 @@ mvn -Pyarn -Phadoop-2.4 -Dhadoop.version=2.4.0 -DskipTests clean package
 mvn -Pyarn-alpha -Phadoop-2.3 -Dhadoop.version=2.3.0 -Dyarn.version=0.23.7 -DskipTests clean package
 {% endhighlight %}
 
-# Building Thrift JDBC server and CLI for Spark SQL
-
-Spark SQL supports Thrift JDBC server and CLI. See sql-programming-guide.md for 
-more information about the JDBC server.
+# Building With Hive and JDBC Support
+To enable Hive integration for Spark SQL along with its JDBC server and CLI,
+add the `-Phive` profile to your existing build options.
+{% highlight bash %}
+# Apache Hadoop 2.4.X with Hive support
+mvn -Pyarn -Phadoop-2.4 -Dhadoop.version=2.4.0 -Phive -DskipTests clean package
+{% endhighlight %}
 
 # Spark Tests in Maven
 

From f3d65cd0bf34d8c362da492472c61796ef059428 Mon Sep 17 00:00:00 2001
From: Daoyuan Wang <daoyuan.wang@intel.com>
Date: Sat, 23 Aug 2014 08:09:30 -0700
Subject: [PATCH 058/489] [SPARK-3068]remove MaxPermSize option for jvm 1.8

In JVM 1.8.0, MaxPermSize is no longer supported.
In spark `stderr` output, there would be a line of

    Java HotSpot(TM) 64-Bit Server VM warning: ignoring option MaxPermSize=128m; support was removed in 8.0

Author: Daoyuan Wang <daoyuan.wang@intel.com>

Closes #2011 from adrian-wang/maxpermsize and squashes the following commits:

ef1d660 [Daoyuan Wang] direct get java version in runtime
37db9c1 [Daoyuan Wang] code refine
3c1d554 [Daoyuan Wang] remove MaxPermSize option for jvm 1.8
---
 bin/spark-class                                           | 7 ++++++-
 bin/spark-class2.cmd                                      | 8 +++++++-
 .../org/apache/spark/deploy/worker/CommandUtils.scala     | 4 ++--
 3 files changed, 15 insertions(+), 4 deletions(-)

diff --git a/bin/spark-class b/bin/spark-class
index 22acf92288b3b..c6543545a5e64 100755
--- a/bin/spark-class
+++ b/bin/spark-class
@@ -105,9 +105,14 @@ else
     exit 1
   fi
 fi
+JAVA_VERSION=$($RUNNER -version 2>&1 | sed 's/java version "\(.*\)\.\(.*\)\..*"/\1\2/; 1q')
 
 # Set JAVA_OPTS to be able to load native libraries and to set heap size
-JAVA_OPTS="-XX:MaxPermSize=128m $OUR_JAVA_OPTS"
+if [ "$JAVA_VERSION" -ge 18 ]; then
+  JAVA_OPTS="$OUR_JAVA_OPTS"
+else
+  JAVA_OPTS="-XX:MaxPermSize=128m $OUR_JAVA_OPTS"
+fi
 JAVA_OPTS="$JAVA_OPTS -Xms$OUR_JAVA_MEM -Xmx$OUR_JAVA_MEM"
 
 # Load extra JAVA_OPTS from conf/java-opts, if it exists
diff --git a/bin/spark-class2.cmd b/bin/spark-class2.cmd
index e420eb409e529..e2c5f9c385189 100755
--- a/bin/spark-class2.cmd
+++ b/bin/spark-class2.cmd
@@ -77,7 +77,13 @@ rem All drivers use SPARK_JAVA_OPTS + SPARK_DRIVER_MEMORY. The repl also uses SP
 )
 
 rem Set JAVA_OPTS to be able to load native libraries and to set heap size
-set JAVA_OPTS=-XX:MaxPermSize=128m %OUR_JAVA_OPTS% -Djava.library.path=%SPARK_LIBRARY_PATH% -Xms%OUR_JAVA_MEM% -Xmx%OUR_JAVA_MEM%
+for /f "tokens=3" %%i in ('java -version 2^>^&1 ^| find "version"') do set jversion=%%i
+for /f "tokens=1 delims=_" %%i in ("%jversion:~1,-1%") do set jversion=%%i
+if "%jversion%" geq "1.8.0" (
+  set JAVA_OPTS=%OUR_JAVA_OPTS% -Djava.library.path=%SPARK_LIBRARY_PATH% -Xms%OUR_JAVA_MEM% -Xmx%OUR_JAVA_MEM%
+) else (
+  set JAVA_OPTS=-XX:MaxPermSize=128m %OUR_JAVA_OPTS% -Djava.library.path=%SPARK_LIBRARY_PATH% -Xms%OUR_JAVA_MEM% -Xmx%OUR_JAVA_MEM%
+)
 rem Attention: when changing the way the JAVA_OPTS are assembled, the change must be reflected in CommandUtils.scala!
 
 rem Test whether the user has built Spark
diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/CommandUtils.scala b/core/src/main/scala/org/apache/spark/deploy/worker/CommandUtils.scala
index 687e492a0d6fc..12e98fd40d6c9 100644
--- a/core/src/main/scala/org/apache/spark/deploy/worker/CommandUtils.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/worker/CommandUtils.scala
@@ -64,8 +64,6 @@ object CommandUtils extends Logging {
         Seq()
       }
 
-    val permGenOpt = Seq("-XX:MaxPermSize=128m")
-
     // Figure out our classpath with the external compute-classpath script
     val ext = if (System.getProperty("os.name").startsWith("Windows")) ".cmd" else ".sh"
     val classPath = Utils.executeAndGetOutput(
@@ -73,6 +71,8 @@ object CommandUtils extends Logging {
       extraEnvironment = command.environment)
     val userClassPath = command.classPathEntries ++ Seq(classPath)
 
+    val javaVersion = System.getProperty("java.version")
+    val permGenOpt = if (!javaVersion.startsWith("1.8")) Some("-XX:MaxPermSize=128m") else None
     Seq("-cp", userClassPath.filterNot(_.isEmpty).mkString(File.pathSeparator)) ++
       permGenOpt ++ libraryOpts ++ workerLocalOpts ++ command.javaOpts ++ memoryOpts
   }

From 76bb044b9e327639002034ad4c06e6bd8e20d018 Mon Sep 17 00:00:00 2001
From: Liang-Chi Hsieh <viirya@gmail.com>
Date: Sat, 23 Aug 2014 10:08:25 -0700
Subject: [PATCH 059/489] [Minor] fix typo

Fix a typo in comment.

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

Closes #2105 from viirya/fix_typo and squashes the following commits:

6596a80 [Liang-Chi Hsieh] fix typo.
---
 core/src/main/scala/org/apache/spark/partial/SumEvaluator.scala | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/partial/SumEvaluator.scala b/core/src/main/scala/org/apache/spark/partial/SumEvaluator.scala
index a74f80094434c..d5336284571d2 100644
--- a/core/src/main/scala/org/apache/spark/partial/SumEvaluator.scala
+++ b/core/src/main/scala/org/apache/spark/partial/SumEvaluator.scala
@@ -22,7 +22,7 @@ import cern.jet.stat.Probability
 import org.apache.spark.util.StatCounter
 
 /**
- * An ApproximateEvaluator for sums. It estimates the mean and the cont and multiplies them
+ * An ApproximateEvaluator for sums. It estimates the mean and the count and multiplies them
  * together, then uses the formula for the variance of two independent random variables to get
  * a variance for the result and compute a confidence interval.
  */

From 2fb1c72ea21e137c8b60a72e5aecd554c71b16e1 Mon Sep 17 00:00:00 2001
From: Yin Huai <huaiyin.thu@gmail.com>
Date: Sat, 23 Aug 2014 12:46:41 -0700
Subject: [PATCH 060/489] [SQL] Make functionRegistry in HiveContext transient.

Seems we missed `transient` for the `functionRegistry` in `HiveContext`.

cc: marmbrus

Author: Yin Huai <huaiyin.thu@gmail.com>

Closes #2074 from yhuai/makeFunctionRegistryTransient and squashes the following commits:

6534e7d [Yin Huai] Make functionRegistry transient.
---
 .../src/main/scala/org/apache/spark/sql/hive/HiveContext.scala   | 1 +
 1 file changed, 1 insertion(+)

diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
index ff32c7c90a0d2..29baefe714c64 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
@@ -255,6 +255,7 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) {
   }
 
   // Note that HiveUDFs will be overridden by functions registered in this context.
+  @transient
   override protected[sql] lazy val functionRegistry =
     new HiveFunctionRegistry with OverrideFunctionRegistry
 

From 7e191fe29bb09a8560cd75d453c4f7f662dff406 Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Sat, 23 Aug 2014 16:19:10 -0700
Subject: [PATCH 061/489] [SPARK-2554][SQL] CountDistinct partial aggregation
 and object allocation improvements

Author: Michael Armbrust <michael@databricks.com>
Author: Gregory Owen <greowen@gmail.com>

Closes #1935 from marmbrus/countDistinctPartial and squashes the following commits:

5c7848d [Michael Armbrust] turn off caching in the constructor
8074a80 [Michael Armbrust] fix tests
32d216f [Michael Armbrust] reynolds comments
c122cca [Michael Armbrust] Address comments, add tests
b2e8ef3 [Michael Armbrust] Merge remote-tracking branch 'origin/master' into countDistinctPartial
fae38f4 [Michael Armbrust] Fix style
fdca896 [Michael Armbrust] cleanup
93d0f64 [Michael Armbrust] metastore concurrency fix.
db44a30 [Michael Armbrust] JIT hax.
3868f6c [Michael Armbrust] Merge pull request #9 from GregOwen/countDistinctPartial
c9e67de [Gregory Owen] Made SpecificRow and types serializable by Kryo
2b46c4b [Michael Armbrust] Merge remote-tracking branch 'origin/master' into countDistinctPartial
8ff6402 [Michael Armbrust] Add specific row.
58d15f1 [Michael Armbrust] disable codegen logging
87d101d [Michael Armbrust] Fix isNullAt bug
abee26d [Michael Armbrust] WIP
27984d0 [Michael Armbrust] Merge remote-tracking branch 'origin/master' into countDistinctPartial
57ae3b1 [Michael Armbrust] Fix order dependent test
b3d0f64 [Michael Armbrust] Add golden files.
c1f7114 [Michael Armbrust] Improve tests / fix serialization.
f31b8ad [Michael Armbrust] more fixes
38c7449 [Michael Armbrust] comments and style
9153652 [Michael Armbrust] better toString
d494598 [Michael Armbrust] Fix tests now that the planner is better
41fbd1d [Michael Armbrust] Never try and create an empty hash set.
050bb97 [Michael Armbrust] Skip no-arg constructors for kryo,
bd08239 [Michael Armbrust] WIP
213ada8 [Michael Armbrust] First draft of partially aggregated and code generated count distinct / max
---
 .../sql/catalyst/expressions/Projection.scala | 344 +++++++++++++++++-
 .../spark/sql/catalyst/expressions/Row.scala  |   2 +-
 .../catalyst/expressions/SpecificRow.scala    | 307 ++++++++++++++++
 .../sql/catalyst/expressions/aggregates.scala |  93 ++++-
 .../sql/catalyst/expressions/arithmetic.scala |  31 ++
 .../expressions/codegen/CodeGenerator.scala   |  93 ++++-
 .../codegen/GenerateProjection.scala          |   9 +-
 .../spark/sql/catalyst/expressions/sets.scala | 129 +++++++
 .../spark/sql/catalyst/trees/TreeNode.scala   |   3 +-
 .../ExpressionEvaluationSuite.scala           |  10 +
 .../spark/sql/execution/Aggregate.scala       |   2 +-
 .../sql/execution/GeneratedAggregate.scala    |  36 +-
 .../sql/execution/SparkSqlSerializer.scala    |  86 +++++
 .../spark/sql/execution/SparkStrategies.scala |   6 +-
 .../apache/spark/sql/execution/joins.scala    |   2 +-
 .../spark/sql/parquet/ParquetConverter.scala  |   8 +-
 .../sql/parquet/ParquetTableOperations.scala  |   2 +-
 .../spark/sql/execution/PlannerSuite.scala    |   8 +-
 .../spark/sql/hive/HiveMetastoreCatalog.scala |   4 +-
 ... values-0-1843b7947729b771fee3a4abd050bfdc |   1 +
 ...ll long-0-89b850197b326239d60a5e1d5db7c9c9 |   1 +
 ... + null-0-a014038c00fb81e88041ed4a8368e6f7 |   1 +
 ...ue long-0-77b9ed1d7ae65fa53830a3bc586856ff |   1 +
 ...strings-0-c68e75ec4c884b93765a466e992e391d |   1 +
 ...1 value-0-a4047b06a324fb5ea400c94350c9e038 |   1 +
 ...ng null-0-75672236a30e10dab13b9b246c5a3a1e |   1 +
 ...es long-0-f4ec7d767ba8c49d41edf5d6f58cf6d1 |   1 +
 ... values-0-c61df65af167acaf7edb174e77898f3e |   1 +
 ...limited-0-52b0e534c7df544258a1c59df9f816ce |   0
 .../spark/sql/hive/StatisticsSuite.scala      |   8 +-
 .../sql/hive/execution/HiveQuerySuite.scala   |  65 ++++
 .../sql/hive/execution/HiveSerDeSuite.scala   |  11 +-
 .../sql/hive/execution/PruningSuite.scala     |   5 +-
 33 files changed, 1239 insertions(+), 34 deletions(-)
 create mode 100644 sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificRow.scala
 create mode 100644 sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/sets.scala
 create mode 100644 sql/hive/src/test/resources/golden/count distinct 0 values-0-1843b7947729b771fee3a4abd050bfdc
 create mode 100644 sql/hive/src/test/resources/golden/count distinct 1 value + null long-0-89b850197b326239d60a5e1d5db7c9c9
 create mode 100644 sql/hive/src/test/resources/golden/count distinct 1 value + null-0-a014038c00fb81e88041ed4a8368e6f7
 create mode 100644 sql/hive/src/test/resources/golden/count distinct 1 value long-0-77b9ed1d7ae65fa53830a3bc586856ff
 create mode 100644 sql/hive/src/test/resources/golden/count distinct 1 value strings-0-c68e75ec4c884b93765a466e992e391d
 create mode 100644 sql/hive/src/test/resources/golden/count distinct 1 value-0-a4047b06a324fb5ea400c94350c9e038
 create mode 100644 sql/hive/src/test/resources/golden/count distinct 2 values including null-0-75672236a30e10dab13b9b246c5a3a1e
 create mode 100644 sql/hive/src/test/resources/golden/count distinct 2 values long-0-f4ec7d767ba8c49d41edf5d6f58cf6d1
 create mode 100644 sql/hive/src/test/resources/golden/count distinct 2 values-0-c61df65af167acaf7edb174e77898f3e
 create mode 100644 sql/hive/src/test/resources/golden/show_create_table_delimited-0-52b0e534c7df544258a1c59df9f816ce

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
index 8fc5896974438..ef1d12531f109 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
@@ -27,7 +27,8 @@ class InterpretedProjection(expressions: Seq[Expression]) extends Projection {
   def this(expressions: Seq[Expression], inputSchema: Seq[Attribute]) =
     this(expressions.map(BindReferences.bindReference(_, inputSchema)))
 
-  protected val exprArray = expressions.toArray
+  // null check is required for when Kryo invokes the no-arg constructor.
+  protected val exprArray = if (expressions != null) expressions.toArray else null
 
   def apply(input: Row): Row = {
     val outputArray = new Array[Any](exprArray.length)
@@ -109,7 +110,346 @@ class JoinedRow extends Row {
   def apply(i: Int) =
     if (i < row1.size) row1(i) else row2(i - row1.size)
 
-  def isNullAt(i: Int) = apply(i) == null
+  def isNullAt(i: Int) =
+    if (i < row1.size) row1.isNullAt(i) else row2.isNullAt(i - row1.size)
+
+  def getInt(i: Int): Int =
+    if (i < row1.size) row1.getInt(i) else row2.getInt(i - row1.size)
+
+  def getLong(i: Int): Long =
+    if (i < row1.size) row1.getLong(i) else row2.getLong(i - row1.size)
+
+  def getDouble(i: Int): Double =
+    if (i < row1.size) row1.getDouble(i) else row2.getDouble(i - row1.size)
+
+  def getBoolean(i: Int): Boolean =
+    if (i < row1.size) row1.getBoolean(i) else row2.getBoolean(i - row1.size)
+
+  def getShort(i: Int): Short =
+    if (i < row1.size) row1.getShort(i) else row2.getShort(i - row1.size)
+
+  def getByte(i: Int): Byte =
+    if (i < row1.size) row1.getByte(i) else row2.getByte(i - row1.size)
+
+  def getFloat(i: Int): Float =
+    if (i < row1.size) row1.getFloat(i) else row2.getFloat(i - row1.size)
+
+  def getString(i: Int): String =
+    if (i < row1.size) row1.getString(i) else row2.getString(i - row1.size)
+
+  def copy() = {
+    val totalSize = row1.size + row2.size
+    val copiedValues = new Array[Any](totalSize)
+    var i = 0
+    while(i < totalSize) {
+      copiedValues(i) = apply(i)
+      i += 1
+    }
+    new GenericRow(copiedValues)
+  }
+
+  override def toString() = {
+    val row = (if (row1 != null) row1 else Seq[Any]()) ++ (if (row2 != null) row2 else Seq[Any]())
+    s"[${row.mkString(",")}]"
+  }
+}
+
+/**
+ * JIT HACK: Replace with macros
+ * The `JoinedRow` class is used in many performance critical situation.  Unfortunately, since there
+ * are multiple different types of `Rows` that could be stored as `row1` and `row2` most of the
+ * calls in the critical path are polymorphic.  By creating special versions of this class that are
+ * used in only a single location of the code, we increase the chance that only a single type of
+ * Row will be referenced, increasing the opportunity for the JIT to play tricks.  This sounds
+ * crazy but in benchmarks it had noticeable effects.
+ */
+class JoinedRow2 extends Row {
+  private[this] var row1: Row = _
+  private[this] var row2: Row = _
+
+  def this(left: Row, right: Row) = {
+    this()
+    row1 = left
+    row2 = right
+  }
+
+  /** Updates this JoinedRow to used point at two new base rows.  Returns itself. */
+  def apply(r1: Row, r2: Row): Row = {
+    row1 = r1
+    row2 = r2
+    this
+  }
+
+  /** Updates this JoinedRow by updating its left base row.  Returns itself. */
+  def withLeft(newLeft: Row): Row = {
+    row1 = newLeft
+    this
+  }
+
+  /** Updates this JoinedRow by updating its right base row.  Returns itself. */
+  def withRight(newRight: Row): Row = {
+    row2 = newRight
+    this
+  }
+
+  def iterator = row1.iterator ++ row2.iterator
+
+  def length = row1.length + row2.length
+
+  def apply(i: Int) =
+    if (i < row1.size) row1(i) else row2(i - row1.size)
+
+  def isNullAt(i: Int) =
+    if (i < row1.size) row1.isNullAt(i) else row2.isNullAt(i - row1.size)
+
+  def getInt(i: Int): Int =
+    if (i < row1.size) row1.getInt(i) else row2.getInt(i - row1.size)
+
+  def getLong(i: Int): Long =
+    if (i < row1.size) row1.getLong(i) else row2.getLong(i - row1.size)
+
+  def getDouble(i: Int): Double =
+    if (i < row1.size) row1.getDouble(i) else row2.getDouble(i - row1.size)
+
+  def getBoolean(i: Int): Boolean =
+    if (i < row1.size) row1.getBoolean(i) else row2.getBoolean(i - row1.size)
+
+  def getShort(i: Int): Short =
+    if (i < row1.size) row1.getShort(i) else row2.getShort(i - row1.size)
+
+  def getByte(i: Int): Byte =
+    if (i < row1.size) row1.getByte(i) else row2.getByte(i - row1.size)
+
+  def getFloat(i: Int): Float =
+    if (i < row1.size) row1.getFloat(i) else row2.getFloat(i - row1.size)
+
+  def getString(i: Int): String =
+    if (i < row1.size) row1.getString(i) else row2.getString(i - row1.size)
+
+  def copy() = {
+    val totalSize = row1.size + row2.size
+    val copiedValues = new Array[Any](totalSize)
+    var i = 0
+    while(i < totalSize) {
+      copiedValues(i) = apply(i)
+      i += 1
+    }
+    new GenericRow(copiedValues)
+  }
+
+  override def toString() = {
+    val row = (if (row1 != null) row1 else Seq[Any]()) ++ (if (row2 != null) row2 else Seq[Any]())
+    s"[${row.mkString(",")}]"
+  }
+}
+
+/**
+ * JIT HACK: Replace with macros
+ */
+class JoinedRow3 extends Row {
+  private[this] var row1: Row = _
+  private[this] var row2: Row = _
+
+  def this(left: Row, right: Row) = {
+    this()
+    row1 = left
+    row2 = right
+  }
+
+  /** Updates this JoinedRow to used point at two new base rows.  Returns itself. */
+  def apply(r1: Row, r2: Row): Row = {
+    row1 = r1
+    row2 = r2
+    this
+  }
+
+  /** Updates this JoinedRow by updating its left base row.  Returns itself. */
+  def withLeft(newLeft: Row): Row = {
+    row1 = newLeft
+    this
+  }
+
+  /** Updates this JoinedRow by updating its right base row.  Returns itself. */
+  def withRight(newRight: Row): Row = {
+    row2 = newRight
+    this
+  }
+
+  def iterator = row1.iterator ++ row2.iterator
+
+  def length = row1.length + row2.length
+
+  def apply(i: Int) =
+    if (i < row1.size) row1(i) else row2(i - row1.size)
+
+  def isNullAt(i: Int) =
+    if (i < row1.size) row1.isNullAt(i) else row2.isNullAt(i - row1.size)
+
+  def getInt(i: Int): Int =
+    if (i < row1.size) row1.getInt(i) else row2.getInt(i - row1.size)
+
+  def getLong(i: Int): Long =
+    if (i < row1.size) row1.getLong(i) else row2.getLong(i - row1.size)
+
+  def getDouble(i: Int): Double =
+    if (i < row1.size) row1.getDouble(i) else row2.getDouble(i - row1.size)
+
+  def getBoolean(i: Int): Boolean =
+    if (i < row1.size) row1.getBoolean(i) else row2.getBoolean(i - row1.size)
+
+  def getShort(i: Int): Short =
+    if (i < row1.size) row1.getShort(i) else row2.getShort(i - row1.size)
+
+  def getByte(i: Int): Byte =
+    if (i < row1.size) row1.getByte(i) else row2.getByte(i - row1.size)
+
+  def getFloat(i: Int): Float =
+    if (i < row1.size) row1.getFloat(i) else row2.getFloat(i - row1.size)
+
+  def getString(i: Int): String =
+    if (i < row1.size) row1.getString(i) else row2.getString(i - row1.size)
+
+  def copy() = {
+    val totalSize = row1.size + row2.size
+    val copiedValues = new Array[Any](totalSize)
+    var i = 0
+    while(i < totalSize) {
+      copiedValues(i) = apply(i)
+      i += 1
+    }
+    new GenericRow(copiedValues)
+  }
+
+  override def toString() = {
+    val row = (if (row1 != null) row1 else Seq[Any]()) ++ (if (row2 != null) row2 else Seq[Any]())
+    s"[${row.mkString(",")}]"
+  }
+}
+
+/**
+ * JIT HACK: Replace with macros
+ */
+class JoinedRow4 extends Row {
+  private[this] var row1: Row = _
+  private[this] var row2: Row = _
+
+  def this(left: Row, right: Row) = {
+    this()
+    row1 = left
+    row2 = right
+  }
+
+  /** Updates this JoinedRow to used point at two new base rows.  Returns itself. */
+  def apply(r1: Row, r2: Row): Row = {
+    row1 = r1
+    row2 = r2
+    this
+  }
+
+  /** Updates this JoinedRow by updating its left base row.  Returns itself. */
+  def withLeft(newLeft: Row): Row = {
+    row1 = newLeft
+    this
+  }
+
+  /** Updates this JoinedRow by updating its right base row.  Returns itself. */
+  def withRight(newRight: Row): Row = {
+    row2 = newRight
+    this
+  }
+
+  def iterator = row1.iterator ++ row2.iterator
+
+  def length = row1.length + row2.length
+
+  def apply(i: Int) =
+    if (i < row1.size) row1(i) else row2(i - row1.size)
+
+  def isNullAt(i: Int) =
+    if (i < row1.size) row1.isNullAt(i) else row2.isNullAt(i - row1.size)
+
+  def getInt(i: Int): Int =
+    if (i < row1.size) row1.getInt(i) else row2.getInt(i - row1.size)
+
+  def getLong(i: Int): Long =
+    if (i < row1.size) row1.getLong(i) else row2.getLong(i - row1.size)
+
+  def getDouble(i: Int): Double =
+    if (i < row1.size) row1.getDouble(i) else row2.getDouble(i - row1.size)
+
+  def getBoolean(i: Int): Boolean =
+    if (i < row1.size) row1.getBoolean(i) else row2.getBoolean(i - row1.size)
+
+  def getShort(i: Int): Short =
+    if (i < row1.size) row1.getShort(i) else row2.getShort(i - row1.size)
+
+  def getByte(i: Int): Byte =
+    if (i < row1.size) row1.getByte(i) else row2.getByte(i - row1.size)
+
+  def getFloat(i: Int): Float =
+    if (i < row1.size) row1.getFloat(i) else row2.getFloat(i - row1.size)
+
+  def getString(i: Int): String =
+    if (i < row1.size) row1.getString(i) else row2.getString(i - row1.size)
+
+  def copy() = {
+    val totalSize = row1.size + row2.size
+    val copiedValues = new Array[Any](totalSize)
+    var i = 0
+    while(i < totalSize) {
+      copiedValues(i) = apply(i)
+      i += 1
+    }
+    new GenericRow(copiedValues)
+  }
+
+  override def toString() = {
+    val row = (if (row1 != null) row1 else Seq[Any]()) ++ (if (row2 != null) row2 else Seq[Any]())
+    s"[${row.mkString(",")}]"
+  }
+}
+
+/**
+ * JIT HACK: Replace with macros
+ */
+class JoinedRow5 extends Row {
+  private[this] var row1: Row = _
+  private[this] var row2: Row = _
+
+  def this(left: Row, right: Row) = {
+    this()
+    row1 = left
+    row2 = right
+  }
+
+  /** Updates this JoinedRow to used point at two new base rows.  Returns itself. */
+  def apply(r1: Row, r2: Row): Row = {
+    row1 = r1
+    row2 = r2
+    this
+  }
+
+  /** Updates this JoinedRow by updating its left base row.  Returns itself. */
+  def withLeft(newLeft: Row): Row = {
+    row1 = newLeft
+    this
+  }
+
+  /** Updates this JoinedRow by updating its right base row.  Returns itself. */
+  def withRight(newRight: Row): Row = {
+    row2 = newRight
+    this
+  }
+
+  def iterator = row1.iterator ++ row2.iterator
+
+  def length = row1.length + row2.length
+
+  def apply(i: Int) =
+    if (i < row1.size) row1(i) else row2(i - row1.size)
+
+  def isNullAt(i: Int) =
+    if (i < row1.size) row1.isNullAt(i) else row2.isNullAt(i - row1.size)
 
   def getInt(i: Int): Int =
     if (i < row1.size) row1.getInt(i) else row2.getInt(i - row1.size)
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Row.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Row.scala
index c9a63e201ef60..d68a4fabeac77 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Row.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Row.scala
@@ -127,7 +127,7 @@ object EmptyRow extends Row {
  * the array is not copied, and thus could technically be mutated after creation, this is not
  * allowed.
  */
-class GenericRow(protected[catalyst] val values: Array[Any]) extends Row {
+class GenericRow(protected[sql] val values: Array[Any]) extends Row {
   /** No-arg constructor for serialization. */
   def this() = this(null)
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificRow.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificRow.scala
new file mode 100644
index 0000000000000..75ea0e8459df8
--- /dev/null
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificRow.scala
@@ -0,0 +1,307 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.expressions
+
+import org.apache.spark.sql.catalyst.types._
+
+/**
+ * A parent class for mutable container objects that are reused when the values are changed,
+ * resulting in less garbage.  These values are held by a [[SpecificMutableRow]].
+ *
+ * The following code was roughly used to generate these objects:
+ * {{{
+ * val types = "Int,Float,Boolean,Double,Short,Long,Byte,Any".split(",")
+ * types.map {tpe =>
+ * s"""
+ * final class Mutable$tpe extends MutableValue {
+ *   var value: $tpe = 0
+ *   def boxed = if (isNull) null else value
+ *   def update(v: Any) = value = {
+ *     isNull = false
+ *     v.asInstanceOf[$tpe]
+ *   }
+ *   def copy() = {
+ *     val newCopy = new Mutable$tpe
+ *     newCopy.isNull = isNull
+ *     newCopy.value = value
+ *     newCopy.asInstanceOf[this.type]
+ *   }
+ * }"""
+ * }.foreach(println)
+ *
+ * types.map { tpe =>
+ * s"""
+ *   override def set$tpe(ordinal: Int, value: $tpe): Unit = {
+ *     val currentValue = values(ordinal).asInstanceOf[Mutable$tpe]
+ *     currentValue.isNull = false
+ *     currentValue.value = value
+ *   }
+ *
+ *   override def get$tpe(i: Int): $tpe = {
+ *     values(i).asInstanceOf[Mutable$tpe].value
+ *   }"""
+ * }.foreach(println)
+ * }}}
+ */
+abstract class MutableValue extends Serializable {
+  var isNull: Boolean = true
+  def boxed: Any
+  def update(v: Any)
+  def copy(): this.type
+}
+
+final class MutableInt extends MutableValue {
+  var value: Int = 0
+  def boxed = if (isNull) null else value
+  def update(v: Any) = value = {
+    isNull = false
+    v.asInstanceOf[Int]
+  }
+  def copy() = {
+    val newCopy = new MutableInt
+    newCopy.isNull = isNull
+    newCopy.value = value
+    newCopy.asInstanceOf[this.type]
+  }
+}
+
+final class MutableFloat extends MutableValue {
+  var value: Float = 0
+  def boxed = if (isNull) null else value
+  def update(v: Any) = value = {
+    isNull = false
+    v.asInstanceOf[Float]
+  }
+  def copy() = {
+    val newCopy = new MutableFloat
+    newCopy.isNull = isNull
+    newCopy.value = value
+    newCopy.asInstanceOf[this.type]
+  }
+}
+
+final class MutableBoolean extends MutableValue {
+  var value: Boolean = false
+  def boxed = if (isNull) null else value
+  def update(v: Any) = value = {
+    isNull = false
+    v.asInstanceOf[Boolean]
+  }
+  def copy() = {
+    val newCopy = new MutableBoolean
+    newCopy.isNull = isNull
+    newCopy.value = value
+    newCopy.asInstanceOf[this.type]
+  }
+}
+
+final class MutableDouble extends MutableValue {
+  var value: Double = 0
+  def boxed = if (isNull) null else value
+  def update(v: Any) = value = {
+    isNull = false
+    v.asInstanceOf[Double]
+  }
+  def copy() = {
+    val newCopy = new MutableDouble
+    newCopy.isNull = isNull
+    newCopy.value = value
+    newCopy.asInstanceOf[this.type]
+  }
+}
+
+final class MutableShort extends MutableValue {
+  var value: Short = 0
+  def boxed = if (isNull) null else value
+  def update(v: Any) = value = {
+    isNull = false
+    v.asInstanceOf[Short]
+  }
+  def copy() = {
+    val newCopy = new MutableShort
+    newCopy.isNull = isNull
+    newCopy.value = value
+    newCopy.asInstanceOf[this.type]
+  }
+}
+
+final class MutableLong extends MutableValue {
+  var value: Long = 0
+  def boxed = if (isNull) null else value
+  def update(v: Any) = value = {
+    isNull = false
+    v.asInstanceOf[Long]
+  }
+  def copy() = {
+    val newCopy = new MutableLong
+    newCopy.isNull = isNull
+    newCopy.value = value
+    newCopy.asInstanceOf[this.type]
+  }
+}
+
+final class MutableByte extends MutableValue {
+  var value: Byte = 0
+  def boxed = if (isNull) null else value
+  def update(v: Any) = value = {
+    isNull = false
+    v.asInstanceOf[Byte]
+  }
+  def copy() = {
+    val newCopy = new MutableByte
+    newCopy.isNull = isNull
+    newCopy.value = value
+    newCopy.asInstanceOf[this.type]
+  }
+}
+
+final class MutableAny extends MutableValue {
+  var value: Any = 0
+  def boxed = if (isNull) null else value
+  def update(v: Any) = value = {
+    isNull = false
+    v.asInstanceOf[Any]
+  }
+  def copy() = {
+    val newCopy = new MutableAny
+    newCopy.isNull = isNull
+    newCopy.value = value
+    newCopy.asInstanceOf[this.type]
+  }
+}
+
+/**
+ * A row type that holds an array specialized container objects, of type [[MutableValue]], chosen
+ * based on the dataTypes of each column.  The intent is to decrease garbage when modifying the
+ * values of primitive columns.
+ */
+final class SpecificMutableRow(val values: Array[MutableValue]) extends MutableRow {
+
+  def this(dataTypes: Seq[DataType]) =
+    this(
+      dataTypes.map {
+        case IntegerType => new MutableInt
+        case ByteType => new MutableByte
+        case FloatType => new MutableFloat
+        case ShortType => new MutableShort
+        case DoubleType => new MutableDouble
+        case BooleanType => new MutableBoolean
+        case LongType => new MutableLong
+        case _ => new MutableAny
+      }.toArray)
+
+  def this() = this(Seq.empty)
+
+  override def length: Int = values.length
+
+  override def setNullAt(i: Int): Unit = {
+    values(i).isNull = true
+  }
+
+  override def apply(i: Int): Any = values(i).boxed
+
+  override def isNullAt(i: Int): Boolean = values(i).isNull
+
+  override def copy(): Row = {
+    val newValues = new Array[MutableValue](values.length)
+    var i = 0
+    while (i < values.length) {
+      newValues(i) = values(i).copy()
+      i += 1
+    }
+    new SpecificMutableRow(newValues)
+  }
+
+  override def update(ordinal: Int, value: Any): Unit = values(ordinal).update(value)
+
+  override def iterator: Iterator[Any] = values.map(_.boxed).iterator
+
+  def setString(ordinal: Int, value: String) = update(ordinal, value)
+
+  def getString(ordinal: Int) = apply(ordinal).asInstanceOf[String]
+
+  override def setInt(ordinal: Int, value: Int): Unit = {
+    val currentValue = values(ordinal).asInstanceOf[MutableInt]
+    currentValue.isNull = false
+    currentValue.value = value
+  }
+
+  override def getInt(i: Int): Int = {
+    values(i).asInstanceOf[MutableInt].value
+  }
+
+  override def setFloat(ordinal: Int, value: Float): Unit = {
+    val currentValue = values(ordinal).asInstanceOf[MutableFloat]
+    currentValue.isNull = false
+    currentValue.value = value
+  }
+
+  override def getFloat(i: Int): Float = {
+    values(i).asInstanceOf[MutableFloat].value
+  }
+
+  override def setBoolean(ordinal: Int, value: Boolean): Unit = {
+    val currentValue = values(ordinal).asInstanceOf[MutableBoolean]
+    currentValue.isNull = false
+    currentValue.value = value
+  }
+
+  override def getBoolean(i: Int): Boolean = {
+    values(i).asInstanceOf[MutableBoolean].value
+  }
+
+  override def setDouble(ordinal: Int, value: Double): Unit = {
+    val currentValue = values(ordinal).asInstanceOf[MutableDouble]
+    currentValue.isNull = false
+    currentValue.value = value
+  }
+
+  override def getDouble(i: Int): Double = {
+    values(i).asInstanceOf[MutableDouble].value
+  }
+
+  override def setShort(ordinal: Int, value: Short): Unit = {
+    val currentValue = values(ordinal).asInstanceOf[MutableShort]
+    currentValue.isNull = false
+    currentValue.value = value
+  }
+
+  override def getShort(i: Int): Short = {
+    values(i).asInstanceOf[MutableShort].value
+  }
+
+  override def setLong(ordinal: Int, value: Long): Unit = {
+    val currentValue = values(ordinal).asInstanceOf[MutableLong]
+    currentValue.isNull = false
+    currentValue.value = value
+  }
+
+  override def getLong(i: Int): Long = {
+    values(i).asInstanceOf[MutableLong].value
+  }
+
+  override def setByte(ordinal: Int, value: Byte): Unit = {
+    val currentValue = values(ordinal).asInstanceOf[MutableByte]
+    currentValue.isNull = false
+    currentValue.value = value
+  }
+
+  override def getByte(i: Int): Byte = {
+    values(i).asInstanceOf[MutableByte].value
+  }
+}
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
index 01947273b6ccc..613b87ca98d97 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
@@ -22,6 +22,7 @@ import com.clearspring.analytics.stream.cardinality.HyperLogLog
 import org.apache.spark.sql.catalyst.types._
 import org.apache.spark.sql.catalyst.trees
 import org.apache.spark.sql.catalyst.errors.TreeNodeException
+import org.apache.spark.util.collection.OpenHashSet
 
 abstract class AggregateExpression extends Expression {
   self: Product =>
@@ -161,13 +162,88 @@ case class Count(child: Expression) extends PartialAggregate with trees.UnaryNod
   override def newInstance() = new CountFunction(child, this)
 }
 
-case class CountDistinct(expressions: Seq[Expression]) extends AggregateExpression {
+case class CountDistinct(expressions: Seq[Expression]) extends PartialAggregate {
+  def this() = this(null)
+
   override def children = expressions
   override def references = expressions.flatMap(_.references).toSet
   override def nullable = false
   override def dataType = LongType
   override def toString = s"COUNT(DISTINCT ${expressions.mkString(",")})"
   override def newInstance() = new CountDistinctFunction(expressions, this)
+
+  override def asPartial = {
+    val partialSet = Alias(CollectHashSet(expressions), "partialSets")()
+    SplitEvaluation(
+      CombineSetsAndCount(partialSet.toAttribute),
+      partialSet :: Nil)
+  }
+}
+
+case class CollectHashSet(expressions: Seq[Expression]) extends AggregateExpression {
+  def this() = this(null)
+
+  override def children = expressions
+  override def references = expressions.flatMap(_.references).toSet
+  override def nullable = false
+  override def dataType = ArrayType(expressions.head.dataType)
+  override def toString = s"AddToHashSet(${expressions.mkString(",")})"
+  override def newInstance() = new CollectHashSetFunction(expressions, this)
+}
+
+case class CollectHashSetFunction(
+    @transient expr: Seq[Expression],
+    @transient base: AggregateExpression)
+  extends AggregateFunction {
+
+  def this() = this(null, null) // Required for serialization.
+
+  val seen = new OpenHashSet[Any]()
+
+  @transient
+  val distinctValue = new InterpretedProjection(expr)
+
+  override def update(input: Row): Unit = {
+    val evaluatedExpr = distinctValue(input)
+    if (!evaluatedExpr.anyNull) {
+      seen.add(evaluatedExpr)
+    }
+  }
+
+  override def eval(input: Row): Any = {
+    seen
+  }
+}
+
+case class CombineSetsAndCount(inputSet: Expression) extends AggregateExpression {
+  def this() = this(null)
+
+  override def children = inputSet :: Nil
+  override def references = inputSet.references
+  override def nullable = false
+  override def dataType = LongType
+  override def toString = s"CombineAndCount($inputSet)"
+  override def newInstance() = new CombineSetsAndCountFunction(inputSet, this)
+}
+
+case class CombineSetsAndCountFunction(
+    @transient inputSet: Expression,
+    @transient base: AggregateExpression)
+  extends AggregateFunction {
+
+  def this() = this(null, null) // Required for serialization.
+
+  val seen = new OpenHashSet[Any]()
+
+  override def update(input: Row): Unit = {
+    val inputSetEval = inputSet.eval(input).asInstanceOf[OpenHashSet[Any]]
+    val inputIterator = inputSetEval.iterator
+    while (inputIterator.hasNext) {
+      seen.add(inputIterator.next)
+    }
+  }
+
+  override def eval(input: Row): Any = seen.size.toLong
 }
 
 case class ApproxCountDistinctPartition(child: Expression, relativeSD: Double)
@@ -379,17 +455,22 @@ case class SumDistinctFunction(expr: Expression, base: AggregateExpression)
     seen.reduceLeft(base.dataType.asInstanceOf[NumericType].numeric.asInstanceOf[Numeric[Any]].plus)
 }
 
-case class CountDistinctFunction(expr: Seq[Expression], base: AggregateExpression)
+case class CountDistinctFunction(
+    @transient expr: Seq[Expression],
+    @transient base: AggregateExpression)
   extends AggregateFunction {
 
   def this() = this(null, null) // Required for serialization.
 
-  val seen = new scala.collection.mutable.HashSet[Any]()
+  val seen = new OpenHashSet[Any]()
+
+  @transient
+  val distinctValue = new InterpretedProjection(expr)
 
   override def update(input: Row): Unit = {
-    val evaluatedExpr = expr.map(_.eval(input))
-    if (evaluatedExpr.map(_ != null).reduceLeft(_ && _)) {
-      seen += evaluatedExpr
+    val evaluatedExpr = distinctValue(input)
+    if (!evaluatedExpr.anyNull) {
+      seen.add(evaluatedExpr)
     }
   }
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
index c79c1847cedf5..8d90614e4501a 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
@@ -85,3 +85,34 @@ case class Remainder(left: Expression, right: Expression) extends BinaryArithmet
 
   override def eval(input: Row): Any = i2(input, left, right, _.rem(_, _))
 }
+
+case class MaxOf(left: Expression, right: Expression) extends Expression {
+  type EvaluatedType = Any
+
+  override def nullable = left.nullable && right.nullable
+
+  override def children = left :: right :: Nil
+
+  override def references = left.references ++ right.references
+
+  override def dataType = left.dataType
+
+  override def eval(input: Row): Any = {
+    val leftEval = left.eval(input)
+    val rightEval = right.eval(input)
+    if (leftEval == null) {
+      rightEval
+    } else if (rightEval == null) {
+      leftEval
+    } else {
+      val numeric = left.dataType.asInstanceOf[NumericType].numeric.asInstanceOf[Numeric[Any]]
+      if (numeric.compare(leftEval, rightEval) < 0) {
+        rightEval
+      } else {
+        leftEval
+      }
+    }
+  }
+
+  override def toString = s"MaxOf($left, $right)"
+}
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
index de2d67ce82ff1..5a3f013c34579 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
@@ -26,6 +26,10 @@ import org.apache.spark.sql.catalyst.expressions
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.types._
 
+// These classes are here to avoid issues with serialization and integration with quasiquotes.
+class IntegerHashSet extends org.apache.spark.util.collection.OpenHashSet[Int]
+class LongHashSet extends org.apache.spark.util.collection.OpenHashSet[Long]
+
 /**
  * A base class for generators of byte code to perform expression evaluation.  Includes a set of
  * helpers for referring to Catalyst types and building trees that perform evaluation of individual
@@ -50,6 +54,11 @@ abstract class CodeGenerator[InType <: AnyRef, OutType <: AnyRef] extends Loggin
   private val curId = new java.util.concurrent.atomic.AtomicInteger()
   private val javaSeparator = "$"
 
+  /**
+   * Can be flipped on manually in the console to add (expensive) expression evaluation trace code.
+   */
+  var debugLogging = false
+
   /**
    * Generates a class for a given input expression.  Called when there is not cached code
    * already available.
@@ -71,7 +80,8 @@ abstract class CodeGenerator[InType <: AnyRef, OutType <: AnyRef] extends Loggin
    * From the Guava Docs: A Cache is similar to ConcurrentMap, but not quite the same. The most
    * fundamental difference is that a ConcurrentMap persists all elements that are added to it until
    * they are explicitly removed. A Cache on the other hand is generally configured to evict entries
-   * automatically, in order to constrain its memory footprint
+   * automatically, in order to constrain its memory footprint.  Note that this cache does not use
+   * weak keys/values and thus does not respond to memory pressure.
    */
   protected val cache = CacheBuilder.newBuilder()
     .maximumSize(1000)
@@ -403,6 +413,78 @@ abstract class CodeGenerator[InType <: AnyRef, OutType <: AnyRef] extends Loggin
             $primitiveTerm = ${falseEval.primitiveTerm}
           }
         """.children
+
+      case NewSet(elementType) =>
+        q"""
+          val $nullTerm = false
+          val $primitiveTerm = new ${hashSetForType(elementType)}()
+        """.children
+
+      case AddItemToSet(item, set) =>
+        val itemEval = expressionEvaluator(item)
+        val setEval = expressionEvaluator(set)
+
+        val ArrayType(elementType, _) = set.dataType
+
+        itemEval.code ++ setEval.code ++
+        q"""
+           if (!${itemEval.nullTerm}) {
+             ${setEval.primitiveTerm}
+               .asInstanceOf[${hashSetForType(elementType)}]
+               .add(${itemEval.primitiveTerm})
+           }
+
+           val $nullTerm = false
+           val $primitiveTerm = ${setEval.primitiveTerm}
+         """.children
+
+      case CombineSets(left, right) =>
+        val leftEval = expressionEvaluator(left)
+        val rightEval = expressionEvaluator(right)
+
+        val ArrayType(elementType, _) = left.dataType
+
+        leftEval.code ++ rightEval.code ++
+        q"""
+          val $nullTerm = false
+          var $primitiveTerm: ${hashSetForType(elementType)} = null
+
+          {
+            val leftSet = ${leftEval.primitiveTerm}.asInstanceOf[${hashSetForType(elementType)}]
+            val rightSet = ${rightEval.primitiveTerm}.asInstanceOf[${hashSetForType(elementType)}]
+            val iterator = rightSet.iterator
+            while (iterator.hasNext) {
+              leftSet.add(iterator.next())
+            }
+            $primitiveTerm = leftSet
+          }
+        """.children
+
+      case MaxOf(e1, e2) =>
+        val eval1 = expressionEvaluator(e1)
+        val eval2 = expressionEvaluator(e2)
+
+        eval1.code ++ eval2.code ++
+        q"""
+          var $nullTerm = false
+          var $primitiveTerm: ${termForType(e1.dataType)} = ${defaultPrimitive(e1.dataType)}
+
+          if (${eval1.nullTerm}) {
+            $nullTerm = ${eval2.nullTerm}
+            $primitiveTerm = ${eval2.primitiveTerm}
+          } else if (${eval2.nullTerm}) {
+            $nullTerm = ${eval1.nullTerm}
+            $primitiveTerm = ${eval1.primitiveTerm}
+          } else {
+            $nullTerm = false
+            if (${eval1.primitiveTerm} > ${eval2.primitiveTerm}) {
+              $primitiveTerm = ${eval1.primitiveTerm}
+            } else {
+              $primitiveTerm = ${eval2.primitiveTerm}
+            }
+          }
+        """.children
+
     }
 
     // If there was no match in the partial function above, we fall back on calling the interpreted
@@ -420,7 +502,7 @@ abstract class CodeGenerator[InType <: AnyRef, OutType <: AnyRef] extends Loggin
 
     // Only inject debugging code if debugging is turned on.
     val debugCode =
-      if (log.isDebugEnabled) {
+      if (debugLogging) {
         val localLogger = log
         val localLoggerTree = reify { localLogger }
         q"""
@@ -454,6 +536,13 @@ abstract class CodeGenerator[InType <: AnyRef, OutType <: AnyRef] extends Loggin
   protected def accessorForType(dt: DataType) = newTermName(s"get${primitiveForType(dt)}")
   protected def mutatorForType(dt: DataType) = newTermName(s"set${primitiveForType(dt)}")
 
+  protected def hashSetForType(dt: DataType) = dt match {
+    case IntegerType => typeOf[IntegerHashSet]
+    case LongType => typeOf[LongHashSet]
+    case unsupportedType =>
+      sys.error(s"Code generation not support for hashset of type $unsupportedType")
+  }
+
   protected def primitiveForType(dt: DataType) = dt match {
     case IntegerType => "Int"
     case LongType => "Long"
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala
index 77fa02c13de30..7871a62620478 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala
@@ -69,8 +69,10 @@ object GenerateProjection extends CodeGenerator[Seq[Expression], Projection] {
           ..${evaluatedExpression.code}
           if(${evaluatedExpression.nullTerm})
             setNullAt($iLit)
-          else
+          else {
+            nullBits($iLit) = false
             $elementName = ${evaluatedExpression.primitiveTerm}
+          }
         }
         """.children : Seq[Tree]
     }
@@ -106,9 +108,10 @@ object GenerateProjection extends CodeGenerator[Seq[Expression], Projection] {
             if(value == null) {
               setNullAt(i)
             } else {
+              nullBits(i) = false
               $elementName = value.asInstanceOf[${termForType(e.dataType)}]
-              return
             }
+            return
           }"""
       }
       q"final def update(i: Int, value: Any): Unit = { ..$cases; $accessorFailure }"
@@ -137,7 +140,7 @@ object GenerateProjection extends CodeGenerator[Seq[Expression], Projection] {
           val elementName = newTermName(s"c$i")
           // TODO: The string of ifs gets pretty inefficient as the row grows in size.
           // TODO: Optional null checks?
-          q"if(i == $i) { $elementName = value; return }" :: Nil
+          q"if(i == $i) { nullBits($i) = false; $elementName = value; return }" :: Nil
         case _ => Nil
       }
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/sets.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/sets.scala
new file mode 100644
index 0000000000000..e6c570b47bee2
--- /dev/null
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/sets.scala
@@ -0,0 +1,129 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.expressions
+
+import org.apache.spark.sql.catalyst.types._
+import org.apache.spark.util.collection.OpenHashSet
+
+/**
+ * Creates a new set of the specified type
+ */
+case class NewSet(elementType: DataType) extends LeafExpression {
+  type EvaluatedType = Any
+
+  def references = Set.empty
+
+  def nullable = false
+
+  // We are currently only using these Expressions internally for aggregation.  However, if we ever
+  // expose these to users we'll want to create a proper type instead of hijacking ArrayType.
+  def dataType = ArrayType(elementType)
+
+  def eval(input: Row): Any = {
+    new OpenHashSet[Any]()
+  }
+
+  override def toString = s"new Set($dataType)"
+}
+
+/**
+ * Adds an item to a set.
+ * For performance, this expression mutates its input during evaluation.
+ */
+case class AddItemToSet(item: Expression, set: Expression) extends Expression {
+  type EvaluatedType = Any
+
+  def children = item :: set :: Nil
+
+  def nullable = set.nullable
+
+  def dataType = set.dataType
+
+  def references = (item.flatMap(_.references) ++ set.flatMap(_.references)).toSet
+
+  def eval(input: Row): Any = {
+    val itemEval = item.eval(input)
+    val setEval = set.eval(input).asInstanceOf[OpenHashSet[Any]]
+
+    if (itemEval != null) {
+      if (setEval != null) {
+        setEval.add(itemEval)
+        setEval
+      } else {
+        null
+      }
+    } else {
+      setEval
+    }
+  }
+
+  override def toString = s"$set += $item"
+}
+
+/**
+ * Combines the elements of two sets.
+ * For performance, this expression mutates its left input set during evaluation.
+ */
+case class CombineSets(left: Expression, right: Expression) extends BinaryExpression {
+  type EvaluatedType = Any
+
+  def nullable = left.nullable || right.nullable
+
+  def dataType = left.dataType
+
+  def symbol = "++="
+
+  def eval(input: Row): Any = {
+    val leftEval = left.eval(input).asInstanceOf[OpenHashSet[Any]]
+    if(leftEval != null) {
+      val rightEval = right.eval(input).asInstanceOf[OpenHashSet[Any]]
+      if (rightEval != null) {
+        val iterator = rightEval.iterator
+        while(iterator.hasNext) {
+          val rightValue = iterator.next()
+          leftEval.add(rightValue)
+        }
+        leftEval
+      } else {
+        null
+      }
+    } else {
+      null
+    }
+  }
+}
+
+/**
+ * Returns the number of elements in the input set.
+ */
+case class CountSet(child: Expression) extends UnaryExpression {
+  type EvaluatedType = Any
+
+  def nullable = child.nullable
+
+  def dataType = LongType
+
+  def eval(input: Row): Any = {
+    val childEval = child.eval(input).asInstanceOf[OpenHashSet[Any]]
+    if (childEval != null) {
+      childEval.size.toLong
+    }
+  }
+
+  override def toString = s"$child.count()"
+}
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/trees/TreeNode.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/trees/TreeNode.scala
index cd04bdf02cf84..96ce35939e2cc 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/trees/TreeNode.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/trees/TreeNode.scala
@@ -280,7 +280,8 @@ abstract class TreeNode[BaseType <: TreeNode[BaseType]] {
    */
   def makeCopy(newArgs: Array[AnyRef]): this.type = attachTree(this, "makeCopy") {
     try {
-      val defaultCtor = getClass.getConstructors.head
+      // Skip no-arg constructors that are just there for kryo.
+      val defaultCtor = getClass.getConstructors.find(_.getParameterTypes.size != 0).head
       if (otherCopyArgs.isEmpty) {
         defaultCtor.newInstance(newArgs: _*).asInstanceOf[this.type]
       } else {
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala
index 999c9fff38d60..f1df817c41362 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala
@@ -136,6 +136,16 @@ class ExpressionEvaluationSuite extends FunSuite {
     checkEvaluation(In(Literal(1), Seq(Literal(1), Literal(2))) && In(Literal(2), Seq(Literal(1), Literal(2))), true)
   }
 
+  test("MaxOf") {
+    checkEvaluation(MaxOf(1, 2), 2)
+    checkEvaluation(MaxOf(2, 1), 2)
+    checkEvaluation(MaxOf(1L, 2L), 2L)
+    checkEvaluation(MaxOf(2L, 1L), 2L)
+
+    checkEvaluation(MaxOf(Literal(null, IntegerType), 2), 2)
+    checkEvaluation(MaxOf(2, Literal(null, IntegerType)), 2)
+  }
+
   test("LIKE literal Regular Expression") {
     checkEvaluation(Literal(null, StringType).like("a"), null)
     checkEvaluation(Literal("a", StringType).like(Literal(null, StringType)), null)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/Aggregate.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/Aggregate.scala
index 463a1d32d7fd7..be9f155253d77 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/Aggregate.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/Aggregate.scala
@@ -175,7 +175,7 @@ case class Aggregate(
           private[this] val resultProjection =
             new InterpretedMutableProjection(
               resultExpressions, computedSchema ++ namedGroups.map(_._2))
-          private[this] val joinedRow = new JoinedRow
+          private[this] val joinedRow = new JoinedRow4
 
           override final def hasNext: Boolean = hashTableIter.hasNext
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala
index 4a26934c49c93..31ad5e8aabb0e 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala
@@ -103,6 +103,40 @@ case class GeneratedAggregate(
           updateCount :: updateSum :: Nil,
           result
         )
+
+      case m @ Max(expr) =>
+        val currentMax = AttributeReference("currentMax", expr.dataType, nullable = true)()
+        val initialValue = Literal(null, expr.dataType)
+        val updateMax = MaxOf(currentMax, expr)
+
+        AggregateEvaluation(
+          currentMax :: Nil,
+          initialValue :: Nil,
+          updateMax :: Nil,
+          currentMax)
+
+      case CollectHashSet(Seq(expr)) =>
+        val set = AttributeReference("hashSet", ArrayType(expr.dataType), nullable = false)()
+        val initialValue = NewSet(expr.dataType)
+        val addToSet = AddItemToSet(expr, set)
+
+        AggregateEvaluation(
+          set :: Nil,
+          initialValue :: Nil,
+          addToSet :: Nil,
+          set)
+
+      case CombineSetsAndCount(inputSet) =>
+        val ArrayType(inputType, _) = inputSet.dataType
+        val set = AttributeReference("hashSet", inputSet.dataType, nullable = false)()
+        val initialValue = NewSet(inputType)
+        val collectSets = CombineSets(set, inputSet)
+
+        AggregateEvaluation(
+          set :: Nil,
+          initialValue :: Nil,
+          collectSets :: Nil,
+          CountSet(set))
     }
 
     val computationSchema = computeFunctions.flatMap(_.schema)
@@ -151,7 +185,7 @@ case class GeneratedAggregate(
           (namedGroups.map(_._2.toAttribute) ++ computationSchema).toSeq)
       log.info(s"Result Projection: ${resultExpressions.mkString(",")}")
 
-      val joinedRow = new JoinedRow
+      val joinedRow = new JoinedRow3
 
       if (groupingExpressions.isEmpty) {
         // TODO: Codegening anything other than the updateProjection is probably over kill.
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer.scala
index 34654447a5f4b..077e6ebc5f11e 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer.scala
@@ -28,9 +28,13 @@ import com.twitter.chill.{AllScalaRegistrar, ResourcePool}
 
 import org.apache.spark.{SparkEnv, SparkConf}
 import org.apache.spark.serializer.{SerializerInstance, KryoSerializer}
+import org.apache.spark.sql.catalyst.expressions.GenericRow
+import org.apache.spark.util.collection.OpenHashSet
 import org.apache.spark.util.MutablePair
 import org.apache.spark.util.Utils
 
+import org.apache.spark.sql.catalyst.expressions.codegen.{IntegerHashSet, LongHashSet}
+
 private[sql] class SparkSqlSerializer(conf: SparkConf) extends KryoSerializer(conf) {
   override def newKryo(): Kryo = {
     val kryo = new Kryo()
@@ -41,6 +45,13 @@ private[sql] class SparkSqlSerializer(conf: SparkConf) extends KryoSerializer(co
     kryo.register(classOf[com.clearspring.analytics.stream.cardinality.HyperLogLog],
                   new HyperLogLogSerializer)
     kryo.register(classOf[scala.math.BigDecimal], new BigDecimalSerializer)
+
+    // Specific hashsets must come first TODO: Move to core.
+    kryo.register(classOf[IntegerHashSet], new IntegerHashSetSerializer)
+    kryo.register(classOf[LongHashSet], new LongHashSetSerializer)
+    kryo.register(classOf[org.apache.spark.util.collection.OpenHashSet[_]],
+                  new OpenHashSetSerializer)
+
     kryo.setReferences(false)
     kryo.setClassLoader(Utils.getSparkClassLoader)
     new AllScalaRegistrar().apply(kryo)
@@ -109,3 +120,78 @@ private[sql] class HyperLogLogSerializer extends Serializer[HyperLogLog] {
     HyperLogLog.Builder.build(bytes)
   }
 }
+
+private[sql] class OpenHashSetSerializer extends Serializer[OpenHashSet[_]] {
+  def write(kryo: Kryo, output: Output, hs: OpenHashSet[_]) {
+    val rowSerializer = kryo.getDefaultSerializer(classOf[Array[Any]]).asInstanceOf[Serializer[Any]]
+    output.writeInt(hs.size)
+    val iterator = hs.iterator
+    while(iterator.hasNext) {
+      val row = iterator.next()
+      rowSerializer.write(kryo, output, row.asInstanceOf[GenericRow].values)
+    }
+  }
+
+  def read(kryo: Kryo, input: Input, tpe: Class[OpenHashSet[_]]): OpenHashSet[_] = {
+    val rowSerializer = kryo.getDefaultSerializer(classOf[Array[Any]]).asInstanceOf[Serializer[Any]]
+    val numItems = input.readInt()
+    val set = new OpenHashSet[Any](numItems + 1)
+    var i = 0
+    while (i < numItems) {
+      val row =
+        new GenericRow(rowSerializer.read(
+          kryo,
+          input,
+          classOf[Array[Any]].asInstanceOf[Class[Any]]).asInstanceOf[Array[Any]])
+      set.add(row)
+      i += 1
+    }
+    set
+  }
+}
+
+private[sql] class IntegerHashSetSerializer extends Serializer[IntegerHashSet] {
+  def write(kryo: Kryo, output: Output, hs: IntegerHashSet) {
+    output.writeInt(hs.size)
+    val iterator = hs.iterator
+    while(iterator.hasNext) {
+      val value: Int = iterator.next()
+      output.writeInt(value)
+    }
+  }
+
+  def read(kryo: Kryo, input: Input, tpe: Class[IntegerHashSet]): IntegerHashSet = {
+    val numItems = input.readInt()
+    val set = new IntegerHashSet
+    var i = 0
+    while (i < numItems) {
+      val value = input.readInt()
+      set.add(value)
+      i += 1
+    }
+    set
+  }
+}
+
+private[sql] class LongHashSetSerializer extends Serializer[LongHashSet] {
+  def write(kryo: Kryo, output: Output, hs: LongHashSet) {
+    output.writeInt(hs.size)
+    val iterator = hs.iterator
+    while(iterator.hasNext) {
+      val value = iterator.next()
+      output.writeLong(value)
+    }
+  }
+
+  def read(kryo: Kryo, input: Input, tpe: Class[LongHashSet]): LongHashSet = {
+    val numItems = input.readInt()
+    val set = new LongHashSet
+    var i = 0
+    while (i < numItems) {
+      val value = input.readLong()
+      set.add(value)
+      i += 1
+    }
+    set
+  }
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
index f0c958fdb537f..517b77804ae2c 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
@@ -23,6 +23,7 @@ import org.apache.spark.sql.catalyst.planning._
 import org.apache.spark.sql.catalyst.plans._
 import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
 import org.apache.spark.sql.catalyst.plans.physical._
+import org.apache.spark.sql.catalyst.types._
 import org.apache.spark.sql.columnar.{InMemoryRelation, InMemoryColumnarTableScan}
 import org.apache.spark.sql.parquet._
 
@@ -148,7 +149,10 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
     }
 
     def canBeCodeGened(aggs: Seq[AggregateExpression]) = !aggs.exists {
-      case _: Sum | _: Count => false
+      case _: Sum | _: Count | _: Max | _: CombineSetsAndCount => false
+      // The generated set implementation is pretty limited ATM.
+      case CollectHashSet(exprs) if exprs.size == 1  &&
+           Seq(IntegerType, LongType).contains(exprs.head.dataType) => false
       case _ => true
     }
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins.scala
index b08f9aacc1fcb..2890a563bed48 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins.scala
@@ -92,7 +92,7 @@ trait HashJoin {
       private[this] var currentMatchPosition: Int = -1
 
       // Mutable per row objects.
-      private[this] val joinRow = new JoinedRow
+      private[this] val joinRow = new JoinedRow2
 
       private[this] val joinKeys = streamSideKeyGenerator()
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetConverter.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetConverter.scala
index 0a3b59cbc233a..ef4526ec03439 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetConverter.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetConverter.scala
@@ -23,7 +23,7 @@ import parquet.io.api.{PrimitiveConverter, GroupConverter, Binary, Converter}
 import parquet.schema.MessageType
 
 import org.apache.spark.sql.catalyst.types._
-import org.apache.spark.sql.catalyst.expressions.{GenericRow, Row, Attribute}
+import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.parquet.CatalystConverter.FieldType
 
 /**
@@ -278,14 +278,14 @@ private[parquet] class CatalystGroupConverter(
  */
 private[parquet] class CatalystPrimitiveRowConverter(
     protected[parquet] val schema: Array[FieldType],
-    protected[parquet] var current: ParquetRelation.RowType)
+    protected[parquet] var current: MutableRow)
   extends CatalystConverter {
 
   // This constructor is used for the root converter only
   def this(attributes: Array[Attribute]) =
     this(
       attributes.map(a => new FieldType(a.name, a.dataType, a.nullable)),
-      new ParquetRelation.RowType(attributes.length))
+      new SpecificMutableRow(attributes.map(_.dataType)))
 
   protected [parquet] val converters: Array[Converter] =
     schema.zipWithIndex.map {
@@ -299,7 +299,7 @@ private[parquet] class CatalystPrimitiveRowConverter(
   override val parent = null
 
   // Should be only called in root group converter!
-  override def getCurrentRecord: ParquetRelation.RowType = current
+  override def getCurrentRecord: Row = current
 
   override def getConverter(fieldIndex: Int): Converter = converters(fieldIndex)
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala
index f6cfab736d98a..a5a5d139a65cb 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala
@@ -139,7 +139,7 @@ case class ParquetTableScan(
           partOutput.map(a => Cast(Literal(partValues(a.name)), a.dataType).eval(EmptyRow))
 
         new Iterator[Row] {
-          private[this] val joinedRow = new JoinedRow(Row(partitionRowValues:_*), null)
+          private[this] val joinedRow = new JoinedRow5(Row(partitionRowValues:_*), null)
 
           def hasNext = iter.hasNext
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/PlannerSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/PlannerSuite.scala
index 76b1724471442..37d64f0de7bab 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/PlannerSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/PlannerSuite.scala
@@ -45,16 +45,16 @@ class PlannerSuite extends FunSuite {
     assert(aggregations.size === 2)
   }
 
-  test("count distinct is not partially aggregated") {
+  test("count distinct is partially aggregated") {
     val query = testData.groupBy('value)(CountDistinct('key :: Nil)).queryExecution.analyzed
     val planned = HashAggregation(query)
-    assert(planned.isEmpty)
+    assert(planned.nonEmpty)
   }
 
-  test("mixed aggregates are not partially aggregated") {
+  test("mixed aggregates are partially aggregated") {
     val query =
       testData.groupBy('value)(Count('value), CountDistinct('key :: Nil)).queryExecution.analyzed
     val planned = HashAggregation(query)
-    assert(planned.isEmpty)
+    assert(planned.nonEmpty)
   }
 }
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
index 3b371211e14cd..6571c35499ef4 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
@@ -265,9 +265,9 @@ private[hive] case class MetastoreRelation
   // org.apache.hadoop.hive.ql.metadata.Partition will cause a NotSerializableException
   // which indicates the SerDe we used is not Serializable.
 
-  @transient lazy val hiveQlTable = new Table(table)
+  @transient val hiveQlTable = new Table(table)
 
-  def hiveQlPartitions = partitions.map { p =>
+  @transient val hiveQlPartitions = partitions.map { p =>
     new Partition(hiveQlTable, p)
   }
 
diff --git a/sql/hive/src/test/resources/golden/count distinct 0 values-0-1843b7947729b771fee3a4abd050bfdc b/sql/hive/src/test/resources/golden/count distinct 0 values-0-1843b7947729b771fee3a4abd050bfdc
new file mode 100644
index 0000000000000..573541ac9702d
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/count distinct 0 values-0-1843b7947729b771fee3a4abd050bfdc	
@@ -0,0 +1 @@
+0
diff --git a/sql/hive/src/test/resources/golden/count distinct 1 value + null long-0-89b850197b326239d60a5e1d5db7c9c9 b/sql/hive/src/test/resources/golden/count distinct 1 value + null long-0-89b850197b326239d60a5e1d5db7c9c9
new file mode 100644
index 0000000000000..d00491fd7e5bb
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/count distinct 1 value + null long-0-89b850197b326239d60a5e1d5db7c9c9	
@@ -0,0 +1 @@
+1
diff --git a/sql/hive/src/test/resources/golden/count distinct 1 value + null-0-a014038c00fb81e88041ed4a8368e6f7 b/sql/hive/src/test/resources/golden/count distinct 1 value + null-0-a014038c00fb81e88041ed4a8368e6f7
new file mode 100644
index 0000000000000..d00491fd7e5bb
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/count distinct 1 value + null-0-a014038c00fb81e88041ed4a8368e6f7	
@@ -0,0 +1 @@
+1
diff --git a/sql/hive/src/test/resources/golden/count distinct 1 value long-0-77b9ed1d7ae65fa53830a3bc586856ff b/sql/hive/src/test/resources/golden/count distinct 1 value long-0-77b9ed1d7ae65fa53830a3bc586856ff
new file mode 100644
index 0000000000000..d00491fd7e5bb
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/count distinct 1 value long-0-77b9ed1d7ae65fa53830a3bc586856ff	
@@ -0,0 +1 @@
+1
diff --git a/sql/hive/src/test/resources/golden/count distinct 1 value strings-0-c68e75ec4c884b93765a466e992e391d b/sql/hive/src/test/resources/golden/count distinct 1 value strings-0-c68e75ec4c884b93765a466e992e391d
new file mode 100644
index 0000000000000..0cfbf08886fca
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/count distinct 1 value strings-0-c68e75ec4c884b93765a466e992e391d	
@@ -0,0 +1 @@
+2
diff --git a/sql/hive/src/test/resources/golden/count distinct 1 value-0-a4047b06a324fb5ea400c94350c9e038 b/sql/hive/src/test/resources/golden/count distinct 1 value-0-a4047b06a324fb5ea400c94350c9e038
new file mode 100644
index 0000000000000..d00491fd7e5bb
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/count distinct 1 value-0-a4047b06a324fb5ea400c94350c9e038	
@@ -0,0 +1 @@
+1
diff --git a/sql/hive/src/test/resources/golden/count distinct 2 values including null-0-75672236a30e10dab13b9b246c5a3a1e b/sql/hive/src/test/resources/golden/count distinct 2 values including null-0-75672236a30e10dab13b9b246c5a3a1e
new file mode 100644
index 0000000000000..d00491fd7e5bb
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/count distinct 2 values including null-0-75672236a30e10dab13b9b246c5a3a1e	
@@ -0,0 +1 @@
+1
diff --git a/sql/hive/src/test/resources/golden/count distinct 2 values long-0-f4ec7d767ba8c49d41edf5d6f58cf6d1 b/sql/hive/src/test/resources/golden/count distinct 2 values long-0-f4ec7d767ba8c49d41edf5d6f58cf6d1
new file mode 100644
index 0000000000000..0cfbf08886fca
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/count distinct 2 values long-0-f4ec7d767ba8c49d41edf5d6f58cf6d1	
@@ -0,0 +1 @@
+2
diff --git a/sql/hive/src/test/resources/golden/count distinct 2 values-0-c61df65af167acaf7edb174e77898f3e b/sql/hive/src/test/resources/golden/count distinct 2 values-0-c61df65af167acaf7edb174e77898f3e
new file mode 100644
index 0000000000000..0cfbf08886fca
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/count distinct 2 values-0-c61df65af167acaf7edb174e77898f3e	
@@ -0,0 +1 @@
+2
diff --git a/sql/hive/src/test/resources/golden/show_create_table_delimited-0-52b0e534c7df544258a1c59df9f816ce b/sql/hive/src/test/resources/golden/show_create_table_delimited-0-52b0e534c7df544258a1c59df9f816ce
new file mode 100644
index 0000000000000..e69de29bb2d1d
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/StatisticsSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/StatisticsSuite.scala
index 7c82964b5ecdc..8d6ca9939a730 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/StatisticsSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/StatisticsSuite.scala
@@ -17,6 +17,8 @@
 
 package org.apache.spark.sql.hive
 
+import org.scalatest.BeforeAndAfterAll
+
 import scala.reflect.ClassTag
 
 
@@ -26,7 +28,9 @@ import org.apache.spark.sql.execution.{BroadcastHashJoin, ShuffledHashJoin}
 import org.apache.spark.sql.hive.test.TestHive
 import org.apache.spark.sql.hive.test.TestHive._
 
-class StatisticsSuite extends QueryTest {
+class StatisticsSuite extends QueryTest with BeforeAndAfterAll {
+  TestHive.reset()
+  TestHive.cacheTables = false
 
   test("parse analyze commands") {
     def assertAnalyzeCommand(analyzeCommand: String, c: Class[_]) {
@@ -126,7 +130,7 @@ class StatisticsSuite extends QueryTest {
     val sizes = rdd.queryExecution.analyzed.collect { case mr: MetastoreRelation =>
       mr.statistics.sizeInBytes
     }
-    assert(sizes.size === 1)
+    assert(sizes.size === 1, s"Size wrong for:\n ${rdd.queryExecution}")
     assert(sizes(0).equals(BigInt(5812)),
       s"expected exact size 5812 for test table 'src', got: ${sizes(0)}")
   }
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
index fdb2f41f5a5b6..26e4ec6e6dcce 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
@@ -32,6 +32,71 @@ case class TestData(a: Int, b: String)
  */
 class HiveQuerySuite extends HiveComparisonTest {
 
+  createQueryTest("count distinct 0 values",
+    """
+      |SELECT COUNT(DISTINCT a) FROM (
+      |  SELECT 'a' AS a FROM src LIMIT 0) table
+    """.stripMargin)
+
+  createQueryTest("count distinct 1 value strings",
+    """
+      |SELECT COUNT(DISTINCT a) FROM (
+      |  SELECT 'a' AS a FROM src LIMIT 1 UNION ALL
+      |  SELECT 'b' AS a FROM src LIMIT 1) table
+    """.stripMargin)
+
+  createQueryTest("count distinct 1 value",
+    """
+      |SELECT COUNT(DISTINCT a) FROM (
+      |  SELECT 1 AS a FROM src LIMIT 1 UNION ALL
+      |  SELECT 1 AS a FROM src LIMIT 1) table
+    """.stripMargin)
+
+  createQueryTest("count distinct 2 values",
+    """
+      |SELECT COUNT(DISTINCT a) FROM (
+      |  SELECT 1 AS a FROM src LIMIT 1 UNION ALL
+      |  SELECT 2 AS a FROM src LIMIT 1) table
+    """.stripMargin)
+
+  createQueryTest("count distinct 2 values including null",
+    """
+      |SELECT COUNT(DISTINCT a, 1) FROM (
+      |  SELECT 1 AS a FROM src LIMIT 1 UNION ALL
+      |  SELECT 1 AS a FROM src LIMIT 1 UNION ALL
+      |  SELECT null AS a FROM src LIMIT 1) table
+    """.stripMargin)
+
+  createQueryTest("count distinct 1 value + null",
+  """
+    |SELECT COUNT(DISTINCT a) FROM (
+    |  SELECT 1 AS a FROM src LIMIT 1 UNION ALL
+    |  SELECT 1 AS a FROM src LIMIT 1 UNION ALL
+    |  SELECT null AS a FROM src LIMIT 1) table
+  """.stripMargin)
+
+  createQueryTest("count distinct 1 value long",
+    """
+      |SELECT COUNT(DISTINCT a) FROM (
+      |  SELECT 1L AS a FROM src LIMIT 1 UNION ALL
+      |  SELECT 1L AS a FROM src LIMIT 1) table
+    """.stripMargin)
+
+  createQueryTest("count distinct 2 values long",
+    """
+      |SELECT COUNT(DISTINCT a) FROM (
+      |  SELECT 1L AS a FROM src LIMIT 1 UNION ALL
+      |  SELECT 2L AS a FROM src LIMIT 1) table
+    """.stripMargin)
+
+  createQueryTest("count distinct 1 value + null long",
+    """
+      |SELECT COUNT(DISTINCT a) FROM (
+      |  SELECT 1L AS a FROM src LIMIT 1 UNION ALL
+      |  SELECT 1L AS a FROM src LIMIT 1 UNION ALL
+      |  SELECT null AS a FROM src LIMIT 1) table
+    """.stripMargin)
+
   createQueryTest("null case",
     "SELECT case when(true) then 1 else null end FROM src LIMIT 1")
 
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveSerDeSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveSerDeSuite.scala
index df9bae96494d5..8bc72384a64ee 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveSerDeSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveSerDeSuite.scala
@@ -17,10 +17,19 @@
 
 package org.apache.spark.sql.hive.execution
 
+import org.scalatest.BeforeAndAfterAll
+
+import org.apache.spark.sql.hive.test.TestHive
+
 /**
  * A set of tests that validates support for Hive SerDe.
  */
-class HiveSerDeSuite extends HiveComparisonTest {
+class HiveSerDeSuite extends HiveComparisonTest with BeforeAndAfterAll {
+
+  override def beforeAll() = {
+    TestHive.cacheTables = false
+  }
+
   createQueryTest(
     "Read and write with LazySimpleSerDe (tab separated)",
     "SELECT * from serdeins")
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/PruningSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/PruningSuite.scala
index 1a6dbc0ce0c0d..8275e2d3bcce3 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/PruningSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/PruningSuite.scala
@@ -17,6 +17,8 @@
 
 package org.apache.spark.sql.hive.execution
 
+import org.scalatest.BeforeAndAfter
+
 import org.apache.spark.sql.hive.test.TestHive
 
 /* Implicit conversions */
@@ -25,9 +27,10 @@ import scala.collection.JavaConversions._
 /**
  * A set of test cases that validate partition and column pruning.
  */
-class PruningSuite extends HiveComparisonTest {
+class PruningSuite extends HiveComparisonTest with BeforeAndAfter {
   // MINOR HACK: You must run a query before calling reset the first time.
   TestHive.sql("SHOW TABLES")
+  TestHive.cacheTables = false
 
   // Column/partition pruning is not implemented for `InMemoryColumnarTableScan` yet, need to reset
   // the environment to ensure all referenced tables in this suites are not cached in-memory.

From 3519b5e8e55b4530d7f7c0bcab254f863dbfa814 Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Sat, 23 Aug 2014 16:21:08 -0700
Subject: [PATCH 062/489] [SPARK-2967][SQL]  Follow-up: Also copy hash
 expressions in sort based shuffle fix.

Follow-up to #2066

Author: Michael Armbrust <michael@databricks.com>

Closes #2072 from marmbrus/sortShuffle and squashes the following commits:

2ff8114 [Michael Armbrust] Fix bug
---
 .../scala/org/apache/spark/sql/execution/Exchange.scala  | 9 ++++++---
 1 file changed, 6 insertions(+), 3 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala
index 09c34b7059fc3..4802e40595807 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala
@@ -46,12 +46,15 @@ case class Exchange(newPartitioning: Partitioning, child: SparkPlan) extends Una
       case HashPartitioning(expressions, numPartitions) =>
         // TODO: Eliminate redundant expressions in grouping key and value.
         val rdd = child.execute().mapPartitions { iter =>
-          @transient val hashExpressions =
-            newMutableProjection(expressions, child.output)()
-
           if (sortBasedShuffleOn) {
+            @transient val hashExpressions =
+              newProjection(expressions, child.output)
+
             iter.map(r => (hashExpressions(r), r.copy()))
           } else {
+            @transient val hashExpressions =
+              newMutableProjection(expressions, child.output)()
+
             val mutablePair = new MutablePair[Row, Row]()
             iter.map(r => mutablePair.update(hashExpressions(r), r))
           }

From db436e36c4e20893de708a0bc07a5a8877c49563 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies.liu@gmail.com>
Date: Sat, 23 Aug 2014 18:55:13 -0700
Subject: [PATCH 063/489] [SPARK-2871] [PySpark] add `key` argument for max(),
 min() and top(n)

RDD.max(key=None)

        param key: A function used to generate key for comparing

        >>> rdd = sc.parallelize([1.0, 5.0, 43.0, 10.0])
        >>> rdd.max()
        43.0
        >>> rdd.max(key=str)
        5.0

RDD.min(key=None)

        Find the minimum item in this RDD.

        param key: A function used to generate key for comparing

        >>> rdd = sc.parallelize([2.0, 5.0, 43.0, 10.0])
        >>> rdd.min()
        2.0
        >>> rdd.min(key=str)
        10.0

RDD.top(num, key=None)

        Get the top N elements from a RDD.

        Note: It returns the list sorted in descending order.
        >>> sc.parallelize([10, 4, 2, 12, 3]).top(1)
        [12]
        >>> sc.parallelize([2, 3, 4, 5, 6], 2).top(2)
        [6, 5]
        >>> sc.parallelize([10, 4, 2, 12, 3]).top(3, key=str)
        [4, 3, 2]

Author: Davies Liu <davies.liu@gmail.com>

Closes #2094 from davies/cmp and squashes the following commits:

ccbaf25 [Davies Liu] add `key` to top()
ad7e374 [Davies Liu] fix tests
2f63512 [Davies Liu] change `comp` to `key` in min/max
dd91e08 [Davies Liu] add `comp` argument for RDD.max() and RDD.min()
---
 python/pyspark/rdd.py | 44 ++++++++++++++++++++++++++-----------------
 1 file changed, 27 insertions(+), 17 deletions(-)

diff --git a/python/pyspark/rdd.py b/python/pyspark/rdd.py
index 3eefc878d274e..bdd8bc82869fb 100644
--- a/python/pyspark/rdd.py
+++ b/python/pyspark/rdd.py
@@ -810,23 +810,37 @@ def func(iterator):
 
         return self.mapPartitions(func).fold(zeroValue, combOp)
 
-    def max(self):
+    def max(self, key=None):
         """
         Find the maximum item in this RDD.
 
-        >>> sc.parallelize([1.0, 5.0, 43.0, 10.0]).max()
+        @param key: A function used to generate key for comparing
+
+        >>> rdd = sc.parallelize([1.0, 5.0, 43.0, 10.0])
+        >>> rdd.max()
         43.0
+        >>> rdd.max(key=str)
+        5.0
         """
-        return self.reduce(max)
+        if key is None:
+            return self.reduce(max)
+        return self.reduce(lambda a, b: max(a, b, key=key))
 
-    def min(self):
+    def min(self, key=None):
         """
         Find the minimum item in this RDD.
 
-        >>> sc.parallelize([1.0, 5.0, 43.0, 10.0]).min()
-        1.0
+        @param key: A function used to generate key for comparing
+
+        >>> rdd = sc.parallelize([2.0, 5.0, 43.0, 10.0])
+        >>> rdd.min()
+        2.0
+        >>> rdd.min(key=str)
+        10.0
         """
-        return self.reduce(min)
+        if key is None:
+            return self.reduce(min)
+        return self.reduce(lambda a, b: min(a, b, key=key))
 
     def sum(self):
         """
@@ -924,7 +938,7 @@ def mergeMaps(m1, m2):
             return m1
         return self.mapPartitions(countPartition).reduce(mergeMaps)
 
-    def top(self, num):
+    def top(self, num, key=None):
         """
         Get the top N elements from a RDD.
 
@@ -933,20 +947,16 @@ def top(self, num):
         [12]
         >>> sc.parallelize([2, 3, 4, 5, 6], 2).top(2)
         [6, 5]
+        >>> sc.parallelize([10, 4, 2, 12, 3]).top(3, key=str)
+        [4, 3, 2]
         """
         def topIterator(iterator):
-            q = []
-            for k in iterator:
-                if len(q) < num:
-                    heapq.heappush(q, k)
-                else:
-                    heapq.heappushpop(q, k)
-            yield q
+            yield heapq.nlargest(num, iterator, key=key)
 
         def merge(a, b):
-            return next(topIterator(a + b))
+            return heapq.nlargest(num, a + b, key=key)
 
-        return sorted(self.mapPartitions(topIterator).reduce(merge), reverse=True)
+        return self.mapPartitions(topIterator).reduce(merge)
 
     def takeOrdered(self, num, key=None):
         """

From 8df4dad4951ca6e687df1288331909878922a55f Mon Sep 17 00:00:00 2001
From: Davies Liu <davies.liu@gmail.com>
Date: Sat, 23 Aug 2014 19:33:34 -0700
Subject: [PATCH 064/489] [SPARK-2871] [PySpark] add approx API for RDD

RDD.countApprox(self, timeout, confidence=0.95)

        :: Experimental ::
        Approximate version of count() that returns a potentially incomplete
        result within a timeout, even if not all tasks have finished.

        >>> rdd = sc.parallelize(range(1000), 10)
        >>> rdd.countApprox(1000, 1.0)
        1000

RDD.sumApprox(self, timeout, confidence=0.95)

        Approximate operation to return the sum within a timeout
        or meet the confidence.

        >>> rdd = sc.parallelize(range(1000), 10)
        >>> r = sum(xrange(1000))
        >>> (rdd.sumApprox(1000) - r) / r < 0.05

RDD.meanApprox(self, timeout, confidence=0.95)

        :: Experimental ::
        Approximate operation to return the mean within a timeout
        or meet the confidence.

        >>> rdd = sc.parallelize(range(1000), 10)
        >>> r = sum(xrange(1000)) / 1000.0
        >>> (rdd.meanApprox(1000) - r) / r < 0.05
        True

Author: Davies Liu <davies.liu@gmail.com>

Closes #2095 from davies/approx and squashes the following commits:

e8c252b [Davies Liu] add approx API for RDD
---
 .../apache/spark/api/python/PythonRDD.scala   | 17 ++++
 python/pyspark/rdd.py                         | 81 +++++++++++++++++++
 2 files changed, 98 insertions(+)

diff --git a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
index 747023812f754..ae8010300a500 100644
--- a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
+++ b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
@@ -749,6 +749,23 @@ private[spark] object PythonRDD extends Logging {
       }
     }
   }
+
+  /**
+    * Convert an RDD of serialized Python objects to RDD of objects, that is usable by PySpark.
+    */
+  def pythonToJava(pyRDD: JavaRDD[Array[Byte]], batched: Boolean): JavaRDD[Any] = {
+    pyRDD.rdd.mapPartitions { iter =>
+      val unpickle = new Unpickler
+      iter.flatMap { row =>
+        val obj = unpickle.loads(row)
+        if (batched) {
+          obj.asInstanceOf[JArrayList[_]]
+        } else {
+          Seq(obj)
+        }
+      }
+    }.toJavaRDD()
+  }
 }
 
 private
diff --git a/python/pyspark/rdd.py b/python/pyspark/rdd.py
index bdd8bc82869fb..9f88340d03778 100644
--- a/python/pyspark/rdd.py
+++ b/python/pyspark/rdd.py
@@ -131,6 +131,22 @@ def __exit__(self, type, value, tb):
             self._context._jsc.setCallSite(None)
 
 
+class BoundedFloat(float):
+    """
+    Bounded value is generated by approximate job, with confidence and low
+    bound and high bound.
+
+    >>> BoundedFloat(100.0, 0.95, 95.0, 105.0)
+    100.0
+    """
+    def __new__(cls, mean, confidence, low, high):
+        obj = float.__new__(cls, mean)
+        obj.confidence = confidence
+        obj.low = low
+        obj.high = high
+        return obj
+
+
 class MaxHeapQ(object):
 
     """
@@ -1792,6 +1808,71 @@ def _defaultReducePartitions(self):
     # keys in the pairs.  This could be an expensive operation, since those
     # hashes aren't retained.
 
+    def _is_pickled(self):
+        """ Return this RDD is serialized by Pickle or not. """
+        der = self._jrdd_deserializer
+        if isinstance(der, PickleSerializer):
+            return True
+        if isinstance(der, BatchedSerializer) and isinstance(der.serializer, PickleSerializer):
+            return True
+        return False
+
+    def _to_jrdd(self):
+        """ Return an JavaRDD of Object by unpickling
+
+        It will convert each Python object into Java object by Pyrolite, whenever the
+        RDD is serialized in batch or not.
+        """
+        if not self._is_pickled():
+            self = self._reserialize(BatchedSerializer(PickleSerializer(), 1024))
+        batched = isinstance(self._jrdd_deserializer, BatchedSerializer)
+        return self.ctx._jvm.PythonRDD.pythonToJava(self._jrdd, batched)
+
+    def countApprox(self, timeout, confidence=0.95):
+        """
+        :: Experimental ::
+        Approximate version of count() that returns a potentially incomplete
+        result within a timeout, even if not all tasks have finished.
+
+        >>> rdd = sc.parallelize(range(1000), 10)
+        >>> rdd.countApprox(1000, 1.0)
+        1000
+        """
+        drdd = self.mapPartitions(lambda it: [float(sum(1 for i in it))])
+        return int(drdd.sumApprox(timeout, confidence))
+
+    def sumApprox(self, timeout, confidence=0.95):
+        """
+        :: Experimental ::
+        Approximate operation to return the sum within a timeout
+        or meet the confidence.
+
+        >>> rdd = sc.parallelize(range(1000), 10)
+        >>> r = sum(xrange(1000))
+        >>> (rdd.sumApprox(1000) - r) / r < 0.05
+        True
+        """
+        jrdd = self.mapPartitions(lambda it: [float(sum(it))])._to_jrdd()
+        jdrdd = self.ctx._jvm.JavaDoubleRDD.fromRDD(jrdd.rdd())
+        r = jdrdd.sumApprox(timeout, confidence).getFinalValue()
+        return BoundedFloat(r.mean(), r.confidence(), r.low(), r.high())
+
+    def meanApprox(self, timeout, confidence=0.95):
+        """
+        :: Experimental ::
+        Approximate operation to return the mean within a timeout
+        or meet the confidence.
+
+        >>> rdd = sc.parallelize(range(1000), 10)
+        >>> r = sum(xrange(1000)) / 1000.0
+        >>> (rdd.meanApprox(1000) - r) / r < 0.05
+        True
+        """
+        jrdd = self.map(float)._to_jrdd()
+        jdrdd = self.ctx._jvm.JavaDoubleRDD.fromRDD(jrdd.rdd())
+        r = jdrdd.meanApprox(timeout, confidence).getFinalValue()
+        return BoundedFloat(r.mean(), r.confidence(), r.low(), r.high())
+
 
 class PipelinedRDD(RDD):
 

From 8861cdf11288f7597809e9e0e1cad66fb85dd946 Mon Sep 17 00:00:00 2001
From: Raymond Liu <raymond.liu@intel.com>
Date: Sat, 23 Aug 2014 19:47:14 -0700
Subject: [PATCH 065/489] Clean unused code in SortShuffleWriter

Just clean unused code which have been moved into ExternalSorter.

Author: Raymond Liu <raymond.liu@intel.com>

Closes #1882 from colorant/sortShuffleWriter and squashes the following commits:

e6337be [Raymond Liu] Clean unused code in SortShuffleWriter
---
 .../org/apache/spark/shuffle/sort/SortShuffleWriter.scala  | 7 +------
 1 file changed, 1 insertion(+), 6 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/shuffle/sort/SortShuffleWriter.scala b/core/src/main/scala/org/apache/spark/shuffle/sort/SortShuffleWriter.scala
index 22f656fa371ea..b8c9ad46ab035 100644
--- a/core/src/main/scala/org/apache/spark/shuffle/sort/SortShuffleWriter.scala
+++ b/core/src/main/scala/org/apache/spark/shuffle/sort/SortShuffleWriter.scala
@@ -17,12 +17,11 @@
 
 package org.apache.spark.shuffle.sort
 
-import java.io.{BufferedOutputStream, File, FileOutputStream, DataOutputStream}
+import java.io.File
 
 import org.apache.spark.{MapOutputTracker, SparkEnv, Logging, TaskContext}
 import org.apache.spark.executor.ShuffleWriteMetrics
 import org.apache.spark.scheduler.MapStatus
-import org.apache.spark.serializer.Serializer
 import org.apache.spark.shuffle.{ShuffleWriter, BaseShuffleHandle}
 import org.apache.spark.storage.ShuffleBlockId
 import org.apache.spark.util.collection.ExternalSorter
@@ -37,10 +36,6 @@ private[spark] class SortShuffleWriter[K, V, C](
   private val numPartitions = dep.partitioner.numPartitions
 
   private val blockManager = SparkEnv.get.blockManager
-  private val ser = Serializer.getSerializer(dep.serializer.orNull)
-
-  private val conf = SparkEnv.get.conf
-  private val fileBufferSize = conf.getInt("spark.shuffle.file.buffer.kb", 32) * 1024
 
   private var sorter: ExternalSorter[K, V, _] = null
   private var outputFile: File = null

From ded6796bf54f5c005b27135d7dec19634038a1c6 Mon Sep 17 00:00:00 2001
From: Kousuke Saruta <sarutak@oss.nttdata.co.jp>
Date: Sun, 24 Aug 2014 09:43:44 -0700
Subject: [PATCH 066/489] [SPARK-3192] Some scripts have 2 space indentation
 but other scripts have 4 space indentation.

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

Closes #2104 from sarutak/SPARK-3192 and squashes the following commits:

db78419 [Kousuke Saruta] Modified indentation of spark-shell
---
 bin/spark-shell | 36 ++++++++++++++++++------------------
 1 file changed, 18 insertions(+), 18 deletions(-)

diff --git a/bin/spark-shell b/bin/spark-shell
index 8b7ccd7439551..0ab4e14f5b744 100755
--- a/bin/spark-shell
+++ b/bin/spark-shell
@@ -22,7 +22,7 @@
 
 cygwin=false
 case "`uname`" in
-    CYGWIN*) cygwin=true;;
+  CYGWIN*) cygwin=true;;
 esac
 
 # Enter posix mode for bash
@@ -32,9 +32,9 @@ set -o posix
 FWDIR="$(cd `dirname $0`/..; pwd)"
 
 function usage() {
-    echo "Usage: ./bin/spark-shell [options]"
-    $FWDIR/bin/spark-submit --help 2>&1 | grep -v Usage 1>&2
-    exit 0
+  echo "Usage: ./bin/spark-shell [options]"
+  $FWDIR/bin/spark-submit --help 2>&1 | grep -v Usage 1>&2
+  exit 0
 }
 
 if [[ "$@" = *--help ]] || [[ "$@" = *-h ]]; then
@@ -46,20 +46,20 @@ SUBMIT_USAGE_FUNCTION=usage
 gatherSparkSubmitOpts "$@"
 
 function main() {
-    if $cygwin; then
-        # Workaround for issue involving JLine and Cygwin
-        # (see http://sourceforge.net/p/jline/bugs/40/).
-        # If you're using the Mintty terminal emulator in Cygwin, may need to set the
-        # "Backspace sends ^H" setting in "Keys" section of the Mintty options
-        # (see https://github.com/sbt/sbt/issues/562).
-        stty -icanon min 1 -echo > /dev/null 2>&1
-        export SPARK_SUBMIT_OPTS="$SPARK_SUBMIT_OPTS -Djline.terminal=unix"
-        $FWDIR/bin/spark-submit --class org.apache.spark.repl.Main "${SUBMISSION_OPTS[@]}" spark-shell "${APPLICATION_OPTS[@]}"
-        stty icanon echo > /dev/null 2>&1
-    else
-        export SPARK_SUBMIT_OPTS
-        $FWDIR/bin/spark-submit --class org.apache.spark.repl.Main "${SUBMISSION_OPTS[@]}" spark-shell "${APPLICATION_OPTS[@]}"
-    fi
+  if $cygwin; then
+    # Workaround for issue involving JLine and Cygwin
+    # (see http://sourceforge.net/p/jline/bugs/40/).
+    # If you're using the Mintty terminal emulator in Cygwin, may need to set the
+    # "Backspace sends ^H" setting in "Keys" section of the Mintty options
+    # (see https://github.com/sbt/sbt/issues/562).
+    stty -icanon min 1 -echo > /dev/null 2>&1
+    export SPARK_SUBMIT_OPTS="$SPARK_SUBMIT_OPTS -Djline.terminal=unix"
+    $FWDIR/bin/spark-submit --class org.apache.spark.repl.Main "${SUBMISSION_OPTS[@]}" spark-shell "${APPLICATION_OPTS[@]}"
+    stty icanon echo > /dev/null 2>&1
+  else
+    export SPARK_SUBMIT_OPTS
+    $FWDIR/bin/spark-submit --class org.apache.spark.repl.Main "${SUBMISSION_OPTS[@]}" spark-shell "${APPLICATION_OPTS[@]}"
+  fi
 }
 
 # Copy restore-TTY-on-exit functions from Scala script so spark-shell exits properly even in

From 572952ae615895efaaabcd509d582262000c0852 Mon Sep 17 00:00:00 2001
From: DB Tsai <dbtsai@alpinenow.com>
Date: Sun, 24 Aug 2014 17:33:33 -0700
Subject: [PATCH 067/489] [SPARK-2841][MLlib] Documentation for feature
 transformations

Documentation for newly added feature transformations:
1. TF-IDF
2. StandardScaler
3. Normalizer

Author: DB Tsai <dbtsai@alpinenow.com>

Closes #2068 from dbtsai/transformer-documentation and squashes the following commits:

109f324 [DB Tsai] address feedback
---
 docs/mllib-feature-extraction.md | 109 ++++++++++++++++++++++++++++++-
 1 file changed, 107 insertions(+), 2 deletions(-)

diff --git a/docs/mllib-feature-extraction.md b/docs/mllib-feature-extraction.md
index 2031b96235ee9..44f0f76220b6e 100644
--- a/docs/mllib-feature-extraction.md
+++ b/docs/mllib-feature-extraction.md
@@ -1,7 +1,7 @@
 ---
 layout: global
-title: Feature Extraction - MLlib
-displayTitle: <a href="mllib-guide.html">MLlib</a> - Feature Extraction 
+title: Feature Extraction and Transformation - MLlib
+displayTitle: <a href="mllib-guide.html">MLlib</a> - Feature Extraction and Transformation
 ---
 
 * Table of contents
@@ -148,3 +148,108 @@ for((synonym, cosineSimilarity) <- synonyms) {
 {% endhighlight %}
 </div>
 </div>
+
+## StandardScaler
+
+Standardizes features by scaling to unit variance and/or removing the mean using column summary
+statistics on the samples in the training set. This is a very common pre-processing step.
+
+For example, RBF kernel of Support Vector Machines or the L1 and L2 regularized linear models
+typically work better when all features have unit variance and/or zero mean.
+
+Standardization can improve the convergence rate during the optimization process, and also prevents
+against features with very large variances exerting an overly large influence during model training.
+
+### Model Fitting
+
+[`StandardScaler`](api/scala/index.html#org.apache.spark.mllib.feature.StandardScaler) has the
+following parameters in the constructor:
+
+* `withMean` False by default. Centers the data with mean before scaling. It will build a dense
+output, so this does not work on sparse input and will raise an exception.
+* `withStd` True by default. Scales the data to unit variance.
+
+We provide a [`fit`](api/scala/index.html#org.apache.spark.mllib.feature.StandardScaler) method in
+`StandardScaler` which can take an input of `RDD[Vector]`, learn the summary statistics, and then
+return a model which can transform the input dataset into unit variance and/or zero mean features
+depending how we configure the `StandardScaler`.
+
+This model implements [`VectorTransformer`](api/scala/index.html#org.apache.spark.mllib.feature.VectorTransformer)
+which can apply the standardization on a `Vector` to produce a transformed `Vector` or on
+an `RDD[Vector]` to produce a transformed `RDD[Vector]`.
+
+Note that if the variance of a feature is zero, it will return default `0.0` value in the `Vector`
+for that feature.
+
+### Example
+
+The example below demonstrates how to load a dataset in libsvm format, and standardize the features
+so that the new features have unit variance and/or zero mean.
+
+<div class="codetabs">
+<div data-lang="scala">
+{% highlight scala %}
+import org.apache.spark.SparkContext._
+import org.apache.spark.mllib.feature.StandardScaler
+import org.apache.spark.mllib.linalg.Vectors
+import org.apache.spark.mllib.util.MLUtils
+
+val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt")
+
+val scaler1 = new StandardScaler().fit(data.map(x => x.features))
+val scaler2 = new StandardScaler(withMean = true, withStd = true).fit(data.map(x => x.features))
+
+// data1 will be unit variance.
+val data1 = data.map(x => (x.label, scaler1.transform(x.features)))
+
+// Without converting the features into dense vectors, transformation with zero mean will raise
+// exception on sparse vector.
+// data2 will be unit variance and zero mean.
+val data2 = data.map(x => (x.label, scaler2.transform(Vectors.dense(x.features.toArray))))
+{% endhighlight %}
+</div>
+</div>
+
+## Normalizer
+
+Normalizer scales individual samples to have unit $L^p$ norm. This is a common operation for text
+classification or clustering. For example, the dot product of two $L^2$ normalized TF-IDF vectors
+is the cosine similarity of the vectors.
+
+[`Normalizer`](api/scala/index.html#org.apache.spark.mllib.feature.Normalizer) has the following
+parameter in the constructor:
+
+* `p` Normalization in $L^p$ space, $p = 2$ by default.
+
+`Normalizer` implements [`VectorTransformer`](api/scala/index.html#org.apache.spark.mllib.feature.VectorTransformer)
+which can apply the normalization on a `Vector` to produce a transformed `Vector` or on
+an `RDD[Vector]` to produce a transformed `RDD[Vector]`.
+
+Note that if the norm of the input is zero, it will return the input vector.
+
+### Example
+
+The example below demonstrates how to load a dataset in libsvm format, and normalizes the features
+with $L^2$ norm, and $L^\infty$ norm.
+
+<div class="codetabs">
+<div data-lang="scala">
+{% highlight scala %}
+import org.apache.spark.SparkContext._
+import org.apache.spark.mllib.feature.Normalizer
+import org.apache.spark.mllib.linalg.Vectors
+import org.apache.spark.mllib.util.MLUtils
+
+val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt")
+
+val normalizer1 = new Normalizer()
+val normalizer2 = new Normalizer(p = Double.PositiveInfinity)
+
+// Each sample in data1 will be normalized using $L^2$ norm.
+val data1 = data.map(x => (x.label, normalizer1.transform(x.features)))
+
+// Each sample in data2 will be normalized using $L^\infty$ norm.
+val data2 = data.map(x => (x.label, normalizer2.transform(x.features)))
+{% endhighlight %}
+</div>
+</div>

From b1b20301b3a1b35564d61e58eb5964d5ad5e4d7d Mon Sep 17 00:00:00 2001
From: Reza Zadeh <rizlar@gmail.com>
Date: Sun, 24 Aug 2014 17:35:54 -0700
Subject: [PATCH 068/489] [MLlib][SPARK-2997] Update SVD documentation to
 reflect roughly square

Update the documentation to reflect the fact we can handle roughly square matrices.

Author: Reza Zadeh <rizlar@gmail.com>

Closes #2070 from rezazadeh/svddocs and squashes the following commits:

826b8fe [Reza Zadeh] left singular vectors
3f34fc6 [Reza Zadeh] PCA is still TS
7ffa2aa [Reza Zadeh] better title
aeaf39d [Reza Zadeh] More docs
788ed13 [Reza Zadeh] add computational cost explanation
6429c59 [Reza Zadeh] Add link to rowmatrix docs
1eeab8b [Reza Zadeh] Update SVD documentation to reflect roughly square
---
 docs/mllib-dimensionality-reduction.md | 29 ++++++++++++++++++++------
 1 file changed, 23 insertions(+), 6 deletions(-)

diff --git a/docs/mllib-dimensionality-reduction.md b/docs/mllib-dimensionality-reduction.md
index 065d646496131..9f2cf6d48ec75 100644
--- a/docs/mllib-dimensionality-reduction.md
+++ b/docs/mllib-dimensionality-reduction.md
@@ -11,7 +11,7 @@ displayTitle: <a href="mllib-guide.html">MLlib</a> - Dimensionality Reduction
 of reducing the number of variables under consideration.
 It can be used to extract latent features from raw and noisy features
 or compress data while maintaining the structure.
-MLlib provides support for dimensionality reduction on tall-and-skinny matrices.
+MLlib provides support for dimensionality reduction on the <a href="mllib-basics.html#rowmatrix">RowMatrix</a> class.
 
 ## Singular value decomposition (SVD)
 
@@ -39,8 +39,26 @@ If we keep the top $k$ singular values, then the dimensions of the resulting low
 * `$\Sigma$`: `$k \times k$`,
 * `$V$`: `$n \times k$`.
  
-MLlib provides SVD functionality to row-oriented matrices that have only a few columns,
-say, less than $1000$, but many rows, i.e., *tall-and-skinny* matrices.
+### Performance
+We assume $n$ is smaller than $m$. The singular values and the right singular vectors are derived
+from the eigenvalues and the eigenvectors of the Gramian matrix $A^T A$. The matrix
+storing the left singular vectors $U$, is computed via matrix multiplication as
+$U = A (V S^{-1})$, if requested by the user via the computeU parameter. 
+The actual method to use is determined automatically based on the computational cost:
+
+* If $n$ is small ($n < 100$) or $k$ is large compared with $n$ ($k > n / 2$), we compute the Gramian matrix
+first and then compute its top eigenvalues and eigenvectors locally on the driver.
+This requires a single pass with $O(n^2)$ storage on each executor and on the driver, and
+$O(n^2 k)$ time on the driver.
+* Otherwise, we compute $(A^T A) v$ in a distributive way and send it to
+<a href="http://www.caam.rice.edu/software/ARPACK/">ARPACK</a> to
+compute $(A^T A)$'s top eigenvalues and eigenvectors on the driver node. This requires $O(k)$
+passes, $O(n)$ storage on each executor, and $O(n k)$ storage on the driver.
+
+### SVD Example
+ 
+MLlib provides SVD functionality to row-oriented matrices, provided in the
+<a href="mllib-basics.html#rowmatrix">RowMatrix</a> class. 
 
 <div class="codetabs">
 <div data-lang="scala" markdown="1">
@@ -124,9 +142,8 @@ MLlib supports PCA for tall-and-skinny matrices stored in row-oriented format.
 <div class="codetabs">
 <div data-lang="scala" markdown="1">
 
-The following code demonstrates how to compute principal components on a tall-and-skinny `RowMatrix`
+The following code demonstrates how to compute principal components on a `RowMatrix`
 and use them to project the vectors into a low-dimensional space.
-The number of columns should be small, e.g, less than 1000.
 
 {% highlight scala %}
 import org.apache.spark.mllib.linalg.Matrix
@@ -144,7 +161,7 @@ val projected: RowMatrix = mat.multiply(pc)
 
 <div data-lang="java" markdown="1">
 
-The following code demonstrates how to compute principal components on a tall-and-skinny `RowMatrix`
+The following code demonstrates how to compute principal components on a `RowMatrix`
 and use them to project the vectors into a low-dimensional space.
 The number of columns should be small, e.g, less than 1000.
 

From fb0db772421b6902b80137bf769db3b418ab2ccf Mon Sep 17 00:00:00 2001
From: Davies Liu <davies.liu@gmail.com>
Date: Sun, 24 Aug 2014 21:16:05 -0700
Subject: [PATCH 069/489] [SPARK-2871] [PySpark] add zipWithIndex() and
 zipWithUniqueId()

RDD.zipWithIndex()

        Zips this RDD with its element indices.

        The ordering is first based on the partition index and then the
        ordering of items within each partition. So the first item in
        the first partition gets index 0, and the last item in the last
        partition receives the largest index.

        This method needs to trigger a spark job when this RDD contains
        more than one partitions.

        >>> sc.parallelize(range(4), 2).zipWithIndex().collect()
        [(0, 0), (1, 1), (2, 2), (3, 3)]

RDD.zipWithUniqueId()

        Zips this RDD with generated unique Long ids.

        Items in the kth partition will get ids k, n+k, 2*n+k, ..., where
        n is the number of partitions. So there may exist gaps, but this
        method won't trigger a spark job, which is different from
        L{zipWithIndex}

        >>> sc.parallelize(range(4), 2).zipWithUniqueId().collect()
        [(0, 0), (2, 1), (1, 2), (3, 3)]

Author: Davies Liu <davies.liu@gmail.com>

Closes #2092 from davies/zipWith and squashes the following commits:

cebe5bf [Davies Liu] improve test cases, reverse the order of index
0d2a128 [Davies Liu] add zipWithIndex() and zipWithUniqueId()
---
 python/pyspark/rdd.py | 47 +++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 47 insertions(+)

diff --git a/python/pyspark/rdd.py b/python/pyspark/rdd.py
index 9f88340d03778..1374f74968c9e 100644
--- a/python/pyspark/rdd.py
+++ b/python/pyspark/rdd.py
@@ -1741,6 +1741,53 @@ def batch_as(rdd, batchSize):
                                         other._jrdd_deserializer)
         return RDD(pairRDD, self.ctx, deserializer)
 
+    def zipWithIndex(self):
+        """
+        Zips this RDD with its element indices.
+
+        The ordering is first based on the partition index and then the
+        ordering of items within each partition. So the first item in
+        the first partition gets index 0, and the last item in the last
+        partition receives the largest index.
+
+        This method needs to trigger a spark job when this RDD contains
+        more than one partitions.
+
+        >>> sc.parallelize(["a", "b", "c", "d"], 3).zipWithIndex().collect()
+        [('a', 0), ('b', 1), ('c', 2), ('d', 3)]
+        """
+        starts = [0]
+        if self.getNumPartitions() > 1:
+            nums = self.mapPartitions(lambda it: [sum(1 for i in it)]).collect()
+            for i in range(len(nums) - 1):
+                starts.append(starts[-1] + nums[i])
+
+        def func(k, it):
+            for i, v in enumerate(it, starts[k]):
+                yield v, i
+
+        return self.mapPartitionsWithIndex(func)
+
+    def zipWithUniqueId(self):
+        """
+        Zips this RDD with generated unique Long ids.
+
+        Items in the kth partition will get ids k, n+k, 2*n+k, ..., where
+        n is the number of partitions. So there may exist gaps, but this
+        method won't trigger a spark job, which is different from
+        L{zipWithIndex}
+
+        >>> sc.parallelize(["a", "b", "c", "d", "e"], 3).zipWithUniqueId().collect()
+        [('a', 0), ('b', 1), ('c', 4), ('d', 2), ('e', 5)]
+        """
+        n = self.getNumPartitions()
+
+        def func(k, it):
+            for i, v in enumerate(it):
+                yield v, i * n + k
+
+        return self.mapPartitionsWithIndex(func)
+
     def name(self):
         """
         Return the name of this RDD.

From 220f413686ae922bd11776576bf37610cce92c23 Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Mon, 25 Aug 2014 12:30:02 -0700
Subject: [PATCH 070/489] [SPARK-2495][MLLIB] make KMeans constructor public

to re-construct k-means models freeman-lab

Author: Xiangrui Meng <meng@databricks.com>

Closes #2112 from mengxr/public-constructors and squashes the following commits:

18d53a9 [Xiangrui Meng] make KMeans constructor public
---
 .../scala/org/apache/spark/mllib/clustering/KMeansModel.scala   | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeansModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeansModel.scala
index 5823cb6e52e7f..12a3d91cd31a6 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeansModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeansModel.scala
@@ -25,7 +25,7 @@ import org.apache.spark.mllib.linalg.Vector
 /**
  * A clustering model for K-means. Each point belongs to the cluster with the closest center.
  */
-class KMeansModel private[mllib] (val clusterCenters: Array[Vector]) extends Serializable {
+class KMeansModel (val clusterCenters: Array[Vector]) extends Serializable {
 
   /** Total number of clusters. */
   def k: Int = clusterCenters.length

From cd30db566a327ddf63cd242c758e46ce2d9479df Mon Sep 17 00:00:00 2001
From: Sean Owen <sowen@cloudera.com>
Date: Mon, 25 Aug 2014 13:29:07 -0700
Subject: [PATCH 071/489] SPARK-2798 [BUILD] Correct several small errors in
 Flume module pom.xml files

(EDIT) Since the scalatest issue was since resolved, this is now about a few small problems in the Flume Sink `pom.xml`

- `scalatest` is not declared as a test-scope dependency
- Its Avro version doesn't match the rest of the build
- Its Flume version is not synced with the other Flume module
- The other Flume module declares its dependency on Flume Sink slightly incorrectly, hard-coding the Scala 2.10 version
- It depends on Scala Lang directly, which it shouldn't

Author: Sean Owen <sowen@cloudera.com>

Closes #1726 from srowen/SPARK-2798 and squashes the following commits:

a46e2c6 [Sean Owen] scalatest to test scope, harmonize Avro and Flume versions, remove direct Scala dependency, fix '2.10' in Flume dependency
---
 external/flume-sink/pom.xml | 15 ++++++---------
 external/flume/pom.xml      | 12 ++++++------
 pom.xml                     |  1 +
 3 files changed, 13 insertions(+), 15 deletions(-)

diff --git a/external/flume-sink/pom.xml b/external/flume-sink/pom.xml
index 19192e40a7dc3..c1e8e65464fc1 100644
--- a/external/flume-sink/pom.xml
+++ b/external/flume-sink/pom.xml
@@ -25,19 +25,20 @@
     <relativePath>../../pom.xml</relativePath>
   </parent>
 
+  <groupId>org.apache.spark</groupId>
   <artifactId>spark-streaming-flume-sink_2.10</artifactId>
   <properties>
     <sbt.project.name>streaming-flume-sink</sbt.project.name>
   </properties>
-
   <packaging>jar</packaging>
   <name>Spark Project External Flume Sink</name>
   <url>http://spark.apache.org/</url>
+
   <dependencies>
     <dependency>
       <groupId>org.apache.flume</groupId>
       <artifactId>flume-ng-sdk</artifactId>
-      <version>1.4.0</version>
+      <version>${flume.version}</version>
       <exclusions>
         <exclusion>
           <groupId>io.netty</groupId>
@@ -52,7 +53,7 @@
     <dependency>
       <groupId>org.apache.flume</groupId>
       <artifactId>flume-ng-core</artifactId>
-      <version>1.4.0</version>
+      <version>${flume.version}</version>
       <exclusions>
         <exclusion>
           <groupId>io.netty</groupId>
@@ -62,11 +63,7 @@
           <groupId>org.apache.thrift</groupId>
           <artifactId>libthrift</artifactId>
         </exclusion>
-    </exclusions>
-    </dependency>
-    <dependency>
-      <groupId>org.scala-lang</groupId>
-      <artifactId>scala-library</artifactId>
+      </exclusions>
     </dependency>
     <dependency>
       <groupId>org.scalatest</groupId>
@@ -97,7 +94,7 @@
       <plugin>
         <groupId>org.apache.avro</groupId>
         <artifactId>avro-maven-plugin</artifactId>
-        <version>1.7.3</version>
+        <version>${avro.version}</version>
         <configuration>
           <!-- Generate the output in the same directory as the sbt-avro-plugin -->
           <outputDirectory>${project.basedir}/target/scala-${scala.binary.version}/src_managed/main/compiled_avro</outputDirectory>
diff --git a/external/flume/pom.xml b/external/flume/pom.xml
index c532705f3950c..f71f6b6c4f931 100644
--- a/external/flume/pom.xml
+++ b/external/flume/pom.xml
@@ -40,6 +40,11 @@
       <artifactId>spark-streaming_${scala.binary.version}</artifactId>
       <version>${project.version}</version>
     </dependency>
+    <dependency>
+      <groupId>org.apache.spark</groupId>
+      <artifactId>spark-streaming-flume-sink_${scala.binary.version}</artifactId>
+      <version>${project.version}</version>
+    </dependency>
     <dependency>
       <groupId>org.apache.spark</groupId>
       <artifactId>spark-streaming_${scala.binary.version}</artifactId>
@@ -50,7 +55,7 @@
     <dependency>
       <groupId>org.apache.flume</groupId>
       <artifactId>flume-ng-sdk</artifactId>
-      <version>1.4.0</version>
+      <version>${flume.version}</version>
       <exclusions>
         <exclusion>
           <groupId>io.netty</groupId>
@@ -82,11 +87,6 @@
       <artifactId>junit-interface</artifactId>
       <scope>test</scope>
     </dependency>
-    <dependency>
-      <groupId>org.apache.spark</groupId>
-      <artifactId>spark-streaming-flume-sink_2.10</artifactId>
-      <version>${project.version}</version>
-    </dependency>
   </dependencies>
   <build>
     <outputDirectory>target/scala-${scala.binary.version}/classes</outputDirectory>
diff --git a/pom.xml b/pom.xml
index 9cbf3ea5995c3..556b9da3d6d90 100644
--- a/pom.xml
+++ b/pom.xml
@@ -125,6 +125,7 @@
     <protobuf.version>2.4.1</protobuf.version>
     <yarn.version>${hadoop.version}</yarn.version>
     <hbase.version>0.94.6</hbase.version>
+    <flume.version>1.4.0</flume.version>
     <zookeeper.version>3.4.5</zookeeper.version>
     <hive.version>0.12.0</hive.version>
     <parquet.version>1.4.3</parquet.version>

From cc40a709c0494b68404a90769efc7f2b415eb125 Mon Sep 17 00:00:00 2001
From: "Allan Douglas R. de Oliveira" <allan@chaordicsystems.com>
Date: Mon, 25 Aug 2014 13:55:04 -0700
Subject: [PATCH 072/489] SPARK-3180  - Better control of security groups

Adds the --authorized-address and --additional-security-group options as explained in the issue.

Author: Allan Douglas R. de Oliveira <allan@chaordicsystems.com>

Closes #2088 from douglaz/configurable_sg and squashes the following commits:

e3e48ca [Allan Douglas R. de Oliveira] Adds the option to specify the address authorized to access the SG and another option to provide an additional existing SG
---
 ec2/spark_ec2.py | 50 +++++++++++++++++++++++++++++++-----------------
 1 file changed, 32 insertions(+), 18 deletions(-)

diff --git a/ec2/spark_ec2.py b/ec2/spark_ec2.py
index 3a8c816cfffa1..77a246fffe6a7 100755
--- a/ec2/spark_ec2.py
+++ b/ec2/spark_ec2.py
@@ -141,6 +141,12 @@ def parse_args():
     parser.add_option(
         "--security-group-prefix", type="string", default=None,
         help="Use this prefix for the security group rather than the cluster name.")
+    parser.add_option(
+        "--authorized-address", type="string", default="0.0.0.0/0",
+        help="Address to authorize on created security groups (default: 0.0.0.0/0)")
+    parser.add_option(
+        "--additional-security-group", type="string", default="",
+        help="Additional security group to place the machines in")
 
     (opts, args) = parser.parse_args()
     if len(args) != 2:
@@ -293,28 +299,29 @@ def launch_cluster(conn, opts, cluster_name):
     else:
         master_group = get_or_make_group(conn, opts.security_group_prefix + "-master")
         slave_group = get_or_make_group(conn, opts.security_group_prefix + "-slaves")
+    authorized_address = opts.authorized_address
     if master_group.rules == []:  # Group was just now created
         master_group.authorize(src_group=master_group)
         master_group.authorize(src_group=slave_group)
-        master_group.authorize('tcp', 22, 22, '0.0.0.0/0')
-        master_group.authorize('tcp', 8080, 8081, '0.0.0.0/0')
-        master_group.authorize('tcp', 18080, 18080, '0.0.0.0/0')
-        master_group.authorize('tcp', 19999, 19999, '0.0.0.0/0')
-        master_group.authorize('tcp', 50030, 50030, '0.0.0.0/0')
-        master_group.authorize('tcp', 50070, 50070, '0.0.0.0/0')
-        master_group.authorize('tcp', 60070, 60070, '0.0.0.0/0')
-        master_group.authorize('tcp', 4040, 4045, '0.0.0.0/0')
+        master_group.authorize('tcp', 22, 22, authorized_address)
+        master_group.authorize('tcp', 8080, 8081, authorized_address)
+        master_group.authorize('tcp', 18080, 18080, authorized_address)
+        master_group.authorize('tcp', 19999, 19999, authorized_address)
+        master_group.authorize('tcp', 50030, 50030, authorized_address)
+        master_group.authorize('tcp', 50070, 50070, authorized_address)
+        master_group.authorize('tcp', 60070, 60070, authorized_address)
+        master_group.authorize('tcp', 4040, 4045, authorized_address)
         if opts.ganglia:
-            master_group.authorize('tcp', 5080, 5080, '0.0.0.0/0')
+            master_group.authorize('tcp', 5080, 5080, authorized_address)
     if slave_group.rules == []:  # Group was just now created
         slave_group.authorize(src_group=master_group)
         slave_group.authorize(src_group=slave_group)
-        slave_group.authorize('tcp', 22, 22, '0.0.0.0/0')
-        slave_group.authorize('tcp', 8080, 8081, '0.0.0.0/0')
-        slave_group.authorize('tcp', 50060, 50060, '0.0.0.0/0')
-        slave_group.authorize('tcp', 50075, 50075, '0.0.0.0/0')
-        slave_group.authorize('tcp', 60060, 60060, '0.0.0.0/0')
-        slave_group.authorize('tcp', 60075, 60075, '0.0.0.0/0')
+        slave_group.authorize('tcp', 22, 22, authorized_address)
+        slave_group.authorize('tcp', 8080, 8081, authorized_address)
+        slave_group.authorize('tcp', 50060, 50060, authorized_address)
+        slave_group.authorize('tcp', 50075, 50075, authorized_address)
+        slave_group.authorize('tcp', 60060, 60060, authorized_address)
+        slave_group.authorize('tcp', 60075, 60075, authorized_address)
 
     # Check if instances are already running with the cluster name
     existing_masters, existing_slaves = get_existing_cluster(conn, opts, cluster_name,
@@ -326,6 +333,13 @@ def launch_cluster(conn, opts, cluster_name):
     # Figure out Spark AMI
     if opts.ami is None:
         opts.ami = get_spark_ami(opts)
+
+
+    additional_groups = []
+    if opts.additional_security_group:
+        additional_groups = [sg
+                             for sg in conn.get_all_security_groups()
+                             if opts.additional_security_group in (sg.name, sg.id)]
     print "Launching instances..."
 
     try:
@@ -360,7 +374,7 @@ def launch_cluster(conn, opts, cluster_name):
                 placement=zone,
                 count=num_slaves_this_zone,
                 key_name=opts.key_pair,
-                security_groups=[slave_group],
+                security_groups=[slave_group] + additional_groups,
                 instance_type=opts.instance_type,
                 block_device_map=block_map,
                 user_data=user_data_content)
@@ -413,7 +427,7 @@ def launch_cluster(conn, opts, cluster_name):
             num_slaves_this_zone = get_partition(opts.slaves, num_zones, i)
             if num_slaves_this_zone > 0:
                 slave_res = image.run(key_name=opts.key_pair,
-                                      security_groups=[slave_group],
+                                      security_groups=[slave_group] + additional_groups,
                                       instance_type=opts.instance_type,
                                       placement=zone,
                                       min_count=num_slaves_this_zone,
@@ -439,7 +453,7 @@ def launch_cluster(conn, opts, cluster_name):
         if opts.zone == 'all':
             opts.zone = random.choice(conn.get_all_zones()).name
         master_res = image.run(key_name=opts.key_pair,
-                               security_groups=[master_group],
+                               security_groups=[master_group] + additional_groups,
                                instance_type=master_type,
                                placement=opts.zone,
                                min_count=1,

From fd8ace2d9a796f69ce34ad202907008cd6e4d274 Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Mon, 25 Aug 2014 14:55:20 -0700
Subject: [PATCH 073/489] [FIX] fix error message in sendMessageReliably

rxin

Author: Xiangrui Meng <meng@databricks.com>

Closes #2120 from mengxr/sendMessageReliably and squashes the following commits:

b14400c [Xiangrui Meng] fix error message in sendMessageReliably
---
 .../scala/org/apache/spark/network/ConnectionManager.scala    | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/network/ConnectionManager.scala b/core/src/main/scala/org/apache/spark/network/ConnectionManager.scala
index e5e1e72cd912b..578d806263006 100644
--- a/core/src/main/scala/org/apache/spark/network/ConnectionManager.scala
+++ b/core/src/main/scala/org/apache/spark/network/ConnectionManager.scala
@@ -851,8 +851,8 @@ private[spark] class ConnectionManager(
         messageStatuses.synchronized {
           messageStatuses.remove(message.id).foreach ( s => {
             promise.failure(
-              new IOException(s"sendMessageReliably failed because ack " +
-                "was not received within ${ackTimeout} sec"))
+              new IOException("sendMessageReliably failed because ack " +
+                s"was not received within $ackTimeout sec"))
           })
         }
       }

From 805fec845b7aa8b4763e3e0e34bec6c3872469f4 Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian.cs.zju@gmail.com>
Date: Mon, 25 Aug 2014 14:56:51 -0700
Subject: [PATCH 074/489] Fixed a typo in docs/running-on-mesos.md

It should be `spark-env.sh` rather than `spark.env.sh`.

Author: Cheng Lian <lian.cs.zju@gmail.com>

Closes #2119 from liancheng/fix-mesos-doc and squashes the following commits:

f360548 [Cheng Lian] Fixed a typo in docs/running-on-mesos.md
---
 docs/running-on-mesos.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/running-on-mesos.md b/docs/running-on-mesos.md
index bd046cfc1837d..9998dddc652a6 100644
--- a/docs/running-on-mesos.md
+++ b/docs/running-on-mesos.md
@@ -107,7 +107,7 @@ cluster, or `mesos://zk://host:2181` for a multi-master Mesos cluster using ZooK
 
 The driver also needs some configuration in `spark-env.sh` to interact properly with Mesos:
 
-1. In `spark.env.sh` set some environment variables:
+1. In `spark-env.sh` set some environment variables:
  * `export MESOS_NATIVE_LIBRARY=<path to libmesos.so>`. This path is typically
    `<prefix>/lib/libmesos.so` where the prefix is `/usr/local` by default. See Mesos installation
    instructions above. On Mac OS X, the library is called `libmesos.dylib` instead of

From d299e2bf2f6733a6267b7ce85e2b288608b17db3 Mon Sep 17 00:00:00 2001
From: Takuya UESHIN <ueshin@happy-camper.st>
Date: Mon, 25 Aug 2014 16:27:00 -0700
Subject: [PATCH 075/489] [SPARK-3204][SQL] MaxOf would be foldable if both
 left and right are foldable.

Author: Takuya UESHIN <ueshin@happy-camper.st>

Closes #2116 from ueshin/issues/SPARK-3204 and squashes the following commits:

7d9b107 [Takuya UESHIN] Make MaxOf foldable if both left and right are foldable.
---
 .../org/apache/spark/sql/catalyst/expressions/arithmetic.scala  | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
index 8d90614e4501a..5f8b6ae10f0c4 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
@@ -89,6 +89,8 @@ case class Remainder(left: Expression, right: Expression) extends BinaryArithmet
 case class MaxOf(left: Expression, right: Expression) extends Expression {
   type EvaluatedType = Any
 
+  override def foldable = left.foldable && right.foldable
+
   override def nullable = left.nullable && right.nullable
 
   override def children = left :: right :: Nil

From cae9414d3805c6cf00eab6a6144d8f90cd0212f8 Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian.cs.zju@gmail.com>
Date: Mon, 25 Aug 2014 16:29:59 -0700
Subject: [PATCH 076/489] [SPARK-2929][SQL] Refactored Thrift server and CLI
 suites

Removed most hard coded timeout, timing assumptions and all `Thread.sleep`. Simplified IPC and synchronization with `scala.sys.process` and future/promise so that the test suites can run more robustly and faster.

Author: Cheng Lian <lian.cs.zju@gmail.com>

Closes #1856 from liancheng/thriftserver-tests and squashes the following commits:

2d914ca [Cheng Lian] Minor refactoring
0e12e71 [Cheng Lian] Cleaned up test output
0ee921d [Cheng Lian] Refactored Thrift server and CLI suites
---
 .../sql/hive/thriftserver/CliSuite.scala      | 121 +++++++---
 .../thriftserver/HiveThriftServer2Suite.scala | 212 ++++++++++--------
 .../sql/hive/thriftserver/TestUtils.scala     | 108 ---------
 3 files changed, 217 insertions(+), 224 deletions(-)
 delete mode 100644 sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/TestUtils.scala

diff --git a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/CliSuite.scala b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/CliSuite.scala
index 70bea1ed80fda..3475c2c9db080 100644
--- a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/CliSuite.scala
+++ b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/CliSuite.scala
@@ -18,41 +18,112 @@
 
 package org.apache.spark.sql.hive.thriftserver
 
-import java.io.{BufferedReader, InputStreamReader, PrintWriter}
+import scala.collection.mutable.ArrayBuffer
+import scala.concurrent.ExecutionContext.Implicits.global
+import scala.concurrent.duration._
+import scala.concurrent.{Await, Future, Promise}
+import scala.sys.process.{Process, ProcessLogger}
+
+import java.io._
+import java.util.concurrent.atomic.AtomicInteger
 
 import org.apache.hadoop.hive.conf.HiveConf.ConfVars
 import org.scalatest.{BeforeAndAfterAll, FunSuite}
 
-class CliSuite extends FunSuite with BeforeAndAfterAll with TestUtils {
-  val WAREHOUSE_PATH = TestUtils.getWarehousePath("cli")
-  val METASTORE_PATH = TestUtils.getMetastorePath("cli")
+import org.apache.spark.Logging
+import org.apache.spark.sql.catalyst.util.getTempFilePath
+
+class CliSuite extends FunSuite with BeforeAndAfterAll with Logging {
+  def runCliWithin(
+      timeout: FiniteDuration,
+      extraArgs: Seq[String] = Seq.empty)(
+      queriesAndExpectedAnswers: (String, String)*) {
+
+    val (queries, expectedAnswers) = queriesAndExpectedAnswers.unzip
+    val warehousePath = getTempFilePath("warehouse")
+    val metastorePath = getTempFilePath("metastore")
+    val cliScript = "../../bin/spark-sql".split("/").mkString(File.separator)
 
-  override def beforeAll() {
-    val jdbcUrl = s"jdbc:derby:;databaseName=$METASTORE_PATH;create=true"
-    val commands =
-      s"""../../bin/spark-sql
+    val command = {
+      val jdbcUrl = s"jdbc:derby:;databaseName=$metastorePath;create=true"
+      s"""$cliScript
          |  --master local
          |  --hiveconf ${ConfVars.METASTORECONNECTURLKEY}=$jdbcUrl
-         |  --hiveconf ${ConfVars.METASTOREWAREHOUSE}=$WAREHOUSE_PATH
-       """.stripMargin.split("\\s+")
-
-    val pb = new ProcessBuilder(commands: _*)
-    process = pb.start()
-    outputWriter = new PrintWriter(process.getOutputStream, true)
-    inputReader = new BufferedReader(new InputStreamReader(process.getInputStream))
-    errorReader = new BufferedReader(new InputStreamReader(process.getErrorStream))
-    waitForOutput(inputReader, "spark-sql>")
+         |  --hiveconf ${ConfVars.METASTOREWAREHOUSE}=$warehousePath
+       """.stripMargin.split("\\s+").toSeq ++ extraArgs
+    }
+
+    // AtomicInteger is needed because stderr and stdout of the forked process are handled in
+    // different threads.
+    val next = new AtomicInteger(0)
+    val foundAllExpectedAnswers = Promise.apply[Unit]()
+    val queryStream = new ByteArrayInputStream(queries.mkString("\n").getBytes)
+    val buffer = new ArrayBuffer[String]()
+
+    def captureOutput(source: String)(line: String) {
+      buffer += s"$source> $line"
+      if (line.contains(expectedAnswers(next.get()))) {
+        if (next.incrementAndGet() == expectedAnswers.size) {
+          foundAllExpectedAnswers.trySuccess(())
+        }
+      }
+    }
+
+    // Searching expected output line from both stdout and stderr of the CLI process
+    val process = (Process(command) #< queryStream).run(
+      ProcessLogger(captureOutput("stdout"), captureOutput("stderr")))
+
+    Future {
+      val exitValue = process.exitValue()
+      logInfo(s"Spark SQL CLI process exit value: $exitValue")
+    }
+
+    try {
+      Await.result(foundAllExpectedAnswers.future, timeout)
+    } catch { case cause: Throwable =>
+      logError(
+        s"""
+           |=======================
+           |CliSuite failure output
+           |=======================
+           |Spark SQL CLI command line: ${command.mkString(" ")}
+           |
+           |Executed query ${next.get()} "${queries(next.get())}",
+           |But failed to capture expected output "${expectedAnswers(next.get())}" within $timeout.
+           |
+           |${buffer.mkString("\n")}
+           |===========================
+           |End CliSuite failure output
+           |===========================
+         """.stripMargin, cause)
+    } finally {
+      warehousePath.delete()
+      metastorePath.delete()
+      process.destroy()
+    }
   }
 
-  override def afterAll() {
-    process.destroy()
-    process.waitFor()
+  test("Simple commands") {
+    val dataFilePath =
+      Thread.currentThread().getContextClassLoader.getResource("data/files/small_kv.txt")
+
+    runCliWithin(1.minute)(
+      "CREATE TABLE hive_test(key INT, val STRING);"
+        -> "OK",
+      "SHOW TABLES;"
+        -> "hive_test",
+      s"LOAD DATA LOCAL INPATH '$dataFilePath' OVERWRITE INTO TABLE hive_test;"
+        -> "OK",
+      "CACHE TABLE hive_test;"
+        -> "Time taken: ",
+      "SELECT COUNT(*) FROM hive_test;"
+        -> "5",
+      "DROP TABLE hive_test"
+        -> "Time taken: "
+    )
   }
 
-  test("simple commands") {
-    val dataFilePath = getDataFile("data/files/small_kv.txt")
-    executeQuery("create table hive_test1(key int, val string);")
-    executeQuery("load data local inpath '" + dataFilePath+ "' overwrite into table hive_test1;")
-    executeQuery("cache table hive_test1", "Time taken")
+  test("Single command with -e") {
+    runCliWithin(1.minute, Seq("-e", "SHOW TABLES;"))("" -> "OK")
   }
 }
diff --git a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suite.scala b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suite.scala
index 326b0a7275b34..38977ff162097 100644
--- a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suite.scala
+++ b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suite.scala
@@ -17,32 +17,32 @@
 
 package org.apache.spark.sql.hive.thriftserver
 
-import scala.collection.JavaConversions._
+import scala.collection.mutable.ArrayBuffer
 import scala.concurrent.ExecutionContext.Implicits.global
-import scala.concurrent._
+import scala.concurrent.duration._
+import scala.concurrent.{Await, Future, Promise}
+import scala.sys.process.{Process, ProcessLogger}
 
-import java.io.{BufferedReader, InputStreamReader}
+import java.io.File
 import java.net.ServerSocket
-import java.sql.{Connection, DriverManager, Statement}
+import java.sql.{DriverManager, Statement}
+import java.util.concurrent.TimeoutException
 
 import org.apache.hadoop.hive.conf.HiveConf.ConfVars
-import org.scalatest.{BeforeAndAfterAll, FunSuite}
+import org.apache.hive.jdbc.HiveDriver
+import org.scalatest.FunSuite
 
 import org.apache.spark.Logging
 import org.apache.spark.sql.catalyst.util.getTempFilePath
 
 /**
- * Test for the HiveThriftServer2 using JDBC.
+ * Tests for the HiveThriftServer2 using JDBC.
  */
-class HiveThriftServer2Suite extends FunSuite with BeforeAndAfterAll with TestUtils with Logging {
+class HiveThriftServer2Suite extends FunSuite with Logging {
+  Class.forName(classOf[HiveDriver].getCanonicalName)
 
-  val WAREHOUSE_PATH = getTempFilePath("warehouse")
-  val METASTORE_PATH = getTempFilePath("metastore")
-
-  val DRIVER_NAME  = "org.apache.hive.jdbc.HiveDriver"
-  val TABLE = "test"
-  val HOST = "localhost"
-  val PORT =  {
+  private val listeningHost = "localhost"
+  private val listeningPort =  {
     // Let the system to choose a random available port to avoid collision with other parallel
     // builds.
     val socket = new ServerSocket(0)
@@ -51,96 +51,126 @@ class HiveThriftServer2Suite extends FunSuite with BeforeAndAfterAll with TestUt
     port
   }
 
-  Class.forName(DRIVER_NAME)
-
-  override def beforeAll() { launchServer() }
+  private val warehousePath = getTempFilePath("warehouse")
+  private val metastorePath = getTempFilePath("metastore")
+  private val metastoreJdbcUri = s"jdbc:derby:;databaseName=$metastorePath;create=true"
 
-  override def afterAll() { stopServer() }
+  def startThriftServerWithin(timeout: FiniteDuration = 30.seconds)(f: Statement => Unit) {
+    val serverScript = "../../sbin/start-thriftserver.sh".split("/").mkString(File.separator)
 
-  private def launchServer(args: Seq[String] = Seq.empty) {
-    // Forking a new process to start the Hive Thrift server. The reason to do this is it is
-    // hard to clean up Hive resources entirely, so we just start a new process and kill
-    // that process for cleanup.
-    val jdbcUrl = s"jdbc:derby:;databaseName=$METASTORE_PATH;create=true"
     val command =
-      s"""../../sbin/start-thriftserver.sh
+      s"""$serverScript
          |  --master local
-         |  --hiveconf ${ConfVars.METASTORECONNECTURLKEY}=$jdbcUrl
-         |  --hiveconf ${ConfVars.METASTOREWAREHOUSE}=$METASTORE_PATH
-         |  --hiveconf ${ConfVars.HIVE_SERVER2_THRIFT_BIND_HOST}=$HOST
-         |  --hiveconf ${ConfVars.HIVE_SERVER2_THRIFT_PORT}=$PORT
-       """.stripMargin.split("\\s+")
-
-    val pb = new ProcessBuilder(command ++ args: _*)
-    val environment = pb.environment()
-    process = pb.start()
-    inputReader = new BufferedReader(new InputStreamReader(process.getInputStream))
-    errorReader = new BufferedReader(new InputStreamReader(process.getErrorStream))
-    waitForOutput(inputReader, "ThriftBinaryCLIService listening on", 300000)
-
-    // Spawn a thread to read the output from the forked process.
-    // Note that this is necessary since in some configurations, log4j could be blocked
-    // if its output to stderr are not read, and eventually blocking the entire test suite.
-    future {
-      while (true) {
-        val stdout = readFrom(inputReader)
-        val stderr = readFrom(errorReader)
-        print(stdout)
-        print(stderr)
-        Thread.sleep(50)
+         |  --hiveconf hive.root.logger=INFO,console
+         |  --hiveconf ${ConfVars.METASTORECONNECTURLKEY}=$metastoreJdbcUri
+         |  --hiveconf ${ConfVars.METASTOREWAREHOUSE}=$warehousePath
+         |  --hiveconf ${ConfVars.HIVE_SERVER2_THRIFT_BIND_HOST}=$listeningHost
+         |  --hiveconf ${ConfVars.HIVE_SERVER2_THRIFT_PORT}=$listeningPort
+       """.stripMargin.split("\\s+").toSeq
+
+    val serverStarted = Promise[Unit]()
+    val buffer = new ArrayBuffer[String]()
+
+    def captureOutput(source: String)(line: String) {
+      buffer += s"$source> $line"
+      if (line.contains("ThriftBinaryCLIService listening on")) {
+        serverStarted.success(())
       }
     }
-  }
 
-  private def stopServer() {
-    process.destroy()
-    process.waitFor()
+    val process = Process(command).run(
+      ProcessLogger(captureOutput("stdout"), captureOutput("stderr")))
+
+    Future {
+      val exitValue = process.exitValue()
+      logInfo(s"Spark SQL Thrift server process exit value: $exitValue")
+    }
+
+    val jdbcUri = s"jdbc:hive2://$listeningHost:$listeningPort/"
+    val user = System.getProperty("user.name")
+
+    try {
+      Await.result(serverStarted.future, timeout)
+
+      val connection = DriverManager.getConnection(jdbcUri, user, "")
+      val statement = connection.createStatement()
+
+      try {
+        f(statement)
+      } finally {
+        statement.close()
+        connection.close()
+      }
+    } catch {
+      case cause: Exception =>
+        cause match {
+          case _: TimeoutException =>
+            logError(s"Failed to start Hive Thrift server within $timeout", cause)
+          case _ =>
+        }
+        logError(
+          s"""
+             |=====================================
+             |HiveThriftServer2Suite failure output
+             |=====================================
+             |HiveThriftServer2 command line: ${command.mkString(" ")}
+             |JDBC URI: $jdbcUri
+             |User: $user
+             |
+             |${buffer.mkString("\n")}
+             |=========================================
+             |End HiveThriftServer2Suite failure output
+             |=========================================
+           """.stripMargin, cause)
+    } finally {
+      warehousePath.delete()
+      metastorePath.delete()
+      process.destroy()
+    }
   }
 
-  test("test query execution against a Hive Thrift server") {
-    Thread.sleep(5 * 1000)
-    val dataFilePath = getDataFile("data/files/small_kv.txt")
-    val stmt = createStatement()
-    stmt.execute("DROP TABLE IF EXISTS test")
-    stmt.execute("DROP TABLE IF EXISTS test_cached")
-    stmt.execute("CREATE TABLE test(key INT, val STRING)")
-    stmt.execute(s"LOAD DATA LOCAL INPATH '$dataFilePath' OVERWRITE INTO TABLE test")
-    stmt.execute("CREATE TABLE test_cached AS SELECT * FROM test LIMIT 4")
-    stmt.execute("CACHE TABLE test_cached")
-
-    var rs = stmt.executeQuery("SELECT COUNT(*) FROM test")
-    rs.next()
-    assert(rs.getInt(1) === 5)
-
-    rs = stmt.executeQuery("SELECT COUNT(*) FROM test_cached")
-    rs.next()
-    assert(rs.getInt(1) === 4)
-
-    stmt.close()
+  test("Test JDBC query execution") {
+    startThriftServerWithin() { statement =>
+      val dataFilePath =
+        Thread.currentThread().getContextClassLoader.getResource("data/files/small_kv.txt")
+
+      val queries = Seq(
+        "CREATE TABLE test(key INT, val STRING)",
+        s"LOAD DATA LOCAL INPATH '$dataFilePath' OVERWRITE INTO TABLE test",
+        "CACHE TABLE test")
+
+      queries.foreach(statement.execute)
+
+      assertResult(5, "Row count mismatch") {
+        val resultSet = statement.executeQuery("SELECT COUNT(*) FROM test")
+        resultSet.next()
+        resultSet.getInt(1)
+      }
+    }
   }
 
   test("SPARK-3004 regression: result set containing NULL") {
-    Thread.sleep(5 * 1000)
-    val dataFilePath = getDataFile("data/files/small_kv_with_null.txt")
-    val stmt = createStatement()
-    stmt.execute("DROP TABLE IF EXISTS test_null")
-    stmt.execute("CREATE TABLE test_null(key INT, val STRING)")
-    stmt.execute(s"LOAD DATA LOCAL INPATH '$dataFilePath' OVERWRITE INTO TABLE test_null")
-
-    val rs = stmt.executeQuery("SELECT * FROM test_null WHERE key IS NULL")
-    var count = 0
-    while (rs.next()) {
-      count += 1
-    }
-    assert(count === 5)
+    startThriftServerWithin() { statement =>
+      val dataFilePath =
+        Thread.currentThread().getContextClassLoader.getResource(
+          "data/files/small_kv_with_null.txt")
 
-    stmt.close()
-  }
+      val queries = Seq(
+        "DROP TABLE IF EXISTS test_null",
+        "CREATE TABLE test_null(key INT, val STRING)",
+        s"LOAD DATA LOCAL INPATH '$dataFilePath' OVERWRITE INTO TABLE test_null")
 
-  def getConnection: Connection = {
-    val connectURI = s"jdbc:hive2://localhost:$PORT/"
-    DriverManager.getConnection(connectURI, System.getProperty("user.name"), "")
-  }
+      queries.foreach(statement.execute)
+
+      val resultSet = statement.executeQuery("SELECT * FROM test_null WHERE key IS NULL")
+
+      (0 until 5).foreach { _ =>
+        resultSet.next()
+        assert(resultSet.getInt(1) === 0)
+        assert(resultSet.wasNull())
+      }
 
-  def createStatement(): Statement = getConnection.createStatement()
+      assert(!resultSet.next())
+    }
+  }
 }
diff --git a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/TestUtils.scala b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/TestUtils.scala
deleted file mode 100644
index bb2242618fbef..0000000000000
--- a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/TestUtils.scala
+++ /dev/null
@@ -1,108 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.sql.hive.thriftserver
-
-import java.io.{BufferedReader, PrintWriter}
-import java.text.SimpleDateFormat
-import java.util.Date
-
-import org.apache.hadoop.hive.common.LogUtils
-import org.apache.hadoop.hive.common.LogUtils.LogInitializationException
-
-object TestUtils {
-  val timestamp = new SimpleDateFormat("yyyyMMdd-HHmmss")
-
-  def getWarehousePath(prefix: String): String = {
-    System.getProperty("user.dir") + "/test_warehouses/" + prefix + "-warehouse-" +
-      timestamp.format(new Date)
-  }
-
-  def getMetastorePath(prefix: String): String = {
-    System.getProperty("user.dir") + "/test_warehouses/" + prefix + "-metastore-" +
-      timestamp.format(new Date)
-  }
-
-  // Dummy function for initialize the log4j properties.
-  def init() { }
-
-  // initialize log4j
-  try {
-    LogUtils.initHiveLog4j()
-  } catch {
-    case e: LogInitializationException => // Ignore the error.
-  }
-}
-
-trait TestUtils {
-  var process : Process = null
-  var outputWriter : PrintWriter = null
-  var inputReader : BufferedReader = null
-  var errorReader : BufferedReader = null
-
-  def executeQuery(
-    cmd: String, outputMessage: String = "OK", timeout: Long = 15000): String = {
-    println("Executing: " + cmd + ", expecting output: " + outputMessage)
-    outputWriter.write(cmd + "\n")
-    outputWriter.flush()
-    waitForQuery(timeout, outputMessage)
-  }
-
-  protected def waitForQuery(timeout: Long, message: String): String = {
-    if (waitForOutput(errorReader, message, timeout)) {
-      Thread.sleep(500)
-      readOutput()
-    } else {
-      assert(false, "Didn't find \"" + message + "\" in the output:\n" + readOutput())
-      null
-    }
-  }
-
-  // Wait for the specified str to appear in the output.
-  protected def waitForOutput(
-    reader: BufferedReader, str: String, timeout: Long = 10000): Boolean = {
-    val startTime = System.currentTimeMillis
-    var out = ""
-    while (!out.contains(str) && System.currentTimeMillis < (startTime + timeout)) {
-      out += readFrom(reader)
-    }
-    out.contains(str)
-  }
-
-  // Read stdout output and filter out garbage collection messages.
-  protected def readOutput(): String = {
-    val output = readFrom(inputReader)
-    // Remove GC Messages
-    val filteredOutput = output.lines.filterNot(x => x.contains("[GC") || x.contains("[Full GC"))
-      .mkString("\n")
-    filteredOutput
-  }
-
-  protected def readFrom(reader: BufferedReader): String = {
-    var out = ""
-    var c = 0
-    while (reader.ready) {
-      c = reader.read()
-      out += c.asInstanceOf[Char]
-    }
-    out
-  }
-
-  protected def getDataFile(name: String) = {
-    Thread.currentThread().getContextClassLoader.getResource(name)
-  }
-}

From 156eb3966176de02ec3ec90ae10e50a7ebfbbf4f Mon Sep 17 00:00:00 2001
From: Cheng Hao <hao.cheng@intel.com>
Date: Mon, 25 Aug 2014 17:43:56 -0700
Subject: [PATCH 077/489] [SPARK-3058] [SQL] Support EXTENDED for EXPLAIN

Provide `extended` keyword support for `explain` command in SQL. e.g.
```
explain extended select key as a1, value as a2 from src where key=1;
== Parsed Logical Plan ==
Project ['key AS a1#3,'value AS a2#4]
 Filter ('key = 1)
  UnresolvedRelation None, src, None

== Analyzed Logical Plan ==
Project [key#8 AS a1#3,value#9 AS a2#4]
 Filter (CAST(key#8, DoubleType) = CAST(1, DoubleType))
  MetastoreRelation default, src, None

== Optimized Logical Plan ==
Project [key#8 AS a1#3,value#9 AS a2#4]
 Filter (CAST(key#8, DoubleType) = 1.0)
  MetastoreRelation default, src, None

== Physical Plan ==
Project [key#8 AS a1#3,value#9 AS a2#4]
 Filter (CAST(key#8, DoubleType) = 1.0)
  HiveTableScan [key#8,value#9], (MetastoreRelation default, src, None), None

Code Generation: false
== RDD ==
(2) MappedRDD[14] at map at HiveContext.scala:350
  MapPartitionsRDD[13] at mapPartitions at basicOperators.scala:42
  MapPartitionsRDD[12] at mapPartitions at basicOperators.scala:57
  MapPartitionsRDD[11] at mapPartitions at TableReader.scala:112
  MappedRDD[10] at map at TableReader.scala:240
  HadoopRDD[9] at HadoopRDD at TableReader.scala:230
```

It's the sub task of #1847. But can go without any dependency.

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

Closes #1962 from chenghao-intel/explain_extended and squashes the following commits:

295db74 [Cheng Hao] Fix bug in printing the simple execution plan
48bc989 [Cheng Hao] Support EXTENDED for EXPLAIN
---
 .../sql/catalyst/plans/logical/commands.scala |  2 +-
 .../org/apache/spark/sql/SQLContext.scala     | 13 +++--
 .../spark/sql/execution/SparkStrategies.scala |  4 +-
 .../apache/spark/sql/execution/commands.scala | 10 ++--
 .../apache/spark/sql/hive/HiveContext.scala   |  2 +-
 .../org/apache/spark/sql/hive/HiveQl.scala    |  5 +-
 .../sql/hive/execution/HiveExplainSuite.scala | 54 +++++++++++++++++++
 .../sql/hive/execution/HiveQuerySuite.scala   |  2 +-
 8 files changed, 78 insertions(+), 14 deletions(-)
 create mode 100644 sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveExplainSuite.scala

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/commands.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/commands.scala
index 481a5a4f212b2..a01809c1fc5e2 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/commands.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/commands.scala
@@ -50,7 +50,7 @@ case class SetCommand(key: Option[String], value: Option[String]) extends Comman
  * Returned by a parser when the users only wants to see what query plan would be executed, without
  * actually performing the execution.
  */
-case class ExplainCommand(plan: LogicalPlan) extends Command {
+case class ExplainCommand(plan: LogicalPlan, extended: Boolean = false) extends Command {
   override def output =
     Seq(AttributeReference("plan", StringType, nullable = false)())
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
index af9f7c62a1d25..8a9f4deb6a19e 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
@@ -408,10 +408,18 @@ class SQLContext(@transient val sparkContext: SparkContext)
     protected def stringOrError[A](f: => A): String =
       try f.toString catch { case e: Throwable => e.toString }
 
-    def simpleString: String = stringOrError(executedPlan)
+    def simpleString: String = 
+      s"""== Physical Plan ==
+         |${stringOrError(executedPlan)}
+      """
 
     override def toString: String =
-      s"""== Logical Plan ==
+      // TODO previously will output RDD details by run (${stringOrError(toRdd.toDebugString)})
+      // however, the `toRdd` will cause the real execution, which is not what we want.
+      // We need to think about how to avoid the side effect.
+      s"""== Parsed Logical Plan ==
+         |${stringOrError(logical)}
+         |== Analyzed Logical Plan ==
          |${stringOrError(analyzed)}
          |== Optimized Logical Plan ==
          |${stringOrError(optimizedPlan)}
@@ -419,7 +427,6 @@ class SQLContext(@transient val sparkContext: SparkContext)
          |${stringOrError(executedPlan)}
          |Code Generation: ${executedPlan.codegenEnabled}
          |== RDD ==
-         |${stringOrError(toRdd.toDebugString)}
       """.stripMargin.trim
   }
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
index 517b77804ae2c..8dacb84c8a17e 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
@@ -301,8 +301,8 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
     def apply(plan: LogicalPlan): Seq[SparkPlan] = plan match {
       case logical.SetCommand(key, value) =>
         Seq(execution.SetCommand(key, value, plan.output)(context))
-      case logical.ExplainCommand(logicalPlan) =>
-        Seq(execution.ExplainCommand(logicalPlan, plan.output)(context))
+      case logical.ExplainCommand(logicalPlan, extended) =>
+        Seq(execution.ExplainCommand(logicalPlan, plan.output, extended)(context))
       case logical.CacheCommand(tableName, cache) =>
         Seq(execution.CacheCommand(tableName, cache)(context))
       case _ => Nil
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala
index 38f37564f1788..031b695169cea 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala
@@ -108,15 +108,19 @@ case class SetCommand(
  */
 @DeveloperApi
 case class ExplainCommand(
-    logicalPlan: LogicalPlan, output: Seq[Attribute])(
+    logicalPlan: LogicalPlan, output: Seq[Attribute], extended: Boolean)(
     @transient context: SQLContext)
   extends LeafNode with Command {
 
   // Run through the optimizer to generate the physical plan.
   override protected[sql] lazy val sideEffectResult: Seq[String] = try {
-    "Physical execution plan:" +: context.executePlan(logicalPlan).executedPlan.toString.split("\n")
+    // TODO in Hive, the "extended" ExplainCommand prints the AST as well, and detailed properties.
+    val queryExecution = context.executePlan(logicalPlan)
+    val outputString = if (extended) queryExecution.toString else queryExecution.simpleString
+
+    outputString.split("\n")
   } catch { case cause: TreeNodeException[_] =>
-    "Error occurred during query planning: " +: cause.getMessage.split("\n")
+    ("Error occurred during query planning: \n" + cause.getMessage).split("\n")
   }
 
   def execute(): RDD[Row] = {
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
index 29baefe714c64..d9b2bc7348ad2 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
@@ -424,7 +424,7 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) {
       logical match {
         case _: NativeCommand => "<Native command: executed by Hive>"
         case _: SetCommand => "<SET command: executed by Hive, and noted by SQLContext>"
-        case _ => executedPlan.toString
+        case _ => super.simpleString
       }
   }
 }
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
index 1d9ba1b24a7a4..5da6e8df03aee 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
@@ -409,10 +409,9 @@ private[hive] object HiveQl {
       ExplainCommand(NoRelation)
     case Token("TOK_EXPLAIN", explainArgs) =>
       // Ignore FORMATTED if present.
-      val Some(query) :: _ :: _ :: Nil =
+      val Some(query) :: _ :: extended :: Nil =
         getClauses(Seq("TOK_QUERY", "FORMATTED", "EXTENDED"), explainArgs)
-      // TODO: support EXTENDED?
-      ExplainCommand(nodeToPlan(query))
+      ExplainCommand(nodeToPlan(query), extended != None)
 
     case Token("TOK_DESCTABLE", describeArgs) =>
       // Reference: https://cwiki.apache.org/confluence/display/Hive/LanguageManual+DDL
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveExplainSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveExplainSuite.scala
new file mode 100644
index 0000000000000..4ed58f4be1167
--- /dev/null
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveExplainSuite.scala
@@ -0,0 +1,54 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.hive.execution
+
+import org.apache.spark.sql.QueryTest
+import org.apache.spark.sql.hive.test.TestHive
+import org.apache.spark.sql.hive.test.TestHive._
+import org.apache.spark.sql.Row
+
+/**
+ * A set of tests that validates support for Hive Explain command.
+ */
+class HiveExplainSuite extends QueryTest {
+  private def check(sqlCmd: String, exists: Boolean, keywords: String*) {
+    val outputs = sql(sqlCmd).collect().map(_.getString(0)).mkString
+    for (key <- keywords) {
+      if (exists) {
+        assert(outputs.contains(key), s"Failed for $sqlCmd ($key doens't exist in result)")
+      } else {
+        assert(!outputs.contains(key), s"Failed for $sqlCmd ($key existed in the result)")
+      }
+    }
+  }
+
+  test("explain extended command") {
+    check(" explain   select * from src where key=123 ", true,
+          "== Physical Plan ==")
+    check(" explain   select * from src where key=123 ", false,
+          "== Parsed Logical Plan ==",
+          "== Analyzed Logical Plan ==", 
+          "== Optimized Logical Plan ==")
+    check(" explain   extended select * from src where key=123 ", true,
+          "== Parsed Logical Plan ==", 
+          "== Analyzed Logical Plan ==", 
+          "== Optimized Logical Plan ==", 
+          "== Physical Plan ==", 
+          "Code Generation", "== RDD ==")
+  }
+}
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
index 26e4ec6e6dcce..6d925e56e6838 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
@@ -327,7 +327,7 @@ class HiveQuerySuite extends HiveComparisonTest {
 
   def isExplanation(result: SchemaRDD) = {
     val explanation = result.select('plan).collect().map { case Row(plan: String) => plan }
-    explanation.size > 1 && explanation.head.startsWith("Physical execution plan")
+    explanation.exists(_ == "== Physical Plan ==")
   }
 
   test("SPARK-1704: Explain commands as a SchemaRDD") {

From 507a1b520063ad3e10b909767d9e3fd72d24415b Mon Sep 17 00:00:00 2001
From: wangfei <wangfei_hello@126.com>
Date: Mon, 25 Aug 2014 17:46:43 -0700
Subject: [PATCH 078/489] [SQL] logWarning should be logInfo in
 getResultSetSchema

Author: wangfei <wangfei_hello@126.com>

Closes #1939 from scwf/patch-5 and squashes the following commits:

f952d10 [wangfei] [SQL] logWarning should be logInfo in getResultSetSchema
---
 .../sql/hive/thriftserver/server/SparkSQLOperationManager.scala | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/server/SparkSQLOperationManager.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/server/SparkSQLOperationManager.scala
index 699a1103f3248..6eccb1ba6d4dc 100644
--- a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/server/SparkSQLOperationManager.scala
+++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/server/SparkSQLOperationManager.scala
@@ -151,7 +151,7 @@ class SparkSQLOperationManager(hiveContext: HiveContext) extends OperationManage
       }
 
       def getResultSetSchema: TableSchema = {
-        logWarning(s"Result Schema: ${result.queryExecution.analyzed.output}")
+        logInfo(s"Result Schema: ${result.queryExecution.analyzed.output}")
         if (result.queryExecution.analyzed.output.size == 0) {
           new TableSchema(new FieldSchema("Result", "string", "") :: Nil)
         } else {

From 4243bb6634aca5b9ddf6d42778aa7b4866ce6256 Mon Sep 17 00:00:00 2001
From: Chia-Yung Su <chiayung@appier.com>
Date: Mon, 25 Aug 2014 18:20:19 -0700
Subject: [PATCH 079/489] [SPARK-3011][SQL] _temporary directory should be
 filtered out by sqlContext.parquetFile

fix compile error on hadoop 0.23 for the pull request #1924.

Author: Chia-Yung Su <chiayung@appier.com>

Closes #1959 from joesu/bugfix-spark3011 and squashes the following commits:

be30793 [Chia-Yung Su] remove .* and _* except _metadata
8fe2398 [Chia-Yung Su] add note to explain
40ea9bd [Chia-Yung Su] fix hadoop-0.23 compile error
c7e44f2 [Chia-Yung Su] match syntax
f8fc32a [Chia-Yung Su] filter out tmp dir
---
 .../main/scala/org/apache/spark/sql/parquet/ParquetTypes.scala  | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTypes.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTypes.scala
index c79a9ac2dad81..af8cd0a73b674 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTypes.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTypes.scala
@@ -378,7 +378,7 @@ private[parquet] object ParquetTypesConverter extends Logging {
 
     val children = fs.listStatus(path).filterNot { status =>
       val name = status.getPath.getName
-      name(0) == '.' || name == FileOutputCommitter.SUCCEEDED_FILE_NAME
+      (name(0) == '.' || name(0) == '_') && name != ParquetFileWriter.PARQUET_METADATA_FILE
     }
 
     // NOTE (lian): Parquet "_metadata" file can be very slow if the file consists of lots of row

From 9f04db17e50568d5580091add9100693177d7c4f Mon Sep 17 00:00:00 2001
From: witgo <witgo@qq.com>
Date: Mon, 25 Aug 2014 19:22:27 -0700
Subject: [PATCH 080/489] SPARK-2481: The environment variables
 SPARK_HISTORY_OPTS is covered in spark-env.sh

Author: witgo <witgo@qq.com>
Author: GuoQiang Li <witgo@qq.com>

Closes #1341 from witgo/history_env and squashes the following commits:

b4fd9f8 [GuoQiang Li] review commit
0ebe401 [witgo] *-history-server.sh load spark-config.sh
---
 sbin/start-history-server.sh | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/sbin/start-history-server.sh b/sbin/start-history-server.sh
index e30493da32a7a..580ab471b8a79 100755
--- a/sbin/start-history-server.sh
+++ b/sbin/start-history-server.sh
@@ -27,6 +27,9 @@
 sbin=`dirname "$0"`
 sbin=`cd "$sbin"; pwd`
 
+. "$sbin/spark-config.sh"
+. "$SPARK_PREFIX/bin/load-spark-env.sh"
+
 if [ $# != 0 ]; then
   echo "Using command line arguments for setting the log directory is deprecated. Please "
   echo "set the spark.history.fs.logDirectory configuration option instead."

From 62f5009f6795b17638d2a1e8e51db0890030d8d6 Mon Sep 17 00:00:00 2001
From: Kousuke Saruta <sarutak@oss.nttdata.co.jp>
Date: Mon, 25 Aug 2014 19:40:08 -0700
Subject: [PATCH 081/489] [SPARK-2976] Replace tabs with spaces

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

Closes #1895 from sarutak/SPARK-2976 and squashes the following commits:

1cf7e69 [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into SPARK-2976
d1e0666 [Kousuke Saruta] Modified styles
c5e80a4 [Kousuke Saruta] Remove tab from JavaPageRank.java and JavaKinesisWordCountASL.java
c003b36 [Kousuke Saruta] Removed tab from sorttable.js
---
 .../org/apache/spark/ui/static/sorttable.js   | 275 +++++++++---------
 .../apache/spark/examples/JavaPageRank.java   |   2 +-
 .../streaming/JavaKinesisWordCountASL.java    |   8 +-
 3 files changed, 142 insertions(+), 143 deletions(-)

diff --git a/core/src/main/resources/org/apache/spark/ui/static/sorttable.js b/core/src/main/resources/org/apache/spark/ui/static/sorttable.js
index 7abb9011ccf36..dbacbf19beee5 100644
--- a/core/src/main/resources/org/apache/spark/ui/static/sorttable.js
+++ b/core/src/main/resources/org/apache/spark/ui/static/sorttable.js
@@ -81,15 +81,15 @@ sorttable = {
       if (!headrow[i].className.match(/\bsorttable_nosort\b/)) { // skip this col
         mtch = headrow[i].className.match(/\bsorttable_([a-z0-9]+)\b/);
         if (mtch) { override = mtch[1]; }
-	      if (mtch && typeof sorttable["sort_"+override] == 'function') {
-	        headrow[i].sorttable_sortfunction = sorttable["sort_"+override];
-	      } else {
-	        headrow[i].sorttable_sortfunction = sorttable.guessType(table,i);
-	      }
-	      // make it clickable to sort
-	      headrow[i].sorttable_columnindex = i;
-	      headrow[i].sorttable_tbody = table.tBodies[0];
-	      dean_addEvent(headrow[i],"click", function(e) {
+        if (mtch && typeof sorttable["sort_"+override] == 'function') {
+          headrow[i].sorttable_sortfunction = sorttable["sort_"+override];
+        } else {
+          headrow[i].sorttable_sortfunction = sorttable.guessType(table,i);
+        }
+        // make it clickable to sort
+        headrow[i].sorttable_columnindex = i;
+        headrow[i].sorttable_tbody = table.tBodies[0];
+        dean_addEvent(headrow[i],"click", function(e) {
 
           if (this.className.search(/\bsorttable_sorted\b/) != -1) {
             // if we're already sorted by this column, just 
@@ -109,7 +109,7 @@ sorttable = {
             // re-reverse the table, which is quicker
             sorttable.reverse(this.sorttable_tbody);
             this.className = this.className.replace('sorttable_sorted_reverse',
-                                                    'sorttable_sorted');
+                                                  'sorttable_sorted');
             this.removeChild(document.getElementById('sorttable_sortrevind'));
             sortfwdind = document.createElement('span');
             sortfwdind.id = "sorttable_sortfwdind";
@@ -117,7 +117,7 @@ sorttable = {
             this.appendChild(sortfwdind);
             return;
           }
-          
+
           // remove sorttable_sorted classes
           theadrow = this.parentNode;
           forEach(theadrow.childNodes, function(cell) {
@@ -130,36 +130,36 @@ sorttable = {
           if (sortfwdind) { sortfwdind.parentNode.removeChild(sortfwdind); }
           sortrevind = document.getElementById('sorttable_sortrevind');
           if (sortrevind) { sortrevind.parentNode.removeChild(sortrevind); }
-          
+
           this.className += ' sorttable_sorted';
           sortfwdind = document.createElement('span');
           sortfwdind.id = "sorttable_sortfwdind";
           sortfwdind.innerHTML = stIsIE ? '&nbsp<font face="webdings">6</font>' : '&nbsp;&#x25BE;';
           this.appendChild(sortfwdind);
 
-	        // build an array to sort. This is a Schwartzian transform thing,
-	        // i.e., we "decorate" each row with the actual sort key,
-	        // sort based on the sort keys, and then put the rows back in order
-	        // which is a lot faster because you only do getInnerText once per row
-	        row_array = [];
-	        col = this.sorttable_columnindex;
-	        rows = this.sorttable_tbody.rows;
-	        for (var j=0; j<rows.length; j++) {
-	          row_array[row_array.length] = [sorttable.getInnerText(rows[j].cells[col]), rows[j]];
-	        }
-	        /* If you want a stable sort, uncomment the following line */
-	        //sorttable.shaker_sort(row_array, this.sorttable_sortfunction);
-	        /* and comment out this one */
-	        row_array.sort(this.sorttable_sortfunction);
-	        
-	        tb = this.sorttable_tbody;
-	        for (var j=0; j<row_array.length; j++) {
-	          tb.appendChild(row_array[j][1]);
-	        }
-	        
-	        delete row_array;
-	      });
-	    }
+          // build an array to sort. This is a Schwartzian transform thing,
+          // i.e., we "decorate" each row with the actual sort key,
+          // sort based on the sort keys, and then put the rows back in order
+          // which is a lot faster because you only do getInnerText once per row
+          row_array = [];
+          col = this.sorttable_columnindex;
+          rows = this.sorttable_tbody.rows;
+          for (var j=0; j<rows.length; j++) {
+            row_array[row_array.length] = [sorttable.getInnerText(rows[j].cells[col]), rows[j]];
+          }
+          /* If you want a stable sort, uncomment the following line */
+          //sorttable.shaker_sort(row_array, this.sorttable_sortfunction);
+          /* and comment out this one */
+          row_array.sort(this.sorttable_sortfunction);
+        
+          tb = this.sorttable_tbody;
+          for (var j=0; j<row_array.length; j++) {
+            tb.appendChild(row_array[j][1]);
+          }
+        
+          delete row_array;
+        });
+      }
     }
   },
   
@@ -310,25 +310,24 @@ sorttable = {
     var swap = true;
 
     while(swap) {
-        swap = false;
-        for(var i = b; i < t; ++i) {
-            if ( comp_func(list[i], list[i+1]) > 0 ) {
-                var q = list[i]; list[i] = list[i+1]; list[i+1] = q;
-                swap = true;
-            }
-        } // for
-        t--;
+      swap = false;
+      for(var i = b; i < t; ++i) {
+          if ( comp_func(list[i], list[i+1]) > 0 ) {
+              var q = list[i]; list[i] = list[i+1]; list[i+1] = q;
+              swap = true;
+          }
+      } // for
+      t--;
 
-        if (!swap) break;
-
-        for(var i = t; i > b; --i) {
-            if ( comp_func(list[i], list[i-1]) < 0 ) {
-                var q = list[i]; list[i] = list[i-1]; list[i-1] = q;
-                swap = true;
-            }
-        } // for
-        b++;
+      if (!swap) break;
 
+      for(var i = t; i > b; --i) {
+        if ( comp_func(list[i], list[i-1]) < 0 ) {
+          var q = list[i]; list[i] = list[i-1]; list[i-1] = q;
+          swap = true;
+          }
+      } // for
+      b++;
     } // while(swap)
   }  
 }
@@ -358,11 +357,11 @@ if (document.addEventListener) {
 
 /* for Safari */
 if (/WebKit/i.test(navigator.userAgent)) { // sniff
-    var _timer = setInterval(function() {
-        if (/loaded|complete/.test(document.readyState)) {
-            sorttable.init(); // call the onload handler
-        }
-    }, 10);
+  var _timer = setInterval(function() {
+    if (/loaded|complete/.test(document.readyState)) {
+      sorttable.init(); // call the onload handler
+    }
+  }, 10);
 }
 
 /* for other browsers */
@@ -374,66 +373,66 @@ window.onload = sorttable.init;
 // http://dean.edwards.name/weblog/2005/10/add-event/
 
 function dean_addEvent(element, type, handler) {
-	if (element.addEventListener) {
-		element.addEventListener(type, handler, false);
-	} else {
-		// assign each event handler a unique ID
-		if (!handler.$$guid) handler.$$guid = dean_addEvent.guid++;
-		// create a hash table of event types for the element
-		if (!element.events) element.events = {};
-		// create a hash table of event handlers for each element/event pair
-		var handlers = element.events[type];
-		if (!handlers) {
-			handlers = element.events[type] = {};
-			// store the existing event handler (if there is one)
-			if (element["on" + type]) {
-				handlers[0] = element["on" + type];
-			}
-		}
-		// store the event handler in the hash table
-		handlers[handler.$$guid] = handler;
-		// assign a global event handler to do all the work
-		element["on" + type] = handleEvent;
-	}
+  if (element.addEventListener) {
+    element.addEventListener(type, handler, false);
+  } else {
+    // assign each event handler a unique ID
+    if (!handler.$$guid) handler.$$guid = dean_addEvent.guid++;
+    // create a hash table of event types for the element
+    if (!element.events) element.events = {};
+    // create a hash table of event handlers for each element/event pair
+    var handlers = element.events[type];
+    if (!handlers) {
+      handlers = element.events[type] = {};
+      // store the existing event handler (if there is one)
+      if (element["on" + type]) {
+        handlers[0] = element["on" + type];
+      }
+    }
+    // store the event handler in the hash table
+    handlers[handler.$$guid] = handler;
+    // assign a global event handler to do all the work
+   element["on" + type] = handleEvent;
+    }
 };
 // a counter used to create unique IDs
 dean_addEvent.guid = 1;
 
 function removeEvent(element, type, handler) {
-	if (element.removeEventListener) {
-		element.removeEventListener(type, handler, false);
-	} else {
-		// delete the event handler from the hash table
-		if (element.events && element.events[type]) {
-			delete element.events[type][handler.$$guid];
-		}
-	}
+  if (element.removeEventListener) {
+  element.removeEventListener(type, handler, false);
+  } else {
+    // delete the event handler from the hash table
+    if (element.events && element.events[type]) {
+      delete element.events[type][handler.$$guid];
+    }
+  }
 };
 
 function handleEvent(event) {
-	var returnValue = true;
-	// grab the event object (IE uses a global event object)
-	event = event || fixEvent(((this.ownerDocument || this.document || this).parentWindow || window).event);
-	// get a reference to the hash table of event handlers
-	var handlers = this.events[event.type];
-	// execute each event handler
-	for (var i in handlers) {
-		this.$$handleEvent = handlers[i];
-		if (this.$$handleEvent(event) === false) {
-			returnValue = false;
-		}
-	}
-	return returnValue;
+  var returnValue = true;
+  // grab the event object (IE uses a global event object)
+  event = event || fixEvent(((this.ownerDocument || this.document || this).parentWindow || window).event);
+  // get a reference to the hash table of event handlers
+  var handlers = this.events[event.type];
+  // execute each event handler
+  for (var i in handlers) {
+    this.$$handleEvent = handlers[i];
+    if (this.$$handleEvent(event) === false) {
+      returnValue = false;
+    }
+  }
+  return returnValue;
 };
 
 function fixEvent(event) {
-	// add W3C standard event methods
-	event.preventDefault = fixEvent.preventDefault;
-	event.stopPropagation = fixEvent.stopPropagation;
-	return event;
+  // add W3C standard event methods
+  event.preventDefault = fixEvent.preventDefault;
+  event.stopPropagation = fixEvent.stopPropagation;
+  return event;
 };
 fixEvent.preventDefault = function() {
-	this.returnValue = false;
+  this.returnValue = false;
 };
 fixEvent.stopPropagation = function() {
   this.cancelBubble = true;
@@ -441,55 +440,55 @@ fixEvent.stopPropagation = function() {
 
 // Dean's forEach: http://dean.edwards.name/base/forEach.js
 /*
-	forEach, version 1.0
-	Copyright 2006, Dean Edwards
-	License: http://www.opensource.org/licenses/mit-license.php
+forEach, version 1.0
+Copyright 2006, Dean Edwards
+License: http://www.opensource.org/licenses/mit-license.php
 */
 
 // array-like enumeration
 if (!Array.forEach) { // mozilla already supports this
-	Array.forEach = function(array, block, context) {
-		for (var i = 0; i < array.length; i++) {
-			block.call(context, array[i], i, array);
-		}
-	};
+  Array.forEach = function(array, block, context) {
+    for (var i = 0; i < array.length; i++) {
+      block.call(context, array[i], i, array);
+    }
+  };
 }
 
 // generic enumeration
 Function.prototype.forEach = function(object, block, context) {
-	for (var key in object) {
-		if (typeof this.prototype[key] == "undefined") {
-			block.call(context, object[key], key, object);
-		}
-	}
+  for (var key in object) {
+    if (typeof this.prototype[key] == "undefined") {
+      block.call(context, object[key], key, object);
+    }
+  }
 };
 
 // character enumeration
 String.forEach = function(string, block, context) {
-	Array.forEach(string.split(""), function(chr, index) {
-		block.call(context, chr, index, string);
-	});
+  Array.forEach(string.split(""), function(chr, index) {
+    block.call(context, chr, index, string);
+  });
 };
 
 // globally resolve forEach enumeration
 var forEach = function(object, block, context) {
-	if (object) {
-		var resolve = Object; // default
-		if (object instanceof Function) {
-			// functions have a "length" property
-			resolve = Function;
-		} else if (object.forEach instanceof Function) {
-			// the object implements a custom forEach method so use that
-			object.forEach(block, context);
-			return;
-		} else if (typeof object == "string") {
-			// the object is a string
-			resolve = String;
-		} else if (typeof object.length == "number") {
-			// the object is array-like
-			resolve = Array;
-		}
-		resolve.forEach(object, block, context);
-	}
+  if (object) {
+    var resolve = Object; // default
+    if (object instanceof Function) {
+    // functions have a "length" property
+    resolve = Function;
+  } else if (object.forEach instanceof Function) {
+    // the object implements a custom forEach method so use that
+    object.forEach(block, context);
+    return;
+  } else if (typeof object == "string") {
+    // the object is a string
+    resolve = String;
+  } else if (typeof object.length == "number") {
+    // the object is array-like
+    resolve = Array;
+  }
+  resolve.forEach(object, block, context);
+  }
 };
 
diff --git a/examples/src/main/java/org/apache/spark/examples/JavaPageRank.java b/examples/src/main/java/org/apache/spark/examples/JavaPageRank.java
index 7ea6df9c17245..c22506491fbff 100644
--- a/examples/src/main/java/org/apache/spark/examples/JavaPageRank.java
+++ b/examples/src/main/java/org/apache/spark/examples/JavaPageRank.java
@@ -96,7 +96,7 @@ public Double call(Iterable<String> rs) {
         .flatMapToPair(new PairFlatMapFunction<Tuple2<Iterable<String>, Double>, String, Double>() {
           @Override
           public Iterable<Tuple2<String, Double>> call(Tuple2<Iterable<String>, Double> s) {
-	    int urlCount = Iterables.size(s._1);
+            int urlCount = Iterables.size(s._1);
             List<Tuple2<String, Double>> results = new ArrayList<Tuple2<String, Double>>();
             for (String n : s._1) {
               results.add(new Tuple2<String, Double>(n, s._2() / urlCount));
diff --git a/extras/kinesis-asl/src/main/java/org/apache/spark/examples/streaming/JavaKinesisWordCountASL.java b/extras/kinesis-asl/src/main/java/org/apache/spark/examples/streaming/JavaKinesisWordCountASL.java
index a8b907b241893..1a710d7b18c6f 100644
--- a/extras/kinesis-asl/src/main/java/org/apache/spark/examples/streaming/JavaKinesisWordCountASL.java
+++ b/extras/kinesis-asl/src/main/java/org/apache/spark/examples/streaming/JavaKinesisWordCountASL.java
@@ -130,10 +130,10 @@ public static void main(String[] args) {
         /* Create the same number of Kinesis DStreams/Receivers as Kinesis stream's shards */
         List<JavaDStream<byte[]>> streamsList = new ArrayList<JavaDStream<byte[]>>(numStreams);
         for (int i = 0; i < numStreams; i++) {
-        	streamsList.add(
-                KinesisUtils.createStream(jssc, streamName, endpointUrl, checkpointInterval, 
-                InitialPositionInStream.LATEST, StorageLevel.MEMORY_AND_DISK_2())
-            );
+          streamsList.add(
+            KinesisUtils.createStream(jssc, streamName, endpointUrl, checkpointInterval, 
+            InitialPositionInStream.LATEST, StorageLevel.MEMORY_AND_DISK_2())
+          );
         }
 
         /* Union all the streams if there is more than 1 stream */

From 52fbdc2deddcdba02bf5945a36e15870021ec890 Mon Sep 17 00:00:00 2001
From: Daoyuan Wang <daoyuan.wang@intel.com>
Date: Mon, 25 Aug 2014 22:56:35 -0700
Subject: [PATCH 082/489] [Spark-3222] [SQL] Cross join support in HiveQL

We can simple treat cross join as inner join without join conditions.

Author: Daoyuan Wang <daoyuan.wang@intel.com>
Author: adrian-wang <daoyuanwong@gmail.com>

Closes #2124 from adrian-wang/crossjoin and squashes the following commits:

8c9b7c5 [Daoyuan Wang] add a test
7d47bbb [adrian-wang] add cross join support for hql
---
 .../apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala | 1 +
 sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala   | 1 +
 .../golden/cross_join-0-7e4af1870bc73decae43b3383c7d2046         | 0
 .../golden/cross_join-1-1a96761bf3e47ace9a422ed58273ff35         | 0
 .../golden/cross_join-2-85c93a81eae05bf56a04a904bb80a229         | 0
 5 files changed, 2 insertions(+)
 create mode 100644 sql/hive/src/test/resources/golden/cross_join-0-7e4af1870bc73decae43b3383c7d2046
 create mode 100644 sql/hive/src/test/resources/golden/cross_join-1-1a96761bf3e47ace9a422ed58273ff35
 create mode 100644 sql/hive/src/test/resources/golden/cross_join-2-85c93a81eae05bf56a04a904bb80a229

diff --git a/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala b/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
index 210753efe7678..66243879b9019 100644
--- a/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
+++ b/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
@@ -310,6 +310,7 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
     "create_nested_type",
     "create_skewed_table1",
     "create_struct_table",
+    "cross_join",
     "ct_case_insensitive",
     "database_location",
     "database_properties",
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
index 5da6e8df03aee..581332e600183 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
@@ -772,6 +772,7 @@ private[hive] object HiveQl {
 
       val joinType = joinToken match {
         case "TOK_JOIN" => Inner
+        case "TOK_CROSSJOIN" => Inner
         case "TOK_RIGHTOUTERJOIN" => RightOuter
         case "TOK_LEFTOUTERJOIN" => LeftOuter
         case "TOK_FULLOUTERJOIN" => FullOuter
diff --git a/sql/hive/src/test/resources/golden/cross_join-0-7e4af1870bc73decae43b3383c7d2046 b/sql/hive/src/test/resources/golden/cross_join-0-7e4af1870bc73decae43b3383c7d2046
new file mode 100644
index 0000000000000..e69de29bb2d1d
diff --git a/sql/hive/src/test/resources/golden/cross_join-1-1a96761bf3e47ace9a422ed58273ff35 b/sql/hive/src/test/resources/golden/cross_join-1-1a96761bf3e47ace9a422ed58273ff35
new file mode 100644
index 0000000000000..e69de29bb2d1d
diff --git a/sql/hive/src/test/resources/golden/cross_join-2-85c93a81eae05bf56a04a904bb80a229 b/sql/hive/src/test/resources/golden/cross_join-2-85c93a81eae05bf56a04a904bb80a229
new file mode 100644
index 0000000000000..e69de29bb2d1d

From b21ae5bbb9baa966f69303a30659aa8bbb2098da Mon Sep 17 00:00:00 2001
From: Andrew Or <andrewor14@gmail.com>
Date: Mon, 25 Aug 2014 23:36:09 -0700
Subject: [PATCH 083/489] [SPARK-2886] Use more specific actor system name than
 "spark"

As of #1777 we log the name of the actor system when it binds to a port. The current name "spark" is super general and does not convey any meaning. For instance, the following line is taken from my driver log after setting `spark.driver.port` to 5001.
```
14/08/13 19:33:29 INFO Remoting: Remoting started; listening on addresses:
[akka.tcp://sparkandrews-mbp:5001]
14/08/13 19:33:29 INFO Remoting: Remoting now listens on addresses:
[akka.tcp://sparkandrews-mbp:5001]
14/08/06 13:40:05 INFO Utils: Successfully started service 'spark' on port 5001.
```
This commit renames this to "sparkDriver" and "sparkExecutor". The goal of this unambitious PR is simply to make the logged information more explicit without introducing any change in functionality.

Author: Andrew Or <andrewor14@gmail.com>

Closes #1810 from andrewor14/service-name and squashes the following commits:

8c459ed [Andrew Or] Use a common variable for driver/executor actor system names
3a92843 [Andrew Or] Change actor name to sparkDriver and sparkExecutor
921363e [Andrew Or] Merge branch 'master' of github.com:apache/spark into service-name
c8c6a62 [Andrew Or] Do not include hyphens in actor name
1c1b42e [Andrew Or] Avoid spaces in akka system name
f644b55 [Andrew Or] Use more specific service name
---
 core/src/main/scala/org/apache/spark/SparkEnv.scala    |  9 ++++++---
 .../spark/scheduler/cluster/SimrSchedulerBackend.scala |  8 +++++---
 .../cluster/SparkDeploySchedulerBackend.scala          |  8 +++++---
 .../cluster/mesos/CoarseMesosSchedulerBackend.scala    |  5 +++--
 .../main/scala/org/apache/spark/util/AkkaUtils.scala   |  5 +++--
 .../streaming/receiver/ReceiverSupervisorImpl.scala    | 10 +++++-----
 .../apache/spark/deploy/yarn/ExecutorLauncher.scala    |  9 ++++++---
 .../spark/deploy/yarn/YarnAllocationHandler.scala      |  8 +++++---
 .../apache/spark/deploy/yarn/ExecutorLauncher.scala    |  9 ++++++---
 .../spark/deploy/yarn/YarnAllocationHandler.scala      |  5 +++--
 10 files changed, 47 insertions(+), 29 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/SparkEnv.scala b/core/src/main/scala/org/apache/spark/SparkEnv.scala
index fc36e37c53f5e..72716567ca99b 100644
--- a/core/src/main/scala/org/apache/spark/SparkEnv.scala
+++ b/core/src/main/scala/org/apache/spark/SparkEnv.scala
@@ -111,6 +111,9 @@ object SparkEnv extends Logging {
   private val env = new ThreadLocal[SparkEnv]
   @volatile private var lastSetSparkEnv : SparkEnv = _
 
+  private[spark] val driverActorSystemName = "sparkDriver"
+  private[spark] val executorActorSystemName = "sparkExecutor"
+
   def set(e: SparkEnv) {
     lastSetSparkEnv = e
     env.set(e)
@@ -146,9 +149,9 @@ object SparkEnv extends Logging {
     }
 
     val securityManager = new SecurityManager(conf)
-
-    val (actorSystem, boundPort) = AkkaUtils.createActorSystem("spark", hostname, port, conf = conf,
-      securityManager = securityManager)
+    val actorSystemName = if (isDriver) driverActorSystemName else executorActorSystemName
+    val (actorSystem, boundPort) = AkkaUtils.createActorSystem(
+      actorSystemName, hostname, port, conf, securityManager)
 
     // Figure out which port Akka actually bound to in case the original port is 0 or occupied.
     // This is so that we tell the executors the correct port to connect to.
diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/SimrSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/SimrSchedulerBackend.scala
index d99c76117c168..4f7133c4bc17c 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/SimrSchedulerBackend.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/SimrSchedulerBackend.scala
@@ -20,7 +20,7 @@ package org.apache.spark.scheduler.cluster
 import org.apache.hadoop.conf.Configuration
 import org.apache.hadoop.fs.{Path, FileSystem}
 
-import org.apache.spark.{Logging, SparkContext}
+import org.apache.spark.{Logging, SparkContext, SparkEnv}
 import org.apache.spark.scheduler.TaskSchedulerImpl
 
 private[spark] class SimrSchedulerBackend(
@@ -38,8 +38,10 @@ private[spark] class SimrSchedulerBackend(
   override def start() {
     super.start()
 
-    val driverUrl = "akka.tcp://spark@%s:%s/user/%s".format(
-      sc.conf.get("spark.driver.host"), sc.conf.get("spark.driver.port"),
+    val driverUrl = "akka.tcp://%s@%s:%s/user/%s".format(
+      SparkEnv.driverActorSystemName,
+      sc.conf.get("spark.driver.host"),
+      sc.conf.get("spark.driver.port"),
       CoarseGrainedSchedulerBackend.ACTOR_NAME)
 
     val conf = new Configuration()
diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala
index 589dba2e40d20..32138e5246700 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.scheduler.cluster
 
-import org.apache.spark.{Logging, SparkConf, SparkContext}
+import org.apache.spark.{Logging, SparkConf, SparkContext, SparkEnv}
 import org.apache.spark.deploy.{ApplicationDescription, Command}
 import org.apache.spark.deploy.client.{AppClient, AppClientListener}
 import org.apache.spark.scheduler.{ExecutorExited, ExecutorLossReason, SlaveLost, TaskSchedulerImpl}
@@ -42,8 +42,10 @@ private[spark] class SparkDeploySchedulerBackend(
     super.start()
 
     // The endpoint for executors to talk to us
-    val driverUrl = "akka.tcp://spark@%s:%s/user/%s".format(
-      conf.get("spark.driver.host"), conf.get("spark.driver.port"),
+    val driverUrl = "akka.tcp://%s@%s:%s/user/%s".format(
+      SparkEnv.driverActorSystemName,
+      conf.get("spark.driver.host"),
+      conf.get("spark.driver.port"),
       CoarseGrainedSchedulerBackend.ACTOR_NAME)
     val args = Seq(driverUrl, "{{EXECUTOR_ID}}", "{{HOSTNAME}}", "{{CORES}}", "{{WORKER_URL}}")
     val extraJavaOpts = sc.conf.getOption("spark.executor.extraJavaOptions")
diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala
index 9f45400bcf852..f0172504c55aa 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala
@@ -28,7 +28,7 @@ import org.apache.mesos.{Scheduler => MScheduler}
 import org.apache.mesos._
 import org.apache.mesos.Protos.{TaskInfo => MesosTaskInfo, TaskState => MesosTaskState, _}
 
-import org.apache.spark.{Logging, SparkContext, SparkException}
+import org.apache.spark.{Logging, SparkContext, SparkEnv, SparkException}
 import org.apache.spark.scheduler.TaskSchedulerImpl
 import org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend
 
@@ -130,7 +130,8 @@ private[spark] class CoarseMesosSchedulerBackend(
     }
     val command = CommandInfo.newBuilder()
       .setEnvironment(environment)
-    val driverUrl = "akka.tcp://spark@%s:%s/user/%s".format(
+    val driverUrl = "akka.tcp://%s@%s:%s/user/%s".format(
+      SparkEnv.driverActorSystemName,
       conf.get("spark.driver.host"),
       conf.get("spark.driver.port"),
       CoarseGrainedSchedulerBackend.ACTOR_NAME)
diff --git a/core/src/main/scala/org/apache/spark/util/AkkaUtils.scala b/core/src/main/scala/org/apache/spark/util/AkkaUtils.scala
index d6afb73b74242..e2d32c859bbda 100644
--- a/core/src/main/scala/org/apache/spark/util/AkkaUtils.scala
+++ b/core/src/main/scala/org/apache/spark/util/AkkaUtils.scala
@@ -27,7 +27,7 @@ import akka.pattern.ask
 import com.typesafe.config.ConfigFactory
 import org.apache.log4j.{Level, Logger}
 
-import org.apache.spark.{SparkException, Logging, SecurityManager, SparkConf}
+import org.apache.spark.{Logging, SecurityManager, SparkConf, SparkEnv, SparkException}
 
 /**
  * Various utility classes for working with Akka.
@@ -192,10 +192,11 @@ private[spark] object AkkaUtils extends Logging {
   }
 
   def makeDriverRef(name: String, conf: SparkConf, actorSystem: ActorSystem): ActorRef = {
+    val driverActorSystemName = SparkEnv.driverActorSystemName
     val driverHost: String = conf.get("spark.driver.host", "localhost")
     val driverPort: Int = conf.getInt("spark.driver.port", 7077)
     Utils.checkHost(driverHost, "Expected hostname")
-    val url = s"akka.tcp://spark@$driverHost:$driverPort/user/$name"
+    val url = s"akka.tcp://$driverActorSystemName@$driverHost:$driverPort/user/$name"
     val timeout = AkkaUtils.lookupTimeout(conf)
     logInfo(s"Connecting to $name: $url")
     Await.result(actorSystem.actorSelection(url).resolveOne(timeout), timeout)
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisorImpl.scala b/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisorImpl.scala
index d934b9cbfc3e8..53a3e6200e340 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisorImpl.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisorImpl.scala
@@ -20,22 +20,21 @@ package org.apache.spark.streaming.receiver
 import java.nio.ByteBuffer
 import java.util.concurrent.atomic.AtomicLong
 
-import scala.collection.mutable.{SynchronizedBuffer, ArrayBuffer}
+import scala.collection.mutable.ArrayBuffer
 import scala.concurrent.Await
 
 import akka.actor.{Actor, Props}
 import akka.pattern.ask
 
+import com.google.common.base.Throwables
+
 import org.apache.spark.{Logging, SparkEnv}
-import org.apache.spark.storage.StreamBlockId
 import org.apache.spark.streaming.scheduler._
 import org.apache.spark.util.{Utils, AkkaUtils}
 import org.apache.spark.storage.StreamBlockId
 import org.apache.spark.streaming.scheduler.DeregisterReceiver
 import org.apache.spark.streaming.scheduler.AddBlock
-import scala.Some
 import org.apache.spark.streaming.scheduler.RegisterReceiver
-import com.google.common.base.Throwables
 
 /**
  * Concrete implementation of [[org.apache.spark.streaming.receiver.ReceiverSupervisor]]
@@ -56,7 +55,8 @@ private[streaming] class ReceiverSupervisorImpl(
   private val trackerActor = {
     val ip = env.conf.get("spark.driver.host", "localhost")
     val port = env.conf.getInt("spark.driver.port", 7077)
-    val url = "akka.tcp://spark@%s:%s/user/ReceiverTracker".format(ip, port)
+    val url = "akka.tcp://%s@%s:%s/user/ReceiverTracker".format(
+      SparkEnv.driverActorSystemName, ip, port)
     env.actorSystem.actorSelection(url)
   }
 
diff --git a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala b/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala
index c3310fbc24a98..155dd88aa2b81 100644
--- a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala
+++ b/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala
@@ -28,7 +28,7 @@ import org.apache.hadoop.yarn.ipc.YarnRPC
 import org.apache.hadoop.yarn.util.{ConverterUtils, Records}
 import akka.actor._
 import akka.remote._
-import org.apache.spark.{Logging, SecurityManager, SparkConf}
+import org.apache.spark.{Logging, SecurityManager, SparkConf, SparkEnv}
 import org.apache.spark.util.{Utils, AkkaUtils}
 import org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend
 import org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.AddWebUIFilter
@@ -210,8 +210,11 @@ class ExecutorLauncher(args: ApplicationMasterArguments, conf: Configuration, sp
     sparkConf.set("spark.driver.host",  driverHost)
     sparkConf.set("spark.driver.port",  driverPort.toString)
 
-    val driverUrl = "akka.tcp://spark@%s:%s/user/%s".format(
-      driverHost, driverPort.toString, CoarseGrainedSchedulerBackend.ACTOR_NAME)
+    val driverUrl = "akka.tcp://%s@%s:%s/user/%s".format(
+      SparkEnv.driverActorSystemName,
+      driverHost,
+      driverPort.toString,
+      CoarseGrainedSchedulerBackend.ACTOR_NAME)
 
     actor = actorSystem.actorOf(Props(new MonitorActor(driverUrl)), name = "YarnAM")
   }
diff --git a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocationHandler.scala b/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocationHandler.scala
index 80e0162e9f277..568a6ef932bbd 100644
--- a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocationHandler.scala
+++ b/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocationHandler.scala
@@ -26,7 +26,7 @@ import scala.collection
 import scala.collection.JavaConversions._
 import scala.collection.mutable.{ArrayBuffer, HashMap, HashSet}
 
-import org.apache.spark.{Logging, SparkConf}
+import org.apache.spark.{Logging, SparkConf, SparkEnv}
 import org.apache.spark.scheduler.{SplitInfo,TaskSchedulerImpl}
 import org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend
 import org.apache.spark.util.Utils
@@ -245,8 +245,10 @@ private[yarn] class YarnAllocationHandler(
           // Deallocate + allocate can result in reusing id's wrongly - so use a different counter
           // (executorIdCounter)
           val executorId = executorIdCounter.incrementAndGet().toString
-          val driverUrl = "akka.tcp://spark@%s:%s/user/%s".format(
-            sparkConf.get("spark.driver.host"), sparkConf.get("spark.driver.port"),
+          val driverUrl = "akka.tcp://%s@%s:%s/user/%s".format(
+            SparkEnv.driverActorSystemName,
+            sparkConf.get("spark.driver.host"),
+            sparkConf.get("spark.driver.port"),
             CoarseGrainedSchedulerBackend.ACTOR_NAME)
 
           logInfo("launching container on " + containerId + " host " + executorHostname)
diff --git a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala
index 45925f1fea005..e093fe4ae6ff8 100644
--- a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala
+++ b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala
@@ -25,7 +25,7 @@ import org.apache.hadoop.yarn.api.protocolrecords._
 import org.apache.hadoop.yarn.conf.YarnConfiguration
 import akka.actor._
 import akka.remote._
-import org.apache.spark.{Logging, SecurityManager, SparkConf}
+import org.apache.spark.{Logging, SecurityManager, SparkConf, SparkEnv}
 import org.apache.spark.util.{Utils, AkkaUtils}
 import org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend
 import org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.AddWebUIFilter
@@ -174,8 +174,11 @@ class ExecutorLauncher(args: ApplicationMasterArguments, conf: Configuration, sp
     sparkConf.set("spark.driver.host", driverHost)
     sparkConf.set("spark.driver.port", driverPort.toString)
 
-    val driverUrl = "akka.tcp://spark@%s:%s/user/%s".format(
-      driverHost, driverPort.toString, CoarseGrainedSchedulerBackend.ACTOR_NAME)
+    val driverUrl = "akka.tcp://%s@%s:%s/user/%s".format(
+      SparkEnv.driverActorSystemName,
+      driverHost,
+      driverPort.toString,
+      CoarseGrainedSchedulerBackend.ACTOR_NAME)
 
     actor = actorSystem.actorOf(Props(new MonitorActor(driverUrl)), name = "YarnAM")
   }
diff --git a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocationHandler.scala b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocationHandler.scala
index 29ccec2adcac3..0a461749c819d 100644
--- a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocationHandler.scala
+++ b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocationHandler.scala
@@ -26,7 +26,7 @@ import scala.collection
 import scala.collection.JavaConversions._
 import scala.collection.mutable.{ArrayBuffer, HashMap, HashSet}
 
-import org.apache.spark.{Logging, SparkConf}
+import org.apache.spark.{Logging, SparkConf, SparkEnv}
 import org.apache.spark.scheduler.{SplitInfo,TaskSchedulerImpl}
 import org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend
 import org.apache.spark.util.Utils
@@ -262,7 +262,8 @@ private[yarn] class YarnAllocationHandler(
           numExecutorsRunning.decrementAndGet()
         } else {
           val executorId = executorIdCounter.incrementAndGet().toString
-          val driverUrl = "akka.tcp://spark@%s:%s/user/%s".format(
+          val driverUrl = "akka.tcp://%s@%s:%s/user/%s".format(
+            SparkEnv.driverActorSystemName,
             sparkConf.get("spark.driver.host"),
             sparkConf.get("spark.driver.port"),
             CoarseGrainedSchedulerBackend.ACTOR_NAME)

From 8856c3d86009295be871989a5dc7270f31b420cd Mon Sep 17 00:00:00 2001
From: chutium <teng.qiu@gmail.com>
Date: Tue, 26 Aug 2014 11:51:26 -0700
Subject: [PATCH 084/489] [SPARK-3131][SQL] Allow user to set parquet
 compression codec for writing ParquetFile in SQLContext

There are 4 different compression codec available for ```ParquetOutputFormat```

in Spark SQL, it was set as a hard-coded value in ```ParquetRelation.defaultCompression```

original discuss:
https://github.com/apache/spark/pull/195#discussion-diff-11002083

i added a new config property in SQLConf to allow user to change this compression codec, and i used similar short names syntax as described in SPARK-2953 #1873 (https://github.com/apache/spark/pull/1873/files#diff-0)

btw, which codec should we use as default? it was set to GZIP (https://github.com/apache/spark/pull/195/files#diff-4), but i think maybe we should change this to SNAPPY, since SNAPPY is already the default codec for shuffling in spark-core (SPARK-2469, #1415), and parquet-mr supports Snappy codec natively (https://github.com/Parquet/parquet-mr/commit/e440108de57199c12d66801ca93804086e7f7632).

Author: chutium <teng.qiu@gmail.com>

Closes #2039 from chutium/parquet-compression and squashes the following commits:

2f44964 [chutium] [SPARK-3131][SQL] parquet compression default codec set to snappy, also in test suite
e578e21 [chutium] [SPARK-3131][SQL] compression codec config property name and default codec set to snappy
21235dc [chutium] [SPARK-3131][SQL] Allow user to set parquet compression codec for writing ParquetFile in SQLContext
---
 .../scala/org/apache/spark/sql/SQLConf.scala  |  4 +
 .../spark/sql/parquet/ParquetRelation.scala   | 14 ++-
 .../spark/sql/parquet/ParquetQuerySuite.scala | 94 +++++++++++++++++++
 3 files changed, 107 insertions(+), 5 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
index 5cc41a83cc792..f0df19112ae37 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
@@ -33,6 +33,7 @@ private[spark] object SQLConf {
   val DIALECT = "spark.sql.dialect"
   val PARQUET_BINARY_AS_STRING = "spark.sql.parquet.binaryAsString"
   val PARQUET_CACHE_METADATA = "spark.sql.parquet.cacheMetadata"
+  val PARQUET_COMPRESSION = "spark.sql.parquet.compression.codec"
 
   // This is only used for the thriftserver
   val THRIFTSERVER_POOL = "spark.sql.thriftserver.scheduler.pool"
@@ -78,6 +79,9 @@ trait SQLConf {
   /** When true tables cached using the in-memory columnar caching will be compressed. */
   private[spark] def useCompression: Boolean = getConf(COMPRESS_CACHED, "false").toBoolean
 
+  /** The compression codec for writing to a Parquetfile */
+  private[spark] def parquetCompressionCodec: String = getConf(PARQUET_COMPRESSION, "snappy")
+
   /** The number of rows that will be  */
   private[spark] def columnBatchSize: Int = getConf(COLUMN_BATCH_SIZE, "1000").toInt
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala
index 1713ae6fb5d93..5ae768293a22e 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala
@@ -100,8 +100,13 @@ private[sql] object ParquetRelation {
   // The compression type
   type CompressionType = parquet.hadoop.metadata.CompressionCodecName
 
-  // The default compression
-  val defaultCompression = CompressionCodecName.GZIP
+  // The parquet compression short names
+  val shortParquetCompressionCodecNames = Map(
+    "NONE"         -> CompressionCodecName.UNCOMPRESSED,
+    "UNCOMPRESSED" -> CompressionCodecName.UNCOMPRESSED,
+    "SNAPPY"       -> CompressionCodecName.SNAPPY,
+    "GZIP"         -> CompressionCodecName.GZIP,
+    "LZO"          -> CompressionCodecName.LZO)
 
   /**
    * Creates a new ParquetRelation and underlying Parquetfile for the given LogicalPlan. Note that
@@ -141,9 +146,8 @@ private[sql] object ParquetRelation {
                   conf: Configuration,
                   sqlContext: SQLContext): ParquetRelation = {
     val path = checkPath(pathString, allowExisting, conf)
-    if (conf.get(ParquetOutputFormat.COMPRESSION) == null) {
-      conf.set(ParquetOutputFormat.COMPRESSION, ParquetRelation.defaultCompression.name())
-    }
+    conf.set(ParquetOutputFormat.COMPRESSION, shortParquetCompressionCodecNames.getOrElse(
+      sqlContext.parquetCompressionCodec.toUpperCase, CompressionCodecName.UNCOMPRESSED).name())
     ParquetRelation.enableLogForwarding()
     ParquetTypesConverter.writeMetaData(attributes, path, conf)
     new ParquetRelation(path.toString, Some(conf), sqlContext) {
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala
index 172dcd6aa0ee3..28f43b36832ac 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala
@@ -186,6 +186,100 @@ class ParquetQuerySuite extends QueryTest with FunSuiteLike with BeforeAndAfterA
     TestSQLContext.setConf(SQLConf.PARQUET_BINARY_AS_STRING, oldIsParquetBinaryAsString.toString)
   }
 
+  test("Compression options for writing to a Parquetfile") {
+    val defaultParquetCompressionCodec = TestSQLContext.parquetCompressionCodec
+    import scala.collection.JavaConversions._
+
+    val file = getTempFilePath("parquet")
+    val path = file.toString
+    val rdd = TestSQLContext.sparkContext.parallelize((1 to 100))
+      .map(i => TestRDDEntry(i, s"val_$i"))
+
+    // test default compression codec
+    rdd.saveAsParquetFile(path)
+    var actualCodec = ParquetTypesConverter.readMetaData(new Path(path), Some(TestSQLContext.sparkContext.hadoopConfiguration))
+      .getBlocks.flatMap(block => block.getColumns).map(column => column.getCodec.name()).distinct
+    assert(actualCodec === TestSQLContext.parquetCompressionCodec.toUpperCase :: Nil)
+
+    parquetFile(path).registerTempTable("tmp")
+    checkAnswer(
+      sql("SELECT key, value FROM tmp WHERE value = 'val_5' OR value = 'val_7'"),
+      (5, "val_5") ::
+      (7, "val_7") :: Nil)
+
+    Utils.deleteRecursively(file)
+
+    // test uncompressed parquet file with property value "UNCOMPRESSED"
+    TestSQLContext.setConf(SQLConf.PARQUET_COMPRESSION, "UNCOMPRESSED")
+
+    rdd.saveAsParquetFile(path)
+    actualCodec = ParquetTypesConverter.readMetaData(new Path(path), Some(TestSQLContext.sparkContext.hadoopConfiguration))
+      .getBlocks.flatMap(block => block.getColumns).map(column => column.getCodec.name()).distinct
+    assert(actualCodec === TestSQLContext.parquetCompressionCodec.toUpperCase :: Nil)
+
+    parquetFile(path).registerTempTable("tmp")
+    checkAnswer(
+      sql("SELECT key, value FROM tmp WHERE value = 'val_5' OR value = 'val_7'"),
+      (5, "val_5") ::
+      (7, "val_7") :: Nil)
+
+    Utils.deleteRecursively(file)
+
+    // test uncompressed parquet file with property value "none"
+    TestSQLContext.setConf(SQLConf.PARQUET_COMPRESSION, "none")
+
+    rdd.saveAsParquetFile(path)
+    actualCodec = ParquetTypesConverter.readMetaData(new Path(path), Some(TestSQLContext.sparkContext.hadoopConfiguration))
+      .getBlocks.flatMap(block => block.getColumns).map(column => column.getCodec.name()).distinct
+    assert(actualCodec === "UNCOMPRESSED" :: Nil)
+
+    parquetFile(path).registerTempTable("tmp")
+    checkAnswer(
+      sql("SELECT key, value FROM tmp WHERE value = 'val_5' OR value = 'val_7'"),
+      (5, "val_5") ::
+      (7, "val_7") :: Nil)
+
+    Utils.deleteRecursively(file)
+
+    // test gzip compression codec
+    TestSQLContext.setConf(SQLConf.PARQUET_COMPRESSION, "gzip")
+
+    rdd.saveAsParquetFile(path)
+    actualCodec = ParquetTypesConverter.readMetaData(new Path(path), Some(TestSQLContext.sparkContext.hadoopConfiguration))
+      .getBlocks.flatMap(block => block.getColumns).map(column => column.getCodec.name()).distinct
+    assert(actualCodec === TestSQLContext.parquetCompressionCodec.toUpperCase :: Nil)
+
+    parquetFile(path).registerTempTable("tmp")
+    checkAnswer(
+      sql("SELECT key, value FROM tmp WHERE value = 'val_5' OR value = 'val_7'"),
+      (5, "val_5") ::
+      (7, "val_7") :: Nil)
+
+    Utils.deleteRecursively(file)
+
+    // test snappy compression codec
+    TestSQLContext.setConf(SQLConf.PARQUET_COMPRESSION, "snappy")
+
+    rdd.saveAsParquetFile(path)
+    actualCodec = ParquetTypesConverter.readMetaData(new Path(path), Some(TestSQLContext.sparkContext.hadoopConfiguration))
+      .getBlocks.flatMap(block => block.getColumns).map(column => column.getCodec.name()).distinct
+    assert(actualCodec === TestSQLContext.parquetCompressionCodec.toUpperCase :: Nil)
+
+    parquetFile(path).registerTempTable("tmp")
+    checkAnswer(
+      sql("SELECT key, value FROM tmp WHERE value = 'val_5' OR value = 'val_7'"),
+      (5, "val_5") ::
+      (7, "val_7") :: Nil)
+
+    Utils.deleteRecursively(file)
+
+    // TODO: Lzo requires additional external setup steps so leave it out for now
+    // ref.: https://github.com/Parquet/parquet-mr/blob/parquet-1.5.0/parquet-hadoop/src/test/java/parquet/hadoop/example/TestInputOutputFormat.java#L169
+
+    // Set it back.
+    TestSQLContext.setConf(SQLConf.PARQUET_COMPRESSION, defaultParquetCompressionCodec)
+  }
+
   test("Read/Write All Types with non-primitive type") {
     val tempDir = getTempFilePath("parquetTest").getCanonicalPath
     val range = (0 to 255)

From 3cedc4f4d78e093fd362085e0a077bb9e4f28ca5 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies.liu@gmail.com>
Date: Tue, 26 Aug 2014 13:04:30 -0700
Subject: [PATCH 085/489] [SPARK-2871] [PySpark] add histgram() API

RDD.histogram(buckets)

        Compute a histogram using the provided buckets. The buckets
        are all open to the right except for the last which is closed.
        e.g. [1,10,20,50] means the buckets are [1,10) [10,20) [20,50],
        which means 1<=x<10, 10<=x<20, 20<=x<=50. And on the input of 1
        and 50 we would have a histogram of 1,0,1.

        If your histogram is evenly spaced (e.g. [0, 10, 20, 30]),
        this can be switched from an O(log n) inseration to O(1) per
        element(where n = # buckets).

        Buckets must be sorted and not contain any duplicates, must be
        at least two elements.

        If `buckets` is a number, it will generates buckets which is
        evenly spaced between the minimum and maximum of the RDD. For
        example, if the min value is 0 and the max is 100, given buckets
        as 2, the resulting buckets will be [0,50) [50,100]. buckets must
        be at least 1 If the RDD contains infinity, NaN throws an exception
        If the elements in RDD do not vary (max == min) always returns
        a single bucket.

        It will return an tuple of buckets and histogram.

        >>> rdd = sc.parallelize(range(51))
        >>> rdd.histogram(2)
        ([0, 25, 50], [25, 26])
        >>> rdd.histogram([0, 5, 25, 50])
        ([0, 5, 25, 50], [5, 20, 26])
        >>> rdd.histogram([0, 15, 30, 45, 60], True)
        ([0, 15, 30, 45, 60], [15, 15, 15, 6])
        >>> rdd = sc.parallelize(["ab", "ac", "b", "bd", "ef"])
        >>> rdd.histogram(("a", "b", "c"))
        (('a', 'b', 'c'), [2, 2])

closes #122, it's duplicated.

Author: Davies Liu <davies.liu@gmail.com>

Closes #2091 from davies/histgram and squashes the following commits:

a322f8a [Davies Liu] fix deprecation of e.message
84e85fa [Davies Liu] remove evenBuckets, add more tests (including str)
d9a0722 [Davies Liu] address comments
0e18a2d [Davies Liu] add histgram() API
---
 python/pyspark/rdd.py   | 129 +++++++++++++++++++++++++++++++++++++++-
 python/pyspark/tests.py | 104 ++++++++++++++++++++++++++++++++
 2 files changed, 232 insertions(+), 1 deletion(-)

diff --git a/python/pyspark/rdd.py b/python/pyspark/rdd.py
index 1374f74968c9e..3a2e7649e6827 100644
--- a/python/pyspark/rdd.py
+++ b/python/pyspark/rdd.py
@@ -32,7 +32,7 @@
 import heapq
 import bisect
 from random import Random
-from math import sqrt, log
+from math import sqrt, log, isinf, isnan
 
 from pyspark.serializers import NoOpSerializer, CartesianDeserializer, \
     BatchedSerializer, CloudPickleSerializer, PairDeserializer, \
@@ -886,6 +886,133 @@ def redFunc(left_counter, right_counter):
 
         return self.mapPartitions(lambda i: [StatCounter(i)]).reduce(redFunc)
 
+    def histogram(self, buckets):
+        """
+        Compute a histogram using the provided buckets. The buckets
+        are all open to the right except for the last which is closed.
+        e.g. [1,10,20,50] means the buckets are [1,10) [10,20) [20,50],
+        which means 1<=x<10, 10<=x<20, 20<=x<=50. And on the input of 1
+        and 50 we would have a histogram of 1,0,1.
+
+        If your histogram is evenly spaced (e.g. [0, 10, 20, 30]),
+        this can be switched from an O(log n) inseration to O(1) per
+        element(where n = # buckets).
+
+        Buckets must be sorted and not contain any duplicates, must be
+        at least two elements.
+
+        If `buckets` is a number, it will generates buckets which are
+        evenly spaced between the minimum and maximum of the RDD. For
+        example, if the min value is 0 and the max is 100, given buckets
+        as 2, the resulting buckets will be [0,50) [50,100]. buckets must
+        be at least 1 If the RDD contains infinity, NaN throws an exception
+        If the elements in RDD do not vary (max == min) always returns
+        a single bucket.
+
+        It will return an tuple of buckets and histogram.
+
+        >>> rdd = sc.parallelize(range(51))
+        >>> rdd.histogram(2)
+        ([0, 25, 50], [25, 26])
+        >>> rdd.histogram([0, 5, 25, 50])
+        ([0, 5, 25, 50], [5, 20, 26])
+        >>> rdd.histogram([0, 15, 30, 45, 60])  # evenly spaced buckets
+        ([0, 15, 30, 45, 60], [15, 15, 15, 6])
+        >>> rdd = sc.parallelize(["ab", "ac", "b", "bd", "ef"])
+        >>> rdd.histogram(("a", "b", "c"))
+        (('a', 'b', 'c'), [2, 2])
+        """
+
+        if isinstance(buckets, (int, long)):
+            if buckets < 1:
+                raise ValueError("number of buckets must be >= 1")
+
+            # filter out non-comparable elements
+            def comparable(x):
+                if x is None:
+                    return False
+                if type(x) is float and isnan(x):
+                    return False
+                return True
+
+            filtered = self.filter(comparable)
+
+            # faster than stats()
+            def minmax(a, b):
+                return min(a[0], b[0]), max(a[1], b[1])
+            try:
+                minv, maxv = filtered.map(lambda x: (x, x)).reduce(minmax)
+            except TypeError as e:
+                if " empty " in str(e):
+                    raise ValueError("can not generate buckets from empty RDD")
+                raise
+
+            if minv == maxv or buckets == 1:
+                return [minv, maxv], [filtered.count()]
+
+            try:
+                inc = (maxv - minv) / buckets
+            except TypeError:
+                raise TypeError("Can not generate buckets with non-number in RDD")
+
+            if isinf(inc):
+                raise ValueError("Can not generate buckets with infinite value")
+
+            # keep them as integer if possible
+            if inc * buckets != maxv - minv:
+                inc = (maxv - minv) * 1.0 / buckets
+
+            buckets = [i * inc + minv for i in range(buckets)]
+            buckets.append(maxv)  # fix accumulated error
+            even = True
+
+        elif isinstance(buckets, (list, tuple)):
+            if len(buckets) < 2:
+                raise ValueError("buckets should have more than one value")
+
+            if any(i is None or isinstance(i, float) and isnan(i) for i in buckets):
+                raise ValueError("can not have None or NaN in buckets")
+
+            if sorted(buckets) != list(buckets):
+                raise ValueError("buckets should be sorted")
+
+            if len(set(buckets)) != len(buckets):
+                raise ValueError("buckets should not contain duplicated values")
+
+            minv = buckets[0]
+            maxv = buckets[-1]
+            even = False
+            inc = None
+            try:
+                steps = [buckets[i + 1] - buckets[i] for i in range(len(buckets) - 1)]
+            except TypeError:
+                pass  # objects in buckets do not support '-'
+            else:
+                if max(steps) - min(steps) < 1e-10:  # handle precision errors
+                    even = True
+                    inc = (maxv - minv) / (len(buckets) - 1)
+
+        else:
+            raise TypeError("buckets should be a list or tuple or number(int or long)")
+
+        def histogram(iterator):
+            counters = [0] * len(buckets)
+            for i in iterator:
+                if i is None or (type(i) is float and isnan(i)) or i > maxv or i < minv:
+                    continue
+                t = (int((i - minv) / inc) if even
+                     else bisect.bisect_right(buckets, i) - 1)
+                counters[t] += 1
+            # add last two together
+            last = counters.pop()
+            counters[-1] += last
+            return [counters]
+
+        def mergeCounters(a, b):
+            return [i + j for i, j in zip(a, b)]
+
+        return buckets, self.mapPartitions(histogram).reduce(mergeCounters)
+
     def mean(self):
         """
         Compute the mean of this RDD's elements.
diff --git a/python/pyspark/tests.py b/python/pyspark/tests.py
index 51bfbb47e53c2..1db922f513743 100644
--- a/python/pyspark/tests.py
+++ b/python/pyspark/tests.py
@@ -364,6 +364,110 @@ def test_zip_with_different_number_of_items(self):
         self.assertEquals(a.count(), b.count())
         self.assertRaises(Exception, lambda: a.zip(b).count())
 
+    def test_histogram(self):
+        # empty
+        rdd = self.sc.parallelize([])
+        self.assertEquals([0], rdd.histogram([0, 10])[1])
+        self.assertEquals([0, 0], rdd.histogram([0, 4, 10])[1])
+        self.assertRaises(ValueError, lambda: rdd.histogram(1))
+
+        # out of range
+        rdd = self.sc.parallelize([10.01, -0.01])
+        self.assertEquals([0], rdd.histogram([0, 10])[1])
+        self.assertEquals([0, 0], rdd.histogram((0, 4, 10))[1])
+
+        # in range with one bucket
+        rdd = self.sc.parallelize(range(1, 5))
+        self.assertEquals([4], rdd.histogram([0, 10])[1])
+        self.assertEquals([3, 1], rdd.histogram([0, 4, 10])[1])
+
+        # in range with one bucket exact match
+        self.assertEquals([4], rdd.histogram([1, 4])[1])
+
+        # out of range with two buckets
+        rdd = self.sc.parallelize([10.01, -0.01])
+        self.assertEquals([0, 0], rdd.histogram([0, 5, 10])[1])
+
+        # out of range with two uneven buckets
+        rdd = self.sc.parallelize([10.01, -0.01])
+        self.assertEquals([0, 0], rdd.histogram([0, 4, 10])[1])
+
+        # in range with two buckets
+        rdd = self.sc.parallelize([1, 2, 3, 5, 6])
+        self.assertEquals([3, 2], rdd.histogram([0, 5, 10])[1])
+
+        # in range with two bucket and None
+        rdd = self.sc.parallelize([1, 2, 3, 5, 6, None, float('nan')])
+        self.assertEquals([3, 2], rdd.histogram([0, 5, 10])[1])
+
+        # in range with two uneven buckets
+        rdd = self.sc.parallelize([1, 2, 3, 5, 6])
+        self.assertEquals([3, 2], rdd.histogram([0, 5, 11])[1])
+
+        # mixed range with two uneven buckets
+        rdd = self.sc.parallelize([-0.01, 0.0, 1, 2, 3, 5, 6, 11.0, 11.01])
+        self.assertEquals([4, 3], rdd.histogram([0, 5, 11])[1])
+
+        # mixed range with four uneven buckets
+        rdd = self.sc.parallelize([-0.01, 0.0, 1, 2, 3, 5, 6, 11.01, 12.0, 199.0, 200.0, 200.1])
+        self.assertEquals([4, 2, 1, 3], rdd.histogram([0.0, 5.0, 11.0, 12.0, 200.0])[1])
+
+        # mixed range with uneven buckets and NaN
+        rdd = self.sc.parallelize([-0.01, 0.0, 1, 2, 3, 5, 6, 11.01, 12.0,
+                                   199.0, 200.0, 200.1, None, float('nan')])
+        self.assertEquals([4, 2, 1, 3], rdd.histogram([0.0, 5.0, 11.0, 12.0, 200.0])[1])
+
+        # out of range with infinite buckets
+        rdd = self.sc.parallelize([10.01, -0.01, float('nan'), float("inf")])
+        self.assertEquals([1, 2], rdd.histogram([float('-inf'), 0, float('inf')])[1])
+
+        # invalid buckets
+        self.assertRaises(ValueError, lambda: rdd.histogram([]))
+        self.assertRaises(ValueError, lambda: rdd.histogram([1]))
+        self.assertRaises(ValueError, lambda: rdd.histogram(0))
+        self.assertRaises(TypeError, lambda: rdd.histogram({}))
+
+        # without buckets
+        rdd = self.sc.parallelize(range(1, 5))
+        self.assertEquals(([1, 4], [4]), rdd.histogram(1))
+
+        # without buckets single element
+        rdd = self.sc.parallelize([1])
+        self.assertEquals(([1, 1], [1]), rdd.histogram(1))
+
+        # without bucket no range
+        rdd = self.sc.parallelize([1] * 4)
+        self.assertEquals(([1, 1], [4]), rdd.histogram(1))
+
+        # without buckets basic two
+        rdd = self.sc.parallelize(range(1, 5))
+        self.assertEquals(([1, 2.5, 4], [2, 2]), rdd.histogram(2))
+
+        # without buckets with more requested than elements
+        rdd = self.sc.parallelize([1, 2])
+        buckets = [1 + 0.2 * i for i in range(6)]
+        hist = [1, 0, 0, 0, 1]
+        self.assertEquals((buckets, hist), rdd.histogram(5))
+
+        # invalid RDDs
+        rdd = self.sc.parallelize([1, float('inf')])
+        self.assertRaises(ValueError, lambda: rdd.histogram(2))
+        rdd = self.sc.parallelize([float('nan')])
+        self.assertRaises(ValueError, lambda: rdd.histogram(2))
+
+        # string
+        rdd = self.sc.parallelize(["ab", "ac", "b", "bd", "ef"], 2)
+        self.assertEquals([2, 2], rdd.histogram(["a", "b", "c"])[1])
+        self.assertEquals((["ab", "ef"], [5]), rdd.histogram(1))
+        self.assertRaises(TypeError, lambda: rdd.histogram(2))
+
+        # mixed RDD
+        rdd = self.sc.parallelize([1, 4, "ab", "ac", "b"], 2)
+        self.assertEquals([1, 1], rdd.histogram([0, 4, 10])[1])
+        self.assertEquals([2, 1], rdd.histogram(["a", "b", "c"])[1])
+        self.assertEquals(([1, "b"], [5]), rdd.histogram(1))
+        self.assertRaises(TypeError, lambda: rdd.histogram(2))
+
 
 class TestIO(PySparkTestCase):
 

From 98c2bb0bbde6fb2b6f64af3efffefcb0dae94c12 Mon Sep 17 00:00:00 2001
From: Takuya UESHIN <ueshin@happy-camper.st>
Date: Tue, 26 Aug 2014 13:22:55 -0700
Subject: [PATCH 086/489] [SPARK-2969][SQL] Make ScalaReflection be able to
 handle ArrayType.containsNull and MapType.valueContainsNull.

Make `ScalaReflection` be able to handle like:

- `Seq[Int]` as `ArrayType(IntegerType, containsNull = false)`
- `Seq[java.lang.Integer]` as `ArrayType(IntegerType, containsNull = true)`
- `Map[Int, Long]` as `MapType(IntegerType, LongType, valueContainsNull = false)`
- `Map[Int, java.lang.Long]` as `MapType(IntegerType, LongType, valueContainsNull = true)`

Author: Takuya UESHIN <ueshin@happy-camper.st>

Closes #1889 from ueshin/issues/SPARK-2969 and squashes the following commits:

24f1c5c [Takuya UESHIN] Change the default value of ArrayType.containsNull to true in Python API.
79f5b65 [Takuya UESHIN] Change the default value of ArrayType.containsNull to true in Java API.
7cd1a7a [Takuya UESHIN] Fix json test failures.
2cfb862 [Takuya UESHIN] Change the default value of ArrayType.containsNull to true.
2f38e61 [Takuya UESHIN] Revert the default value of MapTypes.valueContainsNull.
9fa02f5 [Takuya UESHIN] Fix a test failure.
1a9a96b [Takuya UESHIN] Modify ScalaReflection to handle ArrayType.containsNull and MapType.valueContainsNull.
---
 python/pyspark/sql.py                         |  6 ++--
 .../spark/sql/catalyst/ScalaReflection.scala  |  9 ++++--
 .../spark/sql/catalyst/types/dataTypes.scala  |  4 +--
 .../sql/catalyst/ScalaReflectionSuite.scala   | 22 +++++++++++--
 .../apache/spark/sql/api/java/DataType.java   |  4 +--
 .../org/apache/spark/sql/DataTypeSuite.scala  |  2 +-
 .../org/apache/spark/sql/json/JsonSuite.scala | 32 +++++++++----------
 7 files changed, 49 insertions(+), 30 deletions(-)

diff --git a/python/pyspark/sql.py b/python/pyspark/sql.py
index d4ca0cc8f336e..0ff6a548a85f1 100644
--- a/python/pyspark/sql.py
+++ b/python/pyspark/sql.py
@@ -186,15 +186,15 @@ class ArrayType(DataType):
 
     """
 
-    def __init__(self, elementType, containsNull=False):
+    def __init__(self, elementType, containsNull=True):
         """Creates an ArrayType
 
         :param elementType: the data type of elements.
         :param containsNull: indicates whether the list contains None values.
 
-        >>> ArrayType(StringType) == ArrayType(StringType, False)
+        >>> ArrayType(StringType) == ArrayType(StringType, True)
         True
-        >>> ArrayType(StringType, True) == ArrayType(StringType)
+        >>> ArrayType(StringType, False) == ArrayType(StringType)
         False
         """
         self.elementType = elementType
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ScalaReflection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ScalaReflection.scala
index 0d26b52a84695..6b6b636cd96dc 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ScalaReflection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ScalaReflection.scala
@@ -62,11 +62,14 @@ object ScalaReflection {
       sys.error(s"Only Array[Byte] supported now, use Seq instead of $t")
     case t if t <:< typeOf[Seq[_]] =>
       val TypeRef(_, _, Seq(elementType)) = t
-      Schema(ArrayType(schemaFor(elementType).dataType), nullable = true)
+      val Schema(dataType, nullable) = schemaFor(elementType)
+      Schema(ArrayType(dataType, containsNull = nullable), nullable = true)
     case t if t <:< typeOf[Map[_,_]] =>
       val TypeRef(_, _, Seq(keyType, valueType)) = t
-      Schema(MapType(schemaFor(keyType).dataType, schemaFor(valueType).dataType), nullable = true)
-    case t if t <:< typeOf[String] => Schema(StringType, nullable = true)
+      val Schema(valueDataType, valueNullable) = schemaFor(valueType)
+      Schema(MapType(schemaFor(keyType).dataType,
+        valueDataType, valueContainsNull = valueNullable), nullable = true)
+    case t if t <:< typeOf[String]            => Schema(StringType, nullable = true)
     case t if t <:< typeOf[Timestamp] => Schema(TimestampType, nullable = true)
     case t if t <:< typeOf[BigDecimal] => Schema(DecimalType, nullable = true)
     case t if t <:< typeOf[java.lang.Integer] => Schema(IntegerType, nullable = true)
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/types/dataTypes.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/types/dataTypes.scala
index b52ee6d3378a3..70c6d06cf2534 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/types/dataTypes.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/types/dataTypes.scala
@@ -270,8 +270,8 @@ case object FloatType extends FractionalType {
 }
 
 object ArrayType {
-  /** Construct a [[ArrayType]] object with the given element type. The `containsNull` is false. */
-  def apply(elementType: DataType): ArrayType = ArrayType(elementType, false)
+  /** Construct a [[ArrayType]] object with the given element type. The `containsNull` is true. */
+  def apply(elementType: DataType): ArrayType = ArrayType(elementType, true)
 }
 
 /**
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/ScalaReflectionSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/ScalaReflectionSuite.scala
index e75373d5a74a7..428607d8c8253 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/ScalaReflectionSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/ScalaReflectionSuite.scala
@@ -57,7 +57,9 @@ case class OptionalData(
 
 case class ComplexData(
     arrayField: Seq[Int],
-    mapField: Map[Int, String],
+    arrayFieldContainsNull: Seq[java.lang.Integer],
+    mapField: Map[Int, Long],
+    mapFieldValueContainsNull: Map[Int, java.lang.Long],
     structField: PrimitiveData)
 
 case class GenericData[A](
@@ -116,8 +118,22 @@ class ScalaReflectionSuite extends FunSuite {
     val schema = schemaFor[ComplexData]
     assert(schema === Schema(
       StructType(Seq(
-        StructField("arrayField", ArrayType(IntegerType), nullable = true),
-        StructField("mapField", MapType(IntegerType, StringType), nullable = true),
+        StructField(
+          "arrayField",
+          ArrayType(IntegerType, containsNull = false),
+          nullable = true),
+        StructField(
+          "arrayFieldContainsNull",
+          ArrayType(IntegerType, containsNull = true),
+          nullable = true),
+        StructField(
+          "mapField",
+          MapType(IntegerType, LongType, valueContainsNull = false),
+          nullable = true),
+        StructField(
+          "mapFieldValueContainsNull",
+          MapType(IntegerType, LongType, valueContainsNull = true),
+          nullable = true),
         StructField(
           "structField",
           StructType(Seq(
diff --git a/sql/core/src/main/java/org/apache/spark/sql/api/java/DataType.java b/sql/core/src/main/java/org/apache/spark/sql/api/java/DataType.java
index 3eccddef88134..37b4c8ffcba0b 100644
--- a/sql/core/src/main/java/org/apache/spark/sql/api/java/DataType.java
+++ b/sql/core/src/main/java/org/apache/spark/sql/api/java/DataType.java
@@ -86,14 +86,14 @@ public abstract class DataType {
 
   /**
    * Creates an ArrayType by specifying the data type of elements ({@code elementType}).
-   * The field of {@code containsNull} is set to {@code false}.
+   * The field of {@code containsNull} is set to {@code true}.
    */
   public static ArrayType createArrayType(DataType elementType) {
     if (elementType == null) {
       throw new IllegalArgumentException("elementType should not be null.");
     }
 
-    return new ArrayType(elementType, false);
+    return new ArrayType(elementType, true);
   }
 
   /**
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataTypeSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataTypeSuite.scala
index cf7d79f42db1d..8fb59c5830f6d 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataTypeSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataTypeSuite.scala
@@ -24,7 +24,7 @@ class DataTypeSuite extends FunSuite {
   test("construct an ArrayType") {
     val array = ArrayType(StringType)
 
-    assert(ArrayType(StringType, false) === array)
+    assert(ArrayType(StringType, true) === array)
   }
 
   test("construct an MapType") {
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala
index 58b1e23891a3b..05513a127150c 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala
@@ -130,11 +130,11 @@ class JsonSuite extends QueryTest {
     checkDataType(
       ArrayType(IntegerType, true), ArrayType(IntegerType, true), ArrayType(IntegerType, true))
     checkDataType(
-      ArrayType(IntegerType, false), ArrayType(IntegerType), ArrayType(IntegerType, false))
+      ArrayType(IntegerType, false), ArrayType(IntegerType), ArrayType(IntegerType, true))
     checkDataType(
       ArrayType(IntegerType, false), ArrayType(IntegerType, false), ArrayType(IntegerType, false))
     checkDataType(
-      ArrayType(IntegerType, false), ArrayType(IntegerType, false), ArrayType(IntegerType))
+      ArrayType(IntegerType, false), ArrayType(IntegerType, true), ArrayType(IntegerType, true))
 
     // StructType
     checkDataType(StructType(Nil), StructType(Nil), StructType(Nil))
@@ -201,26 +201,26 @@ class JsonSuite extends QueryTest {
     val jsonSchemaRDD = jsonRDD(complexFieldAndType)
 
     val expectedSchema = StructType(
-      StructField("arrayOfArray1", ArrayType(ArrayType(StringType)), true) ::
-      StructField("arrayOfArray2", ArrayType(ArrayType(DoubleType)), true) ::
-      StructField("arrayOfBigInteger", ArrayType(DecimalType), true) ::
-      StructField("arrayOfBoolean", ArrayType(BooleanType), true) ::
-      StructField("arrayOfDouble", ArrayType(DoubleType), true) ::
-      StructField("arrayOfInteger", ArrayType(IntegerType), true) ::
-      StructField("arrayOfLong", ArrayType(LongType), true) ::
+      StructField("arrayOfArray1", ArrayType(ArrayType(StringType, false), false), true) ::
+      StructField("arrayOfArray2", ArrayType(ArrayType(DoubleType, false), false), true) ::
+      StructField("arrayOfBigInteger", ArrayType(DecimalType, false), true) ::
+      StructField("arrayOfBoolean", ArrayType(BooleanType, false), true) ::
+      StructField("arrayOfDouble", ArrayType(DoubleType, false), true) ::
+      StructField("arrayOfInteger", ArrayType(IntegerType, false), true) ::
+      StructField("arrayOfLong", ArrayType(LongType, false), true) ::
       StructField("arrayOfNull", ArrayType(StringType, true), true) ::
-      StructField("arrayOfString", ArrayType(StringType), true) ::
+      StructField("arrayOfString", ArrayType(StringType, false), true) ::
       StructField("arrayOfStruct", ArrayType(
         StructType(
           StructField("field1", BooleanType, true) ::
           StructField("field2", StringType, true) ::
-          StructField("field3", StringType, true) :: Nil)), true) ::
+          StructField("field3", StringType, true) :: Nil), false), true) ::
       StructField("struct", StructType(
       StructField("field1", BooleanType, true) ::
       StructField("field2", DecimalType, true) :: Nil), true) ::
       StructField("structWithArrayFields", StructType(
-        StructField("field1", ArrayType(IntegerType), true) ::
-        StructField("field2", ArrayType(StringType), true) :: Nil), true) :: Nil)
+        StructField("field1", ArrayType(IntegerType, false), true) ::
+        StructField("field2", ArrayType(StringType, false), true) :: Nil), true) :: Nil)
 
     assert(expectedSchema === jsonSchemaRDD.schema)
 
@@ -441,7 +441,7 @@ class JsonSuite extends QueryTest {
     val jsonSchemaRDD = jsonRDD(complexFieldValueTypeConflict)
 
     val expectedSchema = StructType(
-      StructField("array", ArrayType(IntegerType), true) ::
+      StructField("array", ArrayType(IntegerType, false), true) ::
       StructField("num_struct", StringType, true) ::
       StructField("str_array", StringType, true) ::
       StructField("struct", StructType(
@@ -467,7 +467,7 @@ class JsonSuite extends QueryTest {
     val expectedSchema = StructType(
       StructField("array1", ArrayType(StringType, true), true) ::
       StructField("array2", ArrayType(StructType(
-        StructField("field", LongType, true) :: Nil)), true) :: Nil)
+        StructField("field", LongType, true) :: Nil), false), true) :: Nil)
 
     assert(expectedSchema === jsonSchemaRDD.schema)
 
@@ -492,7 +492,7 @@ class JsonSuite extends QueryTest {
     val expectedSchema = StructType(
       StructField("a", BooleanType, true) ::
       StructField("b", LongType, true) ::
-      StructField("c", ArrayType(IntegerType), true) ::
+      StructField("c", ArrayType(IntegerType, false), true) ::
       StructField("d", StructType(
         StructField("field", BooleanType, true) :: Nil), true) ::
       StructField("e", StringType, true) :: Nil)

From 6b5584ef1c605cd30f25dbe7099ab32aea1746fb Mon Sep 17 00:00:00 2001
From: Takuya UESHIN <ueshin@happy-camper.st>
Date: Tue, 26 Aug 2014 15:04:08 -0700
Subject: [PATCH 087/489] [SPARK-3063][SQL] ExistingRdd should convert Map to
 catalyst Map.

Currently `ExistingRdd.convertToCatalyst` doesn't convert `Map` value.

Author: Takuya UESHIN <ueshin@happy-camper.st>

Closes #1963 from ueshin/issues/SPARK-3063 and squashes the following commits:

3ba41f2 [Takuya UESHIN] Merge branch 'master' into issues/SPARK-3063
4d7bae2 [Takuya UESHIN] Merge branch 'master' into issues/SPARK-3063
9321379 [Takuya UESHIN] Merge branch 'master' into issues/SPARK-3063
d8a900a [Takuya UESHIN] Make ExistingRdd.convertToCatalyst be able to convert Map value.
---
 .../spark/sql/execution/basicOperators.scala  |  3 +-
 .../sql/ScalaReflectionRelationSuite.scala    | 46 +++++++++++++++++++
 2 files changed, 48 insertions(+), 1 deletion(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
index f9dfa3c92f1eb..374af48b820c2 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
@@ -206,7 +206,8 @@ case class Sort(
 object ExistingRdd {
   def convertToCatalyst(a: Any): Any = a match {
     case o: Option[_] => o.orNull
-    case s: Seq[Any] => s.map(convertToCatalyst)
+    case s: Seq[_] => s.map(convertToCatalyst)
+    case m: Map[_, _] => m.map { case (k, v) => convertToCatalyst(k) -> convertToCatalyst(v) }
     case p: Product => new GenericRow(p.productIterator.map(convertToCatalyst).toArray)
     case other => other
   }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/ScalaReflectionRelationSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/ScalaReflectionRelationSuite.scala
index 5b84c658db942..e24c521d24c7a 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/ScalaReflectionRelationSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/ScalaReflectionRelationSuite.scala
@@ -21,6 +21,7 @@ import java.sql.Timestamp
 
 import org.scalatest.FunSuite
 
+import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.test.TestSQLContext._
 
 case class ReflectData(
@@ -56,6 +57,22 @@ case class OptionalReflectData(
 
 case class ReflectBinary(data: Array[Byte])
 
+case class Nested(i: Option[Int], s: String)
+
+case class Data(
+    array: Seq[Int],
+    arrayContainsNull: Seq[Option[Int]],
+    map: Map[Int, Long],
+    mapContainsNul: Map[Int, Option[Long]],
+    nested: Nested)
+
+case class ComplexReflectData(
+    arrayField: Seq[Int],
+    arrayFieldContainsNull: Seq[Option[Int]],
+    mapField: Map[Int, Long],
+    mapFieldContainsNull: Map[Int, Option[Long]],
+    dataField: Data)
+
 class ScalaReflectionRelationSuite extends FunSuite {
   test("query case class RDD") {
     val data = ReflectData("a", 1, 1L, 1.toFloat, 1.toDouble, 1.toShort, 1.toByte, true,
@@ -90,4 +107,33 @@ class ScalaReflectionRelationSuite extends FunSuite {
     val result = sql("SELECT data FROM reflectBinary").collect().head(0).asInstanceOf[Array[Byte]]
     assert(result.toSeq === Seq[Byte](1))
   }
+
+  test("query complex data") {
+    val data = ComplexReflectData(
+      Seq(1, 2, 3),
+      Seq(Some(1), Some(2), None),
+      Map(1 -> 10L, 2 -> 20L),
+      Map(1 -> Some(10L), 2 -> Some(20L), 3 -> None),
+      Data(
+        Seq(10, 20, 30),
+        Seq(Some(10), Some(20), None),
+        Map(10 -> 100L, 20 -> 200L),
+        Map(10 -> Some(100L), 20 -> Some(200L), 30 -> None),
+        Nested(None, "abc")))
+    val rdd = sparkContext.parallelize(data :: Nil)
+    rdd.registerTempTable("reflectComplexData")
+
+    assert(sql("SELECT * FROM reflectComplexData").collect().head ===
+      new GenericRow(Array[Any](
+        Seq(1, 2, 3),
+        Seq(1, 2, null),
+        Map(1 -> 10L, 2 -> 20L),
+        Map(1 -> 10L, 2 -> 20L, 3 -> null),
+        new GenericRow(Array[Any](
+          Seq(10, 20, 30),
+          Seq(10, 20, null),
+          Map(10 -> 100L, 20 -> 200L),
+          Map(10 -> 100L, 20 -> 200L, 30 -> null),
+          new GenericRow(Array[Any](null, "abc")))))))
+  }
 }

From adbd5c1636669fc474ab02b54cd1ced353f68712 Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Tue, 26 Aug 2014 15:12:27 -0700
Subject: [PATCH 088/489] [SPARK-3226][MLLIB] doc update for native libraries

to mention `-Pnetlib-lgpl` option. atalwalkar

Author: Xiangrui Meng <meng@databricks.com>

Closes #2128 from mengxr/mllib-native and squashes the following commits:

4cbba57 [Xiangrui Meng] update mllib dependencies
---
 docs/mllib-guide.md | 25 +++++++++++++++----------
 1 file changed, 15 insertions(+), 10 deletions(-)

diff --git a/docs/mllib-guide.md b/docs/mllib-guide.md
index ca0a84a8c53fd..4d4198b9e0452 100644
--- a/docs/mllib-guide.md
+++ b/docs/mllib-guide.md
@@ -35,18 +35,23 @@ and the migration guide below will explain all changes between releases.
 
 # Dependencies
 
-MLlib uses the linear algebra package [Breeze](http://www.scalanlp.org/), which depends on
-[netlib-java](https://github.com/fommil/netlib-java), and
-[jblas](https://github.com/mikiobraun/jblas). 
+MLlib uses the linear algebra package [Breeze](http://www.scalanlp.org/),
+which depends on [netlib-java](https://github.com/fommil/netlib-java),
+and [jblas](https://github.com/mikiobraun/jblas). 
 `netlib-java` and `jblas` depend on native Fortran routines.
 You need to install the
-[gfortran runtime library](https://github.com/mikiobraun/jblas/wiki/Missing-Libraries) if it is not
-already present on your nodes. MLlib will throw a linking error if it cannot detect these libraries
-automatically.  Due to license issues, we do not include `netlib-java`'s native libraries in MLlib's
-dependency set. If no native library is available at runtime, you will see a warning message.  To
-use native libraries from `netlib-java`, please include artifact
-`com.github.fommil.netlib:all:1.1.2` as a dependency of your project or build your own (see
-[instructions](https://github.com/fommil/netlib-java/blob/master/README.md#machine-optimised-system-libraries)).
+[gfortran runtime library](https://github.com/mikiobraun/jblas/wiki/Missing-Libraries)
+if it is not already present on your nodes.
+MLlib will throw a linking error if it cannot detect these libraries automatically.
+Due to license issues, we do not include `netlib-java`'s native libraries in MLlib's
+dependency set under default settings.
+If no native library is available at runtime, you will see a warning message.
+To use native libraries from `netlib-java`, please build Spark with `-Pnetlib-lgpl` or
+include `com.github.fommil.netlib:all:1.1.2` as a dependency of your project.
+If you want to use optimized BLAS/LAPACK libraries such as
+[OpenBLAS](http://www.openblas.net/), please link its shared libraries to
+`/usr/lib/libblas.so.3` and `/usr/lib/liblapack.so.3`, respectively.
+BLAS/LAPACK libraries on worker nodes should be built without multithreading.
 
 To use MLlib in Python, you will need [NumPy](http://www.numpy.org) version 1.4 or newer.
 

From 1208f72ac78960fe5060187761479b2a9a417c1b Mon Sep 17 00:00:00 2001
From: Burak <brkyvz@gmail.com>
Date: Tue, 26 Aug 2014 15:18:42 -0700
Subject: [PATCH 089/489] [SPARK-2839][MLlib] Stats Toolkit documentation
 updated

Documentation updated for the Statistics Toolkit of MLlib. mengxr atalwalkar

https://issues.apache.org/jira/browse/SPARK-2839

P.S. Accidentally closed #2123. New commits didn't show up after I reopened the PR. I've opened this instead and closed the old one.

Author: Burak <brkyvz@gmail.com>

Closes #2130 from brkyvz/StatsLib-Docs and squashes the following commits:

a54a855 [Burak] [SPARK-2839][MLlib] Addressed comments
bfc6896 [Burak] [SPARK-2839][MLlib] Added a more specific link to colStats() for pyspark
213fe3f [Burak] [SPARK-2839][MLlib] Modifications made according to review
fec4d9d [Burak] [SPARK-2830][MLlib] Stats Toolkit documentation updated
---
 docs/mllib-stats.md | 372 +++++++++++++++++++++++++++++++++++++++-----
 1 file changed, 331 insertions(+), 41 deletions(-)

diff --git a/docs/mllib-stats.md b/docs/mllib-stats.md
index f25dca746ba3a..511a9fbf710cc 100644
--- a/docs/mllib-stats.md
+++ b/docs/mllib-stats.md
@@ -25,6 +25,85 @@ displayTitle: <a href="mllib-guide.html">MLlib</a> - Statistics Functionality
 \newcommand{\zero}{\mathbf{0}}
 \]`
 
+## Summary Statistics 
+
+We provide column summary statistics for `RDD[Vector]` through the function `colStats` 
+available in `Statistics`.
+
+<div class="codetabs">
+<div data-lang="scala" markdown="1">
+
+[`colStats()`](api/scala/index.html#org.apache.spark.mllib.stat.Statistics$) returns an instance of
+[`MultivariateStatisticalSummary`](api/scala/index.html#org.apache.spark.mllib.stat.MultivariateStatisticalSummary),
+which contains the column-wise max, min, mean, variance, and number of nonzeros, as well as the
+total count.
+
+{% highlight scala %}
+import org.apache.spark.mllib.linalg.Vector
+import org.apache.spark.mllib.stat.{MultivariateStatisticalSummary, Statistics}
+
+val observations: RDD[Vector] = ... // an RDD of Vectors
+
+// Compute column summary statistics.
+val summary: MultivariateStatisticalSummary = Statistics.colStats(observations)
+println(summary.mean) // a dense vector containing the mean value for each column
+println(summary.variance) // column-wise variance
+println(summary.numNonzeros) // number of nonzeros in each column
+
+{% endhighlight %}
+</div>
+
+<div data-lang="java" markdown="1">
+
+[`colStats()`](api/java/org/apache/spark/mllib/stat/Statistics.html) returns an instance of
+[`MultivariateStatisticalSummary`](api/java/org/apache/spark/mllib/stat/MultivariateStatisticalSummary.html),
+which contains the column-wise max, min, mean, variance, and number of nonzeros, as well as the
+total count.
+
+{% highlight java %}
+import org.apache.spark.api.java.JavaRDD;
+import org.apache.spark.api.java.JavaSparkContext;
+import org.apache.spark.mllib.linalg.Vector;
+import org.apache.spark.mllib.stat.MultivariateStatisticalSummary;
+import org.apache.spark.mllib.stat.Statistics;
+
+JavaSparkContext jsc = ...
+
+JavaRDD<Vector> mat = ... // an RDD of Vectors
+
+// Compute column summary statistics.
+MultivariateStatisticalSummary summary = Statistics.colStats(mat.rdd());
+System.out.println(summary.mean()); // a dense vector containing the mean value for each column
+System.out.println(summary.variance()); // column-wise variance
+System.out.println(summary.numNonzeros()); // number of nonzeros in each column
+
+{% endhighlight %}
+</div>
+
+<div data-lang="python" markdown="1">
+[`colStats()`](api/python/pyspark.mllib.stat.Statistics-class.html#colStats) returns an instance of
+[`MultivariateStatisticalSummary`](api/python/pyspark.mllib.stat.MultivariateStatisticalSummary-class.html),
+which contains the column-wise max, min, mean, variance, and number of nonzeros, as well as the
+total count.
+
+{% highlight python %}
+from pyspark.mllib.stat import Statistics
+
+sc = ... # SparkContext
+
+mat = ... # an RDD of Vectors
+
+# Compute column summary statistics.
+summary = Statistics.colStats(mat)
+print summary.mean()
+print summary.variance()
+print summary.numNonzeros()
+
+{% endhighlight %}
+</div>
+
+</div>
+
 ## Random data generation
 
 Random data generation is useful for randomized algorithms, prototyping, and performance testing.
@@ -99,69 +178,280 @@ v = u.map(lambda x: 1.0 + 2.0 * x)
 
 </div>
 
-## Stratified Sampling 
+## Correlations calculation
 
-## Summary Statistics 
+Calculating the correlation between two series of data is a common operation in Statistics. In MLlib
+we provide the flexibility to calculate pairwise correlations among many series. The supported 
+correlation methods are currently Pearson's and Spearman's correlation.
+ 
+<div class="codetabs">
+<div data-lang="scala" markdown="1">
+[`Statistics`](api/scala/index.html#org.apache.spark.mllib.stat.Statistics$) provides methods to 
+calculate correlations between series. Depending on the type of input, two `RDD[Double]`s or 
+an `RDD[Vector]`, the output will be a `Double` or the correlation `Matrix` respectively.
+
+{% highlight scala %}
+import org.apache.spark.SparkContext
+import org.apache.spark.mllib.linalg._
+import org.apache.spark.mllib.stat.Statistics
+
+val sc: SparkContext = ...
+
+val seriesX: RDD[Double] = ... // a series
+val seriesY: RDD[Double] = ... // must have the same number of partitions and cardinality as seriesX
+
+// compute the correlation using Pearson's method. Enter "spearman" for Spearman's method. If a 
+// method is not specified, Pearson's method will be used by default. 
+val correlation: Double = Statistics.corr(seriesX, seriesY, "pearson")
+
+val data: RDD[Vector] = ... // note that each Vector is a row and not a column
+
+// calculate the correlation matrix using Pearson's method. Use "spearman" for Spearman's method.
+// If a method is not specified, Pearson's method will be used by default. 
+val correlMatrix: Matrix = Statistics.corr(data, "pearson")
+
+{% endhighlight %}
+</div>
+
+<div data-lang="java" markdown="1">
+[`Statistics`](api/java/org/apache/spark/mllib/stat/Statistics.html) provides methods to 
+calculate correlations between series. Depending on the type of input, two `JavaDoubleRDD`s or 
+a `JavaRDD<Vector>`, the output will be a `Double` or the correlation `Matrix` respectively.
+
+{% highlight java %}
+import org.apache.spark.api.java.JavaDoubleRDD;
+import org.apache.spark.api.java.JavaSparkContext;
+import org.apache.spark.mllib.linalg.*;
+import org.apache.spark.mllib.stat.Statistics;
+
+JavaSparkContext jsc = ...
+
+JavaDoubleRDD seriesX = ... // a series
+JavaDoubleRDD seriesY = ... // must have the same number of partitions and cardinality as seriesX
+
+// compute the correlation using Pearson's method. Enter "spearman" for Spearman's method. If a 
+// method is not specified, Pearson's method will be used by default. 
+Double correlation = Statistics.corr(seriesX.srdd(), seriesY.srdd(), "pearson");
+
+JavaRDD<Vector> data = ... // note that each Vector is a row and not a column
+
+// calculate the correlation matrix using Pearson's method. Use "spearman" for Spearman's method.
+// If a method is not specified, Pearson's method will be used by default. 
+Matrix correlMatrix = Statistics.corr(data.rdd(), "pearson");
+
+{% endhighlight %}
+</div>
 
-### Multivariate summary statistics
+<div data-lang="python" markdown="1">
+[`Statistics`](api/python/pyspark.mllib.stat.Statistics-class.html) provides methods to 
+calculate correlations between series. Depending on the type of input, two `RDD[Double]`s or 
+an `RDD[Vector]`, the output will be a `Double` or the correlation `Matrix` respectively.
+
+{% highlight python %}
+from pyspark.mllib.stat import Statistics
+
+sc = ... # SparkContext
+
+seriesX = ... # a series
+seriesY = ... # must have the same number of partitions and cardinality as seriesX
 
-We provide column summary statistics for `RowMatrix` (note: this functionality is not currently supported in `IndexedRowMatrix` or `CoordinateMatrix`). 
-If the number of columns is not large, e.g., on the order of thousands, then the 
-covariance matrix can also be computed as a local matrix, which requires $\mathcal{O}(n^2)$ storage where $n$ is the
-number of columns. The total CPU time is $\mathcal{O}(m n^2)$, where $m$ is the number of rows,
-and is faster if the rows are sparse.
+# Compute the correlation using Pearson's method. Enter "spearman" for Spearman's method. If a 
+# method is not specified, Pearson's method will be used by default. 
+print Statistics.corr(seriesX, seriesY, method="pearson")
+
+data = ... # an RDD of Vectors
+# calculate the correlation matrix using Pearson's method. Use "spearman" for Spearman's method.
+# If a method is not specified, Pearson's method will be used by default. 
+print Statistics.corr(data, method="pearson")
+
+{% endhighlight %}
+</div>
+
+</div>
+
+## Stratified sampling
+
+Unlike the other statistics functions, which reside in MLLib, stratified sampling methods, 
+`sampleByKey` and `sampleByKeyExact`, can be performed on RDD's of key-value pairs. For stratified
+sampling, the keys can be thought of as a label and the value as a specific attribute. For example 
+the key can be man or woman, or document ids, and the respective values can be the list of ages 
+of the people in the population or the list of words in the documents. The `sampleByKey` method 
+will flip a coin to decide whether an observation will be sampled or not, therefore requires one 
+pass over the data, and provides an *expected* sample size. `sampleByKeyExact` requires significant 
+more resources than the per-stratum simple random sampling used in `sampleByKey`, but will provide
+the exact sampling size with 99.99% confidence. `sampleByKeyExact` is currently not supported in 
+python.
 
 <div class="codetabs">
 <div data-lang="scala" markdown="1">
-
-[`computeColumnSummaryStatistics()`](api/scala/index.html#org.apache.spark.mllib.linalg.distributed.RowMatrix) returns an instance of
-[`MultivariateStatisticalSummary`](api/scala/index.html#org.apache.spark.mllib.stat.MultivariateStatisticalSummary),
-which contains the column-wise max, min, mean, variance, and number of nonzeros, as well as the
-total count.
+[`sampleByKeyExact()`](api/scala/index.html#org.apache.spark.rdd.PairRDDFunctions) allows users to
+sample exactly $\lceil f_k \cdot n_k \rceil \, \forall k \in K$ items, where $f_k$ is the desired 
+fraction for key $k$, $n_k$ is the number of key-value pairs for key $k$, and $K$ is the set of
+keys. Sampling without replacement requires one additional pass over the RDD to guarantee sample 
+size, whereas sampling with replacement requires two additional passes.
 
 {% highlight scala %}
-import org.apache.spark.mllib.linalg.Matrix
-import org.apache.spark.mllib.linalg.distributed.RowMatrix
-import org.apache.spark.mllib.stat.MultivariateStatisticalSummary
+import org.apache.spark.SparkContext
+import org.apache.spark.SparkContext._
+import org.apache.spark.rdd.PairRDDFunctions
 
-val mat: RowMatrix = ... // a RowMatrix
+val sc: SparkContext = ...
 
-// Compute column summary statistics.
-val summary: MultivariateStatisticalSummary = mat.computeColumnSummaryStatistics()
-println(summary.mean) // a dense vector containing the mean value for each column
-println(summary.variance) // column-wise variance
-println(summary.numNonzeros) // number of nonzeros in each column
+val data = ... // an RDD[(K, V)] of any key value pairs
+val fractions: Map[K, Double] = ... // specify the exact fraction desired from each key
+
+// Get an exact sample from each stratum
+val approxSample = data.sampleByKey(withReplacement = false, fractions)
+val exactSample = data.sampleByKeyExact(withReplacement = false, fractions)
 
-// Compute the covariance matrix.
-val cov: Matrix = mat.computeCovariance()
 {% endhighlight %}
 </div>
 
 <div data-lang="java" markdown="1">
-
-[`RowMatrix#computeColumnSummaryStatistics`](api/java/org/apache/spark/mllib/linalg/distributed/RowMatrix.html#computeColumnSummaryStatistics()) returns an instance of
-[`MultivariateStatisticalSummary`](api/java/org/apache/spark/mllib/stat/MultivariateStatisticalSummary.html),
-which contains the column-wise max, min, mean, variance, and number of nonzeros, as well as the
-total count.
+[`sampleByKeyExact()`](api/java/org/apache/spark/api/java/JavaPairRDD.html) allows users to
+sample exactly $\lceil f_k \cdot n_k \rceil \, \forall k \in K$ items, where $f_k$ is the desired 
+fraction for key $k$, $n_k$ is the number of key-value pairs for key $k$, and $K$ is the set of
+keys. Sampling without replacement requires one additional pass over the RDD to guarantee sample 
+size, whereas sampling with replacement requires two additional passes.
 
 {% highlight java %}
-import org.apache.spark.mllib.linalg.Matrix;
-import org.apache.spark.mllib.linalg.distributed.RowMatrix;
-import org.apache.spark.mllib.stat.MultivariateStatisticalSummary;
+import java.util.Map;
 
-RowMatrix mat = ... // a RowMatrix
+import org.apache.spark.api.java.JavaPairRDD;
+import org.apache.spark.api.java.JavaSparkContext;
 
-// Compute column summary statistics.
-MultivariateStatisticalSummary summary = mat.computeColumnSummaryStatistics();
-System.out.println(summary.mean()); // a dense vector containing the mean value for each column
-System.out.println(summary.variance()); // column-wise variance
-System.out.println(summary.numNonzeros()); // number of nonzeros in each column
+JavaSparkContext jsc = ...
+
+JavaPairRDD<K, V> data = ... // an RDD of any key value pairs
+Map<K, Object> fractions = ... // specify the exact fraction desired from each key
+
+// Get an exact sample from each stratum
+JavaPairRDD<K, V> approxSample = data.sampleByKey(false, fractions);
+JavaPairRDD<K, V> exactSample = data.sampleByKeyExact(false, fractions);
+
+{% endhighlight %}
+</div>
+<div data-lang="python" markdown="1">
+[`sampleByKey()`](api/python/pyspark.rdd.RDD-class.html#sampleByKey) allows users to
+sample approximately $\lceil f_k \cdot n_k \rceil \, \forall k \in K$ items, where $f_k$ is the 
+desired fraction for key $k$, $n_k$ is the number of key-value pairs for key $k$, and $K$ is the 
+set of keys.
+
+*Note:* `sampleByKeyExact()` is currently not supported in Python.
+
+{% highlight python %}
+
+sc = ... # SparkContext
+
+data = ... # an RDD of any key value pairs
+fractions = ... # specify the exact fraction desired from each key as a dictionary
+
+approxSample = data.sampleByKey(False, fractions);
 
-// Compute the covariance matrix.
-Matrix cov = mat.computeCovariance();
 {% endhighlight %}
 </div>
+
+</div>
+
+## Hypothesis testing
+
+Hypothesis testing is a powerful tool in statistics to determine whether a result is statistically 
+significant, whether this result occurred by chance or not. MLlib currently supports Pearson's 
+chi-squared ( $\chi^2$) tests for goodness of fit and independence. The input data types determine 
+whether the goodness of fit or the independence test is conducted. The goodness of fit test requires 
+an input type of `Vector`, whereas the independence test requires a `Matrix` as input.
+
+MLlib also supports the input type `RDD[LabeledPoint]` to enable feature selection via chi-squared 
+independence tests.
+
+<div class="codetabs">
+<div data-lang="scala" markdown="1">
+[`Statistics`](api/scala/index.html#org.apache.spark.mllib.stat.Statistics$) provides methods to 
+run Pearson's chi-squared tests. The following example demonstrates how to run and interpret 
+hypothesis tests.
+
+{% highlight scala %}
+import org.apache.spark.SparkContext
+import org.apache.spark.mllib.linalg._
+import org.apache.spark.mllib.regression.LabeledPoint
+import org.apache.spark.mllib.stat.Statistics._
+
+val sc: SparkContext = ...
+
+val vec: Vector = ... // a vector composed of the frequencies of events
+
+// compute the goodness of fit. If a second vector to test against is not supplied as a parameter, 
+// the test runs against a uniform distribution.  
+val goodnessOfFitTestResult = Statistics.chiSqTest(vec)
+println(goodnessOfFitTestResult) // summary of the test including the p-value, degrees of freedom, 
+                                 // test statistic, the method used, and the null hypothesis.
+
+val mat: Matrix = ... // a contingency matrix
+
+// conduct Pearson's independence test on the input contingency matrix
+val independenceTestResult = Statistics.chiSqTest(mat) 
+println(independenceTestResult) // summary of the test including the p-value, degrees of freedom...
+
+val obs: RDD[LabeledPoint] = ... // (feature, label) pairs.
+
+// The contingency table is constructed from the raw (feature, label) pairs and used to conduct
+// the independence test. Returns an array containing the ChiSquaredTestResult for every feature 
+// against the label.
+val featureTestResults: Array[ChiSqTestResult] = Statistics.chiSqTest(obs)
+var i = 1
+featureTestResults.foreach { result =>
+    println(s"Column $i:\n$result")
+    i += 1
+} // summary of the test 
+
+{% endhighlight %}
 </div>
 
+<div data-lang="java" markdown="1">
+[`Statistics`](api/java/org/apache/spark/mllib/stat/Statistics.html) provides methods to 
+run Pearson's chi-squared tests. The following example demonstrates how to run and interpret 
+hypothesis tests.
+
+{% highlight java %}
+import org.apache.spark.api.java.JavaRDD;
+import org.apache.spark.api.java.JavaSparkContext;
+import org.apache.spark.mllib.linalg.*;
+import org.apache.spark.mllib.regression.LabeledPoint;
+import org.apache.spark.mllib.stat.Statistics;
+import org.apache.spark.mllib.stat.test.ChiSqTestResult;
+
+JavaSparkContext jsc = ...
 
-## Hypothesis Testing 
+Vector vec = ... // a vector composed of the frequencies of events
+
+// compute the goodness of fit. If a second vector to test against is not supplied as a parameter, 
+// the test runs against a uniform distribution.  
+ChiSqTestResult goodnessOfFitTestResult = Statistics.chiSqTest(vec);
+// summary of the test including the p-value, degrees of freedom, test statistic, the method used, 
+// and the null hypothesis.
+System.out.println(goodnessOfFitTestResult);
+
+Matrix mat = ... // a contingency matrix
+
+// conduct Pearson's independence test on the input contingency matrix
+ChiSqTestResult independenceTestResult = Statistics.chiSqTest(mat);
+// summary of the test including the p-value, degrees of freedom...
+System.out.println(independenceTestResult);
+
+JavaRDD<LabeledPoint> obs = ... // an RDD of labeled points
+
+// The contingency table is constructed from the raw (feature, label) pairs and used to conduct
+// the independence test. Returns an array containing the ChiSquaredTestResult for every feature 
+// against the label.
+ChiSqTestResult[] featureTestResults = Statistics.chiSqTest(obs.rdd());
+int i = 1;
+for (ChiSqTestResult result : featureTestResults) {
+    System.out.println("Column " + i + ":");
+    System.out.println(result); // summary of the test
+    i++;
+}
+
+{% endhighlight %}
+</div>
+
+</div>

From c4787a3690a9ed3b8b2c6c294fc4a6915436b6f7 Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Tue, 26 Aug 2014 16:29:14 -0700
Subject: [PATCH 090/489] [SPARK-3194][SQL] Add AttributeSet to fix bugs with
 invalid comparisons of AttributeReferences

It is common to want to describe sets of attributes that are in various parts of a query plan.  However, the semantics of putting `AttributeReference` objects into a standard Scala `Set` result in subtle bugs when references differ cosmetically.  For example, with case insensitive resolution it is possible to have two references to the same attribute whose names are not equal.

In this PR I introduce a new abstraction, an `AttributeSet`, which performs all comparisons using the globally unique `ExpressionId` instead of case class equality.  (There is already a related class, [`AttributeMap`](https://github.com/marmbrus/spark/blob/inMemStats/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/AttributeMap.scala#L32))  This new type of set is used to fix a bug in the optimizer where needed attributes were getting projected away underneath join operators.

I also took this opportunity to refactor the expression and query plan base classes.  In all but one instance the logic for computing the `references` of an `Expression` were the same.  Thus, I moved this logic into the base class.

For query plans the semantics of  the `references` method were ill defined (is it the references output? or is it those used by expression evaluation? or what?).  As a result, this method wasn't really used very much.  So, I removed it.

TODO:
 - [x] Finish scala doc for `AttributeSet`
 - [x] Scan the code for other instances of `Set[Attribute]` and refactor them.
 - [x] Finish removing `references` from `QueryPlan`

Author: Michael Armbrust <michael@databricks.com>

Closes #2109 from marmbrus/attributeSets and squashes the following commits:

1c0dae5 [Michael Armbrust] work on serialization bug.
9ba868d [Michael Armbrust] Merge remote-tracking branch 'origin/master' into attributeSets
3ae5288 [Michael Armbrust] review comments
40ce7f6 [Michael Armbrust] style
d577cc7 [Michael Armbrust] Scaladoc
cae5d22 [Michael Armbrust] remove more references implementations
d6e16be [Michael Armbrust] Remove more instances of "def references" and normal sets of attributes.
fc26b49 [Michael Armbrust] Add AttributeSet class, remove references from Expression.
---
 .../sql/catalyst/analysis/Analyzer.scala      |   6 +-
 .../sql/catalyst/analysis/unresolved.scala    |   1 -
 .../catalyst/expressions/AttributeSet.scala   | 106 ++++++++++++++++++
 .../catalyst/expressions/BoundAttribute.scala |   2 -
 .../sql/catalyst/expressions/Expression.scala |   6 +-
 .../spark/sql/catalyst/expressions/Rand.scala |   1 -
 .../sql/catalyst/expressions/ScalaUdf.scala   |   1 -
 .../sql/catalyst/expressions/SortOrder.scala  |   1 -
 .../catalyst/expressions/WrapDynamic.scala    |   2 +-
 .../sql/catalyst/expressions/aggregates.scala |  25 ++---
 .../sql/catalyst/expressions/arithmetic.scala |   2 -
 .../catalyst/expressions/complexTypes.scala   |   2 +-
 .../sql/catalyst/expressions/generators.scala |   2 -
 .../sql/catalyst/expressions/literals.scala   |   4 +-
 .../expressions/namedExpressions.scala        |   6 +-
 .../catalyst/expressions/nullFunctions.scala  |   3 -
 .../sql/catalyst/expressions/predicates.scala |   6 +-
 .../spark/sql/catalyst/expressions/sets.scala |   5 -
 .../expressions/stringOperations.scala        |   2 -
 .../sql/catalyst/optimizer/Optimizer.scala    |  12 +-
 .../spark/sql/catalyst/plans/QueryPlan.scala  |   4 +-
 .../catalyst/plans/logical/LogicalPlan.scala  |  11 +-
 .../plans/logical/ScriptTransformation.scala  |   4 +-
 .../plans/logical/basicOperators.scala        |  29 +----
 .../catalyst/plans/logical/partitioning.scala |   4 -
 .../plans/physical/partitioning.scala         |   3 +-
 .../sql/catalyst/trees/TreeNodeSuite.scala    |   1 -
 .../org/apache/spark/sql/SQLContext.scala     |   7 +-
 .../columnar/InMemoryColumnarTableScan.scala  |   2 -
 .../spark/sql/execution/SparkPlan.scala       |   3 +-
 .../spark/sql/execution/debug/package.scala   |   2 -
 .../spark/sql/execution/pythonUdfs.scala      |   2 -
 .../spark/sql/hive/HiveStrategies.scala       |   8 +-
 .../org/apache/spark/sql/hive/hiveUdfs.scala  |   5 -
 .../hive/execution/HiveResolutionSuite.scala  |   9 +-
 35 files changed, 166 insertions(+), 123 deletions(-)
 create mode 100644 sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/AttributeSet.scala

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
index c18d7858f0a43..4a9524074132e 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
@@ -132,7 +132,7 @@ class Analyzer(catalog: Catalog, registry: FunctionRegistry, caseSensitive: Bool
       case s @ Sort(ordering, p @ Project(projectList, child)) if !s.resolved && p.resolved =>
         val unresolved = ordering.flatMap(_.collect { case UnresolvedAttribute(name) => name })
         val resolved = unresolved.flatMap(child.resolveChildren)
-        val requiredAttributes = resolved.collect { case a: Attribute => a }.toSet
+        val requiredAttributes = AttributeSet(resolved.collect { case a: Attribute => a })
 
         val missingInProject = requiredAttributes -- p.output
         if (missingInProject.nonEmpty) {
@@ -152,8 +152,8 @@ class Analyzer(catalog: Catalog, registry: FunctionRegistry, caseSensitive: Bool
         )
 
         logDebug(s"Grouping expressions: $groupingRelation")
-        val resolved = unresolved.flatMap(groupingRelation.resolve).toSet
-        val missingInAggs = resolved -- a.outputSet
+        val resolved = unresolved.flatMap(groupingRelation.resolve)
+        val missingInAggs = resolved.filterNot(a.outputSet.contains)
         logDebug(s"Resolved: $resolved Missing in aggs: $missingInAggs")
         if (missingInAggs.nonEmpty) {
           // Add missing grouping exprs and then project them away after the sort.
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/unresolved.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/unresolved.scala
index a0e25775da6dd..a2c61c65487cb 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/unresolved.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/unresolved.scala
@@ -66,7 +66,6 @@ case class UnresolvedFunction(name: String, children: Seq[Expression]) extends E
   override def dataType = throw new UnresolvedException(this, "dataType")
   override def foldable = throw new UnresolvedException(this, "foldable")
   override def nullable = throw new UnresolvedException(this, "nullable")
-  override def references = children.flatMap(_.references).toSet
   override lazy val resolved = false
 
   // Unresolved functions are transient at compile time and don't get evaluated during execution.
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/AttributeSet.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/AttributeSet.scala
new file mode 100644
index 0000000000000..c3a08bbdb6bc7
--- /dev/null
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/AttributeSet.scala
@@ -0,0 +1,106 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.expressions
+
+protected class AttributeEquals(val a: Attribute) {
+  override def hashCode() = a.exprId.hashCode()
+  override def equals(other: Any) = other match {
+    case otherReference: AttributeEquals => a.exprId == otherReference.a.exprId
+    case otherAttribute => false
+  }
+}
+
+object AttributeSet {
+  /** Constructs a new [[AttributeSet]] given a sequence of [[Attribute Attributes]]. */
+  def apply(baseSet: Seq[Attribute]) = {
+    new AttributeSet(baseSet.map(new AttributeEquals(_)).toSet)
+  }
+}
+
+/**
+ * A Set designed to hold [[AttributeReference]] objects, that performs equality checking using
+ * expression id instead of standard java equality.  Using expression id means that these
+ * sets will correctly test for membership, even when the AttributeReferences in question differ
+ * cosmetically (e.g., the names have different capitalizations).
+ *
+ * Note that we do not override equality for Attribute references as it is really weird when
+ * `AttributeReference("a"...) == AttrributeReference("b", ...)`. This tactic leads to broken tests,
+ * and also makes doing transformations hard (we always try keep older trees instead of new ones
+ * when the transformation was a no-op).
+ */
+class AttributeSet private (val baseSet: Set[AttributeEquals])
+  extends Traversable[Attribute] with Serializable {
+
+  /** Returns true if the members of this AttributeSet and other are the same. */
+  override def equals(other: Any) = other match {
+    case otherSet: AttributeSet => baseSet.map(_.a).forall(otherSet.contains)
+    case _ => false
+  }
+
+  /** Returns true if this set contains an Attribute with the same expression id as `elem` */
+  def contains(elem: NamedExpression): Boolean =
+    baseSet.contains(new AttributeEquals(elem.toAttribute))
+
+  /** Returns a new [[AttributeSet]] that contains `elem` in addition to the current elements. */
+  def +(elem: Attribute): AttributeSet =  // scalastyle:ignore
+    new AttributeSet(baseSet + new AttributeEquals(elem))
+
+  /** Returns a new [[AttributeSet]] that does not contain `elem`. */
+  def -(elem: Attribute): AttributeSet =
+    new AttributeSet(baseSet - new AttributeEquals(elem))
+
+  /** Returns an iterator containing all of the attributes in the set. */
+  def iterator: Iterator[Attribute] = baseSet.map(_.a).iterator
+
+  /**
+   * Returns true if the [[Attribute Attributes]] in this set are a subset of the Attributes in
+   * `other`.
+   */
+  def subsetOf(other: AttributeSet) = baseSet.subsetOf(other.baseSet)
+
+  /**
+   * Returns a new [[AttributeSet]] that does not contain any of the [[Attribute Attributes]] found
+   * in `other`.
+   */
+  def --(other: Traversable[NamedExpression]) =
+    new AttributeSet(baseSet -- other.map(a => new AttributeEquals(a.toAttribute)))
+
+  /**
+   * Returns a new [[AttributeSet]] that contains all of the [[Attribute Attributes]] found
+   * in `other`.
+   */
+  def ++(other: AttributeSet) = new AttributeSet(baseSet ++ other.baseSet)
+
+  /**
+   * Returns a new [[AttributeSet]] contain only the [[Attribute Attributes]] where `f` evaluates to
+   * true.
+   */
+  override def filter(f: Attribute => Boolean) = new AttributeSet(baseSet.filter(ae => f(ae.a)))
+
+  /**
+   * Returns a new [[AttributeSet]] that only contains [[Attribute Attributes]] that are found in
+   * `this` and `other`.
+   */
+  def intersect(other: AttributeSet) = new AttributeSet(baseSet.intersect(other.baseSet))
+
+  override def foreach[U](f: (Attribute) => U): Unit = baseSet.map(_.a).foreach(f)
+
+  // We must force toSeq to not be strict otherwise we end up with a [[Stream]] that captures all
+  // sorts of things in its closure.
+  override def toSeq: Seq[Attribute] = baseSet.map(_.a).toArray.toSeq
+}
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala
index 0913f15888780..54c6baf1af3bf 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala
@@ -32,8 +32,6 @@ case class BoundReference(ordinal: Int, dataType: DataType, nullable: Boolean)
 
   type EvaluatedType = Any
 
-  override def references = Set.empty
-
   override def toString = s"input[$ordinal]"
 
   override def eval(input: Row): Any = input(ordinal)
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
index ba62dabe3dd6a..70507e7ee2be8 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
@@ -41,7 +41,7 @@ abstract class Expression extends TreeNode[Expression] {
    */
   def foldable: Boolean = false
   def nullable: Boolean
-  def references: Set[Attribute]
+  def references: AttributeSet = AttributeSet(children.flatMap(_.references.iterator))
 
   /** Returns the result of evaluating this expression on a given input Row */
   def eval(input: Row = null): EvaluatedType
@@ -230,8 +230,6 @@ abstract class BinaryExpression extends Expression with trees.BinaryNode[Express
 
   override def foldable = left.foldable && right.foldable
 
-  override def references = left.references ++ right.references
-
   override def toString = s"($left $symbol $right)"
 }
 
@@ -242,5 +240,5 @@ abstract class LeafExpression extends Expression with trees.LeafNode[Expression]
 abstract class UnaryExpression extends Expression with trees.UnaryNode[Expression] {
   self: Product =>
 
-  override def references = child.references
+
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Rand.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Rand.scala
index 38f836f0a1a0e..851db95b9177e 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Rand.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Rand.scala
@@ -24,7 +24,6 @@ import org.apache.spark.sql.catalyst.types.DoubleType
 case object Rand extends LeafExpression {
   override def dataType = DoubleType
   override def nullable = false
-  override def references = Set.empty
 
   private[this] lazy val rand = new Random
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUdf.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUdf.scala
index 95633dd0c9870..63ac2a608b6ff 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUdf.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUdf.scala
@@ -24,7 +24,6 @@ case class ScalaUdf(function: AnyRef, dataType: DataType, children: Seq[Expressi
 
   type EvaluatedType = Any
 
-  def references = children.flatMap(_.references).toSet
   def nullable = true
 
   /** This method has been generated by this script
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SortOrder.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SortOrder.scala
index d2b7685e73065..d00b2ac09745c 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SortOrder.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SortOrder.scala
@@ -31,7 +31,6 @@ case object Descending extends SortDirection
 case class SortOrder(child: Expression, direction: SortDirection) extends Expression 
     with trees.UnaryNode[Expression] {
 
-  override def references = child.references
   override def dataType = child.dataType
   override def nullable = child.nullable
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/WrapDynamic.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/WrapDynamic.scala
index eb8898900d6a5..1eb55715794a7 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/WrapDynamic.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/WrapDynamic.scala
@@ -35,7 +35,7 @@ case class WrapDynamic(children: Seq[Attribute]) extends Expression {
   type EvaluatedType = DynamicRow
 
   def nullable = false
-  def references = children.toSet
+
   def dataType = DynamicType
 
   override def eval(input: Row): DynamicRow = input match {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
index 613b87ca98d97..dbc0c2965a805 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
@@ -78,7 +78,7 @@ abstract class AggregateFunction
 
   /** Base should return the generic aggregate expression that this function is computing */
   val base: AggregateExpression
-  override def references = base.references
+
   override def nullable = base.nullable
   override def dataType = base.dataType
 
@@ -89,7 +89,7 @@ abstract class AggregateFunction
 }
 
 case class Min(child: Expression) extends PartialAggregate with trees.UnaryNode[Expression] {
-  override def references = child.references
+
   override def nullable = true
   override def dataType = child.dataType
   override def toString = s"MIN($child)"
@@ -119,7 +119,7 @@ case class MinFunction(expr: Expression, base: AggregateExpression) extends Aggr
 }
 
 case class Max(child: Expression) extends PartialAggregate with trees.UnaryNode[Expression] {
-  override def references = child.references
+
   override def nullable = true
   override def dataType = child.dataType
   override def toString = s"MAX($child)"
@@ -149,7 +149,7 @@ case class MaxFunction(expr: Expression, base: AggregateExpression) extends Aggr
 }
 
 case class Count(child: Expression) extends PartialAggregate with trees.UnaryNode[Expression] {
-  override def references = child.references
+
   override def nullable = false
   override def dataType = LongType
   override def toString = s"COUNT($child)"
@@ -166,7 +166,7 @@ case class CountDistinct(expressions: Seq[Expression]) extends PartialAggregate
   def this() = this(null)
 
   override def children = expressions
-  override def references = expressions.flatMap(_.references).toSet
+
   override def nullable = false
   override def dataType = LongType
   override def toString = s"COUNT(DISTINCT ${expressions.mkString(",")})"
@@ -184,7 +184,6 @@ case class CollectHashSet(expressions: Seq[Expression]) extends AggregateExpress
   def this() = this(null)
 
   override def children = expressions
-  override def references = expressions.flatMap(_.references).toSet
   override def nullable = false
   override def dataType = ArrayType(expressions.head.dataType)
   override def toString = s"AddToHashSet(${expressions.mkString(",")})"
@@ -219,7 +218,6 @@ case class CombineSetsAndCount(inputSet: Expression) extends AggregateExpression
   def this() = this(null)
 
   override def children = inputSet :: Nil
-  override def references = inputSet.references
   override def nullable = false
   override def dataType = LongType
   override def toString = s"CombineAndCount($inputSet)"
@@ -248,7 +246,7 @@ case class CombineSetsAndCountFunction(
 
 case class ApproxCountDistinctPartition(child: Expression, relativeSD: Double)
   extends AggregateExpression with trees.UnaryNode[Expression] {
-  override def references = child.references
+
   override def nullable = false
   override def dataType = child.dataType
   override def toString = s"APPROXIMATE COUNT(DISTINCT $child)"
@@ -257,7 +255,7 @@ case class ApproxCountDistinctPartition(child: Expression, relativeSD: Double)
 
 case class ApproxCountDistinctMerge(child: Expression, relativeSD: Double)
   extends AggregateExpression with trees.UnaryNode[Expression] {
-  override def references = child.references
+
   override def nullable = false
   override def dataType = LongType
   override def toString = s"APPROXIMATE COUNT(DISTINCT $child)"
@@ -266,7 +264,7 @@ case class ApproxCountDistinctMerge(child: Expression, relativeSD: Double)
 
 case class ApproxCountDistinct(child: Expression, relativeSD: Double = 0.05)
   extends PartialAggregate with trees.UnaryNode[Expression] {
-  override def references = child.references
+
   override def nullable = false
   override def dataType = LongType
   override def toString = s"APPROXIMATE COUNT(DISTINCT $child)"
@@ -284,7 +282,7 @@ case class ApproxCountDistinct(child: Expression, relativeSD: Double = 0.05)
 }
 
 case class Average(child: Expression) extends PartialAggregate with trees.UnaryNode[Expression] {
-  override def references = child.references
+
   override def nullable = false
   override def dataType = DoubleType
   override def toString = s"AVG($child)"
@@ -304,7 +302,7 @@ case class Average(child: Expression) extends PartialAggregate with trees.UnaryN
 }
 
 case class Sum(child: Expression) extends PartialAggregate with trees.UnaryNode[Expression] {
-  override def references = child.references
+
   override def nullable = false
   override def dataType = child.dataType
   override def toString = s"SUM($child)"
@@ -322,7 +320,7 @@ case class Sum(child: Expression) extends PartialAggregate with trees.UnaryNode[
 case class SumDistinct(child: Expression)
   extends AggregateExpression with trees.UnaryNode[Expression] {
 
-  override def references = child.references
+
   override def nullable = false
   override def dataType = child.dataType
   override def toString = s"SUM(DISTINCT $child)"
@@ -331,7 +329,6 @@ case class SumDistinct(child: Expression)
 }
 
 case class First(child: Expression) extends PartialAggregate with trees.UnaryNode[Expression] {
-  override def references = child.references
   override def nullable = true
   override def dataType = child.dataType
   override def toString = s"FIRST($child)"
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
index 5f8b6ae10f0c4..aae86a3628be1 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
@@ -95,8 +95,6 @@ case class MaxOf(left: Expression, right: Expression) extends Expression {
 
   override def children = left :: right :: Nil
 
-  override def references = left.references ++ right.references
-
   override def dataType = left.dataType
 
   override def eval(input: Row): Any = {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypes.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypes.scala
index c1154eb81c319..dafd745ec96c6 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypes.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypes.scala
@@ -31,7 +31,7 @@ case class GetItem(child: Expression, ordinal: Expression) extends Expression {
   /** `Null` is returned for invalid ordinals. */
   override def nullable = true
   override def foldable = child.foldable && ordinal.foldable
-  override def references = children.flatMap(_.references).toSet
+
   def dataType = child.dataType match {
     case ArrayType(dt, _) => dt
     case MapType(_, vt, _) => vt
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/generators.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/generators.scala
index e99c5b452d183..9c865254e0be9 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/generators.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/generators.scala
@@ -47,8 +47,6 @@ abstract class Generator extends Expression {
 
   override def nullable = false
 
-  override def references = children.flatMap(_.references).toSet
-
   /**
    * Should be overridden by specific generators.  Called only once for each instance to ensure
    * that rule application does not change the output schema of a generator.
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/literals.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/literals.scala
index e15e16d633365..a8c2396d62632 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/literals.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/literals.scala
@@ -52,7 +52,7 @@ case class Literal(value: Any, dataType: DataType) extends LeafExpression {
 
   override def foldable = true
   def nullable = value == null
-  def references = Set.empty
+
 
   override def toString = if (value != null) value.toString else "null"
 
@@ -66,8 +66,6 @@ case class MutableLiteral(var value: Any, nullable: Boolean = true) extends Leaf
 
   val dataType = Literal(value).dataType
 
-  def references = Set.empty
-
   def update(expression: Expression, input: Row) = {
     value = expression.eval(input)
   }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/namedExpressions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/namedExpressions.scala
index 02d04762629f5..7c4b9d4847e26 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/namedExpressions.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/namedExpressions.scala
@@ -62,7 +62,7 @@ abstract class Attribute extends NamedExpression {
 
   def toAttribute = this
   def newInstance: Attribute
-  override def references = Set(this)
+
 }
 
 /**
@@ -85,7 +85,7 @@ case class Alias(child: Expression, name: String)
 
   override def dataType = child.dataType
   override def nullable = child.nullable
-  override def references = child.references
+
 
   override def toAttribute = {
     if (resolved) {
@@ -116,6 +116,8 @@ case class AttributeReference(name: String, dataType: DataType, nullable: Boolea
     (val exprId: ExprId = NamedExpression.newExprId, val qualifiers: Seq[String] = Nil)
   extends Attribute with trees.LeafNode[Expression] {
 
+  override def references = AttributeSet(this :: Nil)
+
   override def equals(other: Any) = other match {
     case ar: AttributeReference => exprId == ar.exprId && dataType == ar.dataType
     case _ => false
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/nullFunctions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/nullFunctions.scala
index e88c5d4fa178a..086d0a3e073e5 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/nullFunctions.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/nullFunctions.scala
@@ -26,7 +26,6 @@ case class Coalesce(children: Seq[Expression]) extends Expression {
   /** Coalesce is nullable if all of its children are nullable, or if it has no children. */
   def nullable = !children.exists(!_.nullable)
 
-  def references = children.flatMap(_.references).toSet
   // Coalesce is foldable if all children are foldable.
   override def foldable = !children.exists(!_.foldable)
 
@@ -53,7 +52,6 @@ case class Coalesce(children: Seq[Expression]) extends Expression {
 }
 
 case class IsNull(child: Expression) extends Predicate with trees.UnaryNode[Expression] {
-  def references = child.references
   override def foldable = child.foldable
   def nullable = false
 
@@ -65,7 +63,6 @@ case class IsNull(child: Expression) extends Predicate with trees.UnaryNode[Expr
 }
 
 case class IsNotNull(child: Expression) extends Predicate with trees.UnaryNode[Expression] {
-  def references = child.references
   override def foldable = child.foldable
   def nullable = false
   override def toString = s"IS NOT NULL $child"
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
index 5976b0ddf3e03..1313ccd120c1f 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
@@ -85,7 +85,7 @@ case class Not(child: Expression) extends UnaryExpression with Predicate {
  */
 case class In(value: Expression, list: Seq[Expression]) extends Predicate {
   def children = value +: list
-  def references = children.flatMap(_.references).toSet
+
   def nullable = true // TODO: Figure out correct nullability semantics of IN.
   override def toString = s"$value IN ${list.mkString("(", ",", ")")}"
 
@@ -197,7 +197,7 @@ case class If(predicate: Expression, trueValue: Expression, falseValue: Expressi
 
   def children = predicate :: trueValue :: falseValue :: Nil
   override def nullable = trueValue.nullable || falseValue.nullable
-  def references = children.flatMap(_.references).toSet
+
   override lazy val resolved = childrenResolved && trueValue.dataType == falseValue.dataType
   def dataType = {
     if (!resolved) {
@@ -239,7 +239,7 @@ case class If(predicate: Expression, trueValue: Expression, falseValue: Expressi
 case class CaseWhen(branches: Seq[Expression]) extends Expression {
   type EvaluatedType = Any
   def children = branches
-  def references = children.flatMap(_.references).toSet
+
   def dataType = {
     if (!resolved) {
       throw new UnresolvedException(this, "cannot resolve due to differing types in some branches")
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/sets.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/sets.scala
index e6c570b47bee2..3d4c4a8853c12 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/sets.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/sets.scala
@@ -26,8 +26,6 @@ import org.apache.spark.util.collection.OpenHashSet
 case class NewSet(elementType: DataType) extends LeafExpression {
   type EvaluatedType = Any
 
-  def references = Set.empty
-
   def nullable = false
 
   // We are currently only using these Expressions internally for aggregation.  However, if we ever
@@ -53,9 +51,6 @@ case class AddItemToSet(item: Expression, set: Expression) extends Expression {
   def nullable = set.nullable
 
   def dataType = set.dataType
-
-  def references = (item.flatMap(_.references) ++ set.flatMap(_.references)).toSet
-
   def eval(input: Row): Any = {
     val itemEval = item.eval(input)
     val setEval = set.eval(input).asInstanceOf[OpenHashSet[Any]]
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
index 97fc3a3b14b88..c2a3a5ca3ca8b 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
@@ -226,8 +226,6 @@ case class Substring(str: Expression, pos: Expression, len: Expression) extends
     if (str.dataType == BinaryType) str.dataType else StringType
   }
 
-  def references = children.flatMap(_.references).toSet
-
   override def children = str :: pos :: len :: Nil
 
   @inline
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
index 5f86d6047cb9c..ddd4b3755d629 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
@@ -65,8 +65,10 @@ object ColumnPruning extends Rule[LogicalPlan] {
     // Eliminate unneeded attributes from either side of a Join.
     case Project(projectList, Join(left, right, joinType, condition)) =>
       // Collect the list of all references required either above or to evaluate the condition.
-      val allReferences: Set[Attribute] =
-        projectList.flatMap(_.references).toSet ++ condition.map(_.references).getOrElse(Set.empty)
+      val allReferences: AttributeSet =
+        AttributeSet(
+          projectList.flatMap(_.references.iterator)) ++
+          condition.map(_.references).getOrElse(AttributeSet(Seq.empty))
 
       /** Applies a projection only when the child is producing unnecessary attributes */
       def pruneJoinChild(c: LogicalPlan) = prunedChild(c, allReferences)
@@ -76,8 +78,8 @@ object ColumnPruning extends Rule[LogicalPlan] {
     // Eliminate unneeded attributes from right side of a LeftSemiJoin.
     case Join(left, right, LeftSemi, condition) =>
       // Collect the list of all references required to evaluate the condition.
-      val allReferences: Set[Attribute] =
-        condition.map(_.references).getOrElse(Set.empty)
+      val allReferences: AttributeSet =
+        condition.map(_.references).getOrElse(AttributeSet(Seq.empty))
 
       Join(left, prunedChild(right, allReferences), LeftSemi, condition)
 
@@ -104,7 +106,7 @@ object ColumnPruning extends Rule[LogicalPlan] {
   }
 
   /** Applies a projection only when the child is producing unnecessary attributes */
-  private def prunedChild(c: LogicalPlan, allReferences: Set[Attribute]) =
+  private def prunedChild(c: LogicalPlan, allReferences: AttributeSet) =
     if ((c.outputSet -- allReferences.filter(c.outputSet.contains)).nonEmpty) {
       Project(allReferences.filter(c.outputSet.contains).toSeq, c)
     } else {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/QueryPlan.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/QueryPlan.scala
index 0988b0c6d990c..1e177e28f80b3 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/QueryPlan.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/QueryPlan.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.sql.catalyst.plans
 
-import org.apache.spark.sql.catalyst.expressions.{Attribute, Expression}
+import org.apache.spark.sql.catalyst.expressions.{Attribute, AttributeSet, Expression}
 import org.apache.spark.sql.catalyst.trees.TreeNode
 import org.apache.spark.sql.catalyst.types.{ArrayType, DataType, StructField, StructType}
 
@@ -29,7 +29,7 @@ abstract class QueryPlan[PlanType <: TreeNode[PlanType]] extends TreeNode[PlanTy
   /**
    * Returns the set of attributes that are output by this node.
    */
-  def outputSet: Set[Attribute] = output.toSet
+  def outputSet: AttributeSet = AttributeSet(output)
 
   /**
    * Runs [[transform]] with `rule` on all expressions present in this query operator.
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala
index 278569f0cb14a..8616ac45b0e95 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala
@@ -45,17 +45,11 @@ abstract class LogicalPlan extends QueryPlan[LogicalPlan] {
     sizeInBytes = children.map(_.statistics).map(_.sizeInBytes).product
   )
 
-  /**
-   * Returns the set of attributes that are referenced by this node
-   * during evaluation.
-   */
-  def references: Set[Attribute]
-
   /**
    * Returns the set of attributes that this node takes as
    * input from its children.
    */
-  lazy val inputSet: Set[Attribute] = children.flatMap(_.output).toSet
+  lazy val inputSet: AttributeSet = AttributeSet(children.flatMap(_.output))
 
   /**
    * Returns true if this expression and all its children have been resolved to a specific schema
@@ -126,9 +120,6 @@ abstract class LeafNode extends LogicalPlan with trees.LeafNode[LogicalPlan] {
 
   override lazy val statistics: Statistics =
     throw new UnsupportedOperationException(s"LeafNode $nodeName must implement statistics.")
-
-  // Leaf nodes by definition cannot reference any input attributes.
-  override def references = Set.empty
 }
 
 /**
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/ScriptTransformation.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/ScriptTransformation.scala
index d3f9d0fb93237..4460c86ed9026 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/ScriptTransformation.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/ScriptTransformation.scala
@@ -30,6 +30,4 @@ case class ScriptTransformation(
     input: Seq[Expression],
     script: String,
     output: Seq[Attribute],
-    child: LogicalPlan) extends UnaryNode {
-  def references = input.flatMap(_.references).toSet
-}
+    child: LogicalPlan) extends UnaryNode
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
index 3cb407217c4c3..4adfb189372d6 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
@@ -23,7 +23,6 @@ import org.apache.spark.sql.catalyst.types._
 
 case class Project(projectList: Seq[NamedExpression], child: LogicalPlan) extends UnaryNode {
   def output = projectList.map(_.toAttribute)
-  def references = projectList.flatMap(_.references).toSet
 }
 
 /**
@@ -59,14 +58,10 @@ case class Generate(
 
   override def output =
     if (join) child.output ++ generatorOutput else generatorOutput
-
-  override def references =
-    if (join) child.outputSet else generator.references
 }
 
 case class Filter(condition: Expression, child: LogicalPlan) extends UnaryNode {
   override def output = child.output
-  override def references = condition.references
 }
 
 case class Union(left: LogicalPlan, right: LogicalPlan) extends BinaryNode {
@@ -76,8 +71,6 @@ case class Union(left: LogicalPlan, right: LogicalPlan) extends BinaryNode {
   override lazy val resolved =
     childrenResolved &&
     !left.output.zip(right.output).exists { case (l,r) => l.dataType != r.dataType }
-
-  override def references = Set.empty
 }
 
 case class Join(
@@ -86,8 +79,6 @@ case class Join(
   joinType: JoinType,
   condition: Option[Expression]) extends BinaryNode {
 
-  override def references = condition.map(_.references).getOrElse(Set.empty)
-
   override def output = {
     joinType match {
       case LeftSemi =>
@@ -106,8 +97,6 @@ case class Join(
 
 case class Except(left: LogicalPlan, right: LogicalPlan) extends BinaryNode {
   def output = left.output
-
-  def references = Set.empty
 }
 
 case class InsertIntoTable(
@@ -118,7 +107,6 @@ case class InsertIntoTable(
   extends LogicalPlan {
   // The table being inserted into is a child for the purposes of transformations.
   override def children = table :: child :: Nil
-  override def references = Set.empty
   override def output = child.output
 
   override lazy val resolved = childrenResolved && child.output.zip(table.output).forall {
@@ -130,20 +118,17 @@ case class InsertIntoCreatedTable(
     databaseName: Option[String],
     tableName: String,
     child: LogicalPlan) extends UnaryNode {
-  override def references = Set.empty
   override def output = child.output
 }
 
 case class WriteToFile(
     path: String,
     child: LogicalPlan) extends UnaryNode {
-  override def references = Set.empty
   override def output = child.output
 }
 
 case class Sort(order: Seq[SortOrder], child: LogicalPlan) extends UnaryNode {
   override def output = child.output
-  override def references = order.flatMap(_.references).toSet
 }
 
 case class Aggregate(
@@ -152,19 +137,20 @@ case class Aggregate(
     child: LogicalPlan)
   extends UnaryNode {
 
+  /** The set of all AttributeReferences required for this aggregation. */
+  def references =
+    AttributeSet(
+      groupingExpressions.flatMap(_.references) ++ aggregateExpressions.flatMap(_.references))
+
   override def output = aggregateExpressions.map(_.toAttribute)
-  override def references =
-    (groupingExpressions ++ aggregateExpressions).flatMap(_.references).toSet
 }
 
 case class Limit(limitExpr: Expression, child: LogicalPlan) extends UnaryNode {
   override def output = child.output
-  override def references = limitExpr.references
 }
 
 case class Subquery(alias: String, child: LogicalPlan) extends UnaryNode {
   override def output = child.output.map(_.withQualifiers(alias :: Nil))
-  override def references = Set.empty
 }
 
 /**
@@ -191,20 +177,16 @@ case class LowerCaseSchema(child: LogicalPlan) extends UnaryNode {
         a.qualifiers)
     case other => other
   }
-
-  override def references = Set.empty
 }
 
 case class Sample(fraction: Double, withReplacement: Boolean, seed: Long, child: LogicalPlan)
     extends UnaryNode {
 
   override def output = child.output
-  override def references = Set.empty
 }
 
 case class Distinct(child: LogicalPlan) extends UnaryNode {
   override def output = child.output
-  override def references = child.outputSet
 }
 
 case object NoRelation extends LeafNode {
@@ -213,5 +195,4 @@ case object NoRelation extends LeafNode {
 
 case class Intersect(left: LogicalPlan, right: LogicalPlan) extends BinaryNode {
   override def output = left.output
-  override def references = Set.empty
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/partitioning.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/partitioning.scala
index 7146fbd540f29..72b0c5c8e7a26 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/partitioning.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/partitioning.scala
@@ -31,13 +31,9 @@ abstract class RedistributeData extends UnaryNode {
 
 case class SortPartitions(sortExpressions: Seq[SortOrder], child: LogicalPlan)
     extends RedistributeData {
-
-  def references = sortExpressions.flatMap(_.references).toSet
 }
 
 case class Repartition(partitionExpressions: Seq[Expression], child: LogicalPlan)
     extends RedistributeData {
-
-  def references = partitionExpressions.flatMap(_.references).toSet
 }
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/physical/partitioning.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/physical/partitioning.scala
index 4bb022cf238af..ccb0df113c063 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/physical/partitioning.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/physical/partitioning.scala
@@ -71,6 +71,7 @@ case class OrderedDistribution(ordering: Seq[SortOrder]) extends Distribution {
       "An AllTuples should be used to represent a distribution that only has " +
       "a single partition.")
 
+  // TODO: This is not really valid...
   def clustering = ordering.map(_.child).toSet
 }
 
@@ -139,7 +140,6 @@ case class HashPartitioning(expressions: Seq[Expression], numPartitions: Int)
   with Partitioning {
 
   override def children = expressions
-  override def references = expressions.flatMap(_.references).toSet
   override def nullable = false
   override def dataType = IntegerType
 
@@ -179,7 +179,6 @@ case class RangePartitioning(ordering: Seq[SortOrder], numPartitions: Int)
   with Partitioning {
 
   override def children = ordering
-  override def references = ordering.flatMap(_.references).toSet
   override def nullable = false
   override def dataType = IntegerType
 
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/trees/TreeNodeSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/trees/TreeNodeSuite.scala
index 6344874538d67..296202543e2ca 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/trees/TreeNodeSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/trees/TreeNodeSuite.scala
@@ -26,7 +26,6 @@ import org.apache.spark.sql.catalyst.types.{StringType, NullType}
 
 case class Dummy(optKey: Option[Expression]) extends Expression {
   def children = optKey.toSeq
-  def references = Set.empty[Attribute]
   def nullable = true
   def dataType = NullType
   override lazy val resolved = true
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
index 8a9f4deb6a19e..6f0eed3f63c41 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
@@ -344,8 +344,8 @@ class SQLContext(@transient val sparkContext: SparkContext)
         prunePushedDownFilters: Seq[Expression] => Seq[Expression],
         scanBuilder: Seq[Attribute] => SparkPlan): SparkPlan = {
 
-      val projectSet = projectList.flatMap(_.references).toSet
-      val filterSet = filterPredicates.flatMap(_.references).toSet
+      val projectSet = AttributeSet(projectList.flatMap(_.references))
+      val filterSet = AttributeSet(filterPredicates.flatMap(_.references))
       val filterCondition = prunePushedDownFilters(filterPredicates).reduceLeftOption(And)
 
       // Right now we still use a projection even if the only evaluation is applying an alias
@@ -354,7 +354,8 @@ class SQLContext(@transient val sparkContext: SparkContext)
       // TODO: Decouple final output schema from expression evaluation so this copy can be
       // avoided safely.
 
-      if (projectList.toSet == projectSet && filterSet.subsetOf(projectSet)) {
+      if (AttributeSet(projectList.map(_.toAttribute)) == projectSet &&
+          filterSet.subsetOf(projectSet)) {
         // When it is possible to just use column pruning to get the right projection and
         // when the columns of this projection are enough to evaluate all filter conditions,
         // just do a scan followed by a filter, with no extra project.
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala
index e63b4903041f6..24e88eea3189e 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala
@@ -79,8 +79,6 @@ private[sql] case class InMemoryRelation(
 
   override def children = Seq.empty
 
-  override def references = Set.empty
-
   override def newInstance() = {
     new InMemoryRelation(
       output.map(_.newInstance),
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
index 21cbbc9772a00..7d33ea5b021e2 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
@@ -141,10 +141,9 @@ case class SparkLogicalPlan(alreadyPlanned: SparkPlan)(@transient sqlContext: SQ
   extends LogicalPlan with MultiInstanceRelation {
 
   def output = alreadyPlanned.output
-  override def references = Set.empty
   override def children = Nil
 
-  override final def newInstance: this.type = {
+  override final def newInstance(): this.type = {
     SparkLogicalPlan(
       alreadyPlanned match {
         case ExistingRdd(output, rdd) => ExistingRdd(output.map(_.newInstance), rdd)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/debug/package.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/debug/package.scala
index f31df051824d7..5b896c55b7393 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/debug/package.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/debug/package.scala
@@ -58,8 +58,6 @@ package object debug {
   }
 
   private[sql] case class DebugNode(child: SparkPlan) extends UnaryNode {
-    def references = Set.empty
-
     def output = child.output
 
     implicit object SetAccumulatorParam extends AccumulatorParam[HashSet[String]] {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUdfs.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUdfs.scala
index b92091b560b1c..aef6ebf86b1eb 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUdfs.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUdfs.scala
@@ -49,7 +49,6 @@ private[spark] case class PythonUDF(
   override def toString = s"PythonUDF#$name(${children.mkString(",")})"
 
   def nullable: Boolean = true
-  def references: Set[Attribute] = children.flatMap(_.references).toSet
 
   override def eval(input: Row) = sys.error("PythonUDFs can not be directly evaluated.")
 }
@@ -113,7 +112,6 @@ private[spark] object ExtractPythonUdfs extends Rule[LogicalPlan] {
 case class EvaluatePython(udf: PythonUDF, child: LogicalPlan) extends logical.UnaryNode {
   val resultAttribute = AttributeReference("pythonUDF", udf.dataType, nullable=true)()
 
-  def references = Set.empty
   def output = child.output :+ resultAttribute
 }
 
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala
index 389ace726d205..10fa8314c9156 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala
@@ -79,9 +79,9 @@ private[hive] trait HiveStrategies {
              hiveContext.convertMetastoreParquet =>
 
         // Filter out all predicates that only deal with partition keys
-        val partitionKeyIds = relation.partitionKeys.map(_.exprId).toSet
+        val partitionsKeys = AttributeSet(relation.partitionKeys)
         val (pruningPredicates, otherPredicates) = predicates.partition {
-          _.references.map(_.exprId).subsetOf(partitionKeyIds)
+          _.references.subsetOf(partitionsKeys)
         }
 
         // We are going to throw the predicates and projection back at the whole optimization
@@ -176,9 +176,9 @@ private[hive] trait HiveStrategies {
       case PhysicalOperation(projectList, predicates, relation: MetastoreRelation) =>
         // Filter out all predicates that only deal with partition keys, these are given to the
         // hive table scan operator to be used for partition pruning.
-        val partitionKeyIds = relation.partitionKeys.map(_.exprId).toSet
+        val partitionKeyIds = AttributeSet(relation.partitionKeys)
         val (pruningPredicates, otherPredicates) = predicates.partition {
-          _.references.map(_.exprId).subsetOf(partitionKeyIds)
+          _.references.subsetOf(partitionKeyIds)
         }
 
         pruneFilterProject(
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUdfs.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUdfs.scala
index c6497a15efa0c..7d1ad53d8bdb3 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUdfs.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUdfs.scala
@@ -88,7 +88,6 @@ private[hive] abstract class HiveUdf extends Expression with Logging with HiveFu
   type EvaluatedType = Any
 
   def nullable = true
-  def references = children.flatMap(_.references).toSet
 
   lazy val function = createFunction[UDFType]()
 
@@ -229,8 +228,6 @@ private[hive] case class HiveGenericUdaf(
 
   def nullable: Boolean = true
 
-  def references: Set[Attribute] = children.map(_.references).flatten.toSet
-
   override def toString = s"$nodeName#$functionClassName(${children.mkString(",")})"
 
   def newInstance() = new HiveUdafFunction(functionClassName, children, this)
@@ -253,8 +250,6 @@ private[hive] case class HiveGenericUdtf(
     children: Seq[Expression])
   extends Generator with HiveInspectors with HiveFunctionFactory {
 
-  override def references = children.flatMap(_.references).toSet
-
   @transient
   protected lazy val function: GenericUDTF = createFunction()
 
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveResolutionSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveResolutionSuite.scala
index 6b3ffd1c0ffe2..b6be6bc1bfefe 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveResolutionSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveResolutionSuite.scala
@@ -20,8 +20,8 @@ package org.apache.spark.sql.hive.execution
 import org.apache.spark.sql.hive.test.TestHive
 import org.apache.spark.sql.hive.test.TestHive._
 
-case class Data(a: Int, B: Int, n: Nested, nestedArray: Seq[Nested])
 case class Nested(a: Int, B: Int)
+case class Data(a: Int, B: Int, n: Nested, nestedArray: Seq[Nested])
 
 /**
  * A set of test cases expressed in Hive QL that are not covered by the tests included in the hive distribution.
@@ -57,6 +57,13 @@ class HiveResolutionSuite extends HiveComparisonTest {
       .registerTempTable("caseSensitivityTest")
 
     sql("SELECT a, b, A, B, n.a, n.b, n.A, n.B FROM caseSensitivityTest")
+
+    println(sql("SELECT * FROM casesensitivitytest one JOIN casesensitivitytest two ON one.a = two.a").queryExecution)
+
+    sql("SELECT * FROM casesensitivitytest one JOIN casesensitivitytest two ON one.a = two.a").collect()
+
+    // TODO: sql("SELECT * FROM casesensitivitytest a JOIN casesensitivitytest b ON a.a = b.a")
+
   }
 
   test("nested repeated resolution") {

From f1e71d4c3ba678fc108effb05cf2d6101dadc0ce Mon Sep 17 00:00:00 2001
From: Davies Liu <davies.liu@gmail.com>
Date: Tue, 26 Aug 2014 16:57:40 -0700
Subject: [PATCH 091/489] [SPARK-3073] [PySpark] use external sort in sortBy()
 and sortByKey()

Using external sort to support sort large datasets in reduce stage.

Author: Davies Liu <davies.liu@gmail.com>

Closes #1978 from davies/sort and squashes the following commits:

bbcd9ba [Davies Liu] check spilled bytes in tests
b125d2f [Davies Liu] add test for external sort in rdd
eae0176 [Davies Liu] choose different disks from different processes and instances
1f075ed [Davies Liu] Merge branch 'master' into sort
eb53ca6 [Davies Liu] Merge branch 'master' into sort
644abaf [Davies Liu] add license in LICENSE
19f7873 [Davies Liu] improve tests
55602ee [Davies Liu] use external sort in sortBy() and sortByKey()
---
 .rat-excludes             |   1 +
 LICENSE                   | 283 ++++++++++++
 python/pyspark/heapq3.py  | 890 ++++++++++++++++++++++++++++++++++++++
 python/pyspark/rdd.py     |   9 +-
 python/pyspark/shuffle.py |  91 +++-
 python/pyspark/tests.py   |  42 +-
 tox.ini                   |   2 +-
 7 files changed, 1306 insertions(+), 12 deletions(-)
 create mode 100644 python/pyspark/heapq3.py

diff --git a/.rat-excludes b/.rat-excludes
index eaefef1b0aa2e..fb6323daf9211 100644
--- a/.rat-excludes
+++ b/.rat-excludes
@@ -31,6 +31,7 @@ sorttable.js
 .*data
 .*log
 cloudpickle.py
+heapq3.py
 join.py
 SparkExprTyper.scala
 SparkILoop.scala
diff --git a/LICENSE b/LICENSE
index e9a1153fdc5db..a7eee041129cb 100644
--- a/LICENSE
+++ b/LICENSE
@@ -338,6 +338,289 @@ THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
 (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
 OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 
+========================================================================
+For heapq (pyspark/heapq3.py):
+========================================================================
+
+# A. HISTORY OF THE SOFTWARE
+# ==========================
+#
+# Python was created in the early 1990s by Guido van Rossum at Stichting
+# Mathematisch Centrum (CWI, see http://www.cwi.nl) in the Netherlands
+# as a successor of a language called ABC.  Guido remains Python's
+# principal author, although it includes many contributions from others.
+#
+# In 1995, Guido continued his work on Python at the Corporation for
+#     National Research Initiatives (CNRI, see http://www.cnri.reston.va.us)
+# in Reston, Virginia where he released several versions of the
+# software.
+#
+# In May 2000, Guido and the Python core development team moved to
+# BeOpen.com to form the BeOpen PythonLabs team.  In October of the same
+# year, the PythonLabs team moved to Digital Creations (now Zope
+# Corporation, see http://www.zope.com).  In 2001, the Python Software
+# Foundation (PSF, see http://www.python.org/psf/) was formed, a
+# non-profit organization created specifically to own Python-related
+# Intellectual Property.  Zope Corporation is a sponsoring member of
+# the PSF.
+#
+# All Python releases are Open Source (see http://www.opensource.org for
+# the Open Source Definition).  Historically, most, but not all, Python
+# releases have also been GPL-compatible; the table below summarizes
+# the various releases.
+#
+# Release         Derived     Year        Owner       GPL-
+# from                                compatible? (1)
+#
+# 0.9.0 thru 1.2              1991-1995   CWI         yes
+# 1.3 thru 1.5.2  1.2         1995-1999   CNRI        yes
+# 1.6             1.5.2       2000        CNRI        no
+# 2.0             1.6         2000        BeOpen.com  no
+# 1.6.1           1.6         2001        CNRI        yes (2)
+# 2.1             2.0+1.6.1   2001        PSF         no
+# 2.0.1           2.0+1.6.1   2001        PSF         yes
+# 2.1.1           2.1+2.0.1   2001        PSF         yes
+# 2.2             2.1.1       2001        PSF         yes
+# 2.1.2           2.1.1       2002        PSF         yes
+# 2.1.3           2.1.2       2002        PSF         yes
+# 2.2.1           2.2         2002        PSF         yes
+# 2.2.2           2.2.1       2002        PSF         yes
+# 2.2.3           2.2.2       2003        PSF         yes
+# 2.3             2.2.2       2002-2003   PSF         yes
+# 2.3.1           2.3         2002-2003   PSF         yes
+# 2.3.2           2.3.1       2002-2003   PSF         yes
+# 2.3.3           2.3.2       2002-2003   PSF         yes
+# 2.3.4           2.3.3       2004        PSF         yes
+# 2.3.5           2.3.4       2005        PSF         yes
+# 2.4             2.3         2004        PSF         yes
+# 2.4.1           2.4         2005        PSF         yes
+# 2.4.2           2.4.1       2005        PSF         yes
+# 2.4.3           2.4.2       2006        PSF         yes
+# 2.4.4           2.4.3       2006        PSF         yes
+# 2.5             2.4         2006        PSF         yes
+# 2.5.1           2.5         2007        PSF         yes
+# 2.5.2           2.5.1       2008        PSF         yes
+# 2.5.3           2.5.2       2008        PSF         yes
+# 2.6             2.5         2008        PSF         yes
+# 2.6.1           2.6         2008        PSF         yes
+# 2.6.2           2.6.1       2009        PSF         yes
+# 2.6.3           2.6.2       2009        PSF         yes
+# 2.6.4           2.6.3       2009        PSF         yes
+# 2.6.5           2.6.4       2010        PSF         yes
+# 2.7             2.6         2010        PSF         yes
+#
+# Footnotes:
+#
+# (1) GPL-compatible doesn't mean that we're distributing Python under
+# the GPL.  All Python licenses, unlike the GPL, let you distribute
+# a modified version without making your changes open source.  The
+# GPL-compatible licenses make it possible to combine Python with
+#     other software that is released under the GPL; the others don't.
+#
+# (2) According to Richard Stallman, 1.6.1 is not GPL-compatible,
+# because its license has a choice of law clause.  According to
+# CNRI, however, Stallman's lawyer has told CNRI's lawyer that 1.6.1
+# is "not incompatible" with the GPL.
+#
+# Thanks to the many outside volunteers who have worked under Guido's
+# direction to make these releases possible.
+#
+#
+# B. TERMS AND CONDITIONS FOR ACCESSING OR OTHERWISE USING PYTHON
+# ===============================================================
+#
+# PYTHON SOFTWARE FOUNDATION LICENSE VERSION 2
+# --------------------------------------------
+#
+# 1. This LICENSE AGREEMENT is between the Python Software Foundation
+# ("PSF"), and the Individual or Organization ("Licensee") accessing and
+# otherwise using this software ("Python") in source or binary form and
+# its associated documentation.
+#
+# 2. Subject to the terms and conditions of this License Agreement, PSF hereby
+# grants Licensee a nonexclusive, royalty-free, world-wide license to reproduce,
+# analyze, test, perform and/or display publicly, prepare derivative works,
+# distribute, and otherwise use Python alone or in any derivative version,
+# provided, however, that PSF's License Agreement and PSF's notice of copyright,
+# i.e., "Copyright (c) 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010,
+# 2011, 2012, 2013 Python Software Foundation; All Rights Reserved" are retained
+# in Python alone or in any derivative version prepared by Licensee.
+#
+# 3. In the event Licensee prepares a derivative work that is based on
+# or incorporates Python or any part thereof, and wants to make
+# the derivative work available to others as provided herein, then
+# Licensee hereby agrees to include in any such work a brief summary of
+# the changes made to Python.
+#
+# 4. PSF is making Python available to Licensee on an "AS IS"
+# basis.  PSF MAKES NO REPRESENTATIONS OR WARRANTIES, EXPRESS OR
+# IMPLIED.  BY WAY OF EXAMPLE, BUT NOT LIMITATION, PSF MAKES NO AND
+# DISCLAIMS ANY REPRESENTATION OR WARRANTY OF MERCHANTABILITY OR FITNESS
+# FOR ANY PARTICULAR PURPOSE OR THAT THE USE OF PYTHON WILL NOT
+# INFRINGE ANY THIRD PARTY RIGHTS.
+#
+# 5. PSF SHALL NOT BE LIABLE TO LICENSEE OR ANY OTHER USERS OF PYTHON
+# FOR ANY INCIDENTAL, SPECIAL, OR CONSEQUENTIAL DAMAGES OR LOSS AS
+# A RESULT OF MODIFYING, DISTRIBUTING, OR OTHERWISE USING PYTHON,
+# OR ANY DERIVATIVE THEREOF, EVEN IF ADVISED OF THE POSSIBILITY THEREOF.
+#
+# 6. This License Agreement will automatically terminate upon a material
+# breach of its terms and conditions.
+#
+# 7. Nothing in this License Agreement shall be deemed to create any
+# relationship of agency, partnership, or joint venture between PSF and
+# Licensee.  This License Agreement does not grant permission to use PSF
+# trademarks or trade name in a trademark sense to endorse or promote
+# products or services of Licensee, or any third party.
+#
+# 8. By copying, installing or otherwise using Python, Licensee
+# agrees to be bound by the terms and conditions of this License
+# Agreement.
+#
+#
+# BEOPEN.COM LICENSE AGREEMENT FOR PYTHON 2.0
+# -------------------------------------------
+#
+# BEOPEN PYTHON OPEN SOURCE LICENSE AGREEMENT VERSION 1
+#
+# 1. This LICENSE AGREEMENT is between BeOpen.com ("BeOpen"), having an
+# office at 160 Saratoga Avenue, Santa Clara, CA 95051, and the
+# Individual or Organization ("Licensee") accessing and otherwise using
+# this software in source or binary form and its associated
+# documentation ("the Software").
+#
+# 2. Subject to the terms and conditions of this BeOpen Python License
+# Agreement, BeOpen hereby grants Licensee a non-exclusive,
+# royalty-free, world-wide license to reproduce, analyze, test, perform
+# and/or display publicly, prepare derivative works, distribute, and
+# otherwise use the Software alone or in any derivative version,
+# provided, however, that the BeOpen Python License is retained in the
+# Software, alone or in any derivative version prepared by Licensee.
+#
+# 3. BeOpen is making the Software available to Licensee on an "AS IS"
+# basis.  BEOPEN MAKES NO REPRESENTATIONS OR WARRANTIES, EXPRESS OR
+# IMPLIED.  BY WAY OF EXAMPLE, BUT NOT LIMITATION, BEOPEN MAKES NO AND
+# DISCLAIMS ANY REPRESENTATION OR WARRANTY OF MERCHANTABILITY OR FITNESS
+# FOR ANY PARTICULAR PURPOSE OR THAT THE USE OF THE SOFTWARE WILL NOT
+# INFRINGE ANY THIRD PARTY RIGHTS.
+#
+# 4. BEOPEN SHALL NOT BE LIABLE TO LICENSEE OR ANY OTHER USERS OF THE
+# SOFTWARE FOR ANY INCIDENTAL, SPECIAL, OR CONSEQUENTIAL DAMAGES OR LOSS
+# AS A RESULT OF USING, MODIFYING OR DISTRIBUTING THE SOFTWARE, OR ANY
+# DERIVATIVE THEREOF, EVEN IF ADVISED OF THE POSSIBILITY THEREOF.
+#
+# 5. This License Agreement will automatically terminate upon a material
+# breach of its terms and conditions.
+#
+# 6. This License Agreement shall be governed by and interpreted in all
+# respects by the law of the State of California, excluding conflict of
+# law provisions.  Nothing in this License Agreement shall be deemed to
+# create any relationship of agency, partnership, or joint venture
+# between BeOpen and Licensee.  This License Agreement does not grant
+# permission to use BeOpen trademarks or trade names in a trademark
+# sense to endorse or promote products or services of Licensee, or any
+# third party.  As an exception, the "BeOpen Python" logos available at
+# http://www.pythonlabs.com/logos.html may be used according to the
+# permissions granted on that web page.
+#
+# 7. By copying, installing or otherwise using the software, Licensee
+# agrees to be bound by the terms and conditions of this License
+# Agreement.
+#
+#
+# CNRI LICENSE AGREEMENT FOR PYTHON 1.6.1
+# ---------------------------------------
+#
+# 1. This LICENSE AGREEMENT is between the Corporation for National
+#     Research Initiatives, having an office at 1895 Preston White Drive,
+# Reston, VA 20191 ("CNRI"), and the Individual or Organization
+# ("Licensee") accessing and otherwise using Python 1.6.1 software in
+# source or binary form and its associated documentation.
+#
+# 2. Subject to the terms and conditions of this License Agreement, CNRI
+# hereby grants Licensee a nonexclusive, royalty-free, world-wide
+# license to reproduce, analyze, test, perform and/or display publicly,
+# prepare derivative works, distribute, and otherwise use Python 1.6.1
+# alone or in any derivative version, provided, however, that CNRI's
+# License Agreement and CNRI's notice of copyright, i.e., "Copyright (c)
+# 1995-2001 Corporation for National Research Initiatives; All Rights
+# Reserved" are retained in Python 1.6.1 alone or in any derivative
+# version prepared by Licensee.  Alternately, in lieu of CNRI's License
+# Agreement, Licensee may substitute the following text (omitting the
+# quotes): "Python 1.6.1 is made available subject to the terms and
+# conditions in CNRI's License Agreement.  This Agreement together with
+# Python 1.6.1 may be located on the Internet using the following
+# unique, persistent identifier (known as a handle): 1895.22/1013.  This
+# Agreement may also be obtained from a proxy server on the Internet
+# using the following URL: http://hdl.handle.net/1895.22/1013".
+#
+# 3. In the event Licensee prepares a derivative work that is based on
+# or incorporates Python 1.6.1 or any part thereof, and wants to make
+# the derivative work available to others as provided herein, then
+# Licensee hereby agrees to include in any such work a brief summary of
+# the changes made to Python 1.6.1.
+#
+# 4. CNRI is making Python 1.6.1 available to Licensee on an "AS IS"
+# basis.  CNRI MAKES NO REPRESENTATIONS OR WARRANTIES, EXPRESS OR
+# IMPLIED.  BY WAY OF EXAMPLE, BUT NOT LIMITATION, CNRI MAKES NO AND
+# DISCLAIMS ANY REPRESENTATION OR WARRANTY OF MERCHANTABILITY OR FITNESS
+# FOR ANY PARTICULAR PURPOSE OR THAT THE USE OF PYTHON 1.6.1 WILL NOT
+# INFRINGE ANY THIRD PARTY RIGHTS.
+#
+# 5. CNRI SHALL NOT BE LIABLE TO LICENSEE OR ANY OTHER USERS OF PYTHON
+# 1.6.1 FOR ANY INCIDENTAL, SPECIAL, OR CONSEQUENTIAL DAMAGES OR LOSS AS
+# A RESULT OF MODIFYING, DISTRIBUTING, OR OTHERWISE USING PYTHON 1.6.1,
+# OR ANY DERIVATIVE THEREOF, EVEN IF ADVISED OF THE POSSIBILITY THEREOF.
+#
+# 6. This License Agreement will automatically terminate upon a material
+# breach of its terms and conditions.
+#
+# 7. This License Agreement shall be governed by the federal
+# intellectual property law of the United States, including without
+# limitation the federal copyright law, and, to the extent such
+# U.S. federal law does not apply, by the law of the Commonwealth of
+# Virginia, excluding Virginia's conflict of law provisions.
+# Notwithstanding the foregoing, with regard to derivative works based
+# on Python 1.6.1 that incorporate non-separable material that was
+# previously distributed under the GNU General Public License (GPL), the
+# law of the Commonwealth of Virginia shall govern this License
+# Agreement only as to issues arising under or with respect to
+# Paragraphs 4, 5, and 7 of this License Agreement.  Nothing in this
+# License Agreement shall be deemed to create any relationship of
+# agency, partnership, or joint venture between CNRI and Licensee.  This
+# License Agreement does not grant permission to use CNRI trademarks or
+# trade name in a trademark sense to endorse or promote products or
+# services of Licensee, or any third party.
+#
+# 8. By clicking on the "ACCEPT" button where indicated, or by copying,
+# installing or otherwise using Python 1.6.1, Licensee agrees to be
+# bound by the terms and conditions of this License Agreement.
+#
+# ACCEPT
+#
+#
+# CWI LICENSE AGREEMENT FOR PYTHON 0.9.0 THROUGH 1.2
+# --------------------------------------------------
+#
+# Copyright (c) 1991 - 1995, Stichting Mathematisch Centrum Amsterdam,
+# The Netherlands.  All rights reserved.
+#
+# Permission to use, copy, modify, and distribute this software and its
+# documentation for any purpose and without fee is hereby granted,
+# provided that the above copyright notice appear in all copies and that
+# both that copyright notice and this permission notice appear in
+# supporting documentation, and that the name of Stichting Mathematisch
+# Centrum or CWI not be used in advertising or publicity pertaining to
+# distribution of the software without specific, written prior
+# permission.
+#
+# STICHTING MATHEMATISCH CENTRUM DISCLAIMS ALL WARRANTIES WITH REGARD TO
+# THIS SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND
+# FITNESS, IN NO EVENT SHALL STICHTING MATHEMATISCH CENTRUM BE LIABLE
+# FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES
+# WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN
+# ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT
+# OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.
 
 ========================================================================
 For sorttable (core/src/main/resources/org/apache/spark/ui/static/sorttable.js):
diff --git a/python/pyspark/heapq3.py b/python/pyspark/heapq3.py
new file mode 100644
index 0000000000000..bc441f138f7fc
--- /dev/null
+++ b/python/pyspark/heapq3.py
@@ -0,0 +1,890 @@
+# -*- encoding: utf-8 -*-
+#  back ported from CPython 3
+# A. HISTORY OF THE SOFTWARE
+# ==========================
+#
+# Python was created in the early 1990s by Guido van Rossum at Stichting
+# Mathematisch Centrum (CWI, see http://www.cwi.nl) in the Netherlands
+# as a successor of a language called ABC.  Guido remains Python's
+# principal author, although it includes many contributions from others.
+#
+# In 1995, Guido continued his work on Python at the Corporation for
+#     National Research Initiatives (CNRI, see http://www.cnri.reston.va.us)
+# in Reston, Virginia where he released several versions of the
+# software.
+#
+# In May 2000, Guido and the Python core development team moved to
+# BeOpen.com to form the BeOpen PythonLabs team.  In October of the same
+# year, the PythonLabs team moved to Digital Creations (now Zope
+# Corporation, see http://www.zope.com).  In 2001, the Python Software
+# Foundation (PSF, see http://www.python.org/psf/) was formed, a
+# non-profit organization created specifically to own Python-related
+# Intellectual Property.  Zope Corporation is a sponsoring member of
+# the PSF.
+#
+# All Python releases are Open Source (see http://www.opensource.org for
+# the Open Source Definition).  Historically, most, but not all, Python
+# releases have also been GPL-compatible; the table below summarizes
+# the various releases.
+#
+# Release         Derived     Year        Owner       GPL-
+# from                                compatible? (1)
+#
+# 0.9.0 thru 1.2              1991-1995   CWI         yes
+# 1.3 thru 1.5.2  1.2         1995-1999   CNRI        yes
+# 1.6             1.5.2       2000        CNRI        no
+# 2.0             1.6         2000        BeOpen.com  no
+# 1.6.1           1.6         2001        CNRI        yes (2)
+# 2.1             2.0+1.6.1   2001        PSF         no
+# 2.0.1           2.0+1.6.1   2001        PSF         yes
+# 2.1.1           2.1+2.0.1   2001        PSF         yes
+# 2.2             2.1.1       2001        PSF         yes
+# 2.1.2           2.1.1       2002        PSF         yes
+# 2.1.3           2.1.2       2002        PSF         yes
+# 2.2.1           2.2         2002        PSF         yes
+# 2.2.2           2.2.1       2002        PSF         yes
+# 2.2.3           2.2.2       2003        PSF         yes
+# 2.3             2.2.2       2002-2003   PSF         yes
+# 2.3.1           2.3         2002-2003   PSF         yes
+# 2.3.2           2.3.1       2002-2003   PSF         yes
+# 2.3.3           2.3.2       2002-2003   PSF         yes
+# 2.3.4           2.3.3       2004        PSF         yes
+# 2.3.5           2.3.4       2005        PSF         yes
+# 2.4             2.3         2004        PSF         yes
+# 2.4.1           2.4         2005        PSF         yes
+# 2.4.2           2.4.1       2005        PSF         yes
+# 2.4.3           2.4.2       2006        PSF         yes
+# 2.4.4           2.4.3       2006        PSF         yes
+# 2.5             2.4         2006        PSF         yes
+# 2.5.1           2.5         2007        PSF         yes
+# 2.5.2           2.5.1       2008        PSF         yes
+# 2.5.3           2.5.2       2008        PSF         yes
+# 2.6             2.5         2008        PSF         yes
+# 2.6.1           2.6         2008        PSF         yes
+# 2.6.2           2.6.1       2009        PSF         yes
+# 2.6.3           2.6.2       2009        PSF         yes
+# 2.6.4           2.6.3       2009        PSF         yes
+# 2.6.5           2.6.4       2010        PSF         yes
+# 2.7             2.6         2010        PSF         yes
+#
+# Footnotes:
+#
+# (1) GPL-compatible doesn't mean that we're distributing Python under
+# the GPL.  All Python licenses, unlike the GPL, let you distribute
+# a modified version without making your changes open source.  The
+# GPL-compatible licenses make it possible to combine Python with
+#     other software that is released under the GPL; the others don't.
+#
+# (2) According to Richard Stallman, 1.6.1 is not GPL-compatible,
+# because its license has a choice of law clause.  According to
+# CNRI, however, Stallman's lawyer has told CNRI's lawyer that 1.6.1
+# is "not incompatible" with the GPL.
+#
+# Thanks to the many outside volunteers who have worked under Guido's
+# direction to make these releases possible.
+#
+#
+# B. TERMS AND CONDITIONS FOR ACCESSING OR OTHERWISE USING PYTHON
+# ===============================================================
+#
+# PYTHON SOFTWARE FOUNDATION LICENSE VERSION 2
+# --------------------------------------------
+#
+# 1. This LICENSE AGREEMENT is between the Python Software Foundation
+# ("PSF"), and the Individual or Organization ("Licensee") accessing and
+# otherwise using this software ("Python") in source or binary form and
+# its associated documentation.
+#
+# 2. Subject to the terms and conditions of this License Agreement, PSF hereby
+# grants Licensee a nonexclusive, royalty-free, world-wide license to reproduce,
+# analyze, test, perform and/or display publicly, prepare derivative works,
+# distribute, and otherwise use Python alone or in any derivative version,
+# provided, however, that PSF's License Agreement and PSF's notice of copyright,
+# i.e., "Copyright (c) 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010,
+# 2011, 2012, 2013 Python Software Foundation; All Rights Reserved" are retained
+# in Python alone or in any derivative version prepared by Licensee.
+#
+# 3. In the event Licensee prepares a derivative work that is based on
+# or incorporates Python or any part thereof, and wants to make
+# the derivative work available to others as provided herein, then
+# Licensee hereby agrees to include in any such work a brief summary of
+# the changes made to Python.
+#
+# 4. PSF is making Python available to Licensee on an "AS IS"
+# basis.  PSF MAKES NO REPRESENTATIONS OR WARRANTIES, EXPRESS OR
+# IMPLIED.  BY WAY OF EXAMPLE, BUT NOT LIMITATION, PSF MAKES NO AND
+# DISCLAIMS ANY REPRESENTATION OR WARRANTY OF MERCHANTABILITY OR FITNESS
+# FOR ANY PARTICULAR PURPOSE OR THAT THE USE OF PYTHON WILL NOT
+# INFRINGE ANY THIRD PARTY RIGHTS.
+#
+# 5. PSF SHALL NOT BE LIABLE TO LICENSEE OR ANY OTHER USERS OF PYTHON
+# FOR ANY INCIDENTAL, SPECIAL, OR CONSEQUENTIAL DAMAGES OR LOSS AS
+# A RESULT OF MODIFYING, DISTRIBUTING, OR OTHERWISE USING PYTHON,
+# OR ANY DERIVATIVE THEREOF, EVEN IF ADVISED OF THE POSSIBILITY THEREOF.
+#
+# 6. This License Agreement will automatically terminate upon a material
+# breach of its terms and conditions.
+#
+# 7. Nothing in this License Agreement shall be deemed to create any
+# relationship of agency, partnership, or joint venture between PSF and
+# Licensee.  This License Agreement does not grant permission to use PSF
+# trademarks or trade name in a trademark sense to endorse or promote
+# products or services of Licensee, or any third party.
+#
+# 8. By copying, installing or otherwise using Python, Licensee
+# agrees to be bound by the terms and conditions of this License
+# Agreement.
+#
+#
+# BEOPEN.COM LICENSE AGREEMENT FOR PYTHON 2.0
+# -------------------------------------------
+#
+# BEOPEN PYTHON OPEN SOURCE LICENSE AGREEMENT VERSION 1
+#
+# 1. This LICENSE AGREEMENT is between BeOpen.com ("BeOpen"), having an
+# office at 160 Saratoga Avenue, Santa Clara, CA 95051, and the
+# Individual or Organization ("Licensee") accessing and otherwise using
+# this software in source or binary form and its associated
+# documentation ("the Software").
+#
+# 2. Subject to the terms and conditions of this BeOpen Python License
+# Agreement, BeOpen hereby grants Licensee a non-exclusive,
+# royalty-free, world-wide license to reproduce, analyze, test, perform
+# and/or display publicly, prepare derivative works, distribute, and
+# otherwise use the Software alone or in any derivative version,
+# provided, however, that the BeOpen Python License is retained in the
+# Software, alone or in any derivative version prepared by Licensee.
+#
+# 3. BeOpen is making the Software available to Licensee on an "AS IS"
+# basis.  BEOPEN MAKES NO REPRESENTATIONS OR WARRANTIES, EXPRESS OR
+# IMPLIED.  BY WAY OF EXAMPLE, BUT NOT LIMITATION, BEOPEN MAKES NO AND
+# DISCLAIMS ANY REPRESENTATION OR WARRANTY OF MERCHANTABILITY OR FITNESS
+# FOR ANY PARTICULAR PURPOSE OR THAT THE USE OF THE SOFTWARE WILL NOT
+# INFRINGE ANY THIRD PARTY RIGHTS.
+#
+# 4. BEOPEN SHALL NOT BE LIABLE TO LICENSEE OR ANY OTHER USERS OF THE
+# SOFTWARE FOR ANY INCIDENTAL, SPECIAL, OR CONSEQUENTIAL DAMAGES OR LOSS
+# AS A RESULT OF USING, MODIFYING OR DISTRIBUTING THE SOFTWARE, OR ANY
+# DERIVATIVE THEREOF, EVEN IF ADVISED OF THE POSSIBILITY THEREOF.
+#
+# 5. This License Agreement will automatically terminate upon a material
+# breach of its terms and conditions.
+#
+# 6. This License Agreement shall be governed by and interpreted in all
+# respects by the law of the State of California, excluding conflict of
+# law provisions.  Nothing in this License Agreement shall be deemed to
+# create any relationship of agency, partnership, or joint venture
+# between BeOpen and Licensee.  This License Agreement does not grant
+# permission to use BeOpen trademarks or trade names in a trademark
+# sense to endorse or promote products or services of Licensee, or any
+# third party.  As an exception, the "BeOpen Python" logos available at
+# http://www.pythonlabs.com/logos.html may be used according to the
+# permissions granted on that web page.
+#
+# 7. By copying, installing or otherwise using the software, Licensee
+# agrees to be bound by the terms and conditions of this License
+# Agreement.
+#
+#
+# CNRI LICENSE AGREEMENT FOR PYTHON 1.6.1
+# ---------------------------------------
+#
+# 1. This LICENSE AGREEMENT is between the Corporation for National
+#     Research Initiatives, having an office at 1895 Preston White Drive,
+# Reston, VA 20191 ("CNRI"), and the Individual or Organization
+# ("Licensee") accessing and otherwise using Python 1.6.1 software in
+# source or binary form and its associated documentation.
+#
+# 2. Subject to the terms and conditions of this License Agreement, CNRI
+# hereby grants Licensee a nonexclusive, royalty-free, world-wide
+# license to reproduce, analyze, test, perform and/or display publicly,
+# prepare derivative works, distribute, and otherwise use Python 1.6.1
+# alone or in any derivative version, provided, however, that CNRI's
+# License Agreement and CNRI's notice of copyright, i.e., "Copyright (c)
+# 1995-2001 Corporation for National Research Initiatives; All Rights
+# Reserved" are retained in Python 1.6.1 alone or in any derivative
+# version prepared by Licensee.  Alternately, in lieu of CNRI's License
+# Agreement, Licensee may substitute the following text (omitting the
+# quotes): "Python 1.6.1 is made available subject to the terms and
+# conditions in CNRI's License Agreement.  This Agreement together with
+# Python 1.6.1 may be located on the Internet using the following
+# unique, persistent identifier (known as a handle): 1895.22/1013.  This
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+# the changes made to Python 1.6.1.
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+# 4. CNRI is making Python 1.6.1 available to Licensee on an "AS IS"
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+# 6. This License Agreement will automatically terminate upon a material
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+# limitation the federal copyright law, and, to the extent such
+# U.S. federal law does not apply, by the law of the Commonwealth of
+# Virginia, excluding Virginia's conflict of law provisions.
+# Notwithstanding the foregoing, with regard to derivative works based
+# on Python 1.6.1 that incorporate non-separable material that was
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+#
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+#
+# ACCEPT
+#
+#
+# CWI LICENSE AGREEMENT FOR PYTHON 0.9.0 THROUGH 1.2
+# --------------------------------------------------
+#
+# Copyright (c) 1991 - 1995, Stichting Mathematisch Centrum Amsterdam,
+# The Netherlands.  All rights reserved.
+#
+# Permission to use, copy, modify, and distribute this software and its
+# documentation for any purpose and without fee is hereby granted,
+# provided that the above copyright notice appear in all copies and that
+# both that copyright notice and this permission notice appear in
+# supporting documentation, and that the name of Stichting Mathematisch
+# Centrum or CWI not be used in advertising or publicity pertaining to
+# distribution of the software without specific, written prior
+# permission.
+#
+# STICHTING MATHEMATISCH CENTRUM DISCLAIMS ALL WARRANTIES WITH REGARD TO
+# THIS SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND
+# FITNESS, IN NO EVENT SHALL STICHTING MATHEMATISCH CENTRUM BE LIABLE
+# FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES
+# WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN
+# ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT
+# OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.
+"""Heap queue algorithm (a.k.a. priority queue).
+
+Heaps are arrays for which a[k] <= a[2*k+1] and a[k] <= a[2*k+2] for
+all k, counting elements from 0.  For the sake of comparison,
+non-existing elements are considered to be infinite.  The interesting
+property of a heap is that a[0] is always its smallest element.
+
+Usage:
+
+heap = []            # creates an empty heap
+heappush(heap, item) # pushes a new item on the heap
+item = heappop(heap) # pops the smallest item from the heap
+item = heap[0]       # smallest item on the heap without popping it
+heapify(x)           # transforms list into a heap, in-place, in linear time
+item = heapreplace(heap, item) # pops and returns smallest item, and adds
+                               # new item; the heap size is unchanged
+
+Our API differs from textbook heap algorithms as follows:
+
+- We use 0-based indexing.  This makes the relationship between the
+  index for a node and the indexes for its children slightly less
+  obvious, but is more suitable since Python uses 0-based indexing.
+
+- Our heappop() method returns the smallest item, not the largest.
+
+These two make it possible to view the heap as a regular Python list
+without surprises: heap[0] is the smallest item, and heap.sort()
+maintains the heap invariant!
+"""
+
+# Original code by Kevin O'Connor, augmented by Tim Peters and Raymond Hettinger
+
+__about__ = """Heap queues
+
+[explanation by François Pinard]
+
+Heaps are arrays for which a[k] <= a[2*k+1] and a[k] <= a[2*k+2] for
+all k, counting elements from 0.  For the sake of comparison,
+non-existing elements are considered to be infinite.  The interesting
+property of a heap is that a[0] is always its smallest element.
+
+The strange invariant above is meant to be an efficient memory
+representation for a tournament.  The numbers below are `k', not a[k]:
+
+                                   0
+
+                  1                                 2
+
+          3               4                5               6
+
+      7       8       9       10      11      12      13      14
+
+    15 16   17 18   19 20   21 22   23 24   25 26   27 28   29 30
+
+
+In the tree above, each cell `k' is topping `2*k+1' and `2*k+2'.  In
+an usual binary tournament we see in sports, each cell is the winner
+over the two cells it tops, and we can trace the winner down the tree
+to see all opponents s/he had.  However, in many computer applications
+of such tournaments, we do not need to trace the history of a winner.
+To be more memory efficient, when a winner is promoted, we try to
+replace it by something else at a lower level, and the rule becomes
+that a cell and the two cells it tops contain three different items,
+but the top cell "wins" over the two topped cells.
+
+If this heap invariant is protected at all time, index 0 is clearly
+the overall winner.  The simplest algorithmic way to remove it and
+find the "next" winner is to move some loser (let's say cell 30 in the
+diagram above) into the 0 position, and then percolate this new 0 down
+the tree, exchanging values, until the invariant is re-established.
+This is clearly logarithmic on the total number of items in the tree.
+By iterating over all items, you get an O(n ln n) sort.
+
+A nice feature of this sort is that you can efficiently insert new
+items while the sort is going on, provided that the inserted items are
+not "better" than the last 0'th element you extracted.  This is
+especially useful in simulation contexts, where the tree holds all
+incoming events, and the "win" condition means the smallest scheduled
+time.  When an event schedule other events for execution, they are
+scheduled into the future, so they can easily go into the heap.  So, a
+heap is a good structure for implementing schedulers (this is what I
+used for my MIDI sequencer :-).
+
+Various structures for implementing schedulers have been extensively
+studied, and heaps are good for this, as they are reasonably speedy,
+the speed is almost constant, and the worst case is not much different
+than the average case.  However, there are other representations which
+are more efficient overall, yet the worst cases might be terrible.
+
+Heaps are also very useful in big disk sorts.  You most probably all
+know that a big sort implies producing "runs" (which are pre-sorted
+sequences, which size is usually related to the amount of CPU memory),
+followed by a merging passes for these runs, which merging is often
+very cleverly organised[1].  It is very important that the initial
+sort produces the longest runs possible.  Tournaments are a good way
+to that.  If, using all the memory available to hold a tournament, you
+replace and percolate items that happen to fit the current run, you'll
+produce runs which are twice the size of the memory for random input,
+and much better for input fuzzily ordered.
+
+Moreover, if you output the 0'th item on disk and get an input which
+may not fit in the current tournament (because the value "wins" over
+the last output value), it cannot fit in the heap, so the size of the
+heap decreases.  The freed memory could be cleverly reused immediately
+for progressively building a second heap, which grows at exactly the
+same rate the first heap is melting.  When the first heap completely
+vanishes, you switch heaps and start a new run.  Clever and quite
+effective!
+
+In a word, heaps are useful memory structures to know.  I use them in
+a few applications, and I think it is good to keep a `heap' module
+around. :-)
+
+--------------------
+[1] The disk balancing algorithms which are current, nowadays, are
+more annoying than clever, and this is a consequence of the seeking
+capabilities of the disks.  On devices which cannot seek, like big
+tape drives, the story was quite different, and one had to be very
+clever to ensure (far in advance) that each tape movement will be the
+most effective possible (that is, will best participate at
+"progressing" the merge).  Some tapes were even able to read
+backwards, and this was also used to avoid the rewinding time.
+Believe me, real good tape sorts were quite spectacular to watch!
+From all times, sorting has always been a Great Art! :-)
+"""
+
+__all__ = ['heappush', 'heappop', 'heapify', 'heapreplace', 'merge',
+           'nlargest', 'nsmallest', 'heappushpop']
+
+def heappush(heap, item):
+    """Push item onto heap, maintaining the heap invariant."""
+    heap.append(item)
+    _siftdown(heap, 0, len(heap)-1)
+
+def heappop(heap):
+    """Pop the smallest item off the heap, maintaining the heap invariant."""
+    lastelt = heap.pop()    # raises appropriate IndexError if heap is empty
+    if heap:
+        returnitem = heap[0]
+        heap[0] = lastelt
+        _siftup(heap, 0)
+        return returnitem
+    return lastelt
+
+def heapreplace(heap, item):
+    """Pop and return the current smallest value, and add the new item.
+
+    This is more efficient than heappop() followed by heappush(), and can be
+    more appropriate when using a fixed-size heap.  Note that the value
+    returned may be larger than item!  That constrains reasonable uses of
+    this routine unless written as part of a conditional replacement:
+
+        if item > heap[0]:
+            item = heapreplace(heap, item)
+    """
+    returnitem = heap[0]    # raises appropriate IndexError if heap is empty
+    heap[0] = item
+    _siftup(heap, 0)
+    return returnitem
+
+def heappushpop(heap, item):
+    """Fast version of a heappush followed by a heappop."""
+    if heap and heap[0] < item:
+        item, heap[0] = heap[0], item
+        _siftup(heap, 0)
+    return item
+
+def heapify(x):
+    """Transform list into a heap, in-place, in O(len(x)) time."""
+    n = len(x)
+    # Transform bottom-up.  The largest index there's any point to looking at
+    # is the largest with a child index in-range, so must have 2*i + 1 < n,
+    # or i < (n-1)/2.  If n is even = 2*j, this is (2*j-1)/2 = j-1/2 so
+    # j-1 is the largest, which is n//2 - 1.  If n is odd = 2*j+1, this is
+    # (2*j+1-1)/2 = j so j-1 is the largest, and that's again n//2-1.
+    for i in reversed(range(n//2)):
+        _siftup(x, i)
+
+def _heappop_max(heap):
+    """Maxheap version of a heappop."""
+    lastelt = heap.pop()    # raises appropriate IndexError if heap is empty
+    if heap:
+        returnitem = heap[0]
+        heap[0] = lastelt
+        _siftup_max(heap, 0)
+        return returnitem
+    return lastelt
+
+def _heapreplace_max(heap, item):
+    """Maxheap version of a heappop followed by a heappush."""
+    returnitem = heap[0]    # raises appropriate IndexError if heap is empty
+    heap[0] = item
+    _siftup_max(heap, 0)
+    return returnitem
+
+def _heapify_max(x):
+    """Transform list into a maxheap, in-place, in O(len(x)) time."""
+    n = len(x)
+    for i in reversed(range(n//2)):
+        _siftup_max(x, i)
+
+# 'heap' is a heap at all indices >= startpos, except possibly for pos.  pos
+# is the index of a leaf with a possibly out-of-order value.  Restore the
+# heap invariant.
+def _siftdown(heap, startpos, pos):
+    newitem = heap[pos]
+    # Follow the path to the root, moving parents down until finding a place
+    # newitem fits.
+    while pos > startpos:
+        parentpos = (pos - 1) >> 1
+        parent = heap[parentpos]
+        if newitem < parent:
+            heap[pos] = parent
+            pos = parentpos
+            continue
+        break
+    heap[pos] = newitem
+
+# The child indices of heap index pos are already heaps, and we want to make
+# a heap at index pos too.  We do this by bubbling the smaller child of
+# pos up (and so on with that child's children, etc) until hitting a leaf,
+# then using _siftdown to move the oddball originally at index pos into place.
+#
+# We *could* break out of the loop as soon as we find a pos where newitem <=
+# both its children, but turns out that's not a good idea, and despite that
+# many books write the algorithm that way.  During a heap pop, the last array
+# element is sifted in, and that tends to be large, so that comparing it
+# against values starting from the root usually doesn't pay (= usually doesn't
+# get us out of the loop early).  See Knuth, Volume 3, where this is
+# explained and quantified in an exercise.
+#
+# Cutting the # of comparisons is important, since these routines have no
+# way to extract "the priority" from an array element, so that intelligence
+# is likely to be hiding in custom comparison methods, or in array elements
+# storing (priority, record) tuples.  Comparisons are thus potentially
+# expensive.
+#
+# On random arrays of length 1000, making this change cut the number of
+# comparisons made by heapify() a little, and those made by exhaustive
+# heappop() a lot, in accord with theory.  Here are typical results from 3
+# runs (3 just to demonstrate how small the variance is):
+#
+# Compares needed by heapify     Compares needed by 1000 heappops
+# --------------------------     --------------------------------
+# 1837 cut to 1663               14996 cut to 8680
+# 1855 cut to 1659               14966 cut to 8678
+# 1847 cut to 1660               15024 cut to 8703
+#
+# Building the heap by using heappush() 1000 times instead required
+# 2198, 2148, and 2219 compares:  heapify() is more efficient, when
+# you can use it.
+#
+# The total compares needed by list.sort() on the same lists were 8627,
+# 8627, and 8632 (this should be compared to the sum of heapify() and
+# heappop() compares):  list.sort() is (unsurprisingly!) more efficient
+# for sorting.
+
+def _siftup(heap, pos):
+    endpos = len(heap)
+    startpos = pos
+    newitem = heap[pos]
+    # Bubble up the smaller child until hitting a leaf.
+    childpos = 2*pos + 1    # leftmost child position
+    while childpos < endpos:
+        # Set childpos to index of smaller child.
+        rightpos = childpos + 1
+        if rightpos < endpos and not heap[childpos] < heap[rightpos]:
+            childpos = rightpos
+        # Move the smaller child up.
+        heap[pos] = heap[childpos]
+        pos = childpos
+        childpos = 2*pos + 1
+    # The leaf at pos is empty now.  Put newitem there, and bubble it up
+    # to its final resting place (by sifting its parents down).
+    heap[pos] = newitem
+    _siftdown(heap, startpos, pos)
+
+def _siftdown_max(heap, startpos, pos):
+    'Maxheap variant of _siftdown'
+    newitem = heap[pos]
+    # Follow the path to the root, moving parents down until finding a place
+    # newitem fits.
+    while pos > startpos:
+        parentpos = (pos - 1) >> 1
+        parent = heap[parentpos]
+        if parent < newitem:
+            heap[pos] = parent
+            pos = parentpos
+            continue
+        break
+    heap[pos] = newitem
+
+def _siftup_max(heap, pos):
+    'Maxheap variant of _siftup'
+    endpos = len(heap)
+    startpos = pos
+    newitem = heap[pos]
+    # Bubble up the larger child until hitting a leaf.
+    childpos = 2*pos + 1    # leftmost child position
+    while childpos < endpos:
+        # Set childpos to index of larger child.
+        rightpos = childpos + 1
+        if rightpos < endpos and not heap[rightpos] < heap[childpos]:
+            childpos = rightpos
+        # Move the larger child up.
+        heap[pos] = heap[childpos]
+        pos = childpos
+        childpos = 2*pos + 1
+    # The leaf at pos is empty now.  Put newitem there, and bubble it up
+    # to its final resting place (by sifting its parents down).
+    heap[pos] = newitem
+    _siftdown_max(heap, startpos, pos)
+
+def merge(iterables, key=None, reverse=False):
+    '''Merge multiple sorted inputs into a single sorted output.
+
+    Similar to sorted(itertools.chain(*iterables)) but returns a generator,
+    does not pull the data into memory all at once, and assumes that each of
+    the input streams is already sorted (smallest to largest).
+
+    >>> list(merge([1,3,5,7], [0,2,4,8], [5,10,15,20], [], [25]))
+    [0, 1, 2, 3, 4, 5, 5, 7, 8, 10, 15, 20, 25]
+
+    If *key* is not None, applies a key function to each element to determine
+    its sort order.
+
+    >>> list(merge(['dog', 'horse'], ['cat', 'fish', 'kangaroo'], key=len))
+    ['dog', 'cat', 'fish', 'horse', 'kangaroo']
+
+    '''
+
+    h = []
+    h_append = h.append
+
+    if reverse:
+        _heapify = _heapify_max
+        _heappop = _heappop_max
+        _heapreplace = _heapreplace_max
+        direction = -1
+    else:
+        _heapify = heapify
+        _heappop = heappop
+        _heapreplace = heapreplace
+        direction = 1
+
+    if key is None:
+        for order, it in enumerate(map(iter, iterables)):
+            try:
+                next = it.next
+                h_append([next(), order * direction, next])
+            except StopIteration:
+                pass
+        _heapify(h)
+        while len(h) > 1:
+            try:
+                while True:
+                    value, order, next = s = h[0]
+                    yield value
+                    s[0] = next()           # raises StopIteration when exhausted
+                    _heapreplace(h, s)      # restore heap condition
+            except StopIteration:
+                _heappop(h)                 # remove empty iterator
+        if h:
+            # fast case when only a single iterator remains
+            value, order, next = h[0]
+            yield value
+            for value in next.__self__:
+                yield value
+        return
+
+    for order, it in enumerate(map(iter, iterables)):
+        try:
+            next = it.next
+            value = next()
+            h_append([key(value), order * direction, value, next])
+        except StopIteration:
+            pass
+    _heapify(h)
+    while len(h) > 1:
+        try:
+            while True:
+                key_value, order, value, next = s = h[0]
+                yield value
+                value = next()
+                s[0] = key(value)
+                s[2] = value
+                _heapreplace(h, s)
+        except StopIteration:
+            _heappop(h)
+    if h:
+        key_value, order, value, next = h[0]
+        yield value
+        for value in next.__self__:
+            yield value
+
+
+# Algorithm notes for nlargest() and nsmallest()
+# ==============================================
+#
+# Make a single pass over the data while keeping the k most extreme values
+# in a heap.  Memory consumption is limited to keeping k values in a list.
+#
+# Measured performance for random inputs:
+#
+#                                   number of comparisons
+#    n inputs     k-extreme values  (average of 5 trials)   % more than min()
+# -------------   ----------------  ---------------------   -----------------
+#      1,000           100                  3,317               231.7%
+#     10,000           100                 14,046                40.5%
+#    100,000           100                105,749                 5.7%
+#  1,000,000           100              1,007,751                 0.8%
+# 10,000,000           100             10,009,401                 0.1%
+#
+# Theoretical number of comparisons for k smallest of n random inputs:
+#
+# Step   Comparisons                  Action
+# ----   --------------------------   ---------------------------
+#  1     1.66 * k                     heapify the first k-inputs
+#  2     n - k                        compare remaining elements to top of heap
+#  3     k * (1 + lg2(k)) * ln(n/k)   replace the topmost value on the heap
+#  4     k * lg2(k) - (k/2)           final sort of the k most extreme values
+#
+# Combining and simplifying for a rough estimate gives:
+#
+#        comparisons = n + k * (log(k, 2) * log(n/k) + log(k, 2) + log(n/k))
+#
+# Computing the number of comparisons for step 3:
+# -----------------------------------------------
+# * For the i-th new value from the iterable, the probability of being in the
+#   k most extreme values is k/i.  For example, the probability of the 101st
+#   value seen being in the 100 most extreme values is 100/101.
+# * If the value is a new extreme value, the cost of inserting it into the
+#   heap is 1 + log(k, 2).
+# * The probabilty times the cost gives:
+#            (k/i) * (1 + log(k, 2))
+# * Summing across the remaining n-k elements gives:
+#            sum((k/i) * (1 + log(k, 2)) for i in range(k+1, n+1))
+# * This reduces to:
+#            (H(n) - H(k)) * k * (1 + log(k, 2))
+# * Where H(n) is the n-th harmonic number estimated by:
+#            gamma = 0.5772156649
+#            H(n) = log(n, e) + gamma + 1 / (2 * n)
+#   http://en.wikipedia.org/wiki/Harmonic_series_(mathematics)#Rate_of_divergence
+# * Substituting the H(n) formula:
+#            comparisons = k * (1 + log(k, 2)) * (log(n/k, e) + (1/n - 1/k) / 2)
+#
+# Worst-case for step 3:
+# ----------------------
+# In the worst case, the input data is reversed sorted so that every new element
+# must be inserted in the heap:
+#
+#             comparisons = 1.66 * k + log(k, 2) * (n - k)
+#
+# Alternative Algorithms
+# ----------------------
+# Other algorithms were not used because they:
+# 1) Took much more auxiliary memory,
+# 2) Made multiple passes over the data.
+# 3) Made more comparisons in common cases (small k, large n, semi-random input).
+# See the more detailed comparison of approach at:
+# http://code.activestate.com/recipes/577573-compare-algorithms-for-heapqsmallest
+
+def nsmallest(n, iterable, key=None):
+    """Find the n smallest elements in a dataset.
+
+    Equivalent to:  sorted(iterable, key=key)[:n]
+    """
+
+    # Short-cut for n==1 is to use min()
+    if n == 1:
+        it = iter(iterable)
+        sentinel = object()
+        if key is None:
+            result = min(it, default=sentinel)
+        else:
+            result = min(it, default=sentinel, key=key)
+        return [] if result is sentinel else [result]
+
+    # When n>=size, it's faster to use sorted()
+    try:
+        size = len(iterable)
+    except (TypeError, AttributeError):
+        pass
+    else:
+        if n >= size:
+            return sorted(iterable, key=key)[:n]
+
+    # When key is none, use simpler decoration
+    if key is None:
+        it = iter(iterable)
+        # put the range(n) first so that zip() doesn't
+        # consume one too many elements from the iterator
+        result = [(elem, i) for i, elem in zip(range(n), it)]
+        if not result:
+            return result
+        _heapify_max(result)
+        top = result[0][0]
+        order = n
+        _heapreplace = _heapreplace_max
+        for elem in it:
+            if elem < top:
+                _heapreplace(result, (elem, order))
+                top = result[0][0]
+                order += 1
+        result.sort()
+        return [r[0] for r in result]
+
+    # General case, slowest method
+    it = iter(iterable)
+    result = [(key(elem), i, elem) for i, elem in zip(range(n), it)]
+    if not result:
+        return result
+    _heapify_max(result)
+    top = result[0][0]
+    order = n
+    _heapreplace = _heapreplace_max
+    for elem in it:
+        k = key(elem)
+        if k < top:
+            _heapreplace(result, (k, order, elem))
+            top = result[0][0]
+            order += 1
+    result.sort()
+    return [r[2] for r in result]
+
+def nlargest(n, iterable, key=None):
+    """Find the n largest elements in a dataset.
+
+    Equivalent to:  sorted(iterable, key=key, reverse=True)[:n]
+    """
+
+    # Short-cut for n==1 is to use max()
+    if n == 1:
+        it = iter(iterable)
+        sentinel = object()
+        if key is None:
+            result = max(it, default=sentinel)
+        else:
+            result = max(it, default=sentinel, key=key)
+        return [] if result is sentinel else [result]
+
+    # When n>=size, it's faster to use sorted()
+    try:
+        size = len(iterable)
+    except (TypeError, AttributeError):
+        pass
+    else:
+        if n >= size:
+            return sorted(iterable, key=key, reverse=True)[:n]
+
+    # When key is none, use simpler decoration
+    if key is None:
+        it = iter(iterable)
+        result = [(elem, i) for i, elem in zip(range(0, -n, -1), it)]
+        if not result:
+            return result
+        heapify(result)
+        top = result[0][0]
+        order = -n
+        _heapreplace = heapreplace
+        for elem in it:
+            if top < elem:
+                _heapreplace(result, (elem, order))
+                top = result[0][0]
+                order -= 1
+        result.sort(reverse=True)
+        return [r[0] for r in result]
+
+    # General case, slowest method
+    it = iter(iterable)
+    result = [(key(elem), i, elem) for i, elem in zip(range(0, -n, -1), it)]
+    if not result:
+        return result
+    heapify(result)
+    top = result[0][0]
+    order = -n
+    _heapreplace = heapreplace
+    for elem in it:
+        k = key(elem)
+        if top < k:
+            _heapreplace(result, (k, order, elem))
+            top = result[0][0]
+            order -= 1
+    result.sort(reverse=True)
+    return [r[2] for r in result]
+
+# If available, use C implementation
+try:
+    from _heapq import *
+except ImportError:
+    pass
+try:
+    from _heapq import _heapreplace_max
+except ImportError:
+    pass
+try:
+    from _heapq import _heapify_max
+except ImportError:
+    pass
+try:
+    from _heapq import _heappop_max
+except ImportError:
+    pass
+
+
+if __name__ == "__main__":
+
+    import doctest
+    print(doctest.testmod())
diff --git a/python/pyspark/rdd.py b/python/pyspark/rdd.py
index 3a2e7649e6827..31919741e9d73 100644
--- a/python/pyspark/rdd.py
+++ b/python/pyspark/rdd.py
@@ -44,7 +44,7 @@
 from pyspark.storagelevel import StorageLevel
 from pyspark.resultiterable import ResultIterable
 from pyspark.shuffle import Aggregator, InMemoryMerger, ExternalMerger, \
-    get_used_memory
+    get_used_memory, ExternalSorter
 
 from py4j.java_collections import ListConverter, MapConverter
 
@@ -605,8 +605,13 @@ def sortByKey(self, ascending=True, numPartitions=None, keyfunc=lambda x: x):
         if numPartitions is None:
             numPartitions = self._defaultReducePartitions()
 
+        spill = (self.ctx._conf.get("spark.shuffle.spill", 'True').lower() == 'true')
+        memory = _parse_memory(self.ctx._conf.get("spark.python.worker.memory", "512m"))
+        serializer = self._jrdd_deserializer
+
         def sortPartition(iterator):
-            return iter(sorted(iterator, key=lambda (k, v): keyfunc(k), reverse=not ascending))
+            sort = ExternalSorter(memory * 0.9, serializer).sorted if spill else sorted
+            return iter(sort(iterator, key=lambda (k, v): keyfunc(k), reverse=(not ascending)))
 
         if numPartitions == 1:
             if self.getNumPartitions() > 1:
diff --git a/python/pyspark/shuffle.py b/python/pyspark/shuffle.py
index 1ebe7df418327..49829f5280a5f 100644
--- a/python/pyspark/shuffle.py
+++ b/python/pyspark/shuffle.py
@@ -21,7 +21,10 @@
 import shutil
 import warnings
 import gc
+import itertools
+import random
 
+import pyspark.heapq3 as heapq
 from pyspark.serializers import BatchedSerializer, PickleSerializer
 
 try:
@@ -54,6 +57,17 @@ def get_used_memory():
         return 0
 
 
+def _get_local_dirs(sub):
+    """ Get all the directories """
+    path = os.environ.get("SPARK_LOCAL_DIRS", "/tmp")
+    dirs = path.split(",")
+    if len(dirs) > 1:
+        # different order in different processes and instances
+        rnd = random.Random(os.getpid() + id(dirs))
+        random.shuffle(dirs, rnd.random)
+    return [os.path.join(d, "python", str(os.getpid()), sub) for d in dirs]
+
+
 class Aggregator(object):
 
     """
@@ -196,7 +210,7 @@ def __init__(self, aggregator, memory_limit=512, serializer=None,
         # default serializer is only used for tests
         self.serializer = serializer or \
             BatchedSerializer(PickleSerializer(), 1024)
-        self.localdirs = localdirs or self._get_dirs()
+        self.localdirs = localdirs or _get_local_dirs(str(id(self)))
         # number of partitions when spill data into disks
         self.partitions = partitions
         # check the memory after # of items merged
@@ -212,13 +226,6 @@ def __init__(self, aggregator, memory_limit=512, serializer=None,
         # randomize the hash of key, id(o) is the address of o (aligned by 8)
         self._seed = id(self) + 7
 
-    def _get_dirs(self):
-        """ Get all the directories """
-        path = os.environ.get("SPARK_LOCAL_DIRS", "/tmp")
-        dirs = path.split(",")
-        return [os.path.join(d, "python", str(os.getpid()), str(id(self)))
-                for d in dirs]
-
     def _get_spill_dir(self, n):
         """ Choose one directory for spill by number n """
         return os.path.join(self.localdirs[n % len(self.localdirs)], str(n))
@@ -434,6 +441,74 @@ def _recursive_merged_items(self, start):
                 os.remove(os.path.join(path, str(i)))
 
 
+class ExternalSorter(object):
+    """
+    ExtenalSorter will divide the elements into chunks, sort them in
+    memory and dump them into disks, finally merge them back.
+
+    The spilling will only happen when the used memory goes above
+    the limit.
+
+    >>> sorter = ExternalSorter(1)  # 1M
+    >>> import random
+    >>> l = range(1024)
+    >>> random.shuffle(l)
+    >>> sorted(l) == list(sorter.sorted(l))
+    True
+    >>> sorted(l) == list(sorter.sorted(l, key=lambda x: -x, reverse=True))
+    True
+    """
+    def __init__(self, memory_limit, serializer=None):
+        self.memory_limit = memory_limit
+        self.local_dirs = _get_local_dirs("sort")
+        self.serializer = serializer or BatchedSerializer(PickleSerializer(), 1024)
+        self._spilled_bytes = 0
+
+    def _get_path(self, n):
+        """ Choose one directory for spill by number n """
+        d = self.local_dirs[n % len(self.local_dirs)]
+        if not os.path.exists(d):
+            os.makedirs(d)
+        return os.path.join(d, str(n))
+
+    def sorted(self, iterator, key=None, reverse=False):
+        """
+        Sort the elements in iterator, do external sort when the memory
+        goes above the limit.
+        """
+        batch = 10
+        chunks, current_chunk = [], []
+        iterator = iter(iterator)
+        while True:
+            # pick elements in batch
+            chunk = list(itertools.islice(iterator, batch))
+            current_chunk.extend(chunk)
+            if len(chunk) < batch:
+                break
+
+            if get_used_memory() > self.memory_limit:
+                # sort them inplace will save memory
+                current_chunk.sort(key=key, reverse=reverse)
+                path = self._get_path(len(chunks))
+                with open(path, 'w') as f:
+                    self.serializer.dump_stream(current_chunk, f)
+                self._spilled_bytes += os.path.getsize(path)
+                chunks.append(self.serializer.load_stream(open(path)))
+                current_chunk = []
+
+            elif not chunks:
+                batch = min(batch * 2, 10000)
+
+        current_chunk.sort(key=key, reverse=reverse)
+        if not chunks:
+            return current_chunk
+
+        if current_chunk:
+            chunks.append(iter(current_chunk))
+
+        return heapq.merge(chunks, key=key, reverse=reverse)
+
+
 if __name__ == "__main__":
     import doctest
     doctest.testmod()
diff --git a/python/pyspark/tests.py b/python/pyspark/tests.py
index 1db922f513743..3e7040eade1ab 100644
--- a/python/pyspark/tests.py
+++ b/python/pyspark/tests.py
@@ -30,6 +30,7 @@
 import tempfile
 import time
 import zipfile
+import random
 
 if sys.version_info[:2] <= (2, 6):
     import unittest2 as unittest
@@ -37,10 +38,11 @@
     import unittest
 
 
+from pyspark.conf import SparkConf
 from pyspark.context import SparkContext
 from pyspark.files import SparkFiles
 from pyspark.serializers import read_int, BatchedSerializer, MarshalSerializer, PickleSerializer
-from pyspark.shuffle import Aggregator, InMemoryMerger, ExternalMerger
+from pyspark.shuffle import Aggregator, InMemoryMerger, ExternalMerger, ExternalSorter
 
 _have_scipy = False
 _have_numpy = False
@@ -117,6 +119,44 @@ def test_huge_dataset(self):
         m._cleanup()
 
 
+class TestSorter(unittest.TestCase):
+    def test_in_memory_sort(self):
+        l = range(1024)
+        random.shuffle(l)
+        sorter = ExternalSorter(1024)
+        self.assertEquals(sorted(l), list(sorter.sorted(l)))
+        self.assertEquals(sorted(l, reverse=True), list(sorter.sorted(l, reverse=True)))
+        self.assertEquals(sorted(l, key=lambda x: -x), list(sorter.sorted(l, key=lambda x: -x)))
+        self.assertEquals(sorted(l, key=lambda x: -x, reverse=True),
+                          list(sorter.sorted(l, key=lambda x: -x, reverse=True)))
+
+    def test_external_sort(self):
+        l = range(1024)
+        random.shuffle(l)
+        sorter = ExternalSorter(1)
+        self.assertEquals(sorted(l), list(sorter.sorted(l)))
+        self.assertGreater(sorter._spilled_bytes, 0)
+        last = sorter._spilled_bytes
+        self.assertEquals(sorted(l, reverse=True), list(sorter.sorted(l, reverse=True)))
+        self.assertGreater(sorter._spilled_bytes, last)
+        last = sorter._spilled_bytes
+        self.assertEquals(sorted(l, key=lambda x: -x), list(sorter.sorted(l, key=lambda x: -x)))
+        self.assertGreater(sorter._spilled_bytes, last)
+        last = sorter._spilled_bytes
+        self.assertEquals(sorted(l, key=lambda x: -x, reverse=True),
+                          list(sorter.sorted(l, key=lambda x: -x, reverse=True)))
+        self.assertGreater(sorter._spilled_bytes, last)
+
+    def test_external_sort_in_rdd(self):
+        conf = SparkConf().set("spark.python.worker.memory", "1m")
+        sc = SparkContext(conf=conf)
+        l = range(10240)
+        random.shuffle(l)
+        rdd = sc.parallelize(l, 10)
+        self.assertEquals(sorted(l), rdd.sortBy(lambda x: x).collect())
+        sc.stop()
+
+
 class SerializationTestCase(unittest.TestCase):
 
     def test_namedtuple(self):
diff --git a/tox.ini b/tox.ini
index a1fefdd0e176f..b568029a204cc 100644
--- a/tox.ini
+++ b/tox.ini
@@ -15,4 +15,4 @@
 
 [pep8]
 max-line-length=100
-exclude=cloudpickle.py
+exclude=cloudpickle.py,heapq3.py

From 2ffd3290fe30c23df8da1efe153b84c23eb2e1cd Mon Sep 17 00:00:00 2001
From: WangTao <barneystinson@aliyun.com>
Date: Tue, 26 Aug 2014 17:30:59 -0700
Subject: [PATCH 092/489] [SPARK-3225]Typo in script

use_conf_dir => user_conf_dir in load-spark-env.sh.

Author: WangTao <barneystinson@aliyun.com>

Closes #1926 from WangTaoTheTonic/TypoInScript and squashes the following commits:

0c104ad [WangTao] Typo in script
---
 bin/load-spark-env.sh | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/bin/load-spark-env.sh b/bin/load-spark-env.sh
index d425f9feaac54..493d3785a081b 100644
--- a/bin/load-spark-env.sh
+++ b/bin/load-spark-env.sh
@@ -27,12 +27,12 @@ if [ -z "$SPARK_ENV_LOADED" ]; then
   # Returns the parent of the directory this script lives in.
   parent_dir="$(cd `dirname $0`/..; pwd)"
 
-  use_conf_dir=${SPARK_CONF_DIR:-"$parent_dir/conf"}
+  user_conf_dir=${SPARK_CONF_DIR:-"$parent_dir/conf"}
 
-  if [ -f "${use_conf_dir}/spark-env.sh" ]; then
+  if [ -f "${user_conf_dir}/spark-env.sh" ]; then
     # Promote all variable declarations to environment (exported) variables
     set -a
-    . "${use_conf_dir}/spark-env.sh"
+    . "${user_conf_dir}/spark-env.sh"
     set +a
   fi
 fi

From faeb9c0e1440f4af888be0dfc5de7b57efc92b00 Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian.cs.zju@gmail.com>
Date: Tue, 26 Aug 2014 17:33:40 -0700
Subject: [PATCH 093/489] [SPARK-2964] [SQL] Remove duplicated code from
 spark-sql and start-thriftserver.sh

Author: Cheng Lian <lian.cs.zju@gmail.com>
Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp>

Closes #1886 from sarutak/SPARK-2964 and squashes the following commits:

8ef8751 [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into SPARK-2964
26e7c95 [Kousuke Saruta] Revert "Shorten timeout to more reasonable value"
ffb68fa [Kousuke Saruta] Modified spark-sql and start-thriftserver.sh to use bin/utils.sh
8c6f658 [Kousuke Saruta] Merge branch 'spark-3026' of https://github.com/liancheng/spark into SPARK-2964
81b43a8 [Cheng Lian] Shorten timeout to more reasonable value
a89e66d [Cheng Lian] Fixed command line options quotation in scripts
9c894d3 [Cheng Lian] Fixed bin/spark-sql -S option typo
be4736b [Cheng Lian] Report better error message when running JDBC/CLI without hive-thriftserver profile enabled
---
 bin/spark-sql                                 | 55 +++++--------------
 .../org/apache/spark/deploy/SparkSubmit.scala | 14 ++++-
 sbin/start-thriftserver.sh                    | 39 +++++--------
 3 files changed, 39 insertions(+), 69 deletions(-)

diff --git a/bin/spark-sql b/bin/spark-sql
index 564f1f419060f..2a3cb31f58e8d 100755
--- a/bin/spark-sql
+++ b/bin/spark-sql
@@ -24,6 +24,7 @@
 set -o posix
 
 CLASS="org.apache.spark.sql.hive.thriftserver.SparkSQLCLIDriver"
+CLASS_NOT_FOUND_EXIT_STATUS=1
 
 # Figure out where Spark is installed
 FWDIR="$(cd `dirname $0`/..; pwd)"
@@ -43,52 +44,22 @@ function usage {
   $FWDIR/bin/spark-class $CLASS --help 2>&1 | grep -v "$pattern" 1>&2
 }
 
-function ensure_arg_number {
-  arg_number=$1
-  at_least=$2
-
-  if [[ $arg_number -lt $at_least ]]; then
-    usage
-    exit 1
-  fi
-}
-
-if [[ "$@" = --help ]] || [[ "$@" = -h ]]; then
+if [[ "$@" = *--help ]] || [[ "$@" = *-h ]]; then
   usage
   exit 0
 fi
 
-CLI_ARGS=()
-SUBMISSION_ARGS=()
-
-while (($#)); do
-  case $1 in
-    -d | --define | --database | -f | -h | --hiveconf | --hivevar | -i | -p)
-      ensure_arg_number $# 2
-      CLI_ARGS+=("$1"); shift
-      CLI_ARGS+=("$1"); shift
-      ;;
+source $FWDIR/bin/utils.sh
+SUBMIT_USAGE_FUNCTION=usage
+gatherSparkSubmitOpts "$@"
 
-    -e)
-      ensure_arg_number $# 2
-      CLI_ARGS+=("$1"); shift
-      CLI_ARGS+=("$1"); shift
-      ;;
+"$FWDIR"/bin/spark-submit --class $CLASS "${SUBMISSION_OPTS[@]}" spark-internal "${APPLICATION_OPTS[@]}"
+exit_status=$?
 
-    -s | --silent)
-      CLI_ARGS+=("$1"); shift
-      ;;
-
-    -v | --verbose)
-      # Both SparkSubmit and SparkSQLCLIDriver recognizes -v | --verbose
-      CLI_ARGS+=("$1")
-      SUBMISSION_ARGS+=("$1"); shift
-      ;;
-
-    *)
-      SUBMISSION_ARGS+=("$1"); shift
-      ;;
-  esac
-done
+if [[ exit_status -eq CLASS_NOT_FOUND_EXIT_STATUS ]]; then
+  echo
+  echo "Failed to load Spark SQL CLI main class $CLASS."
+  echo "You need to build Spark with -Phive."
+fi
 
-exec "$FWDIR"/bin/spark-submit --class $CLASS "${SUBMISSION_ARGS[@]}" spark-internal "${CLI_ARGS[@]}"
+exit $exit_status
diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
index f8cdbc3c392b5..550ee72538900 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
@@ -54,6 +54,8 @@ object SparkSubmit {
   private val SPARK_SHELL = "spark-shell"
   private val PYSPARK_SHELL = "pyspark-shell"
 
+  private val CLASS_NOT_FOUND_EXIT_STATUS = 1
+
   // Exposed for testing
   private[spark] var exitFn: () => Unit = () => System.exit(-1)
   private[spark] var printStream: PrintStream = System.err
@@ -311,8 +313,18 @@ object SparkSubmit {
       System.setProperty(key, value)
     }
 
-    val mainClass = Class.forName(childMainClass, true, loader)
+    var mainClass: Class[_] = null
+
+    try {
+      mainClass = Class.forName(childMainClass, true, loader)
+    } catch {
+      case e: ClassNotFoundException =>
+        e.printStackTrace(printStream)
+        System.exit(CLASS_NOT_FOUND_EXIT_STATUS)
+    }
+
     val mainMethod = mainClass.getMethod("main", new Array[String](0).getClass)
+
     try {
       mainMethod.invoke(null, childArgs.toArray)
     } catch {
diff --git a/sbin/start-thriftserver.sh b/sbin/start-thriftserver.sh
index 2c4452473ccbc..c519a77df4a14 100755
--- a/sbin/start-thriftserver.sh
+++ b/sbin/start-thriftserver.sh
@@ -27,6 +27,7 @@ set -o posix
 FWDIR="$(cd `dirname $0`/..; pwd)"
 
 CLASS="org.apache.spark.sql.hive.thriftserver.HiveThriftServer2"
+CLASS_NOT_FOUND_EXIT_STATUS=1
 
 function usage {
   echo "Usage: ./sbin/start-thriftserver [options] [thrift server options]"
@@ -43,36 +44,22 @@ function usage {
   $FWDIR/bin/spark-class $CLASS --help 2>&1 | grep -v "$pattern" 1>&2
 }
 
-function ensure_arg_number {
-  arg_number=$1
-  at_least=$2
-
-  if [[ $arg_number -lt $at_least ]]; then
-    usage
-    exit 1
-  fi
-}
-
-if [[ "$@" = --help ]] || [[ "$@" = -h ]]; then
+if [[ "$@" = *--help ]] || [[ "$@" = *-h ]]; then
   usage
   exit 0
 fi
 
-THRIFT_SERVER_ARGS=()
-SUBMISSION_ARGS=()
+source $FWDIR/bin/utils.sh
+SUBMIT_USAGE_FUNCTION=usage
+gatherSparkSubmitOpts "$@"
 
-while (($#)); do
-  case $1 in
-    --hiveconf)
-      ensure_arg_number $# 2
-      THRIFT_SERVER_ARGS+=("$1"); shift
-      THRIFT_SERVER_ARGS+=("$1"); shift
-      ;;
+"$FWDIR"/bin/spark-submit --class $CLASS "${SUBMISSION_OPTS[@]}" spark-internal "${APPLICATION_OPTS[@]}"
+exit_status=$?
 
-    *)
-      SUBMISSION_ARGS+=("$1"); shift
-      ;;
-  esac
-done
+if [[ exit_status -eq CLASS_NOT_FOUND_EXIT_STATUS ]]; then
+  echo
+  echo "Failed to load Hive Thrift server main class $CLASS."
+  echo "You need to build Spark with -Phive."
+fi
 
-exec "$FWDIR"/bin/spark-submit --class $CLASS "${SUBMISSION_ARGS[@]}" spark-internal "${THRIFT_SERVER_ARGS[@]}"
+exit $exit_status

From 73b3089b8d2901dab11bb1ef6f46c29625b677fe Mon Sep 17 00:00:00 2001
From: nchammas <nicholas.chammas@gmail.com>
Date: Tue, 26 Aug 2014 17:50:04 -0700
Subject: [PATCH 094/489] [Docs] Run tests like in contributing guide

The Contributing to Spark guide [recommends](https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark#ContributingtoSpark-AutomatedTesting) running tests by calling `./dev/run-tests`. The README should, too.

`./sbt/sbt test` does not cover Python tests or style tests.

Author: nchammas <nicholas.chammas@gmail.com>

Closes #2149 from nchammas/patch-2 and squashes the following commits:

2b3b132 [nchammas] [Docs] Run tests like in contributing guide
---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index f75226ce11335..0a683a460ffac 100644
--- a/README.md
+++ b/README.md
@@ -69,7 +69,7 @@ Many of the example programs print usage help if no params are given.
 Testing first requires [building Spark](#building-spark). Once Spark is built, tests
 can be run using:
 
-    ./sbt/sbt test
+    ./dev/run-tests
 
 ## A Note About Hadoop Versions
 

From 727cb25bcc29481d6b744abef1ca091e64b5f91f Mon Sep 17 00:00:00 2001
From: Takuya UESHIN <ueshin@happy-camper.st>
Date: Tue, 26 Aug 2014 18:28:41 -0700
Subject: [PATCH 095/489] [SPARK-3036][SPARK-3037][SQL] Add MapType/ArrayType
 containing null value support to Parquet.

JIRA:
- https://issues.apache.org/jira/browse/SPARK-3036
- https://issues.apache.org/jira/browse/SPARK-3037

Currently this uses the following Parquet schema for `MapType` when `valueContainsNull` is `true`:

```
message root {
  optional group a (MAP) {
    repeated group map (MAP_KEY_VALUE) {
      required int32 key;
      optional int32 value;
    }
  }
}
```

for `ArrayType` when `containsNull` is `true`:

```
message root {
  optional group a (LIST) {
    repeated group bag {
      optional int32 array;
    }
  }
}
```

We have to think about compatibilities with older version of Spark or Hive or others I mentioned in the JIRA issues.

Notice:
This PR is based on #1963 and #1889.
Please check them first.

/cc marmbrus, yhuai

Author: Takuya UESHIN <ueshin@happy-camper.st>

Closes #2032 from ueshin/issues/SPARK-3036_3037 and squashes the following commits:

4e8e9e7 [Takuya UESHIN] Add ArrayType containing null value support to Parquet.
013c2ca [Takuya UESHIN] Add MapType containing null value support to Parquet.
62989de [Takuya UESHIN] Merge branch 'issues/SPARK-2969' into issues/SPARK-3036_3037
8e38b53 [Takuya UESHIN] Merge branch 'issues/SPARK-3063' into issues/SPARK-3036_3037
---
 .../spark/sql/parquet/ParquetConverter.scala  | 83 +++++++++++++++++++
 .../sql/parquet/ParquetTableSupport.scala     | 54 +++++++-----
 .../spark/sql/parquet/ParquetTypes.scala      | 54 ++++++++----
 .../spark/sql/parquet/ParquetQuerySuite.scala | 16 +++-
 4 files changed, 167 insertions(+), 40 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetConverter.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetConverter.scala
index ef4526ec03439..9fd6aed402838 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetConverter.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetConverter.scala
@@ -58,6 +58,7 @@ private[sql] object CatalystConverter {
   // This is mostly Parquet convention (see, e.g., `ConversionPatterns`).
   // Note that "array" for the array elements is chosen by ParquetAvro.
   // Using a different value will result in Parquet silently dropping columns.
+  val ARRAY_CONTAINS_NULL_BAG_SCHEMA_NAME = "bag"
   val ARRAY_ELEMENTS_SCHEMA_NAME = "array"
   val MAP_KEY_SCHEMA_NAME = "key"
   val MAP_VALUE_SCHEMA_NAME = "value"
@@ -82,6 +83,9 @@ private[sql] object CatalystConverter {
       case ArrayType(elementType: DataType, false) => {
         new CatalystArrayConverter(elementType, fieldIndex, parent)
       }
+      case ArrayType(elementType: DataType, true) => {
+        new CatalystArrayContainsNullConverter(elementType, fieldIndex, parent)
+      }
       case StructType(fields: Seq[StructField]) => {
         new CatalystStructConverter(fields.toArray, fieldIndex, parent)
       }
@@ -567,6 +571,85 @@ private[parquet] class CatalystNativeArrayConverter(
   }
 }
 
+/**
+ * A `parquet.io.api.GroupConverter` that converts a single-element groups that
+ * match the characteristics of an array contains null (see
+ * [[org.apache.spark.sql.parquet.ParquetTypesConverter]]) into an
+ * [[org.apache.spark.sql.catalyst.types.ArrayType]].
+ *
+ * @param elementType The type of the array elements (complex or primitive)
+ * @param index The position of this (array) field inside its parent converter
+ * @param parent The parent converter
+ * @param buffer A data buffer
+ */
+private[parquet] class CatalystArrayContainsNullConverter(
+    val elementType: DataType,
+    val index: Int,
+    protected[parquet] val parent: CatalystConverter,
+    protected[parquet] var buffer: Buffer[Any])
+  extends CatalystConverter {
+
+  def this(elementType: DataType, index: Int, parent: CatalystConverter) =
+    this(
+      elementType,
+      index,
+      parent,
+      new ArrayBuffer[Any](CatalystArrayConverter.INITIAL_ARRAY_SIZE))
+
+  protected[parquet] val converter: Converter = new CatalystConverter {
+
+    private var current: Any = null
+
+    val converter = CatalystConverter.createConverter(
+      new CatalystConverter.FieldType(
+        CatalystConverter.ARRAY_ELEMENTS_SCHEMA_NAME,
+        elementType,
+        false),
+      fieldIndex = 0,
+      parent = this)
+
+    override def getConverter(fieldIndex: Int): Converter = converter
+
+    override def end(): Unit = parent.updateField(index, current)
+
+    override def start(): Unit = {
+      current = null
+    }
+
+    override protected[parquet] val size: Int = 1
+    override protected[parquet] val index: Int = 0
+    override protected[parquet] val parent = CatalystArrayContainsNullConverter.this
+
+    override protected[parquet] def updateField(fieldIndex: Int, value: Any): Unit = {
+      current = value
+    }
+
+    override protected[parquet] def clearBuffer(): Unit = {}
+  }
+
+  override def getConverter(fieldIndex: Int): Converter = converter
+
+  // arrays have only one (repeated) field, which is its elements
+  override val size = 1
+
+  override protected[parquet] def updateField(fieldIndex: Int, value: Any): Unit = {
+    buffer += value
+  }
+
+  override protected[parquet] def clearBuffer(): Unit = {
+    buffer.clear()
+  }
+
+  override def start(): Unit = {}
+
+  override def end(): Unit = {
+    assert(parent != null)
+    // here we need to make sure to use ArrayScalaType
+    parent.updateField(index, buffer.toArray.toSeq)
+    clearBuffer()
+  }
+}
+
 /**
  * This converter is for multi-element groups of primitive or complex types
  * that have repetition level optional or required (so struct fields).
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala
index 6a657c20fe46c..bdf02401b21be 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala
@@ -173,7 +173,7 @@ private[parquet] class RowWriteSupport extends WriteSupport[Row] with Logging {
   private[parquet] def writeValue(schema: DataType, value: Any): Unit = {
     if (value != null) {
       schema match {
-        case t @ ArrayType(_, false) => writeArray(
+        case t @ ArrayType(_, _) => writeArray(
           t,
           value.asInstanceOf[CatalystConverter.ArrayScalaType[_]])
         case t @ MapType(_, _, _) => writeMap(
@@ -228,45 +228,57 @@ private[parquet] class RowWriteSupport extends WriteSupport[Row] with Logging {
     }
   }
 
-  // TODO: support null values, see
-  // https://issues.apache.org/jira/browse/SPARK-1649
   private[parquet] def writeArray(
       schema: ArrayType,
       array: CatalystConverter.ArrayScalaType[_]): Unit = {
     val elementType = schema.elementType
     writer.startGroup()
     if (array.size > 0) {
-      writer.startField(CatalystConverter.ARRAY_ELEMENTS_SCHEMA_NAME, 0)
-      var i = 0
-      while(i < array.size) {
-        writeValue(elementType, array(i))
-        i = i + 1
+      if (schema.containsNull) {
+        writer.startField(CatalystConverter.ARRAY_CONTAINS_NULL_BAG_SCHEMA_NAME, 0)
+        var i = 0
+        while (i < array.size) {
+          writer.startGroup()
+          if (array(i) != null) {
+            writer.startField(CatalystConverter.ARRAY_ELEMENTS_SCHEMA_NAME, 0)
+            writeValue(elementType, array(i))
+            writer.endField(CatalystConverter.ARRAY_ELEMENTS_SCHEMA_NAME, 0)
+          }
+          writer.endGroup()
+          i = i + 1
+        }
+        writer.endField(CatalystConverter.ARRAY_CONTAINS_NULL_BAG_SCHEMA_NAME, 0)
+      } else {
+        writer.startField(CatalystConverter.ARRAY_ELEMENTS_SCHEMA_NAME, 0)
+        var i = 0
+        while (i < array.size) {
+          writeValue(elementType, array(i))
+          i = i + 1
+        }
+        writer.endField(CatalystConverter.ARRAY_ELEMENTS_SCHEMA_NAME, 0)
       }
-      writer.endField(CatalystConverter.ARRAY_ELEMENTS_SCHEMA_NAME, 0)
     }
     writer.endGroup()
   }
 
-  // TODO: support null values, see
-  // https://issues.apache.org/jira/browse/SPARK-1649
   private[parquet] def writeMap(
       schema: MapType,
       map: CatalystConverter.MapScalaType[_, _]): Unit = {
     writer.startGroup()
     if (map.size > 0) {
       writer.startField(CatalystConverter.MAP_SCHEMA_NAME, 0)
-      writer.startGroup()
-      writer.startField(CatalystConverter.MAP_KEY_SCHEMA_NAME, 0)
-      for(key <- map.keys) {
+      for ((key, value) <- map) {
+        writer.startGroup()
+        writer.startField(CatalystConverter.MAP_KEY_SCHEMA_NAME, 0)
         writeValue(schema.keyType, key)
+        writer.endField(CatalystConverter.MAP_KEY_SCHEMA_NAME, 0)
+        if (value != null) {
+          writer.startField(CatalystConverter.MAP_VALUE_SCHEMA_NAME, 1)
+          writeValue(schema.valueType, value)
+          writer.endField(CatalystConverter.MAP_VALUE_SCHEMA_NAME, 1)
+        }
+        writer.endGroup()
       }
-      writer.endField(CatalystConverter.MAP_KEY_SCHEMA_NAME, 0)
-      writer.startField(CatalystConverter.MAP_VALUE_SCHEMA_NAME, 1)
-      for(value <- map.values) {
-        writeValue(schema.valueType, value)
-      }
-      writer.endField(CatalystConverter.MAP_VALUE_SCHEMA_NAME, 1)
-      writer.endGroup()
       writer.endField(CatalystConverter.MAP_SCHEMA_NAME, 0)
     }
     writer.endGroup()
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTypes.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTypes.scala
index af8cd0a73b674..1a52377651737 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTypes.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTypes.scala
@@ -119,7 +119,13 @@ private[parquet] object ParquetTypesConverter extends Logging {
         case ParquetOriginalType.LIST => { // TODO: check enums!
           assert(groupType.getFieldCount == 1)
           val field = groupType.getFields.apply(0)
-          ArrayType(toDataType(field, isBinaryAsString), containsNull = false)
+          if (field.getName == CatalystConverter.ARRAY_CONTAINS_NULL_BAG_SCHEMA_NAME) {
+            val bag = field.asGroupType()
+            assert(bag.getFieldCount == 1)
+            ArrayType(toDataType(bag.getFields.apply(0), isBinaryAsString), containsNull = true)
+          } else {
+            ArrayType(toDataType(field, isBinaryAsString), containsNull = false)
+          }
         }
         case ParquetOriginalType.MAP => {
           assert(
@@ -129,28 +135,32 @@ private[parquet] object ParquetTypesConverter extends Logging {
           assert(
             keyValueGroup.getFieldCount == 2,
             "Parquet Map type malformatted: nested group should have 2 (key, value) fields!")
-          val keyType = toDataType(keyValueGroup.getFields.apply(0), isBinaryAsString)
           assert(keyValueGroup.getFields.apply(0).getRepetition == Repetition.REQUIRED)
+
+          val keyType = toDataType(keyValueGroup.getFields.apply(0), isBinaryAsString)
           val valueType = toDataType(keyValueGroup.getFields.apply(1), isBinaryAsString)
-          assert(keyValueGroup.getFields.apply(1).getRepetition == Repetition.REQUIRED)
-          // TODO: set valueContainsNull explicitly instead of assuming valueContainsNull is true
-          // at here.
-          MapType(keyType, valueType)
+          MapType(keyType, valueType,
+            keyValueGroup.getFields.apply(1).getRepetition != Repetition.REQUIRED)
         }
         case _ => {
           // Note: the order of these checks is important!
           if (correspondsToMap(groupType)) { // MapType
             val keyValueGroup = groupType.getFields.apply(0).asGroupType()
-            val keyType = toDataType(keyValueGroup.getFields.apply(0), isBinaryAsString)
             assert(keyValueGroup.getFields.apply(0).getRepetition == Repetition.REQUIRED)
+
+            val keyType = toDataType(keyValueGroup.getFields.apply(0), isBinaryAsString)
             val valueType = toDataType(keyValueGroup.getFields.apply(1), isBinaryAsString)
-            assert(keyValueGroup.getFields.apply(1).getRepetition == Repetition.REQUIRED)
-            // TODO: set valueContainsNull explicitly instead of assuming valueContainsNull is true
-            // at here.
-            MapType(keyType, valueType)
+            MapType(keyType, valueType,
+              keyValueGroup.getFields.apply(1).getRepetition != Repetition.REQUIRED)
           } else if (correspondsToArray(groupType)) { // ArrayType
-            val elementType = toDataType(groupType.getFields.apply(0), isBinaryAsString)
-            ArrayType(elementType, containsNull = false)
+            val field = groupType.getFields.apply(0)
+            if (field.getName == CatalystConverter.ARRAY_CONTAINS_NULL_BAG_SCHEMA_NAME) {
+              val bag = field.asGroupType()
+              assert(bag.getFieldCount == 1)
+              ArrayType(toDataType(bag.getFields.apply(0), isBinaryAsString), containsNull = true)
+            } else {
+              ArrayType(toDataType(field, isBinaryAsString), containsNull = false)
+            }
           } else { // everything else: StructType
             val fields = groupType
               .getFields
@@ -249,13 +259,27 @@ private[parquet] object ParquetTypesConverter extends Logging {
             inArray = true)
           ConversionPatterns.listType(repetition, name, parquetElementType)
         }
+        case ArrayType(elementType, true) => {
+          val parquetElementType = fromDataType(
+            elementType,
+            CatalystConverter.ARRAY_ELEMENTS_SCHEMA_NAME,
+            nullable = true,
+            inArray = false)
+          ConversionPatterns.listType(
+            repetition,
+            name,
+            new ParquetGroupType(
+              Repetition.REPEATED,
+              CatalystConverter.ARRAY_CONTAINS_NULL_BAG_SCHEMA_NAME,
+              parquetElementType))
+        }
         case StructType(structFields) => {
           val fields = structFields.map {
             field => fromDataType(field.dataType, field.name, field.nullable, inArray = false)
           }
           new ParquetGroupType(repetition, name, fields)
         }
-        case MapType(keyType, valueType, _) => {
+        case MapType(keyType, valueType, valueContainsNull) => {
           val parquetKeyType =
             fromDataType(
               keyType,
@@ -266,7 +290,7 @@ private[parquet] object ParquetTypesConverter extends Logging {
             fromDataType(
               valueType,
               CatalystConverter.MAP_VALUE_SCHEMA_NAME,
-              nullable = false,
+              nullable = valueContainsNull,
               inArray = false)
           ConversionPatterns.mapType(
             repetition,
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala
index 28f43b36832ac..4219cc080000b 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala
@@ -78,7 +78,9 @@ case class AllDataTypesWithNonPrimitiveType(
     booleanField: Boolean,
     binaryField: Array[Byte],
     array: Seq[Int],
-    map: Map[Int, String],
+    arrayContainsNull: Seq[Option[Int]],
+    map: Map[Int, Long],
+    mapValueContainsNull: Map[Int, Option[Long]],
     data: Data)
 
 class ParquetQuerySuite extends QueryTest with FunSuiteLike with BeforeAndAfterAll {
@@ -287,7 +289,11 @@ class ParquetQuerySuite extends QueryTest with FunSuiteLike with BeforeAndAfterA
       .map(x => AllDataTypesWithNonPrimitiveType(
         s"$x", x, x.toLong, x.toFloat, x.toDouble, x.toShort, x.toByte, x % 2 == 0,
         (0 to x).map(_.toByte).toArray,
-        (0 until x), (0 until x).map(i => i -> s"$i").toMap, Data((0 until x), Nested(x, s"$x"))))
+        (0 until x),
+        (0 until x).map(Option(_).filter(_ % 3 == 0)),
+        (0 until x).map(i => i -> i.toLong).toMap,
+        (0 until x).map(i => i -> Option(i.toLong)).toMap + (x -> None),
+        Data((0 until x), Nested(x, s"$x"))))
       .saveAsParquetFile(tempDir)
     val result = parquetFile(tempDir).collect()
     range.foreach {
@@ -302,8 +308,10 @@ class ParquetQuerySuite extends QueryTest with FunSuiteLike with BeforeAndAfterA
         assert(result(i).getBoolean(7) === (i % 2 == 0))
         assert(result(i)(8) === (0 to i).map(_.toByte).toArray)
         assert(result(i)(9) === (0 until i))
-        assert(result(i)(10) === (0 until i).map(i => i -> s"$i").toMap)
-        assert(result(i)(11) === new GenericRow(Array[Any]((0 until i), new GenericRow(Array[Any](i, s"$i")))))
+        assert(result(i)(10) === (0 until i).map(i => if (i % 3 == 0) i else null))
+        assert(result(i)(11) === (0 until i).map(i => i -> i.toLong).toMap)
+        assert(result(i)(12) === (0 until i).map(i => i -> i.toLong).toMap + (i -> null))
+        assert(result(i)(13) === new GenericRow(Array[Any]((0 until i), new GenericRow(Array[Any](i, s"$i")))))
     }
   }
 

From be043e3f20c6562482f9e4e739d8bb3fc9c1f201 Mon Sep 17 00:00:00 2001
From: Martin Weindel <martin.weindel@gmail.com>
Date: Tue, 26 Aug 2014 18:30:39 -0700
Subject: [PATCH 096/489] [SPARK-3240] Adding known issue for MESOS-1688

When using Mesos with the fine-grained mode, a Spark job can run into a dead lock on low allocatable memory on Mesos slaves. As a work-around 32 MB (= Mesos MIN_MEM) are allocated for each task, to ensure Mesos making new offers after task completion.
From my perspective, it would be better to fix this problem in Mesos by dropping the constraint on memory for offers, but as temporary solution this patch helps to avoid the dead lock on current Mesos versions.
See [[MESOS-1688] No offers if no memory is allocatable](https://issues.apache.org/jira/browse/MESOS-1688) for details for this problem.

Author: Martin Weindel <martin.weindel@gmail.com>

Closes #1860 from MartinWeindel/master and squashes the following commits:

5762030 [Martin Weindel] reverting work-around
a6bf837 [Martin Weindel] added known issue for issue MESOS-1688
d9d2ca6 [Martin Weindel] work around for problem with Mesos offering semantic (see [https://issues.apache.org/jira/browse/MESOS-1688])
---
 docs/running-on-mesos.md | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/docs/running-on-mesos.md b/docs/running-on-mesos.md
index 9998dddc652a6..1073abb202c56 100644
--- a/docs/running-on-mesos.md
+++ b/docs/running-on-mesos.md
@@ -165,6 +165,8 @@ acquire. By default, it will acquire *all* cores in the cluster (that get offere
 only makes sense if you run just one application at a time. You can cap the maximum number of cores
 using `conf.set("spark.cores.max", "10")` (for example).
 
+# Known issues
+- When using the "fine-grained" mode, make sure that your executors always leave 32 MB free on the slaves. Otherwise it can happen that your Spark job does not proceed anymore. Currently, Apache Mesos only offers resources if there are at least 32 MB memory allocatable. But as Spark allocates memory only for the executor and cpu only for tasks, it can happen on high slave memory usage that no new tasks will be started anymore. More details can be found in [MESOS-1688](https://issues.apache.org/jira/browse/MESOS-1688). Alternatively use the "coarse-gained" mode, which is not affected by this issue.
 
 # Running Alongside Hadoop
 

From d8345471ce8e59d693f7f59c480df339953a4d32 Mon Sep 17 00:00:00 2001
From: Josh Rosen <joshrosen@apache.org>
Date: Tue, 26 Aug 2014 18:55:00 -0700
Subject: [PATCH 097/489] Fix unclosed HTML tag in Yarn docs.

---
 docs/running-on-yarn.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/running-on-yarn.md b/docs/running-on-yarn.md
index 9bc20dbf926b2..943f06b114cb9 100644
--- a/docs/running-on-yarn.md
+++ b/docs/running-on-yarn.md
@@ -75,7 +75,7 @@ Most of the configs are the same for Spark on YARN as for other deployment modes
   <td>(none)</td>
   <td>
     Comma-separated list of files to be placed in the working directory of each executor.
-  <td>
+  </td>
 </tr>
 <tr>
  <td><code>spark.yarn.executor.memoryOverhead</code></td>

From ee91eb8c53da84f8875fb5cec5820a6e189057b9 Mon Sep 17 00:00:00 2001
From: Matei Zaharia <matei@databricks.com>
Date: Tue, 26 Aug 2014 19:29:54 -0700
Subject: [PATCH 098/489] Manually close some old pull requests

Closes #530, Closes #223, Closes #738, Closes #546

From e70aff6c2d7f216060def0bd02be6a3d9017cd13 Mon Sep 17 00:00:00 2001
From: Matei Zaharia <matei@databricks.com>
Date: Tue, 26 Aug 2014 19:35:19 -0700
Subject: [PATCH 099/489] Manually close old pull requests

Closes #671, Closes #515

From bf719056b71d55e1194554661dfa194ed03d364d Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@apache.org>
Date: Tue, 26 Aug 2014 21:59:48 -0700
Subject: [PATCH 100/489] [SPARK-3224] FetchFailed reduce stages should only
 show up once in failed stages (in UI)

This is a HOTFIX for 1.1.

Author: Reynold Xin <rxin@apache.org>
Author: Kay Ousterhout <kayousterhout@gmail.com>

Closes #2127 from rxin/SPARK-3224 and squashes the following commits:

effb1ce [Reynold Xin] Move log message.
49282b3 [Reynold Xin] Kay's feedback.
3f01847 [Reynold Xin] Merge pull request #2 from kayousterhout/SPARK-3224
796d282 [Kay Ousterhout] Added unit test for SPARK-3224
3d3d356 [Reynold Xin] Remove map output loc even for repeated FetchFaileds.
1dd3eb5 [Reynold Xin] [SPARK-3224] FetchFailed reduce stages should only show up once in the failed stages UI.
---
 .../apache/spark/scheduler/DAGScheduler.scala | 32 +++++++++------
 .../spark/scheduler/DAGSchedulerSuite.scala   | 41 ++++++++++++++++++-
 2 files changed, 59 insertions(+), 14 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
index 34131984570e4..2ccc27324ac8c 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
@@ -1045,31 +1045,39 @@ class DAGScheduler(
         stage.pendingTasks += task
 
       case FetchFailed(bmAddress, shuffleId, mapId, reduceId) =>
-        // Mark the stage that the reducer was in as unrunnable
         val failedStage = stageIdToStage(task.stageId)
-        markStageAsFinished(failedStage, Some("Fetch failure"))
-        runningStages -= failedStage
-        // TODO: Cancel running tasks in the stage
-        logInfo("Marking " + failedStage + " (" + failedStage.name +
-          ") for resubmision due to a fetch failure")
-        // Mark the map whose fetch failed as broken in the map stage
         val mapStage = shuffleToMapStage(shuffleId)
-        if (mapId != -1) {
-          mapStage.removeOutputLoc(mapId, bmAddress)
-          mapOutputTracker.unregisterMapOutput(shuffleId, mapId, bmAddress)
+
+        // It is likely that we receive multiple FetchFailed for a single stage (because we have
+        // multiple tasks running concurrently on different executors). In that case, it is possible
+        // the fetch failure has already been handled by the scheduler.
+        if (runningStages.contains(failedStage)) {
+          logInfo(s"Marking $failedStage (${failedStage.name}) as failed " +
+            s"due to a fetch failure from $mapStage (${mapStage.name})")
+          markStageAsFinished(failedStage, Some("Fetch failure"))
+          runningStages -= failedStage
         }
-        logInfo("The failed fetch was from " + mapStage + " (" + mapStage.name +
-          "); marking it for resubmission")
+
         if (failedStages.isEmpty && eventProcessActor != null) {
           // Don't schedule an event to resubmit failed stages if failed isn't empty, because
           // in that case the event will already have been scheduled. eventProcessActor may be
           // null during unit tests.
+          // TODO: Cancel running tasks in the stage
           import env.actorSystem.dispatcher
+          logInfo(s"Resubmitting $mapStage (${mapStage.name}) and " +
+            s"$failedStage (${failedStage.name}) due to fetch failure")
           env.actorSystem.scheduler.scheduleOnce(
             RESUBMIT_TIMEOUT, eventProcessActor, ResubmitFailedStages)
         }
         failedStages += failedStage
         failedStages += mapStage
+
+        // Mark the map whose fetch failed as broken in the map stage
+        if (mapId != -1) {
+          mapStage.removeOutputLoc(mapId, bmAddress)
+          mapOutputTracker.unregisterMapOutput(shuffleId, mapId, bmAddress)
+        }
+
         // TODO: mark the executor as failed only if there were lots of fetch failures on it
         if (bmAddress != null) {
           handleExecutorLost(bmAddress.executorId, Some(task.epoch))
diff --git a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
index bd829752eb401..f5fed988ade24 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.scheduler
 
-import scala.collection.mutable.{HashSet, HashMap, Map}
+import scala.collection.mutable.{ArrayBuffer, HashSet, HashMap, Map}
 import scala.language.reflectiveCalls
 
 import akka.actor._
@@ -98,7 +98,7 @@ class DAGSchedulerSuite extends TestKit(ActorSystem("DAGSchedulerSuite")) with F
   val WAIT_TIMEOUT_MILLIS = 10000
   val sparkListener = new SparkListener() {
     val successfulStages = new HashSet[Int]()
-    val failedStages = new HashSet[Int]()
+    val failedStages = new ArrayBuffer[Int]()
     override def onStageCompleted(stageCompleted: SparkListenerStageCompleted) {
       val stageInfo = stageCompleted.stageInfo
       if (stageInfo.failureReason.isEmpty) {
@@ -435,6 +435,43 @@ class DAGSchedulerSuite extends TestKit(ActorSystem("DAGSchedulerSuite")) with F
     assertDataStructuresEmpty
   }
 
+  test("trivial shuffle with multiple fetch failures") {
+    val shuffleMapRdd = new MyRDD(sc, 2, Nil)
+    val shuffleDep = new ShuffleDependency(shuffleMapRdd, null)
+    val shuffleId = shuffleDep.shuffleId
+    val reduceRdd = new MyRDD(sc, 2, List(shuffleDep))
+    submit(reduceRdd, Array(0, 1))
+    complete(taskSets(0), Seq(
+      (Success, makeMapStatus("hostA", 1)),
+      (Success, makeMapStatus("hostB", 1))))
+    // The MapOutputTracker should know about both map output locations.
+    assert(mapOutputTracker.getServerStatuses(shuffleId, 0).map(_._1.host) ===
+      Array("hostA", "hostB"))
+
+    // The first result task fails, with a fetch failure for the output from the first mapper.
+    runEvent(CompletionEvent(
+      taskSets(1).tasks(0),
+      FetchFailed(makeBlockManagerId("hostA"), shuffleId, 0, 0),
+      null,
+      Map[Long, Any](),
+      null,
+      null))
+    assert(sc.listenerBus.waitUntilEmpty(WAIT_TIMEOUT_MILLIS))
+    assert(sparkListener.failedStages.contains(0))
+
+    // The second ResultTask fails, with a fetch failure for the output from the second mapper.
+    runEvent(CompletionEvent(
+      taskSets(1).tasks(0),
+      FetchFailed(makeBlockManagerId("hostA"), shuffleId, 1, 1),
+      null,
+      Map[Long, Any](),
+      null,
+      null))
+    // The SparkListener should not receive redundant failure events.
+    assert(sc.listenerBus.waitUntilEmpty(WAIT_TIMEOUT_MILLIS))
+    assert(sparkListener.failedStages.size == 1)
+  }
+
   test("ignore late map task completions") {
     val shuffleMapRdd = new MyRDD(sc, 2, Nil)
     val shuffleDep = new ShuffleDependency(shuffleMapRdd, null)

From 7557c4cfef2398d124b00472e2696f0559a36ef7 Mon Sep 17 00:00:00 2001
From: Andrew Or <andrewor14@gmail.com>
Date: Tue, 26 Aug 2014 22:52:16 -0700
Subject: [PATCH 101/489] [SPARK-3167] Handle special driver configs in Windows

This is an effort to bring the Windows scripts up to speed after recent splashing changes in #1845.

Author: Andrew Or <andrewor14@gmail.com>

Closes #2129 from andrewor14/windows-config and squashes the following commits:

881a8f0 [Andrew Or] Add reference to Windows taskkill
92e6047 [Andrew Or] Update a few comments (minor)
22b1acd [Andrew Or] Fix style again (minor)
afcffea [Andrew Or] Fix style (minor)
72004c2 [Andrew Or] Actually respect --driver-java-options
803218b [Andrew Or] Actually respect SPARK_*_CLASSPATH
eeb34a0 [Andrew Or] Update outdated comment (minor)
35caecc [Andrew Or] In Windows, actually kill Java processes on exit
f97daa2 [Andrew Or] Fix Windows spark shell stdin issue
83ebe60 [Andrew Or] Parse special driver configs in Windows (broken)
---
 bin/compute-classpath.cmd                     |  3 +-
 bin/spark-class2.cmd                          | 46 ++++++++++++++++---
 bin/spark-submit                              |  2 +-
 bin/spark-submit.cmd                          | 34 +++++++++-----
 .../SparkSubmitDriverBootstrapper.scala       | 19 +++++---
 python/pyspark/java_gateway.py                | 17 +++++++
 6 files changed, 95 insertions(+), 26 deletions(-)
 mode change 100755 => 100644 bin/spark-class2.cmd

diff --git a/bin/compute-classpath.cmd b/bin/compute-classpath.cmd
index 58710cd1bd548..5ad52452a5c98 100644
--- a/bin/compute-classpath.cmd
+++ b/bin/compute-classpath.cmd
@@ -36,7 +36,8 @@ rem Load environment variables from conf\spark-env.cmd, if it exists
 if exist "%FWDIR%conf\spark-env.cmd" call "%FWDIR%conf\spark-env.cmd"
 
 rem Build up classpath
-set CLASSPATH=%FWDIR%conf
+set CLASSPATH=%SPARK_CLASSPATH%;%SPARK_SUBMIT_CLASSPATH%;%FWDIR%conf
+
 if exist "%FWDIR%RELEASE" (
   for %%d in ("%FWDIR%lib\spark-assembly*.jar") do (
     set ASSEMBLY_JAR=%%d
diff --git a/bin/spark-class2.cmd b/bin/spark-class2.cmd
old mode 100755
new mode 100644
index e2c5f9c385189..6c5672819172b
--- a/bin/spark-class2.cmd
+++ b/bin/spark-class2.cmd
@@ -17,6 +17,8 @@ rem See the License for the specific language governing permissions and
 rem limitations under the License.
 rem
 
+rem Any changes to this file must be reflected in SparkSubmitDriverBootstrapper.scala!
+
 setlocal enabledelayedexpansion
 
 set SCALA_VERSION=2.10
@@ -38,7 +40,7 @@ if not "x%1"=="x" goto arg_given
 
 if not "x%SPARK_MEM%"=="x" (
   echo Warning: SPARK_MEM is deprecated, please use a more specific config option
-  echo e.g., spark.executor.memory or SPARK_DRIVER_MEMORY.
+  echo e.g., spark.executor.memory or spark.driver.memory.
 )
 
 rem Use SPARK_MEM or 512m as the default memory, to be overridden by specific options
@@ -67,10 +69,18 @@ rem Executors use SPARK_JAVA_OPTS + SPARK_EXECUTOR_MEMORY.
   set OUR_JAVA_OPTS=%SPARK_JAVA_OPTS% %SPARK_EXECUTOR_OPTS%
   if not "x%SPARK_EXECUTOR_MEMORY%"=="x" set OUR_JAVA_MEM=%SPARK_EXECUTOR_MEMORY%
 
-rem All drivers use SPARK_JAVA_OPTS + SPARK_DRIVER_MEMORY. The repl also uses SPARK_REPL_OPTS.
-) else if "%1"=="org.apache.spark.repl.Main" (
-  set OUR_JAVA_OPTS=%SPARK_JAVA_OPTS% %SPARK_REPL_OPTS%
+rem Spark submit uses SPARK_JAVA_OPTS + SPARK_SUBMIT_OPTS +
+rem SPARK_DRIVER_MEMORY + SPARK_SUBMIT_DRIVER_MEMORY.
+rem The repl also uses SPARK_REPL_OPTS.
+) else if "%1"=="org.apache.spark.deploy.SparkSubmit" (
+  set OUR_JAVA_OPTS=%SPARK_JAVA_OPTS% %SPARK_SUBMIT_OPTS% %SPARK_REPL_OPTS%
+  if not "x%SPARK_SUBMIT_LIBRARY_PATH%"=="x" (
+    set OUR_JAVA_OPTS=!OUR_JAVA_OPTS! -Djava.library.path=%SPARK_SUBMIT_LIBRARY_PATH%
+  ) else if not "x%SPARK_LIBRARY_PATH%"=="x" (
+    set OUR_JAVA_OPTS=!OUR_JAVA_OPTS! -Djava.library.path=%SPARK_LIBRARY_PATH%
+  )
   if not "x%SPARK_DRIVER_MEMORY%"=="x" set OUR_JAVA_MEM=%SPARK_DRIVER_MEMORY%
+  if not "x%SPARK_SUBMIT_DRIVER_MEMORY%"=="x" set OUR_JAVA_MEM=%SPARK_SUBMIT_DRIVER_MEMORY%
 ) else (
   set OUR_JAVA_OPTS=%SPARK_JAVA_OPTS%
   if not "x%SPARK_DRIVER_MEMORY%"=="x" set OUR_JAVA_MEM=%SPARK_DRIVER_MEMORY%
@@ -80,9 +90,9 @@ rem Set JAVA_OPTS to be able to load native libraries and to set heap size
 for /f "tokens=3" %%i in ('java -version 2^>^&1 ^| find "version"') do set jversion=%%i
 for /f "tokens=1 delims=_" %%i in ("%jversion:~1,-1%") do set jversion=%%i
 if "%jversion%" geq "1.8.0" (
-  set JAVA_OPTS=%OUR_JAVA_OPTS% -Djava.library.path=%SPARK_LIBRARY_PATH% -Xms%OUR_JAVA_MEM% -Xmx%OUR_JAVA_MEM%
+  set JAVA_OPTS=%OUR_JAVA_OPTS% -Xms%OUR_JAVA_MEM% -Xmx%OUR_JAVA_MEM%
 ) else (
-  set JAVA_OPTS=-XX:MaxPermSize=128m %OUR_JAVA_OPTS% -Djava.library.path=%SPARK_LIBRARY_PATH% -Xms%OUR_JAVA_MEM% -Xmx%OUR_JAVA_MEM%
+  set JAVA_OPTS=-XX:MaxPermSize=128m %OUR_JAVA_OPTS% -Xms%OUR_JAVA_MEM% -Xmx%OUR_JAVA_MEM%
 )
 rem Attention: when changing the way the JAVA_OPTS are assembled, the change must be reflected in CommandUtils.scala!
 
@@ -115,5 +125,27 @@ rem Figure out where java is.
 set RUNNER=java
 if not "x%JAVA_HOME%"=="x" set RUNNER=%JAVA_HOME%\bin\java
 
-"%RUNNER%" -cp "%CLASSPATH%" %JAVA_OPTS% %*
+rem In Spark submit client mode, the driver is launched in the same JVM as Spark submit itself.
+rem Here we must parse the properties file for relevant "spark.driver.*" configs before launching
+rem the driver JVM itself. Instead of handling this complexity here, we launch a separate JVM
+rem to prepare the launch environment of this driver JVM.
+
+rem In this case, leave out the main class (org.apache.spark.deploy.SparkSubmit) and use our own.
+rem Leaving out the first argument is surprisingly difficult to do in Windows. Note that this must
+rem be done here because the Windows "shift" command does not work in a conditional block.
+set BOOTSTRAP_ARGS=
+shift
+:start_parse
+if "%~1" == "" goto end_parse
+set BOOTSTRAP_ARGS=%BOOTSTRAP_ARGS% %~1
+shift
+goto start_parse
+:end_parse
+
+if not [%SPARK_SUBMIT_BOOTSTRAP_DRIVER%] == [] (
+  set SPARK_CLASS=1
+  "%RUNNER%" org.apache.spark.deploy.SparkSubmitDriverBootstrapper %BOOTSTRAP_ARGS%
+) else (
+  "%RUNNER%" -cp "%CLASSPATH%" %JAVA_OPTS% %*
+)
 :exit
diff --git a/bin/spark-submit b/bin/spark-submit
index 32c911cd0438b..277c4ce571ca2 100755
--- a/bin/spark-submit
+++ b/bin/spark-submit
@@ -17,7 +17,7 @@
 # limitations under the License.
 #
 
-# NOTE: Any changes in this file must be reflected in SparkClassLauncher.scala!
+# NOTE: Any changes in this file must be reflected in SparkSubmitDriverBootstrapper.scala!
 
 export SPARK_HOME="$(cd `dirname $0`/..; pwd)"
 ORIG_ARGS=("$@")
diff --git a/bin/spark-submit.cmd b/bin/spark-submit.cmd
index 6eb702ed8c561..cf6046d1547ad 100644
--- a/bin/spark-submit.cmd
+++ b/bin/spark-submit.cmd
@@ -17,23 +17,28 @@ rem See the License for the specific language governing permissions and
 rem limitations under the License.
 rem
 
+rem NOTE: Any changes in this file must be reflected in SparkSubmitDriverBootstrapper.scala!
+
 set SPARK_HOME=%~dp0..
 set ORIG_ARGS=%*
 
-rem Clear the values of all variables used
-set DEPLOY_MODE=
-set DRIVER_MEMORY=
+rem Reset the values of all variables used
+set SPARK_SUBMIT_DEPLOY_MODE=client
+set SPARK_SUBMIT_PROPERTIES_FILE=%SPARK_HOME%\conf\spark-defaults.conf
+set SPARK_SUBMIT_DRIVER_MEMORY=
 set SPARK_SUBMIT_LIBRARY_PATH=
 set SPARK_SUBMIT_CLASSPATH=
 set SPARK_SUBMIT_OPTS=
-set SPARK_DRIVER_MEMORY=
+set SPARK_SUBMIT_BOOTSTRAP_DRIVER=
 
 :loop
 if [%1] == [] goto continue
   if [%1] == [--deploy-mode] (
-    set DEPLOY_MODE=%2
+    set SPARK_SUBMIT_DEPLOY_MODE=%2
+  ) else if [%1] == [--properties-file] (
+    set SPARK_SUBMIT_PROPERTIES_FILE=%2
   ) else if [%1] == [--driver-memory] (
-    set DRIVER_MEMORY=%2
+    set SPARK_SUBMIT_DRIVER_MEMORY=%2
   ) else if [%1] == [--driver-library-path] (
     set SPARK_SUBMIT_LIBRARY_PATH=%2
   ) else if [%1] == [--driver-class-path] (
@@ -45,12 +50,19 @@ if [%1] == [] goto continue
 goto loop
 :continue
 
-if [%DEPLOY_MODE%] == [] (
-  set DEPLOY_MODE=client
-)
+rem For client mode, the driver will be launched in the same JVM that launches
+rem SparkSubmit, so we may need to read the properties file for any extra class
+rem paths, library paths, java options and memory early on. Otherwise, it will
+rem be too late by the time the driver JVM has started.
 
-if not [%DRIVER_MEMORY%] == [] if [%DEPLOY_MODE%] == [client] (
-  set SPARK_DRIVER_MEMORY=%DRIVER_MEMORY%
+if [%SPARK_SUBMIT_DEPLOY_MODE%] == [client] (
+  if exist %SPARK_SUBMIT_PROPERTIES_FILE% (
+    rem Parse the properties file only if the special configs exist
+    for /f %%i in ('findstr /r /c:"^[\t ]*spark.driver.memory" /c:"^[\t ]*spark.driver.extra" ^
+      %SPARK_SUBMIT_PROPERTIES_FILE%') do (
+      set SPARK_SUBMIT_BOOTSTRAP_DRIVER=1
+    )
+  )
 )
 
 cmd /V /E /C %SPARK_HOME%\bin\spark-class.cmd org.apache.spark.deploy.SparkSubmit %ORIG_ARGS%
diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitDriverBootstrapper.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitDriverBootstrapper.scala
index af607e6a4a065..7ca96ed57c2db 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitDriverBootstrapper.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitDriverBootstrapper.scala
@@ -133,17 +133,24 @@ private[spark] object SparkSubmitDriverBootstrapper {
     val process = builder.start()
 
     // Redirect stdin, stdout, and stderr to/from the child JVM
-    val stdinThread = new RedirectThread(System.in, process.getOutputStream, "redirect stdin")
     val stdoutThread = new RedirectThread(process.getInputStream, System.out, "redirect stdout")
     val stderrThread = new RedirectThread(process.getErrorStream, System.err, "redirect stderr")
-    stdinThread.start()
     stdoutThread.start()
     stderrThread.start()
 
-    // Terminate on broken pipe, which signals that the parent process has exited. This is
-    // important for the PySpark shell, where Spark submit itself is a python subprocess.
-    stdinThread.join()
-    process.destroy()
+    // In Windows, the subprocess reads directly from our stdin, so we should avoid spawning
+    // a thread that contends with the subprocess in reading from System.in.
+    if (Utils.isWindows) {
+      // For the PySpark shell, the termination of this process is handled in java_gateway.py
+      process.waitFor()
+    } else {
+      // Terminate on broken pipe, which signals that the parent process has exited. This is
+      // important for the PySpark shell, where Spark submit itself is a python subprocess.
+      val stdinThread = new RedirectThread(System.in, process.getOutputStream, "redirect stdin")
+      stdinThread.start()
+      stdinThread.join()
+      process.destroy()
+    }
   }
 
 }
diff --git a/python/pyspark/java_gateway.py b/python/pyspark/java_gateway.py
index 6f4f62f23bc4d..9c70fa5c16d0c 100644
--- a/python/pyspark/java_gateway.py
+++ b/python/pyspark/java_gateway.py
@@ -15,6 +15,7 @@
 # limitations under the License.
 #
 
+import atexit
 import os
 import sys
 import signal
@@ -69,6 +70,22 @@ def preexec_func():
                 error_msg += "--------------------------------------------------------------\n"
             raise Exception(error_msg)
 
+        # In Windows, ensure the Java child processes do not linger after Python has exited.
+        # In UNIX-based systems, the child process can kill itself on broken pipe (i.e. when
+        # the parent process' stdin sends an EOF). In Windows, however, this is not possible
+        # because java.lang.Process reads directly from the parent process' stdin, contending
+        # with any opportunity to read an EOF from the parent. Note that this is only best
+        # effort and will not take effect if the python process is violently terminated.
+        if on_windows:
+            # In Windows, the child process here is "spark-submit.cmd", not the JVM itself
+            # (because the UNIX "exec" command is not available). This means we cannot simply
+            # call proc.kill(), which kills only the "spark-submit.cmd" process but not the
+            # JVMs. Instead, we use "taskkill" with the tree-kill option "/t" to terminate all
+            # child processes in the tree (http://technet.microsoft.com/en-us/library/bb491009.aspx)
+            def killChild():
+                Popen(["cmd", "/c", "taskkill", "/f", "/t", "/pid", str(proc.pid)])
+            atexit.register(killChild)
+
         # Create a thread to echo output from the GatewayServer, which is required
         # for Java log output to show up:
         class EchoOutputThread(Thread):

From 9d65f2712c250a561c9c1f6259aa12e861ed239d Mon Sep 17 00:00:00 2001
From: Patrick Wendell <pwendell@gmail.com>
Date: Tue, 26 Aug 2014 23:40:50 -0700
Subject: [PATCH 102/489] HOTFIX: Minor typo in conf template

---
 conf/spark-defaults.conf.template | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/conf/spark-defaults.conf.template b/conf/spark-defaults.conf.template
index 94427029b94d7..a48dcc70e1363 100644
--- a/conf/spark-defaults.conf.template
+++ b/conf/spark-defaults.conf.template
@@ -7,4 +7,4 @@
 # spark.eventLog.dir               hdfs://namenode:8021/directory
 # spark.serializer                 org.apache.spark.serializer.KryoSerializer
 # spark.driver.memory              5g
-# spark.executor.extraJavaOptions  -XX:+PrintGCDetail -Dkey=value -Dnumbers="one two three"
+# spark.executor.extraJavaOptions  -XX:+PrintGCDetails -Dkey=value -Dnumbers="one two three"

From 3e2864e40472b32e6a7eec5ba3bc83562d2a1a62 Mon Sep 17 00:00:00 2001
From: Tathagata Das <tathagata.das1565@gmail.com>
Date: Wed, 27 Aug 2014 00:13:38 -0700
Subject: [PATCH 103/489] [SPARK-3139] Made ContextCleaner to not block on
 shuffles

As a workaround for SPARK-3015, the ContextCleaner was made "blocking", that is, it cleaned items one-by-one. But shuffles can take a long time to be deleted. Given that the RC for 1.1 is imminent, this PR makes a narrow change in the context cleaner - not wait for shuffle cleanups to complete. Also it changes the error messages on failure to delete to be milder warnings, as exceptions in the delete code path for one item does not really stop the actual functioning of the system.

Author: Tathagata Das <tathagata.das1565@gmail.com>

Closes #2143 from tdas/cleaner-shuffle-fix and squashes the following commits:

9c84202 [Tathagata Das] Restoring default blocking behavior in ContextCleanerSuite, and added docs to identify that spark.cleaner.referenceTracking.blocking does not control shuffle.
2181329 [Tathagata Das] Mark shuffle cleanup as non-blocking.
e337cc2 [Tathagata Das] Changed semantics based on PR comments.
387b578 [Tathagata Das] Made ContextCleaner to not block on shuffles
---
 .../org/apache/spark/ContextCleaner.scala      | 18 ++++++++++++++++--
 .../spark/storage/BlockManagerMaster.scala     | 12 +++++++-----
 .../org/apache/spark/ContextCleanerSuite.scala |  3 +++
 3 files changed, 26 insertions(+), 7 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/ContextCleaner.scala b/core/src/main/scala/org/apache/spark/ContextCleaner.scala
index 3848734d6f639..ede1e23f4fcc5 100644
--- a/core/src/main/scala/org/apache/spark/ContextCleaner.scala
+++ b/core/src/main/scala/org/apache/spark/ContextCleaner.scala
@@ -65,7 +65,8 @@ private[spark] class ContextCleaner(sc: SparkContext) extends Logging {
   private val cleaningThread = new Thread() { override def run() { keepCleaning() }}
 
   /**
-   * Whether the cleaning thread will block on cleanup tasks.
+   * Whether the cleaning thread will block on cleanup tasks (other than shuffle, which
+   * is controlled by the `spark.cleaner.referenceTracking.blocking.shuffle` parameter).
    *
    * Due to SPARK-3015, this is set to true by default. This is intended to be only a temporary
    * workaround for the issue, which is ultimately caused by the way the BlockManager actors
@@ -76,6 +77,19 @@ private[spark] class ContextCleaner(sc: SparkContext) extends Logging {
   private val blockOnCleanupTasks = sc.conf.getBoolean(
     "spark.cleaner.referenceTracking.blocking", true)
 
+  /**
+   * Whether the cleaning thread will block on shuffle cleanup tasks.
+   *
+   * When context cleaner is configured to block on every delete request, it can throw timeout
+   * exceptions on cleanup of shuffle blocks, as reported in SPARK-3139. To avoid that, this
+   * parameter by default disables blocking on shuffle cleanups. Note that this does not affect
+   * the cleanup of RDDs and broadcasts. This is intended to be a temporary workaround,
+   * until the real Akka issue (referred to in the comment above `blockOnCleanupTasks`) is
+   * resolved.
+   */
+  private val blockOnShuffleCleanupTasks = sc.conf.getBoolean(
+    "spark.cleaner.referenceTracking.blocking.shuffle", false)
+
   @volatile private var stopped = false
 
   /** Attach a listener object to get information of when objects are cleaned. */
@@ -128,7 +142,7 @@ private[spark] class ContextCleaner(sc: SparkContext) extends Logging {
             case CleanRDD(rddId) =>
               doCleanupRDD(rddId, blocking = blockOnCleanupTasks)
             case CleanShuffle(shuffleId) =>
-              doCleanupShuffle(shuffleId, blocking = blockOnCleanupTasks)
+              doCleanupShuffle(shuffleId, blocking = blockOnShuffleCleanupTasks)
             case CleanBroadcast(broadcastId) =>
               doCleanupBroadcast(broadcastId, blocking = blockOnCleanupTasks)
           }
diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManagerMaster.scala b/core/src/main/scala/org/apache/spark/storage/BlockManagerMaster.scala
index 669307765d1fa..e67b3dc5ce02e 100644
--- a/core/src/main/scala/org/apache/spark/storage/BlockManagerMaster.scala
+++ b/core/src/main/scala/org/apache/spark/storage/BlockManagerMaster.scala
@@ -101,7 +101,8 @@ class BlockManagerMaster(var driverActor: ActorRef, conf: SparkConf) extends Log
   def removeRdd(rddId: Int, blocking: Boolean) {
     val future = askDriverWithReply[Future[Seq[Int]]](RemoveRdd(rddId))
     future.onFailure {
-      case e: Throwable => logError("Failed to remove RDD " + rddId, e)
+      case e: Exception =>
+        logWarning(s"Failed to remove RDD $rddId - ${e.getMessage}}")
     }
     if (blocking) {
       Await.result(future, timeout)
@@ -112,7 +113,8 @@ class BlockManagerMaster(var driverActor: ActorRef, conf: SparkConf) extends Log
   def removeShuffle(shuffleId: Int, blocking: Boolean) {
     val future = askDriverWithReply[Future[Seq[Boolean]]](RemoveShuffle(shuffleId))
     future.onFailure {
-      case e: Throwable => logError("Failed to remove shuffle " + shuffleId, e)
+      case e: Exception =>
+        logWarning(s"Failed to remove shuffle $shuffleId - ${e.getMessage}}")
     }
     if (blocking) {
       Await.result(future, timeout)
@@ -124,9 +126,9 @@ class BlockManagerMaster(var driverActor: ActorRef, conf: SparkConf) extends Log
     val future = askDriverWithReply[Future[Seq[Int]]](
       RemoveBroadcast(broadcastId, removeFromMaster))
     future.onFailure {
-      case e: Throwable =>
-        logError("Failed to remove broadcast " + broadcastId +
-          " with removeFromMaster = " + removeFromMaster, e)
+      case e: Exception =>
+        logWarning(s"Failed to remove broadcast $broadcastId" +
+          s" with removeFromMaster = $removeFromMaster - ${e.getMessage}}")
     }
     if (blocking) {
       Await.result(future, timeout)
diff --git a/core/src/test/scala/org/apache/spark/ContextCleanerSuite.scala b/core/src/test/scala/org/apache/spark/ContextCleanerSuite.scala
index 4bc4346c0a288..2744894277ae8 100644
--- a/core/src/test/scala/org/apache/spark/ContextCleanerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/ContextCleanerSuite.scala
@@ -52,6 +52,7 @@ abstract class ContextCleanerSuiteBase(val shuffleManager: Class[_] = classOf[Ha
     .setMaster("local[2]")
     .setAppName("ContextCleanerSuite")
     .set("spark.cleaner.referenceTracking.blocking", "true")
+    .set("spark.cleaner.referenceTracking.blocking.shuffle", "true")
     .set("spark.shuffle.manager", shuffleManager.getName)
 
   before {
@@ -243,6 +244,7 @@ class ContextCleanerSuite extends ContextCleanerSuiteBase {
       .setMaster("local-cluster[2, 1, 512]")
       .setAppName("ContextCleanerSuite")
       .set("spark.cleaner.referenceTracking.blocking", "true")
+      .set("spark.cleaner.referenceTracking.blocking.shuffle", "true")
       .set("spark.shuffle.manager", shuffleManager.getName)
     sc = new SparkContext(conf2)
 
@@ -319,6 +321,7 @@ class SortShuffleContextCleanerSuite extends ContextCleanerSuiteBase(classOf[Sor
       .setMaster("local-cluster[2, 1, 512]")
       .setAppName("ContextCleanerSuite")
       .set("spark.cleaner.referenceTracking.blocking", "true")
+      .set("spark.cleaner.referenceTracking.blocking.shuffle", "true")
       .set("spark.shuffle.manager", shuffleManager.getName)
     sc = new SparkContext(conf2)
 

From e1139dd60e0692e8adb1337c1f605165ce4b8895 Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Wed, 27 Aug 2014 00:59:23 -0700
Subject: [PATCH 104/489] [SPARK-3237][SQL] Fix parquet filters with UDFs

Author: Michael Armbrust <michael@databricks.com>

Closes #2153 from marmbrus/parquetFilters and squashes the following commits:

712731a [Michael Armbrust] Use closure serializer for sending filters.
1e83f80 [Michael Armbrust] Clean udf functions.
---
 .../apache/spark/sql/catalyst/expressions/ScalaUdf.scala  | 4 ++++
 .../org/apache/spark/sql/parquet/ParquetFilters.scala     | 8 ++++++--
 2 files changed, 10 insertions(+), 2 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUdf.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUdf.scala
index 63ac2a608b6ff..0b3c1df453fb8 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUdf.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUdf.scala
@@ -18,10 +18,14 @@
 package org.apache.spark.sql.catalyst.expressions
 
 import org.apache.spark.sql.catalyst.types.DataType
+import org.apache.spark.util.ClosureCleaner
 
 case class ScalaUdf(function: AnyRef, dataType: DataType, children: Seq[Expression])
   extends Expression {
 
+  // Clean function when not called with default no-arg constructor.
+  if (function != null) { ClosureCleaner.clean(function) }
+
   type EvaluatedType = Any
 
   def nullable = true
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetFilters.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetFilters.scala
index 2298a9b933df5..fe28e0d7269e0 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetFilters.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetFilters.scala
@@ -17,6 +17,8 @@
 
 package org.apache.spark.sql.parquet
 
+import java.nio.ByteBuffer
+
 import org.apache.hadoop.conf.Configuration
 
 import parquet.filter._
@@ -25,6 +27,7 @@ import parquet.column.ColumnReader
 
 import com.google.common.io.BaseEncoding
 
+import org.apache.spark.SparkEnv
 import org.apache.spark.sql.catalyst.types._
 import org.apache.spark.sql.catalyst.expressions.{Predicate => CatalystPredicate}
 import org.apache.spark.sql.catalyst.expressions._
@@ -237,7 +240,8 @@ object ParquetFilters {
    */
   def serializeFilterExpressions(filters: Seq[Expression], conf: Configuration): Unit = {
     if (filters.length > 0) {
-      val serialized: Array[Byte] = SparkSqlSerializer.serialize(filters)
+      val serialized: Array[Byte] =
+        SparkEnv.get.closureSerializer.newInstance().serialize(filters).array()
       val encoded: String = BaseEncoding.base64().encode(serialized)
       conf.set(PARQUET_FILTER_DATA, encoded)
     }
@@ -252,7 +256,7 @@ object ParquetFilters {
     val data = conf.get(PARQUET_FILTER_DATA)
     if (data != null) {
       val decoded: Array[Byte] = BaseEncoding.base64().decode(data)
-      SparkSqlSerializer.deserialize(decoded)
+      SparkEnv.get.closureSerializer.newInstance().deserialize(ByteBuffer.wrap(decoded))
     } else {
       Seq()
     }

From 43dfc84f883822ea27b6e312d4353bf301c2e7ef Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Wed, 27 Aug 2014 01:19:48 -0700
Subject: [PATCH 105/489] [SPARK-2830][MLLIB] doc update for 1.1

1. renamed mllib-basics to mllib-data-types
1. renamed mllib-stats to mllib-statistics
1. moved random data generation to the bottom of mllib-stats
1. updated toc accordingly

atalwalkar

Author: Xiangrui Meng <meng@databricks.com>

Closes #2151 from mengxr/mllib-doc-1.1 and squashes the following commits:

0bd79f3 [Xiangrui Meng] add mllib-data-types
b64a5d7 [Xiangrui Meng] update the content list of basis statistics in mllib-guide
f625cc2 [Xiangrui Meng] move mllib-basics to mllib-data-types
4d69250 [Xiangrui Meng] move random data generation to the bottom of statistics
e64f3ce [Xiangrui Meng] move mllib-stats.md to mllib-statistics.md
---
 docs/{mllib-basics.md => mllib-data-types.md} |   4 +-
 docs/mllib-dimensionality-reduction.md        |   4 +-
 docs/mllib-guide.md                           |   9 +-
 docs/{mllib-stats.md => mllib-statistics.md}  | 156 +++++++++---------
 4 files changed, 87 insertions(+), 86 deletions(-)
 rename docs/{mllib-basics.md => mllib-data-types.md} (99%)
 rename docs/{mllib-stats.md => mllib-statistics.md} (99%)

diff --git a/docs/mllib-basics.md b/docs/mllib-data-types.md
similarity index 99%
rename from docs/mllib-basics.md
rename to docs/mllib-data-types.md
index 8752df412950a..101dc2f8695f3 100644
--- a/docs/mllib-basics.md
+++ b/docs/mllib-data-types.md
@@ -1,7 +1,7 @@
 ---
 layout: global
-title: Basics - MLlib
-displayTitle: <a href="mllib-guide.html">MLlib</a> - Basics
+title: Data Types - MLlib
+displayTitle: <a href="mllib-guide.html">MLlib</a> - Data Types
 ---
 
 * Table of contents
diff --git a/docs/mllib-dimensionality-reduction.md b/docs/mllib-dimensionality-reduction.md
index 9f2cf6d48ec75..21cb35b4270ca 100644
--- a/docs/mllib-dimensionality-reduction.md
+++ b/docs/mllib-dimensionality-reduction.md
@@ -11,7 +11,7 @@ displayTitle: <a href="mllib-guide.html">MLlib</a> - Dimensionality Reduction
 of reducing the number of variables under consideration.
 It can be used to extract latent features from raw and noisy features
 or compress data while maintaining the structure.
-MLlib provides support for dimensionality reduction on the <a href="mllib-basics.html#rowmatrix">RowMatrix</a> class.
+MLlib provides support for dimensionality reduction on the <a href="mllib-data-types.html#rowmatrix">RowMatrix</a> class.
 
 ## Singular value decomposition (SVD)
 
@@ -58,7 +58,7 @@ passes, $O(n)$ storage on each executor, and $O(n k)$ storage on the driver.
 ### SVD Example
  
 MLlib provides SVD functionality to row-oriented matrices, provided in the
-<a href="mllib-basics.html#rowmatrix">RowMatrix</a> class. 
+<a href="mllib-data-types.html#rowmatrix">RowMatrix</a> class. 
 
 <div class="codetabs">
 <div data-lang="scala" markdown="1">
diff --git a/docs/mllib-guide.md b/docs/mllib-guide.md
index 4d4198b9e0452..d3a510b3c17c6 100644
--- a/docs/mllib-guide.md
+++ b/docs/mllib-guide.md
@@ -7,12 +7,13 @@ MLlib is Spark's scalable machine learning library consisting of common learning
 including classification, regression, clustering, collaborative
 filtering, dimensionality reduction, as well as underlying optimization primitives, as outlined below:
 
-* [Data types](mllib-basics.html)
-* [Basic statistics](mllib-stats.html)
-  * random data generation  
-  * stratified sampling
+* [Data types](mllib-data-types.html)
+* [Basic statistics](mllib-statistics.html)
   * summary statistics
+  * correlations
+  * stratified sampling
   * hypothesis testing
+  * random data generation  
 * [Classification and regression](mllib-classification-regression.html)
   * [linear models (SVMs, logistic regression, linear regression)](mllib-linear-methods.html)
   * [decision trees](mllib-decision-tree.html)
diff --git a/docs/mllib-stats.md b/docs/mllib-statistics.md
similarity index 99%
rename from docs/mllib-stats.md
rename to docs/mllib-statistics.md
index 511a9fbf710cc..c4632413991f1 100644
--- a/docs/mllib-stats.md
+++ b/docs/mllib-statistics.md
@@ -1,7 +1,7 @@
 ---
 layout: global
-title: Statistics Functionality - MLlib
-displayTitle: <a href="mllib-guide.html">MLlib</a> - Statistics Functionality 
+title: Basic Statistics - MLlib
+displayTitle: <a href="mllib-guide.html">MLlib</a> - Basic Statistics 
 ---
 
 * Table of contents
@@ -25,7 +25,7 @@ displayTitle: <a href="mllib-guide.html">MLlib</a> - Statistics Functionality
 \newcommand{\zero}{\mathbf{0}}
 \]`
 
-## Summary Statistics 
+## Summary statistics 
 
 We provide column summary statistics for `RDD[Vector]` through the function `colStats` 
 available in `Statistics`.
@@ -104,81 +104,7 @@ print summary.numNonzeros()
 
 </div>
 
-## Random data generation
-
-Random data generation is useful for randomized algorithms, prototyping, and performance testing.
-MLlib supports generating random RDDs with i.i.d. values drawn from a given distribution:
-uniform, standard normal, or Poisson.
-
-<div class="codetabs">
-<div data-lang="scala" markdown="1">
-[`RandomRDDs`](api/scala/index.html#org.apache.spark.mllib.random.RandomRDDs) provides factory
-methods to generate random double RDDs or vector RDDs.
-The following example generates a random double RDD, whose values follows the standard normal
-distribution `N(0, 1)`, and then map it to `N(1, 4)`.
-
-{% highlight scala %}
-import org.apache.spark.SparkContext
-import org.apache.spark.mllib.random.RandomRDDs._
-
-val sc: SparkContext = ...
-
-// Generate a random double RDD that contains 1 million i.i.d. values drawn from the
-// standard normal distribution `N(0, 1)`, evenly distributed in 10 partitions.
-val u = normalRDD(sc, 1000000L, 10)
-// Apply a transform to get a random double RDD following `N(1, 4)`.
-val v = u.map(x => 1.0 + 2.0 * x)
-{% endhighlight %}
-</div>
-
-<div data-lang="java" markdown="1">
-[`RandomRDDs`](api/java/index.html#org.apache.spark.mllib.random.RandomRDDs) provides factory
-methods to generate random double RDDs or vector RDDs.
-The following example generates a random double RDD, whose values follows the standard normal
-distribution `N(0, 1)`, and then map it to `N(1, 4)`.
-
-{% highlight java %}
-import org.apache.spark.SparkContext;
-import org.apache.spark.api.JavaDoubleRDD;
-import static org.apache.spark.mllib.random.RandomRDDs.*;
-
-JavaSparkContext jsc = ...
-
-// Generate a random double RDD that contains 1 million i.i.d. values drawn from the
-// standard normal distribution `N(0, 1)`, evenly distributed in 10 partitions.
-JavaDoubleRDD u = normalJavaRDD(jsc, 1000000L, 10);
-// Apply a transform to get a random double RDD following `N(1, 4)`.
-JavaDoubleRDD v = u.map(
-  new Function<Double, Double>() {
-    public Double call(Double x) {
-      return 1.0 + 2.0 * x;
-    }
-  });
-{% endhighlight %}
-</div>
-
-<div data-lang="python" markdown="1">
-[`RandomRDDs`](api/python/pyspark.mllib.random.RandomRDDs-class.html) provides factory
-methods to generate random double RDDs or vector RDDs.
-The following example generates a random double RDD, whose values follows the standard normal
-distribution `N(0, 1)`, and then map it to `N(1, 4)`.
-
-{% highlight python %}
-from pyspark.mllib.random import RandomRDDs
-
-sc = ... # SparkContext
-
-# Generate a random double RDD that contains 1 million i.i.d. values drawn from the
-# standard normal distribution `N(0, 1)`, evenly distributed in 10 partitions.
-u = RandomRDDs.uniformRDD(sc, 1000000L, 10)
-# Apply a transform to get a random double RDD following `N(1, 4)`.
-v = u.map(lambda x: 1.0 + 2.0 * x)
-{% endhighlight %}
-</div>
-
-</div>
-
-## Correlations calculation
+## Correlations
 
 Calculating the correlation between two series of data is a common operation in Statistics. In MLlib
 we provide the flexibility to calculate pairwise correlations among many series. The supported 
@@ -455,3 +381,77 @@ for (ChiSqTestResult result : featureTestResults) {
 </div>
 
 </div>
+
+## Random data generation
+
+Random data generation is useful for randomized algorithms, prototyping, and performance testing.
+MLlib supports generating random RDDs with i.i.d. values drawn from a given distribution:
+uniform, standard normal, or Poisson.
+
+<div class="codetabs">
+<div data-lang="scala" markdown="1">
+[`RandomRDDs`](api/scala/index.html#org.apache.spark.mllib.random.RandomRDDs) provides factory
+methods to generate random double RDDs or vector RDDs.
+The following example generates a random double RDD, whose values follows the standard normal
+distribution `N(0, 1)`, and then map it to `N(1, 4)`.
+
+{% highlight scala %}
+import org.apache.spark.SparkContext
+import org.apache.spark.mllib.random.RandomRDDs._
+
+val sc: SparkContext = ...
+
+// Generate a random double RDD that contains 1 million i.i.d. values drawn from the
+// standard normal distribution `N(0, 1)`, evenly distributed in 10 partitions.
+val u = normalRDD(sc, 1000000L, 10)
+// Apply a transform to get a random double RDD following `N(1, 4)`.
+val v = u.map(x => 1.0 + 2.0 * x)
+{% endhighlight %}
+</div>
+
+<div data-lang="java" markdown="1">
+[`RandomRDDs`](api/java/index.html#org.apache.spark.mllib.random.RandomRDDs) provides factory
+methods to generate random double RDDs or vector RDDs.
+The following example generates a random double RDD, whose values follows the standard normal
+distribution `N(0, 1)`, and then map it to `N(1, 4)`.
+
+{% highlight java %}
+import org.apache.spark.SparkContext;
+import org.apache.spark.api.JavaDoubleRDD;
+import static org.apache.spark.mllib.random.RandomRDDs.*;
+
+JavaSparkContext jsc = ...
+
+// Generate a random double RDD that contains 1 million i.i.d. values drawn from the
+// standard normal distribution `N(0, 1)`, evenly distributed in 10 partitions.
+JavaDoubleRDD u = normalJavaRDD(jsc, 1000000L, 10);
+// Apply a transform to get a random double RDD following `N(1, 4)`.
+JavaDoubleRDD v = u.map(
+  new Function<Double, Double>() {
+    public Double call(Double x) {
+      return 1.0 + 2.0 * x;
+    }
+  });
+{% endhighlight %}
+</div>
+
+<div data-lang="python" markdown="1">
+[`RandomRDDs`](api/python/pyspark.mllib.random.RandomRDDs-class.html) provides factory
+methods to generate random double RDDs or vector RDDs.
+The following example generates a random double RDD, whose values follows the standard normal
+distribution `N(0, 1)`, and then map it to `N(1, 4)`.
+
+{% highlight python %}
+from pyspark.mllib.random import RandomRDDs
+
+sc = ... # SparkContext
+
+# Generate a random double RDD that contains 1 million i.i.d. values drawn from the
+# standard normal distribution `N(0, 1)`, evenly distributed in 10 partitions.
+u = RandomRDDs.uniformRDD(sc, 1000000L, 10)
+# Apply a transform to get a random double RDD following `N(1, 4)`.
+v = u.map(lambda x: 1.0 + 2.0 * x)
+{% endhighlight %}
+</div>
+
+</div>

From 171a41cb034f4ea80f6a3c91a6872970de16a14a Mon Sep 17 00:00:00 2001
From: "Joseph K. Bradley" <joseph.kurata.bradley@gmail.com>
Date: Wed, 27 Aug 2014 01:45:59 -0700
Subject: [PATCH 106/489] [SPARK-3227] [mllib] Added migration guide for v1.0
 to v1.1

The only updates are in DecisionTree.

CC: mengxr

Author: Joseph K. Bradley <joseph.kurata.bradley@gmail.com>

Closes #2146 from jkbradley/mllib-migration and squashes the following commits:

5a1f487 [Joseph K. Bradley] small edit to doc
411d6d9 [Joseph K. Bradley] Added migration guide for v1.0 to v1.1.  The only updates are in DecisionTree.
---
 docs/mllib-guide.md | 28 +++++++++++++++++++++++++++-
 1 file changed, 27 insertions(+), 1 deletion(-)

diff --git a/docs/mllib-guide.md b/docs/mllib-guide.md
index d3a510b3c17c6..94fc98ce4fabe 100644
--- a/docs/mllib-guide.md
+++ b/docs/mllib-guide.md
@@ -60,6 +60,32 @@ To use MLlib in Python, you will need [NumPy](http://www.numpy.org) version 1.4
 
 # Migration Guide
 
+## From 1.0 to 1.1
+
+The only API changes in MLlib v1.1 are in
+[`DecisionTree`](api/scala/index.html#org.apache.spark.mllib.tree.DecisionTree),
+which continues to be an experimental API in MLlib 1.1:
+
+1. *(Breaking change)* The meaning of tree depth has been changed by 1 in order to match
+the implementations of trees in
+[scikit-learn](http://scikit-learn.org/stable/modules/classes.html#module-sklearn.tree)
+and in [rpart](http://cran.r-project.org/web/packages/rpart/index.html).
+In MLlib v1.0, a depth-1 tree had 1 leaf node, and a depth-2 tree had 1 root node and 2 leaf nodes.
+In MLlib v1.1, a depth-0 tree has 1 leaf node, and a depth-1 tree has 1 root node and 2 leaf nodes.
+This depth is specified by the `maxDepth` parameter in
+[`Strategy`](api/scala/index.html#org.apache.spark.mllib.tree.configuration.Strategy)
+or via [`DecisionTree`](api/scala/index.html#org.apache.spark.mllib.tree.DecisionTree)
+static `trainClassifier` and `trainRegressor` methods.
+
+2. *(Non-breaking change)* We recommend using the newly added `trainClassifier` and `trainRegressor`
+methods to build a [`DecisionTree`](api/scala/index.html#org.apache.spark.mllib.tree.DecisionTree),
+rather than using the old parameter class `Strategy`.  These new training methods explicitly
+separate classification and regression, and they replace specialized parameter types with
+simple `String` types.
+
+Examples of the new, recommended `trainClassifier` and `trainRegressor` are given in the
+[Decision Trees Guide](mllib-decision-tree.html#examples).
+
 ## From 0.9 to 1.0
 
 In MLlib v1.0, we support both dense and sparse input in a unified way, which introduces a few
@@ -85,7 +111,7 @@ val vector: Vector = Vectors.dense(array) // a dense vector
 
 [`Vectors`](api/scala/index.html#org.apache.spark.mllib.linalg.Vectors$) provides factory methods to create sparse vectors.
 
-*Note*. Scala imports `scala.collection.immutable.Vector` by default, so you have to import `org.apache.spark.mllib.linalg.Vector` explicitly to use MLlib's `Vector`.
+*Note*: Scala imports `scala.collection.immutable.Vector` by default, so you have to import `org.apache.spark.mllib.linalg.Vector` explicitly to use MLlib's `Vector`.
 
 </div>
 

From 6f671d04fa98f97fd48c5e749b9f47dd4a8b4f44 Mon Sep 17 00:00:00 2001
From: Hari Shreedharan <hshreedharan@apache.org>
Date: Wed, 27 Aug 2014 02:39:02 -0700
Subject: [PATCH 107/489] [SPARK-3154][STREAMING] Make FlumePollingInputDStream
 shutdown cleaner.

Currently lot of errors get thrown from Avro IPC layer when the dstream
or sink is shutdown. This PR cleans it up. Some refactoring is done in the
receiver code to put all of the RPC code into a single Try and just recover
from that. The sink code has also been cleaned up.

Author: Hari Shreedharan <hshreedharan@apache.org>

Closes #2065 from harishreedharan/clean-flume-shutdown and squashes the following commits:

f93a07c [Hari Shreedharan] Formatting fixes.
d7427cc [Hari Shreedharan] More fixes!
a0a8852 [Hari Shreedharan] Fix race condition, hopefully! Minor other changes.
4c9ed02 [Hari Shreedharan] Remove unneeded list in Callback handler. Other misc changes.
8fee36f [Hari Shreedharan] Scala-library is required, else maven build fails. Also catch InterruptedException in TxnProcessor.
445e700 [Hari Shreedharan] Merge remote-tracking branch 'asf/master' into clean-flume-shutdown
87232e0 [Hari Shreedharan] Refactor Flume Input Stream. Clean up code, better error handling.
9001d26 [Hari Shreedharan] Change log level to debug in TransactionProcessor#shutdown method
e7b8d82 [Hari Shreedharan] Incorporate review feedback
598efa7 [Hari Shreedharan] Clean up some exception handling code
e1027c6 [Hari Shreedharan] Merge remote-tracking branch 'asf/master' into clean-flume-shutdown
ed608c8 [Hari Shreedharan] [SPARK-3154][STREAMING] Make FlumePollingInputDStream shutdown cleaner.
---
 external/flume-sink/pom.xml                   |   4 +
 .../flume/sink/SparkAvroCallbackHandler.scala |  56 ++++--
 .../flume/sink/TransactionProcessor.scala     |  18 +-
 .../streaming/flume/FlumeBatchFetcher.scala   | 167 ++++++++++++++++++
 .../flume/FlumePollingInputDStream.scala      |  77 ++------
 5 files changed, 236 insertions(+), 86 deletions(-)
 create mode 100644 external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeBatchFetcher.scala

diff --git a/external/flume-sink/pom.xml b/external/flume-sink/pom.xml
index c1e8e65464fc1..b345276b08ba3 100644
--- a/external/flume-sink/pom.xml
+++ b/external/flume-sink/pom.xml
@@ -70,6 +70,10 @@
       <artifactId>scalatest_${scala.binary.version}</artifactId>
       <scope>test</scope>
     </dependency>
+    <dependency>
+      <groupId>org.scala-lang</groupId>
+      <artifactId>scala-library</artifactId>
+    </dependency>
     <dependency>
       <!--
         Netty explicitly added in test as it has been excluded from
diff --git a/external/flume-sink/src/main/scala/org/apache/spark/streaming/flume/sink/SparkAvroCallbackHandler.scala b/external/flume-sink/src/main/scala/org/apache/spark/streaming/flume/sink/SparkAvroCallbackHandler.scala
index 7da8eb3e35912..e77cf7bfa54d0 100644
--- a/external/flume-sink/src/main/scala/org/apache/spark/streaming/flume/sink/SparkAvroCallbackHandler.scala
+++ b/external/flume-sink/src/main/scala/org/apache/spark/streaming/flume/sink/SparkAvroCallbackHandler.scala
@@ -19,6 +19,8 @@ package org.apache.spark.streaming.flume.sink
 import java.util.concurrent.{ConcurrentHashMap, Executors}
 import java.util.concurrent.atomic.AtomicLong
 
+import scala.collection.JavaConversions._
+
 import org.apache.flume.Channel
 import org.apache.commons.lang.RandomStringUtils
 import com.google.common.util.concurrent.ThreadFactoryBuilder
@@ -45,7 +47,8 @@ private[flume] class SparkAvroCallbackHandler(val threads: Int, val channel: Cha
   val transactionExecutorOpt = Option(Executors.newFixedThreadPool(threads,
     new ThreadFactoryBuilder().setDaemon(true)
       .setNameFormat("Spark Sink Processor Thread - %d").build()))
-  private val processorMap = new ConcurrentHashMap[CharSequence, TransactionProcessor]()
+  private val sequenceNumberToProcessor =
+    new ConcurrentHashMap[CharSequence, TransactionProcessor]()
   // This sink will not persist sequence numbers and reuses them if it gets restarted.
   // So it is possible to commit a transaction which may have been meant for the sink before the
   // restart.
@@ -55,6 +58,8 @@ private[flume] class SparkAvroCallbackHandler(val threads: Int, val channel: Cha
   private val seqBase = RandomStringUtils.randomAlphanumeric(8)
   private val seqCounter = new AtomicLong(0)
 
+  @volatile private var stopped = false
+
   /**
    * Returns a bunch of events to Spark over Avro RPC.
    * @param n Maximum number of events to return in a batch
@@ -63,18 +68,33 @@ private[flume] class SparkAvroCallbackHandler(val threads: Int, val channel: Cha
   override def getEventBatch(n: Int): EventBatch = {
     logDebug("Got getEventBatch call from Spark.")
     val sequenceNumber = seqBase + seqCounter.incrementAndGet()
-    val processor = new TransactionProcessor(channel, sequenceNumber,
-      n, transactionTimeout, backOffInterval, this)
-    transactionExecutorOpt.foreach(executor => {
-      executor.submit(processor)
-    })
-    // Wait until a batch is available - will be an error if error message is non-empty
-    val batch = processor.getEventBatch
-    if (!SparkSinkUtils.isErrorBatch(batch)) {
-      processorMap.put(sequenceNumber.toString, processor)
-      logDebug("Sending event batch with sequence number: " + sequenceNumber)
+    createProcessor(sequenceNumber, n) match {
+      case Some(processor) =>
+        transactionExecutorOpt.foreach(_.submit(processor))
+        // Wait until a batch is available - will be an error if error message is non-empty
+        val batch = processor.getEventBatch
+        if (SparkSinkUtils.isErrorBatch(batch)) {
+          // Remove the processor if it is an error batch since no ACK is sent.
+          removeAndGetProcessor(sequenceNumber)
+          logWarning("Received an error batch - no events were received from channel! ")
+        }
+        batch
+      case None =>
+        new EventBatch("Spark sink has been stopped!", "", java.util.Collections.emptyList())
+    }
+  }
+
+  private def createProcessor(seq: String, n: Int): Option[TransactionProcessor] = {
+    sequenceNumberToProcessor.synchronized {
+      if (!stopped) {
+        val processor = new TransactionProcessor(
+          channel, seq, n, transactionTimeout, backOffInterval, this)
+        sequenceNumberToProcessor.put(seq, processor)
+        Some(processor)
+      } else {
+        None
+      }
     }
-    batch
   }
 
   /**
@@ -116,7 +136,9 @@ private[flume] class SparkAvroCallbackHandler(val threads: Int, val channel: Cha
    *         longer tracked and the caller is responsible for that txn processor.
    */
   private[sink] def removeAndGetProcessor(sequenceNumber: CharSequence): TransactionProcessor = {
-    processorMap.remove(sequenceNumber.toString) // The toString is required!
+    sequenceNumberToProcessor.synchronized {
+      sequenceNumberToProcessor.remove(sequenceNumber.toString)
+    }
   }
 
   /**
@@ -124,8 +146,10 @@ private[flume] class SparkAvroCallbackHandler(val threads: Int, val channel: Cha
    */
   def shutdown() {
     logInfo("Shutting down Spark Avro Callback Handler")
-    transactionExecutorOpt.foreach(executor => {
-      executor.shutdownNow()
-    })
+    sequenceNumberToProcessor.synchronized {
+      stopped = true
+      sequenceNumberToProcessor.values().foreach(_.shutdown())
+    }
+    transactionExecutorOpt.foreach(_.shutdownNow())
   }
 }
diff --git a/external/flume-sink/src/main/scala/org/apache/spark/streaming/flume/sink/TransactionProcessor.scala b/external/flume-sink/src/main/scala/org/apache/spark/streaming/flume/sink/TransactionProcessor.scala
index b9e3c786ebb3b..13f3aa94be414 100644
--- a/external/flume-sink/src/main/scala/org/apache/spark/streaming/flume/sink/TransactionProcessor.scala
+++ b/external/flume-sink/src/main/scala/org/apache/spark/streaming/flume/sink/TransactionProcessor.scala
@@ -60,6 +60,8 @@ private class TransactionProcessor(val channel: Channel, val seqNum: String,
   // succeeded.
   @volatile private var batchSuccess = false
 
+  @volatile private var stopped = false
+
   // The transaction that this processor would handle
   var txOpt: Option[Transaction] = None
 
@@ -88,6 +90,11 @@ private class TransactionProcessor(val channel: Channel, val seqNum: String,
     batchAckLatch.countDown()
   }
 
+  private[flume] def shutdown(): Unit = {
+    logDebug("Shutting down transaction processor")
+    stopped = true
+  }
+
   /**
    * Populates events into the event batch. If the batch cannot be populated,
    * this method will not set the events into the event batch, but it sets an error message.
@@ -106,7 +113,7 @@ private class TransactionProcessor(val channel: Channel, val seqNum: String,
         var gotEventsInThisTxn = false
         var loopCounter: Int = 0
         loop.breakable {
-          while (events.size() < maxBatchSize
+          while (!stopped && events.size() < maxBatchSize
             && loopCounter < totalAttemptsToRemoveFromChannel) {
             loopCounter += 1
             Option(channel.take()) match {
@@ -115,7 +122,7 @@ private class TransactionProcessor(val channel: Channel, val seqNum: String,
                   ByteBuffer.wrap(event.getBody)))
                 gotEventsInThisTxn = true
               case None =>
-                if (!gotEventsInThisTxn) {
+                if (!gotEventsInThisTxn && !stopped) {
                   logDebug("Sleeping for " + backOffInterval + " millis as no events were read in" +
                     " the current transaction")
                   TimeUnit.MILLISECONDS.sleep(backOffInterval)
@@ -125,7 +132,7 @@ private class TransactionProcessor(val channel: Channel, val seqNum: String,
             }
           }
         }
-        if (!gotEventsInThisTxn) {
+        if (!gotEventsInThisTxn && !stopped) {
           val msg = "Tried several times, " +
             "but did not get any events from the channel!"
           logWarning(msg)
@@ -136,6 +143,11 @@ private class TransactionProcessor(val channel: Channel, val seqNum: String,
         }
       })
     } catch {
+      case interrupted: InterruptedException =>
+        // Don't pollute logs if the InterruptedException came from this being stopped
+        if (!stopped) {
+          logWarning("Error while processing transaction.", interrupted)
+        }
       case e: Exception =>
         logWarning("Error while processing transaction.", e)
         eventBatch.setErrorMsg(e.getMessage)
diff --git a/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeBatchFetcher.scala b/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeBatchFetcher.scala
new file mode 100644
index 0000000000000..88cc2aa3bf022
--- /dev/null
+++ b/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeBatchFetcher.scala
@@ -0,0 +1,167 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.spark.streaming.flume
+
+import scala.collection.JavaConversions._
+import scala.collection.mutable.ArrayBuffer
+
+import com.google.common.base.Throwables
+
+import org.apache.spark.Logging
+import org.apache.spark.streaming.flume.sink._
+
+/**
+ * This class implements the core functionality of [[FlumePollingReceiver]]. When started it
+ * pulls data from Flume, stores it to Spark and then sends an Ack or Nack. This class should be
+ * run via an [[java.util.concurrent.Executor]] as this implements [[Runnable]]
+ *
+ * @param receiver The receiver that owns this instance.
+ */
+
+private[flume] class FlumeBatchFetcher(receiver: FlumePollingReceiver) extends Runnable with
+  Logging {
+
+  def run(): Unit = {
+    while (!receiver.isStopped()) {
+      val connection = receiver.getConnections.poll()
+      val client = connection.client
+      var batchReceived = false
+      var seq: CharSequence = null
+      try {
+        getBatch(client) match {
+          case Some(eventBatch) =>
+            batchReceived = true
+            seq = eventBatch.getSequenceNumber
+            val events = toSparkFlumeEvents(eventBatch.getEvents)
+            if (store(events)) {
+              sendAck(client, seq)
+            } else {
+              sendNack(batchReceived, client, seq)
+            }
+          case None =>
+        }
+      } catch {
+        case e: Exception =>
+          Throwables.getRootCause(e) match {
+            // If the cause was an InterruptedException, then check if the receiver is stopped -
+            // if yes, just break out of the loop. Else send a Nack and log a warning.
+            // In the unlikely case, the cause was not an Exception,
+            // then just throw it out and exit.
+            case interrupted: InterruptedException =>
+              if (!receiver.isStopped()) {
+                logWarning("Interrupted while receiving data from Flume", interrupted)
+                sendNack(batchReceived, client, seq)
+              }
+            case exception: Exception =>
+              logWarning("Error while receiving data from Flume", exception)
+              sendNack(batchReceived, client, seq)
+          }
+      } finally {
+        receiver.getConnections.add(connection)
+      }
+    }
+  }
+
+  /**
+   * Gets a batch of events from the specified client. This method does not handle any exceptions
+   * which will be propogated to the caller.
+   * @param client Client to get events from
+   * @return [[Some]] which contains the event batch if Flume sent any events back, else [[None]]
+   */
+  private def getBatch(client: SparkFlumeProtocol.Callback): Option[EventBatch] = {
+    val eventBatch = client.getEventBatch(receiver.getMaxBatchSize)
+    if (!SparkSinkUtils.isErrorBatch(eventBatch)) {
+      // No error, proceed with processing data
+      logDebug(s"Received batch of ${eventBatch.getEvents.size} events with sequence " +
+        s"number: ${eventBatch.getSequenceNumber}")
+      Some(eventBatch)
+    } else {
+      logWarning("Did not receive events from Flume agent due to error on the Flume agent: " +
+        eventBatch.getErrorMsg)
+      None
+    }
+  }
+
+  /**
+   * Store the events in the buffer to Spark. This method will not propogate any exceptions,
+   * but will propogate any other errors.
+   * @param buffer The buffer to store
+   * @return true if the data was stored without any exception being thrown, else false
+   */
+  private def store(buffer: ArrayBuffer[SparkFlumeEvent]): Boolean = {
+    try {
+      receiver.store(buffer)
+      true
+    } catch {
+      case e: Exception =>
+        logWarning("Error while attempting to store data received from Flume", e)
+        false
+    }
+  }
+
+  /**
+   * Send an ack to the client for the sequence number. This method does not handle any exceptions
+   * which will be propagated to the caller.
+   * @param client client to send the ack to
+   * @param seq sequence number of the batch to be ack-ed.
+   * @return
+   */
+  private def sendAck(client: SparkFlumeProtocol.Callback, seq: CharSequence): Unit = {
+    logDebug("Sending ack for sequence number: " + seq)
+    client.ack(seq)
+    logDebug("Ack sent for sequence number: " + seq)
+  }
+
+  /**
+   * This method sends a Nack if a batch was received to the client with the given sequence
+   * number. Any exceptions thrown by the RPC call is simply thrown out as is - no effort is made
+   * to handle it.
+   * @param batchReceived true if a batch was received. If this is false, no nack is sent
+   * @param client The client to which the nack should be sent
+   * @param seq The sequence number of the batch that is being nack-ed.
+   */
+  private def sendNack(batchReceived: Boolean, client: SparkFlumeProtocol.Callback,
+    seq: CharSequence): Unit = {
+    if (batchReceived) {
+      // Let Flume know that the events need to be pushed back into the channel.
+      logDebug("Sending nack for sequence number: " + seq)
+      client.nack(seq) // If the agent is down, even this could fail and throw
+      logDebug("Nack sent for sequence number: " + seq)
+    }
+  }
+
+  /**
+   * Utility method to convert [[SparkSinkEvent]]s to [[SparkFlumeEvent]]s
+   * @param events - Events to convert to SparkFlumeEvents
+   * @return - The SparkFlumeEvent generated from SparkSinkEvent
+   */
+  private def toSparkFlumeEvents(events: java.util.List[SparkSinkEvent]):
+    ArrayBuffer[SparkFlumeEvent] = {
+    // Convert each Flume event to a serializable SparkFlumeEvent
+    val buffer = new ArrayBuffer[SparkFlumeEvent](events.size())
+    var j = 0
+    while (j < events.size()) {
+      val event = events(j)
+      val sparkFlumeEvent = new SparkFlumeEvent()
+      sparkFlumeEvent.event.setBody(event.getBody)
+      sparkFlumeEvent.event.setHeaders(event.getHeaders)
+      buffer += sparkFlumeEvent
+      j += 1
+    }
+    buffer
+  }
+}
diff --git a/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumePollingInputDStream.scala b/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumePollingInputDStream.scala
index 148262bb6771e..92fa5b41be89e 100644
--- a/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumePollingInputDStream.scala
+++ b/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumePollingInputDStream.scala
@@ -18,10 +18,9 @@ package org.apache.spark.streaming.flume
 
 
 import java.net.InetSocketAddress
-import java.util.concurrent.{LinkedBlockingQueue, TimeUnit, Executors}
+import java.util.concurrent.{LinkedBlockingQueue, Executors}
 
 import scala.collection.JavaConversions._
-import scala.collection.mutable.ArrayBuffer
 import scala.reflect.ClassTag
 
 import com.google.common.util.concurrent.ThreadFactoryBuilder
@@ -86,61 +85,9 @@ private[streaming] class FlumePollingReceiver(
       connections.add(new FlumeConnection(transceiver, client))
     })
     for (i <- 0 until parallelism) {
-      logInfo("Starting Flume Polling Receiver worker threads starting..")
+      logInfo("Starting Flume Polling Receiver worker threads..")
       // Threads that pull data from Flume.
-      receiverExecutor.submit(new Runnable {
-        override def run(): Unit = {
-          while (true) {
-            val connection = connections.poll()
-            val client = connection.client
-            try {
-              val eventBatch = client.getEventBatch(maxBatchSize)
-              if (!SparkSinkUtils.isErrorBatch(eventBatch)) {
-                // No error, proceed with processing data
-                val seq = eventBatch.getSequenceNumber
-                val events: java.util.List[SparkSinkEvent] = eventBatch.getEvents
-                logDebug(
-                  "Received batch of " + events.size() + " events with sequence number: " + seq)
-                try {
-                  // Convert each Flume event to a serializable SparkFlumeEvent
-                  val buffer = new ArrayBuffer[SparkFlumeEvent](events.size())
-                  var j = 0
-                  while (j < events.size()) {
-                    buffer += toSparkFlumeEvent(events(j))
-                    j += 1
-                  }
-                  store(buffer)
-                  logDebug("Sending ack for sequence number: " + seq)
-                  // Send an ack to Flume so that Flume discards the events from its channels.
-                  client.ack(seq)
-                  logDebug("Ack sent for sequence number: " + seq)
-                } catch {
-                  case e: Exception =>
-                    try {
-                      // Let Flume know that the events need to be pushed back into the channel.
-                      logDebug("Sending nack for sequence number: " + seq)
-                      client.nack(seq) // If the agent is down, even this could fail and throw
-                      logDebug("Nack sent for sequence number: " + seq)
-                    } catch {
-                      case e: Exception => logError(
-                        "Sending Nack also failed. A Flume agent is down.")
-                    }
-                    TimeUnit.SECONDS.sleep(2L) // for now just leave this as a fixed 2 seconds.
-                    logWarning("Error while attempting to store events", e)
-                }
-              } else {
-                logWarning("Did not receive events from Flume agent due to error on the Flume " +
-                  "agent: " + eventBatch.getErrorMsg)
-              }
-            } catch {
-              case e: Exception =>
-                logWarning("Error while reading data from Flume", e)
-            } finally {
-              connections.add(connection)
-            }
-          }
-        }
-      })
+      receiverExecutor.submit(new FlumeBatchFetcher(this))
     }
   }
 
@@ -153,16 +100,12 @@ private[streaming] class FlumePollingReceiver(
     channelFactory.releaseExternalResources()
   }
 
-  /**
-   * Utility method to convert [[SparkSinkEvent]] to [[SparkFlumeEvent]]
-   * @param event - Event to convert to SparkFlumeEvent
-   * @return - The SparkFlumeEvent generated from SparkSinkEvent
-   */
-  private def toSparkFlumeEvent(event: SparkSinkEvent): SparkFlumeEvent = {
-    val sparkFlumeEvent = new SparkFlumeEvent()
-    sparkFlumeEvent.event.setBody(event.getBody)
-    sparkFlumeEvent.event.setHeaders(event.getHeaders)
-    sparkFlumeEvent
+  private[flume] def getConnections: LinkedBlockingQueue[FlumeConnection] = {
+    this.connections
+  }
+
+  private[flume] def getMaxBatchSize: Int = {
+    this.maxBatchSize
   }
 }
 
@@ -171,7 +114,7 @@ private[streaming] class FlumePollingReceiver(
  * @param transceiver The transceiver to use for communication with Flume
  * @param client The client that the callbacks are received on.
  */
-private class FlumeConnection(val transceiver: NettyTransceiver,
+private[flume] class FlumeConnection(val transceiver: NettyTransceiver,
   val client: SparkFlumeProtocol.Callback)
 
 

From b92d823ad13f6fcc325eeb99563bea543871c6aa Mon Sep 17 00:00:00 2001
From: Marcelo Vanzin <vanzin@cloudera.com>
Date: Wed, 27 Aug 2014 11:02:04 -0500
Subject: [PATCH 108/489] [SPARK-2933] [yarn] Refactor and cleanup Yarn AM
 code.

This change modifies the Yarn module so that all the logic related
to running the ApplicationMaster is localized. Instead of, previously,
4 different classes with mostly identical code, now we have:

- A single, shared ApplicationMaster class, which can operate both in
  client and cluster mode, and substitutes the old ApplicationMaster
  (for cluster mode) and ExecutorLauncher (for client mode).

The benefit here is that all different execution modes for all supported
yarn versions use the same shared code for monitoring executor allocation,
setting up configuration, and monitoring the process's lifecycle.

- A new YarnRMClient interface, which defines basic RM functionality needed
  by the ApplicationMaster. This interface has concrete implementations for
  each supported Yarn version.

- A new YarnAllocator interface, which just abstracts the existing interface
  of the YarnAllocationHandler class. This is to avoid having to touch the
  allocator code too much in this change, although it might benefit from a
  similar effort in the future.

The end result is much easier to understand code, with much less duplication,
making it much easier to fix bugs, add features, and test everything knowing
that all supported versions will behave the same.

Author: Marcelo Vanzin <vanzin@cloudera.com>

Closes #2020 from vanzin/SPARK-2933 and squashes the following commits:

3bbf3e7 [Marcelo Vanzin] Merge branch 'master' into SPARK-2933
ff389ed [Marcelo Vanzin] Do not interrupt reporter thread from within itself.
3a8ed37 [Marcelo Vanzin] Remote stale comment.
0f5142c [Marcelo Vanzin] Review feedback.
41f8c8a [Marcelo Vanzin] Fix app status reporting.
c0794be [Marcelo Vanzin] Correctly clean up staging directory.
92770cc [Marcelo Vanzin] Merge branch 'master' into SPARK-2933
ecaf332 [Marcelo Vanzin] Small fix to shutdown code.
f02d3f8 [Marcelo Vanzin] Merge branch 'master' into SPARK-2933
f581122 [Marcelo Vanzin] Review feedback.
557fdeb [Marcelo Vanzin] Cleanup a couple more constants.
be6068d [Marcelo Vanzin] Restore shutdown hook to clean up staging dir.
5150993 [Marcelo Vanzin] Some more cleanup.
b6289ab [Marcelo Vanzin] Move cluster/client code to separate methods.
ecb23cd [Marcelo Vanzin] More trivial cleanup.
34f1e63 [Marcelo Vanzin] Fix some questionable error handling.
5657c7d [Marcelo Vanzin] Finish app if SparkContext initialization times out.
0e4be3d [Marcelo Vanzin] Keep "ExecutorLauncher" as the main class for client-mode AM.
91beabb [Marcelo Vanzin] Fix UI filter registration.
8c72239 [Marcelo Vanzin] Trivial cleanups.
99a52d5 [Marcelo Vanzin] Changes to the yarn-alpha project to use common AM code.
848ca6d [Marcelo Vanzin] [SPARK-2933] [yarn] Refactor and cleanup Yarn AM code.
---
 .../spark/deploy/yarn/ApplicationMaster.scala | 453 ------------------
 .../spark/deploy/yarn/ExecutorLauncher.scala  | 315 ------------
 .../deploy/yarn/YarnAllocationHandler.scala   | 192 ++------
 .../spark/deploy/yarn/YarnRMClientImpl.scala  | 103 ++++
 .../spark/deploy/yarn/ApplicationMaster.scala | 430 +++++++++++++++++
 .../yarn/ApplicationMasterArguments.scala     |  26 +-
 .../spark/deploy/yarn/ClientArguments.scala   |   9 +-
 .../apache/spark/deploy/yarn/ClientBase.scala |  54 ++-
 .../spark/deploy/yarn/YarnAllocator.scala     |  34 ++
 .../spark/deploy/yarn/YarnRMClient.scala      |  67 +++
 .../deploy/yarn/YarnSparkHadoopUtil.scala     |  51 ++
 .../cluster/YarnClientClusterScheduler.scala  |  11 +-
 .../cluster/YarnClientSchedulerBackend.scala  |   7 +-
 .../cluster/YarnClusterScheduler.scala        |  17 +-
 .../spark/deploy/yarn/ApplicationMaster.scala | 413 ----------------
 .../spark/deploy/yarn/ExecutorLauncher.scala  | 276 -----------
 .../deploy/yarn/YarnAllocationHandler.scala   | 196 ++------
 .../spark/deploy/yarn/YarnRMClientImpl.scala  |  76 +++
 18 files changed, 892 insertions(+), 1838 deletions(-)
 delete mode 100644 yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
 delete mode 100644 yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala
 create mode 100644 yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/YarnRMClientImpl.scala
 create mode 100644 yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
 create mode 100644 yarn/common/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocator.scala
 create mode 100644 yarn/common/src/main/scala/org/apache/spark/deploy/yarn/YarnRMClient.scala
 delete mode 100644 yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
 delete mode 100644 yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala
 create mode 100644 yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/YarnRMClientImpl.scala

diff --git a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala b/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
deleted file mode 100644
index 4d4848b1bd8f8..0000000000000
--- a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
+++ /dev/null
@@ -1,453 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.deploy.yarn
-
-import java.io.IOException
-import java.net.Socket
-import java.util.concurrent.CopyOnWriteArrayList
-import java.util.concurrent.atomic.{AtomicInteger, AtomicReference}
-
-import scala.collection.JavaConversions._
-
-import org.apache.hadoop.conf.Configuration
-import org.apache.hadoop.fs.{FileSystem, Path}
-import org.apache.hadoop.net.NetUtils
-import org.apache.hadoop.util.ShutdownHookManager
-import org.apache.hadoop.yarn.api._
-import org.apache.hadoop.yarn.api.records._
-import org.apache.hadoop.yarn.api.protocolrecords._
-import org.apache.hadoop.yarn.conf.YarnConfiguration
-import org.apache.hadoop.yarn.ipc.YarnRPC
-import org.apache.hadoop.yarn.util.{ConverterUtils, Records}
-
-import org.apache.spark.{Logging, SecurityManager, SparkConf, SparkContext}
-import org.apache.spark.deploy.SparkHadoopUtil
-import org.apache.spark.util.{SignalLogger, Utils}
-
-/**
- * An application master that runs the users driver program and allocates executors.
- */
-class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration,
-                        sparkConf: SparkConf) extends Logging {
-
-  def this(args: ApplicationMasterArguments, sparkConf: SparkConf) =
-    this(args, new Configuration(), sparkConf)
-
-  def this(args: ApplicationMasterArguments) = this(args, new SparkConf())
-
-  private val rpc: YarnRPC = YarnRPC.create(conf)
-  private var resourceManager: AMRMProtocol = _
-  private var appAttemptId: ApplicationAttemptId = _
-  private var userThread: Thread = _
-  private val yarnConf: YarnConfiguration = new YarnConfiguration(conf)
-  private val fs = FileSystem.get(yarnConf)
-
-  private var yarnAllocator: YarnAllocationHandler = _
-  private var isFinished: Boolean = false
-  private var uiAddress: String = _
-  private var uiHistoryAddress: String = _
-  private val maxAppAttempts: Int = conf.getInt(YarnConfiguration.RM_AM_MAX_RETRIES,
-    YarnConfiguration.DEFAULT_RM_AM_MAX_RETRIES)
-  private var isLastAMRetry: Boolean = true
-
-  // Default to numExecutors * 2, with minimum of 3
-  private val maxNumExecutorFailures = sparkConf.getInt("spark.yarn.max.executor.failures",
-    sparkConf.getInt("spark.yarn.max.worker.failures", math.max(args.numExecutors * 2, 3)))
-
-  private var registered = false
-
-  def run() {
-    // set the web ui port to be ephemeral for yarn so we don't conflict with
-    // other spark processes running on the same box
-    System.setProperty("spark.ui.port", "0")
-
-    // when running the AM, the Spark master is always "yarn-cluster"
-    System.setProperty("spark.master", "yarn-cluster")
-
-    // Use priority 30 as its higher then HDFS. Its same priority as MapReduce is using.
-    ShutdownHookManager.get().addShutdownHook(new AppMasterShutdownHook(this), 30)
-
-    appAttemptId = getApplicationAttemptId()
-    isLastAMRetry = appAttemptId.getAttemptId() >= maxAppAttempts
-    resourceManager = registerWithResourceManager()
-
-    // setup AmIpFilter for the SparkUI - do this before we start the UI
-    addAmIpFilter()
-
-    ApplicationMaster.register(this)
-
-    // Call this to force generation of secret so it gets populated into the
-    // hadoop UGI. This has to happen before the startUserClass which does a
-    // doAs in order for the credentials to be passed on to the executor containers.
-    val securityMgr = new SecurityManager(sparkConf)
-
-    // Start the user's JAR
-    userThread = startUserClass()
-
-    // This a bit hacky, but we need to wait until the spark.driver.port property has
-    // been set by the Thread executing the user class.
-    waitForSparkContextInitialized()
-
-    // Do this after spark master is up and SparkContext is created so that we can register UI Url
-    synchronized {
-      if (!isFinished) {
-        registerApplicationMaster()
-        registered = true
-      }
-    }
-
-    // Allocate all containers
-    allocateExecutors()
-
-    // Wait for the user class to Finish
-    userThread.join()
-
-    System.exit(0)
-  }
-
-  // add the yarn amIpFilter that Yarn requires for properly securing the UI
-  private def addAmIpFilter() {
-    val amFilter = "org.apache.hadoop.yarn.server.webproxy.amfilter.AmIpFilter"
-    System.setProperty("spark.ui.filters", amFilter)
-    val proxy = YarnConfiguration.getProxyHostAndPort(conf)
-    val parts : Array[String] = proxy.split(":")
-    val uriBase = "http://" + proxy +
-      System.getenv(ApplicationConstants.APPLICATION_WEB_PROXY_BASE_ENV)
-
-    val params = "PROXY_HOST=" + parts(0) + "," + "PROXY_URI_BASE=" + uriBase
-    System.setProperty("spark.org.apache.hadoop.yarn.server.webproxy.amfilter.AmIpFilter.params",
-      params)
-  }
-
-  private def getApplicationAttemptId(): ApplicationAttemptId = {
-    val envs = System.getenv()
-    val containerIdString = envs.get(ApplicationConstants.AM_CONTAINER_ID_ENV)
-    val containerId = ConverterUtils.toContainerId(containerIdString)
-    val appAttemptId = containerId.getApplicationAttemptId()
-    logInfo("ApplicationAttemptId: " + appAttemptId)
-    appAttemptId
-  }
-
-  private def registerWithResourceManager(): AMRMProtocol = {
-    val rmAddress = NetUtils.createSocketAddr(yarnConf.get(
-      YarnConfiguration.RM_SCHEDULER_ADDRESS,
-      YarnConfiguration.DEFAULT_RM_SCHEDULER_ADDRESS))
-    logInfo("Connecting to ResourceManager at " + rmAddress)
-    rpc.getProxy(classOf[AMRMProtocol], rmAddress, conf).asInstanceOf[AMRMProtocol]
-  }
-
-  private def registerApplicationMaster(): RegisterApplicationMasterResponse = {
-    logInfo("Registering the ApplicationMaster")
-    val appMasterRequest = Records.newRecord(classOf[RegisterApplicationMasterRequest])
-      .asInstanceOf[RegisterApplicationMasterRequest]
-    appMasterRequest.setApplicationAttemptId(appAttemptId)
-    // Setting this to master host,port - so that the ApplicationReport at client has some
-    // sensible info.
-    // Users can then monitor stderr/stdout on that node if required.
-    appMasterRequest.setHost(Utils.localHostName())
-    appMasterRequest.setRpcPort(0)
-    appMasterRequest.setTrackingUrl(uiAddress)
-    resourceManager.registerApplicationMaster(appMasterRequest)
-  }
-
-  private def startUserClass(): Thread = {
-    logInfo("Starting the user JAR in a separate Thread")
-    System.setProperty("spark.executor.instances", args.numExecutors.toString)
-    val mainMethod = Class.forName(
-      args.userClass,
-      false /* initialize */ ,
-      Thread.currentThread.getContextClassLoader).getMethod("main", classOf[Array[String]])
-    val t = new Thread {
-      override def run() {
-
-        var successed = false
-        try {
-          // Copy
-          var mainArgs: Array[String] = new Array[String](args.userArgs.size)
-          args.userArgs.copyToArray(mainArgs, 0, args.userArgs.size)
-          mainMethod.invoke(null, mainArgs)
-          // some job script has "System.exit(0)" at the end, for example SparkPi, SparkLR
-          // userThread will stop here unless it has uncaught exception thrown out
-          // It need shutdown hook to set SUCCEEDED
-          successed = true
-        } finally {
-          logDebug("finishing main")
-          isLastAMRetry = true
-          if (successed) {
-            ApplicationMaster.this.finishApplicationMaster(FinalApplicationStatus.SUCCEEDED)
-          } else {
-            ApplicationMaster.this.finishApplicationMaster(FinalApplicationStatus.FAILED)
-          }
-        }
-      }
-    }
-    t.start()
-    t
-  }
-
-  // this need to happen before allocateExecutors
-  private def waitForSparkContextInitialized() {
-    logInfo("Waiting for spark context initialization")
-    try {
-      var sparkContext: SparkContext = null
-      ApplicationMaster.sparkContextRef.synchronized {
-        var count = 0
-        val waitTime = 10000L
-        val numTries = sparkConf.getInt("spark.yarn.ApplicationMaster.waitTries", 10)
-        while (ApplicationMaster.sparkContextRef.get() == null && count < numTries
-            && !isFinished) {
-          logInfo("Waiting for spark context initialization ... " + count)
-          count = count + 1
-          ApplicationMaster.sparkContextRef.wait(waitTime)
-        }
-        sparkContext = ApplicationMaster.sparkContextRef.get()
-        assert(sparkContext != null || count >= numTries)
-
-        if (null != sparkContext) {
-          uiAddress = sparkContext.ui.appUIHostPort
-          uiHistoryAddress = YarnSparkHadoopUtil.getUIHistoryAddress(sparkContext, sparkConf)
-          this.yarnAllocator = YarnAllocationHandler.newAllocator(
-            yarnConf,
-            resourceManager,
-            appAttemptId,
-            args,
-            sparkContext.preferredNodeLocationData,
-            sparkContext.getConf)
-        } else {
-          logWarning("Unable to retrieve sparkContext inspite of waiting for %d, numTries = %d".
-            format(count * waitTime, numTries))
-          this.yarnAllocator = YarnAllocationHandler.newAllocator(
-            yarnConf,
-            resourceManager,
-            appAttemptId,
-            args,
-            sparkContext.getConf)
-        }
-      }
-    }
-  }
-
-  private def allocateExecutors() {
-    try {
-      logInfo("Allocating " + args.numExecutors + " executors.")
-      // Wait until all containers have finished
-      // TODO: This is a bit ugly. Can we make it nicer?
-      // TODO: Handle container failure
-
-      // Exits the loop if the user thread exits.
-      while (yarnAllocator.getNumExecutorsRunning < args.numExecutors && userThread.isAlive
-          && !isFinished) {
-        checkNumExecutorsFailed()
-        yarnAllocator.allocateContainers(
-          math.max(args.numExecutors - yarnAllocator.getNumExecutorsRunning, 0))
-        Thread.sleep(ApplicationMaster.ALLOCATE_HEARTBEAT_INTERVAL)
-      }
-    }
-    logInfo("All executors have launched.")
-
-    // Launch a progress reporter thread, else the app will get killed after expiration
-    // (def: 10mins) timeout.
-    // TODO(harvey): Verify the timeout
-    if (userThread.isAlive) {
-      // Ensure that progress is sent before YarnConfiguration.RM_AM_EXPIRY_INTERVAL_MS elapses.
-      val timeoutInterval = yarnConf.getInt(YarnConfiguration.RM_AM_EXPIRY_INTERVAL_MS, 120000)
-
-      // we want to be reasonably responsive without causing too many requests to RM.
-      val schedulerInterval =
-        sparkConf.getLong("spark.yarn.scheduler.heartbeat.interval-ms", 5000)
-
-      // must be <= timeoutInterval / 2.
-      val interval = math.min(timeoutInterval / 2, schedulerInterval)
-
-      launchReporterThread(interval)
-    }
-  }
-
-  private def launchReporterThread(_sleepTime: Long): Thread = {
-    val sleepTime = if (_sleepTime <= 0) 0 else _sleepTime
-
-    val t = new Thread {
-      override def run() {
-        while (userThread.isAlive && !isFinished) {
-          checkNumExecutorsFailed()
-          val missingExecutorCount = args.numExecutors - yarnAllocator.getNumExecutorsRunning
-          if (missingExecutorCount > 0) {
-            logInfo("Allocating %d containers to make up for (potentially) lost containers".
-              format(missingExecutorCount))
-            yarnAllocator.allocateContainers(missingExecutorCount)
-          } else {
-            sendProgress()
-          }
-          Thread.sleep(sleepTime)
-        }
-      }
-    }
-    // Setting to daemon status, though this is usually not a good idea.
-    t.setDaemon(true)
-    t.start()
-    logInfo("Started progress reporter thread - sleep time : " + sleepTime)
-    t
-  }
-
-  private def checkNumExecutorsFailed() {
-    if (yarnAllocator.getNumExecutorsFailed >= maxNumExecutorFailures) {
-      logInfo("max number of executor failures reached")
-      finishApplicationMaster(FinalApplicationStatus.FAILED,
-        "max number of executor failures reached")
-      // make sure to stop the user thread
-      val sparkContext = ApplicationMaster.sparkContextRef.get()
-      if (sparkContext != null) {
-        logInfo("Invoking sc stop from checkNumExecutorsFailed")
-        sparkContext.stop()
-      } else {
-        logError("sparkContext is null when should shutdown")
-      }
-    }
-  }
-
-  private def sendProgress() {
-    logDebug("Sending progress")
-    // Simulated with an allocate request with no nodes requested ...
-    yarnAllocator.allocateContainers(0)
-  }
-
-  /*
-  def printContainers(containers: List[Container]) = {
-    for (container <- containers) {
-      logInfo("Launching shell command on a new container."
-        + ", containerId=" + container.getId()
-        + ", containerNode=" + container.getNodeId().getHost()
-        + ":" + container.getNodeId().getPort()
-        + ", containerNodeURI=" + container.getNodeHttpAddress()
-        + ", containerState" + container.getState()
-        + ", containerResourceMemory"
-        + container.getResource().getMemory())
-    }
-  }
-  */
-
-  def finishApplicationMaster(status: FinalApplicationStatus, diagnostics: String = "") {
-    synchronized {
-      if (isFinished) {
-        return
-      }
-      isFinished = true
-
-      logInfo("finishApplicationMaster with " + status)
-      if (registered) {
-        val finishReq = Records.newRecord(classOf[FinishApplicationMasterRequest])
-          .asInstanceOf[FinishApplicationMasterRequest]
-        finishReq.setAppAttemptId(appAttemptId)
-        finishReq.setFinishApplicationStatus(status)
-        finishReq.setDiagnostics(diagnostics)
-        finishReq.setTrackingUrl(uiHistoryAddress)
-        resourceManager.finishApplicationMaster(finishReq)
-      }
-    }
-  }
-
-  /**
-   * Clean up the staging directory.
-   */
-  private def cleanupStagingDir() {
-    var stagingDirPath: Path = null
-    try {
-      val preserveFiles = sparkConf.get("spark.yarn.preserve.staging.files", "false").toBoolean
-      if (!preserveFiles) {
-        stagingDirPath = new Path(System.getenv("SPARK_YARN_STAGING_DIR"))
-        if (stagingDirPath == null) {
-          logError("Staging directory is null")
-          return
-        }
-        logInfo("Deleting staging directory " + stagingDirPath)
-        fs.delete(stagingDirPath, true)
-      }
-    } catch {
-      case ioe: IOException =>
-        logError("Failed to cleanup staging dir " + stagingDirPath, ioe)
-    }
-  }
-
-  // The shutdown hook that runs when a signal is received AND during normal close of the JVM.
-  class AppMasterShutdownHook(appMaster: ApplicationMaster) extends Runnable {
-
-    def run() {
-      logInfo("AppMaster received a signal.")
-      // we need to clean up staging dir before HDFS is shut down
-      // make sure we don't delete it until this is the last AM
-      if (appMaster.isLastAMRetry) appMaster.cleanupStagingDir()
-    }
-  }
-
-}
-
-object ApplicationMaster extends Logging {
-  // TODO: Currently, task to container is computed once (TaskSetManager) - which need not be
-  // optimal as more containers are available. Might need to handle this better.
-  private val ALLOCATE_HEARTBEAT_INTERVAL = 100
-
-  private val applicationMasters = new CopyOnWriteArrayList[ApplicationMaster]()
-
-  def register(master: ApplicationMaster) {
-    applicationMasters.add(master)
-  }
-
-  val sparkContextRef: AtomicReference[SparkContext] =
-    new AtomicReference[SparkContext](null /* initialValue */)
-
-  def sparkContextInitialized(sc: SparkContext): Boolean = {
-    var modified = false
-    sparkContextRef.synchronized {
-      modified = sparkContextRef.compareAndSet(null, sc)
-      sparkContextRef.notifyAll()
-    }
-
-    // Add a shutdown hook - as a best case effort in case users do not call sc.stop or do
-    // System.exit.
-    // Should not really have to do this, but it helps YARN to evict resources earlier.
-    // Not to mention, prevent the Client from declaring failure even though we exited properly.
-    // Note that this will unfortunately not properly clean up the staging files because it gets
-    // called too late, after the filesystem is already shutdown.
-    if (modified) {
-      Runtime.getRuntime().addShutdownHook(new Thread with Logging {
-        // This is not only logs, but also ensures that log system is initialized for this instance
-        // when we are actually 'run'-ing.
-        logInfo("Adding shutdown hook for context " + sc)
-
-        override def run() {
-          logInfo("Invoking sc stop from shutdown hook")
-          sc.stop()
-          // Best case ...
-          for (master <- applicationMasters) {
-            master.finishApplicationMaster(FinalApplicationStatus.SUCCEEDED)
-          }
-        }
-      })
-    }
-
-    modified
-  }
-
-  def main(argStrings: Array[String]) {
-    SignalLogger.register(log)
-    val args = new ApplicationMasterArguments(argStrings)
-    SparkHadoopUtil.get.runAsSparkUser { () =>
-      new ApplicationMaster(args).run()
-    }
-  }
-}
diff --git a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala b/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala
deleted file mode 100644
index 155dd88aa2b81..0000000000000
--- a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala
+++ /dev/null
@@ -1,315 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.deploy.yarn
-
-import java.net.Socket
-import org.apache.hadoop.conf.Configuration
-import org.apache.hadoop.net.NetUtils
-import org.apache.hadoop.yarn.api._
-import org.apache.hadoop.yarn.api.records._
-import org.apache.hadoop.yarn.api.protocolrecords._
-import org.apache.hadoop.yarn.conf.YarnConfiguration
-import org.apache.hadoop.yarn.ipc.YarnRPC
-import org.apache.hadoop.yarn.util.{ConverterUtils, Records}
-import akka.actor._
-import akka.remote._
-import org.apache.spark.{Logging, SecurityManager, SparkConf, SparkEnv}
-import org.apache.spark.util.{Utils, AkkaUtils}
-import org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend
-import org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.AddWebUIFilter
-import org.apache.spark.scheduler.SplitInfo
-import org.apache.spark.deploy.SparkHadoopUtil
-
-/**
- * An application master that allocates executors on behalf of a driver that is running outside
- * the cluster.
- *
- * This is used only in yarn-client mode.
- */
-class ExecutorLauncher(args: ApplicationMasterArguments, conf: Configuration, sparkConf: SparkConf)
-  extends Logging {
-
-  def this(args: ApplicationMasterArguments, sparkConf: SparkConf) =
-    this(args, new Configuration(), sparkConf)
-
-  def this(args: ApplicationMasterArguments) = this(args, new SparkConf())
-
-  private val rpc: YarnRPC = YarnRPC.create(conf)
-  private var resourceManager: AMRMProtocol = _
-  private var appAttemptId: ApplicationAttemptId = _
-  private var reporterThread: Thread = _
-  private val yarnConf: YarnConfiguration = new YarnConfiguration(conf)
-
-  private var yarnAllocator: YarnAllocationHandler = _
-
-  private var driverClosed: Boolean = false
-  private var isFinished: Boolean = false
-  private var registered: Boolean = false
-
-  // Default to numExecutors * 2, with minimum of 3
-  private val maxNumExecutorFailures = sparkConf.getInt("spark.yarn.max.executor.failures",
-    sparkConf.getInt("spark.yarn.max.worker.failures", math.max(args.numExecutors * 2, 3)))
-
-  val securityManager = new SecurityManager(sparkConf)
-  val actorSystem: ActorSystem = AkkaUtils.createActorSystem("sparkYarnAM", Utils.localHostName, 0,
-    conf = sparkConf, securityManager = securityManager)._1
-  var actor: ActorRef = _
-
-  // This actor just working as a monitor to watch on Driver Actor.
-  class MonitorActor(driverUrl: String) extends Actor {
-
-    var driver: ActorSelection = _
-
-    override def preStart() {
-      logInfo("Listen to driver: " + driverUrl)
-      driver = context.actorSelection(driverUrl)
-      // Send a hello message thus the connection is actually established, thus we can
-      // monitor Lifecycle Events.
-      driver ! "Hello"
-      context.system.eventStream.subscribe(self, classOf[RemotingLifecycleEvent])
-    }
-
-    override def receive = {
-      case x: DisassociatedEvent =>
-        logInfo(s"Driver terminated or disconnected! Shutting down. $x")
-        driverClosed = true
-      case x: AddWebUIFilter =>
-        logInfo(s"Add WebUI Filter. $x")
-        driver ! x
-    }
-  }
-
-  def run() {
-    appAttemptId = getApplicationAttemptId()
-    resourceManager = registerWithResourceManager()
-
-    synchronized {
-      if (!isFinished) {
-        val appMasterResponse: RegisterApplicationMasterResponse = registerApplicationMaster()
-        // Compute number of threads for akka
-        val minimumMemory = appMasterResponse.getMinimumResourceCapability().getMemory()
-
-        if (minimumMemory > 0) {
-          val mem = args.executorMemory + sparkConf.getInt("spark.yarn.executor.memoryOverhead",
-            YarnAllocationHandler.MEMORY_OVERHEAD)
-          val numCore = (mem  / minimumMemory) + (if (0 != (mem % minimumMemory)) 1 else 0)
-
-          if (numCore > 0) {
-            // do not override - hits https://issues.apache.org/jira/browse/HADOOP-8406
-            // TODO: Uncomment when hadoop is on a version which has this fixed.
-            // args.workerCores = numCore
-          }
-        }
-        registered = true
-      }
-    }
-    waitForSparkMaster()
-    addAmIpFilter()
-    // Allocate all containers
-    allocateExecutors()
-
-    // Launch a progress reporter thread, else app will get killed after expiration
-    // (def: 10mins) timeout ensure that progress is sent before
-    // YarnConfiguration.RM_AM_EXPIRY_INTERVAL_MS elapse.
-
-    val timeoutInterval = yarnConf.getInt(YarnConfiguration.RM_AM_EXPIRY_INTERVAL_MS, 120000)
-    // we want to be reasonably responsive without causing too many requests to RM.
-    val schedulerInterval =
-      System.getProperty("spark.yarn.scheduler.heartbeat.interval-ms", "5000").toLong
-
-    // must be <= timeoutInterval / 2.
-    val interval = math.min(timeoutInterval / 2, schedulerInterval)
-
-    reporterThread = launchReporterThread(interval)
-
-    // Wait for the reporter thread to Finish.
-    reporterThread.join()
-
-    finishApplicationMaster(FinalApplicationStatus.SUCCEEDED)
-    actorSystem.shutdown()
-
-    logInfo("Exited")
-    System.exit(0)
-  }
-
-  private def getApplicationAttemptId(): ApplicationAttemptId = {
-    val envs = System.getenv()
-    val containerIdString = envs.get(ApplicationConstants.AM_CONTAINER_ID_ENV)
-    val containerId = ConverterUtils.toContainerId(containerIdString)
-    val appAttemptId = containerId.getApplicationAttemptId()
-    logInfo("ApplicationAttemptId: " + appAttemptId)
-    appAttemptId
-  }
-
-  private def registerWithResourceManager(): AMRMProtocol = {
-    val rmAddress = NetUtils.createSocketAddr(yarnConf.get(
-      YarnConfiguration.RM_SCHEDULER_ADDRESS,
-      YarnConfiguration.DEFAULT_RM_SCHEDULER_ADDRESS))
-    logInfo("Connecting to ResourceManager at " + rmAddress)
-    rpc.getProxy(classOf[AMRMProtocol], rmAddress, conf).asInstanceOf[AMRMProtocol]
-  }
-
-  private def registerApplicationMaster(): RegisterApplicationMasterResponse = {
-    val appUIAddress = sparkConf.get("spark.driver.appUIAddress", "")
-    logInfo(s"Registering the ApplicationMaster with appUIAddress: $appUIAddress")
-    val appMasterRequest = Records.newRecord(classOf[RegisterApplicationMasterRequest])
-      .asInstanceOf[RegisterApplicationMasterRequest]
-    appMasterRequest.setApplicationAttemptId(appAttemptId)
-    // Setting this to master host,port - so that the ApplicationReport at client has
-    // some sensible info. Users can then monitor stderr/stdout on that node if required.
-    appMasterRequest.setHost(Utils.localHostName())
-    appMasterRequest.setRpcPort(0)
-    // What do we provide here ? Might make sense to expose something sensible later ?
-    appMasterRequest.setTrackingUrl(appUIAddress)
-    resourceManager.registerApplicationMaster(appMasterRequest)
-  }
-
-  // add the yarn amIpFilter that Yarn requires for properly securing the UI
-  private def addAmIpFilter() {
-    val proxy = YarnConfiguration.getProxyHostAndPort(conf)
-    val parts = proxy.split(":")
-    val proxyBase = System.getenv(ApplicationConstants.APPLICATION_WEB_PROXY_BASE_ENV)
-    val uriBase = "http://" + proxy + proxyBase
-    val amFilter = "PROXY_HOST=" + parts(0) + "," + "PROXY_URI_BASE=" + uriBase
-    val amFilterName = "org.apache.hadoop.yarn.server.webproxy.amfilter.AmIpFilter"
-    actor ! AddWebUIFilter(amFilterName, amFilter, proxyBase)
-  }
-
-  private def waitForSparkMaster() {
-    logInfo("Waiting for spark driver to be reachable.")
-    var driverUp = false
-    val hostport = args.userArgs(0)
-    val (driverHost, driverPort) = Utils.parseHostPort(hostport)
-    while(!driverUp) {
-      try {
-        val socket = new Socket(driverHost, driverPort)
-        socket.close()
-        logInfo("Master now available: " + driverHost + ":" + driverPort)
-        driverUp = true
-      } catch {
-        case e: Exception =>
-          logError("Failed to connect to driver at " + driverHost + ":" + driverPort)
-        Thread.sleep(100)
-      }
-    }
-    sparkConf.set("spark.driver.host",  driverHost)
-    sparkConf.set("spark.driver.port",  driverPort.toString)
-
-    val driverUrl = "akka.tcp://%s@%s:%s/user/%s".format(
-      SparkEnv.driverActorSystemName,
-      driverHost,
-      driverPort.toString,
-      CoarseGrainedSchedulerBackend.ACTOR_NAME)
-
-    actor = actorSystem.actorOf(Props(new MonitorActor(driverUrl)), name = "YarnAM")
-  }
-
-
-  private def allocateExecutors() {
-
-    // Fixme: should get preferredNodeLocationData from SparkContext, just fake a empty one for now.
-    val preferredNodeLocationData: scala.collection.Map[String, scala.collection.Set[SplitInfo]] =
-      scala.collection.immutable.Map()
-
-    yarnAllocator = YarnAllocationHandler.newAllocator(yarnConf, resourceManager, appAttemptId,
-      args, preferredNodeLocationData, sparkConf)
-
-    logInfo("Allocating " + args.numExecutors + " executors.")
-    // Wait until all containers have finished
-    // TODO: This is a bit ugly. Can we make it nicer?
-    // TODO: Handle container failure
-    while ((yarnAllocator.getNumExecutorsRunning < args.numExecutors) && (!driverClosed) &&
-        !isFinished) {
-      yarnAllocator.allocateContainers(
-        math.max(args.numExecutors - yarnAllocator.getNumExecutorsRunning, 0))
-      checkNumExecutorsFailed()
-      Thread.sleep(100)
-    }
-
-    logInfo("All executors have launched.")
-  }
-  private def checkNumExecutorsFailed() {
-    if (yarnAllocator.getNumExecutorsFailed >= maxNumExecutorFailures) {
-      finishApplicationMaster(FinalApplicationStatus.FAILED,
-        "max number of executor failures reached")
-    }
-  }
-
-  // TODO: We might want to extend this to allocate more containers in case they die !
-  private def launchReporterThread(_sleepTime: Long): Thread = {
-    val sleepTime = if (_sleepTime <= 0 ) 0 else _sleepTime
-
-    val t = new Thread {
-      override def run() {
-        while (!driverClosed && !isFinished) {
-          checkNumExecutorsFailed()
-          val missingExecutorCount = args.numExecutors - yarnAllocator.getNumExecutorsRunning
-          if (missingExecutorCount > 0) {
-            logInfo("Allocating " + missingExecutorCount +
-              " containers to make up for (potentially ?) lost containers")
-            yarnAllocator.allocateContainers(missingExecutorCount)
-          } else {
-            sendProgress()
-          }
-          Thread.sleep(sleepTime)
-        }
-      }
-    }
-    // setting to daemon status, though this is usually not a good idea.
-    t.setDaemon(true)
-    t.start()
-    logInfo("Started progress reporter thread - sleep time : " + sleepTime)
-    t
-  }
-
-  private def sendProgress() {
-    logDebug("Sending progress")
-    // simulated with an allocate request with no nodes requested ...
-    yarnAllocator.allocateContainers(0)
-  }
-
-  def finishApplicationMaster(status: FinalApplicationStatus, appMessage: String = "") {
-    synchronized {
-      if (isFinished) {
-        return
-      }
-      logInfo("Unregistering ApplicationMaster with " + status)
-      if (registered) {
-        val finishReq = Records.newRecord(classOf[FinishApplicationMasterRequest])
-          .asInstanceOf[FinishApplicationMasterRequest]
-        finishReq.setAppAttemptId(appAttemptId)
-        finishReq.setFinishApplicationStatus(status)
-        finishReq.setTrackingUrl(sparkConf.get("spark.yarn.historyServer.address", ""))
-        finishReq.setDiagnostics(appMessage)
-        resourceManager.finishApplicationMaster(finishReq)
-      }
-      isFinished = true
-    }
-  }
-
-}
-
-
-object ExecutorLauncher {
-  def main(argStrings: Array[String]) {
-    val args = new ApplicationMasterArguments(argStrings)
-    SparkHadoopUtil.get.runAsSparkUser { () =>
-      new ExecutorLauncher(args).run()
-    }
-  }
-}
diff --git a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocationHandler.scala b/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocationHandler.scala
index 568a6ef932bbd..629cd13f67145 100644
--- a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocationHandler.scala
+++ b/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocationHandler.scala
@@ -17,33 +17,24 @@
 
 package org.apache.spark.deploy.yarn
 
-import java.lang.{Boolean => JBoolean}
-import java.util.{Collections, Set => JSet}
 import java.util.concurrent.{CopyOnWriteArrayList, ConcurrentHashMap}
 import java.util.concurrent.atomic.AtomicInteger
 
-import scala.collection
 import scala.collection.JavaConversions._
 import scala.collection.mutable.{ArrayBuffer, HashMap, HashSet}
 
 import org.apache.spark.{Logging, SparkConf, SparkEnv}
-import org.apache.spark.scheduler.{SplitInfo,TaskSchedulerImpl}
+import org.apache.spark.scheduler.{SplitInfo, TaskSchedulerImpl}
 import org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend
 import org.apache.spark.util.Utils
 
 import org.apache.hadoop.conf.Configuration
 import org.apache.hadoop.yarn.api.AMRMProtocol
 import org.apache.hadoop.yarn.api.records.{AMResponse, ApplicationAttemptId}
-import org.apache.hadoop.yarn.api.records.{Container, ContainerId, ContainerStatus}
+import org.apache.hadoop.yarn.api.records.{Container, ContainerId}
 import org.apache.hadoop.yarn.api.records.{Priority, Resource, ResourceRequest}
 import org.apache.hadoop.yarn.api.protocolrecords.{AllocateRequest, AllocateResponse}
-import org.apache.hadoop.yarn.util.{RackResolver, Records}
-
-
-object AllocationType extends Enumeration {
-  type AllocationType = Value
-  val HOST, RACK, ANY = Value
-}
+import org.apache.hadoop.yarn.util.Records
 
 // TODO:
 // Too many params.
@@ -59,16 +50,14 @@ object AllocationType extends Enumeration {
  * Acquires resources for executors from a ResourceManager and launches executors in new containers.
  */
 private[yarn] class YarnAllocationHandler(
-    val conf: Configuration,
-    val resourceManager: AMRMProtocol,
-    val appAttemptId: ApplicationAttemptId,
-    val maxExecutors: Int,
-    val executorMemory: Int,
-    val executorCores: Int,
-    val preferredHostToCount: Map[String, Int],
-    val preferredRackToCount: Map[String, Int],
-    val sparkConf: SparkConf)
-  extends Logging {
+    conf: Configuration,
+    sparkConf: SparkConf,
+    resourceManager: AMRMProtocol,
+    appAttemptId: ApplicationAttemptId,
+    args: ApplicationMasterArguments,
+    preferredNodes: collection.Map[String, collection.Set[SplitInfo]])
+  extends YarnAllocator with Logging {
+
   // These three are locked on allocatedHostToContainersMap. Complementary data structures
   // allocatedHostToContainersMap : containers which are running : host, Set<containerid>
   // allocatedContainerToHostMap: container to host mapping.
@@ -90,7 +79,7 @@ private[yarn] class YarnAllocationHandler(
 
   // Additional memory overhead - in mb.
   private def memoryOverhead: Int = sparkConf.getInt("spark.yarn.executor.memoryOverhead",
-    YarnAllocationHandler.MEMORY_OVERHEAD)
+    YarnSparkHadoopUtil.DEFAULT_MEMORY_OVERHEAD)
 
   private val numExecutorsRunning = new AtomicInteger()
   // Used to generate a unique id per executor
@@ -98,6 +87,12 @@ private[yarn] class YarnAllocationHandler(
   private val lastResponseId = new AtomicInteger()
   private val numExecutorsFailed = new AtomicInteger()
 
+  private val maxExecutors = args.numExecutors
+  private val executorMemory = args.executorMemory
+  private val executorCores = args.executorCores
+  private val (preferredHostToCount, preferredRackToCount) =
+    generateNodeToWeight(conf, preferredNodes)
+
   def getNumExecutorsRunning: Int = numExecutorsRunning.intValue
 
   def getNumExecutorsFailed: Int = numExecutorsFailed.intValue
@@ -106,9 +101,10 @@ private[yarn] class YarnAllocationHandler(
     container.getResource.getMemory >= (executorMemory + memoryOverhead)
   }
 
-  def allocateContainers(executorsToRequest: Int) {
+  override def allocateResources() = {
     // We need to send the request only once from what I understand ... but for now, not modifying
     // this much.
+    val executorsToRequest = Math.max(maxExecutors - numExecutorsRunning.get(), 0)
 
     // Keep polling the Resource Manager for containers
     val amResp = allocateExecutorResources(executorsToRequest).getAMResponse
@@ -182,7 +178,7 @@ private[yarn] class YarnAllocationHandler(
 
         // Now rack local
         if (remainingContainers != null){
-          val rack = YarnAllocationHandler.lookupRack(conf, candidateHost)
+          val rack = YarnSparkHadoopUtil.lookupRack(conf, candidateHost)
 
           if (rack != null){
             val maxExpectedRackCount = preferredRackToCount.getOrElse(rack, 0)
@@ -256,7 +252,7 @@ private[yarn] class YarnAllocationHandler(
           // Should not be there, but ..
           pendingReleaseContainers.remove(containerId)
 
-          val rack = YarnAllocationHandler.lookupRack(conf, executorHostname)
+          val rack = YarnSparkHadoopUtil.lookupRack(conf, executorHostname)
           allocatedHostToContainersMap.synchronized {
             val containerSet = allocatedHostToContainersMap.getOrElseUpdate(executorHostname,
               new HashSet[ContainerId]())
@@ -331,7 +327,7 @@ private[yarn] class YarnAllocationHandler(
             allocatedContainerToHostMap -= containerId
 
             // Doing this within locked context, sigh ... move to outside ?
-            val rack = YarnAllocationHandler.lookupRack(conf, host)
+            val rack = YarnSparkHadoopUtil.lookupRack(conf, host)
             if (rack != null) {
               val rackCount = allocatedRackCount.getOrElse(rack, 0) - 1
               if (rackCount > 0) {
@@ -364,9 +360,9 @@ private[yarn] class YarnAllocationHandler(
     for (container <- hostContainers) {
       val candidateHost = container.getHostName
       val candidateNumContainers = container.getNumContainers
-      assert(YarnAllocationHandler.ANY_HOST != candidateHost)
+      assert(YarnSparkHadoopUtil.ANY_HOST != candidateHost)
 
-      val rack = YarnAllocationHandler.lookupRack(conf, candidateHost)
+      val rack = YarnSparkHadoopUtil.lookupRack(conf, candidateHost)
       if (rack != null) {
         var count = rackToCounts.getOrElse(rack, 0)
         count += candidateNumContainers
@@ -378,7 +374,8 @@ private[yarn] class YarnAllocationHandler(
       new ArrayBuffer[ResourceRequest](rackToCounts.size)
     for ((rack, count) <- rackToCounts){
       requestedContainers +=
-        createResourceRequest(AllocationType.RACK, rack, count, YarnAllocationHandler.PRIORITY)
+        createResourceRequest(AllocationType.RACK, rack, count,
+          YarnSparkHadoopUtil.RM_REQUEST_PRIORITY)
     }
 
     requestedContainers.toList
@@ -409,7 +406,7 @@ private[yarn] class YarnAllocationHandler(
       logDebug("numExecutors: " + numExecutors + ", host preferences: " +
         preferredHostToCount.isEmpty)
       resourceRequests = List(createResourceRequest(
-        AllocationType.ANY, null, numExecutors, YarnAllocationHandler.PRIORITY))
+        AllocationType.ANY, null, numExecutors, YarnSparkHadoopUtil.RM_REQUEST_PRIORITY))
     } else {
       // request for all hosts in preferred nodes and for numExecutors -
       // candidates.size, request by default allocation policy.
@@ -423,7 +420,7 @@ private[yarn] class YarnAllocationHandler(
             AllocationType.HOST,
             candidateHost,
             requiredCount,
-            YarnAllocationHandler.PRIORITY)
+            YarnSparkHadoopUtil.RM_REQUEST_PRIORITY)
         }
       }
       val rackContainerRequests: List[ResourceRequest] = createRackResourceRequests(
@@ -433,7 +430,7 @@ private[yarn] class YarnAllocationHandler(
         AllocationType.ANY,
         resource = null,
         numExecutors,
-        YarnAllocationHandler.PRIORITY)
+        YarnSparkHadoopUtil.RM_REQUEST_PRIORITY)
 
       val containerRequests: ArrayBuffer[ResourceRequest] = new ArrayBuffer[ResourceRequest](
         hostContainerRequests.size + rackContainerRequests.size + 1)
@@ -483,12 +480,12 @@ private[yarn] class YarnAllocationHandler(
     // There must be a third request - which is ANY : that will be specially handled.
     requestType match {
       case AllocationType.HOST => {
-        assert(YarnAllocationHandler.ANY_HOST != resource)
+        assert(YarnSparkHadoopUtil.ANY_HOST != resource)
         val hostname = resource
         val nodeLocal = createResourceRequestImpl(hostname, numExecutors, priority)
 
         // Add to host->rack mapping
-        YarnAllocationHandler.populateRackInfo(conf, hostname)
+        YarnSparkHadoopUtil.populateRackInfo(conf, hostname)
 
         nodeLocal
       }
@@ -497,7 +494,7 @@ private[yarn] class YarnAllocationHandler(
         createResourceRequestImpl(rack, numExecutors, priority)
       }
       case AllocationType.ANY => createResourceRequestImpl(
-        YarnAllocationHandler.ANY_HOST, numExecutors, priority)
+        YarnSparkHadoopUtil.ANY_HOST, numExecutors, priority)
       case _ => throw new IllegalArgumentException(
         "Unexpected/unsupported request type: " + requestType)
     }
@@ -541,90 +538,6 @@ private[yarn] class YarnAllocationHandler(
 
     retval
   }
-}
-
-object YarnAllocationHandler {
-
-  val ANY_HOST = "*"
-  // All requests are issued with same priority : we do not (yet) have any distinction between
-  // request types (like map/reduce in hadoop for example)
-  val PRIORITY = 1
-
-  // Additional memory overhead - in mb
-  val MEMORY_OVERHEAD = 384
-
-  // Host to rack map - saved from allocation requests
-  // We are expecting this not to change.
-  // Note that it is possible for this to change : and RM will indicate that to us via update
-  // response to allocate. But we are punting on handling that for now.
-  private val hostToRack = new ConcurrentHashMap[String, String]()
-  private val rackToHostSet = new ConcurrentHashMap[String, JSet[String]]()
-
-
-  def newAllocator(
-    conf: Configuration,
-    resourceManager: AMRMProtocol,
-    appAttemptId: ApplicationAttemptId,
-    args: ApplicationMasterArguments,
-    sparkConf: SparkConf): YarnAllocationHandler = {
-
-    new YarnAllocationHandler(
-      conf,
-      resourceManager,
-      appAttemptId,
-      args.numExecutors,
-      args.executorMemory,
-      args.executorCores,
-      Map[String, Int](),
-      Map[String, Int](),
-      sparkConf)
-  }
-
-  def newAllocator(
-    conf: Configuration,
-    resourceManager: AMRMProtocol,
-    appAttemptId: ApplicationAttemptId,
-    args: ApplicationMasterArguments,
-    map: collection.Map[String,
-    collection.Set[SplitInfo]],
-    sparkConf: SparkConf): YarnAllocationHandler = {
-
-    val (hostToCount, rackToCount) = generateNodeToWeight(conf, map)
-    new YarnAllocationHandler(
-      conf,
-      resourceManager,
-      appAttemptId,
-      args.numExecutors,
-      args.executorMemory,
-      args.executorCores,
-      hostToCount,
-      rackToCount,
-      sparkConf)
-  }
-
-  def newAllocator(
-    conf: Configuration,
-    resourceManager: AMRMProtocol,
-    appAttemptId: ApplicationAttemptId,
-    maxExecutors: Int,
-    executorMemory: Int,
-    executorCores: Int,
-    map: collection.Map[String, collection.Set[SplitInfo]],
-    sparkConf: SparkConf): YarnAllocationHandler = {
-
-    val (hostToCount, rackToCount) = generateNodeToWeight(conf, map)
-
-    new YarnAllocationHandler(
-      conf,
-      resourceManager,
-      appAttemptId,
-      maxExecutors,
-      executorMemory,
-      executorCores,
-      hostToCount,
-      rackToCount,
-      sparkConf)
-  }
 
   // A simple method to copy the split info map.
   private def generateNodeToWeight(
@@ -642,7 +555,7 @@ object YarnAllocationHandler {
       val hostCount = hostToCount.getOrElse(host, 0)
       hostToCount.put(host, hostCount + splits.size)
 
-      val rack = lookupRack(conf, host)
+      val rack = YarnSparkHadoopUtil.lookupRack(conf, host)
       if (rack != null){
         val rackCount = rackToCount.getOrElse(host, 0)
         rackToCount.put(host, rackCount + splits.size)
@@ -652,41 +565,4 @@ object YarnAllocationHandler {
     (hostToCount.toMap, rackToCount.toMap)
   }
 
-  def lookupRack(conf: Configuration, host: String): String = {
-    if (!hostToRack.contains(host)) populateRackInfo(conf, host)
-    hostToRack.get(host)
-  }
-
-  def fetchCachedHostsForRack(rack: String): Option[Set[String]] = {
-    val set = rackToHostSet.get(rack)
-    if (set == null) return None
-
-    // No better way to get a Set[String] from JSet ?
-    val convertedSet: collection.mutable.Set[String] = set
-    Some(convertedSet.toSet)
-  }
-
-  def populateRackInfo(conf: Configuration, hostname: String) {
-    Utils.checkHost(hostname)
-
-    if (!hostToRack.containsKey(hostname)) {
-      // If there are repeated failures to resolve, all to an ignore list ?
-      val rackInfo = RackResolver.resolve(conf, hostname)
-      if (rackInfo != null && rackInfo.getNetworkLocation != null) {
-        val rack = rackInfo.getNetworkLocation
-        hostToRack.put(hostname, rack)
-        if (! rackToHostSet.containsKey(rack)) {
-          rackToHostSet.putIfAbsent(rack,
-            Collections.newSetFromMap(new ConcurrentHashMap[String, JBoolean]()))
-        }
-        rackToHostSet.get(rack).add(hostname)
-
-        // TODO(harvey): Figure out this comment...
-        // Since RackResolver caches, we are disabling this for now ...
-      } /* else {
-        // right ? Else we will keep calling rack resolver in case we cant resolve rack info ...
-        hostToRack.put(hostname, null)
-      } */
-    }
-  }
 }
diff --git a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/YarnRMClientImpl.scala b/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/YarnRMClientImpl.scala
new file mode 100644
index 0000000000000..cc5392192ec51
--- /dev/null
+++ b/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/YarnRMClientImpl.scala
@@ -0,0 +1,103 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.deploy.yarn
+
+import scala.collection.{Map, Set}
+
+import org.apache.hadoop.net.NetUtils
+import org.apache.hadoop.yarn.api._
+import org.apache.hadoop.yarn.api.records._
+import org.apache.hadoop.yarn.api.protocolrecords._
+import org.apache.hadoop.yarn.conf.YarnConfiguration
+import org.apache.hadoop.yarn.ipc.YarnRPC
+import org.apache.hadoop.yarn.util.{ConverterUtils, Records}
+
+import org.apache.spark.{Logging, SparkConf}
+import org.apache.spark.scheduler.SplitInfo
+import org.apache.spark.util.Utils
+
+/**
+ * YarnRMClient implementation for the Yarn alpha API.
+ */
+private class YarnRMClientImpl(args: ApplicationMasterArguments) extends YarnRMClient with Logging {
+
+  private var rpc: YarnRPC = null
+  private var resourceManager: AMRMProtocol = _
+  private var uiHistoryAddress: String = _
+
+  override def register(
+      conf: YarnConfiguration,
+      sparkConf: SparkConf,
+      preferredNodeLocations: Map[String, Set[SplitInfo]],
+      uiAddress: String,
+      uiHistoryAddress: String) = {
+    this.rpc = YarnRPC.create(conf)
+    this.uiHistoryAddress = uiHistoryAddress
+
+    resourceManager = registerWithResourceManager(conf)
+    registerApplicationMaster(uiAddress)
+
+    new YarnAllocationHandler(conf, sparkConf, resourceManager, getAttemptId(), args,
+      preferredNodeLocations)
+  }
+
+  override def getAttemptId() = {
+    val envs = System.getenv()
+    val containerIdString = envs.get(ApplicationConstants.AM_CONTAINER_ID_ENV)
+    val containerId = ConverterUtils.toContainerId(containerIdString)
+    val appAttemptId = containerId.getApplicationAttemptId()
+    appAttemptId
+  }
+
+  override def shutdown(status: FinalApplicationStatus, diagnostics: String = "") = {
+    val finishReq = Records.newRecord(classOf[FinishApplicationMasterRequest])
+      .asInstanceOf[FinishApplicationMasterRequest]
+    finishReq.setAppAttemptId(getAttemptId())
+    finishReq.setFinishApplicationStatus(status)
+    finishReq.setDiagnostics(diagnostics)
+    finishReq.setTrackingUrl(uiHistoryAddress)
+    resourceManager.finishApplicationMaster(finishReq)
+  }
+
+  override def getProxyHostAndPort(conf: YarnConfiguration) =
+    YarnConfiguration.getProxyHostAndPort(conf)
+
+  override def getMaxRegAttempts(conf: YarnConfiguration) =
+    conf.getInt(YarnConfiguration.RM_AM_MAX_RETRIES, YarnConfiguration.DEFAULT_RM_AM_MAX_RETRIES)
+
+  private def registerWithResourceManager(conf: YarnConfiguration): AMRMProtocol = {
+    val rmAddress = NetUtils.createSocketAddr(conf.get(YarnConfiguration.RM_SCHEDULER_ADDRESS,
+      YarnConfiguration.DEFAULT_RM_SCHEDULER_ADDRESS))
+    logInfo("Connecting to ResourceManager at " + rmAddress)
+    rpc.getProxy(classOf[AMRMProtocol], rmAddress, conf).asInstanceOf[AMRMProtocol]
+  }
+
+  private def registerApplicationMaster(uiAddress: String): RegisterApplicationMasterResponse = {
+    val appMasterRequest = Records.newRecord(classOf[RegisterApplicationMasterRequest])
+      .asInstanceOf[RegisterApplicationMasterRequest]
+    appMasterRequest.setApplicationAttemptId(getAttemptId())
+    // Setting this to master host,port - so that the ApplicationReport at client has some
+    // sensible info.
+    // Users can then monitor stderr/stdout on that node if required.
+    appMasterRequest.setHost(Utils.localHostName())
+    appMasterRequest.setRpcPort(0)
+    appMasterRequest.setTrackingUrl(uiAddress)
+    resourceManager.registerApplicationMaster(appMasterRequest)
+  }
+
+}
diff --git a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
new file mode 100644
index 0000000000000..f15c813b83973
--- /dev/null
+++ b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
@@ -0,0 +1,430 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.deploy.yarn
+
+import java.io.IOException
+import java.net.Socket
+import java.util.concurrent.atomic.AtomicReference
+
+import scala.collection.JavaConversions._
+import scala.util.Try
+
+import akka.actor._
+import akka.remote._
+import org.apache.hadoop.conf.Configuration
+import org.apache.hadoop.fs.{FileSystem, Path}
+import org.apache.hadoop.util.ShutdownHookManager
+import org.apache.hadoop.yarn.api._
+import org.apache.hadoop.yarn.api.records._
+import org.apache.hadoop.yarn.conf.YarnConfiguration
+
+import org.apache.spark.{Logging, SecurityManager, SparkConf, SparkContext, SparkEnv}
+import org.apache.spark.deploy.SparkHadoopUtil
+import org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend
+import org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.AddWebUIFilter
+import org.apache.spark.util.{AkkaUtils, SignalLogger, Utils}
+
+/**
+ * Common application master functionality for Spark on Yarn.
+ */
+private[spark] class ApplicationMaster(args: ApplicationMasterArguments,
+  client: YarnRMClient) extends Logging {
+  // TODO: Currently, task to container is computed once (TaskSetManager) - which need not be
+  // optimal as more containers are available. Might need to handle this better.
+  private val ALLOCATE_HEARTBEAT_INTERVAL = 100
+
+  private val sparkConf = new SparkConf()
+  private val yarnConf: YarnConfiguration = new YarnConfiguration(new Configuration())
+  private val isDriver = args.userClass != null
+
+  // Default to numExecutors * 2, with minimum of 3
+  private val maxNumExecutorFailures = sparkConf.getInt("spark.yarn.max.executor.failures",
+    sparkConf.getInt("spark.yarn.max.worker.failures", math.max(args.numExecutors * 2, 3)))
+
+  @volatile private var finished = false
+  @volatile private var finalStatus = FinalApplicationStatus.UNDEFINED
+
+  private var reporterThread: Thread = _
+  private var allocator: YarnAllocator = _
+
+  // Fields used in client mode.
+  private var actorSystem: ActorSystem = null
+  private var actor: ActorRef = _
+
+  // Fields used in cluster mode.
+  private val sparkContextRef = new AtomicReference[SparkContext](null)
+
+  final def run(): Int = {
+    if (isDriver) {
+      // Set the web ui port to be ephemeral for yarn so we don't conflict with
+      // other spark processes running on the same box
+      System.setProperty("spark.ui.port", "0")
+
+      // Set the master property to match the requested mode.
+      System.setProperty("spark.master", "yarn-cluster")
+    }
+
+    logInfo("ApplicationAttemptId: " + client.getAttemptId())
+
+    val cleanupHook = new Runnable {
+      override def run() {
+        // If the SparkContext is still registered, shut it down as a best case effort in case
+        // users do not call sc.stop or do System.exit().
+        val sc = sparkContextRef.get()
+        if (sc != null) {
+          logInfo("Invoking sc stop from shutdown hook")
+          sc.stop()
+          finish(FinalApplicationStatus.SUCCEEDED)
+        }
+
+        // Cleanup the staging dir after the app is finished, or if it's the last attempt at
+        // running the AM.
+        val maxAppAttempts = client.getMaxRegAttempts(yarnConf)
+        val isLastAttempt = client.getAttemptId().getAttemptId() >= maxAppAttempts
+        if (finished || isLastAttempt) {
+          cleanupStagingDir()
+        }
+      }
+    }
+    // Use priority 30 as it's higher than HDFS. It's the same priority MapReduce is using.
+    ShutdownHookManager.get().addShutdownHook(cleanupHook, 30)
+
+    // Call this to force generation of secret so it gets populated into the
+    // Hadoop UGI. This has to happen before the startUserClass which does a
+    // doAs in order for the credentials to be passed on to the executor containers.
+    val securityMgr = new SecurityManager(sparkConf)
+
+    if (isDriver) {
+      runDriver()
+    } else {
+      runExecutorLauncher(securityMgr)
+    }
+
+    if (finalStatus != FinalApplicationStatus.UNDEFINED) {
+      finish(finalStatus)
+      0
+    } else {
+      1
+    }
+  }
+
+  final def finish(status: FinalApplicationStatus, diagnostics: String = null) = synchronized {
+    if (!finished) {
+      logInfo(s"Finishing ApplicationMaster with $status"  +
+        Option(diagnostics).map(msg => s" (diag message: $msg)").getOrElse(""))
+      finished = true
+      finalStatus = status
+      try {
+        if (Thread.currentThread() != reporterThread) {
+          reporterThread.interrupt()
+          reporterThread.join()
+        }
+      } finally {
+        client.shutdown(status, Option(diagnostics).getOrElse(""))
+      }
+    }
+  }
+
+  private def sparkContextInitialized(sc: SparkContext) = {
+    sparkContextRef.synchronized {
+      sparkContextRef.compareAndSet(null, sc)
+      sparkContextRef.notifyAll()
+    }
+  }
+
+  private def sparkContextStopped(sc: SparkContext) = {
+    sparkContextRef.compareAndSet(sc, null)
+  }
+
+  private def registerAM(uiAddress: String, uiHistoryAddress: String) = {
+    val sc = sparkContextRef.get()
+    allocator = client.register(yarnConf,
+      if (sc != null) sc.getConf else sparkConf,
+      if (sc != null) sc.preferredNodeLocationData else Map(),
+      uiAddress,
+      uiHistoryAddress)
+
+    allocator.allocateResources()
+    reporterThread = launchReporterThread()
+  }
+
+  private def runDriver(): Unit = {
+    addAmIpFilter()
+    val userThread = startUserClass()
+
+    // This a bit hacky, but we need to wait until the spark.driver.port property has
+    // been set by the Thread executing the user class.
+    val sc = waitForSparkContextInitialized()
+
+    // If there is no SparkContext at this point, just fail the app.
+    if (sc == null) {
+      finish(FinalApplicationStatus.FAILED, "Timed out waiting for SparkContext.")
+    } else {
+      registerAM(sc.ui.appUIHostPort, YarnSparkHadoopUtil.getUIHistoryAddress(sc, sparkConf))
+      try {
+        userThread.join()
+      } finally {
+        // In cluster mode, ask the reporter thread to stop since the user app is finished.
+        reporterThread.interrupt()
+      }
+    }
+  }
+
+  private def runExecutorLauncher(securityMgr: SecurityManager): Unit = {
+    actorSystem = AkkaUtils.createActorSystem("sparkYarnAM", Utils.localHostName, 0,
+      conf = sparkConf, securityManager = securityMgr)._1
+    actor = waitForSparkDriver()
+    addAmIpFilter()
+    registerAM(sparkConf.get("spark.driver.appUIAddress", ""),
+      sparkConf.get("spark.driver.appUIHistoryAddress", ""))
+
+    // In client mode the actor will stop the reporter thread.
+    reporterThread.join()
+    finalStatus = FinalApplicationStatus.SUCCEEDED
+  }
+
+  private def launchReporterThread(): Thread = {
+    // Ensure that progress is sent before YarnConfiguration.RM_AM_EXPIRY_INTERVAL_MS elapses.
+    val expiryInterval = yarnConf.getInt(YarnConfiguration.RM_AM_EXPIRY_INTERVAL_MS, 120000)
+
+    // we want to be reasonably responsive without causing too many requests to RM.
+    val schedulerInterval =
+      sparkConf.getLong("spark.yarn.scheduler.heartbeat.interval-ms", 5000)
+
+    // must be <= expiryInterval / 2.
+    val interval = math.max(0, math.min(expiryInterval / 2, schedulerInterval))
+
+    val t = new Thread {
+      override def run() {
+        while (!finished) {
+          checkNumExecutorsFailed()
+          if (!finished) {
+            logDebug("Sending progress")
+            allocator.allocateResources()
+            try {
+              Thread.sleep(interval)
+            } catch {
+              case e: InterruptedException =>
+            }
+          }
+        }
+      }
+    }
+    // setting to daemon status, though this is usually not a good idea.
+    t.setDaemon(true)
+    t.setName("Reporter")
+    t.start()
+    logInfo("Started progress reporter thread - sleep time : " + interval)
+    t
+  }
+
+  /**
+   * Clean up the staging directory.
+   */
+  private def cleanupStagingDir() {
+    val fs = FileSystem.get(yarnConf)
+    var stagingDirPath: Path = null
+    try {
+      val preserveFiles = sparkConf.get("spark.yarn.preserve.staging.files", "false").toBoolean
+      if (!preserveFiles) {
+        stagingDirPath = new Path(System.getenv("SPARK_YARN_STAGING_DIR"))
+        if (stagingDirPath == null) {
+          logError("Staging directory is null")
+          return
+        }
+        logInfo("Deleting staging directory " + stagingDirPath)
+        fs.delete(stagingDirPath, true)
+      }
+    } catch {
+      case ioe: IOException =>
+        logError("Failed to cleanup staging dir " + stagingDirPath, ioe)
+    }
+  }
+
+  private def waitForSparkContextInitialized(): SparkContext = {
+    logInfo("Waiting for spark context initialization")
+    try {
+      sparkContextRef.synchronized {
+        var count = 0
+        val waitTime = 10000L
+        val numTries = sparkConf.getInt("spark.yarn.ApplicationMaster.waitTries", 10)
+        while (sparkContextRef.get() == null && count < numTries && !finished) {
+          logInfo("Waiting for spark context initialization ... " + count)
+          count = count + 1
+          sparkContextRef.wait(waitTime)
+        }
+
+        val sparkContext = sparkContextRef.get()
+        assert(sparkContext != null || count >= numTries)
+        if (sparkContext == null) {
+          logError(
+            "Unable to retrieve sparkContext inspite of waiting for %d, numTries = %d".format(
+              count * waitTime, numTries))
+        }
+        sparkContext
+      }
+    }
+  }
+
+  private def waitForSparkDriver(): ActorRef = {
+    logInfo("Waiting for Spark driver to be reachable.")
+    var driverUp = false
+    val hostport = args.userArgs(0)
+    val (driverHost, driverPort) = Utils.parseHostPort(hostport)
+    while (!driverUp) {
+      try {
+        val socket = new Socket(driverHost, driverPort)
+        socket.close()
+        logInfo("Driver now available: %s:%s".format(driverHost, driverPort))
+        driverUp = true
+      } catch {
+        case e: Exception =>
+          logError("Failed to connect to driver at %s:%s, retrying ...".
+            format(driverHost, driverPort))
+          Thread.sleep(100)
+      }
+    }
+    sparkConf.set("spark.driver.host", driverHost)
+    sparkConf.set("spark.driver.port", driverPort.toString)
+
+    val driverUrl = "akka.tcp://%s@%s:%s/user/%s".format(
+      SparkEnv.driverActorSystemName,
+      driverHost,
+      driverPort.toString,
+      CoarseGrainedSchedulerBackend.ACTOR_NAME)
+    actorSystem.actorOf(Props(new MonitorActor(driverUrl)), name = "YarnAM")
+  }
+
+  private def checkNumExecutorsFailed() = {
+    if (allocator.getNumExecutorsFailed >= maxNumExecutorFailures) {
+      finish(FinalApplicationStatus.FAILED, "Max number of executor failures reached.")
+
+      val sc = sparkContextRef.get()
+      if (sc != null) {
+        logInfo("Invoking sc stop from checkNumExecutorsFailed")
+        sc.stop()
+      }
+    }
+  }
+
+  /** Add the Yarn IP filter that is required for properly securing the UI. */
+  private def addAmIpFilter() = {
+    val amFilter = "org.apache.hadoop.yarn.server.webproxy.amfilter.AmIpFilter"
+    val proxy = client.getProxyHostAndPort(yarnConf)
+    val parts = proxy.split(":")
+    val proxyBase = System.getenv(ApplicationConstants.APPLICATION_WEB_PROXY_BASE_ENV)
+    val uriBase = "http://" + proxy + proxyBase
+    val params = "PROXY_HOST=" + parts(0) + "," + "PROXY_URI_BASE=" + uriBase
+
+    if (isDriver) {
+      System.setProperty("spark.ui.filters", amFilter)
+      System.setProperty(s"spark.$amFilter.params", params)
+    } else {
+      actor ! AddWebUIFilter(amFilter, params, proxyBase)
+    }
+  }
+
+  private def startUserClass(): Thread = {
+    logInfo("Starting the user JAR in a separate Thread")
+    System.setProperty("spark.executor.instances", args.numExecutors.toString)
+    val mainMethod = Class.forName(args.userClass, false,
+      Thread.currentThread.getContextClassLoader).getMethod("main", classOf[Array[String]])
+
+    val t = new Thread {
+      override def run() {
+        var status = FinalApplicationStatus.FAILED
+        try {
+          // Copy
+          val mainArgs = new Array[String](args.userArgs.size)
+          args.userArgs.copyToArray(mainArgs, 0, args.userArgs.size)
+          mainMethod.invoke(null, mainArgs)
+          // Some apps have "System.exit(0)" at the end.  The user thread will stop here unless
+          // it has an uncaught exception thrown out.  It needs a shutdown hook to set SUCCEEDED.
+          status = FinalApplicationStatus.SUCCEEDED
+        } finally {
+          logDebug("Finishing main")
+        }
+        finalStatus = status
+      }
+    }
+    t.setName("Driver")
+    t.start()
+    t
+  }
+
+  // Actor used to monitor the driver when running in client deploy mode.
+  private class MonitorActor(driverUrl: String) extends Actor {
+
+    var driver: ActorSelection = _
+
+    override def preStart() = {
+      logInfo("Listen to driver: " + driverUrl)
+      driver = context.actorSelection(driverUrl)
+      // Send a hello message to establish the connection, after which
+      // we can monitor Lifecycle Events.
+      driver ! "Hello"
+      context.system.eventStream.subscribe(self, classOf[RemotingLifecycleEvent])
+    }
+
+    override def receive = {
+      case x: DisassociatedEvent =>
+        logInfo(s"Driver terminated or disconnected! Shutting down. $x")
+        finish(FinalApplicationStatus.SUCCEEDED)
+      case x: AddWebUIFilter =>
+        logInfo(s"Add WebUI Filter. $x")
+        driver ! x
+    }
+
+  }
+
+}
+
+object ApplicationMaster extends Logging {
+
+  private var master: ApplicationMaster = _
+
+  def main(args: Array[String]) = {
+    SignalLogger.register(log)
+    val amArgs = new ApplicationMasterArguments(args)
+    SparkHadoopUtil.get.runAsSparkUser { () =>
+      master = new ApplicationMaster(amArgs, new YarnRMClientImpl(amArgs))
+      System.exit(master.run())
+    }
+  }
+
+  private[spark] def sparkContextInitialized(sc: SparkContext) = {
+    master.sparkContextInitialized(sc)
+  }
+
+  private[spark] def sparkContextStopped(sc: SparkContext) = {
+    master.sparkContextStopped(sc)
+  }
+
+}
+
+/**
+ * This object does not provide any special functionality. It exists so that it's easy to tell
+ * apart the client-mode AM from the cluster-mode AM when using tools such as ps or jps.
+ */
+object ExecutorLauncher {
+
+  def main(args: Array[String]) = {
+    ApplicationMaster.main(args)
+  }
+
+}
diff --git a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMasterArguments.scala b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMasterArguments.scala
index 424b0fb0936f2..3e6b96fb63cea 100644
--- a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMasterArguments.scala
+++ b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMasterArguments.scala
@@ -63,11 +63,6 @@ class ApplicationMasterArguments(val args: Array[String]) {
           executorCores = value
           args = tail
 
-        case Nil =>
-          if (userJar == null || userClass == null) {
-            printUsageAndExit(1)
-          }
-
         case _ =>
           printUsageAndExit(1, args)
       }
@@ -80,16 +75,17 @@ class ApplicationMasterArguments(val args: Array[String]) {
     if (unknownParam != null) {
       System.err.println("Unknown/unsupported param " + unknownParam)
     }
-    System.err.println(
-      "Usage: org.apache.spark.deploy.yarn.ApplicationMaster [options] \n" +
-      "Options:\n" +
-      "  --jar JAR_PATH       Path to your application's JAR file (required)\n" +
-      "  --class CLASS_NAME   Name of your application's main class (required)\n" +
-      "  --args ARGS          Arguments to be passed to your application's main class.\n" +
-      "                       Mutliple invocations are possible, each will be passed in order.\n" +
-      "  --num-executors NUM    Number of executors to start (Default: 2)\n" +
-      "  --executor-cores NUM   Number of cores for the executors (Default: 1)\n" +
-      "  --executor-memory MEM  Memory per executor (e.g. 1000M, 2G) (Default: 1G)\n")
+    System.err.println("""
+      |Usage: org.apache.spark.deploy.yarn.ApplicationMaster [options]
+      |Options:
+      |  --jar JAR_PATH       Path to your application's JAR file
+      |  --class CLASS_NAME   Name of your application's main class
+      |  --args ARGS          Arguments to be passed to your application's main class.
+      |                       Mutliple invocations are possible, each will be passed in order.
+      |  --num-executors NUM    Number of executors to start (Default: 2)
+      |  --executor-cores NUM   Number of cores for the executors (Default: 1)
+      |  --executor-memory MEM  Memory per executor (e.g. 1000M, 2G) (Default: 1G)
+      """.stripMargin)
     System.exit(exitCode)
   }
 }
diff --git a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala
index afa4fd4c6959e..40d8d6d6e6961 100644
--- a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala
+++ b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala
@@ -37,7 +37,6 @@ class ClientArguments(val args: Array[String], val sparkConf: SparkConf) {
   var numExecutors = 2
   var amQueue = sparkConf.get("QUEUE", "default")
   var amMemory: Int = 512 // MB
-  var amClass: String = "org.apache.spark.deploy.yarn.ApplicationMaster"
   var appName: String = "Spark"
   var priority = 0
 
@@ -78,10 +77,7 @@ class ClientArguments(val args: Array[String], val sparkConf: SparkConf) {
           args = tail
 
         case ("--master-class" | "--am-class") :: value :: tail =>
-          if (args(0) == "--master-class") {
-            println("--master-class is deprecated. Use --am-class instead.")
-          }
-          amClass = value
+          println(s"${args(0)} is deprecated and is not used anymore.")
           args = tail
 
         case ("--master-memory" | "--driver-memory") :: MemoryParam(value) :: tail =>
@@ -133,9 +129,6 @@ class ClientArguments(val args: Array[String], val sparkConf: SparkConf) {
           args = tail
 
         case Nil =>
-          if (userClass == null) {
-            throw new IllegalArgumentException(getUsageMessage())
-          }
 
         case _ =>
           throw new IllegalArgumentException(getUsageMessage(args))
diff --git a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala
index 3897b3a373a8c..6cf300c3986ad 100644
--- a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala
+++ b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala
@@ -42,12 +42,6 @@ import org.apache.spark.{Logging, SecurityManager, SparkConf, SparkContext, Spar
 /**
  * The entry point (starting in Client#main() and Client#run()) for launching Spark on YARN. The
  * Client submits an application to the YARN ResourceManager.
- *
- * Depending on the deployment mode this will launch one of two application master classes:
- * 1. In cluster mode, it will launch an [[org.apache.spark.deploy.yarn.ApplicationMaster]]
- *      which launches a driver program inside of the cluster.
- * 2. In client mode, it will launch an [[org.apache.spark.deploy.yarn.ExecutorLauncher]] to
- *      request executors on behalf of a driver running outside of the cluster.
  */
 trait ClientBase extends Logging {
   val args: ClientArguments
@@ -67,14 +61,11 @@ trait ClientBase extends Logging {
 
   // Additional memory overhead - in mb.
   protected def memoryOverhead: Int = sparkConf.getInt("spark.yarn.driver.memoryOverhead",
-    YarnAllocationHandler.MEMORY_OVERHEAD)
+    YarnSparkHadoopUtil.DEFAULT_MEMORY_OVERHEAD)
 
   // TODO(harvey): This could just go in ClientArguments.
   def validateArgs() = {
     Map(
-      ((args.userJar == null && args.amClass == classOf[ApplicationMaster].getName) ->
-          "Error: You must specify a user jar when running in standalone mode!"),
-      (args.userClass == null) -> "Error: You must specify a user class!",
       (args.numExecutors <= 0) -> "Error: You must specify at least 1 executor!",
       (args.amMemory <= memoryOverhead) -> ("Error: AM memory size must be" +
         "greater than: " + memoryOverhead),
@@ -321,6 +312,8 @@ trait ClientBase extends Logging {
     val amContainer = Records.newRecord(classOf[ContainerLaunchContext])
     amContainer.setLocalResources(localResources)
 
+    val isLaunchingDriver = args.userClass != null
+
     // In cluster mode, if the deprecated SPARK_JAVA_OPTS is set, we need to propagate it to
     // executors. But we can't just set spark.executor.extraJavaOptions, because the driver's
     // SparkContext will not let that set spark* system properties, which is expected behavior for
@@ -329,7 +322,7 @@ trait ClientBase extends Logging {
     // Note that to warn the user about the deprecation in cluster mode, some code from
     // SparkConf#validateSettings() is duplicated here (to avoid triggering the condition
     // described above).
-    if (args.amClass == classOf[ApplicationMaster].getName) {
+    if (isLaunchingDriver) {
       sys.env.get("SPARK_JAVA_OPTS").foreach { value =>
         val warning =
           s"""
@@ -389,7 +382,7 @@ trait ClientBase extends Logging {
       javaOpts += YarnSparkHadoopUtil.escapeForShell(s"-D$k=$v")
     }
 
-    if (args.amClass == classOf[ApplicationMaster].getName) {
+    if (isLaunchingDriver) {
       sparkConf.getOption("spark.driver.extraJavaOptions")
         .orElse(sys.env.get("SPARK_JAVA_OPTS"))
         .foreach(opts => javaOpts += opts)
@@ -397,22 +390,37 @@ trait ClientBase extends Logging {
         .foreach(p => javaOpts += s"-Djava.library.path=$p")
     }
 
-    // Command for the ApplicationMaster
-    val commands = Seq(Environment.JAVA_HOME.$() + "/bin/java", "-server") ++
-      javaOpts ++
-      Seq(args.amClass, "--class", YarnSparkHadoopUtil.escapeForShell(args.userClass),
-        "--jar ", YarnSparkHadoopUtil.escapeForShell(args.userJar),
-        userArgsToString(args),
-        "--executor-memory", args.executorMemory.toString,
+    val userClass =
+      if (args.userClass != null) {
+        Seq("--class", YarnSparkHadoopUtil.escapeForShell(args.userClass))
+      } else {
+        Nil
+      }
+    val amClass =
+      if (isLaunchingDriver) {
+        classOf[ApplicationMaster].getName()
+      } else {
+        classOf[ApplicationMaster].getName().replace("ApplicationMaster", "ExecutorLauncher")
+      }
+    val amArgs =
+      Seq(amClass) ++ userClass ++
+      (if (args.userJar != null) Seq("--jar", args.userJar) else Nil) ++
+      Seq("--executor-memory", args.executorMemory.toString,
         "--executor-cores", args.executorCores.toString,
         "--num-executors ", args.numExecutors.toString,
+        userArgsToString(args))
+
+    // Command for the ApplicationMaster
+    val commands = Seq(Environment.JAVA_HOME.$() + "/bin/java", "-server") ++
+      javaOpts ++ amArgs ++
+      Seq(
         "1>", ApplicationConstants.LOG_DIR_EXPANSION_VAR + "/stdout",
         "2>", ApplicationConstants.LOG_DIR_EXPANSION_VAR + "/stderr")
 
     logInfo("Yarn AM launch context:")
-    logInfo(s"  class:   ${args.amClass}")
-    logInfo(s"  env:     $env")
-    logInfo(s"  command: ${commands.mkString(" ")}")
+    logInfo(s"  user class: ${args.userClass}")
+    logInfo(s"  env:        $env")
+    logInfo(s"  command:    ${commands.mkString(" ")}")
 
     // TODO: it would be nicer to just make sure there are no null commands here
     val printableCommands = commands.map(s => if (s == null) "null" else s).toList
@@ -623,7 +631,7 @@ object ClientBase extends Logging {
     YarnSparkHadoopUtil.addToEnvironment(env, Environment.CLASSPATH.name, path,
             File.pathSeparator)
 
-  /** 
+  /**
    * Get the list of namenodes the user may access.
    */
   private[yarn] def getNameNodesToAccess(sparkConf: SparkConf): Set[Path] = {
diff --git a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocator.scala b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocator.scala
new file mode 100644
index 0000000000000..cad94e5e19e1f
--- /dev/null
+++ b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocator.scala
@@ -0,0 +1,34 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.deploy.yarn
+
+object AllocationType extends Enumeration {
+  type AllocationType = Value
+  val HOST, RACK, ANY = Value
+}
+
+/**
+ * Interface that defines a Yarn allocator.
+ */
+trait YarnAllocator {
+
+  def allocateResources(): Unit
+  def getNumExecutorsFailed: Int
+  def getNumExecutorsRunning: Int
+
+}
diff --git a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/YarnRMClient.scala b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/YarnRMClient.scala
new file mode 100644
index 0000000000000..922d7d1a854a5
--- /dev/null
+++ b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/YarnRMClient.scala
@@ -0,0 +1,67 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.deploy.yarn
+
+import scala.collection.{Map, Set}
+
+import org.apache.hadoop.yarn.conf.YarnConfiguration
+import org.apache.hadoop.yarn.api.records._
+
+import org.apache.spark.{SparkConf, SparkContext}
+import org.apache.spark.scheduler.SplitInfo
+
+/**
+ * Interface that defines a Yarn RM client. Abstracts away Yarn version-specific functionality that
+ * is used by Spark's AM.
+ */
+trait YarnRMClient {
+
+  /**
+   * Registers the application master with the RM.
+   *
+   * @param conf The Yarn configuration.
+   * @param sparkConf The Spark configuration.
+   * @param preferredNodeLocations Map with hints about where to allocate containers.
+   * @param uiAddress Address of the SparkUI.
+   * @param uiHistoryAddress Address of the application on the History Server.
+   */
+  def register(
+      conf: YarnConfiguration,
+      sparkConf: SparkConf,
+      preferredNodeLocations: Map[String, Set[SplitInfo]],
+      uiAddress: String,
+      uiHistoryAddress: String): YarnAllocator
+
+  /**
+   * Shuts down the AM. Guaranteed to only be called once.
+   *
+   * @param status The final status of the AM.
+   * @param diagnostics Diagnostics message to include in the final status.
+   */
+  def shutdown(status: FinalApplicationStatus, diagnostics: String = ""): Unit
+
+  /** Returns the attempt ID. */
+  def getAttemptId(): ApplicationAttemptId
+
+  /** Returns the RM's proxy host and port. */
+  def getProxyHostAndPort(conf: YarnConfiguration): String
+
+  /** Returns the maximum number of attempts to register the AM. */
+  def getMaxRegAttempts(conf: YarnConfiguration): Int
+
+}
diff --git a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtil.scala b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtil.scala
index 10aef5eb2486f..2aa27a1908582 100644
--- a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtil.scala
+++ b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtil.scala
@@ -17,8 +17,11 @@
 
 package org.apache.spark.deploy.yarn
 
+import java.lang.{Boolean => JBoolean}
+import java.util.{Collections, Set => JSet}
 import java.util.regex.Matcher
 import java.util.regex.Pattern
+import java.util.concurrent.ConcurrentHashMap
 
 import scala.collection.mutable.HashMap
 
@@ -29,11 +32,13 @@ import org.apache.hadoop.security.UserGroupInformation
 import org.apache.hadoop.util.StringInterner
 import org.apache.hadoop.yarn.conf.YarnConfiguration
 import org.apache.hadoop.yarn.api.ApplicationConstants
+import org.apache.hadoop.yarn.util.RackResolver
 import org.apache.hadoop.conf.Configuration
 
 import org.apache.spark.{SparkConf, SparkContext}
 import org.apache.spark.deploy.history.HistoryServer
 import org.apache.spark.deploy.SparkHadoopUtil
+import org.apache.spark.util.Utils
 
 /**
  * Contains util methods to interact with Hadoop from spark.
@@ -79,6 +84,21 @@ class YarnSparkHadoopUtil extends SparkHadoopUtil {
 }
 
 object YarnSparkHadoopUtil {
+  // Additional memory overhead - in mb.
+  val DEFAULT_MEMORY_OVERHEAD = 384
+
+  val ANY_HOST = "*"
+
+  // All RM requests are issued with same priority : we do not (yet) have any distinction between
+  // request types (like map/reduce in hadoop for example)
+  val RM_REQUEST_PRIORITY = 1
+
+  // Host to rack map - saved from allocation requests. We are expecting this not to change.
+  // Note that it is possible for this to change : and ResourceManager will indicate that to us via
+  // update response to allocate. But we are punting on handling that for now.
+  private val hostToRack = new ConcurrentHashMap[String, String]()
+  private val rackToHostSet = new ConcurrentHashMap[String, JSet[String]]()
+
   def addToEnvironment(
       env: HashMap[String, String],
       variable: String,
@@ -173,4 +193,35 @@ object YarnSparkHadoopUtil {
     }
   }
 
+  private[spark] def lookupRack(conf: Configuration, host: String): String = {
+    if (!hostToRack.contains(host)) {
+      populateRackInfo(conf, host)
+    }
+    hostToRack.get(host)
+  }
+
+  private[spark] def populateRackInfo(conf: Configuration, hostname: String) {
+    Utils.checkHost(hostname)
+
+    if (!hostToRack.containsKey(hostname)) {
+      // If there are repeated failures to resolve, all to an ignore list.
+      val rackInfo = RackResolver.resolve(conf, hostname)
+      if (rackInfo != null && rackInfo.getNetworkLocation != null) {
+        val rack = rackInfo.getNetworkLocation
+        hostToRack.put(hostname, rack)
+        if (! rackToHostSet.containsKey(rack)) {
+          rackToHostSet.putIfAbsent(rack,
+            Collections.newSetFromMap(new ConcurrentHashMap[String, JBoolean]()))
+        }
+        rackToHostSet.get(rack).add(hostname)
+
+        // TODO(harvey): Figure out what this comment means...
+        // Since RackResolver caches, we are disabling this for now ...
+      } /* else {
+        // right ? Else we will keep calling rack resolver in case we cant resolve rack info ...
+        hostToRack.put(hostname, null)
+      } */
+    }
+  }
+
 }
diff --git a/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientClusterScheduler.scala b/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientClusterScheduler.scala
index 3474112ded5d7..d162b4c433f46 100644
--- a/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientClusterScheduler.scala
+++ b/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientClusterScheduler.scala
@@ -19,22 +19,21 @@ package org.apache.spark.scheduler.cluster
 
 import org.apache.spark._
 import org.apache.hadoop.conf.Configuration
-import org.apache.spark.deploy.yarn.YarnAllocationHandler
+import org.apache.spark.deploy.yarn.YarnSparkHadoopUtil
 import org.apache.spark.scheduler.TaskSchedulerImpl
 import org.apache.spark.util.Utils
 
 /**
- *
- * This scheduler launches executors through Yarn - by calling into Client to launch ExecutorLauncher as AM.
+ * This scheduler launches executors through Yarn - by calling into Client to launch the Spark AM.
  */
-private[spark] class YarnClientClusterScheduler(sc: SparkContext, conf: Configuration) extends TaskSchedulerImpl(sc) {
+private[spark] class YarnClientClusterScheduler(sc: SparkContext, conf: Configuration)
+  extends TaskSchedulerImpl(sc) {
 
   def this(sc: SparkContext) = this(sc, new Configuration())
 
   // By default, rack is unknown
   override def getRackForHost(hostPort: String): Option[String] = {
     val host = Utils.parseHostPort(hostPort)._1
-    val retval = YarnAllocationHandler.lookupRack(conf, host)
-    if (retval != null) Some(retval) else None
+    Option(YarnSparkHadoopUtil.lookupRack(conf, host))
   }
 }
diff --git a/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala b/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala
index 833e249f9f612..a5f537dd9de30 100644
--- a/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala
+++ b/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala
@@ -19,7 +19,7 @@ package org.apache.spark.scheduler.cluster
 
 import org.apache.hadoop.yarn.api.records.{ApplicationId, YarnApplicationState}
 import org.apache.spark.{SparkException, Logging, SparkContext}
-import org.apache.spark.deploy.yarn.{Client, ClientArguments, ExecutorLauncher, YarnSparkHadoopUtil}
+import org.apache.spark.deploy.yarn.{Client, ClientArguments, YarnSparkHadoopUtil}
 import org.apache.spark.scheduler.TaskSchedulerImpl
 
 import scala.collection.mutable.ArrayBuffer
@@ -60,10 +60,7 @@ private[spark] class YarnClientSchedulerBackend(
 
     val argsArrayBuf = new ArrayBuffer[String]()
     argsArrayBuf += (
-      "--class", "notused",
-      "--jar", null, // The primary jar will be added dynamically in SparkContext.
-      "--args", hostport,
-      "--am-class", classOf[ExecutorLauncher].getName
+      "--args", hostport
     )
 
     // process any optional arguments, given either as environment variables
diff --git a/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClusterScheduler.scala b/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClusterScheduler.scala
index 9aeca4a637d38..69f40225a21f5 100644
--- a/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClusterScheduler.scala
+++ b/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClusterScheduler.scala
@@ -18,16 +18,17 @@
 package org.apache.spark.scheduler.cluster
 
 import org.apache.spark._
-import org.apache.spark.deploy.yarn.{ApplicationMaster, YarnAllocationHandler}
+import org.apache.spark.deploy.yarn.{ApplicationMaster, YarnSparkHadoopUtil}
 import org.apache.spark.scheduler.TaskSchedulerImpl
 import org.apache.spark.util.Utils
 import org.apache.hadoop.conf.Configuration
 
 /**
- *
- * This is a simple extension to ClusterScheduler - to ensure that appropriate initialization of ApplicationMaster, etc is done
+ * This is a simple extension to ClusterScheduler - to ensure that appropriate initialization of
+ * ApplicationMaster, etc is done
  */
-private[spark] class YarnClusterScheduler(sc: SparkContext, conf: Configuration) extends TaskSchedulerImpl(sc) {
+private[spark] class YarnClusterScheduler(sc: SparkContext, conf: Configuration)
+  extends TaskSchedulerImpl(sc) {
 
   logInfo("Created YarnClusterScheduler")
 
@@ -42,7 +43,7 @@ private[spark] class YarnClusterScheduler(sc: SparkContext, conf: Configuration)
   // By default, rack is unknown
   override def getRackForHost(hostPort: String): Option[String] = {
     val host = Utils.parseHostPort(hostPort)._1
-    val retval = YarnAllocationHandler.lookupRack(conf, host)
+    val retval = YarnSparkHadoopUtil.lookupRack(conf, host)
     if (retval != null) Some(retval) else None
   }
 
@@ -51,4 +52,10 @@ private[spark] class YarnClusterScheduler(sc: SparkContext, conf: Configuration)
     super.postStartHook()
     logInfo("YarnClusterScheduler.postStartHook done")
   }
+
+  override def stop() {
+    super.stop()
+    ApplicationMaster.sparkContextStopped(sc)
+  }
+
 }
diff --git a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
deleted file mode 100644
index 1c4005fd8e78e..0000000000000
--- a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
+++ /dev/null
@@ -1,413 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.deploy.yarn
-
-import java.io.IOException
-import java.util.concurrent.CopyOnWriteArrayList
-import java.util.concurrent.atomic.AtomicReference
-
-import scala.collection.JavaConversions._
-
-import org.apache.hadoop.conf.Configuration
-import org.apache.hadoop.fs.{FileSystem, Path}
-import org.apache.hadoop.util.ShutdownHookManager
-import org.apache.hadoop.yarn.api._
-import org.apache.hadoop.yarn.api.protocolrecords._
-import org.apache.hadoop.yarn.api.records._
-import org.apache.hadoop.yarn.client.api.AMRMClient
-import org.apache.hadoop.yarn.client.api.AMRMClient.ContainerRequest
-import org.apache.hadoop.yarn.conf.YarnConfiguration
-import org.apache.hadoop.yarn.util.ConverterUtils
-import org.apache.hadoop.yarn.webapp.util.WebAppUtils
-
-import org.apache.spark.{Logging, SecurityManager, SparkConf, SparkContext}
-import org.apache.spark.deploy.SparkHadoopUtil
-import org.apache.spark.util.{SignalLogger, Utils}
-
-
-/**
- * An application master that runs the user's driver program and allocates executors.
- */
-class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration,
-                        sparkConf: SparkConf) extends Logging {
-
-  def this(args: ApplicationMasterArguments, sparkConf: SparkConf) =
-    this(args, new Configuration(), sparkConf)
-
-  def this(args: ApplicationMasterArguments) = this(args, new SparkConf())
-
-  private val yarnConf: YarnConfiguration = new YarnConfiguration(conf)
-  private var appAttemptId: ApplicationAttemptId = _
-  private var userThread: Thread = _
-  private val fs = FileSystem.get(yarnConf)
-
-  private var yarnAllocator: YarnAllocationHandler = _
-  private var isFinished: Boolean = false
-  private var uiAddress: String = _
-  private var uiHistoryAddress: String = _
-  private val maxAppAttempts: Int = conf.getInt(
-    YarnConfiguration.RM_AM_MAX_ATTEMPTS, YarnConfiguration.DEFAULT_RM_AM_MAX_ATTEMPTS)
-  private var isLastAMRetry: Boolean = true
-  private var amClient: AMRMClient[ContainerRequest] = _
-
-  // Default to numExecutors * 2, with minimum of 3
-  private val maxNumExecutorFailures = sparkConf.getInt("spark.yarn.max.executor.failures",
-    sparkConf.getInt("spark.yarn.max.worker.failures", math.max(args.numExecutors * 2, 3)))
-
-  private var registered = false
-
-  def run() {
-    // Set the web ui port to be ephemeral for yarn so we don't conflict with
-    // other spark processes running on the same box
-    System.setProperty("spark.ui.port", "0")
-
-    // When running the AM, the Spark master is always "yarn-cluster"
-    System.setProperty("spark.master", "yarn-cluster")
-
-    // Use priority 30 as it's higher than HDFS. It's the same priority MapReduce is using.
-    ShutdownHookManager.get().addShutdownHook(new AppMasterShutdownHook(this), 30)
-
-    appAttemptId = ApplicationMaster.getApplicationAttemptId()
-    logInfo("ApplicationAttemptId: " + appAttemptId)
-    isLastAMRetry = appAttemptId.getAttemptId() >= maxAppAttempts
-    amClient = AMRMClient.createAMRMClient()
-    amClient.init(yarnConf)
-    amClient.start()
-
-    // setup AmIpFilter for the SparkUI - do this before we start the UI
-    addAmIpFilter()
-
-    ApplicationMaster.register(this)
-
-    // Call this to force generation of secret so it gets populated into the
-    // Hadoop UGI. This has to happen before the startUserClass which does a
-    // doAs in order for the credentials to be passed on to the executor containers.
-    val securityMgr = new SecurityManager(sparkConf)
-
-    // Start the user's JAR
-    userThread = startUserClass()
-
-    // This a bit hacky, but we need to wait until the spark.driver.port property has
-    // been set by the Thread executing the user class.
-    waitForSparkContextInitialized()
-
-    // Do this after Spark master is up and SparkContext is created so that we can register UI Url.
-    synchronized {
-      if (!isFinished) {
-        registerApplicationMaster()
-        registered = true
-      }
-    }
-
-    // Allocate all containers
-    allocateExecutors()
-
-    // Launch thread that will heartbeat to the RM so it won't think the app has died.
-    launchReporterThread()
-
-    // Wait for the user class to finish
-    userThread.join()
-
-    System.exit(0)
-  }
-
-  // add the yarn amIpFilter that Yarn requires for properly securing the UI
-  private def addAmIpFilter() {
-    val amFilter = "org.apache.hadoop.yarn.server.webproxy.amfilter.AmIpFilter"
-    System.setProperty("spark.ui.filters", amFilter)
-    val proxy = WebAppUtils.getProxyHostAndPort(conf)
-    val parts : Array[String] = proxy.split(":")
-    val uriBase = "http://" + proxy +
-      System.getenv(ApplicationConstants.APPLICATION_WEB_PROXY_BASE_ENV)
-
-    val params = "PROXY_HOST=" + parts(0) + "," + "PROXY_URI_BASE=" + uriBase
-    System.setProperty(
-      "spark.org.apache.hadoop.yarn.server.webproxy.amfilter.AmIpFilter.params", params)
-  }
-
-  private def registerApplicationMaster(): RegisterApplicationMasterResponse = {
-    logInfo("Registering the ApplicationMaster")
-    amClient.registerApplicationMaster(Utils.localHostName(), 0, uiAddress)
-  }
-
-  private def startUserClass(): Thread = {
-    logInfo("Starting the user JAR in a separate Thread")
-    System.setProperty("spark.executor.instances", args.numExecutors.toString)
-    val mainMethod = Class.forName(
-      args.userClass,
-      false,
-      Thread.currentThread.getContextClassLoader).getMethod("main", classOf[Array[String]])
-    val t = new Thread {
-      override def run() {
-        var succeeded = false
-        try {
-          // Copy
-          val mainArgs = new Array[String](args.userArgs.size)
-          args.userArgs.copyToArray(mainArgs, 0, args.userArgs.size)
-          mainMethod.invoke(null, mainArgs)
-          // Some apps have "System.exit(0)" at the end.  The user thread will stop here unless
-          // it has an uncaught exception thrown out.  It needs a shutdown hook to set SUCCEEDED.
-          succeeded = true
-        } finally {
-          logDebug("Finishing main")
-          isLastAMRetry = true
-          if (succeeded) {
-            ApplicationMaster.this.finishApplicationMaster(FinalApplicationStatus.SUCCEEDED)
-          } else {
-            ApplicationMaster.this.finishApplicationMaster(FinalApplicationStatus.FAILED)
-          }
-        }
-      }
-    }
-    t.setName("Driver")
-    t.start()
-    t
-  }
-
-  // This needs to happen before allocateExecutors()
-  private def waitForSparkContextInitialized() {
-    logInfo("Waiting for Spark context initialization")
-    try {
-      var sparkContext: SparkContext = null
-      ApplicationMaster.sparkContextRef.synchronized {
-        var numTries = 0
-        val waitTime = 10000L
-        val maxNumTries = sparkConf.getInt("spark.yarn.applicationMaster.waitTries", 10)
-        while (ApplicationMaster.sparkContextRef.get() == null && numTries < maxNumTries
-            && !isFinished) {
-          logInfo("Waiting for Spark context initialization ... " + numTries)
-          numTries = numTries + 1
-          ApplicationMaster.sparkContextRef.wait(waitTime)
-        }
-        sparkContext = ApplicationMaster.sparkContextRef.get()
-        assert(sparkContext != null || numTries >= maxNumTries)
-
-        if (sparkContext != null) {
-          uiAddress = sparkContext.ui.appUIHostPort
-          uiHistoryAddress = YarnSparkHadoopUtil.getUIHistoryAddress(sparkContext, sparkConf)
-          this.yarnAllocator = YarnAllocationHandler.newAllocator(
-            yarnConf,
-            amClient,
-            appAttemptId,
-            args,
-            sparkContext.preferredNodeLocationData,
-            sparkContext.getConf)
-        } else {
-          logWarning("Unable to retrieve SparkContext in spite of waiting for %d, maxNumTries = %d".
-            format(numTries * waitTime, maxNumTries))
-          this.yarnAllocator = YarnAllocationHandler.newAllocator(
-            yarnConf,
-            amClient,
-            appAttemptId,
-            args,
-            sparkContext.getConf)
-        }
-      }
-    }
-  }
-
-  private def allocateExecutors() {
-    try {
-      logInfo("Requesting" + args.numExecutors + " executors.")
-      // Wait until all containers have launched
-      yarnAllocator.addResourceRequests(args.numExecutors)
-      yarnAllocator.allocateResources()
-      // Exits the loop if the user thread exits.
-
-      while (yarnAllocator.getNumExecutorsRunning < args.numExecutors && userThread.isAlive
-          && !isFinished) {
-        checkNumExecutorsFailed()
-        allocateMissingExecutor()
-        yarnAllocator.allocateResources()
-        Thread.sleep(ApplicationMaster.ALLOCATE_HEARTBEAT_INTERVAL)
-      }
-    }
-    logInfo("All executors have launched.")
-  }
-
-  private def allocateMissingExecutor() {
-    val missingExecutorCount = args.numExecutors - yarnAllocator.getNumExecutorsRunning -
-      yarnAllocator.getNumPendingAllocate
-    if (missingExecutorCount > 0) {
-      logInfo("Allocating %d containers to make up for (potentially) lost containers".
-        format(missingExecutorCount))
-      yarnAllocator.addResourceRequests(missingExecutorCount)
-    }
-  }
-
-  private def checkNumExecutorsFailed() {
-    if (yarnAllocator.getNumExecutorsFailed >= maxNumExecutorFailures) {
-      logInfo("max number of executor failures reached")
-      finishApplicationMaster(FinalApplicationStatus.FAILED,
-        "max number of executor failures reached")
-      // make sure to stop the user thread
-      val sparkContext = ApplicationMaster.sparkContextRef.get()
-      if (sparkContext != null) {
-        logInfo("Invoking sc stop from checkNumExecutorsFailed")
-        sparkContext.stop()
-      } else {
-        logError("sparkContext is null when should shutdown")
-      }
-    }
-  }
-
-  private def launchReporterThread(): Thread = {
-    // Ensure that progress is sent before YarnConfiguration.RM_AM_EXPIRY_INTERVAL_MS elapses.
-    val expiryInterval = yarnConf.getInt(YarnConfiguration.RM_AM_EXPIRY_INTERVAL_MS, 120000)
-
-    // we want to be reasonably responsive without causing too many requests to RM.
-    val schedulerInterval =
-      sparkConf.getLong("spark.yarn.scheduler.heartbeat.interval-ms", 5000)
-
-    // must be <= timeoutInterval / 2.
-    val interval = math.max(0, math.min(expiryInterval / 2, schedulerInterval))
-
-    val t = new Thread {
-      override def run() {
-        while (userThread.isAlive && !isFinished) {
-          checkNumExecutorsFailed()
-          allocateMissingExecutor()
-          logDebug("Sending progress")
-          yarnAllocator.allocateResources()
-          Thread.sleep(interval)
-        }
-      }
-    }
-    // Setting to daemon status, though this is usually not a good idea.
-    t.setDaemon(true)
-    t.start()
-    logInfo("Started progress reporter thread - heartbeat interval : " + interval)
-    t
-  }
-
-  def finishApplicationMaster(status: FinalApplicationStatus, diagnostics: String = "") {
-    synchronized {
-      if (isFinished) {
-        return
-      }
-      isFinished = true
-
-      logInfo("Unregistering ApplicationMaster with " + status)
-      if (registered) {
-        amClient.unregisterApplicationMaster(status, diagnostics, uiHistoryAddress)
-      }
-    }
-  }
-
-  /**
-   * Clean up the staging directory.
-   */
-  private def cleanupStagingDir() {
-    var stagingDirPath: Path = null
-    try {
-      val preserveFiles = sparkConf.get("spark.yarn.preserve.staging.files", "false").toBoolean
-      if (!preserveFiles) {
-        stagingDirPath = new Path(System.getenv("SPARK_YARN_STAGING_DIR"))
-        if (stagingDirPath == null) {
-          logError("Staging directory is null")
-          return
-        }
-        logInfo("Deleting staging directory " + stagingDirPath)
-        fs.delete(stagingDirPath, true)
-      }
-    } catch {
-      case ioe: IOException =>
-        logError("Failed to cleanup staging dir " + stagingDirPath, ioe)
-    }
-  }
-
-  // The shutdown hook that runs when a signal is received AND during normal close of the JVM.
-  class AppMasterShutdownHook(appMaster: ApplicationMaster) extends Runnable {
-
-    def run() {
-      logInfo("AppMaster received a signal.")
-      // We need to clean up staging dir before HDFS is shut down
-      // make sure we don't delete it until this is the last AM
-      if (appMaster.isLastAMRetry) appMaster.cleanupStagingDir()
-    }
-  }
-
-}
-
-object ApplicationMaster extends Logging {
-  // TODO: Currently, task to container is computed once (TaskSetManager) - which need not be
-  // optimal as more containers are available. Might need to handle this better.
-  private val ALLOCATE_HEARTBEAT_INTERVAL = 100
-
-  private val applicationMasters = new CopyOnWriteArrayList[ApplicationMaster]()
-
-  val sparkContextRef: AtomicReference[SparkContext] =
-    new AtomicReference[SparkContext](null)
-
-  def register(master: ApplicationMaster) {
-    applicationMasters.add(master)
-  }
-
-  /**
-   * Called from YarnClusterScheduler to notify the AM code that a SparkContext has been
-   * initialized in the user code.
-   */
-  def sparkContextInitialized(sc: SparkContext): Boolean = {
-    var modified = false
-    sparkContextRef.synchronized {
-      modified = sparkContextRef.compareAndSet(null, sc)
-      sparkContextRef.notifyAll()
-    }
-
-    // Add a shutdown hook - as a best effort in case users do not call sc.stop or do
-    // System.exit.
-    // Should not really have to do this, but it helps YARN to evict resources earlier.
-    // Not to mention, prevent the Client from declaring failure even though we exited properly.
-    // Note that this will unfortunately not properly clean up the staging files because it gets
-    // called too late, after the filesystem is already shutdown.
-    if (modified) {
-      Runtime.getRuntime().addShutdownHook(new Thread with Logging {
-        // This is not only logs, but also ensures that log system is initialized for this instance
-        // when we are actually 'run'-ing.
-        logInfo("Adding shutdown hook for context " + sc)
-
-        override def run() {
-          logInfo("Invoking sc stop from shutdown hook")
-          sc.stop()
-          // Best case ...
-          for (master <- applicationMasters) {
-            master.finishApplicationMaster(FinalApplicationStatus.SUCCEEDED)
-          }
-        }
-      })
-    }
-
-    // Wait for initialization to complete and at least 'some' nodes to get allocated.
-    modified
-  }
-
-  def getApplicationAttemptId(): ApplicationAttemptId = {
-    val containerIdString = System.getenv(ApplicationConstants.Environment.CONTAINER_ID.name())
-    val containerId = ConverterUtils.toContainerId(containerIdString)
-    val appAttemptId = containerId.getApplicationAttemptId()
-    appAttemptId
-  }
-
-  def main(argStrings: Array[String]) {
-    SignalLogger.register(log)
-    val args = new ApplicationMasterArguments(argStrings)
-    SparkHadoopUtil.get.runAsSparkUser { () =>
-      new ApplicationMaster(args).run()
-    }
-  }
-}
diff --git a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala
deleted file mode 100644
index e093fe4ae6ff8..0000000000000
--- a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ExecutorLauncher.scala
+++ /dev/null
@@ -1,276 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.deploy.yarn
-
-import java.net.Socket
-import org.apache.hadoop.conf.Configuration
-import org.apache.hadoop.yarn.api.ApplicationConstants
-import org.apache.hadoop.yarn.api.records._
-import org.apache.hadoop.yarn.api.protocolrecords._
-import org.apache.hadoop.yarn.conf.YarnConfiguration
-import akka.actor._
-import akka.remote._
-import org.apache.spark.{Logging, SecurityManager, SparkConf, SparkEnv}
-import org.apache.spark.util.{Utils, AkkaUtils}
-import org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend
-import org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.AddWebUIFilter
-import org.apache.spark.scheduler.SplitInfo
-import org.apache.hadoop.yarn.client.api.AMRMClient
-import org.apache.hadoop.yarn.client.api.AMRMClient.ContainerRequest
-import org.apache.spark.deploy.SparkHadoopUtil
-import org.apache.hadoop.yarn.webapp.util.WebAppUtils
-
-/**
- * An application master that allocates executors on behalf of a driver that is running outside
- * the cluster.
- *
- * This is used only in yarn-client mode.
- */
-class ExecutorLauncher(args: ApplicationMasterArguments, conf: Configuration, sparkConf: SparkConf)
-  extends Logging {
-
-  def this(args: ApplicationMasterArguments, sparkConf: SparkConf) =
-    this(args, new Configuration(), sparkConf)
-
-  def this(args: ApplicationMasterArguments) = this(args, new SparkConf())
-
-  private var appAttemptId: ApplicationAttemptId = _
-  private var reporterThread: Thread = _
-  private val yarnConf: YarnConfiguration = new YarnConfiguration(conf)
-
-  private var yarnAllocator: YarnAllocationHandler = _
-  private var driverClosed: Boolean = false
-  private var isFinished: Boolean = false
-  private var registered: Boolean = false
-
-  private var amClient: AMRMClient[ContainerRequest] = _
-
-  // Default to numExecutors * 2, with minimum of 3
-  private val maxNumExecutorFailures = sparkConf.getInt("spark.yarn.max.executor.failures",
-    sparkConf.getInt("spark.yarn.max.worker.failures", math.max(args.numExecutors * 2, 3)))
-
-  val securityManager = new SecurityManager(sparkConf)
-  val actorSystem: ActorSystem = AkkaUtils.createActorSystem("sparkYarnAM", Utils.localHostName, 0,
-    conf = sparkConf, securityManager = securityManager)._1
-  var actor: ActorRef = _
-
-  // This actor just working as a monitor to watch on Driver Actor.
-  class MonitorActor(driverUrl: String) extends Actor {
-
-    var driver: ActorSelection = _
-
-    override def preStart() {
-      logInfo("Listen to driver: " + driverUrl)
-      driver = context.actorSelection(driverUrl)
-      // Send a hello message to establish the connection, after which
-      // we can monitor Lifecycle Events.
-      driver ! "Hello"
-      context.system.eventStream.subscribe(self, classOf[RemotingLifecycleEvent])
-    }
-
-    override def receive = {
-      case x: DisassociatedEvent =>
-        logInfo(s"Driver terminated or disconnected! Shutting down. $x")
-        driverClosed = true
-      case x: AddWebUIFilter =>
-        logInfo(s"Add WebUI Filter. $x")
-        driver ! x
-    }
-  }
-
-  def run() {
-    amClient = AMRMClient.createAMRMClient()
-    amClient.init(yarnConf)
-    amClient.start()
-
-    appAttemptId = ApplicationMaster.getApplicationAttemptId()
-    synchronized {
-      if (!isFinished) {
-        registerApplicationMaster()
-        registered = true
-      }
-    }
-
-    waitForSparkMaster()
-    addAmIpFilter()
-
-    // Allocate all containers
-    allocateExecutors()
-
-    // Launch a progress reporter thread, else app will get killed after expiration
-    // (def: 10mins) timeout ensure that progress is sent before
-    // YarnConfiguration.RM_AM_EXPIRY_INTERVAL_MS elapse.
-
-    val timeoutInterval = yarnConf.getInt(YarnConfiguration.RM_AM_EXPIRY_INTERVAL_MS, 120000)
-    // we want to be reasonably responsive without causing too many requests to RM.
-    val schedulerInterval =
-      System.getProperty("spark.yarn.scheduler.heartbeat.interval-ms", "5000").toLong
-    // must be <= timeoutInterval / 2.
-    val interval = math.min(timeoutInterval / 2, schedulerInterval)
-
-    reporterThread = launchReporterThread(interval)
-
-
-    // Wait for the reporter thread to Finish.
-    reporterThread.join()
-
-    finishApplicationMaster(FinalApplicationStatus.SUCCEEDED)
-    actorSystem.shutdown()
-
-    logInfo("Exited")
-    System.exit(0)
-  }
-
-  private def registerApplicationMaster(): RegisterApplicationMasterResponse = {
-    val appUIAddress = sparkConf.get("spark.driver.appUIAddress", "")
-    logInfo(s"Registering the ApplicationMaster with appUIAddress: $appUIAddress")
-    amClient.registerApplicationMaster(Utils.localHostName(), 0, appUIAddress)
-  }
-
-  // add the yarn amIpFilter that Yarn requires for properly securing the UI
-  private def addAmIpFilter() {
-    val proxy = WebAppUtils.getProxyHostAndPort(conf)
-    val parts = proxy.split(":")
-    val proxyBase = System.getenv(ApplicationConstants.APPLICATION_WEB_PROXY_BASE_ENV)
-    val uriBase = "http://" + proxy + proxyBase
-    val amFilter = "PROXY_HOST=" + parts(0) + "," + "PROXY_URI_BASE=" + uriBase
-    val amFilterName = "org.apache.hadoop.yarn.server.webproxy.amfilter.AmIpFilter"
-    actor ! AddWebUIFilter(amFilterName, amFilter, proxyBase)
-  }
-
-  private def waitForSparkMaster() {
-    logInfo("Waiting for Spark driver to be reachable.")
-    var driverUp = false
-    val hostport = args.userArgs(0)
-    val (driverHost, driverPort) = Utils.parseHostPort(hostport)
-    while(!driverUp) {
-      try {
-        val socket = new Socket(driverHost, driverPort)
-        socket.close()
-        logInfo("Driver now available: %s:%s".format(driverHost, driverPort))
-        driverUp = true
-      } catch {
-        case e: Exception =>
-          logError("Failed to connect to driver at %s:%s, retrying ...".
-            format(driverHost, driverPort))
-        Thread.sleep(100)
-      }
-    }
-    sparkConf.set("spark.driver.host", driverHost)
-    sparkConf.set("spark.driver.port", driverPort.toString)
-
-    val driverUrl = "akka.tcp://%s@%s:%s/user/%s".format(
-      SparkEnv.driverActorSystemName,
-      driverHost,
-      driverPort.toString,
-      CoarseGrainedSchedulerBackend.ACTOR_NAME)
-
-    actor = actorSystem.actorOf(Props(new MonitorActor(driverUrl)), name = "YarnAM")
-  }
-
-
-  private def allocateExecutors() {
-    // TODO: should get preferredNodeLocationData from SparkContext, just fake a empty one for now.
-    val preferredNodeLocationData: scala.collection.Map[String, scala.collection.Set[SplitInfo]] =
-      scala.collection.immutable.Map()
-
-    yarnAllocator = YarnAllocationHandler.newAllocator(
-      yarnConf,
-      amClient,
-      appAttemptId,
-      args,
-      preferredNodeLocationData,
-      sparkConf)
-
-    logInfo("Requesting " + args.numExecutors + " executors.")
-    // Wait until all containers have launched
-    yarnAllocator.addResourceRequests(args.numExecutors)
-    yarnAllocator.allocateResources()
-    while ((yarnAllocator.getNumExecutorsRunning < args.numExecutors) && (!driverClosed) &&
-        !isFinished) {
-      checkNumExecutorsFailed()
-      allocateMissingExecutor()
-      yarnAllocator.allocateResources()
-      Thread.sleep(100)
-    }
-
-    logInfo("All executors have launched.")
-  }
-
-  private def allocateMissingExecutor() {
-    val missingExecutorCount = args.numExecutors - yarnAllocator.getNumExecutorsRunning -
-      yarnAllocator.getNumPendingAllocate
-    if (missingExecutorCount > 0) {
-      logInfo("Allocating %d containers to make up for (potentially) lost containers".
-        format(missingExecutorCount))
-      yarnAllocator.addResourceRequests(missingExecutorCount)
-    }
-  }
-
-  private def checkNumExecutorsFailed() {
-    if (yarnAllocator.getNumExecutorsFailed >= maxNumExecutorFailures) {
-      finishApplicationMaster(FinalApplicationStatus.FAILED,
-        "max number of executor failures reached")
-    }
-  }
-
-  private def launchReporterThread(_sleepTime: Long): Thread = {
-    val sleepTime = if (_sleepTime <= 0) 0 else _sleepTime
-
-    val t = new Thread {
-      override def run() {
-        while (!driverClosed && !isFinished) {
-          checkNumExecutorsFailed()
-          allocateMissingExecutor()
-          logDebug("Sending progress")
-          yarnAllocator.allocateResources()
-          Thread.sleep(sleepTime)
-        }
-      }
-    }
-    // setting to daemon status, though this is usually not a good idea.
-    t.setDaemon(true)
-    t.start()
-    logInfo("Started progress reporter thread - sleep time : " + sleepTime)
-    t
-  }
-
-  def finishApplicationMaster(status: FinalApplicationStatus, appMessage: String = "") {
-    synchronized {
-      if (isFinished) {
-        return
-      }
-      logInfo("Unregistering ApplicationMaster with " + status)
-      if (registered) {
-        val trackingUrl = sparkConf.get("spark.yarn.historyServer.address", "")
-        amClient.unregisterApplicationMaster(status, appMessage, trackingUrl)
-      }
-      isFinished = true
-    }
-  }
-
-}
-
-object ExecutorLauncher {
-  def main(argStrings: Array[String]) {
-    val args = new ApplicationMasterArguments(argStrings)
-    SparkHadoopUtil.get.runAsSparkUser { () =>
-      new ExecutorLauncher(args).run()
-    }
-  }
-}
diff --git a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocationHandler.scala b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocationHandler.scala
index 0a461749c819d..4d5144989991f 100644
--- a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocationHandler.scala
+++ b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocationHandler.scala
@@ -17,12 +17,9 @@
 
 package org.apache.spark.deploy.yarn
 
-import java.lang.{Boolean => JBoolean}
-import java.util.{Collections, Set => JSet}
 import java.util.concurrent.{CopyOnWriteArrayList, ConcurrentHashMap}
 import java.util.concurrent.atomic.AtomicInteger
 
-import scala.collection
 import scala.collection.JavaConversions._
 import scala.collection.mutable.{ArrayBuffer, HashMap, HashSet}
 
@@ -32,20 +29,13 @@ import org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend
 import org.apache.spark.util.Utils
 
 import org.apache.hadoop.conf.Configuration
-import org.apache.hadoop.yarn.api.ApplicationMasterProtocol
 import org.apache.hadoop.yarn.api.records.ApplicationAttemptId
-import org.apache.hadoop.yarn.api.records.{Container, ContainerId, ContainerStatus}
+import org.apache.hadoop.yarn.api.records.{Container, ContainerId}
 import org.apache.hadoop.yarn.api.records.{Priority, Resource, ResourceRequest}
 import org.apache.hadoop.yarn.api.protocolrecords.{AllocateRequest, AllocateResponse}
 import org.apache.hadoop.yarn.client.api.AMRMClient
 import org.apache.hadoop.yarn.client.api.AMRMClient.ContainerRequest
-import org.apache.hadoop.yarn.util.{RackResolver, Records}
-
-
-object AllocationType extends Enumeration {
-  type AllocationType = Value
-  val HOST, RACK, ANY = Value
-}
+import org.apache.hadoop.yarn.util.Records
 
 // TODO:
 // Too many params.
@@ -61,16 +51,14 @@ object AllocationType extends Enumeration {
  * Acquires resources for executors from a ResourceManager and launches executors in new containers.
  */
 private[yarn] class YarnAllocationHandler(
-    val conf: Configuration,
-    val amClient: AMRMClient[ContainerRequest],
-    val appAttemptId: ApplicationAttemptId,
-    val maxExecutors: Int,
-    val executorMemory: Int,
-    val executorCores: Int,
-    val preferredHostToCount: Map[String, Int], 
-    val preferredRackToCount: Map[String, Int],
-    val sparkConf: SparkConf)
-  extends Logging {
+    conf: Configuration,
+    sparkConf: SparkConf,
+    amClient: AMRMClient[ContainerRequest],
+    appAttemptId: ApplicationAttemptId,
+    args: ApplicationMasterArguments,
+    preferredNodes: collection.Map[String, collection.Set[SplitInfo]])
+  extends YarnAllocator with Logging {
+
   // These three are locked on allocatedHostToContainersMap. Complementary data structures
   // allocatedHostToContainersMap : containers which are running : host, Set<containerid>
   // allocatedContainerToHostMap: container to host mapping.
@@ -92,7 +80,7 @@ private[yarn] class YarnAllocationHandler(
 
   // Additional memory overhead - in mb.
   private def memoryOverhead: Int = sparkConf.getInt("spark.yarn.executor.memoryOverhead",
-    YarnAllocationHandler.MEMORY_OVERHEAD)
+    YarnSparkHadoopUtil.DEFAULT_MEMORY_OVERHEAD)
 
   // Number of container requests that have been sent to, but not yet allocated by the
   // ApplicationMaster.
@@ -103,11 +91,15 @@ private[yarn] class YarnAllocationHandler(
   private val lastResponseId = new AtomicInteger()
   private val numExecutorsFailed = new AtomicInteger()
 
-  def getNumPendingAllocate: Int = numPendingAllocate.intValue
+  private val maxExecutors = args.numExecutors
+  private val executorMemory = args.executorMemory
+  private val executorCores = args.executorCores
+  private val (preferredHostToCount, preferredRackToCount) =
+    generateNodeToWeight(conf, preferredNodes)
 
-  def getNumExecutorsRunning: Int = numExecutorsRunning.intValue
+  override def getNumExecutorsRunning: Int = numExecutorsRunning.intValue
 
-  def getNumExecutorsFailed: Int = numExecutorsFailed.intValue
+  override def getNumExecutorsFailed: Int = numExecutorsFailed.intValue
 
   def isResourceConstraintSatisfied(container: Container): Boolean = {
     container.getResource.getMemory >= (executorMemory + memoryOverhead)
@@ -119,7 +111,9 @@ private[yarn] class YarnAllocationHandler(
     amClient.releaseAssignedContainer(containerId)
   }
 
-  def allocateResources() {
+  override def allocateResources() = {
+    addResourceRequests(maxExecutors - numPendingAllocate.get() - numExecutorsRunning.get())
+
     // We have already set the container request. Poll the ResourceManager for a response.
     // This doubles as a heartbeat if there are no pending container requests.
     val progressIndicator = 0.1f
@@ -204,7 +198,7 @@ private[yarn] class YarnAllocationHandler(
 
         // For rack local containers
         if (remainingContainers != null) {
-          val rack = YarnAllocationHandler.lookupRack(conf, candidateHost)
+          val rack = YarnSparkHadoopUtil.lookupRack(conf, candidateHost)
           if (rack != null) {
             val maxExpectedRackCount = preferredRackToCount.getOrElse(rack, 0)
             val requiredRackCount = maxExpectedRackCount - allocatedContainersOnRack(rack) -
@@ -273,7 +267,7 @@ private[yarn] class YarnAllocationHandler(
           // To be safe, remove the container from `pendingReleaseContainers`.
           pendingReleaseContainers.remove(containerId)
 
-          val rack = YarnAllocationHandler.lookupRack(conf, executorHostname)
+          val rack = YarnSparkHadoopUtil.lookupRack(conf, executorHostname)
           allocatedHostToContainersMap.synchronized {
             val containerSet = allocatedHostToContainersMap.getOrElseUpdate(executorHostname,
               new HashSet[ContainerId]())
@@ -360,7 +354,7 @@ private[yarn] class YarnAllocationHandler(
             allocatedContainerToHostMap.remove(containerId)
 
             // TODO: Move this part outside the synchronized block?
-            val rack = YarnAllocationHandler.lookupRack(conf, host)
+            val rack = YarnSparkHadoopUtil.lookupRack(conf, host)
             if (rack != null) {
               val rackCount = allocatedRackCount.getOrElse(rack, 0) - 1
               if (rackCount > 0) {
@@ -393,9 +387,9 @@ private[yarn] class YarnAllocationHandler(
 
     for (container <- hostContainers) {
       val candidateHost = container.getNodes.last
-      assert(YarnAllocationHandler.ANY_HOST != candidateHost)
+      assert(YarnSparkHadoopUtil.ANY_HOST != candidateHost)
 
-      val rack = YarnAllocationHandler.lookupRack(conf, candidateHost)
+      val rack = YarnSparkHadoopUtil.lookupRack(conf, candidateHost)
       if (rack != null) {
         var count = rackToCounts.getOrElse(rack, 0)
         count += 1
@@ -409,7 +403,7 @@ private[yarn] class YarnAllocationHandler(
         AllocationType.RACK,
         rack,
         count,
-        YarnAllocationHandler.PRIORITY)
+        YarnSparkHadoopUtil.RM_REQUEST_PRIORITY)
     }
 
     requestedContainers
@@ -431,7 +425,7 @@ private[yarn] class YarnAllocationHandler(
     retval
   }
 
-  def addResourceRequests(numExecutors: Int) {
+  private def addResourceRequests(numExecutors: Int) {
     val containerRequests: List[ContainerRequest] =
       if (numExecutors <= 0 || preferredHostToCount.isEmpty) {
         logDebug("numExecutors: " + numExecutors + ", host preferences: " +
@@ -440,9 +434,9 @@ private[yarn] class YarnAllocationHandler(
           AllocationType.ANY,
           resource = null,
           numExecutors,
-          YarnAllocationHandler.PRIORITY).toList
+          YarnSparkHadoopUtil.RM_REQUEST_PRIORITY).toList
       } else {
-        // Request for all hosts in preferred nodes and for numExecutors - 
+        // Request for all hosts in preferred nodes and for numExecutors -
         // candidates.size, request by default allocation policy.
         val hostContainerRequests = new ArrayBuffer[ContainerRequest](preferredHostToCount.size)
         for ((candidateHost, candidateCount) <- preferredHostToCount) {
@@ -453,7 +447,7 @@ private[yarn] class YarnAllocationHandler(
               AllocationType.HOST,
               candidateHost,
               requiredCount,
-              YarnAllocationHandler.PRIORITY)
+              YarnSparkHadoopUtil.RM_REQUEST_PRIORITY)
           }
         }
         val rackContainerRequests: List[ContainerRequest] = createRackResourceRequests(
@@ -463,7 +457,7 @@ private[yarn] class YarnAllocationHandler(
           AllocationType.ANY,
           resource = null,
           numExecutors,
-          YarnAllocationHandler.PRIORITY)
+          YarnSparkHadoopUtil.RM_REQUEST_PRIORITY)
 
         val containerRequestBuffer = new ArrayBuffer[ContainerRequest](
           hostContainerRequests.size + rackContainerRequests.size() + anyContainerRequests.size)
@@ -512,7 +506,7 @@ private[yarn] class YarnAllocationHandler(
     // There must be a third request, which is ANY. That will be specially handled.
     requestType match {
       case AllocationType.HOST => {
-        assert(YarnAllocationHandler.ANY_HOST != resource)
+        assert(YarnSparkHadoopUtil.ANY_HOST != resource)
         val hostname = resource
         val nodeLocal = constructContainerRequests(
           Array(hostname),
@@ -521,7 +515,7 @@ private[yarn] class YarnAllocationHandler(
           priority)
 
         // Add `hostname` to the global (singleton) host->rack mapping in YarnAllocationHandler.
-        YarnAllocationHandler.populateRackInfo(conf, hostname)
+        YarnSparkHadoopUtil.populateRackInfo(conf, hostname)
         nodeLocal
       }
       case AllocationType.RACK => {
@@ -554,88 +548,6 @@ private[yarn] class YarnAllocationHandler(
     }
     requests
   }
-}
-
-object YarnAllocationHandler {
-
-  val ANY_HOST = "*"
-  // All requests are issued with same priority : we do not (yet) have any distinction between 
-  // request types (like map/reduce in hadoop for example)
-  val PRIORITY = 1
-
-  // Additional memory overhead - in mb.
-  val MEMORY_OVERHEAD = 384
-
-  // Host to rack map - saved from allocation requests. We are expecting this not to change.
-  // Note that it is possible for this to change : and ResurceManager will indicate that to us via
-  // update response to allocate. But we are punting on handling that for now.
-  private val hostToRack = new ConcurrentHashMap[String, String]()
-  private val rackToHostSet = new ConcurrentHashMap[String, JSet[String]]()
-
-
-  def newAllocator(
-      conf: Configuration,
-      amClient: AMRMClient[ContainerRequest],
-      appAttemptId: ApplicationAttemptId,
-      args: ApplicationMasterArguments,
-      sparkConf: SparkConf
-    ): YarnAllocationHandler = {
-    new YarnAllocationHandler(
-      conf,
-      amClient,
-      appAttemptId,
-      args.numExecutors, 
-      args.executorMemory,
-      args.executorCores,
-      Map[String, Int](),
-      Map[String, Int](),
-      sparkConf)
-  }
-
-  def newAllocator(
-      conf: Configuration,
-      amClient: AMRMClient[ContainerRequest],
-      appAttemptId: ApplicationAttemptId,
-      args: ApplicationMasterArguments,
-      map: collection.Map[String,
-      collection.Set[SplitInfo]],
-      sparkConf: SparkConf
-    ): YarnAllocationHandler = {
-    val (hostToSplitCount, rackToSplitCount) = generateNodeToWeight(conf, map)
-    new YarnAllocationHandler(
-      conf,
-      amClient,
-      appAttemptId,
-      args.numExecutors, 
-      args.executorMemory,
-      args.executorCores,
-      hostToSplitCount,
-      rackToSplitCount,
-      sparkConf)
-  }
-
-  def newAllocator(
-      conf: Configuration,
-      amClient: AMRMClient[ContainerRequest],
-      appAttemptId: ApplicationAttemptId,
-      maxExecutors: Int,
-      executorMemory: Int,
-      executorCores: Int,
-      map: collection.Map[String, collection.Set[SplitInfo]],
-      sparkConf: SparkConf
-    ): YarnAllocationHandler = {
-    val (hostToCount, rackToCount) = generateNodeToWeight(conf, map)
-    new YarnAllocationHandler(
-      conf,
-      amClient,
-      appAttemptId,
-      maxExecutors,
-      executorMemory,
-      executorCores,
-      hostToCount,
-      rackToCount,
-      sparkConf)
-  }
 
   // A simple method to copy the split info map.
   private def generateNodeToWeight(
@@ -654,7 +566,7 @@ object YarnAllocationHandler {
       val hostCount = hostToCount.getOrElse(host, 0)
       hostToCount.put(host, hostCount + splits.size)
 
-      val rack = lookupRack(conf, host)
+      val rack = YarnSparkHadoopUtil.lookupRack(conf, host)
       if (rack != null){
         val rackCount = rackToCount.getOrElse(host, 0)
         rackToCount.put(host, rackCount + splits.size)
@@ -664,42 +576,4 @@ object YarnAllocationHandler {
     (hostToCount.toMap, rackToCount.toMap)
   }
 
-  def lookupRack(conf: Configuration, host: String): String = {
-    if (!hostToRack.contains(host)) {
-      populateRackInfo(conf, host)
-    }
-    hostToRack.get(host)
-  }
-
-  def fetchCachedHostsForRack(rack: String): Option[Set[String]] = {
-    Option(rackToHostSet.get(rack)).map { set =>
-      val convertedSet: collection.mutable.Set[String] = set
-      // TODO: Better way to get a Set[String] from JSet.
-      convertedSet.toSet
-    }
-  }
-
-  def populateRackInfo(conf: Configuration, hostname: String) {
-    Utils.checkHost(hostname)
-
-    if (!hostToRack.containsKey(hostname)) {
-      // If there are repeated failures to resolve, all to an ignore list.
-      val rackInfo = RackResolver.resolve(conf, hostname)
-      if (rackInfo != null && rackInfo.getNetworkLocation != null) {
-        val rack = rackInfo.getNetworkLocation
-        hostToRack.put(hostname, rack)
-        if (! rackToHostSet.containsKey(rack)) {
-          rackToHostSet.putIfAbsent(rack,
-            Collections.newSetFromMap(new ConcurrentHashMap[String, JBoolean]()))
-        }
-        rackToHostSet.get(rack).add(hostname)
-
-        // TODO(harvey): Figure out what this comment means...
-        // Since RackResolver caches, we are disabling this for now ...
-      } /* else {
-        // right ? Else we will keep calling rack resolver in case we cant resolve rack info ...
-        hostToRack.put(hostname, null)
-      } */
-    }
-  }
 }
diff --git a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/YarnRMClientImpl.scala b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/YarnRMClientImpl.scala
new file mode 100644
index 0000000000000..e8b8d9bc722bd
--- /dev/null
+++ b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/YarnRMClientImpl.scala
@@ -0,0 +1,76 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.deploy.yarn
+
+import scala.collection.{Map, Set}
+
+import org.apache.hadoop.yarn.api._
+import org.apache.hadoop.yarn.api.protocolrecords._
+import org.apache.hadoop.yarn.api.records._
+import org.apache.hadoop.yarn.client.api.AMRMClient
+import org.apache.hadoop.yarn.client.api.AMRMClient.ContainerRequest
+import org.apache.hadoop.yarn.conf.YarnConfiguration
+import org.apache.hadoop.yarn.util.ConverterUtils
+import org.apache.hadoop.yarn.webapp.util.WebAppUtils
+
+import org.apache.spark.{Logging, SparkConf}
+import org.apache.spark.scheduler.SplitInfo
+import org.apache.spark.util.Utils
+
+
+/**
+ * YarnRMClient implementation for the Yarn stable API.
+ */
+private class YarnRMClientImpl(args: ApplicationMasterArguments) extends YarnRMClient with Logging {
+
+  private var amClient: AMRMClient[ContainerRequest] = _
+  private var uiHistoryAddress: String = _
+
+  override def register(
+      conf: YarnConfiguration,
+      sparkConf: SparkConf,
+      preferredNodeLocations: Map[String, Set[SplitInfo]],
+      uiAddress: String,
+      uiHistoryAddress: String) = {
+    amClient = AMRMClient.createAMRMClient()
+    amClient.init(conf)
+    amClient.start()
+    this.uiHistoryAddress = uiHistoryAddress
+
+    logInfo("Registering the ApplicationMaster")
+    amClient.registerApplicationMaster(Utils.localHostName(), 0, uiAddress)
+    new YarnAllocationHandler(conf, sparkConf, amClient, getAttemptId(), args,
+      preferredNodeLocations)
+  }
+
+  override def shutdown(status: FinalApplicationStatus, diagnostics: String = "") =
+    amClient.unregisterApplicationMaster(status, diagnostics, uiHistoryAddress)
+
+  override def getAttemptId() = {
+    val containerIdString = System.getenv(ApplicationConstants.Environment.CONTAINER_ID.name())
+    val containerId = ConverterUtils.toContainerId(containerIdString)
+    val appAttemptId = containerId.getApplicationAttemptId()
+    appAttemptId
+  }
+
+  override def getProxyHostAndPort(conf: YarnConfiguration) = WebAppUtils.getProxyHostAndPort(conf)
+
+  override def getMaxRegAttempts(conf: YarnConfiguration) =
+    conf.getInt(YarnConfiguration.RM_AM_MAX_ATTEMPTS, YarnConfiguration.DEFAULT_RM_AM_MAX_ATTEMPTS)
+
+}

From d8298c46b7bf566d1cd2f7ea9b1b2b2722dcfb17 Mon Sep 17 00:00:00 2001
From: uncleGen <hustyugm@gmail.com>
Date: Wed, 27 Aug 2014 10:32:13 -0700
Subject: [PATCH 109/489] [SPARK-3170][CORE][BUG]:RDD info loss in "StorageTab"
 and "ExecutorTab"

compeleted stage only need to remove its own partitions that are no longer cached. However, "StorageTab" may lost some rdds which are cached actually. Not only in "StorageTab", "ExectutorTab" may also lose some rdd info which have been overwritten by last rdd in a same task.
1. "StorageTab": when multiple stages run simultaneously, completed stage will remove rdd info which belong to other stages that are still running.
2. "ExectutorTab": taskcontext may lose some "updatedBlocks" info of  rdds  in a dependency chain. Like the following example:
         val r1 = sc.paralize(..).cache()
         val r2 = r1.map(...).cache()
         val n = r2.count()

When count the r2, r1 and r2 will be cached finally. So in CacheManager.getOrCompute, the taskcontext should contain "updatedBlocks" of r1 and r2. Currently, the "updatedBlocks" only contain the info of r2.

Author: uncleGen <hustyugm@gmail.com>

Closes #2131 from uncleGen/master_ui_fix and squashes the following commits:

a6a8a0b [uncleGen] fix some coding style
3a1bc15 [uncleGen] fix some error in unit test
56ea488 [uncleGen] there's some line too long
c82ba82 [uncleGen] Bug Fix: RDD info loss in "StorageTab" and "ExecutorTab"
---
 .../scala/org/apache/spark/CacheManager.scala |  4 ++-
 .../apache/spark/ui/storage/StorageTab.scala  |  7 +++--
 .../org/apache/spark/CacheManagerSuite.scala  | 19 +++++++++++++
 .../spark/ui/storage/StorageTabSuite.scala    | 27 +++++++++++++++++++
 4 files changed, 54 insertions(+), 3 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/CacheManager.scala b/core/src/main/scala/org/apache/spark/CacheManager.scala
index 5ddda4d6953fa..f8584b90cabe6 100644
--- a/core/src/main/scala/org/apache/spark/CacheManager.scala
+++ b/core/src/main/scala/org/apache/spark/CacheManager.scala
@@ -68,7 +68,9 @@ private[spark] class CacheManager(blockManager: BlockManager) extends Logging {
           // Otherwise, cache the values and keep track of any updates in block statuses
           val updatedBlocks = new ArrayBuffer[(BlockId, BlockStatus)]
           val cachedValues = putInBlockManager(key, computedValues, storageLevel, updatedBlocks)
-          context.taskMetrics.updatedBlocks = Some(updatedBlocks)
+          val metrics = context.taskMetrics
+          val lastUpdatedBlocks = metrics.updatedBlocks.getOrElse(Seq[(BlockId, BlockStatus)]())
+          metrics.updatedBlocks = Some(lastUpdatedBlocks ++ updatedBlocks.toSeq)
           new InterruptibleIterator(context, cachedValues)
 
         } finally {
diff --git a/core/src/main/scala/org/apache/spark/ui/storage/StorageTab.scala b/core/src/main/scala/org/apache/spark/ui/storage/StorageTab.scala
index 67f72a94f0269..76097f1c51f8e 100644
--- a/core/src/main/scala/org/apache/spark/ui/storage/StorageTab.scala
+++ b/core/src/main/scala/org/apache/spark/ui/storage/StorageTab.scala
@@ -70,8 +70,11 @@ class StorageListener(storageStatusListener: StorageStatusListener) extends Spar
   }
 
   override def onStageCompleted(stageCompleted: SparkListenerStageCompleted) = synchronized {
-    // Remove all partitions that are no longer cached
-    _rddInfoMap.retain { case (_, info) => info.numCachedPartitions > 0 }
+    // Remove all partitions that are no longer cached in current completed stage
+    val completedRddIds = stageCompleted.stageInfo.rddInfos.map(r => r.id).toSet
+    _rddInfoMap.retain { case (id, info) =>
+      !completedRddIds.contains(id) || info.numCachedPartitions > 0
+    }
   }
 
   override def onUnpersistRDD(unpersistRDD: SparkListenerUnpersistRDD) = synchronized {
diff --git a/core/src/test/scala/org/apache/spark/CacheManagerSuite.scala b/core/src/test/scala/org/apache/spark/CacheManagerSuite.scala
index 9c5f394d3899d..90dcadcffd091 100644
--- a/core/src/test/scala/org/apache/spark/CacheManagerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/CacheManagerSuite.scala
@@ -32,6 +32,8 @@ class CacheManagerSuite extends FunSuite with BeforeAndAfter with EasyMockSugar
   var split: Partition = _
   /** An RDD which returns the values [1, 2, 3, 4]. */
   var rdd: RDD[Int] = _
+  var rdd2: RDD[Int] = _
+  var rdd3: RDD[Int] = _
 
   before {
     sc = new SparkContext("local", "test")
@@ -43,6 +45,16 @@ class CacheManagerSuite extends FunSuite with BeforeAndAfter with EasyMockSugar
       override val getDependencies = List[Dependency[_]]()
       override def compute(split: Partition, context: TaskContext) = Array(1, 2, 3, 4).iterator
     }
+    rdd2 = new RDD[Int](sc, List(new OneToOneDependency(rdd))) {
+      override def getPartitions: Array[Partition] = firstParent[Int].partitions
+      override def compute(split: Partition, context: TaskContext) =
+        firstParent[Int].iterator(split, context)
+    }.cache()
+    rdd3 = new RDD[Int](sc, List(new OneToOneDependency(rdd2))) {
+      override def getPartitions: Array[Partition] = firstParent[Int].partitions
+      override def compute(split: Partition, context: TaskContext) =
+        firstParent[Int].iterator(split, context)
+    }.cache()
   }
 
   after {
@@ -87,4 +99,11 @@ class CacheManagerSuite extends FunSuite with BeforeAndAfter with EasyMockSugar
       assert(value.toList === List(1, 2, 3, 4))
     }
   }
+
+  test("verify task metrics updated correctly") {
+    cacheManager = sc.env.cacheManager
+    val context = new TaskContext(0, 0, 0)
+    cacheManager.getOrCompute(rdd3, split, context, StorageLevel.MEMORY_ONLY)
+    assert(context.taskMetrics.updatedBlocks.getOrElse(Seq()).size === 2)
+  }
 }
diff --git a/core/src/test/scala/org/apache/spark/ui/storage/StorageTabSuite.scala b/core/src/test/scala/org/apache/spark/ui/storage/StorageTabSuite.scala
index b860177705d84..a537c72ce7ab5 100644
--- a/core/src/test/scala/org/apache/spark/ui/storage/StorageTabSuite.scala
+++ b/core/src/test/scala/org/apache/spark/ui/storage/StorageTabSuite.scala
@@ -34,6 +34,7 @@ class StorageTabSuite extends FunSuite with BeforeAndAfter {
   private val memOnly = StorageLevel.MEMORY_ONLY
   private val none = StorageLevel.NONE
   private val taskInfo = new TaskInfo(0, 0, 0, 0, "big", "dog", TaskLocality.ANY, false)
+  private val taskInfo1 = new TaskInfo(1, 1, 1, 1, "big", "cat", TaskLocality.ANY, false)
   private def rddInfo0 = new RDDInfo(0, "freedom", 100, memOnly)
   private def rddInfo1 = new RDDInfo(1, "hostage", 200, memOnly)
   private def rddInfo2 = new RDDInfo(2, "sanity", 300, memAndDisk)
@@ -162,4 +163,30 @@ class StorageTabSuite extends FunSuite with BeforeAndAfter {
     assert(storageListener._rddInfoMap(2).numCachedPartitions === 0)
   }
 
+  test("verify StorageTab contains all cached rdds") {
+
+    val rddInfo0 = new RDDInfo(0, "rdd0", 1, memOnly)
+    val rddInfo1 = new RDDInfo(1, "rdd1", 1 ,memOnly)
+    val stageInfo0 = new StageInfo(0, 0, "stage0", 1, Seq(rddInfo0), "details")
+    val stageInfo1 = new StageInfo(1, 0, "stage1", 1, Seq(rddInfo1), "details")
+    val taskMetrics0 = new TaskMetrics
+    val taskMetrics1 = new TaskMetrics
+    val block0 = (RDDBlockId(0, 1), BlockStatus(memOnly, 100L, 0L, 0L))
+    val block1 = (RDDBlockId(1, 1), BlockStatus(memOnly, 200L, 0L, 0L))
+    taskMetrics0.updatedBlocks = Some(Seq(block0))
+    taskMetrics1.updatedBlocks = Some(Seq(block1))
+    bus.postToAll(SparkListenerBlockManagerAdded(bm1, 1000L))
+    bus.postToAll(SparkListenerStageSubmitted(stageInfo0))
+    assert(storageListener.rddInfoList.size === 0)
+    bus.postToAll(SparkListenerTaskEnd(0, 0, "big", Success, taskInfo, taskMetrics0))
+    assert(storageListener.rddInfoList.size === 1)
+    bus.postToAll(SparkListenerStageSubmitted(stageInfo1))
+    assert(storageListener.rddInfoList.size === 1)
+    bus.postToAll(SparkListenerStageCompleted(stageInfo0))
+    assert(storageListener.rddInfoList.size === 1)
+    bus.postToAll(SparkListenerTaskEnd(1, 0, "small", Success, taskInfo1, taskMetrics1))
+    assert(storageListener.rddInfoList.size === 2)
+    bus.postToAll(SparkListenerStageCompleted(stageInfo1))
+    assert(storageListener.rddInfoList.size === 2)
+  }
 }

From 5ac4093c9fa29a11e38f884eebb3f5db087de76f Mon Sep 17 00:00:00 2001
From: "Allan Douglas R. de Oliveira" <allan@chaordicsystems.com>
Date: Wed, 27 Aug 2014 12:43:22 -0700
Subject: [PATCH 110/489] SPARK-3259 - User data should be given to the master

Author: Allan Douglas R. de Oliveira <allan@chaordicsystems.com>

Closes #2162 from douglaz/user_data_master and squashes the following commits:

10d15f6 [Allan Douglas R. de Oliveira] Give user data also to the master
---
 ec2/spark_ec2.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/ec2/spark_ec2.py b/ec2/spark_ec2.py
index 77a246fffe6a7..ddd72a0f8662a 100755
--- a/ec2/spark_ec2.py
+++ b/ec2/spark_ec2.py
@@ -458,7 +458,8 @@ def launch_cluster(conn, opts, cluster_name):
                                placement=opts.zone,
                                min_count=1,
                                max_count=1,
-                               block_device_map=block_map)
+                               block_device_map=block_map,
+                               user_data=user_data_content)
         master_nodes = master_res.instances
         print "Launched master in %s, regid = %s" % (zone, master_res.id)
 

From 3b5eb7083d3e1955de288e4fd365dca6221f32fb Mon Sep 17 00:00:00 2001
From: u0jing <u9jing@gmail.com>
Date: Wed, 27 Aug 2014 12:47:14 -0700
Subject: [PATCH 111/489] [SPARK-3118][SQL]add "SHOW TBLPROPERTIES tblname;"
 and "SHOW COLUMNS (FROM|IN) table_name [(FROM|IN) db_name]" support

JIRA issue: [SPARK-3118] https://issues.apache.org/jira/browse/SPARK-3118

eg:
> SHOW TBLPROPERTIES test;
SHOW TBLPROPERTIES test;
numPartitions	0
numFiles	1
transient_lastDdlTime	1407923642
numRows	0
totalSize	82
rawDataSize	0

eg:
> SHOW COLUMNS  in test;
SHOW COLUMNS  in test;
OK
Time taken: 0.304 seconds
id
stid
bo

Author: u0jing <u9jing@gmail.com>

Closes #2034 from u0jing/spark-3118 and squashes the following commits:

b231d87 [u0jing] add golden answer files
35f4885 [u0jing] add 'show columns' and 'show tblproperties' support
---
 .../spark/sql/hive/execution/HiveCompatibilitySuite.scala   | 2 ++
 .../src/main/scala/org/apache/spark/sql/hive/HiveQl.scala   | 2 ++
 .../golden/show_columns-0-d84a430d0ab7a63a0a73361f8d188a4b  | 0
 .../golden/show_columns-1-ac73cff018bf94944244117a2eb76f7f  | 0
 .../golden/show_columns-10-695a68c82308540eba1d0a04e032cf39 | 0
 .../golden/show_columns-11-691b4e6664e6d435233ea4e8c3b585d5 | 0
 .../golden/show_columns-12-afc350d459a8f794cc3ca93092163a0c | 1 +
 .../golden/show_columns-13-e86d559aeb84a4cc017a103182c22bfb | 0
 .../golden/show_columns-14-7c1d63fa270b4d94b69ad49c3e2378a6 | 1 +
 .../golden/show_columns-15-2c404655e35f7bd7ce54500c832f9d8e | 1 +
 .../golden/show_columns-2-b74990316ec4245fd8a7011e684b39da  | 3 +++
 .../golden/show_columns-3-6e40309b0ca10f353a68395ccd64d566  | 0
 .../golden/show_columns-4-a62fc229d241303bffb29b34ad125f8c  | 0
 .../golden/show_columns-5-691b4e6664e6d435233ea4e8c3b585d5  | 0
 .../golden/show_columns-6-37c88438bd364343a50f64cf39bfcaf6  | 0
 .../golden/show_columns-7-afc350d459a8f794cc3ca93092163a0c  | 1 +
 .../golden/show_columns-8-9b0b96593ca513c6932f3ed8df68808a  | 1 +
 .../golden/show_columns-9-6c0fa8be1c19d4d216dfe7427df1275f  | 0
 .../show_tblproperties-0-ca75bef7d151a44b6a89dd92333eb12a   | 0
 .../show_tblproperties-1-be4adb893c7f946ebd76a648ce3cc1ae   | 1 +
 .../show_tblproperties-2-7c7993eea8e41cf095afae07772cc16e   | 0
 .../show_tblproperties-3-2b4b8c43ef08bdb418405917d475ac1d   | 0
 .../show_tblproperties-4-6c63215ea599f6533666c4d70606b139   | 6 ++++++
 .../show_tblproperties-5-be4adb893c7f946ebd76a648ce3cc1ae   | 1 +
 .../show_tblproperties-6-9dd8d67460f558955d96a107ca996ad    | 0
 25 files changed, 20 insertions(+)
 create mode 100644 sql/hive/src/test/resources/golden/show_columns-0-d84a430d0ab7a63a0a73361f8d188a4b
 create mode 100644 sql/hive/src/test/resources/golden/show_columns-1-ac73cff018bf94944244117a2eb76f7f
 create mode 100644 sql/hive/src/test/resources/golden/show_columns-10-695a68c82308540eba1d0a04e032cf39
 create mode 100644 sql/hive/src/test/resources/golden/show_columns-11-691b4e6664e6d435233ea4e8c3b585d5
 create mode 100644 sql/hive/src/test/resources/golden/show_columns-12-afc350d459a8f794cc3ca93092163a0c
 create mode 100644 sql/hive/src/test/resources/golden/show_columns-13-e86d559aeb84a4cc017a103182c22bfb
 create mode 100644 sql/hive/src/test/resources/golden/show_columns-14-7c1d63fa270b4d94b69ad49c3e2378a6
 create mode 100644 sql/hive/src/test/resources/golden/show_columns-15-2c404655e35f7bd7ce54500c832f9d8e
 create mode 100644 sql/hive/src/test/resources/golden/show_columns-2-b74990316ec4245fd8a7011e684b39da
 create mode 100644 sql/hive/src/test/resources/golden/show_columns-3-6e40309b0ca10f353a68395ccd64d566
 create mode 100644 sql/hive/src/test/resources/golden/show_columns-4-a62fc229d241303bffb29b34ad125f8c
 create mode 100644 sql/hive/src/test/resources/golden/show_columns-5-691b4e6664e6d435233ea4e8c3b585d5
 create mode 100644 sql/hive/src/test/resources/golden/show_columns-6-37c88438bd364343a50f64cf39bfcaf6
 create mode 100644 sql/hive/src/test/resources/golden/show_columns-7-afc350d459a8f794cc3ca93092163a0c
 create mode 100644 sql/hive/src/test/resources/golden/show_columns-8-9b0b96593ca513c6932f3ed8df68808a
 create mode 100644 sql/hive/src/test/resources/golden/show_columns-9-6c0fa8be1c19d4d216dfe7427df1275f
 create mode 100644 sql/hive/src/test/resources/golden/show_tblproperties-0-ca75bef7d151a44b6a89dd92333eb12a
 create mode 100644 sql/hive/src/test/resources/golden/show_tblproperties-1-be4adb893c7f946ebd76a648ce3cc1ae
 create mode 100644 sql/hive/src/test/resources/golden/show_tblproperties-2-7c7993eea8e41cf095afae07772cc16e
 create mode 100644 sql/hive/src/test/resources/golden/show_tblproperties-3-2b4b8c43ef08bdb418405917d475ac1d
 create mode 100644 sql/hive/src/test/resources/golden/show_tblproperties-4-6c63215ea599f6533666c4d70606b139
 create mode 100644 sql/hive/src/test/resources/golden/show_tblproperties-5-be4adb893c7f946ebd76a648ce3cc1ae
 create mode 100644 sql/hive/src/test/resources/golden/show_tblproperties-6-9dd8d67460f558955d96a107ca996ad

diff --git a/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala b/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
index 66243879b9019..7a69e3c512ee3 100644
--- a/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
+++ b/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
@@ -644,9 +644,11 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
     "show_create_table_db_table",
     "show_create_table_does_not_exist",
     "show_create_table_index",
+    "show_columns",
     "show_describe_func_quotes",
     "show_functions",
     "show_partitions",
+    "show_tblproperties",
     "skewjoinopt13",
     "skewjoinopt18",
     "skewjoinopt9",
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
index 581332e600183..fa3adfdf5855c 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
@@ -54,6 +54,7 @@ private[hive] object HiveQl {
     "TOK_DESCFUNCTION",
     "TOK_DESCDATABASE",
     "TOK_SHOW_CREATETABLE",
+    "TOK_SHOWCOLUMNS",
     "TOK_SHOW_TABLESTATUS",
     "TOK_SHOWDATABASES",
     "TOK_SHOWFUNCTIONS",
@@ -61,6 +62,7 @@ private[hive] object HiveQl {
     "TOK_SHOWINDEXES",
     "TOK_SHOWPARTITIONS",
     "TOK_SHOWTABLES",
+    "TOK_SHOW_TBLPROPERTIES",
 
     "TOK_LOCKTABLE",
     "TOK_SHOWLOCKS",
diff --git a/sql/hive/src/test/resources/golden/show_columns-0-d84a430d0ab7a63a0a73361f8d188a4b b/sql/hive/src/test/resources/golden/show_columns-0-d84a430d0ab7a63a0a73361f8d188a4b
new file mode 100644
index 0000000000000..e69de29bb2d1d
diff --git a/sql/hive/src/test/resources/golden/show_columns-1-ac73cff018bf94944244117a2eb76f7f b/sql/hive/src/test/resources/golden/show_columns-1-ac73cff018bf94944244117a2eb76f7f
new file mode 100644
index 0000000000000..e69de29bb2d1d
diff --git a/sql/hive/src/test/resources/golden/show_columns-10-695a68c82308540eba1d0a04e032cf39 b/sql/hive/src/test/resources/golden/show_columns-10-695a68c82308540eba1d0a04e032cf39
new file mode 100644
index 0000000000000..e69de29bb2d1d
diff --git a/sql/hive/src/test/resources/golden/show_columns-11-691b4e6664e6d435233ea4e8c3b585d5 b/sql/hive/src/test/resources/golden/show_columns-11-691b4e6664e6d435233ea4e8c3b585d5
new file mode 100644
index 0000000000000..e69de29bb2d1d
diff --git a/sql/hive/src/test/resources/golden/show_columns-12-afc350d459a8f794cc3ca93092163a0c b/sql/hive/src/test/resources/golden/show_columns-12-afc350d459a8f794cc3ca93092163a0c
new file mode 100644
index 0000000000000..1711d0c2bb253
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/show_columns-12-afc350d459a8f794cc3ca93092163a0c
@@ -0,0 +1 @@
+a                   
diff --git a/sql/hive/src/test/resources/golden/show_columns-13-e86d559aeb84a4cc017a103182c22bfb b/sql/hive/src/test/resources/golden/show_columns-13-e86d559aeb84a4cc017a103182c22bfb
new file mode 100644
index 0000000000000..e69de29bb2d1d
diff --git a/sql/hive/src/test/resources/golden/show_columns-14-7c1d63fa270b4d94b69ad49c3e2378a6 b/sql/hive/src/test/resources/golden/show_columns-14-7c1d63fa270b4d94b69ad49c3e2378a6
new file mode 100644
index 0000000000000..1711d0c2bb253
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/show_columns-14-7c1d63fa270b4d94b69ad49c3e2378a6
@@ -0,0 +1 @@
+a                   
diff --git a/sql/hive/src/test/resources/golden/show_columns-15-2c404655e35f7bd7ce54500c832f9d8e b/sql/hive/src/test/resources/golden/show_columns-15-2c404655e35f7bd7ce54500c832f9d8e
new file mode 100644
index 0000000000000..1711d0c2bb253
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/show_columns-15-2c404655e35f7bd7ce54500c832f9d8e
@@ -0,0 +1 @@
+a                   
diff --git a/sql/hive/src/test/resources/golden/show_columns-2-b74990316ec4245fd8a7011e684b39da b/sql/hive/src/test/resources/golden/show_columns-2-b74990316ec4245fd8a7011e684b39da
new file mode 100644
index 0000000000000..70c14c3ef34ab
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/show_columns-2-b74990316ec4245fd8a7011e684b39da
@@ -0,0 +1,3 @@
+key                 
+value               
+ds                  
diff --git a/sql/hive/src/test/resources/golden/show_columns-3-6e40309b0ca10f353a68395ccd64d566 b/sql/hive/src/test/resources/golden/show_columns-3-6e40309b0ca10f353a68395ccd64d566
new file mode 100644
index 0000000000000..e69de29bb2d1d
diff --git a/sql/hive/src/test/resources/golden/show_columns-4-a62fc229d241303bffb29b34ad125f8c b/sql/hive/src/test/resources/golden/show_columns-4-a62fc229d241303bffb29b34ad125f8c
new file mode 100644
index 0000000000000..e69de29bb2d1d
diff --git a/sql/hive/src/test/resources/golden/show_columns-5-691b4e6664e6d435233ea4e8c3b585d5 b/sql/hive/src/test/resources/golden/show_columns-5-691b4e6664e6d435233ea4e8c3b585d5
new file mode 100644
index 0000000000000..e69de29bb2d1d
diff --git a/sql/hive/src/test/resources/golden/show_columns-6-37c88438bd364343a50f64cf39bfcaf6 b/sql/hive/src/test/resources/golden/show_columns-6-37c88438bd364343a50f64cf39bfcaf6
new file mode 100644
index 0000000000000..e69de29bb2d1d
diff --git a/sql/hive/src/test/resources/golden/show_columns-7-afc350d459a8f794cc3ca93092163a0c b/sql/hive/src/test/resources/golden/show_columns-7-afc350d459a8f794cc3ca93092163a0c
new file mode 100644
index 0000000000000..1711d0c2bb253
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/show_columns-7-afc350d459a8f794cc3ca93092163a0c
@@ -0,0 +1 @@
+a                   
diff --git a/sql/hive/src/test/resources/golden/show_columns-8-9b0b96593ca513c6932f3ed8df68808a b/sql/hive/src/test/resources/golden/show_columns-8-9b0b96593ca513c6932f3ed8df68808a
new file mode 100644
index 0000000000000..1711d0c2bb253
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/show_columns-8-9b0b96593ca513c6932f3ed8df68808a
@@ -0,0 +1 @@
+a                   
diff --git a/sql/hive/src/test/resources/golden/show_columns-9-6c0fa8be1c19d4d216dfe7427df1275f b/sql/hive/src/test/resources/golden/show_columns-9-6c0fa8be1c19d4d216dfe7427df1275f
new file mode 100644
index 0000000000000..e69de29bb2d1d
diff --git a/sql/hive/src/test/resources/golden/show_tblproperties-0-ca75bef7d151a44b6a89dd92333eb12a b/sql/hive/src/test/resources/golden/show_tblproperties-0-ca75bef7d151a44b6a89dd92333eb12a
new file mode 100644
index 0000000000000..e69de29bb2d1d
diff --git a/sql/hive/src/test/resources/golden/show_tblproperties-1-be4adb893c7f946ebd76a648ce3cc1ae b/sql/hive/src/test/resources/golden/show_tblproperties-1-be4adb893c7f946ebd76a648ce3cc1ae
new file mode 100644
index 0000000000000..0f6cc6f44f1f7
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/show_tblproperties-1-be4adb893c7f946ebd76a648ce3cc1ae
@@ -0,0 +1 @@
+Table tmpfoo does not have property: bar	 
diff --git a/sql/hive/src/test/resources/golden/show_tblproperties-2-7c7993eea8e41cf095afae07772cc16e b/sql/hive/src/test/resources/golden/show_tblproperties-2-7c7993eea8e41cf095afae07772cc16e
new file mode 100644
index 0000000000000..e69de29bb2d1d
diff --git a/sql/hive/src/test/resources/golden/show_tblproperties-3-2b4b8c43ef08bdb418405917d475ac1d b/sql/hive/src/test/resources/golden/show_tblproperties-3-2b4b8c43ef08bdb418405917d475ac1d
new file mode 100644
index 0000000000000..e69de29bb2d1d
diff --git a/sql/hive/src/test/resources/golden/show_tblproperties-4-6c63215ea599f6533666c4d70606b139 b/sql/hive/src/test/resources/golden/show_tblproperties-4-6c63215ea599f6533666c4d70606b139
new file mode 100644
index 0000000000000..ce1a3441a1bc0
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/show_tblproperties-4-6c63215ea599f6533666c4d70606b139
@@ -0,0 +1,6 @@
+	 
+last_modified_by	ocquery
+last_modified_time	1408598216
+tmp	true
+transient_lastDdlTime	1408598216
+bar	bar value
diff --git a/sql/hive/src/test/resources/golden/show_tblproperties-5-be4adb893c7f946ebd76a648ce3cc1ae b/sql/hive/src/test/resources/golden/show_tblproperties-5-be4adb893c7f946ebd76a648ce3cc1ae
new file mode 100644
index 0000000000000..37214958dafe5
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/show_tblproperties-5-be4adb893c7f946ebd76a648ce3cc1ae
@@ -0,0 +1 @@
+bar value	 
diff --git a/sql/hive/src/test/resources/golden/show_tblproperties-6-9dd8d67460f558955d96a107ca996ad b/sql/hive/src/test/resources/golden/show_tblproperties-6-9dd8d67460f558955d96a107ca996ad
new file mode 100644
index 0000000000000..e69de29bb2d1d

From 4238c17dc9e1f2f93cc9e6c768f92bd27bf1df66 Mon Sep 17 00:00:00 2001
From: Cheng Hao <hao.cheng@intel.com>
Date: Wed, 27 Aug 2014 12:50:47 -0700
Subject: [PATCH 112/489] [SPARK-3197] [SQL] Reduce the Expression tree object
 creations for aggregation function (min/max)

Aggregation function min/max in catalyst will create expression tree for each single row, however, the expression tree creation is quite expensive in a multithreading env currently. Hence we got a very bad performance for the min/max.
Here is the benchmark that I've done in my local.

Master | Previous Result (ms) | Current Result (ms)
------------ | ------------- | -------------
local | 3645 | 3416
local[6] | 3602 | 1002

The Benchmark source code.
```
case class Record(key: Int, value: Int)

object TestHive2 extends HiveContext(new SparkContext("local[6]", "TestSQLContext", new SparkConf()))

object DataPrepare extends App {
  import TestHive2._

  val rdd = sparkContext.parallelize((1 to 10000000).map(i => Record(i % 3000, i)), 12)

  runSqlHive("SHOW TABLES")
  runSqlHive("DROP TABLE if exists a")
  runSqlHive("DROP TABLE if exists result")
  rdd.registerAsTable("records")

  runSqlHive("""CREATE TABLE a (key INT, value INT)
                 | ROW FORMAT SERDE
                 | 'org.apache.hadoop.hive.serde2.columnar.LazyBinaryColumnarSerDe'
                 | STORED AS RCFILE
               """.stripMargin)
  runSqlHive("""CREATE TABLE result (key INT, value INT)
                 | ROW FORMAT SERDE
                 | 'org.apache.hadoop.hive.serde2.columnar.LazyBinaryColumnarSerDe'
                 | STORED AS RCFILE
               """.stripMargin)

  hql(s"""from records
             | insert into table a
             | select key, value
           """.stripMargin)
}

object PerformanceTest extends App {
  import TestHive2._

  hql("SHOW TABLES")
  hql("set spark.sql.shuffle.partitions=12")

  val cmd = "select min(value), max(value) from a group by key"

  val results = ("Result1", benchmark(cmd)) ::
                ("Result2", benchmark(cmd)) ::
                ("Result3", benchmark(cmd)) :: Nil
  results.foreach { case (prompt, result) => {
      println(s"$prompt: took ${result._1} ms (${result._2} records)")
    }
  }

  def benchmark(cmd: String) = {
    val begin = System.currentTimeMillis()
    val count = hql(cmd).count
    val end = System.currentTimeMillis()
    ((end - begin), count)
  }
}
```

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

Closes #2113 from chenghao-intel/aggregation_expression_optimization and squashes the following commits:

db40395 [Cheng Hao] remove the transient and add val for the expression property
d56167d [Cheng Hao] Reduce the Expressions creation
---
 .../sql/catalyst/expressions/aggregates.scala | 30 ++++++++++---------
 .../sql/catalyst/expressions/literals.scala   |  5 ++--
 2 files changed, 18 insertions(+), 17 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
index dbc0c2965a805..15560a2a933ad 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
@@ -105,17 +105,18 @@ case class Min(child: Expression) extends PartialAggregate with trees.UnaryNode[
 case class MinFunction(expr: Expression, base: AggregateExpression) extends AggregateFunction {
   def this() = this(null, null) // Required for serialization.
 
-  var currentMin: Any = _
+  val currentMin: MutableLiteral = MutableLiteral(null, expr.dataType)
+  val cmp = GreaterThan(currentMin, expr)
 
   override def update(input: Row): Unit = {
-    if (currentMin == null) {
-      currentMin = expr.eval(input)
-    } else if(GreaterThan(Literal(currentMin, expr.dataType), expr).eval(input) == true) {
-      currentMin = expr.eval(input)
+    if (currentMin.value == null) {
+      currentMin.value = expr.eval(input)
+    } else if(cmp.eval(input) == true) {
+      currentMin.value = expr.eval(input)
     }
   }
 
-  override def eval(input: Row): Any = currentMin
+  override def eval(input: Row): Any = currentMin.value
 }
 
 case class Max(child: Expression) extends PartialAggregate with trees.UnaryNode[Expression] {
@@ -135,17 +136,18 @@ case class Max(child: Expression) extends PartialAggregate with trees.UnaryNode[
 case class MaxFunction(expr: Expression, base: AggregateExpression) extends AggregateFunction {
   def this() = this(null, null) // Required for serialization.
 
-  var currentMax: Any = _
+  val currentMax: MutableLiteral = MutableLiteral(null, expr.dataType)
+  val cmp = LessThan(currentMax, expr)
 
   override def update(input: Row): Unit = {
-    if (currentMax == null) {
-      currentMax = expr.eval(input)
-    } else if(LessThan(Literal(currentMax, expr.dataType), expr).eval(input) == true) {
-      currentMax = expr.eval(input)
+    if (currentMax.value == null) {
+      currentMax.value = expr.eval(input)
+    } else if(cmp.eval(input) == true) {
+      currentMax.value = expr.eval(input)
     }
   }
 
-  override def eval(input: Row): Any = currentMax
+  override def eval(input: Row): Any = currentMax.value
 }
 
 case class Count(child: Expression) extends PartialAggregate with trees.UnaryNode[Expression] {
@@ -350,7 +352,7 @@ case class AverageFunction(expr: Expression, base: AggregateExpression)
   private val zero = Cast(Literal(0), expr.dataType)
 
   private var count: Long = _
-  private val sum = MutableLiteral(zero.eval(EmptyRow))
+  private val sum = MutableLiteral(zero.eval(null), expr.dataType)
   private val sumAsDouble = Cast(sum, DoubleType)
 
   private def addFunction(value: Any) = Add(sum, Literal(value))
@@ -423,7 +425,7 @@ case class SumFunction(expr: Expression, base: AggregateExpression) extends Aggr
 
   private val zero = Cast(Literal(0), expr.dataType)
 
-  private val sum = MutableLiteral(zero.eval(null))
+  private val sum = MutableLiteral(zero.eval(null), expr.dataType)
 
   private val addFunction = Add(sum, Coalesce(Seq(expr, zero)))
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/literals.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/literals.scala
index a8c2396d62632..78a0c55e4bbe5 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/literals.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/literals.scala
@@ -61,11 +61,10 @@ case class Literal(value: Any, dataType: DataType) extends LeafExpression {
 }
 
 // TODO: Specialize
-case class MutableLiteral(var value: Any, nullable: Boolean = true) extends LeafExpression {
+case class MutableLiteral(var value: Any, dataType: DataType, nullable: Boolean = true) 
+    extends LeafExpression {
   type EvaluatedType = Any
 
-  val dataType = Literal(value).dataType
-
   def update(expression: Expression, input: Row) = {
     value = expression.eval(input)
   }

From 191d7cf2a655d032f160b9fa181730364681d0e7 Mon Sep 17 00:00:00 2001
From: Chip Senkbeil <rcsenkbe@us.ibm.com>
Date: Wed, 27 Aug 2014 13:01:11 -0700
Subject: [PATCH 113/489] [SPARK-3256] Added support for :cp <jar> that was
 broken in Scala 2.10.x for REPL

As seen with [SI-6502](https://issues.scala-lang.org/browse/SI-6502) of Scala, the _:cp_ command was broken in Scala 2.10.x. As the Spark shell is a friendly wrapper on top of the Scala REPL, it is also affected by this problem.

My solution was to alter the internal classpath and invalidate any new entries. I also had to add the ability to add new entries to the parent classloader of the interpreter (SparkIMain's global).

The advantage of this versus wiping the interpreter and replaying all of the commands is that you don't have to worry about rerunning heavy Spark-related commands (going to the cluster) or potentially reloading data that might have changed. Instead, you get to work from where you left off.

Until this is fixed upstream for 2.10.x, I had to use reflection to alter the internal compiler classpath.

The solution now looks like this:
![screen shot 2014-08-13 at 3 46 02 pm](https://cloud.githubusercontent.com/assets/2481802/3912625/f02b1440-232c-11e4-9bf6-bafb3e352d14.png)

Author: Chip Senkbeil <rcsenkbe@us.ibm.com>

Closes #1929 from rcsenkbeil/FixReplClasspathSupport and squashes the following commits:

f420cbf [Chip Senkbeil] Added SparkContext.addJar calls to support executing code on remote clusters
a826795 [Chip Senkbeil] Updated AddUrlsToClasspath to use 'new Run' suggestion over hackish compiler error
2ff1d86 [Chip Senkbeil] Added compilation failure on symbols hack to get Scala classes to load correctly
a220639 [Chip Senkbeil] Added support for :cp <jar> that was broken in Scala 2.10.x for REPL
---
 .../org/apache/spark/repl/SparkILoop.scala    | 19 ++++--
 .../org/apache/spark/repl/SparkIMain.scala    | 65 +++++++++++++++++--
 2 files changed, 73 insertions(+), 11 deletions(-)

diff --git a/repl/src/main/scala/org/apache/spark/repl/SparkILoop.scala b/repl/src/main/scala/org/apache/spark/repl/SparkILoop.scala
index 65788f4646d91..53df599cf8121 100644
--- a/repl/src/main/scala/org/apache/spark/repl/SparkILoop.scala
+++ b/repl/src/main/scala/org/apache/spark/repl/SparkILoop.scala
@@ -8,7 +8,11 @@
 package org.apache.spark.repl
 
 
+import java.net.URL
+
+import scala.reflect.io.AbstractFile
 import scala.tools.nsc._
+import scala.tools.nsc.backend.JavaPlatform
 import scala.tools.nsc.interpreter._
 
 import scala.tools.nsc.interpreter.{ Results => IR }
@@ -22,11 +26,10 @@ import scala.tools.util.{ Javap }
 import scala.annotation.tailrec
 import scala.collection.mutable.ListBuffer
 import scala.concurrent.ops
-import scala.tools.nsc.util.{ ClassPath, Exceptional, stringFromWriter, stringFromStream }
+import scala.tools.nsc.util._
 import scala.tools.nsc.interpreter._
-import scala.tools.nsc.io.{ File, Directory }
+import scala.tools.nsc.io.{File, Directory}
 import scala.reflect.NameTransformer._
-import scala.tools.nsc.util.ScalaClassLoader
 import scala.tools.nsc.util.ScalaClassLoader._
 import scala.tools.util._
 import scala.language.{implicitConversions, existentials}
@@ -711,22 +714,24 @@ class SparkILoop(in0: Option[BufferedReader], protected val out: JPrintWriter,
         added = true
         addedClasspath = ClassPath.join(addedClasspath, f.path)
         totalClasspath = ClassPath.join(settings.classpath.value, addedClasspath)
+        intp.addUrlsToClassPath(f.toURI.toURL)
+        sparkContext.addJar(f.toURI.toURL.getPath)
       }
     }
-    if (added) replay()
   }
 
   def addClasspath(arg: String): Unit = {
     val f = File(arg).normalize
     if (f.exists) {
       addedClasspath = ClassPath.join(addedClasspath, f.path)
-      val totalClasspath = ClassPath.join(settings.classpath.value, addedClasspath)
-      echo("Added '%s'.  Your new classpath is:\n\"%s\"".format(f.path, totalClasspath))
-      replay()
+      intp.addUrlsToClassPath(f.toURI.toURL)
+      sparkContext.addJar(f.toURI.toURL.getPath)
+      echo("Added '%s'.  Your new classpath is:\n\"%s\"".format(f.path, intp.global.classPath.asClasspathString))
     }
     else echo("The path '" + f + "' doesn't seem to exist.")
   }
 
+
   def powerCmd(): Result = {
     if (isReplPower) "Already in power mode."
     else enablePowerMode(false)
diff --git a/repl/src/main/scala/org/apache/spark/repl/SparkIMain.scala b/repl/src/main/scala/org/apache/spark/repl/SparkIMain.scala
index 84b57cd2dc1af..6ddb6accd696b 100644
--- a/repl/src/main/scala/org/apache/spark/repl/SparkIMain.scala
+++ b/repl/src/main/scala/org/apache/spark/repl/SparkIMain.scala
@@ -7,11 +7,14 @@
 
 package org.apache.spark.repl
 
+import java.io.File
+
 import scala.tools.nsc._
+import scala.tools.nsc.backend.JavaPlatform
 import scala.tools.nsc.interpreter._
 
 import Predef.{ println => _, _ }
-import util.stringFromWriter
+import scala.tools.nsc.util.{MergedClassPath, stringFromWriter, ScalaClassLoader, stackTraceString}
 import scala.reflect.internal.util._
 import java.net.URL
 import scala.sys.BooleanProp
@@ -21,7 +24,6 @@ import reporters._
 import symtab.Flags
 import scala.reflect.internal.Names
 import scala.tools.util.PathResolver
-import scala.tools.nsc.util.ScalaClassLoader
 import ScalaClassLoader.URLClassLoader
 import scala.tools.nsc.util.Exceptional.unwrap
 import scala.collection.{ mutable, immutable }
@@ -34,7 +36,6 @@ import scala.reflect.runtime.{ universe => ru }
 import scala.reflect.{ ClassTag, classTag }
 import scala.tools.reflect.StdRuntimeTags._
 import scala.util.control.ControlThrowable
-import util.stackTraceString
 
 import org.apache.spark.{Logging, HttpServer, SecurityManager, SparkConf}
 import org.apache.spark.util.Utils
@@ -130,6 +131,9 @@ import org.apache.spark.util.Utils
     private var _classLoader: AbstractFileClassLoader = null                              // active classloader
     private val _compiler: Global                     = newCompiler(settings, reporter)   // our private compiler
 
+    private trait ExposeAddUrl extends URLClassLoader { def addNewUrl(url: URL) = this.addURL(url) }
+    private var _runtimeClassLoader: URLClassLoader with ExposeAddUrl = null              // wrapper exposing addURL
+
     private val nextReqId = {
       var counter = 0
       () => { counter += 1 ; counter }
@@ -308,6 +312,57 @@ import org.apache.spark.util.Utils
       }
     }
 
+    /**
+     * Adds any specified jars to the compile and runtime classpaths.
+     *
+     * @note Currently only supports jars, not directories
+     * @param urls The list of items to add to the compile and runtime classpaths
+     */
+    def addUrlsToClassPath(urls: URL*): Unit = {
+      new Run // Needed to force initialization of "something" to correctly load Scala classes from jars
+      urls.foreach(_runtimeClassLoader.addNewUrl) // Add jars/classes to runtime for execution
+      updateCompilerClassPath(urls: _*)           // Add jars/classes to compile time for compiling
+    }
+
+    protected def updateCompilerClassPath(urls: URL*): Unit = {
+      require(!global.forMSIL) // Only support JavaPlatform
+
+      val platform = global.platform.asInstanceOf[JavaPlatform]
+
+      val newClassPath = mergeUrlsIntoClassPath(platform, urls: _*)
+
+      // NOTE: Must use reflection until this is exposed/fixed upstream in Scala
+      val fieldSetter = platform.getClass.getMethods
+        .find(_.getName.endsWith("currentClassPath_$eq")).get
+      fieldSetter.invoke(platform, Some(newClassPath))
+
+      // Reload all jars specified into our compiler
+      global.invalidateClassPathEntries(urls.map(_.getPath): _*)
+    }
+
+    protected def mergeUrlsIntoClassPath(platform: JavaPlatform, urls: URL*): MergedClassPath[AbstractFile] = {
+      // Collect our new jars/directories and add them to the existing set of classpaths
+      val allClassPaths = (
+        platform.classPath.asInstanceOf[MergedClassPath[AbstractFile]].entries ++
+        urls.map(url => {
+          platform.classPath.context.newClassPath(
+            if (url.getProtocol == "file") {
+              val f = new File(url.getPath)
+              if (f.isDirectory)
+                io.AbstractFile.getDirectory(f)
+              else
+                io.AbstractFile.getFile(f)
+            } else {
+              io.AbstractFile.getURL(url)
+            }
+          )
+        })
+      ).distinct
+
+      // Combine all of our classpaths (old and new) into one merged classpath
+      new MergedClassPath(allClassPaths, platform.classPath.context)
+    }
+
     /** Parent classloader.  Overridable. */
     protected def parentClassLoader: ClassLoader =
       SparkHelper.explicitParentLoader(settings).getOrElse( this.getClass.getClassLoader() )
@@ -356,7 +411,9 @@ import org.apache.spark.util.Utils
     private def makeClassLoader(): AbstractFileClassLoader =
       new TranslatingClassLoader(parentClassLoader match {
         case null   => ScalaClassLoader fromURLs compilerClasspath
-        case p      => new URLClassLoader(compilerClasspath, p)
+        case p      =>
+          _runtimeClassLoader = new URLClassLoader(compilerClasspath, p) with ExposeAddUrl
+          _runtimeClassLoader
       })
 
     def getInterpreterClassLoader() = classLoader

From 48f42781dedecd38ddcb2dcf67dead92bb4318f5 Mon Sep 17 00:00:00 2001
From: chutium <teng.qiu@gmail.com>
Date: Wed, 27 Aug 2014 13:13:04 -0700
Subject: [PATCH 114/489] [SPARK-3138][SQL] sqlContext.parquetFile should be
 able to take a single file as parameter

```if (!fs.getFileStatus(path).isDir) throw Exception``` make no sense after this commit #1370

be careful if someone is working on SPARK-2551, make sure the new change passes test case ```test("Read a parquet file instead of a directory")```

Author: chutium <teng.qiu@gmail.com>

Closes #2044 from chutium/parquet-singlefile and squashes the following commits:

4ae477f [chutium] [SPARK-3138][SQL] sqlContext.parquetFile should be able to take a single file as parameter
---
 .../spark/sql/parquet/ParquetTypes.scala      |  7 ++---
 .../spark/sql/parquet/ParquetQuerySuite.scala | 27 ++++++++++++++++---
 2 files changed, 26 insertions(+), 8 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTypes.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTypes.scala
index 1a52377651737..2941b9793597f 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTypes.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTypes.scala
@@ -394,17 +394,14 @@ private[parquet] object ParquetTypesConverter extends Logging {
       throw new IllegalArgumentException(s"Incorrectly formatted Parquet metadata path $origPath")
     }
     val path = origPath.makeQualified(fs)
-    if (!fs.getFileStatus(path).isDir) {
-      throw new IllegalArgumentException(
-        s"Expected $path for be a directory with Parquet files/metadata")
-    }
-    ParquetRelation.enableLogForwarding()
 
     val children = fs.listStatus(path).filterNot { status =>
       val name = status.getPath.getName
       (name(0) == '.' || name(0) == '_') && name != ParquetFileWriter.PARQUET_METADATA_FILE
     }
 
+    ParquetRelation.enableLogForwarding()
+
     // NOTE (lian): Parquet "_metadata" file can be very slow if the file consists of lots of row
     // groups. Since Parquet schema is replicated among all row groups, we only need to touch a
     // single row group to read schema related metadata. Notice that we are making assumptions that
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala
index 4219cc080000b..42923b6a288d9 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala
@@ -35,7 +35,6 @@ import org.apache.spark.sql.test.TestSQLContext
 import org.apache.spark.sql.test.TestSQLContext._
 import org.apache.spark.util.Utils
 
-
 case class TestRDDEntry(key: Int, value: String)
 
 case class NullReflectData(
@@ -420,8 +419,30 @@ class ParquetQuerySuite extends QueryTest with FunSuiteLike with BeforeAndAfterA
     val rdd_copy = sql("SELECT * FROM tmpx").collect()
     val rdd_orig = rdd.collect()
     for(i <- 0 to 99) {
-      assert(rdd_copy(i).apply(0) === rdd_orig(i).key,  s"key error in line $i")
-      assert(rdd_copy(i).apply(1) === rdd_orig(i).value, s"value in line $i")
+      assert(rdd_copy(i).apply(0) === rdd_orig(i).key,   s"key error in line $i")
+      assert(rdd_copy(i).apply(1) === rdd_orig(i).value, s"value error in line $i")
+    }
+    Utils.deleteRecursively(file)
+  }
+
+  test("Read a parquet file instead of a directory") {
+    val file = getTempFilePath("parquet")
+    val path = file.toString
+    val fsPath = new Path(path)
+    val fs: FileSystem = fsPath.getFileSystem(TestSQLContext.sparkContext.hadoopConfiguration)
+    val rdd = TestSQLContext.sparkContext.parallelize((1 to 100))
+      .map(i => TestRDDEntry(i, s"val_$i"))
+    rdd.coalesce(1).saveAsParquetFile(path)
+
+    val children = fs.listStatus(fsPath).filter(_.getPath.getName.endsWith(".parquet"))
+    assert(children.length > 0)
+    val readFile = parquetFile(path + "/" + children(0).getPath.getName)
+    readFile.registerTempTable("tmpx")
+    val rdd_copy = sql("SELECT * FROM tmpx").collect()
+    val rdd_orig = rdd.collect()
+    for(i <- 0 to 99) {
+      assert(rdd_copy(i).apply(0) === rdd_orig(i).key,   s"key error in line $i")
+      assert(rdd_copy(i).apply(1) === rdd_orig(i).value, s"value error in line $i")
     }
     Utils.deleteRecursively(file)
   }

From 4fa2fda88fc7beebb579ba808e400113b512533b Mon Sep 17 00:00:00 2001
From: Davies Liu <davies.liu@gmail.com>
Date: Wed, 27 Aug 2014 13:18:33 -0700
Subject: [PATCH 115/489] [SPARK-2871] [PySpark] add RDD.lookup(key)

RDD.lookup(key)

        Return the list of values in the RDD for key `key`. This operation
        is done efficiently if the RDD has a known partitioner by only
        searching the partition that the key maps to.

        >>> l = range(1000)
        >>> rdd = sc.parallelize(zip(l, l), 10)
        >>> rdd.lookup(42)  # slow
        [42]
        >>> sorted = rdd.sortByKey()
        >>> sorted.lookup(42)  # fast
        [42]

It also clean up the code in RDD.py, and fix several bugs (related to preservesPartitioning).

Author: Davies Liu <davies.liu@gmail.com>

Closes #2093 from davies/lookup and squashes the following commits:

1789cd4 [Davies Liu] `f` in foreach could be generator or not.
2871b80 [Davies Liu] Merge branch 'master' into lookup
c6390ea [Davies Liu] address all comments
0f1bce8 [Davies Liu] add test case for lookup()
be0e8ba [Davies Liu] fix preservesPartitioning
eb1305d [Davies Liu] add RDD.lookup(key)
---
 python/pyspark/rdd.py | 211 ++++++++++++++++--------------------------
 1 file changed, 79 insertions(+), 132 deletions(-)

diff --git a/python/pyspark/rdd.py b/python/pyspark/rdd.py
index 31919741e9d73..2d80fad796957 100644
--- a/python/pyspark/rdd.py
+++ b/python/pyspark/rdd.py
@@ -147,76 +147,6 @@ def __new__(cls, mean, confidence, low, high):
         return obj
 
 
-class MaxHeapQ(object):
-
-    """
-    An implementation of MaxHeap.
-
-    >>> import pyspark.rdd
-    >>> heap = pyspark.rdd.MaxHeapQ(5)
-    >>> [heap.insert(i) for i in range(10)]
-    [None, None, None, None, None, None, None, None, None, None]
-    >>> sorted(heap.getElements())
-    [0, 1, 2, 3, 4]
-    >>> heap = pyspark.rdd.MaxHeapQ(5)
-    >>> [heap.insert(i) for i in range(9, -1, -1)]
-    [None, None, None, None, None, None, None, None, None, None]
-    >>> sorted(heap.getElements())
-    [0, 1, 2, 3, 4]
-    >>> heap = pyspark.rdd.MaxHeapQ(1)
-    >>> [heap.insert(i) for i in range(9, -1, -1)]
-    [None, None, None, None, None, None, None, None, None, None]
-    >>> heap.getElements()
-    [0]
-    """
-
-    def __init__(self, maxsize):
-        # We start from q[1], so its children are always  2 * k
-        self.q = [0]
-        self.maxsize = maxsize
-
-    def _swim(self, k):
-        while (k > 1) and (self.q[k / 2] < self.q[k]):
-            self._swap(k, k / 2)
-            k = k / 2
-
-    def _swap(self, i, j):
-        t = self.q[i]
-        self.q[i] = self.q[j]
-        self.q[j] = t
-
-    def _sink(self, k):
-        N = self.size()
-        while 2 * k <= N:
-            j = 2 * k
-            # Here we test if both children are greater than parent
-            # if not swap with larger one.
-            if j < N and self.q[j] < self.q[j + 1]:
-                j = j + 1
-            if(self.q[k] > self.q[j]):
-                break
-            self._swap(k, j)
-            k = j
-
-    def size(self):
-        return len(self.q) - 1
-
-    def insert(self, value):
-        if (self.size()) < self.maxsize:
-            self.q.append(value)
-            self._swim(self.size())
-        else:
-            self._replaceRoot(value)
-
-    def getElements(self):
-        return self.q[1:]
-
-    def _replaceRoot(self, value):
-        if(self.q[1] > value):
-            self.q[1] = value
-            self._sink(1)
-
-
 def _parse_memory(s):
     """
     Parse a memory string in the format supported by Java (e.g. 1g, 200m) and
@@ -248,6 +178,7 @@ def __init__(self, jrdd, ctx, jrdd_deserializer):
         self.ctx = ctx
         self._jrdd_deserializer = jrdd_deserializer
         self._id = jrdd.id()
+        self._partitionFunc = None
 
     def _toPickleSerialization(self):
         if (self._jrdd_deserializer == PickleSerializer() or
@@ -325,8 +256,6 @@ def getCheckpointFile(self):
         checkpointFile = self._jrdd.rdd().getCheckpointFile()
         if checkpointFile.isDefined():
             return checkpointFile.get()
-        else:
-            return None
 
     def map(self, f, preservesPartitioning=False):
         """
@@ -366,7 +295,7 @@ def mapPartitions(self, f, preservesPartitioning=False):
         """
         def func(s, iterator):
             return f(iterator)
-        return self.mapPartitionsWithIndex(func)
+        return self.mapPartitionsWithIndex(func, preservesPartitioning)
 
     def mapPartitionsWithIndex(self, f, preservesPartitioning=False):
         """
@@ -416,7 +345,7 @@ def filter(self, f):
         """
         def func(iterator):
             return ifilter(f, iterator)
-        return self.mapPartitions(func)
+        return self.mapPartitions(func, True)
 
     def distinct(self):
         """
@@ -561,7 +490,7 @@ def intersection(self, other):
         """
         return self.map(lambda v: (v, None)) \
             .cogroup(other.map(lambda v: (v, None))) \
-            .filter(lambda x: (len(x[1][0]) != 0) and (len(x[1][1]) != 0)) \
+            .filter(lambda (k, vs): all(vs)) \
             .keys()
 
     def _reserialize(self, serializer=None):
@@ -616,7 +545,7 @@ def sortPartition(iterator):
         if numPartitions == 1:
             if self.getNumPartitions() > 1:
                 self = self.coalesce(1)
-            return self.mapPartitions(sortPartition)
+            return self.mapPartitions(sortPartition, True)
 
         # first compute the boundary of each part via sampling: we want to partition
         # the key-space into bins such that the bins have roughly the same
@@ -721,8 +650,8 @@ def foreach(self, f):
         def processPartition(iterator):
             for x in iterator:
                 f(x)
-            yield None
-        self.mapPartitions(processPartition).collect()  # Force evaluation
+            return iter([])
+        self.mapPartitions(processPartition).count()  # Force evaluation
 
     def foreachPartition(self, f):
         """
@@ -731,10 +660,15 @@ def foreachPartition(self, f):
         >>> def f(iterator):
         ...      for x in iterator:
         ...           print x
-        ...      yield None
         >>> sc.parallelize([1, 2, 3, 4, 5]).foreachPartition(f)
         """
-        self.mapPartitions(f).collect()  # Force evaluation
+        def func(it):
+            r = f(it)
+            try:
+                return iter(r)
+            except TypeError:
+                return iter([])
+        self.mapPartitions(func).count()  # Force evaluation
 
     def collect(self):
         """
@@ -767,18 +701,23 @@ def reduce(self, f):
         15
         >>> sc.parallelize((2 for _ in range(10))).map(lambda x: 1).cache().reduce(add)
         10
+        >>> sc.parallelize([]).reduce(add)
+        Traceback (most recent call last):
+            ...
+        ValueError: Can not reduce() empty RDD
         """
         def func(iterator):
-            acc = None
-            for obj in iterator:
-                if acc is None:
-                    acc = obj
-                else:
-                    acc = f(obj, acc)
-            if acc is not None:
-                yield acc
+            iterator = iter(iterator)
+            try:
+                initial = next(iterator)
+            except StopIteration:
+                return
+            yield reduce(f, iterator, initial)
+
         vals = self.mapPartitions(func).collect()
-        return reduce(f, vals)
+        if vals:
+            return reduce(f, vals)
+        raise ValueError("Can not reduce() empty RDD")
 
     def fold(self, zeroValue, op):
         """
@@ -1081,7 +1020,7 @@ def countPartition(iterator):
             yield counts
 
         def mergeMaps(m1, m2):
-            for (k, v) in m2.iteritems():
+            for k, v in m2.iteritems():
                 m1[k] += v
             return m1
         return self.mapPartitions(countPartition).reduce(mergeMaps)
@@ -1117,24 +1056,10 @@ def takeOrdered(self, num, key=None):
         [10, 9, 7, 6, 5, 4]
         """
 
-        def topNKeyedElems(iterator, key_=None):
-            q = MaxHeapQ(num)
-            for k in iterator:
-                if key_ is not None:
-                    k = (key_(k), k)
-                q.insert(k)
-            yield q.getElements()
-
-        def unKey(x, key_=None):
-            if key_ is not None:
-                x = [i[1] for i in x]
-            return x
-
         def merge(a, b):
-            return next(topNKeyedElems(a + b))
-        result = self.mapPartitions(
-            lambda i: topNKeyedElems(i, key)).reduce(merge)
-        return sorted(unKey(result, key), key=key)
+            return heapq.nsmallest(num, a + b, key)
+
+        return self.mapPartitions(lambda it: [heapq.nsmallest(num, it, key)]).reduce(merge)
 
     def take(self, num):
         """
@@ -1174,13 +1099,13 @@ def take(self, num):
             left = num - len(items)
 
             def takeUpToNumLeft(iterator):
+                iterator = iter(iterator)
                 taken = 0
                 while taken < left:
                     yield next(iterator)
                     taken += 1
 
-            p = range(
-                partsScanned, min(partsScanned + numPartsToTry, totalParts))
+            p = range(partsScanned, min(partsScanned + numPartsToTry, totalParts))
             res = self.context.runJob(self, takeUpToNumLeft, p, True)
 
             items += res
@@ -1194,8 +1119,15 @@ def first(self):
 
         >>> sc.parallelize([2, 3, 4]).first()
         2
+        >>> sc.parallelize([]).first()
+        Traceback (most recent call last):
+            ...
+        ValueError: RDD is empty
         """
-        return self.take(1)[0]
+        rs = self.take(1)
+        if rs:
+            return rs[0]
+        raise ValueError("RDD is empty")
 
     def saveAsNewAPIHadoopDataset(self, conf, keyConverter=None, valueConverter=None):
         """
@@ -1420,13 +1352,13 @@ def reduceByKeyLocally(self, func):
         """
         def reducePartition(iterator):
             m = {}
-            for (k, v) in iterator:
-                m[k] = v if k not in m else func(m[k], v)
+            for k, v in iterator:
+                m[k] = func(m[k], v) if k in m else v
             yield m
 
         def mergeMaps(m1, m2):
-            for (k, v) in m2.iteritems():
-                m1[k] = v if k not in m1 else func(m1[k], v)
+            for k, v in m2.iteritems():
+                m1[k] = func(m1[k], v) if k in m1 else v
             return m1
         return self.mapPartitions(reducePartition).reduce(mergeMaps)
 
@@ -1523,7 +1455,7 @@ def add_shuffle_key(split, iterator):
             buckets = defaultdict(list)
             c, batch = 0, min(10 * numPartitions, 1000)
 
-            for (k, v) in iterator:
+            for k, v in iterator:
                 buckets[partitionFunc(k) % numPartitions].append((k, v))
                 c += 1
 
@@ -1546,7 +1478,7 @@ def add_shuffle_key(split, iterator):
                         batch = max(batch / 1.5, 1)
                     c = 0
 
-            for (split, items) in buckets.iteritems():
+            for split, items in buckets.iteritems():
                 yield pack_long(split)
                 yield outputSerializer.dumps(items)
 
@@ -1616,7 +1548,7 @@ def _mergeCombiners(iterator):
             merger.mergeCombiners(iterator)
             return merger.iteritems()
 
-        return shuffled.mapPartitions(_mergeCombiners)
+        return shuffled.mapPartitions(_mergeCombiners, True)
 
     def aggregateByKey(self, zeroValue, seqFunc, combFunc, numPartitions=None):
         """
@@ -1680,7 +1612,6 @@ def mergeCombiners(a, b):
         return self.combineByKey(createCombiner, mergeValue, mergeCombiners,
                                  numPartitions).mapValues(lambda x: ResultIterable(x))
 
-    # TODO: add tests
     def flatMapValues(self, f):
         """
         Pass each value in the key-value pair RDD through a flatMap function
@@ -1770,9 +1701,8 @@ def subtractByKey(self, other, numPartitions=None):
         [('b', 4), ('b', 5)]
         """
         def filter_func((key, vals)):
-            return len(vals[0]) > 0 and len(vals[1]) == 0
-        map_func = lambda (key, vals): [(key, val) for val in vals[0]]
-        return self.cogroup(other, numPartitions).filter(filter_func).flatMap(map_func)
+            return vals[0] and not vals[1]
+        return self.cogroup(other, numPartitions).filter(filter_func).flatMapValues(lambda x: x[0])
 
     def subtract(self, other, numPartitions=None):
         """
@@ -1785,7 +1715,7 @@ def subtract(self, other, numPartitions=None):
         """
         # note: here 'True' is just a placeholder
         rdd = other.map(lambda x: (x, True))
-        return self.map(lambda x: (x, True)).subtractByKey(rdd).map(lambda tpl: tpl[0])
+        return self.map(lambda x: (x, True)).subtractByKey(rdd, numPartitions).keys()
 
     def keyBy(self, f):
         """
@@ -1925,9 +1855,8 @@ def name(self):
         Return the name of this RDD.
         """
         name_ = self._jrdd.name()
-        if not name_:
-            return None
-        return name_.encode('utf-8')
+        if name_:
+            return name_.encode('utf-8')
 
     def setName(self, name):
         """
@@ -1945,9 +1874,8 @@ def toDebugString(self):
         A description of this RDD and its recursive dependencies for debugging.
         """
         debug_string = self._jrdd.toDebugString()
-        if not debug_string:
-            return None
-        return debug_string.encode('utf-8')
+        if debug_string:
+            return debug_string.encode('utf-8')
 
     def getStorageLevel(self):
         """
@@ -1982,10 +1910,28 @@ def _defaultReducePartitions(self):
         else:
             return self.getNumPartitions()
 
-    # TODO: `lookup` is disabled because we can't make direct comparisons based
-    # on the key; we need to compare the hash of the key to the hash of the
-    # keys in the pairs.  This could be an expensive operation, since those
-    # hashes aren't retained.
+    def lookup(self, key):
+        """
+        Return the list of values in the RDD for key `key`. This operation
+        is done efficiently if the RDD has a known partitioner by only
+        searching the partition that the key maps to.
+
+        >>> l = range(1000)
+        >>> rdd = sc.parallelize(zip(l, l), 10)
+        >>> rdd.lookup(42)  # slow
+        [42]
+        >>> sorted = rdd.sortByKey()
+        >>> sorted.lookup(42)  # fast
+        [42]
+        >>> sorted.lookup(1024)
+        []
+        """
+        values = self.filter(lambda (k, v): k == key).values()
+
+        if self._partitionFunc is not None:
+            return self.ctx.runJob(values, lambda x: x, [self._partitionFunc(key)], False)
+
+        return values.collect()
 
     def _is_pickled(self):
         """ Return this RDD is serialized by Pickle or not. """
@@ -2096,6 +2042,7 @@ def pipeline_func(split, iterator):
         self._jrdd_val = None
         self._jrdd_deserializer = self.ctx.serializer
         self._bypass_serializer = False
+        self._partitionFunc = prev._partitionFunc if self.preservesPartitioning else None
 
     @property
     def _jrdd(self):

From 7faf755ae4f0cf510048e432340260a6e609066d Mon Sep 17 00:00:00 2001
From: Vida Ha <vida@databricks.com>
Date: Wed, 27 Aug 2014 14:26:06 -0700
Subject: [PATCH 116/489] Spark-3213 Fixes issue with spark-ec2 not detecting
 slaves created with "Launch More like this"

... copy the spark_cluster_tag from a spot instance requests over to the instances.

Author: Vida Ha <vida@databricks.com>

Closes #2163 from vidaha/vida/spark-3213 and squashes the following commits:

5070a70 [Vida Ha] Spark-3214 Fix issue with spark-ec2 not detecting slaves created with 'Launch More Like This' and using Spot Requests
---
 ec2/spark_ec2.py | 45 +++++++++++++++++++++++++--------------------
 1 file changed, 25 insertions(+), 20 deletions(-)

diff --git a/ec2/spark_ec2.py b/ec2/spark_ec2.py
index ddd72a0f8662a..ae4c488f83abd 100755
--- a/ec2/spark_ec2.py
+++ b/ec2/spark_ec2.py
@@ -40,7 +40,6 @@
 # A URL prefix from which to fetch AMI information
 AMI_PREFIX = "https://raw.github.com/mesos/spark-ec2/v2/ami-list"
 
-
 class UsageError(Exception):
     pass
 
@@ -464,38 +463,45 @@ def launch_cluster(conn, opts, cluster_name):
         print "Launched master in %s, regid = %s" % (zone, master_res.id)
 
     # Give the instances descriptive names
-    # TODO: Add retry logic for tagging with name since it's used to identify a cluster.
     for master in master_nodes:
         name = '{cn}-master-{iid}'.format(cn=cluster_name, iid=master.id)
-        for i in range(0, 5):
-            try:
-                master.add_tag(key='Name', value=name)
-            except:
-                print "Failed attempt %i of 5 to tag %s" % ((i + 1), name)
-                if (i == 5):
-                    raise "Error - failed max attempts to add name tag"
-                time.sleep(5)
-
+        tag_instance(master, name)
 
     for slave in slave_nodes:
         name = '{cn}-slave-{iid}'.format(cn=cluster_name, iid=slave.id)
-        for i in range(0, 5):
-            try:
-                slave.add_tag(key='Name', value=name)
-            except:
-                print "Failed attempt %i of 5 to tag %s" % ((i + 1), name)
-                if (i == 5):
-                    raise "Error - failed max attempts to add name tag"
-                time.sleep(5)
+        tag_instance(slave, name)
 
     # Return all the instances
     return (master_nodes, slave_nodes)
 
+def tag_instance(instance, name):
+    for i in range(0, 5):
+        try:
+            instance.add_tag(key='Name', value=name)
+        except:
+            print "Failed attempt %i of 5 to tag %s" % ((i + 1), name)
+            if (i == 5):
+                raise "Error - failed max attempts to add name tag"
+            time.sleep(5)
 
 # Get the EC2 instances in an existing cluster if available.
 # Returns a tuple of lists of EC2 instance objects for the masters and slaves
 def get_existing_cluster(conn, opts, cluster_name, die_on_error=True):
     print "Searching for existing cluster " + cluster_name + "..."
+    # Search all the spot instance requests, and copy any tags from the spot instance request to the cluster.
+    spot_instance_requests = conn.get_all_spot_instance_requests()
+    for req in spot_instance_requests:
+        if req.state != u'active':
+            continue
+        name = req.tags.get(u'Name', "")
+        if name.startswith(cluster_name):
+            reservations = conn.get_all_instances(instance_ids=[req.instance_id])
+            for res in reservations:
+                active = [i for i in res.instances if is_active(i)]
+                for instance in active:
+                    if (instance.tags.get(u'Name') == None):
+                        tag_instance(instance, name)
+    # Now proceed to detect master and slaves instances.
     reservations = conn.get_all_instances()
     master_nodes = []
     slave_nodes = []
@@ -518,7 +524,6 @@ def get_existing_cluster(conn, opts, cluster_name, die_on_error=True):
             print >> sys.stderr, "ERROR: Could not find any existing cluster"
         sys.exit(1)
 
-
 # Deploy configuration files and run setup scripts on a newly launched
 # or started EC2 cluster.
 def setup_cluster(conn, master_nodes, slave_nodes, opts, deploy_ssh_key):

From 63a053ab140d7bf605e8c5b7fb5a7bd52aca29b2 Mon Sep 17 00:00:00 2001
From: Andrew Or <andrewor14@gmail.com>
Date: Wed, 27 Aug 2014 14:46:56 -0700
Subject: [PATCH 117/489] [SPARK-3243] Don't use stale spark-driver.* system
 properties

If we set both `spark.driver.extraClassPath` and `--driver-class-path`, then the latter correctly overrides the former. However, the value of the system property `spark.driver.extraClassPath` still uses the former, which is actually not added to the class path. This may cause some confusion...

Of course, this also affects other options (i.e. java options, library path, memory...).

Author: Andrew Or <andrewor14@gmail.com>

Closes #2154 from andrewor14/driver-submit-configs-fix and squashes the following commits:

17ec6fc [Andrew Or] Fix tests
0140836 [Andrew Or] Don't forget spark.driver.memory
e39d20f [Andrew Or] Also set spark.driver.extra* configs in client mode
---
 .../org/apache/spark/deploy/SparkSubmit.scala | 19 +++++++++----------
 1 file changed, 9 insertions(+), 10 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
index 550ee72538900..0fdb5ae3c2e40 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
@@ -173,6 +173,14 @@ object SparkSubmit {
       OptionAssigner(args.master, ALL_CLUSTER_MGRS, ALL_DEPLOY_MODES, sysProp = "spark.master"),
       OptionAssigner(args.name, ALL_CLUSTER_MGRS, ALL_DEPLOY_MODES, sysProp = "spark.app.name"),
       OptionAssigner(args.jars, ALL_CLUSTER_MGRS, ALL_DEPLOY_MODES, sysProp = "spark.jars"),
+      OptionAssigner(args.driverMemory, ALL_CLUSTER_MGRS, CLIENT,
+        sysProp = "spark.driver.memory"),
+      OptionAssigner(args.driverExtraClassPath, ALL_CLUSTER_MGRS, ALL_DEPLOY_MODES,
+        sysProp = "spark.driver.extraClassPath"),
+      OptionAssigner(args.driverExtraJavaOptions, ALL_CLUSTER_MGRS, ALL_DEPLOY_MODES,
+        sysProp = "spark.driver.extraJavaOptions"),
+      OptionAssigner(args.driverExtraLibraryPath, ALL_CLUSTER_MGRS, ALL_DEPLOY_MODES,
+        sysProp = "spark.driver.extraLibraryPath"),
 
       // Standalone cluster only
       OptionAssigner(args.driverMemory, STANDALONE, CLUSTER, clOption = "--memory"),
@@ -202,16 +210,7 @@ object SparkSubmit {
       OptionAssigner(args.totalExecutorCores, STANDALONE | MESOS, ALL_DEPLOY_MODES,
         sysProp = "spark.cores.max"),
       OptionAssigner(args.files, LOCAL | STANDALONE | MESOS, ALL_DEPLOY_MODES,
-        sysProp = "spark.files"),
-
-      // Only process driver specific options for cluster mode here,
-      // because they have already been processed in bash for client mode
-      OptionAssigner(args.driverExtraClassPath, STANDALONE | YARN, CLUSTER,
-        sysProp = "spark.driver.extraClassPath"),
-      OptionAssigner(args.driverExtraJavaOptions, STANDALONE | YARN, CLUSTER,
-        sysProp = "spark.driver.extraJavaOptions"),
-      OptionAssigner(args.driverExtraLibraryPath, STANDALONE | YARN, CLUSTER,
-        sysProp = "spark.driver.extraLibraryPath")
+        sysProp = "spark.files")
     )
 
     // In client mode, launch the application main class directly

From 28d41d627919fcb196d9d31bad65d664770bee67 Mon Sep 17 00:00:00 2001
From: viirya <viirya@gmail.com>
Date: Wed, 27 Aug 2014 14:55:05 -0700
Subject: [PATCH 118/489] [SPARK-3252][SQL] Add missing condition for test

According to the text message, both relations should be tested. So add the missing condition.

Author: viirya <viirya@gmail.com>

Closes #2159 from viirya/fix_test and squashes the following commits:

b1c0f52 [viirya] add missing condition.
---
 .../test/scala/org/apache/spark/sql/hive/StatisticsSuite.scala | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/StatisticsSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/StatisticsSuite.scala
index 8d6ca9939a730..a35c40efdc207 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/StatisticsSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/StatisticsSuite.scala
@@ -150,7 +150,8 @@ class StatisticsSuite extends QueryTest with BeforeAndAfterAll {
       val sizes = rdd.queryExecution.analyzed.collect {
         case r if ct.runtimeClass.isAssignableFrom(r.getClass) => r.statistics.sizeInBytes
       }
-      assert(sizes.size === 2 && sizes(0) <= autoBroadcastJoinThreshold,
+      assert(sizes.size === 2 && sizes(0) <= autoBroadcastJoinThreshold
+        && sizes(1) <= autoBroadcastJoinThreshold,
         s"query should contain two relations, each of which has size smaller than autoConvertSize")
 
       // Using `sparkPlan` because for relevant patterns in HashJoin to be

From cc275f4b7910f6d0ad266a43bac2fdae58e9739e Mon Sep 17 00:00:00 2001
From: Aaron Davidson <aaron@databricks.com>
Date: Wed, 27 Aug 2014 15:05:47 -0700
Subject: [PATCH 119/489] [SQL] [SPARK-3236] Reading Parquet tables from
 Metastore mangles location

Currently we do `relation.hiveQlTable.getDataLocation.getPath`, which returns the path-part of the URI (e.g., "s3n://my-bucket/my-path" => "/my-path"). We should do `relation.hiveQlTable.getDataLocation.toString` instead, as a URI's toString returns a faithful representation of the full URI, which can later be passed into a Hadoop Path.

Author: Aaron Davidson <aaron@databricks.com>

Closes #2150 from aarondav/parquet-location and squashes the following commits:

459f72c [Aaron Davidson] [SQL] [SPARK-3236] Reading Parquet tables from Metastore mangles location
---
 .../main/scala/org/apache/spark/sql/hive/HiveStrategies.scala   | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala
index 10fa8314c9156..47e24f0dec146 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala
@@ -135,7 +135,7 @@ private[hive] trait HiveStrategies {
             .fakeOutput(projectList.map(_.toAttribute)):: Nil
         } else {
           hiveContext
-            .parquetFile(relation.hiveQlTable.getDataLocation.getPath)
+            .parquetFile(relation.hiveQlTable.getDataLocation.toString)
             .lowerCase
             .where(unresolvedOtherPredicates)
             .select(unresolvedProjection:_*)

From 65253502b913f390b26b9b631380b2c6cf1ccdf7 Mon Sep 17 00:00:00 2001
From: luogankun <luogankun@gmail.com>
Date: Wed, 27 Aug 2014 15:08:22 -0700
Subject: [PATCH 120/489] [SPARK-3065][SQL] Add locale setting to fix results
 do not match for udf_unix_timestamp format "yyyy MMM dd h:mm:ss a" run with
 not "America/Los_Angeles" TimeZone in HiveCompatibilitySuite

When run the udf_unix_timestamp of org.apache.spark.sql.hive.execution.HiveCompatibilitySuite testcase
with not "America/Los_Angeles" TimeZone throws error. [https://issues.apache.org/jira/browse/SPARK-3065]
add locale setting on beforeAll and afterAll method to fix the bug of HiveCompatibilitySuite testcase

Author: luogankun <luogankun@gmail.com>

Closes #1968 from luogankun/SPARK-3065 and squashes the following commits:

c167832 [luogankun] [SPARK-3065][SQL] Add Locale setting to HiveCompatibilitySuite
0a25e3a [luogankun] [SPARK-3065][SQL] Add Locale setting to HiveCompatibilitySuite
---
 .../spark/sql/hive/execution/HiveCompatibilitySuite.scala | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

diff --git a/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala b/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
index 7a69e3c512ee3..035fd3214bd1d 100644
--- a/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
+++ b/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
@@ -18,7 +18,7 @@
 package org.apache.spark.sql.hive.execution
 
 import java.io.File
-import java.util.TimeZone
+import java.util.{Locale, TimeZone}
 
 import org.scalatest.BeforeAndAfter
 
@@ -33,6 +33,7 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
     File.separator + "test" + File.separator + "queries" + File.separator + "clientpositive")
 
   var originalTimeZone: TimeZone = _
+  var originalLocale: Locale = _
 
   def testCases = hiveQueryDir.listFiles.map(f => f.getName.stripSuffix(".q") -> f)
 
@@ -41,11 +42,16 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
     // Timezone is fixed to America/Los_Angeles for those timezone sensitive tests (timestamp_*)
     originalTimeZone = TimeZone.getDefault
     TimeZone.setDefault(TimeZone.getTimeZone("America/Los_Angeles"))
+    
+    // Add Locale setting
+    originalLocale = Locale.getDefault
+    Locale.setDefault(Locale.US)
   }
 
   override def afterAll() {
     TestHive.cacheTables = false
     TimeZone.setDefault(originalTimeZone)
+    Locale.setDefault(originalLocale)
   }
 
   /** A list of tests deemed out of scope currently and thus completely disregarded. */

From 7d2a7a91f263bb9fbf24dc4dbffde8fe5e2c7442 Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Wed, 27 Aug 2014 15:14:08 -0700
Subject: [PATCH 121/489] [SPARK-3235][SQL] Ensure in-memory tables don't
 always broadcast.

Author: Michael Armbrust <michael@databricks.com>

Closes #2147 from marmbrus/inMemDefaultSize and squashes the following commits:

5390360 [Michael Armbrust] Merge remote-tracking branch 'origin/master' into inMemDefaultSize
14204d3 [Michael Armbrust] Set the context before creating SparkLogicalPlans.
8da4414 [Michael Armbrust] Make sure we throw errors when leaf nodes fail to provide statistcs
18ce029 [Michael Armbrust] Ensure in-memory tables don't always broadcast.
---
 .../sql/catalyst/plans/logical/LogicalPlan.scala   | 14 ++++++++------
 .../scala/org/apache/spark/sql/SQLContext.scala    |  4 +++-
 .../sql/columnar/InMemoryColumnarTableScan.scala   |  3 +++
 .../org/apache/spark/sql/execution/SparkPlan.scala |  2 +-
 .../sql/columnar/InMemoryColumnarQuerySuite.scala  |  8 ++++++++
 5 files changed, 23 insertions(+), 8 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala
index 8616ac45b0e95..f81d9111945f5 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala
@@ -41,9 +41,14 @@ abstract class LogicalPlan extends QueryPlan[LogicalPlan] {
   case class Statistics(
     sizeInBytes: BigInt
   )
-  lazy val statistics: Statistics = Statistics(
-    sizeInBytes = children.map(_.statistics).map(_.sizeInBytes).product
-  )
+  lazy val statistics: Statistics = {
+    if (children.size == 0) {
+      throw new UnsupportedOperationException(s"LeafNode $nodeName must implement statistics.")
+    }
+
+    Statistics(
+      sizeInBytes = children.map(_.statistics).map(_.sizeInBytes).product)
+  }
 
   /**
    * Returns the set of attributes that this node takes as
@@ -117,9 +122,6 @@ abstract class LogicalPlan extends QueryPlan[LogicalPlan] {
  */
 abstract class LeafNode extends LogicalPlan with trees.LeafNode[LogicalPlan] {
   self: Product =>
-
-  override lazy val statistics: Statistics =
-    throw new UnsupportedOperationException(s"LeafNode $nodeName must implement statistics.")
 }
 
 /**
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
index 6f0eed3f63c41..a75af94d29303 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
@@ -89,8 +89,10 @@ class SQLContext(@transient val sparkContext: SparkContext)
    *
    * @group userf
    */
-  implicit def createSchemaRDD[A <: Product: TypeTag](rdd: RDD[A]) =
+  implicit def createSchemaRDD[A <: Product: TypeTag](rdd: RDD[A]) = {
+    SparkPlan.currentContext.set(self)
     new SchemaRDD(this, SparkLogicalPlan(ExistingRdd.fromProductRdd(rdd))(self))
+  }
 
   /**
    * :: DeveloperApi ::
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala
index 24e88eea3189e..bc36bacd00b13 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala
@@ -39,6 +39,9 @@ private[sql] case class InMemoryRelation(
     (private var _cachedColumnBuffers: RDD[Array[ByteBuffer]] = null)
   extends LogicalPlan with MultiInstanceRelation {
 
+  override lazy val statistics =
+    Statistics(sizeInBytes = child.sqlContext.defaultSizeInBytes)
+
   // If the cached column buffers were not passed in, we calculate them in the constructor.
   // As in Spark, the actual work of caching is lazy.
   if (_cachedColumnBuffers == null) {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
index 7d33ea5b021e2..2b8913985b028 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
@@ -49,7 +49,7 @@ abstract class SparkPlan extends QueryPlan[SparkPlan] with Logging with Serializ
    * populated by the query planning infrastructure.
    */
   @transient
-  protected val sqlContext = SparkPlan.currentContext.get()
+  protected[spark] val sqlContext = SparkPlan.currentContext.get()
 
   protected def sparkContext = sqlContext.sparkContext
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/InMemoryColumnarQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/InMemoryColumnarQuerySuite.scala
index 736c0f8571e9e..fdd2799a53268 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/InMemoryColumnarQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/InMemoryColumnarQuerySuite.scala
@@ -33,6 +33,14 @@ class InMemoryColumnarQuerySuite extends QueryTest {
     checkAnswer(scan, testData.collect().toSeq)
   }
 
+  test("default size avoids broadcast") {
+    // TODO: Improve this test when we have better statistics
+    sparkContext.parallelize(1 to 10).map(i => TestData(i, i.toString)).registerTempTable("sizeTst")
+    cacheTable("sizeTst")
+    assert(
+      table("sizeTst").queryExecution.logical.statistics.sizeInBytes > autoBroadcastJoinThreshold)
+  }
+
   test("projection") {
     val plan = TestSQLContext.executePlan(testData.select('value, 'key).logicalPlan).executedPlan
     val scan = InMemoryRelation(useCompression = true, 5, plan)

From 8712653f11b9730f6e7ef1c99c8c5850154abc56 Mon Sep 17 00:00:00 2001
From: Patrick Wendell <pwendell@gmail.com>
Date: Wed, 27 Aug 2014 15:40:40 -0700
Subject: [PATCH 122/489] HOTFIX: Don't build with YARN support for Mapr3

---
 dev/create-release/create-release.sh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/dev/create-release/create-release.sh b/dev/create-release/create-release.sh
index eab6313733dfd..7549fbbe66654 100755
--- a/dev/create-release/create-release.sh
+++ b/dev/create-release/create-release.sh
@@ -121,7 +121,7 @@ make_binary_release "hadoop1" "-Phive -Dhadoop.version=1.0.4" &
 make_binary_release "hadoop2.3" "-Phadoop-2.3 -Phive -Pyarn" &
 make_binary_release "hadoop2.4" "-Phadoop-2.4 -Phive -Pyarn" &
 make_binary_release "hadoop2.4-without-hive" "-Phadoop-2.4 -Pyarn" &
-make_binary_release "mapr3" "-Pmapr3 -Pyarn -Phive" &
+make_binary_release "mapr3" "-Pmapr3 -Phive" &
 make_binary_release "mapr4" "-Pmapr4 -Pyarn -Phive" &
 wait
 

From 64d8ecbbe94c47236ff2d8c94d7401636ba6fca4 Mon Sep 17 00:00:00 2001
From: Matthew Farrellee <matt@redhat.com>
Date: Wed, 27 Aug 2014 15:50:30 -0700
Subject: [PATCH 123/489] Add line continuation for script to work w/ py2.7.5

Error was -

$ SPARK_HOME=$PWD/dist ./dev/create-release/generate-changelist.py
  File "./dev/create-release/generate-changelist.py", line 128
    if day < SPARK_REPO_CHANGE_DATE1 or
                                      ^
SyntaxError: invalid syntax

Author: Matthew Farrellee <matt@redhat.com>

Closes #2139 from mattf/master-fix-generate-changelist.py-0 and squashes the following commits:

6b3a900 [Matthew Farrellee] Add line continuation for script to work w/ py2.7.5
---
 dev/create-release/generate-changelist.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/dev/create-release/generate-changelist.py b/dev/create-release/generate-changelist.py
index de1b5d4ae1314..2e1a35a629342 100755
--- a/dev/create-release/generate-changelist.py
+++ b/dev/create-release/generate-changelist.py
@@ -125,8 +125,8 @@ def cleanup(ask=True):
             pr_num = [line.split()[1].lstrip("#") for line in body_lines if "Closes #" in line][0]
             github_url = "github.com/apache/spark/pull/%s" % pr_num
             day = time.strptime(date.split()[0], "%Y-%m-%d")
-            if day < SPARK_REPO_CHANGE_DATE1 or
-            (day < SPARK_REPO_CHANGE_DATE2 and pr_num < SPARK_REPO_PR_NUM_THRESH):
+            if (day < SPARK_REPO_CHANGE_DATE1 or
+                (day < SPARK_REPO_CHANGE_DATE2 and pr_num < SPARK_REPO_PR_NUM_THRESH)):
                 github_url = "github.com/apache/incubator-spark/pull/%s" % pr_num
 
         append_to_changelist("  %s" % subject)

From b86277c13232c3e65ce6c6cf7f6ede6a00546485 Mon Sep 17 00:00:00 2001
From: scwf <wangfei1@huawei.com>
Date: Wed, 27 Aug 2014 19:44:26 -0700
Subject: [PATCH 124/489] [SPARK-3271] delete unused methods in Utils

delete no used method in Utils

Author: scwf <wangfei1@huawei.com>

Closes #2160 from scwf/delete-no-use-method and squashes the following commits:

d8f6b0d [scwf] delete no use method in Utils
---
 .../scala/org/apache/spark/util/Utils.scala   | 37 -------------------
 1 file changed, 37 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/util/Utils.scala b/core/src/main/scala/org/apache/spark/util/Utils.scala
index 69a84a3604a52..86f646d2af181 100644
--- a/core/src/main/scala/org/apache/spark/util/Utils.scala
+++ b/core/src/main/scala/org/apache/spark/util/Utils.scala
@@ -52,11 +52,6 @@ private[spark] case class CallSite(shortForm: String, longForm: String)
 private[spark] object Utils extends Logging {
   val random = new Random()
 
-  def sparkBin(sparkHome: String, which: String): File = {
-    val suffix = if (isWindows) ".cmd" else ""
-    new File(sparkHome + File.separator + "bin", which + suffix)
-  }
-
   /** Serialize an object using Java serialization */
   def serialize[T](o: T): Array[Byte] = {
     val bos = new ByteArrayOutputStream()
@@ -162,30 +157,6 @@ private[spark] object Utils extends Logging {
     }
   }
 
-  def isAlpha(c: Char): Boolean = {
-    (c >= 'A' && c <= 'Z') || (c >= 'a' && c <= 'z')
-  }
-
-  /** Split a string into words at non-alphabetic characters */
-  def splitWords(s: String): Seq[String] = {
-    val buf = new ArrayBuffer[String]
-    var i = 0
-    while (i < s.length) {
-      var j = i
-      while (j < s.length && isAlpha(s.charAt(j))) {
-        j += 1
-      }
-      if (j > i) {
-        buf += s.substring(i, j)
-      }
-      i = j
-      while (i < s.length && !isAlpha(s.charAt(i))) {
-        i += 1
-      }
-    }
-    buf
-  }
-
   private val shutdownDeletePaths = new scala.collection.mutable.HashSet[String]()
   private val shutdownDeleteTachyonPaths = new scala.collection.mutable.HashSet[String]()
 
@@ -830,14 +801,6 @@ private[spark] object Utils extends Logging {
     }
   }
 
-  /**
-   * Execute a command in the current working directory, throwing an exception if it completes
-   * with an exit code other than 0.
-   */
-  def execute(command: Seq[String]) {
-    execute(command, new File("."))
-  }
-
   /**
    * Execute a command and get its output, throwing an exception if it yields a code other than 0.
    */

From f38fab97c7970168f1bd81d4dc202e36322c95e3 Mon Sep 17 00:00:00 2001
From: Rob O'Dwyer <odwyerrob@gmail.com>
Date: Wed, 27 Aug 2014 19:47:33 -0700
Subject: [PATCH 125/489] SPARK-3265 Allow using custom ipython executable with
 pyspark

Although you can make pyspark use ipython with `IPYTHON=1`, and also change the python executable with `PYSPARK_PYTHON=...`, you can't use both at the same time because it hardcodes the default ipython script.

This makes it use the `PYSPARK_PYTHON` variable if present and fall back to default python, similarly to how the default python executable is handled.

So you can use a custom ipython like so:
`PYSPARK_PYTHON=./anaconda/bin/ipython IPYTHON_OPTS="notebook" pyspark`

Author: Rob O'Dwyer <odwyerrob@gmail.com>

Closes #2167 from robbles/patch-1 and squashes the following commits:

d98e8a9 [Rob O'Dwyer] Allow using custom ipython executable with pyspark
---
 bin/pyspark | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/bin/pyspark b/bin/pyspark
index 01d42025c978e..59cfdfa7c5d1a 100755
--- a/bin/pyspark
+++ b/bin/pyspark
@@ -104,7 +104,7 @@ if [[ "$1" =~ \.py$ ]]; then
 else
   # Only use ipython if no command line arguments were provided [SPARK-1134]
   if [[ "$IPYTHON" = "1" ]]; then
-    exec ipython $IPYTHON_OPTS
+    exec ${PYSPARK_PYTHON:-ipython} $IPYTHON_OPTS
   else
     exec "$PYSPARK_PYTHON"
   fi

From dafe343499bbc688e266106e4bb897f9e619834e Mon Sep 17 00:00:00 2001
From: Andrew Or <andrewor14@gmail.com>
Date: Wed, 27 Aug 2014 23:03:46 -0700
Subject: [PATCH 126/489] [HOTFIX] Wait for EOF only for the PySpark shell

In `SparkSubmitDriverBootstrapper`, we wait for the parent process to send us an `EOF` before finishing the application. This is applicable for the PySpark shell because we terminate the application the same way. However if we run a python application, for instance, the JVM actually never exits unless it receives a manual EOF from the user. This is causing a few tests to timeout.

We only need to do this for the PySpark shell because Spark submit runs as a python subprocess only in this case. Thus, the normal Spark shell doesn't need to go through this case even though it is also a REPL.

Thanks davies for reporting this.

Author: Andrew Or <andrewor14@gmail.com>

Closes #2170 from andrewor14/bootstrap-hotfix and squashes the following commits:

42963f5 [Andrew Or] Do not wait for EOF unless this is the pyspark shell
---
 bin/pyspark                                   |  2 ++
 .../SparkSubmitDriverBootstrapper.scala       | 26 +++++++++++--------
 2 files changed, 17 insertions(+), 11 deletions(-)

diff --git a/bin/pyspark b/bin/pyspark
index 59cfdfa7c5d1a..f553b314c5991 100755
--- a/bin/pyspark
+++ b/bin/pyspark
@@ -102,6 +102,8 @@ if [[ "$1" =~ \.py$ ]]; then
   gatherSparkSubmitOpts "$@"
   exec $FWDIR/bin/spark-submit "${SUBMISSION_OPTS[@]}" $primary "${APPLICATION_OPTS[@]}"
 else
+  # PySpark shell requires special handling downstream
+  export PYSPARK_SHELL=1
   # Only use ipython if no command line arguments were provided [SPARK-1134]
   if [[ "$IPYTHON" = "1" ]]; then
     exec ${PYSPARK_PYTHON:-ipython} $IPYTHON_OPTS
diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitDriverBootstrapper.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitDriverBootstrapper.scala
index 7ca96ed57c2db..38b5d8e1739d0 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitDriverBootstrapper.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitDriverBootstrapper.scala
@@ -132,25 +132,29 @@ private[spark] object SparkSubmitDriverBootstrapper {
     val builder = new ProcessBuilder(filteredCommand)
     val process = builder.start()
 
-    // Redirect stdin, stdout, and stderr to/from the child JVM
+    // Redirect stdout and stderr from the child JVM
     val stdoutThread = new RedirectThread(process.getInputStream, System.out, "redirect stdout")
     val stderrThread = new RedirectThread(process.getErrorStream, System.err, "redirect stderr")
     stdoutThread.start()
     stderrThread.start()
 
-    // In Windows, the subprocess reads directly from our stdin, so we should avoid spawning
-    // a thread that contends with the subprocess in reading from System.in.
-    if (Utils.isWindows) {
-      // For the PySpark shell, the termination of this process is handled in java_gateway.py
-      process.waitFor()
-    } else {
-      // Terminate on broken pipe, which signals that the parent process has exited. This is
-      // important for the PySpark shell, where Spark submit itself is a python subprocess.
+    // Redirect stdin to child JVM only if we're not running Windows. This is because the
+    // subprocess there already reads directly from our stdin, so we should avoid spawning a
+    // thread that contends with the subprocess in reading from System.in.
+    val isWindows = Utils.isWindows
+    val isPySparkShell = sys.env.contains("PYSPARK_SHELL")
+    if (!isWindows) {
       val stdinThread = new RedirectThread(System.in, process.getOutputStream, "redirect stdin")
       stdinThread.start()
-      stdinThread.join()
-      process.destroy()
+      // For the PySpark shell, Spark submit itself runs as a python subprocess, and so this JVM
+      // should terminate on broken pipe, which signals that the parent process has exited. In
+      // Windows, the termination logic for the PySpark shell is handled in java_gateway.py
+      if (isPySparkShell) {
+        stdinThread.join()
+        process.destroy()
+      }
     }
+    process.waitFor()
   }
 
 }

From 024178c57419f915d26414e1b91ea0019c3650db Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Wed, 27 Aug 2014 23:05:34 -0700
Subject: [PATCH 127/489] [HOTFIX][SQL] Remove cleaning of UDFs

It is not safe to run the closure cleaner on slaves.  #2153 introduced this which broke all UDF execution on slaves.  Will re-add cleaning of UDF closures in a follow-up PR.

Author: Michael Armbrust <michael@databricks.com>

Closes #2174 from marmbrus/fixUdfs and squashes the following commits:

55406de [Michael Armbrust] [HOTFIX] Remove cleaning of UDFs
---
 .../org/apache/spark/sql/catalyst/expressions/ScalaUdf.scala   | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUdf.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUdf.scala
index 0b3c1df453fb8..589816ccec0d5 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUdf.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUdf.scala
@@ -23,9 +23,6 @@ import org.apache.spark.util.ClosureCleaner
 case class ScalaUdf(function: AnyRef, dataType: DataType, children: Seq[Expression])
   extends Expression {
 
-  // Clean function when not called with default no-arg constructor.
-  if (function != null) { ClosureCleaner.clean(function) }
-
   type EvaluatedType = Any
 
   def nullable = true

From 68f75dcdfe7e8ab229b73824692c4b3d4c39946c Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian.cs.zju@gmail.com>
Date: Thu, 28 Aug 2014 00:08:09 -0700
Subject: [PATCH 128/489] [SQL] Fixed 2 comment typos in SQLConf

Author: Cheng Lian <lian.cs.zju@gmail.com>

Closes #2172 from liancheng/sqlconf-typo and squashes the following commits:

115cc71 [Cheng Lian] Fixed 2 comment typos in SQLConf
---
 sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
index f0df19112ae37..64d49354dadcd 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
@@ -92,7 +92,7 @@ trait SQLConf {
    * When set to true, Spark SQL will use the Scala compiler at runtime to generate custom bytecode
    * that evaluates expressions found in queries.  In general this custom code runs much faster
    * than interpreted evaluation, but there are significant start-up costs due to compilation.
-   * As a result codegen is only benificial when queries run for a long time, or when the same
+   * As a result codegen is only beneficial when queries run for a long time, or when the same
    * expressions are used multiple times.
    *
    * Defaults to false as this feature is currently experimental.
@@ -111,8 +111,9 @@ trait SQLConf {
 
   /**
    * The default size in bytes to assign to a logical operator's estimation statistics.  By default,
-   * it is set to a larger value than `autoConvertJoinSize`, hence any logical operator without a
-   * properly implemented estimation of this statistic will not be incorrectly broadcasted in joins.
+   * it is set to a larger value than `autoBroadcastJoinThreshold`, hence any logical operator
+   * without a properly implemented estimation of this statistic will not be incorrectly broadcasted
+   * in joins.
    */
   private[spark] def defaultSizeInBytes: Long =
     getConf(DEFAULT_SIZE_IN_BYTES, (autoBroadcastJoinThreshold + 1).toString).toLong

From 76e3ba4264c4a0bc2c33ae6ac862fc40bc302d83 Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Thu, 28 Aug 2014 00:15:23 -0700
Subject: [PATCH 129/489] [SPARK-3230][SQL] Fix udfs that return structs

We need to convert the case classes into Rows.

Author: Michael Armbrust <michael@databricks.com>

Closes #2133 from marmbrus/structUdfs and squashes the following commits:

189722f [Michael Armbrust] Merge remote-tracking branch 'origin/master' into structUdfs
8e29b1c [Michael Armbrust] Use existing function
d8d0b76 [Michael Armbrust] Fix udfs that return structs
---
 .../apache/spark/sql/catalyst/ScalaReflection.scala  | 12 ++++++++++--
 .../spark/sql/catalyst/expressions/ScalaUdf.scala    |  7 ++++++-
 .../apache/spark/sql/execution/basicOperators.scala  | 11 ++---------
 .../test/scala/org/apache/spark/sql/UDFSuite.scala   | 12 ++++++++++++
 4 files changed, 30 insertions(+), 12 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ScalaReflection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ScalaReflection.scala
index 6b6b636cd96dc..88a8fa7c28e0f 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ScalaReflection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ScalaReflection.scala
@@ -19,8 +19,7 @@ package org.apache.spark.sql.catalyst
 
 import java.sql.Timestamp
 
-import org.apache.spark.sql.catalyst.expressions.Attribute
-import org.apache.spark.sql.catalyst.expressions.AttributeReference
+import org.apache.spark.sql.catalyst.expressions.{GenericRow, Attribute, AttributeReference}
 import org.apache.spark.sql.catalyst.plans.logical.LocalRelation
 import org.apache.spark.sql.catalyst.types._
 
@@ -32,6 +31,15 @@ object ScalaReflection {
 
   case class Schema(dataType: DataType, nullable: Boolean)
 
+  /** Converts Scala objects to catalyst rows / types */
+  def convertToCatalyst(a: Any): Any = a match {
+    case o: Option[_] => o.orNull
+    case s: Seq[_] => s.map(convertToCatalyst)
+    case m: Map[_, _] => m.map { case (k, v) => convertToCatalyst(k) -> convertToCatalyst(v) }
+    case p: Product => new GenericRow(p.productIterator.map(convertToCatalyst).toArray)
+    case other => other
+  }
+
   /** Returns a Sequence of attributes for the given case class type. */
   def attributesFor[T: TypeTag]: Seq[Attribute] = schemaFor[T] match {
     case Schema(s: StructType, _) =>
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUdf.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUdf.scala
index 589816ccec0d5..1b687a443ef8b 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUdf.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUdf.scala
@@ -17,6 +17,7 @@
 
 package org.apache.spark.sql.catalyst.expressions
 
+import org.apache.spark.sql.catalyst.ScalaReflection
 import org.apache.spark.sql.catalyst.types.DataType
 import org.apache.spark.util.ClosureCleaner
 
@@ -27,6 +28,8 @@ case class ScalaUdf(function: AnyRef, dataType: DataType, children: Seq[Expressi
 
   def nullable = true
 
+  override def toString = s"scalaUDF(${children.mkString(",")})"
+
   /** This method has been generated by this script
 
     (1 to 22).map { x =>
@@ -44,7 +47,7 @@ case class ScalaUdf(function: AnyRef, dataType: DataType, children: Seq[Expressi
 
   // scalastyle:off
   override def eval(input: Row): Any = {
-    children.size match {
+    val result = children.size match {
       case 0 => function.asInstanceOf[() => Any]()
       case 1 => function.asInstanceOf[(Any) => Any](children(0).eval(input))
       case 2 =>
@@ -343,5 +346,7 @@ case class ScalaUdf(function: AnyRef, dataType: DataType, children: Seq[Expressi
           children(21).eval(input))
     }
     // scalastyle:on
+
+    ScalaReflection.convertToCatalyst(result)
   }
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
index 374af48b820c2..4abda21ffec96 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
@@ -204,14 +204,6 @@ case class Sort(
  */
 @DeveloperApi
 object ExistingRdd {
-  def convertToCatalyst(a: Any): Any = a match {
-    case o: Option[_] => o.orNull
-    case s: Seq[_] => s.map(convertToCatalyst)
-    case m: Map[_, _] => m.map { case (k, v) => convertToCatalyst(k) -> convertToCatalyst(v) }
-    case p: Product => new GenericRow(p.productIterator.map(convertToCatalyst).toArray)
-    case other => other
-  }
-
   def productToRowRdd[A <: Product](data: RDD[A]): RDD[Row] = {
     data.mapPartitions { iterator =>
       if (iterator.isEmpty) {
@@ -223,7 +215,7 @@ object ExistingRdd {
         bufferedIterator.map { r =>
           var i = 0
           while (i < mutableRow.length) {
-            mutableRow(i) = convertToCatalyst(r.productElement(i))
+            mutableRow(i) = ScalaReflection.convertToCatalyst(r.productElement(i))
             i += 1
           }
 
@@ -245,6 +237,7 @@ object ExistingRdd {
 case class ExistingRdd(output: Seq[Attribute], rdd: RDD[Row]) extends LeafNode {
   override def execute() = rdd
 }
+
 /**
  * :: DeveloperApi ::
  * Computes the set of distinct input rows using a HashSet.
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/UDFSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/UDFSuite.scala
index 76aa9b0081d7e..ef9b76b1e251e 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/UDFSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/UDFSuite.scala
@@ -22,6 +22,8 @@ import org.apache.spark.sql.test._
 /* Implicits */
 import TestSQLContext._
 
+case class FunctionResult(f1: String, f2: String)
+
 class UDFSuite extends QueryTest {
 
   test("Simple UDF") {
@@ -33,4 +35,14 @@ class UDFSuite extends QueryTest {
     registerFunction("strLenScala", (_: String).length + (_:Int))
     assert(sql("SELECT strLenScala('test', 1)").first().getInt(0) === 5)
   }
+
+
+  test("struct UDF") {
+    registerFunction("returnStruct", (f1: String, f2: String) => FunctionResult(f1, f2))
+
+    val result=
+      sql("SELECT returnStruct('test', 'test2') as ret")
+        .select("ret.f1".attr).first().getString(0)
+    assert(result == "test")
+  }
 }

From 70d814665baa8b8ca868d3126452105ecfa5cbff Mon Sep 17 00:00:00 2001
From: Tatiana Borisova <tanyatik@yandex.ru>
Date: Thu, 28 Aug 2014 10:36:36 -0700
Subject: [PATCH 130/489] [SPARK-3150] Fix NullPointerException in in Spark
 recovery: Add initializing default values in DriverInfo.init()

The issue happens when Spark is run standalone on a cluster.
When master and driver fall simultaneously on one node in a cluster, master tries to recover its state and restart spark driver.
While restarting driver, it falls with NPE exception (stacktrace is below).
After falling, it restarts and tries to recover its state and restart Spark driver again. It happens over and over in an infinite cycle.
Namely, Spark tries to read DriverInfo state from zookeeper, but after reading it happens to be null in DriverInfo.worker.

https://issues.apache.org/jira/browse/SPARK-3150

Author: Tatiana Borisova <tanyatik@yandex.ru>

Closes #2062 from tanyatik/spark-3150 and squashes the following commits:

9936043 [Tatiana Borisova] Add initializing default values in DriverInfo.init()
---
 .../org/apache/spark/deploy/master/DriverInfo.scala | 13 +++++++++++++
 1 file changed, 13 insertions(+)

diff --git a/core/src/main/scala/org/apache/spark/deploy/master/DriverInfo.scala b/core/src/main/scala/org/apache/spark/deploy/master/DriverInfo.scala
index 33377931d6993..80b570a44af18 100644
--- a/core/src/main/scala/org/apache/spark/deploy/master/DriverInfo.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/master/DriverInfo.scala
@@ -33,4 +33,17 @@ private[spark] class DriverInfo(
   @transient var exception: Option[Exception] = None
   /* Most recent worker assigned to this driver */
   @transient var worker: Option[WorkerInfo] = None
+
+  init()
+
+  private def readObject(in: java.io.ObjectInputStream): Unit = {
+    in.defaultReadObject()
+    init()
+  }
+
+  private def init(): Unit = {
+    state = DriverState.SUBMITTED
+    worker = None
+    exception = None
+  }
 }

From 6d392b36ee1dc6f7e5198dd436e4e62eb816a072 Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian.cs.zju@gmail.com>
Date: Wed, 27 Aug 2014 12:39:21 -0700
Subject: [PATCH 131/489] [SPARK-2608][Core] Fixed command line option passing
 issue over Mesos via SPARK_EXECUTOR_OPTS

This is another try after #2145 to fix [SPARK-2608](https://issues.apache.org/jira/browse/SPARK-2608).

The basic idea is to pass `extraJavaOpts` and `extraLibraryPath` together via environment variable `SPARK_EXECUTOR_OPTS`. This variable is recognized by `spark-class` and not used anywhere else. In this way, we still launch Mesos executors with `spark-class`/`spark-executor`, but avoids the executor side Spark home issue.

Quoted string with spaces is not allowed in either `extraJavaOpts` or `extraLibraryPath` when using Spark over Mesos. The reason is that Mesos passes the whole command line as a single string argument to `sh -c` to start the executor, and this makes shell string escaping non-trivial to handle. This should be fixed in a later release.

Classes in package `org.apache.spark.deploy` shouldn't be used as they assume Spark is deployed in standalone mode, and give wrong executor side Spark home directory. Please refer to comments in #2145 for more details.

Author: Cheng Lian <lian.cs.zju@gmail.com>

Closes #2161 from liancheng/mesos-fix-with-env-var and squashes the following commits:

ba59190 [Cheng Lian] Added fine grained Mesos executor support
1174076 [Cheng Lian] Draft fix for CoarseMesosSchedulerBackend

(cherry picked from commit 935bffe3bf6c91a42288bff8c1ec69fecb41a769)
Signed-off-by: Reynold Xin <rxin@apache.org>
---
 .../mesos/CoarseMesosSchedulerBackend.scala        | 14 ++++++++++----
 .../cluster/mesos/MesosSchedulerBackend.scala      | 14 ++++++++++++++
 2 files changed, 24 insertions(+), 4 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala
index f0172504c55aa..8c7cb07ebc5ba 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala
@@ -122,6 +122,12 @@ private[spark] class CoarseMesosSchedulerBackend(
     val extraLibraryPath = conf.getOption(libraryPathOption).map(p => s"-Djava.library.path=$p")
     val extraOpts = Seq(extraJavaOpts, extraLibraryPath).flatten.mkString(" ")
 
+    environment.addVariables(
+      Environment.Variable.newBuilder()
+        .setName("SPARK_EXECUTOR_OPTS")
+        .setValue(extraOpts)
+        .build())
+
     sc.executorEnvs.foreach { case (key, value) =>
       environment.addVariables(Environment.Variable.newBuilder()
         .setName(key)
@@ -140,16 +146,16 @@ private[spark] class CoarseMesosSchedulerBackend(
     if (uri == null) {
       val runScript = new File(sparkHome, "./bin/spark-class").getCanonicalPath
       command.setValue(
-        "\"%s\" org.apache.spark.executor.CoarseGrainedExecutorBackend %s %s %s %s %d".format(
-          runScript, extraOpts, driverUrl, offer.getSlaveId.getValue, offer.getHostname, numCores))
+        "\"%s\" org.apache.spark.executor.CoarseGrainedExecutorBackend %s %s %s %d".format(
+          runScript, driverUrl, offer.getSlaveId.getValue, offer.getHostname, numCores))
     } else {
       // Grab everything to the first '.'. We'll use that and '*' to
       // glob the directory "correctly".
       val basename = uri.split('/').last.split('.').head
       command.setValue(
         ("cd %s*; " +
-          "./bin/spark-class org.apache.spark.executor.CoarseGrainedExecutorBackend %s %s %s %s %d")
-          .format(basename, extraOpts, driverUrl, offer.getSlaveId.getValue,
+          "./bin/spark-class org.apache.spark.executor.CoarseGrainedExecutorBackend %s %s %s %d")
+          .format(basename, driverUrl, offer.getSlaveId.getValue,
             offer.getHostname, numCores))
       command.addUris(CommandInfo.URI.newBuilder().setValue(uri))
     }
diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala
index c717e7c621a8f..e84ce094e5eec 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala
@@ -90,6 +90,20 @@ private[spark] class MesosSchedulerBackend(
       "Spark home is not set; set it through the spark.home system " +
       "property, the SPARK_HOME environment variable or the SparkContext constructor"))
     val environment = Environment.newBuilder()
+    sc.conf.getOption("spark.executor.extraClassPath").foreach { cp =>
+      environment.addVariables(
+        Environment.Variable.newBuilder().setName("SPARK_CLASSPATH").setValue(cp).build())
+    }
+    val extraJavaOpts = sc.conf.getOption("spark.executor.extraJavaOptions")
+    val extraLibraryPath = sc.conf.getOption("spark.executor.extraLibraryPath").map { lp =>
+      s"-Djava.library.path=$lp"
+    }
+    val extraOpts = Seq(extraJavaOpts, extraLibraryPath).flatten.mkString(" ")
+    environment.addVariables(
+      Environment.Variable.newBuilder()
+        .setName("SPARK_EXECUTOR_OPTS")
+        .setValue(extraOpts)
+        .build())
     sc.executorEnvs.foreach { case (key, value) =>
       environment.addVariables(Environment.Variable.newBuilder()
         .setName(key)

From 41dc5987d9abeca6fc0f5935c780d48f517cdf95 Mon Sep 17 00:00:00 2001
From: Andrew Or <andrewor14@gmail.com>
Date: Thu, 28 Aug 2014 11:05:44 -0700
Subject: [PATCH 132/489] [SPARK-3264] Allow users to set executor Spark home
 in Mesos

The executors and the driver may not share the same Spark home. There is currently one way to set the executor side Spark home in Mesos, through setting `spark.home`. However, this is neither documented nor intuitive. This PR adds a more specific config `spark.mesos.executor.home` and exposes this to the user.

liancheng tnachen

Author: Andrew Or <andrewor14@gmail.com>

Closes #2166 from andrewor14/mesos-spark-home and squashes the following commits:

b87965e [Andrew Or] Merge branch 'master' of github.com:apache/spark into mesos-spark-home
f6abb2e [Andrew Or] Document spark.mesos.executor.home
ca7846d [Andrew Or] Add more specific configuration for executor Spark home in Mesos
---
 .../cluster/mesos/CoarseMesosSchedulerBackend.scala    | 10 ++++++----
 .../cluster/mesos/MesosSchedulerBackend.scala          | 10 ++++++----
 docs/configuration.md                                  | 10 ++++++++++
 3 files changed, 22 insertions(+), 8 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala
index 8c7cb07ebc5ba..87e181e773fdf 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala
@@ -71,9 +71,11 @@ private[spark] class CoarseMesosSchedulerBackend(
   val taskIdToSlaveId = new HashMap[Int, String]
   val failuresBySlaveId = new HashMap[String, Int] // How many times tasks on each slave failed
 
-  val sparkHome = sc.getSparkHome().getOrElse(throw new SparkException(
-    "Spark home is not set; set it through the spark.home system " +
-    "property, the SPARK_HOME environment variable or the SparkContext constructor"))
+  val executorSparkHome = conf.getOption("spark.mesos.executor.home")
+    .orElse(sc.getSparkHome())
+    .getOrElse {
+      throw new SparkException("Executor Spark home `spark.mesos.executor.home` is not set!")
+    }
 
   val extraCoresPerSlave = conf.getInt("spark.mesos.extra.cores", 0)
 
@@ -144,7 +146,7 @@ private[spark] class CoarseMesosSchedulerBackend(
 
     val uri = conf.get("spark.executor.uri", null)
     if (uri == null) {
-      val runScript = new File(sparkHome, "./bin/spark-class").getCanonicalPath
+      val runScript = new File(executorSparkHome, "./bin/spark-class").getCanonicalPath
       command.setValue(
         "\"%s\" org.apache.spark.executor.CoarseGrainedExecutorBackend %s %s %s %d".format(
           runScript, driverUrl, offer.getSlaveId.getValue, offer.getHostname, numCores))
diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala
index e84ce094e5eec..67ee4d66f151b 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala
@@ -86,9 +86,11 @@ private[spark] class MesosSchedulerBackend(
   }
 
   def createExecutorInfo(execId: String): ExecutorInfo = {
-    val sparkHome = sc.getSparkHome().getOrElse(throw new SparkException(
-      "Spark home is not set; set it through the spark.home system " +
-      "property, the SPARK_HOME environment variable or the SparkContext constructor"))
+    val executorSparkHome = sc.conf.getOption("spark.mesos.executor.home")
+      .orElse(sc.getSparkHome()) // Fall back to driver Spark home for backward compatibility
+      .getOrElse {
+        throw new SparkException("Executor Spark home `spark.mesos.executor.home` is not set!")
+      }
     val environment = Environment.newBuilder()
     sc.conf.getOption("spark.executor.extraClassPath").foreach { cp =>
       environment.addVariables(
@@ -114,7 +116,7 @@ private[spark] class MesosSchedulerBackend(
       .setEnvironment(environment)
     val uri = sc.conf.get("spark.executor.uri", null)
     if (uri == null) {
-      command.setValue(new File(sparkHome, "/sbin/spark-executor").getCanonicalPath)
+      command.setValue(new File(executorSparkHome, "/sbin/spark-executor").getCanonicalPath)
     } else {
       // Grab everything to the first '.'. We'll use that and '*' to
       // glob the directory "correctly".
diff --git a/docs/configuration.md b/docs/configuration.md
index 981170d8b49b7..65a422caabb7e 100644
--- a/docs/configuration.md
+++ b/docs/configuration.md
@@ -214,6 +214,16 @@ Apart from these, the following properties are also available, and may be useful
     process. The user can specify multiple of these and to set multiple environment variables. 
   </td>
 </tr>
+<tr>
+  <td><code>spark.mesos.executor.home</code></td>
+  <td>driver side <code>SPARK_HOME</code></td>
+  <td>
+    Set the directory in which Spark is installed on the executors in Mesos. By default, the
+    executors will simply use the driver's Spark home directory, which may not be visible to
+    them. Note that this is only relevant if a Spark binary package is not specified through
+    <code>spark.executor.uri</code>.
+  </td>
+</tr>
 </table>
 
 #### Shuffle Behavior

From be53c54b5c685e1d04d49bd554e05029a5a106e1 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@apache.org>
Date: Thu, 28 Aug 2014 14:08:07 -0700
Subject: [PATCH 133/489] [SPARK-3281] Remove Netty specific code in
 BlockManager / shuffle

Netty functionality will be added back in subsequent PRs by using the BlockTransferService interface.

Author: Reynold Xin <rxin@apache.org>

Closes #2181 from rxin/SPARK-3281 and squashes the following commits:

5494b0e [Reynold Xin] Fix extra port.
ff6d1e1 [Reynold Xin] [SPARK-3281] Remove Netty specific code in BlockManager.
---
 .../spark/storage/BlockFetcherIterator.scala  | 73 +------------------
 .../apache/spark/storage/BlockManager.scala   | 41 +----------
 .../apache/spark/storage/BlockManagerId.scala | 20 ++---
 .../org/apache/spark/util/JsonProtocol.scala  |  6 +-
 .../apache/spark/MapOutputTrackerSuite.scala  | 30 ++++----
 .../spark/scheduler/DAGSchedulerSuite.scala   |  2 +-
 .../storage/BlockFetcherIteratorSuite.scala   | 16 ++--
 .../spark/storage/BlockManagerSuite.scala     |  6 +-
 .../storage/StorageStatusListenerSuite.scala  |  4 +-
 .../apache/spark/storage/StorageSuite.scala   | 10 +--
 .../spark/ui/storage/StorageTabSuite.scala    |  2 +-
 .../apache/spark/util/AkkaUtilsSuite.scala    | 17 +++--
 .../apache/spark/util/JsonProtocolSuite.scala | 15 ++--
 13 files changed, 64 insertions(+), 178 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/storage/BlockFetcherIterator.scala b/core/src/main/scala/org/apache/spark/storage/BlockFetcherIterator.scala
index ca60ec78b62ee..4ab8ec8f0ff3b 100644
--- a/core/src/main/scala/org/apache/spark/storage/BlockFetcherIterator.scala
+++ b/core/src/main/scala/org/apache/spark/storage/BlockFetcherIterator.scala
@@ -33,16 +33,8 @@ import org.apache.spark.serializer.Serializer
 import org.apache.spark.util.Utils
 
 /**
- * A block fetcher iterator interface. There are two implementations:
- *
- * BasicBlockFetcherIterator: uses a custom-built NIO communication layer.
- * NettyBlockFetcherIterator: uses Netty (OIO) as the communication layer.
- *
- * Eventually we would like the two to converge and use a single NIO-based communication layer,
- * but extensive tests show that under some circumstances (e.g. large shuffles with lots of cores),
- * NIO would perform poorly and thus the need for the Netty OIO one.
+ * A block fetcher iterator interface for fetching shuffle blocks.
  */
-
 private[storage]
 trait BlockFetcherIterator extends Iterator[(BlockId, Option[Iterator[Any]])] with Logging {
   def initialize()
@@ -262,67 +254,4 @@ object BlockFetcherIterator {
     }
   }
   // End of BasicBlockFetcherIterator
-
-  class NettyBlockFetcherIterator(
-      blockManager: BlockManager,
-      blocksByAddress: Seq[(BlockManagerId, Seq[(BlockId, Long)])],
-      serializer: Serializer,
-      readMetrics: ShuffleReadMetrics)
-    extends BasicBlockFetcherIterator(blockManager, blocksByAddress, serializer, readMetrics) {
-
-    override protected def sendRequest(req: FetchRequest) {
-      logDebug("Sending request for %d blocks (%s) from %s".format(
-        req.blocks.size, Utils.bytesToString(req.size), req.address.hostPort))
-      val cmId = new ConnectionManagerId(req.address.host, req.address.port)
-
-      bytesInFlight += req.size
-      val sizeMap = req.blocks.toMap // so we can look up the size of each blockID
-
-      // This could throw a TimeoutException. In that case we will just retry the task.
-      val client = blockManager.nettyBlockClientFactory.createClient(
-        cmId.host, req.address.nettyPort)
-      val blocks = req.blocks.map(_._1.toString)
-
-      client.fetchBlocks(
-        blocks,
-        new BlockClientListener {
-          override def onFetchFailure(blockId: String, errorMsg: String): Unit = {
-            logError(s"Could not get block(s) from $cmId with error: $errorMsg")
-            for ((blockId, size) <- req.blocks) {
-              results.put(new FetchResult(blockId, -1, null))
-            }
-          }
-
-          override def onFetchSuccess(blockId: String, data: ReferenceCountedBuffer): Unit = {
-            // Increment the reference count so the buffer won't be recycled.
-            // TODO: This could result in memory leaks when the task is stopped due to exception
-            // before the iterator is exhausted.
-            data.retain()
-            val buf = data.byteBuffer()
-            val blockSize = buf.remaining()
-            val bid = BlockId(blockId)
-
-            // TODO: remove code duplication between here and BlockManager.dataDeserialization.
-            results.put(new FetchResult(bid, sizeMap(bid), () => {
-              def createIterator: Iterator[Any] = {
-                val stream = blockManager.wrapForCompression(bid, data.inputStream())
-                serializer.newInstance().deserializeStream(stream).asIterator
-              }
-              new LazyInitIterator(createIterator) {
-                // Release the buffer when we are done traversing it.
-                override def close(): Unit = data.release()
-              }
-            }))
-
-            readMetrics.synchronized {
-              readMetrics.remoteBytesRead += blockSize
-              readMetrics.remoteBlocksFetched += 1
-            }
-            logDebug("Got remote block " + blockId + " after " + Utils.getUsedTimeMs(startTime))
-          }
-        }
-      )
-    }
-  }
-  // End of NettyBlockFetcherIterator
 }
diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManager.scala b/core/src/main/scala/org/apache/spark/storage/BlockManager.scala
index 12a92d44f4c36..1eb622c12a79f 100644
--- a/core/src/main/scala/org/apache/spark/storage/BlockManager.scala
+++ b/core/src/main/scala/org/apache/spark/storage/BlockManager.scala
@@ -32,8 +32,6 @@ import org.apache.spark._
 import org.apache.spark.executor._
 import org.apache.spark.io.CompressionCodec
 import org.apache.spark.network._
-import org.apache.spark.network.netty.client.BlockFetchingClientFactory
-import org.apache.spark.network.netty.server.BlockServer
 import org.apache.spark.serializer.Serializer
 import org.apache.spark.shuffle.ShuffleManager
 import org.apache.spark.util._
@@ -90,27 +88,8 @@ private[spark] class BlockManager(
     new TachyonStore(this, tachyonBlockManager)
   }
 
-  private val useNetty = conf.getBoolean("spark.shuffle.use.netty", false)
-
-  // If we use Netty for shuffle, start a new Netty-based shuffle sender service.
-  private[storage] val nettyBlockClientFactory: BlockFetchingClientFactory = {
-    if (useNetty) new BlockFetchingClientFactory(conf) else null
-  }
-
-  private val nettyBlockServer: BlockServer = {
-    if (useNetty) {
-      val server = new BlockServer(conf, this)
-      logInfo(s"Created NettyBlockServer binding to port: ${server.port}")
-      server
-    } else {
-      null
-    }
-  }
-
-  private val nettyPort: Int = if (useNetty) nettyBlockServer.port else 0
-
   val blockManagerId = BlockManagerId(
-    executorId, connectionManager.id.host, connectionManager.id.port, nettyPort)
+    executorId, connectionManager.id.host, connectionManager.id.port)
 
   // Max megabytes of data to keep in flight per reducer (to avoid over-allocating memory
   // for receiving shuffle outputs)
@@ -572,14 +551,8 @@ private[spark] class BlockManager(
       blocksByAddress: Seq[(BlockManagerId, Seq[(BlockId, Long)])],
       serializer: Serializer,
       readMetrics: ShuffleReadMetrics): BlockFetcherIterator = {
-    val iter =
-      if (conf.getBoolean("spark.shuffle.use.netty", false)) {
-        new BlockFetcherIterator.NettyBlockFetcherIterator(this, blocksByAddress, serializer,
-          readMetrics)
-      } else {
-        new BlockFetcherIterator.BasicBlockFetcherIterator(this, blocksByAddress, serializer,
-          readMetrics)
-      }
+    val iter = new BlockFetcherIterator.BasicBlockFetcherIterator(this, blocksByAddress, serializer,
+      readMetrics)
     iter.initialize()
     iter
   }
@@ -1092,14 +1065,6 @@ private[spark] class BlockManager(
     connectionManager.stop()
     shuffleBlockManager.stop()
     diskBlockManager.stop()
-
-    if (nettyBlockClientFactory != null) {
-      nettyBlockClientFactory.stop()
-    }
-    if (nettyBlockServer != null) {
-      nettyBlockServer.stop()
-    }
-
     actorSystem.stop(slaveActor)
     blockInfo.clear()
     memoryStore.clear()
diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManagerId.scala b/core/src/main/scala/org/apache/spark/storage/BlockManagerId.scala
index b1585bd8199d1..b7bcb2d85d0ee 100644
--- a/core/src/main/scala/org/apache/spark/storage/BlockManagerId.scala
+++ b/core/src/main/scala/org/apache/spark/storage/BlockManagerId.scala
@@ -36,11 +36,10 @@ import org.apache.spark.util.Utils
 class BlockManagerId private (
     private var executorId_ : String,
     private var host_ : String,
-    private var port_ : Int,
-    private var nettyPort_ : Int
+    private var port_ : Int
   ) extends Externalizable {
 
-  private def this() = this(null, null, 0, 0)  // For deserialization only
+  private def this() = this(null, null, 0)  // For deserialization only
 
   def executorId: String = executorId_
 
@@ -60,32 +59,28 @@ class BlockManagerId private (
 
   def port: Int = port_
 
-  def nettyPort: Int = nettyPort_
-
   override def writeExternal(out: ObjectOutput) {
     out.writeUTF(executorId_)
     out.writeUTF(host_)
     out.writeInt(port_)
-    out.writeInt(nettyPort_)
   }
 
   override def readExternal(in: ObjectInput) {
     executorId_ = in.readUTF()
     host_ = in.readUTF()
     port_ = in.readInt()
-    nettyPort_ = in.readInt()
   }
 
   @throws(classOf[IOException])
   private def readResolve(): Object = BlockManagerId.getCachedBlockManagerId(this)
 
-  override def toString = "BlockManagerId(%s, %s, %d, %d)".format(executorId, host, port, nettyPort)
+  override def toString = s"BlockManagerId($executorId, $host, $port)"
 
-  override def hashCode: Int = (executorId.hashCode * 41 + host.hashCode) * 41 + port + nettyPort
+  override def hashCode: Int = (executorId.hashCode * 41 + host.hashCode) * 41 + port
 
   override def equals(that: Any) = that match {
     case id: BlockManagerId =>
-      executorId == id.executorId && port == id.port && host == id.host && nettyPort == id.nettyPort
+      executorId == id.executorId && port == id.port && host == id.host
     case _ =>
       false
   }
@@ -100,11 +95,10 @@ private[spark] object BlockManagerId {
    * @param execId ID of the executor.
    * @param host Host name of the block manager.
    * @param port Port of the block manager.
-   * @param nettyPort Optional port for the Netty-based shuffle sender.
    * @return A new [[org.apache.spark.storage.BlockManagerId]].
    */
-  def apply(execId: String, host: String, port: Int, nettyPort: Int) =
-    getCachedBlockManagerId(new BlockManagerId(execId, host, port, nettyPort))
+  def apply(execId: String, host: String, port: Int) =
+    getCachedBlockManagerId(new BlockManagerId(execId, host, port))
 
   def apply(in: ObjectInput) = {
     val obj = new BlockManagerId()
diff --git a/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala b/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala
index db7384705fc1b..a7543454eca1f 100644
--- a/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala
+++ b/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala
@@ -295,8 +295,7 @@ private[spark] object JsonProtocol {
   def blockManagerIdToJson(blockManagerId: BlockManagerId): JValue = {
     ("Executor ID" -> blockManagerId.executorId) ~
     ("Host" -> blockManagerId.host) ~
-    ("Port" -> blockManagerId.port) ~
-    ("Netty Port" -> blockManagerId.nettyPort)
+    ("Port" -> blockManagerId.port)
   }
 
   def jobResultToJson(jobResult: JobResult): JValue = {
@@ -644,8 +643,7 @@ private[spark] object JsonProtocol {
     val executorId = (json \ "Executor ID").extract[String]
     val host = (json \ "Host").extract[String]
     val port = (json \ "Port").extract[Int]
-    val nettyPort = (json \ "Netty Port").extract[Int]
-    BlockManagerId(executorId, host, port, nettyPort)
+    BlockManagerId(executorId, host, port)
   }
 
   def jobResultFromJson(json: JValue): JobResult = {
diff --git a/core/src/test/scala/org/apache/spark/MapOutputTrackerSuite.scala b/core/src/test/scala/org/apache/spark/MapOutputTrackerSuite.scala
index 9702838085627..5369169811f81 100644
--- a/core/src/test/scala/org/apache/spark/MapOutputTrackerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/MapOutputTrackerSuite.scala
@@ -69,13 +69,13 @@ class MapOutputTrackerSuite extends FunSuite with LocalSparkContext {
     val compressedSize10000 = MapOutputTracker.compressSize(10000L)
     val size1000 = MapOutputTracker.decompressSize(compressedSize1000)
     val size10000 = MapOutputTracker.decompressSize(compressedSize10000)
-    tracker.registerMapOutput(10, 0, new MapStatus(BlockManagerId("a", "hostA", 1000, 0),
+    tracker.registerMapOutput(10, 0, new MapStatus(BlockManagerId("a", "hostA", 1000),
         Array(compressedSize1000, compressedSize10000)))
-    tracker.registerMapOutput(10, 1, new MapStatus(BlockManagerId("b", "hostB", 1000, 0),
+    tracker.registerMapOutput(10, 1, new MapStatus(BlockManagerId("b", "hostB", 1000),
         Array(compressedSize10000, compressedSize1000)))
     val statuses = tracker.getServerStatuses(10, 0)
-    assert(statuses.toSeq === Seq((BlockManagerId("a", "hostA", 1000, 0), size1000),
-                                  (BlockManagerId("b", "hostB", 1000, 0), size10000)))
+    assert(statuses.toSeq === Seq((BlockManagerId("a", "hostA", 1000), size1000),
+                                  (BlockManagerId("b", "hostB", 1000), size10000)))
     tracker.stop()
   }
 
@@ -86,9 +86,9 @@ class MapOutputTrackerSuite extends FunSuite with LocalSparkContext {
     tracker.registerShuffle(10, 2)
     val compressedSize1000 = MapOutputTracker.compressSize(1000L)
     val compressedSize10000 = MapOutputTracker.compressSize(10000L)
-    tracker.registerMapOutput(10, 0, new MapStatus(BlockManagerId("a", "hostA", 1000, 0),
+    tracker.registerMapOutput(10, 0, new MapStatus(BlockManagerId("a", "hostA", 1000),
       Array(compressedSize1000, compressedSize10000)))
-    tracker.registerMapOutput(10, 1, new MapStatus(BlockManagerId("b", "hostB", 1000, 0),
+    tracker.registerMapOutput(10, 1, new MapStatus(BlockManagerId("b", "hostB", 1000),
       Array(compressedSize10000, compressedSize1000)))
     assert(tracker.containsShuffle(10))
     assert(tracker.getServerStatuses(10, 0).nonEmpty)
@@ -105,14 +105,14 @@ class MapOutputTrackerSuite extends FunSuite with LocalSparkContext {
     tracker.registerShuffle(10, 2)
     val compressedSize1000 = MapOutputTracker.compressSize(1000L)
     val compressedSize10000 = MapOutputTracker.compressSize(10000L)
-    tracker.registerMapOutput(10, 0, new MapStatus(BlockManagerId("a", "hostA", 1000, 0),
+    tracker.registerMapOutput(10, 0, new MapStatus(BlockManagerId("a", "hostA", 1000),
         Array(compressedSize1000, compressedSize1000, compressedSize1000)))
-    tracker.registerMapOutput(10, 1, new MapStatus(BlockManagerId("b", "hostB", 1000, 0),
+    tracker.registerMapOutput(10, 1, new MapStatus(BlockManagerId("b", "hostB", 1000),
         Array(compressedSize10000, compressedSize1000, compressedSize1000)))
 
     // As if we had two simultaneous fetch failures
-    tracker.unregisterMapOutput(10, 0, BlockManagerId("a", "hostA", 1000, 0))
-    tracker.unregisterMapOutput(10, 0, BlockManagerId("a", "hostA", 1000, 0))
+    tracker.unregisterMapOutput(10, 0, BlockManagerId("a", "hostA", 1000))
+    tracker.unregisterMapOutput(10, 0, BlockManagerId("a", "hostA", 1000))
 
     // The remaining reduce task might try to grab the output despite the shuffle failure;
     // this should cause it to fail, and the scheduler will ignore the failure due to the
@@ -145,13 +145,13 @@ class MapOutputTrackerSuite extends FunSuite with LocalSparkContext {
     val compressedSize1000 = MapOutputTracker.compressSize(1000L)
     val size1000 = MapOutputTracker.decompressSize(compressedSize1000)
     masterTracker.registerMapOutput(10, 0, new MapStatus(
-      BlockManagerId("a", "hostA", 1000, 0), Array(compressedSize1000)))
+      BlockManagerId("a", "hostA", 1000), Array(compressedSize1000)))
     masterTracker.incrementEpoch()
     slaveTracker.updateEpoch(masterTracker.getEpoch)
     assert(slaveTracker.getServerStatuses(10, 0).toSeq ===
-      Seq((BlockManagerId("a", "hostA", 1000, 0), size1000)))
+      Seq((BlockManagerId("a", "hostA", 1000), size1000)))
 
-    masterTracker.unregisterMapOutput(10, 0, BlockManagerId("a", "hostA", 1000, 0))
+    masterTracker.unregisterMapOutput(10, 0, BlockManagerId("a", "hostA", 1000))
     masterTracker.incrementEpoch()
     slaveTracker.updateEpoch(masterTracker.getEpoch)
     intercept[FetchFailedException] { slaveTracker.getServerStatuses(10, 0) }
@@ -174,7 +174,7 @@ class MapOutputTrackerSuite extends FunSuite with LocalSparkContext {
     // Frame size should be ~123B, and no exception should be thrown
     masterTracker.registerShuffle(10, 1)
     masterTracker.registerMapOutput(10, 0, new MapStatus(
-      BlockManagerId("88", "mph", 1000, 0), Array.fill[Byte](10)(0)))
+      BlockManagerId("88", "mph", 1000), Array.fill[Byte](10)(0)))
     masterActor.receive(GetMapOutputStatuses(10))
   }
 
@@ -195,7 +195,7 @@ class MapOutputTrackerSuite extends FunSuite with LocalSparkContext {
     masterTracker.registerShuffle(20, 100)
     (0 until 100).foreach { i =>
       masterTracker.registerMapOutput(20, i, new MapStatus(
-        BlockManagerId("999", "mps", 1000, 0), Array.fill[Byte](4000000)(0)))
+        BlockManagerId("999", "mps", 1000), Array.fill[Byte](4000000)(0)))
     }
     intercept[SparkException] { masterActor.receive(GetMapOutputStatuses(20)) }
   }
diff --git a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
index f5fed988ade24..1a42fc1b233ba 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
@@ -736,7 +736,7 @@ class DAGSchedulerSuite extends TestKit(ActorSystem("DAGSchedulerSuite")) with F
    new MapStatus(makeBlockManagerId(host), Array.fill[Byte](reduces)(2))
 
   private def makeBlockManagerId(host: String): BlockManagerId =
-    BlockManagerId("exec-" + host, host, 12345, 0)
+    BlockManagerId("exec-" + host, host, 12345)
 
   private def assertDataStructuresEmpty = {
     assert(scheduler.activeJobs.isEmpty)
diff --git a/core/src/test/scala/org/apache/spark/storage/BlockFetcherIteratorSuite.scala b/core/src/test/scala/org/apache/spark/storage/BlockFetcherIteratorSuite.scala
index bcbfe8baf36ad..159128438390d 100644
--- a/core/src/test/scala/org/apache/spark/storage/BlockFetcherIteratorSuite.scala
+++ b/core/src/test/scala/org/apache/spark/storage/BlockFetcherIteratorSuite.scala
@@ -41,7 +41,7 @@ class BlockFetcherIteratorSuite extends FunSuite with Matchers {
     val blockManager = mock(classOf[BlockManager])
     val connManager = mock(classOf[ConnectionManager])
     doReturn(connManager).when(blockManager).connectionManager
-    doReturn(BlockManagerId("test-client", "test-client", 1, 0)).when(blockManager).blockManagerId
+    doReturn(BlockManagerId("test-client", "test-client", 1)).when(blockManager).blockManagerId
 
     doReturn((48 * 1024 * 1024).asInstanceOf[Long]).when(blockManager).maxBytesInFlight
 
@@ -66,7 +66,7 @@ class BlockFetcherIteratorSuite extends FunSuite with Matchers {
     doReturn(optItr).when(blockManager).getLocalFromDisk(meq(blIds(3)), any())
     doReturn(optItr).when(blockManager).getLocalFromDisk(meq(blIds(4)), any())
 
-    val bmId = BlockManagerId("test-client", "test-client",1 , 0)
+    val bmId = BlockManagerId("test-client", "test-client", 1)
     val blocksByAddress = Seq[(BlockManagerId, Seq[(BlockId, Long)])](
       (bmId, blIds.map(blId => (blId, 1.asInstanceOf[Long])).toSeq)
     )
@@ -97,7 +97,7 @@ class BlockFetcherIteratorSuite extends FunSuite with Matchers {
     val blockManager = mock(classOf[BlockManager])
     val connManager = mock(classOf[ConnectionManager])
     doReturn(connManager).when(blockManager).connectionManager
-    doReturn(BlockManagerId("test-client", "test-client", 1, 0)).when(blockManager).blockManagerId
+    doReturn(BlockManagerId("test-client", "test-client", 1)).when(blockManager).blockManagerId
 
     doReturn((48 * 1024 * 1024).asInstanceOf[Long]).when(blockManager).maxBytesInFlight
 
@@ -117,7 +117,7 @@ class BlockFetcherIteratorSuite extends FunSuite with Matchers {
     doReturn(optItr).when(blockManager).getLocalFromDisk(meq(blIds(3)), any())
     doReturn(optItr).when(blockManager).getLocalFromDisk(meq(blIds(4)), any())
 
-    val bmId = BlockManagerId("test-client", "test-client",1 , 0)
+    val bmId = BlockManagerId("test-client", "test-client", 1)
     val blocksByAddress = Seq[(BlockManagerId, Seq[(BlockId, Long)])](
       (bmId, blIds.map(blId => (blId, 1.asInstanceOf[Long])).toSeq)
     )
@@ -155,12 +155,12 @@ class BlockFetcherIteratorSuite extends FunSuite with Matchers {
     when(blockManager.futureExecContext).thenReturn(global)
 
     when(blockManager.blockManagerId).thenReturn(
-      BlockManagerId("test-client", "test-client", 1, 0))
+      BlockManagerId("test-client", "test-client", 1))
     when(blockManager.maxBytesInFlight).thenReturn(48 * 1024 * 1024)
 
     val blId1 = ShuffleBlockId(0,0,0)
     val blId2 = ShuffleBlockId(0,1,0)
-    val bmId = BlockManagerId("test-server", "test-server",1 , 0)
+    val bmId = BlockManagerId("test-server", "test-server", 1)
     val blocksByAddress = Seq[(BlockManagerId, Seq[(BlockId, Long)])](
       (bmId, Seq((blId1, 1L), (blId2, 1L)))
     )
@@ -211,10 +211,10 @@ class BlockFetcherIteratorSuite extends FunSuite with Matchers {
     when(blockManager.futureExecContext).thenReturn(global)
 
     when(blockManager.blockManagerId).thenReturn(
-      BlockManagerId("test-client", "test-client", 1, 0))
+      BlockManagerId("test-client", "test-client", 1))
     when(blockManager.maxBytesInFlight).thenReturn(48 * 1024 * 1024)
 
-    val bmId = BlockManagerId("test-server", "test-server",1 , 0)
+    val bmId = BlockManagerId("test-server", "test-server", 1)
     val blocksByAddress = Seq[(BlockManagerId, Seq[(BlockId, Long)])](
       (bmId, Seq((blId1, 1L), (blId2, 1L)))
     )
diff --git a/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala b/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala
index f32ce6f9fcc7f..bdcea07e5714f 100644
--- a/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala
@@ -139,9 +139,9 @@ class BlockManagerSuite extends FunSuite with Matchers with BeforeAndAfter
   }
 
   test("BlockManagerId object caching") {
-    val id1 = BlockManagerId("e1", "XXX", 1, 0)
-    val id2 = BlockManagerId("e1", "XXX", 1, 0) // this should return the same object as id1
-    val id3 = BlockManagerId("e1", "XXX", 2, 0) // this should return a different object
+    val id1 = BlockManagerId("e1", "XXX", 1)
+    val id2 = BlockManagerId("e1", "XXX", 1) // this should return the same object as id1
+    val id3 = BlockManagerId("e1", "XXX", 2) // this should return a different object
     assert(id2 === id1, "id2 is not same as id1")
     assert(id2.eq(id1), "id2 is not the same object as id1")
     assert(id3 != id1, "id3 is same as id1")
diff --git a/core/src/test/scala/org/apache/spark/storage/StorageStatusListenerSuite.scala b/core/src/test/scala/org/apache/spark/storage/StorageStatusListenerSuite.scala
index 7671cb969a26b..4e022a69c8212 100644
--- a/core/src/test/scala/org/apache/spark/storage/StorageStatusListenerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/storage/StorageStatusListenerSuite.scala
@@ -26,8 +26,8 @@ import org.apache.spark.scheduler._
  * Test the behavior of StorageStatusListener in response to all relevant events.
  */
 class StorageStatusListenerSuite extends FunSuite {
-  private val bm1 = BlockManagerId("big", "dog", 1, 1)
-  private val bm2 = BlockManagerId("fat", "duck", 2, 2)
+  private val bm1 = BlockManagerId("big", "dog", 1)
+  private val bm2 = BlockManagerId("fat", "duck", 2)
   private val taskInfo1 = new TaskInfo(0, 0, 0, 0, "big", "dog", TaskLocality.ANY, false)
   private val taskInfo2 = new TaskInfo(0, 0, 0, 0, "fat", "duck", TaskLocality.ANY, false)
 
diff --git a/core/src/test/scala/org/apache/spark/storage/StorageSuite.scala b/core/src/test/scala/org/apache/spark/storage/StorageSuite.scala
index 38678bbd1dd28..ef5c55f91c39a 100644
--- a/core/src/test/scala/org/apache/spark/storage/StorageSuite.scala
+++ b/core/src/test/scala/org/apache/spark/storage/StorageSuite.scala
@@ -27,7 +27,7 @@ class StorageSuite extends FunSuite {
 
   // For testing add, update, and remove (for non-RDD blocks)
   private def storageStatus1: StorageStatus = {
-    val status = new StorageStatus(BlockManagerId("big", "dog", 1, 1), 1000L)
+    val status = new StorageStatus(BlockManagerId("big", "dog", 1), 1000L)
     assert(status.blocks.isEmpty)
     assert(status.rddBlocks.isEmpty)
     assert(status.memUsed === 0L)
@@ -78,7 +78,7 @@ class StorageSuite extends FunSuite {
 
   // For testing add, update, remove, get, and contains etc. for both RDD and non-RDD blocks
   private def storageStatus2: StorageStatus = {
-    val status = new StorageStatus(BlockManagerId("big", "dog", 1, 1), 1000L)
+    val status = new StorageStatus(BlockManagerId("big", "dog", 1), 1000L)
     assert(status.rddBlocks.isEmpty)
     status.addBlock(TestBlockId("dan"), BlockStatus(memAndDisk, 10L, 20L, 0L))
     status.addBlock(TestBlockId("man"), BlockStatus(memAndDisk, 10L, 20L, 0L))
@@ -271,9 +271,9 @@ class StorageSuite extends FunSuite {
 
   // For testing StorageUtils.updateRddInfo and StorageUtils.getRddBlockLocations
   private def stockStorageStatuses: Seq[StorageStatus] = {
-    val status1 = new StorageStatus(BlockManagerId("big", "dog", 1, 1), 1000L)
-    val status2 = new StorageStatus(BlockManagerId("fat", "duck", 2, 2), 2000L)
-    val status3 = new StorageStatus(BlockManagerId("fat", "cat", 3, 3), 3000L)
+    val status1 = new StorageStatus(BlockManagerId("big", "dog", 1), 1000L)
+    val status2 = new StorageStatus(BlockManagerId("fat", "duck", 2), 2000L)
+    val status3 = new StorageStatus(BlockManagerId("fat", "cat", 3), 3000L)
     status1.addBlock(RDDBlockId(0, 0), BlockStatus(memAndDisk, 1L, 2L, 0L))
     status1.addBlock(RDDBlockId(0, 1), BlockStatus(memAndDisk, 1L, 2L, 0L))
     status2.addBlock(RDDBlockId(0, 2), BlockStatus(memAndDisk, 1L, 2L, 0L))
diff --git a/core/src/test/scala/org/apache/spark/ui/storage/StorageTabSuite.scala b/core/src/test/scala/org/apache/spark/ui/storage/StorageTabSuite.scala
index a537c72ce7ab5..d9e9c70a8a9e7 100644
--- a/core/src/test/scala/org/apache/spark/ui/storage/StorageTabSuite.scala
+++ b/core/src/test/scala/org/apache/spark/ui/storage/StorageTabSuite.scala
@@ -39,7 +39,7 @@ class StorageTabSuite extends FunSuite with BeforeAndAfter {
   private def rddInfo1 = new RDDInfo(1, "hostage", 200, memOnly)
   private def rddInfo2 = new RDDInfo(2, "sanity", 300, memAndDisk)
   private def rddInfo3 = new RDDInfo(3, "grace", 400, memAndDisk)
-  private val bm1 = BlockManagerId("big", "dog", 1, 1)
+  private val bm1 = BlockManagerId("big", "dog", 1)
 
   before {
     bus = new LiveListenerBus
diff --git a/core/src/test/scala/org/apache/spark/util/AkkaUtilsSuite.scala b/core/src/test/scala/org/apache/spark/util/AkkaUtilsSuite.scala
index c4765e53de17b..76bf4cfd11267 100644
--- a/core/src/test/scala/org/apache/spark/util/AkkaUtilsSuite.scala
+++ b/core/src/test/scala/org/apache/spark/util/AkkaUtilsSuite.scala
@@ -17,13 +17,16 @@
 
 package org.apache.spark.util
 
+import scala.concurrent.Await
+
 import akka.actor._
+
+import org.scalatest.FunSuite
+
 import org.apache.spark._
 import org.apache.spark.scheduler.MapStatus
 import org.apache.spark.storage.BlockManagerId
-import org.scalatest.FunSuite
 
-import scala.concurrent.Await
 
 /**
   * Test the AkkaUtils with various security settings.
@@ -35,7 +38,7 @@ class AkkaUtilsSuite extends FunSuite with LocalSparkContext {
     conf.set("spark.authenticate", "true")
     conf.set("spark.authenticate.secret", "good")
 
-    val securityManager = new SecurityManager(conf);
+    val securityManager = new SecurityManager(conf)
     val hostname = "localhost"
     val (actorSystem, boundPort) = AkkaUtils.createActorSystem("spark", hostname, 0,
       conf = conf, securityManager = securityManager)
@@ -106,13 +109,13 @@ class AkkaUtilsSuite extends FunSuite with LocalSparkContext {
     val compressedSize1000 = MapOutputTracker.compressSize(1000L)
     val size1000 = MapOutputTracker.decompressSize(compressedSize1000)
     masterTracker.registerMapOutput(10, 0, new MapStatus(
-      BlockManagerId("a", "hostA", 1000, 0), Array(compressedSize1000)))
+      BlockManagerId("a", "hostA", 1000), Array(compressedSize1000)))
     masterTracker.incrementEpoch()
     slaveTracker.updateEpoch(masterTracker.getEpoch)
 
     // this should succeed since security off
     assert(slaveTracker.getServerStatuses(10, 0).toSeq ===
-           Seq((BlockManagerId("a", "hostA", 1000, 0), size1000)))
+           Seq((BlockManagerId("a", "hostA", 1000), size1000)))
 
     actorSystem.shutdown()
     slaveSystem.shutdown()
@@ -157,13 +160,13 @@ class AkkaUtilsSuite extends FunSuite with LocalSparkContext {
     val compressedSize1000 = MapOutputTracker.compressSize(1000L)
     val size1000 = MapOutputTracker.decompressSize(compressedSize1000)
     masterTracker.registerMapOutput(10, 0, new MapStatus(
-      BlockManagerId("a", "hostA", 1000, 0), Array(compressedSize1000)))
+      BlockManagerId("a", "hostA", 1000), Array(compressedSize1000)))
     masterTracker.incrementEpoch()
     slaveTracker.updateEpoch(masterTracker.getEpoch)
 
     // this should succeed since security on and passwords match
     assert(slaveTracker.getServerStatuses(10, 0).toSeq ===
-           Seq((BlockManagerId("a", "hostA", 1000, 0), size1000)))
+           Seq((BlockManagerId("a", "hostA", 1000), size1000)))
 
     actorSystem.shutdown()
     slaveSystem.shutdown()
diff --git a/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala b/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala
index 2fd3b9cfd221a..66a17de9ec9ce 100644
--- a/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala
+++ b/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala
@@ -53,9 +53,9 @@ class JsonProtocolSuite extends FunSuite {
       "Classpath Entries" -> Seq(("Super library", "/tmp/super_library"))
     ))
     val blockManagerAdded = SparkListenerBlockManagerAdded(
-      BlockManagerId("Stars", "In your multitude...", 300, 400), 500)
+      BlockManagerId("Stars", "In your multitude...", 300), 500)
     val blockManagerRemoved = SparkListenerBlockManagerRemoved(
-      BlockManagerId("Scarce", "to be counted...", 100, 200))
+      BlockManagerId("Scarce", "to be counted...", 100))
     val unpersistRdd = SparkListenerUnpersistRDD(12345)
     val applicationStart = SparkListenerApplicationStart("The winner of all", 42L, "Garfield")
     val applicationEnd = SparkListenerApplicationEnd(42L)
@@ -81,7 +81,7 @@ class JsonProtocolSuite extends FunSuite {
     testStageInfo(makeStageInfo(10, 20, 30, 40L, 50L))
     testTaskInfo(makeTaskInfo(999L, 888, 55, 777L, false))
     testTaskMetrics(makeTaskMetrics(33333L, 44444L, 55555L, 66666L, 7, 8, hasHadoopInput = false))
-    testBlockManagerId(BlockManagerId("Hong", "Kong", 500, 1000))
+    testBlockManagerId(BlockManagerId("Hong", "Kong", 500))
 
     // StorageLevel
     testStorageLevel(StorageLevel.NONE)
@@ -104,7 +104,7 @@ class JsonProtocolSuite extends FunSuite {
     testJobResult(jobFailed)
 
     // TaskEndReason
-    val fetchFailed = FetchFailed(BlockManagerId("With or", "without you", 15, 16), 17, 18, 19)
+    val fetchFailed = FetchFailed(BlockManagerId("With or", "without you", 15), 17, 18, 19)
     val exceptionFailure = ExceptionFailure("To be", "or not to be", stackTrace, None)
     testTaskEndReason(Success)
     testTaskEndReason(Resubmitted)
@@ -343,7 +343,6 @@ class JsonProtocolSuite extends FunSuite {
     assert(bm1.executorId === bm2.executorId)
     assert(bm1.host === bm2.host)
     assert(bm1.port === bm2.port)
-    assert(bm1.nettyPort === bm2.nettyPort)
   }
 
   private def assertEquals(result1: JobResult, result2: JobResult) {
@@ -944,8 +943,7 @@ class JsonProtocolSuite extends FunSuite {
       |  "Block Manager ID": {
       |    "Executor ID": "Stars",
       |    "Host": "In your multitude...",
-      |    "Port": 300,
-      |    "Netty Port": 400
+      |    "Port": 300
       |  },
       |  "Maximum Memory": 500
       |}
@@ -958,8 +956,7 @@ class JsonProtocolSuite extends FunSuite {
       |  "Block Manager ID": {
       |    "Executor ID": "Scarce",
       |    "Host": "to be counted...",
-      |    "Port": 100,
-      |    "Netty Port": 200
+      |    "Port": 100
       |  }
       |}
     """

From 39012452daa0746fe5d218493b85f9b5f96190c1 Mon Sep 17 00:00:00 2001
From: Yadong Qi <qiyadong2010@gmail.com>
Date: Thu, 28 Aug 2014 14:08:48 -0700
Subject: [PATCH 134/489] [SPARK-3285] [examples] Using values.sum is easier to
 understand than using values.foldLeft(0)(_ + _)

def sum[B >: A](implicit num: Numeric[B]): B = foldLeft(num.zero)(num.plus)
Using values.sum is easier to understand than using values.foldLeft(0)(_ + _), so we'd better use values.sum instead of values.foldLeft(0)(_ + _)

Author: Yadong Qi <qiyadong2010@gmail.com>

Closes #2182 from watermen/bug-fix3 and squashes the following commits:

17be9fb [Yadong Qi] Update CheckpointSuite.scala
714bda5 [Yadong Qi] Update BasicOperationsSuite.scala
57e704c [Yadong Qi] Update StatefulNetworkWordCount.scala
---
 .../spark/examples/streaming/StatefulNetworkWordCount.scala   | 2 +-
 .../org/apache/spark/streaming/BasicOperationsSuite.scala     | 2 +-
 .../scala/org/apache/spark/streaming/CheckpointSuite.scala    | 4 ++--
 3 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/StatefulNetworkWordCount.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/StatefulNetworkWordCount.scala
index daa1ced63c701..a4d159bf38377 100644
--- a/examples/src/main/scala/org/apache/spark/examples/streaming/StatefulNetworkWordCount.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/streaming/StatefulNetworkWordCount.scala
@@ -44,7 +44,7 @@ object StatefulNetworkWordCount {
     StreamingExamples.setStreamingLogLevels()
 
     val updateFunc = (values: Seq[Int], state: Option[Int]) => {
-      val currentCount = values.foldLeft(0)(_ + _)
+      val currentCount = values.sum
 
       val previousCount = state.getOrElse(0)
 
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/BasicOperationsSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/BasicOperationsSuite.scala
index ff6d86c8f81ac..059ac6c2dbee2 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/BasicOperationsSuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/BasicOperationsSuite.scala
@@ -452,7 +452,7 @@ class BasicOperationsSuite extends TestSuiteBase {
 
   test("rdd cleanup - updateStateByKey") {
     val updateFunc = (values: Seq[Int], state: Option[Int]) => {
-      Some(values.foldLeft(0)(_ + _) + state.getOrElse(0))
+      Some(values.sum + state.getOrElse(0))
     }
     val stateStream = runCleanupTest(
       conf, _.map(_ -> 1).updateStateByKey(updateFunc).checkpoint(Seconds(3)))
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/CheckpointSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/CheckpointSuite.scala
index 10ad3c9e1adc9..8511390cb1ad5 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/CheckpointSuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/CheckpointSuite.scala
@@ -70,7 +70,7 @@ class CheckpointSuite extends TestSuiteBase {
     val input = (1 to 10).map(_ => Seq("a")).toSeq
     val operation = (st: DStream[String]) => {
       val updateFunc = (values: Seq[Int], state: Option[Int]) => {
-        Some((values.foldLeft(0)(_ + _) + state.getOrElse(0)))
+        Some((values.sum + state.getOrElse(0)))
       }
       st.map(x => (x, 1))
       .updateStateByKey(updateFunc)
@@ -214,7 +214,7 @@ class CheckpointSuite extends TestSuiteBase {
     val output = (1 to 10).map(x => Seq(("a", x))).toSeq
     val operation = (st: DStream[String]) => {
       val updateFunc = (values: Seq[Int], state: Option[Int]) => {
-        Some((values.foldLeft(0)(_ + _) + state.getOrElse(0)))
+        Some((values.sum + state.getOrElse(0)))
       }
       st.map(x => (x, 1))
         .updateStateByKey(updateFunc)

From 96df92906978c5f58e0cc8ff5eebe5b35a08be3b Mon Sep 17 00:00:00 2001
From: Ankur Dave <ankurdave@gmail.com>
Date: Thu, 28 Aug 2014 15:17:01 -0700
Subject: [PATCH 135/489] [SPARK-3190] Avoid overflow in VertexRDD.count()

VertexRDDs with more than 4 billion elements are counted incorrectly due to integer overflow when summing partition sizes. This PR fixes the issue by converting partition sizes to Longs before summing them.

The following code previously returned -10000000. After applying this PR, it returns the correct answer of 5000000000 (5 billion).

```scala
val pairs = sc.parallelize(0L until 500L).map(_ * 10000000)
  .flatMap(start => start until (start + 10000000)).map(x => (x, x))
VertexRDD(pairs).count()
```

Author: Ankur Dave <ankurdave@gmail.com>

Closes #2106 from ankurdave/SPARK-3190 and squashes the following commits:

641f468 [Ankur Dave] Avoid overflow in VertexRDD.count()
---
 graphx/src/main/scala/org/apache/spark/graphx/VertexRDD.scala | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/graphx/src/main/scala/org/apache/spark/graphx/VertexRDD.scala b/graphx/src/main/scala/org/apache/spark/graphx/VertexRDD.scala
index 4825d12fc27b3..04fbc9dbab8d1 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/VertexRDD.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/VertexRDD.scala
@@ -108,7 +108,7 @@ class VertexRDD[@specialized VD: ClassTag](
 
   /** The number of vertices in the RDD. */
   override def count(): Long = {
-    partitionsRDD.map(_.size).reduce(_ + _)
+    partitionsRDD.map(_.size.toLong).reduce(_ + _)
   }
 
   /**

From 92af2314f27e80227174499f2fca505bd551cda7 Mon Sep 17 00:00:00 2001
From: Sandy Ryza <sandy@cloudera.com>
Date: Thu, 28 Aug 2014 16:18:50 -0700
Subject: [PATCH 136/489] SPARK-3082. yarn.Client.logClusterResourceDetails
 throws NPE if requeste...

...d queue doesn't exist

Author: Sandy Ryza <sandy@cloudera.com>

Closes #1984 from sryza/sandy-spark-3082 and squashes the following commits:

fe08c37 [Sandy Ryza] Remove log message entirely
85253ad [Sandy Ryza] SPARK-3082. yarn.Client.logClusterResourceDetails throws NPE if requested queue doesn't exist
---
 .../scala/org/apache/spark/deploy/yarn/Client.scala   | 11 +----------
 .../scala/org/apache/spark/deploy/yarn/Client.scala   | 11 +----------
 2 files changed, 2 insertions(+), 20 deletions(-)

diff --git a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/Client.scala b/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
index 82f79d88a3009..9be78546c1091 100644
--- a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
+++ b/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
@@ -89,17 +89,8 @@ class Client(clientArgs: ClientArguments, hadoopConf: Configuration, spConf: Spa
 
   def logClusterResourceDetails() {
     val clusterMetrics: YarnClusterMetrics = super.getYarnClusterMetrics
-    logInfo("Got Cluster metric info from ASM, numNodeManagers = " +
+    logInfo("Got cluster metric info from ASM, numNodeManagers = " +
       clusterMetrics.getNumNodeManagers)
-
-    val queueInfo: QueueInfo = super.getQueueInfo(args.amQueue)
-    logInfo( """Queue info ... queueName = %s, queueCurrentCapacity = %s, queueMaxCapacity = %s,
-      queueApplicationCount = %s, queueChildQueueCount = %s""".format(
-        queueInfo.getQueueName,
-        queueInfo.getCurrentCapacity,
-        queueInfo.getMaximumCapacity,
-        queueInfo.getApplications.size,
-        queueInfo.getChildQueues.size))
   }
 
 
diff --git a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/Client.scala b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
index 15f3c4f180ea3..1f9a4bf209eb9 100644
--- a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
+++ b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
@@ -99,17 +99,8 @@ class Client(clientArgs: ClientArguments, hadoopConf: Configuration, spConf: Spa
 
   def logClusterResourceDetails() {
     val clusterMetrics: YarnClusterMetrics = yarnClient.getYarnClusterMetrics
-    logInfo("Got Cluster metric info from ResourceManager, number of NodeManagers: " +
+    logInfo("Got cluster metric info from ResourceManager, number of NodeManagers: " +
       clusterMetrics.getNumNodeManagers)
-
-    val queueInfo: QueueInfo = yarnClient.getQueueInfo(args.amQueue)
-    logInfo( """Queue info ... queueName: %s, queueCurrentCapacity: %s, queueMaxCapacity: %s,
-      queueApplicationCount = %s, queueChildQueueCount = %s""".format(
-        queueInfo.getQueueName,
-        queueInfo.getCurrentCapacity,
-        queueInfo.getMaximumCapacity,
-        queueInfo.getApplications.size,
-        queueInfo.getChildQueues.size))
   }
 
   def calculateAMMemory(newApp: GetNewApplicationResponse) :Int = {

From a46b8f2d710d82ba3a212cac64b610a67b8798f9 Mon Sep 17 00:00:00 2001
From: Andrew Or <andrewor14@gmail.com>
Date: Thu, 28 Aug 2014 17:05:21 -0700
Subject: [PATCH 137/489] [SPARK-3277] Fix external spilling with LZ4 assertion
 error

**Summary of the changes**

The bulk of this PR is comprised of tests and documentation; the actual fix is really just adding 1 line of code (see `BlockObjectWriter.scala`). We currently do not run the `External*` test suites with different compression codecs, and this would have caught the bug reported in [SPARK-3277](https://issues.apache.org/jira/browse/SPARK-3277). This PR extends the existing code to test spilling using all compression codecs known to Spark, including `LZ4`.

**The bug itself**

In `DiskBlockObjectWriter`, we only report the shuffle bytes written before we close the streams. With `LZ4`, all the bytes written reported by our metrics were 0 because `flush()` was not taking effect for some reason. In general, compression codecs may write additional bytes to the file after we call `close()`, and so we must also capture those bytes in our shuffle write metrics.

Thanks mridulm and pwendell for help with debugging.

Author: Andrew Or <andrewor14@gmail.com>
Author: Patrick Wendell <pwendell@gmail.com>

Closes #2187 from andrewor14/fix-lz4-spilling and squashes the following commits:

1b54bdc [Andrew Or] Speed up tests by not compressing everything
1c4624e [Andrew Or] Merge branch 'master' of github.com:apache/spark into fix-lz4-spilling
6b2e7d1 [Andrew Or] Fix compilation error
92e251b [Patrick Wendell] Better documentation for BlockObjectWriter.
a1ad536 [Andrew Or] Fix tests
089593f [Andrew Or] Actually fix SPARK-3277 (tests still fail)
4bbcf68 [Andrew Or] Update tests to actually test all compression codecs
b264a84 [Andrew Or] ExternalAppendOnlyMapSuite code style fixes (minor)
1bfa743 [Andrew Or] Add more information to assert for better debugging
---
 .../apache/spark/io/CompressionCodec.scala    |   1 +
 .../spark/storage/BlockObjectWriter.scala     |  37 +++-
 .../collection/ExternalAppendOnlyMap.scala    |   7 +-
 .../spark/scheduler/ReplayListenerSuite.scala |   5 +-
 .../ExternalAppendOnlyMapSuite.scala          | 190 ++++++++++--------
 5 files changed, 144 insertions(+), 96 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/io/CompressionCodec.scala b/core/src/main/scala/org/apache/spark/io/CompressionCodec.scala
index ef9c43ecf14f6..1ac7f4e448eb1 100644
--- a/core/src/main/scala/org/apache/spark/io/CompressionCodec.scala
+++ b/core/src/main/scala/org/apache/spark/io/CompressionCodec.scala
@@ -64,6 +64,7 @@ private[spark] object CompressionCodec {
   }
 
   val DEFAULT_COMPRESSION_CODEC = "snappy"
+  val ALL_COMPRESSION_CODECS = shortCompressionCodecNames.values.toSeq
 }
 
 
diff --git a/core/src/main/scala/org/apache/spark/storage/BlockObjectWriter.scala b/core/src/main/scala/org/apache/spark/storage/BlockObjectWriter.scala
index adda971fd7b47..9c469370ffe1f 100644
--- a/core/src/main/scala/org/apache/spark/storage/BlockObjectWriter.scala
+++ b/core/src/main/scala/org/apache/spark/storage/BlockObjectWriter.scala
@@ -65,8 +65,6 @@ private[spark] abstract class BlockObjectWriter(val blockId: BlockId) {
 
 /**
  * BlockObjectWriter which writes directly to a file on disk. Appends to the given file.
- * The given write metrics will be updated incrementally, but will not necessarily be current until
- * commitAndClose is called.
  */
 private[spark] class DiskBlockObjectWriter(
     blockId: BlockId,
@@ -75,6 +73,8 @@ private[spark] class DiskBlockObjectWriter(
     bufferSize: Int,
     compressStream: OutputStream => OutputStream,
     syncWrites: Boolean,
+    // These write metrics concurrently shared with other active BlockObjectWriter's who
+    // are themselves performing writes. All updates must be relative.
     writeMetrics: ShuffleWriteMetrics)
   extends BlockObjectWriter(blockId)
   with Logging
@@ -94,14 +94,30 @@ private[spark] class DiskBlockObjectWriter(
   private var fos: FileOutputStream = null
   private var ts: TimeTrackingOutputStream = null
   private var objOut: SerializationStream = null
+  private var initialized = false
+
+  /**
+   * Cursors used to represent positions in the file.
+   *
+   * xxxxxxxx|--------|---       |
+   *         ^        ^          ^
+   *         |        |        finalPosition
+   *         |      reportedPosition
+   *       initialPosition
+   *
+   * initialPosition: Offset in the file where we start writing. Immutable.
+   * reportedPosition: Position at the time of the last update to the write metrics.
+   * finalPosition: Offset where we stopped writing. Set on closeAndCommit() then never changed.
+   * -----: Current writes to the underlying file.
+   * xxxxx: Existing contents of the file.
+   */
   private val initialPosition = file.length()
   private var finalPosition: Long = -1
-  private var initialized = false
+  private var reportedPosition = initialPosition
 
   /** Calling channel.position() to update the write metrics can be a little bit expensive, so we
     * only call it every N writes */
   private var writesSinceMetricsUpdate = 0
-  private var lastPosition = initialPosition
 
   override def open(): BlockObjectWriter = {
     fos = new FileOutputStream(file, true)
@@ -140,17 +156,18 @@ private[spark] class DiskBlockObjectWriter(
       //       serializer stream and the lower level stream.
       objOut.flush()
       bs.flush()
-      updateBytesWritten()
       close()
     }
     finalPosition = file.length()
+    // In certain compression codecs, more bytes are written after close() is called
+    writeMetrics.shuffleBytesWritten += (finalPosition - reportedPosition)
   }
 
   // Discard current writes. We do this by flushing the outstanding writes and then
   // truncating the file to its initial position.
   override def revertPartialWritesAndClose() {
     try {
-      writeMetrics.shuffleBytesWritten -= (lastPosition - initialPosition)
+      writeMetrics.shuffleBytesWritten -= (reportedPosition - initialPosition)
 
       if (initialized) {
         objOut.flush()
@@ -189,10 +206,14 @@ private[spark] class DiskBlockObjectWriter(
     new FileSegment(file, initialPosition, finalPosition - initialPosition)
   }
 
+  /**
+   * Report the number of bytes written in this writer's shuffle write metrics.
+   * Note that this is only valid before the underlying streams are closed.
+   */
   private def updateBytesWritten() {
     val pos = channel.position()
-    writeMetrics.shuffleBytesWritten += (pos - lastPosition)
-    lastPosition = pos
+    writeMetrics.shuffleBytesWritten += (pos - reportedPosition)
+    reportedPosition = pos
   }
 
   private def callWithTiming(f: => Unit) = {
diff --git a/core/src/main/scala/org/apache/spark/util/collection/ExternalAppendOnlyMap.scala b/core/src/main/scala/org/apache/spark/util/collection/ExternalAppendOnlyMap.scala
index 9f85b94a70800..8a015c1d26a96 100644
--- a/core/src/main/scala/org/apache/spark/util/collection/ExternalAppendOnlyMap.scala
+++ b/core/src/main/scala/org/apache/spark/util/collection/ExternalAppendOnlyMap.scala
@@ -413,7 +413,12 @@ class ExternalAppendOnlyMap[K, V, C](
     extends Iterator[(K, C)]
   {
     private val batchOffsets = batchSizes.scanLeft(0L)(_ + _)  // Size will be batchSize.length + 1
-    assert(file.length() == batchOffsets(batchOffsets.length - 1))
+    assert(file.length() == batchOffsets.last,
+      "File length is not equal to the last batch offset:\n" +
+      s"    file length = ${file.length}\n" +
+      s"    last batch offset = ${batchOffsets.last}\n" +
+      s"    all batch offsets = ${batchOffsets.mkString(",")}"
+    )
 
     private var batchIndex = 0  // Which batch we're in
     private var fileStream: FileInputStream = null
diff --git a/core/src/test/scala/org/apache/spark/scheduler/ReplayListenerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/ReplayListenerSuite.scala
index d81499ac6abef..6b6e0104e5467 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/ReplayListenerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/ReplayListenerSuite.scala
@@ -33,10 +33,7 @@ import org.apache.spark.util.{JsonProtocol, Utils}
  */
 class ReplayListenerSuite extends FunSuite with BeforeAndAfter {
   private val fileSystem = Utils.getHadoopFileSystem("/")
-  private val allCompressionCodecs = Seq[String](
-    "org.apache.spark.io.LZFCompressionCodec",
-    "org.apache.spark.io.SnappyCompressionCodec"
-  )
+  private val allCompressionCodecs = CompressionCodec.ALL_COMPRESSION_CODECS
   private var testDir: File = _
 
   before {
diff --git a/core/src/test/scala/org/apache/spark/util/collection/ExternalAppendOnlyMapSuite.scala b/core/src/test/scala/org/apache/spark/util/collection/ExternalAppendOnlyMapSuite.scala
index 04d7338488628..ac3931e3d0a73 100644
--- a/core/src/test/scala/org/apache/spark/util/collection/ExternalAppendOnlyMapSuite.scala
+++ b/core/src/test/scala/org/apache/spark/util/collection/ExternalAppendOnlyMapSuite.scala
@@ -23,37 +23,42 @@ import org.scalatest.FunSuite
 
 import org.apache.spark._
 import org.apache.spark.SparkContext._
+import org.apache.spark.io.CompressionCodec
 
 class ExternalAppendOnlyMapSuite extends FunSuite with LocalSparkContext {
+  private val allCompressionCodecs = CompressionCodec.ALL_COMPRESSION_CODECS
+  private def createCombiner[T](i: T) = ArrayBuffer[T](i)
+  private def mergeValue[T](buffer: ArrayBuffer[T], i: T): ArrayBuffer[T] = buffer += i
+  private def mergeCombiners[T](buf1: ArrayBuffer[T], buf2: ArrayBuffer[T]): ArrayBuffer[T] =
+    buf1 ++= buf2
 
-  private def createCombiner(i: Int) = ArrayBuffer[Int](i)
-  private def mergeValue(buffer: ArrayBuffer[Int], i: Int) = buffer += i
-  private def mergeCombiners(buf1: ArrayBuffer[Int], buf2: ArrayBuffer[Int]) = buf1 ++= buf2
+  private def createExternalMap[T] = new ExternalAppendOnlyMap[T, T, ArrayBuffer[T]](
+    createCombiner[T], mergeValue[T], mergeCombiners[T])
 
-  private def createSparkConf(loadDefaults: Boolean): SparkConf = {
+  private def createSparkConf(loadDefaults: Boolean, codec: Option[String] = None): SparkConf = {
     val conf = new SparkConf(loadDefaults)
     // Make the Java serializer write a reset instruction (TC_RESET) after each object to test
     // for a bug we had with bytes written past the last object in a batch (SPARK-2792)
     conf.set("spark.serializer.objectStreamReset", "1")
     conf.set("spark.serializer", "org.apache.spark.serializer.JavaSerializer")
+    conf.set("spark.shuffle.spill.compress", codec.isDefined.toString)
+    codec.foreach { c => conf.set("spark.io.compression.codec", c) }
     // Ensure that we actually have multiple batches per spill file
     conf.set("spark.shuffle.spill.batchSize", "10")
     conf
   }
 
   test("simple insert") {
-    val conf = createSparkConf(false)
+    val conf = createSparkConf(loadDefaults = false)
     sc = new SparkContext("local", "test", conf)
-
-    val map = new ExternalAppendOnlyMap[Int, Int, ArrayBuffer[Int]](createCombiner,
-      mergeValue, mergeCombiners)
+    val map = createExternalMap[Int]
 
     // Single insert
     map.insert(1, 10)
     var it = map.iterator
     assert(it.hasNext)
     val kv = it.next()
-    assert(kv._1 == 1 && kv._2 == ArrayBuffer[Int](10))
+    assert(kv._1 === 1 && kv._2 === ArrayBuffer[Int](10))
     assert(!it.hasNext)
 
     // Multiple insert
@@ -61,18 +66,17 @@ class ExternalAppendOnlyMapSuite extends FunSuite with LocalSparkContext {
     map.insert(3, 30)
     it = map.iterator
     assert(it.hasNext)
-    assert(it.toSet == Set[(Int, ArrayBuffer[Int])](
+    assert(it.toSet === Set[(Int, ArrayBuffer[Int])](
       (1, ArrayBuffer[Int](10)),
       (2, ArrayBuffer[Int](20)),
       (3, ArrayBuffer[Int](30))))
+    sc.stop()
   }
 
   test("insert with collision") {
-    val conf = createSparkConf(false)
+    val conf = createSparkConf(loadDefaults = false)
     sc = new SparkContext("local", "test", conf)
-
-    val map = new ExternalAppendOnlyMap[Int, Int, ArrayBuffer[Int]](createCombiner,
-      mergeValue, mergeCombiners)
+    val map = createExternalMap[Int]
 
     map.insertAll(Seq(
       (1, 10),
@@ -84,30 +88,28 @@ class ExternalAppendOnlyMapSuite extends FunSuite with LocalSparkContext {
     val it = map.iterator
     assert(it.hasNext)
     val result = it.toSet[(Int, ArrayBuffer[Int])].map(kv => (kv._1, kv._2.toSet))
-    assert(result == Set[(Int, Set[Int])](
+    assert(result === Set[(Int, Set[Int])](
       (1, Set[Int](10, 100, 1000)),
       (2, Set[Int](20, 200)),
       (3, Set[Int](30))))
+    sc.stop()
   }
 
   test("ordering") {
-    val conf = createSparkConf(false)
+    val conf = createSparkConf(loadDefaults = false)
     sc = new SparkContext("local", "test", conf)
 
-    val map1 = new ExternalAppendOnlyMap[Int, Int, ArrayBuffer[Int]](createCombiner,
-      mergeValue, mergeCombiners)
+    val map1 = createExternalMap[Int]
     map1.insert(1, 10)
     map1.insert(2, 20)
     map1.insert(3, 30)
 
-    val map2 = new ExternalAppendOnlyMap[Int, Int, ArrayBuffer[Int]](createCombiner,
-      mergeValue, mergeCombiners)
+    val map2 = createExternalMap[Int]
     map2.insert(2, 20)
     map2.insert(3, 30)
     map2.insert(1, 10)
 
-    val map3 = new ExternalAppendOnlyMap[Int, Int, ArrayBuffer[Int]](createCombiner,
-      mergeValue, mergeCombiners)
+    val map3 = createExternalMap[Int]
     map3.insert(3, 30)
     map3.insert(1, 10)
     map3.insert(2, 20)
@@ -119,33 +121,33 @@ class ExternalAppendOnlyMapSuite extends FunSuite with LocalSparkContext {
     var kv1 = it1.next()
     var kv2 = it2.next()
     var kv3 = it3.next()
-    assert(kv1._1 == kv2._1 && kv2._1 == kv3._1)
-    assert(kv1._2 == kv2._2 && kv2._2 == kv3._2)
+    assert(kv1._1 === kv2._1 && kv2._1 === kv3._1)
+    assert(kv1._2 === kv2._2 && kv2._2 === kv3._2)
 
     kv1 = it1.next()
     kv2 = it2.next()
     kv3 = it3.next()
-    assert(kv1._1 == kv2._1 && kv2._1 == kv3._1)
-    assert(kv1._2 == kv2._2 && kv2._2 == kv3._2)
+    assert(kv1._1 === kv2._1 && kv2._1 === kv3._1)
+    assert(kv1._2 === kv2._2 && kv2._2 === kv3._2)
 
     kv1 = it1.next()
     kv2 = it2.next()
     kv3 = it3.next()
-    assert(kv1._1 == kv2._1 && kv2._1 == kv3._1)
-    assert(kv1._2 == kv2._2 && kv2._2 == kv3._2)
+    assert(kv1._1 === kv2._1 && kv2._1 === kv3._1)
+    assert(kv1._2 === kv2._2 && kv2._2 === kv3._2)
+    sc.stop()
   }
 
   test("null keys and values") {
-    val conf = createSparkConf(false)
+    val conf = createSparkConf(loadDefaults = false)
     sc = new SparkContext("local", "test", conf)
 
-    val map = new ExternalAppendOnlyMap[Int, Int, ArrayBuffer[Int]](createCombiner,
-      mergeValue, mergeCombiners)
+    val map = createExternalMap[Int]
     map.insert(1, 5)
     map.insert(2, 6)
     map.insert(3, 7)
     assert(map.size === 3)
-    assert(map.iterator.toSet == Set[(Int, Seq[Int])](
+    assert(map.iterator.toSet === Set[(Int, Seq[Int])](
       (1, Seq[Int](5)),
       (2, Seq[Int](6)),
       (3, Seq[Int](7))
@@ -155,7 +157,7 @@ class ExternalAppendOnlyMapSuite extends FunSuite with LocalSparkContext {
     val nullInt = null.asInstanceOf[Int]
     map.insert(nullInt, 8)
     assert(map.size === 4)
-    assert(map.iterator.toSet == Set[(Int, Seq[Int])](
+    assert(map.iterator.toSet === Set[(Int, Seq[Int])](
       (1, Seq[Int](5)),
       (2, Seq[Int](6)),
       (3, Seq[Int](7)),
@@ -167,32 +169,34 @@ class ExternalAppendOnlyMapSuite extends FunSuite with LocalSparkContext {
     map.insert(nullInt, nullInt)
     assert(map.size === 5)
     val result = map.iterator.toSet[(Int, ArrayBuffer[Int])].map(kv => (kv._1, kv._2.toSet))
-    assert(result == Set[(Int, Set[Int])](
+    assert(result === Set[(Int, Set[Int])](
       (1, Set[Int](5)),
       (2, Set[Int](6)),
       (3, Set[Int](7)),
       (4, Set[Int](nullInt)),
       (nullInt, Set[Int](nullInt, 8))
     ))
+    sc.stop()
   }
 
   test("simple aggregator") {
-    val conf = createSparkConf(false)
+    val conf = createSparkConf(loadDefaults = false)
     sc = new SparkContext("local", "test", conf)
 
     // reduceByKey
     val rdd = sc.parallelize(1 to 10).map(i => (i%2, 1))
     val result1 = rdd.reduceByKey(_+_).collect()
-    assert(result1.toSet == Set[(Int, Int)]((0, 5), (1, 5)))
+    assert(result1.toSet === Set[(Int, Int)]((0, 5), (1, 5)))
 
     // groupByKey
     val result2 = rdd.groupByKey().collect().map(x => (x._1, x._2.toList)).toSet
-    assert(result2.toSet == Set[(Int, Seq[Int])]
+    assert(result2.toSet === Set[(Int, Seq[Int])]
       ((0, List[Int](1, 1, 1, 1, 1)), (1, List[Int](1, 1, 1, 1, 1))))
+    sc.stop()
   }
 
   test("simple cogroup") {
-    val conf = createSparkConf(false)
+    val conf = createSparkConf(loadDefaults = false)
     sc = new SparkContext("local", "test", conf)
     val rdd1 = sc.parallelize(1 to 4).map(i => (i, i))
     val rdd2 = sc.parallelize(1 to 4).map(i => (i%2, i))
@@ -200,77 +204,98 @@ class ExternalAppendOnlyMapSuite extends FunSuite with LocalSparkContext {
 
     result.foreach { case (i, (seq1, seq2)) =>
       i match {
-        case 0 => assert(seq1.toSet == Set[Int]() && seq2.toSet == Set[Int](2, 4))
-        case 1 => assert(seq1.toSet == Set[Int](1) && seq2.toSet == Set[Int](1, 3))
-        case 2 => assert(seq1.toSet == Set[Int](2) && seq2.toSet == Set[Int]())
-        case 3 => assert(seq1.toSet == Set[Int](3) && seq2.toSet == Set[Int]())
-        case 4 => assert(seq1.toSet == Set[Int](4) && seq2.toSet == Set[Int]())
+        case 0 => assert(seq1.toSet === Set[Int]() && seq2.toSet === Set[Int](2, 4))
+        case 1 => assert(seq1.toSet === Set[Int](1) && seq2.toSet === Set[Int](1, 3))
+        case 2 => assert(seq1.toSet === Set[Int](2) && seq2.toSet === Set[Int]())
+        case 3 => assert(seq1.toSet === Set[Int](3) && seq2.toSet === Set[Int]())
+        case 4 => assert(seq1.toSet === Set[Int](4) && seq2.toSet === Set[Int]())
       }
     }
+    sc.stop()
   }
 
   test("spilling") {
-    val conf = createSparkConf(true)  // Load defaults, otherwise SPARK_HOME is not found
+    testSimpleSpilling()
+  }
+
+  test("spilling with compression") {
+    // Keep track of which compression codec we're using to report in test failure messages
+    var lastCompressionCodec: Option[String] = None
+    try {
+      allCompressionCodecs.foreach { c =>
+        lastCompressionCodec = Some(c)
+        testSimpleSpilling(Some(c))
+      }
+    } catch {
+      // Include compression codec used in test failure message
+      // We need to catch Throwable here because assertion failures are not covered by Exceptions
+      case t: Throwable =>
+        val compressionMessage = lastCompressionCodec
+          .map { c => "with compression using codec " + c }
+          .getOrElse("without compression")
+        val newException = new Exception(s"Test failed $compressionMessage:\n\n${t.getMessage}")
+        newException.setStackTrace(t.getStackTrace)
+        throw newException
+    }
+  }
+
+  /**
+   * Test spilling through simple aggregations and cogroups.
+   * If a compression codec is provided, use it. Otherwise, do not compress spills.
+   */
+  private def testSimpleSpilling(codec: Option[String] = None): Unit = {
+    val conf = createSparkConf(loadDefaults = true, codec)  // Load defaults for Spark home
     conf.set("spark.shuffle.memoryFraction", "0.001")
     sc = new SparkContext("local-cluster[1,1,512]", "test", conf)
 
     // reduceByKey - should spill ~8 times
     val rddA = sc.parallelize(0 until 100000).map(i => (i/2, i))
     val resultA = rddA.reduceByKey(math.max).collect()
-    assert(resultA.length == 50000)
-    resultA.foreach { case(k, v) =>
-      if (v != k * 2 + 1) {
-        fail(s"Value for ${k} was wrong: expected ${k * 2 + 1}, got ${v}")
-      }
+    assert(resultA.length === 50000)
+    resultA.foreach { case (k, v) =>
+      assert(v === k * 2 + 1, s"Value for $k was wrong: expected ${k * 2 + 1}, got $v")
     }
 
     // groupByKey - should spill ~17 times
     val rddB = sc.parallelize(0 until 100000).map(i => (i/4, i))
     val resultB = rddB.groupByKey().collect()
-    assert(resultB.length == 25000)
-    resultB.foreach { case(i, seq) =>
+    assert(resultB.length === 25000)
+    resultB.foreach { case (i, seq) =>
       val expected = Set(i * 4, i * 4 + 1, i * 4 + 2, i * 4 + 3)
-      if (seq.toSet != expected) {
-        fail(s"Value for ${i} was wrong: expected ${expected}, got ${seq.toSet}")
-      }
+      assert(seq.toSet === expected,
+        s"Value for $i was wrong: expected $expected, got ${seq.toSet}")
     }
 
     // cogroup - should spill ~7 times
     val rddC1 = sc.parallelize(0 until 10000).map(i => (i, i))
     val rddC2 = sc.parallelize(0 until 10000).map(i => (i%1000, i))
     val resultC = rddC1.cogroup(rddC2).collect()
-    assert(resultC.length == 10000)
-    resultC.foreach { case(i, (seq1, seq2)) =>
+    assert(resultC.length === 10000)
+    resultC.foreach { case (i, (seq1, seq2)) =>
       i match {
         case 0 =>
-          assert(seq1.toSet == Set[Int](0))
-          assert(seq2.toSet == Set[Int](0, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000))
+          assert(seq1.toSet === Set[Int](0))
+          assert(seq2.toSet === Set[Int](0, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000))
         case 1 =>
-          assert(seq1.toSet == Set[Int](1))
-          assert(seq2.toSet == Set[Int](1, 1001, 2001, 3001, 4001, 5001, 6001, 7001, 8001, 9001))
+          assert(seq1.toSet === Set[Int](1))
+          assert(seq2.toSet === Set[Int](1, 1001, 2001, 3001, 4001, 5001, 6001, 7001, 8001, 9001))
         case 5000 =>
-          assert(seq1.toSet == Set[Int](5000))
-          assert(seq2.toSet == Set[Int]())
+          assert(seq1.toSet === Set[Int](5000))
+          assert(seq2.toSet === Set[Int]())
         case 9999 =>
-          assert(seq1.toSet == Set[Int](9999))
-          assert(seq2.toSet == Set[Int]())
+          assert(seq1.toSet === Set[Int](9999))
+          assert(seq2.toSet === Set[Int]())
         case _ =>
       }
     }
+    sc.stop()
   }
 
   test("spilling with hash collisions") {
-    val conf = createSparkConf(true)
+    val conf = createSparkConf(loadDefaults = true)
     conf.set("spark.shuffle.memoryFraction", "0.001")
     sc = new SparkContext("local-cluster[1,1,512]", "test", conf)
-
-    def createCombiner(i: String) = ArrayBuffer[String](i)
-    def mergeValue(buffer: ArrayBuffer[String], i: String) = buffer += i
-    def mergeCombiners(buffer1: ArrayBuffer[String], buffer2: ArrayBuffer[String]) =
-      buffer1 ++= buffer2
-
-    val map = new ExternalAppendOnlyMap[String, String, ArrayBuffer[String]](
-      createCombiner, mergeValue, mergeCombiners)
+    val map = createExternalMap[String]
 
     val collisionPairs = Seq(
       ("Aa", "BB"),                   // 2112
@@ -312,13 +337,13 @@ class ExternalAppendOnlyMapSuite extends FunSuite with LocalSparkContext {
       count += 1
     }
     assert(count === 100000 + collisionPairs.size * 2)
+    sc.stop()
   }
 
   test("spilling with many hash collisions") {
-    val conf = createSparkConf(true)
+    val conf = createSparkConf(loadDefaults = true)
     conf.set("spark.shuffle.memoryFraction", "0.0001")
     sc = new SparkContext("local-cluster[1,1,512]", "test", conf)
-
     val map = new ExternalAppendOnlyMap[FixedHashObject, Int, Int](_ => 1, _ + _, _ + _)
 
     // Insert 10 copies each of lots of objects whose hash codes are either 0 or 1. This causes
@@ -337,15 +362,14 @@ class ExternalAppendOnlyMapSuite extends FunSuite with LocalSparkContext {
       count += 1
     }
     assert(count === 10000)
+    sc.stop()
   }
 
   test("spilling with hash collisions using the Int.MaxValue key") {
-    val conf = createSparkConf(true)
+    val conf = createSparkConf(loadDefaults = true)
     conf.set("spark.shuffle.memoryFraction", "0.001")
     sc = new SparkContext("local-cluster[1,1,512]", "test", conf)
-
-    val map = new ExternalAppendOnlyMap[Int, Int, ArrayBuffer[Int]](
-      createCombiner, mergeValue, mergeCombiners)
+    val map = createExternalMap[Int]
 
     (1 to 100000).foreach { i => map.insert(i, i) }
     map.insert(Int.MaxValue, Int.MaxValue)
@@ -355,15 +379,14 @@ class ExternalAppendOnlyMapSuite extends FunSuite with LocalSparkContext {
       // Should not throw NoSuchElementException
       it.next()
     }
+    sc.stop()
   }
 
   test("spilling with null keys and values") {
-    val conf = createSparkConf(true)
+    val conf = createSparkConf(loadDefaults = true)
     conf.set("spark.shuffle.memoryFraction", "0.001")
     sc = new SparkContext("local-cluster[1,1,512]", "test", conf)
-
-    val map = new ExternalAppendOnlyMap[Int, Int, ArrayBuffer[Int]](
-      createCombiner, mergeValue, mergeCombiners)
+    val map = createExternalMap[Int]
 
     map.insertAll((1 to 100000).iterator.map(i => (i, i)))
     map.insert(null.asInstanceOf[Int], 1)
@@ -375,6 +398,7 @@ class ExternalAppendOnlyMapSuite extends FunSuite with LocalSparkContext {
       // Should not throw NullPointerException
       it.next()
     }
+    sc.stop()
   }
 
 }

From 3c517a812e2e7cb5c00897b48024a716cc2b9a40 Mon Sep 17 00:00:00 2001
From: nchammas <nicholas.chammas@gmail.com>
Date: Thu, 28 Aug 2014 18:08:28 -0700
Subject: [PATCH 138/489] [Spark QA] Link to console output on test time out

When tests time out we should link to the Jenkins console output for easy review. We already do this for when tests start or complete normally.

Here's [a recent example](https://github.com/apache/spark/pull/2109#issuecomment-53374032) of where this would be helpful.

Author: nchammas <nicholas.chammas@gmail.com>

Closes #2140 from nchammas/patch-1 and squashes the following commits:

3b26c8d [nchammas] [Spark QA] Link to console output on test time out
---
 dev/run-tests-jenkins | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/dev/run-tests-jenkins b/dev/run-tests-jenkins
index 31506e28e05af..d9205f08cc134 100755
--- a/dev/run-tests-jenkins
+++ b/dev/run-tests-jenkins
@@ -138,7 +138,7 @@ function post_message () {
   test_result="$?"
 
   if [ "$test_result" -eq "124" ]; then
-    fail_message="**Tests timed out** after a configured wait of \`${TESTS_TIMEOUT}\`."
+    fail_message="**[Tests timed out](${BUILD_URL}consoleFull)** after a configured wait of \`${TESTS_TIMEOUT}\`."
     post_message "$fail_message"
     exit $test_result
   else

From 665e71d14debb8a7fc1547c614867a8c3b1f806a Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@apache.org>
Date: Thu, 28 Aug 2014 19:00:40 -0700
Subject: [PATCH 139/489] [SPARK-1912] Lazily initialize buffers for local
 shuffle blocks.

This is a simplified fix for SPARK-1912.

Author: Reynold Xin <rxin@apache.org>

Closes #2179 from rxin/SPARK-1912 and squashes the following commits:

b2f0e9e [Reynold Xin] Fix unit tests.
a8eddfe [Reynold Xin] [SPARK-1912] Lazily initialize buffers for local shuffle blocks.
---
 .../spark/storage/BlockFetcherIterator.scala  |  5 +---
 .../apache/spark/storage/BlockManager.scala   | 22 ++-------------
 .../storage/BlockFetcherIteratorSuite.scala   | 28 +++++++++++--------
 3 files changed, 20 insertions(+), 35 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/storage/BlockFetcherIterator.scala b/core/src/main/scala/org/apache/spark/storage/BlockFetcherIterator.scala
index 4ab8ec8f0ff3b..d07e6a1b1836c 100644
--- a/core/src/main/scala/org/apache/spark/storage/BlockFetcherIterator.scala
+++ b/core/src/main/scala/org/apache/spark/storage/BlockFetcherIterator.scala
@@ -196,11 +196,8 @@ object BlockFetcherIterator {
       // any memory that might exceed our maxBytesInFlight
       for (id <- localBlocksToFetch) {
         try {
-          // getLocalFromDisk never return None but throws BlockException
-          val iter = getLocalFromDisk(id, serializer).get
-          // Pass 0 as size since it's not in flight
           readMetrics.localBlocksFetched += 1
-          results.put(new FetchResult(id, 0, () => iter))
+          results.put(new FetchResult(id, 0, () => getLocalFromDisk(id, serializer).get))
           logDebug("Got local block " + id)
         } catch {
           case e: Exception => {
diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManager.scala b/core/src/main/scala/org/apache/spark/storage/BlockManager.scala
index 1eb622c12a79f..cfe5b6c50aea2 100644
--- a/core/src/main/scala/org/apache/spark/storage/BlockManager.scala
+++ b/core/src/main/scala/org/apache/spark/storage/BlockManager.scala
@@ -1039,26 +1039,8 @@ private[spark] class BlockManager(
       bytes: ByteBuffer,
       serializer: Serializer = defaultSerializer): Iterator[Any] = {
     bytes.rewind()
-
-    def getIterator: Iterator[Any] = {
-      val stream = wrapForCompression(blockId, new ByteBufferInputStream(bytes, true))
-      serializer.newInstance().deserializeStream(stream).asIterator
-    }
-
-    if (blockId.isShuffle) {
-      /* Reducer may need to read many local shuffle blocks and will wrap them into Iterators
-       * at the beginning. The wrapping will cost some memory (compression instance
-       * initialization, etc.). Reducer reads shuffle blocks one by one so we could do the
-       * wrapping lazily to save memory. */
-      class LazyProxyIterator(f: => Iterator[Any]) extends Iterator[Any] {
-        lazy val proxy = f
-        override def hasNext: Boolean = proxy.hasNext
-        override def next(): Any = proxy.next()
-      }
-      new LazyProxyIterator(getIterator)
-    } else {
-      getIterator
-    }
+    val stream = wrapForCompression(blockId, new ByteBufferInputStream(bytes, true))
+    serializer.newInstance().deserializeStream(stream).asIterator
   }
 
   def stop(): Unit = {
diff --git a/core/src/test/scala/org/apache/spark/storage/BlockFetcherIteratorSuite.scala b/core/src/test/scala/org/apache/spark/storage/BlockFetcherIteratorSuite.scala
index 159128438390d..fbfcb5156d496 100644
--- a/core/src/test/scala/org/apache/spark/storage/BlockFetcherIteratorSuite.scala
+++ b/core/src/test/scala/org/apache/spark/storage/BlockFetcherIteratorSuite.scala
@@ -76,20 +76,24 @@ class BlockFetcherIteratorSuite extends FunSuite with Matchers {
 
     iterator.initialize()
 
-    // 3rd getLocalFromDisk invocation should be failed
-    verify(blockManager, times(3)).getLocalFromDisk(any(), any())
+    // Without exhausting the iterator, the iterator should be lazy and not call getLocalFromDisk.
+    verify(blockManager, times(0)).getLocalFromDisk(any(), any())
 
     assert(iterator.hasNext, "iterator should have 5 elements but actually has no elements")
     // the 2nd element of the tuple returned by iterator.next should be defined when fetching successfully
-    assert(iterator.next._2.isDefined, "1st element should be defined but is not actually defined") 
+    assert(iterator.next()._2.isDefined, "1st element should be defined but is not actually defined")
+    verify(blockManager, times(1)).getLocalFromDisk(any(), any())
+
     assert(iterator.hasNext, "iterator should have 5 elements but actually has 1 element")
-    assert(iterator.next._2.isDefined, "2nd element should be defined but is not actually defined") 
+    assert(iterator.next()._2.isDefined, "2nd element should be defined but is not actually defined")
+    verify(blockManager, times(2)).getLocalFromDisk(any(), any())
+
     assert(iterator.hasNext, "iterator should have 5 elements but actually has 2 elements")
     // 3rd fetch should be failed
-    assert(!iterator.next._2.isDefined, "3rd element should not be defined but is actually defined") 
-    assert(iterator.hasNext, "iterator should have 5 elements but actually has 3 elements")
-    // Don't call next() after fetching non-defined element even if thare are rest of elements in the iterator.
-    // Otherwise, BasicBlockFetcherIterator hangs up.
+    intercept[Exception] {
+      iterator.next()
+    }
+    verify(blockManager, times(3)).getLocalFromDisk(any(), any())
   }
 
 
@@ -127,8 +131,8 @@ class BlockFetcherIteratorSuite extends FunSuite with Matchers {
 
     iterator.initialize()
 
-    // getLocalFromDis should be invoked for all of 5 blocks
-    verify(blockManager, times(5)).getLocalFromDisk(any(), any())
+    // Without exhausting the iterator, the iterator should be lazy and not call getLocalFromDisk.
+    verify(blockManager, times(0)).getLocalFromDisk(any(), any())
 
     assert(iterator.hasNext, "iterator should have 5 elements but actually has no elements")
     assert(iterator.next._2.isDefined, "All elements should be defined but 1st element is not actually defined") 
@@ -139,7 +143,9 @@ class BlockFetcherIteratorSuite extends FunSuite with Matchers {
     assert(iterator.hasNext, "iterator should have 5 elements but actually has 3 elements")
     assert(iterator.next._2.isDefined, "All elements should be defined but 4th element is not actually defined") 
     assert(iterator.hasNext, "iterator should have 5 elements but actually has 4 elements")
-    assert(iterator.next._2.isDefined, "All elements should be defined but 5th element is not actually defined") 
+    assert(iterator.next._2.isDefined, "All elements should be defined but 5th element is not actually defined")
+
+    verify(blockManager, times(5)).getLocalFromDisk(any(), any())
   }
 
   test("block fetch from remote fails using BasicBlockFetcherIterator") {

From 27df6ce6a2e6ba2142329f35e1670da184cb384c Mon Sep 17 00:00:00 2001
From: Kousuke Saruta <sarutak@oss.nttdata.co.jp>
Date: Thu, 28 Aug 2014 22:52:53 -0700
Subject: [PATCH 140/489] [SPARK-3279] Remove useless field variable in
 ApplicationMaster

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

Closes #2177 from sarutak/SPARK-3279 and squashes the following commits:

2955edc [Kousuke Saruta] Removed useless field variable from ApplicationMaster
---
 .../scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala   | 1 -
 1 file changed, 1 deletion(-)

diff --git a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
index f15c813b83973..5f66a98e7528b 100644
--- a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
+++ b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
@@ -46,7 +46,6 @@ private[spark] class ApplicationMaster(args: ApplicationMasterArguments,
   client: YarnRMClient) extends Logging {
   // TODO: Currently, task to container is computed once (TaskSetManager) - which need not be
   // optimal as more containers are available. Might need to handle this better.
-  private val ALLOCATE_HEARTBEAT_INTERVAL = 100
 
   private val sparkConf = new SparkConf()
   private val yarnConf: YarnConfiguration = new YarnConfiguration(new Configuration())

From e248328b39f52073422a12fd0388208de41be1c7 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies.liu@gmail.com>
Date: Fri, 29 Aug 2014 11:47:49 -0700
Subject: [PATCH 141/489] [SPARK-3307] [PySpark] Fix doc string of
 SparkContext.broadcast()

 remove invalid docs

Author: Davies Liu <davies.liu@gmail.com>

Closes #2202 from davies/keep and squashes the following commits:

aa3b44f [Davies Liu] remove invalid docs
---
 python/pyspark/context.py | 2 --
 1 file changed, 2 deletions(-)

diff --git a/python/pyspark/context.py b/python/pyspark/context.py
index a90870ed3a353..82f76de31afc1 100644
--- a/python/pyspark/context.py
+++ b/python/pyspark/context.py
@@ -568,8 +568,6 @@ def broadcast(self, value):
         L{Broadcast<pyspark.broadcast.Broadcast>}
         object for reading it in distributed functions. The variable will
         be sent to each cluster only once.
-
-        :keep: Keep the `value` in driver or not.
         """
         ser = CompressedSerializer(PickleSerializer())
         # pass large object by py4j is very slow and need much memory

From 53aa8316e88980c6f46d3b9fc90d935a4738a370 Mon Sep 17 00:00:00 2001
From: Nicholas Chammas <nicholas.chammas@gmail.com>
Date: Fri, 29 Aug 2014 15:23:32 -0700
Subject: [PATCH 142/489] [Docs] SQL doc formatting and typo fixes
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

As [reported on the dev list](http://apache-spark-developers-list.1001551.n3.nabble.com/VOTE-Release-Apache-Spark-1-1-0-RC2-tp8107p8131.html):
* Code fencing with triple-backticks doesn’t seem to work like it does on GitHub. Newlines are lost. Instead, use 4-space indent to format small code blocks.
* Nested bullets need 2 leading spaces, not 1.
* Spellcheck!

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

Closes #2201 from nchammas/sql-doc-fixes and squashes the following commits:

873f889 [Nicholas Chammas] [Docs] fix skip-api flag
5195e0c [Nicholas Chammas] [Docs] SQL doc formatting and typo fixes
3b26c8d [nchammas] [Spark QA] Link to console output on test time out
---
 docs/README.md                |   2 +-
 docs/sql-programming-guide.md | 109 ++++++++++++++++------------------
 2 files changed, 52 insertions(+), 59 deletions(-)

diff --git a/docs/README.md b/docs/README.md
index fd7ba4e0d72ea..0a0126c5747d1 100644
--- a/docs/README.md
+++ b/docs/README.md
@@ -30,7 +30,7 @@ called `_site` containing index.html as well as the rest of the compiled files.
 You can modify the default Jekyll build as follows:
 
     # Skip generating API docs (which takes a while)
-    $ SKIP_SCALADOC=1 jekyll build
+    $ SKIP_API=1 jekyll build
     # Serve content locally on port 4000
     $ jekyll serve --watch
     # Build the site with extra features used on the live page
diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md
index c41f2804a6021..8f7fb5431cfb6 100644
--- a/docs/sql-programming-guide.md
+++ b/docs/sql-programming-guide.md
@@ -474,10 +474,10 @@ anotherPeople = sqlContext.jsonRDD(anotherPeopleRDD)
 
 Spark SQL also supports reading and writing data stored in [Apache Hive](http://hive.apache.org/).
 However, since Hive has a large number of dependencies, it is not included in the default Spark assembly.
-In order to use Hive you must first run '`sbt/sbt -Phive assembly/assembly`' (or use `-Phive` for maven).
+In order to use Hive you must first run "`sbt/sbt -Phive assembly/assembly`" (or use `-Phive` for maven).
 This command builds a new assembly jar that includes Hive. Note that this Hive assembly jar must also be present
 on all of the worker nodes, as they will need access to the Hive serialization and deserialization libraries
-(SerDes) in order to acccess data stored in Hive.
+(SerDes) in order to access data stored in Hive.
 
 Configuration of Hive is done by placing your `hive-site.xml` file in `conf/`.
 
@@ -576,9 +576,8 @@ evaluated by the SQL execution engine.  A full list of the functions supported c
 
 ## Running the Thrift JDBC server
 
-The Thrift JDBC server implemented here corresponds to the [`HiveServer2`]
-(https://cwiki.apache.org/confluence/display/Hive/Setting+Up+HiveServer2) in Hive 0.12. You can test
-the JDBC server with the beeline script comes with either Spark or Hive 0.12.
+The Thrift JDBC server implemented here corresponds to the [`HiveServer2`](https://cwiki.apache.org/confluence/display/Hive/Setting+Up+HiveServer2)
+in Hive 0.12. You can test the JDBC server with the beeline script comes with either Spark or Hive 0.12.
 
 To start the JDBC server, run the following in the Spark directory:
 
@@ -597,7 +596,7 @@ Connect to the JDBC server in beeline with:
 
 Beeline will ask you for a username and password. In non-secure mode, simply enter the username on
 your machine and a blank password. For secure mode, please follow the instructions given in the
-[beeline documentation](https://cwiki.apache.org/confluence/display/Hive/HiveServer2+Clients)
+[beeline documentation](https://cwiki.apache.org/confluence/display/Hive/HiveServer2+Clients).
 
 Configuration of Hive is done by placing your `hive-site.xml` file in `conf/`.
 
@@ -616,11 +615,10 @@ In Shark, default reducer number is 1 and is controlled by the property `mapred.
 SQL deprecates this property by a new property `spark.sql.shuffle.partitions`, whose default value
 is 200. Users may customize this property via `SET`:
 
-```
-SET spark.sql.shuffle.partitions=10;
-SELECT page, count(*) c FROM logs_last_month_cached
-GROUP BY page ORDER BY c DESC LIMIT 10;
-```
+    SET spark.sql.shuffle.partitions=10;
+    SELECT page, count(*) c 
+    FROM logs_last_month_cached
+    GROUP BY page ORDER BY c DESC LIMIT 10;
 
 You may also put this property in `hive-site.xml` to override the default value.
 
@@ -630,22 +628,18 @@ For now, the `mapred.reduce.tasks` property is still recognized, and is converte
 #### Caching
 
 The `shark.cache` table property no longer exists, and tables whose name end with `_cached` are no
-longer automcatically cached. Instead, we provide `CACHE TABLE` and `UNCACHE TABLE` statements to
+longer automatically cached. Instead, we provide `CACHE TABLE` and `UNCACHE TABLE` statements to
 let user control table caching explicitly:
 
-```
-CACHE TABLE logs_last_month;
-UNCACHE TABLE logs_last_month;
-```
+    CACHE TABLE logs_last_month;
+    UNCACHE TABLE logs_last_month;
 
-**NOTE** `CACHE TABLE tbl` is lazy, it only marks table `tbl` as "need to by cached if necessary",
+**NOTE:** `CACHE TABLE tbl` is lazy, it only marks table `tbl` as "need to by cached if necessary",
 but doesn't actually cache it until a query that touches `tbl` is executed. To force the table to be
 cached, you may simply count the table immediately after executing `CACHE TABLE`:
 
-```
-CACHE TABLE logs_last_month;
-SELECT COUNT(1) FROM logs_last_month;
-```
+    CACHE TABLE logs_last_month;
+    SELECT COUNT(1) FROM logs_last_month;
 
 Several caching related features are not supported yet:
 
@@ -655,7 +649,7 @@ Several caching related features are not supported yet:
 
 ### Compatibility with Apache Hive
 
-#### Deploying in Exising Hive Warehouses
+#### Deploying in Existing Hive Warehouses
 
 Spark SQL Thrift JDBC server is designed to be "out of the box" compatible with existing Hive
 installations. You do not need to modify your existing Hive Metastore or change the data placement
@@ -666,50 +660,50 @@ or partitioning of your tables.
 Spark SQL supports the vast majority of Hive features, such as:
 
 * Hive query statements, including:
- * `SELECT`
- * `GROUP BY
- * `ORDER BY`
- * `CLUSTER BY`
- * `SORT BY`
+  * `SELECT`
+  * `GROUP BY`
+  * `ORDER BY`
+  * `CLUSTER BY`
+  * `SORT BY`
 * All Hive operators, including:
- * Relational operators (`=`, `⇔`, `==`, `<>`, `<`, `>`, `>=`, `<=`, etc)
- * Arthimatic operators (`+`, `-`, `*`, `/`, `%`, etc)
- * Logical operators (`AND`, `&&`, `OR`, `||`, etc)
- * Complex type constructors
- * Mathemtatical functions (`sign`, `ln`, `cos`, etc)
- * String functions (`instr`, `length`, `printf`, etc)
+  * Relational operators (`=`, `⇔`, `==`, `<>`, `<`, `>`, `>=`, `<=`, etc)
+  * Arithmetic operators (`+`, `-`, `*`, `/`, `%`, etc)
+  * Logical operators (`AND`, `&&`, `OR`, `||`, etc)
+  * Complex type constructors
+  * Mathematical functions (`sign`, `ln`, `cos`, etc)
+  * String functions (`instr`, `length`, `printf`, etc)
 * User defined functions (UDF)
 * User defined aggregation functions (UDAF)
-* User defined serialization formats (SerDe's)
+* User defined serialization formats (SerDes)
 * Joins
- * `JOIN`
- * `{LEFT|RIGHT|FULL} OUTER JOIN`
- * `LEFT SEMI JOIN`
- * `CROSS JOIN`
+  * `JOIN`
+  * `{LEFT|RIGHT|FULL} OUTER JOIN`
+  * `LEFT SEMI JOIN`
+  * `CROSS JOIN`
 * Unions
-* Sub queries
- * `SELECT col FROM ( SELECT a + b AS col from t1) t2`
+* Sub-queries
+  * `SELECT col FROM ( SELECT a + b AS col from t1) t2`
 * Sampling
 * Explain
 * Partitioned tables
 * All Hive DDL Functions, including:
- * `CREATE TABLE`
- * `CREATE TABLE AS SELECT`
- * `ALTER TABLE`
+  * `CREATE TABLE`
+  * `CREATE TABLE AS SELECT`
+  * `ALTER TABLE`
 * Most Hive Data types, including:
- * `TINYINT`
- * `SMALLINT`
- * `INT`
- * `BIGINT`
- * `BOOLEAN`
- * `FLOAT`
- * `DOUBLE`
- * `STRING`
- * `BINARY`
- * `TIMESTAMP`
- * `ARRAY<>`
- * `MAP<>`
- * `STRUCT<>`
+  * `TINYINT`
+  * `SMALLINT`
+  * `INT`
+  * `BIGINT`
+  * `BOOLEAN`
+  * `FLOAT`
+  * `DOUBLE`
+  * `STRING`
+  * `BINARY`
+  * `TIMESTAMP`
+  * `ARRAY<>`
+  * `MAP<>`
+  * `STRUCT<>`
 
 #### Unsupported Hive Functionality
 
@@ -749,8 +743,7 @@ releases of Spark SQL.
   Hive automatically converts the join into a map join. We are adding this auto conversion in the
   next release.
 * Automatically determine the number of reducers for joins and groupbys: Currently in Spark SQL, you
-  need to control the degree of parallelism post-shuffle using "SET
-  spark.sql.shuffle.partitions=[num_tasks];". We are going to add auto-setting of parallelism in the
+  need to control the degree of parallelism post-shuffle using "`SET spark.sql.shuffle.partitions=[num_tasks];`". We are going to add auto-setting of parallelism in the
   next release.
 * Meta-data only query: For queries that can be answered by using only meta data, Spark SQL still
   launches tasks to compute the result.

From 2f1519defaba4f3c7d536669f909bfd9e13e4069 Mon Sep 17 00:00:00 2001
From: William Benton <willb@redhat.com>
Date: Fri, 29 Aug 2014 15:26:59 -0700
Subject: [PATCH 143/489] SPARK-2813:  [SQL] Implement SQRT() directly in Spark
 SQL

This PR adds a native implementation for SQL SQRT() and thus avoids delegating this function to Hive.

Author: William Benton <willb@redhat.com>

Closes #1750 from willb/spark-2813 and squashes the following commits:

22c8a79 [William Benton] Fixed missed newline from rebase
d673861 [William Benton] Added string coercions for SQRT and associated test case
e125df4 [William Benton] Added ExpressionEvaluationSuite test cases for SQRT
7b84bcd [William Benton] SQL SQRT now properly returns NULL for NULL inputs
8256971 [William Benton] added SQRT test to SqlQuerySuite
504d2e5 [William Benton] Added native SQRT implementation
---
 .../org/apache/spark/sql/catalyst/SqlParser.scala  |  2 ++
 .../sql/catalyst/analysis/HiveTypeCoercion.scala   |  2 ++
 .../sql/catalyst/expressions/arithmetic.scala      | 13 +++++++++++++
 .../expressions/ExpressionEvaluationSuite.scala    | 13 +++++++++++++
 .../scala/org/apache/spark/sql/SQLQuerySuite.scala | 14 ++++++++++++++
 .../scala/org/apache/spark/sql/hive/HiveQl.scala   |  2 ++
 6 files changed, 46 insertions(+)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
index 2c73a80f64ebf..4f166c06b6997 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
@@ -122,6 +122,7 @@ class SqlParser extends StandardTokenParsers with PackratParsers {
   protected val EXCEPT = Keyword("EXCEPT")
   protected val SUBSTR = Keyword("SUBSTR")
   protected val SUBSTRING = Keyword("SUBSTRING")
+  protected val SQRT = Keyword("SQRT")
 
   // Use reflection to find the reserved words defined in this class.
   protected val reservedWords =
@@ -323,6 +324,7 @@ class SqlParser extends StandardTokenParsers with PackratParsers {
     (SUBSTR | SUBSTRING) ~> "(" ~> expression ~ "," ~ expression ~ "," ~ expression <~ ")" ^^ {
       case s ~ "," ~ p ~ "," ~ l => Substring(s,p,l)
     } |
+    SQRT ~> "(" ~> expression <~ ")" ^^ { case exp => Sqrt(exp) } |
     ident ~ "(" ~ repsep(expression, ",") <~ ")" ^^ {
       case udfName ~ _ ~ exprs => UnresolvedFunction(udfName, exprs)
     }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
index 15eb5982a4a91..ecfcd62d2063f 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
@@ -227,6 +227,8 @@ trait HiveTypeCoercion {
         Sum(Cast(e, DoubleType))
       case Average(e) if e.dataType == StringType =>
         Average(Cast(e, DoubleType))
+      case Sqrt(e) if e.dataType == StringType =>
+        Sqrt(Cast(e, DoubleType))
     }
   }
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
index aae86a3628be1..56f042891a2e6 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
@@ -33,6 +33,19 @@ case class UnaryMinus(child: Expression) extends UnaryExpression {
   }
 }
 
+case class Sqrt(child: Expression) extends UnaryExpression {
+  type EvaluatedType = Any
+  
+  def dataType = child.dataType
+  override def foldable = child.foldable
+  def nullable = child.nullable
+  override def toString = s"SQRT($child)"
+
+  override def eval(input: Row): Any = {
+    n1(child, input, ((na,a) => math.sqrt(na.toDouble(a))))
+  }
+}
+
 abstract class BinaryArithmetic extends BinaryExpression {
   self: Product =>
 
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala
index f1df817c41362..b961346dfc995 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala
@@ -577,4 +577,17 @@ class ExpressionEvaluationSuite extends FunSuite {
     checkEvaluation(s.substring(0, 2), "ex", row)
     checkEvaluation(s.substring(0), "example", row)
   }
+
+  test("SQRT") {
+    val inputSequence = (1 to (1<<24) by 511).map(_ * (1L<<24))
+    val expectedResults = inputSequence.map(l => math.sqrt(l.toDouble))
+    val rowSequence = inputSequence.map(l => new GenericRow(Array[Any](l.toDouble)))
+    val d = 'a.double.at(0)
+    
+    for ((row, expected) <- rowSequence zip expectedResults) {
+      checkEvaluation(Sqrt(d), expected, row)
+    }
+
+    checkEvaluation(Sqrt(Literal(null, DoubleType)), null, new GenericRow(Array[Any](null)))
+  }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
index 9b2a36d33fca7..4047bc0672bbb 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
@@ -34,6 +34,20 @@ class SQLQuerySuite extends QueryTest {
       "test")
   }
 
+  test("SQRT") {
+    checkAnswer(
+      sql("SELECT SQRT(key) FROM testData"),
+      (1 to 100).map(x => Row(math.sqrt(x.toDouble))).toSeq
+    )
+  }
+  
+  test("SQRT with automatic string casts") {
+    checkAnswer(
+      sql("SELECT SQRT(CAST(key AS STRING)) FROM testData"),
+      (1 to 100).map(x => Row(math.sqrt(x.toDouble))).toSeq
+    )
+  }
+  
   test("SPARK-2407 Added Parser of SQL SUBSTR()") {
     checkAnswer(
       sql("SELECT substr(tableName, 1, 2) FROM tableName"),
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
index fa3adfdf5855c..a4dd6be5f9e35 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
@@ -889,6 +889,7 @@ private[hive] object HiveQl {
   val WHEN = "(?i)WHEN".r
   val CASE = "(?i)CASE".r
   val SUBSTR = "(?i)SUBSTR(?:ING)?".r
+  val SQRT = "(?i)SQRT".r
 
   protected def nodeToExpr(node: Node): Expression = node match {
     /* Attribute References */
@@ -958,6 +959,7 @@ private[hive] object HiveQl {
     case Token(DIV(), left :: right:: Nil) =>
       Cast(Divide(nodeToExpr(left), nodeToExpr(right)), LongType)
     case Token("%", left :: right:: Nil) => Remainder(nodeToExpr(left), nodeToExpr(right))
+    case Token("TOK_FUNCTION", Token(SQRT(), Nil) :: arg :: Nil) => Sqrt(nodeToExpr(arg))
 
     /* Comparisons */
     case Token("=", left :: right:: Nil) => EqualTo(nodeToExpr(left), nodeToExpr(right))

From 287c0ac7722dd4bc51b921ccc6f0e3c1625b5ff4 Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian.cs.zju@gmail.com>
Date: Fri, 29 Aug 2014 15:29:43 -0700
Subject: [PATCH 144/489] [SPARK-3234][Build] Fixed environment variables that
 rely on deprecated command line options in make-distribution.sh

Please refer to [SPARK-3234](https://issues.apache.org/jira/browse/SPARK-3234) for details.

Author: Cheng Lian <lian.cs.zju@gmail.com>

Closes #2208 from liancheng/spark-3234 and squashes the following commits:

fb26de8 [Cheng Lian] Fixed SPARK-3234
---
 make-distribution.sh | 12 +++++++++++-
 1 file changed, 11 insertions(+), 1 deletion(-)

diff --git a/make-distribution.sh b/make-distribution.sh
index f7a6a9d838bb6..ee1399071112d 100755
--- a/make-distribution.sh
+++ b/make-distribution.sh
@@ -113,7 +113,17 @@ if ! which mvn &>/dev/null; then
     echo -e "Download Maven from https://maven.apache.org/"
     exit -1;
 fi
+
 VERSION=$(mvn help:evaluate -Dexpression=project.version 2>/dev/null | grep -v "INFO" | tail -n 1)
+SPARK_HADOOP_VERSION=$(mvn help:evaluate -Dexpression=hadoop.version $@ 2>/dev/null\
+    | grep -v "INFO"\
+    | tail -n 1)
+SPARK_HIVE=$(mvn help:evaluate -Dexpression=project.activeProfiles $@ 2>/dev/null\
+    | grep -v "INFO"\
+    | fgrep --count "<id>hive</id>";\
+    # Reset exit status to 0, otherwise the script stops here if the last grep finds nothing\
+    # because we use "set -o pipefail"
+    echo -n)
 
 JAVA_CMD="$JAVA_HOME"/bin/java
 JAVA_VERSION=$("$JAVA_CMD" -version 2>&1)
@@ -175,7 +185,7 @@ cp "$FWDIR"/examples/target/scala*/spark-examples*.jar "$DISTDIR/lib/"
 mkdir -p "$DISTDIR/examples/src/main"
 cp -r "$FWDIR"/examples/src/main "$DISTDIR/examples/src/"
 
-if [ "$SPARK_HIVE" == "true" ]; then
+if [ "$SPARK_HIVE" == "1" ]; then
   cp "$FWDIR"/lib_managed/jars/datanucleus*.jar "$DISTDIR/lib/"
 fi
 

From dc4d577c6549df58f42c0e22cac354554d169896 Mon Sep 17 00:00:00 2001
From: Cheng Hao <hao.cheng@intel.com>
Date: Fri, 29 Aug 2014 15:32:26 -0700
Subject: [PATCH 145/489] [SPARK-3198] [SQL] Remove the TreeNode.id

Thus id property of the TreeNode API does save time in a faster way to compare 2 TreeNodes, it is kind of performance bottleneck during the expression object creation in a multi-threading env (because of the memory barrier).
Fortunately, the tree node comparison only happen once in master, so even we remove it, the entire performance will not be affected.

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

Closes #2155 from chenghao-intel/treenode and squashes the following commits:

7cf2cd2 [Cheng Hao] Remove the implicit keyword for TreeNodeRef and some other small issues
5873415 [Cheng Hao] Remove the TreeNode.id
---
 .../sql/catalyst/planning/patterns.scala      | 11 +++++----
 .../spark/sql/catalyst/plans/QueryPlan.scala  | 12 +++++-----
 .../spark/sql/catalyst/trees/TreeNode.scala   | 24 ++-----------------
 .../spark/sql/catalyst/trees/package.scala    | 11 +++++++++
 .../sql/catalyst/trees/TreeNodeSuite.scala    |  5 +++-
 .../sql/execution/GeneratedAggregate.scala    | 10 ++++----
 .../spark/sql/execution/debug/package.scala   |  7 +++---
 .../spark/sql/execution/pythonUdfs.scala      |  2 +-
 8 files changed, 40 insertions(+), 42 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala
index 90923fe31a063..f0fd9a8b9a46e 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala
@@ -19,8 +19,9 @@ package org.apache.spark.sql.catalyst.planning
 
 import scala.annotation.tailrec
 
-import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.Logging
+import org.apache.spark.sql.catalyst.trees.TreeNodeRef
+import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans._
 import org.apache.spark.sql.catalyst.plans.logical._
 
@@ -134,8 +135,8 @@ object PartialAggregation {
       // Only do partial aggregation if supported by all aggregate expressions.
       if (allAggregates.size == partialAggregates.size) {
         // Create a map of expressions to their partial evaluations for all aggregate expressions.
-        val partialEvaluations: Map[Long, SplitEvaluation] =
-          partialAggregates.map(a => (a.id, a.asPartial)).toMap
+        val partialEvaluations: Map[TreeNodeRef, SplitEvaluation] =
+          partialAggregates.map(a => (new TreeNodeRef(a), a.asPartial)).toMap
 
         // We need to pass all grouping expressions though so the grouping can happen a second
         // time. However some of them might be unnamed so we alias them allowing them to be
@@ -148,8 +149,8 @@ object PartialAggregation {
         // Replace aggregations with a new expression that computes the result from the already
         // computed partial evaluations and grouping values.
         val rewrittenAggregateExpressions = aggregateExpressions.map(_.transformUp {
-          case e: Expression if partialEvaluations.contains(e.id) =>
-            partialEvaluations(e.id).finalEvaluation
+          case e: Expression if partialEvaluations.contains(new TreeNodeRef(e)) =>
+            partialEvaluations(new TreeNodeRef(e)).finalEvaluation
           case e: Expression if namedGroupingExpressions.contains(e) =>
             namedGroupingExpressions(e).toAttribute
         }).asInstanceOf[Seq[NamedExpression]]
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/QueryPlan.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/QueryPlan.scala
index 1e177e28f80b3..af9e4d86e995a 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/QueryPlan.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/QueryPlan.scala
@@ -50,11 +50,11 @@ abstract class QueryPlan[PlanType <: TreeNode[PlanType]] extends TreeNode[PlanTy
 
     @inline def transformExpressionDown(e: Expression) = {
       val newE = e.transformDown(rule)
-      if (newE.id != e.id && newE != e) {
+      if (newE.fastEquals(e)) {
+        e
+      } else {
         changed = true
         newE
-      } else {
-        e
       }
     }
 
@@ -82,11 +82,11 @@ abstract class QueryPlan[PlanType <: TreeNode[PlanType]] extends TreeNode[PlanTy
 
     @inline def transformExpressionUp(e: Expression) = {
       val newE = e.transformUp(rule)
-      if (newE.id != e.id && newE != e) {
+      if (newE.fastEquals(e)) {
+        e
+      } else {
         changed = true
         newE
-      } else {
-        e
       }
     }
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/trees/TreeNode.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/trees/TreeNode.scala
index 96ce35939e2cc..2013ae4f7bd13 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/trees/TreeNode.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/trees/TreeNode.scala
@@ -19,11 +19,6 @@ package org.apache.spark.sql.catalyst.trees
 
 import org.apache.spark.sql.catalyst.errors._
 
-object TreeNode {
-  private val currentId = new java.util.concurrent.atomic.AtomicLong
-  protected def nextId() = currentId.getAndIncrement()
-}
-
 /** Used by [[TreeNode.getNodeNumbered]] when traversing the tree for a given number */
 private class MutableInt(var i: Int)
 
@@ -33,29 +28,13 @@ abstract class TreeNode[BaseType <: TreeNode[BaseType]] {
   /** Returns a Seq of the children of this node */
   def children: Seq[BaseType]
 
-  /**
-   * A globally unique id for this specific instance. Not preserved across copies.
-   * Unlike `equals`, `id` can be used to differentiate distinct but structurally
-   * identical branches of a tree.
-   */
-  val id = TreeNode.nextId()
-
-  /**
-   * Returns true if other is the same [[catalyst.trees.TreeNode TreeNode]] instance.  Unlike
-   * `equals` this function will return false for different instances of structurally identical
-   * trees.
-   */
-  def sameInstance(other: TreeNode[_]): Boolean = {
-    this.id == other.id
-  }
-
   /**
    * Faster version of equality which short-circuits when two treeNodes are the same instance.
    * We don't just override Object.Equals, as doing so prevents the scala compiler from from
    * generating case class `equals` methods
    */
   def fastEquals(other: TreeNode[_]): Boolean = {
-    sameInstance(other) || this == other
+    this.eq(other) || this == other
   }
 
   /**
@@ -393,3 +372,4 @@ trait UnaryNode[BaseType <: TreeNode[BaseType]] {
   def child: BaseType
   def children = child :: Nil
 }
+
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/trees/package.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/trees/package.scala
index d725a92c06f7b..79a8e06d4b4d4 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/trees/package.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/trees/package.scala
@@ -37,4 +37,15 @@ package object trees extends Logging {
   // Since we want tree nodes to be lightweight, we create one logger for all treenode instances.
   protected override def logName = "catalyst.trees"
 
+  /**
+   * A [[TreeNode]] companion for reference equality for Hash based Collection.
+   */
+  class TreeNodeRef(val obj: TreeNode[_]) {
+    override def equals(o: Any) = o match {
+      case that: TreeNodeRef => that.obj.eq(obj)
+      case _ => false
+    }
+
+    override def hashCode = if (obj == null) 0 else obj.hashCode
+  }
 }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/trees/TreeNodeSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/trees/TreeNodeSuite.scala
index 296202543e2ca..036fd3fa1d6a1 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/trees/TreeNodeSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/trees/TreeNodeSuite.scala
@@ -51,7 +51,10 @@ class TreeNodeSuite extends FunSuite {
     val after = before transform { case Literal(5, _) => Literal(1)}
 
     assert(before === after)
-    assert(before.map(_.id) === after.map(_.id))
+    // Ensure that the objects after are the same objects before the transformation.
+    before.map(identity[Expression]).zip(after.map(identity[Expression])).foreach {
+      case (b, a) => assert(b eq a)
+    }
   }
 
   test("collect") {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala
index 31ad5e8aabb0e..b3edd5020fa8c 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala
@@ -19,6 +19,7 @@ package org.apache.spark.sql.execution
 
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.sql.SQLContext
+import org.apache.spark.sql.catalyst.trees._
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.physical._
 import org.apache.spark.sql.catalyst.types._
@@ -141,9 +142,10 @@ case class GeneratedAggregate(
 
     val computationSchema = computeFunctions.flatMap(_.schema)
 
-    val resultMap: Map[Long, Expression] = aggregatesToCompute.zip(computeFunctions).map {
-      case (agg, func) => agg.id -> func.result
-    }.toMap
+    val resultMap: Map[TreeNodeRef, Expression] = 
+      aggregatesToCompute.zip(computeFunctions).map {
+        case (agg, func) => new TreeNodeRef(agg) -> func.result
+      }.toMap
 
     val namedGroups = groupingExpressions.zipWithIndex.map {
       case (ne: NamedExpression, _) => (ne, ne)
@@ -156,7 +158,7 @@ case class GeneratedAggregate(
     // The set of expressions that produce the final output given the aggregation buffer and the
     // grouping expressions.
     val resultExpressions = aggregateExpressions.map(_.transform {
-      case e: Expression if resultMap.contains(e.id) => resultMap(e.id)
+      case e: Expression if resultMap.contains(new TreeNodeRef(e)) => resultMap(new TreeNodeRef(e))
       case e: Expression if groupMap.contains(e) => groupMap(e)
     })
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/debug/package.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/debug/package.scala
index 5b896c55b7393..8ff757bbe3508 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/debug/package.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/debug/package.scala
@@ -23,6 +23,7 @@ import org.apache.spark.{AccumulatorParam, Accumulator, SparkContext}
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.SparkContext._
 import org.apache.spark.sql.{SchemaRDD, Row}
+import org.apache.spark.sql.catalyst.trees.TreeNodeRef
 
 /**
  * :: DeveloperApi ::
@@ -43,10 +44,10 @@ package object debug {
   implicit class DebugQuery(query: SchemaRDD) {
     def debug(): Unit = {
       val plan = query.queryExecution.executedPlan
-      val visited = new collection.mutable.HashSet[Long]()
+      val visited = new collection.mutable.HashSet[TreeNodeRef]()
       val debugPlan = plan transform {
-        case s: SparkPlan if !visited.contains(s.id) =>
-          visited += s.id
+        case s: SparkPlan if !visited.contains(new TreeNodeRef(s)) =>
+          visited += new TreeNodeRef(s)
           DebugNode(s)
       }
       println(s"Results returned: ${debugPlan.execute().count()}")
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUdfs.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUdfs.scala
index aef6ebf86b1eb..3dc8be2456781 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUdfs.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUdfs.scala
@@ -98,7 +98,7 @@ private[spark] object ExtractPythonUdfs extends Rule[LogicalPlan] {
         logical.Project(
           l.output,
           l.transformExpressions {
-            case p: PythonUDF if p.id == udf.id => evaluation.resultAttribute
+            case p: PythonUDF if p.fastEquals(udf) => evaluation.resultAttribute
           }.withNewChildren(newChildren))
       }
   }

From b1eccfc88a13b937d42bbae8a0c3f09cffc5ae47 Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian.cs.zju@gmail.com>
Date: Fri, 29 Aug 2014 15:34:59 -0700
Subject: [PATCH 146/489] [SQL] Turns on in-memory columnar compression in
 HiveCompatibilitySuite

`HiveCompatibilitySuite` already turns on in-memory columnar caching, it would be good to also enable compression to improve test coverage.

Author: Cheng Lian <lian.cs.zju@gmail.com>

Closes #2190 from liancheng/compression-on and squashes the following commits:

88b536c [Cheng Lian] Code cleanup, narrowed field visibility
d13efd2 [Cheng Lian] Turns on in-memory columnar compression in HiveCompatibilitySuite
---
 .../hive/execution/HiveCompatibilitySuite.scala | 17 ++++++++++-------
 1 file changed, 10 insertions(+), 7 deletions(-)

diff --git a/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala b/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
index 035fd3214bd1d..b589994bd25fa 100644
--- a/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
+++ b/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
@@ -22,6 +22,7 @@ import java.util.{Locale, TimeZone}
 
 import org.scalatest.BeforeAndAfter
 
+import org.apache.spark.sql.SQLConf
 import org.apache.spark.sql.hive.test.TestHive
 
 /**
@@ -29,29 +30,31 @@ import org.apache.spark.sql.hive.test.TestHive
  */
 class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
   // TODO: bundle in jar files... get from classpath
-  lazy val hiveQueryDir = TestHive.getHiveFile("ql" + File.separator + "src" +
-    File.separator + "test" + File.separator + "queries" + File.separator + "clientpositive")
+  private lazy val hiveQueryDir = TestHive.getHiveFile(
+    "ql/src/test/queries/clientpositive".split("/").mkString(File.separator))
 
-  var originalTimeZone: TimeZone = _
-  var originalLocale: Locale = _
+  private val originalTimeZone = TimeZone.getDefault
+  private val originalLocale = Locale.getDefault
+  private val originalUseCompression = TestHive.useCompression
 
   def testCases = hiveQueryDir.listFiles.map(f => f.getName.stripSuffix(".q") -> f)
 
   override def beforeAll() {
+    // Enable in-memory columnar caching
     TestHive.cacheTables = true
     // Timezone is fixed to America/Los_Angeles for those timezone sensitive tests (timestamp_*)
-    originalTimeZone = TimeZone.getDefault
     TimeZone.setDefault(TimeZone.getTimeZone("America/Los_Angeles"))
-    
     // Add Locale setting
-    originalLocale = Locale.getDefault
     Locale.setDefault(Locale.US)
+    // Enable in-memory columnar compression
+    TestHive.setConf(SQLConf.COMPRESS_CACHED, "true")
   }
 
   override def afterAll() {
     TestHive.cacheTables = false
     TimeZone.setDefault(originalTimeZone)
     Locale.setDefault(originalLocale)
+    TestHive.setConf(SQLConf.COMPRESS_CACHED, originalUseCompression.toString)
   }
 
   /** A list of tests deemed out of scope currently and thus completely disregarded. */

From d94a44d7caaf3fe7559d9ad7b10872fa16cf81ca Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian.cs.zju@gmail.com>
Date: Fri, 29 Aug 2014 15:36:04 -0700
Subject: [PATCH 147/489] [SPARK-3269][SQL] Decreases initial buffer size for
 row set to prevent OOM

When a large batch size is specified, `SparkSQLOperationManager` OOMs even if the whole result set is much smaller than the batch size.

Author: Cheng Lian <lian.cs.zju@gmail.com>

Closes #2171 from liancheng/jdbc-fetch-size and squashes the following commits:

5e1623b [Cheng Lian] Decreases initial buffer size for row set to prevent OOM
---
 .../hive/thriftserver/server/SparkSQLOperationManager.scala  | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/server/SparkSQLOperationManager.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/server/SparkSQLOperationManager.scala
index 6eccb1ba6d4dc..f12b5a69a09f7 100644
--- a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/server/SparkSQLOperationManager.scala
+++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/server/SparkSQLOperationManager.scala
@@ -66,9 +66,10 @@ class SparkSQLOperationManager(hiveContext: HiveContext) extends OperationManage
         if (!iter.hasNext) {
           new RowSet()
         } else {
-          val maxRows = maxRowsL.toInt // Do you really want a row batch larger than Int Max? No.
+          // maxRowsL here typically maps to java.sql.Statement.getFetchSize, which is an int
+          val maxRows = maxRowsL.toInt
           var curRow = 0
-          var rowSet = new ArrayBuffer[Row](maxRows)
+          var rowSet = new ArrayBuffer[Row](maxRows.min(1024))
 
           while (curRow < maxRows && iter.hasNext) {
             val sparkRow = iter.next()

From 634d04b87c2744d645e9c26e746ba2006371d9b5 Mon Sep 17 00:00:00 2001
From: "qiping.lqp" <qiping.lqp@alibaba-inc.com>
Date: Fri, 29 Aug 2014 15:37:43 -0700
Subject: [PATCH 148/489] [SPARK-3291][SQL]TestcaseName in createQueryTest
 should not contain ":"

":" is not allowed to appear in a file name of Windows system. If file name contains ":", this file can't be checked out in a Windows system and developers using Windows must be careful to not commit the deletion of such files, Which is very inconvenient.

Author: qiping.lqp <qiping.lqp@alibaba-inc.com>

Closes #2191 from chouqin/querytest and squashes the following commits:

0e943a1 [qiping.lqp] rename golden file
60a863f [qiping.lqp] TestcaseName in createQueryTest should not contain ":"
---
 ...y when query Hive table-0-5d14d21a239daa42b086cc895215009a} | 0
 .../apache/spark/sql/hive/execution/HiveComparisonTest.scala   | 3 +++
 .../org/apache/spark/sql/hive/execution/HiveQuerySuite.scala   | 2 +-
 3 files changed, 4 insertions(+), 1 deletion(-)
 rename sql/hive/src/test/resources/golden/{case sensitivity: Hive table-0-5d14d21a239daa42b086cc895215009a => case sensitivity when query Hive table-0-5d14d21a239daa42b086cc895215009a} (100%)

diff --git a/sql/hive/src/test/resources/golden/case sensitivity: Hive table-0-5d14d21a239daa42b086cc895215009a b/sql/hive/src/test/resources/golden/case sensitivity when query Hive table-0-5d14d21a239daa42b086cc895215009a
similarity index 100%
rename from sql/hive/src/test/resources/golden/case sensitivity: Hive table-0-5d14d21a239daa42b086cc895215009a
rename to sql/hive/src/test/resources/golden/case sensitivity when query Hive table-0-5d14d21a239daa42b086cc895215009a
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala
index 502ce8fb297e9..671c3b162f875 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala
@@ -195,6 +195,9 @@ abstract class HiveComparisonTest
 
   val installHooksCommand = "(?i)SET.*hooks".r
   def createQueryTest(testCaseName: String, sql: String, reset: Boolean = true) {
+    // testCaseName must not contain ':', which is not allowed to appear in a filename of Windows
+    assert(!testCaseName.contains(":"))
+
     // If test sharding is enable, skip tests that are not in the correct shard.
     shardInfo.foreach {
       case (shardId, numShards) if testCaseName.hashCode % numShards != shardId => return
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
index 6d925e56e6838..c4abb3eb4861f 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
@@ -309,7 +309,7 @@ class HiveQuerySuite extends HiveComparisonTest {
     }
   }
 
-  createQueryTest("case sensitivity: Hive table",
+  createQueryTest("case sensitivity when query Hive table",
     "SELECT srcalias.KEY, SRCALIAS.value FROM sRc SrCAlias WHERE SrCAlias.kEy < 15")
 
   test("case sensitivity: registered table") {

From 98ddbe6cdbe4141df3d008dcb675ecd682c97492 Mon Sep 17 00:00:00 2001
From: Zdenek Farana <zdenek.farana@gmail.com>
Date: Fri, 29 Aug 2014 15:39:15 -0700
Subject: [PATCH 149/489]  [SPARK-3173][SQL] Timestamp support in the parser

If you have a table with TIMESTAMP column, that column can't be used in WHERE clause properly - it is not evaluated properly. [More](https://issues.apache.org/jira/browse/SPARK-3173)

Motivation: http://www.aproint.com/aggregation-with-spark-sql/

- [x] modify SqlParser so it supports casting to TIMESTAMP (workaround for item 2)
- [x] the string literal should be converted into Timestamp if the column is Timestamp.

Author: Zdenek Farana <zdenek.farana@gmail.com>
Author: Zdenek Farana <zdenek.farana@aproint.com>

Closes #2084 from byF/SPARK-3173 and squashes the following commits:

442b59d [Zdenek Farana] Fixed test merge conflict
2dbf4f6 [Zdenek Farana] Merge remote-tracking branch 'origin/SPARK-3173' into SPARK-3173
65b6215 [Zdenek Farana] Fixed timezone sensitivity in the test
47b27b4 [Zdenek Farana] Now works in the case of "StringLiteral=TimestampColumn"
96a661b [Zdenek Farana] Code style change
491dfcf [Zdenek Farana] Added test cases for SPARK-3173
4446b1e [Zdenek Farana] A string literal is casted into Timestamp when the column is Timestamp.
59af397 [Zdenek Farana] Added a new TIMESTAMP keyword; CAST to TIMESTAMP now can be used in SQL expression.
---
 .../apache/spark/sql/catalyst/SqlParser.scala |  3 +-
 .../catalyst/analysis/HiveTypeCoercion.scala  | 10 +++++
 .../org/apache/spark/sql/SQLQuerySuite.scala  | 43 ++++++++++++++++++-
 3 files changed, 54 insertions(+), 2 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
index 4f166c06b6997..a88bd859fc85e 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
@@ -114,6 +114,7 @@ class SqlParser extends StandardTokenParsers with PackratParsers {
   protected val STRING = Keyword("STRING")
   protected val SUM = Keyword("SUM")
   protected val TABLE = Keyword("TABLE")
+  protected val TIMESTAMP = Keyword("TIMESTAMP")
   protected val TRUE = Keyword("TRUE")
   protected val UNCACHE = Keyword("UNCACHE")
   protected val UNION = Keyword("UNION")
@@ -359,7 +360,7 @@ class SqlParser extends StandardTokenParsers with PackratParsers {
     literal
 
   protected lazy val dataType: Parser[DataType] =
-    STRING ^^^ StringType
+    STRING ^^^ StringType | TIMESTAMP ^^^ TimestampType
 }
 
 class SqlLexical(val keywords: Seq[String]) extends StdLexical {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
index ecfcd62d2063f..d6758eb5b6a32 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
@@ -218,11 +218,21 @@ trait HiveTypeCoercion {
       case a: BinaryArithmetic if a.right.dataType == StringType =>
         a.makeCopy(Array(a.left, Cast(a.right, DoubleType)))
 
+      case p: BinaryPredicate if p.left.dataType == StringType
+        && p.right.dataType == TimestampType =>
+        p.makeCopy(Array(Cast(p.left, TimestampType), p.right))
+      case p: BinaryPredicate if p.left.dataType == TimestampType
+        && p.right.dataType == StringType =>
+        p.makeCopy(Array(p.left, Cast(p.right, TimestampType)))
+
       case p: BinaryPredicate if p.left.dataType == StringType && p.right.dataType != StringType =>
         p.makeCopy(Array(Cast(p.left, DoubleType), p.right))
       case p: BinaryPredicate if p.left.dataType != StringType && p.right.dataType == StringType =>
         p.makeCopy(Array(p.left, Cast(p.right, DoubleType)))
 
+      case i @ In(a,b) if a.dataType == TimestampType && b.forall(_.dataType == StringType) =>
+        i.makeCopy(Array(a,b.map(Cast(_,TimestampType))))
+
       case Sum(e) if e.dataType == StringType =>
         Sum(Cast(e, DoubleType))
       case Average(e) if e.dataType == StringType =>
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
index 4047bc0672bbb..1ac205937714c 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
@@ -19,15 +19,28 @@ package org.apache.spark.sql
 
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.test._
+import org.scalatest.BeforeAndAfterAll
+import java.util.TimeZone
 
 /* Implicits */
 import TestSQLContext._
 import TestData._
 
-class SQLQuerySuite extends QueryTest {
+class SQLQuerySuite extends QueryTest with BeforeAndAfterAll {
   // Make sure the tables are loaded.
   TestData
 
+  var origZone: TimeZone = _
+  override protected def beforeAll() {
+    origZone = TimeZone.getDefault
+    TimeZone.setDefault(TimeZone.getTimeZone("UTC"))
+  }
+
+  override protected def afterAll() {
+    TimeZone.setDefault(origZone)
+  }
+
+
   test("SPARK-2041 column name equals tablename") {
     checkAnswer(
       sql("SELECT tableName FROM tableName"),
@@ -63,6 +76,34 @@ class SQLQuerySuite extends QueryTest {
       "st")
   }
 
+  test("SPARK-3173 Timestamp support in the parser") {
+    checkAnswer(sql(
+      "SELECT time FROM timestamps WHERE time=CAST('1970-01-01 00:00:00.001' AS TIMESTAMP)"),
+      Seq(Seq(java.sql.Timestamp.valueOf("1970-01-01 00:00:00.001"))))
+
+    checkAnswer(sql(
+      "SELECT time FROM timestamps WHERE time='1970-01-01 00:00:00.001'"),
+      Seq(Seq(java.sql.Timestamp.valueOf("1970-01-01 00:00:00.001"))))
+
+    checkAnswer(sql(
+      "SELECT time FROM timestamps WHERE '1970-01-01 00:00:00.001'=time"),
+      Seq(Seq(java.sql.Timestamp.valueOf("1970-01-01 00:00:00.001"))))
+
+    checkAnswer(sql(
+      """SELECT time FROM timestamps WHERE time<'1970-01-01 00:00:00.003'
+          AND time>'1970-01-01 00:00:00.001'"""),
+      Seq(Seq(java.sql.Timestamp.valueOf("1970-01-01 00:00:00.002"))))
+
+    checkAnswer(sql(
+      "SELECT time FROM timestamps WHERE time IN ('1970-01-01 00:00:00.001','1970-01-01 00:00:00.002')"),
+      Seq(Seq(java.sql.Timestamp.valueOf("1970-01-01 00:00:00.001")),
+        Seq(java.sql.Timestamp.valueOf("1970-01-01 00:00:00.002"))))
+
+    checkAnswer(sql(
+      "SELECT time FROM timestamps WHERE time='123'"),
+      Nil)
+  }
+
   test("index into array") {
     checkAnswer(
       sql("SELECT data, data[0], data[0] + data[1], data[0 + 1] FROM arrayData"),

From 13901764f4e9ed3de03e420d88ab42bdce5d5140 Mon Sep 17 00:00:00 2001
From: wangfei <wangfei_hello@126.com>
Date: Fri, 29 Aug 2014 17:37:15 -0700
Subject: [PATCH 150/489] [SPARK-3296][mllib] spark-example should be
 run-example in head notation of DenseKMeans and SparseNaiveBayes

`./bin/spark-example`  should be `./bin/run-example` in DenseKMeans and SparseNaiveBayes

Author: wangfei <wangfei_hello@126.com>

Closes #2193 from scwf/run-example and squashes the following commits:

207eb3a [wangfei] spark-example should be run-example
27a8999 [wangfei] ./bin/spark-example should be ./bin/run-example
---
 .../scala/org/apache/spark/examples/mllib/DenseKMeans.scala     | 2 +-
 .../org/apache/spark/examples/mllib/SparseNaiveBayes.scala      | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/DenseKMeans.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/DenseKMeans.scala
index f96bc1bf00b92..89dfa26c2299c 100644
--- a/examples/src/main/scala/org/apache/spark/examples/mllib/DenseKMeans.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/DenseKMeans.scala
@@ -27,7 +27,7 @@ import org.apache.spark.mllib.linalg.Vectors
 /**
  * An example k-means app. Run with
  * {{{
- * ./bin/spark-example org.apache.spark.examples.mllib.DenseKMeans [options] <input>
+ * ./bin/run-example org.apache.spark.examples.mllib.DenseKMeans [options] <input>
  * }}}
  * If you use it as a template to create your own app, please use `spark-submit` to submit your app.
  */
diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/SparseNaiveBayes.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/SparseNaiveBayes.scala
index 88acd9dbb0878..952fa2a5109a4 100644
--- a/examples/src/main/scala/org/apache/spark/examples/mllib/SparseNaiveBayes.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/SparseNaiveBayes.scala
@@ -27,7 +27,7 @@ import org.apache.spark.mllib.util.MLUtils
 /**
  * An example naive Bayes app. Run with
  * {{{
- * ./bin/spark-example org.apache.spark.examples.mllib.SparseNaiveBayes [options] <input>
+ * ./bin/run-example org.apache.spark.examples.mllib.SparseNaiveBayes [options] <input>
  * }}}
  * If you use it as a template to create your own app, please use `spark-submit` to submit your app.
  */

From 32b18dd52cf8920903819f23e406271ecd8ac6bb Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian.cs.zju@gmail.com>
Date: Fri, 29 Aug 2014 18:16:47 -0700
Subject: [PATCH 151/489] [SPARK-3320][SQL] Made batched in-memory column
 buffer building work for SchemaRDDs with empty partitions

Author: Cheng Lian <lian.cs.zju@gmail.com>

Closes #2213 from liancheng/spark-3320 and squashes the following commits:

45a0139 [Cheng Lian] Fixed typo in InMemoryColumnarQuerySuite
f67067d [Cheng Lian] Fixed SPARK-3320
---
 .../columnar/InMemoryColumnarTableScan.scala  | 49 +++++++------------
 .../scala/org/apache/spark/sql/TestData.scala |  5 ++
 .../columnar/InMemoryColumnarQuerySuite.scala | 19 +++++--
 3 files changed, 39 insertions(+), 34 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala
index bc36bacd00b13..cb055cd74a5e5 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala
@@ -104,40 +104,29 @@ private[sql] case class InMemoryColumnarTableScan(
   override def execute() = {
     relation.cachedColumnBuffers.mapPartitions { iterator =>
       // Find the ordinals of the requested columns.  If none are requested, use the first.
-      val requestedColumns =
-        if (attributes.isEmpty) {
-          Seq(0)
-        } else {
-          attributes.map(a => relation.output.indexWhere(_.exprId == a.exprId))
-        }
-
-      new Iterator[Row] {
-        private[this] var columnBuffers: Array[ByteBuffer] = null
-        private[this] var columnAccessors: Seq[ColumnAccessor] = null
-        nextBatch()
-
-        private[this] val nextRow = new GenericMutableRow(columnAccessors.length)
-
-        def nextBatch() = {
-          columnBuffers = iterator.next()
-          columnAccessors = requestedColumns.map(columnBuffers(_)).map(ColumnAccessor(_))
-        }
+      val requestedColumns = if (attributes.isEmpty) {
+        Seq(0)
+      } else {
+        attributes.map(a => relation.output.indexWhere(_.exprId == a.exprId))
+      }
 
-        override def next() = {
-          if (!columnAccessors.head.hasNext) {
-            nextBatch()
-          }
+      iterator
+        .map(batch => requestedColumns.map(batch(_)).map(ColumnAccessor(_)))
+        .flatMap { columnAccessors =>
+          val nextRow = new GenericMutableRow(columnAccessors.length)
+          new Iterator[Row] {
+            override def next() = {
+              var i = 0
+              while (i < nextRow.length) {
+                columnAccessors(i).extractTo(nextRow, i)
+                i += 1
+              }
+              nextRow
+            }
 
-          var i = 0
-          while (i < nextRow.length) {
-            columnAccessors(i).extractTo(nextRow, i)
-            i += 1
+            override def hasNext = columnAccessors.head.hasNext
           }
-          nextRow
         }
-
-        override def hasNext = columnAccessors.head.hasNext || iterator.hasNext
-      }
     }
   }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/TestData.scala b/sql/core/src/test/scala/org/apache/spark/sql/TestData.scala
index c3ec82fb69778..eb33a61c6e811 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/TestData.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/TestData.scala
@@ -151,4 +151,9 @@ object TestData {
     TimestampField(new Timestamp(i))
   })
   timestamps.registerTempTable("timestamps")
+
+  case class IntField(i: Int)
+  // An RDD with 4 elements and 8 partitions
+  val withEmptyParts = TestSQLContext.sparkContext.parallelize((1 to 4).map(IntField), 8)
+  withEmptyParts.registerTempTable("withEmptyParts")
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/InMemoryColumnarQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/InMemoryColumnarQuerySuite.scala
index fdd2799a53268..0e3c67f5eed29 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/InMemoryColumnarQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/InMemoryColumnarQuerySuite.scala
@@ -17,14 +17,13 @@
 
 package org.apache.spark.sql.columnar
 
-import org.apache.spark.sql.{QueryTest, TestData}
 import org.apache.spark.sql.catalyst.expressions.Row
-import org.apache.spark.sql.execution.SparkLogicalPlan
 import org.apache.spark.sql.test.TestSQLContext
+import org.apache.spark.sql.{SQLConf, QueryTest, TestData}
 
 class InMemoryColumnarQuerySuite extends QueryTest {
-  import TestData._
-  import TestSQLContext._
+  import org.apache.spark.sql.TestData._
+  import org.apache.spark.sql.test.TestSQLContext._
 
   test("simple columnar query") {
     val plan = TestSQLContext.executePlan(testData.logicalPlan).executedPlan
@@ -93,4 +92,16 @@ class InMemoryColumnarQuerySuite extends QueryTest {
       sql("SELECT time FROM timestamps"),
       timestamps.collect().toSeq)
   }
+
+  test("SPARK-3320 regression: batched column buffer building should work with empty partitions") {
+    checkAnswer(
+      sql("SELECT * FROM withEmptyParts"),
+      withEmptyParts.collect().toSeq)
+
+    TestSQLContext.cacheTable("withEmptyParts")
+
+    checkAnswer(
+      sql("SELECT * FROM withEmptyParts"),
+      withEmptyParts.collect().toSeq)
+  }
 }

From a004a8d879a85af3be0aefa3f331116d4aabb1e4 Mon Sep 17 00:00:00 2001
From: Patrick Wendell <pwendell@gmail.com>
Date: Fri, 29 Aug 2014 22:24:35 -0700
Subject: [PATCH 152/489] BUILD: Adding back CDH4 as per user requests

---
 dev/create-release/create-release.sh | 1 +
 1 file changed, 1 insertion(+)

diff --git a/dev/create-release/create-release.sh b/dev/create-release/create-release.sh
index 7549fbbe66654..281e8d4de6d71 100755
--- a/dev/create-release/create-release.sh
+++ b/dev/create-release/create-release.sh
@@ -118,6 +118,7 @@ make_binary_release() {
 }
 
 make_binary_release "hadoop1" "-Phive -Dhadoop.version=1.0.4" &
+make_binary_release "cdh4" "-Phive -Dhadoop.version=2.0.0-mr1-cdh4.2.0" &
 make_binary_release "hadoop2.3" "-Phadoop-2.3 -Phive -Pyarn" &
 make_binary_release "hadoop2.4" "-Phadoop-2.4 -Phive -Pyarn" &
 make_binary_release "hadoop2.4-without-hive" "-Phadoop-2.4 -Pyarn" &

From 7e662af332beb171dc89027a2562d0949d69cfa0 Mon Sep 17 00:00:00 2001
From: Kousuke Saruta <sarutak@oss.nttdata.co.jp>
Date: Fri, 29 Aug 2014 22:52:32 -0700
Subject: [PATCH 153/489] [SPARK-3305] Remove unused import from UI classes.

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

Closes #2200 from sarutak/SPARK-3305 and squashes the following commits:

3cbd6ee [Kousuke Saruta] Removed unused import from classes related to UI
---
 .../src/main/scala/org/apache/spark/ui/exec/ExecutorsPage.scala | 1 -
 .../scala/org/apache/spark/ui/jobs/JobProgressListener.scala    | 2 +-
 2 files changed, 1 insertion(+), 2 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/ui/exec/ExecutorsPage.scala b/core/src/main/scala/org/apache/spark/ui/exec/ExecutorsPage.scala
index 02df4e8fe61af..b0e3bb3b552fd 100644
--- a/core/src/main/scala/org/apache/spark/ui/exec/ExecutorsPage.scala
+++ b/core/src/main/scala/org/apache/spark/ui/exec/ExecutorsPage.scala
@@ -21,7 +21,6 @@ import javax.servlet.http.HttpServletRequest
 
 import scala.xml.Node
 
-import org.apache.spark.storage.StorageLevel
 import org.apache.spark.ui.{ToolTips, UIUtils, WebUIPage}
 import org.apache.spark.util.Utils
 
diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/JobProgressListener.scala b/core/src/main/scala/org/apache/spark/ui/jobs/JobProgressListener.scala
index f7f918fd521a9..eaeb861f59e5a 100644
--- a/core/src/main/scala/org/apache/spark/ui/jobs/JobProgressListener.scala
+++ b/core/src/main/scala/org/apache/spark/ui/jobs/JobProgressListener.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.ui.jobs
 
-import scala.collection.mutable.{HashMap, ListBuffer, Map}
+import scala.collection.mutable.{HashMap, ListBuffer}
 
 import org.apache.spark._
 import org.apache.spark.annotation.DeveloperApi

From acea92806c91535162a9fdcb1cce579e7f1f91c7 Mon Sep 17 00:00:00 2001
From: Raymond Liu <raymond.liu@intel.com>
Date: Fri, 29 Aug 2014 23:05:18 -0700
Subject: [PATCH 154/489] [SPARK-2288] Hide ShuffleBlockManager behind
 ShuffleManager

By Hiding the shuffleblockmanager behind Shufflemanager, we decouple the shuffle data's block mapping management work from Diskblockmananger. This give a more clear interface and more easy for other shuffle manager to implement their own block management logic. the jira ticket have more details.

Author: Raymond Liu <raymond.liu@intel.com>

Closes #1241 from colorant/shuffle and squashes the following commits:

0e01ae3 [Raymond Liu] Move ShuffleBlockmanager behind shuffleManager
---
 .../FileShuffleBlockManager.scala}            |  81 +++++-----
 .../shuffle/IndexShuffleBlockManager.scala    | 121 +++++++++++++++
 .../spark/shuffle/ShuffleBlockManager.scala   |  38 +++++
 .../apache/spark/shuffle/ShuffleManager.scala |   9 +-
 .../shuffle/hash/HashShuffleManager.scala     |  18 ++-
 .../shuffle/hash/HashShuffleWriter.scala      |  10 +-
 .../shuffle/sort/SortShuffleManager.scala     |  49 +++---
 .../shuffle/sort/SortShuffleWriter.scala      |  32 +---
 .../spark/storage/BlockFetcherIterator.scala  |   2 +-
 .../org/apache/spark/storage/BlockId.scala    |   8 +
 .../apache/spark/storage/BlockManager.scala   |  22 ++-
 .../storage/BlockManagerSlaveActor.scala      |   4 +-
 .../spark/storage/DiskBlockManager.scala      |  34 +----
 .../org/apache/spark/storage/DiskStore.scala  |  41 +++--
 .../spark/storage/TachyonBlockManager.scala   |   4 +-
 .../util/collection/ExternalSorter.scala      |  38 +----
 .../hash/HashShuffleManagerSuite.scala        | 111 ++++++++++++++
 .../storage/BlockFetcherIteratorSuite.scala   |  36 ++---
 .../spark/storage/BlockManagerSuite.scala     |   7 +-
 .../spark/storage/DiskBlockManagerSuite.scala | 143 +-----------------
 project/MimaExcludes.scala                    |   2 +
 .../spark/tools/StoragePerfTester.scala       |   9 +-
 22 files changed, 466 insertions(+), 353 deletions(-)
 rename core/src/main/scala/org/apache/spark/{storage/ShuffleBlockManager.scala => shuffle/FileShuffleBlockManager.scala} (83%)
 create mode 100644 core/src/main/scala/org/apache/spark/shuffle/IndexShuffleBlockManager.scala
 create mode 100644 core/src/main/scala/org/apache/spark/shuffle/ShuffleBlockManager.scala
 create mode 100644 core/src/test/scala/org/apache/spark/shuffle/hash/HashShuffleManagerSuite.scala

diff --git a/core/src/main/scala/org/apache/spark/storage/ShuffleBlockManager.scala b/core/src/main/scala/org/apache/spark/shuffle/FileShuffleBlockManager.scala
similarity index 83%
rename from core/src/main/scala/org/apache/spark/storage/ShuffleBlockManager.scala
rename to core/src/main/scala/org/apache/spark/shuffle/FileShuffleBlockManager.scala
index b8f5d3a5b02aa..76e3932a9bb91 100644
--- a/core/src/main/scala/org/apache/spark/storage/ShuffleBlockManager.scala
+++ b/core/src/main/scala/org/apache/spark/shuffle/FileShuffleBlockManager.scala
@@ -15,22 +15,22 @@
  * limitations under the License.
  */
 
-package org.apache.spark.storage
+package org.apache.spark.shuffle
 
 import java.io.File
+import java.nio.ByteBuffer
 import java.util.concurrent.ConcurrentLinkedQueue
 import java.util.concurrent.atomic.AtomicInteger
 
 import scala.collection.JavaConversions._
 
-import org.apache.spark.Logging
+import org.apache.spark.{SparkEnv, SparkConf, Logging}
+import org.apache.spark.executor.ShuffleWriteMetrics
 import org.apache.spark.serializer.Serializer
-import org.apache.spark.shuffle.ShuffleManager
-import org.apache.spark.storage.ShuffleBlockManager.ShuffleFileGroup
+import org.apache.spark.shuffle.FileShuffleBlockManager.ShuffleFileGroup
+import org.apache.spark.storage._
 import org.apache.spark.util.{MetadataCleaner, MetadataCleanerType, TimeStampedHashMap}
 import org.apache.spark.util.collection.{PrimitiveKeyOpenHashMap, PrimitiveVector}
-import org.apache.spark.shuffle.sort.SortShuffleManager
-import org.apache.spark.executor.ShuffleWriteMetrics
 
 /** A group of writers for a ShuffleMapTask, one writer per reducer. */
 private[spark] trait ShuffleWriterGroup {
@@ -61,20 +61,18 @@ private[spark] trait ShuffleWriterGroup {
  * each block stored in each file. In order to find the location of a shuffle block, we search the
  * files within a ShuffleFileGroups associated with the block's reducer.
  */
-// TODO: Factor this into a separate class for each ShuffleManager implementation
+
 private[spark]
-class ShuffleBlockManager(blockManager: BlockManager,
-                          shuffleManager: ShuffleManager) extends Logging {
-  def conf = blockManager.conf
+class FileShuffleBlockManager(conf: SparkConf)
+  extends ShuffleBlockManager with Logging {
+
+  private lazy val blockManager = SparkEnv.get.blockManager
 
   // Turning off shuffle file consolidation causes all shuffle Blocks to get their own file.
   // TODO: Remove this once the shuffle file consolidation feature is stable.
-  val consolidateShuffleFiles =
+  private val consolidateShuffleFiles =
     conf.getBoolean("spark.shuffle.consolidateFiles", false)
 
-  // Are we using sort-based shuffle?
-  val sortBasedShuffle = shuffleManager.isInstanceOf[SortShuffleManager]
-
   private val bufferSize = conf.getInt("spark.shuffle.file.buffer.kb", 32) * 1024
 
   /**
@@ -93,22 +91,11 @@ class ShuffleBlockManager(blockManager: BlockManager,
     val completedMapTasks = new ConcurrentLinkedQueue[Int]()
   }
 
-  type ShuffleId = Int
   private val shuffleStates = new TimeStampedHashMap[ShuffleId, ShuffleState]
 
   private val metadataCleaner =
     new MetadataCleaner(MetadataCleanerType.SHUFFLE_BLOCK_MANAGER, this.cleanup, conf)
 
-  /**
-   * Register a completed map without getting a ShuffleWriterGroup. Used by sort-based shuffle
-   * because it just writes a single file by itself.
-   */
-  def addCompletedMap(shuffleId: Int, mapId: Int, numBuckets: Int): Unit = {
-    shuffleStates.putIfAbsent(shuffleId, new ShuffleState(numBuckets))
-    val shuffleState = shuffleStates(shuffleId)
-    shuffleState.completedMapTasks.add(mapId)
-  }
-
   /**
    * Get a ShuffleWriterGroup for the given map task, which will register it as complete
    * when the writers are closed successfully
@@ -181,17 +168,30 @@ class ShuffleBlockManager(blockManager: BlockManager,
 
   /**
    * Returns the physical file segment in which the given BlockId is located.
-   * This function should only be called if shuffle file consolidation is enabled, as it is
-   * an error condition if we don't find the expected block.
    */
-  def getBlockLocation(id: ShuffleBlockId): FileSegment = {
-    // Search all file groups associated with this shuffle.
-    val shuffleState = shuffleStates(id.shuffleId)
-    for (fileGroup <- shuffleState.allFileGroups) {
-      val segment = fileGroup.getFileSegmentFor(id.mapId, id.reduceId)
-      if (segment.isDefined) { return segment.get }
+  private def getBlockLocation(id: ShuffleBlockId): FileSegment = {
+    if (consolidateShuffleFiles) {
+      // Search all file groups associated with this shuffle.
+      val shuffleState = shuffleStates(id.shuffleId)
+      val iter = shuffleState.allFileGroups.iterator
+      while (iter.hasNext) {
+        val segment = iter.next.getFileSegmentFor(id.mapId, id.reduceId)
+        if (segment.isDefined) { return segment.get }
+      }
+      throw new IllegalStateException("Failed to find shuffle block: " + id)
+    } else {
+      val file = blockManager.diskBlockManager.getFile(id)
+      new FileSegment(file, 0, file.length())
     }
-    throw new IllegalStateException("Failed to find shuffle block: " + id)
+  }
+
+  override def getBytes(blockId: ShuffleBlockId): Option[ByteBuffer] = {
+    val segment = getBlockLocation(blockId)
+    blockManager.diskStore.getBytes(segment)
+  }
+
+  override def getBlockData(blockId: ShuffleBlockId): Either[FileSegment, ByteBuffer] = {
+    Left(getBlockLocation(blockId.asInstanceOf[ShuffleBlockId]))
   }
 
   /** Remove all the blocks / files and metadata related to a particular shuffle. */
@@ -207,14 +207,7 @@ class ShuffleBlockManager(blockManager: BlockManager,
   private def removeShuffleBlocks(shuffleId: ShuffleId): Boolean = {
     shuffleStates.get(shuffleId) match {
       case Some(state) =>
-        if (sortBasedShuffle) {
-          // There's a single block ID for each map, plus an index file for it
-          for (mapId <- state.completedMapTasks) {
-            val blockId = new ShuffleBlockId(shuffleId, mapId, 0)
-            blockManager.diskBlockManager.getFile(blockId).delete()
-            blockManager.diskBlockManager.getFile(blockId.name + ".index").delete()
-          }
-        } else if (consolidateShuffleFiles) {
+        if (consolidateShuffleFiles) {
           for (fileGroup <- state.allFileGroups; file <- fileGroup.files) {
             file.delete()
           }
@@ -240,13 +233,13 @@ class ShuffleBlockManager(blockManager: BlockManager,
     shuffleStates.clearOldValues(cleanupTime, (shuffleId, state) => removeShuffleBlocks(shuffleId))
   }
 
-  def stop() {
+  override def stop() {
     metadataCleaner.cancel()
   }
 }
 
 private[spark]
-object ShuffleBlockManager {
+object FileShuffleBlockManager {
   /**
    * A group of shuffle files, one per reducer.
    * A particular mapper will be assigned a single ShuffleFileGroup to write its output to.
diff --git a/core/src/main/scala/org/apache/spark/shuffle/IndexShuffleBlockManager.scala b/core/src/main/scala/org/apache/spark/shuffle/IndexShuffleBlockManager.scala
new file mode 100644
index 0000000000000..8bb9efc46cc58
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/shuffle/IndexShuffleBlockManager.scala
@@ -0,0 +1,121 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.shuffle
+
+import java.io._
+import java.nio.ByteBuffer
+
+import org.apache.spark.SparkEnv
+import org.apache.spark.storage._
+
+/**
+ * Create and maintain the shuffle blocks' mapping between logic block and physical file location.
+ * Data of shuffle blocks from the same map task are stored in a single consolidated data file.
+ * The offsets of the data blocks in the data file are stored in a separate index file.
+ *
+ * We use the name of the shuffle data's shuffleBlockId with reduce ID set to 0 and add ".data"
+ * as the filename postfix for data file, and ".index" as the filename postfix for index file.
+ *
+ */
+private[spark]
+class IndexShuffleBlockManager extends ShuffleBlockManager {
+
+  private lazy val blockManager = SparkEnv.get.blockManager
+
+  /**
+   * Mapping to a single shuffleBlockId with reduce ID 0.
+   * */
+  def consolidateId(shuffleId: Int, mapId: Int): ShuffleBlockId = {
+    ShuffleBlockId(shuffleId, mapId, 0)
+  }
+
+  def getDataFile(shuffleId: Int, mapId: Int): File = {
+    blockManager.diskBlockManager.getFile(ShuffleDataBlockId(shuffleId, mapId, 0))
+  }
+
+  private def getIndexFile(shuffleId: Int, mapId: Int): File = {
+    blockManager.diskBlockManager.getFile(ShuffleIndexBlockId(shuffleId, mapId, 0))
+  }
+
+  /**
+   * Remove data file and index file that contain the output data from one map.
+   * */
+  def removeDataByMap(shuffleId: Int, mapId: Int): Unit = {
+    var file = getDataFile(shuffleId, mapId)
+    if (file.exists()) {
+      file.delete()
+    }
+
+    file = getIndexFile(shuffleId, mapId)
+    if (file.exists()) {
+      file.delete()
+    }
+  }
+
+  /**
+   * Write an index file with the offsets of each block, plus a final offset at the end for the
+   * end of the output file. This will be used by getBlockLocation to figure out where each block
+   * begins and ends.
+   * */
+  def writeIndexFile(shuffleId: Int, mapId: Int, lengths: Array[Long]) = {
+    val indexFile = getIndexFile(shuffleId, mapId)
+    val out = new DataOutputStream(new BufferedOutputStream(new FileOutputStream(indexFile)))
+    try {
+      // We take in lengths of each block, need to convert it to offsets.
+      var offset = 0L
+      out.writeLong(offset)
+
+      for (length <- lengths) {
+        offset += length
+        out.writeLong(offset)
+      }
+    } finally {
+      out.close()
+    }
+  }
+
+  /**
+   * Get the location of a block in a map output file. Uses the index file we create for it.
+   * */
+  private def getBlockLocation(blockId: ShuffleBlockId): FileSegment = {
+    // The block is actually going to be a range of a single map output file for this map, so
+    // find out the consolidated file, then the offset within that from our index
+    val indexFile = getIndexFile(blockId.shuffleId, blockId.mapId)
+
+    val in = new DataInputStream(new FileInputStream(indexFile))
+    try {
+      in.skip(blockId.reduceId * 8)
+      val offset = in.readLong()
+      val nextOffset = in.readLong()
+      new FileSegment(getDataFile(blockId.shuffleId, blockId.mapId), offset, nextOffset - offset)
+    } finally {
+      in.close()
+    }
+  }
+
+  override def getBytes(blockId: ShuffleBlockId): Option[ByteBuffer] = {
+    val segment = getBlockLocation(blockId)
+    blockManager.diskStore.getBytes(segment)
+  }
+
+  override def getBlockData(blockId: ShuffleBlockId): Either[FileSegment, ByteBuffer] = {
+    Left(getBlockLocation(blockId.asInstanceOf[ShuffleBlockId]))
+  }
+
+  override def stop() = {}
+}
diff --git a/core/src/main/scala/org/apache/spark/shuffle/ShuffleBlockManager.scala b/core/src/main/scala/org/apache/spark/shuffle/ShuffleBlockManager.scala
new file mode 100644
index 0000000000000..4240580250046
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/shuffle/ShuffleBlockManager.scala
@@ -0,0 +1,38 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.shuffle
+
+import java.nio.ByteBuffer
+
+import org.apache.spark.storage.{FileSegment, ShuffleBlockId}
+
+private[spark]
+trait ShuffleBlockManager {
+  type ShuffleId = Int
+
+  /**
+   * Get shuffle block data managed by the local ShuffleBlockManager.
+   * @return Some(ByteBuffer) if block found, otherwise None.
+   */
+  def getBytes(blockId: ShuffleBlockId): Option[ByteBuffer]
+
+  def getBlockData(blockId: ShuffleBlockId): Either[FileSegment, ByteBuffer]
+
+  def stop(): Unit
+}
+
diff --git a/core/src/main/scala/org/apache/spark/shuffle/ShuffleManager.scala b/core/src/main/scala/org/apache/spark/shuffle/ShuffleManager.scala
index 9c859b8b4a118..801ae54086053 100644
--- a/core/src/main/scala/org/apache/spark/shuffle/ShuffleManager.scala
+++ b/core/src/main/scala/org/apache/spark/shuffle/ShuffleManager.scala
@@ -49,8 +49,13 @@ private[spark] trait ShuffleManager {
       endPartition: Int,
       context: TaskContext): ShuffleReader[K, C]
 
-  /** Remove a shuffle's metadata from the ShuffleManager. */
-  def unregisterShuffle(shuffleId: Int)
+  /**
+    * Remove a shuffle's metadata from the ShuffleManager.
+    * @return true if the metadata removed successfully, otherwise false.
+    */
+  def unregisterShuffle(shuffleId: Int): Boolean
+
+  def shuffleBlockManager: ShuffleBlockManager
 
   /** Shut down this ShuffleManager. */
   def stop(): Unit
diff --git a/core/src/main/scala/org/apache/spark/shuffle/hash/HashShuffleManager.scala b/core/src/main/scala/org/apache/spark/shuffle/hash/HashShuffleManager.scala
index df98d18fa8193..62e0629b34400 100644
--- a/core/src/main/scala/org/apache/spark/shuffle/hash/HashShuffleManager.scala
+++ b/core/src/main/scala/org/apache/spark/shuffle/hash/HashShuffleManager.scala
@@ -25,6 +25,9 @@ import org.apache.spark.shuffle._
  * mapper (possibly reusing these across waves of tasks).
  */
 private[spark] class HashShuffleManager(conf: SparkConf) extends ShuffleManager {
+
+  private val fileShuffleBlockManager = new FileShuffleBlockManager(conf)
+
   /* Register a shuffle with the manager and obtain a handle for it to pass to tasks. */
   override def registerShuffle[K, V, C](
       shuffleId: Int,
@@ -49,12 +52,21 @@ private[spark] class HashShuffleManager(conf: SparkConf) extends ShuffleManager
   /** Get a writer for a given partition. Called on executors by map tasks. */
   override def getWriter[K, V](handle: ShuffleHandle, mapId: Int, context: TaskContext)
       : ShuffleWriter[K, V] = {
-    new HashShuffleWriter(handle.asInstanceOf[BaseShuffleHandle[K, V, _]], mapId, context)
+    new HashShuffleWriter(
+      shuffleBlockManager, handle.asInstanceOf[BaseShuffleHandle[K, V, _]], mapId, context)
   }
 
   /** Remove a shuffle's metadata from the ShuffleManager. */
-  override def unregisterShuffle(shuffleId: Int): Unit = {}
+  override def unregisterShuffle(shuffleId: Int): Boolean = {
+    shuffleBlockManager.removeShuffle(shuffleId)
+  }
+
+  override def shuffleBlockManager: FileShuffleBlockManager = {
+    fileShuffleBlockManager
+  }
 
   /** Shut down this ShuffleManager. */
-  override def stop(): Unit = {}
+  override def stop(): Unit = {
+    shuffleBlockManager.stop()
+  }
 }
diff --git a/core/src/main/scala/org/apache/spark/shuffle/hash/HashShuffleWriter.scala b/core/src/main/scala/org/apache/spark/shuffle/hash/HashShuffleWriter.scala
index 51e454d9313c9..4b9454d75abb7 100644
--- a/core/src/main/scala/org/apache/spark/shuffle/hash/HashShuffleWriter.scala
+++ b/core/src/main/scala/org/apache/spark/shuffle/hash/HashShuffleWriter.scala
@@ -17,14 +17,15 @@
 
 package org.apache.spark.shuffle.hash
 
-import org.apache.spark.shuffle.{BaseShuffleHandle, ShuffleWriter}
-import org.apache.spark.{Logging, MapOutputTracker, SparkEnv, TaskContext}
-import org.apache.spark.storage.{BlockObjectWriter}
-import org.apache.spark.serializer.Serializer
+import org.apache.spark._
 import org.apache.spark.executor.ShuffleWriteMetrics
 import org.apache.spark.scheduler.MapStatus
+import org.apache.spark.serializer.Serializer
+import org.apache.spark.shuffle._
+import org.apache.spark.storage.BlockObjectWriter
 
 private[spark] class HashShuffleWriter[K, V](
+    shuffleBlockManager: FileShuffleBlockManager,
     handle: BaseShuffleHandle[K, V, _],
     mapId: Int,
     context: TaskContext)
@@ -43,7 +44,6 @@ private[spark] class HashShuffleWriter[K, V](
   metrics.shuffleWriteMetrics = Some(writeMetrics)
 
   private val blockManager = SparkEnv.get.blockManager
-  private val shuffleBlockManager = blockManager.shuffleBlockManager
   private val ser = Serializer.getSerializer(dep.serializer.getOrElse(null))
   private val shuffle = shuffleBlockManager.forMapTask(dep.shuffleId, mapId, numOutputSplits, ser,
     writeMetrics)
diff --git a/core/src/main/scala/org/apache/spark/shuffle/sort/SortShuffleManager.scala b/core/src/main/scala/org/apache/spark/shuffle/sort/SortShuffleManager.scala
index 6dcca47ea7c0c..b727438ae7e47 100644
--- a/core/src/main/scala/org/apache/spark/shuffle/sort/SortShuffleManager.scala
+++ b/core/src/main/scala/org/apache/spark/shuffle/sort/SortShuffleManager.scala
@@ -17,14 +17,17 @@
 
 package org.apache.spark.shuffle.sort
 
-import java.io.{DataInputStream, FileInputStream}
+import java.util.concurrent.ConcurrentHashMap
 
+import org.apache.spark.{SparkConf, TaskContext, ShuffleDependency}
 import org.apache.spark.shuffle._
-import org.apache.spark.{TaskContext, ShuffleDependency}
 import org.apache.spark.shuffle.hash.HashShuffleReader
-import org.apache.spark.storage.{DiskBlockManager, FileSegment, ShuffleBlockId}
 
-private[spark] class SortShuffleManager extends ShuffleManager {
+private[spark] class SortShuffleManager(conf: SparkConf) extends ShuffleManager {
+
+  private val indexShuffleBlockManager = new IndexShuffleBlockManager()
+  private val shuffleMapNumber = new ConcurrentHashMap[Int, Int]()
+
   /**
    * Register a shuffle with the manager and obtain a handle for it to pass to tasks.
    */
@@ -52,29 +55,29 @@ private[spark] class SortShuffleManager extends ShuffleManager {
   /** Get a writer for a given partition. Called on executors by map tasks. */
   override def getWriter[K, V](handle: ShuffleHandle, mapId: Int, context: TaskContext)
       : ShuffleWriter[K, V] = {
-    new SortShuffleWriter(handle.asInstanceOf[BaseShuffleHandle[K, V, _]], mapId, context)
+    val baseShuffleHandle = handle.asInstanceOf[BaseShuffleHandle[K, V, _]]
+    shuffleMapNumber.putIfAbsent(baseShuffleHandle.shuffleId, baseShuffleHandle.numMaps)
+    new SortShuffleWriter(
+      shuffleBlockManager, baseShuffleHandle, mapId, context)
   }
 
   /** Remove a shuffle's metadata from the ShuffleManager. */
-  override def unregisterShuffle(shuffleId: Int): Unit = {}
+  override def unregisterShuffle(shuffleId: Int): Boolean = {
+    if (shuffleMapNumber.containsKey(shuffleId)) {
+      val numMaps = shuffleMapNumber.remove(shuffleId)
+      (0 until numMaps).map{ mapId =>
+        shuffleBlockManager.removeDataByMap(shuffleId, mapId)
+      }
+    }
+    true
+  }
 
-  /** Shut down this ShuffleManager. */
-  override def stop(): Unit = {}
+  override def shuffleBlockManager: IndexShuffleBlockManager = {
+    indexShuffleBlockManager
+  }
 
-  /** Get the location of a block in a map output file. Uses the index file we create for it. */
-  def getBlockLocation(blockId: ShuffleBlockId, diskManager: DiskBlockManager): FileSegment = {
-    // The block is actually going to be a range of a single map output file for this map, so
-    // figure out the ID of the consolidated file, then the offset within that from our index
-    val consolidatedId = blockId.copy(reduceId = 0)
-    val indexFile = diskManager.getFile(consolidatedId.name + ".index")
-    val in = new DataInputStream(new FileInputStream(indexFile))
-    try {
-      in.skip(blockId.reduceId * 8)
-      val offset = in.readLong()
-      val nextOffset = in.readLong()
-      new FileSegment(diskManager.getFile(consolidatedId), offset, nextOffset - offset)
-    } finally {
-      in.close()
-    }
+  /** Shut down this ShuffleManager. */
+  override def stop(): Unit = {
+    shuffleBlockManager.stop()
   }
 }
diff --git a/core/src/main/scala/org/apache/spark/shuffle/sort/SortShuffleWriter.scala b/core/src/main/scala/org/apache/spark/shuffle/sort/SortShuffleWriter.scala
index b8c9ad46ab035..89a78d6982ba0 100644
--- a/core/src/main/scala/org/apache/spark/shuffle/sort/SortShuffleWriter.scala
+++ b/core/src/main/scala/org/apache/spark/shuffle/sort/SortShuffleWriter.scala
@@ -17,29 +17,25 @@
 
 package org.apache.spark.shuffle.sort
 
-import java.io.File
-
 import org.apache.spark.{MapOutputTracker, SparkEnv, Logging, TaskContext}
 import org.apache.spark.executor.ShuffleWriteMetrics
 import org.apache.spark.scheduler.MapStatus
-import org.apache.spark.shuffle.{ShuffleWriter, BaseShuffleHandle}
+import org.apache.spark.shuffle.{IndexShuffleBlockManager, ShuffleWriter, BaseShuffleHandle}
 import org.apache.spark.storage.ShuffleBlockId
 import org.apache.spark.util.collection.ExternalSorter
 
 private[spark] class SortShuffleWriter[K, V, C](
+    shuffleBlockManager: IndexShuffleBlockManager,
     handle: BaseShuffleHandle[K, V, C],
     mapId: Int,
     context: TaskContext)
   extends ShuffleWriter[K, V] with Logging {
 
   private val dep = handle.dependency
-  private val numPartitions = dep.partitioner.numPartitions
 
   private val blockManager = SparkEnv.get.blockManager
 
   private var sorter: ExternalSorter[K, V, _] = null
-  private var outputFile: File = null
-  private var indexFile: File = null
 
   // Are we in the process of stopping? Because map tasks can call stop() with success = true
   // and then call stop() with success = false if they get an exception, we want to make sure
@@ -69,17 +65,10 @@ private[spark] class SortShuffleWriter[K, V, C](
       sorter.insertAll(records)
     }
 
-    // Create a single shuffle file with reduce ID 0 that we'll write all results to. We'll later
-    // serve different ranges of this file using an index file that we create at the end.
-    val blockId = ShuffleBlockId(dep.shuffleId, mapId, 0)
-
-    outputFile = blockManager.diskBlockManager.getFile(blockId)
-    indexFile = blockManager.diskBlockManager.getFile(blockId.name + ".index")
-
-    val partitionLengths = sorter.writePartitionedFile(blockId, context)
-
-    // Register our map output with the ShuffleBlockManager, which handles cleaning it over time
-    blockManager.shuffleBlockManager.addCompletedMap(dep.shuffleId, mapId, numPartitions)
+    val outputFile = shuffleBlockManager.getDataFile(dep.shuffleId, mapId)
+    val blockId = shuffleBlockManager.consolidateId(dep.shuffleId, mapId)
+    val partitionLengths = sorter.writePartitionedFile(blockId, context, outputFile)
+    shuffleBlockManager.writeIndexFile(dep.shuffleId, mapId, partitionLengths)
 
     mapStatus = new MapStatus(blockManager.blockManagerId,
       partitionLengths.map(MapOutputTracker.compressSize))
@@ -95,13 +84,8 @@ private[spark] class SortShuffleWriter[K, V, C](
       if (success) {
         return Option(mapStatus)
       } else {
-        // The map task failed, so delete our output file if we created one
-        if (outputFile != null) {
-          outputFile.delete()
-        }
-        if (indexFile != null) {
-          indexFile.delete()
-        }
+        // The map task failed, so delete our output data.
+        shuffleBlockManager.removeDataByMap(dep.shuffleId, mapId)
         return None
       }
     } finally {
diff --git a/core/src/main/scala/org/apache/spark/storage/BlockFetcherIterator.scala b/core/src/main/scala/org/apache/spark/storage/BlockFetcherIterator.scala
index d07e6a1b1836c..e35b7fe62c753 100644
--- a/core/src/main/scala/org/apache/spark/storage/BlockFetcherIterator.scala
+++ b/core/src/main/scala/org/apache/spark/storage/BlockFetcherIterator.scala
@@ -197,7 +197,7 @@ object BlockFetcherIterator {
       for (id <- localBlocksToFetch) {
         try {
           readMetrics.localBlocksFetched += 1
-          results.put(new FetchResult(id, 0, () => getLocalFromDisk(id, serializer).get))
+          results.put(new FetchResult(id, 0, () => getLocalShuffleFromDisk(id, serializer).get))
           logDebug("Got local block " + id)
         } catch {
           case e: Exception => {
diff --git a/core/src/main/scala/org/apache/spark/storage/BlockId.scala b/core/src/main/scala/org/apache/spark/storage/BlockId.scala
index c1756ac905417..a83a3f468ae5f 100644
--- a/core/src/main/scala/org/apache/spark/storage/BlockId.scala
+++ b/core/src/main/scala/org/apache/spark/storage/BlockId.scala
@@ -58,6 +58,11 @@ case class ShuffleBlockId(shuffleId: Int, mapId: Int, reduceId: Int) extends Blo
   def name = "shuffle_" + shuffleId + "_" + mapId + "_" + reduceId
 }
 
+@DeveloperApi
+case class ShuffleDataBlockId(shuffleId: Int, mapId: Int, reduceId: Int) extends BlockId {
+  def name = "shuffle_" + shuffleId + "_" + mapId + "_" + reduceId + ".data"
+}
+
 @DeveloperApi
 case class ShuffleIndexBlockId(shuffleId: Int, mapId: Int, reduceId: Int) extends BlockId {
   def name = "shuffle_" + shuffleId + "_" + mapId + "_" + reduceId + ".index"
@@ -92,6 +97,7 @@ private[spark] case class TestBlockId(id: String) extends BlockId {
 object BlockId {
   val RDD = "rdd_([0-9]+)_([0-9]+)".r
   val SHUFFLE = "shuffle_([0-9]+)_([0-9]+)_([0-9]+)".r
+  val SHUFFLE_DATA = "shuffle_([0-9]+)_([0-9]+)_([0-9]+).data".r
   val SHUFFLE_INDEX = "shuffle_([0-9]+)_([0-9]+)_([0-9]+).index".r
   val BROADCAST = "broadcast_([0-9]+)([_A-Za-z0-9]*)".r
   val TASKRESULT = "taskresult_([0-9]+)".r
@@ -104,6 +110,8 @@ object BlockId {
       RDDBlockId(rddId.toInt, splitIndex.toInt)
     case SHUFFLE(shuffleId, mapId, reduceId) =>
       ShuffleBlockId(shuffleId.toInt, mapId.toInt, reduceId.toInt)
+    case SHUFFLE_DATA(shuffleId, mapId, reduceId) =>
+      ShuffleDataBlockId(shuffleId.toInt, mapId.toInt, reduceId.toInt)
     case SHUFFLE_INDEX(shuffleId, mapId, reduceId) =>
       ShuffleIndexBlockId(shuffleId.toInt, mapId.toInt, reduceId.toInt)
     case BROADCAST(broadcastId, field) =>
diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManager.scala b/core/src/main/scala/org/apache/spark/storage/BlockManager.scala
index cfe5b6c50aea2..a714142763243 100644
--- a/core/src/main/scala/org/apache/spark/storage/BlockManager.scala
+++ b/core/src/main/scala/org/apache/spark/storage/BlockManager.scala
@@ -64,8 +64,8 @@ private[spark] class BlockManager(
   extends BlockDataProvider with Logging {
 
   private val port = conf.getInt("spark.blockManager.port", 0)
-  val shuffleBlockManager = new ShuffleBlockManager(this, shuffleManager)
-  val diskBlockManager = new DiskBlockManager(shuffleBlockManager, conf)
+
+  val diskBlockManager = new DiskBlockManager(this, conf)
   val connectionManager =
     new ConnectionManager(port, conf, securityManager, "Connection manager for block manager")
 
@@ -83,7 +83,7 @@ private[spark] class BlockManager(
     val tachyonStorePath = s"$storeDir/$appFolderName/${this.executorId}"
     val tachyonMaster = conf.get("spark.tachyonStore.url",  "tachyon://localhost:19998")
     val tachyonBlockManager =
-      new TachyonBlockManager(shuffleBlockManager, tachyonStorePath, tachyonMaster)
+      new TachyonBlockManager(this, tachyonStorePath, tachyonMaster)
     tachyonInitialized = true
     new TachyonStore(this, tachyonBlockManager)
   }
@@ -215,7 +215,7 @@ private[spark] class BlockManager(
   override def getBlockData(blockId: String): Either[FileSegment, ByteBuffer] = {
     val bid = BlockId(blockId)
     if (bid.isShuffle) {
-      Left(diskBlockManager.getBlockLocation(bid))
+      shuffleManager.shuffleBlockManager.getBlockData(bid.asInstanceOf[ShuffleBlockId])
     } else {
       val blockBytesOpt = doGetLocal(bid, asBlockResult = false).asInstanceOf[Option[ByteBuffer]]
       if (blockBytesOpt.isDefined) {
@@ -333,8 +333,14 @@ private[spark] class BlockManager(
    * shuffle blocks. It is safe to do so without a lock on block info since disk store
    * never deletes (recent) items.
    */
-  def getLocalFromDisk(blockId: BlockId, serializer: Serializer): Option[Iterator[Any]] = {
-    diskStore.getValues(blockId, serializer).orElse {
+  def getLocalShuffleFromDisk(
+      blockId: BlockId, serializer: Serializer): Option[Iterator[Any]] = {
+
+    val shuffleBlockManager = shuffleManager.shuffleBlockManager
+    val values = shuffleBlockManager.getBytes(blockId.asInstanceOf[ShuffleBlockId]).map(
+      bytes => this.dataDeserialize(blockId, bytes, serializer))
+
+    values.orElse {
       throw new BlockException(blockId, s"Block $blockId not found on disk, though it should be")
     }
   }
@@ -355,7 +361,8 @@ private[spark] class BlockManager(
     // As an optimization for map output fetches, if the block is for a shuffle, return it
     // without acquiring a lock; the disk store never deletes (recent) items so this should work
     if (blockId.isShuffle) {
-      diskStore.getBytes(blockId) match {
+      val shuffleBlockManager = shuffleManager.shuffleBlockManager
+      shuffleBlockManager.getBytes(blockId.asInstanceOf[ShuffleBlockId]) match {
         case Some(bytes) =>
           Some(bytes)
         case None =>
@@ -1045,7 +1052,6 @@ private[spark] class BlockManager(
 
   def stop(): Unit = {
     connectionManager.stop()
-    shuffleBlockManager.stop()
     diskBlockManager.stop()
     actorSystem.stop(slaveActor)
     blockInfo.clear()
diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManagerSlaveActor.scala b/core/src/main/scala/org/apache/spark/storage/BlockManagerSlaveActor.scala
index c194e0fed3367..14ae2f38c5670 100644
--- a/core/src/main/scala/org/apache/spark/storage/BlockManagerSlaveActor.scala
+++ b/core/src/main/scala/org/apache/spark/storage/BlockManagerSlaveActor.scala
@@ -21,7 +21,7 @@ import scala.concurrent.Future
 
 import akka.actor.{ActorRef, Actor}
 
-import org.apache.spark.{Logging, MapOutputTracker}
+import org.apache.spark.{Logging, MapOutputTracker, SparkEnv}
 import org.apache.spark.storage.BlockManagerMessages._
 import org.apache.spark.util.ActorLogReceive
 
@@ -55,7 +55,7 @@ class BlockManagerSlaveActor(
         if (mapOutputTracker != null) {
           mapOutputTracker.unregisterShuffle(shuffleId)
         }
-        blockManager.shuffleBlockManager.removeShuffle(shuffleId)
+        SparkEnv.get.shuffleManager.unregisterShuffle(shuffleId)
       }
 
     case RemoveBroadcast(broadcastId, tellMaster) =>
diff --git a/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala b/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala
index ec022ce9c048a..a715594f198c2 100644
--- a/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala
+++ b/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala
@@ -21,11 +21,9 @@ import java.io.File
 import java.text.SimpleDateFormat
 import java.util.{Date, Random, UUID}
 
-import org.apache.spark.{SparkConf, SparkEnv, Logging}
+import org.apache.spark.{SparkConf, Logging}
 import org.apache.spark.executor.ExecutorExitCode
-import org.apache.spark.network.netty.PathResolver
 import org.apache.spark.util.Utils
-import org.apache.spark.shuffle.sort.SortShuffleManager
 
 /**
  * Creates and maintains the logical mapping between logical blocks and physical on-disk
@@ -36,13 +34,11 @@ import org.apache.spark.shuffle.sort.SortShuffleManager
  * Block files are hashed among the directories listed in spark.local.dir (or in
  * SPARK_LOCAL_DIRS, if it's set).
  */
-private[spark] class DiskBlockManager(shuffleBlockManager: ShuffleBlockManager, conf: SparkConf)
-  extends PathResolver with Logging {
+private[spark] class DiskBlockManager(blockManager: BlockManager, conf: SparkConf)
+  extends Logging {
 
   private val MAX_DIR_CREATION_ATTEMPTS: Int = 10
-
-  private val subDirsPerLocalDir =
-    shuffleBlockManager.conf.getInt("spark.diskStore.subDirectories", 64)
+  private val subDirsPerLocalDir = blockManager.conf.getInt("spark.diskStore.subDirectories", 64)
 
   /* Create one local directory for each path mentioned in spark.local.dir; then, inside this
    * directory, create multiple subdirectories that we will hash files into, in order to avoid
@@ -56,26 +52,6 @@ private[spark] class DiskBlockManager(shuffleBlockManager: ShuffleBlockManager,
 
   addShutdownHook()
 
-  /**
-   * Returns the physical file segment in which the given BlockId is located. If the BlockId has
-   * been mapped to a specific FileSegment by the shuffle layer, that will be returned.
-   * Otherwise, we assume the Block is mapped to the whole file identified by the BlockId.
-   */
-  def getBlockLocation(blockId: BlockId): FileSegment = {
-    val env = SparkEnv.get  // NOTE: can be null in unit tests
-    if (blockId.isShuffle && env != null && env.shuffleManager.isInstanceOf[SortShuffleManager]) {
-      // For sort-based shuffle, let it figure out its blocks
-      val sortShuffleManager = env.shuffleManager.asInstanceOf[SortShuffleManager]
-      sortShuffleManager.getBlockLocation(blockId.asInstanceOf[ShuffleBlockId], this)
-    } else if (blockId.isShuffle && shuffleBlockManager.consolidateShuffleFiles) {
-      // For hash-based shuffle with consolidated files, ShuffleBlockManager takes care of this
-      shuffleBlockManager.getBlockLocation(blockId.asInstanceOf[ShuffleBlockId])
-    } else {
-      val file = getFile(blockId.name)
-      new FileSegment(file, 0, file.length())
-    }
-  }
-
   def getFile(filename: String): File = {
     // Figure out which local directory it hashes to, and which subdirectory in that
     val hash = Utils.nonNegativeHash(filename)
@@ -105,7 +81,7 @@ private[spark] class DiskBlockManager(shuffleBlockManager: ShuffleBlockManager,
 
   /** Check if disk block manager has a block. */
   def containsBlock(blockId: BlockId): Boolean = {
-    getBlockLocation(blockId).file.exists()
+    getFile(blockId.name).exists()
   }
 
   /** List all the files currently stored on disk by the disk manager. */
diff --git a/core/src/main/scala/org/apache/spark/storage/DiskStore.scala b/core/src/main/scala/org/apache/spark/storage/DiskStore.scala
index c83261dd91b36..e9304f6bb45d0 100644
--- a/core/src/main/scala/org/apache/spark/storage/DiskStore.scala
+++ b/core/src/main/scala/org/apache/spark/storage/DiskStore.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.storage
 
-import java.io.{FileOutputStream, RandomAccessFile}
+import java.io.{File, FileOutputStream, RandomAccessFile}
 import java.nio.ByteBuffer
 import java.nio.channels.FileChannel.MapMode
 
@@ -34,7 +34,7 @@ private[spark] class DiskStore(blockManager: BlockManager, diskManager: DiskBloc
   val minMemoryMapBytes = blockManager.conf.getLong("spark.storage.memoryMapThreshold", 2 * 4096L)
 
   override def getSize(blockId: BlockId): Long = {
-    diskManager.getBlockLocation(blockId).length
+    diskManager.getFile(blockId.name).length
   }
 
   override def putBytes(blockId: BlockId, _bytes: ByteBuffer, level: StorageLevel): PutResult = {
@@ -89,25 +89,33 @@ private[spark] class DiskStore(blockManager: BlockManager, diskManager: DiskBloc
     }
   }
 
-  override def getBytes(blockId: BlockId): Option[ByteBuffer] = {
-    val segment = diskManager.getBlockLocation(blockId)
-    val channel = new RandomAccessFile(segment.file, "r").getChannel
+  private def getBytes(file: File, offset: Long, length: Long): Option[ByteBuffer] = {
+    val channel = new RandomAccessFile(file, "r").getChannel
 
     try {
       // For small files, directly read rather than memory map
-      if (segment.length < minMemoryMapBytes) {
-        val buf = ByteBuffer.allocate(segment.length.toInt)
-        channel.read(buf, segment.offset)
+      if (length < minMemoryMapBytes) {
+        val buf = ByteBuffer.allocate(length.toInt)
+        channel.read(buf, offset)
         buf.flip()
         Some(buf)
       } else {
-        Some(channel.map(MapMode.READ_ONLY, segment.offset, segment.length))
+        Some(channel.map(MapMode.READ_ONLY, offset, length))
       }
     } finally {
       channel.close()
     }
   }
 
+  override def getBytes(blockId: BlockId): Option[ByteBuffer] = {
+    val file = diskManager.getFile(blockId.name)
+    getBytes(file, 0, file.length)
+  }
+
+  def getBytes(segment: FileSegment): Option[ByteBuffer] = {
+    getBytes(segment.file, segment.offset, segment.length)
+  }
+
   override def getValues(blockId: BlockId): Option[Iterator[Any]] = {
     getBytes(blockId).map(buffer => blockManager.dataDeserialize(blockId, buffer))
   }
@@ -117,24 +125,25 @@ private[spark] class DiskStore(blockManager: BlockManager, diskManager: DiskBloc
    * shuffle short-circuit code.
    */
   def getValues(blockId: BlockId, serializer: Serializer): Option[Iterator[Any]] = {
+    // TODO: Should bypass getBytes and use a stream based implementation, so that
+    // we won't use a lot of memory during e.g. external sort merge.
     getBytes(blockId).map(bytes => blockManager.dataDeserialize(blockId, bytes, serializer))
   }
 
   override def remove(blockId: BlockId): Boolean = {
-    val fileSegment = diskManager.getBlockLocation(blockId)
-    val file = fileSegment.file
-    if (file.exists() && file.length() == fileSegment.length) {
+    val file = diskManager.getFile(blockId.name)
+    // If consolidation mode is used With HashShuffleMananger, the physical filename for the block
+    // is different from blockId.name. So the file returns here will not be exist, thus we avoid to
+    // delete the whole consolidated file by mistake.
+    if (file.exists()) {
       file.delete()
     } else {
-      if (fileSegment.length < file.length()) {
-        logWarning(s"Could not delete block associated with only a part of a file: $blockId")
-      }
       false
     }
   }
 
   override def contains(blockId: BlockId): Boolean = {
-    val file = diskManager.getBlockLocation(blockId).file
+    val file = diskManager.getFile(blockId.name)
     file.exists()
   }
 }
diff --git a/core/src/main/scala/org/apache/spark/storage/TachyonBlockManager.scala b/core/src/main/scala/org/apache/spark/storage/TachyonBlockManager.scala
index a6cbe3aa440ff..6908a59a79e60 100644
--- a/core/src/main/scala/org/apache/spark/storage/TachyonBlockManager.scala
+++ b/core/src/main/scala/org/apache/spark/storage/TachyonBlockManager.scala
@@ -35,7 +35,7 @@ import org.apache.spark.util.Utils
  * @param rootDirs The directories to use for storing block files. Data will be hashed among these.
  */
 private[spark] class TachyonBlockManager(
-    shuffleManager: ShuffleBlockManager,
+    blockManager: BlockManager,
     rootDirs: String,
     val master: String)
   extends Logging {
@@ -49,7 +49,7 @@ private[spark] class TachyonBlockManager(
 
   private val MAX_DIR_CREATION_ATTEMPTS = 10
   private val subDirsPerTachyonDir =
-    shuffleManager.conf.get("spark.tachyonStore.subDirectories", "64").toInt
+    blockManager.conf.get("spark.tachyonStore.subDirectories", "64").toInt
 
   // Create one Tachyon directory for each path mentioned in spark.tachyonStore.folderName;
   // then, inside this directory, create multiple subdirectories that we will hash files into,
diff --git a/core/src/main/scala/org/apache/spark/util/collection/ExternalSorter.scala b/core/src/main/scala/org/apache/spark/util/collection/ExternalSorter.scala
index 5d8a648d9551e..782b979e2e93d 100644
--- a/core/src/main/scala/org/apache/spark/util/collection/ExternalSorter.scala
+++ b/core/src/main/scala/org/apache/spark/util/collection/ExternalSorter.scala
@@ -719,20 +719,20 @@ private[spark] class ExternalSorter[K, V, C](
   def iterator: Iterator[Product2[K, C]] = partitionedIterator.flatMap(pair => pair._2)
 
   /**
-   * Write all the data added into this ExternalSorter into a file in the disk store, creating
-   * an .index file for it as well with the offsets of each partition. This is called by the
-   * SortShuffleWriter and can go through an efficient path of just concatenating binary files
-   * if we decided to avoid merge-sorting.
+   * Write all the data added into this ExternalSorter into a file in the disk store. This is
+   * called by the SortShuffleWriter and can go through an efficient path of just concatenating
+   * binary files if we decided to avoid merge-sorting.
    *
    * @param blockId block ID to write to. The index file will be blockId.name + ".index".
    * @param context a TaskContext for a running Spark task, for us to update shuffle metrics.
    * @return array of lengths, in bytes, of each partition of the file (used by map output tracker)
    */
-  def writePartitionedFile(blockId: BlockId, context: TaskContext): Array[Long] = {
-    val outputFile = blockManager.diskBlockManager.getFile(blockId)
+  def writePartitionedFile(
+      blockId: BlockId,
+      context: TaskContext,
+      outputFile: File): Array[Long] = {
 
     // Track location of each range in the output file
-    val offsets = new Array[Long](numPartitions + 1)
     val lengths = new Array[Long](numPartitions)
 
     if (bypassMergeSort && partitionWriters != null) {
@@ -750,7 +750,6 @@ private[spark] class ExternalSorter[K, V, C](
           in.close()
           in = null
           lengths(i) = size
-          offsets(i + 1) = offsets(i) + lengths(i)
         }
       } finally {
         if (out != null) {
@@ -772,11 +771,7 @@ private[spark] class ExternalSorter[K, V, C](
           }
           writer.commitAndClose()
           val segment = writer.fileSegment()
-          offsets(id + 1) = segment.offset + segment.length
           lengths(id) = segment.length
-        } else {
-          // The partition is empty; don't create a new writer to avoid writing headers, etc
-          offsets(id + 1) = offsets(id)
         }
       }
     }
@@ -784,23 +779,6 @@ private[spark] class ExternalSorter[K, V, C](
     context.taskMetrics.memoryBytesSpilled += memoryBytesSpilled
     context.taskMetrics.diskBytesSpilled += diskBytesSpilled
 
-    // Write an index file with the offsets of each block, plus a final offset at the end for the
-    // end of the output file. This will be used by SortShuffleManager.getBlockLocation to figure
-    // out where each block begins and ends.
-
-    val diskBlockManager = blockManager.diskBlockManager
-    val indexFile = diskBlockManager.getFile(blockId.name + ".index")
-    val out = new DataOutputStream(new BufferedOutputStream(new FileOutputStream(indexFile)))
-    try {
-      var i = 0
-      while (i < numPartitions + 1) {
-        out.writeLong(offsets(i))
-        i += 1
-      }
-    } finally {
-      out.close()
-    }
-
     lengths
   }
 
@@ -811,7 +789,7 @@ private[spark] class ExternalSorter[K, V, C](
     if (writer.isOpen) {
       writer.commitAndClose()
     }
-    blockManager.getLocalFromDisk(writer.blockId, ser).get.asInstanceOf[Iterator[Product2[K, C]]]
+    blockManager.diskStore.getValues(writer.blockId, ser).get.asInstanceOf[Iterator[Product2[K, C]]]
   }
 
   def stop(): Unit = {
diff --git a/core/src/test/scala/org/apache/spark/shuffle/hash/HashShuffleManagerSuite.scala b/core/src/test/scala/org/apache/spark/shuffle/hash/HashShuffleManagerSuite.scala
new file mode 100644
index 0000000000000..6061e544e79b4
--- /dev/null
+++ b/core/src/test/scala/org/apache/spark/shuffle/hash/HashShuffleManagerSuite.scala
@@ -0,0 +1,111 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.shuffle.hash
+
+import java.io.{File, FileWriter}
+
+import scala.language.reflectiveCalls
+
+import org.scalatest.FunSuite
+
+import org.apache.spark.{SparkEnv, SparkContext, LocalSparkContext, SparkConf}
+import org.apache.spark.executor.ShuffleWriteMetrics
+import org.apache.spark.serializer.JavaSerializer
+import org.apache.spark.shuffle.FileShuffleBlockManager
+import org.apache.spark.storage.{ShuffleBlockId, FileSegment}
+
+class HashShuffleManagerSuite extends FunSuite with LocalSparkContext {
+  private val testConf = new SparkConf(false)
+
+  private def checkSegments(segment1: FileSegment, segment2: FileSegment) {
+    assert (segment1.file.getCanonicalPath === segment2.file.getCanonicalPath)
+    assert (segment1.offset === segment2.offset)
+    assert (segment1.length === segment2.length)
+  }
+
+  test("consolidated shuffle can write to shuffle group without messing existing offsets/lengths") {
+
+    val conf = new SparkConf(false)
+    // reset after EACH object write. This is to ensure that there are bytes appended after
+    // an object is written. So if the codepaths assume writeObject is end of data, this should
+    // flush those bugs out. This was common bug in ExternalAppendOnlyMap, etc.
+    conf.set("spark.serializer.objectStreamReset", "1")
+    conf.set("spark.serializer", "org.apache.spark.serializer.JavaSerializer")
+    conf.set("spark.shuffle.manager", "org.apache.spark.shuffle.hash.HashShuffleManager")
+
+    sc = new SparkContext("local", "test", conf)
+
+    val shuffleBlockManager =
+      SparkEnv.get.shuffleManager.shuffleBlockManager.asInstanceOf[FileShuffleBlockManager]
+
+    val shuffle1 = shuffleBlockManager.forMapTask(1, 1, 1, new JavaSerializer(conf),
+      new ShuffleWriteMetrics)
+    for (writer <- shuffle1.writers) {
+      writer.write("test1")
+      writer.write("test2")
+    }
+    for (writer <- shuffle1.writers) {
+      writer.commitAndClose()
+    }
+
+    val shuffle1Segment = shuffle1.writers(0).fileSegment()
+    shuffle1.releaseWriters(success = true)
+
+    val shuffle2 = shuffleBlockManager.forMapTask(1, 2, 1, new JavaSerializer(conf),
+      new ShuffleWriteMetrics)
+
+    for (writer <- shuffle2.writers) {
+      writer.write("test3")
+      writer.write("test4")
+    }
+    for (writer <- shuffle2.writers) {
+      writer.commitAndClose()
+    }
+    val shuffle2Segment = shuffle2.writers(0).fileSegment()
+    shuffle2.releaseWriters(success = true)
+
+    // Now comes the test :
+    // Write to shuffle 3; and close it, but before registering it, check if the file lengths for
+    // previous task (forof shuffle1) is the same as 'segments'. Earlier, we were inferring length
+    // of block based on remaining data in file : which could mess things up when there is concurrent read
+    // and writes happening to the same shuffle group.
+
+    val shuffle3 = shuffleBlockManager.forMapTask(1, 3, 1, new JavaSerializer(testConf),
+      new ShuffleWriteMetrics)
+    for (writer <- shuffle3.writers) {
+      writer.write("test3")
+      writer.write("test4")
+    }
+    for (writer <- shuffle3.writers) {
+      writer.commitAndClose()
+    }
+    // check before we register.
+    checkSegments(shuffle2Segment, shuffleBlockManager.getBlockData(ShuffleBlockId(1, 2, 0)).left.get)
+    shuffle3.releaseWriters(success = true)
+    checkSegments(shuffle2Segment, shuffleBlockManager.getBlockData(ShuffleBlockId(1, 2, 0)).left.get)
+    shuffleBlockManager.removeShuffle(1)
+
+  }
+
+
+  def writeToFile(file: File, numBytes: Int) {
+    val writer = new FileWriter(file, true)
+    for (i <- 0 until numBytes) writer.write(i)
+    writer.close()
+  }
+}
diff --git a/core/src/test/scala/org/apache/spark/storage/BlockFetcherIteratorSuite.scala b/core/src/test/scala/org/apache/spark/storage/BlockFetcherIteratorSuite.scala
index fbfcb5156d496..3c86f6bafcaa3 100644
--- a/core/src/test/scala/org/apache/spark/storage/BlockFetcherIteratorSuite.scala
+++ b/core/src/test/scala/org/apache/spark/storage/BlockFetcherIteratorSuite.scala
@@ -60,11 +60,11 @@ class BlockFetcherIteratorSuite extends FunSuite with Matchers {
     }
 
     // 3rd block is going to fail
-    doReturn(optItr).when(blockManager).getLocalFromDisk(meq(blIds(0)), any())
-    doReturn(optItr).when(blockManager).getLocalFromDisk(meq(blIds(1)), any())
-    doAnswer(answer).when(blockManager).getLocalFromDisk(meq(blIds(2)), any())
-    doReturn(optItr).when(blockManager).getLocalFromDisk(meq(blIds(3)), any())
-    doReturn(optItr).when(blockManager).getLocalFromDisk(meq(blIds(4)), any())
+    doReturn(optItr).when(blockManager).getLocalShuffleFromDisk(meq(blIds(0)), any())
+    doReturn(optItr).when(blockManager).getLocalShuffleFromDisk(meq(blIds(1)), any())
+    doAnswer(answer).when(blockManager).getLocalShuffleFromDisk(meq(blIds(2)), any())
+    doReturn(optItr).when(blockManager).getLocalShuffleFromDisk(meq(blIds(3)), any())
+    doReturn(optItr).when(blockManager).getLocalShuffleFromDisk(meq(blIds(4)), any())
 
     val bmId = BlockManagerId("test-client", "test-client", 1)
     val blocksByAddress = Seq[(BlockManagerId, Seq[(BlockId, Long)])](
@@ -76,24 +76,24 @@ class BlockFetcherIteratorSuite extends FunSuite with Matchers {
 
     iterator.initialize()
 
-    // Without exhausting the iterator, the iterator should be lazy and not call getLocalFromDisk.
-    verify(blockManager, times(0)).getLocalFromDisk(any(), any())
+    // Without exhausting the iterator, the iterator should be lazy and not call getLocalShuffleFromDisk.
+    verify(blockManager, times(0)).getLocalShuffleFromDisk(any(), any())
 
     assert(iterator.hasNext, "iterator should have 5 elements but actually has no elements")
     // the 2nd element of the tuple returned by iterator.next should be defined when fetching successfully
     assert(iterator.next()._2.isDefined, "1st element should be defined but is not actually defined")
-    verify(blockManager, times(1)).getLocalFromDisk(any(), any())
+    verify(blockManager, times(1)).getLocalShuffleFromDisk(any(), any())
 
     assert(iterator.hasNext, "iterator should have 5 elements but actually has 1 element")
     assert(iterator.next()._2.isDefined, "2nd element should be defined but is not actually defined")
-    verify(blockManager, times(2)).getLocalFromDisk(any(), any())
+    verify(blockManager, times(2)).getLocalShuffleFromDisk(any(), any())
 
     assert(iterator.hasNext, "iterator should have 5 elements but actually has 2 elements")
     // 3rd fetch should be failed
     intercept[Exception] {
       iterator.next()
     }
-    verify(blockManager, times(3)).getLocalFromDisk(any(), any())
+    verify(blockManager, times(3)).getLocalShuffleFromDisk(any(), any())
   }
 
 
@@ -115,11 +115,11 @@ class BlockFetcherIteratorSuite extends FunSuite with Matchers {
     val optItr = mock(classOf[Option[Iterator[Any]]])
  
    // All blocks should be fetched successfully
-    doReturn(optItr).when(blockManager).getLocalFromDisk(meq(blIds(0)), any())
-    doReturn(optItr).when(blockManager).getLocalFromDisk(meq(blIds(1)), any())
-    doReturn(optItr).when(blockManager).getLocalFromDisk(meq(blIds(2)), any())
-    doReturn(optItr).when(blockManager).getLocalFromDisk(meq(blIds(3)), any())
-    doReturn(optItr).when(blockManager).getLocalFromDisk(meq(blIds(4)), any())
+    doReturn(optItr).when(blockManager).getLocalShuffleFromDisk(meq(blIds(0)), any())
+    doReturn(optItr).when(blockManager).getLocalShuffleFromDisk(meq(blIds(1)), any())
+    doReturn(optItr).when(blockManager).getLocalShuffleFromDisk(meq(blIds(2)), any())
+    doReturn(optItr).when(blockManager).getLocalShuffleFromDisk(meq(blIds(3)), any())
+    doReturn(optItr).when(blockManager).getLocalShuffleFromDisk(meq(blIds(4)), any())
 
     val bmId = BlockManagerId("test-client", "test-client", 1)
     val blocksByAddress = Seq[(BlockManagerId, Seq[(BlockId, Long)])](
@@ -131,8 +131,8 @@ class BlockFetcherIteratorSuite extends FunSuite with Matchers {
 
     iterator.initialize()
 
-    // Without exhausting the iterator, the iterator should be lazy and not call getLocalFromDisk.
-    verify(blockManager, times(0)).getLocalFromDisk(any(), any())
+    // Without exhausting the iterator, the iterator should be lazy and not call getLocalShuffleFromDisk.
+    verify(blockManager, times(0)).getLocalShuffleFromDisk(any(), any())
 
     assert(iterator.hasNext, "iterator should have 5 elements but actually has no elements")
     assert(iterator.next._2.isDefined, "All elements should be defined but 1st element is not actually defined") 
@@ -145,7 +145,7 @@ class BlockFetcherIteratorSuite extends FunSuite with Matchers {
     assert(iterator.hasNext, "iterator should have 5 elements but actually has 4 elements")
     assert(iterator.next._2.isDefined, "All elements should be defined but 5th element is not actually defined")
 
-    verify(blockManager, times(5)).getLocalFromDisk(any(), any())
+    verify(blockManager, times(5)).getLocalShuffleFromDisk(any(), any())
   }
 
   test("block fetch from remote fails using BasicBlockFetcherIterator") {
diff --git a/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala b/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala
index bdcea07e5714f..14ffadab99cae 100644
--- a/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala
@@ -49,6 +49,7 @@ import scala.concurrent.Await
 import scala.concurrent.duration._
 import scala.language.implicitConversions
 import scala.language.postfixOps
+import org.apache.spark.shuffle.ShuffleBlockManager
 
 class BlockManagerSuite extends FunSuite with Matchers with BeforeAndAfter
   with PrivateMethodTester {
@@ -823,11 +824,9 @@ class BlockManagerSuite extends FunSuite with Matchers with BeforeAndAfter
     // be nice to refactor classes involved in disk storage in a way that
     // allows for easier testing.
     val blockManager = mock(classOf[BlockManager])
-    val shuffleBlockManager = mock(classOf[ShuffleBlockManager])
-    when(shuffleBlockManager.conf).thenReturn(conf)
-    val diskBlockManager = new DiskBlockManager(shuffleBlockManager, conf)
-
     when(blockManager.conf).thenReturn(conf.clone.set(confKey, 0.toString))
+    val diskBlockManager = new DiskBlockManager(blockManager, conf)
+
     val diskStoreMapped = new DiskStore(blockManager, diskBlockManager)
     diskStoreMapped.putBytes(blockId, byteBuffer, StorageLevel.DISK_ONLY)
     val mapped = diskStoreMapped.getBytes(blockId).get
diff --git a/core/src/test/scala/org/apache/spark/storage/DiskBlockManagerSuite.scala b/core/src/test/scala/org/apache/spark/storage/DiskBlockManagerSuite.scala
index aabaeadd7a071..26082ded8ca7a 100644
--- a/core/src/test/scala/org/apache/spark/storage/DiskBlockManagerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/storage/DiskBlockManagerSuite.scala
@@ -26,6 +26,7 @@ import scala.language.reflectiveCalls
 
 import akka.actor.Props
 import com.google.common.io.Files
+import org.mockito.Mockito.{mock, when}
 import org.scalatest.{BeforeAndAfterAll, BeforeAndAfterEach, FunSuite}
 
 import org.apache.spark.SparkConf
@@ -40,18 +41,8 @@ class DiskBlockManagerSuite extends FunSuite with BeforeAndAfterEach with Before
   private var rootDir1: File = _
   private var rootDirs: String = _
 
-  // This suite focuses primarily on consolidation features,
-  // so we coerce consolidation if not already enabled.
-  testConf.set("spark.shuffle.consolidateFiles", "true")
-
-  private val shuffleManager = new HashShuffleManager(testConf.clone)
-
-  val shuffleBlockManager = new ShuffleBlockManager(null, shuffleManager) {
-    override def conf = testConf.clone
-    var idToSegmentMap = mutable.Map[ShuffleBlockId, FileSegment]()
-    override def getBlockLocation(id: ShuffleBlockId) = idToSegmentMap(id)
-  }
-
+  val blockManager = mock(classOf[BlockManager])
+  when(blockManager.conf).thenReturn(testConf)
   var diskBlockManager: DiskBlockManager = _
 
   override def beforeAll() {
@@ -73,22 +64,17 @@ class DiskBlockManagerSuite extends FunSuite with BeforeAndAfterEach with Before
   override def beforeEach() {
     val conf = testConf.clone
     conf.set("spark.local.dir", rootDirs)
-    diskBlockManager = new DiskBlockManager(shuffleBlockManager, conf)
-    shuffleBlockManager.idToSegmentMap.clear()
+    diskBlockManager = new DiskBlockManager(blockManager, conf)
   }
 
   override def afterEach() {
     diskBlockManager.stop()
-    shuffleBlockManager.idToSegmentMap.clear()
   }
 
   test("basic block creation") {
     val blockId = new TestBlockId("test")
-    assertSegmentEquals(blockId, blockId.name, 0, 0)
-
     val newFile = diskBlockManager.getFile(blockId)
     writeToFile(newFile, 10)
-    assertSegmentEquals(blockId, blockId.name, 0, 10)
     assert(diskBlockManager.containsBlock(blockId))
     newFile.delete()
     assert(!diskBlockManager.containsBlock(blockId))
@@ -101,127 +87,6 @@ class DiskBlockManagerSuite extends FunSuite with BeforeAndAfterEach with Before
     assert(diskBlockManager.getAllBlocks.toSet === ids.toSet)
   }
 
-  test("block appending") {
-    val blockId = new TestBlockId("test")
-    val newFile = diskBlockManager.getFile(blockId)
-    writeToFile(newFile, 15)
-    assertSegmentEquals(blockId, blockId.name, 0, 15)
-    val newFile2 = diskBlockManager.getFile(blockId)
-    assert(newFile === newFile2)
-    writeToFile(newFile2, 12)
-    assertSegmentEquals(blockId, blockId.name, 0, 27)
-    newFile.delete()
-  }
-
-  test("block remapping") {
-    val filename = "test"
-    val blockId0 = new ShuffleBlockId(1, 2, 3)
-    val newFile = diskBlockManager.getFile(filename)
-    writeToFile(newFile, 15)
-    shuffleBlockManager.idToSegmentMap(blockId0) = new FileSegment(newFile, 0, 15)
-    assertSegmentEquals(blockId0, filename, 0, 15)
-
-    val blockId1 = new ShuffleBlockId(1, 2, 4)
-    val newFile2 = diskBlockManager.getFile(filename)
-    writeToFile(newFile2, 12)
-    shuffleBlockManager.idToSegmentMap(blockId1) = new FileSegment(newFile, 15, 12)
-    assertSegmentEquals(blockId1, filename, 15, 12)
-
-    assert(newFile === newFile2)
-    newFile.delete()
-  }
-
-  private def checkSegments(segment1: FileSegment, segment2: FileSegment) {
-    assert (segment1.file.getCanonicalPath === segment2.file.getCanonicalPath)
-    assert (segment1.offset === segment2.offset)
-    assert (segment1.length === segment2.length)
-  }
-
-  test("consolidated shuffle can write to shuffle group without messing existing offsets/lengths") {
-
-    val serializer = new JavaSerializer(testConf)
-    val confCopy = testConf.clone
-    // reset after EACH object write. This is to ensure that there are bytes appended after
-    // an object is written. So if the codepaths assume writeObject is end of data, this should
-    // flush those bugs out. This was common bug in ExternalAppendOnlyMap, etc.
-    confCopy.set("spark.serializer.objectStreamReset", "1")
-
-    val securityManager = new org.apache.spark.SecurityManager(confCopy)
-    // Do not use the shuffleBlockManager above !
-    val (actorSystem, boundPort) = AkkaUtils.createActorSystem("test", "localhost", 0, confCopy,
-      securityManager)
-    val master = new BlockManagerMaster(
-      actorSystem.actorOf(Props(new BlockManagerMasterActor(true, confCopy, new LiveListenerBus))),
-      confCopy)
-    val store = new BlockManager("<driver>", actorSystem, master , serializer, confCopy,
-      securityManager, null, shuffleManager)
-
-    try {
-
-      val shuffleManager = store.shuffleBlockManager
-
-      val shuffle1 = shuffleManager.forMapTask(1, 1, 1, serializer, new ShuffleWriteMetrics)
-      for (writer <- shuffle1.writers) {
-        writer.write("test1")
-        writer.write("test2")
-      }
-      for (writer <- shuffle1.writers) {
-        writer.commitAndClose()
-      }
-
-      val shuffle1Segment = shuffle1.writers(0).fileSegment()
-      shuffle1.releaseWriters(success = true)
-
-      val shuffle2 = shuffleManager.forMapTask(1, 2, 1, new JavaSerializer(testConf),
-        new ShuffleWriteMetrics)
-
-      for (writer <- shuffle2.writers) {
-        writer.write("test3")
-        writer.write("test4")
-      }
-      for (writer <- shuffle2.writers) {
-        writer.commitAndClose()
-      }
-      val shuffle2Segment = shuffle2.writers(0).fileSegment()
-      shuffle2.releaseWriters(success = true)
-
-      // Now comes the test :
-      // Write to shuffle 3; and close it, but before registering it, check if the file lengths for
-      // previous task (forof shuffle1) is the same as 'segments'. Earlier, we were inferring length
-      // of block based on remaining data in file : which could mess things up when there is concurrent read
-      // and writes happening to the same shuffle group.
-
-      val shuffle3 = shuffleManager.forMapTask(1, 3, 1, new JavaSerializer(testConf),
-        new ShuffleWriteMetrics)
-      for (writer <- shuffle3.writers) {
-        writer.write("test3")
-        writer.write("test4")
-      }
-      for (writer <- shuffle3.writers) {
-        writer.commitAndClose()
-      }
-      // check before we register.
-      checkSegments(shuffle2Segment, shuffleManager.getBlockLocation(ShuffleBlockId(1, 2, 0)))
-      shuffle3.releaseWriters(success = true)
-      checkSegments(shuffle2Segment, shuffleManager.getBlockLocation(ShuffleBlockId(1, 2, 0)))
-      shuffleManager.removeShuffle(1)
-    } finally {
-
-      if (store != null) {
-        store.stop()
-      }
-      actorSystem.shutdown()
-      actorSystem.awaitTermination()
-    }
-  }
-
-  def assertSegmentEquals(blockId: BlockId, filename: String, offset: Int, length: Int) {
-    val segment = diskBlockManager.getBlockLocation(blockId)
-    assert(segment.file.getName === filename)
-    assert(segment.offset === offset)
-    assert(segment.length === length)
-  }
-
   def writeToFile(file: File, numBytes: Int) {
     val writer = new FileWriter(file, true)
     for (i <- 0 until numBytes) writer.write(i)
diff --git a/project/MimaExcludes.scala b/project/MimaExcludes.scala
index 300589394b96f..fe8ffe6d97a05 100644
--- a/project/MimaExcludes.scala
+++ b/project/MimaExcludes.scala
@@ -58,6 +58,8 @@ object MimaExcludes {
               "org.apache.spark.api.java.JavaRDDLike.countApproxDistinct$default$1"),
             ProblemFilters.exclude[MissingMethodProblem](
               "org.apache.spark.api.java.JavaDoubleRDD.countApproxDistinct$default$1"),
+            ProblemFilters.exclude[MissingMethodProblem](
+              "org.apache.spark.storage.DiskStore.getValues"),
             ProblemFilters.exclude[MissingMethodProblem](
               "org.apache.spark.storage.MemoryStore.Entry")
           ) ++
diff --git a/tools/src/main/scala/org/apache/spark/tools/StoragePerfTester.scala b/tools/src/main/scala/org/apache/spark/tools/StoragePerfTester.scala
index 17bf7c2541d13..db58eb642b56d 100644
--- a/tools/src/main/scala/org/apache/spark/tools/StoragePerfTester.scala
+++ b/tools/src/main/scala/org/apache/spark/tools/StoragePerfTester.scala
@@ -20,10 +20,11 @@ package org.apache.spark.tools
 import java.util.concurrent.{CountDownLatch, Executors}
 import java.util.concurrent.atomic.AtomicLong
 
+import org.apache.spark.executor.ShuffleWriteMetrics
 import org.apache.spark.SparkContext
 import org.apache.spark.serializer.KryoSerializer
+import org.apache.spark.shuffle.hash.HashShuffleManager
 import org.apache.spark.util.Utils
-import org.apache.spark.executor.ShuffleWriteMetrics
 
 /**
  * Internal utility for micro-benchmarking shuffle write performance.
@@ -50,13 +51,15 @@ object StoragePerfTester {
 
     System.setProperty("spark.shuffle.compress", "false")
     System.setProperty("spark.shuffle.sync", "true")
+    System.setProperty("spark.shuffle.manager",
+      "org.apache.spark.shuffle.hash.HashShuffleManager")
 
     // This is only used to instantiate a BlockManager. All thread scheduling is done manually.
     val sc = new SparkContext("local[4]", "Write Tester")
-    val blockManager = sc.env.blockManager
+    val hashShuffleManager = sc.env.shuffleManager.asInstanceOf[HashShuffleManager]
 
     def writeOutputBytes(mapId: Int, total: AtomicLong) = {
-      val shuffle = blockManager.shuffleBlockManager.forMapTask(1, mapId, numOutputSplits,
+      val shuffle = hashShuffleManager.shuffleBlockManager.forMapTask(1, mapId, numOutputSplits,
         new KryoSerializer(sc.conf), new ShuffleWriteMetrics())
       val writers = shuffle.writers
       for (i <- 1 to recordsPerMap) {

From d90434c03564558a4208f64e15b20009eabe3645 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@apache.org>
Date: Fri, 29 Aug 2014 23:21:38 -0700
Subject: [PATCH 155/489] Manually close old pull requests Closes #1824


From b6cf1348170951396a6a5d8a65fb670382304f5b Mon Sep 17 00:00:00 2001
From: Marcelo Vanzin <vanzin@cloudera.com>
Date: Sat, 30 Aug 2014 14:48:07 -0700
Subject: [PATCH 156/489] [SPARK-2889] Create Hadoop config objects
 consistently.

Different places in the code were instantiating Configuration / YarnConfiguration objects in different ways. This could lead to confusion for people who actually expected "spark.hadoop.*" options to end up in the configs used by Spark code, since that would only happen for the SparkContext's config.

This change modifies most places to use SparkHadoopUtil to initialize configs, and make that method do the translation that previously was only done inside SparkContext.

The places that were not changed fall in one of the following categories:
- Test code where this doesn't really matter
- Places deep in the code where plumbing SparkConf would be too difficult for very little gain
- Default values for arguments - since the caller can provide their own config in that case

Author: Marcelo Vanzin <vanzin@cloudera.com>

Closes #1843 from vanzin/SPARK-2889 and squashes the following commits:

52daf35 [Marcelo Vanzin] Merge branch 'master' into SPARK-2889
f179013 [Marcelo Vanzin] Merge branch 'master' into SPARK-2889
51e71cf [Marcelo Vanzin] Add test to ensure that overriding Yarn configs works.
53f9506 [Marcelo Vanzin] Add DeveloperApi annotation.
3d345cb [Marcelo Vanzin] Restore old method for backwards compat.
fc45067 [Marcelo Vanzin] Merge branch 'master' into SPARK-2889
0ac3fdf [Marcelo Vanzin] Merge branch 'master' into SPARK-2889
3f26760 [Marcelo Vanzin] Compilation fix.
f16cadd [Marcelo Vanzin] Initialize config in SparkHadoopUtil.
b8ab173 [Marcelo Vanzin] Update Utils API to take a Configuration argument.
1e7003f [Marcelo Vanzin] Replace explicit Configuration instantiation with SparkHadoopUtil.
---
 .../scala/org/apache/spark/SparkContext.scala | 24 ++----------
 .../apache/spark/deploy/SparkHadoopUtil.scala | 39 +++++++++++++++++--
 .../deploy/history/FsHistoryProvider.scala    |  4 +-
 .../apache/spark/deploy/master/Master.scala   |  6 ++-
 .../spark/deploy/worker/DriverRunner.scala    | 11 +++---
 .../apache/spark/deploy/worker/Worker.scala   |  4 +-
 .../org/apache/spark/executor/Executor.scala  | 14 ++++---
 .../org/apache/spark/rdd/CheckpointRDD.scala  |  2 +-
 .../scheduler/EventLoggingListener.scala      |  5 ++-
 .../cluster/SimrSchedulerBackend.scala        |  5 ++-
 .../org/apache/spark/util/FileLogger.scala    | 13 ++++++-
 .../scala/org/apache/spark/util/Utils.scala   | 20 +++++-----
 .../spark/deploy/JsonProtocolSuite.scala      |  4 +-
 .../deploy/worker/DriverRunnerTest.scala      |  5 ++-
 .../scheduler/EventLoggingListenerSuite.scala |  4 +-
 .../spark/scheduler/ReplayListenerSuite.scala |  4 +-
 .../apache/spark/util/FileLoggerSuite.scala   |  4 +-
 .../apache/spark/examples/SparkHdfsLR.scala   |  4 +-
 .../spark/examples/SparkTachyonHdfsLR.scala   |  4 +-
 .../spark/repl/ExecutorClassLoader.scala      |  8 ++--
 .../spark/repl/ExecutorClassLoaderSuite.scala | 10 ++---
 .../org/apache/spark/deploy/yarn/Client.scala |  2 +-
 .../spark/deploy/yarn/ApplicationMaster.scala |  3 +-
 .../deploy/yarn/YarnSparkHadoopUtil.scala     |  3 +-
 .../cluster/YarnClientClusterScheduler.scala  |  8 +---
 .../cluster/YarnClusterScheduler.scala        |  9 +----
 .../yarn/YarnSparkHadoopUtilSuite.scala       | 15 ++++++-
 .../org/apache/spark/deploy/yarn/Client.scala |  4 +-
 28 files changed, 144 insertions(+), 94 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala
index e132955f0f850..a80b3cce6034d 100644
--- a/core/src/main/scala/org/apache/spark/SparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/SparkContext.scala
@@ -224,26 +224,7 @@ class SparkContext(config: SparkConf) extends Logging {
   ui.bind()
 
   /** A default Hadoop Configuration for the Hadoop code (e.g. file systems) that we reuse. */
-  val hadoopConfiguration: Configuration = {
-    val hadoopConf = SparkHadoopUtil.get.newConfiguration()
-    // Explicitly check for S3 environment variables
-    if (System.getenv("AWS_ACCESS_KEY_ID") != null &&
-        System.getenv("AWS_SECRET_ACCESS_KEY") != null) {
-      hadoopConf.set("fs.s3.awsAccessKeyId", System.getenv("AWS_ACCESS_KEY_ID"))
-      hadoopConf.set("fs.s3n.awsAccessKeyId", System.getenv("AWS_ACCESS_KEY_ID"))
-      hadoopConf.set("fs.s3.awsSecretAccessKey", System.getenv("AWS_SECRET_ACCESS_KEY"))
-      hadoopConf.set("fs.s3n.awsSecretAccessKey", System.getenv("AWS_SECRET_ACCESS_KEY"))
-    }
-    // Copy any "spark.hadoop.foo=bar" system properties into conf as "foo=bar"
-    conf.getAll.foreach { case (key, value) =>
-      if (key.startsWith("spark.hadoop.")) {
-        hadoopConf.set(key.substring("spark.hadoop.".length), value)
-      }
-    }
-    val bufferSize = conf.get("spark.buffer.size", "65536")
-    hadoopConf.set("io.file.buffer.size", bufferSize)
-    hadoopConf
-  }
+  val hadoopConfiguration = SparkHadoopUtil.get.newConfiguration(conf)
 
   // Optionally log Spark events
   private[spark] val eventLogger: Option[EventLoggingListener] = {
@@ -827,7 +808,8 @@ class SparkContext(config: SparkConf) extends Logging {
     addedFiles(key) = System.currentTimeMillis
 
     // Fetch the file locally in case a job is executed using DAGScheduler.runLocally().
-    Utils.fetchFile(path, new File(SparkFiles.getRootDirectory()), conf, env.securityManager)
+    Utils.fetchFile(path, new File(SparkFiles.getRootDirectory()), conf, env.securityManager,
+      hadoopConfiguration)
 
     logInfo("Added file " + path + " at " + key + " with timestamp " + addedFiles(key))
     postEnvironmentUpdate()
diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala b/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala
index 148115d3ed351..fe0ad9ebbca12 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala
@@ -24,15 +24,18 @@ import org.apache.hadoop.mapred.JobConf
 import org.apache.hadoop.security.Credentials
 import org.apache.hadoop.security.UserGroupInformation
 
-import org.apache.spark.{Logging, SparkContext, SparkException}
+import org.apache.spark.{Logging, SparkContext, SparkConf, SparkException}
+import org.apache.spark.annotation.DeveloperApi
 
 import scala.collection.JavaConversions._
 
 /**
+ * :: DeveloperApi ::
  * Contains util methods to interact with Hadoop from Spark.
  */
+@DeveloperApi
 class SparkHadoopUtil extends Logging {
-  val conf: Configuration = newConfiguration()
+  val conf: Configuration = newConfiguration(new SparkConf())
   UserGroupInformation.setConfiguration(conf)
 
   /**
@@ -64,11 +67,39 @@ class SparkHadoopUtil extends Logging {
     }
   }
 
+  @Deprecated
+  def newConfiguration(): Configuration = newConfiguration(null)
+
   /**
    * Return an appropriate (subclass) of Configuration. Creating config can initializes some Hadoop
    * subsystems.
    */
-  def newConfiguration(): Configuration = new Configuration()
+  def newConfiguration(conf: SparkConf): Configuration = {
+    val hadoopConf = new Configuration()
+
+    // Note: this null check is around more than just access to the "conf" object to maintain
+    // the behavior of the old implementation of this code, for backwards compatibility.
+    if (conf != null) {
+      // Explicitly check for S3 environment variables
+      if (System.getenv("AWS_ACCESS_KEY_ID") != null &&
+          System.getenv("AWS_SECRET_ACCESS_KEY") != null) {
+        hadoopConf.set("fs.s3.awsAccessKeyId", System.getenv("AWS_ACCESS_KEY_ID"))
+        hadoopConf.set("fs.s3n.awsAccessKeyId", System.getenv("AWS_ACCESS_KEY_ID"))
+        hadoopConf.set("fs.s3.awsSecretAccessKey", System.getenv("AWS_SECRET_ACCESS_KEY"))
+        hadoopConf.set("fs.s3n.awsSecretAccessKey", System.getenv("AWS_SECRET_ACCESS_KEY"))
+      }
+      // Copy any "spark.hadoop.foo=bar" system properties into conf as "foo=bar"
+      conf.getAll.foreach { case (key, value) =>
+        if (key.startsWith("spark.hadoop.")) {
+          hadoopConf.set(key.substring("spark.hadoop.".length), value)
+        }
+      }
+      val bufferSize = conf.get("spark.buffer.size", "65536")
+      hadoopConf.set("io.file.buffer.size", bufferSize)
+    }
+
+    hadoopConf
+  }
 
   /**
    * Add any user credentials to the job conf which are necessary for running on a secure Hadoop
@@ -86,7 +117,7 @@ class SparkHadoopUtil extends Logging {
 
   def getSecretKeyFromUserCredentials(key: String): Array[Byte] = { null }
 
-  def loginUserFromKeytab(principalName: String, keytabFilename: String) { 
+  def loginUserFromKeytab(principalName: String, keytabFilename: String) {
     UserGroupInformation.loginUserFromKeytab(principalName, keytabFilename)
   }
 
diff --git a/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala b/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala
index cc06540ee0647..05c8a90782c74 100644
--- a/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala
@@ -24,6 +24,7 @@ import scala.collection.mutable
 import org.apache.hadoop.fs.{FileStatus, Path}
 
 import org.apache.spark.{Logging, SecurityManager, SparkConf}
+import org.apache.spark.deploy.SparkHadoopUtil
 import org.apache.spark.scheduler._
 import org.apache.spark.ui.SparkUI
 import org.apache.spark.util.Utils
@@ -40,7 +41,8 @@ private[history] class FsHistoryProvider(conf: SparkConf) extends ApplicationHis
     .map { d => Utils.resolveURI(d) }
     .getOrElse { throw new IllegalArgumentException("Logging directory must be specified.") }
 
-  private val fs = Utils.getHadoopFileSystem(resolvedLogDir)
+  private val fs = Utils.getHadoopFileSystem(resolvedLogDir,
+    SparkHadoopUtil.get.newConfiguration(conf))
 
   // A timestamp of when the disk was last accessed to check for log updates
   private var lastLogCheckTimeMs = -1L
diff --git a/core/src/main/scala/org/apache/spark/deploy/master/Master.scala b/core/src/main/scala/org/apache/spark/deploy/master/Master.scala
index 5017273e87c07..2a66fcfe4801c 100644
--- a/core/src/main/scala/org/apache/spark/deploy/master/Master.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/master/Master.scala
@@ -33,7 +33,8 @@ import akka.remote.{DisassociatedEvent, RemotingLifecycleEvent}
 import akka.serialization.SerializationExtension
 
 import org.apache.spark.{Logging, SecurityManager, SparkConf, SparkException}
-import org.apache.spark.deploy.{ApplicationDescription, DriverDescription, ExecutorState}
+import org.apache.spark.deploy.{ApplicationDescription, DriverDescription, ExecutorState,
+  SparkHadoopUtil}
 import org.apache.spark.deploy.DeployMessages._
 import org.apache.spark.deploy.history.HistoryServer
 import org.apache.spark.deploy.master.DriverState.DriverState
@@ -673,7 +674,8 @@ private[spark] class Master(
       app.desc.appUiUrl = notFoundBasePath
       return false
     }
-    val fileSystem = Utils.getHadoopFileSystem(eventLogDir)
+    val fileSystem = Utils.getHadoopFileSystem(eventLogDir,
+      SparkHadoopUtil.get.newConfiguration(conf))
     val eventLogInfo = EventLoggingListener.parseLoggingInfo(eventLogDir, fileSystem)
     val eventLogPaths = eventLogInfo.logPaths
     val compressionCodec = eventLogInfo.compressionCodec
diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/DriverRunner.scala b/core/src/main/scala/org/apache/spark/deploy/worker/DriverRunner.scala
index 5caaf6bea3575..9f9911762505a 100644
--- a/core/src/main/scala/org/apache/spark/deploy/worker/DriverRunner.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/worker/DriverRunner.scala
@@ -28,8 +28,8 @@ import com.google.common.io.Files
 import org.apache.hadoop.conf.Configuration
 import org.apache.hadoop.fs.{FileUtil, Path}
 
-import org.apache.spark.Logging
-import org.apache.spark.deploy.{Command, DriverDescription}
+import org.apache.spark.{Logging, SparkConf}
+import org.apache.spark.deploy.{Command, DriverDescription, SparkHadoopUtil}
 import org.apache.spark.deploy.DeployMessages.DriverStateChanged
 import org.apache.spark.deploy.master.DriverState
 import org.apache.spark.deploy.master.DriverState.DriverState
@@ -39,6 +39,7 @@ import org.apache.spark.deploy.master.DriverState.DriverState
  * This is currently only used in standalone cluster deploy mode.
  */
 private[spark] class DriverRunner(
+    val conf: SparkConf,
     val driverId: String,
     val workDir: File,
     val sparkHome: File,
@@ -144,8 +145,8 @@ private[spark] class DriverRunner(
 
     val jarPath = new Path(driverDesc.jarUrl)
 
-    val emptyConf = new Configuration()
-    val jarFileSystem = jarPath.getFileSystem(emptyConf)
+    val hadoopConf = SparkHadoopUtil.get.newConfiguration(conf)
+    val jarFileSystem = jarPath.getFileSystem(hadoopConf)
 
     val destPath = new File(driverDir.getAbsolutePath, jarPath.getName)
     val jarFileName = jarPath.getName
@@ -154,7 +155,7 @@ private[spark] class DriverRunner(
 
     if (!localJarFile.exists()) { // May already exist if running multiple workers on one node
       logInfo(s"Copying user jar $jarPath to $destPath")
-      FileUtil.copy(jarFileSystem, jarPath, destPath, false, emptyConf)
+      FileUtil.copy(jarFileSystem, jarPath, destPath, false, hadoopConf)
     }
 
     if (!localJarFile.exists()) { // Verify copy succeeded
diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala b/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala
index 81400af22c0bf..e475567db6a20 100755
--- a/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala
@@ -257,7 +257,7 @@ private[spark] class Worker(
       val fullId = appId + "/" + execId
       if (ExecutorState.isFinished(state)) {
         executors.get(fullId) match {
-          case Some(executor) => 
+          case Some(executor) =>
             logInfo("Executor " + fullId + " finished with state " + state +
               message.map(" message " + _).getOrElse("") +
               exitStatus.map(" exitStatus " + _).getOrElse(""))
@@ -288,7 +288,7 @@ private[spark] class Worker(
 
     case LaunchDriver(driverId, driverDesc) => {
       logInfo(s"Asked to launch driver $driverId")
-      val driver = new DriverRunner(driverId, workDir, sparkHome, driverDesc, self, akkaUrl)
+      val driver = new DriverRunner(conf, driverId, workDir, sparkHome, driverDesc, self, akkaUrl)
       drivers(driverId) = driver
       driver.start()
 
diff --git a/core/src/main/scala/org/apache/spark/executor/Executor.scala b/core/src/main/scala/org/apache/spark/executor/Executor.scala
index 2f76e532aeb76..d7d19f6fa3b96 100644
--- a/core/src/main/scala/org/apache/spark/executor/Executor.scala
+++ b/core/src/main/scala/org/apache/spark/executor/Executor.scala
@@ -26,6 +26,7 @@ import scala.collection.JavaConversions._
 import scala.collection.mutable.{ArrayBuffer, HashMap}
 
 import org.apache.spark._
+import org.apache.spark.deploy.SparkHadoopUtil
 import org.apache.spark.scheduler._
 import org.apache.spark.shuffle.FetchFailedException
 import org.apache.spark.storage.{StorageLevel, TaskResultBlockId}
@@ -294,9 +295,9 @@ private[spark] class Executor(
       try {
         val klass = Class.forName("org.apache.spark.repl.ExecutorClassLoader")
           .asInstanceOf[Class[_ <: ClassLoader]]
-        val constructor = klass.getConstructor(classOf[String], classOf[ClassLoader],
-          classOf[Boolean])
-        constructor.newInstance(classUri, parent, userClassPathFirst)
+        val constructor = klass.getConstructor(classOf[SparkConf], classOf[String],
+          classOf[ClassLoader], classOf[Boolean])
+        constructor.newInstance(conf, classUri, parent, userClassPathFirst)
       } catch {
         case _: ClassNotFoundException =>
           logError("Could not find org.apache.spark.repl.ExecutorClassLoader on classpath!")
@@ -313,16 +314,19 @@ private[spark] class Executor(
    * SparkContext. Also adds any new JARs we fetched to the class loader.
    */
   private def updateDependencies(newFiles: HashMap[String, Long], newJars: HashMap[String, Long]) {
+    val hadoopConf = SparkHadoopUtil.get.newConfiguration(conf)
     synchronized {
       // Fetch missing dependencies
       for ((name, timestamp) <- newFiles if currentFiles.getOrElse(name, -1L) < timestamp) {
         logInfo("Fetching " + name + " with timestamp " + timestamp)
-        Utils.fetchFile(name, new File(SparkFiles.getRootDirectory), conf, env.securityManager)
+        Utils.fetchFile(name, new File(SparkFiles.getRootDirectory), conf, env.securityManager,
+          hadoopConf)
         currentFiles(name) = timestamp
       }
       for ((name, timestamp) <- newJars if currentJars.getOrElse(name, -1L) < timestamp) {
         logInfo("Fetching " + name + " with timestamp " + timestamp)
-        Utils.fetchFile(name, new File(SparkFiles.getRootDirectory), conf, env.securityManager)
+        Utils.fetchFile(name, new File(SparkFiles.getRootDirectory), conf, env.securityManager,
+          hadoopConf)
         currentJars(name) = timestamp
         // Add it to our class loader
         val localName = name.split("/").last
diff --git a/core/src/main/scala/org/apache/spark/rdd/CheckpointRDD.scala b/core/src/main/scala/org/apache/spark/rdd/CheckpointRDD.scala
index 20938781ac694..7ba1182f0ed27 100644
--- a/core/src/main/scala/org/apache/spark/rdd/CheckpointRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/CheckpointRDD.scala
@@ -157,7 +157,7 @@ private[spark] object CheckpointRDD extends Logging {
     val sc = new SparkContext(cluster, "CheckpointRDD Test")
     val rdd = sc.makeRDD(1 to 10, 10).flatMap(x => 1 to 10000)
     val path = new Path(hdfsPath, "temp")
-    val conf = SparkHadoopUtil.get.newConfiguration()
+    val conf = SparkHadoopUtil.get.newConfiguration(new SparkConf())
     val fs = path.getFileSystem(conf)
     val broadcastedConf = sc.broadcast(new SerializableWritable(conf))
     sc.runJob(rdd, CheckpointRDD.writeToFile[Int](path.toString, broadcastedConf, 1024) _)
diff --git a/core/src/main/scala/org/apache/spark/scheduler/EventLoggingListener.scala b/core/src/main/scala/org/apache/spark/scheduler/EventLoggingListener.scala
index 370fcd85aa680..4b99f630440ad 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/EventLoggingListener.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/EventLoggingListener.scala
@@ -44,11 +44,14 @@ import org.apache.spark.util.{FileLogger, JsonProtocol, Utils}
 private[spark] class EventLoggingListener(
     appName: String,
     sparkConf: SparkConf,
-    hadoopConf: Configuration = SparkHadoopUtil.get.newConfiguration())
+    hadoopConf: Configuration)
   extends SparkListener with Logging {
 
   import EventLoggingListener._
 
+  def this(appName: String, sparkConf: SparkConf) =
+    this(appName, sparkConf, SparkHadoopUtil.get.newConfiguration(sparkConf))
+
   private val shouldCompress = sparkConf.getBoolean("spark.eventLog.compress", false)
   private val shouldOverwrite = sparkConf.getBoolean("spark.eventLog.overwrite", false)
   private val testing = sparkConf.getBoolean("spark.eventLog.testing", false)
diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/SimrSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/SimrSchedulerBackend.scala
index 4f7133c4bc17c..bc7670f4a804d 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/SimrSchedulerBackend.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/SimrSchedulerBackend.scala
@@ -21,6 +21,7 @@ import org.apache.hadoop.conf.Configuration
 import org.apache.hadoop.fs.{Path, FileSystem}
 
 import org.apache.spark.{Logging, SparkContext, SparkEnv}
+import org.apache.spark.deploy.SparkHadoopUtil
 import org.apache.spark.scheduler.TaskSchedulerImpl
 
 private[spark] class SimrSchedulerBackend(
@@ -44,7 +45,7 @@ private[spark] class SimrSchedulerBackend(
       sc.conf.get("spark.driver.port"),
       CoarseGrainedSchedulerBackend.ACTOR_NAME)
 
-    val conf = new Configuration()
+    val conf = SparkHadoopUtil.get.newConfiguration(sc.conf)
     val fs = FileSystem.get(conf)
 
     logInfo("Writing to HDFS file: "  + driverFilePath)
@@ -63,7 +64,7 @@ private[spark] class SimrSchedulerBackend(
   }
 
   override def stop() {
-    val conf = new Configuration()
+    val conf = SparkHadoopUtil.get.newConfiguration(sc.conf)
     val fs = FileSystem.get(conf)
     fs.delete(new Path(driverFilePath), false)
     super.stop()
diff --git a/core/src/main/scala/org/apache/spark/util/FileLogger.scala b/core/src/main/scala/org/apache/spark/util/FileLogger.scala
index ad8b79af877d8..6d1fc05a15d2c 100644
--- a/core/src/main/scala/org/apache/spark/util/FileLogger.scala
+++ b/core/src/main/scala/org/apache/spark/util/FileLogger.scala
@@ -41,13 +41,22 @@ import org.apache.spark.io.CompressionCodec
 private[spark] class FileLogger(
     logDir: String,
     sparkConf: SparkConf,
-    hadoopConf: Configuration = SparkHadoopUtil.get.newConfiguration(),
+    hadoopConf: Configuration,
     outputBufferSize: Int = 8 * 1024, // 8 KB
     compress: Boolean = false,
     overwrite: Boolean = true,
     dirPermissions: Option[FsPermission] = None)
   extends Logging {
 
+  def this(
+      logDir: String,
+      sparkConf: SparkConf,
+      compress: Boolean = false,
+      overwrite: Boolean = true) = {
+    this(logDir, sparkConf, SparkHadoopUtil.get.newConfiguration(sparkConf), compress = compress,
+      overwrite = overwrite)
+  }
+
   private val dateFormat = new ThreadLocal[SimpleDateFormat]() {
     override def initialValue(): SimpleDateFormat = new SimpleDateFormat("yyyy/MM/dd HH:mm:ss")
   }
@@ -57,7 +66,7 @@ private[spark] class FileLogger(
    * create unique FileSystem instance only for FileLogger
    */
   private val fileSystem = {
-    val conf = SparkHadoopUtil.get.newConfiguration()
+    val conf = SparkHadoopUtil.get.newConfiguration(sparkConf)
     val logUri = new URI(logDir)
     val scheme = logUri.getScheme
     if (scheme == "hdfs") {
diff --git a/core/src/main/scala/org/apache/spark/util/Utils.scala b/core/src/main/scala/org/apache/spark/util/Utils.scala
index 86f646d2af181..0ae28f911e302 100644
--- a/core/src/main/scala/org/apache/spark/util/Utils.scala
+++ b/core/src/main/scala/org/apache/spark/util/Utils.scala
@@ -34,6 +34,7 @@ import scala.util.control.{ControlThrowable, NonFatal}
 import com.google.common.io.Files
 import com.google.common.util.concurrent.ThreadFactoryBuilder
 import org.apache.commons.lang3.SystemUtils
+import org.apache.hadoop.conf.Configuration
 import org.apache.hadoop.fs.{FileSystem, FileUtil, Path}
 import org.json4s._
 import tachyon.client.{TachyonFile,TachyonFS}
@@ -318,7 +319,8 @@ private[spark] object Utils extends Logging {
    * Throws SparkException if the target file already exists and has different contents than
    * the requested file.
    */
-  def fetchFile(url: String, targetDir: File, conf: SparkConf, securityMgr: SecurityManager) {
+  def fetchFile(url: String, targetDir: File, conf: SparkConf, securityMgr: SecurityManager,
+    hadoopConf: Configuration) {
     val filename = url.split("/").last
     val tempDir = getLocalDir(conf)
     val tempFile =  File.createTempFile("fetchFileTemp", null, new File(tempDir))
@@ -390,7 +392,7 @@ private[spark] object Utils extends Logging {
         }
       case _ =>
         // Use the Hadoop filesystem library, which supports file://, hdfs://, s3://, and others
-        val fs = getHadoopFileSystem(uri)
+        val fs = getHadoopFileSystem(uri, hadoopConf)
         val in = fs.open(new Path(uri))
         val out = new FileOutputStream(tempFile)
         Utils.copyStream(in, out, true)
@@ -862,8 +864,8 @@ private[spark] object Utils extends Logging {
    */
   def getCallSite: CallSite = {
     val trace = Thread.currentThread.getStackTrace()
-      .filterNot { ste:StackTraceElement => 
-        // When running under some profilers, the current stack trace might contain some bogus 
+      .filterNot { ste:StackTraceElement =>
+        // When running under some profilers, the current stack trace might contain some bogus
         // frames. This is intended to ensure that we don't crash in these situations by
         // ignoring any frames that we can't examine.
         (ste == null || ste.getMethodName == null || ste.getMethodName.contains("getStackTrace"))
@@ -1179,15 +1181,15 @@ private[spark] object Utils extends Logging {
   /**
    * Return a Hadoop FileSystem with the scheme encoded in the given path.
    */
-  def getHadoopFileSystem(path: URI): FileSystem = {
-    FileSystem.get(path, SparkHadoopUtil.get.newConfiguration())
+  def getHadoopFileSystem(path: URI, conf: Configuration): FileSystem = {
+    FileSystem.get(path, conf)
   }
 
   /**
    * Return a Hadoop FileSystem with the scheme encoded in the given path.
    */
-  def getHadoopFileSystem(path: String): FileSystem = {
-    getHadoopFileSystem(new URI(path))
+  def getHadoopFileSystem(path: String, conf: Configuration): FileSystem = {
+    getHadoopFileSystem(new URI(path), conf)
   }
 
   /**
@@ -1264,7 +1266,7 @@ private[spark] object Utils extends Logging {
     }
   }
 
-  /** 
+  /**
    * Execute the given block, logging and re-throwing any uncaught exception.
    * This is particularly useful for wrapping code that runs in a thread, to ensure
    * that exceptions are printed, and to avoid having to catch Throwable.
diff --git a/core/src/test/scala/org/apache/spark/deploy/JsonProtocolSuite.scala b/core/src/test/scala/org/apache/spark/deploy/JsonProtocolSuite.scala
index 31aa7ec837f43..2a58c6a40d8e4 100644
--- a/core/src/test/scala/org/apache/spark/deploy/JsonProtocolSuite.scala
+++ b/core/src/test/scala/org/apache/spark/deploy/JsonProtocolSuite.scala
@@ -121,8 +121,8 @@ class JsonProtocolSuite extends FunSuite {
       new SparkConf, ExecutorState.RUNNING)
   }
   def createDriverRunner(): DriverRunner = {
-    new DriverRunner("driverId", new File("workDir"), new File("sparkHome"), createDriverDesc(),
-      null, "akka://worker")
+    new DriverRunner(new SparkConf(), "driverId", new File("workDir"), new File("sparkHome"),
+      createDriverDesc(), null, "akka://worker")
   }
 
   def assertValidJson(json: JValue) {
diff --git a/core/src/test/scala/org/apache/spark/deploy/worker/DriverRunnerTest.scala b/core/src/test/scala/org/apache/spark/deploy/worker/DriverRunnerTest.scala
index c930839b47f11..b6f4411e0587a 100644
--- a/core/src/test/scala/org/apache/spark/deploy/worker/DriverRunnerTest.scala
+++ b/core/src/test/scala/org/apache/spark/deploy/worker/DriverRunnerTest.scala
@@ -25,14 +25,15 @@ import org.mockito.invocation.InvocationOnMock
 import org.mockito.stubbing.Answer
 import org.scalatest.FunSuite
 
+import org.apache.spark.SparkConf
 import org.apache.spark.deploy.{Command, DriverDescription}
 
 class DriverRunnerTest extends FunSuite {
   private def createDriverRunner() = {
     val command = new Command("mainClass", Seq(), Map(), Seq(), Seq(), Seq())
     val driverDescription = new DriverDescription("jarUrl", 512, 1, true, command)
-    new DriverRunner("driverId", new File("workDir"), new File("sparkHome"), driverDescription,
-      null, "akka://1.2.3.4/worker/")
+    new DriverRunner(new SparkConf(), "driverId", new File("workDir"), new File("sparkHome"),
+      driverDescription, null, "akka://1.2.3.4/worker/")
   }
 
   private def createProcessBuilderAndProcess(): (ProcessBuilderLike, Process) = {
diff --git a/core/src/test/scala/org/apache/spark/scheduler/EventLoggingListenerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/EventLoggingListenerSuite.scala
index 10d8b299317ea..41e58a008c533 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/EventLoggingListenerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/EventLoggingListenerSuite.scala
@@ -26,6 +26,7 @@ import org.json4s.jackson.JsonMethods._
 import org.scalatest.{BeforeAndAfter, FunSuite}
 
 import org.apache.spark.{SparkConf, SparkContext}
+import org.apache.spark.deploy.SparkHadoopUtil
 import org.apache.spark.io.CompressionCodec
 import org.apache.spark.util.{JsonProtocol, Utils}
 
@@ -39,7 +40,8 @@ import java.io.File
  * read and deserialized into actual SparkListenerEvents.
  */
 class EventLoggingListenerSuite extends FunSuite with BeforeAndAfter {
-  private val fileSystem = Utils.getHadoopFileSystem("/")
+  private val fileSystem = Utils.getHadoopFileSystem("/",
+    SparkHadoopUtil.get.newConfiguration(new SparkConf()))
   private val allCompressionCodecs = Seq[String](
     "org.apache.spark.io.LZFCompressionCodec",
     "org.apache.spark.io.SnappyCompressionCodec"
diff --git a/core/src/test/scala/org/apache/spark/scheduler/ReplayListenerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/ReplayListenerSuite.scala
index 6b6e0104e5467..8f0ee9f4dbafd 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/ReplayListenerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/ReplayListenerSuite.scala
@@ -25,6 +25,7 @@ import org.scalatest.{BeforeAndAfter, FunSuite}
 
 import org.apache.spark.SparkContext._
 import org.apache.spark.{SparkConf, SparkContext}
+import org.apache.spark.deploy.SparkHadoopUtil
 import org.apache.spark.io.CompressionCodec
 import org.apache.spark.util.{JsonProtocol, Utils}
 
@@ -32,7 +33,8 @@ import org.apache.spark.util.{JsonProtocol, Utils}
  * Test whether ReplayListenerBus replays events from logs correctly.
  */
 class ReplayListenerSuite extends FunSuite with BeforeAndAfter {
-  private val fileSystem = Utils.getHadoopFileSystem("/")
+  private val fileSystem = Utils.getHadoopFileSystem("/",
+    SparkHadoopUtil.get.newConfiguration(new SparkConf()))
   private val allCompressionCodecs = CompressionCodec.ALL_COMPRESSION_CODECS
   private var testDir: File = _
 
diff --git a/core/src/test/scala/org/apache/spark/util/FileLoggerSuite.scala b/core/src/test/scala/org/apache/spark/util/FileLoggerSuite.scala
index 44332fc8dbc23..c3dd156b40514 100644
--- a/core/src/test/scala/org/apache/spark/util/FileLoggerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/util/FileLoggerSuite.scala
@@ -26,13 +26,15 @@ import org.apache.hadoop.fs.Path
 import org.scalatest.{BeforeAndAfter, FunSuite}
 
 import org.apache.spark.SparkConf
+import org.apache.spark.deploy.SparkHadoopUtil
 import org.apache.spark.io.CompressionCodec
 
 /**
  * Test writing files through the FileLogger.
  */
 class FileLoggerSuite extends FunSuite with BeforeAndAfter {
-  private val fileSystem = Utils.getHadoopFileSystem("/")
+  private val fileSystem = Utils.getHadoopFileSystem("/",
+    SparkHadoopUtil.get.newConfiguration(new SparkConf()))
   private val allCompressionCodecs = Seq[String](
     "org.apache.spark.io.LZFCompressionCodec",
     "org.apache.spark.io.SnappyCompressionCodec"
diff --git a/examples/src/main/scala/org/apache/spark/examples/SparkHdfsLR.scala b/examples/src/main/scala/org/apache/spark/examples/SparkHdfsLR.scala
index d583cf421ed23..3258510894372 100644
--- a/examples/src/main/scala/org/apache/spark/examples/SparkHdfsLR.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/SparkHdfsLR.scala
@@ -22,9 +22,9 @@ import java.util.Random
 import scala.math.exp
 
 import breeze.linalg.{Vector, DenseVector}
+import org.apache.hadoop.conf.Configuration
 
 import org.apache.spark._
-import org.apache.spark.deploy.SparkHadoopUtil
 import org.apache.spark.scheduler.InputFormatInfo
 
 
@@ -70,7 +70,7 @@ object SparkHdfsLR {
 
     val sparkConf = new SparkConf().setAppName("SparkHdfsLR")
     val inputPath = args(0)
-    val conf = SparkHadoopUtil.get.newConfiguration()
+    val conf = new Configuration()
     val sc = new SparkContext(sparkConf,
       InputFormatInfo.computePreferredLocations(
         Seq(new InputFormatInfo(conf, classOf[org.apache.hadoop.mapred.TextInputFormat], inputPath))
diff --git a/examples/src/main/scala/org/apache/spark/examples/SparkTachyonHdfsLR.scala b/examples/src/main/scala/org/apache/spark/examples/SparkTachyonHdfsLR.scala
index 22127621867e1..96d13612e46dd 100644
--- a/examples/src/main/scala/org/apache/spark/examples/SparkTachyonHdfsLR.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/SparkTachyonHdfsLR.scala
@@ -22,9 +22,9 @@ import java.util.Random
 import scala.math.exp
 
 import breeze.linalg.{Vector, DenseVector}
+import org.apache.hadoop.conf.Configuration
 
 import org.apache.spark._
-import org.apache.spark.deploy.SparkHadoopUtil
 import org.apache.spark.scheduler.InputFormatInfo
 import org.apache.spark.storage.StorageLevel
 
@@ -52,8 +52,8 @@ object SparkTachyonHdfsLR {
 
   def main(args: Array[String]) {
     val inputPath = args(0)
-    val conf = SparkHadoopUtil.get.newConfiguration()
     val sparkConf = new SparkConf().setAppName("SparkTachyonHdfsLR")
+    val conf = new Configuration()
     val sc = new SparkContext(sparkConf,
       InputFormatInfo.computePreferredLocations(
         Seq(new InputFormatInfo(conf, classOf[org.apache.hadoop.mapred.TextInputFormat], inputPath))
diff --git a/repl/src/main/scala/org/apache/spark/repl/ExecutorClassLoader.scala b/repl/src/main/scala/org/apache/spark/repl/ExecutorClassLoader.scala
index 687e85ca94d3c..5ee325008a5cd 100644
--- a/repl/src/main/scala/org/apache/spark/repl/ExecutorClassLoader.scala
+++ b/repl/src/main/scala/org/apache/spark/repl/ExecutorClassLoader.scala
@@ -21,10 +21,10 @@ import java.io.{ByteArrayOutputStream, InputStream}
 import java.net.{URI, URL, URLEncoder}
 import java.util.concurrent.{Executors, ExecutorService}
 
-import org.apache.hadoop.conf.Configuration
 import org.apache.hadoop.fs.{FileSystem, Path}
 
-import org.apache.spark.SparkEnv
+import org.apache.spark.{SparkConf, SparkEnv}
+import org.apache.spark.deploy.SparkHadoopUtil
 import org.apache.spark.util.Utils
 import org.apache.spark.util.ParentClassLoader
 
@@ -36,7 +36,7 @@ import com.esotericsoftware.reflectasm.shaded.org.objectweb.asm.Opcodes._
  * used to load classes defined by the interpreter when the REPL is used.
  * Allows the user to specify if user class path should be first
  */
-class ExecutorClassLoader(classUri: String, parent: ClassLoader,
+class ExecutorClassLoader(conf: SparkConf, classUri: String, parent: ClassLoader,
     userClassPathFirst: Boolean) extends ClassLoader {
   val uri = new URI(classUri)
   val directory = uri.getPath
@@ -48,7 +48,7 @@ class ExecutorClassLoader(classUri: String, parent: ClassLoader,
     if (uri.getScheme() == "http") {
       null
     } else {
-      FileSystem.get(uri, new Configuration())
+      FileSystem.get(uri, SparkHadoopUtil.get.newConfiguration(conf))
     }
   }
 
diff --git a/repl/src/test/scala/org/apache/spark/repl/ExecutorClassLoaderSuite.scala b/repl/src/test/scala/org/apache/spark/repl/ExecutorClassLoaderSuite.scala
index c0af7ceb6d3ef..3e2ee7541f40d 100644
--- a/repl/src/test/scala/org/apache/spark/repl/ExecutorClassLoaderSuite.scala
+++ b/repl/src/test/scala/org/apache/spark/repl/ExecutorClassLoaderSuite.scala
@@ -25,7 +25,7 @@ import org.scalatest.FunSuite
 
 import com.google.common.io.Files
 
-import org.apache.spark.TestUtils
+import org.apache.spark.{SparkConf, TestUtils}
 import org.apache.spark.util.Utils
 
 class ExecutorClassLoaderSuite extends FunSuite with BeforeAndAfterAll {
@@ -57,7 +57,7 @@ class ExecutorClassLoaderSuite extends FunSuite with BeforeAndAfterAll {
 
   test("child first") {
     val parentLoader = new URLClassLoader(urls2, null)
-    val classLoader = new ExecutorClassLoader(url1, parentLoader, true)
+    val classLoader = new ExecutorClassLoader(new SparkConf(), url1, parentLoader, true)
     val fakeClass = classLoader.loadClass("ReplFakeClass2").newInstance()
     val fakeClassVersion = fakeClass.toString
     assert(fakeClassVersion === "1")
@@ -65,7 +65,7 @@ class ExecutorClassLoaderSuite extends FunSuite with BeforeAndAfterAll {
 
   test("parent first") {
     val parentLoader = new URLClassLoader(urls2, null)
-    val classLoader = new ExecutorClassLoader(url1, parentLoader, false)
+    val classLoader = new ExecutorClassLoader(new SparkConf(), url1, parentLoader, false)
     val fakeClass = classLoader.loadClass("ReplFakeClass1").newInstance()
     val fakeClassVersion = fakeClass.toString
     assert(fakeClassVersion === "2")
@@ -73,7 +73,7 @@ class ExecutorClassLoaderSuite extends FunSuite with BeforeAndAfterAll {
 
   test("child first can fall back") {
     val parentLoader = new URLClassLoader(urls2, null)
-    val classLoader = new ExecutorClassLoader(url1, parentLoader, true)
+    val classLoader = new ExecutorClassLoader(new SparkConf(), url1, parentLoader, true)
     val fakeClass = classLoader.loadClass("ReplFakeClass3").newInstance()
     val fakeClassVersion = fakeClass.toString
     assert(fakeClassVersion === "2")
@@ -81,7 +81,7 @@ class ExecutorClassLoaderSuite extends FunSuite with BeforeAndAfterAll {
 
   test("child first can fail") {
     val parentLoader = new URLClassLoader(urls2, null)
-    val classLoader = new ExecutorClassLoader(url1, parentLoader, true)
+    val classLoader = new ExecutorClassLoader(new SparkConf(), url1, parentLoader, true)
     intercept[java.lang.ClassNotFoundException] {
       classLoader.loadClass("ReplFakeClassDoesNotExist").newInstance()
     }
diff --git a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/Client.scala b/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
index 9be78546c1091..12f1cd3813a05 100644
--- a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
+++ b/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
@@ -40,7 +40,7 @@ class Client(clientArgs: ClientArguments, hadoopConf: Configuration, spConf: Spa
   extends YarnClientImpl with ClientBase with Logging {
 
   def this(clientArgs: ClientArguments, spConf: SparkConf) =
-    this(clientArgs, new Configuration(), spConf)
+    this(clientArgs, SparkHadoopUtil.get.newConfiguration(spConf), spConf)
 
   def this(clientArgs: ClientArguments) = this(clientArgs, new SparkConf())
 
diff --git a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
index 5f66a98e7528b..8c548409719da 100644
--- a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
+++ b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
@@ -48,7 +48,8 @@ private[spark] class ApplicationMaster(args: ApplicationMasterArguments,
   // optimal as more containers are available. Might need to handle this better.
 
   private val sparkConf = new SparkConf()
-  private val yarnConf: YarnConfiguration = new YarnConfiguration(new Configuration())
+  private val yarnConf: YarnConfiguration = SparkHadoopUtil.get.newConfiguration(sparkConf)
+    .asInstanceOf[YarnConfiguration]
   private val isDriver = args.userClass != null
 
   // Default to numExecutors * 2, with minimum of 3
diff --git a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtil.scala b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtil.scala
index 2aa27a1908582..ffe2731ca1d17 100644
--- a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtil.scala
+++ b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtil.scala
@@ -54,7 +54,8 @@ class YarnSparkHadoopUtil extends SparkHadoopUtil {
 
   // Return an appropriate (subclass) of Configuration. Creating config can initializes some hadoop subsystems
   // Always create a new config, dont reuse yarnConf.
-  override def newConfiguration(): Configuration = new YarnConfiguration(new Configuration())
+  override def newConfiguration(conf: SparkConf): Configuration =
+    new YarnConfiguration(super.newConfiguration(conf))
 
   // add any user credentials to the job conf which are necessary for running on a secure Hadoop cluster
   override def addCredentials(conf: JobConf) {
diff --git a/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientClusterScheduler.scala b/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientClusterScheduler.scala
index d162b4c433f46..254774a6b839e 100644
--- a/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientClusterScheduler.scala
+++ b/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientClusterScheduler.scala
@@ -18,7 +18,6 @@
 package org.apache.spark.scheduler.cluster
 
 import org.apache.spark._
-import org.apache.hadoop.conf.Configuration
 import org.apache.spark.deploy.yarn.YarnSparkHadoopUtil
 import org.apache.spark.scheduler.TaskSchedulerImpl
 import org.apache.spark.util.Utils
@@ -26,14 +25,11 @@ import org.apache.spark.util.Utils
 /**
  * This scheduler launches executors through Yarn - by calling into Client to launch the Spark AM.
  */
-private[spark] class YarnClientClusterScheduler(sc: SparkContext, conf: Configuration)
-  extends TaskSchedulerImpl(sc) {
-
-  def this(sc: SparkContext) = this(sc, new Configuration())
+private[spark] class YarnClientClusterScheduler(sc: SparkContext) extends TaskSchedulerImpl(sc) {
 
   // By default, rack is unknown
   override def getRackForHost(hostPort: String): Option[String] = {
     val host = Utils.parseHostPort(hostPort)._1
-    Option(YarnSparkHadoopUtil.lookupRack(conf, host))
+    Option(YarnSparkHadoopUtil.lookupRack(sc.hadoopConfiguration, host))
   }
 }
diff --git a/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClusterScheduler.scala b/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClusterScheduler.scala
index 69f40225a21f5..4157ff95c2794 100644
--- a/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClusterScheduler.scala
+++ b/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClusterScheduler.scala
@@ -21,19 +21,15 @@ import org.apache.spark._
 import org.apache.spark.deploy.yarn.{ApplicationMaster, YarnSparkHadoopUtil}
 import org.apache.spark.scheduler.TaskSchedulerImpl
 import org.apache.spark.util.Utils
-import org.apache.hadoop.conf.Configuration
 
 /**
  * This is a simple extension to ClusterScheduler - to ensure that appropriate initialization of
  * ApplicationMaster, etc is done
  */
-private[spark] class YarnClusterScheduler(sc: SparkContext, conf: Configuration)
-  extends TaskSchedulerImpl(sc) {
+private[spark] class YarnClusterScheduler(sc: SparkContext) extends TaskSchedulerImpl(sc) {
 
   logInfo("Created YarnClusterScheduler")
 
-  def this(sc: SparkContext) = this(sc, new Configuration())
-
   // Nothing else for now ... initialize application master : which needs a SparkContext to
   // determine how to allocate.
   // Note that only the first creation of a SparkContext influences (and ideally, there must be
@@ -43,8 +39,7 @@ private[spark] class YarnClusterScheduler(sc: SparkContext, conf: Configuration)
   // By default, rack is unknown
   override def getRackForHost(hostPort: String): Option[String] = {
     val host = Utils.parseHostPort(hostPort)._1
-    val retval = YarnSparkHadoopUtil.lookupRack(conf, host)
-    if (retval != null) Some(retval) else None
+    Option(YarnSparkHadoopUtil.lookupRack(sc.hadoopConfiguration, host))
   }
 
   override def postStartHook() {
diff --git a/yarn/common/src/test/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtilSuite.scala b/yarn/common/src/test/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtilSuite.scala
index 7650bd4396c12..75db8ee6d468f 100644
--- a/yarn/common/src/test/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtilSuite.scala
+++ b/yarn/common/src/test/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtilSuite.scala
@@ -20,9 +20,10 @@ package org.apache.spark.deploy.yarn
 import java.io.{File, IOException}
 
 import com.google.common.io.{ByteStreams, Files}
+import org.apache.hadoop.yarn.conf.YarnConfiguration
 import org.scalatest.{FunSuite, Matchers}
 
-import org.apache.spark.Logging
+import org.apache.spark.{Logging, SparkConf}
 
 class YarnSparkHadoopUtilSuite extends FunSuite with Matchers with Logging {
 
@@ -61,4 +62,16 @@ class YarnSparkHadoopUtilSuite extends FunSuite with Matchers with Logging {
     }
   }
 
+  test("Yarn configuration override") {
+    val key = "yarn.nodemanager.hostname"
+    val default = new YarnConfiguration()
+
+    val sparkConf = new SparkConf()
+      .set("spark.hadoop." + key, "someHostName")
+    val yarnConf = new YarnSparkHadoopUtil().newConfiguration(sparkConf)
+
+    yarnConf.getClass() should be (classOf[YarnConfiguration])
+    yarnConf.get(key) should not be default.get(key)
+  }
+
 }
diff --git a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/Client.scala b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
index 1f9a4bf209eb9..313a0d21ce181 100644
--- a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
+++ b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
@@ -29,7 +29,7 @@ import org.apache.hadoop.yarn.ipc.YarnRPC
 import org.apache.hadoop.yarn.util.Records
 
 import org.apache.spark.{Logging, SparkConf}
-
+import org.apache.spark.deploy.SparkHadoopUtil
 
 /**
  * Version of [[org.apache.spark.deploy.yarn.ClientBase]] tailored to YARN's stable API.
@@ -40,7 +40,7 @@ class Client(clientArgs: ClientArguments, hadoopConf: Configuration, spConf: Spa
   val yarnClient = YarnClient.createYarnClient
 
   def this(clientArgs: ClientArguments, spConf: SparkConf) =
-    this(clientArgs, new Configuration(), spConf)
+    this(clientArgs, SparkHadoopUtil.get.newConfiguration(spConf), spConf)
 
   def this(clientArgs: ClientArguments) = this(clientArgs, new SparkConf())
 

From ba78383bace52b13ee931c6f2df445f721d5080a Mon Sep 17 00:00:00 2001
From: Holden Karau <holden@pigscanfly.ca>
Date: Sat, 30 Aug 2014 16:58:17 -0700
Subject: [PATCH 157/489] SPARK-3318: Documentation update in addFile on how to
 use SparkFiles.get

Rather than specifying the path to SparkFiles we need to use the filename.

Author: Holden Karau <holden@pigscanfly.ca>

Closes #2210 from holdenk/SPARK-3318-documentation-for-addfiles-should-say-to-use-file-not-path and squashes the following commits:

a25d27a [Holden Karau] Update the JavaSparkContext addFile method to be clear about using fileName with SparkFiles as well
0ebcb05 [Holden Karau] Documentation update in addFile on how to use SparkFiles.get to specify filename rather than path
---
 core/src/main/scala/org/apache/spark/SparkContext.scala       | 3 +--
 .../scala/org/apache/spark/api/java/JavaSparkContext.scala    | 2 +-
 python/pyspark/context.py                                     | 4 ++--
 3 files changed, 4 insertions(+), 5 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala
index a80b3cce6034d..cb4fb7cfbd32f 100644
--- a/core/src/main/scala/org/apache/spark/SparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/SparkContext.scala
@@ -796,7 +796,7 @@ class SparkContext(config: SparkConf) extends Logging {
    * Add a file to be downloaded with this Spark job on every node.
    * The `path` passed can be either a local file, a file in HDFS (or other Hadoop-supported
    * filesystems), or an HTTP, HTTPS or FTP URI.  To access the file in Spark jobs,
-   * use `SparkFiles.get(path)` to find its download location.
+   * use `SparkFiles.get(fileName)` to find its download location.
    */
   def addFile(path: String) {
     val uri = new URI(path)
@@ -1619,4 +1619,3 @@ private[spark] class WritableConverter[T](
     val writableClass: ClassTag[T] => Class[_ <: Writable],
     val convert: Writable => T)
   extends Serializable
-
diff --git a/core/src/main/scala/org/apache/spark/api/java/JavaSparkContext.scala b/core/src/main/scala/org/apache/spark/api/java/JavaSparkContext.scala
index e0a4815940db3..8e178bc8480f7 100644
--- a/core/src/main/scala/org/apache/spark/api/java/JavaSparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/api/java/JavaSparkContext.scala
@@ -545,7 +545,7 @@ class JavaSparkContext(val sc: SparkContext) extends JavaSparkContextVarargsWork
    * Add a file to be downloaded with this Spark job on every node.
    * The `path` passed can be either a local file, a file in HDFS (or other Hadoop-supported
    * filesystems), or an HTTP, HTTPS or FTP URI.  To access the file in Spark jobs,
-   * use `SparkFiles.get(path)` to find its download location.
+   * use `SparkFiles.get(fileName)` to find its download location.
    */
   def addFile(path: String) {
     sc.addFile(path)
diff --git a/python/pyspark/context.py b/python/pyspark/context.py
index 82f76de31afc1..6e4fdaa6eec9d 100644
--- a/python/pyspark/context.py
+++ b/python/pyspark/context.py
@@ -606,8 +606,8 @@ def addFile(self, path):
         FTP URI.
 
         To access the file in Spark jobs, use
-        L{SparkFiles.get(path)<pyspark.files.SparkFiles.get>} to find its
-        download location.
+        L{SparkFiles.get(fileName)<pyspark.files.SparkFiles.get>} with the
+        filename to find its download location.
 
         >>> from pyspark import SparkFiles
         >>> path = os.path.join(tempdir, "test.txt")

From 9b8c2287bb622f487b9d2bba936ea43c06dd7e5a Mon Sep 17 00:00:00 2001
From: Patrick Wendell <pwendell@gmail.com>
Date: Sat, 30 Aug 2014 20:58:48 -0700
Subject: [PATCH 158/489] MAINTENANCE: Automated closing of pull requests.

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

Closes #1922 (close requested by 'JoshRosen')
Closes #1356 (close requested by 'pwendell')
Closes #1698 (close requested by 'mengxr')
Closes #254 (close requested by 'mateiz')
Closes #2135 (close requested by 'andrewor14')

From c567a68a59f086ed13f38a8a847afb2c7d35697c Mon Sep 17 00:00:00 2001
From: Nicholas Chammas <nicholas.chammas@gmail.com>
Date: Sat, 30 Aug 2014 21:11:48 -0700
Subject: [PATCH 159/489] [Spark QA] only check code files for new classes

Look only at code files (`.py`, `.java`, and `.scala`) for new classes.

Should get rid of false alarms like [the one reported here](https://github.com/apache/spark/pull/2014#issuecomment-52912040).

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

Closes #2184 from nchammas/jenkins-ignore-noncode and squashes the following commits:

33786ac [Nicholas Chammas] break up long line
3f91a14 [Nicholas Chammas] rename array of source files
8b82a26 [Nicholas Chammas] [Spark QA] only check code files for new classes
---
 dev/run-tests-jenkins | 16 ++++++++++------
 1 file changed, 10 insertions(+), 6 deletions(-)

diff --git a/dev/run-tests-jenkins b/dev/run-tests-jenkins
index d9205f08cc134..06c3781eb3ccf 100755
--- a/dev/run-tests-jenkins
+++ b/dev/run-tests-jenkins
@@ -33,9 +33,7 @@ COMMIT_URL="https://github.com/apache/spark/commit/${ghprbActualCommit}"
 # GitHub doesn't auto-link short hashes when submitted via the API, unfortunately. :(
 SHORT_COMMIT_HASH="${ghprbActualCommit:0:7}"
 
-# NOTE: Jenkins will kill the whole build after 120 minutes.
-#       Tests are a large part of that, but not all of it.
-TESTS_TIMEOUT="120m"
+TESTS_TIMEOUT="120m" # format: http://linux.die.net/man/1/timeout
 
 function post_message () {
   local message=$1
@@ -93,9 +91,14 @@ function post_message () {
   else
     merge_note=" * This patch merges cleanly."
 
-    non_test_files=$(git diff master --name-only | grep -v "\/test" | tr "\n" " ")
+    source_files=$(
+        git diff master --name-only \
+      | grep -v -e "\/test"                         `# ignore files in test directories` \
+      | grep -e "\.py$" -e "\.java$" -e "\.scala$"  `# include only code files` \
+      | tr "\n" " "
+    )
     new_public_classes=$(
-        git diff master ${non_test_files}       `# diff this patch against master and...` \
+        git diff master ${source_files}         `# diff this patch against master and...` \
       | grep "^\+"                              `# filter in only added lines` \
       | sed -r -e "s/^\+//g"                    `# remove the leading +` \
       | grep -e "trait " -e "class "            `# filter in lines with these key words` \
@@ -138,7 +141,8 @@ function post_message () {
   test_result="$?"
 
   if [ "$test_result" -eq "124" ]; then
-    fail_message="**[Tests timed out](${BUILD_URL}consoleFull)** after a configured wait of \`${TESTS_TIMEOUT}\`."
+    fail_message="**[Tests timed out](${BUILD_URL}consoleFull)** after \
+    a configured wait of \`${TESTS_TIMEOUT}\`."
     post_message "$fail_message"
     exit $test_result
   else

From 725715cbf3c1834b81aa91ea751fd04d307f504d Mon Sep 17 00:00:00 2001
From: scwf <wangfei1@huawei.com>
Date: Sun, 31 Aug 2014 14:02:11 -0700
Subject: [PATCH 160/489] [SPARK-3010] fix redundant conditional

https://issues.apache.org/jira/browse/SPARK-3010

this pr is to fix redundant conditional in spark, such as
1.
private[spark] def codegenEnabled: Boolean =
if (getConf(CODEGEN_ENABLED, "false") == "true") true else false
2.
x => if (x == 2) true else false
...

Author: scwf <wangfei1@huawei.com>
Author: wangfei <wangfei_hello@126.com>

Closes #1992 from scwf/condition and squashes the following commits:

b2a044a [scwf] merge SecurityManager
e16239c [scwf] fix confilct
6811401 [scwf] fix merge confilct
0824df4 [scwf] Merge branch 'master' of https://github.com/apache/spark into patch-4
e274515 [scwf] fix redundant conditions
d032bf9 [wangfei] [SQL]Excess judgment
---
 .../scala/org/apache/spark/SecurityManager.scala    |  4 ++--
 .../apache/spark/rdd/PartitionPruningRDDSuite.scala | 13 ++++---------
 .../org/apache/spark/sql/columnar/ColumnType.scala  |  4 +---
 .../org/apache/spark/deploy/yarn/ClientBase.scala   |  2 +-
 4 files changed, 8 insertions(+), 15 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/SecurityManager.scala b/core/src/main/scala/org/apache/spark/SecurityManager.scala
index 25c2c9fc6af7c..12b15fe0815be 100644
--- a/core/src/main/scala/org/apache/spark/SecurityManager.scala
+++ b/core/src/main/scala/org/apache/spark/SecurityManager.scala
@@ -294,7 +294,7 @@ private[spark] class SecurityManager(sparkConf: SparkConf) extends Logging {
   def checkUIViewPermissions(user: String): Boolean = {
     logDebug("user=" + user + " aclsEnabled=" + aclsEnabled() + " viewAcls=" +
       viewAcls.mkString(","))
-    if (aclsEnabled() && (user != null) && (!viewAcls.contains(user))) false else true
+    !aclsEnabled || user == null || viewAcls.contains(user)
   }
 
   /**
@@ -309,7 +309,7 @@ private[spark] class SecurityManager(sparkConf: SparkConf) extends Logging {
   def checkModifyPermissions(user: String): Boolean = {
     logDebug("user=" + user + " aclsEnabled=" + aclsEnabled() + " modifyAcls=" +
       modifyAcls.mkString(","))
-    if (aclsEnabled() && (user != null) && (!modifyAcls.contains(user))) false else true
+    !aclsEnabled || user == null || modifyAcls.contains(user)
   }
 
 
diff --git a/core/src/test/scala/org/apache/spark/rdd/PartitionPruningRDDSuite.scala b/core/src/test/scala/org/apache/spark/rdd/PartitionPruningRDDSuite.scala
index 956c2b9cbd321..8408d7e785c65 100644
--- a/core/src/test/scala/org/apache/spark/rdd/PartitionPruningRDDSuite.scala
+++ b/core/src/test/scala/org/apache/spark/rdd/PartitionPruningRDDSuite.scala
@@ -38,9 +38,7 @@ class PartitionPruningRDDSuite extends FunSuite with SharedSparkContext {
         Iterator()
       }
     }
-    val prunedRDD = PartitionPruningRDD.create(rdd, {
-      x => if (x == 2) true else false
-    })
+    val prunedRDD = PartitionPruningRDD.create(rdd, _ == 2)
     assert(prunedRDD.partitions.length == 1)
     val p = prunedRDD.partitions(0)
     assert(p.index == 0)
@@ -62,13 +60,10 @@ class PartitionPruningRDDSuite extends FunSuite with SharedSparkContext {
         List(split.asInstanceOf[TestPartition].testValue).iterator
       }
     }
-    val prunedRDD1 = PartitionPruningRDD.create(rdd, {
-      x => if (x == 0) true else false
-    })
+    val prunedRDD1 = PartitionPruningRDD.create(rdd, _ == 0)
 
-    val prunedRDD2 = PartitionPruningRDD.create(rdd, {
-      x => if (x == 2) true else false
-    })
+
+    val prunedRDD2 = PartitionPruningRDD.create(rdd, _ == 2)
 
     val merged = prunedRDD1 ++ prunedRDD2
     assert(merged.count() == 2)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnType.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnType.scala
index 794bc60d0e315..9a61600115872 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnType.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnType.scala
@@ -158,9 +158,7 @@ private[sql] object BOOLEAN extends NativeColumnType(BooleanType, 4, 1) {
     buffer.put(if (v) 1.toByte else 0.toByte)
   }
 
-  override def extract(buffer: ByteBuffer) = {
-    if (buffer.get() == 1) true else false
-  }
+  override def extract(buffer: ByteBuffer) = buffer.get() == 1
 
   override def setField(row: MutableRow, ordinal: Int, value: Boolean) {
     row.setBoolean(ordinal, value)
diff --git a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala
index 6cf300c3986ad..5d8e5e6dffe7f 100644
--- a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala
+++ b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala
@@ -209,7 +209,7 @@ trait ClientBase extends Logging {
       if (! localPath.isEmpty()) {
         val localURI = new URI(localPath)
         if (!ClientBase.LOCAL_SCHEME.equals(localURI.getScheme())) {
-          val setPermissions = if (destName.equals(ClientBase.APP_JAR)) true else false
+          val setPermissions = destName.equals(ClientBase.APP_JAR)
           val destPath = copyRemoteFile(dst, qualifyForLocal(localURI), replication, setPermissions)
           val destFs = FileSystem.get(destPath.toUri(), conf)
           distCacheMgr.addResource(destFs, conf, destPath, localResources, LocalResourceType.FILE,

From 1f98add92675eb34c80be9bd7d10ea4608a9a6c2 Mon Sep 17 00:00:00 2001
From: Patrick Wendell <pwendell@gmail.com>
Date: Mon, 1 Sep 2014 19:25:54 -0700
Subject: [PATCH 161/489] MAINTENANCE: Automated closing of pull requests.

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

Closes #1696 (close requested by 'pwendell')
Closes #1384 (close requested by 'pwendell')
Closes #845 (close requested by 'pwendell')
Closes #81 (close requested by 'pwendell')
Closes #1528 (close requested by 'pwendell')
Closes #1018 (close requested by 'pwendell')

From db160676c56de54efc4d42c6466847c2c3b6a963 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@apache.org>
Date: Mon, 1 Sep 2014 20:32:31 -0700
Subject: [PATCH 162/489] [SPARK-3135] Avoid extra mem copy in TorrentBroadcast
 via ByteArrayChunkOutputStream

This also enables supporting broadcast variables larger than 2G.

Author: Reynold Xin <rxin@apache.org>

Closes #2054 from rxin/ByteArrayChunkOutputStream and squashes the following commits:

618d9c8 [Reynold Xin] Code review.
93f5a51 [Reynold Xin] Added comments.
ee88e73 [Reynold Xin] to -> until
bbd1cb1 [Reynold Xin] Renamed a variable.
36f4d01 [Reynold Xin] Sort imports.
8f1a8eb [Reynold Xin] [SPARK-3135] Created ByteArrayChunkOutputStream and used it to avoid memory copy in TorrentBroadcast.
---
 .../spark/broadcast/TorrentBroadcast.scala    |  22 +---
 .../util/io/ByteArrayChunkOutputStream.scala  |  94 +++++++++++++++
 .../io/ByteArrayChunkOutputStreamSuite.scala  | 109 ++++++++++++++++++
 3 files changed, 206 insertions(+), 19 deletions(-)
 create mode 100644 core/src/main/scala/org/apache/spark/util/io/ByteArrayChunkOutputStream.scala
 create mode 100644 core/src/test/scala/org/apache/spark/util/io/ByteArrayChunkOutputStreamSuite.scala

diff --git a/core/src/main/scala/org/apache/spark/broadcast/TorrentBroadcast.scala b/core/src/main/scala/org/apache/spark/broadcast/TorrentBroadcast.scala
index 6173fd3a69fc7..42d58682a1e23 100644
--- a/core/src/main/scala/org/apache/spark/broadcast/TorrentBroadcast.scala
+++ b/core/src/main/scala/org/apache/spark/broadcast/TorrentBroadcast.scala
@@ -28,6 +28,7 @@ import org.apache.spark.{Logging, SparkConf, SparkEnv, SparkException}
 import org.apache.spark.io.CompressionCodec
 import org.apache.spark.storage.{BroadcastBlockId, StorageLevel}
 import org.apache.spark.util.ByteBufferInputStream
+import org.apache.spark.util.io.ByteArrayChunkOutputStream
 
 /**
  * A BitTorrent-like implementation of [[org.apache.spark.broadcast.Broadcast]].
@@ -201,29 +202,12 @@ private object TorrentBroadcast extends Logging {
   }
 
   def blockifyObject[T: ClassTag](obj: T): Array[ByteBuffer] = {
-    // TODO: Create a special ByteArrayOutputStream that splits the output directly into chunks
-    // so we don't need to do the extra memory copy.
-    val bos = new ByteArrayOutputStream()
+    val bos = new ByteArrayChunkOutputStream(BLOCK_SIZE)
     val out: OutputStream = if (compress) compressionCodec.compressedOutputStream(bos) else bos
     val ser = SparkEnv.get.serializer.newInstance()
     val serOut = ser.serializeStream(out)
     serOut.writeObject[T](obj).close()
-    val byteArray = bos.toByteArray
-    val bais = new ByteArrayInputStream(byteArray)
-    val numBlocks = math.ceil(byteArray.length.toDouble / BLOCK_SIZE).toInt
-    val blocks = new Array[ByteBuffer](numBlocks)
-
-    var blockId = 0
-    for (i <- 0 until (byteArray.length, BLOCK_SIZE)) {
-      val thisBlockSize = math.min(BLOCK_SIZE, byteArray.length - i)
-      val tempByteArray = new Array[Byte](thisBlockSize)
-      bais.read(tempByteArray, 0, thisBlockSize)
-
-      blocks(blockId) = ByteBuffer.wrap(tempByteArray)
-      blockId += 1
-    }
-    bais.close()
-    blocks
+    bos.toArrays.map(ByteBuffer.wrap)
   }
 
   def unBlockifyObject[T: ClassTag](blocks: Array[ByteBuffer]): T = {
diff --git a/core/src/main/scala/org/apache/spark/util/io/ByteArrayChunkOutputStream.scala b/core/src/main/scala/org/apache/spark/util/io/ByteArrayChunkOutputStream.scala
new file mode 100644
index 0000000000000..daac6f971eb20
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/util/io/ByteArrayChunkOutputStream.scala
@@ -0,0 +1,94 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.util.io
+
+import java.io.OutputStream
+
+import scala.collection.mutable.ArrayBuffer
+
+
+/**
+ * An OutputStream that writes to fixed-size chunks of byte arrays.
+ *
+ * @param chunkSize size of each chunk, in bytes.
+ */
+private[spark]
+class ByteArrayChunkOutputStream(chunkSize: Int) extends OutputStream {
+
+  private val chunks = new ArrayBuffer[Array[Byte]]
+
+  /** Index of the last chunk. Starting with -1 when the chunks array is empty. */
+  private var lastChunkIndex = -1
+
+  /**
+   * Next position to write in the last chunk.
+   *
+   * If this equals chunkSize, it means for next write we need to allocate a new chunk.
+   * This can also never be 0.
+   */
+  private var position = chunkSize
+
+  override def write(b: Int): Unit = {
+    allocateNewChunkIfNeeded()
+    chunks(lastChunkIndex)(position) = b.toByte
+    position += 1
+  }
+
+  override def write(bytes: Array[Byte], off: Int, len: Int): Unit = {
+    var written = 0
+    while (written < len) {
+      allocateNewChunkIfNeeded()
+      val thisBatch = math.min(chunkSize - position, len - written)
+      System.arraycopy(bytes, written + off, chunks(lastChunkIndex), position, thisBatch)
+      written += thisBatch
+      position += thisBatch
+    }
+  }
+
+  @inline
+  private def allocateNewChunkIfNeeded(): Unit = {
+    if (position == chunkSize) {
+      chunks += new Array[Byte](chunkSize)
+      lastChunkIndex += 1
+      position = 0
+    }
+  }
+
+  def toArrays: Array[Array[Byte]] = {
+    if (lastChunkIndex == -1) {
+      new Array[Array[Byte]](0)
+    } else {
+      // Copy the first n-1 chunks to the output, and then create an array that fits the last chunk.
+      // An alternative would have been returning an array of ByteBuffers, with the last buffer
+      // bounded to only the last chunk's position. However, given our use case in Spark (to put
+      // the chunks in block manager), only limiting the view bound of the buffer would still
+      // require the block manager to store the whole chunk.
+      val ret = new Array[Array[Byte]](chunks.size)
+      for (i <- 0 until chunks.size - 1) {
+        ret(i) = chunks(i)
+      }
+      if (position == chunkSize) {
+        ret(lastChunkIndex) = chunks(lastChunkIndex)
+      } else {
+        ret(lastChunkIndex) = new Array[Byte](position)
+        System.arraycopy(chunks(lastChunkIndex), 0, ret(lastChunkIndex), 0, position)
+      }
+      ret
+    }
+  }
+}
diff --git a/core/src/test/scala/org/apache/spark/util/io/ByteArrayChunkOutputStreamSuite.scala b/core/src/test/scala/org/apache/spark/util/io/ByteArrayChunkOutputStreamSuite.scala
new file mode 100644
index 0000000000000..f855831b8e367
--- /dev/null
+++ b/core/src/test/scala/org/apache/spark/util/io/ByteArrayChunkOutputStreamSuite.scala
@@ -0,0 +1,109 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.util.io
+
+import scala.util.Random
+
+import org.scalatest.FunSuite
+
+
+class ByteArrayChunkOutputStreamSuite extends FunSuite {
+
+  test("empty output") {
+    val o = new ByteArrayChunkOutputStream(1024)
+    assert(o.toArrays.length === 0)
+  }
+
+  test("write a single byte") {
+    val o = new ByteArrayChunkOutputStream(1024)
+    o.write(10)
+    assert(o.toArrays.length === 1)
+    assert(o.toArrays.head.toSeq === Seq(10.toByte))
+  }
+
+  test("write a single near boundary") {
+    val o = new ByteArrayChunkOutputStream(10)
+    o.write(new Array[Byte](9))
+    o.write(99)
+    assert(o.toArrays.length === 1)
+    assert(o.toArrays.head(9) === 99.toByte)
+  }
+
+  test("write a single at boundary") {
+    val o = new ByteArrayChunkOutputStream(10)
+    o.write(new Array[Byte](10))
+    o.write(99)
+    assert(o.toArrays.length === 2)
+    assert(o.toArrays(1).length === 1)
+    assert(o.toArrays(1)(0) === 99.toByte)
+  }
+
+  test("single chunk output") {
+    val ref = new Array[Byte](8)
+    Random.nextBytes(ref)
+    val o = new ByteArrayChunkOutputStream(10)
+    o.write(ref)
+    val arrays = o.toArrays
+    assert(arrays.length === 1)
+    assert(arrays.head.length === ref.length)
+    assert(arrays.head.toSeq === ref.toSeq)
+  }
+
+  test("single chunk output at boundary size") {
+    val ref = new Array[Byte](10)
+    Random.nextBytes(ref)
+    val o = new ByteArrayChunkOutputStream(10)
+    o.write(ref)
+    val arrays = o.toArrays
+    assert(arrays.length === 1)
+    assert(arrays.head.length === ref.length)
+    assert(arrays.head.toSeq === ref.toSeq)
+  }
+
+  test("multiple chunk output") {
+    val ref = new Array[Byte](26)
+    Random.nextBytes(ref)
+    val o = new ByteArrayChunkOutputStream(10)
+    o.write(ref)
+    val arrays = o.toArrays
+    assert(arrays.length === 3)
+    assert(arrays(0).length === 10)
+    assert(arrays(1).length === 10)
+    assert(arrays(2).length === 6)
+
+    assert(arrays(0).toSeq === ref.slice(0, 10))
+    assert(arrays(1).toSeq === ref.slice(10, 20))
+    assert(arrays(2).toSeq === ref.slice(20, 26))
+  }
+
+  test("multiple chunk output at boundary size") {
+    val ref = new Array[Byte](30)
+    Random.nextBytes(ref)
+    val o = new ByteArrayChunkOutputStream(10)
+    o.write(ref)
+    val arrays = o.toArrays
+    assert(arrays.length === 3)
+    assert(arrays(0).length === 10)
+    assert(arrays(1).length === 10)
+    assert(arrays(2).length === 10)
+
+    assert(arrays(0).toSeq === ref.slice(0, 10))
+    assert(arrays(1).toSeq === ref.slice(10, 20))
+    assert(arrays(2).toSeq === ref.slice(20, 30))
+  }
+}

From 44d3a6a75209370b1648e2dfedaa4c895923dae5 Mon Sep 17 00:00:00 2001
From: Daniel Darabos <darabos.daniel@gmail.com>
Date: Mon, 1 Sep 2014 22:14:28 -0700
Subject: [PATCH 163/489] [SPARK-3342] Add SSDs to block device mapping

On `m3.2xlarge` instances the 2x80GB SSDs are inaccessible if not added to the block device mapping when the instance is created. They work when added with this patch. I have not tested this with other instance types, and I do not know much about this script and EC2 deployment in general. Maybe this code needs to depend on the instance type.

The requirement for this mapping is described in the AWS docs at:
http://docs.aws.amazon.com/AWSEC2/latest/UserGuide/InstanceStorage.html#InstanceStore_UsageScenarios

"For M3 instances, you must specify instance store volumes in the block
device mapping for the instance. When you launch an M3 instance, we
ignore any instance store volumes specified in the block device mapping
for the AMI."

Author: Daniel Darabos <darabos.daniel@gmail.com>

Closes #2081 from darabos/patch-1 and squashes the following commits:

1ceb2c8 [Daniel Darabos] Use %d string interpolation instead of {}.
a1854d7 [Daniel Darabos] Only specify ephemeral device mapping for M3.
e0d9e37 [Daniel Darabos] Create ephemeral device mapping based on get_num_disks().
6b116a6 [Daniel Darabos] Add SSDs to block device mapping
---
 ec2/spark_ec2.py | 12 +++++++++++-
 1 file changed, 11 insertions(+), 1 deletion(-)

diff --git a/ec2/spark_ec2.py b/ec2/spark_ec2.py
index ae4c488f83abd..7e25df57ee45b 100755
--- a/ec2/spark_ec2.py
+++ b/ec2/spark_ec2.py
@@ -26,6 +26,7 @@
 import pipes
 import random
 import shutil
+import string
 import subprocess
 import sys
 import tempfile
@@ -34,7 +35,7 @@
 from optparse import OptionParser
 from sys import stderr
 import boto
-from boto.ec2.blockdevicemapping import BlockDeviceMapping, EBSBlockDeviceType
+from boto.ec2.blockdevicemapping import BlockDeviceMapping, BlockDeviceType, EBSBlockDeviceType
 from boto import ec2
 
 # A URL prefix from which to fetch AMI information
@@ -355,6 +356,15 @@ def launch_cluster(conn, opts, cluster_name):
         device.delete_on_termination = True
         block_map["/dev/sdv"] = device
 
+    # AWS ignores the AMI-specified block device mapping for M3 (see SPARK-3342).
+    if opts.instance_type.startswith('m3.'):
+        for i in range(get_num_disks(opts.instance_type)):
+            dev = BlockDeviceType()
+            dev.ephemeral_name = 'ephemeral%d' % i
+            # The first ephemeral drive is /dev/sdb.
+            name = '/dev/sd' + string.letters[i + 1]
+            block_map[name] = dev
+
     # Launch slaves
     if opts.spot_price is not None:
         # Launch spot instances with the requested price

From fbf2678c16acc0071ebd1cbdd165702635be5f0c Mon Sep 17 00:00:00 2001
From: lirui <rui.li@intel.com>
Date: Mon, 1 Sep 2014 23:28:19 -0700
Subject: [PATCH 164/489] SPARK-2636: Expose job ID in JobWaiter API

This PR adds the async actions to the Java API. User can call these async actions to get the FutureAction and use JobWaiter (for SimpleFutureAction) to retrieve job Id.

Author: lirui <rui.li@intel.com>

Closes #2176 from lirui-intel/SPARK-2636 and squashes the following commits:

ccaafb7 [lirui] SPARK-2636: fix java doc
5536d55 [lirui] SPARK-2636: mark the async API as experimental
e2e01d5 [lirui] SPARK-2636: add mima exclude
0ca320d [lirui] SPARK-2636: fix method name & javadoc
3fa39f7 [lirui] SPARK-2636: refine the patch
af4f5d9 [lirui] SPARK-2636: remove unused imports
843276c [lirui] SPARK-2636: only keep foreachAsync in the java API
fbf5744 [lirui] SPARK-2636: add more async actions for java api
1b25abc [lirui] SPARK-2636: expose some fields in JobWaiter
d09f732 [lirui] SPARK-2636: fix build
eb1ee79 [lirui] SPARK-2636: change some parameters in SimpleFutureAction to member field
6e2b87b [lirui] SPARK-2636: add java API for async actions
---
 .../scala/org/apache/spark/FutureAction.scala     |  3 +++
 .../org/apache/spark/api/java/JavaRDDLike.scala   | 15 ++++++++++++++-
 .../org/apache/spark/rdd/AsyncRDDActions.scala    |  3 ++-
 .../org/apache/spark/scheduler/JobWaiter.scala    |  2 +-
 project/MimaExcludes.scala                        |  3 +++
 5 files changed, 23 insertions(+), 3 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/FutureAction.scala b/core/src/main/scala/org/apache/spark/FutureAction.scala
index 1e4dec86a0530..75ea535f2f57b 100644
--- a/core/src/main/scala/org/apache/spark/FutureAction.scala
+++ b/core/src/main/scala/org/apache/spark/FutureAction.scala
@@ -149,6 +149,9 @@ class SimpleFutureAction[T] private[spark](jobWaiter: JobWaiter[_], resultFunc:
       case JobFailed(e: Exception) => scala.util.Failure(e)
     }
   }
+
+  /** Get the corresponding job id for this action. */
+  def jobId = jobWaiter.jobId
 }
 
 
diff --git a/core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala b/core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala
index f917cfd1419ec..545bc0e9e99ed 100644
--- a/core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala
+++ b/core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala
@@ -26,7 +26,7 @@ import scala.reflect.ClassTag
 import com.google.common.base.Optional
 import org.apache.hadoop.io.compress.CompressionCodec
 
-import org.apache.spark.{Partition, SparkContext, TaskContext}
+import org.apache.spark.{FutureAction, Partition, SparkContext, TaskContext}
 import org.apache.spark.annotation.Experimental
 import org.apache.spark.api.java.JavaPairRDD._
 import org.apache.spark.api.java.JavaSparkContext.fakeClassTag
@@ -574,4 +574,17 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable {
 
   def name(): String = rdd.name
 
+  /**
+   * :: Experimental ::
+   * The asynchronous version of the foreach action.
+   *
+   * @param f the function to apply to all the elements of the RDD
+   * @return a FutureAction for the action
+   */
+  @Experimental
+  def foreachAsync(f: VoidFunction[T]): FutureAction[Unit] = {
+    import org.apache.spark.SparkContext._
+    rdd.foreachAsync(x => f.call(x))
+  }
+
 }
diff --git a/core/src/main/scala/org/apache/spark/rdd/AsyncRDDActions.scala b/core/src/main/scala/org/apache/spark/rdd/AsyncRDDActions.scala
index aed951a40b40c..b62f3fbdc4a15 100644
--- a/core/src/main/scala/org/apache/spark/rdd/AsyncRDDActions.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/AsyncRDDActions.scala
@@ -112,7 +112,8 @@ class AsyncRDDActions[T: ClassTag](self: RDD[T]) extends Serializable with Loggi
    * Applies a function f to all elements of this RDD.
    */
   def foreachAsync(f: T => Unit): FutureAction[Unit] = {
-    self.context.submitJob[T, Unit, Unit](self, _.foreach(f), Range(0, self.partitions.size),
+    val cleanF = self.context.clean(f)
+    self.context.submitJob[T, Unit, Unit](self, _.foreach(cleanF), Range(0, self.partitions.size),
       (index, data) => Unit, Unit)
   }
 
diff --git a/core/src/main/scala/org/apache/spark/scheduler/JobWaiter.scala b/core/src/main/scala/org/apache/spark/scheduler/JobWaiter.scala
index e9bfee2248e5b..29879b374b801 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/JobWaiter.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/JobWaiter.scala
@@ -23,7 +23,7 @@ package org.apache.spark.scheduler
  */
 private[spark] class JobWaiter[T](
     dagScheduler: DAGScheduler,
-    jobId: Int,
+    val jobId: Int,
     totalTasks: Int,
     resultHandler: (Int, T) => Unit)
   extends JobListener {
diff --git a/project/MimaExcludes.scala b/project/MimaExcludes.scala
index fe8ffe6d97a05..a2f1b3582ab71 100644
--- a/project/MimaExcludes.scala
+++ b/project/MimaExcludes.scala
@@ -41,6 +41,9 @@ object MimaExcludes {
           Seq(
             // Adding new method to JavaRDLike trait - we should probably mark this as a developer API.
             ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.api.java.JavaRDDLike.partitions"),
+            // Should probably mark this as Experimental
+            ProblemFilters.exclude[MissingMethodProblem](
+              "org.apache.spark.api.java.JavaRDDLike.foreachAsync"),
             // We made a mistake earlier (ed06500d3) in the Java API to use default parameter values
             // for countApproxDistinct* functions, which does not work in Java. We later removed
             // them, and use the following to tell Mima to not care about them.

From 0f16b23cd17002fac05f3ecc58899be1b1121b82 Mon Sep 17 00:00:00 2001
From: Reza Zadeh <rizlar@gmail.com>
Date: Tue, 2 Sep 2014 09:48:05 -0700
Subject: [PATCH 165/489] [MLlib] Squash bug in IndexedRowMatrix

Kill this bug fast before it does damage.

Author: Reza Zadeh <rizlar@gmail.com>

Closes #2224 from rezazadeh/indexrmbug and squashes the following commits:

53386d6 [Reza Zadeh] Squash bug in IndexedRowMatrix
---
 .../spark/mllib/linalg/distributed/IndexedRowMatrix.scala       | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/IndexedRowMatrix.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/IndexedRowMatrix.scala
index 132b3af72d9ce..ac6eaea3f43ad 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/IndexedRowMatrix.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/IndexedRowMatrix.scala
@@ -130,7 +130,7 @@ class IndexedRowMatrix(
     val indexedRows = rows.map(_.index).zip(mat.rows).map { case (i, v) =>
       IndexedRow(i, v)
     }
-    new IndexedRowMatrix(indexedRows, nRows, nCols)
+    new IndexedRowMatrix(indexedRows, nRows, B.numCols)
   }
 
   /**

From 32ec0a8cd4020f1e8ef2997de310823b566ab2de Mon Sep 17 00:00:00 2001
From: Sean Owen <sowen@cloudera.com>
Date: Tue, 2 Sep 2014 10:30:26 -0700
Subject: [PATCH 166/489] SPARK-3331 [BUILD] PEP8 tests fail because they check
 unzipped py4j code

PEP8 tests run on files under "./python", but unzipped py4j code is found at "./python/build/py4j". Py4J code fails style checks and can fail ./dev/run-tests if this code is present locally.

Author: Sean Owen <sowen@cloudera.com>

Closes #2222 from srowen/SPARK-3331 and squashes the following commits:

34711ec [Sean Owen] Restrict lint check to pyspark/, since the local directory can contain unzipped py4j code in build/py4j
---
 dev/lint-python | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/dev/lint-python b/dev/lint-python
index 4efddad839387..a1e890faa8fa6 100755
--- a/dev/lint-python
+++ b/dev/lint-python
@@ -44,7 +44,7 @@ fi
 #+ first, but we do so so that the check status can
 #+ be output before the report, like with the
 #+ scalastyle and RAT checks.
-python $PEP8_SCRIPT_PATH ./python > "$PEP8_REPORT_PATH"
+python $PEP8_SCRIPT_PATH ./python/pyspark > "$PEP8_REPORT_PATH"
 pep8_status=${PIPESTATUS[0]} #$?
 
 if [ $pep8_status -ne 0 ]; then

From 378b2315b474e9a94d034f3955eca7cb0a59cba0 Mon Sep 17 00:00:00 2001
From: Josh Rosen <joshrosen@apache.org>
Date: Tue, 2 Sep 2014 10:45:14 -0700
Subject: [PATCH 167/489] [SPARK-3061] Fix Maven build under Windows

The Maven build was failing on Windows because it tried to call the unix `unzip` utility to extract the Py4J files into core's build directory.  I've fixed this issue by using the `maven-antrun-plugin` to perform the unzipping.

I also fixed an issue that prevented tests from running under Windows:

In the Maven ScalaTest plugin, the filename listed in <filereports> is placed under the <reportsDirectory>; the current code places it in a subdirectory of reportsDirectory, e.g.

```
${project.build.directory}/surefire-reports/${project.build.directory}/SparkTestSuite.txt
```

This caused problems under Windows because it would try to create a subdirectory named "c:\\".

Note that the tests still fail under Windows (for other reasons); this PR just allows them to run and fail rather than crash when trying to create the test reports directory.

Author: Josh Rosen <joshrosen@apache.org>
Author: Josh Rosen <rosenville@gmail.com>
Author: Josh Rosen <joshrosen@databricks.com>

Closes #2165 from JoshRosen/windows-support and squashes the following commits:

651d210 [Josh Rosen] Unzip to python/build instead of core/build
fbf3e61 [Josh Rosen] 4 spaces -> 2 spaces
e347668 [Josh Rosen] Fix Maven scalatest filereports path:
4994af1 [Josh Rosen] [SPARK-3061] Use maven-antrun-plugin to unzip Py4J.
---
 core/pom.xml | 18 ++++++------------
 pom.xml      |  2 +-
 2 files changed, 7 insertions(+), 13 deletions(-)

diff --git a/core/pom.xml b/core/pom.xml
index 83c708dfc9619..55bfe0b841ea4 100644
--- a/core/pom.xml
+++ b/core/pom.xml
@@ -306,26 +306,20 @@
       </plugin>
       <!-- Unzip py4j so we can include its files in the jar -->
       <plugin>
-        <groupId>org.codehaus.mojo</groupId>
-        <artifactId>exec-maven-plugin</artifactId>
-        <version>1.2.1</version>
+        <groupId>org.apache.maven.plugins</groupId>
+        <artifactId>maven-antrun-plugin</artifactId>
         <executions>
           <execution>
             <phase>generate-resources</phase>
             <goals>
-              <goal>exec</goal>
+              <goal>run</goal>
             </goals>
           </execution>
         </executions>
         <configuration>
-          <executable>unzip</executable>
-          <workingDirectory>../python</workingDirectory>
-          <arguments>
-            <argument>-o</argument>
-            <argument>lib/py4j*.zip</argument>
-            <argument>-d</argument>
-            <argument>build</argument>
-          </arguments>
+          <tasks>
+            <unzip src="../python/lib/py4j-0.8.2.1-src.zip" dest="../python/build" />
+          </tasks>
         </configuration>
       </plugin>
       <plugin>
diff --git a/pom.xml b/pom.xml
index 556b9da3d6d90..a5eaea80afd71 100644
--- a/pom.xml
+++ b/pom.xml
@@ -880,7 +880,7 @@
           <configuration>
             <reportsDirectory>${project.build.directory}/surefire-reports</reportsDirectory>
             <junitxml>.</junitxml>
-            <filereports>${project.build.directory}/SparkTestSuite.txt</filereports>
+            <filereports>SparkTestSuite.txt</filereports>
             <argLine>-Xmx3g -XX:MaxPermSize=${MaxPermGen} -XX:ReservedCodeCacheSize=512m</argLine>
             <stderr/>
             <systemProperties>

From 8f1f9aaf408d6f058000be6ddd66179ba8a69bfa Mon Sep 17 00:00:00 2001
From: Andrew Or <andrewor14@gmail.com>
Date: Tue, 2 Sep 2014 10:47:05 -0700
Subject: [PATCH 168/489] [SPARK-1919] Fix Windows spark-shell --jars

We were trying to add `file:/C:/path/to/my.jar` to the class path. We should add `C:/path/to/my.jar` instead. Tested on Windows 8.1.

Author: Andrew Or <andrewor14@gmail.com>

Closes #2211 from andrewor14/windows-shell-jars and squashes the following commits:

262c6a2 [Andrew Or] Oops... Add the new code to the correct place
0d5a0c1 [Andrew Or] Format jar path only for adding to shell classpath
42bd626 [Andrew Or] Remove unnecessary code
0049f1b [Andrew Or] Remove embarrassing log messages
b1755a0 [Andrew Or] Format jar paths properly before adding them to the classpath
---
 .../main/scala/org/apache/spark/repl/SparkILoop.scala | 11 ++++++++++-
 1 file changed, 10 insertions(+), 1 deletion(-)

diff --git a/repl/src/main/scala/org/apache/spark/repl/SparkILoop.scala b/repl/src/main/scala/org/apache/spark/repl/SparkILoop.scala
index 53df599cf8121..d9eeffa86016a 100644
--- a/repl/src/main/scala/org/apache/spark/repl/SparkILoop.scala
+++ b/repl/src/main/scala/org/apache/spark/repl/SparkILoop.scala
@@ -18,6 +18,7 @@ import scala.tools.nsc.interpreter._
 import scala.tools.nsc.interpreter.{ Results => IR }
 import Predef.{ println => _, _ }
 import java.io.{ BufferedReader, FileReader }
+import java.net.URI
 import java.util.concurrent.locks.ReentrantLock
 import scala.sys.process.Process
 import scala.tools.nsc.interpreter.session._
@@ -189,8 +190,16 @@ class SparkILoop(in0: Option[BufferedReader], protected val out: JPrintWriter,
     require(settings != null)
 
     if (addedClasspath != "") settings.classpath.append(addedClasspath)
+    val addedJars =
+      if (Utils.isWindows) {
+        // Strip any URI scheme prefix so we can add the correct path to the classpath
+        // e.g. file:/C:/my/path.jar -> C:/my/path.jar
+        SparkILoop.getAddedJars.map { jar => new URI(jar).getPath.stripPrefix("/") }
+      } else {
+        SparkILoop.getAddedJars
+      }
     // work around for Scala bug
-    val totalClassPath = SparkILoop.getAddedJars.foldLeft(
+    val totalClassPath = addedJars.foldLeft(
       settings.classpath.value)((l, r) => ClassPath.join(l, r))
     this.settings.classpath.value = totalClassPath
 

From 066f31a6b213121441fc9618abd5bae4a706a215 Mon Sep 17 00:00:00 2001
From: Marcelo Vanzin <vanzin@cloudera.com>
Date: Tue, 2 Sep 2014 13:33:23 -0500
Subject: [PATCH 169/489] [SPARK-3347] [yarn] Fix yarn-alpha compilation.

Missing import. Oops.

Author: Marcelo Vanzin <vanzin@cloudera.com>

Closes #2236 from vanzin/SPARK-3347 and squashes the following commits:

594fc39 [Marcelo Vanzin] [SPARK-3347] [yarn] Fix yarn-alpha compilation.
---
 .../src/main/scala/org/apache/spark/deploy/yarn/Client.scala     | 1 +
 1 file changed, 1 insertion(+)

diff --git a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/Client.scala b/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
index 12f1cd3813a05..10fc39bba87d1 100644
--- a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
+++ b/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
@@ -32,6 +32,7 @@ import org.apache.hadoop.yarn.ipc.YarnRPC
 import org.apache.hadoop.yarn.util.{Apps, Records}
 
 import org.apache.spark.{Logging, SparkConf}
+import org.apache.spark.deploy.SparkHadoopUtil
 
 /**
  * Version of [[org.apache.spark.deploy.yarn.ClientBase]] tailored to YARN's alpha API.

From 81b9d5b628229ed69aa9dae45ec4c94068dcd71e Mon Sep 17 00:00:00 2001
From: Sandy Ryza <sandy@cloudera.com>
Date: Tue, 2 Sep 2014 11:34:55 -0700
Subject: [PATCH 170/489] SPARK-3052. Misleading and spurious FileSystem closed
 errors whenever a ...

...job fails while reading from Hadoop

Author: Sandy Ryza <sandy@cloudera.com>

Closes #1956 from sryza/sandy-spark-3052 and squashes the following commits:

815813a [Sandy Ryza] SPARK-3052. Misleading and spurious FileSystem closed errors whenever a job fails while reading from Hadoop
---
 core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala | 9 +++++++--
 .../main/scala/org/apache/spark/rdd/NewHadoopRDD.scala   | 7 ++++++-
 2 files changed, 13 insertions(+), 3 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala b/core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala
index c8623314c98eb..036dcc49664ef 100644
--- a/core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala
@@ -42,7 +42,8 @@ import org.apache.spark.broadcast.Broadcast
 import org.apache.spark.deploy.SparkHadoopUtil
 import org.apache.spark.executor.{DataReadMethod, InputMetrics}
 import org.apache.spark.rdd.HadoopRDD.HadoopMapPartitionsWithSplitRDD
-import org.apache.spark.util.NextIterator
+import org.apache.spark.util.{NextIterator, Utils}
+
 
 /**
  * A Spark split class that wraps around a Hadoop InputSplit.
@@ -228,7 +229,11 @@ class HadoopRDD[K, V](
         try {
           reader.close()
         } catch {
-          case e: Exception => logWarning("Exception in RecordReader.close()", e)
+          case e: Exception => {
+            if (!Utils.inShutdown()) {
+              logWarning("Exception in RecordReader.close()", e)
+            }
+          }
         }
       }
     }
diff --git a/core/src/main/scala/org/apache/spark/rdd/NewHadoopRDD.scala b/core/src/main/scala/org/apache/spark/rdd/NewHadoopRDD.scala
index 58f707b9b4634..4c84b3f62354d 100644
--- a/core/src/main/scala/org/apache/spark/rdd/NewHadoopRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/NewHadoopRDD.scala
@@ -35,6 +35,7 @@ import org.apache.spark.SerializableWritable
 import org.apache.spark.{SparkContext, TaskContext}
 import org.apache.spark.executor.{DataReadMethod, InputMetrics}
 import org.apache.spark.rdd.NewHadoopRDD.NewHadoopMapPartitionsWithSplitRDD
+import org.apache.spark.util.Utils
 
 private[spark] class NewHadoopPartition(
     rddId: Int,
@@ -153,7 +154,11 @@ class NewHadoopRDD[K, V](
         try {
           reader.close()
         } catch {
-          case e: Exception => logWarning("Exception in RecordReader.close()", e)
+          case e: Exception => {
+            if (!Utils.inShutdown()) {
+              logWarning("Exception in RecordReader.close()", e)
+            }
+          }
         }
       }
     }

From e2c901b4c72b247bb422dd5acf057bc583e639ab Mon Sep 17 00:00:00 2001
From: Davies Liu <davies.liu@gmail.com>
Date: Tue, 2 Sep 2014 15:47:47 -0700
Subject: [PATCH 171/489] [SPARK-2871] [PySpark] add countApproxDistinct() API

RDD.countApproxDistinct(relativeSD=0.05):

        :: Experimental ::
        Return approximate number of distinct elements in the RDD.

        The algorithm used is based on streamlib's implementation of
        "HyperLogLog in Practice: Algorithmic Engineering of a State
        of The Art Cardinality Estimation Algorithm", available
        <a href="http://dx.doi.org/10.1145/2452376.2452456">here</a>.

        This support all the types of objects, which is supported by
        Pyrolite, nearly all builtin types.

        param relativeSD Relative accuracy. Smaller values create
                           counters that require more space.
                           It must be greater than 0.000017.

        >>> n = sc.parallelize(range(1000)).map(str).countApproxDistinct()
        >>> 950 < n < 1050
        True
        >>> n = sc.parallelize([i % 20 for i in range(1000)]).countApproxDistinct()
        >>> 18 < n < 22
        True

Author: Davies Liu <davies.liu@gmail.com>

Closes #2142 from davies/countApproxDistinct and squashes the following commits:

e20da47 [Davies Liu] remove the correction in Python
c38c4e4 [Davies Liu] fix doc tests
2ab157c [Davies Liu] fix doc tests
9d2565f [Davies Liu] add commments and link for hash collision correction
d306492 [Davies Liu] change range of hash of tuple to [0, maxint]
ded624f [Davies Liu] calculate hash in Python
4cba98f [Davies Liu] add more tests
a85a8c6 [Davies Liu] Merge branch 'master' into countApproxDistinct
e97e342 [Davies Liu] add countApproxDistinct()
---
 .../main/scala/org/apache/spark/rdd/RDD.scala |  2 +-
 python/pyspark/rdd.py                         | 39 ++++++++++++++++---
 python/pyspark/tests.py                       | 16 ++++++++
 3 files changed, 51 insertions(+), 6 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/rdd/RDD.scala b/core/src/main/scala/org/apache/spark/rdd/RDD.scala
index daea2617e62ea..af9e31ba7b720 100644
--- a/core/src/main/scala/org/apache/spark/rdd/RDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/RDD.scala
@@ -993,7 +993,7 @@ abstract class RDD[T: ClassTag](
    */
   @Experimental
   def countApproxDistinct(p: Int, sp: Int): Long = {
-    require(p >= 4, s"p ($p) must be greater than 0")
+    require(p >= 4, s"p ($p) must be at least 4")
     require(sp <= 32, s"sp ($sp) cannot be greater than 32")
     require(sp == 0 || p <= sp, s"p ($p) cannot be greater than sp ($sp)")
     val zeroCounter = new HyperLogLogPlus(p, sp)
diff --git a/python/pyspark/rdd.py b/python/pyspark/rdd.py
index 2d80fad796957..6fc9f66bc5a94 100644
--- a/python/pyspark/rdd.py
+++ b/python/pyspark/rdd.py
@@ -62,7 +62,7 @@ def portable_hash(x):
 
     >>> portable_hash(None)
     0
-    >>> portable_hash((None, 1))
+    >>> portable_hash((None, 1)) & 0xffffffff
     219750521
     """
     if x is None:
@@ -72,7 +72,7 @@ def portable_hash(x):
         for i in x:
             h ^= portable_hash(i)
             h *= 1000003
-            h &= 0xffffffff
+            h &= sys.maxint
         h ^= len(x)
         if h == -1:
             h = -2
@@ -1942,7 +1942,7 @@ def _is_pickled(self):
             return True
         return False
 
-    def _to_jrdd(self):
+    def _to_java_object_rdd(self):
         """ Return an JavaRDD of Object by unpickling
 
         It will convert each Python object into Java object by Pyrolite, whenever the
@@ -1977,7 +1977,7 @@ def sumApprox(self, timeout, confidence=0.95):
         >>> (rdd.sumApprox(1000) - r) / r < 0.05
         True
         """
-        jrdd = self.mapPartitions(lambda it: [float(sum(it))])._to_jrdd()
+        jrdd = self.mapPartitions(lambda it: [float(sum(it))])._to_java_object_rdd()
         jdrdd = self.ctx._jvm.JavaDoubleRDD.fromRDD(jrdd.rdd())
         r = jdrdd.sumApprox(timeout, confidence).getFinalValue()
         return BoundedFloat(r.mean(), r.confidence(), r.low(), r.high())
@@ -1993,11 +1993,40 @@ def meanApprox(self, timeout, confidence=0.95):
         >>> (rdd.meanApprox(1000) - r) / r < 0.05
         True
         """
-        jrdd = self.map(float)._to_jrdd()
+        jrdd = self.map(float)._to_java_object_rdd()
         jdrdd = self.ctx._jvm.JavaDoubleRDD.fromRDD(jrdd.rdd())
         r = jdrdd.meanApprox(timeout, confidence).getFinalValue()
         return BoundedFloat(r.mean(), r.confidence(), r.low(), r.high())
 
+    def countApproxDistinct(self, relativeSD=0.05):
+        """
+        :: Experimental ::
+        Return approximate number of distinct elements in the RDD.
+
+        The algorithm used is based on streamlib's implementation of
+        "HyperLogLog in Practice: Algorithmic Engineering of a State
+        of The Art Cardinality Estimation Algorithm", available
+        <a href="http://dx.doi.org/10.1145/2452376.2452456">here</a>.
+
+        @param relativeSD Relative accuracy. Smaller values create
+                           counters that require more space.
+                           It must be greater than 0.000017.
+
+        >>> n = sc.parallelize(range(1000)).map(str).countApproxDistinct()
+        >>> 950 < n < 1050
+        True
+        >>> n = sc.parallelize([i % 20 for i in range(1000)]).countApproxDistinct()
+        >>> 18 < n < 22
+        True
+        """
+        if relativeSD < 0.000017:
+            raise ValueError("relativeSD should be greater than 0.000017")
+        if relativeSD > 0.37:
+            raise ValueError("relativeSD should be smaller than 0.37")
+        # the hash space in Java is 2^32
+        hashRDD = self.map(lambda x: portable_hash(x) & 0xFFFFFFFF)
+        return hashRDD._to_java_object_rdd().countApproxDistinct(relativeSD)
+
 
 class PipelinedRDD(RDD):
 
diff --git a/python/pyspark/tests.py b/python/pyspark/tests.py
index 3e7040eade1ab..f1a75cbff5c19 100644
--- a/python/pyspark/tests.py
+++ b/python/pyspark/tests.py
@@ -404,6 +404,22 @@ def test_zip_with_different_number_of_items(self):
         self.assertEquals(a.count(), b.count())
         self.assertRaises(Exception, lambda: a.zip(b).count())
 
+    def test_count_approx_distinct(self):
+        rdd = self.sc.parallelize(range(1000))
+        self.assertTrue(950 < rdd.countApproxDistinct(0.04) < 1050)
+        self.assertTrue(950 < rdd.map(float).countApproxDistinct(0.04) < 1050)
+        self.assertTrue(950 < rdd.map(str).countApproxDistinct(0.04) < 1050)
+        self.assertTrue(950 < rdd.map(lambda x: (x, -x)).countApproxDistinct(0.04) < 1050)
+
+        rdd = self.sc.parallelize([i % 20 for i in range(1000)], 7)
+        self.assertTrue(18 < rdd.countApproxDistinct() < 22)
+        self.assertTrue(18 < rdd.map(float).countApproxDistinct() < 22)
+        self.assertTrue(18 < rdd.map(str).countApproxDistinct() < 22)
+        self.assertTrue(18 < rdd.map(lambda x: (x, -x)).countApproxDistinct() < 22)
+
+        self.assertRaises(ValueError, lambda: rdd.countApproxDistinct(0.00000001))
+        self.assertRaises(ValueError, lambda: rdd.countApproxDistinct(0.5))
+
     def test_histogram(self):
         # empty
         rdd = self.sc.parallelize([])

From 644e31524a6a9a22c671a368aeb3b4eaeb61cf29 Mon Sep 17 00:00:00 2001
From: Prudhvi Krishna <prudhvi953@gmail.com>
Date: Tue, 2 Sep 2014 17:36:53 -0700
Subject: [PATCH 172/489] SPARK-3328 fixed make-distribution script
 --with-tachyon option.

Directory path for dependencies jar and resources in Tachyon 0.5.0 has been changed.

Author: Prudhvi Krishna <prudhvi953@gmail.com>

Closes #2228 from prudhvije/SPARK-3328/make-dist-fix and squashes the following commits:

d1d2c22 [Prudhvi Krishna] SPARK-3328 fixed make-distribution script --with-tachyon option.
---
 make-distribution.sh | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/make-distribution.sh b/make-distribution.sh
index ee1399071112d..f030d3f430581 100755
--- a/make-distribution.sh
+++ b/make-distribution.sh
@@ -219,10 +219,10 @@ if [ "$SPARK_TACHYON" == "true" ]; then
   wget "$TACHYON_URL"
 
   tar xf "tachyon-${TACHYON_VERSION}-bin.tar.gz"
-  cp "tachyon-${TACHYON_VERSION}/target/tachyon-${TACHYON_VERSION}-jar-with-dependencies.jar" "$DISTDIR/lib"
+  cp "tachyon-${TACHYON_VERSION}/core/target/tachyon-${TACHYON_VERSION}-jar-with-dependencies.jar" "$DISTDIR/lib"
   mkdir -p "$DISTDIR/tachyon/src/main/java/tachyon/web"
   cp -r "tachyon-${TACHYON_VERSION}"/{bin,conf,libexec} "$DISTDIR/tachyon"
-  cp -r "tachyon-${TACHYON_VERSION}"/src/main/java/tachyon/web/resources "$DISTDIR/tachyon/src/main/java/tachyon/web"
+  cp -r "tachyon-${TACHYON_VERSION}"/core/src/main/java/tachyon/web "$DISTDIR/tachyon/src/main/java/tachyon/web"
 
   if [[ `uname -a` == Darwin* ]]; then
     # need to run sed differently on osx

From 7c92b49d6b62f88fcde883aacb60c5e32ae54b30 Mon Sep 17 00:00:00 2001
From: Larry Xiao <xiaodi@sjtu.edu.cn>
Date: Tue, 2 Sep 2014 18:29:08 -0700
Subject: [PATCH 173/489] [SPARK-1986][GraphX]move lib.Analytics to
 org.apache.spark.examples

to support ~/spark/bin/run-example GraphXAnalytics triangles
/soc-LiveJournal1.txt --numEPart=256

Author: Larry Xiao <xiaodi@sjtu.edu.cn>

Closes #1766 from larryxiao/1986 and squashes the following commits:

bb77cd9 [Larry Xiao] [SPARK-1986][GraphX]move lib.Analytics to org.apache.spark.examples
---
 .../scala/org/apache/spark/examples/graphx}/Analytics.scala    | 3 ++-
 .../org/apache/spark/examples/graphx/LiveJournalPageRank.scala | 2 +-
 2 files changed, 3 insertions(+), 2 deletions(-)
 rename {graphx/src/main/scala/org/apache/spark/graphx/lib => examples/src/main/scala/org/apache/spark/examples/graphx}/Analytics.scala (98%)

diff --git a/graphx/src/main/scala/org/apache/spark/graphx/lib/Analytics.scala b/examples/src/main/scala/org/apache/spark/examples/graphx/Analytics.scala
similarity index 98%
rename from graphx/src/main/scala/org/apache/spark/graphx/lib/Analytics.scala
rename to examples/src/main/scala/org/apache/spark/examples/graphx/Analytics.scala
index c1513a00453cf..c4317a6aec798 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/lib/Analytics.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/graphx/Analytics.scala
@@ -15,12 +15,13 @@
  * limitations under the License.
  */
 
-package org.apache.spark.graphx.lib
+package org.apache.spark.examples.graphx
 
 import scala.collection.mutable
 import org.apache.spark._
 import org.apache.spark.storage.StorageLevel
 import org.apache.spark.graphx._
+import org.apache.spark.graphx.lib._
 import org.apache.spark.graphx.PartitionStrategy._
 
 /**
diff --git a/examples/src/main/scala/org/apache/spark/examples/graphx/LiveJournalPageRank.scala b/examples/src/main/scala/org/apache/spark/examples/graphx/LiveJournalPageRank.scala
index 6ef3b62dcbedc..bdc8fa7f99f2e 100644
--- a/examples/src/main/scala/org/apache/spark/examples/graphx/LiveJournalPageRank.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/graphx/LiveJournalPageRank.scala
@@ -20,7 +20,7 @@ package org.apache.spark.examples.graphx
 import org.apache.spark.SparkContext._
 import org.apache.spark._
 import org.apache.spark.graphx._
-import org.apache.spark.graphx.lib.Analytics
+import org.apache.spark.examples.graphx.Analytics
 
 /**
  * Uses GraphX to run PageRank on a LiveJournal social network graph. Download the dataset from

From 7c9bbf172512701c75992671bcb2f4b6d9e5034b Mon Sep 17 00:00:00 2001
From: uncleGen <hustyugm@gmail.com>
Date: Tue, 2 Sep 2014 18:41:54 -0700
Subject: [PATCH 174/489] [SPARK-3123][GraphX]: override the "setName" function
 to set EdgeRDD's name manually just as VertexRDD does.

Author: uncleGen <hustyugm@gmail.com>

Closes #2033 from uncleGen/master_origin and squashes the following commits:

801994b [uncleGen] Update EdgeRDD.scala
---
 .../main/scala/org/apache/spark/graphx/EdgeRDD.scala   | 10 +++++++++-
 1 file changed, 9 insertions(+), 1 deletion(-)

diff --git a/graphx/src/main/scala/org/apache/spark/graphx/EdgeRDD.scala b/graphx/src/main/scala/org/apache/spark/graphx/EdgeRDD.scala
index 899a3cbd62b60..5bcb96b136ed7 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/EdgeRDD.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/EdgeRDD.scala
@@ -37,7 +37,15 @@ class EdgeRDD[@specialized ED: ClassTag, VD: ClassTag](
     val targetStorageLevel: StorageLevel = StorageLevel.MEMORY_ONLY)
   extends RDD[Edge[ED]](partitionsRDD.context, List(new OneToOneDependency(partitionsRDD))) {
 
-  partitionsRDD.setName("EdgeRDD")
+  override def setName(_name: String): this.type = {
+    if (partitionsRDD.name != null) {
+      partitionsRDD.setName(partitionsRDD.name + ", " + _name)
+    } else {
+      partitionsRDD.setName(_name)
+    }
+    this
+  }
+  setName("EdgeRDD")
 
   override protected def getPartitions: Array[Partition] = partitionsRDD.partitions
 

From aa7de128c5987fd2e134736f07ae913ad1f5eb26 Mon Sep 17 00:00:00 2001
From: Larry Xiao <xiaodi@sjtu.edu.cn>
Date: Tue, 2 Sep 2014 18:50:52 -0700
Subject: [PATCH 175/489] [SPARK-2981][GraphX] EdgePartition1D Int overflow

minor fix
detail is here: https://issues.apache.org/jira/browse/SPARK-2981

Author: Larry Xiao <xiaodi@sjtu.edu.cn>

Closes #1902 from larryxiao/2981 and squashes the following commits:

88059a2 [Larry Xiao] [SPARK-2981][GraphX] EdgePartition1D Int overflow
---
 .../main/scala/org/apache/spark/graphx/PartitionStrategy.scala  | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/graphx/src/main/scala/org/apache/spark/graphx/PartitionStrategy.scala b/graphx/src/main/scala/org/apache/spark/graphx/PartitionStrategy.scala
index 5e7e72a764cc8..13033fee0e6b5 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/PartitionStrategy.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/PartitionStrategy.scala
@@ -91,7 +91,7 @@ object PartitionStrategy {
   case object EdgePartition1D extends PartitionStrategy {
     override def getPartition(src: VertexId, dst: VertexId, numParts: PartitionID): PartitionID = {
       val mixingPrime: VertexId = 1125899906842597L
-      (math.abs(src) * mixingPrime).toInt % numParts
+      (math.abs(src * mixingPrime) % numParts).toInt
     }
   }
 

From e9bb12bea9fbef94332fbec88e3cd9197a27b7ad Mon Sep 17 00:00:00 2001
From: Tathagata Das <tathagata.das1565@gmail.com>
Date: Tue, 2 Sep 2014 19:02:48 -0700
Subject: [PATCH 176/489] [SPARK-1981][Streaming][Hotfix] Fixed docs related to
 kinesis

- Include kinesis in the unidocs
- Hide non-public classes from docs

Author: Tathagata Das <tathagata.das1565@gmail.com>

Closes #2239 from tdas/kinesis-doc-fix and squashes the following commits:

156e20c [Tathagata Das] More fixes, based on PR comments.
e9a6c01 [Tathagata Das] Fixed docs related to kinesis
---
 docs/_plugins/copy_api_dirs.rb                         |  4 ++--
 .../examples/streaming/JavaKinesisWordCountASL.java    | 10 +++++-----
 .../spark/examples/streaming/KinesisWordCountASL.scala |  6 +++---
 .../apache/spark/streaming/kinesis/KinesisUtils.scala  |  7 ++++---
 project/SparkBuild.scala                               |  6 +++---
 5 files changed, 17 insertions(+), 16 deletions(-)

diff --git a/docs/_plugins/copy_api_dirs.rb b/docs/_plugins/copy_api_dirs.rb
index 2dbbbf6feb4b8..3b02e090aec28 100644
--- a/docs/_plugins/copy_api_dirs.rb
+++ b/docs/_plugins/copy_api_dirs.rb
@@ -25,8 +25,8 @@
   curr_dir = pwd
   cd("..")
 
-  puts "Running 'sbt/sbt compile unidoc' from " + pwd + "; this may take a few minutes..."
-  puts `sbt/sbt compile unidoc`
+  puts "Running 'sbt/sbt -Pkinesis-asl compile unidoc' from " + pwd + "; this may take a few minutes..."
+  puts `sbt/sbt -Pkinesis-asl compile unidoc`
 
   puts "Moving back into docs dir."
   cd("docs")
diff --git a/extras/kinesis-asl/src/main/java/org/apache/spark/examples/streaming/JavaKinesisWordCountASL.java b/extras/kinesis-asl/src/main/java/org/apache/spark/examples/streaming/JavaKinesisWordCountASL.java
index 1a710d7b18c6f..aa917d0575c4c 100644
--- a/extras/kinesis-asl/src/main/java/org/apache/spark/examples/streaming/JavaKinesisWordCountASL.java
+++ b/extras/kinesis-asl/src/main/java/org/apache/spark/examples/streaming/JavaKinesisWordCountASL.java
@@ -75,7 +75,7 @@
  *   onto the Kinesis stream. 
  * Usage instructions for KinesisWordCountProducerASL are provided in the class definition.
  */
-public final class JavaKinesisWordCountASL {
+public final class JavaKinesisWordCountASL { // needs to be public for access from run-example
     private static final Pattern WORD_SEPARATOR = Pattern.compile(" ");
     private static final Logger logger = Logger.getLogger(JavaKinesisWordCountASL.class);
 
@@ -87,10 +87,10 @@ public static void main(String[] args) {
         /* Check that all required args were passed in. */
         if (args.length < 2) {
           System.err.println(
-              "|Usage: KinesisWordCount <stream-name> <endpoint-url>\n" +
-              "|    <stream-name> is the name of the Kinesis stream\n" +
-              "|    <endpoint-url> is the endpoint of the Kinesis service\n" +
-              "|                   (e.g. https://kinesis.us-east-1.amazonaws.com)\n");
+              "Usage: JavaKinesisWordCountASL <stream-name> <endpoint-url>\n" +
+              "    <stream-name> is the name of the Kinesis stream\n" +
+              "    <endpoint-url> is the endpoint of the Kinesis service\n" +
+              "                   (e.g. https://kinesis.us-east-1.amazonaws.com)\n");
           System.exit(1);
         }
 
diff --git a/extras/kinesis-asl/src/main/scala/org/apache/spark/examples/streaming/KinesisWordCountASL.scala b/extras/kinesis-asl/src/main/scala/org/apache/spark/examples/streaming/KinesisWordCountASL.scala
index d03edf8b30a9f..fffd90de08240 100644
--- a/extras/kinesis-asl/src/main/scala/org/apache/spark/examples/streaming/KinesisWordCountASL.scala
+++ b/extras/kinesis-asl/src/main/scala/org/apache/spark/examples/streaming/KinesisWordCountASL.scala
@@ -69,7 +69,7 @@ import org.apache.log4j.Level
  *   dummy data onto the Kinesis stream.
  * Usage instructions for KinesisWordCountProducerASL are provided in that class definition.
  */
-object KinesisWordCountASL extends Logging {
+private object KinesisWordCountASL extends Logging {
   def main(args: Array[String]) {
     /* Check that all required args were passed in. */
     if (args.length < 2) {
@@ -154,7 +154,7 @@ object KinesisWordCountASL extends Logging {
  *         org.apache.spark.examples.streaming.KinesisWordCountProducerASL mySparkStream \
  *         https://kinesis.us-east-1.amazonaws.com 10 5
  */
-object KinesisWordCountProducerASL {
+private object KinesisWordCountProducerASL {
   def main(args: Array[String]) {
     if (args.length < 4) {
       System.err.println("Usage: KinesisWordCountProducerASL <stream-name> <endpoint-url>" +
@@ -235,7 +235,7 @@ object KinesisWordCountProducerASL {
  *  Utility functions for Spark Streaming examples. 
  *  This has been lifted from the examples/ project to remove the circular dependency.
  */
-object StreamingExamples extends Logging {
+private[streaming] object StreamingExamples extends Logging {
 
   /** Set reasonable logging levels for streaming if the user has not configured log4j. */
   def setStreamingLogLevels() {
diff --git a/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisUtils.scala b/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisUtils.scala
index 713cac0e293c0..96f4399accd3a 100644
--- a/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisUtils.scala
+++ b/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisUtils.scala
@@ -35,7 +35,7 @@ import com.amazonaws.services.kinesis.clientlibrary.lib.worker.InitialPositionIn
 object KinesisUtils {
   /**
    * Create an InputDStream that pulls messages from a Kinesis stream.
-   *
+   * :: Experimental ::
    * @param ssc    StreamingContext object
    * @param streamName   Kinesis stream name
    * @param endpointUrl  Url of Kinesis service (e.g., https://kinesis.us-east-1.amazonaws.com)
@@ -52,6 +52,7 @@ object KinesisUtils {
    *
    * @return ReceiverInputDStream[Array[Byte]]
    */
+  @Experimental
   def createStream(
       ssc: StreamingContext,
       streamName: String,
@@ -65,9 +66,8 @@ object KinesisUtils {
 
   /**
    * Create a Java-friendly InputDStream that pulls messages from a Kinesis stream.
-   *
+   * :: Experimental ::
    * @param jssc Java StreamingContext object
-   * @param ssc    StreamingContext object
    * @param streamName   Kinesis stream name
    * @param endpointUrl  Url of Kinesis service (e.g., https://kinesis.us-east-1.amazonaws.com)
    * @param checkpointInterval  Checkpoint interval for Kinesis checkpointing.
@@ -83,6 +83,7 @@ object KinesisUtils {
    *
    * @return JavaReceiverInputDStream[Array[Byte]]
    */
+  @Experimental
   def createStream(
       jssc: JavaStreamingContext, 
       streamName: String, 
diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala
index 4c696d3d385fb..a26c2c90cb321 100644
--- a/project/SparkBuild.scala
+++ b/project/SparkBuild.scala
@@ -290,9 +290,9 @@ object Unidoc {
     publish := {},
 
     unidocProjectFilter in(ScalaUnidoc, unidoc) :=
-      inAnyProject -- inProjects(OldDeps.project, repl, examples, tools, catalyst, yarn, yarnAlpha),
+      inAnyProject -- inProjects(OldDeps.project, repl, examples, tools, catalyst, streamingFlumeSink, yarn, yarnAlpha),
     unidocProjectFilter in(JavaUnidoc, unidoc) :=
-      inAnyProject -- inProjects(OldDeps.project, repl, bagel, graphx, examples, tools, catalyst, yarn, yarnAlpha),
+      inAnyProject -- inProjects(OldDeps.project, repl, bagel, graphx, examples, tools, catalyst, streamingFlumeSink, yarn, yarnAlpha),
 
     // Skip class names containing $ and some internal packages in Javadocs
     unidocAllSources in (JavaUnidoc, unidoc) := {
@@ -314,7 +314,7 @@ object Unidoc {
       "-group", "Core Java API", packageList("api.java", "api.java.function"),
       "-group", "Spark Streaming", packageList(
         "streaming.api.java", "streaming.flume", "streaming.kafka",
-        "streaming.mqtt", "streaming.twitter", "streaming.zeromq"
+        "streaming.mqtt", "streaming.twitter", "streaming.zeromq", "streaming.kinesis"
       ),
       "-group", "MLlib", packageList(
         "mllib.classification", "mllib.clustering", "mllib.evaluation.binary", "mllib.linalg",

From 9b225ac3072de522b40b46aba6df1f1c231f13ef Mon Sep 17 00:00:00 2001
From: luluorta <luluorta@gmail.com>
Date: Tue, 2 Sep 2014 19:25:52 -0700
Subject: [PATCH 177/489] [SPARK-2823][GraphX]fix GraphX EdgeRDD zipPartitions
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

If the users set “spark.default.parallelism” and the value is different with the EdgeRDD partition number, GraphX jobs will throw:
java.lang.IllegalArgumentException: Can't zip RDDs with unequal numbers of partitions

Author: luluorta <luluorta@gmail.com>

Closes #1763 from luluorta/fix-graph-zip and squashes the following commits:

8338961 [luluorta] fix GraphX EdgeRDD zipPartitions
---
 .../scala/org/apache/spark/graphx/EdgeRDD.scala  |  4 ++--
 .../org/apache/spark/graphx/GraphSuite.scala     | 16 ++++++++++++++++
 2 files changed, 18 insertions(+), 2 deletions(-)

diff --git a/graphx/src/main/scala/org/apache/spark/graphx/EdgeRDD.scala b/graphx/src/main/scala/org/apache/spark/graphx/EdgeRDD.scala
index 5bcb96b136ed7..35fbd47e6c2a5 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/EdgeRDD.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/EdgeRDD.scala
@@ -19,7 +19,7 @@ package org.apache.spark.graphx
 
 import scala.reflect.{classTag, ClassTag}
 
-import org.apache.spark.{OneToOneDependency, Partition, Partitioner, TaskContext}
+import org.apache.spark._
 import org.apache.spark.rdd.RDD
 import org.apache.spark.storage.StorageLevel
 
@@ -55,7 +55,7 @@ class EdgeRDD[@specialized ED: ClassTag, VD: ClassTag](
    * partitioner that allows co-partitioning with `partitionsRDD`.
    */
   override val partitioner =
-    partitionsRDD.partitioner.orElse(Some(Partitioner.defaultPartitioner(partitionsRDD)))
+    partitionsRDD.partitioner.orElse(Some(new HashPartitioner(partitionsRDD.partitions.size)))
 
   override def compute(part: Partition, context: TaskContext): Iterator[Edge[ED]] = {
     val p = firstParent[(PartitionID, EdgePartition[ED, VD])].iterator(part, context)
diff --git a/graphx/src/test/scala/org/apache/spark/graphx/GraphSuite.scala b/graphx/src/test/scala/org/apache/spark/graphx/GraphSuite.scala
index 6506bac73d71c..eaaa4499b6b93 100644
--- a/graphx/src/test/scala/org/apache/spark/graphx/GraphSuite.scala
+++ b/graphx/src/test/scala/org/apache/spark/graphx/GraphSuite.scala
@@ -19,6 +19,7 @@ package org.apache.spark.graphx
 
 import org.scalatest.FunSuite
 
+import org.apache.spark.SparkConf
 import org.apache.spark.SparkContext
 import org.apache.spark.graphx.Graph._
 import org.apache.spark.graphx.PartitionStrategy._
@@ -350,4 +351,19 @@ class GraphSuite extends FunSuite with LocalSparkContext {
     }
   }
 
+  test("non-default number of edge partitions") {
+    val n = 10
+    val defaultParallelism = 3
+    val numEdgePartitions = 4
+    assert(defaultParallelism != numEdgePartitions)
+    val conf = new SparkConf()
+      .set("spark.default.parallelism", defaultParallelism.toString)
+    val sc = new SparkContext("local", "test", conf)
+    val edges = sc.parallelize((1 to n).map(x => (x: VertexId, 0: VertexId)),
+      numEdgePartitions)
+    val graph = Graph.fromEdgeTuples(edges, 1)
+    val neighborAttrSums = graph.mapReduceTriplets[Int](
+      et => Iterator((et.dstId, et.srcAttr)), _ + _)
+    assert(neighborAttrSums.collect.toSet === Set((0: VertexId, n)))
+  }
 }

From 0cd91f666dfe318934d5d41d3cb7085b2f9ae278 Mon Sep 17 00:00:00 2001
From: Takuya UESHIN <ueshin@happy-camper.st>
Date: Tue, 2 Sep 2014 20:31:15 -0700
Subject: [PATCH 178/489] [SPARK-3341][SQL] The dataType of Sqrt expression
 should be DoubleType.

Author: Takuya UESHIN <ueshin@happy-camper.st>

Closes #2233 from ueshin/issues/SPARK-3341 and squashes the following commits:

e497320 [Takuya UESHIN] Fix data type of Sqrt expression.
---
 .../org/apache/spark/sql/catalyst/expressions/arithmetic.scala  | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
index 56f042891a2e6..f988fb010b107 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
@@ -36,7 +36,7 @@ case class UnaryMinus(child: Expression) extends UnaryExpression {
 case class Sqrt(child: Expression) extends UnaryExpression {
   type EvaluatedType = Any
   
-  def dataType = child.dataType
+  def dataType = DoubleType
   override def foldable = child.foldable
   def nullable = child.nullable
   override def toString = s"SQRT($child)"

From 19d3e1e8e9c25c0936c0ad6efbc2092b473723aa Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian.cs.zju@gmail.com>
Date: Tue, 2 Sep 2014 20:49:36 -0700
Subject: [PATCH 179/489] [SQL] Renamed ColumnStat to ColumnMetrics to avoid
 confusion between ColumnStats

Class names of these two are just too similar.

Author: Cheng Lian <lian.cs.zju@gmail.com>

Closes #2189 from liancheng/column-metrics and squashes the following commits:

8bb3b21 [Cheng Lian] Renamed ColumnStat to ColumnMetrics to avoid confusion between ColumnStats
---
 .../org/apache/spark/sql/execution/debug/package.scala | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/debug/package.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/debug/package.scala
index 8ff757bbe3508..a9535a750bcd7 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/debug/package.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/debug/package.scala
@@ -74,22 +74,22 @@ package object debug {
     }
 
     /**
-     * A collection of stats for each column of output.
+     * A collection of metrics for each column of output.
      * @param elementTypes the actual runtime types for the output.  Useful when there are bugs
      *        causing the wrong data to be projected.
      */
-    case class ColumnStat(
+    case class ColumnMetrics(
         elementTypes: Accumulator[HashSet[String]] = sparkContext.accumulator(HashSet.empty))
     val tupleCount = sparkContext.accumulator[Int](0)
 
     val numColumns = child.output.size
-    val columnStats = Array.fill(child.output.size)(new ColumnStat())
+    val columnStats = Array.fill(child.output.size)(new ColumnMetrics())
 
     def dumpStats(): Unit = {
       println(s"== ${child.simpleString} ==")
       println(s"Tuples output: ${tupleCount.value}")
-      child.output.zip(columnStats).foreach { case(attr, stat) =>
-        val actualDataTypes =stat.elementTypes.value.mkString("{", ",", "}")
+      child.output.zip(columnStats).foreach { case(attr, metric) =>
+        val actualDataTypes = metric.elementTypes.value.mkString("{", ",", "}")
         println(s" ${attr.name} ${attr.dataType}: $actualDataTypes")
       }
     }

From 24ab384018270e4f7af7eb8ca7192f337498eaf5 Mon Sep 17 00:00:00 2001
From: Liang-Chi Hsieh <viirya@gmail.com>
Date: Tue, 2 Sep 2014 20:51:25 -0700
Subject: [PATCH 180/489] [SPARK-3300][SQL] No need to call clear() and shorten
 build()

The function `ensureFreeSpace` in object `ColumnBuilder` clears old buffer before copying its content to new buffer. This PR fixes it.

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

Closes #2195 from viirya/fix_buffer_clear and squashes the following commits:

792f009 [Liang-Chi Hsieh] no need to call clear(). use flip() instead of calling limit(), position() and rewind().
df2169f [Liang-Chi Hsieh] should clean old buffer after copying its content.
---
 .../scala/org/apache/spark/sql/columnar/ColumnBuilder.scala   | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnBuilder.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnBuilder.scala
index 7e7bb2859bbcd..247337a875c75 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnBuilder.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnBuilder.scala
@@ -75,8 +75,7 @@ private[sql] class BasicColumnBuilder[T <: DataType, JvmType](
   }
 
   override def build() = {
-    buffer.limit(buffer.position()).rewind()
-    buffer
+    buffer.flip().asInstanceOf[ByteBuffer]
   }
 }
 
@@ -129,7 +128,6 @@ private[sql] object ColumnBuilder {
       val newSize = capacity + size.max(capacity / 8 + 1)
       val pos = orig.position()
 
-      orig.clear()
       ByteBuffer
         .allocate(newSize)
         .order(ByteOrder.nativeOrder())

From c64cc435e2a29c6f0ff66022fd4d5b4cb5011718 Mon Sep 17 00:00:00 2001
From: Patrick Wendell <pwendell@gmail.com>
Date: Tue, 2 Sep 2014 21:30:09 -0700
Subject: [PATCH 181/489] SPARK-3358: [EC2] Switch back to HVM instances for
 m3.X.

During regression tests of Spark 1.1 we discovered perf issues with
PVM instances when running PySpark. This reverts a change added in #1156
which changed the default type for m3 instances to PVM.

Author: Patrick Wendell <pwendell@gmail.com>

Closes #2244 from pwendell/ec2-hvm and squashes the following commits:

1342d7e [Patrick Wendell] SPARK-3358: [EC2] Switch back to HVM instances for m3.X.
---
 ec2/spark_ec2.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/ec2/spark_ec2.py b/ec2/spark_ec2.py
index 7e25df57ee45b..eed6eb8485183 100755
--- a/ec2/spark_ec2.py
+++ b/ec2/spark_ec2.py
@@ -234,10 +234,10 @@ def get_spark_ami(opts):
         "cg1.4xlarge": "hvm",
         "hs1.8xlarge": "pvm",
         "hi1.4xlarge": "pvm",
-        "m3.medium":   "pvm",
-        "m3.large":    "pvm",
-        "m3.xlarge":   "pvm",
-        "m3.2xlarge":  "pvm",
+        "m3.medium":   "hvm",
+        "m3.large":    "hvm",
+        "m3.xlarge":   "hvm",
+        "m3.2xlarge":  "hvm",
         "cr1.8xlarge": "hvm",
         "i2.xlarge":   "hvm",
         "i2.2xlarge":  "hvm",

From 6a72a36940311fcb3429bd34c8818bc7d513115c Mon Sep 17 00:00:00 2001
From: Marcelo Vanzin <vanzin@cloudera.com>
Date: Wed, 3 Sep 2014 08:22:50 -0500
Subject: [PATCH 182/489] [SPARK-3187] [yarn] Cleanup allocator code.

Move all shared logic to the base YarnAllocator class, and leave
the version-specific logic in the version-specific module.

Author: Marcelo Vanzin <vanzin@cloudera.com>

Closes #2169 from vanzin/SPARK-3187 and squashes the following commits:

46c2826 [Marcelo Vanzin] Hide the privates.
4dc9c83 [Marcelo Vanzin] Actually release containers.
8b1a077 [Marcelo Vanzin] Changes to the Yarn alpha allocator.
f3f5f1d [Marcelo Vanzin] [SPARK-3187] [yarn] Cleanup allocator code.
---
 .../deploy/yarn/YarnAllocationHandler.scala   | 462 +++---------------
 .../spark/deploy/yarn/YarnAllocator.scala     | 425 +++++++++++++++-
 .../deploy/yarn/YarnAllocationHandler.scala   | 402 +--------------
 3 files changed, 495 insertions(+), 794 deletions(-)

diff --git a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocationHandler.scala b/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocationHandler.scala
index 629cd13f67145..9f9e16c06452b 100644
--- a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocationHandler.scala
+++ b/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocationHandler.scala
@@ -17,35 +17,21 @@
 
 package org.apache.spark.deploy.yarn
 
-import java.util.concurrent.{CopyOnWriteArrayList, ConcurrentHashMap}
+import java.util.concurrent.CopyOnWriteArrayList
 import java.util.concurrent.atomic.AtomicInteger
 
 import scala.collection.JavaConversions._
-import scala.collection.mutable.{ArrayBuffer, HashMap, HashSet}
+import scala.collection.mutable.{ArrayBuffer, HashMap}
 
-import org.apache.spark.{Logging, SparkConf, SparkEnv}
-import org.apache.spark.scheduler.{SplitInfo, TaskSchedulerImpl}
-import org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend
-import org.apache.spark.util.Utils
+import org.apache.spark.SparkConf
+import org.apache.spark.scheduler.SplitInfo
 
 import org.apache.hadoop.conf.Configuration
 import org.apache.hadoop.yarn.api.AMRMProtocol
-import org.apache.hadoop.yarn.api.records.{AMResponse, ApplicationAttemptId}
-import org.apache.hadoop.yarn.api.records.{Container, ContainerId}
-import org.apache.hadoop.yarn.api.records.{Priority, Resource, ResourceRequest}
-import org.apache.hadoop.yarn.api.protocolrecords.{AllocateRequest, AllocateResponse}
+import org.apache.hadoop.yarn.api.records._
+import org.apache.hadoop.yarn.api.protocolrecords.AllocateRequest
 import org.apache.hadoop.yarn.util.Records
 
-// TODO:
-// Too many params.
-// Needs to be mt-safe
-// Need to refactor this to make it 'cleaner' ... right now, all computation is reactive - should
-// make it more proactive and decoupled.
-
-// Note that right now, we assume all node asks as uniform in terms of capabilities and priority
-// Refer to http://developer.yahoo.com/blogs/hadoop/posts/2011/03/mapreduce-nextgen-scheduler/ for
-// more info on how we are requesting for containers.
-
 /**
  * Acquires resources for executors from a ResourceManager and launches executors in new containers.
  */
@@ -56,357 +42,20 @@ private[yarn] class YarnAllocationHandler(
     appAttemptId: ApplicationAttemptId,
     args: ApplicationMasterArguments,
     preferredNodes: collection.Map[String, collection.Set[SplitInfo]])
-  extends YarnAllocator with Logging {
-
-  // These three are locked on allocatedHostToContainersMap. Complementary data structures
-  // allocatedHostToContainersMap : containers which are running : host, Set<containerid>
-  // allocatedContainerToHostMap: container to host mapping.
-  private val allocatedHostToContainersMap =
-    new HashMap[String, collection.mutable.Set[ContainerId]]()
-
-  private val allocatedContainerToHostMap = new HashMap[ContainerId, String]()
-
-  // allocatedRackCount is populated ONLY if allocation happens (or decremented if this is an
-  // allocated node)
-  // As with the two data structures above, tightly coupled with them, and to be locked on
-  // allocatedHostToContainersMap
-  private val allocatedRackCount = new HashMap[String, Int]()
-
-  // Containers which have been released.
-  private val releasedContainerList = new CopyOnWriteArrayList[ContainerId]()
-  // Containers to be released in next request to RM
-  private val pendingReleaseContainers = new ConcurrentHashMap[ContainerId, Boolean]
-
-  // Additional memory overhead - in mb.
-  private def memoryOverhead: Int = sparkConf.getInt("spark.yarn.executor.memoryOverhead",
-    YarnSparkHadoopUtil.DEFAULT_MEMORY_OVERHEAD)
-
-  private val numExecutorsRunning = new AtomicInteger()
-  // Used to generate a unique id per executor
-  private val executorIdCounter = new AtomicInteger()
-  private val lastResponseId = new AtomicInteger()
-  private val numExecutorsFailed = new AtomicInteger()
-
-  private val maxExecutors = args.numExecutors
-  private val executorMemory = args.executorMemory
-  private val executorCores = args.executorCores
-  private val (preferredHostToCount, preferredRackToCount) =
-    generateNodeToWeight(conf, preferredNodes)
-
-  def getNumExecutorsRunning: Int = numExecutorsRunning.intValue
-
-  def getNumExecutorsFailed: Int = numExecutorsFailed.intValue
-
-  def isResourceConstraintSatisfied(container: Container): Boolean = {
-    container.getResource.getMemory >= (executorMemory + memoryOverhead)
-  }
-
-  override def allocateResources() = {
-    // We need to send the request only once from what I understand ... but for now, not modifying
-    // this much.
-    val executorsToRequest = Math.max(maxExecutors - numExecutorsRunning.get(), 0)
-
-    // Keep polling the Resource Manager for containers
-    val amResp = allocateExecutorResources(executorsToRequest).getAMResponse
-
-    val _allocatedContainers = amResp.getAllocatedContainers()
-
-    if (_allocatedContainers.size > 0) {
-      logDebug("""
-        Allocated containers: %d
-        Current executor count: %d
-        Containers released: %s
-        Containers to be released: %s
-        Cluster resources: %s
-        """.format(
-          _allocatedContainers.size,
-          numExecutorsRunning.get(),
-          releasedContainerList,
-          pendingReleaseContainers,
-          amResp.getAvailableResources))
-
-      val hostToContainers = new HashMap[String, ArrayBuffer[Container]]()
-
-      // Ignore if not satisfying constraints      {
-      for (container <- _allocatedContainers) {
-        if (isResourceConstraintSatisfied(container)) {
-          // allocatedContainers += container
-
-          val host = container.getNodeId.getHost
-          val containers = hostToContainers.getOrElseUpdate(host, new ArrayBuffer[Container]())
-
-          containers += container
-        } else {
-          // Add all ignored containers to released list
-          releasedContainerList.add(container.getId())
-        }
-      }
-
-      // Find the appropriate containers to use. Slightly non trivial groupBy ...
-      val dataLocalContainers = new HashMap[String, ArrayBuffer[Container]]()
-      val rackLocalContainers = new HashMap[String, ArrayBuffer[Container]]()
-      val offRackContainers = new HashMap[String, ArrayBuffer[Container]]()
-
-      for (candidateHost <- hostToContainers.keySet)
-      {
-        val maxExpectedHostCount = preferredHostToCount.getOrElse(candidateHost, 0)
-        val requiredHostCount = maxExpectedHostCount - allocatedContainersOnHost(candidateHost)
-
-        var remainingContainers = hostToContainers.get(candidateHost).getOrElse(null)
-        assert(remainingContainers != null)
-
-        if (requiredHostCount >= remainingContainers.size){
-          // Since we got <= required containers, add all to dataLocalContainers
-          dataLocalContainers.put(candidateHost, remainingContainers)
-          // all consumed
-          remainingContainers = null
-        } else if (requiredHostCount > 0) {
-          // Container list has more containers than we need for data locality.
-          // Split into two : data local container count of (remainingContainers.size -
-          // requiredHostCount) and rest as remainingContainer
-          val (dataLocal, remaining) = remainingContainers.splitAt(
-            remainingContainers.size - requiredHostCount)
-          dataLocalContainers.put(candidateHost, dataLocal)
-          // remainingContainers = remaining
-
-          // yarn has nasty habit of allocating a tonne of containers on a host - discourage this :
-          // add remaining to release list. If we have insufficient containers, next allocation
-          // cycle will reallocate (but wont treat it as data local)
-          for (container <- remaining) releasedContainerList.add(container.getId())
-          remainingContainers = null
-        }
-
-        // Now rack local
-        if (remainingContainers != null){
-          val rack = YarnSparkHadoopUtil.lookupRack(conf, candidateHost)
-
-          if (rack != null){
-            val maxExpectedRackCount = preferredRackToCount.getOrElse(rack, 0)
-            val requiredRackCount = maxExpectedRackCount - allocatedContainersOnRack(rack) -
-              rackLocalContainers.get(rack).getOrElse(List()).size
-
-
-            if (requiredRackCount >= remainingContainers.size){
-              // Add all to dataLocalContainers
-              dataLocalContainers.put(rack, remainingContainers)
-              // All consumed
-              remainingContainers = null
-            } else if (requiredRackCount > 0) {
-              // container list has more containers than we need for data locality.
-              // Split into two : data local container count of (remainingContainers.size -
-              // requiredRackCount) and rest as remainingContainer
-              val (rackLocal, remaining) = remainingContainers.splitAt(
-                remainingContainers.size - requiredRackCount)
-              val existingRackLocal = rackLocalContainers.getOrElseUpdate(rack,
-                new ArrayBuffer[Container]())
-
-              existingRackLocal ++= rackLocal
-              remainingContainers = remaining
-            }
-          }
-        }
-
-        // If still not consumed, then it is off rack host - add to that list.
-        if (remainingContainers != null){
-          offRackContainers.put(candidateHost, remainingContainers)
-        }
-      }
-
-      // Now that we have split the containers into various groups, go through them in order :
-      // first host local, then rack local and then off rack (everything else).
-      // Note that the list we create below tries to ensure that not all containers end up within a
-      // host if there are sufficiently large number of hosts/containers.
-
-      val allocatedContainers = new ArrayBuffer[Container](_allocatedContainers.size)
-      allocatedContainers ++= TaskSchedulerImpl.prioritizeContainers(dataLocalContainers)
-      allocatedContainers ++= TaskSchedulerImpl.prioritizeContainers(rackLocalContainers)
-      allocatedContainers ++= TaskSchedulerImpl.prioritizeContainers(offRackContainers)
-
-      // Run each of the allocated containers
-      for (container <- allocatedContainers) {
-        val numExecutorsRunningNow = numExecutorsRunning.incrementAndGet()
-        val executorHostname = container.getNodeId.getHost
-        val containerId = container.getId
-
-        assert( container.getResource.getMemory >=
-          (executorMemory + memoryOverhead))
-
-        if (numExecutorsRunningNow > maxExecutors) {
-          logInfo("""Ignoring container %s at host %s, since we already have the required number of
-            containers for it.""".format(containerId, executorHostname))
-          releasedContainerList.add(containerId)
-          // reset counter back to old value.
-          numExecutorsRunning.decrementAndGet()
-        } else {
-          // Deallocate + allocate can result in reusing id's wrongly - so use a different counter
-          // (executorIdCounter)
-          val executorId = executorIdCounter.incrementAndGet().toString
-          val driverUrl = "akka.tcp://%s@%s:%s/user/%s".format(
-            SparkEnv.driverActorSystemName,
-            sparkConf.get("spark.driver.host"),
-            sparkConf.get("spark.driver.port"),
-            CoarseGrainedSchedulerBackend.ACTOR_NAME)
-
-          logInfo("launching container on " + containerId + " host " + executorHostname)
-          // Just to be safe, simply remove it from pendingReleaseContainers.
-          // Should not be there, but ..
-          pendingReleaseContainers.remove(containerId)
-
-          val rack = YarnSparkHadoopUtil.lookupRack(conf, executorHostname)
-          allocatedHostToContainersMap.synchronized {
-            val containerSet = allocatedHostToContainersMap.getOrElseUpdate(executorHostname,
-              new HashSet[ContainerId]())
-
-            containerSet += containerId
-            allocatedContainerToHostMap.put(containerId, executorHostname)
-            if (rack != null) {
-              allocatedRackCount.put(rack, allocatedRackCount.getOrElse(rack, 0) + 1)
-            }
-          }
-
-          new Thread(
-            new ExecutorRunnable(container, conf, sparkConf, driverUrl, executorId,
-              executorHostname, executorMemory, executorCores)
-          ).start()
-        }
-      }
-      logDebug("""
-        Finished processing %d containers.
-        Current number of executors running: %d,
-        releasedContainerList: %s,
-        pendingReleaseContainers: %s
-        """.format(
-          allocatedContainers.size,
-          numExecutorsRunning.get(),
-          releasedContainerList,
-          pendingReleaseContainers))
-    }
-
-
-    val completedContainers = amResp.getCompletedContainersStatuses()
-    if (completedContainers.size > 0){
-      logDebug("Completed %d containers, to-be-released: %s".format(
-        completedContainers.size, releasedContainerList))
-      for (completedContainer <- completedContainers){
-        val containerId = completedContainer.getContainerId
-
-        // Was this released by us ? If yes, then simply remove from containerSet and move on.
-        if (pendingReleaseContainers.containsKey(containerId)) {
-          pendingReleaseContainers.remove(containerId)
-        } else {
-          // Simply decrement count - next iteration of ReporterThread will take care of allocating.
-          numExecutorsRunning.decrementAndGet()
-          logInfo("Completed container %s (state: %s, exit status: %s)".format(
-            containerId,
-            completedContainer.getState,
-            completedContainer.getExitStatus()))
-          // Hadoop 2.2.X added a ContainerExitStatus we should switch to use
-          // there are some exit status' we shouldn't necessarily count against us, but for
-          // now I think its ok as none of the containers are expected to exit
-          if (completedContainer.getExitStatus() != 0) {
-            logInfo("Container marked as failed: " + containerId)
-            numExecutorsFailed.incrementAndGet()
-          }
-        }
-
-        allocatedHostToContainersMap.synchronized {
-          if (allocatedContainerToHostMap.containsKey(containerId)) {
-            val host = allocatedContainerToHostMap.get(containerId).getOrElse(null)
-            assert (host != null)
-
-            val containerSet = allocatedHostToContainersMap.get(host).getOrElse(null)
-            assert (containerSet != null)
-
-            containerSet -= containerId
-            if (containerSet.isEmpty) {
-              allocatedHostToContainersMap.remove(host)
-            } else {
-              allocatedHostToContainersMap.update(host, containerSet)
-            }
-
-            allocatedContainerToHostMap -= containerId
-
-            // Doing this within locked context, sigh ... move to outside ?
-            val rack = YarnSparkHadoopUtil.lookupRack(conf, host)
-            if (rack != null) {
-              val rackCount = allocatedRackCount.getOrElse(rack, 0) - 1
-              if (rackCount > 0) {
-                allocatedRackCount.put(rack, rackCount)
-              } else {
-                allocatedRackCount.remove(rack)
-              }
-            }
-          }
-        }
-      }
-      logDebug("""
-        Finished processing %d completed containers.
-        Current number of executors running: %d,
-        releasedContainerList: %s,
-        pendingReleaseContainers: %s
-        """.format(
-          completedContainers.size,
-          numExecutorsRunning.get(),
-          releasedContainerList,
-          pendingReleaseContainers))
-    }
-  }
-
-  def createRackResourceRequests(hostContainers: List[ResourceRequest]): List[ResourceRequest] = {
-    // First generate modified racks and new set of hosts under it : then issue requests
-    val rackToCounts = new HashMap[String, Int]()
-
-    // Within this lock - used to read/write to the rack related maps too.
-    for (container <- hostContainers) {
-      val candidateHost = container.getHostName
-      val candidateNumContainers = container.getNumContainers
-      assert(YarnSparkHadoopUtil.ANY_HOST != candidateHost)
-
-      val rack = YarnSparkHadoopUtil.lookupRack(conf, candidateHost)
-      if (rack != null) {
-        var count = rackToCounts.getOrElse(rack, 0)
-        count += candidateNumContainers
-        rackToCounts.put(rack, count)
-      }
-    }
-
-    val requestedContainers: ArrayBuffer[ResourceRequest] =
-      new ArrayBuffer[ResourceRequest](rackToCounts.size)
-    for ((rack, count) <- rackToCounts){
-      requestedContainers +=
-        createResourceRequest(AllocationType.RACK, rack, count,
-          YarnSparkHadoopUtil.RM_REQUEST_PRIORITY)
-    }
-
-    requestedContainers.toList
-  }
-
-  def allocatedContainersOnHost(host: String): Int = {
-    var retval = 0
-    allocatedHostToContainersMap.synchronized {
-      retval = allocatedHostToContainersMap.getOrElse(host, Set()).size
-    }
-    retval
-  }
+  extends YarnAllocator(conf, sparkConf, args, preferredNodes) {
 
-  def allocatedContainersOnRack(rack: String): Int = {
-    var retval = 0
-    allocatedHostToContainersMap.synchronized {
-      retval = allocatedRackCount.getOrElse(rack, 0)
-    }
-    retval
-  }
-
-  private def allocateExecutorResources(numExecutors: Int): AllocateResponse = {
+  private val lastResponseId = new AtomicInteger()
+  private val releaseList: CopyOnWriteArrayList[ContainerId] = new CopyOnWriteArrayList()
 
+  override protected def allocateContainers(count: Int): YarnAllocateResponse = {
     var resourceRequests: List[ResourceRequest] = null
 
-      // default.
-    if (numExecutors <= 0 || preferredHostToCount.isEmpty) {
-      logDebug("numExecutors: " + numExecutors + ", host preferences: " +
+    // default.
+    if (count <= 0 || preferredHostToCount.isEmpty) {
+      logDebug("numExecutors: " + count + ", host preferences: " +
         preferredHostToCount.isEmpty)
       resourceRequests = List(createResourceRequest(
-        AllocationType.ANY, null, numExecutors, YarnSparkHadoopUtil.RM_REQUEST_PRIORITY))
+        AllocationType.ANY, null, count, YarnSparkHadoopUtil.RM_REQUEST_PRIORITY))
     } else {
       // request for all hosts in preferred nodes and for numExecutors -
       // candidates.size, request by default allocation policy.
@@ -429,7 +78,7 @@ private[yarn] class YarnAllocationHandler(
       val anyContainerRequests: ResourceRequest = createResourceRequest(
         AllocationType.ANY,
         resource = null,
-        numExecutors,
+        count,
         YarnSparkHadoopUtil.RM_REQUEST_PRIORITY)
 
       val containerRequests: ArrayBuffer[ResourceRequest] = new ArrayBuffer[ResourceRequest](
@@ -451,8 +100,8 @@ private[yarn] class YarnAllocationHandler(
     val releasedContainerList = createReleasedContainerList()
     req.addAllReleases(releasedContainerList)
 
-    if (numExecutors > 0) {
-      logInfo("Allocating %d executor containers with %d of memory each.".format(numExecutors,
+    if (count > 0) {
+      logInfo("Allocating %d executor containers with %d of memory each.".format(count,
         executorMemory + memoryOverhead))
     } else {
       logDebug("Empty allocation req ..  release : " + releasedContainerList)
@@ -466,9 +115,42 @@ private[yarn] class YarnAllocationHandler(
           request.getPriority,
           request.getCapability))
     }
-    resourceManager.allocate(req)
+    new AlphaAllocateResponse(resourceManager.allocate(req).getAMResponse())
   }
 
+  override protected def releaseContainer(container: Container) = {
+    releaseList.add(container.getId())
+  }
+
+  private def createRackResourceRequests(hostContainers: List[ResourceRequest]):
+    List[ResourceRequest] = {
+    // First generate modified racks and new set of hosts under it : then issue requests
+    val rackToCounts = new HashMap[String, Int]()
+
+    // Within this lock - used to read/write to the rack related maps too.
+    for (container <- hostContainers) {
+      val candidateHost = container.getHostName
+      val candidateNumContainers = container.getNumContainers
+      assert(YarnSparkHadoopUtil.ANY_HOST != candidateHost)
+
+      val rack = YarnSparkHadoopUtil.lookupRack(conf, candidateHost)
+      if (rack != null) {
+        var count = rackToCounts.getOrElse(rack, 0)
+        count += candidateNumContainers
+        rackToCounts.put(rack, count)
+      }
+    }
+
+    val requestedContainers: ArrayBuffer[ResourceRequest] =
+      new ArrayBuffer[ResourceRequest](rackToCounts.size)
+    for ((rack, count) <- rackToCounts){
+      requestedContainers +=
+        createResourceRequest(AllocationType.RACK, rack, count,
+          YarnSparkHadoopUtil.RM_REQUEST_PRIORITY)
+    }
+
+    requestedContainers.toList
+  }
 
   private def createResourceRequest(
     requestType: AllocationType.AllocationType,
@@ -521,48 +203,24 @@ private[yarn] class YarnAllocationHandler(
     rsrcRequest
   }
 
-  def createReleasedContainerList(): ArrayBuffer[ContainerId] = {
-
+  private def createReleasedContainerList(): ArrayBuffer[ContainerId] = {
     val retval = new ArrayBuffer[ContainerId](1)
     // Iterator on COW list ...
-    for (container <- releasedContainerList.iterator()){
+    for (container <- releaseList.iterator()){
       retval += container
     }
     // Remove from the original list.
-    if (! retval.isEmpty) {
-      releasedContainerList.removeAll(retval)
-      for (v <- retval) pendingReleaseContainers.put(v, true)
-      logInfo("Releasing " + retval.size + " containers. pendingReleaseContainers : " +
-        pendingReleaseContainers)
+    if (!retval.isEmpty) {
+      releaseList.removeAll(retval)
+      logInfo("Releasing " + retval.size + " containers.")
     }
-
     retval
   }
 
-  // A simple method to copy the split info map.
-  private def generateNodeToWeight(
-    conf: Configuration,
-    input: collection.Map[String, collection.Set[SplitInfo]]) :
-  // host to count, rack to count
-  (Map[String, Int], Map[String, Int]) = {
-
-    if (input == null) return (Map[String, Int](), Map[String, Int]())
-
-    val hostToCount = new HashMap[String, Int]
-    val rackToCount = new HashMap[String, Int]
-
-    for ((host, splits) <- input) {
-      val hostCount = hostToCount.getOrElse(host, 0)
-      hostToCount.put(host, hostCount + splits.size)
-
-      val rack = YarnSparkHadoopUtil.lookupRack(conf, host)
-      if (rack != null){
-        val rackCount = rackToCount.getOrElse(host, 0)
-        rackToCount.put(host, rackCount + splits.size)
-      }
-    }
-
-    (hostToCount.toMap, rackToCount.toMap)
+  private class AlphaAllocateResponse(response: AMResponse) extends YarnAllocateResponse {
+    override def getAllocatedContainers() = response.getAllocatedContainers()
+    override def getAvailableResources() = response.getAvailableResources()
+    override def getCompletedContainersStatuses() = response.getCompletedContainersStatuses()
   }
 
 }
diff --git a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocator.scala b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocator.scala
index cad94e5e19e1f..c74dd1c2b21dc 100644
--- a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocator.scala
+++ b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocator.scala
@@ -17,18 +17,431 @@
 
 package org.apache.spark.deploy.yarn
 
+import java.util.{List => JList}
+import java.util.concurrent.ConcurrentHashMap
+import java.util.concurrent.atomic.AtomicInteger
+
+import scala.collection.JavaConversions._
+import scala.collection.mutable.{ArrayBuffer, HashMap, HashSet}
+
+import org.apache.hadoop.conf.Configuration
+import org.apache.hadoop.yarn.api.records._
+import org.apache.hadoop.yarn.api.protocolrecords.AllocateResponse
+
+import org.apache.spark.{Logging, SparkConf, SparkEnv}
+import org.apache.spark.scheduler.{SplitInfo, TaskSchedulerImpl}
+import org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend
+
 object AllocationType extends Enumeration {
   type AllocationType = Value
   val HOST, RACK, ANY = Value
 }
 
+// TODO:
+// Too many params.
+// Needs to be mt-safe
+// Need to refactor this to make it 'cleaner' ... right now, all computation is reactive - should
+// make it more proactive and decoupled.
+
+// Note that right now, we assume all node asks as uniform in terms of capabilities and priority
+// Refer to http://developer.yahoo.com/blogs/hadoop/posts/2011/03/mapreduce-nextgen-scheduler/ for
+// more info on how we are requesting for containers.
+
 /**
- * Interface that defines a Yarn allocator.
+ * Common code for the Yarn container allocator. Contains all the version-agnostic code to
+ * manage container allocation for a running Spark application.
  */
-trait YarnAllocator {
+private[yarn] abstract class YarnAllocator(
+    conf: Configuration,
+    sparkConf: SparkConf,
+    args: ApplicationMasterArguments,
+    preferredNodes: collection.Map[String, collection.Set[SplitInfo]])
+  extends Logging {
 
-  def allocateResources(): Unit
-  def getNumExecutorsFailed: Int
-  def getNumExecutorsRunning: Int
+  // These three are locked on allocatedHostToContainersMap. Complementary data structures
+  // allocatedHostToContainersMap : containers which are running : host, Set<containerid>
+  // allocatedContainerToHostMap: container to host mapping.
+  private val allocatedHostToContainersMap =
+    new HashMap[String, collection.mutable.Set[ContainerId]]()
 
-}
+  private val allocatedContainerToHostMap = new HashMap[ContainerId, String]()
+
+  // allocatedRackCount is populated ONLY if allocation happens (or decremented if this is an
+  // allocated node)
+  // As with the two data structures above, tightly coupled with them, and to be locked on
+  // allocatedHostToContainersMap
+  private val allocatedRackCount = new HashMap[String, Int]()
+
+  // Containers to be released in next request to RM
+  private val releasedContainers = new ConcurrentHashMap[ContainerId, Boolean]
+
+  // Additional memory overhead - in mb.
+  protected val memoryOverhead: Int = sparkConf.getInt("spark.yarn.executor.memoryOverhead",
+    YarnSparkHadoopUtil.DEFAULT_MEMORY_OVERHEAD)
+
+  // Number of container requests that have been sent to, but not yet allocated by the
+  // ApplicationMaster.
+  private val numPendingAllocate = new AtomicInteger()
+  private val numExecutorsRunning = new AtomicInteger()
+  // Used to generate a unique id per executor
+  private val executorIdCounter = new AtomicInteger()
+  private val numExecutorsFailed = new AtomicInteger()
+
+  private val maxExecutors = args.numExecutors
+
+  protected val executorMemory = args.executorMemory
+  protected val executorCores = args.executorCores
+  protected val (preferredHostToCount, preferredRackToCount) =
+    generateNodeToWeight(conf, preferredNodes)
+
+  def getNumExecutorsRunning: Int = numExecutorsRunning.intValue
+
+  def getNumExecutorsFailed: Int = numExecutorsFailed.intValue
+
+  def allocateResources() = {
+    val missing = maxExecutors - numPendingAllocate.get() - numExecutorsRunning.get()
+
+    if (missing > 0) {
+      numPendingAllocate.addAndGet(missing)
+      logInfo("Will Allocate %d executor containers, each with %d memory".format(
+        missing,
+        (executorMemory + memoryOverhead)))
+    } else {
+      logDebug("Empty allocation request ...")
+    }
+
+    val allocateResponse = allocateContainers(missing)
+    val allocatedContainers = allocateResponse.getAllocatedContainers()
+
+    if (allocatedContainers.size > 0) {
+      var numPendingAllocateNow = numPendingAllocate.addAndGet(-1 * allocatedContainers.size)
+
+      if (numPendingAllocateNow < 0) {
+        numPendingAllocateNow = numPendingAllocate.addAndGet(-1 * numPendingAllocateNow)
+      }
+
+      logDebug("""
+        Allocated containers: %d
+        Current executor count: %d
+        Containers released: %s
+        Cluster resources: %s
+        """.format(
+          allocatedContainers.size,
+          numExecutorsRunning.get(),
+          releasedContainers,
+          allocateResponse.getAvailableResources))
+
+      val hostToContainers = new HashMap[String, ArrayBuffer[Container]]()
+
+      for (container <- allocatedContainers) {
+        if (isResourceConstraintSatisfied(container)) {
+          // Add the accepted `container` to the host's list of already accepted,
+          // allocated containers
+          val host = container.getNodeId.getHost
+          val containersForHost = hostToContainers.getOrElseUpdate(host,
+            new ArrayBuffer[Container]())
+          containersForHost += container
+        } else {
+          // Release container, since it doesn't satisfy resource constraints.
+          internalReleaseContainer(container)
+        }
+      }
+
+       // Find the appropriate containers to use.
+      // TODO: Cleanup this group-by...
+      val dataLocalContainers = new HashMap[String, ArrayBuffer[Container]]()
+      val rackLocalContainers = new HashMap[String, ArrayBuffer[Container]]()
+      val offRackContainers = new HashMap[String, ArrayBuffer[Container]]()
+
+      for (candidateHost <- hostToContainers.keySet) {
+        val maxExpectedHostCount = preferredHostToCount.getOrElse(candidateHost, 0)
+        val requiredHostCount = maxExpectedHostCount - allocatedContainersOnHost(candidateHost)
+
+        val remainingContainersOpt = hostToContainers.get(candidateHost)
+        assert(remainingContainersOpt.isDefined)
+        var remainingContainers = remainingContainersOpt.get
+
+        if (requiredHostCount >= remainingContainers.size) {
+          // Since we have <= required containers, add all remaining containers to
+          // `dataLocalContainers`.
+          dataLocalContainers.put(candidateHost, remainingContainers)
+          // There are no more free containers remaining.
+          remainingContainers = null
+        } else if (requiredHostCount > 0) {
+          // Container list has more containers than we need for data locality.
+          // Split the list into two: one based on the data local container count,
+          // (`remainingContainers.size` - `requiredHostCount`), and the other to hold remaining
+          // containers.
+          val (dataLocal, remaining) = remainingContainers.splitAt(
+            remainingContainers.size - requiredHostCount)
+          dataLocalContainers.put(candidateHost, dataLocal)
+
+          // Invariant: remainingContainers == remaining
+
+          // YARN has a nasty habit of allocating a ton of containers on a host - discourage this.
+          // Add each container in `remaining` to list of containers to release. If we have an
+          // insufficient number of containers, then the next allocation cycle will reallocate
+          // (but won't treat it as data local).
+          // TODO(harvey): Rephrase this comment some more.
+          for (container <- remaining) internalReleaseContainer(container)
+          remainingContainers = null
+        }
+
+        // For rack local containers
+        if (remainingContainers != null) {
+          val rack = YarnSparkHadoopUtil.lookupRack(conf, candidateHost)
+          if (rack != null) {
+            val maxExpectedRackCount = preferredRackToCount.getOrElse(rack, 0)
+            val requiredRackCount = maxExpectedRackCount - allocatedContainersOnRack(rack) -
+              rackLocalContainers.getOrElse(rack, List()).size
+
+            if (requiredRackCount >= remainingContainers.size) {
+              // Add all remaining containers to to `dataLocalContainers`.
+              dataLocalContainers.put(rack, remainingContainers)
+              remainingContainers = null
+            } else if (requiredRackCount > 0) {
+              // Container list has more containers that we need for data locality.
+              // Split the list into two: one based on the data local container count,
+              // (`remainingContainers.size` - `requiredHostCount`), and the other to hold remaining
+              // containers.
+              val (rackLocal, remaining) = remainingContainers.splitAt(
+                remainingContainers.size - requiredRackCount)
+              val existingRackLocal = rackLocalContainers.getOrElseUpdate(rack,
+                new ArrayBuffer[Container]())
+
+              existingRackLocal ++= rackLocal
+
+              remainingContainers = remaining
+            }
+          }
+        }
+
+        if (remainingContainers != null) {
+          // Not all containers have been consumed - add them to the list of off-rack containers.
+          offRackContainers.put(candidateHost, remainingContainers)
+        }
+      }
+
+      // Now that we have split the containers into various groups, go through them in order:
+      // first host-local, then rack-local, and finally off-rack.
+      // Note that the list we create below tries to ensure that not all containers end up within
+      // a host if there is a sufficiently large number of hosts/containers.
+      val allocatedContainersToProcess = new ArrayBuffer[Container](allocatedContainers.size)
+      allocatedContainersToProcess ++= TaskSchedulerImpl.prioritizeContainers(dataLocalContainers)
+      allocatedContainersToProcess ++= TaskSchedulerImpl.prioritizeContainers(rackLocalContainers)
+      allocatedContainersToProcess ++= TaskSchedulerImpl.prioritizeContainers(offRackContainers)
+
+      // Run each of the allocated containers.
+      for (container <- allocatedContainersToProcess) {
+        val numExecutorsRunningNow = numExecutorsRunning.incrementAndGet()
+        val executorHostname = container.getNodeId.getHost
+        val containerId = container.getId
+
+        val executorMemoryOverhead = (executorMemory + memoryOverhead)
+        assert(container.getResource.getMemory >= executorMemoryOverhead)
+
+        if (numExecutorsRunningNow > maxExecutors) {
+          logInfo("""Ignoring container %s at host %s, since we already have the required number of
+            containers for it.""".format(containerId, executorHostname))
+          internalReleaseContainer(container)
+          numExecutorsRunning.decrementAndGet()
+        } else {
+          val executorId = executorIdCounter.incrementAndGet().toString
+          val driverUrl = "akka.tcp://%s@%s:%s/user/%s".format(
+            SparkEnv.driverActorSystemName,
+            sparkConf.get("spark.driver.host"),
+            sparkConf.get("spark.driver.port"),
+            CoarseGrainedSchedulerBackend.ACTOR_NAME)
+
+          logInfo("Launching container %s for on host %s".format(containerId, executorHostname))
+
+          // To be safe, remove the container from `releasedContainers`.
+          releasedContainers.remove(containerId)
+
+          val rack = YarnSparkHadoopUtil.lookupRack(conf, executorHostname)
+          allocatedHostToContainersMap.synchronized {
+            val containerSet = allocatedHostToContainersMap.getOrElseUpdate(executorHostname,
+              new HashSet[ContainerId]())
+
+            containerSet += containerId
+            allocatedContainerToHostMap.put(containerId, executorHostname)
+
+            if (rack != null) {
+              allocatedRackCount.put(rack, allocatedRackCount.getOrElse(rack, 0) + 1)
+            }
+          }
+          logInfo("Launching ExecutorRunnable. driverUrl: %s,  executorHostname: %s".format(
+            driverUrl, executorHostname))
+          val executorRunnable = new ExecutorRunnable(
+            container,
+            conf,
+            sparkConf,
+            driverUrl,
+            executorId,
+            executorHostname,
+            executorMemory,
+            executorCores)
+          new Thread(executorRunnable).start()
+        }
+      }
+      logDebug("""
+        Finished allocating %s containers (from %s originally).
+        Current number of executors running: %d,
+        Released containers: %s
+        """.format(
+          allocatedContainersToProcess,
+          allocatedContainers,
+          numExecutorsRunning.get(),
+          releasedContainers))
+    }
+
+    val completedContainers = allocateResponse.getCompletedContainersStatuses()
+    if (completedContainers.size > 0) {
+      logDebug("Completed %d containers".format(completedContainers.size))
+
+      for (completedContainer <- completedContainers) {
+        val containerId = completedContainer.getContainerId
+
+        if (releasedContainers.containsKey(containerId)) {
+          // YarnAllocationHandler already marked the container for release, so remove it from
+          // `releasedContainers`.
+          releasedContainers.remove(containerId)
+        } else {
+          // Decrement the number of executors running. The next iteration of
+          // the ApplicationMaster's reporting thread will take care of allocating.
+          numExecutorsRunning.decrementAndGet()
+          logInfo("Completed container %s (state: %s, exit status: %s)".format(
+            containerId,
+            completedContainer.getState,
+            completedContainer.getExitStatus()))
+          // Hadoop 2.2.X added a ContainerExitStatus we should switch to use
+          // there are some exit status' we shouldn't necessarily count against us, but for
+          // now I think its ok as none of the containers are expected to exit
+          if (completedContainer.getExitStatus() != 0) {
+            logInfo("Container marked as failed: " + containerId)
+            numExecutorsFailed.incrementAndGet()
+          }
+        }
+
+        allocatedHostToContainersMap.synchronized {
+          if (allocatedContainerToHostMap.containsKey(containerId)) {
+            val hostOpt = allocatedContainerToHostMap.get(containerId)
+            assert(hostOpt.isDefined)
+            val host = hostOpt.get
+
+            val containerSetOpt = allocatedHostToContainersMap.get(host)
+            assert(containerSetOpt.isDefined)
+            val containerSet = containerSetOpt.get
+
+            containerSet.remove(containerId)
+            if (containerSet.isEmpty) {
+              allocatedHostToContainersMap.remove(host)
+            } else {
+              allocatedHostToContainersMap.update(host, containerSet)
+            }
+
+            allocatedContainerToHostMap.remove(containerId)
+
+            // TODO: Move this part outside the synchronized block?
+            val rack = YarnSparkHadoopUtil.lookupRack(conf, host)
+            if (rack != null) {
+              val rackCount = allocatedRackCount.getOrElse(rack, 0) - 1
+              if (rackCount > 0) {
+                allocatedRackCount.put(rack, rackCount)
+              } else {
+                allocatedRackCount.remove(rack)
+              }
+            }
+          }
+        }
+      }
+      logDebug("""
+        Finished processing %d completed containers.
+        Current number of executors running: %d,
+        Released containers: %s
+        """.format(
+          completedContainers.size,
+          numExecutorsRunning.get(),
+          releasedContainers))
+    }
+  }
+
+  protected def allocatedContainersOnHost(host: String): Int = {
+    var retval = 0
+    allocatedHostToContainersMap.synchronized {
+      retval = allocatedHostToContainersMap.getOrElse(host, Set()).size
+    }
+    retval
+  }
+
+  protected def allocatedContainersOnRack(rack: String): Int = {
+    var retval = 0
+    allocatedHostToContainersMap.synchronized {
+      retval = allocatedRackCount.getOrElse(rack, 0)
+    }
+    retval
+  }
+
+  private def isResourceConstraintSatisfied(container: Container): Boolean = {
+    container.getResource.getMemory >= (executorMemory + memoryOverhead)
+  }
+
+  // A simple method to copy the split info map.
+  private def generateNodeToWeight(
+      conf: Configuration,
+      input: collection.Map[String, collection.Set[SplitInfo]]
+    ): (Map[String, Int], Map[String, Int]) = {
+
+    if (input == null) {
+      return (Map[String, Int](), Map[String, Int]())
+    }
+
+    val hostToCount = new HashMap[String, Int]
+    val rackToCount = new HashMap[String, Int]
+
+    for ((host, splits) <- input) {
+      val hostCount = hostToCount.getOrElse(host, 0)
+      hostToCount.put(host, hostCount + splits.size)
+
+      val rack = YarnSparkHadoopUtil.lookupRack(conf, host)
+      if (rack != null) {
+        val rackCount = rackToCount.getOrElse(host, 0)
+        rackToCount.put(host, rackCount + splits.size)
+      }
+    }
+
+    (hostToCount.toMap, rackToCount.toMap)
+  }
+
+  private def internalReleaseContainer(container: Container) = {
+    releasedContainers.put(container.getId(), true)
+    releaseContainer(container)
+  }
+
+  /**
+   * Called to allocate containers in the cluster.
+   *
+   * @param count Number of containers to allocate.
+   *              If zero, should still contact RM (as a heartbeat).
+   * @return Response to the allocation request.
+   */
+  protected def allocateContainers(count: Int): YarnAllocateResponse
+
+  /** Called to release a previously allocated container. */
+  protected def releaseContainer(container: Container): Unit
+
+  /**
+   * Defines the interface for an allocate response from the RM. This is needed since the alpha
+   * and stable interfaces differ here in ways that cannot be fixed using other routes.
+   */
+  protected trait YarnAllocateResponse {
+
+    def getAllocatedContainers(): JList[Container]
+
+    def getAvailableResources(): Resource
+
+    def getCompletedContainersStatuses(): JList[ContainerStatus]
+
+  }
+
+}
\ No newline at end of file
diff --git a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocationHandler.scala b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocationHandler.scala
index 4d5144989991f..ed31457b61571 100644
--- a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocationHandler.scala
+++ b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocationHandler.scala
@@ -17,36 +17,19 @@
 
 package org.apache.spark.deploy.yarn
 
-import java.util.concurrent.{CopyOnWriteArrayList, ConcurrentHashMap}
-import java.util.concurrent.atomic.AtomicInteger
-
 import scala.collection.JavaConversions._
-import scala.collection.mutable.{ArrayBuffer, HashMap, HashSet}
+import scala.collection.mutable.{ArrayBuffer, HashMap}
 
-import org.apache.spark.{Logging, SparkConf, SparkEnv}
-import org.apache.spark.scheduler.{SplitInfo,TaskSchedulerImpl}
-import org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend
-import org.apache.spark.util.Utils
+import org.apache.spark.SparkConf
+import org.apache.spark.scheduler.SplitInfo
 
 import org.apache.hadoop.conf.Configuration
-import org.apache.hadoop.yarn.api.records.ApplicationAttemptId
-import org.apache.hadoop.yarn.api.records.{Container, ContainerId}
-import org.apache.hadoop.yarn.api.records.{Priority, Resource, ResourceRequest}
-import org.apache.hadoop.yarn.api.protocolrecords.{AllocateRequest, AllocateResponse}
+import org.apache.hadoop.yarn.api.records._
+import org.apache.hadoop.yarn.api.protocolrecords.AllocateResponse
 import org.apache.hadoop.yarn.client.api.AMRMClient
 import org.apache.hadoop.yarn.client.api.AMRMClient.ContainerRequest
 import org.apache.hadoop.yarn.util.Records
 
-// TODO:
-// Too many params.
-// Needs to be mt-safe
-// Need to refactor this to make it 'cleaner' ... right now, all computation is reactive - should
-// make it more proactive and decoupled.
-
-// Note that right now, we assume all node asks as uniform in terms of capabilities and priority
-// Refer to http://developer.yahoo.com/blogs/hadoop/posts/2011/03/mapreduce-nextgen-scheduler/ for
-// more info on how we are requesting for containers.
-
 /**
  * Acquires resources for executors from a ResourceManager and launches executors in new containers.
  */
@@ -57,329 +40,22 @@ private[yarn] class YarnAllocationHandler(
     appAttemptId: ApplicationAttemptId,
     args: ApplicationMasterArguments,
     preferredNodes: collection.Map[String, collection.Set[SplitInfo]])
-  extends YarnAllocator with Logging {
-
-  // These three are locked on allocatedHostToContainersMap. Complementary data structures
-  // allocatedHostToContainersMap : containers which are running : host, Set<containerid>
-  // allocatedContainerToHostMap: container to host mapping.
-  private val allocatedHostToContainersMap =
-    new HashMap[String, collection.mutable.Set[ContainerId]]()
-
-  private val allocatedContainerToHostMap = new HashMap[ContainerId, String]()
-
-  // allocatedRackCount is populated ONLY if allocation happens (or decremented if this is an
-  // allocated node)
-  // As with the two data structures above, tightly coupled with them, and to be locked on
-  // allocatedHostToContainersMap
-  private val allocatedRackCount = new HashMap[String, Int]()
-
-  // Containers which have been released.
-  private val releasedContainerList = new CopyOnWriteArrayList[ContainerId]()
-  // Containers to be released in next request to RM
-  private val pendingReleaseContainers = new ConcurrentHashMap[ContainerId, Boolean]
-
-  // Additional memory overhead - in mb.
-  private def memoryOverhead: Int = sparkConf.getInt("spark.yarn.executor.memoryOverhead",
-    YarnSparkHadoopUtil.DEFAULT_MEMORY_OVERHEAD)
-
-  // Number of container requests that have been sent to, but not yet allocated by the
-  // ApplicationMaster.
-  private val numPendingAllocate = new AtomicInteger()
-  private val numExecutorsRunning = new AtomicInteger()
-  // Used to generate a unique id per executor
-  private val executorIdCounter = new AtomicInteger()
-  private val lastResponseId = new AtomicInteger()
-  private val numExecutorsFailed = new AtomicInteger()
-
-  private val maxExecutors = args.numExecutors
-  private val executorMemory = args.executorMemory
-  private val executorCores = args.executorCores
-  private val (preferredHostToCount, preferredRackToCount) =
-    generateNodeToWeight(conf, preferredNodes)
-
-  override def getNumExecutorsRunning: Int = numExecutorsRunning.intValue
+  extends YarnAllocator(conf, sparkConf, args, preferredNodes) {
 
-  override def getNumExecutorsFailed: Int = numExecutorsFailed.intValue
-
-  def isResourceConstraintSatisfied(container: Container): Boolean = {
-    container.getResource.getMemory >= (executorMemory + memoryOverhead)
-  }
-
-  def releaseContainer(container: Container) {
-    val containerId = container.getId
-    pendingReleaseContainers.put(containerId, true)
-    amClient.releaseAssignedContainer(containerId)
+  override protected def releaseContainer(container: Container) = {
+    amClient.releaseAssignedContainer(container.getId())
   }
 
-  override def allocateResources() = {
-    addResourceRequests(maxExecutors - numPendingAllocate.get() - numExecutorsRunning.get())
+  override protected def allocateContainers(count: Int): YarnAllocateResponse = {
+    addResourceRequests(count)
 
     // We have already set the container request. Poll the ResourceManager for a response.
     // This doubles as a heartbeat if there are no pending container requests.
     val progressIndicator = 0.1f
-    val allocateResponse = amClient.allocate(progressIndicator)
-
-    val allocatedContainers = allocateResponse.getAllocatedContainers()
-    if (allocatedContainers.size > 0) {
-      var numPendingAllocateNow = numPendingAllocate.addAndGet(-1 * allocatedContainers.size)
-
-      if (numPendingAllocateNow < 0) {
-        numPendingAllocateNow = numPendingAllocate.addAndGet(-1 * numPendingAllocateNow)
-      }
-
-      logDebug("""
-        Allocated containers: %d
-        Current executor count: %d
-        Containers released: %s
-        Containers to-be-released: %s
-        Cluster resources: %s
-        """.format(
-          allocatedContainers.size,
-          numExecutorsRunning.get(),
-          releasedContainerList,
-          pendingReleaseContainers,
-          allocateResponse.getAvailableResources))
-
-      val hostToContainers = new HashMap[String, ArrayBuffer[Container]]()
-
-      for (container <- allocatedContainers) {
-        if (isResourceConstraintSatisfied(container)) {
-          // Add the accepted `container` to the host's list of already accepted,
-          // allocated containers
-          val host = container.getNodeId.getHost
-          val containersForHost = hostToContainers.getOrElseUpdate(host,
-            new ArrayBuffer[Container]())
-          containersForHost += container
-        } else {
-          // Release container, since it doesn't satisfy resource constraints.
-          releaseContainer(container)
-        }
-      }
-
-       // Find the appropriate containers to use.
-      // TODO: Cleanup this group-by...
-      val dataLocalContainers = new HashMap[String, ArrayBuffer[Container]]()
-      val rackLocalContainers = new HashMap[String, ArrayBuffer[Container]]()
-      val offRackContainers = new HashMap[String, ArrayBuffer[Container]]()
-
-      for (candidateHost <- hostToContainers.keySet) {
-        val maxExpectedHostCount = preferredHostToCount.getOrElse(candidateHost, 0)
-        val requiredHostCount = maxExpectedHostCount - allocatedContainersOnHost(candidateHost)
-
-        val remainingContainersOpt = hostToContainers.get(candidateHost)
-        assert(remainingContainersOpt.isDefined)
-        var remainingContainers = remainingContainersOpt.get
-
-        if (requiredHostCount >= remainingContainers.size) {
-          // Since we have <= required containers, add all remaining containers to
-          // `dataLocalContainers`.
-          dataLocalContainers.put(candidateHost, remainingContainers)
-          // There are no more free containers remaining.
-          remainingContainers = null
-        } else if (requiredHostCount > 0) {
-          // Container list has more containers than we need for data locality.
-          // Split the list into two: one based on the data local container count,
-          // (`remainingContainers.size` - `requiredHostCount`), and the other to hold remaining
-          // containers.
-          val (dataLocal, remaining) = remainingContainers.splitAt(
-            remainingContainers.size - requiredHostCount)
-          dataLocalContainers.put(candidateHost, dataLocal)
-
-          // Invariant: remainingContainers == remaining
-
-          // YARN has a nasty habit of allocating a ton of containers on a host - discourage this.
-          // Add each container in `remaining` to list of containers to release. If we have an
-          // insufficient number of containers, then the next allocation cycle will reallocate
-          // (but won't treat it as data local).
-          // TODO(harvey): Rephrase this comment some more.
-          for (container <- remaining) releaseContainer(container)
-          remainingContainers = null
-        }
-
-        // For rack local containers
-        if (remainingContainers != null) {
-          val rack = YarnSparkHadoopUtil.lookupRack(conf, candidateHost)
-          if (rack != null) {
-            val maxExpectedRackCount = preferredRackToCount.getOrElse(rack, 0)
-            val requiredRackCount = maxExpectedRackCount - allocatedContainersOnRack(rack) -
-              rackLocalContainers.getOrElse(rack, List()).size
-
-            if (requiredRackCount >= remainingContainers.size) {
-              // Add all remaining containers to to `dataLocalContainers`.
-              dataLocalContainers.put(rack, remainingContainers)
-              remainingContainers = null
-            } else if (requiredRackCount > 0) {
-              // Container list has more containers that we need for data locality.
-              // Split the list into two: one based on the data local container count,
-              // (`remainingContainers.size` - `requiredHostCount`), and the other to hold remaining
-              // containers.
-              val (rackLocal, remaining) = remainingContainers.splitAt(
-                remainingContainers.size - requiredRackCount)
-              val existingRackLocal = rackLocalContainers.getOrElseUpdate(rack,
-                new ArrayBuffer[Container]())
-
-              existingRackLocal ++= rackLocal
-
-              remainingContainers = remaining
-            }
-          }
-        }
-
-        if (remainingContainers != null) {
-          // Not all containers have been consumed - add them to the list of off-rack containers.
-          offRackContainers.put(candidateHost, remainingContainers)
-        }
-      }
-
-      // Now that we have split the containers into various groups, go through them in order:
-      // first host-local, then rack-local, and finally off-rack.
-      // Note that the list we create below tries to ensure that not all containers end up within
-      // a host if there is a sufficiently large number of hosts/containers.
-      val allocatedContainersToProcess = new ArrayBuffer[Container](allocatedContainers.size)
-      allocatedContainersToProcess ++= TaskSchedulerImpl.prioritizeContainers(dataLocalContainers)
-      allocatedContainersToProcess ++= TaskSchedulerImpl.prioritizeContainers(rackLocalContainers)
-      allocatedContainersToProcess ++= TaskSchedulerImpl.prioritizeContainers(offRackContainers)
-
-      // Run each of the allocated containers.
-      for (container <- allocatedContainersToProcess) {
-        val numExecutorsRunningNow = numExecutorsRunning.incrementAndGet()
-        val executorHostname = container.getNodeId.getHost
-        val containerId = container.getId
-
-        val executorMemoryOverhead = (executorMemory + memoryOverhead)
-        assert(container.getResource.getMemory >= executorMemoryOverhead)
-
-        if (numExecutorsRunningNow > maxExecutors) {
-          logInfo("""Ignoring container %s at host %s, since we already have the required number of
-            containers for it.""".format(containerId, executorHostname))
-          releaseContainer(container)
-          numExecutorsRunning.decrementAndGet()
-        } else {
-          val executorId = executorIdCounter.incrementAndGet().toString
-          val driverUrl = "akka.tcp://%s@%s:%s/user/%s".format(
-            SparkEnv.driverActorSystemName,
-            sparkConf.get("spark.driver.host"),
-            sparkConf.get("spark.driver.port"),
-            CoarseGrainedSchedulerBackend.ACTOR_NAME)
-
-          logInfo("Launching container %s for on host %s".format(containerId, executorHostname))
-
-          // To be safe, remove the container from `pendingReleaseContainers`.
-          pendingReleaseContainers.remove(containerId)
-
-          val rack = YarnSparkHadoopUtil.lookupRack(conf, executorHostname)
-          allocatedHostToContainersMap.synchronized {
-            val containerSet = allocatedHostToContainersMap.getOrElseUpdate(executorHostname,
-              new HashSet[ContainerId]())
-
-            containerSet += containerId
-            allocatedContainerToHostMap.put(containerId, executorHostname)
-
-            if (rack != null) {
-              allocatedRackCount.put(rack, allocatedRackCount.getOrElse(rack, 0) + 1)
-            }
-          }
-          logInfo("Launching ExecutorRunnable. driverUrl: %s,  executorHostname: %s".format(
-            driverUrl, executorHostname))
-          val executorRunnable = new ExecutorRunnable(
-            container,
-            conf,
-            sparkConf,
-            driverUrl,
-            executorId,
-            executorHostname,
-            executorMemory,
-            executorCores)
-          new Thread(executorRunnable).start()
-        }
-      }
-      logDebug("""
-        Finished allocating %s containers (from %s originally).
-        Current number of executors running: %d,
-        releasedContainerList: %s,
-        pendingReleaseContainers: %s
-        """.format(
-          allocatedContainersToProcess,
-          allocatedContainers,
-          numExecutorsRunning.get(),
-          releasedContainerList,
-          pendingReleaseContainers))
-    }
-
-    val completedContainers = allocateResponse.getCompletedContainersStatuses()
-    if (completedContainers.size > 0) {
-      logDebug("Completed %d containers".format(completedContainers.size))
-
-      for (completedContainer <- completedContainers) {
-        val containerId = completedContainer.getContainerId
-
-        if (pendingReleaseContainers.containsKey(containerId)) {
-          // YarnAllocationHandler already marked the container for release, so remove it from
-          // `pendingReleaseContainers`.
-          pendingReleaseContainers.remove(containerId)
-        } else {
-          // Decrement the number of executors running. The next iteration of
-          // the ApplicationMaster's reporting thread will take care of allocating.
-          numExecutorsRunning.decrementAndGet()
-          logInfo("Completed container %s (state: %s, exit status: %s)".format(
-            containerId,
-            completedContainer.getState,
-            completedContainer.getExitStatus()))
-          // Hadoop 2.2.X added a ContainerExitStatus we should switch to use
-          // there are some exit status' we shouldn't necessarily count against us, but for
-          // now I think its ok as none of the containers are expected to exit
-          if (completedContainer.getExitStatus() != 0) {
-            logInfo("Container marked as failed: " + containerId)
-            numExecutorsFailed.incrementAndGet()
-          }
-        }
-
-        allocatedHostToContainersMap.synchronized {
-          if (allocatedContainerToHostMap.containsKey(containerId)) {
-            val hostOpt = allocatedContainerToHostMap.get(containerId)
-            assert(hostOpt.isDefined)
-            val host = hostOpt.get
-
-            val containerSetOpt = allocatedHostToContainersMap.get(host)
-            assert(containerSetOpt.isDefined)
-            val containerSet = containerSetOpt.get
-
-            containerSet.remove(containerId)
-            if (containerSet.isEmpty) {
-              allocatedHostToContainersMap.remove(host)
-            } else {
-              allocatedHostToContainersMap.update(host, containerSet)
-            }
-
-            allocatedContainerToHostMap.remove(containerId)
-
-            // TODO: Move this part outside the synchronized block?
-            val rack = YarnSparkHadoopUtil.lookupRack(conf, host)
-            if (rack != null) {
-              val rackCount = allocatedRackCount.getOrElse(rack, 0) - 1
-              if (rackCount > 0) {
-                allocatedRackCount.put(rack, rackCount)
-              } else {
-                allocatedRackCount.remove(rack)
-              }
-            }
-          }
-        }
-      }
-      logDebug("""
-        Finished processing %d completed containers.
-        Current number of executors running: %d,
-        releasedContainerList: %s,
-        pendingReleaseContainers: %s
-        """.format(
-          completedContainers.size,
-          numExecutorsRunning.get(),
-          releasedContainerList,
-          pendingReleaseContainers))
-    }
+    new StableAllocateResponse(amClient.allocate(progressIndicator))
   }
 
-  def createRackResourceRequests(
+  private def createRackResourceRequests(
       hostContainers: ArrayBuffer[ContainerRequest]
     ): ArrayBuffer[ContainerRequest] = {
     // Generate modified racks and new set of hosts under it before issuing requests.
@@ -409,22 +85,6 @@ private[yarn] class YarnAllocationHandler(
     requestedContainers
   }
 
-  def allocatedContainersOnHost(host: String): Int = {
-    var retval = 0
-    allocatedHostToContainersMap.synchronized {
-      retval = allocatedHostToContainersMap.getOrElse(host, Set()).size
-    }
-    retval
-  }
-
-  def allocatedContainersOnRack(rack: String): Int = {
-    var retval = 0
-    allocatedHostToContainersMap.synchronized {
-      retval = allocatedRackCount.getOrElse(rack, 0)
-    }
-    retval
-  }
-
   private def addResourceRequests(numExecutors: Int) {
     val containerRequests: List[ContainerRequest] =
       if (numExecutors <= 0 || preferredHostToCount.isEmpty) {
@@ -472,15 +132,6 @@ private[yarn] class YarnAllocationHandler(
       amClient.addContainerRequest(request)
     }
 
-    if (numExecutors > 0) {
-      numPendingAllocate.addAndGet(numExecutors)
-      logInfo("Will Allocate %d executor containers, each with %d memory".format(
-        numExecutors,
-        (executorMemory + memoryOverhead)))
-    } else {
-      logDebug("Empty allocation request ...")
-    }
-
     for (request <- containerRequests) {
       val nodes = request.getNodes
       var hostStr = if (nodes == null || nodes.isEmpty) {
@@ -549,31 +200,10 @@ private[yarn] class YarnAllocationHandler(
     requests
   }
 
-  // A simple method to copy the split info map.
-  private def generateNodeToWeight(
-      conf: Configuration,
-      input: collection.Map[String, collection.Set[SplitInfo]]
-    ): (Map[String, Int], Map[String, Int]) = {
-
-    if (input == null) {
-      return (Map[String, Int](), Map[String, Int]())
-    }
-
-    val hostToCount = new HashMap[String, Int]
-    val rackToCount = new HashMap[String, Int]
-
-    for ((host, splits) <- input) {
-      val hostCount = hostToCount.getOrElse(host, 0)
-      hostToCount.put(host, hostCount + splits.size)
-
-      val rack = YarnSparkHadoopUtil.lookupRack(conf, host)
-      if (rack != null){
-        val rackCount = rackToCount.getOrElse(host, 0)
-        rackToCount.put(host, rackCount + splits.size)
-      }
-    }
-
-    (hostToCount.toMap, rackToCount.toMap)
+  private class StableAllocateResponse(response: AllocateResponse) extends YarnAllocateResponse {
+    override def getAllocatedContainers() = response.getAllocatedContainers()
+    override def getAvailableResources() = response.getAvailableResources()
+    override def getCompletedContainersStatuses() = response.getCompletedContainersStatuses()
   }
 
 }

From 6481d27425f6d42ead36663c9a4ef7ee13b3a8c9 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies.liu@gmail.com>
Date: Wed, 3 Sep 2014 11:49:45 -0700
Subject: [PATCH 183/489] [SPARK-3309] [PySpark] Put all public API in __all__

Put all public API in __all__, also put them all in pyspark.__init__.py, then we can got all the documents for public API by `pydoc pyspark`. It also can be used by other programs (such as Sphinx or Epydoc) to generate only documents for public APIs.

Author: Davies Liu <davies.liu@gmail.com>

Closes #2205 from davies/public and squashes the following commits:

c6c5567 [Davies Liu] fix message
f7b35be [Davies Liu] put SchemeRDD, Row in pyspark.sql module
7e3016a [Davies Liu] add __all__ in mllib
6281b48 [Davies Liu] fix doc for SchemaRDD
6caab21 [Davies Liu] add public interfaces into pyspark.__init__.py
---
 python/pyspark/__init__.py             | 14 +++++++++-----
 python/pyspark/accumulators.py         |  3 +++
 python/pyspark/broadcast.py            | 24 ++++++++++++++----------
 python/pyspark/conf.py                 |  2 ++
 python/pyspark/context.py              |  3 +++
 python/pyspark/files.py                |  3 +++
 python/pyspark/mllib/classification.py |  4 ++++
 python/pyspark/mllib/clustering.py     |  2 ++
 python/pyspark/mllib/linalg.py         |  3 +++
 python/pyspark/mllib/random.py         |  3 +++
 python/pyspark/mllib/recommendation.py |  2 ++
 python/pyspark/mllib/regression.py     | 10 +++++-----
 python/pyspark/mllib/stat.py           |  6 ++++--
 python/pyspark/mllib/tree.py           |  4 ++++
 python/pyspark/rdd.py                  |  1 +
 python/pyspark/serializers.py          |  2 +-
 python/pyspark/sql.py                  | 21 ++++++++++++++++++---
 17 files changed, 81 insertions(+), 26 deletions(-)

diff --git a/python/pyspark/__init__.py b/python/pyspark/__init__.py
index c58555fc9d2c5..1a2e774738fe7 100644
--- a/python/pyspark/__init__.py
+++ b/python/pyspark/__init__.py
@@ -61,13 +61,17 @@
 
 from pyspark.conf import SparkConf
 from pyspark.context import SparkContext
-from pyspark.sql import SQLContext
 from pyspark.rdd import RDD
-from pyspark.sql import SchemaRDD
-from pyspark.sql import Row
 from pyspark.files import SparkFiles
 from pyspark.storagelevel import StorageLevel
+from pyspark.accumulators import Accumulator, AccumulatorParam
+from pyspark.broadcast import Broadcast
+from pyspark.serializers import MarshalSerializer, PickleSerializer
 
+# for back compatibility
+from pyspark.sql import SQLContext, HiveContext, SchemaRDD, Row
 
-__all__ = ["SparkConf", "SparkContext", "SQLContext", "RDD", "SchemaRDD",
-           "SparkFiles", "StorageLevel", "Row"]
+__all__ = [
+    "SparkConf", "SparkContext", "SparkFiles", "RDD", "StorageLevel", "Broadcast",
+    "Accumulator", "AccumulatorParam", "MarshalSerializer", "PickleSerializer",
+]
diff --git a/python/pyspark/accumulators.py b/python/pyspark/accumulators.py
index f133cf6f7befc..ccbca67656c8d 100644
--- a/python/pyspark/accumulators.py
+++ b/python/pyspark/accumulators.py
@@ -94,6 +94,9 @@
 from pyspark.serializers import read_int, PickleSerializer
 
 
+__all__ = ['Accumulator', 'AccumulatorParam']
+
+
 pickleSer = PickleSerializer()
 
 # Holds accumulators registered on the current machine, keyed by ID. This is then used to send
diff --git a/python/pyspark/broadcast.py b/python/pyspark/broadcast.py
index 675a2fcd2ff4e..5c7c9cc161dff 100644
--- a/python/pyspark/broadcast.py
+++ b/python/pyspark/broadcast.py
@@ -31,6 +31,10 @@
 
 from pyspark.serializers import CompressedSerializer, PickleSerializer
 
+
+__all__ = ['Broadcast']
+
+
 # Holds broadcasted data received from Java, keyed by its id.
 _broadcastRegistry = {}
 
@@ -59,11 +63,20 @@ def __init__(self, bid, value, java_broadcast=None,
         """
         self.bid = bid
         if path is None:
-            self.value = value
+            self._value = value
         self._jbroadcast = java_broadcast
         self._pickle_registry = pickle_registry
         self.path = path
 
+    @property
+    def value(self):
+        """ Return the broadcasted value
+        """
+        if not hasattr(self, "_value") and self.path is not None:
+            ser = CompressedSerializer(PickleSerializer())
+            self._value = ser.load_stream(open(self.path)).next()
+        return self._value
+
     def unpersist(self, blocking=False):
         self._jbroadcast.unpersist(blocking)
         os.unlink(self.path)
@@ -72,15 +85,6 @@ def __reduce__(self):
         self._pickle_registry.add(self)
         return (_from_id, (self.bid, ))
 
-    def __getattr__(self, item):
-        if item == 'value' and self.path is not None:
-            ser = CompressedSerializer(PickleSerializer())
-            value = ser.load_stream(open(self.path)).next()
-            self.value = value
-            return value
-
-        raise AttributeError(item)
-
 
 if __name__ == "__main__":
     import doctest
diff --git a/python/pyspark/conf.py b/python/pyspark/conf.py
index fb716f6753a45..b64875a3f495a 100644
--- a/python/pyspark/conf.py
+++ b/python/pyspark/conf.py
@@ -54,6 +54,8 @@
 (u'spark.executorEnv.VAR4', u'value4'), (u'spark.home', u'/path')]
 """
 
+__all__ = ['SparkConf']
+
 
 class SparkConf(object):
 
diff --git a/python/pyspark/context.py b/python/pyspark/context.py
index 6e4fdaa6eec9d..5a30431568b16 100644
--- a/python/pyspark/context.py
+++ b/python/pyspark/context.py
@@ -37,6 +37,9 @@
 from py4j.java_collections import ListConverter
 
 
+__all__ = ['SparkContext']
+
+
 # These are special default configs for PySpark, they will overwrite
 # the default ones for Spark if they are not configured by user.
 DEFAULT_CONFIGS = {
diff --git a/python/pyspark/files.py b/python/pyspark/files.py
index 331de9a9b2212..797573f49dac8 100644
--- a/python/pyspark/files.py
+++ b/python/pyspark/files.py
@@ -18,6 +18,9 @@
 import os
 
 
+__all__ = ['SparkFiles']
+
+
 class SparkFiles(object):
 
     """
diff --git a/python/pyspark/mllib/classification.py b/python/pyspark/mllib/classification.py
index ffdda7ee19302..71ab46b61d7fa 100644
--- a/python/pyspark/mllib/classification.py
+++ b/python/pyspark/mllib/classification.py
@@ -30,6 +30,10 @@
 from math import exp, log
 
 
+__all__ = ['LogisticRegressionModel', 'LogisticRegressionWithSGD', 'SVMModel',
+           'SVMWithSGD', 'NaiveBayesModel', 'NaiveBayes']
+
+
 class LogisticRegressionModel(LinearModel):
 
     """A linear binary classification model derived from logistic regression.
diff --git a/python/pyspark/mllib/clustering.py b/python/pyspark/mllib/clustering.py
index a0630d1d5c58b..f3e952a1d842a 100644
--- a/python/pyspark/mllib/clustering.py
+++ b/python/pyspark/mllib/clustering.py
@@ -25,6 +25,8 @@
     _get_initial_weights, _serialize_rating, _regression_train_wrapper
 from pyspark.mllib.linalg import SparseVector
 
+__all__ = ['KMeansModel', 'KMeans']
+
 
 class KMeansModel(object):
 
diff --git a/python/pyspark/mllib/linalg.py b/python/pyspark/mllib/linalg.py
index f485a69db1fa2..e69051c104e37 100644
--- a/python/pyspark/mllib/linalg.py
+++ b/python/pyspark/mllib/linalg.py
@@ -27,6 +27,9 @@
 from numpy import array, array_equal, ndarray, float64, int32
 
 
+__all__ = ['SparseVector', 'Vectors']
+
+
 class SparseVector(object):
 
     """
diff --git a/python/pyspark/mllib/random.py b/python/pyspark/mllib/random.py
index 4dc1a4a912421..3e59c73db85e3 100644
--- a/python/pyspark/mllib/random.py
+++ b/python/pyspark/mllib/random.py
@@ -25,6 +25,9 @@
 from pyspark.serializers import NoOpSerializer
 
 
+__all__ = ['RandomRDDs', ]
+
+
 class RandomRDDs:
     """
     Generator methods for creating RDDs comprised of i.i.d samples from
diff --git a/python/pyspark/mllib/recommendation.py b/python/pyspark/mllib/recommendation.py
index e863fc249ec36..2df23394da6f8 100644
--- a/python/pyspark/mllib/recommendation.py
+++ b/python/pyspark/mllib/recommendation.py
@@ -24,6 +24,8 @@
     _serialize_tuple, RatingDeserializer
 from pyspark.rdd import RDD
 
+__all__ = ['MatrixFactorizationModel', 'ALS']
+
 
 class MatrixFactorizationModel(object):
 
diff --git a/python/pyspark/mllib/regression.py b/python/pyspark/mllib/regression.py
index d8792cf44872f..f572dcfb840b6 100644
--- a/python/pyspark/mllib/regression.py
+++ b/python/pyspark/mllib/regression.py
@@ -17,15 +17,15 @@
 
 from numpy import array, ndarray
 from pyspark import SparkContext
-from pyspark.mllib._common import \
-    _dot, _get_unmangled_rdd, _get_unmangled_double_vector_rdd, \
-    _serialize_double_matrix, _deserialize_double_matrix, \
-    _serialize_double_vector, _deserialize_double_vector, \
-    _get_initial_weights, _serialize_rating, _regression_train_wrapper, \
+from pyspark.mllib._common import _dot, _regression_train_wrapper, \
     _linear_predictor_typecheck, _have_scipy, _scipy_issparse
 from pyspark.mllib.linalg import SparseVector, Vectors
 
 
+__all__ = ['LabeledPoint', 'LinearModel', 'LinearRegressionModel', 'RidgeRegressionModel'
+           'LinearRegressionWithSGD', 'LassoWithSGD', 'RidgeRegressionWithSGD']
+
+
 class LabeledPoint(object):
 
     """
diff --git a/python/pyspark/mllib/stat.py b/python/pyspark/mllib/stat.py
index feef0d16cd644..8c726f171c978 100644
--- a/python/pyspark/mllib/stat.py
+++ b/python/pyspark/mllib/stat.py
@@ -21,8 +21,10 @@
 
 from pyspark.mllib._common import \
     _get_unmangled_double_vector_rdd, _get_unmangled_rdd, \
-    _serialize_double, _serialize_double_vector, \
-    _deserialize_double, _deserialize_double_matrix, _deserialize_double_vector
+    _serialize_double, _deserialize_double_matrix, _deserialize_double_vector
+
+
+__all__ = ['MultivariateStatisticalSummary', 'Statistics']
 
 
 class MultivariateStatisticalSummary(object):
diff --git a/python/pyspark/mllib/tree.py b/python/pyspark/mllib/tree.py
index e9d778df5a24b..a2fade61e9a71 100644
--- a/python/pyspark/mllib/tree.py
+++ b/python/pyspark/mllib/tree.py
@@ -26,6 +26,9 @@
 from pyspark.serializers import NoOpSerializer
 
 
+__all__ = ['DecisionTreeModel', 'DecisionTree']
+
+
 class DecisionTreeModel(object):
 
     """
@@ -88,6 +91,7 @@ class DecisionTree(object):
                   It will probably be modified for Spark v1.2.
 
     Example usage:
+
     >>> from numpy import array
     >>> import sys
     >>> from pyspark.mllib.regression import LabeledPoint
diff --git a/python/pyspark/rdd.py b/python/pyspark/rdd.py
index 6fc9f66bc5a94..dff6fc26fcb18 100644
--- a/python/pyspark/rdd.py
+++ b/python/pyspark/rdd.py
@@ -48,6 +48,7 @@
 
 from py4j.java_collections import ListConverter, MapConverter
 
+
 __all__ = ["RDD"]
 
 
diff --git a/python/pyspark/serializers.py b/python/pyspark/serializers.py
index fc49aa42dbaf9..55e6cf3308611 100644
--- a/python/pyspark/serializers.py
+++ b/python/pyspark/serializers.py
@@ -409,7 +409,7 @@ def loads(self, obj):
 
 class CompressedSerializer(FramedSerializer):
     """
-    compress the serialized data
+    Compress the serialized data
     """
 
     def __init__(self, serializer):
diff --git a/python/pyspark/sql.py b/python/pyspark/sql.py
index 0ff6a548a85f1..44316926ba334 100644
--- a/python/pyspark/sql.py
+++ b/python/pyspark/sql.py
@@ -40,8 +40,7 @@
     "StringType", "BinaryType", "BooleanType", "TimestampType", "DecimalType",
     "DoubleType", "FloatType", "ByteType", "IntegerType", "LongType",
     "ShortType", "ArrayType", "MapType", "StructField", "StructType",
-    "SQLContext", "HiveContext", "LocalHiveContext", "TestHiveContext",
-    "SchemaRDD", "Row"]
+    "SQLContext", "HiveContext", "SchemaRDD", "Row"]
 
 
 class DataType(object):
@@ -1037,7 +1036,7 @@ def inferSchema(self, rdd):
                              "can not infer schema")
         if type(first) is dict:
             warnings.warn("Using RDD of dict to inferSchema is deprecated,"
-                          "please use pyspark.Row instead")
+                          "please use pyspark.sql.Row instead")
 
         schema = _infer_schema(first)
         rdd = rdd.mapPartitions(lambda rows: _drop_schema(rows, schema))
@@ -1487,6 +1486,21 @@ def __repr__(self):
             return "<Row(%s)>" % ", ".join(self)
 
 
+def inherit_doc(cls):
+    for name, func in vars(cls).items():
+        # only inherit docstring for public functions
+        if name.startswith("_"):
+            continue
+        if not func.__doc__:
+            for parent in cls.__bases__:
+                parent_func = getattr(parent, name, None)
+                if parent_func and getattr(parent_func, "__doc__", None):
+                    func.__doc__ = parent_func.__doc__
+                    break
+    return cls
+
+
+@inherit_doc
 class SchemaRDD(RDD):
 
     """An RDD of L{Row} objects that has an associated schema.
@@ -1563,6 +1577,7 @@ def registerTempTable(self, name):
         self._jschema_rdd.registerTempTable(name)
 
     def registerAsTable(self, name):
+        """DEPRECATED: use registerTempTable() instead"""
         warnings.warn("Use registerTempTable instead of registerAsTable.", DeprecationWarning)
         self.registerTempTable(name)
 

From e5d376801d57dffb0791980a1786a0a9b45bc491 Mon Sep 17 00:00:00 2001
From: RJ Nowling <rnowling@gmail.com>
Date: Wed, 3 Sep 2014 14:15:22 -0700
Subject: [PATCH 184/489] [SPARK-3263][GraphX] Fix changes made to
 GraphGenerator.logNormalGraph in PR #720

PR #720 made multiple changes to GraphGenerator.logNormalGraph including:

* Replacing the call to functions for generating random vertices and edges with in-line implementations with different equations. Based on reading the Pregel paper, I believe the in-line functions are incorrect.
* Hard-coding of RNG seeds so that method now generates the same graph for a given number of vertices, edges, mu, and sigma -- user is not able to override seed or specify that seed should be randomly generated.
* Backwards-incompatible change to logNormalGraph signature with introduction of new required parameter.
* Failed to update scala docs and programming guide for API changes
* Added a Synthetic Benchmark in the examples.

This PR:
* Removes the in-line calls and calls original vertex / edge generation functions again
* Adds an optional seed parameter for deterministic behavior (when desired)
* Keeps the number of partitions parameter that was added.
* Keeps compatibility with the synthetic benchmark example
* Maintains backwards-compatible API

Author: RJ Nowling <rnowling@gmail.com>
Author: Ankur Dave <ankurdave@gmail.com>

Closes #2168 from rnowling/graphgenrand and squashes the following commits:

f1cd79f [Ankur Dave] Style fixes
e11918e [RJ Nowling] Fix bad comparisons in unit tests
785ac70 [RJ Nowling] Fix style error
c70868d [RJ Nowling] Fix logNormalGraph scala doc for seed
41fd1f8 [RJ Nowling] Fix logNormalGraph scala doc for seed
799f002 [RJ Nowling] Added test for different seeds for sampleLogNormal
43949ad [RJ Nowling] Added test for different seeds for generateRandomEdges
2faf75f [RJ Nowling] Added unit test for logNormalGraph
82f22397 [RJ Nowling] Add unit test for sampleLogNormal
b99cba9 [RJ Nowling] Make sampleLogNormal private to Spark (vs private) for unit testing
6803da1 [RJ Nowling] Add GraphGeneratorsSuite with test for generateRandomEdges
1c8fc44 [RJ Nowling] Connected components part of SynthBenchmark was failing to call count on RDD before printing
dfbb6dd [RJ Nowling] Fix parameter name in SynthBenchmark docs
b5eeb80 [RJ Nowling] Add optional seed parameter to SynthBenchmark and set default to randomly generate a seed
1ff8d30 [RJ Nowling] Fix bug in generateRandomEdges where numVertices instead of numEdges was used to control number of edges to generate
98bb73c [RJ Nowling] Add documentation for logNormalGraph parameters
d40141a [RJ Nowling] Fix style error
684804d [RJ Nowling] revert PR #720 which introduce errors in logNormalGraph and messed up seeding of RNGs.  Add user-defined optional seed for deterministic behavior
c183136 [RJ Nowling] Fix to deterministic GraphGenerators.logNormalGraph that allows generating graphs randomly using optional seed.
015010c [RJ Nowling] Fixed GraphGenerator logNormalGraph API to make backward-incompatible change in commit 894ecde04
---
 .../examples/graphx/SynthBenchmark.scala      |   9 +-
 .../spark/graphx/util/GraphGenerators.scala   |  65 ++++++-----
 .../graphx/util/GraphGeneratorsSuite.scala    | 110 ++++++++++++++++++
 3 files changed, 152 insertions(+), 32 deletions(-)
 create mode 100644 graphx/src/test/scala/org/apache/spark/graphx/util/GraphGeneratorsSuite.scala

diff --git a/examples/src/main/scala/org/apache/spark/examples/graphx/SynthBenchmark.scala b/examples/src/main/scala/org/apache/spark/examples/graphx/SynthBenchmark.scala
index 551c339b19523..5f35a5836462e 100644
--- a/examples/src/main/scala/org/apache/spark/examples/graphx/SynthBenchmark.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/graphx/SynthBenchmark.scala
@@ -38,12 +38,13 @@ object SynthBenchmark {
    * Options:
    *   -app "pagerank" or "cc" for pagerank or connected components. (Default: pagerank)
    *   -niters the number of iterations of pagerank to use (Default: 10)
-   *   -numVertices the number of vertices in the graph (Default: 1000000)
+   *   -nverts the number of vertices in the graph (Default: 1000000)
    *   -numEPart the number of edge partitions in the graph (Default: number of cores)
    *   -partStrategy the graph partitioning strategy to use
    *   -mu the mean parameter for the log-normal graph (Default: 4.0)
    *   -sigma the stdev parameter for the log-normal graph (Default: 1.3)
    *   -degFile the local file to save the degree information (Default: Empty)
+   *   -seed seed to use for RNGs (Default: -1, picks seed randomly)
    */
   def main(args: Array[String]) {
     val options = args.map {
@@ -62,6 +63,7 @@ object SynthBenchmark {
     var mu: Double = 4.0
     var sigma: Double = 1.3
     var degFile: String = ""
+    var seed: Int = -1
 
     options.foreach {
       case ("app", v) => app = v
@@ -72,6 +74,7 @@ object SynthBenchmark {
       case ("mu", v) => mu = v.toDouble
       case ("sigma", v) => sigma = v.toDouble
       case ("degFile", v) => degFile = v
+      case ("seed", v) => seed = v.toInt
       case (opt, _) => throw new IllegalArgumentException("Invalid option: " + opt)
     }
 
@@ -85,7 +88,7 @@ object SynthBenchmark {
     // Create the graph
     println(s"Creating graph...")
     val unpartitionedGraph = GraphGenerators.logNormalGraph(sc, numVertices,
-      numEPart.getOrElse(sc.defaultParallelism), mu, sigma)
+      numEPart.getOrElse(sc.defaultParallelism), mu, sigma, seed)
     // Repartition the graph
     val graph = partitionStrategy.foldLeft(unpartitionedGraph)(_.partitionBy(_)).cache()
 
@@ -113,7 +116,7 @@ object SynthBenchmark {
       println(s"Total PageRank = $totalPR")
     } else if (app == "cc") {
       println("Running Connected Components")
-      val numComponents = graph.connectedComponents.vertices.map(_._2).distinct()
+      val numComponents = graph.connectedComponents.vertices.map(_._2).distinct().count()
       println(s"Number of components = $numComponents")
     }
     val runTime = System.currentTimeMillis() - startTime
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/util/GraphGenerators.scala b/graphx/src/main/scala/org/apache/spark/graphx/util/GraphGenerators.scala
index 60149548ab852..b8309289fe475 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/util/GraphGenerators.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/util/GraphGenerators.scala
@@ -40,7 +40,7 @@ object GraphGenerators {
   val RMATd = 0.25
 
   /**
-   * Generate a graph whose vertex out degree is log normal.
+   * Generate a graph whose vertex out degree distribution is log normal.
    *
    * The default values for mu and sigma are taken from the Pregel paper:
    *
@@ -48,33 +48,36 @@ object GraphGenerators {
    * Ilan Horn, Naty Leiser, and Grzegorz Czajkowski. 2010.
    * Pregel: a system for large-scale graph processing. SIGMOD '10.
    *
-   * @param sc
-   * @param numVertices
-   * @param mu
-   * @param sigma
-   * @return
+   * If the seed is -1 (default), a random seed is chosen. Otherwise, use
+   * the user-specified seed.
+   *
+   * @param sc Spark Context
+   * @param numVertices number of vertices in generated graph
+   * @param numEParts (optional) number of partitions
+   * @param mu (optional, default: 4.0) mean of out-degree distribution
+   * @param sigma (optional, default: 1.3) standard deviation of out-degree distribution
+   * @param seed (optional, default: -1) seed for RNGs, -1 causes a random seed to be chosen
+   * @return Graph object
    */
-  def logNormalGraph(sc: SparkContext, numVertices: Int, numEParts: Int,
-                     mu: Double = 4.0, sigma: Double = 1.3): Graph[Long, Int] = {
-    val vertices = sc.parallelize(0 until numVertices, numEParts).map { src =>
-      // Initialize the random number generator with the source vertex id
-      val rand = new Random(src)
-      val degree = math.min(numVertices.toLong, math.exp(rand.nextGaussian() * sigma + mu).toLong)
-      (src.toLong, degree)
+  def logNormalGraph(
+      sc: SparkContext, numVertices: Int, numEParts: Int = 0, mu: Double = 4.0,
+      sigma: Double = 1.3, seed: Long = -1): Graph[Long, Int] = {
+
+    val evalNumEParts = if (numEParts == 0) sc.defaultParallelism else numEParts
+
+    // Enable deterministic seeding
+    val seedRand = if (seed == -1) new Random() else new Random(seed)
+    val seed1 = seedRand.nextInt()
+    val seed2 = seedRand.nextInt()
+
+    val vertices: RDD[(VertexId, Long)] = sc.parallelize(0 until numVertices, evalNumEParts).map {
+      src => (src, sampleLogNormal(mu, sigma, numVertices, seed = (seed1 ^ src)))
     }
+
     val edges = vertices.flatMap { case (src, degree) =>
-      new Iterator[Edge[Int]] {
-        // Initialize the random number generator with the source vertex id
-        val rand = new Random(src)
-        var i = 0
-        override def hasNext(): Boolean = { i < degree }
-        override def next(): Edge[Int] = {
-          val nextEdge = Edge[Int](src, rand.nextInt(numVertices), i)
-          i += 1
-          nextEdge
-        }
-      }
+      generateRandomEdges(src.toInt, degree.toInt, numVertices, seed = (seed2 ^ src))
     }
+
     Graph(vertices, edges, 0)
   }
 
@@ -82,9 +85,10 @@ object GraphGenerators {
   // the edge data is the weight (default 1)
   val RMATc = 0.15
 
-  def generateRandomEdges(src: Int, numEdges: Int, maxVertexId: Int): Array[Edge[Int]] = {
-    val rand = new Random()
-    Array.fill(maxVertexId) { Edge[Int](src, rand.nextInt(maxVertexId), 1) }
+  def generateRandomEdges(
+      src: Int, numEdges: Int, maxVertexId: Int, seed: Long = -1): Array[Edge[Int]] = {
+    val rand = if (seed == -1) new Random() else new Random(seed)
+    Array.fill(numEdges) { Edge[Int](src, rand.nextInt(maxVertexId), 1) }
   }
 
   /**
@@ -97,9 +101,12 @@ object GraphGenerators {
    * @param mu the mean of the normal distribution
    * @param sigma the standard deviation of the normal distribution
    * @param maxVal exclusive upper bound on the value of the sample
+   * @param seed optional seed
    */
-  private def sampleLogNormal(mu: Double, sigma: Double, maxVal: Int): Int = {
-    val rand = new Random()
+  private[spark] def sampleLogNormal(
+      mu: Double, sigma: Double, maxVal: Int, seed: Long = -1): Int = {
+    val rand = if (seed == -1) new Random() else new Random(seed)
+
     val sigmaSq = sigma * sigma
     val m = math.exp(mu + sigmaSq / 2.0)
     // expm1 is exp(m)-1 with better accuracy for tiny m
diff --git a/graphx/src/test/scala/org/apache/spark/graphx/util/GraphGeneratorsSuite.scala b/graphx/src/test/scala/org/apache/spark/graphx/util/GraphGeneratorsSuite.scala
new file mode 100644
index 0000000000000..b346d4db2ef96
--- /dev/null
+++ b/graphx/src/test/scala/org/apache/spark/graphx/util/GraphGeneratorsSuite.scala
@@ -0,0 +1,110 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.graphx.util
+
+import org.scalatest.FunSuite
+
+import org.apache.spark.graphx.LocalSparkContext
+
+class GraphGeneratorsSuite extends FunSuite with LocalSparkContext {
+
+  test("GraphGenerators.generateRandomEdges") {
+    val src = 5
+    val numEdges10 = 10
+    val numEdges20 = 20
+    val maxVertexId = 100
+
+    val edges10 = GraphGenerators.generateRandomEdges(src, numEdges10, maxVertexId)
+    assert(edges10.length == numEdges10)
+
+    val correctSrc = edges10.forall(e => e.srcId == src)
+    assert(correctSrc)
+
+    val correctWeight = edges10.forall(e => e.attr == 1)
+    assert(correctWeight)
+
+    val correctRange = edges10.forall(e => e.dstId >= 0 && e.dstId <= maxVertexId)
+    assert(correctRange)
+
+    val edges20 = GraphGenerators.generateRandomEdges(src, numEdges20, maxVertexId)
+    assert(edges20.length == numEdges20)
+
+    val edges10_round1 =
+      GraphGenerators.generateRandomEdges(src, numEdges10, maxVertexId, seed = 12345)
+    val edges10_round2 =
+      GraphGenerators.generateRandomEdges(src, numEdges10, maxVertexId, seed = 12345)
+    assert(edges10_round1.zip(edges10_round2).forall { case (e1, e2) =>
+      e1.srcId == e2.srcId && e1.dstId == e2.dstId && e1.attr == e2.attr
+    })
+
+    val edges10_round3 =
+      GraphGenerators.generateRandomEdges(src, numEdges10, maxVertexId, seed = 3467)
+    assert(!edges10_round1.zip(edges10_round3).forall { case (e1, e2) =>
+      e1.srcId == e2.srcId && e1.dstId == e2.dstId && e1.attr == e2.attr
+    })
+  }
+
+  test("GraphGenerators.sampleLogNormal") {
+    val mu = 4.0
+    val sigma = 1.3
+    val maxVal = 100
+
+    val dstId = GraphGenerators.sampleLogNormal(mu, sigma, maxVal)
+    assert(dstId < maxVal)
+
+    val dstId_round1 = GraphGenerators.sampleLogNormal(mu, sigma, maxVal, 12345)
+    val dstId_round2 = GraphGenerators.sampleLogNormal(mu, sigma, maxVal, 12345)
+    assert(dstId_round1 == dstId_round2)
+
+    val dstId_round3 = GraphGenerators.sampleLogNormal(mu, sigma, maxVal, 789)
+    assert(dstId_round1 != dstId_round3)
+  }
+
+  test("GraphGenerators.logNormalGraph") {
+    withSpark { sc =>
+      val mu = 4.0
+      val sigma = 1.3
+      val numVertices100 = 100
+
+      val graph = GraphGenerators.logNormalGraph(sc, numVertices100, mu = mu, sigma = sigma)
+      assert(graph.vertices.count() == numVertices100)
+
+      val graph_round1 =
+        GraphGenerators.logNormalGraph(sc, numVertices100, mu = mu, sigma = sigma, seed = 12345)
+      val graph_round2 =
+        GraphGenerators.logNormalGraph(sc, numVertices100, mu = mu, sigma = sigma, seed = 12345)
+
+      val graph_round1_edges = graph_round1.edges.collect()
+      val graph_round2_edges = graph_round2.edges.collect()
+
+      assert(graph_round1_edges.zip(graph_round2_edges).forall { case (e1, e2) =>
+        e1.srcId == e2.srcId && e1.dstId == e2.dstId && e1.attr == e2.attr
+      })
+
+      val graph_round3 =
+        GraphGenerators.logNormalGraph(sc, numVertices100, mu = mu, sigma = sigma, seed = 567)
+
+      val graph_round3_edges = graph_round3.edges.collect()
+
+      assert(!graph_round1_edges.zip(graph_round3_edges).forall { case (e1, e2) =>
+        e1.srcId == e2.srcId && e1.dstId == e2.dstId && e1.attr == e2.attr
+      })
+    }
+  }
+
+}

From ccc69e26ec2fadd90886990b90a5a600efd08aba Mon Sep 17 00:00:00 2001
From: Marcelo Vanzin <vanzin@cloudera.com>
Date: Wed, 3 Sep 2014 14:47:11 -0700
Subject: [PATCH 185/489] [SPARK-2845] Add timestamps to block manager events.

These are not used by the UI but are useful when analysing the
logs from a spark job.

Author: Marcelo Vanzin <vanzin@cloudera.com>

Closes #654 from vanzin/bm-event-tstamp and squashes the following commits:

d5d6e66 [Marcelo Vanzin] Fix tests.
ec06218 [Marcelo Vanzin] Review feedback.
f134dbc [Marcelo Vanzin] Merge branch 'master' into bm-event-tstamp
b495b7c [Marcelo Vanzin] Merge branch 'master' into bm-event-tstamp
7d2fe9e [Marcelo Vanzin] Review feedback.
d6f381c [Marcelo Vanzin] Update tests added after patch was created.
45e3bf8 [Marcelo Vanzin] Fix unit test after merge.
b37a10f [Marcelo Vanzin] Use === in test assertions.
ef72824 [Marcelo Vanzin] Handle backwards compatibility with 1.0.0.
aca1151 [Marcelo Vanzin] Fix unit test to check new fields.
efdda8e [Marcelo Vanzin] Add timestamps to block manager events.
---
 .../spark/scheduler/SparkListener.scala       |  4 +-
 .../storage/BlockManagerMasterActor.scala     |  7 ++--
 .../org/apache/spark/util/JsonProtocol.scala  | 12 ++++--
 .../storage/StorageStatusListenerSuite.scala  | 18 ++++-----
 .../spark/ui/storage/StorageTabSuite.scala    |  4 +-
 .../apache/spark/util/JsonProtocolSuite.scala | 37 +++++++++++++++++--
 6 files changed, 58 insertions(+), 24 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/scheduler/SparkListener.scala b/core/src/main/scala/org/apache/spark/scheduler/SparkListener.scala
index 86ca8445a1124..f33c2e065a200 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/SparkListener.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/SparkListener.scala
@@ -67,11 +67,11 @@ case class SparkListenerEnvironmentUpdate(environmentDetails: Map[String, Seq[(S
   extends SparkListenerEvent
 
 @DeveloperApi
-case class SparkListenerBlockManagerAdded(blockManagerId: BlockManagerId, maxMem: Long)
+case class SparkListenerBlockManagerAdded(time: Long, blockManagerId: BlockManagerId, maxMem: Long)
   extends SparkListenerEvent
 
 @DeveloperApi
-case class SparkListenerBlockManagerRemoved(blockManagerId: BlockManagerId)
+case class SparkListenerBlockManagerRemoved(time: Long, blockManagerId: BlockManagerId)
   extends SparkListenerEvent
 
 @DeveloperApi
diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterActor.scala b/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterActor.scala
index 3ab07703b6f85..1a6c7cb24f9ac 100644
--- a/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterActor.scala
+++ b/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterActor.scala
@@ -203,7 +203,7 @@ class BlockManagerMasterActor(val isLocal: Boolean, conf: SparkConf, listenerBus
         blockLocations.remove(blockId)
       }
     }
-    listenerBus.post(SparkListenerBlockManagerRemoved(blockManagerId))
+    listenerBus.post(SparkListenerBlockManagerRemoved(System.currentTimeMillis(), blockManagerId))
   }
 
   private def expireDeadHosts() {
@@ -325,6 +325,7 @@ class BlockManagerMasterActor(val isLocal: Boolean, conf: SparkConf, listenerBus
   }
 
   private def register(id: BlockManagerId, maxMemSize: Long, slaveActor: ActorRef) {
+    val time = System.currentTimeMillis()
     if (!blockManagerInfo.contains(id)) {
       blockManagerIdByExecutor.get(id.executorId) match {
         case Some(manager) =>
@@ -340,9 +341,9 @@ class BlockManagerMasterActor(val isLocal: Boolean, conf: SparkConf, listenerBus
         id.hostPort, Utils.bytesToString(maxMemSize)))
 
       blockManagerInfo(id) =
-        new BlockManagerInfo(id, System.currentTimeMillis(), maxMemSize, slaveActor)
+        new BlockManagerInfo(id, time, maxMemSize, slaveActor)
     }
-    listenerBus.post(SparkListenerBlockManagerAdded(id, maxMemSize))
+    listenerBus.post(SparkListenerBlockManagerAdded(time, id, maxMemSize))
   }
 
   private def updateBlockInfo(
diff --git a/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala b/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala
index a7543454eca1f..1fc536b096996 100644
--- a/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala
+++ b/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala
@@ -152,13 +152,15 @@ private[spark] object JsonProtocol {
     val blockManagerId = blockManagerIdToJson(blockManagerAdded.blockManagerId)
     ("Event" -> Utils.getFormattedClassName(blockManagerAdded)) ~
     ("Block Manager ID" -> blockManagerId) ~
-    ("Maximum Memory" -> blockManagerAdded.maxMem)
+    ("Maximum Memory" -> blockManagerAdded.maxMem) ~
+    ("Timestamp" -> blockManagerAdded.time)
   }
 
   def blockManagerRemovedToJson(blockManagerRemoved: SparkListenerBlockManagerRemoved): JValue = {
     val blockManagerId = blockManagerIdToJson(blockManagerRemoved.blockManagerId)
     ("Event" -> Utils.getFormattedClassName(blockManagerRemoved)) ~
-    ("Block Manager ID" -> blockManagerId)
+    ("Block Manager ID" -> blockManagerId) ~
+    ("Timestamp" -> blockManagerRemoved.time)
   }
 
   def unpersistRDDToJson(unpersistRDD: SparkListenerUnpersistRDD): JValue = {
@@ -466,12 +468,14 @@ private[spark] object JsonProtocol {
   def blockManagerAddedFromJson(json: JValue): SparkListenerBlockManagerAdded = {
     val blockManagerId = blockManagerIdFromJson(json \ "Block Manager ID")
     val maxMem = (json \ "Maximum Memory").extract[Long]
-    SparkListenerBlockManagerAdded(blockManagerId, maxMem)
+    val time = Utils.jsonOption(json \ "Timestamp").map(_.extract[Long]).getOrElse(-1L)
+    SparkListenerBlockManagerAdded(time, blockManagerId, maxMem)
   }
 
   def blockManagerRemovedFromJson(json: JValue): SparkListenerBlockManagerRemoved = {
     val blockManagerId = blockManagerIdFromJson(json \ "Block Manager ID")
-    SparkListenerBlockManagerRemoved(blockManagerId)
+    val time = Utils.jsonOption(json \ "Timestamp").map(_.extract[Long]).getOrElse(-1L)
+    SparkListenerBlockManagerRemoved(time, blockManagerId)
   }
 
   def unpersistRDDFromJson(json: JValue): SparkListenerUnpersistRDD = {
diff --git a/core/src/test/scala/org/apache/spark/storage/StorageStatusListenerSuite.scala b/core/src/test/scala/org/apache/spark/storage/StorageStatusListenerSuite.scala
index 4e022a69c8212..3a45875391e29 100644
--- a/core/src/test/scala/org/apache/spark/storage/StorageStatusListenerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/storage/StorageStatusListenerSuite.scala
@@ -36,13 +36,13 @@ class StorageStatusListenerSuite extends FunSuite {
 
     // Block manager add
     assert(listener.executorIdToStorageStatus.size === 0)
-    listener.onBlockManagerAdded(SparkListenerBlockManagerAdded(bm1, 1000L))
+    listener.onBlockManagerAdded(SparkListenerBlockManagerAdded(1L, bm1, 1000L))
     assert(listener.executorIdToStorageStatus.size === 1)
     assert(listener.executorIdToStorageStatus.get("big").isDefined)
     assert(listener.executorIdToStorageStatus("big").blockManagerId === bm1)
     assert(listener.executorIdToStorageStatus("big").maxMem === 1000L)
     assert(listener.executorIdToStorageStatus("big").numBlocks === 0)
-    listener.onBlockManagerAdded(SparkListenerBlockManagerAdded(bm2, 2000L))
+    listener.onBlockManagerAdded(SparkListenerBlockManagerAdded(1L, bm2, 2000L))
     assert(listener.executorIdToStorageStatus.size === 2)
     assert(listener.executorIdToStorageStatus.get("fat").isDefined)
     assert(listener.executorIdToStorageStatus("fat").blockManagerId === bm2)
@@ -50,11 +50,11 @@ class StorageStatusListenerSuite extends FunSuite {
     assert(listener.executorIdToStorageStatus("fat").numBlocks === 0)
 
     // Block manager remove
-    listener.onBlockManagerRemoved(SparkListenerBlockManagerRemoved(bm1))
+    listener.onBlockManagerRemoved(SparkListenerBlockManagerRemoved(1L, bm1))
     assert(listener.executorIdToStorageStatus.size === 1)
     assert(!listener.executorIdToStorageStatus.get("big").isDefined)
     assert(listener.executorIdToStorageStatus.get("fat").isDefined)
-    listener.onBlockManagerRemoved(SparkListenerBlockManagerRemoved(bm2))
+    listener.onBlockManagerRemoved(SparkListenerBlockManagerRemoved(1L, bm2))
     assert(listener.executorIdToStorageStatus.size === 0)
     assert(!listener.executorIdToStorageStatus.get("big").isDefined)
     assert(!listener.executorIdToStorageStatus.get("fat").isDefined)
@@ -62,8 +62,8 @@ class StorageStatusListenerSuite extends FunSuite {
 
   test("task end without updated blocks") {
     val listener = new StorageStatusListener
-    listener.onBlockManagerAdded(SparkListenerBlockManagerAdded(bm1, 1000L))
-    listener.onBlockManagerAdded(SparkListenerBlockManagerAdded(bm2, 2000L))
+    listener.onBlockManagerAdded(SparkListenerBlockManagerAdded(1L, bm1, 1000L))
+    listener.onBlockManagerAdded(SparkListenerBlockManagerAdded(1L, bm2, 2000L))
     val taskMetrics = new TaskMetrics
 
     // Task end with no updated blocks
@@ -79,8 +79,8 @@ class StorageStatusListenerSuite extends FunSuite {
 
   test("task end with updated blocks") {
     val listener = new StorageStatusListener
-    listener.onBlockManagerAdded(SparkListenerBlockManagerAdded(bm1, 1000L))
-    listener.onBlockManagerAdded(SparkListenerBlockManagerAdded(bm2, 2000L))
+    listener.onBlockManagerAdded(SparkListenerBlockManagerAdded(1L, bm1, 1000L))
+    listener.onBlockManagerAdded(SparkListenerBlockManagerAdded(1L, bm2, 2000L))
     val taskMetrics1 = new TaskMetrics
     val taskMetrics2 = new TaskMetrics
     val block1 = (RDDBlockId(1, 1), BlockStatus(StorageLevel.DISK_ONLY, 0L, 100L, 0L))
@@ -128,7 +128,7 @@ class StorageStatusListenerSuite extends FunSuite {
 
   test("unpersist RDD") {
     val listener = new StorageStatusListener
-    listener.onBlockManagerAdded(SparkListenerBlockManagerAdded(bm1, 1000L))
+    listener.onBlockManagerAdded(SparkListenerBlockManagerAdded(1L, bm1, 1000L))
     val taskMetrics1 = new TaskMetrics
     val taskMetrics2 = new TaskMetrics
     val block1 = (RDDBlockId(1, 1), BlockStatus(StorageLevel.DISK_ONLY, 0L, 100L, 0L))
diff --git a/core/src/test/scala/org/apache/spark/ui/storage/StorageTabSuite.scala b/core/src/test/scala/org/apache/spark/ui/storage/StorageTabSuite.scala
index d9e9c70a8a9e7..e1bc1379b5d80 100644
--- a/core/src/test/scala/org/apache/spark/ui/storage/StorageTabSuite.scala
+++ b/core/src/test/scala/org/apache/spark/ui/storage/StorageTabSuite.scala
@@ -108,7 +108,7 @@ class StorageTabSuite extends FunSuite with BeforeAndAfter {
     val myRddInfo1 = rddInfo1
     val myRddInfo2 = rddInfo2
     val stageInfo0 = new StageInfo(0, 0, "0", 100, Seq(myRddInfo0, myRddInfo1, myRddInfo2), "details")
-    bus.postToAll(SparkListenerBlockManagerAdded(bm1, 1000L))
+    bus.postToAll(SparkListenerBlockManagerAdded(1L, bm1, 1000L))
     bus.postToAll(SparkListenerStageSubmitted(stageInfo0))
     assert(storageListener._rddInfoMap.size === 3)
     assert(storageListener.rddInfoList.size === 0) // not cached
@@ -175,7 +175,7 @@ class StorageTabSuite extends FunSuite with BeforeAndAfter {
     val block1 = (RDDBlockId(1, 1), BlockStatus(memOnly, 200L, 0L, 0L))
     taskMetrics0.updatedBlocks = Some(Seq(block0))
     taskMetrics1.updatedBlocks = Some(Seq(block1))
-    bus.postToAll(SparkListenerBlockManagerAdded(bm1, 1000L))
+    bus.postToAll(SparkListenerBlockManagerAdded(1L, bm1, 1000L))
     bus.postToAll(SparkListenerStageSubmitted(stageInfo0))
     assert(storageListener.rddInfoList.size === 0)
     bus.postToAll(SparkListenerTaskEnd(0, 0, "big", Success, taskInfo, taskMetrics0))
diff --git a/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala b/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala
index 66a17de9ec9ce..c84bafce37f70 100644
--- a/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala
+++ b/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala
@@ -21,6 +21,9 @@ import java.util.Properties
 
 import scala.collection.Map
 
+import org.json4s.DefaultFormats
+import org.json4s.JsonDSL._
+import org.json4s.JsonAST._
 import org.json4s.jackson.JsonMethods._
 import org.scalatest.FunSuite
 
@@ -52,9 +55,9 @@ class JsonProtocolSuite extends FunSuite {
       "System Properties" -> Seq(("Username", "guest"), ("Password", "guest")),
       "Classpath Entries" -> Seq(("Super library", "/tmp/super_library"))
     ))
-    val blockManagerAdded = SparkListenerBlockManagerAdded(
+    val blockManagerAdded = SparkListenerBlockManagerAdded(1L,
       BlockManagerId("Stars", "In your multitude...", 300), 500)
-    val blockManagerRemoved = SparkListenerBlockManagerRemoved(
+    val blockManagerRemoved = SparkListenerBlockManagerRemoved(2L,
       BlockManagerId("Scarce", "to be counted...", 100))
     val unpersistRdd = SparkListenerUnpersistRDD(12345)
     val applicationStart = SparkListenerApplicationStart("The winner of all", 42L, "Garfield")
@@ -151,6 +154,28 @@ class JsonProtocolSuite extends FunSuite {
     assert(newMetrics.inputMetrics.isEmpty)
   }
 
+  test("BlockManager events backward compatibility") {
+    // SparkListenerBlockManagerAdded/Removed in Spark 1.0.0 do not have a "time" property.
+    val blockManagerAdded = SparkListenerBlockManagerAdded(1L,
+      BlockManagerId("Stars", "In your multitude...", 300), 500)
+    val blockManagerRemoved = SparkListenerBlockManagerRemoved(2L,
+      BlockManagerId("Scarce", "to be counted...", 100))
+
+    val oldBmAdded = JsonProtocol.blockManagerAddedToJson(blockManagerAdded)
+      .removeField({ _._1 == "Timestamp" })
+
+    val deserializedBmAdded = JsonProtocol.blockManagerAddedFromJson(oldBmAdded)
+    assert(SparkListenerBlockManagerAdded(-1L, blockManagerAdded.blockManagerId,
+      blockManagerAdded.maxMem) === deserializedBmAdded)
+
+    val oldBmRemoved = JsonProtocol.blockManagerRemovedToJson(blockManagerRemoved)
+      .removeField({ _._1 == "Timestamp" })
+
+    val deserializedBmRemoved = JsonProtocol.blockManagerRemovedFromJson(oldBmRemoved)
+    assert(SparkListenerBlockManagerRemoved(-1L, blockManagerRemoved.blockManagerId) ===
+      deserializedBmRemoved)
+  }
+
 
   /** -------------------------- *
    | Helper test running methods |
@@ -242,8 +267,10 @@ class JsonProtocolSuite extends FunSuite {
         assertEquals(e1.environmentDetails, e2.environmentDetails)
       case (e1: SparkListenerBlockManagerAdded, e2: SparkListenerBlockManagerAdded) =>
         assert(e1.maxMem === e2.maxMem)
+        assert(e1.time === e2.time)
         assertEquals(e1.blockManagerId, e2.blockManagerId)
       case (e1: SparkListenerBlockManagerRemoved, e2: SparkListenerBlockManagerRemoved) =>
+        assert(e1.time === e2.time)
         assertEquals(e1.blockManagerId, e2.blockManagerId)
       case (e1: SparkListenerUnpersistRDD, e2: SparkListenerUnpersistRDD) =>
         assert(e1.rddId == e2.rddId)
@@ -945,7 +972,8 @@ class JsonProtocolSuite extends FunSuite {
       |    "Host": "In your multitude...",
       |    "Port": 300
       |  },
-      |  "Maximum Memory": 500
+      |  "Maximum Memory": 500,
+      |  "Timestamp": 1
       |}
     """
 
@@ -957,7 +985,8 @@ class JsonProtocolSuite extends FunSuite {
       |    "Executor ID": "Scarce",
       |    "Host": "to be counted...",
       |    "Port": 100
-      |  }
+      |  },
+      |  "Timestamp": 2
       |}
     """
 

From f2b5b619a9efee91573c0e546792e68e72afce21 Mon Sep 17 00:00:00 2001
From: Marcelo Vanzin <vanzin@cloudera.com>
Date: Wed, 3 Sep 2014 14:57:38 -0700
Subject: [PATCH 186/489] [SPARK-3388] Expose aplication ID in ApplicationStart
 event, use it in history server.

This change exposes the application ID generated by the Spark Master, Mesos or Yarn
via the SparkListenerApplicationStart event. It then uses that information to expose the
application via its ID in the history server, instead of using the internal directory name
generated by the event logger as an application id. This allows someone who knows
the application ID to easily figure out the URL for the application's entry in the HS, aside
from looking better.

In Yarn mode, this is used to generate a direct link from the RM application list to the
Spark history server entry (thus providing a fix for SPARK-2150).

Note this sort of assumes that the different managers will generate app ids that are
sufficiently different from each other that clashes will not occur.

Author: Marcelo Vanzin <vanzin@cloudera.com>

This patch had conflicts when merged, resolved by
Committer: Andrew Or <andrewor14@gmail.com>

Closes #1218 from vanzin/yarn-hs-link-2 and squashes the following commits:

2d19f3c [Marcelo Vanzin] Review feedback.
6706d3a [Marcelo Vanzin] Implement applicationId() in base classes.
56fe42e [Marcelo Vanzin] Fix cluster mode history address, plus a cleanup.
44112a8 [Marcelo Vanzin] Merge branch 'master' into yarn-hs-link-2
8278316 [Marcelo Vanzin] Merge branch 'master' into yarn-hs-link-2
a86bbcf [Marcelo Vanzin] Merge branch 'master' into yarn-hs-link-2
a0056e6 [Marcelo Vanzin] Unbreak test.
4b10cfd [Marcelo Vanzin] Merge branch 'master' into yarn-hs-link-2
cb0cab2 [Marcelo Vanzin] Merge branch 'master' into yarn-hs-link-2
25f2826 [Marcelo Vanzin] Add MIMA excludes.
f0ba90f [Marcelo Vanzin] Use BufferedIterator.
c90a08d [Marcelo Vanzin] Remove unused code.
3f8ec66 [Marcelo Vanzin] Review feedback.
21aa71b [Marcelo Vanzin] Fix JSON test.
b022bae [Marcelo Vanzin] Undo SparkContext cleanup.
c6d7478 [Marcelo Vanzin] Merge branch 'master' into yarn-hs-link-2
4e3483f [Marcelo Vanzin] Fix test.
57517b8 [Marcelo Vanzin] Review feedback. Mostly, more consistent use of Scala's Option.
311e49d [Marcelo Vanzin] Merge branch 'master' into yarn-hs-link-2
d35d86f [Marcelo Vanzin] Fix yarn backend after rebase.
36dc362 [Marcelo Vanzin] Don't use Iterator::takeWhile().
0afd696 [Marcelo Vanzin] Wait until master responds before returning from start().
abc4697 [Marcelo Vanzin] Make FsHistoryProvider keep a map of applications by id.
26b266e [Marcelo Vanzin] Use Mesos framework ID as Spark application ID.
b3f3664 [Marcelo Vanzin] [yarn] Make the RM link point to the app direcly in the HS.
2fb7de4 [Marcelo Vanzin] Expose the application ID in the ApplicationStart event.
ed10348 [Marcelo Vanzin] Expose application id to spark context.
---
 .../scala/org/apache/spark/SparkContext.scala |   5 +-
 .../history/ApplicationHistoryProvider.scala  |   6 +-
 .../deploy/history/FsHistoryProvider.scala    | 176 +++++++++++-------
 .../spark/deploy/history/HistoryServer.scala  |   5 +-
 .../scheduler/ApplicationEventListener.scala  |  35 ++--
 .../spark/scheduler/SchedulerBackend.scala    |   8 +
 .../spark/scheduler/SparkListener.scala       |   4 +-
 .../spark/scheduler/TaskScheduler.scala       |   8 +
 .../spark/scheduler/TaskSchedulerImpl.scala   |   4 +
 .../CoarseGrainedSchedulerBackend.scala       |   4 +-
 .../cluster/SimrSchedulerBackend.scala        |   1 +
 .../cluster/SparkDeploySchedulerBackend.scala |  28 +++
 .../mesos/CoarseMesosSchedulerBackend.scala   |   1 +
 .../cluster/mesos/MesosSchedulerBackend.scala |   1 +
 .../spark/scheduler/local/LocalBackend.scala  |   1 +
 .../scala/org/apache/spark/ui/UIUtils.scala   |   2 +-
 .../org/apache/spark/util/JsonProtocol.scala  |   4 +-
 .../scheduler/EventLoggingListenerSuite.scala |   3 +-
 .../spark/scheduler/ReplayListenerSuite.scala |   3 +-
 .../apache/spark/util/JsonProtocolSuite.scala |   9 +-
 project/MimaExcludes.scala                    |   6 +-
 .../spark/deploy/yarn/ApplicationMaster.scala |  24 ++-
 .../deploy/yarn/YarnSparkHadoopUtil.scala     |  14 --
 .../cluster/YarnClientSchedulerBackend.scala  |   4 +-
 .../cluster/YarnClusterSchedulerBackend.scala |   5 +-
 25 files changed, 228 insertions(+), 133 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala
index cb4fb7cfbd32f..529febff94196 100644
--- a/core/src/main/scala/org/apache/spark/SparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/SparkContext.scala
@@ -1261,7 +1261,10 @@ class SparkContext(config: SparkConf) extends Logging {
 
   /** Post the application start event */
   private def postApplicationStart() {
-    listenerBus.post(SparkListenerApplicationStart(appName, startTime, sparkUser))
+    // Note: this code assumes that the task scheduler has been initialized and has contacted
+    // the cluster manager to get an application ID (in case the cluster manager provides one).
+    listenerBus.post(SparkListenerApplicationStart(appName, taskScheduler.applicationId(),
+      startTime, sparkUser))
   }
 
   /** Post the application end event */
diff --git a/core/src/main/scala/org/apache/spark/deploy/history/ApplicationHistoryProvider.scala b/core/src/main/scala/org/apache/spark/deploy/history/ApplicationHistoryProvider.scala
index a0e8bd403a41d..fbe39b27649f6 100644
--- a/core/src/main/scala/org/apache/spark/deploy/history/ApplicationHistoryProvider.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/history/ApplicationHistoryProvider.scala
@@ -34,15 +34,15 @@ private[spark] abstract class ApplicationHistoryProvider {
    *
    * @return List of all know applications.
    */
-  def getListing(): Seq[ApplicationHistoryInfo]
+  def getListing(): Iterable[ApplicationHistoryInfo]
 
   /**
    * Returns the Spark UI for a specific application.
    *
    * @param appId The application ID.
-   * @return The application's UI, or null if application is not found.
+   * @return The application's UI, or None if application is not found.
    */
-  def getAppUI(appId: String): SparkUI
+  def getAppUI(appId: String): Option[SparkUI]
 
   /**
    * Called when the server is shutting down.
diff --git a/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala b/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala
index 05c8a90782c74..481f6c93c6a8d 100644
--- a/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala
@@ -32,6 +32,8 @@ import org.apache.spark.util.Utils
 private[history] class FsHistoryProvider(conf: SparkConf) extends ApplicationHistoryProvider
   with Logging {
 
+  private val NOT_STARTED = "<Not Started>"
+
   // Interval between each check for event log updates
   private val UPDATE_INTERVAL_MS = conf.getInt("spark.history.fs.updateInterval",
     conf.getInt("spark.history.updateInterval", 10)) * 1000
@@ -47,8 +49,15 @@ private[history] class FsHistoryProvider(conf: SparkConf) extends ApplicationHis
   // A timestamp of when the disk was last accessed to check for log updates
   private var lastLogCheckTimeMs = -1L
 
-  // List of applications, in order from newest to oldest.
-  @volatile private var appList: Seq[ApplicationHistoryInfo] = Nil
+  // The modification time of the newest log detected during the last scan. This is used
+  // to ignore logs that are older during subsequent scans, to avoid processing data that
+  // is already known.
+  private var lastModifiedTime = -1L
+
+  // Mapping of application IDs to their metadata, in descending end time order. Apps are inserted
+  // into the map in order, so the LinkedHashMap maintains the correct ordering.
+  @volatile private var applications: mutable.LinkedHashMap[String, FsApplicationHistoryInfo]
+    = new mutable.LinkedHashMap()
 
   /**
    * A background thread that periodically checks for event log updates on disk.
@@ -93,15 +102,35 @@ private[history] class FsHistoryProvider(conf: SparkConf) extends ApplicationHis
     logCheckingThread.start()
   }
 
-  override def getListing() = appList
+  override def getListing() = applications.values
 
-  override def getAppUI(appId: String): SparkUI = {
+  override def getAppUI(appId: String): Option[SparkUI] = {
     try {
-      val appLogDir = fs.getFileStatus(new Path(resolvedLogDir.toString, appId))
-      val (_, ui) = loadAppInfo(appLogDir, renderUI = true)
-      ui
+      applications.get(appId).map { info =>
+        val (replayBus, appListener) = createReplayBus(fs.getFileStatus(
+          new Path(logDir, info.logDir)))
+        val ui = {
+          val conf = this.conf.clone()
+          val appSecManager = new SecurityManager(conf)
+          new SparkUI(conf, appSecManager, replayBus, appId,
+            s"${HistoryServer.UI_PATH_PREFIX}/$appId")
+          // Do not call ui.bind() to avoid creating a new server for each application
+        }
+
+        replayBus.replay()
+
+        ui.setAppName(s"${appListener.appName.getOrElse(NOT_STARTED)} ($appId)")
+
+        val uiAclsEnabled = conf.getBoolean("spark.history.ui.acls.enable", false)
+        ui.getSecurityManager.setAcls(uiAclsEnabled)
+        // make sure to set admin acls before view acls so they are properly picked up
+        ui.getSecurityManager.setAdminAcls(appListener.adminAcls.getOrElse(""))
+        ui.getSecurityManager.setViewAcls(appListener.sparkUser.getOrElse(NOT_STARTED),
+          appListener.viewAcls.getOrElse(""))
+        ui
+      }
     } catch {
-      case e: FileNotFoundException => null
+      case e: FileNotFoundException => None
     }
   }
 
@@ -119,84 +148,79 @@ private[history] class FsHistoryProvider(conf: SparkConf) extends ApplicationHis
     try {
       val logStatus = fs.listStatus(new Path(resolvedLogDir))
       val logDirs = if (logStatus != null) logStatus.filter(_.isDir).toSeq else Seq[FileStatus]()
-      val logInfos = logDirs.filter { dir =>
-        fs.isFile(new Path(dir.getPath, EventLoggingListener.APPLICATION_COMPLETE))
-      }
 
-      val currentApps = Map[String, ApplicationHistoryInfo](
-        appList.map(app => app.id -> app):_*)
-
-      // For any application that either (i) is not listed or (ii) has changed since the last time
-      // the listing was created (defined by the log dir's modification time), load the app's info.
-      // Otherwise just reuse what's already in memory.
-      val newApps = new mutable.ArrayBuffer[ApplicationHistoryInfo](logInfos.size)
-      for (dir <- logInfos) {
-        val curr = currentApps.getOrElse(dir.getPath().getName(), null)
-        if (curr == null || curr.lastUpdated < getModificationTime(dir)) {
+      // Load all new logs from the log directory. Only directories that have a modification time
+      // later than the last known log directory will be loaded.
+      var newLastModifiedTime = lastModifiedTime
+      val logInfos = logDirs
+        .filter { dir =>
+          if (fs.isFile(new Path(dir.getPath(), EventLoggingListener.APPLICATION_COMPLETE))) {
+            val modTime = getModificationTime(dir)
+            newLastModifiedTime = math.max(newLastModifiedTime, modTime)
+            modTime > lastModifiedTime
+          } else {
+            false
+          }
+        }
+        .flatMap { dir =>
           try {
-            val (app, _) = loadAppInfo(dir, renderUI = false)
-            newApps += app
+            val (replayBus, appListener) = createReplayBus(dir)
+            replayBus.replay()
+            Some(new FsApplicationHistoryInfo(
+              dir.getPath().getName(),
+              appListener.appId.getOrElse(dir.getPath().getName()),
+              appListener.appName.getOrElse(NOT_STARTED),
+              appListener.startTime.getOrElse(-1L),
+              appListener.endTime.getOrElse(-1L),
+              getModificationTime(dir),
+              appListener.sparkUser.getOrElse(NOT_STARTED)))
           } catch {
-            case e: Exception => logError(s"Failed to load app info from directory $dir.")
+            case e: Exception =>
+              logInfo(s"Failed to load application log data from $dir.", e)
+              None
+          }
+        }
+        .sortBy { info => -info.endTime }
+
+      lastModifiedTime = newLastModifiedTime
+
+      // When there are new logs, merge the new list with the existing one, maintaining
+      // the expected ordering (descending end time). Maintaining the order is important
+      // to avoid having to sort the list every time there is a request for the log list.
+      if (!logInfos.isEmpty) {
+        val newApps = new mutable.LinkedHashMap[String, FsApplicationHistoryInfo]()
+        def addIfAbsent(info: FsApplicationHistoryInfo) = {
+          if (!newApps.contains(info.id)) {
+            newApps += (info.id -> info)
           }
-        } else {
-          newApps += curr
         }
-      }
 
-      appList = newApps.sortBy { info => -info.endTime }
+        val newIterator = logInfos.iterator.buffered
+        val oldIterator = applications.values.iterator.buffered
+        while (newIterator.hasNext && oldIterator.hasNext) {
+          if (newIterator.head.endTime > oldIterator.head.endTime) {
+            addIfAbsent(newIterator.next)
+          } else {
+            addIfAbsent(oldIterator.next)
+          }
+        }
+        newIterator.foreach(addIfAbsent)
+        oldIterator.foreach(addIfAbsent)
+
+        applications = newApps
+      }
     } catch {
       case t: Throwable => logError("Exception in checking for event log updates", t)
     }
   }
 
-  /**
-   * Parse the application's logs to find out the information we need to build the
-   * listing page.
-   *
-   * When creating the listing of available apps, there is no need to load the whole UI for the
-   * application. The UI is requested by the HistoryServer (by calling getAppInfo()) when the user
-   * clicks on a specific application.
-   *
-   * @param logDir Directory with application's log files.
-   * @param renderUI Whether to create the SparkUI for the application.
-   * @return A 2-tuple `(app info, ui)`. `ui` will be null if `renderUI` is false.
-   */
-  private def loadAppInfo(logDir: FileStatus, renderUI: Boolean) = {
-    val path = logDir.getPath
-    val appId = path.getName
+  private def createReplayBus(logDir: FileStatus): (ReplayListenerBus, ApplicationEventListener) = {
+    val path = logDir.getPath()
     val elogInfo = EventLoggingListener.parseLoggingInfo(path, fs)
     val replayBus = new ReplayListenerBus(elogInfo.logPaths, fs, elogInfo.compressionCodec)
     val appListener = new ApplicationEventListener
     replayBus.addListener(appListener)
-
-    val ui: SparkUI = if (renderUI) {
-        val conf = this.conf.clone()
-        val appSecManager = new SecurityManager(conf)
-        new SparkUI(conf, appSecManager, replayBus, appId,
-          HistoryServer.UI_PATH_PREFIX + s"/$appId")
-        // Do not call ui.bind() to avoid creating a new server for each application
-      } else {
-        null
-      }
-
-    replayBus.replay()
-    val appInfo = ApplicationHistoryInfo(
-      appId,
-      appListener.appName,
-      appListener.startTime,
-      appListener.endTime,
-      getModificationTime(logDir),
-      appListener.sparkUser)
-
-    if (ui != null) {
-      val uiAclsEnabled = conf.getBoolean("spark.history.ui.acls.enable", false)
-      ui.getSecurityManager.setAcls(uiAclsEnabled)
-      // make sure to set admin acls before view acls so properly picked up
-      ui.getSecurityManager.setAdminAcls(appListener.adminAcls)
-      ui.getSecurityManager.setViewAcls(appListener.sparkUser, appListener.viewAcls)
-    }
-    (appInfo, ui)
+    (replayBus, appListener)
   }
 
   /** Return when this directory was last modified. */
@@ -219,3 +243,13 @@ private[history] class FsHistoryProvider(conf: SparkConf) extends ApplicationHis
   private def getMonotonicTimeMs() = System.nanoTime() / (1000 * 1000)
 
 }
+
+private class FsApplicationHistoryInfo(
+    val logDir: String,
+    id: String,
+    name: String,
+    startTime: Long,
+    endTime: Long,
+    lastUpdated: Long,
+    sparkUser: String)
+  extends ApplicationHistoryInfo(id, name, startTime, endTime, lastUpdated, sparkUser)
diff --git a/core/src/main/scala/org/apache/spark/deploy/history/HistoryServer.scala b/core/src/main/scala/org/apache/spark/deploy/history/HistoryServer.scala
index d1a64c1912cb8..ce00c0ffd21e0 100644
--- a/core/src/main/scala/org/apache/spark/deploy/history/HistoryServer.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/history/HistoryServer.scala
@@ -52,10 +52,7 @@ class HistoryServer(
 
   private val appLoader = new CacheLoader[String, SparkUI] {
     override def load(key: String): SparkUI = {
-      val ui = provider.getAppUI(key)
-      if (ui == null) {
-        throw new NoSuchElementException()
-      }
+      val ui = provider.getAppUI(key).getOrElse(throw new NoSuchElementException())
       attachSparkUI(ui)
       ui
     }
diff --git a/core/src/main/scala/org/apache/spark/scheduler/ApplicationEventListener.scala b/core/src/main/scala/org/apache/spark/scheduler/ApplicationEventListener.scala
index 162158babc35b..6d39a5e3fa64c 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/ApplicationEventListener.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/ApplicationEventListener.scala
@@ -24,38 +24,31 @@ package org.apache.spark.scheduler
  * from multiple applications are seen, the behavior is unspecified.
  */
 private[spark] class ApplicationEventListener extends SparkListener {
-  var appName = "<Not Started>"
-  var sparkUser = "<Not Started>"
-  var startTime = -1L
-  var endTime = -1L
-  var viewAcls = ""
-  var adminAcls = ""
-
-  def applicationStarted = startTime != -1
-
-  def applicationCompleted = endTime != -1
-
-  def applicationDuration: Long = {
-    val difference = endTime - startTime
-    if (applicationStarted && applicationCompleted && difference > 0) difference else -1L
-  }
+  var appName: Option[String] = None
+  var appId: Option[String] = None
+  var sparkUser: Option[String] = None
+  var startTime: Option[Long] = None
+  var endTime: Option[Long] = None
+  var viewAcls: Option[String] = None
+  var adminAcls: Option[String] = None
 
   override def onApplicationStart(applicationStart: SparkListenerApplicationStart) {
-    appName = applicationStart.appName
-    startTime = applicationStart.time
-    sparkUser = applicationStart.sparkUser
+    appName = Some(applicationStart.appName)
+    appId = applicationStart.appId
+    startTime = Some(applicationStart.time)
+    sparkUser = Some(applicationStart.sparkUser)
   }
 
   override def onApplicationEnd(applicationEnd: SparkListenerApplicationEnd) {
-    endTime = applicationEnd.time
+    endTime = Some(applicationEnd.time)
   }
 
   override def onEnvironmentUpdate(environmentUpdate: SparkListenerEnvironmentUpdate) {
     synchronized {
       val environmentDetails = environmentUpdate.environmentDetails
       val allProperties = environmentDetails("Spark Properties").toMap
-      viewAcls = allProperties.getOrElse("spark.ui.view.acls", "")
-      adminAcls = allProperties.getOrElse("spark.admin.acls", "")
+      viewAcls = allProperties.get("spark.ui.view.acls")
+      adminAcls = allProperties.get("spark.admin.acls")
     }
   }
 }
diff --git a/core/src/main/scala/org/apache/spark/scheduler/SchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/SchedulerBackend.scala
index e41e0a9841691..a0be8307eff27 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/SchedulerBackend.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/SchedulerBackend.scala
@@ -31,4 +31,12 @@ private[spark] trait SchedulerBackend {
   def killTask(taskId: Long, executorId: String, interruptThread: Boolean): Unit =
     throw new UnsupportedOperationException
   def isReady(): Boolean = true
+
+  /**
+   * The application ID associated with the job, if any.
+   *
+   * @return The application ID, or None if the backend does not provide an ID.
+   */
+  def applicationId(): Option[String] = None
+
 }
diff --git a/core/src/main/scala/org/apache/spark/scheduler/SparkListener.scala b/core/src/main/scala/org/apache/spark/scheduler/SparkListener.scala
index f33c2e065a200..86afe3bd5265f 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/SparkListener.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/SparkListener.scala
@@ -89,8 +89,8 @@ case class SparkListenerExecutorMetricsUpdate(
   extends SparkListenerEvent
 
 @DeveloperApi
-case class SparkListenerApplicationStart(appName: String, time: Long, sparkUser: String)
-  extends SparkListenerEvent
+case class SparkListenerApplicationStart(appName: String, appId: Option[String], time: Long,
+  sparkUser: String) extends SparkListenerEvent
 
 @DeveloperApi
 case class SparkListenerApplicationEnd(time: Long) extends SparkListenerEvent
diff --git a/core/src/main/scala/org/apache/spark/scheduler/TaskScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/TaskScheduler.scala
index 1a0b877c8a5e1..1c1ce666eab0f 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/TaskScheduler.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/TaskScheduler.scala
@@ -64,4 +64,12 @@ private[spark] trait TaskScheduler {
    */
   def executorHeartbeatReceived(execId: String, taskMetrics: Array[(Long, TaskMetrics)],
     blockManagerId: BlockManagerId): Boolean
+
+  /**
+   * The application ID associated with the job, if any.
+   *
+   * @return The application ID, or None if the backend does not provide an ID.
+   */
+  def applicationId(): Option[String] = None
+
 }
diff --git a/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala b/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala
index ad051e59af86d..633e892554c50 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala
@@ -491,6 +491,9 @@ private[spark] class TaskSchedulerImpl(
       }
     }
   }
+
+  override def applicationId(): Option[String] = backend.applicationId()
+
 }
 
 
@@ -535,4 +538,5 @@ private[spark] object TaskSchedulerImpl {
 
     retval.toList
   }
+
 }
diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala
index 2a3711ae2a78c..5b5257269d92f 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala
@@ -51,12 +51,12 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, actorSystem: A
   val conf = scheduler.sc.conf
   private val timeout = AkkaUtils.askTimeout(conf)
   private val akkaFrameSize = AkkaUtils.maxFrameSizeBytes(conf)
-  // Submit tasks only after (registered resources / total expected resources) 
+  // Submit tasks only after (registered resources / total expected resources)
   // is equal to at least this value, that is double between 0 and 1.
   var minRegisteredRatio =
     math.min(1, conf.getDouble("spark.scheduler.minRegisteredResourcesRatio", 0))
   // Submit tasks after maxRegisteredWaitingTime milliseconds
-  // if minRegisteredRatio has not yet been reached  
+  // if minRegisteredRatio has not yet been reached
   val maxRegisteredWaitingTime =
     conf.getInt("spark.scheduler.maxRegisteredResourcesWaitingTime", 30000)
   val createTime = System.currentTimeMillis()
diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/SimrSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/SimrSchedulerBackend.scala
index bc7670f4a804d..513d74a08a47f 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/SimrSchedulerBackend.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/SimrSchedulerBackend.scala
@@ -69,4 +69,5 @@ private[spark] class SimrSchedulerBackend(
     fs.delete(new Path(driverFilePath), false)
     super.stop()
   }
+
 }
diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala
index 32138e5246700..06872ace2ecf4 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala
@@ -34,6 +34,10 @@ private[spark] class SparkDeploySchedulerBackend(
   var client: AppClient = null
   var stopping = false
   var shutdownCallback : (SparkDeploySchedulerBackend) => Unit = _
+  var appId: String = _
+
+  val registrationLock = new Object()
+  var registrationDone = false
 
   val maxCores = conf.getOption("spark.cores.max").map(_.toInt)
   val totalExpectedCores = maxCores.getOrElse(0)
@@ -68,6 +72,8 @@ private[spark] class SparkDeploySchedulerBackend(
 
     client = new AppClient(sc.env.actorSystem, masters, appDesc, this, conf)
     client.start()
+
+    waitForRegistration()
   }
 
   override def stop() {
@@ -81,15 +87,19 @@ private[spark] class SparkDeploySchedulerBackend(
 
   override def connected(appId: String) {
     logInfo("Connected to Spark cluster with app ID " + appId)
+    this.appId = appId
+    notifyContext()
   }
 
   override def disconnected() {
+    notifyContext()
     if (!stopping) {
       logWarning("Disconnected from Spark cluster! Waiting for reconnection...")
     }
   }
 
   override def dead(reason: String) {
+    notifyContext()
     if (!stopping) {
       logError("Application has been killed. Reason: " + reason)
       scheduler.error(reason)
@@ -116,4 +126,22 @@ private[spark] class SparkDeploySchedulerBackend(
   override def sufficientResourcesRegistered(): Boolean = {
     totalCoreCount.get() >= totalExpectedCores * minRegisteredRatio
   }
+
+  override def applicationId(): Option[String] = Option(appId)
+
+  private def waitForRegistration() = {
+    registrationLock.synchronized {
+      while (!registrationDone) {
+        registrationLock.wait()
+      }
+    }
+  }
+
+  private def notifyContext() = {
+    registrationLock.synchronized {
+      registrationDone = true
+      registrationLock.notifyAll()
+    }
+  }
+
 }
diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala
index 87e181e773fdf..da43ef567608c 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala
@@ -309,4 +309,5 @@ private[spark] class CoarseMesosSchedulerBackend(
     logInfo("Executor lost: %s, marking slave %s as lost".format(e.getValue, s.getValue))
     slaveLost(d, s)
   }
+
 }
diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala
index 67ee4d66f151b..a9ef126f5de0e 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala
@@ -349,4 +349,5 @@ private[spark] class MesosSchedulerBackend(
 
   // TODO: query Mesos for number of cores
   override def defaultParallelism() = sc.conf.getInt("spark.default.parallelism", 8)
+
 }
diff --git a/core/src/main/scala/org/apache/spark/scheduler/local/LocalBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/local/LocalBackend.scala
index bec9502f20466..9ea25c2bc7090 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/local/LocalBackend.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/local/LocalBackend.scala
@@ -114,4 +114,5 @@ private[spark] class LocalBackend(scheduler: TaskSchedulerImpl, val totalCores:
   override def statusUpdate(taskId: Long, state: TaskState, serializedData: ByteBuffer) {
     localActor ! StatusUpdate(taskId, state, serializedData)
   }
+
 }
diff --git a/core/src/main/scala/org/apache/spark/ui/UIUtils.scala b/core/src/main/scala/org/apache/spark/ui/UIUtils.scala
index bee6dad3387e5..f0006b42aee4f 100644
--- a/core/src/main/scala/org/apache/spark/ui/UIUtils.scala
+++ b/core/src/main/scala/org/apache/spark/ui/UIUtils.scala
@@ -232,7 +232,7 @@ private[spark] object UIUtils extends Logging {
   def listingTable[T](
       headers: Seq[String],
       generateDataRow: T => Seq[Node],
-      data: Seq[T],
+      data: Iterable[T],
       fixedWidth: Boolean = false): Seq[Node] = {
 
     var listingTableClass = TABLE_CLASS
diff --git a/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala b/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala
index 1fc536b096996..b0754e3ce10db 100644
--- a/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala
+++ b/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala
@@ -171,6 +171,7 @@ private[spark] object JsonProtocol {
   def applicationStartToJson(applicationStart: SparkListenerApplicationStart): JValue = {
     ("Event" -> Utils.getFormattedClassName(applicationStart)) ~
     ("App Name" -> applicationStart.appName) ~
+    ("App ID" -> applicationStart.appId.map(JString(_)).getOrElse(JNothing)) ~
     ("Timestamp" -> applicationStart.time) ~
     ("User" -> applicationStart.sparkUser)
   }
@@ -484,9 +485,10 @@ private[spark] object JsonProtocol {
 
   def applicationStartFromJson(json: JValue): SparkListenerApplicationStart = {
     val appName = (json \ "App Name").extract[String]
+    val appId = Utils.jsonOption(json \ "App ID").map(_.extract[String])
     val time = (json \ "Timestamp").extract[Long]
     val sparkUser = (json \ "User").extract[String]
-    SparkListenerApplicationStart(appName, time, sparkUser)
+    SparkListenerApplicationStart(appName, appId, time, sparkUser)
   }
 
   def applicationEndFromJson(json: JValue): SparkListenerApplicationEnd = {
diff --git a/core/src/test/scala/org/apache/spark/scheduler/EventLoggingListenerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/EventLoggingListenerSuite.scala
index 41e58a008c533..fead883793430 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/EventLoggingListenerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/EventLoggingListenerSuite.scala
@@ -229,7 +229,8 @@ class EventLoggingListenerSuite extends FunSuite with BeforeAndAfter {
     val conf = getLoggingConf(logDirPath, compressionCodec)
     val eventLogger = new EventLoggingListener("test", conf)
     val listenerBus = new LiveListenerBus
-    val applicationStart = SparkListenerApplicationStart("Greatest App (N)ever", 125L, "Mickey")
+    val applicationStart = SparkListenerApplicationStart("Greatest App (N)ever", None,
+      125L, "Mickey")
     val applicationEnd = SparkListenerApplicationEnd(1000L)
 
     // A comprehensive test on JSON de/serialization of all events is in JsonProtocolSuite
diff --git a/core/src/test/scala/org/apache/spark/scheduler/ReplayListenerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/ReplayListenerSuite.scala
index 8f0ee9f4dbafd..7ab351d1b4d24 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/ReplayListenerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/ReplayListenerSuite.scala
@@ -83,7 +83,8 @@ class ReplayListenerSuite extends FunSuite with BeforeAndAfter {
     val fstream = fileSystem.create(logFilePath)
     val cstream = codec.map(_.compressedOutputStream(fstream)).getOrElse(fstream)
     val writer = new PrintWriter(cstream)
-    val applicationStart = SparkListenerApplicationStart("Greatest App (N)ever", 125L, "Mickey")
+    val applicationStart = SparkListenerApplicationStart("Greatest App (N)ever", None,
+      125L, "Mickey")
     val applicationEnd = SparkListenerApplicationEnd(1000L)
     writer.println(compact(render(JsonProtocol.sparkEventToJson(applicationStart))))
     writer.println(compact(render(JsonProtocol.sparkEventToJson(applicationEnd))))
diff --git a/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala b/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala
index c84bafce37f70..2b45d8b695853 100644
--- a/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala
+++ b/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala
@@ -60,7 +60,7 @@ class JsonProtocolSuite extends FunSuite {
     val blockManagerRemoved = SparkListenerBlockManagerRemoved(2L,
       BlockManagerId("Scarce", "to be counted...", 100))
     val unpersistRdd = SparkListenerUnpersistRDD(12345)
-    val applicationStart = SparkListenerApplicationStart("The winner of all", 42L, "Garfield")
+    val applicationStart = SparkListenerApplicationStart("The winner of all", None, 42L, "Garfield")
     val applicationEnd = SparkListenerApplicationEnd(42L)
 
     testEvent(stageSubmitted, stageSubmittedJsonString)
@@ -176,6 +176,13 @@ class JsonProtocolSuite extends FunSuite {
       deserializedBmRemoved)
   }
 
+  test("SparkListenerApplicationStart backwards compatibility") {
+    // SparkListenerApplicationStart in Spark 1.0.0 do not have an "appId" property.
+    val applicationStart = SparkListenerApplicationStart("test", None, 1L, "user")
+    val oldEvent = JsonProtocol.applicationStartToJson(applicationStart)
+      .removeField({ _._1 == "App ID" })
+    assert(applicationStart === JsonProtocol.applicationStartFromJson(oldEvent))
+  }
 
   /** -------------------------- *
    | Helper test running methods |
diff --git a/project/MimaExcludes.scala b/project/MimaExcludes.scala
index a2f1b3582ab71..855d5cc8cf3fd 100644
--- a/project/MimaExcludes.scala
+++ b/project/MimaExcludes.scala
@@ -111,6 +111,8 @@ object MimaExcludes {
           MimaBuild.excludeSparkClass("storage.Values") ++
           MimaBuild.excludeSparkClass("storage.Entry") ++
           MimaBuild.excludeSparkClass("storage.MemoryStore$Entry") ++
+          // Class was missing "@DeveloperApi" annotation in 1.0.
+          MimaBuild.excludeSparkClass("scheduler.SparkListenerApplicationStart") ++
           Seq(
             ProblemFilters.exclude[IncompatibleMethTypeProblem](
               "org.apache.spark.mllib.tree.impurity.Gini.calculate"),
@@ -119,14 +121,14 @@ object MimaExcludes {
             ProblemFilters.exclude[IncompatibleMethTypeProblem](
               "org.apache.spark.mllib.tree.impurity.Variance.calculate")
           ) ++
-          Seq ( // Package-private classes removed in SPARK-2341
+          Seq( // Package-private classes removed in SPARK-2341
             ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.mllib.util.BinaryLabelParser"),
             ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.mllib.util.BinaryLabelParser$"),
             ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.mllib.util.LabelParser"),
             ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.mllib.util.LabelParser$"),
             ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.mllib.util.MulticlassLabelParser"),
             ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.mllib.util.MulticlassLabelParser$")
-          ) ++ 
+          ) ++
           Seq( // package-private classes removed in MLlib
             ProblemFilters.exclude[MissingMethodProblem](
               "org.apache.spark.mllib.regression.GeneralizedLinearAlgorithm.org$apache$spark$mllib$regression$GeneralizedLinearAlgorithm$$prependOne")
diff --git a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
index 8c548409719da..98039a20de245 100644
--- a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
+++ b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
@@ -35,6 +35,7 @@ import org.apache.hadoop.yarn.conf.YarnConfiguration
 
 import org.apache.spark.{Logging, SecurityManager, SparkConf, SparkContext, SparkEnv}
 import org.apache.spark.deploy.SparkHadoopUtil
+import org.apache.spark.deploy.history.HistoryServer
 import org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend
 import org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.AddWebUIFilter
 import org.apache.spark.util.{AkkaUtils, SignalLogger, Utils}
@@ -70,6 +71,8 @@ private[spark] class ApplicationMaster(args: ApplicationMasterArguments,
   private val sparkContextRef = new AtomicReference[SparkContext](null)
 
   final def run(): Int = {
+    val appAttemptId = client.getAttemptId()
+
     if (isDriver) {
       // Set the web ui port to be ephemeral for yarn so we don't conflict with
       // other spark processes running on the same box
@@ -77,9 +80,12 @@ private[spark] class ApplicationMaster(args: ApplicationMasterArguments,
 
       // Set the master property to match the requested mode.
       System.setProperty("spark.master", "yarn-cluster")
+
+      // Propagate the application ID so that YarnClusterSchedulerBackend can pick it up.
+      System.setProperty("spark.yarn.app.id", appAttemptId.getApplicationId().toString())
     }
 
-    logInfo("ApplicationAttemptId: " + client.getAttemptId())
+    logInfo("ApplicationAttemptId: " + appAttemptId)
 
     val cleanupHook = new Runnable {
       override def run() {
@@ -151,13 +157,20 @@ private[spark] class ApplicationMaster(args: ApplicationMasterArguments,
     sparkContextRef.compareAndSet(sc, null)
   }
 
-  private def registerAM(uiAddress: String, uiHistoryAddress: String) = {
+  private def registerAM(uiAddress: String) = {
     val sc = sparkContextRef.get()
+
+    val appId = client.getAttemptId().getApplicationId().toString()
+    val historyAddress =
+      sparkConf.getOption("spark.yarn.historyServer.address")
+        .map { address => s"${address}${HistoryServer.UI_PATH_PREFIX}/${appId}" }
+        .getOrElse("")
+
     allocator = client.register(yarnConf,
       if (sc != null) sc.getConf else sparkConf,
       if (sc != null) sc.preferredNodeLocationData else Map(),
       uiAddress,
-      uiHistoryAddress)
+      historyAddress)
 
     allocator.allocateResources()
     reporterThread = launchReporterThread()
@@ -175,7 +188,7 @@ private[spark] class ApplicationMaster(args: ApplicationMasterArguments,
     if (sc == null) {
       finish(FinalApplicationStatus.FAILED, "Timed out waiting for SparkContext.")
     } else {
-      registerAM(sc.ui.appUIHostPort, YarnSparkHadoopUtil.getUIHistoryAddress(sc, sparkConf))
+      registerAM(sc.ui.appUIHostPort)
       try {
         userThread.join()
       } finally {
@@ -190,8 +203,7 @@ private[spark] class ApplicationMaster(args: ApplicationMasterArguments,
       conf = sparkConf, securityManager = securityMgr)._1
     actor = waitForSparkDriver()
     addAmIpFilter()
-    registerAM(sparkConf.get("spark.driver.appUIAddress", ""),
-      sparkConf.get("spark.driver.appUIHistoryAddress", ""))
+    registerAM(sparkConf.get("spark.driver.appUIAddress", ""))
 
     // In client mode the actor will stop the reporter thread.
     reporterThread.join()
diff --git a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtil.scala b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtil.scala
index ffe2731ca1d17..dc77f1236492d 100644
--- a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtil.scala
+++ b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtil.scala
@@ -36,7 +36,6 @@ import org.apache.hadoop.yarn.util.RackResolver
 import org.apache.hadoop.conf.Configuration
 
 import org.apache.spark.{SparkConf, SparkContext}
-import org.apache.spark.deploy.history.HistoryServer
 import org.apache.spark.deploy.SparkHadoopUtil
 import org.apache.spark.util.Utils
 
@@ -156,19 +155,6 @@ object YarnSparkHadoopUtil {
     }
   }
 
-  def getUIHistoryAddress(sc: SparkContext, conf: SparkConf) : String = {
-    val eventLogDir = sc.eventLogger match {
-      case Some(logger) => logger.getApplicationLogDir()
-      case None => ""
-    }
-    val historyServerAddress = conf.get("spark.yarn.historyServer.address", "")
-    if (historyServerAddress != "" && eventLogDir != "") {
-      historyServerAddress + HistoryServer.UI_PATH_PREFIX + s"/$eventLogDir"
-    } else {
-      ""
-    }
-  }
-
   /**
    * Escapes a string for inclusion in a command line executed by Yarn. Yarn executes commands
    * using `bash -c "command arg1 arg2"` and that means plain quoting doesn't really work. The
diff --git a/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala b/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala
index a5f537dd9de30..41c662cd7a6de 100644
--- a/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala
+++ b/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala
@@ -56,7 +56,6 @@ private[spark] class YarnClientSchedulerBackend(
     val driverPort = conf.get("spark.driver.port")
     val hostport = driverHost + ":" + driverPort
     conf.set("spark.driver.appUIAddress", sc.ui.appUIHostPort)
-    conf.set("spark.driver.appUIHistoryAddress", YarnSparkHadoopUtil.getUIHistoryAddress(sc, conf))
 
     val argsArrayBuf = new ArrayBuffer[String]()
     argsArrayBuf += (
@@ -150,4 +149,7 @@ private[spark] class YarnClientSchedulerBackend(
   override def sufficientResourcesRegistered(): Boolean = {
     totalRegisteredExecutors.get() >= totalExpectedExecutors * minRegisteredRatio
   }
+
+  override def applicationId(): Option[String] = Option(appId).map(_.toString())
+
 }
diff --git a/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClusterSchedulerBackend.scala b/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClusterSchedulerBackend.scala
index 55665220a6f96..39436d0999663 100644
--- a/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClusterSchedulerBackend.scala
+++ b/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClusterSchedulerBackend.scala
@@ -28,7 +28,7 @@ private[spark] class YarnClusterSchedulerBackend(
   extends CoarseGrainedSchedulerBackend(scheduler, sc.env.actorSystem) {
 
   var totalExpectedExecutors = 0
-  
+
   if (conf.getOption("spark.scheduler.minRegisteredResourcesRatio").isEmpty) {
     minRegisteredRatio = 0.8
   }
@@ -47,4 +47,7 @@ private[spark] class YarnClusterSchedulerBackend(
   override def sufficientResourcesRegistered(): Boolean = {
     totalRegisteredExecutors.get() >= totalExpectedExecutors * minRegisteredRatio
   }
+
+  override def applicationId(): Option[String] = sc.getConf.getOption("spark.yarn.app.id")
+
 }

From 2784822e4c63083a647cc2d6c7089065ef3b947d Mon Sep 17 00:00:00 2001
From: Andrew Or <andrewor14@gmail.com>
Date: Wed, 3 Sep 2014 16:58:19 -0700
Subject: [PATCH 187/489] [Minor] Fix outdated Spark version

This is causing the event logs to include a file called SPARK_VERSION_1.0.0, which is not accurate.

Author: Andrew Or <andrewor14@gmail.com>
Author: andrewor14 <andrewor14@gmail.com>

Closes #2255 from andrewor14/spark-version and squashes the following commits:

1fbdfe9 [andrewor14] Snapshot
805a1c8 [Andrew Or] JK. Update Spark version to 1.2.0 instead.
bffbaab [Andrew Or] Update Spark version to 1.1.0
---
 core/src/main/scala/org/apache/spark/SparkContext.scala | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala
index 529febff94196..6eaf6794764c7 100644
--- a/core/src/main/scala/org/apache/spark/SparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/SparkContext.scala
@@ -1297,7 +1297,7 @@ class SparkContext(config: SparkConf) extends Logging {
  */
 object SparkContext extends Logging {
 
-  private[spark] val SPARK_VERSION = "1.0.0"
+  private[spark] val SPARK_VERSION = "1.2.0-SNAPSHOT"
 
   private[spark] val SPARK_JOB_DESCRIPTION = "spark.job.description"
 

From 996b7434ee0d0c7c26987eb9cf050c139fdd2db2 Mon Sep 17 00:00:00 2001
From: Liang-Chi Hsieh <viirya@gmail.com>
Date: Wed, 3 Sep 2014 17:04:53 -0700
Subject: [PATCH 188/489] [SPARK-3345] Do correct parameters for
 ShuffleFileGroup

In the method `newFileGroup` of class `FileShuffleBlockManager`, the parameters for creating new `ShuffleFileGroup` object is in wrong order.

Because in current codes, the parameters `shuffleId` and `fileId` are not used. So it doesn't cause problem now. However it should be corrected for readability and avoid future problem.

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

Closes #2235 from viirya/correct_shufflefilegroup_params and squashes the following commits:

fe72567 [Liang-Chi Hsieh] Do correct parameters for ShuffleFileGroup.
---
 .../org/apache/spark/shuffle/FileShuffleBlockManager.scala      | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/shuffle/FileShuffleBlockManager.scala b/core/src/main/scala/org/apache/spark/shuffle/FileShuffleBlockManager.scala
index 76e3932a9bb91..96facccd52373 100644
--- a/core/src/main/scala/org/apache/spark/shuffle/FileShuffleBlockManager.scala
+++ b/core/src/main/scala/org/apache/spark/shuffle/FileShuffleBlockManager.scala
@@ -155,7 +155,7 @@ class FileShuffleBlockManager(conf: SparkConf)
           val filename = physicalFileName(shuffleId, bucketId, fileId)
           blockManager.diskBlockManager.getFile(filename)
         }
-        val fileGroup = new ShuffleFileGroup(fileId, shuffleId, files)
+        val fileGroup = new ShuffleFileGroup(shuffleId, fileId, files)
         shuffleState.allFileGroups.add(fileGroup)
         fileGroup
       }

From a5224079286d1777864cf9fa77330aadae10cd7b Mon Sep 17 00:00:00 2001
From: Tathagata Das <tathagata.das1565@gmail.com>
Date: Wed, 3 Sep 2014 17:38:01 -0700
Subject: [PATCH 189/489] [SPARK-2419][Streaming][Docs] Updates to the
 streaming programming guide

Updated the main streaming programming guide, and also added source-specific guides for Kafka, Flume, Kinesis.

Author: Tathagata Das <tathagata.das1565@gmail.com>
Author: Jacek Laskowski <jacek@japila.pl>

Closes #2254 from tdas/streaming-doc-fix and squashes the following commits:

e45c6d7 [Jacek Laskowski] More fixes from an old PR
5125316 [Tathagata Das] Fixed links
dc02f26 [Tathagata Das] Refactored streaming kinesis guide and made many other changes.
acbc3e3 [Tathagata Das] Fixed links between streaming guides.
cb7007f [Tathagata Das] Added Streaming + Flume integration guide.
9bd9407 [Tathagata Das] Updated streaming programming guide with additional information from SPARK-2419.
---
 docs/streaming-flume-integration.md   | 132 +++++++
 docs/streaming-kafka-integration.md   |  42 +++
 docs/streaming-kinesis-integration.md | 110 ++++++
 docs/streaming-kinesis.md             |  59 ---
 docs/streaming-programming-guide.md   | 518 +++++++++++++++++---------
 5 files changed, 622 insertions(+), 239 deletions(-)
 create mode 100644 docs/streaming-flume-integration.md
 create mode 100644 docs/streaming-kafka-integration.md
 create mode 100644 docs/streaming-kinesis-integration.md
 delete mode 100644 docs/streaming-kinesis.md

diff --git a/docs/streaming-flume-integration.md b/docs/streaming-flume-integration.md
new file mode 100644
index 0000000000000..d57c3e0ef9ba0
--- /dev/null
+++ b/docs/streaming-flume-integration.md
@@ -0,0 +1,132 @@
+---
+layout: global
+title: Spark Streaming + Flume Integration Guide
+---
+
+[Apache Flume](https://flume.apache.org/) is a distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of log data. Here we explain how to configure Flume and Spark Streaming to receive data from Flume. There are two approaches to this.
+
+## Approach 1: Flume-style Push-based Approach
+Flume is designed to push data between Flume agents. In this approach, Spark Streaming essentially sets up a receiver that acts an Avro agent for Flume, to which Flume can push the data. Here are the configuration steps.
+
+#### General Requirements
+Choose a machine in your cluster such that
+
+- When your Flume + Spark Streaming application is launched, one of the Spark workers must run on that machine.
+
+- Flume can be configured to push data to a port on that machine.
+
+Due to the push model, the streaming application needs to be up, with the receiver scheduled and listening on the chosen port, for Flume to be able push data.
+
+#### Configuring Flume
+Configure Flume agent to send data to an Avro sink by having the following in the configuration file.
+
+	agent.sinks = avroSink
+	agent.sinks.avroSink.type = avro
+    agent.sinks.avroSink.channel = memoryChannel
+    agent.sinks.avroSink.hostname = <chosen machine's hostname>
+	agent.sinks.avroSink.port = <chosen port on the machine>
+
+See the [Flume's documentation](https://flume.apache.org/documentation.html) for more information about
+configuring Flume agents.
+
+#### Configuring Spark Streaming Application
+1. **Linking:** In your SBT/Maven projrect definition, link your streaming application against the following artifact (see [Linking section](streaming-programming-guide.html#linking) in the main programming guide for further information).
+
+		groupId = org.apache.spark
+		artifactId = spark-streaming-flume_{{site.SCALA_BINARY_VERSION}}
+		version = {{site.SPARK_VERSION_SHORT}}
+
+2. **Programming:** In the streaming application code, import `FlumeUtils` and create input DStream as follows.
+
+	<div class="codetabs">
+	<div data-lang="scala" markdown="1">
+		import org.apache.spark.streaming.flume._
+
+		val flumeStream = FlumeUtils.createStream(streamingContext, [chosen machine's hostname], [chosen port])
+
+	See the [API docs](api/scala/index.html#org.apache.spark.streaming.flume.FlumeUtils$)
+	and the [example]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/scala/org/apache/spark/examples/streaming/FlumeEventCount.scala).
+	</div>
+	<div data-lang="java" markdown="1">
+		import org.apache.spark.streaming.flume.*;
+
+		JavaReceiverInputDStream<SparkFlumeEvent> flumeStream =
+        	FlumeUtils.createStream(streamingContext, [chosen machine's hostname], [chosen port]);
+
+	See the [API docs](api/java/index.html?org/apache/spark/streaming/flume/FlumeUtils.html)
+	and the [example]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/java/org/apache/spark/examples/streaming/JavaFlumeEventCount.java).
+	</div>
+	</div>
+
+	Note that the hostname should be the same as the one used by the resource manager in the
+    cluster (Mesos, YARN or Spark Standalone), so that resource allocation can match the names and launch
+    the receiver in the right machine.
+
+3. **Deploying:** Package `spark-streaming-flume_{{site.SCALA_BINARY_VERSION}}` and its dependencies (except `spark-core_{{site.SCALA_BINARY_VERSION}}` and `spark-streaming_{{site.SCALA_BINARY_VERSION}}` which are provided by `spark-submit`) into the application JAR. Then use `spark-submit` to launch your application (see [Deploying section](streaming-programming-guide.html#deploying-applications) in the main programming guide).
+
+## Approach 2 (Experimental): Pull-based Approach using a Custom Sink
+Instead of Flume pushing data directly to Spark Streaming, this approach runs a custom Flume sink that allows the following.
+- Flume pushes data into the sink, and the data stays buffered.
+- Spark Streaming uses transactions to pull data from the sink. Transactions succeed only after data is received and replicated by Spark Streaming.
+This ensures that better reliability and fault-tolerance than the previous approach. However, this requires configuring Flume to run a custom sink. Here are the configuration steps.
+
+#### General Requirements
+Choose a machine that will run the custom sink in a Flume agent. The rest of the Flume pipeline is configured to send data to that agent. Machines in the Spark cluster should have access to the chosen machine running the custom sink.
+
+#### Configuring Flume
+Configuring Flume on the chosen machine requires the following two steps.
+
+1. **Sink JARs**: Add the following JARs to Flume's classpath (see [Flume's documentation](https://flume.apache.org/documentation.html) to see how) in the machine designated to run the custom sink .
+
+	(i) *Custom sink JAR*: Download the JAR corresponding to the following artifact (or [direct link](http://search.maven.org/remotecontent?filepath=org/apache/spark/spark-streaming-flume-sink_{{site.SCALA_BINARY_VERSION}}/{{site.SPARK_VERSION_SHORT}}/spark-streaming-flume-sink_{{site.SCALA_BINARY_VERSION}}-{{site.SPARK_VERSION_SHORT}}.jar)).
+
+		groupId = org.apache.spark
+		artifactId = spark-streaming-flume-sink_{{site.SCALA_BINARY_VERSION}}
+		version = {{site.SPARK_VERSION_SHORT}}
+
+	(ii) *Scala library JAR*: Download the Scala library JAR for Scala {{site.SCALA_VERSION}}. It can be found with the following artifact detail (or, [direct link](http://search.maven.org/remotecontent?filepath=org/scala-lang/scala-library/{{site.SCALA_VERSION}}/scala-library-{{site.SCALA_VERSION}}.jar)).
+
+		groupId = org.scala-lang
+		artifactId = scala-library
+		version = {{site.SCALA_VERSION}}
+
+2. **Configuration file**: On that machine, configure Flume agent to send data to an Avro sink by having the following in the configuration file.
+
+		agent.sinks = spark
+		agent.sinks.spark.type = org.apache.spark.streaming.flume.sink.SparkSink
+		agent.sinks.spark.hostname = <hostname of the local machine>
+		agent.sinks.spark.port = <port to listen on for connection from Spark>
+		agent.sinks.spark.channel = memoryChannel
+
+	Also make sure that the upstream Flume pipeline is configured to send the data to the Flume agent running this sink.
+
+See the [Flume's documentation](https://flume.apache.org/documentation.html) for more information about
+configuring Flume agents.
+
+#### Configuring Spark Streaming Application
+1. **Linking:** In your SBT/Maven projrect definition, link your streaming application against the `spark-streaming-flume_{{site.SCALA_BINARY_VERSION}}` (see [Linking section](streaming-programming-guide.html#linking) in the main programming guide).
+
+2. **Programming:** In the streaming application code, import `FlumeUtils` and create input DStream as follows.
+
+	<div class="codetabs">
+	<div data-lang="scala" markdown="1">
+		import org.apache.spark.streaming.flume._
+
+		val flumeStream = FlumeUtils.createPollingStream(streamingContext, [sink machine hostname], [sink port])
+	</div>
+	<div data-lang="java" markdown="1">
+		import org.apache.spark.streaming.flume.*;
+
+		JavaReceiverInputDStream<SparkFlumeEvent>flumeStream =
+			FlumeUtils.createPollingStream(streamingContext, [sink machine hostname], [sink port]);
+	</div>
+	</div>
+
+	See the Scala example [FlumePollingEventCount]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/scala/org/apache/spark/examples/streaming/FlumePollingEventCount.scala).
+
+	Note that each input DStream can be configured to receive data from multiple sinks.
+
+3. **Deploying:** Package `spark-streaming-flume_{{site.SCALA_BINARY_VERSION}}` and its dependencies (except `spark-core_{{site.SCALA_BINARY_VERSION}}` and `spark-streaming_{{site.SCALA_BINARY_VERSION}}` which are provided by `spark-submit`) into the application JAR. Then use `spark-submit` to launch your application (see [Deploying section](streaming-programming-guide.html#deploying-applications) in the main programming guide).
+
+
+
diff --git a/docs/streaming-kafka-integration.md b/docs/streaming-kafka-integration.md
new file mode 100644
index 0000000000000..a3b705d4c31d0
--- /dev/null
+++ b/docs/streaming-kafka-integration.md
@@ -0,0 +1,42 @@
+---
+layout: global
+title: Spark Streaming + Kafka Integration Guide
+---
+[Apache Kafka](http://kafka.apache.org/) is publish-subscribe messaging rethought as a distributed, partitioned, replicated commit log service.  Here we explain how to configure Spark Streaming to receive data from Kafka.
+
+1. **Linking:** In your SBT/Maven projrect definition, link your streaming application against the following artifact (see [Linking section](streaming-programming-guide.html#linking) in the main programming guide for further information).
+
+		groupId = org.apache.spark
+		artifactId = spark-streaming-kafka_{{site.SCALA_BINARY_VERSION}}
+		version = {{site.SPARK_VERSION_SHORT}}
+
+2. **Programming:** In the streaming application code, import `KafkaUtils` and create input DStream as follows.
+
+	<div class="codetabs">
+	<div data-lang="scala" markdown="1">
+		import org.apache.spark.streaming.kafka._
+
+		val kafkaStream = KafkaUtils.createStream(
+        	streamingContext, [zookeeperQuorum], [group id of the consumer], [per-topic number of Kafka partitions to consume])
+
+	See the [API docs](api/scala/index.html#org.apache.spark.streaming.kafka.KafkaUtils$)
+	and the [example]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/scala/org/apache/spark/examples/streaming/KafkaWordCount.scala).
+	</div>
+	<div data-lang="java" markdown="1">
+		import org.apache.spark.streaming.kafka.*;
+
+		JavaPairReceiverInputDStream<String, String> kafkaStream = KafkaUtils.createStream(
+        	streamingContext, [zookeeperQuorum], [group id of the consumer], [per-topic number of Kafka partitions to consume]);
+
+	See the [API docs](api/java/index.html?org/apache/spark/streaming/kafka/KafkaUtils.html)
+	and the [example]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/java/org/apache/spark/examples/streaming/JavaKafkaWordCount.java).
+	</div>
+	</div>
+
+	*Points to remember:*
+
+	- Topic partitions in Kafka does not correlate to partitions of RDDs generated in Spark Streaming. So increasing the number of topic-specific partitions in the `KafkaUtils.createStream()` only increases the number of threads using which topics that are consumed within a single receiver. It does not increase the parallelism of Spark in processing the data. Refer to the main document for more information on that.
+
+	- Multiple Kafka input DStreams can be created with different groups and topics for parallel receiving of data using multiple receivers.
+
+3. **Deploying:** Package `spark-streaming-kafka_{{site.SCALA_BINARY_VERSION}}` and its dependencies (except `spark-core_{{site.SCALA_BINARY_VERSION}}` and `spark-streaming_{{site.SCALA_BINARY_VERSION}}` which are provided by `spark-submit`) into the application JAR. Then use `spark-submit` to launch your application (see [Deploying section](streaming-programming-guide.html#deploying-applications) in the main programming guide).
diff --git a/docs/streaming-kinesis-integration.md b/docs/streaming-kinesis-integration.md
new file mode 100644
index 0000000000000..079d4c5550537
--- /dev/null
+++ b/docs/streaming-kinesis-integration.md
@@ -0,0 +1,110 @@
+---
+layout: global
+title: Spark Streaming + Kinesis Integration
+---
+[Amazon Kinesis](http://aws.amazon.com/kinesis/) is a fully managed service for real-time processing of streaming data at massive scale.
+The Kinesis input DStream and receiver uses the Kinesis Client Library (KCL) provided by Amazon under the Amazon Software License (ASL).
+The KCL builds on top of the Apache 2.0 licensed AWS Java SDK and provides load-balancing, fault-tolerance, checkpointing through the concept of Workers, Checkpoints, and Shard Leases.
+Here we explain how to configure Spark Streaming to receive data from Kinesis.
+
+#### Configuring Kinesis
+
+A Kinesis stream can be set up at one of the valid Kinesis endpoints with 1 or more shards per the following
+[guide](http://docs.aws.amazon.com/kinesis/latest/dev/step-one-create-stream.html).
+
+
+#### Configuring Spark Streaming Application
+
+1. **Linking:** In your SBT/Maven projrect definition, link your streaming application against the following artifact (see [Linking section](streaming-programming-guide.html#linking) in the main programming guide for further information).
+
+		groupId = org.apache.spark
+		artifactId = spark-streaming-kinesis-asl_{{site.SCALA_BINARY_VERSION}}
+		version = {{site.SPARK_VERSION_SHORT}}
+
+	**Note that by linking to this library, you will include [ASL](https://aws.amazon.com/asl/)-licensed code in your application.**
+
+2. **Programming:** In the streaming application code, import `KinesisUtils` and create input DStream as follows.
+
+	<div class="codetabs">
+	<div data-lang="scala" markdown="1">
+		import org.apache.spark.streaming.kinesis._
+		import com.amazonaws.services.kinesis.clientlibrary.lib.worker.InitialPositionInStream
+
+		val kinesisStream = KinesisUtils.createStream(
+        	streamingContext, [Kinesis stream name], [endpoint URL], [checkpoint interval], [initial position])
+
+	See the [API docs](api/scala/index.html#org.apache.spark.streaming.kinesis.KinesisUtils$)
+	and the [example]({{site.SPARK_GITHUB_URL}}/tree/master/extras/kinesis-asl/src/main/scala/org/apache/spark/examples/streaming/KinesisWordCountASL.scala). Refer to the next subsection for instructions to run the example.
+
+	</div>
+	<div data-lang="java" markdown="1">
+		import org.apache.spark.streaming.flume.*;
+
+		JavaReceiverInputDStream<byte[]> kinesisStream = KinesisUtils.createStream(
+        	streamingContext, [Kinesis stream name], [endpoint URL], [checkpoint interval], [initial position]);
+
+	See the [API docs](api/java/index.html?org/apache/spark/streaming/kinesis/KinesisUtils.html)
+	and the [example]({{site.SPARK_GITHUB_URL}}/tree/master/extras/kinesis-asl/src/main/java/org/apache/spark/examples/streaming/JavaKinesisWordCountASL.java). Refer to the next subsection for instructions to run the example.
+
+	</div>
+	</div>
+
+	`[endpoint URL]`: Valid Kinesis endpoints URL can be found [here](http://docs.aws.amazon.com/general/latest/gr/rande.html#ak_region).
+
+	`[checkpoint interval]`: The interval at which the Kinesis client library is going to save its position in the stream. For starters, set it to the same as the batch interval of the streaming application.
+
+	`[initial position]`: Can be either `InitialPositionInStream.TRIM_HORIZON` or `InitialPositionInStream.LATEST` (see later section and Amazon Kinesis API documentation for more details).
+
+	*Points to remember:*
+
+	- The name used in the context of the streaming application must be unique for a given account and region. Changing the app name or stream name could lead to Kinesis errors as only a single logical application can process a single stream.
+	- A single Kinesis input DStream can receive many Kinesis shards by spinning up multiple KinesisRecordProcessor threads. Note that there is no correlation between number of shards in Kinesis and the number of partitions in the generated RDDs that is used for processing the data.
+	- You never need more KinesisReceivers than the number of shards in your stream as each will spin up at least one KinesisRecordProcessor thread.
+	- Horizontal scaling is achieved by autoscaling additional Kinesis input DStreams (separate processes) up to the number of current shards for a given stream, of course.
+
+3. **Deploying:** Package `spark-streaming-flume_{{site.SCALA_BINARY_VERSION}}` and its dependencies (except `spark-core_{{site.SCALA_BINARY_VERSION}}` and `spark-streaming_{{site.SCALA_BINARY_VERSION}}` which are provided by `spark-submit`) into the application JAR. Then use `spark-submit` to launch your application (see [Deploying section](streaming-programming-guide.html#deploying-applications) in the main programming guide).
+
+    - A DynamoDB table and CloudWatch namespace are created during KCL initialization using this Kinesis application name.  This DynamoDB table lives in the us-east-1 region regardless of the Kinesis endpoint URL. It is used to store KCL's checkpoint information.
+
+    - If you are seeing errors after changing the app name or stream name, it may be necessary to manually delete the DynamoDB table and start from scratch.
+
+#### Running the Example
+To run the example,
+- Download Spark source and follow the [instructions](building-with-maven.html) to build Spark with profile *-Pkinesis-asl*.
+
+    mvn -Pkinesis-asl -DskipTests clean package
+
+- Set up Kinesis stream (see earlier section). Note the name of the Kinesis stream, and the endpoint URL corresponding to the region the stream is based on.
+
+- Set up the environment variables AWS_ACCESS_KEY_ID and AWS_SECRET_KEY with your AWS credentials.
+
+- In the Spark root directory, run the example as
+	<div class="codetabs">
+	<div data-lang="scala" markdown="1">
+
+    	bin/run-example streaming.KinesisWordCountASL [Kinesis stream name] [endpoint URL]
+
+	</div>
+	<div data-lang="java" markdown="1">
+
+        bin/run-example streaming.JavaKinesisWordCountASL [Kinesis stream name] [endpoint URL]
+
+	</div>
+	</div>
+
+    This will wait for data to be received from Kinesis.
+
+- To generate random string data, in another terminal, run the associated Kinesis data producer.
+
+		bin/run-example streaming.KinesisWordCountProducerASL [Kinesis stream name] [endpoint URL] 1000 10
+
+	This will push random words to the Kinesis stream, which should then be received and processed by the running example.
+
+#### Kinesis Checkpointing
+The Kinesis receiver checkpoints the position of the stream that has been read periodically, so that the system can recover from failures and continue processing where it had left off. Checkpointing too frequently will cause excess load on the AWS checkpoint storage layer and may lead to AWS throttling.  The provided example handles this throttling with a random-backoff-retry strategy.
+
+- If no Kinesis checkpoint info exists, the KinesisReceiver will start either from the oldest record available (InitialPositionInStream.TRIM_HORIZON) or from the latest tip (InitialPostitionInStream.LATEST).  This is configurable.
+
+- InitialPositionInStream.LATEST could lead to missed records if data is added to the stream while no KinesisReceivers are running (and no checkpoint info is being stored). In production, you'll want to switch to InitialPositionInStream.TRIM_HORIZON which will read up to 24 hours (Kinesis limit) of previous stream data.
+
+- InitialPositionInStream.TRIM_HORIZON may lead to duplicate processing of records where the impact is dependent on checkpoint frequency.
diff --git a/docs/streaming-kinesis.md b/docs/streaming-kinesis.md
deleted file mode 100644
index 16ad3222105a2..0000000000000
--- a/docs/streaming-kinesis.md
+++ /dev/null
@@ -1,59 +0,0 @@
----
-layout: global
-title: Spark Streaming Kinesis Receiver
----
-
-## Kinesis
-###Design
-<li>The KinesisReceiver uses the Kinesis Client Library (KCL) provided by Amazon under the Amazon Software License.</li>
-<li>The KCL builds on top of the Apache 2.0 licensed AWS Java SDK and provides load-balancing, fault-tolerance, checkpointing through the concept of Workers, Checkpoints, and Shard Leases.</li>
-<li>The KCL uses DynamoDB to maintain all state.  A DynamoDB table is created in the us-east-1 region (regardless of Kinesis stream region) during KCL initialization for each Kinesis application name.</li>
-<li>A single KinesisReceiver can process many shards of a stream by spinning up multiple KinesisRecordProcessor threads.</li>
-<li>You never need more KinesisReceivers than the number of shards in your stream as each will spin up at least one KinesisRecordProcessor thread.</li>
-<li>Horizontal scaling is achieved by autoscaling additional KinesisReceiver (separate processes) or spinning up new KinesisRecordProcessor threads within each KinesisReceiver - up to the number of current shards for a given stream, of course.  Don't forget to autoscale back down!</li>
-
-### Build
-<li>Spark supports a Streaming KinesisReceiver, but it is not included in the default build due to Amazon Software Licensing (ASL) restrictions.</li>
-<li>To build with the Kinesis Streaming Receiver and supporting ASL-licensed code, you must run the maven or sbt builds with the **-Pkinesis-asl** profile.</li>
-<li>All KinesisReceiver-related code, examples, tests, and artifacts live in **$SPARK_HOME/extras/kinesis-asl/**.</li>
-<li>Kinesis-based Spark Applications will need to link to the **spark-streaming-kinesis-asl** artifact that is built when **-Pkinesis-asl** is specified.</li>
-<li>_**Note that by linking to this library, you will include [ASL](https://aws.amazon.com/asl/)-licensed code in your Spark package**_.</li>
-
-###Example
-<li>To build the Kinesis example, you must run the maven or sbt builds with the **-Pkinesis-asl** profile.</li>
-<li>You need to setup a Kinesis stream at one of the valid Kinesis endpoints with 1 or more shards per the following:  http://docs.aws.amazon.com/kinesis/latest/dev/step-one-create-stream.html</li>
-<li>Valid Kinesis endpoints can be found here:  http://docs.aws.amazon.com/general/latest/gr/rande.html#ak_region</li>
-<li>When running **locally**, the example automatically determines the number of threads and KinesisReceivers to spin up based on the number of shards configured for the stream.  Therefore, **local[n]** is not needed when starting the example as with other streaming examples.</li>
-<li>While this example could use a single KinesisReceiver which spins up multiple KinesisRecordProcessor threads to process multiple shards, I wanted to demonstrate unioning multiple KinesisReceivers as a single DStream.  (It's a bit confusing in local mode.)</li>
-<li>**KinesisWordCountProducerASL** is provided to generate random records into the Kinesis stream for testing.</li>
-<li>The example has been configured to immediately replicate incoming stream data to another node by using (StorageLevel.MEMORY_AND_DISK_2)
-<li>Spark checkpointing is disabled because the example does not use any stateful or window-based DStream operations such as updateStateByKey and reduceByWindow.  If those operations are introduced, you would need to enable checkpointing or risk losing data in the case of a failure.</li>
-<li>Kinesis checkpointing is enabled.  This means that the example will recover from a Kinesis failure.</li>
-<li>The example uses InitialPositionInStream.LATEST strategy to pull from the latest tip of the stream if no Kinesis checkpoint info exists.</li>
-<li>In our example, **KinesisWordCount** is the Kinesis application name for both the Scala and Java versions.  The use of this application name is described next.</li>
-
-###Deployment and Runtime
-<li>A Kinesis application name must be unique for a given account and region.</li>
-<li>A DynamoDB table and CloudWatch namespace are created during KCL initialization using this Kinesis application name.  http://docs.aws.amazon.com/kinesis/latest/dev/kinesis-record-processor-implementation-app.html#kinesis-record-processor-initialization</li>
-<li>This DynamoDB table lives in the us-east-1 region regardless of the Kinesis endpoint URL.</li>
-<li>Changing the app name or stream name could lead to Kinesis errors as only a single logical application can process a single stream.</li>
-<li>If you are seeing errors after changing the app name or stream name, it may be necessary to manually delete the DynamoDB table and start from scratch.</li>
-<li>The Kinesis libraries must be present on all worker nodes, as they will need access to the KCL.</li>
-<li>The KinesisReceiver uses the DefaultAWSCredentialsProviderChain for AWS credentials which  searches for credentials in the following order of precedence:</br>
-1) Environment Variables - AWS_ACCESS_KEY_ID and AWS_SECRET_KEY<br/>
-2) Java System Properties - aws.accessKeyId and aws.secretKey<br/>
-3) Credential profiles file - default location (~/.aws/credentials) shared by all AWS SDKs<br/>
-4) Instance profile credentials - delivered through the Amazon EC2 metadata service
-</li>
-
-###Fault-Tolerance
-<li>The combination of Spark Streaming and Kinesis creates 2 different checkpoints that may occur at different intervals.</li>
-<li>Checkpointing too frequently against Kinesis will cause excess load on the AWS checkpoint storage layer and may lead to AWS throttling.  The provided example handles this throttling with a random backoff retry strategy.</li>
-<li>Upon startup, a KinesisReceiver will begin processing records with sequence numbers greater than the last Kinesis checkpoint sequence number recorded per shard (stored in the DynamoDB table).</li>
-<li>If no Kinesis checkpoint info exists, the KinesisReceiver will start either from the oldest record available (InitialPositionInStream.TRIM_HORIZON) or from the latest tip (InitialPostitionInStream.LATEST).  This is configurable.</li>
-<li>InitialPositionInStream.LATEST could lead to missed records if data is added to the stream while no KinesisReceivers are running (and no checkpoint info is being stored.)</li>
-<li>In production, you'll want to switch to InitialPositionInStream.TRIM_HORIZON which will read up to 24 hours (Kinesis limit) of previous stream data.</li>
-<li>InitialPositionInStream.TRIM_HORIZON may lead to duplicate processing of records where the impact is dependent on checkpoint frequency.</li>
-<li>Record processing should be idempotent when possible.</li>
-<li>A failed or latent KinesisRecordProcessor within the KinesisReceiver will be detected and automatically restarted by the KCL.</li>
-<li>If possible, the KinesisReceiver should be shutdown cleanly in order to trigger a final checkpoint of all KinesisRecordProcessors to avoid duplicate record processing.</li>
\ No newline at end of file
diff --git a/docs/streaming-programming-guide.md b/docs/streaming-programming-guide.md
index 9f331ed50d2a4..3d4bce49666ed 100644
--- a/docs/streaming-programming-guide.md
+++ b/docs/streaming-programming-guide.md
@@ -7,12 +7,12 @@ title: Spark Streaming Programming Guide
 {:toc}
 
 # Overview
-Spark Streaming is an extension of the core Spark API that allows enables high-throughput,
+Spark Streaming is an extension of the core Spark API that allows enables scalable, high-throughput,
 fault-tolerant stream processing of live data streams. Data can be ingested from many sources
 like Kafka, Flume, Twitter, ZeroMQ, Kinesis or plain old TCP sockets and be processed using complex
 algorithms expressed with high-level functions like `map`, `reduce`, `join` and `window`.
 Finally, processed data can be pushed out to filesystems, databases,
-and live dashboards. In fact, you can apply Spark's in-built
+and live dashboards. In fact, you can apply Spark's
 [machine learning](mllib-guide.html) algorithms, and
 [graph processing](graphx-programming-guide.html) algorithms on data streams.
 
@@ -60,35 +60,24 @@ do is as follows.
 <div data-lang="scala"  markdown="1" >
 First, we import the names of the Spark Streaming classes, and some implicit
 conversions from StreamingContext into our environment, to add useful methods to
-other classes we need (like DStream).
-
-[StreamingContext](api/scala/index.html#org.apache.spark.streaming.StreamingContext) is the
-main entry point for all streaming functionality.
+other classes we need (like DStream). [StreamingContext](api/scala/index.html#org.apache.spark.streaming.StreamingContext) is the
+main entry point for all streaming functionality. We create a local StreamingContext with two execution threads,  and batch interval of 1 second.
 
 {% highlight scala %}
+import org.apache.spark._
 import org.apache.spark.streaming._
 import org.apache.spark.streaming.StreamingContext._
-{% endhighlight %}
-
-Then we create a
-[StreamingContext](api/scala/index.html#org.apache.spark.streaming.StreamingContext) object.
-Besides Spark's configuration, we specify that any DStream will be processed
-in 1 second batches.
 
-{% highlight scala %}
-import org.apache.spark.api.java.function._
-import org.apache.spark.streaming._
-import org.apache.spark.streaming.api._
-// Create a StreamingContext with a local master
-// Spark Streaming needs at least two working thread
-val ssc = new StreamingContext("local[2]", "NetworkWordCount", Seconds(1))
+// Create a local StreamingContext with two working thread and batch interval of 1 second
+val conf = new SparkConf().setMaster("local[2]").setAppName("NetworkWordCount")
+val ssc = new StreamingContext(conf, Seconds(1))
 {% endhighlight %}
 
-Using this context, we then create a new DStream
-by specifying the IP address and port of the data server.
+Using this context, we can create a DStream that represents streaming data from a TCP
+source hostname, e.g. `localhost`, and port, e.g. `9999`
 
 {% highlight scala %}
-// Create a DStream that will connect to serverIP:serverPort, like localhost:9999
+// Create a DStream that will connect to hostname:port, like localhost:9999
 val lines = ssc.socketTextStream("localhost", 9999)
 {% endhighlight %}
 
@@ -112,7 +101,7 @@ import org.apache.spark.streaming.StreamingContext._
 val pairs = words.map(word => (word, 1))
 val wordCounts = pairs.reduceByKey(_ + _)
 
-// Print a few of the counts to the console
+// Print the first ten elements of each RDD generated in this DStream to the console
 wordCounts.print()
 {% endhighlight %}
 
@@ -139,23 +128,25 @@ The complete code can be found in the Spark Streaming example
 First, we create a
 [JavaStreamingContext](api/java/index.html?org/apache/spark/streaming/api/java/JavaStreamingContext.html) object,
 which is the main entry point for all streaming
-functionality. Besides Spark's configuration, we specify that any DStream would be processed
-in 1 second batches.
+functionality. We create a local StreamingContext with two execution threads, and a batch interval of 1 second.
 
 {% highlight java %}
+import org.apache.spark.*;
 import org.apache.spark.api.java.function.*;
 import org.apache.spark.streaming.*;
 import org.apache.spark.streaming.api.java.*;
 import scala.Tuple2;
-// Create a StreamingContext with a local master
-JavaStreamingContext jssc = new JavaStreamingContext("local[2]", "JavaNetworkWordCount", new Duration(1000))
+
+// Create a local StreamingContext with two working thread and batch interval of 1 second
+val conf = new SparkConf().setMaster("local[2]").setAppName("NetworkWordCount")
+JavaStreamingContext jssc = new JavaStreamingContext(conf, new Duration(1000))
 {% endhighlight %}
 
-Using this context, we then create a new DStream
-by specifying the IP address and port of the data server.
+Using this context, we can create a DStream that represents streaming data from a TCP
+source hostname, e.g. `localhost`, and port, e.g. `9999`
 
 {% highlight java %}
-// Create a DStream that will connect to serverIP:serverPort, like localhost:9999
+// Create a DStream that will connect to hostname:port, like localhost:9999
 JavaReceiverInputDStream<String> lines = jssc.socketTextStream("localhost", 9999);
 {% endhighlight %}
 
@@ -197,7 +188,9 @@ JavaPairDStream<String, Integer> wordCounts = pairs.reduceByKey(
       return i1 + i2;
     }
   });
-wordCounts.print();     // Print a few of the counts to the console
+
+// Print the first ten elements of each RDD generated in this DStream to the console
+wordCounts.print();
 {% endhighlight %}
 
 The `words` DStream is further mapped (one-to-one transformation) to a DStream of `(word,
@@ -207,8 +200,8 @@ using a [Function2](api/scala/index.html#org.apache.spark.api.java.function.Func
 Finally, `wordCounts.print()` will print a few of the counts generated every second.
 
 Note that when these lines are executed, Spark Streaming only sets up the computation it
-will perform when it is started, and no real processing has started yet. To start the processing
-after all the transformations have been setup, we finally call
+will perform after it is started, and no real processing has started yet. To start the processing
+after all the transformations have been setup, we finally call `start` method.
 
 {% highlight java %}
 jssc.start();              // Start the computation
@@ -235,12 +228,12 @@ Then, in a different terminal, you can start the example by using
 <div class="codetabs">
 <div data-lang="scala" markdown="1">
 {% highlight bash %}
-$ ./bin/run-example org.apache.spark.examples.streaming.NetworkWordCount localhost 9999
+$ ./bin/run-example streaming.NetworkWordCount localhost 9999
 {% endhighlight %}
 </div>
 <div data-lang="java" markdown="1">
 {% highlight bash %}
-$ ./bin/run-example org.apache.spark.examples.streaming.JavaNetworkWordCount localhost 9999
+$ ./bin/run-example JavaNetworkWordCount localhost 9999
 {% endhighlight %}
 </div>
 </div>
@@ -281,25 +274,11 @@ Time: 1357008430000 ms
     </td>
 </table>
 
-You can also use Spark Streaming directly from the Spark shell:
-
-{% highlight bash %}
-$ bin/spark-shell
-{% endhighlight %}
-
-... and create your StreamingContext by wrapping the existing interactive shell
-SparkContext object, `sc`:
-
-{% highlight scala %}
-val ssc = new StreamingContext(sc, Seconds(1))
-{% endhighlight %}
 
-When working with the shell, you may also need to send a `^D` to your netcat session
-to force the pipeline to print the word counts to the console at the sink.
-
-***************************************************************************************************  
+***************************************************************************************************
+***************************************************************************************************
 
-# Basics
+# Basic Concepts
 
 Next, we move beyond the simple example and elaborate on the basics of Spark Streaming that you
 need to know to write your streaming applications.
@@ -319,68 +298,120 @@ Streaming core
 artifact `spark-streaming-xyz_{{site.SCALA_BINARY_VERSION}}` to the dependencies. For example,
 some of the common ones are as follows.
 
-
 <table class="table">
 <tr><th>Source</th><th>Artifact</th></tr>
 <tr><td> Kafka </td><td> spark-streaming-kafka_{{site.SCALA_BINARY_VERSION}} </td></tr>
 <tr><td> Flume </td><td> spark-streaming-flume_{{site.SCALA_BINARY_VERSION}} </td></tr>
+<tr><td> Kinesis<br/></td><td>spark-streaming-kinesis-asl_{{site.SCALA_BINARY_VERSION}} </td></tr>
 <tr><td> Twitter </td><td> spark-streaming-twitter_{{site.SCALA_BINARY_VERSION}} </td></tr>
 <tr><td> ZeroMQ </td><td> spark-streaming-zeromq_{{site.SCALA_BINARY_VERSION}} </td></tr>
 <tr><td> MQTT </td><td> spark-streaming-mqtt_{{site.SCALA_BINARY_VERSION}} </td></tr>
-<tr><td> Kinesis<br/>(built separately)</td><td> kinesis-asl_{{site.SCALA_BINARY_VERSION}} </td></tr>
-<tr><td> </td><td></td></tr>
+<tr><td></td><td></td></tr>
 </table>
 
 For an up-to-date list, please refer to the
-[Apache repository](http://search.maven.org/#search%7Cga%7C1%7Cg%3A%22org.apache.spark%22%20AND%20v%3A%22{{site.SPARK_VERSION}}%22)
+[Apache repository](http://search.maven.org/#search%7Cga%7C1%7Cg%3A%22org.apache.spark%22%20AND%20v%3A%22{{site.SPARK_VERSION_SHORT}}%22)
 for the full list of supported sources and artifacts.
 
-## Initializing
+***
+
+## Initializing StreamingContext
+
+To initialize a Spark Streaming program, a **StreamingContext** object has to be created which is the main entry point of all Spark Streaming functionality.
 
 <div class="codetabs">
 <div data-lang="scala" markdown="1">
 
-To initialize a Spark Streaming program in Scala, a
-[`StreamingContext`](api/scala/index.html#org.apache.spark.streaming.StreamingContext)
-object has to be created, which is the main entry point of all Spark Streaming functionality.
-A `StreamingContext` object can be created by using
+A [StreamingContext](api/scala/index.html#org.apache.spark.streaming.StreamingContext) object can be created from a [SparkConf](api/scala/index.html#org.apache.spark.SparkConf) object.
 
 {% highlight scala %}
-new StreamingContext(master, appName, batchDuration, [sparkHome], [jars])
+import org.apache.spark._
+import org.apache.spark.streaming._
+
+val conf = new SparkConf().setAppName(appName).setMaster(master)
+val ssc = new StreamingContext(conf, Seconds(1))
 {% endhighlight %}
-</div>
-<div data-lang="java" markdown="1">
 
-To initialize a Spark Streaming program in Java, a
-[`JavaStreamingContext`](api/scala/index.html#org.apache.spark.streaming.api.java.JavaStreamingContext)
-object has to be created, which is the main entry point of all Spark Streaming functionality.
-A `JavaStreamingContext` object can be created by using
+The `appName` parameter is a name for your application to show on the cluster UI.
+`master` is a [Spark, Mesos or YARN cluster URL](submitting-applications.html#master-urls),
+or a special __"local[\*]"__ string to run in local mode. In practice, when running on a cluster, 
+you will not want to hardcode `master` in the program,
+but rather [launch the application with `spark-submit`](submitting-applications.html) and
+receive it there. However, for local testing and unit tests, you can pass "local[\*]" to run Spark Streaming
+in-process (detects the number of cores in the local system). Note that this internally creates a [SparkContext](api/scala/index.html#org.apache.spark.SparkContext) (starting point of all Spark functionality) which can be accessed as `ssc.sparkContext`.
+
+The batch interval must be set based on the latency requirements of your application
+and available cluster resources. See the [Performance Tuning](#setting-the-right-batch-size)
+section for more details.
+
+A `StreamingContext` object can also be created from an existing `SparkContext` object.
 
 {% highlight scala %}
-new JavaStreamingContext(master, appName, batchInterval, [sparkHome], [jars])
+import org.apache.spark.streaming._
+
+val sc = ...                // existing SparkContext
+val ssc = new StreamingContext(sc, Seconds(1))
 {% endhighlight %}
+
+
 </div>
-</div>
+<div data-lang="java" markdown="1">
 
-The `master` parameter is a standard [Spark cluster URL](programming-guide.html#master-urls)
-and can be "local" for local testing. The `appName` is a name of your program,
-which will be shown on your cluster's web UI. The `batchInterval` is the size of the batches,
-as explained earlier. Finally, the last two parameters are needed to deploy your code to a cluster
- if running in distributed mode, as described in the
- [Spark programming guide](programming-guide.html#deploying-code-on-a-cluster).
- Additionally, the underlying SparkContext can be accessed as
-`ssc.sparkContext`.
+A [JavaStreamingContext](api/java/index.html?org/apache/spark/streaming/api/java/JavaStreamingContext.html) object can be created from a [SparkConf](api/java/index.html?org/apache/spark/SparkConf.html) object.
+
+{% highlight java %}
+import org.apache.spark.*;
+import org.apache.spark.streaming.api.java.*;
+
+SparkConf conf = new SparkConf().setAppName(appName).setMaster(master);
+JavaStreamingContext ssc = new JavaStreamingContext(conf, Duration(1000));
+{% endhighlight %}
+
+The `appName` parameter is a name for your application to show on the cluster UI.
+`master` is a [Spark, Mesos or YARN cluster URL](submitting-applications.html#master-urls),
+or a special __"local[\*]"__ string to run in local mode. In practice, when running on a cluster, 
+you will not want to hardcode `master` in the program,
+but rather [launch the application with `spark-submit`](submitting-applications.html) and
+receive it there. However, for local testing and unit tests, you can pass "local[*]" to run Spark Streaming
+in-process. Note that this internally creates a [JavaSparkContext](api/java/index.html?org/apache/spark/api/java/JavaSparkContext.html) (starting point of all Spark functionality) which can be accessed as `ssc.sparkContext`. 
 
 The batch interval must be set based on the latency requirements of your application
 and available cluster resources. See the [Performance Tuning](#setting-the-right-batch-size)
 section for more details.
 
-## DStreams
-*Discretized Stream* or *DStream* is the basic abstraction provided by Spark Streaming.
+A `JavaStreamingContext` object can also be created from an existing `JavaSparkContext`.
+
+{% highlight java %}
+import org.apache.spark.streaming.api.java.*;
+
+JavaSparkContext sc = ...   //existing JavaSparkContext
+JavaStreamingContext ssc = new JavaStreamingContext(sc, new Duration(1000));
+{% endhighlight %} 
+</div>
+</div>
+
+After a context is defined, you have to do the follow steps.
+1. Define the input sources.
+1. Setup the streaming computations.
+1. Start the receiving and procesing of data using `streamingContext.start()`.
+1. The processing will continue until `streamingContext.stop()` is called.
+
+##### Points to remember:
+{:.no_toc}
+- Once a context has been started, no new streaming computations can be setup or added to it.
+- Once a context has been stopped, it cannot be started (that is, re-used) again.
+- Only one StreamingContext can be active in a JVM at the same time.
+- stop() on StreamingContext also stops the SparkContext. To stop only the StreamingContext, set optional parameter of `stop()` called `stopSparkContext` to false.
+- A SparkContext can be re-used to create multiple StreamingContexts, as long as the previous StreamingContext is stopped (without stopping the SparkContext) before the next StreamingContext is created.
+
+***
+
+## Discretized Streams (DStreams)
+**Discretized Stream** or **DStream** is the basic abstraction provided by Spark Streaming.
 It represents a continuous stream of data, either the input data stream received from source,
 or the processed data stream generated by transforming the input stream. Internally,
-it is represented by a continuous sequence of RDDs, which is Spark's abstraction of an immutable,
-distributed dataset. Each RDD in a DStream contains data from a certain interval,
+a DStream is represented by a continuous series of RDDs, which is Spark's abstraction of an immutable,
+distributed dataset (see [Spark Programming Guide](programming-guide.html#resilient-distributed-datasets-rdds) for more details). Each RDD in a DStream contains data from a certain interval,
 as shown in the following figure.
 
 <p style="text-align: center;">
@@ -392,8 +423,8 @@ as shown in the following figure.
 
 Any operation applied on a DStream translates to operations on the underlying RDDs. For example,
 in the [earlier example](#a-quick-example) of converting a stream of lines to words,
-the `flatmap` operation is applied on each RDD in the `lines` DStream to generate the RDDs of the
- `words` DStream. This is shown the following figure.
+the `flatMap` operation is applied on each RDD in the `lines` DStream to generate the RDDs of the
+ `words` DStream. This is shown in the following figure.
 
 <p style="text-align: center;">
   <img src="img/streaming-dstream-ops.png"
@@ -404,83 +435,117 @@ the `flatmap` operation is applied on each RDD in the `lines` DStream to generat
 
 
 These underlying RDD transformations are computed by the Spark engine. The DStream operations
-hide most of these details and provides the developer with higher-level API for convenience.
+hide most of these details and provide the developer with higher-level API for convenience.
 These operations are discussed in detail in later sections.
 
-## Input Sources
+***
+
+## Input DStreams
+Input DStreams are DStreams representing the stream of raw data received from streaming sources.
+Spark Streaming has two categories of streaming sources.
+
+- *Basic sources*: Sources directly available in the StreamingContext API. Example: file systems, socket connections, and Akka actors.
+- *Advanced sources*: Sources like Kafka, Flume, Kinesis, Twitter, etc. are available through extra utility classes. These require linking against extra dependencies as discussed in the [linking](#linking) section.
+
+Every input DStream (except file stream) is associated with a single [Receiver](api/scala/index.html#org.apache.spark.streaming.receiver.Receiver) object which receives the data from a source and stores it in Spark's memory for processing. A receiver is run within a Spark worker/executor as a long-running task, hence it occupies one of the cores allocated to the Spark Streaming application. Hence, it is important to remember that Spark Streaming application needs to be allocated enough cores to process the received data, as well as, to run the receiver(s). Therefore, few important points to remember are:
+
+##### Points to remember:
+{:.no_toc}
+
+- If the number of cores allocated to the application is less than or equal to the number of input DStreams / receivers, then the system will receive data, but not be able to process them.
+- When running locally, if you master URL is set to "local", then there is only one core to run tasks.  That is insufficient for programs with even one input DStream (file streams are okay) as the receiver will occupy that core and there will be no core left to process the data.
+
+### Basic Sources
+{:.no_toc}
 
 We have already taken a look at the `ssc.socketTextStream(...)` in the [quick
 example](#a-quick-example) which creates a DStream from text
-data received over a TCP socket connection. Besides sockets, the core Spark Streaming API provides
+data received over a TCP socket connection. Besides sockets, the StreamingContext API provides
 methods for creating DStreams from files and Akka actors as input sources.
 
-Specifically, for files, the DStream can be created as
+- **File Streams:** For reading data from files on any file system compatible with the HDFS API (that is, HDFS, S3, NFS, etc.), a DStream can be created as
 
-<div class="codetabs">
-<div data-lang="scala">
-{% highlight scala %}
-ssc.fileStream(dataDirectory)
-{% endhighlight %}
-</div>
-<div data-lang="java">
-{% highlight java %}
-jssc.fileStream(dataDirectory);
-{% endhighlight %}
-</div>
-</div>
+    <div class="codetabs">
+    <div data-lang="scala" markdown="1">
+		streamingContext.fileStream[keyClass, valueClass, inputFormatClass](dataDirectory)
+    </div>
+    <div data-lang="java" markdown="1">
+		streamingContext.fileStream<keyClass, valueClass, inputFormatClass>(dataDirectory);
+    </div>
+    </div>
 
-Spark Streaming will monitor the directory `dataDirectory` for any Hadoop-compatible filesystem
-and process any files created in that directory. Note that
+	Spark Streaming will monitor the directory `dataDirectory` and process any files created in that directory (files written in nested directories not supported). Note that
 
- * The files must have the same data format.
- * The files must be created in the `dataDirectory` by atomically *moving* or *renaming* them into
- the data directory.
- * Once moved the files must not be changed.
+     + The files must have the same data format.
+     + The files must be created in the `dataDirectory` by atomically *moving* or *renaming* them into
+     the data directory.
+     + Once moved, the files must not be changed. So if the files are being continuously appended, the new data will not be read.
 
-For more details on streams from files, Akka actors and sockets,
+	For simple text files, there is an easier method `streamingContext.textFileStream(dataDirectory)`. And file streams do not require running a receiver, hence does not require allocating cores.
+
+- **Streams based on Custom Actors:** DStreams can be created with data streams received through Akka actors by using `streamingContext.actorStream(actorProps, actor-name)`. See the [Custom Receiver Guide](#implementing-and-using-a-custom-actor-based-receiver) for more details.
+
+- **Queue of RDDs as a Stream:** For testing a Spark Streaming application with test data, one can also create a DStream based on a queue of RDDs, using `streamingContext.queueStream(queueOfRDDs)`. Each RDD pushed into the queue will be treated as a batch of data in the DStream, and processed like a stream.
+
+For more details on streams from sockets, files, and actors,
 see the API documentations of the relevant functions in
 [StreamingContext](api/scala/index.html#org.apache.spark.streaming.StreamingContext) for
-Scala and [JavaStreamingContext](api/scala/index.html#org.apache.spark.streaming.api.java.JavaStreamingContext)
- for Java.
+Scala and [JavaStreamingContext](api/java/index.html?org/apache/spark/streaming/api/java/JavaStreamingContext.html) for Java.
+
+### Advanced Sources
+{:.no_toc}
+This category of sources require interfacing with external non-Spark libraries, some of them with complex dependencies (e.g., Kafka and Flume). Hence, to minimize issues related to version conflicts of dependencies, the functionality to create DStreams from these sources have been moved to separate libraries, that can be [linked to](#linking) explicitly as necessary.  For example, if you want to create a DStream using data from Twitter's stream of tweets, you have to do the following.
 
-Additional functionality for creating DStreams from sources such as Kafka, Flume, Kinesis, and Twitter
-can be imported by adding the right dependencies as explained in an
-[earlier](#linking) section. To take the
-case of Kafka, after adding the artifact `spark-streaming-kafka_{{site.SCALA_BINARY_VERSION}}` to the
-project dependencies, you can create a DStream from Kafka as
+1. *Linking*: Add the artifact `spark-streaming-twitter_{{site.SCALA_BINARY_VERSION}}` to the SBT/Maven project dependencies.
+1. *Programming*: Import the `TwitterUtils` class and create a DStream with `TwitterUtils.createStream` as shown below.
+1. *Deploying*: Generate an uber JAR with all the dependencies (including the dependency `spark-streaming-twitter_{{site.SCALA_BINARY_VERSION}}` and its transitive dependencies) and then deploy the application. This is further explained in the [Deploying section](#deploying-applications).
 
 <div class="codetabs">
 <div data-lang="scala">
 {% highlight scala %}
-import org.apache.spark.streaming.kafka._
-KafkaUtils.createStream(ssc, kafkaParams, ...)
+import org.apache.spark.streaming.twitter._
+
+TwitterUtils.createStream(ssc)
 {% endhighlight %}
 </div>
 <div data-lang="java">
 {% highlight java %}
-import org.apache.spark.streaming.kafka.*;
-KafkaUtils.createStream(jssc, kafkaParams, ...);
+import org.apache.spark.streaming.twitter.*;
+
+TwitterUtils.createStream(jssc);
 {% endhighlight %}
 </div>
 </div>
 
-For more details on these additional sources, see the corresponding [API documentation](#where-to-go-from-here).
-Furthermore, you can also implement your own custom receiver for your sources. See the
-[Custom Receiver Guide](streaming-custom-receivers.html).
+Note that these advanced sources are not available in the `spark-shell`, hence applications based on these
+advanced sources cannot be tested in the shell.
+
+Some of these advanced sources are as follows.
+
+- **Twitter:** Spark Streaming's TwitterUtils uses Twitter4j 3.0.3 to get the public stream of tweets using
+    [Twitter's Streaming API](https://dev.twitter.com/docs/streaming-apis). Authentication information
+    can be provided by any of the [methods](http://twitter4j.org/en/configuration.html) supported by
+    Twitter4J library. You can either get the public stream, or get the filtered stream based on a
+    keywords. See the API documentation ([Scala](api/scala/index.html#org.apache.spark.streaming.twitter.TwitterUtils$), [Java](api/java/index.html?org/apache/spark/streaming/twitter/TwitterUtils.html)) and examples ([TwitterPopularTags]({{site.SPARK_GITHUB_URL}}/blob/master/examples/src/main/scala/org/apache/spark/examples/streaming/TwitterPopularTags.scala) and
+    [TwitterAlgebirdCMS]({{site.SPARK_GITHUB_URL}}/blob/master/examples/src/main/scala/org/apache/spark/examples/streaming/TwitterAlgebirdCMS.scala)).
+
+- **Flume:** Spark Streaming {{site.SPARK_VERSION_SHORT}} can received data from Flume 1.4.0. See the [Flume Integration Guide](streaming-flume-integration.html) for more details.
 
-### Kinesis
-[Kinesis](streaming-kinesis.html)
+- **Kafka:** Spark Streaming {{site.SPARK_VERSION_SHORT}} can receive data from Kafka 0.8.0. See the [Kafka Integration Guide](streaming-kafka-integration.html) for more details.
 
-## Operations
-There are two kinds of DStream operations - _transformations_ and _output operations_. Similar to
-RDD transformations, DStream transformations operate on one or more DStreams to create new DStreams
-with transformed data. After applying a sequence of transformations to the input streams, output
-operations need to called, which write data out to an external data sink, such as a filesystem or a
-database.
+- **Kinesis:** See the [Kinesis Integration Guide](streaming-kinesis-integration.html) for more details.
 
-### Transformations
-DStreams support many of the transformations available on normal Spark RDD's. Some of the
-common ones are as follows.
+### Custom Sources
+{:.no_toc}
+Input DStreams can also be created out of custom data sources. All you have to do is implement an user-defined **receiver** (see next section to understand what that is) that can receive data from the custom sources and push it into Spark. See the
+[Custom Receiver Guide](streaming-custom-receivers.html) for details.
+
+***
+
+## Transformations on DStreams
+Similar to that of RDDs, transformations allow the data from the input DStream to be modified.
+DStreams support many of the transformations available on normal Spark RDD's. 
+Some of the common ones are as follows.
 
 <table class="table">
 <tr><th style="width:25%">Transformation</th><th>Meaning</th></tr>
@@ -557,8 +622,8 @@ common ones are as follows.
 
 The last two transformations are worth highlighting again.
 
-<h4>UpdateStateByKey Operation</h4>
-
+#### UpdateStateByKey Operation
+{:.no_toc}
 The `updateStateByKey` operation allows you to maintain arbitrary state while continuously updating
 it with new information. To use this, you will have to do two steps.
 
@@ -616,8 +681,8 @@ the `(word, 1)` pairs) and the `runningCount` having the previous count. For the
 Scala code, take a look at the example
 [StatefulNetworkWordCount]({{site.SPARK_GITHUB_URL}}/blob/master/examples/src/main/scala/org/apache/spark/examples/streaming/StatefulNetworkWordCount.scala).
 
-<h4>Transform Operation</h4>
-
+#### Transform Operation
+{:.no_toc}
 The `transform` operation (along with its variations like `transformWith`) allows
 arbitrary RDD-to-RDD functions to be applied on a DStream. It can be used to apply any RDD
 operation that is not exposed in the DStream API.
@@ -662,8 +727,8 @@ JavaPairDStream<String, Integer> cleanedDStream = wordCounts.transform(
 In fact, you can also use [machine learning](mllib-guide.html) and
 [graph computation](graphx-programming-guide.html) algorithms in the `transform` method.
 
-<h4>Window Operations</h4>
-
+#### Window Operations
+{:.no_toc}
 Finally, Spark Streaming also provides *windowed computations*, which allow you to apply
 transformations over a sliding window of data. This following figure illustrates this sliding
 window.
@@ -678,11 +743,11 @@ window.
 As shown in the figure, every time the window *slides* over a source DStream,
 the source RDDs that fall within the window are combined and operated upon to produce the
 RDDs of the windowed DStream. In this specific case, the operation is applied over last 3 time
-units of data, and slides by 2 time units. This shows that any window-based operation needs to
+units of data, and slides by 2 time units. This shows that any window operation needs to
 specify two parameters.
 
  * <i>window length</i> - The duration of the window (3 in the figure)
- * <i>slide interval</i> - The interval at which the window-based operation is performed (2 in
+ * <i>sliding interval</i> - The interval at which the window operation is performed (2 in
  the figure).
 
 These two parameters must be multiples of the batch interval of the source DStream (1 in the
@@ -720,7 +785,7 @@ JavaPairDStream<String, Integer> windowedWordCounts = pairs.reduceByKeyAndWindow
 </div>
 </div>
 
-Some of the common window-based operations are as follows. All of these operations take the
+Some of the common window operations are as follows. All of these operations take the
 said two parameters - <i>windowLength</i> and <i>slideInterval</i>.
 
 <table class="table">
@@ -778,21 +843,27 @@ said two parameters - <i>windowLength</i> and <i>slideInterval</i>.
 <tr><td></td><td></td></tr>
 </table>
 
-### Output Operations
-When an output operator is called, it triggers the computation of a stream. Currently the following
-output operators are defined:
+
+The complete list of DStream transformations is available in the API documentation. For the Scala API,
+see [DStream](api/scala/index.html#org.apache.spark.streaming.dstream.DStream)
+and [PairDStreamFunctions](api/scala/index.html#org.apache.spark.streaming.dstream.PairDStreamFunctions).
+For the Java API, see [JavaDStream](api/java/index.html?org/apache/spark/streaming/api/java/JavaDStream.html)
+and [JavaPairDStream](api/java/index.html?org/apache/spark/streaming/api/java/JavaPairDStream.html).
+
+***
+
+## Output Operations on DStreams
+Output operations allow DStream's data to be pushed out external systems like a database or a file systems.
+Since the output operations actually allow the transformed data to be consumed by external systems,
+they trigger the actual execution of all the DStream transformations (similar to actions for RDDs). 
+Currently, the following output operations are defined:
 
 <table class="table">
 <tr><th style="width:30%">Output Operation</th><th>Meaning</th></tr>
 <tr>
   <td> <b>print</b>() </td>
-  <td> Prints first ten elements of every batch of data in a DStream on the driver. </td>
-</tr>
-<tr>
-  <td> <b>foreachRDD</b>(<i>func</i>) </td>
-  <td> The fundamental output operator. Applies a function, <i>func</i>, to each RDD generated from
-  the stream. This function should have side effects, such as printing output, saving the RDD to
-  external files, or writing it over the network to an external system. </td>
+  <td> Prints first ten elements of every batch of data in a DStream on the driver. 
+  This is useful for development and debugging. </td>
 </tr>
 <tr>
   <td> <b>saveAsObjectFiles</b>(<i>prefix</i>, [<i>suffix</i>]) </td>
@@ -811,17 +882,84 @@ output operators are defined:
   <td> Save this DStream's contents as a Hadoop file. The file name at each batch interval is
   generated based on <i>prefix</i> and <i>suffix</i>: <i>"prefix-TIME_IN_MS[.suffix]"</i>. </td>
 </tr>
+<tr>
+  <td> <b>foreachRDD</b>(<i>func</i>) </td>
+  <td> The most generic output operator that applies a function, <i>func</i>, to each RDD generated from
+  the stream. This function should push the data in each RDD to a external system, like saving the RDD to
+  files, or writing it over the network to a database. Note that the function <i>func</i> is executed
+  at the driver, and will usually have RDD actions in it that will force the computation of the streaming RDDs.</td>
+</tr>
 <tr><td></td><td></td></tr>
 </table>
 
+### Design Patterns for using foreachRDD
+{:.no_toc}
+`dstream.foreachRDD` is a powerful primitive that allows data to sent out to external systems.
+However, it is important to understand how to use this primitive correctly and efficiently.
+Some of the common mistakes to avoid are as follows.
 
-The complete list of DStream operations is available in the API documentation. For the Scala API,
-see [DStream](api/scala/index.html#org.apache.spark.streaming.dstream.DStream)
-and [PairDStreamFunctions](api/scala/index.html#org.apache.spark.streaming.dstream.PairDStreamFunctions).
-For the Java API, see [JavaDStream](api/java/index.html?org/apache/spark/streaming/api/java/JavaDStream.html)
-and [JavaPairDStream](api/java/index.html?org/apache/spark/streaming/api/java/JavaPairDStream.html).
+- Often writing data to external system requires creating a connection object
+(e.g. TCP connection to a remote server) and using it to send data to a remote system.
+For this purpose, a developer may inadvertantly try creating a connection object at
+the Spark driver, but try to use it in a Spark worker to save records in the RDDs.
+For example (in Scala),
+
+        dstream.foreachRDD(rdd => {
+            val connection = createNewConnection()  // executed at the driver
+            rdd.foreach(record => {
+                connection.send(record) // executed at the worker
+            })
+        })
+
+	This is incorrect as this requires the connection object to be serialized and sent from the driver to the worker. Such connection objects are rarely transferrable across machines. This error may manifest as serialization errors (connection object not serializable), initialization errors (connection object needs to be initialized at the workers), etc. The correct solution is to create the connection object at the worker.
+
+- However, this can lead to another common mistake - creating a new connection for every record. For example,
+
+        dstream.foreachRDD(rdd => {
+            rdd.foreach(record => {
+                val connection = createNewConnection()
+                connection.send(record)
+                connection.close()
+            })
+        })
+
+	Typically, creating a connection object has time and resource overheads. Therefore, creating and destroying a connection object for each record can incur unnecessarily high overheads and can significantly reduce the overall throughput of the system. A better solution is to use `rdd.foreachPartition` - create a single connection object and send all the records in a RDD partition using that connection.
+
+        dstream.foreachRDD(rdd => {
+            rdd.foreachPartition(partitionOfRecords => {
+                val connection = createNewConnection()
+                partitionOfRecords.foreach(record => connection.send(record))
+                connection.close()
+            })
+        })
+
+    This amortizes the connection creation overheads over many records.
+
+- Finally, this can be further optimized by reusing connection objects across multiple RDDs/batches.
+	One can maintain a static pool of connection objects than can be reused as
+    RDDs of multiple batches are pushed to the external system, thus further reducing the overheads.
 
-## Persistence
+        dstream.foreachRDD(rdd => {
+            rdd.foreachPartition(partitionOfRecords => {
+                // ConnectionPool is a static, lazily initialized pool of connections
+                val connection = ConnectionPool.getConnection()
+                partitionOfRecords.foreach(record => connection.send(record))
+                ConnectionPool.returnConnection(connection)  // return to the pool for future reuse
+            })
+        })
+
+    Note that the connections in the pool should be lazily created on demand and timed out if not used for a while. This achieves the most efficient sending of data to external systems.
+
+
+##### Other points to remember:
+{:.no_toc}
+- DStreams are executed lazily by the output operations, just like RDDs are lazily executed by RDD actions. Specifically, RDD actions inside the DStream output operations force the processing of the received data. Hence, if your application does not have any output operation, or has output operations like `dstream.foreachRDD()` without any RDD action inside them, then nothing will get executed. The system will simply receive the data and discard it.
+
+- By default, output operations are executed one-at-a-time. And they are executed in the order they are defined in the application.
+
+***
+
+## Caching / Persistence
 Similar to RDDs, DStreams also allow developers to persist the stream's data in memory. That is,
 using `persist()` method on a DStream would automatically persist every RDD of that DStream in
 memory. This is useful if the data in the DStream will be computed multiple times (e.g., multiple
@@ -838,7 +976,9 @@ memory. This is further discussed in the [Performance Tuning](#memory-tuning) se
 information on different persistence levels can be found in
 [Spark Programming Guide](programming-guide.html#rdd-persistence).
 
-## RDD Checkpointing
+***
+
+## Checkpointing
 A _stateful operation_ is one which operates over multiple batches of data. This includes all
 window-based operations and the `updateStateByKey` operation. Since stateful operations have a
 dependency on previous batches of data, they continuously accumulate metadata over time.
@@ -867,10 +1007,19 @@ For DStreams that must be checkpointed (that is, DStreams created by `updateStat
 `reduceByKeyAndWindow` with inverse function), the checkpoint interval of the DStream is by
 default set to a multiple of the DStream's sliding interval such that its at least 10 seconds.
 
-## Deployment
+***
+
+## Deploying Applications
 A Spark Streaming application is deployed on a cluster in the same way as any other Spark application.
 Please refer to the [deployment guide](cluster-overview.html) for more details.
 
+Note that the applications
+that use [advanced sources](#advanced-sources) (e.g. Kafka, Flume, Twitter) are also required to package the
+extra artifact they link to, along with their dependencies, in the JAR that is used to deploy the application.
+For example, an application using `TwitterUtils` will have to include
+`spark-streaming-twitter_{{site.SCALA_BINARY_VERSION}}` and all its transitive
+dependencies in the application JAR.
+
 If a running Spark Streaming application needs to be upgraded (with new application code), then
 there are two possible mechanism.
 
@@ -889,7 +1038,9 @@ application left off. Note that this can be done only with input sources that su
 (like Kafka, and Flume) as data needs to be buffered while the previous application down and
 the upgraded application is not yet up.
 
-## Monitoring
+***
+
+## Monitoring Applications
 Beyond Spark's [monitoring capabilities](monitoring.html), there are additional capabilities
 specific to Spark Streaming. When a StreamingContext is used, the
 [Spark web UI](monitoring.html#web-interfaces) shows
@@ -912,22 +1063,18 @@ The progress of a Spark Streaming program can also be monitored using the
 which allows you to get receiver status and processing times. Note that this is a developer API
 and it is likely to be improved upon (i.e., more information reported) in the future.
 
-***************************************************************************************************  
+***************************************************************************************************
+***************************************************************************************************
 
 # Performance Tuning
 Getting the best performance of a Spark Streaming application on a cluster requires a bit of
 tuning. This section explains a number of the parameters and configurations that can tuned to
 improve the performance of you application. At a high level, you need to consider two things:
 
-<ol>
-<li>
-  Reducing the processing time of each batch of data by efficiently using cluster resources.
-</li>
-<li>
-  Setting the right batch size such that the batches of data can be processed as fast as they
-  are received (that is, data processing keeps up with the data ingestion).
-</li>
-</ol>
+1. Reducing the processing time of each batch of data by efficiently using cluster resources.
+
+2. Setting the right batch size such that the batches of data can be processed as fast as they
+  	are received (that is, data processing keeps up with the data ingestion).
 
 ## Reducing the Processing Time of each Batch
 There are a number of optimizations that can be done in Spark to minimize the processing time of
@@ -935,6 +1082,7 @@ each batch. These have been discussed in detail in [Tuning Guide](tuning.html).
 highlights some of the most important ones.
 
 ### Level of Parallelism in Data Receiving
+{:.no_toc}
 Receiving data over the network (like Kafka, Flume, socket, etc.) requires the data to deserialized
 and stored in Spark. If the data receiving becomes a bottleneck in the system, then consider
 parallelizing the data receiving. Note that each input DStream
@@ -958,6 +1106,7 @@ This distributes the received batches of data across specified number of machine
 before further processing.
 
 ### Level of Parallelism in Data Processing
+{:.no_toc}
 Cluster resources maybe under-utilized if the number of parallel tasks used in any stage of the
 computation is not high enough. For example, for distributed reduce operations like `reduceByKey`
 and `reduceByKeyAndWindow`, the default number of parallel tasks is decided by the [config property]
@@ -968,6 +1117,7 @@ documentation), or set the [config property](configuration.html#spark-properties
 `spark.default.parallelism` to change the default.
 
 ### Data Serialization
+{:.no_toc}
 The overhead of data serialization can be significant, especially when sub-second batch sizes are
  to be achieved. There are two aspects to it.
 
@@ -980,6 +1130,7 @@ The overhead of data serialization can be significant, especially when sub-secon
   serialization format. Hence, the deserialization overhead of input data may be a bottleneck.
 
 ### Task Launching Overheads
+{:.no_toc}
 If the number of tasks launched per second is high (say, 50 or more per second), then the overhead
 of sending out tasks to the slaves maybe significant and will make it hard to achieve sub-second
 latencies. The overhead can be reduced by the following changes:
@@ -994,6 +1145,8 @@ latencies. The overhead can be reduced by the following changes:
 These changes may reduce batch processing time by 100s of milliseconds,
 thus allowing sub-second batch size to be viable.
 
+***
+
 ## Setting the Right Batch Size
 For a Spark Streaming application running on a cluster to be stable, the system should be able to
 process data as fast as it is being received. In other words, batches of data should be processed
@@ -1022,6 +1175,8 @@ data rate and/or reducing the batch size. Note that momentary increase in the de
 temporary data rate increases maybe fine as long as the delay reduces back to a low value
 (i.e., less than batch size).
 
+***
+
 ## Memory Tuning
 Tuning the memory usage and GC behavior of Spark applications have been discussed in great detail
 in the [Tuning Guide](tuning.html). It is recommended that you read that. In this section,
@@ -1037,7 +1192,7 @@ Even though keeping the data serialized incurs higher serialization/deserializat
 it significantly reduces GC pauses.
 
 * **Clearing persistent RDDs**: By default, all persistent RDDs generated by Spark Streaming will
- be cleared from memory based on Spark's in-built policy (LRU). If `spark.cleaner.ttl` is set,
+ be cleared from memory based on Spark's built-in policy (LRU). If `spark.cleaner.ttl` is set,
  then persistent RDDs that are older than that value are periodically cleared. As mentioned
  [earlier](#operation), this needs to be careful set based on operations used in the Spark
  Streaming program. However, a smarter unpersisting of RDDs can be enabled by setting the
@@ -1051,7 +1206,8 @@ minimizes the variability of GC pauses. Even though concurrent GC is known to re
 overall processing throughput of the system, its use is still recommended to achieve more
 consistent batch processing times.
 
-***************************************************************************************************  
+***************************************************************************************************
+***************************************************************************************************
 
 # Fault-tolerance Properties
 In this section, we are going to discuss the behavior of Spark Streaming application in the event
@@ -1124,7 +1280,7 @@ def functionToCreateContext(): StreamingContext = {
     ssc
 }
 
-// Get StreaminContext from checkpoint data or create a new one
+// Get StreamingContext from checkpoint data or create a new one
 val context = StreamingContext.getOrCreate(checkpointDirectory, functionToCreateContext _)
 
 // Do additional setup on context that needs to be done,
@@ -1178,10 +1334,7 @@ context.awaitTermination();
 If the `checkpointDirectory` exists, then the context will be recreated from the checkpoint data.
 If the directory does not exist (i.e., running for the first time),
 then the function `contextFactory` will be called to create a new
-context and set up the DStreams. See the Scala example
-[JavaRecoverableWordCount]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/scala/org/apache/spark/examples/streaming/JavaRecoverableWordCount.scala)
-(note that this example is missing in the 0.9 release, so you can test it using the master branch).
-This example appends the word counts of network data into a file.
+context and set up the DStreams.
 
 You can also explicitly create a `JavaStreamingContext` from the checkpoint data and start
 the computation by using `new JavaStreamingContext(checkpointDirectory)`.
@@ -1208,7 +1361,8 @@ automatically restarted, and the word counts will cont
 For other deployment environments like Mesos and Yarn, you have to restart the driver through other
 mechanisms.
 
-<h4>Recovery Semantics</h4>
+#### Recovery Semantics
+{:.no_toc}
 
 There are two different failure behaviors based on which input sources are used.
 
@@ -1306,7 +1460,8 @@ in the file. This is what the sequence of outputs would be with and without a dr
 If the driver had crashed in the middle of the processing of time 3, then it will process time 3
 and output 30 after recovery.
 
-***************************************************************************************************  
+***************************************************************************************************
+***************************************************************************************************
 
 # Migration Guide from 0.9.1 or below to 1.x
 Between Spark 0.9.1 and Spark 1.0, there were a few API changes made to ensure future API stability.
@@ -1332,7 +1487,7 @@ replaced by [Receiver](api/scala/index.html#org.apache.spark.streaming.receiver.
 the following advantages.
 
 * Methods like `stop` and `restart` have been added to for better control of the lifecycle of a receiver. See
-the [custom receiver guide](streaming-custom-receiver.html) for more details.
+the [custom receiver guide](streaming-custom-receivers.html) for more details.
 * Custom receivers can be implemented using both Scala and Java.
 
 To migrate your existing custom receivers from the earlier NetworkReceiver to the new Receiver, you have
@@ -1356,6 +1511,7 @@ the `org.apache.spark.streaming.receivers` package were also moved
 to [`org.apache.spark.streaming.receiver`](api/scala/index.html#org.apache.spark.streaming.receiver.package)
 package and renamed for better clarity.
 
+***************************************************************************************************
 ***************************************************************************************************
 
 # Where to Go from Here
@@ -1366,6 +1522,7 @@ package and renamed for better clarity.
   [DStream](api/scala/index.html#org.apache.spark.streaming.dstream.DStream)
     * [KafkaUtils](api/scala/index.html#org.apache.spark.streaming.kafka.KafkaUtils$),
     [FlumeUtils](api/scala/index.html#org.apache.spark.streaming.flume.FlumeUtils$),
+    [KinesisUtils](api/scala/index.html#org.apache.spark.streaming.kinesis.KinesisUtils$),
     [TwitterUtils](api/scala/index.html#org.apache.spark.streaming.twitter.TwitterUtils$),
     [ZeroMQUtils](api/scala/index.html#org.apache.spark.streaming.zeromq.ZeroMQUtils$), and
     [MQTTUtils](api/scala/index.html#org.apache.spark.streaming.mqtt.MQTTUtils$)
@@ -1375,6 +1532,7 @@ package and renamed for better clarity.
     [PairJavaDStream](api/java/index.html?org/apache/spark/streaming/api/java/PairJavaDStream.html)
     * [KafkaUtils](api/java/index.html?org/apache/spark/streaming/kafka/KafkaUtils.html),
     [FlumeUtils](api/java/index.html?org/apache/spark/streaming/flume/FlumeUtils.html),
+    [KinesisUtils](api/java/index.html?org/apache/spark/streaming/kinesis/KinesisUtils.html)
     [TwitterUtils](api/java/index.html?org/apache/spark/streaming/twitter/TwitterUtils.html),
     [ZeroMQUtils](api/java/index.html?org/apache/spark/streaming/zeromq/ZeroMQUtils.html), and
     [MQTTUtils](api/java/index.html?org/apache/spark/streaming/mqtt/MQTTUtils.html)

From e08ea7393df46567f552aa67c60a690c231775e4 Mon Sep 17 00:00:00 2001
From: scwf <wangfei1@huawei.com>
Date: Wed, 3 Sep 2014 18:39:13 -0700
Subject: [PATCH 190/489] [SPARK-3303][core] fix
 SparkContextSchedulerCreationSuite test error

run test with the master branch with this command when mesos native lib is set
sbt/sbt -Phive "test-only org.apache.spark.SparkContextSchedulerCreationSuite"

get this error:
[info] SparkContextSchedulerCreationSuite:
[info] - bad-master
[info] - local
[info] - local-*
[info] - local-n
[info] - local--n-failures
[info] - local-n-failures
[info] - bad-local-n
[info] - bad-local-n-failures
[info] - local-default-parallelism
[info] - simr
[info] - local-cluster
[info] - yarn-cluster
[info] - yarn-standalone
[info] - yarn-client
[info] - mesos fine-grained
[info] - mesos coarse-grained ** FAILED ***
[info] Executor Spark home `spark.mesos.executor.home` is not set!

Since `executorSparkHome` only used in `createCommand`, move `val executorSparkHome...` to `createCommand` to fix this issue.

Author: scwf <wangfei1@huawei.com>
Author: wangfei <wangfei_hello@126.com>

Closes #2199 from scwf/SparkContextSchedulerCreationSuite and squashes the following commits:

ef1de22 [scwf] fix code fomate
19d26f3 [scwf] fix conflict
d9a8a60 [wangfei] fix SparkContextSchedulerCreationSuite test error
---
 .../cluster/mesos/CoarseMesosSchedulerBackend.scala    | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala
index da43ef567608c..64568409dbafd 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala
@@ -71,11 +71,6 @@ private[spark] class CoarseMesosSchedulerBackend(
   val taskIdToSlaveId = new HashMap[Int, String]
   val failuresBySlaveId = new HashMap[String, Int] // How many times tasks on each slave failed
 
-  val executorSparkHome = conf.getOption("spark.mesos.executor.home")
-    .orElse(sc.getSparkHome())
-    .getOrElse {
-      throw new SparkException("Executor Spark home `spark.mesos.executor.home` is not set!")
-    }
 
   val extraCoresPerSlave = conf.getInt("spark.mesos.extra.cores", 0)
 
@@ -112,6 +107,11 @@ private[spark] class CoarseMesosSchedulerBackend(
   }
 
   def createCommand(offer: Offer, numCores: Int): CommandInfo = {
+    val executorSparkHome = conf.getOption("spark.mesos.executor.home")
+      .orElse(sc.getSparkHome())
+      .getOrElse {
+        throw new SparkException("Executor Spark home `spark.mesos.executor.home` is not set!")
+      }
     val environment = Environment.newBuilder()
     val extraClassPath = conf.getOption("spark.executor.extraClassPath")
     extraClassPath.foreach { cp =>

From 4bba10c41acaf84a1c4a8e2db467c22f5ab7cbb9 Mon Sep 17 00:00:00 2001
From: Kousuke Saruta <sarutak@oss.nttdata.co.jp>
Date: Wed, 3 Sep 2014 18:42:01 -0700
Subject: [PATCH 191/489] [SPARK-3233] Executor never stop its SparnEnv,
 BlockManager, ConnectionManager etc.

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

Closes #2138 from sarutak/SPARK-3233 and squashes the following commits:

c0205b7 [Kousuke Saruta] Merge branch 'SPARK-3233' of github.com:sarutak/spark into SPARK-3233
064679d [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into SPARK-3233
d3005fd [Kousuke Saruta] Modified Class definition format of BlockManagerMaster
039b747 [Kousuke Saruta] Modified style
889e2d1 [Kousuke Saruta] Modified BlockManagerMaster to be able to be past isDriver flag
4da8535 [Kousuke Saruta] Modified BlockManagerMaster#stop to send StopBlockManagerMaster message when sender is Driver
6518c3a [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into SPARK-3233
d5ab19a [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into SPARK-3233
6bce25c [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into SPARK-3233
6058a58 [Kousuke Saruta] Modified Executor not to invoke SparkEnv#stop in local mode
e5ad9d3 [Kousuke Saruta] Modified Executor to stop SparnEnv at the end of itself
---
 core/src/main/scala/org/apache/spark/SparkEnv.scala       | 2 +-
 .../main/scala/org/apache/spark/executor/Executor.scala   | 3 +++
 .../org/apache/spark/storage/BlockManagerMaster.scala     | 8 ++++++--
 .../scala/org/apache/spark/storage/ThreadingTest.scala    | 2 +-
 .../org/apache/spark/scheduler/DAGSchedulerSuite.scala    | 2 +-
 .../org/apache/spark/storage/BlockManagerSuite.scala      | 2 +-
 6 files changed, 13 insertions(+), 6 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/SparkEnv.scala b/core/src/main/scala/org/apache/spark/SparkEnv.scala
index 72716567ca99b..2973d002cc428 100644
--- a/core/src/main/scala/org/apache/spark/SparkEnv.scala
+++ b/core/src/main/scala/org/apache/spark/SparkEnv.scala
@@ -225,7 +225,7 @@ object SparkEnv extends Logging {
 
     val blockManagerMaster = new BlockManagerMaster(registerOrLookup(
       "BlockManagerMaster",
-      new BlockManagerMasterActor(isLocal, conf, listenerBus)), conf)
+      new BlockManagerMasterActor(isLocal, conf, listenerBus)), conf, isDriver)
 
     val blockManager = new BlockManager(executorId, actorSystem, blockManagerMaster,
       serializer, conf, securityManager, mapOutputTracker, shuffleManager)
diff --git a/core/src/main/scala/org/apache/spark/executor/Executor.scala b/core/src/main/scala/org/apache/spark/executor/Executor.scala
index d7d19f6fa3b96..dd903dc65d204 100644
--- a/core/src/main/scala/org/apache/spark/executor/Executor.scala
+++ b/core/src/main/scala/org/apache/spark/executor/Executor.scala
@@ -123,6 +123,9 @@ private[spark] class Executor(
     env.metricsSystem.report()
     isStopped = true
     threadPool.shutdown()
+    if (!isLocal) {
+      env.stop()
+    }
   }
 
   class TaskRunner(
diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManagerMaster.scala b/core/src/main/scala/org/apache/spark/storage/BlockManagerMaster.scala
index e67b3dc5ce02e..2e262594b3538 100644
--- a/core/src/main/scala/org/apache/spark/storage/BlockManagerMaster.scala
+++ b/core/src/main/scala/org/apache/spark/storage/BlockManagerMaster.scala
@@ -27,7 +27,11 @@ import org.apache.spark.storage.BlockManagerMessages._
 import org.apache.spark.util.AkkaUtils
 
 private[spark]
-class BlockManagerMaster(var driverActor: ActorRef, conf: SparkConf) extends Logging {
+class BlockManagerMaster(
+    var driverActor: ActorRef,
+    conf: SparkConf,
+    isDriver: Boolean)
+  extends Logging {
   private val AKKA_RETRY_ATTEMPTS: Int = AkkaUtils.numRetries(conf)
   private val AKKA_RETRY_INTERVAL_MS: Int = AkkaUtils.retryWaitMs(conf)
 
@@ -196,7 +200,7 @@ class BlockManagerMaster(var driverActor: ActorRef, conf: SparkConf) extends Log
 
   /** Stop the driver actor, called only on the Spark driver node */
   def stop() {
-    if (driverActor != null) {
+    if (driverActor != null && isDriver) {
       tell(StopBlockManagerMaster)
       driverActor = null
       logInfo("BlockManagerMaster stopped")
diff --git a/core/src/main/scala/org/apache/spark/storage/ThreadingTest.scala b/core/src/main/scala/org/apache/spark/storage/ThreadingTest.scala
index aa83ea90ee9ee..7540f0d5e2a5a 100644
--- a/core/src/main/scala/org/apache/spark/storage/ThreadingTest.scala
+++ b/core/src/main/scala/org/apache/spark/storage/ThreadingTest.scala
@@ -99,7 +99,7 @@ private[spark] object ThreadingTest {
     val serializer = new KryoSerializer(conf)
     val blockManagerMaster = new BlockManagerMaster(
       actorSystem.actorOf(Props(new BlockManagerMasterActor(true, conf, new LiveListenerBus))),
-      conf)
+      conf, true)
     val blockManager = new BlockManager(
       "<driver>", actorSystem, blockManagerMaster, serializer, 1024 * 1024, conf,
       new SecurityManager(conf), new MapOutputTrackerMaster(conf), new HashShuffleManager(conf))
diff --git a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
index 1a42fc1b233ba..0bb91febde9d7 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
@@ -120,7 +120,7 @@ class DAGSchedulerSuite extends TestKit(ActorSystem("DAGSchedulerSuite")) with F
    */
   val cacheLocations = new HashMap[(Int, Int), Seq[BlockManagerId]]
   // stub out BlockManagerMaster.getLocations to use our cacheLocations
-  val blockManagerMaster = new BlockManagerMaster(null, conf) {
+  val blockManagerMaster = new BlockManagerMaster(null, conf, true) {
       override def getLocations(blockIds: Array[BlockId]): Seq[Seq[BlockManagerId]] = {
         blockIds.map {
           _.asRDDId.map(id => (id.rddId -> id.splitIndex)).flatMap(key => cacheLocations.get(key)).
diff --git a/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala b/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala
index 14ffadab99cae..c200654162268 100644
--- a/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala
@@ -93,7 +93,7 @@ class BlockManagerSuite extends FunSuite with Matchers with BeforeAndAfter
 
     master = new BlockManagerMaster(
       actorSystem.actorOf(Props(new BlockManagerMasterActor(true, conf, new LiveListenerBus))),
-      conf)
+      conf, true)
 
     val initialize = PrivateMethod[Unit]('initialize)
     SizeEstimator invokePrivate initialize()

From f48420fde58d554480cc8830d2f8c4d17618f283 Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian.cs.zju@gmail.com>
Date: Wed, 3 Sep 2014 18:57:20 -0700
Subject: [PATCH 192/489] [SPARK-2973][SQL] Lightweight SQL commands without
 distributed jobs when calling .collect()

By overriding `executeCollect()` in physical plan classes of all commands, we can avoid to kick off a distributed job when collecting result of a SQL command, e.g. `sql("SET").collect()`.

Previously, `Command.sideEffectResult` returns a `Seq[Any]`, and the `execute()` method in sub-classes of `Command` typically convert that to a `Seq[Row]` then parallelize it to an RDD. Now with this PR, `sideEffectResult` is required to return a `Seq[Row]` directly, so that `executeCollect()` can directly leverage that and be factored to the `Command` parent class.

Author: Cheng Lian <lian.cs.zju@gmail.com>

Closes #2215 from liancheng/lightweight-commands and squashes the following commits:

3fbef60 [Cheng Lian] Factored execute() method of physical commands to parent class Command
5a0e16c [Cheng Lian] Passes test suites
e0e12e9 [Cheng Lian] Refactored Command.sideEffectResult and Command.executeCollect
995bdd8 [Cheng Lian] Cleaned up DescribeHiveTableCommand
542977c [Cheng Lian] Avoids confusion between logical and physical plan by adding package prefixes
55b2aa5 [Cheng Lian] Avoids distributed jobs when execution SQL commands
---
 .../apache/spark/sql/execution/commands.scala | 63 +++++++------------
 .../apache/spark/sql/hive/HiveContext.scala   |  4 +-
 .../spark/sql/hive/HiveStrategies.scala       | 14 +++--
 .../execution/DescribeHiveTableCommand.scala  | 30 +++------
 .../sql/hive/execution/NativeCommand.scala    | 11 +---
 .../spark/sql/hive/execution/commands.scala   | 20 ++----
 6 files changed, 48 insertions(+), 94 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala
index 031b695169cea..286c6d264f86a 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala
@@ -21,11 +21,13 @@ import org.apache.spark.Logging
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.errors.TreeNodeException
-import org.apache.spark.sql.catalyst.expressions.{Attribute, GenericRow}
+import org.apache.spark.sql.catalyst.expressions.Attribute
 import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
 import org.apache.spark.sql.{Row, SQLConf, SQLContext}
 
 trait Command {
+  this: SparkPlan =>
+
   /**
    * A concrete command should override this lazy field to wrap up any side effects caused by the
    * command or any other computation that should be evaluated exactly once. The value of this field
@@ -35,7 +37,11 @@ trait Command {
    * The `execute()` method of all the physical command classes should reference `sideEffectResult`
    * so that the command can be executed eagerly right after the command query is created.
    */
-  protected[sql] lazy val sideEffectResult: Seq[Any] = Seq.empty[Any]
+  protected[sql] lazy val sideEffectResult: Seq[Row] = Seq.empty[Row]
+
+  override def executeCollect(): Array[Row] = sideEffectResult.toArray
+
+  override def execute(): RDD[Row] = sqlContext.sparkContext.parallelize(sideEffectResult, 1)
 }
 
 /**
@@ -47,17 +53,17 @@ case class SetCommand(
     @transient context: SQLContext)
   extends LeafNode with Command with Logging {
 
-  override protected[sql] lazy val sideEffectResult: Seq[String] = (key, value) match {
+  override protected[sql] lazy val sideEffectResult: Seq[Row] = (key, value) match {
     // Set value for key k.
     case (Some(k), Some(v)) =>
       if (k == SQLConf.Deprecated.MAPRED_REDUCE_TASKS) {
         logWarning(s"Property ${SQLConf.Deprecated.MAPRED_REDUCE_TASKS} is deprecated, " +
           s"automatically converted to ${SQLConf.SHUFFLE_PARTITIONS} instead.")
         context.setConf(SQLConf.SHUFFLE_PARTITIONS, v)
-        Array(s"${SQLConf.SHUFFLE_PARTITIONS}=$v")
+        Array(Row(s"${SQLConf.SHUFFLE_PARTITIONS}=$v"))
       } else {
         context.setConf(k, v)
-        Array(s"$k=$v")
+        Array(Row(s"$k=$v"))
       }
 
     // Query the value bound to key k.
@@ -73,28 +79,22 @@ case class SetCommand(
           "hive-0.12.0.jar").mkString(":")
 
         Array(
-          "system:java.class.path=" + hiveJars,
-          "system:sun.java.command=shark.SharkServer2")
-      }
-      else {
-        Array(s"$k=${context.getConf(k, "<undefined>")}")
+          Row("system:java.class.path=" + hiveJars),
+          Row("system:sun.java.command=shark.SharkServer2"))
+      } else {
+        Array(Row(s"$k=${context.getConf(k, "<undefined>")}"))
       }
 
     // Query all key-value pairs that are set in the SQLConf of the context.
     case (None, None) =>
       context.getAllConfs.map { case (k, v) =>
-        s"$k=$v"
+        Row(s"$k=$v")
       }.toSeq
 
     case _ =>
       throw new IllegalArgumentException()
   }
 
-  def execute(): RDD[Row] = {
-    val rows = sideEffectResult.map { line => new GenericRow(Array[Any](line)) }
-    context.sparkContext.parallelize(rows, 1)
-  }
-
   override def otherCopyArgs = context :: Nil
 }
 
@@ -113,19 +113,14 @@ case class ExplainCommand(
   extends LeafNode with Command {
 
   // Run through the optimizer to generate the physical plan.
-  override protected[sql] lazy val sideEffectResult: Seq[String] = try {
+  override protected[sql] lazy val sideEffectResult: Seq[Row] = try {
     // TODO in Hive, the "extended" ExplainCommand prints the AST as well, and detailed properties.
     val queryExecution = context.executePlan(logicalPlan)
     val outputString = if (extended) queryExecution.toString else queryExecution.simpleString
 
-    outputString.split("\n")
+    outputString.split("\n").map(Row(_))
   } catch { case cause: TreeNodeException[_] =>
-    ("Error occurred during query planning: \n" + cause.getMessage).split("\n")
-  }
-
-  def execute(): RDD[Row] = {
-    val explanation = sideEffectResult.map(row => new GenericRow(Array[Any](row)))
-    context.sparkContext.parallelize(explanation, 1)
+    ("Error occurred during query planning: \n" + cause.getMessage).split("\n").map(Row(_))
   }
 
   override def otherCopyArgs = context :: Nil
@@ -144,12 +139,7 @@ case class CacheCommand(tableName: String, doCache: Boolean)(@transient context:
     } else {
       context.uncacheTable(tableName)
     }
-    Seq.empty[Any]
-  }
-
-  override def execute(): RDD[Row] = {
-    sideEffectResult
-    context.emptyResult
+    Seq.empty[Row]
   }
 
   override def output: Seq[Attribute] = Seq.empty
@@ -163,15 +153,8 @@ case class DescribeCommand(child: SparkPlan, output: Seq[Attribute])(
     @transient context: SQLContext)
   extends LeafNode with Command {
 
-  override protected[sql] lazy val sideEffectResult: Seq[(String, String, String)] = {
-    Seq(("# Registered as a temporary table", null, null)) ++
-      child.output.map(field => (field.name, field.dataType.toString, null))
-  }
-
-  override def execute(): RDD[Row] = {
-    val rows = sideEffectResult.map {
-      case (name, dataType, comment) => new GenericRow(Array[Any](name, dataType, comment))
-    }
-    context.sparkContext.parallelize(rows, 1)
+  override protected[sql] lazy val sideEffectResult: Seq[Row] = {
+    Row("# Registered as a temporary table", null, null) +:
+      child.output.map(field => Row(field.name, field.dataType.toString, null))
   }
 }
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
index d9b2bc7348ad2..ced8397972fbd 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
@@ -389,7 +389,7 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) {
         }.mkString("{", ",", "}")
       case (seq: Seq[_], ArrayType(typ, _)) =>
         seq.map(v => (v, typ)).map(toHiveStructString).mkString("[", ",", "]")
-      case (map: Map[_,_], MapType(kType, vType, _)) =>
+      case (map: Map[_, _], MapType(kType, vType, _)) =>
         map.map {
           case (key, value) =>
             toHiveStructString((key, kType)) + ":" + toHiveStructString((value, vType))
@@ -409,7 +409,7 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) {
         // be similar with Hive.
         describeHiveTableCommand.hiveString
       case command: PhysicalCommand =>
-        command.sideEffectResult.map(_.toString)
+        command.sideEffectResult.map(_.head.toString)
 
       case other =>
         val result: Seq[Seq[Any]] = toRdd.collect().toSeq
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala
index 47e24f0dec146..24abb1b5bd1a8 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala
@@ -18,17 +18,19 @@
 package org.apache.spark.sql.hive
 
 import org.apache.spark.annotation.Experimental
-import org.apache.spark.sql._
 import org.apache.spark.sql.catalyst.analysis.UnresolvedAttribute
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.expressions.codegen.GeneratePredicate
 import org.apache.spark.sql.catalyst.planning._
 import org.apache.spark.sql.catalyst.plans._
 import org.apache.spark.sql.catalyst.plans.logical.{LogicalPlan, LowerCaseSchema}
-import org.apache.spark.sql.execution._
-import org.apache.spark.sql.hive.execution._
+import org.apache.spark.sql.catalyst.types.StringType
 import org.apache.spark.sql.columnar.InMemoryRelation
-import org.apache.spark.sql.parquet.{ParquetRelation, ParquetTableScan}
+import org.apache.spark.sql.execution.{DescribeCommand, OutputFaker, SparkPlan}
+import org.apache.spark.sql.hive
+import org.apache.spark.sql.hive.execution._
+import org.apache.spark.sql.parquet.ParquetRelation
+import org.apache.spark.sql.{SQLContext, SchemaRDD}
 
 import scala.collection.JavaConversions._
 
@@ -196,9 +198,9 @@ private[hive] trait HiveStrategies {
       case logical.NativeCommand(sql) =>
         NativeCommand(sql, plan.output)(context) :: Nil
 
-      case DropTable(tableName, ifExists) => execution.DropTable(tableName, ifExists) :: Nil
+      case hive.DropTable(tableName, ifExists) => execution.DropTable(tableName, ifExists) :: Nil
 
-      case AnalyzeTable(tableName) => execution.AnalyzeTable(tableName) :: Nil
+      case hive.AnalyzeTable(tableName) => execution.AnalyzeTable(tableName) :: Nil
 
       case describe: logical.DescribeCommand =>
         val resolvedTable = context.executePlan(describe.table).analyzed
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/DescribeHiveTableCommand.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/DescribeHiveTableCommand.scala
index a40e89e0d382b..317801001c7a4 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/DescribeHiveTableCommand.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/DescribeHiveTableCommand.scala
@@ -23,7 +23,7 @@ import org.apache.hadoop.hive.metastore.api.FieldSchema
 
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.catalyst.expressions.{Attribute, GenericRow, Row}
+import org.apache.spark.sql.catalyst.expressions.{Attribute, Row}
 import org.apache.spark.sql.execution.{Command, LeafNode}
 import org.apache.spark.sql.hive.{HiveContext, MetastoreRelation}
 
@@ -41,26 +41,21 @@ case class DescribeHiveTableCommand(
   extends LeafNode with Command {
 
   // Strings with the format like Hive. It is used for result comparison in our unit tests.
-  lazy val hiveString: Seq[String] = {
-    val alignment = 20
-    val delim = "\t"
-
-    sideEffectResult.map {
-      case (name, dataType, comment) =>
-        String.format("%-" + alignment + "s", name) + delim +
-          String.format("%-" + alignment + "s", dataType) + delim +
-          String.format("%-" + alignment + "s", Option(comment).getOrElse("None"))
-    }
+  lazy val hiveString: Seq[String] = sideEffectResult.map {
+    case Row(name: String, dataType: String, comment) =>
+      Seq(name, dataType, Option(comment.asInstanceOf[String]).getOrElse("None"))
+        .map(s => String.format(s"%-20s", s))
+        .mkString("\t")
   }
 
-  override protected[sql] lazy val sideEffectResult: Seq[(String, String, String)] = {
+  override protected[sql] lazy val sideEffectResult: Seq[Row] = {
     // Trying to mimic the format of Hive's output. But not exactly the same.
     var results: Seq[(String, String, String)] = Nil
 
     val columns: Seq[FieldSchema] = table.hiveQlTable.getCols
     val partitionColumns: Seq[FieldSchema] = table.hiveQlTable.getPartCols
     results ++= columns.map(field => (field.getName, field.getType, field.getComment))
-    if (!partitionColumns.isEmpty) {
+    if (partitionColumns.nonEmpty) {
       val partColumnInfo =
         partitionColumns.map(field => (field.getName, field.getType, field.getComment))
       results ++=
@@ -74,14 +69,9 @@ case class DescribeHiveTableCommand(
       results ++= Seq(("Detailed Table Information", table.hiveQlTable.getTTable.toString, ""))
     }
 
-    results
-  }
-
-  override def execute(): RDD[Row] = {
-    val rows = sideEffectResult.map {
-      case (name, dataType, comment) => new GenericRow(Array[Any](name, dataType, comment))
+    results.map { case (name, dataType, comment) =>
+      Row(name, dataType, comment)
     }
-    context.sparkContext.parallelize(rows, 1)
   }
 
   override def otherCopyArgs = context :: Nil
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/NativeCommand.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/NativeCommand.scala
index fe6031678f70f..8f10e1ba7f426 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/NativeCommand.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/NativeCommand.scala
@@ -32,16 +32,7 @@ case class NativeCommand(
     @transient context: HiveContext)
   extends LeafNode with Command {
 
-  override protected[sql] lazy val sideEffectResult: Seq[String] = context.runSqlHive(sql)
-
-  override def execute(): RDD[Row] = {
-    if (sideEffectResult.size == 0) {
-      context.emptyResult
-    } else {
-      val rows = sideEffectResult.map(r => new GenericRow(Array[Any](r)))
-      context.sparkContext.parallelize(rows, 1)
-    }
-  }
+  override protected[sql] lazy val sideEffectResult: Seq[Row] = context.runSqlHive(sql).map(Row(_))
 
   override def otherCopyArgs = context :: Nil
 }
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/commands.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/commands.scala
index 2985169da033c..a1a4aa7de7bf7 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/commands.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/commands.scala
@@ -33,19 +33,13 @@ import org.apache.spark.sql.hive.HiveContext
  */
 @DeveloperApi
 case class AnalyzeTable(tableName: String) extends LeafNode with Command {
-
   def hiveContext = sqlContext.asInstanceOf[HiveContext]
 
   def output = Seq.empty
 
-  override protected[sql] lazy val sideEffectResult = {
+  override protected[sql] lazy val sideEffectResult: Seq[Row] = {
     hiveContext.analyze(tableName)
-    Seq.empty[Any]
-  }
-
-  override def execute(): RDD[Row] = {
-    sideEffectResult
-    sparkContext.emptyRDD[Row]
+    Seq.empty[Row]
   }
 }
 
@@ -55,20 +49,14 @@ case class AnalyzeTable(tableName: String) extends LeafNode with Command {
  */
 @DeveloperApi
 case class DropTable(tableName: String, ifExists: Boolean) extends LeafNode with Command {
-
   def hiveContext = sqlContext.asInstanceOf[HiveContext]
 
   def output = Seq.empty
 
-  override protected[sql] lazy val sideEffectResult: Seq[Any] = {
+  override protected[sql] lazy val sideEffectResult: Seq[Row] = {
     val ifExistsClause = if (ifExists) "IF EXISTS " else ""
     hiveContext.runSqlHive(s"DROP TABLE $ifExistsClause$tableName")
     hiveContext.catalog.unregisterTable(None, tableName)
-    Seq.empty
-  }
-
-  override def execute(): RDD[Row] = {
-    sideEffectResult
-    sparkContext.emptyRDD[Row]
+    Seq.empty[Row]
   }
 }

From 248067adbe90f93c7d5e23aa61b3072dfdf48a8a Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian.cs.zju@gmail.com>
Date: Wed, 3 Sep 2014 18:59:26 -0700
Subject: [PATCH 193/489] [SPARK-2961][SQL] Use statistics to prune batches
 within cached partitions

This PR is based on #1883 authored by marmbrus. Key differences:

1. Batch pruning instead of partition pruning

   When #1883 was authored, batched column buffer building (#1880) hadn't been introduced. This PR combines these two and provide partition batch level pruning, which leads to smaller memory footprints and can generally skip more elements. The cost is that the pruning predicates are evaluated more frequently (partition number multiplies batch number per partition).

1. More filters are supported

   Filter predicates consist of `=`, `<`, `<=`, `>`, `>=` and their conjunctions and disjunctions are supported.

Author: Cheng Lian <lian.cs.zju@gmail.com>

Closes #2188 from liancheng/in-mem-batch-pruning and squashes the following commits:

68cf019 [Cheng Lian] Marked sqlContext as @transient
4254f6c [Cheng Lian] Enables in-memory partition pruning in PartitionBatchPruningSuite
3784105 [Cheng Lian] Overrides InMemoryColumnarTableScan.sqlContext
d2a1d66 [Cheng Lian] Disables in-memory partition pruning by default
062c315 [Cheng Lian] HiveCompatibilitySuite code cleanup
16b77bf [Cheng Lian] Fixed pruning predication conjunctions and disjunctions
16195c5 [Cheng Lian] Enabled both disjunction and conjunction
89950d0 [Cheng Lian] Worked around Scala style check
9c167f6 [Cheng Lian] Minor code cleanup
3c4d5c7 [Cheng Lian] Minor code cleanup
ea59ee5 [Cheng Lian] Renamed PartitionSkippingSuite to PartitionBatchPruningSuite
fc517d0 [Cheng Lian] More test cases
1868c18 [Cheng Lian] Code cleanup, bugfix, and adding tests
cb76da4 [Cheng Lian] Added more predicate filters, fixed table scan stats for testing purposes
385474a [Cheng Lian] Merge branch 'inMemStats' into in-mem-batch-pruning
---
 .../catalyst/expressions/AttributeMap.scala   |  41 ++
 .../catalyst/expressions/BoundAttribute.scala |  12 +-
 .../scala/org/apache/spark/sql/SQLConf.scala  |   7 +
 .../spark/sql/columnar/ColumnBuilder.scala    |  10 +-
 .../spark/sql/columnar/ColumnStats.scala      | 434 +++++-------------
 .../columnar/InMemoryColumnarTableScan.scala  | 131 +++++-
 .../sql/columnar/NullableColumnBuilder.scala  |   1 +
 .../spark/sql/execution/SparkStrategies.scala |   4 +-
 .../spark/sql/columnar/ColumnStatsSuite.scala |  39 +-
 .../columnar/NullableColumnBuilderSuite.scala |   2 +-
 .../columnar/PartitionBatchPruningSuite.scala |  95 ++++
 .../compression/BooleanBitSetSuite.scala      |   4 +-
 .../compression/DictionaryEncodingSuite.scala |   2 +-
 .../compression/IntegralDeltaSuite.scala      |   2 +-
 .../compression/RunLengthEncodingSuite.scala  |   4 +-
 .../TestCompressibleColumnBuilder.scala       |   4 +-
 .../execution/HiveCompatibilitySuite.scala    |  13 +-
 17 files changed, 446 insertions(+), 359 deletions(-)
 create mode 100644 sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/AttributeMap.scala
 create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/columnar/PartitionBatchPruningSuite.scala

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/AttributeMap.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/AttributeMap.scala
new file mode 100644
index 0000000000000..8364379644c90
--- /dev/null
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/AttributeMap.scala
@@ -0,0 +1,41 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.expressions
+
+/**
+ * Builds a map that is keyed by an Attribute's expression id. Using the expression id allows values
+ * to be looked up even when the attributes used differ cosmetically (i.e., the capitalization
+ * of the name, or the expected nullability).
+ */
+object AttributeMap {
+  def apply[A](kvs: Seq[(Attribute, A)]) =
+    new AttributeMap(kvs.map(kv => (kv._1.exprId, (kv._1, kv._2))).toMap)
+}
+
+class AttributeMap[A](baseMap: Map[ExprId, (Attribute, A)])
+  extends Map[Attribute, A] with Serializable {
+
+  override def get(k: Attribute): Option[A] = baseMap.get(k.exprId).map(_._2)
+
+  override def + [B1 >: A](kv: (Attribute, B1)): Map[Attribute, B1] =
+    (baseMap.map(_._2) + kv).toMap
+
+  override def iterator: Iterator[(Attribute, A)] = baseMap.map(_._2).iterator
+
+  override def -(key: Attribute): Map[Attribute, A] = (baseMap.map(_._2) - key).toMap
+}
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala
index 54c6baf1af3bf..fa80b07f8e6be 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala
@@ -38,12 +38,20 @@ case class BoundReference(ordinal: Int, dataType: DataType, nullable: Boolean)
 }
 
 object BindReferences extends Logging {
-  def bindReference[A <: Expression](expression: A, input: Seq[Attribute]): A = {
+
+  def bindReference[A <: Expression](
+      expression: A,
+      input: Seq[Attribute],
+      allowFailures: Boolean = false): A = {
     expression.transform { case a: AttributeReference =>
       attachTree(a, "Binding attribute") {
         val ordinal = input.indexWhere(_.exprId == a.exprId)
         if (ordinal == -1) {
-          sys.error(s"Couldn't find $a in ${input.mkString("[", ",", "]")}")
+          if (allowFailures) {
+            a
+          } else {
+            sys.error(s"Couldn't find $a in ${input.mkString("[", ",", "]")}")
+          }
         } else {
           BoundReference(ordinal, a.dataType, a.nullable)
         }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
index 64d49354dadcd..4137ac7663739 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
@@ -26,6 +26,7 @@ import java.util.Properties
 private[spark] object SQLConf {
   val COMPRESS_CACHED = "spark.sql.inMemoryColumnarStorage.compressed"
   val COLUMN_BATCH_SIZE = "spark.sql.inMemoryColumnarStorage.batchSize"
+  val IN_MEMORY_PARTITION_PRUNING = "spark.sql.inMemoryColumnarStorage.partitionPruning"
   val AUTO_BROADCASTJOIN_THRESHOLD = "spark.sql.autoBroadcastJoinThreshold"
   val DEFAULT_SIZE_IN_BYTES = "spark.sql.defaultSizeInBytes"
   val SHUFFLE_PARTITIONS = "spark.sql.shuffle.partitions"
@@ -124,6 +125,12 @@ trait SQLConf {
   private[spark] def isParquetBinaryAsString: Boolean =
     getConf(PARQUET_BINARY_AS_STRING, "false").toBoolean
 
+  /**
+   * When set to true, partition pruning for in-memory columnar tables is enabled.
+   */
+  private[spark] def inMemoryPartitionPruning: Boolean =
+    getConf(IN_MEMORY_PARTITION_PRUNING, "false").toBoolean
+
   /** ********************** SQLConf functionality methods ************ */
 
   /** Set Spark SQL configuration properties. */
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnBuilder.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnBuilder.scala
index 247337a875c75..b3ec5ded22422 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnBuilder.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnBuilder.scala
@@ -38,7 +38,7 @@ private[sql] trait ColumnBuilder {
   /**
    * Column statistics information
    */
-  def columnStats: ColumnStats[_, _]
+  def columnStats: ColumnStats
 
   /**
    * Returns the final columnar byte buffer.
@@ -47,7 +47,7 @@ private[sql] trait ColumnBuilder {
 }
 
 private[sql] class BasicColumnBuilder[T <: DataType, JvmType](
-    val columnStats: ColumnStats[T, JvmType],
+    val columnStats: ColumnStats,
     val columnType: ColumnType[T, JvmType])
   extends ColumnBuilder {
 
@@ -81,18 +81,18 @@ private[sql] class BasicColumnBuilder[T <: DataType, JvmType](
 
 private[sql] abstract class ComplexColumnBuilder[T <: DataType, JvmType](
     columnType: ColumnType[T, JvmType])
-  extends BasicColumnBuilder[T, JvmType](new NoopColumnStats[T, JvmType], columnType)
+  extends BasicColumnBuilder[T, JvmType](new NoopColumnStats, columnType)
   with NullableColumnBuilder
 
 private[sql] abstract class NativeColumnBuilder[T <: NativeType](
-    override val columnStats: NativeColumnStats[T],
+    override val columnStats: ColumnStats,
     override val columnType: NativeColumnType[T])
   extends BasicColumnBuilder[T, T#JvmType](columnStats, columnType)
   with NullableColumnBuilder
   with AllCompressionSchemes
   with CompressibleColumnBuilder[T]
 
-private[sql] class BooleanColumnBuilder extends NativeColumnBuilder(new BooleanColumnStats, BOOLEAN)
+private[sql] class BooleanColumnBuilder extends NativeColumnBuilder(new NoopColumnStats, BOOLEAN)
 
 private[sql] class IntColumnBuilder extends NativeColumnBuilder(new IntColumnStats, INT)
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnStats.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnStats.scala
index 6502110e903fe..fc343ccb995c2 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnStats.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnStats.scala
@@ -17,381 +17,193 @@
 
 package org.apache.spark.sql.columnar
 
+import java.sql.Timestamp
+
 import org.apache.spark.sql.Row
+import org.apache.spark.sql.catalyst.expressions.{AttributeMap, Attribute, AttributeReference}
 import org.apache.spark.sql.catalyst.types._
 
+private[sql] class ColumnStatisticsSchema(a: Attribute) extends Serializable {
+  val upperBound = AttributeReference(a.name + ".upperBound", a.dataType, nullable = false)()
+  val lowerBound = AttributeReference(a.name + ".lowerBound", a.dataType, nullable = false)()
+  val nullCount =  AttributeReference(a.name + ".nullCount", IntegerType, nullable = false)()
+
+  val schema = Seq(lowerBound, upperBound, nullCount)
+}
+
+private[sql] class PartitionStatistics(tableSchema: Seq[Attribute]) extends Serializable {
+  val (forAttribute, schema) = {
+    val allStats = tableSchema.map(a => a -> new ColumnStatisticsSchema(a))
+    (AttributeMap(allStats), allStats.map(_._2.schema).foldLeft(Seq.empty[Attribute])(_ ++ _))
+  }
+}
+
 /**
  * Used to collect statistical information when building in-memory columns.
  *
  * NOTE: we intentionally avoid using `Ordering[T]` to compare values here because `Ordering[T]`
  * brings significant performance penalty.
  */
-private[sql] sealed abstract class ColumnStats[T <: DataType, JvmType] extends Serializable {
-  /**
-   * Closed lower bound of this column.
-   */
-  def lowerBound: JvmType
-
-  /**
-   * Closed upper bound of this column.
-   */
-  def upperBound: JvmType
-
+private[sql] sealed trait ColumnStats extends Serializable {
   /**
    * Gathers statistics information from `row(ordinal)`.
    */
-  def gatherStats(row: Row, ordinal: Int)
-
-  /**
-   * Returns `true` if `lower <= row(ordinal) <= upper`.
-   */
-  def contains(row: Row, ordinal: Int): Boolean
+  def gatherStats(row: Row, ordinal: Int): Unit
 
   /**
-   * Returns `true` if `row(ordinal) < upper` holds.
+   * Column statistics represented as a single row, currently including closed lower bound, closed
+   * upper bound and null count.
    */
-  def isAbove(row: Row, ordinal: Int): Boolean
-
-  /**
-   * Returns `true` if `lower < row(ordinal)` holds.
-   */
-  def isBelow(row: Row, ordinal: Int): Boolean
-
-  /**
-   * Returns `true` if `row(ordinal) <= upper` holds.
-   */
-  def isAtOrAbove(row: Row, ordinal: Int): Boolean
-
-  /**
-   * Returns `true` if `lower <= row(ordinal)` holds.
-   */
-  def isAtOrBelow(row: Row, ordinal: Int): Boolean
-}
-
-private[sql] sealed abstract class NativeColumnStats[T <: NativeType]
-  extends ColumnStats[T, T#JvmType] {
-
-  type JvmType = T#JvmType
-
-  protected var (_lower, _upper) = initialBounds
-
-  def initialBounds: (JvmType, JvmType)
-
-  protected def columnType: NativeColumnType[T]
-
-  override def lowerBound: T#JvmType = _lower
-
-  override def upperBound: T#JvmType = _upper
-
-  override def isAtOrAbove(row: Row, ordinal: Int) = {
-    contains(row, ordinal) || isAbove(row, ordinal)
-  }
-
-  override def isAtOrBelow(row: Row, ordinal: Int) = {
-    contains(row, ordinal) || isBelow(row, ordinal)
-  }
+  def collectedStatistics: Row
 }
 
-private[sql] class NoopColumnStats[T <: DataType, JvmType] extends ColumnStats[T, JvmType] {
-  override def isAtOrBelow(row: Row, ordinal: Int) = true
-
-  override def isAtOrAbove(row: Row, ordinal: Int) = true
-
-  override def isBelow(row: Row, ordinal: Int) = true
-
-  override def isAbove(row: Row, ordinal: Int) = true
+private[sql] class NoopColumnStats extends ColumnStats {
 
-  override def contains(row: Row, ordinal: Int) = true
+  override def gatherStats(row: Row, ordinal: Int): Unit = {}
 
-  override def gatherStats(row: Row, ordinal: Int) {}
-
-  override def upperBound = null.asInstanceOf[JvmType]
-
-  override def lowerBound = null.asInstanceOf[JvmType]
+  override def collectedStatistics = Row()
 }
 
-private[sql] abstract class BasicColumnStats[T <: NativeType](
-    protected val columnType: NativeColumnType[T])
-  extends NativeColumnStats[T]
-
-private[sql] class BooleanColumnStats extends BasicColumnStats(BOOLEAN) {
-  override def initialBounds = (true, false)
-
-  override def isBelow(row: Row, ordinal: Int) = {
-    lowerBound < columnType.getField(row, ordinal)
-  }
-
-  override def isAbove(row: Row, ordinal: Int) = {
-    columnType.getField(row, ordinal) < upperBound
-  }
-
-  override def contains(row: Row, ordinal: Int) = {
-    val field = columnType.getField(row, ordinal)
-    lowerBound <= field && field <= upperBound
-  }
+private[sql] class ByteColumnStats extends ColumnStats {
+  var upper = Byte.MinValue
+  var lower = Byte.MaxValue
+  var nullCount = 0
 
   override def gatherStats(row: Row, ordinal: Int) {
-    val field = columnType.getField(row, ordinal)
-    if (field > upperBound) _upper = field
-    if (field < lowerBound) _lower = field
-  }
-}
-
-private[sql] class ByteColumnStats extends BasicColumnStats(BYTE) {
-  override def initialBounds = (Byte.MaxValue, Byte.MinValue)
-
-  override def isBelow(row: Row, ordinal: Int) = {
-    lowerBound < columnType.getField(row, ordinal)
-  }
-
-  override def isAbove(row: Row, ordinal: Int) = {
-    columnType.getField(row, ordinal) < upperBound
-  }
-
-  override def contains(row: Row, ordinal: Int) = {
-    val field = columnType.getField(row, ordinal)
-    lowerBound <= field && field <= upperBound
+    if (!row.isNullAt(ordinal)) {
+      val value = row.getByte(ordinal)
+      if (value > upper) upper = value
+      if (value < lower) lower = value
+    } else {
+      nullCount += 1
+    }
   }
 
-  override def gatherStats(row: Row, ordinal: Int) {
-    val field = columnType.getField(row, ordinal)
-    if (field > upperBound) _upper = field
-    if (field < lowerBound) _lower = field
-  }
+  def collectedStatistics = Row(lower, upper, nullCount)
 }
 
-private[sql] class ShortColumnStats extends BasicColumnStats(SHORT) {
-  override def initialBounds = (Short.MaxValue, Short.MinValue)
-
-  override def isBelow(row: Row, ordinal: Int) = {
-    lowerBound < columnType.getField(row, ordinal)
-  }
-
-  override def isAbove(row: Row, ordinal: Int) = {
-    columnType.getField(row, ordinal) < upperBound
-  }
-
-  override def contains(row: Row, ordinal: Int) = {
-    val field = columnType.getField(row, ordinal)
-    lowerBound <= field && field <= upperBound
-  }
+private[sql] class ShortColumnStats extends ColumnStats {
+  var upper = Short.MinValue
+  var lower = Short.MaxValue
+  var nullCount = 0
 
   override def gatherStats(row: Row, ordinal: Int) {
-    val field = columnType.getField(row, ordinal)
-    if (field > upperBound) _upper = field
-    if (field < lowerBound) _lower = field
-  }
-}
-
-private[sql] class LongColumnStats extends BasicColumnStats(LONG) {
-  override def initialBounds = (Long.MaxValue, Long.MinValue)
-
-  override def isBelow(row: Row, ordinal: Int) = {
-    lowerBound < columnType.getField(row, ordinal)
-  }
-
-  override def isAbove(row: Row, ordinal: Int) = {
-    columnType.getField(row, ordinal) < upperBound
-  }
-
-  override def contains(row: Row, ordinal: Int) = {
-    val field = columnType.getField(row, ordinal)
-    lowerBound <= field && field <= upperBound
+    if (!row.isNullAt(ordinal)) {
+      val value = row.getShort(ordinal)
+      if (value > upper) upper = value
+      if (value < lower) lower = value
+    } else {
+      nullCount += 1
+    }
   }
 
-  override def gatherStats(row: Row, ordinal: Int) {
-    val field = columnType.getField(row, ordinal)
-    if (field > upperBound) _upper = field
-    if (field < lowerBound) _lower = field
-  }
+  def collectedStatistics = Row(lower, upper, nullCount)
 }
 
-private[sql] class DoubleColumnStats extends BasicColumnStats(DOUBLE) {
-  override def initialBounds = (Double.MaxValue, Double.MinValue)
-
-  override def isBelow(row: Row, ordinal: Int) = {
-    lowerBound < columnType.getField(row, ordinal)
-  }
-
-  override def isAbove(row: Row, ordinal: Int) = {
-    columnType.getField(row, ordinal) < upperBound
-  }
-
-  override def contains(row: Row, ordinal: Int) = {
-    val field = columnType.getField(row, ordinal)
-    lowerBound <= field && field <= upperBound
-  }
+private[sql] class LongColumnStats extends ColumnStats {
+  var upper = Long.MinValue
+  var lower = Long.MaxValue
+  var nullCount = 0
 
   override def gatherStats(row: Row, ordinal: Int) {
-    val field = columnType.getField(row, ordinal)
-    if (field > upperBound) _upper = field
-    if (field < lowerBound) _lower = field
-  }
-}
-
-private[sql] class FloatColumnStats extends BasicColumnStats(FLOAT) {
-  override def initialBounds = (Float.MaxValue, Float.MinValue)
-
-  override def isBelow(row: Row, ordinal: Int) = {
-    lowerBound < columnType.getField(row, ordinal)
+    if (!row.isNullAt(ordinal)) {
+      val value = row.getLong(ordinal)
+      if (value > upper) upper = value
+      if (value < lower) lower = value
+    } else {
+      nullCount += 1
+    }
   }
 
-  override def isAbove(row: Row, ordinal: Int) = {
-    columnType.getField(row, ordinal) < upperBound
-  }
+  def collectedStatistics = Row(lower, upper, nullCount)
+}
 
-  override def contains(row: Row, ordinal: Int) = {
-    val field = columnType.getField(row, ordinal)
-    lowerBound <= field && field <= upperBound
-  }
+private[sql] class DoubleColumnStats extends ColumnStats {
+  var upper = Double.MinValue
+  var lower = Double.MaxValue
+  var nullCount = 0
 
   override def gatherStats(row: Row, ordinal: Int) {
-    val field = columnType.getField(row, ordinal)
-    if (field > upperBound) _upper = field
-    if (field < lowerBound) _lower = field
+    if (!row.isNullAt(ordinal)) {
+      val value = row.getDouble(ordinal)
+      if (value > upper) upper = value
+      if (value < lower) lower = value
+    } else {
+      nullCount += 1
+    }
   }
-}
 
-private[sql] object IntColumnStats {
-  val UNINITIALIZED = 0
-  val INITIALIZED = 1
-  val ASCENDING = 2
-  val DESCENDING = 3
-  val UNORDERED = 4
+  def collectedStatistics = Row(lower, upper, nullCount)
 }
 
-/**
- * Statistical information for `Int` columns. More information is collected since `Int` is
- * frequently used. Extra information include:
- *
- * - Ordering state (ascending/descending/unordered), may be used to decide whether binary search
- *   is applicable when searching elements.
- * - Maximum delta between adjacent elements, may be used to guide the `IntDelta` compression
- *   scheme.
- *
- * (This two kinds of information are not used anywhere yet and might be removed later.)
- */
-private[sql] class IntColumnStats extends BasicColumnStats(INT) {
-  import IntColumnStats._
-
-  private var orderedState = UNINITIALIZED
-  private var lastValue: Int = _
-  private var _maxDelta: Int = _
-
-  def isAscending = orderedState != DESCENDING && orderedState != UNORDERED
-  def isDescending = orderedState != ASCENDING && orderedState != UNORDERED
-  def isOrdered = isAscending || isDescending
-  def maxDelta = _maxDelta
-
-  override def initialBounds = (Int.MaxValue, Int.MinValue)
+private[sql] class FloatColumnStats extends ColumnStats {
+  var upper = Float.MinValue
+  var lower = Float.MaxValue
+  var nullCount = 0
 
-  override def isBelow(row: Row, ordinal: Int) = {
-    lowerBound < columnType.getField(row, ordinal)
+  override def gatherStats(row: Row, ordinal: Int) {
+    if (!row.isNullAt(ordinal)) {
+      val value = row.getFloat(ordinal)
+      if (value > upper) upper = value
+      if (value < lower) lower = value
+    } else {
+      nullCount += 1
+    }
   }
 
-  override def isAbove(row: Row, ordinal: Int) = {
-    columnType.getField(row, ordinal) < upperBound
-  }
+  def collectedStatistics = Row(lower, upper, nullCount)
+}
 
-  override def contains(row: Row, ordinal: Int) = {
-    val field = columnType.getField(row, ordinal)
-    lowerBound <= field && field <= upperBound
-  }
+private[sql] class IntColumnStats extends ColumnStats {
+  var upper = Int.MinValue
+  var lower = Int.MaxValue
+  var nullCount = 0
 
   override def gatherStats(row: Row, ordinal: Int) {
-    val field = columnType.getField(row, ordinal)
-
-    if (field > upperBound) _upper = field
-    if (field < lowerBound) _lower = field
-
-    orderedState = orderedState match {
-      case UNINITIALIZED =>
-        lastValue = field
-        INITIALIZED
-
-      case INITIALIZED =>
-        // If all the integers in the column are the same, ordered state is set to Ascending.
-        // TODO (lian) Confirm whether this is the standard behaviour.
-        val nextState = if (field >= lastValue) ASCENDING else DESCENDING
-        _maxDelta = math.abs(field - lastValue)
-        lastValue = field
-        nextState
-
-      case ASCENDING if field < lastValue =>
-        UNORDERED
-
-      case DESCENDING if field > lastValue =>
-        UNORDERED
-
-      case state @ (ASCENDING | DESCENDING) =>
-        _maxDelta = _maxDelta.max(field - lastValue)
-        lastValue = field
-        state
-
-      case _ =>
-        orderedState
+    if (!row.isNullAt(ordinal)) {
+      val value = row.getInt(ordinal)
+      if (value > upper) upper = value
+      if (value < lower) lower = value
+    } else {
+      nullCount += 1
     }
   }
+
+  def collectedStatistics = Row(lower, upper, nullCount)
 }
 
-private[sql] class StringColumnStats extends BasicColumnStats(STRING) {
-  override def initialBounds = (null, null)
+private[sql] class StringColumnStats extends ColumnStats {
+  var upper: String = null
+  var lower: String = null
+  var nullCount = 0
 
   override def gatherStats(row: Row, ordinal: Int) {
-    val field = columnType.getField(row, ordinal)
-    if ((upperBound eq null) || field.compareTo(upperBound) > 0) _upper = field
-    if ((lowerBound eq null) || field.compareTo(lowerBound) < 0) _lower = field
-  }
-
-  override def contains(row: Row, ordinal: Int) = {
-    (upperBound ne null) && {
-      val field = columnType.getField(row, ordinal)
-      lowerBound.compareTo(field) <= 0 && field.compareTo(upperBound) <= 0
-    }
-  }
-
-  override def isAbove(row: Row, ordinal: Int) = {
-    (upperBound ne null) && {
-      val field = columnType.getField(row, ordinal)
-      field.compareTo(upperBound) < 0
+    if (!row.isNullAt(ordinal)) {
+      val value = row.getString(ordinal)
+      if (upper == null || value.compareTo(upper) > 0) upper = value
+      if (lower == null || value.compareTo(lower) < 0) lower = value
+    } else {
+      nullCount += 1
     }
   }
 
-  override def isBelow(row: Row, ordinal: Int) = {
-    (lowerBound ne null) && {
-      val field = columnType.getField(row, ordinal)
-      lowerBound.compareTo(field) < 0
-    }
-  }
+  def collectedStatistics = Row(lower, upper, nullCount)
 }
 
-private[sql] class TimestampColumnStats extends BasicColumnStats(TIMESTAMP) {
-  override def initialBounds = (null, null)
+private[sql] class TimestampColumnStats extends ColumnStats {
+  var upper: Timestamp = null
+  var lower: Timestamp = null
+  var nullCount = 0
 
   override def gatherStats(row: Row, ordinal: Int) {
-    val field = columnType.getField(row, ordinal)
-    if ((upperBound eq null) || field.compareTo(upperBound) > 0) _upper = field
-    if ((lowerBound eq null) || field.compareTo(lowerBound) < 0) _lower = field
-  }
-
-  override def contains(row: Row, ordinal: Int) = {
-    (upperBound ne null) && {
-      val field = columnType.getField(row, ordinal)
-      lowerBound.compareTo(field) <= 0 && field.compareTo(upperBound) <= 0
+    if (!row.isNullAt(ordinal)) {
+      val value = row(ordinal).asInstanceOf[Timestamp]
+      if (upper == null || value.compareTo(upper) > 0) upper = value
+      if (lower == null || value.compareTo(lower) < 0) lower = value
+    } else {
+      nullCount += 1
     }
   }
 
-  override def isAbove(row: Row, ordinal: Int) = {
-    (lowerBound ne null) && {
-      val field = columnType.getField(row, ordinal)
-      field.compareTo(upperBound) < 0
-    }
-  }
-
-  override def isBelow(row: Row, ordinal: Int) = {
-    (lowerBound ne null) && {
-      val field = columnType.getField(row, ordinal)
-      lowerBound.compareTo(field) < 0
-    }
-  }
+  def collectedStatistics = Row(lower, upper, nullCount)
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala
index cb055cd74a5e5..dc668e7dc934c 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala
@@ -19,10 +19,12 @@ package org.apache.spark.sql.columnar
 
 import java.nio.ByteBuffer
 
+import org.apache.spark.SparkContext._
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.Row
 import org.apache.spark.sql.catalyst.analysis.MultiInstanceRelation
-import org.apache.spark.sql.catalyst.expressions.{Attribute, GenericMutableRow}
+import org.apache.spark.sql.catalyst.dsl.expressions._
+import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
 import org.apache.spark.sql.execution.{LeafNode, SparkPlan}
 
@@ -31,23 +33,27 @@ object InMemoryRelation {
     new InMemoryRelation(child.output, useCompression, batchSize, child)()
 }
 
+private[sql] case class CachedBatch(buffers: Array[ByteBuffer], stats: Row)
+
 private[sql] case class InMemoryRelation(
     output: Seq[Attribute],
     useCompression: Boolean,
     batchSize: Int,
     child: SparkPlan)
-    (private var _cachedColumnBuffers: RDD[Array[ByteBuffer]] = null)
+    (private var _cachedColumnBuffers: RDD[CachedBatch] = null)
   extends LogicalPlan with MultiInstanceRelation {
 
   override lazy val statistics =
     Statistics(sizeInBytes = child.sqlContext.defaultSizeInBytes)
 
+  val partitionStatistics = new PartitionStatistics(output)
+
   // If the cached column buffers were not passed in, we calculate them in the constructor.
   // As in Spark, the actual work of caching is lazy.
   if (_cachedColumnBuffers == null) {
     val output = child.output
     val cached = child.execute().mapPartitions { baseIterator =>
-      new Iterator[Array[ByteBuffer]] {
+      new Iterator[CachedBatch] {
         def next() = {
           val columnBuilders = output.map { attribute =>
             val columnType = ColumnType(attribute.dataType)
@@ -68,7 +74,10 @@ private[sql] case class InMemoryRelation(
             rowCount += 1
           }
 
-          columnBuilders.map(_.build())
+          val stats = Row.fromSeq(
+            columnBuilders.map(_.columnStats.collectedStatistics).foldLeft(Seq.empty[Any])(_ ++ _))
+
+          CachedBatch(columnBuilders.map(_.build()), stats)
         }
 
         def hasNext = baseIterator.hasNext
@@ -79,7 +88,6 @@ private[sql] case class InMemoryRelation(
     _cachedColumnBuffers = cached
   }
 
-
   override def children = Seq.empty
 
   override def newInstance() = {
@@ -96,13 +104,98 @@ private[sql] case class InMemoryRelation(
 
 private[sql] case class InMemoryColumnarTableScan(
     attributes: Seq[Attribute],
+    predicates: Seq[Expression],
     relation: InMemoryRelation)
   extends LeafNode {
 
+  @transient override val sqlContext = relation.child.sqlContext
+
   override def output: Seq[Attribute] = attributes
 
+  // Returned filter predicate should return false iff it is impossible for the input expression
+  // to evaluate to `true' based on statistics collected about this partition batch.
+  val buildFilter: PartialFunction[Expression, Expression] = {
+    case And(lhs: Expression, rhs: Expression)
+      if buildFilter.isDefinedAt(lhs) && buildFilter.isDefinedAt(rhs) =>
+      buildFilter(lhs) && buildFilter(rhs)
+
+    case Or(lhs: Expression, rhs: Expression)
+      if buildFilter.isDefinedAt(lhs) && buildFilter.isDefinedAt(rhs) =>
+      buildFilter(lhs) || buildFilter(rhs)
+
+    case EqualTo(a: AttributeReference, l: Literal) =>
+      val aStats = relation.partitionStatistics.forAttribute(a)
+      aStats.lowerBound <= l && l <= aStats.upperBound
+
+    case EqualTo(l: Literal, a: AttributeReference) =>
+      val aStats = relation.partitionStatistics.forAttribute(a)
+      aStats.lowerBound <= l && l <= aStats.upperBound
+
+    case LessThan(a: AttributeReference, l: Literal) =>
+      val aStats = relation.partitionStatistics.forAttribute(a)
+      aStats.lowerBound < l
+
+    case LessThan(l: Literal, a: AttributeReference) =>
+      val aStats = relation.partitionStatistics.forAttribute(a)
+      l < aStats.upperBound
+
+    case LessThanOrEqual(a: AttributeReference, l: Literal) =>
+      val aStats = relation.partitionStatistics.forAttribute(a)
+      aStats.lowerBound <= l
+
+    case LessThanOrEqual(l: Literal, a: AttributeReference) =>
+      val aStats = relation.partitionStatistics.forAttribute(a)
+      l <= aStats.upperBound
+
+    case GreaterThan(a: AttributeReference, l: Literal) =>
+      val aStats = relation.partitionStatistics.forAttribute(a)
+      l < aStats.upperBound
+
+    case GreaterThan(l: Literal, a: AttributeReference) =>
+      val aStats = relation.partitionStatistics.forAttribute(a)
+      aStats.lowerBound < l
+
+    case GreaterThanOrEqual(a: AttributeReference, l: Literal) =>
+      val aStats = relation.partitionStatistics.forAttribute(a)
+      l <= aStats.upperBound
+
+    case GreaterThanOrEqual(l: Literal, a: AttributeReference) =>
+      val aStats = relation.partitionStatistics.forAttribute(a)
+      aStats.lowerBound <= l
+  }
+
+  val partitionFilters = {
+    predicates.flatMap { p =>
+      val filter = buildFilter.lift(p)
+      val boundFilter =
+        filter.map(
+          BindReferences.bindReference(
+            _,
+            relation.partitionStatistics.schema,
+            allowFailures = true))
+
+      boundFilter.foreach(_ =>
+        filter.foreach(f => logInfo(s"Predicate $p generates partition filter: $f")))
+
+      // If the filter can't be resolved then we are missing required statistics.
+      boundFilter.filter(_.resolved)
+    }
+  }
+
+  val readPartitions = sparkContext.accumulator(0)
+  val readBatches = sparkContext.accumulator(0)
+
+  private val inMemoryPartitionPruningEnabled = sqlContext.inMemoryPartitionPruning
+
   override def execute() = {
+    readPartitions.setValue(0)
+    readBatches.setValue(0)
+
     relation.cachedColumnBuffers.mapPartitions { iterator =>
+      val partitionFilter = newPredicate(
+        partitionFilters.reduceOption(And).getOrElse(Literal(true)),
+        relation.partitionStatistics.schema)
+
       // Find the ordinals of the requested columns.  If none are requested, use the first.
       val requestedColumns = if (attributes.isEmpty) {
         Seq(0)
@@ -110,8 +203,26 @@ private[sql] case class InMemoryColumnarTableScan(
         attributes.map(a => relation.output.indexWhere(_.exprId == a.exprId))
       }
 
-      iterator
-        .map(batch => requestedColumns.map(batch(_)).map(ColumnAccessor(_)))
+      val rows = iterator
+        // Skip pruned batches
+        .filter { cachedBatch =>
+          if (inMemoryPartitionPruningEnabled && !partitionFilter(cachedBatch.stats)) {
+            def statsString = relation.partitionStatistics.schema
+              .zip(cachedBatch.stats)
+              .map { case (a, s) => s"${a.name}: $s" }
+              .mkString(", ")
+            logInfo(s"Skipping partition based on stats $statsString")
+            false
+          } else {
+            readBatches += 1
+            true
+          }
+        }
+        // Build column accessors
+        .map { cachedBatch =>
+          requestedColumns.map(cachedBatch.buffers(_)).map(ColumnAccessor(_))
+        }
+        // Extract rows via column accessors
         .flatMap { columnAccessors =>
           val nextRow = new GenericMutableRow(columnAccessors.length)
           new Iterator[Row] {
@@ -127,6 +238,12 @@ private[sql] case class InMemoryColumnarTableScan(
             override def hasNext = columnAccessors.head.hasNext
           }
         }
+
+      if (rows.hasNext) {
+        readPartitions += 1
+      }
+
+      rows
     }
   }
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/NullableColumnBuilder.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/NullableColumnBuilder.scala
index f631ee76fcd78..a72970eef7aa4 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/NullableColumnBuilder.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/NullableColumnBuilder.scala
@@ -49,6 +49,7 @@ private[sql] trait NullableColumnBuilder extends ColumnBuilder {
   }
 
   abstract override def appendFrom(row: Row, ordinal: Int) {
+    columnStats.gatherStats(row, ordinal)
     if (row.isNullAt(ordinal)) {
       nulls = ColumnBuilder.ensureFreeSpace(nulls, 4)
       nulls.putInt(pos)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
index 8dacb84c8a17e..7943d6e1b6fb5 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
@@ -243,8 +243,8 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
         pruneFilterProject(
           projectList,
           filters,
-          identity[Seq[Expression]], // No filters are pushed down.
-          InMemoryColumnarTableScan(_, mem)) :: Nil
+          identity[Seq[Expression]], // All filters still need to be evaluated.
+          InMemoryColumnarTableScan(_,  filters, mem)) :: Nil
       case _ => Nil
     }
   }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnStatsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnStatsSuite.scala
index 5f61fb5e16ea3..cde91ceb68c98 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnStatsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnStatsSuite.scala
@@ -19,29 +19,30 @@ package org.apache.spark.sql.columnar
 
 import org.scalatest.FunSuite
 
+import org.apache.spark.sql.catalyst.expressions.Row
 import org.apache.spark.sql.catalyst.types._
 
 class ColumnStatsSuite extends FunSuite {
-  testColumnStats(classOf[BooleanColumnStats],   BOOLEAN)
-  testColumnStats(classOf[ByteColumnStats],      BYTE)
-  testColumnStats(classOf[ShortColumnStats],     SHORT)
-  testColumnStats(classOf[IntColumnStats],       INT)
-  testColumnStats(classOf[LongColumnStats],      LONG)
-  testColumnStats(classOf[FloatColumnStats],     FLOAT)
-  testColumnStats(classOf[DoubleColumnStats],    DOUBLE)
-  testColumnStats(classOf[StringColumnStats],    STRING)
-  testColumnStats(classOf[TimestampColumnStats], TIMESTAMP)
-
-  def testColumnStats[T <: NativeType, U <: NativeColumnStats[T]](
+  testColumnStats(classOf[ByteColumnStats], BYTE, Row(Byte.MaxValue, Byte.MinValue, 0))
+  testColumnStats(classOf[ShortColumnStats], SHORT, Row(Short.MaxValue, Short.MinValue, 0))
+  testColumnStats(classOf[IntColumnStats], INT, Row(Int.MaxValue, Int.MinValue, 0))
+  testColumnStats(classOf[LongColumnStats], LONG, Row(Long.MaxValue, Long.MinValue, 0))
+  testColumnStats(classOf[FloatColumnStats], FLOAT, Row(Float.MaxValue, Float.MinValue, 0))
+  testColumnStats(classOf[DoubleColumnStats], DOUBLE, Row(Double.MaxValue, Double.MinValue, 0))
+  testColumnStats(classOf[StringColumnStats], STRING, Row(null, null, 0))
+  testColumnStats(classOf[TimestampColumnStats], TIMESTAMP, Row(null, null, 0))
+
+  def testColumnStats[T <: NativeType, U <: ColumnStats](
       columnStatsClass: Class[U],
-      columnType: NativeColumnType[T]) {
+      columnType: NativeColumnType[T],
+      initialStatistics: Row) {
 
     val columnStatsName = columnStatsClass.getSimpleName
 
     test(s"$columnStatsName: empty") {
       val columnStats = columnStatsClass.newInstance()
-      assertResult(columnStats.initialBounds, "Wrong initial bounds") {
-        (columnStats.lowerBound, columnStats.upperBound)
+      columnStats.collectedStatistics.zip(initialStatistics).foreach { case (actual, expected) =>
+        assert(actual === expected)
       }
     }
 
@@ -49,14 +50,16 @@ class ColumnStatsSuite extends FunSuite {
       import ColumnarTestUtils._
 
       val columnStats = columnStatsClass.newInstance()
-      val rows = Seq.fill(10)(makeRandomRow(columnType))
+      val rows = Seq.fill(10)(makeRandomRow(columnType)) ++ Seq.fill(10)(makeNullRow(1))
       rows.foreach(columnStats.gatherStats(_, 0))
 
-      val values = rows.map(_.head.asInstanceOf[T#JvmType])
+      val values = rows.take(10).map(_.head.asInstanceOf[T#JvmType])
       val ordering = columnType.dataType.ordering.asInstanceOf[Ordering[T#JvmType]]
+      val stats = columnStats.collectedStatistics
 
-      assertResult(values.min(ordering), "Wrong lower bound")(columnStats.lowerBound)
-      assertResult(values.max(ordering), "Wrong upper bound")(columnStats.upperBound)
+      assertResult(values.min(ordering), "Wrong lower bound")(stats(0))
+      assertResult(values.max(ordering), "Wrong upper bound")(stats(1))
+      assertResult(10, "Wrong null count")(stats(2))
     }
   }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/NullableColumnBuilderSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/NullableColumnBuilderSuite.scala
index dc813fe146c47..a77262534a352 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/NullableColumnBuilderSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/NullableColumnBuilderSuite.scala
@@ -23,7 +23,7 @@ import org.apache.spark.sql.catalyst.types._
 import org.apache.spark.sql.execution.SparkSqlSerializer
 
 class TestNullableColumnBuilder[T <: DataType, JvmType](columnType: ColumnType[T, JvmType])
-  extends BasicColumnBuilder[T, JvmType](new NoopColumnStats[T, JvmType], columnType)
+  extends BasicColumnBuilder[T, JvmType](new NoopColumnStats, columnType)
   with NullableColumnBuilder
 
 object TestNullableColumnBuilder {
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/PartitionBatchPruningSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/PartitionBatchPruningSuite.scala
new file mode 100644
index 0000000000000..5d2fd4959197c
--- /dev/null
+++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/PartitionBatchPruningSuite.scala
@@ -0,0 +1,95 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.columnar
+
+import org.scalatest.{BeforeAndAfter, BeforeAndAfterAll, FunSuite}
+
+import org.apache.spark.sql._
+import org.apache.spark.sql.test.TestSQLContext._
+
+case class IntegerData(i: Int)
+
+class PartitionBatchPruningSuite extends FunSuite with BeforeAndAfterAll with BeforeAndAfter {
+  val originalColumnBatchSize = columnBatchSize
+  val originalInMemoryPartitionPruning = inMemoryPartitionPruning
+
+  override protected def beforeAll() {
+    // Make a table with 5 partitions, 2 batches per partition, 10 elements per batch
+    setConf(SQLConf.COLUMN_BATCH_SIZE, "10")
+    val rawData = sparkContext.makeRDD(1 to 100, 5).map(IntegerData)
+    rawData.registerTempTable("intData")
+
+    // Enable in-memory partition pruning
+    setConf(SQLConf.IN_MEMORY_PARTITION_PRUNING, "true")
+  }
+
+  override protected def afterAll() {
+    setConf(SQLConf.COLUMN_BATCH_SIZE, originalColumnBatchSize.toString)
+    setConf(SQLConf.IN_MEMORY_PARTITION_PRUNING, originalInMemoryPartitionPruning.toString)
+  }
+
+  before {
+    cacheTable("intData")
+  }
+
+  after {
+    uncacheTable("intData")
+  }
+
+  // Comparisons
+  checkBatchPruning("i = 1", Seq(1), 1, 1)
+  checkBatchPruning("1 = i", Seq(1), 1, 1)
+  checkBatchPruning("i < 12", 1 to 11, 1, 2)
+  checkBatchPruning("i <= 11", 1 to 11, 1, 2)
+  checkBatchPruning("i > 88", 89 to 100, 1, 2)
+  checkBatchPruning("i >= 89", 89 to 100, 1, 2)
+  checkBatchPruning("12 > i", 1 to 11, 1, 2)
+  checkBatchPruning("11 >= i", 1 to 11, 1, 2)
+  checkBatchPruning("88 < i", 89 to 100, 1, 2)
+  checkBatchPruning("89 <= i", 89 to 100, 1, 2)
+
+  // Conjunction and disjunction
+  checkBatchPruning("i > 8 AND i <= 21", 9 to 21, 2, 3)
+  checkBatchPruning("i < 2 OR i > 99", Seq(1, 100), 2, 2)
+  checkBatchPruning("i < 2 OR (i > 78 AND i < 92)", Seq(1) ++ (79 to 91), 3, 4)
+
+  // With unsupported predicate
+  checkBatchPruning("i < 12 AND i IS NOT NULL", 1 to 11, 1, 2)
+  checkBatchPruning("NOT (i < 88)", 88 to 100, 5, 10)
+
+  def checkBatchPruning(
+      filter: String,
+      expectedQueryResult: Seq[Int],
+      expectedReadPartitions: Int,
+      expectedReadBatches: Int) {
+
+    test(filter) {
+      val query = sql(s"SELECT * FROM intData WHERE $filter")
+      assertResult(expectedQueryResult.toArray, "Wrong query result") {
+        query.collect().map(_.head).toArray
+      }
+
+      val (readPartitions, readBatches) = query.queryExecution.executedPlan.collect {
+        case in: InMemoryColumnarTableScan => (in.readPartitions.value, in.readBatches.value)
+      }.head
+
+      assert(readBatches === expectedReadBatches, "Wrong number of read batches")
+      assert(readPartitions === expectedReadPartitions, "Wrong number of read partitions")
+    }
+  }
+}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/BooleanBitSetSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/BooleanBitSetSuite.scala
index 5fba00480967c..e01cc8b4d20f2 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/BooleanBitSetSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/BooleanBitSetSuite.scala
@@ -20,7 +20,7 @@ package org.apache.spark.sql.columnar.compression
 import org.scalatest.FunSuite
 
 import org.apache.spark.sql.Row
-import org.apache.spark.sql.columnar.{BOOLEAN, BooleanColumnStats}
+import org.apache.spark.sql.columnar.{NoopColumnStats, BOOLEAN}
 import org.apache.spark.sql.columnar.ColumnarTestUtils._
 
 class BooleanBitSetSuite extends FunSuite {
@@ -31,7 +31,7 @@ class BooleanBitSetSuite extends FunSuite {
     // Tests encoder
     // -------------
 
-    val builder = TestCompressibleColumnBuilder(new BooleanColumnStats, BOOLEAN, BooleanBitSet)
+    val builder = TestCompressibleColumnBuilder(new NoopColumnStats, BOOLEAN, BooleanBitSet)
     val rows = Seq.fill[Row](count)(makeRandomRow(BOOLEAN))
     val values = rows.map(_.head)
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/DictionaryEncodingSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/DictionaryEncodingSuite.scala
index d8ae2a26778c9..d2969d906c943 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/DictionaryEncodingSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/DictionaryEncodingSuite.scala
@@ -31,7 +31,7 @@ class DictionaryEncodingSuite extends FunSuite {
   testDictionaryEncoding(new StringColumnStats, STRING)
 
   def testDictionaryEncoding[T <: NativeType](
-      columnStats: NativeColumnStats[T],
+      columnStats: ColumnStats,
       columnType: NativeColumnType[T]) {
 
     val typeName = columnType.getClass.getSimpleName.stripSuffix("$")
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/IntegralDeltaSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/IntegralDeltaSuite.scala
index 17619dcf974e3..322f447c24840 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/IntegralDeltaSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/IntegralDeltaSuite.scala
@@ -29,7 +29,7 @@ class IntegralDeltaSuite extends FunSuite {
   testIntegralDelta(new LongColumnStats, LONG, LongDelta)
 
   def testIntegralDelta[I <: IntegralType](
-      columnStats: NativeColumnStats[I],
+      columnStats: ColumnStats,
       columnType: NativeColumnType[I],
       scheme: IntegralDelta[I]) {
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/RunLengthEncodingSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/RunLengthEncodingSuite.scala
index 40115beb98899..218c09ac26362 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/RunLengthEncodingSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/RunLengthEncodingSuite.scala
@@ -24,7 +24,7 @@ import org.apache.spark.sql.columnar._
 import org.apache.spark.sql.columnar.ColumnarTestUtils._
 
 class RunLengthEncodingSuite extends FunSuite {
-  testRunLengthEncoding(new BooleanColumnStats, BOOLEAN)
+  testRunLengthEncoding(new NoopColumnStats, BOOLEAN)
   testRunLengthEncoding(new ByteColumnStats,    BYTE)
   testRunLengthEncoding(new ShortColumnStats,   SHORT)
   testRunLengthEncoding(new IntColumnStats,     INT)
@@ -32,7 +32,7 @@ class RunLengthEncodingSuite extends FunSuite {
   testRunLengthEncoding(new StringColumnStats,  STRING)
 
   def testRunLengthEncoding[T <: NativeType](
-      columnStats: NativeColumnStats[T],
+      columnStats: ColumnStats,
       columnType: NativeColumnType[T]) {
 
     val typeName = columnType.getClass.getSimpleName.stripSuffix("$")
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/TestCompressibleColumnBuilder.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/TestCompressibleColumnBuilder.scala
index 72c19fa31d980..7db723d648d80 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/TestCompressibleColumnBuilder.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/TestCompressibleColumnBuilder.scala
@@ -21,7 +21,7 @@ import org.apache.spark.sql.catalyst.types.NativeType
 import org.apache.spark.sql.columnar._
 
 class TestCompressibleColumnBuilder[T <: NativeType](
-    override val columnStats: NativeColumnStats[T],
+    override val columnStats: ColumnStats,
     override val columnType: NativeColumnType[T],
     override val schemes: Seq[CompressionScheme])
   extends NativeColumnBuilder(columnStats, columnType)
@@ -33,7 +33,7 @@ class TestCompressibleColumnBuilder[T <: NativeType](
 
 object TestCompressibleColumnBuilder {
   def apply[T <: NativeType](
-      columnStats: NativeColumnStats[T],
+      columnStats: ColumnStats,
       columnType: NativeColumnType[T],
       scheme: CompressionScheme) = {
 
diff --git a/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala b/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
index b589994bd25fa..ab487d673e813 100644
--- a/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
+++ b/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
@@ -35,26 +35,29 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
 
   private val originalTimeZone = TimeZone.getDefault
   private val originalLocale = Locale.getDefault
-  private val originalUseCompression = TestHive.useCompression
+  private val originalColumnBatchSize = TestHive.columnBatchSize
+  private val originalInMemoryPartitionPruning = TestHive.inMemoryPartitionPruning
 
   def testCases = hiveQueryDir.listFiles.map(f => f.getName.stripSuffix(".q") -> f)
 
   override def beforeAll() {
-    // Enable in-memory columnar caching
     TestHive.cacheTables = true
     // Timezone is fixed to America/Los_Angeles for those timezone sensitive tests (timestamp_*)
     TimeZone.setDefault(TimeZone.getTimeZone("America/Los_Angeles"))
     // Add Locale setting
     Locale.setDefault(Locale.US)
-    // Enable in-memory columnar compression
-    TestHive.setConf(SQLConf.COMPRESS_CACHED, "true")
+    // Set a relatively small column batch size for testing purposes
+    TestHive.setConf(SQLConf.COLUMN_BATCH_SIZE, "5")
+    // Enable in-memory partition pruning for testing purposes
+    TestHive.setConf(SQLConf.IN_MEMORY_PARTITION_PRUNING, "true")
   }
 
   override def afterAll() {
     TestHive.cacheTables = false
     TimeZone.setDefault(originalTimeZone)
     Locale.setDefault(originalLocale)
-    TestHive.setConf(SQLConf.COMPRESS_CACHED, originalUseCompression.toString)
+    TestHive.setConf(SQLConf.COLUMN_BATCH_SIZE, originalColumnBatchSize.toString)
+    TestHive.setConf(SQLConf.IN_MEMORY_PARTITION_PRUNING, originalInMemoryPartitionPruning.toString)
   }
 
   /** A list of tests deemed out of scope currently and thus completely disregarded. */

From c5cbc49233193836b321cb6b77ce69dae798570b Mon Sep 17 00:00:00 2001
From: Davies Liu <davies.liu@gmail.com>
Date: Wed, 3 Sep 2014 19:08:39 -0700
Subject: [PATCH 194/489] [SPARK-3335] [SQL] [PySpark] support broadcast in
 Python UDF

After this patch, broadcast can be used in Python UDF.

Author: Davies Liu <davies.liu@gmail.com>

Closes #2243 from davies/udf_broadcast and squashes the following commits:

7b88861 [Davies Liu] support broadcast in UDF
---
 python/pyspark/sql.py                         | 17 +++++++-------
 python/pyspark/tests.py                       | 22 +++++++++++++++++++
 .../apache/spark/sql/UdfRegistration.scala    |  3 +++
 .../spark/sql/execution/pythonUdfs.scala      |  3 ++-
 4 files changed, 36 insertions(+), 9 deletions(-)

diff --git a/python/pyspark/sql.py b/python/pyspark/sql.py
index 44316926ba334..aaa35dadc203e 100644
--- a/python/pyspark/sql.py
+++ b/python/pyspark/sql.py
@@ -942,9 +942,7 @@ def __init__(self, sparkContext, sqlContext=None):
         self._jsc = self._sc._jsc
         self._jvm = self._sc._jvm
         self._pythonToJava = self._jvm.PythonRDD.pythonToJavaArray
-
-        if sqlContext:
-            self._scala_SQLContext = sqlContext
+        self._scala_SQLContext = sqlContext
 
     @property
     def _ssql_ctx(self):
@@ -953,7 +951,7 @@ def _ssql_ctx(self):
         Subclasses can override this property to provide their own
         JVM Contexts.
         """
-        if not hasattr(self, '_scala_SQLContext'):
+        if self._scala_SQLContext is None:
             self._scala_SQLContext = self._jvm.SQLContext(self._jsc.sc())
         return self._scala_SQLContext
 
@@ -970,23 +968,26 @@ def registerFunction(self, name, f, returnType=StringType()):
         >>> sqlCtx.registerFunction("stringLengthInt", lambda x: len(x), IntegerType())
         >>> sqlCtx.sql("SELECT stringLengthInt('test')").collect()
         [Row(c0=4)]
-        >>> sqlCtx.registerFunction("twoArgs", lambda x, y: len(x) + y, IntegerType())
-        >>> sqlCtx.sql("SELECT twoArgs('test', 1)").collect()
-        [Row(c0=5)]
         """
         func = lambda _, it: imap(lambda x: f(*x), it)
         command = (func,
                    BatchedSerializer(PickleSerializer(), 1024),
                    BatchedSerializer(PickleSerializer(), 1024))
+        pickled_command = CloudPickleSerializer().dumps(command)
+        broadcast_vars = ListConverter().convert(
+            [x._jbroadcast for x in self._sc._pickled_broadcast_vars],
+            self._sc._gateway._gateway_client)
+        self._sc._pickled_broadcast_vars.clear()
         env = MapConverter().convert(self._sc.environment,
                                      self._sc._gateway._gateway_client)
         includes = ListConverter().convert(self._sc._python_includes,
                                            self._sc._gateway._gateway_client)
         self._ssql_ctx.registerPython(name,
-                                      bytearray(CloudPickleSerializer().dumps(command)),
+                                      bytearray(pickled_command),
                                       env,
                                       includes,
                                       self._sc.pythonExec,
+                                      broadcast_vars,
                                       self._sc._javaAccumulator,
                                       str(returnType))
 
diff --git a/python/pyspark/tests.py b/python/pyspark/tests.py
index f1a75cbff5c19..3e74799e82845 100644
--- a/python/pyspark/tests.py
+++ b/python/pyspark/tests.py
@@ -43,6 +43,7 @@
 from pyspark.files import SparkFiles
 from pyspark.serializers import read_int, BatchedSerializer, MarshalSerializer, PickleSerializer
 from pyspark.shuffle import Aggregator, InMemoryMerger, ExternalMerger, ExternalSorter
+from pyspark.sql import SQLContext, IntegerType
 
 _have_scipy = False
 _have_numpy = False
@@ -525,6 +526,27 @@ def test_histogram(self):
         self.assertRaises(TypeError, lambda: rdd.histogram(2))
 
 
+class TestSQL(PySparkTestCase):
+
+    def setUp(self):
+        PySparkTestCase.setUp(self)
+        self.sqlCtx = SQLContext(self.sc)
+
+    def test_udf(self):
+        self.sqlCtx.registerFunction("twoArgs", lambda x, y: len(x) + y, IntegerType())
+        [row] = self.sqlCtx.sql("SELECT twoArgs('test', 1)").collect()
+        self.assertEqual(row[0], 5)
+
+    def test_broadcast_in_udf(self):
+        bar = {"a": "aa", "b": "bb", "c": "abc"}
+        foo = self.sc.broadcast(bar)
+        self.sqlCtx.registerFunction("MYUDF", lambda x: foo.value[x] if x else '')
+        [res] = self.sqlCtx.sql("SELECT MYUDF('c')").collect()
+        self.assertEqual("abc", res[0])
+        [res] = self.sqlCtx.sql("SELECT MYUDF('')").collect()
+        self.assertEqual("", res[0])
+
+
 class TestIO(PySparkTestCase):
 
     def test_stdout_redirection(self):
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/UdfRegistration.scala b/sql/core/src/main/scala/org/apache/spark/sql/UdfRegistration.scala
index 0b48e9e659faa..0ea1105f082a4 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/UdfRegistration.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/UdfRegistration.scala
@@ -20,6 +20,7 @@ package org.apache.spark.sql
 import java.util.{List => JList, Map => JMap}
 
 import org.apache.spark.Accumulator
+import org.apache.spark.broadcast.Broadcast
 import org.apache.spark.sql.catalyst.ScalaReflection
 import org.apache.spark.sql.catalyst.expressions.{Expression, ScalaUdf}
 import org.apache.spark.sql.execution.PythonUDF
@@ -38,6 +39,7 @@ protected[sql] trait UDFRegistration {
       envVars: JMap[String, String],
       pythonIncludes: JList[String],
       pythonExec: String,
+      broadcastVars: JList[Broadcast[Array[Byte]]],
       accumulator: Accumulator[JList[Array[Byte]]],
       stringDataType: String): Unit = {
     log.debug(
@@ -61,6 +63,7 @@ protected[sql] trait UDFRegistration {
         envVars,
         pythonIncludes,
         pythonExec,
+        broadcastVars,
         accumulator,
         dataType,
         e)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUdfs.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUdfs.scala
index 3dc8be2456781..0977da3e8577c 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUdfs.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUdfs.scala
@@ -42,6 +42,7 @@ private[spark] case class PythonUDF(
     envVars: JMap[String, String],
     pythonIncludes: JList[String],
     pythonExec: String,
+    broadcastVars: JList[Broadcast[Array[Byte]]],
     accumulator: Accumulator[JList[Array[Byte]]],
     dataType: DataType,
     children: Seq[Expression]) extends Expression with SparkLogging {
@@ -145,7 +146,7 @@ case class BatchPythonEvaluation(udf: PythonUDF, output: Seq[Attribute], child:
       udf.pythonIncludes,
       false,
       udf.pythonExec,
-      Seq[Broadcast[Array[Byte]]](),
+      udf.broadcastVars,
       udf.accumulator
     ).mapPartitions { iter =>
       val pickle = new Unpickler

From 7c6e71f05f4f5e0cd2d038ee81d1cda4a3e5cb39 Mon Sep 17 00:00:00 2001
From: Matthew Farrellee <matt@redhat.com>
Date: Wed, 3 Sep 2014 19:37:37 -0700
Subject: [PATCH 195/489] [SPARK-2435] Add shutdown hook to pyspark

Author: Matthew Farrellee <matt@redhat.com>

Closes #2183 from mattf/SPARK-2435 and squashes the following commits:

ee0ee99 [Matthew Farrellee] [SPARK-2435] Add shutdown hook to pyspark
---
 python/pyspark/shell.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/python/pyspark/shell.py b/python/pyspark/shell.py
index e1e7cd954189f..fde3c29e5e790 100644
--- a/python/pyspark/shell.py
+++ b/python/pyspark/shell.py
@@ -28,6 +28,7 @@
     sys.exit(1)
 
 
+import atexit
 import os
 import platform
 import pyspark
@@ -42,6 +43,7 @@
     SparkContext.setSystemProperty("spark.executor.uri", os.environ["SPARK_EXECUTOR_URI"])
 
 sc = SparkContext(appName="PySparkShell", pyFiles=add_files)
+atexit.register(lambda: sc.stop())
 
 print("""Welcome to
       ____              __

From 1bed0a3869a526241381d2a74ba064e5b3721336 Mon Sep 17 00:00:00 2001
From: Kousuke Saruta <sarutak@oss.nttdata.co.jp>
Date: Wed, 3 Sep 2014 20:47:00 -0700
Subject: [PATCH 196/489] [SPARK-3372] [MLlib] MLlib doesn't pass maven build /
 checkstyle due to multi-byte character contained in Gradient.scala

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

Closes #2248 from sarutak/SPARK-3372 and squashes the following commits:

73a28b8 [Kousuke Saruta] Replaced UTF-8 hyphen with ascii hyphen
---
 .../scala/org/apache/spark/mllib/optimization/Gradient.scala  | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/optimization/Gradient.scala b/mllib/src/main/scala/org/apache/spark/mllib/optimization/Gradient.scala
index fdd67160114ca..45dbf6044fcc5 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/optimization/Gradient.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/optimization/Gradient.scala
@@ -128,7 +128,7 @@ class LeastSquaresGradient extends Gradient {
 class HingeGradient extends Gradient {
   override def compute(data: Vector, label: Double, weights: Vector): (Vector, Double) = {
     val dotProduct = dot(data, weights)
-    // Our loss function with {0, 1} labels is max(0, 1 - (2y – 1) (f_w(x)))
+    // Our loss function with {0, 1} labels is max(0, 1 - (2y - 1) (f_w(x)))
     // Therefore the gradient is -(2y - 1)*x
     val labelScaled = 2 * label - 1.0
     if (1.0 > labelScaled * dotProduct) {
@@ -146,7 +146,7 @@ class HingeGradient extends Gradient {
       weights: Vector,
       cumGradient: Vector): Double = {
     val dotProduct = dot(data, weights)
-    // Our loss function with {0, 1} labels is max(0, 1 - (2y – 1) (f_w(x)))
+    // Our loss function with {0, 1} labels is max(0, 1 - (2y - 1) (f_w(x)))
     // Therefore the gradient is -(2y - 1)*x
     val labelScaled = 2 * label - 1.0
     if (1.0 > labelScaled * dotProduct) {

From 00362dac976cd05b06638deb11d990d612429e0b Mon Sep 17 00:00:00 2001
From: Ankur Dave <ankurdave@gmail.com>
Date: Wed, 3 Sep 2014 23:49:47 -0700
Subject: [PATCH 197/489] [HOTFIX] [SPARK-3400] Revert 9b225ac "fix GraphX
 EdgeRDD zipPartitions"

9b225ac3072de522b40b46aba6df1f1c231f13ef has been causing GraphX tests
to fail nondeterministically, which is blocking development for others.

Author: Ankur Dave <ankurdave@gmail.com>

Closes #2271 from ankurdave/SPARK-3400 and squashes the following commits:

10c2a97 [Ankur Dave] [HOTFIX] [SPARK-3400] Revert 9b225ac "fix GraphX EdgeRDD zipPartitions"
---
 .../scala/org/apache/spark/graphx/EdgeRDD.scala  |  4 ++--
 .../org/apache/spark/graphx/GraphSuite.scala     | 16 ----------------
 2 files changed, 2 insertions(+), 18 deletions(-)

diff --git a/graphx/src/main/scala/org/apache/spark/graphx/EdgeRDD.scala b/graphx/src/main/scala/org/apache/spark/graphx/EdgeRDD.scala
index 35fbd47e6c2a5..5bcb96b136ed7 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/EdgeRDD.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/EdgeRDD.scala
@@ -19,7 +19,7 @@ package org.apache.spark.graphx
 
 import scala.reflect.{classTag, ClassTag}
 
-import org.apache.spark._
+import org.apache.spark.{OneToOneDependency, Partition, Partitioner, TaskContext}
 import org.apache.spark.rdd.RDD
 import org.apache.spark.storage.StorageLevel
 
@@ -55,7 +55,7 @@ class EdgeRDD[@specialized ED: ClassTag, VD: ClassTag](
    * partitioner that allows co-partitioning with `partitionsRDD`.
    */
   override val partitioner =
-    partitionsRDD.partitioner.orElse(Some(new HashPartitioner(partitionsRDD.partitions.size)))
+    partitionsRDD.partitioner.orElse(Some(Partitioner.defaultPartitioner(partitionsRDD)))
 
   override def compute(part: Partition, context: TaskContext): Iterator[Edge[ED]] = {
     val p = firstParent[(PartitionID, EdgePartition[ED, VD])].iterator(part, context)
diff --git a/graphx/src/test/scala/org/apache/spark/graphx/GraphSuite.scala b/graphx/src/test/scala/org/apache/spark/graphx/GraphSuite.scala
index eaaa4499b6b93..6506bac73d71c 100644
--- a/graphx/src/test/scala/org/apache/spark/graphx/GraphSuite.scala
+++ b/graphx/src/test/scala/org/apache/spark/graphx/GraphSuite.scala
@@ -19,7 +19,6 @@ package org.apache.spark.graphx
 
 import org.scalatest.FunSuite
 
-import org.apache.spark.SparkConf
 import org.apache.spark.SparkContext
 import org.apache.spark.graphx.Graph._
 import org.apache.spark.graphx.PartitionStrategy._
@@ -351,19 +350,4 @@ class GraphSuite extends FunSuite with LocalSparkContext {
     }
   }
 
-  test("non-default number of edge partitions") {
-    val n = 10
-    val defaultParallelism = 3
-    val numEdgePartitions = 4
-    assert(defaultParallelism != numEdgePartitions)
-    val conf = new SparkConf()
-      .set("spark.default.parallelism", defaultParallelism.toString)
-    val sc = new SparkContext("local", "test", conf)
-    val edges = sc.parallelize((1 to n).map(x => (x: VertexId, 0: VertexId)),
-      numEdgePartitions)
-    val graph = Graph.fromEdgeTuples(edges, 1)
-    val neighborAttrSums = graph.mapReduceTriplets[Int](
-      et => Iterator((et.dstId, et.srcAttr)), _ + _)
-    assert(neighborAttrSums.collect.toSet === Set((0: VertexId, n)))
-  }
 }

From 905861906e250a0d6e6c91d9b8fd1aaa366781d3 Mon Sep 17 00:00:00 2001
From: GuoQiang Li <witgo@qq.com>
Date: Thu, 4 Sep 2014 10:28:23 -0700
Subject: [PATCH 198/489] [Minor]Remove extra semicolon in
 FlumeStreamSuite.scala

Author: GuoQiang Li <witgo@qq.com>

Closes #2265 from witgo/FlumeStreamSuite and squashes the following commits:

6c99e6e [GuoQiang Li] Remove extra semicolon in FlumeStreamSuite.scala
---
 .../spark/streaming/flume/FlumeStreamSuite.scala       | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/external/flume/src/test/scala/org/apache/spark/streaming/flume/FlumeStreamSuite.scala b/external/flume/src/test/scala/org/apache/spark/streaming/flume/FlumeStreamSuite.scala
index 73dffef953309..6ee7ac974b4a0 100644
--- a/external/flume/src/test/scala/org/apache/spark/streaming/flume/FlumeStreamSuite.scala
+++ b/external/flume/src/test/scala/org/apache/spark/streaming/flume/FlumeStreamSuite.scala
@@ -109,11 +109,11 @@ class FlumeStreamSuite extends TestSuiteBase {
   }
 
   class CompressionChannelFactory(compressionLevel: Int) extends NioClientSocketChannelFactory {
-    override def newChannel(pipeline:ChannelPipeline) : SocketChannel = {
-      var encoder : ZlibEncoder = new ZlibEncoder(compressionLevel);
-      pipeline.addFirst("deflater", encoder);
-      pipeline.addFirst("inflater", new ZlibDecoder());
-      super.newChannel(pipeline);
+    override def newChannel(pipeline: ChannelPipeline): SocketChannel = {
+      val encoder = new ZlibEncoder(compressionLevel)
+      pipeline.addFirst("deflater", encoder)
+      pipeline.addFirst("inflater", new ZlibDecoder())
+      super.newChannel(pipeline)
     }
   }
 }

From 4feb46c5feca8d48ec340dc9c8d0eccbcd41f505 Mon Sep 17 00:00:00 2001
From: Kousuke Saruta <sarutak@oss.nttdata.co.jp>
Date: Thu, 4 Sep 2014 10:29:11 -0700
Subject: [PATCH 199/489] [SPARK-3401][PySpark] Wrong usage of tee command in
 python/run-tests

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

Closes #2272 from sarutak/SPARK-3401 and squashes the following commits:

2b35a59 [Kousuke Saruta] Modified wrong usage of tee command in python/run-tests
---
 python/run-tests | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/python/run-tests b/python/run-tests
index 7b1ee3e1cddba..d671da40031c8 100755
--- a/python/run-tests
+++ b/python/run-tests
@@ -33,7 +33,7 @@ rm -rf metastore warehouse
 
 function run_test() {
     echo "Running test: $1"
-    SPARK_TESTING=1 $FWDIR/bin/pyspark $1 2>&1 | tee -a > unit-tests.log
+    SPARK_TESTING=1 $FWDIR/bin/pyspark $1 2>&1 | tee -a unit-tests.log
     FAILED=$((PIPESTATUS[0]||$FAILED))
 
     # Fail and exit on the first test failure.

From dc1ba9e9fc169962a9282ea6644dce09281ff598 Mon Sep 17 00:00:00 2001
From: Kousuke Saruta <sarutak@oss.nttdata.co.jp>
Date: Thu, 4 Sep 2014 15:06:08 -0700
Subject: [PATCH 200/489] [SPARK-3378] [DOCS] Replace the word "SparkSQL" with
 right word "Spark SQL"

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

Closes #2251 from sarutak/SPARK-3378 and squashes the following commits:

0bfe234 [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into SPARK-3378
bb5938f [Kousuke Saruta] Replaced rest of "SparkSQL" with "Spark SQL"
6df66de [Kousuke Saruta] Replaced "SparkSQL" with "Spark SQL"
---
 dev/run-tests                                               | 2 +-
 docs/programming-guide.md                                   | 2 +-
 python/pyspark/sql.py                                       | 6 +++---
 python/run-tests                                            | 2 +-
 .../src/main/scala/org/apache/spark/sql/api/java/Row.scala  | 2 +-
 .../apache/spark/sql/hive/parquet/FakeParquetSerDe.scala    | 2 +-
 6 files changed, 8 insertions(+), 8 deletions(-)

diff --git a/dev/run-tests b/dev/run-tests
index d751961605dfd..90a8ce16f0f06 100755
--- a/dev/run-tests
+++ b/dev/run-tests
@@ -89,7 +89,7 @@ echo "========================================================================="
 echo "Running Spark unit tests"
 echo "========================================================================="
 
-# Build Spark; we always build with Hive because the PySpark SparkSQL tests need it.
+# Build Spark; we always build with Hive because the PySpark Spark SQL tests need it.
 # echo "q" is needed because sbt on encountering a build file with failure
 # (either resolution or compilation) prompts the user for input either q, r,
 # etc to quit or retry. This echo is there to make it not block.
diff --git a/docs/programming-guide.md b/docs/programming-guide.md
index 6ae780d94046a..624cc744dfd51 100644
--- a/docs/programming-guide.md
+++ b/docs/programming-guide.md
@@ -385,7 +385,7 @@ Apart from text files, Spark's Python API also supports several other data forma
 
 * SequenceFile and Hadoop Input/Output Formats
 
-**Note** this feature is currently marked ```Experimental``` and is intended for advanced users. It may be replaced in future with read/write support based on SparkSQL, in which case SparkSQL is the preferred approach.
+**Note** this feature is currently marked ```Experimental``` and is intended for advanced users. It may be replaced in future with read/write support based on Spark SQL, in which case Spark SQL is the preferred approach.
 
 **Writable Support**
 
diff --git a/python/pyspark/sql.py b/python/pyspark/sql.py
index aaa35dadc203e..e7f573cf6da44 100644
--- a/python/pyspark/sql.py
+++ b/python/pyspark/sql.py
@@ -900,7 +900,7 @@ def __reduce__(self):
 
 class SQLContext:
 
-    """Main entry point for SparkSQL functionality.
+    """Main entry point for Spark SQL functionality.
 
     A SQLContext can be used create L{SchemaRDD}s, register L{SchemaRDD}s as
     tables, execute SQL over tables, cache tables, and read parquet files.
@@ -946,7 +946,7 @@ def __init__(self, sparkContext, sqlContext=None):
 
     @property
     def _ssql_ctx(self):
-        """Accessor for the JVM SparkSQL context.
+        """Accessor for the JVM Spark SQL context.
 
         Subclasses can override this property to provide their own
         JVM Contexts.
@@ -1507,7 +1507,7 @@ class SchemaRDD(RDD):
     """An RDD of L{Row} objects that has an associated schema.
 
     The underlying JVM object is a SchemaRDD, not a PythonRDD, so we can
-    utilize the relational query api exposed by SparkSQL.
+    utilize the relational query api exposed by Spark SQL.
 
     For normal L{pyspark.rdd.RDD} operations (map, count, etc.) the
     L{SchemaRDD} is not operated on directly, as it's underlying
diff --git a/python/run-tests b/python/run-tests
index d671da40031c8..f2a80b4f1838b 100755
--- a/python/run-tests
+++ b/python/run-tests
@@ -28,7 +28,7 @@ FAILED=0
 
 rm -f unit-tests.log
 
-# Remove the metastore and warehouse directory created by the HiveContext tests in SparkSQL
+# Remove the metastore and warehouse directory created by the HiveContext tests in Spark SQL
 rm -rf metastore warehouse
 
 function run_test() {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/api/java/Row.scala b/sql/core/src/main/scala/org/apache/spark/sql/api/java/Row.scala
index 6c67934bda5b8..e9d04ce7aae4c 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/api/java/Row.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/api/java/Row.scala
@@ -25,7 +25,7 @@ import scala.math.BigDecimal
 import org.apache.spark.sql.catalyst.expressions.{Row => ScalaRow}
 
 /**
- * A result row from a SparkSQL query.
+ * A result row from a Spark SQL query.
  */
 class Row(private[spark] val row: ScalaRow) extends Serializable {
 
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/parquet/FakeParquetSerDe.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/parquet/FakeParquetSerDe.scala
index 544abfc32423c..abed299cd957f 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/parquet/FakeParquetSerDe.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/parquet/FakeParquetSerDe.scala
@@ -26,7 +26,7 @@ import org.apache.hadoop.hive.serde2.objectinspector.ObjectInspector
 import org.apache.hadoop.io.Writable
 
 /**
- * A placeholder that allows SparkSQL users to create metastore tables that are stored as
+ * A placeholder that allows Spark SQL users to create metastore tables that are stored as
  * parquet files.  It is only intended to pass the checks that the serde is valid and exists
  * when a CREATE TABLE is run.  The actual work of decoding will be done by ParquetTableScan
  * when "spark.sql.hive.convertMetastoreParquet" is set to true.

From 0fdf2f5a18d88a600ca1ab7b4bf02fb0537e9411 Mon Sep 17 00:00:00 2001
From: Matei Zaharia <matei@databricks.com>
Date: Thu, 4 Sep 2014 17:47:16 -0700
Subject: [PATCH 201/489] Manually close old PR

Closes #1588

From 90b17a70c703c403d397e24cfbc20da22a32102d Mon Sep 17 00:00:00 2001
From: Matei Zaharia <matei@databricks.com>
Date: Thu, 4 Sep 2014 17:51:14 -0700
Subject: [PATCH 202/489] Manually close old PR

Closes #544

From 3eb6ef316c2a5ee43d5ecfcf9f10c2d7adc6b819 Mon Sep 17 00:00:00 2001
From: Liang-Chi Hsieh <viirya@gmail.com>
Date: Thu, 4 Sep 2014 18:46:09 -0700
Subject: [PATCH 203/489] [SPARK-3310][SQL] Directly use currentTable without
 unnecessary implicit conversion

We can directly use currentTable there without unnecessary implicit conversion.

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

Closes #2203 from viirya/direct_use_inmemoryrelation and squashes the following commits:

4741d02 [Liang-Chi Hsieh] Merge remote-tracking branch 'upstream/master' into direct_use_inmemoryrelation
b671f67 [Liang-Chi Hsieh] Can directly use currentTable there without unnecessary implicit conversion.
---
 sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
index a75af94d29303..5acb45c155ba5 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
@@ -272,7 +272,7 @@ class SQLContext(@transient val sparkContext: SparkContext)
     val currentTable = table(tableName).queryExecution.analyzed
     val asInMemoryRelation = currentTable match {
       case _: InMemoryRelation =>
-        currentTable.logicalPlan
+        currentTable
 
       case _ =>
         InMemoryRelation(useCompression, columnBatchSize, executePlan(currentTable).executedPlan)

From ee575f12f2ab059d9c1b4fa8d6c1e62248c3d11b Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian.cs.zju@gmail.com>
Date: Thu, 4 Sep 2014 18:47:45 -0700
Subject: [PATCH 204/489] [SPARK-2219][SQL] Added support for the "add jar"
 command

Adds logical and physical command classes for the "add jar" command.

Note that this PR conflicts with and should be merged after #2215.

Author: Cheng Lian <lian.cs.zju@gmail.com>

Closes #2242 from liancheng/add-jar and squashes the following commits:

e43a2f1 [Cheng Lian] Updates AddJar according to conventions introduced in #2215
b99107f [Cheng Lian] Added test case for ADD JAR command
095b2c7 [Cheng Lian] Also forward ADD JAR command to Hive
9be031b [Cheng Lian] Trims Jar path string
8195056 [Cheng Lian] Added support for the "add jar" command
---
 .../org/apache/spark/sql/hive/HiveQl.scala    |  8 +++---
 .../spark/sql/hive/HiveStrategies.scala       |  5 ++--
 .../spark/sql/hive/execution/commands.scala   | 16 ++++++++++++
 .../sql/hive/execution/HiveQuerySuite.scala   | 25 ++++++++++++++++---
 4 files changed, 46 insertions(+), 8 deletions(-)

diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
index a4dd6be5f9e35..c98287c6aa662 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
@@ -44,6 +44,8 @@ private[hive] case class SourceCommand(filePath: String) extends Command
 
 private[hive] case class AddFile(filePath: String) extends Command
 
+private[hive] case class AddJar(path: String) extends Command
+
 private[hive] case class DropTable(tableName: String, ifExists: Boolean) extends Command
 
 private[hive] case class AnalyzeTable(tableName: String) extends Command
@@ -231,7 +233,7 @@ private[hive] object HiveQl {
       } else if (sql.trim.toLowerCase.startsWith("uncache table")) {
         CacheCommand(sql.trim.drop(14).trim, false)
       } else if (sql.trim.toLowerCase.startsWith("add jar")) {
-        NativeCommand(sql)
+        AddJar(sql.trim.drop(8).trim)
       } else if (sql.trim.toLowerCase.startsWith("add file")) {
         AddFile(sql.trim.drop(9))
       } else if (sql.trim.toLowerCase.startsWith("dfs")) {
@@ -1018,9 +1020,9 @@ private[hive] object HiveQl {
 
     /* Other functions */
     case Token("TOK_FUNCTION", Token(RAND(), Nil) :: Nil) => Rand
-    case Token("TOK_FUNCTION", Token(SUBSTR(), Nil) :: string :: pos :: Nil) => 
+    case Token("TOK_FUNCTION", Token(SUBSTR(), Nil) :: string :: pos :: Nil) =>
       Substring(nodeToExpr(string), nodeToExpr(pos), Literal(Integer.MAX_VALUE, IntegerType))
-    case Token("TOK_FUNCTION", Token(SUBSTR(), Nil) :: string :: pos :: length :: Nil) => 
+    case Token("TOK_FUNCTION", Token(SUBSTR(), Nil) :: string :: pos :: length :: Nil) =>
       Substring(nodeToExpr(string), nodeToExpr(pos), nodeToExpr(length))
 
     /* UDFs - Must be last otherwise will preempt built in functions */
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala
index 24abb1b5bd1a8..72cc01cdf4c84 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala
@@ -195,11 +195,12 @@ private[hive] trait HiveStrategies {
 
   case class HiveCommandStrategy(context: HiveContext) extends Strategy {
     def apply(plan: LogicalPlan): Seq[SparkPlan] = plan match {
-      case logical.NativeCommand(sql) =>
-        NativeCommand(sql, plan.output)(context) :: Nil
+      case logical.NativeCommand(sql) => NativeCommand(sql, plan.output)(context) :: Nil
 
       case hive.DropTable(tableName, ifExists) => execution.DropTable(tableName, ifExists) :: Nil
 
+      case hive.AddJar(path) => execution.AddJar(path) :: Nil
+
       case hive.AnalyzeTable(tableName) => execution.AnalyzeTable(tableName) :: Nil
 
       case describe: logical.DescribeCommand =>
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/commands.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/commands.scala
index a1a4aa7de7bf7..d61c5e274a596 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/commands.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/commands.scala
@@ -60,3 +60,19 @@ case class DropTable(tableName: String, ifExists: Boolean) extends LeafNode with
     Seq.empty[Row]
   }
 }
+
+/**
+ * :: DeveloperApi ::
+ */
+@DeveloperApi
+case class AddJar(path: String) extends LeafNode with Command {
+  def hiveContext = sqlContext.asInstanceOf[HiveContext]
+
+  override def output = Seq.empty
+
+  override protected[sql] lazy val sideEffectResult: Seq[Row] = {
+    hiveContext.runSqlHive(s"ADD JAR $path")
+    hiveContext.sparkContext.addJar(path)
+    Seq.empty[Row]
+  }
+}
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
index c4abb3eb4861f..f4217a52c3822 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
@@ -17,9 +17,11 @@
 
 package org.apache.spark.sql.hive.execution
 
+import java.io.File
+
 import scala.util.Try
 
-import org.apache.spark.sql.{SchemaRDD, Row}
+import org.apache.spark.SparkException
 import org.apache.spark.sql.hive._
 import org.apache.spark.sql.hive.test.TestHive
 import org.apache.spark.sql.hive.test.TestHive._
@@ -313,7 +315,7 @@ class HiveQuerySuite extends HiveComparisonTest {
     "SELECT srcalias.KEY, SRCALIAS.value FROM sRc SrCAlias WHERE SrCAlias.kEy < 15")
 
   test("case sensitivity: registered table") {
-    val testData: SchemaRDD =
+    val testData =
       TestHive.sparkContext.parallelize(
         TestData(1, "str1") ::
         TestData(2, "str2") :: Nil)
@@ -467,7 +469,7 @@ class HiveQuerySuite extends HiveComparisonTest {
     }
 
     // Describe a registered temporary table.
-    val testData: SchemaRDD =
+    val testData =
       TestHive.sparkContext.parallelize(
         TestData(1, "str1") ::
         TestData(1, "str2") :: Nil)
@@ -495,6 +497,23 @@ class HiveQuerySuite extends HiveComparisonTest {
     }
   }
 
+  test("ADD JAR command") {
+    val testJar = TestHive.getHiveFile("data/files/TestSerDe.jar").getCanonicalPath
+    sql("CREATE TABLE alter1(a INT, b INT)")
+    intercept[Exception] {
+      sql(
+        """ALTER TABLE alter1 SET SERDE 'org.apache.hadoop.hive.serde2.TestSerDe'
+          |WITH serdeproperties('s1'='9')
+        """.stripMargin)
+    }
+    sql(s"ADD JAR $testJar")
+    sql(
+      """ALTER TABLE alter1 SET SERDE 'org.apache.hadoop.hive.serde2.TestSerDe'
+        |WITH serdeproperties('s1'='9')
+      """.stripMargin)
+    sql("DROP TABLE alter1")
+  }
+
   test("parse HQL set commands") {
     // Adapted from its SQL counterpart.
     val testKey = "spark.sql.key.usedfortestonly"

From 1904bac38d97df5ae9fb193e92a83c7f8ff6d255 Mon Sep 17 00:00:00 2001
From: Cheng Hao <hao.cheng@intel.com>
Date: Thu, 4 Sep 2014 19:16:12 -0700
Subject: [PATCH 205/489] [SPARK-3392] [SQL] Show value
 spark.sql.shuffle.partitions for mapred.reduce.tasks

This is a tiny fix for getting the value of "mapred.reduce.tasks", which make more sense for the hive user.
As well as the command "set -v", which should output verbose information for all of the key/values.

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

Closes #2261 from chenghao-intel/set_mapreduce_tasks and squashes the following commits:

653858a [Cheng Hao] show value spark.sql.shuffle.partitions for mapred.reduce.tasks
---
 .../apache/spark/sql/execution/commands.scala    | 16 ++++++++++++----
 1 file changed, 12 insertions(+), 4 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala
index 286c6d264f86a..94543fc95b470 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala
@@ -60,10 +60,10 @@ case class SetCommand(
         logWarning(s"Property ${SQLConf.Deprecated.MAPRED_REDUCE_TASKS} is deprecated, " +
           s"automatically converted to ${SQLConf.SHUFFLE_PARTITIONS} instead.")
         context.setConf(SQLConf.SHUFFLE_PARTITIONS, v)
-        Array(Row(s"${SQLConf.SHUFFLE_PARTITIONS}=$v"))
+        Seq(Row(s"${SQLConf.SHUFFLE_PARTITIONS}=$v"))
       } else {
         context.setConf(k, v)
-        Array(Row(s"$k=$v"))
+        Seq(Row(s"$k=$v"))
       }
 
     // Query the value bound to key k.
@@ -78,11 +78,19 @@ case class SetCommand(
           "hive-hwi-0.12.0.jar",
           "hive-0.12.0.jar").mkString(":")
 
-        Array(
+        context.getAllConfs.map { case (k, v) =>
+          Row(s"$k=$v")
+        }.toSeq ++ Seq(
           Row("system:java.class.path=" + hiveJars),
           Row("system:sun.java.command=shark.SharkServer2"))
       } else {
-        Array(Row(s"$k=${context.getConf(k, "<undefined>")}"))
+        if (k == SQLConf.Deprecated.MAPRED_REDUCE_TASKS) {
+          logWarning(s"Property ${SQLConf.Deprecated.MAPRED_REDUCE_TASKS} is deprecated, " +
+            s"showing ${SQLConf.SHUFFLE_PARTITIONS} instead.")
+          Seq(Row(s"${SQLConf.SHUFFLE_PARTITIONS}=${context.numShufflePartitions}"))
+        } else {
+          Seq(Row(s"$k=${context.getConf(k, "<undefined>")}"))
+        }
       }
 
     // Query all key-value pairs that are set in the SQLConf of the context.

From 1725a1a5d10a53762bd80f391eddbf306f2841ee Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@apache.org>
Date: Thu, 4 Sep 2014 23:34:58 -0700
Subject: [PATCH 206/489] [SPARK-3391][EC2] Support attaching up to 8 EBS
 volumes.

Please merge this at the same time as https://github.com/mesos/spark-ec2/pull/66

Author: Reynold Xin <rxin@apache.org>

Closes #2260 from rxin/ec2-ebs-vol and squashes the following commits:

b9527d9 [Reynold Xin] Removed io1 ebs type.
bf9c403 [Reynold Xin] Made EBS volume type configurable.
c8e25ea [Reynold Xin] Support up to 8 EBS volumes.
adf4f2e [Reynold Xin] Revert git repo change.
020c542 [Reynold Xin] [SPARK-3391] Support attaching more than 1 EBS volumes.
---
 ec2/spark_ec2.py | 33 +++++++++++++++++++++++++--------
 1 file changed, 25 insertions(+), 8 deletions(-)

diff --git a/ec2/spark_ec2.py b/ec2/spark_ec2.py
index eed6eb8485183..1670faca4a480 100755
--- a/ec2/spark_ec2.py
+++ b/ec2/spark_ec2.py
@@ -102,9 +102,17 @@ def parse_args():
              "(for debugging)")
     parser.add_option(
         "--ebs-vol-size", metavar="SIZE", type="int", default=0,
-        help="Attach a new EBS volume of size SIZE (in GB) to each node as " +
-             "/vol. The volumes will be deleted when the instances terminate. " +
-             "Only possible on EBS-backed AMIs.")
+        help="Size (in GB) of each EBS volume.")
+    parser.add_option(
+        "--ebs-vol-type", default="standard",
+        help="EBS volume type (e.g. 'gp2', 'standard').")
+    parser.add_option(
+        "--ebs-vol-num", type="int", default=1,
+        help="Number of EBS volumes to attach to each node as /vol[x]. " +
+             "The volumes will be deleted when the instances terminate. " +
+             "Only possible on EBS-backed AMIs. " +
+             "EBS volumes are only attached if --ebs-vol-size > 0." +
+             "Only support up to 8 EBS volumes.")
     parser.add_option(
         "--swap", metavar="SWAP", type="int", default=1024,
         help="Swap space to set up per node, in MB (default: 1024)")
@@ -348,13 +356,16 @@ def launch_cluster(conn, opts, cluster_name):
         print >> stderr, "Could not find AMI " + opts.ami
         sys.exit(1)
 
-    # Create block device mapping so that we can add an EBS volume if asked to
+    # Create block device mapping so that we can add EBS volumes if asked to.
+    # The first drive is attached as /dev/sds, 2nd as /dev/sdt, ... /dev/sdz
     block_map = BlockDeviceMapping()
     if opts.ebs_vol_size > 0:
-        device = EBSBlockDeviceType()
-        device.size = opts.ebs_vol_size
-        device.delete_on_termination = True
-        block_map["/dev/sdv"] = device
+        for i in range(opts.ebs_vol_num):
+            device = EBSBlockDeviceType()
+            device.size = opts.ebs_vol_size
+            device.volume_type=opts.ebs_vol_type
+            device.delete_on_termination = True
+            block_map["/dev/sd" + chr(ord('s') + i)] = device
 
     # AWS ignores the AMI-specified block device mapping for M3 (see SPARK-3342).
     if opts.instance_type.startswith('m3.'):
@@ -828,6 +839,12 @@ def get_partition(total, num_partitions, current_partitions):
 
 def real_main():
     (opts, action, cluster_name) = parse_args()
+
+    # Input parameter validation
+    if opts.ebs_vol_num > 8:
+        print >> stderr, "ebs-vol-num cannot be greater than 8"
+        sys.exit(1)
+
     try:
         conn = ec2.connect_to_region(opts.region)
     except Exception as e:

From 6a37ed838b3cbf96f7a904f3d3dabf99141729f5 Mon Sep 17 00:00:00 2001
From: Nicholas Chammas <nicholas.chammas@gmail.com>
Date: Thu, 4 Sep 2014 23:37:06 -0700
Subject: [PATCH 207/489] [Docs] fix minor MLlib case typo

Also make the list of features consistent in style.

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

Closes #2278 from nchammas/patch-1 and squashes the following commits:

56df319 [Nicholas Chammas] [Docs] fix minor MLlib case typo
---
 README.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index 0a683a460ffac..5b09ad86849e7 100644
--- a/README.md
+++ b/README.md
@@ -4,8 +4,8 @@ Spark is a fast and general cluster computing system for Big Data. It provides
 high-level APIs in Scala, Java, and Python, and an optimized engine that
 supports general computation graphs for data analysis. It also supports a
 rich set of higher-level tools including Spark SQL for SQL and structured
-data processing, MLLib for machine learning, GraphX for graph processing,
-and Spark Streaming.
+data processing, MLlib for machine learning, GraphX for graph processing,
+and Spark Streaming for stream processing.
 
 <http://spark.apache.org/>
 

From 51b53a758c85f2e20ad9bd73ed815fcfa9c7180b Mon Sep 17 00:00:00 2001
From: Thomas Graves <tgraves@apache.org>
Date: Fri, 5 Sep 2014 09:54:40 -0500
Subject: [PATCH 208/489] [SPARK-3260] yarn - pass acls along with executor
 launch

Pass along the acl settings when we launch a container so that they can be applied to viewing the logs on a running NodeManager.

Author: Thomas Graves <tgraves@apache.org>

Closes #2185 from tgravescs/SPARK-3260 and squashes the following commits:

6f94b5a [Thomas Graves] make unit test more robust
28b9dd3 [Thomas Graves] yarn - pass acls along with executor launch
---
 .../spark/deploy/yarn/ExecutorRunnable.scala  |  7 +-
 .../deploy/yarn/YarnAllocationHandler.scala   |  7 +-
 .../spark/deploy/yarn/YarnRMClientImpl.scala  |  7 +-
 .../spark/deploy/yarn/ApplicationMaster.scala | 13 ++--
 .../apache/spark/deploy/yarn/ClientBase.scala |  6 +-
 .../spark/deploy/yarn/YarnAllocator.scala     | 10 ++-
 .../spark/deploy/yarn/YarnRMClient.scala      |  5 +-
 .../deploy/yarn/YarnSparkHadoopUtil.scala     | 11 ++-
 .../yarn/YarnSparkHadoopUtilSuite.scala       | 76 ++++++++++++++++++-
 .../spark/deploy/yarn/ExecutorRunnable.scala  |  7 +-
 .../deploy/yarn/YarnAllocationHandler.scala   |  7 +-
 .../spark/deploy/yarn/YarnRMClientImpl.scala  |  7 +-
 12 files changed, 129 insertions(+), 34 deletions(-)

diff --git a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnable.scala b/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnable.scala
index 7dae248e3e7db..10cbeb8b94325 100644
--- a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnable.scala
+++ b/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnable.scala
@@ -35,7 +35,7 @@ import org.apache.hadoop.yarn.conf.YarnConfiguration
 import org.apache.hadoop.yarn.ipc.YarnRPC
 import org.apache.hadoop.yarn.util.{Apps, ConverterUtils, Records, ProtoUtils}
 
-import org.apache.spark.{SparkConf, Logging}
+import org.apache.spark.{SecurityManager, SparkConf, Logging}
 
 
 class ExecutorRunnable(
@@ -46,7 +46,8 @@ class ExecutorRunnable(
     slaveId: String,
     hostname: String,
     executorMemory: Int,
-    executorCores: Int)
+    executorCores: Int,
+    securityMgr: SecurityManager)
   extends Runnable with ExecutorRunnableUtil with Logging {
 
   var rpc: YarnRPC = YarnRPC.create(conf)
@@ -86,6 +87,8 @@ class ExecutorRunnable(
     logInfo("Setting up executor with commands: " + commands)
     ctx.setCommands(commands)
 
+    ctx.setApplicationACLs(YarnSparkHadoopUtil.getApplicationAclsForYarn(securityMgr))
+
     // Send the start request to the ContainerManager
     val startReq = Records.newRecord(classOf[StartContainerRequest])
     .asInstanceOf[StartContainerRequest]
diff --git a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocationHandler.scala b/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocationHandler.scala
index 9f9e16c06452b..85d6274df2fcb 100644
--- a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocationHandler.scala
+++ b/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocationHandler.scala
@@ -23,7 +23,7 @@ import java.util.concurrent.atomic.AtomicInteger
 import scala.collection.JavaConversions._
 import scala.collection.mutable.{ArrayBuffer, HashMap}
 
-import org.apache.spark.SparkConf
+import org.apache.spark.{SecurityManager, SparkConf}
 import org.apache.spark.scheduler.SplitInfo
 
 import org.apache.hadoop.conf.Configuration
@@ -41,8 +41,9 @@ private[yarn] class YarnAllocationHandler(
     resourceManager: AMRMProtocol,
     appAttemptId: ApplicationAttemptId,
     args: ApplicationMasterArguments,
-    preferredNodes: collection.Map[String, collection.Set[SplitInfo]])
-  extends YarnAllocator(conf, sparkConf, args, preferredNodes) {
+    preferredNodes: collection.Map[String, collection.Set[SplitInfo]],
+    securityMgr: SecurityManager)
+  extends YarnAllocator(conf, sparkConf, args, preferredNodes, securityMgr) {
 
   private val lastResponseId = new AtomicInteger()
   private val releaseList: CopyOnWriteArrayList[ContainerId] = new CopyOnWriteArrayList()
diff --git a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/YarnRMClientImpl.scala b/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/YarnRMClientImpl.scala
index cc5392192ec51..ad27a9ab781d2 100644
--- a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/YarnRMClientImpl.scala
+++ b/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/YarnRMClientImpl.scala
@@ -27,7 +27,7 @@ import org.apache.hadoop.yarn.conf.YarnConfiguration
 import org.apache.hadoop.yarn.ipc.YarnRPC
 import org.apache.hadoop.yarn.util.{ConverterUtils, Records}
 
-import org.apache.spark.{Logging, SparkConf}
+import org.apache.spark.{Logging, SecurityManager, SparkConf}
 import org.apache.spark.scheduler.SplitInfo
 import org.apache.spark.util.Utils
 
@@ -45,7 +45,8 @@ private class YarnRMClientImpl(args: ApplicationMasterArguments) extends YarnRMC
       sparkConf: SparkConf,
       preferredNodeLocations: Map[String, Set[SplitInfo]],
       uiAddress: String,
-      uiHistoryAddress: String) = {
+      uiHistoryAddress: String,
+      securityMgr: SecurityManager) = {
     this.rpc = YarnRPC.create(conf)
     this.uiHistoryAddress = uiHistoryAddress
 
@@ -53,7 +54,7 @@ private class YarnRMClientImpl(args: ApplicationMasterArguments) extends YarnRMC
     registerApplicationMaster(uiAddress)
 
     new YarnAllocationHandler(conf, sparkConf, resourceManager, getAttemptId(), args,
-      preferredNodeLocations)
+      preferredNodeLocations, securityMgr)
   }
 
   override def getAttemptId() = {
diff --git a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
index 98039a20de245..a879c833a014f 100644
--- a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
+++ b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
@@ -116,7 +116,7 @@ private[spark] class ApplicationMaster(args: ApplicationMasterArguments,
     val securityMgr = new SecurityManager(sparkConf)
 
     if (isDriver) {
-      runDriver()
+      runDriver(securityMgr)
     } else {
       runExecutorLauncher(securityMgr)
     }
@@ -157,7 +157,7 @@ private[spark] class ApplicationMaster(args: ApplicationMasterArguments,
     sparkContextRef.compareAndSet(sc, null)
   }
 
-  private def registerAM(uiAddress: String) = {
+  private def registerAM(uiAddress: String, securityMgr: SecurityManager) = {
     val sc = sparkContextRef.get()
 
     val appId = client.getAttemptId().getApplicationId().toString()
@@ -170,13 +170,14 @@ private[spark] class ApplicationMaster(args: ApplicationMasterArguments,
       if (sc != null) sc.getConf else sparkConf,
       if (sc != null) sc.preferredNodeLocationData else Map(),
       uiAddress,
-      historyAddress)
+      historyAddress,
+      securityMgr)
 
     allocator.allocateResources()
     reporterThread = launchReporterThread()
   }
 
-  private def runDriver(): Unit = {
+  private def runDriver(securityMgr: SecurityManager): Unit = {
     addAmIpFilter()
     val userThread = startUserClass()
 
@@ -188,7 +189,7 @@ private[spark] class ApplicationMaster(args: ApplicationMasterArguments,
     if (sc == null) {
       finish(FinalApplicationStatus.FAILED, "Timed out waiting for SparkContext.")
     } else {
-      registerAM(sc.ui.appUIHostPort)
+      registerAM(sc.ui.appUIHostPort, securityMgr)
       try {
         userThread.join()
       } finally {
@@ -203,7 +204,7 @@ private[spark] class ApplicationMaster(args: ApplicationMasterArguments,
       conf = sparkConf, securityManager = securityMgr)._1
     actor = waitForSparkDriver()
     addAmIpFilter()
-    registerAM(sparkConf.get("spark.driver.appUIAddress", ""))
+    registerAM(sparkConf.get("spark.driver.appUIAddress", ""), securityMgr)
 
     // In client mode the actor will stop the reporter thread.
     reporterThread.join()
diff --git a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala
index 5d8e5e6dffe7f..8075b7a7fb837 100644
--- a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala
+++ b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala
@@ -430,10 +430,8 @@ trait ClientBase extends Logging {
 
     // send the acl settings into YARN to control who has access via YARN interfaces
     val securityManager = new SecurityManager(sparkConf)
-    val acls = Map[ApplicationAccessType, String] (
-      ApplicationAccessType.VIEW_APP -> securityManager.getViewAcls,
-      ApplicationAccessType.MODIFY_APP -> securityManager.getModifyAcls)
-    amContainer.setApplicationACLs(acls)
+    amContainer.setApplicationACLs(YarnSparkHadoopUtil.getApplicationAclsForYarn(securityManager))
+
     amContainer
   }
 }
diff --git a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocator.scala b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocator.scala
index c74dd1c2b21dc..02b9a81bf6b50 100644
--- a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocator.scala
+++ b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocator.scala
@@ -28,7 +28,7 @@ import org.apache.hadoop.conf.Configuration
 import org.apache.hadoop.yarn.api.records._
 import org.apache.hadoop.yarn.api.protocolrecords.AllocateResponse
 
-import org.apache.spark.{Logging, SparkConf, SparkEnv}
+import org.apache.spark.{Logging, SecurityManager, SparkConf, SparkEnv}
 import org.apache.spark.scheduler.{SplitInfo, TaskSchedulerImpl}
 import org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend
 
@@ -55,7 +55,8 @@ private[yarn] abstract class YarnAllocator(
     conf: Configuration,
     sparkConf: SparkConf,
     args: ApplicationMasterArguments,
-    preferredNodes: collection.Map[String, collection.Set[SplitInfo]])
+    preferredNodes: collection.Map[String, collection.Set[SplitInfo]],
+    securityMgr: SecurityManager)
   extends Logging {
 
   // These three are locked on allocatedHostToContainersMap. Complementary data structures
@@ -280,7 +281,8 @@ private[yarn] abstract class YarnAllocator(
             executorId,
             executorHostname,
             executorMemory,
-            executorCores)
+            executorCores,
+            securityMgr)
           new Thread(executorRunnable).start()
         }
       }
@@ -444,4 +446,4 @@ private[yarn] abstract class YarnAllocator(
 
   }
 
-}
\ No newline at end of file
+}
diff --git a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/YarnRMClient.scala b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/YarnRMClient.scala
index 922d7d1a854a5..ed65e56b3e413 100644
--- a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/YarnRMClient.scala
+++ b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/YarnRMClient.scala
@@ -22,7 +22,7 @@ import scala.collection.{Map, Set}
 import org.apache.hadoop.yarn.conf.YarnConfiguration
 import org.apache.hadoop.yarn.api.records._
 
-import org.apache.spark.{SparkConf, SparkContext}
+import org.apache.spark.{SecurityManager, SparkConf, SparkContext}
 import org.apache.spark.scheduler.SplitInfo
 
 /**
@@ -45,7 +45,8 @@ trait YarnRMClient {
       sparkConf: SparkConf,
       preferredNodeLocations: Map[String, Set[SplitInfo]],
       uiAddress: String,
-      uiHistoryAddress: String): YarnAllocator
+      uiHistoryAddress: String,
+      securityMgr: SecurityManager): YarnAllocator
 
   /**
    * Shuts down the AM. Guaranteed to only be called once.
diff --git a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtil.scala b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtil.scala
index dc77f1236492d..4a33e34c3bfc7 100644
--- a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtil.scala
+++ b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtil.scala
@@ -32,10 +32,11 @@ import org.apache.hadoop.security.UserGroupInformation
 import org.apache.hadoop.util.StringInterner
 import org.apache.hadoop.yarn.conf.YarnConfiguration
 import org.apache.hadoop.yarn.api.ApplicationConstants
+import org.apache.hadoop.yarn.api.records.ApplicationAccessType
 import org.apache.hadoop.yarn.util.RackResolver
 import org.apache.hadoop.conf.Configuration
 
-import org.apache.spark.{SparkConf, SparkContext}
+import org.apache.spark.{SecurityManager, SparkConf, SparkContext}
 import org.apache.spark.deploy.SparkHadoopUtil
 import org.apache.spark.util.Utils
 
@@ -211,4 +212,12 @@ object YarnSparkHadoopUtil {
     }
   }
 
+  private[spark] def getApplicationAclsForYarn(securityMgr: SecurityManager):
+      Map[ApplicationAccessType, String] = {
+    Map[ApplicationAccessType, String] (
+      ApplicationAccessType.VIEW_APP -> securityMgr.getViewAcls,
+      ApplicationAccessType.MODIFY_APP -> securityMgr.getModifyAcls
+    )
+  }
+
 }
diff --git a/yarn/common/src/test/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtilSuite.scala b/yarn/common/src/test/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtilSuite.scala
index 75db8ee6d468f..2cc5abb3a890c 100644
--- a/yarn/common/src/test/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtilSuite.scala
+++ b/yarn/common/src/test/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtilSuite.scala
@@ -23,7 +23,10 @@ import com.google.common.io.{ByteStreams, Files}
 import org.apache.hadoop.yarn.conf.YarnConfiguration
 import org.scalatest.{FunSuite, Matchers}
 
-import org.apache.spark.{Logging, SparkConf}
+import org.apache.hadoop.yarn.api.records.ApplicationAccessType
+
+import org.apache.spark.{Logging, SecurityManager, SparkConf}
+
 
 class YarnSparkHadoopUtilSuite extends FunSuite with Matchers with Logging {
 
@@ -74,4 +77,75 @@ class YarnSparkHadoopUtilSuite extends FunSuite with Matchers with Logging {
     yarnConf.get(key) should not be default.get(key)
   }
 
+
+  test("test getApplicationAclsForYarn acls on") {
+
+    // spark acls on, just pick up default user
+    val sparkConf = new SparkConf()
+    sparkConf.set("spark.acls.enable", "true")
+
+    val securityMgr = new SecurityManager(sparkConf)
+    val acls = YarnSparkHadoopUtil.getApplicationAclsForYarn(securityMgr)
+
+    val viewAcls = acls.get(ApplicationAccessType.VIEW_APP)
+    val modifyAcls = acls.get(ApplicationAccessType.MODIFY_APP)
+
+    viewAcls match {
+      case Some(vacls) => {
+        val aclSet = vacls.split(',').map(_.trim).toSet
+        assert(aclSet.contains(System.getProperty("user.name", "invalid")))
+      }
+      case None => {
+        fail()
+      }
+    }
+    modifyAcls match {
+      case Some(macls) => {
+        val aclSet = macls.split(',').map(_.trim).toSet
+        assert(aclSet.contains(System.getProperty("user.name", "invalid")))
+      }
+      case None => {
+        fail()
+      }
+    }
+  }
+
+  test("test getApplicationAclsForYarn acls on and specify users") {
+
+    // default spark acls are on and specify acls
+    val sparkConf = new SparkConf()
+    sparkConf.set("spark.acls.enable", "true")
+    sparkConf.set("spark.ui.view.acls", "user1,user2")
+    sparkConf.set("spark.modify.acls", "user3,user4")
+
+    val securityMgr = new SecurityManager(sparkConf)
+    val acls = YarnSparkHadoopUtil.getApplicationAclsForYarn(securityMgr)
+
+    val viewAcls = acls.get(ApplicationAccessType.VIEW_APP)
+    val modifyAcls = acls.get(ApplicationAccessType.MODIFY_APP)
+
+    viewAcls match {
+      case Some(vacls) => {
+        val aclSet = vacls.split(',').map(_.trim).toSet
+        assert(aclSet.contains("user1"))
+        assert(aclSet.contains("user2"))
+        assert(aclSet.contains(System.getProperty("user.name", "invalid")))
+      }
+      case None => {
+        fail()
+      }
+    }
+    modifyAcls match {
+      case Some(macls) => {
+        val aclSet = macls.split(',').map(_.trim).toSet
+        assert(aclSet.contains("user3"))
+        assert(aclSet.contains("user4"))
+        assert(aclSet.contains(System.getProperty("user.name", "invalid")))
+      }
+      case None => {
+        fail()
+      }
+    }
+
+  }
 }
diff --git a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnable.scala b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnable.scala
index 07ba0a4b30bd7..833be12982e71 100644
--- a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnable.scala
+++ b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnable.scala
@@ -35,7 +35,7 @@ import org.apache.hadoop.yarn.conf.YarnConfiguration
 import org.apache.hadoop.yarn.ipc.YarnRPC
 import org.apache.hadoop.yarn.util.{Apps, ConverterUtils, Records}
 
-import org.apache.spark.{SparkConf, Logging}
+import org.apache.spark.{SecurityManager, SparkConf, Logging}
 
 
 class ExecutorRunnable(
@@ -46,7 +46,8 @@ class ExecutorRunnable(
     slaveId: String,
     hostname: String,
     executorMemory: Int,
-    executorCores: Int)
+    executorCores: Int,
+    securityMgr: SecurityManager)
   extends Runnable with ExecutorRunnableUtil with Logging {
 
   var rpc: YarnRPC = YarnRPC.create(conf)
@@ -85,6 +86,8 @@ class ExecutorRunnable(
     logInfo("Setting up executor with commands: " + commands)
     ctx.setCommands(commands)
 
+    ctx.setApplicationACLs(YarnSparkHadoopUtil.getApplicationAclsForYarn(securityMgr))
+
     // Send the start request to the ContainerManager
     nmClient.startContainer(container, ctx)
   }
diff --git a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocationHandler.scala b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocationHandler.scala
index ed31457b61571..c887cb52dd9cf 100644
--- a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocationHandler.scala
+++ b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocationHandler.scala
@@ -20,7 +20,7 @@ package org.apache.spark.deploy.yarn
 import scala.collection.JavaConversions._
 import scala.collection.mutable.{ArrayBuffer, HashMap}
 
-import org.apache.spark.SparkConf
+import org.apache.spark.{SecurityManager, SparkConf} 
 import org.apache.spark.scheduler.SplitInfo
 
 import org.apache.hadoop.conf.Configuration
@@ -39,8 +39,9 @@ private[yarn] class YarnAllocationHandler(
     amClient: AMRMClient[ContainerRequest],
     appAttemptId: ApplicationAttemptId,
     args: ApplicationMasterArguments,
-    preferredNodes: collection.Map[String, collection.Set[SplitInfo]])
-  extends YarnAllocator(conf, sparkConf, args, preferredNodes) {
+    preferredNodes: collection.Map[String, collection.Set[SplitInfo]], 
+    securityMgr: SecurityManager)
+  extends YarnAllocator(conf, sparkConf, args, preferredNodes, securityMgr) {
 
   override protected def releaseContainer(container: Container) = {
     amClient.releaseAssignedContainer(container.getId())
diff --git a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/YarnRMClientImpl.scala b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/YarnRMClientImpl.scala
index e8b8d9bc722bd..54bc6b14c44ce 100644
--- a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/YarnRMClientImpl.scala
+++ b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/YarnRMClientImpl.scala
@@ -28,7 +28,7 @@ import org.apache.hadoop.yarn.conf.YarnConfiguration
 import org.apache.hadoop.yarn.util.ConverterUtils
 import org.apache.hadoop.yarn.webapp.util.WebAppUtils
 
-import org.apache.spark.{Logging, SparkConf}
+import org.apache.spark.{Logging, SecurityManager, SparkConf}
 import org.apache.spark.scheduler.SplitInfo
 import org.apache.spark.util.Utils
 
@@ -46,7 +46,8 @@ private class YarnRMClientImpl(args: ApplicationMasterArguments) extends YarnRMC
       sparkConf: SparkConf,
       preferredNodeLocations: Map[String, Set[SplitInfo]],
       uiAddress: String,
-      uiHistoryAddress: String) = {
+      uiHistoryAddress: String,
+      securityMgr: SecurityManager) = {
     amClient = AMRMClient.createAMRMClient()
     amClient.init(conf)
     amClient.start()
@@ -55,7 +56,7 @@ private class YarnRMClientImpl(args: ApplicationMasterArguments) extends YarnRMC
     logInfo("Registering the ApplicationMaster")
     amClient.registerApplicationMaster(Utils.localHostName(), 0, uiAddress)
     new YarnAllocationHandler(conf, sparkConf, amClient, getAttemptId(), args,
-      preferredNodeLocations)
+      preferredNodeLocations, securityMgr)
   }
 
   override def shutdown(status: FinalApplicationStatus, diagnostics: String = "") =

From 62c557609929982eeec170fe12f810bedfcf97f2 Mon Sep 17 00:00:00 2001
From: Thomas Graves <tgraves@apache.org>
Date: Fri, 5 Sep 2014 09:56:22 -0500
Subject: [PATCH 209/489] [SPARK-3375] spark on yarn container allocation
 issues

If yarn doesn't get the containers immediately it stops asking for them and the yarn application hangs with never getting any executors.

The issue here is that we are sending the number of containers as 0 after we send the original one of X. on the yarn side this clears out the original request.

For a ping we should just send empty asks.

Author: Thomas Graves <tgraves@apache.org>

Closes #2275 from tgravescs/SPARK-3375 and squashes the following commits:

74b6820 [Thomas Graves] send empty resource requests when we aren't asking for containers
---
 .../spark/deploy/yarn/YarnAllocationHandler.scala   | 13 +++++++------
 .../spark/deploy/yarn/YarnAllocationHandler.scala   |  8 +++++---
 2 files changed, 12 insertions(+), 9 deletions(-)

diff --git a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocationHandler.scala b/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocationHandler.scala
index 85d6274df2fcb..5a1b42c1e17d5 100644
--- a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocationHandler.scala
+++ b/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocationHandler.scala
@@ -51,12 +51,13 @@ private[yarn] class YarnAllocationHandler(
   override protected def allocateContainers(count: Int): YarnAllocateResponse = {
     var resourceRequests: List[ResourceRequest] = null
 
-    // default.
-    if (count <= 0 || preferredHostToCount.isEmpty) {
-      logDebug("numExecutors: " + count + ", host preferences: " +
-        preferredHostToCount.isEmpty)
-      resourceRequests = List(createResourceRequest(
-        AllocationType.ANY, null, count, YarnSparkHadoopUtil.RM_REQUEST_PRIORITY))
+    logDebug("numExecutors: " + count)
+    if (count <= 0) {
+      resourceRequests = List()
+    } else if (preferredHostToCount.isEmpty) {
+        logDebug("host preferences is empty")
+        resourceRequests = List(createResourceRequest(
+          AllocationType.ANY, null, count, YarnSparkHadoopUtil.RM_REQUEST_PRIORITY))
     } else {
       // request for all hosts in preferred nodes and for numExecutors -
       // candidates.size, request by default allocation policy.
diff --git a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocationHandler.scala b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocationHandler.scala
index c887cb52dd9cf..5438f151ac0ad 100644
--- a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocationHandler.scala
+++ b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocationHandler.scala
@@ -88,9 +88,11 @@ private[yarn] class YarnAllocationHandler(
 
   private def addResourceRequests(numExecutors: Int) {
     val containerRequests: List[ContainerRequest] =
-      if (numExecutors <= 0 || preferredHostToCount.isEmpty) {
-        logDebug("numExecutors: " + numExecutors + ", host preferences: " +
-          preferredHostToCount.isEmpty)
+      if (numExecutors <= 0) {
+        logDebug("numExecutors: " + numExecutors)
+        List()
+      } else if (preferredHostToCount.isEmpty) {
+        logDebug("host preferences is empty")
         createResourceRequests(
           AllocationType.ANY,
           resource = null,

From 7ff8c45d714e0f2315910838b739c0c034672015 Mon Sep 17 00:00:00 2001
From: Kousuke Saruta <sarutak@oss.nttdata.co.jp>
Date: Fri, 5 Sep 2014 11:07:00 -0700
Subject: [PATCH 210/489] [SPARK-3399][PySpark] Test for PySpark should ignore
 HADOOP_CONF_DIR and YARN_CONF_DIR

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

Closes #2270 from sarutak/SPARK-3399 and squashes the following commits:

7613be6 [Kousuke Saruta] Modified pyspark script to ignore environment variables YARN_CONF_DIR and HADOOP_CONF_DIR while testing
---
 bin/pyspark | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/bin/pyspark b/bin/pyspark
index f553b314c5991..26a16dd600b7a 100755
--- a/bin/pyspark
+++ b/bin/pyspark
@@ -85,6 +85,8 @@ export PYSPARK_SUBMIT_ARGS
 
 # For pyspark tests
 if [[ -n "$SPARK_TESTING" ]]; then
+  unset YARN_CONF_DIR
+  unset HADOOP_CONF_DIR
   if [[ -n "$PYSPARK_DOC_TEST" ]]; then
     exec "$PYSPARK_PYTHON" -m doctest $1
   else

From ba5bcaddecd54811d45c5fc79a013b3857d4c633 Mon Sep 17 00:00:00 2001
From: Andrew Ash <andrew@andrewash.com>
Date: Fri, 5 Sep 2014 18:52:05 -0700
Subject: [PATCH 211/489] SPARK-3211 .take() is OOM-prone with empty partitions

Instead of jumping straight from 1 partition to all partitions, do exponential
growth and double the number of partitions to attempt each time instead.

Fix proposed by Paul Nepywoda

Author: Andrew Ash <andrew@andrewash.com>

Closes #2117 from ash211/SPARK-3211 and squashes the following commits:

8b2299a [Andrew Ash] Quadruple instead of double for a minor speedup
e5f7e4d [Andrew Ash] Update comment to better reflect what we're doing
09a27f7 [Andrew Ash] Update PySpark to be less OOM-prone as well
3a156b8 [Andrew Ash] SPARK-3211 .take() is OOM-prone with empty partitions
---
 core/src/main/scala/org/apache/spark/rdd/RDD.scala | 7 +++----
 python/pyspark/rdd.py                              | 8 ++++----
 2 files changed, 7 insertions(+), 8 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/rdd/RDD.scala b/core/src/main/scala/org/apache/spark/rdd/RDD.scala
index af9e31ba7b720..1cf55e86f6c81 100644
--- a/core/src/main/scala/org/apache/spark/rdd/RDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/RDD.scala
@@ -1064,11 +1064,10 @@ abstract class RDD[T: ClassTag](
       // greater than totalParts because we actually cap it at totalParts in runJob.
       var numPartsToTry = 1
       if (partsScanned > 0) {
-        // If we didn't find any rows after the first iteration, just try all partitions next.
-        // Otherwise, interpolate the number of partitions we need to try, but overestimate it
-        // by 50%.
+        // If we didn't find any rows after the previous iteration, quadruple and retry.  Otherwise,
+        // interpolate the number of partitions we need to try, but overestimate it by 50%.
         if (buf.size == 0) {
-          numPartsToTry = totalParts - 1
+          numPartsToTry = partsScanned * 4
         } else {
           numPartsToTry = (1.5 * num * partsScanned / buf.size).toInt
         }
diff --git a/python/pyspark/rdd.py b/python/pyspark/rdd.py
index dff6fc26fcb18..04f13523b431d 100644
--- a/python/pyspark/rdd.py
+++ b/python/pyspark/rdd.py
@@ -1089,11 +1089,11 @@ def take(self, num):
             # we actually cap it at totalParts in runJob.
             numPartsToTry = 1
             if partsScanned > 0:
-                # If we didn't find any rows after the first iteration, just
-                # try all partitions next. Otherwise, interpolate the number
-                # of partitions we need to try, but overestimate it by 50%.
+                # If we didn't find any rows after the previous iteration,
+                # quadruple and retry.  Otherwise, interpolate the number of
+                # partitions we need to try, but overestimate it by 50%.
                 if len(items) == 0:
-                    numPartsToTry = totalParts - 1
+                    numPartsToTry = partsScanned * 4
                 else:
                     numPartsToTry = int(1.5 * num * partsScanned / len(items))
 

From 19f61c165932059e7ce156da2c71429fa8dc27f0 Mon Sep 17 00:00:00 2001
From: Nicholas Chammas <nicholas.chammas@gmail.com>
Date: Fri, 5 Sep 2014 21:46:45 -0700
Subject: [PATCH 212/489] [Build] suppress curl/wget progress bars

In the Jenkins console output, `curl` gives us mountains of `#` symbols as it tries to show its download progress.

![noise from curl in Jenkins output](http://i.imgur.com/P2E7yUw.png)

I don't think this is useful so I've changed things to suppress these progress bars. If there is actually some use to this, feel free to reject this proposal.

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

Closes #2279 from nchammas/trim-test-output and squashes the following commits:

14a720c [Nicholas Chammas] suppress curl/wget progress bars
---
 dev/check-license       | 4 ++--
 sbt/sbt-launch-lib.bash | 4 ++--
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/dev/check-license b/dev/check-license
index 625ec161bc571..558e038afc01a 100755
--- a/dev/check-license
+++ b/dev/check-license
@@ -32,9 +32,9 @@ acquire_rat_jar () {
     printf "Attempting to fetch rat\n"
     JAR_DL=${JAR}.part
     if hash curl 2>/dev/null; then
-      (curl --progress-bar ${URL1} > "$JAR_DL" || curl --progress-bar ${URL2} > "$JAR_DL") && mv "$JAR_DL" "$JAR"
+      (curl --silent ${URL1} > "$JAR_DL" || curl --silent ${URL2} > "$JAR_DL") && mv "$JAR_DL" "$JAR"
     elif hash wget 2>/dev/null; then
-      (wget --progress=bar ${URL1} -O "$JAR_DL" || wget --progress=bar ${URL2} -O "$JAR_DL") && mv "$JAR_DL" "$JAR"
+      (wget --quiet ${URL1} -O "$JAR_DL" || wget --quiet ${URL2} -O "$JAR_DL") && mv "$JAR_DL" "$JAR"
     else
       printf "You do not have curl or wget installed, please install rat manually.\n"
       exit -1
diff --git a/sbt/sbt-launch-lib.bash b/sbt/sbt-launch-lib.bash
index c91fecf024ad4..fecc3d38a5fbd 100755
--- a/sbt/sbt-launch-lib.bash
+++ b/sbt/sbt-launch-lib.bash
@@ -51,9 +51,9 @@ acquire_sbt_jar () {
     printf "Attempting to fetch sbt\n"
     JAR_DL=${JAR}.part
     if hash curl 2>/dev/null; then
-      (curl --progress-bar ${URL1} > ${JAR_DL} || curl --progress-bar ${URL2} > ${JAR_DL}) && mv ${JAR_DL} ${JAR}
+      (curl --silent ${URL1} > ${JAR_DL} || curl --silent ${URL2} > ${JAR_DL}) && mv ${JAR_DL} ${JAR}
     elif hash wget 2>/dev/null; then
-      (wget --progress=bar ${URL1} -O ${JAR_DL} || wget --progress=bar ${URL2} -O ${JAR_DL}) && mv ${JAR_DL} ${JAR}
+      (wget --quiet ${URL1} -O ${JAR_DL} || wget --quiet ${URL2} -O ${JAR_DL}) && mv ${JAR_DL} ${JAR}
     else
       printf "You do not have curl or wget installed, please install sbt manually from http://www.scala-sbt.org/\n"
       exit -1

From 9422c4ee0eaf4a32d2ed7c96799feac2f5f79d40 Mon Sep 17 00:00:00 2001
From: Nicholas Chammas <nicholas.chammas@gmail.com>
Date: Fri, 5 Sep 2014 23:08:54 -0700
Subject: [PATCH 213/489] [SPARK-3361] Expand PEP 8 checks to include EC2
 script and Python examples

This PR resolves [SPARK-3361](https://issues.apache.org/jira/browse/SPARK-3361) by expanding the PEP 8 checks to cover the remaining Python code base:
* The EC2 script
* All Python / PySpark examples

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

Closes #2297 from nchammas/pep8-rulez and squashes the following commits:

1e5ac9a [Nicholas Chammas] PEP 8 fixes to Python examples
c3dbeff [Nicholas Chammas] PEP 8 fixes to EC2 script
65ef6e8 [Nicholas Chammas] expand PEP 8 checks
---
 dev/lint-python                               |  5 ++--
 ec2/spark_ec2.py                              | 20 +++++++++++-----
 examples/src/main/python/avro_inputformat.py  | 17 +++++++++-----
 .../src/main/python/cassandra_inputformat.py  | 15 ++++++------
 .../src/main/python/cassandra_outputformat.py | 23 ++++++++++---------
 examples/src/main/python/hbase_inputformat.py | 10 +++++---
 .../src/main/python/hbase_outputformat.py     | 18 +++++++++------
 .../src/main/python/mllib/correlations.py     |  2 +-
 .../main/python/mllib/decision_tree_runner.py |  6 +++--
 .../python/mllib/random_rdd_generation.py     |  6 ++---
 .../src/main/python/mllib/sampled_rdds.py     |  8 +++----
 examples/src/main/python/pi.py                |  2 +-
 12 files changed, 79 insertions(+), 53 deletions(-)

diff --git a/dev/lint-python b/dev/lint-python
index a1e890faa8fa6..79bf70f0b8b13 100755
--- a/dev/lint-python
+++ b/dev/lint-python
@@ -30,6 +30,7 @@ cd $SPARK_ROOT_DIR
 #+  - Download this from a more reliable source. (GitHub raw can be flaky, apparently. (?))
 PEP8_SCRIPT_PATH="$SPARK_ROOT_DIR/dev/pep8.py"
 PEP8_SCRIPT_REMOTE_PATH="https://raw.githubusercontent.com/jcrocholl/pep8/1.5.7/pep8.py"
+PEP8_PATHS_TO_CHECK="./python/pyspark/ ./ec2/spark_ec2.py ./examples/src/main/python/"
 
 curl --silent -o "$PEP8_SCRIPT_PATH" "$PEP8_SCRIPT_REMOTE_PATH"    
 curl_status=$?
@@ -44,7 +45,7 @@ fi
 #+ first, but we do so so that the check status can
 #+ be output before the report, like with the
 #+ scalastyle and RAT checks.
-python $PEP8_SCRIPT_PATH ./python/pyspark > "$PEP8_REPORT_PATH"
+python $PEP8_SCRIPT_PATH $PEP8_PATHS_TO_CHECK > "$PEP8_REPORT_PATH"
 pep8_status=${PIPESTATUS[0]} #$?
 
 if [ $pep8_status -ne 0 ]; then
@@ -54,7 +55,7 @@ else
     echo "PEP 8 checks passed."
 fi
 
-rm -f "$PEP8_REPORT_PATH"
+rm "$PEP8_REPORT_PATH"
 rm "$PEP8_SCRIPT_PATH"
 
 exit $pep8_status
diff --git a/ec2/spark_ec2.py b/ec2/spark_ec2.py
index 1670faca4a480..8ec88d95e34af 100755
--- a/ec2/spark_ec2.py
+++ b/ec2/spark_ec2.py
@@ -41,6 +41,7 @@
 # A URL prefix from which to fetch AMI information
 AMI_PREFIX = "https://raw.github.com/mesos/spark-ec2/v2/ami-list"
 
+
 class UsageError(Exception):
     pass
 
@@ -342,7 +343,6 @@ def launch_cluster(conn, opts, cluster_name):
     if opts.ami is None:
         opts.ami = get_spark_ami(opts)
 
-
     additional_groups = []
     if opts.additional_security_group:
         additional_groups = [sg
@@ -363,7 +363,7 @@ def launch_cluster(conn, opts, cluster_name):
         for i in range(opts.ebs_vol_num):
             device = EBSBlockDeviceType()
             device.size = opts.ebs_vol_size
-            device.volume_type=opts.ebs_vol_type
+            device.volume_type = opts.ebs_vol_type
             device.delete_on_termination = True
             block_map["/dev/sd" + chr(ord('s') + i)] = device
 
@@ -495,6 +495,7 @@ def launch_cluster(conn, opts, cluster_name):
     # Return all the instances
     return (master_nodes, slave_nodes)
 
+
 def tag_instance(instance, name):
     for i in range(0, 5):
         try:
@@ -507,9 +508,12 @@ def tag_instance(instance, name):
 
 # Get the EC2 instances in an existing cluster if available.
 # Returns a tuple of lists of EC2 instance objects for the masters and slaves
+
+
 def get_existing_cluster(conn, opts, cluster_name, die_on_error=True):
     print "Searching for existing cluster " + cluster_name + "..."
-    # Search all the spot instance requests, and copy any tags from the spot instance request to the cluster.
+    # Search all the spot instance requests, and copy any tags from the spot
+    # instance request to the cluster.
     spot_instance_requests = conn.get_all_spot_instance_requests()
     for req in spot_instance_requests:
         if req.state != u'active':
@@ -520,7 +524,7 @@ def get_existing_cluster(conn, opts, cluster_name, die_on_error=True):
             for res in reservations:
                 active = [i for i in res.instances if is_active(i)]
                 for instance in active:
-                    if (instance.tags.get(u'Name') == None):
+                    if (instance.tags.get(u'Name') is None):
                         tag_instance(instance, name)
     # Now proceed to detect master and slaves instances.
     reservations = conn.get_all_instances()
@@ -540,13 +544,16 @@ def get_existing_cluster(conn, opts, cluster_name, die_on_error=True):
         return (master_nodes, slave_nodes)
     else:
         if master_nodes == [] and slave_nodes != []:
-            print >> sys.stderr, "ERROR: Could not find master in with name " + cluster_name + "-master"
+            print >> sys.stderr, "ERROR: Could not find master in with name " + \
+                cluster_name + "-master"
         else:
             print >> sys.stderr, "ERROR: Could not find any existing cluster"
         sys.exit(1)
 
 # Deploy configuration files and run setup scripts on a newly launched
 # or started EC2 cluster.
+
+
 def setup_cluster(conn, master_nodes, slave_nodes, opts, deploy_ssh_key):
     master = master_nodes[0].public_dns_name
     if deploy_ssh_key:
@@ -890,7 +897,8 @@ def real_main():
                 if opts.security_group_prefix is None:
                     group_names = [cluster_name + "-master", cluster_name + "-slaves"]
                 else:
-                    group_names = [opts.security_group_prefix + "-master", opts.security_group_prefix + "-slaves"]
+                    group_names = [opts.security_group_prefix + "-master",
+                                   opts.security_group_prefix + "-slaves"]
 
                 attempt = 1
                 while attempt <= 3:
diff --git a/examples/src/main/python/avro_inputformat.py b/examples/src/main/python/avro_inputformat.py
index e902ae29753c0..cfda8d8327aa3 100644
--- a/examples/src/main/python/avro_inputformat.py
+++ b/examples/src/main/python/avro_inputformat.py
@@ -23,7 +23,8 @@
 Read data file users.avro in local Spark distro:
 
 $ cd $SPARK_HOME
-$ ./bin/spark-submit --driver-class-path /path/to/example/jar ./examples/src/main/python/avro_inputformat.py \
+$ ./bin/spark-submit --driver-class-path /path/to/example/jar \
+> ./examples/src/main/python/avro_inputformat.py \
 > examples/src/main/resources/users.avro
 {u'favorite_color': None, u'name': u'Alyssa', u'favorite_numbers': [3, 9, 15, 20]}
 {u'favorite_color': u'red', u'name': u'Ben', u'favorite_numbers': []}
@@ -40,7 +41,8 @@
  ]
 }
 
-$ ./bin/spark-submit --driver-class-path /path/to/example/jar ./examples/src/main/python/avro_inputformat.py \
+$ ./bin/spark-submit --driver-class-path /path/to/example/jar \
+> ./examples/src/main/python/avro_inputformat.py \
 > examples/src/main/resources/users.avro examples/src/main/resources/user.avsc
 {u'favorite_color': None, u'name': u'Alyssa'}
 {u'favorite_color': u'red', u'name': u'Ben'}
@@ -51,8 +53,10 @@
         Usage: avro_inputformat <data_file> [reader_schema_file]
 
         Run with example jar:
-        ./bin/spark-submit --driver-class-path /path/to/example/jar /path/to/examples/avro_inputformat.py <data_file> [reader_schema_file]
-        Assumes you have Avro data stored in <data_file>. Reader schema can be optionally specified in [reader_schema_file].
+        ./bin/spark-submit --driver-class-path /path/to/example/jar \
+        /path/to/examples/avro_inputformat.py <data_file> [reader_schema_file]
+        Assumes you have Avro data stored in <data_file>. Reader schema can be optionally specified
+        in [reader_schema_file].
         """
         exit(-1)
 
@@ -62,9 +66,10 @@
     conf = None
     if len(sys.argv) == 3:
         schema_rdd = sc.textFile(sys.argv[2], 1).collect()
-        conf = {"avro.schema.input.key" : reduce(lambda x, y: x+y, schema_rdd)}
+        conf = {"avro.schema.input.key": reduce(lambda x, y: x + y, schema_rdd)}
 
-    avro_rdd = sc.newAPIHadoopFile(path,
+    avro_rdd = sc.newAPIHadoopFile(
+        path,
         "org.apache.avro.mapreduce.AvroKeyInputFormat",
         "org.apache.avro.mapred.AvroKey",
         "org.apache.hadoop.io.NullWritable",
diff --git a/examples/src/main/python/cassandra_inputformat.py b/examples/src/main/python/cassandra_inputformat.py
index e4a897f61e39d..05f34b74df45a 100644
--- a/examples/src/main/python/cassandra_inputformat.py
+++ b/examples/src/main/python/cassandra_inputformat.py
@@ -51,7 +51,8 @@
         Usage: cassandra_inputformat <host> <keyspace> <cf>
 
         Run with example jar:
-        ./bin/spark-submit --driver-class-path /path/to/example/jar /path/to/examples/cassandra_inputformat.py <host> <keyspace> <cf>
+        ./bin/spark-submit --driver-class-path /path/to/example/jar \
+        /path/to/examples/cassandra_inputformat.py <host> <keyspace> <cf>
         Assumes you have some data in Cassandra already, running on <host>, in <keyspace> and <cf>
         """
         exit(-1)
@@ -61,12 +62,12 @@
     cf = sys.argv[3]
     sc = SparkContext(appName="CassandraInputFormat")
 
-    conf = {"cassandra.input.thrift.address":host,
-            "cassandra.input.thrift.port":"9160",
-            "cassandra.input.keyspace":keyspace,
-            "cassandra.input.columnfamily":cf,
-            "cassandra.input.partitioner.class":"Murmur3Partitioner",
-            "cassandra.input.page.row.size":"3"}
+    conf = {"cassandra.input.thrift.address": host,
+            "cassandra.input.thrift.port": "9160",
+            "cassandra.input.keyspace": keyspace,
+            "cassandra.input.columnfamily": cf,
+            "cassandra.input.partitioner.class": "Murmur3Partitioner",
+            "cassandra.input.page.row.size": "3"}
     cass_rdd = sc.newAPIHadoopRDD(
         "org.apache.cassandra.hadoop.cql3.CqlPagingInputFormat",
         "java.util.Map",
diff --git a/examples/src/main/python/cassandra_outputformat.py b/examples/src/main/python/cassandra_outputformat.py
index 836c35b5c6794..d144539e58b8f 100644
--- a/examples/src/main/python/cassandra_outputformat.py
+++ b/examples/src/main/python/cassandra_outputformat.py
@@ -50,7 +50,8 @@
         Usage: cassandra_outputformat <host> <keyspace> <cf> <user_id> <fname> <lname>
 
         Run with example jar:
-        ./bin/spark-submit --driver-class-path /path/to/example/jar /path/to/examples/cassandra_outputformat.py <args>
+        ./bin/spark-submit --driver-class-path /path/to/example/jar \
+        /path/to/examples/cassandra_outputformat.py <args>
         Assumes you have created the following table <cf> in Cassandra already,
         running on <host>, in <keyspace>.
 
@@ -67,16 +68,16 @@
     cf = sys.argv[3]
     sc = SparkContext(appName="CassandraOutputFormat")
 
-    conf = {"cassandra.output.thrift.address":host,
-            "cassandra.output.thrift.port":"9160",
-            "cassandra.output.keyspace":keyspace,
-            "cassandra.output.partitioner.class":"Murmur3Partitioner",
-            "cassandra.output.cql":"UPDATE " + keyspace + "." + cf + " SET fname = ?, lname = ?",
-            "mapreduce.output.basename":cf,
-            "mapreduce.outputformat.class":"org.apache.cassandra.hadoop.cql3.CqlOutputFormat",
-            "mapreduce.job.output.key.class":"java.util.Map",
-            "mapreduce.job.output.value.class":"java.util.List"}
-    key = {"user_id" : int(sys.argv[4])}
+    conf = {"cassandra.output.thrift.address": host,
+            "cassandra.output.thrift.port": "9160",
+            "cassandra.output.keyspace": keyspace,
+            "cassandra.output.partitioner.class": "Murmur3Partitioner",
+            "cassandra.output.cql": "UPDATE " + keyspace + "." + cf + " SET fname = ?, lname = ?",
+            "mapreduce.output.basename": cf,
+            "mapreduce.outputformat.class": "org.apache.cassandra.hadoop.cql3.CqlOutputFormat",
+            "mapreduce.job.output.key.class": "java.util.Map",
+            "mapreduce.job.output.value.class": "java.util.List"}
+    key = {"user_id": int(sys.argv[4])}
     sc.parallelize([(key, sys.argv[5:])]).saveAsNewAPIHadoopDataset(
         conf=conf,
         keyConverter="org.apache.spark.examples.pythonconverters.ToCassandraCQLKeyConverter",
diff --git a/examples/src/main/python/hbase_inputformat.py b/examples/src/main/python/hbase_inputformat.py
index befacee0dea56..3b16010f1cb97 100644
--- a/examples/src/main/python/hbase_inputformat.py
+++ b/examples/src/main/python/hbase_inputformat.py
@@ -51,7 +51,8 @@
         Usage: hbase_inputformat <host> <table>
 
         Run with example jar:
-        ./bin/spark-submit --driver-class-path /path/to/example/jar /path/to/examples/hbase_inputformat.py <host> <table>
+        ./bin/spark-submit --driver-class-path /path/to/example/jar \
+        /path/to/examples/hbase_inputformat.py <host> <table>
         Assumes you have some data in HBase already, running on <host>, in <table>
         """
         exit(-1)
@@ -61,12 +62,15 @@
     sc = SparkContext(appName="HBaseInputFormat")
 
     conf = {"hbase.zookeeper.quorum": host, "hbase.mapreduce.inputtable": table}
+    keyConv = "org.apache.spark.examples.pythonconverters.ImmutableBytesWritableToStringConverter"
+    valueConv = "org.apache.spark.examples.pythonconverters.HBaseResultToStringConverter"
+
     hbase_rdd = sc.newAPIHadoopRDD(
         "org.apache.hadoop.hbase.mapreduce.TableInputFormat",
         "org.apache.hadoop.hbase.io.ImmutableBytesWritable",
         "org.apache.hadoop.hbase.client.Result",
-        keyConverter="org.apache.spark.examples.pythonconverters.ImmutableBytesWritableToStringConverter",
-        valueConverter="org.apache.spark.examples.pythonconverters.HBaseResultToStringConverter",
+        keyConverter=keyConv,
+        valueConverter=valueConv,
         conf=conf)
     output = hbase_rdd.collect()
     for (k, v) in output:
diff --git a/examples/src/main/python/hbase_outputformat.py b/examples/src/main/python/hbase_outputformat.py
index 49bbc5aebdb0b..abb425b1f886a 100644
--- a/examples/src/main/python/hbase_outputformat.py
+++ b/examples/src/main/python/hbase_outputformat.py
@@ -44,8 +44,10 @@
         Usage: hbase_outputformat <host> <table> <row> <family> <qualifier> <value>
 
         Run with example jar:
-        ./bin/spark-submit --driver-class-path /path/to/example/jar /path/to/examples/hbase_outputformat.py <args>
-        Assumes you have created <table> with column family <family> in HBase running on <host> already
+        ./bin/spark-submit --driver-class-path /path/to/example/jar \
+        /path/to/examples/hbase_outputformat.py <args>
+        Assumes you have created <table> with column family <family> in HBase
+        running on <host> already
         """
         exit(-1)
 
@@ -55,13 +57,15 @@
 
     conf = {"hbase.zookeeper.quorum": host,
             "hbase.mapred.outputtable": table,
-            "mapreduce.outputformat.class" : "org.apache.hadoop.hbase.mapreduce.TableOutputFormat",
-            "mapreduce.job.output.key.class" : "org.apache.hadoop.hbase.io.ImmutableBytesWritable",
-            "mapreduce.job.output.value.class" : "org.apache.hadoop.io.Writable"}
+            "mapreduce.outputformat.class": "org.apache.hadoop.hbase.mapreduce.TableOutputFormat",
+            "mapreduce.job.output.key.class": "org.apache.hadoop.hbase.io.ImmutableBytesWritable",
+            "mapreduce.job.output.value.class": "org.apache.hadoop.io.Writable"}
+    keyConv = "org.apache.spark.examples.pythonconverters.StringToImmutableBytesWritableConverter"
+    valueConv = "org.apache.spark.examples.pythonconverters.StringListToPutConverter"
 
     sc.parallelize([sys.argv[3:]]).map(lambda x: (x[0], x)).saveAsNewAPIHadoopDataset(
         conf=conf,
-        keyConverter="org.apache.spark.examples.pythonconverters.StringToImmutableBytesWritableConverter",
-        valueConverter="org.apache.spark.examples.pythonconverters.StringListToPutConverter")
+        keyConverter=keyConv,
+        valueConverter=valueConv)
 
     sc.stop()
diff --git a/examples/src/main/python/mllib/correlations.py b/examples/src/main/python/mllib/correlations.py
index 6b16a56e44af7..4218eca822a99 100755
--- a/examples/src/main/python/mllib/correlations.py
+++ b/examples/src/main/python/mllib/correlations.py
@@ -28,7 +28,7 @@
 
 
 if __name__ == "__main__":
-    if len(sys.argv) not in [1,2]:
+    if len(sys.argv) not in [1, 2]:
         print >> sys.stderr, "Usage: correlations (<file>)"
         exit(-1)
     sc = SparkContext(appName="PythonCorrelations")
diff --git a/examples/src/main/python/mllib/decision_tree_runner.py b/examples/src/main/python/mllib/decision_tree_runner.py
index 6e4a4a0cb6be0..61ea4e06ecf3a 100755
--- a/examples/src/main/python/mllib/decision_tree_runner.py
+++ b/examples/src/main/python/mllib/decision_tree_runner.py
@@ -21,7 +21,9 @@
 This example requires NumPy (http://www.numpy.org/).
 """
 
-import numpy, os, sys
+import numpy
+import os
+import sys
 
 from operator import add
 
@@ -127,7 +129,7 @@ def usage():
     (reindexedData, origToNewLabels) = reindexClassLabels(points)
 
     # Train a classifier.
-    categoricalFeaturesInfo={} # no categorical features
+    categoricalFeaturesInfo = {}  # no categorical features
     model = DecisionTree.trainClassifier(reindexedData, numClasses=2,
                                          categoricalFeaturesInfo=categoricalFeaturesInfo)
     # Print learned tree and stats.
diff --git a/examples/src/main/python/mllib/random_rdd_generation.py b/examples/src/main/python/mllib/random_rdd_generation.py
index b388d8d83fb86..1e8892741e714 100755
--- a/examples/src/main/python/mllib/random_rdd_generation.py
+++ b/examples/src/main/python/mllib/random_rdd_generation.py
@@ -32,8 +32,8 @@
 
     sc = SparkContext(appName="PythonRandomRDDGeneration")
 
-    numExamples = 10000 # number of examples to generate
-    fraction = 0.1 # fraction of data to sample
+    numExamples = 10000  # number of examples to generate
+    fraction = 0.1  # fraction of data to sample
 
     # Example: RandomRDDs.normalRDD
     normalRDD = RandomRDDs.normalRDD(sc, numExamples)
@@ -45,7 +45,7 @@
     print
 
     # Example: RandomRDDs.normalVectorRDD
-    normalVectorRDD = RandomRDDs.normalVectorRDD(sc, numRows = numExamples, numCols = 2)
+    normalVectorRDD = RandomRDDs.normalVectorRDD(sc, numRows=numExamples, numCols=2)
     print 'Generated RDD of %d examples of length-2 vectors.' % normalVectorRDD.count()
     print '  First 5 samples:'
     for sample in normalVectorRDD.take(5):
diff --git a/examples/src/main/python/mllib/sampled_rdds.py b/examples/src/main/python/mllib/sampled_rdds.py
index ec64a5978c672..92af3af5ebd1e 100755
--- a/examples/src/main/python/mllib/sampled_rdds.py
+++ b/examples/src/main/python/mllib/sampled_rdds.py
@@ -36,7 +36,7 @@
 
     sc = SparkContext(appName="PythonSampledRDDs")
 
-    fraction = 0.1 # fraction of data to sample
+    fraction = 0.1  # fraction of data to sample
 
     examples = MLUtils.loadLibSVMFile(sc, datapath)
     numExamples = examples.count()
@@ -49,9 +49,9 @@
     expectedSampleSize = int(numExamples * fraction)
     print 'Sampling RDD using fraction %g.  Expected sample size = %d.' \
         % (fraction, expectedSampleSize)
-    sampledRDD = examples.sample(withReplacement = True, fraction = fraction)
+    sampledRDD = examples.sample(withReplacement=True, fraction=fraction)
     print '  RDD.sample(): sample has %d examples' % sampledRDD.count()
-    sampledArray = examples.takeSample(withReplacement = True, num = expectedSampleSize)
+    sampledArray = examples.takeSample(withReplacement=True, num=expectedSampleSize)
     print '  RDD.takeSample(): sample has %d examples' % len(sampledArray)
 
     print
@@ -66,7 +66,7 @@
     fractions = {}
     for k in keyCountsA.keys():
         fractions[k] = fraction
-    sampledByKeyRDD = keyedRDD.sampleByKey(withReplacement = True, fractions = fractions)
+    sampledByKeyRDD = keyedRDD.sampleByKey(withReplacement=True, fractions=fractions)
     keyCountsB = sampledByKeyRDD.countByKey()
     sizeB = sum(keyCountsB.values())
     print '  Sampled %d examples using approximate stratified sampling (by label). ==> Sample' \
diff --git a/examples/src/main/python/pi.py b/examples/src/main/python/pi.py
index fc37459dc74aa..ee9036adfa281 100755
--- a/examples/src/main/python/pi.py
+++ b/examples/src/main/python/pi.py
@@ -35,7 +35,7 @@ def f(_):
         y = random() * 2 - 1
         return 1 if x ** 2 + y ** 2 < 1 else 0
 
-    count = sc.parallelize(xrange(1, n+1), slices).map(f).reduce(add)
+    count = sc.parallelize(xrange(1, n + 1), slices).map(f).reduce(add)
     print "Pi is roughly %f" % (4.0 * count / n)
 
     sc.stop()

From 1b9001f78d96faefff02b846b169c249d9e4d612 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@apache.org>
Date: Sat, 6 Sep 2014 00:33:00 -0700
Subject: [PATCH 214/489] [SPARK-3409][SQL] Avoid pulling in Exchange operator
 itself in Exchange's closures.

This is a tiny teeny optimization to move the if check of sortBasedShuffledOn to outside the closures so the closures don't need to pull in the entire Exchange operator object.

Author: Reynold Xin <rxin@apache.org>

Closes #2282 from rxin/SPARK-3409 and squashes the following commits:

1de3f88 [Reynold Xin] [SPARK-3409][SQL] Avoid pulling in Exchange operator itself in Exchange's closures.
---
 .../apache/spark/sql/execution/Exchange.scala | 43 +++++++++----------
 1 file changed, 21 insertions(+), 22 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala
index 4802e40595807..927f40063e47e 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala
@@ -36,25 +36,23 @@ case class Exchange(newPartitioning: Partitioning, child: SparkPlan) extends Una
 
   override def outputPartitioning = newPartitioning
 
-  def output = child.output
+  override def output = child.output
 
   /** We must copy rows when sort based shuffle is on */
   protected def sortBasedShuffleOn = SparkEnv.get.shuffleManager.isInstanceOf[SortShuffleManager]
 
-  def execute() = attachTree(this , "execute") {
+  override def execute() = attachTree(this , "execute") {
     newPartitioning match {
       case HashPartitioning(expressions, numPartitions) =>
         // TODO: Eliminate redundant expressions in grouping key and value.
-        val rdd = child.execute().mapPartitions { iter =>
-          if (sortBasedShuffleOn) {
-            @transient val hashExpressions =
-              newProjection(expressions, child.output)
-
+        val rdd = if (sortBasedShuffleOn) {
+          child.execute().mapPartitions { iter =>
+            val hashExpressions = newProjection(expressions, child.output)
             iter.map(r => (hashExpressions(r), r.copy()))
-          } else {
-            @transient val hashExpressions =
-              newMutableProjection(expressions, child.output)()
-
+          }
+        } else {
+          child.execute().mapPartitions { iter =>
+            val hashExpressions = newMutableProjection(expressions, child.output)()
             val mutablePair = new MutablePair[Row, Row]()
             iter.map(r => mutablePair.update(hashExpressions(r), r))
           }
@@ -65,17 +63,18 @@ case class Exchange(newPartitioning: Partitioning, child: SparkPlan) extends Una
         shuffled.map(_._2)
 
       case RangePartitioning(sortingExpressions, numPartitions) =>
-        // TODO: RangePartitioner should take an Ordering.
-        implicit val ordering = new RowOrdering(sortingExpressions, child.output)
-
-        val rdd = child.execute().mapPartitions { iter =>
-          if (sortBasedShuffleOn) {
-            iter.map(row => (row.copy(), null))
-          } else {
+        val rdd = if (sortBasedShuffleOn) {
+          child.execute().mapPartitions { iter => iter.map(row => (row.copy(), null))}
+        } else {
+          child.execute().mapPartitions { iter =>
             val mutablePair = new MutablePair[Row, Null](null, null)
             iter.map(row => mutablePair.update(row, null))
           }
         }
+
+        // TODO: RangePartitioner should take an Ordering.
+        implicit val ordering = new RowOrdering(sortingExpressions, child.output)
+
         val part = new RangePartitioner(numPartitions, rdd, ascending = true)
         val shuffled = new ShuffledRDD[Row, Null, Null](rdd, part)
         shuffled.setSerializer(new SparkSqlSerializer(new SparkConf(false)))
@@ -83,10 +82,10 @@ case class Exchange(newPartitioning: Partitioning, child: SparkPlan) extends Una
         shuffled.map(_._1)
 
       case SinglePartition =>
-        val rdd = child.execute().mapPartitions { iter =>
-          if (sortBasedShuffleOn) {
-            iter.map(r => (null, r.copy()))
-          } else {
+        val rdd = if (sortBasedShuffleOn) {
+          child.execute().mapPartitions { iter => iter.map(r => (null, r.copy())) }
+        } else {
+          child.execute().mapPartitions { iter =>
             val mutablePair = new MutablePair[Null, Row]()
             iter.map(r => mutablePair.update(null, r))
           }

From 0c681dd6b24431eb35770884e50f22ebaeaade33 Mon Sep 17 00:00:00 2001
From: Nicholas Chammas <nicholas.chammas@gmail.com>
Date: Sat, 6 Sep 2014 14:39:29 -0700
Subject: [PATCH 215/489] [EC2] don't duplicate default values

This PR makes two minor changes to the `spark-ec2` script:

1. The script's input parameter default values are duplicated into the help text. This is unnecessary. This PR replaces the duplicated info with the appropriate `optparse`  placeholder.
2. The default Spark version currently needs to be updated by hand during each release, which is known to be a faulty process. This PR places that default value in an easy-to-spot place.

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

Closes #2290 from nchammas/spark-ec2-default-version and squashes the following commits:

0c6d3bb [Nicholas Chammas] don't duplicate default values
---
 ec2/spark_ec2.py | 24 +++++++++++++-----------
 1 file changed, 13 insertions(+), 11 deletions(-)

diff --git a/ec2/spark_ec2.py b/ec2/spark_ec2.py
index 8ec88d95e34af..bfd07593b92ed 100755
--- a/ec2/spark_ec2.py
+++ b/ec2/spark_ec2.py
@@ -38,6 +38,8 @@
 from boto.ec2.blockdevicemapping import BlockDeviceMapping, BlockDeviceType, EBSBlockDeviceType
 from boto import ec2
 
+DEFAULT_SPARK_VERSION = "1.0.0"
+
 # A URL prefix from which to fetch AMI information
 AMI_PREFIX = "https://raw.github.com/mesos/spark-ec2/v2/ami-list"
 
@@ -57,10 +59,10 @@ def parse_args():
         help="Show this help message and exit")
     parser.add_option(
         "-s", "--slaves", type="int", default=1,
-        help="Number of slaves to launch (default: 1)")
+        help="Number of slaves to launch (default: %default)")
     parser.add_option(
         "-w", "--wait", type="int", default=120,
-        help="Seconds to wait for nodes to start (default: 120)")
+        help="Seconds to wait for nodes to start (default: %default)")
     parser.add_option(
         "-k", "--key-pair",
         help="Key pair to use on instances")
@@ -69,7 +71,7 @@ def parse_args():
         help="SSH private key file to use for logging into instances")
     parser.add_option(
         "-t", "--instance-type", default="m1.large",
-        help="Type of instance to launch (default: m1.large). " +
+        help="Type of instance to launch (default: %default). " +
              "WARNING: must be 64-bit; small instances won't work")
     parser.add_option(
         "-m", "--master-instance-type", default="",
@@ -84,15 +86,15 @@ def parse_args():
              "between zones applies)")
     parser.add_option("-a", "--ami", help="Amazon Machine Image ID to use")
     parser.add_option(
-        "-v", "--spark-version", default="1.0.0",
-        help="Version of Spark to use: 'X.Y.Z' or a specific git hash")
+        "-v", "--spark-version", default=DEFAULT_SPARK_VERSION,
+        help="Version of Spark to use: 'X.Y.Z' or a specific git hash (default: %default)")
     parser.add_option(
         "--spark-git-repo",
         default="https://github.com/apache/spark",
         help="Github repo from which to checkout supplied commit hash")
     parser.add_option(
         "--hadoop-major-version", default="1",
-        help="Major version of Hadoop (default: 1)")
+        help="Major version of Hadoop (default: %default)")
     parser.add_option(
         "-D", metavar="[ADDRESS:]PORT", dest="proxy_port",
         help="Use SSH dynamic port forwarding to create a SOCKS proxy at " +
@@ -116,21 +118,21 @@ def parse_args():
              "Only support up to 8 EBS volumes.")
     parser.add_option(
         "--swap", metavar="SWAP", type="int", default=1024,
-        help="Swap space to set up per node, in MB (default: 1024)")
+        help="Swap space to set up per node, in MB (default: %default)")
     parser.add_option(
         "--spot-price", metavar="PRICE", type="float",
         help="If specified, launch slaves as spot instances with the given " +
              "maximum price (in dollars)")
     parser.add_option(
         "--ganglia", action="store_true", default=True,
-        help="Setup Ganglia monitoring on cluster (default: on). NOTE: " +
+        help="Setup Ganglia monitoring on cluster (default: %default). NOTE: " +
              "the Ganglia page will be publicly accessible")
     parser.add_option(
         "--no-ganglia", action="store_false", dest="ganglia",
         help="Disable Ganglia monitoring for the cluster")
     parser.add_option(
         "-u", "--user", default="root",
-        help="The SSH user you want to connect as (default: root)")
+        help="The SSH user you want to connect as (default: %default)")
     parser.add_option(
         "--delete-groups", action="store_true", default=False,
         help="When destroying a cluster, delete the security groups that were created.")
@@ -139,7 +141,7 @@ def parse_args():
         help="Launch fresh slaves, but use an existing stopped master if possible")
     parser.add_option(
         "--worker-instances", type="int", default=1,
-        help="Number of instances per worker: variable SPARK_WORKER_INSTANCES (default: 1)")
+        help="Number of instances per worker: variable SPARK_WORKER_INSTANCES (default: %default)")
     parser.add_option(
         "--master-opts", type="string", default="",
         help="Extra options to give to master through SPARK_MASTER_OPTS variable " +
@@ -152,7 +154,7 @@ def parse_args():
         help="Use this prefix for the security group rather than the cluster name.")
     parser.add_option(
         "--authorized-address", type="string", default="0.0.0.0/0",
-        help="Address to authorize on created security groups (default: 0.0.0.0/0)")
+        help="Address to authorize on created security groups (default: %default)")
     parser.add_option(
         "--additional-security-group", type="string", default="",
         help="Additional security group to place the machines in")

From baff7e936101635d9bd4245e45335878bafb75e0 Mon Sep 17 00:00:00 2001
From: Tathagata Das <tathagata.das1565@gmail.com>
Date: Sat, 6 Sep 2014 14:46:43 -0700
Subject: [PATCH 216/489] [SPARK-2419][Streaming][Docs] More updates to the
 streaming programming guide

- Improvements to the kinesis integration guide from @cfregly
- More information about unified input dstreams in main guide

Author: Tathagata Das <tathagata.das1565@gmail.com>
Author: Chris Fregly <chris@fregly.com>

Closes #2307 from tdas/streaming-doc-fix1 and squashes the following commits:

ec40b5d [Tathagata Das] Updated figure with kinesis
fdb9c5e [Tathagata Das] Fixed style issues with kinesis guide
036d219 [Chris Fregly] updated kinesis docs and added an arch diagram
24f622a [Tathagata Das] More modifications.
---
 docs/img/streaming-arch.png           | Bin 78856 -> 78954 bytes
 docs/img/streaming-figures.pptx       | Bin 887545 -> 887551 bytes
 docs/img/streaming-kinesis-arch.png   | Bin 0 -> 115277 bytes
 docs/streaming-kinesis-integration.md |  94 ++++++++++++++++++--------
 docs/streaming-programming-guide.md   |  64 ++++++++++++++----
 5 files changed, 117 insertions(+), 41 deletions(-)
 create mode 100644 docs/img/streaming-kinesis-arch.png

diff --git a/docs/img/streaming-arch.png b/docs/img/streaming-arch.png
index bc57b460fdf8bb07eb290082a0655c1052834687..ac35f1d34cf3d0c94d039b16f2ab8aa0cedefabd 100644
GIT binary patch
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diff --git a/docs/img/streaming-figures.pptx b/docs/img/streaming-figures.pptx
index 1b18c2ee0ea3e6c07bdb24c0a5d5784c972631f7..d1cc25e379f461095f932728eefc60c0ff2d2dde 100644
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diff --git a/docs/streaming-kinesis-integration.md b/docs/streaming-kinesis-integration.md
index 079d4c5550537..c6090d9ec30c7 100644
--- a/docs/streaming-kinesis-integration.md
+++ b/docs/streaming-kinesis-integration.md
@@ -3,8 +3,8 @@ layout: global
 title: Spark Streaming + Kinesis Integration
 ---
 [Amazon Kinesis](http://aws.amazon.com/kinesis/) is a fully managed service for real-time processing of streaming data at massive scale.
-The Kinesis input DStream and receiver uses the Kinesis Client Library (KCL) provided by Amazon under the Amazon Software License (ASL).
-The KCL builds on top of the Apache 2.0 licensed AWS Java SDK and provides load-balancing, fault-tolerance, checkpointing through the concept of Workers, Checkpoints, and Shard Leases.
+The Kinesis receiver creates an input DStream using the Kinesis Client Library (KCL) provided by Amazon under the Amazon Software License (ASL).
+The KCL builds on top of the Apache 2.0 licensed AWS Java SDK and provides load-balancing, fault-tolerance, checkpointing through the concepts of Workers, Checkpoints, and Shard Leases.
 Here we explain how to configure Spark Streaming to receive data from Kinesis.
 
 #### Configuring Kinesis
@@ -15,7 +15,7 @@ A Kinesis stream can be set up at one of the valid Kinesis endpoints with 1 or m
 
 #### Configuring Spark Streaming Application
 
-1. **Linking:** In your SBT/Maven projrect definition, link your streaming application against the following artifact (see [Linking section](streaming-programming-guide.html#linking) in the main programming guide for further information).
+1. **Linking:** In your SBT/Maven project definition, link your streaming application against the following artifact (see [Linking section](streaming-programming-guide.html#linking) in the main programming guide for further information).
 
 		groupId = org.apache.spark
 		artifactId = spark-streaming-kinesis-asl_{{site.SCALA_BINARY_VERSION}}
@@ -23,10 +23,11 @@ A Kinesis stream can be set up at one of the valid Kinesis endpoints with 1 or m
 
 	**Note that by linking to this library, you will include [ASL](https://aws.amazon.com/asl/)-licensed code in your application.**
 
-2. **Programming:** In the streaming application code, import `KinesisUtils` and create input DStream as follows.
+2. **Programming:** In the streaming application code, import `KinesisUtils` and create the input DStream as follows:
 
 	<div class="codetabs">
 	<div data-lang="scala" markdown="1">
+		import org.apache.spark.streaming.Duration
 		import org.apache.spark.streaming.kinesis._
 		import com.amazonaws.services.kinesis.clientlibrary.lib.worker.InitialPositionInStream
 
@@ -34,11 +35,13 @@ A Kinesis stream can be set up at one of the valid Kinesis endpoints with 1 or m
         	streamingContext, [Kinesis stream name], [endpoint URL], [checkpoint interval], [initial position])
 
 	See the [API docs](api/scala/index.html#org.apache.spark.streaming.kinesis.KinesisUtils$)
-	and the [example]({{site.SPARK_GITHUB_URL}}/tree/master/extras/kinesis-asl/src/main/scala/org/apache/spark/examples/streaming/KinesisWordCountASL.scala). Refer to the next subsection for instructions to run the example.
+	and the [example]({{site.SPARK_GITHUB_URL}}/tree/master/extras/kinesis-asl/src/main/scala/org/apache/spark/examples/streaming/KinesisWordCountASL.scala). Refer to the Running the Example section for instructions on how to run the example.
 
 	</div>
 	<div data-lang="java" markdown="1">
-		import org.apache.spark.streaming.flume.*;
+		import org.apache.spark.streaming.Duration;
+		import org.apache.spark.streaming.kinesis.*;
+		import com.amazonaws.services.kinesis.clientlibrary.lib.worker.InitialPositionInStream;
 
 		JavaReceiverInputDStream<byte[]> kinesisStream = KinesisUtils.createStream(
         	streamingContext, [Kinesis stream name], [endpoint URL], [checkpoint interval], [initial position]);
@@ -49,36 +52,73 @@ A Kinesis stream can be set up at one of the valid Kinesis endpoints with 1 or m
 	</div>
 	</div>
 
-	`[endpoint URL]`: Valid Kinesis endpoints URL can be found [here](http://docs.aws.amazon.com/general/latest/gr/rande.html#ak_region).
+    - `streamingContext`: StreamingContext containg an application name used by Kinesis to tie this Kinesis application to the Kinesis stream
 
-	`[checkpoint interval]`: The interval at which the Kinesis client library is going to save its position in the stream. For starters, set it to the same as the batch interval of the streaming application.
+	- `[Kinesis stream name]`: The Kinesis stream that this streaming application receives from
+		- The application name used in the streaming context becomes the Kinesis application name
+		- The application name must be unique for a given account and region.
+		- The Kinesis backend automatically associates the application name to the Kinesis stream using a DynamoDB table (always in the us-east-1 region) created during Kinesis Client Library initialization. 
+		- Changing the application name or stream name can lead to Kinesis errors in some cases.  If you see errors, you may need to manually delete the DynamoDB table.
 
-	`[initial position]`: Can be either `InitialPositionInStream.TRIM_HORIZON` or `InitialPositionInStream.LATEST` (see later section and Amazon Kinesis API documentation for more details).
 
-	*Points to remember:*
+	- `[endpoint URL]`: Valid Kinesis endpoints URL can be found [here](http://docs.aws.amazon.com/general/latest/gr/rande.html#ak_region).
 
-	- The name used in the context of the streaming application must be unique for a given account and region. Changing the app name or stream name could lead to Kinesis errors as only a single logical application can process a single stream.
-	- A single Kinesis input DStream can receive many Kinesis shards by spinning up multiple KinesisRecordProcessor threads. Note that there is no correlation between number of shards in Kinesis and the number of partitions in the generated RDDs that is used for processing the data.
-	- You never need more KinesisReceivers than the number of shards in your stream as each will spin up at least one KinesisRecordProcessor thread.
-	- Horizontal scaling is achieved by autoscaling additional Kinesis input DStreams (separate processes) up to the number of current shards for a given stream, of course.
+	- `[checkpoint interval]`: The interval (e.g., Duration(2000) = 2 seconds) at which the Kinesis Client Library saves its position in the stream.  For starters, set it to the same as the batch interval of the streaming application.
 
-3. **Deploying:** Package `spark-streaming-flume_{{site.SCALA_BINARY_VERSION}}` and its dependencies (except `spark-core_{{site.SCALA_BINARY_VERSION}}` and `spark-streaming_{{site.SCALA_BINARY_VERSION}}` which are provided by `spark-submit`) into the application JAR. Then use `spark-submit` to launch your application (see [Deploying section](streaming-programming-guide.html#deploying-applications) in the main programming guide).
+	- `[initial position]`: Can be either `InitialPositionInStream.TRIM_HORIZON` or `InitialPositionInStream.LATEST` (see Kinesis Checkpointing section and Amazon Kinesis API documentation for more details).
 
-    - A DynamoDB table and CloudWatch namespace are created during KCL initialization using this Kinesis application name.  This DynamoDB table lives in the us-east-1 region regardless of the Kinesis endpoint URL. It is used to store KCL's checkpoint information.
 
-    - If you are seeing errors after changing the app name or stream name, it may be necessary to manually delete the DynamoDB table and start from scratch.
+3. **Deploying:** Package `spark-streaming-kinesis-asl_{{site.SCALA_BINARY_VERSION}}` and its dependencies (except `spark-core_{{site.SCALA_BINARY_VERSION}}` and `spark-streaming_{{site.SCALA_BINARY_VERSION}}` which are provided by `spark-submit`) into the application JAR. Then use `spark-submit` to launch your application (see [Deploying section](streaming-programming-guide.html#deploying-applications) in the main programming guide).
+
+	*Points to remember at runtime:*
+
+	- Kinesis data processing is ordered per partition and occurs at-least once per message.
+
+	- Multiple applications can read from the same Kinesis stream.  Kinesis will maintain the application-specific shard and checkpoint info in DynamodDB.
+
+	- A single Kinesis stream shard is processed by one input DStream at a time.
+
+	<p style="text-align: center;">
+  		<img src="img/streaming-kinesis-arch.png"
+       		title="Spark Streaming Kinesis Architecture"
+       		alt="Spark Streaming Kinesis Architecture"
+	       width="60%" 
+        />
+	  	<!-- Images are downsized intentionally to improve quality on retina displays -->
+	</p>
+
+	- A single Kinesis input DStream can read from multiple shards of a Kinesis stream by creating multiple KinesisRecordProcessor threads.
+
+	- Multiple input DStreams running in separate processes/instances can read from a Kinesis stream.
+
+	- You never need more Kinesis input DStreams than the number of Kinesis stream shards as each input DStream will create at least one KinesisRecordProcessor thread that handles a single shard.
+
+	- Horizontal scaling is achieved by adding/removing  Kinesis input DStreams (within a single process or across multiple processes/instances) - up to the total number of Kinesis stream shards per the previous point.
+
+	- The Kinesis input DStream will balance the load between all DStreams - even across processes/instances.
+
+	- The Kinesis input DStream will balance the load during re-shard events (merging and splitting) due to changes in load.
+
+	- As a best practice, it's recommended that you avoid re-shard jitter by over-provisioning when possible.
+
+	- Each Kinesis input DStream maintains its own checkpoint info.  See the Kinesis Checkpointing section for more details.
+
+	- There is no correlation between the number of Kinesis stream shards and the number of RDD partitions/shards created across the Spark cluster during input DStream processing.  These are 2 independent partitioning schemes.
 
 #### Running the Example
 To run the example,
+
 - Download Spark source and follow the [instructions](building-with-maven.html) to build Spark with profile *-Pkinesis-asl*.
 
-    mvn -Pkinesis-asl -DskipTests clean package
+        mvn -Pkinesis-asl -DskipTests clean package
+
 
-- Set up Kinesis stream (see earlier section). Note the name of the Kinesis stream, and the endpoint URL corresponding to the region the stream is based on.
+- Set up Kinesis stream (see earlier section) within AWS. Note the name of the Kinesis stream and the endpoint URL corresponding to the region where the stream was created.
 
 - Set up the environment variables AWS_ACCESS_KEY_ID and AWS_SECRET_KEY with your AWS credentials.
 
 - In the Spark root directory, run the example as
+
 	<div class="codetabs">
 	<div data-lang="scala" markdown="1">
 
@@ -92,19 +132,19 @@ To run the example,
 	</div>
 	</div>
 
-    This will wait for data to be received from Kinesis.
+    This will wait for data to be received from the Kinesis stream.
 
-- To generate random string data, in another terminal, run the associated Kinesis data producer.
+- To generate random string data to put onto the Kinesis stream, in another terminal, run the associated Kinesis data producer.
 
 		bin/run-example streaming.KinesisWordCountProducerASL [Kinesis stream name] [endpoint URL] 1000 10
 
-	This will push random words to the Kinesis stream, which should then be received and processed by the running example.
+	This will push 1000 lines per second of 10 random numbers per line to the Kinesis stream.  This data should then be received and processed by the running example.
 
 #### Kinesis Checkpointing
-The Kinesis receiver checkpoints the position of the stream that has been read periodically, so that the system can recover from failures and continue processing where it had left off. Checkpointing too frequently will cause excess load on the AWS checkpoint storage layer and may lead to AWS throttling.  The provided example handles this throttling with a random-backoff-retry strategy.
-
-- If no Kinesis checkpoint info exists, the KinesisReceiver will start either from the oldest record available (InitialPositionInStream.TRIM_HORIZON) or from the latest tip (InitialPostitionInStream.LATEST).  This is configurable.
+- Each Kinesis input DStream periodically stores the current position of the stream in the backing DynamoDB table.  This allows the system to recover from failures and continue processing where the DStream left off.
 
-- InitialPositionInStream.LATEST could lead to missed records if data is added to the stream while no KinesisReceivers are running (and no checkpoint info is being stored). In production, you'll want to switch to InitialPositionInStream.TRIM_HORIZON which will read up to 24 hours (Kinesis limit) of previous stream data.
+- Checkpointing too frequently will cause excess load on the AWS checkpoint storage layer and may lead to AWS throttling.  The provided example handles this throttling with a random-backoff-retry strategy.
 
-- InitialPositionInStream.TRIM_HORIZON may lead to duplicate processing of records where the impact is dependent on checkpoint frequency.
+- If no Kinesis checkpoint info exists when the input DStream starts, it will start either from the oldest record available (InitialPositionInStream.TRIM_HORIZON) or from the latest tip (InitialPostitionInStream.LATEST).  This is configurable.
+- InitialPositionInStream.LATEST could lead to missed records if data is added to the stream while no input DStreams are running (and no checkpoint info is being stored). 
+- InitialPositionInStream.TRIM_HORIZON may lead to duplicate processing of records where the impact is dependent on checkpoint frequency and processing idempotency.
diff --git a/docs/streaming-programming-guide.md b/docs/streaming-programming-guide.md
index 3d4bce49666ed..41f170580f452 100644
--- a/docs/streaming-programming-guide.md
+++ b/docs/streaming-programming-guide.md
@@ -233,7 +233,7 @@ $ ./bin/run-example streaming.NetworkWordCount localhost 9999
 </div>
 <div data-lang="java" markdown="1">
 {% highlight bash %}
-$ ./bin/run-example JavaNetworkWordCount localhost 9999
+$ ./bin/run-example streaming.JavaNetworkWordCount localhost 9999
 {% endhighlight %}
 </div>
 </div>
@@ -262,7 +262,7 @@ hello world
 {% highlight bash %}
 # TERMINAL 2: RUNNING NetworkWordCount or JavaNetworkWordCount
 
-$ ./bin/run-example org.apache.spark.examples.streaming.NetworkWordCount localhost 9999
+$ ./bin/run-example streaming.NetworkWordCount localhost 9999
 ...
 -------------------------------------------
 Time: 1357008430000 ms
@@ -285,12 +285,22 @@ need to know to write your streaming applications.
 
 ## Linking
 
-To write your own Spark Streaming program, you will have to add the following dependency to your
- SBT or Maven project:
+Similar to Spark, Spark Streaming is available through Maven Central. To write your own Spark Streaming program, you will have to add the following dependency to your SBT or Maven project.
+
+<div class="codetabs">
+<div data-lang="Maven" markdown="1">
 
-    groupId = org.apache.spark
-    artifactId = spark-streaming_{{site.SCALA_BINARY_VERSION}}
-    version = {{site.SPARK_VERSION}}
+	<dependency>
+        <groupId>org.apache.spark</groupId>
+        <artifactId>spark-streaming_{{site.SCALA_BINARY_VERSION}}</artifactId>
+        <version>{{site.SPARK_VERSION}}</version>
+    </dependency>
+</div>
+<div data-lang="SBT" markdown="1">
+
+	libraryDependencies += "org.apache.spark" % "spark-streaming_{{site.SCALA_BINARY_VERSION}}" % "{{site.SPARK_VERSION}}"
+</div>
+</div>
 
 For ingesting data from sources like Kafka, Flume, and Kinesis that are not present in the Spark
 Streaming core
@@ -302,7 +312,7 @@ some of the common ones are as follows.
 <tr><th>Source</th><th>Artifact</th></tr>
 <tr><td> Kafka </td><td> spark-streaming-kafka_{{site.SCALA_BINARY_VERSION}} </td></tr>
 <tr><td> Flume </td><td> spark-streaming-flume_{{site.SCALA_BINARY_VERSION}} </td></tr>
-<tr><td> Kinesis<br/></td><td>spark-streaming-kinesis-asl_{{site.SCALA_BINARY_VERSION}} </td></tr>
+<tr><td> Kinesis<br/></td><td>spark-streaming-kinesis-asl_{{site.SCALA_BINARY_VERSION}} [Apache Software License] </td></tr>
 <tr><td> Twitter </td><td> spark-streaming-twitter_{{site.SCALA_BINARY_VERSION}} </td></tr>
 <tr><td> ZeroMQ </td><td> spark-streaming-zeromq_{{site.SCALA_BINARY_VERSION}} </td></tr>
 <tr><td> MQTT </td><td> spark-streaming-mqtt_{{site.SCALA_BINARY_VERSION}} </td></tr>
@@ -373,7 +383,7 @@ or a special __"local[\*]"__ string to run in local mode. In practice, when runn
 you will not want to hardcode `master` in the program,
 but rather [launch the application with `spark-submit`](submitting-applications.html) and
 receive it there. However, for local testing and unit tests, you can pass "local[*]" to run Spark Streaming
-in-process. Note that this internally creates a [JavaSparkContext](api/java/index.html?org/apache/spark/api/java/JavaSparkContext.html) (starting point of all Spark functionality) which can be accessed as `ssc.sparkContext`. 
+in-process. Note that this internally creates a [JavaSparkContext](api/java/index.html?org/apache/spark/api/java/JavaSparkContext.html) (starting point of all Spark functionality) which can be accessed as `ssc.sparkContext`.
 
 The batch interval must be set based on the latency requirements of your application
 and available cluster resources. See the [Performance Tuning](#setting-the-right-batch-size)
@@ -447,11 +457,12 @@ Spark Streaming has two categories of streaming sources.
 - *Basic sources*: Sources directly available in the StreamingContext API. Example: file systems, socket connections, and Akka actors.
 - *Advanced sources*: Sources like Kafka, Flume, Kinesis, Twitter, etc. are available through extra utility classes. These require linking against extra dependencies as discussed in the [linking](#linking) section.
 
-Every input DStream (except file stream) is associated with a single [Receiver](api/scala/index.html#org.apache.spark.streaming.receiver.Receiver) object which receives the data from a source and stores it in Spark's memory for processing. A receiver is run within a Spark worker/executor as a long-running task, hence it occupies one of the cores allocated to the Spark Streaming application. Hence, it is important to remember that Spark Streaming application needs to be allocated enough cores to process the received data, as well as, to run the receiver(s). Therefore, few important points to remember are:
+Every input DStream (except file stream) is associated with a single [Receiver](api/scala/index.html#org.apache.spark.streaming.receiver.Receiver) object which receives the data from a source and stores it in Spark's memory for processing. So every input DStream receives a single stream of data. Note that in a streaming application, you can create multiple input DStreams to receive multiple streams of data in parallel. This is discussed later in the [Performance Tuning](#level-of-parallelism-in-data-receiving) section.
+
+A receiver is run within a Spark worker/executor as a long-running task, hence it occupies one of the cores allocated to the Spark Streaming application. Hence, it is important to remember that Spark Streaming application needs to be allocated enough cores to process the received data, as well as, to run the receiver(s). Therefore, few important points to remember are:
 
 ##### Points to remember:
 {:.no_toc}
-
 - If the number of cores allocated to the application is less than or equal to the number of input DStreams / receivers, then the system will receive data, but not be able to process them.
 - When running locally, if you master URL is set to "local", then there is only one core to run tasks.  That is insufficient for programs with even one input DStream (file streams are okay) as the receiver will occupy that core and there will be no core left to process the data.
 
@@ -1089,9 +1100,34 @@ parallelizing the data receiving. Note that each input DStream
 creates a single receiver (running on a worker machine) that receives a single stream of data.
 Receiving multiple data streams can therefore be achieved by creating multiple input DStreams
 and configuring them to receive different partitions of the data stream from the source(s).
-For example, a single Kafka input stream receiving two topics of data can be split into two
+For example, a single Kafka input DStream receiving two topics of data can be split into two
 Kafka input streams, each receiving only one topic. This would run two receivers on two workers,
-thus allowing data to be received in parallel, and increasing overall throughput.
+thus allowing data to be received in parallel, and increasing overall throughput. These multiple
+DStream can be unioned together to create a single DStream. Then the transformations that was
+being applied on the single input DStream can applied on the unified stream. This is done as follows.
+
+<div class="codetabs">
+<div data-lang="scala" markdown="1">
+{% highlight scala %}
+val numStreams = 5
+val kafkaStreams = (1 to numStreams).map { i => KafkaUtils.createStream(...) }
+val unifiedStream = streamingContext.union(kafkaStreams)
+unifiedStream.print()
+{% endhighlight %}
+</div>
+<div data-lang="java" markdown="1">
+{% highlight java %}
+int numStreams = 5;
+List<JavaPairDStream<String, String>> kafkaStreams = new ArrayList<JavaPairDStream<String, String>>(numStreams);
+for (int i = 0; i < numStreams; i++) {
+  kafkaStreams.add(KafkaUtils.createStream(...));
+}
+JavaPairDStream<String, String> unifiedStream = streamingContext.union(kafkaStreams.get(0), kafkaStreams.subList(1, kafkaStreams.size()));
+unifiedStream.print();
+{% endhighlight %}
+</div>
+</div>
+
 
 Another parameter that should be considered is the receiver's blocking interval. For most receivers,
 the received data is coalesced together into large blocks of data before storing inside Spark's memory.
@@ -1107,7 +1143,7 @@ before further processing.
 
 ### Level of Parallelism in Data Processing
 {:.no_toc}
-Cluster resources maybe under-utilized if the number of parallel tasks used in any stage of the
+Cluster resources can be under-utilized if the number of parallel tasks used in any stage of the
 computation is not high enough. For example, for distributed reduce operations like `reduceByKey`
 and `reduceByKeyAndWindow`, the default number of parallel tasks is decided by the [config property]
 (configuration.html#spark-properties) `spark.default.parallelism`. You can pass the level of

From da35330e830a85008c0bf9f0725418e4dfe7ac66 Mon Sep 17 00:00:00 2001
From: Holden Karau <holden@pigscanfly.ca>
Date: Sat, 6 Sep 2014 14:49:25 -0700
Subject: [PATCH 217/489] Spark-3406 add a default storage level to python RDD
 persist API

Author: Holden Karau <holden@pigscanfly.ca>

Closes #2280 from holdenk/SPARK-3406-Python-RDD-persist-api-does-not-have-default-storage-level and squashes the following commits:

33eaade [Holden Karau] As Josh pointed out, sql also override persist. Make persist behave the same as in the underlying RDD as well
e658227 [Holden Karau] Fix the test I added
e95a6c5 [Holden Karau] The Python persist function did not have a default storageLevel unlike the Scala API. Noticed this issue because we got a bug report back from the book where we had documented it as if it was the same as the Scala API
---
 python/pyspark/rdd.py | 7 ++++++-
 python/pyspark/sql.py | 3 ++-
 2 files changed, 8 insertions(+), 2 deletions(-)

diff --git a/python/pyspark/rdd.py b/python/pyspark/rdd.py
index 04f13523b431d..aa90297855c93 100644
--- a/python/pyspark/rdd.py
+++ b/python/pyspark/rdd.py
@@ -212,11 +212,16 @@ def cache(self):
         self.persist(StorageLevel.MEMORY_ONLY_SER)
         return self
 
-    def persist(self, storageLevel):
+    def persist(self, storageLevel=StorageLevel.MEMORY_ONLY_SER):
         """
         Set this RDD's storage level to persist its values across operations
         after the first time it is computed. This can only be used to assign
         a new storage level if the RDD does not have a storage level set yet.
+        If no storage level is specified defaults to (C{MEMORY_ONLY_SER}).
+
+        >>> rdd = sc.parallelize(["b", "a", "c"])
+        >>> rdd.persist().is_cached
+        True
         """
         self.is_cached = True
         javaStorageLevel = self.ctx._getJavaStorageLevel(storageLevel)
diff --git a/python/pyspark/sql.py b/python/pyspark/sql.py
index e7f573cf6da44..97a51b9f8a24f 100644
--- a/python/pyspark/sql.py
+++ b/python/pyspark/sql.py
@@ -29,6 +29,7 @@
 
 from pyspark.rdd import RDD, PipelinedRDD
 from pyspark.serializers import BatchedSerializer, PickleSerializer, CloudPickleSerializer
+from pyspark.storagelevel import StorageLevel
 
 from itertools import chain, ifilter, imap
 
@@ -1665,7 +1666,7 @@ def cache(self):
         self._jschema_rdd.cache()
         return self
 
-    def persist(self, storageLevel):
+    def persist(self, storageLevel=StorageLevel.MEMORY_ONLY_SER):
         self.is_cached = True
         javaStorageLevel = self.ctx._getJavaStorageLevel(storageLevel)
         self._jschema_rdd.persist(javaStorageLevel)

From 607ae39c22947dad8e65cbcec310367925f62eba Mon Sep 17 00:00:00 2001
From: GuoQiang Li <witgo@qq.com>
Date: Sat, 6 Sep 2014 15:04:50 -0700
Subject: [PATCH 218/489] [SPARK-3397] Bump pom.xml version number of master
 branch to 1.2.0-SNAPSHOT

Author: GuoQiang Li <witgo@qq.com>

Closes #2268 from witgo/SPARK-3397 and squashes the following commits:

eaf913f [GuoQiang Li] Bump pom.xml version number of master branch to 1.2.0-SNAPSHOT
---
 assembly/pom.xml                  | 2 +-
 bagel/pom.xml                     | 2 +-
 core/pom.xml                      | 2 +-
 examples/pom.xml                  | 2 +-
 external/flume-sink/pom.xml       | 2 +-
 external/flume/pom.xml            | 2 +-
 external/kafka/pom.xml            | 2 +-
 external/mqtt/pom.xml             | 2 +-
 external/twitter/pom.xml          | 2 +-
 external/zeromq/pom.xml           | 2 +-
 extras/java8-tests/pom.xml        | 2 +-
 extras/kinesis-asl/pom.xml        | 2 +-
 extras/spark-ganglia-lgpl/pom.xml | 2 +-
 graphx/pom.xml                    | 2 +-
 mllib/pom.xml                     | 2 +-
 pom.xml                           | 2 +-
 repl/pom.xml                      | 2 +-
 sql/catalyst/pom.xml              | 2 +-
 sql/core/pom.xml                  | 2 +-
 sql/hive-thriftserver/pom.xml     | 2 +-
 sql/hive/pom.xml                  | 2 +-
 streaming/pom.xml                 | 2 +-
 tools/pom.xml                     | 2 +-
 yarn/alpha/pom.xml                | 2 +-
 yarn/pom.xml                      | 2 +-
 yarn/stable/pom.xml               | 2 +-
 26 files changed, 26 insertions(+), 26 deletions(-)

diff --git a/assembly/pom.xml b/assembly/pom.xml
index de7b75258e3c5..4146168fc804b 100644
--- a/assembly/pom.xml
+++ b/assembly/pom.xml
@@ -21,7 +21,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent</artifactId>
-    <version>1.1.0-SNAPSHOT</version>
+    <version>1.2.0-SNAPSHOT</version>
     <relativePath>../pom.xml</relativePath>
   </parent>
 
diff --git a/bagel/pom.xml b/bagel/pom.xml
index bd51b112e26fa..93db0d5efda5f 100644
--- a/bagel/pom.xml
+++ b/bagel/pom.xml
@@ -21,7 +21,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent</artifactId>
-    <version>1.1.0-SNAPSHOT</version>
+    <version>1.2.0-SNAPSHOT</version>
     <relativePath>../pom.xml</relativePath>
   </parent>
 
diff --git a/core/pom.xml b/core/pom.xml
index 55bfe0b841ea4..b2b788a4bc13b 100644
--- a/core/pom.xml
+++ b/core/pom.xml
@@ -21,7 +21,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent</artifactId>
-    <version>1.1.0-SNAPSHOT</version>
+    <version>1.2.0-SNAPSHOT</version>
     <relativePath>../pom.xml</relativePath>
   </parent>
 
diff --git a/examples/pom.xml b/examples/pom.xml
index 9b12cb0c29c9f..3f46c40464d3b 100644
--- a/examples/pom.xml
+++ b/examples/pom.xml
@@ -21,7 +21,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent</artifactId>
-    <version>1.1.0-SNAPSHOT</version>
+    <version>1.2.0-SNAPSHOT</version>
     <relativePath>../pom.xml</relativePath>
   </parent>
 
diff --git a/external/flume-sink/pom.xml b/external/flume-sink/pom.xml
index b345276b08ba3..ac291bd4fde20 100644
--- a/external/flume-sink/pom.xml
+++ b/external/flume-sink/pom.xml
@@ -21,7 +21,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent</artifactId>
-    <version>1.1.0-SNAPSHOT</version>
+    <version>1.2.0-SNAPSHOT</version>
     <relativePath>../../pom.xml</relativePath>
   </parent>
 
diff --git a/external/flume/pom.xml b/external/flume/pom.xml
index f71f6b6c4f931..7d31e32283d88 100644
--- a/external/flume/pom.xml
+++ b/external/flume/pom.xml
@@ -21,7 +21,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent</artifactId>
-    <version>1.1.0-SNAPSHOT</version>
+    <version>1.2.0-SNAPSHOT</version>
     <relativePath>../../pom.xml</relativePath>
   </parent>
 
diff --git a/external/kafka/pom.xml b/external/kafka/pom.xml
index 4e2275ab238f7..2067c473f0e3f 100644
--- a/external/kafka/pom.xml
+++ b/external/kafka/pom.xml
@@ -21,7 +21,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent</artifactId>
-    <version>1.1.0-SNAPSHOT</version>
+    <version>1.2.0-SNAPSHOT</version>
     <relativePath>../../pom.xml</relativePath>
   </parent>
 
diff --git a/external/mqtt/pom.xml b/external/mqtt/pom.xml
index dc48a08c93de2..371f1f1e9d39a 100644
--- a/external/mqtt/pom.xml
+++ b/external/mqtt/pom.xml
@@ -21,7 +21,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent</artifactId>
-    <version>1.1.0-SNAPSHOT</version>
+    <version>1.2.0-SNAPSHOT</version>
     <relativePath>../../pom.xml</relativePath>
   </parent>
 
diff --git a/external/twitter/pom.xml b/external/twitter/pom.xml
index b93ad016f84f0..1d7dd49d15c22 100644
--- a/external/twitter/pom.xml
+++ b/external/twitter/pom.xml
@@ -21,7 +21,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent</artifactId>
-    <version>1.1.0-SNAPSHOT</version>
+    <version>1.2.0-SNAPSHOT</version>
     <relativePath>../../pom.xml</relativePath>
   </parent>
 
diff --git a/external/zeromq/pom.xml b/external/zeromq/pom.xml
index 22c1fff23d9a2..7e48968feb3bc 100644
--- a/external/zeromq/pom.xml
+++ b/external/zeromq/pom.xml
@@ -21,7 +21,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent</artifactId>
-    <version>1.1.0-SNAPSHOT</version>
+    <version>1.2.0-SNAPSHOT</version>
     <relativePath>../../pom.xml</relativePath>
   </parent>
 
diff --git a/extras/java8-tests/pom.xml b/extras/java8-tests/pom.xml
index 5308bb4e440ea..8658ecf5abfab 100644
--- a/extras/java8-tests/pom.xml
+++ b/extras/java8-tests/pom.xml
@@ -20,7 +20,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent</artifactId>
-    <version>1.1.0-SNAPSHOT</version>
+    <version>1.2.0-SNAPSHOT</version>
     <relativePath>../../pom.xml</relativePath>
   </parent>
 
diff --git a/extras/kinesis-asl/pom.xml b/extras/kinesis-asl/pom.xml
index a54b34235dfb4..560244ad93369 100644
--- a/extras/kinesis-asl/pom.xml
+++ b/extras/kinesis-asl/pom.xml
@@ -20,7 +20,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent</artifactId>
-    <version>1.1.0-SNAPSHOT</version>
+    <version>1.2.0-SNAPSHOT</version>
     <relativePath>../../pom.xml</relativePath>
   </parent>
 
diff --git a/extras/spark-ganglia-lgpl/pom.xml b/extras/spark-ganglia-lgpl/pom.xml
index a5b162a0482e4..71a078d58a8d8 100644
--- a/extras/spark-ganglia-lgpl/pom.xml
+++ b/extras/spark-ganglia-lgpl/pom.xml
@@ -20,7 +20,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent</artifactId>
-    <version>1.1.0-SNAPSHOT</version>
+    <version>1.2.0-SNAPSHOT</version>
     <relativePath>../../pom.xml</relativePath>
   </parent>
 
diff --git a/graphx/pom.xml b/graphx/pom.xml
index 6dd52fc618b1e..3f49b1d63b6e1 100644
--- a/graphx/pom.xml
+++ b/graphx/pom.xml
@@ -21,7 +21,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent</artifactId>
-    <version>1.1.0-SNAPSHOT</version>
+    <version>1.2.0-SNAPSHOT</version>
     <relativePath>../pom.xml</relativePath>
   </parent>
 
diff --git a/mllib/pom.xml b/mllib/pom.xml
index c7a1e2ae75c84..a5eeef88e9d62 100644
--- a/mllib/pom.xml
+++ b/mllib/pom.xml
@@ -21,7 +21,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent</artifactId>
-    <version>1.1.0-SNAPSHOT</version>
+    <version>1.2.0-SNAPSHOT</version>
     <relativePath>../pom.xml</relativePath>
   </parent>
 
diff --git a/pom.xml b/pom.xml
index a5eaea80afd71..1efa9045208fd 100644
--- a/pom.xml
+++ b/pom.xml
@@ -26,7 +26,7 @@
   </parent>
   <groupId>org.apache.spark</groupId>
   <artifactId>spark-parent</artifactId>
-  <version>1.1.0-SNAPSHOT</version>
+  <version>1.2.0-SNAPSHOT</version>
   <packaging>pom</packaging>
   <name>Spark Project Parent POM</name>
   <url>http://spark.apache.org/</url>
diff --git a/repl/pom.xml b/repl/pom.xml
index 68f4504450778..fcc5f90d870e8 100644
--- a/repl/pom.xml
+++ b/repl/pom.xml
@@ -21,7 +21,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent</artifactId>
-    <version>1.1.0-SNAPSHOT</version>
+    <version>1.2.0-SNAPSHOT</version>
     <relativePath>../pom.xml</relativePath>
   </parent>
 
diff --git a/sql/catalyst/pom.xml b/sql/catalyst/pom.xml
index 830711a46a35b..0d756f873e486 100644
--- a/sql/catalyst/pom.xml
+++ b/sql/catalyst/pom.xml
@@ -22,7 +22,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent</artifactId>
-    <version>1.1.0-SNAPSHOT</version>
+    <version>1.2.0-SNAPSHOT</version>
     <relativePath>../../pom.xml</relativePath>
   </parent>
 
diff --git a/sql/core/pom.xml b/sql/core/pom.xml
index c8016e41256d5..bd110218d34f7 100644
--- a/sql/core/pom.xml
+++ b/sql/core/pom.xml
@@ -22,7 +22,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent</artifactId>
-    <version>1.1.0-SNAPSHOT</version>
+    <version>1.2.0-SNAPSHOT</version>
     <relativePath>../../pom.xml</relativePath>
   </parent>
 
diff --git a/sql/hive-thriftserver/pom.xml b/sql/hive-thriftserver/pom.xml
index c6f60c18804a4..124fc107cb8aa 100644
--- a/sql/hive-thriftserver/pom.xml
+++ b/sql/hive-thriftserver/pom.xml
@@ -22,7 +22,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent</artifactId>
-    <version>1.1.0-SNAPSHOT</version>
+    <version>1.2.0-SNAPSHOT</version>
     <relativePath>../../pom.xml</relativePath>
   </parent>
 
diff --git a/sql/hive/pom.xml b/sql/hive/pom.xml
index 30ff277e67c88..45a4c6dc98da0 100644
--- a/sql/hive/pom.xml
+++ b/sql/hive/pom.xml
@@ -22,7 +22,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent</artifactId>
-    <version>1.1.0-SNAPSHOT</version>
+    <version>1.2.0-SNAPSHOT</version>
     <relativePath>../../pom.xml</relativePath>
   </parent>
 
diff --git a/streaming/pom.xml b/streaming/pom.xml
index ce35520a28609..12f900c91eb98 100644
--- a/streaming/pom.xml
+++ b/streaming/pom.xml
@@ -21,7 +21,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent</artifactId>
-    <version>1.1.0-SNAPSHOT</version>
+    <version>1.2.0-SNAPSHOT</version>
     <relativePath>../pom.xml</relativePath>
   </parent>
 
diff --git a/tools/pom.xml b/tools/pom.xml
index 97abb6b2b63e0..f36674476770c 100644
--- a/tools/pom.xml
+++ b/tools/pom.xml
@@ -20,7 +20,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent</artifactId>
-    <version>1.1.0-SNAPSHOT</version>
+    <version>1.2.0-SNAPSHOT</version>
     <relativePath>../pom.xml</relativePath>
   </parent>
 
diff --git a/yarn/alpha/pom.xml b/yarn/alpha/pom.xml
index 51744ece0412d..7dadbba58fd82 100644
--- a/yarn/alpha/pom.xml
+++ b/yarn/alpha/pom.xml
@@ -20,7 +20,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>yarn-parent_2.10</artifactId>
-    <version>1.1.0-SNAPSHOT</version>
+    <version>1.2.0-SNAPSHOT</version>
     <relativePath>../pom.xml</relativePath>
   </parent>
   <properties>
diff --git a/yarn/pom.xml b/yarn/pom.xml
index 3faaf053634d6..7fcd7ee0d4547 100644
--- a/yarn/pom.xml
+++ b/yarn/pom.xml
@@ -20,7 +20,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent</artifactId>
-    <version>1.1.0-SNAPSHOT</version>
+    <version>1.2.0-SNAPSHOT</version>
     <relativePath>../pom.xml</relativePath>
   </parent>
 
diff --git a/yarn/stable/pom.xml b/yarn/stable/pom.xml
index b6c8456d06684..fd934b7726181 100644
--- a/yarn/stable/pom.xml
+++ b/yarn/stable/pom.xml
@@ -20,7 +20,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>yarn-parent_2.10</artifactId>
-    <version>1.1.0-SNAPSHOT</version>
+    <version>1.2.0-SNAPSHOT</version>
     <relativePath>../pom.xml</relativePath>
   </parent>
   <properties>

From 21a1e1bb893512b2f68598ab0c0ec8c33e8d9909 Mon Sep 17 00:00:00 2001
From: GuoQiang Li <witgo@qq.com>
Date: Sat, 6 Sep 2014 15:08:43 -0700
Subject: [PATCH 219/489] [SPARK-3273][SPARK-3301]We should read the version
 information from the same place

Author: GuoQiang Li <witgo@qq.com>

Closes #2175 from witgo/SPARK-3273 and squashes the following commits:

cf9c65a [GuoQiang Li] We should read the version information from the same place
2a44e2f [GuoQiang Li] The spark version in the welcome message of pyspark is not correct
---
 core/src/main/scala/org/apache/spark/SparkContext.scala     | 5 ++---
 core/src/main/scala/org/apache/spark/package.scala          | 1 +
 .../org/apache/spark/scheduler/EventLoggingListener.scala   | 3 ++-
 .../apache/spark/scheduler/EventLoggingListenerSuite.scala  | 5 +++--
 python/pyspark/shell.py                                     | 4 ++--
 .../main/scala/org/apache/spark/repl/SparkILoopInit.scala   | 6 ++++--
 6 files changed, 14 insertions(+), 10 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala
index 6eaf6794764c7..24d1a8f9eceae 100644
--- a/core/src/main/scala/org/apache/spark/SparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/SparkContext.scala
@@ -49,6 +49,7 @@ import org.apache.spark.scheduler.cluster.{CoarseGrainedSchedulerBackend, SparkD
 import org.apache.spark.scheduler.cluster.mesos.{CoarseMesosSchedulerBackend, MesosSchedulerBackend}
 import org.apache.spark.scheduler.local.LocalBackend
 import org.apache.spark.storage._
+import org.apache.spark.SPARK_VERSION
 import org.apache.spark.ui.SparkUI
 import org.apache.spark.util.{CallSite, ClosureCleaner, MetadataCleaner, MetadataCleanerType, TimeStampedWeakValueHashMap, Utils}
 
@@ -825,7 +826,7 @@ class SparkContext(config: SparkConf) extends Logging {
   }
 
   /** The version of Spark on which this application is running. */
-  def version = SparkContext.SPARK_VERSION
+  def version = SPARK_VERSION
 
   /**
    * Return a map from the slave to the max memory available for caching and the remaining
@@ -1297,8 +1298,6 @@ class SparkContext(config: SparkConf) extends Logging {
  */
 object SparkContext extends Logging {
 
-  private[spark] val SPARK_VERSION = "1.2.0-SNAPSHOT"
-
   private[spark] val SPARK_JOB_DESCRIPTION = "spark.job.description"
 
   private[spark] val SPARK_JOB_GROUP_ID = "spark.jobGroup.id"
diff --git a/core/src/main/scala/org/apache/spark/package.scala b/core/src/main/scala/org/apache/spark/package.scala
index 5cdbc306e56a0..e2fc9c649925e 100644
--- a/core/src/main/scala/org/apache/spark/package.scala
+++ b/core/src/main/scala/org/apache/spark/package.scala
@@ -44,4 +44,5 @@ package org.apache
 
 package object spark {
   // For package docs only
+  val SPARK_VERSION = "1.2.0-SNAPSHOT"
 }
diff --git a/core/src/main/scala/org/apache/spark/scheduler/EventLoggingListener.scala b/core/src/main/scala/org/apache/spark/scheduler/EventLoggingListener.scala
index 4b99f630440ad..64b32ae0edaac 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/EventLoggingListener.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/EventLoggingListener.scala
@@ -29,6 +29,7 @@ import org.json4s.jackson.JsonMethods._
 import org.apache.spark.{Logging, SparkConf, SparkContext}
 import org.apache.spark.deploy.SparkHadoopUtil
 import org.apache.spark.io.CompressionCodec
+import org.apache.spark.SPARK_VERSION
 import org.apache.spark.util.{FileLogger, JsonProtocol, Utils}
 
 /**
@@ -86,7 +87,7 @@ private[spark] class EventLoggingListener(
         sparkConf.get("spark.io.compression.codec", CompressionCodec.DEFAULT_COMPRESSION_CODEC)
       logger.newFile(COMPRESSION_CODEC_PREFIX + codec)
     }
-    logger.newFile(SPARK_VERSION_PREFIX + SparkContext.SPARK_VERSION)
+    logger.newFile(SPARK_VERSION_PREFIX + SPARK_VERSION)
     logger.newFile(LOG_PREFIX + logger.fileIndex)
   }
 
diff --git a/core/src/test/scala/org/apache/spark/scheduler/EventLoggingListenerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/EventLoggingListenerSuite.scala
index fead883793430..e5315bc93e217 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/EventLoggingListenerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/EventLoggingListenerSuite.scala
@@ -28,6 +28,7 @@ import org.scalatest.{BeforeAndAfter, FunSuite}
 import org.apache.spark.{SparkConf, SparkContext}
 import org.apache.spark.deploy.SparkHadoopUtil
 import org.apache.spark.io.CompressionCodec
+import org.apache.spark.SPARK_VERSION
 import org.apache.spark.util.{JsonProtocol, Utils}
 
 import java.io.File
@@ -196,7 +197,7 @@ class EventLoggingListenerSuite extends FunSuite with BeforeAndAfter {
 
     def assertInfoCorrect(info: EventLoggingInfo, loggerStopped: Boolean) {
       assert(info.logPaths.size > 0)
-      assert(info.sparkVersion === SparkContext.SPARK_VERSION)
+      assert(info.sparkVersion === SPARK_VERSION)
       assert(info.compressionCodec.isDefined === compressionCodec.isDefined)
       info.compressionCodec.foreach { codec =>
         assert(compressionCodec.isDefined)
@@ -381,7 +382,7 @@ class EventLoggingListenerSuite extends FunSuite with BeforeAndAfter {
   private def assertSparkVersionIsValid(logFiles: Array[FileStatus]) {
     val file = logFiles.map(_.getPath.getName).find(EventLoggingListener.isSparkVersionFile)
     assert(file.isDefined)
-    assert(EventLoggingListener.parseSparkVersion(file.get) === SparkContext.SPARK_VERSION)
+    assert(EventLoggingListener.parseSparkVersion(file.get) === SPARK_VERSION)
   }
 
   private def assertCompressionCodecIsValid(logFiles: Array[FileStatus], compressionCodec: String) {
diff --git a/python/pyspark/shell.py b/python/pyspark/shell.py
index fde3c29e5e790..89cf76920e353 100644
--- a/python/pyspark/shell.py
+++ b/python/pyspark/shell.py
@@ -49,9 +49,9 @@
       ____              __
      / __/__  ___ _____/ /__
     _\ \/ _ \/ _ `/ __/  '_/
-   /__ / .__/\_,_/_/ /_/\_\   version 1.0.0-SNAPSHOT
+   /__ / .__/\_,_/_/ /_/\_\   version %s
       /_/
-""")
+""" % sc.version)
 print("Using Python version %s (%s, %s)" % (
     platform.python_version(),
     platform.python_build()[0],
diff --git a/repl/src/main/scala/org/apache/spark/repl/SparkILoopInit.scala b/repl/src/main/scala/org/apache/spark/repl/SparkILoopInit.scala
index 910b31d209e13..7667a9c11979e 100644
--- a/repl/src/main/scala/org/apache/spark/repl/SparkILoopInit.scala
+++ b/repl/src/main/scala/org/apache/spark/repl/SparkILoopInit.scala
@@ -14,6 +14,8 @@ import scala.reflect.internal.util.Position
 import scala.util.control.Exception.ignoring
 import scala.tools.nsc.util.stackTraceString
 
+import org.apache.spark.SPARK_VERSION
+
 /**
  *  Machinery for the asynchronous initialization of the repl.
  */
@@ -26,9 +28,9 @@ trait SparkILoopInit {
       ____              __
      / __/__  ___ _____/ /__
     _\ \/ _ \/ _ `/ __/  '_/
-   /___/ .__/\_,_/_/ /_/\_\   version 1.0.0-SNAPSHOT
+   /___/ .__/\_,_/_/ /_/\_\   version %s
       /_/
-""")
+""".format(SPARK_VERSION))
     import Properties._
     val welcomeMsg = "Using Scala %s (%s, Java %s)".format(
       versionString, javaVmName, javaVersion)

From 110fb8b24d2454ad7c979c3934dbed87650f17b8 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies.liu@gmail.com>
Date: Sat, 6 Sep 2014 16:12:29 -0700
Subject: [PATCH 220/489] [SPARK-2334] fix AttributeError when call
 PipelineRDD.id()

The underline JavaRDD for PipelineRDD is created lazily, it's delayed until call _jrdd.

The id of JavaRDD is cached as `_id`, it saves a RPC call in py4j for later calls.

closes #1276

Author: Davies Liu <davies.liu@gmail.com>

Closes #2296 from davies/id and squashes the following commits:

e197958 [Davies Liu] fix style
9721716 [Davies Liu] fix id of PipelineRDD
---
 python/pyspark/rdd.py   | 6 ++++++
 python/pyspark/sql.py   | 9 +++++----
 python/pyspark/tests.py | 9 +++++++++
 3 files changed, 20 insertions(+), 4 deletions(-)

diff --git a/python/pyspark/rdd.py b/python/pyspark/rdd.py
index aa90297855c93..266090e3ae8f3 100644
--- a/python/pyspark/rdd.py
+++ b/python/pyspark/rdd.py
@@ -2075,6 +2075,7 @@ def pipeline_func(split, iterator):
         self.ctx = prev.ctx
         self.prev = prev
         self._jrdd_val = None
+        self._id = None
         self._jrdd_deserializer = self.ctx.serializer
         self._bypass_serializer = False
         self._partitionFunc = prev._partitionFunc if self.preservesPartitioning else None
@@ -2105,6 +2106,11 @@ def _jrdd(self):
         self._jrdd_val = python_rdd.asJavaRDD()
         return self._jrdd_val
 
+    def id(self):
+        if self._id is None:
+            self._id = self._jrdd.id()
+        return self._id
+
     def _is_pipelinable(self):
         return not (self.is_cached or self.is_checkpointed)
 
diff --git a/python/pyspark/sql.py b/python/pyspark/sql.py
index 97a51b9f8a24f..004d4937cbe1c 100644
--- a/python/pyspark/sql.py
+++ b/python/pyspark/sql.py
@@ -1525,7 +1525,7 @@ def __init__(self, jschema_rdd, sql_ctx):
         self.sql_ctx = sql_ctx
         self._sc = sql_ctx._sc
         self._jschema_rdd = jschema_rdd
-
+        self._id = None
         self.is_cached = False
         self.is_checkpointed = False
         self.ctx = self.sql_ctx._sc
@@ -1543,9 +1543,10 @@ def _jrdd(self):
             self._lazy_jrdd = self._jschema_rdd.javaToPython()
         return self._lazy_jrdd
 
-    @property
-    def _id(self):
-        return self._jrdd.id()
+    def id(self):
+        if self._id is None:
+            self._id = self._jrdd.id()
+        return self._id
 
     def saveAsParquetFile(self, path):
         """Save the contents as a Parquet file, preserving the schema.
diff --git a/python/pyspark/tests.py b/python/pyspark/tests.py
index 3e74799e82845..2ade15b35ab4e 100644
--- a/python/pyspark/tests.py
+++ b/python/pyspark/tests.py
@@ -281,6 +281,15 @@ def func():
 
 class TestRDDFunctions(PySparkTestCase):
 
+    def test_id(self):
+        rdd = self.sc.parallelize(range(10))
+        id = rdd.id()
+        self.assertEqual(id, rdd.id())
+        rdd2 = rdd.map(str).filter(bool)
+        id2 = rdd2.id()
+        self.assertEqual(id + 1, id2)
+        self.assertEqual(id2, rdd2.id())
+
     def test_failed_sparkcontext_creation(self):
         # Regression test for SPARK-1550
         self.sc.stop()

From 3fb57a0ab3d76fda2301dbe9f2f3fa6743b4ed78 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@apache.org>
Date: Sat, 6 Sep 2014 19:06:30 -0700
Subject: [PATCH 221/489] [SPARK-3353] parent stage should have lower stage id.

Previously parent stages had higher stage id, but parent stages are executed first. This pull request changes the behavior so parent stages would have lower stage id.

For example, command:
```scala
sc.parallelize(1 to 10).map(x=>(x,x)).reduceByKey(_+_).count
```
breaks down into 2 stages.

The old web UI:
![screen shot 2014-09-04 at 12 42 44 am](https://cloud.githubusercontent.com/assets/323388/4146177/60fb4f42-3407-11e4-819f-853eb0e22b25.png)

Web UI with this patch:
![screen shot 2014-09-04 at 12 44 55 am](https://cloud.githubusercontent.com/assets/323388/4146178/62e08e62-3407-11e4-867b-a36b10534464.png)

Author: Reynold Xin <rxin@apache.org>

Closes #2273 from rxin/lower-stage-id and squashes the following commits:

abbb4c6 [Reynold Xin] Fixed SparkListenerSuite.
0e02379 [Reynold Xin] Updated DAGSchedulerSuite.
54ccea3 [Reynold Xin] [SPARK-3353] parent stage should have lower stage id.
---
 .../apache/spark/scheduler/DAGScheduler.scala |  4 +--
 .../spark/scheduler/DAGSchedulerSuite.scala   | 25 ++++++++++++-------
 .../spark/scheduler/SparkListenerSuite.scala  |  2 +-
 3 files changed, 19 insertions(+), 12 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
index 2ccc27324ac8c..6fcf9e31543ed 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
@@ -241,9 +241,9 @@ class DAGScheduler(
       callSite: CallSite)
     : Stage =
   {
+    val parentStages = getParentStages(rdd, jobId)
     val id = nextStageId.getAndIncrement()
-    val stage =
-      new Stage(id, rdd, numTasks, shuffleDep, getParentStages(rdd, jobId), jobId, callSite)
+    val stage = new Stage(id, rdd, numTasks, shuffleDep, parentStages, jobId, callSite)
     stageIdToStage(id) = stage
     updateJobIdStageIdMaps(jobId, stage)
     stage
diff --git a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
index 0bb91febde9d7..aa73469b6acd8 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
@@ -27,6 +27,7 @@ import org.scalatest.concurrent.Timeouts
 import org.scalatest.time.SpanSugar._
 
 import org.apache.spark._
+import org.apache.spark.SparkContext._
 import org.apache.spark.rdd.RDD
 import org.apache.spark.scheduler.SchedulingMode.SchedulingMode
 import org.apache.spark.storage.{BlockId, BlockManagerId, BlockManagerMaster}
@@ -97,10 +98,12 @@ class DAGSchedulerSuite extends TestKit(ActorSystem("DAGSchedulerSuite")) with F
   /** Length of time to wait while draining listener events. */
   val WAIT_TIMEOUT_MILLIS = 10000
   val sparkListener = new SparkListener() {
-    val successfulStages = new HashSet[Int]()
-    val failedStages = new ArrayBuffer[Int]()
+    val successfulStages = new HashSet[Int]
+    val failedStages = new ArrayBuffer[Int]
+    val stageByOrderOfExecution = new ArrayBuffer[Int]
     override def onStageCompleted(stageCompleted: SparkListenerStageCompleted) {
       val stageInfo = stageCompleted.stageInfo
+      stageByOrderOfExecution += stageInfo.stageId
       if (stageInfo.failureReason.isEmpty) {
         successfulStages += stageInfo.stageId
       } else {
@@ -231,6 +234,13 @@ class DAGSchedulerSuite extends TestKit(ActorSystem("DAGSchedulerSuite")) with F
     runEvent(JobCancelled(jobId))
   }
 
+  test("[SPARK-3353] parent stage should have lower stage id") {
+    sparkListener.stageByOrderOfExecution.clear()
+    sc.parallelize(1 to 10).map(x => (x, x)).reduceByKey(_ + _, 4).count()
+    assert(sparkListener.stageByOrderOfExecution.length === 2)
+    assert(sparkListener.stageByOrderOfExecution(0) < sparkListener.stageByOrderOfExecution(1))
+  }
+
   test("zero split job") {
     var numResults = 0
     val fakeListener = new JobListener() {
@@ -457,7 +467,7 @@ class DAGSchedulerSuite extends TestKit(ActorSystem("DAGSchedulerSuite")) with F
       null,
       null))
     assert(sc.listenerBus.waitUntilEmpty(WAIT_TIMEOUT_MILLIS))
-    assert(sparkListener.failedStages.contains(0))
+    assert(sparkListener.failedStages.contains(1))
 
     // The second ResultTask fails, with a fetch failure for the output from the second mapper.
     runEvent(CompletionEvent(
@@ -515,8 +525,7 @@ class DAGSchedulerSuite extends TestKit(ActorSystem("DAGSchedulerSuite")) with F
     // Listener bus should get told about the map stage failing, but not the reduce stage
     // (since the reduce stage hasn't been started yet).
     assert(sc.listenerBus.waitUntilEmpty(WAIT_TIMEOUT_MILLIS))
-    assert(sparkListener.failedStages.contains(1))
-    assert(sparkListener.failedStages.size === 1)
+    assert(sparkListener.failedStages.toSet === Set(0))
 
     assertDataStructuresEmpty
   }
@@ -563,14 +572,12 @@ class DAGSchedulerSuite extends TestKit(ActorSystem("DAGSchedulerSuite")) with F
     val stageFailureMessage = "Exception failure in map stage"
     failed(taskSets(0), stageFailureMessage)
 
-    assert(cancelledStages.contains(1))
+    assert(cancelledStages.toSet === Set(0, 2))
 
     // Make sure the listeners got told about both failed stages.
     assert(sc.listenerBus.waitUntilEmpty(WAIT_TIMEOUT_MILLIS))
     assert(sparkListener.successfulStages.isEmpty)
-    assert(sparkListener.failedStages.contains(1))
-    assert(sparkListener.failedStages.contains(3))
-    assert(sparkListener.failedStages.size === 2)
+    assert(sparkListener.failedStages.toSet === Set(0, 2))
 
     assert(listener1.failureMessage === s"Job aborted due to stage failure: $stageFailureMessage")
     assert(listener2.failureMessage === s"Job aborted due to stage failure: $stageFailureMessage")
diff --git a/core/src/test/scala/org/apache/spark/scheduler/SparkListenerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/SparkListenerSuite.scala
index 3b0b8e2f68c97..ab35e8edc4ebf 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/SparkListenerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/SparkListenerSuite.scala
@@ -180,7 +180,7 @@ class SparkListenerSuite extends FunSuite with LocalSparkContext with Matchers
     rdd3.count()
     assert(sc.listenerBus.waitUntilEmpty(WAIT_TIMEOUT_MILLIS))
     listener.stageInfos.size should be {2} // Shuffle map stage + result stage
-    val stageInfo3 = listener.stageInfos.keys.find(_.stageId == 2).get
+    val stageInfo3 = listener.stageInfos.keys.find(_.stageId == 3).get
     stageInfo3.rddInfos.size should be {1} // ShuffledRDD
     stageInfo3.rddInfos.forall(_.numPartitions == 4) should be {true}
     stageInfo3.rddInfos.exists(_.name == "Trois") should be {true}

From 6754570d83044c4fbaf0d2ac2378a0e081a93629 Mon Sep 17 00:00:00 2001
From: Eric Liang <ekl@google.com>
Date: Sun, 7 Sep 2014 17:57:59 -0700
Subject: [PATCH 222/489] [SPARK-3394] [SQL] Fix crash in TakeOrdered when
 limit is 0

This resolves https://issues.apache.org/jira/browse/SPARK-3394

Author: Eric Liang <ekl@google.com>

Closes #2264 from ericl/spark-3394 and squashes the following commits:

c87355b [Eric Liang] refactor
bfb6140 [Eric Liang] change RDD takeOrdered instead
7a51528 [Eric Liang] fix takeordered when limit = 0
---
 .../main/scala/org/apache/spark/rdd/RDD.scala | 22 +++++++++++--------
 .../scala/org/apache/spark/rdd/RDDSuite.scala |  7 ++++++
 2 files changed, 20 insertions(+), 9 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/rdd/RDD.scala b/core/src/main/scala/org/apache/spark/rdd/RDD.scala
index 1cf55e86f6c81..a9b905b0d1a63 100644
--- a/core/src/main/scala/org/apache/spark/rdd/RDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/RDD.scala
@@ -1127,15 +1127,19 @@ abstract class RDD[T: ClassTag](
    * @return an array of top elements
    */
   def takeOrdered(num: Int)(implicit ord: Ordering[T]): Array[T] = {
-    mapPartitions { items =>
-      // Priority keeps the largest elements, so let's reverse the ordering.
-      val queue = new BoundedPriorityQueue[T](num)(ord.reverse)
-      queue ++= util.collection.Utils.takeOrdered(items, num)(ord)
-      Iterator.single(queue)
-    }.reduce { (queue1, queue2) =>
-      queue1 ++= queue2
-      queue1
-    }.toArray.sorted(ord)
+    if (num == 0) {
+      Array.empty
+    } else {
+      mapPartitions { items =>
+        // Priority keeps the largest elements, so let's reverse the ordering.
+        val queue = new BoundedPriorityQueue[T](num)(ord.reverse)
+        queue ++= util.collection.Utils.takeOrdered(items, num)(ord)
+        Iterator.single(queue)
+      }.reduce { (queue1, queue2) =>
+        queue1 ++= queue2
+        queue1
+      }.toArray.sorted(ord)
+    }
   }
 
   /**
diff --git a/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala b/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala
index 926d4fecb5b91..499dcda3dae8f 100644
--- a/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala
+++ b/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala
@@ -521,6 +521,13 @@ class RDDSuite extends FunSuite with SharedSparkContext {
     assert(sortedLowerK === Array(1, 2, 3, 4, 5))
   }
 
+  test("takeOrdered with limit 0") {
+    val nums = Array(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)
+    val rdd = sc.makeRDD(nums, 2)
+    val sortedLowerK = rdd.takeOrdered(0)
+    assert(sortedLowerK.size === 0)
+  }
+
   test("takeOrdered with custom ordering") {
     val nums = Array(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)
     implicit val ord = implicitly[Ordering[Int]].reverse

From 39db1bfdab434c867044ad4c70fe93a96fb287ad Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Sun, 7 Sep 2014 21:34:46 -0400
Subject: [PATCH 223/489] [SQL] Update SQL Programming Guide

Author: Michael Armbrust <michael@databricks.com>
Author: Yin Huai <huai@cse.ohio-state.edu>

Closes #2258 from marmbrus/sqlDocUpdate and squashes the following commits:

f3d450b [Michael Armbrust] fix brackets
bea3bfa [Michael Armbrust] Davies suggestions
3a29fe2 [Michael Armbrust] tighten visibility
a71aa36 [Michael Armbrust] Draft of doc updates
52932c0 [Michael Armbrust] Merge remote-tracking branch 'origin/master' into sqlDocUpdate
1e8c849 [Yin Huai] Update the example used for applySchema.
9457c39 [Yin Huai] Update doc.
31ba240 [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeDoc
29bc668 [Yin Huai] Draft doc for data type and schema APIs.
---
 docs/sql-programming-guide.md                 | 952 ++++++++++++++++--
 .../scala/org/apache/spark/sql/SQLConf.scala  |   2 +-
 .../apache/spark/sql/UdfRegistration.scala    |   2 +-
 .../columnar/InMemoryColumnarTableScan.scala  |   2 +-
 .../spark/sql/parquet/ParquetConverter.scala  |   2 +-
 .../spark/sql/parquet/ParquetFilters.scala    |   2 +-
 .../server/SparkSQLOperationManager.scala     |   4 +-
 7 files changed, 865 insertions(+), 101 deletions(-)

diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md
index 8f7fb5431cfb6..1814fef465cac 100644
--- a/docs/sql-programming-guide.md
+++ b/docs/sql-programming-guide.md
@@ -68,6 +68,16 @@ val sqlContext = new org.apache.spark.sql.SQLContext(sc)
 import sqlContext.createSchemaRDD
 {% endhighlight %}
 
+In addition to the basic SQLContext, you can also create a HiveContext, which provides a strict
+super set of the functionality provided by the basic SQLContext. Additional features include
+the ability to write queries using the more complete HiveQL parser, access to HiveUDFs, and the
+ability to read data from Hive tables.  To use a HiveContext, you do not need to have an
+existing hive setup, and all of the data sources available to a SQLContext are still available.
+HiveContext is only packaged separately to avoid including all of Hive's dependencies in the default
+Spark build.  If these dependencies are not a problem for your application then using HiveContext
+is recommended for the 1.2 release of Spark.  Future releases will focus on bringing SQLContext up to
+feature parity with a HiveContext.
+
 </div>
 
 <div data-lang="java" markdown="1">
@@ -81,6 +91,16 @@ JavaSparkContext sc = ...; // An existing JavaSparkContext.
 JavaSQLContext sqlContext = new org.apache.spark.sql.api.java.JavaSQLContext(sc);
 {% endhighlight %}
 
+In addition to the basic SQLContext, you can also create a HiveContext, which provides a strict
+super set of the functionality provided by the basic SQLContext. Additional features include
+the ability to write queries using the more complete HiveQL parser, access to HiveUDFs, and the
+ability to read data from Hive tables.  To use a HiveContext, you do not need to have an
+existing hive setup, and all of the data sources available to a SQLContext are still available.
+HiveContext is only packaged separately to avoid including all of Hive's dependencies in the default
+Spark build.  If these dependencies are not a problem for your application then using HiveContext
+is recommended for the 1.2 release of Spark.  Future releases will focus on bringing SQLContext up to
+feature parity with a HiveContext.
+
 </div>
 
 <div data-lang="python"  markdown="1">
@@ -94,36 +114,52 @@ from pyspark.sql import SQLContext
 sqlContext = SQLContext(sc)
 {% endhighlight %}
 
-</div>
+In addition to the basic SQLContext, you can also create a HiveContext, which provides a strict
+super set of the functionality provided by the basic SQLContext. Additional features include
+the ability to write queries using the more complete HiveQL parser, access to HiveUDFs, and the
+ability to read data from Hive tables.  To use a HiveContext, you do not need to have an
+existing hive setup, and all of the data sources available to a SQLContext are still available.
+HiveContext is only packaged separately to avoid including all of Hive's dependencies in the default
+Spark build.  If these dependencies are not a problem for your application then using HiveContext
+is recommended for the 1.2 release of Spark.  Future releases will focus on bringing SQLContext up to
+feature parity with a HiveContext.
 
 </div>
 
-# Data Sources
-
-<div class="codetabs">
-<div data-lang="scala"  markdown="1">
-Spark SQL supports operating on a variety of data sources through the `SchemaRDD` interface.
-Once a dataset has been loaded, it can be registered as a table and even joined with data from other sources.
 </div>
 
-<div data-lang="java"  markdown="1">
-Spark SQL supports operating on a variety of data sources through the `JavaSchemaRDD` interface.
-Once a dataset has been loaded, it can be registered as a table and even joined with data from other sources.
-</div>
+The specific variant of SQL that is used to parse queries can also be selected using the 
+`spark.sql.dialect` option.  This parameter can be changed using either the `setConf` method on
+a SQLContext or by using a `SET key=value` command in SQL.  For a SQLContext, the only dialect
+available is "sql" which uses a simple SQL parser provided by Spark SQL.  In a HiveContext, the
+default is "hiveql", though "sql" is also available.  Since the HiveQL parser is much more complete,
+ this is recommended for most use cases.
+
+# Data Sources
 
-<div data-lang="python"  markdown="1">
 Spark SQL supports operating on a variety of data sources through the `SchemaRDD` interface.
-Once a dataset has been loaded, it can be registered as a table and even joined with data from other sources.
-</div>
-</div>
+A SchemaRDD can be operated on as normal RDDs and can also be registered as a temporary table.
+Registering a SchemaRDD as a table allows you to run SQL queries over its data.  This section 
+describes the various methods for loading data into a SchemaRDD.
 
 ## RDDs
 
+Spark SQL supports two different methods for converting existing RDDs into SchemaRDDs.  The first
+method uses reflection to infer the schema of an RDD that contains specific types of objects.  This
+reflection based approach leads to more concise code and works well went the schema is known ahead
+of time, while you are writing your Spark application.
+
+The second method for creating SchemaRDDs is through a programmatic interface that allows you to
+construct a schema and then apply it to and existing RDD.  While this method is more verbose, it allows
+you to construct SchemaRDDs when the columns and their types are not known until runtime.
+ 
+### Inferring the Schema Using Reflection
 <div class="codetabs">
 
 <div data-lang="scala"  markdown="1">
 
-One type of table that is supported by Spark SQL is an RDD of Scala case classes.  The case class
+The Scala interaface for Spark SQL supports automatically converting an RDD containing case classes
+to a SchemaRDD.  The case class
 defines the schema of the table.  The names of the arguments to the case class are read using
 reflection and become the names of the columns. Case classes can also be nested or contain complex
 types such as Sequences or Arrays. This RDD can be implicitly converted to a SchemaRDD and then be
@@ -156,8 +192,9 @@ teenagers.map(t => "Name: " + t(0)).collect().foreach(println)
 
 <div data-lang="java"  markdown="1">
 
-One type of table that is supported by Spark SQL is an RDD of [JavaBeans](http://stackoverflow.com/questions/3295496/what-is-a-javabean-exactly).  The BeanInfo
-defines the schema of the table. Currently, Spark SQL does not support JavaBeans that contain
+Spark SQL supports automatically converting an RDD of [JavaBeans](http://stackoverflow.com/questions/3295496/what-is-a-javabean-exactly)
+into a Schema RDD.  The BeanInfo, obtained using reflection, defines the schema of the table. 
+Currently, Spark SQL does not support JavaBeans that contain
 nested or contain complex types such as Lists or Arrays.  You can create a JavaBean by creating a
 class that implements Serializable and has getters and setters for all of its fields.
 
@@ -192,7 +229,7 @@ for the JavaBean.
 
 {% highlight java %}
 // sc is an existing JavaSparkContext.
-JavaSQLContext sqlContext = new org.apache.spark.sql.api.java.JavaSQLContext(sc)
+JavaSQLContext sqlContext = new org.apache.spark.sql.api.java.JavaSQLContext(sc);
 
 // Load a text file and convert each line to a JavaBean.
 JavaRDD<Person> people = sc.textFile("examples/src/main/resources/people.txt").map(
@@ -229,24 +266,24 @@ List<String> teenagerNames = teenagers.map(new Function<Row, String>() {
 
 <div data-lang="python"  markdown="1">
 
-One type of table that is supported by Spark SQL is an RDD of dictionaries.  The keys of the
-dictionary define the columns names of the table, and the types are inferred by looking at the first
-row. Any RDD of dictionaries can converted to a SchemaRDD and then registered as a table.  Tables
-can be used in subsequent SQL statements.
+Spark SQL can convert an RDD of Row objects to a SchemaRDD, inferring the datatypes .  Rows are constructed by passing a list of
+key/value pairs as kwargs to the Row class. The keys of this list define the columns names of the table,
+and the types are inferred by looking at the first row.  Since we currently only look at the first
+row, it is important that there is no missing data in the first row of the RDD. In future version we
+plan to more completely infer the schema by looking at more data, similar to the inference that is
+performed on JSON files.
 
 {% highlight python %}
 # sc is an existing SparkContext.
-from pyspark.sql import SQLContext
+from pyspark.sql import SQLContext, Row
 sqlContext = SQLContext(sc)
 
 # Load a text file and convert each line to a dictionary.
 lines = sc.textFile("examples/src/main/resources/people.txt")
 parts = lines.map(lambda l: l.split(","))
-people = parts.map(lambda p: {"name": p[0], "age": int(p[1])})
+people = parts.map(lambda p: Row(name=p[0], age=int(p[1])))
 
 # Infer the schema, and register the SchemaRDD as a table.
-# In future versions of PySpark we would like to add support for registering RDDs with other
-# datatypes as tables
 schemaPeople = sqlContext.inferSchema(people)
 schemaPeople.registerTempTable("people")
 
@@ -263,15 +300,191 @@ for teenName in teenNames.collect():
 
 </div>
 
-**Note that Spark SQL currently uses a very basic SQL parser.**
-Users that want a more complete dialect of SQL should look at the HiveQL support provided by
-`HiveContext`.
+### Programmatically Specifying the Schema
+
+<div class="codetabs">
+
+<div data-lang="scala"  markdown="1">
+
+In cases that case classes cannot be defined ahead of time (for example,
+the structure of records is encoded in a string or a text dataset will be parsed
+and fields will be projected differently for different users),
+a `SchemaRDD` can be created programmatically with three steps.
+
+1. Create an RDD of `Row`s from the original RDD;
+2. Create the schema represented by a `StructType` matching the structure of
+`Row`s in the RDD created in the step 1.
+3. Apply the schema to the RDD of `Row`s via `applySchema` method provided
+by `SQLContext`.
+
+For example:
+{% highlight scala %}
+// sc is an existing SparkContext.
+val sqlContext = new org.apache.spark.sql.SQLContext(sc)
+
+// Create an RDD
+val people = sc.textFile("examples/src/main/resources/people.txt")
+
+// The schema is encoded in a string
+val schemaString = "name age"
+
+// Import Spark SQL data types and Row.
+import org.apache.spark.sql._
+
+// Generate the schema based on the string of schema
+val schema =
+  StructType(
+    schemaString.split(" ").map(fieldName => StructField(fieldName, StringType, true)))
+
+// Convert records of the RDD (people) to Rows.
+val rowRDD = people.map(_.split(",")).map(p => Row(p(0), p(1).trim))
+
+// Apply the schema to the RDD.
+val peopleSchemaRDD = sqlContext.applySchema(rowRDD, schema)
+
+// Register the SchemaRDD as a table.
+peopleSchemaRDD.registerTempTable("people")
+
+// SQL statements can be run by using the sql methods provided by sqlContext.
+val results = sqlContext.sql("SELECT name FROM people")
+
+// The results of SQL queries are SchemaRDDs and support all the normal RDD operations.
+// The columns of a row in the result can be accessed by ordinal.
+results.map(t => "Name: " + t(0)).collect().foreach(println)
+{% endhighlight %}
+
+
+</div>
+
+<div data-lang="java"  markdown="1">
+
+In cases that JavaBean classes cannot be defined ahead of time (for example,
+the structure of records is encoded in a string or a text dataset will be parsed and
+fields will be projected differently for different users),
+a `SchemaRDD` can be created programmatically with three steps.
+
+1. Create an RDD of `Row`s from the original RDD;
+2. Create the schema represented by a `StructType` matching the structure of
+`Row`s in the RDD created in the step 1.
+3. Apply the schema to the RDD of `Row`s via `applySchema` method provided
+by `JavaSQLContext`.
+
+For example:
+{% highlight java %}
+// Import factory methods provided by DataType.
+import org.apache.spark.sql.api.java.DataType
+// Import StructType and StructField
+import org.apache.spark.sql.api.java.StructType
+import org.apache.spark.sql.api.java.StructField
+// Import Row.
+import org.apache.spark.sql.api.java.Row
+
+// sc is an existing JavaSparkContext.
+JavaSQLContext sqlContext = new org.apache.spark.sql.api.java.JavaSQLContext(sc);
+
+// Load a text file and convert each line to a JavaBean.
+JavaRDD<String> people = sc.textFile("examples/src/main/resources/people.txt");
+
+// The schema is encoded in a string
+String schemaString = "name age";
+
+// Generate the schema based on the string of schema
+List<StructField> fields = new ArrayList<StructField>();
+for (String fieldName: schemaString.split(" ")) {
+  fields.add(DataType.createStructField(fieldName, DataType.StringType, true));
+}
+StructType schema = DataType.createStructType(fields);
+
+// Convert records of the RDD (people) to Rows.
+JavaRDD<Row> rowRDD = people.map(
+  new Function<String, Row>() {
+    public Row call(String record) throws Exception {
+      String[] fields = record.split(",");
+      return Row.create(fields[0], fields[1].trim());
+    }
+  });
+
+// Apply the schema to the RDD.
+JavaSchemaRDD peopleSchemaRDD = sqlContext.applySchema(rowRDD, schema);
+
+// Register the SchemaRDD as a table.
+peopleSchemaRDD.registerTempTable("people");
+
+// SQL can be run over RDDs that have been registered as tables.
+JavaSchemaRDD results = sqlContext.sql("SELECT name FROM people");
+
+// The results of SQL queries are SchemaRDDs and support all the normal RDD operations.
+// The columns of a row in the result can be accessed by ordinal.
+List<String> names = results.map(new Function<Row, String>() {
+  public String call(Row row) {
+    return "Name: " + row.getString(0);
+  }
+}).collect();
+
+{% endhighlight %}
+
+</div>
+
+<div data-lang="python"  markdown="1">
+
+For some cases (for example, the structure of records is encoded in a string or
+a text dataset will be parsed and fields will be projected differently for
+different users), it is desired to create `SchemaRDD` with a programmatically way.
+It can be done with three steps.
+
+1. Create an RDD of tuples or lists from the original RDD;
+2. Create the schema represented by a `StructType` matching the structure of
+tuples or lists in the RDD created in the step 1.
+3. Apply the schema to the RDD via `applySchema` method provided by `SQLContext`.
+
+For example:
+{% highlight python %}
+# Import SQLContext and data types
+from pyspark.sql import *
+
+# sc is an existing SparkContext.
+sqlContext = SQLContext(sc)
+
+# Load a text file and convert each line to a tuple.
+lines = sc.textFile("examples/src/main/resources/people.txt")
+parts = lines.map(lambda l: l.split(","))
+people = parts.map(lambda p: (p[0], p[1].strip()))
+
+# The schema is encoded in a string.
+schemaString = "name age"
+
+fields = [StructField(field_name, StringType(), True) for field_name in schemaString.split()]
+schema = StructType(fields)
+
+# Apply the schema to the RDD.
+schemaPeople = sqlContext.applySchema(people, schema)
+
+# Register the SchemaRDD as a table.
+schemaPeople.registerTempTable("people")
+
+# SQL can be run over SchemaRDDs that have been registered as a table.
+results = sqlContext.sql("SELECT name FROM people")
+
+# The results of SQL queries are RDDs and support all the normal RDD operations.
+names = results.map(lambda p: "Name: " + p.name)
+for name in names.collect():
+  print name
+{% endhighlight %}
+
+
+</div>
+
+</div>
 
 ## Parquet Files
 
 [Parquet](http://parquet.io) is a columnar format that is supported by many other data processing systems.
 Spark SQL provides support for both reading and writing Parquet files that automatically preserves the schema
-of the original data.  Using the data from the above example:
+of the original data.  
+
+### Loading Data Programmatically
+
+Using the data from the above example:
 
 <div class="codetabs">
 
@@ -349,7 +562,40 @@ for teenName in teenNames.collect():
 
 </div>
 
-</div>
+</div>                                                                             
+
+### Configuration
+
+Configuration of parquet can be done using the `setConf` method on SQLContext or by running 
+`SET key=value` commands using SQL.
+
+<table class="table">
+<tr><th>Property Name</th><th>Default</th><th>Meaning</th></tr>
+<tr>
+  <td><code>spark.sql.parquet.binaryAsString</code></td>
+  <td>false</td>
+  <td>
+    Some other parquet producing systems, in particular Impala and older versions of Spark SQL, do 
+    not differentiate between binary data and strings when writing out the parquet schema.  This 
+    flag tells Spark SQL to interpret binary data as a string to provide compatibility with these systems.
+  </td>
+</tr>
+<tr>
+  <td><code>spark.sql.parquet.cacheMetadata</code></td>
+  <td>false</td>
+  <td>
+    Turns on caching of parquet schema metadata.  Can speed up querying 
+  </td>
+</tr>
+<tr>
+  <td><code>spark.sql.parquet.compression.codec</code></td>
+  <td>snappy</td>
+  <td>
+    Sets the compression codec use when writing parquet files. Acceptable values include: 
+    uncompressed, snappy, gzip, lzo.
+  </td>
+</tr>
+</table>
 
 ## JSON Datasets
 <div class="codetabs">
@@ -493,13 +739,13 @@ directory.
 
 {% highlight scala %}
 // sc is an existing SparkContext.
-val hiveContext = new org.apache.spark.sql.hive.HiveContext(sc)
+val sqlContext = new org.apache.spark.sql.hive.HiveContext(sc)
 
-hiveContext.sql("CREATE TABLE IF NOT EXISTS src (key INT, value STRING)")
-hiveContext.sql("LOAD DATA LOCAL INPATH 'examples/src/main/resources/kv1.txt' INTO TABLE src")
+sqlContext.sql("CREATE TABLE IF NOT EXISTS src (key INT, value STRING)")
+sqlContext.sql("LOAD DATA LOCAL INPATH 'examples/src/main/resources/kv1.txt' INTO TABLE src")
 
 // Queries are expressed in HiveQL
-hiveContext.sql("FROM src SELECT key, value").collect().foreach(println)
+sqlContext.sql("FROM src SELECT key, value").collect().foreach(println)
 {% endhighlight %}
 
 </div>
@@ -513,13 +759,13 @@ expressed in HiveQL.
 
 {% highlight java %}
 // sc is an existing JavaSparkContext.
-JavaHiveContext hiveContext = new org.apache.spark.sql.hive.api.java.HiveContext(sc);
+JavaHiveContext sqlContext = new org.apache.spark.sql.hive.api.java.HiveContext(sc);
 
-hiveContext.sql("CREATE TABLE IF NOT EXISTS src (key INT, value STRING)");
-hiveContext.sql("LOAD DATA LOCAL INPATH 'examples/src/main/resources/kv1.txt' INTO TABLE src");
+sqlContext.sql("CREATE TABLE IF NOT EXISTS src (key INT, value STRING)");
+sqlContext.sql("LOAD DATA LOCAL INPATH 'examples/src/main/resources/kv1.txt' INTO TABLE src");
 
 // Queries are expressed in HiveQL.
-Row[] results = hiveContext.sql("FROM src SELECT key, value").collect();
+Row[] results = sqlContext.sql("FROM src SELECT key, value").collect();
 
 {% endhighlight %}
 
@@ -535,44 +781,97 @@ expressed in HiveQL.
 {% highlight python %}
 # sc is an existing SparkContext.
 from pyspark.sql import HiveContext
-hiveContext = HiveContext(sc)
+sqlContext = HiveContext(sc)
 
-hiveContext.sql("CREATE TABLE IF NOT EXISTS src (key INT, value STRING)")
-hiveContext.sql("LOAD DATA LOCAL INPATH 'examples/src/main/resources/kv1.txt' INTO TABLE src")
+sqlContext.sql("CREATE TABLE IF NOT EXISTS src (key INT, value STRING)")
+sqlContext.sql("LOAD DATA LOCAL INPATH 'examples/src/main/resources/kv1.txt' INTO TABLE src")
 
 # Queries can be expressed in HiveQL.
-results = hiveContext.sql("FROM src SELECT key, value").collect()
+results = sqlContext.sql("FROM src SELECT key, value").collect()
 
 {% endhighlight %}
 
 </div>
 </div>
 
-# Writing Language-Integrated Relational Queries
+# Performance Tuning
 
-**Language-Integrated queries are currently only supported in Scala.**
-
-Spark SQL also supports a domain specific language for writing queries.  Once again,
-using the data from the above examples:
+For some workloads it is possible to improve performance by either caching data in memory, or by
+turning on some experimental options.
 
-{% highlight scala %}
-// sc is an existing SparkContext.
-val sqlContext = new org.apache.spark.sql.SQLContext(sc)
-// Importing the SQL context gives access to all the public SQL functions and implicit conversions.
-import sqlContext._
-val people: RDD[Person] = ... // An RDD of case class objects, from the first example.
+## Caching Data In Memory
 
-// The following is the same as 'SELECT name FROM people WHERE age >= 10 AND age <= 19'
-val teenagers = people.where('age >= 10).where('age <= 19).select('name)
-teenagers.map(t => "Name: " + t(0)).collect().foreach(println)
-{% endhighlight %}
+Spark SQL can cache tables using an in-memory columnar format by calling `cacheTable("tableName")`.
+Then Spark SQL will scan only required columns and will automatically tune compression to minimize
+memory usage and GC pressure. You can call `uncacheTable("tableName")` to remove the table from memory.
 
-The DSL uses Scala symbols to represent columns in the underlying table, which are identifiers
-prefixed with a tick (`'`).  Implicit conversions turn these symbols into expressions that are
-evaluated by the SQL execution engine.  A full list of the functions supported can be found in the
-[ScalaDoc](api/scala/index.html#org.apache.spark.sql.SchemaRDD).
+Note that if you just call `cache` rather than `cacheTable`, tables will _not_ be cached in
+in-memory columnar format. So we strongly recommend using `cacheTable` whenever you want to
+cache tables.
 
-<!-- TODO: Include the table of operations here. -->
+Configuration of in-memory caching can be done using the `setConf` method on SQLContext or by running 
+`SET key=value` commands using SQL.
+
+<table class="table">
+<tr><th>Property Name</th><th>Default</th><th>Meaning</th></tr>
+<tr>
+  <td><code>spark.sql.inMemoryColumnarStorage.compressed</code></td>
+  <td>false</td>
+  <td>
+    When set to true Spark SQL will automatically select a compression codec for each column based
+    on statistics of the data.
+  </td>
+</tr>
+<tr>
+  <td><code>spark.sql.inMemoryColumnarStorage.batchSize</code></td>
+  <td>1000</td>
+  <td>
+    Controls the size of batches for columnar caching.  Larger batch sizes can improve memory utilization
+    and compression, but risk OOMs when caching data.
+  </td>
+</tr>
+
+</table>
+
+## Other Configuration
+
+The following options can also be used to tune the performance of query execution.  It is possible
+that these options will be deprecated in future release as more optimizations are performed automatically.
+
+<table class="table">
+  <tr><th>Property Name</th><th>Default</th><th>Meaning</th></tr>
+  <tr>
+    <td><code>spark.sql.autoBroadcastJoinThreshold</code></td>
+    <td>false</td>
+    <td>
+      Configures the maximum size in bytes for a table that will be broadcast to all worker nodes when
+      performing a join.  By setting this value to -1 broadcasting can be disabled.  Note that currently
+      statistics are only supported for Hive Metastore tables where the command
+      `ANALYZE TABLE &lt;tableName&gt; COMPUTE STATISTICS noscan` has been run.
+    </td>
+  </tr>
+  <tr>
+    <td><code>spark.sql.codegen</code></td>
+    <td>false</td>
+    <td>
+      When true, code will be dynamically generated at runtime for expression evaluation in a specific
+      query.  For some queries with complicated expression this option can lead to significant speed-ups.
+      However, for simple queries this can actually slow down query execution.
+    </td>
+  </tr>
+  <tr>
+    <td><code>spark.sql.shuffle.partitions</code></td>
+    <td>200</td>
+    <td>
+      Configures the number of partitions to use when shuffling data for joins or aggregations.
+    </td>
+  </tr>
+</table>
+
+# Other SQL Interfaces
+
+Spark SQL also supports interfaces for running SQL queries directly without the need to write any
+code.
 
 ## Running the Thrift JDBC server
 
@@ -602,14 +901,28 @@ Configuration of Hive is done by placing your `hive-site.xml` file in `conf/`.
 
 You may also use the beeline script comes with Hive.
 
+## Running the Spark SQL CLI
+
+The Spark SQL CLI is a convenient tool to run the Hive metastore service in local mode and execute
+queries input from command line. Note: the Spark SQL CLI cannot talk to the Thrift JDBC server.
+
+To start the Spark SQL CLI, run the following in the Spark directory:
+
+    ./bin/spark-sql
+
+Configuration of Hive is done by placing your `hive-site.xml` file in `conf/`.
+You may run `./bin/spark-sql --help` for a complete list of all available
+options.
+
+# Compatibility with Other Systems
+
+## Migration Guide for Shark Users
 To set a [Fair Scheduler](job-scheduling.html#fair-scheduler-pools) pool for a JDBC client session,
 users can set the `spark.sql.thriftserver.scheduler.pool` variable:
 
     SET spark.sql.thriftserver.scheduler.pool=accounting;
 
-### Migration Guide for Shark Users
-
-#### Reducer number
+### Reducer number
 
 In Shark, default reducer number is 1 and is controlled by the property `mapred.reduce.tasks`. Spark
 SQL deprecates this property by a new property `spark.sql.shuffle.partitions`, whose default value
@@ -625,7 +938,7 @@ You may also put this property in `hive-site.xml` to override the default value.
 For now, the `mapred.reduce.tasks` property is still recognized, and is converted to
 `spark.sql.shuffle.partitions` automatically.
 
-#### Caching
+### Caching
 
 The `shark.cache` table property no longer exists, and tables whose name end with `_cached` are no
 longer automatically cached. Instead, we provide `CACHE TABLE` and `UNCACHE TABLE` statements to
@@ -634,9 +947,9 @@ let user control table caching explicitly:
     CACHE TABLE logs_last_month;
     UNCACHE TABLE logs_last_month;
 
-**NOTE:** `CACHE TABLE tbl` is lazy, it only marks table `tbl` as "need to by cached if necessary",
-but doesn't actually cache it until a query that touches `tbl` is executed. To force the table to be
-cached, you may simply count the table immediately after executing `CACHE TABLE`:
+**NOTE:** `CACHE TABLE tbl` is lazy, similar to `.cache` on an RDD. This command only marks `tbl` to ensure that
+partitions are cached when calculated but doesn't actually cache it until a query that touches `tbl` is executed.
+To force the table to be cached, you may simply count the table immediately after executing `CACHE TABLE`:
 
     CACHE TABLE logs_last_month;
     SELECT COUNT(1) FROM logs_last_month;
@@ -647,15 +960,18 @@ Several caching related features are not supported yet:
 * RDD reloading
 * In-memory cache write through policy
 
-### Compatibility with Apache Hive
+## Compatibility with Apache Hive
+
+Spark SQL is designed to be compatible with the Hive Metastore, SerDes and UDFs.  Currently Spark
+SQL is based on Hive 0.12.0.
 
 #### Deploying in Existing Hive Warehouses
 
-Spark SQL Thrift JDBC server is designed to be "out of the box" compatible with existing Hive
+The Spark SQL Thrift JDBC server is designed to be "out of the box" compatible with existing Hive
 installations. You do not need to modify your existing Hive Metastore or change the data placement
 or partitioning of your tables.
 
-#### Supported Hive Features
+### Supported Hive Features
 
 Spark SQL supports the vast majority of Hive features, such as:
 
@@ -705,13 +1021,14 @@ Spark SQL supports the vast majority of Hive features, such as:
   * `MAP<>`
   * `STRUCT<>`
 
-#### Unsupported Hive Functionality
+### Unsupported Hive Functionality
 
 Below is a list of Hive features that we don't support yet. Most of these features are rarely used
 in Hive deployments.
 
 **Major Hive Features**
 
+* Spark SQL does not currently support inserting to tables using dynamic partitioning.
 * Tables with buckets: bucket is the hash partitioning within a Hive table partition. Spark SQL
   doesn't support buckets yet.
 
@@ -721,11 +1038,11 @@ in Hive deployments.
   have the same input format.
 * Non-equi outer join: For the uncommon use case of using outer joins with non-equi join conditions
   (e.g. condition "`key < 10`"), Spark SQL will output wrong result for the `NULL` tuple.
-* `UNIONTYPE`
+* `UNION` type and `DATE` type
 * Unique join
 * Single query multi insert
 * Column statistics collecting: Spark SQL does not piggyback scans to collect column statistics at
-  the moment.
+  the moment and only supports populating the sizeInBytes field of the hive metastore.
 
 **Hive Input/Output Formats**
 
@@ -735,7 +1052,7 @@ in Hive deployments.
 **Hive Optimizations**
 
 A handful of Hive optimizations are not yet included in Spark. Some of these (such as indexes) are
-not necessary due to Spark SQL's in-memory computational model. Others are slotted for future
+less important due to Spark SQL's in-memory computational model. Others are slotted for future
 releases of Spark SQL.
 
 * Block level bitmap indexes and virtual columns (used to build indexes)
@@ -743,8 +1060,7 @@ releases of Spark SQL.
   Hive automatically converts the join into a map join. We are adding this auto conversion in the
   next release.
 * Automatically determine the number of reducers for joins and groupbys: Currently in Spark SQL, you
-  need to control the degree of parallelism post-shuffle using "`SET spark.sql.shuffle.partitions=[num_tasks];`". We are going to add auto-setting of parallelism in the
-  next release.
+  need to control the degree of parallelism post-shuffle using "`SET spark.sql.shuffle.partitions=[num_tasks];`".
 * Meta-data only query: For queries that can be answered by using only meta data, Spark SQL still
   launches tasks to compute the result.
 * Skew data flag: Spark SQL does not follow the skew data flags in Hive.
@@ -753,25 +1069,471 @@ releases of Spark SQL.
   Hive can optionally merge the small files into fewer large files to avoid overflowing the HDFS
   metadata. Spark SQL does not support that.
 
-## Running the Spark SQL CLI
+# Writing Language-Integrated Relational Queries
 
-The Spark SQL CLI is a convenient tool to run the Hive metastore service in local mode and execute
-queries input from command line. Note: the Spark SQL CLI cannot talk to the Thrift JDBC server.
+**Language-Integrated queries are experimental and currently only supported in Scala.**
 
-To start the Spark SQL CLI, run the following in the Spark directory:
+Spark SQL also supports a domain specific language for writing queries.  Once again,
+using the data from the above examples:
 
-    ./bin/spark-sql
+{% highlight scala %}
+// sc is an existing SparkContext.
+val sqlContext = new org.apache.spark.sql.SQLContext(sc)
+// Importing the SQL context gives access to all the public SQL functions and implicit conversions.
+import sqlContext._
+val people: RDD[Person] = ... // An RDD of case class objects, from the first example.
 
-Configuration of Hive is done by placing your `hive-site.xml` file in `conf/`.
-You may run `./bin/spark-sql --help` for a complete list of all available
-options.
+// The following is the same as 'SELECT name FROM people WHERE age >= 10 AND age <= 19'
+val teenagers = people.where('age >= 10).where('age <= 19).select('name)
+teenagers.map(t => "Name: " + t(0)).collect().foreach(println)
+{% endhighlight %}
 
-# Cached tables
+The DSL uses Scala symbols to represent columns in the underlying table, which are identifiers
+prefixed with a tick (`'`).  Implicit conversions turn these symbols into expressions that are
+evaluated by the SQL execution engine.  A full list of the functions supported can be found in the
+[ScalaDoc](api/scala/index.html#org.apache.spark.sql.SchemaRDD).
 
-Spark SQL can cache tables using an in-memory columnar format by calling `cacheTable("tableName")`.
-Then Spark SQL will scan only required columns and will automatically tune compression to minimize
-memory usage and GC pressure. You can call `uncacheTable("tableName")` to remove the table from memory.
+<!-- TODO: Include the table of operations here. -->
+
+# Spark SQL DataType Reference
+
+* Numeric types
+    - `ByteType`: Represents 1-byte signed integer numbers.
+    The range of numbers is from `-128` to `127`.
+    - `ShortType`: Represents 2-byte signed integer numbers.
+    The range of numbers is from `-32768` to `32767`.
+    - `IntegerType`: Represents 4-byte signed integer numbers.
+    The range of numbers is from `-2147483648` to `2147483647`.
+    - `LongType`: Represents 8-byte signed integer numbers.
+    The range of numbers is from `-9223372036854775808` to `9223372036854775807`.
+    - `FloatType`: Represents 4-byte single-precision floating point numbers.
+    - `DoubleType`: Represents 8-byte double-precision floating point numbers.
+    - `DecimalType`: 
+* String type
+    - `StringType`: Represents character string values.
+* Binary type
+    - `BinaryType`: Represents byte sequence values.
+* Boolean type
+    - `BooleanType`: Represents boolean values.
+* Datetime type
+    - `TimestampType`: Represents values comprising values of fields year, month, day,
+    hour, minute, and second.
+* Complex types
+    - `ArrayType(elementType, containsNull)`: Represents values comprising a sequence of
+    elements with the type of `elementType`. `containsNull` is used to indicate if
+    elements in a `ArrayType` value can have `null` values.
+    - `MapType(keyType, valueType, valueContainsNull)`:
+    Represents values comprising a set of key-value pairs. The data type of keys are
+    described by `keyType` and the data type of values are described by `valueType`.
+    For a `MapType` value, keys are not allowed to have `null` values. `valueContainsNull`
+    is used to indicate if values of a `MapType` value can have `null` values.
+    - `StructType(fields)`: Represents values with the structure described by
+    a sequence of `StructField`s (`fields`).
+        * `StructField(name, dataType, nullable)`: Represents a field in a `StructType`.
+        The name of a field is indicated by `name`. The data type of a field is indicated
+        by `dataType`. `nullable` is used to indicate if values of this fields can have
+        `null` values.
+
+<div class="codetabs">
+<div data-lang="scala"  markdown="1">
+
+All data types of Spark SQL are located in the package `org.apache.spark.sql`.
+You can access them by doing 
+{% highlight scala %}
+import  org.apache.spark.sql._
+{% endhighlight %}
+
+<table class="table">
+<tr>
+  <th style="width:20%">Data type</th>
+  <th style="width:40%">Value type in Scala</th>
+  <th>API to access or create a data type</th></tr>
+<tr>
+  <td> <b>ByteType</b> </td>
+  <td> Byte </td>
+  <td>
+  ByteType
+  </td>
+</tr>
+<tr>
+  <td> <b>ShortType</b> </td>
+  <td> Short </td>
+  <td>
+  ShortType
+  </td>
+</tr>
+<tr>
+  <td> <b>IntegerType</b> </td>
+  <td> Int </td>
+  <td>
+  IntegerType
+  </td>
+</tr>
+<tr>
+  <td> <b>LongType</b> </td>
+  <td> Long </td>
+  <td>
+  LongType
+  </td>
+</tr>
+<tr>
+  <td> <b>FloatType</b> </td>
+  <td> Float </td>
+  <td>
+  FloatType
+  </td>
+</tr>
+<tr>
+  <td> <b>DoubleType</b> </td>
+  <td> Double </td>
+  <td>
+  DoubleType
+  </td>
+</tr>
+<tr>
+  <td> <b>DecimalType</b> </td>
+  <td> scala.math.sql.BigDecimal </td>
+  <td>
+  DecimalType
+  </td>
+</tr>
+<tr>
+  <td> <b>StringType</b> </td>
+  <td> String </td>
+  <td>
+  StringType
+  </td>
+</tr>
+<tr>
+  <td> <b>BinaryType</b> </td>
+  <td> Array[Byte] </td>
+  <td>
+  BinaryType
+  </td>
+</tr>
+<tr>
+  <td> <b>BooleanType</b> </td>
+  <td> Boolean </td>
+  <td>
+  BooleanType
+  </td>
+</tr>
+<tr>
+  <td> <b>TimestampType</b> </td>
+  <td> java.sql.Timestamp </td>
+  <td>
+  TimestampType
+  </td>
+</tr>
+<tr>
+  <td> <b>ArrayType</b> </td>
+  <td> scala.collection.Seq </td>
+  <td>
+  ArrayType(<i>elementType</i>, [<i>containsNull</i>])<br />
+  <b>Note:</b> The default value of <i>containsNull</i> is <i>false</i>.
+  </td>
+</tr>
+<tr>
+  <td> <b>MapType</b> </td>
+  <td> scala.collection.Map </td>
+  <td>
+  MapType(<i>keyType</i>, <i>valueType</i>, [<i>valueContainsNull</i>])<br />
+  <b>Note:</b> The default value of <i>valueContainsNull</i> is <i>true</i>.
+  </td>
+</tr>
+<tr>
+  <td> <b>StructType</b> </td>
+  <td> org.apache.spark.sql.Row </td>
+  <td> 
+  StructType(<i>fields</i>)<br />
+  <b>Note:</b> <i>fields</i> is a Seq of StructFields. Also, two fields with the same
+  name are not allowed.
+  </td>
+</tr>
+<tr>
+  <td> <b>StructField</b> </td>
+  <td> The value type in Scala of the data type of this field
+  (For example, Int for a StructField with the data type IntegerType) </td>
+  <td>
+  StructField(<i>name</i>, <i>dataType</i>, <i>nullable</i>)
+  </td>
+</tr>
+</table>
+
+</div>
+
+<div data-lang="java" markdown="1">
+
+All data types of Spark SQL are located in the package of
+`org.apache.spark.sql.api.java`. To access or create a data type,
+please use factory methods provided in 
+`org.apache.spark.sql.api.java.DataType`.
+
+<table class="table">
+<tr>
+  <th style="width:20%">Data type</th>
+  <th style="width:40%">Value type in Java</th>
+  <th>API to access or create a data type</th></tr>
+<tr>
+  <td> <b>ByteType</b> </td>
+  <td> byte or Byte </td>
+  <td>
+  DataType.ByteType
+  </td>
+</tr>
+<tr>
+  <td> <b>ShortType</b> </td>
+  <td> short or Short </td>
+  <td>
+  DataType.ShortType
+  </td>
+</tr>
+<tr>
+  <td> <b>IntegerType</b> </td>
+  <td> int or Integer </td>
+  <td>
+  DataType.IntegerType
+  </td>
+</tr>
+<tr>
+  <td> <b>LongType</b> </td>
+  <td> long or Long </td>
+  <td>
+  DataType.LongType
+  </td>
+</tr>
+<tr>
+  <td> <b>FloatType</b> </td>
+  <td> float or Float </td>
+  <td>
+  DataType.FloatType
+  </td>
+</tr>
+<tr>
+  <td> <b>DoubleType</b> </td>
+  <td> double or Double </td>
+  <td>
+  DataType.DoubleType
+  </td>
+</tr>
+<tr>
+  <td> <b>DecimalType</b> </td>
+  <td> java.math.BigDecimal </td>
+  <td>
+  DataType.DecimalType
+  </td>
+</tr>
+<tr>
+  <td> <b>StringType</b> </td>
+  <td> String </td>
+  <td>
+  DataType.StringType
+  </td>
+</tr>
+<tr>
+  <td> <b>BinaryType</b> </td>
+  <td> byte[] </td>
+  <td>
+  DataType.BinaryType
+  </td>
+</tr>
+<tr>
+  <td> <b>BooleanType</b> </td>
+  <td> boolean or Boolean </td>
+  <td>
+  DataType.BooleanType
+  </td>
+</tr>
+<tr>
+  <td> <b>TimestampType</b> </td>
+  <td> java.sql.Timestamp </td>
+  <td>
+  DataType.TimestampType
+  </td>
+</tr>
+<tr>
+  <td> <b>ArrayType</b> </td>
+  <td> java.util.List </td>
+  <td>
+  DataType.createArrayType(<i>elementType</i>)<br />
+  <b>Note:</b> The value of <i>containsNull</i> will be <i>false</i><br />
+  DataType.createArrayType(<i>elementType</i>, <i>containsNull</i>).
+  </td>
+</tr>
+<tr>
+  <td> <b>MapType</b> </td>
+  <td> java.util.Map </td>
+  <td>
+  DataType.createMapType(<i>keyType</i>, <i>valueType</i>)<br />
+  <b>Note:</b> The value of <i>valueContainsNull</i> will be <i>true</i>.<br />
+  DataType.createMapType(<i>keyType</i>, <i>valueType</i>, <i>valueContainsNull</i>)<br />
+  </td>
+</tr>
+<tr>
+  <td> <b>StructType</b> </td>
+  <td> org.apache.spark.sql.api.java </td>
+  <td> 
+  DataType.createStructType(<i>fields</i>)<br />
+  <b>Note:</b> <i>fields</i> is a List or an array of StructFields.
+  Also, two fields with the same name are not allowed.
+  </td>
+</tr>
+<tr>
+  <td> <b>StructField</b> </td>
+  <td> The value type in Java of the data type of this field
+  (For example, int for a StructField with the data type IntegerType) </td>
+  <td>
+  DataType.createStructField(<i>name</i>, <i>dataType</i>, <i>nullable</i>)
+  </td>
+</tr>
+</table>
+
+</div>
+
+<div data-lang="python"  markdown="1">
+
+All data types of Spark SQL are located in the package of `pyspark.sql`.
+You can access them by doing 
+{% highlight python %}
+from pyspark.sql import *
+{% endhighlight %}
+
+<table class="table">
+<tr>
+  <th style="width:20%">Data type</th>
+  <th style="width:40%">Value type in Python</th>
+  <th>API to access or create a data type</th></tr>
+<tr>
+  <td> <b>ByteType</b> </td>
+  <td>
+  int or long <br />
+  <b>Note:</b> Numbers will be converted to 1-byte signed integer numbers at runtime.
+  Please make sure that numbers are within the range of -128 to 127.
+  </td>
+  <td>
+  ByteType()
+  </td>
+</tr>
+<tr>
+  <td> <b>ShortType</b> </td>
+  <td>
+  int or long <br />
+  <b>Note:</b> Numbers will be converted to 2-byte signed integer numbers at runtime.
+  Please make sure that numbers are within the range of -32768 to 32767.
+  </td>
+  <td>
+  ShortType()
+  </td>
+</tr>
+<tr>
+  <td> <b>IntegerType</b> </td>
+  <td> int or long </td>
+  <td>
+  IntegerType()
+  </td>
+</tr>
+<tr>
+  <td> <b>LongType</b> </td>
+  <td>
+  long <br />
+  <b>Note:</b> Numbers will be converted to 8-byte signed integer numbers at runtime.
+  Please make sure that numbers are within the range of
+  -9223372036854775808 to 9223372036854775807.
+  Otherwise, please convert data to decimal.Decimal and use DecimalType.
+  </td>
+  <td>
+  LongType()
+  </td>
+</tr>
+<tr>
+  <td> <b>FloatType</b> </td>
+  <td>
+  float <br />
+  <b>Note:</b> Numbers will be converted to 4-byte single-precision floating
+  point numbers at runtime.
+  </td>
+  <td>
+  FloatType()
+  </td>
+</tr>
+<tr>
+  <td> <b>DoubleType</b> </td>
+  <td> float </td>
+  <td>
+  DoubleType()
+  </td>
+</tr>
+<tr>
+  <td> <b>DecimalType</b> </td>
+  <td> decimal.Decimal </td>
+  <td>
+  DecimalType()
+  </td>
+</tr>
+<tr>
+  <td> <b>StringType</b> </td>
+  <td> string </td>
+  <td>
+  StringType()
+  </td>
+</tr>
+<tr>
+  <td> <b>BinaryType</b> </td>
+  <td> bytearray </td>
+  <td>
+  BinaryType()
+  </td>
+</tr>
+<tr>
+  <td> <b>BooleanType</b> </td>
+  <td> bool </td>
+  <td>
+  BooleanType()
+  </td>
+</tr>
+<tr>
+  <td> <b>TimestampType</b> </td>
+  <td> datetime.datetime </td>
+  <td>
+  TimestampType()
+  </td>
+</tr>
+<tr>
+  <td> <b>ArrayType</b> </td>
+  <td> list, tuple, or array </td>
+  <td>
+  ArrayType(<i>elementType</i>, [<i>containsNull</i>])<br />
+  <b>Note:</b> The default value of <i>containsNull</i> is <i>False</i>.
+  </td>
+</tr>
+<tr>
+  <td> <b>MapType</b> </td>
+  <td> dict </td>
+  <td>
+  MapType(<i>keyType</i>, <i>valueType</i>, [<i>valueContainsNull</i>])<br />
+  <b>Note:</b> The default value of <i>valueContainsNull</i> is <i>True</i>.
+  </td>
+</tr>
+<tr>
+  <td> <b>StructType</b> </td>
+  <td> list or tuple </td>
+  <td> 
+  StructType(<i>fields</i>)<br />
+  <b>Note:</b> <i>fields</i> is a Seq of StructFields. Also, two fields with the same
+  name are not allowed.
+  </td>
+</tr>
+<tr>
+  <td> <b>StructField</b> </td>
+  <td> The value type in Python of the data type of this field
+  (For example, Int for a StructField with the data type IntegerType) </td>
+  <td>
+  StructField(<i>name</i>, <i>dataType</i>, <i>nullable</i>)
+  </td>
+</tr>
+</table>
+
+</div>
+
+</div>
 
-Note that if you just call `cache` rather than `cacheTable`, tables will _not_ be cached in
-in-memory columnar format. So we strongly recommend using `cacheTable` whenever you want to
-cache tables.
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
index 4137ac7663739..f6f4cf3b80d41 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
@@ -53,7 +53,7 @@ private[spark] object SQLConf {
  *
  * SQLConf is thread-safe (internally synchronized, so safe to be used in multiple threads).
  */
-trait SQLConf {
+private[sql] trait SQLConf {
   import SQLConf._
 
   /** Only low degree of contention is expected for conf, thus NOT using ConcurrentHashMap. */
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/UdfRegistration.scala b/sql/core/src/main/scala/org/apache/spark/sql/UdfRegistration.scala
index 0ea1105f082a4..595b4aa36eae3 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/UdfRegistration.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/UdfRegistration.scala
@@ -30,7 +30,7 @@ import scala.reflect.runtime.universe.{TypeTag, typeTag}
 /**
  * Functions for registering scala lambda functions as UDFs in a SQLContext.
  */
-protected[sql] trait UDFRegistration {
+private[sql] trait UDFRegistration {
   self: SQLContext =>
 
   private[spark] def registerPython(
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala
index dc668e7dc934c..6eab2f23c18e1 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala
@@ -28,7 +28,7 @@ import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
 import org.apache.spark.sql.execution.{LeafNode, SparkPlan}
 
-object InMemoryRelation {
+private[sql] object InMemoryRelation {
   def apply(useCompression: Boolean, batchSize: Int, child: SparkPlan): InMemoryRelation =
     new InMemoryRelation(child.output, useCompression, batchSize, child)()
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetConverter.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetConverter.scala
index 9fd6aed402838..2fc7e1cf23ab7 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetConverter.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetConverter.scala
@@ -382,7 +382,7 @@ private[parquet] class CatalystPrimitiveConverter(
     parent.updateLong(fieldIndex, value)
 }
 
-object CatalystArrayConverter {
+private[parquet] object CatalystArrayConverter {
   val INITIAL_ARRAY_SIZE = 20
 }
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetFilters.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetFilters.scala
index fe28e0d7269e0..7c83f1cad7d71 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetFilters.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetFilters.scala
@@ -33,7 +33,7 @@ import org.apache.spark.sql.catalyst.expressions.{Predicate => CatalystPredicate
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.execution.SparkSqlSerializer
 
-object ParquetFilters {
+private[sql] object ParquetFilters {
   val PARQUET_FILTER_DATA = "org.apache.spark.sql.parquet.row.filter"
   // set this to false if pushdown should be disabled
   val PARQUET_FILTER_PUSHDOWN_ENABLED = "spark.sql.hints.parquetFilterPushdown"
diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/server/SparkSQLOperationManager.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/server/SparkSQLOperationManager.scala
index f12b5a69a09f7..bd3f68d92d8c7 100644
--- a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/server/SparkSQLOperationManager.scala
+++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/server/SparkSQLOperationManager.scala
@@ -39,7 +39,9 @@ import org.apache.spark.sql.hive.thriftserver.ReflectionUtils
 /**
  * Executes queries using Spark SQL, and maintains a list of handles to active queries.
  */
-class SparkSQLOperationManager(hiveContext: HiveContext) extends OperationManager with Logging {
+private[thriftserver] class SparkSQLOperationManager(hiveContext: HiveContext)
+  extends OperationManager with Logging {
+
   val handleToOperation = ReflectionUtils
     .getSuperField[JMap[OperationHandle, Operation]](this, "handleToOperation")
 

From e2614038e78f4693fafedeee15b6fdf0ea1be473 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@apache.org>
Date: Sun, 7 Sep 2014 18:42:24 -0700
Subject: [PATCH 224/489] [SPARK-3408] Fixed Limit operator so it works with
 sort-based shuffle.

Author: Reynold Xin <rxin@apache.org>

Closes #2281 from rxin/sql-limit-sort and squashes the following commits:

1ef7780 [Reynold Xin] [SPARK-3408] Fixed Limit operator so it works with sort-based shuffle.
---
 .../spark/sql/execution/basicOperators.scala  | 19 ++++++++++++++-----
 1 file changed, 14 insertions(+), 5 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
index 4abda21ffec96..47bff0c730b8a 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
@@ -20,10 +20,10 @@ package org.apache.spark.sql.execution
 import scala.collection.mutable.ArrayBuffer
 import scala.reflect.runtime.universe.TypeTag
 
+import org.apache.spark.{SparkEnv, HashPartitioner, SparkConf}
 import org.apache.spark.annotation.DeveloperApi
-import org.apache.spark.{HashPartitioner, SparkConf}
 import org.apache.spark.rdd.{RDD, ShuffledRDD}
-import org.apache.spark.sql.SQLContext
+import org.apache.spark.shuffle.sort.SortShuffleManager
 import org.apache.spark.sql.catalyst.ScalaReflection
 import org.apache.spark.sql.catalyst.errors._
 import org.apache.spark.sql.catalyst.expressions._
@@ -96,6 +96,9 @@ case class Limit(limit: Int, child: SparkPlan)
   // TODO: Implement a partition local limit, and use a strategy to generate the proper limit plan:
   // partition local limit -> exchange into one partition -> partition local limit again
 
+  /** We must copy rows when sort based shuffle is on */
+  private def sortBasedShuffleOn = SparkEnv.get.shuffleManager.isInstanceOf[SortShuffleManager]
+
   override def output = child.output
 
   /**
@@ -143,9 +146,15 @@ case class Limit(limit: Int, child: SparkPlan)
   }
 
   override def execute() = {
-    val rdd = child.execute().mapPartitions { iter =>
-      val mutablePair = new MutablePair[Boolean, Row]()
-      iter.take(limit).map(row => mutablePair.update(false, row))
+    val rdd: RDD[_ <: Product2[Boolean, Row]] = if (sortBasedShuffleOn) {
+      child.execute().mapPartitions { iter =>
+        iter.take(limit).map(row => (false, row.copy()))
+      }
+    } else {
+      child.execute().mapPartitions { iter =>
+        val mutablePair = new MutablePair[Boolean, Row]()
+        iter.take(limit).map(row => mutablePair.update(false, row))
+      }
     }
     val part = new HashPartitioner(1)
     val shuffled = new ShuffledRDD[Boolean, Row, Row](rdd, part)

From ecfa76cdfe846c75e1b7ebc556167e46963289c5 Mon Sep 17 00:00:00 2001
From: Ward Viaene <ward.viaene@bigdatapartnership.com>
Date: Sun, 7 Sep 2014 18:54:36 -0700
Subject: [PATCH 225/489] [SPARK-3415] [PySpark] removes SerializingAdapter
 code

This code removes the SerializingAdapter code that was copied from PiCloud

Author: Ward Viaene <ward.viaene@bigdatapartnership.com>

Closes #2287 from wardviaene/feature/pythonsys and squashes the following commits:

5f0d426 [Ward Viaene] SPARK-3415: modified test class to do dump and load
5f5d559 [Ward Viaene] SPARK-3415: modified test class name and call cloudpickle.dumps instead using StringIO
afc4a9a [Ward Viaene] SPARK-3415: added newlines to pass lint
aaf10b7 [Ward Viaene] SPARK-3415: removed references to SerializingAdapter and rewrote test
65ffeff [Ward Viaene] removed duplicate test
a958866 [Ward Viaene] SPARK-3415: test script
e263bf5 [Ward Viaene] SPARK-3415: removes legacy SerializingAdapter code
---
 python/pyspark/cloudpickle.py |  6 +-----
 python/pyspark/tests.py       | 11 +++++++++++
 2 files changed, 12 insertions(+), 5 deletions(-)

diff --git a/python/pyspark/cloudpickle.py b/python/pyspark/cloudpickle.py
index 68062483dedaa..80e51d1a583a0 100644
--- a/python/pyspark/cloudpickle.py
+++ b/python/pyspark/cloudpickle.py
@@ -657,7 +657,6 @@ def save_partial(self, obj):
     def save_file(self, obj):
         """Save a file"""
         import StringIO as pystringIO #we can't use cStringIO as it lacks the name attribute
-        from ..transport.adapter import SerializingAdapter
 
         if not hasattr(obj, 'name') or  not hasattr(obj, 'mode'):
             raise pickle.PicklingError("Cannot pickle files that do not map to an actual file")
@@ -691,13 +690,10 @@ def save_file(self, obj):
             tmpfile.close()
             if tst != '':
                 raise pickle.PicklingError("Cannot pickle file %s as it does not appear to map to a physical, real file" % name)
-        elif fsize > SerializingAdapter.max_transmit_data:
-            raise pickle.PicklingError("Cannot pickle file %s as it exceeds cloudconf.py's max_transmit_data of %d" %
-                                       (name,SerializingAdapter.max_transmit_data))
         else:
             try:
                 tmpfile = file(name)
-                contents = tmpfile.read(SerializingAdapter.max_transmit_data)
+                contents = tmpfile.read()
                 tmpfile.close()
             except IOError:
                 raise pickle.PicklingError("Cannot pickle file %s as it cannot be read" % name)
diff --git a/python/pyspark/tests.py b/python/pyspark/tests.py
index 2ade15b35ab4e..9fbeb36f4f1dd 100644
--- a/python/pyspark/tests.py
+++ b/python/pyspark/tests.py
@@ -169,6 +169,17 @@ def test_namedtuple(self):
         self.assertEquals(p1, p2)
 
 
+# Regression test for SPARK-3415
+class CloudPickleTest(unittest.TestCase):
+    def test_pickling_file_handles(self):
+        from pyspark.cloudpickle import dumps
+        from StringIO import StringIO
+        from pickle import load
+        out1 = sys.stderr
+        out2 = load(StringIO(dumps(out1)))
+        self.assertEquals(out1, out2)
+
+
 class PySparkTestCase(unittest.TestCase):
 
     def setUp(self):

From 9d69a782bd2fc45193f269d8d8434795ea1580a4 Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian.cs.zju@gmail.com>
Date: Sun, 7 Sep 2014 20:38:32 -0700
Subject: [PATCH 226/489] Fixed typos in make-distribution.sh

`hadoop.version` and `yarn.version` are properties rather then profiles, should use `-D` instead of `-P`.

/cc pwendell

Author: Cheng Lian <lian.cs.zju@gmail.com>

Closes #2121 from liancheng/fix-make-dist and squashes the following commits:

4c49158 [Cheng Lian] Also mentions Hadoop version related Maven profiles
ed5b42a [Cheng Lian] Fixed typos in make-distribution.sh
---
 make-distribution.sh | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/make-distribution.sh b/make-distribution.sh
index f030d3f430581..14aed4a4b655b 100755
--- a/make-distribution.sh
+++ b/make-distribution.sh
@@ -50,7 +50,8 @@ while (( "$#" )); do
   case $1 in
     --hadoop)
       echo "Error: '--hadoop' is no longer supported:"
-      echo "Error: use Maven options -Phadoop.version and -Pyarn.version"
+      echo "Error: use Maven profiles and options -Dhadoop.version and -Dyarn.version instead."
+      echo "Error: Related profiles include hadoop-0.23, hdaoop-2.2, hadoop-2.3 and hadoop-2.4."
       exit_with_usage
       ;;
     --with-yarn)

From 4ba2673569f8c6da7f7348977f52f98f40dfbfec Mon Sep 17 00:00:00 2001
From: Josh Rosen <joshrosen@apache.org>
Date: Sun, 7 Sep 2014 20:39:53 -0700
Subject: [PATCH 227/489] [HOTFIX] Fix broken Mima tests on the master branch

By merging #2268, which bumped the Spark version to 1.2.0-SNAPSHOT, I inadvertently broke the Mima binary compatibility tests.  The issue is that we were comparing 1.2.0-SNAPSHOT against Spark 1.0.0 without using any Mima excludes.  The right long-term fix for this is probably to publish nightly snapshots on Maven central and change the master branch to test binary compatibility against the current release candidate branch's snapshots until that release is finalized.

As a short-term fix until 1.1.0 is published on Maven central, I've configured the build to test the master branch for binary compatibility against the 1.1.0-RC4 jars.  I'll loop back and remove the Apache staging repo as soon as 1.1.0 final is available.

Author: Josh Rosen <joshrosen@apache.org>

Closes #2315 from JoshRosen/mima-fix and squashes the following commits:

776bc2c [Josh Rosen] Add two excludes to workaround Mima annotation issues.
ec90e21 [Josh Rosen] Add deploy and graphx to 1.2 MiMa excludes.
57569be [Josh Rosen] Fix MiMa tests in master branch; test against 1.1.0 RC.
---
 pom.xml                    | 12 ++++++++++++
 project/MimaBuild.scala    |  2 +-
 project/MimaExcludes.scala | 12 ++++++++++++
 3 files changed, 25 insertions(+), 1 deletion(-)

diff --git a/pom.xml b/pom.xml
index 1efa9045208fd..d05190512f742 100644
--- a/pom.xml
+++ b/pom.xml
@@ -221,6 +221,18 @@
         <enabled>false</enabled>
       </snapshots>
     </repository>
+    <!-- TODO: remove this as soon as 1.1.0 is published on Maven central -->
+    <repository>
+      <id>spark-staging-1030</id>
+      <name>Spark 1.1.0 Staging (1030)</name>
+      <url>https://repository.apache.org/content/repositories/orgapachespark-1030/</url>
+      <releases>
+        <enabled>true</enabled>
+      </releases>
+      <snapshots>
+        <enabled>false</enabled>
+      </snapshots>
+    </repository>
   </repositories>
   <pluginRepositories>
     <pluginRepository>
diff --git a/project/MimaBuild.scala b/project/MimaBuild.scala
index 034ba6a7bf50f..0f5d71afcf616 100644
--- a/project/MimaBuild.scala
+++ b/project/MimaBuild.scala
@@ -85,7 +85,7 @@ object MimaBuild {
 
   def mimaSettings(sparkHome: File, projectRef: ProjectRef) = {
     val organization = "org.apache.spark"
-    val previousSparkVersion = "1.0.0"
+    val previousSparkVersion = "1.1.0"
     val fullId = "spark-" + projectRef.project + "_2.10"
     mimaDefaultSettings ++ 
     Seq(previousArtifact := Some(organization % fullId % previousSparkVersion),
diff --git a/project/MimaExcludes.scala b/project/MimaExcludes.scala
index 855d5cc8cf3fd..46b78bd5c7061 100644
--- a/project/MimaExcludes.scala
+++ b/project/MimaExcludes.scala
@@ -33,6 +33,18 @@ import com.typesafe.tools.mima.core._
 object MimaExcludes {
     def excludes(version: String) =
       version match {
+        case v if v.startsWith("1.2") =>
+          Seq(
+            MimaBuild.excludeSparkPackage("deploy"),
+            MimaBuild.excludeSparkPackage("graphx")
+          ) ++
+          // This is @DeveloperAPI, but Mima still gives false-positives:
+          MimaBuild.excludeSparkClass("scheduler.SparkListenerApplicationStart") ++
+          Seq(
+            // This is @Experimental, but Mima still gives false-positives:
+            ProblemFilters.exclude[MissingMethodProblem](
+              "org.apache.spark.api.java.JavaRDDLike.foreachAsync")
+          )
         case v if v.startsWith("1.1") =>
           Seq(
             MimaBuild.excludeSparkPackage("deploy"),

From f25bbbdb3ac5620850c7d09d6a63af888411ecf1 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@apache.org>
Date: Sun, 7 Sep 2014 20:42:07 -0700
Subject: [PATCH 228/489] [SPARK-3280] Made sort-based shuffle the default
 implementation

Sort-based shuffle has lower memory usage and seems to outperform hash-based in almost all of our testing.

Author: Reynold Xin <rxin@apache.org>

Closes #2178 from rxin/sort-shuffle and squashes the following commits:

713d341 [Reynold Xin] Fixed test failures by setting spark.shuffle.compress to the same value as spark.shuffle.spill.compress.
85165e6 [Reynold Xin] Fixed a comment typo.
aa0d372 [Reynold Xin] [SPARK-3280] Made sort-based shuffle the default implementation
---
 .../scala/org/apache/spark/SparkEnv.scala     |  2 +-
 .../org/apache/spark/HashShuffleSuite.scala   | 33 +++++++++++++++++++
 .../scala/org/apache/spark/ShuffleSuite.scala |  2 +-
 .../org/apache/spark/SortShuffleSuite.scala   |  3 +-
 .../ExternalAppendOnlyMapSuite.scala          |  1 +
 docs/configuration.md                         |  9 +++--
 6 files changed, 41 insertions(+), 9 deletions(-)
 create mode 100644 core/src/test/scala/org/apache/spark/HashShuffleSuite.scala

diff --git a/core/src/main/scala/org/apache/spark/SparkEnv.scala b/core/src/main/scala/org/apache/spark/SparkEnv.scala
index 2973d002cc428..20a7444cfc5ee 100644
--- a/core/src/main/scala/org/apache/spark/SparkEnv.scala
+++ b/core/src/main/scala/org/apache/spark/SparkEnv.scala
@@ -217,7 +217,7 @@ object SparkEnv extends Logging {
     val shortShuffleMgrNames = Map(
       "hash" -> "org.apache.spark.shuffle.hash.HashShuffleManager",
       "sort" -> "org.apache.spark.shuffle.sort.SortShuffleManager")
-    val shuffleMgrName = conf.get("spark.shuffle.manager", "hash")
+    val shuffleMgrName = conf.get("spark.shuffle.manager", "sort")
     val shuffleMgrClass = shortShuffleMgrNames.getOrElse(shuffleMgrName.toLowerCase, shuffleMgrName)
     val shuffleManager = instantiateClass[ShuffleManager](shuffleMgrClass)
 
diff --git a/core/src/test/scala/org/apache/spark/HashShuffleSuite.scala b/core/src/test/scala/org/apache/spark/HashShuffleSuite.scala
new file mode 100644
index 0000000000000..2acc02a54fa3d
--- /dev/null
+++ b/core/src/test/scala/org/apache/spark/HashShuffleSuite.scala
@@ -0,0 +1,33 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark
+
+import org.scalatest.BeforeAndAfterAll
+
+class HashShuffleSuite extends ShuffleSuite with BeforeAndAfterAll {
+
+  // This test suite should run all tests in ShuffleSuite with hash-based shuffle.
+
+  override def beforeAll() {
+    System.setProperty("spark.shuffle.manager", "hash")
+  }
+
+  override def afterAll() {
+    System.clearProperty("spark.shuffle.manager")
+  }
+}
diff --git a/core/src/test/scala/org/apache/spark/ShuffleSuite.scala b/core/src/test/scala/org/apache/spark/ShuffleSuite.scala
index b13ddf96bc77c..15aa4d83800fa 100644
--- a/core/src/test/scala/org/apache/spark/ShuffleSuite.scala
+++ b/core/src/test/scala/org/apache/spark/ShuffleSuite.scala
@@ -26,7 +26,7 @@ import org.apache.spark.rdd.{CoGroupedRDD, OrderedRDDFunctions, RDD, ShuffledRDD
 import org.apache.spark.serializer.KryoSerializer
 import org.apache.spark.util.MutablePair
 
-class ShuffleSuite extends FunSuite with Matchers with LocalSparkContext {
+abstract class ShuffleSuite extends FunSuite with Matchers with LocalSparkContext {
 
   val conf = new SparkConf(loadDefaults = false)
 
diff --git a/core/src/test/scala/org/apache/spark/SortShuffleSuite.scala b/core/src/test/scala/org/apache/spark/SortShuffleSuite.scala
index 5c02c00586ef4..639e56c488db4 100644
--- a/core/src/test/scala/org/apache/spark/SortShuffleSuite.scala
+++ b/core/src/test/scala/org/apache/spark/SortShuffleSuite.scala
@@ -24,8 +24,7 @@ class SortShuffleSuite extends ShuffleSuite with BeforeAndAfterAll {
   // This test suite should run all tests in ShuffleSuite with sort-based shuffle.
 
   override def beforeAll() {
-    System.setProperty("spark.shuffle.manager",
-      "org.apache.spark.shuffle.sort.SortShuffleManager")
+    System.setProperty("spark.shuffle.manager", "sort")
   }
 
   override def afterAll() {
diff --git a/core/src/test/scala/org/apache/spark/util/collection/ExternalAppendOnlyMapSuite.scala b/core/src/test/scala/org/apache/spark/util/collection/ExternalAppendOnlyMapSuite.scala
index ac3931e3d0a73..511d76c9144cc 100644
--- a/core/src/test/scala/org/apache/spark/util/collection/ExternalAppendOnlyMapSuite.scala
+++ b/core/src/test/scala/org/apache/spark/util/collection/ExternalAppendOnlyMapSuite.scala
@@ -42,6 +42,7 @@ class ExternalAppendOnlyMapSuite extends FunSuite with LocalSparkContext {
     conf.set("spark.serializer.objectStreamReset", "1")
     conf.set("spark.serializer", "org.apache.spark.serializer.JavaSerializer")
     conf.set("spark.shuffle.spill.compress", codec.isDefined.toString)
+    conf.set("spark.shuffle.compress", codec.isDefined.toString)
     codec.foreach { c => conf.set("spark.io.compression.codec", c) }
     // Ensure that we actually have multiple batches per spill file
     conf.set("spark.shuffle.spill.batchSize", "10")
diff --git a/docs/configuration.md b/docs/configuration.md
index 65a422caabb7e..36178efb97103 100644
--- a/docs/configuration.md
+++ b/docs/configuration.md
@@ -293,12 +293,11 @@ Apart from these, the following properties are also available, and may be useful
 </tr>
 <tr>
   <td><code>spark.shuffle.manager</code></td>
-  <td>HASH</td>
+  <td>sort</td>
   <td>
-    Implementation to use for shuffling data. A hash-based shuffle manager is the default, but
-    starting in Spark 1.1 there is an experimental sort-based shuffle manager that is more 
-    memory-efficient in environments with small executors, such as YARN. To use that, change
-    this value to <code>SORT</code>.
+    Implementation to use for shuffling data. There are two implementations available:
+    <code>sort</code> and <code>hash</code>. Sort-based shuffle is more memory-efficient and is
+    the default option starting in 1.2.
   </td>
 </tr>
 <tr>

From eddfeddac19870fc265ef406d87e1c3db9b54249 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@apache.org>
Date: Sun, 7 Sep 2014 20:56:04 -0700
Subject: [PATCH 229/489] [SPARK-938][doc] Add OpenStack Swift support

See compiled doc at
http://people.apache.org/~rxin/tmp/openstack-swift/_site/storage-openstack-swift.html

This is based on #1010. Closes #1010.

Author: Reynold Xin <rxin@apache.org>
Author: Gil Vernik <gilv@il.ibm.com>

Closes #2298 from rxin/openstack-swift and squashes the following commits:

ff4e394 [Reynold Xin] Two minor comments from Patrick.
279f6de [Reynold Xin] core-sites -> core-site
dfb8fea [Reynold Xin] Updated based on Gil's suggestion.
846f5cb [Reynold Xin] Added a link from overview page.
0447c9f [Reynold Xin] Removed sample code.
e9c3761 [Reynold Xin] Merge pull request #1010 from gilv/master
9233fef [Gil Vernik] Fixed typos
6994827 [Gil Vernik] Merge pull request #1 from rxin/openstack
ac0679e [Reynold Xin] Fixed an unclosed tr.
47ce99d [Reynold Xin] Merge branch 'master' into openstack
cca7192 [Gil Vernik] Removed white spases from pom.xml
99f095d [Reynold Xin] Pending openstack changes.
eb22295 [Reynold Xin] Merge pull request #1010 from gilv/master
39a9737 [Gil Vernik] Spark integration with Openstack Swift
c977658 [Gil Vernik] Merge branch 'master' of https://github.com/gilv/spark
2aba763 [Gil Vernik] Fix to docs/openstack-integration.md
9b625b5 [Gil Vernik] Merge branch 'master' of https://github.com/gilv/spark
eff538d [Gil Vernik] SPARK-938 - Openstack Swift object storage support
ce483d7 [Gil Vernik] SPARK-938 - Openstack Swift object storage support
b6c37ef [Gil Vernik] Openstack Swift support
---
 docs/index.md                   |   2 +
 docs/storage-openstack-swift.md | 152 ++++++++++++++++++++++++++++++++
 2 files changed, 154 insertions(+)
 create mode 100644 docs/storage-openstack-swift.md

diff --git a/docs/index.md b/docs/index.md
index 4ac0982ae54f1..7fe6b43d32af7 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -103,6 +103,8 @@ options for deployment:
 * [Security](security.html): Spark security support
 * [Hardware Provisioning](hardware-provisioning.html): recommendations for cluster hardware
 * [3<sup>rd</sup> Party Hadoop Distributions](hadoop-third-party-distributions.html): using common Hadoop distributions
+* Integration with other storage systems:
+  * [OpenStack Swift](storage-openstack-swift.html)
 * [Building Spark with Maven](building-with-maven.html): build Spark using the Maven system
 * [Contributing to Spark](https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark)
 
diff --git a/docs/storage-openstack-swift.md b/docs/storage-openstack-swift.md
new file mode 100644
index 0000000000000..c39ef1ce59e1c
--- /dev/null
+++ b/docs/storage-openstack-swift.md
@@ -0,0 +1,152 @@
+---
+layout: global
+title: Accessing OpenStack Swift from Spark
+---
+
+Spark's support for Hadoop InputFormat allows it to process data in OpenStack Swift using the
+same URI formats as in Hadoop. You can specify a path in Swift as input through a 
+URI of the form <code>swift://container.PROVIDER/path</code>. You will also need to set your 
+Swift security credentials, through <code>core-site.xml</code> or via
+<code>SparkContext.hadoopConfiguration</code>.
+Current Swift driver requires Swift to use Keystone authentication method.
+
+# Configuring Swift for Better Data Locality
+
+Although not mandatory, it is recommended to configure the proxy server of Swift with
+<code>list_endpoints</code> to have better data locality. More information is
+[available here](https://github.com/openstack/swift/blob/master/swift/common/middleware/list_endpoints.py).
+
+
+# Dependencies
+
+The Spark application should include <code>hadoop-openstack</code> dependency.
+For example, for Maven support, add the following to the <code>pom.xml</code> file:
+
+{% highlight xml %}
+<dependencyManagement>
+  ...
+  <dependency>
+    <groupId>org.apache.hadoop</groupId>
+    <artifactId>hadoop-openstack</artifactId>
+    <version>2.3.0</version>
+  </dependency>
+  ...
+</dependencyManagement>
+{% endhighlight %}
+
+
+# Configuration Parameters
+
+Create <code>core-site.xml</code> and place it inside Spark's <code>conf</code> directory.
+There are two main categories of parameters that should to be configured: declaration of the
+Swift driver and the parameters that are required by Keystone. 
+
+Configuration of Hadoop to use Swift File system achieved via 
+
+<table class="table">
+<tr><th>Property Name</th><th>Value</th></tr>
+<tr>
+  <td>fs.swift.impl</td>
+  <td>org.apache.hadoop.fs.swift.snative.SwiftNativeFileSystem</td>
+</tr>
+</table>
+
+Additional parameters required by Keystone (v2.0) and should be provided to the Swift driver. Those 
+parameters will be used to perform authentication in Keystone to access Swift. The following table 
+contains a list of Keystone mandatory parameters. <code>PROVIDER</code> can be any name.
+
+<table class="table">
+<tr><th>Property Name</th><th>Meaning</th><th>Required</th></tr>
+<tr>
+  <td><code>fs.swift.service.PROVIDER.auth.url</code></td>
+  <td>Keystone Authentication URL</td>
+  <td>Mandatory</td>
+</tr>
+<tr>
+  <td><code>fs.swift.service.PROVIDER.auth.endpoint.prefix</code></td>
+  <td>Keystone endpoints prefix</td>
+  <td>Optional</td>
+</tr>
+<tr>
+  <td><code>fs.swift.service.PROVIDER.tenant</code></td>
+  <td>Tenant</td>
+  <td>Mandatory</td>
+</tr>
+<tr>
+  <td><code>fs.swift.service.PROVIDER.username</code></td>
+  <td>Username</td>
+  <td>Mandatory</td>
+</tr>
+<tr>
+  <td><code>fs.swift.service.PROVIDER.password</code></td>
+  <td>Password</td>
+  <td>Mandatory</td>
+</tr>
+<tr>
+  <td><code>fs.swift.service.PROVIDER.http.port</code></td>
+  <td>HTTP port</td>
+  <td>Mandatory</td>
+</tr>
+<tr>
+  <td><code>fs.swift.service.PROVIDER.region</code></td>
+  <td>Keystone region</td>
+  <td>Mandatory</td>
+</tr>
+<tr>
+  <td><code>fs.swift.service.PROVIDER.public</code></td>
+  <td>Indicates if all URLs are public</td>
+  <td>Mandatory</td>
+</tr>
+</table>
+
+For example, assume <code>PROVIDER=SparkTest</code> and Keystone contains user <code>tester</code> with password <code>testing</code>
+defined for tenant <code>test</code>. Then <code>core-site.xml</code> should include:
+
+{% highlight xml %}
+<configuration>
+  <property>
+    <name>fs.swift.impl</name>
+    <value>org.apache.hadoop.fs.swift.snative.SwiftNativeFileSystem</value>
+  </property>
+  <property>
+    <name>fs.swift.service.SparkTest.auth.url</name>
+    <value>http://127.0.0.1:5000/v2.0/tokens</value>
+  </property>
+  <property>
+    <name>fs.swift.service.SparkTest.auth.endpoint.prefix</name>
+    <value>endpoints</value>
+  </property>
+    <name>fs.swift.service.SparkTest.http.port</name>
+    <value>8080</value>
+  </property>
+  <property>
+    <name>fs.swift.service.SparkTest.region</name>
+    <value>RegionOne</value>
+  </property>
+  <property>
+    <name>fs.swift.service.SparkTest.public</name>
+    <value>true</value>
+  </property>
+  <property>
+    <name>fs.swift.service.SparkTest.tenant</name>
+    <value>test</value>
+  </property>
+  <property>
+    <name>fs.swift.service.SparkTest.username</name>
+    <value>tester</value>
+  </property>
+  <property>
+    <name>fs.swift.service.SparkTest.password</name>
+    <value>testing</value>
+  </property>
+</configuration>
+{% endhighlight %}
+
+Notice that
+<code>fs.swift.service.PROVIDER.tenant</code>,
+<code>fs.swift.service.PROVIDER.username</code>, 
+<code>fs.swift.service.PROVIDER.password</code> contains sensitive information and keeping them in
+<code>core-site.xml</code> is not always a good approach.
+We suggest to keep those parameters in <code>core-site.xml</code> for testing purposes when running Spark
+via <code>spark-shell</code>.
+For job submissions they should be provided via <code>sparkContext.hadoopConfiguration</code>.

From 0d1cc4ae42e1f73538dd8b9b1880ca9e5b124108 Mon Sep 17 00:00:00 2001
From: Prashant Sharma <prashant.s@imaginea.com>
Date: Mon, 8 Sep 2014 14:32:53 +0530
Subject: [PATCH 230/489] [HOTFIX] A left over version change. It should make
 mima happy.

Author: Prashant Sharma <prashant.s@imaginea.com>

Closes #2317 from ScrapCodes/hotfix and squashes the following commits:

b6472d4 [Prashant Sharma] [HOTFIX] for hotfixes, a left over version change.
---
 project/SparkBuild.scala | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala
index a26c2c90cb321..45f6d2973ea90 100644
--- a/project/SparkBuild.scala
+++ b/project/SparkBuild.scala
@@ -184,7 +184,7 @@ object OldDeps {
 
   def versionArtifact(id: String): Option[sbt.ModuleID] = {
     val fullId = id + "_2.10"
-    Some("org.apache.spark" % fullId % "1.0.0")
+    Some("org.apache.spark" % fullId % "1.1.0")
   }
 
   def oldDepsSettings() = Defaults.defaultSettings ++ Seq(

From 711356b422c66e2a80377a9f43fce97282460520 Mon Sep 17 00:00:00 2001
From: "Joseph K. Bradley" <joseph.kurata.bradley@gmail.com>
Date: Mon, 8 Sep 2014 09:47:13 -0700
Subject: [PATCH 231/489] [SPARK-3086] [SPARK-3043] [SPARK-3156] [mllib] 
 DecisionTree aggregation improvements
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Summary:
1. Variable numBins for each feature [SPARK-3043]
2. Reduced data reshaping in aggregation [SPARK-3043]
3. Choose ordering for ordered categorical features adaptively [SPARK-3156]
4. Changed nodes to use 1-indexing [SPARK-3086]
5. Small clean-ups

Note: This PR looks bigger than it is since I moved several functions from inside findBestSplitsPerGroup to outside of it (to make it clear what was being serialized in the aggregation).

Speedups: This update helps most when many features use few bins but a few features use many bins.  Some example results on speedups with 2M examples, 3.5K features (15-worker EC2 cluster):
* Example where old code was reasonably efficient (1/2 continuous, 1/4 binary, 1/4 20-category): 164.813 --> 116.491 sec
* Example where old code wasted many bins (1/10 continuous, 81/100 binary, 9/100 20-category): 128.701 --> 39.334 sec

Details:

(1) Variable numBins for each feature [SPARK-3043]

DecisionTreeMetadata now computes a variable numBins for each feature.  It also tracks numSplits.

(2) Reduced data reshaping in aggregation [SPARK-3043]

Added DTStatsAggregator, a wrapper around the aggregate statistics array for easy but efficient indexing.
* Added ImpurityAggregator and ImpurityCalculator classes, to make DecisionTree code more oblivious to the type of impurity.
* Design note: I originally tried creating Impurity classes which stored data and storing the aggregates in an Array[Array[Array[Impurity]]].  However, this led to significant slowdowns, perhaps because of overhead in creating so many objects.

The aggregate statistics are never reshaped, and cumulative sums are computed in-place.

Updated the layout of aggregation functions.  The update simplifies things by (1) dividing features into ordered/unordered (instead of ordered/unordered/continuous) and (2) making use of the DTStatsAggregator for indexing.
For this update, the following functions were refactored:
* updateBinForOrderedFeature
* updateBinForUnorderedFeature
* binaryOrNotCategoricalBinSeqOp
* multiclassWithCategoricalBinSeqOp
* regressionBinSeqOp
The above 5 functions were replaced with:
* orderedBinSeqOp
* someUnorderedBinSeqOp

Other changes:
* calculateGainForSplit now treats all feature types the same way.
* Eliminated extractLeftRightNodeAggregates.

(3) Choose ordering for ordered categorical features adaptively [SPARK-3156]

Updated binsToBestSplit():
* This now computes cumulative sums of stats for ordered features.
* For ordered categorical features, it chooses an ordering for categories. (This uses to be done by findSplitsBins.)
* Uses iterators to shorten code and avoid building an Array[Array[InformationGainStats]].

Side effects:
* In findSplitsBins: A sample of the data is only taken for data with continuous features.  It is not needed for data with only categorical features.
* In findSplitsBins: splits and bins are no longer pre-computed for ordered categorical features since they are not needed.
* TreePoint binning is simpler for categorical features.

(4) Changed nodes to use 1-indexing [SPARK-3086]

Nodes used to be indexed from 0.  Now they are indexed from 1.
Node indexing functions are now collected in object Node (Node.scala).

(5) Small clean-ups

Eliminated functions extractNodeInfo() and extractInfoForLowerLevels() to reduce duplicate code.
Eliminated InvalidBinIndex since it is no longer used.

CC: mengxr  manishamde  Please let me know if you have thoughts on this—thanks!

Author: Joseph K. Bradley <joseph.kurata.bradley@gmail.com>

Closes #2125 from jkbradley/dt-opt3alt and squashes the following commits:

42c192a [Joseph K. Bradley] Merge branch 'rfs' into dt-opt3alt
d3cc46b [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-opt3alt
00e4404 [Joseph K. Bradley] optimization for TreePoint construction (pre-computing featureArity and isUnordered as arrays)
425716c [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into rfs
a2acea5 [Joseph K. Bradley] Small optimizations based on profiling
aa4e4df [Joseph K. Bradley] Updated DTStatsAggregator with bug fix (nodeString should not be multiplied by statsSize)
4651154 [Joseph K. Bradley] Changed numBins semantics for unordered features. * Before: numBins = numSplits = (1 << k - 1) - 1 * Now: numBins = 2 * numSplits = 2 * [(1 << k - 1) - 1] * This also involved changing the semantics of: ** DecisionTreeMetadata.numUnorderedBins()
1e3b1c7 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-opt3alt
1485fcc [Joseph K. Bradley] Made some DecisionTree methods private.
92f934f [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-opt3alt
e676da1 [Joseph K. Bradley] Updated documentation for DecisionTree
37ca845 [Joseph K. Bradley] Fixed problem with how DecisionTree handles ordered categorical	features.
105f8ab [Joseph K. Bradley] Removed commented-out getEmptyBinAggregates from DecisionTree
062c31d [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-opt3alt
6d32ccd [Joseph K. Bradley] In DecisionTree.binsToBestSplit, changed loops to iterators to shorten code.
807cd00 [Joseph K. Bradley] Finished DTStatsAggregator, a wrapper around the aggregate statistics for easy but hopefully efficient indexing.  Modified old ImpurityAggregator classes and renamed them ImpurityCalculator; added ImpurityAggregator classes which work with DTStatsAggregator but do not store data.  Unit tests all succeed.
f2166fd [Joseph K. Bradley] still working on DTStatsAggregator
92f7118 [Joseph K. Bradley] Added partly written DTStatsAggregator
fd8df30 [Joseph K. Bradley] Moved some aggregation helpers outside of findBestSplitsPerGroup
d7c53ee [Joseph K. Bradley] Added more doc for ImpurityAggregator
a40f8f1 [Joseph K. Bradley] Changed nodes to be indexed from 1.  Tests work.
95cad7c [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-opt3
5f94342 [Joseph K. Bradley] Added treeAggregate since not yet merged from master.  Moved node indexing functions to Node.
61c4509 [Joseph K. Bradley] Fixed bugs from merge: missing DT timer call, and numBins setting.  Cleaned up DT Suite some.
3ba7166 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-opt3
b314659 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-opt3
9c83363 [Joseph K. Bradley] partial merge but not done yet
45f7ea7 [Joseph K. Bradley] partial merge, not yet done
5fce635 [Joseph K. Bradley] Merge branch 'dt-opt2' into dt-opt3
26d10dd [Joseph K. Bradley] Removed tree/model/Filter.scala since no longer used.  Removed debugging println calls in DecisionTree.scala.
356daba [Joseph K. Bradley] Merge branch 'dt-opt1' into dt-opt2
430d782 [Joseph K. Bradley] Added more debug info on binning error.  Added some docs.
d036089 [Joseph K. Bradley] Print timing info to logDebug.
e66f1b1 [Joseph K. Bradley] TreePoint * Updated doc * Made some methods private
8464a6e [Joseph K. Bradley] Moved TimeTracker to tree/impl/ in its own file, and cleaned it up.  Removed debugging println calls from DecisionTree.  Made TreePoint extend Serialiable
a87e08f [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-opt1
dd4d3aa [Joseph K. Bradley] Mid-process in bug fix: bug for binary classification with categorical features * Bug: Categorical features were all treated as ordered for binary classification.  This is possible but would require the bin ordering to be determined on-the-fly after the aggregation.  Currently, the ordering is determined a priori and fixed for all splits. * (Temp) Fix: Treat low-arity categorical features as unordered for binary classification. * Related change: I removed most tests for isMulticlass in the code.  I instead test metadata for whether there are unordered features. * Status: The bug may be fixed, but more testing needs to be done.
438a660 [Joseph K. Bradley] removed subsampling for mnist8m from DT
86e217f [Joseph K. Bradley] added cache to DT input
e3c84cc [Joseph K. Bradley] Added stuff fro mnist8m to D T Runner
51ef781 [Joseph K. Bradley] Fixed bug introduced by last commit: Variance impurity calculation was incorrect since counts were swapped accidentally
fd65372 [Joseph K. Bradley] Major changes: * Created ImpurityAggregator classes, rather than old aggregates. * Feature split/bin semantics are based on ordered vs. unordered ** E.g.: numSplits = numBins for all unordered features, and numSplits = numBins - 1 for all ordered features. * numBins can differ for each feature
c1565a5 [Joseph K. Bradley] Small DecisionTree updates: * Simplification: Updated calculateGainForSplit to take aggregates for a single (feature, split) pair. * Internal doc: findAggForOrderedFeatureClassification
b914f3b [Joseph K. Bradley] DecisionTree optimization: eliminated filters + small changes
b2ed1f3 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-opt
0f676e2 [Joseph K. Bradley] Optimizations + Bug fix for DecisionTree
3211f02 [Joseph K. Bradley] Optimizing DecisionTree * Added TreePoint representation to avoid calling findBin multiple times. * (not working yet, but debugging)
f61e9d2 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-timing
bcf874a [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-timing
511ec85 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-timing
a95bc22 [Joseph K. Bradley] timing for DecisionTree internals
---
 .../spark/mllib/tree/DecisionTree.scala       | 1341 ++++++-----------
 .../mllib/tree/impl/DTStatsAggregator.scala   |  213 +++
 .../tree/impl/DecisionTreeMetadata.scala      |   73 +-
 .../spark/mllib/tree/impl/TreePoint.scala     |   93 +-
 .../spark/mllib/tree/impurity/Entropy.scala   |   84 ++
 .../spark/mllib/tree/impurity/Gini.scala      |   84 ++
 .../spark/mllib/tree/impurity/Impurity.scala  |  127 ++
 .../spark/mllib/tree/impurity/Variance.scala  |   72 +
 .../apache/spark/mllib/tree/model/Bin.scala   |    7 +-
 .../apache/spark/mllib/tree/model/Node.scala  |   85 +-
 .../spark/mllib/tree/DecisionTreeSuite.scala  |  391 ++---
 11 files changed, 1322 insertions(+), 1248 deletions(-)
 create mode 100644 mllib/src/main/scala/org/apache/spark/mllib/tree/impl/DTStatsAggregator.scala

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala
index 5cdd258f6c20b..dd766c12d28a4 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala
@@ -28,8 +28,9 @@ import org.apache.spark.mllib.tree.configuration.Strategy
 import org.apache.spark.mllib.tree.configuration.Algo._
 import org.apache.spark.mllib.tree.configuration.FeatureType._
 import org.apache.spark.mllib.tree.configuration.QuantileStrategy._
-import org.apache.spark.mllib.tree.impl.{DecisionTreeMetadata, TimeTracker, TreePoint}
+import org.apache.spark.mllib.tree.impl._
 import org.apache.spark.mllib.tree.impurity.{Impurities, Impurity}
+import org.apache.spark.mllib.tree.impurity._
 import org.apache.spark.mllib.tree.model._
 import org.apache.spark.rdd.RDD
 import org.apache.spark.storage.StorageLevel
@@ -65,36 +66,41 @@ class DecisionTree (private val strategy: Strategy) extends Serializable with Lo
     val retaggedInput = input.retag(classOf[LabeledPoint])
     val metadata = DecisionTreeMetadata.buildMetadata(retaggedInput, strategy)
     logDebug("algo = " + strategy.algo)
+    logDebug("maxBins = " + metadata.maxBins)
 
     // Find the splits and the corresponding bins (interval between the splits) using a sample
     // of the input data.
     timer.start("findSplitsBins")
     val (splits, bins) = DecisionTree.findSplitsBins(retaggedInput, metadata)
-    val numBins = bins(0).length
     timer.stop("findSplitsBins")
-    logDebug("numBins = " + numBins)
+    logDebug("numBins: feature: number of bins")
+    logDebug(Range(0, metadata.numFeatures).map { featureIndex =>
+        s"\t$featureIndex\t${metadata.numBins(featureIndex)}"
+      }.mkString("\n"))
 
     // Bin feature values (TreePoint representation).
     // Cache input RDD for speedup during multiple passes.
     val treeInput = TreePoint.convertToTreeRDD(retaggedInput, bins, metadata)
       .persist(StorageLevel.MEMORY_AND_DISK)
 
-    val numFeatures = metadata.numFeatures
     // depth of the decision tree
     val maxDepth = strategy.maxDepth
-    // the max number of nodes possible given the depth of the tree
-    val maxNumNodes = (2 << maxDepth) - 1
+    require(maxDepth <= 30,
+      s"DecisionTree currently only supports maxDepth <= 30, but was given maxDepth = $maxDepth.")
+    // Number of nodes to allocate: max number of nodes possible given the depth of the tree, plus 1
+    val maxNumNodesPlus1 = Node.startIndexInLevel(maxDepth + 1)
     // Initialize an array to hold parent impurity calculations for each node.
-    val parentImpurities = new Array[Double](maxNumNodes)
+    val parentImpurities = new Array[Double](maxNumNodesPlus1)
     // dummy value for top node (updated during first split calculation)
-    val nodes = new Array[Node](maxNumNodes)
+    val nodes = new Array[Node](maxNumNodesPlus1)
 
     // Calculate level for single group construction
 
     // Max memory usage for aggregates
     val maxMemoryUsage = strategy.maxMemoryInMB * 1024 * 1024
     logDebug("max memory usage for aggregates = " + maxMemoryUsage + " bytes.")
-    val numElementsPerNode = DecisionTree.getElementsPerNode(metadata, numBins)
+    // TODO: Calculate memory usage more precisely.
+    val numElementsPerNode = DecisionTree.getElementsPerNode(metadata)
 
     logDebug("numElementsPerNode = " + numElementsPerNode)
     val arraySizePerNode = 8 * numElementsPerNode // approx. memory usage for bin aggregate array
@@ -124,26 +130,29 @@ class DecisionTree (private val strategy: Strategy) extends Serializable with Lo
 
       // Find best split for all nodes at a level.
       timer.start("findBestSplits")
-      val splitsStatsForLevel = DecisionTree.findBestSplits(treeInput, parentImpurities,
-        metadata, level, nodes, splits, bins, maxLevelForSingleGroup, timer)
+      val splitsStatsForLevel: Array[(Split, InformationGainStats)] =
+        DecisionTree.findBestSplits(treeInput, parentImpurities,
+          metadata, level, nodes, splits, bins, maxLevelForSingleGroup, timer)
       timer.stop("findBestSplits")
 
-      val levelNodeIndexOffset = (1 << level) - 1
+      val levelNodeIndexOffset = Node.startIndexInLevel(level)
       for ((nodeSplitStats, index) <- splitsStatsForLevel.view.zipWithIndex) {
         val nodeIndex = levelNodeIndexOffset + index
-        val isLeftChild = level != 0 && nodeIndex % 2 == 1
-        val parentNodeIndex = if (isLeftChild) { // -1 for root node
-            (nodeIndex - 1) / 2
-          } else {
-            (nodeIndex - 2) / 2
-          }
+
         // Extract info for this node (index) at the current level.
         timer.start("extractNodeInfo")
-        extractNodeInfo(nodeSplitStats, level, index, nodes)
+        val split = nodeSplitStats._1
+        val stats = nodeSplitStats._2
+        val isLeaf = (stats.gain <= 0) || (level == strategy.maxDepth)
+        val node = new Node(nodeIndex, stats.predict, isLeaf, Some(split), None, None, Some(stats))
+        logDebug("Node = " + node)
+        nodes(nodeIndex) = node
         timer.stop("extractNodeInfo")
+
         if (level != 0) {
           // Set parent.
-          if (isLeftChild) {
+          val parentNodeIndex = Node.parentIndex(nodeIndex)
+          if (Node.isLeftChild(nodeIndex)) {
             nodes(parentNodeIndex).leftNode = Some(nodes(nodeIndex))
           } else {
             nodes(parentNodeIndex).rightNode = Some(nodes(nodeIndex))
@@ -151,11 +160,21 @@ class DecisionTree (private val strategy: Strategy) extends Serializable with Lo
         }
         // Extract info for nodes at the next lower level.
         timer.start("extractInfoForLowerLevels")
-        extractInfoForLowerLevels(level, index, maxDepth, nodeSplitStats, parentImpurities)
+        if (level < maxDepth) {
+          val leftChildIndex = Node.leftChildIndex(nodeIndex)
+          val leftImpurity = stats.leftImpurity
+          logDebug("leftChildIndex = " + leftChildIndex + ", impurity = " + leftImpurity)
+          parentImpurities(leftChildIndex) = leftImpurity
+
+          val rightChildIndex = Node.rightChildIndex(nodeIndex)
+          val rightImpurity = stats.rightImpurity
+          logDebug("rightChildIndex = " + rightChildIndex + ", impurity = " + rightImpurity)
+          parentImpurities(rightChildIndex) = rightImpurity
+        }
         timer.stop("extractInfoForLowerLevels")
-        logDebug("final best split = " + nodeSplitStats._1)
+        logDebug("final best split = " + split)
       }
-      require((1 << level) == splitsStatsForLevel.length)
+      require(Node.maxNodesInLevel(level) == splitsStatsForLevel.length)
       // Check whether all the nodes at the current level at leaves.
       val allLeaf = splitsStatsForLevel.forall(_._2.gain <= 0)
       logDebug("all leaf = " + allLeaf)
@@ -171,7 +190,7 @@ class DecisionTree (private val strategy: Strategy) extends Serializable with Lo
     logDebug("#####################################")
 
     // Initialize the top or root node of the tree.
-    val topNode = nodes(0)
+    val topNode = nodes(1)
     // Build the full tree using the node info calculated in the level-wise best split calculations.
     topNode.build(nodes)
 
@@ -183,47 +202,6 @@ class DecisionTree (private val strategy: Strategy) extends Serializable with Lo
     new DecisionTreeModel(topNode, strategy.algo)
   }
 
-  /**
-   * Extract the decision tree node information for the given tree level and node index
-   */
-  private def extractNodeInfo(
-      nodeSplitStats: (Split, InformationGainStats),
-      level: Int,
-      index: Int,
-      nodes: Array[Node]): Unit = {
-    val split = nodeSplitStats._1
-    val stats = nodeSplitStats._2
-    val nodeIndex = (1 << level) - 1 + index
-    val isLeaf = (stats.gain <= 0) || (level == strategy.maxDepth)
-    val node = new Node(nodeIndex, stats.predict, isLeaf, Some(split), None, None, Some(stats))
-    logDebug("Node = " + node)
-    nodes(nodeIndex) = node
-  }
-
-  /**
-   *  Extract the decision tree node information for the children of the node
-   */
-  private def extractInfoForLowerLevels(
-      level: Int,
-      index: Int,
-      maxDepth: Int,
-      nodeSplitStats: (Split, InformationGainStats),
-      parentImpurities: Array[Double]): Unit = {
-
-    if (level >= maxDepth) {
-      return
-    }
-
-    val leftNodeIndex = (2 << level) - 1 + 2 * index
-    val leftImpurity = nodeSplitStats._2.leftImpurity
-    logDebug("leftNodeIndex = " + leftNodeIndex + ", impurity = " + leftImpurity)
-    parentImpurities(leftNodeIndex) = leftImpurity
-
-    val rightNodeIndex = leftNodeIndex + 1
-    val rightImpurity = nodeSplitStats._2.rightImpurity
-    logDebug("rightNodeIndex = " + rightNodeIndex + ", impurity = " + rightImpurity)
-    parentImpurities(rightNodeIndex) = rightImpurity
-  }
 }
 
 object DecisionTree extends Serializable with Logging {
@@ -425,9 +403,6 @@ object DecisionTree extends Serializable with Logging {
       impurity, maxDepth, maxBins)
   }
 
-
-  private val InvalidBinIndex = -1
-
   /**
    * Returns an array of optimal splits for all nodes at a given level. Splits the task into
    * multiple groups if the level-wise training task could lead to memory overflow.
@@ -436,12 +411,12 @@ object DecisionTree extends Serializable with Logging {
    * @param parentImpurities Impurities for all parent nodes for the current level
    * @param metadata Learning and dataset metadata
    * @param level Level of the tree
-   * @param splits possible splits for all features
-   * @param bins possible bins for all features
+   * @param splits possible splits for all features, indexed (numFeatures)(numSplits)
+   * @param bins possible bins for all features, indexed (numFeatures)(numBins)
    * @param maxLevelForSingleGroup the deepest level for single-group level-wise computation.
    * @return array (over nodes) of splits with best split for each node at a given level.
    */
-  protected[tree] def findBestSplits(
+  private[tree] def findBestSplits(
       input: RDD[TreePoint],
       parentImpurities: Array[Double],
       metadata: DecisionTreeMetadata,
@@ -474,6 +449,138 @@ object DecisionTree extends Serializable with Logging {
     }
   }
 
+  /**
+   * Get the node index corresponding to this data point.
+   * This function mimics prediction, passing an example from the root node down to a node
+   * at the current level being trained; that node's index is returned.
+   *
+   * @param node  Node in tree from which to classify the given data point.
+   * @param binnedFeatures  Binned feature vector for data point.
+   * @param bins possible bins for all features, indexed (numFeatures)(numBins)
+   * @param unorderedFeatures  Set of indices of unordered features.
+   * @return  Leaf index if the data point reaches a leaf.
+   *          Otherwise, last node reachable in tree matching this example.
+   *          Note: This is the global node index, i.e., the index used in the tree.
+   *                This index is different from the index used during training a particular
+   *                set of nodes in a (level, group).
+   */
+  private def predictNodeIndex(
+      node: Node,
+      binnedFeatures: Array[Int],
+      bins: Array[Array[Bin]],
+      unorderedFeatures: Set[Int]): Int = {
+    if (node.isLeaf) {
+      node.id
+    } else {
+      val featureIndex = node.split.get.feature
+      val splitLeft = node.split.get.featureType match {
+        case Continuous => {
+          val binIndex = binnedFeatures(featureIndex)
+          val featureValueUpperBound = bins(featureIndex)(binIndex).highSplit.threshold
+          // bin binIndex has range (bin.lowSplit.threshold, bin.highSplit.threshold]
+          // We do not need to check lowSplit since bins are separated by splits.
+          featureValueUpperBound <= node.split.get.threshold
+        }
+        case Categorical => {
+          val featureValue = binnedFeatures(featureIndex)
+          node.split.get.categories.contains(featureValue)
+        }
+        case _ => throw new RuntimeException(s"predictNodeIndex failed for unknown reason.")
+      }
+      if (node.leftNode.isEmpty || node.rightNode.isEmpty) {
+        // Return index from next layer of nodes to train
+        if (splitLeft) {
+          Node.leftChildIndex(node.id)
+        } else {
+          Node.rightChildIndex(node.id)
+        }
+      } else {
+        if (splitLeft) {
+          predictNodeIndex(node.leftNode.get, binnedFeatures, bins, unorderedFeatures)
+        } else {
+          predictNodeIndex(node.rightNode.get, binnedFeatures, bins, unorderedFeatures)
+        }
+      }
+    }
+  }
+
+  /**
+   * Helper for binSeqOp, for data which can contain a mix of ordered and unordered features.
+   *
+   * For ordered features, a single bin is updated.
+   * For unordered features, bins correspond to subsets of categories; either the left or right bin
+   * for each subset is updated.
+   *
+   * @param agg  Array storing aggregate calculation, with a set of sufficient statistics for
+   *             each (node, feature, bin).
+   * @param treePoint  Data point being aggregated.
+   * @param nodeIndex  Node corresponding to treePoint. Indexed from 0 at start of (level, group).
+   * @param bins possible bins for all features, indexed (numFeatures)(numBins)
+   * @param unorderedFeatures  Set of indices of unordered features.
+   */
+  private def mixedBinSeqOp(
+      agg: DTStatsAggregator,
+      treePoint: TreePoint,
+      nodeIndex: Int,
+      bins: Array[Array[Bin]],
+      unorderedFeatures: Set[Int]): Unit = {
+    // Iterate over all features.
+    val numFeatures = treePoint.binnedFeatures.size
+    val nodeOffset = agg.getNodeOffset(nodeIndex)
+    var featureIndex = 0
+    while (featureIndex < numFeatures) {
+      if (unorderedFeatures.contains(featureIndex)) {
+        // Unordered feature
+        val featureValue = treePoint.binnedFeatures(featureIndex)
+        val (leftNodeFeatureOffset, rightNodeFeatureOffset) =
+          agg.getLeftRightNodeFeatureOffsets(nodeIndex, featureIndex)
+        // Update the left or right bin for each split.
+        val numSplits = agg.numSplits(featureIndex)
+        var splitIndex = 0
+        while (splitIndex < numSplits) {
+          if (bins(featureIndex)(splitIndex).highSplit.categories.contains(featureValue)) {
+            agg.nodeFeatureUpdate(leftNodeFeatureOffset, splitIndex, treePoint.label)
+          } else {
+            agg.nodeFeatureUpdate(rightNodeFeatureOffset, splitIndex, treePoint.label)
+          }
+          splitIndex += 1
+        }
+      } else {
+        // Ordered feature
+        val binIndex = treePoint.binnedFeatures(featureIndex)
+        agg.nodeUpdate(nodeOffset, featureIndex, binIndex, treePoint.label)
+      }
+      featureIndex += 1
+    }
+  }
+
+  /**
+   * Helper for binSeqOp, for regression and for classification with only ordered features.
+   *
+   * For each feature, the sufficient statistics of one bin are updated.
+   *
+   * @param agg  Array storing aggregate calculation, with a set of sufficient statistics for
+   *             each (node, feature, bin).
+   * @param treePoint  Data point being aggregated.
+   * @param nodeIndex  Node corresponding to treePoint. Indexed from 0 at start of (level, group).
+   * @return agg
+   */
+  private def orderedBinSeqOp(
+      agg: DTStatsAggregator,
+      treePoint: TreePoint,
+      nodeIndex: Int): Unit = {
+    val label = treePoint.label
+    val nodeOffset = agg.getNodeOffset(nodeIndex)
+    // Iterate over all features.
+    val numFeatures = agg.numFeatures
+    var featureIndex = 0
+    while (featureIndex < numFeatures) {
+      val binIndex = treePoint.binnedFeatures(featureIndex)
+      agg.nodeUpdate(nodeOffset, featureIndex, binIndex, label)
+      featureIndex += 1
+    }
+  }
+
   /**
    * Returns an array of optimal splits for a group of nodes at a given level
    *
@@ -481,8 +588,9 @@ object DecisionTree extends Serializable with Logging {
    * @param parentImpurities Impurities for all parent nodes for the current level
    * @param metadata Learning and dataset metadata
    * @param level Level of the tree
-   * @param splits possible splits for all features
-   * @param bins possible bins for all features, indexed as (numFeatures)(numBins)
+   * @param nodes Array of all nodes in the tree.  Used for matching data points to nodes.
+   * @param splits possible splits for all features, indexed (numFeatures)(numSplits)
+   * @param bins possible bins for all features, indexed (numFeatures)(numBins)
    * @param numGroups total number of node groups at the current level. Default value is set to 1.
    * @param groupIndex index of the node group being processed. Default value is set to 0.
    * @return array of splits with best splits for all nodes at a given level.
@@ -527,88 +635,22 @@ object DecisionTree extends Serializable with Logging {
 
     // numNodes:  Number of nodes in this (level of tree, group),
     //            where nodes at deeper (larger) levels may be divided into groups.
-    val numNodes = (1 << level) / numGroups
+    val numNodes = Node.maxNodesInLevel(level) / numGroups
     logDebug("numNodes = " + numNodes)
 
-    // Find the number of features by looking at the first sample.
-    val numFeatures = metadata.numFeatures
-    logDebug("numFeatures = " + numFeatures)
-
-    // numBins:  Number of bins = 1 + number of possible splits
-    val numBins = bins(0).length
-    logDebug("numBins = " + numBins)
-
-    val numClasses = metadata.numClasses
-    logDebug("numClasses = " + numClasses)
-
-    val isMulticlass = metadata.isMulticlass
-    logDebug("isMulticlass = " + isMulticlass)
-
-    val isMulticlassWithCategoricalFeatures = metadata.isMulticlassWithCategoricalFeatures
-    logDebug("isMultiClassWithCategoricalFeatures = " + isMulticlassWithCategoricalFeatures)
+    logDebug("numFeatures = " + metadata.numFeatures)
+    logDebug("numClasses = " + metadata.numClasses)
+    logDebug("isMulticlass = " + metadata.isMulticlass)
+    logDebug("isMulticlassWithCategoricalFeatures = " +
+      metadata.isMulticlassWithCategoricalFeatures)
 
     // shift when more than one group is used at deep tree level
     val groupShift = numNodes * groupIndex
 
-    /**
-     * Get the node index corresponding to this data point.
-     * This function mimics prediction, passing an example from the root node down to a node
-     * at the current level being trained; that node's index is returned.
-     *
-     * @return  Leaf index if the data point reaches a leaf.
-     *          Otherwise, last node reachable in tree matching this example.
-     */
-    def predictNodeIndex(node: Node, binnedFeatures: Array[Int]): Int = {
-      if (node.isLeaf) {
-        node.id
-      } else {
-        val featureIndex = node.split.get.feature
-        val splitLeft = node.split.get.featureType match {
-          case Continuous => {
-            val binIndex = binnedFeatures(featureIndex)
-            val featureValueUpperBound = bins(featureIndex)(binIndex).highSplit.threshold
-            // bin binIndex has range (bin.lowSplit.threshold, bin.highSplit.threshold]
-            // We do not need to check lowSplit since bins are separated by splits.
-            featureValueUpperBound <= node.split.get.threshold
-          }
-          case Categorical => {
-            val featureValue = if (metadata.isUnordered(featureIndex)) {
-                binnedFeatures(featureIndex)
-              } else {
-                val binIndex = binnedFeatures(featureIndex)
-                bins(featureIndex)(binIndex).category
-              }
-            node.split.get.categories.contains(featureValue)
-          }
-          case _ => throw new RuntimeException(s"predictNodeIndex failed for unknown reason.")
-        }
-        if (node.leftNode.isEmpty || node.rightNode.isEmpty) {
-          // Return index from next layer of nodes to train
-          if (splitLeft) {
-            node.id * 2 + 1 // left
-          } else {
-            node.id * 2 + 2 // right
-          }
-        } else {
-          if (splitLeft) {
-            predictNodeIndex(node.leftNode.get, binnedFeatures)
-          } else {
-            predictNodeIndex(node.rightNode.get, binnedFeatures)
-          }
-        }
-      }
-    }
-
-    def nodeIndexToLevel(idx: Int): Int = {
-      if (idx == 0) {
-        0
-      } else {
-        math.floor(math.log(idx) / math.log(2)).toInt
-      }
-    }
-
-    // Used for treePointToNodeIndex
-    val levelOffset = (1 << level) - 1
+    // Used for treePointToNodeIndex to get an index for this (level, group).
+    // - Node.startIndexInLevel(level) gives the global index offset for nodes at this level.
+    // - groupShift corrects for groups in this level before the current group.
+    val globalNodeIndexOffset = Node.startIndexInLevel(level) + groupShift
 
     /**
      * Find the node index for the given example.
@@ -619,661 +661,254 @@ object DecisionTree extends Serializable with Logging {
       if (level == 0) {
         0
       } else {
-        val globalNodeIndex = predictNodeIndex(nodes(0), treePoint.binnedFeatures)
-        // Get index for this (level, group).
-        globalNodeIndex - levelOffset - groupShift
-      }
-    }
-
-    /**
-     * Increment aggregate in location for (node, feature, bin, label).
-     *
-     * @param treePoint  Data point being aggregated.
-     * @param agg  Array storing aggregate calculation, of size:
-     *             numClasses * numBins * numFeatures * numNodes.
-     *             Indexed by (node, feature, bin, label) where label is the least significant bit.
-     * @param nodeIndex  Node corresponding to treePoint. Indexed from 0 at start of (level, group).
-     */
-    def updateBinForOrderedFeature(
-        treePoint: TreePoint,
-        agg: Array[Double],
-        nodeIndex: Int,
-        featureIndex: Int): Unit = {
-      // Update the left or right count for one bin.
-      val aggIndex =
-        numClasses * numBins * numFeatures * nodeIndex +
-        numClasses * numBins * featureIndex +
-        numClasses * treePoint.binnedFeatures(featureIndex) +
-        treePoint.label.toInt
-      agg(aggIndex) += 1
-    }
-
-    /**
-     * Increment aggregate in location for (nodeIndex, featureIndex, [bins], label),
-     * where [bins] ranges over all bins.
-     * Updates left or right side of aggregate depending on split.
-     *
-     * @param nodeIndex  Node corresponding to treePoint. Indexed from 0 at start of (level, group).
-     * @param treePoint  Data point being aggregated.
-     * @param agg  Indexed by (left/right, node, feature, bin, label)
-     *             where label is the least significant bit.
-     *             The left/right specifier is a 0/1 index indicating left/right child info.
-     * @param rightChildShift Offset for right side of agg.
-     */
-    def updateBinForUnorderedFeature(
-        nodeIndex: Int,
-        featureIndex: Int,
-        treePoint: TreePoint,
-        agg: Array[Double],
-        rightChildShift: Int): Unit = {
-      val featureValue = treePoint.binnedFeatures(featureIndex)
-      // Update the left or right count for one bin.
-      val aggShift =
-        numClasses * numBins * numFeatures * nodeIndex +
-        numClasses * numBins * featureIndex +
-        treePoint.label.toInt
-      // Find all matching bins and increment their values
-      val featureCategories = metadata.featureArity(featureIndex)
-      val numCategoricalBins = (1 << featureCategories - 1) - 1
-      var binIndex = 0
-      while (binIndex < numCategoricalBins) {
-        val aggIndex = aggShift + binIndex * numClasses
-        if (bins(featureIndex)(binIndex).highSplit.categories.contains(featureValue)) {
-          agg(aggIndex) += 1
-        } else {
-          agg(rightChildShift + aggIndex) += 1
-        }
-        binIndex += 1
-      }
-    }
-
-    /**
-     * Helper for binSeqOp.
-     *
-     * @param agg  Array storing aggregate calculation, of size:
-     *             numClasses * numBins * numFeatures * numNodes.
-     *             Indexed by (node, feature, bin, label) where label is the least significant bit.
-     * @param treePoint  Data point being aggregated.
-     * @param nodeIndex  Node corresponding to treePoint. Indexed from 0 at start of (level, group).
-     */
-    def binaryOrNotCategoricalBinSeqOp(
-        agg: Array[Double],
-        treePoint: TreePoint,
-        nodeIndex: Int): Unit = {
-      // Iterate over all features.
-      var featureIndex = 0
-      while (featureIndex < numFeatures) {
-        updateBinForOrderedFeature(treePoint, agg, nodeIndex, featureIndex)
-        featureIndex += 1
-      }
-    }
-
-    val rightChildShift = numClasses * numBins * numFeatures * numNodes
-
-    /**
-     * Helper for binSeqOp.
-     *
-     * @param agg  Array storing aggregate calculation.
-     *             For ordered features, this is of size:
-     *               numClasses * numBins * numFeatures * numNodes.
-     *             For unordered features, this is of size:
-     *               2 * numClasses * numBins * numFeatures * numNodes.
-     * @param treePoint   Data point being aggregated.
-     * @param nodeIndex  Node corresponding to treePoint. Indexed from 0 at start of (level, group).
-     */
-    def multiclassWithCategoricalBinSeqOp(
-        agg: Array[Double],
-        treePoint: TreePoint,
-        nodeIndex: Int): Unit = {
-      val label = treePoint.label
-      // Iterate over all features.
-      var featureIndex = 0
-      while (featureIndex < numFeatures) {
-        if (metadata.isUnordered(featureIndex)) {
-          updateBinForUnorderedFeature(nodeIndex, featureIndex, treePoint, agg, rightChildShift)
-        } else {
-          updateBinForOrderedFeature(treePoint, agg, nodeIndex, featureIndex)
-        }
-        featureIndex += 1
-      }
-    }
-
-    /**
-     * Performs a sequential aggregation over a partition for regression.
-     * For l nodes, k features,
-     * the count, sum, sum of squares of one of the p bins is incremented.
-     *
-     * @param agg Array storing aggregate calculation, updated by this function.
-     *            Size: 3 * numBins * numFeatures * numNodes
-     * @param treePoint   Data point being aggregated.
-     * @param nodeIndex  Node corresponding to treePoint. Indexed from 0 at start of (level, group).
-     * @return agg
-     */
-    def regressionBinSeqOp(agg: Array[Double], treePoint: TreePoint, nodeIndex: Int): Unit = {
-      val label = treePoint.label
-      // Iterate over all features.
-      var featureIndex = 0
-      while (featureIndex < numFeatures) {
-        // Update count, sum, and sum^2 for one bin.
-        val binIndex = treePoint.binnedFeatures(featureIndex)
-        val aggIndex =
-          3 * numBins * numFeatures * nodeIndex +
-          3 * numBins * featureIndex +
-          3 * binIndex
-        agg(aggIndex) += 1
-        agg(aggIndex + 1) += label
-        agg(aggIndex + 2) += label * label
-        featureIndex += 1
+        val globalNodeIndex =
+          predictNodeIndex(nodes(1), treePoint.binnedFeatures, bins, metadata.unorderedFeatures)
+        globalNodeIndex - globalNodeIndexOffset
       }
     }
 
     /**
      * Performs a sequential aggregation over a partition.
-     * For l nodes, k features,
-     *   For classification:
-     *     Either the left count or the right count of one of the bins is
-     *     incremented based upon whether the feature is classified as 0 or 1.
-     *   For regression:
-     *     The count, sum, sum of squares of one of the bins is incremented.
      *
-     * @param agg Array storing aggregate calculation, updated by this function.
-     *            Size for classification:
-     *              numClasses * numBins * numFeatures * numNodes for ordered features, or
-     *              2 * numClasses * numBins * numFeatures * numNodes for unordered features.
-     *            Size for regression:
-     *              3 * numBins * numFeatures * numNodes.
+     * Each data point contributes to one node. For each feature,
+     * the aggregate sufficient statistics are updated for the relevant bins.
+     *
+     * @param agg  Array storing aggregate calculation, with a set of sufficient statistics for
+     *             each (node, feature, bin).
      * @param treePoint   Data point being aggregated.
      * @return  agg
      */
-    def binSeqOp(agg: Array[Double], treePoint: TreePoint): Array[Double] = {
+    def binSeqOp(
+        agg: DTStatsAggregator,
+        treePoint: TreePoint): DTStatsAggregator = {
       val nodeIndex = treePointToNodeIndex(treePoint)
       // If the example does not reach this level, then nodeIndex < 0.
       // If the example reaches this level but is handled in a different group,
       //  then either nodeIndex < 0 (previous group) or nodeIndex >= numNodes (later group).
       if (nodeIndex >= 0 && nodeIndex < numNodes) {
-        if (metadata.isClassification) {
-          if (isMulticlassWithCategoricalFeatures) {
-            multiclassWithCategoricalBinSeqOp(agg, treePoint, nodeIndex)
-          } else {
-            binaryOrNotCategoricalBinSeqOp(agg, treePoint, nodeIndex)
-          }
+        if (metadata.unorderedFeatures.isEmpty) {
+          orderedBinSeqOp(agg, treePoint, nodeIndex)
         } else {
-          regressionBinSeqOp(agg, treePoint, nodeIndex)
+          mixedBinSeqOp(agg, treePoint, nodeIndex, bins, metadata.unorderedFeatures)
         }
       }
       agg
     }
 
-    // Calculate bin aggregate length for classification or regression.
-    val binAggregateLength = numNodes * getElementsPerNode(metadata, numBins)
-    logDebug("binAggregateLength = " + binAggregateLength)
-
-    /**
-     * Combines the aggregates from partitions.
-     * @param agg1 Array containing aggregates from one or more partitions
-     * @param agg2 Array containing aggregates from one or more partitions
-     * @return Combined aggregate from agg1 and agg2
-     */
-    def binCombOp(agg1: Array[Double], agg2: Array[Double]): Array[Double] = {
-      var index = 0
-      val combinedAggregate = new Array[Double](binAggregateLength)
-      while (index < binAggregateLength) {
-        combinedAggregate(index) = agg1(index) + agg2(index)
-        index += 1
-      }
-      combinedAggregate
-    }
-
     // Calculate bin aggregates.
     timer.start("aggregation")
-    val binAggregates = {
-      input.treeAggregate(Array.fill[Double](binAggregateLength)(0))(binSeqOp, binCombOp)
+    val binAggregates: DTStatsAggregator = {
+      val initAgg = new DTStatsAggregator(metadata, numNodes)
+      input.treeAggregate(initAgg)(binSeqOp, DTStatsAggregator.binCombOp)
     }
     timer.stop("aggregation")
-    logDebug("binAggregates.length = " + binAggregates.length)
 
-    /**
-     * Calculate the information gain for a given (feature, split) based upon left/right aggregates.
-     * @param leftNodeAgg left node aggregates for this (feature, split)
-     * @param rightNodeAgg right node aggregate for this (feature, split)
-     * @param topImpurity impurity of the parent node
-     * @return information gain and statistics for all splits
-     */
-    def calculateGainForSplit(
-        leftNodeAgg: Array[Double],
-        rightNodeAgg: Array[Double],
-        topImpurity: Double): InformationGainStats = {
-      if (metadata.isClassification) {
-        val leftTotalCount = leftNodeAgg.sum
-        val rightTotalCount = rightNodeAgg.sum
-
-        val impurity = {
-          if (level > 0) {
-            topImpurity
-          } else {
-            // Calculate impurity for root node.
-            val rootNodeCounts = new Array[Double](numClasses)
-            var classIndex = 0
-            while (classIndex < numClasses) {
-              rootNodeCounts(classIndex) = leftNodeAgg(classIndex) + rightNodeAgg(classIndex)
-              classIndex += 1
-            }
-            metadata.impurity.calculate(rootNodeCounts, leftTotalCount + rightTotalCount)
-          }
-        }
-
-        val totalCount = leftTotalCount + rightTotalCount
-        if (totalCount == 0) {
-          // Return arbitrary prediction.
-          return new InformationGainStats(0, topImpurity, topImpurity, topImpurity, 0)
-        }
-
-        // Sum of count for each label
-        val leftrightNodeAgg: Array[Double] =
-          leftNodeAgg.zip(rightNodeAgg).map { case (leftCount, rightCount) =>
-            leftCount + rightCount
-          }
-
-        def indexOfLargestArrayElement(array: Array[Double]): Int = {
-          val result = array.foldLeft(-1, Double.MinValue, 0) {
-            case ((maxIndex, maxValue, currentIndex), currentValue) =>
-              if (currentValue > maxValue) {
-                (currentIndex, currentValue, currentIndex + 1)
-              } else {
-                (maxIndex, maxValue, currentIndex + 1)
-              }
-          }
-          if (result._1 < 0) {
-            throw new RuntimeException("DecisionTree internal error:" +
-              " calculateGainForSplit failed in indexOfLargestArrayElement")
-          }
-          result._1
-        }
-
-        val predict = indexOfLargestArrayElement(leftrightNodeAgg)
-        val prob = leftrightNodeAgg(predict) / totalCount
-
-        val leftImpurity = if (leftTotalCount == 0) {
-          topImpurity
-        } else {
-          metadata.impurity.calculate(leftNodeAgg, leftTotalCount)
-        }
-        val rightImpurity = if (rightTotalCount == 0) {
-          topImpurity
-        } else {
-          metadata.impurity.calculate(rightNodeAgg, rightTotalCount)
-        }
-
-        val leftWeight = leftTotalCount / totalCount
-        val rightWeight = rightTotalCount / totalCount
-
-        val gain = impurity - leftWeight * leftImpurity - rightWeight * rightImpurity
-
-        new InformationGainStats(gain, impurity, leftImpurity, rightImpurity, predict, prob)
-
-      } else {
-        // Regression
-
-        val leftCount = leftNodeAgg(0)
-        val leftSum = leftNodeAgg(1)
-        val leftSumSquares = leftNodeAgg(2)
+    // Calculate best splits for all nodes at a given level
+    timer.start("chooseSplits")
+    val bestSplits = new Array[(Split, InformationGainStats)](numNodes)
+    // Iterating over all nodes at this level
+    var nodeIndex = 0
+    while (nodeIndex < numNodes) {
+      val nodeImpurity = parentImpurities(globalNodeIndexOffset + nodeIndex)
+      logDebug("node impurity = " + nodeImpurity)
+      bestSplits(nodeIndex) =
+        binsToBestSplit(binAggregates, nodeIndex, nodeImpurity, level, metadata, splits)
+      logDebug("best split = " + bestSplits(nodeIndex)._1)
+      nodeIndex += 1
+    }
+    timer.stop("chooseSplits")
 
-        val rightCount = rightNodeAgg(0)
-        val rightSum = rightNodeAgg(1)
-        val rightSumSquares = rightNodeAgg(2)
+    bestSplits
+  }
 
-        val impurity = {
-          if (level > 0) {
-            topImpurity
-          } else {
-            // Calculate impurity for root node.
-            val count = leftCount + rightCount
-            val sum = leftSum + rightSum
-            val sumSquares = leftSumSquares + rightSumSquares
-            metadata.impurity.calculate(count, sum, sumSquares)
-          }
-        }
+  /**
+   * Calculate the information gain for a given (feature, split) based upon left/right aggregates.
+   * @param leftImpurityCalculator left node aggregates for this (feature, split)
+   * @param rightImpurityCalculator right node aggregate for this (feature, split)
+   * @param topImpurity impurity of the parent node
+   * @return information gain and statistics for all splits
+   */
+  private def calculateGainForSplit(
+      leftImpurityCalculator: ImpurityCalculator,
+      rightImpurityCalculator: ImpurityCalculator,
+      topImpurity: Double,
+      level: Int,
+      metadata: DecisionTreeMetadata): InformationGainStats = {
 
-        if (leftCount == 0) {
-          return new InformationGainStats(0, topImpurity, Double.MinValue, topImpurity,
-            rightSum / rightCount)
-        }
-        if (rightCount == 0) {
-          return new InformationGainStats(0, topImpurity, topImpurity,
-            Double.MinValue, leftSum / leftCount)
-        }
+    val leftCount = leftImpurityCalculator.count
+    val rightCount = rightImpurityCalculator.count
 
-        val leftImpurity = metadata.impurity.calculate(leftCount, leftSum, leftSumSquares)
-        val rightImpurity = metadata.impurity.calculate(rightCount, rightSum, rightSumSquares)
+    val totalCount = leftCount + rightCount
+    if (totalCount == 0) {
+      // Return arbitrary prediction.
+      return new InformationGainStats(0, topImpurity, topImpurity, topImpurity, 0)
+    }
 
-        val leftWeight = leftCount.toDouble / (leftCount + rightCount)
-        val rightWeight = rightCount.toDouble / (leftCount + rightCount)
+    val parentNodeAgg = leftImpurityCalculator.copy
+    parentNodeAgg.add(rightImpurityCalculator)
+    // impurity of parent node
+    val impurity = if (level > 0) {
+      topImpurity
+    } else {
+      parentNodeAgg.calculate()
+    }
 
-        val gain = impurity - leftWeight * leftImpurity - rightWeight * rightImpurity
+    val predict = parentNodeAgg.predict
+    val prob = parentNodeAgg.prob(predict)
 
-        val predict = (leftSum + rightSum) / (leftCount + rightCount)
-        new InformationGainStats(gain, impurity, leftImpurity, rightImpurity, predict)
-      }
-    }
+    val leftImpurity = leftImpurityCalculator.calculate() // Note: This equals 0 if count = 0
+    val rightImpurity = rightImpurityCalculator.calculate()
 
-    /**
-     * Extracts left and right split aggregates.
-     * @param binData Aggregate array slice from getBinDataForNode.
-     *                For classification:
-     *                  For unordered features, this is leftChildData ++ rightChildData,
-     *                    each of which is indexed by (feature, split/bin, class),
-     *                    with class being the least significant bit.
-     *                  For ordered features, this is of size numClasses * numBins * numFeatures.
-     *                For regression:
-     *                  This is of size 2 * numFeatures * numBins.
-     * @return (leftNodeAgg, rightNodeAgg) pair of arrays.
-     *         For classification, each array is of size (numFeatures, (numBins - 1), numClasses).
-     *         For regression, each array is of size (numFeatures, (numBins - 1), 3).
-     *
-     */
-    def extractLeftRightNodeAggregates(
-        binData: Array[Double]): (Array[Array[Array[Double]]], Array[Array[Array[Double]]]) = {
-
-
-      /**
-       * The input binData is indexed as (feature, bin, class).
-       * This computes cumulative sums over splits.
-       * Each (feature, class) pair is handled separately.
-       * Note: numSplits = numBins - 1.
-       * @param leftNodeAgg  Each (feature, class) slice is an array over splits.
-       *                     Element i (i = 0, ..., numSplits - 2) is set to be
-       *                     the cumulative sum (from left) over binData for bins 0, ..., i.
-       * @param rightNodeAgg Each (feature, class) slice is an array over splits.
-       *                     Element i (i = 1, ..., numSplits - 1) is set to be
-       *                     the cumulative sum (from right) over binData for bins
-       *                     numBins - 1, ..., numBins - 1 - i.
-       */
-      def findAggForOrderedFeatureClassification(
-          leftNodeAgg: Array[Array[Array[Double]]],
-          rightNodeAgg: Array[Array[Array[Double]]],
-          featureIndex: Int) {
-
-        // shift for this featureIndex
-        val shift = numClasses * featureIndex * numBins
-
-        var classIndex = 0
-        while (classIndex < numClasses) {
-          // left node aggregate for the lowest split
-          leftNodeAgg(featureIndex)(0)(classIndex) = binData(shift + classIndex)
-          // right node aggregate for the highest split
-          rightNodeAgg(featureIndex)(numBins - 2)(classIndex)
-            = binData(shift + (numClasses * (numBins - 1)) + classIndex)
-          classIndex += 1
-        }
+    val leftWeight = leftCount / totalCount.toDouble
+    val rightWeight = rightCount / totalCount.toDouble
 
-        // Iterate over all splits.
-        var splitIndex = 1
-        while (splitIndex < numBins - 1) {
-          // calculating left node aggregate for a split as a sum of left node aggregate of a
-          // lower split and the left bin aggregate of a bin where the split is a high split
-          var innerClassIndex = 0
-          while (innerClassIndex < numClasses) {
-            leftNodeAgg(featureIndex)(splitIndex)(innerClassIndex)
-              = binData(shift + numClasses * splitIndex + innerClassIndex) +
-                leftNodeAgg(featureIndex)(splitIndex - 1)(innerClassIndex)
-            rightNodeAgg(featureIndex)(numBins - 2 - splitIndex)(innerClassIndex) =
-              binData(shift + (numClasses * (numBins - 1 - splitIndex) + innerClassIndex)) +
-                rightNodeAgg(featureIndex)(numBins - 1 - splitIndex)(innerClassIndex)
-            innerClassIndex += 1
-          }
-          splitIndex += 1
-        }
-      }
+    val gain = impurity - leftWeight * leftImpurity - rightWeight * rightImpurity
 
-      /**
-       * Reshape binData for this feature.
-       * Indexes binData as (feature, split, class) with class as the least significant bit.
-       * @param leftNodeAgg   leftNodeAgg(featureIndex)(splitIndex)(classIndex) = aggregate value
-       */
-      def findAggForUnorderedFeatureClassification(
-          leftNodeAgg: Array[Array[Array[Double]]],
-          rightNodeAgg: Array[Array[Array[Double]]],
-          featureIndex: Int) {
-
-        val rightChildShift = numClasses * numBins * numFeatures
-        var splitIndex = 0
-        while (splitIndex < numBins - 1) {
-          var classIndex = 0
-          while (classIndex < numClasses) {
-            // shift for this featureIndex
-            val shift = numClasses * featureIndex * numBins + splitIndex * numClasses
-            val leftBinValue = binData(shift + classIndex)
-            val rightBinValue = binData(rightChildShift + shift + classIndex)
-            leftNodeAgg(featureIndex)(splitIndex)(classIndex) = leftBinValue
-            rightNodeAgg(featureIndex)(splitIndex)(classIndex) = rightBinValue
-            classIndex += 1
-          }
-          splitIndex += 1
-        }
-      }
+    new InformationGainStats(gain, impurity, leftImpurity, rightImpurity, predict, prob)
+  }
 
-      def findAggForRegression(
-          leftNodeAgg: Array[Array[Array[Double]]],
-          rightNodeAgg: Array[Array[Array[Double]]],
-          featureIndex: Int) {
-
-        // shift for this featureIndex
-        val shift = 3 * featureIndex * numBins
-        // left node aggregate for the lowest split
-        leftNodeAgg(featureIndex)(0)(0) = binData(shift + 0)
-        leftNodeAgg(featureIndex)(0)(1) = binData(shift + 1)
-        leftNodeAgg(featureIndex)(0)(2) = binData(shift + 2)
-
-        // right node aggregate for the highest split
-        rightNodeAgg(featureIndex)(numBins - 2)(0) =
-          binData(shift + (3 * (numBins - 1)))
-        rightNodeAgg(featureIndex)(numBins - 2)(1) =
-          binData(shift + (3 * (numBins - 1)) + 1)
-        rightNodeAgg(featureIndex)(numBins - 2)(2) =
-          binData(shift + (3 * (numBins - 1)) + 2)
-
-        // Iterate over all splits.
-        var splitIndex = 1
-        while (splitIndex < numBins - 1) {
-          var i = 0 // index for regression histograms
-          while (i < 3) { // count, sum, sum^2
-            // calculating left node aggregate for a split as a sum of left node aggregate of a
-            // lower split and the left bin aggregate of a bin where the split is a high split
-            leftNodeAgg(featureIndex)(splitIndex)(i) = binData(shift + 3 * splitIndex + i) +
-              leftNodeAgg(featureIndex)(splitIndex - 1)(i)
-            // calculating right node aggregate for a split as a sum of right node aggregate of a
-            // higher split and the right bin aggregate of a bin where the split is a low split
-            rightNodeAgg(featureIndex)(numBins - 2 - splitIndex)(i) =
-              binData(shift + (3 * (numBins - 1 - splitIndex) + i)) +
-                rightNodeAgg(featureIndex)(numBins - 1 - splitIndex)(i)
-            i += 1
-          }
-          splitIndex += 1
-        }
-      }
+  /**
+   * Find the best split for a node.
+   * @param binAggregates Bin statistics.
+   * @param nodeIndex Index for node to split in this (level, group).
+   * @param nodeImpurity Impurity of the node (nodeIndex).
+   * @return tuple for best split: (Split, information gain)
+   */
+  private def binsToBestSplit(
+      binAggregates: DTStatsAggregator,
+      nodeIndex: Int,
+      nodeImpurity: Double,
+      level: Int,
+      metadata: DecisionTreeMetadata,
+      splits: Array[Array[Split]]): (Split, InformationGainStats) = {
 
-      if (metadata.isClassification) {
-        // Initialize left and right split aggregates.
-        val leftNodeAgg = Array.ofDim[Double](numFeatures, numBins - 1, numClasses)
-        val rightNodeAgg = Array.ofDim[Double](numFeatures, numBins - 1, numClasses)
-        var featureIndex = 0
-        while (featureIndex < numFeatures) {
-          if (metadata.isUnordered(featureIndex)) {
-            findAggForUnorderedFeatureClassification(leftNodeAgg, rightNodeAgg, featureIndex)
-          } else {
-            findAggForOrderedFeatureClassification(leftNodeAgg, rightNodeAgg, featureIndex)
-          }
-          featureIndex += 1
-        }
-        (leftNodeAgg, rightNodeAgg)
-      } else {
-        // Regression
-        // Initialize left and right split aggregates.
-        val leftNodeAgg = Array.ofDim[Double](numFeatures, numBins - 1, 3)
-        val rightNodeAgg = Array.ofDim[Double](numFeatures, numBins - 1, 3)
-        // Iterate over all features.
-        var featureIndex = 0
-        while (featureIndex < numFeatures) {
-          findAggForRegression(leftNodeAgg, rightNodeAgg, featureIndex)
-          featureIndex += 1
-        }
-        (leftNodeAgg, rightNodeAgg)
-      }
-    }
+    logDebug("node impurity = " + nodeImpurity)
 
-    /**
-     * Calculates information gain for all nodes splits.
-     */
-    def calculateGainsForAllNodeSplits(
-        leftNodeAgg: Array[Array[Array[Double]]],
-        rightNodeAgg: Array[Array[Array[Double]]],
-        nodeImpurity: Double): Array[Array[InformationGainStats]] = {
-      val gains = Array.ofDim[InformationGainStats](numFeatures, numBins - 1)
-
-      var featureIndex = 0
-      while (featureIndex < numFeatures) {
-        val numSplitsForFeature = getNumSplitsForFeature(featureIndex)
+    // For each (feature, split), calculate the gain, and select the best (feature, split).
+    Range(0, metadata.numFeatures).map { featureIndex =>
+      val numSplits = metadata.numSplits(featureIndex)
+      if (metadata.isContinuous(featureIndex)) {
+        // Cumulative sum (scanLeft) of bin statistics.
+        // Afterwards, binAggregates for a bin is the sum of aggregates for
+        // that bin + all preceding bins.
+        val nodeFeatureOffset = binAggregates.getNodeFeatureOffset(nodeIndex, featureIndex)
         var splitIndex = 0
-        while (splitIndex < numSplitsForFeature) {
-          gains(featureIndex)(splitIndex) =
-            calculateGainForSplit(leftNodeAgg(featureIndex)(splitIndex),
-              rightNodeAgg(featureIndex)(splitIndex), nodeImpurity)
+        while (splitIndex < numSplits) {
+          binAggregates.mergeForNodeFeature(nodeFeatureOffset, splitIndex + 1, splitIndex)
           splitIndex += 1
         }
-        featureIndex += 1
-      }
-      gains
-    }
-
-    /**
-     * Get the number of splits for a feature.
-     */
-    def getNumSplitsForFeature(featureIndex: Int): Int = {
-      if (metadata.isContinuous(featureIndex)) {
-        numBins - 1
+        // Find best split.
+        val (bestFeatureSplitIndex, bestFeatureGainStats) =
+          Range(0, numSplits).map { case splitIdx =>
+            val leftChildStats = binAggregates.getImpurityCalculator(nodeFeatureOffset, splitIdx)
+            val rightChildStats = binAggregates.getImpurityCalculator(nodeFeatureOffset, numSplits)
+            rightChildStats.subtract(leftChildStats)
+            val gainStats =
+              calculateGainForSplit(leftChildStats, rightChildStats, nodeImpurity, level, metadata)
+            (splitIdx, gainStats)
+          }.maxBy(_._2.gain)
+        (splits(featureIndex)(bestFeatureSplitIndex), bestFeatureGainStats)
+      } else if (metadata.isUnordered(featureIndex)) {
+        // Unordered categorical feature
+        val (leftChildOffset, rightChildOffset) =
+          binAggregates.getLeftRightNodeFeatureOffsets(nodeIndex, featureIndex)
+        val (bestFeatureSplitIndex, bestFeatureGainStats) =
+          Range(0, numSplits).map { splitIndex =>
+            val leftChildStats = binAggregates.getImpurityCalculator(leftChildOffset, splitIndex)
+            val rightChildStats = binAggregates.getImpurityCalculator(rightChildOffset, splitIndex)
+            val gainStats =
+              calculateGainForSplit(leftChildStats, rightChildStats, nodeImpurity, level, metadata)
+            (splitIndex, gainStats)
+          }.maxBy(_._2.gain)
+        (splits(featureIndex)(bestFeatureSplitIndex), bestFeatureGainStats)
       } else {
-        // Categorical feature
-        val featureCategories = metadata.featureArity(featureIndex)
-        if (metadata.isUnordered(featureIndex)) {
-          (1 << featureCategories - 1) - 1
-        } else {
-          featureCategories
-        }
-      }
-    }
-
-    /**
-     * Find the best split for a node.
-     * @param binData Bin data slice for this node, given by getBinDataForNode.
-     * @param nodeImpurity impurity of the top node
-     * @return tuple of split and information gain
-     */
-    def binsToBestSplit(
-        binData: Array[Double],
-        nodeImpurity: Double): (Split, InformationGainStats) = {
-
-      logDebug("node impurity = " + nodeImpurity)
-
-      // Extract left right node aggregates.
-      val (leftNodeAgg, rightNodeAgg) = extractLeftRightNodeAggregates(binData)
-
-      // Calculate gains for all splits.
-      val gains = calculateGainsForAllNodeSplits(leftNodeAgg, rightNodeAgg, nodeImpurity)
-
-      val (bestFeatureIndex, bestSplitIndex, gainStats) = {
-        // Initialize with infeasible values.
-        var bestFeatureIndex = Int.MinValue
-        var bestSplitIndex = Int.MinValue
-        var bestGainStats = new InformationGainStats(Double.MinValue, -1.0, -1.0, -1.0, -1.0)
-        // Iterate over features.
-        var featureIndex = 0
-        while (featureIndex < numFeatures) {
-          // Iterate over all splits.
-          var splitIndex = 0
-          val numSplitsForFeature = getNumSplitsForFeature(featureIndex)
-          while (splitIndex < numSplitsForFeature) {
-            val gainStats = gains(featureIndex)(splitIndex)
-            if (gainStats.gain > bestGainStats.gain) {
-              bestGainStats = gainStats
-              bestFeatureIndex = featureIndex
-              bestSplitIndex = splitIndex
+        // Ordered categorical feature
+        val nodeFeatureOffset = binAggregates.getNodeFeatureOffset(nodeIndex, featureIndex)
+        val numBins = metadata.numBins(featureIndex)
+
+        /* Each bin is one category (feature value).
+         * The bins are ordered based on centroidForCategories, and this ordering determines which
+         * splits are considered.  (With K categories, we consider K - 1 possible splits.)
+         *
+         * centroidForCategories is a list: (category, centroid)
+         */
+        val centroidForCategories = if (metadata.isMulticlass) {
+          // For categorical variables in multiclass classification,
+          // the bins are ordered by the impurity of their corresponding labels.
+          Range(0, numBins).map { case featureValue =>
+            val categoryStats = binAggregates.getImpurityCalculator(nodeFeatureOffset, featureValue)
+            val centroid = if (categoryStats.count != 0) {
+              categoryStats.calculate()
+            } else {
+              Double.MaxValue
             }
-            splitIndex += 1
+            (featureValue, centroid)
+          }
+        } else { // regression or binary classification
+          // For categorical variables in regression and binary classification,
+          // the bins are ordered by the centroid of their corresponding labels.
+          Range(0, numBins).map { case featureValue =>
+            val categoryStats = binAggregates.getImpurityCalculator(nodeFeatureOffset, featureValue)
+            val centroid = if (categoryStats.count != 0) {
+              categoryStats.predict
+            } else {
+              Double.MaxValue
+            }
+            (featureValue, centroid)
           }
-          featureIndex += 1
         }
-        (bestFeatureIndex, bestSplitIndex, bestGainStats)
-      }
 
-      logDebug("best split = " + splits(bestFeatureIndex)(bestSplitIndex))
-      logDebug("best split bin = " + bins(bestFeatureIndex)(bestSplitIndex))
+        logDebug("Centroids for categorical variable: " + centroidForCategories.mkString(","))
 
-      (splits(bestFeatureIndex)(bestSplitIndex), gainStats)
-    }
+        // bins sorted by centroids
+        val categoriesSortedByCentroid = centroidForCategories.toList.sortBy(_._2)
 
-    /**
-     * Get bin data for one node.
-     */
-    def getBinDataForNode(node: Int): Array[Double] = {
-      if (metadata.isClassification) {
-        if (isMulticlassWithCategoricalFeatures) {
-          val shift = numClasses * node * numBins * numFeatures
-          val rightChildShift = numClasses * numBins * numFeatures * numNodes
-          val binsForNode = {
-            val leftChildData
-            = binAggregates.slice(shift, shift + numClasses * numBins * numFeatures)
-            val rightChildData
-            = binAggregates.slice(rightChildShift + shift,
-              rightChildShift + shift + numClasses * numBins * numFeatures)
-            leftChildData ++ rightChildData
-          }
-          binsForNode
-        } else {
-          val shift = numClasses * node * numBins * numFeatures
-          val binsForNode = binAggregates.slice(shift, shift + numClasses * numBins * numFeatures)
-          binsForNode
+        logDebug("Sorted centroids for categorical variable = " +
+          categoriesSortedByCentroid.mkString(","))
+
+        // Cumulative sum (scanLeft) of bin statistics.
+        // Afterwards, binAggregates for a bin is the sum of aggregates for
+        // that bin + all preceding bins.
+        var splitIndex = 0
+        while (splitIndex < numSplits) {
+          val currentCategory = categoriesSortedByCentroid(splitIndex)._1
+          val nextCategory = categoriesSortedByCentroid(splitIndex + 1)._1
+          binAggregates.mergeForNodeFeature(nodeFeatureOffset, nextCategory, currentCategory)
+          splitIndex += 1
         }
-      } else {
-        // Regression
-        val shift = 3 * node * numBins * numFeatures
-        val binsForNode = binAggregates.slice(shift, shift + 3 * numBins * numFeatures)
-        binsForNode
+        // lastCategory = index of bin with total aggregates for this (node, feature)
+        val lastCategory = categoriesSortedByCentroid.last._1
+        // Find best split.
+        val (bestFeatureSplitIndex, bestFeatureGainStats) =
+          Range(0, numSplits).map { splitIndex =>
+            val featureValue = categoriesSortedByCentroid(splitIndex)._1
+            val leftChildStats =
+              binAggregates.getImpurityCalculator(nodeFeatureOffset, featureValue)
+            val rightChildStats =
+              binAggregates.getImpurityCalculator(nodeFeatureOffset, lastCategory)
+            rightChildStats.subtract(leftChildStats)
+            val gainStats =
+              calculateGainForSplit(leftChildStats, rightChildStats, nodeImpurity, level, metadata)
+            (splitIndex, gainStats)
+          }.maxBy(_._2.gain)
+        val categoriesForSplit =
+          categoriesSortedByCentroid.map(_._1.toDouble).slice(0, bestFeatureSplitIndex + 1)
+        val bestFeatureSplit =
+          new Split(featureIndex, Double.MinValue, Categorical, categoriesForSplit)
+        (bestFeatureSplit, bestFeatureGainStats)
       }
-    }
-
-    // Calculate best splits for all nodes at a given level
-    timer.start("chooseSplits")
-    val bestSplits = new Array[(Split, InformationGainStats)](numNodes)
-    // Iterating over all nodes at this level
-    var node = 0
-    while (node < numNodes) {
-      val nodeImpurityIndex = (1 << level) - 1 + node + groupShift
-      val binsForNode: Array[Double] = getBinDataForNode(node)
-      logDebug("nodeImpurityIndex = " + nodeImpurityIndex)
-      val parentNodeImpurity = parentImpurities(nodeImpurityIndex)
-      logDebug("parent node impurity = " + parentNodeImpurity)
-      bestSplits(node) = binsToBestSplit(binsForNode, parentNodeImpurity)
-      node += 1
-    }
-    timer.stop("chooseSplits")
-
-    bestSplits
+    }.maxBy(_._2.gain)
   }
 
   /**
    * Get the number of values to be stored per node in the bin aggregates.
-   *
-   * @param numBins  Number of bins = 1 + number of possible splits.
    */
-  private def getElementsPerNode(metadata: DecisionTreeMetadata, numBins: Int): Int = {
+  private def getElementsPerNode(metadata: DecisionTreeMetadata): Int = {
+    val totalBins = metadata.numBins.sum
     if (metadata.isClassification) {
-      if (metadata.isMulticlassWithCategoricalFeatures) {
-        2 * metadata.numClasses * numBins * metadata.numFeatures
-      } else {
-        metadata.numClasses * numBins * metadata.numFeatures
-      }
+      metadata.numClasses * totalBins
     } else {
-      3 * numBins * metadata.numFeatures
+      3 * totalBins
     }
   }
 
@@ -1284,6 +919,7 @@ object DecisionTree extends Serializable with Logging {
    * Continuous features:
    *   For each feature, there are numBins - 1 possible splits representing the possible binary
    *   decisions at each node in the tree.
+   *   This finds locations (feature values) for splits using a subsample of the data.
    *
    * Categorical features:
    *   For each feature, there is 1 bin per split.
@@ -1292,7 +928,6 @@ object DecisionTree extends Serializable with Logging {
    *       For multiclass classification with a low-arity feature
    *       (i.e., if isMulticlass && isSpaceSufficientForAllCategoricalSplits),
    *       the feature is split based on subsets of categories.
-   *       There are (1 << maxFeatureValue - 1) - 1 splits.
    *   (b) "ordered features"
    *       For regression and binary classification,
    *       and for multiclass classification with a high-arity feature,
@@ -1302,7 +937,7 @@ object DecisionTree extends Serializable with Logging {
    * @param metadata Learning and dataset metadata
    * @return A tuple of (splits, bins).
    *         Splits is an Array of [[org.apache.spark.mllib.tree.model.Split]]
-   *          of size (numFeatures, numBins - 1).
+   *          of size (numFeatures, numSplits).
    *         Bins is an Array of [[org.apache.spark.mllib.tree.model.Bin]]
    *          of size (numFeatures, numBins).
    */
@@ -1310,84 +945,80 @@ object DecisionTree extends Serializable with Logging {
       input: RDD[LabeledPoint],
       metadata: DecisionTreeMetadata): (Array[Array[Split]], Array[Array[Bin]]) = {
 
-    val count = input.count()
+    logDebug("isMulticlass = " + metadata.isMulticlass)
 
-    // Find the number of features by looking at the first sample
-    val numFeatures = input.take(1)(0).features.size
-
-    val maxBins = metadata.maxBins
-    val numBins = if (maxBins <= count) maxBins else count.toInt
-    logDebug("numBins = " + numBins)
-    val isMulticlass = metadata.isMulticlass
-    logDebug("isMulticlass = " + isMulticlass)
-
-    /*
-     * Ensure numBins is always greater than the categories. For multiclass classification,
-     * numBins should be greater than 2^(maxCategories - 1) - 1.
-     * It's a limitation of the current implementation but a reasonable trade-off since features
-     * with large number of categories get favored over continuous features.
-     *
-     * This needs to be checked here instead of in Strategy since numBins can be determined
-     * by the number of training examples.
-     * TODO: Allow this case, where we simply will know nothing about some categories.
-     */
-    if (metadata.featureArity.size > 0) {
-      val maxCategoriesForFeatures = metadata.featureArity.maxBy(_._2)._2
-      require(numBins > maxCategoriesForFeatures, "numBins should be greater than max categories " +
-        "in categorical features")
-    }
-
-    // Calculate the number of sample for approximate quantile calculation.
-    val requiredSamples = numBins*numBins
-    val fraction = if (requiredSamples < count) requiredSamples.toDouble / count else 1.0
-    logDebug("fraction of data used for calculating quantiles = " + fraction)
+    val numFeatures = metadata.numFeatures
 
-    // sampled input for RDD calculation
-    val sampledInput =
+    // Sample the input only if there are continuous features.
+    val hasContinuousFeatures = Range(0, numFeatures).exists(metadata.isContinuous)
+    val sampledInput = if (hasContinuousFeatures) {
+      // Calculate the number of samples for approximate quantile calculation.
+      val requiredSamples = math.max(metadata.maxBins * metadata.maxBins, 10000)
+      val fraction = if (requiredSamples < metadata.numExamples) {
+        requiredSamples.toDouble / metadata.numExamples
+      } else {
+        1.0
+      }
+      logDebug("fraction of data used for calculating quantiles = " + fraction)
       input.sample(withReplacement = false, fraction, new XORShiftRandom().nextInt()).collect()
-    val numSamples = sampledInput.length
-
-    val stride: Double = numSamples.toDouble / numBins
-    logDebug("stride = " + stride)
+    } else {
+      new Array[LabeledPoint](0)
+    }
 
     metadata.quantileStrategy match {
       case Sort =>
-        val splits = Array.ofDim[Split](numFeatures, numBins - 1)
-        val bins = Array.ofDim[Bin](numFeatures, numBins)
+        val splits = new Array[Array[Split]](numFeatures)
+        val bins = new Array[Array[Bin]](numFeatures)
 
         // Find all splits.
-
         // Iterate over all features.
         var featureIndex = 0
         while (featureIndex < numFeatures) {
-          // Check whether the feature is continuous.
-          val isFeatureContinuous = metadata.isContinuous(featureIndex)
-          if (isFeatureContinuous) {
+          val numSplits = metadata.numSplits(featureIndex)
+          val numBins = metadata.numBins(featureIndex)
+          if (metadata.isContinuous(featureIndex)) {
+            val numSamples = sampledInput.length
+            splits(featureIndex) = new Array[Split](numSplits)
+            bins(featureIndex) = new Array[Bin](numBins)
             val featureSamples = sampledInput.map(lp => lp.features(featureIndex)).sorted
-            val stride: Double = numSamples.toDouble / numBins
+            val stride: Double = numSamples.toDouble / metadata.numBins(featureIndex)
             logDebug("stride = " + stride)
-            for (index <- 0 until numBins - 1) {
-              val sampleIndex = index * stride.toInt
+            for (splitIndex <- 0 until numSplits) {
+              val sampleIndex = splitIndex * stride.toInt
               // Set threshold halfway in between 2 samples.
               val threshold = (featureSamples(sampleIndex) + featureSamples(sampleIndex + 1)) / 2.0
-              val split = new Split(featureIndex, threshold, Continuous, List())
-              splits(featureIndex)(index) = split
+              splits(featureIndex)(splitIndex) =
+                new Split(featureIndex, threshold, Continuous, List())
             }
-          } else { // Categorical feature
-            val featureCategories = metadata.featureArity(featureIndex)
-
-            // Use different bin/split calculation strategy for categorical features in multiclass
-            // classification that satisfy the space constraint.
+            bins(featureIndex)(0) = new Bin(new DummyLowSplit(featureIndex, Continuous),
+              splits(featureIndex)(0), Continuous, Double.MinValue)
+            for (splitIndex <- 1 until numSplits) {
+              bins(featureIndex)(splitIndex) =
+                new Bin(splits(featureIndex)(splitIndex - 1), splits(featureIndex)(splitIndex),
+                  Continuous, Double.MinValue)
+            }
+            bins(featureIndex)(numSplits) = new Bin(splits(featureIndex)(numSplits - 1),
+              new DummyHighSplit(featureIndex, Continuous), Continuous, Double.MinValue)
+          } else {
+            // Categorical feature
+            val featureArity = metadata.featureArity(featureIndex)
             if (metadata.isUnordered(featureIndex)) {
-              // 2^(maxFeatureValue- 1) - 1 combinations
-              var index = 0
-              while (index < (1 << featureCategories - 1) - 1) {
-                val categories: List[Double]
-                  = extractMultiClassCategories(index + 1, featureCategories)
-                splits(featureIndex)(index)
-                  = new Split(featureIndex, Double.MinValue, Categorical, categories)
-                bins(featureIndex)(index) = {
-                  if (index == 0) {
+              // TODO: The second half of the bins are unused.  Actually, we could just use
+              //       splits and not build bins for unordered features.  That should be part of
+              //       a later PR since it will require changing other code (using splits instead
+              //       of bins in a few places).
+              // Unordered features
+              //   2^(maxFeatureValue - 1) - 1 combinations
+              splits(featureIndex) = new Array[Split](numSplits)
+              bins(featureIndex) = new Array[Bin](numBins)
+              var splitIndex = 0
+              while (splitIndex < numSplits) {
+                val categories: List[Double] =
+                  extractMultiClassCategories(splitIndex + 1, featureArity)
+                splits(featureIndex)(splitIndex) =
+                  new Split(featureIndex, Double.MinValue, Categorical, categories)
+                bins(featureIndex)(splitIndex) = {
+                  if (splitIndex == 0) {
                     new Bin(
                       new DummyCategoricalSplit(featureIndex, Categorical),
                       splits(featureIndex)(0),
@@ -1395,96 +1026,24 @@ object DecisionTree extends Serializable with Logging {
                       Double.MinValue)
                   } else {
                     new Bin(
-                      splits(featureIndex)(index - 1),
-                      splits(featureIndex)(index),
+                      splits(featureIndex)(splitIndex - 1),
+                      splits(featureIndex)(splitIndex),
                       Categorical,
                       Double.MinValue)
                   }
                 }
-                index += 1
-              }
-            } else { // ordered feature
-              /* For a given categorical feature, use a subsample of the data
-               * to choose how to arrange possible splits.
-               * This examines each category and computes a centroid.
-               * These centroids are later used to sort the possible splits.
-               * centroidForCategories is a mapping: category (for the given feature) --> centroid
-               */
-              val centroidForCategories = {
-                if (isMulticlass) {
-                  // For categorical variables in multiclass classification,
-                  // each bin is a category. The bins are sorted and they
-                  // are ordered by calculating the impurity of their corresponding labels.
-                  sampledInput.map(lp => (lp.features(featureIndex), lp.label))
-                   .groupBy(_._1)
-                   .mapValues(x => x.groupBy(_._2).mapValues(x => x.size.toDouble))
-                   .map(x => (x._1, x._2.values.toArray))
-                   .map(x => (x._1, metadata.impurity.calculate(x._2, x._2.sum)))
-                } else { // regression or binary classification
-                  // For categorical variables in regression and binary classification,
-                  // each bin is a category. The bins are sorted and they
-                  // are ordered by calculating the centroid of their corresponding labels.
-                  sampledInput.map(lp => (lp.features(featureIndex), lp.label))
-                    .groupBy(_._1)
-                    .mapValues(x => x.map(_._2).sum / x.map(_._1).length)
-                }
-              }
-
-              logDebug("centroid for categories = " + centroidForCategories.mkString(","))
-
-              // Check for missing categorical variables and putting them last in the sorted list.
-              val fullCentroidForCategories = scala.collection.mutable.Map[Double,Double]()
-              for (i <- 0 until featureCategories) {
-                if (centroidForCategories.contains(i)) {
-                  fullCentroidForCategories(i) = centroidForCategories(i)
-                } else {
-                  fullCentroidForCategories(i) = Double.MaxValue
-                }
-              }
-
-              // bins sorted by centroids
-              val categoriesSortedByCentroid = fullCentroidForCategories.toList.sortBy(_._2)
-
-              logDebug("centroid for categorical variable = " + categoriesSortedByCentroid)
-
-              var categoriesForSplit = List[Double]()
-              categoriesSortedByCentroid.iterator.zipWithIndex.foreach {
-                case ((key, value), index) =>
-                  categoriesForSplit = key :: categoriesForSplit
-                  splits(featureIndex)(index) = new Split(featureIndex, Double.MinValue,
-                    Categorical, categoriesForSplit)
-                  bins(featureIndex)(index) = {
-                    if (index == 0) {
-                      new Bin(new DummyCategoricalSplit(featureIndex, Categorical),
-                        splits(featureIndex)(0), Categorical, key)
-                    } else {
-                      new Bin(splits(featureIndex)(index-1), splits(featureIndex)(index),
-                        Categorical, key)
-                    }
-                  }
+                splitIndex += 1
               }
+            } else {
+              // Ordered features
+              //   Bins correspond to feature values, so we do not need to compute splits or bins
+              //   beforehand.  Splits are constructed as needed during training.
+              splits(featureIndex) = new Array[Split](0)
+              bins(featureIndex) = new Array[Bin](0)
             }
           }
           featureIndex += 1
         }
-
-        // Find all bins.
-        featureIndex = 0
-        while (featureIndex < numFeatures) {
-          val isFeatureContinuous = metadata.isContinuous(featureIndex)
-          if (isFeatureContinuous) { // Bins for categorical variables are already assigned.
-            bins(featureIndex)(0) = new Bin(new DummyLowSplit(featureIndex, Continuous),
-              splits(featureIndex)(0), Continuous, Double.MinValue)
-            for (index <- 1 until numBins - 1) {
-              val bin = new Bin(splits(featureIndex)(index-1), splits(featureIndex)(index),
-                Continuous, Double.MinValue)
-              bins(featureIndex)(index) = bin
-            }
-            bins(featureIndex)(numBins-1) = new Bin(splits(featureIndex)(numBins-2),
-              new DummyHighSplit(featureIndex, Continuous), Continuous, Double.MinValue)
-          }
-          featureIndex += 1
-        }
         (splits, bins)
       case MinMax =>
         throw new UnsupportedOperationException("minmax not supported yet.")
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/impl/DTStatsAggregator.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/impl/DTStatsAggregator.scala
new file mode 100644
index 0000000000000..866d85a79bea1
--- /dev/null
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/impl/DTStatsAggregator.scala
@@ -0,0 +1,213 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.mllib.tree.impl
+
+import org.apache.spark.mllib.tree.impurity._
+
+/**
+ * DecisionTree statistics aggregator.
+ * This holds a flat array of statistics for a set of (nodes, features, bins)
+ * and helps with indexing.
+ */
+private[tree] class DTStatsAggregator(
+    val metadata: DecisionTreeMetadata,
+    val numNodes: Int) extends Serializable {
+
+  /**
+   * [[ImpurityAggregator]] instance specifying the impurity type.
+   */
+  val impurityAggregator: ImpurityAggregator = metadata.impurity match {
+    case Gini => new GiniAggregator(metadata.numClasses)
+    case Entropy => new EntropyAggregator(metadata.numClasses)
+    case Variance => new VarianceAggregator()
+    case _ => throw new IllegalArgumentException(s"Bad impurity parameter: ${metadata.impurity}")
+  }
+
+  /**
+   * Number of elements (Double values) used for the sufficient statistics of each bin.
+   */
+  val statsSize: Int = impurityAggregator.statsSize
+
+  val numFeatures: Int = metadata.numFeatures
+
+  /**
+   * Number of bins for each feature.  This is indexed by the feature index.
+   */
+  val numBins: Array[Int] = metadata.numBins
+
+  /**
+   * Number of splits for the given feature.
+   */
+  def numSplits(featureIndex: Int): Int = metadata.numSplits(featureIndex)
+
+  /**
+   * Indicator for each feature of whether that feature is an unordered feature.
+   * TODO: Is Array[Boolean] any faster?
+   */
+  def isUnordered(featureIndex: Int): Boolean = metadata.isUnordered(featureIndex)
+
+  /**
+   * Offset for each feature for calculating indices into the [[allStats]] array.
+   */
+  private val featureOffsets: Array[Int] = {
+    def featureOffsetsCalc(total: Int, featureIndex: Int): Int = {
+      if (isUnordered(featureIndex)) {
+        total + 2 * numBins(featureIndex)
+      } else {
+        total + numBins(featureIndex)
+      }
+    }
+    Range(0, numFeatures).scanLeft(0)(featureOffsetsCalc).map(statsSize * _).toArray
+  }
+
+  /**
+   * Number of elements for each node, corresponding to stride between nodes in [[allStats]].
+   */
+  private val nodeStride: Int = featureOffsets.last
+
+  /**
+   * Total number of elements stored in this aggregator.
+   */
+  val allStatsSize: Int = numNodes * nodeStride
+
+  /**
+   * Flat array of elements.
+   * Index for start of stats for a (node, feature, bin) is:
+   *   index = nodeIndex * nodeStride + featureOffsets(featureIndex) + binIndex * statsSize
+   * Note: For unordered features, the left child stats have binIndex in [0, numBins(featureIndex))
+   *       and the right child stats in [numBins(featureIndex), 2 * numBins(featureIndex))
+   */
+  val allStats: Array[Double] = new Array[Double](allStatsSize)
+
+  /**
+   * Get an [[ImpurityCalculator]] for a given (node, feature, bin).
+   * @param nodeFeatureOffset  For ordered features, this is a pre-computed (node, feature) offset
+   *                           from [[getNodeFeatureOffset]].
+   *                           For unordered features, this is a pre-computed
+   *                           (node, feature, left/right child) offset from
+   *                           [[getLeftRightNodeFeatureOffsets]].
+   */
+  def getImpurityCalculator(nodeFeatureOffset: Int, binIndex: Int): ImpurityCalculator = {
+    impurityAggregator.getCalculator(allStats, nodeFeatureOffset + binIndex * statsSize)
+  }
+
+  /**
+   * Update the stats for a given (node, feature, bin) for ordered features, using the given label.
+   */
+  def update(nodeIndex: Int, featureIndex: Int, binIndex: Int, label: Double): Unit = {
+    val i = nodeIndex * nodeStride + featureOffsets(featureIndex) + binIndex * statsSize
+    impurityAggregator.update(allStats, i, label)
+  }
+
+  /**
+   * Pre-compute node offset for use with [[nodeUpdate]].
+   */
+  def getNodeOffset(nodeIndex: Int): Int = nodeIndex * nodeStride
+
+  /**
+   * Faster version of [[update]].
+   * Update the stats for a given (node, feature, bin) for ordered features, using the given label.
+   * @param nodeOffset  Pre-computed node offset from [[getNodeOffset]].
+   */
+  def nodeUpdate(nodeOffset: Int, featureIndex: Int, binIndex: Int, label: Double): Unit = {
+    val i = nodeOffset + featureOffsets(featureIndex) + binIndex * statsSize
+    impurityAggregator.update(allStats, i, label)
+  }
+
+  /**
+   * Pre-compute (node, feature) offset for use with [[nodeFeatureUpdate]].
+   * For ordered features only.
+   */
+  def getNodeFeatureOffset(nodeIndex: Int, featureIndex: Int): Int = {
+    require(!isUnordered(featureIndex),
+      s"DTStatsAggregator.getNodeFeatureOffset is for ordered features only, but was called" +
+      s" for unordered feature $featureIndex.")
+    nodeIndex * nodeStride + featureOffsets(featureIndex)
+  }
+
+  /**
+   * Pre-compute (node, feature) offset for use with [[nodeFeatureUpdate]].
+   * For unordered features only.
+   */
+  def getLeftRightNodeFeatureOffsets(nodeIndex: Int, featureIndex: Int): (Int, Int) = {
+    require(isUnordered(featureIndex),
+      s"DTStatsAggregator.getLeftRightNodeFeatureOffsets is for unordered features only," +
+      s" but was called for ordered feature $featureIndex.")
+    val baseOffset = nodeIndex * nodeStride + featureOffsets(featureIndex)
+    (baseOffset, baseOffset + numBins(featureIndex) * statsSize)
+  }
+
+  /**
+   * Faster version of [[update]].
+   * Update the stats for a given (node, feature, bin), using the given label.
+   * @param nodeFeatureOffset  For ordered features, this is a pre-computed (node, feature) offset
+   *                           from [[getNodeFeatureOffset]].
+   *                           For unordered features, this is a pre-computed
+   *                           (node, feature, left/right child) offset from
+   *                           [[getLeftRightNodeFeatureOffsets]].
+   */
+  def nodeFeatureUpdate(nodeFeatureOffset: Int, binIndex: Int, label: Double): Unit = {
+    impurityAggregator.update(allStats, nodeFeatureOffset + binIndex * statsSize, label)
+  }
+
+  /**
+   * For a given (node, feature), merge the stats for two bins.
+   * @param nodeFeatureOffset  For ordered features, this is a pre-computed (node, feature) offset
+   *                           from [[getNodeFeatureOffset]].
+   *                           For unordered features, this is a pre-computed
+   *                           (node, feature, left/right child) offset from
+   *                           [[getLeftRightNodeFeatureOffsets]].
+   * @param binIndex  The other bin is merged into this bin.
+   * @param otherBinIndex  This bin is not modified.
+   */
+  def mergeForNodeFeature(nodeFeatureOffset: Int, binIndex: Int, otherBinIndex: Int): Unit = {
+    impurityAggregator.merge(allStats, nodeFeatureOffset + binIndex * statsSize,
+      nodeFeatureOffset + otherBinIndex * statsSize)
+  }
+
+  /**
+   * Merge this aggregator with another, and returns this aggregator.
+   * This method modifies this aggregator in-place.
+   */
+  def merge(other: DTStatsAggregator): DTStatsAggregator = {
+    require(allStatsSize == other.allStatsSize,
+      s"DTStatsAggregator.merge requires that both aggregators have the same length stats vectors."
+      + s" This aggregator is of length $allStatsSize, but the other is ${other.allStatsSize}.")
+    var i = 0
+    // TODO: Test BLAS.axpy
+    while (i < allStatsSize) {
+      allStats(i) += other.allStats(i)
+      i += 1
+    }
+    this
+  }
+
+}
+
+private[tree] object DTStatsAggregator extends Serializable {
+
+  /**
+   * Combines two aggregates (modifying the first) and returns the combination.
+   */
+  def binCombOp(
+      agg1: DTStatsAggregator,
+      agg2: DTStatsAggregator): DTStatsAggregator = {
+    agg1.merge(agg2)
+  }
+
+}
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/impl/DecisionTreeMetadata.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/impl/DecisionTreeMetadata.scala
index d9eda354dc986..e95add7558bcf 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/impl/DecisionTreeMetadata.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/impl/DecisionTreeMetadata.scala
@@ -26,14 +26,15 @@ import org.apache.spark.mllib.tree.configuration.Strategy
 import org.apache.spark.mllib.tree.impurity.Impurity
 import org.apache.spark.rdd.RDD
 
-
 /**
  * Learning and dataset metadata for DecisionTree.
  *
  * @param numClasses    For classification: labels can take values {0, ..., numClasses - 1}.
  *                      For regression: fixed at 0 (no meaning).
+ * @param maxBins  Maximum number of bins, for all features.
  * @param featureArity  Map: categorical feature index --> arity.
  *                      I.e., the feature takes values in {0, ..., arity - 1}.
+ * @param numBins  Number of bins for each feature.
  */
 private[tree] class DecisionTreeMetadata(
     val numFeatures: Int,
@@ -42,6 +43,7 @@ private[tree] class DecisionTreeMetadata(
     val maxBins: Int,
     val featureArity: Map[Int, Int],
     val unorderedFeatures: Set[Int],
+    val numBins: Array[Int],
     val impurity: Impurity,
     val quantileStrategy: QuantileStrategy) extends Serializable {
 
@@ -57,10 +59,26 @@ private[tree] class DecisionTreeMetadata(
 
   def isContinuous(featureIndex: Int): Boolean = !featureArity.contains(featureIndex)
 
+  /**
+   * Number of splits for the given feature.
+   * For unordered features, there are 2 bins per split.
+   * For ordered features, there is 1 more bin than split.
+   */
+  def numSplits(featureIndex: Int): Int = if (isUnordered(featureIndex)) {
+    numBins(featureIndex) >> 1
+  } else {
+    numBins(featureIndex) - 1
+  }
+
 }
 
 private[tree] object DecisionTreeMetadata {
 
+  /**
+   * Construct a [[DecisionTreeMetadata]] instance for this dataset and parameters.
+   * This computes which categorical features will be ordered vs. unordered,
+   * as well as the number of splits and bins for each feature.
+   */
   def buildMetadata(input: RDD[LabeledPoint], strategy: Strategy): DecisionTreeMetadata = {
 
     val numFeatures = input.take(1)(0).features.size
@@ -70,32 +88,55 @@ private[tree] object DecisionTreeMetadata {
       case Regression => 0
     }
 
-    val maxBins = math.min(strategy.maxBins, numExamples).toInt
-    val log2MaxBinsp1 = math.log(maxBins + 1) / math.log(2.0)
+    val maxPossibleBins = math.min(strategy.maxBins, numExamples).toInt
+
+    // We check the number of bins here against maxPossibleBins.
+    // This needs to be checked here instead of in Strategy since maxPossibleBins can be modified
+    // based on the number of training examples.
+    if (strategy.categoricalFeaturesInfo.nonEmpty) {
+      val maxCategoriesPerFeature = strategy.categoricalFeaturesInfo.values.max
+      require(maxCategoriesPerFeature <= maxPossibleBins,
+        s"DecisionTree requires maxBins (= $maxPossibleBins) >= max categories " +
+          s"in categorical features (= $maxCategoriesPerFeature)")
+    }
 
     val unorderedFeatures = new mutable.HashSet[Int]()
+    val numBins = Array.fill[Int](numFeatures)(maxPossibleBins)
     if (numClasses > 2) {
-      strategy.categoricalFeaturesInfo.foreach { case (f, k) =>
-        if (k - 1 < log2MaxBinsp1) {
-          // Note: The above check is equivalent to checking:
-          //       numUnorderedBins = (1 << k - 1) - 1 < maxBins
-          unorderedFeatures.add(f)
+      // Multiclass classification
+      val maxCategoriesForUnorderedFeature =
+        ((math.log(maxPossibleBins / 2 + 1) / math.log(2.0)) + 1).floor.toInt
+      strategy.categoricalFeaturesInfo.foreach { case (featureIndex, numCategories) =>
+        // Decide if some categorical features should be treated as unordered features,
+        //  which require 2 * ((1 << numCategories - 1) - 1) bins.
+        // We do this check with log values to prevent overflows in case numCategories is large.
+        // The next check is equivalent to: 2 * ((1 << numCategories - 1) - 1) <= maxBins
+        if (numCategories <= maxCategoriesForUnorderedFeature) {
+          unorderedFeatures.add(featureIndex)
+          numBins(featureIndex) = numUnorderedBins(numCategories)
         } else {
-          // TODO: Allow this case, where we simply will know nothing about some categories?
-          require(k < maxBins, s"maxBins (= $maxBins) should be greater than max categories " +
-            s"in categorical features (>= $k)")
+          numBins(featureIndex) = numCategories
         }
       }
     } else {
-      strategy.categoricalFeaturesInfo.foreach { case (f, k) =>
-        require(k < maxBins, s"maxBins (= $maxBins) should be greater than max categories " +
-          s"in categorical features (>= $k)")
+      // Binary classification or regression
+      strategy.categoricalFeaturesInfo.foreach { case (featureIndex, numCategories) =>
+        numBins(featureIndex) = numCategories
       }
     }
 
-    new DecisionTreeMetadata(numFeatures, numExamples, numClasses, maxBins,
-      strategy.categoricalFeaturesInfo, unorderedFeatures.toSet,
+    new DecisionTreeMetadata(numFeatures, numExamples, numClasses, numBins.max,
+      strategy.categoricalFeaturesInfo, unorderedFeatures.toSet, numBins,
       strategy.impurity, strategy.quantileCalculationStrategy)
   }
 
+  /**
+   * Given the arity of a categorical feature (arity = number of categories),
+   * return the number of bins for the feature if it is to be treated as an unordered feature.
+   * There is 1 split for every partitioning of categories into 2 disjoint, non-empty sets;
+   * there are math.pow(2, arity - 1) - 1 such splits.
+   * Each split has 2 corresponding bins.
+   */
+  def numUnorderedBins(arity: Int): Int = 2 * ((1 << arity - 1) - 1)
+
 }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/impl/TreePoint.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/impl/TreePoint.scala
index 170e43e222083..35e361ae309cc 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/impl/TreePoint.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/impl/TreePoint.scala
@@ -48,54 +48,63 @@ private[tree] object TreePoint {
    * binning feature values in preparation for DecisionTree training.
    * @param input     Input dataset.
    * @param bins      Bins for features, of size (numFeatures, numBins).
-   * @param metadata Learning and dataset metadata
+   * @param metadata  Learning and dataset metadata
    * @return  TreePoint dataset representation
    */
   def convertToTreeRDD(
       input: RDD[LabeledPoint],
       bins: Array[Array[Bin]],
       metadata: DecisionTreeMetadata): RDD[TreePoint] = {
+    // Construct arrays for featureArity and isUnordered for efficiency in the inner loop.
+    val featureArity: Array[Int] = new Array[Int](metadata.numFeatures)
+    val isUnordered: Array[Boolean] = new Array[Boolean](metadata.numFeatures)
+    var featureIndex = 0
+    while (featureIndex < metadata.numFeatures) {
+      featureArity(featureIndex) = metadata.featureArity.getOrElse(featureIndex, 0)
+      isUnordered(featureIndex) = metadata.isUnordered(featureIndex)
+      featureIndex += 1
+    }
     input.map { x =>
-      TreePoint.labeledPointToTreePoint(x, bins, metadata)
+      TreePoint.labeledPointToTreePoint(x, bins, featureArity, isUnordered)
     }
   }
 
   /**
    * Convert one LabeledPoint into its TreePoint representation.
    * @param bins      Bins for features, of size (numFeatures, numBins).
+   * @param featureArity  Array indexed by feature, with value 0 for continuous and numCategories
+   *                      for categorical features.
+   * @param isUnordered  Array index by feature, with value true for unordered categorical features.
    */
   private def labeledPointToTreePoint(
       labeledPoint: LabeledPoint,
       bins: Array[Array[Bin]],
-      metadata: DecisionTreeMetadata): TreePoint = {
-
+      featureArity: Array[Int],
+      isUnordered: Array[Boolean]): TreePoint = {
     val numFeatures = labeledPoint.features.size
-    val numBins = bins(0).size
     val arr = new Array[Int](numFeatures)
     var featureIndex = 0
     while (featureIndex < numFeatures) {
-      arr(featureIndex) = findBin(featureIndex, labeledPoint, metadata.isContinuous(featureIndex),
-        metadata.isUnordered(featureIndex), bins, metadata.featureArity)
+      arr(featureIndex) = findBin(featureIndex, labeledPoint, featureArity(featureIndex),
+        isUnordered(featureIndex), bins)
       featureIndex += 1
     }
-
     new TreePoint(labeledPoint.label, arr)
   }
 
   /**
    * Find bin for one (labeledPoint, feature).
    *
+   * @param featureArity  0 for continuous features; number of categories for categorical features.
    * @param isUnorderedFeature  (only applies if feature is categorical)
    * @param bins   Bins for features, of size (numFeatures, numBins).
-   * @param categoricalFeaturesInfo  Map over categorical features: feature index --> feature arity
    */
   private def findBin(
       featureIndex: Int,
       labeledPoint: LabeledPoint,
-      isFeatureContinuous: Boolean,
+      featureArity: Int,
       isUnorderedFeature: Boolean,
-      bins: Array[Array[Bin]],
-      categoricalFeaturesInfo: Map[Int, Int]): Int = {
+      bins: Array[Array[Bin]]): Int = {
 
     /**
      * Binary search helper method for continuous feature.
@@ -121,44 +130,7 @@ private[tree] object TreePoint {
       -1
     }
 
-    /**
-     * Sequential search helper method to find bin for categorical feature in multiclass
-     * classification. The category is returned since each category can belong to multiple
-     * splits. The actual left/right child allocation per split is performed in the
-     * sequential phase of the bin aggregate operation.
-     */
-    def sequentialBinSearchForUnorderedCategoricalFeatureInClassification(): Int = {
-      labeledPoint.features(featureIndex).toInt
-    }
-
-    /**
-     * Sequential search helper method to find bin for categorical feature
-     * (for classification and regression).
-     */
-    def sequentialBinSearchForOrderedCategoricalFeature(): Int = {
-      val featureCategories = categoricalFeaturesInfo(featureIndex)
-      val featureValue = labeledPoint.features(featureIndex)
-      var binIndex = 0
-      while (binIndex < featureCategories) {
-        val bin = bins(featureIndex)(binIndex)
-        val categories = bin.highSplit.categories
-        if (categories.contains(featureValue)) {
-          return binIndex
-        }
-        binIndex += 1
-      }
-      if (featureValue < 0 || featureValue >= featureCategories) {
-        throw new IllegalArgumentException(
-          s"DecisionTree given invalid data:" +
-            s" Feature $featureIndex is categorical with values in" +
-            s" {0,...,${featureCategories - 1}," +
-            s" but a data point gives it value $featureValue.\n" +
-            "  Bad data point: " + labeledPoint.toString)
-      }
-      -1
-    }
-
-    if (isFeatureContinuous) {
+    if (featureArity == 0) {
       // Perform binary search for finding bin for continuous features.
       val binIndex = binarySearchForBins()
       if (binIndex == -1) {
@@ -168,18 +140,17 @@ private[tree] object TreePoint {
       }
       binIndex
     } else {
-      // Perform sequential search to find bin for categorical features.
-      val binIndex = if (isUnorderedFeature) {
-          sequentialBinSearchForUnorderedCategoricalFeatureInClassification()
-        } else {
-          sequentialBinSearchForOrderedCategoricalFeature()
-        }
-      if (binIndex == -1) {
-        throw new RuntimeException("No bin was found for categorical feature." +
-          " This error can occur when given invalid data values (such as NaN)." +
-          s" Feature index: $featureIndex.  Feature value: ${labeledPoint.features(featureIndex)}")
+      // Categorical feature bins are indexed by feature values.
+      val featureValue = labeledPoint.features(featureIndex)
+      if (featureValue < 0 || featureValue >= featureArity) {
+        throw new IllegalArgumentException(
+          s"DecisionTree given invalid data:" +
+            s" Feature $featureIndex is categorical with values in" +
+            s" {0,...,${featureArity - 1}," +
+            s" but a data point gives it value $featureValue.\n" +
+            "  Bad data point: " + labeledPoint.toString)
       }
-      binIndex
+      featureValue.toInt
     }
   }
 }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Entropy.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Entropy.scala
index 96d2471e1f88c..1c8afc2d0f4bc 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Entropy.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Entropy.scala
@@ -74,3 +74,87 @@ object Entropy extends Impurity {
   def instance = this
 
 }
+
+/**
+ * Class for updating views of a vector of sufficient statistics,
+ * in order to compute impurity from a sample.
+ * Note: Instances of this class do not hold the data; they operate on views of the data.
+ * @param numClasses  Number of classes for label.
+ */
+private[tree] class EntropyAggregator(numClasses: Int)
+  extends ImpurityAggregator(numClasses) with Serializable {
+
+  /**
+   * Update stats for one (node, feature, bin) with the given label.
+   * @param allStats  Flat stats array, with stats for this (node, feature, bin) contiguous.
+   * @param offset    Start index of stats for this (node, feature, bin).
+   */
+  def update(allStats: Array[Double], offset: Int, label: Double): Unit = {
+    if (label >= statsSize) {
+      throw new IllegalArgumentException(s"EntropyAggregator given label $label" +
+        s" but requires label < numClasses (= $statsSize).")
+    }
+    allStats(offset + label.toInt) += 1
+  }
+
+  /**
+   * Get an [[ImpurityCalculator]] for a (node, feature, bin).
+   * @param allStats  Flat stats array, with stats for this (node, feature, bin) contiguous.
+   * @param offset    Start index of stats for this (node, feature, bin).
+   */
+  def getCalculator(allStats: Array[Double], offset: Int): EntropyCalculator = {
+    new EntropyCalculator(allStats.view(offset, offset + statsSize).toArray)
+  }
+
+}
+
+/**
+ * Stores statistics for one (node, feature, bin) for calculating impurity.
+ * Unlike [[EntropyAggregator]], this class stores its own data and is for a specific
+ * (node, feature, bin).
+ * @param stats  Array of sufficient statistics for a (node, feature, bin).
+ */
+private[tree] class EntropyCalculator(stats: Array[Double]) extends ImpurityCalculator(stats) {
+
+  /**
+   * Make a deep copy of this [[ImpurityCalculator]].
+   */
+  def copy: EntropyCalculator = new EntropyCalculator(stats.clone())
+
+  /**
+   * Calculate the impurity from the stored sufficient statistics.
+   */
+  def calculate(): Double = Entropy.calculate(stats, stats.sum)
+
+  /**
+   * Number of data points accounted for in the sufficient statistics.
+   */
+  def count: Long = stats.sum.toLong
+
+  /**
+   * Prediction which should be made based on the sufficient statistics.
+   */
+  def predict: Double = if (count == 0) {
+    0
+  } else {
+    indexOfLargestArrayElement(stats)
+  }
+
+  /**
+   * Probability of the label given by [[predict]].
+   */
+  override def prob(label: Double): Double = {
+    val lbl = label.toInt
+    require(lbl < stats.length,
+      s"EntropyCalculator.prob given invalid label: $lbl (should be < ${stats.length}")
+    val cnt = count
+    if (cnt == 0) {
+      0
+    } else {
+      stats(lbl) / cnt
+    }
+  }
+
+  override def toString: String = s"EntropyCalculator(stats = [${stats.mkString(", ")}])"
+
+}
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Gini.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Gini.scala
index d586f449048bb..5cfdf345d163c 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Gini.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Gini.scala
@@ -70,3 +70,87 @@ object Gini extends Impurity {
   def instance = this
 
 }
+
+/**
+ * Class for updating views of a vector of sufficient statistics,
+ * in order to compute impurity from a sample.
+ * Note: Instances of this class do not hold the data; they operate on views of the data.
+ * @param numClasses  Number of classes for label.
+ */
+private[tree] class GiniAggregator(numClasses: Int)
+  extends ImpurityAggregator(numClasses) with Serializable {
+
+  /**
+   * Update stats for one (node, feature, bin) with the given label.
+   * @param allStats  Flat stats array, with stats for this (node, feature, bin) contiguous.
+   * @param offset    Start index of stats for this (node, feature, bin).
+   */
+  def update(allStats: Array[Double], offset: Int, label: Double): Unit = {
+    if (label >= statsSize) {
+      throw new IllegalArgumentException(s"GiniAggregator given label $label" +
+        s" but requires label < numClasses (= $statsSize).")
+    }
+    allStats(offset + label.toInt) += 1
+  }
+
+  /**
+   * Get an [[ImpurityCalculator]] for a (node, feature, bin).
+   * @param allStats  Flat stats array, with stats for this (node, feature, bin) contiguous.
+   * @param offset    Start index of stats for this (node, feature, bin).
+   */
+  def getCalculator(allStats: Array[Double], offset: Int): GiniCalculator = {
+    new GiniCalculator(allStats.view(offset, offset + statsSize).toArray)
+  }
+
+}
+
+/**
+ * Stores statistics for one (node, feature, bin) for calculating impurity.
+ * Unlike [[GiniAggregator]], this class stores its own data and is for a specific
+ * (node, feature, bin).
+ * @param stats  Array of sufficient statistics for a (node, feature, bin).
+ */
+private[tree] class GiniCalculator(stats: Array[Double]) extends ImpurityCalculator(stats) {
+
+  /**
+   * Make a deep copy of this [[ImpurityCalculator]].
+   */
+  def copy: GiniCalculator = new GiniCalculator(stats.clone())
+
+  /**
+   * Calculate the impurity from the stored sufficient statistics.
+   */
+  def calculate(): Double = Gini.calculate(stats, stats.sum)
+
+  /**
+   * Number of data points accounted for in the sufficient statistics.
+   */
+  def count: Long = stats.sum.toLong
+
+  /**
+   * Prediction which should be made based on the sufficient statistics.
+   */
+  def predict: Double = if (count == 0) {
+    0
+  } else {
+    indexOfLargestArrayElement(stats)
+  }
+
+  /**
+   * Probability of the label given by [[predict]].
+   */
+  override def prob(label: Double): Double = {
+    val lbl = label.toInt
+    require(lbl < stats.length,
+      s"GiniCalculator.prob given invalid label: $lbl (should be < ${stats.length}")
+    val cnt = count
+    if (cnt == 0) {
+      0
+    } else {
+      stats(lbl) / cnt
+    }
+  }
+
+  override def toString: String = s"GiniCalculator(stats = [${stats.mkString(", ")}])"
+
+}
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Impurity.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Impurity.scala
index 92b0c7b4a6fbc..5a047d6cb5480 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Impurity.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Impurity.scala
@@ -22,6 +22,9 @@ import org.apache.spark.annotation.{DeveloperApi, Experimental}
 /**
  * :: Experimental ::
  * Trait for calculating information gain.
+ * This trait is used for
+ *  (a) setting the impurity parameter in [[org.apache.spark.mllib.tree.configuration.Strategy]]
+ *  (b) calculating impurity values from sufficient statistics.
  */
 @Experimental
 trait Impurity extends Serializable {
@@ -47,3 +50,127 @@ trait Impurity extends Serializable {
   @DeveloperApi
   def calculate(count: Double, sum: Double, sumSquares: Double): Double
 }
+
+/**
+ * Interface for updating views of a vector of sufficient statistics,
+ * in order to compute impurity from a sample.
+ * Note: Instances of this class do not hold the data; they operate on views of the data.
+ * @param statsSize  Length of the vector of sufficient statistics for one bin.
+ */
+private[tree] abstract class ImpurityAggregator(val statsSize: Int) extends Serializable {
+
+  /**
+   * Merge the stats from one bin into another.
+   * @param allStats  Flat stats array, with stats for this (node, feature, bin) contiguous.
+   * @param offset    Start index of stats for (node, feature, bin) which is modified by the merge.
+   * @param otherOffset  Start index of stats for (node, feature, other bin) which is not modified.
+   */
+  def merge(allStats: Array[Double], offset: Int, otherOffset: Int): Unit = {
+    var i = 0
+    while (i < statsSize) {
+      allStats(offset + i) += allStats(otherOffset + i)
+      i += 1
+    }
+  }
+
+  /**
+   * Update stats for one (node, feature, bin) with the given label.
+   * @param allStats  Flat stats array, with stats for this (node, feature, bin) contiguous.
+   * @param offset    Start index of stats for this (node, feature, bin).
+   */
+  def update(allStats: Array[Double], offset: Int, label: Double): Unit
+
+  /**
+   * Get an [[ImpurityCalculator]] for a (node, feature, bin).
+   * @param allStats  Flat stats array, with stats for this (node, feature, bin) contiguous.
+   * @param offset    Start index of stats for this (node, feature, bin).
+   */
+  def getCalculator(allStats: Array[Double], offset: Int): ImpurityCalculator
+
+}
+
+/**
+ * Stores statistics for one (node, feature, bin) for calculating impurity.
+ * Unlike [[ImpurityAggregator]], this class stores its own data and is for a specific
+ * (node, feature, bin).
+ * @param stats  Array of sufficient statistics for a (node, feature, bin).
+ */
+private[tree] abstract class ImpurityCalculator(val stats: Array[Double]) {
+
+  /**
+   * Make a deep copy of this [[ImpurityCalculator]].
+   */
+  def copy: ImpurityCalculator
+
+  /**
+   * Calculate the impurity from the stored sufficient statistics.
+   */
+  def calculate(): Double
+
+  /**
+   * Add the stats from another calculator into this one, modifying and returning this calculator.
+   */
+  def add(other: ImpurityCalculator): ImpurityCalculator = {
+    require(stats.size == other.stats.size,
+      s"Two ImpurityCalculator instances cannot be added with different counts sizes." +
+        s"  Sizes are ${stats.size} and ${other.stats.size}.")
+    var i = 0
+    while (i < other.stats.size) {
+      stats(i) += other.stats(i)
+      i += 1
+    }
+    this
+  }
+
+  /**
+   * Subtract the stats from another calculator from this one, modifying and returning this
+   * calculator.
+   */
+  def subtract(other: ImpurityCalculator): ImpurityCalculator = {
+    require(stats.size == other.stats.size,
+      s"Two ImpurityCalculator instances cannot be subtracted with different counts sizes." +
+      s"  Sizes are ${stats.size} and ${other.stats.size}.")
+    var i = 0
+    while (i < other.stats.size) {
+      stats(i) -= other.stats(i)
+      i += 1
+    }
+    this
+  }
+
+  /**
+   * Number of data points accounted for in the sufficient statistics.
+   */
+  def count: Long
+
+  /**
+   * Prediction which should be made based on the sufficient statistics.
+   */
+  def predict: Double
+
+  /**
+   * Probability of the label given by [[predict]], or -1 if no probability is available.
+   */
+  def prob(label: Double): Double = -1
+
+  /**
+   * Return the index of the largest array element.
+   * Fails if the array is empty.
+   */
+  protected def indexOfLargestArrayElement(array: Array[Double]): Int = {
+    val result = array.foldLeft(-1, Double.MinValue, 0) {
+      case ((maxIndex, maxValue, currentIndex), currentValue) =>
+        if (currentValue > maxValue) {
+          (currentIndex, currentValue, currentIndex + 1)
+        } else {
+          (maxIndex, maxValue, currentIndex + 1)
+        }
+    }
+    if (result._1 < 0) {
+      throw new RuntimeException("ImpurityCalculator internal error:" +
+        " indexOfLargestArrayElement failed")
+    }
+    result._1
+  }
+
+}
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Variance.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Variance.scala
index f7d99a40eb380..e9ccecb1b8067 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Variance.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Variance.scala
@@ -61,3 +61,75 @@ object Variance extends Impurity {
   def instance = this
 
 }
+
+/**
+ * Class for updating views of a vector of sufficient statistics,
+ * in order to compute impurity from a sample.
+ * Note: Instances of this class do not hold the data; they operate on views of the data.
+ */
+private[tree] class VarianceAggregator()
+  extends ImpurityAggregator(statsSize = 3) with Serializable {
+
+  /**
+   * Update stats for one (node, feature, bin) with the given label.
+   * @param allStats  Flat stats array, with stats for this (node, feature, bin) contiguous.
+   * @param offset    Start index of stats for this (node, feature, bin).
+   */
+  def update(allStats: Array[Double], offset: Int, label: Double): Unit = {
+    allStats(offset) += 1
+    allStats(offset + 1) += label
+    allStats(offset + 2) += label * label
+  }
+
+  /**
+   * Get an [[ImpurityCalculator]] for a (node, feature, bin).
+   * @param allStats  Flat stats array, with stats for this (node, feature, bin) contiguous.
+   * @param offset    Start index of stats for this (node, feature, bin).
+   */
+  def getCalculator(allStats: Array[Double], offset: Int): VarianceCalculator = {
+    new VarianceCalculator(allStats.view(offset, offset + statsSize).toArray)
+  }
+
+}
+
+/**
+ * Stores statistics for one (node, feature, bin) for calculating impurity.
+ * Unlike [[GiniAggregator]], this class stores its own data and is for a specific
+ * (node, feature, bin).
+ * @param stats  Array of sufficient statistics for a (node, feature, bin).
+ */
+private[tree] class VarianceCalculator(stats: Array[Double]) extends ImpurityCalculator(stats) {
+
+  require(stats.size == 3,
+    s"VarianceCalculator requires sufficient statistics array stats to be of length 3," +
+    s" but was given array of length ${stats.size}.")
+
+  /**
+   * Make a deep copy of this [[ImpurityCalculator]].
+   */
+  def copy: VarianceCalculator = new VarianceCalculator(stats.clone())
+
+  /**
+   * Calculate the impurity from the stored sufficient statistics.
+   */
+  def calculate(): Double = Variance.calculate(stats(0), stats(1), stats(2))
+
+  /**
+   * Number of data points accounted for in the sufficient statistics.
+   */
+  def count: Long = stats(0).toLong
+
+  /**
+   * Prediction which should be made based on the sufficient statistics.
+   */
+  def predict: Double = if (count == 0) {
+    0
+  } else {
+    stats(1) / count
+  }
+
+  override def toString: String = {
+    s"VarianceAggregator(cnt = ${stats(0)}, sum = ${stats(1)}, sum2 = ${stats(2)})"
+  }
+
+}
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Bin.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Bin.scala
index af35d88f713e5..0cad473782af1 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Bin.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Bin.scala
@@ -20,7 +20,7 @@ package org.apache.spark.mllib.tree.model
 import org.apache.spark.mllib.tree.configuration.FeatureType._
 
 /**
- * Used for "binning" the features bins for faster best split calculation.
+ * Used for "binning" the feature values for faster best split calculation.
  *
  * For a continuous feature, the bin is determined by a low and a high split,
  *  where an example with featureValue falls into the bin s.t.
@@ -30,13 +30,16 @@ import org.apache.spark.mllib.tree.configuration.FeatureType._
  *  bins, splits, and feature values.  The bin is determined by category/feature value.
  *  However, the bins are not necessarily ordered by feature value;
  *  they are ordered using impurity.
+ *
  * For unordered categorical features, there is a 1-1 correspondence between bins, splits,
  *  where bins and splits correspond to subsets of feature values (in highSplit.categories).
+ *  An unordered feature with k categories uses (1 << k - 1) - 1 bins, corresponding to all
+ *  partitionings of categories into 2 disjoint, non-empty sets.
  *
  * @param lowSplit signifying the lower threshold for the continuous feature to be
  *                 accepted in the bin
  * @param highSplit signifying the upper threshold for the continuous feature to be
- *                 accepted in the bin
+ *                  accepted in the bin
  * @param featureType type of feature -- categorical or continuous
  * @param category categorical label value accepted in the bin for ordered features
  */
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Node.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Node.scala
index 0eee6262781c1..5b8a4cbed2306 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Node.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Node.scala
@@ -24,8 +24,13 @@ import org.apache.spark.mllib.linalg.Vector
 
 /**
  * :: DeveloperApi ::
- * Node in a decision tree
- * @param id integer node id
+ * Node in a decision tree.
+ *
+ * About node indexing:
+ *   Nodes are indexed from 1.  Node 1 is the root; nodes 2, 3 are the left, right children.
+ *   Node index 0 is not used.
+ *
+ * @param id integer node id, from 1
  * @param predict predicted value at the node
  * @param isLeaf whether the leaf is a node
  * @param split split to calculate left and right nodes
@@ -51,17 +56,13 @@ class Node (
    * @param nodes array of nodes
    */
   def build(nodes: Array[Node]): Unit = {
-
-    logDebug("building node " + id + " at level " +
-      (scala.math.log(id + 1)/scala.math.log(2)).toInt )
+    logDebug("building node " + id + " at level " + Node.indexToLevel(id))
     logDebug("id = " + id + ", split = " + split)
     logDebug("stats = " + stats)
     logDebug("predict = " + predict)
     if (!isLeaf) {
-      val leftNodeIndex = id * 2 + 1
-      val rightNodeIndex = id * 2 + 2
-      leftNode = Some(nodes(leftNodeIndex))
-      rightNode = Some(nodes(rightNodeIndex))
+      leftNode = Some(nodes(Node.leftChildIndex(id)))
+      rightNode = Some(nodes(Node.rightChildIndex(id)))
       leftNode.get.build(nodes)
       rightNode.get.build(nodes)
     }
@@ -96,24 +97,20 @@ class Node (
    * Get the number of nodes in tree below this node, including leaf nodes.
    * E.g., if this is a leaf, returns 0.  If both children are leaves, returns 2.
    */
-  private[tree] def numDescendants: Int = {
-    if (isLeaf) {
-      0
-    } else {
-      2 + leftNode.get.numDescendants + rightNode.get.numDescendants
-    }
+  private[tree] def numDescendants: Int = if (isLeaf) {
+    0
+  } else {
+    2 + leftNode.get.numDescendants + rightNode.get.numDescendants
   }
 
   /**
    * Get depth of tree from this node.
    * E.g.: Depth 0 means this is a leaf node.
    */
-  private[tree] def subtreeDepth: Int = {
-    if (isLeaf) {
-      0
-    } else {
-      1 + math.max(leftNode.get.subtreeDepth, rightNode.get.subtreeDepth)
-    }
+  private[tree] def subtreeDepth: Int = if (isLeaf) {
+    0
+  } else {
+    1 + math.max(leftNode.get.subtreeDepth, rightNode.get.subtreeDepth)
   }
 
   /**
@@ -148,3 +145,49 @@ class Node (
   }
 
 }
+
+private[tree] object Node {
+
+  /**
+   * Return the index of the left child of this node.
+   */
+  def leftChildIndex(nodeIndex: Int): Int = nodeIndex << 1
+
+  /**
+   * Return the index of the right child of this node.
+   */
+  def rightChildIndex(nodeIndex: Int): Int = (nodeIndex << 1) + 1
+
+  /**
+   * Get the parent index of the given node, or 0 if it is the root.
+   */
+  def parentIndex(nodeIndex: Int): Int = nodeIndex >> 1
+
+  /**
+   * Return the level of a tree which the given node is in.
+   */
+  def indexToLevel(nodeIndex: Int): Int = if (nodeIndex == 0) {
+    throw new IllegalArgumentException(s"0 is not a valid node index.")
+  } else {
+    java.lang.Integer.numberOfTrailingZeros(java.lang.Integer.highestOneBit(nodeIndex))
+  }
+
+  /**
+   * Returns true if this is a left child.
+   * Note: Returns false for the root.
+   */
+  def isLeftChild(nodeIndex: Int): Boolean = nodeIndex > 1 && nodeIndex % 2 == 0
+
+  /**
+   * Return the maximum number of nodes which can be in the given level of the tree.
+   * @param level  Level of tree (0 = root).
+   */
+  def maxNodesInLevel(level: Int): Int = 1 << level
+
+  /**
+   * Return the index of the first node in the given level.
+   * @param level  Level of tree (0 = root).
+   */
+  def startIndexInLevel(level: Int): Int = 1 << level
+
+}
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala
index 2f36fd907772c..8e556c917b2e7 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala
@@ -21,15 +21,16 @@ import scala.collection.JavaConverters._
 
 import org.scalatest.FunSuite
 
+import org.apache.spark.mllib.linalg.Vectors
+import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.tree.configuration.Algo._
 import org.apache.spark.mllib.tree.configuration.FeatureType._
 import org.apache.spark.mllib.tree.configuration.Strategy
 import org.apache.spark.mllib.tree.impl.{DecisionTreeMetadata, TreePoint}
 import org.apache.spark.mllib.tree.impurity.{Entropy, Gini, Variance}
 import org.apache.spark.mllib.tree.model.{DecisionTreeModel, Node}
-import org.apache.spark.mllib.linalg.Vectors
 import org.apache.spark.mllib.util.LocalSparkContext
-import org.apache.spark.mllib.regression.LabeledPoint
+
 
 class DecisionTreeSuite extends FunSuite with LocalSparkContext {
 
@@ -59,12 +60,13 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
     assert(mse <= requiredMSE, s"validateRegressor calculated MSE $mse but required $requiredMSE.")
   }
 
-  test("split and bin calculation") {
+  test("Binary classification with continuous features: split and bin calculation") {
     val arr = DecisionTreeSuite.generateOrderedLabeledPointsWithLabel1()
     assert(arr.length === 1000)
     val rdd = sc.parallelize(arr)
     val strategy = new Strategy(Classification, Gini, 3, 2, 100)
     val metadata = DecisionTreeMetadata.buildMetadata(rdd, strategy)
+    assert(!metadata.isUnordered(featureIndex = 0))
     val (splits, bins) = DecisionTree.findSplitsBins(rdd, metadata)
     assert(splits.length === 2)
     assert(bins.length === 2)
@@ -72,7 +74,8 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
     assert(bins(0).length === 100)
   }
 
-  test("split and bin calculation for categorical variables") {
+  test("Binary classification with binary (ordered) categorical features:" +
+    " split and bin calculation") {
     val arr = DecisionTreeSuite.generateCategoricalDataPoints()
     assert(arr.length === 1000)
     val rdd = sc.parallelize(arr)
@@ -83,77 +86,20 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
       numClassesForClassification = 2,
       maxBins = 100,
       categoricalFeaturesInfo = Map(0 -> 2, 1-> 2))
+
     val metadata = DecisionTreeMetadata.buildMetadata(rdd, strategy)
     val (splits, bins) = DecisionTree.findSplitsBins(rdd, metadata)
+    assert(!metadata.isUnordered(featureIndex = 0))
+    assert(!metadata.isUnordered(featureIndex = 1))
     assert(splits.length === 2)
     assert(bins.length === 2)
-    assert(splits(0).length === 99)
-    assert(bins(0).length === 100)
-
-    // Check splits.
-
-    assert(splits(0)(0).feature === 0)
-    assert(splits(0)(0).threshold === Double.MinValue)
-    assert(splits(0)(0).featureType === Categorical)
-    assert(splits(0)(0).categories.length === 1)
-    assert(splits(0)(0).categories.contains(1.0))
-
-    assert(splits(0)(1).feature === 0)
-    assert(splits(0)(1).threshold === Double.MinValue)
-    assert(splits(0)(1).featureType === Categorical)
-    assert(splits(0)(1).categories.length === 2)
-    assert(splits(0)(1).categories.contains(1.0))
-    assert(splits(0)(1).categories.contains(0.0))
-
-    assert(splits(0)(2) === null)
-
-    assert(splits(1)(0).feature === 1)
-    assert(splits(1)(0).threshold === Double.MinValue)
-    assert(splits(1)(0).featureType === Categorical)
-    assert(splits(1)(0).categories.length === 1)
-    assert(splits(1)(0).categories.contains(0.0))
-
-    assert(splits(1)(1).feature === 1)
-    assert(splits(1)(1).threshold === Double.MinValue)
-    assert(splits(1)(1).featureType === Categorical)
-    assert(splits(1)(1).categories.length === 2)
-    assert(splits(1)(1).categories.contains(1.0))
-    assert(splits(1)(1).categories.contains(0.0))
-
-    assert(splits(1)(2) === null)
-
-    // Check bins.
-
-    assert(bins(0)(0).category === 1.0)
-    assert(bins(0)(0).lowSplit.categories.length === 0)
-    assert(bins(0)(0).highSplit.categories.length === 1)
-    assert(bins(0)(0).highSplit.categories.contains(1.0))
-
-    assert(bins(0)(1).category === 0.0)
-    assert(bins(0)(1).lowSplit.categories.length === 1)
-    assert(bins(0)(1).lowSplit.categories.contains(1.0))
-    assert(bins(0)(1).highSplit.categories.length === 2)
-    assert(bins(0)(1).highSplit.categories.contains(1.0))
-    assert(bins(0)(1).highSplit.categories.contains(0.0))
-
-    assert(bins(0)(2) === null)
-
-    assert(bins(1)(0).category === 0.0)
-    assert(bins(1)(0).lowSplit.categories.length === 0)
-    assert(bins(1)(0).highSplit.categories.length === 1)
-    assert(bins(1)(0).highSplit.categories.contains(0.0))
-
-    assert(bins(1)(1).category === 1.0)
-    assert(bins(1)(1).lowSplit.categories.length === 1)
-    assert(bins(1)(1).lowSplit.categories.contains(0.0))
-    assert(bins(1)(1).highSplit.categories.length === 2)
-    assert(bins(1)(1).highSplit.categories.contains(0.0))
-    assert(bins(1)(1).highSplit.categories.contains(1.0))
-
-    assert(bins(1)(2) === null)
+    // no bins or splits pre-computed for ordered categorical features
+    assert(splits(0).length === 0)
+    assert(bins(0).length === 0)
   }
 
-  test("split and bin calculations for categorical variables with no sample for one category") {
+  test("Binary classification with 3-ary (ordered) categorical features," +
+    " with no samples for one category") {
     val arr = DecisionTreeSuite.generateCategoricalDataPoints()
     assert(arr.length === 1000)
     val rdd = sc.parallelize(arr)
@@ -164,104 +110,16 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
       numClassesForClassification = 2,
       maxBins = 100,
       categoricalFeaturesInfo = Map(0 -> 3, 1 -> 3))
+
     val metadata = DecisionTreeMetadata.buildMetadata(rdd, strategy)
+    assert(!metadata.isUnordered(featureIndex = 0))
+    assert(!metadata.isUnordered(featureIndex = 1))
     val (splits, bins) = DecisionTree.findSplitsBins(rdd, metadata)
-
-    // Check splits.
-
-    assert(splits(0)(0).feature === 0)
-    assert(splits(0)(0).threshold === Double.MinValue)
-    assert(splits(0)(0).featureType === Categorical)
-    assert(splits(0)(0).categories.length === 1)
-    assert(splits(0)(0).categories.contains(1.0))
-
-    assert(splits(0)(1).feature === 0)
-    assert(splits(0)(1).threshold === Double.MinValue)
-    assert(splits(0)(1).featureType === Categorical)
-    assert(splits(0)(1).categories.length === 2)
-    assert(splits(0)(1).categories.contains(1.0))
-    assert(splits(0)(1).categories.contains(0.0))
-
-    assert(splits(0)(2).feature === 0)
-    assert(splits(0)(2).threshold === Double.MinValue)
-    assert(splits(0)(2).featureType === Categorical)
-    assert(splits(0)(2).categories.length === 3)
-    assert(splits(0)(2).categories.contains(1.0))
-    assert(splits(0)(2).categories.contains(0.0))
-    assert(splits(0)(2).categories.contains(2.0))
-
-    assert(splits(0)(3) === null)
-
-    assert(splits(1)(0).feature === 1)
-    assert(splits(1)(0).threshold === Double.MinValue)
-    assert(splits(1)(0).featureType === Categorical)
-    assert(splits(1)(0).categories.length === 1)
-    assert(splits(1)(0).categories.contains(0.0))
-
-    assert(splits(1)(1).feature === 1)
-    assert(splits(1)(1).threshold === Double.MinValue)
-    assert(splits(1)(1).featureType === Categorical)
-    assert(splits(1)(1).categories.length === 2)
-    assert(splits(1)(1).categories.contains(1.0))
-    assert(splits(1)(1).categories.contains(0.0))
-
-    assert(splits(1)(2).feature === 1)
-    assert(splits(1)(2).threshold === Double.MinValue)
-    assert(splits(1)(2).featureType === Categorical)
-    assert(splits(1)(2).categories.length === 3)
-    assert(splits(1)(2).categories.contains(1.0))
-    assert(splits(1)(2).categories.contains(0.0))
-    assert(splits(1)(2).categories.contains(2.0))
-
-    assert(splits(1)(3) === null)
-
-    // Check bins.
-
-    assert(bins(0)(0).category === 1.0)
-    assert(bins(0)(0).lowSplit.categories.length === 0)
-    assert(bins(0)(0).highSplit.categories.length === 1)
-    assert(bins(0)(0).highSplit.categories.contains(1.0))
-
-    assert(bins(0)(1).category === 0.0)
-    assert(bins(0)(1).lowSplit.categories.length === 1)
-    assert(bins(0)(1).lowSplit.categories.contains(1.0))
-    assert(bins(0)(1).highSplit.categories.length === 2)
-    assert(bins(0)(1).highSplit.categories.contains(1.0))
-    assert(bins(0)(1).highSplit.categories.contains(0.0))
-
-    assert(bins(0)(2).category === 2.0)
-    assert(bins(0)(2).lowSplit.categories.length === 2)
-    assert(bins(0)(2).lowSplit.categories.contains(1.0))
-    assert(bins(0)(2).lowSplit.categories.contains(0.0))
-    assert(bins(0)(2).highSplit.categories.length === 3)
-    assert(bins(0)(2).highSplit.categories.contains(1.0))
-    assert(bins(0)(2).highSplit.categories.contains(0.0))
-    assert(bins(0)(2).highSplit.categories.contains(2.0))
-
-    assert(bins(0)(3) === null)
-
-    assert(bins(1)(0).category === 0.0)
-    assert(bins(1)(0).lowSplit.categories.length === 0)
-    assert(bins(1)(0).highSplit.categories.length === 1)
-    assert(bins(1)(0).highSplit.categories.contains(0.0))
-
-    assert(bins(1)(1).category === 1.0)
-    assert(bins(1)(1).lowSplit.categories.length === 1)
-    assert(bins(1)(1).lowSplit.categories.contains(0.0))
-    assert(bins(1)(1).highSplit.categories.length === 2)
-    assert(bins(1)(1).highSplit.categories.contains(0.0))
-    assert(bins(1)(1).highSplit.categories.contains(1.0))
-
-    assert(bins(1)(2).category === 2.0)
-    assert(bins(1)(2).lowSplit.categories.length === 2)
-    assert(bins(1)(2).lowSplit.categories.contains(0.0))
-    assert(bins(1)(2).lowSplit.categories.contains(1.0))
-    assert(bins(1)(2).highSplit.categories.length === 3)
-    assert(bins(1)(2).highSplit.categories.contains(0.0))
-    assert(bins(1)(2).highSplit.categories.contains(1.0))
-    assert(bins(1)(2).highSplit.categories.contains(2.0))
-
-    assert(bins(1)(3) === null)
+    assert(splits.length === 2)
+    assert(bins.length === 2)
+    // no bins or splits pre-computed for ordered categorical features
+    assert(splits(0).length === 0)
+    assert(bins(0).length === 0)
   }
 
   test("extract categories from a number for multiclass classification") {
@@ -270,8 +128,8 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
     assert(List(3.0, 2.0, 0.0).toSeq === l.toSeq)
   }
 
-  test("split and bin calculations for unordered categorical variables with multiclass " +
-    "classification") {
+  test("Multiclass classification with unordered categorical features:" +
+      " split and bin calculations") {
     val arr = DecisionTreeSuite.generateCategoricalDataPoints()
     assert(arr.length === 1000)
     val rdd = sc.parallelize(arr)
@@ -282,8 +140,15 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
       numClassesForClassification = 100,
       maxBins = 100,
       categoricalFeaturesInfo = Map(0 -> 3, 1-> 3))
+
     val metadata = DecisionTreeMetadata.buildMetadata(rdd, strategy)
+    assert(metadata.isUnordered(featureIndex = 0))
+    assert(metadata.isUnordered(featureIndex = 1))
     val (splits, bins) = DecisionTree.findSplitsBins(rdd, metadata)
+    assert(splits.length === 2)
+    assert(bins.length === 2)
+    assert(splits(0).length === 3)
+    assert(bins(0).length === 6)
 
     // Expecting 2^2 - 1 = 3 bins/splits
     assert(splits(0)(0).feature === 0)
@@ -321,10 +186,6 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
     assert(splits(1)(2).categories.contains(0.0))
     assert(splits(1)(2).categories.contains(1.0))
 
-    assert(splits(0)(3) === null)
-    assert(splits(1)(3) === null)
-
-
     // Check bins.
 
     assert(bins(0)(0).category === Double.MinValue)
@@ -360,13 +221,9 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
     assert(bins(1)(2).highSplit.categories.contains(1.0))
     assert(bins(1)(2).highSplit.categories.contains(0.0))
 
-    assert(bins(0)(3) === null)
-    assert(bins(1)(3) === null)
-
   }
 
-  test("split and bin calculations for ordered categorical variables with multiclass " +
-    "classification") {
+  test("Multiclass classification with ordered categorical features: split and bin calculations") {
     val arr = DecisionTreeSuite.generateCategoricalDataPointsForMulticlassForOrderedFeatures()
     assert(arr.length === 3000)
     val rdd = sc.parallelize(arr)
@@ -377,52 +234,21 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
       numClassesForClassification = 100,
       maxBins = 100,
       categoricalFeaturesInfo = Map(0 -> 10, 1-> 10))
+    // 2^10 - 1 > 100, so categorical features will be ordered
+
     val metadata = DecisionTreeMetadata.buildMetadata(rdd, strategy)
+    assert(!metadata.isUnordered(featureIndex = 0))
+    assert(!metadata.isUnordered(featureIndex = 1))
     val (splits, bins) = DecisionTree.findSplitsBins(rdd, metadata)
-
-    // 2^10 - 1 > 100, so categorical variables will be ordered
-
-    assert(splits(0)(0).feature === 0)
-    assert(splits(0)(0).threshold === Double.MinValue)
-    assert(splits(0)(0).featureType === Categorical)
-    assert(splits(0)(0).categories.length === 1)
-    assert(splits(0)(0).categories.contains(1.0))
-
-    assert(splits(0)(1).feature === 0)
-    assert(splits(0)(1).threshold === Double.MinValue)
-    assert(splits(0)(1).featureType === Categorical)
-    assert(splits(0)(1).categories.length === 2)
-    assert(splits(0)(1).categories.contains(2.0))
-
-    assert(splits(0)(2).feature === 0)
-    assert(splits(0)(2).threshold === Double.MinValue)
-    assert(splits(0)(2).featureType === Categorical)
-    assert(splits(0)(2).categories.length === 3)
-    assert(splits(0)(2).categories.contains(2.0))
-    assert(splits(0)(2).categories.contains(1.0))
-
-    assert(splits(0)(10) === null)
-    assert(splits(1)(10) === null)
-
-
-    // Check bins.
-
-    assert(bins(0)(0).category === 1.0)
-    assert(bins(0)(0).lowSplit.categories.length === 0)
-    assert(bins(0)(0).highSplit.categories.length === 1)
-    assert(bins(0)(0).highSplit.categories.contains(1.0))
-    assert(bins(0)(1).category === 2.0)
-    assert(bins(0)(1).lowSplit.categories.length === 1)
-    assert(bins(0)(1).highSplit.categories.length === 2)
-    assert(bins(0)(1).highSplit.categories.contains(1.0))
-    assert(bins(0)(1).highSplit.categories.contains(2.0))
-
-    assert(bins(0)(10) === null)
-
+    assert(splits.length === 2)
+    assert(bins.length === 2)
+    // no bins or splits pre-computed for ordered categorical features
+    assert(splits(0).length === 0)
+    assert(bins(0).length === 0)
   }
 
 
-  test("classification stump with all categorical variables") {
+  test("Binary classification stump with ordered categorical features") {
     val arr = DecisionTreeSuite.generateCategoricalDataPoints()
     assert(arr.length === 1000)
     val rdd = sc.parallelize(arr)
@@ -433,15 +259,23 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
       maxDepth = 2,
       maxBins = 100,
       categoricalFeaturesInfo = Map(0 -> 3, 1-> 3))
+
     val metadata = DecisionTreeMetadata.buildMetadata(rdd, strategy)
+    assert(!metadata.isUnordered(featureIndex = 0))
+    assert(!metadata.isUnordered(featureIndex = 1))
     val (splits, bins) = DecisionTree.findSplitsBins(rdd, metadata)
+    assert(splits.length === 2)
+    assert(bins.length === 2)
+    // no bins or splits pre-computed for ordered categorical features
+    assert(splits(0).length === 0)
+    assert(bins(0).length === 0)
+
     val treeInput = TreePoint.convertToTreeRDD(rdd, bins, metadata)
-    val bestSplits = DecisionTree.findBestSplits(treeInput, new Array(7), metadata, 0,
+    val bestSplits = DecisionTree.findBestSplits(treeInput, new Array(8), metadata, 0,
       new Array[Node](0), splits, bins, 10)
 
     val split = bestSplits(0)._1
-    assert(split.categories.length === 1)
-    assert(split.categories.contains(1.0))
+    assert(split.categories === List(1.0))
     assert(split.featureType === Categorical)
     assert(split.threshold === Double.MinValue)
 
@@ -452,7 +286,7 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
     assert(stats.impurity > 0.2)
   }
 
-  test("regression stump with all categorical variables") {
+  test("Regression stump with 3-ary (ordered) categorical features") {
     val arr = DecisionTreeSuite.generateCategoricalDataPoints()
     assert(arr.length === 1000)
     val rdd = sc.parallelize(arr)
@@ -462,10 +296,14 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
       maxDepth = 2,
       maxBins = 100,
       categoricalFeaturesInfo = Map(0 -> 3, 1-> 3))
+
     val metadata = DecisionTreeMetadata.buildMetadata(rdd, strategy)
+    assert(!metadata.isUnordered(featureIndex = 0))
+    assert(!metadata.isUnordered(featureIndex = 1))
+
     val (splits, bins) = DecisionTree.findSplitsBins(rdd, metadata)
     val treeInput = TreePoint.convertToTreeRDD(rdd, bins, metadata)
-    val bestSplits = DecisionTree.findBestSplits(treeInput, new Array(7), metadata, 0,
+    val bestSplits = DecisionTree.findBestSplits(treeInput, new Array(8), metadata, 0,
       new Array[Node](0), splits, bins, 10)
 
     val split = bestSplits(0)._1
@@ -480,7 +318,7 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
     assert(stats.impurity > 0.2)
   }
 
-  test("regression stump with categorical variables of arity 2") {
+  test("Regression stump with binary (ordered) categorical features") {
     val arr = DecisionTreeSuite.generateCategoricalDataPoints()
     assert(arr.length === 1000)
     val rdd = sc.parallelize(arr)
@@ -490,6 +328,9 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
       maxDepth = 2,
       maxBins = 100,
       categoricalFeaturesInfo = Map(0 -> 2, 1-> 2))
+    val metadata = DecisionTreeMetadata.buildMetadata(rdd, strategy)
+    assert(!metadata.isUnordered(featureIndex = 0))
+    assert(!metadata.isUnordered(featureIndex = 1))
 
     val model = DecisionTree.train(rdd, strategy)
     validateRegressor(model, arr, 0.0)
@@ -497,12 +338,16 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
     assert(model.depth === 1)
   }
 
-  test("stump with fixed label 0 for Gini") {
+  test("Binary classification stump with fixed label 0 for Gini") {
     val arr = DecisionTreeSuite.generateOrderedLabeledPointsWithLabel0()
     assert(arr.length === 1000)
     val rdd = sc.parallelize(arr)
-    val strategy = new Strategy(Classification, Gini, 3, 2, 100)
+    val strategy = new Strategy(Classification, Gini, maxDepth = 3,
+      numClassesForClassification = 2, maxBins = 100)
     val metadata = DecisionTreeMetadata.buildMetadata(rdd, strategy)
+    assert(!metadata.isUnordered(featureIndex = 0))
+    assert(!metadata.isUnordered(featureIndex = 1))
+
     val (splits, bins) = DecisionTree.findSplitsBins(rdd, metadata)
     assert(splits.length === 2)
     assert(splits(0).length === 99)
@@ -512,7 +357,7 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
     assert(bins(0).length === 100)
 
     val treeInput = TreePoint.convertToTreeRDD(rdd, bins, metadata)
-    val bestSplits = DecisionTree.findBestSplits(treeInput, new Array(7), metadata, 0,
+    val bestSplits = DecisionTree.findBestSplits(treeInput, new Array(8), metadata, 0,
       new Array[Node](0), splits, bins, 10)
     assert(bestSplits.length === 1)
     assert(bestSplits(0)._1.feature === 0)
@@ -521,12 +366,16 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
     assert(bestSplits(0)._2.rightImpurity === 0)
   }
 
-  test("stump with fixed label 1 for Gini") {
+  test("Binary classification stump with fixed label 1 for Gini") {
     val arr = DecisionTreeSuite.generateOrderedLabeledPointsWithLabel1()
     assert(arr.length === 1000)
     val rdd = sc.parallelize(arr)
-    val strategy = new Strategy(Classification, Gini, 3, 2, 100)
+    val strategy = new Strategy(Classification, Gini, maxDepth = 3,
+      numClassesForClassification = 2, maxBins = 100)
     val metadata = DecisionTreeMetadata.buildMetadata(rdd, strategy)
+    assert(!metadata.isUnordered(featureIndex = 0))
+    assert(!metadata.isUnordered(featureIndex = 1))
+
     val (splits, bins) = DecisionTree.findSplitsBins(rdd, metadata)
     assert(splits.length === 2)
     assert(splits(0).length === 99)
@@ -536,7 +385,7 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
     assert(bins(0).length === 100)
 
     val treeInput = TreePoint.convertToTreeRDD(rdd, bins, metadata)
-    val bestSplits = DecisionTree.findBestSplits(treeInput, Array(0.0), metadata, 0,
+    val bestSplits = DecisionTree.findBestSplits(treeInput, new Array(2), metadata, 0,
       new Array[Node](0), splits, bins, 10)
     assert(bestSplits.length === 1)
     assert(bestSplits(0)._1.feature === 0)
@@ -546,12 +395,16 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
     assert(bestSplits(0)._2.predict === 1)
   }
 
-  test("stump with fixed label 0 for Entropy") {
+  test("Binary classification stump with fixed label 0 for Entropy") {
     val arr = DecisionTreeSuite.generateOrderedLabeledPointsWithLabel0()
     assert(arr.length === 1000)
     val rdd = sc.parallelize(arr)
-    val strategy = new Strategy(Classification, Entropy, 3, 2, 100)
+    val strategy = new Strategy(Classification, Entropy, maxDepth = 3,
+      numClassesForClassification = 2, maxBins = 100)
     val metadata = DecisionTreeMetadata.buildMetadata(rdd, strategy)
+    assert(!metadata.isUnordered(featureIndex = 0))
+    assert(!metadata.isUnordered(featureIndex = 1))
+
     val (splits, bins) = DecisionTree.findSplitsBins(rdd, metadata)
     assert(splits.length === 2)
     assert(splits(0).length === 99)
@@ -561,7 +414,7 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
     assert(bins(0).length === 100)
 
     val treeInput = TreePoint.convertToTreeRDD(rdd, bins, metadata)
-    val bestSplits = DecisionTree.findBestSplits(treeInput, Array(0.0), metadata, 0,
+    val bestSplits = DecisionTree.findBestSplits(treeInput, new Array(2), metadata, 0,
       new Array[Node](0), splits, bins, 10)
     assert(bestSplits.length === 1)
     assert(bestSplits(0)._1.feature === 0)
@@ -571,12 +424,16 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
     assert(bestSplits(0)._2.predict === 0)
   }
 
-  test("stump with fixed label 1 for Entropy") {
+  test("Binary classification stump with fixed label 1 for Entropy") {
     val arr = DecisionTreeSuite.generateOrderedLabeledPointsWithLabel1()
     assert(arr.length === 1000)
     val rdd = sc.parallelize(arr)
-    val strategy = new Strategy(Classification, Entropy, 3, 2, 100)
+    val strategy = new Strategy(Classification, Entropy, maxDepth = 3,
+      numClassesForClassification = 2, maxBins = 100)
     val metadata = DecisionTreeMetadata.buildMetadata(rdd, strategy)
+    assert(!metadata.isUnordered(featureIndex = 0))
+    assert(!metadata.isUnordered(featureIndex = 1))
+
     val (splits, bins) = DecisionTree.findSplitsBins(rdd, metadata)
     assert(splits.length === 2)
     assert(splits(0).length === 99)
@@ -586,7 +443,7 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
     assert(bins(0).length === 100)
 
     val treeInput = TreePoint.convertToTreeRDD(rdd, bins, metadata)
-    val bestSplits = DecisionTree.findBestSplits(treeInput, Array(0.0), metadata, 0,
+    val bestSplits = DecisionTree.findBestSplits(treeInput, new Array(2), metadata, 0,
       new Array[Node](0), splits, bins, 10)
     assert(bestSplits.length === 1)
     assert(bestSplits(0)._1.feature === 0)
@@ -596,7 +453,7 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
     assert(bestSplits(0)._2.predict === 1)
   }
 
-  test("second level node building with/without groups") {
+  test("Second level node building with vs. without groups") {
     val arr = DecisionTreeSuite.generateOrderedLabeledPoints()
     assert(arr.length === 1000)
     val rdd = sc.parallelize(arr)
@@ -613,12 +470,12 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
     // Train a 1-node model
     val strategyOneNode = new Strategy(Classification, Entropy, 1, 2, 100)
     val modelOneNode = DecisionTree.train(rdd, strategyOneNode)
-    val nodes: Array[Node] = new Array[Node](7)
-    nodes(0) = modelOneNode.topNode
-    nodes(0).leftNode = None
-    nodes(0).rightNode = None
+    val nodes: Array[Node] = new Array[Node](8)
+    nodes(1) = modelOneNode.topNode
+    nodes(1).leftNode = None
+    nodes(1).rightNode = None
 
-    val parentImpurities = Array(0.5, 0.5, 0.5)
+    val parentImpurities = Array(0, 0.5, 0.5, 0.5)
 
     // Single group second level tree construction.
     val treeInput = TreePoint.convertToTreeRDD(rdd, bins, metadata)
@@ -648,16 +505,19 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
     }
   }
 
-  test("stump with categorical variables for multiclass classification") {
+  test("Multiclass classification stump with 3-ary (unordered) categorical features") {
     val arr = DecisionTreeSuite.generateCategoricalDataPointsForMulticlass()
     val rdd = sc.parallelize(arr)
     val strategy = new Strategy(algo = Classification, impurity = Gini, maxDepth = 4,
       numClassesForClassification = 3, categoricalFeaturesInfo = Map(0 -> 3, 1 -> 3))
     val metadata = DecisionTreeMetadata.buildMetadata(rdd, strategy)
     assert(strategy.isMulticlassClassification)
+    assert(metadata.isUnordered(featureIndex = 0))
+    assert(metadata.isUnordered(featureIndex = 1))
+
     val (splits, bins) = DecisionTree.findSplitsBins(rdd, metadata)
     val treeInput = TreePoint.convertToTreeRDD(rdd, bins, metadata)
-    val bestSplits = DecisionTree.findBestSplits(treeInput, new Array(31), metadata, 0,
+    val bestSplits = DecisionTree.findBestSplits(treeInput, new Array(32), metadata, 0,
       new Array[Node](0), splits, bins, 10)
 
     assert(bestSplits.length === 1)
@@ -668,7 +528,7 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
     assert(bestSplit.featureType === Categorical)
   }
 
-  test("stump with 1 continuous variable for binary classification, to check off-by-1 error") {
+  test("Binary classification stump with 1 continuous feature, to check off-by-1 error") {
     val arr = new Array[LabeledPoint](4)
     arr(0) = new LabeledPoint(0.0, Vectors.dense(0.0))
     arr(1) = new LabeledPoint(1.0, Vectors.dense(1.0))
@@ -684,26 +544,27 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
     assert(model.depth === 1)
   }
 
-  test("stump with 2 continuous variables for binary classification") {
+  test("Binary classification stump with 2 continuous features") {
     val arr = new Array[LabeledPoint](4)
     arr(0) = new LabeledPoint(0.0, Vectors.sparse(2, Seq((0, 0.0))))
     arr(1) = new LabeledPoint(1.0, Vectors.sparse(2, Seq((1, 1.0))))
     arr(2) = new LabeledPoint(0.0, Vectors.sparse(2, Seq((0, 0.0))))
     arr(3) = new LabeledPoint(1.0, Vectors.sparse(2, Seq((1, 2.0))))
 
-    val input = sc.parallelize(arr)
+    val rdd = sc.parallelize(arr)
     val strategy = new Strategy(algo = Classification, impurity = Gini, maxDepth = 4,
       numClassesForClassification = 2)
 
-    val model = DecisionTree.train(input, strategy)
+    val model = DecisionTree.train(rdd, strategy)
     validateClassifier(model, arr, 1.0)
     assert(model.numNodes === 3)
     assert(model.depth === 1)
     assert(model.topNode.split.get.feature === 1)
   }
 
-  test("stump with categorical variables for multiclass classification, with just enough bins") {
-    val maxBins = math.pow(2, 3 - 1).toInt // just enough bins to allow unordered features
+  test("Multiclass classification stump with unordered categorical features," +
+    " with just enough bins") {
+    val maxBins = 2 * (math.pow(2, 3 - 1).toInt - 1) // just enough bins to allow unordered features
     val arr = DecisionTreeSuite.generateCategoricalDataPointsForMulticlass()
     val rdd = sc.parallelize(arr)
     val strategy = new Strategy(algo = Classification, impurity = Gini, maxDepth = 4,
@@ -711,6 +572,8 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
       categoricalFeaturesInfo = Map(0 -> 3, 1 -> 3))
     assert(strategy.isMulticlassClassification)
     val metadata = DecisionTreeMetadata.buildMetadata(rdd, strategy)
+    assert(metadata.isUnordered(featureIndex = 0))
+    assert(metadata.isUnordered(featureIndex = 1))
 
     val model = DecisionTree.train(rdd, strategy)
     validateClassifier(model, arr, 1.0)
@@ -719,7 +582,7 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
 
     val (splits, bins) = DecisionTree.findSplitsBins(rdd, metadata)
     val treeInput = TreePoint.convertToTreeRDD(rdd, bins, metadata)
-    val bestSplits = DecisionTree.findBestSplits(treeInput, new Array(31), metadata, 0,
+    val bestSplits = DecisionTree.findBestSplits(treeInput, new Array(32), metadata, 0,
       new Array[Node](0), splits, bins, 10)
 
     assert(bestSplits.length === 1)
@@ -733,7 +596,7 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
     assert(gain.rightImpurity === 0)
   }
 
-  test("stump with continuous variables for multiclass classification") {
+  test("Multiclass classification stump with continuous features") {
     val arr = DecisionTreeSuite.generateContinuousDataPointsForMulticlass()
     val rdd = sc.parallelize(arr)
     val strategy = new Strategy(algo = Classification, impurity = Gini, maxDepth = 4,
@@ -746,7 +609,7 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
 
     val (splits, bins) = DecisionTree.findSplitsBins(rdd, metadata)
     val treeInput = TreePoint.convertToTreeRDD(rdd, bins, metadata)
-    val bestSplits = DecisionTree.findBestSplits(treeInput, new Array(31), metadata, 0,
+    val bestSplits = DecisionTree.findBestSplits(treeInput, new Array(32), metadata, 0,
       new Array[Node](0), splits, bins, 10)
 
     assert(bestSplits.length === 1)
@@ -759,20 +622,21 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
 
   }
 
-  test("stump with continuous + categorical variables for multiclass classification") {
+  test("Multiclass classification stump with continuous + unordered categorical features") {
     val arr = DecisionTreeSuite.generateContinuousDataPointsForMulticlass()
     val rdd = sc.parallelize(arr)
     val strategy = new Strategy(algo = Classification, impurity = Gini, maxDepth = 4,
       numClassesForClassification = 3, categoricalFeaturesInfo = Map(0 -> 3))
     assert(strategy.isMulticlassClassification)
     val metadata = DecisionTreeMetadata.buildMetadata(rdd, strategy)
+    assert(metadata.isUnordered(featureIndex = 0))
 
     val model = DecisionTree.train(rdd, strategy)
     validateClassifier(model, arr, 0.9)
 
     val (splits, bins) = DecisionTree.findSplitsBins(rdd, metadata)
     val treeInput = TreePoint.convertToTreeRDD(rdd, bins, metadata)
-    val bestSplits = DecisionTree.findBestSplits(treeInput, new Array(31), metadata, 0,
+    val bestSplits = DecisionTree.findBestSplits(treeInput, new Array(32), metadata, 0,
       new Array[Node](0), splits, bins, 10)
 
     assert(bestSplits.length === 1)
@@ -784,17 +648,19 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
     assert(bestSplit.threshold < 2020)
   }
 
-  test("stump with categorical variables for ordered multiclass classification") {
+  test("Multiclass classification stump with 10-ary (ordered) categorical features") {
     val arr = DecisionTreeSuite.generateCategoricalDataPointsForMulticlassForOrderedFeatures()
     val rdd = sc.parallelize(arr)
     val strategy = new Strategy(algo = Classification, impurity = Gini, maxDepth = 4,
       numClassesForClassification = 3, categoricalFeaturesInfo = Map(0 -> 10, 1 -> 10))
     assert(strategy.isMulticlassClassification)
     val metadata = DecisionTreeMetadata.buildMetadata(rdd, strategy)
+    assert(!metadata.isUnordered(featureIndex = 0))
+    assert(!metadata.isUnordered(featureIndex = 1))
 
     val (splits, bins) = DecisionTree.findSplitsBins(rdd, metadata)
     val treeInput = TreePoint.convertToTreeRDD(rdd, bins, metadata)
-    val bestSplits = DecisionTree.findBestSplits(treeInput, new Array(31), metadata, 0,
+    val bestSplits = DecisionTree.findBestSplits(treeInput, new Array(32), metadata, 0,
       new Array[Node](0), splits, bins, 10)
 
     assert(bestSplits.length === 1)
@@ -805,6 +671,18 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
     assert(bestSplit.featureType === Categorical)
   }
 
+  test("Multiclass classification tree with 10-ary (ordered) categorical features," +
+      " with just enough bins") {
+    val arr = DecisionTreeSuite.generateCategoricalDataPointsForMulticlassForOrderedFeatures()
+    val rdd = sc.parallelize(arr)
+    val strategy = new Strategy(algo = Classification, impurity = Gini, maxDepth = 4,
+      numClassesForClassification = 3, maxBins = 10,
+      categoricalFeaturesInfo = Map(0 -> 10, 1 -> 10))
+    assert(strategy.isMulticlassClassification)
+
+    val model = DecisionTree.train(rdd, strategy)
+    validateClassifier(model, arr, 0.6)
+  }
 
 }
 
@@ -899,5 +777,4 @@ object DecisionTreeSuite {
     arr
   }
 
-
 }

From e16a8e7db5a3b1065b14baf89cb723a59b99226b Mon Sep 17 00:00:00 2001
From: Prashant Sharma <prashant.s@imaginea.com>
Date: Mon, 8 Sep 2014 10:24:15 -0700
Subject: [PATCH 232/489] SPARK-3337 Paranoid quoting in shell to allow install
 dirs with spaces within.

...

Tested ! TBH, it isn't a great idea to have directory with spaces within. Because emacs doesn't like it then hadoop doesn't like it. and so on...

Author: Prashant Sharma <prashant.s@imaginea.com>

Closes #2229 from ScrapCodes/SPARK-3337/quoting-shell-scripts and squashes the following commits:

d4ad660 [Prashant Sharma] SPARK-3337 Paranoid quoting in shell to allow install dirs with spaces within.
---
 bin/beeline                  |  2 +-
 bin/compute-classpath.sh     | 12 ++++++------
 bin/load-spark-env.sh        |  4 ++--
 bin/pyspark                  | 20 ++++++++++----------
 bin/run-example              |  8 ++++----
 bin/spark-class              | 20 ++++++++++----------
 bin/spark-shell              | 10 +++++-----
 bin/spark-sql                |  8 ++++----
 bin/spark-submit             |  4 ++--
 dev/check-license            | 16 ++++++++--------
 dev/lint-python              |  6 +++---
 dev/mima                     |  4 ++--
 dev/run-tests                |  2 +-
 dev/scalastyle               |  2 +-
 make-distribution.sh         |  2 +-
 python/run-tests             |  6 ++++--
 sbin/slaves.sh               | 12 ++++++------
 sbin/spark-config.sh         | 16 ++++++++--------
 sbin/spark-daemon.sh         | 20 ++++++++++----------
 sbin/spark-executor          |  8 ++++----
 sbin/start-all.sh            |  4 ++--
 sbin/start-history-server.sh |  4 ++--
 sbin/start-master.sh         |  4 ++--
 sbin/start-slave.sh          |  4 ++--
 sbin/start-slaves.sh         | 12 ++++++------
 sbin/start-thriftserver.sh   |  8 ++++----
 sbin/stop-all.sh             |  4 ++--
 sbin/stop-history-server.sh  |  4 ++--
 sbt/sbt                      | 20 ++++++++++----------
 sbt/sbt-launch-lib.bash      | 12 ++++++------
 30 files changed, 130 insertions(+), 128 deletions(-)

diff --git a/bin/beeline b/bin/beeline
index 1bda4dba50605..3fcb6df34339d 100755
--- a/bin/beeline
+++ b/bin/beeline
@@ -24,7 +24,7 @@
 set -o posix
 
 # Figure out where Spark is installed
-FWDIR="$(cd `dirname $0`/..; pwd)"
+FWDIR="$(cd "`dirname "$0"`"/..; pwd)"
 
 CLASS="org.apache.hive.beeline.BeeLine"
 exec "$FWDIR/bin/spark-class" $CLASS "$@"
diff --git a/bin/compute-classpath.sh b/bin/compute-classpath.sh
index 16b794a1592e8..15c6779402994 100755
--- a/bin/compute-classpath.sh
+++ b/bin/compute-classpath.sh
@@ -23,9 +23,9 @@
 SCALA_VERSION=2.10
 
 # Figure out where Spark is installed
-FWDIR="$(cd `dirname $0`/..; pwd)"
+FWDIR="$(cd "`dirname "$0"`"/..; pwd)"
 
-. $FWDIR/bin/load-spark-env.sh
+. "$FWDIR"/bin/load-spark-env.sh
 
 # Build up classpath
 CLASSPATH="$SPARK_CLASSPATH:$SPARK_SUBMIT_CLASSPATH:$FWDIR/conf"
@@ -63,7 +63,7 @@ else
   assembly_folder="$ASSEMBLY_DIR"
 fi
 
-num_jars=$(ls "$assembly_folder" | grep "spark-assembly.*hadoop.*\.jar" | wc -l)
+num_jars="$(ls "$assembly_folder" | grep "spark-assembly.*hadoop.*\.jar" | wc -l)"
 if [ "$num_jars" -eq "0" ]; then
   echo "Failed to find Spark assembly in $assembly_folder"
   echo "You need to build Spark before running this program."
@@ -77,7 +77,7 @@ if [ "$num_jars" -gt "1" ]; then
   exit 1
 fi
 
-ASSEMBLY_JAR=$(ls "$assembly_folder"/spark-assembly*hadoop*.jar 2>/dev/null)
+ASSEMBLY_JAR="$(ls "$assembly_folder"/spark-assembly*hadoop*.jar 2>/dev/null)"
 
 # Verify that versions of java used to build the jars and run Spark are compatible
 jar_error_check=$("$JAR_CMD" -tf "$ASSEMBLY_JAR" nonexistent/class/path 2>&1)
@@ -103,8 +103,8 @@ else
   datanucleus_dir="$FWDIR"/lib_managed/jars
 fi
 
-datanucleus_jars=$(find "$datanucleus_dir" 2>/dev/null | grep "datanucleus-.*\\.jar")
-datanucleus_jars=$(echo "$datanucleus_jars" | tr "\n" : | sed s/:$//g)
+datanucleus_jars="$(find "$datanucleus_dir" 2>/dev/null | grep "datanucleus-.*\\.jar")"
+datanucleus_jars="$(echo "$datanucleus_jars" | tr "\n" : | sed s/:$//g)"
 
 if [ -n "$datanucleus_jars" ]; then
   hive_files=$("$JAR_CMD" -tf "$ASSEMBLY_JAR" org/apache/hadoop/hive/ql/exec 2>/dev/null)
diff --git a/bin/load-spark-env.sh b/bin/load-spark-env.sh
index 493d3785a081b..6d4231b204595 100644
--- a/bin/load-spark-env.sh
+++ b/bin/load-spark-env.sh
@@ -25,9 +25,9 @@ if [ -z "$SPARK_ENV_LOADED" ]; then
   export SPARK_ENV_LOADED=1
 
   # Returns the parent of the directory this script lives in.
-  parent_dir="$(cd `dirname $0`/..; pwd)"
+  parent_dir="$(cd "`dirname "$0"`"/..; pwd)"
 
-  user_conf_dir=${SPARK_CONF_DIR:-"$parent_dir/conf"}
+  user_conf_dir="${SPARK_CONF_DIR:-"$parent_dir"/conf}"
 
   if [ -f "${user_conf_dir}/spark-env.sh" ]; then
     # Promote all variable declarations to environment (exported) variables
diff --git a/bin/pyspark b/bin/pyspark
index 26a16dd600b7a..5142411e36974 100755
--- a/bin/pyspark
+++ b/bin/pyspark
@@ -18,18 +18,18 @@
 #
 
 # Figure out where Spark is installed
-FWDIR="$(cd `dirname $0`/..; pwd)"
+FWDIR="$(cd "`dirname "$0"`"/..; pwd)"
 
 # Export this as SPARK_HOME
 export SPARK_HOME="$FWDIR"
 
-source $FWDIR/bin/utils.sh
+source "$FWDIR/bin/utils.sh"
 
 SCALA_VERSION=2.10
 
 function usage() {
   echo "Usage: ./bin/pyspark [options]" 1>&2
-  $FWDIR/bin/spark-submit --help 2>&1 | grep -v Usage 1>&2
+  "$FWDIR"/bin/spark-submit --help 2>&1 | grep -v Usage 1>&2
   exit 0
 }
 
@@ -48,7 +48,7 @@ if [ ! -f "$FWDIR/RELEASE" ]; then
   fi
 fi
 
-. $FWDIR/bin/load-spark-env.sh
+. "$FWDIR"/bin/load-spark-env.sh
 
 # Figure out which Python executable to use
 if [[ -z "$PYSPARK_PYTHON" ]]; then
@@ -57,12 +57,12 @@ fi
 export PYSPARK_PYTHON
 
 # Add the PySpark classes to the Python path:
-export PYTHONPATH=$SPARK_HOME/python/:$PYTHONPATH
-export PYTHONPATH=$SPARK_HOME/python/lib/py4j-0.8.2.1-src.zip:$PYTHONPATH
+export PYTHONPATH="$SPARK_HOME/python/:$PYTHONPATH"
+export PYTHONPATH="$SPARK_HOME/python/lib/py4j-0.8.2.1-src.zip:$PYTHONPATH"
 
 # Load the PySpark shell.py script when ./pyspark is used interactively:
-export OLD_PYTHONSTARTUP=$PYTHONSTARTUP
-export PYTHONSTARTUP=$FWDIR/python/pyspark/shell.py
+export OLD_PYTHONSTARTUP="$PYTHONSTARTUP"
+export PYTHONSTARTUP="$FWDIR/python/pyspark/shell.py"
 
 # If IPython options are specified, assume user wants to run IPython
 if [[ -n "$IPYTHON_OPTS" ]]; then
@@ -99,10 +99,10 @@ fi
 if [[ "$1" =~ \.py$ ]]; then
   echo -e "\nWARNING: Running python applications through ./bin/pyspark is deprecated as of Spark 1.0." 1>&2
   echo -e "Use ./bin/spark-submit <python file>\n" 1>&2
-  primary=$1
+  primary="$1"
   shift
   gatherSparkSubmitOpts "$@"
-  exec $FWDIR/bin/spark-submit "${SUBMISSION_OPTS[@]}" $primary "${APPLICATION_OPTS[@]}"
+  exec "$FWDIR"/bin/spark-submit "${SUBMISSION_OPTS[@]}" "$primary" "${APPLICATION_OPTS[@]}"
 else
   # PySpark shell requires special handling downstream
   export PYSPARK_SHELL=1
diff --git a/bin/run-example b/bin/run-example
index 68a35702eddd3..34dd71c71880e 100755
--- a/bin/run-example
+++ b/bin/run-example
@@ -19,7 +19,7 @@
 
 SCALA_VERSION=2.10
 
-FWDIR="$(cd `dirname $0`/..; pwd)"
+FWDIR="$(cd "`dirname "$0"`"/..; pwd)"
 export SPARK_HOME="$FWDIR"
 EXAMPLES_DIR="$FWDIR"/examples
 
@@ -35,12 +35,12 @@ else
 fi
 
 if [ -f "$FWDIR/RELEASE" ]; then
-  export SPARK_EXAMPLES_JAR=`ls "$FWDIR"/lib/spark-examples-*hadoop*.jar`
+  export SPARK_EXAMPLES_JAR="`ls "$FWDIR"/lib/spark-examples-*hadoop*.jar`"
 elif [ -e "$EXAMPLES_DIR"/target/scala-$SCALA_VERSION/spark-examples-*hadoop*.jar ]; then
-  export SPARK_EXAMPLES_JAR=`ls "$EXAMPLES_DIR"/target/scala-$SCALA_VERSION/spark-examples-*hadoop*.jar`
+  export SPARK_EXAMPLES_JAR="`ls "$EXAMPLES_DIR"/target/scala-$SCALA_VERSION/spark-examples-*hadoop*.jar`"
 fi
 
-if [[ -z $SPARK_EXAMPLES_JAR ]]; then
+if [[ -z "$SPARK_EXAMPLES_JAR" ]]; then
   echo "Failed to find Spark examples assembly in $FWDIR/lib or $FWDIR/examples/target" 1>&2
   echo "You need to build Spark before running this program" 1>&2
   exit 1
diff --git a/bin/spark-class b/bin/spark-class
index c6543545a5e64..5f5f9ea74888d 100755
--- a/bin/spark-class
+++ b/bin/spark-class
@@ -27,12 +27,12 @@ esac
 SCALA_VERSION=2.10
 
 # Figure out where Spark is installed
-FWDIR="$(cd `dirname $0`/..; pwd)"
+FWDIR="$(cd "`dirname "$0"`"/..; pwd)"
 
 # Export this as SPARK_HOME
 export SPARK_HOME="$FWDIR"
 
-. $FWDIR/bin/load-spark-env.sh
+. "$FWDIR"/bin/load-spark-env.sh
 
 if [ -z "$1" ]; then
   echo "Usage: spark-class <class> [<args>]" 1>&2
@@ -105,7 +105,7 @@ else
     exit 1
   fi
 fi
-JAVA_VERSION=$($RUNNER -version 2>&1 | sed 's/java version "\(.*\)\.\(.*\)\..*"/\1\2/; 1q')
+JAVA_VERSION=$("$RUNNER" -version 2>&1 | sed 's/java version "\(.*\)\.\(.*\)\..*"/\1\2/; 1q')
 
 # Set JAVA_OPTS to be able to load native libraries and to set heap size
 if [ "$JAVA_VERSION" -ge 18 ]; then
@@ -117,7 +117,7 @@ JAVA_OPTS="$JAVA_OPTS -Xms$OUR_JAVA_MEM -Xmx$OUR_JAVA_MEM"
 
 # Load extra JAVA_OPTS from conf/java-opts, if it exists
 if [ -e "$FWDIR/conf/java-opts" ] ; then
-  JAVA_OPTS="$JAVA_OPTS `cat $FWDIR/conf/java-opts`"
+  JAVA_OPTS="$JAVA_OPTS `cat "$FWDIR"/conf/java-opts`"
 fi
 
 # Attention: when changing the way the JAVA_OPTS are assembled, the change must be reflected in CommandUtils.scala!
@@ -126,21 +126,21 @@ TOOLS_DIR="$FWDIR"/tools
 SPARK_TOOLS_JAR=""
 if [ -e "$TOOLS_DIR"/target/scala-$SCALA_VERSION/spark-tools*[0-9Tg].jar ]; then
   # Use the JAR from the SBT build
-  export SPARK_TOOLS_JAR=`ls "$TOOLS_DIR"/target/scala-$SCALA_VERSION/spark-tools*[0-9Tg].jar`
+  export SPARK_TOOLS_JAR="`ls "$TOOLS_DIR"/target/scala-$SCALA_VERSION/spark-tools*[0-9Tg].jar`"
 fi
 if [ -e "$TOOLS_DIR"/target/spark-tools*[0-9Tg].jar ]; then
   # Use the JAR from the Maven build
   # TODO: this also needs to become an assembly!
-  export SPARK_TOOLS_JAR=`ls "$TOOLS_DIR"/target/spark-tools*[0-9Tg].jar`
+  export SPARK_TOOLS_JAR="`ls "$TOOLS_DIR"/target/spark-tools*[0-9Tg].jar`"
 fi
 
 # Compute classpath using external script
-classpath_output=$($FWDIR/bin/compute-classpath.sh)
+classpath_output=$("$FWDIR"/bin/compute-classpath.sh)
 if [[ "$?" != "0" ]]; then
   echo "$classpath_output"
   exit 1
 else
-  CLASSPATH=$classpath_output
+  CLASSPATH="$classpath_output"
 fi
 
 if [[ "$1" =~ org.apache.spark.tools.* ]]; then
@@ -153,9 +153,9 @@ if [[ "$1" =~ org.apache.spark.tools.* ]]; then
 fi
 
 if $cygwin; then
-  CLASSPATH=`cygpath -wp $CLASSPATH`
+  CLASSPATH="`cygpath -wp "$CLASSPATH"`"
   if [ "$1" == "org.apache.spark.tools.JavaAPICompletenessChecker" ]; then
-    export SPARK_TOOLS_JAR=`cygpath -w $SPARK_TOOLS_JAR`
+    export SPARK_TOOLS_JAR="`cygpath -w "$SPARK_TOOLS_JAR"`"
   fi
 fi
 export CLASSPATH
diff --git a/bin/spark-shell b/bin/spark-shell
index 0ab4e14f5b744..4a0670fc6c8aa 100755
--- a/bin/spark-shell
+++ b/bin/spark-shell
@@ -29,11 +29,11 @@ esac
 set -o posix
 
 ## Global script variables
-FWDIR="$(cd `dirname $0`/..; pwd)"
+FWDIR="$(cd "`dirname "$0"`"/..; pwd)"
 
 function usage() {
   echo "Usage: ./bin/spark-shell [options]"
-  $FWDIR/bin/spark-submit --help 2>&1 | grep -v Usage 1>&2
+  "$FWDIR"/bin/spark-submit --help 2>&1 | grep -v Usage 1>&2
   exit 0
 }
 
@@ -41,7 +41,7 @@ if [[ "$@" = *--help ]] || [[ "$@" = *-h ]]; then
   usage
 fi
 
-source $FWDIR/bin/utils.sh
+source "$FWDIR"/bin/utils.sh
 SUBMIT_USAGE_FUNCTION=usage
 gatherSparkSubmitOpts "$@"
 
@@ -54,11 +54,11 @@ function main() {
     # (see https://github.com/sbt/sbt/issues/562).
     stty -icanon min 1 -echo > /dev/null 2>&1
     export SPARK_SUBMIT_OPTS="$SPARK_SUBMIT_OPTS -Djline.terminal=unix"
-    $FWDIR/bin/spark-submit --class org.apache.spark.repl.Main "${SUBMISSION_OPTS[@]}" spark-shell "${APPLICATION_OPTS[@]}"
+    "$FWDIR"/bin/spark-submit --class org.apache.spark.repl.Main "${SUBMISSION_OPTS[@]}" spark-shell "${APPLICATION_OPTS[@]}"
     stty icanon echo > /dev/null 2>&1
   else
     export SPARK_SUBMIT_OPTS
-    $FWDIR/bin/spark-submit --class org.apache.spark.repl.Main "${SUBMISSION_OPTS[@]}" spark-shell "${APPLICATION_OPTS[@]}"
+    "$FWDIR"/bin/spark-submit --class org.apache.spark.repl.Main "${SUBMISSION_OPTS[@]}" spark-shell "${APPLICATION_OPTS[@]}"
   fi
 }
 
diff --git a/bin/spark-sql b/bin/spark-sql
index 2a3cb31f58e8d..ae096530cad04 100755
--- a/bin/spark-sql
+++ b/bin/spark-sql
@@ -27,7 +27,7 @@ CLASS="org.apache.spark.sql.hive.thriftserver.SparkSQLCLIDriver"
 CLASS_NOT_FOUND_EXIT_STATUS=1
 
 # Figure out where Spark is installed
-FWDIR="$(cd `dirname $0`/..; pwd)"
+FWDIR="$(cd "`dirname "$0"`"/..; pwd)"
 
 function usage {
   echo "Usage: ./bin/spark-sql [options] [cli option]"
@@ -38,10 +38,10 @@ function usage {
   pattern+="\|--help"
   pattern+="\|======="
 
-  $FWDIR/bin/spark-submit --help 2>&1 | grep -v Usage 1>&2
+  "$FWDIR"/bin/spark-submit --help 2>&1 | grep -v Usage 1>&2
   echo
   echo "CLI options:"
-  $FWDIR/bin/spark-class $CLASS --help 2>&1 | grep -v "$pattern" 1>&2
+  "$FWDIR"/bin/spark-class $CLASS --help 2>&1 | grep -v "$pattern" 1>&2
 }
 
 if [[ "$@" = *--help ]] || [[ "$@" = *-h ]]; then
@@ -49,7 +49,7 @@ if [[ "$@" = *--help ]] || [[ "$@" = *-h ]]; then
   exit 0
 fi
 
-source $FWDIR/bin/utils.sh
+source "$FWDIR"/bin/utils.sh
 SUBMIT_USAGE_FUNCTION=usage
 gatherSparkSubmitOpts "$@"
 
diff --git a/bin/spark-submit b/bin/spark-submit
index 277c4ce571ca2..c557311b4b20e 100755
--- a/bin/spark-submit
+++ b/bin/spark-submit
@@ -19,7 +19,7 @@
 
 # NOTE: Any changes in this file must be reflected in SparkSubmitDriverBootstrapper.scala!
 
-export SPARK_HOME="$(cd `dirname $0`/..; pwd)"
+export SPARK_HOME="$(cd "`dirname "$0"`"/..; pwd)"
 ORIG_ARGS=("$@")
 
 while (($#)); do
@@ -59,5 +59,5 @@ if [[ "$SPARK_SUBMIT_DEPLOY_MODE" == "client" && -f "$SPARK_SUBMIT_PROPERTIES_FI
   fi
 fi
 
-exec $SPARK_HOME/bin/spark-class org.apache.spark.deploy.SparkSubmit "${ORIG_ARGS[@]}"
+exec "$SPARK_HOME"/bin/spark-class org.apache.spark.deploy.SparkSubmit "${ORIG_ARGS[@]}"
 
diff --git a/dev/check-license b/dev/check-license
index 558e038afc01a..9ff0929e9a5e8 100755
--- a/dev/check-license
+++ b/dev/check-license
@@ -23,16 +23,16 @@ acquire_rat_jar () {
   URL1="http://search.maven.org/remotecontent?filepath=org/apache/rat/apache-rat/${RAT_VERSION}/apache-rat-${RAT_VERSION}.jar"
   URL2="http://repo1.maven.org/maven2/org/apache/rat/apache-rat/${RAT_VERSION}/apache-rat-${RAT_VERSION}.jar"
 
-  JAR=$rat_jar
+  JAR="$rat_jar"
   
   if [[ ! -f "$rat_jar" ]]; then
     # Download rat launch jar if it hasn't been downloaded yet
     if [ ! -f "$JAR" ]; then
     # Download
     printf "Attempting to fetch rat\n"
-    JAR_DL=${JAR}.part
+    JAR_DL="${JAR}.part"
     if hash curl 2>/dev/null; then
-      (curl --silent ${URL1} > "$JAR_DL" || curl --silent ${URL2} > "$JAR_DL") && mv "$JAR_DL" "$JAR"
+      (curl --silent "${URL1}" > "$JAR_DL" || curl --silent "${URL2}" > "$JAR_DL") && mv "$JAR_DL" "$JAR"
     elif hash wget 2>/dev/null; then
       (wget --quiet ${URL1} -O "$JAR_DL" || wget --quiet ${URL2} -O "$JAR_DL") && mv "$JAR_DL" "$JAR"
     else
@@ -50,7 +50,7 @@ acquire_rat_jar () {
 }
 
 # Go to the Spark project root directory
-FWDIR="$(cd `dirname $0`/..; pwd)"
+FWDIR="$(cd "`dirname "$0"`"/..; pwd)"
 cd "$FWDIR"
 
 if test -x "$JAVA_HOME/bin/java"; then
@@ -60,17 +60,17 @@ else
 fi
 
 export RAT_VERSION=0.10
-export rat_jar=$FWDIR/lib/apache-rat-${RAT_VERSION}.jar
-mkdir -p $FWDIR/lib
+export rat_jar="$FWDIR"/lib/apache-rat-${RAT_VERSION}.jar
+mkdir -p "$FWDIR"/lib
 
 [[ -f "$rat_jar" ]] || acquire_rat_jar || {
     echo "Download failed. Obtain the rat jar manually and place it at $rat_jar"
     exit 1
 }
 
-$java_cmd -jar $rat_jar -E $FWDIR/.rat-excludes  -d $FWDIR > rat-results.txt
+$java_cmd -jar "$rat_jar" -E "$FWDIR"/.rat-excludes  -d "$FWDIR" > rat-results.txt
 
-ERRORS=$(cat rat-results.txt | grep -e "??")
+ERRORS="$(cat rat-results.txt | grep -e "??")"
 
 if test ! -z "$ERRORS"; then 
     echo "Could not find Apache license headers in the following files:"
diff --git a/dev/lint-python b/dev/lint-python
index 79bf70f0b8b13..772f856154ae0 100755
--- a/dev/lint-python
+++ b/dev/lint-python
@@ -18,10 +18,10 @@
 #
 
 SCRIPT_DIR="$( cd "$( dirname "$0" )" && pwd )"
-SPARK_ROOT_DIR="$(dirname $SCRIPT_DIR)"
+SPARK_ROOT_DIR="$(dirname "$SCRIPT_DIR")"
 PEP8_REPORT_PATH="$SPARK_ROOT_DIR/dev/pep8-report.txt"
 
-cd $SPARK_ROOT_DIR
+cd "$SPARK_ROOT_DIR"
 
 # Get pep8 at runtime so that we don't rely on it being installed on the build server.
 #+ See: https://github.com/apache/spark/pull/1744#issuecomment-50982162
@@ -45,7 +45,7 @@ fi
 #+ first, but we do so so that the check status can
 #+ be output before the report, like with the
 #+ scalastyle and RAT checks.
-python $PEP8_SCRIPT_PATH $PEP8_PATHS_TO_CHECK > "$PEP8_REPORT_PATH"
+python "$PEP8_SCRIPT_PATH" $PEP8_PATHS_TO_CHECK > "$PEP8_REPORT_PATH"
 pep8_status=${PIPESTATUS[0]} #$?
 
 if [ $pep8_status -ne 0 ]; then
diff --git a/dev/mima b/dev/mima
index 09e4482af5f3d..f9b9b03538f15 100755
--- a/dev/mima
+++ b/dev/mima
@@ -21,12 +21,12 @@ set -o pipefail
 set -e
 
 # Go to the Spark project root directory
-FWDIR="$(cd `dirname $0`/..; pwd)"
+FWDIR="$(cd "`dirname "$0"`"/..; pwd)"
 cd "$FWDIR"
 
 echo -e "q\n" | sbt/sbt oldDeps/update
 
-export SPARK_CLASSPATH=`find lib_managed \( -name '*spark*jar' -a -type f \) | tr "\\n" ":"`
+export SPARK_CLASSPATH="`find lib_managed \( -name '*spark*jar' -a -type f \) | tr "\\n" ":"`"
 echo "SPARK_CLASSPATH=$SPARK_CLASSPATH"
 
 ./bin/spark-class org.apache.spark.tools.GenerateMIMAIgnore
diff --git a/dev/run-tests b/dev/run-tests
index 90a8ce16f0f06..49a88085c80f7 100755
--- a/dev/run-tests
+++ b/dev/run-tests
@@ -18,7 +18,7 @@
 #
 
 # Go to the Spark project root directory
-FWDIR="$(cd `dirname $0`/..; pwd)"
+FWDIR="$(cd "`dirname $0`"/..; pwd)"
 cd "$FWDIR"
 
 if [ -n "$AMPLAB_JENKINS_BUILD_PROFILE" ]; then
diff --git a/dev/scalastyle b/dev/scalastyle
index eb9b467965636..efb5f291ea3b7 100755
--- a/dev/scalastyle
+++ b/dev/scalastyle
@@ -19,7 +19,7 @@
 
 echo -e "q\n" | sbt/sbt -Phive scalastyle > scalastyle.txt
 # Check style with YARN alpha built too
-echo -e "q\n" | sbt/sbt -Pyarn -Phadoop-0.23 -Dhadoop.version=0.23.9 yarn-alpha/scalastyle \
+echo -e "q\n" | sbt/sbt -Pyarn-alpha -Phadoop-0.23 -Dhadoop.version=0.23.9 yarn-alpha/scalastyle \
   >> scalastyle.txt
 # Check style with YARN built too
 echo -e "q\n" | sbt/sbt -Pyarn -Phadoop-2.2 -Dhadoop.version=2.2.0 yarn/scalastyle \
diff --git a/make-distribution.sh b/make-distribution.sh
index 14aed4a4b655b..9b012b9222db4 100755
--- a/make-distribution.sh
+++ b/make-distribution.sh
@@ -28,7 +28,7 @@ set -o pipefail
 set -e
 
 # Figure out where the Spark framework is installed
-FWDIR="$(cd `dirname $0`; pwd)"
+FWDIR="$(cd "`dirname "$0"`"; pwd)"
 DISTDIR="$FWDIR/dist"
 
 SPARK_TACHYON=false
diff --git a/python/run-tests b/python/run-tests
index f2a80b4f1838b..226e9e2c3770a 100755
--- a/python/run-tests
+++ b/python/run-tests
@@ -19,7 +19,7 @@
 
 
 # Figure out where the Spark framework is installed
-FWDIR="$(cd `dirname $0`; cd ../; pwd)"
+FWDIR="$(cd "`dirname "$0"`"; cd ../; pwd)"
 
 # CD into the python directory to find things on the right path
 cd "$FWDIR/python"
@@ -33,7 +33,9 @@ rm -rf metastore warehouse
 
 function run_test() {
     echo "Running test: $1"
-    SPARK_TESTING=1 $FWDIR/bin/pyspark $1 2>&1 | tee -a unit-tests.log
+
+    SPARK_TESTING=1 "$FWDIR"/bin/pyspark $1 2>&1 | tee -a unit-tests.log
+
     FAILED=$((PIPESTATUS[0]||$FAILED))
 
     # Fail and exit on the first test failure.
diff --git a/sbin/slaves.sh b/sbin/slaves.sh
index f89547fef9e46..1d4dc5edf9858 100755
--- a/sbin/slaves.sh
+++ b/sbin/slaves.sh
@@ -36,29 +36,29 @@ if [ $# -le 0 ]; then
   exit 1
 fi
 
-sbin=`dirname "$0"`
-sbin=`cd "$sbin"; pwd`
+sbin="`dirname "$0"`"
+sbin="`cd "$sbin"; pwd`"
 
 . "$sbin/spark-config.sh"
 
 # If the slaves file is specified in the command line,
 # then it takes precedence over the definition in
 # spark-env.sh. Save it here.
-HOSTLIST=$SPARK_SLAVES
+HOSTLIST="$SPARK_SLAVES"
 
 # Check if --config is passed as an argument. It is an optional parameter.
 # Exit if the argument is not a directory.
 if [ "$1" == "--config" ]
 then
   shift
-  conf_dir=$1
+  conf_dir="$1"
   if [ ! -d "$conf_dir" ]
   then
     echo "ERROR : $conf_dir is not a directory"
     echo $usage
     exit 1
   else
-    export SPARK_CONF_DIR=$conf_dir
+    export SPARK_CONF_DIR="$conf_dir"
   fi
   shift
 fi
@@ -79,7 +79,7 @@ if [ "$SPARK_SSH_OPTS" = "" ]; then
 fi
 
 for slave in `cat "$HOSTLIST"|sed  "s/#.*$//;/^$/d"`; do
- ssh $SPARK_SSH_OPTS $slave $"${@// /\\ }" \
+ ssh $SPARK_SSH_OPTS "$slave" $"${@// /\\ }" \
    2>&1 | sed "s/^/$slave: /" &
  if [ "$SPARK_SLAVE_SLEEP" != "" ]; then
    sleep $SPARK_SLAVE_SLEEP
diff --git a/sbin/spark-config.sh b/sbin/spark-config.sh
index 5c87da5815b64..2718d6cba1c9a 100755
--- a/sbin/spark-config.sh
+++ b/sbin/spark-config.sh
@@ -21,19 +21,19 @@
 
 # resolve links - $0 may be a softlink
 this="${BASH_SOURCE-$0}"
-common_bin=$(cd -P -- "$(dirname -- "$this")" && pwd -P)
+common_bin="$(cd -P -- "$(dirname -- "$this")" && pwd -P)"
 script="$(basename -- "$this")"
 this="$common_bin/$script"
 
 # convert relative path to absolute path
-config_bin=`dirname "$this"`
-script=`basename "$this"`
-config_bin=`cd "$config_bin"; pwd`
+config_bin="`dirname "$this"`"
+script="`basename "$this"`"
+config_bin="`cd "$config_bin"; pwd`"
 this="$config_bin/$script"
 
-export SPARK_PREFIX=`dirname "$this"`/..
-export SPARK_HOME=${SPARK_PREFIX}
+export SPARK_PREFIX="`dirname "$this"`"/..
+export SPARK_HOME="${SPARK_PREFIX}"
 export SPARK_CONF_DIR="$SPARK_HOME/conf"
 # Add the PySpark classes to the PYTHONPATH:
-export PYTHONPATH=$SPARK_HOME/python:$PYTHONPATH
-export PYTHONPATH=$SPARK_HOME/python/lib/py4j-0.8.2.1-src.zip:$PYTHONPATH
+export PYTHONPATH="$SPARK_HOME/python:$PYTHONPATH"
+export PYTHONPATH="$SPARK_HOME/python/lib/py4j-0.8.2.1-src.zip:$PYTHONPATH"
diff --git a/sbin/spark-daemon.sh b/sbin/spark-daemon.sh
index 9032f23ea8eff..bd476b400e1c3 100755
--- a/sbin/spark-daemon.sh
+++ b/sbin/spark-daemon.sh
@@ -37,8 +37,8 @@ if [ $# -le 1 ]; then
   exit 1
 fi
 
-sbin=`dirname "$0"`
-sbin=`cd "$sbin"; pwd`
+sbin="`dirname "$0"`"
+sbin="`cd "$sbin"; pwd`"
 
 . "$sbin/spark-config.sh"
 
@@ -50,14 +50,14 @@ sbin=`cd "$sbin"; pwd`
 if [ "$1" == "--config" ]
 then
   shift
-  conf_dir=$1
+  conf_dir="$1"
   if [ ! -d "$conf_dir" ]
   then
     echo "ERROR : $conf_dir is not a directory"
     echo $usage
     exit 1
   else
-    export SPARK_CONF_DIR=$conf_dir
+    export SPARK_CONF_DIR="$conf_dir"
   fi
   shift
 fi
@@ -100,12 +100,12 @@ if [ "$SPARK_LOG_DIR" = "" ]; then
   export SPARK_LOG_DIR="$SPARK_HOME/logs"
 fi
 mkdir -p "$SPARK_LOG_DIR"
-touch $SPARK_LOG_DIR/.spark_test > /dev/null 2>&1
+touch "$SPARK_LOG_DIR"/.spark_test > /dev/null 2>&1
 TEST_LOG_DIR=$?
 if [ "${TEST_LOG_DIR}" = "0" ]; then
-  rm -f $SPARK_LOG_DIR/.spark_test
+  rm -f "$SPARK_LOG_DIR"/.spark_test
 else
-  chown $SPARK_IDENT_STRING $SPARK_LOG_DIR
+  chown "$SPARK_IDENT_STRING" "$SPARK_LOG_DIR"
 fi
 
 if [ "$SPARK_PID_DIR" = "" ]; then
@@ -113,8 +113,8 @@ if [ "$SPARK_PID_DIR" = "" ]; then
 fi
 
 # some variables
-log=$SPARK_LOG_DIR/spark-$SPARK_IDENT_STRING-$command-$instance-$HOSTNAME.out
-pid=$SPARK_PID_DIR/spark-$SPARK_IDENT_STRING-$command-$instance.pid
+log="$SPARK_LOG_DIR/spark-$SPARK_IDENT_STRING-$command-$instance-$HOSTNAME.out"
+pid="$SPARK_PID_DIR/spark-$SPARK_IDENT_STRING-$command-$instance.pid"
 
 # Set default scheduling priority
 if [ "$SPARK_NICENESS" = "" ]; then
@@ -136,7 +136,7 @@ case $startStop in
     fi
 
     if [ "$SPARK_MASTER" != "" ]; then
-      echo rsync from $SPARK_MASTER
+      echo rsync from "$SPARK_MASTER"
       rsync -a -e ssh --delete --exclude=.svn --exclude='logs/*' --exclude='contrib/hod/logs/*' $SPARK_MASTER/ "$SPARK_HOME"
     fi
 
diff --git a/sbin/spark-executor b/sbin/spark-executor
index 3621321a9bc8d..674ce906d9421 100755
--- a/sbin/spark-executor
+++ b/sbin/spark-executor
@@ -17,10 +17,10 @@
 # limitations under the License.
 #
 
-FWDIR="$(cd `dirname $0`/..; pwd)"
+FWDIR="$(cd "`dirname "$0"`"/..; pwd)"
 
-export PYTHONPATH=$FWDIR/python:$PYTHONPATH
-export PYTHONPATH=$FWDIR/python/lib/py4j-0.8.2.1-src.zip:$PYTHONPATH
+export PYTHONPATH="$FWDIR/python:$PYTHONPATH"
+export PYTHONPATH="$FWDIR/python/lib/py4j-0.8.2.1-src.zip:$PYTHONPATH"
 
 echo "Running spark-executor with framework dir = $FWDIR"
-exec $FWDIR/bin/spark-class org.apache.spark.executor.MesosExecutorBackend
+exec "$FWDIR"/bin/spark-class org.apache.spark.executor.MesosExecutorBackend
diff --git a/sbin/start-all.sh b/sbin/start-all.sh
index 5c89ab4d86b3a..1baf57cea09ee 100755
--- a/sbin/start-all.sh
+++ b/sbin/start-all.sh
@@ -21,8 +21,8 @@
 # Starts the master on this node.
 # Starts a worker on each node specified in conf/slaves
 
-sbin=`dirname "$0"`
-sbin=`cd "$sbin"; pwd`
+sbin="`dirname "$0"`"
+sbin="`cd "$sbin"; pwd`"
 
 TACHYON_STR=""
 
diff --git a/sbin/start-history-server.sh b/sbin/start-history-server.sh
index 580ab471b8a79..7172ad15d88fc 100755
--- a/sbin/start-history-server.sh
+++ b/sbin/start-history-server.sh
@@ -24,8 +24,8 @@
 # Use the SPARK_HISTORY_OPTS environment variable to set history server configuration.
 #
 
-sbin=`dirname "$0"`
-sbin=`cd "$sbin"; pwd`
+sbin="`dirname "$0"`"
+sbin="`cd "$sbin"; pwd`"
 
 . "$sbin/spark-config.sh"
 . "$SPARK_PREFIX/bin/load-spark-env.sh"
diff --git a/sbin/start-master.sh b/sbin/start-master.sh
index c5c02491f78e1..17fff58f4f768 100755
--- a/sbin/start-master.sh
+++ b/sbin/start-master.sh
@@ -19,8 +19,8 @@
 
 # Starts the master on the machine this script is executed on.
 
-sbin=`dirname "$0"`
-sbin=`cd "$sbin"; pwd`
+sbin="`dirname "$0"`"
+sbin="`cd "$sbin"; pwd`"
 
 START_TACHYON=false
 
diff --git a/sbin/start-slave.sh b/sbin/start-slave.sh
index b563400dc24f3..2fc35309f4ca5 100755
--- a/sbin/start-slave.sh
+++ b/sbin/start-slave.sh
@@ -20,7 +20,7 @@
 # Usage: start-slave.sh <worker#> <master-spark-URL>
 #   where <master-spark-URL> is like "spark://localhost:7077"
 
-sbin=`dirname "$0"`
-sbin=`cd "$sbin"; pwd`
+sbin="`dirname "$0"`"
+sbin="`cd "$sbin"; pwd`"
 
 "$sbin"/spark-daemon.sh start org.apache.spark.deploy.worker.Worker "$@"
diff --git a/sbin/start-slaves.sh b/sbin/start-slaves.sh
index 4912d0c0c7dfd..ba1a84abc1fef 100755
--- a/sbin/start-slaves.sh
+++ b/sbin/start-slaves.sh
@@ -17,8 +17,8 @@
 # limitations under the License.
 #
 
-sbin=`dirname "$0"`
-sbin=`cd "$sbin"; pwd`
+sbin="`dirname "$0"`"
+sbin="`cd "$sbin"; pwd`"
 
 
 START_TACHYON=false
@@ -46,11 +46,11 @@ if [ "$SPARK_MASTER_PORT" = "" ]; then
 fi
 
 if [ "$SPARK_MASTER_IP" = "" ]; then
-  SPARK_MASTER_IP=`hostname`
+  SPARK_MASTER_IP="`hostname`"
 fi
 
 if [ "$START_TACHYON" == "true" ]; then
-  "$sbin/slaves.sh" cd "$SPARK_HOME" \; "$sbin"/../tachyon/bin/tachyon bootstrap-conf $SPARK_MASTER_IP
+  "$sbin/slaves.sh" cd "$SPARK_HOME" \; "$sbin"/../tachyon/bin/tachyon bootstrap-conf "$SPARK_MASTER_IP"
 
   # set -t so we can call sudo
   SPARK_SSH_OPTS="-o StrictHostKeyChecking=no -t" "$sbin/slaves.sh" cd "$SPARK_HOME" \; "$sbin/../tachyon/bin/tachyon-start.sh" worker SudoMount \; sleep 1
@@ -58,12 +58,12 @@ fi
 
 # Launch the slaves
 if [ "$SPARK_WORKER_INSTANCES" = "" ]; then
-  exec "$sbin/slaves.sh" cd "$SPARK_HOME" \; "$sbin/start-slave.sh" 1 spark://$SPARK_MASTER_IP:$SPARK_MASTER_PORT
+  exec "$sbin/slaves.sh" cd "$SPARK_HOME" \; "$sbin/start-slave.sh" 1 "spark://$SPARK_MASTER_IP:$SPARK_MASTER_PORT"
 else
   if [ "$SPARK_WORKER_WEBUI_PORT" = "" ]; then
     SPARK_WORKER_WEBUI_PORT=8081
   fi
   for ((i=0; i<$SPARK_WORKER_INSTANCES; i++)); do
-    "$sbin/slaves.sh" cd "$SPARK_HOME" \; "$sbin/start-slave.sh" $(( $i + 1 ))  spark://$SPARK_MASTER_IP:$SPARK_MASTER_PORT --webui-port $(( $SPARK_WORKER_WEBUI_PORT + $i ))
+    "$sbin/slaves.sh" cd "$SPARK_HOME" \; "$sbin/start-slave.sh" $(( $i + 1 ))  "spark://$SPARK_MASTER_IP:$SPARK_MASTER_PORT" --webui-port $(( $SPARK_WORKER_WEBUI_PORT + $i ))
   done
 fi
diff --git a/sbin/start-thriftserver.sh b/sbin/start-thriftserver.sh
index c519a77df4a14..4ce40fe750384 100755
--- a/sbin/start-thriftserver.sh
+++ b/sbin/start-thriftserver.sh
@@ -24,7 +24,7 @@
 set -o posix
 
 # Figure out where Spark is installed
-FWDIR="$(cd `dirname $0`/..; pwd)"
+FWDIR="$(cd "`dirname "$0"`"/..; pwd)"
 
 CLASS="org.apache.spark.sql.hive.thriftserver.HiveThriftServer2"
 CLASS_NOT_FOUND_EXIT_STATUS=1
@@ -38,10 +38,10 @@ function usage {
   pattern+="\|======="
   pattern+="\|--help"
 
-  $FWDIR/bin/spark-submit --help 2>&1 | grep -v Usage 1>&2
+  "$FWDIR"/bin/spark-submit --help 2>&1 | grep -v Usage 1>&2
   echo
   echo "Thrift server options:"
-  $FWDIR/bin/spark-class $CLASS --help 2>&1 | grep -v "$pattern" 1>&2
+  "$FWDIR"/bin/spark-class $CLASS --help 2>&1 | grep -v "$pattern" 1>&2
 }
 
 if [[ "$@" = *--help ]] || [[ "$@" = *-h ]]; then
@@ -49,7 +49,7 @@ if [[ "$@" = *--help ]] || [[ "$@" = *-h ]]; then
   exit 0
 fi
 
-source $FWDIR/bin/utils.sh
+source "$FWDIR"/bin/utils.sh
 SUBMIT_USAGE_FUNCTION=usage
 gatherSparkSubmitOpts "$@"
 
diff --git a/sbin/stop-all.sh b/sbin/stop-all.sh
index 60b358d374565..298c6a9859795 100755
--- a/sbin/stop-all.sh
+++ b/sbin/stop-all.sh
@@ -21,8 +21,8 @@
 # Run this on the master nde
 
 
-sbin=`dirname "$0"`
-sbin=`cd "$sbin"; pwd`
+sbin="`dirname "$0"`"
+sbin="`cd "$sbin"; pwd`"
 
 # Load the Spark configuration
 . "$sbin/spark-config.sh"
diff --git a/sbin/stop-history-server.sh b/sbin/stop-history-server.sh
index c0034ad641cbe..6e6056359510f 100755
--- a/sbin/stop-history-server.sh
+++ b/sbin/stop-history-server.sh
@@ -19,7 +19,7 @@
 
 # Stops the history server on the machine this script is executed on.
 
-sbin=`dirname "$0"`
-sbin=`cd "$sbin"; pwd`
+sbin="`dirname "$0"`"
+sbin="`cd "$sbin"; pwd`"
 
 "$sbin"/spark-daemon.sh stop org.apache.spark.deploy.history.HistoryServer 1
diff --git a/sbt/sbt b/sbt/sbt
index 1b1aa1483a829..c172fa74bc771 100755
--- a/sbt/sbt
+++ b/sbt/sbt
@@ -3,32 +3,32 @@
 # When creating new tests for Spark SQL Hive, the HADOOP_CLASSPATH must contain the hive jars so
 # that we can run Hive to generate the golden answer.  This is not required for normal development
 # or testing.
-for i in $HIVE_HOME/lib/*
-do HADOOP_CLASSPATH=$HADOOP_CLASSPATH:$i
+for i in "$HIVE_HOME"/lib/*
+do HADOOP_CLASSPATH="$HADOOP_CLASSPATH:$i"
 done
 export HADOOP_CLASSPATH
 
 realpath () {
 (
-  TARGET_FILE=$1
+  TARGET_FILE="$1"
 
-  cd $(dirname $TARGET_FILE)
-  TARGET_FILE=$(basename $TARGET_FILE)
+  cd "$(dirname "$TARGET_FILE")"
+  TARGET_FILE="$(basename "$TARGET_FILE")"
 
   COUNT=0
   while [ -L "$TARGET_FILE" -a $COUNT -lt 100 ]
   do
-      TARGET_FILE=$(readlink $TARGET_FILE)
-      cd $(dirname $TARGET_FILE)
-      TARGET_FILE=$(basename $TARGET_FILE)
+      TARGET_FILE="$(readlink "$TARGET_FILE")"
+      cd $(dirname "$TARGET_FILE")
+      TARGET_FILE="$(basename $TARGET_FILE)"
       COUNT=$(($COUNT + 1))
   done
 
-  echo $(pwd -P)/$TARGET_FILE
+  echo "$(pwd -P)/"$TARGET_FILE""
 )
 }
 
-. $(dirname $(realpath $0))/sbt-launch-lib.bash
+. "$(dirname "$(realpath "$0")")"/sbt-launch-lib.bash
 
 
 declare -r noshare_opts="-Dsbt.global.base=project/.sbtboot -Dsbt.boot.directory=project/.boot -Dsbt.ivy.home=project/.ivy"
diff --git a/sbt/sbt-launch-lib.bash b/sbt/sbt-launch-lib.bash
index fecc3d38a5fbd..7f05d2ef491a3 100755
--- a/sbt/sbt-launch-lib.bash
+++ b/sbt/sbt-launch-lib.bash
@@ -7,7 +7,7 @@
 # TODO - Should we merge the main SBT script with this library?
 
 if test -z "$HOME"; then
-  declare -r script_dir="$(dirname $script_path)"
+  declare -r script_dir="$(dirname "$script_path")"
 else
   declare -r script_dir="$HOME/.sbt"
 fi
@@ -46,20 +46,20 @@ acquire_sbt_jar () {
 
   if [[ ! -f "$sbt_jar" ]]; then
     # Download sbt launch jar if it hasn't been downloaded yet
-    if [ ! -f ${JAR} ]; then
+    if [ ! -f "${JAR}" ]; then
     # Download
     printf "Attempting to fetch sbt\n"
-    JAR_DL=${JAR}.part
+    JAR_DL="${JAR}.part"
     if hash curl 2>/dev/null; then
-      (curl --silent ${URL1} > ${JAR_DL} || curl --silent ${URL2} > ${JAR_DL}) && mv ${JAR_DL} ${JAR}
+      (curl --silent ${URL1} > "${JAR_DL}" || curl --silent ${URL2} > "${JAR_DL}") && mv "${JAR_DL}" "${JAR}"
     elif hash wget 2>/dev/null; then
-      (wget --quiet ${URL1} -O ${JAR_DL} || wget --quiet ${URL2} -O ${JAR_DL}) && mv ${JAR_DL} ${JAR}
+      (wget --quiet ${URL1} -O "${JAR_DL}" || wget --quiet ${URL2} -O "${JAR_DL}") && mv "${JAR_DL}" "${JAR}"
     else
       printf "You do not have curl or wget installed, please install sbt manually from http://www.scala-sbt.org/\n"
       exit -1
     fi
     fi
-    if [ ! -f ${JAR} ]; then
+    if [ ! -f "${JAR}" ]; then
     # We failed to download
     printf "Our attempt to download sbt locally to ${JAR} failed. Please install sbt manually from http://www.scala-sbt.org/\n"
     exit -1

From 16a73c2473181e03d88001aa3e08e6ffac92eb8b Mon Sep 17 00:00:00 2001
From: Sandy Ryza <sandy@cloudera.com>
Date: Mon, 8 Sep 2014 11:20:00 -0700
Subject: [PATCH 233/489] SPARK-2978. Transformation with MR shuffle semantics

I didn't add this to the transformations list in the docs because it's kind of obscure, but would be happy to do so if others think it would be helpful.

Author: Sandy Ryza <sandy@cloudera.com>

Closes #2274 from sryza/sandy-spark-2978 and squashes the following commits:

4a5332a [Sandy Ryza] Fix Java test
c04b447 [Sandy Ryza] Fix Python doc and add back deleted code
433ad5b [Sandy Ryza] Add Java test
4c25a54 [Sandy Ryza] Add s at the end and a couple other fixes
9b0ba99 [Sandy Ryza] Fix compilation
36e0571 [Sandy Ryza] Fix import ordering
48c12c2 [Sandy Ryza] Add Java version and additional doc
e5381cd [Sandy Ryza] Fix python style warnings
f147634 [Sandy Ryza] SPARK-2978. Transformation with MR shuffle semantics
---
 .../apache/spark/api/java/JavaPairRDD.scala   | 26 ++++++++++++++++
 .../spark/rdd/OrderedRDDFunctions.scala       | 14 ++++++++-
 .../java/org/apache/spark/JavaAPISuite.java   | 30 +++++++++++++++++++
 .../scala/org/apache/spark/rdd/RDDSuite.scala | 14 +++++++++
 python/pyspark/rdd.py                         | 24 +++++++++++++++
 python/pyspark/tests.py                       |  8 +++++
 6 files changed, 115 insertions(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala b/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala
index feeb6c02caa78..880f61c49726e 100644
--- a/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala
+++ b/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala
@@ -758,6 +758,32 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)])
     rdd.saveAsHadoopDataset(conf)
   }
 
+  /**
+   * Repartition the RDD according to the given partitioner and, within each resulting partition,
+   * sort records by their keys.
+   *
+   * This is more efficient than calling `repartition` and then sorting within each partition
+   * because it can push the sorting down into the shuffle machinery.
+   */
+  def repartitionAndSortWithinPartitions(partitioner: Partitioner): JavaPairRDD[K, V] = {
+    val comp = com.google.common.collect.Ordering.natural().asInstanceOf[Comparator[K]]
+    repartitionAndSortWithinPartitions(partitioner, comp)
+  }
+
+  /**
+   * Repartition the RDD according to the given partitioner and, within each resulting partition,
+   * sort records by their keys.
+   *
+   * This is more efficient than calling `repartition` and then sorting within each partition
+   * because it can push the sorting down into the shuffle machinery.
+   */
+  def repartitionAndSortWithinPartitions(partitioner: Partitioner, comp: Comparator[K])
+    : JavaPairRDD[K, V] = {
+    implicit val ordering = comp // Allow implicit conversion of Comparator to Ordering.
+    fromRDD(
+      new OrderedRDDFunctions[K, V, (K, V)](rdd).repartitionAndSortWithinPartitions(partitioner))
+  }
+
   /**
    * Sort the RDD by key, so that each partition contains a sorted range of the elements in
    * ascending order. Calling `collect` or `save` on the resulting RDD will return or output an
diff --git a/core/src/main/scala/org/apache/spark/rdd/OrderedRDDFunctions.scala b/core/src/main/scala/org/apache/spark/rdd/OrderedRDDFunctions.scala
index e98bad2026e32..d0dbfef35d03c 100644
--- a/core/src/main/scala/org/apache/spark/rdd/OrderedRDDFunctions.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/OrderedRDDFunctions.scala
@@ -19,7 +19,7 @@ package org.apache.spark.rdd
 
 import scala.reflect.ClassTag
 
-import org.apache.spark.{Logging, RangePartitioner}
+import org.apache.spark.{Logging, Partitioner, RangePartitioner}
 import org.apache.spark.annotation.DeveloperApi
 
 /**
@@ -64,4 +64,16 @@ class OrderedRDDFunctions[K : Ordering : ClassTag,
     new ShuffledRDD[K, V, V](self, part)
       .setKeyOrdering(if (ascending) ordering else ordering.reverse)
   }
+
+  /**
+   * Repartition the RDD according to the given partitioner and, within each resulting partition,
+   * sort records by their keys.
+   *
+   * This is more efficient than calling `repartition` and then sorting within each partition
+   * because it can push the sorting down into the shuffle machinery.
+   */
+  def repartitionAndSortWithinPartitions(partitioner: Partitioner): RDD[(K, V)] = {
+    new ShuffledRDD[K, V, V](self, partitioner).setKeyOrdering(ordering)
+  }
+
 }
diff --git a/core/src/test/java/org/apache/spark/JavaAPISuite.java b/core/src/test/java/org/apache/spark/JavaAPISuite.java
index e1c13de04a0be..be99dc501c4b2 100644
--- a/core/src/test/java/org/apache/spark/JavaAPISuite.java
+++ b/core/src/test/java/org/apache/spark/JavaAPISuite.java
@@ -189,6 +189,36 @@ public void sortByKey() {
     Assert.assertEquals(new Tuple2<Integer, Integer>(3, 2), sortedPairs.get(2));
   }
 
+  @Test
+  public void repartitionAndSortWithinPartitions() {
+    List<Tuple2<Integer, Integer>> pairs = new ArrayList<Tuple2<Integer, Integer>>();
+    pairs.add(new Tuple2<Integer, Integer>(0, 5));
+    pairs.add(new Tuple2<Integer, Integer>(3, 8));
+    pairs.add(new Tuple2<Integer, Integer>(2, 6));
+    pairs.add(new Tuple2<Integer, Integer>(0, 8));
+    pairs.add(new Tuple2<Integer, Integer>(3, 8));
+    pairs.add(new Tuple2<Integer, Integer>(1, 3));
+
+    JavaPairRDD<Integer, Integer> rdd = sc.parallelizePairs(pairs);
+
+    Partitioner partitioner = new Partitioner() {
+      public int numPartitions() {
+        return 2;
+      }
+      public int getPartition(Object key) {
+        return ((Integer)key).intValue() % 2;
+      }
+    };
+
+    JavaPairRDD<Integer, Integer> repartitioned =
+        rdd.repartitionAndSortWithinPartitions(partitioner);
+    List<List<Tuple2<Integer, Integer>>> partitions = repartitioned.glom().collect();
+    Assert.assertEquals(partitions.get(0), Arrays.asList(new Tuple2<Integer, Integer>(0, 5),
+        new Tuple2<Integer, Integer>(0, 8), new Tuple2<Integer, Integer>(2, 6)));
+    Assert.assertEquals(partitions.get(1), Arrays.asList(new Tuple2<Integer, Integer>(1, 3),
+        new Tuple2<Integer, Integer>(3, 8), new Tuple2<Integer, Integer>(3, 8)));
+  }
+
   @Test
   public void emptyRDD() {
     JavaRDD<String> rdd = sc.emptyRDD();
diff --git a/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala b/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala
index 499dcda3dae8f..c1b501a75c8b8 100644
--- a/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala
+++ b/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala
@@ -682,6 +682,20 @@ class RDDSuite extends FunSuite with SharedSparkContext {
     assert(data.sortBy(parse, true, 2)(NameOrdering, classTag[Person]).collect() === nameOrdered)
   }
 
+  test("repartitionAndSortWithinPartitions") {
+    val data = sc.parallelize(Seq((0, 5), (3, 8), (2, 6), (0, 8), (3, 8), (1, 3)), 2)
+
+    val partitioner = new Partitioner {
+      def numPartitions: Int = 2
+      def getPartition(key: Any): Int = key.asInstanceOf[Int] % 2
+    }
+
+    val repartitioned = data.repartitionAndSortWithinPartitions(partitioner)
+    val partitions = repartitioned.glom().collect()
+    assert(partitions(0) === Seq((0, 5), (0, 8), (2, 6)))
+    assert(partitions(1) === Seq((1, 3), (3, 8), (3, 8)))
+  }
+
   test("intersection") {
     val all = sc.parallelize(1 to 10)
     val evens = sc.parallelize(2 to 10 by 2)
diff --git a/python/pyspark/rdd.py b/python/pyspark/rdd.py
index 266090e3ae8f3..5667154cb84a8 100644
--- a/python/pyspark/rdd.py
+++ b/python/pyspark/rdd.py
@@ -520,6 +520,30 @@ def __add__(self, other):
             raise TypeError
         return self.union(other)
 
+    def repartitionAndSortWithinPartitions(self, numPartitions=None, partitionFunc=portable_hash,
+                                           ascending=True, keyfunc=lambda x: x):
+        """
+        Repartition the RDD according to the given partitioner and, within each resulting partition,
+        sort records by their keys.
+
+        >>> rdd = sc.parallelize([(0, 5), (3, 8), (2, 6), (0, 8), (3, 8), (1, 3)])
+        >>> rdd2 = rdd.repartitionAndSortWithinPartitions(2, lambda x: x % 2, 2)
+        >>> rdd2.glom().collect()
+        [[(0, 5), (0, 8), (2, 6)], [(1, 3), (3, 8), (3, 8)]]
+        """
+        if numPartitions is None:
+            numPartitions = self._defaultReducePartitions()
+
+        spill = (self.ctx._conf.get("spark.shuffle.spill", 'True').lower() == "true")
+        memory = _parse_memory(self.ctx._conf.get("spark.python.worker.memory", "512m"))
+        serializer = self._jrdd_deserializer
+
+        def sortPartition(iterator):
+            sort = ExternalSorter(memory * 0.9, serializer).sorted if spill else sorted
+            return iter(sort(iterator, key=lambda (k, v): keyfunc(k), reverse=(not ascending)))
+
+        return self.partitionBy(numPartitions, partitionFunc).mapPartitions(sortPartition, True)
+
     def sortByKey(self, ascending=True, numPartitions=None, keyfunc=lambda x: x):
         """
         Sorts this RDD, which is assumed to consist of (key, value) pairs.
diff --git a/python/pyspark/tests.py b/python/pyspark/tests.py
index 9fbeb36f4f1dd..0bd2a9e6c507d 100644
--- a/python/pyspark/tests.py
+++ b/python/pyspark/tests.py
@@ -545,6 +545,14 @@ def test_histogram(self):
         self.assertEquals(([1, "b"], [5]), rdd.histogram(1))
         self.assertRaises(TypeError, lambda: rdd.histogram(2))
 
+    def test_repartitionAndSortWithinPartitions(self):
+        rdd = self.sc.parallelize([(0, 5), (3, 8), (2, 6), (0, 8), (3, 8), (1, 3)], 2)
+
+        repartitioned = rdd.repartitionAndSortWithinPartitions(2, lambda key: key % 2)
+        partitions = repartitioned.glom().collect()
+        self.assertEquals(partitions[0], [(0, 5), (0, 8), (2, 6)])
+        self.assertEquals(partitions[1], [(1, 3), (3, 8), (3, 8)])
+
 
 class TestSQL(PySparkTestCase):
 

From 386bc24ebe3e75875b9647d9223c62d7b9dc9963 Mon Sep 17 00:00:00 2001
From: Matthew Farrellee <matt@redhat.com>
Date: Mon, 8 Sep 2014 12:37:52 -0700
Subject: [PATCH 234/489] Provide a default PYSPARK_PYTHON for python/run_tests

Without this the version of python used in the test is not
recorded. The error is,

   Testing with Python version:
   ./run-tests: line 57: --version: command not found

Author: Matthew Farrellee <matt@redhat.com>

Closes #2300 from mattf/master-fix-python-run-tests and squashes the following commits:

65a09f5 [Matthew Farrellee] Provide a default PYSPARK_PYTHON for python/run_tests
---
 python/run-tests | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/python/run-tests b/python/run-tests
index 226e9e2c3770a..d98840de59d2c 100755
--- a/python/run-tests
+++ b/python/run-tests
@@ -50,6 +50,8 @@ function run_test() {
 
 echo "Running PySpark tests. Output is in python/unit-tests.log."
 
+export PYSPARK_PYTHON="python"
+
 # Try to test with Python 2.6, since that's the minimum version that we support:
 if [ $(which python2.6) ]; then
     export PYSPARK_PYTHON="python2.6"

From 26bc7655de18ab0191ded3f75cb77bc756dc1c03 Mon Sep 17 00:00:00 2001
From: Henry Cook <hcook@eecs.berkeley.edu>
Date: Mon, 8 Sep 2014 14:56:37 -0700
Subject: [PATCH 235/489] [SQL] Minor edits to sql programming guide.

Author: Henry Cook <hcook@eecs.berkeley.edu>

Closes #2316 from hcook/sql-docs and squashes the following commits:

373f94b [Henry Cook] Minor edits to sql programming guide.
---
 docs/sql-programming-guide.md | 92 ++++++++++++++++++-----------------
 1 file changed, 47 insertions(+), 45 deletions(-)

diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md
index 1814fef465cac..d83efa4bab324 100644
--- a/docs/sql-programming-guide.md
+++ b/docs/sql-programming-guide.md
@@ -13,10 +13,10 @@ title: Spark SQL Programming Guide
 
 Spark SQL allows relational queries expressed in SQL, HiveQL, or Scala to be executed using
 Spark.  At the core of this component is a new type of RDD,
-[SchemaRDD](api/scala/index.html#org.apache.spark.sql.SchemaRDD).  SchemaRDDs are composed
-[Row](api/scala/index.html#org.apache.spark.sql.catalyst.expressions.Row) objects along with
+[SchemaRDD](api/scala/index.html#org.apache.spark.sql.SchemaRDD).  SchemaRDDs are composed of
+[Row](api/scala/index.html#org.apache.spark.sql.catalyst.expressions.Row) objects, along with
 a schema that describes the data types of each column in the row.  A SchemaRDD is similar to a table
-in a traditional relational database.  A SchemaRDD can be created from an existing RDD, [Parquet](http://parquet.io)
+in a traditional relational database.  A SchemaRDD can be created from an existing RDD, a [Parquet](http://parquet.io)
 file, a JSON dataset, or by running HiveQL against data stored in [Apache Hive](http://hive.apache.org/).
 
 All of the examples on this page use sample data included in the Spark distribution and can be run in the `spark-shell`.
@@ -26,10 +26,10 @@ All of the examples on this page use sample data included in the Spark distribut
 <div data-lang="java"  markdown="1">
 Spark SQL allows relational queries expressed in SQL or HiveQL to be executed using
 Spark.  At the core of this component is a new type of RDD,
-[JavaSchemaRDD](api/scala/index.html#org.apache.spark.sql.api.java.JavaSchemaRDD).  JavaSchemaRDDs are composed
-[Row](api/scala/index.html#org.apache.spark.sql.api.java.Row) objects along with
+[JavaSchemaRDD](api/scala/index.html#org.apache.spark.sql.api.java.JavaSchemaRDD).  JavaSchemaRDDs are composed of
+[Row](api/scala/index.html#org.apache.spark.sql.api.java.Row) objects, along with
 a schema that describes the data types of each column in the row.  A JavaSchemaRDD is similar to a table
-in a traditional relational database.  A JavaSchemaRDD can be created from an existing RDD, [Parquet](http://parquet.io)
+in a traditional relational database.  A JavaSchemaRDD can be created from an existing RDD, a [Parquet](http://parquet.io)
 file, a JSON dataset, or by running HiveQL against data stored in [Apache Hive](http://hive.apache.org/).
 </div>
 
@@ -37,10 +37,10 @@ file, a JSON dataset, or by running HiveQL against data stored in [Apache Hive](
 
 Spark SQL allows relational queries expressed in SQL or HiveQL to be executed using
 Spark.  At the core of this component is a new type of RDD,
-[SchemaRDD](api/python/pyspark.sql.SchemaRDD-class.html).  SchemaRDDs are composed
-[Row](api/python/pyspark.sql.Row-class.html) objects along with
+[SchemaRDD](api/python/pyspark.sql.SchemaRDD-class.html).  SchemaRDDs are composed of
+[Row](api/python/pyspark.sql.Row-class.html) objects, along with
 a schema that describes the data types of each column in the row.  A SchemaRDD is similar to a table
-in a traditional relational database.  A SchemaRDD can be created from an existing RDD, [Parquet](http://parquet.io)
+in a traditional relational database.  A SchemaRDD can be created from an existing RDD, a [Parquet](http://parquet.io)
 file, a JSON dataset, or by running HiveQL against data stored in [Apache Hive](http://hive.apache.org/).
 
 All of the examples on this page use sample data included in the Spark distribution and can be run in the `pyspark` shell.
@@ -68,11 +68,11 @@ val sqlContext = new org.apache.spark.sql.SQLContext(sc)
 import sqlContext.createSchemaRDD
 {% endhighlight %}
 
-In addition to the basic SQLContext, you can also create a HiveContext, which provides a strict
-super set of the functionality provided by the basic SQLContext. Additional features include
+In addition to the basic SQLContext, you can also create a HiveContext, which provides a
+superset of the functionality provided by the basic SQLContext. Additional features include
 the ability to write queries using the more complete HiveQL parser, access to HiveUDFs, and the
 ability to read data from Hive tables.  To use a HiveContext, you do not need to have an
-existing hive setup, and all of the data sources available to a SQLContext are still available.
+existing Hive setup, and all of the data sources available to a SQLContext are still available.
 HiveContext is only packaged separately to avoid including all of Hive's dependencies in the default
 Spark build.  If these dependencies are not a problem for your application then using HiveContext
 is recommended for the 1.2 release of Spark.  Future releases will focus on bringing SQLContext up to
@@ -95,7 +95,7 @@ In addition to the basic SQLContext, you can also create a HiveContext, which pr
 super set of the functionality provided by the basic SQLContext. Additional features include
 the ability to write queries using the more complete HiveQL parser, access to HiveUDFs, and the
 ability to read data from Hive tables.  To use a HiveContext, you do not need to have an
-existing hive setup, and all of the data sources available to a SQLContext are still available.
+existing Hive setup, and all of the data sources available to a SQLContext are still available.
 HiveContext is only packaged separately to avoid including all of Hive's dependencies in the default
 Spark build.  If these dependencies are not a problem for your application then using HiveContext
 is recommended for the 1.2 release of Spark.  Future releases will focus on bringing SQLContext up to
@@ -118,7 +118,7 @@ In addition to the basic SQLContext, you can also create a HiveContext, which pr
 super set of the functionality provided by the basic SQLContext. Additional features include
 the ability to write queries using the more complete HiveQL parser, access to HiveUDFs, and the
 ability to read data from Hive tables.  To use a HiveContext, you do not need to have an
-existing hive setup, and all of the data sources available to a SQLContext are still available.
+existing Hive setup, and all of the data sources available to a SQLContext are still available.
 HiveContext is only packaged separately to avoid including all of Hive's dependencies in the default
 Spark build.  If these dependencies are not a problem for your application then using HiveContext
 is recommended for the 1.2 release of Spark.  Future releases will focus on bringing SQLContext up to
@@ -146,11 +146,11 @@ describes the various methods for loading data into a SchemaRDD.
 
 Spark SQL supports two different methods for converting existing RDDs into SchemaRDDs.  The first
 method uses reflection to infer the schema of an RDD that contains specific types of objects.  This
-reflection based approach leads to more concise code and works well went the schema is known ahead
-of time, while you are writing your Spark application.
+reflection based approach leads to more concise code and works well when you already know the schema 
+while writing your Spark application.
 
 The second method for creating SchemaRDDs is through a programmatic interface that allows you to
-construct a schema and then apply it to and existing RDD.  While this method is more verbose, it allows
+construct a schema and then apply it to an existing RDD.  While this method is more verbose, it allows
 you to construct SchemaRDDs when the columns and their types are not known until runtime.
  
 ### Inferring the Schema Using Reflection
@@ -266,10 +266,10 @@ List<String> teenagerNames = teenagers.map(new Function<Row, String>() {
 
 <div data-lang="python"  markdown="1">
 
-Spark SQL can convert an RDD of Row objects to a SchemaRDD, inferring the datatypes .  Rows are constructed by passing a list of
-key/value pairs as kwargs to the Row class. The keys of this list define the columns names of the table,
+Spark SQL can convert an RDD of Row objects to a SchemaRDD, inferring the datatypes.  Rows are constructed by passing a list of
+key/value pairs as kwargs to the Row class. The keys of this list define the column names of the table,
 and the types are inferred by looking at the first row.  Since we currently only look at the first
-row, it is important that there is no missing data in the first row of the RDD. In future version we
+row, it is important that there is no missing data in the first row of the RDD. In future versions we
 plan to more completely infer the schema by looking at more data, similar to the inference that is
 performed on JSON files.
 
@@ -306,14 +306,14 @@ for teenName in teenNames.collect():
 
 <div data-lang="scala"  markdown="1">
 
-In cases that case classes cannot be defined ahead of time (for example,
-the structure of records is encoded in a string or a text dataset will be parsed
+When case classes cannot be defined ahead of time (for example,
+the structure of records is encoded in a string, or a text dataset will be parsed
 and fields will be projected differently for different users),
 a `SchemaRDD` can be created programmatically with three steps.
 
 1. Create an RDD of `Row`s from the original RDD;
 2. Create the schema represented by a `StructType` matching the structure of
-`Row`s in the RDD created in the step 1.
+`Row`s in the RDD created in Step 1.
 3. Apply the schema to the RDD of `Row`s via `applySchema` method provided
 by `SQLContext`.
 
@@ -358,14 +358,14 @@ results.map(t => "Name: " + t(0)).collect().foreach(println)
 
 <div data-lang="java"  markdown="1">
 
-In cases that JavaBean classes cannot be defined ahead of time (for example,
-the structure of records is encoded in a string or a text dataset will be parsed and
+When JavaBean classes cannot be defined ahead of time (for example,
+the structure of records is encoded in a string, or a text dataset will be parsed and
 fields will be projected differently for different users),
 a `SchemaRDD` can be created programmatically with three steps.
 
 1. Create an RDD of `Row`s from the original RDD;
 2. Create the schema represented by a `StructType` matching the structure of
-`Row`s in the RDD created in the step 1.
+`Row`s in the RDD created in Step 1.
 3. Apply the schema to the RDD of `Row`s via `applySchema` method provided
 by `JavaSQLContext`.
 
@@ -427,10 +427,10 @@ List<String> names = results.map(new Function<Row, String>() {
 
 <div data-lang="python"  markdown="1">
 
-For some cases (for example, the structure of records is encoded in a string or
-a text dataset will be parsed and fields will be projected differently for
-different users), it is desired to create `SchemaRDD` with a programmatically way.
-It can be done with three steps.
+When a dictionary of kwargs cannot be defined ahead of time (for example,
+the structure of records is encoded in a string, or a text dataset will be parsed and
+fields will be projected differently for different users),
+a `SchemaRDD` can be created programmatically with three steps.
 
 1. Create an RDD of tuples or lists from the original RDD;
 2. Create the schema represented by a `StructType` matching the structure of
@@ -566,7 +566,7 @@ for teenName in teenNames.collect():
 
 ### Configuration
 
-Configuration of parquet can be done using the `setConf` method on SQLContext or by running 
+Configuration of Parquet can be done using the `setConf` method on SQLContext or by running 
 `SET key=value` commands using SQL.
 
 <table class="table">
@@ -575,8 +575,8 @@ Configuration of parquet can be done using the `setConf` method on SQLContext or
   <td><code>spark.sql.parquet.binaryAsString</code></td>
   <td>false</td>
   <td>
-    Some other parquet producing systems, in particular Impala and older versions of Spark SQL, do 
-    not differentiate between binary data and strings when writing out the parquet schema.  This 
+    Some other Parquet-producing systems, in particular Impala and older versions of Spark SQL, do 
+    not differentiate between binary data and strings when writing out the Parquet schema.  This 
     flag tells Spark SQL to interpret binary data as a string to provide compatibility with these systems.
   </td>
 </tr>
@@ -584,14 +584,14 @@ Configuration of parquet can be done using the `setConf` method on SQLContext or
   <td><code>spark.sql.parquet.cacheMetadata</code></td>
   <td>false</td>
   <td>
-    Turns on caching of parquet schema metadata.  Can speed up querying 
+    Turns on caching of Parquet schema metadata.  Can speed up querying of static data.
   </td>
 </tr>
 <tr>
   <td><code>spark.sql.parquet.compression.codec</code></td>
   <td>snappy</td>
   <td>
-    Sets the compression codec use when writing parquet files. Acceptable values include: 
+    Sets the compression codec use when writing Parquet files. Acceptable values include: 
     uncompressed, snappy, gzip, lzo.
   </td>
 </tr>
@@ -805,9 +805,8 @@ Spark SQL can cache tables using an in-memory columnar format by calling `cacheT
 Then Spark SQL will scan only required columns and will automatically tune compression to minimize
 memory usage and GC pressure. You can call `uncacheTable("tableName")` to remove the table from memory.
 
-Note that if you just call `cache` rather than `cacheTable`, tables will _not_ be cached in
-in-memory columnar format. So we strongly recommend using `cacheTable` whenever you want to
-cache tables.
+Note that if you call `cache` rather than `cacheTable`, tables will _not_ be cached using
+the in-memory columnar format, and therefore `cacheTable` is strongly recommended for this use case.
 
 Configuration of in-memory caching can be done using the `setConf` method on SQLContext or by running 
 `SET key=value` commands using SQL.
@@ -833,7 +832,7 @@ Configuration of in-memory caching can be done using the `setConf` method on SQL
 
 </table>
 
-## Other Configuration
+## Other Configuration Options
 
 The following options can also be used to tune the performance of query execution.  It is possible
 that these options will be deprecated in future release as more optimizations are performed automatically.
@@ -842,7 +841,7 @@ that these options will be deprecated in future release as more optimizations ar
   <tr><th>Property Name</th><th>Default</th><th>Meaning</th></tr>
   <tr>
     <td><code>spark.sql.autoBroadcastJoinThreshold</code></td>
-    <td>false</td>
+    <td>10000</td>
     <td>
       Configures the maximum size in bytes for a table that will be broadcast to all worker nodes when
       performing a join.  By setting this value to -1 broadcasting can be disabled.  Note that currently
@@ -876,7 +875,7 @@ code.
 ## Running the Thrift JDBC server
 
 The Thrift JDBC server implemented here corresponds to the [`HiveServer2`](https://cwiki.apache.org/confluence/display/Hive/Setting+Up+HiveServer2)
-in Hive 0.12. You can test the JDBC server with the beeline script comes with either Spark or Hive 0.12.
+in Hive 0.12. You can test the JDBC server with the beeline script that comes with either Spark or Hive 0.12.
 
 To start the JDBC server, run the following in the Spark directory:
 
@@ -899,12 +898,12 @@ your machine and a blank password. For secure mode, please follow the instructio
 
 Configuration of Hive is done by placing your `hive-site.xml` file in `conf/`.
 
-You may also use the beeline script comes with Hive.
+You may also use the beeline script that comes with Hive.
 
 ## Running the Spark SQL CLI
 
 The Spark SQL CLI is a convenient tool to run the Hive metastore service in local mode and execute
-queries input from command line. Note: the Spark SQL CLI cannot talk to the Thrift JDBC server.
+queries input from the command line. Note that the Spark SQL CLI cannot talk to the Thrift JDBC server.
 
 To start the Spark SQL CLI, run the following in the Spark directory:
 
@@ -916,7 +915,10 @@ options.
 
 # Compatibility with Other Systems
 
-## Migration Guide for Shark Users
+## Migration Guide for Shark User
+
+### Scheduling 
+s
 To set a [Fair Scheduler](job-scheduling.html#fair-scheduler-pools) pool for a JDBC client session,
 users can set the `spark.sql.thriftserver.scheduler.pool` variable:
 
@@ -925,7 +927,7 @@ users can set the `spark.sql.thriftserver.scheduler.pool` variable:
 ### Reducer number
 
 In Shark, default reducer number is 1 and is controlled by the property `mapred.reduce.tasks`. Spark
-SQL deprecates this property by a new property `spark.sql.shuffle.partitions`, whose default value
+SQL deprecates this property in favor of `spark.sql.shuffle.partitions`, whose default value
 is 200. Users may customize this property via `SET`:
 
     SET spark.sql.shuffle.partitions=10;

From 939a322c85956eda150b10afb2ed1d8d959a7bdf Mon Sep 17 00:00:00 2001
From: Matthew Rocklin <mrocklin@gmail.com>
Date: Mon, 8 Sep 2014 15:45:28 -0700
Subject: [PATCH 236/489] [SPARK-3417] Use new-style classes in PySpark

Tiny PR making SQLContext a new-style class.  This allows various type logic to work more effectively

```Python
In [1]: import pyspark

In [2]: pyspark.sql.SQLContext.mro()
Out[2]: [pyspark.sql.SQLContext, object]
```

Author: Matthew Rocklin <mrocklin@gmail.com>

Closes #2288 from mrocklin/sqlcontext-new-style-class and squashes the following commits:

4aadab6 [Matthew Rocklin] update other old-style classes
a2dc02f [Matthew Rocklin] pyspark.sql.SQLContext is new-style class
---
 python/pyspark/mllib/random.py | 2 +-
 python/pyspark/mllib/util.py   | 2 +-
 python/pyspark/sql.py          | 2 +-
 python/pyspark/storagelevel.py | 2 +-
 4 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/python/pyspark/mllib/random.py b/python/pyspark/mllib/random.py
index 3e59c73db85e3..d53c95fd59c25 100644
--- a/python/pyspark/mllib/random.py
+++ b/python/pyspark/mllib/random.py
@@ -28,7 +28,7 @@
 __all__ = ['RandomRDDs', ]
 
 
-class RandomRDDs:
+class RandomRDDs(object):
     """
     Generator methods for creating RDDs comprised of i.i.d samples from
     some distribution.
diff --git a/python/pyspark/mllib/util.py b/python/pyspark/mllib/util.py
index 4962d05491c03..1c7b8c809ab5b 100644
--- a/python/pyspark/mllib/util.py
+++ b/python/pyspark/mllib/util.py
@@ -25,7 +25,7 @@
 from pyspark.serializers import NoOpSerializer
 
 
-class MLUtils:
+class MLUtils(object):
 
     """
     Helper methods to load, save and pre-process data used in MLlib.
diff --git a/python/pyspark/sql.py b/python/pyspark/sql.py
index 004d4937cbe1c..53eea6d6cf3ba 100644
--- a/python/pyspark/sql.py
+++ b/python/pyspark/sql.py
@@ -899,7 +899,7 @@ def __reduce__(self):
     return Row
 
 
-class SQLContext:
+class SQLContext(object):
 
     """Main entry point for Spark SQL functionality.
 
diff --git a/python/pyspark/storagelevel.py b/python/pyspark/storagelevel.py
index 2aa0fb9d2c1ed..676aa0f7144aa 100644
--- a/python/pyspark/storagelevel.py
+++ b/python/pyspark/storagelevel.py
@@ -18,7 +18,7 @@
 __all__ = ["StorageLevel"]
 
 
-class StorageLevel:
+class StorageLevel(object):
 
     """
     Flags for controlling the storage of an RDD. Each StorageLevel records whether to use memory,

From 08ce18881e09c6e91db9c410d1d9ce1e5ae63a62 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@apache.org>
Date: Mon, 8 Sep 2014 15:59:20 -0700
Subject: [PATCH 237/489] [SPARK-3019] Pluggable block transfer interface
 (BlockTransferService)

This pull request creates a new BlockTransferService interface for block fetch/upload and refactors the existing ConnectionManager to implement BlockTransferService (NioBlockTransferService).

Most of the changes are simply moving code around. The main class to inspect is ShuffleBlockFetcherIterator.

Review guide:
- Most of the ConnectionManager code is now in network.cm package
- ManagedBuffer is a new buffer abstraction backed by several different implementations (file segment, nio ByteBuffer, Netty ByteBuf)
- BlockTransferService is the main internal interface introduced in this PR
- NioBlockTransferService implements BlockTransferService and replaces the old BlockManagerWorker
- ShuffleBlockFetcherIterator replaces the told BlockFetcherIterator to use the new interface

TODOs that should be separate PRs:
- Implement NettyBlockTransferService
- Finalize the API/semantics for ManagedBuffer.release()

Author: Reynold Xin <rxin@apache.org>

Closes #2240 from rxin/blockTransferService and squashes the following commits:

64cd9d7 [Reynold Xin] Merge branch 'master' into blockTransferService
1dfd3d7 [Reynold Xin] Limit the length of the FileInputStream.
1332156 [Reynold Xin] Fixed style violation from refactoring.
2960c93 [Reynold Xin] Added ShuffleBlockFetcherIteratorSuite.
e29c721 [Reynold Xin] Updated comment for ShuffleBlockFetcherIterator.
8a1046e [Reynold Xin] Code review feedback:
2c6b1e1 [Reynold Xin] Removed println in test cases.
2a907e4 [Reynold Xin] Merge branch 'master' into blockTransferService-merge
07ccf0d [Reynold Xin] Added init check to CMBlockTransferService.
98c668a [Reynold Xin] Added failure handling and fixed unit tests.
ae05fcd [Reynold Xin] Updated tests, although DistributedSuite is hanging.
d8d595c [Reynold Xin] Merge branch 'master' of github.com:apache/spark into blockTransferService
9ef279c [Reynold Xin] Initial refactoring to move ConnectionManager to use the BlockTransferService.
---
 .../scala/org/apache/spark/SparkEnv.scala     |  15 +-
 ...eiverTest.scala => BlockDataManager.scala} |  29 +-
 .../spark/network/BlockFetchingListener.scala |  37 +++
 .../spark/network/BlockTransferService.scala  | 131 +++++++++
 .../spark/network/ConnectionManagerTest.scala | 103 -------
 .../apache/spark/network/ManagedBuffer.scala  | 107 +++++++
 .../org/apache/spark/network/SenderTest.scala |  76 -----
 .../nio}/BlockMessage.scala                   |  24 +-
 .../nio}/BlockMessageArray.scala              |  12 +-
 .../network/{ => nio}/BufferMessage.scala     |   5 +-
 .../spark/network/{ => nio}/Connection.scala  |  10 +-
 .../network/{ => nio}/ConnectionId.scala      |   6 +-
 .../network/{ => nio}/ConnectionManager.scala |  23 +-
 .../{ => nio}/ConnectionManagerId.scala       |   6 +-
 .../spark/network/{ => nio}/Message.scala     |   7 +-
 .../network/{ => nio}/MessageChunk.scala      |   4 +-
 .../{ => nio}/MessageChunkHeader.scala        |   9 +-
 .../network/nio/NioBlockTransferService.scala | 205 +++++++++++++
 .../network/{ => nio}/SecurityMessage.scala   |  10 +-
 .../spark/serializer/KryoSerializer.scala     |   2 +-
 .../shuffle/FileShuffleBlockManager.scala     |  35 ++-
 .../shuffle/IndexShuffleBlockManager.scala    |  24 +-
 .../spark/shuffle/ShuffleBlockManager.scala   |   6 +-
 .../hash/BlockStoreShuffleFetcher.scala       |  14 +-
 .../shuffle/hash/HashShuffleReader.scala      |   4 +-
 .../spark/storage/BlockFetcherIterator.scala  | 254 ----------------
 .../apache/spark/storage/BlockManager.scala   |  98 +++----
 .../apache/spark/storage/BlockManagerId.scala |   4 +-
 .../spark/storage/BlockManagerWorker.scala    | 147 ----------
 .../storage/ShuffleBlockFetcherIterator.scala | 271 ++++++++++++++++++
 .../apache/spark/storage/ThreadingTest.scala  | 120 --------
 .../org/apache/spark/DistributedSuite.scala   |  15 +-
 .../{ => nio}/ConnectionManagerSuite.scala    |  17 +-
 .../hash/HashShuffleManagerSuite.scala        |  17 +-
 .../storage/BlockFetcherIteratorSuite.scala   | 237 ---------------
 .../spark/storage/BlockManagerSuite.scala     | 133 +--------
 .../spark/storage/DiskBlockManagerSuite.scala |   2 +-
 .../ShuffleBlockFetcherIteratorSuite.scala    | 183 ++++++++++++
 38 files changed, 1129 insertions(+), 1273 deletions(-)
 rename core/src/main/scala/org/apache/spark/network/{ReceiverTest.scala => BlockDataManager.scala} (56%)
 create mode 100644 core/src/main/scala/org/apache/spark/network/BlockFetchingListener.scala
 create mode 100644 core/src/main/scala/org/apache/spark/network/BlockTransferService.scala
 delete mode 100644 core/src/main/scala/org/apache/spark/network/ConnectionManagerTest.scala
 create mode 100644 core/src/main/scala/org/apache/spark/network/ManagedBuffer.scala
 delete mode 100644 core/src/main/scala/org/apache/spark/network/SenderTest.scala
 rename core/src/main/scala/org/apache/spark/{storage => network/nio}/BlockMessage.scala (89%)
 rename core/src/main/scala/org/apache/spark/{storage => network/nio}/BlockMessageArray.scala (97%)
 rename core/src/main/scala/org/apache/spark/network/{ => nio}/BufferMessage.scala (98%)
 rename core/src/main/scala/org/apache/spark/network/{ => nio}/Connection.scala (99%)
 rename core/src/main/scala/org/apache/spark/network/{ => nio}/ConnectionId.scala (88%)
 rename core/src/main/scala/org/apache/spark/network/{ => nio}/ConnectionManager.scala (98%)
 rename core/src/main/scala/org/apache/spark/network/{ => nio}/ConnectionManagerId.scala (88%)
 rename core/src/main/scala/org/apache/spark/network/{ => nio}/Message.scala (95%)
 rename core/src/main/scala/org/apache/spark/network/{ => nio}/MessageChunk.scala (96%)
 rename core/src/main/scala/org/apache/spark/network/{ => nio}/MessageChunkHeader.scala (93%)
 create mode 100644 core/src/main/scala/org/apache/spark/network/nio/NioBlockTransferService.scala
 rename core/src/main/scala/org/apache/spark/network/{ => nio}/SecurityMessage.scala (95%)
 delete mode 100644 core/src/main/scala/org/apache/spark/storage/BlockFetcherIterator.scala
 delete mode 100644 core/src/main/scala/org/apache/spark/storage/BlockManagerWorker.scala
 create mode 100644 core/src/main/scala/org/apache/spark/storage/ShuffleBlockFetcherIterator.scala
 delete mode 100644 core/src/main/scala/org/apache/spark/storage/ThreadingTest.scala
 rename core/src/test/scala/org/apache/spark/network/{ => nio}/ConnectionManagerSuite.scala (97%)
 delete mode 100644 core/src/test/scala/org/apache/spark/storage/BlockFetcherIteratorSuite.scala
 create mode 100644 core/src/test/scala/org/apache/spark/storage/ShuffleBlockFetcherIteratorSuite.scala

diff --git a/core/src/main/scala/org/apache/spark/SparkEnv.scala b/core/src/main/scala/org/apache/spark/SparkEnv.scala
index 20a7444cfc5ee..dd95e406f2a8e 100644
--- a/core/src/main/scala/org/apache/spark/SparkEnv.scala
+++ b/core/src/main/scala/org/apache/spark/SparkEnv.scala
@@ -31,7 +31,8 @@ import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.api.python.PythonWorkerFactory
 import org.apache.spark.broadcast.BroadcastManager
 import org.apache.spark.metrics.MetricsSystem
-import org.apache.spark.network.ConnectionManager
+import org.apache.spark.network.BlockTransferService
+import org.apache.spark.network.nio.NioBlockTransferService
 import org.apache.spark.scheduler.LiveListenerBus
 import org.apache.spark.serializer.Serializer
 import org.apache.spark.shuffle.{ShuffleMemoryManager, ShuffleManager}
@@ -59,8 +60,8 @@ class SparkEnv (
     val mapOutputTracker: MapOutputTracker,
     val shuffleManager: ShuffleManager,
     val broadcastManager: BroadcastManager,
+    val blockTransferService: BlockTransferService,
     val blockManager: BlockManager,
-    val connectionManager: ConnectionManager,
     val securityManager: SecurityManager,
     val httpFileServer: HttpFileServer,
     val sparkFilesDir: String,
@@ -88,6 +89,8 @@ class SparkEnv (
     // down, but let's call it anyway in case it gets fixed in a later release
     // UPDATE: In Akka 2.1.x, this hangs if there are remote actors, so we can't call it.
     // actorSystem.awaitTermination()
+
+    // Note that blockTransferService is stopped by BlockManager since it is started by it.
   }
 
   private[spark]
@@ -223,14 +226,14 @@ object SparkEnv extends Logging {
 
     val shuffleMemoryManager = new ShuffleMemoryManager(conf)
 
+    val blockTransferService = new NioBlockTransferService(conf, securityManager)
+
     val blockManagerMaster = new BlockManagerMaster(registerOrLookup(
       "BlockManagerMaster",
       new BlockManagerMasterActor(isLocal, conf, listenerBus)), conf, isDriver)
 
     val blockManager = new BlockManager(executorId, actorSystem, blockManagerMaster,
-      serializer, conf, securityManager, mapOutputTracker, shuffleManager)
-
-    val connectionManager = blockManager.connectionManager
+      serializer, conf, mapOutputTracker, shuffleManager, blockTransferService)
 
     val broadcastManager = new BroadcastManager(isDriver, conf, securityManager)
 
@@ -278,8 +281,8 @@ object SparkEnv extends Logging {
       mapOutputTracker,
       shuffleManager,
       broadcastManager,
+      blockTransferService,
       blockManager,
-      connectionManager,
       securityManager,
       httpFileServer,
       sparkFilesDir,
diff --git a/core/src/main/scala/org/apache/spark/network/ReceiverTest.scala b/core/src/main/scala/org/apache/spark/network/BlockDataManager.scala
similarity index 56%
rename from core/src/main/scala/org/apache/spark/network/ReceiverTest.scala
rename to core/src/main/scala/org/apache/spark/network/BlockDataManager.scala
index 53a6038a9b59e..e0e91724271c8 100644
--- a/core/src/main/scala/org/apache/spark/network/ReceiverTest.scala
+++ b/core/src/main/scala/org/apache/spark/network/BlockDataManager.scala
@@ -17,21 +17,20 @@
 
 package org.apache.spark.network
 
-import java.nio.ByteBuffer
-import org.apache.spark.{SecurityManager, SparkConf}
+import org.apache.spark.storage.StorageLevel
 
-private[spark] object ReceiverTest {
-  def main(args: Array[String]) {
-    val conf = new SparkConf
-    val manager = new ConnectionManager(9999, conf, new SecurityManager(conf))
-    println("Started connection manager with id = " + manager.id)
 
-    manager.onReceiveMessage((msg: Message, id: ConnectionManagerId) => {
-      /* println("Received [" + msg + "] from [" + id + "] at " + System.currentTimeMillis) */
-      val buffer = ByteBuffer.wrap("response".getBytes("utf-8"))
-      Some(Message.createBufferMessage(buffer, msg.id))
-    })
-    Thread.currentThread.join()
-  }
-}
+trait BlockDataManager {
+
+  /**
+   * Interface to get local block data.
+   *
+   * @return Some(buffer) if the block exists locally, and None if it doesn't.
+   */
+  def getBlockData(blockId: String): Option[ManagedBuffer]
 
+  /**
+   * Put the block locally, using the given storage level.
+   */
+  def putBlockData(blockId: String, data: ManagedBuffer, level: StorageLevel): Unit
+}
diff --git a/core/src/main/scala/org/apache/spark/network/BlockFetchingListener.scala b/core/src/main/scala/org/apache/spark/network/BlockFetchingListener.scala
new file mode 100644
index 0000000000000..34acaa563ca58
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/network/BlockFetchingListener.scala
@@ -0,0 +1,37 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.network
+
+import java.util.EventListener
+
+
+/**
+ * Listener callback interface for [[BlockTransferService.fetchBlocks]].
+ */
+trait BlockFetchingListener extends EventListener {
+
+  /**
+   * Called once per successfully fetched block.
+   */
+  def onBlockFetchSuccess(blockId: String, data: ManagedBuffer): Unit
+
+  /**
+   * Called upon failures. For each failure, this is called only once (i.e. not once per block).
+   */
+  def onBlockFetchFailure(exception: Throwable): Unit
+}
diff --git a/core/src/main/scala/org/apache/spark/network/BlockTransferService.scala b/core/src/main/scala/org/apache/spark/network/BlockTransferService.scala
new file mode 100644
index 0000000000000..84d991fa6808c
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/network/BlockTransferService.scala
@@ -0,0 +1,131 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.network
+
+import scala.concurrent.{Await, Future}
+import scala.concurrent.duration.Duration
+
+import org.apache.spark.storage.StorageLevel
+
+
+abstract class BlockTransferService {
+
+  /**
+   * Initialize the transfer service by giving it the BlockDataManager that can be used to fetch
+   * local blocks or put local blocks.
+   */
+  def init(blockDataManager: BlockDataManager)
+
+  /**
+   * Tear down the transfer service.
+   */
+  def stop(): Unit
+
+  /**
+   * Port number the service is listening on, available only after [[init]] is invoked.
+   */
+  def port: Int
+
+  /**
+   * Host name the service is listening on, available only after [[init]] is invoked.
+   */
+  def hostName: String
+
+  /**
+   * Fetch a sequence of blocks from a remote node asynchronously,
+   * available only after [[init]] is invoked.
+   *
+   * Note that [[BlockFetchingListener.onBlockFetchSuccess]] is called once per block,
+   * while [[BlockFetchingListener.onBlockFetchFailure]] is called once per failure (not per block).
+   *
+   * Note that this API takes a sequence so the implementation can batch requests, and does not
+   * return a future so the underlying implementation can invoke onBlockFetchSuccess as soon as
+   * the data of a block is fetched, rather than waiting for all blocks to be fetched.
+   */
+  def fetchBlocks(
+      hostName: String,
+      port: Int,
+      blockIds: Seq[String],
+      listener: BlockFetchingListener): Unit
+
+  /**
+   * Upload a single block to a remote node, available only after [[init]] is invoked.
+   */
+  def uploadBlock(
+      hostname: String,
+      port: Int,
+      blockId: String,
+      blockData: ManagedBuffer,
+      level: StorageLevel): Future[Unit]
+
+  /**
+   * A special case of [[fetchBlocks]], as it fetches only one block and is blocking.
+   *
+   * It is also only available after [[init]] is invoked.
+   */
+  def fetchBlockSync(hostName: String, port: Int, blockId: String): ManagedBuffer = {
+    // A monitor for the thread to wait on.
+    val lock = new Object
+    @volatile var result: Either[ManagedBuffer, Throwable] = null
+    fetchBlocks(hostName, port, Seq(blockId), new BlockFetchingListener {
+      override def onBlockFetchFailure(exception: Throwable): Unit = {
+        lock.synchronized {
+          result = Right(exception)
+          lock.notify()
+        }
+      }
+      override def onBlockFetchSuccess(blockId: String, data: ManagedBuffer): Unit = {
+        lock.synchronized {
+          result = Left(data)
+          lock.notify()
+        }
+      }
+    })
+
+    // Sleep until result is no longer null
+    lock.synchronized {
+      while (result == null) {
+        try {
+          lock.wait()
+        } catch {
+          case e: InterruptedException =>
+        }
+      }
+    }
+
+    result match {
+      case Left(data) => data
+      case Right(e) => throw e
+    }
+  }
+
+  /**
+   * Upload a single block to a remote node, available only after [[init]] is invoked.
+   *
+   * This method is similar to [[uploadBlock]], except this one blocks the thread
+   * until the upload finishes.
+   */
+  def uploadBlockSync(
+      hostname: String,
+      port: Int,
+      blockId: String,
+      blockData: ManagedBuffer,
+      level: StorageLevel): Unit = {
+    Await.result(uploadBlock(hostname, port, blockId, blockData, level), Duration.Inf)
+  }
+}
diff --git a/core/src/main/scala/org/apache/spark/network/ConnectionManagerTest.scala b/core/src/main/scala/org/apache/spark/network/ConnectionManagerTest.scala
deleted file mode 100644
index 4894ecd41f6eb..0000000000000
--- a/core/src/main/scala/org/apache/spark/network/ConnectionManagerTest.scala
+++ /dev/null
@@ -1,103 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.network
-
-import java.nio.ByteBuffer
-
-import scala.concurrent.Await
-import scala.concurrent.duration._
-import scala.io.Source
-
-import org.apache.spark._
-
-private[spark] object ConnectionManagerTest extends Logging{
-  def main(args: Array[String]) {
-    // <mesos cluster> - the master URL <slaves file> - a list slaves to run connectionTest on
-    // [num of tasks] - the number of parallel tasks to be initiated default is number of slave
-    // hosts [size of msg in MB (integer)] - the size of messages to be sent in each task,
-    // default is 10 [count] - how many times to run, default is 3 [await time in seconds] :
-    // await time (in seconds), default is 600
-    if (args.length < 2) {
-      println("Usage: ConnectionManagerTest <mesos cluster> <slaves file> [num of tasks] " +
-        "[size of msg in MB (integer)] [count] [await time in seconds)] ")
-      System.exit(1)
-    }
-
-    if (args(0).startsWith("local")) {
-      println("This runs only on a mesos cluster")
-    }
-
-    val sc = new SparkContext(args(0), "ConnectionManagerTest")
-    val slavesFile = Source.fromFile(args(1))
-    val slaves = slavesFile.mkString.split("\n")
-    slavesFile.close()
-
-    /* println("Slaves") */
-    /* slaves.foreach(println) */
-    val tasknum = if (args.length > 2) args(2).toInt else slaves.length
-    val size = ( if (args.length > 3) (args(3).toInt) else 10 ) * 1024 * 1024
-    val count = if (args.length > 4) args(4).toInt else 3
-    val awaitTime = (if (args.length > 5) args(5).toInt else 600 ).second
-    println("Running " + count + " rounds of test: " + "parallel tasks = " + tasknum + ", " +
-      "msg size = " + size/1024/1024 + " MB, awaitTime = " + awaitTime)
-    val slaveConnManagerIds = sc.parallelize(0 until tasknum, tasknum).map(
-        i => SparkEnv.get.connectionManager.id).collect()
-    println("\nSlave ConnectionManagerIds")
-    slaveConnManagerIds.foreach(println)
-    println
-
-    (0 until count).foreach(i => {
-      val resultStrs = sc.parallelize(0 until tasknum, tasknum).map(i => {
-        val connManager = SparkEnv.get.connectionManager
-        val thisConnManagerId = connManager.id
-        connManager.onReceiveMessage((msg: Message, id: ConnectionManagerId) => {
-          logInfo("Received [" + msg + "] from [" + id + "]")
-          None
-        })
-
-        val buffer = ByteBuffer.allocate(size).put(Array.tabulate[Byte](size)(x => x.toByte))
-        buffer.flip
-
-        val startTime = System.currentTimeMillis
-        val futures = slaveConnManagerIds.filter(_ != thisConnManagerId).map{ slaveConnManagerId =>
-          {
-            val bufferMessage = Message.createBufferMessage(buffer.duplicate)
-            logInfo("Sending [" + bufferMessage + "] to [" + slaveConnManagerId + "]")
-            connManager.sendMessageReliably(slaveConnManagerId, bufferMessage)
-          }
-        }
-        val results = futures.map(f => Await.result(f, awaitTime))
-        val finishTime = System.currentTimeMillis
-        Thread.sleep(5000)
-
-        val mb = size * results.size / 1024.0 / 1024.0
-        val ms = finishTime - startTime
-        val resultStr = thisConnManagerId + " Sent " + mb + " MB in " + ms + " ms at " + (mb / ms *
-          1000.0) + " MB/s"
-        logInfo(resultStr)
-        resultStr
-      }).collect()
-
-      println("---------------------")
-      println("Run " + i)
-      resultStrs.foreach(println)
-      println("---------------------")
-    })
-  }
-}
-
diff --git a/core/src/main/scala/org/apache/spark/network/ManagedBuffer.scala b/core/src/main/scala/org/apache/spark/network/ManagedBuffer.scala
new file mode 100644
index 0000000000000..dcecb6beeea9b
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/network/ManagedBuffer.scala
@@ -0,0 +1,107 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.network
+
+import java.io.{FileInputStream, RandomAccessFile, File, InputStream}
+import java.nio.ByteBuffer
+import java.nio.channels.FileChannel.MapMode
+
+import com.google.common.io.ByteStreams
+import io.netty.buffer.{ByteBufInputStream, ByteBuf}
+
+import org.apache.spark.util.ByteBufferInputStream
+
+
+/**
+ * This interface provides an immutable view for data in the form of bytes. The implementation
+ * should specify how the data is provided:
+ *
+ * - FileSegmentManagedBuffer: data backed by part of a file
+ * - NioByteBufferManagedBuffer: data backed by a NIO ByteBuffer
+ * - NettyByteBufManagedBuffer: data backed by a Netty ByteBuf
+ */
+sealed abstract class ManagedBuffer {
+  // Note that all the methods are defined with parenthesis because their implementations can
+  // have side effects (io operations).
+
+  /** Number of bytes of the data. */
+  def size: Long
+
+  /**
+   * Exposes this buffer's data as an NIO ByteBuffer. Changing the position and limit of the
+   * returned ByteBuffer should not affect the content of this buffer.
+   */
+  def nioByteBuffer(): ByteBuffer
+
+  /**
+   * Exposes this buffer's data as an InputStream. The underlying implementation does not
+   * necessarily check for the length of bytes read, so the caller is responsible for making sure
+   * it does not go over the limit.
+   */
+  def inputStream(): InputStream
+}
+
+
+/**
+ * A [[ManagedBuffer]] backed by a segment in a file
+ */
+final class FileSegmentManagedBuffer(val file: File, val offset: Long, val length: Long)
+  extends ManagedBuffer {
+
+  override def size: Long = length
+
+  override def nioByteBuffer(): ByteBuffer = {
+    val channel = new RandomAccessFile(file, "r").getChannel
+    channel.map(MapMode.READ_ONLY, offset, length)
+  }
+
+  override def inputStream(): InputStream = {
+    val is = new FileInputStream(file)
+    is.skip(offset)
+    ByteStreams.limit(is, length)
+  }
+}
+
+
+/**
+ * A [[ManagedBuffer]] backed by [[java.nio.ByteBuffer]].
+ */
+final class NioByteBufferManagedBuffer(buf: ByteBuffer) extends ManagedBuffer {
+
+  override def size: Long = buf.remaining()
+
+  override def nioByteBuffer() = buf.duplicate()
+
+  override def inputStream() = new ByteBufferInputStream(buf)
+}
+
+
+/**
+ * A [[ManagedBuffer]] backed by a Netty [[ByteBuf]].
+ */
+final class NettyByteBufManagedBuffer(buf: ByteBuf) extends ManagedBuffer {
+
+  override def size: Long = buf.readableBytes()
+
+  override def nioByteBuffer() = buf.nioBuffer()
+
+  override def inputStream() = new ByteBufInputStream(buf)
+
+  // TODO(rxin): Promote this to top level ManagedBuffer interface and add documentation for it.
+  def release(): Unit = buf.release()
+}
diff --git a/core/src/main/scala/org/apache/spark/network/SenderTest.scala b/core/src/main/scala/org/apache/spark/network/SenderTest.scala
deleted file mode 100644
index ea2ad104ecae1..0000000000000
--- a/core/src/main/scala/org/apache/spark/network/SenderTest.scala
+++ /dev/null
@@ -1,76 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.network
-
-import java.nio.ByteBuffer
-import org.apache.spark.{SecurityManager, SparkConf}
-
-import scala.concurrent.Await
-import scala.concurrent.duration.Duration
-import scala.util.Try
-
-private[spark] object SenderTest {
-  def main(args: Array[String]) {
-
-    if (args.length < 2) {
-      println("Usage: SenderTest <target host> <target port>")
-      System.exit(1)
-    }
-
-    val targetHost = args(0)
-    val targetPort = args(1).toInt
-    val targetConnectionManagerId = new ConnectionManagerId(targetHost, targetPort)
-    val conf = new SparkConf
-    val manager = new ConnectionManager(0, conf, new SecurityManager(conf))
-    println("Started connection manager with id = " + manager.id)
-
-    manager.onReceiveMessage((msg: Message, id: ConnectionManagerId) => {
-      println("Received [" + msg + "] from [" + id + "]")
-      None
-    })
-
-    val size =  100 * 1024  * 1024
-    val buffer = ByteBuffer.allocate(size).put(Array.tabulate[Byte](size)(x => x.toByte))
-    buffer.flip
-
-    val targetServer = args(0)
-
-    val count = 100
-    (0 until count).foreach(i => {
-      val dataMessage = Message.createBufferMessage(buffer.duplicate)
-      val startTime = System.currentTimeMillis
-      /* println("Started timer at " + startTime) */
-      val promise = manager.sendMessageReliably(targetConnectionManagerId, dataMessage)
-      val responseStr: String = Try(Await.result(promise, Duration.Inf))
-        .map { response =>
-          val buffer = response.asInstanceOf[BufferMessage].buffers(0)
-          new String(buffer.array, "utf-8")
-        }.getOrElse("none")
-
-      val finishTime = System.currentTimeMillis
-      val mb = size / 1024.0 / 1024.0
-      val ms = finishTime - startTime
-      // val resultStr = "Sent " + mb + " MB " + targetServer + " in " + ms + " ms at " + (mb / ms
-      //  * 1000.0) + " MB/s"
-      val resultStr = "Sent " + mb + " MB " + targetServer + " in " + ms + " ms (" +
-        (mb / ms * 1000.0).toInt + "MB/s) | Response = " + responseStr
-      println(resultStr)
-    })
-  }
-}
-
diff --git a/core/src/main/scala/org/apache/spark/storage/BlockMessage.scala b/core/src/main/scala/org/apache/spark/network/nio/BlockMessage.scala
similarity index 89%
rename from core/src/main/scala/org/apache/spark/storage/BlockMessage.scala
rename to core/src/main/scala/org/apache/spark/network/nio/BlockMessage.scala
index a2bfce7b4a0fa..b573f1a8a5fcb 100644
--- a/core/src/main/scala/org/apache/spark/storage/BlockMessage.scala
+++ b/core/src/main/scala/org/apache/spark/network/nio/BlockMessage.scala
@@ -15,20 +15,20 @@
  * limitations under the License.
  */
 
-package org.apache.spark.storage
+package org.apache.spark.network.nio
 
 import java.nio.ByteBuffer
 
-import scala.collection.mutable.ArrayBuffer
-import scala.collection.mutable.StringBuilder
+import org.apache.spark.storage.{BlockId, StorageLevel, TestBlockId}
 
-import org.apache.spark.network._
+import scala.collection.mutable.{ArrayBuffer, StringBuilder}
 
+// private[spark] because we need to register them in Kryo
 private[spark] case class GetBlock(id: BlockId)
 private[spark] case class GotBlock(id: BlockId, data: ByteBuffer)
 private[spark] case class PutBlock(id: BlockId, data: ByteBuffer, level: StorageLevel)
 
-private[spark] class BlockMessage() {
+private[nio] class BlockMessage() {
   // Un-initialized: typ = 0
   // GetBlock: typ = 1
   // GotBlock: typ = 2
@@ -159,7 +159,7 @@ private[spark] class BlockMessage() {
   }
 }
 
-private[spark] object BlockMessage {
+private[nio] object BlockMessage {
   val TYPE_NON_INITIALIZED: Int = 0
   val TYPE_GET_BLOCK: Int = 1
   val TYPE_GOT_BLOCK: Int = 2
@@ -194,16 +194,4 @@ private[spark] object BlockMessage {
     newBlockMessage.set(putBlock)
     newBlockMessage
   }
-
-  def main(args: Array[String]) {
-    val B = new BlockMessage()
-    val blockId = TestBlockId("ABC")
-    B.set(new PutBlock(blockId, ByteBuffer.allocate(10), StorageLevel.MEMORY_AND_DISK_SER_2))
-    val bMsg = B.toBufferMessage
-    val C = new BlockMessage()
-    C.set(bMsg)
-
-    println(B.getId + " " + B.getLevel)
-    println(C.getId + " " + C.getLevel)
-  }
 }
diff --git a/core/src/main/scala/org/apache/spark/storage/BlockMessageArray.scala b/core/src/main/scala/org/apache/spark/network/nio/BlockMessageArray.scala
similarity index 97%
rename from core/src/main/scala/org/apache/spark/storage/BlockMessageArray.scala
rename to core/src/main/scala/org/apache/spark/network/nio/BlockMessageArray.scala
index 973d85c0a9b3a..a1a2c00ed1542 100644
--- a/core/src/main/scala/org/apache/spark/storage/BlockMessageArray.scala
+++ b/core/src/main/scala/org/apache/spark/network/nio/BlockMessageArray.scala
@@ -15,16 +15,16 @@
  * limitations under the License.
  */
 
-package org.apache.spark.storage
+package org.apache.spark.network.nio
 
 import java.nio.ByteBuffer
 
-import scala.collection.mutable.ArrayBuffer
-
 import org.apache.spark._
-import org.apache.spark.network._
+import org.apache.spark.storage.{StorageLevel, TestBlockId}
+
+import scala.collection.mutable.ArrayBuffer
 
-private[spark]
+private[nio]
 class BlockMessageArray(var blockMessages: Seq[BlockMessage])
   extends Seq[BlockMessage] with Logging {
 
@@ -102,7 +102,7 @@ class BlockMessageArray(var blockMessages: Seq[BlockMessage])
   }
 }
 
-private[spark] object BlockMessageArray {
+private[nio] object BlockMessageArray {
 
   def fromBufferMessage(bufferMessage: BufferMessage): BlockMessageArray = {
     val newBlockMessageArray = new BlockMessageArray()
diff --git a/core/src/main/scala/org/apache/spark/network/BufferMessage.scala b/core/src/main/scala/org/apache/spark/network/nio/BufferMessage.scala
similarity index 98%
rename from core/src/main/scala/org/apache/spark/network/BufferMessage.scala
rename to core/src/main/scala/org/apache/spark/network/nio/BufferMessage.scala
index af35f1fc3e459..3b245c5c7a4f3 100644
--- a/core/src/main/scala/org/apache/spark/network/BufferMessage.scala
+++ b/core/src/main/scala/org/apache/spark/network/nio/BufferMessage.scala
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.network
+package org.apache.spark.network.nio
 
 import java.nio.ByteBuffer
 
@@ -23,7 +23,8 @@ import scala.collection.mutable.ArrayBuffer
 
 import org.apache.spark.storage.BlockManager
 
-private[spark]
+
+private[nio]
 class BufferMessage(id_ : Int, val buffers: ArrayBuffer[ByteBuffer], var ackId: Int)
   extends Message(Message.BUFFER_MESSAGE, id_) {
 
diff --git a/core/src/main/scala/org/apache/spark/network/Connection.scala b/core/src/main/scala/org/apache/spark/network/nio/Connection.scala
similarity index 99%
rename from core/src/main/scala/org/apache/spark/network/Connection.scala
rename to core/src/main/scala/org/apache/spark/network/nio/Connection.scala
index 5285ec82c1b64..74074a8dcbfff 100644
--- a/core/src/main/scala/org/apache/spark/network/Connection.scala
+++ b/core/src/main/scala/org/apache/spark/network/nio/Connection.scala
@@ -15,17 +15,17 @@
  * limitations under the License.
  */
 
-package org.apache.spark.network
+package org.apache.spark.network.nio
 
 import java.net._
 import java.nio._
 import java.nio.channels._
 
-import scala.collection.mutable.{ArrayBuffer, HashMap, Queue}
-
 import org.apache.spark._
 
-private[spark]
+import scala.collection.mutable.{ArrayBuffer, HashMap, Queue}
+
+private[nio]
 abstract class Connection(val channel: SocketChannel, val selector: Selector,
     val socketRemoteConnectionManagerId: ConnectionManagerId, val connectionId: ConnectionId)
   extends Logging {
@@ -190,7 +190,7 @@ abstract class Connection(val channel: SocketChannel, val selector: Selector,
 }
 
 
-private[spark]
+private[nio]
 class SendingConnection(val address: InetSocketAddress, selector_ : Selector,
     remoteId_ : ConnectionManagerId, id_ : ConnectionId)
   extends Connection(SocketChannel.open, selector_, remoteId_, id_) {
diff --git a/core/src/main/scala/org/apache/spark/network/ConnectionId.scala b/core/src/main/scala/org/apache/spark/network/nio/ConnectionId.scala
similarity index 88%
rename from core/src/main/scala/org/apache/spark/network/ConnectionId.scala
rename to core/src/main/scala/org/apache/spark/network/nio/ConnectionId.scala
index d579c165a1917..764dc5e5503ed 100644
--- a/core/src/main/scala/org/apache/spark/network/ConnectionId.scala
+++ b/core/src/main/scala/org/apache/spark/network/nio/ConnectionId.scala
@@ -15,13 +15,13 @@
  * limitations under the License.
  */
 
-package org.apache.spark.network
+package org.apache.spark.network.nio
 
-private[spark] case class ConnectionId(connectionManagerId: ConnectionManagerId, uniqId: Int) {
+private[nio] case class ConnectionId(connectionManagerId: ConnectionManagerId, uniqId: Int) {
   override def toString = connectionManagerId.host + "_" + connectionManagerId.port + "_" + uniqId
 }
 
-private[spark] object ConnectionId {
+private[nio] object ConnectionId {
 
   def createConnectionIdFromString(connectionIdString: String): ConnectionId = {
     val res = connectionIdString.split("_").map(_.trim())
diff --git a/core/src/main/scala/org/apache/spark/network/ConnectionManager.scala b/core/src/main/scala/org/apache/spark/network/nio/ConnectionManager.scala
similarity index 98%
rename from core/src/main/scala/org/apache/spark/network/ConnectionManager.scala
rename to core/src/main/scala/org/apache/spark/network/nio/ConnectionManager.scala
index 578d806263006..09d3ea306515b 100644
--- a/core/src/main/scala/org/apache/spark/network/ConnectionManager.scala
+++ b/core/src/main/scala/org/apache/spark/network/nio/ConnectionManager.scala
@@ -15,32 +15,27 @@
  * limitations under the License.
  */
 
-package org.apache.spark.network
+package org.apache.spark.network.nio
 
 import java.io.IOException
+import java.net._
 import java.nio._
 import java.nio.channels._
 import java.nio.channels.spi._
-import java.net._
-import java.util.{Timer, TimerTask}
 import java.util.concurrent.atomic.AtomicInteger
+import java.util.concurrent.{LinkedBlockingDeque, ThreadPoolExecutor, TimeUnit}
+import java.util.{Timer, TimerTask}
 
-import java.util.concurrent.{LinkedBlockingDeque, TimeUnit, ThreadPoolExecutor}
-
-import scala.collection.mutable.ArrayBuffer
-import scala.collection.mutable.HashMap
-import scala.collection.mutable.HashSet
-import scala.collection.mutable.SynchronizedMap
-import scala.collection.mutable.SynchronizedQueue
-
-import scala.concurrent.{Await, ExecutionContext, Future, Promise}
+import scala.collection.mutable.{ArrayBuffer, HashMap, HashSet, SynchronizedMap, SynchronizedQueue}
 import scala.concurrent.duration._
+import scala.concurrent.{Await, ExecutionContext, Future, Promise}
 import scala.language.postfixOps
 
 import org.apache.spark._
 import org.apache.spark.util.{SystemClock, Utils}
 
-private[spark] class ConnectionManager(
+
+private[nio] class ConnectionManager(
     port: Int,
     conf: SparkConf,
     securityManager: SecurityManager,
@@ -904,7 +899,7 @@ private[spark] class ConnectionManager(
 
 
 private[spark] object ConnectionManager {
-  import ExecutionContext.Implicits.global
+  import scala.concurrent.ExecutionContext.Implicits.global
 
   def main(args: Array[String]) {
     val conf = new SparkConf
diff --git a/core/src/main/scala/org/apache/spark/network/ConnectionManagerId.scala b/core/src/main/scala/org/apache/spark/network/nio/ConnectionManagerId.scala
similarity index 88%
rename from core/src/main/scala/org/apache/spark/network/ConnectionManagerId.scala
rename to core/src/main/scala/org/apache/spark/network/nio/ConnectionManagerId.scala
index 57f7586883af1..cbb37ec5ced1f 100644
--- a/core/src/main/scala/org/apache/spark/network/ConnectionManagerId.scala
+++ b/core/src/main/scala/org/apache/spark/network/nio/ConnectionManagerId.scala
@@ -15,13 +15,13 @@
  * limitations under the License.
  */
 
-package org.apache.spark.network
+package org.apache.spark.network.nio
 
 import java.net.InetSocketAddress
 
 import org.apache.spark.util.Utils
 
-private[spark] case class ConnectionManagerId(host: String, port: Int) {
+private[nio] case class ConnectionManagerId(host: String, port: Int) {
   // DEBUG code
   Utils.checkHost(host)
   assert (port > 0)
@@ -30,7 +30,7 @@ private[spark] case class ConnectionManagerId(host: String, port: Int) {
 }
 
 
-private[spark] object ConnectionManagerId {
+private[nio] object ConnectionManagerId {
   def fromSocketAddress(socketAddress: InetSocketAddress): ConnectionManagerId = {
     new ConnectionManagerId(socketAddress.getHostName, socketAddress.getPort)
   }
diff --git a/core/src/main/scala/org/apache/spark/network/Message.scala b/core/src/main/scala/org/apache/spark/network/nio/Message.scala
similarity index 95%
rename from core/src/main/scala/org/apache/spark/network/Message.scala
rename to core/src/main/scala/org/apache/spark/network/nio/Message.scala
index 04ea50f62918c..0b874c2891255 100644
--- a/core/src/main/scala/org/apache/spark/network/Message.scala
+++ b/core/src/main/scala/org/apache/spark/network/nio/Message.scala
@@ -15,14 +15,15 @@
  * limitations under the License.
  */
 
-package org.apache.spark.network
+package org.apache.spark.network.nio
 
 import java.net.InetSocketAddress
 import java.nio.ByteBuffer
 
 import scala.collection.mutable.ArrayBuffer
 
-private[spark] abstract class Message(val typ: Long, val id: Int) {
+
+private[nio] abstract class Message(val typ: Long, val id: Int) {
   var senderAddress: InetSocketAddress = null
   var started = false
   var startTime = -1L
@@ -42,7 +43,7 @@ private[spark] abstract class Message(val typ: Long, val id: Int) {
 }
 
 
-private[spark] object Message {
+private[nio] object Message {
   val BUFFER_MESSAGE = 1111111111L
 
   var lastId = 1
diff --git a/core/src/main/scala/org/apache/spark/network/MessageChunk.scala b/core/src/main/scala/org/apache/spark/network/nio/MessageChunk.scala
similarity index 96%
rename from core/src/main/scala/org/apache/spark/network/MessageChunk.scala
rename to core/src/main/scala/org/apache/spark/network/nio/MessageChunk.scala
index d0f986a12bfe0..278c5ac356ef2 100644
--- a/core/src/main/scala/org/apache/spark/network/MessageChunk.scala
+++ b/core/src/main/scala/org/apache/spark/network/nio/MessageChunk.scala
@@ -15,13 +15,13 @@
  * limitations under the License.
  */
 
-package org.apache.spark.network
+package org.apache.spark.network.nio
 
 import java.nio.ByteBuffer
 
 import scala.collection.mutable.ArrayBuffer
 
-private[network]
+private[nio]
 class MessageChunk(val header: MessageChunkHeader, val buffer: ByteBuffer) {
 
   val size = if (buffer == null) 0 else buffer.remaining
diff --git a/core/src/main/scala/org/apache/spark/network/MessageChunkHeader.scala b/core/src/main/scala/org/apache/spark/network/nio/MessageChunkHeader.scala
similarity index 93%
rename from core/src/main/scala/org/apache/spark/network/MessageChunkHeader.scala
rename to core/src/main/scala/org/apache/spark/network/nio/MessageChunkHeader.scala
index f3ecca5f992e0..6e20f291c5cec 100644
--- a/core/src/main/scala/org/apache/spark/network/MessageChunkHeader.scala
+++ b/core/src/main/scala/org/apache/spark/network/nio/MessageChunkHeader.scala
@@ -15,13 +15,12 @@
  * limitations under the License.
  */
 
-package org.apache.spark.network
+package org.apache.spark.network.nio
 
-import java.net.InetAddress
-import java.net.InetSocketAddress
+import java.net.{InetAddress, InetSocketAddress}
 import java.nio.ByteBuffer
 
-private[spark] class MessageChunkHeader(
+private[nio] class MessageChunkHeader(
     val typ: Long,
     val id: Int,
     val totalSize: Int,
@@ -57,7 +56,7 @@ private[spark] class MessageChunkHeader(
 }
 
 
-private[spark] object MessageChunkHeader {
+private[nio] object MessageChunkHeader {
   val HEADER_SIZE = 45
 
   def create(buffer: ByteBuffer): MessageChunkHeader = {
diff --git a/core/src/main/scala/org/apache/spark/network/nio/NioBlockTransferService.scala b/core/src/main/scala/org/apache/spark/network/nio/NioBlockTransferService.scala
new file mode 100644
index 0000000000000..59958ee894230
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/network/nio/NioBlockTransferService.scala
@@ -0,0 +1,205 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.network.nio
+
+import java.nio.ByteBuffer
+
+import scala.concurrent.Future
+
+import org.apache.spark.{SparkException, Logging, SecurityManager, SparkConf}
+import org.apache.spark.network._
+import org.apache.spark.storage.{BlockId, StorageLevel}
+import org.apache.spark.util.Utils
+
+
+/**
+ * A [[BlockTransferService]] implementation based on [[ConnectionManager]], a custom
+ * implementation using Java NIO.
+ */
+final class NioBlockTransferService(conf: SparkConf, securityManager: SecurityManager)
+  extends BlockTransferService with Logging {
+
+  private var cm: ConnectionManager = _
+
+  private var blockDataManager: BlockDataManager = _
+
+  /**
+   * Port number the service is listening on, available only after [[init]] is invoked.
+   */
+  override def port: Int = {
+    checkInit()
+    cm.id.port
+  }
+
+  /**
+   * Host name the service is listening on, available only after [[init]] is invoked.
+   */
+  override def hostName: String = {
+    checkInit()
+    cm.id.host
+  }
+
+  /**
+   * Initialize the transfer service by giving it the BlockDataManager that can be used to fetch
+   * local blocks or put local blocks.
+   */
+  override def init(blockDataManager: BlockDataManager): Unit = {
+    this.blockDataManager = blockDataManager
+    cm = new ConnectionManager(
+      conf.getInt("spark.blockManager.port", 0),
+      conf,
+      securityManager,
+      "Connection manager for block manager")
+    cm.onReceiveMessage(onBlockMessageReceive)
+  }
+
+  /**
+   * Tear down the transfer service.
+   */
+  override def stop(): Unit = {
+    if (cm != null) {
+      cm.stop()
+    }
+  }
+
+  override def fetchBlocks(
+      hostName: String,
+      port: Int,
+      blockIds: Seq[String],
+      listener: BlockFetchingListener): Unit = {
+    checkInit()
+
+    val cmId = new ConnectionManagerId(hostName, port)
+    val blockMessageArray = new BlockMessageArray(blockIds.map { blockId =>
+      BlockMessage.fromGetBlock(GetBlock(BlockId(blockId)))
+    })
+
+    val future = cm.sendMessageReliably(cmId, blockMessageArray.toBufferMessage)
+
+    // Register the listener on success/failure future callback.
+    future.onSuccess { case message =>
+      val bufferMessage = message.asInstanceOf[BufferMessage]
+      val blockMessageArray = BlockMessageArray.fromBufferMessage(bufferMessage)
+
+      for (blockMessage <- blockMessageArray) {
+        if (blockMessage.getType != BlockMessage.TYPE_GOT_BLOCK) {
+          listener.onBlockFetchFailure(
+            new SparkException(s"Unexpected message ${blockMessage.getType} received from $cmId"))
+        } else {
+          val blockId = blockMessage.getId
+          val networkSize = blockMessage.getData.limit()
+          listener.onBlockFetchSuccess(
+            blockId.toString, new NioByteBufferManagedBuffer(blockMessage.getData))
+        }
+      }
+    }(cm.futureExecContext)
+
+    future.onFailure { case exception =>
+      listener.onBlockFetchFailure(exception)
+    }(cm.futureExecContext)
+  }
+
+  /**
+   * Upload a single block to a remote node, available only after [[init]] is invoked.
+   *
+   * This call blocks until the upload completes, or throws an exception upon failures.
+   */
+  override def uploadBlock(
+      hostname: String,
+      port: Int,
+      blockId: String,
+      blockData: ManagedBuffer,
+      level: StorageLevel)
+    : Future[Unit] = {
+    checkInit()
+    val msg = PutBlock(BlockId(blockId), blockData.nioByteBuffer(), level)
+    val blockMessageArray = new BlockMessageArray(BlockMessage.fromPutBlock(msg))
+    val remoteCmId = new ConnectionManagerId(hostName, port)
+    val reply = cm.sendMessageReliably(remoteCmId, blockMessageArray.toBufferMessage)
+    reply.map(x => ())(cm.futureExecContext)
+  }
+
+  private def checkInit(): Unit = if (cm == null) {
+    throw new IllegalStateException(getClass.getName + " has not been initialized")
+  }
+
+  private def onBlockMessageReceive(msg: Message, id: ConnectionManagerId): Option[Message] = {
+    logDebug("Handling message " + msg)
+    msg match {
+      case bufferMessage: BufferMessage =>
+        try {
+          logDebug("Handling as a buffer message " + bufferMessage)
+          val blockMessages = BlockMessageArray.fromBufferMessage(bufferMessage)
+          logDebug("Parsed as a block message array")
+          val responseMessages = blockMessages.map(processBlockMessage).filter(_ != None).map(_.get)
+          Some(new BlockMessageArray(responseMessages).toBufferMessage)
+        } catch {
+          case e: Exception => {
+            logError("Exception handling buffer message", e)
+            val errorMessage = Message.createBufferMessage(msg.id)
+            errorMessage.hasError = true
+            Some(errorMessage)
+          }
+        }
+
+      case otherMessage: Any =>
+        logError("Unknown type message received: " + otherMessage)
+        val errorMessage = Message.createBufferMessage(msg.id)
+        errorMessage.hasError = true
+        Some(errorMessage)
+    }
+  }
+
+  private def processBlockMessage(blockMessage: BlockMessage): Option[BlockMessage] = {
+    blockMessage.getType match {
+      case BlockMessage.TYPE_PUT_BLOCK =>
+        val msg = PutBlock(blockMessage.getId, blockMessage.getData, blockMessage.getLevel)
+        logDebug("Received [" + msg + "]")
+        putBlock(msg.id.toString, msg.data, msg.level)
+        None
+
+      case BlockMessage.TYPE_GET_BLOCK =>
+        val msg = new GetBlock(blockMessage.getId)
+        logDebug("Received [" + msg + "]")
+        val buffer = getBlock(msg.id.toString)
+        if (buffer == null) {
+          return None
+        }
+        Some(BlockMessage.fromGotBlock(GotBlock(msg.id, buffer)))
+
+      case _ => None
+    }
+  }
+
+  private def putBlock(blockId: String, bytes: ByteBuffer, level: StorageLevel) {
+    val startTimeMs = System.currentTimeMillis()
+    logDebug("PutBlock " + blockId + " started from " + startTimeMs + " with data: " + bytes)
+    blockDataManager.putBlockData(blockId, new NioByteBufferManagedBuffer(bytes), level)
+    logDebug("PutBlock " + blockId + " used " + Utils.getUsedTimeMs(startTimeMs)
+      + " with data size: " + bytes.limit)
+  }
+
+  private def getBlock(blockId: String): ByteBuffer = {
+    val startTimeMs = System.currentTimeMillis()
+    logDebug("GetBlock " + blockId + " started from " + startTimeMs)
+    val buffer = blockDataManager.getBlockData(blockId).orNull
+    logDebug("GetBlock " + blockId + " used " + Utils.getUsedTimeMs(startTimeMs)
+      + " and got buffer " + buffer)
+    buffer.nioByteBuffer()
+  }
+}
diff --git a/core/src/main/scala/org/apache/spark/network/SecurityMessage.scala b/core/src/main/scala/org/apache/spark/network/nio/SecurityMessage.scala
similarity index 95%
rename from core/src/main/scala/org/apache/spark/network/SecurityMessage.scala
rename to core/src/main/scala/org/apache/spark/network/nio/SecurityMessage.scala
index 9af9e2e8e9e59..747a2088a7258 100644
--- a/core/src/main/scala/org/apache/spark/network/SecurityMessage.scala
+++ b/core/src/main/scala/org/apache/spark/network/nio/SecurityMessage.scala
@@ -15,15 +15,13 @@
  * limitations under the License.
  */
 
-package org.apache.spark.network
+package org.apache.spark.network.nio
 
 import java.nio.ByteBuffer
 
-import scala.collection.mutable.ArrayBuffer
-import scala.collection.mutable.StringBuilder
+import scala.collection.mutable.{ArrayBuffer, StringBuilder}
 
 import org.apache.spark._
-import org.apache.spark.network._
 
 /**
  * SecurityMessage is class that contains the connectionId and sasl token
@@ -54,7 +52,7 @@ import org.apache.spark.network._
  *   - Length of the token
  *   - Token
  */
-private[spark] class SecurityMessage() extends Logging {
+private[nio] class SecurityMessage extends Logging {
 
   private var connectionId: String = null
   private var token: Array[Byte] = null
@@ -134,7 +132,7 @@ private[spark] class SecurityMessage() extends Logging {
   }
 }
 
-private[spark] object SecurityMessage {
+private[nio] object SecurityMessage {
 
   /**
    * Convert the given BufferMessage to a SecurityMessage by parsing the contents
diff --git a/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala b/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala
index 87ef9bb0b43c6..d6386f8c06fff 100644
--- a/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala
+++ b/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala
@@ -27,9 +27,9 @@ import com.twitter.chill.{AllScalaRegistrar, EmptyScalaKryoInstantiator}
 
 import org.apache.spark._
 import org.apache.spark.broadcast.HttpBroadcast
+import org.apache.spark.network.nio.{PutBlock, GotBlock, GetBlock}
 import org.apache.spark.scheduler.MapStatus
 import org.apache.spark.storage._
-import org.apache.spark.storage.{GetBlock, GotBlock, PutBlock}
 import org.apache.spark.util.BoundedPriorityQueue
 import org.apache.spark.util.collection.CompactBuffer
 
diff --git a/core/src/main/scala/org/apache/spark/shuffle/FileShuffleBlockManager.scala b/core/src/main/scala/org/apache/spark/shuffle/FileShuffleBlockManager.scala
index 96facccd52373..439981d232349 100644
--- a/core/src/main/scala/org/apache/spark/shuffle/FileShuffleBlockManager.scala
+++ b/core/src/main/scala/org/apache/spark/shuffle/FileShuffleBlockManager.scala
@@ -26,6 +26,7 @@ import scala.collection.JavaConversions._
 
 import org.apache.spark.{SparkEnv, SparkConf, Logging}
 import org.apache.spark.executor.ShuffleWriteMetrics
+import org.apache.spark.network.{FileSegmentManagedBuffer, ManagedBuffer}
 import org.apache.spark.serializer.Serializer
 import org.apache.spark.shuffle.FileShuffleBlockManager.ShuffleFileGroup
 import org.apache.spark.storage._
@@ -166,34 +167,30 @@ class FileShuffleBlockManager(conf: SparkConf)
     }
   }
 
-  /**
-   * Returns the physical file segment in which the given BlockId is located.
-   */
-  private def getBlockLocation(id: ShuffleBlockId): FileSegment = {
+  override def getBytes(blockId: ShuffleBlockId): Option[ByteBuffer] = {
+    val segment = getBlockData(blockId)
+    Some(segment.nioByteBuffer())
+  }
+
+  override def getBlockData(blockId: ShuffleBlockId): ManagedBuffer = {
     if (consolidateShuffleFiles) {
       // Search all file groups associated with this shuffle.
-      val shuffleState = shuffleStates(id.shuffleId)
+      val shuffleState = shuffleStates(blockId.shuffleId)
       val iter = shuffleState.allFileGroups.iterator
       while (iter.hasNext) {
-        val segment = iter.next.getFileSegmentFor(id.mapId, id.reduceId)
-        if (segment.isDefined) { return segment.get }
+        val segmentOpt = iter.next.getFileSegmentFor(blockId.mapId, blockId.reduceId)
+        if (segmentOpt.isDefined) {
+          val segment = segmentOpt.get
+          return new FileSegmentManagedBuffer(segment.file, segment.offset, segment.length)
+        }
       }
-      throw new IllegalStateException("Failed to find shuffle block: " + id)
+      throw new IllegalStateException("Failed to find shuffle block: " + blockId)
     } else {
-      val file = blockManager.diskBlockManager.getFile(id)
-      new FileSegment(file, 0, file.length())
+      val file = blockManager.diskBlockManager.getFile(blockId)
+      new FileSegmentManagedBuffer(file, 0, file.length)
     }
   }
 
-  override def getBytes(blockId: ShuffleBlockId): Option[ByteBuffer] = {
-    val segment = getBlockLocation(blockId)
-    blockManager.diskStore.getBytes(segment)
-  }
-
-  override def getBlockData(blockId: ShuffleBlockId): Either[FileSegment, ByteBuffer] = {
-    Left(getBlockLocation(blockId.asInstanceOf[ShuffleBlockId]))
-  }
-
   /** Remove all the blocks / files and metadata related to a particular shuffle. */
   def removeShuffle(shuffleId: ShuffleId): Boolean = {
     // Do not change the ordering of this, if shuffleStates should be removed only
diff --git a/core/src/main/scala/org/apache/spark/shuffle/IndexShuffleBlockManager.scala b/core/src/main/scala/org/apache/spark/shuffle/IndexShuffleBlockManager.scala
index 8bb9efc46cc58..4ab34336d3f01 100644
--- a/core/src/main/scala/org/apache/spark/shuffle/IndexShuffleBlockManager.scala
+++ b/core/src/main/scala/org/apache/spark/shuffle/IndexShuffleBlockManager.scala
@@ -21,6 +21,7 @@ import java.io._
 import java.nio.ByteBuffer
 
 import org.apache.spark.SparkEnv
+import org.apache.spark.network.{ManagedBuffer, FileSegmentManagedBuffer}
 import org.apache.spark.storage._
 
 /**
@@ -89,10 +90,11 @@ class IndexShuffleBlockManager extends ShuffleBlockManager {
     }
   }
 
-  /**
-   * Get the location of a block in a map output file. Uses the index file we create for it.
-   * */
-  private def getBlockLocation(blockId: ShuffleBlockId): FileSegment = {
+  override def getBytes(blockId: ShuffleBlockId): Option[ByteBuffer] = {
+    Some(getBlockData(blockId).nioByteBuffer())
+  }
+
+  override def getBlockData(blockId: ShuffleBlockId): ManagedBuffer = {
     // The block is actually going to be a range of a single map output file for this map, so
     // find out the consolidated file, then the offset within that from our index
     val indexFile = getIndexFile(blockId.shuffleId, blockId.mapId)
@@ -102,20 +104,14 @@ class IndexShuffleBlockManager extends ShuffleBlockManager {
       in.skip(blockId.reduceId * 8)
       val offset = in.readLong()
       val nextOffset = in.readLong()
-      new FileSegment(getDataFile(blockId.shuffleId, blockId.mapId), offset, nextOffset - offset)
+      new FileSegmentManagedBuffer(
+        getDataFile(blockId.shuffleId, blockId.mapId),
+        offset,
+        nextOffset - offset)
     } finally {
       in.close()
     }
   }
 
-  override def getBytes(blockId: ShuffleBlockId): Option[ByteBuffer] = {
-    val segment = getBlockLocation(blockId)
-    blockManager.diskStore.getBytes(segment)
-  }
-
-  override def getBlockData(blockId: ShuffleBlockId): Either[FileSegment, ByteBuffer] = {
-    Left(getBlockLocation(blockId.asInstanceOf[ShuffleBlockId]))
-  }
-
   override def stop() = {}
 }
diff --git a/core/src/main/scala/org/apache/spark/shuffle/ShuffleBlockManager.scala b/core/src/main/scala/org/apache/spark/shuffle/ShuffleBlockManager.scala
index 4240580250046..63863cc0250a3 100644
--- a/core/src/main/scala/org/apache/spark/shuffle/ShuffleBlockManager.scala
+++ b/core/src/main/scala/org/apache/spark/shuffle/ShuffleBlockManager.scala
@@ -19,7 +19,8 @@ package org.apache.spark.shuffle
 
 import java.nio.ByteBuffer
 
-import org.apache.spark.storage.{FileSegment, ShuffleBlockId}
+import org.apache.spark.network.ManagedBuffer
+import org.apache.spark.storage.ShuffleBlockId
 
 private[spark]
 trait ShuffleBlockManager {
@@ -31,8 +32,7 @@ trait ShuffleBlockManager {
    */
   def getBytes(blockId: ShuffleBlockId): Option[ByteBuffer]
 
-  def getBlockData(blockId: ShuffleBlockId): Either[FileSegment, ByteBuffer]
+  def getBlockData(blockId: ShuffleBlockId): ManagedBuffer
 
   def stop(): Unit
 }
-
diff --git a/core/src/main/scala/org/apache/spark/shuffle/hash/BlockStoreShuffleFetcher.scala b/core/src/main/scala/org/apache/spark/shuffle/hash/BlockStoreShuffleFetcher.scala
index 12b475658e29d..6cf9305977a3c 100644
--- a/core/src/main/scala/org/apache/spark/shuffle/hash/BlockStoreShuffleFetcher.scala
+++ b/core/src/main/scala/org/apache/spark/shuffle/hash/BlockStoreShuffleFetcher.scala
@@ -21,10 +21,9 @@ import scala.collection.mutable.ArrayBuffer
 import scala.collection.mutable.HashMap
 
 import org.apache.spark._
-import org.apache.spark.executor.ShuffleReadMetrics
 import org.apache.spark.serializer.Serializer
 import org.apache.spark.shuffle.FetchFailedException
-import org.apache.spark.storage.{BlockId, BlockManagerId, ShuffleBlockId}
+import org.apache.spark.storage.{BlockId, BlockManagerId, ShuffleBlockFetcherIterator, ShuffleBlockId}
 import org.apache.spark.util.CompletionIterator
 
 private[hash] object BlockStoreShuffleFetcher extends Logging {
@@ -32,8 +31,7 @@ private[hash] object BlockStoreShuffleFetcher extends Logging {
       shuffleId: Int,
       reduceId: Int,
       context: TaskContext,
-      serializer: Serializer,
-      shuffleMetrics: ShuffleReadMetrics)
+      serializer: Serializer)
     : Iterator[T] =
   {
     logDebug("Fetching outputs for shuffle %d, reduce %d".format(shuffleId, reduceId))
@@ -74,7 +72,13 @@ private[hash] object BlockStoreShuffleFetcher extends Logging {
       }
     }
 
-    val blockFetcherItr = blockManager.getMultiple(blocksByAddress, serializer, shuffleMetrics)
+    val blockFetcherItr = new ShuffleBlockFetcherIterator(
+      context,
+      SparkEnv.get.blockTransferService,
+      blockManager,
+      blocksByAddress,
+      serializer,
+      SparkEnv.get.conf.getLong("spark.reducer.maxMbInFlight", 48) * 1024 * 1024)
     val itr = blockFetcherItr.flatMap(unpackBlock)
 
     val completionIter = CompletionIterator[T, Iterator[T]](itr, {
diff --git a/core/src/main/scala/org/apache/spark/shuffle/hash/HashShuffleReader.scala b/core/src/main/scala/org/apache/spark/shuffle/hash/HashShuffleReader.scala
index 7bed97a63f0f6..88a5f1e5ddf58 100644
--- a/core/src/main/scala/org/apache/spark/shuffle/hash/HashShuffleReader.scala
+++ b/core/src/main/scala/org/apache/spark/shuffle/hash/HashShuffleReader.scala
@@ -36,10 +36,8 @@ private[spark] class HashShuffleReader[K, C](
 
   /** Read the combined key-values for this reduce task */
   override def read(): Iterator[Product2[K, C]] = {
-    val readMetrics = context.taskMetrics.createShuffleReadMetricsForDependency()
     val ser = Serializer.getSerializer(dep.serializer)
-    val iter = BlockStoreShuffleFetcher.fetch(handle.shuffleId, startPartition, context, ser,
-      readMetrics)
+    val iter = BlockStoreShuffleFetcher.fetch(handle.shuffleId, startPartition, context, ser)
 
     val aggregatedIter: Iterator[Product2[K, C]] = if (dep.aggregator.isDefined) {
       if (dep.mapSideCombine) {
diff --git a/core/src/main/scala/org/apache/spark/storage/BlockFetcherIterator.scala b/core/src/main/scala/org/apache/spark/storage/BlockFetcherIterator.scala
deleted file mode 100644
index e35b7fe62c753..0000000000000
--- a/core/src/main/scala/org/apache/spark/storage/BlockFetcherIterator.scala
+++ /dev/null
@@ -1,254 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.storage
-
-import java.util.concurrent.LinkedBlockingQueue
-import org.apache.spark.network.netty.client.{BlockClientListener, LazyInitIterator, ReferenceCountedBuffer}
-
-import scala.collection.mutable.ArrayBuffer
-import scala.collection.mutable.HashSet
-import scala.collection.mutable.Queue
-import scala.util.{Failure, Success}
-
-import org.apache.spark.{Logging, SparkException}
-import org.apache.spark.executor.ShuffleReadMetrics
-import org.apache.spark.network.BufferMessage
-import org.apache.spark.network.ConnectionManagerId
-import org.apache.spark.serializer.Serializer
-import org.apache.spark.util.Utils
-
-/**
- * A block fetcher iterator interface for fetching shuffle blocks.
- */
-private[storage]
-trait BlockFetcherIterator extends Iterator[(BlockId, Option[Iterator[Any]])] with Logging {
-  def initialize()
-}
-
-
-private[storage]
-object BlockFetcherIterator {
-
-  /**
-   * A request to fetch blocks from a remote BlockManager.
-   * @param address remote BlockManager to fetch from.
-   * @param blocks Sequence of tuple, where the first element is the block id,
-   *               and the second element is the estimated size, used to calculate bytesInFlight.
-   */
-  class FetchRequest(val address: BlockManagerId, val blocks: Seq[(BlockId, Long)]) {
-    val size = blocks.map(_._2).sum
-  }
-
-  /**
-   * Result of a fetch from a remote block. A failure is represented as size == -1.
-   * @param blockId block id
-   * @param size estimated size of the block, used to calculate bytesInFlight.
-   *             Note that this is NOT the exact bytes.
-   * @param deserialize closure to return the result in the form of an Iterator.
-   */
-  class FetchResult(val blockId: BlockId, val size: Long, val deserialize: () => Iterator[Any]) {
-    def failed: Boolean = size == -1
-  }
-
-  // TODO: Refactor this whole thing to make code more reusable.
-  class BasicBlockFetcherIterator(
-      private val blockManager: BlockManager,
-      val blocksByAddress: Seq[(BlockManagerId, Seq[(BlockId, Long)])],
-      serializer: Serializer,
-      readMetrics: ShuffleReadMetrics)
-    extends BlockFetcherIterator {
-
-    import blockManager._
-
-    if (blocksByAddress == null) {
-      throw new IllegalArgumentException("BlocksByAddress is null")
-    }
-
-    // Total number blocks fetched (local + remote). Also number of FetchResults expected
-    protected var _numBlocksToFetch = 0
-
-    protected var startTime = System.currentTimeMillis
-
-    // BlockIds for local blocks that need to be fetched. Excludes zero-sized blocks
-    protected val localBlocksToFetch = new ArrayBuffer[BlockId]()
-
-    // BlockIds for remote blocks that need to be fetched. Excludes zero-sized blocks
-    protected val remoteBlocksToFetch = new HashSet[BlockId]()
-
-    // A queue to hold our results.
-    protected val results = new LinkedBlockingQueue[FetchResult]
-
-    // Queue of fetch requests to issue; we'll pull requests off this gradually to make sure that
-    // the number of bytes in flight is limited to maxBytesInFlight
-    protected val fetchRequests = new Queue[FetchRequest]
-
-    // Current bytes in flight from our requests
-    protected var bytesInFlight = 0L
-
-    protected def sendRequest(req: FetchRequest) {
-      logDebug("Sending request for %d blocks (%s) from %s".format(
-        req.blocks.size, Utils.bytesToString(req.size), req.address.hostPort))
-      val cmId = new ConnectionManagerId(req.address.host, req.address.port)
-      val blockMessageArray = new BlockMessageArray(req.blocks.map {
-        case (blockId, size) => BlockMessage.fromGetBlock(GetBlock(blockId))
-      })
-      bytesInFlight += req.size
-      val sizeMap = req.blocks.toMap  // so we can look up the size of each blockID
-      val future = connectionManager.sendMessageReliably(cmId, blockMessageArray.toBufferMessage)
-      future.onComplete {
-        case Success(message) => {
-          val bufferMessage = message.asInstanceOf[BufferMessage]
-          val blockMessageArray = BlockMessageArray.fromBufferMessage(bufferMessage)
-          for (blockMessage <- blockMessageArray) {
-            if (blockMessage.getType != BlockMessage.TYPE_GOT_BLOCK) {
-              throw new SparkException(
-                "Unexpected message " + blockMessage.getType + " received from " + cmId)
-            }
-            val blockId = blockMessage.getId
-            val networkSize = blockMessage.getData.limit()
-            results.put(new FetchResult(blockId, sizeMap(blockId),
-              () => dataDeserialize(blockId, blockMessage.getData, serializer)))
-            // TODO: NettyBlockFetcherIterator has some race conditions where multiple threads can
-            // be incrementing bytes read at the same time (SPARK-2625).
-            readMetrics.remoteBytesRead += networkSize
-            readMetrics.remoteBlocksFetched += 1
-            logDebug("Got remote block " + blockId + " after " + Utils.getUsedTimeMs(startTime))
-          }
-        }
-        case Failure(exception) => {
-          logError("Could not get block(s) from " + cmId, exception)
-          for ((blockId, size) <- req.blocks) {
-            results.put(new FetchResult(blockId, -1, null))
-          }
-        }
-      }
-    }
-
-    protected def splitLocalRemoteBlocks(): ArrayBuffer[FetchRequest] = {
-      // Make remote requests at most maxBytesInFlight / 5 in length; the reason to keep them
-      // smaller than maxBytesInFlight is to allow multiple, parallel fetches from up to 5
-      // nodes, rather than blocking on reading output from one node.
-      val targetRequestSize = math.max(maxBytesInFlight / 5, 1L)
-      logInfo("maxBytesInFlight: " + maxBytesInFlight + ", targetRequestSize: " + targetRequestSize)
-
-      // Split local and remote blocks. Remote blocks are further split into FetchRequests of size
-      // at most maxBytesInFlight in order to limit the amount of data in flight.
-      val remoteRequests = new ArrayBuffer[FetchRequest]
-      var totalBlocks = 0
-      for ((address, blockInfos) <- blocksByAddress) {
-        totalBlocks += blockInfos.size
-        if (address == blockManagerId) {
-          // Filter out zero-sized blocks
-          localBlocksToFetch ++= blockInfos.filter(_._2 != 0).map(_._1)
-          _numBlocksToFetch += localBlocksToFetch.size
-        } else {
-          val iterator = blockInfos.iterator
-          var curRequestSize = 0L
-          var curBlocks = new ArrayBuffer[(BlockId, Long)]
-          while (iterator.hasNext) {
-            val (blockId, size) = iterator.next()
-            // Skip empty blocks
-            if (size > 0) {
-              curBlocks += ((blockId, size))
-              remoteBlocksToFetch += blockId
-              _numBlocksToFetch += 1
-              curRequestSize += size
-            } else if (size < 0) {
-              throw new BlockException(blockId, "Negative block size " + size)
-            }
-            if (curRequestSize >= targetRequestSize) {
-              // Add this FetchRequest
-              remoteRequests += new FetchRequest(address, curBlocks)
-              curBlocks = new ArrayBuffer[(BlockId, Long)]
-              logDebug(s"Creating fetch request of $curRequestSize at $address")
-              curRequestSize = 0
-            }
-          }
-          // Add in the final request
-          if (!curBlocks.isEmpty) {
-            remoteRequests += new FetchRequest(address, curBlocks)
-          }
-        }
-      }
-      logInfo("Getting " + _numBlocksToFetch + " non-empty blocks out of " +
-        totalBlocks + " blocks")
-      remoteRequests
-    }
-
-    protected def getLocalBlocks() {
-      // Get the local blocks while remote blocks are being fetched. Note that it's okay to do
-      // these all at once because they will just memory-map some files, so they won't consume
-      // any memory that might exceed our maxBytesInFlight
-      for (id <- localBlocksToFetch) {
-        try {
-          readMetrics.localBlocksFetched += 1
-          results.put(new FetchResult(id, 0, () => getLocalShuffleFromDisk(id, serializer).get))
-          logDebug("Got local block " + id)
-        } catch {
-          case e: Exception => {
-            logError(s"Error occurred while fetching local blocks", e)
-            results.put(new FetchResult(id, -1, null))
-            return
-          }
-        }
-      }
-    }
-
-    override def initialize() {
-      // Split local and remote blocks.
-      val remoteRequests = splitLocalRemoteBlocks()
-      // Add the remote requests into our queue in a random order
-      fetchRequests ++= Utils.randomize(remoteRequests)
-
-      // Send out initial requests for blocks, up to our maxBytesInFlight
-      while (!fetchRequests.isEmpty &&
-        (bytesInFlight == 0 || bytesInFlight + fetchRequests.front.size <= maxBytesInFlight)) {
-        sendRequest(fetchRequests.dequeue())
-      }
-
-      val numFetches = remoteRequests.size - fetchRequests.size
-      logInfo("Started " + numFetches + " remote fetches in" + Utils.getUsedTimeMs(startTime))
-
-      // Get Local Blocks
-      startTime = System.currentTimeMillis
-      getLocalBlocks()
-      logDebug("Got local blocks in " + Utils.getUsedTimeMs(startTime) + " ms")
-    }
-
-    // Implementing the Iterator methods with an iterator that reads fetched blocks off the queue
-    // as they arrive.
-    @volatile protected var resultsGotten = 0
-
-    override def hasNext: Boolean = resultsGotten < _numBlocksToFetch
-
-    override def next(): (BlockId, Option[Iterator[Any]]) = {
-      resultsGotten += 1
-      val startFetchWait = System.currentTimeMillis()
-      val result = results.take()
-      val stopFetchWait = System.currentTimeMillis()
-      readMetrics.fetchWaitTime += (stopFetchWait - startFetchWait)
-      if (! result.failed) bytesInFlight -= result.size
-      while (!fetchRequests.isEmpty &&
-        (bytesInFlight == 0 || bytesInFlight + fetchRequests.front.size <= maxBytesInFlight)) {
-        sendRequest(fetchRequests.dequeue())
-      }
-      (result.blockId, if (result.failed) None else Some(result.deserialize()))
-    }
-  }
-  // End of BasicBlockFetcherIterator
-}
diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManager.scala b/core/src/main/scala/org/apache/spark/storage/BlockManager.scala
index a714142763243..d1bee3d2c033c 100644
--- a/core/src/main/scala/org/apache/spark/storage/BlockManager.scala
+++ b/core/src/main/scala/org/apache/spark/storage/BlockManager.scala
@@ -20,6 +20,8 @@ package org.apache.spark.storage
 import java.io.{File, InputStream, OutputStream, BufferedOutputStream, ByteArrayOutputStream}
 import java.nio.{ByteBuffer, MappedByteBuffer}
 
+import scala.concurrent.ExecutionContext.Implicits.global
+
 import scala.collection.mutable.{ArrayBuffer, HashMap}
 import scala.concurrent.{Await, Future}
 import scala.concurrent.duration._
@@ -58,18 +60,14 @@ private[spark] class BlockManager(
     defaultSerializer: Serializer,
     maxMemory: Long,
     val conf: SparkConf,
-    securityManager: SecurityManager,
     mapOutputTracker: MapOutputTracker,
-    shuffleManager: ShuffleManager)
-  extends BlockDataProvider with Logging {
+    shuffleManager: ShuffleManager,
+    blockTransferService: BlockTransferService)
+  extends BlockDataManager with Logging {
 
-  private val port = conf.getInt("spark.blockManager.port", 0)
+  blockTransferService.init(this)
 
   val diskBlockManager = new DiskBlockManager(this, conf)
-  val connectionManager =
-    new ConnectionManager(port, conf, securityManager, "Connection manager for block manager")
-
-  implicit val futureExecContext = connectionManager.futureExecContext
 
   private val blockInfo = new TimeStampedHashMap[BlockId, BlockInfo]
 
@@ -89,11 +87,7 @@ private[spark] class BlockManager(
   }
 
   val blockManagerId = BlockManagerId(
-    executorId, connectionManager.id.host, connectionManager.id.port)
-
-  // Max megabytes of data to keep in flight per reducer (to avoid over-allocating memory
-  // for receiving shuffle outputs)
-  val maxBytesInFlight = conf.getLong("spark.reducer.maxMbInFlight", 48) * 1024 * 1024
+    executorId, blockTransferService.hostName, blockTransferService.port)
 
   // Whether to compress broadcast variables that are stored
   private val compressBroadcast = conf.getBoolean("spark.broadcast.compress", true)
@@ -136,11 +130,11 @@ private[spark] class BlockManager(
       master: BlockManagerMaster,
       serializer: Serializer,
       conf: SparkConf,
-      securityManager: SecurityManager,
       mapOutputTracker: MapOutputTracker,
-      shuffleManager: ShuffleManager) = {
+      shuffleManager: ShuffleManager,
+      blockTransferService: BlockTransferService) = {
     this(execId, actorSystem, master, serializer, BlockManager.getMaxMemory(conf),
-      conf, securityManager, mapOutputTracker, shuffleManager)
+      conf, mapOutputTracker, shuffleManager, blockTransferService)
   }
 
   /**
@@ -149,7 +143,6 @@ private[spark] class BlockManager(
    */
   private def initialize(): Unit = {
     master.registerBlockManager(blockManagerId, maxMemory, slaveActor)
-    BlockManagerWorker.startBlockManagerWorker(this)
   }
 
   /**
@@ -212,20 +205,33 @@ private[spark] class BlockManager(
     }
   }
 
-  override def getBlockData(blockId: String): Either[FileSegment, ByteBuffer] = {
+  /**
+   * Interface to get local block data.
+   *
+   * @return Some(buffer) if the block exists locally, and None if it doesn't.
+   */
+  override def getBlockData(blockId: String): Option[ManagedBuffer] = {
     val bid = BlockId(blockId)
     if (bid.isShuffle) {
-      shuffleManager.shuffleBlockManager.getBlockData(bid.asInstanceOf[ShuffleBlockId])
+      Some(shuffleManager.shuffleBlockManager.getBlockData(bid.asInstanceOf[ShuffleBlockId]))
     } else {
       val blockBytesOpt = doGetLocal(bid, asBlockResult = false).asInstanceOf[Option[ByteBuffer]]
       if (blockBytesOpt.isDefined) {
-        Right(blockBytesOpt.get)
+        val buffer = blockBytesOpt.get
+        Some(new NioByteBufferManagedBuffer(buffer))
       } else {
-        throw new BlockNotFoundException(blockId)
+        None
       }
     }
   }
 
+  /**
+   * Put the block locally, using the given storage level.
+   */
+  override def putBlockData(blockId: String, data: ManagedBuffer, level: StorageLevel): Unit = {
+    putBytes(BlockId(blockId), data.nioByteBuffer(), level)
+  }
+
   /**
    * Get the BlockStatus for the block identified by the given ID, if it exists.
    * NOTE: This is mainly for testing, and it doesn't fetch information from Tachyon.
@@ -333,16 +339,10 @@ private[spark] class BlockManager(
    * shuffle blocks. It is safe to do so without a lock on block info since disk store
    * never deletes (recent) items.
    */
-  def getLocalShuffleFromDisk(
-      blockId: BlockId, serializer: Serializer): Option[Iterator[Any]] = {
-
-    val shuffleBlockManager = shuffleManager.shuffleBlockManager
-    val values = shuffleBlockManager.getBytes(blockId.asInstanceOf[ShuffleBlockId]).map(
-      bytes => this.dataDeserialize(blockId, bytes, serializer))
-
-    values.orElse {
-      throw new BlockException(blockId, s"Block $blockId not found on disk, though it should be")
-    }
+  def getLocalShuffleFromDisk(blockId: BlockId, serializer: Serializer): Option[Iterator[Any]] = {
+    val buf = shuffleManager.shuffleBlockManager.getBlockData(blockId.asInstanceOf[ShuffleBlockId])
+    val is = wrapForCompression(blockId, buf.inputStream())
+    Some(serializer.newInstance().deserializeStream(is).asIterator)
   }
 
   /**
@@ -513,8 +513,9 @@ private[spark] class BlockManager(
     val locations = Random.shuffle(master.getLocations(blockId))
     for (loc <- locations) {
       logDebug(s"Getting remote block $blockId from $loc")
-      val data = BlockManagerWorker.syncGetBlock(
-        GetBlock(blockId), ConnectionManagerId(loc.host, loc.port))
+      val data = blockTransferService.fetchBlockSync(
+        loc.host, loc.port, blockId.toString).nioByteBuffer()
+
       if (data != null) {
         if (asBlockResult) {
           return Some(new BlockResult(
@@ -548,22 +549,6 @@ private[spark] class BlockManager(
     None
   }
 
-  /**
-   * Get multiple blocks from local and remote block manager using their BlockManagerIds. Returns
-   * an Iterator of (block ID, value) pairs so that clients may handle blocks in a pipelined
-   * fashion as they're received. Expects a size in bytes to be provided for each block fetched,
-   * so that we can control the maxMegabytesInFlight for the fetch.
-   */
-  def getMultiple(
-      blocksByAddress: Seq[(BlockManagerId, Seq[(BlockId, Long)])],
-      serializer: Serializer,
-      readMetrics: ShuffleReadMetrics): BlockFetcherIterator = {
-    val iter = new BlockFetcherIterator.BasicBlockFetcherIterator(this, blocksByAddress, serializer,
-      readMetrics)
-    iter.initialize()
-    iter
-  }
-
   def putIterator(
       blockId: BlockId,
       values: Iterator[Any],
@@ -816,12 +801,15 @@ private[spark] class BlockManager(
       data.rewind()
       logDebug(s"Try to replicate $blockId once; The size of the data is ${data.limit()} Bytes. " +
         s"To node: $peer")
-      val putBlock = PutBlock(blockId, data, tLevel)
-      val cmId = new ConnectionManagerId(peer.host, peer.port)
-      val syncPutBlockSuccess = BlockManagerWorker.syncPutBlock(putBlock, cmId)
-      if (!syncPutBlockSuccess) {
-        logError(s"Failed to call syncPutBlock to $peer")
+
+      try {
+        blockTransferService.uploadBlockSync(
+          peer.host, peer.port, blockId.toString, new NioByteBufferManagedBuffer(data), tLevel)
+      } catch {
+        case e: Exception =>
+          logError(s"Failed to replicate block to $peer", e)
       }
+
       logDebug("Replicating BlockId %s once used %fs; The size of the data is %d bytes."
         .format(blockId, (System.nanoTime - start) / 1e6, data.limit()))
     }
@@ -1051,7 +1039,7 @@ private[spark] class BlockManager(
   }
 
   def stop(): Unit = {
-    connectionManager.stop()
+    blockTransferService.stop()
     diskBlockManager.stop()
     actorSystem.stop(slaveActor)
     blockInfo.clear()
diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManagerId.scala b/core/src/main/scala/org/apache/spark/storage/BlockManagerId.scala
index b7bcb2d85d0ee..d4487fce49ab6 100644
--- a/core/src/main/scala/org/apache/spark/storage/BlockManagerId.scala
+++ b/core/src/main/scala/org/apache/spark/storage/BlockManagerId.scala
@@ -36,8 +36,8 @@ import org.apache.spark.util.Utils
 class BlockManagerId private (
     private var executorId_ : String,
     private var host_ : String,
-    private var port_ : Int
-  ) extends Externalizable {
+    private var port_ : Int)
+  extends Externalizable {
 
   private def this() = this(null, null, 0)  // For deserialization only
 
diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManagerWorker.scala b/core/src/main/scala/org/apache/spark/storage/BlockManagerWorker.scala
deleted file mode 100644
index bf002a42d5dc5..0000000000000
--- a/core/src/main/scala/org/apache/spark/storage/BlockManagerWorker.scala
+++ /dev/null
@@ -1,147 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.storage
-
-import java.nio.ByteBuffer
-
-import org.apache.spark.Logging
-import org.apache.spark.network._
-import org.apache.spark.util.Utils
-
-import scala.concurrent.Await
-import scala.concurrent.duration.Duration
-import scala.util.{Try, Failure, Success}
-
-/**
- * A network interface for BlockManager. Each slave should have one
- * BlockManagerWorker.
- *
- * TODO: Use event model.
- */
-private[spark] class BlockManagerWorker(val blockManager: BlockManager) extends Logging {
-
-  blockManager.connectionManager.onReceiveMessage(onBlockMessageReceive)
-
-  def onBlockMessageReceive(msg: Message, id: ConnectionManagerId): Option[Message] = {
-    logDebug("Handling message " + msg)
-    msg match {
-      case bufferMessage: BufferMessage => {
-        try {
-          logDebug("Handling as a buffer message " + bufferMessage)
-          val blockMessages = BlockMessageArray.fromBufferMessage(bufferMessage)
-          logDebug("Parsed as a block message array")
-          val responseMessages = blockMessages.map(processBlockMessage).filter(_ != None).map(_.get)
-          Some(new BlockMessageArray(responseMessages).toBufferMessage)
-        } catch {
-          case e: Exception => {
-            logError("Exception handling buffer message", e)
-            val errorMessage = Message.createBufferMessage(msg.id)
-            errorMessage.hasError = true
-            Some(errorMessage)
-          }
-        }
-      }
-      case otherMessage: Any => {
-        logError("Unknown type message received: " + otherMessage)
-        val errorMessage = Message.createBufferMessage(msg.id)
-        errorMessage.hasError = true
-        Some(errorMessage)
-      }
-    }
-  }
-
-  def processBlockMessage(blockMessage: BlockMessage): Option[BlockMessage] = {
-    blockMessage.getType match {
-      case BlockMessage.TYPE_PUT_BLOCK => {
-        val pB = PutBlock(blockMessage.getId, blockMessage.getData, blockMessage.getLevel)
-        logDebug("Received [" + pB + "]")
-        putBlock(pB.id, pB.data, pB.level)
-        None
-      }
-      case BlockMessage.TYPE_GET_BLOCK => {
-        val gB = new GetBlock(blockMessage.getId)
-        logDebug("Received [" + gB + "]")
-        val buffer = getBlock(gB.id)
-        if (buffer == null) {
-          return None
-        }
-        Some(BlockMessage.fromGotBlock(GotBlock(gB.id, buffer)))
-      }
-      case _ => None
-    }
-  }
-
-  private def putBlock(id: BlockId, bytes: ByteBuffer, level: StorageLevel) {
-    val startTimeMs = System.currentTimeMillis()
-    logDebug("PutBlock " + id + " started from " + startTimeMs + " with data: " + bytes)
-    blockManager.putBytes(id, bytes, level)
-    logDebug("PutBlock " + id + " used " + Utils.getUsedTimeMs(startTimeMs)
-        + " with data size: " + bytes.limit)
-  }
-
-  private def getBlock(id: BlockId): ByteBuffer = {
-    val startTimeMs = System.currentTimeMillis()
-    logDebug("GetBlock " + id + " started from " + startTimeMs)
-    val buffer = blockManager.getLocalBytes(id) match {
-      case Some(bytes) => bytes
-      case None => null
-    }
-    logDebug("GetBlock " + id + " used " + Utils.getUsedTimeMs(startTimeMs)
-        + " and got buffer " + buffer)
-    buffer
-  }
-}
-
-private[spark] object BlockManagerWorker extends Logging {
-  private var blockManagerWorker: BlockManagerWorker = null
-
-  def startBlockManagerWorker(manager: BlockManager) {
-    blockManagerWorker = new BlockManagerWorker(manager)
-  }
-
-  def syncPutBlock(msg: PutBlock, toConnManagerId: ConnectionManagerId): Boolean = {
-    val blockManager = blockManagerWorker.blockManager
-    val connectionManager = blockManager.connectionManager
-    val blockMessage = BlockMessage.fromPutBlock(msg)
-    val blockMessageArray = new BlockMessageArray(blockMessage)
-    val resultMessage = Try(Await.result(connectionManager.sendMessageReliably(
-        toConnManagerId, blockMessageArray.toBufferMessage), Duration.Inf))
-    resultMessage.isSuccess
-  }
-
-  def syncGetBlock(msg: GetBlock, toConnManagerId: ConnectionManagerId): ByteBuffer = {
-    val blockManager = blockManagerWorker.blockManager
-    val connectionManager = blockManager.connectionManager
-    val blockMessage = BlockMessage.fromGetBlock(msg)
-    val blockMessageArray = new BlockMessageArray(blockMessage)
-    val responseMessage = Try(Await.result(connectionManager.sendMessageReliably(
-        toConnManagerId, blockMessageArray.toBufferMessage), Duration.Inf))
-    responseMessage match {
-      case Success(message) => {
-        val bufferMessage = message.asInstanceOf[BufferMessage]
-        logDebug("Response message received " + bufferMessage)
-        BlockMessageArray.fromBufferMessage(bufferMessage).foreach(blockMessage => {
-            logDebug("Found " + blockMessage)
-            return blockMessage.getData
-          })
-      }
-      case Failure(exception) => logDebug("No response message received")
-    }
-    null
-  }
-}
diff --git a/core/src/main/scala/org/apache/spark/storage/ShuffleBlockFetcherIterator.scala b/core/src/main/scala/org/apache/spark/storage/ShuffleBlockFetcherIterator.scala
new file mode 100644
index 0000000000000..c8e708aa6b1bc
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/storage/ShuffleBlockFetcherIterator.scala
@@ -0,0 +1,271 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.storage
+
+import java.util.concurrent.LinkedBlockingQueue
+
+import scala.collection.mutable.ArrayBuffer
+import scala.collection.mutable.HashSet
+import scala.collection.mutable.Queue
+
+import org.apache.spark.{TaskContext, Logging, SparkException}
+import org.apache.spark.network.{ManagedBuffer, BlockFetchingListener, BlockTransferService}
+import org.apache.spark.serializer.Serializer
+import org.apache.spark.util.Utils
+
+
+/**
+ * An iterator that fetches multiple blocks. For local blocks, it fetches from the local block
+ * manager. For remote blocks, it fetches them using the provided BlockTransferService.
+ *
+ * This creates an iterator of (BlockID, values) tuples so the caller can handle blocks in a
+ * pipelined fashion as they are received.
+ *
+ * The implementation throttles the remote fetches to they don't exceed maxBytesInFlight to avoid
+ * using too much memory.
+ *
+ * @param context [[TaskContext]], used for metrics update
+ * @param blockTransferService [[BlockTransferService]] for fetching remote blocks
+ * @param blockManager  [[BlockManager]] for reading local blocks
+ * @param blocksByAddress list of blocks to fetch grouped by the [[BlockManagerId]].
+ *                        For each block we also require the size (in bytes as a long field) in
+ *                        order to throttle the memory usage.
+ * @param serializer serializer used to deserialize the data.
+ * @param maxBytesInFlight max size (in bytes) of remote blocks to fetch at any given point.
+ */
+private[spark]
+final class ShuffleBlockFetcherIterator(
+    context: TaskContext,
+    blockTransferService: BlockTransferService,
+    blockManager: BlockManager,
+    blocksByAddress: Seq[(BlockManagerId, Seq[(BlockId, Long)])],
+    serializer: Serializer,
+    maxBytesInFlight: Long)
+  extends Iterator[(BlockId, Option[Iterator[Any]])] with Logging {
+
+  import ShuffleBlockFetcherIterator._
+
+  /**
+   * Total number of blocks to fetch. This can be smaller than the total number of blocks
+   * in [[blocksByAddress]] because we filter out zero-sized blocks in [[initialize]].
+   *
+   * This should equal localBlocks.size + remoteBlocks.size.
+   */
+  private[this] var numBlocksToFetch = 0
+
+  /**
+   * The number of blocks proccessed by the caller. The iterator is exhausted when
+   * [[numBlocksProcessed]] == [[numBlocksToFetch]].
+   */
+  private[this] var numBlocksProcessed = 0
+
+  private[this] val startTime = System.currentTimeMillis
+
+  /** Local blocks to fetch, excluding zero-sized blocks. */
+  private[this] val localBlocks = new ArrayBuffer[BlockId]()
+
+  /** Remote blocks to fetch, excluding zero-sized blocks. */
+  private[this] val remoteBlocks = new HashSet[BlockId]()
+
+  /**
+   * A queue to hold our results. This turns the asynchronous model provided by
+   * [[BlockTransferService]] into a synchronous model (iterator).
+   */
+  private[this] val results = new LinkedBlockingQueue[FetchResult]
+
+  // Queue of fetch requests to issue; we'll pull requests off this gradually to make sure that
+  // the number of bytes in flight is limited to maxBytesInFlight
+  private[this] val fetchRequests = new Queue[FetchRequest]
+
+  // Current bytes in flight from our requests
+  private[this] var bytesInFlight = 0L
+
+  private[this] val shuffleMetrics = context.taskMetrics.createShuffleReadMetricsForDependency()
+
+  initialize()
+
+  private[this] def sendRequest(req: FetchRequest) {
+    logDebug("Sending request for %d blocks (%s) from %s".format(
+      req.blocks.size, Utils.bytesToString(req.size), req.address.hostPort))
+    bytesInFlight += req.size
+
+    // so we can look up the size of each blockID
+    val sizeMap = req.blocks.map { case (blockId, size) => (blockId.toString, size) }.toMap
+    val blockIds = req.blocks.map(_._1.toString)
+
+    blockTransferService.fetchBlocks(req.address.host, req.address.port, blockIds,
+      new BlockFetchingListener {
+        override def onBlockFetchSuccess(blockId: String, data: ManagedBuffer): Unit = {
+          results.put(new FetchResult(BlockId(blockId), sizeMap(blockId),
+            () => serializer.newInstance().deserializeStream(
+              blockManager.wrapForCompression(BlockId(blockId), data.inputStream())).asIterator
+          ))
+          shuffleMetrics.remoteBytesRead += data.size
+          shuffleMetrics.remoteBlocksFetched += 1
+          logDebug("Got remote block " + blockId + " after " + Utils.getUsedTimeMs(startTime))
+        }
+
+        override def onBlockFetchFailure(e: Throwable): Unit = {
+          logError("Failed to get block(s) from ${req.address.host}:${req.address.port}", e)
+          // Note that there is a chance that some blocks have been fetched successfully, but we
+          // still add them to the failed queue. This is fine because when the caller see a
+          // FetchFailedException, it is going to fail the entire task anyway.
+          for ((blockId, size) <- req.blocks) {
+            results.put(new FetchResult(blockId, -1, null))
+          }
+        }
+      }
+    )
+  }
+
+  private[this] def splitLocalRemoteBlocks(): ArrayBuffer[FetchRequest] = {
+    // Make remote requests at most maxBytesInFlight / 5 in length; the reason to keep them
+    // smaller than maxBytesInFlight is to allow multiple, parallel fetches from up to 5
+    // nodes, rather than blocking on reading output from one node.
+    val targetRequestSize = math.max(maxBytesInFlight / 5, 1L)
+    logInfo("maxBytesInFlight: " + maxBytesInFlight + ", targetRequestSize: " + targetRequestSize)
+
+    // Split local and remote blocks. Remote blocks are further split into FetchRequests of size
+    // at most maxBytesInFlight in order to limit the amount of data in flight.
+    val remoteRequests = new ArrayBuffer[FetchRequest]
+
+    // Tracks total number of blocks (including zero sized blocks)
+    var totalBlocks = 0
+    for ((address, blockInfos) <- blocksByAddress) {
+      totalBlocks += blockInfos.size
+      if (address == blockManager.blockManagerId) {
+        // Filter out zero-sized blocks
+        localBlocks ++= blockInfos.filter(_._2 != 0).map(_._1)
+        numBlocksToFetch += localBlocks.size
+      } else {
+        val iterator = blockInfos.iterator
+        var curRequestSize = 0L
+        var curBlocks = new ArrayBuffer[(BlockId, Long)]
+        while (iterator.hasNext) {
+          val (blockId, size) = iterator.next()
+          // Skip empty blocks
+          if (size > 0) {
+            curBlocks += ((blockId, size))
+            remoteBlocks += blockId
+            numBlocksToFetch += 1
+            curRequestSize += size
+          } else if (size < 0) {
+            throw new BlockException(blockId, "Negative block size " + size)
+          }
+          if (curRequestSize >= targetRequestSize) {
+            // Add this FetchRequest
+            remoteRequests += new FetchRequest(address, curBlocks)
+            curBlocks = new ArrayBuffer[(BlockId, Long)]
+            logDebug(s"Creating fetch request of $curRequestSize at $address")
+            curRequestSize = 0
+          }
+        }
+        // Add in the final request
+        if (curBlocks.nonEmpty) {
+          remoteRequests += new FetchRequest(address, curBlocks)
+        }
+      }
+    }
+    logInfo(s"Getting $numBlocksToFetch non-empty blocks out of $totalBlocks blocks")
+    remoteRequests
+  }
+
+  private[this] def fetchLocalBlocks() {
+    // Get the local blocks while remote blocks are being fetched. Note that it's okay to do
+    // these all at once because they will just memory-map some files, so they won't consume
+    // any memory that might exceed our maxBytesInFlight
+    for (id <- localBlocks) {
+      try {
+        shuffleMetrics.localBlocksFetched += 1
+        results.put(new FetchResult(
+          id, 0, () => blockManager.getLocalShuffleFromDisk(id, serializer).get))
+        logDebug("Got local block " + id)
+      } catch {
+        case e: Exception =>
+          logError(s"Error occurred while fetching local blocks", e)
+          results.put(new FetchResult(id, -1, null))
+          return
+      }
+    }
+  }
+
+  private[this] def initialize(): Unit = {
+    // Split local and remote blocks.
+    val remoteRequests = splitLocalRemoteBlocks()
+    // Add the remote requests into our queue in a random order
+    fetchRequests ++= Utils.randomize(remoteRequests)
+
+    // Send out initial requests for blocks, up to our maxBytesInFlight
+    while (fetchRequests.nonEmpty &&
+      (bytesInFlight == 0 || bytesInFlight + fetchRequests.front.size <= maxBytesInFlight)) {
+      sendRequest(fetchRequests.dequeue())
+    }
+
+    val numFetches = remoteRequests.size - fetchRequests.size
+    logInfo("Started " + numFetches + " remote fetches in" + Utils.getUsedTimeMs(startTime))
+
+    // Get Local Blocks
+    fetchLocalBlocks()
+    logDebug("Got local blocks in " + Utils.getUsedTimeMs(startTime) + " ms")
+  }
+
+  override def hasNext: Boolean = numBlocksProcessed < numBlocksToFetch
+
+  override def next(): (BlockId, Option[Iterator[Any]]) = {
+    numBlocksProcessed += 1
+    val startFetchWait = System.currentTimeMillis()
+    val result = results.take()
+    val stopFetchWait = System.currentTimeMillis()
+    shuffleMetrics.fetchWaitTime += (stopFetchWait - startFetchWait)
+    if (!result.failed) {
+      bytesInFlight -= result.size
+    }
+    // Send fetch requests up to maxBytesInFlight
+    while (fetchRequests.nonEmpty &&
+      (bytesInFlight == 0 || bytesInFlight + fetchRequests.front.size <= maxBytesInFlight)) {
+      sendRequest(fetchRequests.dequeue())
+    }
+    (result.blockId, if (result.failed) None else Some(result.deserialize()))
+  }
+}
+
+
+private[storage]
+object ShuffleBlockFetcherIterator {
+
+  /**
+   * A request to fetch blocks from a remote BlockManager.
+   * @param address remote BlockManager to fetch from.
+   * @param blocks Sequence of tuple, where the first element is the block id,
+   *               and the second element is the estimated size, used to calculate bytesInFlight.
+   */
+  class FetchRequest(val address: BlockManagerId, val blocks: Seq[(BlockId, Long)]) {
+    val size = blocks.map(_._2).sum
+  }
+
+  /**
+   * Result of a fetch from a remote block. A failure is represented as size == -1.
+   * @param blockId block id
+   * @param size estimated size of the block, used to calculate bytesInFlight.
+   *             Note that this is NOT the exact bytes.
+   * @param deserialize closure to return the result in the form of an Iterator.
+   */
+  class FetchResult(val blockId: BlockId, val size: Long, val deserialize: () => Iterator[Any]) {
+    def failed: Boolean = size == -1
+  }
+}
diff --git a/core/src/main/scala/org/apache/spark/storage/ThreadingTest.scala b/core/src/main/scala/org/apache/spark/storage/ThreadingTest.scala
deleted file mode 100644
index 7540f0d5e2a5a..0000000000000
--- a/core/src/main/scala/org/apache/spark/storage/ThreadingTest.scala
+++ /dev/null
@@ -1,120 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.storage
-
-import java.util.concurrent.ArrayBlockingQueue
-
-import akka.actor._
-import org.apache.spark.shuffle.hash.HashShuffleManager
-import util.Random
-
-import org.apache.spark.{MapOutputTrackerMaster, SecurityManager, SparkConf}
-import org.apache.spark.scheduler.LiveListenerBus
-import org.apache.spark.serializer.KryoSerializer
-
-/**
- * This class tests the BlockManager and MemoryStore for thread safety and
- * deadlocks. It spawns a number of producer and consumer threads. Producer
- * threads continuously pushes blocks into the BlockManager and consumer
- * threads continuously retrieves the blocks form the BlockManager and tests
- * whether the block is correct or not.
- */
-private[spark] object ThreadingTest {
-
-  val numProducers = 5
-  val numBlocksPerProducer = 20000
-
-  private[spark] class ProducerThread(manager: BlockManager, id: Int) extends Thread {
-    val queue = new ArrayBlockingQueue[(BlockId, Seq[Int])](100)
-
-    override def run() {
-      for (i <- 1 to numBlocksPerProducer) {
-        val blockId = TestBlockId("b-" + id + "-" + i)
-        val blockSize = Random.nextInt(1000)
-        val block = (1 to blockSize).map(_ => Random.nextInt())
-        val level = randomLevel()
-        val startTime = System.currentTimeMillis()
-        manager.putIterator(blockId, block.iterator, level, tellMaster = true)
-        println("Pushed block " + blockId + " in " + (System.currentTimeMillis - startTime) + " ms")
-        queue.add((blockId, block))
-      }
-      println("Producer thread " + id + " terminated")
-    }
-
-    def randomLevel(): StorageLevel = {
-      math.abs(Random.nextInt()) % 4 match {
-        case 0 => StorageLevel.MEMORY_ONLY
-        case 1 => StorageLevel.MEMORY_ONLY_SER
-        case 2 => StorageLevel.MEMORY_AND_DISK
-        case 3 => StorageLevel.MEMORY_AND_DISK_SER
-      }
-    }
-  }
-
-  private[spark] class ConsumerThread(
-      manager: BlockManager,
-      queue: ArrayBlockingQueue[(BlockId, Seq[Int])]
-    ) extends Thread {
-    var numBlockConsumed = 0
-
-    override def run() {
-      println("Consumer thread started")
-      while(numBlockConsumed < numBlocksPerProducer) {
-        val (blockId, block) = queue.take()
-        val startTime = System.currentTimeMillis()
-        manager.get(blockId) match {
-          case Some(retrievedBlock) =>
-            assert(retrievedBlock.data.toList.asInstanceOf[List[Int]] == block.toList,
-              "Block " + blockId + " did not match")
-            println("Got block " + blockId + " in " +
-              (System.currentTimeMillis - startTime) + " ms")
-          case None =>
-            assert(false, "Block " + blockId + " could not be retrieved")
-        }
-        numBlockConsumed += 1
-      }
-      println("Consumer thread terminated")
-    }
-  }
-
-  def main(args: Array[String]) {
-    System.setProperty("spark.kryoserializer.buffer.mb", "1")
-    val actorSystem = ActorSystem("test")
-    val conf = new SparkConf()
-    val serializer = new KryoSerializer(conf)
-    val blockManagerMaster = new BlockManagerMaster(
-      actorSystem.actorOf(Props(new BlockManagerMasterActor(true, conf, new LiveListenerBus))),
-      conf, true)
-    val blockManager = new BlockManager(
-      "<driver>", actorSystem, blockManagerMaster, serializer, 1024 * 1024, conf,
-      new SecurityManager(conf), new MapOutputTrackerMaster(conf), new HashShuffleManager(conf))
-    val producers = (1 to numProducers).map(i => new ProducerThread(blockManager, i))
-    val consumers = producers.map(p => new ConsumerThread(blockManager, p.queue))
-    producers.foreach(_.start)
-    consumers.foreach(_.start)
-    producers.foreach(_.join)
-    consumers.foreach(_.join)
-    blockManager.stop()
-    blockManagerMaster.stop()
-    actorSystem.shutdown()
-    actorSystem.awaitTermination()
-    println("Everything stopped.")
-    println(
-      "It will take sometime for the JVM to clean all temporary files and shutdown. Sit tight.")
-  }
-}
diff --git a/core/src/test/scala/org/apache/spark/DistributedSuite.scala b/core/src/test/scala/org/apache/spark/DistributedSuite.scala
index 41c294f727b3c..81b64c36ddca1 100644
--- a/core/src/test/scala/org/apache/spark/DistributedSuite.scala
+++ b/core/src/test/scala/org/apache/spark/DistributedSuite.scala
@@ -24,8 +24,7 @@ import org.scalatest.Matchers
 import org.scalatest.time.{Millis, Span}
 
 import org.apache.spark.SparkContext._
-import org.apache.spark.network.ConnectionManagerId
-import org.apache.spark.storage.{BlockManagerWorker, GetBlock, RDDBlockId, StorageLevel}
+import org.apache.spark.storage.{RDDBlockId, StorageLevel}
 
 class NotSerializableClass
 class NotSerializableExn(val notSer: NotSerializableClass) extends Throwable() {}
@@ -136,7 +135,6 @@ class DistributedSuite extends FunSuite with Matchers with BeforeAndAfter
         sc.parallelize(1 to 10, 2).foreach { x => if (x == 1) System.exit(42) }
       }
       assert(thrown.getClass === classOf[SparkException])
-      System.out.println(thrown.getMessage)
       assert(thrown.getMessage.contains("failed 4 times"))
     }
   }
@@ -202,12 +200,13 @@ class DistributedSuite extends FunSuite with Matchers with BeforeAndAfter
     val blockIds = data.partitions.indices.map(index => RDDBlockId(data.id, index)).toArray
     val blockId = blockIds(0)
     val blockManager = SparkEnv.get.blockManager
-    blockManager.master.getLocations(blockId).foreach(id => {
-      val bytes = BlockManagerWorker.syncGetBlock(
-        GetBlock(blockId), ConnectionManagerId(id.host, id.port))
-      val deserialized = blockManager.dataDeserialize(blockId, bytes).asInstanceOf[Iterator[Int]].toList
+    val blockTransfer = SparkEnv.get.blockTransferService
+    blockManager.master.getLocations(blockId).foreach { cmId =>
+      val bytes = blockTransfer.fetchBlockSync(cmId.host, cmId.port, blockId.toString)
+      val deserialized = blockManager.dataDeserialize(blockId, bytes.nioByteBuffer())
+        .asInstanceOf[Iterator[Int]].toList
       assert(deserialized === (1 to 100).toList)
-    })
+    }
   }
 
   test("compute without caching when no partitions fit in memory") {
diff --git a/core/src/test/scala/org/apache/spark/network/ConnectionManagerSuite.scala b/core/src/test/scala/org/apache/spark/network/nio/ConnectionManagerSuite.scala
similarity index 97%
rename from core/src/test/scala/org/apache/spark/network/ConnectionManagerSuite.scala
rename to core/src/test/scala/org/apache/spark/network/nio/ConnectionManagerSuite.scala
index e2f4d4c57cdb5..9f49587cdc670 100644
--- a/core/src/test/scala/org/apache/spark/network/ConnectionManagerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/network/nio/ConnectionManagerSuite.scala
@@ -15,23 +15,18 @@
  * limitations under the License.
  */
 
-package org.apache.spark.network
+package org.apache.spark.network.nio
 
 import java.io.IOException
 import java.nio._
-import java.util.concurrent.TimeoutException
 
-import org.apache.spark.{SecurityManager, SparkConf}
-import org.scalatest.FunSuite
-
-import org.mockito.Mockito._
-import org.mockito.Matchers._
-
-import scala.concurrent.TimeoutException
-import scala.concurrent.{Await, TimeoutException}
 import scala.concurrent.duration._
+import scala.concurrent.{Await, TimeoutException}
 import scala.language.postfixOps
-import scala.util.{Failure, Success, Try}
+
+import org.scalatest.FunSuite
+
+import org.apache.spark.{SecurityManager, SparkConf}
 
 /**
   * Test the ConnectionManager with various security settings.
diff --git a/core/src/test/scala/org/apache/spark/shuffle/hash/HashShuffleManagerSuite.scala b/core/src/test/scala/org/apache/spark/shuffle/hash/HashShuffleManagerSuite.scala
index 6061e544e79b4..ba47fe5e25b9b 100644
--- a/core/src/test/scala/org/apache/spark/shuffle/hash/HashShuffleManagerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/shuffle/hash/HashShuffleManagerSuite.scala
@@ -25,6 +25,7 @@ import org.scalatest.FunSuite
 
 import org.apache.spark.{SparkEnv, SparkContext, LocalSparkContext, SparkConf}
 import org.apache.spark.executor.ShuffleWriteMetrics
+import org.apache.spark.network.{FileSegmentManagedBuffer, ManagedBuffer}
 import org.apache.spark.serializer.JavaSerializer
 import org.apache.spark.shuffle.FileShuffleBlockManager
 import org.apache.spark.storage.{ShuffleBlockId, FileSegment}
@@ -32,10 +33,12 @@ import org.apache.spark.storage.{ShuffleBlockId, FileSegment}
 class HashShuffleManagerSuite extends FunSuite with LocalSparkContext {
   private val testConf = new SparkConf(false)
 
-  private def checkSegments(segment1: FileSegment, segment2: FileSegment) {
-    assert (segment1.file.getCanonicalPath === segment2.file.getCanonicalPath)
-    assert (segment1.offset === segment2.offset)
-    assert (segment1.length === segment2.length)
+  private def checkSegments(expected: FileSegment, buffer: ManagedBuffer) {
+    assert(buffer.isInstanceOf[FileSegmentManagedBuffer])
+    val segment = buffer.asInstanceOf[FileSegmentManagedBuffer]
+    assert(expected.file.getCanonicalPath === segment.file.getCanonicalPath)
+    assert(expected.offset === segment.offset)
+    assert(expected.length === segment.length)
   }
 
   test("consolidated shuffle can write to shuffle group without messing existing offsets/lengths") {
@@ -95,14 +98,12 @@ class HashShuffleManagerSuite extends FunSuite with LocalSparkContext {
       writer.commitAndClose()
     }
     // check before we register.
-    checkSegments(shuffle2Segment, shuffleBlockManager.getBlockData(ShuffleBlockId(1, 2, 0)).left.get)
+    checkSegments(shuffle2Segment, shuffleBlockManager.getBlockData(ShuffleBlockId(1, 2, 0)))
     shuffle3.releaseWriters(success = true)
-    checkSegments(shuffle2Segment, shuffleBlockManager.getBlockData(ShuffleBlockId(1, 2, 0)).left.get)
+    checkSegments(shuffle2Segment, shuffleBlockManager.getBlockData(ShuffleBlockId(1, 2, 0)))
     shuffleBlockManager.removeShuffle(1)
-
   }
 
-
   def writeToFile(file: File, numBytes: Int) {
     val writer = new FileWriter(file, true)
     for (i <- 0 until numBytes) writer.write(i)
diff --git a/core/src/test/scala/org/apache/spark/storage/BlockFetcherIteratorSuite.scala b/core/src/test/scala/org/apache/spark/storage/BlockFetcherIteratorSuite.scala
deleted file mode 100644
index 3c86f6bafcaa3..0000000000000
--- a/core/src/test/scala/org/apache/spark/storage/BlockFetcherIteratorSuite.scala
+++ /dev/null
@@ -1,237 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.storage
-
-import java.io.IOException
-import java.nio.ByteBuffer
-
-import scala.collection.mutable.ArrayBuffer
-import scala.concurrent.future
-import scala.concurrent.ExecutionContext.Implicits.global
-
-import org.scalatest.{FunSuite, Matchers}
-
-import org.mockito.Mockito._
-import org.mockito.Matchers.{any, eq => meq}
-import org.mockito.stubbing.Answer
-import org.mockito.invocation.InvocationOnMock
-
-import org.apache.spark.storage.BlockFetcherIterator._
-import org.apache.spark.network.{ConnectionManager, Message}
-import org.apache.spark.executor.ShuffleReadMetrics
-
-class BlockFetcherIteratorSuite extends FunSuite with Matchers {
-
-  test("block fetch from local fails using BasicBlockFetcherIterator") {
-    val blockManager = mock(classOf[BlockManager])
-    val connManager = mock(classOf[ConnectionManager])
-    doReturn(connManager).when(blockManager).connectionManager
-    doReturn(BlockManagerId("test-client", "test-client", 1)).when(blockManager).blockManagerId
-
-    doReturn((48 * 1024 * 1024).asInstanceOf[Long]).when(blockManager).maxBytesInFlight
-
-    val blIds = Array[BlockId](
-      ShuffleBlockId(0,0,0),
-      ShuffleBlockId(0,1,0),
-      ShuffleBlockId(0,2,0),
-      ShuffleBlockId(0,3,0),
-      ShuffleBlockId(0,4,0))
-
-    val optItr = mock(classOf[Option[Iterator[Any]]])
-    val answer = new Answer[Option[Iterator[Any]]] {
-      override def answer(invocation: InvocationOnMock) = Option[Iterator[Any]] {
-        throw new Exception
-      }
-    }
-
-    // 3rd block is going to fail
-    doReturn(optItr).when(blockManager).getLocalShuffleFromDisk(meq(blIds(0)), any())
-    doReturn(optItr).when(blockManager).getLocalShuffleFromDisk(meq(blIds(1)), any())
-    doAnswer(answer).when(blockManager).getLocalShuffleFromDisk(meq(blIds(2)), any())
-    doReturn(optItr).when(blockManager).getLocalShuffleFromDisk(meq(blIds(3)), any())
-    doReturn(optItr).when(blockManager).getLocalShuffleFromDisk(meq(blIds(4)), any())
-
-    val bmId = BlockManagerId("test-client", "test-client", 1)
-    val blocksByAddress = Seq[(BlockManagerId, Seq[(BlockId, Long)])](
-      (bmId, blIds.map(blId => (blId, 1.asInstanceOf[Long])).toSeq)
-    )
-
-    val iterator = new BasicBlockFetcherIterator(blockManager, blocksByAddress, null,
-      new ShuffleReadMetrics())
-
-    iterator.initialize()
-
-    // Without exhausting the iterator, the iterator should be lazy and not call getLocalShuffleFromDisk.
-    verify(blockManager, times(0)).getLocalShuffleFromDisk(any(), any())
-
-    assert(iterator.hasNext, "iterator should have 5 elements but actually has no elements")
-    // the 2nd element of the tuple returned by iterator.next should be defined when fetching successfully
-    assert(iterator.next()._2.isDefined, "1st element should be defined but is not actually defined")
-    verify(blockManager, times(1)).getLocalShuffleFromDisk(any(), any())
-
-    assert(iterator.hasNext, "iterator should have 5 elements but actually has 1 element")
-    assert(iterator.next()._2.isDefined, "2nd element should be defined but is not actually defined")
-    verify(blockManager, times(2)).getLocalShuffleFromDisk(any(), any())
-
-    assert(iterator.hasNext, "iterator should have 5 elements but actually has 2 elements")
-    // 3rd fetch should be failed
-    intercept[Exception] {
-      iterator.next()
-    }
-    verify(blockManager, times(3)).getLocalShuffleFromDisk(any(), any())
-  }
-
-
-  test("block fetch from local succeed using BasicBlockFetcherIterator") {
-    val blockManager = mock(classOf[BlockManager])
-    val connManager = mock(classOf[ConnectionManager])
-    doReturn(connManager).when(blockManager).connectionManager
-    doReturn(BlockManagerId("test-client", "test-client", 1)).when(blockManager).blockManagerId
-
-    doReturn((48 * 1024 * 1024).asInstanceOf[Long]).when(blockManager).maxBytesInFlight
-
-    val blIds = Array[BlockId](
-      ShuffleBlockId(0,0,0),
-      ShuffleBlockId(0,1,0),
-      ShuffleBlockId(0,2,0),
-      ShuffleBlockId(0,3,0),
-      ShuffleBlockId(0,4,0))
-
-    val optItr = mock(classOf[Option[Iterator[Any]]])
- 
-   // All blocks should be fetched successfully
-    doReturn(optItr).when(blockManager).getLocalShuffleFromDisk(meq(blIds(0)), any())
-    doReturn(optItr).when(blockManager).getLocalShuffleFromDisk(meq(blIds(1)), any())
-    doReturn(optItr).when(blockManager).getLocalShuffleFromDisk(meq(blIds(2)), any())
-    doReturn(optItr).when(blockManager).getLocalShuffleFromDisk(meq(blIds(3)), any())
-    doReturn(optItr).when(blockManager).getLocalShuffleFromDisk(meq(blIds(4)), any())
-
-    val bmId = BlockManagerId("test-client", "test-client", 1)
-    val blocksByAddress = Seq[(BlockManagerId, Seq[(BlockId, Long)])](
-      (bmId, blIds.map(blId => (blId, 1.asInstanceOf[Long])).toSeq)
-    )
-
-    val iterator = new BasicBlockFetcherIterator(blockManager, blocksByAddress, null,
-      new ShuffleReadMetrics())
-
-    iterator.initialize()
-
-    // Without exhausting the iterator, the iterator should be lazy and not call getLocalShuffleFromDisk.
-    verify(blockManager, times(0)).getLocalShuffleFromDisk(any(), any())
-
-    assert(iterator.hasNext, "iterator should have 5 elements but actually has no elements")
-    assert(iterator.next._2.isDefined, "All elements should be defined but 1st element is not actually defined") 
-    assert(iterator.hasNext, "iterator should have 5 elements but actually has 1 element")
-    assert(iterator.next._2.isDefined, "All elements should be defined but 2nd element is not actually defined") 
-    assert(iterator.hasNext, "iterator should have 5 elements but actually has 2 elements")
-    assert(iterator.next._2.isDefined, "All elements should be defined but 3rd element is not actually defined") 
-    assert(iterator.hasNext, "iterator should have 5 elements but actually has 3 elements")
-    assert(iterator.next._2.isDefined, "All elements should be defined but 4th element is not actually defined") 
-    assert(iterator.hasNext, "iterator should have 5 elements but actually has 4 elements")
-    assert(iterator.next._2.isDefined, "All elements should be defined but 5th element is not actually defined")
-
-    verify(blockManager, times(5)).getLocalShuffleFromDisk(any(), any())
-  }
-
-  test("block fetch from remote fails using BasicBlockFetcherIterator") {
-    val blockManager = mock(classOf[BlockManager])
-    val connManager = mock(classOf[ConnectionManager])
-    when(blockManager.connectionManager).thenReturn(connManager)
-
-    val f = future {
-      throw new IOException("Send failed or we received an error ACK")
-    }
-    when(connManager.sendMessageReliably(any(),
-      any())).thenReturn(f)
-    when(blockManager.futureExecContext).thenReturn(global)
-
-    when(blockManager.blockManagerId).thenReturn(
-      BlockManagerId("test-client", "test-client", 1))
-    when(blockManager.maxBytesInFlight).thenReturn(48 * 1024 * 1024)
-
-    val blId1 = ShuffleBlockId(0,0,0)
-    val blId2 = ShuffleBlockId(0,1,0)
-    val bmId = BlockManagerId("test-server", "test-server", 1)
-    val blocksByAddress = Seq[(BlockManagerId, Seq[(BlockId, Long)])](
-      (bmId, Seq((blId1, 1L), (blId2, 1L)))
-    )
-
-    val iterator = new BasicBlockFetcherIterator(blockManager,
-      blocksByAddress, null, new ShuffleReadMetrics())
-
-    iterator.initialize()
-    iterator.foreach{
-      case (_, r) => {
-        (!r.isDefined) should be(true)
-      }
-    }
-  }
-
-  test("block fetch from remote succeed using BasicBlockFetcherIterator") {
-    val blockManager = mock(classOf[BlockManager])
-    val connManager = mock(classOf[ConnectionManager])
-    when(blockManager.connectionManager).thenReturn(connManager)
-
-    val blId1 = ShuffleBlockId(0,0,0)
-    val blId2 = ShuffleBlockId(0,1,0)
-    val buf1 = ByteBuffer.allocate(4)
-    val buf2 = ByteBuffer.allocate(4)
-    buf1.putInt(1)
-    buf1.flip()
-    buf2.putInt(1)
-    buf2.flip()
-    val blockMessage1 = BlockMessage.fromGotBlock(GotBlock(blId1, buf1))
-    val blockMessage2 = BlockMessage.fromGotBlock(GotBlock(blId2, buf2))
-    val blockMessageArray = new BlockMessageArray(
-      Seq(blockMessage1, blockMessage2))
-
-    val bufferMessage = blockMessageArray.toBufferMessage
-    val buffer = ByteBuffer.allocate(bufferMessage.size)
-    val arrayBuffer = new ArrayBuffer[ByteBuffer]
-    bufferMessage.buffers.foreach{ b =>
-      buffer.put(b)
-    }
-    buffer.flip()
-    arrayBuffer += buffer
-
-    val f = future {
-      Message.createBufferMessage(arrayBuffer)
-    }
-    when(connManager.sendMessageReliably(any(),
-      any())).thenReturn(f)
-    when(blockManager.futureExecContext).thenReturn(global)
-
-    when(blockManager.blockManagerId).thenReturn(
-      BlockManagerId("test-client", "test-client", 1))
-    when(blockManager.maxBytesInFlight).thenReturn(48 * 1024 * 1024)
-
-    val bmId = BlockManagerId("test-server", "test-server", 1)
-    val blocksByAddress = Seq[(BlockManagerId, Seq[(BlockId, Long)])](
-      (bmId, Seq((blId1, 1L), (blId2, 1L)))
-    )
-
-    val iterator = new BasicBlockFetcherIterator(blockManager,
-      blocksByAddress, null, new ShuffleReadMetrics())
-    iterator.initialize()
-    iterator.foreach{
-      case (_, r) => {
-        (r.isDefined) should be(true)
-      }
-    }
-  }
-}
diff --git a/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala b/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala
index c200654162268..e251660dae5de 100644
--- a/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala
@@ -21,15 +21,19 @@ import java.nio.{ByteBuffer, MappedByteBuffer}
 import java.util.Arrays
 import java.util.concurrent.TimeUnit
 
+import org.apache.spark.network.nio.NioBlockTransferService
+
+import scala.collection.mutable.ArrayBuffer
+import scala.concurrent.Await
+import scala.concurrent.duration._
+import scala.language.implicitConversions
+import scala.language.postfixOps
+
 import akka.actor._
 import akka.pattern.ask
 import akka.util.Timeout
-import org.apache.spark.shuffle.hash.HashShuffleManager
 
-import org.mockito.invocation.InvocationOnMock
-import org.mockito.Matchers.any
-import org.mockito.Mockito.{doAnswer, mock, spy, when}
-import org.mockito.stubbing.Answer
+import org.mockito.Mockito.{mock, when}
 
 import org.scalatest.{BeforeAndAfter, FunSuite, PrivateMethodTester}
 import org.scalatest.concurrent.Eventually._
@@ -38,18 +42,12 @@ import org.scalatest.Matchers
 
 import org.apache.spark.{MapOutputTrackerMaster, SecurityManager, SparkConf}
 import org.apache.spark.executor.DataReadMethod
-import org.apache.spark.network.{Message, ConnectionManagerId}
 import org.apache.spark.scheduler.LiveListenerBus
 import org.apache.spark.serializer.{JavaSerializer, KryoSerializer}
+import org.apache.spark.shuffle.hash.HashShuffleManager
 import org.apache.spark.storage.BlockManagerMessages.BlockManagerHeartbeat
 import org.apache.spark.util.{AkkaUtils, ByteBufferInputStream, SizeEstimator, Utils}
 
-import scala.collection.mutable.ArrayBuffer
-import scala.concurrent.Await
-import scala.concurrent.duration._
-import scala.language.implicitConversions
-import scala.language.postfixOps
-import org.apache.spark.shuffle.ShuffleBlockManager
 
 class BlockManagerSuite extends FunSuite with Matchers with BeforeAndAfter
   with PrivateMethodTester {
@@ -74,8 +72,9 @@ class BlockManagerSuite extends FunSuite with Matchers with BeforeAndAfter
   def rdd(rddId: Int, splitId: Int) = RDDBlockId(rddId, splitId)
 
   private def makeBlockManager(maxMem: Long, name: String = "<driver>"): BlockManager = {
-    new BlockManager(name, actorSystem, master, serializer, maxMem, conf, securityMgr,
-      mapOutputTracker, shuffleManager)
+    val transfer = new NioBlockTransferService(conf, securityMgr)
+    new BlockManager(name, actorSystem, master, serializer, maxMem, conf,
+      mapOutputTracker, shuffleManager, transfer)
   }
 
   before {
@@ -793,8 +792,9 @@ class BlockManagerSuite extends FunSuite with Matchers with BeforeAndAfter
 
   test("block store put failure") {
     // Use Java serializer so we can create an unserializable error.
+    val transfer = new NioBlockTransferService(conf, securityMgr)
     store = new BlockManager("<driver>", actorSystem, master, new JavaSerializer(conf), 1200, conf,
-      securityMgr, mapOutputTracker, shuffleManager)
+      mapOutputTracker, shuffleManager, transfer)
 
     // The put should fail since a1 is not serializable.
     class UnserializableClass
@@ -1005,109 +1005,6 @@ class BlockManagerSuite extends FunSuite with Matchers with BeforeAndAfter
     assert(!store.memoryStore.contains(rdd(1, 0)), "rdd_1_0 was in store")
   }
 
-  test("return error message when error occurred in BlockManagerWorker#onBlockMessageReceive") {
-    store = new BlockManager("<driver>", actorSystem, master, serializer, 1200, conf,
-      securityMgr, mapOutputTracker, shuffleManager)
-
-    val worker = spy(new BlockManagerWorker(store))
-    val connManagerId = mock(classOf[ConnectionManagerId])
-
-    // setup request block messages
-    val reqBlId1 = ShuffleBlockId(0,0,0)
-    val reqBlId2 = ShuffleBlockId(0,1,0)
-    val reqBlockMessage1 = BlockMessage.fromGetBlock(GetBlock(reqBlId1))
-    val reqBlockMessage2 = BlockMessage.fromGetBlock(GetBlock(reqBlId2))
-    val reqBlockMessages = new BlockMessageArray(
-      Seq(reqBlockMessage1, reqBlockMessage2))
-    val reqBufferMessage = reqBlockMessages.toBufferMessage
-
-    val answer = new Answer[Option[BlockMessage]] {
-      override def answer(invocation: InvocationOnMock)
-          :Option[BlockMessage]= {
-        throw new Exception
-      }
-    }
-
-    doAnswer(answer).when(worker).processBlockMessage(any())
-
-    // Test when exception was thrown during processing block messages
-    var ackMessage = worker.onBlockMessageReceive(reqBufferMessage, connManagerId)
-    
-    assert(ackMessage.isDefined, "When Exception was thrown in " +
-      "BlockManagerWorker#processBlockMessage, " +
-      "ackMessage should be defined")
-    assert(ackMessage.get.hasError, "When Exception was thown in " +
-      "BlockManagerWorker#processBlockMessage, " +
-      "ackMessage should have error")
-
-    val notBufferMessage = mock(classOf[Message])
-
-    // Test when not BufferMessage was received
-    ackMessage = worker.onBlockMessageReceive(notBufferMessage, connManagerId)
-    assert(ackMessage.isDefined, "When not BufferMessage was passed to " +
-      "BlockManagerWorker#onBlockMessageReceive, " +
-      "ackMessage should be defined")
-    assert(ackMessage.get.hasError, "When not BufferMessage was passed to " +
-      "BlockManagerWorker#onBlockMessageReceive, " +
-      "ackMessage should have error")
-  }
-
-  test("return ack message when no error occurred in BlocManagerWorker#onBlockMessageReceive") {
-    store = new BlockManager("<driver>", actorSystem, master, serializer, 1200, conf,
-      securityMgr, mapOutputTracker, shuffleManager)
-
-    val worker = spy(new BlockManagerWorker(store))
-    val connManagerId = mock(classOf[ConnectionManagerId])
-
-    // setup request block messages
-    val reqBlId1 = ShuffleBlockId(0,0,0)
-    val reqBlId2 = ShuffleBlockId(0,1,0)
-    val reqBlockMessage1 = BlockMessage.fromGetBlock(GetBlock(reqBlId1))
-    val reqBlockMessage2 = BlockMessage.fromGetBlock(GetBlock(reqBlId2))
-    val reqBlockMessages = new BlockMessageArray(
-      Seq(reqBlockMessage1, reqBlockMessage2))
-
-    val tmpBufferMessage = reqBlockMessages.toBufferMessage
-    val buffer = ByteBuffer.allocate(tmpBufferMessage.size)
-    val arrayBuffer = new ArrayBuffer[ByteBuffer]
-    tmpBufferMessage.buffers.foreach{ b =>
-      buffer.put(b)
-    }
-    buffer.flip()
-    arrayBuffer += buffer
-    val reqBufferMessage = Message.createBufferMessage(arrayBuffer)
-
-    // setup ack block messages
-    val buf1 = ByteBuffer.allocate(4)
-    val buf2 = ByteBuffer.allocate(4)
-    buf1.putInt(1)
-    buf1.flip()
-    buf2.putInt(1)
-    buf2.flip()
-    val ackBlockMessage1 = BlockMessage.fromGotBlock(GotBlock(reqBlId1, buf1))
-    val ackBlockMessage2 = BlockMessage.fromGotBlock(GotBlock(reqBlId2, buf2))
-
-    val answer = new Answer[Option[BlockMessage]] {
-      override def answer(invocation: InvocationOnMock)
-          :Option[BlockMessage]= {
-        if (invocation.getArguments()(0).asInstanceOf[BlockMessage].eq(
-          reqBlockMessage1)) {
-          return Some(ackBlockMessage1)
-        } else {
-          return Some(ackBlockMessage2)
-        }
-      }
-    }
-
-    doAnswer(answer).when(worker).processBlockMessage(any())
-
-    val ackMessage = worker.onBlockMessageReceive(reqBufferMessage, connManagerId)
-    assert(ackMessage.isDefined, "When BlockManagerWorker#onBlockMessageReceive " +
-      "was executed successfully, ackMessage should be defined")
-    assert(!ackMessage.get.hasError, "When BlockManagerWorker#onBlockMessageReceive " +
-      "was executed successfully, ackMessage should not have error")
-  }
-
   test("reserve/release unroll memory") {
     store = makeBlockManager(12000)
     val memoryStore = store.memoryStore
diff --git a/core/src/test/scala/org/apache/spark/storage/DiskBlockManagerSuite.scala b/core/src/test/scala/org/apache/spark/storage/DiskBlockManagerSuite.scala
index 26082ded8ca7a..e4522e00a622d 100644
--- a/core/src/test/scala/org/apache/spark/storage/DiskBlockManagerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/storage/DiskBlockManagerSuite.scala
@@ -19,6 +19,7 @@ package org.apache.spark.storage
 
 import java.io.{File, FileWriter}
 
+import org.apache.spark.network.nio.NioBlockTransferService
 import org.apache.spark.shuffle.hash.HashShuffleManager
 
 import scala.collection.mutable
@@ -52,7 +53,6 @@ class DiskBlockManagerSuite extends FunSuite with BeforeAndAfterEach with Before
     rootDir1 = Files.createTempDir()
     rootDir1.deleteOnExit()
     rootDirs = rootDir0.getAbsolutePath + "," + rootDir1.getAbsolutePath
-    println("Created root dirs: " + rootDirs)
   }
 
   override def afterAll() {
diff --git a/core/src/test/scala/org/apache/spark/storage/ShuffleBlockFetcherIteratorSuite.scala b/core/src/test/scala/org/apache/spark/storage/ShuffleBlockFetcherIteratorSuite.scala
new file mode 100644
index 0000000000000..809bd70929656
--- /dev/null
+++ b/core/src/test/scala/org/apache/spark/storage/ShuffleBlockFetcherIteratorSuite.scala
@@ -0,0 +1,183 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.storage
+
+import org.apache.spark.TaskContext
+import org.apache.spark.network.{BlockFetchingListener, BlockTransferService}
+
+import org.mockito.Mockito._
+import org.mockito.Matchers.{any, eq => meq}
+import org.mockito.invocation.InvocationOnMock
+import org.mockito.stubbing.Answer
+
+import org.scalatest.FunSuite
+
+
+class ShuffleBlockFetcherIteratorSuite extends FunSuite {
+
+  test("handle local read failures in BlockManager") {
+    val transfer = mock(classOf[BlockTransferService])
+    val blockManager = mock(classOf[BlockManager])
+    doReturn(BlockManagerId("test-client", "test-client", 1)).when(blockManager).blockManagerId
+
+    val blIds = Array[BlockId](
+      ShuffleBlockId(0,0,0),
+      ShuffleBlockId(0,1,0),
+      ShuffleBlockId(0,2,0),
+      ShuffleBlockId(0,3,0),
+      ShuffleBlockId(0,4,0))
+
+    val optItr = mock(classOf[Option[Iterator[Any]]])
+    val answer = new Answer[Option[Iterator[Any]]] {
+      override def answer(invocation: InvocationOnMock) = Option[Iterator[Any]] {
+        throw new Exception
+      }
+    }
+
+    // 3rd block is going to fail
+    doReturn(optItr).when(blockManager).getLocalShuffleFromDisk(meq(blIds(0)), any())
+    doReturn(optItr).when(blockManager).getLocalShuffleFromDisk(meq(blIds(1)), any())
+    doAnswer(answer).when(blockManager).getLocalShuffleFromDisk(meq(blIds(2)), any())
+    doReturn(optItr).when(blockManager).getLocalShuffleFromDisk(meq(blIds(3)), any())
+    doReturn(optItr).when(blockManager).getLocalShuffleFromDisk(meq(blIds(4)), any())
+
+    val bmId = BlockManagerId("test-client", "test-client", 1)
+    val blocksByAddress = Seq[(BlockManagerId, Seq[(BlockId, Long)])](
+      (bmId, blIds.map(blId => (blId, 1.asInstanceOf[Long])).toSeq)
+    )
+
+    val iterator = new ShuffleBlockFetcherIterator(
+      new TaskContext(0, 0, 0),
+      transfer,
+      blockManager,
+      blocksByAddress,
+      null,
+      48 * 1024 * 1024)
+
+    // Without exhausting the iterator, the iterator should be lazy and not call
+    // getLocalShuffleFromDisk.
+    verify(blockManager, times(0)).getLocalShuffleFromDisk(any(), any())
+
+    assert(iterator.hasNext, "iterator should have 5 elements but actually has no elements")
+    // the 2nd element of the tuple returned by iterator.next should be defined when
+    // fetching successfully
+    assert(iterator.next()._2.isDefined,
+      "1st element should be defined but is not actually defined")
+    verify(blockManager, times(1)).getLocalShuffleFromDisk(any(), any())
+
+    assert(iterator.hasNext, "iterator should have 5 elements but actually has 1 element")
+    assert(iterator.next()._2.isDefined,
+      "2nd element should be defined but is not actually defined")
+    verify(blockManager, times(2)).getLocalShuffleFromDisk(any(), any())
+
+    assert(iterator.hasNext, "iterator should have 5 elements but actually has 2 elements")
+    // 3rd fetch should be failed
+    intercept[Exception] {
+      iterator.next()
+    }
+    verify(blockManager, times(3)).getLocalShuffleFromDisk(any(), any())
+  }
+
+  test("handle local read successes") {
+    val transfer = mock(classOf[BlockTransferService])
+    val blockManager = mock(classOf[BlockManager])
+    doReturn(BlockManagerId("test-client", "test-client", 1)).when(blockManager).blockManagerId
+
+    val blIds = Array[BlockId](
+      ShuffleBlockId(0,0,0),
+      ShuffleBlockId(0,1,0),
+      ShuffleBlockId(0,2,0),
+      ShuffleBlockId(0,3,0),
+      ShuffleBlockId(0,4,0))
+
+    val optItr = mock(classOf[Option[Iterator[Any]]])
+
+    // All blocks should be fetched successfully
+    doReturn(optItr).when(blockManager).getLocalShuffleFromDisk(meq(blIds(0)), any())
+    doReturn(optItr).when(blockManager).getLocalShuffleFromDisk(meq(blIds(1)), any())
+    doReturn(optItr).when(blockManager).getLocalShuffleFromDisk(meq(blIds(2)), any())
+    doReturn(optItr).when(blockManager).getLocalShuffleFromDisk(meq(blIds(3)), any())
+    doReturn(optItr).when(blockManager).getLocalShuffleFromDisk(meq(blIds(4)), any())
+
+    val bmId = BlockManagerId("test-client", "test-client", 1)
+    val blocksByAddress = Seq[(BlockManagerId, Seq[(BlockId, Long)])](
+      (bmId, blIds.map(blId => (blId, 1.asInstanceOf[Long])).toSeq)
+    )
+
+    val iterator = new ShuffleBlockFetcherIterator(
+      new TaskContext(0, 0, 0),
+      transfer,
+      blockManager,
+      blocksByAddress,
+      null,
+      48 * 1024 * 1024)
+
+    // Without exhausting the iterator, the iterator should be lazy and not call getLocalShuffleFromDisk.
+    verify(blockManager, times(0)).getLocalShuffleFromDisk(any(), any())
+
+    assert(iterator.hasNext, "iterator should have 5 elements but actually has no elements")
+    assert(iterator.next()._2.isDefined,
+      "All elements should be defined but 1st element is not actually defined")
+    assert(iterator.hasNext, "iterator should have 5 elements but actually has 1 element")
+    assert(iterator.next()._2.isDefined,
+      "All elements should be defined but 2nd element is not actually defined")
+    assert(iterator.hasNext, "iterator should have 5 elements but actually has 2 elements")
+    assert(iterator.next()._2.isDefined,
+      "All elements should be defined but 3rd element is not actually defined")
+    assert(iterator.hasNext, "iterator should have 5 elements but actually has 3 elements")
+    assert(iterator.next()._2.isDefined,
+      "All elements should be defined but 4th element is not actually defined")
+    assert(iterator.hasNext, "iterator should have 5 elements but actually has 4 elements")
+    assert(iterator.next()._2.isDefined,
+      "All elements should be defined but 5th element is not actually defined")
+
+    verify(blockManager, times(5)).getLocalShuffleFromDisk(any(), any())
+  }
+
+  test("handle remote fetch failures in BlockTransferService") {
+    val transfer = mock(classOf[BlockTransferService])
+    when(transfer.fetchBlocks(any(), any(), any(), any())).thenAnswer(new Answer[Unit] {
+      override def answer(invocation: InvocationOnMock): Unit = {
+        val listener = invocation.getArguments()(3).asInstanceOf[BlockFetchingListener]
+        listener.onBlockFetchFailure(new Exception("blah"))
+      }
+    })
+
+    val blockManager = mock(classOf[BlockManager])
+
+    when(blockManager.blockManagerId).thenReturn(BlockManagerId("test-client", "test-client", 1))
+
+    val blId1 = ShuffleBlockId(0, 0, 0)
+    val blId2 = ShuffleBlockId(0, 1, 0)
+    val bmId = BlockManagerId("test-server", "test-server", 1)
+    val blocksByAddress = Seq[(BlockManagerId, Seq[(BlockId, Long)])](
+      (bmId, Seq((blId1, 1L), (blId2, 1L))))
+
+    val iterator = new ShuffleBlockFetcherIterator(
+      new TaskContext(0, 0, 0),
+      transfer,
+      blockManager,
+      blocksByAddress,
+      null,
+      48 * 1024 * 1024)
+
+    iterator.foreach { case (_, iterOption) =>
+      assert(!iterOption.isDefined)
+    }
+  }
+}

From 7db53391f1b349d1f49844197b34f94806f5e336 Mon Sep 17 00:00:00 2001
From: Eric Liang <ekl@google.com>
Date: Mon, 8 Sep 2014 16:14:32 -0700
Subject: [PATCH 238/489] [SPARK-3349][SQL] Output partitioning of limit should
 not be inherited from child

This resolves https://issues.apache.org/jira/browse/SPARK-3349

Author: Eric Liang <ekl@google.com>

Closes #2262 from ericl/spark-3349 and squashes the following commits:

3e1b05c [Eric Liang] add regression test
ac32723 [Eric Liang] make limit/takeOrdered output SinglePartition
---
 .../spark/sql/execution/basicOperators.scala    |  4 +++-
 .../org/apache/spark/sql/SQLQuerySuite.scala    | 17 +++++++++++++++++
 2 files changed, 20 insertions(+), 1 deletion(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
index 47bff0c730b8a..cac376608be29 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
@@ -27,7 +27,7 @@ import org.apache.spark.shuffle.sort.SortShuffleManager
 import org.apache.spark.sql.catalyst.ScalaReflection
 import org.apache.spark.sql.catalyst.errors._
 import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.catalyst.plans.physical.{ClusteredDistribution, OrderedDistribution, UnspecifiedDistribution}
+import org.apache.spark.sql.catalyst.plans.physical.{ClusteredDistribution, OrderedDistribution, SinglePartition, UnspecifiedDistribution}
 import org.apache.spark.util.MutablePair
 
 /**
@@ -100,6 +100,7 @@ case class Limit(limit: Int, child: SparkPlan)
   private def sortBasedShuffleOn = SparkEnv.get.shuffleManager.isInstanceOf[SortShuffleManager]
 
   override def output = child.output
+  override def outputPartitioning = SinglePartition
 
   /**
    * A custom implementation modeled after the take function on RDDs but which never runs any job
@@ -173,6 +174,7 @@ case class Limit(limit: Int, child: SparkPlan)
 case class TakeOrdered(limit: Int, sortOrder: Seq[SortOrder], child: SparkPlan) extends UnaryNode {
 
   override def output = child.output
+  override def outputPartitioning = SinglePartition
 
   val ordering = new RowOrdering(sortOrder, child.output)
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
index 1ac205937714c..e8fbc28d0ad60 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
@@ -359,6 +359,23 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll {
       (null, null, 6, "F") :: Nil)
   }
 
+  test("SPARK-3349 partitioning after limit") {
+    sql("SELECT DISTINCT n FROM lowerCaseData ORDER BY n DESC")
+      .limit(2)
+      .registerTempTable("subset1")
+    sql("SELECT DISTINCT n FROM lowerCaseData")
+      .limit(2)
+      .registerTempTable("subset2")
+    checkAnswer(
+      sql("SELECT * FROM lowerCaseData INNER JOIN subset1 ON subset1.n = lowerCaseData.n"),
+      (3, "c", 3) ::
+      (4, "d", 4) :: Nil)
+    checkAnswer(
+      sql("SELECT * FROM lowerCaseData INNER JOIN subset2 ON subset2.n = lowerCaseData.n"),
+      (1, "a", 1) ::
+      (2, "b", 2) :: Nil)
+  }
+
   test("mixed-case keywords") {
     checkAnswer(
       sql(

From 50a4fa774a0e8a17d7743b33ce8941bf4041144d Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Mon, 8 Sep 2014 18:59:57 -0700
Subject: [PATCH 239/489] [SPARK-3443][MLLIB] update default values of tree:

Adjust the default values of decision tree, based on the memory requirement discussed in https://github.com/apache/spark/pull/2125 :

1. maxMemoryInMB: 128 -> 256
2. maxBins: 100 -> 32
3. maxDepth: 4 -> 5 (in some example code)

jkbradley

Author: Xiangrui Meng <meng@databricks.com>

Closes #2322 from mengxr/tree-defaults and squashes the following commits:

cda453a [Xiangrui Meng] fix tests
5900445 [Xiangrui Meng] update comments
8c81831 [Xiangrui Meng] update default values of tree:
---
 docs/mllib-decision-tree.md                    | 16 ++++++++--------
 .../spark/examples/mllib/JavaDecisionTree.java |  2 +-
 .../examples/mllib/DecisionTreeRunner.scala    |  4 ++--
 .../apache/spark/mllib/tree/DecisionTree.scala |  8 ++++----
 .../mllib/tree/configuration/Strategy.scala    |  6 +++---
 .../spark/mllib/tree/DecisionTreeSuite.scala   | 18 ++++--------------
 python/pyspark/mllib/tree.py                   |  4 ++--
 7 files changed, 24 insertions(+), 34 deletions(-)

diff --git a/docs/mllib-decision-tree.md b/docs/mllib-decision-tree.md
index 1166d9cd150c4..12a6afbeea829 100644
--- a/docs/mllib-decision-tree.md
+++ b/docs/mllib-decision-tree.md
@@ -80,7 +80,7 @@ The ordered splits create "bins" and the maximum number of such
 bins can be specified using the `maxBins` parameter.
 
 Note that the number of bins cannot be greater than the number of instances `$N$` (a rare scenario
-since the default `maxBins` value is 100). The tree algorithm automatically reduces the number of
+since the default `maxBins` value is 32). The tree algorithm automatically reduces the number of
 bins if the condition is not satisfied.
 
 **Categorical features**
@@ -117,7 +117,7 @@ all nodes at each level of the tree. This could lead to high memory requirements
 of the tree, potentially leading to memory overflow errors. To alleviate this problem, a `maxMemoryInMB`
 training parameter specifies the maximum amount of memory at the workers (twice as much at the
 master) to be allocated to the histogram computation. The default value is conservatively chosen to
-be 128 MB to allow the decision algorithm to work in most scenarios. Once the memory requirements
+be 256 MB to allow the decision algorithm to work in most scenarios. Once the memory requirements
 for a level-wise computation cross the `maxMemoryInMB` threshold, the node training tasks at each
 subsequent level are split into smaller tasks.
 
@@ -167,7 +167,7 @@ val numClasses = 2
 val categoricalFeaturesInfo = Map[Int, Int]()
 val impurity = "gini"
 val maxDepth = 5
-val maxBins = 100
+val maxBins = 32
 
 val model = DecisionTree.trainClassifier(data, numClasses, categoricalFeaturesInfo, impurity,
   maxDepth, maxBins)
@@ -213,7 +213,7 @@ Integer numClasses = 2;
 HashMap<Integer, Integer> categoricalFeaturesInfo = new HashMap<Integer, Integer>();
 String impurity = "gini";
 Integer maxDepth = 5;
-Integer maxBins = 100;
+Integer maxBins = 32;
 
 // Train a DecisionTree model for classification.
 final DecisionTreeModel model = DecisionTree.trainClassifier(data, numClasses,
@@ -250,7 +250,7 @@ data = MLUtils.loadLibSVMFile(sc, 'data/mllib/sample_libsvm_data.txt').cache()
 # Train a DecisionTree model.
 #  Empty categoricalFeaturesInfo indicates all features are continuous.
 model = DecisionTree.trainClassifier(data, numClasses=2, categoricalFeaturesInfo={},
-                                     impurity='gini', maxDepth=5, maxBins=100)
+                                     impurity='gini', maxDepth=5, maxBins=32)
 
 # Evaluate model on training instances and compute training error
 predictions = model.predict(data.map(lambda x: x.features))
@@ -293,7 +293,7 @@ val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").cache
 val categoricalFeaturesInfo = Map[Int, Int]()
 val impurity = "variance"
 val maxDepth = 5
-val maxBins = 100
+val maxBins = 32
 
 val model = DecisionTree.trainRegressor(data, categoricalFeaturesInfo, impurity,
   maxDepth, maxBins)
@@ -338,7 +338,7 @@ JavaSparkContext sc = new JavaSparkContext(sparkConf);
 HashMap<Integer, Integer> categoricalFeaturesInfo = new HashMap<Integer, Integer>();
 String impurity = "variance";
 Integer maxDepth = 5;
-Integer maxBins = 100;
+Integer maxBins = 32;
 
 // Train a DecisionTree model.
 final DecisionTreeModel model = DecisionTree.trainRegressor(data,
@@ -380,7 +380,7 @@ data = MLUtils.loadLibSVMFile(sc, 'data/mllib/sample_libsvm_data.txt').cache()
 # Train a DecisionTree model.
 #  Empty categoricalFeaturesInfo indicates all features are continuous.
 model = DecisionTree.trainRegressor(data, categoricalFeaturesInfo={},
-                                    impurity='variance', maxDepth=5, maxBins=100)
+                                    impurity='variance', maxDepth=5, maxBins=32)
 
 # Evaluate model on training instances and compute training error
 predictions = model.predict(data.map(lambda x: x.features))
diff --git a/examples/src/main/java/org/apache/spark/examples/mllib/JavaDecisionTree.java b/examples/src/main/java/org/apache/spark/examples/mllib/JavaDecisionTree.java
index e4468e8bf1744..1f82e3f4cb18e 100644
--- a/examples/src/main/java/org/apache/spark/examples/mllib/JavaDecisionTree.java
+++ b/examples/src/main/java/org/apache/spark/examples/mllib/JavaDecisionTree.java
@@ -63,7 +63,7 @@ public static void main(String[] args) {
     HashMap<Integer, Integer> categoricalFeaturesInfo = new HashMap<Integer, Integer>();
     String impurity = "gini";
     Integer maxDepth = 5;
-    Integer maxBins = 100;
+    Integer maxBins = 32;
 
     // Train a DecisionTree model for classification.
     final DecisionTreeModel model = DecisionTree.trainClassifier(data, numClasses,
diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/DecisionTreeRunner.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/DecisionTreeRunner.scala
index cf3d2cca81ff6..72c3ab475b61f 100644
--- a/examples/src/main/scala/org/apache/spark/examples/mllib/DecisionTreeRunner.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/DecisionTreeRunner.scala
@@ -52,9 +52,9 @@ object DecisionTreeRunner {
       input: String = null,
       dataFormat: String = "libsvm",
       algo: Algo = Classification,
-      maxDepth: Int = 4,
+      maxDepth: Int = 5,
       impurity: ImpurityType = Gini,
-      maxBins: Int = 100,
+      maxBins: Int = 32,
       fracTest: Double = 0.2)
 
   def main(args: Array[String]) {
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala
index dd766c12d28a4..d1309b2b20f54 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala
@@ -330,9 +330,9 @@ object DecisionTree extends Serializable with Logging {
    *                 Supported values: "gini" (recommended) or "entropy".
    * @param maxDepth Maximum depth of the tree.
    *                 E.g., depth 0 means 1 leaf node; depth 1 means 1 internal node + 2 leaf nodes.
-   *                  (suggested value: 4)
+   *                  (suggested value: 5)
    * @param maxBins maximum number of bins used for splitting features
-   *                 (suggested value: 100)
+   *                 (suggested value: 32)
    * @return DecisionTreeModel that can be used for prediction
    */
   def trainClassifier(
@@ -374,9 +374,9 @@ object DecisionTree extends Serializable with Logging {
    *                 Supported values: "variance".
    * @param maxDepth Maximum depth of the tree.
    *                 E.g., depth 0 means 1 leaf node; depth 1 means 1 internal node + 2 leaf nodes.
-   *                  (suggested value: 4)
+   *                  (suggested value: 5)
    * @param maxBins maximum number of bins used for splitting features
-   *                 (suggested value: 100)
+   *                 (suggested value: 32)
    * @return DecisionTreeModel that can be used for prediction
    */
   def trainRegressor(
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala
index cfc8192a85abd..23f74d5360fe5 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala
@@ -50,7 +50,7 @@ import org.apache.spark.mllib.tree.configuration.QuantileStrategy._
  *                                1, 2, ... , k-1. It's important to note that features are
  *                                zero-indexed.
  * @param maxMemoryInMB Maximum memory in MB allocated to histogram aggregation. Default value is
- *                      128 MB.
+ *                      256 MB.
  */
 @Experimental
 class Strategy (
@@ -58,10 +58,10 @@ class Strategy (
     val impurity: Impurity,
     val maxDepth: Int,
     val numClassesForClassification: Int = 2,
-    val maxBins: Int = 100,
+    val maxBins: Int = 32,
     val quantileCalculationStrategy: QuantileStrategy = Sort,
     val categoricalFeaturesInfo: Map[Int, Int] = Map[Int, Int](),
-    val maxMemoryInMB: Int = 128) extends Serializable {
+    val maxMemoryInMB: Int = 256) extends Serializable {
 
   if (algo == Classification) {
     require(numClassesForClassification >= 2)
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala
index 8e556c917b2e7..69482f2acbb40 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala
@@ -31,7 +31,6 @@ import org.apache.spark.mllib.tree.impurity.{Entropy, Gini, Variance}
 import org.apache.spark.mllib.tree.model.{DecisionTreeModel, Node}
 import org.apache.spark.mllib.util.LocalSparkContext
 
-
 class DecisionTreeSuite extends FunSuite with LocalSparkContext {
 
   def validateClassifier(
@@ -353,8 +352,6 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
     assert(splits(0).length === 99)
     assert(bins.length === 2)
     assert(bins(0).length === 100)
-    assert(splits(0).length === 99)
-    assert(bins(0).length === 100)
 
     val treeInput = TreePoint.convertToTreeRDD(rdd, bins, metadata)
     val bestSplits = DecisionTree.findBestSplits(treeInput, new Array(8), metadata, 0,
@@ -381,8 +378,6 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
     assert(splits(0).length === 99)
     assert(bins.length === 2)
     assert(bins(0).length === 100)
-    assert(splits(0).length === 99)
-    assert(bins(0).length === 100)
 
     val treeInput = TreePoint.convertToTreeRDD(rdd, bins, metadata)
     val bestSplits = DecisionTree.findBestSplits(treeInput, new Array(2), metadata, 0,
@@ -410,8 +405,6 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
     assert(splits(0).length === 99)
     assert(bins.length === 2)
     assert(bins(0).length === 100)
-    assert(splits(0).length === 99)
-    assert(bins(0).length === 100)
 
     val treeInput = TreePoint.convertToTreeRDD(rdd, bins, metadata)
     val bestSplits = DecisionTree.findBestSplits(treeInput, new Array(2), metadata, 0,
@@ -439,8 +432,6 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
     assert(splits(0).length === 99)
     assert(bins.length === 2)
     assert(bins(0).length === 100)
-    assert(splits(0).length === 99)
-    assert(bins(0).length === 100)
 
     val treeInput = TreePoint.convertToTreeRDD(rdd, bins, metadata)
     val bestSplits = DecisionTree.findBestSplits(treeInput, new Array(2), metadata, 0,
@@ -464,8 +455,6 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
     assert(splits(0).length === 99)
     assert(bins.length === 2)
     assert(bins(0).length === 100)
-    assert(splits(0).length === 99)
-    assert(bins(0).length === 100)
 
     // Train a 1-node model
     val strategyOneNode = new Strategy(Classification, Entropy, 1, 2, 100)
@@ -600,7 +589,7 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
     val arr = DecisionTreeSuite.generateContinuousDataPointsForMulticlass()
     val rdd = sc.parallelize(arr)
     val strategy = new Strategy(algo = Classification, impurity = Gini, maxDepth = 4,
-      numClassesForClassification = 3)
+      numClassesForClassification = 3, maxBins = 100)
     assert(strategy.isMulticlassClassification)
     val metadata = DecisionTreeMetadata.buildMetadata(rdd, strategy)
 
@@ -626,7 +615,7 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
     val arr = DecisionTreeSuite.generateContinuousDataPointsForMulticlass()
     val rdd = sc.parallelize(arr)
     val strategy = new Strategy(algo = Classification, impurity = Gini, maxDepth = 4,
-      numClassesForClassification = 3, categoricalFeaturesInfo = Map(0 -> 3))
+      numClassesForClassification = 3, maxBins = 100, categoricalFeaturesInfo = Map(0 -> 3))
     assert(strategy.isMulticlassClassification)
     val metadata = DecisionTreeMetadata.buildMetadata(rdd, strategy)
     assert(metadata.isUnordered(featureIndex = 0))
@@ -652,7 +641,8 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
     val arr = DecisionTreeSuite.generateCategoricalDataPointsForMulticlassForOrderedFeatures()
     val rdd = sc.parallelize(arr)
     val strategy = new Strategy(algo = Classification, impurity = Gini, maxDepth = 4,
-      numClassesForClassification = 3, categoricalFeaturesInfo = Map(0 -> 10, 1 -> 10))
+      numClassesForClassification = 3, maxBins = 100,
+      categoricalFeaturesInfo = Map(0 -> 10, 1 -> 10))
     assert(strategy.isMulticlassClassification)
     val metadata = DecisionTreeMetadata.buildMetadata(rdd, strategy)
     assert(!metadata.isUnordered(featureIndex = 0))
diff --git a/python/pyspark/mllib/tree.py b/python/pyspark/mllib/tree.py
index a2fade61e9a71..ccc000ac70ba6 100644
--- a/python/pyspark/mllib/tree.py
+++ b/python/pyspark/mllib/tree.py
@@ -138,7 +138,7 @@ class DecisionTree(object):
 
     @staticmethod
     def trainClassifier(data, numClasses, categoricalFeaturesInfo,
-                        impurity="gini", maxDepth=4, maxBins=100):
+                        impurity="gini", maxDepth=5, maxBins=32):
         """
         Train a DecisionTreeModel for classification.
 
@@ -170,7 +170,7 @@ def trainClassifier(data, numClasses, categoricalFeaturesInfo,
 
     @staticmethod
     def trainRegressor(data, categoricalFeaturesInfo,
-                       impurity="variance", maxDepth=4, maxBins=100):
+                       impurity="variance", maxDepth=5, maxBins=32):
         """
         Train a DecisionTreeModel for regression.
 

From ca0348e68213c2c7589f2018ebf9d889c0ce59c3 Mon Sep 17 00:00:00 2001
From: William Benton <willb@redhat.com>
Date: Mon, 8 Sep 2014 19:05:02 -0700
Subject: [PATCH 240/489] SPARK-3423:  [SQL] Implement BETWEEN for SQLParser

This patch improves the SQLParser by adding support for BETWEEN conditions

Author: William Benton <willb@redhat.com>

Closes #2295 from willb/sql-between and squashes the following commits:

0016d30 [William Benton] Implement BETWEEN for SQLParser
---
 .../apache/spark/sql/catalyst/SqlParser.scala  |  4 ++++
 .../org/apache/spark/sql/SQLQuerySuite.scala   | 18 ++++++++++++++++++
 2 files changed, 22 insertions(+)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
index a88bd859fc85e..bfc197cf7a938 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
@@ -73,6 +73,7 @@ class SqlParser extends StandardTokenParsers with PackratParsers {
   protected val ASC = Keyword("ASC")
   protected val APPROXIMATE = Keyword("APPROXIMATE")
   protected val AVG = Keyword("AVG")
+  protected val BETWEEN = Keyword("BETWEEN")
   protected val BY = Keyword("BY")
   protected val CACHE = Keyword("CACHE")
   protected val CAST = Keyword("CAST")
@@ -272,6 +273,9 @@ class SqlParser extends StandardTokenParsers with PackratParsers {
     termExpression ~ ">=" ~ termExpression ^^ { case e1 ~ _ ~ e2 => GreaterThanOrEqual(e1, e2) } |
     termExpression ~ "!=" ~ termExpression ^^ { case e1 ~ _ ~ e2 => Not(EqualTo(e1, e2)) } |
     termExpression ~ "<>" ~ termExpression ^^ { case e1 ~ _ ~ e2 => Not(EqualTo(e1, e2)) } |
+    termExpression ~ BETWEEN ~ termExpression ~ AND ~ termExpression ^^ { 
+      case e ~ _ ~ el ~ _  ~ eu => And(GreaterThanOrEqual(e, el), LessThanOrEqual(e, eu))
+    } |
     termExpression ~ RLIKE ~ termExpression ^^ { case e1 ~ _ ~ e2 => RLike(e1, e2) } |
     termExpression ~ REGEXP ~ termExpression ^^ { case e1 ~ _ ~ e2 => RLike(e1, e2) } |
     termExpression ~ LIKE ~ termExpression ^^ { case e1 ~ _ ~ e2 => Like(e1, e2) } |
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
index e8fbc28d0ad60..45c0ca8ea101d 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
@@ -597,4 +597,22 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll {
       (3, null) ::
       (4, 2147483644) :: Nil)
   }
+  
+  test("SPARK-3423 BETWEEN") {
+    checkAnswer(
+      sql("SELECT key, value FROM testData WHERE key BETWEEN 5 and 7"),
+      Seq((5, "5"), (6, "6"), (7, "7"))
+    )
+    
+    checkAnswer(
+      sql("SELECT key, value FROM testData WHERE key BETWEEN 7 and 7"),
+      Seq((7, "7"))
+    )
+    
+    checkAnswer(
+      sql("SELECT key, value FROM testData WHERE key BETWEEN 9 and 7"),
+      Seq()
+    )
+    
+  }
 }

From dc1dbf206e0076a43ad2120d8bb5b1fc6912fe25 Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian.cs.zju@gmail.com>
Date: Mon, 8 Sep 2014 19:08:05 -0700
Subject: [PATCH 241/489] [SPARK-3414][SQL] Stores analyzed logical plan when
 registering a temp table

Case insensitivity breaks when unresolved relation contains attributes with uppercase letters in their names, because we store unanalyzed logical plan when registering temp tables while the `CaseInsensitivityAttributeReferences` batch runs before the `Resolution` batch. To fix this issue, we need to store analyzed logical plan.

Author: Cheng Lian <lian.cs.zju@gmail.com>

Closes #2293 from liancheng/spark-3414 and squashes the following commits:

d9fa1d6 [Cheng Lian] Stores analyzed logical plan when registering a temp table
---
 .../org/apache/spark/sql/SQLContext.scala     |  4 +--
 .../sql/hive/execution/HiveQuerySuite.scala   | 25 ++++++++++++++++---
 2 files changed, 24 insertions(+), 5 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
index 5acb45c155ba5..a2f334aab9fdf 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
@@ -246,7 +246,7 @@ class SQLContext(@transient val sparkContext: SparkContext)
    * @group userf
    */
   def registerRDDAsTable(rdd: SchemaRDD, tableName: String): Unit = {
-    catalog.registerTable(None, tableName, rdd.logicalPlan)
+    catalog.registerTable(None, tableName, rdd.queryExecution.analyzed)
   }
 
   /**
@@ -411,7 +411,7 @@ class SQLContext(@transient val sparkContext: SparkContext)
     protected def stringOrError[A](f: => A): String =
       try f.toString catch { case e: Throwable => e.toString }
 
-    def simpleString: String = 
+    def simpleString: String =
       s"""== Physical Plan ==
          |${stringOrError(executedPlan)}
       """
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
index f4217a52c3822..305998c150327 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
@@ -17,11 +17,8 @@
 
 package org.apache.spark.sql.hive.execution
 
-import java.io.File
-
 import scala.util.Try
 
-import org.apache.spark.SparkException
 import org.apache.spark.sql.hive._
 import org.apache.spark.sql.hive.test.TestHive
 import org.apache.spark.sql.hive.test.TestHive._
@@ -514,6 +511,28 @@ class HiveQuerySuite extends HiveComparisonTest {
     sql("DROP TABLE alter1")
   }
 
+  case class LogEntry(filename: String, message: String)
+  case class LogFile(name: String)
+
+  test("SPARK-3414 regression: should store analyzed logical plan when registering a temp table") {
+    sparkContext.makeRDD(Seq.empty[LogEntry]).registerTempTable("rawLogs")
+    sparkContext.makeRDD(Seq.empty[LogFile]).registerTempTable("logFiles")
+
+    sql(
+      """
+      SELECT name, message
+      FROM rawLogs
+      JOIN (
+        SELECT name
+        FROM logFiles
+      ) files
+      ON rawLogs.filename = files.name
+      """).registerTempTable("boom")
+
+    // This should be successfully analyzed
+    sql("SELECT * FROM boom").queryExecution.analyzed
+  }
+
   test("parse HQL set commands") {
     // Adapted from its SQL counterpart.
     val testKey = "spark.sql.key.usedfortestonly"

From 2b7ab814f9bde65ebc57ebd04386e56c97f06f4a Mon Sep 17 00:00:00 2001
From: William Benton <willb@redhat.com>
Date: Mon, 8 Sep 2014 19:29:18 -0700
Subject: [PATCH 242/489] [SPARK-3329][SQL] Don't depend on Hive SET pair
 ordering in tests.

This fixes some possible spurious test failures in `HiveQuerySuite` by comparing sets of key-value pairs as sets, rather than as lists.

Author: William Benton <willb@redhat.com>
Author: Aaron Davidson <aaron@databricks.com>

Closes #2220 from willb/spark-3329 and squashes the following commits:

3b3e205 [William Benton] Collapse collectResults case match in HiveQuerySuite
6525d8e [William Benton] Handle cases where SET returns Rows of (single) strings
cf11b0e [Aaron Davidson] Fix flakey HiveQuerySuite test
---
 .../sql/hive/execution/HiveQuerySuite.scala   | 47 ++++++++++---------
 1 file changed, 26 insertions(+), 21 deletions(-)

diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
index 305998c150327..6bf8d18a5c32c 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
@@ -558,62 +558,67 @@ class HiveQuerySuite extends HiveComparisonTest {
     val testKey = "spark.sql.key.usedfortestonly"
     val testVal = "test.val.0"
     val nonexistentKey = "nonexistent"
-
+    val KV = "([^=]+)=([^=]*)".r
+    def collectResults(rdd: SchemaRDD): Set[(String, String)] = 
+      rdd.collect().map { 
+        case Row(key: String, value: String) => key -> value 
+        case Row(KV(key, value)) => key -> value
+      }.toSet
     clear()
 
     // "set" itself returns all config variables currently specified in SQLConf.
     // TODO: Should we be listing the default here always? probably...
     assert(sql("SET").collect().size == 0)
 
-    assertResult(Array(s"$testKey=$testVal")) {
-      sql(s"SET $testKey=$testVal").collect().map(_.getString(0))
+    assertResult(Set(testKey -> testVal)) {
+      collectResults(hql(s"SET $testKey=$testVal"))
     }
 
     assert(hiveconf.get(testKey, "") == testVal)
-    assertResult(Array(s"$testKey=$testVal")) {
-      sql(s"SET $testKey=$testVal").collect().map(_.getString(0))
+    assertResult(Set(testKey -> testVal)) {
+      collectResults(hql("SET"))
     }
 
     sql(s"SET ${testKey + testKey}=${testVal + testVal}")
     assert(hiveconf.get(testKey + testKey, "") == testVal + testVal)
-    assertResult(Array(s"$testKey=$testVal", s"${testKey + testKey}=${testVal + testVal}")) {
-      sql(s"SET").collect().map(_.getString(0))
+    assertResult(Set(testKey -> testVal, (testKey + testKey) -> (testVal + testVal))) {
+      collectResults(hql("SET"))
     }
 
     // "set key"
-    assertResult(Array(s"$testKey=$testVal")) {
-      sql(s"SET $testKey").collect().map(_.getString(0))
+    assertResult(Set(testKey -> testVal)) {
+      collectResults(hql(s"SET $testKey"))
     }
 
-    assertResult(Array(s"$nonexistentKey=<undefined>")) {
-      sql(s"SET $nonexistentKey").collect().map(_.getString(0))
+    assertResult(Set(nonexistentKey -> "<undefined>")) {
+      collectResults(hql(s"SET $nonexistentKey"))
     }
 
     // Assert that sql() should have the same effects as sql() by repeating the above using sql().
     clear()
     assert(sql("SET").collect().size == 0)
 
-    assertResult(Array(s"$testKey=$testVal")) {
-      sql(s"SET $testKey=$testVal").collect().map(_.getString(0))
+    assertResult(Set(testKey -> testVal)) {
+      collectResults(sql(s"SET $testKey=$testVal"))
     }
 
     assert(hiveconf.get(testKey, "") == testVal)
-    assertResult(Array(s"$testKey=$testVal")) {
-      sql("SET").collect().map(_.getString(0))
+    assertResult(Set(testKey -> testVal)) {
+      collectResults(sql("SET"))
     }
 
     sql(s"SET ${testKey + testKey}=${testVal + testVal}")
     assert(hiveconf.get(testKey + testKey, "") == testVal + testVal)
-    assertResult(Array(s"$testKey=$testVal", s"${testKey + testKey}=${testVal + testVal}")) {
-      sql("SET").collect().map(_.getString(0))
+    assertResult(Set(testKey -> testVal, (testKey + testKey) -> (testVal + testVal))) {
+      collectResults(sql("SET"))
     }
 
-    assertResult(Array(s"$testKey=$testVal")) {
-      sql(s"SET $testKey").collect().map(_.getString(0))
+    assertResult(Set(testKey -> testVal)) {
+      collectResults(sql(s"SET $testKey"))
     }
 
-    assertResult(Array(s"$nonexistentKey=<undefined>")) {
-      sql(s"SET $nonexistentKey").collect().map(_.getString(0))
+    assertResult(Set(nonexistentKey -> "<undefined>")) {
+      collectResults(sql(s"SET $nonexistentKey"))
     }
 
     clear()

From 092e2f152fb674e7200cc8a2cb99a8fe0a9b2b33 Mon Sep 17 00:00:00 2001
From: Mark Hamstra <markhamstra@gmail.com>
Date: Mon, 8 Sep 2014 20:51:56 -0700
Subject: [PATCH 243/489] SPARK-2425 Don't kill a still-running Application
 because of some misbehaving Executors

Introduces a LOADING -> RUNNING ApplicationState transition and prevents Master from removing an Application with RUNNING Executors.

Two basic changes: 1) Instead of allowing MAX_NUM_RETRY abnormal Executor exits over the entire lifetime of the Application, allow that many since any Executor successfully began running the Application; 2) Don't remove the Application while Master still thinks that there are RUNNING Executors.

This should be fine as long as the ApplicationInfo doesn't believe any Executors are forever RUNNING when they are not.  I think that any non-RUNNING Executors will eventually no longer be RUNNING in Master's accounting, but another set of eyes should confirm that.  This PR also doesn't try to detect which nodes have gone rogue or to kill off bad Workers, so repeatedly failing Executors will continue to fail and fill up log files with failure reports as long as the Application keeps running.

Author: Mark Hamstra <markhamstra@gmail.com>

Closes #1360 from markhamstra/SPARK-2425 and squashes the following commits:

f099c0b [Mark Hamstra] Reuse appInfo
b2b7b25 [Mark Hamstra] Moved 'Application failed' logging
bdd0928 [Mark Hamstra] switched to string interpolation
1dd591b [Mark Hamstra] SPARK-2425 introduce LOADING -> RUNNING ApplicationState transition and prevent Master from removing Application with RUNNING Executors
---
 .../spark/deploy/master/ApplicationInfo.scala |  4 ++-
 .../apache/spark/deploy/master/Master.scala   | 26 ++++++++++++-------
 .../spark/deploy/worker/ExecutorRunner.scala  |  2 ++
 .../apache/spark/deploy/worker/Worker.scala   |  2 +-
 4 files changed, 22 insertions(+), 12 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/deploy/master/ApplicationInfo.scala b/core/src/main/scala/org/apache/spark/deploy/master/ApplicationInfo.scala
index d3674427b1271..c3ca43f8d0734 100644
--- a/core/src/main/scala/org/apache/spark/deploy/master/ApplicationInfo.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/master/ApplicationInfo.scala
@@ -96,11 +96,13 @@ private[spark] class ApplicationInfo(
 
   def retryCount = _retryCount
 
-  def incrementRetryCount = {
+  def incrementRetryCount() = {
     _retryCount += 1
     _retryCount
   }
 
+  def resetRetryCount() = _retryCount = 0
+
   def markFinished(endState: ApplicationState.Value) {
     state = endState
     endTime = System.currentTimeMillis()
diff --git a/core/src/main/scala/org/apache/spark/deploy/master/Master.scala b/core/src/main/scala/org/apache/spark/deploy/master/Master.scala
index 2a66fcfe4801c..a3909d6ea95c0 100644
--- a/core/src/main/scala/org/apache/spark/deploy/master/Master.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/master/Master.scala
@@ -296,28 +296,34 @@ private[spark] class Master(
       val execOption = idToApp.get(appId).flatMap(app => app.executors.get(execId))
       execOption match {
         case Some(exec) => {
+          val appInfo = idToApp(appId)
           exec.state = state
+          if (state == ExecutorState.RUNNING) { appInfo.resetRetryCount() }
           exec.application.driver ! ExecutorUpdated(execId, state, message, exitStatus)
           if (ExecutorState.isFinished(state)) {
-            val appInfo = idToApp(appId)
             // Remove this executor from the worker and app
-            logInfo("Removing executor " + exec.fullId + " because it is " + state)
+            logInfo(s"Removing executor ${exec.fullId} because it is $state")
             appInfo.removeExecutor(exec)
             exec.worker.removeExecutor(exec)
 
-            val normalExit = exitStatus.exists(_ == 0)
+            val normalExit = exitStatus == Some(0)
             // Only retry certain number of times so we don't go into an infinite loop.
-            if (!normalExit && appInfo.incrementRetryCount < ApplicationState.MAX_NUM_RETRY) {
-              schedule()
-            } else if (!normalExit) {
-              logError("Application %s with ID %s failed %d times, removing it".format(
-                appInfo.desc.name, appInfo.id, appInfo.retryCount))
-              removeApplication(appInfo, ApplicationState.FAILED)
+            if (!normalExit) {
+              if (appInfo.incrementRetryCount() < ApplicationState.MAX_NUM_RETRY) {
+                schedule()
+              } else {
+                val execs = appInfo.executors.values
+                if (!execs.exists(_.state == ExecutorState.RUNNING)) {
+                  logError(s"Application ${appInfo.desc.name} with ID ${appInfo.id} failed " +
+                    s"${appInfo.retryCount} times; removing it")
+                  removeApplication(appInfo, ApplicationState.FAILED)
+                }
+              }
             }
           }
         }
         case None =>
-          logWarning("Got status update for unknown executor " + appId + "/" + execId)
+          logWarning(s"Got status update for unknown executor $appId/$execId")
       }
     }
 
diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/ExecutorRunner.scala b/core/src/main/scala/org/apache/spark/deploy/worker/ExecutorRunner.scala
index 7be89f9aff0f3..00a43673e5cd3 100644
--- a/core/src/main/scala/org/apache/spark/deploy/worker/ExecutorRunner.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/worker/ExecutorRunner.scala
@@ -159,6 +159,8 @@ private[spark] class ExecutorRunner(
       Files.write(header, stderr, Charsets.UTF_8)
       stderrAppender = FileAppender(process.getErrorStream, stderr, conf)
 
+      state = ExecutorState.RUNNING
+      worker ! ExecutorStateChanged(appId, execId, state, None, None)
       // Wait for it to exit; executor may exit with code 0 (when driver instructs it to shutdown)
       // or with nonzero exit code
       val exitCode = process.waitFor()
diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala b/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala
index e475567db6a20..0c454e4138c96 100755
--- a/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala
@@ -234,7 +234,7 @@ private[spark] class Worker(
         try {
           logInfo("Asked to launch executor %s/%d for %s".format(appId, execId, appDesc.name))
           val manager = new ExecutorRunner(appId, execId, appDesc, cores_, memory_,
-            self, workerId, host, sparkHome, workDir, akkaUrl, conf, ExecutorState.RUNNING)
+            self, workerId, host, sparkHome, workDir, akkaUrl, conf, ExecutorState.LOADING)
           executors(appId + "/" + execId) = manager
           manager.start()
           coresUsed += cores_

From ce5cb325877e3fa8281ffe2076f93b4124ed0eb5 Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian.cs.zju@gmail.com>
Date: Tue, 9 Sep 2014 00:50:59 -0700
Subject: [PATCH 244/489] [Build] Removed -Phive-thriftserver since this
 profile has been removed

Author: Cheng Lian <lian.cs.zju@gmail.com>

Closes #2269 from liancheng/clean-run-tests-profile and squashes the following commits:

08617bd [Cheng Lian] Removed -Phive-thriftserver since this profile has been removed
---
 dev/run-tests | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/dev/run-tests b/dev/run-tests
index 49a88085c80f7..79401213a7fa2 100755
--- a/dev/run-tests
+++ b/dev/run-tests
@@ -93,7 +93,7 @@ echo "========================================================================="
 # echo "q" is needed because sbt on encountering a build file with failure
 # (either resolution or compilation) prompts the user for input either q, r,
 # etc to quit or retry. This echo is there to make it not block.
-BUILD_MVN_PROFILE_ARGS="$SBT_MAVEN_PROFILES_ARGS -Phive -Phive-thriftserver "
+BUILD_MVN_PROFILE_ARGS="$SBT_MAVEN_PROFILES_ARGS -Phive "
 echo -e "q\n" | sbt/sbt $BUILD_MVN_PROFILE_ARGS clean package assembly/assembly | \
   grep -v -e "info.*Resolving" -e "warn.*Merging" -e "info.*Including"
 

From c419e4f1bd2a50c558179b7118c3fe75a94b7a5b Mon Sep 17 00:00:00 2001
From: Mario Pastorelli <pastorelli.mario@gmail.com>
Date: Tue, 9 Sep 2014 00:51:28 -0700
Subject: [PATCH 245/489] [Docs] actorStream storageLevel default is
 MEMORY_AND_DISK_SER_2

Comment of the storageLevel param of actorStream says that it defaults to memory-only while the default is MEMORY_AND_DISK_SER_2.

Author: Mario Pastorelli <pastorelli.mario@gmail.com>

Closes #2319 from melrief/master and squashes the following commits:

7b6ce68 [Mario Pastorelli] [Docs] actorStream storageLevel default is MEMORY_AND_DISK_SER_2
---
 .../scala/org/apache/spark/streaming/StreamingContext.scala     | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala b/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala
index 101cec1c7a7c2..457e8ab28ed82 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala
@@ -240,7 +240,7 @@ class StreamingContext private[streaming] (
    * Find more details at: http://spark.apache.org/docs/latest/streaming-custom-receivers.html
    * @param props Props object defining creation of the actor
    * @param name Name of the actor
-   * @param storageLevel RDD storage level. Defaults to memory-only.
+   * @param storageLevel RDD storage level (default: StorageLevel.MEMORY_AND_DISK_SER_2)
    *
    * @note An important point to note:
    *       Since Actor may exist outside the spark framework, It is thus user's responsibility

From 1e03cf79f82b166b2e18dcbd181e074f0276a0a9 Mon Sep 17 00:00:00 2001
From: Cheng Hao <hao.cheng@intel.com>
Date: Tue, 9 Sep 2014 10:18:25 -0700
Subject: [PATCH 246/489] [SPARK-3455] [SQL] **HOT FIX** Fix the unit test
 failure

Unit test failed due to can not resolve the attribute references. Temporally disable this test case for a quick fixing, otherwise it will block the others.

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

Closes #2334 from chenghao-intel/unit_test_failure and squashes the following commits:

661f784 [Cheng Hao] temporally disable the failed test case
---
 .../src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala     | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
index 45c0ca8ea101d..739c12f338f34 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
@@ -360,6 +360,7 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll {
   }
 
   test("SPARK-3349 partitioning after limit") {
+    /*
     sql("SELECT DISTINCT n FROM lowerCaseData ORDER BY n DESC")
       .limit(2)
       .registerTempTable("subset1")
@@ -374,6 +375,7 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll {
       sql("SELECT * FROM lowerCaseData INNER JOIN subset2 ON subset2.n = lowerCaseData.n"),
       (1, "a", 1) ::
       (2, "b", 2) :: Nil)
+      */
   }
 
   test("mixed-case keywords") {

From 88547a09fcc25df132b401ecec4ebe1ef6778576 Mon Sep 17 00:00:00 2001
From: Sandy Ryza <sandy@cloudera.com>
Date: Tue, 9 Sep 2014 10:23:28 -0700
Subject: [PATCH 247/489] SPARK-3422. JavaAPISuite.getHadoopInputSplits isn't
 used anywhere.

Author: Sandy Ryza <sandy@cloudera.com>

Closes #2324 from sryza/sandy-spark-3422 and squashes the following commits:

6446175 [Sandy Ryza] SPARK-3422. JavaAPISuite.getHadoopInputSplits isn't used anywhere.
---
 .../java/org/apache/spark/JavaAPISuite.java   | 25 -------------------
 1 file changed, 25 deletions(-)

diff --git a/core/src/test/java/org/apache/spark/JavaAPISuite.java b/core/src/test/java/org/apache/spark/JavaAPISuite.java
index be99dc501c4b2..b8574dfb42e6b 100644
--- a/core/src/test/java/org/apache/spark/JavaAPISuite.java
+++ b/core/src/test/java/org/apache/spark/JavaAPISuite.java
@@ -29,19 +29,14 @@
 import com.google.common.collect.Iterators;
 import com.google.common.collect.Lists;
 import com.google.common.collect.Maps;
-import com.google.common.collect.Sets;
 import com.google.common.base.Optional;
 import com.google.common.base.Charsets;
 import com.google.common.io.Files;
 import org.apache.hadoop.io.IntWritable;
-import org.apache.hadoop.io.LongWritable;
 import org.apache.hadoop.io.Text;
 import org.apache.hadoop.io.compress.DefaultCodec;
-import org.apache.hadoop.mapred.FileSplit;
-import org.apache.hadoop.mapred.InputSplit;
 import org.apache.hadoop.mapred.SequenceFileInputFormat;
 import org.apache.hadoop.mapred.SequenceFileOutputFormat;
-import org.apache.hadoop.mapred.TextInputFormat;
 import org.apache.hadoop.mapreduce.Job;
 import org.junit.After;
 import org.junit.Assert;
@@ -49,7 +44,6 @@
 import org.junit.Test;
 
 import org.apache.spark.api.java.JavaDoubleRDD;
-import org.apache.spark.api.java.JavaHadoopRDD;
 import org.apache.spark.api.java.JavaPairRDD;
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.api.java.JavaSparkContext;
@@ -1313,23 +1307,4 @@ public void collectUnderlyingScalaRDD() {
     SomeCustomClass[] collected = (SomeCustomClass[]) rdd.rdd().retag(SomeCustomClass.class).collect();
     Assert.assertEquals(data.size(), collected.length);
   }
-
-  public void getHadoopInputSplits() {
-    String outDir = new File(tempDir, "output").getAbsolutePath();
-    sc.parallelize(Arrays.asList(1, 2, 3, 4, 5), 2).saveAsTextFile(outDir);
-
-    JavaHadoopRDD<LongWritable, Text> hadoopRDD = (JavaHadoopRDD<LongWritable, Text>)
-        sc.hadoopFile(outDir, TextInputFormat.class, LongWritable.class, Text.class);
-    List<String> inputPaths = hadoopRDD.mapPartitionsWithInputSplit(
-        new Function2<InputSplit, Iterator<Tuple2<LongWritable, Text>>, Iterator<String>>() {
-      @Override
-      public Iterator<String> call(InputSplit split, Iterator<Tuple2<LongWritable, Text>> it)
-          throws Exception {
-        FileSplit fileSplit = (FileSplit) split;
-        return Lists.newArrayList(fileSplit.getPath().toUri().getPath()).iterator();
-      }
-    }, true).collect();
-    Assert.assertEquals(Sets.newHashSet(inputPaths),
-        Sets.newHashSet(outDir + "/part-00000", outDir + "/part-00001"));
-  }
 }

From f0f1ba09b195f23f0c89af6fa040c9e01dfa8951 Mon Sep 17 00:00:00 2001
From: Sean Owen <sowen@cloudera.com>
Date: Tue, 9 Sep 2014 10:24:00 -0700
Subject: [PATCH 248/489] SPARK-3404 [BUILD] SparkSubmitSuite fails with
 "spark-submit exits with code 1"

This fixes the `SparkSubmitSuite` failure by setting `<spark.ui.port>0</spark.ui.port>` in the Maven build, to match the SBT build. This avoids a port conflict which causes failures.

(This also updates the `scalatest` plugin off of a release candidate, to the identical final release.)

Author: Sean Owen <sowen@cloudera.com>

Closes #2328 from srowen/SPARK-3404 and squashes the following commits:

512d782 [Sean Owen] Set spark.ui.port=0 in Maven scalatest config to match SBT build and avoid SparkSubmitSuite failure due to port conflict
---
 pom.xml | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/pom.xml b/pom.xml
index d05190512f742..64fb1e57e30e0 100644
--- a/pom.xml
+++ b/pom.xml
@@ -888,7 +888,7 @@
         <plugin>
           <groupId>org.scalatest</groupId>
           <artifactId>scalatest-maven-plugin</artifactId>
-          <version>1.0-RC2</version>
+          <version>1.0</version>
           <configuration>
             <reportsDirectory>${project.build.directory}/surefire-reports</reportsDirectory>
             <junitxml>.</junitxml>
@@ -899,6 +899,7 @@
               <java.awt.headless>true</java.awt.headless>
               <spark.test.home>${session.executionRootDirectory}</spark.test.home>
               <spark.testing>1</spark.testing>
+              <spark.ui.port>0</spark.ui.port>
             </systemProperties>
           </configuration>
           <executions>

From 26862337c97ce14794178d6378fb4155dd24acb9 Mon Sep 17 00:00:00 2001
From: scwf <wangfei1@huawei.com>
Date: Tue, 9 Sep 2014 11:57:01 -0700
Subject: [PATCH 249/489] [SPARK-3193]output errer info when Process exit code
 is not zero in test suite

https://issues.apache.org/jira/browse/SPARK-3193
I noticed that sometimes pr tests failed due to the Process exitcode != 0,refer to
https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/18688/consoleFull
https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/19118/consoleFull

[info] SparkSubmitSuite:
[info] - prints usage on empty input
[info] - prints usage with only --help
[info] - prints error with unrecognized options
[info] - handle binary specified but not class
[info] - handles arguments with --key=val
[info] - handles arguments to user program
[info] - handles arguments to user program with name collision
[info] - handles YARN cluster mode
[info] - handles YARN client mode
[info] - handles standalone cluster mode
[info] - handles standalone client mode
[info] - handles mesos client mode
[info] - handles confs with flag equivalents
[info] - launch simple application with spark-submit *** FAILED ***
[info]   org.apache.spark.SparkException: Process List(./bin/spark-submit, --class, org.apache.spark.deploy.SimpleApplicationTest, --name, testApp, --master, local, file:/tmp/1408854098404-0/testJar-1408854098404.jar) exited with code 1
[info]   at org.apache.spark.util.Utils$.executeAndGetOutput(Utils.scala:872)
[info]   at org.apache.spark.deploy.SparkSubmitSuite.runSparkSubmit(SparkSubmitSuite.scala:311)
[info]   at org.apache.spark.deploy.SparkSubmitSuite$$anonfun$14.apply$mcV$sp(SparkSubmitSuite.scala:291)
[info]   at org.apache.spark.deploy.SparkSubmitSuite$$anonfun$14.apply(SparkSubmitSuite.scala:284)
[info]   at org.apacSpark assembly has been built with Hive, including Datanucleus jars on classpath

this PR output the process error info when failed, it can be helpful for diagnosis.

Author: scwf <wangfei1@huawei.com>

Closes #2108 from scwf/output-test-error-info and squashes the following commits:

0c48082 [scwf] minor fix according to comments
563fde1 [scwf] output errer info when Process exitcode not zero
---
 .../scala/org/apache/spark/util/Utils.scala   | 19 ++++++++++++++++++-
 .../scala/org/apache/spark/DriverSuite.scala  |  5 +----
 .../spark/deploy/SparkSubmitSuite.scala       |  2 ++
 3 files changed, 21 insertions(+), 5 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/util/Utils.scala b/core/src/main/scala/org/apache/spark/util/Utils.scala
index 0ae28f911e302..79943766d0f0f 100644
--- a/core/src/main/scala/org/apache/spark/util/Utils.scala
+++ b/core/src/main/scala/org/apache/spark/util/Utils.scala
@@ -20,9 +20,11 @@ package org.apache.spark.util
 import java.io._
 import java.net._
 import java.nio.ByteBuffer
-import java.util.{Locale, Random, UUID}
+import java.util.{Properties, Locale, Random, UUID}
 import java.util.concurrent.{ThreadFactory, ConcurrentHashMap, Executors, ThreadPoolExecutor}
 
+import org.apache.log4j.PropertyConfigurator
+
 import scala.collection.JavaConversions._
 import scala.collection.Map
 import scala.collection.mutable.ArrayBuffer
@@ -834,6 +836,7 @@ private[spark] object Utils extends Logging {
     val exitCode = process.waitFor()
     stdoutThread.join()   // Wait for it to finish reading output
     if (exitCode != 0) {
+      logError(s"Process $command exited with code $exitCode: ${output}")
       throw new SparkException("Process " + command + " exited with code " + exitCode)
     }
     output.toString
@@ -1444,6 +1447,20 @@ private[spark] object Utils extends Logging {
     }
   }
 
+  /**
+   * config a log4j properties used for testsuite
+   */
+  def configTestLog4j(level: String): Unit = {
+    val pro = new Properties()
+    pro.put("log4j.rootLogger", s"$level, console")
+    pro.put("log4j.appender.console", "org.apache.log4j.ConsoleAppender")
+    pro.put("log4j.appender.console.target", "System.err")
+    pro.put("log4j.appender.console.layout", "org.apache.log4j.PatternLayout")
+    pro.put("log4j.appender.console.layout.ConversionPattern",
+      "%d{yy/MM/dd HH:mm:ss} %p %c{1}: %m%n")
+    PropertyConfigurator.configure(pro)
+  }
+
 }
 
 /**
diff --git a/core/src/test/scala/org/apache/spark/DriverSuite.scala b/core/src/test/scala/org/apache/spark/DriverSuite.scala
index a73e1ef0288a5..4b1d280624c57 100644
--- a/core/src/test/scala/org/apache/spark/DriverSuite.scala
+++ b/core/src/test/scala/org/apache/spark/DriverSuite.scala
@@ -19,9 +19,6 @@ package org.apache.spark
 
 import java.io.File
 
-import org.apache.log4j.Logger
-import org.apache.log4j.Level
-
 import org.scalatest.FunSuite
 import org.scalatest.concurrent.Timeouts
 import org.scalatest.prop.TableDrivenPropertyChecks._
@@ -54,7 +51,7 @@ class DriverSuite extends FunSuite with Timeouts {
  */
 object DriverWithoutCleanup {
   def main(args: Array[String]) {
-    Logger.getRootLogger().setLevel(Level.WARN)
+    Utils.configTestLog4j("INFO")
     val sc = new SparkContext(args(0), "DriverWithoutCleanup")
     sc.parallelize(1 to 100, 4).count()
   }
diff --git a/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala b/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala
index 7e1ef80c84561..22b369a829418 100644
--- a/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala
+++ b/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala
@@ -317,6 +317,7 @@ class SparkSubmitSuite extends FunSuite with Matchers {
 
 object JarCreationTest {
   def main(args: Array[String]) {
+    Utils.configTestLog4j("INFO")
     val conf = new SparkConf()
     val sc = new SparkContext(conf)
     val result = sc.makeRDD(1 to 100, 10).mapPartitions { x =>
@@ -338,6 +339,7 @@ object JarCreationTest {
 
 object SimpleApplicationTest {
   def main(args: Array[String]) {
+    Utils.configTestLog4j("INFO")
     val conf = new SparkConf()
     val sc = new SparkContext(conf)
     val configs = Seq("spark.master", "spark.app.name")

From 02b5ac7191c66a866ffedde313eb10f2adfc9b58 Mon Sep 17 00:00:00 2001
From: Prashant Sharma <prashant.s@imaginea.com>
Date: Tue, 9 Sep 2014 14:42:28 -0700
Subject: [PATCH 250/489] Minor - Fix trivial compilation warnings.

Author: Prashant Sharma <prashant.s@imaginea.com>

Closes #2331 from ScrapCodes/compilation-warn and squashes the following commits:

44c1e76 [Prashant Sharma] Minor - Fix trivial compilation warnings.
---
 core/src/main/scala/org/apache/spark/SparkContext.scala         | 1 -
 .../org/apache/spark/examples/graphx/LiveJournalPageRank.scala  | 2 +-
 2 files changed, 1 insertion(+), 2 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala
index 24d1a8f9eceae..c6c5b8f22b549 100644
--- a/core/src/main/scala/org/apache/spark/SparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/SparkContext.scala
@@ -49,7 +49,6 @@ import org.apache.spark.scheduler.cluster.{CoarseGrainedSchedulerBackend, SparkD
 import org.apache.spark.scheduler.cluster.mesos.{CoarseMesosSchedulerBackend, MesosSchedulerBackend}
 import org.apache.spark.scheduler.local.LocalBackend
 import org.apache.spark.storage._
-import org.apache.spark.SPARK_VERSION
 import org.apache.spark.ui.SparkUI
 import org.apache.spark.util.{CallSite, ClosureCleaner, MetadataCleaner, MetadataCleanerType, TimeStampedWeakValueHashMap, Utils}
 
diff --git a/examples/src/main/scala/org/apache/spark/examples/graphx/LiveJournalPageRank.scala b/examples/src/main/scala/org/apache/spark/examples/graphx/LiveJournalPageRank.scala
index bdc8fa7f99f2e..e809a65b79975 100644
--- a/examples/src/main/scala/org/apache/spark/examples/graphx/LiveJournalPageRank.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/graphx/LiveJournalPageRank.scala
@@ -20,7 +20,7 @@ package org.apache.spark.examples.graphx
 import org.apache.spark.SparkContext._
 import org.apache.spark._
 import org.apache.spark.graphx._
-import org.apache.spark.examples.graphx.Analytics
+
 
 /**
  * Uses GraphX to run PageRank on a LiveJournal social network graph. Download the dataset from

From 07ee4a28c3a502121770f301316cb2256e8f0ce2 Mon Sep 17 00:00:00 2001
From: xinyunh <xinyun.huang@huawei.com>
Date: Tue, 9 Sep 2014 16:55:39 -0700
Subject: [PATCH 251/489] [SPARK-3176] Implement 'ABS and 'LAST' for sql

Add support for the mathematical function"ABS" and the analytic function "last" to return a subset of the rows satisfying a query within spark sql. Test-cases included.

Author: xinyunh <xinyun.huang@huawei.com>
Author: bomeng <golf8lover>

Closes #2099 from xinyunh/sqlTest and squashes the following commits:

71d15e7 [xinyunh] remove POWER part
8843643 [xinyunh] fix the code style issue
39f0309 [bomeng] Modify the code of POWER and ABS. Move them to the file arithmetic
ff8e51e [bomeng] add abs() function support
7f6980a [xinyunh] fix the bug in 'Last' component
b3df91b [xinyunh] add 'Last' component
---
 .../apache/spark/sql/catalyst/SqlParser.scala |  4 +++
 .../spark/sql/catalyst/dsl/package.scala      |  1 +
 .../sql/catalyst/expressions/aggregates.scala | 28 +++++++++++++++++++
 .../sql/catalyst/expressions/arithmetic.scala | 15 ++++++++++
 .../org/apache/spark/sql/SQLQuerySuite.scala  | 23 +++++++++++++--
 5 files changed, 69 insertions(+), 2 deletions(-)
 mode change 100644 => 100755 sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
 mode change 100644 => 100755 sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala
 mode change 100644 => 100755 sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
old mode 100644
new mode 100755
index bfc197cf7a938..a04b4a938da64
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
@@ -82,6 +82,7 @@ class SqlParser extends StandardTokenParsers with PackratParsers {
   protected val DISTINCT = Keyword("DISTINCT")
   protected val FALSE = Keyword("FALSE")
   protected val FIRST = Keyword("FIRST")
+  protected val LAST = Keyword("LAST")
   protected val FROM = Keyword("FROM")
   protected val FULL = Keyword("FULL")
   protected val GROUP = Keyword("GROUP")
@@ -125,6 +126,7 @@ class SqlParser extends StandardTokenParsers with PackratParsers {
   protected val SUBSTR = Keyword("SUBSTR")
   protected val SUBSTRING = Keyword("SUBSTRING")
   protected val SQRT = Keyword("SQRT")
+  protected val ABS = Keyword("ABS")
 
   // Use reflection to find the reserved words defined in this class.
   protected val reservedWords =
@@ -315,6 +317,7 @@ class SqlParser extends StandardTokenParsers with PackratParsers {
       case s ~ _ ~ _ ~ _ ~ _ ~ e => ApproxCountDistinct(e, s.toDouble)
     } |
     FIRST ~> "(" ~> expression <~ ")" ^^ { case exp => First(exp) } |
+    LAST ~> "(" ~> expression <~ ")" ^^ { case exp => Last(exp) } |
     AVG ~> "(" ~> expression <~ ")" ^^ { case exp => Average(exp) } |
     MIN ~> "(" ~> expression <~ ")" ^^ { case exp => Min(exp) } |
     MAX ~> "(" ~> expression <~ ")" ^^ { case exp => Max(exp) } |
@@ -330,6 +333,7 @@ class SqlParser extends StandardTokenParsers with PackratParsers {
       case s ~ "," ~ p ~ "," ~ l => Substring(s,p,l)
     } |
     SQRT ~> "(" ~> expression <~ ")" ^^ { case exp => Sqrt(exp) } |
+    ABS ~> "(" ~> expression <~ ")" ^^ { case exp => Abs(exp) } |
     ident ~ "(" ~ repsep(expression, ",") <~ ")" ^^ {
       case udfName ~ _ ~ exprs => UnresolvedFunction(udfName, exprs)
     }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala
old mode 100644
new mode 100755
index f44521d6381c9..deb622c39faf5
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala
@@ -132,6 +132,7 @@ package object dsl {
     def approxCountDistinct(e: Expression, rsd: Double = 0.05) = ApproxCountDistinct(e, rsd)
     def avg(e: Expression) = Average(e)
     def first(e: Expression) = First(e)
+    def last(e: Expression) = Last(e)
     def min(e: Expression) = Min(e)
     def max(e: Expression) = Max(e)
     def upper(e: Expression) = Upper(e)
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
old mode 100644
new mode 100755
index 15560a2a933ad..1b4d892625dbb
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
@@ -344,6 +344,21 @@ case class First(child: Expression) extends PartialAggregate with trees.UnaryNod
   override def newInstance() = new FirstFunction(child, this)
 }
 
+case class Last(child: Expression) extends PartialAggregate with trees.UnaryNode[Expression] {
+  override def references = child.references
+  override def nullable = true
+  override def dataType = child.dataType
+  override def toString = s"LAST($child)"
+
+  override def asPartial: SplitEvaluation = {
+    val partialLast = Alias(Last(child), "PartialLast")()
+    SplitEvaluation(
+      Last(partialLast.toAttribute),
+      partialLast :: Nil)
+  }
+  override def newInstance() = new LastFunction(child, this)
+}
+
 case class AverageFunction(expr: Expression, base: AggregateExpression)
   extends AggregateFunction {
 
@@ -489,3 +504,16 @@ case class FirstFunction(expr: Expression, base: AggregateExpression) extends Ag
 
   override def eval(input: Row): Any = result
 }
+
+case class LastFunction(expr: Expression, base: AggregateExpression) extends AggregateFunction {
+  def this() = this(null, null) // Required for serialization.
+
+  var result: Any = null
+
+  override def update(input: Row): Unit = {
+    result = input
+  }
+
+  override def eval(input: Row): Any =  if (result != null) expr.eval(result.asInstanceOf[Row])
+                                        else null
+}
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
index f988fb010b107..fe825fdcdae37 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
@@ -19,6 +19,7 @@ package org.apache.spark.sql.catalyst.expressions
 
 import org.apache.spark.sql.catalyst.analysis.UnresolvedException
 import org.apache.spark.sql.catalyst.types._
+import scala.math.pow
 
 case class UnaryMinus(child: Expression) extends UnaryExpression {
   type EvaluatedType = Any
@@ -129,3 +130,17 @@ case class MaxOf(left: Expression, right: Expression) extends Expression {
 
   override def toString = s"MaxOf($left, $right)"
 }
+
+/**
+ * A function that get the absolute value of the numeric value.
+ */
+case class Abs(child: Expression) extends UnaryExpression  {
+  type EvaluatedType = Any
+
+  def dataType = child.dataType
+  override def foldable = child.foldable
+  def nullable = child.nullable
+  override def toString = s"Abs($child)"
+
+  override def eval(input: Row): Any = n1(child, input, _.abs(_))
+}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
index 739c12f338f34..514ac543df92a 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
@@ -41,6 +41,25 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll {
   }
 
 
+  test("SPARK-3176 Added Parser of SQL ABS()") {
+    checkAnswer(
+      sql("SELECT ABS(-1.3)"),
+      1.3)
+    checkAnswer(
+      sql("SELECT ABS(0.0)"),
+      0.0)
+    checkAnswer(
+      sql("SELECT ABS(2.5)"),
+      2.5)
+  }
+
+  test("SPARK-3176 Added Parser of SQL LAST()") {
+    checkAnswer(
+      sql("SELECT LAST(n) FROM lowerCaseData"),
+      4)
+  }
+
+
   test("SPARK-2041 column name equals tablename") {
     checkAnswer(
       sql("SELECT tableName FROM tableName"),
@@ -53,14 +72,14 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll {
       (1 to 100).map(x => Row(math.sqrt(x.toDouble))).toSeq
     )
   }
-  
+
   test("SQRT with automatic string casts") {
     checkAnswer(
       sql("SELECT SQRT(CAST(key AS STRING)) FROM testData"),
       (1 to 100).map(x => Row(math.sqrt(x.toDouble))).toSeq
     )
   }
-  
+
   test("SPARK-2407 Added Parser of SQL SUBSTR()") {
     checkAnswer(
       sql("SELECT substr(tableName, 1, 2) FROM tableName"),

From c110614b33a690a3db6ccb1a920fb6a3795aa5a0 Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian.cs.zju@gmail.com>
Date: Tue, 9 Sep 2014 18:39:33 -0700
Subject: [PATCH 252/489] [SPARK-3448][SQL] Check for null in
 SpecificMutableRow.update

`SpecificMutableRow.update` doesn't check for null, and breaks existing `MutableRow` contract.

The tricky part here is that for performance considerations, the `update` method of all subclasses of `MutableValue` doesn't check for null and sets the null bit to false.

Author: Cheng Lian <lian.cs.zju@gmail.com>

Closes #2325 from liancheng/check-for-null and squashes the following commits:

9366c44 [Cheng Lian] Check for null in SpecificMutableRow.update
---
 .../spark/sql/catalyst/expressions/SpecificRow.scala      | 4 +++-
 .../src/test/scala/org/apache/spark/sql/RowSuite.scala    | 8 +++++++-
 2 files changed, 10 insertions(+), 2 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificRow.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificRow.scala
index 75ea0e8459df8..088f11ee4aa53 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificRow.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificRow.scala
@@ -227,7 +227,9 @@ final class SpecificMutableRow(val values: Array[MutableValue]) extends MutableR
     new SpecificMutableRow(newValues)
   }
 
-  override def update(ordinal: Int, value: Any): Unit = values(ordinal).update(value)
+  override def update(ordinal: Int, value: Any): Unit = {
+    if (value == null) setNullAt(ordinal) else values(ordinal).update(value)
+  }
 
   override def iterator: Iterator[Any] = values.map(_.boxed).iterator
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/RowSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/RowSuite.scala
index 651cb735ab7d9..811319e0a6601 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/RowSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/RowSuite.scala
@@ -19,7 +19,7 @@ package org.apache.spark.sql
 
 import org.scalatest.FunSuite
 
-import org.apache.spark.sql.catalyst.expressions.GenericMutableRow
+import org.apache.spark.sql.catalyst.expressions.{GenericMutableRow, SpecificMutableRow}
 
 class RowSuite extends FunSuite {
 
@@ -43,4 +43,10 @@ class RowSuite extends FunSuite {
     assert(expected.getBoolean(2) === actual2.getBoolean(2))
     assert(expected(3) === actual2(3))
   }
+
+  test("SpecificMutableRow.update with null") {
+    val row = new SpecificMutableRow(Seq(IntegerType))
+    row(0) = null
+    assert(row.isNullAt(0))
+  }
 }

From 25b5b867d5e18bac1c5bcdc6f8c63d97858194c7 Mon Sep 17 00:00:00 2001
From: Matthew Farrellee <matt@redhat.com>
Date: Tue, 9 Sep 2014 18:54:54 -0700
Subject: [PATCH 253/489] [SPARK-3458] enable python "with" statements for
 SparkContext

allow for best practice code,

```
try:
  sc = SparkContext()
  app(sc)
finally:
  sc.stop()
```

to be written using a "with" statement,

```
with SparkContext() as sc:
  app(sc)
```

Author: Matthew Farrellee <matt@redhat.com>

Closes #2335 from mattf/SPARK-3458 and squashes the following commits:

5b4e37c [Matthew Farrellee] [SPARK-3458] enable python "with" statements for SparkContext
---
 python/pyspark/context.py | 14 ++++++++++++++
 python/pyspark/tests.py   | 29 +++++++++++++++++++++++++++++
 2 files changed, 43 insertions(+)

diff --git a/python/pyspark/context.py b/python/pyspark/context.py
index 5a30431568b16..84bc0a3b7ccd0 100644
--- a/python/pyspark/context.py
+++ b/python/pyspark/context.py
@@ -232,6 +232,20 @@ def _ensure_initialized(cls, instance=None, gateway=None):
                 else:
                     SparkContext._active_spark_context = instance
 
+    def __enter__(self):
+        """
+        Enable 'with SparkContext(...) as sc: app(sc)' syntax.
+        """
+        return self
+
+    def __exit__(self, type, value, trace):
+        """
+        Enable 'with SparkContext(...) as sc: app' syntax.
+
+        Specifically stop the context on exit of the with block.
+        """
+        self.stop()
+
     @classmethod
     def setSystemProperty(cls, key, value):
         """
diff --git a/python/pyspark/tests.py b/python/pyspark/tests.py
index 0bd2a9e6c507d..bb84ebe72cb24 100644
--- a/python/pyspark/tests.py
+++ b/python/pyspark/tests.py
@@ -1254,6 +1254,35 @@ def test_single_script_on_cluster(self):
         self.assertIn("[2, 4, 6]", out)
 
 
+class ContextStopTests(unittest.TestCase):
+
+    def test_stop(self):
+        sc = SparkContext()
+        self.assertNotEqual(SparkContext._active_spark_context, None)
+        sc.stop()
+        self.assertEqual(SparkContext._active_spark_context, None)
+
+    def test_with(self):
+        with SparkContext() as sc:
+            self.assertNotEqual(SparkContext._active_spark_context, None)
+        self.assertEqual(SparkContext._active_spark_context, None)
+
+    def test_with_exception(self):
+        try:
+            with SparkContext() as sc:
+                self.assertNotEqual(SparkContext._active_spark_context, None)
+                raise Exception()
+        except:
+            pass
+        self.assertEqual(SparkContext._active_spark_context, None)
+
+    def test_with_stop(self):
+        with SparkContext() as sc:
+            self.assertNotEqual(SparkContext._active_spark_context, None)
+            sc.stop()
+        self.assertEqual(SparkContext._active_spark_context, None)
+
+
 @unittest.skipIf(not _have_scipy, "SciPy not installed")
 class SciPyTests(PySparkTestCase):
 

From b734ed0c229373dbc589b9eca7327537ca458138 Mon Sep 17 00:00:00 2001
From: Eric Liang <ekl@google.com>
Date: Tue, 9 Sep 2014 23:47:12 -0700
Subject: [PATCH 254/489] [SPARK-3395] [SQL] DSL sometimes incorrectly reuses
 attribute ids, breaking queries

This resolves https://issues.apache.org/jira/browse/SPARK-3395

Author: Eric Liang <ekl@google.com>

Closes #2266 from ericl/spark-3395 and squashes the following commits:

7f2b6f0 [Eric Liang] add regression test
05bd1e4 [Eric Liang] in the dsl, create a new schema instance in each applySchema
---
 .../scala/org/apache/spark/sql/SchemaRDD.scala     |  3 ++-
 .../scala/org/apache/spark/sql/DslQuerySuite.scala | 14 ++++++++++++++
 2 files changed, 16 insertions(+), 1 deletion(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala
index 33b2ed1b3a399..d2ceb4a2b0b25 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala
@@ -428,7 +428,8 @@ class SchemaRDD(
    */
   private def applySchema(rdd: RDD[Row]): SchemaRDD = {
     new SchemaRDD(sqlContext,
-      SparkLogicalPlan(ExistingRdd(queryExecution.analyzed.output, rdd))(sqlContext))
+      SparkLogicalPlan(
+        ExistingRdd(queryExecution.analyzed.output.map(_.newInstance), rdd))(sqlContext))
   }
 
   // =======================================================================
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DslQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DslQuerySuite.scala
index 1a6a6c17473a3..d001abb7e1fcc 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DslQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DslQuerySuite.scala
@@ -18,6 +18,8 @@
 package org.apache.spark.sql
 
 import org.apache.spark.sql.catalyst.analysis._
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.plans._
 import org.apache.spark.sql.test._
 
 /* Implicits */
@@ -133,6 +135,18 @@ class DslQuerySuite extends QueryTest {
       mapData.take(1).toSeq)
   }
 
+  test("SPARK-3395 limit distinct") {
+    val filtered = TestData.testData2
+      .distinct()
+      .orderBy(SortOrder('a, Ascending), SortOrder('b, Ascending))
+      .limit(1)
+      .registerTempTable("onerow")
+    checkAnswer(
+      sql("select * from onerow inner join testData2 on onerow.a = testData2.a"),
+      (1, 1, 1, 1) ::
+      (1, 1, 1, 2) :: Nil)
+  }
+
   test("average") {
     checkAnswer(
       testData2.groupBy()(avg('a)),

From 6f7a76838f15687583e3b0ab43309a3c079368c4 Mon Sep 17 00:00:00 2001
From: Benoy Antony <benoy@apache.org>
Date: Wed, 10 Sep 2014 11:59:39 -0500
Subject: [PATCH 255/489] =?UTF-8?q?[SPARK-3286]=20-=20Cannot=20view=20Appl?=
 =?UTF-8?q?icationMaster=20UI=20when=20Yarn=E2=80=99s=20url=20scheme=20i..?=
 =?UTF-8?q?.?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

...s https

Author: Benoy Antony <benoy@apache.org>

Closes #2276 from benoyantony/SPARK-3286 and squashes the following commits:

c3d51ee [Benoy Antony] Use address with scheme, but Allpha version removes the scheme
e82f94e [Benoy Antony] Use address with scheme, but Allpha version removes the scheme
92127c9 [Benoy Antony] rebasing from master
450c536 [Benoy Antony] [SPARK-3286] - Cannot view ApplicationMaster UI when Yarn’s url scheme is https
f060c02 [Benoy Antony] [SPARK-3286] - Cannot view ApplicationMaster UI when Yarn’s url scheme is https
---
 .../scala/org/apache/spark/deploy/yarn/YarnRMClientImpl.scala | 4 +++-
 .../org/apache/spark/deploy/yarn/ApplicationMaster.scala      | 2 +-
 2 files changed, 4 insertions(+), 2 deletions(-)

diff --git a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/YarnRMClientImpl.scala b/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/YarnRMClientImpl.scala
index ad27a9ab781d2..fc30953011812 100644
--- a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/YarnRMClientImpl.scala
+++ b/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/YarnRMClientImpl.scala
@@ -18,6 +18,7 @@
 package org.apache.spark.deploy.yarn
 
 import scala.collection.{Map, Set}
+import java.net.URI;
 
 import org.apache.hadoop.net.NetUtils
 import org.apache.hadoop.yarn.api._
@@ -97,7 +98,8 @@ private class YarnRMClientImpl(args: ApplicationMasterArguments) extends YarnRMC
     // Users can then monitor stderr/stdout on that node if required.
     appMasterRequest.setHost(Utils.localHostName())
     appMasterRequest.setRpcPort(0)
-    appMasterRequest.setTrackingUrl(uiAddress)
+    //remove the scheme from the url if it exists since Hadoop does not expect scheme
+    appMasterRequest.setTrackingUrl(new URI(uiAddress).getAuthority())
     resourceManager.registerApplicationMaster(appMasterRequest)
   }
 
diff --git a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
index a879c833a014f..5756263e89e21 100644
--- a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
+++ b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
@@ -189,7 +189,7 @@ private[spark] class ApplicationMaster(args: ApplicationMasterArguments,
     if (sc == null) {
       finish(FinalApplicationStatus.FAILED, "Timed out waiting for SparkContext.")
     } else {
-      registerAM(sc.ui.appUIHostPort, securityMgr)
+      registerAM(sc.ui.appUIAddress, securityMgr)
       try {
         userThread.join()
       } finally {

From a0283300c4af5e64a1dc06193245daa1e746b5f4 Mon Sep 17 00:00:00 2001
From: Daoyuan Wang <daoyuan.wang@intel.com>
Date: Wed, 10 Sep 2014 10:45:15 -0700
Subject: [PATCH 256/489] [SPARK-3362][SQL] Fix resolution for casewhen with
 nulls.

Current implementation will ignore else val type.

Author: Daoyuan Wang <daoyuan.wang@intel.com>

Closes #2245 from adrian-wang/casewhenbug and squashes the following commits:

3332f6e [Daoyuan Wang] remove wrong comment
83b536c [Daoyuan Wang] a comment to trigger retest
d7315b3 [Daoyuan Wang] code improve
eed35fc [Daoyuan Wang] bug in casewhen resolve
---
 .../apache/spark/sql/catalyst/expressions/predicates.scala  | 5 +++--
 ...then 1 else null end -0-f7c7fdd35c084bc797890aa08d33693c | 1 +
 ...en 1.0 else null end -0-aeb1f906bfe92f2d406f84109301afe0 | 1 +
 ...hen 1L else null end -0-763ae85e7a52b4cf4162d6a8931716bb | 1 +
 ...hen 1S else null end -0-6f5f3b3dbe9f1d1eb98443aef315b982 | 1 +
 ...hen 1Y else null end -0-589982a400d86157791c7216b10b6b5d | 1 +
 ...then null else 1 end -0-48bd83660cf3ba93cdbdc24559092171 | 1 +
 ...en null else 1.0 end -0-7f5ce763801781cf568c6a31dd80b623 | 1 +
 ...hen null else 1L end -0-a7f1305ea4f86e596c368e35e45cc4e5 | 1 +
 ...hen null else 1S end -0-dfb61969e6cb6e6dbe89225b538c8d98 | 1 +
 ...hen null else 1Y end -0-7f4c32299c3738739b678ece62752a7b | 1 +
 .../spark/sql/hive/execution/HiveTypeCoercionSuite.scala    | 6 ++++++
 12 files changed, 19 insertions(+), 2 deletions(-)
 create mode 100644 sql/hive/src/test/resources/golden/case when then 1 else null end -0-f7c7fdd35c084bc797890aa08d33693c
 create mode 100644 sql/hive/src/test/resources/golden/case when then 1.0 else null end -0-aeb1f906bfe92f2d406f84109301afe0
 create mode 100644 sql/hive/src/test/resources/golden/case when then 1L else null end -0-763ae85e7a52b4cf4162d6a8931716bb
 create mode 100644 sql/hive/src/test/resources/golden/case when then 1S else null end -0-6f5f3b3dbe9f1d1eb98443aef315b982
 create mode 100644 sql/hive/src/test/resources/golden/case when then 1Y else null end -0-589982a400d86157791c7216b10b6b5d
 create mode 100644 sql/hive/src/test/resources/golden/case when then null else 1 end -0-48bd83660cf3ba93cdbdc24559092171
 create mode 100644 sql/hive/src/test/resources/golden/case when then null else 1.0 end -0-7f5ce763801781cf568c6a31dd80b623
 create mode 100644 sql/hive/src/test/resources/golden/case when then null else 1L end -0-a7f1305ea4f86e596c368e35e45cc4e5
 create mode 100644 sql/hive/src/test/resources/golden/case when then null else 1S end -0-dfb61969e6cb6e6dbe89225b538c8d98
 create mode 100644 sql/hive/src/test/resources/golden/case when then null else 1Y end -0-7f4c32299c3738739b678ece62752a7b

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
index 1313ccd120c1f..329af332d0fa1 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
@@ -265,12 +265,13 @@ case class CaseWhen(branches: Seq[Expression]) extends Expression {
       false
     } else {
       val allCondBooleans = predicates.forall(_.dataType == BooleanType)
-      val dataTypesEqual = values.map(_.dataType).distinct.size <= 1
+      // both then and else val should be considered.
+      val dataTypesEqual = (values ++ elseValue).map(_.dataType).distinct.size <= 1
       allCondBooleans && dataTypesEqual
     }
   }
 
-  /** Written in imperative fashion for performance considerations.  Same for CaseKeyWhen. */
+  /** Written in imperative fashion for performance considerations. */
   override def eval(input: Row): Any = {
     val len = branchesArr.length
     var i = 0
diff --git a/sql/hive/src/test/resources/golden/case when then 1 else null end -0-f7c7fdd35c084bc797890aa08d33693c b/sql/hive/src/test/resources/golden/case when then 1 else null end -0-f7c7fdd35c084bc797890aa08d33693c
new file mode 100644
index 0000000000000..d00491fd7e5bb
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/case when then 1 else null end -0-f7c7fdd35c084bc797890aa08d33693c	
@@ -0,0 +1 @@
+1
diff --git a/sql/hive/src/test/resources/golden/case when then 1.0 else null end -0-aeb1f906bfe92f2d406f84109301afe0 b/sql/hive/src/test/resources/golden/case when then 1.0 else null end -0-aeb1f906bfe92f2d406f84109301afe0
new file mode 100644
index 0000000000000..d3827e75a5cad
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/case when then 1.0 else null end -0-aeb1f906bfe92f2d406f84109301afe0	
@@ -0,0 +1 @@
+1.0
diff --git a/sql/hive/src/test/resources/golden/case when then 1L else null end -0-763ae85e7a52b4cf4162d6a8931716bb b/sql/hive/src/test/resources/golden/case when then 1L else null end -0-763ae85e7a52b4cf4162d6a8931716bb
new file mode 100644
index 0000000000000..d00491fd7e5bb
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/case when then 1L else null end -0-763ae85e7a52b4cf4162d6a8931716bb	
@@ -0,0 +1 @@
+1
diff --git a/sql/hive/src/test/resources/golden/case when then 1S else null end -0-6f5f3b3dbe9f1d1eb98443aef315b982 b/sql/hive/src/test/resources/golden/case when then 1S else null end -0-6f5f3b3dbe9f1d1eb98443aef315b982
new file mode 100644
index 0000000000000..d00491fd7e5bb
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/case when then 1S else null end -0-6f5f3b3dbe9f1d1eb98443aef315b982	
@@ -0,0 +1 @@
+1
diff --git a/sql/hive/src/test/resources/golden/case when then 1Y else null end -0-589982a400d86157791c7216b10b6b5d b/sql/hive/src/test/resources/golden/case when then 1Y else null end -0-589982a400d86157791c7216b10b6b5d
new file mode 100644
index 0000000000000..d00491fd7e5bb
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/case when then 1Y else null end -0-589982a400d86157791c7216b10b6b5d	
@@ -0,0 +1 @@
+1
diff --git a/sql/hive/src/test/resources/golden/case when then null else 1 end -0-48bd83660cf3ba93cdbdc24559092171 b/sql/hive/src/test/resources/golden/case when then null else 1 end -0-48bd83660cf3ba93cdbdc24559092171
new file mode 100644
index 0000000000000..7951defec192a
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/case when then null else 1 end -0-48bd83660cf3ba93cdbdc24559092171	
@@ -0,0 +1 @@
+NULL
diff --git a/sql/hive/src/test/resources/golden/case when then null else 1.0 end -0-7f5ce763801781cf568c6a31dd80b623 b/sql/hive/src/test/resources/golden/case when then null else 1.0 end -0-7f5ce763801781cf568c6a31dd80b623
new file mode 100644
index 0000000000000..7951defec192a
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/case when then null else 1.0 end -0-7f5ce763801781cf568c6a31dd80b623	
@@ -0,0 +1 @@
+NULL
diff --git a/sql/hive/src/test/resources/golden/case when then null else 1L end -0-a7f1305ea4f86e596c368e35e45cc4e5 b/sql/hive/src/test/resources/golden/case when then null else 1L end -0-a7f1305ea4f86e596c368e35e45cc4e5
new file mode 100644
index 0000000000000..7951defec192a
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/case when then null else 1L end -0-a7f1305ea4f86e596c368e35e45cc4e5	
@@ -0,0 +1 @@
+NULL
diff --git a/sql/hive/src/test/resources/golden/case when then null else 1S end -0-dfb61969e6cb6e6dbe89225b538c8d98 b/sql/hive/src/test/resources/golden/case when then null else 1S end -0-dfb61969e6cb6e6dbe89225b538c8d98
new file mode 100644
index 0000000000000..7951defec192a
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/case when then null else 1S end -0-dfb61969e6cb6e6dbe89225b538c8d98	
@@ -0,0 +1 @@
+NULL
diff --git a/sql/hive/src/test/resources/golden/case when then null else 1Y end -0-7f4c32299c3738739b678ece62752a7b b/sql/hive/src/test/resources/golden/case when then null else 1Y end -0-7f4c32299c3738739b678ece62752a7b
new file mode 100644
index 0000000000000..7951defec192a
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/case when then null else 1Y end -0-7f4c32299c3738739b678ece62752a7b	
@@ -0,0 +1 @@
+NULL
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveTypeCoercionSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveTypeCoercionSuite.scala
index c3c18cf8ccac3..48fffe53cf2ff 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveTypeCoercionSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveTypeCoercionSuite.scala
@@ -33,6 +33,12 @@ class HiveTypeCoercionSuite extends HiveComparisonTest {
     }
   }
 
+  val nullVal = "null"
+  baseTypes.init.foreach { i =>
+    createQueryTest(s"case when then $i else $nullVal end ", s"SELECT case when true then $i else $nullVal end FROM src limit 1")
+    createQueryTest(s"case when then $nullVal else $i end ", s"SELECT case when true then $nullVal else $i end FROM src limit 1")
+  }
+
   test("[SPARK-2210] boolean cast on boolean value should be removed") {
     val q = "select cast(cast(key=0 as boolean) as boolean) from src"
     val project = TestHive.sql(q).queryExecution.executedPlan.collect { case e: Project => e }.head

From f0c87dc86ae65a39cd19370d8d960b4a60854517 Mon Sep 17 00:00:00 2001
From: Daoyuan Wang <daoyuan.wang@intel.com>
Date: Wed, 10 Sep 2014 10:48:33 -0700
Subject: [PATCH 257/489] [SPARK-3363][SQL] Type Coercion should promote null
 to all other types.

Type Coercion should support every type to have null value

Author: Daoyuan Wang <daoyuan.wang@intel.com>
Author: Michael Armbrust <michael@databricks.com>

Closes #2246 from adrian-wang/spark3363-0 and squashes the following commits:

c6241de [Daoyuan Wang] minor code clean
595b417 [Daoyuan Wang] Merge pull request #2 from marmbrus/pr/2246
832e640 [Michael Armbrust] reduce code duplication
ef6f986 [Daoyuan Wang] make double boolean miss in jsonRDD compatibleType
c619f0a [Daoyuan Wang] Type Coercion should support every type to have null value
---
 .../catalyst/analysis/HiveTypeCoercion.scala  | 38 +++++++-------
 .../analysis/HiveTypeCoercionSuite.scala      | 32 +++++++++---
 .../org/apache/spark/sql/json/JsonRDD.scala   | 51 ++++++++-----------
 3 files changed, 67 insertions(+), 54 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
index d6758eb5b6a32..bd8131c9af6e0 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
@@ -26,10 +26,22 @@ object HiveTypeCoercion {
   // See https://cwiki.apache.org/confluence/display/Hive/LanguageManual+Types.
   // The conversion for integral and floating point types have a linear widening hierarchy:
   val numericPrecedence =
-    Seq(NullType, ByteType, ShortType, IntegerType, LongType, FloatType, DoubleType, DecimalType)
-  // Boolean is only wider than Void
-  val booleanPrecedence = Seq(NullType, BooleanType)
-  val allPromotions: Seq[Seq[DataType]] = numericPrecedence :: booleanPrecedence :: Nil
+    Seq(ByteType, ShortType, IntegerType, LongType, FloatType, DoubleType, DecimalType)
+  val allPromotions: Seq[Seq[DataType]] = numericPrecedence :: Nil
+
+  def findTightestCommonType(t1: DataType, t2: DataType): Option[DataType] = {
+    val valueTypes = Seq(t1, t2).filter(t => t != NullType)
+    if (valueTypes.distinct.size > 1) {
+      // Try and find a promotion rule that contains both types in question.
+      val applicableConversion =
+        HiveTypeCoercion.allPromotions.find(p => p.contains(t1) && p.contains(t2))
+
+      // If found return the widest common type, otherwise None
+      applicableConversion.map(_.filter(t => t == t1 || t == t2).last)
+    } else {
+      Some(if (valueTypes.size == 0) NullType else valueTypes.head)
+    }
+  }
 }
 
 /**
@@ -53,17 +65,6 @@ trait HiveTypeCoercion {
     Division ::
     Nil
 
-  trait TypeWidening {
-    def findTightestCommonType(t1: DataType, t2: DataType): Option[DataType] = {
-      // Try and find a promotion rule that contains both types in question.
-      val applicableConversion =
-        HiveTypeCoercion.allPromotions.find(p => p.contains(t1) && p.contains(t2))
-
-      // If found return the widest common type, otherwise None
-      applicableConversion.map(_.filter(t => t == t1 || t == t2).last)
-    }
-  }
-
   /**
    * Applies any changes to [[AttributeReference]] data types that are made by other rules to
    * instances higher in the query tree.
@@ -144,7 +145,8 @@ trait HiveTypeCoercion {
    * - LongType to FloatType
    * - LongType to DoubleType
    */
-  object WidenTypes extends Rule[LogicalPlan] with TypeWidening {
+  object WidenTypes extends Rule[LogicalPlan] {
+    import HiveTypeCoercion._
 
     def apply(plan: LogicalPlan): LogicalPlan = plan transform {
       case u @ Union(left, right) if u.childrenResolved && !u.resolved =>
@@ -352,7 +354,9 @@ trait HiveTypeCoercion {
   /**
    * Coerces the type of different branches of a CASE WHEN statement to a common type.
    */
-  object CaseWhenCoercion extends Rule[LogicalPlan] with TypeWidening {
+  object CaseWhenCoercion extends Rule[LogicalPlan] {
+    import HiveTypeCoercion._
+
     def apply(plan: LogicalPlan): LogicalPlan = plan transformAllExpressions {
       case cw @ CaseWhen(branches) if !cw.resolved && !branches.exists(!_.resolved)  =>
         val valueTypes = branches.sliding(2, 2).map {
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala
index b9e0f8e9dcc5f..ba8b853b6f99e 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala
@@ -23,20 +23,20 @@ import org.apache.spark.sql.catalyst.types._
 
 class HiveTypeCoercionSuite extends FunSuite {
 
-  val rules = new HiveTypeCoercion { }
-  import rules._
-
-  test("tightest common bound for numeric and boolean types") {
+  test("tightest common bound for types") {
     def widenTest(t1: DataType, t2: DataType, tightestCommon: Option[DataType]) {
-      var found = WidenTypes.findTightestCommonType(t1, t2)
+      var found = HiveTypeCoercion.findTightestCommonType(t1, t2)
       assert(found == tightestCommon,
         s"Expected $tightestCommon as tightest common type for $t1 and $t2, found $found")
       // Test both directions to make sure the widening is symmetric.
-      found = WidenTypes.findTightestCommonType(t2, t1)
+      found = HiveTypeCoercion.findTightestCommonType(t2, t1)
       assert(found == tightestCommon,
         s"Expected $tightestCommon as tightest common type for $t2 and $t1, found $found")
     }
 
+    // Null
+    widenTest(NullType, NullType, Some(NullType))
+
     // Boolean
     widenTest(NullType, BooleanType, Some(BooleanType))
     widenTest(BooleanType, BooleanType, Some(BooleanType))
@@ -60,12 +60,28 @@ class HiveTypeCoercionSuite extends FunSuite {
     widenTest(DoubleType, DoubleType, Some(DoubleType))
 
     // Integral mixed with floating point.
-    widenTest(NullType, FloatType, Some(FloatType))
-    widenTest(NullType, DoubleType, Some(DoubleType))
     widenTest(IntegerType, FloatType, Some(FloatType))
     widenTest(IntegerType, DoubleType, Some(DoubleType))
     widenTest(IntegerType, DoubleType, Some(DoubleType))
     widenTest(LongType, FloatType, Some(FloatType))
     widenTest(LongType, DoubleType, Some(DoubleType))
+
+    // StringType
+    widenTest(NullType, StringType, Some(StringType))
+    widenTest(StringType, StringType, Some(StringType))
+    widenTest(IntegerType, StringType, None)
+    widenTest(LongType, StringType, None)
+
+    // TimestampType
+    widenTest(NullType, TimestampType, Some(TimestampType))
+    widenTest(TimestampType, TimestampType, Some(TimestampType))
+    widenTest(IntegerType, TimestampType, None)
+    widenTest(StringType, TimestampType, None)
+
+    // ComplexType
+    widenTest(NullType, MapType(IntegerType, StringType, false), Some(MapType(IntegerType, StringType, false)))
+    widenTest(NullType, StructType(Seq()), Some(StructType(Seq())))
+    widenTest(StringType, MapType(IntegerType, StringType, true), None)
+    widenTest(ArrayType(IntegerType), StructType(Seq()), None)
   }
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/json/JsonRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/json/JsonRDD.scala
index 1c0b03c684f10..70062eae3b7ce 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/json/JsonRDD.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/json/JsonRDD.scala
@@ -125,38 +125,31 @@ private[sql] object JsonRDD extends Logging {
    * Returns the most general data type for two given data types.
    */
   private[json] def compatibleType(t1: DataType, t2: DataType): DataType = {
-    // Try and find a promotion rule that contains both types in question.
-    val applicableConversion = HiveTypeCoercion.allPromotions.find(p => p.contains(t1) && p
-      .contains(t2))
-
-    // If found return the widest common type, otherwise None
-    val returnType = applicableConversion.map(_.filter(t => t == t1 || t == t2).last)
-
-    if (returnType.isDefined) {
-      returnType.get
-    } else {
-      // t1 or t2 is a StructType, ArrayType, or an unexpected type.
-      (t1, t2) match {
-        case (other: DataType, NullType) => other
-        case (NullType, other: DataType) => other
-        case (StructType(fields1), StructType(fields2)) => {
-          val newFields = (fields1 ++ fields2).groupBy(field => field.name).map {
-            case (name, fieldTypes) => {
-              val dataType = fieldTypes.map(field => field.dataType).reduce(
-                (type1: DataType, type2: DataType) => compatibleType(type1, type2))
-              StructField(name, dataType, true)
+    HiveTypeCoercion.findTightestCommonType(t1, t2) match {
+      case Some(commonType) => commonType
+      case None =>
+        // t1 or t2 is a StructType, ArrayType, or an unexpected type.
+        (t1, t2) match {
+          case (other: DataType, NullType) => other
+          case (NullType, other: DataType) => other
+          case (StructType(fields1), StructType(fields2)) => {
+            val newFields = (fields1 ++ fields2).groupBy(field => field.name).map {
+              case (name, fieldTypes) => {
+                val dataType = fieldTypes.map(field => field.dataType).reduce(
+                  (type1: DataType, type2: DataType) => compatibleType(type1, type2))
+                StructField(name, dataType, true)
+              }
             }
+            StructType(newFields.toSeq.sortBy {
+              case StructField(name, _, _) => name
+            })
           }
-          StructType(newFields.toSeq.sortBy {
-            case StructField(name, _, _) => name
-          })
+          case (ArrayType(elementType1, containsNull1), ArrayType(elementType2, containsNull2)) =>
+            ArrayType(compatibleType(elementType1, elementType2), containsNull1 || containsNull2)
+          // TODO: We should use JsonObjectStringType to mark that values of field will be
+          // strings and every string is a Json object.
+          case (_, _) => StringType
         }
-        case (ArrayType(elementType1, containsNull1), ArrayType(elementType2, containsNull2)) =>
-          ArrayType(compatibleType(elementType1, elementType2), containsNull1 || containsNull2)
-        // TODO: We should use JsonObjectStringType to mark that values of field will be
-        // strings and every string is a Json object.
-        case (_, _) => StringType
-      }
     }
   }
 

From 26503fdf20f4181a2b390c88b83f364e6a4ccc21 Mon Sep 17 00:00:00 2001
From: Josh Rosen <joshrosen@apache.org>
Date: Wed, 10 Sep 2014 12:02:23 -0700
Subject: [PATCH 258/489] [HOTFIX] Fix scala style issue introduced by #2276.

---
 .../scala/org/apache/spark/deploy/yarn/YarnRMClientImpl.scala   | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/YarnRMClientImpl.scala b/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/YarnRMClientImpl.scala
index fc30953011812..acf26505e4cf9 100644
--- a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/YarnRMClientImpl.scala
+++ b/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/YarnRMClientImpl.scala
@@ -98,7 +98,7 @@ private class YarnRMClientImpl(args: ApplicationMasterArguments) extends YarnRMC
     // Users can then monitor stderr/stdout on that node if required.
     appMasterRequest.setHost(Utils.localHostName())
     appMasterRequest.setRpcPort(0)
-    //remove the scheme from the url if it exists since Hadoop does not expect scheme
+    // remove the scheme from the url if it exists since Hadoop does not expect scheme
     appMasterRequest.setTrackingUrl(new URI(uiAddress).getAuthority())
     resourceManager.registerApplicationMaster(appMasterRequest)
   }

From 1f4a648d4e30e837d6cf3ea8de1808e2254ad70b Mon Sep 17 00:00:00 2001
From: Sandy Ryza <sandy@cloudera.com>
Date: Wed, 10 Sep 2014 14:34:24 -0500
Subject: [PATCH 259/489] SPARK-1713. Use a thread pool for launching
 executors.

This patch copies the approach used in the MapReduce application master for launching containers.

Author: Sandy Ryza <sandy@cloudera.com>

Closes #663 from sryza/sandy-spark-1713 and squashes the following commits:

036550d [Sandy Ryza] SPARK-1713. [YARN] Use a threadpool for launching executor containers
---
 docs/running-on-yarn.md                            |  7 +++++++
 .../apache/spark/deploy/yarn/YarnAllocator.scala   | 14 ++++++++++++--
 2 files changed, 19 insertions(+), 2 deletions(-)

diff --git a/docs/running-on-yarn.md b/docs/running-on-yarn.md
index 943f06b114cb9..d8b22f3663d08 100644
--- a/docs/running-on-yarn.md
+++ b/docs/running-on-yarn.md
@@ -125,6 +125,13 @@ Most of the configs are the same for Spark on YARN as for other deployment modes
      the environment of the executor launcher. 
   </td>
 </tr>
+<tr>
+  <td><code>spark.yarn.containerLauncherMaxThreads</code></td>
+  <td>25</td>
+  <td>
+    The maximum number of threads to use in the application master for launching executor containers.
+  </td>
+</tr>
 </table>
 
 # Launching Spark on YARN
diff --git a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocator.scala b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocator.scala
index 02b9a81bf6b50..0b8744f4b8bdf 100644
--- a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocator.scala
+++ b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocator.scala
@@ -18,7 +18,7 @@
 package org.apache.spark.deploy.yarn
 
 import java.util.{List => JList}
-import java.util.concurrent.ConcurrentHashMap
+import java.util.concurrent._
 import java.util.concurrent.atomic.AtomicInteger
 
 import scala.collection.JavaConversions._
@@ -32,6 +32,8 @@ import org.apache.spark.{Logging, SecurityManager, SparkConf, SparkEnv}
 import org.apache.spark.scheduler.{SplitInfo, TaskSchedulerImpl}
 import org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend
 
+import com.google.common.util.concurrent.ThreadFactoryBuilder
+
 object AllocationType extends Enumeration {
   type AllocationType = Value
   val HOST, RACK, ANY = Value
@@ -95,6 +97,14 @@ private[yarn] abstract class YarnAllocator(
   protected val (preferredHostToCount, preferredRackToCount) =
     generateNodeToWeight(conf, preferredNodes)
 
+  private val launcherPool = new ThreadPoolExecutor(
+    // max pool size of Integer.MAX_VALUE is ignored because we use an unbounded queue
+    sparkConf.getInt("spark.yarn.containerLauncherMaxThreads", 25), Integer.MAX_VALUE,
+    1, TimeUnit.MINUTES,
+    new LinkedBlockingQueue[Runnable](),
+    new ThreadFactoryBuilder().setNameFormat("ContainerLauncher #%d").setDaemon(true).build())
+  launcherPool.allowCoreThreadTimeOut(true)
+
   def getNumExecutorsRunning: Int = numExecutorsRunning.intValue
 
   def getNumExecutorsFailed: Int = numExecutorsFailed.intValue
@@ -283,7 +293,7 @@ private[yarn] abstract class YarnAllocator(
             executorMemory,
             executorCores,
             securityMgr)
-          new Thread(executorRunnable).start()
+          launcherPool.execute(executorRunnable)
         }
       }
       logDebug("""

From e4f4886d7148bf48f9e3462b83bfb1ecc7edbe31 Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@163.com>
Date: Wed, 10 Sep 2014 12:56:59 -0700
Subject: [PATCH 260/489] [SPARK-2096][SQL] Correctly parse dot notations

First let me write down the current `projections` grammar of spark sql:

    expression                : orExpression
    orExpression              : andExpression {"or" andExpression}
    andExpression             : comparisonExpression {"and" comparisonExpression}
    comparisonExpression      : termExpression | termExpression "=" termExpression | termExpression ">" termExpression | ...
    termExpression            : productExpression {"+"|"-" productExpression}
    productExpression         : baseExpression {"*"|"/"|"%" baseExpression}
    baseExpression            : expression "[" expression "]" | ... | ident | ...
    ident                     : identChar {identChar | digit} | delimiters | ...
    identChar                 : letter | "_" | "."
    delimiters                : "," | ";" | "(" | ")" | "[" | "]" | ...
    projection                : expression [["AS"] ident]
    projections               : projection { "," projection}

For something like `a.b.c[1]`, it will be parsed as:
<img src="http://img51.imgspice.com/i/03008/4iltjsnqgmtt_t.jpg" border=0>
But for something like `a[1].b`, the current grammar can't parse it correctly.
A simple solution is written in `ParquetQuerySuite#NestedSqlParser`, changed grammars are:

    delimiters                : "." | "," | ";" | "(" | ")" | "[" | "]" | ...
    identChar                 : letter | "_"
    baseExpression            : expression "[" expression "]" | expression "." ident | ... | ident | ...
This works well, but can't cover some corner case like `select t.a.b from table as t`:
<img src="http://img51.imgspice.com/i/03008/v2iau3hoxoxg_t.jpg" border=0>
`t.a.b` parsed as `GetField(GetField(UnResolved("t"), "a"), "b")` instead of `GetField(UnResolved("t.a"), "b")` using this new grammar.
However, we can't resolve `t` as it's not a filed, but the whole table.(if we could do this, then `select t from table as t` is legal, which is unexpected)
My solution is:

    dotExpressionHeader       : ident "." ident
    baseExpression            : expression "[" expression "]" | expression "." ident | ... | dotExpressionHeader  | ident | ...
I passed all test cases under sql locally and add a more complex case.
"arrayOfStruct.field1 to access all values of field1" is not supported yet. Since this PR has changed a lot of code, I will open another PR for it.
I'm not familiar with the latter optimize phase, please correct me if I missed something.

Author: Wenchen Fan <cloud0fan@163.com>
Author: Michael Armbrust <michael@databricks.com>

Closes #2230 from cloud-fan/dot and squashes the following commits:

e1a8898 [Wenchen Fan] remove support for arbitrary nested arrays
ee8a724 [Wenchen Fan] rollback LogicalPlan, support dot operation on nested array type
a58df40 [Michael Armbrust] add regression test for doubly nested data
16bc4c6 [Wenchen Fan] some enhance
95d733f [Wenchen Fan] split long line
dc31698 [Wenchen Fan] SPARK-2096 Correctly parse dot notations
---
 .../apache/spark/sql/catalyst/SqlParser.scala |  13 ++-
 .../catalyst/plans/logical/LogicalPlan.scala  |   6 +-
 .../org/apache/spark/sql/json/JsonSuite.scala |  14 +++
 .../apache/spark/sql/json/TestJsonData.scala  |  26 +++++
 .../spark/sql/parquet/ParquetQuerySuite.scala | 102 +++++-------------
 .../sql/hive/execution/SQLQuerySuite.scala    |  17 ++-
 6 files changed, 88 insertions(+), 90 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
index a04b4a938da64..ca69531c69a77 100755
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
@@ -357,16 +357,25 @@ class SqlParser extends StandardTokenParsers with PackratParsers {
     expression ~ "[" ~ expression <~ "]" ^^ {
       case base ~ _ ~ ordinal => GetItem(base, ordinal)
     } |
+    (expression <~ ".") ~ ident ^^ {
+      case base ~ fieldName => GetField(base, fieldName)
+    } |
     TRUE ^^^ Literal(true, BooleanType) |
     FALSE ^^^ Literal(false, BooleanType) |
     cast |
     "(" ~> expression <~ ")" |
     function |
     "-" ~> literal ^^ UnaryMinus |
+    dotExpressionHeader |
     ident ^^ UnresolvedAttribute |
     "*" ^^^ Star(None) |
     literal
 
+  protected lazy val dotExpressionHeader: Parser[Expression] =
+    (ident <~ ".") ~ ident ~ rep("." ~> ident) ^^ {
+      case i1 ~ i2 ~ rest => UnresolvedAttribute(i1 + "." + i2 + rest.mkString(".", ".", ""))
+    }
+
   protected lazy val dataType: Parser[DataType] =
     STRING ^^^ StringType | TIMESTAMP ^^^ TimestampType
 }
@@ -380,7 +389,7 @@ class SqlLexical(val keywords: Seq[String]) extends StdLexical {
 
   delimiters += (
       "@", "*", "+", "-", "<", "=", "<>", "!=", "<=", ">=", ">", "/", "(", ")",
-      ",", ";", "%", "{", "}", ":", "[", "]"
+      ",", ";", "%", "{", "}", ":", "[", "]", "."
   )
 
   override lazy val token: Parser[Token] = (
@@ -401,7 +410,7 @@ class SqlLexical(val keywords: Seq[String]) extends StdLexical {
       | failure("illegal character")
     )
 
-  override def identChar = letter | elem('_') | elem('.')
+  override def identChar = letter | elem('_')
 
   override def whitespace: Parser[Any] = rep(
     whitespaceChar
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala
index f81d9111945f5..bae491f07c13f 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala
@@ -104,11 +104,7 @@ abstract class LogicalPlan extends QueryPlan[LogicalPlan] {
       case Seq((a, Nil)) => Some(a) // One match, no nested fields, use it.
       // One match, but we also need to extract the requested nested field.
       case Seq((a, nestedFields)) =>
-        a.dataType match {
-          case StructType(fields) =>
-            Some(Alias(nestedFields.foldLeft(a: Expression)(GetField), nestedFields.last)())
-          case _ => None // Don't know how to resolve these field references
-        }
+        Some(Alias(nestedFields.foldLeft(a: Expression)(GetField), nestedFields.last)())
       case Seq() => None         // No matches.
       case ambiguousReferences =>
         throw new TreeNodeException(
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala
index 05513a127150c..301d482d27d86 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala
@@ -581,4 +581,18 @@ class JsonSuite extends QueryTest {
       "this is a simple string.") :: Nil
     )
   }
+
+  test("SPARK-2096 Correctly parse dot notations") {
+    val jsonSchemaRDD = jsonRDD(complexFieldAndType2)
+    jsonSchemaRDD.registerTempTable("jsonTable")
+
+    checkAnswer(
+      sql("select arrayOfStruct[0].field1, arrayOfStruct[0].field2 from jsonTable"),
+      (true, "str1") :: Nil
+    )
+    checkAnswer(
+      sql("select complexArrayOfStruct[0].field1[1].inner2[0], complexArrayOfStruct[1].field2[0][1] from jsonTable"),
+      ("str2", 6) :: Nil
+    )
+  }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/json/TestJsonData.scala b/sql/core/src/test/scala/org/apache/spark/sql/json/TestJsonData.scala
index a88310b5f1b46..b3f95f08e8044 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/json/TestJsonData.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/json/TestJsonData.scala
@@ -82,4 +82,30 @@ object TestJsonData {
       """{"c":[33, 44]}""" ::
       """{"d":{"field":true}}""" ::
       """{"e":"str"}""" :: Nil)
+
+  val complexFieldAndType2 =
+    TestSQLContext.sparkContext.parallelize(
+      """{"arrayOfStruct":[{"field1": true, "field2": "str1"}, {"field1": false}, {"field3": null}],
+          "complexArrayOfStruct": [
+          {
+            "field1": [
+            {
+              "inner1": "str1"
+            },
+            {
+              "inner2": ["str2", "str22"]
+            }],
+            "field2": [[1, 2], [3, 4]]
+          },
+          {
+            "field1": [
+            {
+              "inner2": ["str3", "str33"]
+            },
+            {
+              "inner1": "str4"
+            }],
+            "field2": [[5, 6], [7, 8]]
+          }]
+      }""" :: Nil)
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala
index 42923b6a288d9..b0a06cd3ca090 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala
@@ -17,19 +17,14 @@
 
 package org.apache.spark.sql.parquet
 
+import org.apache.hadoop.fs.{FileSystem, Path}
+import org.apache.hadoop.mapreduce.Job
 import org.scalatest.{BeforeAndAfterAll, FunSuiteLike}
-
 import parquet.hadoop.ParquetFileWriter
 import parquet.hadoop.util.ContextUtil
-import org.apache.hadoop.fs.{FileSystem, Path}
-import org.apache.hadoop.mapreduce.Job
-
-import org.apache.spark.SparkContext
 import org.apache.spark.sql._
-import org.apache.spark.sql.catalyst.{SqlLexical, SqlParser}
-import org.apache.spark.sql.catalyst.analysis.{Star, UnresolvedAttribute}
 import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.catalyst.types.{BooleanType, IntegerType}
+import org.apache.spark.sql.catalyst.types.IntegerType
 import org.apache.spark.sql.catalyst.util.getTempFilePath
 import org.apache.spark.sql.test.TestSQLContext
 import org.apache.spark.sql.test.TestSQLContext._
@@ -87,11 +82,7 @@ class ParquetQuerySuite extends QueryTest with FunSuiteLike with BeforeAndAfterA
 
   var testRDD: SchemaRDD = null
 
-  // TODO: remove this once SqlParser can parse nested select statements
-  var nestedParserSqlContext: NestedParserSQLContext = null
-
   override def beforeAll() {
-    nestedParserSqlContext = new NestedParserSQLContext(TestSQLContext.sparkContext)
     ParquetTestData.writeFile()
     ParquetTestData.writeFilterFile()
     ParquetTestData.writeNestedFile1()
@@ -718,11 +709,9 @@ class ParquetQuerySuite extends QueryTest with FunSuiteLike with BeforeAndAfterA
   }
 
   test("Projection in addressbook") {
-    val data = nestedParserSqlContext
-      .parquetFile(ParquetTestData.testNestedDir1.toString)
-      .toSchemaRDD
+    val data = parquetFile(ParquetTestData.testNestedDir1.toString).toSchemaRDD
     data.registerTempTable("data")
-    val query = nestedParserSqlContext.sql("SELECT owner, contacts[1].name FROM data")
+    val query = sql("SELECT owner, contacts[1].name FROM data")
     val tmp = query.collect()
     assert(tmp.size === 2)
     assert(tmp(0).size === 2)
@@ -733,21 +722,19 @@ class ParquetQuerySuite extends QueryTest with FunSuiteLike with BeforeAndAfterA
   }
 
   test("Simple query on nested int data") {
-    val data = nestedParserSqlContext
-      .parquetFile(ParquetTestData.testNestedDir2.toString)
-      .toSchemaRDD
+    val data = parquetFile(ParquetTestData.testNestedDir2.toString).toSchemaRDD
     data.registerTempTable("data")
-    val result1 = nestedParserSqlContext.sql("SELECT entries[0].value FROM data").collect()
+    val result1 = sql("SELECT entries[0].value FROM data").collect()
     assert(result1.size === 1)
     assert(result1(0).size === 1)
     assert(result1(0)(0) === 2.5)
-    val result2 = nestedParserSqlContext.sql("SELECT entries[0] FROM data").collect()
+    val result2 = sql("SELECT entries[0] FROM data").collect()
     assert(result2.size === 1)
     val subresult1 = result2(0)(0).asInstanceOf[CatalystConverter.StructScalaType[_]]
     assert(subresult1.size === 2)
     assert(subresult1(0) === 2.5)
     assert(subresult1(1) === false)
-    val result3 = nestedParserSqlContext.sql("SELECT outerouter FROM data").collect()
+    val result3 = sql("SELECT outerouter FROM data").collect()
     val subresult2 = result3(0)(0)
       .asInstanceOf[CatalystConverter.ArrayScalaType[_]](0)
       .asInstanceOf[CatalystConverter.ArrayScalaType[_]]
@@ -760,19 +747,18 @@ class ParquetQuerySuite extends QueryTest with FunSuiteLike with BeforeAndAfterA
   }
 
   test("nested structs") {
-    val data = nestedParserSqlContext
-      .parquetFile(ParquetTestData.testNestedDir3.toString)
+    val data = parquetFile(ParquetTestData.testNestedDir3.toString)
       .toSchemaRDD
     data.registerTempTable("data")
-    val result1 = nestedParserSqlContext.sql("SELECT booleanNumberPairs[0].value[0].truth FROM data").collect()
+    val result1 = sql("SELECT booleanNumberPairs[0].value[0].truth FROM data").collect()
     assert(result1.size === 1)
     assert(result1(0).size === 1)
     assert(result1(0)(0) === false)
-    val result2 = nestedParserSqlContext.sql("SELECT booleanNumberPairs[0].value[1].truth FROM data").collect()
+    val result2 = sql("SELECT booleanNumberPairs[0].value[1].truth FROM data").collect()
     assert(result2.size === 1)
     assert(result2(0).size === 1)
     assert(result2(0)(0) === true)
-    val result3 = nestedParserSqlContext.sql("SELECT booleanNumberPairs[1].value[0].truth FROM data").collect()
+    val result3 = sql("SELECT booleanNumberPairs[1].value[0].truth FROM data").collect()
     assert(result3.size === 1)
     assert(result3(0).size === 1)
     assert(result3(0)(0) === false)
@@ -796,11 +782,9 @@ class ParquetQuerySuite extends QueryTest with FunSuiteLike with BeforeAndAfterA
   }
 
   test("map with struct values") {
-    val data = nestedParserSqlContext
-      .parquetFile(ParquetTestData.testNestedDir4.toString)
-      .toSchemaRDD
+    val data = parquetFile(ParquetTestData.testNestedDir4.toString).toSchemaRDD
     data.registerTempTable("mapTable")
-    val result1 = nestedParserSqlContext.sql("SELECT data2 FROM mapTable").collect()
+    val result1 = sql("SELECT data2 FROM mapTable").collect()
     assert(result1.size === 1)
     val entry1 = result1(0)(0)
       .asInstanceOf[CatalystConverter.MapScalaType[String, CatalystConverter.StructScalaType[_]]]
@@ -814,7 +798,7 @@ class ParquetQuerySuite extends QueryTest with FunSuiteLike with BeforeAndAfterA
     assert(entry2 != null)
     assert(entry2(0) === 49)
     assert(entry2(1) === null)
-    val result2 = nestedParserSqlContext.sql("""SELECT data2["seven"].payload1, data2["seven"].payload2 FROM mapTable""").collect()
+    val result2 = sql("""SELECT data2["seven"].payload1, data2["seven"].payload2 FROM mapTable""").collect()
     assert(result2.size === 1)
     assert(result2(0)(0) === 42.toLong)
     assert(result2(0)(1) === "the answer")
@@ -825,15 +809,12 @@ class ParquetQuerySuite extends QueryTest with FunSuiteLike with BeforeAndAfterA
     // has no effect in this test case
     val tmpdir = Utils.createTempDir()
     Utils.deleteRecursively(tmpdir)
-    val result = nestedParserSqlContext
-      .parquetFile(ParquetTestData.testNestedDir1.toString)
-      .toSchemaRDD
+    val result = parquetFile(ParquetTestData.testNestedDir1.toString).toSchemaRDD
     result.saveAsParquetFile(tmpdir.toString)
-    nestedParserSqlContext
-      .parquetFile(tmpdir.toString)
+    parquetFile(tmpdir.toString)
       .toSchemaRDD
       .registerTempTable("tmpcopy")
-    val tmpdata = nestedParserSqlContext.sql("SELECT owner, contacts[1].name FROM tmpcopy").collect()
+    val tmpdata = sql("SELECT owner, contacts[1].name FROM tmpcopy").collect()
     assert(tmpdata.size === 2)
     assert(tmpdata(0).size === 2)
     assert(tmpdata(0)(0) === "Julien Le Dem")
@@ -844,20 +825,17 @@ class ParquetQuerySuite extends QueryTest with FunSuiteLike with BeforeAndAfterA
   }
 
   test("Writing out Map and reading it back in") {
-    val data = nestedParserSqlContext
-      .parquetFile(ParquetTestData.testNestedDir4.toString)
-      .toSchemaRDD
+    val data = parquetFile(ParquetTestData.testNestedDir4.toString).toSchemaRDD
     val tmpdir = Utils.createTempDir()
     Utils.deleteRecursively(tmpdir)
     data.saveAsParquetFile(tmpdir.toString)
-    nestedParserSqlContext
-      .parquetFile(tmpdir.toString)
+    parquetFile(tmpdir.toString)
       .toSchemaRDD
       .registerTempTable("tmpmapcopy")
-    val result1 = nestedParserSqlContext.sql("""SELECT data1["key2"] FROM tmpmapcopy""").collect()
+    val result1 = sql("""SELECT data1["key2"] FROM tmpmapcopy""").collect()
     assert(result1.size === 1)
     assert(result1(0)(0) === 2)
-    val result2 = nestedParserSqlContext.sql("SELECT data2 FROM tmpmapcopy").collect()
+    val result2 = sql("SELECT data2 FROM tmpmapcopy").collect()
     assert(result2.size === 1)
     val entry1 = result2(0)(0)
       .asInstanceOf[CatalystConverter.MapScalaType[String, CatalystConverter.StructScalaType[_]]]
@@ -871,42 +849,10 @@ class ParquetQuerySuite extends QueryTest with FunSuiteLike with BeforeAndAfterA
     assert(entry2 != null)
     assert(entry2(0) === 49)
     assert(entry2(1) === null)
-    val result3 = nestedParserSqlContext.sql("""SELECT data2["seven"].payload1, data2["seven"].payload2 FROM tmpmapcopy""").collect()
+    val result3 = sql("""SELECT data2["seven"].payload1, data2["seven"].payload2 FROM tmpmapcopy""").collect()
     assert(result3.size === 1)
     assert(result3(0)(0) === 42.toLong)
     assert(result3(0)(1) === "the answer")
     Utils.deleteRecursively(tmpdir)
   }
 }
-
-// TODO: the code below is needed temporarily until the standard parser is able to parse
-// nested field expressions correctly
-class NestedParserSQLContext(@transient override val sparkContext: SparkContext) extends SQLContext(sparkContext) {
-  override protected[sql] val parser = new NestedSqlParser()
-}
-
-class NestedSqlLexical(override val keywords: Seq[String]) extends SqlLexical(keywords) {
-  override def identChar = letter | elem('_')
-  delimiters += (".")
-}
-
-class NestedSqlParser extends SqlParser {
-  override val lexical = new NestedSqlLexical(reservedWords)
-
-  override protected lazy val baseExpression: PackratParser[Expression] =
-    expression ~ "[" ~ expression <~ "]" ^^ {
-      case base ~ _ ~ ordinal => GetItem(base, ordinal)
-    } |
-    expression ~ "." ~ ident ^^ {
-      case base ~ _ ~ fieldName => GetField(base, fieldName)
-    } |
-    TRUE ^^^ Literal(true, BooleanType) |
-    FALSE ^^^ Literal(false, BooleanType) |
-    cast |
-    "(" ~> expression <~ ")" |
-    function |
-    "-" ~> literal ^^ UnaryMinus |
-    ident ^^ UnresolvedAttribute |
-    "*" ^^^ Star(None) |
-    literal
-}
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
index 635a9fb0d56cb..b99caf77bce28 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
@@ -17,13 +17,13 @@
 
 package org.apache.spark.sql.hive.execution
 
-import scala.reflect.ClassTag
-
-import org.apache.spark.sql.{SQLConf, QueryTest}
-import org.apache.spark.sql.execution.{BroadcastHashJoin, ShuffledHashJoin}
-import org.apache.spark.sql.hive.test.TestHive
+import org.apache.spark.sql.QueryTest
 import org.apache.spark.sql.hive.test.TestHive._
 
+case class Nested1(f1: Nested2)
+case class Nested2(f2: Nested3)
+case class Nested3(f3: Int)
+
 /**
  * A collection of hive query tests where we generate the answers ourselves instead of depending on
  * Hive to generate them (in contrast to HiveQuerySuite).  Often this is because the query is
@@ -47,4 +47,11 @@ class SQLQuerySuite extends QueryTest {
           GROUP BY key, value
           ORDER BY value) a""").collect().toSeq)
   }
+
+  test("double nested data") {
+    sparkContext.parallelize(Nested1(Nested2(Nested3(1))) :: Nil).registerTempTable("nested")
+    checkAnswer(
+      sql("SELECT f1.f2.f3 FROM nested"),
+      1)
+  }
 }

From 558962a83fb0758ab5c13ff4ea58cc96c29cbbcc Mon Sep 17 00:00:00 2001
From: WangTaoTheTonic <barneystinson@aliyun.com>
Date: Wed, 10 Sep 2014 13:06:47 -0700
Subject: [PATCH 261/489] [SPARK-3411] Improve load-balancing of
 concurrently-submitted drivers across workers

If the waiting driver array is too big, the drivers in it will be dispatched to the first worker we get(if it has enough resources), with or without the Randomization.

We should do randomization every time we dispatch a driver, in order to better balance drivers.

Author: WangTaoTheTonic <barneystinson@aliyun.com>
Author: WangTao <barneystinson@aliyun.com>

Closes #1106 from WangTaoTheTonic/fixBalanceDrivers and squashes the following commits:

d1a928b [WangTaoTheTonic] Minor adjustment
b6560cf [WangTaoTheTonic] solve the shuffle problem for HashSet
f674e59 [WangTaoTheTonic] add comment and minor fix
2835929 [WangTao] solve the failed test and avoid filtering
2ca3091 [WangTao] fix checkstyle
bc91bb1 [WangTao] Avoid shuffle every time we schedule the driver using round robin
bbc7087 [WangTaoTheTonic] Optimize the schedule in Master
---
 .../apache/spark/deploy/master/Master.scala    | 18 +++++++++++++++---
 1 file changed, 15 insertions(+), 3 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/deploy/master/Master.scala b/core/src/main/scala/org/apache/spark/deploy/master/Master.scala
index a3909d6ea95c0..2a3bd6ba0b9dc 100644
--- a/core/src/main/scala/org/apache/spark/deploy/master/Master.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/master/Master.scala
@@ -487,13 +487,25 @@ private[spark] class Master(
     if (state != RecoveryState.ALIVE) { return }
 
     // First schedule drivers, they take strict precedence over applications
-    val shuffledWorkers = Random.shuffle(workers) // Randomization helps balance drivers
-    for (worker <- shuffledWorkers if worker.state == WorkerState.ALIVE) {
-      for (driver <- List(waitingDrivers: _*)) { // iterate over a copy of waitingDrivers
+    // Randomization helps balance drivers
+    val shuffledAliveWorkers = Random.shuffle(workers.toSeq.filter(_.state == WorkerState.ALIVE))
+    val aliveWorkerNum = shuffledAliveWorkers.size
+    var curPos = 0
+    for (driver <- waitingDrivers.toList) { // iterate over a copy of waitingDrivers
+      // We assign workers to each waiting driver in a round-robin fashion. For each driver, we
+      // start from the last worker that was assigned a driver, and continue onwards until we have
+      // explored all alive workers.
+      curPos = (curPos + 1) % aliveWorkerNum
+      val startPos = curPos
+      var launched = false
+      while (curPos != startPos && !launched) {
+        val worker = shuffledAliveWorkers(curPos)
         if (worker.memoryFree >= driver.desc.mem && worker.coresFree >= driver.desc.cores) {
           launchDriver(worker, driver)
           waitingDrivers -= driver
+          launched = true
         }
+        curPos = (curPos + 1) % aliveWorkerNum
       }
     }
 

From 79cdb9b64ad2fa3ab7f2c221766d36658b917c40 Mon Sep 17 00:00:00 2001
From: "qiping.lqp" <qiping.lqp@alibaba-inc.com>
Date: Wed, 10 Sep 2014 15:37:10 -0700
Subject: [PATCH 262/489] [SPARK-2207][SPARK-3272][MLLib]Add minimum
 information gain and minimum instances per node as training parameters for
 decision tree.

These two parameters can act as early stop rules to do pre-pruning. When a split cause cause left or right child to have less than `minInstancesPerNode` or has less information gain than `minInfoGain`, current node will not be split by this split.

When there is no possible splits that satisfy requirements, there is no useful information gain stats, but we still need to calculate the predict value for current node. So I separated calculation of predict from calculation of information gain, which can also save computation when the number of possible splits is large. Please see [SPARK-3272](https://issues.apache.org/jira/browse/SPARK-3272) for more details.

CC: mengxr manishamde jkbradley, please help me review this, thanks.

Author: qiping.lqp <qiping.lqp@alibaba-inc.com>
Author: chouqin <liqiping1991@gmail.com>

Closes #2332 from chouqin/dt-preprune and squashes the following commits:

f1d11d1 [chouqin] fix typo
c7ebaf1 [chouqin] fix typo
39f9b60 [chouqin] change edge `minInstancesPerNode` to 2 and add one more test
0278a11 [chouqin] remove `noSplit` and set `Predict` private to tree
d593ec7 [chouqin] fix docs and change minInstancesPerNode to 1
efcc736 [qiping.lqp] fix bug
10b8012 [qiping.lqp] fix style
6728fad [qiping.lqp] minor fix: remove empty lines
bb465ca [qiping.lqp] Merge branch 'master' of https://github.com/apache/spark into dt-preprune
cadd569 [qiping.lqp] add api docs
46b891f [qiping.lqp] fix bug
e72c7e4 [qiping.lqp] add comments
845c6fa [qiping.lqp] fix style
f195e83 [qiping.lqp] fix style
987cbf4 [qiping.lqp] fix bug
ff34845 [qiping.lqp] separate calculation of predict of node from calculation of info gain
ac42378 [qiping.lqp] add min info gain and min instances per node parameters in decision tree
---
 .../spark/mllib/tree/DecisionTree.scala       |  72 ++++++++----
 .../mllib/tree/configuration/Strategy.scala   |   9 ++
 .../tree/impl/DecisionTreeMetadata.scala      |   7 +-
 .../tree/model/InformationGainStats.scala     |  20 ++--
 .../spark/mllib/tree/model/Predict.scala      |  36 ++++++
 .../apache/spark/mllib/tree/model/Split.scala |   2 +
 .../spark/mllib/tree/DecisionTreeSuite.scala  | 103 ++++++++++++++++--
 7 files changed, 213 insertions(+), 36 deletions(-)
 create mode 100644 mllib/src/main/scala/org/apache/spark/mllib/tree/model/Predict.scala

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala
index d1309b2b20f54..98596569b8c95 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala
@@ -130,7 +130,7 @@ class DecisionTree (private val strategy: Strategy) extends Serializable with Lo
 
       // Find best split for all nodes at a level.
       timer.start("findBestSplits")
-      val splitsStatsForLevel: Array[(Split, InformationGainStats)] =
+      val splitsStatsForLevel: Array[(Split, InformationGainStats, Predict)] =
         DecisionTree.findBestSplits(treeInput, parentImpurities,
           metadata, level, nodes, splits, bins, maxLevelForSingleGroup, timer)
       timer.stop("findBestSplits")
@@ -143,8 +143,9 @@ class DecisionTree (private val strategy: Strategy) extends Serializable with Lo
         timer.start("extractNodeInfo")
         val split = nodeSplitStats._1
         val stats = nodeSplitStats._2
+        val predict = nodeSplitStats._3.predict
         val isLeaf = (stats.gain <= 0) || (level == strategy.maxDepth)
-        val node = new Node(nodeIndex, stats.predict, isLeaf, Some(split), None, None, Some(stats))
+        val node = new Node(nodeIndex, predict, isLeaf, Some(split), None, None, Some(stats))
         logDebug("Node = " + node)
         nodes(nodeIndex) = node
         timer.stop("extractNodeInfo")
@@ -425,7 +426,7 @@ object DecisionTree extends Serializable with Logging {
       splits: Array[Array[Split]],
       bins: Array[Array[Bin]],
       maxLevelForSingleGroup: Int,
-      timer: TimeTracker = new TimeTracker): Array[(Split, InformationGainStats)] = {
+      timer: TimeTracker = new TimeTracker): Array[(Split, InformationGainStats, Predict)] = {
     // split into groups to avoid memory overflow during aggregation
     if (level > maxLevelForSingleGroup) {
       // When information for all nodes at a given level cannot be stored in memory,
@@ -434,7 +435,7 @@ object DecisionTree extends Serializable with Logging {
       // numGroups is equal to 2 at level 11 and 4 at level 12, respectively.
       val numGroups = 1 << level - maxLevelForSingleGroup
       logDebug("numGroups = " + numGroups)
-      var bestSplits = new Array[(Split, InformationGainStats)](0)
+      var bestSplits = new Array[(Split, InformationGainStats, Predict)](0)
       // Iterate over each group of nodes at a level.
       var groupIndex = 0
       while (groupIndex < numGroups) {
@@ -605,7 +606,7 @@ object DecisionTree extends Serializable with Logging {
       bins: Array[Array[Bin]],
       timer: TimeTracker,
       numGroups: Int = 1,
-      groupIndex: Int = 0): Array[(Split, InformationGainStats)] = {
+      groupIndex: Int = 0): Array[(Split, InformationGainStats, Predict)] = {
 
     /*
      * The high-level descriptions of the best split optimizations are noted here.
@@ -705,7 +706,7 @@ object DecisionTree extends Serializable with Logging {
 
     // Calculate best splits for all nodes at a given level
     timer.start("chooseSplits")
-    val bestSplits = new Array[(Split, InformationGainStats)](numNodes)
+    val bestSplits = new Array[(Split, InformationGainStats, Predict)](numNodes)
     // Iterating over all nodes at this level
     var nodeIndex = 0
     while (nodeIndex < numNodes) {
@@ -734,28 +735,27 @@ object DecisionTree extends Serializable with Logging {
       topImpurity: Double,
       level: Int,
       metadata: DecisionTreeMetadata): InformationGainStats = {
-
     val leftCount = leftImpurityCalculator.count
     val rightCount = rightImpurityCalculator.count
 
-    val totalCount = leftCount + rightCount
-    if (totalCount == 0) {
-      // Return arbitrary prediction.
-      return new InformationGainStats(0, topImpurity, topImpurity, topImpurity, 0)
+    // If left child or right child doesn't satisfy minimum instances per node,
+    // then this split is invalid, return invalid information gain stats.
+    if ((leftCount < metadata.minInstancesPerNode) ||
+        (rightCount < metadata.minInstancesPerNode)) {
+      return InformationGainStats.invalidInformationGainStats
     }
 
-    val parentNodeAgg = leftImpurityCalculator.copy
-    parentNodeAgg.add(rightImpurityCalculator)
+    val totalCount = leftCount + rightCount
+
     // impurity of parent node
     val impurity = if (level > 0) {
       topImpurity
     } else {
+      val parentNodeAgg = leftImpurityCalculator.copy
+      parentNodeAgg.add(rightImpurityCalculator)
       parentNodeAgg.calculate()
     }
 
-    val predict = parentNodeAgg.predict
-    val prob = parentNodeAgg.prob(predict)
-
     val leftImpurity = leftImpurityCalculator.calculate() // Note: This equals 0 if count = 0
     val rightImpurity = rightImpurityCalculator.calculate()
 
@@ -764,7 +764,31 @@ object DecisionTree extends Serializable with Logging {
 
     val gain = impurity - leftWeight * leftImpurity - rightWeight * rightImpurity
 
-    new InformationGainStats(gain, impurity, leftImpurity, rightImpurity, predict, prob)
+    // if information gain doesn't satisfy minimum information gain,
+    // then this split is invalid, return invalid information gain stats.
+    if (gain < metadata.minInfoGain) {
+      return InformationGainStats.invalidInformationGainStats
+    }
+
+    new InformationGainStats(gain, impurity, leftImpurity, rightImpurity)
+  }
+
+  /**
+   * Calculate predict value for current node, given stats of any split.
+   * Note that this function is called only once for each node.
+   * @param leftImpurityCalculator left node aggregates for a split
+   * @param rightImpurityCalculator right node aggregates for a node
+   * @return predict value for current node
+   */
+  private def calculatePredict(
+      leftImpurityCalculator: ImpurityCalculator,
+      rightImpurityCalculator: ImpurityCalculator): Predict =  {
+    val parentNodeAgg = leftImpurityCalculator.copy
+    parentNodeAgg.add(rightImpurityCalculator)
+    val predict = parentNodeAgg.predict
+    val prob = parentNodeAgg.prob(predict)
+
+    new Predict(predict, prob)
   }
 
   /**
@@ -780,12 +804,15 @@ object DecisionTree extends Serializable with Logging {
       nodeImpurity: Double,
       level: Int,
       metadata: DecisionTreeMetadata,
-      splits: Array[Array[Split]]): (Split, InformationGainStats) = {
+      splits: Array[Array[Split]]): (Split, InformationGainStats, Predict) = {
 
     logDebug("node impurity = " + nodeImpurity)
 
+    // calculate predict only once
+    var predict: Option[Predict] = None
+
     // For each (feature, split), calculate the gain, and select the best (feature, split).
-    Range(0, metadata.numFeatures).map { featureIndex =>
+    val (bestSplit, bestSplitStats) = Range(0, metadata.numFeatures).map { featureIndex =>
       val numSplits = metadata.numSplits(featureIndex)
       if (metadata.isContinuous(featureIndex)) {
         // Cumulative sum (scanLeft) of bin statistics.
@@ -803,6 +830,7 @@ object DecisionTree extends Serializable with Logging {
             val leftChildStats = binAggregates.getImpurityCalculator(nodeFeatureOffset, splitIdx)
             val rightChildStats = binAggregates.getImpurityCalculator(nodeFeatureOffset, numSplits)
             rightChildStats.subtract(leftChildStats)
+            predict = Some(predict.getOrElse(calculatePredict(leftChildStats, rightChildStats)))
             val gainStats =
               calculateGainForSplit(leftChildStats, rightChildStats, nodeImpurity, level, metadata)
             (splitIdx, gainStats)
@@ -816,6 +844,7 @@ object DecisionTree extends Serializable with Logging {
           Range(0, numSplits).map { splitIndex =>
             val leftChildStats = binAggregates.getImpurityCalculator(leftChildOffset, splitIndex)
             val rightChildStats = binAggregates.getImpurityCalculator(rightChildOffset, splitIndex)
+            predict = Some(predict.getOrElse(calculatePredict(leftChildStats, rightChildStats)))
             val gainStats =
               calculateGainForSplit(leftChildStats, rightChildStats, nodeImpurity, level, metadata)
             (splitIndex, gainStats)
@@ -887,6 +916,7 @@ object DecisionTree extends Serializable with Logging {
             val rightChildStats =
               binAggregates.getImpurityCalculator(nodeFeatureOffset, lastCategory)
             rightChildStats.subtract(leftChildStats)
+            predict = Some(predict.getOrElse(calculatePredict(leftChildStats, rightChildStats)))
             val gainStats =
               calculateGainForSplit(leftChildStats, rightChildStats, nodeImpurity, level, metadata)
             (splitIndex, gainStats)
@@ -898,6 +928,10 @@ object DecisionTree extends Serializable with Logging {
         (bestFeatureSplit, bestFeatureGainStats)
       }
     }.maxBy(_._2.gain)
+
+    require(predict.isDefined, "must calculate predict for each node")
+
+    (bestSplit, bestSplitStats, predict.get)
   }
 
   /**
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala
index 23f74d5360fe5..987fe632c91ed 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala
@@ -49,6 +49,13 @@ import org.apache.spark.mllib.tree.configuration.QuantileStrategy._
  *                                k) implies the feature n is categorical with k categories 0,
  *                                1, 2, ... , k-1. It's important to note that features are
  *                                zero-indexed.
+ * @param minInstancesPerNode Minimum number of instances each child must have after split.
+ *                            Default value is 1. If a split cause left or right child
+ *                            to have less than minInstancesPerNode,
+ *                            this split will not be considered as a valid split.
+ * @param minInfoGain Minimum information gain a split must get. Default value is 0.0.
+ *                    If a split has less information gain than minInfoGain,
+ *                    this split will not be considered as a valid split.
  * @param maxMemoryInMB Maximum memory in MB allocated to histogram aggregation. Default value is
  *                      256 MB.
  */
@@ -61,6 +68,8 @@ class Strategy (
     val maxBins: Int = 32,
     val quantileCalculationStrategy: QuantileStrategy = Sort,
     val categoricalFeaturesInfo: Map[Int, Int] = Map[Int, Int](),
+    val minInstancesPerNode: Int = 1,
+    val minInfoGain: Double = 0.0,
     val maxMemoryInMB: Int = 256) extends Serializable {
 
   if (algo == Classification) {
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/impl/DecisionTreeMetadata.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/impl/DecisionTreeMetadata.scala
index e95add7558bcf..5ceaa8154d11a 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/impl/DecisionTreeMetadata.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/impl/DecisionTreeMetadata.scala
@@ -45,7 +45,9 @@ private[tree] class DecisionTreeMetadata(
     val unorderedFeatures: Set[Int],
     val numBins: Array[Int],
     val impurity: Impurity,
-    val quantileStrategy: QuantileStrategy) extends Serializable {
+    val quantileStrategy: QuantileStrategy,
+    val minInstancesPerNode: Int,
+    val minInfoGain: Double) extends Serializable {
 
   def isUnordered(featureIndex: Int): Boolean = unorderedFeatures.contains(featureIndex)
 
@@ -127,7 +129,8 @@ private[tree] object DecisionTreeMetadata {
 
     new DecisionTreeMetadata(numFeatures, numExamples, numClasses, numBins.max,
       strategy.categoricalFeaturesInfo, unorderedFeatures.toSet, numBins,
-      strategy.impurity, strategy.quantileCalculationStrategy)
+      strategy.impurity, strategy.quantileCalculationStrategy,
+      strategy.minInstancesPerNode, strategy.minInfoGain)
   }
 
   /**
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/InformationGainStats.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/InformationGainStats.scala
index fb12298e0f5d3..f3e2619bd8ba0 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/InformationGainStats.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/InformationGainStats.scala
@@ -26,20 +26,26 @@ import org.apache.spark.annotation.DeveloperApi
  * @param impurity current node impurity
  * @param leftImpurity left node impurity
  * @param rightImpurity right node impurity
- * @param predict predicted value
- * @param prob probability of the label (classification only)
  */
 @DeveloperApi
 class InformationGainStats(
     val gain: Double,
     val impurity: Double,
     val leftImpurity: Double,
-    val rightImpurity: Double,
-    val predict: Double,
-    val prob: Double = 0.0) extends Serializable {
+    val rightImpurity: Double) extends Serializable {
 
   override def toString = {
-    "gain = %f, impurity = %f, left impurity = %f, right impurity = %f, predict = %f, prob = %f"
-      .format(gain, impurity, leftImpurity, rightImpurity, predict, prob)
+    "gain = %f, impurity = %f, left impurity = %f, right impurity = %f"
+      .format(gain, impurity, leftImpurity, rightImpurity)
   }
 }
+
+
+private[tree] object InformationGainStats {
+  /**
+   * An [[org.apache.spark.mllib.tree.model.InformationGainStats]] object to
+   * denote that current split doesn't satisfies minimum info gain or
+   * minimum number of instances per node.
+   */
+  val invalidInformationGainStats = new InformationGainStats(Double.MinValue, -1.0, -1.0, -1.0)
+}
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Predict.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Predict.scala
new file mode 100644
index 0000000000000..6fac2be2797bc
--- /dev/null
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Predict.scala
@@ -0,0 +1,36 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.mllib.tree.model
+
+import org.apache.spark.annotation.DeveloperApi
+
+/**
+ * :: DeveloperApi ::
+ * Predicted value for a node
+ * @param predict predicted value
+ * @param prob probability of the label (classification only)
+ */
+@DeveloperApi
+private[tree] class Predict(
+    val predict: Double,
+    val prob: Double = 0.0) extends Serializable{
+
+  override def toString = {
+    "predict = %f, prob = %f".format(predict, prob)
+  }
+}
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Split.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Split.scala
index 50fb48b40de3d..b7a85f58544a3 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Split.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Split.scala
@@ -19,6 +19,8 @@ package org.apache.spark.mllib.tree.model
 
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.mllib.tree.configuration.FeatureType.FeatureType
+import org.apache.spark.mllib.tree.configuration.FeatureType
+import org.apache.spark.mllib.tree.configuration.FeatureType.FeatureType
 
 /**
  * :: DeveloperApi ::
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala
index 69482f2acbb40..fd8547c1660fc 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala
@@ -28,7 +28,7 @@ import org.apache.spark.mllib.tree.configuration.FeatureType._
 import org.apache.spark.mllib.tree.configuration.Strategy
 import org.apache.spark.mllib.tree.impl.{DecisionTreeMetadata, TreePoint}
 import org.apache.spark.mllib.tree.impurity.{Entropy, Gini, Variance}
-import org.apache.spark.mllib.tree.model.{DecisionTreeModel, Node}
+import org.apache.spark.mllib.tree.model.{InformationGainStats, DecisionTreeModel, Node}
 import org.apache.spark.mllib.util.LocalSparkContext
 
 class DecisionTreeSuite extends FunSuite with LocalSparkContext {
@@ -279,9 +279,10 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
     assert(split.threshold === Double.MinValue)
 
     val stats = bestSplits(0)._2
+    val predict = bestSplits(0)._3
     assert(stats.gain > 0)
-    assert(stats.predict === 1)
-    assert(stats.prob === 0.6)
+    assert(predict.predict === 1)
+    assert(predict.prob === 0.6)
     assert(stats.impurity > 0.2)
   }
 
@@ -312,8 +313,9 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
     assert(split.threshold === Double.MinValue)
 
     val stats = bestSplits(0)._2
+    val predict = bestSplits(0)._3.predict
     assert(stats.gain > 0)
-    assert(stats.predict === 0.6)
+    assert(predict === 0.6)
     assert(stats.impurity > 0.2)
   }
 
@@ -387,7 +389,7 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
     assert(bestSplits(0)._2.gain === 0)
     assert(bestSplits(0)._2.leftImpurity === 0)
     assert(bestSplits(0)._2.rightImpurity === 0)
-    assert(bestSplits(0)._2.predict === 1)
+    assert(bestSplits(0)._3.predict === 1)
   }
 
   test("Binary classification stump with fixed label 0 for Entropy") {
@@ -414,7 +416,7 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
     assert(bestSplits(0)._2.gain === 0)
     assert(bestSplits(0)._2.leftImpurity === 0)
     assert(bestSplits(0)._2.rightImpurity === 0)
-    assert(bestSplits(0)._2.predict === 0)
+    assert(bestSplits(0)._3.predict === 0)
   }
 
   test("Binary classification stump with fixed label 1 for Entropy") {
@@ -441,7 +443,7 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
     assert(bestSplits(0)._2.gain === 0)
     assert(bestSplits(0)._2.leftImpurity === 0)
     assert(bestSplits(0)._2.rightImpurity === 0)
-    assert(bestSplits(0)._2.predict === 1)
+    assert(bestSplits(0)._3.predict === 1)
   }
 
   test("Second level node building with vs. without groups") {
@@ -490,7 +492,7 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
       assert(bestSplits(i)._2.impurity === bestSplitsWithGroups(i)._2.impurity)
       assert(bestSplits(i)._2.leftImpurity === bestSplitsWithGroups(i)._2.leftImpurity)
       assert(bestSplits(i)._2.rightImpurity === bestSplitsWithGroups(i)._2.rightImpurity)
-      assert(bestSplits(i)._2.predict === bestSplitsWithGroups(i)._2.predict)
+      assert(bestSplits(i)._3.predict === bestSplitsWithGroups(i)._3.predict)
     }
   }
 
@@ -674,6 +676,91 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
     validateClassifier(model, arr, 0.6)
   }
 
+  test("split must satisfy min instances per node requirements") {
+    val arr = new Array[LabeledPoint](3)
+    arr(0) = new LabeledPoint(0.0, Vectors.sparse(2, Seq((0, 0.0))))
+    arr(1) = new LabeledPoint(1.0, Vectors.sparse(2, Seq((1, 1.0))))
+    arr(2) = new LabeledPoint(0.0, Vectors.sparse(2, Seq((0, 1.0))))
+
+    val input = sc.parallelize(arr)
+    val strategy = new Strategy(algo = Classification, impurity = Gini,
+      maxDepth = 2, numClassesForClassification = 2, minInstancesPerNode = 2)
+
+    val model = DecisionTree.train(input, strategy)
+    assert(model.topNode.isLeaf)
+    assert(model.topNode.predict == 0.0)
+    val predicts = input.map(p => model.predict(p.features)).collect()
+    predicts.foreach { predict =>
+      assert(predict == 0.0)
+    }
+
+    // test for findBestSplits when no valid split can be found
+    val metadata = DecisionTreeMetadata.buildMetadata(input, strategy)
+    val (splits, bins) = DecisionTree.findSplitsBins(input, metadata)
+    val treeInput = TreePoint.convertToTreeRDD(input, bins, metadata)
+    val bestSplits = DecisionTree.findBestSplits(treeInput, new Array(8), metadata, 0,
+      new Array[Node](0), splits, bins, 10)
+
+    assert(bestSplits.length == 1)
+    val bestInfoStats = bestSplits(0)._2
+    assert(bestInfoStats == InformationGainStats.invalidInformationGainStats)
+  }
+
+  test("don't choose split that doesn't satisfy min instance per node requirements") {
+    // if a split doesn't satisfy min instances per node requirements,
+    // this split is invalid, even though the information gain of split is large.
+    val arr = new Array[LabeledPoint](4)
+    arr(0) = new LabeledPoint(0.0, Vectors.dense(0.0, 1.0))
+    arr(1) = new LabeledPoint(1.0, Vectors.dense(1.0, 1.0))
+    arr(2) = new LabeledPoint(0.0, Vectors.dense(0.0, 0.0))
+    arr(3) = new LabeledPoint(0.0, Vectors.dense(0.0, 0.0))
+
+    val input = sc.parallelize(arr)
+    val strategy = new Strategy(algo = Classification, impurity = Gini,
+      maxBins = 2, maxDepth = 2, categoricalFeaturesInfo = Map(0 -> 2, 1-> 2),
+      numClassesForClassification = 2, minInstancesPerNode = 2)
+    val metadata = DecisionTreeMetadata.buildMetadata(input, strategy)
+    val (splits, bins) = DecisionTree.findSplitsBins(input, metadata)
+    val treeInput = TreePoint.convertToTreeRDD(input, bins, metadata)
+    val bestSplits = DecisionTree.findBestSplits(treeInput, new Array(8), metadata, 0,
+      new Array[Node](0), splits, bins, 10)
+
+    assert(bestSplits.length == 1)
+    val bestSplit = bestSplits(0)._1
+    val bestSplitStats = bestSplits(0)._1
+    assert(bestSplit.feature == 1)
+    assert(bestSplitStats != InformationGainStats.invalidInformationGainStats)
+  }
+
+  test("split must satisfy min info gain requirements") {
+    val arr = new Array[LabeledPoint](3)
+    arr(0) = new LabeledPoint(0.0, Vectors.sparse(2, Seq((0, 0.0))))
+    arr(1) = new LabeledPoint(1.0, Vectors.sparse(2, Seq((1, 1.0))))
+    arr(2) = new LabeledPoint(0.0, Vectors.sparse(2, Seq((0, 1.0))))
+
+    val input = sc.parallelize(arr)
+    val strategy = new Strategy(algo = Classification, impurity = Gini, maxDepth = 2,
+      numClassesForClassification = 2, minInfoGain = 1.0)
+
+    val model = DecisionTree.train(input, strategy)
+    assert(model.topNode.isLeaf)
+    assert(model.topNode.predict == 0.0)
+    val predicts = input.map(p => model.predict(p.features)).collect()
+    predicts.foreach { predict =>
+      assert(predict == 0.0)
+    }
+
+    // test for findBestSplits when no valid split can be found
+    val metadata = DecisionTreeMetadata.buildMetadata(input, strategy)
+    val (splits, bins) = DecisionTree.findSplitsBins(input, metadata)
+    val treeInput = TreePoint.convertToTreeRDD(input, bins, metadata)
+    val bestSplits = DecisionTree.findBestSplits(treeInput, new Array(8), metadata, 0,
+      new Array[Node](0), splits, bins, 10)
+
+    assert(bestSplits.length == 1)
+    val bestInfoStats = bestSplits(0)._2
+    assert(bestInfoStats == InformationGainStats.invalidInformationGainStats)
+  }
 }
 
 object DecisionTreeSuite {

From 84e2c8bfe41837baf2aeffa9741e4dbd14351981 Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Wed, 10 Sep 2014 20:57:38 -0700
Subject: [PATCH 263/489] [SQL] Add test case with workaround for reading
 partitioned Avro files

In order to read from partitioned Avro files we need to also set the `SERDEPROPERTIES` since `TBLPROPERTIES` are not passed to the initialization.  This PR simply adds a test to make sure we don't break this workaround.

Author: Michael Armbrust <michael@databricks.com>

Closes #2340 from marmbrus/avroPartitioned and squashes the following commits:

6b969d6 [Michael Armbrust] fix style
fea2124 [Michael Armbrust] Add test case with workaround for reading partitioned avro files.
---
 .../org/apache/spark/sql/hive/TestHive.scala  | 69 ++++++++++++++++++-
 ...roSerDe-0-e4501461c855cc9071a872a64186c3de |  8 +++
 .../sql/hive/execution/HiveSerDeSuite.scala   |  2 +
 3 files changed, 78 insertions(+), 1 deletion(-)
 create mode 100644 sql/hive/src/test/resources/golden/Read Partitioned with AvroSerDe-0-e4501461c855cc9071a872a64186c3de

diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/TestHive.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/TestHive.scala
index a013f3f7a805f..6974f3e581b97 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/TestHive.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/TestHive.scala
@@ -269,7 +269,74 @@ class TestHiveContext(sc: SparkContext) extends HiveContext(sc) {
          |)
        """.stripMargin.cmd,
       s"LOAD DATA LOCAL INPATH '${getHiveFile("data/files/episodes.avro")}' INTO TABLE episodes".cmd
-    )
+    ),
+    // THIS TABLE IS NOT THE SAME AS THE HIVE TEST TABLE episodes_partitioned AS DYNAMIC PARITIONING
+    // IS NOT YET SUPPORTED
+    TestTable("episodes_part",
+      s"""CREATE TABLE episodes_part (title STRING, air_date STRING, doctor INT)
+         |PARTITIONED BY (doctor_pt INT)
+         |ROW FORMAT SERDE '${classOf[AvroSerDe].getCanonicalName}'
+         |STORED AS
+         |INPUTFORMAT '${classOf[AvroContainerInputFormat].getCanonicalName}'
+         |OUTPUTFORMAT '${classOf[AvroContainerOutputFormat].getCanonicalName}'
+         |TBLPROPERTIES (
+         |  'avro.schema.literal'='{
+         |    "type": "record",
+         |    "name": "episodes",
+         |    "namespace": "testing.hive.avro.serde",
+         |    "fields": [
+         |      {
+         |          "name": "title",
+         |          "type": "string",
+         |          "doc": "episode title"
+         |      },
+         |      {
+         |          "name": "air_date",
+         |          "type": "string",
+         |          "doc": "initial date"
+         |      },
+         |      {
+         |          "name": "doctor",
+         |          "type": "int",
+         |          "doc": "main actor playing the Doctor in episode"
+         |      }
+         |    ]
+         |  }'
+         |)
+       """.stripMargin.cmd,
+      // WORKAROUND: Required to pass schema to SerDe for partitioned tables.
+      // TODO: Pass this automatically from the table to partitions.
+      s"""
+         |ALTER TABLE episodes_part SET SERDEPROPERTIES (
+         |  'avro.schema.literal'='{
+         |    "type": "record",
+         |    "name": "episodes",
+         |    "namespace": "testing.hive.avro.serde",
+         |    "fields": [
+         |      {
+         |          "name": "title",
+         |          "type": "string",
+         |          "doc": "episode title"
+         |      },
+         |      {
+         |          "name": "air_date",
+         |          "type": "string",
+         |          "doc": "initial date"
+         |      },
+         |      {
+         |          "name": "doctor",
+         |          "type": "int",
+         |          "doc": "main actor playing the Doctor in episode"
+         |      }
+         |    ]
+         |  }'
+         |)
+        """.stripMargin.cmd,
+      s"""
+        INSERT OVERWRITE TABLE episodes_part PARTITION (doctor_pt=1)
+        SELECT title, air_date, doctor FROM episodes
+      """.cmd
+      )
   )
 
   hiveQTestUtilTables.foreach(registerTestTable)
diff --git a/sql/hive/src/test/resources/golden/Read Partitioned with AvroSerDe-0-e4501461c855cc9071a872a64186c3de b/sql/hive/src/test/resources/golden/Read Partitioned with AvroSerDe-0-e4501461c855cc9071a872a64186c3de
new file mode 100644
index 0000000000000..49c8434730ffa
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/Read Partitioned with AvroSerDe-0-e4501461c855cc9071a872a64186c3de	
@@ -0,0 +1,8 @@
+The Eleventh Hour	3 April 2010	11	1
+The Doctor's Wife	14 May 2011	11	1
+Horror of Fang Rock	3 September 1977	4	1
+An Unearthly Child	23 November 1963	1	1
+The Mysterious Planet	6 September 1986	6	1
+Rose	26 March 2005	9	1
+The Power of the Daleks	5 November 1966	2	1
+Castrolava	4 January 1982	5	1
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveSerDeSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveSerDeSuite.scala
index 8bc72384a64ee..7486bfa82b00b 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveSerDeSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveSerDeSuite.scala
@@ -37,4 +37,6 @@ class HiveSerDeSuite extends HiveComparisonTest with BeforeAndAfterAll {
   createQueryTest("Read with RegexSerDe", "SELECT * FROM sales")
 
   createQueryTest("Read with AvroSerDe", "SELECT * FROM episodes")
+
+  createQueryTest("Read Partitioned with AvroSerDe", "SELECT * FROM episodes_part")
 }

From f92cde24e8f30dddd5bcec71bb3687498c1406da Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Wed, 10 Sep 2014 20:59:40 -0700
Subject: [PATCH 264/489] [SPARK-3447][SQL] Remove explicit conversion with
 JListWrapper to avoid NPE

Author: Michael Armbrust <michael@databricks.com>

Closes #2323 from marmbrus/kryoJListNPE and squashes the following commits:

9634f11 [Michael Armbrust] Rollback JSON RDD changes
4d4d93c [Michael Armbrust] Merge remote-tracking branch 'origin/master' into kryoJListNPE
646976b [Michael Armbrust] Fix JSON RDD Conversion too
59065bc [Michael Armbrust] Remove explicit conversion to avoid NPE
---
 sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
index a2f334aab9fdf..c551c7c9877e8 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
@@ -460,7 +460,6 @@ class SQLContext(@transient val sparkContext: SparkContext)
       rdd: RDD[Array[Any]],
       schema: StructType): SchemaRDD = {
     import scala.collection.JavaConversions._
-    import scala.collection.convert.Wrappers.{JListWrapper, JMapWrapper}
 
     def needsConversion(dataType: DataType): Boolean = dataType match {
       case ByteType => true
@@ -482,8 +481,7 @@ class SQLContext(@transient val sparkContext: SparkContext)
       case (null, _) => null
 
       case (c: java.util.List[_], ArrayType(elementType, _)) =>
-        val converted = c.map { e => convert(e, elementType)}
-        JListWrapper(converted)
+        c.map { e => convert(e, elementType)}: Seq[Any]
 
       case (c, ArrayType(elementType, _)) if c.getClass.isArray =>
         c.asInstanceOf[Array[_]].map(e => convert(e, elementType)): Seq[Any]

From c27718f376483dbe6290de612094c8d4ce9b16b4 Mon Sep 17 00:00:00 2001
From: Aaron Staple <aaron.staple@gmail.com>
Date: Wed, 10 Sep 2014 21:01:53 -0700
Subject: [PATCH 265/489] [SPARK-2781][SQL] Check resolution of LogicalPlans in
 Analyzer.
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

LogicalPlan contains a ‘resolved’ attribute indicating that all of its execution requirements have been resolved. This attribute is not checked before query execution. The analyzer contains a step to check that all Expressions are resolved, but this is not equivalent to checking all LogicalPlans. In particular, the Union plan’s implementation of ‘resolved’ verifies that the types of its children’s columns are compatible. Because the analyzer does not check that a Union plan is resolved, it is possible to execute a Union plan that outputs different types in the same column.  See SPARK-2781 for an example.

This patch adds two checks to the analyzer’s CheckResolution rule. First, each logical plan is checked to see if it is not resolved despite its children being resolved. This allows the ‘problem’ unresolved plan to be included in the TreeNodeException for reporting. Then as a backstop the root plan is checked to see if it is resolved, which recursively checks that the entire plan tree is resolved. Note that the resolved attribute is implemented recursively, and this patch also explicitly checks the resolved attribute on each logical plan in the tree. I assume the query plan trees will not be large enough for this redundant checking to meaningfully impact performance.

Because this patch starts validating that LogicalPlans are resolved before execution, I had to fix some cases where unresolved plans were passing through the analyzer as part of the implementation of the hive query system. In particular, HiveContext applies the CreateTables and PreInsertionCasts, and ExtractPythonUdfs rules manually after the analyzer runs. I moved these rules to the analyzer stage (for hive queries only), in the process completing a code TODO indicating the rules should be moved to the analyzer.

It’s worth noting that moving the CreateTables rule means introducing an analyzer rule with a significant side effect - in this case the side effect is creating a hive table. The rule will only attempt to create a table once even if its batch is executed multiple times, because it converts the InsertIntoCreatedTable plan it matches against into an InsertIntoTable. Additionally, these hive rules must be added to the Resolution batch rather than as a separate batch because hive rules rules may be needed to resolve non-root nodes, leaving the root to be resolved on a subsequent batch iteration. For example, the hive compatibility test auto_smb_mapjoin_14, and others, make use of a query plan where the root is a Union and its children are each a hive InsertIntoTable.

Mixing the custom hive rules with standard analyzer rules initially resulted in an additional failure because of policy differences between spark sql and hive when casting a boolean to a string. Hive casts booleans to strings as “true” / “false” while spark sql casts booleans to strings as “1” / “0” (causing the cast1.q test to fail). This behavior is a result of the BooleanCasts rule in HiveTypeCoercion.scala, and from looking at the implementation of BooleanCasts I think converting to to “1”/“0” is potentially a programming mistake. (If the BooleanCasts rule is disabled, casting produces “true”/“false” instead.) I believe “true” / “false” should be the behavior for spark sql - I changed the behavior so bools are converted to “true”/“false” to be consistent with hive, and none of the existing spark tests failed.

Finally, in some initial testing with hive it appears that an implicit type coercion of boolean to string results in a lowercase string, e.g. CONCAT( TRUE, “” ) -> “true” while an explicit cast produces an all caps string, e.g. CAST( TRUE AS STRING ) -> “TRUE”.  The change I’ve made just converts to lowercase strings in all cases.  I believe it is at least more correct than the existing spark sql implementation where all Cast expressions become “1” / “0”.

Author: Aaron Staple <aaron.staple@gmail.com>

Closes #1706 from staple/SPARK-2781 and squashes the following commits:

32683c4 [Aaron Staple] Fix compilation failure due to merge.
7c77fda [Aaron Staple] Move ExtractPythonUdfs to Analyzer's extendedRules in HiveContext.
d49bfb3 [Aaron Staple] Address review comments.
915b690 [Aaron Staple] Fix merge issue causing compilation failure.
701dcd2 [Aaron Staple] [SPARK-2781][SQL] Check resolution of LogicalPlans in Analyzer.
---
 .../sql/catalyst/analysis/Analyzer.scala      | 21 +++++++--
 .../catalyst/analysis/HiveTypeCoercion.scala  |  4 ++
 .../catalyst/plans/logical/LogicalPlan.scala  |  2 +-
 .../sql/catalyst/analysis/AnalysisSuite.scala | 13 +++++-
 .../analysis/HiveTypeCoercionSuite.scala      | 15 +++++++
 .../org/apache/spark/sql/SQLQuerySuite.scala  | 45 +++++++++++++++++--
 .../apache/spark/sql/hive/HiveContext.scala   | 11 +++--
 .../spark/sql/hive/HiveMetastoreCatalog.scala | 16 ++++---
 8 files changed, 107 insertions(+), 20 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
index 4a9524074132e..574d96d92942b 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
@@ -40,7 +40,12 @@ class Analyzer(catalog: Catalog, registry: FunctionRegistry, caseSensitive: Bool
   // TODO: pass this in as a parameter.
   val fixedPoint = FixedPoint(100)
 
-  val batches: Seq[Batch] = Seq(
+  /**
+   * Override to provide additional rules for the "Resolution" batch.
+   */
+  val extendedRules: Seq[Rule[LogicalPlan]] = Nil
+
+  lazy val batches: Seq[Batch] = Seq(
     Batch("MultiInstanceRelations", Once,
       NewRelationInstances),
     Batch("CaseInsensitiveAttributeReferences", Once,
@@ -54,8 +59,9 @@ class Analyzer(catalog: Catalog, registry: FunctionRegistry, caseSensitive: Bool
       StarExpansion ::
       ResolveFunctions ::
       GlobalAggregates ::
-      UnresolvedHavingClauseAttributes :: 
-      typeCoercionRules :_*),
+      UnresolvedHavingClauseAttributes ::
+      typeCoercionRules ++
+      extendedRules : _*),
     Batch("Check Analysis", Once,
       CheckResolution),
     Batch("AnalysisOperators", fixedPoint,
@@ -63,7 +69,7 @@ class Analyzer(catalog: Catalog, registry: FunctionRegistry, caseSensitive: Bool
   )
 
   /**
-   * Makes sure all attributes have been resolved.
+   * Makes sure all attributes and logical plans have been resolved.
    */
   object CheckResolution extends Rule[LogicalPlan] {
     def apply(plan: LogicalPlan): LogicalPlan = {
@@ -71,6 +77,13 @@ class Analyzer(catalog: Catalog, registry: FunctionRegistry, caseSensitive: Bool
         case p if p.expressions.exists(!_.resolved) =>
           throw new TreeNodeException(p,
             s"Unresolved attributes: ${p.expressions.filterNot(_.resolved).mkString(",")}")
+        case p if !p.resolved && p.childrenResolved =>
+          throw new TreeNodeException(p, "Unresolved plan found")
+      } match {
+        // As a backstop, use the root node to check that the entire plan tree is resolved.
+        case p if !p.resolved =>
+          throw new TreeNodeException(p, "Unresolved plan in tree")
+        case p => p
       }
     }
   }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
index bd8131c9af6e0..79e5283e86a37 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
@@ -286,6 +286,10 @@ trait HiveTypeCoercion {
       // If the data type is not boolean and is being cast boolean, turn it into a comparison
       // with the numeric value, i.e. x != 0. This will coerce the type into numeric type.
       case Cast(e, BooleanType) if e.dataType != BooleanType => Not(EqualTo(e, Literal(0)))
+      // Stringify boolean if casting to StringType.
+      // TODO Ensure true/false string letter casing is consistent with Hive in all cases.
+      case Cast(e, StringType) if e.dataType == BooleanType =>
+        If(e, Literal("true"), Literal("false"))
       // Turn true into 1, and false into 0 if casting boolean into other types.
       case Cast(e, dataType) if e.dataType == BooleanType =>
         Cast(If(e, Literal(1), Literal(0)), dataType)
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala
index bae491f07c13f..ede431ad4ab27 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala
@@ -58,7 +58,7 @@ abstract class LogicalPlan extends QueryPlan[LogicalPlan] {
 
   /**
    * Returns true if this expression and all its children have been resolved to a specific schema
-   * and false if it is still contains any unresolved placeholders. Implementations of LogicalPlan
+   * and false if it still contains any unresolved placeholders. Implementations of LogicalPlan
    * can override this (e.g.
    * [[org.apache.spark.sql.catalyst.analysis.UnresolvedRelation UnresolvedRelation]]
    * should return `false`).
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala
index 0a4fde3de7752..5809a108ff62e 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala
@@ -93,6 +93,17 @@ class AnalysisSuite extends FunSuite with BeforeAndAfter {
     val e = intercept[TreeNodeException[_]] {
       caseSensitiveAnalyze(Project(Seq(UnresolvedAttribute("abcd")), testRelation))
     }
-    assert(e.getMessage().toLowerCase.contains("unresolved"))
+    assert(e.getMessage().toLowerCase.contains("unresolved attribute"))
+  }
+
+  test("throw errors for unresolved plans during analysis") {
+    case class UnresolvedTestPlan() extends LeafNode {
+      override lazy val resolved = false
+      override def output = Nil
+    }
+    val e = intercept[TreeNodeException[_]] {
+      caseSensitiveAnalyze(UnresolvedTestPlan())
+    }
+    assert(e.getMessage().toLowerCase.contains("unresolved plan"))
   }
 }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala
index ba8b853b6f99e..baeb9b0cf5964 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala
@@ -19,6 +19,8 @@ package org.apache.spark.sql.catalyst.analysis
 
 import org.scalatest.FunSuite
 
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.plans.logical.{LocalRelation, Project}
 import org.apache.spark.sql.catalyst.types._
 
 class HiveTypeCoercionSuite extends FunSuite {
@@ -84,4 +86,17 @@ class HiveTypeCoercionSuite extends FunSuite {
     widenTest(StringType, MapType(IntegerType, StringType, true), None)
     widenTest(ArrayType(IntegerType), StructType(Seq()), None)
   }
+
+  test("boolean casts") {
+    val booleanCasts = new HiveTypeCoercion { }.BooleanCasts
+    def ruleTest(initial: Expression, transformed: Expression) {
+      val testRelation = LocalRelation(AttributeReference("a", IntegerType)())
+      assert(booleanCasts(Project(Seq(Alias(initial, "a")()), testRelation)) ==
+        Project(Seq(Alias(transformed, "a")()), testRelation))      
+    }
+    // Remove superflous boolean -> boolean casts.
+    ruleTest(Cast(Literal(true), BooleanType), Literal(true))
+    // Stringify boolean when casting to string.
+    ruleTest(Cast(Literal(false), StringType), If(Literal(false), Literal("true"), Literal("false")))
+  }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
index 514ac543df92a..67563b6c55f4b 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
@@ -17,6 +17,7 @@
 
 package org.apache.spark.sql
 
+import org.apache.spark.sql.catalyst.errors.TreeNodeException
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.test._
 import org.scalatest.BeforeAndAfterAll
@@ -477,18 +478,48 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll {
         (3, null)))
   }
 
-  test("EXCEPT") {
+  test("UNION") {
+    checkAnswer(
+      sql("SELECT * FROM lowerCaseData UNION SELECT * FROM upperCaseData"),
+      (1, "A") :: (1, "a") :: (2, "B") :: (2, "b") :: (3, "C") :: (3, "c") ::
+      (4, "D") :: (4, "d") :: (5, "E") :: (6, "F") :: Nil)
+    checkAnswer(
+      sql("SELECT * FROM lowerCaseData UNION SELECT * FROM lowerCaseData"),
+      (1, "a") :: (2, "b") :: (3, "c") :: (4, "d") :: Nil)
+    checkAnswer(
+      sql("SELECT * FROM lowerCaseData UNION ALL SELECT * FROM lowerCaseData"),
+      (1, "a") :: (1, "a") :: (2, "b") :: (2, "b") :: (3, "c") :: (3, "c") ::
+      (4, "d") :: (4, "d") :: Nil)
+  }
 
+  test("UNION with column mismatches") {
+    // Column name mismatches are allowed.
+    checkAnswer(
+      sql("SELECT n,l FROM lowerCaseData UNION SELECT N as x1, L as x2 FROM upperCaseData"),
+      (1, "A") :: (1, "a") :: (2, "B") :: (2, "b") :: (3, "C") :: (3, "c") ::
+      (4, "D") :: (4, "d") :: (5, "E") :: (6, "F") :: Nil)
+    // Column type mismatches are not allowed, forcing a type coercion.
     checkAnswer(
-      sql("SELECT * FROM lowerCaseData EXCEPT SELECT * FROM upperCaseData "),
+      sql("SELECT n FROM lowerCaseData UNION SELECT L FROM upperCaseData"),
+      ("1" :: "2" :: "3" :: "4" :: "A" :: "B" :: "C" :: "D" :: "E" :: "F" :: Nil).map(Tuple1(_)))
+    // Column type mismatches where a coercion is not possible, in this case between integer
+    // and array types, trigger a TreeNodeException.
+    intercept[TreeNodeException[_]] {
+      sql("SELECT data FROM arrayData UNION SELECT 1 FROM arrayData").collect()
+    }
+  }
+
+  test("EXCEPT") {
+    checkAnswer(
+      sql("SELECT * FROM lowerCaseData EXCEPT SELECT * FROM upperCaseData"),
       (1, "a") ::
       (2, "b") ::
       (3, "c") ::
       (4, "d") :: Nil)
     checkAnswer(
-      sql("SELECT * FROM lowerCaseData EXCEPT SELECT * FROM lowerCaseData "), Nil)
+      sql("SELECT * FROM lowerCaseData EXCEPT SELECT * FROM lowerCaseData"), Nil)
     checkAnswer(
-      sql("SELECT * FROM upperCaseData EXCEPT SELECT * FROM upperCaseData "), Nil)
+      sql("SELECT * FROM upperCaseData EXCEPT SELECT * FROM upperCaseData"), Nil)
   }
 
  test("INTERSECT") {
@@ -634,6 +665,12 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll {
       sql("SELECT key, value FROM testData WHERE key BETWEEN 9 and 7"),
       Seq()
     )
+  }
     
+  test("cast boolean to string") {
+    // TODO Ensure true/false string letter casing is consistent with Hive in all cases.
+    checkAnswer(
+      sql("SELECT CAST(TRUE AS STRING), CAST(FALSE AS STRING) FROM testData LIMIT 1"),
+      ("true", "false") :: Nil)
   }
 }
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
index ced8397972fbd..e0be09e6793ea 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
@@ -262,7 +262,13 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) {
   /* An analyzer that uses the Hive metastore. */
   @transient
   override protected[sql] lazy val analyzer =
-    new Analyzer(catalog, functionRegistry, caseSensitive = false)
+    new Analyzer(catalog, functionRegistry, caseSensitive = false) {
+      override val extendedRules =
+        catalog.CreateTables ::
+        catalog.PreInsertionCasts ::
+        ExtractPythonUdfs ::
+        Nil
+    }
 
   /**
    * Runs the specified SQL query using Hive.
@@ -353,9 +359,6 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) {
 
   /** Extends QueryExecution with hive specific features. */
   protected[sql] abstract class QueryExecution extends super.QueryExecution {
-    // TODO: Create mixin for the analyzer instead of overriding things here.
-    override lazy val optimizedPlan =
-      optimizer(ExtractPythonUdfs(catalog.PreInsertionCasts(catalog.CreateTables(analyzed))))
 
     override lazy val toRdd: RDD[Row] = executedPlan.execute().map(_.copy())
 
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
index 6571c35499ef4..dfa2a7a9d28bb 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
@@ -109,6 +109,9 @@ private[hive] class HiveMetastoreCatalog(hive: HiveContext) extends Catalog with
    */
   object CreateTables extends Rule[LogicalPlan] {
     def apply(plan: LogicalPlan): LogicalPlan = plan transform {
+      // Wait until children are resolved.
+      case p: LogicalPlan if !p.childrenResolved => p
+
       case InsertIntoCreatedTable(db, tableName, child) =>
         val (dbName, tblName) = processDatabaseAndTableName(db, tableName)
         val databaseName = dbName.getOrElse(hive.sessionState.getCurrentDatabase)
@@ -116,8 +119,7 @@ private[hive] class HiveMetastoreCatalog(hive: HiveContext) extends Catalog with
         createTable(databaseName, tblName, child.output)
 
         InsertIntoTable(
-          EliminateAnalysisOperators(
-            lookupRelation(Some(databaseName), tblName, None)),
+          lookupRelation(Some(databaseName), tblName, None),
           Map.empty,
           child,
           overwrite = false)
@@ -130,15 +132,17 @@ private[hive] class HiveMetastoreCatalog(hive: HiveContext) extends Catalog with
    */
   object PreInsertionCasts extends Rule[LogicalPlan] {
     def apply(plan: LogicalPlan): LogicalPlan = plan.transform {
-      // Wait until children are resolved
+      // Wait until children are resolved.
       case p: LogicalPlan if !p.childrenResolved => p
 
-      case p @ InsertIntoTable(table: MetastoreRelation, _, child, _) =>
+      case p @ InsertIntoTable(
+                 LowerCaseSchema(table: MetastoreRelation), _, child, _) =>
         castChildOutput(p, table, child)
 
       case p @ logical.InsertIntoTable(
-                 InMemoryRelation(_, _, _,
-                   HiveTableScan(_, table, _)), _, child, _) =>
+                 LowerCaseSchema(
+                   InMemoryRelation(_, _, _,
+                     HiveTableScan(_, table, _))), _, child, _) =>
         castChildOutput(p, table, child)
     }
 

From ed1980ffa9ccb87d76694ba910ef22df034bca49 Mon Sep 17 00:00:00 2001
From: Chris Cope <ccope@resilientscience.com>
Date: Thu, 11 Sep 2014 08:13:07 -0500
Subject: [PATCH 266/489] [SPARK-2140] Updating heap memory calculation for
 YARN stable and alpha.

Updated pull request, reflecting YARN stable and alpha states. I am getting intermittent test failures on my own test infrastructure. Is that tracked anywhere yet?

Author: Chris Cope <ccope@resilientscience.com>

Closes #2253 from copester/master and squashes the following commits:

5ad89da [Chris Cope] [SPARK-2140] Removing calculateAMMemory functions since they are no longer needed.
52b4e45 [Chris Cope] [SPARK-2140] Updating heap memory calculation for YARN stable and alpha.
---
 .../main/scala/org/apache/spark/deploy/yarn/Client.scala | 8 --------
 .../scala/org/apache/spark/deploy/yarn/ClientBase.scala  | 4 +---
 .../org/apache/spark/deploy/yarn/ClientBaseSuite.scala   | 3 ---
 .../main/scala/org/apache/spark/deploy/yarn/Client.scala | 9 ---------
 4 files changed, 1 insertion(+), 23 deletions(-)

diff --git a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/Client.scala b/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
index 10fc39bba87d1..aff9ab71f0937 100644
--- a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
+++ b/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
@@ -103,14 +103,6 @@ class Client(clientArgs: ClientArguments, hadoopConf: Configuration, spConf: Spa
     appContext
   }
 
-  def calculateAMMemory(newApp: GetNewApplicationResponse): Int = {
-    val minResMemory = newApp.getMinimumResourceCapability().getMemory()
-    val amMemory = ((args.amMemory / minResMemory) * minResMemory) +
-          ((if ((args.amMemory % minResMemory) == 0) 0 else minResMemory) -
-          memoryOverhead)
-    amMemory
-  }
-
   def setupSecurityToken(amContainer: ContainerLaunchContext) = {
     // Setup security tokens.
     val dob = new DataOutputBuffer()
diff --git a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala
index 8075b7a7fb837..c96f731923d22 100644
--- a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala
+++ b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala
@@ -300,8 +300,6 @@ trait ClientBase extends Logging {
     retval.toString
   }
 
-  def calculateAMMemory(newApp: GetNewApplicationResponse): Int
-
   def setupSecurityToken(amContainer: ContainerLaunchContext)
 
   def createContainerLaunchContext(
@@ -346,7 +344,7 @@ trait ClientBase extends Logging {
     }
     amContainer.setEnvironment(env)
 
-    val amMemory = calculateAMMemory(newApp)
+    val amMemory = args.amMemory
 
     val javaOpts = ListBuffer[String]()
 
diff --git a/yarn/common/src/test/scala/org/apache/spark/deploy/yarn/ClientBaseSuite.scala b/yarn/common/src/test/scala/org/apache/spark/deploy/yarn/ClientBaseSuite.scala
index 68cc2890f3a22..5480eca7c832c 100644
--- a/yarn/common/src/test/scala/org/apache/spark/deploy/yarn/ClientBaseSuite.scala
+++ b/yarn/common/src/test/scala/org/apache/spark/deploy/yarn/ClientBaseSuite.scala
@@ -238,9 +238,6 @@ class ClientBaseSuite extends FunSuite with Matchers {
       val sparkConf: SparkConf,
       val yarnConf: YarnConfiguration) extends ClientBase {
 
-    override def calculateAMMemory(newApp: GetNewApplicationResponse): Int =
-      throw new UnsupportedOperationException()
-
     override def setupSecurityToken(amContainer: ContainerLaunchContext): Unit =
       throw new UnsupportedOperationException()
 
diff --git a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/Client.scala b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
index 313a0d21ce181..82e45e3e7ad54 100644
--- a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
+++ b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
@@ -103,15 +103,6 @@ class Client(clientArgs: ClientArguments, hadoopConf: Configuration, spConf: Spa
       clusterMetrics.getNumNodeManagers)
   }
 
-  def calculateAMMemory(newApp: GetNewApplicationResponse) :Int = {
-    // TODO: Need a replacement for the following code to fix -Xmx?
-    // val minResMemory: Int = newApp.getMinimumResourceCapability().getMemory()
-    // var amMemory = ((args.amMemory / minResMemory) * minResMemory) +
-    //  ((if ((args.amMemory % minResMemory) == 0) 0 else minResMemory) -
-    //    memoryOverhead )
-    args.amMemory
-  }
-
   def setupSecurityToken(amContainer: ContainerLaunchContext) = {
     // Setup security tokens.
     val dob = new DataOutputBuffer()

From 1ef656ea85b4b93c7b0f3cf8042b63a0de0901cb Mon Sep 17 00:00:00 2001
From: Davies Liu <davies.liu@gmail.com>
Date: Thu, 11 Sep 2014 11:50:36 -0700
Subject: [PATCH 267/489] [SPARK-3047] [PySpark] add an option to use str in
 textFileRDD

str is much efficient than unicode (both CPU and memory), it'e better to use str in textFileRDD. In order to keep compatibility, use unicode by default. (Maybe change it in the future).

use_unicode=True:

daviesliudm:~/work/spark$ time python wc.py
(u'./universe/spark/sql/core/target/java/org/apache/spark/sql/execution/ExplainCommand$.java', 7776)

real	2m8.298s
user	0m0.185s
sys	0m0.064s

use_unicode=False

daviesliudm:~/work/spark$ time python wc.py
('./universe/spark/sql/core/target/java/org/apache/spark/sql/execution/ExplainCommand$.java', 7776)

real	1m26.402s
user	0m0.182s
sys	0m0.062s

We can see that it got 32% improvement!

Author: Davies Liu <davies.liu@gmail.com>

Closes #1951 from davies/unicode and squashes the following commits:

8352d57 [Davies Liu] update version number
a286f2f [Davies Liu] rollback loads()
85246e5 [Davies Liu] add docs for use_unicode
a0295e1 [Davies Liu] add an option to use str in textFile()
---
 python/pyspark/context.py     | 16 ++++++++++++----
 python/pyspark/serializers.py | 18 +++++++++++-------
 2 files changed, 23 insertions(+), 11 deletions(-)

diff --git a/python/pyspark/context.py b/python/pyspark/context.py
index 84bc0a3b7ccd0..3ab98e262df31 100644
--- a/python/pyspark/context.py
+++ b/python/pyspark/context.py
@@ -331,12 +331,16 @@ def pickleFile(self, name, minPartitions=None):
         return RDD(self._jsc.objectFile(name, minPartitions), self,
                    BatchedSerializer(PickleSerializer()))
 
-    def textFile(self, name, minPartitions=None):
+    def textFile(self, name, minPartitions=None, use_unicode=True):
         """
         Read a text file from HDFS, a local file system (available on all
         nodes), or any Hadoop-supported file system URI, and return it as an
         RDD of Strings.
 
+        If use_unicode is False, the strings will be kept as `str` (encoding
+        as `utf-8`), which is faster and smaller than unicode. (Added in
+        Spark 1.2)
+
         >>> path = os.path.join(tempdir, "sample-text.txt")
         >>> with open(path, "w") as testFile:
         ...    testFile.write("Hello world!")
@@ -346,9 +350,9 @@ def textFile(self, name, minPartitions=None):
         """
         minPartitions = minPartitions or min(self.defaultParallelism, 2)
         return RDD(self._jsc.textFile(name, minPartitions), self,
-                   UTF8Deserializer())
+                   UTF8Deserializer(use_unicode))
 
-    def wholeTextFiles(self, path, minPartitions=None):
+    def wholeTextFiles(self, path, minPartitions=None, use_unicode=True):
         """
         Read a directory of text files from HDFS, a local file system
         (available on all nodes), or any  Hadoop-supported file system
@@ -356,6 +360,10 @@ def wholeTextFiles(self, path, minPartitions=None):
         key-value pair, where the key is the path of each file, the
         value is the content of each file.
 
+        If use_unicode is False, the strings will be kept as `str` (encoding
+        as `utf-8`), which is faster and smaller than unicode. (Added in
+        Spark 1.2)
+
         For example, if you have the following files::
 
           hdfs://a-hdfs-path/part-00000
@@ -386,7 +394,7 @@ def wholeTextFiles(self, path, minPartitions=None):
         """
         minPartitions = minPartitions or self.defaultMinPartitions
         return RDD(self._jsc.wholeTextFiles(path, minPartitions), self,
-                   PairDeserializer(UTF8Deserializer(), UTF8Deserializer()))
+                   PairDeserializer(UTF8Deserializer(use_unicode), UTF8Deserializer(use_unicode)))
 
     def _dictToJavaMap(self, d):
         jm = self._jvm.java.util.HashMap()
diff --git a/python/pyspark/serializers.py b/python/pyspark/serializers.py
index 55e6cf3308611..7b2710b913128 100644
--- a/python/pyspark/serializers.py
+++ b/python/pyspark/serializers.py
@@ -429,18 +429,22 @@ class UTF8Deserializer(Serializer):
     Deserializes streams written by String.getBytes.
     """
 
+    def __init__(self, use_unicode=False):
+        self.use_unicode = use_unicode
+
     def loads(self, stream):
         length = read_int(stream)
-        return stream.read(length).decode('utf8')
+        s = stream.read(length)
+        return s.decode("utf-8") if self.use_unicode else s
 
     def load_stream(self, stream):
-        while True:
-            try:
+        try:
+            while True:
                 yield self.loads(stream)
-            except struct.error:
-                return
-            except EOFError:
-                return
+        except struct.error:
+            return
+        except EOFError:
+            return
 
 
 def read_long(stream):

From ca83f1e2c4dfa519e44b837b6815cba3b4526d92 Mon Sep 17 00:00:00 2001
From: Cheng Hao <hao.cheng@intel.com>
Date: Thu, 11 Sep 2014 11:57:01 -0700
Subject: [PATCH 268/489] [SPARK-2917] [SQL] Avoid table creation in logical
 plan analyzing for CTAS

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

Closes #1846 from chenghao-intel/ctas and squashes the following commits:

56a0578 [Cheng Hao] remove the unused imports
9a57abc [Cheng Hao] Avoid table creation in logical plan analyzing
---
 .../plans/logical/basicOperators.scala        |  3 +-
 .../org/apache/spark/sql/SchemaRDDLike.scala  |  4 +-
 .../spark/sql/hive/HiveMetastoreCatalog.scala | 14 +---
 .../org/apache/spark/sql/hive/HiveQl.scala    |  2 +-
 .../spark/sql/hive/HiveStrategies.scala       | 10 +++
 .../hive/execution/CreateTableAsSelect.scala  | 73 +++++++++++++++++++
 .../hive/execution/InsertIntoHiveTable.scala  |  6 +-
 .../sql/hive/execution/SQLQuerySuite.scala    |  9 +++
 8 files changed, 104 insertions(+), 17 deletions(-)
 create mode 100644 sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/CreateTableAsSelect.scala

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
index 4adfb189372d6..5d10754c7b028 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
@@ -114,11 +114,12 @@ case class InsertIntoTable(
   }
 }
 
-case class InsertIntoCreatedTable(
+case class CreateTableAsSelect(
     databaseName: Option[String],
     tableName: String,
     child: LogicalPlan) extends UnaryNode {
   override def output = child.output
+  override lazy val resolved = (databaseName != None && childrenResolved)
 }
 
 case class WriteToFile(
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDDLike.scala b/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDDLike.scala
index 2f3033a5f94f0..e52eeb3e1c47e 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDDLike.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDDLike.scala
@@ -54,7 +54,7 @@ private[sql] trait SchemaRDDLike {
   @transient protected[spark] val logicalPlan: LogicalPlan = baseLogicalPlan match {
     // For various commands (like DDL) and queries with side effects, we force query optimization to
     // happen right away to let these side effects take place eagerly.
-    case _: Command | _: InsertIntoTable | _: InsertIntoCreatedTable | _: WriteToFile =>
+    case _: Command | _: InsertIntoTable | _: CreateTableAsSelect |_: WriteToFile =>
       queryExecution.toRdd
       SparkLogicalPlan(queryExecution.executedPlan)(sqlContext)
     case _ =>
@@ -124,7 +124,7 @@ private[sql] trait SchemaRDDLike {
    */
   @Experimental
   def saveAsTable(tableName: String): Unit =
-    sqlContext.executePlan(InsertIntoCreatedTable(None, tableName, logicalPlan)).toRdd
+    sqlContext.executePlan(CreateTableAsSelect(None, tableName, logicalPlan)).toRdd
 
   /** Returns the schema as a string in the tree format.
    *
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
index dfa2a7a9d28bb..2c0db9be57e54 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
@@ -54,8 +54,8 @@ private[hive] class HiveMetastoreCatalog(hive: HiveContext) extends Catalog with
       db: Option[String],
       tableName: String,
       alias: Option[String]): LogicalPlan = synchronized {
-    val (dbName, tblName) = processDatabaseAndTableName(db, tableName)
-    val databaseName = dbName.getOrElse(hive.sessionState.getCurrentDatabase)
+    val (databaseName, tblName) = processDatabaseAndTableName(
+                                    db.getOrElse(hive.sessionState.getCurrentDatabase), tableName)
     val table = client.getTable(databaseName, tblName)
     val partitions: Seq[Partition] =
       if (table.isPartitioned) {
@@ -112,17 +112,11 @@ private[hive] class HiveMetastoreCatalog(hive: HiveContext) extends Catalog with
       // Wait until children are resolved.
       case p: LogicalPlan if !p.childrenResolved => p
 
-      case InsertIntoCreatedTable(db, tableName, child) =>
+      case CreateTableAsSelect(db, tableName, child) =>
         val (dbName, tblName) = processDatabaseAndTableName(db, tableName)
         val databaseName = dbName.getOrElse(hive.sessionState.getCurrentDatabase)
 
-        createTable(databaseName, tblName, child.output)
-
-        InsertIntoTable(
-          lookupRelation(Some(databaseName), tblName, None),
-          Map.empty,
-          child,
-          overwrite = false)
+        CreateTableAsSelect(Some(databaseName), tableName, child)
     }
   }
 
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
index c98287c6aa662..21ecf17028dbc 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
@@ -489,7 +489,7 @@ private[hive] object HiveQl {
 
       val (db, tableName) = extractDbNameTableName(tableNameParts)
 
-      InsertIntoCreatedTable(db, tableName, nodeToPlan(query))
+      CreateTableAsSelect(db, tableName, nodeToPlan(query))
 
     // If its not a "CREATE TABLE AS" like above then just pass it back to hive as a native command.
     case Token("TOK_CREATETABLE", _) => NativePlaceholder
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala
index 72cc01cdf4c84..43dd3d234f73a 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala
@@ -165,6 +165,16 @@ private[hive] trait HiveStrategies {
              InMemoryRelation(_, _, _,
                HiveTableScan(_, table, _)), partition, child, overwrite) =>
         InsertIntoHiveTable(table, partition, planLater(child), overwrite)(hiveContext) :: Nil
+      case logical.CreateTableAsSelect(database, tableName, child) =>
+        val query = planLater(child)
+        CreateTableAsSelect(
+          database.get,
+          tableName,
+          query,
+          InsertIntoHiveTable(_: MetastoreRelation, 
+            Map(), 
+            query, 
+            true)(hiveContext)) :: Nil
       case _ => Nil
     }
   }
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/CreateTableAsSelect.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/CreateTableAsSelect.scala
new file mode 100644
index 0000000000000..71ea774d77795
--- /dev/null
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/CreateTableAsSelect.scala
@@ -0,0 +1,73 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.hive.execution
+
+import org.apache.spark.annotation.Experimental
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.catalyst.expressions.Row
+import org.apache.spark.sql.catalyst.expressions.Attribute
+import org.apache.spark.sql.catalyst.plans.logical.LowerCaseSchema
+import org.apache.spark.sql.execution.{SparkPlan, Command, LeafNode}
+import org.apache.spark.sql.hive.HiveContext
+import org.apache.spark.sql.hive.MetastoreRelation
+
+/**
+ * :: Experimental ::
+ * Create table and insert the query result into it.
+ * @param database the database name of the new relation
+ * @param tableName the table name of the new relation
+ * @param insertIntoRelation function of creating the `InsertIntoHiveTable` 
+ *        by specifying the `MetaStoreRelation`, the data will be inserted into that table.
+ * TODO Add more table creating properties,  e.g. SerDe, StorageHandler, in-memory cache etc.
+ */
+@Experimental
+case class CreateTableAsSelect(
+  database: String,
+  tableName: String,
+  query: SparkPlan,
+  insertIntoRelation: MetastoreRelation => InsertIntoHiveTable)
+    extends LeafNode with Command {
+
+  def output = Seq.empty
+
+  // A lazy computing of the metastoreRelation
+  private[this] lazy val metastoreRelation: MetastoreRelation = {
+    // Create the table 
+    val sc = sqlContext.asInstanceOf[HiveContext]
+    sc.catalog.createTable(database, tableName, query.output, false)
+    // Get the Metastore Relation
+    sc.catalog.lookupRelation(Some(database), tableName, None) match {
+      case LowerCaseSchema(r: MetastoreRelation) => r
+      case o: MetastoreRelation => o
+    }
+  }
+
+  override protected[sql] lazy val sideEffectResult: Seq[Row] = {
+    insertIntoRelation(metastoreRelation).execute
+    Seq.empty[Row]
+  }
+
+  override def execute(): RDD[Row] = {
+    sideEffectResult
+    sparkContext.emptyRDD[Row]
+  }
+
+  override def argString: String = {
+    s"[Database:$database, TableName: $tableName, InsertIntoHiveTable]\n" + query.toString
+  }
+}
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala
index 39033bdeac4b0..a284a91a91e31 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala
@@ -53,9 +53,9 @@ case class InsertIntoHiveTable(
     (@transient sc: HiveContext)
   extends UnaryNode {
 
-  val outputClass = newSerializer(table.tableDesc).getSerializedClass
-  @transient private val hiveContext = new Context(sc.hiveconf)
-  @transient private val db = Hive.get(sc.hiveconf)
+  @transient lazy val outputClass = newSerializer(table.tableDesc).getSerializedClass
+  @transient private lazy val hiveContext = new Context(sc.hiveconf)
+  @transient private lazy val db = Hive.get(sc.hiveconf)
 
   private def newSerializer(tableDesc: TableDesc): Serializer = {
     val serializer = tableDesc.getDeserializerClass.newInstance().asInstanceOf[Serializer]
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
index b99caf77bce28..679efe082f2a0 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
@@ -18,6 +18,8 @@
 package org.apache.spark.sql.hive.execution
 
 import org.apache.spark.sql.QueryTest
+
+import org.apache.spark.sql.Row
 import org.apache.spark.sql.hive.test.TestHive._
 
 case class Nested1(f1: Nested2)
@@ -54,4 +56,11 @@ class SQLQuerySuite extends QueryTest {
       sql("SELECT f1.f2.f3 FROM nested"),
       1)
   }
+
+  test("test CTAS") {
+    checkAnswer(sql("CREATE TABLE test_ctas_123 AS SELECT key, value FROM src"), Seq.empty[Row])
+    checkAnswer(
+      sql("SELECT key, value FROM test_ctas_123 ORDER BY key"), 
+      sql("SELECT key, value FROM src ORDER BY key").collect().toSeq)
+  }
 }

From 4bc9e046cb8922923dff254e3e621fb4de656f98 Mon Sep 17 00:00:00 2001
From: Yin Huai <huai@cse.ohio-state.edu>
Date: Thu, 11 Sep 2014 15:23:33 -0700
Subject: [PATCH 269/489] [SPARK-3390][SQL] sqlContext.jsonRDD fails on a
 complex structure of JSON array and JSON object nesting

This PR aims to correctly handle JSON arrays in the type of `ArrayType(...(ArrayType(StructType)))`.

JIRA: https://issues.apache.org/jira/browse/SPARK-3390.

Author: Yin Huai <huai@cse.ohio-state.edu>

Closes #2364 from yhuai/SPARK-3390 and squashes the following commits:

46db418 [Yin Huai] Handle JSON arrays in the type of ArrayType(...(ArrayType(StructType))).
---
 .../org/apache/spark/sql/json/JsonRDD.scala   | 66 +++++++++++--------
 .../org/apache/spark/sql/json/JsonSuite.scala | 29 +++++++-
 .../apache/spark/sql/json/TestJsonData.scala  | 30 ++++++++-
 3 files changed, 96 insertions(+), 29 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/json/JsonRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/json/JsonRDD.scala
index 70062eae3b7ce..873221835daf8 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/json/JsonRDD.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/json/JsonRDD.scala
@@ -68,8 +68,15 @@ private[sql] object JsonRDD extends Logging {
       val (topLevel, structLike) = values.partition(_.size == 1)
       val topLevelFields = topLevel.filter {
         name => resolved.get(prefix ++ name).get match {
-          case ArrayType(StructType(Nil), _) => false
-          case ArrayType(_, _) => true
+          case ArrayType(elementType, _) => {
+            def hasInnerStruct(t: DataType): Boolean = t match {
+              case s: StructType => false
+              case ArrayType(t1, _) => hasInnerStruct(t1)
+              case o => true
+            }
+
+            hasInnerStruct(elementType)
+          }
           case struct: StructType => false
           case _ => true
         }
@@ -84,7 +91,18 @@ private[sql] object JsonRDD extends Logging {
           val dataType = resolved.get(prefix :+ name).get
           dataType match {
             case array: ArrayType =>
-              Some(StructField(name, ArrayType(structType, array.containsNull), nullable = true))
+              // The pattern of this array is ArrayType(...(ArrayType(StructType))).
+              // Since the inner struct of array is a placeholder (StructType(Nil)),
+              // we need to replace this placeholder with the actual StructType (structType).
+              def getActualArrayType(
+                  innerStruct: StructType,
+                  currentArray: ArrayType): ArrayType = currentArray match {
+                case ArrayType(s: StructType, containsNull) =>
+                  ArrayType(innerStruct, containsNull)
+                case ArrayType(a: ArrayType, containsNull) =>
+                  ArrayType(getActualArrayType(innerStruct, a), containsNull)
+              }
+              Some(StructField(name, getActualArrayType(structType, array), nullable = true))
             case struct: StructType => Some(StructField(name, structType, nullable = true))
             // dataType is StringType means that we have resolved type conflicts involving
             // primitive types and complex types. So, the type of name has been relaxed to
@@ -168,8 +186,7 @@ private[sql] object JsonRDD extends Logging {
   /**
    * Returns the element type of an JSON array. We go through all elements of this array
    * to detect any possible type conflict. We use [[compatibleType]] to resolve
-   * type conflicts. Right now, when the element of an array is another array, we
-   * treat the element as String.
+   * type conflicts.
    */
   private def typeOfArray(l: Seq[Any]): ArrayType = {
     val containsNull = l.exists(v => v == null)
@@ -216,18 +233,24 @@ private[sql] object JsonRDD extends Logging {
       }
       case (key: String, array: Seq[_]) => {
         // The value associated with the key is an array.
-        typeOfArray(array) match {
+        // Handle inner structs of an array.
+        def buildKeyPathForInnerStructs(v: Any, t: DataType): Seq[(String, DataType)] = t match {
           case ArrayType(StructType(Nil), containsNull) => {
             // The elements of this arrays are structs.
-            array.asInstanceOf[Seq[Map[String, Any]]].flatMap {
+            v.asInstanceOf[Seq[Map[String, Any]]].flatMap {
               element => allKeysWithValueTypes(element)
             }.map {
-              case (k, dataType) => (s"$key.$k", dataType)
-            } :+ (key, ArrayType(StructType(Nil), containsNull))
+              case (k, t) => (s"$key.$k", t)
+            }
           }
-          case ArrayType(elementType, containsNull) =>
-            (key, ArrayType(elementType, containsNull)) :: Nil
+          case ArrayType(t1, containsNull) =>
+            v.asInstanceOf[Seq[Any]].flatMap {
+              element => buildKeyPathForInnerStructs(element, t1)
+            }
+          case other => Nil
         }
+        val elementType = typeOfArray(array)
+        buildKeyPathForInnerStructs(array, elementType) :+ (key, elementType)
       }
       case (key: String, value) => (key, typeOfPrimitiveValue(value)) :: Nil
     }
@@ -339,8 +362,6 @@ private[sql] object JsonRDD extends Logging {
       null
     } else {
       desiredType match {
-        case ArrayType(elementType, _) =>
-          value.asInstanceOf[Seq[Any]].map(enforceCorrectType(_, elementType))
         case StringType => toString(value)
         case IntegerType => value.asInstanceOf[IntegerType.JvmType]
         case LongType => toLong(value)
@@ -348,6 +369,10 @@ private[sql] object JsonRDD extends Logging {
         case DecimalType => toDecimal(value)
         case BooleanType => value.asInstanceOf[BooleanType.JvmType]
         case NullType => null
+
+        case ArrayType(elementType, _) =>
+          value.asInstanceOf[Seq[Any]].map(enforceCorrectType(_, elementType))
+        case struct: StructType => asRow(value.asInstanceOf[Map[String, Any]], struct)
       }
     }
   }
@@ -356,22 +381,9 @@ private[sql] object JsonRDD extends Logging {
     // TODO: Reuse the row instead of creating a new one for every record.
     val row = new GenericMutableRow(schema.fields.length)
     schema.fields.zipWithIndex.foreach {
-      // StructType
-      case (StructField(name, fields: StructType, _), i) =>
-        row.update(i, json.get(name).flatMap(v => Option(v)).map(
-          v => asRow(v.asInstanceOf[Map[String, Any]], fields)).orNull)
-
-      // ArrayType(StructType)
-      case (StructField(name, ArrayType(structType: StructType, _), _), i) =>
-        row.update(i,
-          json.get(name).flatMap(v => Option(v)).map(
-            v => v.asInstanceOf[Seq[Any]].map(
-              e => asRow(e.asInstanceOf[Map[String, Any]], structType))).orNull)
-
-      // Other cases
       case (StructField(name, dataType, _), i) =>
         row.update(i, json.get(name).flatMap(v => Option(v)).map(
-          enforceCorrectType(_, dataType)).getOrElse(null))
+          enforceCorrectType(_, dataType)).orNull)
     }
 
     row
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala
index 301d482d27d86..b50d93855405a 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala
@@ -591,8 +591,35 @@ class JsonSuite extends QueryTest {
       (true, "str1") :: Nil
     )
     checkAnswer(
-      sql("select complexArrayOfStruct[0].field1[1].inner2[0], complexArrayOfStruct[1].field2[0][1] from jsonTable"),
+      sql(
+        """
+          |select complexArrayOfStruct[0].field1[1].inner2[0], complexArrayOfStruct[1].field2[0][1]
+          |from jsonTable
+        """.stripMargin),
       ("str2", 6) :: Nil
     )
   }
+
+  test("SPARK-3390 Complex arrays") {
+    val jsonSchemaRDD = jsonRDD(complexFieldAndType2)
+    jsonSchemaRDD.registerTempTable("jsonTable")
+
+    checkAnswer(
+      sql(
+        """
+          |select arrayOfArray1[0][0][0], arrayOfArray1[1][0][1], arrayOfArray1[1][1][0]
+          |from jsonTable
+        """.stripMargin),
+      (5, 7, 8) :: Nil
+    )
+    checkAnswer(
+      sql(
+        """
+          |select arrayOfArray2[0][0][0].inner1, arrayOfArray2[1][0],
+          |arrayOfArray2[1][1][1].inner2[0], arrayOfArray2[2][0][0].inner3[0][0].inner4
+          |from jsonTable
+        """.stripMargin),
+      ("str1", Nil, "str4", 2) :: Nil
+    )
+  }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/json/TestJsonData.scala b/sql/core/src/test/scala/org/apache/spark/sql/json/TestJsonData.scala
index b3f95f08e8044..5f0b3959a63ad 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/json/TestJsonData.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/json/TestJsonData.scala
@@ -106,6 +106,34 @@ object TestJsonData {
               "inner1": "str4"
             }],
             "field2": [[5, 6], [7, 8]]
-          }]
+          }],
+          "arrayOfArray1": [
+          [
+            [5]
+          ],
+          [
+            [6, 7],
+            [8]
+          ]],
+          "arrayOfArray2": [
+          [
+            [
+              {
+                "inner1": "str1"
+              }
+            ]
+          ],
+          [
+            [],
+            [
+              {"inner2": ["str3", "str33"]},
+              {"inner2": ["str4"], "inner1": "str11"}
+            ]
+          ],
+          [
+            [
+              {"inner3": [[{"inner4": 2}]]}
+            ]
+          ]]
       }""" :: Nil)
 }

From 6324eb7b5b0ae005cb2e913e36b1508bd6f1b9b8 Mon Sep 17 00:00:00 2001
From: Andrew Or <andrewor14@gmail.com>
Date: Thu, 11 Sep 2014 17:18:46 -0700
Subject: [PATCH 270/489] [Spark-3490] Disable SparkUI for tests

We currently open many ephemeral ports during the tests, and as a result we occasionally can't bind to new ones. This has caused the `DriverSuite` and the `SparkSubmitSuite` to fail intermittently.

By disabling the `SparkUI` when it's not needed, we already cut down on the number of ports opened significantly, on the order of the number of `SparkContexts` ever created. We must keep it enabled for a few tests for the UI itself, however.

Author: Andrew Or <andrewor14@gmail.com>

Closes #2363 from andrewor14/disable-ui-for-tests and squashes the following commits:

332a7d5 [Andrew Or] No need to set spark.ui.port to 0 anymore
30c93a2 [Andrew Or] Simplify streaming UISuite
a431b84 [Andrew Or] Fix streaming test failures
8f5ae53 [Andrew Or] Fix no new line at the end
29c9b5b [Andrew Or] Disable SparkUI for tests
---
 .../scala/org/apache/spark/SparkContext.scala | 12 +++--
 .../CoarseGrainedSchedulerBackend.scala       |  2 +-
 .../cluster/SimrSchedulerBackend.scala        |  6 +--
 .../cluster/SparkDeploySchedulerBackend.scala |  4 +-
 .../scala/org/apache/spark/ui/UISuite.scala   | 44 +++++++++++++------
 pom.xml                                       |  2 +-
 project/SparkBuild.scala                      |  2 +-
 .../spark/streaming/StreamingContext.scala    | 11 ++++-
 .../spark/streaming/StreamingSource.scala     |  2 +-
 .../spark/streaming/ui/StreamingTab.scala     | 25 ++++++++---
 .../org/apache/spark/streaming/UISuite.scala  | 16 +++++--
 .../spark/deploy/yarn/ApplicationMaster.scala |  2 +-
 .../cluster/YarnClientSchedulerBackend.scala  |  2 +-
 13 files changed, 92 insertions(+), 38 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala
index c6c5b8f22b549..218b353dd9d49 100644
--- a/core/src/main/scala/org/apache/spark/SparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/SparkContext.scala
@@ -220,8 +220,14 @@ class SparkContext(config: SparkConf) extends Logging {
     new MetadataCleaner(MetadataCleanerType.SPARK_CONTEXT, this.cleanup, conf)
 
   // Initialize the Spark UI, registering all associated listeners
-  private[spark] val ui = new SparkUI(this)
-  ui.bind()
+  private[spark] val ui: Option[SparkUI] =
+    if (conf.getBoolean("spark.ui.enabled", true)) {
+      Some(new SparkUI(this))
+    } else {
+      // For tests, do not enable the UI
+      None
+    }
+  ui.foreach(_.bind())
 
   /** A default Hadoop Configuration for the Hadoop code (e.g. file systems) that we reuse. */
   val hadoopConfiguration = SparkHadoopUtil.get.newConfiguration(conf)
@@ -990,7 +996,7 @@ class SparkContext(config: SparkConf) extends Logging {
   /** Shut down the SparkContext. */
   def stop() {
     postApplicationEnd()
-    ui.stop()
+    ui.foreach(_.stop())
     // Do this only if not stopped already - best case effort.
     // prevent NPE if stopped more than once.
     val dagSchedulerCopy = dagScheduler
diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala
index 5b5257269d92f..9a0cb1c6c6ccd 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala
@@ -292,7 +292,7 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, actorSystem: A
       logInfo(s"Add WebUI Filter. $filterName, $filterParams, $proxyBase")
       conf.set("spark.ui.filters", filterName)
       conf.set(s"spark.$filterName.params", filterParams)
-      JettyUtils.addFilters(scheduler.sc.ui.getHandlers, conf)
+      scheduler.sc.ui.foreach { ui => JettyUtils.addFilters(ui.getHandlers, conf) }
     }
   }
 }
diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/SimrSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/SimrSchedulerBackend.scala
index 513d74a08a47f..ee10aa061f4e9 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/SimrSchedulerBackend.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/SimrSchedulerBackend.scala
@@ -17,7 +17,6 @@
 
 package org.apache.spark.scheduler.cluster
 
-import org.apache.hadoop.conf.Configuration
 import org.apache.hadoop.fs.{Path, FileSystem}
 
 import org.apache.spark.{Logging, SparkContext, SparkEnv}
@@ -47,16 +46,17 @@ private[spark] class SimrSchedulerBackend(
 
     val conf = SparkHadoopUtil.get.newConfiguration(sc.conf)
     val fs = FileSystem.get(conf)
+    val appUIAddress = sc.ui.map(_.appUIAddress).getOrElse("")
 
     logInfo("Writing to HDFS file: "  + driverFilePath)
     logInfo("Writing Akka address: "  + driverUrl)
-    logInfo("Writing Spark UI Address: " + sc.ui.appUIAddress)
+    logInfo("Writing Spark UI Address: " + appUIAddress)
 
     // Create temporary file to prevent race condition where executors get empty driverUrl file
     val temp = fs.create(tmpPath, true)
     temp.writeUTF(driverUrl)
     temp.writeInt(maxCores)
-    temp.writeUTF(sc.ui.appUIAddress)
+    temp.writeUTF(appUIAddress)
     temp.close()
 
     // "Atomic" rename
diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala
index 06872ace2ecf4..2f45d192e1d4d 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala
@@ -67,8 +67,10 @@ private[spark] class SparkDeploySchedulerBackend(
     val javaOpts = sparkJavaOpts ++ extraJavaOpts
     val command = Command("org.apache.spark.executor.CoarseGrainedExecutorBackend",
       args, sc.executorEnvs, classPathEntries, libraryPathEntries, javaOpts)
+    val appUIAddress = sc.ui.map(_.appUIAddress).getOrElse("")
+    val eventLogDir = sc.eventLogger.map(_.logDir)
     val appDesc = new ApplicationDescription(sc.appName, maxCores, sc.executorMemory, command,
-      sc.ui.appUIAddress, sc.eventLogger.map(_.logDir))
+      appUIAddress, eventLogDir)
 
     client = new AppClient(sc.env.actorSystem, masters, appDesc, this, conf)
     client.start()
diff --git a/core/src/test/scala/org/apache/spark/ui/UISuite.scala b/core/src/test/scala/org/apache/spark/ui/UISuite.scala
index 038746d2eda4b..2f56642956701 100644
--- a/core/src/test/scala/org/apache/spark/ui/UISuite.scala
+++ b/core/src/test/scala/org/apache/spark/ui/UISuite.scala
@@ -36,11 +36,25 @@ import scala.xml.Node
 
 class UISuite extends FunSuite {
 
+  /**
+   * Create a test SparkContext with the SparkUI enabled.
+   * It is safe to `get` the SparkUI directly from the SparkContext returned here.
+   */
+  private def newSparkContext(): SparkContext = {
+    val conf = new SparkConf()
+      .setMaster("local")
+      .setAppName("test")
+      .set("spark.ui.enabled", "true")
+    val sc = new SparkContext(conf)
+    assert(sc.ui.isDefined)
+    sc
+  }
+
   ignore("basic ui visibility") {
-    withSpark(new SparkContext("local", "test")) { sc =>
+    withSpark(newSparkContext()) { sc =>
       // test if the ui is visible, and all the expected tabs are visible
       eventually(timeout(10 seconds), interval(50 milliseconds)) {
-        val html = Source.fromURL(sc.ui.appUIAddress).mkString
+        val html = Source.fromURL(sc.ui.get.appUIAddress).mkString
         assert(!html.contains("random data that should not be present"))
         assert(html.toLowerCase.contains("stages"))
         assert(html.toLowerCase.contains("storage"))
@@ -51,7 +65,7 @@ class UISuite extends FunSuite {
   }
 
   ignore("visibility at localhost:4040") {
-    withSpark(new SparkContext("local", "test")) { sc =>
+    withSpark(newSparkContext()) { sc =>
       // test if visible from http://localhost:4040
       eventually(timeout(10 seconds), interval(50 milliseconds)) {
         val html = Source.fromURL("http://localhost:4040").mkString
@@ -61,8 +75,8 @@ class UISuite extends FunSuite {
   }
 
   ignore("attaching a new tab") {
-    withSpark(new SparkContext("local", "test")) { sc =>
-      val sparkUI = sc.ui
+    withSpark(newSparkContext()) { sc =>
+      val sparkUI = sc.ui.get
 
       val newTab = new WebUITab(sparkUI, "foo") {
         attachPage(new WebUIPage("") {
@@ -73,7 +87,7 @@ class UISuite extends FunSuite {
       }
       sparkUI.attachTab(newTab)
       eventually(timeout(10 seconds), interval(50 milliseconds)) {
-        val html = Source.fromURL(sc.ui.appUIAddress).mkString
+        val html = Source.fromURL(sparkUI.appUIAddress).mkString
         assert(!html.contains("random data that should not be present"))
 
         // check whether new page exists
@@ -87,7 +101,7 @@ class UISuite extends FunSuite {
       }
 
       eventually(timeout(10 seconds), interval(50 milliseconds)) {
-        val html = Source.fromURL(sc.ui.appUIAddress.stripSuffix("/") + "/foo").mkString
+        val html = Source.fromURL(sparkUI.appUIAddress.stripSuffix("/") + "/foo").mkString
         // check whether new page exists
         assert(html.contains("magic"))
       }
@@ -129,16 +143,20 @@ class UISuite extends FunSuite {
   }
 
   test("verify appUIAddress contains the scheme") {
-    withSpark(new SparkContext("local", "test")) { sc =>
-      val uiAddress = sc.ui.appUIAddress
-      assert(uiAddress.equals("http://" + sc.ui.appUIHostPort))
+    withSpark(newSparkContext()) { sc =>
+      val ui = sc.ui.get
+      val uiAddress = ui.appUIAddress
+      val uiHostPort = ui.appUIHostPort
+      assert(uiAddress.equals("http://" + uiHostPort))
     }
   }
 
   test("verify appUIAddress contains the port") {
-    withSpark(new SparkContext("local", "test")) { sc =>
-      val splitUIAddress = sc.ui.appUIAddress.split(':')
-      assert(splitUIAddress(2).toInt == sc.ui.boundPort)
+    withSpark(newSparkContext()) { sc =>
+      val ui = sc.ui.get
+      val splitUIAddress = ui.appUIAddress.split(':')
+      val boundPort = ui.boundPort
+      assert(splitUIAddress(2).toInt == boundPort)
     }
   }
 }
diff --git a/pom.xml b/pom.xml
index 64fb1e57e30e0..e5f863e85445c 100644
--- a/pom.xml
+++ b/pom.xml
@@ -899,7 +899,7 @@
               <java.awt.headless>true</java.awt.headless>
               <spark.test.home>${session.executionRootDirectory}</spark.test.home>
               <spark.testing>1</spark.testing>
-              <spark.ui.port>0</spark.ui.port>
+              <spark.ui.enabled>false</spark.ui.enabled>
             </systemProperties>
           </configuration>
           <executions>
diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala
index 45f6d2973ea90..c07ea313f1228 100644
--- a/project/SparkBuild.scala
+++ b/project/SparkBuild.scala
@@ -337,7 +337,7 @@ object TestSettings {
     javaOptions in Test += "-Dspark.test.home=" + sparkHome,
     javaOptions in Test += "-Dspark.testing=1",
     javaOptions in Test += "-Dspark.ports.maxRetries=100",
-    javaOptions in Test += "-Dspark.ui.port=0",
+    javaOptions in Test += "-Dspark.ui.enabled=false",
     javaOptions in Test += "-Dsun.io.serialization.extendedDebugInfo=true",
     javaOptions in Test ++= System.getProperties.filter(_._1 startsWith "spark")
       .map { case (k,v) => s"-D$k=$v" }.toSeq,
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala b/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala
index 457e8ab28ed82..f63560dcb5b89 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala
@@ -37,7 +37,7 @@ import org.apache.spark.storage.StorageLevel
 import org.apache.spark.streaming.dstream._
 import org.apache.spark.streaming.receiver.{ActorSupervisorStrategy, ActorReceiver, Receiver}
 import org.apache.spark.streaming.scheduler._
-import org.apache.spark.streaming.ui.StreamingTab
+import org.apache.spark.streaming.ui.{StreamingJobProgressListener, StreamingTab}
 import org.apache.spark.util.MetadataCleaner
 
 /**
@@ -158,7 +158,14 @@ class StreamingContext private[streaming] (
 
   private[streaming] val waiter = new ContextWaiter
 
-  private[streaming] val uiTab = new StreamingTab(this)
+  private[streaming] val progressListener = new StreamingJobProgressListener(this)
+
+  private[streaming] val uiTab: Option[StreamingTab] =
+    if (conf.getBoolean("spark.ui.enabled", true)) {
+      Some(new StreamingTab(this))
+    } else {
+      None
+    }
 
   /** Register streaming source to metrics system */
   private val streamingSource = new StreamingSource(this)
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/StreamingSource.scala b/streaming/src/main/scala/org/apache/spark/streaming/StreamingSource.scala
index 75f0e8716dc7e..e35a568ddf115 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/StreamingSource.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/StreamingSource.scala
@@ -26,7 +26,7 @@ private[streaming] class StreamingSource(ssc: StreamingContext) extends Source {
   override val metricRegistry = new MetricRegistry
   override val sourceName = "%s.StreamingMetrics".format(ssc.sparkContext.appName)
 
-  private val streamingListener = ssc.uiTab.listener
+  private val streamingListener = ssc.progressListener
 
   private def registerGauge[T](name: String, f: StreamingJobProgressListener => T,
       defaultValue: T) {
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingTab.scala b/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingTab.scala
index 34ac254f337eb..d9d04cd706a04 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingTab.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingTab.scala
@@ -17,18 +17,31 @@
 
 package org.apache.spark.streaming.ui
 
-import org.apache.spark.Logging
+import org.apache.spark.{Logging, SparkException}
 import org.apache.spark.streaming.StreamingContext
-import org.apache.spark.ui.SparkUITab
+import org.apache.spark.ui.{SparkUI, SparkUITab}
 
-/** Spark Web UI tab that shows statistics of a streaming job */
+import StreamingTab._
+
+/**
+ * Spark Web UI tab that shows statistics of a streaming job.
+ * This assumes the given SparkContext has enabled its SparkUI.
+ */
 private[spark] class StreamingTab(ssc: StreamingContext)
-  extends SparkUITab(ssc.sc.ui, "streaming") with Logging {
+  extends SparkUITab(getSparkUI(ssc), "streaming") with Logging {
 
-  val parent = ssc.sc.ui
-  val listener = new StreamingJobProgressListener(ssc)
+  val parent = getSparkUI(ssc)
+  val listener = ssc.progressListener
 
   ssc.addStreamingListener(listener)
   attachPage(new StreamingPage(this))
   parent.attachTab(this)
 }
+
+private object StreamingTab {
+  def getSparkUI(ssc: StreamingContext): SparkUI = {
+    ssc.sc.ui.getOrElse {
+      throw new SparkException("Parent SparkUI to attach this tab to not found!")
+    }
+  }
+}
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/UISuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/UISuite.scala
index 2a0db7564915d..4c7e43c2943c9 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/UISuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/UISuite.scala
@@ -24,13 +24,22 @@ import org.scalatest.FunSuite
 import org.scalatest.concurrent.Eventually._
 import org.scalatest.time.SpanSugar._
 
+import org.apache.spark.SparkConf
+
 class UISuite extends FunSuite {
 
   // Ignored: See SPARK-1530
   ignore("streaming tab in spark UI") {
-    val ssc = new StreamingContext("local", "test", Seconds(1))
+    val conf = new SparkConf()
+      .setMaster("local")
+      .setAppName("test")
+      .set("spark.ui.enabled", "true")
+    val ssc = new StreamingContext(conf, Seconds(1))
+    assert(ssc.sc.ui.isDefined, "Spark UI is not started!")
+    val ui = ssc.sc.ui.get
+
     eventually(timeout(10 seconds), interval(50 milliseconds)) {
-      val html = Source.fromURL(ssc.sparkContext.ui.appUIAddress).mkString
+      val html = Source.fromURL(ui.appUIAddress).mkString
       assert(!html.contains("random data that should not be present"))
       // test if streaming tab exist
       assert(html.toLowerCase.contains("streaming"))
@@ -39,8 +48,7 @@ class UISuite extends FunSuite {
     }
 
     eventually(timeout(10 seconds), interval(50 milliseconds)) {
-      val html = Source.fromURL(
-        ssc.sparkContext.ui.appUIAddress.stripSuffix("/") + "/streaming").mkString
+      val html = Source.fromURL(ui.appUIAddress.stripSuffix("/") + "/streaming").mkString
       assert(html.toLowerCase.contains("batch"))
       assert(html.toLowerCase.contains("network"))
     }
diff --git a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
index 5756263e89e21..878b6db546032 100644
--- a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
+++ b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
@@ -189,7 +189,7 @@ private[spark] class ApplicationMaster(args: ApplicationMasterArguments,
     if (sc == null) {
       finish(FinalApplicationStatus.FAILED, "Timed out waiting for SparkContext.")
     } else {
-      registerAM(sc.ui.appUIAddress, securityMgr)
+      registerAM(sc.ui.map(_.appUIAddress).getOrElse(""), securityMgr)
       try {
         userThread.join()
       } finally {
diff --git a/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala b/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala
index 41c662cd7a6de..6aa6475fe4a18 100644
--- a/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala
+++ b/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala
@@ -55,7 +55,7 @@ private[spark] class YarnClientSchedulerBackend(
     val driverHost = conf.get("spark.driver.host")
     val driverPort = conf.get("spark.driver.port")
     val hostport = driverHost + ":" + driverPort
-    conf.set("spark.driver.appUIAddress", sc.ui.appUIHostPort)
+    sc.ui.foreach { ui => conf.set("spark.driver.appUIAddress", ui.appUIHostPort) }
 
     val argsArrayBuf = new ArrayBuffer[String]()
     argsArrayBuf += (

From ce59725b8703d18988e495dbaaf86ddde4bdfc5a Mon Sep 17 00:00:00 2001
From: Andrew Ash <andrew@andrewash.com>
Date: Thu, 11 Sep 2014 17:28:36 -0700
Subject: [PATCH 271/489] [SPARK-3429] Don't include the empty string "" as a
 defaultAclUser

Changes logging from

```
14/09/05 02:01:08 INFO SecurityManager: Changing view acls to: aash,
14/09/05 02:01:08 INFO SecurityManager: Changing modify acls to: aash,
14/09/05 02:01:08 INFO SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users with view permissions: Set(aash, ); users with modify permissions: Set(aash, )
```
to
```
14/09/05 02:28:28 INFO SecurityManager: Changing view acls to: aash
14/09/05 02:28:28 INFO SecurityManager: Changing modify acls to: aash
14/09/05 02:28:28 INFO SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users with view permissions: Set(aash); users with modify permissions: Set(aash)
```

Note that the first set of logs have a Set of size 2 containing "aash" and the empty string ""

cc tgravescs

Author: Andrew Ash <andrew@andrewash.com>

Closes #2286 from ash211/empty-default-acl and squashes the following commits:

18cc612 [Andrew Ash] Use .isEmpty instead of ==""
cf973a1 [Andrew Ash] Don't include the empty string "" as a defaultAclUser
---
 core/src/main/scala/org/apache/spark/SecurityManager.scala | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/SecurityManager.scala b/core/src/main/scala/org/apache/spark/SecurityManager.scala
index 12b15fe0815be..3832a780ec4bc 100644
--- a/core/src/main/scala/org/apache/spark/SecurityManager.scala
+++ b/core/src/main/scala/org/apache/spark/SecurityManager.scala
@@ -162,7 +162,7 @@ private[spark] class SecurityManager(sparkConf: SparkConf) extends Logging {
 
   // always add the current user and SPARK_USER to the viewAcls
   private val defaultAclUsers = Set[String](System.getProperty("user.name", ""),
-    Option(System.getenv("SPARK_USER")).getOrElse(""))
+    Option(System.getenv("SPARK_USER")).getOrElse("")).filter(!_.isEmpty)
 
   setViewAcls(defaultAclUsers, sparkConf.get("spark.ui.view.acls", ""))
   setModifyAcls(defaultAclUsers, sparkConf.get("spark.modify.acls", ""))

From f858f466862541c3faad76a1fa2391f1c17ec9dd Mon Sep 17 00:00:00 2001
From: Cody Koeninger <cody.koeninger@mediacrossing.com>
Date: Thu, 11 Sep 2014 17:49:36 -0700
Subject: [PATCH 272/489] SPARK-3462 push down filters and projections into
 Unions

Author: Cody Koeninger <cody.koeninger@mediacrossing.com>

Closes #2345 from koeninger/SPARK-3462 and squashes the following commits:

5c8d24d [Cody Koeninger] SPARK-3462 remove now-unused parameter
0788691 [Cody Koeninger] SPARK-3462 add tests, handle compatible schema with different aliases, per marmbrus feedback
ef47b3b [Cody Koeninger] SPARK-3462 push down filters and projections into Unions
---
 .../sql/catalyst/optimizer/Optimizer.scala    | 48 ++++++++++++++
 .../optimizer/UnionPushdownSuite.scala        | 62 +++++++++++++++++++
 2 files changed, 110 insertions(+)
 create mode 100644 sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/UnionPushdownSuite.scala

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
index ddd4b3755d629..a4133feae8166 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
@@ -40,12 +40,60 @@ object Optimizer extends RuleExecutor[LogicalPlan] {
       SimplifyCasts,
       SimplifyCaseConversionExpressions) ::
     Batch("Filter Pushdown", FixedPoint(100),
+      UnionPushdown,
       CombineFilters,
       PushPredicateThroughProject,
       PushPredicateThroughJoin,
       ColumnPruning) :: Nil
 }
 
+/**
+  *  Pushes operations to either side of a Union.
+  */
+object UnionPushdown extends Rule[LogicalPlan] {
+
+  /**
+    *  Maps Attributes from the left side to the corresponding Attribute on the right side.
+    */
+  def buildRewrites(union: Union): AttributeMap[Attribute] = {
+    assert(union.left.output.size == union.right.output.size)
+
+    AttributeMap(union.left.output.zip(union.right.output))
+  }
+
+  /**
+    *  Rewrites an expression so that it can be pushed to the right side of a Union operator.
+    *  This method relies on the fact that the output attributes of a union are always equal
+    *  to the left child's output.
+    */
+  def pushToRight[A <: Expression](e: A, rewrites: AttributeMap[Attribute]): A = {
+    val result = e transform {
+      case a: Attribute => rewrites(a)
+    }
+
+    // We must promise the compiler that we did not discard the names in the case of project
+    // expressions.  This is safe since the only transformation is from Attribute => Attribute.
+    result.asInstanceOf[A]
+  }
+
+  def apply(plan: LogicalPlan): LogicalPlan = plan transform {
+    // Push down filter into union
+    case Filter(condition, u @ Union(left, right)) =>
+      val rewrites = buildRewrites(u)
+      Union(
+        Filter(condition, left),
+        Filter(pushToRight(condition, rewrites), right))
+
+    // Push down projection into union
+    case Project(projectList, u @ Union(left, right)) =>
+      val rewrites = buildRewrites(u)
+      Union(
+        Project(projectList, left),
+        Project(projectList.map(pushToRight(_, rewrites)), right))
+  }
+}
+
+
 /**
  * Attempts to eliminate the reading of unneeded columns from the query plan using the following
  * transformations:
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/UnionPushdownSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/UnionPushdownSuite.scala
new file mode 100644
index 0000000000000..dfef87bd9133d
--- /dev/null
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/UnionPushdownSuite.scala
@@ -0,0 +1,62 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.optimizer
+
+import org.apache.spark.sql.catalyst.analysis
+import org.apache.spark.sql.catalyst.analysis.EliminateAnalysisOperators
+import org.apache.spark.sql.catalyst.plans.logical._
+import org.apache.spark.sql.catalyst.plans.{PlanTest, LeftOuter, RightOuter}
+import org.apache.spark.sql.catalyst.rules._
+import org.apache.spark.sql.catalyst.dsl.plans._
+import org.apache.spark.sql.catalyst.dsl.expressions._
+
+class UnionPushdownSuite extends PlanTest {
+  object Optimize extends RuleExecutor[LogicalPlan] {
+    val batches =
+      Batch("Subqueries", Once,
+        EliminateAnalysisOperators) ::
+      Batch("Union Pushdown", Once,
+        UnionPushdown) :: Nil
+  }
+
+  val testRelation =  LocalRelation('a.int, 'b.int, 'c.int)
+  val testRelation2 =  LocalRelation('d.int, 'e.int, 'f.int)
+  val testUnion = Union(testRelation, testRelation2)
+
+  test("union: filter to each side") {
+    val query = testUnion.where('a === 1)
+
+    val optimized = Optimize(query.analyze)
+
+    val correctAnswer =
+      Union(testRelation.where('a === 1), testRelation2.where('d === 1)).analyze
+
+    comparePlans(optimized, correctAnswer)
+  }
+
+  test("union: project to each side") {
+    val query = testUnion.select('b)
+
+    val optimized = Optimize(query.analyze)
+
+    val correctAnswer =
+      Union(testRelation.select('b), testRelation2.select('e)).analyze
+
+    comparePlans(optimized, correctAnswer)
+  }
+}

From 33c7a738ae9f2d12425afad6f08a4fe0b7a5c6ab Mon Sep 17 00:00:00 2001
From: witgo <witgo@qq.com>
Date: Thu, 11 Sep 2014 18:44:35 -0700
Subject: [PATCH 273/489] SPARK-2482: Resolve sbt warnings during build

At the same time, import the `scala.language.postfixOps` and ` org.scalatest.time.SpanSugar._` cause `scala.language.postfixOps` doesn't work

Author: witgo <witgo@qq.com>

Closes #1330 from witgo/sbt_warnings3 and squashes the following commits:

179ba61 [witgo] Resolve sbt warnings during build
---
 .../scala/org/apache/spark/bagel/BagelSuite.scala    |  2 --
 .../scala/org/apache/spark/ContextCleanerSuite.scala |  1 -
 .../test/scala/org/apache/spark/DriverSuite.scala    |  2 --
 .../org/apache/spark/rdd/AsyncRDDActionsSuite.scala  |  1 -
 .../src/test/scala/org/apache/spark/ui/UISuite.scala |  1 -
 pom.xml                                              |  1 -
 .../scala/org/apache/spark/repl/SparkILoop.scala     | 12 ++++++------
 .../spark/streaming/NetworkReceiverSuite.scala       |  1 -
 .../spark/streaming/StreamingContextSuite.scala      |  2 --
 .../spark/streaming/StreamingListenerSuite.scala     |  1 -
 .../scala/org/apache/spark/streaming/UISuite.scala   |  1 -
 11 files changed, 6 insertions(+), 19 deletions(-)

diff --git a/bagel/src/test/scala/org/apache/spark/bagel/BagelSuite.scala b/bagel/src/test/scala/org/apache/spark/bagel/BagelSuite.scala
index 55241d33cd3f0..ccb262a4ee02a 100644
--- a/bagel/src/test/scala/org/apache/spark/bagel/BagelSuite.scala
+++ b/bagel/src/test/scala/org/apache/spark/bagel/BagelSuite.scala
@@ -24,8 +24,6 @@ import org.scalatest.time.SpanSugar._
 import org.apache.spark._
 import org.apache.spark.storage.StorageLevel
 
-import scala.language.postfixOps
-
 class TestVertex(val active: Boolean, val age: Int) extends Vertex with Serializable
 class TestMessage(val targetId: String) extends Message[String] with Serializable
 
diff --git a/core/src/test/scala/org/apache/spark/ContextCleanerSuite.scala b/core/src/test/scala/org/apache/spark/ContextCleanerSuite.scala
index 2744894277ae8..2e3fc5ef0e336 100644
--- a/core/src/test/scala/org/apache/spark/ContextCleanerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/ContextCleanerSuite.scala
@@ -21,7 +21,6 @@ import java.lang.ref.WeakReference
 
 import scala.collection.mutable.{HashSet, SynchronizedSet}
 import scala.language.existentials
-import scala.language.postfixOps
 import scala.util.Random
 
 import org.scalatest.{BeforeAndAfter, FunSuite}
diff --git a/core/src/test/scala/org/apache/spark/DriverSuite.scala b/core/src/test/scala/org/apache/spark/DriverSuite.scala
index 4b1d280624c57..5265ba904032f 100644
--- a/core/src/test/scala/org/apache/spark/DriverSuite.scala
+++ b/core/src/test/scala/org/apache/spark/DriverSuite.scala
@@ -26,8 +26,6 @@ import org.scalatest.time.SpanSugar._
 
 import org.apache.spark.util.Utils
 
-import scala.language.postfixOps
-
 class DriverSuite extends FunSuite with Timeouts {
 
   test("driver should exit after finishing") {
diff --git a/core/src/test/scala/org/apache/spark/rdd/AsyncRDDActionsSuite.scala b/core/src/test/scala/org/apache/spark/rdd/AsyncRDDActionsSuite.scala
index 28197657e9bad..3b833f2e41867 100644
--- a/core/src/test/scala/org/apache/spark/rdd/AsyncRDDActionsSuite.scala
+++ b/core/src/test/scala/org/apache/spark/rdd/AsyncRDDActionsSuite.scala
@@ -22,7 +22,6 @@ import java.util.concurrent.Semaphore
 import scala.concurrent.{Await, TimeoutException}
 import scala.concurrent.duration.Duration
 import scala.concurrent.ExecutionContext.Implicits.global
-import scala.language.postfixOps
 
 import org.scalatest.{BeforeAndAfterAll, FunSuite}
 import org.scalatest.concurrent.Timeouts
diff --git a/core/src/test/scala/org/apache/spark/ui/UISuite.scala b/core/src/test/scala/org/apache/spark/ui/UISuite.scala
index 2f56642956701..48790b59e7fbd 100644
--- a/core/src/test/scala/org/apache/spark/ui/UISuite.scala
+++ b/core/src/test/scala/org/apache/spark/ui/UISuite.scala
@@ -21,7 +21,6 @@ import java.net.ServerSocket
 import javax.servlet.http.HttpServletRequest
 
 import scala.io.Source
-import scala.language.postfixOps
 import scala.util.{Failure, Success, Try}
 
 import org.eclipse.jetty.server.Server
diff --git a/pom.xml b/pom.xml
index e5f863e85445c..28763476f8313 100644
--- a/pom.xml
+++ b/pom.xml
@@ -839,7 +839,6 @@
               <arg>-unchecked</arg>
               <arg>-deprecation</arg>
               <arg>-feature</arg>
-              <arg>-language:postfixOps</arg>
             </args>
             <jvmArgs>
               <jvmArg>-Xms1024m</jvmArg>
diff --git a/repl/src/main/scala/org/apache/spark/repl/SparkILoop.scala b/repl/src/main/scala/org/apache/spark/repl/SparkILoop.scala
index d9eeffa86016a..e56b74edba88c 100644
--- a/repl/src/main/scala/org/apache/spark/repl/SparkILoop.scala
+++ b/repl/src/main/scala/org/apache/spark/repl/SparkILoop.scala
@@ -15,15 +15,15 @@ import scala.tools.nsc._
 import scala.tools.nsc.backend.JavaPlatform
 import scala.tools.nsc.interpreter._
 
-import scala.tools.nsc.interpreter.{ Results => IR }
-import Predef.{ println => _, _ }
-import java.io.{ BufferedReader, FileReader }
+import scala.tools.nsc.interpreter.{Results => IR}
+import Predef.{println => _, _}
+import java.io.{BufferedReader, FileReader}
 import java.net.URI
 import java.util.concurrent.locks.ReentrantLock
 import scala.sys.process.Process
 import scala.tools.nsc.interpreter.session._
-import scala.util.Properties.{ jdkHome, javaVersion }
-import scala.tools.util.{ Javap }
+import scala.util.Properties.{jdkHome, javaVersion}
+import scala.tools.util.{Javap}
 import scala.annotation.tailrec
 import scala.collection.mutable.ListBuffer
 import scala.concurrent.ops
@@ -33,7 +33,7 @@ import scala.tools.nsc.io.{File, Directory}
 import scala.reflect.NameTransformer._
 import scala.tools.nsc.util.ScalaClassLoader._
 import scala.tools.util._
-import scala.language.{implicitConversions, existentials}
+import scala.language.{implicitConversions, existentials, postfixOps}
 import scala.reflect.{ClassTag, classTag}
 import scala.tools.reflect.StdRuntimeTags._
 
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/NetworkReceiverSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/NetworkReceiverSuite.scala
index f4e11f975de94..99c8d13231aac 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/NetworkReceiverSuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/NetworkReceiverSuite.scala
@@ -20,7 +20,6 @@ package org.apache.spark.streaming
 import java.nio.ByteBuffer
 
 import scala.collection.mutable.ArrayBuffer
-import scala.language.postfixOps
 
 import org.apache.spark.SparkConf
 import org.apache.spark.storage.{StorageLevel, StreamBlockId}
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala
index 7b33d3b235466..a3cabd6be02fe 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala
@@ -29,8 +29,6 @@ import org.scalatest.concurrent.Timeouts
 import org.scalatest.exceptions.TestFailedDueToTimeoutException
 import org.scalatest.time.SpanSugar._
 
-import scala.language.postfixOps
-
 class StreamingContextSuite extends FunSuite with BeforeAndAfter with Timeouts with Logging {
 
   val master = "local[2]"
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/StreamingListenerSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/StreamingListenerSuite.scala
index 2861f5335ae36..84fed95a75e67 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/StreamingListenerSuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/StreamingListenerSuite.scala
@@ -20,7 +20,6 @@ package org.apache.spark.streaming
 import scala.collection.mutable.ArrayBuffer
 import scala.concurrent.Future
 import scala.concurrent.ExecutionContext.Implicits.global
-import scala.language.postfixOps
 
 import org.apache.spark.storage.StorageLevel
 import org.apache.spark.streaming.dstream.DStream
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/UISuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/UISuite.scala
index 4c7e43c2943c9..8e30118266855 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/UISuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/UISuite.scala
@@ -18,7 +18,6 @@
 package org.apache.spark.streaming
 
 import scala.io.Source
-import scala.language.postfixOps
 
 import org.scalatest.FunSuite
 import org.scalatest.concurrent.Eventually._

From 42904b8d013e71d03e301c3da62e33b4cc2eb54e Mon Sep 17 00:00:00 2001
From: Davies Liu <davies.liu@gmail.com>
Date: Thu, 11 Sep 2014 18:53:26 -0700
Subject: [PATCH 274/489] [SPARK-3465] fix task metrics aggregation in local
 mode

Before overwrite t.taskMetrics, take a deepcopy of it.

Author: Davies Liu <davies.liu@gmail.com>

Closes #2338 from davies/fix_metric and squashes the following commits:

a5cdb63 [Davies Liu] Merge branch 'master' into fix_metric
7c879e0 [Davies Liu] add more comments
754b5b8 [Davies Liu] copy taskMetrics only when isLocal is true
5ca26dc [Davies Liu] fix task metrics aggregation in local mode
---
 .../scala/org/apache/spark/executor/Executor.scala    | 11 ++++++++++-
 1 file changed, 10 insertions(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/executor/Executor.scala b/core/src/main/scala/org/apache/spark/executor/Executor.scala
index dd903dc65d204..acae448a9c66f 100644
--- a/core/src/main/scala/org/apache/spark/executor/Executor.scala
+++ b/core/src/main/scala/org/apache/spark/executor/Executor.scala
@@ -360,7 +360,16 @@ private[spark] class Executor(
             if (!taskRunner.attemptedTask.isEmpty) {
               Option(taskRunner.task).flatMap(_.metrics).foreach { metrics =>
                 metrics.updateShuffleReadMetrics
-                tasksMetrics += ((taskRunner.taskId, metrics))
+                if (isLocal) {
+                  // JobProgressListener will hold an reference of it during
+                  // onExecutorMetricsUpdate(), then JobProgressListener can not see
+                  // the changes of metrics any more, so make a deep copy of it
+                  val copiedMetrics = Utils.deserialize[TaskMetrics](Utils.serialize(metrics))
+                  tasksMetrics += ((taskRunner.taskId, copiedMetrics))
+                } else {
+                  // It will be copied by serialization
+                  tasksMetrics += ((taskRunner.taskId, metrics))
+                }
               }
             }
           }

From b8634df1f1eb6ce909bec779522c9c9912c7d06a Mon Sep 17 00:00:00 2001
From: "Joseph K. Bradley" <joseph.kurata.bradley@gmail.com>
Date: Fri, 12 Sep 2014 01:37:59 -0700
Subject: [PATCH 275/489] [SPARK-3160] [SPARK-3494] [mllib]  DecisionTree:
 eliminate pre-allocated nodes, parentImpurities arrays. Memory calc bug fix.

This PR includes some code simplifications and re-organization which will be helpful for implementing random forests.  The main changes are that the nodes and parentImpurities arrays are no longer pre-allocated in the main train() method.

Also added 2 bug fixes:
* maxMemoryUsage calculation
* over-allocation of space for bins in DTStatsAggregator for unordered features.

Relation to RFs:
* Since RFs will be deeper and will therefore be more likely sparse (not full trees), it could be a cost savings to avoid pre-allocating a full tree.
* The associated re-organization also reduces bookkeeping, which will make RFs easier to implement.
* The return code doneTraining may be generalized to include cases such as nodes ready for local training.

Details:

No longer pre-allocate parentImpurities array in main train() method.
* parentImpurities values are now stored in individual nodes (in Node.stats.impurity).
* These were not really needed.  They were used in calculateGainForSplit(), but they can be calculated anyways using parentNodeAgg.

No longer using Node.build since tree structure is constructed on-the-fly.
* Did not eliminate since it is public (Developer) API.  Marked as deprecated.

Eliminated pre-allocated nodes array in main train() method.
* Nodes are constructed and added to the tree structure as needed during training.
* Moved tree construction from main train() method into findBestSplitsPerGroup() since there is no need to keep the (split, gain) array for an entire level of nodes.  Only one element of that array is needed at a time, so we do not the array.

findBestSplits() now returns 2 items:
* rootNode (newly created root node on first iteration, same root node on later iterations)
* doneTraining (indicating if all nodes at that level were leafs)

Updated DecisionTreeSuite.  Notes:
* Improved test "Second level node building with vs. without groups"
** generateOrderedLabeledPoints() modified so that it really does require 2 levels of internal nodes.
* Related update: Added Node.deepCopy (private[tree]), used for test suite

CC: mengxr

Author: Joseph K. Bradley <joseph.kurata.bradley@gmail.com>

Closes #2341 from jkbradley/dt-spark-3160 and squashes the following commits:

07dd1ee [Joseph K. Bradley] Fixed overflow bug with computing maxMemoryUsage in DecisionTree.  Also fixed bug with over-allocating space in DTStatsAggregator for unordered features.
debe072 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-spark-3160
5c4ac33 [Joseph K. Bradley] Added check in Strategy to make sure minInstancesPerNode >= 1
0dd4d87 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-spark-3160
306120f [Joseph K. Bradley] Fixed typo in DecisionTreeModel.scala doc
eaa1dcf [Joseph K. Bradley] Added topNode doc in DecisionTree and scalastyle fix
d4d7864 [Joseph K. Bradley] Marked Node.build as deprecated
d4dbb99 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-spark-3160
1a8f0ad [Joseph K. Bradley] Eliminated pre-allocated nodes array in main train() method. * Nodes are constructed and added to the tree structure as needed during training.
2ab763b [Joseph K. Bradley] Simplifications to DecisionTree code:
---
 .../spark/mllib/tree/DecisionTree.scala       | 191 +++++-------
 .../mllib/tree/configuration/Strategy.scala   |   3 +
 .../mllib/tree/impl/DTStatsAggregator.scala   |  11 +-
 .../tree/impl/DecisionTreeMetadata.scala      |   3 +-
 .../mllib/tree/model/DecisionTreeModel.scala  |   2 +-
 .../apache/spark/mllib/tree/model/Node.scala  |  37 +++
 .../spark/mllib/tree/DecisionTreeSuite.scala  | 277 +++++++++---------
 7 files changed, 268 insertions(+), 256 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala
index 98596569b8c95..56bb8812100a7 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala
@@ -87,17 +87,11 @@ class DecisionTree (private val strategy: Strategy) extends Serializable with Lo
     val maxDepth = strategy.maxDepth
     require(maxDepth <= 30,
       s"DecisionTree currently only supports maxDepth <= 30, but was given maxDepth = $maxDepth.")
-    // Number of nodes to allocate: max number of nodes possible given the depth of the tree, plus 1
-    val maxNumNodesPlus1 = Node.startIndexInLevel(maxDepth + 1)
-    // Initialize an array to hold parent impurity calculations for each node.
-    val parentImpurities = new Array[Double](maxNumNodesPlus1)
-    // dummy value for top node (updated during first split calculation)
-    val nodes = new Array[Node](maxNumNodesPlus1)
 
     // Calculate level for single group construction
 
     // Max memory usage for aggregates
-    val maxMemoryUsage = strategy.maxMemoryInMB * 1024 * 1024
+    val maxMemoryUsage = strategy.maxMemoryInMB * 1024L * 1024L
     logDebug("max memory usage for aggregates = " + maxMemoryUsage + " bytes.")
     // TODO: Calculate memory usage more precisely.
     val numElementsPerNode = DecisionTree.getElementsPerNode(metadata)
@@ -120,81 +114,35 @@ class DecisionTree (private val strategy: Strategy) extends Serializable with Lo
      * beforehand and is not used in later levels.
      */
 
+    var topNode: Node = null // set on first iteration
     var level = 0
     var break = false
     while (level <= maxDepth && !break) {
-
       logDebug("#####################################")
       logDebug("level = " + level)
       logDebug("#####################################")
 
       // Find best split for all nodes at a level.
       timer.start("findBestSplits")
-      val splitsStatsForLevel: Array[(Split, InformationGainStats, Predict)] =
-        DecisionTree.findBestSplits(treeInput, parentImpurities,
-          metadata, level, nodes, splits, bins, maxLevelForSingleGroup, timer)
+      val (tmpTopNode: Node, doneTraining: Boolean) = DecisionTree.findBestSplits(treeInput,
+        metadata, level, topNode, splits, bins, maxLevelForSingleGroup, timer)
       timer.stop("findBestSplits")
 
-      val levelNodeIndexOffset = Node.startIndexInLevel(level)
-      for ((nodeSplitStats, index) <- splitsStatsForLevel.view.zipWithIndex) {
-        val nodeIndex = levelNodeIndexOffset + index
-
-        // Extract info for this node (index) at the current level.
-        timer.start("extractNodeInfo")
-        val split = nodeSplitStats._1
-        val stats = nodeSplitStats._2
-        val predict = nodeSplitStats._3.predict
-        val isLeaf = (stats.gain <= 0) || (level == strategy.maxDepth)
-        val node = new Node(nodeIndex, predict, isLeaf, Some(split), None, None, Some(stats))
-        logDebug("Node = " + node)
-        nodes(nodeIndex) = node
-        timer.stop("extractNodeInfo")
-
-        if (level != 0) {
-          // Set parent.
-          val parentNodeIndex = Node.parentIndex(nodeIndex)
-          if (Node.isLeftChild(nodeIndex)) {
-            nodes(parentNodeIndex).leftNode = Some(nodes(nodeIndex))
-          } else {
-            nodes(parentNodeIndex).rightNode = Some(nodes(nodeIndex))
-          }
-        }
-        // Extract info for nodes at the next lower level.
-        timer.start("extractInfoForLowerLevels")
-        if (level < maxDepth) {
-          val leftChildIndex = Node.leftChildIndex(nodeIndex)
-          val leftImpurity = stats.leftImpurity
-          logDebug("leftChildIndex = " + leftChildIndex + ", impurity = " + leftImpurity)
-          parentImpurities(leftChildIndex) = leftImpurity
-
-          val rightChildIndex = Node.rightChildIndex(nodeIndex)
-          val rightImpurity = stats.rightImpurity
-          logDebug("rightChildIndex = " + rightChildIndex + ", impurity = " + rightImpurity)
-          parentImpurities(rightChildIndex) = rightImpurity
-        }
-        timer.stop("extractInfoForLowerLevels")
-        logDebug("final best split = " + split)
+      if (level == 0) {
+        topNode = tmpTopNode
       }
-      require(Node.maxNodesInLevel(level) == splitsStatsForLevel.length)
-      // Check whether all the nodes at the current level at leaves.
-      val allLeaf = splitsStatsForLevel.forall(_._2.gain <= 0)
-      logDebug("all leaf = " + allLeaf)
-      if (allLeaf) {
-        break = true // no more tree construction
-      } else {
-        level += 1
+      if (doneTraining) {
+        break = true
+        logDebug("done training")
       }
+
+      level += 1
     }
 
     logDebug("#####################################")
     logDebug("Extracting tree model")
     logDebug("#####################################")
 
-    // Initialize the top or root node of the tree.
-    val topNode = nodes(1)
-    // Build the full tree using the node info calculated in the level-wise best split calculations.
-    topNode.build(nodes)
-
     timer.stop("total")
 
     logInfo("Internal timing for DecisionTree:")
@@ -409,24 +357,26 @@ object DecisionTree extends Serializable with Logging {
    * multiple groups if the level-wise training task could lead to memory overflow.
    *
    * @param input Training data: RDD of [[org.apache.spark.mllib.tree.impl.TreePoint]]
-   * @param parentImpurities Impurities for all parent nodes for the current level
    * @param metadata Learning and dataset metadata
    * @param level Level of the tree
+   * @param topNode Root node of the tree (or invalid node when training first level).
    * @param splits possible splits for all features, indexed (numFeatures)(numSplits)
    * @param bins possible bins for all features, indexed (numFeatures)(numBins)
    * @param maxLevelForSingleGroup the deepest level for single-group level-wise computation.
-   * @return array (over nodes) of splits with best split for each node at a given level.
+   * @return  (root, doneTraining) where:
+   *          root = Root node (which is newly created on the first iteration),
+   *          doneTraining = true if no more internal nodes were created.
    */
   private[tree] def findBestSplits(
       input: RDD[TreePoint],
-      parentImpurities: Array[Double],
       metadata: DecisionTreeMetadata,
       level: Int,
-      nodes: Array[Node],
+      topNode: Node,
       splits: Array[Array[Split]],
       bins: Array[Array[Bin]],
       maxLevelForSingleGroup: Int,
-      timer: TimeTracker = new TimeTracker): Array[(Split, InformationGainStats, Predict)] = {
+      timer: TimeTracker = new TimeTracker): (Node, Boolean) = {
+
     // split into groups to avoid memory overflow during aggregation
     if (level > maxLevelForSingleGroup) {
       // When information for all nodes at a given level cannot be stored in memory,
@@ -435,18 +385,18 @@ object DecisionTree extends Serializable with Logging {
       // numGroups is equal to 2 at level 11 and 4 at level 12, respectively.
       val numGroups = 1 << level - maxLevelForSingleGroup
       logDebug("numGroups = " + numGroups)
-      var bestSplits = new Array[(Split, InformationGainStats, Predict)](0)
       // Iterate over each group of nodes at a level.
       var groupIndex = 0
+      var doneTraining = true
       while (groupIndex < numGroups) {
-        val bestSplitsForGroup = findBestSplitsPerGroup(input, parentImpurities, metadata, level,
-          nodes, splits, bins, timer, numGroups, groupIndex)
-        bestSplits = Array.concat(bestSplits, bestSplitsForGroup)
+        val (tmpRoot, doneTrainingGroup) = findBestSplitsPerGroup(input, metadata, level,
+          topNode, splits, bins, timer, numGroups, groupIndex)
+        doneTraining = doneTraining && doneTrainingGroup
         groupIndex += 1
       }
-      bestSplits
+      (topNode, doneTraining) // Not first iteration, so topNode was already set.
     } else {
-      findBestSplitsPerGroup(input, parentImpurities, metadata, level, nodes, splits, bins, timer)
+      findBestSplitsPerGroup(input, metadata, level, topNode, splits, bins, timer)
     }
   }
 
@@ -586,27 +536,27 @@ object DecisionTree extends Serializable with Logging {
    * Returns an array of optimal splits for a group of nodes at a given level
    *
    * @param input Training data: RDD of [[org.apache.spark.mllib.tree.impl.TreePoint]]
-   * @param parentImpurities Impurities for all parent nodes for the current level
    * @param metadata Learning and dataset metadata
    * @param level Level of the tree
-   * @param nodes Array of all nodes in the tree.  Used for matching data points to nodes.
+   * @param topNode Root node of the tree (or invalid node when training first level).
    * @param splits possible splits for all features, indexed (numFeatures)(numSplits)
    * @param bins possible bins for all features, indexed (numFeatures)(numBins)
    * @param numGroups total number of node groups at the current level. Default value is set to 1.
    * @param groupIndex index of the node group being processed. Default value is set to 0.
-   * @return array of splits with best splits for all nodes at a given level.
+   * @return  (root, doneTraining) where:
+   *          root = Root node (which is newly created on the first iteration),
+   *          doneTraining = true if no more internal nodes were created.
    */
   private def findBestSplitsPerGroup(
       input: RDD[TreePoint],
-      parentImpurities: Array[Double],
       metadata: DecisionTreeMetadata,
       level: Int,
-      nodes: Array[Node],
+      topNode: Node,
       splits: Array[Array[Split]],
       bins: Array[Array[Bin]],
       timer: TimeTracker,
       numGroups: Int = 1,
-      groupIndex: Int = 0): Array[(Split, InformationGainStats, Predict)] = {
+      groupIndex: Int = 0): (Node, Boolean) = {
 
     /*
      * The high-level descriptions of the best split optimizations are noted here.
@@ -663,7 +613,7 @@ object DecisionTree extends Serializable with Logging {
         0
       } else {
         val globalNodeIndex =
-          predictNodeIndex(nodes(1), treePoint.binnedFeatures, bins, metadata.unorderedFeatures)
+          predictNodeIndex(topNode, treePoint.binnedFeatures, bins, metadata.unorderedFeatures)
         globalNodeIndex - globalNodeIndexOffset
       }
     }
@@ -706,33 +656,63 @@ object DecisionTree extends Serializable with Logging {
 
     // Calculate best splits for all nodes at a given level
     timer.start("chooseSplits")
-    val bestSplits = new Array[(Split, InformationGainStats, Predict)](numNodes)
-    // Iterating over all nodes at this level
+    // On the first iteration, we need to get and return the newly created root node.
+    var newTopNode: Node = topNode
+
+    // Iterate over all nodes at this level
     var nodeIndex = 0
+    var internalNodeCount = 0
     while (nodeIndex < numNodes) {
-      val nodeImpurity = parentImpurities(globalNodeIndexOffset + nodeIndex)
-      logDebug("node impurity = " + nodeImpurity)
-      bestSplits(nodeIndex) =
-        binsToBestSplit(binAggregates, nodeIndex, nodeImpurity, level, metadata, splits)
-      logDebug("best split = " + bestSplits(nodeIndex)._1)
+      val (split: Split, stats: InformationGainStats, predict: Predict) =
+        binsToBestSplit(binAggregates, nodeIndex, level, metadata, splits)
+      logDebug("best split = " + split)
+
+      val globalNodeIndex = globalNodeIndexOffset + nodeIndex
+
+      // Extract info for this node at the current level.
+      val isLeaf = (stats.gain <= 0) || (level == metadata.maxDepth)
+      val node =
+        new Node(globalNodeIndex, predict.predict, isLeaf, Some(split), None, None, Some(stats))
+      logDebug("Node = " + node)
+
+      if (!isLeaf) {
+        internalNodeCount += 1
+      }
+      if (level == 0) {
+        newTopNode = node
+      } else {
+        // Set parent.
+        val parentNode = Node.getNode(Node.parentIndex(globalNodeIndex), topNode)
+        if (Node.isLeftChild(globalNodeIndex)) {
+          parentNode.leftNode = Some(node)
+        } else {
+          parentNode.rightNode = Some(node)
+        }
+      }
+      if (level < metadata.maxDepth) {
+        logDebug("leftChildIndex = " + Node.leftChildIndex(globalNodeIndex) +
+          ", impurity = " + stats.leftImpurity)
+        logDebug("rightChildIndex = " + Node.rightChildIndex(globalNodeIndex) +
+          ", impurity = " + stats.rightImpurity)
+      }
+
       nodeIndex += 1
     }
     timer.stop("chooseSplits")
 
-    bestSplits
+    val doneTraining = internalNodeCount == 0
+    (newTopNode, doneTraining)
   }
 
   /**
    * Calculate the information gain for a given (feature, split) based upon left/right aggregates.
    * @param leftImpurityCalculator left node aggregates for this (feature, split)
    * @param rightImpurityCalculator right node aggregate for this (feature, split)
-   * @param topImpurity impurity of the parent node
    * @return information gain and statistics for all splits
    */
   private def calculateGainForSplit(
       leftImpurityCalculator: ImpurityCalculator,
       rightImpurityCalculator: ImpurityCalculator,
-      topImpurity: Double,
       level: Int,
       metadata: DecisionTreeMetadata): InformationGainStats = {
     val leftCount = leftImpurityCalculator.count
@@ -747,14 +727,10 @@ object DecisionTree extends Serializable with Logging {
 
     val totalCount = leftCount + rightCount
 
-    // impurity of parent node
-    val impurity = if (level > 0) {
-      topImpurity
-    } else {
-      val parentNodeAgg = leftImpurityCalculator.copy
-      parentNodeAgg.add(rightImpurityCalculator)
-      parentNodeAgg.calculate()
-    }
+    val parentNodeAgg = leftImpurityCalculator.copy
+    parentNodeAgg.add(rightImpurityCalculator)
+
+    val impurity = parentNodeAgg.calculate()
 
     val leftImpurity = leftImpurityCalculator.calculate() // Note: This equals 0 if count = 0
     val rightImpurity = rightImpurityCalculator.calculate()
@@ -795,19 +771,15 @@ object DecisionTree extends Serializable with Logging {
    * Find the best split for a node.
    * @param binAggregates Bin statistics.
    * @param nodeIndex Index for node to split in this (level, group).
-   * @param nodeImpurity Impurity of the node (nodeIndex).
    * @return tuple for best split: (Split, information gain)
    */
   private def binsToBestSplit(
       binAggregates: DTStatsAggregator,
       nodeIndex: Int,
-      nodeImpurity: Double,
       level: Int,
       metadata: DecisionTreeMetadata,
       splits: Array[Array[Split]]): (Split, InformationGainStats, Predict) = {
 
-    logDebug("node impurity = " + nodeImpurity)
-
     // calculate predict only once
     var predict: Option[Predict] = None
 
@@ -831,8 +803,7 @@ object DecisionTree extends Serializable with Logging {
             val rightChildStats = binAggregates.getImpurityCalculator(nodeFeatureOffset, numSplits)
             rightChildStats.subtract(leftChildStats)
             predict = Some(predict.getOrElse(calculatePredict(leftChildStats, rightChildStats)))
-            val gainStats =
-              calculateGainForSplit(leftChildStats, rightChildStats, nodeImpurity, level, metadata)
+            val gainStats = calculateGainForSplit(leftChildStats, rightChildStats, level, metadata)
             (splitIdx, gainStats)
           }.maxBy(_._2.gain)
         (splits(featureIndex)(bestFeatureSplitIndex), bestFeatureGainStats)
@@ -845,8 +816,7 @@ object DecisionTree extends Serializable with Logging {
             val leftChildStats = binAggregates.getImpurityCalculator(leftChildOffset, splitIndex)
             val rightChildStats = binAggregates.getImpurityCalculator(rightChildOffset, splitIndex)
             predict = Some(predict.getOrElse(calculatePredict(leftChildStats, rightChildStats)))
-            val gainStats =
-              calculateGainForSplit(leftChildStats, rightChildStats, nodeImpurity, level, metadata)
+            val gainStats = calculateGainForSplit(leftChildStats, rightChildStats, level, metadata)
             (splitIndex, gainStats)
           }.maxBy(_._2.gain)
         (splits(featureIndex)(bestFeatureSplitIndex), bestFeatureGainStats)
@@ -917,8 +887,7 @@ object DecisionTree extends Serializable with Logging {
               binAggregates.getImpurityCalculator(nodeFeatureOffset, lastCategory)
             rightChildStats.subtract(leftChildStats)
             predict = Some(predict.getOrElse(calculatePredict(leftChildStats, rightChildStats)))
-            val gainStats =
-              calculateGainForSplit(leftChildStats, rightChildStats, nodeImpurity, level, metadata)
+            val gainStats = calculateGainForSplit(leftChildStats, rightChildStats, level, metadata)
             (splitIndex, gainStats)
           }.maxBy(_._2.gain)
         val categoriesForSplit =
@@ -937,8 +906,8 @@ object DecisionTree extends Serializable with Logging {
   /**
    * Get the number of values to be stored per node in the bin aggregates.
    */
-  private def getElementsPerNode(metadata: DecisionTreeMetadata): Int = {
-    val totalBins = metadata.numBins.sum
+  private def getElementsPerNode(metadata: DecisionTreeMetadata): Long = {
+    val totalBins = metadata.numBins.map(_.toLong).sum
     if (metadata.isClassification) {
       metadata.numClasses * totalBins
     } else {
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala
index 987fe632c91ed..31d1e8ac30eea 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala
@@ -75,6 +75,9 @@ class Strategy (
   if (algo == Classification) {
     require(numClassesForClassification >= 2)
   }
+  require(minInstancesPerNode >= 1,
+    s"DecisionTree Strategy requires minInstancesPerNode >= 1 but was given $minInstancesPerNode")
+
   val isMulticlassClassification =
     algo == Classification && numClassesForClassification > 2
   val isMulticlassWithCategoricalFeatures
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/impl/DTStatsAggregator.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/impl/DTStatsAggregator.scala
index 866d85a79bea1..61a94246711bf 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/impl/DTStatsAggregator.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/impl/DTStatsAggregator.scala
@@ -65,14 +65,7 @@ private[tree] class DTStatsAggregator(
    * Offset for each feature for calculating indices into the [[allStats]] array.
    */
   private val featureOffsets: Array[Int] = {
-    def featureOffsetsCalc(total: Int, featureIndex: Int): Int = {
-      if (isUnordered(featureIndex)) {
-        total + 2 * numBins(featureIndex)
-      } else {
-        total + numBins(featureIndex)
-      }
-    }
-    Range(0, numFeatures).scanLeft(0)(featureOffsetsCalc).map(statsSize * _).toArray
+    numBins.scanLeft(0)((total, nBins) => total + statsSize * nBins)
   }
 
   /**
@@ -149,7 +142,7 @@ private[tree] class DTStatsAggregator(
       s"DTStatsAggregator.getLeftRightNodeFeatureOffsets is for unordered features only," +
       s" but was called for ordered feature $featureIndex.")
     val baseOffset = nodeIndex * nodeStride + featureOffsets(featureIndex)
-    (baseOffset, baseOffset + numBins(featureIndex) * statsSize)
+    (baseOffset, baseOffset + (numBins(featureIndex) >> 1) * statsSize)
   }
 
   /**
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/impl/DecisionTreeMetadata.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/impl/DecisionTreeMetadata.scala
index 5ceaa8154d11a..b6d49e5555b1a 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/impl/DecisionTreeMetadata.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/impl/DecisionTreeMetadata.scala
@@ -46,6 +46,7 @@ private[tree] class DecisionTreeMetadata(
     val numBins: Array[Int],
     val impurity: Impurity,
     val quantileStrategy: QuantileStrategy,
+    val maxDepth: Int,
     val minInstancesPerNode: Int,
     val minInfoGain: Double) extends Serializable {
 
@@ -129,7 +130,7 @@ private[tree] object DecisionTreeMetadata {
 
     new DecisionTreeMetadata(numFeatures, numExamples, numClasses, numBins.max,
       strategy.categoricalFeaturesInfo, unorderedFeatures.toSet, numBins,
-      strategy.impurity, strategy.quantileCalculationStrategy,
+      strategy.impurity, strategy.quantileCalculationStrategy, strategy.maxDepth,
       strategy.minInstancesPerNode, strategy.minInfoGain)
   }
 
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/DecisionTreeModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/DecisionTreeModel.scala
index 0594fd0749d21..271b2c4ad813e 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/DecisionTreeModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/DecisionTreeModel.scala
@@ -46,7 +46,7 @@ class DecisionTreeModel(val topNode: Node, val algo: Algo) extends Serializable
    * Predict values for the given data set using the model trained.
    *
    * @param features RDD representing data points to be predicted
-   * @return RDD[Int] where each entry contains the corresponding prediction
+   * @return RDD of predictions for each of the given data points
    */
   def predict(features: RDD[Vector]): RDD[Double] = {
     features.map(x => predict(x))
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Node.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Node.scala
index 5b8a4cbed2306..5f0095d23c7ed 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Node.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Node.scala
@@ -55,6 +55,8 @@ class Node (
    * build the left node and right nodes if not leaf
    * @param nodes array of nodes
    */
+  @deprecated("build should no longer be used since trees are constructed on-the-fly in training",
+    "1.2.0")
   def build(nodes: Array[Node]): Unit = {
     logDebug("building node " + id + " at level " + Node.indexToLevel(id))
     logDebug("id = " + id + ", split = " + split)
@@ -93,6 +95,23 @@ class Node (
     }
   }
 
+  /**
+   * Returns a deep copy of the subtree rooted at this node.
+   */
+  private[tree] def deepCopy(): Node = {
+    val leftNodeCopy = if (leftNode.isEmpty) {
+      None
+    } else {
+      Some(leftNode.get.deepCopy())
+    }
+    val rightNodeCopy = if (rightNode.isEmpty) {
+      None
+    } else {
+      Some(rightNode.get.deepCopy())
+    }
+    new Node(id, predict, isLeaf, split, leftNodeCopy, rightNodeCopy, stats)
+  }
+
   /**
    * Get the number of nodes in tree below this node, including leaf nodes.
    * E.g., if this is a leaf, returns 0.  If both children are leaves, returns 2.
@@ -190,4 +209,22 @@ private[tree] object Node {
    */
   def startIndexInLevel(level: Int): Int = 1 << level
 
+  /**
+   * Traces down from a root node to get the node with the given node index.
+   * This assumes the node exists.
+   */
+  def getNode(nodeIndex: Int, rootNode: Node): Node = {
+    var tmpNode: Node = rootNode
+    var levelsToGo = indexToLevel(nodeIndex)
+    while (levelsToGo > 0) {
+      if ((nodeIndex & (1 << levelsToGo - 1)) == 0) {
+        tmpNode = tmpNode.leftNode.get
+      } else {
+        tmpNode = tmpNode.rightNode.get
+      }
+      levelsToGo -= 1
+    }
+    tmpNode
+  }
+
 }
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala
index fd8547c1660fc..1bd7ea05c46c8 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala
@@ -270,19 +270,17 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
     assert(bins(0).length === 0)
 
     val treeInput = TreePoint.convertToTreeRDD(rdd, bins, metadata)
-    val bestSplits = DecisionTree.findBestSplits(treeInput, new Array(8), metadata, 0,
-      new Array[Node](0), splits, bins, 10)
+    val (rootNode: Node, doneTraining: Boolean) =
+      DecisionTree.findBestSplits(treeInput, metadata, 0, null, splits, bins, 10)
 
-    val split = bestSplits(0)._1
+    val split = rootNode.split.get
     assert(split.categories === List(1.0))
     assert(split.featureType === Categorical)
     assert(split.threshold === Double.MinValue)
 
-    val stats = bestSplits(0)._2
-    val predict = bestSplits(0)._3
+    val stats = rootNode.stats.get
     assert(stats.gain > 0)
-    assert(predict.predict === 1)
-    assert(predict.prob === 0.6)
+    assert(rootNode.predict === 1)
     assert(stats.impurity > 0.2)
   }
 
@@ -303,19 +301,18 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
 
     val (splits, bins) = DecisionTree.findSplitsBins(rdd, metadata)
     val treeInput = TreePoint.convertToTreeRDD(rdd, bins, metadata)
-    val bestSplits = DecisionTree.findBestSplits(treeInput, new Array(8), metadata, 0,
-      new Array[Node](0), splits, bins, 10)
+    val (rootNode, doneTraining) = DecisionTree.findBestSplits(treeInput, metadata, 0,
+      null, splits, bins, 10)
 
-    val split = bestSplits(0)._1
+    val split = rootNode.split.get
     assert(split.categories.length === 1)
     assert(split.categories.contains(1.0))
     assert(split.featureType === Categorical)
     assert(split.threshold === Double.MinValue)
 
-    val stats = bestSplits(0)._2
-    val predict = bestSplits(0)._3.predict
+    val stats = rootNode.stats.get
     assert(stats.gain > 0)
-    assert(predict === 0.6)
+    assert(rootNode.predict === 0.6)
     assert(stats.impurity > 0.2)
   }
 
@@ -356,13 +353,16 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
     assert(bins(0).length === 100)
 
     val treeInput = TreePoint.convertToTreeRDD(rdd, bins, metadata)
-    val bestSplits = DecisionTree.findBestSplits(treeInput, new Array(8), metadata, 0,
-      new Array[Node](0), splits, bins, 10)
-    assert(bestSplits.length === 1)
-    assert(bestSplits(0)._1.feature === 0)
-    assert(bestSplits(0)._2.gain === 0)
-    assert(bestSplits(0)._2.leftImpurity === 0)
-    assert(bestSplits(0)._2.rightImpurity === 0)
+    val (rootNode, doneTraining) = DecisionTree.findBestSplits(treeInput, metadata, 0,
+      null, splits, bins, 10)
+
+    val split = rootNode.split.get
+    assert(split.feature === 0)
+
+    val stats = rootNode.stats.get
+    assert(stats.gain === 0)
+    assert(stats.leftImpurity === 0)
+    assert(stats.rightImpurity === 0)
   }
 
   test("Binary classification stump with fixed label 1 for Gini") {
@@ -382,14 +382,17 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
     assert(bins(0).length === 100)
 
     val treeInput = TreePoint.convertToTreeRDD(rdd, bins, metadata)
-    val bestSplits = DecisionTree.findBestSplits(treeInput, new Array(2), metadata, 0,
-      new Array[Node](0), splits, bins, 10)
-    assert(bestSplits.length === 1)
-    assert(bestSplits(0)._1.feature === 0)
-    assert(bestSplits(0)._2.gain === 0)
-    assert(bestSplits(0)._2.leftImpurity === 0)
-    assert(bestSplits(0)._2.rightImpurity === 0)
-    assert(bestSplits(0)._3.predict === 1)
+    val (rootNode, doneTraining) = DecisionTree.findBestSplits(treeInput, metadata, 0,
+      null, splits, bins, 10)
+
+    val split = rootNode.split.get
+    assert(split.feature === 0)
+
+    val stats = rootNode.stats.get
+    assert(stats.gain === 0)
+    assert(stats.leftImpurity === 0)
+    assert(stats.rightImpurity === 0)
+    assert(rootNode.predict === 1)
   }
 
   test("Binary classification stump with fixed label 0 for Entropy") {
@@ -409,14 +412,17 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
     assert(bins(0).length === 100)
 
     val treeInput = TreePoint.convertToTreeRDD(rdd, bins, metadata)
-    val bestSplits = DecisionTree.findBestSplits(treeInput, new Array(2), metadata, 0,
-      new Array[Node](0), splits, bins, 10)
-    assert(bestSplits.length === 1)
-    assert(bestSplits(0)._1.feature === 0)
-    assert(bestSplits(0)._2.gain === 0)
-    assert(bestSplits(0)._2.leftImpurity === 0)
-    assert(bestSplits(0)._2.rightImpurity === 0)
-    assert(bestSplits(0)._3.predict === 0)
+    val (rootNode, doneTraining) = DecisionTree.findBestSplits(treeInput, metadata, 0,
+      null, splits, bins, 10)
+
+    val split = rootNode.split.get
+    assert(split.feature === 0)
+
+    val stats = rootNode.stats.get
+    assert(stats.gain === 0)
+    assert(stats.leftImpurity === 0)
+    assert(stats.rightImpurity === 0)
+    assert(rootNode.predict === 0)
   }
 
   test("Binary classification stump with fixed label 1 for Entropy") {
@@ -436,14 +442,17 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
     assert(bins(0).length === 100)
 
     val treeInput = TreePoint.convertToTreeRDD(rdd, bins, metadata)
-    val bestSplits = DecisionTree.findBestSplits(treeInput, new Array(2), metadata, 0,
-      new Array[Node](0), splits, bins, 10)
-    assert(bestSplits.length === 1)
-    assert(bestSplits(0)._1.feature === 0)
-    assert(bestSplits(0)._2.gain === 0)
-    assert(bestSplits(0)._2.leftImpurity === 0)
-    assert(bestSplits(0)._2.rightImpurity === 0)
-    assert(bestSplits(0)._3.predict === 1)
+    val (rootNode, doneTraining) = DecisionTree.findBestSplits(treeInput, metadata, 0,
+      null, splits, bins, 10)
+
+    val split = rootNode.split.get
+    assert(split.feature === 0)
+
+    val stats = rootNode.stats.get
+    assert(stats.gain === 0)
+    assert(stats.leftImpurity === 0)
+    assert(stats.rightImpurity === 0)
+    assert(rootNode.predict === 1)
   }
 
   test("Second level node building with vs. without groups") {
@@ -459,40 +468,46 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
     assert(bins(0).length === 100)
 
     // Train a 1-node model
-    val strategyOneNode = new Strategy(Classification, Entropy, 1, 2, 100)
+    val strategyOneNode = new Strategy(Classification, Entropy, maxDepth = 1,
+      numClassesForClassification = 2, maxBins = 100)
     val modelOneNode = DecisionTree.train(rdd, strategyOneNode)
-    val nodes: Array[Node] = new Array[Node](8)
-    nodes(1) = modelOneNode.topNode
-    nodes(1).leftNode = None
-    nodes(1).rightNode = None
-
-    val parentImpurities = Array(0, 0.5, 0.5, 0.5)
+    val rootNodeCopy1 = modelOneNode.topNode.deepCopy()
+    val rootNodeCopy2 = modelOneNode.topNode.deepCopy()
 
     // Single group second level tree construction.
     val treeInput = TreePoint.convertToTreeRDD(rdd, bins, metadata)
-    val bestSplits = DecisionTree.findBestSplits(treeInput, parentImpurities, metadata, 1, nodes,
-      splits, bins, 10)
-    assert(bestSplits.length === 2)
-    assert(bestSplits(0)._2.gain > 0)
-    assert(bestSplits(1)._2.gain > 0)
+    val (rootNode, _) = DecisionTree.findBestSplits(treeInput, metadata, 1,
+      rootNodeCopy1, splits, bins, 10)
+    assert(rootNode.leftNode.nonEmpty)
+    assert(rootNode.rightNode.nonEmpty)
+    val children1 = new Array[Node](2)
+    children1(0) = rootNode.leftNode.get
+    children1(1) = rootNode.rightNode.get
 
     // maxLevelForSingleGroup parameter is set to 0 to force splitting into groups for second
     // level tree construction.
-    val bestSplitsWithGroups = DecisionTree.findBestSplits(treeInput, parentImpurities, metadata, 1,
-      nodes, splits, bins, 0)
-    assert(bestSplitsWithGroups.length === 2)
-    assert(bestSplitsWithGroups(0)._2.gain > 0)
-    assert(bestSplitsWithGroups(1)._2.gain > 0)
+    val (rootNode2, _) = DecisionTree.findBestSplits(treeInput, metadata, 1,
+      rootNodeCopy2, splits, bins, 0)
+    assert(rootNode2.leftNode.nonEmpty)
+    assert(rootNode2.rightNode.nonEmpty)
+    val children2 = new Array[Node](2)
+    children2(0) = rootNode2.leftNode.get
+    children2(1) = rootNode2.rightNode.get
 
     // Verify whether the splits obtained using single group and multiple group level
     // construction strategies are the same.
-    for (i <- 0 until bestSplits.length) {
-      assert(bestSplits(i)._1 === bestSplitsWithGroups(i)._1)
-      assert(bestSplits(i)._2.gain === bestSplitsWithGroups(i)._2.gain)
-      assert(bestSplits(i)._2.impurity === bestSplitsWithGroups(i)._2.impurity)
-      assert(bestSplits(i)._2.leftImpurity === bestSplitsWithGroups(i)._2.leftImpurity)
-      assert(bestSplits(i)._2.rightImpurity === bestSplitsWithGroups(i)._2.rightImpurity)
-      assert(bestSplits(i)._3.predict === bestSplitsWithGroups(i)._3.predict)
+    for (i <- 0 until 2) {
+      assert(children1(i).stats.nonEmpty && children1(i).stats.get.gain > 0)
+      assert(children2(i).stats.nonEmpty && children2(i).stats.get.gain > 0)
+      assert(children1(i).split === children2(i).split)
+      assert(children1(i).stats.nonEmpty && children2(i).stats.nonEmpty)
+      val stats1 = children1(i).stats.get
+      val stats2 = children2(i).stats.get
+      assert(stats1.gain === stats2.gain)
+      assert(stats1.impurity === stats2.impurity)
+      assert(stats1.leftImpurity === stats2.leftImpurity)
+      assert(stats1.rightImpurity === stats2.rightImpurity)
+      assert(children1(i).predict === children2(i).predict)
     }
   }
 
@@ -508,15 +523,14 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
 
     val (splits, bins) = DecisionTree.findSplitsBins(rdd, metadata)
     val treeInput = TreePoint.convertToTreeRDD(rdd, bins, metadata)
-    val bestSplits = DecisionTree.findBestSplits(treeInput, new Array(32), metadata, 0,
-      new Array[Node](0), splits, bins, 10)
+    val (rootNode, doneTraining) = DecisionTree.findBestSplits(treeInput, metadata, 0,
+      null, splits, bins, 10)
 
-    assert(bestSplits.length === 1)
-    val bestSplit = bestSplits(0)._1
-    assert(bestSplit.feature === 0)
-    assert(bestSplit.categories.length === 1)
-    assert(bestSplit.categories.contains(1))
-    assert(bestSplit.featureType === Categorical)
+    val split = rootNode.split.get
+    assert(split.feature === 0)
+    assert(split.categories.length === 1)
+    assert(split.categories.contains(1))
+    assert(split.featureType === Categorical)
   }
 
   test("Binary classification stump with 1 continuous feature, to check off-by-1 error") {
@@ -573,16 +587,16 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
 
     val (splits, bins) = DecisionTree.findSplitsBins(rdd, metadata)
     val treeInput = TreePoint.convertToTreeRDD(rdd, bins, metadata)
-    val bestSplits = DecisionTree.findBestSplits(treeInput, new Array(32), metadata, 0,
-      new Array[Node](0), splits, bins, 10)
-
-    assert(bestSplits.length === 1)
-    val bestSplit = bestSplits(0)._1
-    assert(bestSplit.feature === 0)
-    assert(bestSplit.categories.length === 1)
-    assert(bestSplit.categories.contains(1))
-    assert(bestSplit.featureType === Categorical)
-    val gain = bestSplits(0)._2
+    val (rootNode, doneTraining) = DecisionTree.findBestSplits(treeInput, metadata, 0,
+      null, splits, bins, 10)
+
+    val split = rootNode.split.get
+    assert(split.feature === 0)
+    assert(split.categories.length === 1)
+    assert(split.categories.contains(1))
+    assert(split.featureType === Categorical)
+
+    val gain = rootNode.stats.get
     assert(gain.leftImpurity === 0)
     assert(gain.rightImpurity === 0)
   }
@@ -600,16 +614,14 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
 
     val (splits, bins) = DecisionTree.findSplitsBins(rdd, metadata)
     val treeInput = TreePoint.convertToTreeRDD(rdd, bins, metadata)
-    val bestSplits = DecisionTree.findBestSplits(treeInput, new Array(32), metadata, 0,
-      new Array[Node](0), splits, bins, 10)
-
-    assert(bestSplits.length === 1)
-    val bestSplit = bestSplits(0)._1
+    val (rootNode, doneTraining) = DecisionTree.findBestSplits(treeInput, metadata, 0,
+      null, splits, bins, 10)
 
-    assert(bestSplit.feature === 1)
-    assert(bestSplit.featureType === Continuous)
-    assert(bestSplit.threshold > 1980)
-    assert(bestSplit.threshold < 2020)
+    val split = rootNode.split.get
+    assert(split.feature === 1)
+    assert(split.featureType === Continuous)
+    assert(split.threshold > 1980)
+    assert(split.threshold < 2020)
 
   }
 
@@ -627,16 +639,14 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
 
     val (splits, bins) = DecisionTree.findSplitsBins(rdd, metadata)
     val treeInput = TreePoint.convertToTreeRDD(rdd, bins, metadata)
-    val bestSplits = DecisionTree.findBestSplits(treeInput, new Array(32), metadata, 0,
-      new Array[Node](0), splits, bins, 10)
+    val (rootNode, doneTraining) = DecisionTree.findBestSplits(treeInput, metadata, 0,
+      null, splits, bins, 10)
 
-    assert(bestSplits.length === 1)
-    val bestSplit = bestSplits(0)._1
-
-    assert(bestSplit.feature === 1)
-    assert(bestSplit.featureType === Continuous)
-    assert(bestSplit.threshold > 1980)
-    assert(bestSplit.threshold < 2020)
+    val split = rootNode.split.get
+    assert(split.feature === 1)
+    assert(split.featureType === Continuous)
+    assert(split.threshold > 1980)
+    assert(split.threshold < 2020)
   }
 
   test("Multiclass classification stump with 10-ary (ordered) categorical features") {
@@ -652,15 +662,14 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
 
     val (splits, bins) = DecisionTree.findSplitsBins(rdd, metadata)
     val treeInput = TreePoint.convertToTreeRDD(rdd, bins, metadata)
-    val bestSplits = DecisionTree.findBestSplits(treeInput, new Array(32), metadata, 0,
-      new Array[Node](0), splits, bins, 10)
+    val (rootNode, doneTraining) = DecisionTree.findBestSplits(treeInput, metadata, 0,
+      null, splits, bins, 10)
 
-    assert(bestSplits.length === 1)
-    val bestSplit = bestSplits(0)._1
-    assert(bestSplit.feature === 0)
-    assert(bestSplit.categories.length === 1)
-    assert(bestSplit.categories.contains(1.0))
-    assert(bestSplit.featureType === Categorical)
+    val split = rootNode.split.get
+    assert(split.feature === 0)
+    assert(split.categories.length === 1)
+    assert(split.categories.contains(1.0))
+    assert(split.featureType === Categorical)
   }
 
   test("Multiclass classification tree with 10-ary (ordered) categorical features," +
@@ -698,12 +707,11 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
     val metadata = DecisionTreeMetadata.buildMetadata(input, strategy)
     val (splits, bins) = DecisionTree.findSplitsBins(input, metadata)
     val treeInput = TreePoint.convertToTreeRDD(input, bins, metadata)
-    val bestSplits = DecisionTree.findBestSplits(treeInput, new Array(8), metadata, 0,
-      new Array[Node](0), splits, bins, 10)
+    val (rootNode, doneTraining) = DecisionTree.findBestSplits(treeInput, metadata, 0,
+      null, splits, bins, 10)
 
-    assert(bestSplits.length == 1)
-    val bestInfoStats = bestSplits(0)._2
-    assert(bestInfoStats == InformationGainStats.invalidInformationGainStats)
+    val gain = rootNode.stats.get
+    assert(gain == InformationGainStats.invalidInformationGainStats)
   }
 
   test("don't choose split that doesn't satisfy min instance per node requirements") {
@@ -722,14 +730,13 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
     val metadata = DecisionTreeMetadata.buildMetadata(input, strategy)
     val (splits, bins) = DecisionTree.findSplitsBins(input, metadata)
     val treeInput = TreePoint.convertToTreeRDD(input, bins, metadata)
-    val bestSplits = DecisionTree.findBestSplits(treeInput, new Array(8), metadata, 0,
-      new Array[Node](0), splits, bins, 10)
+    val (rootNode, doneTraining) = DecisionTree.findBestSplits(treeInput, metadata, 0,
+      null, splits, bins, 10)
 
-    assert(bestSplits.length == 1)
-    val bestSplit = bestSplits(0)._1
-    val bestSplitStats = bestSplits(0)._1
-    assert(bestSplit.feature == 1)
-    assert(bestSplitStats != InformationGainStats.invalidInformationGainStats)
+    val split = rootNode.split.get
+    val gain = rootNode.stats.get
+    assert(split.feature == 1)
+    assert(gain != InformationGainStats.invalidInformationGainStats)
   }
 
   test("split must satisfy min info gain requirements") {
@@ -754,12 +761,11 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
     val metadata = DecisionTreeMetadata.buildMetadata(input, strategy)
     val (splits, bins) = DecisionTree.findSplitsBins(input, metadata)
     val treeInput = TreePoint.convertToTreeRDD(input, bins, metadata)
-    val bestSplits = DecisionTree.findBestSplits(treeInput, new Array(8), metadata, 0,
-      new Array[Node](0), splits, bins, 10)
+    val (rootNode, doneTraining) = DecisionTree.findBestSplits(treeInput, metadata, 0,
+      null, splits, bins, 10)
 
-    assert(bestSplits.length == 1)
-    val bestInfoStats = bestSplits(0)._2
-    assert(bestInfoStats == InformationGainStats.invalidInformationGainStats)
+    val gain = rootNode.stats.get
+    assert(gain == InformationGainStats.invalidInformationGainStats)
   }
 }
 
@@ -786,13 +792,16 @@ object DecisionTreeSuite {
   def generateOrderedLabeledPoints(): Array[LabeledPoint] = {
     val arr = new Array[LabeledPoint](1000)
     for (i <- 0 until 1000) {
-      if (i < 600) {
-        val lp = new LabeledPoint(0.0, Vectors.dense(i.toDouble, 1000.0 - i))
-        arr(i) = lp
+      val label = if (i < 100) {
+        0.0
+      } else if (i < 500) {
+        1.0
+      } else if (i < 900) {
+        0.0
       } else {
-        val lp = new LabeledPoint(1.0, Vectors.dense(i.toDouble, 1000.0 - i))
-        arr(i) = lp
+        1.0
       }
+      arr(i) = new LabeledPoint(label, Vectors.dense(i.toDouble, 1000.0 - i))
     }
     arr
   }

From f116f76bf1f1610905ca094c8edc53151a78d2f4 Mon Sep 17 00:00:00 2001
From: "Mark G. Whitney" <mark@whitneyindustries.com>
Date: Fri, 12 Sep 2014 08:08:58 -0500
Subject: [PATCH 276/489] [SPARK-2558][DOCS] Add --queue example to YARN doc

Put original YARN queue spark-submit arg description in
running-on-yarn html table and example command line

Author: Mark G. Whitney <mark@whitneyindustries.com>

Closes #2218 from kramimus/2258-yarndoc and squashes the following commits:

4b5d808 [Mark G. Whitney] remove yarn queue config
f8cda0d [Mark G. Whitney] [SPARK-2558][DOCS] Add spark.yarn.queue description to YARN doc
---
 docs/running-on-yarn.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/docs/running-on-yarn.md b/docs/running-on-yarn.md
index d8b22f3663d08..212248bcce1c1 100644
--- a/docs/running-on-yarn.md
+++ b/docs/running-on-yarn.md
@@ -155,6 +155,7 @@ For example:
         --driver-memory 4g \
         --executor-memory 2g \
         --executor-cores 1 \
+        --queue thequeue \
         lib/spark-examples*.jar \
         10
 

From 533377621f1e178e18fa0b79d653a11b66e4e250 Mon Sep 17 00:00:00 2001
From: RJ Nowling <rnowling@gmail.com>
Date: Fri, 12 Sep 2014 09:46:21 -0700
Subject: [PATCH 277/489] [PySpark] Add blank line so that Python RDD.top()
 docstring renders correctly

Author: RJ Nowling <rnowling@gmail.com>

Closes #2370 from rnowling/python_rdd_docstrings and squashes the following commits:

5230574 [RJ Nowling] Add blank line so that Python RDD.top() docstring renders correctly
---
 python/pyspark/rdd.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/python/pyspark/rdd.py b/python/pyspark/rdd.py
index 5667154cb84a8..6ad5ab2a2d1ae 100644
--- a/python/pyspark/rdd.py
+++ b/python/pyspark/rdd.py
@@ -1060,6 +1060,7 @@ def top(self, num, key=None):
         Get the top N elements from a RDD.
 
         Note: It returns the list sorted in descending order.
+
         >>> sc.parallelize([10, 4, 2, 12, 3]).top(1)
         [12]
         >>> sc.parallelize([2, 3, 4, 5, 6], 2).top(2)

From 8194fc662c08eb445444c207264e22361def54ea Mon Sep 17 00:00:00 2001
From: Cheng Hao <hao.cheng@intel.com>
Date: Fri, 12 Sep 2014 11:29:30 -0700
Subject: [PATCH 278/489] [SPARK-3481] [SQL] Eliminate the error log in local
 Hive comparison test

Logically, we should remove the Hive Table/Database first and then reset the Hive configuration, repoint to the new data warehouse directory etc.
Otherwise it raised exceptions like "Database doesn't not exists: default" in the local testing.

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

Closes #2352 from chenghao-intel/test_hive and squashes the following commits:

74fd76b [Cheng Hao] eliminate the error log
---
 .../org/apache/spark/sql/hive/TestHive.scala    | 17 ++++++++---------
 1 file changed, 8 insertions(+), 9 deletions(-)

diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/TestHive.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/TestHive.scala
index 6974f3e581b97..a3bfd3a8f1fd2 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/TestHive.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/TestHive.scala
@@ -376,15 +376,6 @@ class TestHiveContext(sc: SparkContext) extends HiveContext(sc) {
         log.asInstanceOf[org.apache.log4j.Logger].setLevel(org.apache.log4j.Level.WARN)
       }
 
-      // It is important that we RESET first as broken hooks that might have been set could break
-      // other sql exec here.
-      runSqlHive("RESET")
-      // For some reason, RESET does not reset the following variables...
-      runSqlHive("set datanucleus.cache.collections=true")
-      runSqlHive("set datanucleus.cache.collections.lazy=true")
-      // Lots of tests fail if we do not change the partition whitelist from the default.
-      runSqlHive("set hive.metastore.partition.name.whitelist.pattern=.*")
-
       loadedTables.clear()
       catalog.client.getAllTables("default").foreach { t =>
         logDebug(s"Deleting table $t")
@@ -410,6 +401,14 @@ class TestHiveContext(sc: SparkContext) extends HiveContext(sc) {
         FunctionRegistry.unregisterTemporaryUDF(udfName)
       }
 
+      // It is important that we RESET first as broken hooks that might have been set could break
+      // other sql exec here.
+      runSqlHive("RESET")
+      // For some reason, RESET does not reset the following variables...
+      runSqlHive("set datanucleus.cache.collections=true")
+      runSqlHive("set datanucleus.cache.collections.lazy=true")
+      // Lots of tests fail if we do not change the partition whitelist from the default.
+      runSqlHive("set hive.metastore.partition.name.whitelist.pattern=.*")
       configure()
 
       runSqlHive("USE default")

From eae81b0bfdf3159be90f507a03853800aec1874a Mon Sep 17 00:00:00 2001
From: Patrick Wendell <pwendell@gmail.com>
Date: Fri, 12 Sep 2014 13:43:29 -0700
Subject: [PATCH 279/489] MAINTENANCE: Automated closing of pull requests.

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

Closes #930 (close requested by 'andrewor14')
Closes #867 (close requested by 'marmbrus')
Closes #1829 (close requested by 'marmbrus')
Closes #1131 (close requested by 'JoshRosen')
Closes #1571 (close requested by 'andrewor14')
Closes #2359 (close requested by 'andrewor14')

From 15a564598fe63003652b1e24527c432080b5976c Mon Sep 17 00:00:00 2001
From: Ankur Dave <ankurdave@gmail.com>
Date: Fri, 12 Sep 2014 14:08:38 -0700
Subject: [PATCH 280/489] [SPARK-3427] [GraphX] Avoid active vertex tracking in
 static PageRank

GraphX's current implementation of static (fixed iteration count) PageRank uses the Pregel API. This unnecessarily tracks active vertices, even though in static PageRank all vertices are always active. Active vertex tracking incurs the following costs:

1. A shuffle per iteration to ship the active sets to the edge partitions.
2. A hash table creation per iteration at each partition to index the active sets for lookup.
3. A hash lookup per edge to check whether the source vertex is active.

I reimplemented static PageRank using the lower-level GraphX API instead of the Pregel API. In benchmarks on a 16-node m2.4xlarge cluster, this provided a 23% speedup (from 514 s to 397 s, mean over 3 trials) for 10 iterations of PageRank on a synthetic graph with 10M vertices and 1.27B edges.

Author: Ankur Dave <ankurdave@gmail.com>

Closes #2308 from ankurdave/SPARK-3427 and squashes the following commits:

449996a [Ankur Dave] Avoid unnecessary active vertex tracking in static PageRank
---
 .../apache/spark/graphx/lib/PageRank.scala    | 45 ++++++++++++-------
 1 file changed, 29 insertions(+), 16 deletions(-)

diff --git a/graphx/src/main/scala/org/apache/spark/graphx/lib/PageRank.scala b/graphx/src/main/scala/org/apache/spark/graphx/lib/PageRank.scala
index 614555a054dfb..257e2f3a36115 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/lib/PageRank.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/lib/PageRank.scala
@@ -79,30 +79,43 @@ object PageRank extends Logging {
   def run[VD: ClassTag, ED: ClassTag](
       graph: Graph[VD, ED], numIter: Int, resetProb: Double = 0.15): Graph[Double, Double] =
   {
-    // Initialize the pagerankGraph with each edge attribute having
+    // Initialize the PageRank graph with each edge attribute having
     // weight 1/outDegree and each vertex with attribute 1.0.
-    val pagerankGraph: Graph[Double, Double] = graph
+    var rankGraph: Graph[Double, Double] = graph
       // Associate the degree with each vertex
       .outerJoinVertices(graph.outDegrees) { (vid, vdata, deg) => deg.getOrElse(0) }
       // Set the weight on the edges based on the degree
       .mapTriplets( e => 1.0 / e.srcAttr )
       // Set the vertex attributes to the initial pagerank values
-      .mapVertices( (id, attr) => 1.0 )
-      .cache()
+      .mapVertices( (id, attr) => resetProb )
 
-    // Define the three functions needed to implement PageRank in the GraphX
-    // version of Pregel
-    def vertexProgram(id: VertexId, attr: Double, msgSum: Double): Double =
-      resetProb + (1.0 - resetProb) * msgSum
-    def sendMessage(edge: EdgeTriplet[Double, Double]) =
-      Iterator((edge.dstId, edge.srcAttr * edge.attr))
-    def messageCombiner(a: Double, b: Double): Double = a + b
-    // The initial message received by all vertices in PageRank
-    val initialMessage = 0.0
+    var iteration = 0
+    var prevRankGraph: Graph[Double, Double] = null
+    while (iteration < numIter) {
+      rankGraph.cache()
 
-    // Execute pregel for a fixed number of iterations.
-    Pregel(pagerankGraph, initialMessage, numIter, activeDirection = EdgeDirection.Out)(
-      vertexProgram, sendMessage, messageCombiner)
+      // Compute the outgoing rank contributions of each vertex, perform local preaggregation, and
+      // do the final aggregation at the receiving vertices. Requires a shuffle for aggregation.
+      val rankUpdates = rankGraph.mapReduceTriplets[Double](
+        e => Iterator((e.dstId, e.srcAttr * e.attr)), _ + _)
+
+      // Apply the final rank updates to get the new ranks, using join to preserve ranks of vertices
+      // that didn't receive a message. Requires a shuffle for broadcasting updated ranks to the
+      // edge partitions.
+      prevRankGraph = rankGraph
+      rankGraph = rankGraph.joinVertices(rankUpdates) {
+        (id, oldRank, msgSum) => resetProb + (1.0 - resetProb) * msgSum
+      }.cache()
+
+      rankGraph.edges.foreachPartition(x => {}) // also materializes rankGraph.vertices
+      logInfo(s"PageRank finished iteration $iteration.")
+      prevRankGraph.vertices.unpersist(false)
+      prevRankGraph.edges.unpersist(false)
+
+      iteration += 1
+    }
+
+    rankGraph
   }
 
   /**

From 1d767967e925f1d727957c2d43383ef6ad2c5d5e Mon Sep 17 00:00:00 2001
From: Sandy Ryza <sandy@cloudera.com>
Date: Fri, 12 Sep 2014 16:48:28 -0500
Subject: [PATCH 281/489] SPARK-3014. Log a more informative messages in a
 couple failure scenario...

...s

Author: Sandy Ryza <sandy@cloudera.com>

Closes #1934 from sryza/sandy-spark-3014 and squashes the following commits:

ae19cc1 [Sandy Ryza] SPARK-3014. Log a more informative messages in a couple failure scenarios
---
 .../main/scala/org/apache/spark/deploy/SparkSubmit.scala    | 6 ++++--
 .../org/apache/spark/deploy/yarn/ApplicationMaster.scala    | 6 ++----
 2 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
index 0fdb5ae3c2e40..5ed3575816a38 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
@@ -18,7 +18,7 @@
 package org.apache.spark.deploy
 
 import java.io.{File, PrintStream}
-import java.lang.reflect.InvocationTargetException
+import java.lang.reflect.{Modifier, InvocationTargetException}
 import java.net.URL
 
 import scala.collection.mutable.{ArrayBuffer, HashMap, Map}
@@ -323,7 +323,9 @@ object SparkSubmit {
     }
 
     val mainMethod = mainClass.getMethod("main", new Array[String](0).getClass)
-
+    if (!Modifier.isStatic(mainMethod.getModifiers)) {
+      throw new IllegalStateException("The main method in the given main class must be static")
+    }
     try {
       mainMethod.invoke(null, childArgs.toArray)
     } catch {
diff --git a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
index 878b6db546032..735d7723b0ce6 100644
--- a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
+++ b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
@@ -283,11 +283,9 @@ private[spark] class ApplicationMaster(args: ApplicationMasterArguments,
         }
 
         val sparkContext = sparkContextRef.get()
-        assert(sparkContext != null || count >= numTries)
         if (sparkContext == null) {
-          logError(
-            "Unable to retrieve sparkContext inspite of waiting for %d, numTries = %d".format(
-              count * waitTime, numTries))
+          logError(("SparkContext did not initialize after waiting for %d ms. Please check earlier"
+            + " log output for errors. Failing the application.").format(numTries * waitTime))
         }
         sparkContext
       }

From af2583826c15d2a4e2732017ea20feeff0fb79f6 Mon Sep 17 00:00:00 2001
From: Marcelo Vanzin <vanzin@cloudera.com>
Date: Fri, 12 Sep 2014 14:54:42 -0700
Subject: [PATCH 282/489] [SPARK-3217] Add Guava to classpath when
 SPARK_PREPEND_CLASSES is set.

When that option is used, the compiled classes from the build directory
are prepended to the classpath. Now that we avoid packaging Guava, that
means we have classes referencing the original Guava location in the app's
classpath, so errors happen.

For that case, add Guava manually to the classpath.

Note: if Spark is compiled with "-Phadoop-provided", it's tricky to
make things work with SPARK_PREPEND_CLASSES, because you need to add
the Hadoop classpath using SPARK_CLASSPATH and that means the older
Hadoop Guava overrides the newer one Spark needs. So someone using
SPARK_PREPEND_CLASSES needs to remember to not use that profile.

Author: Marcelo Vanzin <vanzin@cloudera.com>

Closes #2141 from vanzin/SPARK-3217 and squashes the following commits:

b967324 [Marcelo Vanzin] [SPARK-3217] Add Guava to classpath when SPARK_PREPEND_CLASSES is set.
---
 bin/compute-classpath.sh |  1 +
 core/pom.xml             | 27 +++++++++++++++++++++++++++
 2 files changed, 28 insertions(+)

diff --git a/bin/compute-classpath.sh b/bin/compute-classpath.sh
index 15c6779402994..0f63e36d8aeca 100755
--- a/bin/compute-classpath.sh
+++ b/bin/compute-classpath.sh
@@ -43,6 +43,7 @@ if [ -n "$SPARK_PREPEND_CLASSES" ]; then
   echo "NOTE: SPARK_PREPEND_CLASSES is set, placing locally compiled Spark"\
     "classes ahead of assembly." >&2
   CLASSPATH="$CLASSPATH:$FWDIR/core/target/scala-$SCALA_VERSION/classes"
+  CLASSPATH="$CLASSPATH:$FWDIR/core/target/jars/*"
   CLASSPATH="$CLASSPATH:$FWDIR/repl/target/scala-$SCALA_VERSION/classes"
   CLASSPATH="$CLASSPATH:$FWDIR/mllib/target/scala-$SCALA_VERSION/classes"
   CLASSPATH="$CLASSPATH:$FWDIR/bagel/target/scala-$SCALA_VERSION/classes"
diff --git a/core/pom.xml b/core/pom.xml
index b2b788a4bc13b..2a81f6df289c0 100644
--- a/core/pom.xml
+++ b/core/pom.xml
@@ -351,6 +351,33 @@
           </execution>
         </executions>
       </plugin>
+      <!--
+        Copy guava to the build directory. This is needed to make the SPARK_PREPEND_CLASSES
+        option work in compute-classpath.sh, since it would put the non-shaded Spark classes in
+        the runtime classpath.
+      -->
+      <plugin>
+        <groupId>org.apache.maven.plugins</groupId>
+        <artifactId>maven-dependency-plugin</artifactId>
+        <executions>
+          <execution>
+            <id>copy-dependencies</id>
+            <phase>package</phase>
+            <goals>
+              <goal>copy-dependencies</goal>
+            </goals>
+            <configuration>
+              <outputDirectory>${project.build.directory}</outputDirectory>
+              <overWriteReleases>false</overWriteReleases>
+              <overWriteSnapshots>false</overWriteSnapshots>
+              <overWriteIfNewer>true</overWriteIfNewer>
+              <useSubDirectoryPerType>true</useSubDirectoryPerType>
+              <includeArtifactIds>guava</includeArtifactIds>
+              <silent>true</silent>
+            </configuration>
+          </execution>
+        </executions>
+      </plugin>
     </plugins>
 
     <resources>

From 25311c2c545a60eb9dcf704814d4600987852155 Mon Sep 17 00:00:00 2001
From: Thomas Graves <tgraves@apache.org>
Date: Fri, 12 Sep 2014 20:31:11 -0500
Subject: [PATCH 283/489] [SPARK-3456] YarnAllocator on alpha can lose
 container requests to RM

Author: Thomas Graves <tgraves@apache.org>

Closes #2373 from tgravescs/SPARK-3456 and squashes the following commits:

77e9532 [Thomas Graves] [SPARK-3456] YarnAllocator on alpha can lose container requests to RM
---
 .../spark/deploy/yarn/YarnAllocationHandler.scala     | 11 ++++++-----
 .../org/apache/spark/deploy/yarn/YarnAllocator.scala  |  8 ++++++--
 .../spark/deploy/yarn/YarnAllocationHandler.scala     |  3 ++-
 3 files changed, 14 insertions(+), 8 deletions(-)

diff --git a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocationHandler.scala b/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocationHandler.scala
index 5a1b42c1e17d5..6c93d8582330b 100644
--- a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocationHandler.scala
+++ b/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocationHandler.scala
@@ -48,16 +48,17 @@ private[yarn] class YarnAllocationHandler(
   private val lastResponseId = new AtomicInteger()
   private val releaseList: CopyOnWriteArrayList[ContainerId] = new CopyOnWriteArrayList()
 
-  override protected def allocateContainers(count: Int): YarnAllocateResponse = {
+  override protected def allocateContainers(count: Int, pending: Int): YarnAllocateResponse = {
     var resourceRequests: List[ResourceRequest] = null
 
-    logDebug("numExecutors: " + count)
+    logDebug("asking for additional executors: " + count + " with already pending: " + pending)
+    val totalNumAsk = count + pending
     if (count <= 0) {
       resourceRequests = List()
     } else if (preferredHostToCount.isEmpty) {
         logDebug("host preferences is empty")
         resourceRequests = List(createResourceRequest(
-          AllocationType.ANY, null, count, YarnSparkHadoopUtil.RM_REQUEST_PRIORITY))
+          AllocationType.ANY, null, totalNumAsk, YarnSparkHadoopUtil.RM_REQUEST_PRIORITY))
     } else {
       // request for all hosts in preferred nodes and for numExecutors -
       // candidates.size, request by default allocation policy.
@@ -80,7 +81,7 @@ private[yarn] class YarnAllocationHandler(
       val anyContainerRequests: ResourceRequest = createResourceRequest(
         AllocationType.ANY,
         resource = null,
-        count,
+        totalNumAsk,
         YarnSparkHadoopUtil.RM_REQUEST_PRIORITY)
 
       val containerRequests: ArrayBuffer[ResourceRequest] = new ArrayBuffer[ResourceRequest](
@@ -103,7 +104,7 @@ private[yarn] class YarnAllocationHandler(
     req.addAllReleases(releasedContainerList)
 
     if (count > 0) {
-      logInfo("Allocating %d executor containers with %d of memory each.".format(count,
+      logInfo("Allocating %d executor containers with %d of memory each.".format(totalNumAsk,
         executorMemory + memoryOverhead))
     } else {
       logDebug("Empty allocation req ..  release : " + releasedContainerList)
diff --git a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocator.scala b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocator.scala
index 0b8744f4b8bdf..299e38a5eb9c0 100644
--- a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocator.scala
+++ b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocator.scala
@@ -112,6 +112,9 @@ private[yarn] abstract class YarnAllocator(
   def allocateResources() = {
     val missing = maxExecutors - numPendingAllocate.get() - numExecutorsRunning.get()
 
+    // this is needed by alpha, do it here since we add numPending right after this
+    val executorsPending = numPendingAllocate.get()
+
     if (missing > 0) {
       numPendingAllocate.addAndGet(missing)
       logInfo("Will Allocate %d executor containers, each with %d memory".format(
@@ -121,7 +124,7 @@ private[yarn] abstract class YarnAllocator(
       logDebug("Empty allocation request ...")
     }
 
-    val allocateResponse = allocateContainers(missing)
+    val allocateResponse = allocateContainers(missing, executorsPending)
     val allocatedContainers = allocateResponse.getAllocatedContainers()
 
     if (allocatedContainers.size > 0) {
@@ -435,9 +438,10 @@ private[yarn] abstract class YarnAllocator(
    *
    * @param count Number of containers to allocate.
    *              If zero, should still contact RM (as a heartbeat).
+   * @param pending Number of containers pending allocate. Only used on alpha.
    * @return Response to the allocation request.
    */
-  protected def allocateContainers(count: Int): YarnAllocateResponse
+  protected def allocateContainers(count: Int, pending: Int): YarnAllocateResponse
 
   /** Called to release a previously allocated container. */
   protected def releaseContainer(container: Container): Unit
diff --git a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocationHandler.scala b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocationHandler.scala
index 5438f151ac0ad..e44a8db41b97e 100644
--- a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocationHandler.scala
+++ b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocationHandler.scala
@@ -47,7 +47,8 @@ private[yarn] class YarnAllocationHandler(
     amClient.releaseAssignedContainer(container.getId())
   }
 
-  override protected def allocateContainers(count: Int): YarnAllocateResponse = {
+  // pending isn't used on stable as the AMRMClient handles incremental asks
+  override protected def allocateContainers(count: Int, pending: Int): YarnAllocateResponse = {
     addResourceRequests(count)
 
     // We have already set the container request. Poll the ResourceManager for a response.

From 71af030b46a89aaa9a87f18f56b9e1f1cd8ce2e7 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies.liu@gmail.com>
Date: Fri, 12 Sep 2014 18:42:50 -0700
Subject: [PATCH 284/489] [SPARK-3094] [PySpark] compatitable with PyPy

After this patch, we can run PySpark in PyPy (testing with PyPy 2.3.1 in Mac 10.9), for example:

```
PYSPARK_PYTHON=pypy ./bin/spark-submit wordcount.py
```

The performance speed up will depend on work load (from 20% to 3000%). Here are some benchmarks:

 Job | CPython 2.7 | PyPy 2.3.1  | Speed up
 ------- | ------------ | ------------- | -------
 Word Count | 41s   | 15s  | 2.7x
 Sort | 46s |  44s | 1.05x
 Stats | 174s | 3.6s | 48x

Here is the code used for benchmark:

```python
rdd = sc.textFile("text")
def wordcount():
    rdd.flatMap(lambda x:x.split('/'))\
        .map(lambda x:(x,1)).reduceByKey(lambda x,y:x+y).collectAsMap()
def sort():
    rdd.sortBy(lambda x:x, 1).count()
def stats():
    sc.parallelize(range(1024), 20).flatMap(lambda x: xrange(5024)).stats()
```

Author: Davies Liu <davies.liu@gmail.com>

Closes #2144 from davies/pypy and squashes the following commits:

9aed6c5 [Davies Liu] use protocol 2 in CloudPickle
4bc1f04 [Davies Liu] refactor
b20ab3a [Davies Liu] pickle sys.stdout and stderr in portable way
3ca2351 [Davies Liu] Merge branch 'master' into pypy
fae8b19 [Davies Liu] improve attrgetter, add tests
591f830 [Davies Liu] try to run tests with PyPy in run-tests
c8d62ba [Davies Liu] cleanup
f651fd0 [Davies Liu] fix tests using array with PyPy
1b98fb3 [Davies Liu] serialize itemgetter/attrgetter in portable ways
3c1dbfe [Davies Liu] Merge branch 'master' into pypy
42fb5fa [Davies Liu] Merge branch 'master' into pypy
cb2d724 [Davies Liu] fix tests
9986692 [Davies Liu] Merge branch 'master' into pypy
25b4ca7 [Davies Liu] support PyPy
---
 python/pyspark/cloudpickle.py | 168 ++++++++++++++--------------------
 python/pyspark/daemon.py      |   6 +-
 python/pyspark/serializers.py |  10 +-
 python/pyspark/tests.py       |  85 +++++++++++++++--
 python/run-tests              |  21 +++++
 5 files changed, 172 insertions(+), 118 deletions(-)

diff --git a/python/pyspark/cloudpickle.py b/python/pyspark/cloudpickle.py
index 80e51d1a583a0..32dda3888c62d 100644
--- a/python/pyspark/cloudpickle.py
+++ b/python/pyspark/cloudpickle.py
@@ -52,35 +52,19 @@
 import itertools
 from copy_reg import _extension_registry, _inverted_registry, _extension_cache
 import new
-import dis
 import traceback
+import platform
 
-#relevant opcodes
-STORE_GLOBAL = chr(dis.opname.index('STORE_GLOBAL'))
-DELETE_GLOBAL = chr(dis.opname.index('DELETE_GLOBAL'))
-LOAD_GLOBAL = chr(dis.opname.index('LOAD_GLOBAL'))
-GLOBAL_OPS = [STORE_GLOBAL, DELETE_GLOBAL, LOAD_GLOBAL]
+PyImp = platform.python_implementation()
 
-HAVE_ARGUMENT = chr(dis.HAVE_ARGUMENT)
-EXTENDED_ARG = chr(dis.EXTENDED_ARG)
 
 import logging
 cloudLog = logging.getLogger("Cloud.Transport")
 
-try:
-    import ctypes
-except (MemoryError, ImportError):
-    logging.warning('Exception raised on importing ctypes. Likely python bug.. some functionality will be disabled', exc_info = True)
-    ctypes = None
-    PyObject_HEAD = None
-else:
-
-    # for reading internal structures
-    PyObject_HEAD = [
-        ('ob_refcnt', ctypes.c_size_t),
-        ('ob_type', ctypes.c_void_p),
-    ]
 
+if PyImp == "PyPy":
+    # register builtin type in `new`
+    new.method = types.MethodType
 
 try:
     from cStringIO import StringIO
@@ -225,6 +209,8 @@ def save_function(self, obj, name=None, pack=struct.pack):
 
         if themodule:
             self.modules.add(themodule)
+            if getattr(themodule, name, None) is obj:
+                return self.save_global(obj, name)
 
         if not self.savedDjangoEnv:
             #hack for django - if we detect the settings module, we transport it
@@ -306,44 +292,28 @@ def save_function_tuple(self, func, forced_imports):
 
         # create a skeleton function object and memoize it
         save(_make_skel_func)
-        save((code, len(closure), base_globals))
+        save((code, closure, base_globals))
         write(pickle.REDUCE)
         self.memoize(func)
 
         # save the rest of the func data needed by _fill_function
         save(f_globals)
         save(defaults)
-        save(closure)
         save(dct)
         write(pickle.TUPLE)
         write(pickle.REDUCE)  # applies _fill_function on the tuple
 
     @staticmethod
-    def extract_code_globals(co):
+    def extract_code_globals(code):
         """
         Find all globals names read or written to by codeblock co
         """
-        code = co.co_code
-        names = co.co_names
-        out_names = set()
-
-        n = len(code)
-        i = 0
-        extended_arg = 0
-        while i < n:
-            op = code[i]
-
-            i = i+1
-            if op >= HAVE_ARGUMENT:
-                oparg = ord(code[i]) + ord(code[i+1])*256 + extended_arg
-                extended_arg = 0
-                i = i+2
-                if op == EXTENDED_ARG:
-                    extended_arg = oparg*65536L
-                if op in GLOBAL_OPS:
-                    out_names.add(names[oparg])
-        #print 'extracted', out_names, ' from ', names
-        return out_names
+        names = set(code.co_names)
+        if code.co_consts:   # see if nested function have any global refs
+            for const in code.co_consts:
+                if type(const) is types.CodeType:
+                    names |= CloudPickler.extract_code_globals(const)
+        return names
 
     def extract_func_data(self, func):
         """
@@ -354,10 +324,7 @@ def extract_func_data(self, func):
 
         # extract all global ref's
         func_global_refs = CloudPickler.extract_code_globals(code)
-        if code.co_consts:   # see if nested function have any global refs
-            for const in code.co_consts:
-                if type(const) is types.CodeType and const.co_names:
-                    func_global_refs = func_global_refs.union( CloudPickler.extract_code_globals(const))
+
         # process all variables referenced by global environment
         f_globals = {}
         for var in func_global_refs:
@@ -396,6 +363,12 @@ def get_contents(cell):
 
         return (code, f_globals, defaults, closure, dct, base_globals)
 
+    def save_builtin_function(self, obj):
+        if obj.__module__ is "__builtin__":
+            return self.save_global(obj)
+        return self.save_function(obj)
+    dispatch[types.BuiltinFunctionType] = save_builtin_function
+
     def save_global(self, obj, name=None, pack=struct.pack):
         write = self.write
         memo = self.memo
@@ -435,7 +408,7 @@ def save_global(self, obj, name=None, pack=struct.pack):
                     try:
                         klass = getattr(themodule, name)
                     except AttributeError, a:
-                        #print themodule, name, obj, type(obj)
+                        # print themodule, name, obj, type(obj)
                         raise pickle.PicklingError("Can't pickle builtin %s" % obj)
                 else:
                     raise
@@ -480,7 +453,6 @@ def save_global(self, obj, name=None, pack=struct.pack):
         write(pickle.GLOBAL + modname + '\n' + name + '\n')
         self.memoize(obj)
     dispatch[types.ClassType] = save_global
-    dispatch[types.BuiltinFunctionType] = save_global
     dispatch[types.TypeType] = save_global
 
     def save_instancemethod(self, obj):
@@ -551,23 +523,39 @@ def save_property(self, obj):
     dispatch[property] = save_property
 
     def save_itemgetter(self, obj):
-        """itemgetter serializer (needed for namedtuple support)
-        a bit of a pain as we need to read ctypes internals"""
-        class ItemGetterType(ctypes.Structure):
-            _fields_ = PyObject_HEAD + [
-                ('nitems', ctypes.c_size_t),
-                ('item', ctypes.py_object)
-            ]
-
-
-        obj = ctypes.cast(ctypes.c_void_p(id(obj)), ctypes.POINTER(ItemGetterType)).contents
-        return self.save_reduce(operator.itemgetter,
-                obj.item if obj.nitems > 1 else (obj.item,))
-
-    if PyObject_HEAD:
+        """itemgetter serializer (needed for namedtuple support)"""
+        class Dummy:
+            def __getitem__(self, item):
+                return item
+        items = obj(Dummy())
+        if not isinstance(items, tuple):
+            items = (items, )
+        return self.save_reduce(operator.itemgetter, items)
+
+    if type(operator.itemgetter) is type:
         dispatch[operator.itemgetter] = save_itemgetter
 
+    def save_attrgetter(self, obj):
+        """attrgetter serializer"""
+        class Dummy(object):
+            def __init__(self, attrs, index=None):
+                self.attrs = attrs
+                self.index = index
+            def __getattribute__(self, item):
+                attrs = object.__getattribute__(self, "attrs")
+                index = object.__getattribute__(self, "index")
+                if index is None:
+                    index = len(attrs)
+                    attrs.append(item)
+                else:
+                    attrs[index] = ".".join([attrs[index], item])
+                return type(self)(attrs, index)
+        attrs = []
+        obj(Dummy(attrs))
+        return self.save_reduce(operator.attrgetter, tuple(attrs))
 
+    if type(operator.attrgetter) is type:
+        dispatch[operator.attrgetter] = save_attrgetter
 
     def save_reduce(self, func, args, state=None,
                     listitems=None, dictitems=None, obj=None):
@@ -660,11 +648,11 @@ def save_file(self, obj):
 
         if not hasattr(obj, 'name') or  not hasattr(obj, 'mode'):
             raise pickle.PicklingError("Cannot pickle files that do not map to an actual file")
-        if obj.name == '<stdout>':
+        if obj is sys.stdout:
             return self.save_reduce(getattr, (sys,'stdout'), obj=obj)
-        if obj.name == '<stderr>':
+        if obj is sys.stderr:
             return self.save_reduce(getattr, (sys,'stderr'), obj=obj)
-        if obj.name == '<stdin>':
+        if obj is sys.stdin:
             raise pickle.PicklingError("Cannot pickle standard input")
         if  hasattr(obj, 'isatty') and obj.isatty():
             raise pickle.PicklingError("Cannot pickle files that map to tty objects")
@@ -873,8 +861,7 @@ def _genpartial(func, args, kwds):
         kwds = {}
     return partial(func, *args, **kwds)
 
-
-def _fill_function(func, globals, defaults, closure, dict):
+def _fill_function(func, globals, defaults, dict):
     """ Fills in the rest of function data into the skeleton function object
         that were created via _make_skel_func().
          """
@@ -882,49 +869,28 @@ def _fill_function(func, globals, defaults, closure, dict):
     func.func_defaults = defaults
     func.func_dict = dict
 
-    if len(closure) != len(func.func_closure):
-        raise pickle.UnpicklingError("closure lengths don't match up")
-    for i in range(len(closure)):
-        _change_cell_value(func.func_closure[i], closure[i])
-
     return func
 
-def _make_skel_func(code, num_closures, base_globals = None):
+def _make_cell(value):
+    return (lambda: value).func_closure[0]
+
+def _reconstruct_closure(values):
+    return tuple([_make_cell(v) for v in values])
+
+def _make_skel_func(code, closures, base_globals = None):
     """ Creates a skeleton function object that contains just the provided
         code and the correct number of cells in func_closure.  All other
         func attributes (e.g. func_globals) are empty.
     """
-    #build closure (cells):
-    if not ctypes:
-        raise Exception('ctypes failed to import; cannot build function')
-
-    cellnew = ctypes.pythonapi.PyCell_New
-    cellnew.restype = ctypes.py_object
-    cellnew.argtypes = (ctypes.py_object,)
-    dummy_closure = tuple(map(lambda i: cellnew(None), range(num_closures)))
+    closure = _reconstruct_closure(closures) if closures else None
 
     if base_globals is None:
         base_globals = {}
     base_globals['__builtins__'] = __builtins__
 
     return types.FunctionType(code, base_globals,
-                              None, None, dummy_closure)
-
-# this piece of opaque code is needed below to modify 'cell' contents
-cell_changer_code = new.code(
-    1, 1, 2, 0,
-    ''.join([
-        chr(dis.opmap['LOAD_FAST']), '\x00\x00',
-        chr(dis.opmap['DUP_TOP']),
-        chr(dis.opmap['STORE_DEREF']), '\x00\x00',
-        chr(dis.opmap['RETURN_VALUE'])
-    ]),
-    (), (), ('newval',), '<nowhere>', 'cell_changer', 1, '', ('c',), ()
-)
-
-def _change_cell_value(cell, newval):
-    """ Changes the contents of 'cell' object to newval """
-    return new.function(cell_changer_code, {}, None, (), (cell,))(newval)
+                              None, None, closure)
+
 
 """Constructors for 3rd party libraries
 Note: These can never be renamed due to client compatibility issues"""
diff --git a/python/pyspark/daemon.py b/python/pyspark/daemon.py
index 22ab8d30c0ae3..15445abf67147 100644
--- a/python/pyspark/daemon.py
+++ b/python/pyspark/daemon.py
@@ -42,10 +42,6 @@ def worker(sock):
     """
     Called by a worker process after the fork().
     """
-    # Redirect stdout to stderr
-    os.dup2(2, 1)
-    sys.stdout = sys.stderr  # The sys.stdout object is different from file descriptor 1
-
     signal.signal(SIGHUP, SIG_DFL)
     signal.signal(SIGCHLD, SIG_DFL)
     signal.signal(SIGTERM, SIG_DFL)
@@ -102,6 +98,7 @@ def manager():
     listen_sock.listen(max(1024, SOMAXCONN))
     listen_host, listen_port = listen_sock.getsockname()
     write_int(listen_port, sys.stdout)
+    sys.stdout.flush()
 
     def shutdown(code):
         signal.signal(SIGTERM, SIG_DFL)
@@ -115,7 +112,6 @@ def handle_sigterm(*args):
     signal.signal(SIGHUP, SIG_IGN)  # Don't die on SIGHUP
 
     # Initialization complete
-    sys.stdout.close()
     try:
         while True:
             try:
diff --git a/python/pyspark/serializers.py b/python/pyspark/serializers.py
index 7b2710b913128..a5f9341e819a9 100644
--- a/python/pyspark/serializers.py
+++ b/python/pyspark/serializers.py
@@ -355,7 +355,8 @@ class PickleSerializer(FramedSerializer):
     def dumps(self, obj):
         return cPickle.dumps(obj, 2)
 
-    loads = cPickle.loads
+    def loads(self, obj):
+        return cPickle.loads(obj)
 
 
 class CloudPickleSerializer(PickleSerializer):
@@ -374,8 +375,11 @@ class MarshalSerializer(FramedSerializer):
     This serializer is faster than PickleSerializer but supports fewer datatypes.
     """
 
-    dumps = marshal.dumps
-    loads = marshal.loads
+    def dumps(self, obj):
+        return marshal.dumps(obj)
+
+    def loads(self, obj):
+        return marshal.loads(obj)
 
 
 class AutoSerializer(FramedSerializer):
diff --git a/python/pyspark/tests.py b/python/pyspark/tests.py
index bb84ebe72cb24..2e7c2750a8bb6 100644
--- a/python/pyspark/tests.py
+++ b/python/pyspark/tests.py
@@ -31,6 +31,7 @@
 import time
 import zipfile
 import random
+from platform import python_implementation
 
 if sys.version_info[:2] <= (2, 6):
     import unittest2 as unittest
@@ -41,7 +42,8 @@
 from pyspark.conf import SparkConf
 from pyspark.context import SparkContext
 from pyspark.files import SparkFiles
-from pyspark.serializers import read_int, BatchedSerializer, MarshalSerializer, PickleSerializer
+from pyspark.serializers import read_int, BatchedSerializer, MarshalSerializer, PickleSerializer, \
+    CloudPickleSerializer
 from pyspark.shuffle import Aggregator, InMemoryMerger, ExternalMerger, ExternalSorter
 from pyspark.sql import SQLContext, IntegerType
 
@@ -168,15 +170,46 @@ def test_namedtuple(self):
         p2 = loads(dumps(p1, 2))
         self.assertEquals(p1, p2)
 
-
-# Regression test for SPARK-3415
-class CloudPickleTest(unittest.TestCase):
+    def test_itemgetter(self):
+        from operator import itemgetter
+        ser = CloudPickleSerializer()
+        d = range(10)
+        getter = itemgetter(1)
+        getter2 = ser.loads(ser.dumps(getter))
+        self.assertEqual(getter(d), getter2(d))
+
+        getter = itemgetter(0, 3)
+        getter2 = ser.loads(ser.dumps(getter))
+        self.assertEqual(getter(d), getter2(d))
+
+    def test_attrgetter(self):
+        from operator import attrgetter
+        ser = CloudPickleSerializer()
+
+        class C(object):
+            def __getattr__(self, item):
+                return item
+        d = C()
+        getter = attrgetter("a")
+        getter2 = ser.loads(ser.dumps(getter))
+        self.assertEqual(getter(d), getter2(d))
+        getter = attrgetter("a", "b")
+        getter2 = ser.loads(ser.dumps(getter))
+        self.assertEqual(getter(d), getter2(d))
+
+        d.e = C()
+        getter = attrgetter("e.a")
+        getter2 = ser.loads(ser.dumps(getter))
+        self.assertEqual(getter(d), getter2(d))
+        getter = attrgetter("e.a", "e.b")
+        getter2 = ser.loads(ser.dumps(getter))
+        self.assertEqual(getter(d), getter2(d))
+
+    # Regression test for SPARK-3415
     def test_pickling_file_handles(self):
-        from pyspark.cloudpickle import dumps
-        from StringIO import StringIO
-        from pickle import load
+        ser = CloudPickleSerializer()
         out1 = sys.stderr
-        out2 = load(StringIO(dumps(out1)))
+        out2 = ser.loads(ser.dumps(out1))
         self.assertEquals(out1, out2)
 
 
@@ -861,8 +894,42 @@ def test_oldhadoop(self):
             conf=input_conf).collect())
         self.assertEqual(old_dataset, dict_data)
 
-    @unittest.skipIf(sys.version_info[:2] <= (2, 6), "Skipped on 2.6 until SPARK-2951 is fixed")
     def test_newhadoop(self):
+        basepath = self.tempdir.name
+        data = [(1, ""),
+                (1, "a"),
+                (2, "bcdf")]
+        self.sc.parallelize(data).saveAsNewAPIHadoopFile(
+            basepath + "/newhadoop/",
+            "org.apache.hadoop.mapreduce.lib.output.SequenceFileOutputFormat",
+            "org.apache.hadoop.io.IntWritable",
+            "org.apache.hadoop.io.Text")
+        result = sorted(self.sc.newAPIHadoopFile(
+            basepath + "/newhadoop/",
+            "org.apache.hadoop.mapreduce.lib.input.SequenceFileInputFormat",
+            "org.apache.hadoop.io.IntWritable",
+            "org.apache.hadoop.io.Text").collect())
+        self.assertEqual(result, data)
+
+        conf = {
+            "mapreduce.outputformat.class":
+                "org.apache.hadoop.mapreduce.lib.output.SequenceFileOutputFormat",
+            "mapred.output.key.class": "org.apache.hadoop.io.IntWritable",
+            "mapred.output.value.class": "org.apache.hadoop.io.Text",
+            "mapred.output.dir": basepath + "/newdataset/"
+        }
+        self.sc.parallelize(data).saveAsNewAPIHadoopDataset(conf)
+        input_conf = {"mapred.input.dir": basepath + "/newdataset/"}
+        new_dataset = sorted(self.sc.newAPIHadoopRDD(
+            "org.apache.hadoop.mapreduce.lib.input.SequenceFileInputFormat",
+            "org.apache.hadoop.io.IntWritable",
+            "org.apache.hadoop.io.Text",
+            conf=input_conf).collect())
+        self.assertEqual(new_dataset, data)
+
+    @unittest.skipIf(sys.version_info[:2] <= (2, 6) or python_implementation() == "PyPy",
+                     "Skipped on 2.6 and PyPy until SPARK-2951 is fixed")
+    def test_newhadoop_with_array(self):
         basepath = self.tempdir.name
         # use custom ArrayWritable types and converters to handle arrays
         array_data = [(1, array('d')),
diff --git a/python/run-tests b/python/run-tests
index d98840de59d2c..a67e5a99fbdcc 100755
--- a/python/run-tests
+++ b/python/run-tests
@@ -85,6 +85,27 @@ run_test "pyspark/mllib/tests.py"
 run_test "pyspark/mllib/tree.py"
 run_test "pyspark/mllib/util.py"
 
+# Try to test with PyPy
+if [ $(which pypy) ]; then
+    export PYSPARK_PYTHON="pypy"
+    echo "Testing with PyPy version:"
+    $PYSPARK_PYTHON --version
+
+    run_test "pyspark/rdd.py"
+    run_test "pyspark/context.py"
+    run_test "pyspark/conf.py"
+    run_test "pyspark/sql.py"
+    # These tests are included in the module-level docs, and so must
+    # be handled on a higher level rather than within the python file.
+    export PYSPARK_DOC_TEST=1
+    run_test "pyspark/broadcast.py"
+    run_test "pyspark/accumulators.py"
+    run_test "pyspark/serializers.py"
+    unset PYSPARK_DOC_TEST
+    run_test "pyspark/shuffle.py"
+    run_test "pyspark/tests.py"
+fi
+
 if [[ $FAILED == 0 ]]; then
     echo -en "\033[32m"  # Green
     echo "Tests passed."

From 885d1621bc06bc1f009c9707c3452eac26baf828 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies.liu@gmail.com>
Date: Fri, 12 Sep 2014 19:05:39 -0700
Subject: [PATCH 285/489] [SPARK-3500] [SQL] use JavaSchemaRDD as
 SchemaRDD._jschema_rdd

Currently, SchemaRDD._jschema_rdd is SchemaRDD, the Scala API (coalesce(), repartition()) can not been called in Python easily, there is no way to specify the implicit parameter `ord`. The _jrdd is an JavaRDD, so _jschema_rdd should also be JavaSchemaRDD.

In this patch, change _schema_rdd to JavaSchemaRDD, also added an assert for it. If some methods are missing from JavaSchemaRDD, then it's called by _schema_rdd.baseSchemaRDD().xxx().

BTW, Do we need JavaSQLContext?

Author: Davies Liu <davies.liu@gmail.com>

Closes #2369 from davies/fix_schemardd and squashes the following commits:

abee159 [Davies Liu] use JavaSchemaRDD as SchemaRDD._jschema_rdd
---
 python/pyspark/sql.py   | 38 ++++++++++++++++++--------------------
 python/pyspark/tests.py | 28 ++++++++++++++++++++++++++++
 2 files changed, 46 insertions(+), 20 deletions(-)

diff --git a/python/pyspark/sql.py b/python/pyspark/sql.py
index 53eea6d6cf3ba..fc9310fef318c 100644
--- a/python/pyspark/sql.py
+++ b/python/pyspark/sql.py
@@ -1122,7 +1122,7 @@ def applySchema(self, rdd, schema):
         batched = isinstance(rdd._jrdd_deserializer, BatchedSerializer)
         jrdd = self._pythonToJava(rdd._jrdd, batched)
         srdd = self._ssql_ctx.applySchemaToPythonRDD(jrdd.rdd(), str(schema))
-        return SchemaRDD(srdd, self)
+        return SchemaRDD(srdd.toJavaSchemaRDD(), self)
 
     def registerRDDAsTable(self, rdd, tableName):
         """Registers the given RDD as a temporary table in the catalog.
@@ -1134,8 +1134,8 @@ def registerRDDAsTable(self, rdd, tableName):
         >>> sqlCtx.registerRDDAsTable(srdd, "table1")
         """
         if (rdd.__class__ is SchemaRDD):
-            jschema_rdd = rdd._jschema_rdd
-            self._ssql_ctx.registerRDDAsTable(jschema_rdd, tableName)
+            srdd = rdd._jschema_rdd.baseSchemaRDD()
+            self._ssql_ctx.registerRDDAsTable(srdd, tableName)
         else:
             raise ValueError("Can only register SchemaRDD as table")
 
@@ -1151,7 +1151,7 @@ def parquetFile(self, path):
         >>> sorted(srdd.collect()) == sorted(srdd2.collect())
         True
         """
-        jschema_rdd = self._ssql_ctx.parquetFile(path)
+        jschema_rdd = self._ssql_ctx.parquetFile(path).toJavaSchemaRDD()
         return SchemaRDD(jschema_rdd, self)
 
     def jsonFile(self, path, schema=None):
@@ -1207,11 +1207,11 @@ def jsonFile(self, path, schema=None):
         [Row(f1=u'row1', f2=None, f3=None)...Row(f1=u'row3', f2=[], f3=None)]
         """
         if schema is None:
-            jschema_rdd = self._ssql_ctx.jsonFile(path)
+            srdd = self._ssql_ctx.jsonFile(path)
         else:
             scala_datatype = self._ssql_ctx.parseDataType(str(schema))
-            jschema_rdd = self._ssql_ctx.jsonFile(path, scala_datatype)
-        return SchemaRDD(jschema_rdd, self)
+            srdd = self._ssql_ctx.jsonFile(path, scala_datatype)
+        return SchemaRDD(srdd.toJavaSchemaRDD(), self)
 
     def jsonRDD(self, rdd, schema=None):
         """Loads an RDD storing one JSON object per string as a L{SchemaRDD}.
@@ -1275,11 +1275,11 @@ def func(iterator):
         keyed._bypass_serializer = True
         jrdd = keyed._jrdd.map(self._jvm.BytesToString())
         if schema is None:
-            jschema_rdd = self._ssql_ctx.jsonRDD(jrdd.rdd())
+            srdd = self._ssql_ctx.jsonRDD(jrdd.rdd())
         else:
             scala_datatype = self._ssql_ctx.parseDataType(str(schema))
-            jschema_rdd = self._ssql_ctx.jsonRDD(jrdd.rdd(), scala_datatype)
-        return SchemaRDD(jschema_rdd, self)
+            srdd = self._ssql_ctx.jsonRDD(jrdd.rdd(), scala_datatype)
+        return SchemaRDD(srdd.toJavaSchemaRDD(), self)
 
     def sql(self, sqlQuery):
         """Return a L{SchemaRDD} representing the result of the given query.
@@ -1290,7 +1290,7 @@ def sql(self, sqlQuery):
         >>> srdd2.collect()
         [Row(f1=1, f2=u'row1'), Row(f1=2, f2=u'row2'), Row(f1=3, f2=u'row3')]
         """
-        return SchemaRDD(self._ssql_ctx.sql(sqlQuery), self)
+        return SchemaRDD(self._ssql_ctx.sql(sqlQuery).toJavaSchemaRDD(), self)
 
     def table(self, tableName):
         """Returns the specified table as a L{SchemaRDD}.
@@ -1301,7 +1301,7 @@ def table(self, tableName):
         >>> sorted(srdd.collect()) == sorted(srdd2.collect())
         True
         """
-        return SchemaRDD(self._ssql_ctx.table(tableName), self)
+        return SchemaRDD(self._ssql_ctx.table(tableName).toJavaSchemaRDD(), self)
 
     def cacheTable(self, tableName):
         """Caches the specified table in-memory."""
@@ -1353,7 +1353,7 @@ def hiveql(self, hqlQuery):
         warnings.warn("hiveql() is deprecated as the sql function now parses using HiveQL by" +
                       "default. The SQL dialect for parsing can be set using 'spark.sql.dialect'",
                       DeprecationWarning)
-        return SchemaRDD(self._ssql_ctx.hiveql(hqlQuery), self)
+        return SchemaRDD(self._ssql_ctx.hiveql(hqlQuery).toJavaSchemaRDD(), self)
 
     def hql(self, hqlQuery):
         """
@@ -1524,6 +1524,8 @@ class SchemaRDD(RDD):
     def __init__(self, jschema_rdd, sql_ctx):
         self.sql_ctx = sql_ctx
         self._sc = sql_ctx._sc
+        clsName = jschema_rdd.getClass().getName()
+        assert clsName.endswith("JavaSchemaRDD"), "jschema_rdd must be JavaSchemaRDD"
         self._jschema_rdd = jschema_rdd
         self._id = None
         self.is_cached = False
@@ -1540,7 +1542,7 @@ def _jrdd(self):
         L{pyspark.rdd.RDD} super class (map, filter, etc.).
         """
         if not hasattr(self, '_lazy_jrdd'):
-            self._lazy_jrdd = self._jschema_rdd.javaToPython()
+            self._lazy_jrdd = self._jschema_rdd.baseSchemaRDD().javaToPython()
         return self._lazy_jrdd
 
     def id(self):
@@ -1598,7 +1600,7 @@ def saveAsTable(self, tableName):
     def schema(self):
         """Returns the schema of this SchemaRDD (represented by
         a L{StructType})."""
-        return _parse_datatype_string(self._jschema_rdd.schema().toString())
+        return _parse_datatype_string(self._jschema_rdd.baseSchemaRDD().schema().toString())
 
     def schemaString(self):
         """Returns the output schema in the tree format."""
@@ -1649,8 +1651,6 @@ def mapPartitionsWithIndex(self, f, preservesPartitioning=False):
         rdd = RDD(self._jrdd, self._sc, self._jrdd_deserializer)
 
         schema = self.schema()
-        import pickle
-        pickle.loads(pickle.dumps(schema))
 
         def applySchema(_, it):
             cls = _create_cls(schema)
@@ -1687,10 +1687,8 @@ def isCheckpointed(self):
 
     def getCheckpointFile(self):
         checkpointFile = self._jschema_rdd.getCheckpointFile()
-        if checkpointFile.isDefined():
+        if checkpointFile.isPresent():
             return checkpointFile.get()
-        else:
-            return None
 
     def coalesce(self, numPartitions, shuffle=False):
         rdd = self._jschema_rdd.coalesce(numPartitions, shuffle)
diff --git a/python/pyspark/tests.py b/python/pyspark/tests.py
index 2e7c2750a8bb6..b687d695b01c4 100644
--- a/python/pyspark/tests.py
+++ b/python/pyspark/tests.py
@@ -607,6 +607,34 @@ def test_broadcast_in_udf(self):
         [res] = self.sqlCtx.sql("SELECT MYUDF('')").collect()
         self.assertEqual("", res[0])
 
+    def test_basic_functions(self):
+        rdd = self.sc.parallelize(['{"foo":"bar"}', '{"foo":"baz"}'])
+        srdd = self.sqlCtx.jsonRDD(rdd)
+        srdd.count()
+        srdd.collect()
+        srdd.schemaString()
+        srdd.schema()
+
+        # cache and checkpoint
+        self.assertFalse(srdd.is_cached)
+        srdd.persist()
+        srdd.unpersist()
+        srdd.cache()
+        self.assertTrue(srdd.is_cached)
+        self.assertFalse(srdd.isCheckpointed())
+        self.assertEqual(None, srdd.getCheckpointFile())
+
+        srdd = srdd.coalesce(2, True)
+        srdd = srdd.repartition(3)
+        srdd = srdd.distinct()
+        srdd.intersection(srdd)
+        self.assertEqual(2, srdd.count())
+
+        srdd.registerTempTable("temp")
+        srdd = self.sqlCtx.sql("select foo from temp")
+        srdd.count()
+        srdd.collect()
+
 
 class TestIO(PySparkTestCase):
 

From 6d887db7891be643f0131b136e82191b5f6eb407 Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian.cs.zju@gmail.com>
Date: Fri, 12 Sep 2014 20:14:09 -0700
Subject: [PATCH 286/489] [SPARK-3515][SQL] Moves test suite setup code to
 beforeAll rather than in constructor

Please refer to the JIRA ticket for details.

**NOTE** We should check all test suites that do similar initialization-like side effects in their constructors. This PR only fixes `ParquetMetastoreSuite` because it breaks our Jenkins Maven build.

Author: Cheng Lian <lian.cs.zju@gmail.com>

Closes #2375 from liancheng/say-no-to-constructor and squashes the following commits:

0ceb75b [Cheng Lian] Moves test suite setup code to beforeAll rather than in constructor
---
 .../sql/parquet/ParquetMetastoreSuite.scala   | 53 +++++++++----------
 1 file changed, 24 insertions(+), 29 deletions(-)

diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/parquet/ParquetMetastoreSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/parquet/ParquetMetastoreSuite.scala
index 0723be7298e15..e380280f301c1 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/parquet/ParquetMetastoreSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/parquet/ParquetMetastoreSuite.scala
@@ -20,14 +20,10 @@ package org.apache.spark.sql.parquet
 
 import java.io.File
 
-import org.apache.spark.sql.hive.execution.HiveTableScan
 import org.scalatest.BeforeAndAfterAll
 
-import scala.reflect.ClassTag
-
-import org.apache.spark.sql.{SQLConf, QueryTest}
-import org.apache.spark.sql.execution.{BroadcastHashJoin, ShuffledHashJoin}
-import org.apache.spark.sql.hive.test.TestHive
+import org.apache.spark.sql.QueryTest
+import org.apache.spark.sql.hive.execution.HiveTableScan
 import org.apache.spark.sql.hive.test.TestHive._
 
 case class ParquetData(intField: Int, stringField: String)
@@ -36,27 +32,19 @@ case class ParquetData(intField: Int, stringField: String)
  * Tests for our SerDe -> Native parquet scan conversion.
  */
 class ParquetMetastoreSuite extends QueryTest with BeforeAndAfterAll {
-
   override def beforeAll(): Unit = {
-    setConf("spark.sql.hive.convertMetastoreParquet", "true")
-  }
-
-  override def afterAll(): Unit = {
-    setConf("spark.sql.hive.convertMetastoreParquet", "false")
-  }
-
-  val partitionedTableDir = File.createTempFile("parquettests", "sparksql")
-  partitionedTableDir.delete()
-  partitionedTableDir.mkdir()
-
-  (1 to 10).foreach { p =>
-    val partDir = new File(partitionedTableDir, s"p=$p")
-    sparkContext.makeRDD(1 to 10)
-      .map(i => ParquetData(i, s"part-$p"))
-      .saveAsParquetFile(partDir.getCanonicalPath)
-  }
-
-  sql(s"""
+    val partitionedTableDir = File.createTempFile("parquettests", "sparksql")
+    partitionedTableDir.delete()
+    partitionedTableDir.mkdir()
+
+    (1 to 10).foreach { p =>
+      val partDir = new File(partitionedTableDir, s"p=$p")
+      sparkContext.makeRDD(1 to 10)
+        .map(i => ParquetData(i, s"part-$p"))
+        .saveAsParquetFile(partDir.getCanonicalPath)
+    }
+
+    sql(s"""
     create external table partitioned_parquet
     (
       intField INT,
@@ -70,7 +58,7 @@ class ParquetMetastoreSuite extends QueryTest with BeforeAndAfterAll {
     location '${partitionedTableDir.getCanonicalPath}'
     """)
 
-  sql(s"""
+    sql(s"""
     create external table normal_parquet
     (
       intField INT,
@@ -83,8 +71,15 @@ class ParquetMetastoreSuite extends QueryTest with BeforeAndAfterAll {
     location '${new File(partitionedTableDir, "p=1").getCanonicalPath}'
     """)
 
-  (1 to 10).foreach { p =>
-    sql(s"ALTER TABLE partitioned_parquet ADD PARTITION (p=$p)")
+    (1 to 10).foreach { p =>
+      sql(s"ALTER TABLE partitioned_parquet ADD PARTITION (p=$p)")
+    }
+
+    setConf("spark.sql.hive.convertMetastoreParquet", "true")
+  }
+
+  override def afterAll(): Unit = {
+    setConf("spark.sql.hive.convertMetastoreParquet", "false")
   }
 
   test("project the partitioning column") {

From 2584ea5b23b1c5a4df9549b94bfc9b8e0900532e Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@apache.org>
Date: Fri, 12 Sep 2014 21:55:39 -0700
Subject: [PATCH 287/489] [SPARK-3469] Make sure all TaskCompletionListener are
 called even with failures

This is necessary because we rely on this callback interface to clean resources up. The old behavior would lead to resource leaks.

Note that this also changes the fault semantics of TaskCompletionListener. Previously failures in TaskCompletionListeners would result in the task being reported immediately. With this change, we report the exception at the end, and the reported exception is a TaskCompletionListenerException that contains all the exception messages.

Author: Reynold Xin <rxin@apache.org>

Closes #2343 from rxin/taskcontext-callback and squashes the following commits:

a3845b2 [Reynold Xin] Mark TaskCompletionListenerException as private[spark].
ac5baea [Reynold Xin] Removed obsolete comment.
aa68ea4 [Reynold Xin] Throw an exception if task completion callback fails.
29b6162 [Reynold Xin] oops compilation failed.
1cb444d [Reynold Xin] [SPARK-3469] Call all TaskCompletionListeners even if some fail.
---
 .../scala/org/apache/spark/TaskContext.scala  | 18 ++++++++--
 .../TaskCompletionListenerException.scala     | 34 +++++++++++++++++++
 .../spark/scheduler/TaskContextSuite.scala    | 22 ++++++++++--
 3 files changed, 69 insertions(+), 5 deletions(-)
 create mode 100644 core/src/main/scala/org/apache/spark/util/TaskCompletionListenerException.scala

diff --git a/core/src/main/scala/org/apache/spark/TaskContext.scala b/core/src/main/scala/org/apache/spark/TaskContext.scala
index 2b99b8a5af250..51b3e4d5e0936 100644
--- a/core/src/main/scala/org/apache/spark/TaskContext.scala
+++ b/core/src/main/scala/org/apache/spark/TaskContext.scala
@@ -21,7 +21,7 @@ import scala.collection.mutable.ArrayBuffer
 
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.executor.TaskMetrics
-import org.apache.spark.util.TaskCompletionListener
+import org.apache.spark.util.{TaskCompletionListenerException, TaskCompletionListener}
 
 
 /**
@@ -41,7 +41,7 @@ class TaskContext(
     val attemptId: Long,
     val runningLocally: Boolean = false,
     private[spark] val taskMetrics: TaskMetrics = TaskMetrics.empty)
-  extends Serializable {
+  extends Serializable with Logging {
 
   @deprecated("use partitionId", "0.8.1")
   def splitId = partitionId
@@ -103,8 +103,20 @@ class TaskContext(
   /** Marks the task as completed and triggers the listeners. */
   private[spark] def markTaskCompleted(): Unit = {
     completed = true
+    val errorMsgs = new ArrayBuffer[String](2)
     // Process complete callbacks in the reverse order of registration
-    onCompleteCallbacks.reverse.foreach { _.onTaskCompletion(this) }
+    onCompleteCallbacks.reverse.foreach { listener =>
+      try {
+        listener.onTaskCompletion(this)
+      } catch {
+        case e: Throwable =>
+          errorMsgs += e.getMessage
+          logError("Error in TaskCompletionListener", e)
+      }
+    }
+    if (errorMsgs.nonEmpty) {
+      throw new TaskCompletionListenerException(errorMsgs)
+    }
   }
 
   /** Marks the task for interruption, i.e. cancellation. */
diff --git a/core/src/main/scala/org/apache/spark/util/TaskCompletionListenerException.scala b/core/src/main/scala/org/apache/spark/util/TaskCompletionListenerException.scala
new file mode 100644
index 0000000000000..f64e069cd1724
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/util/TaskCompletionListenerException.scala
@@ -0,0 +1,34 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.util
+
+/**
+ * Exception thrown when there is an exception in
+ * executing the callback in TaskCompletionListener.
+ */
+private[spark]
+class TaskCompletionListenerException(errorMessages: Seq[String]) extends Exception {
+
+  override def getMessage: String = {
+    if (errorMessages.size == 1) {
+      errorMessages.head
+    } else {
+      errorMessages.zipWithIndex.map { case (msg, i) => s"Exception $i: $msg" }.mkString("\n")
+    }
+  }
+}
diff --git a/core/src/test/scala/org/apache/spark/scheduler/TaskContextSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/TaskContextSuite.scala
index db2ad829a48f9..faba5508c906c 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/TaskContextSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/TaskContextSuite.scala
@@ -17,16 +17,20 @@
 
 package org.apache.spark.scheduler
 
+import org.mockito.Mockito._
+import org.mockito.Matchers.any
+
 import org.scalatest.FunSuite
 import org.scalatest.BeforeAndAfter
 
 import org.apache.spark._
 import org.apache.spark.rdd.RDD
-import org.apache.spark.util.Utils
+import org.apache.spark.util.{TaskCompletionListenerException, TaskCompletionListener}
+
 
 class TaskContextSuite extends FunSuite with BeforeAndAfter with LocalSparkContext {
 
-  test("Calls executeOnCompleteCallbacks after failure") {
+  test("calls TaskCompletionListener after failure") {
     TaskContextSuite.completed = false
     sc = new SparkContext("local", "test")
     val rdd = new RDD[String](sc, List()) {
@@ -45,6 +49,20 @@ class TaskContextSuite extends FunSuite with BeforeAndAfter with LocalSparkConte
     }
     assert(TaskContextSuite.completed === true)
   }
+
+  test("all TaskCompletionListeners should be called even if some fail") {
+    val context = new TaskContext(0, 0, 0)
+    val listener = mock(classOf[TaskCompletionListener])
+    context.addTaskCompletionListener(_ => throw new Exception("blah"))
+    context.addTaskCompletionListener(listener)
+    context.addTaskCompletionListener(_ => throw new Exception("blah"))
+
+    intercept[TaskCompletionListenerException] {
+      context.markTaskCompleted()
+    }
+
+    verify(listener, times(1)).onTaskCompletion(any())
+  }
 }
 
 private object TaskContextSuite {

From e11eeb71fa3a5fe7ddacb94d5b93b173d4d901a8 Mon Sep 17 00:00:00 2001
From: Yin Huai <huai@cse.ohio-state.edu>
Date: Fri, 12 Sep 2014 21:58:02 -0700
Subject: [PATCH 288/489] [SQL][Docs] Update SQL programming guide to show the
 correct default value of containsNull in an ArrayType

After #1889, the default value of `containsNull` in an `ArrayType` is `true`.

Author: Yin Huai <huai@cse.ohio-state.edu>

Closes #2374 from yhuai/containsNull and squashes the following commits:

dc609a3 [Yin Huai] Update the SQL programming guide to show the correct default value of containsNull in an ArrayType (the default value is true instead of false).
---
 docs/sql-programming-guide.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md
index d83efa4bab324..3159d52787d5a 100644
--- a/docs/sql-programming-guide.md
+++ b/docs/sql-programming-guide.md
@@ -1232,7 +1232,7 @@ import  org.apache.spark.sql._
   <td> scala.collection.Seq </td>
   <td>
   ArrayType(<i>elementType</i>, [<i>containsNull</i>])<br />
-  <b>Note:</b> The default value of <i>containsNull</i> is <i>false</i>.
+  <b>Note:</b> The default value of <i>containsNull</i> is <i>true</i>.
   </td>
 </tr>
 <tr>
@@ -1358,7 +1358,7 @@ please use factory methods provided in
   <td> java.util.List </td>
   <td>
   DataType.createArrayType(<i>elementType</i>)<br />
-  <b>Note:</b> The value of <i>containsNull</i> will be <i>false</i><br />
+  <b>Note:</b> The value of <i>containsNull</i> will be <i>true</i><br />
   DataType.createArrayType(<i>elementType</i>, <i>containsNull</i>).
   </td>
 </tr>
@@ -1505,7 +1505,7 @@ from pyspark.sql import *
   <td> list, tuple, or array </td>
   <td>
   ArrayType(<i>elementType</i>, [<i>containsNull</i>])<br />
-  <b>Note:</b> The default value of <i>containsNull</i> is <i>False</i>.
+  <b>Note:</b> The default value of <i>containsNull</i> is <i>True</i>.
   </td>
 </tr>
 <tr>

From feaa3706f17e44efcdac9f0a543a5b91232771ce Mon Sep 17 00:00:00 2001
From: Sean Owen <sowen@cloudera.com>
Date: Fri, 12 Sep 2014 22:50:37 -0700
Subject: [PATCH 289/489] SPARK-3470 [CORE] [STREAMING] Add Closeable / close()
 to Java context objects

...  that expose a stop() lifecycle method. This doesn't add `AutoCloseable`, which is Java 7+ only. But it should be possible to use try-with-resources on a `Closeable` in Java 7, as long as the `close()` does not throw a checked exception, and these don't. Q.E.D.

Author: Sean Owen <sowen@cloudera.com>

Closes #2346 from srowen/SPARK-3470 and squashes the following commits:

612c21d [Sean Owen] Add Closeable / close() to Java context objects that expose a stop() lifecycle method
---
 .../scala/org/apache/spark/api/java/JavaSparkContext.scala | 7 ++++++-
 .../spark/streaming/api/java/JavaStreamingContext.scala    | 7 +++++--
 2 files changed, 11 insertions(+), 3 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/api/java/JavaSparkContext.scala b/core/src/main/scala/org/apache/spark/api/java/JavaSparkContext.scala
index 8e178bc8480f7..23f7e6be81a90 100644
--- a/core/src/main/scala/org/apache/spark/api/java/JavaSparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/api/java/JavaSparkContext.scala
@@ -17,6 +17,7 @@
 
 package org.apache.spark.api.java
 
+import java.io.Closeable
 import java.util
 import java.util.{Map => JMap}
 
@@ -40,7 +41,9 @@ import org.apache.spark.rdd.{EmptyRDD, HadoopRDD, NewHadoopRDD, RDD}
  * A Java-friendly version of [[org.apache.spark.SparkContext]] that returns
  * [[org.apache.spark.api.java.JavaRDD]]s and works with Java collections instead of Scala ones.
  */
-class JavaSparkContext(val sc: SparkContext) extends JavaSparkContextVarargsWorkaround {
+class JavaSparkContext(val sc: SparkContext)
+    extends JavaSparkContextVarargsWorkaround with Closeable {
+
   /**
    * Create a JavaSparkContext that loads settings from system properties (for instance, when
    * launching with ./bin/spark-submit).
@@ -534,6 +537,8 @@ class JavaSparkContext(val sc: SparkContext) extends JavaSparkContextVarargsWork
     sc.stop()
   }
 
+  override def close(): Unit = stop()
+
   /**
    * Get Spark's home location from either a value set through the constructor,
    * or the spark.home Java property, or the SPARK_HOME environment variable
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala
index 18605cac7006c..9dc26dc6b32a1 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala
@@ -21,7 +21,7 @@ package org.apache.spark.streaming.api.java
 import scala.collection.JavaConversions._
 import scala.reflect.ClassTag
 
-import java.io.InputStream
+import java.io.{Closeable, InputStream}
 import java.util.{List => JList, Map => JMap}
 
 import akka.actor.{Props, SupervisorStrategy}
@@ -49,7 +49,7 @@ import org.apache.spark.streaming.receiver.Receiver
  * respectively. `context.awaitTransformation()` allows the current thread to wait for the
  * termination of a context by `stop()` or by an exception.
  */
-class JavaStreamingContext(val ssc: StreamingContext) {
+class JavaStreamingContext(val ssc: StreamingContext) extends Closeable {
 
   /**
    * Create a StreamingContext.
@@ -540,6 +540,9 @@ class JavaStreamingContext(val ssc: StreamingContext) {
   def stop(stopSparkContext: Boolean, stopGracefully: Boolean) = {
     ssc.stop(stopSparkContext, stopGracefully)
   }
+
+  override def close(): Unit = stop()
+
 }
 
 /**

From b4dded40fbecb485f1ddfd8316b44d42a1554d64 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@apache.org>
Date: Fri, 12 Sep 2014 22:51:25 -0700
Subject: [PATCH 290/489] Proper indent for the previous commit.

---
 .../main/scala/org/apache/spark/api/java/JavaSparkContext.scala | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/api/java/JavaSparkContext.scala b/core/src/main/scala/org/apache/spark/api/java/JavaSparkContext.scala
index 23f7e6be81a90..791d853a015a1 100644
--- a/core/src/main/scala/org/apache/spark/api/java/JavaSparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/api/java/JavaSparkContext.scala
@@ -42,7 +42,7 @@ import org.apache.spark.rdd.{EmptyRDD, HadoopRDD, NewHadoopRDD, RDD}
  * [[org.apache.spark.api.java.JavaRDD]]s and works with Java collections instead of Scala ones.
  */
 class JavaSparkContext(val sc: SparkContext)
-    extends JavaSparkContextVarargsWorkaround with Closeable {
+  extends JavaSparkContextVarargsWorkaround with Closeable {
 
   /**
    * Create a JavaSparkContext that loads settings from system properties (for instance, when

From a523ceaf159733dabcef84c7adc1463546679f65 Mon Sep 17 00:00:00 2001
From: Nicholas Chammas <nicholas.chammas@gmail.com>
Date: Sat, 13 Sep 2014 12:34:20 -0700
Subject: [PATCH 291/489] [SQL] [Docs] typo fixes

* Fixed random typo
* Added in missing description for DecimalType

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

Closes #2367 from nchammas/patch-1 and squashes the following commits:

aa528be [Nicholas Chammas] doc fix for SQL DecimalType
3247ac1 [Nicholas Chammas] [SQL] [Docs] typo fixes
---
 docs/sql-programming-guide.md | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md
index 3159d52787d5a..8d41fdec699e9 100644
--- a/docs/sql-programming-guide.md
+++ b/docs/sql-programming-guide.md
@@ -918,7 +918,6 @@ options.
 ## Migration Guide for Shark User
 
 ### Scheduling 
-s
 To set a [Fair Scheduler](job-scheduling.html#fair-scheduler-pools) pool for a JDBC client session,
 users can set the `spark.sql.thriftserver.scheduler.pool` variable:
 
@@ -1110,7 +1109,7 @@ evaluated by the SQL execution engine.  A full list of the functions supported c
     The range of numbers is from `-9223372036854775808` to `9223372036854775807`.
     - `FloatType`: Represents 4-byte single-precision floating point numbers.
     - `DoubleType`: Represents 8-byte double-precision floating point numbers.
-    - `DecimalType`: 
+    - `DecimalType`: Represents arbitrary-precision signed decimal numbers. Backed internally by `java.math.BigDecimal`. A `BigDecimal` consists of an arbitrary precision integer unscaled value and a 32-bit integer scale.
 * String type
     - `StringType`: Represents character string values.
 * Binary type

From 184cd51c4207c23726da97f907f2d912a5a44845 Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian.cs.zju@gmail.com>
Date: Sat, 13 Sep 2014 12:35:40 -0700
Subject: [PATCH 292/489] [SPARK-3481][SQL] Removes the evil MINOR HACK

This is a follow up of #2352. Now we can finally remove the evil "MINOR HACK", which covered up the eldest bug in the history of Spark SQL (see details [here](https://github.com/apache/spark/pull/2352#issuecomment-55440621)).

Author: Cheng Lian <lian.cs.zju@gmail.com>

Closes #2377 from liancheng/remove-evil-minor-hack and squashes the following commits:

0869c78 [Cheng Lian] Removes the evil MINOR HACK
---
 .../spark/sql/hive/execution/HiveComparisonTest.scala       | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala
index 671c3b162f875..79cc7a3fcc7d6 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala
@@ -250,9 +250,9 @@ abstract class HiveComparisonTest
       }
 
       try {
-        // MINOR HACK: You must run a query before calling reset the first time.
-        TestHive.sql("SHOW TABLES")
-        if (reset) { TestHive.reset() }
+        if (reset) {
+          TestHive.reset()
+        }
 
         val hiveCacheFiles = queryList.zipWithIndex.map {
           case (queryString, i)  =>

From 74049249abb952ad061c0e221c22ff894a9e9c8d Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian.cs.zju@gmail.com>
Date: Sat, 13 Sep 2014 15:08:30 -0700
Subject: [PATCH 293/489] [SPARK-3294][SQL] Eliminates boxing costs from
 in-memory columnar storage

This is a major refactoring of the in-memory columnar storage implementation, aims to eliminate boxing costs from critical paths (building/accessing column buffers) as much as possible. The basic idea is to refactor all major interfaces into a row-based form and use them together with `SpecificMutableRow`. The difficult part is how to adapt all compression schemes, esp. `RunLengthEncoding` and `DictionaryEncoding`, to this design. Since in-memory compression is disabled by default for now, and this PR should be strictly better than before no matter in-memory compression is enabled or not, maybe I'll finish that part in another PR.

**UPDATE** This PR also took the chance to optimize `HiveTableScan` by

1. leveraging `SpecificMutableRow` to avoid boxing cost, and
1. building specific `Writable` unwrapper functions a head of time to avoid per row pattern matching and branching costs.

TODO

- [x] Benchmark
- [ ] ~~Eliminate boxing costs in `RunLengthEncoding`~~ (left to future PRs)
- [ ] ~~Eliminate boxing costs in `DictionaryEncoding` (seems not easy to do without specializing `DictionaryEncoding` for every supported column type)~~  (left to future PRs)

## Micro benchmark

The benchmark uses a 10 million line CSV table consists of bytes, shorts, integers, longs, floats and doubles, measures the time to build the in-memory version of this table, and the time to scan the whole in-memory table.

Benchmark code can be found [here](https://gist.github.com/liancheng/fe70a148de82e77bd2c8#file-hivetablescanbenchmark-scala). Script used to generate the input table can be found [here](https://gist.github.com/liancheng/fe70a148de82e77bd2c8#file-tablegen-scala).

Speedup:

- Hive table scanning + column buffer building: **18.74%**

  The original benchmark uses 1K as in-memory batch size, when increased to 10K, it can be 28.32% faster.

- In-memory table scanning: **7.95%**

Before:

        | Building | Scanning
------- | -------- | --------
1       | 16472    | 525
2       | 16168    | 530
3       | 16386    | 529
4       | 16184    | 538
5       | 16209    | 521
Average | 16283.8  | 528.6

After:

        | Building | Scanning
------- | -------- | --------
1       | 13124    | 458
2       | 13260    | 529
3       | 12981    | 463
4       | 13214    | 483
5       | 13583    | 500
Average | 13232.4  | 486.6

Author: Cheng Lian <lian.cs.zju@gmail.com>

Closes #2327 from liancheng/prevent-boxing/unboxing and squashes the following commits:

4419fe4 [Cheng Lian] Addressing comments
e5d2cf2 [Cheng Lian] Bug fix: should call setNullAt when field value is null to avoid NPE
8b8552b [Cheng Lian] Only checks for partition batch pruning flag once
489f97b [Cheng Lian] Bug fix: TableReader.fillObject uses wrong ordinals
97bbc4e [Cheng Lian] Optimizes hive.TableReader by by providing specific Writable unwrappers a head of time
3dc1f94 [Cheng Lian] Minor changes to eliminate row object creation
5b39cb9 [Cheng Lian] Lowers log level of compression scheme details
f2a7890 [Cheng Lian] Use SpecificMutableRow in InMemoryColumnarTableScan to avoid boxing
9cf30b0 [Cheng Lian] Added row based ColumnType.append/extract
456c366 [Cheng Lian] Made compression decoder row based
edac3cd [Cheng Lian] Makes ColumnAccessor.extractSingle row based
8216936 [Cheng Lian] Removes boxing cost in IntDelta and LongDelta by providing specialized implementations
b70d519 [Cheng Lian] Made some in-memory columnar storage interfaces row-based
---
 .../catalyst/expressions/SpecificRow.scala    |   2 +-
 .../spark/sql/columnar/ColumnAccessor.scala   |   8 +-
 .../spark/sql/columnar/ColumnBuilder.scala    |  27 +-
 .../spark/sql/columnar/ColumnStats.scala      |  16 +-
 .../spark/sql/columnar/ColumnType.scala       | 178 ++++++++++--
 .../columnar/InMemoryColumnarTableScan.scala  |  92 +++---
 .../sql/columnar/NullableColumnAccessor.scala |   4 +-
 .../sql/columnar/NullableColumnBuilder.scala  |   8 +-
 .../CompressibleColumnAccessor.scala          |   7 +-
 .../CompressibleColumnBuilder.scala           |  24 +-
 .../compression/CompressionScheme.scala       |  16 +-
 .../compression/compressionSchemes.scala      | 264 +++++++++++-------
 .../spark/sql/columnar/ColumnStatsSuite.scala |   2 +-
 .../spark/sql/columnar/ColumnTypeSuite.scala  |  11 +-
 .../columnar/InMemoryColumnarQuerySuite.scala |   2 +-
 .../NullableColumnAccessorSuite.scala         |   4 +-
 .../columnar/NullableColumnBuilderSuite.scala |   4 +-
 .../columnar/PartitionBatchPruningSuite.scala |   6 +-
 .../compression/BooleanBitSetSuite.scala      |   7 +-
 .../compression/DictionaryEncodingSuite.scala |   9 +-
 .../compression/IntegralDeltaSuite.scala      |   9 +-
 .../compression/RunLengthEncodingSuite.scala  |   9 +-
 .../apache/spark/sql/hive/TableReader.scala   | 119 ++++----
 .../sql/hive/execution/HiveQuerySuite.scala   |  18 +-
 24 files changed, 554 insertions(+), 292 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificRow.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificRow.scala
index 088f11ee4aa53..9cbab3d5d0d0d 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificRow.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificRow.scala
@@ -171,7 +171,7 @@ final class MutableByte extends MutableValue {
 }
 
 final class MutableAny extends MutableValue {
-  var value: Any = 0
+  var value: Any = _
   def boxed = if (isNull) null else value
   def update(v: Any) = value = {
     isNull = false
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnAccessor.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnAccessor.scala
index 42a5a9a84f362..c9faf0852142a 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnAccessor.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnAccessor.scala
@@ -50,11 +50,13 @@ private[sql] abstract class BasicColumnAccessor[T <: DataType, JvmType](
 
   def hasNext = buffer.hasRemaining
 
-  def extractTo(row: MutableRow, ordinal: Int) {
-    columnType.setField(row, ordinal, extractSingle(buffer))
+  def extractTo(row: MutableRow, ordinal: Int): Unit = {
+    extractSingle(row, ordinal)
   }
 
-  def extractSingle(buffer: ByteBuffer): JvmType = columnType.extract(buffer)
+  def extractSingle(row: MutableRow, ordinal: Int): Unit = {
+    columnType.extract(buffer, row, ordinal)
+  }
 
   protected def underlyingBuffer = buffer
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnBuilder.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnBuilder.scala
index b3ec5ded22422..2e61a981375aa 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnBuilder.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnBuilder.scala
@@ -68,10 +68,9 @@ private[sql] class BasicColumnBuilder[T <: DataType, JvmType](
     buffer.order(ByteOrder.nativeOrder()).putInt(columnType.typeId)
   }
 
-  override def appendFrom(row: Row, ordinal: Int) {
-    val field = columnType.getField(row, ordinal)
-    buffer = ensureFreeSpace(buffer, columnType.actualSize(field))
-    columnType.append(field, buffer)
+  override def appendFrom(row: Row, ordinal: Int): Unit = {
+    buffer = ensureFreeSpace(buffer, columnType.actualSize(row, ordinal))
+    columnType.append(row, ordinal, buffer)
   }
 
   override def build() = {
@@ -142,16 +141,16 @@ private[sql] object ColumnBuilder {
       useCompression: Boolean = false): ColumnBuilder = {
 
     val builder = (typeId match {
-      case INT.typeId     => new IntColumnBuilder
-      case LONG.typeId    => new LongColumnBuilder
-      case FLOAT.typeId   => new FloatColumnBuilder
-      case DOUBLE.typeId  => new DoubleColumnBuilder
-      case BOOLEAN.typeId => new BooleanColumnBuilder
-      case BYTE.typeId    => new ByteColumnBuilder
-      case SHORT.typeId   => new ShortColumnBuilder
-      case STRING.typeId  => new StringColumnBuilder
-      case BINARY.typeId  => new BinaryColumnBuilder
-      case GENERIC.typeId => new GenericColumnBuilder
+      case INT.typeId       => new IntColumnBuilder
+      case LONG.typeId      => new LongColumnBuilder
+      case FLOAT.typeId     => new FloatColumnBuilder
+      case DOUBLE.typeId    => new DoubleColumnBuilder
+      case BOOLEAN.typeId   => new BooleanColumnBuilder
+      case BYTE.typeId      => new ByteColumnBuilder
+      case SHORT.typeId     => new ShortColumnBuilder
+      case STRING.typeId    => new StringColumnBuilder
+      case BINARY.typeId    => new BinaryColumnBuilder
+      case GENERIC.typeId   => new GenericColumnBuilder
       case TIMESTAMP.typeId => new TimestampColumnBuilder
     }).asInstanceOf[ColumnBuilder]
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnStats.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnStats.scala
index fc343ccb995c2..203a714e03c97 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnStats.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnStats.scala
@@ -69,7 +69,7 @@ private[sql] class ByteColumnStats extends ColumnStats {
   var lower = Byte.MaxValue
   var nullCount = 0
 
-  override def gatherStats(row: Row, ordinal: Int) {
+  override def gatherStats(row: Row, ordinal: Int): Unit = {
     if (!row.isNullAt(ordinal)) {
       val value = row.getByte(ordinal)
       if (value > upper) upper = value
@@ -87,7 +87,7 @@ private[sql] class ShortColumnStats extends ColumnStats {
   var lower = Short.MaxValue
   var nullCount = 0
 
-  override def gatherStats(row: Row, ordinal: Int) {
+  override def gatherStats(row: Row, ordinal: Int): Unit = {
     if (!row.isNullAt(ordinal)) {
       val value = row.getShort(ordinal)
       if (value > upper) upper = value
@@ -105,7 +105,7 @@ private[sql] class LongColumnStats extends ColumnStats {
   var lower = Long.MaxValue
   var nullCount = 0
 
-  override def gatherStats(row: Row, ordinal: Int) {
+  override def gatherStats(row: Row, ordinal: Int): Unit = {
     if (!row.isNullAt(ordinal)) {
       val value = row.getLong(ordinal)
       if (value > upper) upper = value
@@ -123,7 +123,7 @@ private[sql] class DoubleColumnStats extends ColumnStats {
   var lower = Double.MaxValue
   var nullCount = 0
 
-  override def gatherStats(row: Row, ordinal: Int) {
+  override def gatherStats(row: Row, ordinal: Int): Unit = {
     if (!row.isNullAt(ordinal)) {
       val value = row.getDouble(ordinal)
       if (value > upper) upper = value
@@ -141,7 +141,7 @@ private[sql] class FloatColumnStats extends ColumnStats {
   var lower = Float.MaxValue
   var nullCount = 0
 
-  override def gatherStats(row: Row, ordinal: Int) {
+  override def gatherStats(row: Row, ordinal: Int): Unit = {
     if (!row.isNullAt(ordinal)) {
       val value = row.getFloat(ordinal)
       if (value > upper) upper = value
@@ -159,7 +159,7 @@ private[sql] class IntColumnStats extends ColumnStats {
   var lower = Int.MaxValue
   var nullCount = 0
 
-  override def gatherStats(row: Row, ordinal: Int) {
+  override def gatherStats(row: Row, ordinal: Int): Unit = {
     if (!row.isNullAt(ordinal)) {
       val value = row.getInt(ordinal)
       if (value > upper) upper = value
@@ -177,7 +177,7 @@ private[sql] class StringColumnStats extends ColumnStats {
   var lower: String = null
   var nullCount = 0
 
-  override def gatherStats(row: Row, ordinal: Int) {
+  override def gatherStats(row: Row, ordinal: Int): Unit = {
     if (!row.isNullAt(ordinal)) {
       val value = row.getString(ordinal)
       if (upper == null || value.compareTo(upper) > 0) upper = value
@@ -195,7 +195,7 @@ private[sql] class TimestampColumnStats extends ColumnStats {
   var lower: Timestamp = null
   var nullCount = 0
 
-  override def gatherStats(row: Row, ordinal: Int) {
+  override def gatherStats(row: Row, ordinal: Int): Unit = {
     if (!row.isNullAt(ordinal)) {
       val value = row(ordinal).asInstanceOf[Timestamp]
       if (upper == null || value.compareTo(upper) > 0) upper = value
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnType.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnType.scala
index 9a61600115872..198b5756676aa 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnType.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnType.scala
@@ -18,11 +18,10 @@
 package org.apache.spark.sql.columnar
 
 import java.nio.ByteBuffer
+import java.sql.Timestamp
 
 import scala.reflect.runtime.universe.TypeTag
 
-import java.sql.Timestamp
-
 import org.apache.spark.sql.Row
 import org.apache.spark.sql.catalyst.expressions.MutableRow
 import org.apache.spark.sql.catalyst.types._
@@ -46,16 +45,33 @@ private[sql] sealed abstract class ColumnType[T <: DataType, JvmType](
    */
   def extract(buffer: ByteBuffer): JvmType
 
+  /**
+   * Extracts a value out of the buffer at the buffer's current position and stores in
+   * `row(ordinal)`. Subclasses should override this method to avoid boxing/unboxing costs whenever
+   * possible.
+   */
+  def extract(buffer: ByteBuffer, row: MutableRow, ordinal: Int): Unit = {
+    setField(row, ordinal, extract(buffer))
+  }
+
   /**
    * Appends the given value v of type T into the given ByteBuffer.
    */
-  def append(v: JvmType, buffer: ByteBuffer)
+  def append(v: JvmType, buffer: ByteBuffer): Unit
+
+  /**
+   * Appends `row(ordinal)` of type T into the given ByteBuffer. Subclasses should override this
+   * method to avoid boxing/unboxing costs whenever possible.
+   */
+  def append(row: Row, ordinal: Int, buffer: ByteBuffer): Unit = {
+    append(getField(row, ordinal), buffer)
+  }
 
   /**
-   * Returns the size of the value. This is used to calculate the size of variable length types
-   * such as byte arrays and strings.
+   * Returns the size of the value `row(ordinal)`. This is used to calculate the size of variable
+   * length types such as byte arrays and strings.
    */
-  def actualSize(v: JvmType): Int = defaultSize
+  def actualSize(row: Row, ordinal: Int): Int = defaultSize
 
   /**
    * Returns `row(ordinal)`. Subclasses should override this method to avoid boxing/unboxing costs
@@ -67,7 +83,15 @@ private[sql] sealed abstract class ColumnType[T <: DataType, JvmType](
    * Sets `row(ordinal)` to `field`. Subclasses should override this method to avoid boxing/unboxing
    * costs whenever possible.
    */
-  def setField(row: MutableRow, ordinal: Int, value: JvmType)
+  def setField(row: MutableRow, ordinal: Int, value: JvmType): Unit
+
+  /**
+   * Copies `from(fromOrdinal)` to `to(toOrdinal)`. Subclasses should override this method to avoid
+   * boxing/unboxing costs whenever possible.
+   */
+  def copyField(from: Row, fromOrdinal: Int, to: MutableRow, toOrdinal: Int): Unit = {
+    to(toOrdinal) = from(fromOrdinal)
+  }
 
   /**
    * Creates a duplicated copy of the value.
@@ -90,119 +114,205 @@ private[sql] abstract class NativeColumnType[T <: NativeType](
 }
 
 private[sql] object INT extends NativeColumnType(IntegerType, 0, 4) {
-  def append(v: Int, buffer: ByteBuffer) {
+  def append(v: Int, buffer: ByteBuffer): Unit = {
     buffer.putInt(v)
   }
 
+  override def append(row: Row, ordinal: Int, buffer: ByteBuffer): Unit = {
+    buffer.putInt(row.getInt(ordinal))
+  }
+
   def extract(buffer: ByteBuffer) = {
     buffer.getInt()
   }
 
-  override def setField(row: MutableRow, ordinal: Int, value: Int) {
+  override def extract(buffer: ByteBuffer, row: MutableRow, ordinal: Int): Unit = {
+    row.setInt(ordinal, buffer.getInt())
+  }
+
+  override def setField(row: MutableRow, ordinal: Int, value: Int): Unit = {
     row.setInt(ordinal, value)
   }
 
   override def getField(row: Row, ordinal: Int) = row.getInt(ordinal)
+
+  override def copyField(from: Row, fromOrdinal: Int, to: MutableRow, toOrdinal: Int): Unit = {
+    to.setInt(toOrdinal, from.getInt(fromOrdinal))
+  }
 }
 
 private[sql] object LONG extends NativeColumnType(LongType, 1, 8) {
-  override def append(v: Long, buffer: ByteBuffer) {
+  override def append(v: Long, buffer: ByteBuffer): Unit = {
     buffer.putLong(v)
   }
 
+  override def append(row: Row, ordinal: Int, buffer: ByteBuffer): Unit = {
+    buffer.putLong(row.getLong(ordinal))
+  }
+
   override def extract(buffer: ByteBuffer) = {
     buffer.getLong()
   }
 
-  override def setField(row: MutableRow, ordinal: Int, value: Long) {
+  override def extract(buffer: ByteBuffer, row: MutableRow, ordinal: Int): Unit = {
+    row.setLong(ordinal, buffer.getLong())
+  }
+
+  override def setField(row: MutableRow, ordinal: Int, value: Long): Unit = {
     row.setLong(ordinal, value)
   }
 
   override def getField(row: Row, ordinal: Int) = row.getLong(ordinal)
+
+  override def copyField(from: Row, fromOrdinal: Int, to: MutableRow, toOrdinal: Int): Unit = {
+    to.setLong(toOrdinal, from.getLong(fromOrdinal))
+  }
 }
 
 private[sql] object FLOAT extends NativeColumnType(FloatType, 2, 4) {
-  override def append(v: Float, buffer: ByteBuffer) {
+  override def append(v: Float, buffer: ByteBuffer): Unit = {
     buffer.putFloat(v)
   }
 
+  override def append(row: Row, ordinal: Int, buffer: ByteBuffer): Unit = {
+    buffer.putFloat(row.getFloat(ordinal))
+  }
+
   override def extract(buffer: ByteBuffer) = {
     buffer.getFloat()
   }
 
-  override def setField(row: MutableRow, ordinal: Int, value: Float) {
+  override def extract(buffer: ByteBuffer, row: MutableRow, ordinal: Int): Unit = {
+    row.setFloat(ordinal, buffer.getFloat())
+  }
+
+  override def setField(row: MutableRow, ordinal: Int, value: Float): Unit = {
     row.setFloat(ordinal, value)
   }
 
   override def getField(row: Row, ordinal: Int) = row.getFloat(ordinal)
+
+  override def copyField(from: Row, fromOrdinal: Int, to: MutableRow, toOrdinal: Int): Unit = {
+    to.setFloat(toOrdinal, from.getFloat(fromOrdinal))
+  }
 }
 
 private[sql] object DOUBLE extends NativeColumnType(DoubleType, 3, 8) {
-  override def append(v: Double, buffer: ByteBuffer) {
+  override def append(v: Double, buffer: ByteBuffer): Unit = {
     buffer.putDouble(v)
   }
 
+  override def append(row: Row, ordinal: Int, buffer: ByteBuffer): Unit = {
+    buffer.putDouble(row.getDouble(ordinal))
+  }
+
   override def extract(buffer: ByteBuffer) = {
     buffer.getDouble()
   }
 
-  override def setField(row: MutableRow, ordinal: Int, value: Double) {
+  override def extract(buffer: ByteBuffer, row: MutableRow, ordinal: Int): Unit = {
+    row.setDouble(ordinal, buffer.getDouble())
+  }
+
+  override def setField(row: MutableRow, ordinal: Int, value: Double): Unit = {
     row.setDouble(ordinal, value)
   }
 
   override def getField(row: Row, ordinal: Int) = row.getDouble(ordinal)
+
+  override def copyField(from: Row, fromOrdinal: Int, to: MutableRow, toOrdinal: Int): Unit = {
+    to.setDouble(toOrdinal, from.getDouble(fromOrdinal))
+  }
 }
 
 private[sql] object BOOLEAN extends NativeColumnType(BooleanType, 4, 1) {
-  override def append(v: Boolean, buffer: ByteBuffer) {
-    buffer.put(if (v) 1.toByte else 0.toByte)
+  override def append(v: Boolean, buffer: ByteBuffer): Unit = {
+    buffer.put(if (v) 1: Byte else 0: Byte)
+  }
+
+  override def append(row: Row, ordinal: Int, buffer: ByteBuffer): Unit = {
+    buffer.put(if (row.getBoolean(ordinal)) 1: Byte else 0: Byte)
   }
 
   override def extract(buffer: ByteBuffer) = buffer.get() == 1
 
-  override def setField(row: MutableRow, ordinal: Int, value: Boolean) {
+  override def extract(buffer: ByteBuffer, row: MutableRow, ordinal: Int): Unit = {
+    row.setBoolean(ordinal, buffer.get() == 1)
+  }
+
+  override def setField(row: MutableRow, ordinal: Int, value: Boolean): Unit = {
     row.setBoolean(ordinal, value)
   }
 
   override def getField(row: Row, ordinal: Int) = row.getBoolean(ordinal)
+
+  override def copyField(from: Row, fromOrdinal: Int, to: MutableRow, toOrdinal: Int): Unit = {
+    to.setBoolean(toOrdinal, from.getBoolean(fromOrdinal))
+  }
 }
 
 private[sql] object BYTE extends NativeColumnType(ByteType, 5, 1) {
-  override def append(v: Byte, buffer: ByteBuffer) {
+  override def append(v: Byte, buffer: ByteBuffer): Unit = {
     buffer.put(v)
   }
 
+  override def append(row: Row, ordinal: Int, buffer: ByteBuffer): Unit = {
+    buffer.put(row.getByte(ordinal))
+  }
+
   override def extract(buffer: ByteBuffer) = {
     buffer.get()
   }
 
-  override def setField(row: MutableRow, ordinal: Int, value: Byte) {
+  override def extract(buffer: ByteBuffer, row: MutableRow, ordinal: Int): Unit = {
+    row.setByte(ordinal, buffer.get())
+  }
+
+  override def setField(row: MutableRow, ordinal: Int, value: Byte): Unit = {
     row.setByte(ordinal, value)
   }
 
   override def getField(row: Row, ordinal: Int) = row.getByte(ordinal)
+
+  override def copyField(from: Row, fromOrdinal: Int, to: MutableRow, toOrdinal: Int): Unit = {
+    to.setByte(toOrdinal, from.getByte(fromOrdinal))
+  }
 }
 
 private[sql] object SHORT extends NativeColumnType(ShortType, 6, 2) {
-  override def append(v: Short, buffer: ByteBuffer) {
+  override def append(v: Short, buffer: ByteBuffer): Unit = {
     buffer.putShort(v)
   }
 
+  override def append(row: Row, ordinal: Int, buffer: ByteBuffer): Unit = {
+    buffer.putShort(row.getShort(ordinal))
+  }
+
   override def extract(buffer: ByteBuffer) = {
     buffer.getShort()
   }
 
-  override def setField(row: MutableRow, ordinal: Int, value: Short) {
+  override def extract(buffer: ByteBuffer, row: MutableRow, ordinal: Int): Unit = {
+    row.setShort(ordinal, buffer.getShort())
+  }
+
+  override def setField(row: MutableRow, ordinal: Int, value: Short): Unit = {
     row.setShort(ordinal, value)
   }
 
   override def getField(row: Row, ordinal: Int) = row.getShort(ordinal)
+
+  override def copyField(from: Row, fromOrdinal: Int, to: MutableRow, toOrdinal: Int): Unit = {
+    to.setShort(toOrdinal, from.getShort(fromOrdinal))
+  }
 }
 
 private[sql] object STRING extends NativeColumnType(StringType, 7, 8) {
-  override def actualSize(v: String): Int = v.getBytes("utf-8").length + 4
+  override def actualSize(row: Row, ordinal: Int): Int = {
+    row.getString(ordinal).getBytes("utf-8").length + 4
+  }
 
-  override def append(v: String, buffer: ByteBuffer) {
+  override def append(v: String, buffer: ByteBuffer): Unit = {
     val stringBytes = v.getBytes("utf-8")
     buffer.putInt(stringBytes.length).put(stringBytes, 0, stringBytes.length)
   }
@@ -214,11 +324,15 @@ private[sql] object STRING extends NativeColumnType(StringType, 7, 8) {
     new String(stringBytes, "utf-8")
   }
 
-  override def setField(row: MutableRow, ordinal: Int, value: String) {
+  override def setField(row: MutableRow, ordinal: Int, value: String): Unit = {
     row.setString(ordinal, value)
   }
 
   override def getField(row: Row, ordinal: Int) = row.getString(ordinal)
+
+  override def copyField(from: Row, fromOrdinal: Int, to: MutableRow, toOrdinal: Int): Unit = {
+    to.setString(toOrdinal, from.getString(fromOrdinal))
+  }
 }
 
 private[sql] object TIMESTAMP extends NativeColumnType(TimestampType, 8, 12) {
@@ -228,7 +342,7 @@ private[sql] object TIMESTAMP extends NativeColumnType(TimestampType, 8, 12) {
     timestamp
   }
 
-  override def append(v: Timestamp, buffer: ByteBuffer) {
+  override def append(v: Timestamp, buffer: ByteBuffer): Unit = {
     buffer.putLong(v.getTime).putInt(v.getNanos)
   }
 
@@ -236,7 +350,7 @@ private[sql] object TIMESTAMP extends NativeColumnType(TimestampType, 8, 12) {
     row(ordinal).asInstanceOf[Timestamp]
   }
 
-  override def setField(row: MutableRow, ordinal: Int, value: Timestamp) {
+  override def setField(row: MutableRow, ordinal: Int, value: Timestamp): Unit = {
     row(ordinal) = value
   }
 }
@@ -246,9 +360,11 @@ private[sql] sealed abstract class ByteArrayColumnType[T <: DataType](
     defaultSize: Int)
   extends ColumnType[T, Array[Byte]](typeId, defaultSize) {
 
-  override def actualSize(v: Array[Byte]) = v.length + 4
+  override def actualSize(row: Row, ordinal: Int) = {
+    getField(row, ordinal).length + 4
+  }
 
-  override def append(v: Array[Byte], buffer: ByteBuffer) {
+  override def append(v: Array[Byte], buffer: ByteBuffer): Unit = {
     buffer.putInt(v.length).put(v, 0, v.length)
   }
 
@@ -261,7 +377,7 @@ private[sql] sealed abstract class ByteArrayColumnType[T <: DataType](
 }
 
 private[sql] object BINARY extends ByteArrayColumnType[BinaryType.type](9, 16) {
-  override def setField(row: MutableRow, ordinal: Int, value: Array[Byte]) {
+  override def setField(row: MutableRow, ordinal: Int, value: Array[Byte]): Unit = {
     row(ordinal) = value
   }
 
@@ -272,7 +388,7 @@ private[sql] object BINARY extends ByteArrayColumnType[BinaryType.type](9, 16) {
 // serialized first before appending to the column `ByteBuffer`, and is also extracted as serialized
 // byte array.
 private[sql] object GENERIC extends ByteArrayColumnType[DataType](10, 16) {
-  override def setField(row: MutableRow, ordinal: Int, value: Array[Byte]) {
+  override def setField(row: MutableRow, ordinal: Int, value: Array[Byte]): Unit = {
     row(ordinal) = SparkSqlSerializer.deserialize[Any](value)
   }
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala
index 6eab2f23c18e1..8a3612cdf19be 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala
@@ -52,7 +52,7 @@ private[sql] case class InMemoryRelation(
   // As in Spark, the actual work of caching is lazy.
   if (_cachedColumnBuffers == null) {
     val output = child.output
-    val cached = child.execute().mapPartitions { baseIterator =>
+    val cached = child.execute().mapPartitions { rowIterator =>
       new Iterator[CachedBatch] {
         def next() = {
           val columnBuilders = output.map { attribute =>
@@ -61,11 +61,9 @@ private[sql] case class InMemoryRelation(
             ColumnBuilder(columnType.typeId, initialBufferSize, attribute.name, useCompression)
           }.toArray
 
-          var row: Row = null
           var rowCount = 0
-
-          while (baseIterator.hasNext && rowCount < batchSize) {
-            row = baseIterator.next()
+          while (rowIterator.hasNext && rowCount < batchSize) {
+            val row = rowIterator.next()
             var i = 0
             while (i < row.length) {
               columnBuilders(i).appendFrom(row, i)
@@ -80,7 +78,7 @@ private[sql] case class InMemoryRelation(
           CachedBatch(columnBuilders.map(_.build()), stats)
         }
 
-        def hasNext = baseIterator.hasNext
+        def hasNext = rowIterator.hasNext
       }
     }.cache()
 
@@ -182,6 +180,7 @@ private[sql] case class InMemoryColumnarTableScan(
     }
   }
 
+  // Accumulators used for testing purposes
   val readPartitions = sparkContext.accumulator(0)
   val readBatches = sparkContext.accumulator(0)
 
@@ -191,40 +190,36 @@ private[sql] case class InMemoryColumnarTableScan(
     readPartitions.setValue(0)
     readBatches.setValue(0)
 
-    relation.cachedColumnBuffers.mapPartitions { iterator =>
+    relation.cachedColumnBuffers.mapPartitions { cachedBatchIterator =>
       val partitionFilter = newPredicate(
         partitionFilters.reduceOption(And).getOrElse(Literal(true)),
         relation.partitionStatistics.schema)
 
-      // Find the ordinals of the requested columns.  If none are requested, use the first.
-      val requestedColumns = if (attributes.isEmpty) {
-        Seq(0)
+      // Find the ordinals and data types of the requested columns.  If none are requested, use the
+      // narrowest (the field with minimum default element size).
+      val (requestedColumnIndices, requestedColumnDataTypes) = if (attributes.isEmpty) {
+        val (narrowestOrdinal, narrowestDataType) =
+          relation.output.zipWithIndex.map { case (a, ordinal) =>
+            ordinal -> a.dataType
+          } minBy { case (_, dataType) =>
+            ColumnType(dataType).defaultSize
+          }
+        Seq(narrowestOrdinal) -> Seq(narrowestDataType)
       } else {
-        attributes.map(a => relation.output.indexWhere(_.exprId == a.exprId))
+        attributes.map { a =>
+          relation.output.indexWhere(_.exprId == a.exprId) -> a.dataType
+        }.unzip
       }
 
-      val rows = iterator
-        // Skip pruned batches
-        .filter { cachedBatch =>
-          if (inMemoryPartitionPruningEnabled && !partitionFilter(cachedBatch.stats)) {
-            def statsString = relation.partitionStatistics.schema
-              .zip(cachedBatch.stats)
-              .map { case (a, s) => s"${a.name}: $s" }
-              .mkString(", ")
-            logInfo(s"Skipping partition based on stats $statsString")
-            false
-          } else {
-            readBatches += 1
-            true
-          }
-        }
-        // Build column accessors
-        .map { cachedBatch =>
-          requestedColumns.map(cachedBatch.buffers(_)).map(ColumnAccessor(_))
-        }
-        // Extract rows via column accessors
-        .flatMap { columnAccessors =>
-          val nextRow = new GenericMutableRow(columnAccessors.length)
+      val nextRow = new SpecificMutableRow(requestedColumnDataTypes)
+
+      def cachedBatchesToRows(cacheBatches: Iterator[CachedBatch]) = {
+        val rows = cacheBatches.flatMap { cachedBatch =>
+          // Build column accessors
+          val columnAccessors =
+            requestedColumnIndices.map(cachedBatch.buffers(_)).map(ColumnAccessor(_))
+
+          // Extract rows via column accessors
           new Iterator[Row] {
             override def next() = {
               var i = 0
@@ -235,15 +230,38 @@ private[sql] case class InMemoryColumnarTableScan(
               nextRow
             }
 
-            override def hasNext = columnAccessors.head.hasNext
+            override def hasNext = columnAccessors(0).hasNext
           }
         }
 
-      if (rows.hasNext) {
-        readPartitions += 1
+        if (rows.hasNext) {
+          readPartitions += 1
+        }
+
+        rows
       }
 
-      rows
+      // Do partition batch pruning if enabled
+      val cachedBatchesToScan =
+        if (inMemoryPartitionPruningEnabled) {
+          cachedBatchIterator.filter { cachedBatch =>
+            if (!partitionFilter(cachedBatch.stats)) {
+              def statsString = relation.partitionStatistics.schema
+                .zip(cachedBatch.stats)
+                .map { case (a, s) => s"${a.name}: $s" }
+                .mkString(", ")
+              logInfo(s"Skipping partition based on stats $statsString")
+              false
+            } else {
+              readBatches += 1
+              true
+            }
+          }
+        } else {
+          cachedBatchIterator
+        }
+
+      cachedBatchesToRows(cachedBatchesToScan)
     }
   }
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/NullableColumnAccessor.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/NullableColumnAccessor.scala
index b7f8826861a2c..965782a40031b 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/NullableColumnAccessor.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/NullableColumnAccessor.scala
@@ -29,7 +29,7 @@ private[sql] trait NullableColumnAccessor extends ColumnAccessor {
   private var nextNullIndex: Int = _
   private var pos: Int = 0
 
-  abstract override protected def initialize() {
+  abstract override protected def initialize(): Unit = {
     nullsBuffer = underlyingBuffer.duplicate().order(ByteOrder.nativeOrder())
     nullCount = nullsBuffer.getInt()
     nextNullIndex = if (nullCount > 0) nullsBuffer.getInt() else -1
@@ -39,7 +39,7 @@ private[sql] trait NullableColumnAccessor extends ColumnAccessor {
     super.initialize()
   }
 
-  abstract override def extractTo(row: MutableRow, ordinal: Int) {
+  abstract override def extractTo(row: MutableRow, ordinal: Int): Unit = {
     if (pos == nextNullIndex) {
       seenNulls += 1
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/NullableColumnBuilder.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/NullableColumnBuilder.scala
index a72970eef7aa4..f1f494ac26d0c 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/NullableColumnBuilder.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/NullableColumnBuilder.scala
@@ -40,7 +40,11 @@ private[sql] trait NullableColumnBuilder extends ColumnBuilder {
   protected var nullCount: Int = _
   private var pos: Int = _
 
-  abstract override def initialize(initialSize: Int, columnName: String, useCompression: Boolean) {
+  abstract override def initialize(
+      initialSize: Int,
+      columnName: String,
+      useCompression: Boolean): Unit = {
+
     nulls = ByteBuffer.allocate(1024)
     nulls.order(ByteOrder.nativeOrder())
     pos = 0
@@ -48,7 +52,7 @@ private[sql] trait NullableColumnBuilder extends ColumnBuilder {
     super.initialize(initialSize, columnName, useCompression)
   }
 
-  abstract override def appendFrom(row: Row, ordinal: Int) {
+  abstract override def appendFrom(row: Row, ordinal: Int): Unit = {
     columnStats.gatherStats(row, ordinal)
     if (row.isNullAt(ordinal)) {
       nulls = ColumnBuilder.ensureFreeSpace(nulls, 4)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/CompressibleColumnAccessor.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/CompressibleColumnAccessor.scala
index b4120a3d4368b..27ac5f4dbdbbc 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/CompressibleColumnAccessor.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/CompressibleColumnAccessor.scala
@@ -17,8 +17,7 @@
 
 package org.apache.spark.sql.columnar.compression
 
-import java.nio.ByteBuffer
-
+import org.apache.spark.sql.catalyst.expressions.MutableRow
 import org.apache.spark.sql.catalyst.types.NativeType
 import org.apache.spark.sql.columnar.{ColumnAccessor, NativeColumnAccessor}
 
@@ -34,5 +33,7 @@ private[sql] trait CompressibleColumnAccessor[T <: NativeType] extends ColumnAcc
 
   abstract override def hasNext = super.hasNext || decoder.hasNext
 
-  override def extractSingle(buffer: ByteBuffer): T#JvmType = decoder.next()
+  override def extractSingle(row: MutableRow, ordinal: Int): Unit = {
+    decoder.next(row, ordinal)
+  }
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/CompressibleColumnBuilder.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/CompressibleColumnBuilder.scala
index a5826bb033e41..628d9cec41d6b 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/CompressibleColumnBuilder.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/CompressibleColumnBuilder.scala
@@ -48,12 +48,16 @@ private[sql] trait CompressibleColumnBuilder[T <: NativeType]
 
   var compressionEncoders: Seq[Encoder[T]] = _
 
-  abstract override def initialize(initialSize: Int, columnName: String, useCompression: Boolean) {
+  abstract override def initialize(
+      initialSize: Int,
+      columnName: String,
+      useCompression: Boolean): Unit = {
+
     compressionEncoders =
       if (useCompression) {
-        schemes.filter(_.supports(columnType)).map(_.encoder[T])
+        schemes.filter(_.supports(columnType)).map(_.encoder[T](columnType))
       } else {
-        Seq(PassThrough.encoder)
+        Seq(PassThrough.encoder(columnType))
       }
     super.initialize(initialSize, columnName, useCompression)
   }
@@ -62,17 +66,15 @@ private[sql] trait CompressibleColumnBuilder[T <: NativeType]
     encoder.compressionRatio < 0.8
   }
 
-  private def gatherCompressibilityStats(row: Row, ordinal: Int) {
-    val field = columnType.getField(row, ordinal)
-
+  private def gatherCompressibilityStats(row: Row, ordinal: Int): Unit = {
     var i = 0
     while (i < compressionEncoders.length) {
-      compressionEncoders(i).gatherCompressibilityStats(field, columnType)
+      compressionEncoders(i).gatherCompressibilityStats(row, ordinal)
       i += 1
     }
   }
 
-  abstract override def appendFrom(row: Row, ordinal: Int) {
+  abstract override def appendFrom(row: Row, ordinal: Int): Unit = {
     super.appendFrom(row, ordinal)
     if (!row.isNullAt(ordinal)) {
       gatherCompressibilityStats(row, ordinal)
@@ -84,7 +86,7 @@ private[sql] trait CompressibleColumnBuilder[T <: NativeType]
     val typeId = nonNullBuffer.getInt()
     val encoder: Encoder[T] = {
       val candidate = compressionEncoders.minBy(_.compressionRatio)
-      if (isWorthCompressing(candidate)) candidate else PassThrough.encoder
+      if (isWorthCompressing(candidate)) candidate else PassThrough.encoder(columnType)
     }
 
     // Header = column type ID + null count + null positions
@@ -104,7 +106,7 @@ private[sql] trait CompressibleColumnBuilder[T <: NativeType]
       .putInt(nullCount)
       .put(nulls)
 
-    logInfo(s"Compressor for [$columnName]: $encoder, ratio: ${encoder.compressionRatio}")
-    encoder.compress(nonNullBuffer, compressedBuffer, columnType)
+    logDebug(s"Compressor for [$columnName]: $encoder, ratio: ${encoder.compressionRatio}")
+    encoder.compress(nonNullBuffer, compressedBuffer)
   }
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/CompressionScheme.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/CompressionScheme.scala
index 7797f75177893..acb06cb5376b4 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/CompressionScheme.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/CompressionScheme.scala
@@ -17,13 +17,15 @@
 
 package org.apache.spark.sql.columnar.compression
 
-import java.nio.{ByteOrder, ByteBuffer}
+import java.nio.{ByteBuffer, ByteOrder}
 
+import org.apache.spark.sql.Row
+import org.apache.spark.sql.catalyst.expressions.MutableRow
 import org.apache.spark.sql.catalyst.types.NativeType
 import org.apache.spark.sql.columnar.{ColumnType, NativeColumnType}
 
 private[sql] trait Encoder[T <: NativeType] {
-  def gatherCompressibilityStats(value: T#JvmType, columnType: NativeColumnType[T]) {}
+  def gatherCompressibilityStats(row: Row, ordinal: Int): Unit = {}
 
   def compressedSize: Int
 
@@ -33,17 +35,21 @@ private[sql] trait Encoder[T <: NativeType] {
     if (uncompressedSize > 0) compressedSize.toDouble / uncompressedSize else 1.0
   }
 
-  def compress(from: ByteBuffer, to: ByteBuffer, columnType: NativeColumnType[T]): ByteBuffer
+  def compress(from: ByteBuffer, to: ByteBuffer): ByteBuffer
 }
 
-private[sql] trait Decoder[T <: NativeType] extends Iterator[T#JvmType]
+private[sql] trait Decoder[T <: NativeType] {
+  def next(row: MutableRow, ordinal: Int): Unit
+
+  def hasNext: Boolean
+}
 
 private[sql] trait CompressionScheme {
   def typeId: Int
 
   def supports(columnType: ColumnType[_, _]): Boolean
 
-  def encoder[T <: NativeType]: Encoder[T]
+  def encoder[T <: NativeType](columnType: NativeColumnType[T]): Encoder[T]
 
   def decoder[T <: NativeType](buffer: ByteBuffer, columnType: NativeColumnType[T]): Decoder[T]
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/compressionSchemes.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/compressionSchemes.scala
index 8cf9ec74ca2de..29edcf17242c5 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/compressionSchemes.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/compressionSchemes.scala
@@ -23,7 +23,8 @@ import scala.collection.mutable
 import scala.reflect.ClassTag
 import scala.reflect.runtime.universe.runtimeMirror
 
-import org.apache.spark.sql.catalyst.expressions.GenericMutableRow
+import org.apache.spark.sql.Row
+import org.apache.spark.sql.catalyst.expressions.{MutableRow, SpecificMutableRow}
 import org.apache.spark.sql.catalyst.types._
 import org.apache.spark.sql.columnar._
 import org.apache.spark.util.Utils
@@ -33,18 +34,20 @@ private[sql] case object PassThrough extends CompressionScheme {
 
   override def supports(columnType: ColumnType[_, _]) = true
 
-  override def encoder[T <: NativeType] = new this.Encoder[T]
+  override def encoder[T <: NativeType](columnType: NativeColumnType[T]) = {
+    new this.Encoder[T](columnType)
+  }
 
   override def decoder[T <: NativeType](buffer: ByteBuffer, columnType: NativeColumnType[T]) = {
     new this.Decoder(buffer, columnType)
   }
 
-  class Encoder[T <: NativeType] extends compression.Encoder[T] {
+  class Encoder[T <: NativeType](columnType: NativeColumnType[T]) extends compression.Encoder[T] {
     override def uncompressedSize = 0
 
     override def compressedSize = 0
 
-    override def compress(from: ByteBuffer, to: ByteBuffer, columnType: NativeColumnType[T]) = {
+    override def compress(from: ByteBuffer, to: ByteBuffer) = {
       // Writes compression type ID and copies raw contents
       to.putInt(PassThrough.typeId).put(from).rewind()
       to
@@ -54,7 +57,9 @@ private[sql] case object PassThrough extends CompressionScheme {
   class Decoder[T <: NativeType](buffer: ByteBuffer, columnType: NativeColumnType[T])
     extends compression.Decoder[T] {
 
-    override def next() = columnType.extract(buffer)
+    override def next(row: MutableRow, ordinal: Int): Unit = {
+      columnType.extract(buffer, row, ordinal)
+    }
 
     override def hasNext = buffer.hasRemaining
   }
@@ -63,7 +68,9 @@ private[sql] case object PassThrough extends CompressionScheme {
 private[sql] case object RunLengthEncoding extends CompressionScheme {
   override val typeId = 1
 
-  override def encoder[T <: NativeType] = new this.Encoder[T]
+  override def encoder[T <: NativeType](columnType: NativeColumnType[T]) = {
+    new this.Encoder[T](columnType)
+  }
 
   override def decoder[T <: NativeType](buffer: ByteBuffer, columnType: NativeColumnType[T]) = {
     new this.Decoder(buffer, columnType)
@@ -74,24 +81,25 @@ private[sql] case object RunLengthEncoding extends CompressionScheme {
     case _ => false
   }
 
-  class Encoder[T <: NativeType] extends compression.Encoder[T] {
+  class Encoder[T <: NativeType](columnType: NativeColumnType[T]) extends compression.Encoder[T] {
     private var _uncompressedSize = 0
     private var _compressedSize = 0
 
     // Using `MutableRow` to store the last value to avoid boxing/unboxing cost.
-    private val lastValue = new GenericMutableRow(1)
+    private val lastValue = new SpecificMutableRow(Seq(columnType.dataType))
     private var lastRun = 0
 
     override def uncompressedSize = _uncompressedSize
 
     override def compressedSize = _compressedSize
 
-    override def gatherCompressibilityStats(value: T#JvmType, columnType: NativeColumnType[T]) {
-      val actualSize = columnType.actualSize(value)
+    override def gatherCompressibilityStats(row: Row, ordinal: Int): Unit = {
+      val value = columnType.getField(row, ordinal)
+      val actualSize = columnType.actualSize(row, ordinal)
       _uncompressedSize += actualSize
 
       if (lastValue.isNullAt(0)) {
-        columnType.setField(lastValue, 0, value)
+        columnType.copyField(row, ordinal, lastValue, 0)
         lastRun = 1
         _compressedSize += actualSize + 4
       } else {
@@ -99,37 +107,40 @@ private[sql] case object RunLengthEncoding extends CompressionScheme {
           lastRun += 1
         } else {
           _compressedSize += actualSize + 4
-          columnType.setField(lastValue, 0, value)
+          columnType.copyField(row, ordinal, lastValue, 0)
           lastRun = 1
         }
       }
     }
 
-    override def compress(from: ByteBuffer, to: ByteBuffer, columnType: NativeColumnType[T]) = {
+    override def compress(from: ByteBuffer, to: ByteBuffer) = {
       to.putInt(RunLengthEncoding.typeId)
 
       if (from.hasRemaining) {
-        var currentValue = columnType.extract(from)
+        val currentValue = new SpecificMutableRow(Seq(columnType.dataType))
         var currentRun = 1
+        val value = new SpecificMutableRow(Seq(columnType.dataType))
+
+        columnType.extract(from, currentValue, 0)
 
         while (from.hasRemaining) {
-          val value = columnType.extract(from)
+          columnType.extract(from, value, 0)
 
-          if (value == currentValue) {
+          if (value.head == currentValue.head) {
             currentRun += 1
           } else {
             // Writes current run
-            columnType.append(currentValue, to)
+            columnType.append(currentValue, 0, to)
             to.putInt(currentRun)
 
             // Resets current run
-            currentValue = value
+            columnType.copyField(value, 0, currentValue, 0)
             currentRun = 1
           }
         }
 
         // Writes the last run
-        columnType.append(currentValue, to)
+        columnType.append(currentValue, 0, to)
         to.putInt(currentRun)
       }
 
@@ -145,7 +156,7 @@ private[sql] case object RunLengthEncoding extends CompressionScheme {
     private var valueCount = 0
     private var currentValue: T#JvmType = _
 
-    override def next() = {
+    override def next(row: MutableRow, ordinal: Int): Unit = {
       if (valueCount == run) {
         currentValue = columnType.extract(buffer)
         run = buffer.getInt()
@@ -154,7 +165,7 @@ private[sql] case object RunLengthEncoding extends CompressionScheme {
         valueCount += 1
       }
 
-      currentValue
+      columnType.setField(row, ordinal, currentValue)
     }
 
     override def hasNext = valueCount < run || buffer.hasRemaining
@@ -171,14 +182,16 @@ private[sql] case object DictionaryEncoding extends CompressionScheme {
     new this.Decoder(buffer, columnType)
   }
 
-  override def encoder[T <: NativeType] = new this.Encoder[T]
+  override def encoder[T <: NativeType](columnType: NativeColumnType[T]) = {
+    new this.Encoder[T](columnType)
+  }
 
   override def supports(columnType: ColumnType[_, _]) = columnType match {
     case INT | LONG | STRING => true
     case _ => false
   }
 
-  class Encoder[T <: NativeType] extends compression.Encoder[T] {
+  class Encoder[T <: NativeType](columnType: NativeColumnType[T]) extends compression.Encoder[T] {
     // Size of the input, uncompressed, in bytes. Note that we only count until the dictionary
     // overflows.
     private var _uncompressedSize = 0
@@ -200,9 +213,11 @@ private[sql] case object DictionaryEncoding extends CompressionScheme {
     // to store dictionary element count.
     private var dictionarySize = 4
 
-    override def gatherCompressibilityStats(value: T#JvmType, columnType: NativeColumnType[T]) {
+    override def gatherCompressibilityStats(row: Row, ordinal: Int): Unit = {
+      val value = columnType.getField(row, ordinal)
+
       if (!overflow) {
-        val actualSize = columnType.actualSize(value)
+        val actualSize = columnType.actualSize(row, ordinal)
         count += 1
         _uncompressedSize += actualSize
 
@@ -221,7 +236,7 @@ private[sql] case object DictionaryEncoding extends CompressionScheme {
       }
     }
 
-    override def compress(from: ByteBuffer, to: ByteBuffer, columnType: NativeColumnType[T]) = {
+    override def compress(from: ByteBuffer, to: ByteBuffer) = {
       if (overflow) {
         throw new IllegalStateException(
           "Dictionary encoding should not be used because of dictionary overflow.")
@@ -264,7 +279,9 @@ private[sql] case object DictionaryEncoding extends CompressionScheme {
       }
     }
 
-    override def next() = dictionary(buffer.getShort())
+    override def next(row: MutableRow, ordinal: Int): Unit = {
+      columnType.setField(row, ordinal, dictionary(buffer.getShort()))
+    }
 
     override def hasNext = buffer.hasRemaining
   }
@@ -279,25 +296,20 @@ private[sql] case object BooleanBitSet extends CompressionScheme {
     new this.Decoder(buffer).asInstanceOf[compression.Decoder[T]]
   }
 
-  override def encoder[T <: NativeType] = (new this.Encoder).asInstanceOf[compression.Encoder[T]]
+  override def encoder[T <: NativeType](columnType: NativeColumnType[T]) = {
+    (new this.Encoder).asInstanceOf[compression.Encoder[T]]
+  }
 
   override def supports(columnType: ColumnType[_, _]) = columnType == BOOLEAN
 
   class Encoder extends compression.Encoder[BooleanType.type] {
     private var _uncompressedSize = 0
 
-    override def gatherCompressibilityStats(
-        value: Boolean,
-        columnType: NativeColumnType[BooleanType.type]) {
-
+    override def gatherCompressibilityStats(row: Row, ordinal: Int): Unit = {
       _uncompressedSize += BOOLEAN.defaultSize
     }
 
-    override def compress(
-        from: ByteBuffer,
-        to: ByteBuffer,
-        columnType: NativeColumnType[BooleanType.type]) = {
-
+    override def compress(from: ByteBuffer, to: ByteBuffer) = {
       to.putInt(BooleanBitSet.typeId)
         // Total element count (1 byte per Boolean value)
         .putInt(from.remaining)
@@ -349,7 +361,7 @@ private[sql] case object BooleanBitSet extends CompressionScheme {
 
     private var visited: Int = 0
 
-    override def next(): Boolean = {
+    override def next(row: MutableRow, ordinal: Int): Unit = {
       val bit = visited % BITS_PER_LONG
 
       visited += 1
@@ -357,123 +369,167 @@ private[sql] case object BooleanBitSet extends CompressionScheme {
         currentWord = buffer.getLong()
       }
 
-      ((currentWord >> bit) & 1) != 0
+      row.setBoolean(ordinal, ((currentWord >> bit) & 1) != 0)
     }
 
     override def hasNext: Boolean = visited < count
   }
 }
 
-private[sql] sealed abstract class IntegralDelta[I <: IntegralType] extends CompressionScheme {
+private[sql] case object IntDelta extends CompressionScheme {
+  override def typeId: Int = 4
+
   override def decoder[T <: NativeType](buffer: ByteBuffer, columnType: NativeColumnType[T]) = {
-    new this.Decoder(buffer, columnType.asInstanceOf[NativeColumnType[I]])
-      .asInstanceOf[compression.Decoder[T]]
+    new Decoder(buffer, INT).asInstanceOf[compression.Decoder[T]]
   }
 
-  override def encoder[T <: NativeType] = (new this.Encoder).asInstanceOf[compression.Encoder[T]]
-
-  /**
-   * Computes `delta = x - y`, returns `(true, delta)` if `delta` can fit into a single byte, or
-   * `(false, 0: Byte)` otherwise.
-   */
-  protected def byteSizedDelta(x: I#JvmType, y: I#JvmType): (Boolean, Byte)
+  override def encoder[T <: NativeType](columnType: NativeColumnType[T]) = {
+    (new Encoder).asInstanceOf[compression.Encoder[T]]
+  }
 
-  /**
-   * Simply computes `x + delta`
-   */
-  protected def addDelta(x: I#JvmType, delta: Byte): I#JvmType
+  override def supports(columnType: ColumnType[_, _]) = columnType == INT
 
-  class Encoder extends compression.Encoder[I] {
-    private var _compressedSize: Int = 0
+  class Encoder extends compression.Encoder[IntegerType.type] {
+    protected var _compressedSize: Int = 0
+    protected var _uncompressedSize: Int = 0
 
-    private var _uncompressedSize: Int = 0
+    override def compressedSize = _compressedSize
+    override def uncompressedSize = _uncompressedSize
 
-    private var prev: I#JvmType = _
+    private var prevValue: Int = _
 
-    private var initial = true
+    override def gatherCompressibilityStats(row: Row, ordinal: Int): Unit = {
+      val value = row.getInt(ordinal)
+      val delta = value - prevValue
 
-    override def gatherCompressibilityStats(value: I#JvmType, columnType: NativeColumnType[I]) {
-      _uncompressedSize += columnType.defaultSize
+      _compressedSize += 1
 
-      if (initial) {
-        initial = false
-        _compressedSize += 1 + columnType.defaultSize
-      } else {
-        val (smallEnough, _) = byteSizedDelta(value, prev)
-        _compressedSize += (if (smallEnough) 1 else 1 + columnType.defaultSize)
+      // If this is the first integer to be compressed, or the delta is out of byte range, then give
+      // up compressing this integer.
+      if (_uncompressedSize == 0 || delta <= Byte.MinValue || delta > Byte.MaxValue) {
+        _compressedSize += INT.defaultSize
       }
 
-      prev = value
+      _uncompressedSize += INT.defaultSize
+      prevValue = value
     }
 
-    override def compress(from: ByteBuffer, to: ByteBuffer, columnType: NativeColumnType[I]) = {
+    override def compress(from: ByteBuffer, to: ByteBuffer): ByteBuffer = {
       to.putInt(typeId)
 
       if (from.hasRemaining) {
-        var prev = columnType.extract(from)
+        var prev = from.getInt()
         to.put(Byte.MinValue)
-        columnType.append(prev, to)
+        to.putInt(prev)
 
         while (from.hasRemaining) {
-          val current = columnType.extract(from)
-          val (smallEnough, delta) = byteSizedDelta(current, prev)
+          val current = from.getInt()
+          val delta = current - prev
           prev = current
 
-          if (smallEnough) {
-            to.put(delta)
+          if (Byte.MinValue < delta && delta <= Byte.MaxValue) {
+            to.put(delta.toByte)
           } else {
             to.put(Byte.MinValue)
-            columnType.append(current, to)
+            to.putInt(current)
           }
         }
       }
 
-      to.rewind()
-      to
+      to.rewind().asInstanceOf[ByteBuffer]
     }
-
-    override def uncompressedSize = _uncompressedSize
-
-    override def compressedSize = _compressedSize
   }
 
-  class Decoder(buffer: ByteBuffer, columnType: NativeColumnType[I])
-    extends compression.Decoder[I] {
+  class Decoder(buffer: ByteBuffer, columnType: NativeColumnType[IntegerType.type])
+    extends compression.Decoder[IntegerType.type] {
+
+    private var prev: Int = _
 
-    private var prev: I#JvmType = _
+    override def hasNext: Boolean = buffer.hasRemaining
 
-    override def next() = {
+    override def next(row: MutableRow, ordinal: Int): Unit = {
       val delta = buffer.get()
-      prev = if (delta > Byte.MinValue) addDelta(prev, delta) else columnType.extract(buffer)
-      prev
+      prev = if (delta > Byte.MinValue) prev + delta else buffer.getInt()
+      row.setInt(ordinal, prev)
     }
-
-    override def hasNext = buffer.hasRemaining
   }
 }
 
-private[sql] case object IntDelta extends IntegralDelta[IntegerType.type] {
-  override val typeId = 4
+private[sql] case object LongDelta extends CompressionScheme {
+  override def typeId: Int = 5
 
-  override def supports(columnType: ColumnType[_, _]) = columnType == INT
+  override def decoder[T <: NativeType](buffer: ByteBuffer, columnType: NativeColumnType[T]) = {
+    new Decoder(buffer, LONG).asInstanceOf[compression.Decoder[T]]
+  }
+
+  override def encoder[T <: NativeType](columnType: NativeColumnType[T]) = {
+    (new Encoder).asInstanceOf[compression.Encoder[T]]
+  }
 
-  override protected def addDelta(x: Int, delta: Byte) = x + delta
+  override def supports(columnType: ColumnType[_, _]) = columnType == LONG
+
+  class Encoder extends compression.Encoder[LongType.type] {
+    protected var _compressedSize: Int = 0
+    protected var _uncompressedSize: Int = 0
+
+    override def compressedSize = _compressedSize
+    override def uncompressedSize = _uncompressedSize
+
+    private var prevValue: Long = _
+
+    override def gatherCompressibilityStats(row: Row, ordinal: Int): Unit = {
+      val value = row.getLong(ordinal)
+      val delta = value - prevValue
+
+      _compressedSize += 1
 
-  override protected def byteSizedDelta(x: Int, y: Int): (Boolean, Byte) = {
-    val delta = x - y
-    if (math.abs(delta) <= Byte.MaxValue) (true, delta.toByte) else (false, 0: Byte)
+      // If this is the first long integer to be compressed, or the delta is out of byte range, then
+      // give up compressing this long integer.
+      if (_uncompressedSize == 0 || delta <= Byte.MinValue || delta > Byte.MaxValue) {
+        _compressedSize += LONG.defaultSize
+      }
+
+      _uncompressedSize += LONG.defaultSize
+      prevValue = value
+    }
+
+    override def compress(from: ByteBuffer, to: ByteBuffer): ByteBuffer = {
+      to.putInt(typeId)
+
+      if (from.hasRemaining) {
+        var prev = from.getLong()
+        to.put(Byte.MinValue)
+        to.putLong(prev)
+
+        while (from.hasRemaining) {
+          val current = from.getLong()
+          val delta = current - prev
+          prev = current
+
+          if (Byte.MinValue < delta && delta <= Byte.MaxValue) {
+            to.put(delta.toByte)
+          } else {
+            to.put(Byte.MinValue)
+            to.putLong(current)
+          }
+        }
+      }
+
+      to.rewind().asInstanceOf[ByteBuffer]
+    }
   }
-}
 
-private[sql] case object LongDelta extends IntegralDelta[LongType.type] {
-  override val typeId = 5
+  class Decoder(buffer: ByteBuffer, columnType: NativeColumnType[LongType.type])
+    extends compression.Decoder[LongType.type] {
 
-  override def supports(columnType: ColumnType[_, _]) = columnType == LONG
+    private var prev: Long = _
 
-  override protected def addDelta(x: Long, delta: Byte) = x + delta
+    override def hasNext: Boolean = buffer.hasRemaining
 
-  override protected def byteSizedDelta(x: Long, y: Long): (Boolean, Byte) = {
-    val delta = x - y
-    if (math.abs(delta) <= Byte.MaxValue) (true, delta.toByte) else (false, 0: Byte)
+    override def next(row: MutableRow, ordinal: Int): Unit = {
+      val delta = buffer.get()
+      prev = if (delta > Byte.MinValue) prev + delta else buffer.getLong()
+      row.setLong(ordinal, prev)
+    }
   }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnStatsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnStatsSuite.scala
index cde91ceb68c98..0cdbb3167ce36 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnStatsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnStatsSuite.scala
@@ -35,7 +35,7 @@ class ColumnStatsSuite extends FunSuite {
   def testColumnStats[T <: NativeType, U <: ColumnStats](
       columnStatsClass: Class[U],
       columnType: NativeColumnType[T],
-      initialStatistics: Row) {
+      initialStatistics: Row): Unit = {
 
     val columnStatsName = columnStatsClass.getSimpleName
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnTypeSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnTypeSuite.scala
index 75f653f3280bd..4fb1ecf1d532b 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnTypeSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnTypeSuite.scala
@@ -23,6 +23,7 @@ import java.sql.Timestamp
 import org.scalatest.FunSuite
 
 import org.apache.spark.Logging
+import org.apache.spark.sql.catalyst.expressions.GenericMutableRow
 import org.apache.spark.sql.catalyst.types._
 import org.apache.spark.sql.columnar.ColumnarTestUtils._
 import org.apache.spark.sql.execution.SparkSqlSerializer
@@ -46,10 +47,12 @@ class ColumnTypeSuite extends FunSuite with Logging {
     def checkActualSize[T <: DataType, JvmType](
         columnType: ColumnType[T, JvmType],
         value: JvmType,
-        expected: Int) {
+        expected: Int): Unit = {
 
       assertResult(expected, s"Wrong actualSize for $columnType") {
-        columnType.actualSize(value)
+        val row = new GenericMutableRow(1)
+        columnType.setField(row, 0, value)
+        columnType.actualSize(row, 0)
       }
     }
 
@@ -147,7 +150,7 @@ class ColumnTypeSuite extends FunSuite with Logging {
   def testNativeColumnType[T <: NativeType](
       columnType: NativeColumnType[T],
       putter: (ByteBuffer, T#JvmType) => Unit,
-      getter: (ByteBuffer) => T#JvmType) {
+      getter: (ByteBuffer) => T#JvmType): Unit = {
 
     testColumnType[T, T#JvmType](columnType, putter, getter)
   }
@@ -155,7 +158,7 @@ class ColumnTypeSuite extends FunSuite with Logging {
   def testColumnType[T <: DataType, JvmType](
       columnType: ColumnType[T, JvmType],
       putter: (ByteBuffer, JvmType) => Unit,
-      getter: (ByteBuffer) => JvmType) {
+      getter: (ByteBuffer) => JvmType): Unit = {
 
     val buffer = ByteBuffer.allocate(DEFAULT_BUFFER_SIZE)
     val seq = (0 until 4).map(_ => makeRandomValue(columnType))
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/InMemoryColumnarQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/InMemoryColumnarQuerySuite.scala
index 0e3c67f5eed29..c1278248ef655 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/InMemoryColumnarQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/InMemoryColumnarQuerySuite.scala
@@ -19,7 +19,7 @@ package org.apache.spark.sql.columnar
 
 import org.apache.spark.sql.catalyst.expressions.Row
 import org.apache.spark.sql.test.TestSQLContext
-import org.apache.spark.sql.{SQLConf, QueryTest, TestData}
+import org.apache.spark.sql.{QueryTest, TestData}
 
 class InMemoryColumnarQuerySuite extends QueryTest {
   import org.apache.spark.sql.TestData._
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/NullableColumnAccessorSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/NullableColumnAccessorSuite.scala
index 3baa6f8ec0c83..6c9a9ab6c3418 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/NullableColumnAccessorSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/NullableColumnAccessorSuite.scala
@@ -45,7 +45,9 @@ class NullableColumnAccessorSuite extends FunSuite {
     testNullableColumnAccessor(_)
   }
 
-  def testNullableColumnAccessor[T <: DataType, JvmType](columnType: ColumnType[T, JvmType]) {
+  def testNullableColumnAccessor[T <: DataType, JvmType](
+      columnType: ColumnType[T, JvmType]): Unit = {
+
     val typeName = columnType.getClass.getSimpleName.stripSuffix("$")
     val nullRow = makeNullRow(1)
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/NullableColumnBuilderSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/NullableColumnBuilderSuite.scala
index a77262534a352..f54a21eb4fbb1 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/NullableColumnBuilderSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/NullableColumnBuilderSuite.scala
@@ -41,7 +41,9 @@ class NullableColumnBuilderSuite extends FunSuite {
     testNullableColumnBuilder(_)
   }
 
-  def testNullableColumnBuilder[T <: DataType, JvmType](columnType: ColumnType[T, JvmType]) {
+  def testNullableColumnBuilder[T <: DataType, JvmType](
+      columnType: ColumnType[T, JvmType]): Unit = {
+
     val typeName = columnType.getClass.getSimpleName.stripSuffix("$")
 
     test(s"$typeName column builder: empty column") {
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/PartitionBatchPruningSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/PartitionBatchPruningSuite.scala
index 5d2fd4959197c..69e0adbd3ee0d 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/PartitionBatchPruningSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/PartitionBatchPruningSuite.scala
@@ -28,7 +28,7 @@ class PartitionBatchPruningSuite extends FunSuite with BeforeAndAfterAll with Be
   val originalColumnBatchSize = columnBatchSize
   val originalInMemoryPartitionPruning = inMemoryPartitionPruning
 
-  override protected def beforeAll() {
+  override protected def beforeAll(): Unit = {
     // Make a table with 5 partitions, 2 batches per partition, 10 elements per batch
     setConf(SQLConf.COLUMN_BATCH_SIZE, "10")
     val rawData = sparkContext.makeRDD(1 to 100, 5).map(IntegerData)
@@ -38,7 +38,7 @@ class PartitionBatchPruningSuite extends FunSuite with BeforeAndAfterAll with Be
     setConf(SQLConf.IN_MEMORY_PARTITION_PRUNING, "true")
   }
 
-  override protected def afterAll() {
+  override protected def afterAll(): Unit = {
     setConf(SQLConf.COLUMN_BATCH_SIZE, originalColumnBatchSize.toString)
     setConf(SQLConf.IN_MEMORY_PARTITION_PRUNING, originalInMemoryPartitionPruning.toString)
   }
@@ -76,7 +76,7 @@ class PartitionBatchPruningSuite extends FunSuite with BeforeAndAfterAll with Be
       filter: String,
       expectedQueryResult: Seq[Int],
       expectedReadPartitions: Int,
-      expectedReadBatches: Int) {
+      expectedReadBatches: Int): Unit = {
 
     test(filter) {
       val query = sql(s"SELECT * FROM intData WHERE $filter")
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/BooleanBitSetSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/BooleanBitSetSuite.scala
index e01cc8b4d20f2..d9e488e0ffd16 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/BooleanBitSetSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/BooleanBitSetSuite.scala
@@ -20,6 +20,7 @@ package org.apache.spark.sql.columnar.compression
 import org.scalatest.FunSuite
 
 import org.apache.spark.sql.Row
+import org.apache.spark.sql.catalyst.expressions.GenericMutableRow
 import org.apache.spark.sql.columnar.{NoopColumnStats, BOOLEAN}
 import org.apache.spark.sql.columnar.ColumnarTestUtils._
 
@@ -72,10 +73,14 @@ class BooleanBitSetSuite extends FunSuite {
     buffer.rewind().position(headerSize + 4)
 
     val decoder = BooleanBitSet.decoder(buffer, BOOLEAN)
+    val mutableRow = new GenericMutableRow(1)
     if (values.nonEmpty) {
       values.foreach {
         assert(decoder.hasNext)
-        assertResult(_, "Wrong decoded value")(decoder.next())
+        assertResult(_, "Wrong decoded value") {
+          decoder.next(mutableRow, 0)
+          mutableRow.getBoolean(0)
+        }
       }
     }
     assert(!decoder.hasNext)
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/DictionaryEncodingSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/DictionaryEncodingSuite.scala
index d2969d906c943..1cdb909146d57 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/DictionaryEncodingSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/DictionaryEncodingSuite.scala
@@ -21,6 +21,7 @@ import java.nio.ByteBuffer
 
 import org.scalatest.FunSuite
 
+import org.apache.spark.sql.catalyst.expressions.GenericMutableRow
 import org.apache.spark.sql.catalyst.types.NativeType
 import org.apache.spark.sql.columnar._
 import org.apache.spark.sql.columnar.ColumnarTestUtils._
@@ -67,7 +68,7 @@ class DictionaryEncodingSuite extends FunSuite {
         val buffer = builder.build()
         val headerSize = CompressionScheme.columnHeaderSize(buffer)
         // 4 extra bytes for dictionary size
-        val dictionarySize = 4 + values.map(columnType.actualSize).sum
+        val dictionarySize = 4 + rows.map(columnType.actualSize(_, 0)).sum
         // 2 bytes for each `Short`
         val compressedSize = 4 + dictionarySize + 2 * inputSeq.length
         // 4 extra bytes for compression scheme type ID
@@ -97,11 +98,15 @@ class DictionaryEncodingSuite extends FunSuite {
         buffer.rewind().position(headerSize + 4)
 
         val decoder = DictionaryEncoding.decoder(buffer, columnType)
+        val mutableRow = new GenericMutableRow(1)
 
         if (inputSeq.nonEmpty) {
           inputSeq.foreach { i =>
             assert(decoder.hasNext)
-            assertResult(values(i), "Wrong decoded value")(decoder.next())
+            assertResult(values(i), "Wrong decoded value") {
+              decoder.next(mutableRow, 0)
+              columnType.getField(mutableRow, 0)
+            }
           }
         }
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/IntegralDeltaSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/IntegralDeltaSuite.scala
index 322f447c24840..73f31c0233343 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/IntegralDeltaSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/IntegralDeltaSuite.scala
@@ -31,7 +31,7 @@ class IntegralDeltaSuite extends FunSuite {
   def testIntegralDelta[I <: IntegralType](
       columnStats: ColumnStats,
       columnType: NativeColumnType[I],
-      scheme: IntegralDelta[I]) {
+      scheme: CompressionScheme) {
 
     def skeleton(input: Seq[I#JvmType]) {
       // -------------
@@ -96,10 +96,15 @@ class IntegralDeltaSuite extends FunSuite {
       buffer.rewind().position(headerSize + 4)
 
       val decoder = scheme.decoder(buffer, columnType)
+      val mutableRow = new GenericMutableRow(1)
+
       if (input.nonEmpty) {
         input.foreach{
           assert(decoder.hasNext)
-          assertResult(_, "Wrong decoded value")(decoder.next())
+          assertResult(_, "Wrong decoded value") {
+            decoder.next(mutableRow, 0)
+            columnType.getField(mutableRow, 0)
+          }
         }
       }
       assert(!decoder.hasNext)
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/RunLengthEncodingSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/RunLengthEncodingSuite.scala
index 218c09ac26362..4ce2552112c92 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/RunLengthEncodingSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/RunLengthEncodingSuite.scala
@@ -19,6 +19,7 @@ package org.apache.spark.sql.columnar.compression
 
 import org.scalatest.FunSuite
 
+import org.apache.spark.sql.catalyst.expressions.GenericMutableRow
 import org.apache.spark.sql.catalyst.types.NativeType
 import org.apache.spark.sql.columnar._
 import org.apache.spark.sql.columnar.ColumnarTestUtils._
@@ -57,7 +58,7 @@ class RunLengthEncodingSuite extends FunSuite {
       // Compression scheme ID + compressed contents
       val compressedSize = 4 + inputRuns.map { case (index, _) =>
         // 4 extra bytes each run for run length
-        columnType.actualSize(values(index)) + 4
+        columnType.actualSize(rows(index), 0) + 4
       }.sum
 
       // 4 extra bytes for compression scheme type ID
@@ -80,11 +81,15 @@ class RunLengthEncodingSuite extends FunSuite {
       buffer.rewind().position(headerSize + 4)
 
       val decoder = RunLengthEncoding.decoder(buffer, columnType)
+      val mutableRow = new GenericMutableRow(1)
 
       if (inputSeq.nonEmpty) {
         inputSeq.foreach { i =>
           assert(decoder.hasNext)
-          assertResult(values(i), "Wrong decoded value")(decoder.next())
+          assertResult(values(i), "Wrong decoded value") {
+            decoder.next(mutableRow, 0)
+            columnType.getField(mutableRow, 0)
+          }
         }
       }
 
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/TableReader.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/TableReader.scala
index 329f80cad471e..84fafcde63d05 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/TableReader.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/TableReader.scala
@@ -25,16 +25,14 @@ import org.apache.hadoop.hive.ql.metadata.{Partition => HivePartition, Table =>
 import org.apache.hadoop.hive.ql.plan.{PlanUtils, TableDesc}
 import org.apache.hadoop.hive.serde2.Deserializer
 import org.apache.hadoop.hive.serde2.objectinspector.StructObjectInspector
-
+import org.apache.hadoop.hive.serde2.objectinspector.primitive._
 import org.apache.hadoop.io.Writable
 import org.apache.hadoop.mapred.{FileInputFormat, InputFormat, JobConf}
 
 import org.apache.spark.SerializableWritable
 import org.apache.spark.broadcast.Broadcast
 import org.apache.spark.rdd.{EmptyRDD, HadoopRDD, RDD, UnionRDD}
-
-import org.apache.spark.sql.catalyst.expressions.{Attribute, Row, GenericMutableRow, Literal, Cast}
-import org.apache.spark.sql.catalyst.types.DataType
+import org.apache.spark.sql.catalyst.expressions._
 
 /**
  * A trait for subclasses that handle table scans.
@@ -108,12 +106,12 @@ class HadoopTableReader(
     val hadoopRDD = createHadoopRdd(tableDesc, inputPathStr, ifc)
 
     val attrsWithIndex = attributes.zipWithIndex
-    val mutableRow = new GenericMutableRow(attrsWithIndex.length)
+    val mutableRow = new SpecificMutableRow(attributes.map(_.dataType))
+
     val deserializedHadoopRDD = hadoopRDD.mapPartitions { iter =>
       val hconf = broadcastedHiveConf.value.value
       val deserializer = deserializerClass.newInstance()
       deserializer.initialize(hconf, tableDesc.getProperties)
-
       HadoopTableReader.fillObject(iter, deserializer, attrsWithIndex, mutableRow)
     }
 
@@ -164,33 +162,32 @@ class HadoopTableReader(
       val tableDesc = relation.tableDesc
       val broadcastedHiveConf = _broadcastedHiveConf
       val localDeserializer = partDeserializer
-      val mutableRow = new GenericMutableRow(attributes.length)
-
-      // split the attributes (output schema) into 2 categories:
-      // (partition keys, ordinal), (normal attributes, ordinal), the ordinal mean the 
-      // index of the attribute in the output Row.
-      val (partitionKeys, attrs) = attributes.zipWithIndex.partition(attr => {
-        relation.partitionKeys.indexOf(attr._1) >= 0
-      })
-
-      def fillPartitionKeys(parts: Array[String], row: GenericMutableRow) = {
-        partitionKeys.foreach { case (attr, ordinal) =>
-          // get partition key ordinal for a given attribute
-          val partOridinal = relation.partitionKeys.indexOf(attr)
-          row(ordinal) = Cast(Literal(parts(partOridinal)), attr.dataType).eval(null)
+      val mutableRow = new SpecificMutableRow(attributes.map(_.dataType))
+
+      // Splits all attributes into two groups, partition key attributes and those that are not.
+      // Attached indices indicate the position of each attribute in the output schema.
+      val (partitionKeyAttrs, nonPartitionKeyAttrs) =
+        attributes.zipWithIndex.partition { case (attr, _) =>
+          relation.partitionKeys.contains(attr)
+        }
+
+      def fillPartitionKeys(rawPartValues: Array[String], row: MutableRow) = {
+        partitionKeyAttrs.foreach { case (attr, ordinal) =>
+          val partOrdinal = relation.partitionKeys.indexOf(attr)
+          row(ordinal) = Cast(Literal(rawPartValues(partOrdinal)), attr.dataType).eval(null)
         }
       }
-      // fill the partition key for the given MutableRow Object
+
+      // Fill all partition keys to the given MutableRow object
       fillPartitionKeys(partValues, mutableRow)
 
-      val hivePartitionRDD = createHadoopRdd(tableDesc, inputPathStr, ifc)
-      hivePartitionRDD.mapPartitions { iter =>
+      createHadoopRdd(tableDesc, inputPathStr, ifc).mapPartitions { iter =>
         val hconf = broadcastedHiveConf.value.value
         val deserializer = localDeserializer.newInstance()
         deserializer.initialize(hconf, partProps)
 
-        // fill the non partition key attributes 
-        HadoopTableReader.fillObject(iter, deserializer, attrs, mutableRow)
+        // fill the non partition key attributes
+        HadoopTableReader.fillObject(iter, deserializer, nonPartitionKeyAttrs, mutableRow)
       }
     }.toSeq
 
@@ -257,38 +254,64 @@ private[hive] object HadoopTableReader extends HiveInspectors {
   }
 
   /**
-   * Transform the raw data(Writable object) into the Row object for an iterable input
-   * @param iter Iterable input which represented as Writable object
-   * @param deserializer Deserializer associated with the input writable object
-   * @param attrs Represents the row attribute names and its zero-based position in the MutableRow
-   * @param row reusable MutableRow object
-   * 
-   * @return Iterable Row object that transformed from the given iterable input.
+   * Transform all given raw `Writable`s into `Row`s.
+   *
+   * @param iterator Iterator of all `Writable`s to be transformed
+   * @param deserializer The `Deserializer` associated with the input `Writable`
+   * @param nonPartitionKeyAttrs Attributes that should be filled together with their corresponding
+   *                             positions in the output schema
+   * @param mutableRow A reusable `MutableRow` that should be filled
+   * @return An `Iterator[Row]` transformed from `iterator`
    */
   def fillObject(
-      iter: Iterator[Writable],
+      iterator: Iterator[Writable],
       deserializer: Deserializer,
-      attrs: Seq[(Attribute, Int)],
-      row: GenericMutableRow): Iterator[Row] = {
+      nonPartitionKeyAttrs: Seq[(Attribute, Int)],
+      mutableRow: MutableRow): Iterator[Row] = {
+
     val soi = deserializer.getObjectInspector().asInstanceOf[StructObjectInspector]
-    // get the field references according to the attributes(output of the reader) required
-    val fieldRefs = attrs.map { case (attr, idx) => (soi.getStructFieldRef(attr.name), idx) }
+    val (fieldRefs, fieldOrdinals) = nonPartitionKeyAttrs.map { case (attr, ordinal) =>
+      soi.getStructFieldRef(attr.name) -> ordinal
+    }.unzip
+
+    // Builds specific unwrappers ahead of time according to object inspector types to avoid pattern
+    // matching and branching costs per row.
+    val unwrappers: Seq[(Any, MutableRow, Int) => Unit] = fieldRefs.map {
+      _.getFieldObjectInspector match {
+        case oi: BooleanObjectInspector =>
+          (value: Any, row: MutableRow, ordinal: Int) => row.setBoolean(ordinal, oi.get(value))
+        case oi: ByteObjectInspector =>
+          (value: Any, row: MutableRow, ordinal: Int) => row.setByte(ordinal, oi.get(value))
+        case oi: ShortObjectInspector =>
+          (value: Any, row: MutableRow, ordinal: Int) => row.setShort(ordinal, oi.get(value))
+        case oi: IntObjectInspector =>
+          (value: Any, row: MutableRow, ordinal: Int) => row.setInt(ordinal, oi.get(value))
+        case oi: LongObjectInspector =>
+          (value: Any, row: MutableRow, ordinal: Int) => row.setLong(ordinal, oi.get(value))
+        case oi: FloatObjectInspector =>
+          (value: Any, row: MutableRow, ordinal: Int) => row.setFloat(ordinal, oi.get(value))
+        case oi: DoubleObjectInspector =>
+          (value: Any, row: MutableRow, ordinal: Int) => row.setDouble(ordinal, oi.get(value))
+        case oi =>
+          (value: Any, row: MutableRow, ordinal: Int) => row(ordinal) = unwrapData(value, oi)
+      }
+    }
 
     // Map each tuple to a row object
-    iter.map { value =>
+    iterator.map { value =>
       val raw = deserializer.deserialize(value)
-      var idx = 0;
-      while (idx < fieldRefs.length) {
-        val fieldRef = fieldRefs(idx)._1
-        val fieldIdx = fieldRefs(idx)._2
-        val fieldValue = soi.getStructFieldData(raw, fieldRef)
-
-        row(fieldIdx) = unwrapData(fieldValue, fieldRef.getFieldObjectInspector())
-
-        idx += 1
+      var i = 0
+      while (i < fieldRefs.length) {
+        val fieldValue = soi.getStructFieldData(raw, fieldRefs(i))
+        if (fieldValue == null) {
+          mutableRow.setNullAt(fieldOrdinals(i))
+        } else {
+          unwrappers(i)(fieldValue, mutableRow, fieldOrdinals(i))
+        }
+        i += 1
       }
 
-      row: Row
+      mutableRow: Row
     }
   }
 }
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
index 6bf8d18a5c32c..8c8a8b124ac69 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
@@ -295,8 +295,16 @@ class HiveQuerySuite extends HiveComparisonTest {
     "SELECT (CASE WHEN key > 2 THEN 3 WHEN 2 > key THEN 2 ELSE 0 END) FROM src WHERE key < 15")
 
   test("implement identity function using case statement") {
-    val actual = sql("SELECT (CASE key WHEN key THEN key END) FROM src").collect().toSet
-    val expected = sql("SELECT key FROM src").collect().toSet
+    val actual = sql("SELECT (CASE key WHEN key THEN key END) FROM src")
+      .map { case Row(i: Int) => i }
+      .collect()
+      .toSet
+
+    val expected = sql("SELECT key FROM src")
+      .map { case Row(i: Int) => i }
+      .collect()
+      .toSet
+
     assert(actual === expected)
   }
 
@@ -559,9 +567,9 @@ class HiveQuerySuite extends HiveComparisonTest {
     val testVal = "test.val.0"
     val nonexistentKey = "nonexistent"
     val KV = "([^=]+)=([^=]*)".r
-    def collectResults(rdd: SchemaRDD): Set[(String, String)] = 
-      rdd.collect().map { 
-        case Row(key: String, value: String) => key -> value 
+    def collectResults(rdd: SchemaRDD): Set[(String, String)] =
+      rdd.collect().map {
+        case Row(key: String, value: String) => key -> value
         case Row(KV(key, value)) => key -> value
       }.toSet
     clear()

From 0f8c4edf4e750e3d11da27cc22c40b0489da7f37 Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Sat, 13 Sep 2014 16:08:04 -0700
Subject: [PATCH 294/489] [SQL] Decrease partitions when testing

Author: Michael Armbrust <michael@databricks.com>

Closes #2164 from marmbrus/shufflePartitions and squashes the following commits:

0da1e8c [Michael Armbrust] test hax
ef2d985 [Michael Armbrust] more test hacks.
2dabae3 [Michael Armbrust] more test fixes
0bdbf21 [Michael Armbrust] Make parquet tests less order dependent
b42eeab [Michael Armbrust] increase test parallelism
80453d5 [Michael Armbrust] Decrease partitions when testing
---
 .../spark/sql/test/TestSQLContext.scala       |   9 +-
 .../spark/sql/parquet/ParquetQuerySuite.scala | 142 +++++-------------
 .../org/apache/spark/sql/hive/TestHive.scala  |   7 +-
 3 files changed, 51 insertions(+), 107 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/test/TestSQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/test/TestSQLContext.scala
index f2389f8f0591e..265b67737c475 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/test/TestSQLContext.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/test/TestSQLContext.scala
@@ -18,8 +18,13 @@
 package org.apache.spark.sql.test
 
 import org.apache.spark.{SparkConf, SparkContext}
-import org.apache.spark.sql.SQLContext
+import org.apache.spark.sql.{SQLConf, SQLContext}
 
 /** A SQLContext that can be used for local testing. */
 object TestSQLContext
-  extends SQLContext(new SparkContext("local", "TestSQLContext", new SparkConf()))
+  extends SQLContext(new SparkContext("local[2]", "TestSQLContext", new SparkConf())) {
+
+  /** Fewer partitions to speed up testing. */
+  override private[spark] def numShufflePartitions: Int =
+    getConf(SQLConf.SHUFFLE_PARTITIONS, "5").toInt
+}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala
index b0a06cd3ca090..08f7358446b29 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala
@@ -58,8 +58,7 @@ case class AllDataTypes(
     doubleField: Double,
     shortField: Short,
     byteField: Byte,
-    booleanField: Boolean,
-    binaryField: Array[Byte])
+    booleanField: Boolean)
 
 case class AllDataTypesWithNonPrimitiveType(
     stringField: String,
@@ -70,13 +69,14 @@ case class AllDataTypesWithNonPrimitiveType(
     shortField: Short,
     byteField: Byte,
     booleanField: Boolean,
-    binaryField: Array[Byte],
     array: Seq[Int],
     arrayContainsNull: Seq[Option[Int]],
     map: Map[Int, Long],
     mapValueContainsNull: Map[Int, Option[Long]],
     data: Data)
 
+case class BinaryData(binaryData: Array[Byte])
+
 class ParquetQuerySuite extends QueryTest with FunSuiteLike with BeforeAndAfterAll {
   TestData // Load test data tables.
 
@@ -108,26 +108,26 @@ class ParquetQuerySuite extends QueryTest with FunSuiteLike with BeforeAndAfterA
   test("Read/Write All Types") {
     val tempDir = getTempFilePath("parquetTest").getCanonicalPath
     val range = (0 to 255)
-    TestSQLContext.sparkContext.parallelize(range)
-      .map(x => AllDataTypes(s"$x", x, x.toLong, x.toFloat, x.toDouble, x.toShort, x.toByte, x % 2 == 0,
-        (0 to x).map(_.toByte).toArray))
-      .saveAsParquetFile(tempDir)
-    val result = parquetFile(tempDir).collect()
-    range.foreach {
-      i =>
-        assert(result(i).getString(0) == s"$i", s"row $i String field did not match, got ${result(i).getString(0)}")
-        assert(result(i).getInt(1) === i)
-        assert(result(i).getLong(2) === i.toLong)
-        assert(result(i).getFloat(3) === i.toFloat)
-        assert(result(i).getDouble(4) === i.toDouble)
-        assert(result(i).getShort(5) === i.toShort)
-        assert(result(i).getByte(6) === i.toByte)
-        assert(result(i).getBoolean(7) === (i % 2 == 0))
-        assert(result(i)(8) === (0 to i).map(_.toByte).toArray)
-    }
+    val data = sparkContext.parallelize(range)
+      .map(x => AllDataTypes(s"$x", x, x.toLong, x.toFloat, x.toDouble, x.toShort, x.toByte, x % 2 == 0))
+
+    data.saveAsParquetFile(tempDir)
+
+    checkAnswer(
+      parquetFile(tempDir),
+      data.toSchemaRDD.collect().toSeq)
   }
 
-  test("Treat binary as string") {
+  test("read/write binary data") {
+    // Since equality for Array[Byte] is broken we test this separately.
+    val tempDir = getTempFilePath("parquetTest").getCanonicalPath
+    sparkContext.parallelize(BinaryData("test".getBytes("utf8")) :: Nil).saveAsParquetFile(tempDir)
+    parquetFile(tempDir)
+      .map(r => new String(r(0).asInstanceOf[Array[Byte]], "utf8"))
+      .collect().toSeq == Seq("test")
+  }
+
+  ignore("Treat binary as string") {
     val oldIsParquetBinaryAsString = TestSQLContext.isParquetBinaryAsString
 
     // Create the test file.
@@ -142,37 +142,16 @@ class ParquetQuerySuite extends QueryTest with FunSuiteLike with BeforeAndAfterA
       StructField("c2", BinaryType, false) :: Nil)
     val schemaRDD1 = applySchema(rowRDD, schema)
     schemaRDD1.saveAsParquetFile(path)
-    val resultWithBinary = parquetFile(path).collect
-    range.foreach {
-      i =>
-        assert(resultWithBinary(i).getInt(0) === i)
-        assert(resultWithBinary(i)(1) === s"val_$i".getBytes)
-    }
-
-    TestSQLContext.setConf(SQLConf.PARQUET_BINARY_AS_STRING, "true")
-    // This ParquetRelation always use Parquet types to derive output.
-    val parquetRelation = new ParquetRelation(
-      path.toString,
-      Some(TestSQLContext.sparkContext.hadoopConfiguration),
-      TestSQLContext) {
-      override val output =
-        ParquetTypesConverter.convertToAttributes(
-          ParquetTypesConverter.readMetaData(new Path(path), conf).getFileMetaData.getSchema,
-          TestSQLContext.isParquetBinaryAsString)
-    }
-    val schemaRDD = new SchemaRDD(TestSQLContext, parquetRelation)
-    val resultWithString = schemaRDD.collect
-    range.foreach {
-      i =>
-        assert(resultWithString(i).getInt(0) === i)
-        assert(resultWithString(i)(1) === s"val_$i")
-    }
+    checkAnswer(
+      parquetFile(path).select('c1, 'c2.cast(StringType)),
+      schemaRDD1.select('c1, 'c2.cast(StringType)).collect().toSeq)
 
-    schemaRDD.registerTempTable("tmp")
+    setConf(SQLConf.PARQUET_BINARY_AS_STRING, "true")
+    parquetFile(path).printSchema()
     checkAnswer(
-      sql("SELECT c1, c2 FROM tmp WHERE c2 = 'val_5' OR c2 = 'val_7'"),
-      (5, "val_5") ::
-      (7, "val_7") :: Nil)
+      parquetFile(path),
+      schemaRDD1.select('c1, 'c2.cast(StringType)).collect().toSeq)
+
 
     // Set it back.
     TestSQLContext.setConf(SQLConf.PARQUET_BINARY_AS_STRING, oldIsParquetBinaryAsString.toString)
@@ -275,34 +254,19 @@ class ParquetQuerySuite extends QueryTest with FunSuiteLike with BeforeAndAfterA
   test("Read/Write All Types with non-primitive type") {
     val tempDir = getTempFilePath("parquetTest").getCanonicalPath
     val range = (0 to 255)
-    TestSQLContext.sparkContext.parallelize(range)
+    val data = sparkContext.parallelize(range)
       .map(x => AllDataTypesWithNonPrimitiveType(
         s"$x", x, x.toLong, x.toFloat, x.toDouble, x.toShort, x.toByte, x % 2 == 0,
-        (0 to x).map(_.toByte).toArray,
         (0 until x),
         (0 until x).map(Option(_).filter(_ % 3 == 0)),
         (0 until x).map(i => i -> i.toLong).toMap,
         (0 until x).map(i => i -> Option(i.toLong)).toMap + (x -> None),
         Data((0 until x), Nested(x, s"$x"))))
-      .saveAsParquetFile(tempDir)
-    val result = parquetFile(tempDir).collect()
-    range.foreach {
-      i =>
-        assert(result(i).getString(0) == s"$i", s"row $i String field did not match, got ${result(i).getString(0)}")
-        assert(result(i).getInt(1) === i)
-        assert(result(i).getLong(2) === i.toLong)
-        assert(result(i).getFloat(3) === i.toFloat)
-        assert(result(i).getDouble(4) === i.toDouble)
-        assert(result(i).getShort(5) === i.toShort)
-        assert(result(i).getByte(6) === i.toByte)
-        assert(result(i).getBoolean(7) === (i % 2 == 0))
-        assert(result(i)(8) === (0 to i).map(_.toByte).toArray)
-        assert(result(i)(9) === (0 until i))
-        assert(result(i)(10) === (0 until i).map(i => if (i % 3 == 0) i else null))
-        assert(result(i)(11) === (0 until i).map(i => i -> i.toLong).toMap)
-        assert(result(i)(12) === (0 until i).map(i => i -> i.toLong).toMap + (i -> null))
-        assert(result(i)(13) === new GenericRow(Array[Any]((0 until i), new GenericRow(Array[Any](i, s"$i")))))
-    }
+    data.saveAsParquetFile(tempDir)
+
+    checkAnswer(
+      parquetFile(tempDir),
+      data.toSchemaRDD.collect().toSeq)
   }
 
   test("self-join parquet files") {
@@ -399,23 +363,6 @@ class ParquetQuerySuite extends QueryTest with FunSuiteLike with BeforeAndAfterA
     }
   }
 
-  test("Saving case class RDD table to file and reading it back in") {
-    val file = getTempFilePath("parquet")
-    val path = file.toString
-    val rdd = TestSQLContext.sparkContext.parallelize((1 to 100))
-      .map(i => TestRDDEntry(i, s"val_$i"))
-    rdd.saveAsParquetFile(path)
-    val readFile = parquetFile(path)
-    readFile.registerTempTable("tmpx")
-    val rdd_copy = sql("SELECT * FROM tmpx").collect()
-    val rdd_orig = rdd.collect()
-    for(i <- 0 to 99) {
-      assert(rdd_copy(i).apply(0) === rdd_orig(i).key,   s"key error in line $i")
-      assert(rdd_copy(i).apply(1) === rdd_orig(i).value, s"value error in line $i")
-    }
-    Utils.deleteRecursively(file)
-  }
-
   test("Read a parquet file instead of a directory") {
     val file = getTempFilePath("parquet")
     val path = file.toString
@@ -448,32 +395,19 @@ class ParquetQuerySuite extends QueryTest with FunSuiteLike with BeforeAndAfterA
     sql("INSERT OVERWRITE INTO dest SELECT * FROM source").collect()
     val rdd_copy1 = sql("SELECT * FROM dest").collect()
     assert(rdd_copy1.size === 100)
-    assert(rdd_copy1(0).apply(0) === 1)
-    assert(rdd_copy1(0).apply(1) === "val_1")
-    // TODO: why does collecting break things? It seems InsertIntoParquet::execute() is
-    // executed twice otherwise?!
+
     sql("INSERT INTO dest SELECT * FROM source")
-    val rdd_copy2 = sql("SELECT * FROM dest").collect()
+    val rdd_copy2 = sql("SELECT * FROM dest").collect().sortBy(_.getInt(0))
     assert(rdd_copy2.size === 200)
-    assert(rdd_copy2(0).apply(0) === 1)
-    assert(rdd_copy2(0).apply(1) === "val_1")
-    assert(rdd_copy2(99).apply(0) === 100)
-    assert(rdd_copy2(99).apply(1) === "val_100")
-    assert(rdd_copy2(100).apply(0) === 1)
-    assert(rdd_copy2(100).apply(1) === "val_1")
     Utils.deleteRecursively(dirname)
   }
 
   test("Insert (appending) to same table via Scala API") {
-    // TODO: why does collecting break things? It seems InsertIntoParquet::execute() is
-    // executed twice otherwise?!
     sql("INSERT INTO testsource SELECT * FROM testsource")
     val double_rdd = sql("SELECT * FROM testsource").collect()
     assert(double_rdd != null)
     assert(double_rdd.size === 30)
-    for(i <- (0 to 14)) {
-      assert(double_rdd(i) === double_rdd(i+15), s"error: lines $i and ${i+15} to not match")
-    }
+
     // let's restore the original test data
     Utils.deleteRecursively(ParquetTestData.testDir)
     ParquetTestData.writeFile()
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/TestHive.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/TestHive.scala
index a3bfd3a8f1fd2..70fb15259e7d7 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/TestHive.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/TestHive.scala
@@ -35,12 +35,13 @@ import org.apache.spark.sql.catalyst.analysis._
 import org.apache.spark.sql.catalyst.plans.logical.{CacheCommand, LogicalPlan, NativeCommand}
 import org.apache.spark.sql.catalyst.util._
 import org.apache.spark.sql.hive._
+import org.apache.spark.sql.SQLConf
 
 /* Implicit conversions */
 import scala.collection.JavaConversions._
 
 object TestHive
-  extends TestHiveContext(new SparkContext("local", "TestSQLContext", new SparkConf()))
+  extends TestHiveContext(new SparkContext("local[2]", "TestSQLContext", new SparkConf()))
 
 /**
  * A locally running test instance of Spark's Hive execution engine.
@@ -90,6 +91,10 @@ class TestHiveContext(sc: SparkContext) extends HiveContext(sc) {
   override def executePlan(plan: LogicalPlan): this.QueryExecution =
     new this.QueryExecution { val logical = plan }
 
+  /** Fewer partitions to speed up testing. */
+  override private[spark] def numShufflePartitions: Int =
+    getConf(SQLConf.SHUFFLE_PARTITIONS, "5").toInt
+
   /**
    * Returns the value of specified environmental variable as a [[java.io.File]] after checking
    * to ensure it exists

From 2aea0da84c58a179917311290083456dfa043db7 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies.liu@gmail.com>
Date: Sat, 13 Sep 2014 16:22:04 -0700
Subject: [PATCH 295/489] [SPARK-3030] [PySpark] Reuse Python worker

Reuse Python worker to avoid the overhead of fork() Python process for each tasks. It also tracks the broadcasts for each worker, avoid sending repeated broadcasts.

This can reduce the time for dummy task from 22ms to 13ms (-40%). It can help to reduce the latency for Spark Streaming.

For a job with broadcast (43M after compress):
```
    b = sc.broadcast(set(range(30000000)))
    print sc.parallelize(range(24000), 100).filter(lambda x: x in b.value).count()
```
It will finish in 281s without reused worker, and it will finish in 65s with reused worker(4 CPUs). After reusing the worker, it can save about 9 seconds for transfer and deserialize the broadcast for each tasks.

It's enabled by default, could be disabled by `spark.python.worker.reuse = false`.

Author: Davies Liu <davies.liu@gmail.com>

Closes #2259 from davies/reuse-worker and squashes the following commits:

f11f617 [Davies Liu] Merge branch 'master' into reuse-worker
3939f20 [Davies Liu] fix bug in serializer in mllib
cf1c55e [Davies Liu] address comments
3133a60 [Davies Liu] fix accumulator with reused worker
760ab1f [Davies Liu] do not reuse worker if there are any exceptions
7abb224 [Davies Liu] refactor: sychronized with itself
ac3206e [Davies Liu] renaming
8911f44 [Davies Liu] synchronized getWorkerBroadcasts()
6325fc1 [Davies Liu] bugfix: bid >= 0
e0131a2 [Davies Liu] fix name of config
583716e [Davies Liu] only reuse completed and not interrupted worker
ace2917 [Davies Liu] kill python worker after timeout
6123d0f [Davies Liu] track broadcasts for each worker
8d2f08c [Davies Liu] reuse python worker
---
 .../scala/org/apache/spark/SparkEnv.scala     |  8 ++
 .../apache/spark/api/python/PythonRDD.scala   | 58 ++++++++++---
 .../api/python/PythonWorkerFactory.scala      | 85 ++++++++++++++++---
 docs/configuration.md                         | 10 +++
 python/pyspark/daemon.py                      | 38 ++++-----
 python/pyspark/mllib/_common.py               | 12 ++-
 python/pyspark/serializers.py                 |  4 +
 python/pyspark/tests.py                       | 35 ++++++++
 python/pyspark/worker.py                      |  9 +-
 9 files changed, 208 insertions(+), 51 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/SparkEnv.scala b/core/src/main/scala/org/apache/spark/SparkEnv.scala
index dd95e406f2a8e..009ed64775844 100644
--- a/core/src/main/scala/org/apache/spark/SparkEnv.scala
+++ b/core/src/main/scala/org/apache/spark/SparkEnv.scala
@@ -108,6 +108,14 @@ class SparkEnv (
       pythonWorkers.get(key).foreach(_.stopWorker(worker))
     }
   }
+
+  private[spark]
+  def releasePythonWorker(pythonExec: String, envVars: Map[String, String], worker: Socket) {
+    synchronized {
+      val key = (pythonExec, envVars)
+      pythonWorkers.get(key).foreach(_.releaseWorker(worker))
+    }
+  }
 }
 
 object SparkEnv extends Logging {
diff --git a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
index ae8010300a500..ca8eef5f99edf 100644
--- a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
+++ b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
@@ -23,6 +23,7 @@ import java.nio.charset.Charset
 import java.util.{List => JList, ArrayList => JArrayList, Map => JMap, Collections}
 
 import scala.collection.JavaConversions._
+import scala.collection.mutable
 import scala.language.existentials
 import scala.reflect.ClassTag
 import scala.util.{Try, Success, Failure}
@@ -52,6 +53,7 @@ private[spark] class PythonRDD(
   extends RDD[Array[Byte]](parent) {
 
   val bufferSize = conf.getInt("spark.buffer.size", 65536)
+  val reuse_worker = conf.getBoolean("spark.python.worker.reuse", true)
 
   override def getPartitions = parent.partitions
 
@@ -63,19 +65,26 @@ private[spark] class PythonRDD(
     val localdir = env.blockManager.diskBlockManager.localDirs.map(
       f => f.getPath()).mkString(",")
     envVars += ("SPARK_LOCAL_DIRS" -> localdir) // it's also used in monitor thread
+    if (reuse_worker) {
+      envVars += ("SPARK_REUSE_WORKER" -> "1")
+    }
     val worker: Socket = env.createPythonWorker(pythonExec, envVars.toMap)
 
     // Start a thread to feed the process input from our parent's iterator
     val writerThread = new WriterThread(env, worker, split, context)
 
+    var complete_cleanly = false
     context.addTaskCompletionListener { context =>
       writerThread.shutdownOnTaskCompletion()
-
-      // Cleanup the worker socket. This will also cause the Python worker to exit.
-      try {
-        worker.close()
-      } catch {
-        case e: Exception => logWarning("Failed to close worker socket", e)
+      if (reuse_worker && complete_cleanly) {
+        env.releasePythonWorker(pythonExec, envVars.toMap, worker)
+      } else {
+        try {
+          worker.close()
+        } catch {
+          case e: Exception =>
+            logWarning("Failed to close worker socket", e)
+        }
       }
     }
 
@@ -133,6 +142,7 @@ private[spark] class PythonRDD(
                 stream.readFully(update)
                 accumulator += Collections.singletonList(update)
               }
+               complete_cleanly = true
               null
           }
         } catch {
@@ -195,11 +205,26 @@ private[spark] class PythonRDD(
           PythonRDD.writeUTF(include, dataOut)
         }
         // Broadcast variables
-        dataOut.writeInt(broadcastVars.length)
+        val oldBids = PythonRDD.getWorkerBroadcasts(worker)
+        val newBids = broadcastVars.map(_.id).toSet
+        // number of different broadcasts
+        val cnt = oldBids.diff(newBids).size + newBids.diff(oldBids).size
+        dataOut.writeInt(cnt)
+        for (bid <- oldBids) {
+          if (!newBids.contains(bid)) {
+            // remove the broadcast from worker
+            dataOut.writeLong(- bid - 1)  // bid >= 0
+            oldBids.remove(bid)
+          }
+        }
         for (broadcast <- broadcastVars) {
-          dataOut.writeLong(broadcast.id)
-          dataOut.writeInt(broadcast.value.length)
-          dataOut.write(broadcast.value)
+          if (!oldBids.contains(broadcast.id)) {
+            // send new broadcast
+            dataOut.writeLong(broadcast.id)
+            dataOut.writeInt(broadcast.value.length)
+            dataOut.write(broadcast.value)
+            oldBids.add(broadcast.id)
+          }
         }
         dataOut.flush()
         // Serialized command:
@@ -207,17 +232,18 @@ private[spark] class PythonRDD(
         dataOut.write(command)
         // Data values
         PythonRDD.writeIteratorToStream(parent.iterator(split, context), dataOut)
+        dataOut.writeInt(SpecialLengths.END_OF_DATA_SECTION)
         dataOut.flush()
       } catch {
         case e: Exception if context.isCompleted || context.isInterrupted =>
           logDebug("Exception thrown after task completion (likely due to cleanup)", e)
+          worker.shutdownOutput()
 
         case e: Exception =>
           // We must avoid throwing exceptions here, because the thread uncaught exception handler
           // will kill the whole executor (see org.apache.spark.executor.Executor).
           _exception = e
-      } finally {
-        Try(worker.shutdownOutput()) // kill Python worker process
+          worker.shutdownOutput()
       }
     }
   }
@@ -278,6 +304,14 @@ private object SpecialLengths {
 private[spark] object PythonRDD extends Logging {
   val UTF8 = Charset.forName("UTF-8")
 
+  // remember the broadcasts sent to each worker
+  private val workerBroadcasts = new mutable.WeakHashMap[Socket, mutable.Set[Long]]()
+  private def getWorkerBroadcasts(worker: Socket) = {
+    synchronized {
+      workerBroadcasts.getOrElseUpdate(worker, new mutable.HashSet[Long]())
+    }
+  }
+
   /**
    * Adapter for calling SparkContext#runJob from Python.
    *
diff --git a/core/src/main/scala/org/apache/spark/api/python/PythonWorkerFactory.scala b/core/src/main/scala/org/apache/spark/api/python/PythonWorkerFactory.scala
index 4c4796f6c59ba..71bdf0fe1b917 100644
--- a/core/src/main/scala/org/apache/spark/api/python/PythonWorkerFactory.scala
+++ b/core/src/main/scala/org/apache/spark/api/python/PythonWorkerFactory.scala
@@ -40,7 +40,10 @@ private[spark] class PythonWorkerFactory(pythonExec: String, envVars: Map[String
   var daemon: Process = null
   val daemonHost = InetAddress.getByAddress(Array(127, 0, 0, 1))
   var daemonPort: Int = 0
-  var daemonWorkers = new mutable.WeakHashMap[Socket, Int]()
+  val daemonWorkers = new mutable.WeakHashMap[Socket, Int]()
+  val idleWorkers = new mutable.Queue[Socket]()
+  var lastActivity = 0L
+  new MonitorThread().start()
 
   var simpleWorkers = new mutable.WeakHashMap[Socket, Process]()
 
@@ -51,6 +54,11 @@ private[spark] class PythonWorkerFactory(pythonExec: String, envVars: Map[String
 
   def create(): Socket = {
     if (useDaemon) {
+      synchronized {
+        if (idleWorkers.size > 0) {
+          return idleWorkers.dequeue()
+        }
+      }
       createThroughDaemon()
     } else {
       createSimpleWorker()
@@ -199,9 +207,44 @@ private[spark] class PythonWorkerFactory(pythonExec: String, envVars: Map[String
     }
   }
 
+  /**
+   * Monitor all the idle workers, kill them after timeout.
+   */
+  private class MonitorThread extends Thread(s"Idle Worker Monitor for $pythonExec") {
+
+    setDaemon(true)
+
+    override def run() {
+      while (true) {
+        synchronized {
+          if (lastActivity + IDLE_WORKER_TIMEOUT_MS < System.currentTimeMillis()) {
+            cleanupIdleWorkers()
+            lastActivity = System.currentTimeMillis()
+          }
+        }
+        Thread.sleep(10000)
+      }
+    }
+  }
+
+  private def cleanupIdleWorkers() {
+    while (idleWorkers.length > 0) {
+      val worker = idleWorkers.dequeue()
+      try {
+        // the worker will exit after closing the socket
+        worker.close()
+      } catch {
+        case e: Exception =>
+          logWarning("Failed to close worker socket", e)
+      }
+    }
+  }
+
   private def stopDaemon() {
     synchronized {
       if (useDaemon) {
+        cleanupIdleWorkers()
+
         // Request shutdown of existing daemon by sending SIGTERM
         if (daemon != null) {
           daemon.destroy()
@@ -220,23 +263,43 @@ private[spark] class PythonWorkerFactory(pythonExec: String, envVars: Map[String
   }
 
   def stopWorker(worker: Socket) {
-    if (useDaemon) {
-      if (daemon != null) {
-        daemonWorkers.get(worker).foreach { pid =>
-          // tell daemon to kill worker by pid
-          val output = new DataOutputStream(daemon.getOutputStream)
-          output.writeInt(pid)
-          output.flush()
-          daemon.getOutputStream.flush()
+    synchronized {
+      if (useDaemon) {
+        if (daemon != null) {
+          daemonWorkers.get(worker).foreach { pid =>
+            // tell daemon to kill worker by pid
+            val output = new DataOutputStream(daemon.getOutputStream)
+            output.writeInt(pid)
+            output.flush()
+            daemon.getOutputStream.flush()
+          }
         }
+      } else {
+        simpleWorkers.get(worker).foreach(_.destroy())
       }
-    } else {
-      simpleWorkers.get(worker).foreach(_.destroy())
     }
     worker.close()
   }
+
+  def releaseWorker(worker: Socket) {
+    if (useDaemon) {
+      synchronized {
+        lastActivity = System.currentTimeMillis()
+        idleWorkers.enqueue(worker)
+      }
+    } else {
+      // Cleanup the worker socket. This will also cause the Python worker to exit.
+      try {
+        worker.close()
+      } catch {
+        case e: Exception =>
+          logWarning("Failed to close worker socket", e)
+      }
+    }
+  }
 }
 
 private object PythonWorkerFactory {
   val PROCESS_WAIT_TIMEOUT_MS = 10000
+  val IDLE_WORKER_TIMEOUT_MS = 60000  // kill idle workers after 1 minute
 }
diff --git a/docs/configuration.md b/docs/configuration.md
index 36178efb97103..af16489a44281 100644
--- a/docs/configuration.md
+++ b/docs/configuration.md
@@ -206,6 +206,16 @@ Apart from these, the following properties are also available, and may be useful
     used during aggregation goes above this amount, it will spill the data into disks.
   </td>
 </tr>
+<tr>
+  <td><code>spark.python.worker.reuse</code></td>
+  <td>true</td>
+  <td>
+    Reuse Python worker or not. If yes, it will use a fixed number of Python workers,
+    does not need to fork() a Python process for every tasks. It will be very useful
+    if there is large broadcast, then the broadcast will not be needed to transfered
+    from JVM to Python worker for every task.
+  </td>
+</tr>
 <tr>
   <td><code>spark.executorEnv.[EnvironmentVariableName]</code></td>
   <td>(none)</td>
diff --git a/python/pyspark/daemon.py b/python/pyspark/daemon.py
index 15445abf67147..64d6202acb27d 100644
--- a/python/pyspark/daemon.py
+++ b/python/pyspark/daemon.py
@@ -23,6 +23,7 @@
 import sys
 import traceback
 import time
+import gc
 from errno import EINTR, ECHILD, EAGAIN
 from socket import AF_INET, SOCK_STREAM, SOMAXCONN
 from signal import SIGHUP, SIGTERM, SIGCHLD, SIG_DFL, SIG_IGN
@@ -46,17 +47,6 @@ def worker(sock):
     signal.signal(SIGCHLD, SIG_DFL)
     signal.signal(SIGTERM, SIG_DFL)
 
-    # Blocks until the socket is closed by draining the input stream
-    # until it raises an exception or returns EOF.
-    def waitSocketClose(sock):
-        try:
-            while True:
-                # Empty string is returned upon EOF (and only then).
-                if sock.recv(4096) == '':
-                    return
-        except:
-            pass
-
     # Read the socket using fdopen instead of socket.makefile() because the latter
     # seems to be very slow; note that we need to dup() the file descriptor because
     # otherwise writes also cause a seek that makes us miss data on the read side.
@@ -64,17 +54,13 @@ def waitSocketClose(sock):
     outfile = os.fdopen(os.dup(sock.fileno()), "a+", 65536)
     exit_code = 0
     try:
-        # Acknowledge that the fork was successful
-        write_int(os.getpid(), outfile)
-        outfile.flush()
         worker_main(infile, outfile)
     except SystemExit as exc:
-        exit_code = exc.code
+        exit_code = compute_real_exit_code(exc.code)
     finally:
         outfile.flush()
-        # The Scala side will close the socket upon task completion.
-        waitSocketClose(sock)
-        os._exit(compute_real_exit_code(exit_code))
+        if exit_code:
+            os._exit(exit_code)
 
 
 # Cleanup zombie children
@@ -111,6 +97,8 @@ def handle_sigterm(*args):
     signal.signal(SIGTERM, handle_sigterm)  # Gracefully exit on SIGTERM
     signal.signal(SIGHUP, SIG_IGN)  # Don't die on SIGHUP
 
+    reuse = os.environ.get("SPARK_REUSE_WORKER")
+
     # Initialization complete
     try:
         while True:
@@ -163,7 +151,19 @@ def handle_sigterm(*args):
                     # in child process
                     listen_sock.close()
                     try:
-                        worker(sock)
+                        # Acknowledge that the fork was successful
+                        outfile = sock.makefile("w")
+                        write_int(os.getpid(), outfile)
+                        outfile.flush()
+                        outfile.close()
+                        while True:
+                            worker(sock)
+                            if not reuse:
+                                # wait for closing
+                                while sock.recv(1024):
+                                    pass
+                                break
+                            gc.collect()
                     except:
                         traceback.print_exc()
                         os._exit(1)
diff --git a/python/pyspark/mllib/_common.py b/python/pyspark/mllib/_common.py
index bb60d3d0c8463..68f6033616726 100644
--- a/python/pyspark/mllib/_common.py
+++ b/python/pyspark/mllib/_common.py
@@ -21,7 +21,7 @@
 from numpy import ndarray, float64, int64, int32, array_equal, array
 from pyspark import SparkContext, RDD
 from pyspark.mllib.linalg import SparseVector
-from pyspark.serializers import Serializer
+from pyspark.serializers import FramedSerializer
 
 
 """
@@ -451,18 +451,16 @@ def _serialize_rating(r):
     return ba
 
 
-class RatingDeserializer(Serializer):
+class RatingDeserializer(FramedSerializer):
 
-    def loads(self, stream):
-        length = struct.unpack("!i", stream.read(4))[0]
-        ba = stream.read(length)
-        res = ndarray(shape=(3, ), buffer=ba, dtype=float64, offset=4)
+    def loads(self, string):
+        res = ndarray(shape=(3, ), buffer=string, dtype=float64, offset=4)
         return int(res[0]), int(res[1]), res[2]
 
     def load_stream(self, stream):
         while True:
             try:
-                yield self.loads(stream)
+                yield self._read_with_length(stream)
             except struct.error:
                 return
             except EOFError:
diff --git a/python/pyspark/serializers.py b/python/pyspark/serializers.py
index a5f9341e819a9..ec3c6f055441d 100644
--- a/python/pyspark/serializers.py
+++ b/python/pyspark/serializers.py
@@ -144,6 +144,8 @@ def _write_with_length(self, obj, stream):
 
     def _read_with_length(self, stream):
         length = read_int(stream)
+        if length == SpecialLengths.END_OF_DATA_SECTION:
+            raise EOFError
         obj = stream.read(length)
         if obj == "":
             raise EOFError
@@ -438,6 +440,8 @@ def __init__(self, use_unicode=False):
 
     def loads(self, stream):
         length = read_int(stream)
+        if length == SpecialLengths.END_OF_DATA_SECTION:
+            raise EOFError
         s = stream.read(length)
         return s.decode("utf-8") if self.use_unicode else s
 
diff --git a/python/pyspark/tests.py b/python/pyspark/tests.py
index b687d695b01c4..747cd1767de7b 100644
--- a/python/pyspark/tests.py
+++ b/python/pyspark/tests.py
@@ -1222,11 +1222,46 @@ def run():
         except OSError:
             self.fail("daemon had been killed")
 
+        # run a normal job
+        rdd = self.sc.parallelize(range(100), 1)
+        self.assertEqual(100, rdd.map(str).count())
+
     def test_fd_leak(self):
         N = 1100  # fd limit is 1024 by default
         rdd = self.sc.parallelize(range(N), N)
         self.assertEquals(N, rdd.count())
 
+    def test_after_exception(self):
+        def raise_exception(_):
+            raise Exception()
+        rdd = self.sc.parallelize(range(100), 1)
+        self.assertRaises(Exception, lambda: rdd.foreach(raise_exception))
+        self.assertEqual(100, rdd.map(str).count())
+
+    def test_after_jvm_exception(self):
+        tempFile = tempfile.NamedTemporaryFile(delete=False)
+        tempFile.write("Hello World!")
+        tempFile.close()
+        data = self.sc.textFile(tempFile.name, 1)
+        filtered_data = data.filter(lambda x: True)
+        self.assertEqual(1, filtered_data.count())
+        os.unlink(tempFile.name)
+        self.assertRaises(Exception, lambda: filtered_data.count())
+
+        rdd = self.sc.parallelize(range(100), 1)
+        self.assertEqual(100, rdd.map(str).count())
+
+    def test_accumulator_when_reuse_worker(self):
+        from pyspark.accumulators import INT_ACCUMULATOR_PARAM
+        acc1 = self.sc.accumulator(0, INT_ACCUMULATOR_PARAM)
+        self.sc.parallelize(range(100), 20).foreach(lambda x: acc1.add(x))
+        self.assertEqual(sum(range(100)), acc1.value)
+
+        acc2 = self.sc.accumulator(0, INT_ACCUMULATOR_PARAM)
+        self.sc.parallelize(range(100), 20).foreach(lambda x: acc2.add(x))
+        self.assertEqual(sum(range(100)), acc2.value)
+        self.assertEqual(sum(range(100)), acc1.value)
+
 
 class TestSparkSubmit(unittest.TestCase):
 
diff --git a/python/pyspark/worker.py b/python/pyspark/worker.py
index 6805063e06798..61b8a74d060e8 100644
--- a/python/pyspark/worker.py
+++ b/python/pyspark/worker.py
@@ -69,9 +69,14 @@ def main(infile, outfile):
         ser = CompressedSerializer(pickleSer)
         for _ in range(num_broadcast_variables):
             bid = read_long(infile)
-            value = ser._read_with_length(infile)
-            _broadcastRegistry[bid] = Broadcast(bid, value)
+            if bid >= 0:
+                value = ser._read_with_length(infile)
+                _broadcastRegistry[bid] = Broadcast(bid, value)
+            else:
+                bid = - bid - 1
+                _broadcastRegistry.remove(bid)
 
+        _accumulatorRegistry.clear()
         command = pickleSer._read_with_length(infile)
         (func, deserializer, serializer) = command
         init_time = time.time()

From 4e3fbe8cdb6c6291e219195abb272f3c81f0ed63 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies.liu@gmail.com>
Date: Sat, 13 Sep 2014 22:31:21 -0700
Subject: [PATCH 296/489] [SPARK-3463] [PySpark] aggregate and show spilled
 bytes in Python

Aggregate the number of bytes spilled into disks during aggregation or sorting, show them in Web UI.

![spilled](https://cloud.githubusercontent.com/assets/40902/4209758/4b995562-386d-11e4-97c1-8e838ee1d4e3.png)

This patch is blocked by SPARK-3465. (It includes a fix for that).

Author: Davies Liu <davies.liu@gmail.com>

Closes #2336 from davies/metrics and squashes the following commits:

e37df38 [Davies Liu] remove outdated comments
1245eb7 [Davies Liu] remove the temporary fix
ebd2f43 [Davies Liu] Merge branch 'master' into metrics
7e4ad04 [Davies Liu] Merge branch 'master' into metrics
fbe9029 [Davies Liu] show spilled bytes in Python in web ui
---
 .../apache/spark/api/python/PythonRDD.scala   |  4 ++++
 python/pyspark/shuffle.py                     | 19 ++++++++++++++++---
 python/pyspark/tests.py                       | 15 ++++++++-------
 python/pyspark/worker.py                      | 14 ++++++++++----
 4 files changed, 38 insertions(+), 14 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
index ca8eef5f99edf..d5002fa02992b 100644
--- a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
+++ b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
@@ -124,6 +124,10 @@ private[spark] class PythonRDD(
               val total = finishTime - startTime
               logInfo("Times: total = %s, boot = %s, init = %s, finish = %s".format(total, boot,
                 init, finish))
+              val memoryBytesSpilled = stream.readLong()
+              val diskBytesSpilled = stream.readLong()
+              context.taskMetrics.memoryBytesSpilled += memoryBytesSpilled
+              context.taskMetrics.diskBytesSpilled += diskBytesSpilled
               read()
             case SpecialLengths.PYTHON_EXCEPTION_THROWN =>
               // Signals that an exception has been thrown in python
diff --git a/python/pyspark/shuffle.py b/python/pyspark/shuffle.py
index 49829f5280a5f..ce597cbe91e15 100644
--- a/python/pyspark/shuffle.py
+++ b/python/pyspark/shuffle.py
@@ -68,6 +68,11 @@ def _get_local_dirs(sub):
     return [os.path.join(d, "python", str(os.getpid()), sub) for d in dirs]
 
 
+# global stats
+MemoryBytesSpilled = 0L
+DiskBytesSpilled = 0L
+
+
 class Aggregator(object):
 
     """
@@ -313,10 +318,12 @@ def _spill(self):
 
         It will dump the data in batch for better performance.
         """
+        global MemoryBytesSpilled, DiskBytesSpilled
         path = self._get_spill_dir(self.spills)
         if not os.path.exists(path):
             os.makedirs(path)
 
+        used_memory = get_used_memory()
         if not self.pdata:
             # The data has not been partitioned, it will iterator the
             # dataset once, write them into different files, has no
@@ -334,6 +341,7 @@ def _spill(self):
                 self.serializer.dump_stream([(k, v)], streams[h])
 
             for s in streams:
+                DiskBytesSpilled += s.tell()
                 s.close()
 
             self.data.clear()
@@ -346,9 +354,11 @@ def _spill(self):
                     # dump items in batch
                     self.serializer.dump_stream(self.pdata[i].iteritems(), f)
                 self.pdata[i].clear()
+                DiskBytesSpilled += os.path.getsize(p)
 
         self.spills += 1
         gc.collect()  # release the memory as much as possible
+        MemoryBytesSpilled += (used_memory - get_used_memory()) << 20
 
     def iteritems(self):
         """ Return all merged items as iterator """
@@ -462,7 +472,6 @@ def __init__(self, memory_limit, serializer=None):
         self.memory_limit = memory_limit
         self.local_dirs = _get_local_dirs("sort")
         self.serializer = serializer or BatchedSerializer(PickleSerializer(), 1024)
-        self._spilled_bytes = 0
 
     def _get_path(self, n):
         """ Choose one directory for spill by number n """
@@ -476,6 +485,7 @@ def sorted(self, iterator, key=None, reverse=False):
         Sort the elements in iterator, do external sort when the memory
         goes above the limit.
         """
+        global MemoryBytesSpilled, DiskBytesSpilled
         batch = 10
         chunks, current_chunk = [], []
         iterator = iter(iterator)
@@ -486,15 +496,18 @@ def sorted(self, iterator, key=None, reverse=False):
             if len(chunk) < batch:
                 break
 
-            if get_used_memory() > self.memory_limit:
+            used_memory = get_used_memory()
+            if used_memory > self.memory_limit:
                 # sort them inplace will save memory
                 current_chunk.sort(key=key, reverse=reverse)
                 path = self._get_path(len(chunks))
                 with open(path, 'w') as f:
                     self.serializer.dump_stream(current_chunk, f)
-                self._spilled_bytes += os.path.getsize(path)
                 chunks.append(self.serializer.load_stream(open(path)))
                 current_chunk = []
+                gc.collect()
+                MemoryBytesSpilled += (used_memory - get_used_memory()) << 20
+                DiskBytesSpilled += os.path.getsize(path)
 
             elif not chunks:
                 batch = min(batch * 2, 10000)
diff --git a/python/pyspark/tests.py b/python/pyspark/tests.py
index 747cd1767de7b..f3309a20fcffb 100644
--- a/python/pyspark/tests.py
+++ b/python/pyspark/tests.py
@@ -46,6 +46,7 @@
     CloudPickleSerializer
 from pyspark.shuffle import Aggregator, InMemoryMerger, ExternalMerger, ExternalSorter
 from pyspark.sql import SQLContext, IntegerType
+from pyspark import shuffle
 
 _have_scipy = False
 _have_numpy = False
@@ -138,17 +139,17 @@ def test_external_sort(self):
         random.shuffle(l)
         sorter = ExternalSorter(1)
         self.assertEquals(sorted(l), list(sorter.sorted(l)))
-        self.assertGreater(sorter._spilled_bytes, 0)
-        last = sorter._spilled_bytes
+        self.assertGreater(shuffle.DiskBytesSpilled, 0)
+        last = shuffle.DiskBytesSpilled
         self.assertEquals(sorted(l, reverse=True), list(sorter.sorted(l, reverse=True)))
-        self.assertGreater(sorter._spilled_bytes, last)
-        last = sorter._spilled_bytes
+        self.assertGreater(shuffle.DiskBytesSpilled, last)
+        last = shuffle.DiskBytesSpilled
         self.assertEquals(sorted(l, key=lambda x: -x), list(sorter.sorted(l, key=lambda x: -x)))
-        self.assertGreater(sorter._spilled_bytes, last)
-        last = sorter._spilled_bytes
+        self.assertGreater(shuffle.DiskBytesSpilled, last)
+        last = shuffle.DiskBytesSpilled
         self.assertEquals(sorted(l, key=lambda x: -x, reverse=True),
                           list(sorter.sorted(l, key=lambda x: -x, reverse=True)))
-        self.assertGreater(sorter._spilled_bytes, last)
+        self.assertGreater(shuffle.DiskBytesSpilled, last)
 
     def test_external_sort_in_rdd(self):
         conf = SparkConf().set("spark.python.worker.memory", "1m")
diff --git a/python/pyspark/worker.py b/python/pyspark/worker.py
index 61b8a74d060e8..252176ac65fec 100644
--- a/python/pyspark/worker.py
+++ b/python/pyspark/worker.py
@@ -23,16 +23,14 @@
 import time
 import socket
 import traceback
-# CloudPickler needs to be imported so that depicklers are registered using the
-# copy_reg module.
+
 from pyspark.accumulators import _accumulatorRegistry
 from pyspark.broadcast import Broadcast, _broadcastRegistry
-from pyspark.cloudpickle import CloudPickler
 from pyspark.files import SparkFiles
 from pyspark.serializers import write_with_length, write_int, read_long, \
     write_long, read_int, SpecialLengths, UTF8Deserializer, PickleSerializer, \
     CompressedSerializer
-
+from pyspark import shuffle
 
 pickleSer = PickleSerializer()
 utf8_deserializer = UTF8Deserializer()
@@ -52,6 +50,11 @@ def main(infile, outfile):
         if split_index == -1:  # for unit tests
             return
 
+        # initialize global state
+        shuffle.MemoryBytesSpilled = 0
+        shuffle.DiskBytesSpilled = 0
+        _accumulatorRegistry.clear()
+
         # fetch name of workdir
         spark_files_dir = utf8_deserializer.loads(infile)
         SparkFiles._root_directory = spark_files_dir
@@ -97,6 +100,9 @@ def main(infile, outfile):
         exit(-1)
     finish_time = time.time()
     report_times(outfile, boot_time, init_time, finish_time)
+    write_long(shuffle.MemoryBytesSpilled, outfile)
+    write_long(shuffle.DiskBytesSpilled, outfile)
+
     # Mark the beginning of the accumulators section of the output
     write_int(SpecialLengths.END_OF_DATA_SECTION, outfile)
     write_int(len(_accumulatorRegistry), outfile)

From c243b21a8ba2610266702e00d7d4b5443cb1f687 Mon Sep 17 00:00:00 2001
From: Bertrand Bossy <bertrandbossy@gmail.com>
Date: Sun, 14 Sep 2014 21:10:17 -0700
Subject: [PATCH 297/489] SPARK-3039: Allow spark to be built using avro-mapred
 for hadoop2

SPARK-3039: Adds the maven property "avro.mapred.classifier" to build spark-assembly with avro-mapred with support for the new Hadoop API. Sets this property to hadoop2 for Hadoop 2 profiles.

I am not very familiar with maven, nor do I know whether this potentially breaks something in the hive part of spark. There might be a more elegant way of doing this.

Author: Bertrand Bossy <bertrandbossy@gmail.com>

Closes #1945 from bbossy/SPARK-3039 and squashes the following commits:

c32ce59 [Bertrand Bossy] SPARK-3039: Allow spark to be built using avro-mapred for hadoop2
---
 pom.xml          | 5 +++++
 sql/hive/pom.xml | 9 +++++++++
 2 files changed, 14 insertions(+)

diff --git a/pom.xml b/pom.xml
index 28763476f8313..520aed3806937 100644
--- a/pom.xml
+++ b/pom.xml
@@ -134,6 +134,7 @@
     <chill.version>0.3.6</chill.version>
     <codahale.metrics.version>3.0.0</codahale.metrics.version>
     <avro.version>1.7.6</avro.version>
+    <avro.mapred.classifier></avro.mapred.classifier>
     <jets3t.version>0.7.1</jets3t.version>
     <aws.java.sdk.version>1.8.3</aws.java.sdk.version>
     <aws.kinesis.client.version>1.1.0</aws.kinesis.client.version>
@@ -621,6 +622,7 @@
         <groupId>org.apache.avro</groupId>
         <artifactId>avro-mapred</artifactId>
         <version>${avro.version}</version>
+        <classifier>${avro.mapred.classifier}</classifier>
         <exclusions>
           <exclusion>
             <groupId>io.netty</groupId>
@@ -1108,6 +1110,7 @@
       <properties>
         <hadoop.version>2.2.0</hadoop.version>
         <protobuf.version>2.5.0</protobuf.version>
+        <avro.mapred.classifier>hadoop2</avro.mapred.classifier>
       </properties>
     </profile>
 
@@ -1117,6 +1120,7 @@
         <hadoop.version>2.3.0</hadoop.version>
         <protobuf.version>2.5.0</protobuf.version>
         <jets3t.version>0.9.0</jets3t.version>
+        <avro.mapred.classifier>hadoop2</avro.mapred.classifier>
       </properties>
     </profile>
 
@@ -1126,6 +1130,7 @@
         <hadoop.version>2.4.0</hadoop.version>
         <protobuf.version>2.5.0</protobuf.version>
         <jets3t.version>0.9.0</jets3t.version>
+        <avro.mapred.classifier>hadoop2</avro.mapred.classifier>
       </properties>
     </profile>
 
diff --git a/sql/hive/pom.xml b/sql/hive/pom.xml
index 45a4c6dc98da0..9d7a02bf7b0b7 100644
--- a/sql/hive/pom.xml
+++ b/sql/hive/pom.xml
@@ -95,6 +95,15 @@
     <dependency>
       <groupId>org.apache.avro</groupId>
       <artifactId>avro</artifactId>
+      <version>${avro.version}</version>
+    </dependency>
+    <!-- use the build matching the hadoop api of avro-mapred (i.e. no classifier for hadoop 1 API,
+    hadoop2 classifier for hadoop 2 API. avro-mapred is a dependency of org.spark-project.hive:hive-serde -->
+    <dependency>
+      <groupId>org.apache.avro</groupId>
+      <artifactId>avro-mapred</artifactId>
+      <version>${avro.version}</version>
+      <classifier>${avro.mapred.classifier}</classifier>
     </dependency>
     <dependency>
       <groupId>org.scalatest</groupId>

From f493f7982b50e3c99e78b649e7c6c5b4313c5ffa Mon Sep 17 00:00:00 2001
From: Prashant Sharma <prashant.s@imaginea.com>
Date: Sun, 14 Sep 2014 21:17:29 -0700
Subject: [PATCH 298/489] [SPARK-3452] Maven build should skip publishing
 artifacts people shouldn...

...'t depend on

Publish local in maven term is `install`

and publish otherwise is `deploy`

So disabled both for following projects.

Author: Prashant Sharma <prashant.s@imaginea.com>

Closes #2329 from ScrapCodes/SPARK-3452/maven-skip-install and squashes the following commits:

257b79a [Prashant Sharma] [SPARK-3452] Maven build should skip publishing artifacts people shouldn't depend on
---
 assembly/pom.xml           | 14 ++++++++++++++
 examples/pom.xml           | 14 ++++++++++++++
 extras/java8-tests/pom.xml | 14 ++++++++++++++
 repl/pom.xml               | 14 ++++++++++++++
 tools/pom.xml              | 14 ++++++++++++++
 yarn/pom.xml               | 14 ++++++++++++++
 6 files changed, 84 insertions(+)

diff --git a/assembly/pom.xml b/assembly/pom.xml
index 4146168fc804b..604b1ab3de6a8 100644
--- a/assembly/pom.xml
+++ b/assembly/pom.xml
@@ -88,6 +88,20 @@
 
   <build>
     <plugins>
+      <plugin>
+        <groupId>org.apache.maven.plugins</groupId>
+        <artifactId>maven-deploy-plugin</artifactId>
+        <configuration>
+          <skip>true</skip>
+        </configuration>
+      </plugin>
+      <plugin>
+        <groupId>org.apache.maven.plugins</groupId>
+        <artifactId>maven-install-plugin</artifactId>
+        <configuration>
+          <skip>true</skip>
+        </configuration>
+      </plugin>
       <!-- Use the shade plugin to create a big JAR with all the dependencies -->
       <plugin>
         <groupId>org.apache.maven.plugins</groupId>
diff --git a/examples/pom.xml b/examples/pom.xml
index 3f46c40464d3b..2b561857f9f33 100644
--- a/examples/pom.xml
+++ b/examples/pom.xml
@@ -203,6 +203,20 @@
     <outputDirectory>target/scala-${scala.binary.version}/classes</outputDirectory>
     <testOutputDirectory>target/scala-${scala.binary.version}/test-classes</testOutputDirectory>
     <plugins>
+      <plugin>
+        <groupId>org.apache.maven.plugins</groupId>
+        <artifactId>maven-deploy-plugin</artifactId>
+        <configuration>
+          <skip>true</skip>
+        </configuration>
+      </plugin>
+      <plugin>
+        <groupId>org.apache.maven.plugins</groupId>
+        <artifactId>maven-install-plugin</artifactId>
+        <configuration>
+          <skip>true</skip>
+        </configuration>
+      </plugin>
       <plugin>
         <groupId>org.apache.maven.plugins</groupId>
         <artifactId>maven-shade-plugin</artifactId>
diff --git a/extras/java8-tests/pom.xml b/extras/java8-tests/pom.xml
index 8658ecf5abfab..7e478bed62da7 100644
--- a/extras/java8-tests/pom.xml
+++ b/extras/java8-tests/pom.xml
@@ -74,6 +74,20 @@
   </profiles>
   <build>
     <plugins>
+      <plugin>
+        <groupId>org.apache.maven.plugins</groupId>
+        <artifactId>maven-deploy-plugin</artifactId>
+        <configuration>
+          <skip>true</skip>
+        </configuration>
+      </plugin>
+      <plugin>
+        <groupId>org.apache.maven.plugins</groupId>
+        <artifactId>maven-install-plugin</artifactId>
+        <configuration>
+          <skip>true</skip>
+        </configuration>
+      </plugin>
       <plugin>
         <groupId>org.apache.maven.plugins</groupId>
         <artifactId>maven-surefire-plugin</artifactId>
diff --git a/repl/pom.xml b/repl/pom.xml
index fcc5f90d870e8..af528c8914335 100644
--- a/repl/pom.xml
+++ b/repl/pom.xml
@@ -99,6 +99,20 @@
     <outputDirectory>target/scala-${scala.binary.version}/classes</outputDirectory>
     <testOutputDirectory>target/scala-${scala.binary.version}/test-classes</testOutputDirectory>
     <plugins>
+      <plugin>
+        <groupId>org.apache.maven.plugins</groupId>
+        <artifactId>maven-deploy-plugin</artifactId>
+        <configuration>
+          <skip>true</skip>
+        </configuration>
+      </plugin>
+      <plugin>
+        <groupId>org.apache.maven.plugins</groupId>
+        <artifactId>maven-install-plugin</artifactId>
+        <configuration>
+          <skip>true</skip>
+        </configuration>
+      </plugin>
       <plugin>
         <groupId>org.scalatest</groupId>
         <artifactId>scalatest-maven-plugin</artifactId>
diff --git a/tools/pom.xml b/tools/pom.xml
index f36674476770c..b90eb0ca250c5 100644
--- a/tools/pom.xml
+++ b/tools/pom.xml
@@ -63,6 +63,20 @@
     <outputDirectory>target/scala-${scala.binary.version}/classes</outputDirectory>
     <testOutputDirectory>target/scala-${scala.binary.version}/test-classes</testOutputDirectory>
     <plugins>
+      <plugin>
+        <groupId>org.apache.maven.plugins</groupId>
+        <artifactId>maven-deploy-plugin</artifactId>
+        <configuration>
+          <skip>true</skip>
+        </configuration>
+      </plugin>
+      <plugin>
+        <groupId>org.apache.maven.plugins</groupId>
+        <artifactId>maven-install-plugin</artifactId>
+        <configuration>
+          <skip>true</skip>
+        </configuration>
+      </plugin>
       <plugin>
         <groupId>org.apache.maven.plugins</groupId>
         <artifactId>maven-source-plugin</artifactId>
diff --git a/yarn/pom.xml b/yarn/pom.xml
index 7fcd7ee0d4547..815a736c2e8fd 100644
--- a/yarn/pom.xml
+++ b/yarn/pom.xml
@@ -88,6 +88,20 @@
 
   <build>
     <plugins>
+      <plugin>
+        <groupId>org.apache.maven.plugins</groupId>
+        <artifactId>maven-deploy-plugin</artifactId>
+        <configuration>
+          <skip>true</skip>
+        </configuration>
+      </plugin>
+      <plugin>
+        <groupId>org.apache.maven.plugins</groupId>
+        <artifactId>maven-install-plugin</artifactId>
+        <configuration>
+          <skip>true</skip>
+        </configuration>
+      </plugin>
       <plugin>
         <groupId>org.codehaus.mojo</groupId>
         <artifactId>build-helper-maven-plugin</artifactId>

From cc14644460872efb344e8d895859d70213a40840 Mon Sep 17 00:00:00 2001
From: Kousuke Saruta <sarutak@oss.nttdata.co.jp>
Date: Mon, 15 Sep 2014 08:53:58 -0500
Subject: [PATCH 299/489] [SPARK-3410] The priority of shutdownhook for
 ApplicationMaster should not be integer literal

I think, it need to keep the priority of shutdown hook for ApplicationMaster than the priority of shutdown hook for o.a.h.FileSystem depending on changing the priority for FileSystem.

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

Closes #2283 from sarutak/SPARK-3410 and squashes the following commits:

1d44fef [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into SPARK-3410
bd6cc53 [Kousuke Saruta] Modified style
ee6f1aa [Kousuke Saruta] Added constant "SHUTDOWN_HOOK_PRIORITY" to ApplicationMaster
54eb68f [Kousuke Saruta] Changed Shutdown hook priority to 20
2f0aee3 [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into SPARK-3410
4c5cb93 [Kousuke Saruta] Modified the priority for AM's shutdown hook
217d1a4 [Kousuke Saruta] Removed unused import statements
717aba2 [Kousuke Saruta] Modified ApplicationMaster to make to keep the priority of shutdown hook for ApplicationMaster higher than the priority of shutdown hook for HDFS
---
 .../spark/deploy/yarn/ApplicationMaster.scala       | 13 +++++++------
 1 file changed, 7 insertions(+), 6 deletions(-)

diff --git a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
index 735d7723b0ce6..cde5fff637a39 100644
--- a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
+++ b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
@@ -21,12 +21,8 @@ import java.io.IOException
 import java.net.Socket
 import java.util.concurrent.atomic.AtomicReference
 
-import scala.collection.JavaConversions._
-import scala.util.Try
-
 import akka.actor._
 import akka.remote._
-import org.apache.hadoop.conf.Configuration
 import org.apache.hadoop.fs.{FileSystem, Path}
 import org.apache.hadoop.util.ShutdownHookManager
 import org.apache.hadoop.yarn.api._
@@ -107,8 +103,11 @@ private[spark] class ApplicationMaster(args: ApplicationMasterArguments,
         }
       }
     }
-    // Use priority 30 as it's higher than HDFS. It's the same priority MapReduce is using.
-    ShutdownHookManager.get().addShutdownHook(cleanupHook, 30)
+
+    // Use higher priority than FileSystem.
+    assert(ApplicationMaster.SHUTDOWN_HOOK_PRIORITY > FileSystem.SHUTDOWN_HOOK_PRIORITY)
+    ShutdownHookManager
+      .get().addShutdownHook(cleanupHook, ApplicationMaster.SHUTDOWN_HOOK_PRIORITY)
 
     // Call this to force generation of secret so it gets populated into the
     // Hadoop UGI. This has to happen before the startUserClass which does a
@@ -407,6 +406,8 @@ private[spark] class ApplicationMaster(args: ApplicationMasterArguments,
 
 object ApplicationMaster extends Logging {
 
+  val SHUTDOWN_HOOK_PRIORITY: Int = 30
+
   private var master: ApplicationMaster = _
 
   def main(args: Array[String]) = {

From fe2b1d6a209db9fe96b1c6630677955b94bd48c9 Mon Sep 17 00:00:00 2001
From: Matthew Farrellee <matt@redhat.com>
Date: Mon, 15 Sep 2014 10:57:53 -0700
Subject: [PATCH 300/489] [SPARK-3425] do not set MaxPermSize for OpenJDK 1.8

Closes #2387

Author: Matthew Farrellee <matt@redhat.com>

Closes #2301 from mattf/SPARK-3425 and squashes the following commits:

20f3c09 [Matthew Farrellee] [SPARK-3425] do not set MaxPermSize for OpenJDK 1.8
---
 bin/spark-class       | 2 +-
 dev/merge_spark_pr.py | 4 ++--
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/bin/spark-class b/bin/spark-class
index 5f5f9ea74888d..613dc9c4566f2 100755
--- a/bin/spark-class
+++ b/bin/spark-class
@@ -105,7 +105,7 @@ else
     exit 1
   fi
 fi
-JAVA_VERSION=$("$RUNNER" -version 2>&1 | sed 's/java version "\(.*\)\.\(.*\)\..*"/\1\2/; 1q')
+JAVA_VERSION=$("$RUNNER" -version 2>&1 | sed 's/.* version "\(.*\)\.\(.*\)\..*"/\1\2/; 1q')
 
 # Set JAVA_OPTS to be able to load native libraries and to set heap size
 if [ "$JAVA_VERSION" -ge 18 ]; then
diff --git a/dev/merge_spark_pr.py b/dev/merge_spark_pr.py
index d48c8bde12905..a8e92e36fe0d8 100755
--- a/dev/merge_spark_pr.py
+++ b/dev/merge_spark_pr.py
@@ -44,9 +44,9 @@
 # Remote name which points to Apache git
 PUSH_REMOTE_NAME = os.environ.get("PUSH_REMOTE_NAME", "apache")
 # ASF JIRA username
-JIRA_USERNAME = os.environ.get("JIRA_USERNAME", "")
+JIRA_USERNAME = os.environ.get("JIRA_USERNAME", "pwendell")
 # ASF JIRA password
-JIRA_PASSWORD = os.environ.get("JIRA_PASSWORD", "")
+JIRA_PASSWORD = os.environ.get("JIRA_PASSWORD", "35500")
 
 GITHUB_BASE = "https://github.com/apache/spark/pull"
 GITHUB_API_BASE = "https://api.github.com/repos/apache/spark"

From e59fac1f97c3fbeeb6defd12625a49763a353156 Mon Sep 17 00:00:00 2001
From: Kousuke Saruta <sarutak@oss.nttdata.co.jp>
Date: Mon, 15 Sep 2014 16:11:41 -0700
Subject: [PATCH 301/489] [SPARK-3518] Remove wasted statement in JsonProtocol

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

Closes #2380 from sarutak/SPARK-3518 and squashes the following commits:

8a1464e [Kousuke Saruta] Replaced a variable with simple field reference
c660fbc [Kousuke Saruta] Removed useless statement in JsonProtocol.scala
---
 core/src/main/scala/org/apache/spark/util/JsonProtocol.scala | 1 -
 1 file changed, 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala b/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala
index b0754e3ce10db..c4dddb2d1037e 100644
--- a/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala
+++ b/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala
@@ -205,7 +205,6 @@ private[spark] object JsonProtocol {
   }
 
   def taskInfoToJson(taskInfo: TaskInfo): JValue = {
-    val accumUpdateMap = taskInfo.accumulables
     ("Task ID" -> taskInfo.taskId) ~
     ("Index" -> taskInfo.index) ~
     ("Attempt" -> taskInfo.attempt) ~

From 37d925280cdfdda8f6f7174c67a614056eea5d69 Mon Sep 17 00:00:00 2001
From: yantangzhai <tyz0303@163.com>
Date: Mon, 15 Sep 2014 16:57:38 -0700
Subject: [PATCH 302/489] [SPARK-2714] DAGScheduler logs jobid when runJob
 finishes

DAGScheduler logs jobid when runJob finishes

Author: yantangzhai <tyz0303@163.com>

Closes #1617 from YanTangZhai/SPARK-2714 and squashes the following commits:

0a0243f [yantangzhai] [SPARK-2714] DAGScheduler logs jobid when runJob finishes
fbb1150 [yantangzhai] [SPARK-2714] DAGScheduler logs jobid when runJob finishes
7aec2a9 [yantangzhai] [SPARK-2714] DAGScheduler logs jobid when runJob finishes
fb42f0f [yantangzhai] [SPARK-2714] DAGScheduler logs jobid when runJob finishes
090d908 [yantangzhai] [SPARK-2714] DAGScheduler logs jobid when runJob finishes
---
 core/src/main/scala/org/apache/spark/SparkContext.scala  | 3 ---
 .../scala/org/apache/spark/scheduler/DAGScheduler.scala  | 9 +++++++--
 2 files changed, 7 insertions(+), 5 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala
index 218b353dd9d49..428f019b02a23 100644
--- a/core/src/main/scala/org/apache/spark/SparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/SparkContext.scala
@@ -1072,11 +1072,8 @@ class SparkContext(config: SparkConf) extends Logging {
     val callSite = getCallSite
     val cleanedFunc = clean(func)
     logInfo("Starting job: " + callSite.shortForm)
-    val start = System.nanoTime
     dagScheduler.runJob(rdd, cleanedFunc, partitions, callSite, allowLocal,
       resultHandler, localProperties.get)
-    logInfo(
-      "Job finished: " + callSite.shortForm + ", took " + (System.nanoTime - start) / 1e9 + " s")
     rdd.doCheckpoint()
   }
 
diff --git a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
index 6fcf9e31543ed..b2774dfc47553 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
@@ -507,11 +507,16 @@ class DAGScheduler(
       resultHandler: (Int, U) => Unit,
       properties: Properties = null)
   {
+    val start = System.nanoTime
     val waiter = submitJob(rdd, func, partitions, callSite, allowLocal, resultHandler, properties)
     waiter.awaitResult() match {
-      case JobSucceeded => {}
+      case JobSucceeded => {
+        logInfo("Job %d finished: %s, took %f s".format
+          (waiter.jobId, callSite.shortForm, (System.nanoTime - start) / 1e9))
+      }
       case JobFailed(exception: Exception) =>
-        logInfo("Failed to run " + callSite.shortForm)
+        logInfo("Job %d failed: %s, took %f s".format
+          (waiter.jobId, callSite.shortForm, (System.nanoTime - start) / 1e9))
         throw exception
     }
   }

From 3b93128139e8d303f1d7bfd04e9a99a11a5b6404 Mon Sep 17 00:00:00 2001
From: Christoph Sawade <christoph@sawade.me>
Date: Mon, 15 Sep 2014 17:39:31 -0700
Subject: [PATCH 303/489] [SPARK-3396][MLLIB] Use SquaredL2Updater in
 LogisticRegressionWithSGD

SimpleUpdater ignores the regularizer, which leads to an unregularized
LogReg. To enable the common L2 regularizer (and the corresponding
regularization parameter) for logistic regression the SquaredL2Updater
has to be used in SGD (see, e.g., [SVMWithSGD])

Author: Christoph Sawade <christoph@sawade.me>

Closes #2398 from BigCrunsh/fix-regparam-logreg and squashes the following commits:

0820c04 [Christoph Sawade] Use SquaredL2Updater in LogisticRegressionWithSGD
---
 .../classification/LogisticRegression.scala   |  2 +-
 .../LogisticRegressionSuite.scala             | 44 +++++++++++++++++--
 2 files changed, 42 insertions(+), 4 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala b/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala
index 486bdbfa9cb47..84d3c7cebd7c8 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala
@@ -84,7 +84,7 @@ class LogisticRegressionWithSGD private (
   extends GeneralizedLinearAlgorithm[LogisticRegressionModel] with Serializable {
 
   private val gradient = new LogisticGradient()
-  private val updater = new SimpleUpdater()
+  private val updater = new SquaredL2Updater()
   override val optimizer = new GradientDescent(gradient, updater)
     .setStepSize(stepSize)
     .setNumIterations(numIterations)
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/classification/LogisticRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/classification/LogisticRegressionSuite.scala
index 862178694a50e..e954baaf7d91e 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/classification/LogisticRegressionSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/classification/LogisticRegressionSuite.scala
@@ -43,7 +43,7 @@ object LogisticRegressionSuite {
       offset: Double,
       scale: Double,
       nPoints: Int,
-      seed: Int): Seq[LabeledPoint]  = {
+      seed: Int): Seq[LabeledPoint] = {
     val rnd = new Random(seed)
     val x1 = Array.fill[Double](nPoints)(rnd.nextGaussian())
 
@@ -58,12 +58,15 @@ object LogisticRegressionSuite {
 }
 
 class LogisticRegressionSuite extends FunSuite with LocalSparkContext with Matchers {
-  def validatePrediction(predictions: Seq[Double], input: Seq[LabeledPoint]) {
+  def validatePrediction(
+      predictions: Seq[Double],
+      input: Seq[LabeledPoint],
+      expectedAcc: Double = 0.83) {
     val numOffPredictions = predictions.zip(input).count { case (prediction, expected) =>
       prediction != expected.label
     }
     // At least 83% of the predictions should be on.
-    ((input.length - numOffPredictions).toDouble / input.length) should be > 0.83
+    ((input.length - numOffPredictions).toDouble / input.length) should be > expectedAcc
   }
 
   // Test if we can correctly learn A, B where Y = logistic(A + B*X)
@@ -155,6 +158,41 @@ class LogisticRegressionSuite extends FunSuite with LocalSparkContext with Match
     validatePrediction(validationData.map(row => model.predict(row.features)), validationData)
   }
 
+  test("logistic regression with initial weights and non-default regularization parameter") {
+    val nPoints = 10000
+    val A = 2.0
+    val B = -1.5
+
+    val testData = LogisticRegressionSuite.generateLogisticInput(A, B, nPoints, 42)
+
+    val initialB = -1.0
+    val initialWeights = Vectors.dense(initialB)
+
+    val testRDD = sc.parallelize(testData, 2)
+    testRDD.cache()
+
+    // Use half as many iterations as the previous test.
+    val lr = new LogisticRegressionWithSGD().setIntercept(true)
+    lr.optimizer.
+      setStepSize(10.0).
+      setNumIterations(10).
+      setRegParam(1.0)
+
+    val model = lr.run(testRDD, initialWeights)
+
+    // Test the weights
+    assert(model.weights(0) ~== -430000.0 relTol 20000.0)
+    assert(model.intercept ~== 370000.0 relTol 20000.0)
+
+    val validationData = LogisticRegressionSuite.generateLogisticInput(A, B, nPoints, 17)
+    val validationRDD = sc.parallelize(validationData, 2)
+    // Test prediction on RDD.
+    validatePrediction(model.predict(validationRDD.map(_.features)).collect(), validationData, 0.8)
+
+    // Test prediction on Array.
+    validatePrediction(validationData.map(row => model.predict(row.features)), validationData, 0.8)
+  }
+
   test("logistic regression with initial weights with LBFGS") {
     val nPoints = 10000
     val A = 2.0

From 983d6a9c48b69c5f0542922aa8b133f69eb1034d Mon Sep 17 00:00:00 2001
From: Reza Zadeh <rizlar@gmail.com>
Date: Mon, 15 Sep 2014 17:41:15 -0700
Subject: [PATCH 304/489] [MLlib] Update SVD documentation in IndexedRowMatrix

Updating this to reflect the newest SVD via ARPACK

Author: Reza Zadeh <rizlar@gmail.com>

Closes #2389 from rezazadeh/irmdocs and squashes the following commits:

7fa1313 [Reza Zadeh] Update svd docs
715da25 [Reza Zadeh] Updated computeSVD documentation IndexedRowMatrix
---
 .../mllib/linalg/distributed/IndexedRowMatrix.scala  | 12 ++++--------
 1 file changed, 4 insertions(+), 8 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/IndexedRowMatrix.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/IndexedRowMatrix.scala
index ac6eaea3f43ad..5c1acca0ec532 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/IndexedRowMatrix.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/IndexedRowMatrix.scala
@@ -76,16 +76,12 @@ class IndexedRowMatrix(
   }
 
   /**
-   * Computes the singular value decomposition of this matrix.
+   * Computes the singular value decomposition of this IndexedRowMatrix.
    * Denote this matrix by A (m x n), this will compute matrices U, S, V such that A = U * S * V'.
    *
-   * There is no restriction on m, but we require `n^2` doubles to fit in memory.
-   * Further, n should be less than m.
-
-   * The decomposition is computed by first computing A'A = V S^2 V',
-   * computing svd locally on that (since n x n is small), from which we recover S and V.
-   * Then we compute U via easy matrix multiplication as U =  A * (V * S^-1).
-   * Note that this approach requires `O(n^3)` time on the master node.
+   * The cost and implementation of this method is identical to that in
+   * [[org.apache.spark.mllib.linalg.distributed.RowMatrix]]
+   * With the addition of indices.
    *
    * At most k largest non-zero singular values and associated vectors are returned.
    * If there are k such values, then the dimensions of the return will be:

From fdb302f49c021227026909bdcdade7496059013f Mon Sep 17 00:00:00 2001
From: "qiping.lqp" <qiping.lqp@alibaba-inc.com>
Date: Mon, 15 Sep 2014 17:43:26 -0700
Subject: [PATCH 305/489] [SPARK-3516] [mllib] DecisionTree: Add
 minInstancesPerNode, minInfoGain params to example and Python API

Added minInstancesPerNode, minInfoGain params to:
* DecisionTreeRunner.scala example
* Python API (tree.py)

Also:
* Fixed typo in tree suite test "do not choose split that does not satisfy min instance per node requirements"
* small style fixes

CC: mengxr

Author: qiping.lqp <qiping.lqp@alibaba-inc.com>
Author: Joseph K. Bradley <joseph.kurata.bradley@gmail.com>
Author: chouqin <liqiping1991@gmail.com>

Closes #2349 from jkbradley/chouqin-dt-preprune and squashes the following commits:

61b2e72 [Joseph K. Bradley] Added max of 10GB for maxMemoryInMB in Strategy.
a95e7c8 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into chouqin-dt-preprune
95c479d [Joseph K. Bradley] * Fixed typo in tree suite test "do not choose split that does not satisfy min instance per node requirements" * small style fixes
e2628b6 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into chouqin-dt-preprune
19b01af [Joseph K. Bradley] Merge remote-tracking branch 'chouqin/dt-preprune' into chouqin-dt-preprune
f1d11d1 [chouqin] fix typo
c7ebaf1 [chouqin] fix typo
39f9b60 [chouqin] change edge `minInstancesPerNode` to 2 and add one more test
c6e2dfc [Joseph K. Bradley] Added minInstancesPerNode and minInfoGain parameters to DecisionTreeRunner.scala and to Python API in tree.py
0278a11 [chouqin] remove `noSplit` and set `Predict` private to tree
d593ec7 [chouqin] fix docs and change minInstancesPerNode to 1
efcc736 [qiping.lqp] fix bug
10b8012 [qiping.lqp] fix style
6728fad [qiping.lqp] minor fix: remove empty lines
bb465ca [qiping.lqp] Merge branch 'master' of https://github.com/apache/spark into dt-preprune
cadd569 [qiping.lqp] add api docs
46b891f [qiping.lqp] fix bug
e72c7e4 [qiping.lqp] add comments
845c6fa [qiping.lqp] fix style
f195e83 [qiping.lqp] fix style
987cbf4 [qiping.lqp] fix bug
ff34845 [qiping.lqp] separate calculation of predict of node from calculation of info gain
ac42378 [qiping.lqp] add min info gain and min instances per node parameters in decision tree
---
 .../examples/mllib/DecisionTreeRunner.scala      | 13 ++++++++++++-
 .../spark/mllib/api/python/PythonMLLibAPI.scala  |  8 ++++++--
 .../apache/spark/mllib/tree/DecisionTree.scala   |  4 ++--
 .../mllib/tree/configuration/Strategy.scala      |  2 ++
 .../apache/spark/mllib/tree/model/Predict.scala  |  6 +-----
 .../spark/mllib/tree/DecisionTreeSuite.scala     |  4 ++--
 python/pyspark/mllib/tree.py                     | 16 ++++++++++++----
 7 files changed, 37 insertions(+), 16 deletions(-)

diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/DecisionTreeRunner.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/DecisionTreeRunner.scala
index 72c3ab475b61f..4683e6eb966be 100644
--- a/examples/src/main/scala/org/apache/spark/examples/mllib/DecisionTreeRunner.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/DecisionTreeRunner.scala
@@ -55,6 +55,8 @@ object DecisionTreeRunner {
       maxDepth: Int = 5,
       impurity: ImpurityType = Gini,
       maxBins: Int = 32,
+      minInstancesPerNode: Int = 1,
+      minInfoGain: Double = 0.0,
       fracTest: Double = 0.2)
 
   def main(args: Array[String]) {
@@ -75,6 +77,13 @@ object DecisionTreeRunner {
       opt[Int]("maxBins")
         .text(s"max number of bins, default: ${defaultParams.maxBins}")
         .action((x, c) => c.copy(maxBins = x))
+      opt[Int]("minInstancesPerNode")
+        .text(s"min number of instances required at child nodes to create the parent split," +
+        s" default: ${defaultParams.minInstancesPerNode}")
+        .action((x, c) => c.copy(minInstancesPerNode = x))
+      opt[Double]("minInfoGain")
+        .text(s"min info gain required to create a split, default: ${defaultParams.minInfoGain}")
+        .action((x, c) => c.copy(minInfoGain = x))
       opt[Double]("fracTest")
         .text(s"fraction of data to hold out for testing, default: ${defaultParams.fracTest}")
         .action((x, c) => c.copy(fracTest = x))
@@ -179,7 +188,9 @@ object DecisionTreeRunner {
           impurity = impurityCalculator,
           maxDepth = params.maxDepth,
           maxBins = params.maxBins,
-          numClassesForClassification = numClasses)
+          numClassesForClassification = numClasses,
+          minInstancesPerNode = params.minInstancesPerNode,
+          minInfoGain = params.minInfoGain)
     val model = DecisionTree.train(training, strategy)
 
     println(model)
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
index 4343124f102a0..fa0fa69f38634 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
@@ -303,7 +303,9 @@ class PythonMLLibAPI extends Serializable {
       categoricalFeaturesInfoJMap: java.util.Map[Int, Int],
       impurityStr: String,
       maxDepth: Int,
-      maxBins: Int): DecisionTreeModel = {
+      maxBins: Int,
+      minInstancesPerNode: Int,
+      minInfoGain: Double): DecisionTreeModel = {
 
     val data = dataBytesJRDD.rdd.map(SerDe.deserializeLabeledPoint)
 
@@ -316,7 +318,9 @@ class PythonMLLibAPI extends Serializable {
       maxDepth = maxDepth,
       numClassesForClassification = numClasses,
       maxBins = maxBins,
-      categoricalFeaturesInfo = categoricalFeaturesInfoJMap.asScala.toMap)
+      categoricalFeaturesInfo = categoricalFeaturesInfoJMap.asScala.toMap,
+      minInstancesPerNode = minInstancesPerNode,
+      minInfoGain = minInfoGain)
 
     DecisionTree.train(data, strategy)
   }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala
index 56bb8812100a7..c7f2576c822b1 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala
@@ -389,7 +389,7 @@ object DecisionTree extends Serializable with Logging {
       var groupIndex = 0
       var doneTraining = true
       while (groupIndex < numGroups) {
-        val (tmpRoot, doneTrainingGroup) = findBestSplitsPerGroup(input, metadata, level,
+        val (_, doneTrainingGroup) = findBestSplitsPerGroup(input, metadata, level,
           topNode, splits, bins, timer, numGroups, groupIndex)
         doneTraining = doneTraining && doneTrainingGroup
         groupIndex += 1
@@ -898,7 +898,7 @@ object DecisionTree extends Serializable with Logging {
       }
     }.maxBy(_._2.gain)
 
-    require(predict.isDefined, "must calculate predict for each node")
+    assert(predict.isDefined, "must calculate predict for each node")
 
     (bestSplit, bestSplitStats, predict.get)
   }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala
index 31d1e8ac30eea..caaccbfb8ad16 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala
@@ -77,6 +77,8 @@ class Strategy (
   }
   require(minInstancesPerNode >= 1,
     s"DecisionTree Strategy requires minInstancesPerNode >= 1 but was given $minInstancesPerNode")
+  require(maxMemoryInMB <= 10240,
+    s"DecisionTree Strategy requires maxMemoryInMB <= 10240, but was given $maxMemoryInMB")
 
   val isMulticlassClassification =
     algo == Classification && numClassesForClassification > 2
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Predict.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Predict.scala
index 6fac2be2797bc..d8476b5cd7bc7 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Predict.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Predict.scala
@@ -17,18 +17,14 @@
 
 package org.apache.spark.mllib.tree.model
 
-import org.apache.spark.annotation.DeveloperApi
-
 /**
- * :: DeveloperApi ::
  * Predicted value for a node
  * @param predict predicted value
  * @param prob probability of the label (classification only)
  */
-@DeveloperApi
 private[tree] class Predict(
     val predict: Double,
-    val prob: Double = 0.0) extends Serializable{
+    val prob: Double = 0.0) extends Serializable {
 
   override def toString = {
     "predict = %f, prob = %f".format(predict, prob)
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala
index 1bd7ea05c46c8..2b2e579b992f6 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala
@@ -714,8 +714,8 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
     assert(gain == InformationGainStats.invalidInformationGainStats)
   }
 
-  test("don't choose split that doesn't satisfy min instance per node requirements") {
-    // if a split doesn't satisfy min instances per node requirements,
+  test("do not choose split that does not satisfy min instance per node requirements") {
+    // if a split does not satisfy min instances per node requirements,
     // this split is invalid, even though the information gain of split is large.
     val arr = new Array[LabeledPoint](4)
     arr(0) = new LabeledPoint(0.0, Vectors.dense(0.0, 1.0))
diff --git a/python/pyspark/mllib/tree.py b/python/pyspark/mllib/tree.py
index ccc000ac70ba6..5b13ab682bbfc 100644
--- a/python/pyspark/mllib/tree.py
+++ b/python/pyspark/mllib/tree.py
@@ -138,7 +138,8 @@ class DecisionTree(object):
 
     @staticmethod
     def trainClassifier(data, numClasses, categoricalFeaturesInfo,
-                        impurity="gini", maxDepth=5, maxBins=32):
+                        impurity="gini", maxDepth=5, maxBins=32, minInstancesPerNode=1,
+                        minInfoGain=0.0):
         """
         Train a DecisionTreeModel for classification.
 
@@ -154,6 +155,9 @@ def trainClassifier(data, numClasses, categoricalFeaturesInfo,
                          E.g., depth 0 means 1 leaf node.
                          Depth 1 means 1 internal node + 2 leaf nodes.
         :param maxBins: Number of bins used for finding splits at each node.
+        :param minInstancesPerNode: Min number of instances required at child nodes to create
+                                    the parent split
+        :param minInfoGain: Min info gain required to create a split
         :return: DecisionTreeModel
         """
         sc = data.context
@@ -164,13 +168,14 @@ def trainClassifier(data, numClasses, categoricalFeaturesInfo,
         model = sc._jvm.PythonMLLibAPI().trainDecisionTreeModel(
             dataBytes._jrdd, "classification",
             numClasses, categoricalFeaturesInfoJMap,
-            impurity, maxDepth, maxBins)
+            impurity, maxDepth, maxBins, minInstancesPerNode, minInfoGain)
         dataBytes.unpersist()
         return DecisionTreeModel(sc, model)
 
     @staticmethod
     def trainRegressor(data, categoricalFeaturesInfo,
-                       impurity="variance", maxDepth=5, maxBins=32):
+                       impurity="variance", maxDepth=5, maxBins=32, minInstancesPerNode=1,
+                       minInfoGain=0.0):
         """
         Train a DecisionTreeModel for regression.
 
@@ -185,6 +190,9 @@ def trainRegressor(data, categoricalFeaturesInfo,
                          E.g., depth 0 means 1 leaf node.
                          Depth 1 means 1 internal node + 2 leaf nodes.
         :param maxBins: Number of bins used for finding splits at each node.
+        :param minInstancesPerNode: Min number of instances required at child nodes to create
+                                    the parent split
+        :param minInfoGain: Min info gain required to create a split
         :return: DecisionTreeModel
         """
         sc = data.context
@@ -195,7 +203,7 @@ def trainRegressor(data, categoricalFeaturesInfo,
         model = sc._jvm.PythonMLLibAPI().trainDecisionTreeModel(
             dataBytes._jrdd, "regression",
             0, categoricalFeaturesInfoJMap,
-            impurity, maxDepth, maxBins)
+            impurity, maxDepth, maxBins, minInstancesPerNode, minInfoGain)
         dataBytes.unpersist()
         return DecisionTreeModel(sc, model)
 

From da33acb8b681eca5e787d546fe922af76a151398 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies.liu@gmail.com>
Date: Mon, 15 Sep 2014 18:57:25 -0700
Subject: [PATCH 306/489] [SPARK-2951] [PySpark] support unpickle array.array
 for Python 2.6

Pyrolite can not unpickle array.array which pickled by Python 2.6, this patch fix it by extend Pyrolite.

There is a bug in Pyrolite when unpickle array of float/double, this patch workaround it by reverse the endianness for float/double. This workaround should be removed after Pyrolite have a new release to fix this issue.

I had send an PR to Pyrolite to fix it:  https://github.com/irmen/Pyrolite/pull/11

Author: Davies Liu <davies.liu@gmail.com>

Closes #2365 from davies/pickle and squashes the following commits:

f44f771 [Davies Liu] enable tests about array
3908f5c [Davies Liu] Merge branch 'master' into pickle
c77c87b [Davies Liu] cleanup debugging code
60e4e2f [Davies Liu] support unpickle array.array for Python 2.6
---
 .../apache/spark/api/python/SerDeUtil.scala   | 51 +++++++++++++++++++
 python/pyspark/context.py                     |  1 +
 python/pyspark/tests.py                       |  2 -
 3 files changed, 52 insertions(+), 2 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/api/python/SerDeUtil.scala b/core/src/main/scala/org/apache/spark/api/python/SerDeUtil.scala
index efc9009c088a8..6668797f5f8be 100644
--- a/core/src/main/scala/org/apache/spark/api/python/SerDeUtil.scala
+++ b/core/src/main/scala/org/apache/spark/api/python/SerDeUtil.scala
@@ -17,6 +17,8 @@
 
 package org.apache.spark.api.python
 
+import java.nio.ByteOrder
+
 import scala.collection.JavaConversions._
 import scala.util.Failure
 import scala.util.Try
@@ -28,6 +30,55 @@ import org.apache.spark.rdd.RDD
 
 /** Utilities for serialization / deserialization between Python and Java, using Pickle. */
 private[python] object SerDeUtil extends Logging {
+  // Unpickle array.array generated by Python 2.6
+  class ArrayConstructor extends net.razorvine.pickle.objects.ArrayConstructor {
+    //  /* Description of types */
+    //  static struct arraydescr descriptors[] = {
+    //    {'c', sizeof(char), c_getitem, c_setitem},
+    //    {'b', sizeof(char), b_getitem, b_setitem},
+    //    {'B', sizeof(char), BB_getitem, BB_setitem},
+    //    #ifdef Py_USING_UNICODE
+    //      {'u', sizeof(Py_UNICODE), u_getitem, u_setitem},
+    //    #endif
+    //    {'h', sizeof(short), h_getitem, h_setitem},
+    //    {'H', sizeof(short), HH_getitem, HH_setitem},
+    //    {'i', sizeof(int), i_getitem, i_setitem},
+    //    {'I', sizeof(int), II_getitem, II_setitem},
+    //    {'l', sizeof(long), l_getitem, l_setitem},
+    //    {'L', sizeof(long), LL_getitem, LL_setitem},
+    //    {'f', sizeof(float), f_getitem, f_setitem},
+    //    {'d', sizeof(double), d_getitem, d_setitem},
+    //    {'\0', 0, 0, 0} /* Sentinel */
+    //  };
+    // TODO: support Py_UNICODE with 2 bytes
+    // FIXME: unpickle array of float is wrong in Pyrolite, so we reverse the
+    // machine code for float/double here to workaround it.
+    // we should fix this after Pyrolite fix them
+    val machineCodes: Map[Char, Int] = if (ByteOrder.nativeOrder().equals(ByteOrder.BIG_ENDIAN)) {
+      Map('c' -> 1, 'B' -> 0, 'b' -> 1, 'H' -> 3, 'h' -> 5, 'I' -> 7, 'i' -> 9,
+        'L' -> 11, 'l' -> 13, 'f' -> 14, 'd' -> 16, 'u' -> 21
+      )
+    } else {
+      Map('c' -> 1, 'B' -> 0, 'b' -> 1, 'H' -> 2, 'h' -> 4, 'I' -> 6, 'i' -> 8,
+        'L' -> 10, 'l' -> 12, 'f' -> 15, 'd' -> 17, 'u' -> 20
+      )
+    }
+    override def construct(args: Array[Object]): Object = {
+      if (args.length == 1) {
+        construct(args ++ Array(""))
+      } else if (args.length == 2 && args(1).isInstanceOf[String]) {
+        val typecode = args(0).asInstanceOf[String].charAt(0)
+        val data: String = args(1).asInstanceOf[String]
+        construct(typecode, machineCodes(typecode), data.getBytes("ISO-8859-1"))
+      } else {
+        super.construct(args)
+      }
+    }
+  }
+
+  def initialize() = {
+    Unpickler.registerConstructor("array", "array", new ArrayConstructor())
+  }
 
   private def checkPickle(t: (Any, Any)): (Boolean, Boolean) = {
     val pickle = new Pickler
diff --git a/python/pyspark/context.py b/python/pyspark/context.py
index 3ab98e262df31..ea28e8cd8c89f 100644
--- a/python/pyspark/context.py
+++ b/python/pyspark/context.py
@@ -214,6 +214,7 @@ def _ensure_initialized(cls, instance=None, gateway=None):
                 SparkContext._gateway = gateway or launch_gateway()
                 SparkContext._jvm = SparkContext._gateway.jvm
                 SparkContext._writeToFile = SparkContext._jvm.PythonRDD.writeToFile
+                SparkContext._jvm.SerDeUtil.initialize()
 
             if instance:
                 if (SparkContext._active_spark_context and
diff --git a/python/pyspark/tests.py b/python/pyspark/tests.py
index f3309a20fcffb..f255b44359fec 100644
--- a/python/pyspark/tests.py
+++ b/python/pyspark/tests.py
@@ -956,8 +956,6 @@ def test_newhadoop(self):
             conf=input_conf).collect())
         self.assertEqual(new_dataset, data)
 
-    @unittest.skipIf(sys.version_info[:2] <= (2, 6) or python_implementation() == "PyPy",
-                     "Skipped on 2.6 and PyPy until SPARK-2951 is fixed")
     def test_newhadoop_with_array(self):
         basepath = self.tempdir.name
         # use custom ArrayWritable types and converters to handle arrays

From 60050f42885582a699fc7a6fa0529964162bb8a3 Mon Sep 17 00:00:00 2001
From: Aaron Staple <aaron.staple@gmail.com>
Date: Mon, 15 Sep 2014 19:28:17 -0700
Subject: [PATCH 307/489] [SPARK-1087] Move python traceback utilities into new
 traceback_utils.py file.

Also made some cosmetic cleanups.

Author: Aaron Staple <aaron.staple@gmail.com>

Closes #2385 from staple/SPARK-1087 and squashes the following commits:

7b3bb13 [Aaron Staple] Address review comments, cosmetic cleanups.
10ba6e1 [Aaron Staple] [SPARK-1087] Move python traceback utilities into new traceback_utils.py file.
---
 python/pyspark/context.py         |  8 +---
 python/pyspark/rdd.py             | 58 ++---------------------
 python/pyspark/traceback_utils.py | 78 +++++++++++++++++++++++++++++++
 3 files changed, 83 insertions(+), 61 deletions(-)
 create mode 100644 python/pyspark/traceback_utils.py

diff --git a/python/pyspark/context.py b/python/pyspark/context.py
index ea28e8cd8c89f..a33aae87f65e8 100644
--- a/python/pyspark/context.py
+++ b/python/pyspark/context.py
@@ -20,7 +20,6 @@
 import sys
 from threading import Lock
 from tempfile import NamedTemporaryFile
-from collections import namedtuple
 
 from pyspark import accumulators
 from pyspark.accumulators import Accumulator
@@ -33,6 +32,7 @@
 from pyspark.storagelevel import StorageLevel
 from pyspark import rdd
 from pyspark.rdd import RDD
+from pyspark.traceback_utils import CallSite, first_spark_call
 
 from py4j.java_collections import ListConverter
 
@@ -99,11 +99,7 @@ def __init__(self, master=None, appName=None, sparkHome=None, pyFiles=None,
             ...
         ValueError:...
         """
-        if rdd._extract_concise_traceback() is not None:
-            self._callsite = rdd._extract_concise_traceback()
-        else:
-            tempNamedTuple = namedtuple("Callsite", "function file linenum")
-            self._callsite = tempNamedTuple(function=None, file=None, linenum=None)
+        self._callsite = first_spark_call() or CallSite(None, None, None)
         SparkContext._ensure_initialized(self, gateway=gateway)
         try:
             self._do_init(master, appName, sparkHome, pyFiles, environment, batchSize, serializer,
diff --git a/python/pyspark/rdd.py b/python/pyspark/rdd.py
index 6ad5ab2a2d1ae..21f182b0ff137 100644
--- a/python/pyspark/rdd.py
+++ b/python/pyspark/rdd.py
@@ -18,13 +18,11 @@
 from base64 import standard_b64encode as b64enc
 import copy
 from collections import defaultdict
-from collections import namedtuple
 from itertools import chain, ifilter, imap
 import operator
 import os
 import sys
 import shlex
-import traceback
 from subprocess import Popen, PIPE
 from tempfile import NamedTemporaryFile
 from threading import Thread
@@ -45,6 +43,7 @@
 from pyspark.resultiterable import ResultIterable
 from pyspark.shuffle import Aggregator, InMemoryMerger, ExternalMerger, \
     get_used_memory, ExternalSorter
+from pyspark.traceback_utils import SCCallSiteSync
 
 from py4j.java_collections import ListConverter, MapConverter
 
@@ -81,57 +80,6 @@ def portable_hash(x):
     return hash(x)
 
 
-def _extract_concise_traceback():
-    """
-    This function returns the traceback info for a callsite, returns a dict
-    with function name, file name and line number
-    """
-    tb = traceback.extract_stack()
-    callsite = namedtuple("Callsite", "function file linenum")
-    if len(tb) == 0:
-        return None
-    file, line, module, what = tb[len(tb) - 1]
-    sparkpath = os.path.dirname(file)
-    first_spark_frame = len(tb) - 1
-    for i in range(0, len(tb)):
-        file, line, fun, what = tb[i]
-        if file.startswith(sparkpath):
-            first_spark_frame = i
-            break
-    if first_spark_frame == 0:
-        file, line, fun, what = tb[0]
-        return callsite(function=fun, file=file, linenum=line)
-    sfile, sline, sfun, swhat = tb[first_spark_frame]
-    ufile, uline, ufun, uwhat = tb[first_spark_frame - 1]
-    return callsite(function=sfun, file=ufile, linenum=uline)
-
-_spark_stack_depth = 0
-
-
-class _JavaStackTrace(object):
-
-    def __init__(self, sc):
-        tb = _extract_concise_traceback()
-        if tb is not None:
-            self._traceback = "%s at %s:%s" % (
-                tb.function, tb.file, tb.linenum)
-        else:
-            self._traceback = "Error! Could not extract traceback info"
-        self._context = sc
-
-    def __enter__(self):
-        global _spark_stack_depth
-        if _spark_stack_depth == 0:
-            self._context._jsc.setCallSite(self._traceback)
-        _spark_stack_depth += 1
-
-    def __exit__(self, type, value, tb):
-        global _spark_stack_depth
-        _spark_stack_depth -= 1
-        if _spark_stack_depth == 0:
-            self._context._jsc.setCallSite(None)
-
-
 class BoundedFloat(float):
     """
     Bounded value is generated by approximate job, with confidence and low
@@ -704,7 +652,7 @@ def collect(self):
         """
         Return a list that contains all of the elements in this RDD.
         """
-        with _JavaStackTrace(self.context) as st:
+        with SCCallSiteSync(self.context) as css:
             bytesInJava = self._jrdd.collect().iterator()
         return list(self._collect_iterator_through_file(bytesInJava))
 
@@ -1515,7 +1463,7 @@ def add_shuffle_key(split, iterator):
 
         keyed = self.mapPartitionsWithIndex(add_shuffle_key)
         keyed._bypass_serializer = True
-        with _JavaStackTrace(self.context) as st:
+        with SCCallSiteSync(self.context) as css:
             pairRDD = self.ctx._jvm.PairwiseRDD(
                 keyed._jrdd.rdd()).asJavaPairRDD()
             partitioner = self.ctx._jvm.PythonPartitioner(numPartitions,
diff --git a/python/pyspark/traceback_utils.py b/python/pyspark/traceback_utils.py
new file mode 100644
index 0000000000000..bb8646df2b0bf
--- /dev/null
+++ b/python/pyspark/traceback_utils.py
@@ -0,0 +1,78 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from collections import namedtuple
+import os
+import traceback
+
+
+CallSite = namedtuple("CallSite", "function file linenum")
+
+
+def first_spark_call():
+    """
+    Return a CallSite representing the first Spark call in the current call stack.
+    """
+    tb = traceback.extract_stack()
+    if len(tb) == 0:
+        return None
+    file, line, module, what = tb[len(tb) - 1]
+    sparkpath = os.path.dirname(file)
+    first_spark_frame = len(tb) - 1
+    for i in range(0, len(tb)):
+        file, line, fun, what = tb[i]
+        if file.startswith(sparkpath):
+            first_spark_frame = i
+            break
+    if first_spark_frame == 0:
+        file, line, fun, what = tb[0]
+        return CallSite(function=fun, file=file, linenum=line)
+    sfile, sline, sfun, swhat = tb[first_spark_frame]
+    ufile, uline, ufun, uwhat = tb[first_spark_frame - 1]
+    return CallSite(function=sfun, file=ufile, linenum=uline)
+
+
+class SCCallSiteSync(object):
+    """
+    Helper for setting the spark context call site.
+
+    Example usage:
+    from pyspark.context import SCCallSiteSync
+    with SCCallSiteSync(<relevant SparkContext>) as css:
+        <a Spark call>
+    """
+
+    _spark_stack_depth = 0
+
+    def __init__(self, sc):
+        call_site = first_spark_call()
+        if call_site is not None:
+            self._call_site = "%s at %s:%s" % (
+                call_site.function, call_site.file, call_site.linenum)
+        else:
+            self._call_site = "Error! Could not extract traceback info"
+        self._context = sc
+
+    def __enter__(self):
+        if SCCallSiteSync._spark_stack_depth == 0:
+            self._context._jsc.setCallSite(self._call_site)
+        SCCallSiteSync._spark_stack_depth += 1
+
+    def __exit__(self, type, value, tb):
+        SCCallSiteSync._spark_stack_depth -= 1
+        if SCCallSiteSync._spark_stack_depth == 0:
+            self._context._jsc.setCallSite(None)

From d428ac6a221d2dce19c43442abf197f2ade6658f Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@apache.org>
Date: Mon, 15 Sep 2014 21:09:58 -0700
Subject: [PATCH 308/489] [SPARK-3540] Add reboot-slaves functionality to the
 ec2 script

Tested on a real cluster.

Author: Reynold Xin <rxin@apache.org>

Closes #2404 from rxin/ec2-reboot-slaves and squashes the following commits:

00a2dbd [Reynold Xin] Allow rebooting slaves.
---
 ec2/spark_ec2.py | 16 +++++++++++++++-
 1 file changed, 15 insertions(+), 1 deletion(-)

diff --git a/ec2/spark_ec2.py b/ec2/spark_ec2.py
index bfd07593b92ed..5682e96aa8770 100755
--- a/ec2/spark_ec2.py
+++ b/ec2/spark_ec2.py
@@ -52,7 +52,7 @@ class UsageError(Exception):
 def parse_args():
     parser = OptionParser(
         usage="spark-ec2 [options] <action> <cluster_name>"
-        + "\n\n<action> can be: launch, destroy, login, stop, start, get-master",
+        + "\n\n<action> can be: launch, destroy, login, stop, start, get-master, reboot-slaves",
         add_help_option=False)
     parser.add_option(
         "-h", "--help", action="help",
@@ -950,6 +950,20 @@ def real_main():
         subprocess.check_call(
             ssh_command(opts) + proxy_opt + ['-t', '-t', "%s@%s" % (opts.user, master)])
 
+    elif action == "reboot-slaves":
+        response = raw_input(
+            "Are you sure you want to reboot the cluster " +
+            cluster_name + " slaves?\n" +
+            "Reboot cluster slaves " + cluster_name + " (y/N): ")
+        if response == "y":
+            (master_nodes, slave_nodes) = get_existing_cluster(
+                conn, opts, cluster_name, die_on_error=False)
+            print "Rebooting slaves..."
+            for inst in slave_nodes:
+                if inst.state not in ["shutting-down", "terminated"]:
+                    print "Rebooting " + inst.id
+                    inst.reboot()
+
     elif action == "get-master":
         (master_nodes, slave_nodes) = get_existing_cluster(conn, opts, cluster_name)
         print master_nodes[0].public_dns_name

From ecf0c02935815f0d4018c0e30ec4c784e60a5db0 Mon Sep 17 00:00:00 2001
From: Prashant Sharma <prashant@apache.org>
Date: Mon, 15 Sep 2014 21:14:00 -0700
Subject: [PATCH 309/489] [SPARK-3433][BUILD] Fix for Mima false-positives with
 @DeveloperAPI and @Experimental annotations.

Actually false positive reported was due to mima generator not picking up the new jars in presence of old jars(theoretically this should not have happened.). So as a workaround, ran them both separately and just append them together.

Author: Prashant Sharma <prashant@apache.org>
Author: Prashant Sharma <prashant.s@imaginea.com>

Closes #2285 from ScrapCodes/mima-fix and squashes the following commits:

093c76f [Prashant Sharma] Update mima
59012a8 [Prashant Sharma] Update mima
35b6c71 [Prashant Sharma] SPARK-3433 Fix for Mima false-positives with @DeveloperAPI and @Experimental annotations.
---
 dev/mima                                           |  8 ++++++++
 project/MimaBuild.scala                            |  6 ++++++
 project/MimaExcludes.scala                         |  8 +-------
 project/SparkBuild.scala                           |  2 +-
 .../apache/spark/tools/GenerateMIMAIgnore.scala    | 14 ++++++++++----
 5 files changed, 26 insertions(+), 12 deletions(-)

diff --git a/dev/mima b/dev/mima
index f9b9b03538f15..40603166c21ae 100755
--- a/dev/mima
+++ b/dev/mima
@@ -25,11 +25,19 @@ FWDIR="$(cd "`dirname "$0"`"/..; pwd)"
 cd "$FWDIR"
 
 echo -e "q\n" | sbt/sbt oldDeps/update
+rm -f .generated-mima*
+
+# Generate Mima Ignore is called twice, first with latest built jars 
+# on the classpath and then again with previous version jars on the classpath.
+# Because of a bug in GenerateMIMAIgnore that when old jars are ahead on classpath
+# it did not process the new classes (which are in assembly jar). 
+./bin/spark-class org.apache.spark.tools.GenerateMIMAIgnore
 
 export SPARK_CLASSPATH="`find lib_managed \( -name '*spark*jar' -a -type f \) | tr "\\n" ":"`"
 echo "SPARK_CLASSPATH=$SPARK_CLASSPATH"
 
 ./bin/spark-class org.apache.spark.tools.GenerateMIMAIgnore
+
 echo -e "q\n" | sbt/sbt mima-report-binary-issues | grep -v -e "info.*Resolving"
 ret_val=$?
 
diff --git a/project/MimaBuild.scala b/project/MimaBuild.scala
index 0f5d71afcf616..39f8ba4745737 100644
--- a/project/MimaBuild.scala
+++ b/project/MimaBuild.scala
@@ -30,6 +30,12 @@ object MimaBuild {
 
   def excludeMember(fullName: String) = Seq(
       ProblemFilters.exclude[MissingMethodProblem](fullName),
+      // Sometimes excluded methods have default arguments and 
+      // they are translated into public methods/fields($default$) in generated
+      // bytecode. It is not possible to exhustively list everything.
+      // But this should be okay.
+      ProblemFilters.exclude[MissingMethodProblem](fullName+"$default$2"),
+      ProblemFilters.exclude[MissingMethodProblem](fullName+"$default$1"),
       ProblemFilters.exclude[MissingFieldProblem](fullName),
       ProblemFilters.exclude[IncompatibleResultTypeProblem](fullName),
       ProblemFilters.exclude[IncompatibleMethTypeProblem](fullName),
diff --git a/project/MimaExcludes.scala b/project/MimaExcludes.scala
index 46b78bd5c7061..2f1e05dfcc7b1 100644
--- a/project/MimaExcludes.scala
+++ b/project/MimaExcludes.scala
@@ -37,14 +37,8 @@ object MimaExcludes {
           Seq(
             MimaBuild.excludeSparkPackage("deploy"),
             MimaBuild.excludeSparkPackage("graphx")
-          ) ++
-          // This is @DeveloperAPI, but Mima still gives false-positives:
-          MimaBuild.excludeSparkClass("scheduler.SparkListenerApplicationStart") ++
-          Seq(
-            // This is @Experimental, but Mima still gives false-positives:
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.api.java.JavaRDDLike.foreachAsync")
           )
+
         case v if v.startsWith("1.1") =>
           Seq(
             MimaBuild.excludeSparkPackage("deploy"),
diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala
index c07ea313f1228..ab9f8ba120e83 100644
--- a/project/SparkBuild.scala
+++ b/project/SparkBuild.scala
@@ -187,7 +187,7 @@ object OldDeps {
     Some("org.apache.spark" % fullId % "1.1.0")
   }
 
-  def oldDepsSettings() = Defaults.defaultSettings ++ Seq(
+  def oldDepsSettings() = Defaults.coreDefaultSettings ++ Seq(
     name := "old-deps",
     scalaVersion := "2.10.4",
     retrieveManaged := true,
diff --git a/tools/src/main/scala/org/apache/spark/tools/GenerateMIMAIgnore.scala b/tools/src/main/scala/org/apache/spark/tools/GenerateMIMAIgnore.scala
index bcf6d43ab34eb..595ded6ae67fa 100644
--- a/tools/src/main/scala/org/apache/spark/tools/GenerateMIMAIgnore.scala
+++ b/tools/src/main/scala/org/apache/spark/tools/GenerateMIMAIgnore.scala
@@ -24,6 +24,7 @@ import scala.collection.mutable
 import scala.collection.JavaConversions._
 import scala.reflect.runtime.universe.runtimeMirror
 import scala.reflect.runtime.{universe => unv}
+import scala.util.Try
 
 /**
  * A tool for generating classes to be excluded during binary checking with MIMA. It is expected
@@ -121,12 +122,17 @@ object GenerateMIMAIgnore {
   }
 
   def main(args: Array[String]) {
+    import scala.tools.nsc.io.File
     val (privateClasses, privateMembers) = privateWithin("org.apache.spark")
-    scala.tools.nsc.io.File(".generated-mima-class-excludes").
-      writeAll(privateClasses.mkString("\n"))
+    val previousContents = Try(File(".generated-mima-class-excludes").lines()).
+      getOrElse(Iterator.empty).mkString("\n")
+    File(".generated-mima-class-excludes")
+      .writeAll(previousContents + privateClasses.mkString("\n"))
     println("Created : .generated-mima-class-excludes in current directory.")
-    scala.tools.nsc.io.File(".generated-mima-member-excludes").
-      writeAll(privateMembers.mkString("\n"))
+    val previousMembersContents = Try(File(".generated-mima-member-excludes").lines)
+      .getOrElse(Iterator.empty).mkString("\n")
+    File(".generated-mima-member-excludes").writeAll(previousMembersContents +
+      privateMembers.mkString("\n"))
     println("Created : .generated-mima-member-excludes in current directory.")
   }
 

From febafefa5aaee3b3eda5e1b45a75bc6d8e7fb13f Mon Sep 17 00:00:00 2001
From: Ye Xianjin <advancedxy@gmail.com>
Date: Mon, 15 Sep 2014 21:53:38 -0700
Subject: [PATCH 310/489] [SPARK-3040] pick up a more proper local ip address
 for Utils.findLocalIpAddress method

Short version: NetworkInterface.getNetworkInterfaces returns ifs in reverse order compared to ifconfig output. It may pick up ip address associated with tun0 or virtual network interface.
See [SPARK_3040](https://issues.apache.org/jira/browse/SPARK-3040) for more detail

Author: Ye Xianjin <advancedxy@gmail.com>

Closes #1946 from advancedxy/SPARK-3040 and squashes the following commits:

f33f6b2 [Ye Xianjin] add windows support
087a785 [Ye Xianjin] reverse the Networkinterface.getNetworkInterfaces output order to get a more proper local ip address.
---
 core/src/main/scala/org/apache/spark/util/Utils.scala | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/util/Utils.scala b/core/src/main/scala/org/apache/spark/util/Utils.scala
index 79943766d0f0f..c76b7af18481d 100644
--- a/core/src/main/scala/org/apache/spark/util/Utils.scala
+++ b/core/src/main/scala/org/apache/spark/util/Utils.scala
@@ -530,7 +530,12 @@ private[spark] object Utils extends Logging {
       if (address.isLoopbackAddress) {
         // Address resolves to something like 127.0.1.1, which happens on Debian; try to find
         // a better address using the local network interfaces
-        for (ni <- NetworkInterface.getNetworkInterfaces) {
+        // getNetworkInterfaces returns ifs in reverse order compared to ifconfig output order
+        // on unix-like system. On windows, it returns in index order.
+        // It's more proper to pick ip address following system output order.
+        val activeNetworkIFs = NetworkInterface.getNetworkInterfaces.toList
+        val reOrderedNetworkIFs = if (isWindows) activeNetworkIFs else activeNetworkIFs.reverse
+        for (ni <- reOrderedNetworkIFs) {
           for (addr <- ni.getInetAddresses if !addr.isLinkLocalAddress &&
                !addr.isLoopbackAddress && addr.isInstanceOf[Inet4Address]) {
             // We've found an address that looks reasonable!

From 61e21fe7f478e7b06b72851f26b87d99cbbdf117 Mon Sep 17 00:00:00 2001
From: Sean Owen <sowen@cloudera.com>
Date: Tue, 16 Sep 2014 09:18:03 -0700
Subject: [PATCH 311/489] SPARK-3069 [DOCS] Build instructions in README are
 outdated

Here's my crack at Bertrand's suggestion. The Github `README.md` contains build info that's outdated. It should just point to the current online docs, and reflect that Maven is the primary build now.

(Incidentally, the stanza at the end about contributions of original work should go in https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark too. It won't hurt to be crystal clear about the agreement to license, given that ICLAs are not required of anyone here.)

Author: Sean Owen <sowen@cloudera.com>

Closes #2014 from srowen/SPARK-3069 and squashes the following commits:

501507e [Sean Owen] Note that Zinc is for Maven builds too
db2bd97 [Sean Owen] sbt -> sbt/sbt and add note about zinc
be82027 [Sean Owen] Fix additional occurrences of building-with-maven -> building-spark
91c921f [Sean Owen] Move building-with-maven to building-spark and create a redirect. Update doc links to building-spark.html Add jekyll-redirect-from plugin and make associated config changes (including fixing pygments deprecation). Add example of SBT to README.md
999544e [Sean Owen] Change "Building Spark with Maven" title to "Building Spark"; reinstate tl;dr info about dev/run-tests in README.md; add brief note about building with SBT
c18d140 [Sean Owen] Optionally, remove the copy of contributing text from main README.md
8e83934 [Sean Owen] Add CONTRIBUTING.md to trigger notice on new pull request page
b1c04a1 [Sean Owen] Refer to current online documentation for building, and remove slightly outdated copy in README.md
---
 CONTRIBUTING.md                               | 12 +++
 README.md                                     | 78 ++++---------------
 docs/README.md                                |  5 +-
 docs/_config.yml                              |  4 +-
 docs/_layouts/global.html                     |  2 +-
 ...ilding-with-maven.md => building-spark.md} | 20 ++++-
 docs/hadoop-third-party-distributions.md      |  2 +-
 docs/index.md                                 |  4 +-
 docs/running-on-yarn.md                       |  2 +-
 docs/streaming-kinesis-integration.md         |  2 +-
 make-distribution.sh                          |  2 +-
 11 files changed, 60 insertions(+), 73 deletions(-)
 create mode 100644 CONTRIBUTING.md
 rename docs/{building-with-maven.md => building-spark.md} (87%)

diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
new file mode 100644
index 0000000000000..c6b4aa5344757
--- /dev/null
+++ b/CONTRIBUTING.md
@@ -0,0 +1,12 @@
+## Contributing to Spark
+
+Contributions via GitHub pull requests are gladly accepted from their original
+author. Along with any pull requests, please state that the contribution is
+your original work and that you license the work to the project under the
+project's open source license. Whether or not you state this explicitly, by
+submitting any copyrighted material via pull request, email, or other means
+you agree to license the material under the project's open source license and
+warrant that you have the legal authority to do so.
+
+Please see [Contributing to Spark wiki page](https://cwiki.apache.org/SPARK/Contributing+to+Spark)
+for more information.
diff --git a/README.md b/README.md
index 5b09ad86849e7..b05bbfb5a594c 100644
--- a/README.md
+++ b/README.md
@@ -13,16 +13,19 @@ and Spark Streaming for stream processing.
 ## Online Documentation
 
 You can find the latest Spark documentation, including a programming
-guide, on the project webpage at <http://spark.apache.org/documentation.html>.
+guide, on the [project web page](http://spark.apache.org/documentation.html).
 This README file only contains basic setup instructions.
 
 ## Building Spark
 
-Spark is built on Scala 2.10. To build Spark and its example programs, run:
+Spark is built using [Apache Maven](http://maven.apache.org/).
+To build Spark and its example programs, run:
 
-    ./sbt/sbt assembly
+    mvn -DskipTests clean package
 
 (You do not need to do this if you downloaded a pre-built package.)
+More detailed documentation is available from the project site, at
+["Building Spark"](http://spark.apache.org/docs/latest/building-spark.html).
 
 ## Interactive Scala Shell
 
@@ -71,73 +74,24 @@ can be run using:
 
     ./dev/run-tests
 
+Please see the guidance on how to 
+[run all automated tests](https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark#ContributingtoSpark-AutomatedTesting)
+
 ## A Note About Hadoop Versions
 
 Spark uses the Hadoop core library to talk to HDFS and other Hadoop-supported
 storage systems. Because the protocols have changed in different versions of
 Hadoop, you must build Spark against the same version that your cluster runs.
-You can change the version by setting `-Dhadoop.version` when building Spark.
-
-For Apache Hadoop versions 1.x, Cloudera CDH MRv1, and other Hadoop
-versions without YARN, use:
-
-    # Apache Hadoop 1.2.1
-    $ sbt/sbt -Dhadoop.version=1.2.1 assembly
-
-    # Cloudera CDH 4.2.0 with MapReduce v1
-    $ sbt/sbt -Dhadoop.version=2.0.0-mr1-cdh4.2.0 assembly
-
-For Apache Hadoop 2.2.X, 2.1.X, 2.0.X, 0.23.x, Cloudera CDH MRv2, and other Hadoop versions
-with YARN, also set `-Pyarn`:
-
-    # Apache Hadoop 2.0.5-alpha
-    $ sbt/sbt -Dhadoop.version=2.0.5-alpha -Pyarn assembly
-
-    # Cloudera CDH 4.2.0 with MapReduce v2
-    $ sbt/sbt -Dhadoop.version=2.0.0-cdh4.2.0 -Pyarn assembly
-
-    # Apache Hadoop 2.2.X and newer
-    $ sbt/sbt -Dhadoop.version=2.2.0 -Pyarn assembly
-
-When developing a Spark application, specify the Hadoop version by adding the
-"hadoop-client" artifact to your project's dependencies. For example, if you're
-using Hadoop 1.2.1 and build your application using SBT, add this entry to
-`libraryDependencies`:
-
-    "org.apache.hadoop" % "hadoop-client" % "1.2.1"
 
-If your project is built with Maven, add this to your POM file's `<dependencies>` section:
-
-    <dependency>
-      <groupId>org.apache.hadoop</groupId>
-      <artifactId>hadoop-client</artifactId>
-      <version>1.2.1</version>
-    </dependency>
-
-
-## A Note About Thrift JDBC server and CLI for Spark SQL
-
-Spark SQL supports Thrift JDBC server and CLI.
-See sql-programming-guide.md for more information about using the JDBC server and CLI.
-You can use those features by setting `-Phive` when building Spark as follows.
-
-    $ sbt/sbt -Phive  assembly
+Please refer to the build documentation at
+["Specifying the Hadoop Version"](http://spark.apache.org/docs/latest/building-spark.html#specifying-the-hadoop-version)
+for detailed guidance on building for a particular distribution of Hadoop, including
+building for particular Hive and Hive Thriftserver distributions. See also
+["Third Party Hadoop Distributions"](http://spark.apache.org/docs/latest/hadoop-third-party-distributions.html)
+for guidance on building a Spark application that works with a particular
+distribution.
 
 ## Configuration
 
 Please refer to the [Configuration guide](http://spark.apache.org/docs/latest/configuration.html)
 in the online documentation for an overview on how to configure Spark.
-
-
-## Contributing to Spark
-
-Contributions via GitHub pull requests are gladly accepted from their original
-author. Along with any pull requests, please state that the contribution is
-your original work and that you license the work to the project under the
-project's open source license. Whether or not you state this explicitly, by
-submitting any copyrighted material via pull request, email, or other means
-you agree to license the material under the project's open source license and
-warrant that you have the legal authority to do so.
-
-Please see [Contributing to Spark wiki page](https://cwiki.apache.org/SPARK/Contributing+to+Spark)
-for more information.
diff --git a/docs/README.md b/docs/README.md
index 0a0126c5747d1..fdc89d2eb767a 100644
--- a/docs/README.md
+++ b/docs/README.md
@@ -23,8 +23,9 @@ The markdown code can be compiled to HTML using the [Jekyll tool](http://jekyllr
 To use the `jekyll` command, you will need to have Jekyll installed. 
 The easiest way to do this is via a Ruby Gem, see the 
 [jekyll installation instructions](http://jekyllrb.com/docs/installation).
-If not already installed, you need to install `kramdown` with `sudo gem install kramdown`.
-Execute `jekyll` from the `docs/` directory. Compiling the site with Jekyll will create a directory
+If not already installed, you need to install `kramdown` and `jekyll-redirect-from` Gems
+with `sudo gem install kramdown jekyll-redirect-from`.
+Execute `jekyll build` from the `docs/` directory. Compiling the site with Jekyll will create a directory
 called `_site` containing index.html as well as the rest of the compiled files.
 
 You can modify the default Jekyll build as follows:
diff --git a/docs/_config.yml b/docs/_config.yml
index 45b78fe724a50..d3ea2625c7448 100644
--- a/docs/_config.yml
+++ b/docs/_config.yml
@@ -1,5 +1,7 @@
-pygments: true
+highlighter: pygments
 markdown: kramdown
+gems:
+  - jekyll-redirect-from
 
 # These allow the documentation to be updated with nerw releases
 # of Spark, Scala, and Mesos.
diff --git a/docs/_layouts/global.html b/docs/_layouts/global.html
index b30ab1e5218c0..a53e8a775b71f 100755
--- a/docs/_layouts/global.html
+++ b/docs/_layouts/global.html
@@ -109,7 +109,7 @@
                                 <li><a href="hardware-provisioning.html">Hardware Provisioning</a></li>
                                 <li><a href="hadoop-third-party-distributions.html">3<sup>rd</sup>-Party Hadoop Distros</a></li>
                                 <li class="divider"></li>
-                                <li><a href="building-with-maven.html">Building Spark with Maven</a></li>
+                                <li><a href="building-spark.html">Building Spark</a></li>
                                 <li><a href="https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark">Contributing to Spark</a></li>
                             </ul>
                         </li>
diff --git a/docs/building-with-maven.md b/docs/building-spark.md
similarity index 87%
rename from docs/building-with-maven.md
rename to docs/building-spark.md
index bce7412c7d4c9..2378092d4a1a8 100644
--- a/docs/building-with-maven.md
+++ b/docs/building-spark.md
@@ -1,6 +1,7 @@
 ---
 layout: global
-title: Building Spark with Maven
+title: Building Spark
+redirect_from: "building-with-maven.html"
 ---
 
 * This will become a table of contents (this text will be scraped).
@@ -159,4 +160,21 @@ then ship it over to the cluster. We are investigating the exact cause for this.
 
 The assembly jar produced by `mvn package` will, by default, include all of Spark's dependencies, including Hadoop and some of its ecosystem projects. On YARN deployments, this causes multiple versions of these to appear on executor classpaths: the version packaged in the Spark assembly and the version on each node, included with yarn.application.classpath.  The `hadoop-provided` profile builds the assembly without including Hadoop-ecosystem projects, like ZooKeeper and Hadoop itself. 
 
+# Building with SBT
 
+Maven is the official recommendation for packaging Spark, and is the "build of reference".
+But SBT is supported for day-to-day development since it can provide much faster iterative
+compilation. More advanced developers may wish to use SBT.
+
+The SBT build is derived from the Maven POM files, and so the same Maven profiles and variables
+can be set to control the SBT build. For example:
+
+    sbt/sbt -Pyarn -Phadoop-2.3 compile
+
+# Speeding up Compilation with Zinc
+
+[Zinc](https://github.com/typesafehub/zinc) is a long-running server version of SBT's incremental
+compiler. When run locally as a background process, it speeds up builds of Scala-based projects
+like Spark. Developers who regularly recompile Spark with Maven will be the most interested in
+Zinc. The project site gives instructions for building and running `zinc`; OS X users can
+install it using `brew install zinc`.
\ No newline at end of file
diff --git a/docs/hadoop-third-party-distributions.md b/docs/hadoop-third-party-distributions.md
index ab1023b8f1842..dd73e9dc54440 100644
--- a/docs/hadoop-third-party-distributions.md
+++ b/docs/hadoop-third-party-distributions.md
@@ -11,7 +11,7 @@ with these distributions:
 
 When compiling Spark, you'll need to specify the Hadoop version by defining the `hadoop.version`
 property. For certain versions, you will need to specify additional profiles. For more detail,
-see the guide on [building with maven](building-with-maven.html#specifying-the-hadoop-version):
+see the guide on [building with maven](building-spark.html#specifying-the-hadoop-version):
 
     mvn -Dhadoop.version=1.0.4 -DskipTests clean package
     mvn -Phadoop-2.2 -Dhadoop.version=2.2.0 -DskipTests clean package
diff --git a/docs/index.md b/docs/index.md
index 7fe6b43d32af7..e8ebadbd4e427 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -12,7 +12,7 @@ It also supports a rich set of higher-level tools including [Spark SQL](sql-prog
 
 Get Spark from the [downloads page](http://spark.apache.org/downloads.html) of the project website. This documentation is for Spark version {{site.SPARK_VERSION}}. The downloads page 
 contains Spark packages for many popular HDFS versions. If you'd like to build Spark from 
-scratch, visit [building Spark with Maven](building-with-maven.html).
+scratch, visit [Building Spark](building-spark.html).
 
 Spark runs on both Windows and UNIX-like systems (e.g. Linux, Mac OS). It's easy to run
 locally on one machine --- all you need is to have `java` installed on your system `PATH`,
@@ -105,7 +105,7 @@ options for deployment:
 * [3<sup>rd</sup> Party Hadoop Distributions](hadoop-third-party-distributions.html): using common Hadoop distributions
 * Integration with other storage systems:
   * [OpenStack Swift](storage-openstack-swift.html)
-* [Building Spark with Maven](building-with-maven.html): build Spark using the Maven system
+* [Building Spark](building-spark.html): build Spark using the Maven system
 * [Contributing to Spark](https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark)
 
 **External Resources:**
diff --git a/docs/running-on-yarn.md b/docs/running-on-yarn.md
index 212248bcce1c1..74bcc2eeb65f6 100644
--- a/docs/running-on-yarn.md
+++ b/docs/running-on-yarn.md
@@ -11,7 +11,7 @@ was added to Spark in version 0.6.0, and improved in subsequent releases.
 
 Running Spark-on-YARN requires a binary distribution of Spark which is built with YARN support.
 Binary distributions can be downloaded from the Spark project website. 
-To build Spark yourself, refer to the [building with Maven guide](building-with-maven.html).
+To build Spark yourself, refer to [Building Spark](building-spark.html).
 
 # Configuration
 
diff --git a/docs/streaming-kinesis-integration.md b/docs/streaming-kinesis-integration.md
index c6090d9ec30c7..379eb513d521e 100644
--- a/docs/streaming-kinesis-integration.md
+++ b/docs/streaming-kinesis-integration.md
@@ -108,7 +108,7 @@ A Kinesis stream can be set up at one of the valid Kinesis endpoints with 1 or m
 #### Running the Example
 To run the example,
 
-- Download Spark source and follow the [instructions](building-with-maven.html) to build Spark with profile *-Pkinesis-asl*.
+- Download Spark source and follow the [instructions](building-spark.html) to build Spark with profile *-Pkinesis-asl*.
 
         mvn -Pkinesis-asl -DskipTests clean package
 
diff --git a/make-distribution.sh b/make-distribution.sh
index 9b012b9222db4..884659954a491 100755
--- a/make-distribution.sh
+++ b/make-distribution.sh
@@ -40,7 +40,7 @@ function exit_with_usage {
   echo ""
   echo "usage:"
   echo "./make-distribution.sh [--name] [--tgz] [--with-tachyon] <maven build options>"
-  echo "See Spark's \"Building with Maven\" doc for correct Maven options."
+  echo "See Spark's \"Building Spark\" doc for correct Maven options."
   echo ""
   exit 1
 }

From 7b8008f5a4d413b61aa88fbc60959e98e59f17dd Mon Sep 17 00:00:00 2001
From: Prashant Sharma <prashant.s@imaginea.com>
Date: Tue, 16 Sep 2014 09:21:03 -0700
Subject: [PATCH 312/489] [SPARK-2182] Scalastyle rule blocking non ascii
 characters.

...erators.

Author: Prashant Sharma <prashant.s@imaginea.com>

Closes #2358 from ScrapCodes/scalastyle-unicode and squashes the following commits:

12a20f2 [Prashant Sharma] [SPARK-2182] Scalastyle rule blocking (non keyboard typeable) unicode operators.
---
 .../scalastyle/NonASCIICharacterChecker.scala | 39 +++++++++++++++++++
 scalastyle-config.xml                         |  1 +
 2 files changed, 40 insertions(+)
 create mode 100644 project/spark-style/src/main/scala/org/apache/spark/scalastyle/NonASCIICharacterChecker.scala

diff --git a/project/spark-style/src/main/scala/org/apache/spark/scalastyle/NonASCIICharacterChecker.scala b/project/spark-style/src/main/scala/org/apache/spark/scalastyle/NonASCIICharacterChecker.scala
new file mode 100644
index 0000000000000..3d43c35299555
--- /dev/null
+++ b/project/spark-style/src/main/scala/org/apache/spark/scalastyle/NonASCIICharacterChecker.scala
@@ -0,0 +1,39 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+
+package org.apache.spark.scalastyle
+
+import java.util.regex.Pattern
+
+import org.scalastyle.{PositionError, ScalariformChecker, ScalastyleError}
+
+import scalariform.lexer.Token
+import scalariform.parser.CompilationUnit
+
+class NonASCIICharacterChecker extends ScalariformChecker {
+  val errorKey: String = "non.ascii.character.disallowed"
+
+  override def verify(ast: CompilationUnit): List[ScalastyleError] = {
+    ast.tokens.filter(hasNonAsciiChars).map(x => PositionError(x.offset)).toList
+  }
+
+  private def hasNonAsciiChars(x: Token) =
+    x.rawText.trim.nonEmpty && !Pattern.compile( """\p{ASCII}+""", Pattern.DOTALL)
+    .matcher(x.text.trim).matches()
+
+}
diff --git a/scalastyle-config.xml b/scalastyle-config.xml
index 76ba1ecca33ab..c54f8b72ebf42 100644
--- a/scalastyle-config.xml
+++ b/scalastyle-config.xml
@@ -140,5 +140,6 @@
  <!-- <check level="error" class="org.scalastyle.scalariform.PublicMethodsHaveTypeChecker" enabled="true"></check> -->
  <check level="error" class="org.scalastyle.file.NewLineAtEofChecker" enabled="true"></check>
  <check level="error" class="org.scalastyle.file.NoNewLineAtEofChecker" enabled="false"></check>
+ <check level="error" class="org.apache.spark.scalastyle.NonASCIICharacterChecker" enabled="true"></check>
  <check level="error" class="org.apache.spark.scalastyle.SparkSpaceAfterCommentStartChecker" enabled="true"></check>
 </scalastyle>

From 86d253ec4e2ed94c68687d575f9e2dfbb44463e1 Mon Sep 17 00:00:00 2001
From: Cheng Hao <hao.cheng@intel.com>
Date: Tue, 16 Sep 2014 11:21:30 -0700
Subject: [PATCH 313/489] [SPARK-3527] [SQL] Strip the string message

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

Closes #2392 from chenghao-intel/trim and squashes the following commits:

e52024f [Cheng Hao] trim the string message
---
 sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
index c551c7c9877e8..7dbaf7faff0c0 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
@@ -414,7 +414,7 @@ class SQLContext(@transient val sparkContext: SparkContext)
     def simpleString: String =
       s"""== Physical Plan ==
          |${stringOrError(executedPlan)}
-      """
+      """.stripMargin.trim
 
     override def toString: String =
       // TODO previously will output RDD details by run (${stringOrError(toRdd.toDebugString)})

From 9d5fa763d8559ac412a18d7a2f43c4368a0af897 Mon Sep 17 00:00:00 2001
From: Matthew Farrellee <matt@redhat.com>
Date: Tue, 16 Sep 2014 11:39:57 -0700
Subject: [PATCH 314/489] [SPARK-3519] add distinct(n) to PySpark

Added missing rdd.distinct(numPartitions) and associated tests

Author: Matthew Farrellee <matt@redhat.com>

Closes #2383 from mattf/SPARK-3519 and squashes the following commits:

30b837a [Matthew Farrellee] Combine test cases to save on JVM startups
6bc4a2c [Matthew Farrellee] [SPARK-3519] add distinct(n) to SchemaRDD in PySpark
7a17f2b [Matthew Farrellee] [SPARK-3519] add distinct(n) to PySpark
---
 python/pyspark/rdd.py   |  4 ++--
 python/pyspark/sql.py   |  7 +++++--
 python/pyspark/tests.py | 17 +++++++++++++++++
 3 files changed, 24 insertions(+), 4 deletions(-)

diff --git a/python/pyspark/rdd.py b/python/pyspark/rdd.py
index 21f182b0ff137..cb09c191bed71 100644
--- a/python/pyspark/rdd.py
+++ b/python/pyspark/rdd.py
@@ -301,7 +301,7 @@ def func(iterator):
             return ifilter(f, iterator)
         return self.mapPartitions(func, True)
 
-    def distinct(self):
+    def distinct(self, numPartitions=None):
         """
         Return a new RDD containing the distinct elements in this RDD.
 
@@ -309,7 +309,7 @@ def distinct(self):
         [1, 2, 3]
         """
         return self.map(lambda x: (x, None)) \
-                   .reduceByKey(lambda x, _: x) \
+                   .reduceByKey(lambda x, _: x, numPartitions) \
                    .map(lambda (x, _): x)
 
     def sample(self, withReplacement, fraction, seed=None):
diff --git a/python/pyspark/sql.py b/python/pyspark/sql.py
index fc9310fef318c..eac55cbe15193 100644
--- a/python/pyspark/sql.py
+++ b/python/pyspark/sql.py
@@ -1694,8 +1694,11 @@ def coalesce(self, numPartitions, shuffle=False):
         rdd = self._jschema_rdd.coalesce(numPartitions, shuffle)
         return SchemaRDD(rdd, self.sql_ctx)
 
-    def distinct(self):
-        rdd = self._jschema_rdd.distinct()
+    def distinct(self, numPartitions=None):
+        if numPartitions is None:
+            rdd = self._jschema_rdd.distinct()
+        else:
+            rdd = self._jschema_rdd.distinct(numPartitions)
         return SchemaRDD(rdd, self.sql_ctx)
 
     def intersection(self, other):
diff --git a/python/pyspark/tests.py b/python/pyspark/tests.py
index f255b44359fec..0b3854347ad2e 100644
--- a/python/pyspark/tests.py
+++ b/python/pyspark/tests.py
@@ -587,6 +587,14 @@ def test_repartitionAndSortWithinPartitions(self):
         self.assertEquals(partitions[0], [(0, 5), (0, 8), (2, 6)])
         self.assertEquals(partitions[1], [(1, 3), (3, 8), (3, 8)])
 
+    def test_distinct(self):
+        rdd = self.sc.parallelize((1, 2, 3)*10, 10)
+        self.assertEquals(rdd.getNumPartitions(), 10)
+        self.assertEquals(rdd.distinct().count(), 3)
+        result = rdd.distinct(5)
+        self.assertEquals(result.getNumPartitions(), 5)
+        self.assertEquals(result.count(), 3)
+
 
 class TestSQL(PySparkTestCase):
 
@@ -636,6 +644,15 @@ def test_basic_functions(self):
         srdd.count()
         srdd.collect()
 
+    def test_distinct(self):
+        rdd = self.sc.parallelize(['{"a": 1}', '{"b": 2}', '{"c": 3}']*10, 10)
+        srdd = self.sqlCtx.jsonRDD(rdd)
+        self.assertEquals(srdd.getNumPartitions(), 10)
+        self.assertEquals(srdd.distinct().count(), 3)
+        result = srdd.distinct(5)
+        self.assertEquals(result.getNumPartitions(), 5)
+        self.assertEquals(result.count(), 3)
+
 
 class TestIO(PySparkTestCase):
 

From 7583699873fb4f252c6ce65db1096783ef438731 Mon Sep 17 00:00:00 2001
From: Yin Huai <huai@cse.ohio-state.edu>
Date: Tue, 16 Sep 2014 11:40:28 -0700
Subject: [PATCH 315/489] [SPARK-3308][SQL] Ability to read JSON Arrays as
 tables

This PR aims to support reading top level JSON arrays and take every element in such an array as a row (an empty array will not generate a row).

JIRA: https://issues.apache.org/jira/browse/SPARK-3308

Author: Yin Huai <huai@cse.ohio-state.edu>

Closes #2400 from yhuai/SPARK-3308 and squashes the following commits:

990077a [Yin Huai] Handle top level JSON arrays.
---
 .../org/apache/spark/sql/json/JsonRDD.scala     | 10 +++++++---
 .../org/apache/spark/sql/json/JsonSuite.scala   | 17 +++++++++++++++++
 .../apache/spark/sql/json/TestJsonData.scala    |  7 +++++++
 3 files changed, 31 insertions(+), 3 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/json/JsonRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/json/JsonRDD.scala
index 873221835daf8..0f27fd13e7379 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/json/JsonRDD.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/json/JsonRDD.scala
@@ -287,9 +287,13 @@ private[sql] object JsonRDD extends Logging {
       // the ObjectMapper will take the last value associated with this duplicate key.
       // For example: for {"key": 1, "key":2}, we will get "key"->2.
       val mapper = new ObjectMapper()
-      iter.map { record =>
-        val parsed = scalafy(mapper.readValue(record, classOf[java.util.Map[String, Any]]))
-        parsed.asInstanceOf[Map[String, Any]]
+      iter.flatMap { record =>
+        val parsed = mapper.readValue(record, classOf[Object]) match {
+          case map: java.util.Map[_, _] => scalafy(map).asInstanceOf[Map[String, Any]] :: Nil
+          case list: java.util.List[_] => scalafy(list).asInstanceOf[Seq[Map[String, Any]]]
+        }
+
+        parsed
       }
     })
   }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala
index b50d93855405a..685e788207725 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala
@@ -622,4 +622,21 @@ class JsonSuite extends QueryTest {
       ("str1", Nil, "str4", 2) :: Nil
     )
   }
+
+  test("SPARK-3308 Read top level JSON arrays") {
+    val jsonSchemaRDD = jsonRDD(jsonArray)
+    jsonSchemaRDD.registerTempTable("jsonTable")
+
+    checkAnswer(
+      sql(
+        """
+          |select a, b, c
+          |from jsonTable
+        """.stripMargin),
+      ("str_a_1", null, null) ::
+      ("str_a_2", null, null) ::
+      (null, "str_b_3", null) ::
+      ("str_a_4", "str_b_4", "str_c_4") ::Nil
+    )
+  }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/json/TestJsonData.scala b/sql/core/src/test/scala/org/apache/spark/sql/json/TestJsonData.scala
index 5f0b3959a63ad..fc833b8b54e4c 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/json/TestJsonData.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/json/TestJsonData.scala
@@ -136,4 +136,11 @@ object TestJsonData {
             ]
           ]]
       }""" :: Nil)
+
+  val jsonArray =
+    TestSQLContext.sparkContext.parallelize(
+      """[{"a":"str_a_1"}]""" ::
+      """[{"a":"str_a_2"}, {"b":"str_b_3"}]""" ::
+      """{"b":"str_b_4", "a":"str_a_4", "c":"str_c_4"}""" ::
+      """[]""" :: Nil)
 }

From 30f288ae34a67307aa45b7aecbd0d02a0a14fe69 Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Tue, 16 Sep 2014 11:42:26 -0700
Subject: [PATCH 316/489] [SPARK-2890][SQL] Allow reading of data when case
 insensitive resolution could cause possible ambiguity.

Throwing an error in the constructor makes it possible to run queries, even when there is no actual ambiguity.  Remove this check in favor of throwing an error in analysis when they query is actually is ambiguous.

Also took the opportunity to add test cases that would have caught a subtle bug in my first attempt at fixing this and refactor some other test code.

Author: Michael Armbrust <michael@databricks.com>

Closes #2209 from marmbrus/sameNameStruct and squashes the following commits:

729cca4 [Michael Armbrust] Better tests.
a003aeb [Michael Armbrust] Remove error (it'll be caught in analysis).
---
 .../spark/sql/catalyst/types/dataTypes.scala  |  4 --
 .../sql/hive/execution/HiveUdfSuite.scala     | 67 ++++++++++++-------
 2 files changed, 44 insertions(+), 27 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/types/dataTypes.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/types/dataTypes.scala
index 70c6d06cf2534..49520b7678e90 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/types/dataTypes.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/types/dataTypes.scala
@@ -308,13 +308,9 @@ case class StructField(name: String, dataType: DataType, nullable: Boolean) {
 object StructType {
   protected[sql] def fromAttributes(attributes: Seq[Attribute]): StructType =
     StructType(attributes.map(a => StructField(a.name, a.dataType, a.nullable)))
-
-  private def validateFields(fields: Seq[StructField]): Boolean =
-    fields.map(field => field.name).distinct.size == fields.size
 }
 
 case class StructType(fields: Seq[StructField]) extends DataType {
-  require(StructType.validateFields(fields), "Found fields with the same name.")
 
   /**
    * Returns all field names in a [[Seq]].
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUdfSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUdfSuite.scala
index b6b8592344ef5..cc125d539c3c2 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUdfSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUdfSuite.scala
@@ -17,47 +17,68 @@
 
 package org.apache.spark.sql.hive.execution
 
-import org.apache.spark.sql.hive.test.TestHive
-import org.apache.hadoop.conf.Configuration
-import org.apache.spark.SparkContext._
+import java.io.{DataOutput, DataInput}
 import java.util
-import org.apache.hadoop.fs.{FileSystem, Path}
+import java.util.Properties
+
+import org.apache.spark.util.Utils
+
+import scala.collection.JavaConversions._
+
+import org.apache.hadoop.conf.Configuration
 import org.apache.hadoop.hive.serde2.{SerDeStats, AbstractSerDe}
-import org.apache.hadoop.io.{NullWritable, Writable}
+import org.apache.hadoop.io.Writable
 import org.apache.hadoop.hive.serde2.objectinspector.{ObjectInspectorFactory, ObjectInspector}
-import java.util.Properties
+
 import org.apache.hadoop.hive.serde2.objectinspector.primitive.PrimitiveObjectInspectorFactory
-import scala.collection.JavaConversions._
-import java.io.{DataOutput, DataInput}
 import org.apache.hadoop.hive.ql.udf.generic.GenericUDF
 import org.apache.hadoop.hive.ql.udf.generic.GenericUDF.DeferredObject
 
+import org.apache.spark.sql.Row
+import org.apache.spark.sql.hive.test.TestHive
+import org.apache.spark.sql.hive.test.TestHive._
+
+case class Fields(f1: Int, f2: Int, f3: Int, f4: Int, f5: Int)
+
 /**
  * A test suite for Hive custom UDFs.
  */
 class HiveUdfSuite extends HiveComparisonTest {
 
-  TestHive.sql(
-    """
+  test("spark sql udf test that returns a struct") {
+    registerFunction("getStruct", (_: Int) => Fields(1, 2, 3, 4, 5))
+    assert(sql(
+      """
+        |SELECT getStruct(1).f1,
+        |       getStruct(1).f2,
+        |       getStruct(1).f3,
+        |       getStruct(1).f4,
+        |       getStruct(1).f5 FROM src LIMIT 1
+      """.stripMargin).first() === Row(1, 2, 3, 4, 5))
+  }
+
+  test("hive struct udf") {
+    sql(
+      """
       |CREATE EXTERNAL TABLE hiveUdfTestTable (
       |   pair STRUCT<id: INT, value: INT>
       |)
       |PARTITIONED BY (partition STRING)
       |ROW FORMAT SERDE '%s'
       |STORED AS SEQUENCEFILE
-    """.stripMargin.format(classOf[PairSerDe].getName)
-  )
-
-  TestHive.sql(
-    "ALTER TABLE hiveUdfTestTable ADD IF NOT EXISTS PARTITION(partition='testUdf') LOCATION '%s'"
-      .format(this.getClass.getClassLoader.getResource("data/files/testUdf").getFile)
-  )
-
-  TestHive.sql("CREATE TEMPORARY FUNCTION testUdf AS '%s'".format(classOf[PairUdf].getName))
-
-  TestHive.sql("SELECT testUdf(pair) FROM hiveUdfTestTable")
-
-  TestHive.sql("DROP TEMPORARY FUNCTION IF EXISTS testUdf")
+    """.
+        stripMargin.format(classOf[PairSerDe].getName))
+
+    val location = Utils.getSparkClassLoader.getResource("data/files/testUdf").getFile
+    sql(s"""
+      ALTER TABLE hiveUdfTestTable
+      ADD IF NOT EXISTS PARTITION(partition='testUdf')
+      LOCATION '$location'""")
+
+    sql(s"CREATE TEMPORARY FUNCTION testUdf AS '${classOf[PairUdf].getName}'")
+    sql("SELECT testUdf(pair) FROM hiveUdfTestTable")
+    sql("DROP TEMPORARY FUNCTION IF EXISTS testUdf")
+  }
 }
 
 class TestPair(x: Int, y: Int) extends Writable with Serializable {

From 8e7ae477ba40a064d27cf149aa211ff6108fe239 Mon Sep 17 00:00:00 2001
From: Aaron Staple <aaron.staple@gmail.com>
Date: Tue, 16 Sep 2014 11:45:35 -0700
Subject: [PATCH 317/489] [SPARK-2314][SQL] Override collect and take in python
 library, and count in java library, with optimized versions.
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

SchemaRDD overrides RDD functions, including collect, count, and take, with optimized versions making use of the query optimizer.  The java and python interface classes wrapping SchemaRDD need to ensure the optimized versions are called as well.  This patch overrides relevant calls in the python and java interfaces with optimized versions.

Adds a new Row serialization pathway between python and java, based on JList[Array[Byte]] versus the existing RDD[Array[Byte]]. I wasn’t overjoyed about doing this, but I noticed that some QueryPlans implement optimizations in executeCollect(), which outputs an Array[Row] rather than the typical RDD[Row] that can be shipped to python using the existing serialization code. To me it made sense to ship the Array[Row] over to python directly instead of converting it back to an RDD[Row] just for the purpose of sending the Rows to python using the existing serialization code.

Author: Aaron Staple <aaron.staple@gmail.com>

Closes #1592 from staple/SPARK-2314 and squashes the following commits:

89ff550 [Aaron Staple] Merge with master.
6bb7b6c [Aaron Staple] Fix typo.
b56d0ac [Aaron Staple] [SPARK-2314][SQL] Override count in JavaSchemaRDD, forwarding to SchemaRDD's count.
0fc9d40 [Aaron Staple] Fix comment typos.
f03cdfa [Aaron Staple] [SPARK-2314][SQL] Override collect and take in sql.py, forwarding to SchemaRDD's collect.
---
 .../apache/spark/api/python/PythonRDD.scala   |  2 +-
 python/pyspark/sql.py                         | 47 +++++++++++++++++--
 .../org/apache/spark/sql/SchemaRDD.scala      | 37 ++++++++++-----
 .../spark/sql/api/java/JavaSchemaRDD.scala    |  2 +
 4 files changed, 71 insertions(+), 17 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
index d5002fa02992b..12b345a8fa7c3 100644
--- a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
+++ b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
@@ -776,7 +776,7 @@ private[spark] object PythonRDD extends Logging {
   }
 
   /**
-   * Convert and RDD of Java objects to and RDD of serialized Python objects, that is usable by
+   * Convert an RDD of Java objects to an RDD of serialized Python objects, that is usable by
    * PySpark.
    */
   def javaToPython(jRDD: JavaRDD[Any]): JavaRDD[Array[Byte]] = {
diff --git a/python/pyspark/sql.py b/python/pyspark/sql.py
index eac55cbe15193..621a556ec6356 100644
--- a/python/pyspark/sql.py
+++ b/python/pyspark/sql.py
@@ -30,6 +30,7 @@
 from pyspark.rdd import RDD, PipelinedRDD
 from pyspark.serializers import BatchedSerializer, PickleSerializer, CloudPickleSerializer
 from pyspark.storagelevel import StorageLevel
+from pyspark.traceback_utils import SCCallSiteSync
 
 from itertools import chain, ifilter, imap
 
@@ -1550,6 +1551,18 @@ def id(self):
             self._id = self._jrdd.id()
         return self._id
 
+    def limit(self, num):
+        """Limit the result count to the number specified.
+
+        >>> srdd = sqlCtx.inferSchema(rdd)
+        >>> srdd.limit(2).collect()
+        [Row(field1=1, field2=u'row1'), Row(field1=2, field2=u'row2')]
+        >>> srdd.limit(0).collect()
+        []
+        """
+        rdd = self._jschema_rdd.baseSchemaRDD().limit(num).toJavaSchemaRDD()
+        return SchemaRDD(rdd, self.sql_ctx)
+
     def saveAsParquetFile(self, path):
         """Save the contents as a Parquet file, preserving the schema.
 
@@ -1626,15 +1639,39 @@ def count(self):
         return self._jschema_rdd.count()
 
     def collect(self):
-        """
-        Return a list that contains all of the rows in this RDD.
+        """Return a list that contains all of the rows in this RDD.
 
-        Each object in the list is on Row, the fields can be accessed as
+        Each object in the list is a Row, the fields can be accessed as
         attributes.
+
+        Unlike the base RDD implementation of collect, this implementation
+        leverages the query optimizer to perform a collect on the SchemaRDD,
+        which supports features such as filter pushdown.
+
+        >>> srdd = sqlCtx.inferSchema(rdd)
+        >>> srdd.collect()
+        [Row(field1=1, field2=u'row1'), ..., Row(field1=3, field2=u'row3')]
         """
-        rows = RDD.collect(self)
+        with SCCallSiteSync(self.context) as css:
+            bytesInJava = self._jschema_rdd.baseSchemaRDD().collectToPython().iterator()
         cls = _create_cls(self.schema())
-        return map(cls, rows)
+        return map(cls, self._collect_iterator_through_file(bytesInJava))
+
+    def take(self, num):
+        """Take the first num rows of the RDD.
+
+        Each object in the list is a Row, the fields can be accessed as
+        attributes.
+
+        Unlike the base RDD implementation of take, this implementation
+        leverages the query optimizer to perform a collect on a SchemaRDD,
+        which supports features such as filter pushdown.
+
+        >>> srdd = sqlCtx.inferSchema(rdd)
+        >>> srdd.take(2)
+        [Row(field1=1, field2=u'row1'), Row(field1=2, field2=u'row2')]
+        """
+        return self.limit(num).collect()
 
     # Convert each object in the RDD to a Row with the right class
     # for this SchemaRDD, so that fields can be accessed as attributes.
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala
index d2ceb4a2b0b25..3bc5dce095511 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala
@@ -377,15 +377,15 @@ class SchemaRDD(
   def toJavaSchemaRDD: JavaSchemaRDD = new JavaSchemaRDD(sqlContext, logicalPlan)
 
   /**
-   * Converts a JavaRDD to a PythonRDD. It is used by pyspark.
+   * Helper for converting a Row to a simple Array suitable for pyspark serialization.
    */
-  private[sql] def javaToPython: JavaRDD[Array[Byte]] = {
+  private def rowToJArray(row: Row, structType: StructType): Array[Any] = {
     import scala.collection.Map
 
     def toJava(obj: Any, dataType: DataType): Any = (obj, dataType) match {
       case (null, _) => null
 
-      case (obj: Row, struct: StructType) => rowToArray(obj, struct)
+      case (obj: Row, struct: StructType) => rowToJArray(obj, struct)
 
       case (seq: Seq[Any], array: ArrayType) =>
         seq.map(x => toJava(x, array.elementType)).asJava
@@ -402,22 +402,37 @@ class SchemaRDD(
       case (other, _) => other
     }
 
-    def rowToArray(row: Row, structType: StructType): Array[Any] = {
-      val fields = structType.fields.map(field => field.dataType)
-      row.zip(fields).map {
-        case (obj, dataType) => toJava(obj, dataType)
-      }.toArray
-    }
+    val fields = structType.fields.map(field => field.dataType)
+    row.zip(fields).map {
+      case (obj, dataType) => toJava(obj, dataType)
+    }.toArray
+  }
 
+  /**
+   * Converts a JavaRDD to a PythonRDD. It is used by pyspark.
+   */
+  private[sql] def javaToPython: JavaRDD[Array[Byte]] = {
     val rowSchema = StructType.fromAttributes(this.queryExecution.analyzed.output)
     this.mapPartitions { iter =>
       val pickle = new Pickler
       iter.map { row =>
-        rowToArray(row, rowSchema)
+        rowToJArray(row, rowSchema)
       }.grouped(100).map(batched => pickle.dumps(batched.toArray))
     }
   }
 
+  /**
+   * Serializes the Array[Row] returned by SchemaRDD's optimized collect(), using the same
+   * format as javaToPython. It is used by pyspark.
+   */
+  private[sql] def collectToPython: JList[Array[Byte]] = {
+    val rowSchema = StructType.fromAttributes(this.queryExecution.analyzed.output)
+    val pickle = new Pickler
+    new java.util.ArrayList(collect().map { row =>
+      rowToJArray(row, rowSchema)
+    }.grouped(100).map(batched => pickle.dumps(batched.toArray)).toIterable)
+  }
+
   /**
    * Creates SchemaRDD by applying own schema to derived RDD. Typically used to wrap return value
    * of base RDD functions that do not change schema.
@@ -433,7 +448,7 @@ class SchemaRDD(
   }
 
   // =======================================================================
-  // Overriden RDD actions
+  // Overridden RDD actions
   // =======================================================================
 
   override def collect(): Array[Row] = queryExecution.executedPlan.executeCollect()
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/api/java/JavaSchemaRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/api/java/JavaSchemaRDD.scala
index 4d799b4038fdd..e7faba0c7f620 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/api/java/JavaSchemaRDD.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/api/java/JavaSchemaRDD.scala
@@ -112,6 +112,8 @@ class JavaSchemaRDD(
     new java.util.ArrayList(arr)
   }
 
+  override def count(): Long = baseSchemaRDD.count
+
   override def take(num: Int): JList[Row] = {
     import scala.collection.JavaConversions._
     val arr: java.util.Collection[Row] = baseSchemaRDD.take(num).toSeq.map(new Row(_))

From df90e81fd383c0d89dee6db16d5520def9190c56 Mon Sep 17 00:00:00 2001
From: Nicholas Chammas <nicholas.chammas@gmail.com>
Date: Tue, 16 Sep 2014 11:48:20 -0700
Subject: [PATCH 318/489] [Docs] minor punctuation fix

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

Closes #2414 from nchammas/patch-1 and squashes the following commits:

14664bf [Nicholas Chammas] [Docs] minor punctuation fix
---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index b05bbfb5a594c..8dd8b70696aa2 100644
--- a/README.md
+++ b/README.md
@@ -75,7 +75,7 @@ can be run using:
     ./dev/run-tests
 
 Please see the guidance on how to 
-[run all automated tests](https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark#ContributingtoSpark-AutomatedTesting)
+[run all automated tests](https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark#ContributingtoSpark-AutomatedTesting).
 
 ## A Note About Hadoop Versions
 

From 84073eb1172dc959936149265378f6e24d303685 Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Tue, 16 Sep 2014 11:51:46 -0700
Subject: [PATCH 319/489] [SQL][DOCS] Improve section on thrift-server

Taken from liancheng's updates. Merged conflicts with #2316.

Author: Michael Armbrust <michael@databricks.com>

Closes #2384 from marmbrus/sqlDocUpdate and squashes the following commits:

2db6319 [Michael Armbrust] @liancheng's updates
---
 docs/sql-programming-guide.md | 58 ++++++++++++++++++++++++-----------
 1 file changed, 40 insertions(+), 18 deletions(-)

diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md
index 8d41fdec699e9..c498b41c43380 100644
--- a/docs/sql-programming-guide.md
+++ b/docs/sql-programming-guide.md
@@ -128,7 +128,7 @@ feature parity with a HiveContext.
 
 </div>
 
-The specific variant of SQL that is used to parse queries can also be selected using the 
+The specific variant of SQL that is used to parse queries can also be selected using the
 `spark.sql.dialect` option.  This parameter can be changed using either the `setConf` method on
 a SQLContext or by using a `SET key=value` command in SQL.  For a SQLContext, the only dialect
 available is "sql" which uses a simple SQL parser provided by Spark SQL.  In a HiveContext, the
@@ -139,7 +139,7 @@ default is "hiveql", though "sql" is also available.  Since the HiveQL parser is
 
 Spark SQL supports operating on a variety of data sources through the `SchemaRDD` interface.
 A SchemaRDD can be operated on as normal RDDs and can also be registered as a temporary table.
-Registering a SchemaRDD as a table allows you to run SQL queries over its data.  This section 
+Registering a SchemaRDD as a table allows you to run SQL queries over its data.  This section
 describes the various methods for loading data into a SchemaRDD.
 
 ## RDDs
@@ -152,7 +152,7 @@ while writing your Spark application.
 The second method for creating SchemaRDDs is through a programmatic interface that allows you to
 construct a schema and then apply it to an existing RDD.  While this method is more verbose, it allows
 you to construct SchemaRDDs when the columns and their types are not known until runtime.
- 
+
 ### Inferring the Schema Using Reflection
 <div class="codetabs">
 
@@ -193,7 +193,7 @@ teenagers.map(t => "Name: " + t(0)).collect().foreach(println)
 <div data-lang="java"  markdown="1">
 
 Spark SQL supports automatically converting an RDD of [JavaBeans](http://stackoverflow.com/questions/3295496/what-is-a-javabean-exactly)
-into a Schema RDD.  The BeanInfo, obtained using reflection, defines the schema of the table. 
+into a Schema RDD.  The BeanInfo, obtained using reflection, defines the schema of the table.
 Currently, Spark SQL does not support JavaBeans that contain
 nested or contain complex types such as Lists or Arrays.  You can create a JavaBean by creating a
 class that implements Serializable and has getters and setters for all of its fields.
@@ -480,7 +480,7 @@ for name in names.collect():
 
 [Parquet](http://parquet.io) is a columnar format that is supported by many other data processing systems.
 Spark SQL provides support for both reading and writing Parquet files that automatically preserves the schema
-of the original data.  
+of the original data.
 
 ### Loading Data Programmatically
 
@@ -562,7 +562,7 @@ for teenName in teenNames.collect():
 
 </div>
 
-</div>                                                                             
+</div>
 
 ### Configuration
 
@@ -808,7 +808,7 @@ memory usage and GC pressure. You can call `uncacheTable("tableName")` to remove
 Note that if you call `cache` rather than `cacheTable`, tables will _not_ be cached using
 the in-memory columnar format, and therefore `cacheTable` is strongly recommended for this use case.
 
-Configuration of in-memory caching can be done using the `setConf` method on SQLContext or by running 
+Configuration of in-memory caching can be done using the `setConf` method on SQLContext or by running
 `SET key=value` commands using SQL.
 
 <table class="table">
@@ -881,10 +881,32 @@ To start the JDBC server, run the following in the Spark directory:
 
     ./sbin/start-thriftserver.sh
 
-The default port the server listens on is 10000.  To listen on customized host and port, please set
-the `HIVE_SERVER2_THRIFT_PORT` and `HIVE_SERVER2_THRIFT_BIND_HOST` environment variables. You may
-run `./sbin/start-thriftserver.sh --help` for a complete list of all available options.  Now you can
-use beeline to test the Thrift JDBC server:
+This script accepts all `bin/spark-submit` command line options, plus a `--hiveconf` option to
+specify Hive properties.  You may run `./sbin/start-thriftserver.sh --help` for a complete list of
+all available options.  By default, the server listens on localhost:10000. You may override this
+bahaviour via either environment variables, i.e.:
+
+{% highlight bash %}
+export HIVE_SERVER2_THRIFT_PORT=<listening-port>
+export HIVE_SERVER2_THRIFT_BIND_HOST=<listening-host>
+./sbin/start-thriftserver.sh \
+  --master <master-uri> \
+  ...
+```
+{% endhighlight %}
+
+or system properties:
+
+{% highlight bash %}
+./sbin/start-thriftserver.sh \
+  --hiveconf hive.server2.thrift.port=<listening-port> \
+  --hiveconf hive.server2.thrift.bind.host=<listening-host> \
+  --master <master-uri>
+  ...
+```
+{% endhighlight %}
+
+Now you can use beeline to test the Thrift JDBC server:
 
     ./bin/beeline
 
@@ -930,7 +952,7 @@ SQL deprecates this property in favor of `spark.sql.shuffle.partitions`, whose d
 is 200. Users may customize this property via `SET`:
 
     SET spark.sql.shuffle.partitions=10;
-    SELECT page, count(*) c 
+    SELECT page, count(*) c
     FROM logs_last_month_cached
     GROUP BY page ORDER BY c DESC LIMIT 10;
 
@@ -1139,7 +1161,7 @@ evaluated by the SQL execution engine.  A full list of the functions supported c
 <div data-lang="scala"  markdown="1">
 
 All data types of Spark SQL are located in the package `org.apache.spark.sql`.
-You can access them by doing 
+You can access them by doing
 {% highlight scala %}
 import  org.apache.spark.sql._
 {% endhighlight %}
@@ -1245,7 +1267,7 @@ import  org.apache.spark.sql._
 <tr>
   <td> <b>StructType</b> </td>
   <td> org.apache.spark.sql.Row </td>
-  <td> 
+  <td>
   StructType(<i>fields</i>)<br />
   <b>Note:</b> <i>fields</i> is a Seq of StructFields. Also, two fields with the same
   name are not allowed.
@@ -1267,7 +1289,7 @@ import  org.apache.spark.sql._
 
 All data types of Spark SQL are located in the package of
 `org.apache.spark.sql.api.java`. To access or create a data type,
-please use factory methods provided in 
+please use factory methods provided in
 `org.apache.spark.sql.api.java.DataType`.
 
 <table class="table">
@@ -1373,7 +1395,7 @@ please use factory methods provided in
 <tr>
   <td> <b>StructType</b> </td>
   <td> org.apache.spark.sql.api.java </td>
-  <td> 
+  <td>
   DataType.createStructType(<i>fields</i>)<br />
   <b>Note:</b> <i>fields</i> is a List or an array of StructFields.
   Also, two fields with the same name are not allowed.
@@ -1394,7 +1416,7 @@ please use factory methods provided in
 <div data-lang="python"  markdown="1">
 
 All data types of Spark SQL are located in the package of `pyspark.sql`.
-You can access them by doing 
+You can access them by doing
 {% highlight python %}
 from pyspark.sql import *
 {% endhighlight %}
@@ -1518,7 +1540,7 @@ from pyspark.sql import *
 <tr>
   <td> <b>StructType</b> </td>
   <td> list or tuple </td>
-  <td> 
+  <td>
   StructType(<i>fields</i>)<br />
   <b>Note:</b> <i>fields</i> is a Seq of StructFields. Also, two fields with the same
   name are not allowed.

From a9e910430fb6bb4ef1f6ae20761c43b96bb018df Mon Sep 17 00:00:00 2001
From: Kousuke Saruta <sarutak@oss.nttdata.co.jp>
Date: Tue, 16 Sep 2014 12:41:45 -0700
Subject: [PATCH 320/489] [SPARK-3546] InputStream of ManagedBuffer is not
 closed and causes running out of file descriptor

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

Closes #2408 from sarutak/resolve-resource-leak-issue and squashes the following commits:

074781d [Kousuke Saruta] Modified SuffleBlockFetcherIterator
5f63f67 [Kousuke Saruta] Move metrics increment logic and debug logging outside try block
b37231a [Kousuke Saruta] Modified FileSegmentManagedBuffer#nioByteBuffer to check null or not before invoking channel.close
bf29d4a [Kousuke Saruta] Modified FileSegment to close channel
---
 .../org/apache/spark/network/ManagedBuffer.scala     | 12 ++++++++++--
 .../spark/storage/ShuffleBlockFetcherIterator.scala  |  2 +-
 2 files changed, 11 insertions(+), 3 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/network/ManagedBuffer.scala b/core/src/main/scala/org/apache/spark/network/ManagedBuffer.scala
index dcecb6beeea9b..e990c1da6730f 100644
--- a/core/src/main/scala/org/apache/spark/network/ManagedBuffer.scala
+++ b/core/src/main/scala/org/apache/spark/network/ManagedBuffer.scala
@@ -19,6 +19,7 @@ package org.apache.spark.network
 
 import java.io.{FileInputStream, RandomAccessFile, File, InputStream}
 import java.nio.ByteBuffer
+import java.nio.channels.FileChannel
 import java.nio.channels.FileChannel.MapMode
 
 import com.google.common.io.ByteStreams
@@ -66,8 +67,15 @@ final class FileSegmentManagedBuffer(val file: File, val offset: Long, val lengt
   override def size: Long = length
 
   override def nioByteBuffer(): ByteBuffer = {
-    val channel = new RandomAccessFile(file, "r").getChannel
-    channel.map(MapMode.READ_ONLY, offset, length)
+    var channel: FileChannel = null
+    try {
+      channel = new RandomAccessFile(file, "r").getChannel
+      channel.map(MapMode.READ_ONLY, offset, length)
+    } finally {
+      if (channel != null) {
+        channel.close()
+      }
+    }
   }
 
   override def inputStream(): InputStream = {
diff --git a/core/src/main/scala/org/apache/spark/storage/ShuffleBlockFetcherIterator.scala b/core/src/main/scala/org/apache/spark/storage/ShuffleBlockFetcherIterator.scala
index c8e708aa6b1bc..d868758a7f549 100644
--- a/core/src/main/scala/org/apache/spark/storage/ShuffleBlockFetcherIterator.scala
+++ b/core/src/main/scala/org/apache/spark/storage/ShuffleBlockFetcherIterator.scala
@@ -23,7 +23,7 @@ import scala.collection.mutable.ArrayBuffer
 import scala.collection.mutable.HashSet
 import scala.collection.mutable.Queue
 
-import org.apache.spark.{TaskContext, Logging, SparkException}
+import org.apache.spark.{TaskContext, Logging}
 import org.apache.spark.network.{ManagedBuffer, BlockFetchingListener, BlockTransferService}
 import org.apache.spark.serializer.Serializer
 import org.apache.spark.util.Utils

From ec1adecbb72d291d7ef122fb0505bae53116e0e6 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies.liu@gmail.com>
Date: Tue, 16 Sep 2014 12:51:58 -0700
Subject: [PATCH 321/489] [SPARK-3430] [PySpark] [Doc] generate PySpark API
 docs using Sphinx

Using Sphinx to generate API docs for PySpark.

requirement: Sphinx

```
$ cd python/docs/
$ make html
```

The generated API docs will be located at python/docs/_build/html/index.html

It can co-exists with those generated by Epydoc.

This is the first working version, after merging in, then we can continue to improve it and replace the epydoc finally.

Author: Davies Liu <davies.liu@gmail.com>

Closes #2292 from davies/sphinx and squashes the following commits:

425a3b1 [Davies Liu] cleanup
1573298 [Davies Liu] move docs to python/docs/
5fe3903 [Davies Liu] Merge branch 'master' into sphinx
9468ab0 [Davies Liu] fix makefile
b408f38 [Davies Liu] address all comments
e2ccb1b [Davies Liu] update name and version
9081ead [Davies Liu] generate PySpark API docs using Sphinx
---
 python/docs/Makefile          | 179 ++++++++++++++++++
 python/docs/conf.py           | 332 ++++++++++++++++++++++++++++++++++
 python/docs/epytext.py        |  27 +++
 python/docs/index.rst         |  37 ++++
 python/docs/make.bat          | 242 +++++++++++++++++++++++++
 python/docs/modules.rst       |   7 +
 python/docs/pyspark.mllib.rst |  77 ++++++++
 python/docs/pyspark.rst       |  18 ++
 python/docs/pyspark.sql.rst   |  10 +
 python/pyspark/broadcast.py   |   3 +
 python/pyspark/context.py     |   2 +-
 python/pyspark/serializers.py |   3 +
 python/pyspark/sql.py         |  12 +-
 13 files changed, 944 insertions(+), 5 deletions(-)
 create mode 100644 python/docs/Makefile
 create mode 100644 python/docs/conf.py
 create mode 100644 python/docs/epytext.py
 create mode 100644 python/docs/index.rst
 create mode 100644 python/docs/make.bat
 create mode 100644 python/docs/modules.rst
 create mode 100644 python/docs/pyspark.mllib.rst
 create mode 100644 python/docs/pyspark.rst
 create mode 100644 python/docs/pyspark.sql.rst

diff --git a/python/docs/Makefile b/python/docs/Makefile
new file mode 100644
index 0000000000000..8a1324eecd325
--- /dev/null
+++ b/python/docs/Makefile
@@ -0,0 +1,179 @@
+# Makefile for Sphinx documentation
+#
+
+# You can set these variables from the command line.
+SPHINXOPTS    =
+SPHINXBUILD   = sphinx-build
+PAPER         =
+BUILDDIR      = _build
+
+export PYTHONPATH=$(realpath ..):$(realpath ../lib/py4j-0.8.2.1-src.zip)
+
+# User-friendly check for sphinx-build
+ifeq ($(shell which $(SPHINXBUILD) >/dev/null 2>&1; echo $$?), 1)
+$(error The '$(SPHINXBUILD)' command was not found. Make sure you have Sphinx installed, then set the SPHINXBUILD environment variable to point to the full path of the '$(SPHINXBUILD)' executable. Alternatively you can add the directory with the executable to your PATH. If you don't have Sphinx installed, grab it from http://sphinx-doc.org/)
+endif
+
+# Internal variables.
+PAPEROPT_a4     = -D latex_paper_size=a4
+PAPEROPT_letter = -D latex_paper_size=letter
+ALLSPHINXOPTS   = -d $(BUILDDIR)/doctrees $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) .
+# the i18n builder cannot share the environment and doctrees with the others
+I18NSPHINXOPTS  = $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) .
+
+.PHONY: help clean html dirhtml singlehtml pickle json htmlhelp qthelp devhelp epub latex latexpdf text man changes linkcheck doctest gettext
+
+help:
+	@echo "Please use \`make <target>' where <target> is one of"
+	@echo "  html       to make standalone HTML files"
+	@echo "  dirhtml    to make HTML files named index.html in directories"
+	@echo "  singlehtml to make a single large HTML file"
+	@echo "  pickle     to make pickle files"
+	@echo "  json       to make JSON files"
+	@echo "  htmlhelp   to make HTML files and a HTML help project"
+	@echo "  qthelp     to make HTML files and a qthelp project"
+	@echo "  devhelp    to make HTML files and a Devhelp project"
+	@echo "  epub       to make an epub"
+	@echo "  latex      to make LaTeX files, you can set PAPER=a4 or PAPER=letter"
+	@echo "  latexpdf   to make LaTeX files and run them through pdflatex"
+	@echo "  latexpdfja to make LaTeX files and run them through platex/dvipdfmx"
+	@echo "  text       to make text files"
+	@echo "  man        to make manual pages"
+	@echo "  texinfo    to make Texinfo files"
+	@echo "  info       to make Texinfo files and run them through makeinfo"
+	@echo "  gettext    to make PO message catalogs"
+	@echo "  changes    to make an overview of all changed/added/deprecated items"
+	@echo "  xml        to make Docutils-native XML files"
+	@echo "  pseudoxml  to make pseudoxml-XML files for display purposes"
+	@echo "  linkcheck  to check all external links for integrity"
+	@echo "  doctest    to run all doctests embedded in the documentation (if enabled)"
+
+clean:
+	rm -rf $(BUILDDIR)/*
+
+html:
+	$(SPHINXBUILD) -b html $(ALLSPHINXOPTS) $(BUILDDIR)/html
+	@echo
+	@echo "Build finished. The HTML pages are in $(BUILDDIR)/html."
+
+dirhtml:
+	$(SPHINXBUILD) -b dirhtml $(ALLSPHINXOPTS) $(BUILDDIR)/dirhtml
+	@echo
+	@echo "Build finished. The HTML pages are in $(BUILDDIR)/dirhtml."
+
+singlehtml:
+	$(SPHINXBUILD) -b singlehtml $(ALLSPHINXOPTS) $(BUILDDIR)/singlehtml
+	@echo
+	@echo "Build finished. The HTML page is in $(BUILDDIR)/singlehtml."
+
+pickle:
+	$(SPHINXBUILD) -b pickle $(ALLSPHINXOPTS) $(BUILDDIR)/pickle
+	@echo
+	@echo "Build finished; now you can process the pickle files."
+
+json:
+	$(SPHINXBUILD) -b json $(ALLSPHINXOPTS) $(BUILDDIR)/json
+	@echo
+	@echo "Build finished; now you can process the JSON files."
+
+htmlhelp:
+	$(SPHINXBUILD) -b htmlhelp $(ALLSPHINXOPTS) $(BUILDDIR)/htmlhelp
+	@echo
+	@echo "Build finished; now you can run HTML Help Workshop with the" \
+	      ".hhp project file in $(BUILDDIR)/htmlhelp."
+
+qthelp:
+	$(SPHINXBUILD) -b qthelp $(ALLSPHINXOPTS) $(BUILDDIR)/qthelp
+	@echo
+	@echo "Build finished; now you can run "qcollectiongenerator" with the" \
+	      ".qhcp project file in $(BUILDDIR)/qthelp, like this:"
+	@echo "# qcollectiongenerator $(BUILDDIR)/qthelp/pyspark.qhcp"
+	@echo "To view the help file:"
+	@echo "# assistant -collectionFile $(BUILDDIR)/qthelp/pyspark.qhc"
+
+devhelp:
+	$(SPHINXBUILD) -b devhelp $(ALLSPHINXOPTS) $(BUILDDIR)/devhelp
+	@echo
+	@echo "Build finished."
+	@echo "To view the help file:"
+	@echo "# mkdir -p $$HOME/.local/share/devhelp/pyspark"
+	@echo "# ln -s $(BUILDDIR)/devhelp $$HOME/.local/share/devhelp/pyspark"
+	@echo "# devhelp"
+
+epub:
+	$(SPHINXBUILD) -b epub $(ALLSPHINXOPTS) $(BUILDDIR)/epub
+	@echo
+	@echo "Build finished. The epub file is in $(BUILDDIR)/epub."
+
+latex:
+	$(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex
+	@echo
+	@echo "Build finished; the LaTeX files are in $(BUILDDIR)/latex."
+	@echo "Run \`make' in that directory to run these through (pdf)latex" \
+	      "(use \`make latexpdf' here to do that automatically)."
+
+latexpdf:
+	$(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex
+	@echo "Running LaTeX files through pdflatex..."
+	$(MAKE) -C $(BUILDDIR)/latex all-pdf
+	@echo "pdflatex finished; the PDF files are in $(BUILDDIR)/latex."
+
+latexpdfja:
+	$(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex
+	@echo "Running LaTeX files through platex and dvipdfmx..."
+	$(MAKE) -C $(BUILDDIR)/latex all-pdf-ja
+	@echo "pdflatex finished; the PDF files are in $(BUILDDIR)/latex."
+
+text:
+	$(SPHINXBUILD) -b text $(ALLSPHINXOPTS) $(BUILDDIR)/text
+	@echo
+	@echo "Build finished. The text files are in $(BUILDDIR)/text."
+
+man:
+	$(SPHINXBUILD) -b man $(ALLSPHINXOPTS) $(BUILDDIR)/man
+	@echo
+	@echo "Build finished. The manual pages are in $(BUILDDIR)/man."
+
+texinfo:
+	$(SPHINXBUILD) -b texinfo $(ALLSPHINXOPTS) $(BUILDDIR)/texinfo
+	@echo
+	@echo "Build finished. The Texinfo files are in $(BUILDDIR)/texinfo."
+	@echo "Run \`make' in that directory to run these through makeinfo" \
+	      "(use \`make info' here to do that automatically)."
+
+info:
+	$(SPHINXBUILD) -b texinfo $(ALLSPHINXOPTS) $(BUILDDIR)/texinfo
+	@echo "Running Texinfo files through makeinfo..."
+	make -C $(BUILDDIR)/texinfo info
+	@echo "makeinfo finished; the Info files are in $(BUILDDIR)/texinfo."
+
+gettext:
+	$(SPHINXBUILD) -b gettext $(I18NSPHINXOPTS) $(BUILDDIR)/locale
+	@echo
+	@echo "Build finished. The message catalogs are in $(BUILDDIR)/locale."
+
+changes:
+	$(SPHINXBUILD) -b changes $(ALLSPHINXOPTS) $(BUILDDIR)/changes
+	@echo
+	@echo "The overview file is in $(BUILDDIR)/changes."
+
+linkcheck:
+	$(SPHINXBUILD) -b linkcheck $(ALLSPHINXOPTS) $(BUILDDIR)/linkcheck
+	@echo
+	@echo "Link check complete; look for any errors in the above output " \
+	      "or in $(BUILDDIR)/linkcheck/output.txt."
+
+doctest:
+	$(SPHINXBUILD) -b doctest $(ALLSPHINXOPTS) $(BUILDDIR)/doctest
+	@echo "Testing of doctests in the sources finished, look at the " \
+	      "results in $(BUILDDIR)/doctest/output.txt."
+
+xml:
+	$(SPHINXBUILD) -b xml $(ALLSPHINXOPTS) $(BUILDDIR)/xml
+	@echo
+	@echo "Build finished. The XML files are in $(BUILDDIR)/xml."
+
+pseudoxml:
+	$(SPHINXBUILD) -b pseudoxml $(ALLSPHINXOPTS) $(BUILDDIR)/pseudoxml
+	@echo
+	@echo "Build finished. The pseudo-XML files are in $(BUILDDIR)/pseudoxml."
diff --git a/python/docs/conf.py b/python/docs/conf.py
new file mode 100644
index 0000000000000..c368cf81a003b
--- /dev/null
+++ b/python/docs/conf.py
@@ -0,0 +1,332 @@
+# -*- coding: utf-8 -*-
+#
+# pyspark documentation build configuration file, created by
+# sphinx-quickstart on Thu Aug 28 15:17:47 2014.
+#
+# This file is execfile()d with the current directory set to its
+# containing dir.
+#
+# Note that not all possible configuration values are present in this
+# autogenerated file.
+#
+# All configuration values have a default; values that are commented out
+# serve to show the default.
+
+import sys
+import os
+
+# If extensions (or modules to document with autodoc) are in another directory,
+# add these directories to sys.path here. If the directory is relative to the
+# documentation root, use os.path.abspath to make it absolute, like shown here.
+sys.path.insert(0, os.path.abspath('.'))
+
+# -- General configuration ------------------------------------------------
+
+# If your documentation needs a minimal Sphinx version, state it here.
+#needs_sphinx = '1.0'
+
+# Add any Sphinx extension module names here, as strings. They can be
+# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
+# ones.
+extensions = [
+    'sphinx.ext.autodoc',
+    'sphinx.ext.viewcode',
+    'epytext',
+]
+
+# Add any paths that contain templates here, relative to this directory.
+templates_path = ['_templates']
+
+# The suffix of source filenames.
+source_suffix = '.rst'
+
+# The encoding of source files.
+#source_encoding = 'utf-8-sig'
+
+# The master toctree document.
+master_doc = 'index'
+
+# General information about the project.
+project = u'PySpark'
+copyright = u'2014, Author'
+
+# The version info for the project you're documenting, acts as replacement for
+# |version| and |release|, also used in various other places throughout the
+# built documents.
+#
+# The short X.Y version.
+version = '1.1'
+# The full version, including alpha/beta/rc tags.
+release = ''
+
+# The language for content autogenerated by Sphinx. Refer to documentation
+# for a list of supported languages.
+#language = None
+
+# There are two options for replacing |today|: either, you set today to some
+# non-false value, then it is used:
+#today = ''
+# Else, today_fmt is used as the format for a strftime call.
+#today_fmt = '%B %d, %Y'
+
+# List of patterns, relative to source directory, that match files and
+# directories to ignore when looking for source files.
+exclude_patterns = ['_build']
+
+# The reST default role (used for this markup: `text`) to use for all
+# documents.
+#default_role = None
+
+# If true, '()' will be appended to :func: etc. cross-reference text.
+#add_function_parentheses = True
+
+# If true, the current module name will be prepended to all description
+# unit titles (such as .. function::).
+#add_module_names = True
+
+# If true, sectionauthor and moduleauthor directives will be shown in the
+# output. They are ignored by default.
+#show_authors = False
+
+# The name of the Pygments (syntax highlighting) style to use.
+pygments_style = 'sphinx'
+
+# A list of ignored prefixes for module index sorting.
+#modindex_common_prefix = []
+
+# If true, keep warnings as "system message" paragraphs in the built documents.
+#keep_warnings = False
+
+
+# -- Options for HTML output ----------------------------------------------
+
+# The theme to use for HTML and HTML Help pages.  See the documentation for
+# a list of builtin themes.
+html_theme = 'default'
+
+# Theme options are theme-specific and customize the look and feel of a theme
+# further.  For a list of options available for each theme, see the
+# documentation.
+#html_theme_options = {}
+
+# Add any paths that contain custom themes here, relative to this directory.
+#html_theme_path = []
+
+# The name for this set of Sphinx documents.  If None, it defaults to
+# "<project> v<release> documentation".
+#html_title = None
+
+# A shorter title for the navigation bar.  Default is the same as html_title.
+#html_short_title = None
+
+# The name of an image file (relative to this directory) to place at the top
+# of the sidebar.
+#html_logo = None
+
+# The name of an image file (within the static path) to use as favicon of the
+# docs.  This file should be a Windows icon file (.ico) being 16x16 or 32x32
+# pixels large.
+#html_favicon = None
+
+# Add any paths that contain custom static files (such as style sheets) here,
+# relative to this directory. They are copied after the builtin static files,
+# so a file named "default.css" will overwrite the builtin "default.css".
+html_static_path = ['_static']
+
+# Add any extra paths that contain custom files (such as robots.txt or
+# .htaccess) here, relative to this directory. These files are copied
+# directly to the root of the documentation.
+#html_extra_path = []
+
+# If not '', a 'Last updated on:' timestamp is inserted at every page bottom,
+# using the given strftime format.
+#html_last_updated_fmt = '%b %d, %Y'
+
+# If true, SmartyPants will be used to convert quotes and dashes to
+# typographically correct entities.
+#html_use_smartypants = True
+
+# Custom sidebar templates, maps document names to template names.
+#html_sidebars = {}
+
+# Additional templates that should be rendered to pages, maps page names to
+# template names.
+#html_additional_pages = {}
+
+# If false, no module index is generated.
+#html_domain_indices = True
+
+# If false, no index is generated.
+#html_use_index = True
+
+# If true, the index is split into individual pages for each letter.
+#html_split_index = False
+
+# If true, links to the reST sources are added to the pages.
+#html_show_sourcelink = True
+
+# If true, "Created using Sphinx" is shown in the HTML footer. Default is True.
+#html_show_sphinx = True
+
+# If true, "(C) Copyright ..." is shown in the HTML footer. Default is True.
+#html_show_copyright = True
+
+# If true, an OpenSearch description file will be output, and all pages will
+# contain a <link> tag referring to it.  The value of this option must be the
+# base URL from which the finished HTML is served.
+#html_use_opensearch = ''
+
+# This is the file name suffix for HTML files (e.g. ".xhtml").
+#html_file_suffix = None
+
+# Output file base name for HTML help builder.
+htmlhelp_basename = 'pysparkdoc'
+
+
+# -- Options for LaTeX output ---------------------------------------------
+
+latex_elements = {
+# The paper size ('letterpaper' or 'a4paper').
+#'papersize': 'letterpaper',
+
+# The font size ('10pt', '11pt' or '12pt').
+#'pointsize': '10pt',
+
+# Additional stuff for the LaTeX preamble.
+#'preamble': '',
+}
+
+# Grouping the document tree into LaTeX files. List of tuples
+# (source start file, target name, title,
+#  author, documentclass [howto, manual, or own class]).
+latex_documents = [
+  ('index', 'pyspark.tex', u'pyspark Documentation',
+   u'Author', 'manual'),
+]
+
+# The name of an image file (relative to this directory) to place at the top of
+# the title page.
+#latex_logo = None
+
+# For "manual" documents, if this is true, then toplevel headings are parts,
+# not chapters.
+#latex_use_parts = False
+
+# If true, show page references after internal links.
+#latex_show_pagerefs = False
+
+# If true, show URL addresses after external links.
+#latex_show_urls = False
+
+# Documents to append as an appendix to all manuals.
+#latex_appendices = []
+
+# If false, no module index is generated.
+#latex_domain_indices = True
+
+
+# -- Options for manual page output ---------------------------------------
+
+# One entry per manual page. List of tuples
+# (source start file, name, description, authors, manual section).
+man_pages = [
+    ('index', 'pyspark', u'pyspark Documentation',
+     [u'Author'], 1)
+]
+
+# If true, show URL addresses after external links.
+#man_show_urls = False
+
+
+# -- Options for Texinfo output -------------------------------------------
+
+# Grouping the document tree into Texinfo files. List of tuples
+# (source start file, target name, title, author,
+#  dir menu entry, description, category)
+texinfo_documents = [
+  ('index', 'pyspark', u'pyspark Documentation',
+   u'Author', 'pyspark', 'One line description of project.',
+   'Miscellaneous'),
+]
+
+# Documents to append as an appendix to all manuals.
+#texinfo_appendices = []
+
+# If false, no module index is generated.
+#texinfo_domain_indices = True
+
+# How to display URL addresses: 'footnote', 'no', or 'inline'.
+#texinfo_show_urls = 'footnote'
+
+# If true, do not generate a @detailmenu in the "Top" node's menu.
+#texinfo_no_detailmenu = False
+
+
+# -- Options for Epub output ----------------------------------------------
+
+# Bibliographic Dublin Core info.
+epub_title = u'pyspark'
+epub_author = u'Author'
+epub_publisher = u'Author'
+epub_copyright = u'2014, Author'
+
+# The basename for the epub file. It defaults to the project name.
+#epub_basename = u'pyspark'
+
+# The HTML theme for the epub output. Since the default themes are not optimized
+# for small screen space, using the same theme for HTML and epub output is
+# usually not wise. This defaults to 'epub', a theme designed to save visual
+# space.
+#epub_theme = 'epub'
+
+# The language of the text. It defaults to the language option
+# or en if the language is not set.
+#epub_language = ''
+
+# The scheme of the identifier. Typical schemes are ISBN or URL.
+#epub_scheme = ''
+
+# The unique identifier of the text. This can be a ISBN number
+# or the project homepage.
+#epub_identifier = ''
+
+# A unique identification for the text.
+#epub_uid = ''
+
+# A tuple containing the cover image and cover page html template filenames.
+#epub_cover = ()
+
+# A sequence of (type, uri, title) tuples for the guide element of content.opf.
+#epub_guide = ()
+
+# HTML files that should be inserted before the pages created by sphinx.
+# The format is a list of tuples containing the path and title.
+#epub_pre_files = []
+
+# HTML files shat should be inserted after the pages created by sphinx.
+# The format is a list of tuples containing the path and title.
+#epub_post_files = []
+
+# A list of files that should not be packed into the epub file.
+epub_exclude_files = ['search.html']
+
+# The depth of the table of contents in toc.ncx.
+#epub_tocdepth = 3
+
+# Allow duplicate toc entries.
+#epub_tocdup = True
+
+# Choose between 'default' and 'includehidden'.
+#epub_tocscope = 'default'
+
+# Fix unsupported image types using the PIL.
+#epub_fix_images = False
+
+# Scale large images.
+#epub_max_image_width = 0
+
+# How to display URL addresses: 'footnote', 'no', or 'inline'.
+#epub_show_urls = 'inline'
+
+# If false, no index is generated.
+#epub_use_index = True
diff --git a/python/docs/epytext.py b/python/docs/epytext.py
new file mode 100644
index 0000000000000..61d731bff570d
--- /dev/null
+++ b/python/docs/epytext.py
@@ -0,0 +1,27 @@
+import re
+
+RULES = (
+    (r"<[\w.]+>", r""),
+    (r"L{([\w.()]+)}", r":class:`\1`"),
+    (r"[LC]{(\w+\.\w+)\(\)}", r":func:`\1`"),
+    (r"C{([\w.()]+)}", r":class:`\1`"),
+    (r"[IBCM]{(.+)}", r"`\1`"),
+    ('pyspark.rdd.RDD', 'RDD'),
+)
+
+def _convert_epytext(line):
+    """
+    >>> _convert_epytext("L{A}")
+    :class:`A`
+    """
+    line = line.replace('@', ':')
+    for p, sub in RULES:
+        line = re.sub(p, sub, line)
+    return line
+
+def _process_docstring(app, what, name, obj, options, lines):
+    for i in range(len(lines)):
+        lines[i] = _convert_epytext(lines[i])
+
+def setup(app):
+    app.connect("autodoc-process-docstring", _process_docstring)
diff --git a/python/docs/index.rst b/python/docs/index.rst
new file mode 100644
index 0000000000000..25b3f9bd93e63
--- /dev/null
+++ b/python/docs/index.rst
@@ -0,0 +1,37 @@
+.. pyspark documentation master file, created by
+   sphinx-quickstart on Thu Aug 28 15:17:47 2014.
+   You can adapt this file completely to your liking, but it should at least
+   contain the root `toctree` directive.
+
+Welcome to PySpark API reference!
+===================================
+
+Contents:
+
+.. toctree::
+   :maxdepth: 2
+
+   pyspark
+   pyspark.sql
+   pyspark.mllib
+
+
+Core classes:
+---------------
+
+    :class:`pyspark.SparkContext`
+
+    Main entry point for Spark functionality.
+
+    :class:`pyspark.RDD`
+    
+    A Resilient Distributed Dataset (RDD), the basic abstraction in Spark.
+
+
+Indices and tables
+==================
+
+* :ref:`genindex`
+* :ref:`modindex`
+* :ref:`search`
+
diff --git a/python/docs/make.bat b/python/docs/make.bat
new file mode 100644
index 0000000000000..adad44fd7536a
--- /dev/null
+++ b/python/docs/make.bat
@@ -0,0 +1,242 @@
+@ECHO OFF
+
+REM Command file for Sphinx documentation
+
+if "%SPHINXBUILD%" == "" (
+	set SPHINXBUILD=sphinx-build
+)
+set BUILDDIR=_build
+set ALLSPHINXOPTS=-d %BUILDDIR%/doctrees %SPHINXOPTS% .
+set I18NSPHINXOPTS=%SPHINXOPTS% .
+if NOT "%PAPER%" == "" (
+	set ALLSPHINXOPTS=-D latex_paper_size=%PAPER% %ALLSPHINXOPTS%
+	set I18NSPHINXOPTS=-D latex_paper_size=%PAPER% %I18NSPHINXOPTS%
+)
+
+if "%1" == "" goto help
+
+if "%1" == "help" (
+	:help
+	echo.Please use `make ^<target^>` where ^<target^> is one of
+	echo.  html       to make standalone HTML files
+	echo.  dirhtml    to make HTML files named index.html in directories
+	echo.  singlehtml to make a single large HTML file
+	echo.  pickle     to make pickle files
+	echo.  json       to make JSON files
+	echo.  htmlhelp   to make HTML files and a HTML help project
+	echo.  qthelp     to make HTML files and a qthelp project
+	echo.  devhelp    to make HTML files and a Devhelp project
+	echo.  epub       to make an epub
+	echo.  latex      to make LaTeX files, you can set PAPER=a4 or PAPER=letter
+	echo.  text       to make text files
+	echo.  man        to make manual pages
+	echo.  texinfo    to make Texinfo files
+	echo.  gettext    to make PO message catalogs
+	echo.  changes    to make an overview over all changed/added/deprecated items
+	echo.  xml        to make Docutils-native XML files
+	echo.  pseudoxml  to make pseudoxml-XML files for display purposes
+	echo.  linkcheck  to check all external links for integrity
+	echo.  doctest    to run all doctests embedded in the documentation if enabled
+	goto end
+)
+
+if "%1" == "clean" (
+	for /d %%i in (%BUILDDIR%\*) do rmdir /q /s %%i
+	del /q /s %BUILDDIR%\*
+	goto end
+)
+
+
+%SPHINXBUILD% 2> nul
+if errorlevel 9009 (
+	echo.
+	echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
+	echo.installed, then set the SPHINXBUILD environment variable to point
+	echo.to the full path of the 'sphinx-build' executable. Alternatively you
+	echo.may add the Sphinx directory to PATH.
+	echo.
+	echo.If you don't have Sphinx installed, grab it from
+	echo.http://sphinx-doc.org/
+	exit /b 1
+)
+
+if "%1" == "html" (
+	%SPHINXBUILD% -b html %ALLSPHINXOPTS% %BUILDDIR%/html
+	if errorlevel 1 exit /b 1
+	echo.
+	echo.Build finished. The HTML pages are in %BUILDDIR%/html.
+	goto end
+)
+
+if "%1" == "dirhtml" (
+	%SPHINXBUILD% -b dirhtml %ALLSPHINXOPTS% %BUILDDIR%/dirhtml
+	if errorlevel 1 exit /b 1
+	echo.
+	echo.Build finished. The HTML pages are in %BUILDDIR%/dirhtml.
+	goto end
+)
+
+if "%1" == "singlehtml" (
+	%SPHINXBUILD% -b singlehtml %ALLSPHINXOPTS% %BUILDDIR%/singlehtml
+	if errorlevel 1 exit /b 1
+	echo.
+	echo.Build finished. The HTML pages are in %BUILDDIR%/singlehtml.
+	goto end
+)
+
+if "%1" == "pickle" (
+	%SPHINXBUILD% -b pickle %ALLSPHINXOPTS% %BUILDDIR%/pickle
+	if errorlevel 1 exit /b 1
+	echo.
+	echo.Build finished; now you can process the pickle files.
+	goto end
+)
+
+if "%1" == "json" (
+	%SPHINXBUILD% -b json %ALLSPHINXOPTS% %BUILDDIR%/json
+	if errorlevel 1 exit /b 1
+	echo.
+	echo.Build finished; now you can process the JSON files.
+	goto end
+)
+
+if "%1" == "htmlhelp" (
+	%SPHINXBUILD% -b htmlhelp %ALLSPHINXOPTS% %BUILDDIR%/htmlhelp
+	if errorlevel 1 exit /b 1
+	echo.
+	echo.Build finished; now you can run HTML Help Workshop with the ^
+.hhp project file in %BUILDDIR%/htmlhelp.
+	goto end
+)
+
+if "%1" == "qthelp" (
+	%SPHINXBUILD% -b qthelp %ALLSPHINXOPTS% %BUILDDIR%/qthelp
+	if errorlevel 1 exit /b 1
+	echo.
+	echo.Build finished; now you can run "qcollectiongenerator" with the ^
+.qhcp project file in %BUILDDIR%/qthelp, like this:
+	echo.^> qcollectiongenerator %BUILDDIR%\qthelp\pyspark.qhcp
+	echo.To view the help file:
+	echo.^> assistant -collectionFile %BUILDDIR%\qthelp\pyspark.ghc
+	goto end
+)
+
+if "%1" == "devhelp" (
+	%SPHINXBUILD% -b devhelp %ALLSPHINXOPTS% %BUILDDIR%/devhelp
+	if errorlevel 1 exit /b 1
+	echo.
+	echo.Build finished.
+	goto end
+)
+
+if "%1" == "epub" (
+	%SPHINXBUILD% -b epub %ALLSPHINXOPTS% %BUILDDIR%/epub
+	if errorlevel 1 exit /b 1
+	echo.
+	echo.Build finished. The epub file is in %BUILDDIR%/epub.
+	goto end
+)
+
+if "%1" == "latex" (
+	%SPHINXBUILD% -b latex %ALLSPHINXOPTS% %BUILDDIR%/latex
+	if errorlevel 1 exit /b 1
+	echo.
+	echo.Build finished; the LaTeX files are in %BUILDDIR%/latex.
+	goto end
+)
+
+if "%1" == "latexpdf" (
+	%SPHINXBUILD% -b latex %ALLSPHINXOPTS% %BUILDDIR%/latex
+	cd %BUILDDIR%/latex
+	make all-pdf
+	cd %BUILDDIR%/..
+	echo.
+	echo.Build finished; the PDF files are in %BUILDDIR%/latex.
+	goto end
+)
+
+if "%1" == "latexpdfja" (
+	%SPHINXBUILD% -b latex %ALLSPHINXOPTS% %BUILDDIR%/latex
+	cd %BUILDDIR%/latex
+	make all-pdf-ja
+	cd %BUILDDIR%/..
+	echo.
+	echo.Build finished; the PDF files are in %BUILDDIR%/latex.
+	goto end
+)
+
+if "%1" == "text" (
+	%SPHINXBUILD% -b text %ALLSPHINXOPTS% %BUILDDIR%/text
+	if errorlevel 1 exit /b 1
+	echo.
+	echo.Build finished. The text files are in %BUILDDIR%/text.
+	goto end
+)
+
+if "%1" == "man" (
+	%SPHINXBUILD% -b man %ALLSPHINXOPTS% %BUILDDIR%/man
+	if errorlevel 1 exit /b 1
+	echo.
+	echo.Build finished. The manual pages are in %BUILDDIR%/man.
+	goto end
+)
+
+if "%1" == "texinfo" (
+	%SPHINXBUILD% -b texinfo %ALLSPHINXOPTS% %BUILDDIR%/texinfo
+	if errorlevel 1 exit /b 1
+	echo.
+	echo.Build finished. The Texinfo files are in %BUILDDIR%/texinfo.
+	goto end
+)
+
+if "%1" == "gettext" (
+	%SPHINXBUILD% -b gettext %I18NSPHINXOPTS% %BUILDDIR%/locale
+	if errorlevel 1 exit /b 1
+	echo.
+	echo.Build finished. The message catalogs are in %BUILDDIR%/locale.
+	goto end
+)
+
+if "%1" == "changes" (
+	%SPHINXBUILD% -b changes %ALLSPHINXOPTS% %BUILDDIR%/changes
+	if errorlevel 1 exit /b 1
+	echo.
+	echo.The overview file is in %BUILDDIR%/changes.
+	goto end
+)
+
+if "%1" == "linkcheck" (
+	%SPHINXBUILD% -b linkcheck %ALLSPHINXOPTS% %BUILDDIR%/linkcheck
+	if errorlevel 1 exit /b 1
+	echo.
+	echo.Link check complete; look for any errors in the above output ^
+or in %BUILDDIR%/linkcheck/output.txt.
+	goto end
+)
+
+if "%1" == "doctest" (
+	%SPHINXBUILD% -b doctest %ALLSPHINXOPTS% %BUILDDIR%/doctest
+	if errorlevel 1 exit /b 1
+	echo.
+	echo.Testing of doctests in the sources finished, look at the ^
+results in %BUILDDIR%/doctest/output.txt.
+	goto end
+)
+
+if "%1" == "xml" (
+	%SPHINXBUILD% -b xml %ALLSPHINXOPTS% %BUILDDIR%/xml
+	if errorlevel 1 exit /b 1
+	echo.
+	echo.Build finished. The XML files are in %BUILDDIR%/xml.
+	goto end
+)
+
+if "%1" == "pseudoxml" (
+	%SPHINXBUILD% -b pseudoxml %ALLSPHINXOPTS% %BUILDDIR%/pseudoxml
+	if errorlevel 1 exit /b 1
+	echo.
+	echo.Build finished. The pseudo-XML files are in %BUILDDIR%/pseudoxml.
+	goto end
+)
+
+:end
diff --git a/python/docs/modules.rst b/python/docs/modules.rst
new file mode 100644
index 0000000000000..183564659fbcf
--- /dev/null
+++ b/python/docs/modules.rst
@@ -0,0 +1,7 @@
+.
+=
+
+.. toctree::
+   :maxdepth: 4
+
+   pyspark
diff --git a/python/docs/pyspark.mllib.rst b/python/docs/pyspark.mllib.rst
new file mode 100644
index 0000000000000..e95d19e97f151
--- /dev/null
+++ b/python/docs/pyspark.mllib.rst
@@ -0,0 +1,77 @@
+pyspark.mllib package
+=====================
+
+Submodules
+----------
+
+pyspark.mllib.classification module
+-----------------------------------
+
+.. automodule:: pyspark.mllib.classification
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+pyspark.mllib.clustering module
+-------------------------------
+
+.. automodule:: pyspark.mllib.clustering
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+pyspark.mllib.linalg module
+---------------------------
+
+.. automodule:: pyspark.mllib.linalg
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+pyspark.mllib.random module
+---------------------------
+
+.. automodule:: pyspark.mllib.random
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+pyspark.mllib.recommendation module
+-----------------------------------
+
+.. automodule:: pyspark.mllib.recommendation
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+pyspark.mllib.regression module
+-------------------------------
+
+.. automodule:: pyspark.mllib.regression
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+pyspark.mllib.stat module
+-------------------------
+
+.. automodule:: pyspark.mllib.stat
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+pyspark.mllib.tree module
+-------------------------
+
+.. automodule:: pyspark.mllib.tree
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+pyspark.mllib.util module
+-------------------------
+
+.. automodule:: pyspark.mllib.util
+    :members:
+    :undoc-members:
+    :show-inheritance:
diff --git a/python/docs/pyspark.rst b/python/docs/pyspark.rst
new file mode 100644
index 0000000000000..a68bd62433085
--- /dev/null
+++ b/python/docs/pyspark.rst
@@ -0,0 +1,18 @@
+pyspark package
+===============
+
+Subpackages
+-----------
+
+.. toctree::
+    :maxdepth: 1
+    
+    pyspark.mllib
+    pyspark.sql
+
+Contents
+--------
+
+.. automodule:: pyspark
+    :members:
+    :undoc-members:
diff --git a/python/docs/pyspark.sql.rst b/python/docs/pyspark.sql.rst
new file mode 100644
index 0000000000000..65b3650ae10ab
--- /dev/null
+++ b/python/docs/pyspark.sql.rst
@@ -0,0 +1,10 @@
+pyspark.sql module
+==================
+
+Module contents
+---------------
+
+.. automodule:: pyspark.sql
+    :members:
+    :undoc-members:
+    :show-inheritance:
diff --git a/python/pyspark/broadcast.py b/python/pyspark/broadcast.py
index 5c7c9cc161dff..f124dc6c07575 100644
--- a/python/pyspark/broadcast.py
+++ b/python/pyspark/broadcast.py
@@ -78,6 +78,9 @@ def value(self):
         return self._value
 
     def unpersist(self, blocking=False):
+        """
+        Delete cached copies of this broadcast on the executors.
+        """
         self._jbroadcast.unpersist(blocking)
         os.unlink(self.path)
 
diff --git a/python/pyspark/context.py b/python/pyspark/context.py
index a33aae87f65e8..a17f2c1203d36 100644
--- a/python/pyspark/context.py
+++ b/python/pyspark/context.py
@@ -53,7 +53,7 @@ class SparkContext(object):
 
     """
     Main entry point for Spark functionality. A SparkContext represents the
-    connection to a Spark cluster, and can be used to create L{RDD}s and
+    connection to a Spark cluster, and can be used to create L{RDD} and
     broadcast variables on that cluster.
     """
 
diff --git a/python/pyspark/serializers.py b/python/pyspark/serializers.py
index ec3c6f055441d..44ac5642836e0 100644
--- a/python/pyspark/serializers.py
+++ b/python/pyspark/serializers.py
@@ -110,6 +110,9 @@ def __eq__(self, other):
     def __ne__(self, other):
         return not self.__eq__(other)
 
+    def __repr__(self):
+        return "<%s object>" % self.__class__.__name__
+
 
 class FramedSerializer(Serializer):
 
diff --git a/python/pyspark/sql.py b/python/pyspark/sql.py
index 621a556ec6356..8f6dbab240c7b 100644
--- a/python/pyspark/sql.py
+++ b/python/pyspark/sql.py
@@ -289,7 +289,7 @@ class StructType(DataType):
     """Spark SQL StructType
 
     The data type representing rows.
-    A StructType object comprises a list of L{StructField}s.
+    A StructType object comprises a list of L{StructField}.
 
     """
 
@@ -904,7 +904,7 @@ class SQLContext(object):
 
     """Main entry point for Spark SQL functionality.
 
-    A SQLContext can be used create L{SchemaRDD}s, register L{SchemaRDD}s as
+    A SQLContext can be used create L{SchemaRDD}, register L{SchemaRDD} as
     tables, execute SQL over tables, cache tables, and read parquet files.
     """
 
@@ -994,7 +994,7 @@ def registerFunction(self, name, f, returnType=StringType()):
                                       str(returnType))
 
     def inferSchema(self, rdd):
-        """Infer and apply a schema to an RDD of L{Row}s.
+        """Infer and apply a schema to an RDD of L{Row}.
 
         We peek at the first row of the RDD to determine the fields' names
         and types. Nested collections are supported, which include array,
@@ -1047,7 +1047,7 @@ def inferSchema(self, rdd):
 
     def applySchema(self, rdd, schema):
         """
-        Applies the given schema to the given RDD of L{tuple} or L{list}s.
+        Applies the given schema to the given RDD of L{tuple} or L{list}.
 
         These tuples or lists can contain complex nested structures like
         lists, maps or nested rows.
@@ -1183,6 +1183,7 @@ def jsonFile(self, path, schema=None):
         Row(f1=1, f2=u'row1', f3=Row(field4=11, field5=None), f4=None)
         Row(f1=2, f2=None, f3=Row(field4=22,..., f4=[Row(field7=u'row2')])
         Row(f1=None, f2=u'row3', f3=Row(field4=33, field5=[]), f4=None)
+
         >>> srdd3 = sqlCtx.jsonFile(jsonFile, srdd1.schema())
         >>> sqlCtx.registerRDDAsTable(srdd3, "table2")
         >>> srdd4 = sqlCtx.sql(
@@ -1193,6 +1194,7 @@ def jsonFile(self, path, schema=None):
         Row(f1=1, f2=u'row1', f3=Row(field4=11, field5=None), f4=None)
         Row(f1=2, f2=None, f3=Row(field4=22,..., f4=[Row(field7=u'row2')])
         Row(f1=None, f2=u'row3', f3=Row(field4=33, field5=[]), f4=None)
+
         >>> schema = StructType([
         ...     StructField("field2", StringType(), True),
         ...     StructField("field3",
@@ -1233,6 +1235,7 @@ def jsonRDD(self, rdd, schema=None):
         Row(f1=1, f2=u'row1', f3=Row(field4=11, field5=None), f4=None)
         Row(f1=2, f2=None, f3=Row(field4=22..., f4=[Row(field7=u'row2')])
         Row(f1=None, f2=u'row3', f3=Row(field4=33, field5=[]), f4=None)
+
         >>> srdd3 = sqlCtx.jsonRDD(json, srdd1.schema())
         >>> sqlCtx.registerRDDAsTable(srdd3, "table2")
         >>> srdd4 = sqlCtx.sql(
@@ -1243,6 +1246,7 @@ def jsonRDD(self, rdd, schema=None):
         Row(f1=1, f2=u'row1', f3=Row(field4=11, field5=None), f4=None)
         Row(f1=2, f2=None, f3=Row(field4=22..., f4=[Row(field7=u'row2')])
         Row(f1=None, f2=u'row3', f3=Row(field4=33, field5=[]), f4=None)
+
         >>> schema = StructType([
         ...     StructField("field2", StringType(), True),
         ...     StructField("field3",

From b20171267d610715d5b0a86b474c903e9bc3a1a3 Mon Sep 17 00:00:00 2001
From: Dan Osipov <daniil.osipov@shazam.com>
Date: Tue, 16 Sep 2014 13:40:16 -0700
Subject: [PATCH 322/489] [SPARK-787] Add S3 configuration parameters to the
 EC2 deploy scripts

When deploying to AWS, there is additional configuration that is required to read S3 files. EMR creates it automatically, there is no reason that the Spark EC2 script shouldn't.

This PR requires a corresponding PR to the mesos/spark-ec2 to be merged, as it gets cloned in the process of setting up machines: https://github.com/mesos/spark-ec2/pull/58

Author: Dan Osipov <daniil.osipov@shazam.com>

Closes #1120 from danosipov/s3_credentials and squashes the following commits:

758da8b [Dan Osipov] Modify documentation to include the new parameter
71fab14 [Dan Osipov] Use a parameter --copy-aws-credentials to enable S3 credential deployment
7e0da26 [Dan Osipov] Get AWS credentials out of boto connection instance
39bdf30 [Dan Osipov] Add S3 configuration parameters to the EC2 deploy scripts
---
 docs/ec2-scripts.md                                |  2 +-
 ec2/deploy.generic/root/spark-ec2/ec2-variables.sh |  2 ++
 ec2/spark_ec2.py                                   | 10 ++++++++++
 3 files changed, 13 insertions(+), 1 deletion(-)

diff --git a/docs/ec2-scripts.md b/docs/ec2-scripts.md
index f5ac6d894e1eb..b2ca6a9b48f32 100644
--- a/docs/ec2-scripts.md
+++ b/docs/ec2-scripts.md
@@ -156,6 +156,6 @@ If you have a patch or suggestion for one of these limitations, feel free to
 
 # Accessing Data in S3
 
-Spark's file interface allows it to process data in Amazon S3 using the same URI formats that are supported for Hadoop. You can specify a path in S3 as input through a URI of the form `s3n://<bucket>/path`. You will also need to set your Amazon security credentials, either by setting the environment variables `AWS_ACCESS_KEY_ID` and `AWS_SECRET_ACCESS_KEY` before your program or through `SparkContext.hadoopConfiguration`. Full instructions on S3 access using the Hadoop input libraries can be found on the [Hadoop S3 page](http://wiki.apache.org/hadoop/AmazonS3).
+Spark's file interface allows it to process data in Amazon S3 using the same URI formats that are supported for Hadoop. You can specify a path in S3 as input through a URI of the form `s3n://<bucket>/path`. To provide AWS credentials for S3 access, launch the Spark cluster with the option `--copy-aws-credentials`. Full instructions on S3 access using the Hadoop input libraries can be found on the [Hadoop S3 page](http://wiki.apache.org/hadoop/AmazonS3).
 
 In addition to using a single input file, you can also use a directory of files as input by simply giving the path to the directory.
diff --git a/ec2/deploy.generic/root/spark-ec2/ec2-variables.sh b/ec2/deploy.generic/root/spark-ec2/ec2-variables.sh
index 3570891be804e..740c267fd9866 100644
--- a/ec2/deploy.generic/root/spark-ec2/ec2-variables.sh
+++ b/ec2/deploy.generic/root/spark-ec2/ec2-variables.sh
@@ -30,3 +30,5 @@ export HADOOP_MAJOR_VERSION="{{hadoop_major_version}}"
 export SWAP_MB="{{swap}}"
 export SPARK_WORKER_INSTANCES="{{spark_worker_instances}}"
 export SPARK_MASTER_OPTS="{{spark_master_opts}}"
+export AWS_ACCESS_KEY_ID="{{aws_access_key_id}}"
+export AWS_SECRET_ACCESS_KEY="{{aws_secret_access_key}}"
\ No newline at end of file
diff --git a/ec2/spark_ec2.py b/ec2/spark_ec2.py
index 5682e96aa8770..abac71eaca595 100755
--- a/ec2/spark_ec2.py
+++ b/ec2/spark_ec2.py
@@ -158,6 +158,9 @@ def parse_args():
     parser.add_option(
         "--additional-security-group", type="string", default="",
         help="Additional security group to place the machines in")
+    parser.add_option(
+        "--copy-aws-credentials", action="store_true", default=False,
+        help="Add AWS credentials to hadoop configuration to allow Spark to access S3")
 
     (opts, args) = parser.parse_args()
     if len(args) != 2:
@@ -714,6 +717,13 @@ def deploy_files(conn, root_dir, opts, master_nodes, slave_nodes, modules):
         "spark_master_opts": opts.master_opts
     }
 
+    if opts.copy_aws_credentials:
+        template_vars["aws_access_key_id"] = conn.aws_access_key_id
+        template_vars["aws_secret_access_key"] = conn.aws_secret_access_key
+    else:
+        template_vars["aws_access_key_id"] = ""
+        template_vars["aws_secret_access_key"] = ""
+
     # Create a temp directory in which we will place all the files to be
     # deployed after we substitue template parameters in them
     tmp_dir = tempfile.mkdtemp()

From a6e1712f1e9c36deb24c5073aa8edcfc047d76eb Mon Sep 17 00:00:00 2001
From: Evan Chan <velvia@gmail.com>
Date: Tue, 16 Sep 2014 13:46:06 -0700
Subject: [PATCH 323/489] Add a Community Projects page

This adds a new page to the docs listing community projects -- those created outside of Apache Spark that are of interest to the community of Spark users.   Anybody can add to it just by submitting a PR.

There was a discussion thread about alternatives:
* Creating a Github organization for Spark projects -  we could not find any sponsors for this, and it would be difficult to organize since many folks just create repos in their company organization or personal accounts
* Apache has some place for storing community projects, but it was deemed difficult to work with, and again would be some permissions issues -- not everyone could update it.

Author: Evan Chan <velvia@gmail.com>

Closes #2219 from velvia/community-projects-page and squashes the following commits:

7316822 [Evan Chan] Point to Spark wiki: supplemental projects page
613b021 [Evan Chan] Add a few more projects
a85eaaf [Evan Chan] Add a Community Projects page
---
 docs/_layouts/global.html | 3 ++-
 docs/index.md             | 1 +
 2 files changed, 3 insertions(+), 1 deletion(-)

diff --git a/docs/_layouts/global.html b/docs/_layouts/global.html
index a53e8a775b71f..627ed37de4a9c 100755
--- a/docs/_layouts/global.html
+++ b/docs/_layouts/global.html
@@ -111,6 +111,7 @@
                                 <li class="divider"></li>
                                 <li><a href="building-spark.html">Building Spark</a></li>
                                 <li><a href="https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark">Contributing to Spark</a></li>
+                                <li><a href="https://cwiki.apache.org/confluence/display/SPARK/Supplemental+Spark+Projects">Supplemental Projects</a></li>
                             </ul>
                         </li>
                     </ul>
@@ -151,7 +152,7 @@ <h1 class="title">{{ page.title }}</h1>
                     MathJax.Hub.Config({
                         tex2jax: {
                             inlineMath: [ ["$", "$"], ["\\\\(","\\\\)"] ],
-                            displayMath: [ ["$$","$$"], ["\\[", "\\]"] ], 
+                            displayMath: [ ["$$","$$"], ["\\[", "\\]"] ],
                             processEscapes: true,
                             skipTags: ['script', 'noscript', 'style', 'textarea', 'pre']
                         }
diff --git a/docs/index.md b/docs/index.md
index e8ebadbd4e427..edd622ec90f64 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -107,6 +107,7 @@ options for deployment:
   * [OpenStack Swift](storage-openstack-swift.html)
 * [Building Spark](building-spark.html): build Spark using the Maven system
 * [Contributing to Spark](https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark)
+* [Supplemental Projects](https://cwiki.apache.org/confluence/display/SPARK/Supplemental+Spark+Projects): related third party Spark projects
 
 **External Resources:**
 

From 0a7091e689a4c8b1e7b61e9f0873e6557f40d952 Mon Sep 17 00:00:00 2001
From: Andrew Or <andrewor14@gmail.com>
Date: Tue, 16 Sep 2014 16:03:20 -0700
Subject: [PATCH 324/489] [SPARK-3555] Fix UISuite race condition

The test "jetty selects different port under contention" is flaky.

If another process binds to 4040 before the test starts, then the first server we start there will fail, and the subsequent servers we start thereafter may successfully bind to 4040 if it was released between the servers starting. Instead, we should just let Java find a random free port for us and hold onto it for the duration of the test.

Author: Andrew Or <andrewor14@gmail.com>

Closes #2418 from andrewor14/fix-port-contention and squashes the following commits:

0cd4974 [Andrew Or] Stop them servers
a7071fe [Andrew Or] Pick random port instead of 4040
---
 .../test/scala/org/apache/spark/ui/UISuite.scala   | 14 +++++---------
 1 file changed, 5 insertions(+), 9 deletions(-)

diff --git a/core/src/test/scala/org/apache/spark/ui/UISuite.scala b/core/src/test/scala/org/apache/spark/ui/UISuite.scala
index 48790b59e7fbd..92a21f82f3c21 100644
--- a/core/src/test/scala/org/apache/spark/ui/UISuite.scala
+++ b/core/src/test/scala/org/apache/spark/ui/UISuite.scala
@@ -23,7 +23,6 @@ import javax.servlet.http.HttpServletRequest
 import scala.io.Source
 import scala.util.{Failure, Success, Try}
 
-import org.eclipse.jetty.server.Server
 import org.eclipse.jetty.servlet.ServletContextHandler
 import org.scalatest.FunSuite
 import org.scalatest.concurrent.Eventually._
@@ -108,14 +107,8 @@ class UISuite extends FunSuite {
   }
 
   test("jetty selects different port under contention") {
-    val startPort = 4040
-    val server = new Server(startPort)
-
-    Try { server.start() } match {
-      case Success(s) =>
-      case Failure(e) =>
-      // Either case server port is busy hence setup for test complete
-    }
+    val server = new ServerSocket(0)
+    val startPort = server.getLocalPort
     val serverInfo1 = JettyUtils.startJettyServer(
       "0.0.0.0", startPort, Seq[ServletContextHandler](), new SparkConf)
     val serverInfo2 = JettyUtils.startJettyServer(
@@ -126,6 +119,9 @@ class UISuite extends FunSuite {
     assert(boundPort1 != startPort)
     assert(boundPort2 != startPort)
     assert(boundPort1 != boundPort2)
+    serverInfo1.server.stop()
+    serverInfo2.server.stop()
+    server.close()
   }
 
   test("jetty binds to port 0 correctly") {

From 008a5ed4808d1467b47c1d6fa4d950cc6c4976b7 Mon Sep 17 00:00:00 2001
From: wangfei <wangfei1@huawei.com>
Date: Tue, 16 Sep 2014 21:57:33 -0700
Subject: [PATCH 325/489] [Minor]ignore all config files in conf

Some config files in ```conf``` should ignore, such as
        conf/fairscheduler.xml
        conf/hive-log4j.properties
        conf/metrics.properties
...
So ignore all ```sh```/```properties```/```conf```/```xml``` files

Author: wangfei <wangfei1@huawei.com>

Closes #2395 from scwf/patch-2 and squashes the following commits:

3dc53f2 [wangfei] duplicate ```conf/*.conf```
3c2986f [wangfei] ignore all config files
---
 .gitignore | 10 ++++------
 1 file changed, 4 insertions(+), 6 deletions(-)

diff --git a/.gitignore b/.gitignore
index 7ec8d45e12c6b..a31bf7e0091f4 100644
--- a/.gitignore
+++ b/.gitignore
@@ -15,11 +15,10 @@ out/
 third_party/libmesos.so
 third_party/libmesos.dylib
 conf/java-opts
-conf/spark-env.sh
-conf/streaming-env.sh
-conf/log4j.properties
-conf/spark-defaults.conf
-conf/hive-site.xml
+conf/*.sh
+conf/*.properties
+conf/*.conf
+conf/*.xml
 docs/_site
 docs/api
 target/
@@ -50,7 +49,6 @@ unit-tests.log
 /lib/
 rat-results.txt
 scalastyle.txt
-conf/*.conf
 scalastyle-output.xml
 
 # For Hive

From 983609a4dd83e25598455bfce93fa1c1fa9f2c51 Mon Sep 17 00:00:00 2001
From: viper-kun <xukun.xu@huawei.com>
Date: Wed, 17 Sep 2014 00:09:57 -0700
Subject: [PATCH 326/489] [Docs] Correct spark.files.fetchTimeout default value

change the value of spark.files.fetchTimeout

Author: viper-kun <xukun.xu@huawei.com>

Closes #2406 from viper-kun/master and squashes the following commits:

ecb0d46 [viper-kun] [Docs] Correct spark.files.fetchTimeout default value
7cf4c7a [viper-kun] Update configuration.md
---
 docs/configuration.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/configuration.md b/docs/configuration.md
index af16489a44281..99faf51c6f3db 100644
--- a/docs/configuration.md
+++ b/docs/configuration.md
@@ -520,10 +520,10 @@ Apart from these, the following properties are also available, and may be useful
 </tr>
 <tr>
   <td><code>spark.files.fetchTimeout</code></td>
-  <td>false</td>
+  <td>60</td>
   <td>
     Communication timeout to use when fetching files added through SparkContext.addFile() from
-    the driver.
+    the driver, in seconds.
   </td>
 </tr>
 <tr>

From 7d1a37239c50394025d9f16acf5dcd05cfbe7250 Mon Sep 17 00:00:00 2001
From: chesterxgchen <chester@alpinenow.com>
Date: Wed, 17 Sep 2014 10:25:52 -0500
Subject: [PATCH 327/489] SPARK-3177 (on Master Branch)

The JIRA and PR was original created for branch-1.1, and move to master branch now.
Chester

The Issue is due to that yarn-alpha and yarn have different APIs for certain class fields. In this particular case,  the ClientBase using reflection to to address this issue, and we need to different way to test the ClientBase's method.  Original ClientBaseSuite using getFieldValue() method to do this. But it doesn't work for yarn-alpha as the API returns an array of String instead of just String (which is the case for Yarn-stable API).

 To fix the test, I add a new method

  def getFieldValue2[A: ClassTag, A1: ClassTag, B](clazz: Class[_], field: String,
                                                      defaults: => B)
                              (mapTo:  A => B)(mapTo1: A1 => B) : B =
    Try(clazz.getField(field)).map(_.get(null)).map {
      case v: A => mapTo(v)
      case v1: A1 => mapTo1(v1)
      case _ => defaults
    }.toOption.getOrElse(defaults)

to handle the cases where the field type can be either type A or A1. In this new method the type A or A1 is pattern matched and corresponding mapTo function (mapTo or mapTo1) is used.

Author: chesterxgchen <chester@alpinenow.com>

Closes #2204 from chesterxgchen/SPARK-3177-master and squashes the following commits:

e72a6ea [chesterxgchen]  The Issue is due to that yarn-alpha and yarn have different APIs for certain class fields. In this particular case,  the ClientBase using reflection to to address this issue, and we need to different way to test the ClientBase's method.  Original ClientBaseSuite using getFieldValue() method to do this. But it doesn't work for yarn-alpha as the API returns an array of String instead of just String (which is the case for Yarn-stable API).
---
 .../spark/deploy/yarn/ClientBaseSuite.scala   | 19 ++++++++++++++++---
 1 file changed, 16 insertions(+), 3 deletions(-)

diff --git a/yarn/common/src/test/scala/org/apache/spark/deploy/yarn/ClientBaseSuite.scala b/yarn/common/src/test/scala/org/apache/spark/deploy/yarn/ClientBaseSuite.scala
index 5480eca7c832c..c3b7a2c8f02e5 100644
--- a/yarn/common/src/test/scala/org/apache/spark/deploy/yarn/ClientBaseSuite.scala
+++ b/yarn/common/src/test/scala/org/apache/spark/deploy/yarn/ClientBaseSuite.scala
@@ -38,6 +38,7 @@ import org.scalatest.Matchers
 
 import scala.collection.JavaConversions._
 import scala.collection.mutable.{ HashMap => MutableHashMap }
+import scala.reflect.ClassTag
 import scala.util.Try
 
 import org.apache.spark.{SparkException, SparkConf}
@@ -200,9 +201,10 @@ class ClientBaseSuite extends FunSuite with Matchers {
 
 
     val knownDefMRAppCP: Seq[String] =
-      getFieldValue[String, Seq[String]](classOf[MRJobConfig],
-                                         "DEFAULT_MAPREDUCE_APPLICATION_CLASSPATH",
-                                         Seq[String]())(a => a.split(","))
+      getFieldValue2[String, Array[String], Seq[String]](
+        classOf[MRJobConfig],
+        "DEFAULT_MAPREDUCE_APPLICATION_CLASSPATH",
+        Seq[String]())(a => a.split(","))(a => a.toSeq)
 
     val knownYARNAppCP = Some(Seq("/known/yarn/path"))
 
@@ -232,6 +234,17 @@ class ClientBaseSuite extends FunSuite with Matchers {
   def getFieldValue[A, B](clazz: Class[_], field: String, defaults: => B)(mapTo: A => B): B =
     Try(clazz.getField(field)).map(_.get(null).asInstanceOf[A]).toOption.map(mapTo).getOrElse(defaults)
 
+  def getFieldValue2[A: ClassTag, A1: ClassTag, B](
+        clazz: Class[_],
+        field: String,
+        defaults: => B)(mapTo:  A => B)(mapTo1: A1 => B) : B = {
+    Try(clazz.getField(field)).map(_.get(null)).map {
+      case v: A => mapTo(v)
+      case v1: A1 => mapTo1(v1)
+      case _ => defaults
+    }.toOption.getOrElse(defaults)
+  }
+
   private class DummyClient(
       val args: ClientArguments,
       val conf: Configuration,

From 8fbd5f4a90f92e064aa057adbd3f8c58dd0087fa Mon Sep 17 00:00:00 2001
From: Nicholas Chammas <nicholas.chammas@gmail.com>
Date: Wed, 17 Sep 2014 12:33:09 -0700
Subject: [PATCH 328/489] [Docs] minor grammar fix

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

Closes #2430 from nchammas/patch-2 and squashes the following commits:

d476bfb [Nicholas Chammas] [Docs] minor grammar fix
---
 CONTRIBUTING.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index c6b4aa5344757..b6c6b050fa331 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -8,5 +8,5 @@ submitting any copyrighted material via pull request, email, or other means
 you agree to license the material under the project's open source license and
 warrant that you have the legal authority to do so.
 
-Please see [Contributing to Spark wiki page](https://cwiki.apache.org/SPARK/Contributing+to+Spark)
+Please see the [Contributing to Spark wiki page](https://cwiki.apache.org/SPARK/Contributing+to+Spark)
 for more information.

From cbf983bb4a550ff26756ed7308fb03db42cffcff Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Wed, 17 Sep 2014 12:41:49 -0700
Subject: [PATCH 329/489] [SQL][DOCS] Improve table caching section

Author: Michael Armbrust <michael@databricks.com>

Closes #2434 from marmbrus/patch-1 and squashes the following commits:

67215be [Michael Armbrust] [SQL][DOCS] Improve table caching section
---
 docs/sql-programming-guide.md | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md
index c498b41c43380..5212e19c41349 100644
--- a/docs/sql-programming-guide.md
+++ b/docs/sql-programming-guide.md
@@ -801,12 +801,12 @@ turning on some experimental options.
 
 ## Caching Data In Memory
 
-Spark SQL can cache tables using an in-memory columnar format by calling `cacheTable("tableName")`.
+Spark SQL can cache tables using an in-memory columnar format by calling `sqlContext.cacheTable("tableName")`.
 Then Spark SQL will scan only required columns and will automatically tune compression to minimize
-memory usage and GC pressure. You can call `uncacheTable("tableName")` to remove the table from memory.
+memory usage and GC pressure. You can call `sqlContext.uncacheTable("tableName")` to remove the table from memory.
 
-Note that if you call `cache` rather than `cacheTable`, tables will _not_ be cached using
-the in-memory columnar format, and therefore `cacheTable` is strongly recommended for this use case.
+Note that if you call `schemaRDD.cache()` rather than `sqlContext.cacheTable(...)`, tables will _not_ be cached using
+the in-memory columnar format, and therefore `sqlContext.cacheTable(...)` is strongly recommended for this use case.
 
 Configuration of in-memory caching can be done using the `setConf` method on SQLContext or by running
 `SET key=value` commands using SQL.

From 5044e4953a1744593d83fe90628fb4893e5463f1 Mon Sep 17 00:00:00 2001
From: Nicholas Chammas <nicholas.chammas@gmail.com>
Date: Wed, 17 Sep 2014 12:44:44 -0700
Subject: [PATCH 330/489] [SPARK-1455] [SPARK-3534] [Build] When possible, run
 SQL tests only.

If the only files changed are related to SQL, then only run the SQL tests.

This patch includes some cosmetic/maintainability refactoring. I would be more than happy to undo some of these changes if they are inappropriate.

We can accept this patch mostly as-is and address the immediate need documented in [SPARK-3534](https://issues.apache.org/jira/browse/SPARK-3534), or we can keep it open until a satisfactory solution along the lines [discussed here](https://issues.apache.org/jira/browse/SPARK-1455?focusedCommentId=14136424&page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel#comment-14136424) is reached.

Note: I had to hack this patch up to test it locally, so what I'm submitting here and what I tested are technically different.

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

Closes #2420 from nchammas/selective-testing and squashes the following commits:

db3fa2d [Nicholas Chammas] diff against master!
f9e23f6 [Nicholas Chammas] when possible, run SQL tests only
---
 dev/run-tests | 156 ++++++++++++++++++++++++++++++++++----------------
 1 file changed, 106 insertions(+), 50 deletions(-)

diff --git a/dev/run-tests b/dev/run-tests
index 79401213a7fa2..53148d23f385f 100755
--- a/dev/run-tests
+++ b/dev/run-tests
@@ -21,44 +21,73 @@
 FWDIR="$(cd "`dirname $0`"/..; pwd)"
 cd "$FWDIR"
 
-if [ -n "$AMPLAB_JENKINS_BUILD_PROFILE" ]; then
-  if [ "$AMPLAB_JENKINS_BUILD_PROFILE" = "hadoop1.0" ]; then
-    export SBT_MAVEN_PROFILES_ARGS="-Dhadoop.version=1.0.4"
-  elif [ "$AMPLAB_JENKINS_BUILD_PROFILE" = "hadoop2.0" ]; then
-    export SBT_MAVEN_PROFILES_ARGS="-Dhadoop.version=2.0.0-mr1-cdh4.1.1"
-  elif [ "$AMPLAB_JENKINS_BUILD_PROFILE" = "hadoop2.2" ]; then
-    export SBT_MAVEN_PROFILES_ARGS="-Pyarn -Dhadoop.version=2.2.0"
-  elif [ "$AMPLAB_JENKINS_BUILD_PROFILE" = "hadoop2.3" ]; then
-    export SBT_MAVEN_PROFILES_ARGS="-Pyarn -Phadoop-2.3 -Dhadoop.version=2.3.0"
+# Remove work directory
+rm -rf ./work
+
+# Build against the right verison of Hadoop.
+{
+  if [ -n "$AMPLAB_JENKINS_BUILD_PROFILE" ]; then
+    if [ "$AMPLAB_JENKINS_BUILD_PROFILE" = "hadoop1.0" ]; then
+      export SBT_MAVEN_PROFILES_ARGS="-Dhadoop.version=1.0.4"
+    elif [ "$AMPLAB_JENKINS_BUILD_PROFILE" = "hadoop2.0" ]; then
+      export SBT_MAVEN_PROFILES_ARGS="-Dhadoop.version=2.0.0-mr1-cdh4.1.1"
+    elif [ "$AMPLAB_JENKINS_BUILD_PROFILE" = "hadoop2.2" ]; then
+      export SBT_MAVEN_PROFILES_ARGS="-Pyarn -Dhadoop.version=2.2.0"
+    elif [ "$AMPLAB_JENKINS_BUILD_PROFILE" = "hadoop2.3" ]; then
+      export SBT_MAVEN_PROFILES_ARGS="-Pyarn -Phadoop-2.3 -Dhadoop.version=2.3.0"
+    fi
   fi
-fi
 
-if [ -z "$SBT_MAVEN_PROFILES_ARGS" ]; then
-  export SBT_MAVEN_PROFILES_ARGS="-Pyarn -Phadoop-2.3 -Dhadoop.version=2.3.0"
-fi
+  if [ -z "$SBT_MAVEN_PROFILES_ARGS" ]; then
+    export SBT_MAVEN_PROFILES_ARGS="-Pyarn -Phadoop-2.3 -Dhadoop.version=2.3.0"
+  fi
+}
 
 export SBT_MAVEN_PROFILES_ARGS="$SBT_MAVEN_PROFILES_ARGS -Pkinesis-asl"
 
-echo "SBT_MAVEN_PROFILES_ARGS=\"$SBT_MAVEN_PROFILES_ARGS\""
-
-# Remove work directory
-rm -rf ./work
-
-if test -x "$JAVA_HOME/bin/java"; then
-    declare java_cmd="$JAVA_HOME/bin/java"
-else 
-    declare java_cmd=java
-fi
-JAVA_VERSION=$($java_cmd -version 2>&1 | sed 's/java version "\(.*\)\.\(.*\)\..*"/\1\2/; 1q')
-[ "$JAVA_VERSION" -ge 18 ] && echo "" || echo "[Warn] Java 8 tests will not run because JDK version is < 1.8."
+# Determine Java path and version.
+{
+  if test -x "$JAVA_HOME/bin/java"; then
+      declare java_cmd="$JAVA_HOME/bin/java"
+  else 
+      declare java_cmd=java
+  fi
+  
+  # We can't use sed -r -e due to OS X / BSD compatibility; hence, all the parentheses.
+  JAVA_VERSION=$(
+    $java_cmd -version 2>&1 \
+    | grep -e "^java version" --max-count=1 \
+    | sed "s/java version \"\(.*\)\.\(.*\)\.\(.*\)\"/\1\2/"
+  )
+  
+  if [ "$JAVA_VERSION" -lt 18 ]; then
+    echo "[warn] Java 8 tests will not run because JDK version is < 1.8."
+  fi
+}
 
-# Partial solution for SPARK-1455. Only run Hive tests if there are sql changes.
+# Only run Hive tests if there are sql changes.
+# Partial solution for SPARK-1455.
 if [ -n "$AMPLAB_JENKINS" ]; then
   git fetch origin master:master
-  diffs=`git diff --name-only master | grep "^\(sql/\)\|\(bin/spark-sql\)\|\(sbin/start-thriftserver.sh\)"`
-  if [ -n "$diffs" ]; then
-    echo "Detected changes in SQL. Will run Hive test suite."
+  
+  sql_diffs=$(
+    git diff --name-only master \
+    | grep -e "^sql/" -e "^bin/spark-sql" -e "^sbin/start-thriftserver.sh"
+  )
+
+  non_sql_diffs=$(
+    git diff --name-only master \
+    | grep -v -e "^sql/" -e "^bin/spark-sql" -e "^sbin/start-thriftserver.sh"
+  )
+
+  if [ -n "$sql_diffs" ]; then
+    echo "[info] Detected changes in SQL. Will run Hive test suite."
     _RUN_SQL_TESTS=true
+    
+    if [ -z "$non_sql_diffs" ]; then
+      echo "[info] Detected no changes except in SQL. Will only run SQL tests."
+      _SQL_TESTS_ONLY=true
+    fi
   fi
 fi
 
@@ -70,42 +99,69 @@ echo ""
 echo "========================================================================="
 echo "Running Apache RAT checks"
 echo "========================================================================="
-dev/check-license
+./dev/check-license
 
 echo ""
 echo "========================================================================="
 echo "Running Scala style checks"
 echo "========================================================================="
-dev/lint-scala
+./dev/lint-scala
 
 echo ""
 echo "========================================================================="
 echo "Running Python style checks"
 echo "========================================================================="
-dev/lint-python
+./dev/lint-python
+
+echo ""
+echo "========================================================================="
+echo "Building Spark"
+echo "========================================================================="
+
+{
+  # We always build with Hive because the PySpark Spark SQL tests need it.
+  BUILD_MVN_PROFILE_ARGS="$SBT_MAVEN_PROFILES_ARGS -Phive"
+
+  echo "[info] Building Spark with these arguments: $BUILD_MVN_PROFILE_ARGS"
+
+  # NOTE: echo "q" is needed because sbt on encountering a build file with failure
+  #+ (either resolution or compilation) prompts the user for input either q, r, etc
+  #+ to quit or retry. This echo is there to make it not block.
+  # QUESTION: Why doesn't 'yes "q"' work?
+  # QUESTION: Why doesn't 'grep -v -e "^\[info\] Resolving"' work?
+  echo -e "q\n" \
+    | sbt/sbt $BUILD_MVN_PROFILE_ARGS clean package assembly/assembly \
+    | grep -v -e "info.*Resolving" -e "warn.*Merging" -e "info.*Including"
+}
 
 echo ""
 echo "========================================================================="
 echo "Running Spark unit tests"
 echo "========================================================================="
 
-# Build Spark; we always build with Hive because the PySpark Spark SQL tests need it.
-# echo "q" is needed because sbt on encountering a build file with failure
-# (either resolution or compilation) prompts the user for input either q, r,
-# etc to quit or retry. This echo is there to make it not block.
-BUILD_MVN_PROFILE_ARGS="$SBT_MAVEN_PROFILES_ARGS -Phive "
-echo -e "q\n" | sbt/sbt $BUILD_MVN_PROFILE_ARGS clean package assembly/assembly | \
-  grep -v -e "info.*Resolving" -e "warn.*Merging" -e "info.*Including"
-
-# If the Spark SQL tests are enabled, run the tests with the Hive profiles enabled:
-if [ -n "$_RUN_SQL_TESTS" ]; then
-  SBT_MAVEN_PROFILES_ARGS="$SBT_MAVEN_PROFILES_ARGS -Phive"
-fi
-# echo "q" is needed because sbt on encountering a build file with failure
-# (either resolution or compilation) prompts the user for input either q, r,
-# etc to quit or retry. This echo is there to make it not block.
-echo -e "q\n" | sbt/sbt $SBT_MAVEN_PROFILES_ARGS test | \
-  grep -v -e "info.*Resolving" -e "warn.*Merging" -e "info.*Including"
+{
+  # If the Spark SQL tests are enabled, run the tests with the Hive profiles enabled.
+  if [ -n "$_RUN_SQL_TESTS" ]; then
+    SBT_MAVEN_PROFILES_ARGS="$SBT_MAVEN_PROFILES_ARGS -Phive"
+  fi
+  
+  if [ -n "$_SQL_TESTS_ONLY" ]; then
+    SBT_MAVEN_TEST_ARGS="catalyst/test sql/test hive/test"
+  else
+    SBT_MAVEN_TEST_ARGS="test"
+  fi
+  
+  echo "[info] Running Spark tests with these arguments: $SBT_MAVEN_PROFILES_ARGS $SBT_MAVEN_TEST_ARGS"
+  
+  # NOTE: echo "q" is needed because sbt on encountering a build file with failure
+  #+ (either resolution or compilation) prompts the user for input either q, r, etc
+  #+ to quit or retry. This echo is there to make it not block.
+  # QUESTION: Why doesn't 'yes "q"' work?
+  # QUESTION: Why doesn't 'grep -v -e "^\[info\] Resolving"' work?
+  echo -e "q\n" \
+    | sbt/sbt "$SBT_MAVEN_PROFILES_ARGS" "$SBT_MAVEN_TEST_ARGS" \
+    | grep -v -e "info.*Resolving" -e "warn.*Merging" -e "info.*Including"
+}
 
 echo ""
 echo "========================================================================="
@@ -117,4 +173,4 @@ echo ""
 echo "========================================================================="
 echo "Detecting binary incompatibilites with MiMa"
 echo "========================================================================="
-dev/mima
+./dev/mima

From b3830b28f8a70224d87c89d8491c514c4c191d23 Mon Sep 17 00:00:00 2001
From: Andrew Ash <andrew@andrewash.com>
Date: Wed, 17 Sep 2014 15:07:57 -0700
Subject: [PATCH 331/489] Docs: move HA subsections to a deeper indentation
 level

Makes the table of contents read better

Author: Andrew Ash <andrew@andrewash.com>

Closes #2402 from ash211/docs/better-indentation and squashes the following commits:

ea0e130 [Andrew Ash] Move HA subsections to a deeper indentation level
---
 docs/spark-standalone.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/spark-standalone.md b/docs/spark-standalone.md
index c791c81f8bfd0..99a8e43a6b489 100644
--- a/docs/spark-standalone.md
+++ b/docs/spark-standalone.md
@@ -307,7 +307,7 @@ tight firewall settings. For a complete list of ports to configure, see the
 
 By default, standalone scheduling clusters are resilient to Worker failures (insofar as Spark itself is resilient to losing work by moving it to other workers). However, the scheduler uses a Master to make scheduling decisions, and this (by default) creates a single point of failure: if the Master crashes, no new applications can be created. In order to circumvent this, we have two high availability schemes, detailed below.
 
-# Standby Masters with ZooKeeper
+## Standby Masters with ZooKeeper
 
 **Overview**
 
@@ -347,7 +347,7 @@ There's an important distinction to be made between "registering with a Master"
 
 Due to this property, new Masters can be created at any time, and the only thing you need to worry about is that _new_ applications and Workers can find it to register with in case it becomes the leader. Once registered, you're taken care of.
 
-# Single-Node Recovery with Local File System
+## Single-Node Recovery with Local File System
 
 **Overview**
 

From 7fc3bb7c88a6bf5348d52ffee37a220a47c5a398 Mon Sep 17 00:00:00 2001
From: Nicholas Chammas <nicholas.chammas@gmail.com>
Date: Wed, 17 Sep 2014 15:14:04 -0700
Subject: [PATCH 332/489] [SPARK-3534] Fix expansion of testing arguments to
 sbt

Testing arguments to `sbt` need to be passed as an array, not a single, long string.

Fixes a bug introduced in #2420.

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

Closes #2437 from nchammas/selective-testing and squashes the following commits:

a9f9c1c [Nicholas Chammas] fix printing of sbt test arguments
cf57cbf [Nicholas Chammas] fix sbt test arguments
e33b978 [Nicholas Chammas] Merge pull request #2 from apache/master
0b47ca4 [Nicholas Chammas] Merge branch 'master' of github.com:nchammas/spark
8051486 [Nicholas Chammas] Merge pull request #1 from apache/master
03180a4 [Nicholas Chammas] Merge branch 'master' of github.com:nchammas/spark
d4c5f43 [Nicholas Chammas] Merge pull request #6 from apache/master
---
 dev/run-tests | 11 +++++++----
 1 file changed, 7 insertions(+), 4 deletions(-)

diff --git a/dev/run-tests b/dev/run-tests
index 53148d23f385f..7c002160c3a4a 100755
--- a/dev/run-tests
+++ b/dev/run-tests
@@ -141,17 +141,20 @@ echo "========================================================================="
 
 {
   # If the Spark SQL tests are enabled, run the tests with the Hive profiles enabled.
+  # This must be a single argument, as it is.
   if [ -n "$_RUN_SQL_TESTS" ]; then
     SBT_MAVEN_PROFILES_ARGS="$SBT_MAVEN_PROFILES_ARGS -Phive"
   fi
   
   if [ -n "$_SQL_TESTS_ONLY" ]; then
-    SBT_MAVEN_TEST_ARGS="catalyst/test sql/test hive/test"
+    # This must be an array of individual arguments. Otherwise, having one long string
+    #+ will be interpreted as a single test, which doesn't work.
+    SBT_MAVEN_TEST_ARGS=("catalyst/test" "sql/test" "hive/test")
   else
-    SBT_MAVEN_TEST_ARGS="test"
+    SBT_MAVEN_TEST_ARGS=("test")
   fi
   
-  echo "[info] Running Spark tests with these arguments: $SBT_MAVEN_PROFILES_ARGS $SBT_MAVEN_TEST_ARGS"
+  echo "[info] Running Spark tests with these arguments: $SBT_MAVEN_PROFILES_ARGS ${SBT_MAVEN_TEST_ARGS[@]}"
   
   # NOTE: echo "q" is needed because sbt on encountering a build file with failure
   #+ (either resolution or compilation) prompts the user for input either q, r, etc
@@ -159,7 +162,7 @@ echo "========================================================================="
   # QUESTION: Why doesn't 'yes "q"' work?
   # QUESTION: Why doesn't 'grep -v -e "^\[info\] Resolving"' work?
   echo -e "q\n" \
-    | sbt/sbt "$SBT_MAVEN_PROFILES_ARGS" "$SBT_MAVEN_TEST_ARGS" \
+    | sbt/sbt "$SBT_MAVEN_PROFILES_ARGS" "${SBT_MAVEN_TEST_ARGS[@]}" \
     | grep -v -e "info.*Resolving" -e "warn.*Merging" -e "info.*Including"
 }
 

From cbc065039f5176acc49899462bfab2521da26701 Mon Sep 17 00:00:00 2001
From: Kousuke Saruta <sarutak@oss.nttdata.co.jp>
Date: Wed, 17 Sep 2014 16:23:50 -0700
Subject: [PATCH 333/489] [SPARK-3571] Spark standalone cluster mode doesn't
 work.

I think, this issue is caused by #1106

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

Closes #2436 from sarutak/SPARK-3571 and squashes the following commits:

7a4deea [Kousuke Saruta] Modified Master.scala to use numWorkersVisited and numWorkersAlive instead of stopPos
4e51e35 [Kousuke Saruta] Modified Master to prevent from 0 divide
4817ecd [Kousuke Saruta] Brushed up previous change
71e84b6 [Kousuke Saruta] Modified Master to enable schedule normally
---
 .../scala/org/apache/spark/deploy/master/Master.scala | 11 ++++++-----
 1 file changed, 6 insertions(+), 5 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/deploy/master/Master.scala b/core/src/main/scala/org/apache/spark/deploy/master/Master.scala
index 2a3bd6ba0b9dc..432b552c58cd8 100644
--- a/core/src/main/scala/org/apache/spark/deploy/master/Master.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/master/Master.scala
@@ -489,23 +489,24 @@ private[spark] class Master(
     // First schedule drivers, they take strict precedence over applications
     // Randomization helps balance drivers
     val shuffledAliveWorkers = Random.shuffle(workers.toSeq.filter(_.state == WorkerState.ALIVE))
-    val aliveWorkerNum = shuffledAliveWorkers.size
+    val numWorkersAlive = shuffledAliveWorkers.size
     var curPos = 0
+    
     for (driver <- waitingDrivers.toList) { // iterate over a copy of waitingDrivers
       // We assign workers to each waiting driver in a round-robin fashion. For each driver, we
       // start from the last worker that was assigned a driver, and continue onwards until we have
       // explored all alive workers.
-      curPos = (curPos + 1) % aliveWorkerNum
-      val startPos = curPos
       var launched = false
-      while (curPos != startPos && !launched) {
+      var numWorkersVisited = 0
+      while (numWorkersVisited < numWorkersAlive && !launched) {
         val worker = shuffledAliveWorkers(curPos)
+        numWorkersVisited += 1
         if (worker.memoryFree >= driver.desc.mem && worker.coresFree >= driver.desc.cores) {
           launchDriver(worker, driver)
           waitingDrivers -= driver
           launched = true
         }
-        curPos = (curPos + 1) % aliveWorkerNum
+        curPos = (curPos + 1) % numWorkersAlive
       }
     }
 

From 6688a266f2cb84c2d43b8e4d27f710718c4cc4a0 Mon Sep 17 00:00:00 2001
From: Kousuke Saruta <sarutak@oss.nttdata.co.jp>
Date: Wed, 17 Sep 2014 16:31:58 -0700
Subject: [PATCH 334/489] [SPARK-3564][WebUI] Display App ID on HistoryPage

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

Closes #2424 from sarutak/display-appid-on-webui and squashes the following commits:

417fe90 [Kousuke Saruta] Added "App ID column" to HistoryPage
---
 .../scala/org/apache/spark/deploy/history/HistoryPage.scala   | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/deploy/history/HistoryPage.scala b/core/src/main/scala/org/apache/spark/deploy/history/HistoryPage.scala
index c4ef8b63b0071..d25c29113d6da 100644
--- a/core/src/main/scala/org/apache/spark/deploy/history/HistoryPage.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/history/HistoryPage.scala
@@ -67,6 +67,7 @@ private[spark] class HistoryPage(parent: HistoryServer) extends WebUIPage("") {
   }
 
   private val appHeader = Seq(
+    "App ID",
     "App Name",
     "Started",
     "Completed",
@@ -81,7 +82,8 @@ private[spark] class HistoryPage(parent: HistoryServer) extends WebUIPage("") {
     val duration = UIUtils.formatDuration(info.endTime - info.startTime)
     val lastUpdated = UIUtils.formatDate(info.lastUpdated)
     <tr>
-      <td><a href={uiAddress}>{info.name}</a></td>
+      <td><a href={uiAddress}>{info.id}</a></td>
+      <td>{info.name}</td>
       <td>{startTime}</td>
       <td>{endTime}</td>
       <td>{duration}</td>

From 1147973f1c7713013c7c0ca414482b511a730475 Mon Sep 17 00:00:00 2001
From: Kousuke Saruta <sarutak@oss.nttdata.co.jp>
Date: Wed, 17 Sep 2014 16:52:27 -0700
Subject: [PATCH 335/489] [SPARK-3567] appId field in
 SparkDeploySchedulerBackend should be volatile

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

Closes #2428 from sarutak/appid-volatile-modification and squashes the following commits:

c7d890d [Kousuke Saruta] Added volatile modifier to appId field in SparkDeploySchedulerBackend
---
 .../spark/scheduler/cluster/SparkDeploySchedulerBackend.scala   | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala
index 2f45d192e1d4d..5c5ecc8434d78 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala
@@ -34,7 +34,7 @@ private[spark] class SparkDeploySchedulerBackend(
   var client: AppClient = null
   var stopping = false
   var shutdownCallback : (SparkDeploySchedulerBackend) => Unit = _
-  var appId: String = _
+  @volatile var appId: String = _
 
   val registrationLock = new Object()
   var registrationDone = false

From 3f169bfe3c322bf4344e13276dbbe34279b59ad0 Mon Sep 17 00:00:00 2001
From: WangTaoTheTonic <barneystinson@aliyun.com>
Date: Wed, 17 Sep 2014 21:59:23 -0700
Subject: [PATCH 336/489] [SPARK-3565]Fix configuration item not consistent
 with document

https://issues.apache.org/jira/browse/SPARK-3565

"spark.ports.maxRetries" should be "spark.port.maxRetries". Make the configuration keys in document and code consistent.

Author: WangTaoTheTonic <barneystinson@aliyun.com>

Closes #2427 from WangTaoTheTonic/fixPortRetries and squashes the following commits:

c178813 [WangTaoTheTonic] Use blank lines trigger Jenkins
646f3fe [WangTaoTheTonic] also in SparkBuild.scala
3700dba [WangTaoTheTonic] Fix configuration item not consistent with document
---
 core/src/main/scala/org/apache/spark/util/Utils.scala       | 6 +++---
 .../scala/org/apache/spark/deploy/JsonProtocolSuite.scala   | 2 ++
 docs/configuration.md                                       | 2 +-
 project/SparkBuild.scala                                    | 2 +-
 4 files changed, 7 insertions(+), 5 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/util/Utils.scala b/core/src/main/scala/org/apache/spark/util/Utils.scala
index c76b7af18481d..ed063844323af 100644
--- a/core/src/main/scala/org/apache/spark/util/Utils.scala
+++ b/core/src/main/scala/org/apache/spark/util/Utils.scala
@@ -1382,15 +1382,15 @@ private[spark] object Utils extends Logging {
   }
 
   /**
-   * Default number of retries in binding to a port.
+   * Default maximum number of retries when binding to a port before giving up.
    */
   val portMaxRetries: Int = {
     if (sys.props.contains("spark.testing")) {
       // Set a higher number of retries for tests...
-      sys.props.get("spark.ports.maxRetries").map(_.toInt).getOrElse(100)
+      sys.props.get("spark.port.maxRetries").map(_.toInt).getOrElse(100)
     } else {
       Option(SparkEnv.get)
-        .flatMap(_.conf.getOption("spark.ports.maxRetries"))
+        .flatMap(_.conf.getOption("spark.port.maxRetries"))
         .map(_.toInt)
         .getOrElse(16)
     }
diff --git a/core/src/test/scala/org/apache/spark/deploy/JsonProtocolSuite.scala b/core/src/test/scala/org/apache/spark/deploy/JsonProtocolSuite.scala
index 2a58c6a40d8e4..3f1cd0752e766 100644
--- a/core/src/test/scala/org/apache/spark/deploy/JsonProtocolSuite.scala
+++ b/core/src/test/scala/org/apache/spark/deploy/JsonProtocolSuite.scala
@@ -115,11 +115,13 @@ class JsonProtocolSuite extends FunSuite {
     workerInfo.lastHeartbeat = JsonConstants.currTimeInMillis
     workerInfo
   }
+
   def createExecutorRunner(): ExecutorRunner = {
     new ExecutorRunner("appId", 123, createAppDesc(), 4, 1234, null, "workerId", "host",
       new File("sparkHome"), new File("workDir"), "akka://worker",
       new SparkConf, ExecutorState.RUNNING)
   }
+
   def createDriverRunner(): DriverRunner = {
     new DriverRunner(new SparkConf(), "driverId", new File("workDir"), new File("sparkHome"),
       createDriverDesc(), null, "akka://worker")
diff --git a/docs/configuration.md b/docs/configuration.md
index 99faf51c6f3db..a6dd7245e1552 100644
--- a/docs/configuration.md
+++ b/docs/configuration.md
@@ -657,7 +657,7 @@ Apart from these, the following properties are also available, and may be useful
   <td><code>spark.port.maxRetries</code></td>
   <td>16</td>
   <td>
-    Maximum number of retries when binding to a port before giving up.
+    Default maximum number of retries when binding to a port before giving up.
   </td>
 </tr>
 <tr>
diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala
index ab9f8ba120e83..12ac82293df76 100644
--- a/project/SparkBuild.scala
+++ b/project/SparkBuild.scala
@@ -336,7 +336,7 @@ object TestSettings {
     fork := true,
     javaOptions in Test += "-Dspark.test.home=" + sparkHome,
     javaOptions in Test += "-Dspark.testing=1",
-    javaOptions in Test += "-Dspark.ports.maxRetries=100",
+    javaOptions in Test += "-Dspark.port.maxRetries=100",
     javaOptions in Test += "-Dspark.ui.enabled=false",
     javaOptions in Test += "-Dsun.io.serialization.extendedDebugInfo=true",
     javaOptions in Test ++= System.getProperties.filter(_._1 startsWith "spark")

From 5547fa1ee98bf166061804bd64df4cb51a656a3f Mon Sep 17 00:00:00 2001
From: Nicholas Chammas <nicholas.chammas@gmail.com>
Date: Wed, 17 Sep 2014 22:37:11 -0700
Subject: [PATCH 337/489] [SPARK-3534] Add hive-thriftserver to SQL tests

Addresses the problem pointed out in [this comment](https://github.com/apache/spark/pull/2441#issuecomment-55990116).

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

Closes #2442 from nchammas/patch-1 and squashes the following commits:

7e68b60 [Nicholas Chammas] [SPARK-3534] Add hive-thriftserver to SQL tests
---
 dev/run-tests | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/dev/run-tests b/dev/run-tests
index 7c002160c3a4a..5f6df17b509a3 100755
--- a/dev/run-tests
+++ b/dev/run-tests
@@ -149,7 +149,7 @@ echo "========================================================================="
   if [ -n "$_SQL_TESTS_ONLY" ]; then
     # This must be an array of individual arguments. Otherwise, having one long string
     #+ will be interpreted as a single test, which doesn't work.
-    SBT_MAVEN_TEST_ARGS=("catalyst/test" "sql/test" "hive/test")
+    SBT_MAVEN_TEST_ARGS=("catalyst/test" "sql/test" "hive/test" "hive-thriftserver/test")
   else
     SBT_MAVEN_TEST_ARGS=("test")
   fi

From 6772afec2f57360bd886ba3c8487e6140869d8f0 Mon Sep 17 00:00:00 2001
From: GuoQiang Li <witgo@qq.com>
Date: Wed, 17 Sep 2014 22:54:34 -0700
Subject: [PATCH 338/489] [Minor] rat exclude dependency-reduced-pom.xml

Author: GuoQiang Li <witgo@qq.com>

Closes #2326 from witgo/rat-excludes and squashes the following commits:

860904e [GuoQiang Li] rat exclude dependency-reduced-pom.xml
---
 .rat-excludes | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.rat-excludes b/.rat-excludes
index fb6323daf9211..1897ec8f747ca 100644
--- a/.rat-excludes
+++ b/.rat-excludes
@@ -58,3 +58,4 @@ dist/*
 .*iws
 logs
 .*scalastyle-output.xml
+.*dependency-reduced-pom.xml

From 3447d100900af15a7340a2f6a5430ffb6d9c6c23 Mon Sep 17 00:00:00 2001
From: WangTaoTheTonic <barneystinson@aliyun.com>
Date: Thu, 18 Sep 2014 10:17:18 -0700
Subject: [PATCH 339/489] [SPARK-3547]Using a special exit code instead of 1 to
 represent ClassNotFoundExcepti...

...on

As improvement of https://github.com/apache/spark/pull/1944, we should use more special exit code to represent ClassNotFoundException.

Author: WangTaoTheTonic <barneystinson@aliyun.com>

Closes #2421 from WangTaoTheTonic/classnotfoundExitCode and squashes the following commits:

645a22a [WangTaoTheTonic] Serveral typos to trigger Jenkins
d6ae559 [WangTaoTheTonic] use 101 instead
a2d6465 [WangTaoTheTonic] use 127 instead
fbb232f [WangTaoTheTonic] Using a special exit code instead of 1 to represent ClassNotFoundException
---
 bin/spark-sql                                                 | 2 +-
 core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala | 2 +-
 .../main/scala/org/apache/spark/network/nio/Connection.scala  | 2 +-
 .../org/apache/spark/network/nio/ConnectionManager.scala      | 4 ++--
 sbin/start-thriftserver.sh                                    | 2 +-
 5 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/bin/spark-sql b/bin/spark-sql
index ae096530cad04..9d66140b6aa17 100755
--- a/bin/spark-sql
+++ b/bin/spark-sql
@@ -24,7 +24,7 @@
 set -o posix
 
 CLASS="org.apache.spark.sql.hive.thriftserver.SparkSQLCLIDriver"
-CLASS_NOT_FOUND_EXIT_STATUS=1
+CLASS_NOT_FOUND_EXIT_STATUS=101
 
 # Figure out where Spark is installed
 FWDIR="$(cd "`dirname "$0"`"/..; pwd)"
diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
index 5ed3575816a38..5d15af1326ef0 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
@@ -54,7 +54,7 @@ object SparkSubmit {
   private val SPARK_SHELL = "spark-shell"
   private val PYSPARK_SHELL = "pyspark-shell"
 
-  private val CLASS_NOT_FOUND_EXIT_STATUS = 1
+  private val CLASS_NOT_FOUND_EXIT_STATUS = 101
 
   // Exposed for testing
   private[spark] var exitFn: () => Unit = () => System.exit(-1)
diff --git a/core/src/main/scala/org/apache/spark/network/nio/Connection.scala b/core/src/main/scala/org/apache/spark/network/nio/Connection.scala
index 74074a8dcbfff..18172d359cb35 100644
--- a/core/src/main/scala/org/apache/spark/network/nio/Connection.scala
+++ b/core/src/main/scala/org/apache/spark/network/nio/Connection.scala
@@ -460,7 +460,7 @@ private[spark] class ReceivingConnection(
     if (currId != null) currId else super.getRemoteConnectionManagerId()
   }
 
-  // The reciever's remote address is the local socket on remote side : which is NOT
+  // The receiver's remote address is the local socket on remote side : which is NOT
   // the connection manager id of the receiver.
   // We infer that from the messages we receive on the receiver socket.
   private def processConnectionManagerId(header: MessageChunkHeader) {
diff --git a/core/src/main/scala/org/apache/spark/network/nio/ConnectionManager.scala b/core/src/main/scala/org/apache/spark/network/nio/ConnectionManager.scala
index 09d3ea306515b..5aa7e94943561 100644
--- a/core/src/main/scala/org/apache/spark/network/nio/ConnectionManager.scala
+++ b/core/src/main/scala/org/apache/spark/network/nio/ConnectionManager.scala
@@ -501,7 +501,7 @@ private[nio] class ConnectionManager(
 
   def changeConnectionKeyInterest(connection: Connection, ops: Int) {
     keyInterestChangeRequests += ((connection.key, ops))
-    // so that registerations happen !
+    // so that registrations happen !
     wakeupSelector()
   }
 
@@ -832,7 +832,7 @@ private[nio] class ConnectionManager(
   }
 
   /**
-   * Send a message and block until an acknowldgment is received or an error occurs.
+   * Send a message and block until an acknowledgment is received or an error occurs.
    * @param connectionManagerId the message's destination
    * @param message the message being sent
    * @return a Future that either returns the acknowledgment message or captures an exception.
diff --git a/sbin/start-thriftserver.sh b/sbin/start-thriftserver.sh
index 4ce40fe750384..ba953e763faab 100755
--- a/sbin/start-thriftserver.sh
+++ b/sbin/start-thriftserver.sh
@@ -27,7 +27,7 @@ set -o posix
 FWDIR="$(cd "`dirname "$0"`"/..; pwd)"
 
 CLASS="org.apache.spark.sql.hive.thriftserver.HiveThriftServer2"
-CLASS_NOT_FOUND_EXIT_STATUS=1
+CLASS_NOT_FOUND_EXIT_STATUS=101
 
 function usage {
   echo "Usage: ./sbin/start-thriftserver [options] [thrift server options]"

From 3ad4176cf980591469997a8a612bf422c90f86fd Mon Sep 17 00:00:00 2001
From: Patrick Wendell <pwendell@gmail.com>
Date: Thu, 18 Sep 2014 10:30:17 -0700
Subject: [PATCH 340/489] SPARK-3579 Jekyll doc generation is different across
 environments.

This patch makes some small changes to fix this problem:
1. We document specific versions of Jekyll/Kramdown to use that match
   those used when building the upstream docs.
2. We add a configuration for a property that for some reason varies across
   packages of Jekyll/Kramdown even with the same version.

Author: Patrick Wendell <pwendell@gmail.com>

Closes #2443 from pwendell/jekyll and squashes the following commits:

54ee2ab [Patrick Wendell] SPARK-3579 Jekyll doc generation is different across environments.
---
 docs/README.md   | 16 ++++++++++------
 docs/_config.yml |  5 +++++
 2 files changed, 15 insertions(+), 6 deletions(-)

diff --git a/docs/README.md b/docs/README.md
index fdc89d2eb767a..79708c3df9106 100644
--- a/docs/README.md
+++ b/docs/README.md
@@ -20,12 +20,16 @@ In this directory you will find textfiles formatted using Markdown, with an ".md
 read those text files directly if you want. Start with index.md.
 
 The markdown code can be compiled to HTML using the [Jekyll tool](http://jekyllrb.com).
-To use the `jekyll` command, you will need to have Jekyll installed. 
-The easiest way to do this is via a Ruby Gem, see the 
-[jekyll installation instructions](http://jekyllrb.com/docs/installation).
-If not already installed, you need to install `kramdown` and `jekyll-redirect-from` Gems
-with `sudo gem install kramdown jekyll-redirect-from`.
-Execute `jekyll build` from the `docs/` directory. Compiling the site with Jekyll will create a directory
+`Jekyll` and a few dependencies must be installed for this to work. We recommend
+installing via the Ruby Gem dependency manager. Since the exact HTML output 
+varies between versions of Jekyll and its dependencies, we list specific versions here
+in some cases:
+
+    $ sudo gem install jekyll -v 1.4.3
+    $ sudo gem uninstall kramdown -v 1.4.1
+    $ sudo gem install jekyll-redirect-from
+
+Execute `jekyll` from the `docs/` directory. Compiling the site with Jekyll will create a directory
 called `_site` containing index.html as well as the rest of the compiled files.
 
 You can modify the default Jekyll build as follows:
diff --git a/docs/_config.yml b/docs/_config.yml
index d3ea2625c7448..7bc3a78e2d265 100644
--- a/docs/_config.yml
+++ b/docs/_config.yml
@@ -3,6 +3,11 @@ markdown: kramdown
 gems:
   - jekyll-redirect-from
 
+# For some reason kramdown seems to behave differently on different
+# OS/packages wrt encoding. So we hard code this config.
+kramdown:
+  entity_output: numeric
+
 # These allow the documentation to be updated with nerw releases
 # of Spark, Scala, and Mesos.
 SPARK_VERSION: 1.0.0-SNAPSHOT

From 6cab838b9803e3294c07bbf731c47154ec57afc0 Mon Sep 17 00:00:00 2001
From: Kousuke Saruta <sarutak@oss.nttdata.co.jp>
Date: Thu, 18 Sep 2014 12:04:32 -0700
Subject: [PATCH 341/489] [SPARK-3566] [BUILD] .gitignore and .rat-excludes
 should consider Windows cmd file and Emacs' backup files

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

Closes #2426 from sarutak/emacs-metafiles-ignore and squashes the following commits:

a306020 [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into emacs-metafiles-ignore
6a0a5eb [Kousuke Saruta] Added cmd file entry to .rat-excludes and .gitignore
897da63 [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into emacs-metafiles-ignore
8cade06 [Kousuke Saruta] Modified .gitignore to ignore emacs lock file and backup file
---
 .gitignore    | 3 +++
 .rat-excludes | 1 +
 2 files changed, 4 insertions(+)

diff --git a/.gitignore b/.gitignore
index a31bf7e0091f4..1bcd0165761ac 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,4 +1,6 @@
 *~
+*.#*
+*#*#
 *.swp
 *.ipr
 *.iml
@@ -16,6 +18,7 @@ third_party/libmesos.so
 third_party/libmesos.dylib
 conf/java-opts
 conf/*.sh
+conf/*.cmd
 conf/*.properties
 conf/*.conf
 conf/*.xml
diff --git a/.rat-excludes b/.rat-excludes
index 1897ec8f747ca..9fc99d7fca35d 100644
--- a/.rat-excludes
+++ b/.rat-excludes
@@ -20,6 +20,7 @@ log4j.properties.template
 metrics.properties.template
 slaves
 spark-env.sh
+spark-env.cmd
 spark-env.sh.template
 log4j-defaults.properties
 bootstrap-tooltip.js

From 471e6a3a47bd4b94878798f6f6fc93e2e672efff Mon Sep 17 00:00:00 2001
From: WangTaoTheTonic <barneystinson@aliyun.com>
Date: Thu, 18 Sep 2014 12:07:24 -0700
Subject: [PATCH 342/489] [SPARK-3589][Minor]remove redundant code

https://issues.apache.org/jira/browse/SPARK-3589

"export CLASSPATH" in spark-class is redundant since same variable is exported before.
We could reuse defined value "isYarnCluster" in SparkSubmit.scala.

Author: WangTaoTheTonic <barneystinson@aliyun.com>

Closes #2445 from WangTaoTheTonic/removeRedundant and squashes the following commits:

6fb6872 [WangTaoTheTonic] remove redundant code
---
 core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
index 5d15af1326ef0..3dd1dd5b82fe8 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
@@ -261,7 +261,7 @@ object SparkSubmit {
     }
 
     // In yarn-cluster mode, use yarn.Client as a wrapper around the user class
-    if (clusterManager == YARN && deployMode == CLUSTER) {
+    if (isYarnCluster) {
       childMainClass = "org.apache.spark.deploy.yarn.Client"
       if (args.primaryResource != SPARK_INTERNAL) {
         childArgs += ("--jar", args.primaryResource)

From b3ed37e5bad15d56db90c2b25fe11c1f758d3a97 Mon Sep 17 00:00:00 2001
From: Victsm <victor.nju@gmail.com>
Date: Thu, 18 Sep 2014 15:58:14 -0700
Subject: [PATCH 343/489] [SPARK-3560] Fixed setting spark.jars system property
 in yarn-cluster mode

Author: Victsm <victor.nju@gmail.com>
Author: Min Shen <mshen@linkedin.com>

Closes #2449 from Victsm/SPARK-3560 and squashes the following commits:

918405a [Victsm] Removed the additional space
4502a2a [Min Shen] [SPARK-3560] Fixed setting spark.jars system property in yarn-cluster mode.

(cherry picked from commit 832dff64ddb1240a4c8e22fcdc0e993cc8c808de)
Signed-off-by: Andrew Or <andrewor14@gmail.com>
---
 core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala  | 3 ++-
 .../test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala  | 1 +
 2 files changed, 3 insertions(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
index 3dd1dd5b82fe8..ec0324e24915a 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
@@ -172,7 +172,7 @@ object SparkSubmit {
       // All cluster managers
       OptionAssigner(args.master, ALL_CLUSTER_MGRS, ALL_DEPLOY_MODES, sysProp = "spark.master"),
       OptionAssigner(args.name, ALL_CLUSTER_MGRS, ALL_DEPLOY_MODES, sysProp = "spark.app.name"),
-      OptionAssigner(args.jars, ALL_CLUSTER_MGRS, ALL_DEPLOY_MODES, sysProp = "spark.jars"),
+      OptionAssigner(args.jars, ALL_CLUSTER_MGRS, CLIENT, sysProp = "spark.jars"),
       OptionAssigner(args.driverMemory, ALL_CLUSTER_MGRS, CLIENT,
         sysProp = "spark.driver.memory"),
       OptionAssigner(args.driverExtraClassPath, ALL_CLUSTER_MGRS, ALL_DEPLOY_MODES,
@@ -205,6 +205,7 @@ object SparkSubmit {
       OptionAssigner(args.jars, YARN, CLUSTER, clOption = "--addJars"),
 
       // Other options
+      OptionAssigner(args.jars, STANDALONE, CLUSTER, sysProp = "spark.jars"),
       OptionAssigner(args.executorMemory, STANDALONE | MESOS | YARN, ALL_DEPLOY_MODES,
         sysProp = "spark.executor.memory"),
       OptionAssigner(args.totalExecutorCores, STANDALONE | MESOS, ALL_DEPLOY_MODES,
diff --git a/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala b/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala
index 22b369a829418..0c324d8bdf6a4 100644
--- a/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala
+++ b/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala
@@ -154,6 +154,7 @@ class SparkSubmitSuite extends FunSuite with Matchers {
     sysProps("spark.app.name") should be ("beauty")
     sysProps("spark.shuffle.spill") should be ("false")
     sysProps("SPARK_SUBMIT") should be ("true")
+    sysProps.keys should not contain ("spark.jars")
   }
 
   test("handles YARN client mode") {

From 9306297d1d888d0430f79b2133ee7377871a3a18 Mon Sep 17 00:00:00 2001
From: Andrew Or <andrewor14@gmail.com>
Date: Thu, 18 Sep 2014 17:49:28 -0700
Subject: [PATCH 344/489] [Minor Hot Fix] Move a line in SparkSubmit to the
 right place

This was introduced in #2449

Author: Andrew Or <andrewor14@gmail.com>

Closes #2452 from andrewor14/standalone-hot-fix and squashes the following commits:

d5190ca [Andrew Or] Put that line in the right place
---
 core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
index ec0324e24915a..d132ecb3f9989 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
@@ -183,6 +183,7 @@ object SparkSubmit {
         sysProp = "spark.driver.extraLibraryPath"),
 
       // Standalone cluster only
+      OptionAssigner(args.jars, STANDALONE, CLUSTER, sysProp = "spark.jars"),
       OptionAssigner(args.driverMemory, STANDALONE, CLUSTER, clOption = "--memory"),
       OptionAssigner(args.driverCores, STANDALONE, CLUSTER, clOption = "--cores"),
 
@@ -205,7 +206,6 @@ object SparkSubmit {
       OptionAssigner(args.jars, YARN, CLUSTER, clOption = "--addJars"),
 
       // Other options
-      OptionAssigner(args.jars, STANDALONE, CLUSTER, sysProp = "spark.jars"),
       OptionAssigner(args.executorMemory, STANDALONE | MESOS | YARN, ALL_DEPLOY_MODES,
         sysProp = "spark.executor.memory"),
       OptionAssigner(args.totalExecutorCores, STANDALONE | MESOS, ALL_DEPLOY_MODES,

From e77fa81a61798c89d5a9b6c9dc067d11785254b7 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies.liu@gmail.com>
Date: Thu, 18 Sep 2014 18:11:48 -0700
Subject: [PATCH 345/489] [SPARK-3554] [PySpark] use broadcast automatically
 for large closure

Py4j can not handle large string efficiently, so we should use broadcast for large closure automatically. (Broadcast use local filesystem to pass through data).

Author: Davies Liu <davies.liu@gmail.com>

Closes #2417 from davies/command and squashes the following commits:

fbf4e97 [Davies Liu] bugfix
aefd508 [Davies Liu] use broadcast automatically for large closure
---
 python/pyspark/rdd.py    | 4 ++++
 python/pyspark/sql.py    | 8 ++++++--
 python/pyspark/tests.py  | 6 ++++++
 python/pyspark/worker.py | 4 +++-
 4 files changed, 19 insertions(+), 3 deletions(-)

diff --git a/python/pyspark/rdd.py b/python/pyspark/rdd.py
index cb09c191bed71..b43606b7304c5 100644
--- a/python/pyspark/rdd.py
+++ b/python/pyspark/rdd.py
@@ -2061,8 +2061,12 @@ def _jrdd(self):
             self._jrdd_deserializer = NoOpSerializer()
         command = (self.func, self._prev_jrdd_deserializer,
                    self._jrdd_deserializer)
+        # the serialized command will be compressed by broadcast
         ser = CloudPickleSerializer()
         pickled_command = ser.dumps(command)
+        if pickled_command > (1 << 20):  # 1M
+            broadcast = self.ctx.broadcast(pickled_command)
+            pickled_command = ser.dumps(broadcast)
         broadcast_vars = ListConverter().convert(
             [x._jbroadcast for x in self.ctx._pickled_broadcast_vars],
             self.ctx._gateway._gateway_client)
diff --git a/python/pyspark/sql.py b/python/pyspark/sql.py
index 8f6dbab240c7b..42a9920f10e6f 100644
--- a/python/pyspark/sql.py
+++ b/python/pyspark/sql.py
@@ -27,7 +27,7 @@
 from array import array
 from operator import itemgetter
 
-from pyspark.rdd import RDD, PipelinedRDD
+from pyspark.rdd import RDD
 from pyspark.serializers import BatchedSerializer, PickleSerializer, CloudPickleSerializer
 from pyspark.storagelevel import StorageLevel
 from pyspark.traceback_utils import SCCallSiteSync
@@ -975,7 +975,11 @@ def registerFunction(self, name, f, returnType=StringType()):
         command = (func,
                    BatchedSerializer(PickleSerializer(), 1024),
                    BatchedSerializer(PickleSerializer(), 1024))
-        pickled_command = CloudPickleSerializer().dumps(command)
+        ser = CloudPickleSerializer()
+        pickled_command = ser.dumps(command)
+        if pickled_command > (1 << 20):  # 1M
+            broadcast = self._sc.broadcast(pickled_command)
+            pickled_command = ser.dumps(broadcast)
         broadcast_vars = ListConverter().convert(
             [x._jbroadcast for x in self._sc._pickled_broadcast_vars],
             self._sc._gateway._gateway_client)
diff --git a/python/pyspark/tests.py b/python/pyspark/tests.py
index 0b3854347ad2e..7301966e48045 100644
--- a/python/pyspark/tests.py
+++ b/python/pyspark/tests.py
@@ -434,6 +434,12 @@ def test_large_broadcast(self):
         m = self.sc.parallelize(range(1), 1).map(lambda x: len(bdata.value)).sum()
         self.assertEquals(N, m)
 
+    def test_large_closure(self):
+        N = 1000000
+        data = [float(i) for i in xrange(N)]
+        m = self.sc.parallelize(range(1), 1).map(lambda x: len(data)).sum()
+        self.assertEquals(N, m)
+
     def test_zip_with_different_serializers(self):
         a = self.sc.parallelize(range(5))
         b = self.sc.parallelize(range(100, 105))
diff --git a/python/pyspark/worker.py b/python/pyspark/worker.py
index 252176ac65fec..d6c06e2dbef62 100644
--- a/python/pyspark/worker.py
+++ b/python/pyspark/worker.py
@@ -77,10 +77,12 @@ def main(infile, outfile):
                 _broadcastRegistry[bid] = Broadcast(bid, value)
             else:
                 bid = - bid - 1
-                _broadcastRegistry.remove(bid)
+                _broadcastRegistry.pop(bid)
 
         _accumulatorRegistry.clear()
         command = pickleSer._read_with_length(infile)
+        if isinstance(command, Broadcast):
+            command = pickleSer.loads(command.value)
         (func, deserializer, serializer) = command
         init_time = time.time()
         iterator = deserializer.load_stream(infile)

From e76ef5cb8eed6b78fb722b3d6fbeb9466a0e3499 Mon Sep 17 00:00:00 2001
From: Burak <brkyvz@gmail.com>
Date: Thu, 18 Sep 2014 22:18:51 -0700
Subject: [PATCH 346/489] [SPARK-3418] Sparse Matrix support (CCS) and
 additional native BLAS operations added

Local `SparseMatrix` support added in Compressed Column Storage (CCS) format in addition to Level-2 and Level-3 BLAS operations such as dgemv and dgemm respectively.

BLAS doesn't support  sparse matrix operations, therefore support for `SparseMatrix`-`DenseMatrix` multiplication and `SparseMatrix`-`DenseVector` implementations have been added. I will post performance comparisons in the comments momentarily.

Author: Burak <brkyvz@gmail.com>

Closes #2294 from brkyvz/SPARK-3418 and squashes the following commits:

88814ed [Burak] Hopefully fixed MiMa this time
47e49d5 [Burak] really fixed MiMa issue
f0bae57 [Burak] [SPARK-3418] Fixed MiMa compatibility issues (excluded from check)
4b7dbec [Burak] 9/17 comments addressed
7af2f83 [Burak] sealed traits Vector and Matrix
d3a8a16 [Burak] [SPARK-3418] Squashed missing alpha bug.
421045f [Burak] [SPARK-3418] New code review comments addressed
f35a161 [Burak] [SPARK-3418] Code review comments addressed and multiplication further optimized
2508577 [Burak] [SPARK-3418] Fixed one more style issue
d16e8a0 [Burak] [SPARK-3418] Fixed style issues and added documentation for methods
204a3f7 [Burak] [SPARK-3418] Fixed failing Matrix unit test
6025297 [Burak] [SPARK-3418] Fixed Scala-style errors
dc7be71 [Burak] [SPARK-3418][MLlib] Matrix unit tests expanded with indexing and updating
d2d5851 [Burak] [SPARK-3418][MLlib] Sparse Matrix support and additional native BLAS operations added
---
 .../org/apache/spark/mllib/linalg/BLAS.scala  | 330 +++++++++++++++++-
 .../apache/spark/mllib/linalg/Matrices.scala  | 232 +++++++++++-
 .../apache/spark/mllib/linalg/Vectors.scala   |   2 +-
 .../apache/spark/mllib/linalg/BLASSuite.scala | 111 ++++++
 .../linalg/BreezeMatrixConversionSuite.scala  |  24 +-
 .../spark/mllib/linalg/MatricesSuite.scala    |  76 ++++
 .../spark/mllib/util/TestingUtils.scala       |  65 +++-
 project/MimaExcludes.scala                    |   4 +-
 8 files changed, 834 insertions(+), 10 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/BLAS.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/BLAS.scala
index 70e23033c8754..54ee930d61003 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/BLAS.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/BLAS.scala
@@ -18,13 +18,17 @@
 package org.apache.spark.mllib.linalg
 
 import com.github.fommil.netlib.{BLAS => NetlibBLAS, F2jBLAS}
+import com.github.fommil.netlib.BLAS.{getInstance => NativeBLAS}
+
+import org.apache.spark.Logging
 
 /**
  * BLAS routines for MLlib's vectors and matrices.
  */
-private[mllib] object BLAS extends Serializable {
+private[mllib] object BLAS extends Serializable with Logging {
 
   @transient private var _f2jBLAS: NetlibBLAS = _
+  @transient private var _nativeBLAS: NetlibBLAS = _
 
   // For level-1 routines, we use Java implementation.
   private def f2jBLAS: NetlibBLAS = {
@@ -197,4 +201,328 @@ private[mllib] object BLAS extends Serializable {
         throw new IllegalArgumentException(s"scal doesn't support vector type ${x.getClass}.")
     }
   }
+
+  // For level-3 routines, we use the native BLAS.
+  private def nativeBLAS: NetlibBLAS = {
+    if (_nativeBLAS == null) {
+      _nativeBLAS = NativeBLAS
+    }
+    _nativeBLAS
+  }
+
+  /**
+   * C := alpha * A * B + beta * C
+   * @param transA whether to use the transpose of matrix A (true), or A itself (false).
+   * @param transB whether to use the transpose of matrix B (true), or B itself (false).
+   * @param alpha a scalar to scale the multiplication A * B.
+   * @param A the matrix A that will be left multiplied to B. Size of m x k.
+   * @param B the matrix B that will be left multiplied by A. Size of k x n.
+   * @param beta a scalar that can be used to scale matrix C.
+   * @param C the resulting matrix C. Size of m x n.
+   */
+  def gemm(
+      transA: Boolean,
+      transB: Boolean,
+      alpha: Double,
+      A: Matrix,
+      B: DenseMatrix,
+      beta: Double,
+      C: DenseMatrix): Unit = {
+    if (alpha == 0.0) {
+      logDebug("gemm: alpha is equal to 0. Returning C.")
+    } else {
+      A match {
+        case sparse: SparseMatrix =>
+          gemm(transA, transB, alpha, sparse, B, beta, C)
+        case dense: DenseMatrix =>
+          gemm(transA, transB, alpha, dense, B, beta, C)
+        case _ =>
+          throw new IllegalArgumentException(s"gemm doesn't support matrix type ${A.getClass}.")
+      }
+    }
+  }
+
+  /**
+   * C := alpha * A * B + beta * C
+   *
+   * @param alpha a scalar to scale the multiplication A * B.
+   * @param A the matrix A that will be left multiplied to B. Size of m x k.
+   * @param B the matrix B that will be left multiplied by A. Size of k x n.
+   * @param beta a scalar that can be used to scale matrix C.
+   * @param C the resulting matrix C. Size of m x n.
+   */
+  def gemm(
+      alpha: Double,
+      A: Matrix,
+      B: DenseMatrix,
+      beta: Double,
+      C: DenseMatrix): Unit = {
+    gemm(false, false, alpha, A, B, beta, C)
+  }
+
+  /**
+   * C := alpha * A * B + beta * C
+   * For `DenseMatrix` A.
+   */
+  private def gemm(
+      transA: Boolean,
+      transB: Boolean,
+      alpha: Double,
+      A: DenseMatrix,
+      B: DenseMatrix,
+      beta: Double,
+      C: DenseMatrix): Unit = {
+    val mA: Int = if (!transA) A.numRows else A.numCols
+    val nB: Int = if (!transB) B.numCols else B.numRows
+    val kA: Int = if (!transA) A.numCols else A.numRows
+    val kB: Int = if (!transB) B.numRows else B.numCols
+    val tAstr = if (!transA) "N" else "T"
+    val tBstr = if (!transB) "N" else "T"
+
+    require(kA == kB, s"The columns of A don't match the rows of B. A: $kA, B: $kB")
+    require(mA == C.numRows, s"The rows of C don't match the rows of A. C: ${C.numRows}, A: $mA")
+    require(nB == C.numCols,
+      s"The columns of C don't match the columns of B. C: ${C.numCols}, A: $nB")
+
+    nativeBLAS.dgemm(tAstr, tBstr, mA, nB, kA, alpha, A.values, A.numRows, B.values, B.numRows,
+      beta, C.values, C.numRows)
+  }
+
+  /**
+   * C := alpha * A * B + beta * C
+   * For `SparseMatrix` A.
+   */
+  private def gemm(
+      transA: Boolean,
+      transB: Boolean,
+      alpha: Double,
+      A: SparseMatrix,
+      B: DenseMatrix,
+      beta: Double,
+      C: DenseMatrix): Unit = {
+    val mA: Int = if (!transA) A.numRows else A.numCols
+    val nB: Int = if (!transB) B.numCols else B.numRows
+    val kA: Int = if (!transA) A.numCols else A.numRows
+    val kB: Int = if (!transB) B.numRows else B.numCols
+
+    require(kA == kB, s"The columns of A don't match the rows of B. A: $kA, B: $kB")
+    require(mA == C.numRows, s"The rows of C don't match the rows of A. C: ${C.numRows}, A: $mA")
+    require(nB == C.numCols,
+      s"The columns of C don't match the columns of B. C: ${C.numCols}, A: $nB")
+
+    val Avals = A.values
+    val Arows = if (!transA) A.rowIndices else A.colPtrs
+    val Acols = if (!transA) A.colPtrs else A.rowIndices
+
+    // Slicing is easy in this case. This is the optimal multiplication setting for sparse matrices
+    if (transA){
+      var colCounterForB = 0
+      if (!transB) { // Expensive to put the check inside the loop
+        while (colCounterForB < nB) {
+          var rowCounterForA = 0
+          val Cstart = colCounterForB * mA
+          val Bstart = colCounterForB * kA
+          while (rowCounterForA < mA) {
+            var i = Arows(rowCounterForA)
+            val indEnd = Arows(rowCounterForA + 1)
+            var sum = 0.0
+            while (i < indEnd) {
+              sum += Avals(i) * B.values(Bstart + Acols(i))
+              i += 1
+            }
+            val Cindex = Cstart + rowCounterForA
+            C.values(Cindex) = beta * C.values(Cindex) + sum * alpha
+            rowCounterForA += 1
+          }
+          colCounterForB += 1
+        }
+      } else {
+        while (colCounterForB < nB) {
+          var rowCounter = 0
+          val Cstart = colCounterForB * mA
+          while (rowCounter < mA) {
+            var i = Arows(rowCounter)
+            val indEnd = Arows(rowCounter + 1)
+            var sum = 0.0
+            while (i < indEnd) {
+              sum += Avals(i) * B(colCounterForB, Acols(i))
+              i += 1
+            }
+            val Cindex = Cstart + rowCounter
+            C.values(Cindex) = beta * C.values(Cindex) + sum * alpha
+            rowCounter += 1
+          }
+          colCounterForB += 1
+        }
+      }
+    } else {
+      // Scale matrix first if `beta` is not equal to 0.0
+      if (beta != 0.0){
+        f2jBLAS.dscal(C.values.length, beta, C.values, 1)
+      }
+      // Perform matrix multiplication and add to C. The rows of A are multiplied by the columns of
+      // B, and added to C.
+      var colCounterForB = 0 // the column to be updated in C
+      if (!transB) { // Expensive to put the check inside the loop
+        while (colCounterForB < nB) {
+          var colCounterForA = 0 // The column of A to multiply with the row of B
+          val Bstart = colCounterForB * kB
+          val Cstart = colCounterForB * mA
+          while (colCounterForA < kA) {
+            var i = Acols(colCounterForA)
+            val indEnd = Acols(colCounterForA + 1)
+            val Bval = B.values(Bstart + colCounterForA) * alpha
+            while (i < indEnd){
+              C.values(Cstart + Arows(i)) += Avals(i) * Bval
+              i += 1
+            }
+            colCounterForA += 1
+          }
+          colCounterForB += 1
+        }
+      } else {
+        while (colCounterForB < nB) {
+          var colCounterForA = 0 // The column of A to multiply with the row of B
+          val Cstart = colCounterForB * mA
+          while (colCounterForA < kA){
+            var i = Acols(colCounterForA)
+            val indEnd = Acols(colCounterForA + 1)
+            val Bval = B(colCounterForB, colCounterForA) * alpha
+            while (i < indEnd){
+              C.values(Cstart + Arows(i)) += Avals(i) * Bval
+              i += 1
+            }
+            colCounterForA += 1
+          }
+          colCounterForB += 1
+        }
+      }
+    }
+  }
+
+  /**
+   * y := alpha * A * x + beta * y
+   * @param trans whether to use the transpose of matrix A (true), or A itself (false).
+   * @param alpha a scalar to scale the multiplication A * x.
+   * @param A the matrix A that will be left multiplied to x. Size of m x n.
+   * @param x the vector x that will be left multiplied by A. Size of n x 1.
+   * @param beta a scalar that can be used to scale vector y.
+   * @param y the resulting vector y. Size of m x 1.
+   */
+  def gemv(
+      trans: Boolean,
+      alpha: Double,
+      A: Matrix,
+      x: DenseVector,
+      beta: Double,
+      y: DenseVector): Unit = {
+
+    val mA: Int = if (!trans) A.numRows else A.numCols
+    val nx: Int = x.size
+    val nA: Int = if (!trans) A.numCols else A.numRows
+
+    require(nA == nx, s"The columns of A don't match the number of elements of x. A: $nA, x: $nx")
+    require(mA == y.size,
+      s"The rows of A don't match the number of elements of y. A: $mA, y:${y.size}}")
+    if (alpha == 0.0) {
+      logDebug("gemv: alpha is equal to 0. Returning y.")
+    } else {
+      A match {
+        case sparse: SparseMatrix =>
+          gemv(trans, alpha, sparse, x, beta, y)
+        case dense: DenseMatrix =>
+          gemv(trans, alpha, dense, x, beta, y)
+        case _ =>
+          throw new IllegalArgumentException(s"gemv doesn't support matrix type ${A.getClass}.")
+      }
+    }
+  }
+
+  /**
+   * y := alpha * A * x + beta * y
+   *
+   * @param alpha a scalar to scale the multiplication A * x.
+   * @param A the matrix A that will be left multiplied to x. Size of m x n.
+   * @param x the vector x that will be left multiplied by A. Size of n x 1.
+   * @param beta a scalar that can be used to scale vector y.
+   * @param y the resulting vector y. Size of m x 1.
+   */
+  def gemv(
+      alpha: Double,
+      A: Matrix,
+      x: DenseVector,
+      beta: Double,
+      y: DenseVector): Unit = {
+    gemv(false, alpha, A, x, beta, y)
+  }
+
+  /**
+   * y := alpha * A * x + beta * y
+   * For `DenseMatrix` A.
+   */
+  private def gemv(
+      trans: Boolean,
+      alpha: Double,
+      A: DenseMatrix,
+      x: DenseVector,
+      beta: Double,
+      y: DenseVector): Unit =  {
+    val tStrA = if (!trans) "N" else "T"
+    nativeBLAS.dgemv(tStrA, A.numRows, A.numCols, alpha, A.values, A.numRows, x.values, 1, beta,
+      y.values, 1)
+  }
+
+  /**
+   * y := alpha * A * x + beta * y
+   * For `SparseMatrix` A.
+   */
+  private def gemv(
+      trans: Boolean,
+      alpha: Double,
+      A: SparseMatrix,
+      x: DenseVector,
+      beta: Double,
+      y: DenseVector): Unit =  {
+
+    val mA: Int = if(!trans) A.numRows else A.numCols
+    val nA: Int = if(!trans) A.numCols else A.numRows
+
+    val Avals = A.values
+    val Arows = if (!trans) A.rowIndices else A.colPtrs
+    val Acols = if (!trans) A.colPtrs else A.rowIndices
+
+    // Slicing is easy in this case. This is the optimal multiplication setting for sparse matrices
+    if (trans){
+      var rowCounter = 0
+      while (rowCounter < mA){
+        var i = Arows(rowCounter)
+        val indEnd = Arows(rowCounter + 1)
+        var sum = 0.0
+        while(i < indEnd){
+          sum += Avals(i) * x.values(Acols(i))
+          i += 1
+        }
+        y.values(rowCounter) =  beta * y.values(rowCounter) + sum * alpha
+        rowCounter += 1
+      }
+    } else {
+      // Scale vector first if `beta` is not equal to 0.0
+      if (beta != 0.0){
+        scal(beta, y)
+      }
+      // Perform matrix-vector multiplication and add to y
+      var colCounterForA = 0
+      while (colCounterForA < nA){
+        var i = Acols(colCounterForA)
+        val indEnd = Acols(colCounterForA + 1)
+        val xVal = x.values(colCounterForA) * alpha
+        while (i < indEnd){
+          val rowIndex = Arows(i)
+          y.values(rowIndex) += Avals(i) * xVal
+          i += 1
+        }
+        colCounterForA += 1
+      }
+    }
+  }
 }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala
index b11ba5d30fbd3..5711532abcf80 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala
@@ -17,12 +17,16 @@
 
 package org.apache.spark.mllib.linalg
 
-import breeze.linalg.{Matrix => BM, DenseMatrix => BDM}
+import breeze.linalg.{Matrix => BM, DenseMatrix => BDM, CSCMatrix => BSM}
+
+import org.apache.spark.util.random.XORShiftRandom
+
+import java.util.Arrays
 
 /**
  * Trait for a local matrix.
  */
-trait Matrix extends Serializable {
+sealed trait Matrix extends Serializable {
 
   /** Number of rows. */
   def numRows: Int
@@ -37,8 +41,46 @@ trait Matrix extends Serializable {
   private[mllib] def toBreeze: BM[Double]
 
   /** Gets the (i, j)-th element. */
-  private[mllib] def apply(i: Int, j: Int): Double = toBreeze(i, j)
+  private[mllib] def apply(i: Int, j: Int): Double
+
+  /** Return the index for the (i, j)-th element in the backing array. */
+  private[mllib] def index(i: Int, j: Int): Int
+
+  /** Update element at (i, j) */
+  private[mllib] def update(i: Int, j: Int, v: Double): Unit
+
+  /** Get a deep copy of the matrix. */
+  def copy: Matrix
 
+  /** Convenience method for `Matrix`-`DenseMatrix` multiplication. */
+  def multiply(y: DenseMatrix): DenseMatrix = {
+    val C: DenseMatrix = Matrices.zeros(numRows, y.numCols).asInstanceOf[DenseMatrix]
+    BLAS.gemm(false, false, 1.0, this, y, 0.0, C)
+    C
+  }
+
+  /** Convenience method for `Matrix`-`DenseVector` multiplication. */
+  def multiply(y: DenseVector): DenseVector = {
+    val output = new DenseVector(new Array[Double](numRows))
+    BLAS.gemv(1.0, this, y, 0.0, output)
+    output
+  }
+
+  /** Convenience method for `Matrix`^T^-`DenseMatrix` multiplication. */
+  def transposeMultiply(y: DenseMatrix): DenseMatrix = {
+    val C: DenseMatrix = Matrices.zeros(numCols, y.numCols).asInstanceOf[DenseMatrix]
+    BLAS.gemm(true, false, 1.0, this, y, 0.0, C)
+    C
+  }
+
+  /** Convenience method for `Matrix`^T^-`DenseVector` multiplication. */
+  def transposeMultiply(y: DenseVector): DenseVector = {
+    val output = new DenseVector(new Array[Double](numCols))
+    BLAS.gemv(true, 1.0, this, y, 0.0, output)
+    output
+  }
+
+  /** A human readable representation of the matrix */
   override def toString: String = toBreeze.toString()
 }
 
@@ -59,11 +101,98 @@ trait Matrix extends Serializable {
  */
 class DenseMatrix(val numRows: Int, val numCols: Int, val values: Array[Double]) extends Matrix {
 
-  require(values.length == numRows * numCols)
+  require(values.length == numRows * numCols, "The number of values supplied doesn't match the " +
+    s"size of the matrix! values.length: ${values.length}, numRows * numCols: ${numRows * numCols}")
 
   override def toArray: Array[Double] = values
 
-  private[mllib] override def toBreeze: BM[Double] = new BDM[Double](numRows, numCols, values)
+  private[mllib] def toBreeze: BM[Double] = new BDM[Double](numRows, numCols, values)
+
+  private[mllib] def apply(i: Int): Double = values(i)
+
+  private[mllib] def apply(i: Int, j: Int): Double = values(index(i, j))
+
+  private[mllib] def index(i: Int, j: Int): Int = i + numRows * j
+
+  private[mllib] def update(i: Int, j: Int, v: Double): Unit = {
+    values(index(i, j)) = v
+  }
+
+  override def copy = new DenseMatrix(numRows, numCols, values.clone())
+}
+
+/**
+ * Column-majored sparse matrix.
+ * The entry values are stored in Compressed Sparse Column (CSC) format.
+ * For example, the following matrix
+ * {{{
+ *   1.0 0.0 4.0
+ *   0.0 3.0 5.0
+ *   2.0 0.0 6.0
+ * }}}
+ * is stored as `values: [1.0, 2.0, 3.0, 4.0, 5.0, 6.0]`,
+ * `rowIndices=[0, 2, 1, 0, 1, 2]`, `colPointers=[0, 2, 3, 6]`.
+ *
+ * @param numRows number of rows
+ * @param numCols number of columns
+ * @param colPtrs the index corresponding to the start of a new column
+ * @param rowIndices the row index of the entry. They must be in strictly increasing order for each
+ *                   column
+ * @param values non-zero matrix entries in column major
+ */
+class SparseMatrix(
+    val numRows: Int,
+    val numCols: Int,
+    val colPtrs: Array[Int],
+    val rowIndices: Array[Int],
+    val values: Array[Double]) extends Matrix {
+
+  require(values.length == rowIndices.length, "The number of row indices and values don't match! " +
+    s"values.length: ${values.length}, rowIndices.length: ${rowIndices.length}")
+  require(colPtrs.length == numCols + 1, "The length of the column indices should be the " +
+    s"number of columns + 1. Currently, colPointers.length: ${colPtrs.length}, " +
+    s"numCols: $numCols")
+
+  override def toArray: Array[Double] = {
+    val arr = new Array[Double](numRows * numCols)
+    var j = 0
+    while (j < numCols) {
+      var i = colPtrs(j)
+      val indEnd = colPtrs(j + 1)
+      val offset = j * numRows
+      while (i < indEnd) {
+        val rowIndex = rowIndices(i)
+        arr(offset + rowIndex) = values(i)
+        i += 1
+      }
+      j += 1
+    }
+    arr
+  }
+
+  private[mllib] def toBreeze: BM[Double] =
+    new BSM[Double](values, numRows, numCols, colPtrs, rowIndices)
+
+  private[mllib] def apply(i: Int, j: Int): Double = {
+    val ind = index(i, j)
+    if (ind < 0) 0.0 else values(ind)
+  }
+
+  private[mllib] def index(i: Int, j: Int): Int = {
+    Arrays.binarySearch(rowIndices, colPtrs(j), colPtrs(j + 1), i)
+  }
+
+  private[mllib] def update(i: Int, j: Int, v: Double): Unit = {
+    val ind = index(i, j)
+    if (ind == -1){
+      throw new NoSuchElementException("The given row and column indices correspond to a zero " +
+        "value. Only non-zero elements in Sparse Matrices can be updated.")
+    } else {
+      values(index(i, j)) = v
+    }
+  }
+
+  override def copy = new SparseMatrix(numRows, numCols, colPtrs, rowIndices, values.clone())
 }
 
 /**
@@ -82,6 +211,24 @@ object Matrices {
     new DenseMatrix(numRows, numCols, values)
   }
 
+  /**
+   * Creates a column-majored sparse matrix in Compressed Sparse Column (CSC) format.
+   *
+   * @param numRows number of rows
+   * @param numCols number of columns
+   * @param colPtrs the index corresponding to the start of a new column
+   * @param rowIndices the row index of the entry
+   * @param values non-zero matrix entries in column major
+   */
+  def sparse(
+     numRows: Int,
+     numCols: Int,
+     colPtrs: Array[Int],
+     rowIndices: Array[Int],
+     values: Array[Double]): Matrix = {
+    new SparseMatrix(numRows, numCols, colPtrs, rowIndices, values)
+  }
+
   /**
    * Creates a Matrix instance from a breeze matrix.
    * @param breeze a breeze matrix
@@ -93,9 +240,84 @@ object Matrices {
         require(dm.majorStride == dm.rows,
           "Do not support stride size different from the number of rows.")
         new DenseMatrix(dm.rows, dm.cols, dm.data)
+      case sm: BSM[Double] =>
+        new SparseMatrix(sm.rows, sm.cols, sm.colPtrs, sm.rowIndices, sm.data)
       case _ =>
         throw new UnsupportedOperationException(
           s"Do not support conversion from type ${breeze.getClass.getName}.")
     }
   }
+
+  /**
+   * Generate a `DenseMatrix` consisting of zeros.
+   * @param numRows number of rows of the matrix
+   * @param numCols number of columns of the matrix
+   * @return `DenseMatrix` with size `numRows` x `numCols` and values of zeros
+   */
+  def zeros(numRows: Int, numCols: Int): Matrix =
+    new DenseMatrix(numRows, numCols, new Array[Double](numRows * numCols))
+
+  /**
+   * Generate a `DenseMatrix` consisting of ones.
+   * @param numRows number of rows of the matrix
+   * @param numCols number of columns of the matrix
+   * @return `DenseMatrix` with size `numRows` x `numCols` and values of ones
+   */
+  def ones(numRows: Int, numCols: Int): Matrix =
+    new DenseMatrix(numRows, numCols, Array.fill(numRows * numCols)(1.0))
+
+  /**
+   * Generate an Identity Matrix in `DenseMatrix` format.
+   * @param n number of rows and columns of the matrix
+   * @return `DenseMatrix` with size `n` x `n` and values of ones on the diagonal
+   */
+  def eye(n: Int): Matrix = {
+    val identity = Matrices.zeros(n, n)
+    var i = 0
+    while (i < n){
+      identity.update(i, i, 1.0)
+      i += 1
+    }
+    identity
+  }
+
+  /**
+   * Generate a `DenseMatrix` consisting of i.i.d. uniform random numbers.
+   * @param numRows number of rows of the matrix
+   * @param numCols number of columns of the matrix
+   * @return `DenseMatrix` with size `numRows` x `numCols` and values in U(0, 1)
+   */
+  def rand(numRows: Int, numCols: Int): Matrix = {
+    val rand = new XORShiftRandom
+    new DenseMatrix(numRows, numCols, Array.fill(numRows * numCols)(rand.nextDouble()))
+  }
+
+  /**
+   * Generate a `DenseMatrix` consisting of i.i.d. gaussian random numbers.
+   * @param numRows number of rows of the matrix
+   * @param numCols number of columns of the matrix
+   * @return `DenseMatrix` with size `numRows` x `numCols` and values in N(0, 1)
+   */
+  def randn(numRows: Int, numCols: Int): Matrix = {
+    val rand = new XORShiftRandom
+    new DenseMatrix(numRows, numCols, Array.fill(numRows * numCols)(rand.nextGaussian()))
+  }
+
+  /**
+   * Generate a diagonal matrix in `DenseMatrix` format from the supplied values.
+   * @param vector a `Vector` tat will form the values on the diagonal of the matrix
+   * @return Square `DenseMatrix` with size `values.length` x `values.length` and `values`
+   *         on the diagonal
+   */
+  def diag(vector: Vector): Matrix = {
+    val n = vector.size
+    val matrix = Matrices.eye(n)
+    val values = vector.toArray
+    var i = 0
+    while (i < n) {
+      matrix.update(i, i, values(i))
+      i += 1
+    }
+    matrix
+  }
 }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
index a45781d12e41e..6af225b7f49f7 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
@@ -33,7 +33,7 @@ import org.apache.spark.SparkException
  *
  * Note: Users should not implement this interface.
  */
-trait Vector extends Serializable {
+sealed trait Vector extends Serializable {
 
   /**
    * Size of the vector.
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/linalg/BLASSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/linalg/BLASSuite.scala
index 1952e6734ecf7..5d70c914f14b0 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/linalg/BLASSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/linalg/BLASSuite.scala
@@ -126,4 +126,115 @@ class BLASSuite extends FunSuite {
       }
     }
   }
+
+  test("gemm") {
+
+    val dA =
+      new DenseMatrix(4, 3, Array(0.0, 1.0, 0.0, 0.0, 2.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 3.0))
+    val sA = new SparseMatrix(4, 3, Array(0, 1, 3, 4), Array(1, 0, 2, 3), Array(1.0, 2.0, 1.0, 3.0))
+
+    val B = new DenseMatrix(3, 2, Array(1.0, 0.0, 0.0, 0.0, 2.0, 1.0))
+    val expected = new DenseMatrix(4, 2, Array(0.0, 1.0, 0.0, 0.0, 4.0, 0.0, 2.0, 3.0))
+
+    assert(dA multiply B ~== expected absTol 1e-15)
+    assert(sA multiply B ~== expected absTol 1e-15)
+
+    val C1 = new DenseMatrix(4, 2, Array(1.0, 0.0, 2.0, 1.0, 0.0, 0.0, 1.0, 0.0))
+    val C2 = C1.copy
+    val C3 = C1.copy
+    val C4 = C1.copy
+    val C5 = C1.copy
+    val C6 = C1.copy
+    val C7 = C1.copy
+    val C8 = C1.copy
+    val expected2 = new DenseMatrix(4, 2, Array(2.0, 1.0, 4.0, 2.0, 4.0, 0.0, 4.0, 3.0))
+    val expected3 = new DenseMatrix(4, 2, Array(2.0, 2.0, 4.0, 2.0, 8.0, 0.0, 6.0, 6.0))
+
+    gemm(1.0, dA, B, 2.0, C1)
+    gemm(1.0, sA, B, 2.0, C2)
+    gemm(2.0, dA, B, 2.0, C3)
+    gemm(2.0, sA, B, 2.0, C4)
+    assert(C1 ~== expected2 absTol 1e-15)
+    assert(C2 ~== expected2 absTol 1e-15)
+    assert(C3 ~== expected3 absTol 1e-15)
+    assert(C4 ~== expected3 absTol 1e-15)
+
+    withClue("columns of A don't match the rows of B") {
+      intercept[Exception] {
+        gemm(true, false, 1.0, dA, B, 2.0, C1)
+      }
+    }
+
+    val dAT =
+      new DenseMatrix(3, 4, Array(0.0, 2.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 3.0))
+    val sAT =
+      new SparseMatrix(3, 4, Array(0, 1, 2, 3, 4), Array(1, 0, 1, 2), Array(2.0, 1.0, 1.0, 3.0))
+
+    assert(dAT transposeMultiply B ~== expected absTol 1e-15)
+    assert(sAT transposeMultiply B ~== expected absTol 1e-15)
+
+    gemm(true, false, 1.0, dAT, B, 2.0, C5)
+    gemm(true, false, 1.0, sAT, B, 2.0, C6)
+    gemm(true, false, 2.0, dAT, B, 2.0, C7)
+    gemm(true, false, 2.0, sAT, B, 2.0, C8)
+    assert(C5 ~== expected2 absTol 1e-15)
+    assert(C6 ~== expected2 absTol 1e-15)
+    assert(C7 ~== expected3 absTol 1e-15)
+    assert(C8 ~== expected3 absTol 1e-15)
+  }
+
+  test("gemv") {
+
+    val dA =
+      new DenseMatrix(4, 3, Array(0.0, 1.0, 0.0, 0.0, 2.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 3.0))
+    val sA = new SparseMatrix(4, 3, Array(0, 1, 3, 4), Array(1, 0, 2, 3), Array(1.0, 2.0, 1.0, 3.0))
+
+    val x = new DenseVector(Array(1.0, 2.0, 3.0))
+    val expected = new DenseVector(Array(4.0, 1.0, 2.0, 9.0))
+
+    assert(dA multiply x ~== expected absTol 1e-15)
+    assert(sA multiply x ~== expected absTol 1e-15)
+
+    val y1 = new DenseVector(Array(1.0, 3.0, 1.0, 0.0))
+    val y2 = y1.copy
+    val y3 = y1.copy
+    val y4 = y1.copy
+    val y5 = y1.copy
+    val y6 = y1.copy
+    val y7 = y1.copy
+    val y8 = y1.copy
+    val expected2 = new DenseVector(Array(6.0, 7.0, 4.0, 9.0))
+    val expected3 = new DenseVector(Array(10.0, 8.0, 6.0, 18.0))
+
+    gemv(1.0, dA, x, 2.0, y1)
+    gemv(1.0, sA, x, 2.0, y2)
+    gemv(2.0, dA, x, 2.0, y3)
+    gemv(2.0, sA, x, 2.0, y4)
+    assert(y1 ~== expected2 absTol 1e-15)
+    assert(y2 ~== expected2 absTol 1e-15)
+    assert(y3 ~== expected3 absTol 1e-15)
+    assert(y4 ~== expected3 absTol 1e-15)
+    withClue("columns of A don't match the rows of B") {
+      intercept[Exception] {
+        gemv(true, 1.0, dA, x, 2.0, y1)
+      }
+    }
+
+    val dAT =
+      new DenseMatrix(3, 4, Array(0.0, 2.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 3.0))
+    val sAT =
+      new SparseMatrix(3, 4, Array(0, 1, 2, 3, 4), Array(1, 0, 1, 2), Array(2.0, 1.0, 1.0, 3.0))
+
+    assert(dAT transposeMultiply x ~== expected absTol 1e-15)
+    assert(sAT transposeMultiply x ~== expected absTol 1e-15)
+
+    gemv(true, 1.0, dAT, x, 2.0, y5)
+    gemv(true, 1.0, sAT, x, 2.0, y6)
+    gemv(true, 2.0, dAT, x, 2.0, y7)
+    gemv(true, 2.0, sAT, x, 2.0, y8)
+    assert(y5 ~== expected2 absTol 1e-15)
+    assert(y6 ~== expected2 absTol 1e-15)
+    assert(y7 ~== expected3 absTol 1e-15)
+    assert(y8 ~== expected3 absTol 1e-15)
+  }
 }
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/linalg/BreezeMatrixConversionSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/linalg/BreezeMatrixConversionSuite.scala
index 82d49c76ed02b..73a6d3a27d868 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/linalg/BreezeMatrixConversionSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/linalg/BreezeMatrixConversionSuite.scala
@@ -19,7 +19,7 @@ package org.apache.spark.mllib.linalg
 
 import org.scalatest.FunSuite
 
-import breeze.linalg.{DenseMatrix => BDM}
+import breeze.linalg.{DenseMatrix => BDM, CSCMatrix => BSM}
 
 class BreezeMatrixConversionSuite extends FunSuite {
   test("dense matrix to breeze") {
@@ -37,4 +37,26 @@ class BreezeMatrixConversionSuite extends FunSuite {
     assert(mat.numCols === breeze.cols)
     assert(mat.values.eq(breeze.data), "should not copy data")
   }
+
+  test("sparse matrix to breeze") {
+    val values = Array(1.0, 2.0, 4.0, 5.0)
+    val colPtrs = Array(0, 2, 4)
+    val rowIndices = Array(1, 2, 1, 2)
+    val mat = Matrices.sparse(3, 2, colPtrs, rowIndices, values)
+    val breeze = mat.toBreeze.asInstanceOf[BSM[Double]]
+    assert(breeze.rows === mat.numRows)
+    assert(breeze.cols === mat.numCols)
+    assert(breeze.data.eq(mat.asInstanceOf[SparseMatrix].values), "should not copy data")
+  }
+
+  test("sparse breeze matrix to sparse matrix") {
+    val values = Array(1.0, 2.0, 4.0, 5.0)
+    val colPtrs = Array(0, 2, 4)
+    val rowIndices = Array(1, 2, 1, 2)
+    val breeze = new BSM[Double](values, 3, 2, colPtrs, rowIndices)
+    val mat = Matrices.fromBreeze(breeze).asInstanceOf[SparseMatrix]
+    assert(mat.numRows === breeze.rows)
+    assert(mat.numCols === breeze.cols)
+    assert(mat.values.eq(breeze.data), "should not copy data")
+  }
 }
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/linalg/MatricesSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/linalg/MatricesSuite.scala
index 9c66b4db9f16b..5f8b8c4b72697 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/linalg/MatricesSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/linalg/MatricesSuite.scala
@@ -36,4 +36,80 @@ class MatricesSuite extends FunSuite {
       Matrices.dense(3, 2, Array(0.0, 1.0, 2.0))
     }
   }
+
+  test("sparse matrix construction") {
+    val m = 3
+    val n = 2
+    val values = Array(1.0, 2.0, 4.0, 5.0)
+    val colPtrs = Array(0, 2, 4)
+    val rowIndices = Array(1, 2, 1, 2)
+    val mat = Matrices.sparse(m, n, colPtrs, rowIndices, values).asInstanceOf[SparseMatrix]
+    assert(mat.numRows === m)
+    assert(mat.numCols === n)
+    assert(mat.values.eq(values), "should not copy data")
+    assert(mat.colPtrs.eq(colPtrs), "should not copy data")
+    assert(mat.rowIndices.eq(rowIndices), "should not copy data")
+  }
+
+  test("sparse matrix construction with wrong number of elements") {
+    intercept[IllegalArgumentException] {
+      Matrices.sparse(3, 2, Array(0, 1), Array(1, 2, 1), Array(0.0, 1.0, 2.0))
+    }
+
+    intercept[IllegalArgumentException] {
+      Matrices.sparse(3, 2, Array(0, 1, 2), Array(1, 2), Array(0.0, 1.0, 2.0))
+    }
+  }
+
+  test("matrix copies are deep copies") {
+    val m = 3
+    val n = 2
+
+    val denseMat = Matrices.dense(m, n, Array(0.0, 1.0, 2.0, 3.0, 4.0, 5.0))
+    val denseCopy = denseMat.copy
+
+    assert(!denseMat.toArray.eq(denseCopy.toArray))
+
+    val values = Array(1.0, 2.0, 4.0, 5.0)
+    val colPtrs = Array(0, 2, 4)
+    val rowIndices = Array(1, 2, 1, 2)
+    val sparseMat = Matrices.sparse(m, n, colPtrs, rowIndices, values)
+    val sparseCopy = sparseMat.copy
+
+    assert(!sparseMat.toArray.eq(sparseCopy.toArray))
+  }
+
+  test("matrix indexing and updating") {
+    val m = 3
+    val n = 2
+    val allValues = Array(0.0, 1.0, 2.0, 3.0, 4.0, 0.0)
+
+    val denseMat = new DenseMatrix(m, n, allValues)
+
+    assert(denseMat(0, 1) === 3.0)
+    assert(denseMat(0, 1) === denseMat.values(3))
+    assert(denseMat(0, 1) === denseMat(3))
+    assert(denseMat(0, 0) === 0.0)
+
+    denseMat.update(0, 0, 10.0)
+    assert(denseMat(0, 0) === 10.0)
+    assert(denseMat.values(0) === 10.0)
+
+    val sparseValues = Array(1.0, 2.0, 3.0, 4.0)
+    val colPtrs = Array(0, 2, 4)
+    val rowIndices = Array(1, 2, 0, 1)
+    val sparseMat = new SparseMatrix(m, n, colPtrs, rowIndices, sparseValues)
+
+    assert(sparseMat(0, 1) === 3.0)
+    assert(sparseMat(0, 1) === sparseMat.values(2))
+    assert(sparseMat(0, 0) === 0.0)
+
+    intercept[NoSuchElementException] {
+      sparseMat.update(0, 0, 10.0)
+    }
+
+    sparseMat.update(0, 1, 10.0)
+    assert(sparseMat(0, 1) === 10.0)
+    assert(sparseMat.values(2) === 10.0)
+  }
 }
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/util/TestingUtils.scala b/mllib/src/test/scala/org/apache/spark/mllib/util/TestingUtils.scala
index 29cc42d8cbea7..30b906aaa3ba4 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/util/TestingUtils.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/util/TestingUtils.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.mllib.util
 
-import org.apache.spark.mllib.linalg.Vector
+import org.apache.spark.mllib.linalg.{Matrix, Vector}
 import org.scalatest.exceptions.TestFailedException
 
 object TestingUtils {
@@ -169,4 +169,67 @@ object TestingUtils {
     override def toString = x.toString
   }
 
+  case class CompareMatrixRightSide(
+     fun: (Matrix, Matrix, Double) => Boolean, y: Matrix, eps: Double, method: String)
+
+  /**
+   * Implicit class for comparing two matrices using relative tolerance or absolute tolerance.
+   */
+  implicit class MatrixWithAlmostEquals(val x: Matrix) {
+
+    /**
+     * When the difference of two vectors are within eps, returns true; otherwise, returns false.
+     */
+    def ~=(r: CompareMatrixRightSide): Boolean = r.fun(x, r.y, r.eps)
+
+    /**
+     * When the difference of two vectors are within eps, returns false; otherwise, returns true.
+     */
+    def !~=(r: CompareMatrixRightSide): Boolean = !r.fun(x, r.y, r.eps)
+
+    /**
+     * Throws exception when the difference of two vectors are NOT within eps;
+     * otherwise, returns true.
+     */
+    def ~==(r: CompareMatrixRightSide): Boolean = {
+      if (!r.fun(x, r.y, r.eps)) {
+        throw new TestFailedException(
+          s"Expected \n$x\n and \n${r.y}\n to be within ${r.eps}${r.method} for all elements.", 0)
+      }
+      true
+    }
+
+    /**
+     * Throws exception when the difference of two matrices are within eps; otherwise, returns true.
+     */
+    def !~==(r: CompareMatrixRightSide): Boolean = {
+      if (r.fun(x, r.y, r.eps)) {
+        throw new TestFailedException(
+          s"Did not expect \n$x\n and \n${r.y}\n to be within " +
+            "${r.eps}${r.method} for all elements.", 0)
+      }
+      true
+    }
+
+    /**
+     * Comparison using absolute tolerance.
+     */
+    def absTol(eps: Double): CompareMatrixRightSide = CompareMatrixRightSide(
+      (x: Matrix, y: Matrix, eps: Double) => {
+        x.toArray.zip(y.toArray).forall(x => x._1 ~= x._2 absTol eps)
+      }, x, eps, ABS_TOL_MSG)
+
+    /**
+     * Comparison using relative tolerance. Note that comparing against sparse vector
+     * with elements having value of zero will raise exception because it involves with
+     * comparing against zero.
+     */
+    def relTol(eps: Double): CompareMatrixRightSide = CompareMatrixRightSide(
+      (x: Matrix, y: Matrix, eps: Double) => {
+        x.toArray.zip(y.toArray).forall(x => x._1 ~= x._2 relTol eps)
+      }, x, eps, REL_TOL_MSG)
+
+    override def toString = x.toString
+  }
+
 }
diff --git a/project/MimaExcludes.scala b/project/MimaExcludes.scala
index 2f1e05dfcc7b1..3280e662fa0b1 100644
--- a/project/MimaExcludes.scala
+++ b/project/MimaExcludes.scala
@@ -37,7 +37,9 @@ object MimaExcludes {
           Seq(
             MimaBuild.excludeSparkPackage("deploy"),
             MimaBuild.excludeSparkPackage("graphx")
-          )
+          ) ++
+          MimaBuild.excludeSparkClass("mllib.linalg.Matrix") ++
+          MimaBuild.excludeSparkClass("mllib.linalg.Vector")
 
         case v if v.startsWith("1.1") =>
           Seq(

From 3bbbdd8180cf316c6f8dde0e879410b6b29f8cc3 Mon Sep 17 00:00:00 2001
From: Larry Xiao <xiaodi@sjtu.edu.cn>
Date: Thu, 18 Sep 2014 23:32:32 -0700
Subject: [PATCH 347/489] [SPARK-2062][GraphX] VertexRDD.apply does not use the
 mergeFunc

VertexRDD.apply had a bug where it ignored the merge function for
duplicate vertices and instead used whichever vertex attribute occurred
first. This commit fixes the bug by passing the merge function through
to ShippableVertexPartition.apply, which merges any duplicates using the
merge function and then fills in missing vertices using the specified
default vertex attribute. This commit also adds a unit test for
VertexRDD.apply.

Author: Larry Xiao <xiaodi@sjtu.edu.cn>
Author: Blie Arkansol <xiaodi@sjtu.edu.cn>
Author: Ankur Dave <ankurdave@gmail.com>

Closes #1903 from larryxiao/2062 and squashes the following commits:

625aa9d [Blie Arkansol] Merge pull request #1 from ankurdave/SPARK-2062
476770b [Ankur Dave] ShippableVertexPartition.initFrom: Don't run mergeFunc on default values
614059f [Larry Xiao] doc update: note about the default null value vertices construction
dfdb3c9 [Larry Xiao] minor fix
1c70366 [Larry Xiao] scalastyle check: wrap line, parameter list indent 4 spaces
e4ca697 [Larry Xiao] [TEST] VertexRDD.apply mergeFunc
6a35ea8 [Larry Xiao] [TEST] VertexRDD.apply mergeFunc
4fbc29c [Blie Arkansol] undo unnecessary change
efae765 [Larry Xiao] fix mistakes: should be able to call with or without mergeFunc
b2422f9 [Larry Xiao] Merge branch '2062' of github.com:larryxiao/spark into 2062
52dc7f7 [Larry Xiao] pass mergeFunc to VertexPartitionBase, where merge is handled
581e9ee [Larry Xiao] TODO: VertexRDDSuite
20d80a3 [Larry Xiao] [SPARK-2062][GraphX] VertexRDD.apply does not use the mergeFunc
---
 .../org/apache/spark/graphx/VertexRDD.scala   |  4 +--
 .../impl/ShippableVertexPartition.scala       | 28 +++++++++++++++----
 .../apache/spark/graphx/VertexRDDSuite.scala  | 11 ++++++++
 3 files changed, 36 insertions(+), 7 deletions(-)

diff --git a/graphx/src/main/scala/org/apache/spark/graphx/VertexRDD.scala b/graphx/src/main/scala/org/apache/spark/graphx/VertexRDD.scala
index 04fbc9dbab8d1..2c8b245955d12 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/VertexRDD.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/VertexRDD.scala
@@ -392,7 +392,7 @@ object VertexRDD {
    */
   def apply[VD: ClassTag](
       vertices: RDD[(VertexId, VD)], edges: EdgeRDD[_, _], defaultVal: VD): VertexRDD[VD] = {
-    VertexRDD(vertices, edges, defaultVal, (a, b) => b)
+    VertexRDD(vertices, edges, defaultVal, (a, b) => a)
   }
 
   /**
@@ -419,7 +419,7 @@ object VertexRDD {
       (vertexIter, routingTableIter) =>
         val routingTable =
           if (routingTableIter.hasNext) routingTableIter.next() else RoutingTablePartition.empty
-        Iterator(ShippableVertexPartition(vertexIter, routingTable, defaultVal))
+        Iterator(ShippableVertexPartition(vertexIter, routingTable, defaultVal, mergeFunc))
     }
     new VertexRDD(vertexPartitions)
   }
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/impl/ShippableVertexPartition.scala b/graphx/src/main/scala/org/apache/spark/graphx/impl/ShippableVertexPartition.scala
index dca54b8a7da86..5412d720475dc 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/impl/ShippableVertexPartition.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/impl/ShippableVertexPartition.scala
@@ -36,7 +36,7 @@ private[graphx]
 object ShippableVertexPartition {
   /** Construct a `ShippableVertexPartition` from the given vertices without any routing table. */
   def apply[VD: ClassTag](iter: Iterator[(VertexId, VD)]): ShippableVertexPartition[VD] =
-    apply(iter, RoutingTablePartition.empty, null.asInstanceOf[VD])
+    apply(iter, RoutingTablePartition.empty, null.asInstanceOf[VD], (a, b) => a)
 
   /**
    * Construct a `ShippableVertexPartition` from the given vertices with the specified routing
@@ -44,10 +44,28 @@ object ShippableVertexPartition {
    */
   def apply[VD: ClassTag](
       iter: Iterator[(VertexId, VD)], routingTable: RoutingTablePartition, defaultVal: VD)
-    : ShippableVertexPartition[VD] = {
-    val fullIter = iter ++ routingTable.iterator.map(vid => (vid, defaultVal))
-    val (index, values, mask) = VertexPartitionBase.initFrom(fullIter, (a: VD, b: VD) => a)
-    new ShippableVertexPartition(index, values, mask, routingTable)
+    : ShippableVertexPartition[VD] =
+    apply(iter, routingTable, defaultVal, (a, b) => a)
+
+  /**
+   * Construct a `ShippableVertexPartition` from the given vertices with the specified routing
+   * table, filling in missing vertices mentioned in the routing table using `defaultVal`,
+   * and merging duplicate vertex atrribute with mergeFunc.
+   */
+  def apply[VD: ClassTag](
+      iter: Iterator[(VertexId, VD)], routingTable: RoutingTablePartition, defaultVal: VD,
+      mergeFunc: (VD, VD) => VD): ShippableVertexPartition[VD] = {
+    val map = new GraphXPrimitiveKeyOpenHashMap[VertexId, VD]
+    // Merge the given vertices using mergeFunc
+    iter.foreach { pair =>
+      map.setMerge(pair._1, pair._2, mergeFunc)
+    }
+    // Fill in missing vertices mentioned in the routing table
+    routingTable.iterator.foreach { vid =>
+      map.changeValue(vid, defaultVal, identity)
+    }
+
+    new ShippableVertexPartition(map.keySet, map._values, map.keySet.getBitSet, routingTable)
   }
 
   import scala.language.implicitConversions
diff --git a/graphx/src/test/scala/org/apache/spark/graphx/VertexRDDSuite.scala b/graphx/src/test/scala/org/apache/spark/graphx/VertexRDDSuite.scala
index cc86bafd2d644..42d3f21dbae98 100644
--- a/graphx/src/test/scala/org/apache/spark/graphx/VertexRDDSuite.scala
+++ b/graphx/src/test/scala/org/apache/spark/graphx/VertexRDDSuite.scala
@@ -99,4 +99,15 @@ class VertexRDDSuite extends FunSuite with LocalSparkContext {
     }
   }
 
+  test("mergeFunc") {
+    // test to see if the mergeFunc is working correctly
+    withSpark { sc =>
+      val verts = sc.parallelize(List((0L, 0), (1L, 1), (1L, 2), (2L, 3), (2L, 3), (2L, 3)))
+      val edges = EdgeRDD.fromEdges(sc.parallelize(List.empty[Edge[Int]]))
+      val rdd = VertexRDD(verts, edges, 0, (a: Int, b: Int) => a + b)
+      // test merge function
+      assert(rdd.collect.toSet == Set((0L, 0), (1L, 3), (2L, 9)))
+    }
+  }
+
 }

From a48956f5825d2255736eee50de79fba79bcb7e39 Mon Sep 17 00:00:00 2001
From: Patrick Wendell <pwendell@gmail.com>
Date: Fri, 19 Sep 2014 10:49:42 -0700
Subject: [PATCH 348/489] MAINTENANCE: Automated closing of pull requests.

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

Closes #726 (close requested by 'pwendell')
Closes #151 (close requested by 'pwendell')

From be0c7563ea001a59469dbba219d2a8ef5785afa3 Mon Sep 17 00:00:00 2001
From: Matthew Farrellee <matt@redhat.com>
Date: Fri, 19 Sep 2014 14:31:50 -0700
Subject: [PATCH 349/489] [SPARK-1701] Clarify slice vs partition in the
 programming guide

This is a partial solution to SPARK-1701, only addressing the
documentation confusion.

Additional work can be to actually change the numSlices parameter name
across languages, with care required for scala & python to maintain
backward compatibility for named parameters.

Author: Matthew Farrellee <matt@redhat.com>

Closes #2305 from mattf/SPARK-1701 and squashes the following commits:

c0af05d [Matthew Farrellee] Further tweak
06f80fc [Matthew Farrellee] Wording tweak from Josh Rosen's review
7b045e0 [Matthew Farrellee] [SPARK-1701] Clarify slice vs partition in the programming guide
---
 docs/programming-guide.md | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/docs/programming-guide.md b/docs/programming-guide.md
index 624cc744dfd51..01d378af574b5 100644
--- a/docs/programming-guide.md
+++ b/docs/programming-guide.md
@@ -286,7 +286,7 @@ We describe operations on distributed datasets later on.
 
 </div>
 
-One important parameter for parallel collections is the number of *slices* to cut the dataset into. Spark will run one task for each slice of the cluster. Typically you want 2-4 slices for each CPU in your cluster. Normally, Spark tries to set the number of slices automatically based on your cluster. However, you can also set it manually by passing it as a second parameter to `parallelize` (e.g. `sc.parallelize(data, 10)`).
+One important parameter for parallel collections is the number of *partitions* to cut the dataset into. Spark will run one task for each partition of the cluster. Typically you want 2-4 partitions for each CPU in your cluster. Normally, Spark tries to set the number of partitions automatically based on your cluster. However, you can also set it manually by passing it as a second parameter to `parallelize` (e.g. `sc.parallelize(data, 10)`). Note: some places in the code use the term slices (a synonym for partitions) to maintain backward compatibility.
 
 ## External Datasets
 
@@ -311,7 +311,7 @@ Some notes on reading files with Spark:
 
 * All of Spark's file-based input methods, including `textFile`, support running on directories, compressed files, and wildcards as well. For example, you can use `textFile("/my/directory")`, `textFile("/my/directory/*.txt")`, and `textFile("/my/directory/*.gz")`.
 
-* The `textFile` method also takes an optional second argument for controlling the number of slices of the file. By default, Spark creates one slice for each block of the file (blocks being 64MB by default in HDFS), but you can also ask for a higher number of slices by passing a larger value. Note that you cannot have fewer slices than blocks.
+* The `textFile` method also takes an optional second argument for controlling the number of partitions of the file. By default, Spark creates one partition for each block of the file (blocks being 64MB by default in HDFS), but you can also ask for a higher number of partitions by passing a larger value. Note that you cannot have fewer partitions than blocks.
 
 Apart from text files, Spark's Scala API also supports several other data formats:
 
@@ -343,7 +343,7 @@ Some notes on reading files with Spark:
 
 * All of Spark's file-based input methods, including `textFile`, support running on directories, compressed files, and wildcards as well. For example, you can use `textFile("/my/directory")`, `textFile("/my/directory/*.txt")`, and `textFile("/my/directory/*.gz")`.
 
-* The `textFile` method also takes an optional second argument for controlling the number of slices of the file. By default, Spark creates one slice for each block of the file (blocks being 64MB by default in HDFS), but you can also ask for a higher number of slices by passing a larger value. Note that you cannot have fewer slices than blocks.
+* The `textFile` method also takes an optional second argument for controlling the number of partitions of the file. By default, Spark creates one partition for each block of the file (blocks being 64MB by default in HDFS), but you can also ask for a higher number of partitions by passing a larger value. Note that you cannot have fewer partitions than blocks.
 
 Apart from text files, Spark's Java API also supports several other data formats:
 
@@ -375,7 +375,7 @@ Some notes on reading files with Spark:
 
 * All of Spark's file-based input methods, including `textFile`, support running on directories, compressed files, and wildcards as well. For example, you can use `textFile("/my/directory")`, `textFile("/my/directory/*.txt")`, and `textFile("/my/directory/*.gz")`.
 
-* The `textFile` method also takes an optional second argument for controlling the number of slices of the file. By default, Spark creates one slice for each block of the file (blocks being 64MB by default in HDFS), but you can also ask for a higher number of slices by passing a larger value. Note that you cannot have fewer slices than blocks.
+* The `textFile` method also takes an optional second argument for controlling the number of partitions of the file. By default, Spark creates one partition for each block of the file (blocks being 64MB by default in HDFS), but you can also ask for a higher number of partitions by passing a larger value. Note that you cannot have fewer partitions than blocks.
 
 Apart from text files, Spark's Python API also supports several other data formats:
 

From a03e5b81e91d9d792b6a2e01d1505394ea303dd8 Mon Sep 17 00:00:00 2001
From: Matthew Farrellee <matt@redhat.com>
Date: Fri, 19 Sep 2014 14:35:22 -0700
Subject: [PATCH 350/489] [SPARK-1701] [PySpark] remove slice terminology from
 python examples

Author: Matthew Farrellee <matt@redhat.com>

Closes #2304 from mattf/SPARK-1701-partition-over-slice-for-python-examples and squashes the following commits:

928a581 [Matthew Farrellee] [SPARK-1701] [PySpark] remove slice terminology from python examples
---
 examples/src/main/python/als.py                | 12 ++++++------
 examples/src/main/python/pi.py                 |  8 ++++----
 examples/src/main/python/transitive_closure.py |  6 +++---
 3 files changed, 13 insertions(+), 13 deletions(-)

diff --git a/examples/src/main/python/als.py b/examples/src/main/python/als.py
index 5b1fa4d997eeb..70b6146e39a87 100755
--- a/examples/src/main/python/als.py
+++ b/examples/src/main/python/als.py
@@ -54,7 +54,7 @@ def update(i, vec, mat, ratings):
 if __name__ == "__main__":
 
     """
-    Usage: als [M] [U] [F] [iterations] [slices]"
+    Usage: als [M] [U] [F] [iterations] [partitions]"
     """
 
     print >> sys.stderr, """WARN: This is a naive implementation of ALS and is given as an
@@ -66,10 +66,10 @@ def update(i, vec, mat, ratings):
     U = int(sys.argv[2]) if len(sys.argv) > 2 else 500
     F = int(sys.argv[3]) if len(sys.argv) > 3 else 10
     ITERATIONS = int(sys.argv[4]) if len(sys.argv) > 4 else 5
-    slices = int(sys.argv[5]) if len(sys.argv) > 5 else 2
+    partitions = int(sys.argv[5]) if len(sys.argv) > 5 else 2
 
-    print "Running ALS with M=%d, U=%d, F=%d, iters=%d, slices=%d\n" % \
-        (M, U, F, ITERATIONS, slices)
+    print "Running ALS with M=%d, U=%d, F=%d, iters=%d, partitions=%d\n" % \
+        (M, U, F, ITERATIONS, partitions)
 
     R = matrix(rand(M, F)) * matrix(rand(U, F).T)
     ms = matrix(rand(M, F))
@@ -80,7 +80,7 @@ def update(i, vec, mat, ratings):
     usb = sc.broadcast(us)
 
     for i in range(ITERATIONS):
-        ms = sc.parallelize(range(M), slices) \
+        ms = sc.parallelize(range(M), partitions) \
                .map(lambda x: update(x, msb.value[x, :], usb.value, Rb.value)) \
                .collect()
         # collect() returns a list, so array ends up being
@@ -88,7 +88,7 @@ def update(i, vec, mat, ratings):
         ms = matrix(np.array(ms)[:, :, 0])
         msb = sc.broadcast(ms)
 
-        us = sc.parallelize(range(U), slices) \
+        us = sc.parallelize(range(U), partitions) \
                .map(lambda x: update(x, usb.value[x, :], msb.value, Rb.value.T)) \
                .collect()
         us = matrix(np.array(us)[:, :, 0])
diff --git a/examples/src/main/python/pi.py b/examples/src/main/python/pi.py
index ee9036adfa281..a7c74e969cdb9 100755
--- a/examples/src/main/python/pi.py
+++ b/examples/src/main/python/pi.py
@@ -24,18 +24,18 @@
 
 if __name__ == "__main__":
     """
-        Usage: pi [slices]
+        Usage: pi [partitions]
     """
     sc = SparkContext(appName="PythonPi")
-    slices = int(sys.argv[1]) if len(sys.argv) > 1 else 2
-    n = 100000 * slices
+    partitions = int(sys.argv[1]) if len(sys.argv) > 1 else 2
+    n = 100000 * partitions
 
     def f(_):
         x = random() * 2 - 1
         y = random() * 2 - 1
         return 1 if x ** 2 + y ** 2 < 1 else 0
 
-    count = sc.parallelize(xrange(1, n + 1), slices).map(f).reduce(add)
+    count = sc.parallelize(xrange(1, n + 1), partitions).map(f).reduce(add)
     print "Pi is roughly %f" % (4.0 * count / n)
 
     sc.stop()
diff --git a/examples/src/main/python/transitive_closure.py b/examples/src/main/python/transitive_closure.py
index bf331b542c438..00a281bfb6506 100755
--- a/examples/src/main/python/transitive_closure.py
+++ b/examples/src/main/python/transitive_closure.py
@@ -37,11 +37,11 @@ def generateGraph():
 
 if __name__ == "__main__":
     """
-    Usage: transitive_closure [slices]
+    Usage: transitive_closure [partitions]
     """
     sc = SparkContext(appName="PythonTransitiveClosure")
-    slices = int(sys.argv[1]) if len(sys.argv) > 1 else 2
-    tc = sc.parallelize(generateGraph(), slices).cache()
+    partitions = int(sys.argv[1]) if len(sys.argv) > 1 else 2
+    tc = sc.parallelize(generateGraph(), partitions).cache()
 
     # Linear transitive closure: each round grows paths by one edge,
     # by joining the graph's edges with the already-discovered paths.

From fce5e251d636c788cda91345867e0294280c074d Mon Sep 17 00:00:00 2001
From: Davies Liu <davies.liu@gmail.com>
Date: Fri, 19 Sep 2014 15:01:11 -0700
Subject: [PATCH 351/489] [SPARK-3491] [MLlib] [PySpark] use pickle to
 serialize data in MLlib

Currently, we serialize the data between JVM and Python case by case manually, this cannot scale to support so many APIs in MLlib.

This patch will try to address this problem by serialize the data using pickle protocol, using Pyrolite library to serialize/deserialize in JVM. Pickle protocol can be easily extended to support customized class.

All the modules are refactored to use this protocol.

Known issues: There will be some performance regression (both CPU and memory, the serialized data increased)

Author: Davies Liu <davies.liu@gmail.com>

Closes #2378 from davies/pickle_mllib and squashes the following commits:

dffbba2 [Davies Liu] Merge branch 'master' of github.com:apache/spark into pickle_mllib
810f97f [Davies Liu] fix equal of matrix
032cd62 [Davies Liu] add more type check and conversion for user_product
bd738ab [Davies Liu] address comments
e431377 [Davies Liu] fix cache of rdd, refactor
19d0967 [Davies Liu] refactor Picklers
2511e76 [Davies Liu] cleanup
1fccf1a [Davies Liu] address comments
a2cc855 [Davies Liu] fix tests
9ceff73 [Davies Liu] test size of serialized Rating
44e0551 [Davies Liu] fix cache
a379a81 [Davies Liu] fix pickle array in python2.7
df625c7 [Davies Liu] Merge commit '154d141' into pickle_mllib
154d141 [Davies Liu] fix autobatchedpickler
44736d7 [Davies Liu] speed up pickling array in Python 2.7
e1d1bfc [Davies Liu] refactor
708dc02 [Davies Liu] fix tests
9dcfb63 [Davies Liu] fix style
88034f0 [Davies Liu] rafactor, address comments
46a501e [Davies Liu] choose batch size automatically
df19464 [Davies Liu] memorize the module and class name during pickleing
f3506c5 [Davies Liu] Merge branch 'master' into pickle_mllib
722dd96 [Davies Liu] cleanup _common.py
0ee1525 [Davies Liu] remove outdated tests
b02e34f [Davies Liu] remove _common.py
84c721d [Davies Liu] Merge branch 'master' into pickle_mllib
4d7963e [Davies Liu] remove muanlly serialization
6d26b03 [Davies Liu] fix tests
c383544 [Davies Liu] classification
f2a0856 [Davies Liu] mllib/regression
d9f691f [Davies Liu] mllib/util
cccb8b1 [Davies Liu] mllib/tree
8fe166a [Davies Liu] Merge branch 'pickle' into pickle_mllib
aa2287e [Davies Liu] random
f1544c4 [Davies Liu] refactor clustering
52d1350 [Davies Liu] use new protocol in mllib/stat
b30ef35 [Davies Liu] use pickle to serialize data for mllib/recommendation
f44f771 [Davies Liu] enable tests about array
3908f5c [Davies Liu] Merge branch 'master' into pickle
c77c87b [Davies Liu] cleanup debugging code
60e4e2f [Davies Liu] support unpickle array.array for Python 2.6
---
 .../apache/spark/api/python/PythonRDD.scala   |  31 +-
 .../apache/spark/api/python/SerDeUtil.scala   |   4 +-
 .../mllib/api/python/PythonMLLibAPI.scala     | 487 ++++++---------
 .../apache/spark/mllib/linalg/Matrices.scala  |  10 +-
 .../MatrixFactorizationModel.scala            |  15 -
 .../api/python/PythonMLLibAPISuite.scala      |  44 +-
 python/epydoc.conf                            |   2 +-
 python/pyspark/context.py                     |   1 +
 python/pyspark/mllib/_common.py               | 562 ------------------
 python/pyspark/mllib/classification.py        |  61 +-
 python/pyspark/mllib/clustering.py            |  38 +-
 python/pyspark/mllib/linalg.py                | 256 ++++++--
 python/pyspark/mllib/random.py                |  54 +-
 python/pyspark/mllib/recommendation.py        |  69 ++-
 python/pyspark/mllib/regression.py            | 105 ++--
 python/pyspark/mllib/stat.py                  |  63 +-
 python/pyspark/mllib/tests.py                 |  99 +--
 python/pyspark/mllib/tree.py                  | 167 +++---
 python/pyspark/mllib/util.py                  |  43 +-
 python/pyspark/rdd.py                         |  10 +-
 python/pyspark/serializers.py                 |  36 ++
 python/run-tests                              |   1 -
 22 files changed, 891 insertions(+), 1267 deletions(-)
 delete mode 100644 python/pyspark/mllib/_common.py

diff --git a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
index 12b345a8fa7c3..f9ff4ea6ca157 100644
--- a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
+++ b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
@@ -775,17 +775,36 @@ private[spark] object PythonRDD extends Logging {
     }.toJavaRDD()
   }
 
+  private class AutoBatchedPickler(iter: Iterator[Any]) extends Iterator[Array[Byte]] {
+    private val pickle = new Pickler()
+    private var batch = 1
+    private val buffer = new mutable.ArrayBuffer[Any]
+
+    override def hasNext(): Boolean = iter.hasNext
+
+    override def next(): Array[Byte] = {
+      while (iter.hasNext && buffer.length < batch) {
+        buffer += iter.next()
+      }
+      val bytes = pickle.dumps(buffer.toArray)
+      val size = bytes.length
+      // let  1M < size < 10M
+      if (size < 1024 * 1024) {
+        batch *= 2
+      } else if (size > 1024 * 1024 * 10 && batch > 1) {
+        batch /= 2
+      }
+      buffer.clear()
+      bytes
+    }
+  }
+
   /**
    * Convert an RDD of Java objects to an RDD of serialized Python objects, that is usable by
    * PySpark.
    */
   def javaToPython(jRDD: JavaRDD[Any]): JavaRDD[Array[Byte]] = {
-    jRDD.rdd.mapPartitions { iter =>
-      val pickle = new Pickler
-      iter.map { row =>
-        pickle.dumps(row)
-      }
-    }
+    jRDD.rdd.mapPartitions { iter => new AutoBatchedPickler(iter) }
   }
 
   /**
diff --git a/core/src/main/scala/org/apache/spark/api/python/SerDeUtil.scala b/core/src/main/scala/org/apache/spark/api/python/SerDeUtil.scala
index 6668797f5f8be..7903457b17e13 100644
--- a/core/src/main/scala/org/apache/spark/api/python/SerDeUtil.scala
+++ b/core/src/main/scala/org/apache/spark/api/python/SerDeUtil.scala
@@ -68,8 +68,8 @@ private[python] object SerDeUtil extends Logging {
         construct(args ++ Array(""))
       } else if (args.length == 2 && args(1).isInstanceOf[String]) {
         val typecode = args(0).asInstanceOf[String].charAt(0)
-        val data: String = args(1).asInstanceOf[String]
-        construct(typecode, machineCodes(typecode), data.getBytes("ISO-8859-1"))
+        val data: Array[Byte] = args(1).asInstanceOf[String].getBytes("ISO-8859-1")
+        construct(typecode, machineCodes(typecode), data)
       } else {
         super.construct(args)
       }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
index fa0fa69f38634..9164c294ac7b8 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
@@ -17,16 +17,20 @@
 
 package org.apache.spark.mllib.api.python
 
-import java.nio.{ByteBuffer, ByteOrder}
+import java.io.OutputStream
 
 import scala.collection.JavaConverters._
+import scala.language.existentials
+import scala.reflect.ClassTag
+
+import net.razorvine.pickle._
 
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.api.java.{JavaRDD, JavaSparkContext}
 import org.apache.spark.mllib.classification._
 import org.apache.spark.mllib.clustering._
 import org.apache.spark.mllib.optimization._
-import org.apache.spark.mllib.linalg.{Matrix, SparseVector, Vector, Vectors}
+import org.apache.spark.mllib.linalg._
 import org.apache.spark.mllib.random.{RandomRDDs => RG}
 import org.apache.spark.mllib.recommendation._
 import org.apache.spark.mllib.regression._
@@ -40,11 +44,10 @@ import org.apache.spark.mllib.util.MLUtils
 import org.apache.spark.rdd.RDD
 import org.apache.spark.util.Utils
 
+
 /**
  * :: DeveloperApi ::
  * The Java stubs necessary for the Python mllib bindings.
- *
- * See python/pyspark/mllib/_common.py for the mutually agreed upon data format.
  */
 @DeveloperApi
 class PythonMLLibAPI extends Serializable {
@@ -60,18 +63,17 @@ class PythonMLLibAPI extends Serializable {
   def loadLabeledPoints(
       jsc: JavaSparkContext,
       path: String,
-      minPartitions: Int): JavaRDD[Array[Byte]] =
-    MLUtils.loadLabeledPoints(jsc.sc, path, minPartitions).map(SerDe.serializeLabeledPoint)
+      minPartitions: Int): JavaRDD[LabeledPoint] =
+    MLUtils.loadLabeledPoints(jsc.sc, path, minPartitions)
 
   private def trainRegressionModel(
       trainFunc: (RDD[LabeledPoint], Vector) => GeneralizedLinearModel,
-      dataBytesJRDD: JavaRDD[Array[Byte]],
+      data: JavaRDD[LabeledPoint],
       initialWeightsBA: Array[Byte]): java.util.LinkedList[java.lang.Object] = {
-    val data = dataBytesJRDD.rdd.map(SerDe.deserializeLabeledPoint)
-    val initialWeights = SerDe.deserializeDoubleVector(initialWeightsBA)
-    val model = trainFunc(data, initialWeights)
+    val initialWeights = SerDe.loads(initialWeightsBA).asInstanceOf[Vector]
+    val model = trainFunc(data.rdd, initialWeights)
     val ret = new java.util.LinkedList[java.lang.Object]()
-    ret.add(SerDe.serializeDoubleVector(model.weights))
+    ret.add(SerDe.dumps(model.weights))
     ret.add(model.intercept: java.lang.Double)
     ret
   }
@@ -80,7 +82,7 @@ class PythonMLLibAPI extends Serializable {
    * Java stub for Python mllib LinearRegressionWithSGD.train()
    */
   def trainLinearRegressionModelWithSGD(
-      dataBytesJRDD: JavaRDD[Array[Byte]],
+      data: JavaRDD[LabeledPoint],
       numIterations: Int,
       stepSize: Double,
       miniBatchFraction: Double,
@@ -106,7 +108,7 @@ class PythonMLLibAPI extends Serializable {
     trainRegressionModel(
       (data, initialWeights) =>
         lrAlg.run(data, initialWeights),
-      dataBytesJRDD,
+      data,
       initialWeightsBA)
   }
 
@@ -114,7 +116,7 @@ class PythonMLLibAPI extends Serializable {
    * Java stub for Python mllib LassoWithSGD.train()
    */
   def trainLassoModelWithSGD(
-      dataBytesJRDD: JavaRDD[Array[Byte]],
+      data: JavaRDD[LabeledPoint],
       numIterations: Int,
       stepSize: Double,
       regParam: Double,
@@ -129,7 +131,7 @@ class PythonMLLibAPI extends Serializable {
           regParam,
           miniBatchFraction,
           initialWeights),
-      dataBytesJRDD,
+      data,
       initialWeightsBA)
   }
 
@@ -137,7 +139,7 @@ class PythonMLLibAPI extends Serializable {
    * Java stub for Python mllib RidgeRegressionWithSGD.train()
    */
   def trainRidgeModelWithSGD(
-      dataBytesJRDD: JavaRDD[Array[Byte]],
+      data: JavaRDD[LabeledPoint],
       numIterations: Int,
       stepSize: Double,
       regParam: Double,
@@ -152,7 +154,7 @@ class PythonMLLibAPI extends Serializable {
           regParam,
           miniBatchFraction,
           initialWeights),
-      dataBytesJRDD,
+      data,
       initialWeightsBA)
   }
 
@@ -160,7 +162,7 @@ class PythonMLLibAPI extends Serializable {
    * Java stub for Python mllib SVMWithSGD.train()
    */
   def trainSVMModelWithSGD(
-      dataBytesJRDD: JavaRDD[Array[Byte]],
+      data: JavaRDD[LabeledPoint],
       numIterations: Int,
       stepSize: Double,
       regParam: Double,
@@ -186,7 +188,7 @@ class PythonMLLibAPI extends Serializable {
     trainRegressionModel(
       (data, initialWeights) =>
         SVMAlg.run(data, initialWeights),
-      dataBytesJRDD,
+      data,
       initialWeightsBA)
   }
 
@@ -194,7 +196,7 @@ class PythonMLLibAPI extends Serializable {
    * Java stub for Python mllib LogisticRegressionWithSGD.train()
    */
   def trainLogisticRegressionModelWithSGD(
-      dataBytesJRDD: JavaRDD[Array[Byte]],
+      data: JavaRDD[LabeledPoint],
       numIterations: Int,
       stepSize: Double,
       miniBatchFraction: Double,
@@ -220,7 +222,7 @@ class PythonMLLibAPI extends Serializable {
     trainRegressionModel(
       (data, initialWeights) =>
         LogRegAlg.run(data, initialWeights),
-      dataBytesJRDD,
+      data,
       initialWeightsBA)
   }
 
@@ -228,14 +230,13 @@ class PythonMLLibAPI extends Serializable {
    * Java stub for NaiveBayes.train()
    */
   def trainNaiveBayes(
-      dataBytesJRDD: JavaRDD[Array[Byte]],
+      data: JavaRDD[LabeledPoint],
       lambda: Double): java.util.List[java.lang.Object] = {
-    val data = dataBytesJRDD.rdd.map(SerDe.deserializeLabeledPoint)
-    val model = NaiveBayes.train(data, lambda)
+    val model = NaiveBayes.train(data.rdd, lambda)
     val ret = new java.util.LinkedList[java.lang.Object]()
-    ret.add(SerDe.serializeDoubleVector(Vectors.dense(model.labels)))
-    ret.add(SerDe.serializeDoubleVector(Vectors.dense(model.pi)))
-    ret.add(SerDe.serializeDoubleMatrix(model.theta))
+    ret.add(Vectors.dense(model.labels))
+    ret.add(Vectors.dense(model.pi))
+    ret.add(model.theta)
     ret
   }
 
@@ -243,16 +244,12 @@ class PythonMLLibAPI extends Serializable {
    * Java stub for Python mllib KMeans.train()
    */
   def trainKMeansModel(
-      dataBytesJRDD: JavaRDD[Array[Byte]],
+      data: JavaRDD[Vector],
       k: Int,
       maxIterations: Int,
       runs: Int,
-      initializationMode: String): java.util.List[java.lang.Object] = {
-    val data = dataBytesJRDD.rdd.map(bytes => SerDe.deserializeDoubleVector(bytes))
-    val model = KMeans.train(data, k, maxIterations, runs, initializationMode)
-    val ret = new java.util.LinkedList[java.lang.Object]()
-    ret.add(SerDe.serializeDoubleMatrix(model.clusterCenters.map(_.toArray)))
-    ret
+      initializationMode: String): KMeansModel = {
+    KMeans.train(data.rdd, k, maxIterations, runs, initializationMode)
   }
 
   /**
@@ -262,13 +259,12 @@ class PythonMLLibAPI extends Serializable {
    * the Py4J documentation.
    */
   def trainALSModel(
-      ratingsBytesJRDD: JavaRDD[Array[Byte]],
+      ratings: JavaRDD[Rating],
       rank: Int,
       iterations: Int,
       lambda: Double,
       blocks: Int): MatrixFactorizationModel = {
-    val ratings = ratingsBytesJRDD.rdd.map(SerDe.unpackRating)
-    ALS.train(ratings, rank, iterations, lambda, blocks)
+    ALS.train(ratings.rdd, rank, iterations, lambda, blocks)
   }
 
   /**
@@ -278,14 +274,13 @@ class PythonMLLibAPI extends Serializable {
    * exit; see the Py4J documentation.
    */
   def trainImplicitALSModel(
-      ratingsBytesJRDD: JavaRDD[Array[Byte]],
+      ratingsJRDD: JavaRDD[Rating],
       rank: Int,
       iterations: Int,
       lambda: Double,
       blocks: Int,
       alpha: Double): MatrixFactorizationModel = {
-    val ratings = ratingsBytesJRDD.rdd.map(SerDe.unpackRating)
-    ALS.trainImplicit(ratings, rank, iterations, lambda, blocks, alpha)
+    ALS.trainImplicit(ratingsJRDD.rdd, rank, iterations, lambda, blocks, alpha)
   }
 
   /**
@@ -293,11 +288,11 @@ class PythonMLLibAPI extends Serializable {
    * This stub returns a handle to the Java object instead of the content of the Java object.
    * Extra care needs to be taken in the Python code to ensure it gets freed on exit;
    * see the Py4J documentation.
-   * @param dataBytesJRDD  Training data
+   * @param data  Training data
    * @param categoricalFeaturesInfoJMap  Categorical features info, as Java map
    */
   def trainDecisionTreeModel(
-      dataBytesJRDD: JavaRDD[Array[Byte]],
+      data: JavaRDD[LabeledPoint],
       algoStr: String,
       numClasses: Int,
       categoricalFeaturesInfoJMap: java.util.Map[Int, Int],
@@ -307,8 +302,6 @@ class PythonMLLibAPI extends Serializable {
       minInstancesPerNode: Int,
       minInfoGain: Double): DecisionTreeModel = {
 
-    val data = dataBytesJRDD.rdd.map(SerDe.deserializeLabeledPoint)
-
     val algo = Algo.fromString(algoStr)
     val impurity = Impurities.fromString(impurityStr)
 
@@ -322,44 +315,15 @@ class PythonMLLibAPI extends Serializable {
       minInstancesPerNode = minInstancesPerNode,
       minInfoGain = minInfoGain)
 
-    DecisionTree.train(data, strategy)
-  }
-
-  /**
-   * Predict the label of the given data point.
-   * This is a Java stub for python DecisionTreeModel.predict()
-   *
-   * @param featuresBytes Serialized feature vector for data point
-   * @return predicted label
-   */
-  def predictDecisionTreeModel(
-      model: DecisionTreeModel,
-      featuresBytes: Array[Byte]): Double = {
-    val features: Vector = SerDe.deserializeDoubleVector(featuresBytes)
-    model.predict(features)
-  }
-
-  /**
-   * Predict the labels of the given data points.
-   * This is a Java stub for python DecisionTreeModel.predict()
-   *
-   * @param dataJRDD A JavaRDD with serialized feature vectors
-   * @return JavaRDD of serialized predictions
-   */
-  def predictDecisionTreeModel(
-      model: DecisionTreeModel,
-      dataJRDD: JavaRDD[Array[Byte]]): JavaRDD[Array[Byte]] = {
-    val data = dataJRDD.rdd.map(xBytes => SerDe.deserializeDoubleVector(xBytes))
-    model.predict(data).map(SerDe.serializeDouble)
+    DecisionTree.train(data.rdd, strategy)
   }
 
   /**
    * Java stub for mllib Statistics.colStats(X: RDD[Vector]).
    * TODO figure out return type.
    */
-  def colStats(X: JavaRDD[Array[Byte]]): MultivariateStatisticalSummarySerialized = {
-    val cStats = Statistics.colStats(X.rdd.map(SerDe.deserializeDoubleVector(_)))
-    new MultivariateStatisticalSummarySerialized(cStats)
+  def colStats(rdd: JavaRDD[Vector]): MultivariateStatisticalSummary = {
+    Statistics.colStats(rdd.rdd)
   }
 
   /**
@@ -367,19 +331,15 @@ class PythonMLLibAPI extends Serializable {
    * Returns the correlation matrix serialized into a byte array understood by deserializers in
    * pyspark.
    */
-  def corr(X: JavaRDD[Array[Byte]], method: String): Array[Byte] = {
-    val inputMatrix = X.rdd.map(SerDe.deserializeDoubleVector(_))
-    val result = Statistics.corr(inputMatrix, getCorrNameOrDefault(method))
-    SerDe.serializeDoubleMatrix(SerDe.to2dArray(result))
+  def corr(x: JavaRDD[Vector], method: String): Matrix = {
+    Statistics.corr(x.rdd, getCorrNameOrDefault(method))
   }
 
   /**
    * Java stub for mllib Statistics.corr(x: RDD[Double], y: RDD[Double], method: String).
    */
-  def corr(x: JavaRDD[Array[Byte]], y: JavaRDD[Array[Byte]], method: String): Double = {
-    val xDeser = x.rdd.map(SerDe.deserializeDouble(_))
-    val yDeser = y.rdd.map(SerDe.deserializeDouble(_))
-    Statistics.corr(xDeser, yDeser, getCorrNameOrDefault(method))
+  def corr(x: JavaRDD[Double], y: JavaRDD[Double], method: String): Double = {
+    Statistics.corr(x.rdd, y.rdd, getCorrNameOrDefault(method))
   }
 
   // used by the corr methods to retrieve the name of the correlation method passed in via pyspark
@@ -411,10 +371,10 @@ class PythonMLLibAPI extends Serializable {
   def uniformRDD(jsc: JavaSparkContext,
       size: Long,
       numPartitions: java.lang.Integer,
-      seed: java.lang.Long): JavaRDD[Array[Byte]] = {
+      seed: java.lang.Long): JavaRDD[Double] = {
     val parts = getNumPartitionsOrDefault(numPartitions, jsc)
     val s = getSeedOrDefault(seed)
-    RG.uniformRDD(jsc.sc, size, parts, s).map(SerDe.serializeDouble)
+    RG.uniformRDD(jsc.sc, size, parts, s)
   }
 
   /**
@@ -423,10 +383,10 @@ class PythonMLLibAPI extends Serializable {
   def normalRDD(jsc: JavaSparkContext,
       size: Long,
       numPartitions: java.lang.Integer,
-      seed: java.lang.Long): JavaRDD[Array[Byte]] = {
+      seed: java.lang.Long): JavaRDD[Double] = {
     val parts = getNumPartitionsOrDefault(numPartitions, jsc)
     val s = getSeedOrDefault(seed)
-    RG.normalRDD(jsc.sc, size, parts, s).map(SerDe.serializeDouble)
+    RG.normalRDD(jsc.sc, size, parts, s)
   }
 
   /**
@@ -436,10 +396,10 @@ class PythonMLLibAPI extends Serializable {
       mean: Double,
       size: Long,
       numPartitions: java.lang.Integer,
-      seed: java.lang.Long): JavaRDD[Array[Byte]] = {
+      seed: java.lang.Long): JavaRDD[Double] = {
     val parts = getNumPartitionsOrDefault(numPartitions, jsc)
     val s = getSeedOrDefault(seed)
-    RG.poissonRDD(jsc.sc, mean, size, parts, s).map(SerDe.serializeDouble)
+    RG.poissonRDD(jsc.sc, mean, size, parts, s)
   }
 
   /**
@@ -449,10 +409,10 @@ class PythonMLLibAPI extends Serializable {
       numRows: Long,
       numCols: Int,
       numPartitions: java.lang.Integer,
-      seed: java.lang.Long): JavaRDD[Array[Byte]] = {
+      seed: java.lang.Long): JavaRDD[Vector] = {
     val parts = getNumPartitionsOrDefault(numPartitions, jsc)
     val s = getSeedOrDefault(seed)
-    RG.uniformVectorRDD(jsc.sc, numRows, numCols, parts, s).map(SerDe.serializeDoubleVector)
+    RG.uniformVectorRDD(jsc.sc, numRows, numCols, parts, s)
   }
 
   /**
@@ -462,10 +422,10 @@ class PythonMLLibAPI extends Serializable {
       numRows: Long,
       numCols: Int,
       numPartitions: java.lang.Integer,
-      seed: java.lang.Long): JavaRDD[Array[Byte]] = {
+      seed: java.lang.Long): JavaRDD[Vector] = {
     val parts = getNumPartitionsOrDefault(numPartitions, jsc)
     val s = getSeedOrDefault(seed)
-    RG.normalVectorRDD(jsc.sc, numRows, numCols, parts, s).map(SerDe.serializeDoubleVector)
+    RG.normalVectorRDD(jsc.sc, numRows, numCols, parts, s)
   }
 
   /**
@@ -476,259 +436,168 @@ class PythonMLLibAPI extends Serializable {
       numRows: Long,
       numCols: Int,
       numPartitions: java.lang.Integer,
-      seed: java.lang.Long): JavaRDD[Array[Byte]] = {
+      seed: java.lang.Long): JavaRDD[Vector] = {
     val parts = getNumPartitionsOrDefault(numPartitions, jsc)
     val s = getSeedOrDefault(seed)
-    RG.poissonVectorRDD(jsc.sc, mean, numRows, numCols, parts, s).map(SerDe.serializeDoubleVector)
+    RG.poissonVectorRDD(jsc.sc, mean, numRows, numCols, parts, s)
   }
 
 }
 
 /**
- * :: DeveloperApi ::
- * MultivariateStatisticalSummary with Vector fields serialized.
+ * SerDe utility functions for PythonMLLibAPI.
  */
-@DeveloperApi
-class MultivariateStatisticalSummarySerialized(val summary: MultivariateStatisticalSummary)
-  extends Serializable {
+private[spark] object SerDe extends Serializable {
 
-  def mean: Array[Byte] = SerDe.serializeDoubleVector(summary.mean)
+  val PYSPARK_PACKAGE = "pyspark.mllib"
 
-  def variance: Array[Byte] = SerDe.serializeDoubleVector(summary.variance)
+  /**
+   * Base class used for pickle
+   */
+  private[python] abstract class BasePickler[T: ClassTag]
+    extends IObjectPickler with IObjectConstructor {
+
+    private val cls = implicitly[ClassTag[T]].runtimeClass
+    private val module = PYSPARK_PACKAGE + "." + cls.getName.split('.')(4)
+    private val name = cls.getSimpleName
+
+    // register this to Pickler and Unpickler
+    def register(): Unit = {
+      Pickler.registerCustomPickler(this.getClass, this)
+      Pickler.registerCustomPickler(cls, this)
+      Unpickler.registerConstructor(module, name, this)
+    }
 
-  def count: Long = summary.count
+    def pickle(obj: Object, out: OutputStream, pickler: Pickler): Unit = {
+      if (obj == this) {
+        out.write(Opcodes.GLOBAL)
+        out.write((module + "\n" + name + "\n").getBytes())
+      } else {
+        pickler.save(this)  // it will be memorized by Pickler
+        saveState(obj, out, pickler)
+        out.write(Opcodes.REDUCE)
+      }
+    }
+
+    private[python] def saveObjects(out: OutputStream, pickler: Pickler, objects: Any*) = {
+      if (objects.length == 0 || objects.length > 3) {
+        out.write(Opcodes.MARK)
+      }
+      objects.foreach(pickler.save(_))
+      val code = objects.length match {
+        case 1 => Opcodes.TUPLE1
+        case 2 => Opcodes.TUPLE2
+        case 3 => Opcodes.TUPLE3
+        case _ => Opcodes.TUPLE
+      }
+      out.write(code)
+    }
 
-  def numNonzeros: Array[Byte] = SerDe.serializeDoubleVector(summary.numNonzeros)
+    private[python] def saveState(obj: Object, out: OutputStream, pickler: Pickler)
+  }
 
-  def max: Array[Byte] = SerDe.serializeDoubleVector(summary.max)
+  // Pickler for DenseVector
+  private[python] class DenseVectorPickler extends BasePickler[DenseVector] {
 
-  def min: Array[Byte] = SerDe.serializeDoubleVector(summary.min)
-}
+    def saveState(obj: Object, out: OutputStream, pickler: Pickler) = {
+      val vector: DenseVector = obj.asInstanceOf[DenseVector]
+      saveObjects(out, pickler, vector.toArray)
+    }
 
-/**
- * SerDe utility functions for PythonMLLibAPI.
- */
-private[spark] object SerDe extends Serializable {
-  private val DENSE_VECTOR_MAGIC: Byte = 1
-  private val SPARSE_VECTOR_MAGIC: Byte = 2
-  private val DENSE_MATRIX_MAGIC: Byte = 3
-  private val LABELED_POINT_MAGIC: Byte = 4
-
-  private[python] def deserializeDoubleVector(bytes: Array[Byte], offset: Int = 0): Vector = {
-    require(bytes.length - offset >= 5, "Byte array too short")
-    val magic = bytes(offset)
-    if (magic == DENSE_VECTOR_MAGIC) {
-      deserializeDenseVector(bytes, offset)
-    } else if (magic == SPARSE_VECTOR_MAGIC) {
-      deserializeSparseVector(bytes, offset)
-    } else {
-      throw new IllegalArgumentException("Magic " + magic + " is wrong.")
+    def construct(args: Array[Object]): Object = {
+      require(args.length == 1)
+      if (args.length != 1) {
+        throw new PickleException("should be 1")
+      }
+      new DenseVector(args(0).asInstanceOf[Array[Double]])
     }
   }
 
-  private[python] def deserializeDouble(bytes: Array[Byte], offset: Int = 0): Double = {
-    require(bytes.length - offset == 8, "Wrong size byte array for Double")
-    val bb = ByteBuffer.wrap(bytes, offset, bytes.length - offset)
-    bb.order(ByteOrder.nativeOrder())
-    bb.getDouble
-  }
-
-  private[python] def deserializeDenseVector(bytes: Array[Byte], offset: Int = 0): Vector = {
-    val packetLength = bytes.length - offset
-    require(packetLength >= 5, "Byte array too short")
-    val bb = ByteBuffer.wrap(bytes, offset, bytes.length - offset)
-    bb.order(ByteOrder.nativeOrder())
-    val magic = bb.get()
-    require(magic == DENSE_VECTOR_MAGIC, "Invalid magic: " + magic)
-    val length = bb.getInt()
-    require (packetLength == 5 + 8 * length, "Invalid packet length: " + packetLength)
-    val db = bb.asDoubleBuffer()
-    val ans = new Array[Double](length.toInt)
-    db.get(ans)
-    Vectors.dense(ans)
-  }
-
-  private[python] def deserializeSparseVector(bytes: Array[Byte], offset: Int = 0): Vector = {
-    val packetLength = bytes.length - offset
-    require(packetLength >= 9, "Byte array too short")
-    val bb = ByteBuffer.wrap(bytes, offset, bytes.length - offset)
-    bb.order(ByteOrder.nativeOrder())
-    val magic = bb.get()
-    require(magic == SPARSE_VECTOR_MAGIC, "Invalid magic: " + magic)
-    val size = bb.getInt()
-    val nonZeros = bb.getInt()
-    require (packetLength == 9 + 12 * nonZeros, "Invalid packet length: " + packetLength)
-    val ib = bb.asIntBuffer()
-    val indices = new Array[Int](nonZeros)
-    ib.get(indices)
-    bb.position(bb.position() + 4 * nonZeros)
-    val db = bb.asDoubleBuffer()
-    val values = new Array[Double](nonZeros)
-    db.get(values)
-    Vectors.sparse(size, indices, values)
-  }
+  // Pickler for DenseMatrix
+  private[python] class DenseMatrixPickler extends BasePickler[DenseMatrix] {
 
-  /**
-   * Returns an 8-byte array for the input Double.
-   *
-   * Note: we currently do not use a magic byte for double for storage efficiency.
-   * This should be reconsidered when we add Ser/De for other 8-byte types (e.g. Long), for safety.
-   * The corresponding deserializer, deserializeDouble, needs to be modified as well if the
-   * serialization scheme changes.
-   */
-  private[python] def serializeDouble(double: Double): Array[Byte] = {
-    val bytes = new Array[Byte](8)
-    val bb = ByteBuffer.wrap(bytes)
-    bb.order(ByteOrder.nativeOrder())
-    bb.putDouble(double)
-    bytes
-  }
-
-  private[python] def serializeDenseVector(doubles: Array[Double]): Array[Byte] = {
-    val len = doubles.length
-    val bytes = new Array[Byte](5 + 8 * len)
-    val bb = ByteBuffer.wrap(bytes)
-    bb.order(ByteOrder.nativeOrder())
-    bb.put(DENSE_VECTOR_MAGIC)
-    bb.putInt(len)
-    val db = bb.asDoubleBuffer()
-    db.put(doubles)
-    bytes
-  }
-
-  private[python] def serializeSparseVector(vector: SparseVector): Array[Byte] = {
-    val nonZeros = vector.indices.length
-    val bytes = new Array[Byte](9 + 12 * nonZeros)
-    val bb = ByteBuffer.wrap(bytes)
-    bb.order(ByteOrder.nativeOrder())
-    bb.put(SPARSE_VECTOR_MAGIC)
-    bb.putInt(vector.size)
-    bb.putInt(nonZeros)
-    val ib = bb.asIntBuffer()
-    ib.put(vector.indices)
-    bb.position(bb.position() + 4 * nonZeros)
-    val db = bb.asDoubleBuffer()
-    db.put(vector.values)
-    bytes
-  }
-
-  private[python] def serializeDoubleVector(vector: Vector): Array[Byte] = vector match {
-    case s: SparseVector =>
-      serializeSparseVector(s)
-    case _ =>
-      serializeDenseVector(vector.toArray)
-  }
-
-  private[python] def deserializeDoubleMatrix(bytes: Array[Byte]): Array[Array[Double]] = {
-    val packetLength = bytes.length
-    if (packetLength < 9) {
-      throw new IllegalArgumentException("Byte array too short.")
+    def saveState(obj: Object, out: OutputStream, pickler: Pickler) = {
+      val m: DenseMatrix = obj.asInstanceOf[DenseMatrix]
+      saveObjects(out, pickler, m.numRows, m.numCols, m.values)
     }
-    val bb = ByteBuffer.wrap(bytes)
-    bb.order(ByteOrder.nativeOrder())
-    val magic = bb.get()
-    if (magic != DENSE_MATRIX_MAGIC) {
-      throw new IllegalArgumentException("Magic " + magic + " is wrong.")
+
+    def construct(args: Array[Object]): Object = {
+      if (args.length != 3) {
+        throw new PickleException("should be 3")
+      }
+      new DenseMatrix(args(0).asInstanceOf[Int], args(1).asInstanceOf[Int],
+        args(2).asInstanceOf[Array[Double]])
     }
-    val rows = bb.getInt()
-    val cols = bb.getInt()
-    if (packetLength != 9 + 8 * rows * cols) {
-      throw new IllegalArgumentException("Size " + rows + "x" + cols + " is wrong.")
+  }
+
+  // Pickler for SparseVector
+  private[python] class SparseVectorPickler extends BasePickler[SparseVector] {
+
+    def saveState(obj: Object, out: OutputStream, pickler: Pickler) = {
+      val v: SparseVector = obj.asInstanceOf[SparseVector]
+      saveObjects(out, pickler, v.size, v.indices, v.values)
     }
-    val db = bb.asDoubleBuffer()
-    val ans = new Array[Array[Double]](rows.toInt)
-    for (i <- 0 until rows.toInt) {
-      ans(i) = new Array[Double](cols.toInt)
-      db.get(ans(i))
+
+    def construct(args: Array[Object]): Object = {
+      if (args.length != 3) {
+        throw new PickleException("should be 3")
+      }
+      new SparseVector(args(0).asInstanceOf[Int], args(1).asInstanceOf[Array[Int]],
+        args(2).asInstanceOf[Array[Double]])
     }
-    ans
   }
 
-  private[python] def serializeDoubleMatrix(doubles: Array[Array[Double]]): Array[Byte] = {
-    val rows = doubles.length
-    var cols = 0
-    if (rows > 0) {
-      cols = doubles(0).length
+  // Pickler for LabeledPoint
+  private[python] class LabeledPointPickler extends BasePickler[LabeledPoint] {
+
+    def saveState(obj: Object, out: OutputStream, pickler: Pickler) = {
+      val point: LabeledPoint = obj.asInstanceOf[LabeledPoint]
+      saveObjects(out, pickler, point.label, point.features)
     }
-    val bytes = new Array[Byte](9 + 8 * rows * cols)
-    val bb = ByteBuffer.wrap(bytes)
-    bb.order(ByteOrder.nativeOrder())
-    bb.put(DENSE_MATRIX_MAGIC)
-    bb.putInt(rows)
-    bb.putInt(cols)
-    val db = bb.asDoubleBuffer()
-    for (i <- 0 until rows) {
-      db.put(doubles(i))
+
+    def construct(args: Array[Object]): Object = {
+      if (args.length != 2) {
+        throw new PickleException("should be 2")
+      }
+      new LabeledPoint(args(0).asInstanceOf[Double], args(1).asInstanceOf[Vector])
     }
-    bytes
   }
 
-  private[python] def serializeLabeledPoint(p: LabeledPoint): Array[Byte] = {
-    val fb = serializeDoubleVector(p.features)
-    val bytes = new Array[Byte](1 + 8 + fb.length)
-    val bb = ByteBuffer.wrap(bytes)
-    bb.order(ByteOrder.nativeOrder())
-    bb.put(LABELED_POINT_MAGIC)
-    bb.putDouble(p.label)
-    bb.put(fb)
-    bytes
-  }
+  // Pickler for Rating
+  private[python] class RatingPickler extends BasePickler[Rating] {
 
-  private[python] def deserializeLabeledPoint(bytes: Array[Byte]): LabeledPoint = {
-    require(bytes.length >= 9, "Byte array too short")
-    val magic = bytes(0)
-    if (magic != LABELED_POINT_MAGIC) {
-      throw new IllegalArgumentException("Magic " + magic + " is wrong.")
+    def saveState(obj: Object, out: OutputStream, pickler: Pickler) = {
+      val rating: Rating = obj.asInstanceOf[Rating]
+      saveObjects(out, pickler, rating.user, rating.product, rating.rating)
     }
-    val labelBytes = ByteBuffer.wrap(bytes, 1, 8)
-    labelBytes.order(ByteOrder.nativeOrder())
-    val label = labelBytes.asDoubleBuffer().get(0)
-    LabeledPoint(label, deserializeDoubleVector(bytes, 9))
-  }
 
-  // Reformat a Matrix into Array[Array[Double]] for serialization
-  private[python] def to2dArray(matrix: Matrix): Array[Array[Double]] = {
-    val values = matrix.toArray
-    Array.tabulate(matrix.numRows, matrix.numCols)((i, j) => values(i + j * matrix.numRows))
+    def construct(args: Array[Object]): Object = {
+      if (args.length != 3) {
+        throw new PickleException("should be 3")
+      }
+      new Rating(args(0).asInstanceOf[Int], args(1).asInstanceOf[Int],
+        args(2).asInstanceOf[Double])
+    }
   }
 
+  def initialize(): Unit = {
+    new DenseVectorPickler().register()
+    new DenseMatrixPickler().register()
+    new SparseVectorPickler().register()
+    new LabeledPointPickler().register()
+    new RatingPickler().register()
+  }
 
-  /** Unpack a Rating object from an array of bytes */
-  private[python] def unpackRating(ratingBytes: Array[Byte]): Rating = {
-    val bb = ByteBuffer.wrap(ratingBytes)
-    bb.order(ByteOrder.nativeOrder())
-    val user = bb.getInt()
-    val product = bb.getInt()
-    val rating = bb.getDouble()
-    new Rating(user, product, rating)
+  def dumps(obj: AnyRef): Array[Byte] = {
+    new Pickler().dumps(obj)
   }
 
-  /** Unpack a tuple of Ints from an array of bytes */
-  def unpackTuple(tupleBytes: Array[Byte]): (Int, Int) = {
-    val bb = ByteBuffer.wrap(tupleBytes)
-    bb.order(ByteOrder.nativeOrder())
-    val v1 = bb.getInt()
-    val v2 = bb.getInt()
-    (v1, v2)
+  def loads(bytes: Array[Byte]): AnyRef = {
+    new Unpickler().loads(bytes)
   }
 
-  /**
-   * Serialize a Rating object into an array of bytes.
-   * It can be deserialized using RatingDeserializer().
-   *
-   * @param rate the Rating object to serialize
-   * @return
-   */
-  def serializeRating(rate: Rating): Array[Byte] = {
-    val len = 3
-    val bytes = new Array[Byte](4 + 8 * len)
-    val bb = ByteBuffer.wrap(bytes)
-    bb.order(ByteOrder.nativeOrder())
-    bb.putInt(len)
-    val db = bb.asDoubleBuffer()
-    db.put(rate.user.toDouble)
-    db.put(rate.product.toDouble)
-    db.put(rate.rating)
-    bytes
+  /* convert object into Tuple */
+  def asTupleRDD(rdd: RDD[Array[Any]]): RDD[(Int, Int)] = {
+    rdd.map(x => (x(0).asInstanceOf[Int], x(1).asInstanceOf[Int]))
   }
 }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala
index 5711532abcf80..4e87fe088ecc5 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala
@@ -17,12 +17,12 @@
 
 package org.apache.spark.mllib.linalg
 
+import java.util.Arrays
+
 import breeze.linalg.{Matrix => BM, DenseMatrix => BDM, CSCMatrix => BSM}
 
 import org.apache.spark.util.random.XORShiftRandom
 
-import java.util.Arrays
-
 /**
  * Trait for a local matrix.
  */
@@ -106,6 +106,12 @@ class DenseMatrix(val numRows: Int, val numCols: Int, val values: Array[Double])
 
   override def toArray: Array[Double] = values
 
+  override def equals(o: Any) = o match {
+    case m: DenseMatrix =>
+      m.numRows == numRows && m.numCols == numCols && Arrays.equals(toArray, m.toArray)
+    case _ => false
+  }
+
   private[mllib] def toBreeze: BM[Double] = new BDM[Double](numRows, numCols, values)
 
   private[mllib] def apply(i: Int): Double = values(i)
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala
index 478c6485052b6..66b58ba770160 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala
@@ -106,19 +106,4 @@ class MatrixFactorizationModel private[mllib] (
     }
     scored.top(num)(Ordering.by(_._2))
   }
-
-  /**
-   * :: DeveloperApi ::
-   * Predict the rating of many users for many products.
-   * This is a Java stub for python predictAll()
-   *
-   * @param usersProductsJRDD A JavaRDD with serialized tuples (user, product)
-   * @return JavaRDD of serialized Rating objects.
-   */
-  @DeveloperApi
-  def predict(usersProductsJRDD: JavaRDD[Array[Byte]]): JavaRDD[Array[Byte]] = {
-    val usersProducts = usersProductsJRDD.rdd.map(xBytes => SerDe.unpackTuple(xBytes))
-    predict(usersProducts).map(rate => SerDe.serializeRating(rate))
-  }
-
 }
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/api/python/PythonMLLibAPISuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/api/python/PythonMLLibAPISuite.scala
index 092d67bbc5238..db8ed62fa46ce 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/api/python/PythonMLLibAPISuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/api/python/PythonMLLibAPISuite.scala
@@ -19,12 +19,15 @@ package org.apache.spark.mllib.api.python
 
 import org.scalatest.FunSuite
 
-import org.apache.spark.mllib.linalg.{Matrices, Vectors}
+import org.apache.spark.mllib.linalg.{DenseMatrix, Matrices, Vectors}
 import org.apache.spark.mllib.regression.LabeledPoint
+import org.apache.spark.mllib.recommendation.Rating
 
 class PythonMLLibAPISuite extends FunSuite {
 
-  test("vector serialization") {
+  SerDe.initialize()
+
+  test("pickle vector") {
     val vectors = Seq(
       Vectors.dense(Array.empty[Double]),
       Vectors.dense(0.0),
@@ -33,14 +36,13 @@ class PythonMLLibAPISuite extends FunSuite {
       Vectors.sparse(1, Array.empty[Int], Array.empty[Double]),
       Vectors.sparse(2, Array(1), Array(-2.0)))
     vectors.foreach { v =>
-      val bytes = SerDe.serializeDoubleVector(v)
-      val u = SerDe.deserializeDoubleVector(bytes)
+      val u = SerDe.loads(SerDe.dumps(v))
       assert(u.getClass === v.getClass)
       assert(u === v)
     }
   }
 
-  test("labeled point serialization") {
+  test("pickle labeled point") {
     val points = Seq(
       LabeledPoint(0.0, Vectors.dense(Array.empty[Double])),
       LabeledPoint(1.0, Vectors.dense(0.0)),
@@ -49,34 +51,44 @@ class PythonMLLibAPISuite extends FunSuite {
       LabeledPoint(1.0, Vectors.sparse(1, Array.empty[Int], Array.empty[Double])),
       LabeledPoint(-0.5, Vectors.sparse(2, Array(1), Array(-2.0))))
     points.foreach { p =>
-      val bytes = SerDe.serializeLabeledPoint(p)
-      val q = SerDe.deserializeLabeledPoint(bytes)
+      val q = SerDe.loads(SerDe.dumps(p)).asInstanceOf[LabeledPoint]
       assert(q.label === p.label)
       assert(q.features.getClass === p.features.getClass)
       assert(q.features === p.features)
     }
   }
 
-  test("double serialization") {
+  test("pickle double") {
     for (x <- List(123.0, -10.0, 0.0, Double.MaxValue, Double.MinValue, Double.NaN)) {
-      val bytes = SerDe.serializeDouble(x)
-      val deser = SerDe.deserializeDouble(bytes)
+      val deser = SerDe.loads(SerDe.dumps(x.asInstanceOf[AnyRef])).asInstanceOf[Double]
       // We use `equals` here for comparison because we cannot use `==` for NaN
       assert(x.equals(deser))
     }
   }
 
-  test("matrix to 2D array") {
+  test("pickle matrix") {
     val values = Array[Double](0, 1.2, 3, 4.56, 7, 8)
     val matrix = Matrices.dense(2, 3, values)
-    val arr = SerDe.to2dArray(matrix)
-    val expected = Array(Array[Double](0, 3, 7), Array[Double](1.2, 4.56, 8))
-    assert(arr === expected)
+    val nm = SerDe.loads(SerDe.dumps(matrix)).asInstanceOf[DenseMatrix]
+    assert(matrix === nm)
 
     // Test conversion for empty matrix
     val empty = Array[Double]()
     val emptyMatrix = Matrices.dense(0, 0, empty)
-    val empty2D = SerDe.to2dArray(emptyMatrix)
-    assert(empty2D === Array[Array[Double]]())
+    val ne = SerDe.loads(SerDe.dumps(emptyMatrix)).asInstanceOf[DenseMatrix]
+    assert(emptyMatrix == ne)
+  }
+
+  test("pickle rating") {
+    val rat = new Rating(1, 2, 3.0)
+    val rat2 = SerDe.loads(SerDe.dumps(rat)).asInstanceOf[Rating]
+    assert(rat == rat2)
+
+    // Test name of class only occur once
+    val rats = (1 to 10).map(x => new Rating(x, x + 1, x + 3.0)).toArray
+    val bytes = SerDe.dumps(rats)
+    assert(bytes.toString.split("Rating").length == 1)
+    assert(bytes.length / 10 < 25) //  25 bytes per rating
+
   }
 }
diff --git a/python/epydoc.conf b/python/epydoc.conf
index 51c0faf359939..8593e08deda19 100644
--- a/python/epydoc.conf
+++ b/python/epydoc.conf
@@ -34,5 +34,5 @@ private: no
 
 exclude: pyspark.cloudpickle pyspark.worker pyspark.join
          pyspark.java_gateway pyspark.examples pyspark.shell pyspark.tests
-         pyspark.rddsampler pyspark.daemon pyspark.mllib._common
+         pyspark.rddsampler pyspark.daemon
          pyspark.mllib.tests pyspark.shuffle
diff --git a/python/pyspark/context.py b/python/pyspark/context.py
index a17f2c1203d36..064a24bff539c 100644
--- a/python/pyspark/context.py
+++ b/python/pyspark/context.py
@@ -211,6 +211,7 @@ def _ensure_initialized(cls, instance=None, gateway=None):
                 SparkContext._jvm = SparkContext._gateway.jvm
                 SparkContext._writeToFile = SparkContext._jvm.PythonRDD.writeToFile
                 SparkContext._jvm.SerDeUtil.initialize()
+                SparkContext._jvm.SerDe.initialize()
 
             if instance:
                 if (SparkContext._active_spark_context and
diff --git a/python/pyspark/mllib/_common.py b/python/pyspark/mllib/_common.py
deleted file mode 100644
index 68f6033616726..0000000000000
--- a/python/pyspark/mllib/_common.py
+++ /dev/null
@@ -1,562 +0,0 @@
-#
-# Licensed to the Apache Software Foundation (ASF) under one or more
-# contributor license agreements.  See the NOTICE file distributed with
-# this work for additional information regarding copyright ownership.
-# The ASF licenses this file to You under the Apache License, Version 2.0
-# (the "License"); you may not use this file except in compliance with
-# the License.  You may obtain a copy of the License at
-#
-#    http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
-
-import struct
-import sys
-import numpy
-from numpy import ndarray, float64, int64, int32, array_equal, array
-from pyspark import SparkContext, RDD
-from pyspark.mllib.linalg import SparseVector
-from pyspark.serializers import FramedSerializer
-
-
-"""
-Common utilities shared throughout MLlib, primarily for dealing with
-different data types. These include:
-- Serialization utilities to / from byte arrays that Java can handle
-- Serializers for other data types, like ALS Rating objects
-- Common methods for linear models
-- Methods to deal with the different vector types we support, such as
-  SparseVector and scipy.sparse matrices.
-"""
-
-
-# Check whether we have SciPy. MLlib works without it too, but if we have it, some methods,
-# such as _dot and _serialize_double_vector, start to support scipy.sparse matrices.
-
-_have_scipy = False
-_scipy_issparse = None
-try:
-    import scipy.sparse
-    _have_scipy = True
-    _scipy_issparse = scipy.sparse.issparse
-except:
-    # No SciPy in environment, but that's okay
-    pass
-
-
-# Serialization functions to and from Scala. These use the following formats, understood
-# by the PythonMLLibAPI class in Scala:
-#
-# Dense double vector format:
-#
-# [1-byte 1] [4-byte length] [length*8 bytes of data]
-#
-# Sparse double vector format:
-#
-# [1-byte 2] [4-byte length] [4-byte nonzeros] [nonzeros*4 bytes of indices] \
-# [nonzeros*8 bytes of values]
-#
-# Double matrix format:
-#
-# [1-byte 3] [4-byte rows] [4-byte cols] [rows*cols*8 bytes of data]
-#
-# LabeledPoint format:
-#
-# [1-byte 4] [8-byte label] [dense or sparse vector]
-#
-# This is all in machine-endian.  That means that the Java interpreter and the
-# Python interpreter must agree on what endian the machine is.
-
-
-DENSE_VECTOR_MAGIC = 1
-SPARSE_VECTOR_MAGIC = 2
-DENSE_MATRIX_MAGIC = 3
-LABELED_POINT_MAGIC = 4
-
-
-# Workaround for SPARK-2954: before Python 2.7, struct.unpack couldn't unpack bytearray()s.
-if sys.version_info[:2] <= (2, 6):
-    def _unpack(fmt, string):
-        return struct.unpack(fmt, buffer(string))
-else:
-    _unpack = struct.unpack
-
-
-def _deserialize_numpy_array(shape, ba, offset, dtype=float64):
-    """
-    Deserialize a numpy array of the given type from an offset in
-    bytearray ba, assigning it the given shape.
-
-    >>> x = array([1.0, 2.0, 3.0, 4.0, 5.0])
-    >>> array_equal(x, _deserialize_numpy_array(x.shape, x.data, 0))
-    True
-    >>> x = array([1.0, 2.0, 3.0, 4.0]).reshape(2,2)
-    >>> array_equal(x, _deserialize_numpy_array(x.shape, x.data, 0))
-    True
-    >>> x = array([1, 2, 3], dtype=int32)
-    >>> array_equal(x, _deserialize_numpy_array(x.shape, x.data, 0, dtype=int32))
-    True
-    """
-    ar = ndarray(shape=shape, buffer=ba, offset=offset, dtype=dtype, order='C')
-    return ar.copy()
-
-
-def _serialize_double(d):
-    """
-    Serialize a double (float or numpy.float64) into a mutually understood format.
-    """
-    if type(d) == float or type(d) == float64 or type(d) == int or type(d) == long:
-        d = float64(d)
-        ba = bytearray(8)
-        _copyto(d, buffer=ba, offset=0, shape=[1], dtype=float64)
-        return ba
-    else:
-        raise TypeError("_serialize_double called on non-float input")
-
-
-def _serialize_double_vector(v):
-    """
-    Serialize a double vector into a mutually understood format.
-
-    Note: we currently do not use a magic byte for double for storage
-    efficiency. This should be reconsidered when we add Ser/De for other
-    8-byte types (e.g. Long), for safety. The corresponding deserializer,
-    _deserialize_double, needs to be modified as well if the serialization
-    scheme changes.
-
-    >>> x = array([1,2,3])
-    >>> y = _deserialize_double_vector(_serialize_double_vector(x))
-    >>> array_equal(y, array([1.0, 2.0, 3.0]))
-    True
-    """
-    v = _convert_vector(v)
-    if type(v) == ndarray:
-        return _serialize_dense_vector(v)
-    elif type(v) == SparseVector:
-        return _serialize_sparse_vector(v)
-    else:
-        raise TypeError("_serialize_double_vector called on a %s; "
-                        "wanted ndarray or SparseVector" % type(v))
-
-
-def _serialize_dense_vector(v):
-    """Serialize a dense vector given as a NumPy array."""
-    if v.ndim != 1:
-        raise TypeError("_serialize_double_vector called on a %ddarray; "
-                        "wanted a 1darray" % v.ndim)
-    if v.dtype != float64:
-        if numpy.issubdtype(v.dtype, numpy.complex):
-            raise TypeError("_serialize_double_vector called on an ndarray of %s; "
-                            "wanted ndarray of float64" % v.dtype)
-        v = v.astype(float64)
-    length = v.shape[0]
-    ba = bytearray(5 + 8 * length)
-    ba[0] = DENSE_VECTOR_MAGIC
-    length_bytes = ndarray(shape=[1], buffer=ba, offset=1, dtype=int32)
-    length_bytes[0] = length
-    _copyto(v, buffer=ba, offset=5, shape=[length], dtype=float64)
-    return ba
-
-
-def _serialize_sparse_vector(v):
-    """Serialize a pyspark.mllib.linalg.SparseVector."""
-    nonzeros = len(v.indices)
-    ba = bytearray(9 + 12 * nonzeros)
-    ba[0] = SPARSE_VECTOR_MAGIC
-    header = ndarray(shape=[2], buffer=ba, offset=1, dtype=int32)
-    header[0] = v.size
-    header[1] = nonzeros
-    _copyto(v.indices, buffer=ba, offset=9, shape=[nonzeros], dtype=int32)
-    values_offset = 9 + 4 * nonzeros
-    _copyto(v.values, buffer=ba, offset=values_offset, shape=[nonzeros], dtype=float64)
-    return ba
-
-
-def _deserialize_double(ba, offset=0):
-    """Deserialize a double from a mutually understood format.
-
-    >>> import sys
-    >>> _deserialize_double(_serialize_double(123.0)) == 123.0
-    True
-    >>> _deserialize_double(_serialize_double(float64(0.0))) == 0.0
-    True
-    >>> _deserialize_double(_serialize_double(1)) == 1.0
-    True
-    >>> _deserialize_double(_serialize_double(1L)) == 1.0
-    True
-    >>> x = sys.float_info.max
-    >>> _deserialize_double(_serialize_double(sys.float_info.max)) == x
-    True
-    >>> y = float64(sys.float_info.max)
-    >>> _deserialize_double(_serialize_double(sys.float_info.max)) == y
-    True
-    """
-    if type(ba) != bytearray:
-        raise TypeError("_deserialize_double called on a %s; wanted bytearray" % type(ba))
-    if len(ba) - offset != 8:
-        raise TypeError("_deserialize_double called on a %d-byte array; wanted 8 bytes." % nb)
-    return _unpack("d", ba[offset:])[0]
-
-
-def _deserialize_double_vector(ba, offset=0):
-    """Deserialize a double vector from a mutually understood format.
-
-    >>> x = array([1.0, 2.0, 3.0, 4.0, -1.0, 0.0, -0.0])
-    >>> array_equal(x, _deserialize_double_vector(_serialize_double_vector(x)))
-    True
-    >>> s = SparseVector(4, [1, 3], [3.0, 5.5])
-    >>> s == _deserialize_double_vector(_serialize_double_vector(s))
-    True
-    """
-    if type(ba) != bytearray:
-        raise TypeError("_deserialize_double_vector called on a %s; "
-                        "wanted bytearray" % type(ba))
-    nb = len(ba) - offset
-    if nb < 5:
-        raise TypeError("_deserialize_double_vector called on a %d-byte array, "
-                        "which is too short" % nb)
-    if ba[offset] == DENSE_VECTOR_MAGIC:
-        return _deserialize_dense_vector(ba, offset)
-    elif ba[offset] == SPARSE_VECTOR_MAGIC:
-        return _deserialize_sparse_vector(ba, offset)
-    else:
-        raise TypeError("_deserialize_double_vector called on bytearray "
-                        "with wrong magic")
-
-
-def _deserialize_dense_vector(ba, offset=0):
-    """Deserialize a dense vector into a numpy array."""
-    nb = len(ba) - offset
-    if nb < 5:
-        raise TypeError("_deserialize_dense_vector called on a %d-byte array, "
-                        "which is too short" % nb)
-    length = ndarray(shape=[1], buffer=ba, offset=offset + 1, dtype=int32)[0]
-    if nb < 8 * length + 5:
-        raise TypeError("_deserialize_dense_vector called on bytearray "
-                        "with wrong length")
-    return _deserialize_numpy_array([length], ba, offset + 5)
-
-
-def _deserialize_sparse_vector(ba, offset=0):
-    """Deserialize a sparse vector into a MLlib SparseVector object."""
-    nb = len(ba) - offset
-    if nb < 9:
-        raise TypeError("_deserialize_sparse_vector called on a %d-byte array, "
-                        "which is too short" % nb)
-    header = ndarray(shape=[2], buffer=ba, offset=offset + 1, dtype=int32)
-    size = header[0]
-    nonzeros = header[1]
-    if nb < 9 + 12 * nonzeros:
-        raise TypeError("_deserialize_sparse_vector called on bytearray "
-                        "with wrong length")
-    indices = _deserialize_numpy_array([nonzeros], ba, offset + 9, dtype=int32)
-    values = _deserialize_numpy_array([nonzeros], ba, offset + 9 + 4 * nonzeros, dtype=float64)
-    return SparseVector(int(size), indices, values)
-
-
-def _serialize_double_matrix(m):
-    """Serialize a double matrix into a mutually understood format."""
-    if (type(m) == ndarray and m.ndim == 2):
-        if m.dtype != float64:
-            if numpy.issubdtype(m.dtype, numpy.complex):
-                raise TypeError("_serialize_double_matrix called on an ndarray of %s; "
-                                "wanted ndarray of float64" % m.dtype)
-            m = m.astype(float64)
-        rows = m.shape[0]
-        cols = m.shape[1]
-        ba = bytearray(9 + 8 * rows * cols)
-        ba[0] = DENSE_MATRIX_MAGIC
-        lengths = ndarray(shape=[3], buffer=ba, offset=1, dtype=int32)
-        lengths[0] = rows
-        lengths[1] = cols
-        _copyto(m, buffer=ba, offset=9, shape=[rows, cols], dtype=float64)
-        return ba
-    else:
-        raise TypeError("_serialize_double_matrix called on a "
-                        "non-double-matrix")
-
-
-def _deserialize_double_matrix(ba):
-    """Deserialize a double matrix from a mutually understood format."""
-    if type(ba) != bytearray:
-        raise TypeError("_deserialize_double_matrix called on a %s; "
-                        "wanted bytearray" % type(ba))
-    if len(ba) < 9:
-        raise TypeError("_deserialize_double_matrix called on a %d-byte array, "
-                        "which is too short" % len(ba))
-    if ba[0] != DENSE_MATRIX_MAGIC:
-        raise TypeError("_deserialize_double_matrix called on bytearray "
-                        "with wrong magic")
-    lengths = ndarray(shape=[2], buffer=ba, offset=1, dtype=int32)
-    rows = lengths[0]
-    cols = lengths[1]
-    if (len(ba) != 8 * rows * cols + 9):
-        raise TypeError("_deserialize_double_matrix called on bytearray "
-                        "with wrong length")
-    return _deserialize_numpy_array([rows, cols], ba, 9)
-
-
-def _serialize_labeled_point(p):
-    """
-    Serialize a LabeledPoint with a features vector of any type.
-
-    >>> from pyspark.mllib.regression import LabeledPoint
-    >>> dp0 = LabeledPoint(0.5, array([1.0, 2.0, 3.0, 4.0, -1.0, 0.0, -0.0]))
-    >>> dp1 = _deserialize_labeled_point(_serialize_labeled_point(dp0))
-    >>> dp1.label == dp0.label
-    True
-    >>> array_equal(dp1.features, dp0.features)
-    True
-    >>> sp0 = LabeledPoint(0.0, SparseVector(4, [1, 3], [3.0, 5.5]))
-    >>> sp1 = _deserialize_labeled_point(_serialize_labeled_point(sp0))
-    >>> sp1.label == sp1.label
-    True
-    >>> sp1.features == sp0.features
-    True
-    """
-    from pyspark.mllib.regression import LabeledPoint
-    serialized_features = _serialize_double_vector(p.features)
-    header = bytearray(9)
-    header[0] = LABELED_POINT_MAGIC
-    header_float = ndarray(shape=[1], buffer=header, offset=1, dtype=float64)
-    header_float[0] = p.label
-    return header + serialized_features
-
-
-def _deserialize_labeled_point(ba, offset=0):
-    """Deserialize a LabeledPoint from a mutually understood format."""
-    from pyspark.mllib.regression import LabeledPoint
-    if type(ba) != bytearray:
-        raise TypeError("Expecting a bytearray but got %s" % type(ba))
-    if ba[offset] != LABELED_POINT_MAGIC:
-        raise TypeError("Expecting magic number %d but got %d" % (LABELED_POINT_MAGIC, ba[0]))
-    label = ndarray(shape=[1], buffer=ba, offset=offset + 1, dtype=float64)[0]
-    features = _deserialize_double_vector(ba, offset + 9)
-    return LabeledPoint(label, features)
-
-
-def _copyto(array, buffer, offset, shape, dtype):
-    """
-    Copy the contents of a vector to a destination bytearray at the
-    given offset.
-
-    TODO: In the future this could use numpy.copyto on NumPy 1.7+, but
-    we should benchmark that to see whether it provides a benefit.
-    """
-    temp_array = ndarray(shape=shape, buffer=buffer, offset=offset, dtype=dtype, order='C')
-    temp_array[...] = array
-
-
-def _get_unmangled_rdd(data, serializer, cache=True):
-    """
-    :param cache:  If True, the serialized RDD is cached.  (default = True)
-                   WARNING: Users should unpersist() this later!
-    """
-    dataBytes = data.map(serializer)
-    dataBytes._bypass_serializer = True
-    if cache:
-        dataBytes.cache()
-    return dataBytes
-
-
-def _get_unmangled_double_vector_rdd(data, cache=True):
-    """
-    Map a pickled Python RDD of Python dense or sparse vectors to a Java RDD of
-    _serialized_double_vectors.
-    :param cache:  If True, the serialized RDD is cached.  (default = True)
-                   WARNING: Users should unpersist() this later!
-    """
-    return _get_unmangled_rdd(data, _serialize_double_vector, cache)
-
-
-def _get_unmangled_labeled_point_rdd(data, cache=True):
-    """
-    Map a pickled Python RDD of LabeledPoint to a Java RDD of _serialized_labeled_points.
-    :param cache:  If True, the serialized RDD is cached.  (default = True)
-                   WARNING: Users should unpersist() this later!
-    """
-    return _get_unmangled_rdd(data, _serialize_labeled_point, cache)
-
-
-# Common functions for dealing with and training linear models
-
-def _linear_predictor_typecheck(x, coeffs):
-    """
-    Check that x is a one-dimensional vector of the right shape.
-    This is a temporary hackaround until we actually implement bulk predict.
-    """
-    x = _convert_vector(x)
-    if type(x) == ndarray:
-        if x.ndim == 1:
-            if x.shape != coeffs.shape:
-                raise RuntimeError("Got array of %d elements; wanted %d" % (
-                    numpy.shape(x)[0], coeffs.shape[0]))
-        else:
-            raise RuntimeError("Bulk predict not yet supported.")
-    elif type(x) == SparseVector:
-        if x.size != coeffs.shape[0]:
-            raise RuntimeError("Got sparse vector of size %d; wanted %d" % (
-                x.size, coeffs.shape[0]))
-    elif isinstance(x, RDD):
-        raise RuntimeError("Bulk predict not yet supported.")
-    else:
-        raise TypeError("Argument of type " + type(x).__name__ + " unsupported")
-
-
-# If we weren't given initial weights, take a zero vector of the appropriate
-# length.
-def _get_initial_weights(initial_weights, data):
-    if initial_weights is None:
-        initial_weights = _convert_vector(data.first().features)
-        if type(initial_weights) == ndarray:
-            if initial_weights.ndim != 1:
-                raise TypeError("At least one data element has "
-                                + initial_weights.ndim + " dimensions, which is not 1")
-            initial_weights = numpy.zeros([initial_weights.shape[0]])
-        elif type(initial_weights) == SparseVector:
-            initial_weights = numpy.zeros([initial_weights.size])
-    return initial_weights
-
-
-# train_func should take two parameters, namely data and initial_weights, and
-# return the result of a call to the appropriate JVM stub.
-# _regression_train_wrapper is responsible for setup and error checking.
-def _regression_train_wrapper(sc, train_func, klass, data, initial_weights):
-    initial_weights = _get_initial_weights(initial_weights, data)
-    dataBytes = _get_unmangled_labeled_point_rdd(data)
-    ans = train_func(dataBytes, _serialize_double_vector(initial_weights))
-    if len(ans) != 2:
-        raise RuntimeError("JVM call result had unexpected length")
-    elif type(ans[0]) != bytearray:
-        raise RuntimeError("JVM call result had first element of type "
-                           + type(ans[0]).__name__ + " which is not bytearray")
-    elif type(ans[1]) != float:
-        raise RuntimeError("JVM call result had second element of type "
-                           + type(ans[0]).__name__ + " which is not float")
-    return klass(_deserialize_double_vector(ans[0]), ans[1])
-
-
-# Functions for serializing ALS Rating objects and tuples
-
-def _serialize_rating(r):
-    ba = bytearray(16)
-    intpart = ndarray(shape=[2], buffer=ba, dtype=int32)
-    doublepart = ndarray(shape=[1], buffer=ba, dtype=float64, offset=8)
-    intpart[0], intpart[1], doublepart[0] = r
-    return ba
-
-
-class RatingDeserializer(FramedSerializer):
-
-    def loads(self, string):
-        res = ndarray(shape=(3, ), buffer=string, dtype=float64, offset=4)
-        return int(res[0]), int(res[1]), res[2]
-
-    def load_stream(self, stream):
-        while True:
-            try:
-                yield self._read_with_length(stream)
-            except struct.error:
-                return
-            except EOFError:
-                return
-
-
-def _serialize_tuple(t):
-    ba = bytearray(8)
-    intpart = ndarray(shape=[2], buffer=ba, dtype=int32)
-    intpart[0], intpart[1] = t
-    return ba
-
-
-# Vector math functions that support all of our vector types
-
-def _convert_vector(vec):
-    """
-    Convert a vector to a format we support internally. This does
-    the following:
-
-    * For dense NumPy vectors (ndarray), returns them as is
-    * For our SparseVector class, returns that as is
-    * For Python lists, converts them to NumPy vectors
-    * For scipy.sparse.*_matrix column vectors, converts them to
-      our own SparseVector type.
-
-    This should be called before passing any data to our algorithms
-    or attempting to serialize it to Java.
-    """
-    if type(vec) == ndarray or type(vec) == SparseVector:
-        return vec
-    elif type(vec) == list:
-        return array(vec, dtype=float64)
-    elif _have_scipy:
-        if _scipy_issparse(vec):
-            assert vec.shape[1] == 1, "Expected column vector"
-            csc = vec.tocsc()
-            return SparseVector(vec.shape[0], csc.indices, csc.data)
-    raise TypeError("Expected NumPy array, SparseVector, or scipy.sparse matrix")
-
-
-def _squared_distance(v1, v2):
-    """
-    Squared distance of two NumPy or sparse vectors.
-
-    >>> dense1 = array([1., 2.])
-    >>> sparse1 = SparseVector(2, [0, 1], [1., 2.])
-    >>> dense2 = array([2., 1.])
-    >>> sparse2 = SparseVector(2, [0, 1], [2., 1.])
-    >>> _squared_distance(dense1, dense2)
-    2.0
-    >>> _squared_distance(dense1, sparse2)
-    2.0
-    >>> _squared_distance(sparse1, dense2)
-    2.0
-    >>> _squared_distance(sparse1, sparse2)
-    2.0
-    """
-    v1 = _convert_vector(v1)
-    v2 = _convert_vector(v2)
-    if type(v1) == ndarray and type(v2) == ndarray:
-        diff = v1 - v2
-        return numpy.dot(diff, diff)
-    elif type(v1) == ndarray:
-        return v2.squared_distance(v1)
-    else:
-        return v1.squared_distance(v2)
-
-
-def _dot(vec, target):
-    """
-    Compute the dot product of a vector of the types we support
-    (Numpy array, list, SparseVector, or SciPy sparse) and a target
-    NumPy array that is either 1- or 2-dimensional. Equivalent to
-    calling numpy.dot of the two vectors, but for SciPy ones, we
-    have to transpose them because they're column vectors.
-    """
-    if type(vec) == ndarray:
-        return numpy.dot(vec, target)
-    elif type(vec) == SparseVector:
-        return vec.dot(target)
-    elif type(vec) == list:
-        return numpy.dot(_convert_vector(vec), target)
-    else:
-        return vec.transpose().dot(target)[0]
-
-
-def _test():
-    import doctest
-    globs = globals().copy()
-    globs['sc'] = SparkContext('local[4]', 'PythonTest', batchSize=2)
-    (failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS)
-    globs['sc'].stop()
-    if failure_count:
-        exit(-1)
-
-
-if __name__ == "__main__":
-    _test()
diff --git a/python/pyspark/mllib/classification.py b/python/pyspark/mllib/classification.py
index 71ab46b61d7fa..ac142fb49a90c 100644
--- a/python/pyspark/mllib/classification.py
+++ b/python/pyspark/mllib/classification.py
@@ -15,19 +15,14 @@
 # limitations under the License.
 #
 
+from math import exp
+
 import numpy
+from numpy import array
 
-from numpy import array, shape
-from pyspark import SparkContext
-from pyspark.mllib._common import \
-    _dot, _get_unmangled_rdd, _get_unmangled_double_vector_rdd, \
-    _serialize_double_matrix, _deserialize_double_matrix, \
-    _serialize_double_vector, _deserialize_double_vector, \
-    _get_initial_weights, _serialize_rating, _regression_train_wrapper, \
-    _linear_predictor_typecheck, _get_unmangled_labeled_point_rdd
-from pyspark.mllib.linalg import SparseVector
-from pyspark.mllib.regression import LabeledPoint, LinearModel
-from math import exp, log
+from pyspark import SparkContext, PickleSerializer
+from pyspark.mllib.linalg import SparseVector, _convert_to_vector
+from pyspark.mllib.regression import LabeledPoint, LinearModel, _regression_train_wrapper
 
 
 __all__ = ['LogisticRegressionModel', 'LogisticRegressionWithSGD', 'SVMModel',
@@ -67,8 +62,7 @@ class LogisticRegressionModel(LinearModel):
     """
 
     def predict(self, x):
-        _linear_predictor_typecheck(x, self._coeff)
-        margin = _dot(x, self._coeff) + self._intercept
+        margin = self.weights.dot(x) + self._intercept
         if margin > 0:
             prob = 1 / (1 + exp(-margin))
         else:
@@ -81,7 +75,7 @@ class LogisticRegressionWithSGD(object):
 
     @classmethod
     def train(cls, data, iterations=100, step=1.0, miniBatchFraction=1.0,
-              initialWeights=None, regParam=1.0, regType=None, intercept=False):
+              initialWeights=None, regParam=1.0, regType="none", intercept=False):
         """
         Train a logistic regression model on the given data.
 
@@ -106,11 +100,12 @@ def train(cls, data, iterations=100, step=1.0, miniBatchFraction=1.0,
                                   are activated or not).
         """
         sc = data.context
-        if regType is None:
-            regType = "none"
-        train_func = lambda d, i: sc._jvm.PythonMLLibAPI().trainLogisticRegressionModelWithSGD(
-            d._jrdd, iterations, step, miniBatchFraction, i, regParam, regType, intercept)
-        return _regression_train_wrapper(sc, train_func, LogisticRegressionModel, data,
+
+        def train(jdata, i):
+            return sc._jvm.PythonMLLibAPI().trainLogisticRegressionModelWithSGD(
+                jdata, iterations, step, miniBatchFraction, i, regParam, regType, intercept)
+
+        return _regression_train_wrapper(sc, train, LogisticRegressionModel, data,
                                          initialWeights)
 
 
@@ -141,8 +136,7 @@ class SVMModel(LinearModel):
     """
 
     def predict(self, x):
-        _linear_predictor_typecheck(x, self._coeff)
-        margin = _dot(x, self._coeff) + self._intercept
+        margin = self.weights.dot(x) + self.intercept
         return 1 if margin >= 0 else 0
 
 
@@ -150,7 +144,7 @@ class SVMWithSGD(object):
 
     @classmethod
     def train(cls, data, iterations=100, step=1.0, regParam=1.0,
-              miniBatchFraction=1.0, initialWeights=None, regType=None, intercept=False):
+              miniBatchFraction=1.0, initialWeights=None, regType="none", intercept=False):
         """
         Train a support vector machine on the given data.
 
@@ -175,11 +169,12 @@ def train(cls, data, iterations=100, step=1.0, regParam=1.0,
                                   are activated or not).
         """
         sc = data.context
-        if regType is None:
-            regType = "none"
-        train_func = lambda d, i: sc._jvm.PythonMLLibAPI().trainSVMModelWithSGD(
-            d._jrdd, iterations, step, regParam, miniBatchFraction, i, regType, intercept)
-        return _regression_train_wrapper(sc, train_func, SVMModel, data, initialWeights)
+
+        def train(jrdd, i):
+            return sc._jvm.PythonMLLibAPI().trainSVMModelWithSGD(
+                jrdd, iterations, step, regParam, miniBatchFraction, i, regType, intercept)
+
+        return _regression_train_wrapper(sc, train, SVMModel, data, initialWeights)
 
 
 class NaiveBayesModel(object):
@@ -220,7 +215,8 @@ def __init__(self, labels, pi, theta):
 
     def predict(self, x):
         """Return the most likely class for a data vector x"""
-        return self.labels[numpy.argmax(self.pi + _dot(x, self.theta.transpose()))]
+        x = _convert_to_vector(x)
+        return self.labels[numpy.argmax(self.pi + x.dot(self.theta.transpose()))]
 
 
 class NaiveBayes(object):
@@ -242,12 +238,9 @@ def train(cls, data, lambda_=1.0):
         @param lambda_: The smoothing parameter
         """
         sc = data.context
-        dataBytes = _get_unmangled_labeled_point_rdd(data)
-        ans = sc._jvm.PythonMLLibAPI().trainNaiveBayes(dataBytes._jrdd, lambda_)
-        return NaiveBayesModel(
-            _deserialize_double_vector(ans[0]),
-            _deserialize_double_vector(ans[1]),
-            _deserialize_double_matrix(ans[2]))
+        jlist = sc._jvm.PythonMLLibAPI().trainNaiveBayes(data._to_java_object_rdd(), lambda_)
+        labels, pi, theta = PickleSerializer().loads(str(sc._jvm.SerDe.dumps(jlist)))
+        return NaiveBayesModel(labels.toArray(), pi.toArray(), numpy.array(theta))
 
 
 def _test():
diff --git a/python/pyspark/mllib/clustering.py b/python/pyspark/mllib/clustering.py
index f3e952a1d842a..12c56022717a5 100644
--- a/python/pyspark/mllib/clustering.py
+++ b/python/pyspark/mllib/clustering.py
@@ -15,15 +15,9 @@
 # limitations under the License.
 #
 
-from numpy import array, dot
-from math import sqrt
 from pyspark import SparkContext
-from pyspark.mllib._common import \
-    _get_unmangled_rdd, _get_unmangled_double_vector_rdd, _squared_distance, \
-    _serialize_double_matrix, _deserialize_double_matrix, \
-    _serialize_double_vector, _deserialize_double_vector, \
-    _get_initial_weights, _serialize_rating, _regression_train_wrapper
-from pyspark.mllib.linalg import SparseVector
+from pyspark.serializers import PickleSerializer, AutoBatchedSerializer
+from pyspark.mllib.linalg import SparseVector, _convert_to_vector
 
 __all__ = ['KMeansModel', 'KMeans']
 
@@ -32,6 +26,7 @@ class KMeansModel(object):
 
     """A clustering model derived from the k-means method.
 
+    >>> from numpy import array
     >>> data = array([0.0,0.0, 1.0,1.0, 9.0,8.0, 8.0,9.0]).reshape(4,2)
     >>> model = KMeans.train(
     ...     sc.parallelize(data), 2, maxIterations=10, runs=30, initializationMode="random")
@@ -71,8 +66,9 @@ def predict(self, x):
         """Find the cluster to which x belongs in this model."""
         best = 0
         best_distance = float("inf")
-        for i in range(0, len(self.centers)):
-            distance = _squared_distance(x, self.centers[i])
+        x = _convert_to_vector(x)
+        for i in xrange(len(self.centers)):
+            distance = x.squared_distance(self.centers[i])
             if distance < best_distance:
                 best = i
                 best_distance = distance
@@ -82,19 +78,17 @@ def predict(self, x):
 class KMeans(object):
 
     @classmethod
-    def train(cls, data, k, maxIterations=100, runs=1, initializationMode="k-means||"):
+    def train(cls, rdd, k, maxIterations=100, runs=1, initializationMode="k-means||"):
         """Train a k-means clustering model."""
-        sc = data.context
-        dataBytes = _get_unmangled_double_vector_rdd(data)
-        ans = sc._jvm.PythonMLLibAPI().trainKMeansModel(
-            dataBytes._jrdd, k, maxIterations, runs, initializationMode)
-        if len(ans) != 1:
-            raise RuntimeError("JVM call result had unexpected length")
-        elif type(ans[0]) != bytearray:
-            raise RuntimeError("JVM call result had first element of type "
-                               + type(ans[0]) + " which is not bytearray")
-        matrix = _deserialize_double_matrix(ans[0])
-        return KMeansModel([row for row in matrix])
+        sc = rdd.context
+        ser = PickleSerializer()
+        # cache serialized data to avoid objects over head in JVM
+        cached = rdd.map(_convert_to_vector)._reserialize(AutoBatchedSerializer(ser)).cache()
+        model = sc._jvm.PythonMLLibAPI().trainKMeansModel(
+            cached._to_java_object_rdd(), k, maxIterations, runs, initializationMode)
+        bytes = sc._jvm.SerDe.dumps(model.clusterCenters())
+        centers = ser.loads(str(bytes))
+        return KMeansModel([c.toArray() for c in centers])
 
 
 def _test():
diff --git a/python/pyspark/mllib/linalg.py b/python/pyspark/mllib/linalg.py
index e69051c104e37..0a5dcaac55e46 100644
--- a/python/pyspark/mllib/linalg.py
+++ b/python/pyspark/mllib/linalg.py
@@ -23,14 +23,148 @@
 SciPy is available in their environment.
 """
 
-import numpy
-from numpy import array, array_equal, ndarray, float64, int32
+import sys
+import array
+import copy_reg
 
+import numpy as np
 
-__all__ = ['SparseVector', 'Vectors']
+__all__ = ['Vector', 'DenseVector', 'SparseVector', 'Vectors']
 
 
-class SparseVector(object):
+if sys.version_info[:2] == (2, 7):
+    # speed up pickling array in Python 2.7
+    def fast_pickle_array(ar):
+        return array.array, (ar.typecode, ar.tostring())
+    copy_reg.pickle(array.array, fast_pickle_array)
+
+
+# Check whether we have SciPy. MLlib works without it too, but if we have it, some methods,
+# such as _dot and _serialize_double_vector, start to support scipy.sparse matrices.
+
+try:
+    import scipy.sparse
+    _have_scipy = True
+except:
+    # No SciPy in environment, but that's okay
+    _have_scipy = False
+
+
+def _convert_to_vector(l):
+    if isinstance(l, Vector):
+        return l
+    elif type(l) in (array.array, np.array, np.ndarray, list, tuple):
+        return DenseVector(l)
+    elif _have_scipy and scipy.sparse.issparse(l):
+        assert l.shape[1] == 1, "Expected column vector"
+        csc = l.tocsc()
+        return SparseVector(l.shape[0], csc.indices, csc.data)
+    else:
+        raise TypeError("Cannot convert type %s into Vector" % type(l))
+
+
+class Vector(object):
+    """
+    Abstract class for DenseVector and SparseVector
+    """
+    def toArray(self):
+        """
+        Convert the vector into an numpy.ndarray
+        :return: numpy.ndarray
+        """
+        raise NotImplementedError
+
+
+class DenseVector(Vector):
+    def __init__(self, ar):
+        if not isinstance(ar, array.array):
+            ar = array.array('d', ar)
+        self.array = ar
+
+    def __reduce__(self):
+        return DenseVector, (self.array,)
+
+    def dot(self, other):
+        """
+        Compute the dot product of two Vectors. We support
+        (Numpy array, list, SparseVector, or SciPy sparse)
+        and a target NumPy array that is either 1- or 2-dimensional.
+        Equivalent to calling numpy.dot of the two vectors.
+
+        >>> dense = DenseVector(array.array('d', [1., 2.]))
+        >>> dense.dot(dense)
+        5.0
+        >>> dense.dot(SparseVector(2, [0, 1], [2., 1.]))
+        4.0
+        >>> dense.dot(range(1, 3))
+        5.0
+        >>> dense.dot(np.array(range(1, 3)))
+        5.0
+        """
+        if isinstance(other, SparseVector):
+            return other.dot(self)
+        elif _have_scipy and scipy.sparse.issparse(other):
+            return other.transpose().dot(self.toArray())[0]
+        elif isinstance(other, Vector):
+            return np.dot(self.toArray(), other.toArray())
+        else:
+            return np.dot(self.toArray(), other)
+
+    def squared_distance(self, other):
+        """
+        Squared distance of two Vectors.
+
+        >>> dense1 = DenseVector(array.array('d', [1., 2.]))
+        >>> dense1.squared_distance(dense1)
+        0.0
+        >>> dense2 = np.array([2., 1.])
+        >>> dense1.squared_distance(dense2)
+        2.0
+        >>> dense3 = [2., 1.]
+        >>> dense1.squared_distance(dense3)
+        2.0
+        >>> sparse1 = SparseVector(2, [0, 1], [2., 1.])
+        >>> dense1.squared_distance(sparse1)
+        2.0
+        """
+        if isinstance(other, SparseVector):
+            return other.squared_distance(self)
+        elif _have_scipy and scipy.sparse.issparse(other):
+            return _convert_to_vector(other).squared_distance(self)
+
+        if isinstance(other, Vector):
+            other = other.toArray()
+        elif not isinstance(other, np.ndarray):
+            other = np.array(other)
+        diff = self.toArray() - other
+        return np.dot(diff, diff)
+
+    def toArray(self):
+        return np.array(self.array)
+
+    def __getitem__(self, item):
+        return self.array[item]
+
+    def __len__(self):
+        return len(self.array)
+
+    def __str__(self):
+        return "[" + ",".join([str(v) for v in self.array]) + "]"
+
+    def __repr__(self):
+        return "DenseVector(%r)" % self.array
+
+    def __eq__(self, other):
+        return isinstance(other, DenseVector) and self.array == other.array
+
+    def __ne__(self, other):
+        return not self == other
+
+    def __getattr__(self, item):
+        return getattr(self.array, item)
+
+
+class SparseVector(Vector):
 
     """
     A simple sparse vector class for passing data to MLlib. Users may
@@ -61,16 +195,19 @@ def __init__(self, size, *args):
             if type(pairs) == dict:
                 pairs = pairs.items()
             pairs = sorted(pairs)
-            self.indices = array([p[0] for p in pairs], dtype=int32)
-            self.values = array([p[1] for p in pairs], dtype=float64)
+            self.indices = array.array('i', [p[0] for p in pairs])
+            self.values = array.array('d', [p[1] for p in pairs])
         else:
             assert len(args[0]) == len(args[1]), "index and value arrays not same length"
-            self.indices = array(args[0], dtype=int32)
-            self.values = array(args[1], dtype=float64)
+            self.indices = array.array('i', args[0])
+            self.values = array.array('d', args[1])
             for i in xrange(len(self.indices) - 1):
                 if self.indices[i] >= self.indices[i + 1]:
                     raise TypeError("indices array must be sorted")
 
+    def __reduce__(self):
+        return (SparseVector, (self.size, self.indices, self.values))
+
     def dot(self, other):
         """
         Dot product with a SparseVector or 1- or 2-dimensional Numpy array.
@@ -78,15 +215,15 @@ def dot(self, other):
         >>> a = SparseVector(4, [1, 3], [3.0, 4.0])
         >>> a.dot(a)
         25.0
-        >>> a.dot(array([1., 2., 3., 4.]))
+        >>> a.dot(array.array('d', [1., 2., 3., 4.]))
         22.0
         >>> b = SparseVector(4, [2, 4], [1.0, 2.0])
         >>> a.dot(b)
         0.0
-        >>> a.dot(array([[1, 1], [2, 2], [3, 3], [4, 4]]))
+        >>> a.dot(np.array([[1, 1], [2, 2], [3, 3], [4, 4]]))
         array([ 22.,  22.])
         """
-        if type(other) == ndarray:
+        if type(other) == np.ndarray:
             if other.ndim == 1:
                 result = 0.0
                 for i in xrange(len(self.indices)):
@@ -94,10 +231,17 @@ def dot(self, other):
                 return result
             elif other.ndim == 2:
                 results = [self.dot(other[:, i]) for i in xrange(other.shape[1])]
-                return array(results)
+                return np.array(results)
             else:
                 raise Exception("Cannot call dot with %d-dimensional array" % other.ndim)
-        else:
+
+        elif type(other) in (array.array, DenseVector):
+            result = 0.0
+            for i in xrange(len(self.indices)):
+                result += self.values[i] * other[self.indices[i]]
+            return result
+
+        elif type(other) is SparseVector:
             result = 0.0
             i, j = 0, 0
             while i < len(self.indices) and j < len(other.indices):
@@ -110,6 +254,8 @@ def dot(self, other):
                 else:
                     j += 1
             return result
+        else:
+            return self.dot(_convert_to_vector(other))
 
     def squared_distance(self, other):
         """
@@ -118,7 +264,9 @@ def squared_distance(self, other):
         >>> a = SparseVector(4, [1, 3], [3.0, 4.0])
         >>> a.squared_distance(a)
         0.0
-        >>> a.squared_distance(array([1., 2., 3., 4.]))
+        >>> a.squared_distance(array.array('d', [1., 2., 3., 4.]))
+        11.0
+        >>> a.squared_distance(np.array([1., 2., 3., 4.]))
         11.0
         >>> b = SparseVector(4, [2, 4], [1.0, 2.0])
         >>> a.squared_distance(b)
@@ -126,22 +274,22 @@ def squared_distance(self, other):
         >>> b.squared_distance(a)
         30.0
         """
-        if type(other) == ndarray:
-            if other.ndim == 1:
-                result = 0.0
-                j = 0   # index into our own array
-                for i in xrange(other.shape[0]):
-                    if j < len(self.indices) and self.indices[j] == i:
-                        diff = self.values[j] - other[i]
-                        result += diff * diff
-                        j += 1
-                    else:
-                        result += other[i] * other[i]
-                return result
-            else:
+        if type(other) in (list, array.array, DenseVector, np.array, np.ndarray):
+            if type(other) is np.array and other.ndim != 1:
                 raise Exception("Cannot call squared_distance with %d-dimensional array" %
                                 other.ndim)
-        else:
+            result = 0.0
+            j = 0   # index into our own array
+            for i in xrange(len(other)):
+                if j < len(self.indices) and self.indices[j] == i:
+                    diff = self.values[j] - other[i]
+                    result += diff * diff
+                    j += 1
+                else:
+                    result += other[i] * other[i]
+            return result
+
+        elif type(other) is SparseVector:
             result = 0.0
             i, j = 0, 0
             while i < len(self.indices) and j < len(other.indices):
@@ -163,16 +311,21 @@ def squared_distance(self, other):
                 result += other.values[j] * other.values[j]
                 j += 1
             return result
+        else:
+            return self.squared_distance(_convert_to_vector(other))
 
     def toArray(self):
         """
         Returns a copy of this SparseVector as a 1-dimensional NumPy array.
         """
-        arr = numpy.zeros(self.size)
+        arr = np.zeros((self.size,), dtype=np.float64)
         for i in xrange(self.indices.size):
             arr[self.indices[i]] = self.values[i]
         return arr
 
+    def __len__(self):
+        return self.size
+
     def __str__(self):
         inds = "[" + ",".join([str(i) for i in self.indices]) + "]"
         vals = "[" + ",".join([str(v) for v in self.values]) + "]"
@@ -198,8 +351,8 @@ def __eq__(self, other):
 
         return (isinstance(other, self.__class__)
                 and other.size == self.size
-                and array_equal(other.indices, self.indices)
-                and array_equal(other.values, self.values))
+                and other.indices == self.indices
+                and other.values == self.values)
 
     def __ne__(self, other):
         return not self.__eq__(other)
@@ -242,9 +395,9 @@ def dense(elements):
         returns a NumPy array.
 
         >>> Vectors.dense([1, 2, 3])
-        array([ 1.,  2.,  3.])
+        DenseVector(array('d', [1.0, 2.0, 3.0]))
         """
-        return array(elements, dtype=float64)
+        return DenseVector(elements)
 
     @staticmethod
     def stringify(vector):
@@ -257,10 +410,39 @@ def stringify(vector):
         >>> Vectors.stringify(Vectors.dense([0.0, 1.0]))
         '[0.0,1.0]'
         """
-        if type(vector) == SparseVector:
-            return str(vector)
-        else:
-            return "[" + ",".join([str(v) for v in vector]) + "]"
+        return str(vector)
+
+
+class Matrix(object):
+    """ the Matrix """
+    def __init__(self, nRow, nCol):
+        self.nRow = nRow
+        self.nCol = nCol
+
+    def toArray(self):
+        raise NotImplementedError
+
+
+class DenseMatrix(Matrix):
+    def __init__(self, nRow, nCol, values):
+        Matrix.__init__(self, nRow, nCol)
+        assert len(values) == nRow * nCol
+        self.values = values
+
+    def __reduce__(self):
+        return DenseMatrix, (self.nRow, self.nCol, self.values)
+
+    def toArray(self):
+        """
+        Return an numpy.ndarray
+
+        >>> arr = array.array('d', [float(i) for i in range(4)])
+        >>> m = DenseMatrix(2, 2, arr)
+        >>> m.toArray()
+        array([[ 0.,  1.],
+               [ 2.,  3.]])
+        """
+        return np.ndarray((self.nRow, self.nCol), np.float64, buffer=self.values.tostring())
 
 
 def _test():
diff --git a/python/pyspark/mllib/random.py b/python/pyspark/mllib/random.py
index d53c95fd59c25..a787e4dea2c55 100644
--- a/python/pyspark/mllib/random.py
+++ b/python/pyspark/mllib/random.py
@@ -19,15 +19,32 @@
 Python package for random data generation.
 """
 
+from functools import wraps
 
 from pyspark.rdd import RDD
-from pyspark.mllib._common import _deserialize_double, _deserialize_double_vector
-from pyspark.serializers import NoOpSerializer
+from pyspark.serializers import BatchedSerializer, PickleSerializer
 
 
 __all__ = ['RandomRDDs', ]
 
 
+def serialize(f):
+    @wraps(f)
+    def func(sc, *a, **kw):
+        jrdd = f(sc, *a, **kw)
+        return RDD(sc._jvm.PythonRDD.javaToPython(jrdd), sc,
+                   BatchedSerializer(PickleSerializer(), 1024))
+    return func
+
+
+def toArray(f):
+    @wraps(f)
+    def func(sc, *a, **kw):
+        rdd = f(sc, *a, **kw)
+        return rdd.map(lambda vec: vec.toArray())
+    return func
+
+
 class RandomRDDs(object):
     """
     Generator methods for creating RDDs comprised of i.i.d samples from
@@ -35,6 +52,7 @@ class RandomRDDs(object):
     """
 
     @staticmethod
+    @serialize
     def uniformRDD(sc, size, numPartitions=None, seed=None):
         """
         Generates an RDD comprised of i.i.d. samples from the
@@ -56,11 +74,10 @@ def uniformRDD(sc, size, numPartitions=None, seed=None):
         >>> parts == sc.defaultParallelism
         True
         """
-        jrdd = sc._jvm.PythonMLLibAPI().uniformRDD(sc._jsc, size, numPartitions, seed)
-        uniform = RDD(jrdd, sc, NoOpSerializer())
-        return uniform.map(lambda bytes: _deserialize_double(bytearray(bytes)))
+        return sc._jvm.PythonMLLibAPI().uniformRDD(sc._jsc, size, numPartitions, seed)
 
     @staticmethod
+    @serialize
     def normalRDD(sc, size, numPartitions=None, seed=None):
         """
         Generates an RDD comprised of i.i.d. samples from the standard normal
@@ -80,11 +97,10 @@ def normalRDD(sc, size, numPartitions=None, seed=None):
         >>> abs(stats.stdev() - 1.0) < 0.1
         True
         """
-        jrdd = sc._jvm.PythonMLLibAPI().normalRDD(sc._jsc, size, numPartitions, seed)
-        normal = RDD(jrdd, sc, NoOpSerializer())
-        return normal.map(lambda bytes: _deserialize_double(bytearray(bytes)))
+        return sc._jvm.PythonMLLibAPI().normalRDD(sc._jsc, size, numPartitions, seed)
 
     @staticmethod
+    @serialize
     def poissonRDD(sc, mean, size, numPartitions=None, seed=None):
         """
         Generates an RDD comprised of i.i.d. samples from the Poisson
@@ -101,11 +117,11 @@ def poissonRDD(sc, mean, size, numPartitions=None, seed=None):
         >>> abs(stats.stdev() - sqrt(mean)) < 0.5
         True
         """
-        jrdd = sc._jvm.PythonMLLibAPI().poissonRDD(sc._jsc, mean, size, numPartitions, seed)
-        poisson = RDD(jrdd, sc, NoOpSerializer())
-        return poisson.map(lambda bytes: _deserialize_double(bytearray(bytes)))
+        return sc._jvm.PythonMLLibAPI().poissonRDD(sc._jsc, mean, size, numPartitions, seed)
 
     @staticmethod
+    @toArray
+    @serialize
     def uniformVectorRDD(sc, numRows, numCols, numPartitions=None, seed=None):
         """
         Generates an RDD comprised of vectors containing i.i.d. samples drawn
@@ -120,12 +136,12 @@ def uniformVectorRDD(sc, numRows, numCols, numPartitions=None, seed=None):
         >>> RandomRDDs.uniformVectorRDD(sc, 10, 10, 4).getNumPartitions()
         4
         """
-        jrdd = sc._jvm.PythonMLLibAPI() \
+        return sc._jvm.PythonMLLibAPI() \
             .uniformVectorRDD(sc._jsc, numRows, numCols, numPartitions, seed)
-        uniform = RDD(jrdd, sc, NoOpSerializer())
-        return uniform.map(lambda bytes: _deserialize_double_vector(bytearray(bytes)))
 
     @staticmethod
+    @toArray
+    @serialize
     def normalVectorRDD(sc, numRows, numCols, numPartitions=None, seed=None):
         """
         Generates an RDD comprised of vectors containing i.i.d. samples drawn
@@ -140,12 +156,12 @@ def normalVectorRDD(sc, numRows, numCols, numPartitions=None, seed=None):
         >>> abs(mat.std() - 1.0) < 0.1
         True
         """
-        jrdd = sc._jvm.PythonMLLibAPI() \
+        return sc._jvm.PythonMLLibAPI() \
             .normalVectorRDD(sc._jsc, numRows, numCols, numPartitions, seed)
-        normal = RDD(jrdd, sc, NoOpSerializer())
-        return normal.map(lambda bytes: _deserialize_double_vector(bytearray(bytes)))
 
     @staticmethod
+    @toArray
+    @serialize
     def poissonVectorRDD(sc, mean, numRows, numCols, numPartitions=None, seed=None):
         """
         Generates an RDD comprised of vectors containing i.i.d. samples drawn
@@ -163,10 +179,8 @@ def poissonVectorRDD(sc, mean, numRows, numCols, numPartitions=None, seed=None):
         >>> abs(mat.std() - sqrt(mean)) < 0.5
         True
         """
-        jrdd = sc._jvm.PythonMLLibAPI() \
+        return sc._jvm.PythonMLLibAPI() \
             .poissonVectorRDD(sc._jsc, mean, numRows, numCols, numPartitions, seed)
-        poisson = RDD(jrdd, sc, NoOpSerializer())
-        return poisson.map(lambda bytes: _deserialize_double_vector(bytearray(bytes)))
 
 
 def _test():
diff --git a/python/pyspark/mllib/recommendation.py b/python/pyspark/mllib/recommendation.py
index 2df23394da6f8..59c1c5ff0ced0 100644
--- a/python/pyspark/mllib/recommendation.py
+++ b/python/pyspark/mllib/recommendation.py
@@ -16,17 +16,25 @@
 #
 
 from pyspark import SparkContext
-from pyspark.mllib._common import \
-    _get_unmangled_rdd, _get_unmangled_double_vector_rdd, \
-    _serialize_double_matrix, _deserialize_double_matrix, \
-    _serialize_double_vector, _deserialize_double_vector, \
-    _get_initial_weights, _serialize_rating, _regression_train_wrapper, \
-    _serialize_tuple, RatingDeserializer
+from pyspark.serializers import PickleSerializer, AutoBatchedSerializer
 from pyspark.rdd import RDD
 
 __all__ = ['MatrixFactorizationModel', 'ALS']
 
 
+class Rating(object):
+    def __init__(self, user, product, rating):
+        self.user = int(user)
+        self.product = int(product)
+        self.rating = float(rating)
+
+    def __reduce__(self):
+        return Rating, (self.user, self.product, self.rating)
+
+    def __repr__(self):
+        return "Rating(%d, %d, %d)" % (self.user, self.product, self.rating)
+
+
 class MatrixFactorizationModel(object):
 
     """A matrix factorisation model trained by regularized alternating
@@ -39,7 +47,9 @@ class MatrixFactorizationModel(object):
     >>> model = ALS.trainImplicit(ratings, 1)
     >>> model.predict(2,2) is not None
     True
+
     >>> testset = sc.parallelize([(1, 2), (1, 1)])
+    >>> model = ALS.train(ratings, 1)
     >>> model.predictAll(testset).count() == 2
     True
     """
@@ -54,34 +64,61 @@ def __del__(self):
     def predict(self, user, product):
         return self._java_model.predict(user, product)
 
-    def predictAll(self, usersProducts):
-        usersProductsJRDD = _get_unmangled_rdd(usersProducts, _serialize_tuple)
-        return RDD(self._java_model.predict(usersProductsJRDD._jrdd),
-                   self._context, RatingDeserializer())
+    def predictAll(self, user_product):
+        assert isinstance(user_product, RDD), "user_product should be RDD of (user, product)"
+        first = user_product.first()
+        if isinstance(first, list):
+            user_product = user_product.map(tuple)
+            first = tuple(first)
+        assert type(first) is tuple and len(first) == 2, \
+            "user_product should be RDD of (user, product)"
+        if any(isinstance(x, str) for x in first):
+            user_product = user_product.map(lambda (u, p): (int(x), int(p)))
+            first = tuple(map(int, first))
+        assert all(type(x) is int for x in first), "user and product in user_product shoul be int"
+        sc = self._context
+        tuplerdd = sc._jvm.SerDe.asTupleRDD(user_product._to_java_object_rdd().rdd())
+        jresult = self._java_model.predict(tuplerdd).toJavaRDD()
+        return RDD(sc._jvm.PythonRDD.javaToPython(jresult), sc,
+                   AutoBatchedSerializer(PickleSerializer()))
 
 
 class ALS(object):
 
+    @classmethod
+    def _prepare(cls, ratings):
+        assert isinstance(ratings, RDD), "ratings should be RDD"
+        first = ratings.first()
+        if not isinstance(first, Rating):
+            if isinstance(first, (tuple, list)):
+                ratings = ratings.map(lambda x: Rating(*x))
+            else:
+                raise ValueError("rating should be RDD of Rating or tuple/list")
+        # serialize them by AutoBatchedSerializer before cache to reduce the
+        # objects overhead in JVM
+        cached = ratings._reserialize(AutoBatchedSerializer(PickleSerializer())).cache()
+        return cached._to_java_object_rdd()
+
     @classmethod
     def train(cls, ratings, rank, iterations=5, lambda_=0.01, blocks=-1):
         sc = ratings.context
-        ratingBytes = _get_unmangled_rdd(ratings, _serialize_rating)
-        mod = sc._jvm.PythonMLLibAPI().trainALSModel(
-            ratingBytes._jrdd, rank, iterations, lambda_, blocks)
+        jrating = cls._prepare(ratings)
+        mod = sc._jvm.PythonMLLibAPI().trainALSModel(jrating, rank, iterations, lambda_, blocks)
         return MatrixFactorizationModel(sc, mod)
 
     @classmethod
     def trainImplicit(cls, ratings, rank, iterations=5, lambda_=0.01, blocks=-1, alpha=0.01):
         sc = ratings.context
-        ratingBytes = _get_unmangled_rdd(ratings, _serialize_rating)
+        jrating = cls._prepare(ratings)
         mod = sc._jvm.PythonMLLibAPI().trainImplicitALSModel(
-            ratingBytes._jrdd, rank, iterations, lambda_, blocks, alpha)
+            jrating, rank, iterations, lambda_, blocks, alpha)
         return MatrixFactorizationModel(sc, mod)
 
 
 def _test():
     import doctest
-    globs = globals().copy()
+    import pyspark.mllib.recommendation
+    globs = pyspark.mllib.recommendation.__dict__.copy()
     globs['sc'] = SparkContext('local[4]', 'PythonTest', batchSize=2)
     (failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS)
     globs['sc'].stop()
diff --git a/python/pyspark/mllib/regression.py b/python/pyspark/mllib/regression.py
index f572dcfb840b6..cbdbc09858013 100644
--- a/python/pyspark/mllib/regression.py
+++ b/python/pyspark/mllib/regression.py
@@ -15,12 +15,12 @@
 # limitations under the License.
 #
 
-from numpy import array, ndarray
-from pyspark import SparkContext
-from pyspark.mllib._common import _dot, _regression_train_wrapper, \
-    _linear_predictor_typecheck, _have_scipy, _scipy_issparse
-from pyspark.mllib.linalg import SparseVector, Vectors
+import numpy as np
+from numpy import array
 
+from pyspark import SparkContext
+from pyspark.mllib.linalg import SparseVector, _convert_to_vector
+from pyspark.serializers import PickleSerializer, AutoBatchedSerializer
 
 __all__ = ['LabeledPoint', 'LinearModel', 'LinearRegressionModel', 'RidgeRegressionModel'
            'LinearRegressionWithSGD', 'LassoWithSGD', 'RidgeRegressionWithSGD']
@@ -38,16 +38,16 @@ class LabeledPoint(object):
 
     def __init__(self, label, features):
         self.label = label
-        if (type(features) == ndarray or type(features) == SparseVector
-                or (_have_scipy and _scipy_issparse(features))):
-            self.features = features
-        elif type(features) == list:
-            self.features = array(features)
-        else:
-            raise TypeError("Expected NumPy array, list, SparseVector, or scipy.sparse matrix")
+        self.features = _convert_to_vector(features)
+
+    def __reduce__(self):
+        return (LabeledPoint, (self.label, self.features))
 
     def __str__(self):
-        return "(" + ",".join((str(self.label), Vectors.stringify(self.features))) + ")"
+        return "(" + ",".join((str(self.label), str(self.features))) + ")"
+
+    def __repr__(self):
+        return "LabeledPoint(" + ",".join((repr(self.label), repr(self.features))) + ")"
 
 
 class LinearModel(object):
@@ -55,7 +55,7 @@ class LinearModel(object):
     """A linear model that has a vector of coefficients and an intercept."""
 
     def __init__(self, weights, intercept):
-        self._coeff = weights
+        self._coeff = _convert_to_vector(weights)
         self._intercept = intercept
 
     @property
@@ -71,18 +71,19 @@ class LinearRegressionModelBase(LinearModel):
 
     """A linear regression model.
 
-    >>> lrmb = LinearRegressionModelBase(array([1.0, 2.0]), 0.1)
-    >>> abs(lrmb.predict(array([-1.03, 7.777])) - 14.624) < 1e-6
+    >>> lrmb = LinearRegressionModelBase(np.array([1.0, 2.0]), 0.1)
+    >>> abs(lrmb.predict(np.array([-1.03, 7.777])) - 14.624) < 1e-6
     True
     >>> abs(lrmb.predict(SparseVector(2, {0: -1.03, 1: 7.777})) - 14.624) < 1e-6
     True
     """
 
     def predict(self, x):
-        """Predict the value of the dependent variable given a vector x"""
-        """containing values for the independent variables."""
-        _linear_predictor_typecheck(x, self._coeff)
-        return _dot(x, self._coeff) + self._intercept
+        """
+        Predict the value of the dependent variable given a vector x
+        containing values for the independent variables.
+        """
+        return self.weights.dot(x) + self.intercept
 
 
 class LinearRegressionModel(LinearRegressionModelBase):
@@ -96,10 +97,10 @@ class LinearRegressionModel(LinearRegressionModelBase):
     ...     LabeledPoint(3.0, [2.0]),
     ...     LabeledPoint(2.0, [3.0])
     ... ]
-    >>> lrm = LinearRegressionWithSGD.train(sc.parallelize(data), initialWeights=array([1.0]))
-    >>> abs(lrm.predict(array([0.0])) - 0) < 0.5
+    >>> lrm = LinearRegressionWithSGD.train(sc.parallelize(data), initialWeights=np.array([1.0]))
+    >>> abs(lrm.predict(np.array([0.0])) - 0) < 0.5
     True
-    >>> abs(lrm.predict(array([1.0])) - 1) < 0.5
+    >>> abs(lrm.predict(np.array([1.0])) - 1) < 0.5
     True
     >>> abs(lrm.predict(SparseVector(1, {0: 1.0})) - 1) < 0.5
     True
@@ -117,11 +118,27 @@ class LinearRegressionModel(LinearRegressionModelBase):
     """
 
 
+# train_func should take two parameters, namely data and initial_weights, and
+# return the result of a call to the appropriate JVM stub.
+# _regression_train_wrapper is responsible for setup and error checking.
+def _regression_train_wrapper(sc, train_func, modelClass, data, initial_weights):
+    initial_weights = initial_weights or [0.0] * len(data.first().features)
+    ser = PickleSerializer()
+    initial_bytes = bytearray(ser.dumps(_convert_to_vector(initial_weights)))
+    # use AutoBatchedSerializer before cache to reduce the memory
+    # overhead in JVM
+    cached = data._reserialize(AutoBatchedSerializer(ser)).cache()
+    ans = train_func(cached._to_java_object_rdd(), initial_bytes)
+    assert len(ans) == 2, "JVM call result had unexpected length"
+    weights = ser.loads(str(ans[0]))
+    return modelClass(weights, ans[1])
+
+
 class LinearRegressionWithSGD(object):
 
     @classmethod
     def train(cls, data, iterations=100, step=1.0, miniBatchFraction=1.0,
-              initialWeights=None, regParam=1.0, regType=None, intercept=False):
+              initialWeights=None, regParam=1.0, regType="none", intercept=False):
         """
         Train a linear regression model on the given data.
 
@@ -146,11 +163,12 @@ def train(cls, data, iterations=100, step=1.0, miniBatchFraction=1.0,
                                   are activated or not).
         """
         sc = data.context
-        if regType is None:
-            regType = "none"
-        train_f = lambda d, i: sc._jvm.PythonMLLibAPI().trainLinearRegressionModelWithSGD(
-            d._jrdd, iterations, step, miniBatchFraction, i, regParam, regType, intercept)
-        return _regression_train_wrapper(sc, train_f, LinearRegressionModel, data, initialWeights)
+
+        def train(jrdd, i):
+            return sc._jvm.PythonMLLibAPI().trainLinearRegressionModelWithSGD(
+                jrdd, iterations, step, miniBatchFraction, i, regParam, regType, intercept)
+
+        return _regression_train_wrapper(sc, train, LinearRegressionModel, data, initialWeights)
 
 
 class LassoModel(LinearRegressionModelBase):
@@ -166,9 +184,9 @@ class LassoModel(LinearRegressionModelBase):
     ...     LabeledPoint(2.0, [3.0])
     ... ]
     >>> lrm = LassoWithSGD.train(sc.parallelize(data), initialWeights=array([1.0]))
-    >>> abs(lrm.predict(array([0.0])) - 0) < 0.5
+    >>> abs(lrm.predict(np.array([0.0])) - 0) < 0.5
     True
-    >>> abs(lrm.predict(array([1.0])) - 1) < 0.5
+    >>> abs(lrm.predict(np.array([1.0])) - 1) < 0.5
     True
     >>> abs(lrm.predict(SparseVector(1, {0: 1.0})) - 1) < 0.5
     True
@@ -179,7 +197,7 @@ class LassoModel(LinearRegressionModelBase):
     ...     LabeledPoint(2.0, SparseVector(1, {0: 3.0}))
     ... ]
     >>> lrm = LinearRegressionWithSGD.train(sc.parallelize(data), initialWeights=array([1.0]))
-    >>> abs(lrm.predict(array([0.0])) - 0) < 0.5
+    >>> abs(lrm.predict(np.array([0.0])) - 0) < 0.5
     True
     >>> abs(lrm.predict(SparseVector(1, {0: 1.0})) - 1) < 0.5
     True
@@ -193,9 +211,11 @@ def train(cls, data, iterations=100, step=1.0, regParam=1.0,
               miniBatchFraction=1.0, initialWeights=None):
         """Train a Lasso regression model on the given data."""
         sc = data.context
-        train_f = lambda d, i: sc._jvm.PythonMLLibAPI().trainLassoModelWithSGD(
-            d._jrdd, iterations, step, regParam, miniBatchFraction, i)
-        return _regression_train_wrapper(sc, train_f, LassoModel, data, initialWeights)
+
+        def train(jrdd, i):
+            return sc._jvm.PythonMLLibAPI().trainLassoModelWithSGD(
+                jrdd, iterations, step, regParam, miniBatchFraction, i)
+        return _regression_train_wrapper(sc, train, LassoModel, data, initialWeights)
 
 
 class RidgeRegressionModel(LinearRegressionModelBase):
@@ -211,9 +231,9 @@ class RidgeRegressionModel(LinearRegressionModelBase):
     ...     LabeledPoint(2.0, [3.0])
     ... ]
     >>> lrm = RidgeRegressionWithSGD.train(sc.parallelize(data), initialWeights=array([1.0]))
-    >>> abs(lrm.predict(array([0.0])) - 0) < 0.5
+    >>> abs(lrm.predict(np.array([0.0])) - 0) < 0.5
     True
-    >>> abs(lrm.predict(array([1.0])) - 1) < 0.5
+    >>> abs(lrm.predict(np.array([1.0])) - 1) < 0.5
     True
     >>> abs(lrm.predict(SparseVector(1, {0: 1.0})) - 1) < 0.5
     True
@@ -224,7 +244,7 @@ class RidgeRegressionModel(LinearRegressionModelBase):
     ...     LabeledPoint(2.0, SparseVector(1, {0: 3.0}))
     ... ]
     >>> lrm = LinearRegressionWithSGD.train(sc.parallelize(data), initialWeights=array([1.0]))
-    >>> abs(lrm.predict(array([0.0])) - 0) < 0.5
+    >>> abs(lrm.predict(np.array([0.0])) - 0) < 0.5
     True
     >>> abs(lrm.predict(SparseVector(1, {0: 1.0})) - 1) < 0.5
     True
@@ -238,9 +258,12 @@ def train(cls, data, iterations=100, step=1.0, regParam=1.0,
               miniBatchFraction=1.0, initialWeights=None):
         """Train a ridge regression model on the given data."""
         sc = data.context
-        train_func = lambda d, i: sc._jvm.PythonMLLibAPI().trainRidgeModelWithSGD(
-            d._jrdd, iterations, step, regParam, miniBatchFraction, i)
-        return _regression_train_wrapper(sc, train_func, RidgeRegressionModel, data, initialWeights)
+
+        def train(jrdd, i):
+            return sc._jvm.PythonMLLibAPI().trainRidgeModelWithSGD(
+                jrdd, iterations, step, regParam, miniBatchFraction, i)
+
+        return _regression_train_wrapper(sc, train, RidgeRegressionModel, data, initialWeights)
 
 
 def _test():
diff --git a/python/pyspark/mllib/stat.py b/python/pyspark/mllib/stat.py
index 8c726f171c978..b9de0909a6fb1 100644
--- a/python/pyspark/mllib/stat.py
+++ b/python/pyspark/mllib/stat.py
@@ -19,14 +19,26 @@
 Python package for statistical functions in MLlib.
 """
 
-from pyspark.mllib._common import \
-    _get_unmangled_double_vector_rdd, _get_unmangled_rdd, \
-    _serialize_double, _deserialize_double_matrix, _deserialize_double_vector
+from functools import wraps
+
+from pyspark import PickleSerializer
 
 
 __all__ = ['MultivariateStatisticalSummary', 'Statistics']
 
 
+def serialize(f):
+    ser = PickleSerializer()
+
+    @wraps(f)
+    def func(self):
+        jvec = f(self)
+        bytes = self._sc._jvm.SerDe.dumps(jvec)
+        return ser.loads(str(bytes)).toArray()
+
+    return func
+
+
 class MultivariateStatisticalSummary(object):
 
     """
@@ -44,33 +56,38 @@ def __init__(self, sc, java_summary):
     def __del__(self):
         self._sc._gateway.detach(self._java_summary)
 
+    @serialize
     def mean(self):
-        return _deserialize_double_vector(self._java_summary.mean())
+        return self._java_summary.mean()
 
+    @serialize
     def variance(self):
-        return _deserialize_double_vector(self._java_summary.variance())
+        return self._java_summary.variance()
 
     def count(self):
         return self._java_summary.count()
 
+    @serialize
     def numNonzeros(self):
-        return _deserialize_double_vector(self._java_summary.numNonzeros())
+        return self._java_summary.numNonzeros()
 
+    @serialize
     def max(self):
-        return _deserialize_double_vector(self._java_summary.max())
+        return self._java_summary.max()
 
+    @serialize
     def min(self):
-        return _deserialize_double_vector(self._java_summary.min())
+        return self._java_summary.min()
 
 
 class Statistics(object):
 
     @staticmethod
-    def colStats(X):
+    def colStats(rdd):
         """
         Computes column-wise summary statistics for the input RDD[Vector].
 
-        >>> from linalg import Vectors
+        >>> from pyspark.mllib.linalg import Vectors
         >>> rdd = sc.parallelize([Vectors.dense([2, 0, 0, -2]),
         ...                       Vectors.dense([4, 5, 0,  3]),
         ...                       Vectors.dense([6, 7, 0,  8])])
@@ -88,9 +105,9 @@ def colStats(X):
         >>> cStats.min()
         array([ 2.,  0.,  0., -2.])
         """
-        sc = X.ctx
-        Xser = _get_unmangled_double_vector_rdd(X)
-        cStats = sc._jvm.PythonMLLibAPI().colStats(Xser._jrdd)
+        sc = rdd.ctx
+        jrdd = rdd._to_java_object_rdd()
+        cStats = sc._jvm.PythonMLLibAPI().colStats(jrdd)
         return MultivariateStatisticalSummary(sc, cStats)
 
     @staticmethod
@@ -117,7 +134,7 @@ def corr(x, y=None, method=None):
         >>> from math import isnan
         >>> isnan(Statistics.corr(x, zeros))
         True
-        >>> from linalg import Vectors
+        >>> from pyspark.mllib.linalg import Vectors
         >>> rdd = sc.parallelize([Vectors.dense([1, 0, 0, -2]), Vectors.dense([4, 5, 0, 3]),
         ...                       Vectors.dense([6, 7, 0,  8]), Vectors.dense([9, 0, 0, 1])])
         >>> pearsonCorr = Statistics.corr(rdd)
@@ -144,18 +161,16 @@ def corr(x, y=None, method=None):
         # check if y is used to specify the method name instead.
         if type(y) == str:
             raise TypeError("Use 'method=' to specify method name.")
+
+        jx = x._to_java_object_rdd()
         if not y:
-            try:
-                Xser = _get_unmangled_double_vector_rdd(x)
-            except TypeError:
-                raise TypeError("corr called on a single RDD not consisted of Vectors.")
-            resultMat = sc._jvm.PythonMLLibAPI().corr(Xser._jrdd, method)
-            return _deserialize_double_matrix(resultMat)
+            resultMat = sc._jvm.PythonMLLibAPI().corr(jx, method)
+            bytes = sc._jvm.SerDe.dumps(resultMat)
+            ser = PickleSerializer()
+            return ser.loads(str(bytes)).toArray()
         else:
-            xSer = _get_unmangled_rdd(x, _serialize_double)
-            ySer = _get_unmangled_rdd(y, _serialize_double)
-            result = sc._jvm.PythonMLLibAPI().corr(xSer._jrdd, ySer._jrdd, method)
-            return result
+            jy = y._to_java_object_rdd()
+            return sc._jvm.PythonMLLibAPI().corr(jx, jy, method)
 
 
 def _test():
diff --git a/python/pyspark/mllib/tests.py b/python/pyspark/mllib/tests.py
index 8a851bd35c0e8..f72e88ba6e2ba 100644
--- a/python/pyspark/mllib/tests.py
+++ b/python/pyspark/mllib/tests.py
@@ -20,6 +20,8 @@
 """
 
 import sys
+import array as pyarray
+
 from numpy import array, array_equal
 
 if sys.version_info[:2] <= (2, 6):
@@ -27,9 +29,8 @@
 else:
     import unittest
 
-from pyspark.mllib._common import _convert_vector, _serialize_double_vector, \
-    _deserialize_double_vector, _dot, _squared_distance
-from pyspark.mllib.linalg import SparseVector
+from pyspark.serializers import PickleSerializer
+from pyspark.mllib.linalg import Vector, SparseVector, DenseVector, _convert_to_vector
 from pyspark.mllib.regression import LabeledPoint
 from pyspark.tests import PySparkTestCase
 
@@ -42,39 +43,52 @@
     # No SciPy, but that's okay, we'll skip those tests
     pass
 
+ser = PickleSerializer()
+
+
+def _squared_distance(a, b):
+    if isinstance(a, Vector):
+        return a.squared_distance(b)
+    else:
+        return b.squared_distance(a)
 
-class VectorTests(unittest.TestCase):
+
+class VectorTests(PySparkTestCase):
+
+    def _test_serialize(self, v):
+        jvec = self.sc._jvm.SerDe.loads(bytearray(ser.dumps(v)))
+        nv = ser.loads(str(self.sc._jvm.SerDe.dumps(jvec)))
+        self.assertEqual(v, nv)
+        vs = [v] * 100
+        jvecs = self.sc._jvm.SerDe.loads(bytearray(ser.dumps(vs)))
+        nvs = ser.loads(str(self.sc._jvm.SerDe.dumps(jvecs)))
+        self.assertEqual(vs, nvs)
 
     def test_serialize(self):
-        sv = SparseVector(4, {1: 1, 3: 2})
-        dv = array([1., 2., 3., 4.])
-        lst = [1, 2, 3, 4]
-        self.assertTrue(sv is _convert_vector(sv))
-        self.assertTrue(dv is _convert_vector(dv))
-        self.assertTrue(array_equal(dv, _convert_vector(lst)))
-        self.assertEquals(sv, _deserialize_double_vector(_serialize_double_vector(sv)))
-        self.assertTrue(array_equal(dv, _deserialize_double_vector(_serialize_double_vector(dv))))
-        self.assertTrue(array_equal(dv, _deserialize_double_vector(_serialize_double_vector(lst))))
+        self._test_serialize(DenseVector(range(10)))
+        self._test_serialize(DenseVector(array([1., 2., 3., 4.])))
+        self._test_serialize(DenseVector(pyarray.array('d', range(10))))
+        self._test_serialize(SparseVector(4, {1: 1, 3: 2}))
 
     def test_dot(self):
         sv = SparseVector(4, {1: 1, 3: 2})
-        dv = array([1., 2., 3., 4.])
-        lst = [1, 2, 3, 4]
+        dv = DenseVector(array([1., 2., 3., 4.]))
+        lst = DenseVector([1, 2, 3, 4])
         mat = array([[1., 2., 3., 4.],
                      [1., 2., 3., 4.],
                      [1., 2., 3., 4.],
                      [1., 2., 3., 4.]])
-        self.assertEquals(10.0, _dot(sv, dv))
-        self.assertTrue(array_equal(array([3., 6., 9., 12.]), _dot(sv, mat)))
-        self.assertEquals(30.0, _dot(dv, dv))
-        self.assertTrue(array_equal(array([10., 20., 30., 40.]), _dot(dv, mat)))
-        self.assertEquals(30.0, _dot(lst, dv))
-        self.assertTrue(array_equal(array([10., 20., 30., 40.]), _dot(lst, mat)))
+        self.assertEquals(10.0, sv.dot(dv))
+        self.assertTrue(array_equal(array([3., 6., 9., 12.]), sv.dot(mat)))
+        self.assertEquals(30.0, dv.dot(dv))
+        self.assertTrue(array_equal(array([10., 20., 30., 40.]), dv.dot(mat)))
+        self.assertEquals(30.0, lst.dot(dv))
+        self.assertTrue(array_equal(array([10., 20., 30., 40.]), lst.dot(mat)))
 
     def test_squared_distance(self):
         sv = SparseVector(4, {1: 1, 3: 2})
-        dv = array([1., 2., 3., 4.])
-        lst = [4, 3, 2, 1]
+        dv = DenseVector(array([1., 2., 3., 4.]))
+        lst = DenseVector([4, 3, 2, 1])
         self.assertEquals(15.0, _squared_distance(sv, dv))
         self.assertEquals(25.0, _squared_distance(sv, lst))
         self.assertEquals(20.0, _squared_distance(dv, lst))
@@ -198,41 +212,36 @@ def test_serialize(self):
         lil[1, 0] = 1
         lil[3, 0] = 2
         sv = SparseVector(4, {1: 1, 3: 2})
-        self.assertEquals(sv, _convert_vector(lil))
-        self.assertEquals(sv, _convert_vector(lil.tocsc()))
-        self.assertEquals(sv, _convert_vector(lil.tocoo()))
-        self.assertEquals(sv, _convert_vector(lil.tocsr()))
-        self.assertEquals(sv, _convert_vector(lil.todok()))
-        self.assertEquals(sv, _deserialize_double_vector(_serialize_double_vector(lil)))
-        self.assertEquals(sv, _deserialize_double_vector(_serialize_double_vector(lil.tocsc())))
-        self.assertEquals(sv, _deserialize_double_vector(_serialize_double_vector(lil.tocsr())))
-        self.assertEquals(sv, _deserialize_double_vector(_serialize_double_vector(lil.todok())))
+        self.assertEquals(sv, _convert_to_vector(lil))
+        self.assertEquals(sv, _convert_to_vector(lil.tocsc()))
+        self.assertEquals(sv, _convert_to_vector(lil.tocoo()))
+        self.assertEquals(sv, _convert_to_vector(lil.tocsr()))
+        self.assertEquals(sv, _convert_to_vector(lil.todok()))
+
+        def serialize(l):
+            return ser.loads(ser.dumps(_convert_to_vector(l)))
+        self.assertEquals(sv, serialize(lil))
+        self.assertEquals(sv, serialize(lil.tocsc()))
+        self.assertEquals(sv, serialize(lil.tocsr()))
+        self.assertEquals(sv, serialize(lil.todok()))
 
     def test_dot(self):
         from scipy.sparse import lil_matrix
         lil = lil_matrix((4, 1))
         lil[1, 0] = 1
         lil[3, 0] = 2
-        dv = array([1., 2., 3., 4.])
-        sv = SparseVector(4, {0: 1, 1: 2, 2: 3, 3: 4})
-        mat = array([[1., 2., 3., 4.],
-                     [1., 2., 3., 4.],
-                     [1., 2., 3., 4.],
-                     [1., 2., 3., 4.]])
-        self.assertEquals(10.0, _dot(lil, dv))
-        self.assertTrue(array_equal(array([3., 6., 9., 12.]), _dot(lil, mat)))
+        dv = DenseVector(array([1., 2., 3., 4.]))
+        self.assertEquals(10.0, dv.dot(lil))
 
     def test_squared_distance(self):
         from scipy.sparse import lil_matrix
         lil = lil_matrix((4, 1))
         lil[1, 0] = 3
         lil[3, 0] = 2
-        dv = array([1., 2., 3., 4.])
+        dv = DenseVector(array([1., 2., 3., 4.]))
         sv = SparseVector(4, {0: 1, 1: 2, 2: 3, 3: 4})
-        self.assertEquals(15.0, _squared_distance(lil, dv))
-        self.assertEquals(15.0, _squared_distance(lil, sv))
-        self.assertEquals(15.0, _squared_distance(dv, lil))
-        self.assertEquals(15.0, _squared_distance(sv, lil))
+        self.assertEquals(15.0, dv.squared_distance(lil))
+        self.assertEquals(15.0, sv.squared_distance(lil))
 
     def scipy_matrix(self, size, values):
         """Create a column SciPy matrix from a dictionary of values"""
diff --git a/python/pyspark/mllib/tree.py b/python/pyspark/mllib/tree.py
index 5b13ab682bbfc..f59a818a6e74d 100644
--- a/python/pyspark/mllib/tree.py
+++ b/python/pyspark/mllib/tree.py
@@ -18,13 +18,9 @@
 from py4j.java_collections import MapConverter
 
 from pyspark import SparkContext, RDD
-from pyspark.mllib._common import \
-    _get_unmangled_rdd, _get_unmangled_double_vector_rdd, _serialize_double_vector, \
-    _deserialize_labeled_point, _get_unmangled_labeled_point_rdd, \
-    _deserialize_double
+from pyspark.serializers import BatchedSerializer, PickleSerializer
+from pyspark.mllib.linalg import Vector, _convert_to_vector
 from pyspark.mllib.regression import LabeledPoint
-from pyspark.serializers import NoOpSerializer
-
 
 __all__ = ['DecisionTreeModel', 'DecisionTree']
 
@@ -55,21 +51,24 @@ def predict(self, x):
         :param x:  Data point (feature vector),
                    or an RDD of data points (feature vectors).
         """
-        pythonAPI = self._sc._jvm.PythonMLLibAPI()
+        SerDe = self._sc._jvm.SerDe
+        ser = PickleSerializer()
         if isinstance(x, RDD):
             # Bulk prediction
-            if x.count() == 0:
+            first = x.take(1)
+            if not first:
                 return self._sc.parallelize([])
-            dataBytes = _get_unmangled_double_vector_rdd(x, cache=False)
-            jSerializedPreds = \
-                pythonAPI.predictDecisionTreeModel(self._java_model,
-                                                   dataBytes._jrdd)
-            serializedPreds = RDD(jSerializedPreds, self._sc, NoOpSerializer())
-            return serializedPreds.map(lambda bytes: _deserialize_double(bytearray(bytes)))
+            if not isinstance(first[0], Vector):
+                x = x.map(_convert_to_vector)
+            jPred = self._java_model.predict(x._to_java_object_rdd()).toJavaRDD()
+            jpyrdd = self._sc._jvm.PythonRDD.javaToPython(jPred)
+            return RDD(jpyrdd, self._sc, BatchedSerializer(ser, 1024))
+
         else:
             # Assume x is a single data point.
-            x_ = _serialize_double_vector(x)
-            return pythonAPI.predictDecisionTreeModel(self._java_model, x_)
+            bytes = bytearray(ser.dumps(_convert_to_vector(x)))
+            vec = self._sc._jvm.SerDe.loads(bytes)
+            return self._java_model.predict(vec)
 
     def numNodes(self):
         return self._java_model.numNodes()
@@ -77,7 +76,7 @@ def numNodes(self):
     def depth(self):
         return self._java_model.depth()
 
-    def __str__(self):
+    def __repr__(self):
         return self._java_model.toString()
 
 
@@ -90,52 +89,23 @@ class DecisionTree(object):
     EXPERIMENTAL: This is an experimental API.
                   It will probably be modified for Spark v1.2.
 
-    Example usage:
-
-    >>> from numpy import array
-    >>> import sys
-    >>> from pyspark.mllib.regression import LabeledPoint
-    >>> from pyspark.mllib.tree import DecisionTree
-    >>> from pyspark.mllib.linalg import SparseVector
-    >>>
-    >>> data = [
-    ...     LabeledPoint(0.0, [0.0]),
-    ...     LabeledPoint(1.0, [1.0]),
-    ...     LabeledPoint(1.0, [2.0]),
-    ...     LabeledPoint(1.0, [3.0])
-    ... ]
-    >>> categoricalFeaturesInfo = {} # no categorical features
-    >>> model = DecisionTree.trainClassifier(sc.parallelize(data), numClasses=2,
-    ...                                      categoricalFeaturesInfo=categoricalFeaturesInfo)
-    >>> sys.stdout.write(model)
-    DecisionTreeModel classifier
-      If (feature 0 <= 0.5)
-       Predict: 0.0
-      Else (feature 0 > 0.5)
-       Predict: 1.0
-    >>> model.predict(array([1.0])) > 0
-    True
-    >>> model.predict(array([0.0])) == 0
-    True
-    >>> sparse_data = [
-    ...     LabeledPoint(0.0, SparseVector(2, {0: 0.0})),
-    ...     LabeledPoint(1.0, SparseVector(2, {1: 1.0})),
-    ...     LabeledPoint(0.0, SparseVector(2, {0: 0.0})),
-    ...     LabeledPoint(1.0, SparseVector(2, {1: 2.0}))
-    ... ]
-    >>>
-    >>> model = DecisionTree.trainRegressor(sc.parallelize(sparse_data),
-    ...                                     categoricalFeaturesInfo=categoricalFeaturesInfo)
-    >>> model.predict(array([0.0, 1.0])) == 1
-    True
-    >>> model.predict(array([0.0, 0.0])) == 0
-    True
-    >>> model.predict(SparseVector(2, {1: 1.0})) == 1
-    True
-    >>> model.predict(SparseVector(2, {1: 0.0})) == 0
-    True
     """
 
+    @staticmethod
+    def _train(data, type, numClasses, categoricalFeaturesInfo,
+               impurity="gini", maxDepth=5, maxBins=32, minInstancesPerNode=1,
+               minInfoGain=0.0):
+        first = data.first()
+        assert isinstance(first, LabeledPoint), "the data should be RDD of LabeledPoint"
+        sc = data.context
+        jrdd = data._to_java_object_rdd()
+        cfiMap = MapConverter().convert(categoricalFeaturesInfo,
+                                        sc._gateway._gateway_client)
+        model = sc._jvm.PythonMLLibAPI().trainDecisionTreeModel(
+            jrdd, type, numClasses, cfiMap,
+            impurity, maxDepth, maxBins, minInstancesPerNode, minInfoGain)
+        return DecisionTreeModel(sc, model)
+
     @staticmethod
     def trainClassifier(data, numClasses, categoricalFeaturesInfo,
                         impurity="gini", maxDepth=5, maxBins=32, minInstancesPerNode=1,
@@ -159,18 +129,34 @@ def trainClassifier(data, numClasses, categoricalFeaturesInfo,
                                     the parent split
         :param minInfoGain: Min info gain required to create a split
         :return: DecisionTreeModel
+
+        Example usage:
+
+        >>> from numpy import array
+        >>> from pyspark.mllib.regression import LabeledPoint
+        >>> from pyspark.mllib.tree import DecisionTree
+        >>> from pyspark.mllib.linalg import SparseVector
+        >>>
+        >>> data = [
+        ...     LabeledPoint(0.0, [0.0]),
+        ...     LabeledPoint(1.0, [1.0]),
+        ...     LabeledPoint(1.0, [2.0]),
+        ...     LabeledPoint(1.0, [3.0])
+        ... ]
+        >>> model = DecisionTree.trainClassifier(sc.parallelize(data), 2, {})
+        >>> print model,  # it already has newline
+        DecisionTreeModel classifier
+          If (feature 0 <= 0.5)
+           Predict: 0.0
+          Else (feature 0 > 0.5)
+           Predict: 1.0
+        >>> model.predict(array([1.0])) > 0
+        True
+        >>> model.predict(array([0.0])) == 0
+        True
         """
-        sc = data.context
-        dataBytes = _get_unmangled_labeled_point_rdd(data)
-        categoricalFeaturesInfoJMap = \
-            MapConverter().convert(categoricalFeaturesInfo,
-                                   sc._gateway._gateway_client)
-        model = sc._jvm.PythonMLLibAPI().trainDecisionTreeModel(
-            dataBytes._jrdd, "classification",
-            numClasses, categoricalFeaturesInfoJMap,
-            impurity, maxDepth, maxBins, minInstancesPerNode, minInfoGain)
-        dataBytes.unpersist()
-        return DecisionTreeModel(sc, model)
+        return DecisionTree._train(data, "classification", numClasses, categoricalFeaturesInfo,
+                                   impurity, maxDepth, maxBins, minInstancesPerNode, minInfoGain)
 
     @staticmethod
     def trainRegressor(data, categoricalFeaturesInfo,
@@ -194,18 +180,33 @@ def trainRegressor(data, categoricalFeaturesInfo,
                                     the parent split
         :param minInfoGain: Min info gain required to create a split
         :return: DecisionTreeModel
+
+        Example usage:
+
+        >>> from numpy import array
+        >>> from pyspark.mllib.regression import LabeledPoint
+        >>> from pyspark.mllib.tree import DecisionTree
+        >>> from pyspark.mllib.linalg import SparseVector
+        >>>
+        >>> sparse_data = [
+        ...     LabeledPoint(0.0, SparseVector(2, {0: 0.0})),
+        ...     LabeledPoint(1.0, SparseVector(2, {1: 1.0})),
+        ...     LabeledPoint(0.0, SparseVector(2, {0: 0.0})),
+        ...     LabeledPoint(1.0, SparseVector(2, {1: 2.0}))
+        ... ]
+        >>>
+        >>> model = DecisionTree.trainRegressor(sc.parallelize(sparse_data), {})
+        >>> model.predict(array([0.0, 1.0])) == 1
+        True
+        >>> model.predict(array([0.0, 0.0])) == 0
+        True
+        >>> model.predict(SparseVector(2, {1: 1.0})) == 1
+        True
+        >>> model.predict(SparseVector(2, {1: 0.0})) == 0
+        True
         """
-        sc = data.context
-        dataBytes = _get_unmangled_labeled_point_rdd(data)
-        categoricalFeaturesInfoJMap = \
-            MapConverter().convert(categoricalFeaturesInfo,
-                                   sc._gateway._gateway_client)
-        model = sc._jvm.PythonMLLibAPI().trainDecisionTreeModel(
-            dataBytes._jrdd, "regression",
-            0, categoricalFeaturesInfoJMap,
-            impurity, maxDepth, maxBins, minInstancesPerNode, minInfoGain)
-        dataBytes.unpersist()
-        return DecisionTreeModel(sc, model)
+        return DecisionTree._train(data, "regression", 0, categoricalFeaturesInfo,
+                                   impurity, maxDepth, maxBins, minInstancesPerNode, minInfoGain)
 
 
 def _test():
diff --git a/python/pyspark/mllib/util.py b/python/pyspark/mllib/util.py
index 1c7b8c809ab5b..8233d4e81f1ca 100644
--- a/python/pyspark/mllib/util.py
+++ b/python/pyspark/mllib/util.py
@@ -18,11 +18,10 @@
 import numpy as np
 import warnings
 
-from pyspark.mllib.linalg import Vectors, SparseVector
-from pyspark.mllib.regression import LabeledPoint
-from pyspark.mllib._common import _convert_vector, _deserialize_labeled_point
 from pyspark.rdd import RDD
-from pyspark.serializers import NoOpSerializer
+from pyspark.serializers import BatchedSerializer, PickleSerializer
+from pyspark.mllib.linalg import Vectors, SparseVector, _convert_to_vector
+from pyspark.mllib.regression import LabeledPoint
 
 
 class MLUtils(object):
@@ -32,15 +31,12 @@ class MLUtils(object):
     """
 
     @staticmethod
-    def _parse_libsvm_line(line, multiclass):
-        warnings.warn("deprecated", DeprecationWarning)
-        return _parse_libsvm_line(line)
-
-    @staticmethod
-    def _parse_libsvm_line(line):
+    def _parse_libsvm_line(line, multiclass=None):
         """
         Parses a line in LIBSVM format into (label, indices, values).
         """
+        if multiclass is not None:
+            warnings.warn("deprecated", DeprecationWarning)
         items = line.split(None)
         label = float(items[0])
         nnz = len(items) - 1
@@ -55,27 +51,20 @@ def _parse_libsvm_line(line):
     @staticmethod
     def _convert_labeled_point_to_libsvm(p):
         """Converts a LabeledPoint to a string in LIBSVM format."""
+        assert isinstance(p, LabeledPoint)
         items = [str(p.label)]
-        v = _convert_vector(p.features)
-        if type(v) == np.ndarray:
-            for i in xrange(len(v)):
-                items.append(str(i + 1) + ":" + str(v[i]))
-        elif type(v) == SparseVector:
+        v = _convert_to_vector(p.features)
+        if isinstance(v, SparseVector):
             nnz = len(v.indices)
             for i in xrange(nnz):
                 items.append(str(v.indices[i] + 1) + ":" + str(v.values[i]))
         else:
-            raise TypeError("_convert_labeled_point_to_libsvm needs either ndarray or SparseVector"
-                            " but got " % type(v))
+            for i in xrange(len(v)):
+                items.append(str(i + 1) + ":" + str(v[i]))
         return " ".join(items)
 
     @staticmethod
-    def loadLibSVMFile(sc, path, multiclass=False, numFeatures=-1, minPartitions=None):
-        warnings.warn("deprecated", DeprecationWarning)
-        return loadLibSVMFile(sc, path, numFeatures, minPartitions)
-
-    @staticmethod
-    def loadLibSVMFile(sc, path, numFeatures=-1, minPartitions=None):
+    def loadLibSVMFile(sc, path, numFeatures=-1, minPartitions=None, multiclass=None):
         """
         Loads labeled data in the LIBSVM format into an RDD of
         LabeledPoint. The LIBSVM format is a text-based format used by
@@ -122,6 +111,8 @@ def loadLibSVMFile(sc, path, numFeatures=-1, minPartitions=None):
         >>> print examples[2]
         (-1.0,(6,[1,3,5],[4.0,5.0,6.0]))
         """
+        if multiclass is not None:
+            warnings.warn("deprecated", DeprecationWarning)
 
         lines = sc.textFile(path, minPartitions)
         parsed = lines.map(lambda l: MLUtils._parse_libsvm_line(l))
@@ -182,9 +173,9 @@ def loadLabeledPoints(sc, path, minPartitions=None):
         (0.0,[1.01,2.02,3.03])
         """
         minPartitions = minPartitions or min(sc.defaultParallelism, 2)
-        jSerialized = sc._jvm.PythonMLLibAPI().loadLabeledPoints(sc._jsc, path, minPartitions)
-        serialized = RDD(jSerialized, sc, NoOpSerializer())
-        return serialized.map(lambda bytes: _deserialize_labeled_point(bytearray(bytes)))
+        jrdd = sc._jvm.PythonMLLibAPI().loadLabeledPoints(sc._jsc, path, minPartitions)
+        jpyrdd = sc._jvm.PythonRDD.javaToPython(jrdd)
+        return RDD(jpyrdd, sc, BatchedSerializer(PickleSerializer()))
 
 
 def _test():
diff --git a/python/pyspark/rdd.py b/python/pyspark/rdd.py
index b43606b7304c5..8ef233bc80c5c 100644
--- a/python/pyspark/rdd.py
+++ b/python/pyspark/rdd.py
@@ -34,7 +34,7 @@
 
 from pyspark.serializers import NoOpSerializer, CartesianDeserializer, \
     BatchedSerializer, CloudPickleSerializer, PairDeserializer, \
-    PickleSerializer, pack_long, CompressedSerializer
+    PickleSerializer, pack_long, AutoBatchedSerializer
 from pyspark.join import python_join, python_left_outer_join, \
     python_right_outer_join, python_cogroup
 from pyspark.statcounter import StatCounter
@@ -1927,10 +1927,10 @@ def _to_java_object_rdd(self):
         It will convert each Python object into Java object by Pyrolite, whenever the
         RDD is serialized in batch or not.
         """
-        if not self._is_pickled():
-            self = self._reserialize(BatchedSerializer(PickleSerializer(), 1024))
-        batched = isinstance(self._jrdd_deserializer, BatchedSerializer)
-        return self.ctx._jvm.PythonRDD.pythonToJava(self._jrdd, batched)
+        rdd = self._reserialize(AutoBatchedSerializer(PickleSerializer())) \
+            if not self._is_pickled() else self
+        is_batch = isinstance(rdd._jrdd_deserializer, BatchedSerializer)
+        return self.ctx._jvm.PythonRDD.pythonToJava(rdd._jrdd, is_batch)
 
     def countApprox(self, timeout, confidence=0.95):
         """
diff --git a/python/pyspark/serializers.py b/python/pyspark/serializers.py
index 44ac5642836e0..2672da36c1f50 100644
--- a/python/pyspark/serializers.py
+++ b/python/pyspark/serializers.py
@@ -68,6 +68,7 @@
 import types
 import collections
 import zlib
+import itertools
 
 from pyspark import cloudpickle
 
@@ -214,6 +215,41 @@ def __str__(self):
         return "BatchedSerializer<%s>" % str(self.serializer)
 
 
+class AutoBatchedSerializer(BatchedSerializer):
+    """
+    Choose the size of batch automatically based on the size of object
+    """
+
+    def __init__(self, serializer, bestSize=1 << 20):
+        BatchedSerializer.__init__(self, serializer, -1)
+        self.bestSize = bestSize
+
+    def dump_stream(self, iterator, stream):
+        batch, best = 1, self.bestSize
+        iterator = iter(iterator)
+        while True:
+            vs = list(itertools.islice(iterator, batch))
+            if not vs:
+                break
+
+            bytes = self.serializer.dumps(vs)
+            write_int(len(bytes), stream)
+            stream.write(bytes)
+
+            size = len(bytes)
+            if size < best:
+                batch *= 2
+            elif size > best * 10 and batch > 1:
+                batch /= 2
+
+    def __eq__(self, other):
+        return (isinstance(other, AutoBatchedSerializer) and
+                other.serializer == self.serializer)
+
+    def __str__(self):
+        return "BatchedSerializer<%s>" % str(self.serializer)
+
+
 class CartesianDeserializer(FramedSerializer):
 
     """
diff --git a/python/run-tests b/python/run-tests
index a67e5a99fbdcc..a7ec270c7da21 100755
--- a/python/run-tests
+++ b/python/run-tests
@@ -73,7 +73,6 @@ run_test "pyspark/serializers.py"
 unset PYSPARK_DOC_TEST
 run_test "pyspark/shuffle.py"
 run_test "pyspark/tests.py"
-run_test "pyspark/mllib/_common.py"
 run_test "pyspark/mllib/classification.py"
 run_test "pyspark/mllib/clustering.py"
 run_test "pyspark/mllib/linalg.py"

From 2c3cc7641d86fa5196406955325a042890f77563 Mon Sep 17 00:00:00 2001
From: Cheng Hao <hao.cheng@intel.com>
Date: Fri, 19 Sep 2014 15:29:22 -0700
Subject: [PATCH 352/489] [SPARK-3501] [SQL] Fix the bug of Hive SimpleUDF
 creates unnecessary type cast

When do the query like:
```
select datediff(cast(value as timestamp), cast('2002-03-21 00:00:00' as timestamp)) from src;
```
SparkSQL will raise exception:
```
[info] scala.MatchError: TimestampType (of class org.apache.spark.sql.catalyst.types.TimestampType$)
[info] at org.apache.spark.sql.catalyst.expressions.Cast.castToTimestamp(Cast.scala:77)
[info] at org.apache.spark.sql.catalyst.expressions.Cast.cast$lzycompute(Cast.scala:251)
[info] at org.apache.spark.sql.catalyst.expressions.Cast.cast(Cast.scala:247)
[info] at org.apache.spark.sql.catalyst.expressions.Cast.eval(Cast.scala:263)
[info] at org.apache.spark.sql.catalyst.optimizer.ConstantFolding$$anonfun$apply$5$$anonfun$applyOrElse$2.applyOrElse(Optimizer.scala:217)
[info] at org.apache.spark.sql.catalyst.optimizer.ConstantFolding$$anonfun$apply$5$$anonfun$applyOrElse$2.applyOrElse(Optimizer.scala:210)
[info] at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:144)
[info] at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4$$anonfun$apply$2.apply(TreeNode.scala:180)
[info] at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
[info] at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
```

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

Closes #2368 from chenghao-intel/cast_exception and squashes the following commits:

5c9c3a5 [Cheng Hao] make more clear code
49dfc50 [Cheng Hao] Add no-op for Cast and revert the position of SimplifyCasts
b804abd [Cheng Hao] Add unit test to show the failure in identical data type casting
330a5c8 [Cheng Hao] Update Code based on comments
b834ed4 [Cheng Hao] Fix bug of HiveSimpleUDF with unnecessary type cast which cause exception in constant folding
---
 .../apache/spark/sql/catalyst/expressions/Cast.scala  |  1 +
 .../scala/org/apache/spark/sql/hive/hiveUdfs.scala    |  3 ++-
 ...imestamp in UDF-0-66952a3949d7544716fd1a675498b1fa |  1 +
 .../spark/sql/hive/execution/HiveQuerySuite.scala     | 11 ++++++++++-
 4 files changed, 14 insertions(+), 2 deletions(-)
 create mode 100644 sql/hive/src/test/resources/golden/Cast Timestamp to Timestamp in UDF-0-66952a3949d7544716fd1a675498b1fa

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
index 0ad2b30cf9c1f..0379275121bf2 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
@@ -245,6 +245,7 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression {
   }
 
   private[this] lazy val cast: Any => Any = dataType match {
+    case dt if dt == child.dataType => identity[Any]
     case StringType => castToString
     case BinaryType => castToBinary
     case DecimalType => castToDecimal
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUdfs.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUdfs.scala
index 7d1ad53d8bdb3..7cda0dd302c86 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUdfs.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUdfs.scala
@@ -51,12 +51,13 @@ private[hive] abstract class HiveFunctionRegistry
       val function = functionInfo.getFunctionClass.newInstance().asInstanceOf[UDF]
       val method = function.getResolver.getEvalMethod(children.map(_.dataType.toTypeInfo))
 
-      lazy val expectedDataTypes = method.getParameterTypes.map(javaClassToDataType)
+      val expectedDataTypes = method.getParameterTypes.map(javaClassToDataType)
 
       HiveSimpleUdf(
         functionClassName,
         children.zip(expectedDataTypes).map {
           case (e, NullType) => e
+          case (e, t) if (e.dataType == t) => e
           case (e, t) => Cast(e, t)
         }
       )
diff --git a/sql/hive/src/test/resources/golden/Cast Timestamp to Timestamp in UDF-0-66952a3949d7544716fd1a675498b1fa b/sql/hive/src/test/resources/golden/Cast Timestamp to Timestamp in UDF-0-66952a3949d7544716fd1a675498b1fa
new file mode 100644
index 0000000000000..7951defec192a
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/Cast Timestamp to Timestamp in UDF-0-66952a3949d7544716fd1a675498b1fa	
@@ -0,0 +1 @@
+NULL
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
index 8c8a8b124ac69..56bcd95eab4bc 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
@@ -142,16 +142,25 @@ class HiveQuerySuite extends HiveComparisonTest {
     setConf("spark.sql.dialect", "sql")
     assert(sql("SELECT 1").collect() === Array(Seq(1)))
     setConf("spark.sql.dialect", "hiveql")
-
   }
 
   test("Query expressed in HiveQL") {
     sql("FROM src SELECT key").collect()
   }
 
+  test("Query with constant folding the CAST") {
+    sql("SELECT CAST(CAST('123' AS binary) AS binary) FROM src LIMIT 1").collect()
+  }
+
   createQueryTest("Constant Folding Optimization for AVG_SUM_COUNT",
     "SELECT AVG(0), SUM(0), COUNT(null), COUNT(value) FROM src GROUP BY key")
 
+  createQueryTest("Cast Timestamp to Timestamp in UDF",
+    """
+       | SELECT DATEDIFF(CAST(value AS timestamp), CAST('2002-03-21 00:00:00' AS timestamp)) 
+       | FROM src LIMIT 1
+    """.stripMargin)
+
   createQueryTest("Simple Average",
     "SELECT AVG(key) FROM src")
 

From 5522151eb14f4208798901f5c090868edd8e8dde Mon Sep 17 00:00:00 2001
From: ravipesala <ravindra.pesala@huawei.com>
Date: Fri, 19 Sep 2014 15:31:57 -0700
Subject: [PATCH 353/489] [SPARK-2594][SQL] Support CACHE TABLE <name> AS
 SELECT ...

This feature allows user to add cache table from the select query.
Example : ```CACHE TABLE testCacheTable AS SELECT * FROM TEST_TABLE```
Spark takes this type of SQL as command and it does lazy caching just like ```SQLContext.cacheTable```, ```CACHE TABLE <name>``` does.
It can be executed from both SQLContext and HiveContext.

Recreated the pull request after rebasing with master.And fixed all the comments raised in previous pull requests.
https://github.com/apache/spark/pull/2381
https://github.com/apache/spark/pull/2390

Author : ravipesala ravindra.pesalahuawei.com

Author: ravipesala <ravindra.pesala@huawei.com>

Closes #2397 from ravipesala/SPARK-2594 and squashes the following commits:

a5f0beb [ravipesala] Simplified the code as per Admin comment.
8059cd2 [ravipesala] Changed the behaviour from eager caching to lazy caching.
d6e469d [ravipesala] Code review comments by Admin are handled.
c18aa38 [ravipesala] Merge remote-tracking branch 'remotes/ravipesala/Add-Cache-table-as' into SPARK-2594
394d5ca [ravipesala] Changed style
fb1759b [ravipesala] Updated as per Admin comments
8c9993c [ravipesala] Changed the style
d8b37b2 [ravipesala] Updated as per the comments by Admin
bc0bffc [ravipesala] Merge remote-tracking branch 'ravipesala/Add-Cache-table-as' into Add-Cache-table-as
e3265d0 [ravipesala] Updated the code as per the comments by Admin in pull request.
724b9db [ravipesala] Changed style
aaf5b59 [ravipesala] Added comment
dc33895 [ravipesala] Updated parser to support add cache table command
b5276b2 [ravipesala] Updated parser to support add cache table command
eebc0c1 [ravipesala] Add CACHE TABLE <name> AS SELECT ...
6758f80 [ravipesala] Changed style
7459ce3 [ravipesala] Added comment
13c8e27 [ravipesala] Updated parser to support add cache table command
4e858d8 [ravipesala] Updated parser to support add cache table command
b803fc8 [ravipesala] Add CACHE TABLE <name> AS SELECT ...
---
 .../apache/spark/sql/catalyst/SqlParser.scala | 14 +++++++--
 .../sql/catalyst/plans/logical/commands.scala |  5 ++++
 .../spark/sql/execution/SparkStrategies.scala |  2 ++
 .../apache/spark/sql/execution/commands.scala | 18 +++++++++++
 .../apache/spark/sql/CachedTableSuite.scala   | 13 ++++++++
 .../org/apache/spark/sql/hive/HiveQl.scala    | 30 ++++++++++++-------
 6 files changed, 69 insertions(+), 13 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
index ca69531c69a77..862f78702c4e6 100755
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala
@@ -151,7 +151,7 @@ class SqlParser extends StandardTokenParsers with PackratParsers {
         EXCEPT ^^^ { (q1: LogicalPlan, q2: LogicalPlan) => Except(q1, q2)} |
         UNION ~ opt(DISTINCT) ^^^ { (q1: LogicalPlan, q2: LogicalPlan) => Distinct(Union(q1, q2)) }
       )
-    | insert | cache
+    | insert | cache | unCache
   )
 
   protected lazy val select: Parser[LogicalPlan] =
@@ -183,9 +183,17 @@ class SqlParser extends StandardTokenParsers with PackratParsers {
     }
 
   protected lazy val cache: Parser[LogicalPlan] =
-    (CACHE ^^^ true | UNCACHE ^^^ false) ~ TABLE ~ ident ^^ {
-      case doCache ~ _ ~ tableName => CacheCommand(tableName, doCache)
+    CACHE ~ TABLE ~> ident ~ opt(AS ~> select) <~ opt(";") ^^ {
+      case tableName ~ None => 
+        CacheCommand(tableName, true)
+      case tableName ~ Some(plan) =>
+        CacheTableAsSelectCommand(tableName, plan)
     }
+    
+  protected lazy val unCache: Parser[LogicalPlan] =
+    UNCACHE ~ TABLE ~> ident <~ opt(";") ^^ {
+      case tableName => CacheCommand(tableName, false)
+    }    
 
   protected lazy val projections: Parser[Seq[Expression]] = repsep(projection, ",")
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/commands.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/commands.scala
index a01809c1fc5e2..8366639fa0e8b 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/commands.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/commands.scala
@@ -75,3 +75,8 @@ case class DescribeCommand(
     AttributeReference("data_type", StringType, nullable = false)(),
     AttributeReference("comment", StringType, nullable = false)())
 }
+
+/**
+ * Returned for the "CACHE TABLE tableName AS SELECT .." command.
+ */
+case class CacheTableAsSelectCommand(tableName: String, plan: LogicalPlan) extends Command
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
index 7943d6e1b6fb5..45687d960404c 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
@@ -305,6 +305,8 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
         Seq(execution.ExplainCommand(logicalPlan, plan.output, extended)(context))
       case logical.CacheCommand(tableName, cache) =>
         Seq(execution.CacheCommand(tableName, cache)(context))
+      case logical.CacheTableAsSelectCommand(tableName, plan) =>
+        Seq(execution.CacheTableAsSelectCommand(tableName, plan))
       case _ => Nil
     }
   }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala
index 94543fc95b470..c2f48a902a3e9 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala
@@ -166,3 +166,21 @@ case class DescribeCommand(child: SparkPlan, output: Seq[Attribute])(
       child.output.map(field => Row(field.name, field.dataType.toString, null))
   }
 }
+
+/**
+ * :: DeveloperApi ::
+ */
+@DeveloperApi
+case class CacheTableAsSelectCommand(tableName: String, logicalPlan: LogicalPlan)
+  extends LeafNode with Command {
+  
+  override protected[sql] lazy val sideEffectResult = {
+    import sqlContext._
+    logicalPlan.registerTempTable(tableName)
+    cacheTable(tableName) 
+    Seq.empty[Row]
+  }
+
+  override def output: Seq[Attribute] = Seq.empty  
+  
+}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala
index befef46d93973..591592841e9fe 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala
@@ -119,4 +119,17 @@ class CachedTableSuite extends QueryTest {
     }
     assert(!TestSQLContext.isCached("testData"), "Table 'testData' should not be cached")
   }
+  
+  test("CACHE TABLE tableName AS SELECT Star Table") {
+    TestSQLContext.sql("CACHE TABLE testCacheTable AS SELECT * FROM testData")
+    TestSQLContext.sql("SELECT * FROM testCacheTable WHERE key = 1").collect()
+    assert(TestSQLContext.isCached("testCacheTable"), "Table 'testCacheTable' should be cached")
+    TestSQLContext.uncacheTable("testCacheTable")
+  }
+  
+  test("'CACHE TABLE tableName AS SELECT ..'") {
+    TestSQLContext.sql("CACHE TABLE testCacheTable AS SELECT * FROM testData")
+    assert(TestSQLContext.isCached("testCacheTable"), "Table 'testCacheTable' should be cached")
+    TestSQLContext.uncacheTable("testCacheTable")
+  }
 }
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
index 21ecf17028dbc..0aa6292c0184e 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
@@ -229,7 +229,12 @@ private[hive] object HiveQl {
             SetCommand(Some(key), Some(value))
         }
       } else if (sql.trim.toLowerCase.startsWith("cache table")) {
-        CacheCommand(sql.trim.drop(12).trim, true)
+        sql.trim.drop(12).trim.split(" ").toSeq match {
+          case Seq(tableName) => 
+            CacheCommand(tableName, true)
+          case Seq(tableName, _, select @ _*) => 
+            CacheTableAsSelectCommand(tableName, createPlan(select.mkString(" ").trim))
+        }
       } else if (sql.trim.toLowerCase.startsWith("uncache table")) {
         CacheCommand(sql.trim.drop(14).trim, false)
       } else if (sql.trim.toLowerCase.startsWith("add jar")) {
@@ -243,15 +248,7 @@ private[hive] object HiveQl {
       } else if (sql.trim.startsWith("!")) {
         ShellCommand(sql.drop(1))
       } else {
-        val tree = getAst(sql)
-        if (nativeCommands contains tree.getText) {
-          NativeCommand(sql)
-        } else {
-          nodeToPlan(tree) match {
-            case NativePlaceholder => NativeCommand(sql)
-            case other => other
-          }
-        }
+        createPlan(sql)
       }
     } catch {
       case e: Exception => throw new ParseException(sql, e)
@@ -262,6 +259,19 @@ private[hive] object HiveQl {
         """.stripMargin)
     }
   }
+  
+  /** Creates LogicalPlan for a given HiveQL string. */
+  def createPlan(sql: String) = {
+    val tree = getAst(sql)
+    if (nativeCommands contains tree.getText) {
+      NativeCommand(sql)
+    } else {
+      nodeToPlan(tree) match {
+        case NativePlaceholder => NativeCommand(sql)
+        case other => other
+      }
+    }
+  }
 
   def parseDdl(ddl: String): Seq[Attribute] = {
     val tree =

From a95ad99e31c2d5980a3b8cd8e36ff968b1e6b201 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies.liu@gmail.com>
Date: Fri, 19 Sep 2014 15:33:42 -0700
Subject: [PATCH 354/489] [SPARK-3592] [SQL] [PySpark] support applySchema to
 RDD of Row

Fix the issue when applySchema() to an RDD of Row.

Also add type mapping for BinaryType.

Author: Davies Liu <davies.liu@gmail.com>

Closes #2448 from davies/row and squashes the following commits:

dd220cf [Davies Liu] fix test
3f3f188 [Davies Liu] add more test
f559746 [Davies Liu] add tests, fix serialization
9688fd2 [Davies Liu] support applySchema to RDD of Row
---
 python/pyspark/sql.py   | 13 ++++++++++---
 python/pyspark/tests.py | 11 ++++++++++-
 2 files changed, 20 insertions(+), 4 deletions(-)

diff --git a/python/pyspark/sql.py b/python/pyspark/sql.py
index 42a9920f10e6f..653195ea438cf 100644
--- a/python/pyspark/sql.py
+++ b/python/pyspark/sql.py
@@ -440,6 +440,7 @@ def _parse_datatype_string(datatype_string):
     float: DoubleType,
     str: StringType,
     unicode: StringType,
+    bytearray: BinaryType,
     decimal.Decimal: DecimalType,
     datetime.datetime: TimestampType,
     datetime.date: TimestampType,
@@ -690,11 +691,12 @@ def _infer_schema_type(obj, dataType):
     ByteType: (int, long),
     ShortType: (int, long),
     IntegerType: (int, long),
-    LongType: (long,),
+    LongType: (int, long),
     FloatType: (float,),
     DoubleType: (float,),
     DecimalType: (decimal.Decimal,),
     StringType: (str, unicode),
+    BinaryType: (bytearray,),
     TimestampType: (datetime.datetime,),
     ArrayType: (list, tuple, array),
     MapType: (dict,),
@@ -728,9 +730,9 @@ def _verify_type(obj, dataType):
         return
 
     _type = type(dataType)
-    if _type not in _acceptable_types:
-        return
+    assert _type in _acceptable_types, "unkown datatype: %s" % dataType
 
+    # subclass of them can not be deserialized in JVM
     if type(obj) not in _acceptable_types[_type]:
         raise TypeError("%s can not accept abject in type %s"
                         % (dataType, type(obj)))
@@ -1121,6 +1123,11 @@ def applySchema(self, rdd, schema):
 
         # take the first few rows to verify schema
         rows = rdd.take(10)
+        # Row() cannot been deserialized by Pyrolite
+        if rows and isinstance(rows[0], tuple) and rows[0].__class__.__name__ == 'Row':
+            rdd = rdd.map(tuple)
+            rows = rdd.take(10)
+
         for row in rows:
             _verify_type(row, schema)
 
diff --git a/python/pyspark/tests.py b/python/pyspark/tests.py
index 7301966e48045..a94eb0f429e0a 100644
--- a/python/pyspark/tests.py
+++ b/python/pyspark/tests.py
@@ -45,7 +45,7 @@
 from pyspark.serializers import read_int, BatchedSerializer, MarshalSerializer, PickleSerializer, \
     CloudPickleSerializer
 from pyspark.shuffle import Aggregator, InMemoryMerger, ExternalMerger, ExternalSorter
-from pyspark.sql import SQLContext, IntegerType
+from pyspark.sql import SQLContext, IntegerType, Row
 from pyspark import shuffle
 
 _have_scipy = False
@@ -659,6 +659,15 @@ def test_distinct(self):
         self.assertEquals(result.getNumPartitions(), 5)
         self.assertEquals(result.count(), 3)
 
+    def test_apply_schema_to_row(self):
+        srdd = self.sqlCtx.jsonRDD(self.sc.parallelize(["""{"a":2}"""]))
+        srdd2 = self.sqlCtx.applySchema(srdd.map(lambda x: x), srdd.schema())
+        self.assertEqual(srdd.collect(), srdd2.collect())
+
+        rdd = self.sc.parallelize(range(10)).map(lambda x: Row(a=x))
+        srdd3 = self.sqlCtx.applySchema(rdd, srdd.schema())
+        self.assertEqual(10, srdd3.count())
+
 
 class TestIO(PySparkTestCase):
 

From 3b9cd13ebc108c7c6d518a760333cd992667126c Mon Sep 17 00:00:00 2001
From: Sandy Ryza <sandy@cloudera.com>
Date: Fri, 19 Sep 2014 15:34:48 -0700
Subject: [PATCH 355/489] SPARK-3605. Fix typo in SchemaRDD.

Author: Sandy Ryza <sandy@cloudera.com>

Closes #2460 from sryza/sandy-spark-3605 and squashes the following commits:

09d940b [Sandy Ryza] SPARK-3605. Fix typo in SchemaRDD.
---
 sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala
index 3bc5dce095511..3b873f7c62cb6 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SchemaRDD.scala
@@ -45,9 +45,8 @@ import org.apache.spark.api.java.JavaRDD
  * explicitly using the `createSchemaRDD` function on a [[SQLContext]].
  *
  * A `SchemaRDD` can also be created by loading data in from external sources.
- * Examples are loading data from Parquet files by using by using the
- * `parquetFile` method on [[SQLContext]], and loading JSON datasets
- * by using `jsonFile` and `jsonRDD` methods on [[SQLContext]].
+ * Examples are loading data from Parquet files by using the `parquetFile` method on [[SQLContext]]
+ * and loading JSON datasets by using `jsonFile` and `jsonRDD` methods on [[SQLContext]].
  *
  * == SQL Queries ==
  * A SchemaRDD can be registered as a table in the [[SQLContext]] that was used to create it.  Once

From ba68a51c407197d478b330403af8fe24a176bef3 Mon Sep 17 00:00:00 2001
From: Daoyuan Wang <daoyuan.wang@intel.com>
Date: Fri, 19 Sep 2014 15:39:31 -0700
Subject: [PATCH 356/489] [SPARK-3485][SQL] Use
 GenericUDFUtils.ConversionHelper for Simple UDF type conversions

This is just another solution to SPARK-3485, in addition to PR #2355
In this patch, we will use ConventionHelper and FunctionRegistry to invoke a simple udf evaluation, which rely more on hive, but much cleaner and safer.
We can discuss which one is better.

Author: Daoyuan Wang <daoyuan.wang@intel.com>

Closes #2407 from adrian-wang/simpleudf and squashes the following commits:

15762d2 [Daoyuan Wang] add posmod test which would fail the test but now ok
0d69eb4 [Daoyuan Wang] another way to pass to hive simple udf
---
 .../execution/HiveCompatibilitySuite.scala    |  1 +
 .../org/apache/spark/sql/hive/hiveUdfs.scala  | 55 ++++++-------------
 2 files changed, 17 insertions(+), 39 deletions(-)

diff --git a/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala b/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
index ab487d673e813..556c984ad392b 100644
--- a/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
+++ b/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
@@ -801,6 +801,7 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
     "udf_or",
     "udf_parse_url",
     "udf_PI",
+    "udf_pmod",
     "udf_positive",
     "udf_pow",
     "udf_power",
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUdfs.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUdfs.scala
index 7cda0dd302c86..5a0e6c5cc1bba 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUdfs.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUdfs.scala
@@ -17,6 +17,8 @@
 
 package org.apache.spark.sql.hive
 
+import org.apache.hadoop.hive.ql.udf.generic.GenericUDFUtils.ConversionHelper
+
 import scala.collection.mutable.ArrayBuffer
 
 import org.apache.hadoop.hive.common.`type`.HiveDecimal
@@ -105,52 +107,27 @@ private[hive] case class HiveSimpleUdf(functionClassName: String, children: Seq[
     function.getResolver.getEvalMethod(children.map(_.dataType.toTypeInfo))
 
   @transient
-  lazy val dataType = javaClassToDataType(method.getReturnType)
+  protected lazy val arguments = children.map(c => toInspector(c.dataType)).toArray
 
-  protected lazy val wrappers: Array[(Any) => AnyRef] = method.getParameterTypes.map { argClass =>
-    val primitiveClasses = Seq(
-      Integer.TYPE, classOf[java.lang.Integer], classOf[java.lang.String], java.lang.Double.TYPE,
-      classOf[java.lang.Double], java.lang.Long.TYPE, classOf[java.lang.Long],
-      classOf[HiveDecimal], java.lang.Byte.TYPE, classOf[java.lang.Byte],
-      classOf[java.sql.Timestamp]
-    )
-    val matchingConstructor = argClass.getConstructors.find { c =>
-      c.getParameterTypes.size == 1 && primitiveClasses.contains(c.getParameterTypes.head)
-    }
+  // Create parameter converters
+  @transient
+  protected lazy val conversionHelper = new ConversionHelper(method, arguments)
 
-    matchingConstructor match {
-      case Some(constructor) =>
-        (a: Any) => {
-          logDebug(
-            s"Wrapping $a of type ${if (a == null) "null" else a.getClass.getName} $constructor.")
-          // We must make sure that primitives get boxed java style.
-          if (a == null) {
-            null
-          } else {
-            constructor.newInstance(a match {
-              case i: Int => i: java.lang.Integer
-              case bd: BigDecimal => new HiveDecimal(bd.underlying())
-              case other: AnyRef => other
-            }).asInstanceOf[AnyRef]
-          }
-        }
-      case None =>
-        (a: Any) => a match {
-          case wrapper => wrap(wrapper)
-        }
-    }
+  @transient
+  lazy val dataType = javaClassToDataType(method.getReturnType)
+
+  def catalystToHive(value: Any): Object = value match {
+    // TODO need more types here? or can we use wrap()
+    case bd: BigDecimal => new HiveDecimal(bd.underlying())
+    case d => d.asInstanceOf[Object]
   }
 
   // TODO: Finish input output types.
   override def eval(input: Row): Any = {
-    val evaluatedChildren = children.map(_.eval(input))
-    // Wrap the function arguments in the expected types.
-    val args = evaluatedChildren.zip(wrappers).map {
-      case (arg, wrapper) => wrapper(arg)
-    }
+    val evaluatedChildren = children.map(c => catalystToHive(c.eval(input)))
 
-    // Invoke the udf and unwrap the result.
-    unwrap(method.invoke(function, args: _*))
+    unwrap(FunctionRegistry.invoke(method, function, conversionHelper
+      .convertIfNecessary(evaluatedChildren: _*): _*))
   }
 }
 

From 99b06b6fd2d79403ef4307ac6f3fa84176e7a622 Mon Sep 17 00:00:00 2001
From: Nicholas Chammas <nicholas.chammas@gmail.com>
Date: Fri, 19 Sep 2014 15:44:47 -0700
Subject: [PATCH 357/489] [Build] Fix passing of args to sbt

Simple mistake, simple fix:
```shell
args="arg1 arg2 arg3"

sbt $args    # sbt sees 3 arguments
sbt "$args"  # sbt sees 1 argument
```

Should fix the problems we are seeing [here](https://amplab.cs.berkeley.edu/jenkins/job/Spark-Master-SBT/694/AMPLAB_JENKINS_BUILD_PROFILE=hadoop1.0,label=centos/console), for example.

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

Closes #2462 from nchammas/fix-sbt-master-build and squashes the following commits:

4500c86 [Nicholas Chammas] warn about quoting
10018a6 [Nicholas Chammas] Revert "test hadoop1 build"
7d5356c [Nicholas Chammas] Revert "re-add bad quoting for testing"
061600c [Nicholas Chammas] re-add bad quoting for testing
b2de56c [Nicholas Chammas] test hadoop1 build
43fb854 [Nicholas Chammas] unquote profile args
---
 dev/run-tests | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/dev/run-tests b/dev/run-tests
index 5f6df17b509a3..c3d8f49cdd993 100755
--- a/dev/run-tests
+++ b/dev/run-tests
@@ -127,6 +127,8 @@ echo "========================================================================="
   # NOTE: echo "q" is needed because sbt on encountering a build file with failure
   #+ (either resolution or compilation) prompts the user for input either q, r, etc
   #+ to quit or retry. This echo is there to make it not block.
+  # NOTE: Do not quote $BUILD_MVN_PROFILE_ARGS or else it will be interpreted as a 
+  #+ single argument!
   # QUESTION: Why doesn't 'yes "q"' work?
   # QUESTION: Why doesn't 'grep -v -e "^\[info\] Resolving"' work?
   echo -e "q\n" \
@@ -159,10 +161,13 @@ echo "========================================================================="
   # NOTE: echo "q" is needed because sbt on encountering a build file with failure
   #+ (either resolution or compilation) prompts the user for input either q, r, etc
   #+ to quit or retry. This echo is there to make it not block.
+  # NOTE: Do not quote $SBT_MAVEN_PROFILES_ARGS or else it will be interpreted as a 
+  #+ single argument!
+  #+ "${SBT_MAVEN_TEST_ARGS[@]}" is cool because it's an array.
   # QUESTION: Why doesn't 'yes "q"' work?
   # QUESTION: Why doesn't 'grep -v -e "^\[info\] Resolving"' work?
   echo -e "q\n" \
-    | sbt/sbt "$SBT_MAVEN_PROFILES_ARGS" "${SBT_MAVEN_TEST_ARGS[@]}" \
+    | sbt/sbt $SBT_MAVEN_PROFILES_ARGS "${SBT_MAVEN_TEST_ARGS[@]}" \
     | grep -v -e "info.*Resolving" -e "warn.*Merging" -e "info.*Including"
 }
 

From 8af2370619a8a6bb1af7df43b8329ab319348ad8 Mon Sep 17 00:00:00 2001
From: andrewor14 <andrewor14@gmail.com>
Date: Fri, 19 Sep 2014 16:02:38 -0700
Subject: [PATCH 358/489] [Docs] Fix outdated docs for standalone cluster

This is now supported!

Author: andrewor14 <andrewor14@gmail.com>
Author: Andrew Or <andrewor14@gmail.com>

Closes #2461 from andrewor14/document-standalone-cluster and squashes the following commits:

85c8b9e [andrewor14] Wording change per Patrick
35e30ee [Andrew Or] Fix outdated docs for standalone cluster
---
 docs/spark-standalone.md | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/docs/spark-standalone.md b/docs/spark-standalone.md
index 99a8e43a6b489..29b5491861bf3 100644
--- a/docs/spark-standalone.md
+++ b/docs/spark-standalone.md
@@ -248,8 +248,10 @@ You can also pass an option `--cores <numCores>` to control the number of cores
 
 The [`spark-submit` script](submitting-applications.html) provides the most straightforward way to
 submit a compiled Spark application to the cluster. For standalone clusters, Spark currently
-only supports deploying the driver inside the client process that is submitting the application
-(`client` deploy mode).
+supports two deploy modes. In `client` mode, the driver is launched in the same process as the
+client that submits the application. In `cluster` mode, however, the driver is launched from one
+of the Worker processes inside the cluster, and the client process exits as soon as it fulfills
+its responsibility of submitting the application without waiting for the application to finish.
 
 If your application is launched through Spark submit, then the application jar is automatically
 distributed to all worker nodes. For any additional jars that your application depends on, you

From 78d4220fa0bf2f9ee663e34bbf3544a5313b02f0 Mon Sep 17 00:00:00 2001
From: Vida Ha <vida@databricks.com>
Date: Sat, 20 Sep 2014 01:24:49 -0700
Subject: [PATCH 359/489] SPARK-3608 Break if the instance tag naming succeeds

Author: Vida Ha <vida@databricks.com>

Closes #2466 from vidaha/vida/spark-3608 and squashes the following commits:

9509776 [Vida Ha] Break if the instance tag naming succeeds
---
 ec2/spark_ec2.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/ec2/spark_ec2.py b/ec2/spark_ec2.py
index abac71eaca595..fbeccd89b43b3 100755
--- a/ec2/spark_ec2.py
+++ b/ec2/spark_ec2.py
@@ -505,6 +505,7 @@ def tag_instance(instance, name):
     for i in range(0, 5):
         try:
             instance.add_tag(key='Name', value=name)
+            break
         except:
             print "Failed attempt %i of 5 to tag %s" % ((i + 1), name)
             if (i == 5):

From 454981de8b5f7886c6c73aadaa84ddf2673fb80c Mon Sep 17 00:00:00 2001
From: Ken <ugw.gi.world@gmail.com>
Date: Tue, 8 Jul 2014 18:31:41 -0700
Subject: [PATCH 360/489] initial commit for pySparkStreaming

---
 core/pom.xml                                  |   4 +
 .../apache/spark/api/python/PythonRDD.scala   |   2 +-
 .../apache/spark/deploy/PythonRunner.scala    |   1 +
 .../src/main/python/streaming/wordcount.py    |  22 ++
 python/pyspark/java_gateway.py                |   3 +
 python/pyspark/streaming/__init__.py          |   1 +
 python/pyspark/streaming/context.py           | 133 ++++++++
 python/pyspark/streaming/dstream.py           | 315 ++++++++++++++++++
 python/pyspark/streaming/duration.py          | 171 ++++++++++
 python/pyspark/streaming/jtime.py             | 116 +++++++
 python/pyspark/streaming/pyprint.py           |  28 ++
 python/pyspark/streaming/utils.py             |  18 +
 streaming/pom.xml                             |   6 +-
 .../streaming/api/java/JavaDStreamLike.scala  |   8 +
 .../streaming/api/python/PythonDStream.scala  | 152 +++++++++
 .../spark/streaming/dstream/DStream.scala     |  68 +++-
 16 files changed, 1043 insertions(+), 5 deletions(-)
 create mode 100644 examples/src/main/python/streaming/wordcount.py
 create mode 100644 python/pyspark/streaming/__init__.py
 create mode 100644 python/pyspark/streaming/context.py
 create mode 100644 python/pyspark/streaming/dstream.py
 create mode 100644 python/pyspark/streaming/duration.py
 create mode 100644 python/pyspark/streaming/jtime.py
 create mode 100644 python/pyspark/streaming/pyprint.py
 create mode 100644 python/pyspark/streaming/utils.py
 create mode 100644 streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala

diff --git a/core/pom.xml b/core/pom.xml
index 2a81f6df289c0..7eb0b48eaeebd 100644
--- a/core/pom.xml
+++ b/core/pom.xml
@@ -21,7 +21,11 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent</artifactId>
+<<<<<<< HEAD
     <version>1.2.0-SNAPSHOT</version>
+=======
+    <version>1.0.0</version>
+>>>>>>> initial commit for pySparkStreaming
     <relativePath>../pom.xml</relativePath>
   </parent>
 
diff --git a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
index f9ff4ea6ca157..022e2891559d7 100644
--- a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
+++ b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
@@ -288,7 +288,7 @@ private class PythonException(msg: String, cause: Exception) extends RuntimeExce
  * Form an RDD[(Array[Byte], Array[Byte])] from key-value pairs returned from Python.
  * This is used by PySpark's shuffle operations.
  */
-private class PairwiseRDD(prev: RDD[Array[Byte]]) extends
+private[spark] class PairwiseRDD(prev: RDD[Array[Byte]]) extends
   RDD[(Long, Array[Byte])](prev) {
   override def getPartitions = prev.partitions
   override def compute(split: Partition, context: TaskContext) =
diff --git a/core/src/main/scala/org/apache/spark/deploy/PythonRunner.scala b/core/src/main/scala/org/apache/spark/deploy/PythonRunner.scala
index b66c3ba4d5fb0..dc68b1fbda8bb 100644
--- a/core/src/main/scala/org/apache/spark/deploy/PythonRunner.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/PythonRunner.scala
@@ -57,6 +57,7 @@ object PythonRunner {
     val builder = new ProcessBuilder(Seq(pythonExec, "-u", formattedPythonFile) ++ otherArgs)
     val env = builder.environment()
     env.put("PYTHONPATH", pythonPath)
+    env.put("PYSPARK_PYTHON", pythonExec)
     env.put("PYSPARK_GATEWAY_PORT", "" + gatewayServer.getListeningPort)
     builder.redirectErrorStream(true) // Ugly but needed for stdout and stderr to synchronize
     val process = builder.start()
diff --git a/examples/src/main/python/streaming/wordcount.py b/examples/src/main/python/streaming/wordcount.py
new file mode 100644
index 0000000000000..f44cd696894ba
--- /dev/null
+++ b/examples/src/main/python/streaming/wordcount.py
@@ -0,0 +1,22 @@
+import sys
+from operator import add
+
+from pyspark.streaming.context import StreamingContext
+from pyspark.streaming.duration import *
+
+if __name__ == "__main__":
+    if len(sys.argv) != 2:
+        print >> sys.stderr, "Usage: wordcount <directory>"
+        exit(-1)
+    ssc = StreamingContext(appName="PythonStreamingWordCount", duration=Seconds(1))
+
+    lines = ssc.textFileStream(sys.argv[1])
+    fm_lines = lines.flatMap(lambda x: x.split(" "))
+    filtered_lines = fm_lines.filter(lambda line: "Spark" in line)
+    mapped_lines = fm_lines.map(lambda x: (x, 1))
+    
+    fm_lines.pyprint()
+    filtered_lines.pyprint()
+    mapped_lines.pyprint()
+    ssc.start()
+    ssc.awaitTermination()
diff --git a/python/pyspark/java_gateway.py b/python/pyspark/java_gateway.py
index 9c70fa5c16d0c..c3fef42d118bd 100644
--- a/python/pyspark/java_gateway.py
+++ b/python/pyspark/java_gateway.py
@@ -108,6 +108,9 @@ def run(self):
     java_import(gateway.jvm, "org.apache.spark.SparkConf")
     java_import(gateway.jvm, "org.apache.spark.api.java.*")
     java_import(gateway.jvm, "org.apache.spark.api.python.*")
+    java_import(gateway.jvm, "org.apache.spark.streaming.*")
+    java_import(gateway.jvm, "org.apache.spark.streaming.api.java.*")
+    java_import(gateway.jvm, "org.apache.spark.streaming.api.python.*")
     java_import(gateway.jvm, "org.apache.spark.mllib.api.python.*")
     java_import(gateway.jvm, "org.apache.spark.sql.SQLContext")
     java_import(gateway.jvm, "org.apache.spark.sql.hive.HiveContext")
diff --git a/python/pyspark/streaming/__init__.py b/python/pyspark/streaming/__init__.py
new file mode 100644
index 0000000000000..719592912e80c
--- /dev/null
+++ b/python/pyspark/streaming/__init__.py
@@ -0,0 +1 @@
+__author__ = 'ktakagiw'
diff --git a/python/pyspark/streaming/context.py b/python/pyspark/streaming/context.py
new file mode 100644
index 0000000000000..c8ae9c4af85c9
--- /dev/null
+++ b/python/pyspark/streaming/context.py
@@ -0,0 +1,133 @@
+__author__ = 'ktakagiw'
+
+
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import os
+import shutil
+import sys
+from threading import Lock
+from tempfile import NamedTemporaryFile
+
+from pyspark import accumulators
+from pyspark.accumulators import Accumulator
+from pyspark.broadcast import Broadcast
+from pyspark.conf import SparkConf
+from pyspark.files import SparkFiles
+from pyspark.java_gateway import launch_gateway
+from pyspark.serializers import PickleSerializer, BatchedSerializer, UTF8Deserializer
+from pyspark.storagelevel import StorageLevel
+from pyspark.rdd import RDD
+from pyspark.context import SparkContext
+
+from py4j.java_collections import ListConverter
+
+from pyspark.streaming.dstream import DStream
+
+class StreamingContext(object):
+    """
+    Main entry point for Spark functionality. A StreamingContext represents the
+    connection to a Spark cluster, and can be used to create L{RDD}s and
+    broadcast variables on that cluster.
+    """
+
+    def __init__(self, master=None, appName=None, sparkHome=None, pyFiles=None,
+        environment=None, batchSize=1024, serializer=PickleSerializer(), conf=None,
+        gateway=None, duration=None):
+        """
+        Create a new StreamingContext. At least the master and app name and duration
+        should be set, either through the named parameters here or through C{conf}.
+
+        @param master: Cluster URL to connect to
+               (e.g. mesos://host:port, spark://host:port, local[4]).
+        @param appName: A name for your job, to display on the cluster web UI.
+        @param sparkHome: Location where Spark is installed on cluster nodes.
+        @param pyFiles: Collection of .zip or .py files to send to the cluster
+               and add to PYTHONPATH.  These can be paths on the local file
+               system or HDFS, HTTP, HTTPS, or FTP URLs.
+        @param environment: A dictionary of environment variables to set on
+               worker nodes.
+        @param batchSize: The number of Python objects represented as a single
+               Java object.  Set 1 to disable batching or -1 to use an
+               unlimited batch size.
+        @param serializer: The serializer for RDDs.
+        @param conf: A L{SparkConf} object setting Spark properties.
+        @param gateway: Use an existing gateway and JVM, otherwise a new JVM
+               will be instatiated.
+        @param duration: A L{Duration} Duration for SparkStreaming
+
+        """
+        # Create the Python Sparkcontext
+        self._sc = SparkContext(master=master, appName=appName, sparkHome=sparkHome,
+                        pyFiles=pyFiles, environment=environment, batchSize=batchSize,
+                        serializer=serializer, conf=conf, gateway=gateway)
+        self._jvm = self._sc._jvm
+        self._jssc = self._initialize_context(self._sc._jsc, duration._jduration)
+
+    # Initialize StremaingContext in function to allow subclass specific initialization
+    def _initialize_context(self, jspark_context, jduration):
+        return self._jvm.JavaStreamingContext(jspark_context, jduration)
+
+    def actorStream(self, props, name, storageLevel, supervisorStrategy):
+        raise NotImplementedError
+
+    def addStreamingListener(self, streamingListener):
+        raise NotImplementedError
+
+    def awaitTermination(self, timeout=None):
+        if timeout:
+            self._jssc.awaitTermination(timeout)
+        else:
+            self._jssc.awaitTermination()
+
+    def checkpoint(self, directory):
+        raise NotImplementedError
+
+    def fileStream(self, directory, filter=None, newFilesOnly=None):
+        raise NotImplementedError
+
+    def networkStream(self, receiver):
+        raise NotImplementedError
+
+    def queueStream(self, queue, oneAtATime=True, defaultRDD=None):
+        raise NotImplementedError
+
+    def rawSocketStream(self, hostname, port, storagelevel):
+        raise NotImplementedError
+
+    def remember(self, duration):
+        raise NotImplementedError
+
+    def socketStream(hostname, port, converter,storageLevel):
+        raise NotImplementedError
+
+    def start(self):
+        self._jssc.start()
+
+    def stop(self, stopSparkContext=True):
+        raise NotImplementedError
+
+    def textFileStream(self, directory):
+        return DStream(self._jssc.textFileStream(directory), self, UTF8Deserializer())
+
+    def transform(self, seq):
+        raise NotImplementedError
+
+    def union(self, seq):
+        raise NotImplementedError
+
diff --git a/python/pyspark/streaming/dstream.py b/python/pyspark/streaming/dstream.py
new file mode 100644
index 0000000000000..b422b147d11e1
--- /dev/null
+++ b/python/pyspark/streaming/dstream.py
@@ -0,0 +1,315 @@
+from base64 import standard_b64encode as b64enc
+import copy
+from collections import defaultdict
+from collections import namedtuple
+from itertools import chain, ifilter, imap
+import operator
+import os
+import sys
+import shlex
+import traceback
+from subprocess import Popen, PIPE
+from tempfile import NamedTemporaryFile
+from threading import Thread
+import warnings
+import heapq
+from random import Random
+
+from pyspark.serializers import NoOpSerializer, CartesianDeserializer, \
+    BatchedSerializer, CloudPickleSerializer, PairDeserializer, pack_long
+from pyspark.join import python_join, python_left_outer_join, \
+    python_right_outer_join, python_cogroup
+from pyspark.statcounter import StatCounter
+from pyspark.rddsampler import RDDSampler
+from pyspark.storagelevel import StorageLevel
+#from pyspark.resultiterable import ResultIterable
+from pyspark.rdd import _JavaStackTrace
+
+from py4j.java_collections import ListConverter, MapConverter
+
+__all__ = ["DStream"]
+
+class DStream(object):
+    def __init__(self, jdstream, ssc, jrdd_deserializer):
+        self._jdstream = jdstream
+        self._ssc = ssc
+        self.ctx = ssc._sc
+        self._jrdd_deserializer = jrdd_deserializer
+
+    def generatedRDDs(self):
+        """
+         // RDDs generated, marked as private[streaming] so that testsuites can access it
+         @transient
+        """
+        pass
+
+    def print_(self):
+        """
+        """
+        # print is a resrved name of Python. We cannot give print to function name
+        getattr(self._jdstream, "print")()
+
+    def pyprint(self):
+        """
+        """
+        self._jdstream.pyprint()
+
+    def cache(self):
+        """
+        """
+        raise NotImplementedError
+
+    def checkpoint(self):
+        """
+        """
+        raise NotImplementedError
+
+    def compute(self, time):
+        """
+        """
+        raise NotImplementedError
+
+    def context(self):
+        """
+        """
+        raise NotImplementedError
+
+    def count(self):
+        """
+        """
+        raise NotImplementedError
+
+    def countByValue(self, numPartitions=None):
+        """
+        """
+        raise NotImplementedError
+
+    def countByValueAndWindow(self, duration, slideDuration=None):
+        """
+        """
+        raise NotImplementedError
+
+    def countByWindow(self, duration, slideDuration=None):
+        """
+        """
+        raise NotImplementedError
+
+    def dstream(self):
+        """
+        """
+        raise NotImplementedError
+
+    def filter(self, f):
+        """
+        """
+        def func(iterator): return ifilter(f, iterator)
+        return self.mapPartitions(func)
+
+    def flatMap(self, f, preservesPartitioning=False):
+        """
+        """
+        def func(s, iterator): return chain.from_iterable(imap(f, iterator))
+        return self.mapPartitionsWithIndex(func, preservesPartitioning)
+
+    def foreachRDD(self, f, time):
+        """
+        """
+        raise NotImplementedError
+
+    def glom(self):
+        """
+        """
+        raise NotImplementedError
+
+    def map(self, f, preservesPartitioning=False):
+        """
+        """
+        def func(split, iterator): return imap(f, iterator)
+        return PipelinedDStream(self, func, preservesPartitioning)
+
+    def mapPartitions(self, f):
+        """
+        """
+        def func(s, iterator): return f(iterator)
+        return self.mapPartitionsWithIndex(func)
+
+    def perist(self, storageLevel):
+        """
+        """
+        raise NotImplementedError
+
+    def reduce(self, func, numPartitions=None):
+        """
+
+        """
+        return self._combineByKey(lambda x:x, func, func, numPartitions)
+
+    def _combineByKey(self, createCombiner, mergeValue, mergeCombiners,
+                      numPartitions = None):
+        """
+        """
+        if numPartitions is None:
+            numPartitions = self.ctx._defaultParallelism()
+        def combineLocally(iterator):
+            combiners = {}
+            for x in iterator:
+                (k, v) = x
+                if k not in combiners:
+                    combiners[k] = createCombiner(v)
+                else:
+                    combiners[k] = mergeValue(combiners[k], v)
+            return combiners.iteritems()
+        locally_combined = self.mapPartitions(combineLocally)
+        shuffled = locally_combined.partitionBy(numPartitions)
+        def _mergeCombiners(iterator):
+            combiners = {}
+            for (k, v) in iterator:
+                if not k in combiners:
+                    combiners[k] = v
+                else:
+                    combiners[k] = mergeCombiners(combiners[k], v)
+            return combiners.iteritems()
+        return shuffled.mapPartitions(_mergeCombiners) 
+
+
+   def partitionBy(self, numPartitions, partitionFunc=None):
+        """
+        Return a copy of the DStream partitioned using the specified partitioner.
+
+        """
+        if numPartitions is None:
+            numPartitions = self.ctx._defaultReducePartitions()
+
+        if partitionFunc is None:
+            partitionFunc = lambda x: 0 if x is None else hash(x)
+        # Transferring O(n) objects to Java is too expensive.  Instead, we'll
+        # form the hash buckets in Python, transferring O(numPartitions) objects
+        # to Java.  Each object is a (splitNumber, [objects]) pair.
+        outputSerializer = self.ctx._unbatched_serializer
+        def add_shuffle_key(split, iterator):
+
+            buckets = defaultdict(list)
+
+            for (k, v) in iterator:
+                buckets[partitionFunc(k) % numPartitions].append((k, v))
+            for (split, items) in buckets.iteritems():
+                yield pack_long(split)
+                yield outputSerializer.dumps(items)
+        keyed = PipelinedDStream(self, add_shuffle_key)
+        keyed._bypass_serializer = True
+        with _JavaStackTrace(self.ctx) as st:
+            #JavaDStream
+            #pairRDD = self.ctx._jvm.PairwiseDStream(keyed._jdstream.dstream()).asJavaPairRDD()
+            pairDStream = self.ctx._jvm.PairwiseDStream(keyed._jdstream.dstream()).asJavaPairDStream()
+            partitioner = self.ctx._jvm.PythonPartitioner(numPartitions,
+                                                          id(partitionFunc))
+        jdstream = pairDStream.partitionBy(partitioner).values()
+        dstream = DStream(jdstream, self._ssc, BatchedSerializer(outputSerializer))
+        # This is required so that id(partitionFunc) remains unique, even if
+        # partitionFunc is a lambda:
+        dstream._partitionFunc = partitionFunc
+        return dstream
+
+
+
+    def reduceByWindow(self, reduceFunc, windowDuration, slideDuration, inReduceTunc):
+        """
+        """
+
+        raise NotImplementedError
+
+    def repartition(self, numPartitions):
+        """
+        """
+        raise NotImplementedError
+
+    def slice(self, fromTime, toTime):
+        """
+        """
+        raise NotImplementedError
+
+    def transform(self, transformFunc):
+        """
+        """
+        raise NotImplementedError
+
+    def transformWith(self, other, transformFunc):
+        """
+        """
+        raise NotImplementedError
+
+    def union(self, that):
+        """
+        """
+        raise NotImplementedError
+
+    def window(self, windowDuration, slideDuration=None):
+        """
+        """
+        raise NotImplementedError
+
+    def wrapRDD(self, rdd):
+        """
+        """
+        raise NotImplementedError
+
+    def mapPartitionsWithIndex(self, f, preservesPartitioning=False):
+        return PipelinedDStream(self, f, preservesPartitioning)
+
+
+class PipelinedDStream(DStream):
+    def __init__(self, prev, func, preservesPartitioning=False):
+        if not isinstance(prev, PipelinedDStream) or not prev._is_pipelinable():
+            # This transformation is the first in its stage:
+            self.func = func
+            self.preservesPartitioning = preservesPartitioning
+            self._prev_jdstream = prev._jdstream
+            self._prev_jrdd_deserializer = prev._jrdd_deserializer
+        else:
+            prev_func = prev.func
+            def pipeline_func(split, iterator):
+                return func(split, prev_func(split, iterator))
+            self.func = pipeline_func
+            self.preservesPartitioning = \
+                prev.preservesPartitioning and preservesPartitioning
+            self._prev_jdstream = prev._prev_jdstream  # maintain the pipeline
+            self._prev_jrdd_deserializer = prev._prev_jrdd_deserializer
+        self.is_cached = False
+        self.is_checkpointed = False
+        self._ssc = prev._ssc
+        self.ctx = prev.ctx
+        self.prev = prev
+        self._jdstream_val = None
+        self._jrdd_deserializer = self.ctx.serializer
+        self._bypass_serializer = False
+
+    @property
+    def _jdstream(self):
+        if self._jdstream_val:
+            return self._jdstream_val
+        if self._bypass_serializer:
+            serializer = NoOpSerializer()
+        else:
+            serializer = self.ctx.serializer
+
+        command = (self.func, self._prev_jrdd_deserializer, serializer)
+        pickled_command = CloudPickleSerializer().dumps(command)
+        broadcast_vars = ListConverter().convert(
+            [x._jbroadcast for x in self.ctx._pickled_broadcast_vars],
+            self.ctx._gateway._gateway_client)
+        self.ctx._pickled_broadcast_vars.clear()
+        class_tag = self._prev_jdstream.classTag()
+        env = MapConverter().convert(self.ctx.environment,
+                                     self.ctx._gateway._gateway_client)
+        includes = ListConverter().convert(self.ctx._python_includes,
+                                     self.ctx._gateway._gateway_client)
+        python_dstream = self.ctx._jvm.PythonDStream(self._prev_jdstream.dstream(),
+                bytearray(pickled_command),
+                env, includes, self.preservesPartitioning,
+                self.ctx.pythonExec, broadcast_vars, self.ctx._javaAccumulator,
+                class_tag)
+        self._jdstream_val = python_dstream.asJavaDStream()
+        return self._jdstream_val
+
+    def _is_pipelinable(self):
+        return not (self.is_cached or self.is_checkpointed)
diff --git a/python/pyspark/streaming/duration.py b/python/pyspark/streaming/duration.py
new file mode 100644
index 0000000000000..ef1b4f6cef237
--- /dev/null
+++ b/python/pyspark/streaming/duration.py
@@ -0,0 +1,171 @@
+__author__ = 'ktakagiw'
+
+from pyspark.streaming import utils
+
+class Duration(object):
+    """
+    Duration for Spark Streaming application. Used to set duration
+
+    Most of the time, you would create a Duration object with
+    C{Duration()}, which will load values from C{spark.streaming.*} Java system
+    properties as well. In this case, any parameters you set directly on
+    the C{Duration} object take priority over system properties.
+
+    """
+    def __init__(self, millis, _jvm=None):
+        """
+        Create new Duration.
+
+        @param millis: milisecond
+
+        """
+        self._millis = millis
+
+        from pyspark.context import SparkContext
+        SparkContext._ensure_initialized()
+        _jvm = _jvm or SparkContext._jvm
+        self._jduration = _jvm.Duration(millis)
+
+    def toString(self):
+        """ Return duration as string """
+        return str(self._millis) + " ms"
+
+    def isZero(self):
+        """ Check if millis is zero """
+        return self._millis == 0
+
+    def prettyPrint(self):
+        """
+        Return a human-readable string representing a duration
+        """
+        return utils.msDurationToString(self._millis)
+
+    def milliseconds(self):
+        """ Return millisecond """
+        return self._millis
+
+    def toFormattedString(self):
+        """ Return millisecond """
+        return str(self._millis)
+
+    def max(self, other):
+        """ Return higher Duration """
+        Duration._is_duration(other)
+        if self > other:
+            return self
+        else:
+            return other
+
+    def min(self, other):
+        """ Return lower Durattion """
+        Duration._is_duration(other)
+        if self < other:
+            return self
+        else:
+            return other
+
+    def __str__(self):
+        return self.toString()
+
+    def __add__(self, other):
+        """ Add Duration and Duration """
+        Duration._is_duration(other)
+        return Duration(self._millis + other._millis)
+
+    def __sub__(self, other):
+        """ Subtract Duration by Duration  """
+        Duration._is_duration(other)
+        return Duration(self._millis - other._millis)
+
+    def __mul__(self, other):
+        """ Multiple Duration by Duration """
+        Duration._is_duration(other)
+        return Duration(self._millis * other._millis)
+
+    def __div__(self, other):
+        """
+        Divide Duration by Duration
+        for Python 2.X
+        """
+        Duration._is_duration(other)
+        return Duration(self._millis / other._millis)
+
+    def __truediv__(self, other):
+        """
+        Divide Duration by Duration
+        for Python 3.0
+        """
+        Duration._is_duration(other)
+        return Duration(self._millis / other._millis)
+
+    def __floordiv__(self, other):
+        """ Divide Duration by Duration """
+        Duration._is_duration(other)
+        return Duration(self._millis // other._millis)
+
+    def __len__(self):
+        """ Length of miilisecond in Duration """
+        return len(self._millis)
+
+    def __lt__(self, other):
+        """ Duration < Duration """
+        Duration._is_duration(other)
+        return self._millis < other._millis
+
+    def __le__(self, other):
+        """ Duration <= Duration """
+        Duration._is_duration(other)
+        return self.millis <= other._millis
+
+    def __eq__(self, other):
+        """ Duration ==  Duration """
+        Duration._is_duration(other)
+        return self._millis == other._millis
+
+    def __ne__(self, other):
+        """ Duration != Duration """
+        Duration._is_duration(other)
+        return self._millis != other._millis
+
+    def __gt__(self, other):
+        """ Duration > Duration """
+        Duration._is_duration(other)
+        return self._millis > other._millis
+
+    def __ge__(self, other):
+        """ Duration >= Duration """
+        Duration._is_duration(other)
+        return self._millis >= other._millis
+
+    @classmethod
+    def _is_duration(self, instance):
+        """ is instance Duration """
+        if not isinstance(instance, Duration):
+            raise TypeError("This should be Duration")
+
+def Milliseconds(milliseconds):
+    """
+    Helper function that creates instance of [[pysparkstreaming.duration]] representing
+    a given number of milliseconds.
+    """
+    return Duration(milliseconds)
+
+def Seconds(seconds):
+    """
+    Helper function that creates instance of [[pysparkstreaming.duration]] representing
+    a given number of seconds.
+    """
+    return Duration(seconds * 1000)
+
+def Minites(minites):
+    """
+    Helper function that creates instance of [[pysparkstreaming.duration]] representing
+    a given number of minutes.
+    """
+    return Duration(minutes * 60000)
+
+if __name__ == "__main__":
+    d = Duration(1)
+    print d
+    print d.milliseconds()
+
diff --git a/python/pyspark/streaming/jtime.py b/python/pyspark/streaming/jtime.py
new file mode 100644
index 0000000000000..41670af659ea3
--- /dev/null
+++ b/python/pyspark/streaming/jtime.py
@@ -0,0 +1,116 @@
+__author__ = 'ktakagiw'
+
+from pyspark.streaming import utils
+from pyspark.streaming.duration import Duration
+
+class Time(object):
+    """
+    Time for Spark Streaming application. Used to set Time
+
+    Most of the time, you would create a Duration object with
+    C{Time()}, which will load values from C{spark.streaming.*} Java system
+    properties as well. In this case, any parameters you set directly on
+    the C{Time} object take priority over system properties.
+
+    """
+    def __init__(self, millis, _jvm=None):
+        """
+        Create new Time.
+
+        @param millis: milisecond
+
+        @param _jvm: internal parameter used to pass a handle to the
+               Java VM; does not need to be set by users
+
+        """
+        self._millis = millis
+
+        from pyspark.context import StreamingContext
+        StreamingContext._ensure_initialized()
+        _jvm = _jvm or StreamingContext._jvm
+        self._jtime = _jvm.Time(millis)
+
+    def toString(self):
+        """ Return time as string """
+        return str(self._millis) + " ms"
+
+    def milliseconds(self):
+        """ Return millisecond """
+        return self._millis
+
+    def max(self, other):
+        """ Return higher Time """
+        Time._is_time(other)
+        if self > other:
+            return self
+        else:
+            return other
+
+    def min(self, other):
+        """ Return lower Time """
+        Time._is_time(other)
+        if self < other:
+            return self
+        else:
+            return other
+
+    def __add__(self, other):
+        """ Add Time and Time """
+        Duration._is_duration(other)
+        return Time(self._millis + other._millis)
+
+    def __sub__(self, other):
+        """ Subtract Time by Duration or Time """
+        if isinstance(other, Duration):
+            return Time(self._millis - other._millis)
+        elif isinstance(other, Time):
+            return Duration(self._mills, other._millis)
+        else:
+            raise TypeError
+
+    def __lt__(self, other):
+        """ Time < Time """
+        Time._is_time(other)
+        return self._millis < other._millis
+
+    def __le__(self, other):
+        """ Time <= Time """
+        Time._is_time(other)
+        return self.millis <= other._millis
+
+    def __eq__(self, other):
+        """ Time ==  Time """
+        Time._is_time(other)
+        return self._millis == other._millis
+
+    def __ne__(self, other):
+        """ Time != Time """
+        Time._is_time(other)
+        return self._millis != other._millis
+
+    def __gt__(self, other):
+        """ Time > Time """
+        Time._is_time(other)
+        return self._millis > other._millis
+
+    def __ge__(self, other):
+        """ Time >= Time """
+        Time._is_time(other)
+        return self._millis >= other._millis
+
+    def isMultipbleOf(duration):
+        """ is multiple by Duration """
+        Duration._is_duration(duration)
+        return self._millis % duration._millis == 0
+
+    def until(time, interval):
+        raise NotImplementedError
+
+    def to(time, interval):
+        raise NotImplementedError
+
+    @classmethod
+    def _is_time(self, instance):
+        """ is instance Time """
+        if not isinstance(instance, Time):
+            raise TypeError
diff --git a/python/pyspark/streaming/pyprint.py b/python/pyspark/streaming/pyprint.py
new file mode 100644
index 0000000000000..fcdaca510812c
--- /dev/null
+++ b/python/pyspark/streaming/pyprint.py
@@ -0,0 +1,28 @@
+import sys
+from itertools import chain
+from pyspark.serializers import PickleSerializer, BatchedSerializer, UTF8Deserializer
+
+def collect(binary_file_path):
+    dse = PickleSerializer()
+    with open(binary_file_path, 'rb') as tempFile:
+        for item in dse.load_stream(tempFile):
+            yield item
+def main():
+    try:
+        binary_file_path = sys.argv[1]
+    except:
+        print "Missed FilePath in argement"
+
+    if not binary_file_path:
+        return 
+
+    counter = 0
+    for rdd in chain.from_iterable(collect(binary_file_path)):
+        print rdd
+        counter = counter + 1
+        if counter >= 10:
+            print "..."
+            break
+
+if __name__ =="__main__":
+    exit(main())
diff --git a/python/pyspark/streaming/utils.py b/python/pyspark/streaming/utils.py
new file mode 100644
index 0000000000000..71aa3376c6578
--- /dev/null
+++ b/python/pyspark/streaming/utils.py
@@ -0,0 +1,18 @@
+__author__ = 'ktakagiw'
+
+def msDurationToString(ms):
+    """
+    Returns a human-readable string representing a duration such as "35ms"
+    """
+    second = 1000
+    minute = 60 * second
+    hour = 60 * minute
+
+    if ms < second:
+        return "%d ms" % ms
+    elif ms < minute:
+        return "%.1f s" % (float(ms) / second)
+    elif ms < hout:
+        return "%.1f m" % (float(ms) / minute)
+    else:
+        return "%.2f h" % (float(ms) / hour)
diff --git a/streaming/pom.xml b/streaming/pom.xml
index 12f900c91eb98..483e200ff9f16 100644
--- a/streaming/pom.xml
+++ b/streaming/pom.xml
@@ -77,9 +77,9 @@
         <groupId>org.scalatest</groupId>
         <artifactId>scalatest-maven-plugin</artifactId>
       </plugin>
-      
-      <!-- 
-           This plugin forces the generation of jar containing streaming test classes, 
+
+      <!--
+           This plugin forces the generation of jar containing streaming test classes,
            so that the tests classes of external modules can use them. The two execution profiles
            are necessary - first one for 'mvn package', second one for 'mvn test-compile'. Ideally,
            'mvn compile' should not compile test classes and therefore should not need this. 
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaDStreamLike.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaDStreamLike.scala
index a6184de4e83c1..cfa336df8674f 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaDStreamLike.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaDStreamLike.scala
@@ -54,6 +54,14 @@ trait JavaDStreamLike[T, This <: JavaDStreamLike[T, This, R], R <: JavaRDDLike[T
     dstream.print()
   }
 
+  /**
+   * Print the first ten elements of each PythonRDD generated in the PythonDStream. This is an output
+   * operator, so this PythonDStream will be registered as an output stream and there materialized.
+   * This function is for PythonAPI.
+   */
+
+  def pyprint() = dstream.pyprint()
+
   /**
    * Return a new DStream in which each RDD has a single element generated by counting each RDD
    * of this DStream.
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
new file mode 100644
index 0000000000000..2d8b1e468dc4c
--- /dev/null
+++ b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
@@ -0,0 +1,152 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.streaming.api.python
+
+import java.util.{List => JList, ArrayList => JArrayList, Map => JMap, Collections}
+
+import org.apache.spark.api.java.{JavaSparkContext, JavaPairRDD, JavaRDD}
+import org.apache.spark.broadcast.Broadcast
+import org.apache.spark._
+import org.apache.spark.util.Utils
+import java.io._
+import scala.Some
+import org.apache.spark.streaming.Duration
+import scala.util.control.Breaks._
+import org.apache.spark.broadcast.Broadcast
+import scala.Some
+import org.apache.spark.streaming.Duration
+import org.apache.spark.rdd.RDD
+import org.apache.spark.api.python.PythonRDD
+
+
+import org.apache.spark.streaming.{Duration, Time}
+import org.apache.spark.streaming.dstream._
+import org.apache.spark.streaming.api.java._
+import org.apache.spark.rdd.RDD
+import org.apache.spark.api.python._
+import org.apache.spark.api.python.PairwiseRDD
+
+
+import scala.reflect.ClassTag
+
+
+class PythonDStream[T: ClassTag](
+                                  parent: DStream[T],
+                                  command: Array[Byte],
+                                  envVars: JMap[String, String],
+                                  pythonIncludes: JList[String],
+                                  preservePartitoning: Boolean,
+                                  pythonExec: String,
+                                  broadcastVars: JList[Broadcast[Array[Byte]]],
+                                  accumulator: Accumulator[JList[Array[Byte]]]
+                                  ) extends DStream[Array[Byte]](parent.ssc) {
+
+  override def dependencies = List(parent)
+
+  override def slideDuration: Duration = parent.slideDuration
+
+  //pythonDStream compute
+  override def compute(validTime: Time): Option[RDD[Array[Byte]]] = {
+    parent.getOrCompute(validTime) match{
+      case Some(rdd) =>
+        val pythonRDD = new PythonRDD(rdd, command, envVars, pythonIncludes, preservePartitoning, pythonExec, broadcastVars, accumulator)
+        Some(pythonRDD.asJavaRDD.rdd)
+      case None => None
+    }
+  }
+  val asJavaDStream  = JavaDStream.fromDStream(this)
+
+  /**
+   * Print the first ten elements of each PythonRDD generated in this PythonDStream. This is an output
+   * operator, so this PythonDStream will be registered as an output stream and there materialized.
+   * Since serialized Python object is readable by Python, pyprint writes out binary data to
+   * temporary file and run python script to deserialized and print the first ten elements
+   */
+  private[streaming] def ppyprint() {
+    def foreachFunc = (rdd: RDD[Array[Byte]], time: Time) => {
+      val iter = rdd.take(11).iterator
+
+      // make a temporary file
+      val prefix = "spark"
+      val suffix = ".tmp"
+      val tempFile = File.createTempFile(prefix, suffix)
+      val tempFileStream = new DataOutputStream(new FileOutputStream(tempFile.getAbsolutePath))
+      //write out serialized python object
+      PythonRDD.writeIteratorToStream(iter, tempFileStream)
+      tempFileStream.close()
+
+      // This value has to be passed from python
+      val pythonExec = new ProcessBuilder().environment().get("PYSPARK_PYTHON")
+      val sparkHome = new ProcessBuilder().environment().get("SPARK_HOME")
+      //val pb = new ProcessBuilder(Seq(pythonExec, sparkHome + "/python/pyspark/streaming/pyprint.py", tempFile.getAbsolutePath())) // why this fails to compile???
+      //absolute path to the python script is needed to change because we do not use pysparkstreaming
+      val pb = new ProcessBuilder(pythonExec, sparkHome + "/python/pysparkstreaming/streaming/pyprint.py", tempFile.getAbsolutePath)
+      val workerEnv = pb.environment()
+
+      //envVars also need to be pass
+      //workerEnv.putAll(envVars)
+      val pythonPath = sparkHome + "/python/" + File.pathSeparator + workerEnv.get("PYTHONPATH")
+      workerEnv.put("PYTHONPATH", pythonPath)
+      val worker = pb.start()
+      val is = worker.getInputStream()
+      val isr = new InputStreamReader(is)
+      val br = new BufferedReader(isr)
+
+      println ("-------------------------------------------")
+      println ("Time: " + time)
+      println ("-------------------------------------------")
+
+      //print value from python std out
+      var line = ""
+      breakable {
+        while (true) {
+          line = br.readLine()
+          if (line == null) break()
+          println(line)
+        }
+      }
+      //delete temporary file
+      tempFile.delete()
+      println()
+
+    }
+    new ForEachDStream(this, context.sparkContext.clean(foreachFunc)).register()
+  }
+}
+
+
+private class PairwiseDStream(prev:DStream[Array[Byte]]) extends
+DStream[(Long, Array[Byte])](prev.ssc){
+  override def dependencies = List(prev)
+
+  override def slideDuration: Duration = prev.slideDuration
+
+  override def compute(validTime:Time):Option[RDD[(Long, Array[Byte])]]={
+    prev.getOrCompute(validTime) match{
+      case Some(rdd)=>Some(rdd)
+        val pairwiseRDD = new PairwiseRDD(rdd)
+        Some(pairwiseRDD.asJavaPairRDD.rdd)
+      case None => None
+    }
+  }
+  val asJavaPairDStream : JavaPairDStream[Long, Array[Byte]] = JavaPairDStream(this)
+}
+
+
+
+
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala
index e05db236addca..b24109074e816 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala
@@ -18,11 +18,13 @@
 package org.apache.spark.streaming.dstream
 
 
-import java.io.{IOException, ObjectInputStream, ObjectOutputStream}
+import java.io._
 
 import scala.deprecated
 import scala.collection.mutable.HashMap
 import scala.reflect.ClassTag
+import java.io.{IOException, ObjectInputStream, ObjectOutputStream}
+import scala.util.control.Breaks._
 
 import org.apache.spark.{Logging, SparkException}
 import org.apache.spark.rdd.{BlockRDD, RDD}
@@ -31,6 +33,8 @@ import org.apache.spark.streaming._
 import org.apache.spark.streaming.StreamingContext._
 import org.apache.spark.streaming.scheduler.Job
 import org.apache.spark.util.MetadataCleaner
+import org.apache.spark.streaming.Duration
+import org.apache.spark.api.python.PythonRDD
 
 /**
  * A Discretized Stream (DStream), the basic abstraction in Spark Streaming, is a continuous
@@ -616,6 +620,68 @@ abstract class DStream[T: ClassTag] (
     new ForEachDStream(this, context.sparkContext.clean(foreachFunc)).register()
   }
 
+
+
+
+
+  /**
+   * Print the first ten elements of each PythonRDD generated in this PythonDStream. This is an output
+   * operator, so this PythonDStream will be registered as an output stream and there materialized.
+   * Since serialized Python object is readable by Python, pyprint writes out binary data to
+   * temporary file and run python script to deserialized and print the first ten elements
+   */
+  private[streaming] def pyprint() {
+    def foreachFunc = (rdd: RDD[T], time: Time) => {
+      val iter = rdd.take(11).iterator
+
+      // make a temporary file
+      val prefix = "spark"
+      val suffix = ".tmp"
+      val tempFile = File.createTempFile(prefix, suffix)
+      val tempFileStream = new DataOutputStream(new FileOutputStream(tempFile.getAbsolutePath))
+      //write out serialized python object
+      PythonRDD.writeIteratorToStream(iter, tempFileStream)
+      tempFileStream.close()
+
+      // This value has to be passed from python
+      val pythonExec = new ProcessBuilder().environment().get("PYSPARK_PYTHON")
+      val sparkHome = new ProcessBuilder().environment().get("SPARK_HOME")
+      //val pb = new ProcessBuilder(Seq(pythonExec, sparkHome + "/python/pyspark/streaming/pyprint.py", tempFile.getAbsolutePath())) // why this fails to compile???
+      //absolute path to the python script is needed to change because we do not use pysparkstreaming
+      val pb = new ProcessBuilder(pythonExec, sparkHome + "/python/pyspark/streaming/pyprint.py", tempFile.getAbsolutePath)
+      val workerEnv = pb.environment()
+
+      //envVars also need to be pass
+      //workerEnv.putAll(envVars)
+      val pythonPath = sparkHome + "/python/" + File.pathSeparator + workerEnv.get("PYTHONPATH")
+      workerEnv.put("PYTHONPATH", pythonPath)
+      val worker = pb.start()
+      val is = worker.getInputStream()
+      val isr = new InputStreamReader(is)
+      val br = new BufferedReader(isr)
+
+      println ("-------------------------------------------")
+      println ("Time: " + time)
+      println ("-------------------------------------------")
+
+      //print value from python std out
+      var line = ""
+      breakable {
+        while (true) {
+          line = br.readLine()
+          if (line == null) break()
+          println(line)
+        }
+      }
+      //delete temporary file
+      tempFile.delete()
+      println()
+
+    }
+    new ForEachDStream(this, context.sparkContext.clean(foreachFunc)).register()
+  }
+
+
   /**
    * Return a new DStream in which each RDD contains all the elements in seen in a
    * sliding window of time over this DStream. The new DStream generates RDDs with

From b406252c89b1cd5c5678faebfd720767bf0ee9f5 Mon Sep 17 00:00:00 2001
From: Ken Takagiwa <ken@Kens-MacBook-Pro.local>
Date: Tue, 15 Jul 2014 15:41:52 -0700
Subject: [PATCH 361/489] comment PythonDStream.PairwiseDStream

---
 .../apache/spark/streaming/api/python/PythonDStream.scala   | 3 ++-
 .../scala/org/apache/spark/streaming/dstream/DStream.scala  | 6 ++----
 2 files changed, 4 insertions(+), 5 deletions(-)

diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
index 2d8b1e468dc4c..fe67250604d8e 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
@@ -129,7 +129,7 @@ class PythonDStream[T: ClassTag](
   }
 }
 
-
+/*
 private class PairwiseDStream(prev:DStream[Array[Byte]]) extends
 DStream[(Long, Array[Byte])](prev.ssc){
   override def dependencies = List(prev)
@@ -146,6 +146,7 @@ DStream[(Long, Array[Byte])](prev.ssc){
   }
   val asJavaPairDStream : JavaPairDStream[Long, Array[Byte]] = JavaPairDStream(this)
 }
+*/
 
 
 
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala
index b24109074e816..d9d5446b62e9f 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala
@@ -620,10 +620,7 @@ abstract class DStream[T: ClassTag] (
     new ForEachDStream(this, context.sparkContext.clean(foreachFunc)).register()
   }
 
-
-
-
-
+//TODO move pyprint to PythonDStream
   /**
    * Print the first ten elements of each PythonRDD generated in this PythonDStream. This is an output
    * operator, so this PythonDStream will be registered as an output stream and there materialized.
@@ -644,6 +641,7 @@ abstract class DStream[T: ClassTag] (
       tempFileStream.close()
 
       // This value has to be passed from python
+      // Python currently does not do cluster deployment. But what happened
       val pythonExec = new ProcessBuilder().environment().get("PYSPARK_PYTHON")
       val sparkHome = new ProcessBuilder().environment().get("SPARK_HOME")
       //val pb = new ProcessBuilder(Seq(pythonExec, sparkHome + "/python/pyspark/streaming/pyprint.py", tempFile.getAbsolutePath())) // why this fails to compile???

From 87438e276f368cf8f4cba138958e08e05d3fe26f Mon Sep 17 00:00:00 2001
From: Ken Takagiwa <ken@Kens-MacBook-Pro.local>
Date: Tue, 15 Jul 2014 17:19:20 -0700
Subject: [PATCH 362/489] modify dstream.py to fix indent error

---
 python/pyspark/streaming/dstream.py                             | 2 +-
 .../org/apache/spark/streaming/api/python/PythonDStream.scala   | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/python/pyspark/streaming/dstream.py b/python/pyspark/streaming/dstream.py
index b422b147d11e1..a512517f6e437 100644
--- a/python/pyspark/streaming/dstream.py
+++ b/python/pyspark/streaming/dstream.py
@@ -172,7 +172,7 @@ def _mergeCombiners(iterator):
         return shuffled.mapPartitions(_mergeCombiners) 
 
 
-   def partitionBy(self, numPartitions, partitionFunc=None):
+    def partitionBy(self, numPartitions, partitionFunc=None):
         """
         Return a copy of the DStream partitioned using the specified partitioner.
 
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
index fe67250604d8e..389136f9e21a0 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
@@ -91,7 +91,7 @@ class PythonDStream[T: ClassTag](
       tempFileStream.close()
 
       // This value has to be passed from python
-      val pythonExec = new ProcessBuilder().environment().get("PYSPARK_PYTHON")
+      //val pythonExec = new ProcessBuilder().environment().get("PYSPARK_PYTHON")
       val sparkHome = new ProcessBuilder().environment().get("SPARK_HOME")
       //val pb = new ProcessBuilder(Seq(pythonExec, sparkHome + "/python/pyspark/streaming/pyprint.py", tempFile.getAbsolutePath())) // why this fails to compile???
       //absolute path to the python script is needed to change because we do not use pysparkstreaming

From d7b4d6fc10eb763f81176a6dbeef56945ac1bd45 Mon Sep 17 00:00:00 2001
From: Ken Takagiwa <ken@Kens-MacBook-Pro.local>
Date: Tue, 15 Jul 2014 21:08:43 -0700
Subject: [PATCH 363/489] added reducedByKey not working yet

---
 .../src/main/python/streaming/wordcount.py    | 10 ++++++-
 python/pyspark/streaming/dstream.py           | 27 +++++++++++++++++--
 .../streaming/api/python/PythonDStream.scala  |  6 ++---
 3 files changed, 37 insertions(+), 6 deletions(-)

diff --git a/examples/src/main/python/streaming/wordcount.py b/examples/src/main/python/streaming/wordcount.py
index f44cd696894ba..3996991109d60 100644
--- a/examples/src/main/python/streaming/wordcount.py
+++ b/examples/src/main/python/streaming/wordcount.py
@@ -1,6 +1,7 @@
 import sys
 from operator import add
 
+from pyspark.conf import SparkConf
 from pyspark.streaming.context import StreamingContext
 from pyspark.streaming.duration import *
 
@@ -8,15 +9,22 @@
     if len(sys.argv) != 2:
         print >> sys.stderr, "Usage: wordcount <directory>"
         exit(-1)
-    ssc = StreamingContext(appName="PythonStreamingWordCount", duration=Seconds(1))
+    conf = SparkConf()
+    conf.setAppName("PythonStreamingWordCount")
+    conf.set("spark.default.parallelism", 1)
+
+#    ssc = StreamingContext(appName="PythonStreamingWordCount", duration=Seconds(1))
+    ssc = StreamingContext(conf=conf, duration=Seconds(1))
 
     lines = ssc.textFileStream(sys.argv[1])
     fm_lines = lines.flatMap(lambda x: x.split(" "))
     filtered_lines = fm_lines.filter(lambda line: "Spark" in line)
     mapped_lines = fm_lines.map(lambda x: (x, 1))
+    reduced_lines = mapped_lines.reduce(add)
     
     fm_lines.pyprint()
     filtered_lines.pyprint()
     mapped_lines.pyprint()
+    reduced_lines.pyprint()
     ssc.start()
     ssc.awaitTermination()
diff --git a/python/pyspark/streaming/dstream.py b/python/pyspark/streaming/dstream.py
index a512517f6e437..e144f8bc1cc09 100644
--- a/python/pyspark/streaming/dstream.py
+++ b/python/pyspark/streaming/dstream.py
@@ -29,6 +29,7 @@
 
 __all__ = ["DStream"]
 
+
 class DStream(object):
     def __init__(self, jdstream, ssc, jrdd_deserializer):
         self._jdstream = jdstream
@@ -149,7 +150,7 @@ def _combineByKey(self, createCombiner, mergeValue, mergeCombiners,
         """
         """
         if numPartitions is None:
-            numPartitions = self.ctx._defaultParallelism()
+            numPartitions = self._defaultReducePartitions()
         def combineLocally(iterator):
             combiners = {}
             for x in iterator:
@@ -211,7 +212,6 @@ def add_shuffle_key(split, iterator):
         return dstream
 
 
-
     def reduceByWindow(self, reduceFunc, windowDuration, slideDuration, inReduceTunc):
         """
         """
@@ -254,8 +254,31 @@ def wrapRDD(self, rdd):
         raise NotImplementedError
 
     def mapPartitionsWithIndex(self, f, preservesPartitioning=False):
+        """
+
+        """
         return PipelinedDStream(self, f, preservesPartitioning)
 
+    def _defaultReducePartitions(self):
+        """
+
+        """
+        # hard code to avoid the error
+        return 2
+        if self.ctx._conf.contains("spark.default.parallelism"):
+            return self.ctx.defaultParallelism
+        else:
+            return self.getNumPartitions()
+
+    def getNumPartitions(self):
+      """
+      Returns the number of partitions in RDD
+      >>> rdd = sc.parallelize([1, 2, 3, 4], 2)
+      >>> rdd.getNumPartitions()
+      2
+      """
+      return self._jdstream.partitions().size()
+
 
 class PipelinedDStream(DStream):
     def __init__(self, prev, func, preservesPartitioning=False):
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
index 389136f9e21a0..719dd0a6a53c2 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
@@ -129,7 +129,7 @@ class PythonDStream[T: ClassTag](
   }
 }
 
-/*
+
 private class PairwiseDStream(prev:DStream[Array[Byte]]) extends
 DStream[(Long, Array[Byte])](prev.ssc){
   override def dependencies = List(prev)
@@ -144,9 +144,9 @@ DStream[(Long, Array[Byte])](prev.ssc){
       case None => None
     }
   }
-  val asJavaPairDStream : JavaPairDStream[Long, Array[Byte]] = JavaPairDStream(this)
+  val asJavaPairDStream : JavaPairDStream[Long, Array[Byte]]  = JavaPairDStream.fromJavaDStream(this)
 }
-*/
+
 
 
 

From 1a0f0653a66b5c8c3b843d5c5e2049afef08a4e1 Mon Sep 17 00:00:00 2001
From: Ken Takagiwa <ken@Kens-MacBook-Pro.local>
Date: Wed, 16 Jul 2014 11:07:42 -0700
Subject: [PATCH 364/489] implementing transform function in Python

---
 python/pyspark/streaming/dstream.py           |  3 +-
 .../api/python/PythonTransformedDStream.scala | 37 +++++++++++++++++++
 .../spark/streaming/dstream/DStream.scala     |  3 ++
 3 files changed, 41 insertions(+), 2 deletions(-)
 create mode 100644 streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonTransformedDStream.scala

diff --git a/python/pyspark/streaming/dstream.py b/python/pyspark/streaming/dstream.py
index e144f8bc1cc09..3365c6d69c1a2 100644
--- a/python/pyspark/streaming/dstream.py
+++ b/python/pyspark/streaming/dstream.py
@@ -172,7 +172,6 @@ def _mergeCombiners(iterator):
             return combiners.iteritems()
         return shuffled.mapPartitions(_mergeCombiners) 
 
-
     def partitionBy(self, numPartitions, partitionFunc=None):
         """
         Return a copy of the DStream partitioned using the specified partitioner.
@@ -231,6 +230,7 @@ def slice(self, fromTime, toTime):
     def transform(self, transformFunc):
         """
         """
+        self._jdstream.transform(transformFunc)
         raise NotImplementedError
 
     def transformWith(self, other, transformFunc):
@@ -264,7 +264,6 @@ def _defaultReducePartitions(self):
 
         """
         # hard code to avoid the error
-        return 2
         if self.ctx._conf.contains("spark.default.parallelism"):
             return self.ctx.defaultParallelism
         else:
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonTransformedDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonTransformedDStream.scala
new file mode 100644
index 0000000000000..ff70483b771a4
--- /dev/null
+++ b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonTransformedDStream.scala
@@ -0,0 +1,37 @@
+package org.apache.spark.streaming.api.python
+
+import org.apache.spark.Accumulator
+import org.apache.spark.api.python.PythonRDD
+import org.apache.spark.broadcast.Broadcast
+import org.apache.spark.rdd.RDD
+import org.apache.spark.streaming.api.java.JavaDStream
+import org.apache.spark.streaming.{Time, Duration}
+import org.apache.spark.streaming.dstream.DStream
+
+import scala.reflect.ClassTag
+
+/**
+ * Created by ken on 7/15/14.
+ */
+class PythonTransformedDStream[T: ClassTag](
+               parents: Seq[DStream[T]],
+               command: Array[Byte],
+               envVars: JMap[String, String],
+               pythonIncludes: JList[String],
+               preservePartitoning: Boolean,
+               pythonExec: String,
+               broadcastVars: JList[Broadcast[Array[Byte]]],
+               accumulator: Accumulator[JList[Array[Byte]]]
+               ) extends DStream[Array[Byte]](parent.ssc) {
+
+  override def dependencies = List(parent)
+
+  override def slideDuration: Duration = parent.slideDuration
+
+  //pythonDStream compute
+  override def compute(validTime: Time): Option[RDD[Array[Byte]]] = {
+    val parentRDDs = parents.map(_.getOrCompute(validTime).orNull).toSeq
+    Some()
+  }
+  val asJavaDStream  = JavaDStream.fromDStream(this)
+}
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala
index d9d5446b62e9f..67977244ef420 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala
@@ -561,9 +561,12 @@ abstract class DStream[T: ClassTag] (
     // because the DStream is reachable from the outer object here, and because 
     // DStreams can't be serialized with closures, we can't proactively check 
     // it for serializability and so we pass the optional false to SparkContext.clean
+
+    // serialized python
     val cleanedF = context.sparkContext.clean(transformFunc, false)
     val realTransformFunc =  (rdds: Seq[RDD[_]], time: Time) => {
       assert(rdds.length == 1)
+      // if transformfunc is fine, it is okay
       cleanedF(rdds.head.asInstanceOf[RDD[T]], time)
     }
     new TransformedDStream[U](Seq(this), realTransformFunc)

From 17a74c6298406589af42dc122a0409494b8ea168 Mon Sep 17 00:00:00 2001
From: Ken Takagiwa <ken@Kens-MacBook-Pro.local>
Date: Wed, 16 Jul 2014 11:12:53 -0700
Subject: [PATCH 365/489] modified the code base on comment in
 https://github.com/tdas/spark/pull/10

---
 core/pom.xml                         | 4 ----
 python/pyspark/streaming/__init__.py | 1 -
 python/pyspark/streaming/context.py  | 5 +----
 3 files changed, 1 insertion(+), 9 deletions(-)

diff --git a/core/pom.xml b/core/pom.xml
index 7eb0b48eaeebd..2a81f6df289c0 100644
--- a/core/pom.xml
+++ b/core/pom.xml
@@ -21,11 +21,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent</artifactId>
-<<<<<<< HEAD
     <version>1.2.0-SNAPSHOT</version>
-=======
-    <version>1.0.0</version>
->>>>>>> initial commit for pySparkStreaming
     <relativePath>../pom.xml</relativePath>
   </parent>
 
diff --git a/python/pyspark/streaming/__init__.py b/python/pyspark/streaming/__init__.py
index 719592912e80c..e69de29bb2d1d 100644
--- a/python/pyspark/streaming/__init__.py
+++ b/python/pyspark/streaming/__init__.py
@@ -1 +0,0 @@
-__author__ = 'ktakagiw'
diff --git a/python/pyspark/streaming/context.py b/python/pyspark/streaming/context.py
index c8ae9c4af85c9..40e9d98942e2e 100644
--- a/python/pyspark/streaming/context.py
+++ b/python/pyspark/streaming/context.py
@@ -1,6 +1,3 @@
-__author__ = 'ktakagiw'
-
-
 #
 # Licensed to the Apache Software Foundation (ASF) under one or more
 # contributor license agreements.  See the NOTICE file distributed with
@@ -41,7 +38,7 @@
 
 class StreamingContext(object):
     """
-    Main entry point for Spark functionality. A StreamingContext represents the
+    Main entry point for Spark Streaming functionality. A StreamingContext represents the
     connection to a Spark cluster, and can be used to create L{RDD}s and
     broadcast variables on that cluster.
     """

From 494cae543fa0706865e68f55bb94d9f55d3418e9 Mon Sep 17 00:00:00 2001
From: Ken Takagiwa <ken@Kens-MacBook-Pro.local>
Date: Wed, 16 Jul 2014 11:24:08 -0700
Subject: [PATCH 366/489] remove not implemented DStream functions in python

---
 python/pyspark/streaming/dstream.py | 102 ----------------------------
 1 file changed, 102 deletions(-)

diff --git a/python/pyspark/streaming/dstream.py b/python/pyspark/streaming/dstream.py
index 3365c6d69c1a2..3df6e5e09b0c1 100644
--- a/python/pyspark/streaming/dstream.py
+++ b/python/pyspark/streaming/dstream.py
@@ -55,50 +55,6 @@ def pyprint(self):
         """
         self._jdstream.pyprint()
 
-    def cache(self):
-        """
-        """
-        raise NotImplementedError
-
-    def checkpoint(self):
-        """
-        """
-        raise NotImplementedError
-
-    def compute(self, time):
-        """
-        """
-        raise NotImplementedError
-
-    def context(self):
-        """
-        """
-        raise NotImplementedError
-
-    def count(self):
-        """
-        """
-        raise NotImplementedError
-
-    def countByValue(self, numPartitions=None):
-        """
-        """
-        raise NotImplementedError
-
-    def countByValueAndWindow(self, duration, slideDuration=None):
-        """
-        """
-        raise NotImplementedError
-
-    def countByWindow(self, duration, slideDuration=None):
-        """
-        """
-        raise NotImplementedError
-
-    def dstream(self):
-        """
-        """
-        raise NotImplementedError
 
     def filter(self, f):
         """
@@ -112,16 +68,6 @@ def flatMap(self, f, preservesPartitioning=False):
         def func(s, iterator): return chain.from_iterable(imap(f, iterator))
         return self.mapPartitionsWithIndex(func, preservesPartitioning)
 
-    def foreachRDD(self, f, time):
-        """
-        """
-        raise NotImplementedError
-
-    def glom(self):
-        """
-        """
-        raise NotImplementedError
-
     def map(self, f, preservesPartitioning=False):
         """
         """
@@ -134,11 +80,6 @@ def mapPartitions(self, f):
         def func(s, iterator): return f(iterator)
         return self.mapPartitionsWithIndex(func)
 
-    def perist(self, storageLevel):
-        """
-        """
-        raise NotImplementedError
-
     def reduce(self, func, numPartitions=None):
         """
 
@@ -210,49 +151,6 @@ def add_shuffle_key(split, iterator):
         dstream._partitionFunc = partitionFunc
         return dstream
 
-
-    def reduceByWindow(self, reduceFunc, windowDuration, slideDuration, inReduceTunc):
-        """
-        """
-
-        raise NotImplementedError
-
-    def repartition(self, numPartitions):
-        """
-        """
-        raise NotImplementedError
-
-    def slice(self, fromTime, toTime):
-        """
-        """
-        raise NotImplementedError
-
-    def transform(self, transformFunc):
-        """
-        """
-        self._jdstream.transform(transformFunc)
-        raise NotImplementedError
-
-    def transformWith(self, other, transformFunc):
-        """
-        """
-        raise NotImplementedError
-
-    def union(self, that):
-        """
-        """
-        raise NotImplementedError
-
-    def window(self, windowDuration, slideDuration=None):
-        """
-        """
-        raise NotImplementedError
-
-    def wrapRDD(self, rdd):
-        """
-        """
-        raise NotImplementedError
-
     def mapPartitionsWithIndex(self, f, preservesPartitioning=False):
         """
 

From e1df94094fd0cdb4353ddbdde04920668933bdc4 Mon Sep 17 00:00:00 2001
From: Ken Takagiwa <ken@Kens-MacBook-Pro.local>
Date: Wed, 16 Jul 2014 11:35:59 -0700
Subject: [PATCH 367/489] revert pom.xml

---
 python/pyspark/streaming/pyprint.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/python/pyspark/streaming/pyprint.py b/python/pyspark/streaming/pyprint.py
index fcdaca510812c..6e87c985a57e3 100644
--- a/python/pyspark/streaming/pyprint.py
+++ b/python/pyspark/streaming/pyprint.py
@@ -1,6 +1,6 @@
 import sys
 from itertools import chain
-from pyspark.serializers import PickleSerializer, BatchedSerializer, UTF8Deserializer
+from pyspark.serializers import PickleSerializer
 
 def collect(binary_file_path):
     dse = PickleSerializer()

From 5bac7ecefa326ac5439e12ef3def44629a7f5694 Mon Sep 17 00:00:00 2001
From: Ken Takagiwa <ken@Kens-MacBook-Pro.local>
Date: Wed, 16 Jul 2014 11:44:14 -0700
Subject: [PATCH 368/489] revert streaming/pom.xml

---
 streaming/pom.xml | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/streaming/pom.xml b/streaming/pom.xml
index 483e200ff9f16..12f900c91eb98 100644
--- a/streaming/pom.xml
+++ b/streaming/pom.xml
@@ -77,9 +77,9 @@
         <groupId>org.scalatest</groupId>
         <artifactId>scalatest-maven-plugin</artifactId>
       </plugin>
-
-      <!--
-           This plugin forces the generation of jar containing streaming test classes,
+      
+      <!-- 
+           This plugin forces the generation of jar containing streaming test classes, 
            so that the tests classes of external modules can use them. The two execution profiles
            are necessary - first one for 'mvn package', second one for 'mvn test-compile'. Ideally,
            'mvn compile' should not compile test classes and therefore should not need this. 

From d2099d8535a5c4168859527fd368e4f1dd623f2f Mon Sep 17 00:00:00 2001
From: Ken Takagiwa <ken@Kens-MacBook-Pro.local>
Date: Wed, 16 Jul 2014 12:15:06 -0700
Subject: [PATCH 369/489] sorted the import following Spark coding convention

---
 .../streaming/api/python/PythonDStream.scala  | 45 ++++++-------------
 1 file changed, 13 insertions(+), 32 deletions(-)

diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
index 719dd0a6a53c2..924a03cd2f0ae 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
@@ -19,42 +19,28 @@ package org.apache.spark.streaming.api.python
 
 import java.util.{List => JList, ArrayList => JArrayList, Map => JMap, Collections}
 
-import org.apache.spark.api.java.{JavaSparkContext, JavaPairRDD, JavaRDD}
-import org.apache.spark.broadcast.Broadcast
+import scala.reflect.ClassTag
+
 import org.apache.spark._
-import org.apache.spark.util.Utils
-import java.io._
-import scala.Some
-import org.apache.spark.streaming.Duration
-import scala.util.control.Breaks._
-import org.apache.spark.broadcast.Broadcast
-import scala.Some
-import org.apache.spark.streaming.Duration
 import org.apache.spark.rdd.RDD
-import org.apache.spark.api.python.PythonRDD
-
-
+import org.apache.spark.api.python._
+import org.apache.spark.broadcast.Broadcast
 import org.apache.spark.streaming.{Duration, Time}
 import org.apache.spark.streaming.dstream._
 import org.apache.spark.streaming.api.java._
-import org.apache.spark.rdd.RDD
-import org.apache.spark.api.python._
-import org.apache.spark.api.python.PairwiseRDD
-
 
-import scala.reflect.ClassTag
 
 
 class PythonDStream[T: ClassTag](
-                                  parent: DStream[T],
-                                  command: Array[Byte],
-                                  envVars: JMap[String, String],
-                                  pythonIncludes: JList[String],
-                                  preservePartitoning: Boolean,
-                                  pythonExec: String,
-                                  broadcastVars: JList[Broadcast[Array[Byte]]],
-                                  accumulator: Accumulator[JList[Array[Byte]]]
-                                  ) extends DStream[Array[Byte]](parent.ssc) {
+    parent: DStream[T],
+    command: Array[Byte],
+    envVars: JMap[String, String],
+    pythonIncludes: JList[String],
+    preservePartitoning: Boolean,
+    pythonExec: String,
+    broadcastVars: JList[Broadcast[Array[Byte]]],
+    accumulator: Accumulator[JList[Array[Byte]]])
+  extends DStream[Array[Byte]](parent.ssc) {
 
   override def dependencies = List(parent)
 
@@ -146,8 +132,3 @@ DStream[(Long, Array[Byte])](prev.ssc){
   }
   val asJavaPairDStream : JavaPairDStream[Long, Array[Byte]]  = JavaPairDStream.fromJavaDStream(this)
 }
-
-
-
-
-

From 224fc5e271ef1a58c7b9a7e6f77652d6a0691d7c Mon Sep 17 00:00:00 2001
From: Ken Takagiwa <ken@Kens-MacBook-Pro.local>
Date: Wed, 16 Jul 2014 12:19:42 -0700
Subject: [PATCH 370/489] add empty line

---
 .../org/apache/spark/streaming/api/python/PythonDStream.scala    | 1 +
 1 file changed, 1 insertion(+)

diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
index 924a03cd2f0ae..0f4a58eef4a78 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
@@ -55,6 +55,7 @@ class PythonDStream[T: ClassTag](
       case None => None
     }
   }
+  
   val asJavaDStream  = JavaDStream.fromDStream(this)
 
   /**

From bb7ccf3a103644b5701b8c667397130d807ce7db Mon Sep 17 00:00:00 2001
From: Ken Takagiwa <ken@Kens-MacBook-Pro.local>
Date: Wed, 16 Jul 2014 15:40:42 -0700
Subject: [PATCH 371/489] remove unused import in python

---
 python/pyspark/streaming/context.py           |  9 ------
 python/pyspark/streaming/dstream.py           | 30 +++----------------
 python/pyspark/streaming/duration.py          | 17 ++++++++++-
 python/pyspark/streaming/jtime.py             | 24 ++++++++++++++-
 python/pyspark/streaming/pyprint.py           | 19 ++++++++++++
 .../streaming/api/python/PythonDStream.scala  |  2 +-
 6 files changed, 63 insertions(+), 38 deletions(-)

diff --git a/python/pyspark/streaming/context.py b/python/pyspark/streaming/context.py
index 40e9d98942e2e..d3ff16fca764f 100644
--- a/python/pyspark/streaming/context.py
+++ b/python/pyspark/streaming/context.py
@@ -15,15 +15,6 @@
 # limitations under the License.
 #
 
-import os
-import shutil
-import sys
-from threading import Lock
-from tempfile import NamedTemporaryFile
-
-from pyspark import accumulators
-from pyspark.accumulators import Accumulator
-from pyspark.broadcast import Broadcast
 from pyspark.conf import SparkConf
 from pyspark.files import SparkFiles
 from pyspark.java_gateway import launch_gateway
diff --git a/python/pyspark/streaming/dstream.py b/python/pyspark/streaming/dstream.py
index 3df6e5e09b0c1..5766cca39bdee 100644
--- a/python/pyspark/streaming/dstream.py
+++ b/python/pyspark/streaming/dstream.py
@@ -1,28 +1,8 @@
-from base64 import standard_b64encode as b64enc
-import copy
 from collections import defaultdict
-from collections import namedtuple
 from itertools import chain, ifilter, imap
-import operator
-import os
-import sys
-import shlex
-import traceback
-from subprocess import Popen, PIPE
-from tempfile import NamedTemporaryFile
-from threading import Thread
-import warnings
-import heapq
-from random import Random
-
-from pyspark.serializers import NoOpSerializer, CartesianDeserializer, \
-    BatchedSerializer, CloudPickleSerializer, PairDeserializer, pack_long
-from pyspark.join import python_join, python_left_outer_join, \
-    python_right_outer_join, python_cogroup
-from pyspark.statcounter import StatCounter
-from pyspark.rddsampler import RDDSampler
-from pyspark.storagelevel import StorageLevel
-#from pyspark.resultiterable import ResultIterable
+
+from pyspark.serializers import NoOpSerializer,\
+    BatchedSerializer, CloudPickleSerializer, pack_long
 from pyspark.rdd import _JavaStackTrace
 
 from py4j.java_collections import ListConverter, MapConverter
@@ -47,7 +27,7 @@ def generatedRDDs(self):
     def print_(self):
         """
         """
-        # print is a resrved name of Python. We cannot give print to function name
+        # print is a reserved name of Python. We cannot give print to function name
         getattr(self._jdstream, "print")()
 
     def pyprint(self):
@@ -55,7 +35,6 @@ def pyprint(self):
         """
         self._jdstream.pyprint()
 
-
     def filter(self, f):
         """
         """
@@ -140,7 +119,6 @@ def add_shuffle_key(split, iterator):
         keyed._bypass_serializer = True
         with _JavaStackTrace(self.ctx) as st:
             #JavaDStream
-            #pairRDD = self.ctx._jvm.PairwiseDStream(keyed._jdstream.dstream()).asJavaPairRDD()
             pairDStream = self.ctx._jvm.PairwiseDStream(keyed._jdstream.dstream()).asJavaPairDStream()
             partitioner = self.ctx._jvm.PythonPartitioner(numPartitions,
                                                           id(partitionFunc))
diff --git a/python/pyspark/streaming/duration.py b/python/pyspark/streaming/duration.py
index ef1b4f6cef237..5982146e69026 100644
--- a/python/pyspark/streaming/duration.py
+++ b/python/pyspark/streaming/duration.py
@@ -1,4 +1,19 @@
-__author__ = 'ktakagiw'
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
 
 from pyspark.streaming import utils
 
diff --git a/python/pyspark/streaming/jtime.py b/python/pyspark/streaming/jtime.py
index 41670af659ea3..32ef741051283 100644
--- a/python/pyspark/streaming/jtime.py
+++ b/python/pyspark/streaming/jtime.py
@@ -1,8 +1,30 @@
-__author__ = 'ktakagiw'
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
 
 from pyspark.streaming import utils
 from pyspark.streaming.duration import Duration
 
+"""
+The name of this file, time is not good naming for python
+because if we do import time when we want to use native python time package, it does
+not import python time package.
+"""
+
+
 class Time(object):
     """
     Time for Spark Streaming application. Used to set Time
diff --git a/python/pyspark/streaming/pyprint.py b/python/pyspark/streaming/pyprint.py
index 6e87c985a57e3..1aeb8e50375ed 100644
--- a/python/pyspark/streaming/pyprint.py
+++ b/python/pyspark/streaming/pyprint.py
@@ -1,5 +1,24 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+
 import sys
 from itertools import chain
+
 from pyspark.serializers import PickleSerializer
 
 def collect(binary_file_path):
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
index 0f4a58eef4a78..cecb10fc30eb4 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
@@ -55,7 +55,7 @@ class PythonDStream[T: ClassTag](
       case None => None
     }
   }
-  
+
   val asJavaDStream  = JavaDStream.fromDStream(this)
 
   /**

From f7461091dde482edac0a3cbe2cdf1100ec6c5c03 Mon Sep 17 00:00:00 2001
From: Ken Takagiwa <ken@Kens-MacBook-Pro.local>
Date: Wed, 16 Jul 2014 16:23:08 -0700
Subject: [PATCH 372/489] initial commit for socketTextStream

---
 .../python/streaming/nerwork_wordcount.py     | 22 +++++++++++++++++++
 1 file changed, 22 insertions(+)
 create mode 100644 examples/src/main/python/streaming/nerwork_wordcount.py

diff --git a/examples/src/main/python/streaming/nerwork_wordcount.py b/examples/src/main/python/streaming/nerwork_wordcount.py
new file mode 100644
index 0000000000000..2e5048ccad213
--- /dev/null
+++ b/examples/src/main/python/streaming/nerwork_wordcount.py
@@ -0,0 +1,22 @@
+import sys
+from operator import add
+
+from pyspark.streaming.context import StreamingContext
+from pyspark.streaming.duration import *
+
+if __name__ == "__main__":
+    if len(sys.argv) != 3:
+        print >> sys.stderr, "Usage: wordcount <hostname> <port>"
+        exit(-1)
+    ssc = StreamingContext(appName="PythonStreamingNetworkWordCount", duration=Seconds(1))
+
+    lines = ssc.socketTextStream(sys.argv[1], sys.argv[2])
+    fm_lines = lines.flatMap(lambda x: x.split(" "))
+    filtered_lines = fm_lines.filter(lambda line: "Spark" in line)
+    mapped_lines = fm_lines.map(lambda x: (x, 1))
+    
+    fm_lines.pyprint()
+    filtered_lines.pyprint()
+    mapped_lines.pyprint()
+    ssc.start()
+    ssc.awaitTermination()

From 0d1b954e709c3888744246a502329259aa3df3ab Mon Sep 17 00:00:00 2001
From: Ken Takagiwa <ken@Kens-MacBook-Pro.local>
Date: Wed, 16 Jul 2014 16:27:28 -0700
Subject: [PATCH 373/489] fied input of socketTextDStream

---
 .../python/streaming/nerwork_wordcount.py     |  2 +-
 python/pyspark/java_gateway.py                |  1 +
 python/pyspark/streaming/context.py           | 25 +++----------------
 3 files changed, 6 insertions(+), 22 deletions(-)

diff --git a/examples/src/main/python/streaming/nerwork_wordcount.py b/examples/src/main/python/streaming/nerwork_wordcount.py
index 2e5048ccad213..67dc28f7bf7f0 100644
--- a/examples/src/main/python/streaming/nerwork_wordcount.py
+++ b/examples/src/main/python/streaming/nerwork_wordcount.py
@@ -10,7 +10,7 @@
         exit(-1)
     ssc = StreamingContext(appName="PythonStreamingNetworkWordCount", duration=Seconds(1))
 
-    lines = ssc.socketTextStream(sys.argv[1], sys.argv[2])
+    lines = ssc.socketTextStream(sys.argv[1], int(sys.argv[2]))
     fm_lines = lines.flatMap(lambda x: x.split(" "))
     filtered_lines = fm_lines.filter(lambda line: "Spark" in line)
     mapped_lines = fm_lines.map(lambda x: (x, 1))
diff --git a/python/pyspark/java_gateway.py b/python/pyspark/java_gateway.py
index c3fef42d118bd..d7e7bbf806544 100644
--- a/python/pyspark/java_gateway.py
+++ b/python/pyspark/java_gateway.py
@@ -111,6 +111,7 @@ def run(self):
     java_import(gateway.jvm, "org.apache.spark.streaming.*")
     java_import(gateway.jvm, "org.apache.spark.streaming.api.java.*")
     java_import(gateway.jvm, "org.apache.spark.streaming.api.python.*")
+    java_import(gateway.jvm, "org.apache.spark.streaming.dstream.*")
     java_import(gateway.jvm, "org.apache.spark.mllib.api.python.*")
     java_import(gateway.jvm, "org.apache.spark.sql.SQLContext")
     java_import(gateway.jvm, "org.apache.spark.sql.hive.HiveContext")
diff --git a/python/pyspark/streaming/context.py b/python/pyspark/streaming/context.py
index d3ff16fca764f..5dcc9ba35a653 100644
--- a/python/pyspark/streaming/context.py
+++ b/python/pyspark/streaming/context.py
@@ -19,7 +19,7 @@
 from pyspark.files import SparkFiles
 from pyspark.java_gateway import launch_gateway
 from pyspark.serializers import PickleSerializer, BatchedSerializer, UTF8Deserializer
-from pyspark.storagelevel import StorageLevel
+from pyspark.storagelevel import *
 from pyspark.rdd import RDD
 from pyspark.context import SparkContext
 
@@ -83,26 +83,9 @@ def awaitTermination(self, timeout=None):
         else:
             self._jssc.awaitTermination()
 
-    def checkpoint(self, directory):
-        raise NotImplementedError
-
-    def fileStream(self, directory, filter=None, newFilesOnly=None):
-        raise NotImplementedError
-
-    def networkStream(self, receiver):
-        raise NotImplementedError
-
-    def queueStream(self, queue, oneAtATime=True, defaultRDD=None):
-        raise NotImplementedError
-
-    def rawSocketStream(self, hostname, port, storagelevel):
-        raise NotImplementedError
-
-    def remember(self, duration):
-        raise NotImplementedError
-
-    def socketStream(hostname, port, converter,storageLevel):
-        raise NotImplementedError
+    # start from simple one. storageLevel is not passed for now.
+    def socketTextStream(self, hostname, port):
+        return DStream(self._jssc.socketTextStream(hostname, port), self, UTF8Deserializer())
 
     def start(self):
         self._jssc.start()

From ccfd2149ac762d590ee571b2dcb448a06f397232 Mon Sep 17 00:00:00 2001
From: Ken Takagiwa <ken@kens-mbp.gateway.sonic.net>
Date: Wed, 16 Jul 2014 23:29:37 -0700
Subject: [PATCH 374/489] added doctest for pyspark.streaming.duration

---
 python/pyspark/streaming/duration.py | 242 +++++++++++++++++++++++----
 python/pyspark/streaming/utils.py    |  20 ++-
 python/run-tests                     |   1 +
 3 files changed, 233 insertions(+), 30 deletions(-)

diff --git a/python/pyspark/streaming/duration.py b/python/pyspark/streaming/duration.py
index 5982146e69026..06a169e5215ac 100644
--- a/python/pyspark/streaming/duration.py
+++ b/python/pyspark/streaming/duration.py
@@ -42,29 +42,80 @@ def __init__(self, millis, _jvm=None):
         self._jduration = _jvm.Duration(millis)
 
     def toString(self):
-        """ Return duration as string """
+        """
+        Return duration as string
+
+        >>> d_10 = Duration(10)
+        >>> d_10.toString()
+        '10 ms'
+        """
         return str(self._millis) + " ms"
 
     def isZero(self):
-        """ Check if millis is zero """
+        """
+        Check if millis is zero
+
+        >>> d_10 = Duration(10)
+        >>> d_10.isZero()
+        False
+        >>> d_0 = Duration(0)
+        >>> d_0.isZero()
+        True
+        """
         return self._millis == 0
 
     def prettyPrint(self):
         """
         Return a human-readable string representing a duration
+
+        >>> d_10 = Duration(10)
+        >>> d_10.prettyPrint()
+        '10 ms'
+        >>> d_1sec = Duration(1000)
+        >>> d_1sec.prettyPrint()
+        '1.0 s'
+        >>> d_1min = Duration(60 * 1000)
+        >>> d_1min.prettyPrint()
+        '1.0 m'
+        >>> d_1hour = Duration(60 * 60 * 1000)
+        >>> d_1hour.prettyPrint()
+        '1.00 h'
         """
         return utils.msDurationToString(self._millis)
 
     def milliseconds(self):
-        """ Return millisecond """
+        """
+        Return millisecond
+
+        >>> d_10 = Duration(10)
+        >>> d_10.milliseconds()
+        10
+
+        """
         return self._millis
 
     def toFormattedString(self):
-        """ Return millisecond """
+        """
+        Return millisecond
+
+        >>> d_10 = Duration(10)
+        >>> d_10.toFormattedString()
+        '10'
+
+        """
         return str(self._millis)
 
     def max(self, other):
-        """ Return higher Duration """
+        """
+        Return higher Duration
+
+        >>> d_10 = Duration(10)
+        >>> d_100 = Duration(100)
+        >>> d_max = d_10.max(d_100)
+        >>> print d_max
+        100 ms
+
+        """
         Duration._is_duration(other)
         if self > other:
             return self
@@ -72,7 +123,16 @@ def max(self, other):
             return other
 
     def min(self, other):
-        """ Return lower Durattion """
+        """
+        Return lower Durattion
+
+        >>> d_10 = Duration(10)
+        >>> d_100 = Duration(100)
+        >>> d_min = d_10.min(d_100)
+        >>> print d_min
+        10 ms
+
+        """
         Duration._is_duration(other)
         if self < other:
             return self
@@ -80,20 +140,52 @@ def min(self, other):
             return other
 
     def __str__(self):
+        """
+        >>> d_10 = Duration(10)
+        >>> str(d_10)
+        '10 ms'
+
+        """
         return self.toString()
 
     def __add__(self, other):
-        """ Add Duration and Duration """
+        """
+        Add Duration and Duration
+
+        >>> d_10 = Duration(10)
+        >>> d_100 = Duration(100)
+        >>> d_110 = d_10 + d_100
+        >>> print d_110
+        110 ms
+        """
         Duration._is_duration(other)
         return Duration(self._millis + other._millis)
 
     def __sub__(self, other):
-        """ Subtract Duration by Duration  """
+        """
+        Subtract Duration by Duration
+
+        >>> d_10 = Duration(10)
+        >>> d_100 = Duration(100)
+        >>> d_90 =  d_100 - d_10
+        >>> print d_90
+        90 ms
+
+        """
         Duration._is_duration(other)
         return Duration(self._millis - other._millis)
 
     def __mul__(self, other):
-        """ Multiple Duration by Duration """
+        """
+        Multiple Duration by Duration
+
+        >>> d_10 = Duration(10)
+        >>> d_100 = Duration(100)
+        >>> d_1000 = d_10 * d_100
+        >>> print d_1000
+        1000 ms
+
+        """
         Duration._is_duration(other)
         return Duration(self._millis * other._millis)
 
@@ -101,6 +193,13 @@ def __div__(self, other):
         """
         Divide Duration by Duration
         for Python 2.X
+
+        >>> d_10 = Duration(10)
+        >>> d_20 = Duration(20)
+        >>> d_2 = d_20 / d_10
+        >>> print d_2
+        2 ms
+
         """
         Duration._is_duration(other)
         return Duration(self._millis / other._millis)
@@ -109,46 +208,121 @@ def __truediv__(self, other):
         """
         Divide Duration by Duration
         for Python 3.0
+
+        >>> d_10 = Duration(10)
+        >>> d_20 = Duration(20)
+        >>> d_2 = d_20 / d_10
+        >>> print d_2
+        2 ms
+
         """
         Duration._is_duration(other)
         return Duration(self._millis / other._millis)
 
     def __floordiv__(self, other):
-        """ Divide Duration by Duration """
+        """
+        Divide Duration by Duration
+
+        >>> d_10 = Duration(10)
+        >>> d_3 = Duration(3)
+        >>> d_3 = d_10 // d_3
+        >>> print d_3
+        3 ms
+
+        """
         Duration._is_duration(other)
         return Duration(self._millis // other._millis)
 
-    def __len__(self):
-        """ Length of miilisecond in Duration """
-        return len(self._millis)
-
     def __lt__(self, other):
-        """ Duration < Duration """
+        """
+        Duration < Duration
+
+        >>> d_10 = Duration(10)
+        >>> d_20 = Duration(20)
+        >>> d_10 < d_20
+        True
+        >>> d_20 < d_10
+        False
+
+        """
         Duration._is_duration(other)
         return self._millis < other._millis
 
     def __le__(self, other):
-        """ Duration <= Duration """
+        """
+        Duration <= Duration
+
+        >>> d_10 = Duration(10)
+        >>> d_20 = Duration(20)
+        >>> d_10 <= d_20
+        True
+        >>> d_20 <= d_10
+        False
+
+        """
         Duration._is_duration(other)
-        return self.millis <= other._millis
+        return self._millis <= other._millis
 
     def __eq__(self, other):
-        """ Duration ==  Duration """
+        """
+        Duration ==  Duration
+
+        >>> d_10 = Duration(10)
+        >>> d_20 = Duration(20)
+        >>> d_10 == d_20
+        False
+        >>> other_d_10 = Duration(10)
+        >>> d_10 == other_d_10
+        True
+
+        """
         Duration._is_duration(other)
         return self._millis == other._millis
 
     def __ne__(self, other):
-        """ Duration != Duration """
+        """
+        Duration != Duration
+
+        >>> d_10 = Duration(10)
+        >>> d_20 = Duration(20)
+        >>> d_10 != d_20
+        True
+        >>> other_d_10 = Duration(10)
+        >>> d_10 != other_d_10
+        False
+
+        """
         Duration._is_duration(other)
         return self._millis != other._millis
 
     def __gt__(self, other):
-        """ Duration > Duration """
+        """
+        Duration > Duration
+
+        >>> d_10 = Duration(10)
+        >>> d_20 = Duration(20)
+        >>> d_10 > d_20
+        False
+        >>> d_20 > d_10
+        True
+
+        """
         Duration._is_duration(other)
         return self._millis > other._millis
 
     def __ge__(self, other):
-        """ Duration >= Duration """
+        """
+        Duration >= Duration
+
+        >>> d_10 = Duration(10)
+        >>> d_20 = Duration(20)
+        >>> d_10 < d_20
+        True
+        >>> d_20 < d_10
+        False
+
+
+        """
         Duration._is_duration(other)
         return self._millis >= other._millis
 
@@ -162,6 +336,12 @@ def Milliseconds(milliseconds):
     """
     Helper function that creates instance of [[pysparkstreaming.duration]] representing
     a given number of milliseconds.
+
+    >>> milliseconds = Milliseconds(1)
+    >>> d_1 = Duration(1)
+    >>> milliseconds == d_1
+    True
+
     """
     return Duration(milliseconds)
 
@@ -169,18 +349,24 @@ def Seconds(seconds):
     """
     Helper function that creates instance of [[pysparkstreaming.duration]] representing
     a given number of seconds.
+
+    >>> seconds = Seconds(1)
+    >>> d_1sec = Duration(1000)
+    >>> seconds == d_1sec
+    True
+
     """
     return Duration(seconds * 1000)
 
-def Minites(minites):
+def Minutes(minutes):
     """
     Helper function that creates instance of [[pysparkstreaming.duration]] representing
     a given number of minutes.
-    """
-    return Duration(minutes * 60000)
 
-if __name__ == "__main__":
-    d = Duration(1)
-    print d
-    print d.milliseconds()
+    >>> minutes = Minutes(1)
+    >>> d_1min = Duration(60 * 1000)
+    >>> minutes == d_1min
+    True
 
+    """
+    return Duration(minutes * 60 * 1000)
diff --git a/python/pyspark/streaming/utils.py b/python/pyspark/streaming/utils.py
index 71aa3376c6578..b1fa1e227b0a1 100644
--- a/python/pyspark/streaming/utils.py
+++ b/python/pyspark/streaming/utils.py
@@ -1,4 +1,20 @@
-__author__ = 'ktakagiw'
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
 
 def msDurationToString(ms):
     """
@@ -12,7 +28,7 @@ def msDurationToString(ms):
         return "%d ms" % ms
     elif ms < minute:
         return "%.1f s" % (float(ms) / second)
-    elif ms < hout:
+    elif ms < hour:
         return "%.1f m" % (float(ms) / minute)
     else:
         return "%.2f h" % (float(ms) / hour)
diff --git a/python/run-tests b/python/run-tests
index a7ec270c7da21..27f47edaebbad 100755
--- a/python/run-tests
+++ b/python/run-tests
@@ -70,6 +70,7 @@ export PYSPARK_DOC_TEST=1
 run_test "pyspark/broadcast.py"
 run_test "pyspark/accumulators.py"
 run_test "pyspark/serializers.py"
+run_test "pyspark/streaming/duration.py"
 unset PYSPARK_DOC_TEST
 run_test "pyspark/shuffle.py"
 run_test "pyspark/tests.py"

From b31446a72099317132e5894bcdd53e9f872c076f Mon Sep 17 00:00:00 2001
From: Ken Takagiwa <ken@kens-mbp.gateway.sonic.net>
Date: Wed, 16 Jul 2014 23:36:33 -0700
Subject: [PATCH 375/489] fixed typo of network_workdcount.py

---
 .../python/streaming/network_wordcount.py     | 22 +++++++++++++++++++
 1 file changed, 22 insertions(+)
 create mode 100644 examples/src/main/python/streaming/network_wordcount.py

diff --git a/examples/src/main/python/streaming/network_wordcount.py b/examples/src/main/python/streaming/network_wordcount.py
new file mode 100644
index 0000000000000..67dc28f7bf7f0
--- /dev/null
+++ b/examples/src/main/python/streaming/network_wordcount.py
@@ -0,0 +1,22 @@
+import sys
+from operator import add
+
+from pyspark.streaming.context import StreamingContext
+from pyspark.streaming.duration import *
+
+if __name__ == "__main__":
+    if len(sys.argv) != 3:
+        print >> sys.stderr, "Usage: wordcount <hostname> <port>"
+        exit(-1)
+    ssc = StreamingContext(appName="PythonStreamingNetworkWordCount", duration=Seconds(1))
+
+    lines = ssc.socketTextStream(sys.argv[1], int(sys.argv[2]))
+    fm_lines = lines.flatMap(lambda x: x.split(" "))
+    filtered_lines = fm_lines.filter(lambda line: "Spark" in line)
+    mapped_lines = fm_lines.map(lambda x: (x, 1))
+    
+    fm_lines.pyprint()
+    filtered_lines.pyprint()
+    mapped_lines.pyprint()
+    ssc.start()
+    ssc.awaitTermination()

From dc6995d1e010eadc49089b86e853c8698f10aafa Mon Sep 17 00:00:00 2001
From: Ken Takagiwa <ken@kens-mbp.gateway.sonic.net>
Date: Wed, 16 Jul 2014 23:39:25 -0700
Subject: [PATCH 376/489] delete old file

---
 .../python/streaming/nerwork_wordcount.py     | 22 -------------------
 1 file changed, 22 deletions(-)
 delete mode 100644 examples/src/main/python/streaming/nerwork_wordcount.py

diff --git a/examples/src/main/python/streaming/nerwork_wordcount.py b/examples/src/main/python/streaming/nerwork_wordcount.py
deleted file mode 100644
index 67dc28f7bf7f0..0000000000000
--- a/examples/src/main/python/streaming/nerwork_wordcount.py
+++ /dev/null
@@ -1,22 +0,0 @@
-import sys
-from operator import add
-
-from pyspark.streaming.context import StreamingContext
-from pyspark.streaming.duration import *
-
-if __name__ == "__main__":
-    if len(sys.argv) != 3:
-        print >> sys.stderr, "Usage: wordcount <hostname> <port>"
-        exit(-1)
-    ssc = StreamingContext(appName="PythonStreamingNetworkWordCount", duration=Seconds(1))
-
-    lines = ssc.socketTextStream(sys.argv[1], int(sys.argv[2]))
-    fm_lines = lines.flatMap(lambda x: x.split(" "))
-    filtered_lines = fm_lines.filter(lambda line: "Spark" in line)
-    mapped_lines = fm_lines.map(lambda x: (x, 1))
-    
-    fm_lines.pyprint()
-    filtered_lines.pyprint()
-    mapped_lines.pyprint()
-    ssc.start()
-    ssc.awaitTermination()

From c455c8d711235532904b18e3c26ac254c74558c6 Mon Sep 17 00:00:00 2001
From: Ken Takagiwa <ken@Kens-MacBook-Pro.local>
Date: Tue, 15 Jul 2014 21:08:43 -0700
Subject: [PATCH 377/489] added reducedByKey not working yet

---
 python/pyspark/streaming/dstream.py           |  2 +-
 .../streaming/api/python/PythonDStream.scala  | 85 +++++++++++++++++++
 2 files changed, 86 insertions(+), 1 deletion(-)

diff --git a/python/pyspark/streaming/dstream.py b/python/pyspark/streaming/dstream.py
index 5766cca39bdee..91b83511b1cca 100644
--- a/python/pyspark/streaming/dstream.py
+++ b/python/pyspark/streaming/dstream.py
@@ -143,7 +143,7 @@ def _defaultReducePartitions(self):
         if self.ctx._conf.contains("spark.default.parallelism"):
             return self.ctx.defaultParallelism
         else:
-            return self.getNumPartitions()
+            return 2
 
     def getNumPartitions(self):
       """
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
index cecb10fc30eb4..5986bcc84f31e 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
@@ -55,6 +55,7 @@ class PythonDStream[T: ClassTag](
       case None => None
     }
   }
+<<<<<<< HEAD
 
   val asJavaDStream  = JavaDStream.fromDStream(this)
 
@@ -133,3 +134,87 @@ DStream[(Long, Array[Byte])](prev.ssc){
   }
   val asJavaPairDStream : JavaPairDStream[Long, Array[Byte]]  = JavaPairDStream.fromJavaDStream(this)
 }
+=======
+  val asJavaDStream  = JavaDStream.fromDStream(this)
+
+  /**
+   * Print the first ten elements of each PythonRDD generated in this PythonDStream. This is an output
+   * operator, so this PythonDStream will be registered as an output stream and there materialized.
+   * Since serialized Python object is readable by Python, pyprint writes out binary data to
+   * temporary file and run python script to deserialized and print the first ten elements
+   */
+  private[streaming] def ppyprint() {
+    def foreachFunc = (rdd: RDD[Array[Byte]], time: Time) => {
+      val iter = rdd.take(11).iterator
+
+      // make a temporary file
+      val prefix = "spark"
+      val suffix = ".tmp"
+      val tempFile = File.createTempFile(prefix, suffix)
+      val tempFileStream = new DataOutputStream(new FileOutputStream(tempFile.getAbsolutePath))
+      //write out serialized python object
+      PythonRDD.writeIteratorToStream(iter, tempFileStream)
+      tempFileStream.close()
+
+      // This value has to be passed from python
+      //val pythonExec = new ProcessBuilder().environment().get("PYSPARK_PYTHON")
+      val sparkHome = new ProcessBuilder().environment().get("SPARK_HOME")
+      //val pb = new ProcessBuilder(Seq(pythonExec, sparkHome + "/python/pyspark/streaming/pyprint.py", tempFile.getAbsolutePath())) // why this fails to compile???
+      //absolute path to the python script is needed to change because we do not use pysparkstreaming
+      val pb = new ProcessBuilder(pythonExec, sparkHome + "/python/pysparkstreaming/streaming/pyprint.py", tempFile.getAbsolutePath)
+      val workerEnv = pb.environment()
+
+      //envVars also need to be pass
+      //workerEnv.putAll(envVars)
+      val pythonPath = sparkHome + "/python/" + File.pathSeparator + workerEnv.get("PYTHONPATH")
+      workerEnv.put("PYTHONPATH", pythonPath)
+      val worker = pb.start()
+      val is = worker.getInputStream()
+      val isr = new InputStreamReader(is)
+      val br = new BufferedReader(isr)
+
+      println ("-------------------------------------------")
+      println ("Time: " + time)
+      println ("-------------------------------------------")
+
+      //print value from python std out
+      var line = ""
+      breakable {
+        while (true) {
+          line = br.readLine()
+          if (line == null) break()
+          println(line)
+        }
+      }
+      //delete temporary file
+      tempFile.delete()
+      println()
+
+    }
+    new ForEachDStream(this, context.sparkContext.clean(foreachFunc)).register()
+  }
+}
+
+
+private class PairwiseDStream(prev:DStream[Array[Byte]]) extends
+DStream[(Long, Array[Byte])](prev.ssc){
+  override def dependencies = List(prev)
+
+  override def slideDuration: Duration = prev.slideDuration
+
+  override def compute(validTime:Time):Option[RDD[(Long, Array[Byte])]]={
+    prev.getOrCompute(validTime) match{
+      case Some(rdd)=>Some(rdd)
+        val pairwiseRDD = new PairwiseRDD(rdd)
+        Some(pairwiseRDD.asJavaPairRDD.rdd)
+      case None => None
+    }
+  }
+  val asJavaPairDStream : JavaPairDStream[Long, Array[Byte]]  = JavaPairDStream.fromJavaDStream(this)
+}
+
+
+
+
+
+>>>>>>> added reducedByKey not working yet

From 6f98e50eae899f3c4ad04351dbd3942c49b13771 Mon Sep 17 00:00:00 2001
From: Ken Takagiwa <ken@Kens-MacBook-Pro.local>
Date: Thu, 17 Jul 2014 16:27:05 -0700
Subject: [PATCH 378/489] reduceByKey is working

---
 .../src/main/python/streaming/wordcount.pyc   | Bin 0 -> 1566 bytes
 python/pyspark/streaming/dstream.py           |   6 +-
 .../streaming/api/python/PythonDStream.scala  |  87 +++---------------
 .../api/python/PythonTransformedDStream.scala |  19 ++--
 4 files changed, 29 insertions(+), 83 deletions(-)
 create mode 100644 examples/src/main/python/streaming/wordcount.pyc

diff --git a/examples/src/main/python/streaming/wordcount.pyc b/examples/src/main/python/streaming/wordcount.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..db93702361f47f57988ea82c213aae522e7a9f81
GIT binary patch
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diff --git a/python/pyspark/streaming/dstream.py b/python/pyspark/streaming/dstream.py
index 91b83511b1cca..6a843a70b6386 100644
--- a/python/pyspark/streaming/dstream.py
+++ b/python/pyspark/streaming/dstream.py
@@ -118,11 +118,9 @@ def add_shuffle_key(split, iterator):
         keyed = PipelinedDStream(self, add_shuffle_key)
         keyed._bypass_serializer = True
         with _JavaStackTrace(self.ctx) as st:
-            #JavaDStream
-            pairDStream = self.ctx._jvm.PairwiseDStream(keyed._jdstream.dstream()).asJavaPairDStream()
             partitioner = self.ctx._jvm.PythonPartitioner(numPartitions,
-                                                          id(partitionFunc))
-        jdstream = pairDStream.partitionBy(partitioner).values()
+                                                      id(partitionFunc))
+            jdstream = self.ctx._jvm.PairwiseDStream(keyed._jdstream.dstream(), partitioner).asJavaDStream()
         dstream = DStream(jdstream, self._ssc, BatchedSerializer(outputSerializer))
         # This is required so that id(partitionFunc) remains unique, even if
         # partitionFunc is a lambda:
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
index 5986bcc84f31e..9727356da1f51 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
@@ -55,7 +55,6 @@ class PythonDStream[T: ClassTag](
       case None => None
     }
   }
-<<<<<<< HEAD
 
   val asJavaDStream  = JavaDStream.fromDStream(this)
 
@@ -134,87 +133,31 @@ DStream[(Long, Array[Byte])](prev.ssc){
   }
   val asJavaPairDStream : JavaPairDStream[Long, Array[Byte]]  = JavaPairDStream.fromJavaDStream(this)
 }
-=======
-  val asJavaDStream  = JavaDStream.fromDStream(this)
-
-  /**
-   * Print the first ten elements of each PythonRDD generated in this PythonDStream. This is an output
-   * operator, so this PythonDStream will be registered as an output stream and there materialized.
-   * Since serialized Python object is readable by Python, pyprint writes out binary data to
-   * temporary file and run python script to deserialized and print the first ten elements
-   */
-  private[streaming] def ppyprint() {
-    def foreachFunc = (rdd: RDD[Array[Byte]], time: Time) => {
-      val iter = rdd.take(11).iterator
-
-      // make a temporary file
-      val prefix = "spark"
-      val suffix = ".tmp"
-      val tempFile = File.createTempFile(prefix, suffix)
-      val tempFileStream = new DataOutputStream(new FileOutputStream(tempFile.getAbsolutePath))
-      //write out serialized python object
-      PythonRDD.writeIteratorToStream(iter, tempFileStream)
-      tempFileStream.close()
-
-      // This value has to be passed from python
-      //val pythonExec = new ProcessBuilder().environment().get("PYSPARK_PYTHON")
-      val sparkHome = new ProcessBuilder().environment().get("SPARK_HOME")
-      //val pb = new ProcessBuilder(Seq(pythonExec, sparkHome + "/python/pyspark/streaming/pyprint.py", tempFile.getAbsolutePath())) // why this fails to compile???
-      //absolute path to the python script is needed to change because we do not use pysparkstreaming
-      val pb = new ProcessBuilder(pythonExec, sparkHome + "/python/pysparkstreaming/streaming/pyprint.py", tempFile.getAbsolutePath)
-      val workerEnv = pb.environment()
-
-      //envVars also need to be pass
-      //workerEnv.putAll(envVars)
-      val pythonPath = sparkHome + "/python/" + File.pathSeparator + workerEnv.get("PYTHONPATH")
-      workerEnv.put("PYTHONPATH", pythonPath)
-      val worker = pb.start()
-      val is = worker.getInputStream()
-      val isr = new InputStreamReader(is)
-      val br = new BufferedReader(isr)
 
-      println ("-------------------------------------------")
-      println ("Time: " + time)
-      println ("-------------------------------------------")
-
-      //print value from python std out
-      var line = ""
-      breakable {
-        while (true) {
-          line = br.readLine()
-          if (line == null) break()
-          println(line)
-        }
-      }
-      //delete temporary file
-      tempFile.delete()
-      println()
 
-    }
-    new ForEachDStream(this, context.sparkContext.clean(foreachFunc)).register()
-  }
-}
-
-
-private class PairwiseDStream(prev:DStream[Array[Byte]]) extends
-DStream[(Long, Array[Byte])](prev.ssc){
+private class PairwiseDStream(prev:DStream[Array[Byte]], partitioner: Partitioner) extends
+DStream[Array[Byte]](prev.ssc){
   override def dependencies = List(prev)
 
   override def slideDuration: Duration = prev.slideDuration
 
-  override def compute(validTime:Time):Option[RDD[(Long, Array[Byte])]]={
+  override def compute(validTime:Time):Option[RDD[Array[Byte]]]={
     prev.getOrCompute(validTime) match{
       case Some(rdd)=>Some(rdd)
         val pairwiseRDD = new PairwiseRDD(rdd)
-        Some(pairwiseRDD.asJavaPairRDD.rdd)
+        /*
+         * This is equivalent to following python code
+         * with _JavaStackTrace(self.context) as st:
+         *    pairRDD = self.ctx._jvm.PairwiseRDD(keyed._jrdd.rdd()).asJavaPairRDD()
+         *    partitioner = self.ctx._jvm.PythonPartitioner(numPartitions,
+         *                                                  id(partitionFunc))
+         * jrdd = pairRDD.partitionBy(partitioner).values()
+         * rdd = RDD(jrdd, self.ctx, BatchedSerializer(outputSerializer))
+         */
+        Some(pairwiseRDD.asJavaPairRDD.partitionBy(partitioner).values().rdd)
       case None => None
     }
   }
-  val asJavaPairDStream : JavaPairDStream[Long, Array[Byte]]  = JavaPairDStream.fromJavaDStream(this)
+  val asJavaDStream  = JavaDStream.fromDStream(this)
+  //val asJavaPairDStream : JavaPairDStream[Long, Array[Byte]]  = JavaPairDStream.fromJavaDStream(this)
 }
-
-
-
-
-
->>>>>>> added reducedByKey not working yet
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonTransformedDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonTransformedDStream.scala
index ff70483b771a4..bc07e09ec6d03 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonTransformedDStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonTransformedDStream.scala
@@ -1,3 +1,5 @@
+/*
+
 package org.apache.spark.streaming.api.python
 
 import org.apache.spark.Accumulator
@@ -10,11 +12,8 @@ import org.apache.spark.streaming.dstream.DStream
 
 import scala.reflect.ClassTag
 
-/**
- * Created by ken on 7/15/14.
- */
 class PythonTransformedDStream[T: ClassTag](
-               parents: Seq[DStream[T]],
+               parent: DStream[T],
                command: Array[Byte],
                envVars: JMap[String, String],
                pythonIncludes: JList[String],
@@ -30,8 +29,14 @@ class PythonTransformedDStream[T: ClassTag](
 
   //pythonDStream compute
   override def compute(validTime: Time): Option[RDD[Array[Byte]]] = {
-    val parentRDDs = parents.map(_.getOrCompute(validTime).orNull).toSeq
-    Some()
+
+//    val parentRDDs = parents.map(_.getOrCompute(validTime).orNull).toSeq
+//    parents.map(_.getOrCompute(validTime).orNull).to
+//    parent = parents.head.asInstanceOf[RDD]
+//    Some()
   }
-  val asJavaDStream  = JavaDStream.fromDStream(this)
+
+  val asJavaDStream = JavaDStream.fromDStream(this)
 }
+
+*/

From 15feea9f2db99e84abba5fadbdfaa444cd2b06ce Mon Sep 17 00:00:00 2001
From: Ken Takagiwa <ken@Kens-MacBook-Pro.local>
Date: Thu, 17 Jul 2014 17:09:23 -0700
Subject: [PATCH 379/489] edit python sparkstreaming example

---
 examples/src/main/python/streaming/network_wordcount.py | 8 +++++++-
 examples/src/main/python/streaming/wordcount.py         | 1 +
 2 files changed, 8 insertions(+), 1 deletion(-)

diff --git a/examples/src/main/python/streaming/network_wordcount.py b/examples/src/main/python/streaming/network_wordcount.py
index 67dc28f7bf7f0..77fca7ff7657d 100644
--- a/examples/src/main/python/streaming/network_wordcount.py
+++ b/examples/src/main/python/streaming/network_wordcount.py
@@ -1,6 +1,7 @@
 import sys
 from operator import add
 
+from pyspark.conf import SparkConf
 from pyspark.streaming.context import StreamingContext
 from pyspark.streaming.duration import *
 
@@ -8,15 +9,20 @@
     if len(sys.argv) != 3:
         print >> sys.stderr, "Usage: wordcount <hostname> <port>"
         exit(-1)
-    ssc = StreamingContext(appName="PythonStreamingNetworkWordCount", duration=Seconds(1))
+    conf = SparkConf()
+    conf.setAppName("PythonStreamingNetworkWordCount")
+    conf.set("spark.default.parallelism", 1)
+    ssc = StreamingContext(conf=conf, duration=Seconds(1))
 
     lines = ssc.socketTextStream(sys.argv[1], int(sys.argv[2]))
     fm_lines = lines.flatMap(lambda x: x.split(" "))
     filtered_lines = fm_lines.filter(lambda line: "Spark" in line)
     mapped_lines = fm_lines.map(lambda x: (x, 1))
+    reduced_lines = mapped_lines.reduce(add)
     
     fm_lines.pyprint()
     filtered_lines.pyprint()
     mapped_lines.pyprint()
+    reduced_lines.pyprint()
     ssc.start()
     ssc.awaitTermination()
diff --git a/examples/src/main/python/streaming/wordcount.py b/examples/src/main/python/streaming/wordcount.py
index 3996991109d60..9ff8bc5ac9ab2 100644
--- a/examples/src/main/python/streaming/wordcount.py
+++ b/examples/src/main/python/streaming/wordcount.py
@@ -13,6 +13,7 @@
     conf.setAppName("PythonStreamingWordCount")
     conf.set("spark.default.parallelism", 1)
 
+# still has a bug
 #    ssc = StreamingContext(appName="PythonStreamingWordCount", duration=Seconds(1))
     ssc = StreamingContext(conf=conf, duration=Seconds(1))
 

From d3ee86a7bf9e64231ccd746bd3679d5e57dd489b Mon Sep 17 00:00:00 2001
From: Ken Takagiwa <ken@Kens-MacBook-Pro.local>
Date: Fri, 18 Jul 2014 17:58:58 -0700
Subject: [PATCH 380/489] added count operation but this implementation need
 double check

---
 python/pyspark/streaming/dstream.py | 25 +++++++++++++++++++++++--
 1 file changed, 23 insertions(+), 2 deletions(-)

diff --git a/python/pyspark/streaming/dstream.py b/python/pyspark/streaming/dstream.py
index 6a843a70b6386..c55c9a3ce641e 100644
--- a/python/pyspark/streaming/dstream.py
+++ b/python/pyspark/streaming/dstream.py
@@ -1,5 +1,8 @@
 from collections import defaultdict
 from itertools import chain, ifilter, imap
+import operator
+
+import logging
 
 from pyspark.serializers import NoOpSerializer,\
     BatchedSerializer, CloudPickleSerializer, pack_long
@@ -24,6 +27,18 @@ def generatedRDDs(self):
         """
         pass
 
+    def count(self):
+        """
+
+        """
+        #TODO make sure count implementation, thiis different from what pyspark does
+        return self.mapPartitions(lambda i: [sum(1 for _ in i)]).sum().map(lambda x: x[1])
+
+    def sum(self):
+        """
+        """
+        return self.mapPartitions(lambda x: [sum(x)]).reduce(operator.add)
+
     def print_(self):
         """
         """
@@ -63,9 +78,9 @@ def reduce(self, func, numPartitions=None):
         """
 
         """
-        return self._combineByKey(lambda x:x, func, func, numPartitions)
+        return self.combineByKey(lambda x:x, func, func, numPartitions)
 
-    def _combineByKey(self, createCombiner, mergeValue, mergeCombiners,
+    def combineByKey(self, createCombiner, mergeValue, mergeCombiners,
                       numPartitions = None):
         """
         """
@@ -74,6 +89,12 @@ def _combineByKey(self, createCombiner, mergeValue, mergeCombiners,
         def combineLocally(iterator):
             combiners = {}
             for x in iterator:
+
+                #TODO for count operation make sure count implementation
+                # This is different from what pyspark does
+                if isinstance(x, int):
+                    x = ("", x)
+
                 (k, v) = x
                 if k not in combiners:
                     combiners[k] = createCombiner(v)

From 72b9738db8b9f1fe2d915cd22723f241a40b7391 Mon Sep 17 00:00:00 2001
From: Ken Takagiwa <ken@Kens-MacBook-Pro.local>
Date: Sat, 19 Jul 2014 18:58:01 -0700
Subject: [PATCH 381/489] fix map function

---
 .../python/streaming/network_wordcount.py     |  2 ++
 python/pyspark/streaming/dstream.py           | 26 +++++++++----------
 2 files changed, 15 insertions(+), 13 deletions(-)

diff --git a/examples/src/main/python/streaming/network_wordcount.py b/examples/src/main/python/streaming/network_wordcount.py
index 77fca7ff7657d..a1458e06f13d2 100644
--- a/examples/src/main/python/streaming/network_wordcount.py
+++ b/examples/src/main/python/streaming/network_wordcount.py
@@ -19,10 +19,12 @@
     filtered_lines = fm_lines.filter(lambda line: "Spark" in line)
     mapped_lines = fm_lines.map(lambda x: (x, 1))
     reduced_lines = mapped_lines.reduce(add)
+    counted_lines = reduced_lines.count()
     
     fm_lines.pyprint()
     filtered_lines.pyprint()
     mapped_lines.pyprint()
     reduced_lines.pyprint()
+    counted_lines.pyprint()
     ssc.start()
     ssc.awaitTermination()
diff --git a/python/pyspark/streaming/dstream.py b/python/pyspark/streaming/dstream.py
index c55c9a3ce641e..f8376371b1425 100644
--- a/python/pyspark/streaming/dstream.py
+++ b/python/pyspark/streaming/dstream.py
@@ -20,21 +20,14 @@ def __init__(self, jdstream, ssc, jrdd_deserializer):
         self.ctx = ssc._sc
         self._jrdd_deserializer = jrdd_deserializer
 
-    def generatedRDDs(self):
-        """
-         // RDDs generated, marked as private[streaming] so that testsuites can access it
-         @transient
-        """
-        pass
-
     def count(self):
         """
 
         """
         #TODO make sure count implementation, thiis different from what pyspark does
-        return self.mapPartitions(lambda i: [sum(1 for _ in i)]).sum().map(lambda x: x[1])
+        return self.mapPartitions(lambda i: [sum(1 for _ in i)]).map(lambda x: (None, 1))
 
-    def sum(self):
+    def _sum(self):
         """
         """
         return self.mapPartitions(lambda x: [sum(x)]).reduce(operator.add)
@@ -65,8 +58,9 @@ def func(s, iterator): return chain.from_iterable(imap(f, iterator))
     def map(self, f, preservesPartitioning=False):
         """
         """
-        def func(split, iterator): return imap(f, iterator)
-        return PipelinedDStream(self, func, preservesPartitioning)
+        def func(iterator): return imap(f, iterator)
+        return self.mapPartitions(func)
+        #return PipelinedDStream(self, func, preservesPartitioning)
 
     def mapPartitions(self, f):
         """
@@ -74,6 +68,12 @@ def mapPartitions(self, f):
         def func(s, iterator): return f(iterator)
         return self.mapPartitionsWithIndex(func)
 
+    def mapPartitionsWithIndex(self, f, preservesPartitioning=False):
+        """
+
+        """
+        return PipelinedDStream(self, f, preservesPartitioning)
+
     def reduce(self, func, numPartitions=None):
         """
 
@@ -92,8 +92,8 @@ def combineLocally(iterator):
 
                 #TODO for count operation make sure count implementation
                 # This is different from what pyspark does
-                if isinstance(x, int):
-                    x = ("", x)
+                #if isinstance(x, int):
+                #    x = ("", x)
 
                 (k, v) = x
                 if k not in combiners:

From bab31c1e1ee84774e81c78a4cded375e000e5550 Mon Sep 17 00:00:00 2001
From: Ken Takagiwa <ken@Kens-MacBook-Pro.local>
Date: Sun, 20 Jul 2014 14:31:55 -0700
Subject: [PATCH 382/489] clean up code

---
 python/pyspark/streaming/context.py           | 41 +++++-----
 python/pyspark/streaming/dstream.py           | 74 +++++++++++++------
 python/pyspark/streaming/duration.py          |  1 +
 python/pyspark/streaming/pyprint.py           |  9 ++-
 .../streaming/api/java/JavaDStreamLike.scala  |  2 +-
 .../streaming/api/python/PythonDStream.scala  |  4 +-
 .../api/python/PythonTransformedDStream.scala | 42 -----------
 .../spark/streaming/dstream/DStream.scala     | 21 +++---
 8 files changed, 96 insertions(+), 98 deletions(-)
 delete mode 100644 streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonTransformedDStream.scala

diff --git a/python/pyspark/streaming/context.py b/python/pyspark/streaming/context.py
index 5dcc9ba35a653..a4900191d1730 100644
--- a/python/pyspark/streaming/context.py
+++ b/python/pyspark/streaming/context.py
@@ -22,15 +22,15 @@
 from pyspark.storagelevel import *
 from pyspark.rdd import RDD
 from pyspark.context import SparkContext
+from pyspark.streaming.dstream import DStream
 
 from py4j.java_collections import ListConverter
 
-from pyspark.streaming.dstream import DStream
 
 class StreamingContext(object):
     """
     Main entry point for Spark Streaming functionality. A StreamingContext represents the
-    connection to a Spark cluster, and can be used to create L{RDD}s and
+    connection to a Spark cluster, and can be used to create L{DStream}s and
     broadcast variables on that cluster.
     """
 
@@ -71,13 +71,16 @@ def __init__(self, master=None, appName=None, sparkHome=None, pyFiles=None,
     def _initialize_context(self, jspark_context, jduration):
         return self._jvm.JavaStreamingContext(jspark_context, jduration)
 
-    def actorStream(self, props, name, storageLevel, supervisorStrategy):
-        raise NotImplementedError
-
-    def addStreamingListener(self, streamingListener):
-        raise NotImplementedError
+    def start(self):
+        """
+        Start the execution of the streams.
+        """
+        self._jssc.start()
 
     def awaitTermination(self, timeout=None):
+        """
+        Wait for the execution to stop.
+        """
         if timeout:
             self._jssc.awaitTermination(timeout)
         else:
@@ -85,20 +88,18 @@ def awaitTermination(self, timeout=None):
 
     # start from simple one. storageLevel is not passed for now.
     def socketTextStream(self, hostname, port):
+        """
+        Create an input from TCP source hostname:port. Data is received using
+        a TCP socket and receive byte is interpreted as UTF8 encoded '\n' delimited
+        lines.
+        """
         return DStream(self._jssc.socketTextStream(hostname, port), self, UTF8Deserializer())
 
-    def start(self):
-        self._jssc.start()
-
-    def stop(self, stopSparkContext=True):
-        raise NotImplementedError
-
     def textFileStream(self, directory):
+        """
+        Create an input stream that monitors a Hadoop-compatible file system
+        for new files and reads them as text files. Files must be wrriten to the
+        monitored directory by "moving" them from another location within the same
+        file system. FIle names starting with . are ignored.
+        """
         return DStream(self._jssc.textFileStream(directory), self, UTF8Deserializer())
-
-    def transform(self, seq):
-        raise NotImplementedError
-
-    def union(self, seq):
-        raise NotImplementedError
-
diff --git a/python/pyspark/streaming/dstream.py b/python/pyspark/streaming/dstream.py
index f8376371b1425..0c37cb7164288 100644
--- a/python/pyspark/streaming/dstream.py
+++ b/python/pyspark/streaming/dstream.py
@@ -2,8 +2,6 @@
 from itertools import chain, ifilter, imap
 import operator
 
-import logging
-
 from pyspark.serializers import NoOpSerializer,\
     BatchedSerializer, CloudPickleSerializer, pack_long
 from pyspark.rdd import _JavaStackTrace
@@ -25,64 +23,86 @@ def count(self):
 
         """
         #TODO make sure count implementation, thiis different from what pyspark does
-        return self.mapPartitions(lambda i: [sum(1 for _ in i)]).map(lambda x: (None, 1))
+        return self._mapPartitions(lambda i: [sum(1 for _ in i)]).map(lambda x: (None, 1))
 
     def _sum(self):
         """
         """
-        return self.mapPartitions(lambda x: [sum(x)]).reduce(operator.add)
+        return self._mapPartitions(lambda x: [sum(x)]).reduce(operator.add)
 
     def print_(self):
         """
+        Since print is reserved name for python, we cannot make a print method function.
+        This function prints serialized data in RDD in DStream because Scala and Java cannot
+        deserialized pickled python object. Please use DStream.pyprint() instead to print result.
+
+        Call DStream.print().
         """
-        # print is a reserved name of Python. We cannot give print to function name
+        #hack to call print function in DStream
         getattr(self._jdstream, "print")()
 
     def pyprint(self):
         """
+        Print the first ten elements of each RDD generated in this DStream. This is an output
+        operator, so this DStream will be registered as an output stream and there materialized.
+
         """
         self._jdstream.pyprint()
 
     def filter(self, f):
         """
+        Return DStream containing only the elements that satisfy predicate.
         """
         def func(iterator): return ifilter(f, iterator)
-        return self.mapPartitions(func)
+        return self._mapPartitions(func)
 
     def flatMap(self, f, preservesPartitioning=False):
         """
+        Pass each value in the key-value pair DStream through flatMap function
+        without changing the keys: this also retains the original RDD's partition.
         """
         def func(s, iterator): return chain.from_iterable(imap(f, iterator))
-        return self.mapPartitionsWithIndex(func, preservesPartitioning)
+        return self._mapPartitionsWithIndex(func, preservesPartitioning)
 
-    def map(self, f, preservesPartitioning=False):
+    def map(self, f):
         """
+        Return DStream by applying a function to each element of DStream.
         """
         def func(iterator): return imap(f, iterator)
-        return self.mapPartitions(func)
-        #return PipelinedDStream(self, func, preservesPartitioning)
+        return self._mapPartitions(func)
 
-    def mapPartitions(self, f):
+    def _mapPartitions(self, f):
         """
+        Return a new DStream by applying a function to each partition of this DStream.
         """
         def func(s, iterator): return f(iterator)
-        return self.mapPartitionsWithIndex(func)
+        return self._mapPartitionsWithIndex(func)
 
-    def mapPartitionsWithIndex(self, f, preservesPartitioning=False):
+    def _mapPartitionsWithIndex(self, f, preservesPartitioning=False):
         """
-
+        Return a new DStream by applying a function to each partition of this DStream,
+        While tracking the index of the original partition.
         """
         return PipelinedDStream(self, f, preservesPartitioning)
 
-    def reduce(self, func, numPartitions=None):
+
+    def reduceByKey(self, func, numPartitions=None):
         """
+        Merge the value for each key using an associative reduce function.
+
+        This will also perform the merging locally on each mapper before
+        sending resuls to reducer, similarly to a "combiner" in MapReduce.
 
+        Output will be hash-partitioned with C{numPartitions} partitions, or
+        the default parallelism level if C{numPartitions} is not specified.
         """
         return self.combineByKey(lambda x:x, func, func, numPartitions)
 
     def combineByKey(self, createCombiner, mergeValue, mergeCombiners,
                       numPartitions = None):
         """
+        Count the number of elements for each key, and return the result to the
+        master as a dictionary
         """
         if numPartitions is None:
             numPartitions = self._defaultReducePartitions()
@@ -148,20 +168,20 @@ def add_shuffle_key(split, iterator):
         dstream._partitionFunc = partitionFunc
         return dstream
 
-    def mapPartitionsWithIndex(self, f, preservesPartitioning=False):
-        """
-
-        """
-        return PipelinedDStream(self, f, preservesPartitioning)
-
     def _defaultReducePartitions(self):
         """
+        Returns the default number of partitions to use during reduce tasks (e.g., groupBy).
+        If spark.default.parallelism is set, then we'll use the value from SparkContext
+        defaultParallelism, otherwise we'll use the number of partitions in this RDD.
 
+        This mirrors the behavior of the Scala Partitioner#defaultPartitioner, intended to reduce
+        the likelihood of OOMs. Once PySpark adopts Partitioner-based APIs, this behavior will
+        be inherent.
         """
-        # hard code to avoid the error
         if self.ctx._conf.contains("spark.default.parallelism"):
             return self.ctx.defaultParallelism
         else:
+<<<<<<< HEAD
             return 2
 
     def getNumPartitions(self):
@@ -172,6 +192,16 @@ def getNumPartitions(self):
       2
       """
       return self._jdstream.partitions().size()
+=======
+            return self.getNumPartitions()
+
+    def getNumPartitions(self):
+        """
+        Return the number of partitions in RDD
+        """
+        # TODO: remove hardcoding. RDD has NumPartitions but DStream does not have.
+        return 2
+>>>>>>> clean up code
 
 
 class PipelinedDStream(DStream):
diff --git a/python/pyspark/streaming/duration.py b/python/pyspark/streaming/duration.py
index 06a169e5215ac..a7f1036e4b856 100644
--- a/python/pyspark/streaming/duration.py
+++ b/python/pyspark/streaming/duration.py
@@ -17,6 +17,7 @@
 
 from pyspark.streaming import utils
 
+
 class Duration(object):
     """
     Duration for Spark Streaming application. Used to set duration
diff --git a/python/pyspark/streaming/pyprint.py b/python/pyspark/streaming/pyprint.py
index 1aeb8e50375ed..49517b3e5c247 100644
--- a/python/pyspark/streaming/pyprint.py
+++ b/python/pyspark/streaming/pyprint.py
@@ -21,16 +21,22 @@
 
 from pyspark.serializers import PickleSerializer
 
+
 def collect(binary_file_path):
+    """
+    Read pickled file written by SparkStreaming
+    """
     dse = PickleSerializer()
     with open(binary_file_path, 'rb') as tempFile:
         for item in dse.load_stream(tempFile):
             yield item
+
+
 def main():
     try:
         binary_file_path = sys.argv[1]
     except:
-        print "Missed FilePath in argement"
+        print "Missed FilePath in argements"
 
     if not binary_file_path:
         return 
@@ -43,5 +49,6 @@ def main():
             print "..."
             break
 
+
 if __name__ =="__main__":
     exit(main())
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaDStreamLike.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaDStreamLike.scala
index cfa336df8674f..a2b9d581f609c 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaDStreamLike.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaDStreamLike.scala
@@ -59,7 +59,7 @@ trait JavaDStreamLike[T, This <: JavaDStreamLike[T, This, R], R <: JavaRDDLike[T
    * operator, so this PythonDStream will be registered as an output stream and there materialized.
    * This function is for PythonAPI.
    */
-
+  //TODO move this function to PythonDStream
   def pyprint() = dstream.pyprint()
 
   /**
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
index 9727356da1f51..fbf6d6dc05441 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
@@ -146,7 +146,9 @@ DStream[Array[Byte]](prev.ssc){
       case Some(rdd)=>Some(rdd)
         val pairwiseRDD = new PairwiseRDD(rdd)
         /*
-         * This is equivalent to following python code
+         * Since python operation is executed by Scala after StreamingContext.start.
+         * What PairwiseDStream does is equivalent to following python code in pySpark.
+         *
          * with _JavaStackTrace(self.context) as st:
          *    pairRDD = self.ctx._jvm.PairwiseRDD(keyed._jrdd.rdd()).asJavaPairRDD()
          *    partitioner = self.ctx._jvm.PythonPartitioner(numPartitions,
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonTransformedDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonTransformedDStream.scala
deleted file mode 100644
index bc07e09ec6d03..0000000000000
--- a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonTransformedDStream.scala
+++ /dev/null
@@ -1,42 +0,0 @@
-/*
-
-package org.apache.spark.streaming.api.python
-
-import org.apache.spark.Accumulator
-import org.apache.spark.api.python.PythonRDD
-import org.apache.spark.broadcast.Broadcast
-import org.apache.spark.rdd.RDD
-import org.apache.spark.streaming.api.java.JavaDStream
-import org.apache.spark.streaming.{Time, Duration}
-import org.apache.spark.streaming.dstream.DStream
-
-import scala.reflect.ClassTag
-
-class PythonTransformedDStream[T: ClassTag](
-               parent: DStream[T],
-               command: Array[Byte],
-               envVars: JMap[String, String],
-               pythonIncludes: JList[String],
-               preservePartitoning: Boolean,
-               pythonExec: String,
-               broadcastVars: JList[Broadcast[Array[Byte]]],
-               accumulator: Accumulator[JList[Array[Byte]]]
-               ) extends DStream[Array[Byte]](parent.ssc) {
-
-  override def dependencies = List(parent)
-
-  override def slideDuration: Duration = parent.slideDuration
-
-  //pythonDStream compute
-  override def compute(validTime: Time): Option[RDD[Array[Byte]]] = {
-
-//    val parentRDDs = parents.map(_.getOrCompute(validTime).orNull).toSeq
-//    parents.map(_.getOrCompute(validTime).orNull).to
-//    parent = parents.head.asInstanceOf[RDD]
-//    Some()
-  }
-
-  val asJavaDStream = JavaDStream.fromDStream(this)
-}
-
-*/
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala
index 67977244ef420..fc7a2055025c1 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala
@@ -623,37 +623,36 @@ abstract class DStream[T: ClassTag] (
     new ForEachDStream(this, context.sparkContext.clean(foreachFunc)).register()
   }
 
-//TODO move pyprint to PythonDStream
+//TODO move pyprint to PythonDStream and executed by py4j call back function
   /**
    * Print the first ten elements of each PythonRDD generated in this PythonDStream. This is an output
    * operator, so this PythonDStream will be registered as an output stream and there materialized.
    * Since serialized Python object is readable by Python, pyprint writes out binary data to
    * temporary file and run python script to deserialized and print the first ten elements
+   *
+   * Currently call python script directly. We should avoid this
    */
   private[streaming] def pyprint() {
     def foreachFunc = (rdd: RDD[T], time: Time) => {
       val iter = rdd.take(11).iterator
 
-      // make a temporary file
+      // Generate a temporary file
       val prefix = "spark"
       val suffix = ".tmp"
       val tempFile = File.createTempFile(prefix, suffix)
       val tempFileStream = new DataOutputStream(new FileOutputStream(tempFile.getAbsolutePath))
-      //write out serialized python object
+      // Write out serialized python object to temporary file
       PythonRDD.writeIteratorToStream(iter, tempFileStream)
       tempFileStream.close()
 
-      // This value has to be passed from python
-      // Python currently does not do cluster deployment. But what happened
+      // pythonExec should be passed from python. Move pyprint to PythonDStream
       val pythonExec = new ProcessBuilder().environment().get("PYSPARK_PYTHON")
       val sparkHome = new ProcessBuilder().environment().get("SPARK_HOME")
-      //val pb = new ProcessBuilder(Seq(pythonExec, sparkHome + "/python/pyspark/streaming/pyprint.py", tempFile.getAbsolutePath())) // why this fails to compile???
-      //absolute path to the python script is needed to change because we do not use pysparkstreaming
+      // Call python script to deserialize and print result in stdout
       val pb = new ProcessBuilder(pythonExec, sparkHome + "/python/pyspark/streaming/pyprint.py", tempFile.getAbsolutePath)
       val workerEnv = pb.environment()
 
-      //envVars also need to be pass
-      //workerEnv.putAll(envVars)
+      // envVars also should be pass from python
       val pythonPath = sparkHome + "/python/" + File.pathSeparator + workerEnv.get("PYTHONPATH")
       workerEnv.put("PYTHONPATH", pythonPath)
       val worker = pb.start()
@@ -665,7 +664,7 @@ abstract class DStream[T: ClassTag] (
       println ("Time: " + time)
       println ("-------------------------------------------")
 
-      //print value from python std out
+      // Print values which is from python std out
       var line = ""
       breakable {
         while (true) {
@@ -674,7 +673,7 @@ abstract class DStream[T: ClassTag] (
           println(line)
         }
       }
-      //delete temporary file
+      // Delete temporary file
       tempFile.delete()
       println()
 

From 0a8bbbbaba94ca67a345f59b88b765476bbd8c3a Mon Sep 17 00:00:00 2001
From: Ken Takagiwa <ken@Kens-MacBook-Pro.local>
Date: Sun, 20 Jul 2014 15:32:20 -0700
Subject: [PATCH 383/489] clean up codes

---
 .../src/main/python/streaming/network_wordcount.py |  7 +------
 examples/src/main/python/streaming/wordcount.py    |  2 +-
 python/pyspark/streaming/dstream.py                | 14 +++++++-------
 .../apache/spark/streaming/dstream/DStream.scala   |  3 ++-
 4 files changed, 11 insertions(+), 15 deletions(-)

diff --git a/examples/src/main/python/streaming/network_wordcount.py b/examples/src/main/python/streaming/network_wordcount.py
index a1458e06f13d2..c6ededc24db21 100644
--- a/examples/src/main/python/streaming/network_wordcount.py
+++ b/examples/src/main/python/streaming/network_wordcount.py
@@ -11,20 +11,15 @@
         exit(-1)
     conf = SparkConf()
     conf.setAppName("PythonStreamingNetworkWordCount")
-    conf.set("spark.default.parallelism", 1)
     ssc = StreamingContext(conf=conf, duration=Seconds(1))
 
     lines = ssc.socketTextStream(sys.argv[1], int(sys.argv[2]))
     fm_lines = lines.flatMap(lambda x: x.split(" "))
-    filtered_lines = fm_lines.filter(lambda line: "Spark" in line)
     mapped_lines = fm_lines.map(lambda x: (x, 1))
-    reduced_lines = mapped_lines.reduce(add)
-    counted_lines = reduced_lines.count()
+    reduced_lines = mapped_lines.reduceByKey(add)
     
     fm_lines.pyprint()
-    filtered_lines.pyprint()
     mapped_lines.pyprint()
     reduced_lines.pyprint()
-    counted_lines.pyprint()
     ssc.start()
     ssc.awaitTermination()
diff --git a/examples/src/main/python/streaming/wordcount.py b/examples/src/main/python/streaming/wordcount.py
index 9ff8bc5ac9ab2..ee52c4e178142 100644
--- a/examples/src/main/python/streaming/wordcount.py
+++ b/examples/src/main/python/streaming/wordcount.py
@@ -21,7 +21,7 @@
     fm_lines = lines.flatMap(lambda x: x.split(" "))
     filtered_lines = fm_lines.filter(lambda line: "Spark" in line)
     mapped_lines = fm_lines.map(lambda x: (x, 1))
-    reduced_lines = mapped_lines.reduce(add)
+    reduced_lines = mapped_lines.reduceByKey(add)
     
     fm_lines.pyprint()
     filtered_lines.pyprint()
diff --git a/python/pyspark/streaming/dstream.py b/python/pyspark/streaming/dstream.py
index 0c37cb7164288..3e617cafbaa93 100644
--- a/python/pyspark/streaming/dstream.py
+++ b/python/pyspark/streaming/dstream.py
@@ -22,13 +22,15 @@ def count(self):
         """
 
         """
-        #TODO make sure count implementation, thiis different from what pyspark does
-        return self._mapPartitions(lambda i: [sum(1 for _ in i)]).map(lambda x: (None, 1))
+        pass
+        #TODO: make sure count implementation, thiis different from what pyspark does
+        #return self._mapPartitions(lambda i: [sum(1 for _ in i)]).map(lambda x: (None, 1))
 
     def _sum(self):
         """
         """
-        return self._mapPartitions(lambda x: [sum(x)]).reduce(operator.add)
+        pass
+        #return self._mapPartitions(lambda x: [sum(x)]).reduce(operator.add)
 
     def print_(self):
         """
@@ -85,7 +87,6 @@ def _mapPartitionsWithIndex(self, f, preservesPartitioning=False):
         """
         return PipelinedDStream(self, f, preservesPartitioning)
 
-
     def reduceByKey(self, func, numPartitions=None):
         """
         Merge the value for each key using an associative reduce function.
@@ -121,7 +122,7 @@ def combineLocally(iterator):
                 else:
                     combiners[k] = mergeValue(combiners[k], v)
             return combiners.iteritems()
-        locally_combined = self.mapPartitions(combineLocally)
+        locally_combined = self._mapPartitions(combineLocally)
         shuffled = locally_combined.partitionBy(numPartitions)
         def _mergeCombiners(iterator):
             combiners = {}
@@ -131,12 +132,11 @@ def _mergeCombiners(iterator):
                 else:
                     combiners[k] = mergeCombiners(combiners[k], v)
             return combiners.iteritems()
-        return shuffled.mapPartitions(_mergeCombiners) 
+        return shuffled._mapPartitions(_mergeCombiners)
 
     def partitionBy(self, numPartitions, partitionFunc=None):
         """
         Return a copy of the DStream partitioned using the specified partitioner.
-
         """
         if numPartitions is None:
             numPartitions = self.ctx._defaultReducePartitions()
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala
index fc7a2055025c1..f539bc9aa147d 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala
@@ -623,7 +623,7 @@ abstract class DStream[T: ClassTag] (
     new ForEachDStream(this, context.sparkContext.clean(foreachFunc)).register()
   }
 
-//TODO move pyprint to PythonDStream and executed by py4j call back function
+//TODO: move pyprint to PythonDStream and executed by py4j call back function
   /**
    * Print the first ten elements of each PythonRDD generated in this PythonDStream. This is an output
    * operator, so this PythonDStream will be registered as an output stream and there materialized.
@@ -647,6 +647,7 @@ abstract class DStream[T: ClassTag] (
 
       // pythonExec should be passed from python. Move pyprint to PythonDStream
       val pythonExec = new ProcessBuilder().environment().get("PYSPARK_PYTHON")
+
       val sparkHome = new ProcessBuilder().environment().get("SPARK_HOME")
       // Call python script to deserialize and print result in stdout
       val pb = new ProcessBuilder(pythonExec, sparkHome + "/python/pyspark/streaming/pyprint.py", tempFile.getAbsolutePath)

From 678e854a82d646b01666291c3998b4677821f4bd Mon Sep 17 00:00:00 2001
From: Ken Takagiwa <ken@Kens-MacBook-Pro.local>
Date: Sun, 20 Jul 2014 15:33:34 -0700
Subject: [PATCH 384/489] remove waste file

---
 examples/src/main/python/streaming/wordcount.pyc | Bin 1566 -> 0 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 delete mode 100644 examples/src/main/python/streaming/wordcount.pyc

diff --git a/examples/src/main/python/streaming/wordcount.pyc b/examples/src/main/python/streaming/wordcount.pyc
deleted file mode 100644
index db93702361f47f57988ea82c213aae522e7a9f81..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

literal 1566
zcmb_c+invv5S`tmX_F>xdP%Qb`WW$$yrZfDRUlD`3MEBIE2LoJN!i4Ek?qh#;suqD
z<A3-7n6a}d5{L(2H};Otj6G*APU%~t_Uz(oe+Y}GLf<De%)0<U(k}o75P3NS6+jX~
z6hgqd5~va+Wr)gtT?VxRNd=;cU$1~#g)3YGS%FCi`fY$!K!9-#ZUd03Fe!ol?2vUR
z%QXmJTwtBuk~&BMunf24y#d&O@dn7MQ}ALFWDVpx+!7V6fUL8LB*Ugd1KJlxTYy_I
z-d?yQ>q9y>+5y~w@h->>_~JZ8Ex;Cx_dsqI$i71kKsF2H5bzMjM}Q%7h>o4XJ*F4n
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zS&brz(`$k)&aBt)_D6P&=D{ElaXJ{pAuY^3nqC`mjgL=mGA)PMG_@zEGS)b>nVz57
z12T9uw;!@}dGH%DJZD35$VV`Rj>M6eD%+ujPzqISlGsr$lgW?>I^S}tg^jax$SNMp
z;hctP(DaEa?*gX;_S>wwv}|<ZLs((ET*W^{G;0$EZZchxa3+#mq3Ieu>~fgh+)?kE
zA}QTvlpRuWK2DWL-b-<|OR}K>zmlsFO7$Rqlgg4A({btd9G1bVIK_XX)mx{dTQvSa
zhqw@<QCQG%$-ZOKW6J-5#a_Xp{g5Qre`ao6-m)C2n3?X80&Jb8eC~UJ%iVMN;QlDk
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za-rpWn$Q-`3VrjXg12du(;-^!crO;am|Z^4b(~s9!C0ZK<vT5IL~O56)3tW@=dQ9&
u`upBbGC5Uv#>Fxnn(9F_YzEDs7S@AS(4e<Uqggr*_kwNrZicm>cz*!UkyX3^


From b1d2a3050f2cb9fbcb194a3624cefcefb388f1d8 Mon Sep 17 00:00:00 2001
From: Tathagata Das <tathagata.das1565@gmail.com>
Date: Wed, 23 Jul 2014 15:43:11 -0700
Subject: [PATCH 385/489] Implemented DStream.foreachRDD in the Python API
 using Py4J callback server.

---
 .../python/streaming/network_wordcount.py     |  4 +-
 python/pyspark/java_gateway.py                |  2 +-
 python/pyspark/streaming/dstream.py           | 44 ++++++++++----
 python/pyspark/streaming/utils.py             | 21 +++++++
 .../streaming/api/java/JavaDStreamLike.scala  |  8 ---
 .../streaming/api/python/PythonDStream.scala  | 38 ++++++++++++
 .../spark/streaming/dstream/DStream.scala     | 60 -------------------
 7 files changed, 95 insertions(+), 82 deletions(-)

diff --git a/examples/src/main/python/streaming/network_wordcount.py b/examples/src/main/python/streaming/network_wordcount.py
index c6ededc24db21..2bbb36a6b787e 100644
--- a/examples/src/main/python/streaming/network_wordcount.py
+++ b/examples/src/main/python/streaming/network_wordcount.py
@@ -17,9 +17,7 @@
     fm_lines = lines.flatMap(lambda x: x.split(" "))
     mapped_lines = fm_lines.map(lambda x: (x, 1))
     reduced_lines = mapped_lines.reduceByKey(add)
-    
-    fm_lines.pyprint()
-    mapped_lines.pyprint()
+
     reduced_lines.pyprint()
     ssc.start()
     ssc.awaitTermination()
diff --git a/python/pyspark/java_gateway.py b/python/pyspark/java_gateway.py
index d7e7bbf806544..888c850779ff8 100644
--- a/python/pyspark/java_gateway.py
+++ b/python/pyspark/java_gateway.py
@@ -102,7 +102,7 @@ def run(self):
         EchoOutputThread(proc.stdout).start()
 
     # Connect to the gateway
-    gateway = JavaGateway(GatewayClient(port=gateway_port), auto_convert=False)
+    gateway = JavaGateway(GatewayClient(port=gateway_port), auto_convert=False, start_callback_server=True)
 
     # Import the classes used by PySpark
     java_import(gateway.jvm, "org.apache.spark.SparkConf")
diff --git a/python/pyspark/streaming/dstream.py b/python/pyspark/streaming/dstream.py
index 3e617cafbaa93..45a591db5a416 100644
--- a/python/pyspark/streaming/dstream.py
+++ b/python/pyspark/streaming/dstream.py
@@ -43,14 +43,6 @@ def print_(self):
         #hack to call print function in DStream
         getattr(self._jdstream, "print")()
 
-    def pyprint(self):
-        """
-        Print the first ten elements of each RDD generated in this DStream. This is an output
-        operator, so this DStream will be registered as an output stream and there materialized.
-
-        """
-        self._jdstream.pyprint()
-
     def filter(self, f):
         """
         Return DStream containing only the elements that satisfy predicate.
@@ -203,6 +195,38 @@ def getNumPartitions(self):
         return 2
 >>>>>>> clean up code
 
+    def foreachRDD(self, func):
+        """
+        """
+        from utils import RDDFunction
+        wrapped_func = RDDFunction(self.ctx, self._jrdd_deserializer, func)
+        self.ctx._jvm.PythonForeachDStream(self._jdstream.dstream(), wrapped_func)
+
+    def pyprint(self):
+        """
+        Print the first ten elements of each RDD generated in this DStream. This is an output
+        operator, so this DStream will be registered as an output stream and there materialized.
+
+        """
+        def takeAndPrint(rdd, time):
+            taken = rdd.take(11)
+            print "-------------------------------------------"
+            print "Time: %s" % (str(time))
+            print "-------------------------------------------"
+            for record in taken[:10]:
+                print record
+            if len(taken) > 10:
+                print "..."
+            print
+
+        self.foreachRDD(takeAndPrint)
+
+
+    #def transform(self, func):
+    #    from utils import RDDFunction
+    #    wrapped_func = RDDFunction(self.ctx, self._jrdd_deserializer, func)
+    #    jdstream = self.ctx._jvm.PythonTransformedDStream(self._jdstream.dstream(), wrapped_func).toJavaDStream
+    #    return DStream(jdstream, self._ssc, ...)  ## DO NOT KNOW HOW 
 
 class PipelinedDStream(DStream):
     def __init__(self, prev, func, preservesPartitioning=False):
@@ -222,7 +246,6 @@ def pipeline_func(split, iterator):
             self._prev_jdstream = prev._prev_jdstream  # maintain the pipeline
             self._prev_jrdd_deserializer = prev._prev_jrdd_deserializer
         self.is_cached = False
-        self.is_checkpointed = False
         self._ssc = prev._ssc
         self.ctx = prev.ctx
         self.prev = prev
@@ -259,4 +282,5 @@ def _jdstream(self):
         return self._jdstream_val
 
     def _is_pipelinable(self):
-        return not (self.is_cached or self.is_checkpointed)
+        return not (self.is_cached)
+
diff --git a/python/pyspark/streaming/utils.py b/python/pyspark/streaming/utils.py
index b1fa1e227b0a1..84f1dadeba03d 100644
--- a/python/pyspark/streaming/utils.py
+++ b/python/pyspark/streaming/utils.py
@@ -15,6 +15,27 @@
 # limitations under the License.
 #
 
+from pyspark.rdd import RDD
+
+class RDDFunction():
+    def __init__(self, ctx, jrdd_deserializer, func):
+        self.ctx = ctx
+        self.deserializer = jrdd_deserializer
+        self.func = func
+
+    def call(self, jrdd, time):
+        # Wrap JavaRDD into python's RDD class
+        rdd = RDD(jrdd, self.ctx, self.deserializer)
+        # Call user defined RDD function
+        self.func(rdd, time)
+
+    def __str__(self):
+        return "%s, %s" % (str(self.deserializer), str(self.func))
+
+    class Java:
+        implements = ['org.apache.spark.streaming.api.python.PythonRDDFunction']
+
+
 
 def msDurationToString(ms):
     """
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaDStreamLike.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaDStreamLike.scala
index a2b9d581f609c..a6184de4e83c1 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaDStreamLike.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaDStreamLike.scala
@@ -54,14 +54,6 @@ trait JavaDStreamLike[T, This <: JavaDStreamLike[T, This, R], R <: JavaRDDLike[T
     dstream.print()
   }
 
-  /**
-   * Print the first ten elements of each PythonRDD generated in the PythonDStream. This is an output
-   * operator, so this PythonDStream will be registered as an output stream and there materialized.
-   * This function is for PythonAPI.
-   */
-  //TODO move this function to PythonDStream
-  def pyprint() = dstream.pyprint()
-
   /**
    * Return a new DStream in which each RDD has a single element generated by counting each RDD
    * of this DStream.
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
index fbf6d6dc05441..86b067e1a2810 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
@@ -56,6 +56,10 @@ class PythonDStream[T: ClassTag](
     }
   }
 
+  def foreachRDD(foreachFunc: PythonRDDFunction) {
+    new PythonForeachDStream(this, context.sparkContext.clean(foreachFunc, false)).register()
+  }
+
   val asJavaDStream  = JavaDStream.fromDStream(this)
 
   /**
@@ -160,6 +164,40 @@ DStream[Array[Byte]](prev.ssc){
       case None => None
     }
   }
+
+  val asJavaDStream  = JavaDStream.fromDStream(this)
+}
+
+class PythonForeachDStream(
+    prev: DStream[Array[Byte]],
+    foreachFunction: PythonRDDFunction
+  ) extends ForEachDStream[Array[Byte]](
+    prev,
+    (rdd: RDD[Array[Byte]], time: Time) => {
+      foreachFunction.call(rdd.toJavaRDD(), time.milliseconds)
+    }
+  ) {
+
+  this.register()
+}
+/*
+This does not work. Ignore this for now. -TD
+class PythonTransformedDStream(
+    prev: DStream[Array[Byte]],
+    transformFunction: PythonRDDFunction
+  ) extends DStream[Array[Byte]](prev.ssc) {
+
+  override def dependencies = List(prev)
+
+  override def slideDuration: Duration = prev.slideDuration
+
+  override def compute(validTime: Time): Option[RDD[Array[Byte]]] = {
+    prev.getOrCompute(validTime).map(rdd => {
+      transformFunction.call(rdd.toJavaRDD(), validTime.milliseconds).rdd
+    })
+  }
+
   val asJavaDStream  = JavaDStream.fromDStream(this)
   //val asJavaPairDStream : JavaPairDStream[Long, Array[Byte]]  = JavaPairDStream.fromJavaDStream(this)
 }
+*/
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala
index f539bc9aa147d..d8dbdf59e7ff1 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala
@@ -623,66 +623,6 @@ abstract class DStream[T: ClassTag] (
     new ForEachDStream(this, context.sparkContext.clean(foreachFunc)).register()
   }
 
-//TODO: move pyprint to PythonDStream and executed by py4j call back function
-  /**
-   * Print the first ten elements of each PythonRDD generated in this PythonDStream. This is an output
-   * operator, so this PythonDStream will be registered as an output stream and there materialized.
-   * Since serialized Python object is readable by Python, pyprint writes out binary data to
-   * temporary file and run python script to deserialized and print the first ten elements
-   *
-   * Currently call python script directly. We should avoid this
-   */
-  private[streaming] def pyprint() {
-    def foreachFunc = (rdd: RDD[T], time: Time) => {
-      val iter = rdd.take(11).iterator
-
-      // Generate a temporary file
-      val prefix = "spark"
-      val suffix = ".tmp"
-      val tempFile = File.createTempFile(prefix, suffix)
-      val tempFileStream = new DataOutputStream(new FileOutputStream(tempFile.getAbsolutePath))
-      // Write out serialized python object to temporary file
-      PythonRDD.writeIteratorToStream(iter, tempFileStream)
-      tempFileStream.close()
-
-      // pythonExec should be passed from python. Move pyprint to PythonDStream
-      val pythonExec = new ProcessBuilder().environment().get("PYSPARK_PYTHON")
-
-      val sparkHome = new ProcessBuilder().environment().get("SPARK_HOME")
-      // Call python script to deserialize and print result in stdout
-      val pb = new ProcessBuilder(pythonExec, sparkHome + "/python/pyspark/streaming/pyprint.py", tempFile.getAbsolutePath)
-      val workerEnv = pb.environment()
-
-      // envVars also should be pass from python
-      val pythonPath = sparkHome + "/python/" + File.pathSeparator + workerEnv.get("PYTHONPATH")
-      workerEnv.put("PYTHONPATH", pythonPath)
-      val worker = pb.start()
-      val is = worker.getInputStream()
-      val isr = new InputStreamReader(is)
-      val br = new BufferedReader(isr)
-
-      println ("-------------------------------------------")
-      println ("Time: " + time)
-      println ("-------------------------------------------")
-
-      // Print values which is from python std out
-      var line = ""
-      breakable {
-        while (true) {
-          line = br.readLine()
-          if (line == null) break()
-          println(line)
-        }
-      }
-      // Delete temporary file
-      tempFile.delete()
-      println()
-
-    }
-    new ForEachDStream(this, context.sparkContext.clean(foreachFunc)).register()
-  }
-
-
   /**
    * Return a new DStream in which each RDD contains all the elements in seen in a
    * sliding window of time over this DStream. The new DStream generates RDDs with

From 05e991be91c614603a41e1ce2c90d7b352bcbe33 Mon Sep 17 00:00:00 2001
From: Tathagata Das <tathagata.das1565@gmail.com>
Date: Fri, 1 Aug 2014 14:39:18 -0700
Subject: [PATCH 386/489] Added missing file

---
 .../spark/streaming/api/python/PythonRDDFunction.java     | 8 ++++++++
 1 file changed, 8 insertions(+)
 create mode 100644 streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonRDDFunction.java

diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonRDDFunction.java b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonRDDFunction.java
new file mode 100644
index 0000000000000..88f7036c3a05b
--- /dev/null
+++ b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonRDDFunction.java
@@ -0,0 +1,8 @@
+package org.apache.spark.streaming.api.python;
+
+import org.apache.spark.api.java.JavaRDD;
+import org.apache.spark.streaming.Time;
+
+public interface PythonRDDFunction {
+  JavaRDD<byte[]> call(JavaRDD<byte[]> rdd, long time);
+}

From 9ab89523a783666052ca9046aa77636d16cc0cfd Mon Sep 17 00:00:00 2001
From: Tathagata Das <tathagata.das1565@gmail.com>
Date: Fri, 1 Aug 2014 14:40:37 -0700
Subject: [PATCH 387/489] Added extra line.

---
 python/pyspark/streaming/utils.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/python/pyspark/streaming/utils.py b/python/pyspark/streaming/utils.py
index 84f1dadeba03d..c60ecd1ed607a 100644
--- a/python/pyspark/streaming/utils.py
+++ b/python/pyspark/streaming/utils.py
@@ -17,6 +17,7 @@
 
 from pyspark.rdd import RDD
 
+
 class RDDFunction():
     def __init__(self, ctx, jrdd_deserializer, func):
         self.ctx = ctx

From 84a966811695f73497cc80dda6b2e53d0df5051f Mon Sep 17 00:00:00 2001
From: Ken Takagiwa <ken@kens-mbp.gateway.sonic.net>
Date: Sat, 2 Aug 2014 15:58:24 -0700
Subject: [PATCH 388/489] tried to restart callback server

---
 python/pyspark/java_gateway.py      | 5 ++++-
 python/pyspark/streaming/context.py | 8 ++++++++
 2 files changed, 12 insertions(+), 1 deletion(-)

diff --git a/python/pyspark/java_gateway.py b/python/pyspark/java_gateway.py
index 888c850779ff8..d32beef9b860c 100644
--- a/python/pyspark/java_gateway.py
+++ b/python/pyspark/java_gateway.py
@@ -102,7 +102,10 @@ def run(self):
         EchoOutputThread(proc.stdout).start()
 
     # Connect to the gateway
-    gateway = JavaGateway(GatewayClient(port=gateway_port), auto_convert=False, start_callback_server=True)
+    # If start_callback_server is True, it looks like callback server is not killed
+    # process is hang up and test case does not move forward.
+    #gateway = JavaGateway(GatewayClient(port=gateway_port), auto_convert=False, start_callback_server=True)
+    gateway = JavaGateway(GatewayClient(port=gateway_port), auto_convert=False, start_callback_server=False)
 
     # Import the classes used by PySpark
     java_import(gateway.jvm, "org.apache.spark.SparkConf")
diff --git a/python/pyspark/streaming/context.py b/python/pyspark/streaming/context.py
index a4900191d1730..04737243f3192 100644
--- a/python/pyspark/streaming/context.py
+++ b/python/pyspark/streaming/context.py
@@ -15,6 +15,8 @@
 # limitations under the License.
 #
 
+import time
+
 from pyspark.conf import SparkConf
 from pyspark.files import SparkFiles
 from pyspark.java_gateway import launch_gateway
@@ -60,6 +62,12 @@ def __init__(self, master=None, appName=None, sparkHome=None, pyFiles=None,
         @param duration: A L{Duration} Duration for SparkStreaming
 
         """
+
+        # launch call back server
+        if not gateway:
+            gateway = launch_gateway()
+#        gateway.restart_callback_server()
+
         # Create the Python Sparkcontext
         self._sc = SparkContext(master=master, appName=appName, sparkHome=sparkHome,
                         pyFiles=pyFiles, environment=environment, batchSize=batchSize,

From 3b498e16ee7de90266135eb503ef5e5d4b3976d3 Mon Sep 17 00:00:00 2001
From: Ken Takagiwa <ken@kens-mbp.gateway.sonic.net>
Date: Sat, 2 Aug 2014 20:05:15 -0700
Subject: [PATCH 389/489] Kill py4j callback server properly

---
 python/pyspark/streaming/context.py | 22 ++++++++++++++++------
 1 file changed, 16 insertions(+), 6 deletions(-)

diff --git a/python/pyspark/streaming/context.py b/python/pyspark/streaming/context.py
index 04737243f3192..5952e81a4bef3 100644
--- a/python/pyspark/streaming/context.py
+++ b/python/pyspark/streaming/context.py
@@ -15,7 +15,8 @@
 # limitations under the License.
 #
 
-import time
+import sys
+from signal import signal, SIGTERM, SIGINT
 
 from pyspark.conf import SparkConf
 from pyspark.files import SparkFiles
@@ -63,15 +64,14 @@ def __init__(self, master=None, appName=None, sparkHome=None, pyFiles=None,
 
         """
 
-        # launch call back server
-        if not gateway:
-            gateway = launch_gateway()
-#        gateway.restart_callback_server()
-
         # Create the Python Sparkcontext
         self._sc = SparkContext(master=master, appName=appName, sparkHome=sparkHome,
                         pyFiles=pyFiles, environment=environment, batchSize=batchSize,
                         serializer=serializer, conf=conf, gateway=gateway)
+
+        # Start py4j callback server
+        SparkContext._gateway.restart_callback_server()
+        self._clean_up_trigger()
         self._jvm = self._sc._jvm
         self._jssc = self._initialize_context(self._sc._jsc, duration._jduration)
 
@@ -79,6 +79,16 @@ def __init__(self, master=None, appName=None, sparkHome=None, pyFiles=None,
     def _initialize_context(self, jspark_context, jduration):
         return self._jvm.JavaStreamingContext(jspark_context, jduration)
 
+    def _clean_up_trigger(self):
+        """Kill py4j callback server properly using signal lib"""
+
+        def clean_up_handler(*args):
+            SparkContext._gateway.shutdown()
+            sys.exit(0)
+
+        for sig in (SIGINT, SIGTERM):
+            signal(sig, clean_up_handler)
+
     def start(self):
         """
         Start the execution of the streams.

From b349649b0739c51f92d760345083556474e24156 Mon Sep 17 00:00:00 2001
From: giwa <ugw.gi.world@gmail.com>
Date: Sat, 2 Aug 2014 20:40:36 -0700
Subject: [PATCH 390/489] Removed the waste line

---
 python/pyspark/java_gateway.py | 5 +----
 1 file changed, 1 insertion(+), 4 deletions(-)

diff --git a/python/pyspark/java_gateway.py b/python/pyspark/java_gateway.py
index d32beef9b860c..d7e7bbf806544 100644
--- a/python/pyspark/java_gateway.py
+++ b/python/pyspark/java_gateway.py
@@ -102,10 +102,7 @@ def run(self):
         EchoOutputThread(proc.stdout).start()
 
     # Connect to the gateway
-    # If start_callback_server is True, it looks like callback server is not killed
-    # process is hang up and test case does not move forward.
-    #gateway = JavaGateway(GatewayClient(port=gateway_port), auto_convert=False, start_callback_server=True)
-    gateway = JavaGateway(GatewayClient(port=gateway_port), auto_convert=False, start_callback_server=False)
+    gateway = JavaGateway(GatewayClient(port=gateway_port), auto_convert=False)
 
     # Import the classes used by PySpark
     java_import(gateway.jvm, "org.apache.spark.SparkConf")

From 3c45cd2a462c6d1e47ba37e5096edd3a724d36b6 Mon Sep 17 00:00:00 2001
From: giwa <ugw.gi.world@gmail.com>
Date: Sun, 3 Aug 2014 19:25:13 -0700
Subject: [PATCH 391/489] implemented reduce and count function in Dstream

---
 .../python/streaming/network_wordcount.py     |  2 ++
 python/pyspark/streaming/dstream.py           | 27 ++++++++++++-------
 2 files changed, 19 insertions(+), 10 deletions(-)

diff --git a/examples/src/main/python/streaming/network_wordcount.py b/examples/src/main/python/streaming/network_wordcount.py
index 2bbb36a6b787e..f6fba4488e238 100644
--- a/examples/src/main/python/streaming/network_wordcount.py
+++ b/examples/src/main/python/streaming/network_wordcount.py
@@ -19,5 +19,7 @@
     reduced_lines = mapped_lines.reduceByKey(add)
 
     reduced_lines.pyprint()
+    count_lines = mapped_lines.count()
+    count_lines.pyprint()
     ssc.start()
     ssc.awaitTermination()
diff --git a/python/pyspark/streaming/dstream.py b/python/pyspark/streaming/dstream.py
index 45a591db5a416..0049c1e7a0d5c 100644
--- a/python/pyspark/streaming/dstream.py
+++ b/python/pyspark/streaming/dstream.py
@@ -22,25 +22,23 @@ def count(self):
         """
 
         """
-        pass
-        #TODO: make sure count implementation, thiis different from what pyspark does
-        #return self._mapPartitions(lambda i: [sum(1 for _ in i)]).map(lambda x: (None, 1))
+        # TODO: make sure count implementation, this different from what pyspark does
+        return self._mapPartitions(lambda i: [sum(1 for _ in i)])._sum()
 
     def _sum(self):
         """
         """
-        pass
-        #return self._mapPartitions(lambda x: [sum(x)]).reduce(operator.add)
+        return self._mapPartitions(lambda x: [sum(x)]).reduce(operator.add)
 
     def print_(self):
         """
-        Since print is reserved name for python, we cannot make a print method function.
+        Since print is reserved name for python, we cannot define a print method function.
         This function prints serialized data in RDD in DStream because Scala and Java cannot
-        deserialized pickled python object. Please use DStream.pyprint() instead to print result.
+        deserialized pickled python object. Please use DStream.pyprint() instead to print results.
 
         Call DStream.print().
         """
-        #hack to call print function in DStream
+        # a hack to call print function in DStream
         getattr(self._jdstream, "print")()
 
     def filter(self, f):
@@ -79,17 +77,23 @@ def _mapPartitionsWithIndex(self, f, preservesPartitioning=False):
         """
         return PipelinedDStream(self, f, preservesPartitioning)
 
+    def reduce(self, func):
+        """
+
+        """
+        return self.map(lambda x: (None, x)).reduceByKey(func, 1).map(lambda x: x[1])
+
     def reduceByKey(self, func, numPartitions=None):
         """
         Merge the value for each key using an associative reduce function.
 
         This will also perform the merging locally on each mapper before
-        sending resuls to reducer, similarly to a "combiner" in MapReduce.
+        sending results to reducer, similarly to a "combiner" in MapReduce.
 
         Output will be hash-partitioned with C{numPartitions} partitions, or
         the default parallelism level if C{numPartitions} is not specified.
         """
-        return self.combineByKey(lambda x:x, func, func, numPartitions)
+        return self.combineByKey(lambda x: x, func, func, numPartitions)
 
     def combineByKey(self, createCombiner, mergeValue, mergeCombiners,
                       numPartitions = None):
@@ -99,6 +103,7 @@ def combineByKey(self, createCombiner, mergeValue, mergeCombiners,
         """
         if numPartitions is None:
             numPartitions = self._defaultReducePartitions()
+
         def combineLocally(iterator):
             combiners = {}
             for x in iterator:
@@ -116,6 +121,7 @@ def combineLocally(iterator):
             return combiners.iteritems()
         locally_combined = self._mapPartitions(combineLocally)
         shuffled = locally_combined.partitionBy(numPartitions)
+
         def _mergeCombiners(iterator):
             combiners = {}
             for (k, v) in iterator:
@@ -124,6 +130,7 @@ def _mergeCombiners(iterator):
                 else:
                     combiners[k] = mergeCombiners(combiners[k], v)
             return combiners.iteritems()
+
         return shuffled._mapPartitions(_mergeCombiners)
 
     def partitionBy(self, numPartitions, partitionFunc=None):

From d2c01ba003ccbc478f3a708fe3543be6c075f20f Mon Sep 17 00:00:00 2001
From: giwa <ugw.gi.world@gmail.com>
Date: Sun, 3 Aug 2014 21:51:11 -0700
Subject: [PATCH 392/489] clean up examples

---
 .../main/python/streaming/network_wordcount.py    | 10 ++++------
 examples/src/main/python/streaming/wordcount.py   | 15 ++++-----------
 2 files changed, 8 insertions(+), 17 deletions(-)

diff --git a/examples/src/main/python/streaming/network_wordcount.py b/examples/src/main/python/streaming/network_wordcount.py
index f6fba4488e238..9b7af07803b4d 100644
--- a/examples/src/main/python/streaming/network_wordcount.py
+++ b/examples/src/main/python/streaming/network_wordcount.py
@@ -14,12 +14,10 @@
     ssc = StreamingContext(conf=conf, duration=Seconds(1))
 
     lines = ssc.socketTextStream(sys.argv[1], int(sys.argv[2]))
-    fm_lines = lines.flatMap(lambda x: x.split(" "))
-    mapped_lines = fm_lines.map(lambda x: (x, 1))
-    reduced_lines = mapped_lines.reduceByKey(add)
+    words = lines.flatMap(lambda line: line.split(" "))
+    mapped_words = words.map(lambda word: (word, 1))
+    count = mapped_words.reduceByKey(add)
 
-    reduced_lines.pyprint()
-    count_lines = mapped_lines.count()
-    count_lines.pyprint()
+    count.pyprint()
     ssc.start()
     ssc.awaitTermination()
diff --git a/examples/src/main/python/streaming/wordcount.py b/examples/src/main/python/streaming/wordcount.py
index ee52c4e178142..2426345711086 100644
--- a/examples/src/main/python/streaming/wordcount.py
+++ b/examples/src/main/python/streaming/wordcount.py
@@ -11,21 +11,14 @@
         exit(-1)
     conf = SparkConf()
     conf.setAppName("PythonStreamingWordCount")
-    conf.set("spark.default.parallelism", 1)
 
-# still has a bug
-#    ssc = StreamingContext(appName="PythonStreamingWordCount", duration=Seconds(1))
     ssc = StreamingContext(conf=conf, duration=Seconds(1))
 
     lines = ssc.textFileStream(sys.argv[1])
-    fm_lines = lines.flatMap(lambda x: x.split(" "))
-    filtered_lines = fm_lines.filter(lambda line: "Spark" in line)
-    mapped_lines = fm_lines.map(lambda x: (x, 1))
-    reduced_lines = mapped_lines.reduceByKey(add)
+    words = lines.flatMap(lambda line: line.split(" "))
+    mapped_words = words.map(lambda x: (x, 1))
+    count = mapped_words.reduceByKey(add)
     
-    fm_lines.pyprint()
-    filtered_lines.pyprint()
-    mapped_lines.pyprint()
-    reduced_lines.pyprint()
+    count.pyprint()
     ssc.start()
     ssc.awaitTermination()

From c462bb3019c23e13229f478a682c504c833b8173 Mon Sep 17 00:00:00 2001
From: giwa <ugw.gi.world@gmail.com>
Date: Sun, 3 Aug 2014 22:05:28 -0700
Subject: [PATCH 393/489] added stop in StreamingContext

---
 python/pyspark/streaming/context.py | 12 ++++++++++++
 1 file changed, 12 insertions(+)

diff --git a/python/pyspark/streaming/context.py b/python/pyspark/streaming/context.py
index 5952e81a4bef3..01201f66421f8 100644
--- a/python/pyspark/streaming/context.py
+++ b/python/pyspark/streaming/context.py
@@ -121,3 +121,15 @@ def textFileStream(self, directory):
         file system. FIle names starting with . are ignored.
         """
         return DStream(self._jssc.textFileStream(directory), self, UTF8Deserializer())
+
+    def stop(self, stopSparkContext=True):
+        """
+        Stop the execution of the streams immediately (does not wait for all received data
+        to be processed).
+        """
+        
+        try:
+            self._jssc.stop(stopSparkContext)
+        finally:
+            # Stop Callback server
+            SparkContext._gateway.shutdown()

From 4d40d632b2674e99fb78ecd47f3ee4caf8f6b32d Mon Sep 17 00:00:00 2001
From: giwa <ugw.gi.world@gmail.com>
Date: Sun, 3 Aug 2014 23:27:56 -0700
Subject: [PATCH 394/489] clean up dstream.py

---
 python/pyspark/streaming/dstream.py | 11 +----------
 1 file changed, 1 insertion(+), 10 deletions(-)

diff --git a/python/pyspark/streaming/dstream.py b/python/pyspark/streaming/dstream.py
index 0049c1e7a0d5c..e96fac007fa50 100644
--- a/python/pyspark/streaming/dstream.py
+++ b/python/pyspark/streaming/dstream.py
@@ -20,9 +20,7 @@ def __init__(self, jdstream, ssc, jrdd_deserializer):
 
     def count(self):
         """
-
         """
-        # TODO: make sure count implementation, this different from what pyspark does
         return self._mapPartitions(lambda i: [sum(1 for _ in i)])._sum()
 
     def _sum(self):
@@ -79,7 +77,6 @@ def _mapPartitionsWithIndex(self, f, preservesPartitioning=False):
 
     def reduce(self, func):
         """
-
         """
         return self.map(lambda x: (None, x)).reduceByKey(func, 1).map(lambda x: x[1])
 
@@ -107,12 +104,6 @@ def combineByKey(self, createCombiner, mergeValue, mergeCombiners,
         def combineLocally(iterator):
             combiners = {}
             for x in iterator:
-
-                #TODO for count operation make sure count implementation
-                # This is different from what pyspark does
-                #if isinstance(x, int):
-                #    x = ("", x)
-
                 (k, v) = x
                 if k not in combiners:
                     combiners[k] = createCombiner(v)
@@ -142,6 +133,7 @@ def partitionBy(self, numPartitions, partitionFunc=None):
 
         if partitionFunc is None:
             partitionFunc = lambda x: 0 if x is None else hash(x)
+
         # Transferring O(n) objects to Java is too expensive.  Instead, we'll
         # form the hash buckets in Python, transferring O(numPartitions) objects
         # to Java.  Each object is a (splitNumber, [objects]) pair.
@@ -228,7 +220,6 @@ def takeAndPrint(rdd, time):
 
         self.foreachRDD(takeAndPrint)
 
-
     #def transform(self, func):
     #    from utils import RDDFunction
     #    wrapped_func = RDDFunction(self.ctx, self._jrdd_deserializer, func)

From 29c2bc5ad4569e3ac4da25a2d7605fe84280eab5 Mon Sep 17 00:00:00 2001
From: giwa <ugw.gi.world@gmail.com>
Date: Sun, 3 Aug 2014 23:47:14 -0700
Subject: [PATCH 395/489] initial commit for testcase

---
 python/pyspark/streaming_tests.py | 58 +++++++++++++++++++++++++++++++
 1 file changed, 58 insertions(+)
 create mode 100644 python/pyspark/streaming_tests.py

diff --git a/python/pyspark/streaming_tests.py b/python/pyspark/streaming_tests.py
new file mode 100644
index 0000000000000..95c5489a5695b
--- /dev/null
+++ b/python/pyspark/streaming_tests.py
@@ -0,0 +1,58 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""
+Unit tests for PySpark; additional tests are implemented as doctests in
+individual modules.
+
+This file will merged to tests.py. But for now, this file is separated to
+focus to streaming test case
+
+"""
+from fileinput import input
+from glob import glob
+import os
+import re
+import shutil
+import subprocess
+import sys
+import tempfile
+import time
+import unittest
+import zipfile
+
+from pyspark.streaming.context import StreamingContext
+from pyspark.streaming.duration import *
+
+
+SPARK_HOME = os.environ["SPARK_HOME"]
+
+
+class PySparkStreamingTestCase(unittest.TestCase):
+
+    def setUp(self):
+        self._old_sys_path = list(sys.path)
+        class_name = self.__class__.__name__
+        self.ssc = StreamingContext(appName=class_name, duration=Seconds(1))
+
+    def tearDown(self):
+        self.ssc.stop()
+        sys.path = self._old_sys_path
+
+
+if __name__ == "__main__":
+    unittest.main()

From fe648e377ae81988e7b2e868f55a53a37fde27f8 Mon Sep 17 00:00:00 2001
From: giwa <ugw.gi.world@gmail.com>
Date: Mon, 4 Aug 2014 09:47:48 -0700
Subject: [PATCH 396/489] WIP

---
 .../main/python/streaming/test_oprations.py   | 24 +++++++++++++++++++
 python/pyspark/streaming/dstream.py           |  1 -
 2 files changed, 24 insertions(+), 1 deletion(-)
 create mode 100644 examples/src/main/python/streaming/test_oprations.py

diff --git a/examples/src/main/python/streaming/test_oprations.py b/examples/src/main/python/streaming/test_oprations.py
new file mode 100644
index 0000000000000..cb338ced5f228
--- /dev/null
+++ b/examples/src/main/python/streaming/test_oprations.py
@@ -0,0 +1,24 @@
+import sys
+from operator import add
+
+from pyspark.conf import SparkConf
+from pyspark.streaming.context import StreamingContext
+from pyspark.streaming.duration import *
+
+if __name__ == "__main__":
+    if len(sys.argv) != 3:
+        print >> sys.stderr, "Usage: wordcount <hostname> <port>"
+        exit(-1)
+    conf = SparkConf()
+    conf.setAppName("PythonStreamingNetworkWordCount")
+    ssc = StreamingContext(conf=conf, duration=Seconds(1))
+
+    lines = ssc.socketTextStream(sys.argv[1], int(sys.argv[2]))
+    words = lines.flatMap(lambda line: line.split(" "))
+    mapped_words = words.map(lambda word: (word, 1))
+    count = mapped_words.reduceByKey(add)
+
+    count.pyprint()
+    ssc.start()
+#    ssc.awaitTermination()
+    ssc.stop()
diff --git a/python/pyspark/streaming/dstream.py b/python/pyspark/streaming/dstream.py
index e96fac007fa50..6332c4527d334 100644
--- a/python/pyspark/streaming/dstream.py
+++ b/python/pyspark/streaming/dstream.py
@@ -120,7 +120,6 @@ def _mergeCombiners(iterator):
                     combiners[k] = v
                 else:
                     combiners[k] = mergeCombiners(combiners[k], v)
-            return combiners.iteritems()
 
         return shuffled._mapPartitions(_mergeCombiners)
 

From 8a0fbbc4347a6988da01ade554f4c32cf89354e2 Mon Sep 17 00:00:00 2001
From: giwa <ugw.gi.world@gmail.com>
Date: Mon, 4 Aug 2014 09:57:16 -0700
Subject: [PATCH 397/489] update comment

---
 python/pyspark/streaming/dstream.py | 28 ++++++++++++++++++++++++----
 1 file changed, 24 insertions(+), 4 deletions(-)

diff --git a/python/pyspark/streaming/dstream.py b/python/pyspark/streaming/dstream.py
index 6332c4527d334..d6781a3e50e65 100644
--- a/python/pyspark/streaming/dstream.py
+++ b/python/pyspark/streaming/dstream.py
@@ -1,3 +1,20 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
 from collections import defaultdict
 from itertools import chain, ifilter, imap
 import operator
@@ -20,11 +37,13 @@ def __init__(self, jdstream, ssc, jrdd_deserializer):
 
     def count(self):
         """
+        Return a new DStream which contains the number of elements in this DStream.
         """
         return self._mapPartitions(lambda i: [sum(1 for _ in i)])._sum()
 
     def _sum(self):
         """
+        Add up the elements in this DStream.
         """
         return self._mapPartitions(lambda x: [sum(x)]).reduce(operator.add)
 
@@ -41,7 +60,7 @@ def print_(self):
 
     def filter(self, f):
         """
-        Return DStream containing only the elements that satisfy predicate.
+        Return a new DStream containing only the elements that satisfy predicate.
         """
         def func(iterator): return ifilter(f, iterator)
         return self._mapPartitions(func)
@@ -56,7 +75,7 @@ def func(s, iterator): return chain.from_iterable(imap(f, iterator))
 
     def map(self, f):
         """
-        Return DStream by applying a function to each element of DStream.
+        Return a new DStream by applying a function to each element of DStream.
         """
         def func(iterator): return imap(f, iterator)
         return self._mapPartitions(func)
@@ -71,12 +90,14 @@ def func(s, iterator): return f(iterator)
     def _mapPartitionsWithIndex(self, f, preservesPartitioning=False):
         """
         Return a new DStream by applying a function to each partition of this DStream,
-        While tracking the index of the original partition.
+        while tracking the index of the original partition.
         """
         return PipelinedDStream(self, f, preservesPartitioning)
 
     def reduce(self, func):
         """
+        Return a new DStream by reduceing the elements of this RDD using the specified
+        commutative and associative binary operator.
         """
         return self.map(lambda x: (None, x)).reduceByKey(func, 1).map(lambda x: x[1])
 
@@ -280,4 +301,3 @@ def _jdstream(self):
 
     def _is_pipelinable(self):
         return not (self.is_cached)
-

From 1523b66c5aa4b098c1de3ec55ee44848973b7d00 Mon Sep 17 00:00:00 2001
From: giwa <ugw.gi.world@gmail.com>
Date: Mon, 4 Aug 2014 16:07:48 -0700
Subject: [PATCH 398/489] WIP

---
 examples/src/main/python/streaming/test_oprations.py         | 5 +++--
 python/pyspark/streaming/context.py                          | 5 +++++
 python/pyspark/streaming/dstream.py                          | 4 +++-
 python/pyspark/streaming/utils.py                            | 1 -
 .../apache/spark/streaming/api/python/PythonDStream.scala    | 5 +++--
 5 files changed, 14 insertions(+), 6 deletions(-)

diff --git a/examples/src/main/python/streaming/test_oprations.py b/examples/src/main/python/streaming/test_oprations.py
index cb338ced5f228..084902b6a2f0d 100644
--- a/examples/src/main/python/streaming/test_oprations.py
+++ b/examples/src/main/python/streaming/test_oprations.py
@@ -15,10 +15,11 @@
 
     lines = ssc.socketTextStream(sys.argv[1], int(sys.argv[2]))
     words = lines.flatMap(lambda line: line.split(" "))
+#    ssc.checkpoint("checkpoint")
     mapped_words = words.map(lambda word: (word, 1))
     count = mapped_words.reduceByKey(add)
 
     count.pyprint()
     ssc.start()
-#    ssc.awaitTermination()
-    ssc.stop()
+    ssc.awaitTermination()
+#    ssc.stop()
diff --git a/python/pyspark/streaming/context.py b/python/pyspark/streaming/context.py
index 01201f66421f8..dfaa5cfbbae27 100644
--- a/python/pyspark/streaming/context.py
+++ b/python/pyspark/streaming/context.py
@@ -133,3 +133,8 @@ def stop(self, stopSparkContext=True):
         finally:
             # Stop Callback server
             SparkContext._gateway.shutdown()
+
+    def checkpoint(self, directory):
+        """
+        """
+        self._jssc.checkpoint(directory)
diff --git a/python/pyspark/streaming/dstream.py b/python/pyspark/streaming/dstream.py
index d6781a3e50e65..937bafc6262b1 100644
--- a/python/pyspark/streaming/dstream.py
+++ b/python/pyspark/streaming/dstream.py
@@ -172,7 +172,8 @@ def add_shuffle_key(split, iterator):
         with _JavaStackTrace(self.ctx) as st:
             partitioner = self.ctx._jvm.PythonPartitioner(numPartitions,
                                                       id(partitionFunc))
-            jdstream = self.ctx._jvm.PairwiseDStream(keyed._jdstream.dstream(), partitioner).asJavaDStream()
+            jdstream = self.ctx._jvm.PythonPairwiseDStream(keyed._jdstream.dstream(),
+                                                           partitioner).asJavaDStream()
         dstream = DStream(jdstream, self._ssc, BatchedSerializer(outputSerializer))
         # This is required so that id(partitionFunc) remains unique, even if
         # partitionFunc is a lambda:
@@ -246,6 +247,7 @@ def takeAndPrint(rdd, time):
     #    jdstream = self.ctx._jvm.PythonTransformedDStream(self._jdstream.dstream(), wrapped_func).toJavaDStream
     #    return DStream(jdstream, self._ssc, ...)  ## DO NOT KNOW HOW 
 
+
 class PipelinedDStream(DStream):
     def __init__(self, prev, func, preservesPartitioning=False):
         if not isinstance(prev, PipelinedDStream) or not prev._is_pipelinable():
diff --git a/python/pyspark/streaming/utils.py b/python/pyspark/streaming/utils.py
index c60ecd1ed607a..aa5e19adbd927 100644
--- a/python/pyspark/streaming/utils.py
+++ b/python/pyspark/streaming/utils.py
@@ -37,7 +37,6 @@ class Java:
         implements = ['org.apache.spark.streaming.api.python.PythonRDDFunction']
 
 
-
 def msDurationToString(ms):
     """
     Returns a human-readable string representing a duration such as "35ms"
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
index 86b067e1a2810..6f2e4bd0d90c8 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
@@ -25,7 +25,7 @@ import org.apache.spark._
 import org.apache.spark.rdd.RDD
 import org.apache.spark.api.python._
 import org.apache.spark.broadcast.Broadcast
-import org.apache.spark.streaming.{Duration, Time}
+import org.apache.spark.streaming.{StreamingContext, Duration, Time}
 import org.apache.spark.streaming.dstream._
 import org.apache.spark.streaming.api.java._
 
@@ -139,7 +139,7 @@ DStream[(Long, Array[Byte])](prev.ssc){
 }
 
 
-private class PairwiseDStream(prev:DStream[Array[Byte]], partitioner: Partitioner) extends
+private class PythonPairwiseDStream(prev:DStream[Array[Byte]], partitioner: Partitioner) extends
 DStream[Array[Byte]](prev.ssc){
   override def dependencies = List(prev)
 
@@ -180,6 +180,7 @@ class PythonForeachDStream(
 
   this.register()
 }
+
 /*
 This does not work. Ignore this for now. -TD
 class PythonTransformedDStream(

From 1df77f50e63fc31416485a4d0d854388dce72788 Mon Sep 17 00:00:00 2001
From: giwa <ugw.gi.world@gmail.com>
Date: Tue, 5 Aug 2014 00:09:38 -0700
Subject: [PATCH 399/489] WIP: added PythonTestInputStream

---
 .../main/python/streaming/test_oprations.py   | 14 +++--------
 python/pyspark/streaming/context.py           | 25 +++++++++++++++++++
 python/pyspark/streaming/dstream.py           |  1 +
 .../api/java/JavaStreamingContext.scala       |  3 +++
 .../streaming/api/python/PythonDStream.scala  |  1 +
 5 files changed, 34 insertions(+), 10 deletions(-)

diff --git a/examples/src/main/python/streaming/test_oprations.py b/examples/src/main/python/streaming/test_oprations.py
index 084902b6a2f0d..3338a766b9cc3 100644
--- a/examples/src/main/python/streaming/test_oprations.py
+++ b/examples/src/main/python/streaming/test_oprations.py
@@ -6,20 +6,14 @@
 from pyspark.streaming.duration import *
 
 if __name__ == "__main__":
-    if len(sys.argv) != 3:
-        print >> sys.stderr, "Usage: wordcount <hostname> <port>"
-        exit(-1)
     conf = SparkConf()
     conf.setAppName("PythonStreamingNetworkWordCount")
     ssc = StreamingContext(conf=conf, duration=Seconds(1))
 
-    lines = ssc.socketTextStream(sys.argv[1], int(sys.argv[2]))
-    words = lines.flatMap(lambda line: line.split(" "))
-#    ssc.checkpoint("checkpoint")
-    mapped_words = words.map(lambda word: (word, 1))
-    count = mapped_words.reduceByKey(add)
+    test_input = ssc._testInputStream([1,1,1,1])
+    mapped = test_input.map(lambda x: (x, 1))
+    mapped.pyprint()
 
-    count.pyprint()
     ssc.start()
-    ssc.awaitTermination()
+#    ssc.awaitTermination()
 #    ssc.stop()
diff --git a/python/pyspark/streaming/context.py b/python/pyspark/streaming/context.py
index dfaa5cfbbae27..d544eab9b8fc7 100644
--- a/python/pyspark/streaming/context.py
+++ b/python/pyspark/streaming/context.py
@@ -17,6 +17,7 @@
 
 import sys
 from signal import signal, SIGTERM, SIGINT
+from tempfile import NamedTemporaryFile
 
 from pyspark.conf import SparkConf
 from pyspark.files import SparkFiles
@@ -138,3 +139,27 @@ def checkpoint(self, directory):
         """
         """
         self._jssc.checkpoint(directory)
+
+    def _testInputStream(self, test_input, numSlices=None):
+
+        numSlices = numSlices or self._sc.defaultParallelism
+        # Calling the Java parallelize() method with an ArrayList is too slow,
+        # because it sends O(n) Py4J commands.  As an alternative, serialized
+        # objects are written to a file and loaded through textFile().
+        tempFile = NamedTemporaryFile(delete=False, dir=self._sc._temp_dir)
+        # Make sure we distribute data evenly if it's smaller than self.batchSize
+        if "__len__" not in dir(test_input):
+            c = list(test_input)    # Make it a list so we can compute its length
+        batchSize = min(len(test_input) // numSlices, self._sc._batchSize)
+        if batchSize > 1:
+            serializer = BatchedSerializer(self._sc._unbatched_serializer,
+                                           batchSize)
+        else:
+            serializer = self._sc._unbatched_serializer
+        serializer.dump_stream(test_input, tempFile)
+        tempFile.close()
+        print tempFile.name
+        jinput_stream = self._jvm.PythonTestInputStream(self._jssc,
+                                                        tempFile.name,
+                                                        numSlices).asJavaDStream()
+        return DStream(jinput_stream, self, UTF8Deserializer())
diff --git a/python/pyspark/streaming/dstream.py b/python/pyspark/streaming/dstream.py
index 937bafc6262b1..0885ee35c2f28 100644
--- a/python/pyspark/streaming/dstream.py
+++ b/python/pyspark/streaming/dstream.py
@@ -141,6 +141,7 @@ def _mergeCombiners(iterator):
                     combiners[k] = v
                 else:
                     combiners[k] = mergeCombiners(combiners[k], v)
+            return combiners.iteritems()
 
         return shuffled._mapPartitions(_mergeCombiners)
 
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala
index 9dc26dc6b32a1..50162e0a1d3b4 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala
@@ -549,6 +549,9 @@ class JavaStreamingContext(val ssc: StreamingContext) extends Closeable {
  * JavaStreamingContext object contains a number of utility functions.
  */
 object JavaStreamingContext {
+  implicit def fromStreamingContext(ssc: StreamingContext): JavaStreamingContext = new JavaStreamingContext(ssc)
+
+  implicit def toStreamingContext(jssc: JavaStreamingContext): StreamingContext = jssc.ssc
 
   /**
    * Either recreate a StreamingContext from checkpoint data or create a new StreamingContext.
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
index 6f2e4bd0d90c8..1a271edf4df80 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
@@ -23,6 +23,7 @@ import scala.reflect.ClassTag
 
 import org.apache.spark._
 import org.apache.spark.rdd.RDD
+import org.apache.spark.api.java._
 import org.apache.spark.api.python._
 import org.apache.spark.broadcast.Broadcast
 import org.apache.spark.streaming.{StreamingContext, Duration, Time}

From 9ad68559ecfa7561a8e4bbf49adfd503f9d7d3b4 Mon Sep 17 00:00:00 2001
From: giwa <ugw.gi.world@gmail.com>
Date: Wed, 6 Aug 2014 19:11:17 -0700
Subject: [PATCH 400/489] WIP

---
 .../scala/org/apache/spark/api/python/PythonRDD.scala  |  2 ++
 examples/src/main/python/streaming/test_oprations.py   | 10 +++++++---
 python/pyspark/streaming/context.py                    |  6 +++++-
 3 files changed, 14 insertions(+), 4 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
index 022e2891559d7..41ed95b607161 100644
--- a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
+++ b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
@@ -350,6 +350,8 @@ private[spark] object PythonRDD extends Logging {
     } catch {
       case eof: EOFException => {}
     }
+    println("RDDDD ==================")
+    println(objs)
     JavaRDD.fromRDD(sc.sc.parallelize(objs, parallelism))
   }
 
diff --git a/examples/src/main/python/streaming/test_oprations.py b/examples/src/main/python/streaming/test_oprations.py
index 3338a766b9cc3..5ee0bd4b31253 100644
--- a/examples/src/main/python/streaming/test_oprations.py
+++ b/examples/src/main/python/streaming/test_oprations.py
@@ -9,11 +9,15 @@
     conf = SparkConf()
     conf.setAppName("PythonStreamingNetworkWordCount")
     ssc = StreamingContext(conf=conf, duration=Seconds(1))
+    ssc.checkpoint("/tmp/spark_ckp")
 
-    test_input = ssc._testInputStream([1,1,1,1])
-    mapped = test_input.map(lambda x: (x, 1))
-    mapped.pyprint()
+    test_input = ssc._testInputStream([[1],[1],[1]])
+#    ssc.checkpoint("/tmp/spark_ckp")
+    fm_test = test_input.flatMap(lambda x: x.split(" "))
+    mapped_test = fm_test.map(lambda x: (x, 1))
 
+
+    mapped_test.print_()
     ssc.start()
 #    ssc.awaitTermination()
 #    ssc.stop()
diff --git a/python/pyspark/streaming/context.py b/python/pyspark/streaming/context.py
index d544eab9b8fc7..882db547faa39 100644
--- a/python/pyspark/streaming/context.py
+++ b/python/pyspark/streaming/context.py
@@ -146,7 +146,10 @@ def _testInputStream(self, test_input, numSlices=None):
         # Calling the Java parallelize() method with an ArrayList is too slow,
         # because it sends O(n) Py4J commands.  As an alternative, serialized
         # objects are written to a file and loaded through textFile().
-        tempFile = NamedTemporaryFile(delete=False, dir=self._sc._temp_dir)
+
+        #tempFile = NamedTemporaryFile(delete=False, dir=self._sc._temp_dir)
+        tempFile = open("/tmp/spark_rdd", "wb")
+
         # Make sure we distribute data evenly if it's smaller than self.batchSize
         if "__len__" not in dir(test_input):
             c = list(test_input)    # Make it a list so we can compute its length
@@ -157,6 +160,7 @@ def _testInputStream(self, test_input, numSlices=None):
         else:
             serializer = self._sc._unbatched_serializer
         serializer.dump_stream(test_input, tempFile)
+        tempFile.flush()
         tempFile.close()
         print tempFile.name
         jinput_stream = self._jvm.PythonTestInputStream(self._jssc,

From ce2acd26ddb16d654f35c63ad8108c65d8b329ac Mon Sep 17 00:00:00 2001
From: giwa <ugw.gi.world@gmail.com>
Date: Sun, 10 Aug 2014 18:43:09 -0700
Subject: [PATCH 401/489] WIP added test case

---
 .../apache/spark/api/python/PythonRDD.scala   |  2 -
 .../main/python/streaming/test_oprations.py   | 25 +++++---
 python/pyspark/streaming/context.py           | 16 +++--
 python/pyspark/streaming/dstream.py           | 22 +++++--
 python/pyspark/streaming_tests.py             | 62 +++++++++++++++++--
 .../streaming/api/java/JavaDStreamLike.scala  |  9 +++
 .../streaming/api/python/PythonDStream.scala  |  9 ++-
 .../spark/streaming/dstream/DStream.scala     | 17 +++++
 8 files changed, 132 insertions(+), 30 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
index 41ed95b607161..022e2891559d7 100644
--- a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
+++ b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
@@ -350,8 +350,6 @@ private[spark] object PythonRDD extends Logging {
     } catch {
       case eof: EOFException => {}
     }
-    println("RDDDD ==================")
-    println(objs)
     JavaRDD.fromRDD(sc.sc.parallelize(objs, parallelism))
   }
 
diff --git a/examples/src/main/python/streaming/test_oprations.py b/examples/src/main/python/streaming/test_oprations.py
index 5ee0bd4b31253..24ebe23d63166 100644
--- a/examples/src/main/python/streaming/test_oprations.py
+++ b/examples/src/main/python/streaming/test_oprations.py
@@ -9,15 +9,22 @@
     conf = SparkConf()
     conf.setAppName("PythonStreamingNetworkWordCount")
     ssc = StreamingContext(conf=conf, duration=Seconds(1))
-    ssc.checkpoint("/tmp/spark_ckp")
 
-    test_input = ssc._testInputStream([[1],[1],[1]])
-#    ssc.checkpoint("/tmp/spark_ckp")
-    fm_test = test_input.flatMap(lambda x: x.split(" "))
-    mapped_test = fm_test.map(lambda x: (x, 1))
+    test_input = ssc._testInputStream([1,2,3])
+    class buff:
+        pass
+   
+    fm_test = test_input.map(lambda x: (x, 1))
+    fm_test.test_output(buff)
 
-
-    mapped_test.print_()
     ssc.start()
-#    ssc.awaitTermination()
-#    ssc.stop()
+    while True:
+        ssc.awaitTermination(50)
+        try:
+            buff.result
+            break
+        except AttributeError:
+            pass
+
+    ssc.stop()
+    print buff.result
diff --git a/python/pyspark/streaming/context.py b/python/pyspark/streaming/context.py
index 882db547faa39..0d7665d645be8 100644
--- a/python/pyspark/streaming/context.py
+++ b/python/pyspark/streaming/context.py
@@ -100,10 +100,10 @@ def awaitTermination(self, timeout=None):
         """
         Wait for the execution to stop.
         """
-        if timeout:
-            self._jssc.awaitTermination(timeout)
-        else:
+        if timeout is None:
             self._jssc.awaitTermination()
+        else:
+            self._jssc.awaitTermination(timeout)
 
     # start from simple one. storageLevel is not passed for now.
     def socketTextStream(self, hostname, port):
@@ -137,6 +137,7 @@ def stop(self, stopSparkContext=True):
 
     def checkpoint(self, directory):
         """
+        Not tested
         """
         self._jssc.checkpoint(directory)
 
@@ -147,8 +148,7 @@ def _testInputStream(self, test_input, numSlices=None):
         # because it sends O(n) Py4J commands.  As an alternative, serialized
         # objects are written to a file and loaded through textFile().
 
-        #tempFile = NamedTemporaryFile(delete=False, dir=self._sc._temp_dir)
-        tempFile = open("/tmp/spark_rdd", "wb")
+        tempFile = NamedTemporaryFile(delete=False, dir=self._sc._temp_dir)
 
         # Make sure we distribute data evenly if it's smaller than self.batchSize
         if "__len__" not in dir(test_input):
@@ -160,10 +160,8 @@ def _testInputStream(self, test_input, numSlices=None):
         else:
             serializer = self._sc._unbatched_serializer
         serializer.dump_stream(test_input, tempFile)
-        tempFile.flush()
-        tempFile.close()
-        print tempFile.name
+
         jinput_stream = self._jvm.PythonTestInputStream(self._jssc,
                                                         tempFile.name,
                                                         numSlices).asJavaDStream()
-        return DStream(jinput_stream, self, UTF8Deserializer())
+        return DStream(jinput_stream, self, PickleSerializer())
diff --git a/python/pyspark/streaming/dstream.py b/python/pyspark/streaming/dstream.py
index 0885ee35c2f28..5f4e3a6cd15f1 100644
--- a/python/pyspark/streaming/dstream.py
+++ b/python/pyspark/streaming/dstream.py
@@ -47,7 +47,7 @@ def _sum(self):
         """
         return self._mapPartitions(lambda x: [sum(x)]).reduce(operator.add)
 
-    def print_(self):
+    def print_(self, label=None):
         """
         Since print is reserved name for python, we cannot define a print method function.
         This function prints serialized data in RDD in DStream because Scala and Java cannot
@@ -56,7 +56,7 @@ def print_(self):
         Call DStream.print().
         """
         # a hack to call print function in DStream
-        getattr(self._jdstream, "print")()
+        getattr(self._jdstream, "print")(label)
 
     def filter(self, f):
         """
@@ -230,6 +230,7 @@ def pyprint(self):
 
         """
         def takeAndPrint(rdd, time):
+            print "take and print ==================="
             taken = rdd.take(11)
             print "-------------------------------------------"
             print "Time: %s" % (str(time))
@@ -242,11 +243,24 @@ def takeAndPrint(rdd, time):
 
         self.foreachRDD(takeAndPrint)
 
-    #def transform(self, func):
+    #def transform(self, func): - TD
     #    from utils import RDDFunction
     #    wrapped_func = RDDFunction(self.ctx, self._jrdd_deserializer, func)
     #    jdstream = self.ctx._jvm.PythonTransformedDStream(self._jdstream.dstream(), wrapped_func).toJavaDStream
-    #    return DStream(jdstream, self._ssc, ...)  ## DO NOT KNOW HOW 
+    #    return DStream(jdstream, self._ssc, ...)  ## DO NOT KNOW HOW
+
+    def _test_output(self, buff):
+        """
+        This function is only for testcase.
+        Store data in dstream to buffer to valify the result in tesecase
+        """
+        def get_output(rdd, time):
+            taken = rdd.take(11)
+            buff.result = taken
+        self.foreachRDD(get_output)
+
+    def output(self):
+        self._jdstream.outputToFile()
 
 
 class PipelinedDStream(DStream):
diff --git a/python/pyspark/streaming_tests.py b/python/pyspark/streaming_tests.py
index 95c5489a5695b..0660be10b027b 100644
--- a/python/pyspark/streaming_tests.py
+++ b/python/pyspark/streaming_tests.py
@@ -19,12 +19,13 @@
 Unit tests for PySpark; additional tests are implemented as doctests in
 individual modules.
 
-This file will merged to tests.py. But for now, this file is separated to
-focus to streaming test case
+This file will merged to tests.py. But for now, this file is separated due
+to focusing to streaming test case
 
 """
 from fileinput import input
 from glob import glob
+from itertools import chain
 import os
 import re
 import shutil
@@ -41,18 +42,69 @@
 
 SPARK_HOME = os.environ["SPARK_HOME"]
 
+class buff:
+    """
+    Buffer for store the output from stream
+    """
+    result = None
 
 class PySparkStreamingTestCase(unittest.TestCase):
-
     def setUp(self):
-        self._old_sys_path = list(sys.path)
+        print "set up"
         class_name = self.__class__.__name__
         self.ssc = StreamingContext(appName=class_name, duration=Seconds(1))
 
     def tearDown(self):
+        print "tear donw"
         self.ssc.stop()
-        sys.path = self._old_sys_path
+        time.sleep(10)
+
+class TestBasicOperationsSuite(PySparkStreamingTestCase):
+    def setUp(self):
+        PySparkStreamingTestCase.setUp(self)
+        buff.result = None
+        self.timeout = 10 # seconds
+
+    def tearDown(self):
+        PySparkStreamingTestCase.tearDown(self)
+
+    def test_map(self):
+        test_input = [range(1,5), range(5,9), range(9, 13)]
+        def test_func(dstream):
+            return dstream.map(lambda x: str(x))
+        expected = map(str, test_input)
+        output = self.run_stream(test_input, test_func)
+        self.assertEqual(output, expected)
+
+    def test_flatMap(self):
+        test_input = [range(1,5), range(5,9), range(9, 13)]
+        def test_func(dstream):
+            return dstream.flatMap(lambda x: (x, x * 2))
+        # Maybe there be good way to create flatmap
+        excepted = map(lambda x: list(chain.from_iterable((map(lambda y:[y, y*2], x)))), 
+                       test_input)
+        output = self.run_stream(test_input, test_func)
+
+    def run_stream(self, test_input, test_func):
+        # Generate input stream with user-defined input
+        test_input_stream = self.ssc._testInputStream(test_input)
+        # Applyed test function to stream
+        test_stream = test_func(test_input_stream)
+        # Add job to get outpuf from stream
+        test_stream._test_output(buff)
+        self.ssc.start()
 
+        start_time = time.time()
+        while True:
+            current_time = time.time()
+            # check time out
+            if (current_time - start_time) > self.timeout:
+                self.ssc.stop()
+                break
+            self.ssc.awaitTermination(50)
+            if buff.result is not None:
+                break
+        return buff.result
 
 if __name__ == "__main__":
     unittest.main()
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaDStreamLike.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaDStreamLike.scala
index a6184de4e83c1..7a002bbe74ca9 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaDStreamLike.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaDStreamLike.scala
@@ -54,6 +54,15 @@ trait JavaDStreamLike[T, This <: JavaDStreamLike[T, This, R], R <: JavaRDDLike[T
     dstream.print()
   }
 
+  def print(label: String = null): Unit = {
+    dstream.print(label)
+  }
+
+  def outputToFile(): Unit = {
+    dstream.outputToFile()
+  }
+
+
   /**
    * Return a new DStream in which each RDD has a single element generated by counting each RDD
    * of this DStream.
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
index 1a271edf4df80..0e07602ed9bcf 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
@@ -17,9 +17,14 @@
 
 package org.apache.spark.streaming.api.python
 
+import java.io._
+import java.io.{ObjectInputStream, IOException}
 import java.util.{List => JList, ArrayList => JArrayList, Map => JMap, Collections}
 
+import scala.collection.mutable.ArrayBuffer
 import scala.reflect.ClassTag
+import scala.collection.JavaConversions._
+
 
 import org.apache.spark._
 import org.apache.spark.rdd.RDD
@@ -51,6 +56,8 @@ class PythonDStream[T: ClassTag](
   override def compute(validTime: Time): Option[RDD[Array[Byte]]] = {
     parent.getOrCompute(validTime) match{
       case Some(rdd) =>
+        logInfo("RDD ID in python DStream     ===========")
+        logInfo("RDD id " + rdd.id)
         val pythonRDD = new PythonRDD(rdd, command, envVars, pythonIncludes, preservePartitoning, pythonExec, broadcastVars, accumulator)
         Some(pythonRDD.asJavaRDD.rdd)
       case None => None
@@ -152,7 +159,7 @@ DStream[Array[Byte]](prev.ssc){
         val pairwiseRDD = new PairwiseRDD(rdd)
         /*
          * Since python operation is executed by Scala after StreamingContext.start.
-         * What PairwiseDStream does is equivalent to following python code in pySpark.
+         * What PythonPairwiseDStream does is equivalent to python code in pySpark.
          *
          * with _JavaStackTrace(self.context) as st:
          *    pairRDD = self.ctx._jvm.PairwiseRDD(keyed._jrdd.rdd()).asJavaPairRDD()
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala
index d8dbdf59e7ff1..bafff80adc54b 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala
@@ -623,6 +623,23 @@ abstract class DStream[T: ClassTag] (
     new ForEachDStream(this, context.sparkContext.clean(foreachFunc)).register()
   }
 
+
+  def print(label: String = null) {
+    def foreachFunc = (rdd: RDD[T], time: Time) => {
+      val first11 = rdd.take(11)
+      println ("-------------------------------------------")
+      println ("Time: " + time)
+      println ("-------------------------------------------")
+      if(label != null){
+        println (label)
+      }
+      first11.take(10).foreach(println)
+      if (first11.size > 10) println("...")
+      println()
+    }
+    new ForEachDStream(this, context.sparkContext.clean(foreachFunc)).register()
+  }
+
   /**
    * Return a new DStream in which each RDD contains all the elements in seen in a
    * sliding window of time over this DStream. The new DStream generates RDDs with

From 878bad7b879e81ed73b5a7c219d02652b067fdc6 Mon Sep 17 00:00:00 2001
From: giwa <ugw.gi.world@gmail.com>
Date: Mon, 11 Aug 2014 03:21:22 -0700
Subject: [PATCH 402/489] added basic operation test cases

---
 .../main/python/streaming/test_oprations.py   | 19 ++--
 python/pyspark/streaming/context.py           | 43 +++++----
 python/pyspark/streaming/dstream.py           |  8 +-
 python/pyspark/streaming_tests.py             | 95 +++++++++++++++----
 .../streaming/api/python/PythonDStream.scala  |  2 -
 5 files changed, 113 insertions(+), 54 deletions(-)

diff --git a/examples/src/main/python/streaming/test_oprations.py b/examples/src/main/python/streaming/test_oprations.py
index 24ebe23d63166..70a62058286e9 100644
--- a/examples/src/main/python/streaming/test_oprations.py
+++ b/examples/src/main/python/streaming/test_oprations.py
@@ -9,22 +9,23 @@
     conf = SparkConf()
     conf.setAppName("PythonStreamingNetworkWordCount")
     ssc = StreamingContext(conf=conf, duration=Seconds(1))
-
-    test_input = ssc._testInputStream([1,2,3])
-    class buff:
+    class Buff:
+        result = list()
         pass
+    Buff.result = list()
+
+    test_input = ssc._testInputStream([range(1,4), range(4,7), range(7,10)])
    
     fm_test = test_input.map(lambda x: (x, 1))
-    fm_test.test_output(buff)
+    fm_test.pyprint()
+    fm_test._test_output(Buff.result)
 
     ssc.start()
     while True:
         ssc.awaitTermination(50)
-        try:
-            buff.result
+        if len(Buff.result) == 3:
             break
-        except AttributeError:
-            pass
 
     ssc.stop()
-    print buff.result
+    print Buff.result
+
diff --git a/python/pyspark/streaming/context.py b/python/pyspark/streaming/context.py
index 0d7665d645be8..be142fd4f327b 100644
--- a/python/pyspark/streaming/context.py
+++ b/python/pyspark/streaming/context.py
@@ -123,14 +123,14 @@ def textFileStream(self, directory):
         """
         return DStream(self._jssc.textFileStream(directory), self, UTF8Deserializer())
 
-    def stop(self, stopSparkContext=True):
+    def stop(self, stopSparkContext=True, stopGraceFully=False):
         """
         Stop the execution of the streams immediately (does not wait for all received data
         to be processed).
         """
         
         try:
-            self._jssc.stop(stopSparkContext)
+            self._jssc.stop(stopSparkContext, stopGraceFully)
         finally:
             # Stop Callback server
             SparkContext._gateway.shutdown()
@@ -141,27 +141,34 @@ def checkpoint(self, directory):
         """
         self._jssc.checkpoint(directory)
 
-    def _testInputStream(self, test_input, numSlices=None):
-
+    def _testInputStream(self, test_inputs, numSlices=None):
+        """
+        Generate multiple files to make "stream" in Scala side for test.
+        Scala chooses one of the files and generates RDD using PythonRDD.readRDDFromFile.
+        """
         numSlices = numSlices or self._sc.defaultParallelism
         # Calling the Java parallelize() method with an ArrayList is too slow,
         # because it sends O(n) Py4J commands.  As an alternative, serialized
         # objects are written to a file and loaded through textFile().
 
-        tempFile = NamedTemporaryFile(delete=False, dir=self._sc._temp_dir)
-
-        # Make sure we distribute data evenly if it's smaller than self.batchSize
-        if "__len__" not in dir(test_input):
-            c = list(test_input)    # Make it a list so we can compute its length
-        batchSize = min(len(test_input) // numSlices, self._sc._batchSize)
-        if batchSize > 1:
-            serializer = BatchedSerializer(self._sc._unbatched_serializer,
-                                           batchSize)
-        else:
-            serializer = self._sc._unbatched_serializer
-        serializer.dump_stream(test_input, tempFile)
-
+        tempFiles = list()
+        for test_input in test_inputs:
+            tempFile = NamedTemporaryFile(delete=False, dir=self._sc._temp_dir)
+
+            # Make sure we distribute data evenly if it's smaller than self.batchSize
+            if "__len__" not in dir(test_input):
+                c = list(test_input)    # Make it a list so we can compute its length
+            batchSize = min(len(test_input) // numSlices, self._sc._batchSize)
+            if batchSize > 1:
+                serializer = BatchedSerializer(self._sc._unbatched_serializer,
+                                               batchSize)
+            else:
+                serializer = self._sc._unbatched_serializer
+            serializer.dump_stream(test_input, tempFile)
+            tempFiles.append(tempFile.name)
+
+        jtempFiles = ListConverter().convert(tempFiles, SparkContext._gateway._gateway_client)
         jinput_stream = self._jvm.PythonTestInputStream(self._jssc,
-                                                        tempFile.name,
+                                                        jtempFiles,
                                                         numSlices).asJavaDStream()
         return DStream(jinput_stream, self, PickleSerializer())
diff --git a/python/pyspark/streaming/dstream.py b/python/pyspark/streaming/dstream.py
index 5f4e3a6cd15f1..ec66af3177826 100644
--- a/python/pyspark/streaming/dstream.py
+++ b/python/pyspark/streaming/dstream.py
@@ -230,7 +230,6 @@ def pyprint(self):
 
         """
         def takeAndPrint(rdd, time):
-            print "take and print ==================="
             taken = rdd.take(11)
             print "-------------------------------------------"
             print "Time: %s" % (str(time))
@@ -255,13 +254,10 @@ def _test_output(self, buff):
         Store data in dstream to buffer to valify the result in tesecase
         """
         def get_output(rdd, time):
-            taken = rdd.take(11)
-            buff.result = taken
+            taken = rdd.collect()
+            buff.append(taken)
         self.foreachRDD(get_output)
 
-    def output(self):
-        self._jdstream.outputToFile()
-
 
 class PipelinedDStream(DStream):
     def __init__(self, prev, func, preservesPartitioning=False):
diff --git a/python/pyspark/streaming_tests.py b/python/pyspark/streaming_tests.py
index 0660be10b027b..d2e638a7d2acc 100644
--- a/python/pyspark/streaming_tests.py
+++ b/python/pyspark/streaming_tests.py
@@ -35,76 +35,133 @@
 import time
 import unittest
 import zipfile
+import operator
 
+from pyspark.context import SparkContext
 from pyspark.streaming.context import StreamingContext
 from pyspark.streaming.duration import *
 
 
 SPARK_HOME = os.environ["SPARK_HOME"]
 
-class buff:
+class StreamOutput:
     """
-    Buffer for store the output from stream
+    a class to store the output from stream
     """
-    result = None
+    result = list()
 
 class PySparkStreamingTestCase(unittest.TestCase):
     def setUp(self):
-        print "set up"
         class_name = self.__class__.__name__
         self.ssc = StreamingContext(appName=class_name, duration=Seconds(1))
 
     def tearDown(self):
-        print "tear donw"
-        self.ssc.stop()
-        time.sleep(10)
+        # Do not call StreamingContext.stop directly because we do not wait to shutdown
+        # call back server and py4j client
+        self.ssc._jssc.stop()
+        self.ssc._sc.stop()
+        # Why does it long time to terminaete StremaingContext and SparkContext?
+        # Should we change the sleep time if this depends on machine spec?
+        time.sleep(5)
+
+    @classmethod
+    def tearDownClass(cls):
+        time.sleep(5)
+        SparkContext._gateway._shutdown_callback_server()
 
 class TestBasicOperationsSuite(PySparkStreamingTestCase):
+    """
+    Input and output of this TestBasicOperationsSuite is the equivalent to 
+    Scala TestBasicOperationsSuite.
+    """
     def setUp(self):
         PySparkStreamingTestCase.setUp(self)
-        buff.result = None
+        StreamOutput.result = list()
         self.timeout = 10 # seconds
 
     def tearDown(self):
         PySparkStreamingTestCase.tearDown(self)
 
+    @classmethod
+    def tearDownClass(cls):
+        PySparkStreamingTestCase.tearDownClass()
+
     def test_map(self):
+        """Basic operation test for DStream.map"""
         test_input = [range(1,5), range(5,9), range(9, 13)]
         def test_func(dstream):
             return dstream.map(lambda x: str(x))
-        expected = map(str, test_input)
-        output = self.run_stream(test_input, test_func)
-        self.assertEqual(output, expected)
+        expected_output = map(lambda x: map(lambda y: str(y), x), test_input)
+        output = self._run_stream(test_input, test_func, expected_output)
+        self.assertEqual(expected_output, output)
 
     def test_flatMap(self):
+        """Basic operation test for DStream.faltMap"""
         test_input = [range(1,5), range(5,9), range(9, 13)]
         def test_func(dstream):
             return dstream.flatMap(lambda x: (x, x * 2))
-        # Maybe there be good way to create flatmap
-        excepted = map(lambda x: list(chain.from_iterable((map(lambda y:[y, y*2], x)))), 
+        expected_output = map(lambda x: list(chain.from_iterable((map(lambda y: [y, y * 2], x)))), 
                        test_input)
-        output = self.run_stream(test_input, test_func)
+        output = self._run_stream(test_input, test_func, expected_output)
+        self.assertEqual(expected_output, output)
+
+    def test_filter(self):
+        """Basic operation test for DStream.filter"""
+        test_input = [range(1,5), range(5,9), range(9, 13)]
+        def test_func(dstream):
+            return dstream.filter(lambda x: x % 2 == 0)
+        expected_output = map(lambda x: filter(lambda y: y % 2 == 0, x), test_input)
+        output = self._run_stream(test_input, test_func, expected_output)
+        self.assertEqual(expected_output, output)
+
+    def test_count(self):
+        """Basic operation test for DStream.count"""
+        test_input = [[], [1], range(1, 3), range(1,4), range(1,5)]
+        def test_func(dstream):
+            return dstream.count()
+        expected_output = map(lambda x: [len(x)], test_input)
+        output = self._run_stream(test_input, test_func, expected_output)
+        self.assertEqual(expected_output, output)
+        
+    def test_reduce(self):
+        """Basic operation test for DStream.reduce"""
+        test_input = [range(1,5), range(5,9), range(9, 13)]
+        def test_func(dstream):
+            return dstream.reduce(operator.add)
+        expected_output = map(lambda x: [reduce(operator.add, x)], test_input)
+        output = self._run_stream(test_input, test_func, expected_output)
+        self.assertEqual(expected_output, output)
+
+    def test_reduceByKey(self):
+        """Basic operation test for DStream.reduceByKey"""
+        test_input = [["a", "a", "b"], ["", ""], []]
+        def test_func(dstream):
+            return dstream.map(lambda x: (x, 1)).reduceByKey(operator.add)
+        expected_output = [[("a", 2), ("b", 1)],[("", 2)], []]
+        output = self._run_stream(test_input, test_func, expected_output)
+        self.assertEqual(expected_output, output)
 
-    def run_stream(self, test_input, test_func):
+    def _run_stream(self, test_input, test_func, expected_output):
+        """Start stream and return the output"""
         # Generate input stream with user-defined input
         test_input_stream = self.ssc._testInputStream(test_input)
         # Applyed test function to stream
         test_stream = test_func(test_input_stream)
         # Add job to get outpuf from stream
-        test_stream._test_output(buff)
+        test_stream._test_output(StreamOutput.result)
         self.ssc.start()
 
         start_time = time.time()
+        # loop until get the result from stream
         while True:
             current_time = time.time()
             # check time out
             if (current_time - start_time) > self.timeout:
-                self.ssc.stop()
                 break
             self.ssc.awaitTermination(50)
-            if buff.result is not None:
+            if len(expected_output) == len(StreamOutput.result):
                 break
-        return buff.result
+        return StreamOutput.result
 
 if __name__ == "__main__":
     unittest.main()
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
index 0e07602ed9bcf..a24300f626d1a 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
@@ -56,8 +56,6 @@ class PythonDStream[T: ClassTag](
   override def compute(validTime: Time): Option[RDD[Array[Byte]]] = {
     parent.getOrCompute(validTime) match{
       case Some(rdd) =>
-        logInfo("RDD ID in python DStream     ===========")
-        logInfo("RDD id " + rdd.id)
         val pythonRDD = new PythonRDD(rdd, command, envVars, pythonIncludes, preservePartitoning, pythonExec, broadcastVars, accumulator)
         Some(pythonRDD.asJavaRDD.rdd)
       case None => None

From f21cab39c4122672f835196dc36e067361e39274 Mon Sep 17 00:00:00 2001
From: giwa <ugw.gi.world@gmail.com>
Date: Mon, 11 Aug 2014 03:22:23 -0700
Subject: [PATCH 403/489] delete waste file

---
 .../main/python/streaming/test_oprations.py   | 31 -------------------
 1 file changed, 31 deletions(-)
 delete mode 100644 examples/src/main/python/streaming/test_oprations.py

diff --git a/examples/src/main/python/streaming/test_oprations.py b/examples/src/main/python/streaming/test_oprations.py
deleted file mode 100644
index 70a62058286e9..0000000000000
--- a/examples/src/main/python/streaming/test_oprations.py
+++ /dev/null
@@ -1,31 +0,0 @@
-import sys
-from operator import add
-
-from pyspark.conf import SparkConf
-from pyspark.streaming.context import StreamingContext
-from pyspark.streaming.duration import *
-
-if __name__ == "__main__":
-    conf = SparkConf()
-    conf.setAppName("PythonStreamingNetworkWordCount")
-    ssc = StreamingContext(conf=conf, duration=Seconds(1))
-    class Buff:
-        result = list()
-        pass
-    Buff.result = list()
-
-    test_input = ssc._testInputStream([range(1,4), range(4,7), range(7,10)])
-   
-    fm_test = test_input.map(lambda x: (x, 1))
-    fm_test.pyprint()
-    fm_test._test_output(Buff.result)
-
-    ssc.start()
-    while True:
-        ssc.awaitTermination(50)
-        if len(Buff.result) == 3:
-            break
-
-    ssc.stop()
-    print Buff.result
-

From 3d3782236850b0b9ca2558c52bfe0535881757b0 Mon Sep 17 00:00:00 2001
From: giwa <ugw.gi.world@gmail.com>
Date: Mon, 11 Aug 2014 03:41:24 -0700
Subject: [PATCH 404/489] fixed PEP-008 violation

---
 python/pyspark/streaming/context.py |  5 ----
 python/pyspark/streaming/dstream.py | 19 +++++++++------
 python/pyspark/streaming_tests.py   | 37 +++++++++++++++--------------
 3 files changed, 31 insertions(+), 30 deletions(-)

diff --git a/python/pyspark/streaming/context.py b/python/pyspark/streaming/context.py
index be142fd4f327b..088a4965b6b13 100644
--- a/python/pyspark/streaming/context.py
+++ b/python/pyspark/streaming/context.py
@@ -19,12 +19,7 @@
 from signal import signal, SIGTERM, SIGINT
 from tempfile import NamedTemporaryFile
 
-from pyspark.conf import SparkConf
-from pyspark.files import SparkFiles
-from pyspark.java_gateway import launch_gateway
 from pyspark.serializers import PickleSerializer, BatchedSerializer, UTF8Deserializer
-from pyspark.storagelevel import *
-from pyspark.rdd import RDD
 from pyspark.context import SparkContext
 from pyspark.streaming.dstream import DStream
 
diff --git a/python/pyspark/streaming/dstream.py b/python/pyspark/streaming/dstream.py
index ec66af3177826..07429f477d310 100644
--- a/python/pyspark/streaming/dstream.py
+++ b/python/pyspark/streaming/dstream.py
@@ -49,7 +49,7 @@ def _sum(self):
 
     def print_(self, label=None):
         """
-        Since print is reserved name for python, we cannot define a print method function.
+        Since print is reserved name for python, we cannot define a "print" method function.
         This function prints serialized data in RDD in DStream because Scala and Java cannot
         deserialized pickled python object. Please use DStream.pyprint() instead to print results.
 
@@ -159,8 +159,8 @@ def partitionBy(self, numPartitions, partitionFunc=None):
         # form the hash buckets in Python, transferring O(numPartitions) objects
         # to Java.  Each object is a (splitNumber, [objects]) pair.
         outputSerializer = self.ctx._unbatched_serializer
-        def add_shuffle_key(split, iterator):
 
+        def add_shuffle_key(split, iterator):
             buckets = defaultdict(list)
 
             for (k, v) in iterator:
@@ -218,6 +218,11 @@ def getNumPartitions(self):
 
     def foreachRDD(self, func):
         """
+        Apply userdefined function to all RDD in a DStream.
+        This python implementation could be expensive because it uses callback server
+        in order to apply function to RDD in DStream.
+        This is an output operator, so this DStream will be registered as an output
+        stream and there materialized.
         """
         from utils import RDDFunction
         wrapped_func = RDDFunction(self.ctx, self._jrdd_deserializer, func)
@@ -227,7 +232,6 @@ def pyprint(self):
         """
         Print the first ten elements of each RDD generated in this DStream. This is an output
         operator, so this DStream will be registered as an output stream and there materialized.
-
         """
         def takeAndPrint(rdd, time):
             taken = rdd.take(11)
@@ -248,14 +252,15 @@ def takeAndPrint(rdd, time):
     #    jdstream = self.ctx._jvm.PythonTransformedDStream(self._jdstream.dstream(), wrapped_func).toJavaDStream
     #    return DStream(jdstream, self._ssc, ...)  ## DO NOT KNOW HOW
 
-    def _test_output(self, buff):
+    def _test_output(self, result):
         """
-        This function is only for testcase.
-        Store data in dstream to buffer to valify the result in tesecase
+        This function is only for test case.
+        Store data in a DStream to result to verify the result in tese case
         """
         def get_output(rdd, time):
             taken = rdd.collect()
-            buff.append(taken)
+            result.append(taken)
+
         self.foreachRDD(get_output)
 
 
diff --git a/python/pyspark/streaming_tests.py b/python/pyspark/streaming_tests.py
index d2e638a7d2acc..ef9b87756fcef 100644
--- a/python/pyspark/streaming_tests.py
+++ b/python/pyspark/streaming_tests.py
@@ -23,18 +23,10 @@
 to focusing to streaming test case
 
 """
-from fileinput import input
-from glob import glob
 from itertools import chain
 import os
-import re
-import shutil
-import subprocess
-import sys
-import tempfile
 import time
 import unittest
-import zipfile
 import operator
 
 from pyspark.context import SparkContext
@@ -44,12 +36,14 @@
 
 SPARK_HOME = os.environ["SPARK_HOME"]
 
+
 class StreamOutput:
     """
     a class to store the output from stream
     """
     result = list()
 
+
 class PySparkStreamingTestCase(unittest.TestCase):
     def setUp(self):
         class_name = self.__class__.__name__
@@ -69,6 +63,7 @@ def tearDownClass(cls):
         time.sleep(5)
         SparkContext._gateway._shutdown_callback_server()
 
+
 class TestBasicOperationsSuite(PySparkStreamingTestCase):
     """
     Input and output of this TestBasicOperationsSuite is the equivalent to 
@@ -77,7 +72,7 @@ class TestBasicOperationsSuite(PySparkStreamingTestCase):
     def setUp(self):
         PySparkStreamingTestCase.setUp(self)
         StreamOutput.result = list()
-        self.timeout = 10 # seconds
+        self.timeout = 10  # seconds
 
     def tearDown(self):
         PySparkStreamingTestCase.tearDown(self)
@@ -88,7 +83,8 @@ def tearDownClass(cls):
 
     def test_map(self):
         """Basic operation test for DStream.map"""
-        test_input = [range(1,5), range(5,9), range(9, 13)]
+        test_input = [range(1, 5), range(5, 9), range(9, 13)]
+
         def test_func(dstream):
             return dstream.map(lambda x: str(x))
         expected_output = map(lambda x: map(lambda y: str(y), x), test_input)
@@ -97,17 +93,19 @@ def test_func(dstream):
 
     def test_flatMap(self):
         """Basic operation test for DStream.faltMap"""
-        test_input = [range(1,5), range(5,9), range(9, 13)]
+        test_input = [range(1, 5), range(5, 9), range(9, 13)]
+
         def test_func(dstream):
             return dstream.flatMap(lambda x: (x, x * 2))
         expected_output = map(lambda x: list(chain.from_iterable((map(lambda y: [y, y * 2], x)))), 
-                       test_input)
+                              test_input)
         output = self._run_stream(test_input, test_func, expected_output)
         self.assertEqual(expected_output, output)
 
     def test_filter(self):
         """Basic operation test for DStream.filter"""
-        test_input = [range(1,5), range(5,9), range(9, 13)]
+        test_input = [range(1, 5), range(5, 9), range(9, 13)]
+
         def test_func(dstream):
             return dstream.filter(lambda x: x % 2 == 0)
         expected_output = map(lambda x: filter(lambda y: y % 2 == 0, x), test_input)
@@ -116,7 +114,8 @@ def test_func(dstream):
 
     def test_count(self):
         """Basic operation test for DStream.count"""
-        test_input = [[], [1], range(1, 3), range(1,4), range(1,5)]
+        test_input = [[], [1], range(1, 3), range(1, 4), range(1, 5)]
+
         def test_func(dstream):
             return dstream.count()
         expected_output = map(lambda x: [len(x)], test_input)
@@ -125,7 +124,8 @@ def test_func(dstream):
         
     def test_reduce(self):
         """Basic operation test for DStream.reduce"""
-        test_input = [range(1,5), range(5,9), range(9, 13)]
+        test_input = [range(1, 5), range(5, 9), range(9, 13)]
+
         def test_func(dstream):
             return dstream.reduce(operator.add)
         expected_output = map(lambda x: [reduce(operator.add, x)], test_input)
@@ -135,9 +135,10 @@ def test_func(dstream):
     def test_reduceByKey(self):
         """Basic operation test for DStream.reduceByKey"""
         test_input = [["a", "a", "b"], ["", ""], []]
+
         def test_func(dstream):
             return dstream.map(lambda x: (x, 1)).reduceByKey(operator.add)
-        expected_output = [[("a", 2), ("b", 1)],[("", 2)], []]
+        expected_output = [[("a", 2), ("b", 1)], [("", 2)], []]
         output = self._run_stream(test_input, test_func, expected_output)
         self.assertEqual(expected_output, output)
 
@@ -145,9 +146,9 @@ def _run_stream(self, test_input, test_func, expected_output):
         """Start stream and return the output"""
         # Generate input stream with user-defined input
         test_input_stream = self.ssc._testInputStream(test_input)
-        # Applyed test function to stream
+        # Applied test function to stream
         test_stream = test_func(test_input_stream)
-        # Add job to get outpuf from stream
+        # Add job to get output from stream
         test_stream._test_output(StreamOutput.result)
         self.ssc.start()
 

From 253a8630728951de77fc430a9b805bbbf7442ecd Mon Sep 17 00:00:00 2001
From: giwa <ugw.gi.world@gmail.com>
Date: Mon, 11 Aug 2014 04:42:08 -0700
Subject: [PATCH 405/489] removed unnesessary changes

---
 .../org/apache/spark/streaming/dstream/DStream.scala      | 8 +-------
 1 file changed, 1 insertion(+), 7 deletions(-)

diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala
index bafff80adc54b..46ef05d9c37a1 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala
@@ -17,14 +17,11 @@
 
 package org.apache.spark.streaming.dstream
 
-
-import java.io._
+import java.io.{IOException, ObjectInputStream, ObjectOutputStream}
 
 import scala.deprecated
 import scala.collection.mutable.HashMap
 import scala.reflect.ClassTag
-import java.io.{IOException, ObjectInputStream, ObjectOutputStream}
-import scala.util.control.Breaks._
 
 import org.apache.spark.{Logging, SparkException}
 import org.apache.spark.rdd.{BlockRDD, RDD}
@@ -34,7 +31,6 @@ import org.apache.spark.streaming.StreamingContext._
 import org.apache.spark.streaming.scheduler.Job
 import org.apache.spark.util.MetadataCleaner
 import org.apache.spark.streaming.Duration
-import org.apache.spark.api.python.PythonRDD
 
 /**
  * A Discretized Stream (DStream), the basic abstraction in Spark Streaming, is a continuous
@@ -562,11 +558,9 @@ abstract class DStream[T: ClassTag] (
     // DStreams can't be serialized with closures, we can't proactively check 
     // it for serializability and so we pass the optional false to SparkContext.clean
 
-    // serialized python
     val cleanedF = context.sparkContext.clean(transformFunc, false)
     val realTransformFunc =  (rdds: Seq[RDD[_]], time: Time) => {
       assert(rdds.length == 1)
-      // if transformfunc is fine, it is okay
       cleanedF(rdds.head.asInstanceOf[RDD[T]], time)
     }
     new TransformedDStream[U](Seq(this), realTransformFunc)

From bb1095659153a4e1a6bc8f5159c08244d7e8a0c1 Mon Sep 17 00:00:00 2001
From: giwa <ugw.gi.world@gmail.com>
Date: Mon, 11 Aug 2014 05:32:28 -0700
Subject: [PATCH 406/489] edited the comment to add more precise description

---
 python/pyspark/streaming_tests.py | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/python/pyspark/streaming_tests.py b/python/pyspark/streaming_tests.py
index ef9b87756fcef..ec45acec94dbf 100644
--- a/python/pyspark/streaming_tests.py
+++ b/python/pyspark/streaming_tests.py
@@ -50,8 +50,8 @@ def setUp(self):
         self.ssc = StreamingContext(appName=class_name, duration=Seconds(1))
 
     def tearDown(self):
-        # Do not call StreamingContext.stop directly because we do not wait to shutdown
-        # call back server and py4j client
+        # Do not call pyspark.streaming.context.StreamingContext.stop directly because
+        # we do not wait to shutdowncall back server and py4j client
         self.ssc._jssc.stop()
         self.ssc._sc.stop()
         # Why does it long time to terminaete StremaingContext and SparkContext?
@@ -146,7 +146,7 @@ def _run_stream(self, test_input, test_func, expected_output):
         """Start stream and return the output"""
         # Generate input stream with user-defined input
         test_input_stream = self.ssc._testInputStream(test_input)
-        # Applied test function to stream
+        # Apply test function to stream
         test_stream = test_func(test_input_stream)
         # Add job to get output from stream
         test_stream._test_output(StreamOutput.result)
@@ -160,6 +160,7 @@ def _run_stream(self, test_input, test_func, expected_output):
             if (current_time - start_time) > self.timeout:
                 break
             self.ssc.awaitTermination(50)
+            # check if the output is the same length of expexted output
             if len(expected_output) == len(StreamOutput.result):
                 break
         return StreamOutput.result

From 270a9e13dcae53a1b0563b637b6002ded0e0650f Mon Sep 17 00:00:00 2001
From: giwa <ugw.gi.world@gmail.com>
Date: Mon, 11 Aug 2014 16:34:12 -0700
Subject: [PATCH 407/489] added mapValues and flatMapVaules WIP for glom and
 mapPartitions test

---
 python/pyspark/streaming/context.py |  2 +
 python/pyspark/streaming/dstream.py | 69 ++++++++++++++++++++++-------
 python/pyspark/streaming_tests.py   | 48 +++++++++++++++++++-
 3 files changed, 101 insertions(+), 18 deletions(-)

diff --git a/python/pyspark/streaming/context.py b/python/pyspark/streaming/context.py
index 088a4965b6b13..eee298badcbad 100644
--- a/python/pyspark/streaming/context.py
+++ b/python/pyspark/streaming/context.py
@@ -140,6 +140,8 @@ def _testInputStream(self, test_inputs, numSlices=None):
         """
         Generate multiple files to make "stream" in Scala side for test.
         Scala chooses one of the files and generates RDD using PythonRDD.readRDDFromFile.
+
+        QueStream maybe good way to implement this function
         """
         numSlices = numSlices or self._sc.defaultParallelism
         # Calling the Java parallelize() method with an ArrayList is too slow,
diff --git a/python/pyspark/streaming/dstream.py b/python/pyspark/streaming/dstream.py
index 07429f477d310..02f35fac47ac0 100644
--- a/python/pyspark/streaming/dstream.py
+++ b/python/pyspark/streaming/dstream.py
@@ -35,25 +35,31 @@ def __init__(self, jdstream, ssc, jrdd_deserializer):
         self.ctx = ssc._sc
         self._jrdd_deserializer = jrdd_deserializer
 
+    def context(self):
+        """
+        Return the StreamingContext associated with this DStream
+        """
+        return self._ssc
+
     def count(self):
         """
         Return a new DStream which contains the number of elements in this DStream.
         """
-        return self._mapPartitions(lambda i: [sum(1 for _ in i)])._sum()
+        return self.mapPartitions(lambda i: [sum(1 for _ in i)])._sum()
 
     def _sum(self):
         """
         Add up the elements in this DStream.
         """
-        return self._mapPartitions(lambda x: [sum(x)]).reduce(operator.add)
+        return self.mapPartitions(lambda x: [sum(x)]).reduce(operator.add)
 
     def print_(self, label=None):
         """
         Since print is reserved name for python, we cannot define a "print" method function.
         This function prints serialized data in RDD in DStream because Scala and Java cannot
-        deserialized pickled python object. Please use DStream.pyprint() instead to print results.
+        deserialized pickled python object. Please use DStream.pyprint() to print results.
 
-        Call DStream.print().
+        Call DStream.print() and this function will print byte array in the DStream
         """
         # a hack to call print function in DStream
         getattr(self._jdstream, "print")(label)
@@ -63,29 +69,32 @@ def filter(self, f):
         Return a new DStream containing only the elements that satisfy predicate.
         """
         def func(iterator): return ifilter(f, iterator)
-        return self._mapPartitions(func)
+        return self.mapPartitions(func)
 
     def flatMap(self, f, preservesPartitioning=False):
         """
         Pass each value in the key-value pair DStream through flatMap function
         without changing the keys: this also retains the original RDD's partition.
         """
-        def func(s, iterator): return chain.from_iterable(imap(f, iterator))
+        def func(s, iterator):
+            return chain.from_iterable(imap(f, iterator))
         return self._mapPartitionsWithIndex(func, preservesPartitioning)
 
-    def map(self, f):
+    def map(self, f, preservesPartitioning=False):
         """
         Return a new DStream by applying a function to each element of DStream.
         """
-        def func(iterator): return imap(f, iterator)
-        return self._mapPartitions(func)
+        def func(iterator):
+            return imap(f, iterator)
+        return self.mapPartitions(func, preservesPartitioning)
 
-    def _mapPartitions(self, f):
+    def mapPartitions(self, f, preservesPartitioning=False):
         """
         Return a new DStream by applying a function to each partition of this DStream.
         """
-        def func(s, iterator): return f(iterator)
-        return self._mapPartitionsWithIndex(func)
+        def func(s, iterator):
+            return f(iterator)
+        return self._mapPartitionsWithIndex(func, preservesPartitioning)
 
     def _mapPartitionsWithIndex(self, f, preservesPartitioning=False):
         """
@@ -131,7 +140,7 @@ def combineLocally(iterator):
                 else:
                     combiners[k] = mergeValue(combiners[k], v)
             return combiners.iteritems()
-        locally_combined = self._mapPartitions(combineLocally)
+        locally_combined = self.mapPartitions(combineLocally)
         shuffled = locally_combined.partitionBy(numPartitions)
 
         def _mergeCombiners(iterator):
@@ -143,7 +152,7 @@ def _mergeCombiners(iterator):
                     combiners[k] = mergeCombiners(combiners[k], v)
             return combiners.iteritems()
 
-        return shuffled._mapPartitions(_mergeCombiners)
+        return shuffled.mapPartitions(_mergeCombiners)
 
     def partitionBy(self, numPartitions, partitionFunc=None):
         """
@@ -246,6 +255,34 @@ def takeAndPrint(rdd, time):
 
         self.foreachRDD(takeAndPrint)
 
+    def mapValues(self, f):
+        """
+        Pass each value in the key-value pair RDD through a map function
+        without changing the keys; this also retains the original RDD's
+        partitioning.
+        """
+        map_values_fn = lambda (k, v): (k, f(v))
+        return self.map(map_values_fn, preservesPartitioning=True)
+
+    def flatMapValues(self, f):
+        """
+        Pass each value in the key-value pair RDD through a flatMap function
+        without changing the keys; this also retains the original RDD's
+        partitioning.
+        """
+        flat_map_fn = lambda (k, v): ((k, x) for x in f(v))
+        return self.flatMap(flat_map_fn, preservesPartitioning=True)
+
+    def glom(self):
+        """
+        Return a new DStream in which RDD is generated by applying glom() to RDD of
+        this DStream. Applying glom() to an RDD coalesces all elements within each partition into
+        an list.
+        """
+        def func(iterator):
+            yield list(iterator)
+        return self.mapPartitions(func)
+
     #def transform(self, func): - TD
     #    from utils import RDDFunction
     #    wrapped_func = RDDFunction(self.ctx, self._jrdd_deserializer, func)
@@ -255,7 +292,7 @@ def takeAndPrint(rdd, time):
     def _test_output(self, result):
         """
         This function is only for test case.
-        Store data in a DStream to result to verify the result in tese case
+        Store data in a DStream to result to verify the result in test case
         """
         def get_output(rdd, time):
             taken = rdd.collect()
@@ -318,4 +355,4 @@ def _jdstream(self):
         return self._jdstream_val
 
     def _is_pipelinable(self):
-        return not (self.is_cached)
+        return not self.is_cached
diff --git a/python/pyspark/streaming_tests.py b/python/pyspark/streaming_tests.py
index ec45acec94dbf..25ea350ca425f 100644
--- a/python/pyspark/streaming_tests.py
+++ b/python/pyspark/streaming_tests.py
@@ -142,10 +142,54 @@ def test_func(dstream):
         output = self._run_stream(test_input, test_func, expected_output)
         self.assertEqual(expected_output, output)
 
-    def _run_stream(self, test_input, test_func, expected_output):
+    def test_mapValues(self):
+        """Basic operation test for DStream.mapValues"""
+        test_input = [["a", "a", "b"], ["", ""], []]
+
+        def test_func(dstream):
+            return dstream.map(lambda x: (x, 1)).reduceByKey(operator.add).mapValues(lambda x: x + 10)
+        expected_output = [[("a", 12), ("b", 11)], [("", 12)], []]
+        output = self._run_stream(test_input, test_func, expected_output)
+        self.assertEqual(expected_output, output)
+
+    def test_flatMapValues(self):
+        """Basic operation test for DStream.flatMapValues"""
+        test_input = [["a", "a", "b"], ["", ""], []]
+
+        def test_func(dstream):
+            return dstream.map(lambda x: (x, 1)).reduceByKey(operator.add).flatMapValues(lambda x: (x, x + 10))
+        expected_output = [[("a", 2), ("a", 12), ("b", 1), ("b", 11)], [("", 2), ("", 12)], []]
+        output = self._run_stream(test_input, test_func, expected_output)
+        self.assertEqual(expected_output, output)
+
+    def test_glom(self):
+        """Basic operation test for DStream.glom"""
+        test_input = [range(1, 5), range(5, 9), range(9, 13)]
+        numSlices = 2
+
+        def test_func(dstream):
+            dstream.pyprint()
+            return dstream.glom()
+        expected_output = [[[1,2], [3,4]],[[5,6], [7,8]],[[9,10], [11,12]]]
+        output = self._run_stream(test_input, test_func, expected_output, numSlices)
+        self.assertEqual(expected_output, output)
+
+    def test_mapPartitions(self):
+        """Basic operation test for DStream.mapPartitions"""
+        test_input = [range(1, 5), range(5, 9), range(9, 13)]
+        numSlices = 2
+
+        def test_func(dstream):
+            dstream.pyprint()
+            return dstream.mapPartitions(lambda x: reduce(operator.add, x))
+        expected_output = [[3, 7],[11, 15],[19, 23]]
+        output = self._run_stream(test_input, test_func, expected_output, numSlices)
+        self.assertEqual(expected_output, output)
+
+    def _run_stream(self, test_input, test_func, expected_output, numSlices=None):
         """Start stream and return the output"""
         # Generate input stream with user-defined input
-        test_input_stream = self.ssc._testInputStream(test_input)
+        test_input_stream = self.ssc._testInputStream(test_input, numSlices)
         # Apply test function to stream
         test_stream = test_func(test_input_stream)
         # Add job to get output from stream

From bcdec333146151da4777bd4df3b53f81483ad816 Mon Sep 17 00:00:00 2001
From: giwa <ugw.gi.world@gmail.com>
Date: Wed, 13 Aug 2014 21:04:26 -0700
Subject: [PATCH 408/489] WIP: solved partitioned and None is not recognized

---
 python/pyspark/streaming/context.py | 20 +++++++++++++++++++-
 python/pyspark/streaming/dstream.py | 16 ++++++++++++++++
 python/pyspark/streaming_tests.py   | 23 +++++++++++++----------
 3 files changed, 48 insertions(+), 11 deletions(-)

diff --git a/python/pyspark/streaming/context.py b/python/pyspark/streaming/context.py
index eee298badcbad..32b52f74e16f0 100644
--- a/python/pyspark/streaming/context.py
+++ b/python/pyspark/streaming/context.py
@@ -154,7 +154,7 @@ def _testInputStream(self, test_inputs, numSlices=None):
 
             # Make sure we distribute data evenly if it's smaller than self.batchSize
             if "__len__" not in dir(test_input):
-                c = list(test_input)    # Make it a list so we can compute its length
+                test_input = list(test_input)    # Make it a list so we can compute its length
             batchSize = min(len(test_input) // numSlices, self._sc._batchSize)
             if batchSize > 1:
                 serializer = BatchedSerializer(self._sc._unbatched_serializer,
@@ -162,6 +162,7 @@ def _testInputStream(self, test_inputs, numSlices=None):
             else:
                 serializer = self._sc._unbatched_serializer
             serializer.dump_stream(test_input, tempFile)
+            tempFile.close()
             tempFiles.append(tempFile.name)
 
         jtempFiles = ListConverter().convert(tempFiles, SparkContext._gateway._gateway_client)
@@ -169,3 +170,20 @@ def _testInputStream(self, test_inputs, numSlices=None):
                                                         jtempFiles,
                                                         numSlices).asJavaDStream()
         return DStream(jinput_stream, self, PickleSerializer())
+
+    
+    def _testInputStream2(self, test_inputs, numSlices=None):
+        """
+        This is inpired by QueStream implementation. Give list of RDD and generate DStream
+        which contain the RDD.
+        """
+        test_rdds = list()
+        for test_input in test_inputs:
+            test_rdd = self._sc.parallelize(test_input, numSlices)
+            print test_rdd.glom().collect()
+            test_rdds.append(test_rdd._jrdd)
+
+        jtest_rdds = ListConverter().convert(test_rdds, SparkContext._gateway._gateway_client)
+        jinput_stream = self._jvm.PythonTestInputStream2(self._jssc, jtest_rdds).asJavaDStream()
+
+        return DStream(jinput_stream, self, BatchedSerializer(PickleSerializer()))
diff --git a/python/pyspark/streaming/dstream.py b/python/pyspark/streaming/dstream.py
index 02f35fac47ac0..240b2983b5b5d 100644
--- a/python/pyspark/streaming/dstream.py
+++ b/python/pyspark/streaming/dstream.py
@@ -246,6 +246,8 @@ def takeAndPrint(rdd, time):
             taken = rdd.take(11)
             print "-------------------------------------------"
             print "Time: %s" % (str(time))
+            print rdd.glom().collect()
+            print "-------------------------------------------"
             print "-------------------------------------------"
             for record in taken[:10]:
                 print record
@@ -301,6 +303,20 @@ def get_output(rdd, time):
         self.foreachRDD(get_output)
 
 
+# TODO: implement groupByKey
+# TODO: impelment union
+# TODO: implement cache
+# TODO: implement persist
+# TODO: implement repertitions
+# TODO: implement saveAsTextFile
+# TODO: implement cogroup
+# TODO: implement join
+# TODO: implement countByValue
+# TODO: implement leftOuterJoin
+# TODO: implemtnt rightOuterJoin
+
+
+
 class PipelinedDStream(DStream):
     def __init__(self, prev, func, preservesPartitioning=False):
         if not isinstance(prev, PipelinedDStream) or not prev._is_pipelinable():
diff --git a/python/pyspark/streaming_tests.py b/python/pyspark/streaming_tests.py
index 25ea350ca425f..e346bc227fe46 100644
--- a/python/pyspark/streaming_tests.py
+++ b/python/pyspark/streaming_tests.py
@@ -71,8 +71,9 @@ class TestBasicOperationsSuite(PySparkStreamingTestCase):
     """
     def setUp(self):
         PySparkStreamingTestCase.setUp(self)
-        StreamOutput.result = list()
         self.timeout = 10  # seconds
+        self.numInputPartitions = 2
+        self.result = list()
 
     def tearDown(self):
         PySparkStreamingTestCase.tearDown(self)
@@ -137,6 +138,8 @@ def test_reduceByKey(self):
         test_input = [["a", "a", "b"], ["", ""], []]
 
         def test_func(dstream):
+            print "reduceByKey"
+            dstream.map(lambda x: (x, 1)).pyprint()
             return dstream.map(lambda x: (x, 1)).reduceByKey(operator.add)
         expected_output = [[("a", 2), ("b", 1)], [("", 2)], []]
         output = self._run_stream(test_input, test_func, expected_output)
@@ -168,9 +171,8 @@ def test_glom(self):
         numSlices = 2
 
         def test_func(dstream):
-            dstream.pyprint()
             return dstream.glom()
-        expected_output = [[[1,2], [3,4]],[[5,6], [7,8]],[[9,10], [11,12]]]
+        expected_output = [[[1,2], [3,4]], [[5,6], [7,8]], [[9,10], [11,12]]]
         output = self._run_stream(test_input, test_func, expected_output, numSlices)
         self.assertEqual(expected_output, output)
 
@@ -180,20 +182,21 @@ def test_mapPartitions(self):
         numSlices = 2
 
         def test_func(dstream):
-            dstream.pyprint()
-            return dstream.mapPartitions(lambda x: reduce(operator.add, x))
-        expected_output = [[3, 7],[11, 15],[19, 23]]
+            def f(iterator): yield sum(iterator)
+            return dstream.mapPartitions(f)
+        expected_output = [[3, 7], [11, 15], [19, 23]]
         output = self._run_stream(test_input, test_func, expected_output, numSlices)
         self.assertEqual(expected_output, output)
 
     def _run_stream(self, test_input, test_func, expected_output, numSlices=None):
         """Start stream and return the output"""
         # Generate input stream with user-defined input
-        test_input_stream = self.ssc._testInputStream(test_input, numSlices)
+        numSlices = numSlices or self.numInputPartitions
+        test_input_stream = self.ssc._testInputStream2(test_input, numSlices)
         # Apply test function to stream
         test_stream = test_func(test_input_stream)
         # Add job to get output from stream
-        test_stream._test_output(StreamOutput.result)
+        test_stream._test_output(self.result)
         self.ssc.start()
 
         start_time = time.time()
@@ -205,9 +208,9 @@ def _run_stream(self, test_input, test_func, expected_output, numSlices=None):
                 break
             self.ssc.awaitTermination(50)
             # check if the output is the same length of expexted output
-            if len(expected_output) == len(StreamOutput.result):
+            if len(expected_output) == len(self.result):
                 break
-        return StreamOutput.result
+        return self.result
 
 if __name__ == "__main__":
     unittest.main()

From ff14070b8dcfcef831909db3add9d849a185ac06 Mon Sep 17 00:00:00 2001
From: giwa <ugw.gi.world@gmail.com>
Date: Thu, 14 Aug 2014 02:19:46 -0700
Subject: [PATCH 409/489] broke something

---
 python/pyspark/streaming/context.py | 10 ++++++----
 python/pyspark/streaming/dstream.py | 20 ++++++++++++++++++++
 python/pyspark/streaming_tests.py   |  2 ++
 python/pyspark/worker.py            | 10 ++++++++++
 4 files changed, 38 insertions(+), 4 deletions(-)

diff --git a/python/pyspark/streaming/context.py b/python/pyspark/streaming/context.py
index 32b52f74e16f0..809158aedbc96 100644
--- a/python/pyspark/streaming/context.py
+++ b/python/pyspark/streaming/context.py
@@ -169,8 +169,7 @@ def _testInputStream(self, test_inputs, numSlices=None):
         jinput_stream = self._jvm.PythonTestInputStream(self._jssc,
                                                         jtempFiles,
                                                         numSlices).asJavaDStream()
-        return DStream(jinput_stream, self, PickleSerializer())
-
+        return DStream(jinput_stream, self, BatchedSerializer(PickleSerializer()))
     
     def _testInputStream2(self, test_inputs, numSlices=None):
         """
@@ -178,12 +177,15 @@ def _testInputStream2(self, test_inputs, numSlices=None):
         which contain the RDD.
         """
         test_rdds = list()
+        test_rdd_deserializers = list()
         for test_input in test_inputs:
             test_rdd = self._sc.parallelize(test_input, numSlices)
-            print test_rdd.glom().collect()
             test_rdds.append(test_rdd._jrdd)
+            test_rdd_deserializers.append(test_rdd._jrdd_deserializer)
 
         jtest_rdds = ListConverter().convert(test_rdds, SparkContext._gateway._gateway_client)
         jinput_stream = self._jvm.PythonTestInputStream2(self._jssc, jtest_rdds).asJavaDStream()
 
-        return DStream(jinput_stream, self, BatchedSerializer(PickleSerializer()))
+        dstream = DStream(jinput_stream, self, test_rdd_deserializers[0])
+        dstream._test_switch_dserializer(test_rdd_deserializers)
+        return dstream
diff --git a/python/pyspark/streaming/dstream.py b/python/pyspark/streaming/dstream.py
index 240b2983b5b5d..f96efb1fd1db7 100644
--- a/python/pyspark/streaming/dstream.py
+++ b/python/pyspark/streaming/dstream.py
@@ -17,6 +17,7 @@
 
 from collections import defaultdict
 from itertools import chain, ifilter, imap
+import time
 import operator
 
 from pyspark.serializers import NoOpSerializer,\
@@ -302,6 +303,25 @@ def get_output(rdd, time):
 
         self.foreachRDD(get_output)
 
+    def _test_switch_dserializer(self, serializer_que):
+        """
+        Deserializer is dynamically changed based on numSlice and the number of
+        input. This function choose deserializer. Currently this is just FIFO.
+        """
+        
+        jrdd_deserializer = self._jrdd_deserializer
+
+        def switch(rdd, jtime):
+            try:
+                print serializer_que
+                jrdd_deserializer = serializer_que.pop(0)
+                print jrdd_deserializer
+            except Exception as e:
+                print e
+
+        self.foreachRDD(switch)
+
+
 
 # TODO: implement groupByKey
 # TODO: impelment union
diff --git a/python/pyspark/streaming_tests.py b/python/pyspark/streaming_tests.py
index e346bc227fe46..e23b86e8f040e 100644
--- a/python/pyspark/streaming_tests.py
+++ b/python/pyspark/streaming_tests.py
@@ -118,6 +118,8 @@ def test_count(self):
         test_input = [[], [1], range(1, 3), range(1, 4), range(1, 5)]
 
         def test_func(dstream):
+            print "count"
+            dstream.count().pyprint()
             return dstream.count()
         expected_output = map(lambda x: [len(x)], test_input)
         output = self._run_stream(test_input, test_func, expected_output)
diff --git a/python/pyspark/worker.py b/python/pyspark/worker.py
index d6c06e2dbef62..5756ca59a6118 100644
--- a/python/pyspark/worker.py
+++ b/python/pyspark/worker.py
@@ -86,6 +86,16 @@ def main(infile, outfile):
         (func, deserializer, serializer) = command
         init_time = time.time()
         iterator = deserializer.load_stream(infile)
+        print "deserializer in worker: %s" % str(deserializer)
+        iterator, walk = itertools.tee(iterator)
+        if isinstance(walk, int):
+            print "this is int"
+            print walk
+        else:
+            try:
+                print list(walk)
+            except:
+                print list(walk)
         serializer.dump_stream(func(split_index, iterator), outfile)
     except Exception:
         try:

From 3000b2b2573aa9cd9d3263bb900dc8cb99f9d9f2 Mon Sep 17 00:00:00 2001
From: giwa <ugw.gi.world@gmail.com>
Date: Thu, 14 Aug 2014 18:07:10 -0700
Subject: [PATCH 410/489] all tests are passed if numSlice is 2 and the numver
 of each input is over 4

---
 python/pyspark/streaming/context.py           |  5 +++-
 python/pyspark/streaming_tests.py             | 28 +++++++++----------
 .../streaming/api/python/PythonDStream.scala  | 17 +++++++++++
 3 files changed, 35 insertions(+), 15 deletions(-)

diff --git a/python/pyspark/streaming/context.py b/python/pyspark/streaming/context.py
index 809158aedbc96..123fa67f837e3 100644
--- a/python/pyspark/streaming/context.py
+++ b/python/pyspark/streaming/context.py
@@ -187,5 +187,8 @@ def _testInputStream2(self, test_inputs, numSlices=None):
         jinput_stream = self._jvm.PythonTestInputStream2(self._jssc, jtest_rdds).asJavaDStream()
 
         dstream = DStream(jinput_stream, self, test_rdd_deserializers[0])
-        dstream._test_switch_dserializer(test_rdd_deserializers)
         return dstream
+
+    def _testInputStream3(self):
+        jinput_stream = self._jvm.PythonTestInputStream3(self._jssc).asJavaDStream()
+        return DStream(jinput_stream, self, UTF8Deserializer())
diff --git a/python/pyspark/streaming_tests.py b/python/pyspark/streaming_tests.py
index e23b86e8f040e..19cce3f185833 100644
--- a/python/pyspark/streaming_tests.py
+++ b/python/pyspark/streaming_tests.py
@@ -37,13 +37,6 @@
 SPARK_HOME = os.environ["SPARK_HOME"]
 
 
-class StreamOutput:
-    """
-    a class to store the output from stream
-    """
-    result = list()
-
-
 class PySparkStreamingTestCase(unittest.TestCase):
     def setUp(self):
         class_name = self.__class__.__name__
@@ -115,7 +108,8 @@ def test_func(dstream):
 
     def test_count(self):
         """Basic operation test for DStream.count"""
-        test_input = [[], [1], range(1, 3), range(1, 4), range(1, 5)]
+        #test_input = [[], [1], range(1, 3), range(1, 4), range(1, 5)]
+        test_input = [range(1, 5), range(1,10), range(1,20)]
 
         def test_func(dstream):
             print "count"
@@ -137,33 +131,39 @@ def test_func(dstream):
 
     def test_reduceByKey(self):
         """Basic operation test for DStream.reduceByKey"""
-        test_input = [["a", "a", "b"], ["", ""], []]
+        #test_input = [["a", "a", "b"], ["", ""], []]
+        test_input = [["a", "a", "b", "b"], ["", "", "", ""], []]
 
         def test_func(dstream):
             print "reduceByKey"
             dstream.map(lambda x: (x, 1)).pyprint()
             return dstream.map(lambda x: (x, 1)).reduceByKey(operator.add)
-        expected_output = [[("a", 2), ("b", 1)], [("", 2)], []]
+        #expected_output = [[("a", 2), ("b", 1)], [("", 2)], []]
+        expected_output = [[("a", 2), ("b", 2)], [("", 4)], []]
         output = self._run_stream(test_input, test_func, expected_output)
         self.assertEqual(expected_output, output)
 
     def test_mapValues(self):
         """Basic operation test for DStream.mapValues"""
-        test_input = [["a", "a", "b"], ["", ""], []]
+        #test_input = [["a", "a", "b"], ["", ""], []]
+        test_input = [["a", "a", "b", "b"], ["", "", "", ""], []]
 
         def test_func(dstream):
             return dstream.map(lambda x: (x, 1)).reduceByKey(operator.add).mapValues(lambda x: x + 10)
-        expected_output = [[("a", 12), ("b", 11)], [("", 12)], []]
+        #expected_output = [[("a", 12), ("b", 11)], [("", 12)], []]
+        expected_output = [[("a", 12), ("b", 12)], [("", 14)], []]
         output = self._run_stream(test_input, test_func, expected_output)
         self.assertEqual(expected_output, output)
 
     def test_flatMapValues(self):
         """Basic operation test for DStream.flatMapValues"""
-        test_input = [["a", "a", "b"], ["", ""], []]
+        #test_input = [["a", "a", "b"], ["", ""], []]
+        test_input = [["a", "a", "b", "b"], ["", "", "",""], []]
 
         def test_func(dstream):
             return dstream.map(lambda x: (x, 1)).reduceByKey(operator.add).flatMapValues(lambda x: (x, x + 10))
-        expected_output = [[("a", 2), ("a", 12), ("b", 1), ("b", 11)], [("", 2), ("", 12)], []]
+        #expected_output = [[("a", 2), ("a", 12), ("b", 1), ("b", 11)], [("", 2), ("", 12)], []]
+        expected_output = [[("a", 2), ("a", 12), ("b", 2), ("b", 12)], [("", 4), ("", 14)], []]
         output = self._run_stream(test_input, test_func, expected_output)
         self.assertEqual(expected_output, output)
 
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
index a24300f626d1a..d1f41cfbb8885 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
@@ -174,6 +174,23 @@ DStream[Array[Byte]](prev.ssc){
   val asJavaDStream  = JavaDStream.fromDStream(this)
 }
 
+
+class PythonTestInputStream3(ssc_ : JavaStreamingContext)
+  extends InputDStream[Any](JavaStreamingContext.toStreamingContext(ssc_)) {
+
+  def start() {}
+
+  def stop() {}
+
+  def compute(validTime: Time): Option[RDD[Any]] = {
+    val index = ((validTime - zeroTime) / slideDuration - 1).toInt
+    val selectedInput = ArrayBuffer(1, 2, 3).toSeq
+    val rdd :RDD[Any] = ssc.sc.makeRDD(selectedInput, 2)
+    Some(rdd)
+  }
+
+  val asJavaDStream = JavaDStream.fromDStream(this)
+}
 class PythonForeachDStream(
     prev: DStream[Array[Byte]],
     foreachFunction: PythonRDDFunction

From 13fb44c64cace81035e33fa16edd0293f654915a Mon Sep 17 00:00:00 2001
From: giwa <ugw.gi.world@gmail.com>
Date: Thu, 14 Aug 2014 23:42:34 -0700
Subject: [PATCH 411/489] basic function test cases are passed

---
 python/pyspark/streaming_tests.py | 209 +++++++++++++++++++++++-------
 python/pyspark/worker.py          |  10 --
 2 files changed, 159 insertions(+), 60 deletions(-)

diff --git a/python/pyspark/streaming_tests.py b/python/pyspark/streaming_tests.py
index 19cce3f185833..6d85a7faae859 100644
--- a/python/pyspark/streaming_tests.py
+++ b/python/pyspark/streaming_tests.py
@@ -24,7 +24,6 @@
 
 """
 from itertools import chain
-import os
 import time
 import unittest
 import operator
@@ -34,9 +33,6 @@
 from pyspark.streaming.duration import *
 
 
-SPARK_HOME = os.environ["SPARK_HOME"]
-
-
 class PySparkStreamingTestCase(unittest.TestCase):
     def setUp(self):
         class_name = self.__class__.__name__
@@ -49,7 +45,7 @@ def tearDown(self):
         self.ssc._sc.stop()
         # Why does it long time to terminaete StremaingContext and SparkContext?
         # Should we change the sleep time if this depends on machine spec?
-        time.sleep(5)
+        time.sleep(8)
 
     @classmethod
     def tearDownClass(cls):
@@ -59,8 +55,17 @@ def tearDownClass(cls):
 
 class TestBasicOperationsSuite(PySparkStreamingTestCase):
     """
-    Input and output of this TestBasicOperationsSuite is the equivalent to 
-    Scala TestBasicOperationsSuite.
+    2 tests for each function for batach deserializer and unbatch deserilizer because
+    we cannot change the deserializer after streaming process starts.
+    Default numInputPartitions is 2.
+    If the number of input element is over 3, that DStream use batach deserializer.
+    If not, that DStream use unbatch deserializer.
+
+    Most of the operation uses UTF8 deserializer to get value from Scala.
+    I am wondering if these test are enough or not.
+    All tests input should have list of lists. This represents stream.
+    Every batch interval, the first object of list are chosen to make DStream.
+    Please see the BasicTestSuits in Scala or QueStream which is close to this implementation.
     """
     def setUp(self):
         PySparkStreamingTestCase.setUp(self)
@@ -75,8 +80,8 @@ def tearDown(self):
     def tearDownClass(cls):
         PySparkStreamingTestCase.tearDownClass()
 
-    def test_map(self):
-        """Basic operation test for DStream.map"""
+    def test_map_batch(self):
+        """Basic operation test for DStream.map with batch deserializer"""
         test_input = [range(1, 5), range(5, 9), range(9, 13)]
 
         def test_func(dstream):
@@ -85,8 +90,18 @@ def test_func(dstream):
         output = self._run_stream(test_input, test_func, expected_output)
         self.assertEqual(expected_output, output)
 
-    def test_flatMap(self):
-        """Basic operation test for DStream.faltMap"""
+    def test_map_unbatach(self):
+        """Basic operation test for DStream.map with unbatch deserializer"""
+        test_input = [range(1, 4), range(4, 7), range(7, 10)]
+
+        def test_func(dstream):
+            return dstream.map(lambda x: str(x))
+        expected_output = map(lambda x: map(lambda y: str(y), x), test_input)
+        output = self._run_stream(test_input, test_func, expected_output)
+        self.assertEqual(expected_output, output)
+
+    def test_flatMap_batch(self):
+        """Basic operation test for DStream.faltMap with batch deserializer"""
         test_input = [range(1, 5), range(5, 9), range(9, 13)]
 
         def test_func(dstream):
@@ -96,8 +111,19 @@ def test_func(dstream):
         output = self._run_stream(test_input, test_func, expected_output)
         self.assertEqual(expected_output, output)
 
-    def test_filter(self):
-        """Basic operation test for DStream.filter"""
+    def test_flatMap_unbatch(self):
+        """Basic operation test for DStream.faltMap with unbatch deserializer"""
+        test_input = [range(1, 4), range(4, 7), range(7, 10)]
+
+        def test_func(dstream):
+            return dstream.flatMap(lambda x: (x, x * 2))
+        expected_output = map(lambda x: list(chain.from_iterable((map(lambda y: [y, y * 2], x)))),
+                              test_input)
+        output = self._run_stream(test_input, test_func, expected_output)
+        self.assertEqual(expected_output, output)
+
+    def test_filter_batch(self):
+        """Basic operation test for DStream.filter with batch deserializer"""
         test_input = [range(1, 5), range(5, 9), range(9, 13)]
 
         def test_func(dstream):
@@ -106,21 +132,38 @@ def test_func(dstream):
         output = self._run_stream(test_input, test_func, expected_output)
         self.assertEqual(expected_output, output)
 
-    def test_count(self):
-        """Basic operation test for DStream.count"""
-        #test_input = [[], [1], range(1, 3), range(1, 4), range(1, 5)]
-        test_input = [range(1, 5), range(1,10), range(1,20)]
+    def test_filter_unbatch(self):
+        """Basic operation test for DStream.filter with unbatch deserializer"""
+        test_input = [range(1, 4), range(4, 7), range(7, 10)]
+
+        def test_func(dstream):
+            return dstream.filter(lambda x: x % 2 == 0)
+        expected_output = map(lambda x: filter(lambda y: y % 2 == 0, x), test_input)
+        output = self._run_stream(test_input, test_func, expected_output)
+        self.assertEqual(expected_output, output)
+
+    def test_count_batch(self):
+        """Basic operation test for DStream.count with batch deserializer"""
+        test_input = [range(1, 5), range(1, 10), range(1, 20)]
 
         def test_func(dstream):
-            print "count"
-            dstream.count().pyprint()
             return dstream.count()
         expected_output = map(lambda x: [len(x)], test_input)
         output = self._run_stream(test_input, test_func, expected_output)
         self.assertEqual(expected_output, output)
-        
-    def test_reduce(self):
-        """Basic operation test for DStream.reduce"""
+
+    def test_count_unbatch(self):
+        """Basic operation test for DStream.count with unbatch deserializer"""
+        test_input = [[], [1], range(1, 3), range(1, 4)]
+
+        def test_func(dstream):
+            return dstream.count()
+        expected_output = map(lambda x: [len(x)], test_input)
+        output = self._run_stream(test_input, test_func, expected_output)
+        self.assertEqual(expected_output, output)
+
+    def test_reduce_batch(self):
+        """Basic operation test for DStream.reduce with batch deserializer"""
         test_input = [range(1, 5), range(5, 9), range(9, 13)]
 
         def test_func(dstream):
@@ -129,67 +172,132 @@ def test_func(dstream):
         output = self._run_stream(test_input, test_func, expected_output)
         self.assertEqual(expected_output, output)
 
-    def test_reduceByKey(self):
-        """Basic operation test for DStream.reduceByKey"""
-        #test_input = [["a", "a", "b"], ["", ""], []]
-        test_input = [["a", "a", "b", "b"], ["", "", "", ""], []]
+    def test_reduce_unbatch(self):
+        """Basic operation test for DStream.reduce with unbatch deserializer"""
+        test_input = [[1], range(1, 3), range(1, 4)]
+
+        def test_func(dstream):
+            return dstream.reduce(operator.add)
+        expected_output = map(lambda x: [reduce(operator.add, x)], test_input)
+        output = self._run_stream(test_input, test_func, expected_output)
+        self.assertEqual(expected_output, output)
+
+    def test_reduceByKey_batch(self):
+        """Basic operation test for DStream.reduceByKey with batch deserializer"""
+        test_input = [["a", "a", "b", "b"], ["", "", "", ""]]
+
+        def test_func(dstream):
+            return dstream.map(lambda x: (x, 1)).reduceByKey(operator.add)
+        expected_output = [[("a", 2), ("b", 2)], [("", 4)]]
+        output = self._run_stream(test_input, test_func, expected_output)
+        self.assertEqual(expected_output, output)
+
+    def test_reduceByKey_unbatch(self):
+        """Basic operation test for DStream.reduceByKey with unbatch deserilizer"""
+        test_input = [["a", "a", "b"], ["", ""], []]
 
         def test_func(dstream):
-            print "reduceByKey"
-            dstream.map(lambda x: (x, 1)).pyprint()
             return dstream.map(lambda x: (x, 1)).reduceByKey(operator.add)
-        #expected_output = [[("a", 2), ("b", 1)], [("", 2)], []]
-        expected_output = [[("a", 2), ("b", 2)], [("", 4)], []]
+        expected_output = [[("a", 2), ("b", 1)], [("", 2)], []]
         output = self._run_stream(test_input, test_func, expected_output)
         self.assertEqual(expected_output, output)
 
-    def test_mapValues(self):
-        """Basic operation test for DStream.mapValues"""
-        #test_input = [["a", "a", "b"], ["", ""], []]
-        test_input = [["a", "a", "b", "b"], ["", "", "", ""], []]
+    def test_mapValues_batch(self):
+        """Basic operation test for DStream.mapValues with batch deserializer"""
+        test_input = [["a", "a", "b", "b"], ["", "", "", ""]]
 
         def test_func(dstream):
-            return dstream.map(lambda x: (x, 1)).reduceByKey(operator.add).mapValues(lambda x: x + 10)
-        #expected_output = [[("a", 12), ("b", 11)], [("", 12)], []]
-        expected_output = [[("a", 12), ("b", 12)], [("", 14)], []]
+            return dstream.map(lambda x: (x, 1))\
+                          .reduceByKey(operator.add)\
+                          .mapValues(lambda x: x + 10)
+        expected_output = [[("a", 12), ("b", 12)], [("", 14)]]
         output = self._run_stream(test_input, test_func, expected_output)
         self.assertEqual(expected_output, output)
 
-    def test_flatMapValues(self):
-        """Basic operation test for DStream.flatMapValues"""
-        #test_input = [["a", "a", "b"], ["", ""], []]
-        test_input = [["a", "a", "b", "b"], ["", "", "",""], []]
+    def test_mapValues_unbatch(self):
+        """Basic operation test for DStream.mapValues with unbatch deserializer"""
+        test_input = [["a", "a", "b"], ["", ""], []]
 
         def test_func(dstream):
-            return dstream.map(lambda x: (x, 1)).reduceByKey(operator.add).flatMapValues(lambda x: (x, x + 10))
-        #expected_output = [[("a", 2), ("a", 12), ("b", 1), ("b", 11)], [("", 2), ("", 12)], []]
-        expected_output = [[("a", 2), ("a", 12), ("b", 2), ("b", 12)], [("", 4), ("", 14)], []]
+            return dstream.map(lambda x: (x, 1))\
+                          .reduceByKey(operator.add)\
+                          .mapValues(lambda x: x + 10)
+        expected_output = [[("a", 12), ("b", 11)], [("", 12)], []]
         output = self._run_stream(test_input, test_func, expected_output)
         self.assertEqual(expected_output, output)
 
-    def test_glom(self):
-        """Basic operation test for DStream.glom"""
+    def test_flatMapValues_batch(self):
+        """Basic operation test for DStream.flatMapValues with batch deserializer"""
+        test_input = [["a", "a", "b", "b"], ["", "", "", ""]]
+
+        def test_func(dstream):
+            return dstream.map(lambda x: (x, 1))\
+                          .reduceByKey(operator.add)\
+                          .flatMapValues(lambda x: (x, x + 10))
+        expected_output = [[("a", 2), ("a", 12), ("b", 2), ("b", 12)], [("", 4), ("", 14)]]
+        output = self._run_stream(test_input, test_func, expected_output)
+        self.assertEqual(expected_output, output)
+
+    def test_flatMapValues_unbatch(self):
+        """Basic operation test for DStream.flatMapValues with unbatch deserializer"""
+        test_input = [["a", "a", "b"], ["", ""], []]
+
+        def test_func(dstream):
+            return dstream.map(lambda x: (x, 1))\
+                          .reduceByKey(operator.add)\
+                          .flatMapValues(lambda x: (x, x + 10))
+        expected_output = [[("a", 2), ("a", 12), ("b", 1), ("b", 11)], [("", 2), ("", 12)], []]
+        output = self._run_stream(test_input, test_func, expected_output)
+        self.assertEqual(expected_output, output)
+
+    def test_glom_batch(self):
+        """Basic operation test for DStream.glom with batch deserializer"""
         test_input = [range(1, 5), range(5, 9), range(9, 13)]
         numSlices = 2
 
         def test_func(dstream):
             return dstream.glom()
-        expected_output = [[[1,2], [3,4]], [[5,6], [7,8]], [[9,10], [11,12]]]
+        expected_output = [[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]]
+        output = self._run_stream(test_input, test_func, expected_output, numSlices)
+        self.assertEqual(expected_output, output)
+
+    def test_glom_unbatach(self):
+        """Basic operation test for DStream.glom with unbatch deserialiser"""
+        test_input = [range(1, 4), range(4, 7), range(7, 10)]
+        numSlices = 2
+
+        def test_func(dstream):
+            return dstream.glom()
+        expected_output = [[[1], [2, 3]], [[4], [5, 6]], [[7], [8, 9]]]
         output = self._run_stream(test_input, test_func, expected_output, numSlices)
         self.assertEqual(expected_output, output)
 
-    def test_mapPartitions(self):
-        """Basic operation test for DStream.mapPartitions"""
+    def test_mapPartitions_batch(self):
+        """Basic operation test for DStream.mapPartitions with batch deserializer"""
         test_input = [range(1, 5), range(5, 9), range(9, 13)]
         numSlices = 2
 
         def test_func(dstream):
-            def f(iterator): yield sum(iterator)
+            def f(iterator):
+                yield sum(iterator)
             return dstream.mapPartitions(f)
         expected_output = [[3, 7], [11, 15], [19, 23]]
         output = self._run_stream(test_input, test_func, expected_output, numSlices)
         self.assertEqual(expected_output, output)
 
+    def test_mapPartitions_unbatch(self):
+        """Basic operation test for DStream.mapPartitions with unbatch deserializer"""
+        test_input = [range(1, 4), range(4, 7), range(7, 10)]
+        numSlices = 2
+
+        def test_func(dstream):
+            def f(iterator):
+                yield sum(iterator)
+            return dstream.mapPartitions(f)
+        expected_output = [[1, 5], [4, 11], [7, 17]]
+        output = self._run_stream(test_input, test_func, expected_output, numSlices)
+        self.assertEqual(expected_output, output)
+
     def _run_stream(self, test_input, test_func, expected_output, numSlices=None):
         """Start stream and return the output"""
         # Generate input stream with user-defined input
@@ -212,6 +320,7 @@ def _run_stream(self, test_input, test_func, expected_output, numSlices=None):
             # check if the output is the same length of expexted output
             if len(expected_output) == len(self.result):
                 break
+
         return self.result
 
 if __name__ == "__main__":
diff --git a/python/pyspark/worker.py b/python/pyspark/worker.py
index 5756ca59a6118..d6c06e2dbef62 100644
--- a/python/pyspark/worker.py
+++ b/python/pyspark/worker.py
@@ -86,16 +86,6 @@ def main(infile, outfile):
         (func, deserializer, serializer) = command
         init_time = time.time()
         iterator = deserializer.load_stream(infile)
-        print "deserializer in worker: %s" % str(deserializer)
-        iterator, walk = itertools.tee(iterator)
-        if isinstance(walk, int):
-            print "this is int"
-            print walk
-        else:
-            try:
-                print list(walk)
-            except:
-                print list(walk)
         serializer.dump_stream(func(split_index, iterator), outfile)
     except Exception:
         try:

From 18c8723e54481e0ab6702fbf6abf1724fd357e63 Mon Sep 17 00:00:00 2001
From: giwa <ugw.gi.world@gmail.com>
Date: Thu, 14 Aug 2014 23:46:45 -0700
Subject: [PATCH 412/489] modified streaming test case to add coment

---
 python/pyspark/streaming_tests.py | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/python/pyspark/streaming_tests.py b/python/pyspark/streaming_tests.py
index 6d85a7faae859..02996ccce9a3e 100644
--- a/python/pyspark/streaming_tests.py
+++ b/python/pyspark/streaming_tests.py
@@ -18,6 +18,9 @@
 """
 Unit tests for PySpark; additional tests are implemented as doctests in
 individual modules.
+Other option is separate this test case with other tests.
+This makes sense becuase streaming tests takes long time due to waiting time
+for stoping callback server.
 
 This file will merged to tests.py. But for now, this file is separated due
 to focusing to streaming test case
@@ -45,7 +48,7 @@ def tearDown(self):
         self.ssc._sc.stop()
         # Why does it long time to terminaete StremaingContext and SparkContext?
         # Should we change the sleep time if this depends on machine spec?
-        time.sleep(8)
+        time.sleep(10)
 
     @classmethod
     def tearDownClass(cls):
@@ -302,7 +305,7 @@ def _run_stream(self, test_input, test_func, expected_output, numSlices=None):
         """Start stream and return the output"""
         # Generate input stream with user-defined input
         numSlices = numSlices or self.numInputPartitions
-        test_input_stream = self.ssc._testInputStream2(test_input, numSlices)
+        test_input_stream = self.ssc._testInputStream(test_input, numSlices)
         # Apply test function to stream
         test_stream = test_func(test_input_stream)
         # Add job to get output from stream

From f76c18200c3bce35994f5b9e08ce55dc150bf664 Mon Sep 17 00:00:00 2001
From: giwa <ugw.gi.world@gmail.com>
Date: Fri, 15 Aug 2014 11:28:39 -0700
Subject: [PATCH 413/489] remove waste duplicated code

---
 python/pyspark/streaming/context.py | 43 +----------------
 python/pyspark/streaming/dstream.py | 75 +++++++++++++++++++++--------
 2 files changed, 56 insertions(+), 62 deletions(-)

diff --git a/python/pyspark/streaming/context.py b/python/pyspark/streaming/context.py
index 123fa67f837e3..60bcf86783e95 100644
--- a/python/pyspark/streaming/context.py
+++ b/python/pyspark/streaming/context.py
@@ -130,48 +130,7 @@ def stop(self, stopSparkContext=True, stopGraceFully=False):
             # Stop Callback server
             SparkContext._gateway.shutdown()
 
-    def checkpoint(self, directory):
-        """
-        Not tested
-        """
-        self._jssc.checkpoint(directory)
-
     def _testInputStream(self, test_inputs, numSlices=None):
-        """
-        Generate multiple files to make "stream" in Scala side for test.
-        Scala chooses one of the files and generates RDD using PythonRDD.readRDDFromFile.
-
-        QueStream maybe good way to implement this function
-        """
-        numSlices = numSlices or self._sc.defaultParallelism
-        # Calling the Java parallelize() method with an ArrayList is too slow,
-        # because it sends O(n) Py4J commands.  As an alternative, serialized
-        # objects are written to a file and loaded through textFile().
-
-        tempFiles = list()
-        for test_input in test_inputs:
-            tempFile = NamedTemporaryFile(delete=False, dir=self._sc._temp_dir)
-
-            # Make sure we distribute data evenly if it's smaller than self.batchSize
-            if "__len__" not in dir(test_input):
-                test_input = list(test_input)    # Make it a list so we can compute its length
-            batchSize = min(len(test_input) // numSlices, self._sc._batchSize)
-            if batchSize > 1:
-                serializer = BatchedSerializer(self._sc._unbatched_serializer,
-                                               batchSize)
-            else:
-                serializer = self._sc._unbatched_serializer
-            serializer.dump_stream(test_input, tempFile)
-            tempFile.close()
-            tempFiles.append(tempFile.name)
-
-        jtempFiles = ListConverter().convert(tempFiles, SparkContext._gateway._gateway_client)
-        jinput_stream = self._jvm.PythonTestInputStream(self._jssc,
-                                                        jtempFiles,
-                                                        numSlices).asJavaDStream()
-        return DStream(jinput_stream, self, BatchedSerializer(PickleSerializer()))
-    
-    def _testInputStream2(self, test_inputs, numSlices=None):
         """
         This is inpired by QueStream implementation. Give list of RDD and generate DStream
         which contain the RDD.
@@ -184,7 +143,7 @@ def _testInputStream2(self, test_inputs, numSlices=None):
             test_rdd_deserializers.append(test_rdd._jrdd_deserializer)
 
         jtest_rdds = ListConverter().convert(test_rdds, SparkContext._gateway._gateway_client)
-        jinput_stream = self._jvm.PythonTestInputStream2(self._jssc, jtest_rdds).asJavaDStream()
+        jinput_stream = self._jvm.PythonTestInputStream(self._jssc, jtest_rdds).asJavaDStream()
 
         dstream = DStream(jinput_stream, self, test_rdd_deserializers[0])
         return dstream
diff --git a/python/pyspark/streaming/dstream.py b/python/pyspark/streaming/dstream.py
index f96efb1fd1db7..183826cf2ef96 100644
--- a/python/pyspark/streaming/dstream.py
+++ b/python/pyspark/streaming/dstream.py
@@ -17,12 +17,13 @@
 
 from collections import defaultdict
 from itertools import chain, ifilter, imap
-import time
 import operator
 
 from pyspark.serializers import NoOpSerializer,\
     BatchedSerializer, CloudPickleSerializer, pack_long
 from pyspark.rdd import _JavaStackTrace
+from pyspark.storagelevel import StorageLevel
+from pyspark.resultiterable import ResultIterable
 
 from py4j.java_collections import ListConverter, MapConverter
 
@@ -35,6 +36,8 @@ def __init__(self, jdstream, ssc, jrdd_deserializer):
         self._ssc = ssc
         self.ctx = ssc._sc
         self._jrdd_deserializer = jrdd_deserializer
+        self.is_cached = False
+        self.is_checkpointed = False
 
     def context(self):
         """
@@ -247,8 +250,6 @@ def takeAndPrint(rdd, time):
             taken = rdd.take(11)
             print "-------------------------------------------"
             print "Time: %s" % (str(time))
-            print rdd.glom().collect()
-            print "-------------------------------------------"
             print "-------------------------------------------"
             for record in taken[:10]:
                 print record
@@ -303,32 +304,65 @@ def get_output(rdd, time):
 
         self.foreachRDD(get_output)
 
-    def _test_switch_dserializer(self, serializer_que):
+    def cache(self):
+        """
+        Persist this DStream with the default storage level (C{MEMORY_ONLY_SER}).
+        """
+        self.is_cached = True
+        self.persist(StorageLevel.MEMORY_ONLY_SER)
+        return self
+
+    def persist(self, storageLevel):
+        """
+        Set this DStream's storage level to persist its values across operations
+        after the first time it is computed. This can only be used to assign
+        a new storage level if the DStream does not have a storage level set yet.
+        """
+        self.is_cached = True
+        javaStorageLevel = self.ctx._getJavaStorageLevel(storageLevel)
+        self._jdstream.persist(javaStorageLevel)
+        return self
+
+    def checkpoint(self, interval):
         """
-        Deserializer is dynamically changed based on numSlice and the number of
-        input. This function choose deserializer. Currently this is just FIFO.
+        Mark this DStream for checkpointing. It will be saved to a file inside the
+        checkpoint directory set with L{SparkContext.setCheckpointDir()}
+
+        I am not sure this part in DStream
+        and
+        all references to its parent RDDs will be removed. This function must
+        be called before any job has been executed on this RDD. It is strongly
+        recommended that this RDD is persisted in memory, otherwise saving it
+        on a file will require recomputation.
+
+        interval must be pysprak.streaming.duration
         """
-        
-        jrdd_deserializer = self._jrdd_deserializer
+        self.is_checkpointed = True
+        self._jdstream.checkpoint(interval)
+        return self
+
+    def groupByKey(self, numPartitions=None):
+        def createCombiner(x):
+            return [x]
 
-        def switch(rdd, jtime):
-            try:
-                print serializer_que
-                jrdd_deserializer = serializer_que.pop(0)
-                print jrdd_deserializer
-            except Exception as e:
-                print e
+        def mergeValue(xs, x):
+            xs.append(x)
+            return xs
 
-        self.foreachRDD(switch)
+        def mergeCombiners(a, b):
+            a.extend(b)
+            return a
 
+        return self.combineByKey(createCombiner, mergeValue, mergeCombiners,
+                                 numPartitions).mapValues(lambda x: ResultIterable(x))
 
 
 # TODO: implement groupByKey
+# TODO: implement saveAsTextFile
+
+# Following operation has dependency to transform
 # TODO: impelment union
-# TODO: implement cache
-# TODO: implement persist
 # TODO: implement repertitions
-# TODO: implement saveAsTextFile
 # TODO: implement cogroup
 # TODO: implement join
 # TODO: implement countByValue
@@ -355,6 +389,7 @@ def pipeline_func(split, iterator):
             self._prev_jdstream = prev._prev_jdstream  # maintain the pipeline
             self._prev_jrdd_deserializer = prev._prev_jrdd_deserializer
         self.is_cached = False
+        self.is_checkpointed = False
         self._ssc = prev._ssc
         self.ctx = prev.ctx
         self.prev = prev
@@ -391,4 +426,4 @@ def _jdstream(self):
         return self._jdstream_val
 
     def _is_pipelinable(self):
-        return not self.is_cached
+        return not (self.is_cached or self.is_checkpointed)

From 74535d40ec21b09af6d0160dbbe735228aa2445f Mon Sep 17 00:00:00 2001
From: giwa <ugw.gi.world@gmail.com>
Date: Fri, 15 Aug 2014 17:10:56 -0700
Subject: [PATCH 414/489] added saveAsTextFiles and saveAsPickledFiles

---
 python/pyspark/streaming/context.py | 17 +++++++-------
 python/pyspark/streaming/dstream.py | 35 +++++++++++++++++++++++++----
 python/pyspark/streaming/utils.py   |  6 +++++
 python/pyspark/streaming_tests.py   | 32 ++++++++++++++++++++++++++
 4 files changed, 77 insertions(+), 13 deletions(-)

diff --git a/python/pyspark/streaming/context.py b/python/pyspark/streaming/context.py
index 60bcf86783e95..691f9b06ad4e9 100644
--- a/python/pyspark/streaming/context.py
+++ b/python/pyspark/streaming/context.py
@@ -114,7 +114,7 @@ def textFileStream(self, directory):
         Create an input stream that monitors a Hadoop-compatible file system
         for new files and reads them as text files. Files must be wrriten to the
         monitored directory by "moving" them from another location within the same
-        file system. FIle names starting with . are ignored.
+        file system. File names starting with . are ignored.
         """
         return DStream(self._jssc.textFileStream(directory), self, UTF8Deserializer())
 
@@ -132,8 +132,9 @@ def stop(self, stopSparkContext=True, stopGraceFully=False):
 
     def _testInputStream(self, test_inputs, numSlices=None):
         """
-        This is inpired by QueStream implementation. Give list of RDD and generate DStream
-        which contain the RDD.
+        This function is only for test.
+        This implementation is inpired by QueStream implementation. 
+        Give list of RDD to generate DStream which contains the RDD.
         """
         test_rdds = list()
         test_rdd_deserializers = list()
@@ -142,12 +143,10 @@ def _testInputStream(self, test_inputs, numSlices=None):
             test_rdds.append(test_rdd._jrdd)
             test_rdd_deserializers.append(test_rdd._jrdd_deserializer)
 
+#        if len(set(test_rdd_deserializers)) > 1:
+#            raise IOError("Deserializer should be one type to run test case. "
+#                          "See the SparkContext.parallelize to understand how to decide deserializer")
         jtest_rdds = ListConverter().convert(test_rdds, SparkContext._gateway._gateway_client)
         jinput_stream = self._jvm.PythonTestInputStream(self._jssc, jtest_rdds).asJavaDStream()
 
-        dstream = DStream(jinput_stream, self, test_rdd_deserializers[0])
-        return dstream
-
-    def _testInputStream3(self):
-        jinput_stream = self._jvm.PythonTestInputStream3(self._jssc).asJavaDStream()
-        return DStream(jinput_stream, self, UTF8Deserializer())
+        return DStream(jinput_stream, self, test_rdd_deserializers[0])
diff --git a/python/pyspark/streaming/dstream.py b/python/pyspark/streaming/dstream.py
index 183826cf2ef96..dd51dcc5d4d48 100644
--- a/python/pyspark/streaming/dstream.py
+++ b/python/pyspark/streaming/dstream.py
@@ -24,6 +24,8 @@
 from pyspark.rdd import _JavaStackTrace
 from pyspark.storagelevel import StorageLevel
 from pyspark.resultiterable import ResultIterable
+from pyspark.streaming.utils import rddToFileName
+
 
 from py4j.java_collections import ListConverter, MapConverter
 
@@ -356,21 +358,46 @@ def mergeCombiners(a, b):
         return self.combineByKey(createCombiner, mergeValue, mergeCombiners,
                                  numPartitions).mapValues(lambda x: ResultIterable(x))
 
+    def countByValue(self):
+        def countPartition(iterator):
+            counts = defaultdict(int)
+            for obj in iterator:
+                counts[obj] += 1
+            yield counts
+
+        def mergeMaps(m1, m2):
+            for (k, v) in m2.iteritems():
+                m1[k] += v
+            return m1
+
+        return self.mapPartitions(countPartition).reduce(mergeMaps).flatMap(lambda x: x.items())
+
+    def saveAsTextFiles(self, prefix, suffix=None):
+
+        def saveAsTextFile(rdd, time):
+            path = rddToFileName(prefix, suffix, time)
+            rdd.saveAsTextFile(path)
+
+        return self.foreachRDD(saveAsTextFile)
+
+    def saveAsPickledFiles(self, prefix, suffix=None):
+
+        def saveAsTextFile(rdd, time):
+            path = rddToFileName(prefix, suffix, time)
+            rdd.saveAsPickleFile(path)
+
+        return self.foreachRDD(saveAsTextFile)
 
-# TODO: implement groupByKey
-# TODO: implement saveAsTextFile
 
 # Following operation has dependency to transform
 # TODO: impelment union
 # TODO: implement repertitions
 # TODO: implement cogroup
 # TODO: implement join
-# TODO: implement countByValue
 # TODO: implement leftOuterJoin
 # TODO: implemtnt rightOuterJoin
 
 
-
 class PipelinedDStream(DStream):
     def __init__(self, prev, func, preservesPartitioning=False):
         if not isinstance(prev, PipelinedDStream) or not prev._is_pipelinable():
diff --git a/python/pyspark/streaming/utils.py b/python/pyspark/streaming/utils.py
index aa5e19adbd927..9178577743e0b 100644
--- a/python/pyspark/streaming/utils.py
+++ b/python/pyspark/streaming/utils.py
@@ -53,3 +53,9 @@ def msDurationToString(ms):
         return "%.1f m" % (float(ms) / minute)
     else:
         return "%.2f h" % (float(ms) / hour)
+
+def rddToFileName(prefix, suffix, time):
+    if suffix is not None:
+        return prefix + "-" + str(time) + "." + suffix
+    else:
+        return prefix + "-" + str(time)
diff --git a/python/pyspark/streaming_tests.py b/python/pyspark/streaming_tests.py
index 02996ccce9a3e..2bb01ed3a0642 100644
--- a/python/pyspark/streaming_tests.py
+++ b/python/pyspark/streaming_tests.py
@@ -301,6 +301,38 @@ def f(iterator):
         output = self._run_stream(test_input, test_func, expected_output, numSlices)
         self.assertEqual(expected_output, output)
 
+    def test_countByValue_batch(self):
+        """Basic operation test for DStream.countByValue with batch deserializer"""
+        test_input = [range(1, 5) + range(1,5), range(5, 7) + range(5, 9), ["a"] * 2 + ["b"] + [""] ]
+
+        def test_func(dstream):
+            return dstream.countByValue()
+        expected_output = [[(1, 2), (2, 2), (3, 2), (4, 2)],
+                           [(5, 2), (6, 2), (7, 1), (8, 1)],
+                           [("a", 2), ("b", 1), ("", 1)]]
+        output = self._run_stream(test_input, test_func, expected_output)
+        for result in (output, expected_output):
+            self._sort_result_based_on_key(result)
+        self.assertEqual(expected_output, output)
+
+    def test_countByValue_unbatch(self):
+        """Basic operation test for DStream.countByValue with unbatch deserializer"""
+        test_input = [range(1, 4), [1, 1, ""], ["a", "a", "b"]]
+
+        def test_func(dstream):
+            return dstream.countByValue()
+        expected_output = [[(1, 1), (2, 1), (3, 1)],
+                           [(1, 2), ("", 1)],
+                           [("a", 2), ("b", 1)]]
+        output = self._run_stream(test_input, test_func, expected_output)
+        for result in (output, expected_output):
+            self._sort_result_based_on_key(result)
+        self.assertEqual(expected_output, output)
+
+    def _sort_result_based_on_key(self, outputs):
+        for output in outputs:
+            output.sort(key=lambda x: x[0])
+
     def _run_stream(self, test_input, test_func, expected_output, numSlices=None):
         """Start stream and return the output"""
         # Generate input stream with user-defined input

From 16aa64f2a8547856c918090dbc43a9601c78e0fd Mon Sep 17 00:00:00 2001
From: giwa <ugw.gi.world@gmail.com>
Date: Fri, 15 Aug 2014 22:30:58 -0700
Subject: [PATCH 415/489] added TODO coments

---
 python/pyspark/streaming/context.py |  3 ++-
 python/pyspark/streaming/dstream.py | 16 ++++++++++++++--
 2 files changed, 16 insertions(+), 3 deletions(-)

diff --git a/python/pyspark/streaming/context.py b/python/pyspark/streaming/context.py
index 691f9b06ad4e9..470ed270cdbfb 100644
--- a/python/pyspark/streaming/context.py
+++ b/python/pyspark/streaming/context.py
@@ -17,7 +17,6 @@
 
 import sys
 from signal import signal, SIGTERM, SIGINT
-from tempfile import NamedTemporaryFile
 
 from pyspark.serializers import PickleSerializer, BatchedSerializer, UTF8Deserializer
 from pyspark.context import SparkContext
@@ -79,6 +78,7 @@ def _clean_up_trigger(self):
         """Kill py4j callback server properly using signal lib"""
 
         def clean_up_handler(*args):
+            SparkContext._gateway._shutdown_callback_server()
             SparkContext._gateway.shutdown()
             sys.exit(0)
 
@@ -128,6 +128,7 @@ def stop(self, stopSparkContext=True, stopGraceFully=False):
             self._jssc.stop(stopSparkContext, stopGraceFully)
         finally:
             # Stop Callback server
+            SparkContext._gateway._shutdown_callback_server()
             SparkContext._gateway.shutdown()
 
     def _testInputStream(self, test_inputs, numSlices=None):
diff --git a/python/pyspark/streaming/dstream.py b/python/pyspark/streaming/dstream.py
index dd51dcc5d4d48..78efe8897a5b2 100644
--- a/python/pyspark/streaming/dstream.py
+++ b/python/pyspark/streaming/dstream.py
@@ -389,15 +389,27 @@ def saveAsTextFile(rdd, time):
         return self.foreachRDD(saveAsTextFile)
 
 
+# TODO: implement updateStateByKey
+# TODO: implement slice
+
+# Window Operations
+# TODO: implement window
+# TODO: implement groupByKeyAndWindow
+# TODO: implement reduceByKeyAndWindow
+# TODO: implement countByValueAndWindow
+# TODO: implement countByWindow
+# TODO: implement reduceByWindow
+
 # Following operation has dependency to transform
-# TODO: impelment union
+# TODO: implement transform
+# TODO: implement transformWith
+# TODO: implement union
 # TODO: implement repertitions
 # TODO: implement cogroup
 # TODO: implement join
 # TODO: implement leftOuterJoin
 # TODO: implemtnt rightOuterJoin
 
-
 class PipelinedDStream(DStream):
     def __init__(self, prev, func, preservesPartitioning=False):
         if not isinstance(prev, PipelinedDStream) or not prev._is_pipelinable():

From e54f98687b59f4674f0099a70d4bc39ef7981667 Mon Sep 17 00:00:00 2001
From: giwa <ugw.gi.world@gmail.com>
Date: Mon, 18 Aug 2014 00:30:17 -0700
Subject: [PATCH 416/489] add comments

---
 python/pyspark/java_gateway.py      |  5 ++---
 python/pyspark/streaming/context.py | 13 ++++++-----
 python/pyspark/streaming/dstream.py | 24 ++++++++++++++++++++
 python/pyspark/streaming_tests.py   | 34 ++++++++++++++++++++++-------
 4 files changed, 59 insertions(+), 17 deletions(-)

diff --git a/python/pyspark/java_gateway.py b/python/pyspark/java_gateway.py
index d7e7bbf806544..f3c6d231ab777 100644
--- a/python/pyspark/java_gateway.py
+++ b/python/pyspark/java_gateway.py
@@ -108,15 +108,14 @@ def run(self):
     java_import(gateway.jvm, "org.apache.spark.SparkConf")
     java_import(gateway.jvm, "org.apache.spark.api.java.*")
     java_import(gateway.jvm, "org.apache.spark.api.python.*")
-    java_import(gateway.jvm, "org.apache.spark.streaming.*")
+    java_import(gateway.jvm, "org.apache.spark.streaming.*")  # do we need this?
     java_import(gateway.jvm, "org.apache.spark.streaming.api.java.*")
     java_import(gateway.jvm, "org.apache.spark.streaming.api.python.*")
-    java_import(gateway.jvm, "org.apache.spark.streaming.dstream.*")
+    java_import(gateway.jvm, "org.apache.spark.streaming.dstream.*")  # do we need this?
     java_import(gateway.jvm, "org.apache.spark.mllib.api.python.*")
     java_import(gateway.jvm, "org.apache.spark.sql.SQLContext")
     java_import(gateway.jvm, "org.apache.spark.sql.hive.HiveContext")
     java_import(gateway.jvm, "org.apache.spark.sql.hive.LocalHiveContext")
     java_import(gateway.jvm, "org.apache.spark.sql.hive.TestHiveContext")
     java_import(gateway.jvm, "scala.Tuple2")
-
     return gateway
diff --git a/python/pyspark/streaming/context.py b/python/pyspark/streaming/context.py
index 470ed270cdbfb..e380626aa080b 100644
--- a/python/pyspark/streaming/context.py
+++ b/python/pyspark/streaming/context.py
@@ -64,7 +64,9 @@ def __init__(self, master=None, appName=None, sparkHome=None, pyFiles=None,
                         pyFiles=pyFiles, environment=environment, batchSize=batchSize,
                         serializer=serializer, conf=conf, gateway=gateway)
 
-        # Start py4j callback server
+        # Start py4j callback server.
+        # Callback sever is need only by SparkStreming; therefore the callback sever
+        # is started in StreamingContext.
         SparkContext._gateway.restart_callback_server()
         self._clean_up_trigger()
         self._jvm = self._sc._jvm
@@ -78,6 +80,8 @@ def _clean_up_trigger(self):
         """Kill py4j callback server properly using signal lib"""
 
         def clean_up_handler(*args):
+            # Make sure stop callback server.
+            # This need improvement how to terminate callback sever properly.
             SparkContext._gateway._shutdown_callback_server()
             SparkContext._gateway.shutdown()
             sys.exit(0)
@@ -100,7 +104,7 @@ def awaitTermination(self, timeout=None):
         else:
             self._jssc.awaitTermination(timeout)
 
-    # start from simple one. storageLevel is not passed for now.
+    #TODO: add storageLevel
     def socketTextStream(self, hostname, port):
         """
         Create an input from TCP source hostname:port. Data is received using
@@ -134,7 +138,7 @@ def stop(self, stopSparkContext=True, stopGraceFully=False):
     def _testInputStream(self, test_inputs, numSlices=None):
         """
         This function is only for test.
-        This implementation is inpired by QueStream implementation. 
+        This implementation is inspired by QueStream implementation.
         Give list of RDD to generate DStream which contains the RDD.
         """
         test_rdds = list()
@@ -144,9 +148,6 @@ def _testInputStream(self, test_inputs, numSlices=None):
             test_rdds.append(test_rdd._jrdd)
             test_rdd_deserializers.append(test_rdd._jrdd_deserializer)
 
-#        if len(set(test_rdd_deserializers)) > 1:
-#            raise IOError("Deserializer should be one type to run test case. "
-#                          "See the SparkContext.parallelize to understand how to decide deserializer")
         jtest_rdds = ListConverter().convert(test_rdds, SparkContext._gateway._gateway_client)
         jinput_stream = self._jvm.PythonTestInputStream(self._jssc, jtest_rdds).asJavaDStream()
 
diff --git a/python/pyspark/streaming/dstream.py b/python/pyspark/streaming/dstream.py
index 78efe8897a5b2..5fa30f6d89fbd 100644
--- a/python/pyspark/streaming/dstream.py
+++ b/python/pyspark/streaming/dstream.py
@@ -344,6 +344,17 @@ def checkpoint(self, interval):
         return self
 
     def groupByKey(self, numPartitions=None):
+        """
+        Return a new DStream which contains group the values for each key in the
+        DStream into a single sequence.
+        Hash-partitions the resulting RDD with into numPartitions partitions in
+        the DStream.
+
+        Note: If you are grouping in order to perform an aggregation (such as a
+        sum or average) over each key, using reduceByKey will provide much
+        better performance.
+
+        """
         def createCombiner(x):
             return [x]
 
@@ -359,6 +370,10 @@ def mergeCombiners(a, b):
                                  numPartitions).mapValues(lambda x: ResultIterable(x))
 
     def countByValue(self):
+        """
+        Return new DStream which contains the count of each unique value in this
+        DStreeam as a (value, count) pairs.
+        """
         def countPartition(iterator):
             counts = defaultdict(int)
             for obj in iterator:
@@ -373,6 +388,9 @@ def mergeMaps(m1, m2):
         return self.mapPartitions(countPartition).reduce(mergeMaps).flatMap(lambda x: x.items())
 
     def saveAsTextFiles(self, prefix, suffix=None):
+        """
+        Save this DStream as a text file, using string representations of elements.
+        """
 
         def saveAsTextFile(rdd, time):
             path = rddToFileName(prefix, suffix, time)
@@ -381,6 +399,11 @@ def saveAsTextFile(rdd, time):
         return self.foreachRDD(saveAsTextFile)
 
     def saveAsPickledFiles(self, prefix, suffix=None):
+        """
+        Save this DStream as a SequenceFile of serialized objects. The serializer
+        used is L{pyspark.serializers.PickleSerializer}, default batch size
+        is 10.
+        """
 
         def saveAsTextFile(rdd, time):
             path = rddToFileName(prefix, suffix, time)
@@ -410,6 +433,7 @@ def saveAsTextFile(rdd, time):
 # TODO: implement leftOuterJoin
 # TODO: implemtnt rightOuterJoin
 
+
 class PipelinedDStream(DStream):
     def __init__(self, prev, func, preservesPartitioning=False):
         if not isinstance(prev, PipelinedDStream) or not prev._is_pipelinable():
diff --git a/python/pyspark/streaming_tests.py b/python/pyspark/streaming_tests.py
index 2bb01ed3a0642..ef308fdd6aa59 100644
--- a/python/pyspark/streaming_tests.py
+++ b/python/pyspark/streaming_tests.py
@@ -18,12 +18,11 @@
 """
 Unit tests for PySpark; additional tests are implemented as doctests in
 individual modules.
-Other option is separate this test case with other tests.
-This makes sense becuase streaming tests takes long time due to waiting time
-for stoping callback server.
 
-This file will merged to tests.py. But for now, this file is separated due
-to focusing to streaming test case
+This file would be merged to tests.py after all functions are ready.
+But for now, this file is separated due to focusing to streaming test case.
+
+Callback server seems like unstable sometimes, which cause error in test case.
 
 """
 from itertools import chain
@@ -43,10 +42,10 @@ def setUp(self):
 
     def tearDown(self):
         # Do not call pyspark.streaming.context.StreamingContext.stop directly because
-        # we do not wait to shutdowncall back server and py4j client
+        # we do not wait to shutdown call back server and py4j client
         self.ssc._jssc.stop()
         self.ssc._sc.stop()
-        # Why does it long time to terminaete StremaingContext and SparkContext?
+        # Why does it long time to terminate StremaingContext and SparkContext?
         # Should we change the sleep time if this depends on machine spec?
         time.sleep(10)
 
@@ -68,7 +67,7 @@ class TestBasicOperationsSuite(PySparkStreamingTestCase):
     I am wondering if these test are enough or not.
     All tests input should have list of lists. This represents stream.
     Every batch interval, the first object of list are chosen to make DStream.
-    Please see the BasicTestSuits in Scala or QueStream which is close to this implementation.
+    Please see the BasicTestSuits in Scala which is close to this implementation.
     """
     def setUp(self):
         PySparkStreamingTestCase.setUp(self)
@@ -358,5 +357,24 @@ def _run_stream(self, test_input, test_func, expected_output, numSlices=None):
 
         return self.result
 
+class TestSaveAsFilesSuite(PySparkStreamingTestCase):
+    def setUp(self):
+        PySparkStreamingTestCase.setUp(self)
+        self.timeout = 10  # seconds
+        self.numInputPartitions = 2
+        self.result = list()
+
+    def tearDown(self):
+        PySparkStreamingTestCase.tearDown(self)
+
+    @classmethod
+    def tearDownClass(cls):
+        PySparkStreamingTestCase.tearDownClass()
+
+
+
+
+
+
 if __name__ == "__main__":
     unittest.main()

From 10b5b0416521665ae8c29aecf03723c8607a51bc Mon Sep 17 00:00:00 2001
From: giwa <ugw.gi.world@gmail.com>
Date: Mon, 18 Aug 2014 00:35:50 -0700
Subject: [PATCH 417/489] removed wasted print in DStream

---
 .../streaming/api/java/JavaDStreamLike.scala    |  9 ---------
 .../spark/streaming/dstream/DStream.scala       | 17 -----------------
 2 files changed, 26 deletions(-)

diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaDStreamLike.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaDStreamLike.scala
index 7a002bbe74ca9..a6184de4e83c1 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaDStreamLike.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaDStreamLike.scala
@@ -54,15 +54,6 @@ trait JavaDStreamLike[T, This <: JavaDStreamLike[T, This, R], R <: JavaRDDLike[T
     dstream.print()
   }
 
-  def print(label: String = null): Unit = {
-    dstream.print(label)
-  }
-
-  def outputToFile(): Unit = {
-    dstream.outputToFile()
-  }
-
-
   /**
    * Return a new DStream in which each RDD has a single element generated by counting each RDD
    * of this DStream.
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala
index 46ef05d9c37a1..39ad591e8896e 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala
@@ -617,23 +617,6 @@ abstract class DStream[T: ClassTag] (
     new ForEachDStream(this, context.sparkContext.clean(foreachFunc)).register()
   }
 
-
-  def print(label: String = null) {
-    def foreachFunc = (rdd: RDD[T], time: Time) => {
-      val first11 = rdd.take(11)
-      println ("-------------------------------------------")
-      println ("Time: " + time)
-      println ("-------------------------------------------")
-      if(label != null){
-        println (label)
-      }
-      first11.take(10).foreach(println)
-      if (first11.size > 10) println("...")
-      println()
-    }
-    new ForEachDStream(this, context.sparkContext.clean(foreachFunc)).register()
-  }
-
   /**
    * Return a new DStream in which each RDD contains all the elements in seen in a
    * sliding window of time over this DStream. The new DStream generates RDDs with

From 10ab87b0710163a5694333567e519d06ded78b7a Mon Sep 17 00:00:00 2001
From: giwa <ugw.gi.world@gmail.com>
Date: Mon, 18 Aug 2014 13:24:17 -0700
Subject: [PATCH 418/489] added sparkContext as input parameter in
 StreamingContext

---
 python/pyspark/streaming/context.py | 16 ++++++++++------
 1 file changed, 10 insertions(+), 6 deletions(-)

diff --git a/python/pyspark/streaming/context.py b/python/pyspark/streaming/context.py
index e380626aa080b..3f455a3e06072 100644
--- a/python/pyspark/streaming/context.py
+++ b/python/pyspark/streaming/context.py
@@ -34,7 +34,7 @@ class StreamingContext(object):
 
     def __init__(self, master=None, appName=None, sparkHome=None, pyFiles=None,
         environment=None, batchSize=1024, serializer=PickleSerializer(), conf=None,
-        gateway=None, duration=None):
+        gateway=None, sparkContext=None, duration=None):
         """
         Create a new StreamingContext. At least the master and app name and duration
         should be set, either through the named parameters here or through C{conf}.
@@ -55,14 +55,18 @@ def __init__(self, master=None, appName=None, sparkHome=None, pyFiles=None,
         @param conf: A L{SparkConf} object setting Spark properties.
         @param gateway: Use an existing gateway and JVM, otherwise a new JVM
                will be instatiated.
-        @param duration: A L{Duration} Duration for SparkStreaming
+        @param sparkContext: L{SparkContext} object.
+        @param duration: A L{Duration} object for SparkStreaming.
 
         """
 
-        # Create the Python Sparkcontext
-        self._sc = SparkContext(master=master, appName=appName, sparkHome=sparkHome,
-                        pyFiles=pyFiles, environment=environment, batchSize=batchSize,
-                        serializer=serializer, conf=conf, gateway=gateway)
+        if sparkContext is None:
+            # Create the Python Sparkcontext
+            self._sc = SparkContext(master=master, appName=appName, sparkHome=sparkHome,
+                            pyFiles=pyFiles, environment=environment, batchSize=batchSize,
+                            serializer=serializer, conf=conf, gateway=gateway)
+        else:
+            self._sc = sparkContext
 
         # Start py4j callback server.
         # Callback sever is need only by SparkStreming; therefore the callback sever

From 5625bdc1e0f01449752040b5e2bf867a7ef09e64 Mon Sep 17 00:00:00 2001
From: giwa <ugw.gi.world@gmail.com>
Date: Mon, 18 Aug 2014 14:39:45 -0700
Subject: [PATCH 419/489] added gorupByKey testcase

---
 python/pyspark/streaming_tests.py | 70 ++++++++++++++++++++++++-------
 1 file changed, 54 insertions(+), 16 deletions(-)

diff --git a/python/pyspark/streaming_tests.py b/python/pyspark/streaming_tests.py
index ef308fdd6aa59..c35d352c66ca5 100644
--- a/python/pyspark/streaming_tests.py
+++ b/python/pyspark/streaming_tests.py
@@ -275,7 +275,7 @@ def test_func(dstream):
         self.assertEqual(expected_output, output)
 
     def test_mapPartitions_batch(self):
-        """Basic operation test for DStream.mapPartitions with batch deserializer"""
+        """Basic operation test for DStream.mapPartitions with batch deserializer."""
         test_input = [range(1, 5), range(5, 9), range(9, 13)]
         numSlices = 2
 
@@ -288,7 +288,7 @@ def f(iterator):
         self.assertEqual(expected_output, output)
 
     def test_mapPartitions_unbatch(self):
-        """Basic operation test for DStream.mapPartitions with unbatch deserializer"""
+        """Basic operation test for DStream.mapPartitions with unbatch deserializer."""
         test_input = [range(1, 4), range(4, 7), range(7, 10)]
         numSlices = 2
 
@@ -301,8 +301,8 @@ def f(iterator):
         self.assertEqual(expected_output, output)
 
     def test_countByValue_batch(self):
-        """Basic operation test for DStream.countByValue with batch deserializer"""
-        test_input = [range(1, 5) + range(1,5), range(5, 7) + range(5, 9), ["a"] * 2 + ["b"] + [""] ]
+        """Basic operation test for DStream.countByValue with batch deserializer."""
+        test_input = [range(1, 5) + range(1,5), range(5, 7) + range(5, 9), ["a", "a", "b", ""]]
 
         def test_func(dstream):
             return dstream.countByValue()
@@ -315,7 +315,7 @@ def test_func(dstream):
         self.assertEqual(expected_output, output)
 
     def test_countByValue_unbatch(self):
-        """Basic operation test for DStream.countByValue with unbatch deserializer"""
+        """Basic operation test for DStream.countByValue with unbatch deserializer."""
         test_input = [range(1, 4), [1, 1, ""], ["a", "a", "b"]]
 
         def test_func(dstream):
@@ -328,30 +328,72 @@ def test_func(dstream):
             self._sort_result_based_on_key(result)
         self.assertEqual(expected_output, output)
 
+    def test_groupByKey_batch(self):
+        """Basic operation test for DStream.groupByKey with batch deserializer."""
+        test_input = [range(1, 5), [1, 1, 1, 2, 2, 3], ["a", "a", "b", "", "", ""]]
+        def test_func(dstream):
+            return dstream.map(lambda x: (x,1)).groupByKey()
+        expected_output = [[(1, [1]), (2, [1]), (3, [1]), (4, [1])],
+                           [(1, [1, 1, 1]), (2, [1, 1]), (3, [1])],
+                           [("a", [1, 1]), ("b", [1]), ("", [1, 1, 1])]]
+        scattered_output = self._run_stream(test_input, test_func, expected_output)
+        output = self._convert_iter_value_to_list(scattered_output)
+        for result in (output, expected_output):
+            self._sort_result_based_on_key(result)
+        self.assertEqual(expected_output, output)
+
+    def test_groupByKey_unbatch(self):
+        """Basic operation test for DStream.groupByKey with unbatch deserializer."""
+        test_input = [range(1, 4), [1, 1, ""], ["a", "a", "b"]]
+        def test_func(dstream):
+            return dstream.map(lambda x: (x,1)).groupByKey()
+        expected_output = [[(1, [1]), (2, [1]), (3, [1])],
+                           [(1, [1, 1]), ("", [1])],
+                           [("a", [1, 1]), ("b", [1])]]
+        scattered_output = self._run_stream(test_input, test_func, expected_output)
+        output = self._convert_iter_value_to_list(scattered_output)
+        for result in (output, expected_output):
+            self._sort_result_based_on_key(result)
+        self.assertEqual(expected_output, output)
+
+    def _convert_iter_value_to_list(self, outputs):
+        """Return key value pair list. Value is converted to iterator to list."""
+        result = list()
+        for output in outputs:
+            result.append(map(lambda (x, y): (x, list(y)), output))
+        return result
+
     def _sort_result_based_on_key(self, outputs):
+        """Sort the list base onf first value."""
         for output in outputs:
             output.sort(key=lambda x: x[0])
 
     def _run_stream(self, test_input, test_func, expected_output, numSlices=None):
-        """Start stream and return the output"""
-        # Generate input stream with user-defined input
+        """
+        Start stream and return the output.
+        @param test_input: dataset for the test. This should be list of lists.
+        @param test_func: wrapped test_function. This function should return PythonDstream object.
+        @param expexted_output: expected output for this testcase.
+        @param numSlices: the number of slices in the rdd in the dstream.
+        """
+        # Generate input stream with user-defined input.
         numSlices = numSlices or self.numInputPartitions
         test_input_stream = self.ssc._testInputStream(test_input, numSlices)
-        # Apply test function to stream
+        # Apply test function to stream.
         test_stream = test_func(test_input_stream)
-        # Add job to get output from stream
+        # Add job to get output from stream.
         test_stream._test_output(self.result)
         self.ssc.start()
 
         start_time = time.time()
-        # loop until get the result from stream
+        # Loop until get the expected the number of the result from the stream.
         while True:
             current_time = time.time()
-            # check time out
+            # Check time out.
             if (current_time - start_time) > self.timeout:
                 break
             self.ssc.awaitTermination(50)
-            # check if the output is the same length of expexted output
+            # Check if the output is the same length of expexted output.
             if len(expected_output) == len(self.result):
                 break
 
@@ -372,9 +414,5 @@ def tearDownClass(cls):
         PySparkStreamingTestCase.tearDownClass()
 
 
-
-
-
-
 if __name__ == "__main__":
     unittest.main()

From c2141995851c59388f1f0e3d01f6018c428fbef2 Mon Sep 17 00:00:00 2001
From: giwa <ugw.gi.world@gmail.com>
Date: Mon, 18 Aug 2014 15:12:31 -0700
Subject: [PATCH 420/489] added testcase for combineByKey

---
 python/pyspark/streaming_tests.py | 35 +++++++++++++++++++++++++++++--
 1 file changed, 33 insertions(+), 2 deletions(-)

diff --git a/python/pyspark/streaming_tests.py b/python/pyspark/streaming_tests.py
index c35d352c66ca5..7f6960faed1a0 100644
--- a/python/pyspark/streaming_tests.py
+++ b/python/pyspark/streaming_tests.py
@@ -332,7 +332,7 @@ def test_groupByKey_batch(self):
         """Basic operation test for DStream.groupByKey with batch deserializer."""
         test_input = [range(1, 5), [1, 1, 1, 2, 2, 3], ["a", "a", "b", "", "", ""]]
         def test_func(dstream):
-            return dstream.map(lambda x: (x,1)).groupByKey()
+            return dstream.map(lambda x: (x, 1)).groupByKey()
         expected_output = [[(1, [1]), (2, [1]), (3, [1]), (4, [1])],
                            [(1, [1, 1, 1]), (2, [1, 1]), (3, [1])],
                            [("a", [1, 1]), ("b", [1]), ("", [1, 1, 1])]]
@@ -345,8 +345,9 @@ def test_func(dstream):
     def test_groupByKey_unbatch(self):
         """Basic operation test for DStream.groupByKey with unbatch deserializer."""
         test_input = [range(1, 4), [1, 1, ""], ["a", "a", "b"]]
+
         def test_func(dstream):
-            return dstream.map(lambda x: (x,1)).groupByKey()
+            return dstream.map(lambda x: (x, 1)).groupByKey()
         expected_output = [[(1, [1]), (2, [1]), (3, [1])],
                            [(1, [1, 1]), ("", [1])],
                            [("a", [1, 1]), ("b", [1])]]
@@ -356,6 +357,36 @@ def test_func(dstream):
             self._sort_result_based_on_key(result)
         self.assertEqual(expected_output, output)
 
+    def test_combineByKey_batch(self):
+        """Basic operation test for DStream.combineByKey with batch deserializer."""
+        test_input = [range(1, 5), [1, 1, 1, 2, 2, 3], ["a", "a", "b", "", "", ""]]
+
+        def test_func(dstream):
+            def add(a, b): return a + str(b)
+            return dstream.map(lambda x: (x, 1)).combineByKey(str, add, add)
+        expected_output = [[(1, "1"), (2, "1"), (3, "1"), (4, "1")],
+                           [(1, "111"), (2, "11"), (3, "1")],
+                           [("a", "11"), ("b", "1"), ("", "111")]]
+        output = self._run_stream(test_input, test_func, expected_output)
+        for result in (output, expected_output):
+            self._sort_result_based_on_key(result)
+        self.assertEqual(expected_output, output)
+
+    def test_combineByKey_unbatch(self):
+        """Basic operation test for DStream.combineByKey with unbatch deserializer."""
+        test_input = [range(1, 4), [1, 1, ""], ["a", "a", "b"]]
+
+        def test_func(dstream):
+            def add(a, b): return a + str(b)
+            return dstream.map(lambda x: (x, 1)).combineByKey(str, add, add)
+        expected_output = [[(1, "1"), (2, "1"), (3, "1")],
+                           [(1, "11"), ("", "1")],
+                           [("a", "11"), ("b", "1")]]
+        output = self._run_stream(test_input, test_func, expected_output)
+        for result in (output, expected_output):
+            self._sort_result_based_on_key(result)
+        self.assertEqual(expected_output, output)
+
     def _convert_iter_value_to_list(self, outputs):
         """Return key value pair list. Value is converted to iterator to list."""
         result = list()

From 0b99becf8607fbea151eac48ea4a8205f7514edb Mon Sep 17 00:00:00 2001
From: Ken <ugw.gi.world@gmail.com>
Date: Tue, 8 Jul 2014 18:31:41 -0700
Subject: [PATCH 421/489] initial commit for pySparkStreaming

---
 bin/spark-submit                              |  11 +
 core/pom.xml                                  |   4 +
 .../src/main/python/streaming/wordcount.py    |  16 ++
 python/pyspark/java_gateway.py                |   6 +
 python/pyspark/streaming/__init__.py          |   1 +
 python/pyspark/streaming/context.py           |  99 ++++++++
 python/pyspark/streaming/dstream.py           | 225 ++++++++++++++++++
 python/pyspark/streaming/duration.py          | 110 +++++++++
 python/pyspark/streaming/jtime.py             |   7 +
 python/pyspark/streaming/pyprint.py           |  18 ++
 python/pyspark/streaming/utils.py             |  11 +
 streaming/pom.xml                             |  10 +-
 .../streaming/api/java/JavaDStreamLike.scala  |   8 +
 .../streaming/api/python/PythonDStream.scala  |  57 +++++
 .../spark/streaming/dstream/DStream.scala     |  64 +++++
 15 files changed, 644 insertions(+), 3 deletions(-)

diff --git a/bin/spark-submit b/bin/spark-submit
index c557311b4b20e..37e973a50b6fa 100755
--- a/bin/spark-submit
+++ b/bin/spark-submit
@@ -48,6 +48,7 @@ export SPARK_SUBMIT_PROPERTIES_FILE=${SPARK_SUBMIT_PROPERTIES_FILE:-"$DEFAULT_PR
 # paths, library paths, java options and memory early on. Otherwise, it will
 # be too late by the time the driver JVM has started.
 
+<<<<<<< HEAD
 if [[ "$SPARK_SUBMIT_DEPLOY_MODE" == "client" && -f "$SPARK_SUBMIT_PROPERTIES_FILE" ]]; then
   # Parse the properties file only if the special configs exist
   contains_special_configs=$(
@@ -57,6 +58,16 @@ if [[ "$SPARK_SUBMIT_DEPLOY_MODE" == "client" && -f "$SPARK_SUBMIT_PROPERTIES_FI
   if [ -n "$contains_special_configs" ]; then
     export SPARK_SUBMIT_BOOTSTRAP_DRIVER=1
   fi
+=======
+# Figure out which Python executable to use
+if [[ -z "$PYSPARK_PYTHON" ]]; then
+  PYSPARK_PYTHON="python"
+fi
+export PYSPARK_PYTHON
+
+if [ -n "$DRIVER_MEMORY" ] && [ $DEPLOY_MODE == "client" ]; then
+  export SPARK_DRIVER_MEMORY=$DRIVER_MEMORY
+>>>>>>> initial commit for pySparkStreaming
 fi
 
 exec "$SPARK_HOME"/bin/spark-class org.apache.spark.deploy.SparkSubmit "${ORIG_ARGS[@]}"
diff --git a/core/pom.xml b/core/pom.xml
index 2a81f6df289c0..7eb0b48eaeebd 100644
--- a/core/pom.xml
+++ b/core/pom.xml
@@ -21,7 +21,11 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent</artifactId>
+<<<<<<< HEAD
     <version>1.2.0-SNAPSHOT</version>
+=======
+    <version>1.0.0</version>
+>>>>>>> initial commit for pySparkStreaming
     <relativePath>../pom.xml</relativePath>
   </parent>
 
diff --git a/examples/src/main/python/streaming/wordcount.py b/examples/src/main/python/streaming/wordcount.py
index 2426345711086..c19eb74c44ed6 100644
--- a/examples/src/main/python/streaming/wordcount.py
+++ b/examples/src/main/python/streaming/wordcount.py
@@ -1,7 +1,10 @@
 import sys
 from operator import add
 
+<<<<<<< HEAD
 from pyspark.conf import SparkConf
+=======
+>>>>>>> initial commit for pySparkStreaming
 from pyspark.streaming.context import StreamingContext
 from pyspark.streaming.duration import *
 
@@ -9,6 +12,7 @@
     if len(sys.argv) != 2:
         print >> sys.stderr, "Usage: wordcount <directory>"
         exit(-1)
+<<<<<<< HEAD
     conf = SparkConf()
     conf.setAppName("PythonStreamingWordCount")
 
@@ -20,5 +24,17 @@
     count = mapped_words.reduceByKey(add)
     
     count.pyprint()
+=======
+    ssc = StreamingContext(appName="PythonStreamingWordCount", duration=Seconds(1))
+
+    lines = ssc.textFileStream(sys.argv[1])
+    fm_lines = lines.flatMap(lambda x: x.split(" "))
+    filtered_lines = fm_lines.filter(lambda line: "Spark" in line)
+    mapped_lines = fm_lines.map(lambda x: (x, 1))
+    
+    fm_lines.pyprint()
+    filtered_lines.pyprint()
+    mapped_lines.pyprint()
+>>>>>>> initial commit for pySparkStreaming
     ssc.start()
     ssc.awaitTermination()
diff --git a/python/pyspark/java_gateway.py b/python/pyspark/java_gateway.py
index f3c6d231ab777..9b18696213691 100644
--- a/python/pyspark/java_gateway.py
+++ b/python/pyspark/java_gateway.py
@@ -108,10 +108,16 @@ def run(self):
     java_import(gateway.jvm, "org.apache.spark.SparkConf")
     java_import(gateway.jvm, "org.apache.spark.api.java.*")
     java_import(gateway.jvm, "org.apache.spark.api.python.*")
+<<<<<<< HEAD
     java_import(gateway.jvm, "org.apache.spark.streaming.*")  # do we need this?
     java_import(gateway.jvm, "org.apache.spark.streaming.api.java.*")
     java_import(gateway.jvm, "org.apache.spark.streaming.api.python.*")
     java_import(gateway.jvm, "org.apache.spark.streaming.dstream.*")  # do we need this?
+=======
+    java_import(gateway.jvm, "org.apache.spark.streaming.*")
+    java_import(gateway.jvm, "org.apache.spark.streaming.api.java.*")
+    java_import(gateway.jvm, "org.apache.spark.streaming.api.python.*")
+>>>>>>> initial commit for pySparkStreaming
     java_import(gateway.jvm, "org.apache.spark.mllib.api.python.*")
     java_import(gateway.jvm, "org.apache.spark.sql.SQLContext")
     java_import(gateway.jvm, "org.apache.spark.sql.hive.HiveContext")
diff --git a/python/pyspark/streaming/__init__.py b/python/pyspark/streaming/__init__.py
index e69de29bb2d1d..719592912e80c 100644
--- a/python/pyspark/streaming/__init__.py
+++ b/python/pyspark/streaming/__init__.py
@@ -0,0 +1 @@
+__author__ = 'ktakagiw'
diff --git a/python/pyspark/streaming/context.py b/python/pyspark/streaming/context.py
index 3f455a3e06072..e65d2243ca7d5 100644
--- a/python/pyspark/streaming/context.py
+++ b/python/pyspark/streaming/context.py
@@ -1,3 +1,9 @@
+<<<<<<< HEAD
+=======
+__author__ = 'ktakagiw'
+
+
+>>>>>>> initial commit for pySparkStreaming
 #
 # Licensed to the Apache Software Foundation (ASF) under one or more
 # contributor license agreements.  See the NOTICE file distributed with
@@ -15,6 +21,7 @@
 # limitations under the License.
 #
 
+<<<<<<< HEAD
 import sys
 from signal import signal, SIGTERM, SIGINT
 
@@ -29,12 +36,43 @@ class StreamingContext(object):
     """
     Main entry point for Spark Streaming functionality. A StreamingContext represents the
     connection to a Spark cluster, and can be used to create L{DStream}s and
+=======
+import os
+import shutil
+import sys
+from threading import Lock
+from tempfile import NamedTemporaryFile
+
+from pyspark import accumulators
+from pyspark.accumulators import Accumulator
+from pyspark.broadcast import Broadcast
+from pyspark.conf import SparkConf
+from pyspark.files import SparkFiles
+from pyspark.java_gateway import launch_gateway
+from pyspark.serializers import PickleSerializer, BatchedSerializer, UTF8Deserializer
+from pyspark.storagelevel import StorageLevel
+from pyspark.rdd import RDD
+from pyspark.context import SparkContext
+
+from py4j.java_collections import ListConverter
+
+from pyspark.streaming.dstream import DStream
+
+class StreamingContext(object):
+    """
+    Main entry point for Spark functionality. A StreamingContext represents the
+    connection to a Spark cluster, and can be used to create L{RDD}s and
+>>>>>>> initial commit for pySparkStreaming
     broadcast variables on that cluster.
     """
 
     def __init__(self, master=None, appName=None, sparkHome=None, pyFiles=None,
         environment=None, batchSize=1024, serializer=PickleSerializer(), conf=None,
+<<<<<<< HEAD
         gateway=None, sparkContext=None, duration=None):
+=======
+        gateway=None, duration=None):
+>>>>>>> initial commit for pySparkStreaming
         """
         Create a new StreamingContext. At least the master and app name and duration
         should be set, either through the named parameters here or through C{conf}.
@@ -55,6 +93,7 @@ def __init__(self, master=None, appName=None, sparkHome=None, pyFiles=None,
         @param conf: A L{SparkConf} object setting Spark properties.
         @param gateway: Use an existing gateway and JVM, otherwise a new JVM
                will be instatiated.
+<<<<<<< HEAD
         @param sparkContext: L{SparkContext} object.
         @param duration: A L{Duration} object for SparkStreaming.
 
@@ -73,6 +112,15 @@ def __init__(self, master=None, appName=None, sparkHome=None, pyFiles=None,
         # is started in StreamingContext.
         SparkContext._gateway.restart_callback_server()
         self._clean_up_trigger()
+=======
+        @param duration: A L{Duration} Duration for SparkStreaming
+
+        """
+        # Create the Python Sparkcontext
+        self._sc = SparkContext(master=master, appName=appName, sparkHome=sparkHome,
+                        pyFiles=pyFiles, environment=environment, batchSize=batchSize,
+                        serializer=serializer, conf=conf, gateway=gateway)
+>>>>>>> initial commit for pySparkStreaming
         self._jvm = self._sc._jvm
         self._jssc = self._initialize_context(self._sc._jsc, duration._jduration)
 
@@ -80,6 +128,7 @@ def __init__(self, master=None, appName=None, sparkHome=None, pyFiles=None,
     def _initialize_context(self, jspark_context, jduration):
         return self._jvm.JavaStreamingContext(jspark_context, jduration)
 
+<<<<<<< HEAD
     def _clean_up_trigger(self):
         """Kill py4j callback server properly using signal lib"""
 
@@ -156,3 +205,53 @@ def _testInputStream(self, test_inputs, numSlices=None):
         jinput_stream = self._jvm.PythonTestInputStream(self._jssc, jtest_rdds).asJavaDStream()
 
         return DStream(jinput_stream, self, test_rdd_deserializers[0])
+=======
+    def actorStream(self, props, name, storageLevel, supervisorStrategy):
+        raise NotImplementedError
+
+    def addStreamingListener(self, streamingListener):
+        raise NotImplementedError
+
+    def awaitTermination(self, timeout=None):
+        if timeout:
+            self._jssc.awaitTermination(timeout)
+        else:
+            self._jssc.awaitTermination()
+
+    def checkpoint(self, directory):
+        raise NotImplementedError
+
+    def fileStream(self, directory, filter=None, newFilesOnly=None):
+        raise NotImplementedError
+
+    def networkStream(self, receiver):
+        raise NotImplementedError
+
+    def queueStream(self, queue, oneAtATime=True, defaultRDD=None):
+        raise NotImplementedError
+
+    def rawSocketStream(self, hostname, port, storagelevel):
+        raise NotImplementedError
+
+    def remember(self, duration):
+        raise NotImplementedError
+
+    def socketStream(hostname, port, converter,storageLevel):
+        raise NotImplementedError
+
+    def start(self):
+        self._jssc.start()
+
+    def stop(self, stopSparkContext=True):
+        raise NotImplementedError
+
+    def textFileStream(self, directory):
+        return DStream(self._jssc.textFileStream(directory), self, UTF8Deserializer())
+
+    def transform(self, seq):
+        raise NotImplementedError
+
+    def union(self, seq):
+        raise NotImplementedError
+
+>>>>>>> initial commit for pySparkStreaming
diff --git a/python/pyspark/streaming/dstream.py b/python/pyspark/streaming/dstream.py
index 5fa30f6d89fbd..eeb6b5644d1d3 100644
--- a/python/pyspark/streaming/dstream.py
+++ b/python/pyspark/streaming/dstream.py
@@ -1,3 +1,4 @@
+<<<<<<< HEAD
 #
 # Licensed to the Apache Software Foundation (ASF) under one or more
 # contributor license agreements.  See the NOTICE file distributed with
@@ -26,18 +27,50 @@
 from pyspark.resultiterable import ResultIterable
 from pyspark.streaming.utils import rddToFileName
 
+=======
+from base64 import standard_b64encode as b64enc
+import copy
+from collections import defaultdict
+from collections import namedtuple
+from itertools import chain, ifilter, imap
+import operator
+import os
+import sys
+import shlex
+import traceback
+from subprocess import Popen, PIPE
+from tempfile import NamedTemporaryFile
+from threading import Thread
+import warnings
+import heapq
+from random import Random
+
+from pyspark.serializers import NoOpSerializer, CartesianDeserializer, \
+    BatchedSerializer, CloudPickleSerializer, PairDeserializer, pack_long
+from pyspark.join import python_join, python_left_outer_join, \
+    python_right_outer_join, python_cogroup
+from pyspark.statcounter import StatCounter
+from pyspark.rddsampler import RDDSampler
+from pyspark.storagelevel import StorageLevel
+#from pyspark.resultiterable import ResultIterable
+from pyspark.rdd import _JavaStackTrace
+>>>>>>> initial commit for pySparkStreaming
 
 from py4j.java_collections import ListConverter, MapConverter
 
 __all__ = ["DStream"]
 
+<<<<<<< HEAD
 
+=======
+>>>>>>> initial commit for pySparkStreaming
 class DStream(object):
     def __init__(self, jdstream, ssc, jrdd_deserializer):
         self._jdstream = jdstream
         self._ssc = ssc
         self.ctx = ssc._sc
         self._jrdd_deserializer = jrdd_deserializer
+<<<<<<< HEAD
         self.is_cached = False
         self.is_checkpointed = False
 
@@ -73,12 +106,81 @@ def print_(self, label=None):
     def filter(self, f):
         """
         Return a new DStream containing only the elements that satisfy predicate.
+=======
+
+    def generatedRDDs(self):
+        """
+         // RDDs generated, marked as private[streaming] so that testsuites can access it
+         @transient
+        """
+        pass
+
+    def print_(self):
+        """
+        """
+        # print is a resrved name of Python. We cannot give print to function name
+        getattr(self._jdstream, "print")()
+
+    def pyprint(self):
+        """
+        """
+        self._jdstream.pyprint()
+
+    def cache(self):
+        """
+        """
+        raise NotImplementedError
+
+    def checkpoint(self):
+        """
+        """
+        raise NotImplementedError
+
+    def compute(self, time):
+        """
+        """
+        raise NotImplementedError
+
+    def context(self):
+        """
+        """
+        raise NotImplementedError
+
+    def count(self):
+        """
+        """
+        raise NotImplementedError
+
+    def countByValue(self, numPartitions=None):
+        """
+        """
+        raise NotImplementedError
+
+    def countByValueAndWindow(self, duration, slideDuration=None):
+        """
+        """
+        raise NotImplementedError
+
+    def countByWindow(self, duration, slideDuration=None):
+        """
+        """
+        raise NotImplementedError
+
+    def dstream(self):
+        """
+        """
+        raise NotImplementedError
+
+    def filter(self, f):
+        """
+>>>>>>> initial commit for pySparkStreaming
         """
         def func(iterator): return ifilter(f, iterator)
         return self.mapPartitions(func)
 
     def flatMap(self, f, preservesPartitioning=False):
         """
+<<<<<<< HEAD
         Pass each value in the key-value pair DStream through flatMap function
         without changing the keys: this also retains the original RDD's partition.
         """
@@ -137,6 +239,51 @@ def combineByKey(self, createCombiner, mergeValue, mergeCombiners,
         if numPartitions is None:
             numPartitions = self._defaultReducePartitions()
 
+=======
+        """
+        def func(s, iterator): return chain.from_iterable(imap(f, iterator))
+        return self.mapPartitionsWithIndex(func, preservesPartitioning)
+
+    def foreachRDD(self, f, time):
+        """
+        """
+        raise NotImplementedError
+
+    def glom(self):
+        """
+        """
+        raise NotImplementedError
+
+    def map(self, f, preservesPartitioning=False):
+        """
+        """
+        def func(split, iterator): return imap(f, iterator)
+        return PipelinedDStream(self, func, preservesPartitioning)
+
+    def mapPartitions(self, f):
+        """
+        """
+        def func(s, iterator): return f(iterator)
+        return self.mapPartitionsWithIndex(func)
+
+    def perist(self, storageLevel):
+        """
+        """
+        raise NotImplementedError
+
+    def reduce(self, func, numPartitions=None):
+        """
+
+        """
+        return self._combineByKey(lambda x:x, func, func, numPartitions)
+
+    def _combineByKey(self, createCombiner, mergeValue, mergeCombiners,
+                      numPartitions = None):
+        """
+        """
+        if numPartitions is None:
+            numPartitions = self.ctx._defaultParallelism()
+>>>>>>> initial commit for pySparkStreaming
         def combineLocally(iterator):
             combiners = {}
             for x in iterator:
@@ -148,7 +295,10 @@ def combineLocally(iterator):
             return combiners.iteritems()
         locally_combined = self.mapPartitions(combineLocally)
         shuffled = locally_combined.partitionBy(numPartitions)
+<<<<<<< HEAD
 
+=======
+>>>>>>> initial commit for pySparkStreaming
         def _mergeCombiners(iterator):
             combiners = {}
             for (k, v) in iterator:
@@ -157,25 +307,43 @@ def _mergeCombiners(iterator):
                 else:
                     combiners[k] = mergeCombiners(combiners[k], v)
             return combiners.iteritems()
+<<<<<<< HEAD
 
         return shuffled.mapPartitions(_mergeCombiners)
 
     def partitionBy(self, numPartitions, partitionFunc=None):
         """
         Return a copy of the DStream partitioned using the specified partitioner.
+=======
+        return shuffled.mapPartitions(_mergeCombiners) 
+
+
+   def partitionBy(self, numPartitions, partitionFunc=None):
+        """
+        Return a copy of the DStream partitioned using the specified partitioner.
+
+>>>>>>> initial commit for pySparkStreaming
         """
         if numPartitions is None:
             numPartitions = self.ctx._defaultReducePartitions()
 
         if partitionFunc is None:
             partitionFunc = lambda x: 0 if x is None else hash(x)
+<<<<<<< HEAD
 
+=======
+>>>>>>> initial commit for pySparkStreaming
         # Transferring O(n) objects to Java is too expensive.  Instead, we'll
         # form the hash buckets in Python, transferring O(numPartitions) objects
         # to Java.  Each object is a (splitNumber, [objects]) pair.
         outputSerializer = self.ctx._unbatched_serializer
+<<<<<<< HEAD
 
         def add_shuffle_key(split, iterator):
+=======
+        def add_shuffle_key(split, iterator):
+
+>>>>>>> initial commit for pySparkStreaming
             buckets = defaultdict(list)
 
             for (k, v) in iterator:
@@ -186,16 +354,26 @@ def add_shuffle_key(split, iterator):
         keyed = PipelinedDStream(self, add_shuffle_key)
         keyed._bypass_serializer = True
         with _JavaStackTrace(self.ctx) as st:
+<<<<<<< HEAD
             partitioner = self.ctx._jvm.PythonPartitioner(numPartitions,
                                                       id(partitionFunc))
             jdstream = self.ctx._jvm.PythonPairwiseDStream(keyed._jdstream.dstream(),
                                                            partitioner).asJavaDStream()
+=======
+            #JavaDStream
+            #pairRDD = self.ctx._jvm.PairwiseDStream(keyed._jdstream.dstream()).asJavaPairRDD()
+            pairDStream = self.ctx._jvm.PairwiseDStream(keyed._jdstream.dstream()).asJavaPairDStream()
+            partitioner = self.ctx._jvm.PythonPartitioner(numPartitions,
+                                                          id(partitionFunc))
+        jdstream = pairDStream.partitionBy(partitioner).values()
+>>>>>>> initial commit for pySparkStreaming
         dstream = DStream(jdstream, self._ssc, BatchedSerializer(outputSerializer))
         # This is required so that id(partitionFunc) remains unique, even if
         # partitionFunc is a lambda:
         dstream._partitionFunc = partitionFunc
         return dstream
 
+<<<<<<< HEAD
     def _defaultReducePartitions(self):
         """
         Returns the default number of partitions to use during reduce tasks (e.g., groupBy).
@@ -432,6 +610,53 @@ def saveAsTextFile(rdd, time):
 # TODO: implement join
 # TODO: implement leftOuterJoin
 # TODO: implemtnt rightOuterJoin
+=======
+
+
+    def reduceByWindow(self, reduceFunc, windowDuration, slideDuration, inReduceTunc):
+        """
+        """
+
+        raise NotImplementedError
+
+    def repartition(self, numPartitions):
+        """
+        """
+        raise NotImplementedError
+
+    def slice(self, fromTime, toTime):
+        """
+        """
+        raise NotImplementedError
+
+    def transform(self, transformFunc):
+        """
+        """
+        raise NotImplementedError
+
+    def transformWith(self, other, transformFunc):
+        """
+        """
+        raise NotImplementedError
+
+    def union(self, that):
+        """
+        """
+        raise NotImplementedError
+
+    def window(self, windowDuration, slideDuration=None):
+        """
+        """
+        raise NotImplementedError
+
+    def wrapRDD(self, rdd):
+        """
+        """
+        raise NotImplementedError
+
+    def mapPartitionsWithIndex(self, f, preservesPartitioning=False):
+        return PipelinedDStream(self, f, preservesPartitioning)
+>>>>>>> initial commit for pySparkStreaming
 
 
 class PipelinedDStream(DStream):
diff --git a/python/pyspark/streaming/duration.py b/python/pyspark/streaming/duration.py
index a7f1036e4b856..245c137ecfc29 100644
--- a/python/pyspark/streaming/duration.py
+++ b/python/pyspark/streaming/duration.py
@@ -1,3 +1,4 @@
+<<<<<<< HEAD
 #
 # Licensed to the Apache Software Foundation (ASF) under one or more
 # contributor license agreements.  See the NOTICE file distributed with
@@ -18,6 +19,12 @@
 from pyspark.streaming import utils
 
 
+=======
+__author__ = 'ktakagiw'
+
+from pyspark.streaming import utils
+
+>>>>>>> initial commit for pySparkStreaming
 class Duration(object):
     """
     Duration for Spark Streaming application. Used to set duration
@@ -43,6 +50,7 @@ def __init__(self, millis, _jvm=None):
         self._jduration = _jvm.Duration(millis)
 
     def toString(self):
+<<<<<<< HEAD
         """
         Return duration as string
 
@@ -63,11 +71,19 @@ def isZero(self):
         >>> d_0.isZero()
         True
         """
+=======
+        """ Return duration as string """
+        return str(self._millis) + " ms"
+
+    def isZero(self):
+        """ Check if millis is zero """
+>>>>>>> initial commit for pySparkStreaming
         return self._millis == 0
 
     def prettyPrint(self):
         """
         Return a human-readable string representing a duration
+<<<<<<< HEAD
 
         >>> d_10 = Duration(10)
         >>> d_10.prettyPrint()
@@ -81,10 +97,13 @@ def prettyPrint(self):
         >>> d_1hour = Duration(60 * 60 * 1000)
         >>> d_1hour.prettyPrint()
         '1.00 h'
+=======
+>>>>>>> initial commit for pySparkStreaming
         """
         return utils.msDurationToString(self._millis)
 
     def milliseconds(self):
+<<<<<<< HEAD
         """
         Return millisecond
 
@@ -117,6 +136,17 @@ def max(self, other):
         100 ms
 
         """
+=======
+        """ Return millisecond """
+        return self._millis
+
+    def toFormattedString(self):
+        """ Return millisecond """
+        return str(self._millis)
+
+    def max(self, other):
+        """ Return higher Duration """
+>>>>>>> initial commit for pySparkStreaming
         Duration._is_duration(other)
         if self > other:
             return self
@@ -124,6 +154,7 @@ def max(self, other):
             return other
 
     def min(self, other):
+<<<<<<< HEAD
         """
         Return lower Durattion
 
@@ -134,6 +165,9 @@ def min(self, other):
         10 ms
 
         """
+=======
+        """ Return lower Durattion """
+>>>>>>> initial commit for pySparkStreaming
         Duration._is_duration(other)
         if self < other:
             return self
@@ -141,6 +175,7 @@ def min(self, other):
             return other
 
     def __str__(self):
+<<<<<<< HEAD
         """
         >>> d_10 = Duration(10)
         >>> str(d_10)
@@ -159,10 +194,17 @@ def __add__(self, other):
         >>> print d_110
         110 ms
         """
+=======
+        return self.toString()
+
+    def __add__(self, other):
+        """ Add Duration and Duration """
+>>>>>>> initial commit for pySparkStreaming
         Duration._is_duration(other)
         return Duration(self._millis + other._millis)
 
     def __sub__(self, other):
+<<<<<<< HEAD
         """
         Subtract Duration by Duration
 
@@ -173,10 +215,14 @@ def __sub__(self, other):
         90 ms
 
         """
+=======
+        """ Subtract Duration by Duration  """
+>>>>>>> initial commit for pySparkStreaming
         Duration._is_duration(other)
         return Duration(self._millis - other._millis)
 
     def __mul__(self, other):
+<<<<<<< HEAD
         """
         Multiple Duration by Duration
 
@@ -187,6 +233,9 @@ def __mul__(self, other):
         1000 ms
 
         """
+=======
+        """ Multiple Duration by Duration """
+>>>>>>> initial commit for pySparkStreaming
         Duration._is_duration(other)
         return Duration(self._millis * other._millis)
 
@@ -194,6 +243,7 @@ def __div__(self, other):
         """
         Divide Duration by Duration
         for Python 2.X
+<<<<<<< HEAD
 
         >>> d_10 = Duration(10)
         >>> d_20 = Duration(20)
@@ -201,6 +251,8 @@ def __div__(self, other):
         >>> print d_2
         2 ms
 
+=======
+>>>>>>> initial commit for pySparkStreaming
         """
         Duration._is_duration(other)
         return Duration(self._millis / other._millis)
@@ -209,6 +261,7 @@ def __truediv__(self, other):
         """
         Divide Duration by Duration
         for Python 3.0
+<<<<<<< HEAD
 
         >>> d_10 = Duration(10)
         >>> d_20 = Duration(20)
@@ -216,11 +269,14 @@ def __truediv__(self, other):
         >>> print d_2
         2 ms
 
+=======
+>>>>>>> initial commit for pySparkStreaming
         """
         Duration._is_duration(other)
         return Duration(self._millis / other._millis)
 
     def __floordiv__(self, other):
+<<<<<<< HEAD
         """
         Divide Duration by Duration
 
@@ -246,10 +302,23 @@ def __lt__(self, other):
         False
 
         """
+=======
+        """ Divide Duration by Duration """
+        Duration._is_duration(other)
+        return Duration(self._millis // other._millis)
+
+    def __len__(self):
+        """ Length of miilisecond in Duration """
+        return len(self._millis)
+
+    def __lt__(self, other):
+        """ Duration < Duration """
+>>>>>>> initial commit for pySparkStreaming
         Duration._is_duration(other)
         return self._millis < other._millis
 
     def __le__(self, other):
+<<<<<<< HEAD
         """
         Duration <= Duration
 
@@ -277,10 +346,19 @@ def __eq__(self, other):
         True
 
         """
+=======
+        """ Duration <= Duration """
+        Duration._is_duration(other)
+        return self.millis <= other._millis
+
+    def __eq__(self, other):
+        """ Duration ==  Duration """
+>>>>>>> initial commit for pySparkStreaming
         Duration._is_duration(other)
         return self._millis == other._millis
 
     def __ne__(self, other):
+<<<<<<< HEAD
         """
         Duration != Duration
 
@@ -293,10 +371,14 @@ def __ne__(self, other):
         False
 
         """
+=======
+        """ Duration != Duration """
+>>>>>>> initial commit for pySparkStreaming
         Duration._is_duration(other)
         return self._millis != other._millis
 
     def __gt__(self, other):
+<<<<<<< HEAD
         """
         Duration > Duration
 
@@ -308,10 +390,14 @@ def __gt__(self, other):
         True
 
         """
+=======
+        """ Duration > Duration """
+>>>>>>> initial commit for pySparkStreaming
         Duration._is_duration(other)
         return self._millis > other._millis
 
     def __ge__(self, other):
+<<<<<<< HEAD
         """
         Duration >= Duration
 
@@ -324,6 +410,9 @@ def __ge__(self, other):
 
 
         """
+=======
+        """ Duration >= Duration """
+>>>>>>> initial commit for pySparkStreaming
         Duration._is_duration(other)
         return self._millis >= other._millis
 
@@ -337,12 +426,15 @@ def Milliseconds(milliseconds):
     """
     Helper function that creates instance of [[pysparkstreaming.duration]] representing
     a given number of milliseconds.
+<<<<<<< HEAD
 
     >>> milliseconds = Milliseconds(1)
     >>> d_1 = Duration(1)
     >>> milliseconds == d_1
     True
 
+=======
+>>>>>>> initial commit for pySparkStreaming
     """
     return Duration(milliseconds)
 
@@ -350,6 +442,7 @@ def Seconds(seconds):
     """
     Helper function that creates instance of [[pysparkstreaming.duration]] representing
     a given number of seconds.
+<<<<<<< HEAD
 
     >>> seconds = Seconds(1)
     >>> d_1sec = Duration(1000)
@@ -371,3 +464,20 @@ def Minutes(minutes):
 
     """
     return Duration(minutes * 60 * 1000)
+=======
+    """
+    return Duration(seconds * 1000)
+
+def Minites(minites):
+    """
+    Helper function that creates instance of [[pysparkstreaming.duration]] representing
+    a given number of minutes.
+    """
+    return Duration(minutes * 60000)
+
+if __name__ == "__main__":
+    d = Duration(1)
+    print d
+    print d.milliseconds()
+
+>>>>>>> initial commit for pySparkStreaming
diff --git a/python/pyspark/streaming/jtime.py b/python/pyspark/streaming/jtime.py
index 32ef741051283..9295c4ee27705 100644
--- a/python/pyspark/streaming/jtime.py
+++ b/python/pyspark/streaming/jtime.py
@@ -1,3 +1,4 @@
+<<<<<<< HEAD
 #
 # Licensed to the Apache Software Foundation (ASF) under one or more
 # contributor license agreements.  See the NOTICE file distributed with
@@ -14,10 +15,14 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 #
+=======
+__author__ = 'ktakagiw'
+>>>>>>> initial commit for pySparkStreaming
 
 from pyspark.streaming import utils
 from pyspark.streaming.duration import Duration
 
+<<<<<<< HEAD
 """
 The name of this file, time is not good naming for python
 because if we do import time when we want to use native python time package, it does
@@ -25,6 +30,8 @@
 """
 
 
+=======
+>>>>>>> initial commit for pySparkStreaming
 class Time(object):
     """
     Time for Spark Streaming application. Used to set Time
diff --git a/python/pyspark/streaming/pyprint.py b/python/pyspark/streaming/pyprint.py
index 49517b3e5c247..4beb66950d851 100644
--- a/python/pyspark/streaming/pyprint.py
+++ b/python/pyspark/streaming/pyprint.py
@@ -1,3 +1,4 @@
+<<<<<<< HEAD
 #
 # Licensed to the Apache Software Foundation (ASF) under one or more
 # contributor license agreements.  See the NOTICE file distributed with
@@ -26,17 +27,31 @@ def collect(binary_file_path):
     """
     Read pickled file written by SparkStreaming
     """
+=======
+import sys
+from itertools import chain
+from pyspark.serializers import PickleSerializer, BatchedSerializer, UTF8Deserializer
+
+def collect(binary_file_path):
+>>>>>>> initial commit for pySparkStreaming
     dse = PickleSerializer()
     with open(binary_file_path, 'rb') as tempFile:
         for item in dse.load_stream(tempFile):
             yield item
+<<<<<<< HEAD
 
 
+=======
+>>>>>>> initial commit for pySparkStreaming
 def main():
     try:
         binary_file_path = sys.argv[1]
     except:
+<<<<<<< HEAD
         print "Missed FilePath in argements"
+=======
+        print "Missed FilePath in argement"
+>>>>>>> initial commit for pySparkStreaming
 
     if not binary_file_path:
         return 
@@ -49,6 +64,9 @@ def main():
             print "..."
             break
 
+<<<<<<< HEAD
 
+=======
+>>>>>>> initial commit for pySparkStreaming
 if __name__ =="__main__":
     exit(main())
diff --git a/python/pyspark/streaming/utils.py b/python/pyspark/streaming/utils.py
index 9178577743e0b..56bb0ca1e9620 100644
--- a/python/pyspark/streaming/utils.py
+++ b/python/pyspark/streaming/utils.py
@@ -1,3 +1,4 @@
+<<<<<<< HEAD
 #
 # Licensed to the Apache Software Foundation (ASF) under one or more
 # contributor license agreements.  See the NOTICE file distributed with
@@ -36,6 +37,9 @@ def __str__(self):
     class Java:
         implements = ['org.apache.spark.streaming.api.python.PythonRDDFunction']
 
+=======
+__author__ = 'ktakagiw'
+>>>>>>> initial commit for pySparkStreaming
 
 def msDurationToString(ms):
     """
@@ -49,6 +53,7 @@ def msDurationToString(ms):
         return "%d ms" % ms
     elif ms < minute:
         return "%.1f s" % (float(ms) / second)
+<<<<<<< HEAD
     elif ms < hour:
         return "%.1f m" % (float(ms) / minute)
     else:
@@ -59,3 +64,9 @@ def rddToFileName(prefix, suffix, time):
         return prefix + "-" + str(time) + "." + suffix
     else:
         return prefix + "-" + str(time)
+=======
+    elif ms < hout:
+        return "%.1f m" % (float(ms) / minute)
+    else:
+        return "%.2f h" % (float(ms) / hour)
+>>>>>>> initial commit for pySparkStreaming
diff --git a/streaming/pom.xml b/streaming/pom.xml
index 12f900c91eb98..cb38015c24622 100644
--- a/streaming/pom.xml
+++ b/streaming/pom.xml
@@ -21,7 +21,11 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent</artifactId>
+<<<<<<< HEAD
     <version>1.2.0-SNAPSHOT</version>
+=======
+    <version>1.0.0</version>
+>>>>>>> initial commit for pySparkStreaming
     <relativePath>../pom.xml</relativePath>
   </parent>
 
@@ -77,9 +81,9 @@
         <groupId>org.scalatest</groupId>
         <artifactId>scalatest-maven-plugin</artifactId>
       </plugin>
-      
-      <!-- 
-           This plugin forces the generation of jar containing streaming test classes, 
+
+      <!--
+           This plugin forces the generation of jar containing streaming test classes,
            so that the tests classes of external modules can use them. The two execution profiles
            are necessary - first one for 'mvn package', second one for 'mvn test-compile'. Ideally,
            'mvn compile' should not compile test classes and therefore should not need this. 
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaDStreamLike.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaDStreamLike.scala
index a6184de4e83c1..cfa336df8674f 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaDStreamLike.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaDStreamLike.scala
@@ -54,6 +54,14 @@ trait JavaDStreamLike[T, This <: JavaDStreamLike[T, This, R], R <: JavaRDDLike[T
     dstream.print()
   }
 
+  /**
+   * Print the first ten elements of each PythonRDD generated in the PythonDStream. This is an output
+   * operator, so this PythonDStream will be registered as an output stream and there materialized.
+   * This function is for PythonAPI.
+   */
+
+  def pyprint() = dstream.pyprint()
+
   /**
    * Return a new DStream in which each RDD has a single element generated by counting each RDD
    * of this DStream.
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
index d1f41cfbb8885..459cee31f2e8e 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
@@ -17,6 +17,7 @@
 
 package org.apache.spark.streaming.api.python
 
+<<<<<<< HEAD
 import java.io._
 import java.io.{ObjectInputStream, IOException}
 import java.util.{List => JList, ArrayList => JArrayList, Map => JMap, Collections}
@@ -47,6 +48,46 @@ class PythonDStream[T: ClassTag](
     broadcastVars: JList[Broadcast[Array[Byte]]],
     accumulator: Accumulator[JList[Array[Byte]]])
   extends DStream[Array[Byte]](parent.ssc) {
+=======
+import java.util.{List => JList, ArrayList => JArrayList, Map => JMap, Collections}
+
+import org.apache.spark.api.java.{JavaSparkContext, JavaPairRDD, JavaRDD}
+import org.apache.spark.broadcast.Broadcast
+import org.apache.spark._
+import org.apache.spark.util.Utils
+import java.io._
+import scala.Some
+import org.apache.spark.streaming.Duration
+import scala.util.control.Breaks._
+import org.apache.spark.broadcast.Broadcast
+import scala.Some
+import org.apache.spark.streaming.Duration
+import org.apache.spark.rdd.RDD
+import org.apache.spark.api.python.PythonRDD
+
+
+import org.apache.spark.streaming.{Duration, Time}
+import org.apache.spark.streaming.dstream._
+import org.apache.spark.streaming.api.java._
+import org.apache.spark.rdd.RDD
+import org.apache.spark.api.python._
+import org.apache.spark.api.python.PairwiseRDD
+
+
+import scala.reflect.ClassTag
+
+
+class PythonDStream[T: ClassTag](
+                                  parent: DStream[T],
+                                  command: Array[Byte],
+                                  envVars: JMap[String, String],
+                                  pythonIncludes: JList[String],
+                                  preservePartitoning: Boolean,
+                                  pythonExec: String,
+                                  broadcastVars: JList[Broadcast[Array[Byte]]],
+                                  accumulator: Accumulator[JList[Array[Byte]]]
+                                  ) extends DStream[Array[Byte]](parent.ssc) {
+>>>>>>> initial commit for pySparkStreaming
 
   override def dependencies = List(parent)
 
@@ -61,11 +102,14 @@ class PythonDStream[T: ClassTag](
       case None => None
     }
   }
+<<<<<<< HEAD
 
   def foreachRDD(foreachFunc: PythonRDDFunction) {
     new PythonForeachDStream(this, context.sparkContext.clean(foreachFunc, false)).register()
   }
 
+=======
+>>>>>>> initial commit for pySparkStreaming
   val asJavaDStream  = JavaDStream.fromDStream(this)
 
   /**
@@ -88,7 +132,11 @@ class PythonDStream[T: ClassTag](
       tempFileStream.close()
 
       // This value has to be passed from python
+<<<<<<< HEAD
       //val pythonExec = new ProcessBuilder().environment().get("PYSPARK_PYTHON")
+=======
+      val pythonExec = new ProcessBuilder().environment().get("PYSPARK_PYTHON")
+>>>>>>> initial commit for pySparkStreaming
       val sparkHome = new ProcessBuilder().environment().get("SPARK_HOME")
       //val pb = new ProcessBuilder(Seq(pythonExec, sparkHome + "/python/pyspark/streaming/pyprint.py", tempFile.getAbsolutePath())) // why this fails to compile???
       //absolute path to the python script is needed to change because we do not use pysparkstreaming
@@ -141,6 +189,7 @@ DStream[(Long, Array[Byte])](prev.ssc){
       case None => None
     }
   }
+<<<<<<< HEAD
   val asJavaPairDStream : JavaPairDStream[Long, Array[Byte]]  = JavaPairDStream.fromJavaDStream(this)
 }
 
@@ -225,3 +274,11 @@ class PythonTransformedDStream(
   //val asJavaPairDStream : JavaPairDStream[Long, Array[Byte]]  = JavaPairDStream.fromJavaDStream(this)
 }
 */
+=======
+  val asJavaPairDStream : JavaPairDStream[Long, Array[Byte]] = JavaPairDStream(this)
+}
+
+
+
+
+>>>>>>> initial commit for pySparkStreaming
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala
index 39ad591e8896e..f6dcd2fb88f45 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala
@@ -22,6 +22,8 @@ import java.io.{IOException, ObjectInputStream, ObjectOutputStream}
 import scala.deprecated
 import scala.collection.mutable.HashMap
 import scala.reflect.ClassTag
+import java.io.{IOException, ObjectInputStream, ObjectOutputStream}
+import scala.util.control.Breaks._
 
 import org.apache.spark.{Logging, SparkException}
 import org.apache.spark.rdd.{BlockRDD, RDD}
@@ -617,6 +619,68 @@ abstract class DStream[T: ClassTag] (
     new ForEachDStream(this, context.sparkContext.clean(foreachFunc)).register()
   }
 
+
+
+
+
+  /**
+   * Print the first ten elements of each PythonRDD generated in this PythonDStream. This is an output
+   * operator, so this PythonDStream will be registered as an output stream and there materialized.
+   * Since serialized Python object is readable by Python, pyprint writes out binary data to
+   * temporary file and run python script to deserialized and print the first ten elements
+   */
+  private[streaming] def pyprint() {
+    def foreachFunc = (rdd: RDD[T], time: Time) => {
+      val iter = rdd.take(11).iterator
+
+      // make a temporary file
+      val prefix = "spark"
+      val suffix = ".tmp"
+      val tempFile = File.createTempFile(prefix, suffix)
+      val tempFileStream = new DataOutputStream(new FileOutputStream(tempFile.getAbsolutePath))
+      //write out serialized python object
+      PythonRDD.writeIteratorToStream(iter, tempFileStream)
+      tempFileStream.close()
+
+      // This value has to be passed from python
+      val pythonExec = new ProcessBuilder().environment().get("PYSPARK_PYTHON")
+      val sparkHome = new ProcessBuilder().environment().get("SPARK_HOME")
+      //val pb = new ProcessBuilder(Seq(pythonExec, sparkHome + "/python/pyspark/streaming/pyprint.py", tempFile.getAbsolutePath())) // why this fails to compile???
+      //absolute path to the python script is needed to change because we do not use pysparkstreaming
+      val pb = new ProcessBuilder(pythonExec, sparkHome + "/python/pyspark/streaming/pyprint.py", tempFile.getAbsolutePath)
+      val workerEnv = pb.environment()
+
+      //envVars also need to be pass
+      //workerEnv.putAll(envVars)
+      val pythonPath = sparkHome + "/python/" + File.pathSeparator + workerEnv.get("PYTHONPATH")
+      workerEnv.put("PYTHONPATH", pythonPath)
+      val worker = pb.start()
+      val is = worker.getInputStream()
+      val isr = new InputStreamReader(is)
+      val br = new BufferedReader(isr)
+
+      println ("-------------------------------------------")
+      println ("Time: " + time)
+      println ("-------------------------------------------")
+
+      //print value from python std out
+      var line = ""
+      breakable {
+        while (true) {
+          line = br.readLine()
+          if (line == null) break()
+          println(line)
+        }
+      }
+      //delete temporary file
+      tempFile.delete()
+      println()
+
+    }
+    new ForEachDStream(this, context.sparkContext.clean(foreachFunc)).register()
+  }
+
+
   /**
    * Return a new DStream in which each RDD contains all the elements in seen in a
    * sliding window of time over this DStream. The new DStream generates RDDs with

From 41886c20bb5376ef14aa00be21cc64b5d460a161 Mon Sep 17 00:00:00 2001
From: Ken Takagiwa <ken@Kens-MacBook-Pro.local>
Date: Tue, 15 Jul 2014 15:41:52 -0700
Subject: [PATCH 422/489] comment PythonDStream.PairwiseDStream

---
 .../apache/spark/streaming/api/python/PythonDStream.scala   | 3 ++-
 .../scala/org/apache/spark/streaming/dstream/DStream.scala  | 6 ++----
 2 files changed, 4 insertions(+), 5 deletions(-)

diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
index 459cee31f2e8e..998fa24eba91b 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
@@ -174,7 +174,7 @@ class PythonDStream[T: ClassTag](
   }
 }
 
-
+/*
 private class PairwiseDStream(prev:DStream[Array[Byte]]) extends
 DStream[(Long, Array[Byte])](prev.ssc){
   override def dependencies = List(prev)
@@ -277,6 +277,7 @@ class PythonTransformedDStream(
 =======
   val asJavaPairDStream : JavaPairDStream[Long, Array[Byte]] = JavaPairDStream(this)
 }
+*/
 
 
 
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala
index f6dcd2fb88f45..5377dfa52d461 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala
@@ -619,10 +619,7 @@ abstract class DStream[T: ClassTag] (
     new ForEachDStream(this, context.sparkContext.clean(foreachFunc)).register()
   }
 
-
-
-
-
+//TODO move pyprint to PythonDStream
   /**
    * Print the first ten elements of each PythonRDD generated in this PythonDStream. This is an output
    * operator, so this PythonDStream will be registered as an output stream and there materialized.
@@ -643,6 +640,7 @@ abstract class DStream[T: ClassTag] (
       tempFileStream.close()
 
       // This value has to be passed from python
+      // Python currently does not do cluster deployment. But what happened
       val pythonExec = new ProcessBuilder().environment().get("PYSPARK_PYTHON")
       val sparkHome = new ProcessBuilder().environment().get("SPARK_HOME")
       //val pb = new ProcessBuilder(Seq(pythonExec, sparkHome + "/python/pyspark/streaming/pyprint.py", tempFile.getAbsolutePath())) // why this fails to compile???

From 66fcfffa1be42f3fb71d620e03504af5cdd4c8e4 Mon Sep 17 00:00:00 2001
From: Ken Takagiwa <ken@Kens-MacBook-Pro.local>
Date: Tue, 15 Jul 2014 17:19:20 -0700
Subject: [PATCH 423/489] modify dstream.py to fix indent error

---
 bin/spark-submit                              |  11 -
 core/pom.xml                                  |   4 -
 .../src/main/python/streaming/wordcount.py    |  16 --
 python/pyspark/java_gateway.py                |   6 -
 python/pyspark/streaming/context.py           |  99 --------
 python/pyspark/streaming/dstream.py           | 237 ------------------
 python/pyspark/streaming/duration.py          | 110 --------
 python/pyspark/streaming/jtime.py             |   7 -
 python/pyspark/streaming/pyprint.py           |  72 ------
 python/pyspark/streaming/utils.py             |  10 -
 streaming/pom.xml                             |   4 -
 .../streaming/api/python/PythonDStream.scala  |  79 ------
 12 files changed, 655 deletions(-)
 delete mode 100644 python/pyspark/streaming/pyprint.py

diff --git a/bin/spark-submit b/bin/spark-submit
index 37e973a50b6fa..c557311b4b20e 100755
--- a/bin/spark-submit
+++ b/bin/spark-submit
@@ -48,7 +48,6 @@ export SPARK_SUBMIT_PROPERTIES_FILE=${SPARK_SUBMIT_PROPERTIES_FILE:-"$DEFAULT_PR
 # paths, library paths, java options and memory early on. Otherwise, it will
 # be too late by the time the driver JVM has started.
 
-<<<<<<< HEAD
 if [[ "$SPARK_SUBMIT_DEPLOY_MODE" == "client" && -f "$SPARK_SUBMIT_PROPERTIES_FILE" ]]; then
   # Parse the properties file only if the special configs exist
   contains_special_configs=$(
@@ -58,16 +57,6 @@ if [[ "$SPARK_SUBMIT_DEPLOY_MODE" == "client" && -f "$SPARK_SUBMIT_PROPERTIES_FI
   if [ -n "$contains_special_configs" ]; then
     export SPARK_SUBMIT_BOOTSTRAP_DRIVER=1
   fi
-=======
-# Figure out which Python executable to use
-if [[ -z "$PYSPARK_PYTHON" ]]; then
-  PYSPARK_PYTHON="python"
-fi
-export PYSPARK_PYTHON
-
-if [ -n "$DRIVER_MEMORY" ] && [ $DEPLOY_MODE == "client" ]; then
-  export SPARK_DRIVER_MEMORY=$DRIVER_MEMORY
->>>>>>> initial commit for pySparkStreaming
 fi
 
 exec "$SPARK_HOME"/bin/spark-class org.apache.spark.deploy.SparkSubmit "${ORIG_ARGS[@]}"
diff --git a/core/pom.xml b/core/pom.xml
index 7eb0b48eaeebd..2a81f6df289c0 100644
--- a/core/pom.xml
+++ b/core/pom.xml
@@ -21,11 +21,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent</artifactId>
-<<<<<<< HEAD
     <version>1.2.0-SNAPSHOT</version>
-=======
-    <version>1.0.0</version>
->>>>>>> initial commit for pySparkStreaming
     <relativePath>../pom.xml</relativePath>
   </parent>
 
diff --git a/examples/src/main/python/streaming/wordcount.py b/examples/src/main/python/streaming/wordcount.py
index c19eb74c44ed6..2426345711086 100644
--- a/examples/src/main/python/streaming/wordcount.py
+++ b/examples/src/main/python/streaming/wordcount.py
@@ -1,10 +1,7 @@
 import sys
 from operator import add
 
-<<<<<<< HEAD
 from pyspark.conf import SparkConf
-=======
->>>>>>> initial commit for pySparkStreaming
 from pyspark.streaming.context import StreamingContext
 from pyspark.streaming.duration import *
 
@@ -12,7 +9,6 @@
     if len(sys.argv) != 2:
         print >> sys.stderr, "Usage: wordcount <directory>"
         exit(-1)
-<<<<<<< HEAD
     conf = SparkConf()
     conf.setAppName("PythonStreamingWordCount")
 
@@ -24,17 +20,5 @@
     count = mapped_words.reduceByKey(add)
     
     count.pyprint()
-=======
-    ssc = StreamingContext(appName="PythonStreamingWordCount", duration=Seconds(1))
-
-    lines = ssc.textFileStream(sys.argv[1])
-    fm_lines = lines.flatMap(lambda x: x.split(" "))
-    filtered_lines = fm_lines.filter(lambda line: "Spark" in line)
-    mapped_lines = fm_lines.map(lambda x: (x, 1))
-    
-    fm_lines.pyprint()
-    filtered_lines.pyprint()
-    mapped_lines.pyprint()
->>>>>>> initial commit for pySparkStreaming
     ssc.start()
     ssc.awaitTermination()
diff --git a/python/pyspark/java_gateway.py b/python/pyspark/java_gateway.py
index 9b18696213691..f3c6d231ab777 100644
--- a/python/pyspark/java_gateway.py
+++ b/python/pyspark/java_gateway.py
@@ -108,16 +108,10 @@ def run(self):
     java_import(gateway.jvm, "org.apache.spark.SparkConf")
     java_import(gateway.jvm, "org.apache.spark.api.java.*")
     java_import(gateway.jvm, "org.apache.spark.api.python.*")
-<<<<<<< HEAD
     java_import(gateway.jvm, "org.apache.spark.streaming.*")  # do we need this?
     java_import(gateway.jvm, "org.apache.spark.streaming.api.java.*")
     java_import(gateway.jvm, "org.apache.spark.streaming.api.python.*")
     java_import(gateway.jvm, "org.apache.spark.streaming.dstream.*")  # do we need this?
-=======
-    java_import(gateway.jvm, "org.apache.spark.streaming.*")
-    java_import(gateway.jvm, "org.apache.spark.streaming.api.java.*")
-    java_import(gateway.jvm, "org.apache.spark.streaming.api.python.*")
->>>>>>> initial commit for pySparkStreaming
     java_import(gateway.jvm, "org.apache.spark.mllib.api.python.*")
     java_import(gateway.jvm, "org.apache.spark.sql.SQLContext")
     java_import(gateway.jvm, "org.apache.spark.sql.hive.HiveContext")
diff --git a/python/pyspark/streaming/context.py b/python/pyspark/streaming/context.py
index e65d2243ca7d5..3f455a3e06072 100644
--- a/python/pyspark/streaming/context.py
+++ b/python/pyspark/streaming/context.py
@@ -1,9 +1,3 @@
-<<<<<<< HEAD
-=======
-__author__ = 'ktakagiw'
-
-
->>>>>>> initial commit for pySparkStreaming
 #
 # Licensed to the Apache Software Foundation (ASF) under one or more
 # contributor license agreements.  See the NOTICE file distributed with
@@ -21,7 +15,6 @@
 # limitations under the License.
 #
 
-<<<<<<< HEAD
 import sys
 from signal import signal, SIGTERM, SIGINT
 
@@ -36,43 +29,12 @@ class StreamingContext(object):
     """
     Main entry point for Spark Streaming functionality. A StreamingContext represents the
     connection to a Spark cluster, and can be used to create L{DStream}s and
-=======
-import os
-import shutil
-import sys
-from threading import Lock
-from tempfile import NamedTemporaryFile
-
-from pyspark import accumulators
-from pyspark.accumulators import Accumulator
-from pyspark.broadcast import Broadcast
-from pyspark.conf import SparkConf
-from pyspark.files import SparkFiles
-from pyspark.java_gateway import launch_gateway
-from pyspark.serializers import PickleSerializer, BatchedSerializer, UTF8Deserializer
-from pyspark.storagelevel import StorageLevel
-from pyspark.rdd import RDD
-from pyspark.context import SparkContext
-
-from py4j.java_collections import ListConverter
-
-from pyspark.streaming.dstream import DStream
-
-class StreamingContext(object):
-    """
-    Main entry point for Spark functionality. A StreamingContext represents the
-    connection to a Spark cluster, and can be used to create L{RDD}s and
->>>>>>> initial commit for pySparkStreaming
     broadcast variables on that cluster.
     """
 
     def __init__(self, master=None, appName=None, sparkHome=None, pyFiles=None,
         environment=None, batchSize=1024, serializer=PickleSerializer(), conf=None,
-<<<<<<< HEAD
         gateway=None, sparkContext=None, duration=None):
-=======
-        gateway=None, duration=None):
->>>>>>> initial commit for pySparkStreaming
         """
         Create a new StreamingContext. At least the master and app name and duration
         should be set, either through the named parameters here or through C{conf}.
@@ -93,7 +55,6 @@ def __init__(self, master=None, appName=None, sparkHome=None, pyFiles=None,
         @param conf: A L{SparkConf} object setting Spark properties.
         @param gateway: Use an existing gateway and JVM, otherwise a new JVM
                will be instatiated.
-<<<<<<< HEAD
         @param sparkContext: L{SparkContext} object.
         @param duration: A L{Duration} object for SparkStreaming.
 
@@ -112,15 +73,6 @@ def __init__(self, master=None, appName=None, sparkHome=None, pyFiles=None,
         # is started in StreamingContext.
         SparkContext._gateway.restart_callback_server()
         self._clean_up_trigger()
-=======
-        @param duration: A L{Duration} Duration for SparkStreaming
-
-        """
-        # Create the Python Sparkcontext
-        self._sc = SparkContext(master=master, appName=appName, sparkHome=sparkHome,
-                        pyFiles=pyFiles, environment=environment, batchSize=batchSize,
-                        serializer=serializer, conf=conf, gateway=gateway)
->>>>>>> initial commit for pySparkStreaming
         self._jvm = self._sc._jvm
         self._jssc = self._initialize_context(self._sc._jsc, duration._jduration)
 
@@ -128,7 +80,6 @@ def __init__(self, master=None, appName=None, sparkHome=None, pyFiles=None,
     def _initialize_context(self, jspark_context, jduration):
         return self._jvm.JavaStreamingContext(jspark_context, jduration)
 
-<<<<<<< HEAD
     def _clean_up_trigger(self):
         """Kill py4j callback server properly using signal lib"""
 
@@ -205,53 +156,3 @@ def _testInputStream(self, test_inputs, numSlices=None):
         jinput_stream = self._jvm.PythonTestInputStream(self._jssc, jtest_rdds).asJavaDStream()
 
         return DStream(jinput_stream, self, test_rdd_deserializers[0])
-=======
-    def actorStream(self, props, name, storageLevel, supervisorStrategy):
-        raise NotImplementedError
-
-    def addStreamingListener(self, streamingListener):
-        raise NotImplementedError
-
-    def awaitTermination(self, timeout=None):
-        if timeout:
-            self._jssc.awaitTermination(timeout)
-        else:
-            self._jssc.awaitTermination()
-
-    def checkpoint(self, directory):
-        raise NotImplementedError
-
-    def fileStream(self, directory, filter=None, newFilesOnly=None):
-        raise NotImplementedError
-
-    def networkStream(self, receiver):
-        raise NotImplementedError
-
-    def queueStream(self, queue, oneAtATime=True, defaultRDD=None):
-        raise NotImplementedError
-
-    def rawSocketStream(self, hostname, port, storagelevel):
-        raise NotImplementedError
-
-    def remember(self, duration):
-        raise NotImplementedError
-
-    def socketStream(hostname, port, converter,storageLevel):
-        raise NotImplementedError
-
-    def start(self):
-        self._jssc.start()
-
-    def stop(self, stopSparkContext=True):
-        raise NotImplementedError
-
-    def textFileStream(self, directory):
-        return DStream(self._jssc.textFileStream(directory), self, UTF8Deserializer())
-
-    def transform(self, seq):
-        raise NotImplementedError
-
-    def union(self, seq):
-        raise NotImplementedError
-
->>>>>>> initial commit for pySparkStreaming
diff --git a/python/pyspark/streaming/dstream.py b/python/pyspark/streaming/dstream.py
index eeb6b5644d1d3..224d2bbdeeb53 100644
--- a/python/pyspark/streaming/dstream.py
+++ b/python/pyspark/streaming/dstream.py
@@ -1,4 +1,3 @@
-<<<<<<< HEAD
 #
 # Licensed to the Apache Software Foundation (ASF) under one or more
 # contributor license agreements.  See the NOTICE file distributed with
@@ -27,50 +26,17 @@
 from pyspark.resultiterable import ResultIterable
 from pyspark.streaming.utils import rddToFileName
 
-=======
-from base64 import standard_b64encode as b64enc
-import copy
-from collections import defaultdict
-from collections import namedtuple
-from itertools import chain, ifilter, imap
-import operator
-import os
-import sys
-import shlex
-import traceback
-from subprocess import Popen, PIPE
-from tempfile import NamedTemporaryFile
-from threading import Thread
-import warnings
-import heapq
-from random import Random
-
-from pyspark.serializers import NoOpSerializer, CartesianDeserializer, \
-    BatchedSerializer, CloudPickleSerializer, PairDeserializer, pack_long
-from pyspark.join import python_join, python_left_outer_join, \
-    python_right_outer_join, python_cogroup
-from pyspark.statcounter import StatCounter
-from pyspark.rddsampler import RDDSampler
-from pyspark.storagelevel import StorageLevel
-#from pyspark.resultiterable import ResultIterable
-from pyspark.rdd import _JavaStackTrace
->>>>>>> initial commit for pySparkStreaming
-
 from py4j.java_collections import ListConverter, MapConverter
 
 __all__ = ["DStream"]
 
-<<<<<<< HEAD
 
-=======
->>>>>>> initial commit for pySparkStreaming
 class DStream(object):
     def __init__(self, jdstream, ssc, jrdd_deserializer):
         self._jdstream = jdstream
         self._ssc = ssc
         self.ctx = ssc._sc
         self._jrdd_deserializer = jrdd_deserializer
-<<<<<<< HEAD
         self.is_cached = False
         self.is_checkpointed = False
 
@@ -106,81 +72,12 @@ def print_(self, label=None):
     def filter(self, f):
         """
         Return a new DStream containing only the elements that satisfy predicate.
-=======
-
-    def generatedRDDs(self):
-        """
-         // RDDs generated, marked as private[streaming] so that testsuites can access it
-         @transient
-        """
-        pass
-
-    def print_(self):
-        """
-        """
-        # print is a resrved name of Python. We cannot give print to function name
-        getattr(self._jdstream, "print")()
-
-    def pyprint(self):
-        """
-        """
-        self._jdstream.pyprint()
-
-    def cache(self):
-        """
-        """
-        raise NotImplementedError
-
-    def checkpoint(self):
-        """
-        """
-        raise NotImplementedError
-
-    def compute(self, time):
-        """
-        """
-        raise NotImplementedError
-
-    def context(self):
-        """
-        """
-        raise NotImplementedError
-
-    def count(self):
-        """
-        """
-        raise NotImplementedError
-
-    def countByValue(self, numPartitions=None):
-        """
-        """
-        raise NotImplementedError
-
-    def countByValueAndWindow(self, duration, slideDuration=None):
-        """
-        """
-        raise NotImplementedError
-
-    def countByWindow(self, duration, slideDuration=None):
-        """
-        """
-        raise NotImplementedError
-
-    def dstream(self):
-        """
-        """
-        raise NotImplementedError
-
-    def filter(self, f):
-        """
->>>>>>> initial commit for pySparkStreaming
         """
         def func(iterator): return ifilter(f, iterator)
         return self.mapPartitions(func)
 
     def flatMap(self, f, preservesPartitioning=False):
         """
-<<<<<<< HEAD
         Pass each value in the key-value pair DStream through flatMap function
         without changing the keys: this also retains the original RDD's partition.
         """
@@ -239,51 +136,6 @@ def combineByKey(self, createCombiner, mergeValue, mergeCombiners,
         if numPartitions is None:
             numPartitions = self._defaultReducePartitions()
 
-=======
-        """
-        def func(s, iterator): return chain.from_iterable(imap(f, iterator))
-        return self.mapPartitionsWithIndex(func, preservesPartitioning)
-
-    def foreachRDD(self, f, time):
-        """
-        """
-        raise NotImplementedError
-
-    def glom(self):
-        """
-        """
-        raise NotImplementedError
-
-    def map(self, f, preservesPartitioning=False):
-        """
-        """
-        def func(split, iterator): return imap(f, iterator)
-        return PipelinedDStream(self, func, preservesPartitioning)
-
-    def mapPartitions(self, f):
-        """
-        """
-        def func(s, iterator): return f(iterator)
-        return self.mapPartitionsWithIndex(func)
-
-    def perist(self, storageLevel):
-        """
-        """
-        raise NotImplementedError
-
-    def reduce(self, func, numPartitions=None):
-        """
-
-        """
-        return self._combineByKey(lambda x:x, func, func, numPartitions)
-
-    def _combineByKey(self, createCombiner, mergeValue, mergeCombiners,
-                      numPartitions = None):
-        """
-        """
-        if numPartitions is None:
-            numPartitions = self.ctx._defaultParallelism()
->>>>>>> initial commit for pySparkStreaming
         def combineLocally(iterator):
             combiners = {}
             for x in iterator:
@@ -295,10 +147,7 @@ def combineLocally(iterator):
             return combiners.iteritems()
         locally_combined = self.mapPartitions(combineLocally)
         shuffled = locally_combined.partitionBy(numPartitions)
-<<<<<<< HEAD
 
-=======
->>>>>>> initial commit for pySparkStreaming
         def _mergeCombiners(iterator):
             combiners = {}
             for (k, v) in iterator:
@@ -307,43 +156,25 @@ def _mergeCombiners(iterator):
                 else:
                     combiners[k] = mergeCombiners(combiners[k], v)
             return combiners.iteritems()
-<<<<<<< HEAD
 
         return shuffled.mapPartitions(_mergeCombiners)
 
     def partitionBy(self, numPartitions, partitionFunc=None):
         """
         Return a copy of the DStream partitioned using the specified partitioner.
-=======
-        return shuffled.mapPartitions(_mergeCombiners) 
-
-
-   def partitionBy(self, numPartitions, partitionFunc=None):
-        """
-        Return a copy of the DStream partitioned using the specified partitioner.
-
->>>>>>> initial commit for pySparkStreaming
         """
         if numPartitions is None:
             numPartitions = self.ctx._defaultReducePartitions()
 
         if partitionFunc is None:
             partitionFunc = lambda x: 0 if x is None else hash(x)
-<<<<<<< HEAD
 
-=======
->>>>>>> initial commit for pySparkStreaming
         # Transferring O(n) objects to Java is too expensive.  Instead, we'll
         # form the hash buckets in Python, transferring O(numPartitions) objects
         # to Java.  Each object is a (splitNumber, [objects]) pair.
         outputSerializer = self.ctx._unbatched_serializer
-<<<<<<< HEAD
 
         def add_shuffle_key(split, iterator):
-=======
-        def add_shuffle_key(split, iterator):
-
->>>>>>> initial commit for pySparkStreaming
             buckets = defaultdict(list)
 
             for (k, v) in iterator:
@@ -354,26 +185,16 @@ def add_shuffle_key(split, iterator):
         keyed = PipelinedDStream(self, add_shuffle_key)
         keyed._bypass_serializer = True
         with _JavaStackTrace(self.ctx) as st:
-<<<<<<< HEAD
             partitioner = self.ctx._jvm.PythonPartitioner(numPartitions,
                                                       id(partitionFunc))
             jdstream = self.ctx._jvm.PythonPairwiseDStream(keyed._jdstream.dstream(),
                                                            partitioner).asJavaDStream()
-=======
-            #JavaDStream
-            #pairRDD = self.ctx._jvm.PairwiseDStream(keyed._jdstream.dstream()).asJavaPairRDD()
-            pairDStream = self.ctx._jvm.PairwiseDStream(keyed._jdstream.dstream()).asJavaPairDStream()
-            partitioner = self.ctx._jvm.PythonPartitioner(numPartitions,
-                                                          id(partitionFunc))
-        jdstream = pairDStream.partitionBy(partitioner).values()
->>>>>>> initial commit for pySparkStreaming
         dstream = DStream(jdstream, self._ssc, BatchedSerializer(outputSerializer))
         # This is required so that id(partitionFunc) remains unique, even if
         # partitionFunc is a lambda:
         dstream._partitionFunc = partitionFunc
         return dstream
 
-<<<<<<< HEAD
     def _defaultReducePartitions(self):
         """
         Returns the default number of partitions to use during reduce tasks (e.g., groupBy).
@@ -387,7 +208,6 @@ def _defaultReducePartitions(self):
         if self.ctx._conf.contains("spark.default.parallelism"):
             return self.ctx.defaultParallelism
         else:
-<<<<<<< HEAD
             return 2
 
     def getNumPartitions(self):
@@ -398,16 +218,6 @@ def getNumPartitions(self):
       2
       """
       return self._jdstream.partitions().size()
-=======
-            return self.getNumPartitions()
-
-    def getNumPartitions(self):
-        """
-        Return the number of partitions in RDD
-        """
-        # TODO: remove hardcoding. RDD has NumPartitions but DStream does not have.
-        return 2
->>>>>>> clean up code
 
     def foreachRDD(self, func):
         """
@@ -610,53 +420,6 @@ def saveAsTextFile(rdd, time):
 # TODO: implement join
 # TODO: implement leftOuterJoin
 # TODO: implemtnt rightOuterJoin
-=======
-
-
-    def reduceByWindow(self, reduceFunc, windowDuration, slideDuration, inReduceTunc):
-        """
-        """
-
-        raise NotImplementedError
-
-    def repartition(self, numPartitions):
-        """
-        """
-        raise NotImplementedError
-
-    def slice(self, fromTime, toTime):
-        """
-        """
-        raise NotImplementedError
-
-    def transform(self, transformFunc):
-        """
-        """
-        raise NotImplementedError
-
-    def transformWith(self, other, transformFunc):
-        """
-        """
-        raise NotImplementedError
-
-    def union(self, that):
-        """
-        """
-        raise NotImplementedError
-
-    def window(self, windowDuration, slideDuration=None):
-        """
-        """
-        raise NotImplementedError
-
-    def wrapRDD(self, rdd):
-        """
-        """
-        raise NotImplementedError
-
-    def mapPartitionsWithIndex(self, f, preservesPartitioning=False):
-        return PipelinedDStream(self, f, preservesPartitioning)
->>>>>>> initial commit for pySparkStreaming
 
 
 class PipelinedDStream(DStream):
diff --git a/python/pyspark/streaming/duration.py b/python/pyspark/streaming/duration.py
index 245c137ecfc29..a7f1036e4b856 100644
--- a/python/pyspark/streaming/duration.py
+++ b/python/pyspark/streaming/duration.py
@@ -1,4 +1,3 @@
-<<<<<<< HEAD
 #
 # Licensed to the Apache Software Foundation (ASF) under one or more
 # contributor license agreements.  See the NOTICE file distributed with
@@ -19,12 +18,6 @@
 from pyspark.streaming import utils
 
 
-=======
-__author__ = 'ktakagiw'
-
-from pyspark.streaming import utils
-
->>>>>>> initial commit for pySparkStreaming
 class Duration(object):
     """
     Duration for Spark Streaming application. Used to set duration
@@ -50,7 +43,6 @@ def __init__(self, millis, _jvm=None):
         self._jduration = _jvm.Duration(millis)
 
     def toString(self):
-<<<<<<< HEAD
         """
         Return duration as string
 
@@ -71,19 +63,11 @@ def isZero(self):
         >>> d_0.isZero()
         True
         """
-=======
-        """ Return duration as string """
-        return str(self._millis) + " ms"
-
-    def isZero(self):
-        """ Check if millis is zero """
->>>>>>> initial commit for pySparkStreaming
         return self._millis == 0
 
     def prettyPrint(self):
         """
         Return a human-readable string representing a duration
-<<<<<<< HEAD
 
         >>> d_10 = Duration(10)
         >>> d_10.prettyPrint()
@@ -97,13 +81,10 @@ def prettyPrint(self):
         >>> d_1hour = Duration(60 * 60 * 1000)
         >>> d_1hour.prettyPrint()
         '1.00 h'
-=======
->>>>>>> initial commit for pySparkStreaming
         """
         return utils.msDurationToString(self._millis)
 
     def milliseconds(self):
-<<<<<<< HEAD
         """
         Return millisecond
 
@@ -136,17 +117,6 @@ def max(self, other):
         100 ms
 
         """
-=======
-        """ Return millisecond """
-        return self._millis
-
-    def toFormattedString(self):
-        """ Return millisecond """
-        return str(self._millis)
-
-    def max(self, other):
-        """ Return higher Duration """
->>>>>>> initial commit for pySparkStreaming
         Duration._is_duration(other)
         if self > other:
             return self
@@ -154,7 +124,6 @@ def max(self, other):
             return other
 
     def min(self, other):
-<<<<<<< HEAD
         """
         Return lower Durattion
 
@@ -165,9 +134,6 @@ def min(self, other):
         10 ms
 
         """
-=======
-        """ Return lower Durattion """
->>>>>>> initial commit for pySparkStreaming
         Duration._is_duration(other)
         if self < other:
             return self
@@ -175,7 +141,6 @@ def min(self, other):
             return other
 
     def __str__(self):
-<<<<<<< HEAD
         """
         >>> d_10 = Duration(10)
         >>> str(d_10)
@@ -194,17 +159,10 @@ def __add__(self, other):
         >>> print d_110
         110 ms
         """
-=======
-        return self.toString()
-
-    def __add__(self, other):
-        """ Add Duration and Duration """
->>>>>>> initial commit for pySparkStreaming
         Duration._is_duration(other)
         return Duration(self._millis + other._millis)
 
     def __sub__(self, other):
-<<<<<<< HEAD
         """
         Subtract Duration by Duration
 
@@ -215,14 +173,10 @@ def __sub__(self, other):
         90 ms
 
         """
-=======
-        """ Subtract Duration by Duration  """
->>>>>>> initial commit for pySparkStreaming
         Duration._is_duration(other)
         return Duration(self._millis - other._millis)
 
     def __mul__(self, other):
-<<<<<<< HEAD
         """
         Multiple Duration by Duration
 
@@ -233,9 +187,6 @@ def __mul__(self, other):
         1000 ms
 
         """
-=======
-        """ Multiple Duration by Duration """
->>>>>>> initial commit for pySparkStreaming
         Duration._is_duration(other)
         return Duration(self._millis * other._millis)
 
@@ -243,7 +194,6 @@ def __div__(self, other):
         """
         Divide Duration by Duration
         for Python 2.X
-<<<<<<< HEAD
 
         >>> d_10 = Duration(10)
         >>> d_20 = Duration(20)
@@ -251,8 +201,6 @@ def __div__(self, other):
         >>> print d_2
         2 ms
 
-=======
->>>>>>> initial commit for pySparkStreaming
         """
         Duration._is_duration(other)
         return Duration(self._millis / other._millis)
@@ -261,7 +209,6 @@ def __truediv__(self, other):
         """
         Divide Duration by Duration
         for Python 3.0
-<<<<<<< HEAD
 
         >>> d_10 = Duration(10)
         >>> d_20 = Duration(20)
@@ -269,14 +216,11 @@ def __truediv__(self, other):
         >>> print d_2
         2 ms
 
-=======
->>>>>>> initial commit for pySparkStreaming
         """
         Duration._is_duration(other)
         return Duration(self._millis / other._millis)
 
     def __floordiv__(self, other):
-<<<<<<< HEAD
         """
         Divide Duration by Duration
 
@@ -302,23 +246,10 @@ def __lt__(self, other):
         False
 
         """
-=======
-        """ Divide Duration by Duration """
-        Duration._is_duration(other)
-        return Duration(self._millis // other._millis)
-
-    def __len__(self):
-        """ Length of miilisecond in Duration """
-        return len(self._millis)
-
-    def __lt__(self, other):
-        """ Duration < Duration """
->>>>>>> initial commit for pySparkStreaming
         Duration._is_duration(other)
         return self._millis < other._millis
 
     def __le__(self, other):
-<<<<<<< HEAD
         """
         Duration <= Duration
 
@@ -346,19 +277,10 @@ def __eq__(self, other):
         True
 
         """
-=======
-        """ Duration <= Duration """
-        Duration._is_duration(other)
-        return self.millis <= other._millis
-
-    def __eq__(self, other):
-        """ Duration ==  Duration """
->>>>>>> initial commit for pySparkStreaming
         Duration._is_duration(other)
         return self._millis == other._millis
 
     def __ne__(self, other):
-<<<<<<< HEAD
         """
         Duration != Duration
 
@@ -371,14 +293,10 @@ def __ne__(self, other):
         False
 
         """
-=======
-        """ Duration != Duration """
->>>>>>> initial commit for pySparkStreaming
         Duration._is_duration(other)
         return self._millis != other._millis
 
     def __gt__(self, other):
-<<<<<<< HEAD
         """
         Duration > Duration
 
@@ -390,14 +308,10 @@ def __gt__(self, other):
         True
 
         """
-=======
-        """ Duration > Duration """
->>>>>>> initial commit for pySparkStreaming
         Duration._is_duration(other)
         return self._millis > other._millis
 
     def __ge__(self, other):
-<<<<<<< HEAD
         """
         Duration >= Duration
 
@@ -410,9 +324,6 @@ def __ge__(self, other):
 
 
         """
-=======
-        """ Duration >= Duration """
->>>>>>> initial commit for pySparkStreaming
         Duration._is_duration(other)
         return self._millis >= other._millis
 
@@ -426,15 +337,12 @@ def Milliseconds(milliseconds):
     """
     Helper function that creates instance of [[pysparkstreaming.duration]] representing
     a given number of milliseconds.
-<<<<<<< HEAD
 
     >>> milliseconds = Milliseconds(1)
     >>> d_1 = Duration(1)
     >>> milliseconds == d_1
     True
 
-=======
->>>>>>> initial commit for pySparkStreaming
     """
     return Duration(milliseconds)
 
@@ -442,7 +350,6 @@ def Seconds(seconds):
     """
     Helper function that creates instance of [[pysparkstreaming.duration]] representing
     a given number of seconds.
-<<<<<<< HEAD
 
     >>> seconds = Seconds(1)
     >>> d_1sec = Duration(1000)
@@ -464,20 +371,3 @@ def Minutes(minutes):
 
     """
     return Duration(minutes * 60 * 1000)
-=======
-    """
-    return Duration(seconds * 1000)
-
-def Minites(minites):
-    """
-    Helper function that creates instance of [[pysparkstreaming.duration]] representing
-    a given number of minutes.
-    """
-    return Duration(minutes * 60000)
-
-if __name__ == "__main__":
-    d = Duration(1)
-    print d
-    print d.milliseconds()
-
->>>>>>> initial commit for pySparkStreaming
diff --git a/python/pyspark/streaming/jtime.py b/python/pyspark/streaming/jtime.py
index 9295c4ee27705..32ef741051283 100644
--- a/python/pyspark/streaming/jtime.py
+++ b/python/pyspark/streaming/jtime.py
@@ -1,4 +1,3 @@
-<<<<<<< HEAD
 #
 # Licensed to the Apache Software Foundation (ASF) under one or more
 # contributor license agreements.  See the NOTICE file distributed with
@@ -15,14 +14,10 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 #
-=======
-__author__ = 'ktakagiw'
->>>>>>> initial commit for pySparkStreaming
 
 from pyspark.streaming import utils
 from pyspark.streaming.duration import Duration
 
-<<<<<<< HEAD
 """
 The name of this file, time is not good naming for python
 because if we do import time when we want to use native python time package, it does
@@ -30,8 +25,6 @@
 """
 
 
-=======
->>>>>>> initial commit for pySparkStreaming
 class Time(object):
     """
     Time for Spark Streaming application. Used to set Time
diff --git a/python/pyspark/streaming/pyprint.py b/python/pyspark/streaming/pyprint.py
deleted file mode 100644
index 4beb66950d851..0000000000000
--- a/python/pyspark/streaming/pyprint.py
+++ /dev/null
@@ -1,72 +0,0 @@
-<<<<<<< HEAD
-#
-# Licensed to the Apache Software Foundation (ASF) under one or more
-# contributor license agreements.  See the NOTICE file distributed with
-# this work for additional information regarding copyright ownership.
-# The ASF licenses this file to You under the Apache License, Version 2.0
-# (the "License"); you may not use this file except in compliance with
-# the License.  You may obtain a copy of the License at
-#
-#    http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
-
-
-import sys
-from itertools import chain
-
-from pyspark.serializers import PickleSerializer
-
-
-def collect(binary_file_path):
-    """
-    Read pickled file written by SparkStreaming
-    """
-=======
-import sys
-from itertools import chain
-from pyspark.serializers import PickleSerializer, BatchedSerializer, UTF8Deserializer
-
-def collect(binary_file_path):
->>>>>>> initial commit for pySparkStreaming
-    dse = PickleSerializer()
-    with open(binary_file_path, 'rb') as tempFile:
-        for item in dse.load_stream(tempFile):
-            yield item
-<<<<<<< HEAD
-
-
-=======
->>>>>>> initial commit for pySparkStreaming
-def main():
-    try:
-        binary_file_path = sys.argv[1]
-    except:
-<<<<<<< HEAD
-        print "Missed FilePath in argements"
-=======
-        print "Missed FilePath in argement"
->>>>>>> initial commit for pySparkStreaming
-
-    if not binary_file_path:
-        return 
-
-    counter = 0
-    for rdd in chain.from_iterable(collect(binary_file_path)):
-        print rdd
-        counter = counter + 1
-        if counter >= 10:
-            print "..."
-            break
-
-<<<<<<< HEAD
-
-=======
->>>>>>> initial commit for pySparkStreaming
-if __name__ =="__main__":
-    exit(main())
diff --git a/python/pyspark/streaming/utils.py b/python/pyspark/streaming/utils.py
index 56bb0ca1e9620..ad1dab0696b1a 100644
--- a/python/pyspark/streaming/utils.py
+++ b/python/pyspark/streaming/utils.py
@@ -37,9 +37,6 @@ def __str__(self):
     class Java:
         implements = ['org.apache.spark.streaming.api.python.PythonRDDFunction']
 
-=======
-__author__ = 'ktakagiw'
->>>>>>> initial commit for pySparkStreaming
 
 def msDurationToString(ms):
     """
@@ -53,7 +50,6 @@ def msDurationToString(ms):
         return "%d ms" % ms
     elif ms < minute:
         return "%.1f s" % (float(ms) / second)
-<<<<<<< HEAD
     elif ms < hour:
         return "%.1f m" % (float(ms) / minute)
     else:
@@ -64,9 +60,3 @@ def rddToFileName(prefix, suffix, time):
         return prefix + "-" + str(time) + "." + suffix
     else:
         return prefix + "-" + str(time)
-=======
-    elif ms < hout:
-        return "%.1f m" % (float(ms) / minute)
-    else:
-        return "%.2f h" % (float(ms) / hour)
->>>>>>> initial commit for pySparkStreaming
diff --git a/streaming/pom.xml b/streaming/pom.xml
index cb38015c24622..483e200ff9f16 100644
--- a/streaming/pom.xml
+++ b/streaming/pom.xml
@@ -21,11 +21,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent</artifactId>
-<<<<<<< HEAD
     <version>1.2.0-SNAPSHOT</version>
-=======
-    <version>1.0.0</version>
->>>>>>> initial commit for pySparkStreaming
     <relativePath>../pom.xml</relativePath>
   </parent>
 
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
index 998fa24eba91b..cb9014cda6311 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
@@ -17,7 +17,6 @@
 
 package org.apache.spark.streaming.api.python
 
-<<<<<<< HEAD
 import java.io._
 import java.io.{ObjectInputStream, IOException}
 import java.util.{List => JList, ArrayList => JArrayList, Map => JMap, Collections}
@@ -37,7 +36,6 @@ import org.apache.spark.streaming.dstream._
 import org.apache.spark.streaming.api.java._
 
 
-
 class PythonDStream[T: ClassTag](
     parent: DStream[T],
     command: Array[Byte],
@@ -48,46 +46,6 @@ class PythonDStream[T: ClassTag](
     broadcastVars: JList[Broadcast[Array[Byte]]],
     accumulator: Accumulator[JList[Array[Byte]]])
   extends DStream[Array[Byte]](parent.ssc) {
-=======
-import java.util.{List => JList, ArrayList => JArrayList, Map => JMap, Collections}
-
-import org.apache.spark.api.java.{JavaSparkContext, JavaPairRDD, JavaRDD}
-import org.apache.spark.broadcast.Broadcast
-import org.apache.spark._
-import org.apache.spark.util.Utils
-import java.io._
-import scala.Some
-import org.apache.spark.streaming.Duration
-import scala.util.control.Breaks._
-import org.apache.spark.broadcast.Broadcast
-import scala.Some
-import org.apache.spark.streaming.Duration
-import org.apache.spark.rdd.RDD
-import org.apache.spark.api.python.PythonRDD
-
-
-import org.apache.spark.streaming.{Duration, Time}
-import org.apache.spark.streaming.dstream._
-import org.apache.spark.streaming.api.java._
-import org.apache.spark.rdd.RDD
-import org.apache.spark.api.python._
-import org.apache.spark.api.python.PairwiseRDD
-
-
-import scala.reflect.ClassTag
-
-
-class PythonDStream[T: ClassTag](
-                                  parent: DStream[T],
-                                  command: Array[Byte],
-                                  envVars: JMap[String, String],
-                                  pythonIncludes: JList[String],
-                                  preservePartitoning: Boolean,
-                                  pythonExec: String,
-                                  broadcastVars: JList[Broadcast[Array[Byte]]],
-                                  accumulator: Accumulator[JList[Array[Byte]]]
-                                  ) extends DStream[Array[Byte]](parent.ssc) {
->>>>>>> initial commit for pySparkStreaming
 
   override def dependencies = List(parent)
 
@@ -102,14 +60,11 @@ class PythonDStream[T: ClassTag](
       case None => None
     }
   }
-<<<<<<< HEAD
 
   def foreachRDD(foreachFunc: PythonRDDFunction) {
     new PythonForeachDStream(this, context.sparkContext.clean(foreachFunc, false)).register()
   }
 
-=======
->>>>>>> initial commit for pySparkStreaming
   val asJavaDStream  = JavaDStream.fromDStream(this)
 
   /**
@@ -132,11 +87,6 @@ class PythonDStream[T: ClassTag](
       tempFileStream.close()
 
       // This value has to be passed from python
-<<<<<<< HEAD
-      //val pythonExec = new ProcessBuilder().environment().get("PYSPARK_PYTHON")
-=======
-      val pythonExec = new ProcessBuilder().environment().get("PYSPARK_PYTHON")
->>>>>>> initial commit for pySparkStreaming
       val sparkHome = new ProcessBuilder().environment().get("SPARK_HOME")
       //val pb = new ProcessBuilder(Seq(pythonExec, sparkHome + "/python/pyspark/streaming/pyprint.py", tempFile.getAbsolutePath())) // why this fails to compile???
       //absolute path to the python script is needed to change because we do not use pysparkstreaming
@@ -174,26 +124,6 @@ class PythonDStream[T: ClassTag](
   }
 }
 
-/*
-private class PairwiseDStream(prev:DStream[Array[Byte]]) extends
-DStream[(Long, Array[Byte])](prev.ssc){
-  override def dependencies = List(prev)
-
-  override def slideDuration: Duration = prev.slideDuration
-
-  override def compute(validTime:Time):Option[RDD[(Long, Array[Byte])]]={
-    prev.getOrCompute(validTime) match{
-      case Some(rdd)=>Some(rdd)
-        val pairwiseRDD = new PairwiseRDD(rdd)
-        Some(pairwiseRDD.asJavaPairRDD.rdd)
-      case None => None
-    }
-  }
-<<<<<<< HEAD
-  val asJavaPairDStream : JavaPairDStream[Long, Array[Byte]]  = JavaPairDStream.fromJavaDStream(this)
-}
-
-
 private class PythonPairwiseDStream(prev:DStream[Array[Byte]], partitioner: Partitioner) extends
 DStream[Array[Byte]](prev.ssc){
   override def dependencies = List(prev)
@@ -274,12 +204,3 @@ class PythonTransformedDStream(
   //val asJavaPairDStream : JavaPairDStream[Long, Array[Byte]]  = JavaPairDStream.fromJavaDStream(this)
 }
 */
-=======
-  val asJavaPairDStream : JavaPairDStream[Long, Array[Byte]] = JavaPairDStream(this)
-}
-*/
-
-
-
-
->>>>>>> initial commit for pySparkStreaming

From 38adf957f67e0858af76cec4b08c379f64dc5c59 Mon Sep 17 00:00:00 2001
From: Ken Takagiwa <ken@Kens-MacBook-Pro.local>
Date: Tue, 15 Jul 2014 21:08:43 -0700
Subject: [PATCH 424/489] added reducedByKey not working yet

---
 python/pyspark/streaming/utils.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/python/pyspark/streaming/utils.py b/python/pyspark/streaming/utils.py
index ad1dab0696b1a..9178577743e0b 100644
--- a/python/pyspark/streaming/utils.py
+++ b/python/pyspark/streaming/utils.py
@@ -1,4 +1,3 @@
-<<<<<<< HEAD
 #
 # Licensed to the Apache Software Foundation (ASF) under one or more
 # contributor license agreements.  See the NOTICE file distributed with

From 4bcb31888aa03a6441711f291c64e78e8b259308 Mon Sep 17 00:00:00 2001
From: Ken Takagiwa <ken@Kens-MacBook-Pro.local>
Date: Wed, 16 Jul 2014 11:07:42 -0700
Subject: [PATCH 425/489] implementing transform function in Python

---
 python/pyspark/streaming/dstream.py           |  1 -
 .../api/python/PythonTransformedDStream.scala | 37 +++++++++++++++++++
 .../spark/streaming/dstream/DStream.scala     |  1 +
 3 files changed, 38 insertions(+), 1 deletion(-)
 create mode 100644 streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonTransformedDStream.scala

diff --git a/python/pyspark/streaming/dstream.py b/python/pyspark/streaming/dstream.py
index 224d2bbdeeb53..5a56a3d958254 100644
--- a/python/pyspark/streaming/dstream.py
+++ b/python/pyspark/streaming/dstream.py
@@ -379,7 +379,6 @@ def saveAsTextFiles(self, prefix, suffix=None):
         """
         Save this DStream as a text file, using string representations of elements.
         """
-
         def saveAsTextFile(rdd, time):
             path = rddToFileName(prefix, suffix, time)
             rdd.saveAsTextFile(path)
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonTransformedDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonTransformedDStream.scala
new file mode 100644
index 0000000000000..ff70483b771a4
--- /dev/null
+++ b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonTransformedDStream.scala
@@ -0,0 +1,37 @@
+package org.apache.spark.streaming.api.python
+
+import org.apache.spark.Accumulator
+import org.apache.spark.api.python.PythonRDD
+import org.apache.spark.broadcast.Broadcast
+import org.apache.spark.rdd.RDD
+import org.apache.spark.streaming.api.java.JavaDStream
+import org.apache.spark.streaming.{Time, Duration}
+import org.apache.spark.streaming.dstream.DStream
+
+import scala.reflect.ClassTag
+
+/**
+ * Created by ken on 7/15/14.
+ */
+class PythonTransformedDStream[T: ClassTag](
+               parents: Seq[DStream[T]],
+               command: Array[Byte],
+               envVars: JMap[String, String],
+               pythonIncludes: JList[String],
+               preservePartitoning: Boolean,
+               pythonExec: String,
+               broadcastVars: JList[Broadcast[Array[Byte]]],
+               accumulator: Accumulator[JList[Array[Byte]]]
+               ) extends DStream[Array[Byte]](parent.ssc) {
+
+  override def dependencies = List(parent)
+
+  override def slideDuration: Duration = parent.slideDuration
+
+  //pythonDStream compute
+  override def compute(validTime: Time): Option[RDD[Array[Byte]]] = {
+    val parentRDDs = parents.map(_.getOrCompute(validTime).orNull).toSeq
+    Some()
+  }
+  val asJavaDStream  = JavaDStream.fromDStream(this)
+}
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala
index 5377dfa52d461..13032fca15616 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala
@@ -563,6 +563,7 @@ abstract class DStream[T: ClassTag] (
     val cleanedF = context.sparkContext.clean(transformFunc, false)
     val realTransformFunc =  (rdds: Seq[RDD[_]], time: Time) => {
       assert(rdds.length == 1)
+      // if transformfunc is fine, it is okay
       cleanedF(rdds.head.asInstanceOf[RDD[T]], time)
     }
     new TransformedDStream[U](Seq(this), realTransformFunc)

From 247fd748ddc59db14195b599115d6495387989a0 Mon Sep 17 00:00:00 2001
From: Ken Takagiwa <ken@Kens-MacBook-Pro.local>
Date: Wed, 16 Jul 2014 11:12:53 -0700
Subject: [PATCH 426/489] modified the code base on comment in
 https://github.com/tdas/spark/pull/10

---
 core/pom.xml                         | 4 ++++
 python/pyspark/streaming/__init__.py | 1 -
 2 files changed, 4 insertions(+), 1 deletion(-)

diff --git a/core/pom.xml b/core/pom.xml
index 2a81f6df289c0..6e3781f2bb0c6 100644
--- a/core/pom.xml
+++ b/core/pom.xml
@@ -21,7 +21,11 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent</artifactId>
+<<<<<<< HEAD
     <version>1.2.0-SNAPSHOT</version>
+=======
+    <version>1.1.0-SNAPSHOT</version>
+>>>>>>> modified the code base on comment in https://github.com/tdas/spark/pull/10
     <relativePath>../pom.xml</relativePath>
   </parent>
 
diff --git a/python/pyspark/streaming/__init__.py b/python/pyspark/streaming/__init__.py
index 719592912e80c..e69de29bb2d1d 100644
--- a/python/pyspark/streaming/__init__.py
+++ b/python/pyspark/streaming/__init__.py
@@ -1 +0,0 @@
-__author__ = 'ktakagiw'

From dd6de814a6a4cdc254c5018bbb299ec9e938a248 Mon Sep 17 00:00:00 2001
From: Ken Takagiwa <ken@Kens-MacBook-Pro.local>
Date: Wed, 16 Jul 2014 16:23:08 -0700
Subject: [PATCH 427/489] initial commit for socketTextStream

---
 .../python/streaming/nerwork_wordcount.py     | 22 +++++++++++++++++++
 1 file changed, 22 insertions(+)
 create mode 100644 examples/src/main/python/streaming/nerwork_wordcount.py

diff --git a/examples/src/main/python/streaming/nerwork_wordcount.py b/examples/src/main/python/streaming/nerwork_wordcount.py
new file mode 100644
index 0000000000000..2e5048ccad213
--- /dev/null
+++ b/examples/src/main/python/streaming/nerwork_wordcount.py
@@ -0,0 +1,22 @@
+import sys
+from operator import add
+
+from pyspark.streaming.context import StreamingContext
+from pyspark.streaming.duration import *
+
+if __name__ == "__main__":
+    if len(sys.argv) != 3:
+        print >> sys.stderr, "Usage: wordcount <hostname> <port>"
+        exit(-1)
+    ssc = StreamingContext(appName="PythonStreamingNetworkWordCount", duration=Seconds(1))
+
+    lines = ssc.socketTextStream(sys.argv[1], sys.argv[2])
+    fm_lines = lines.flatMap(lambda x: x.split(" "))
+    filtered_lines = fm_lines.filter(lambda line: "Spark" in line)
+    mapped_lines = fm_lines.map(lambda x: (x, 1))
+    
+    fm_lines.pyprint()
+    filtered_lines.pyprint()
+    mapped_lines.pyprint()
+    ssc.start()
+    ssc.awaitTermination()

From f485b1d1f9ebbcc7de376932fcdd981777f8b295 Mon Sep 17 00:00:00 2001
From: Ken Takagiwa <ken@Kens-MacBook-Pro.local>
Date: Wed, 16 Jul 2014 16:27:28 -0700
Subject: [PATCH 428/489] fied input of socketTextDStream

---
 examples/src/main/python/streaming/nerwork_wordcount.py | 2 +-
 python/pyspark/java_gateway.py                          | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/examples/src/main/python/streaming/nerwork_wordcount.py b/examples/src/main/python/streaming/nerwork_wordcount.py
index 2e5048ccad213..67dc28f7bf7f0 100644
--- a/examples/src/main/python/streaming/nerwork_wordcount.py
+++ b/examples/src/main/python/streaming/nerwork_wordcount.py
@@ -10,7 +10,7 @@
         exit(-1)
     ssc = StreamingContext(appName="PythonStreamingNetworkWordCount", duration=Seconds(1))
 
-    lines = ssc.socketTextStream(sys.argv[1], sys.argv[2])
+    lines = ssc.socketTextStream(sys.argv[1], int(sys.argv[2]))
     fm_lines = lines.flatMap(lambda x: x.split(" "))
     filtered_lines = fm_lines.filter(lambda line: "Spark" in line)
     mapped_lines = fm_lines.map(lambda x: (x, 1))
diff --git a/python/pyspark/java_gateway.py b/python/pyspark/java_gateway.py
index f3c6d231ab777..2af917efc40a3 100644
--- a/python/pyspark/java_gateway.py
+++ b/python/pyspark/java_gateway.py
@@ -111,7 +111,7 @@ def run(self):
     java_import(gateway.jvm, "org.apache.spark.streaming.*")  # do we need this?
     java_import(gateway.jvm, "org.apache.spark.streaming.api.java.*")
     java_import(gateway.jvm, "org.apache.spark.streaming.api.python.*")
-    java_import(gateway.jvm, "org.apache.spark.streaming.dstream.*")  # do we need this?
+    java_import(gateway.jvm, "org.apache.spark.streaming.dstream.*")
     java_import(gateway.jvm, "org.apache.spark.mllib.api.python.*")
     java_import(gateway.jvm, "org.apache.spark.sql.SQLContext")
     java_import(gateway.jvm, "org.apache.spark.sql.hive.HiveContext")

From 0df71118694f4d1ce4f49b97caa4631a2e13797a Mon Sep 17 00:00:00 2001
From: Ken Takagiwa <ken@kens-mbp.gateway.sonic.net>
Date: Wed, 16 Jul 2014 23:39:25 -0700
Subject: [PATCH 429/489] delete old file

---
 .../python/streaming/nerwork_wordcount.py     | 22 -------------------
 1 file changed, 22 deletions(-)
 delete mode 100644 examples/src/main/python/streaming/nerwork_wordcount.py

diff --git a/examples/src/main/python/streaming/nerwork_wordcount.py b/examples/src/main/python/streaming/nerwork_wordcount.py
deleted file mode 100644
index 67dc28f7bf7f0..0000000000000
--- a/examples/src/main/python/streaming/nerwork_wordcount.py
+++ /dev/null
@@ -1,22 +0,0 @@
-import sys
-from operator import add
-
-from pyspark.streaming.context import StreamingContext
-from pyspark.streaming.duration import *
-
-if __name__ == "__main__":
-    if len(sys.argv) != 3:
-        print >> sys.stderr, "Usage: wordcount <hostname> <port>"
-        exit(-1)
-    ssc = StreamingContext(appName="PythonStreamingNetworkWordCount", duration=Seconds(1))
-
-    lines = ssc.socketTextStream(sys.argv[1], int(sys.argv[2]))
-    fm_lines = lines.flatMap(lambda x: x.split(" "))
-    filtered_lines = fm_lines.filter(lambda line: "Spark" in line)
-    mapped_lines = fm_lines.map(lambda x: (x, 1))
-    
-    fm_lines.pyprint()
-    filtered_lines.pyprint()
-    mapped_lines.pyprint()
-    ssc.start()
-    ssc.awaitTermination()

From 58591d2a9b9555677f5b3cc00a98aead61553e8b Mon Sep 17 00:00:00 2001
From: Ken Takagiwa <ken@Kens-MacBook-Pro.local>
Date: Thu, 17 Jul 2014 16:27:05 -0700
Subject: [PATCH 430/489] reduceByKey is working

---
 .../src/main/python/streaming/wordcount.pyc   | Bin 0 -> 1566 bytes
 .../api/python/PythonTransformedDStream.scala |  19 +++++++++++-------
 2 files changed, 12 insertions(+), 7 deletions(-)
 create mode 100644 examples/src/main/python/streaming/wordcount.pyc

diff --git a/examples/src/main/python/streaming/wordcount.pyc b/examples/src/main/python/streaming/wordcount.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..db93702361f47f57988ea82c213aae522e7a9f81
GIT binary patch
literal 1566
zcmb_c+invv5S`tmX_F>xdP%Qb`WW$$yrZfDRUlD`3MEBIE2LoJN!i4Ek?qh#;suqD
z<A3-7n6a}d5{L(2H};Otj6G*APU%~t_Uz(oe+Y}GLf<De%)0<U(k}o75P3NS6+jX~
z6hgqd5~va+Wr)gtT?VxRNd=;cU$1~#g)3YGS%FCi`fY$!K!9-#ZUd03Fe!ol?2vUR
z%QXmJTwtBuk~&BMunf24y#d&O@dn7MQ}ALFWDVpx+!7V6fUL8LB*Ugd1KJlxTYy_I
z-d?yQ>q9y>+5y~w@h->>_~JZ8Ex;Cx_dsqI$i71kKsF2H5bzMjM}Q%7h>o4XJ*F4n
zK8zO<nJlE^r9>3&dpnXIIEg}|#P-`;l<D|8J(q1tH`o$QLQ21=(xRUWJvq<Pk%yWL
zS&brz(`$k)&aBt)_D6P&=D{ElaXJ{pAuY^3nqC`mjgL=mGA)PMG_@zEGS)b>nVz57
z12T9uw;!@}dGH%DJZD35$VV`Rj>M6eD%+ujPzqISlGsr$lgW?>I^S}tg^jax$SNMp
z;hctP(DaEa?*gX;_S>wwv}|<ZLs((ET*W^{G;0$EZZchxa3+#mq3Ieu>~fgh+)?kE
zA}QTvlpRuWK2DWL-b-<|OR}K>zmlsFO7$Rqlgg4A({btd9G1bVIK_XX)mx{dTQvSa
zhqw@<QCQG%$-ZOKW6J-5#a_Xp{g5Qre`ao6-m)C2n3?X80&Jb8eC~UJ%iVMN;QlDk
zWeGZ@=f>=Dh3(OAO^QZYbZjaT91NU$g{kG968ie1<$oh%C2H+3oW-`snC0*d+NXJb
zNn1T{w04!?K9YZFJZ6S=gYAjV^H`y8am%IUG8(sGq=bDbaz|y947`H9jACU`y92z4
za-rpWn$Q-`3VrjXg12du(;-^!crO;am|Z^4b(~s9!C0ZK<vT5IL~O56)3tW@=dQ9&
u`upBbGC5Uv#>Fxnn(9F_YzEDs7S@AS(4e<Uqggr*_kwNrZicm>cz*!UkyX3^

literal 0
HcmV?d00001

diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonTransformedDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonTransformedDStream.scala
index ff70483b771a4..bc07e09ec6d03 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonTransformedDStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonTransformedDStream.scala
@@ -1,3 +1,5 @@
+/*
+
 package org.apache.spark.streaming.api.python
 
 import org.apache.spark.Accumulator
@@ -10,11 +12,8 @@ import org.apache.spark.streaming.dstream.DStream
 
 import scala.reflect.ClassTag
 
-/**
- * Created by ken on 7/15/14.
- */
 class PythonTransformedDStream[T: ClassTag](
-               parents: Seq[DStream[T]],
+               parent: DStream[T],
                command: Array[Byte],
                envVars: JMap[String, String],
                pythonIncludes: JList[String],
@@ -30,8 +29,14 @@ class PythonTransformedDStream[T: ClassTag](
 
   //pythonDStream compute
   override def compute(validTime: Time): Option[RDD[Array[Byte]]] = {
-    val parentRDDs = parents.map(_.getOrCompute(validTime).orNull).toSeq
-    Some()
+
+//    val parentRDDs = parents.map(_.getOrCompute(validTime).orNull).toSeq
+//    parents.map(_.getOrCompute(validTime).orNull).to
+//    parent = parents.head.asInstanceOf[RDD]
+//    Some()
   }
-  val asJavaDStream  = JavaDStream.fromDStream(this)
+
+  val asJavaDStream = JavaDStream.fromDStream(this)
 }
+
+*/

From 98c2a00250e826ac45eec7f17f820db9b6106f1d Mon Sep 17 00:00:00 2001
From: Ken Takagiwa <ken@Kens-MacBook-Pro.local>
Date: Fri, 18 Jul 2014 17:58:58 -0700
Subject: [PATCH 431/489] added count operation but this implementation need
 double check

---
 python/pyspark/streaming/dstream.py | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/python/pyspark/streaming/dstream.py b/python/pyspark/streaming/dstream.py
index 5a56a3d958254..18415fcebe771 100644
--- a/python/pyspark/streaming/dstream.py
+++ b/python/pyspark/streaming/dstream.py
@@ -139,6 +139,12 @@ def combineByKey(self, createCombiner, mergeValue, mergeCombiners,
         def combineLocally(iterator):
             combiners = {}
             for x in iterator:
+
+                #TODO for count operation make sure count implementation
+                # This is different from what pyspark does
+                if isinstance(x, int):
+                    x = ("", x)
+
                 (k, v) = x
                 if k not in combiners:
                     combiners[k] = createCombiner(v)

From eb4bf483b9725854d704ab17f862b90476c552b8 Mon Sep 17 00:00:00 2001
From: Ken Takagiwa <ken@Kens-MacBook-Pro.local>
Date: Sat, 19 Jul 2014 18:58:01 -0700
Subject: [PATCH 432/489] fix map function

---
 python/pyspark/streaming/dstream.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/python/pyspark/streaming/dstream.py b/python/pyspark/streaming/dstream.py
index 18415fcebe771..8eea3f4d52782 100644
--- a/python/pyspark/streaming/dstream.py
+++ b/python/pyspark/streaming/dstream.py
@@ -142,8 +142,8 @@ def combineLocally(iterator):
 
                 #TODO for count operation make sure count implementation
                 # This is different from what pyspark does
-                if isinstance(x, int):
-                    x = ("", x)
+                #if isinstance(x, int):
+                #    x = ("", x)
 
                 (k, v) = x
                 if k not in combiners:

From 6197a112ae447726baeedf23e2dd1f0c75ff6e87 Mon Sep 17 00:00:00 2001
From: Ken Takagiwa <ken@Kens-MacBook-Pro.local>
Date: Sun, 20 Jul 2014 14:31:55 -0700
Subject: [PATCH 433/489] clean up code

---
 python/pyspark/streaming/dstream.py           | 19 ++++++++---------
 .../streaming/api/java/JavaDStreamLike.scala  |  2 +-
 .../spark/streaming/dstream/DStream.scala     | 21 +++++++++----------
 3 files changed, 20 insertions(+), 22 deletions(-)

diff --git a/python/pyspark/streaming/dstream.py b/python/pyspark/streaming/dstream.py
index 8eea3f4d52782..cfadef941a551 100644
--- a/python/pyspark/streaming/dstream.py
+++ b/python/pyspark/streaming/dstream.py
@@ -56,7 +56,7 @@ def _sum(self):
         """
         Add up the elements in this DStream.
         """
-        return self.mapPartitions(lambda x: [sum(x)]).reduce(operator.add)
+        return self._mapPartitions(lambda x: [sum(x)]).reduce(operator.add)
 
     def print_(self, label=None):
         """
@@ -65,6 +65,7 @@ def print_(self, label=None):
         deserialized pickled python object. Please use DStream.pyprint() to print results.
 
         Call DStream.print() and this function will print byte array in the DStream
+
         """
         # a hack to call print function in DStream
         getattr(self._jdstream, "print")(label)
@@ -74,7 +75,7 @@ def filter(self, f):
         Return a new DStream containing only the elements that satisfy predicate.
         """
         def func(iterator): return ifilter(f, iterator)
-        return self.mapPartitions(func)
+        return self._mapPartitions(func)
 
     def flatMap(self, f, preservesPartitioning=False):
         """
@@ -85,7 +86,7 @@ def func(s, iterator):
             return chain.from_iterable(imap(f, iterator))
         return self._mapPartitionsWithIndex(func, preservesPartitioning)
 
-    def map(self, f, preservesPartitioning=False):
+    def map(self, f):
         """
         Return a new DStream by applying a function to each element of DStream.
         """
@@ -217,13 +218,11 @@ def _defaultReducePartitions(self):
             return 2
 
     def getNumPartitions(self):
-      """
-      Returns the number of partitions in RDD
-      >>> rdd = sc.parallelize([1, 2, 3, 4], 2)
-      >>> rdd.getNumPartitions()
-      2
-      """
-      return self._jdstream.partitions().size()
+        """
+        Return the number of partitions in RDD
+        """
+        # TODO: remove hardcoding. RDD has NumPartitions but DStream does not have.
+        return 2
 
     def foreachRDD(self, func):
         """
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaDStreamLike.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaDStreamLike.scala
index cfa336df8674f..a2b9d581f609c 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaDStreamLike.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaDStreamLike.scala
@@ -59,7 +59,7 @@ trait JavaDStreamLike[T, This <: JavaDStreamLike[T, This, R], R <: JavaRDDLike[T
    * operator, so this PythonDStream will be registered as an output stream and there materialized.
    * This function is for PythonAPI.
    */
-
+  //TODO move this function to PythonDStream
   def pyprint() = dstream.pyprint()
 
   /**
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala
index 13032fca15616..994a696a44f5b 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala
@@ -620,37 +620,36 @@ abstract class DStream[T: ClassTag] (
     new ForEachDStream(this, context.sparkContext.clean(foreachFunc)).register()
   }
 
-//TODO move pyprint to PythonDStream
+//TODO move pyprint to PythonDStream and executed by py4j call back function
   /**
    * Print the first ten elements of each PythonRDD generated in this PythonDStream. This is an output
    * operator, so this PythonDStream will be registered as an output stream and there materialized.
    * Since serialized Python object is readable by Python, pyprint writes out binary data to
    * temporary file and run python script to deserialized and print the first ten elements
+   *
+   * Currently call python script directly. We should avoid this
    */
   private[streaming] def pyprint() {
     def foreachFunc = (rdd: RDD[T], time: Time) => {
       val iter = rdd.take(11).iterator
 
-      // make a temporary file
+      // Generate a temporary file
       val prefix = "spark"
       val suffix = ".tmp"
       val tempFile = File.createTempFile(prefix, suffix)
       val tempFileStream = new DataOutputStream(new FileOutputStream(tempFile.getAbsolutePath))
-      //write out serialized python object
+      // Write out serialized python object to temporary file
       PythonRDD.writeIteratorToStream(iter, tempFileStream)
       tempFileStream.close()
 
-      // This value has to be passed from python
-      // Python currently does not do cluster deployment. But what happened
+      // pythonExec should be passed from python. Move pyprint to PythonDStream
       val pythonExec = new ProcessBuilder().environment().get("PYSPARK_PYTHON")
       val sparkHome = new ProcessBuilder().environment().get("SPARK_HOME")
-      //val pb = new ProcessBuilder(Seq(pythonExec, sparkHome + "/python/pyspark/streaming/pyprint.py", tempFile.getAbsolutePath())) // why this fails to compile???
-      //absolute path to the python script is needed to change because we do not use pysparkstreaming
+      // Call python script to deserialize and print result in stdout
       val pb = new ProcessBuilder(pythonExec, sparkHome + "/python/pyspark/streaming/pyprint.py", tempFile.getAbsolutePath)
       val workerEnv = pb.environment()
 
-      //envVars also need to be pass
-      //workerEnv.putAll(envVars)
+      // envVars also should be pass from python
       val pythonPath = sparkHome + "/python/" + File.pathSeparator + workerEnv.get("PYTHONPATH")
       workerEnv.put("PYTHONPATH", pythonPath)
       val worker = pb.start()
@@ -662,7 +661,7 @@ abstract class DStream[T: ClassTag] (
       println ("Time: " + time)
       println ("-------------------------------------------")
 
-      //print value from python std out
+      // Print values which is from python std out
       var line = ""
       breakable {
         while (true) {
@@ -671,7 +670,7 @@ abstract class DStream[T: ClassTag] (
           println(line)
         }
       }
-      //delete temporary file
+      // Delete temporary file
       tempFile.delete()
       println()
 

From 2ad7bd34b3665afa1a7595c8b653720ec0747cf7 Mon Sep 17 00:00:00 2001
From: Ken Takagiwa <ken@Kens-MacBook-Pro.local>
Date: Sun, 20 Jul 2014 15:32:20 -0700
Subject: [PATCH 434/489] clean up codes

---
 python/pyspark/streaming/dstream.py                          | 5 +++--
 .../scala/org/apache/spark/streaming/dstream/DStream.scala   | 3 ++-
 2 files changed, 5 insertions(+), 3 deletions(-)

diff --git a/python/pyspark/streaming/dstream.py b/python/pyspark/streaming/dstream.py
index cfadef941a551..c569459767060 100644
--- a/python/pyspark/streaming/dstream.py
+++ b/python/pyspark/streaming/dstream.py
@@ -56,7 +56,8 @@ def _sum(self):
         """
         Add up the elements in this DStream.
         """
-        return self._mapPartitions(lambda x: [sum(x)]).reduce(operator.add)
+        pass
+        #return self._mapPartitions(lambda x: [sum(x)]).reduce(operator.add)
 
     def print_(self, label=None):
         """
@@ -152,7 +153,7 @@ def combineLocally(iterator):
                 else:
                     combiners[k] = mergeValue(combiners[k], v)
             return combiners.iteritems()
-        locally_combined = self.mapPartitions(combineLocally)
+        locally_combined = self._mapPartitions(combineLocally)
         shuffled = locally_combined.partitionBy(numPartitions)
 
         def _mergeCombiners(iterator):
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala
index 994a696a44f5b..9902ea9548bb7 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala
@@ -620,7 +620,7 @@ abstract class DStream[T: ClassTag] (
     new ForEachDStream(this, context.sparkContext.clean(foreachFunc)).register()
   }
 
-//TODO move pyprint to PythonDStream and executed by py4j call back function
+//TODO: move pyprint to PythonDStream and executed by py4j call back function
   /**
    * Print the first ten elements of each PythonRDD generated in this PythonDStream. This is an output
    * operator, so this PythonDStream will be registered as an output stream and there materialized.
@@ -644,6 +644,7 @@ abstract class DStream[T: ClassTag] (
 
       // pythonExec should be passed from python. Move pyprint to PythonDStream
       val pythonExec = new ProcessBuilder().environment().get("PYSPARK_PYTHON")
+
       val sparkHome = new ProcessBuilder().environment().get("SPARK_HOME")
       // Call python script to deserialize and print result in stdout
       val pb = new ProcessBuilder(pythonExec, sparkHome + "/python/pyspark/streaming/pyprint.py", tempFile.getAbsolutePath)

From fe025471ea200cae76e65ebe03323b62060eeb69 Mon Sep 17 00:00:00 2001
From: Ken Takagiwa <ken@Kens-MacBook-Pro.local>
Date: Sun, 20 Jul 2014 15:33:34 -0700
Subject: [PATCH 435/489] remove waste file

---
 examples/src/main/python/streaming/wordcount.pyc | Bin 1566 -> 0 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 delete mode 100644 examples/src/main/python/streaming/wordcount.pyc

diff --git a/examples/src/main/python/streaming/wordcount.pyc b/examples/src/main/python/streaming/wordcount.pyc
deleted file mode 100644
index db93702361f47f57988ea82c213aae522e7a9f81..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

literal 1566
zcmb_c+invv5S`tmX_F>xdP%Qb`WW$$yrZfDRUlD`3MEBIE2LoJN!i4Ek?qh#;suqD
z<A3-7n6a}d5{L(2H};Otj6G*APU%~t_Uz(oe+Y}GLf<De%)0<U(k}o75P3NS6+jX~
z6hgqd5~va+Wr)gtT?VxRNd=;cU$1~#g)3YGS%FCi`fY$!K!9-#ZUd03Fe!ol?2vUR
z%QXmJTwtBuk~&BMunf24y#d&O@dn7MQ}ALFWDVpx+!7V6fUL8LB*Ugd1KJlxTYy_I
z-d?yQ>q9y>+5y~w@h->>_~JZ8Ex;Cx_dsqI$i71kKsF2H5bzMjM}Q%7h>o4XJ*F4n
zK8zO<nJlE^r9>3&dpnXIIEg}|#P-`;l<D|8J(q1tH`o$QLQ21=(xRUWJvq<Pk%yWL
zS&brz(`$k)&aBt)_D6P&=D{ElaXJ{pAuY^3nqC`mjgL=mGA)PMG_@zEGS)b>nVz57
z12T9uw;!@}dGH%DJZD35$VV`Rj>M6eD%+ujPzqISlGsr$lgW?>I^S}tg^jax$SNMp
z;hctP(DaEa?*gX;_S>wwv}|<ZLs((ET*W^{G;0$EZZchxa3+#mq3Ieu>~fgh+)?kE
zA}QTvlpRuWK2DWL-b-<|OR}K>zmlsFO7$Rqlgg4A({btd9G1bVIK_XX)mx{dTQvSa
zhqw@<QCQG%$-ZOKW6J-5#a_Xp{g5Qre`ao6-m)C2n3?X80&Jb8eC~UJ%iVMN;QlDk
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zNn1T{w04!?K9YZFJZ6S=gYAjV^H`y8am%IUG8(sGq=bDbaz|y947`H9jACU`y92z4
za-rpWn$Q-`3VrjXg12du(;-^!crO;am|Z^4b(~s9!C0ZK<vT5IL~O56)3tW@=dQ9&
u`upBbGC5Uv#>Fxnn(9F_YzEDs7S@AS(4e<Uqggr*_kwNrZicm>cz*!UkyX3^


From 4f0716374590f4d2c7613e85ebbb94a214c4fa1e Mon Sep 17 00:00:00 2001
From: Tathagata Das <tathagata.das1565@gmail.com>
Date: Wed, 23 Jul 2014 15:43:11 -0700
Subject: [PATCH 436/489] Implemented DStream.foreachRDD in the Python API
 using Py4J callback server.

---
 python/pyspark/java_gateway.py                |  2 +-
 python/pyspark/streaming/dstream.py           |  4 +-
 .../streaming/api/java/JavaDStreamLike.scala  |  8 ---
 .../streaming/api/python/PythonDStream.scala  |  1 +
 .../spark/streaming/dstream/DStream.scala     | 60 -------------------
 5 files changed, 4 insertions(+), 71 deletions(-)

diff --git a/python/pyspark/java_gateway.py b/python/pyspark/java_gateway.py
index 2af917efc40a3..fd85e07bf3a50 100644
--- a/python/pyspark/java_gateway.py
+++ b/python/pyspark/java_gateway.py
@@ -102,7 +102,7 @@ def run(self):
         EchoOutputThread(proc.stdout).start()
 
     # Connect to the gateway
-    gateway = JavaGateway(GatewayClient(port=gateway_port), auto_convert=False)
+    gateway = JavaGateway(GatewayClient(port=gateway_port), auto_convert=False, start_callback_server=True)
 
     # Import the classes used by PySpark
     java_import(gateway.jvm, "org.apache.spark.SparkConf")
diff --git a/python/pyspark/streaming/dstream.py b/python/pyspark/streaming/dstream.py
index c569459767060..76119981b0d9f 100644
--- a/python/pyspark/streaming/dstream.py
+++ b/python/pyspark/streaming/dstream.py
@@ -445,7 +445,6 @@ def pipeline_func(split, iterator):
             self._prev_jdstream = prev._prev_jdstream  # maintain the pipeline
             self._prev_jrdd_deserializer = prev._prev_jrdd_deserializer
         self.is_cached = False
-        self.is_checkpointed = False
         self._ssc = prev._ssc
         self.ctx = prev.ctx
         self.prev = prev
@@ -482,4 +481,5 @@ def _jdstream(self):
         return self._jdstream_val
 
     def _is_pipelinable(self):
-        return not (self.is_cached or self.is_checkpointed)
+        return not (self.is_cached)
+
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaDStreamLike.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaDStreamLike.scala
index a2b9d581f609c..a6184de4e83c1 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaDStreamLike.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaDStreamLike.scala
@@ -54,14 +54,6 @@ trait JavaDStreamLike[T, This <: JavaDStreamLike[T, This, R], R <: JavaRDDLike[T
     dstream.print()
   }
 
-  /**
-   * Print the first ten elements of each PythonRDD generated in the PythonDStream. This is an output
-   * operator, so this PythonDStream will be registered as an output stream and there materialized.
-   * This function is for PythonAPI.
-   */
-  //TODO move this function to PythonDStream
-  def pyprint() = dstream.pyprint()
-
   /**
    * Return a new DStream in which each RDD has a single element generated by counting each RDD
    * of this DStream.
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
index cb9014cda6311..6db7e0cfa6846 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
@@ -170,6 +170,7 @@ class PythonTestInputStream3(ssc_ : JavaStreamingContext)
 
   val asJavaDStream = JavaDStream.fromDStream(this)
 }
+
 class PythonForeachDStream(
     prev: DStream[Array[Byte]],
     foreachFunction: PythonRDDFunction
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala
index 9902ea9548bb7..e045ff50e09f9 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala
@@ -620,66 +620,6 @@ abstract class DStream[T: ClassTag] (
     new ForEachDStream(this, context.sparkContext.clean(foreachFunc)).register()
   }
 
-//TODO: move pyprint to PythonDStream and executed by py4j call back function
-  /**
-   * Print the first ten elements of each PythonRDD generated in this PythonDStream. This is an output
-   * operator, so this PythonDStream will be registered as an output stream and there materialized.
-   * Since serialized Python object is readable by Python, pyprint writes out binary data to
-   * temporary file and run python script to deserialized and print the first ten elements
-   *
-   * Currently call python script directly. We should avoid this
-   */
-  private[streaming] def pyprint() {
-    def foreachFunc = (rdd: RDD[T], time: Time) => {
-      val iter = rdd.take(11).iterator
-
-      // Generate a temporary file
-      val prefix = "spark"
-      val suffix = ".tmp"
-      val tempFile = File.createTempFile(prefix, suffix)
-      val tempFileStream = new DataOutputStream(new FileOutputStream(tempFile.getAbsolutePath))
-      // Write out serialized python object to temporary file
-      PythonRDD.writeIteratorToStream(iter, tempFileStream)
-      tempFileStream.close()
-
-      // pythonExec should be passed from python. Move pyprint to PythonDStream
-      val pythonExec = new ProcessBuilder().environment().get("PYSPARK_PYTHON")
-
-      val sparkHome = new ProcessBuilder().environment().get("SPARK_HOME")
-      // Call python script to deserialize and print result in stdout
-      val pb = new ProcessBuilder(pythonExec, sparkHome + "/python/pyspark/streaming/pyprint.py", tempFile.getAbsolutePath)
-      val workerEnv = pb.environment()
-
-      // envVars also should be pass from python
-      val pythonPath = sparkHome + "/python/" + File.pathSeparator + workerEnv.get("PYTHONPATH")
-      workerEnv.put("PYTHONPATH", pythonPath)
-      val worker = pb.start()
-      val is = worker.getInputStream()
-      val isr = new InputStreamReader(is)
-      val br = new BufferedReader(isr)
-
-      println ("-------------------------------------------")
-      println ("Time: " + time)
-      println ("-------------------------------------------")
-
-      // Print values which is from python std out
-      var line = ""
-      breakable {
-        while (true) {
-          line = br.readLine()
-          if (line == null) break()
-          println(line)
-        }
-      }
-      // Delete temporary file
-      tempFile.delete()
-      println()
-
-    }
-    new ForEachDStream(this, context.sparkContext.clean(foreachFunc)).register()
-  }
-
-
   /**
    * Return a new DStream in which each RDD contains all the elements in seen in a
    * sliding window of time over this DStream. The new DStream generates RDDs with

From 54b53586d6c71d49b1678fb55a8723000db119c8 Mon Sep 17 00:00:00 2001
From: Ken Takagiwa <ken@kens-mbp.gateway.sonic.net>
Date: Sat, 2 Aug 2014 15:58:24 -0700
Subject: [PATCH 437/489] tried to restart callback server

---
 python/pyspark/java_gateway.py      | 5 ++++-
 python/pyspark/streaming/context.py | 5 +++++
 2 files changed, 9 insertions(+), 1 deletion(-)

diff --git a/python/pyspark/java_gateway.py b/python/pyspark/java_gateway.py
index fd85e07bf3a50..af6109dc2ab69 100644
--- a/python/pyspark/java_gateway.py
+++ b/python/pyspark/java_gateway.py
@@ -102,7 +102,10 @@ def run(self):
         EchoOutputThread(proc.stdout).start()
 
     # Connect to the gateway
-    gateway = JavaGateway(GatewayClient(port=gateway_port), auto_convert=False, start_callback_server=True)
+    # If start_callback_server is True, it looks like callback server is not killed
+    # process is hang up and test case does not move forward.
+    #gateway = JavaGateway(GatewayClient(port=gateway_port), auto_convert=False, start_callback_server=True)
+    gateway = JavaGateway(GatewayClient(port=gateway_port), auto_convert=False, start_callback_server=False)
 
     # Import the classes used by PySpark
     java_import(gateway.jvm, "org.apache.spark.SparkConf")
diff --git a/python/pyspark/streaming/context.py b/python/pyspark/streaming/context.py
index 3f455a3e06072..0e32b80cbee31 100644
--- a/python/pyspark/streaming/context.py
+++ b/python/pyspark/streaming/context.py
@@ -18,6 +18,11 @@
 import sys
 from signal import signal, SIGTERM, SIGINT
 
+import time
+
+from pyspark.conf import SparkConf
+from pyspark.files import SparkFiles
+from pyspark.java_gateway import launch_gateway
 from pyspark.serializers import PickleSerializer, BatchedSerializer, UTF8Deserializer
 from pyspark.context import SparkContext
 from pyspark.streaming.dstream import DStream

From 88f75062dfe41f87f4e556755073da9153b9f414 Mon Sep 17 00:00:00 2001
From: Ken Takagiwa <ken@kens-mbp.gateway.sonic.net>
Date: Sat, 2 Aug 2014 20:05:15 -0700
Subject: [PATCH 438/489] Kill py4j callback server properly

---
 python/pyspark/streaming/context.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/python/pyspark/streaming/context.py b/python/pyspark/streaming/context.py
index 0e32b80cbee31..a953fe382259c 100644
--- a/python/pyspark/streaming/context.py
+++ b/python/pyspark/streaming/context.py
@@ -17,8 +17,11 @@
 
 import sys
 from signal import signal, SIGTERM, SIGINT
+<<<<<<< HEAD
 
 import time
+=======
+>>>>>>> Kill py4j callback server properly
 
 from pyspark.conf import SparkConf
 from pyspark.files import SparkFiles

From 1b83354d8446e6a3c2a31854d7b7e7108089fde9 Mon Sep 17 00:00:00 2001
From: giwa <ugw.gi.world@gmail.com>
Date: Sat, 2 Aug 2014 20:40:36 -0700
Subject: [PATCH 439/489] Removed the waste line

---
 python/pyspark/java_gateway.py | 5 +----
 1 file changed, 1 insertion(+), 4 deletions(-)

diff --git a/python/pyspark/java_gateway.py b/python/pyspark/java_gateway.py
index af6109dc2ab69..2af917efc40a3 100644
--- a/python/pyspark/java_gateway.py
+++ b/python/pyspark/java_gateway.py
@@ -102,10 +102,7 @@ def run(self):
         EchoOutputThread(proc.stdout).start()
 
     # Connect to the gateway
-    # If start_callback_server is True, it looks like callback server is not killed
-    # process is hang up and test case does not move forward.
-    #gateway = JavaGateway(GatewayClient(port=gateway_port), auto_convert=False, start_callback_server=True)
-    gateway = JavaGateway(GatewayClient(port=gateway_port), auto_convert=False, start_callback_server=False)
+    gateway = JavaGateway(GatewayClient(port=gateway_port), auto_convert=False)
 
     # Import the classes used by PySpark
     java_import(gateway.jvm, "org.apache.spark.SparkConf")

From 92e333e2add1af1a621bf5595de7a72eed19a358 Mon Sep 17 00:00:00 2001
From: giwa <ugw.gi.world@gmail.com>
Date: Sun, 3 Aug 2014 19:25:13 -0700
Subject: [PATCH 440/489] implemented reduce and count function in Dstream

---
 python/pyspark/streaming/dstream.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/python/pyspark/streaming/dstream.py b/python/pyspark/streaming/dstream.py
index 76119981b0d9f..9bce826e4a650 100644
--- a/python/pyspark/streaming/dstream.py
+++ b/python/pyspark/streaming/dstream.py
@@ -56,8 +56,7 @@ def _sum(self):
         """
         Add up the elements in this DStream.
         """
-        pass
-        #return self._mapPartitions(lambda x: [sum(x)]).reduce(operator.add)
+        return self._mapPartitions(lambda x: [sum(x)]).reduce(operator.add)
 
     def print_(self, label=None):
         """

From 0b09cff8f186f6edc21d67e3c0887994ca3e0c45 Mon Sep 17 00:00:00 2001
From: giwa <ugw.gi.world@gmail.com>
Date: Sun, 3 Aug 2014 22:05:28 -0700
Subject: [PATCH 441/489] added stop in StreamingContext

---
 python/pyspark/streaming/context.py | 4 ----
 1 file changed, 4 deletions(-)

diff --git a/python/pyspark/streaming/context.py b/python/pyspark/streaming/context.py
index a953fe382259c..1e284b58941c4 100644
--- a/python/pyspark/streaming/context.py
+++ b/python/pyspark/streaming/context.py
@@ -17,11 +17,7 @@
 
 import sys
 from signal import signal, SIGTERM, SIGINT
-<<<<<<< HEAD
 
-import time
-=======
->>>>>>> Kill py4j callback server properly
 
 from pyspark.conf import SparkConf
 from pyspark.files import SparkFiles

From 932372a7876c7ba0ad59e9ebc48a13efda84e80a Mon Sep 17 00:00:00 2001
From: giwa <ugw.gi.world@gmail.com>
Date: Sun, 3 Aug 2014 23:27:56 -0700
Subject: [PATCH 442/489] clean up dstream.py

---
 python/pyspark/streaming/dstream.py | 6 ------
 1 file changed, 6 deletions(-)

diff --git a/python/pyspark/streaming/dstream.py b/python/pyspark/streaming/dstream.py
index 9bce826e4a650..01ab35523bbcf 100644
--- a/python/pyspark/streaming/dstream.py
+++ b/python/pyspark/streaming/dstream.py
@@ -140,12 +140,6 @@ def combineByKey(self, createCombiner, mergeValue, mergeCombiners,
         def combineLocally(iterator):
             combiners = {}
             for x in iterator:
-
-                #TODO for count operation make sure count implementation
-                # This is different from what pyspark does
-                #if isinstance(x, int):
-                #    x = ("", x)
-
                 (k, v) = x
                 if k not in combiners:
                     combiners[k] = createCombiner(v)

From 376e3ac3b1991d920f01e413791b6365cfe490bc Mon Sep 17 00:00:00 2001
From: giwa <ugw.gi.world@gmail.com>
Date: Mon, 4 Aug 2014 09:47:48 -0700
Subject: [PATCH 443/489] WIP

---
 .../main/python/streaming/test_oprations.py   | 24 +++++++++++++++++++
 python/pyspark/streaming/dstream.py           |  1 -
 2 files changed, 24 insertions(+), 1 deletion(-)
 create mode 100644 examples/src/main/python/streaming/test_oprations.py

diff --git a/examples/src/main/python/streaming/test_oprations.py b/examples/src/main/python/streaming/test_oprations.py
new file mode 100644
index 0000000000000..cb338ced5f228
--- /dev/null
+++ b/examples/src/main/python/streaming/test_oprations.py
@@ -0,0 +1,24 @@
+import sys
+from operator import add
+
+from pyspark.conf import SparkConf
+from pyspark.streaming.context import StreamingContext
+from pyspark.streaming.duration import *
+
+if __name__ == "__main__":
+    if len(sys.argv) != 3:
+        print >> sys.stderr, "Usage: wordcount <hostname> <port>"
+        exit(-1)
+    conf = SparkConf()
+    conf.setAppName("PythonStreamingNetworkWordCount")
+    ssc = StreamingContext(conf=conf, duration=Seconds(1))
+
+    lines = ssc.socketTextStream(sys.argv[1], int(sys.argv[2]))
+    words = lines.flatMap(lambda line: line.split(" "))
+    mapped_words = words.map(lambda word: (word, 1))
+    count = mapped_words.reduceByKey(add)
+
+    count.pyprint()
+    ssc.start()
+#    ssc.awaitTermination()
+    ssc.stop()
diff --git a/python/pyspark/streaming/dstream.py b/python/pyspark/streaming/dstream.py
index 01ab35523bbcf..aab514bedaa3c 100644
--- a/python/pyspark/streaming/dstream.py
+++ b/python/pyspark/streaming/dstream.py
@@ -156,7 +156,6 @@ def _mergeCombiners(iterator):
                     combiners[k] = v
                 else:
                     combiners[k] = mergeCombiners(combiners[k], v)
-            return combiners.iteritems()
 
         return shuffled.mapPartitions(_mergeCombiners)
 

From 193472604849779c825467a5ffadc01e1171da91 Mon Sep 17 00:00:00 2001
From: giwa <ugw.gi.world@gmail.com>
Date: Mon, 4 Aug 2014 09:57:16 -0700
Subject: [PATCH 444/489] update comment

---
 python/pyspark/streaming/dstream.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/python/pyspark/streaming/dstream.py b/python/pyspark/streaming/dstream.py
index aab514bedaa3c..1ad2e36ad21cf 100644
--- a/python/pyspark/streaming/dstream.py
+++ b/python/pyspark/streaming/dstream.py
@@ -474,4 +474,3 @@ def _jdstream(self):
 
     def _is_pipelinable(self):
         return not (self.is_cached)
-

From 019ef3848680afc0f7d75ec169b5d378797fcb5e Mon Sep 17 00:00:00 2001
From: giwa <ugw.gi.world@gmail.com>
Date: Mon, 4 Aug 2014 16:07:48 -0700
Subject: [PATCH 445/489] WIP

---
 examples/src/main/python/streaming/test_oprations.py | 5 +++--
 python/pyspark/streaming/dstream.py                  | 1 +
 2 files changed, 4 insertions(+), 2 deletions(-)

diff --git a/examples/src/main/python/streaming/test_oprations.py b/examples/src/main/python/streaming/test_oprations.py
index cb338ced5f228..084902b6a2f0d 100644
--- a/examples/src/main/python/streaming/test_oprations.py
+++ b/examples/src/main/python/streaming/test_oprations.py
@@ -15,10 +15,11 @@
 
     lines = ssc.socketTextStream(sys.argv[1], int(sys.argv[2]))
     words = lines.flatMap(lambda line: line.split(" "))
+#    ssc.checkpoint("checkpoint")
     mapped_words = words.map(lambda word: (word, 1))
     count = mapped_words.reduceByKey(add)
 
     count.pyprint()
     ssc.start()
-#    ssc.awaitTermination()
-    ssc.stop()
+    ssc.awaitTermination()
+#    ssc.stop()
diff --git a/python/pyspark/streaming/dstream.py b/python/pyspark/streaming/dstream.py
index 1ad2e36ad21cf..be6da7f2aad68 100644
--- a/python/pyspark/streaming/dstream.py
+++ b/python/pyspark/streaming/dstream.py
@@ -419,6 +419,7 @@ def saveAsTextFile(rdd, time):
 # TODO: implemtnt rightOuterJoin
 
 
+
 class PipelinedDStream(DStream):
     def __init__(self, prev, func, preservesPartitioning=False):
         if not isinstance(prev, PipelinedDStream) or not prev._is_pipelinable():

From 5c04a5f46e68e5fdbaa5c36fbd54fc6a0e7ee7b4 Mon Sep 17 00:00:00 2001
From: giwa <ugw.gi.world@gmail.com>
Date: Tue, 5 Aug 2014 00:09:38 -0700
Subject: [PATCH 446/489] WIP: added PythonTestInputStream

---
 .../src/main/python/streaming/test_oprations.py    | 14 ++++----------
 python/pyspark/streaming/context.py                |  1 +
 python/pyspark/streaming/dstream.py                |  1 +
 3 files changed, 6 insertions(+), 10 deletions(-)

diff --git a/examples/src/main/python/streaming/test_oprations.py b/examples/src/main/python/streaming/test_oprations.py
index 084902b6a2f0d..3338a766b9cc3 100644
--- a/examples/src/main/python/streaming/test_oprations.py
+++ b/examples/src/main/python/streaming/test_oprations.py
@@ -6,20 +6,14 @@
 from pyspark.streaming.duration import *
 
 if __name__ == "__main__":
-    if len(sys.argv) != 3:
-        print >> sys.stderr, "Usage: wordcount <hostname> <port>"
-        exit(-1)
     conf = SparkConf()
     conf.setAppName("PythonStreamingNetworkWordCount")
     ssc = StreamingContext(conf=conf, duration=Seconds(1))
 
-    lines = ssc.socketTextStream(sys.argv[1], int(sys.argv[2]))
-    words = lines.flatMap(lambda line: line.split(" "))
-#    ssc.checkpoint("checkpoint")
-    mapped_words = words.map(lambda word: (word, 1))
-    count = mapped_words.reduceByKey(add)
+    test_input = ssc._testInputStream([1,1,1,1])
+    mapped = test_input.map(lambda x: (x, 1))
+    mapped.pyprint()
 
-    count.pyprint()
     ssc.start()
-    ssc.awaitTermination()
+#    ssc.awaitTermination()
 #    ssc.stop()
diff --git a/python/pyspark/streaming/context.py b/python/pyspark/streaming/context.py
index 1e284b58941c4..765c4d5b96c74 100644
--- a/python/pyspark/streaming/context.py
+++ b/python/pyspark/streaming/context.py
@@ -17,6 +17,7 @@
 
 import sys
 from signal import signal, SIGTERM, SIGINT
+from tempfile import NamedTemporaryFile
 
 
 from pyspark.conf import SparkConf
diff --git a/python/pyspark/streaming/dstream.py b/python/pyspark/streaming/dstream.py
index be6da7f2aad68..a1cccac2eed4e 100644
--- a/python/pyspark/streaming/dstream.py
+++ b/python/pyspark/streaming/dstream.py
@@ -156,6 +156,7 @@ def _mergeCombiners(iterator):
                     combiners[k] = v
                 else:
                     combiners[k] = mergeCombiners(combiners[k], v)
+            return combiners.iteritems()
 
         return shuffled.mapPartitions(_mergeCombiners)
 

From bd3ba53a4e9df4ffd322a08f93e4b07abccfdb47 Mon Sep 17 00:00:00 2001
From: giwa <ugw.gi.world@gmail.com>
Date: Wed, 6 Aug 2014 19:11:17 -0700
Subject: [PATCH 447/489] WIP

---
 .../scala/org/apache/spark/api/python/PythonRDD.scala  |  2 ++
 examples/src/main/python/streaming/test_oprations.py   | 10 +++++++---
 2 files changed, 9 insertions(+), 3 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
index 022e2891559d7..41ed95b607161 100644
--- a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
+++ b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
@@ -350,6 +350,8 @@ private[spark] object PythonRDD extends Logging {
     } catch {
       case eof: EOFException => {}
     }
+    println("RDDDD ==================")
+    println(objs)
     JavaRDD.fromRDD(sc.sc.parallelize(objs, parallelism))
   }
 
diff --git a/examples/src/main/python/streaming/test_oprations.py b/examples/src/main/python/streaming/test_oprations.py
index 3338a766b9cc3..5ee0bd4b31253 100644
--- a/examples/src/main/python/streaming/test_oprations.py
+++ b/examples/src/main/python/streaming/test_oprations.py
@@ -9,11 +9,15 @@
     conf = SparkConf()
     conf.setAppName("PythonStreamingNetworkWordCount")
     ssc = StreamingContext(conf=conf, duration=Seconds(1))
+    ssc.checkpoint("/tmp/spark_ckp")
 
-    test_input = ssc._testInputStream([1,1,1,1])
-    mapped = test_input.map(lambda x: (x, 1))
-    mapped.pyprint()
+    test_input = ssc._testInputStream([[1],[1],[1]])
+#    ssc.checkpoint("/tmp/spark_ckp")
+    fm_test = test_input.flatMap(lambda x: x.split(" "))
+    mapped_test = fm_test.map(lambda x: (x, 1))
 
+
+    mapped_test.print_()
     ssc.start()
 #    ssc.awaitTermination()
 #    ssc.stop()

From 9cde7c96b9c10befd90bbe87f9da4b2f40d643a0 Mon Sep 17 00:00:00 2001
From: giwa <ugw.gi.world@gmail.com>
Date: Sun, 10 Aug 2014 18:43:09 -0700
Subject: [PATCH 448/489] WIP added test case

---
 .../apache/spark/api/python/PythonRDD.scala   |  2 --
 .../main/python/streaming/test_oprations.py   | 25 ++++++++++++-------
 python/pyspark/streaming/dstream.py           |  2 +-
 python/pyspark/streaming_tests.py             | 11 ++++++++
 python/pyspark/worker.py                      |  2 +-
 .../streaming/api/java/JavaDStreamLike.scala  |  9 +++++++
 .../streaming/api/python/PythonDStream.scala  |  2 ++
 .../spark/streaming/dstream/DStream.scala     | 17 +++++++++++++
 8 files changed, 57 insertions(+), 13 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
index 41ed95b607161..022e2891559d7 100644
--- a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
+++ b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
@@ -350,8 +350,6 @@ private[spark] object PythonRDD extends Logging {
     } catch {
       case eof: EOFException => {}
     }
-    println("RDDDD ==================")
-    println(objs)
     JavaRDD.fromRDD(sc.sc.parallelize(objs, parallelism))
   }
 
diff --git a/examples/src/main/python/streaming/test_oprations.py b/examples/src/main/python/streaming/test_oprations.py
index 5ee0bd4b31253..24ebe23d63166 100644
--- a/examples/src/main/python/streaming/test_oprations.py
+++ b/examples/src/main/python/streaming/test_oprations.py
@@ -9,15 +9,22 @@
     conf = SparkConf()
     conf.setAppName("PythonStreamingNetworkWordCount")
     ssc = StreamingContext(conf=conf, duration=Seconds(1))
-    ssc.checkpoint("/tmp/spark_ckp")
 
-    test_input = ssc._testInputStream([[1],[1],[1]])
-#    ssc.checkpoint("/tmp/spark_ckp")
-    fm_test = test_input.flatMap(lambda x: x.split(" "))
-    mapped_test = fm_test.map(lambda x: (x, 1))
+    test_input = ssc._testInputStream([1,2,3])
+    class buff:
+        pass
+   
+    fm_test = test_input.map(lambda x: (x, 1))
+    fm_test.test_output(buff)
 
-
-    mapped_test.print_()
     ssc.start()
-#    ssc.awaitTermination()
-#    ssc.stop()
+    while True:
+        ssc.awaitTermination(50)
+        try:
+            buff.result
+            break
+        except AttributeError:
+            pass
+
+    ssc.stop()
+    print buff.result
diff --git a/python/pyspark/streaming/dstream.py b/python/pyspark/streaming/dstream.py
index a1cccac2eed4e..4c51a862a58fa 100644
--- a/python/pyspark/streaming/dstream.py
+++ b/python/pyspark/streaming/dstream.py
@@ -236,6 +236,7 @@ def pyprint(self):
         operator, so this DStream will be registered as an output stream and there materialized.
         """
         def takeAndPrint(rdd, time):
+            print "take and print ==================="
             taken = rdd.take(11)
             print "-------------------------------------------"
             print "Time: %s" % (str(time))
@@ -420,7 +421,6 @@ def saveAsTextFile(rdd, time):
 # TODO: implemtnt rightOuterJoin
 
 
-
 class PipelinedDStream(DStream):
     def __init__(self, prev, func, preservesPartitioning=False):
         if not isinstance(prev, PipelinedDStream) or not prev._is_pipelinable():
diff --git a/python/pyspark/streaming_tests.py b/python/pyspark/streaming_tests.py
index 7f6960faed1a0..d81f9b023ada2 100644
--- a/python/pyspark/streaming_tests.py
+++ b/python/pyspark/streaming_tests.py
@@ -444,6 +444,17 @@ def tearDown(self):
     def tearDownClass(cls):
         PySparkStreamingTestCase.tearDownClass()
 
+        start_time = time.time()
+        while True:
+            current_time = time.time()
+            # check time out
+            if (current_time - start_time) > self.timeout:
+                self.ssc.stop()
+                break
+            self.ssc.awaitTermination(50)
+            if buff.result is not None:
+                break
+        return buff.result
 
 if __name__ == "__main__":
     unittest.main()
diff --git a/python/pyspark/worker.py b/python/pyspark/worker.py
index d6c06e2dbef62..90ea7b453d401 100644
--- a/python/pyspark/worker.py
+++ b/python/pyspark/worker.py
@@ -61,7 +61,7 @@ def main(infile, outfile):
         SparkFiles._is_running_on_worker = True
 
         # fetch names of includes (*.zip and *.egg files) and construct PYTHONPATH
-        sys.path.append(spark_files_dir)  # *.py files that were added will be copied here
+        sys.path.append(spark_files_dir) # *.py files that were added will be copied here
         num_python_includes = read_int(infile)
         for _ in range(num_python_includes):
             filename = utf8_deserializer.loads(infile)
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaDStreamLike.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaDStreamLike.scala
index a6184de4e83c1..7a002bbe74ca9 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaDStreamLike.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaDStreamLike.scala
@@ -54,6 +54,15 @@ trait JavaDStreamLike[T, This <: JavaDStreamLike[T, This, R], R <: JavaRDDLike[T
     dstream.print()
   }
 
+  def print(label: String = null): Unit = {
+    dstream.print(label)
+  }
+
+  def outputToFile(): Unit = {
+    dstream.outputToFile()
+  }
+
+
   /**
    * Return a new DStream in which each RDD has a single element generated by counting each RDD
    * of this DStream.
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
index 6db7e0cfa6846..289363206ca5a 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
@@ -55,6 +55,8 @@ class PythonDStream[T: ClassTag](
   override def compute(validTime: Time): Option[RDD[Array[Byte]]] = {
     parent.getOrCompute(validTime) match{
       case Some(rdd) =>
+        logInfo("RDD ID in python DStream     ===========")
+        logInfo("RDD id " + rdd.id)
         val pythonRDD = new PythonRDD(rdd, command, envVars, pythonIncludes, preservePartitoning, pythonExec, broadcastVars, accumulator)
         Some(pythonRDD.asJavaRDD.rdd)
       case None => None
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala
index e045ff50e09f9..15ead505eac5b 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala
@@ -620,6 +620,23 @@ abstract class DStream[T: ClassTag] (
     new ForEachDStream(this, context.sparkContext.clean(foreachFunc)).register()
   }
 
+
+  def print(label: String = null) {
+    def foreachFunc = (rdd: RDD[T], time: Time) => {
+      val first11 = rdd.take(11)
+      println ("-------------------------------------------")
+      println ("Time: " + time)
+      println ("-------------------------------------------")
+      if(label != null){
+        println (label)
+      }
+      first11.take(10).foreach(println)
+      if (first11.size > 10) println("...")
+      println()
+    }
+    new ForEachDStream(this, context.sparkContext.clean(foreachFunc)).register()
+  }
+
   /**
    * Return a new DStream in which each RDD contains all the elements in seen in a
    * sliding window of time over this DStream. The new DStream generates RDDs with

From b3b0362fb162b9029ff14a5896f2b82ace191de5 Mon Sep 17 00:00:00 2001
From: giwa <ugw.gi.world@gmail.com>
Date: Mon, 11 Aug 2014 03:21:22 -0700
Subject: [PATCH 449/489] added basic operation test cases

---
 .../main/python/streaming/test_oprations.py   | 19 +++++-----
 python/pyspark/streaming/context.py           | 36 +++++++++++++++++++
 python/pyspark/streaming/dstream.py           |  1 -
 python/pyspark/streaming_tests.py             |  5 ++-
 .../streaming/api/python/PythonDStream.scala  |  2 --
 5 files changed, 48 insertions(+), 15 deletions(-)

diff --git a/examples/src/main/python/streaming/test_oprations.py b/examples/src/main/python/streaming/test_oprations.py
index 24ebe23d63166..70a62058286e9 100644
--- a/examples/src/main/python/streaming/test_oprations.py
+++ b/examples/src/main/python/streaming/test_oprations.py
@@ -9,22 +9,23 @@
     conf = SparkConf()
     conf.setAppName("PythonStreamingNetworkWordCount")
     ssc = StreamingContext(conf=conf, duration=Seconds(1))
-
-    test_input = ssc._testInputStream([1,2,3])
-    class buff:
+    class Buff:
+        result = list()
         pass
+    Buff.result = list()
+
+    test_input = ssc._testInputStream([range(1,4), range(4,7), range(7,10)])
    
     fm_test = test_input.map(lambda x: (x, 1))
-    fm_test.test_output(buff)
+    fm_test.pyprint()
+    fm_test._test_output(Buff.result)
 
     ssc.start()
     while True:
         ssc.awaitTermination(50)
-        try:
-            buff.result
+        if len(Buff.result) == 3:
             break
-        except AttributeError:
-            pass
 
     ssc.stop()
-    print buff.result
+    print Buff.result
+
diff --git a/python/pyspark/streaming/context.py b/python/pyspark/streaming/context.py
index 765c4d5b96c74..e1ea6bda7adac 100644
--- a/python/pyspark/streaming/context.py
+++ b/python/pyspark/streaming/context.py
@@ -150,6 +150,7 @@ def _testInputStream(self, test_inputs, numSlices=None):
         This implementation is inspired by QueStream implementation.
         Give list of RDD to generate DStream which contains the RDD.
         """
+<<<<<<< HEAD
         test_rdds = list()
         test_rdd_deserializers = list()
         for test_input in test_inputs:
@@ -161,3 +162,38 @@ def _testInputStream(self, test_inputs, numSlices=None):
         jinput_stream = self._jvm.PythonTestInputStream(self._jssc, jtest_rdds).asJavaDStream()
 
         return DStream(jinput_stream, self, test_rdd_deserializers[0])
+=======
+        self._jssc.checkpoint(directory)
+
+    def _testInputStream(self, test_inputs, numSlices=None):
+        """
+        Generate multiple files to make "stream" in Scala side for test.
+        Scala chooses one of the files and generates RDD using PythonRDD.readRDDFromFile.
+        """
+        numSlices = numSlices or self._sc.defaultParallelism
+        # Calling the Java parallelize() method with an ArrayList is too slow,
+        # because it sends O(n) Py4J commands.  As an alternative, serialized
+        # objects are written to a file and loaded through textFile().
+
+        tempFiles = list()
+        for test_input in test_inputs:
+            tempFile = NamedTemporaryFile(delete=False, dir=self._sc._temp_dir)
+
+            # Make sure we distribute data evenly if it's smaller than self.batchSize
+            if "__len__" not in dir(test_input):
+                c = list(test_input)    # Make it a list so we can compute its length
+            batchSize = min(len(test_input) // numSlices, self._sc._batchSize)
+            if batchSize > 1:
+                serializer = BatchedSerializer(self._sc._unbatched_serializer,
+                                               batchSize)
+            else:
+                serializer = self._sc._unbatched_serializer
+            serializer.dump_stream(test_input, tempFile)
+            tempFiles.append(tempFile.name)
+
+        jtempFiles = ListConverter().convert(tempFiles, SparkContext._gateway._gateway_client)
+        jinput_stream = self._jvm.PythonTestInputStream(self._jssc,
+                                                        jtempFiles,
+                                                        numSlices).asJavaDStream()
+        return DStream(jinput_stream, self, PickleSerializer())
+>>>>>>> added basic operation test cases
diff --git a/python/pyspark/streaming/dstream.py b/python/pyspark/streaming/dstream.py
index 4c51a862a58fa..c1ace350b1bf5 100644
--- a/python/pyspark/streaming/dstream.py
+++ b/python/pyspark/streaming/dstream.py
@@ -236,7 +236,6 @@ def pyprint(self):
         operator, so this DStream will be registered as an output stream and there materialized.
         """
         def takeAndPrint(rdd, time):
-            print "take and print ==================="
             taken = rdd.take(11)
             print "-------------------------------------------"
             print "Time: %s" % (str(time))
diff --git a/python/pyspark/streaming_tests.py b/python/pyspark/streaming_tests.py
index d81f9b023ada2..0682a68a419ac 100644
--- a/python/pyspark/streaming_tests.py
+++ b/python/pyspark/streaming_tests.py
@@ -449,12 +449,11 @@ def tearDownClass(cls):
             current_time = time.time()
             # check time out
             if (current_time - start_time) > self.timeout:
-                self.ssc.stop()
                 break
             self.ssc.awaitTermination(50)
-            if buff.result is not None:
+            if len(expected_output) == len(StreamOutput.result):
                 break
-        return buff.result
+        return StreamOutput.result
 
 if __name__ == "__main__":
     unittest.main()
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
index 289363206ca5a..6db7e0cfa6846 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
@@ -55,8 +55,6 @@ class PythonDStream[T: ClassTag](
   override def compute(validTime: Time): Option[RDD[Array[Byte]]] = {
     parent.getOrCompute(validTime) match{
       case Some(rdd) =>
-        logInfo("RDD ID in python DStream     ===========")
-        logInfo("RDD id " + rdd.id)
         val pythonRDD = new PythonRDD(rdd, command, envVars, pythonIncludes, preservePartitoning, pythonExec, broadcastVars, accumulator)
         Some(pythonRDD.asJavaRDD.rdd)
       case None => None

From 99410bed9b8884dc91e498229f424ed4e7606a45 Mon Sep 17 00:00:00 2001
From: giwa <ugw.gi.world@gmail.com>
Date: Mon, 11 Aug 2014 03:22:23 -0700
Subject: [PATCH 450/489] delete waste file

---
 .../main/python/streaming/test_oprations.py   | 31 -------------------
 1 file changed, 31 deletions(-)
 delete mode 100644 examples/src/main/python/streaming/test_oprations.py

diff --git a/examples/src/main/python/streaming/test_oprations.py b/examples/src/main/python/streaming/test_oprations.py
deleted file mode 100644
index 70a62058286e9..0000000000000
--- a/examples/src/main/python/streaming/test_oprations.py
+++ /dev/null
@@ -1,31 +0,0 @@
-import sys
-from operator import add
-
-from pyspark.conf import SparkConf
-from pyspark.streaming.context import StreamingContext
-from pyspark.streaming.duration import *
-
-if __name__ == "__main__":
-    conf = SparkConf()
-    conf.setAppName("PythonStreamingNetworkWordCount")
-    ssc = StreamingContext(conf=conf, duration=Seconds(1))
-    class Buff:
-        result = list()
-        pass
-    Buff.result = list()
-
-    test_input = ssc._testInputStream([range(1,4), range(4,7), range(7,10)])
-   
-    fm_test = test_input.map(lambda x: (x, 1))
-    fm_test.pyprint()
-    fm_test._test_output(Buff.result)
-
-    ssc.start()
-    while True:
-        ssc.awaitTermination(50)
-        if len(Buff.result) == 3:
-            break
-
-    ssc.stop()
-    print Buff.result
-

From c1d546e5bd4ef3ecb04f6514c1f96762fe346586 Mon Sep 17 00:00:00 2001
From: giwa <ugw.gi.world@gmail.com>
Date: Mon, 11 Aug 2014 03:41:24 -0700
Subject: [PATCH 451/489] fixed PEP-008 violation

---
 python/pyspark/streaming/context.py | 4 ----
 1 file changed, 4 deletions(-)

diff --git a/python/pyspark/streaming/context.py b/python/pyspark/streaming/context.py
index e1ea6bda7adac..4497818c8a250 100644
--- a/python/pyspark/streaming/context.py
+++ b/python/pyspark/streaming/context.py
@@ -19,10 +19,6 @@
 from signal import signal, SIGTERM, SIGINT
 from tempfile import NamedTemporaryFile
 
-
-from pyspark.conf import SparkConf
-from pyspark.files import SparkFiles
-from pyspark.java_gateway import launch_gateway
 from pyspark.serializers import PickleSerializer, BatchedSerializer, UTF8Deserializer
 from pyspark.context import SparkContext
 from pyspark.streaming.dstream import DStream

From af610d3ba888f411162ef19384409929a1592e3c Mon Sep 17 00:00:00 2001
From: giwa <ugw.gi.world@gmail.com>
Date: Mon, 11 Aug 2014 04:42:08 -0700
Subject: [PATCH 452/489] removed unnesessary changes

---
 .../scala/org/apache/spark/streaming/dstream/DStream.scala     | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala
index 15ead505eac5b..46ef05d9c37a1 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala
@@ -22,8 +22,6 @@ import java.io.{IOException, ObjectInputStream, ObjectOutputStream}
 import scala.deprecated
 import scala.collection.mutable.HashMap
 import scala.reflect.ClassTag
-import java.io.{IOException, ObjectInputStream, ObjectOutputStream}
-import scala.util.control.Breaks._
 
 import org.apache.spark.{Logging, SparkException}
 import org.apache.spark.rdd.{BlockRDD, RDD}
@@ -563,7 +561,6 @@ abstract class DStream[T: ClassTag] (
     val cleanedF = context.sparkContext.clean(transformFunc, false)
     val realTransformFunc =  (rdds: Seq[RDD[_]], time: Time) => {
       assert(rdds.length == 1)
-      // if transformfunc is fine, it is okay
       cleanedF(rdds.head.asInstanceOf[RDD[T]], time)
     }
     new TransformedDStream[U](Seq(this), realTransformFunc)

From 953deb0d9c635b7271d6f13f2628dd2bead03d03 Mon Sep 17 00:00:00 2001
From: giwa <ugw.gi.world@gmail.com>
Date: Mon, 11 Aug 2014 05:32:28 -0700
Subject: [PATCH 453/489] edited the comment to add more precise description

---
 python/pyspark/streaming/context.py |  36 ---
 python/pyspark/streaming_tests.py   | 459 ----------------------------
 2 files changed, 495 deletions(-)
 delete mode 100644 python/pyspark/streaming_tests.py

diff --git a/python/pyspark/streaming/context.py b/python/pyspark/streaming/context.py
index 4497818c8a250..1668bfcd41a57 100644
--- a/python/pyspark/streaming/context.py
+++ b/python/pyspark/streaming/context.py
@@ -146,7 +146,6 @@ def _testInputStream(self, test_inputs, numSlices=None):
         This implementation is inspired by QueStream implementation.
         Give list of RDD to generate DStream which contains the RDD.
         """
-<<<<<<< HEAD
         test_rdds = list()
         test_rdd_deserializers = list()
         for test_input in test_inputs:
@@ -158,38 +157,3 @@ def _testInputStream(self, test_inputs, numSlices=None):
         jinput_stream = self._jvm.PythonTestInputStream(self._jssc, jtest_rdds).asJavaDStream()
 
         return DStream(jinput_stream, self, test_rdd_deserializers[0])
-=======
-        self._jssc.checkpoint(directory)
-
-    def _testInputStream(self, test_inputs, numSlices=None):
-        """
-        Generate multiple files to make "stream" in Scala side for test.
-        Scala chooses one of the files and generates RDD using PythonRDD.readRDDFromFile.
-        """
-        numSlices = numSlices or self._sc.defaultParallelism
-        # Calling the Java parallelize() method with an ArrayList is too slow,
-        # because it sends O(n) Py4J commands.  As an alternative, serialized
-        # objects are written to a file and loaded through textFile().
-
-        tempFiles = list()
-        for test_input in test_inputs:
-            tempFile = NamedTemporaryFile(delete=False, dir=self._sc._temp_dir)
-
-            # Make sure we distribute data evenly if it's smaller than self.batchSize
-            if "__len__" not in dir(test_input):
-                c = list(test_input)    # Make it a list so we can compute its length
-            batchSize = min(len(test_input) // numSlices, self._sc._batchSize)
-            if batchSize > 1:
-                serializer = BatchedSerializer(self._sc._unbatched_serializer,
-                                               batchSize)
-            else:
-                serializer = self._sc._unbatched_serializer
-            serializer.dump_stream(test_input, tempFile)
-            tempFiles.append(tempFile.name)
-
-        jtempFiles = ListConverter().convert(tempFiles, SparkContext._gateway._gateway_client)
-        jinput_stream = self._jvm.PythonTestInputStream(self._jssc,
-                                                        jtempFiles,
-                                                        numSlices).asJavaDStream()
-        return DStream(jinput_stream, self, PickleSerializer())
->>>>>>> added basic operation test cases
diff --git a/python/pyspark/streaming_tests.py b/python/pyspark/streaming_tests.py
deleted file mode 100644
index 0682a68a419ac..0000000000000
--- a/python/pyspark/streaming_tests.py
+++ /dev/null
@@ -1,459 +0,0 @@
-#
-# Licensed to the Apache Software Foundation (ASF) under one or more
-# contributor license agreements.  See the NOTICE file distributed with
-# this work for additional information regarding copyright ownership.
-# The ASF licenses this file to You under the Apache License, Version 2.0
-# (the "License"); you may not use this file except in compliance with
-# the License.  You may obtain a copy of the License at
-#
-#    http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
-
-"""
-Unit tests for PySpark; additional tests are implemented as doctests in
-individual modules.
-
-This file would be merged to tests.py after all functions are ready.
-But for now, this file is separated due to focusing to streaming test case.
-
-Callback server seems like unstable sometimes, which cause error in test case.
-
-"""
-from itertools import chain
-import time
-import unittest
-import operator
-
-from pyspark.context import SparkContext
-from pyspark.streaming.context import StreamingContext
-from pyspark.streaming.duration import *
-
-
-class PySparkStreamingTestCase(unittest.TestCase):
-    def setUp(self):
-        class_name = self.__class__.__name__
-        self.ssc = StreamingContext(appName=class_name, duration=Seconds(1))
-
-    def tearDown(self):
-        # Do not call pyspark.streaming.context.StreamingContext.stop directly because
-        # we do not wait to shutdown call back server and py4j client
-        self.ssc._jssc.stop()
-        self.ssc._sc.stop()
-        # Why does it long time to terminate StremaingContext and SparkContext?
-        # Should we change the sleep time if this depends on machine spec?
-        time.sleep(10)
-
-    @classmethod
-    def tearDownClass(cls):
-        time.sleep(5)
-        SparkContext._gateway._shutdown_callback_server()
-
-
-class TestBasicOperationsSuite(PySparkStreamingTestCase):
-    """
-    2 tests for each function for batach deserializer and unbatch deserilizer because
-    we cannot change the deserializer after streaming process starts.
-    Default numInputPartitions is 2.
-    If the number of input element is over 3, that DStream use batach deserializer.
-    If not, that DStream use unbatch deserializer.
-
-    Most of the operation uses UTF8 deserializer to get value from Scala.
-    I am wondering if these test are enough or not.
-    All tests input should have list of lists. This represents stream.
-    Every batch interval, the first object of list are chosen to make DStream.
-    Please see the BasicTestSuits in Scala which is close to this implementation.
-    """
-    def setUp(self):
-        PySparkStreamingTestCase.setUp(self)
-        self.timeout = 10  # seconds
-        self.numInputPartitions = 2
-        self.result = list()
-
-    def tearDown(self):
-        PySparkStreamingTestCase.tearDown(self)
-
-    @classmethod
-    def tearDownClass(cls):
-        PySparkStreamingTestCase.tearDownClass()
-
-    def test_map_batch(self):
-        """Basic operation test for DStream.map with batch deserializer"""
-        test_input = [range(1, 5), range(5, 9), range(9, 13)]
-
-        def test_func(dstream):
-            return dstream.map(lambda x: str(x))
-        expected_output = map(lambda x: map(lambda y: str(y), x), test_input)
-        output = self._run_stream(test_input, test_func, expected_output)
-        self.assertEqual(expected_output, output)
-
-    def test_map_unbatach(self):
-        """Basic operation test for DStream.map with unbatch deserializer"""
-        test_input = [range(1, 4), range(4, 7), range(7, 10)]
-
-        def test_func(dstream):
-            return dstream.map(lambda x: str(x))
-        expected_output = map(lambda x: map(lambda y: str(y), x), test_input)
-        output = self._run_stream(test_input, test_func, expected_output)
-        self.assertEqual(expected_output, output)
-
-    def test_flatMap_batch(self):
-        """Basic operation test for DStream.faltMap with batch deserializer"""
-        test_input = [range(1, 5), range(5, 9), range(9, 13)]
-
-        def test_func(dstream):
-            return dstream.flatMap(lambda x: (x, x * 2))
-        expected_output = map(lambda x: list(chain.from_iterable((map(lambda y: [y, y * 2], x)))), 
-                              test_input)
-        output = self._run_stream(test_input, test_func, expected_output)
-        self.assertEqual(expected_output, output)
-
-    def test_flatMap_unbatch(self):
-        """Basic operation test for DStream.faltMap with unbatch deserializer"""
-        test_input = [range(1, 4), range(4, 7), range(7, 10)]
-
-        def test_func(dstream):
-            return dstream.flatMap(lambda x: (x, x * 2))
-        expected_output = map(lambda x: list(chain.from_iterable((map(lambda y: [y, y * 2], x)))),
-                              test_input)
-        output = self._run_stream(test_input, test_func, expected_output)
-        self.assertEqual(expected_output, output)
-
-    def test_filter_batch(self):
-        """Basic operation test for DStream.filter with batch deserializer"""
-        test_input = [range(1, 5), range(5, 9), range(9, 13)]
-
-        def test_func(dstream):
-            return dstream.filter(lambda x: x % 2 == 0)
-        expected_output = map(lambda x: filter(lambda y: y % 2 == 0, x), test_input)
-        output = self._run_stream(test_input, test_func, expected_output)
-        self.assertEqual(expected_output, output)
-
-    def test_filter_unbatch(self):
-        """Basic operation test for DStream.filter with unbatch deserializer"""
-        test_input = [range(1, 4), range(4, 7), range(7, 10)]
-
-        def test_func(dstream):
-            return dstream.filter(lambda x: x % 2 == 0)
-        expected_output = map(lambda x: filter(lambda y: y % 2 == 0, x), test_input)
-        output = self._run_stream(test_input, test_func, expected_output)
-        self.assertEqual(expected_output, output)
-
-    def test_count_batch(self):
-        """Basic operation test for DStream.count with batch deserializer"""
-        test_input = [range(1, 5), range(1, 10), range(1, 20)]
-
-        def test_func(dstream):
-            return dstream.count()
-        expected_output = map(lambda x: [len(x)], test_input)
-        output = self._run_stream(test_input, test_func, expected_output)
-        self.assertEqual(expected_output, output)
-
-    def test_count_unbatch(self):
-        """Basic operation test for DStream.count with unbatch deserializer"""
-        test_input = [[], [1], range(1, 3), range(1, 4)]
-
-        def test_func(dstream):
-            return dstream.count()
-        expected_output = map(lambda x: [len(x)], test_input)
-        output = self._run_stream(test_input, test_func, expected_output)
-        self.assertEqual(expected_output, output)
-
-    def test_reduce_batch(self):
-        """Basic operation test for DStream.reduce with batch deserializer"""
-        test_input = [range(1, 5), range(5, 9), range(9, 13)]
-
-        def test_func(dstream):
-            return dstream.reduce(operator.add)
-        expected_output = map(lambda x: [reduce(operator.add, x)], test_input)
-        output = self._run_stream(test_input, test_func, expected_output)
-        self.assertEqual(expected_output, output)
-
-    def test_reduce_unbatch(self):
-        """Basic operation test for DStream.reduce with unbatch deserializer"""
-        test_input = [[1], range(1, 3), range(1, 4)]
-
-        def test_func(dstream):
-            return dstream.reduce(operator.add)
-        expected_output = map(lambda x: [reduce(operator.add, x)], test_input)
-        output = self._run_stream(test_input, test_func, expected_output)
-        self.assertEqual(expected_output, output)
-
-    def test_reduceByKey_batch(self):
-        """Basic operation test for DStream.reduceByKey with batch deserializer"""
-        test_input = [["a", "a", "b", "b"], ["", "", "", ""]]
-
-        def test_func(dstream):
-            return dstream.map(lambda x: (x, 1)).reduceByKey(operator.add)
-        expected_output = [[("a", 2), ("b", 2)], [("", 4)]]
-        output = self._run_stream(test_input, test_func, expected_output)
-        self.assertEqual(expected_output, output)
-
-    def test_reduceByKey_unbatch(self):
-        """Basic operation test for DStream.reduceByKey with unbatch deserilizer"""
-        test_input = [["a", "a", "b"], ["", ""], []]
-
-        def test_func(dstream):
-            return dstream.map(lambda x: (x, 1)).reduceByKey(operator.add)
-        expected_output = [[("a", 2), ("b", 1)], [("", 2)], []]
-        output = self._run_stream(test_input, test_func, expected_output)
-        self.assertEqual(expected_output, output)
-
-    def test_mapValues_batch(self):
-        """Basic operation test for DStream.mapValues with batch deserializer"""
-        test_input = [["a", "a", "b", "b"], ["", "", "", ""]]
-
-        def test_func(dstream):
-            return dstream.map(lambda x: (x, 1))\
-                          .reduceByKey(operator.add)\
-                          .mapValues(lambda x: x + 10)
-        expected_output = [[("a", 12), ("b", 12)], [("", 14)]]
-        output = self._run_stream(test_input, test_func, expected_output)
-        self.assertEqual(expected_output, output)
-
-    def test_mapValues_unbatch(self):
-        """Basic operation test for DStream.mapValues with unbatch deserializer"""
-        test_input = [["a", "a", "b"], ["", ""], []]
-
-        def test_func(dstream):
-            return dstream.map(lambda x: (x, 1))\
-                          .reduceByKey(operator.add)\
-                          .mapValues(lambda x: x + 10)
-        expected_output = [[("a", 12), ("b", 11)], [("", 12)], []]
-        output = self._run_stream(test_input, test_func, expected_output)
-        self.assertEqual(expected_output, output)
-
-    def test_flatMapValues_batch(self):
-        """Basic operation test for DStream.flatMapValues with batch deserializer"""
-        test_input = [["a", "a", "b", "b"], ["", "", "", ""]]
-
-        def test_func(dstream):
-            return dstream.map(lambda x: (x, 1))\
-                          .reduceByKey(operator.add)\
-                          .flatMapValues(lambda x: (x, x + 10))
-        expected_output = [[("a", 2), ("a", 12), ("b", 2), ("b", 12)], [("", 4), ("", 14)]]
-        output = self._run_stream(test_input, test_func, expected_output)
-        self.assertEqual(expected_output, output)
-
-    def test_flatMapValues_unbatch(self):
-        """Basic operation test for DStream.flatMapValues with unbatch deserializer"""
-        test_input = [["a", "a", "b"], ["", ""], []]
-
-        def test_func(dstream):
-            return dstream.map(lambda x: (x, 1))\
-                          .reduceByKey(operator.add)\
-                          .flatMapValues(lambda x: (x, x + 10))
-        expected_output = [[("a", 2), ("a", 12), ("b", 1), ("b", 11)], [("", 2), ("", 12)], []]
-        output = self._run_stream(test_input, test_func, expected_output)
-        self.assertEqual(expected_output, output)
-
-    def test_glom_batch(self):
-        """Basic operation test for DStream.glom with batch deserializer"""
-        test_input = [range(1, 5), range(5, 9), range(9, 13)]
-        numSlices = 2
-
-        def test_func(dstream):
-            return dstream.glom()
-        expected_output = [[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]]
-        output = self._run_stream(test_input, test_func, expected_output, numSlices)
-        self.assertEqual(expected_output, output)
-
-    def test_glom_unbatach(self):
-        """Basic operation test for DStream.glom with unbatch deserialiser"""
-        test_input = [range(1, 4), range(4, 7), range(7, 10)]
-        numSlices = 2
-
-        def test_func(dstream):
-            return dstream.glom()
-        expected_output = [[[1], [2, 3]], [[4], [5, 6]], [[7], [8, 9]]]
-        output = self._run_stream(test_input, test_func, expected_output, numSlices)
-        self.assertEqual(expected_output, output)
-
-    def test_mapPartitions_batch(self):
-        """Basic operation test for DStream.mapPartitions with batch deserializer."""
-        test_input = [range(1, 5), range(5, 9), range(9, 13)]
-        numSlices = 2
-
-        def test_func(dstream):
-            def f(iterator):
-                yield sum(iterator)
-            return dstream.mapPartitions(f)
-        expected_output = [[3, 7], [11, 15], [19, 23]]
-        output = self._run_stream(test_input, test_func, expected_output, numSlices)
-        self.assertEqual(expected_output, output)
-
-    def test_mapPartitions_unbatch(self):
-        """Basic operation test for DStream.mapPartitions with unbatch deserializer."""
-        test_input = [range(1, 4), range(4, 7), range(7, 10)]
-        numSlices = 2
-
-        def test_func(dstream):
-            def f(iterator):
-                yield sum(iterator)
-            return dstream.mapPartitions(f)
-        expected_output = [[1, 5], [4, 11], [7, 17]]
-        output = self._run_stream(test_input, test_func, expected_output, numSlices)
-        self.assertEqual(expected_output, output)
-
-    def test_countByValue_batch(self):
-        """Basic operation test for DStream.countByValue with batch deserializer."""
-        test_input = [range(1, 5) + range(1,5), range(5, 7) + range(5, 9), ["a", "a", "b", ""]]
-
-        def test_func(dstream):
-            return dstream.countByValue()
-        expected_output = [[(1, 2), (2, 2), (3, 2), (4, 2)],
-                           [(5, 2), (6, 2), (7, 1), (8, 1)],
-                           [("a", 2), ("b", 1), ("", 1)]]
-        output = self._run_stream(test_input, test_func, expected_output)
-        for result in (output, expected_output):
-            self._sort_result_based_on_key(result)
-        self.assertEqual(expected_output, output)
-
-    def test_countByValue_unbatch(self):
-        """Basic operation test for DStream.countByValue with unbatch deserializer."""
-        test_input = [range(1, 4), [1, 1, ""], ["a", "a", "b"]]
-
-        def test_func(dstream):
-            return dstream.countByValue()
-        expected_output = [[(1, 1), (2, 1), (3, 1)],
-                           [(1, 2), ("", 1)],
-                           [("a", 2), ("b", 1)]]
-        output = self._run_stream(test_input, test_func, expected_output)
-        for result in (output, expected_output):
-            self._sort_result_based_on_key(result)
-        self.assertEqual(expected_output, output)
-
-    def test_groupByKey_batch(self):
-        """Basic operation test for DStream.groupByKey with batch deserializer."""
-        test_input = [range(1, 5), [1, 1, 1, 2, 2, 3], ["a", "a", "b", "", "", ""]]
-        def test_func(dstream):
-            return dstream.map(lambda x: (x, 1)).groupByKey()
-        expected_output = [[(1, [1]), (2, [1]), (3, [1]), (4, [1])],
-                           [(1, [1, 1, 1]), (2, [1, 1]), (3, [1])],
-                           [("a", [1, 1]), ("b", [1]), ("", [1, 1, 1])]]
-        scattered_output = self._run_stream(test_input, test_func, expected_output)
-        output = self._convert_iter_value_to_list(scattered_output)
-        for result in (output, expected_output):
-            self._sort_result_based_on_key(result)
-        self.assertEqual(expected_output, output)
-
-    def test_groupByKey_unbatch(self):
-        """Basic operation test for DStream.groupByKey with unbatch deserializer."""
-        test_input = [range(1, 4), [1, 1, ""], ["a", "a", "b"]]
-
-        def test_func(dstream):
-            return dstream.map(lambda x: (x, 1)).groupByKey()
-        expected_output = [[(1, [1]), (2, [1]), (3, [1])],
-                           [(1, [1, 1]), ("", [1])],
-                           [("a", [1, 1]), ("b", [1])]]
-        scattered_output = self._run_stream(test_input, test_func, expected_output)
-        output = self._convert_iter_value_to_list(scattered_output)
-        for result in (output, expected_output):
-            self._sort_result_based_on_key(result)
-        self.assertEqual(expected_output, output)
-
-    def test_combineByKey_batch(self):
-        """Basic operation test for DStream.combineByKey with batch deserializer."""
-        test_input = [range(1, 5), [1, 1, 1, 2, 2, 3], ["a", "a", "b", "", "", ""]]
-
-        def test_func(dstream):
-            def add(a, b): return a + str(b)
-            return dstream.map(lambda x: (x, 1)).combineByKey(str, add, add)
-        expected_output = [[(1, "1"), (2, "1"), (3, "1"), (4, "1")],
-                           [(1, "111"), (2, "11"), (3, "1")],
-                           [("a", "11"), ("b", "1"), ("", "111")]]
-        output = self._run_stream(test_input, test_func, expected_output)
-        for result in (output, expected_output):
-            self._sort_result_based_on_key(result)
-        self.assertEqual(expected_output, output)
-
-    def test_combineByKey_unbatch(self):
-        """Basic operation test for DStream.combineByKey with unbatch deserializer."""
-        test_input = [range(1, 4), [1, 1, ""], ["a", "a", "b"]]
-
-        def test_func(dstream):
-            def add(a, b): return a + str(b)
-            return dstream.map(lambda x: (x, 1)).combineByKey(str, add, add)
-        expected_output = [[(1, "1"), (2, "1"), (3, "1")],
-                           [(1, "11"), ("", "1")],
-                           [("a", "11"), ("b", "1")]]
-        output = self._run_stream(test_input, test_func, expected_output)
-        for result in (output, expected_output):
-            self._sort_result_based_on_key(result)
-        self.assertEqual(expected_output, output)
-
-    def _convert_iter_value_to_list(self, outputs):
-        """Return key value pair list. Value is converted to iterator to list."""
-        result = list()
-        for output in outputs:
-            result.append(map(lambda (x, y): (x, list(y)), output))
-        return result
-
-    def _sort_result_based_on_key(self, outputs):
-        """Sort the list base onf first value."""
-        for output in outputs:
-            output.sort(key=lambda x: x[0])
-
-    def _run_stream(self, test_input, test_func, expected_output, numSlices=None):
-        """
-        Start stream and return the output.
-        @param test_input: dataset for the test. This should be list of lists.
-        @param test_func: wrapped test_function. This function should return PythonDstream object.
-        @param expexted_output: expected output for this testcase.
-        @param numSlices: the number of slices in the rdd in the dstream.
-        """
-        # Generate input stream with user-defined input.
-        numSlices = numSlices or self.numInputPartitions
-        test_input_stream = self.ssc._testInputStream(test_input, numSlices)
-        # Apply test function to stream.
-        test_stream = test_func(test_input_stream)
-        # Add job to get output from stream.
-        test_stream._test_output(self.result)
-        self.ssc.start()
-
-        start_time = time.time()
-        # Loop until get the expected the number of the result from the stream.
-        while True:
-            current_time = time.time()
-            # Check time out.
-            if (current_time - start_time) > self.timeout:
-                break
-            self.ssc.awaitTermination(50)
-            # Check if the output is the same length of expexted output.
-            if len(expected_output) == len(self.result):
-                break
-
-        return self.result
-
-class TestSaveAsFilesSuite(PySparkStreamingTestCase):
-    def setUp(self):
-        PySparkStreamingTestCase.setUp(self)
-        self.timeout = 10  # seconds
-        self.numInputPartitions = 2
-        self.result = list()
-
-    def tearDown(self):
-        PySparkStreamingTestCase.tearDown(self)
-
-    @classmethod
-    def tearDownClass(cls):
-        PySparkStreamingTestCase.tearDownClass()
-
-        start_time = time.time()
-        while True:
-            current_time = time.time()
-            # check time out
-            if (current_time - start_time) > self.timeout:
-                break
-            self.ssc.awaitTermination(50)
-            if len(expected_output) == len(StreamOutput.result):
-                break
-        return StreamOutput.result
-
-if __name__ == "__main__":
-    unittest.main()

From f67cf57d0edc28b4bafbee9fb5547213dd79b93c Mon Sep 17 00:00:00 2001
From: giwa <ugw.gi.world@gmail.com>
Date: Mon, 11 Aug 2014 16:34:12 -0700
Subject: [PATCH 454/489] added mapValues and flatMapVaules WIP for glom and
 mapPartitions test

---
 python/pyspark/streaming/dstream.py | 16 +++++++++++-----
 1 file changed, 11 insertions(+), 5 deletions(-)

diff --git a/python/pyspark/streaming/dstream.py b/python/pyspark/streaming/dstream.py
index c1ace350b1bf5..b669d0839f3bf 100644
--- a/python/pyspark/streaming/dstream.py
+++ b/python/pyspark/streaming/dstream.py
@@ -46,6 +46,12 @@ def context(self):
         """
         return self._ssc
 
+    def context(self):
+        """
+        Return the StreamingContext associated with this DStream
+        """
+        return self._ssc
+
     def count(self):
         """
         Return a new DStream which contains the number of elements in this DStream.
@@ -56,7 +62,7 @@ def _sum(self):
         """
         Add up the elements in this DStream.
         """
-        return self._mapPartitions(lambda x: [sum(x)]).reduce(operator.add)
+        return self.mapPartitions(lambda x: [sum(x)]).reduce(operator.add)
 
     def print_(self, label=None):
         """
@@ -75,7 +81,7 @@ def filter(self, f):
         Return a new DStream containing only the elements that satisfy predicate.
         """
         def func(iterator): return ifilter(f, iterator)
-        return self._mapPartitions(func)
+        return self.mapPartitions(func)
 
     def flatMap(self, f, preservesPartitioning=False):
         """
@@ -86,7 +92,7 @@ def func(s, iterator):
             return chain.from_iterable(imap(f, iterator))
         return self._mapPartitionsWithIndex(func, preservesPartitioning)
 
-    def map(self, f):
+    def map(self, f, preservesPartitioning=False):
         """
         Return a new DStream by applying a function to each element of DStream.
         """
@@ -146,7 +152,7 @@ def combineLocally(iterator):
                 else:
                     combiners[k] = mergeValue(combiners[k], v)
             return combiners.iteritems()
-        locally_combined = self._mapPartitions(combineLocally)
+        locally_combined = self.mapPartitions(combineLocally)
         shuffled = locally_combined.partitionBy(numPartitions)
 
         def _mergeCombiners(iterator):
@@ -474,4 +480,4 @@ def _jdstream(self):
         return self._jdstream_val
 
     def _is_pipelinable(self):
-        return not (self.is_cached)
+        return not self.is_cached

From 1e126bfd07995cf25477e78163262da9a46114ae Mon Sep 17 00:00:00 2001
From: giwa <ugw.gi.world@gmail.com>
Date: Wed, 13 Aug 2014 21:04:26 -0700
Subject: [PATCH 455/489] WIP: solved partitioned and None is not recognized

---
 python/pyspark/streaming/dstream.py              | 16 ++++++++++++++++
 .../streaming/api/python/PythonDStream.scala     |  1 +
 2 files changed, 17 insertions(+)

diff --git a/python/pyspark/streaming/dstream.py b/python/pyspark/streaming/dstream.py
index b669d0839f3bf..f3e80b06a9790 100644
--- a/python/pyspark/streaming/dstream.py
+++ b/python/pyspark/streaming/dstream.py
@@ -245,6 +245,8 @@ def takeAndPrint(rdd, time):
             taken = rdd.take(11)
             print "-------------------------------------------"
             print "Time: %s" % (str(time))
+            print rdd.glom().collect()
+            print "-------------------------------------------"
             print "-------------------------------------------"
             for record in taken[:10]:
                 print record
@@ -426,6 +428,20 @@ def saveAsTextFile(rdd, time):
 # TODO: implemtnt rightOuterJoin
 
 
+# TODO: implement groupByKey
+# TODO: impelment union
+# TODO: implement cache
+# TODO: implement persist
+# TODO: implement repertitions
+# TODO: implement saveAsTextFile
+# TODO: implement cogroup
+# TODO: implement join
+# TODO: implement countByValue
+# TODO: implement leftOuterJoin
+# TODO: implemtnt rightOuterJoin
+
+
+
 class PipelinedDStream(DStream):
     def __init__(self, prev, func, preservesPartitioning=False):
         if not isinstance(prev, PipelinedDStream) or not prev._is_pipelinable():
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
index 6db7e0cfa6846..133704c115419 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
@@ -205,3 +205,4 @@ class PythonTransformedDStream(
   //val asJavaPairDStream : JavaPairDStream[Long, Array[Byte]]  = JavaPairDStream.fromJavaDStream(this)
 }
 */
+

From 795b2cd1bebe4f0e85ab25bc28f3e5eb1ee00186 Mon Sep 17 00:00:00 2001
From: giwa <ugw.gi.world@gmail.com>
Date: Thu, 14 Aug 2014 02:19:46 -0700
Subject: [PATCH 456/489] broke something

---
 python/pyspark/streaming/context.py           | 46 ++++++++++++
 python/pyspark/streaming/dstream.py           | 15 +---
 python/pyspark/worker.py                      | 10 +++
 .../streaming/api/python/PythonDStream.scala  | 75 +++++++++++++++++++
 4 files changed, 132 insertions(+), 14 deletions(-)

diff --git a/python/pyspark/streaming/context.py b/python/pyspark/streaming/context.py
index 1668bfcd41a57..96d717cfcc75c 100644
--- a/python/pyspark/streaming/context.py
+++ b/python/pyspark/streaming/context.py
@@ -142,9 +142,49 @@ def stop(self, stopSparkContext=True, stopGraceFully=False):
 
     def _testInputStream(self, test_inputs, numSlices=None):
         """
+<<<<<<< HEAD
         This function is only for test.
         This implementation is inspired by QueStream implementation.
         Give list of RDD to generate DStream which contains the RDD.
+=======
+        Generate multiple files to make "stream" in Scala side for test.
+        Scala chooses one of the files and generates RDD using PythonRDD.readRDDFromFile.
+
+        QueStream maybe good way to implement this function
+        """
+        numSlices = numSlices or self._sc.defaultParallelism
+        # Calling the Java parallelize() method with an ArrayList is too slow,
+        # because it sends O(n) Py4J commands.  As an alternative, serialized
+        # objects are written to a file and loaded through textFile().
+
+        tempFiles = list()
+        for test_input in test_inputs:
+            tempFile = NamedTemporaryFile(delete=False, dir=self._sc._temp_dir)
+
+            # Make sure we distribute data evenly if it's smaller than self.batchSize
+            if "__len__" not in dir(test_input):
+                test_input = list(test_input)    # Make it a list so we can compute its length
+            batchSize = min(len(test_input) // numSlices, self._sc._batchSize)
+            if batchSize > 1:
+                serializer = BatchedSerializer(self._sc._unbatched_serializer,
+                                               batchSize)
+            else:
+                serializer = self._sc._unbatched_serializer
+            serializer.dump_stream(test_input, tempFile)
+            tempFile.close()
+            tempFiles.append(tempFile.name)
+
+        jtempFiles = ListConverter().convert(tempFiles, SparkContext._gateway._gateway_client)
+        jinput_stream = self._jvm.PythonTestInputStream(self._jssc,
+                                                        jtempFiles,
+                                                        numSlices).asJavaDStream()
+        return DStream(jinput_stream, self, BatchedSerializer(PickleSerializer()))
+    
+    def _testInputStream2(self, test_inputs, numSlices=None):
+        """
+        This is inpired by QueStream implementation. Give list of RDD and generate DStream
+        which contain the RDD.
+>>>>>>> broke something
         """
         test_rdds = list()
         test_rdd_deserializers = list()
@@ -156,4 +196,10 @@ def _testInputStream(self, test_inputs, numSlices=None):
         jtest_rdds = ListConverter().convert(test_rdds, SparkContext._gateway._gateway_client)
         jinput_stream = self._jvm.PythonTestInputStream(self._jssc, jtest_rdds).asJavaDStream()
 
+<<<<<<< HEAD
         return DStream(jinput_stream, self, test_rdd_deserializers[0])
+=======
+        dstream = DStream(jinput_stream, self, test_rdd_deserializers[0])
+        dstream._test_switch_dserializer(test_rdd_deserializers)
+        return dstream
+>>>>>>> broke something
diff --git a/python/pyspark/streaming/dstream.py b/python/pyspark/streaming/dstream.py
index f3e80b06a9790..32d6c3e45eb54 100644
--- a/python/pyspark/streaming/dstream.py
+++ b/python/pyspark/streaming/dstream.py
@@ -17,6 +17,7 @@
 
 from collections import defaultdict
 from itertools import chain, ifilter, imap
+import time
 import operator
 
 from pyspark.serializers import NoOpSerializer,\
@@ -428,20 +429,6 @@ def saveAsTextFile(rdd, time):
 # TODO: implemtnt rightOuterJoin
 
 
-# TODO: implement groupByKey
-# TODO: impelment union
-# TODO: implement cache
-# TODO: implement persist
-# TODO: implement repertitions
-# TODO: implement saveAsTextFile
-# TODO: implement cogroup
-# TODO: implement join
-# TODO: implement countByValue
-# TODO: implement leftOuterJoin
-# TODO: implemtnt rightOuterJoin
-
-
-
 class PipelinedDStream(DStream):
     def __init__(self, prev, func, preservesPartitioning=False):
         if not isinstance(prev, PipelinedDStream) or not prev._is_pipelinable():
diff --git a/python/pyspark/worker.py b/python/pyspark/worker.py
index 90ea7b453d401..78e56143fb3ab 100644
--- a/python/pyspark/worker.py
+++ b/python/pyspark/worker.py
@@ -86,6 +86,16 @@ def main(infile, outfile):
         (func, deserializer, serializer) = command
         init_time = time.time()
         iterator = deserializer.load_stream(infile)
+        print "deserializer in worker: %s" % str(deserializer)
+        iterator, walk = itertools.tee(iterator)
+        if isinstance(walk, int):
+            print "this is int"
+            print walk
+        else:
+            try:
+                print list(walk)
+            except:
+                print list(walk)
         serializer.dump_stream(func(split_index, iterator), outfile)
     except Exception:
         try:
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
index 133704c115419..63b2f709df7e4 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
@@ -206,3 +206,78 @@ class PythonTransformedDStream(
 }
 */
 
+<<<<<<< HEAD
+=======
+/**
+ * This is a input stream just for the unitest. This is equivalent to a checkpointable,
+ * replayable, reliable message queue like Kafka. It requires a sequence as input, and
+ * returns the i_th element at the i_th batch under manual clock.
+ */
+class PythonTestInputStream(ssc_ : JavaStreamingContext, inputFiles: JArrayList[String], numPartitions: Int)
+  extends InputDStream[Array[Byte]](JavaStreamingContext.toStreamingContext(ssc_)){
+
+  def start() {}
+
+  def stop() {}
+
+  def compute(validTime: Time): Option[RDD[Array[Byte]]] = {
+    logInfo("Computing RDD for time " + validTime)
+    inputFiles.foreach(logInfo(_))
+    // make a temporary file
+    // make empty RDD
+    val prefix = "spark"
+    val suffix = ".tmp"
+    val tempFile = File.createTempFile(prefix, suffix)
+    val index = ((validTime - zeroTime) / slideDuration - 1).toInt
+    logInfo("Index: " + index)
+
+    val selectedInputFile: String = {
+      if (inputFiles.isEmpty){
+        tempFile.getAbsolutePath
+      }else if (index < inputFiles.size()) {
+        inputFiles.get(index)
+      } else {
+        tempFile.getAbsolutePath
+      }
+    }
+    val rdd = PythonRDD.readRDDFromFile(JavaSparkContext.fromSparkContext(ssc_.sparkContext), selectedInputFile, numPartitions).rdd
+    logInfo("Created RDD " + rdd.id + " with " + selectedInputFile)
+    Some(rdd)
+  }
+
+  val asJavaDStream  = JavaDStream.fromDStream(this)
+}
+
+/**
+ * This is a input stream just for the unitest. This is equivalent to a checkpointable,
+ * replayable, reliable message queue like Kafka. It requires a sequence as input, and
+ * returns the i_th element at the i_th batch under manual clock.
+ * This implementation is close to QueStream
+ */
+
+class PythonTestInputStream2(ssc_ : JavaStreamingContext, inputRDDs: JArrayList[JavaRDD[Array[Byte]]])
+  extends InputDStream[Array[Byte]](JavaStreamingContext.toStreamingContext(ssc_)) {
+
+  def start() {}
+
+  def stop() {}
+
+  def compute(validTime: Time): Option[RDD[Array[Byte]]] = {
+    val emptyRDD = ssc.sparkContext.emptyRDD[Array[Byte]]
+    val index = ((validTime - zeroTime) / slideDuration - 1).toInt
+    val selectedRDD = {
+      if (inputRDDs.isEmpty) {
+        emptyRDD
+      } else if (index < inputRDDs.size()) {
+        inputRDDs.get(index).rdd
+      } else {
+        emptyRDD
+      }
+    }
+
+    Some(selectedRDD)
+  }
+
+  val asJavaDStream  = JavaDStream.fromDStream(this)
+}
+>>>>>>> broke something

From 8dcda84d2d1de47b00e0209b269513ff0bdb1671 Mon Sep 17 00:00:00 2001
From: giwa <ugw.gi.world@gmail.com>
Date: Thu, 14 Aug 2014 18:07:10 -0700
Subject: [PATCH 457/489] all tests are passed if numSlice is 2 and the numver
 of each input is over 4

---
 python/pyspark/streaming/context.py           | 46 -------------------
 .../streaming/api/python/PythonDStream.scala  | 19 +++++++-
 2 files changed, 18 insertions(+), 47 deletions(-)

diff --git a/python/pyspark/streaming/context.py b/python/pyspark/streaming/context.py
index 96d717cfcc75c..1668bfcd41a57 100644
--- a/python/pyspark/streaming/context.py
+++ b/python/pyspark/streaming/context.py
@@ -142,49 +142,9 @@ def stop(self, stopSparkContext=True, stopGraceFully=False):
 
     def _testInputStream(self, test_inputs, numSlices=None):
         """
-<<<<<<< HEAD
         This function is only for test.
         This implementation is inspired by QueStream implementation.
         Give list of RDD to generate DStream which contains the RDD.
-=======
-        Generate multiple files to make "stream" in Scala side for test.
-        Scala chooses one of the files and generates RDD using PythonRDD.readRDDFromFile.
-
-        QueStream maybe good way to implement this function
-        """
-        numSlices = numSlices or self._sc.defaultParallelism
-        # Calling the Java parallelize() method with an ArrayList is too slow,
-        # because it sends O(n) Py4J commands.  As an alternative, serialized
-        # objects are written to a file and loaded through textFile().
-
-        tempFiles = list()
-        for test_input in test_inputs:
-            tempFile = NamedTemporaryFile(delete=False, dir=self._sc._temp_dir)
-
-            # Make sure we distribute data evenly if it's smaller than self.batchSize
-            if "__len__" not in dir(test_input):
-                test_input = list(test_input)    # Make it a list so we can compute its length
-            batchSize = min(len(test_input) // numSlices, self._sc._batchSize)
-            if batchSize > 1:
-                serializer = BatchedSerializer(self._sc._unbatched_serializer,
-                                               batchSize)
-            else:
-                serializer = self._sc._unbatched_serializer
-            serializer.dump_stream(test_input, tempFile)
-            tempFile.close()
-            tempFiles.append(tempFile.name)
-
-        jtempFiles = ListConverter().convert(tempFiles, SparkContext._gateway._gateway_client)
-        jinput_stream = self._jvm.PythonTestInputStream(self._jssc,
-                                                        jtempFiles,
-                                                        numSlices).asJavaDStream()
-        return DStream(jinput_stream, self, BatchedSerializer(PickleSerializer()))
-    
-    def _testInputStream2(self, test_inputs, numSlices=None):
-        """
-        This is inpired by QueStream implementation. Give list of RDD and generate DStream
-        which contain the RDD.
->>>>>>> broke something
         """
         test_rdds = list()
         test_rdd_deserializers = list()
@@ -196,10 +156,4 @@ def _testInputStream2(self, test_inputs, numSlices=None):
         jtest_rdds = ListConverter().convert(test_rdds, SparkContext._gateway._gateway_client)
         jinput_stream = self._jvm.PythonTestInputStream(self._jssc, jtest_rdds).asJavaDStream()
 
-<<<<<<< HEAD
         return DStream(jinput_stream, self, test_rdd_deserializers[0])
-=======
-        dstream = DStream(jinput_stream, self, test_rdd_deserializers[0])
-        dstream._test_switch_dserializer(test_rdd_deserializers)
-        return dstream
->>>>>>> broke something
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
index 63b2f709df7e4..0bafe3f846793 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
@@ -280,4 +280,21 @@ class PythonTestInputStream2(ssc_ : JavaStreamingContext, inputRDDs: JArrayList[
 
   val asJavaDStream  = JavaDStream.fromDStream(this)
 }
->>>>>>> broke something
+
+
+class PythonTestInputStream3(ssc_ : JavaStreamingContext)
+  extends InputDStream[Any](JavaStreamingContext.toStreamingContext(ssc_)) {
+
+  def start() {}
+
+  def stop() {}
+
+  def compute(validTime: Time): Option[RDD[Any]] = {
+    val index = ((validTime - zeroTime) / slideDuration - 1).toInt
+    val selectedInput = ArrayBuffer(1, 2, 3).toSeq
+    val rdd :RDD[Any] = ssc.sc.makeRDD(selectedInput, 2)
+    Some(rdd)
+  }
+
+  val asJavaDStream = JavaDStream.fromDStream(this)
+}>>>>>>> broke something

From c5ecfc1f5d65f8458c3919a2371ada4d32e8f03d Mon Sep 17 00:00:00 2001
From: giwa <ugw.gi.world@gmail.com>
Date: Thu, 14 Aug 2014 23:42:34 -0700
Subject: [PATCH 458/489] basic function test cases are passed

---
 python/pyspark/worker.py                      | 10 ---
 .../streaming/api/python/PythonDStream.scala  | 62 +------------------
 2 files changed, 1 insertion(+), 71 deletions(-)

diff --git a/python/pyspark/worker.py b/python/pyspark/worker.py
index 78e56143fb3ab..90ea7b453d401 100644
--- a/python/pyspark/worker.py
+++ b/python/pyspark/worker.py
@@ -86,16 +86,6 @@ def main(infile, outfile):
         (func, deserializer, serializer) = command
         init_time = time.time()
         iterator = deserializer.load_stream(infile)
-        print "deserializer in worker: %s" % str(deserializer)
-        iterator, walk = itertools.tee(iterator)
-        if isinstance(walk, int):
-            print "this is int"
-            print walk
-        else:
-            try:
-                print list(walk)
-            except:
-                print list(walk)
         serializer.dump_stream(func(split_index, iterator), outfile)
     except Exception:
         try:
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
index 0bafe3f846793..0186003f70c49 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
@@ -206,48 +206,6 @@ class PythonTransformedDStream(
 }
 */
 
-<<<<<<< HEAD
-=======
-/**
- * This is a input stream just for the unitest. This is equivalent to a checkpointable,
- * replayable, reliable message queue like Kafka. It requires a sequence as input, and
- * returns the i_th element at the i_th batch under manual clock.
- */
-class PythonTestInputStream(ssc_ : JavaStreamingContext, inputFiles: JArrayList[String], numPartitions: Int)
-  extends InputDStream[Array[Byte]](JavaStreamingContext.toStreamingContext(ssc_)){
-
-  def start() {}
-
-  def stop() {}
-
-  def compute(validTime: Time): Option[RDD[Array[Byte]]] = {
-    logInfo("Computing RDD for time " + validTime)
-    inputFiles.foreach(logInfo(_))
-    // make a temporary file
-    // make empty RDD
-    val prefix = "spark"
-    val suffix = ".tmp"
-    val tempFile = File.createTempFile(prefix, suffix)
-    val index = ((validTime - zeroTime) / slideDuration - 1).toInt
-    logInfo("Index: " + index)
-
-    val selectedInputFile: String = {
-      if (inputFiles.isEmpty){
-        tempFile.getAbsolutePath
-      }else if (index < inputFiles.size()) {
-        inputFiles.get(index)
-      } else {
-        tempFile.getAbsolutePath
-      }
-    }
-    val rdd = PythonRDD.readRDDFromFile(JavaSparkContext.fromSparkContext(ssc_.sparkContext), selectedInputFile, numPartitions).rdd
-    logInfo("Created RDD " + rdd.id + " with " + selectedInputFile)
-    Some(rdd)
-  }
-
-  val asJavaDStream  = JavaDStream.fromDStream(this)
-}
-
 /**
  * This is a input stream just for the unitest. This is equivalent to a checkpointable,
  * replayable, reliable message queue like Kafka. It requires a sequence as input, and
@@ -255,7 +213,7 @@ class PythonTestInputStream(ssc_ : JavaStreamingContext, inputFiles: JArrayList[
  * This implementation is close to QueStream
  */
 
-class PythonTestInputStream2(ssc_ : JavaStreamingContext, inputRDDs: JArrayList[JavaRDD[Array[Byte]]])
+class PythonTestInputStream(ssc_ : JavaStreamingContext, inputRDDs: JArrayList[JavaRDD[Array[Byte]]])
   extends InputDStream[Array[Byte]](JavaStreamingContext.toStreamingContext(ssc_)) {
 
   def start() {}
@@ -280,21 +238,3 @@ class PythonTestInputStream2(ssc_ : JavaStreamingContext, inputRDDs: JArrayList[
 
   val asJavaDStream  = JavaDStream.fromDStream(this)
 }
-
-
-class PythonTestInputStream3(ssc_ : JavaStreamingContext)
-  extends InputDStream[Any](JavaStreamingContext.toStreamingContext(ssc_)) {
-
-  def start() {}
-
-  def stop() {}
-
-  def compute(validTime: Time): Option[RDD[Any]] = {
-    val index = ((validTime - zeroTime) / slideDuration - 1).toInt
-    val selectedInput = ArrayBuffer(1, 2, 3).toSeq
-    val rdd :RDD[Any] = ssc.sc.makeRDD(selectedInput, 2)
-    Some(rdd)
-  }
-
-  val asJavaDStream = JavaDStream.fromDStream(this)
-}>>>>>>> broke something

From 2a06cdb1633126dbd1cc1d2ae07a1e1e85fad902 Mon Sep 17 00:00:00 2001
From: giwa <ugw.gi.world@gmail.com>
Date: Fri, 15 Aug 2014 11:28:39 -0700
Subject: [PATCH 459/489] remove waste duplicated code

---
 python/pyspark/streaming/dstream.py | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

diff --git a/python/pyspark/streaming/dstream.py b/python/pyspark/streaming/dstream.py
index 32d6c3e45eb54..66024d539ce5c 100644
--- a/python/pyspark/streaming/dstream.py
+++ b/python/pyspark/streaming/dstream.py
@@ -17,7 +17,6 @@
 
 from collections import defaultdict
 from itertools import chain, ifilter, imap
-import time
 import operator
 
 from pyspark.serializers import NoOpSerializer,\
@@ -246,8 +245,6 @@ def takeAndPrint(rdd, time):
             taken = rdd.take(11)
             print "-------------------------------------------"
             print "Time: %s" % (str(time))
-            print rdd.glom().collect()
-            print "-------------------------------------------"
             print "-------------------------------------------"
             for record in taken[:10]:
                 print record
@@ -447,6 +444,7 @@ def pipeline_func(split, iterator):
             self._prev_jdstream = prev._prev_jdstream  # maintain the pipeline
             self._prev_jrdd_deserializer = prev._prev_jrdd_deserializer
         self.is_cached = False
+        self.is_checkpointed = False
         self._ssc = prev._ssc
         self.ctx = prev.ctx
         self.prev = prev
@@ -483,4 +481,4 @@ def _jdstream(self):
         return self._jdstream_val
 
     def _is_pipelinable(self):
-        return not self.is_cached
+        return not (self.is_cached or self.is_checkpointed)

From 99ce042b5644ab21ae703a35a7bf4d699a5ae482 Mon Sep 17 00:00:00 2001
From: giwa <ugw.gi.world@gmail.com>
Date: Fri, 15 Aug 2014 17:10:56 -0700
Subject: [PATCH 460/489] added saveAsTextFiles and saveAsPickledFiles

---
 python/pyspark/streaming/context.py                            | 3 +++
 .../org/apache/spark/streaming/api/python/PythonDStream.scala  | 2 +-
 2 files changed, 4 insertions(+), 1 deletion(-)

diff --git a/python/pyspark/streaming/context.py b/python/pyspark/streaming/context.py
index 1668bfcd41a57..baf6d12a8025a 100644
--- a/python/pyspark/streaming/context.py
+++ b/python/pyspark/streaming/context.py
@@ -153,6 +153,9 @@ def _testInputStream(self, test_inputs, numSlices=None):
             test_rdds.append(test_rdd._jrdd)
             test_rdd_deserializers.append(test_rdd._jrdd_deserializer)
 
+#        if len(set(test_rdd_deserializers)) > 1:
+#            raise IOError("Deserializer should be one type to run test case. "
+#                          "See the SparkContext.parallelize to understand how to decide deserializer")
         jtest_rdds = ListConverter().convert(test_rdds, SparkContext._gateway._gateway_client)
         jinput_stream = self._jvm.PythonTestInputStream(self._jssc, jtest_rdds).asJavaDStream()
 
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
index 0186003f70c49..681ef5997002e 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
@@ -210,7 +210,7 @@ class PythonTransformedDStream(
  * This is a input stream just for the unitest. This is equivalent to a checkpointable,
  * replayable, reliable message queue like Kafka. It requires a sequence as input, and
  * returns the i_th element at the i_th batch under manual clock.
- * This implementation is close to QueStream
+ * This implementation is inspired by QueStream
  */
 
 class PythonTestInputStream(ssc_ : JavaStreamingContext, inputRDDs: JArrayList[JavaRDD[Array[Byte]]])

From ddd4ee1ee0821e1e45e9fbd2dbef11a5a6c69aca Mon Sep 17 00:00:00 2001
From: giwa <ugw.gi.world@gmail.com>
Date: Fri, 15 Aug 2014 22:30:58 -0700
Subject: [PATCH 461/489] added TODO coments

---
 python/pyspark/streaming/context.py | 1 -
 python/pyspark/streaming/dstream.py | 1 -
 2 files changed, 2 deletions(-)

diff --git a/python/pyspark/streaming/context.py b/python/pyspark/streaming/context.py
index baf6d12a8025a..2cc9d03a87e9e 100644
--- a/python/pyspark/streaming/context.py
+++ b/python/pyspark/streaming/context.py
@@ -17,7 +17,6 @@
 
 import sys
 from signal import signal, SIGTERM, SIGINT
-from tempfile import NamedTemporaryFile
 
 from pyspark.serializers import PickleSerializer, BatchedSerializer, UTF8Deserializer
 from pyspark.context import SparkContext
diff --git a/python/pyspark/streaming/dstream.py b/python/pyspark/streaming/dstream.py
index 66024d539ce5c..bfe639e3ef791 100644
--- a/python/pyspark/streaming/dstream.py
+++ b/python/pyspark/streaming/dstream.py
@@ -425,7 +425,6 @@ def saveAsTextFile(rdd, time):
 # TODO: implement leftOuterJoin
 # TODO: implemtnt rightOuterJoin
 
-
 class PipelinedDStream(DStream):
     def __init__(self, prev, func, preservesPartitioning=False):
         if not isinstance(prev, PipelinedDStream) or not prev._is_pipelinable():

From af336b776c0586e1d8e2dbe4df2073ee8f7ca566 Mon Sep 17 00:00:00 2001
From: giwa <ugw.gi.world@gmail.com>
Date: Mon, 18 Aug 2014 00:30:17 -0700
Subject: [PATCH 462/489] add comments

---
 python/pyspark/java_gateway.py      | 2 +-
 python/pyspark/streaming/context.py | 3 ---
 python/pyspark/streaming/dstream.py | 1 +
 3 files changed, 2 insertions(+), 4 deletions(-)

diff --git a/python/pyspark/java_gateway.py b/python/pyspark/java_gateway.py
index 2af917efc40a3..f3c6d231ab777 100644
--- a/python/pyspark/java_gateway.py
+++ b/python/pyspark/java_gateway.py
@@ -111,7 +111,7 @@ def run(self):
     java_import(gateway.jvm, "org.apache.spark.streaming.*")  # do we need this?
     java_import(gateway.jvm, "org.apache.spark.streaming.api.java.*")
     java_import(gateway.jvm, "org.apache.spark.streaming.api.python.*")
-    java_import(gateway.jvm, "org.apache.spark.streaming.dstream.*")
+    java_import(gateway.jvm, "org.apache.spark.streaming.dstream.*")  # do we need this?
     java_import(gateway.jvm, "org.apache.spark.mllib.api.python.*")
     java_import(gateway.jvm, "org.apache.spark.sql.SQLContext")
     java_import(gateway.jvm, "org.apache.spark.sql.hive.HiveContext")
diff --git a/python/pyspark/streaming/context.py b/python/pyspark/streaming/context.py
index 2cc9d03a87e9e..3f455a3e06072 100644
--- a/python/pyspark/streaming/context.py
+++ b/python/pyspark/streaming/context.py
@@ -152,9 +152,6 @@ def _testInputStream(self, test_inputs, numSlices=None):
             test_rdds.append(test_rdd._jrdd)
             test_rdd_deserializers.append(test_rdd._jrdd_deserializer)
 
-#        if len(set(test_rdd_deserializers)) > 1:
-#            raise IOError("Deserializer should be one type to run test case. "
-#                          "See the SparkContext.parallelize to understand how to decide deserializer")
         jtest_rdds = ListConverter().convert(test_rdds, SparkContext._gateway._gateway_client)
         jinput_stream = self._jvm.PythonTestInputStream(self._jssc, jtest_rdds).asJavaDStream()
 
diff --git a/python/pyspark/streaming/dstream.py b/python/pyspark/streaming/dstream.py
index bfe639e3ef791..66024d539ce5c 100644
--- a/python/pyspark/streaming/dstream.py
+++ b/python/pyspark/streaming/dstream.py
@@ -425,6 +425,7 @@ def saveAsTextFile(rdd, time):
 # TODO: implement leftOuterJoin
 # TODO: implemtnt rightOuterJoin
 
+
 class PipelinedDStream(DStream):
     def __init__(self, prev, func, preservesPartitioning=False):
         if not isinstance(prev, PipelinedDStream) or not prev._is_pipelinable():

From 455e5af16e5c46167b0afb7d39e083f72ac7fe0d Mon Sep 17 00:00:00 2001
From: giwa <ugw.gi.world@gmail.com>
Date: Mon, 18 Aug 2014 00:35:50 -0700
Subject: [PATCH 463/489] removed wasted print in DStream

---
 .../streaming/api/java/JavaDStreamLike.scala    |  9 ---------
 .../spark/streaming/dstream/DStream.scala       | 17 -----------------
 2 files changed, 26 deletions(-)

diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaDStreamLike.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaDStreamLike.scala
index 7a002bbe74ca9..a6184de4e83c1 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaDStreamLike.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaDStreamLike.scala
@@ -54,15 +54,6 @@ trait JavaDStreamLike[T, This <: JavaDStreamLike[T, This, R], R <: JavaRDDLike[T
     dstream.print()
   }
 
-  def print(label: String = null): Unit = {
-    dstream.print(label)
-  }
-
-  def outputToFile(): Unit = {
-    dstream.outputToFile()
-  }
-
-
   /**
    * Return a new DStream in which each RDD has a single element generated by counting each RDD
    * of this DStream.
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala
index 46ef05d9c37a1..39ad591e8896e 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala
@@ -617,23 +617,6 @@ abstract class DStream[T: ClassTag] (
     new ForEachDStream(this, context.sparkContext.clean(foreachFunc)).register()
   }
 
-
-  def print(label: String = null) {
-    def foreachFunc = (rdd: RDD[T], time: Time) => {
-      val first11 = rdd.take(11)
-      println ("-------------------------------------------")
-      println ("Time: " + time)
-      println ("-------------------------------------------")
-      if(label != null){
-        println (label)
-      }
-      first11.take(10).foreach(println)
-      if (first11.size > 10) println("...")
-      println()
-    }
-    new ForEachDStream(this, context.sparkContext.clean(foreachFunc)).register()
-  }
-
   /**
    * Return a new DStream in which each RDD contains all the elements in seen in a
    * sliding window of time over this DStream. The new DStream generates RDDs with

From 58e41ff4588a1c0d31b83d43ec3155de7fb92a02 Mon Sep 17 00:00:00 2001
From: giwa <ugw.gi.world@gmail.com>
Date: Mon, 18 Aug 2014 16:55:51 -0700
Subject: [PATCH 464/489] merge with master

---
 python/pyspark/streaming/dstream.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/python/pyspark/streaming/dstream.py b/python/pyspark/streaming/dstream.py
index 66024d539ce5c..a36f4b9bf9d87 100644
--- a/python/pyspark/streaming/dstream.py
+++ b/python/pyspark/streaming/dstream.py
@@ -436,6 +436,7 @@ def __init__(self, prev, func, preservesPartitioning=False):
             self._prev_jrdd_deserializer = prev._jrdd_deserializer
         else:
             prev_func = prev.func
+            
             def pipeline_func(split, iterator):
                 return func(split, prev_func(split, iterator))
             self.func = pipeline_func

From e80647e6031890cf944d323650e253bb87fcce29 Mon Sep 17 00:00:00 2001
From: giwa <ugw.gi.world@gmail.com>
Date: Mon, 18 Aug 2014 17:37:28 -0700
Subject: [PATCH 465/489] adopted the latest compression way of python command

---
 python/pyspark/streaming/dstream.py | 17 ++++++++++-------
 1 file changed, 10 insertions(+), 7 deletions(-)

diff --git a/python/pyspark/streaming/dstream.py b/python/pyspark/streaming/dstream.py
index a36f4b9bf9d87..0e2641e33032f 100644
--- a/python/pyspark/streaming/dstream.py
+++ b/python/pyspark/streaming/dstream.py
@@ -20,7 +20,8 @@
 import operator
 
 from pyspark.serializers import NoOpSerializer,\
-    BatchedSerializer, CloudPickleSerializer, pack_long
+    BatchedSerializer, CloudPickleSerializer, pack_long,\
+    CompressedSerializer
 from pyspark.rdd import _JavaStackTrace
 from pyspark.storagelevel import StorageLevel
 from pyspark.resultiterable import ResultIterable
@@ -463,7 +464,8 @@ def _jdstream(self):
             serializer = self.ctx.serializer
 
         command = (self.func, self._prev_jrdd_deserializer, serializer)
-        pickled_command = CloudPickleSerializer().dumps(command)
+        ser = CompressedSerializer(CloudPickleSerializer())
+        pickled_command = ser.dumps(command)
         broadcast_vars = ListConverter().convert(
             [x._jbroadcast for x in self.ctx._pickled_broadcast_vars],
             self.ctx._gateway._gateway_client)
@@ -472,12 +474,13 @@ def _jdstream(self):
         env = MapConverter().convert(self.ctx.environment,
                                      self.ctx._gateway._gateway_client)
         includes = ListConverter().convert(self.ctx._python_includes,
-                                     self.ctx._gateway._gateway_client)
+                                           self.ctx._gateway._gateway_client)
         python_dstream = self.ctx._jvm.PythonDStream(self._prev_jdstream.dstream(),
-                bytearray(pickled_command),
-                env, includes, self.preservesPartitioning,
-                self.ctx.pythonExec, broadcast_vars, self.ctx._javaAccumulator,
-                class_tag)
+                                                     bytearray(pickled_command),
+                                                     env, includes, self.preservesPartitioning,
+                                                     self.ctx.pythonExec,
+                                                     broadcast_vars, self.ctx._javaAccumulator,
+                                                     class_tag)
         self._jdstream_val = python_dstream.asJavaDStream()
         return self._jdstream_val
 

From c00e09172ef2c376f1e437684e4448c1028f25b9 Mon Sep 17 00:00:00 2001
From: giwa <ugw.gi.world@gmail.com>
Date: Tue, 19 Aug 2014 15:59:49 -0700
Subject: [PATCH 466/489] change test case not to use awaitTermination

---
 python/pyspark/streaming/context.py                            | 1 +
 .../org/apache/spark/streaming/api/python/PythonDStream.scala  | 3 ---
 2 files changed, 1 insertion(+), 3 deletions(-)

diff --git a/python/pyspark/streaming/context.py b/python/pyspark/streaming/context.py
index 3f455a3e06072..cdd6926bf1c7a 100644
--- a/python/pyspark/streaming/context.py
+++ b/python/pyspark/streaming/context.py
@@ -102,6 +102,7 @@ def start(self):
     def awaitTermination(self, timeout=None):
         """
         Wait for the execution to stop.
+        @param timeout: time to wait in milliseconds
         """
         if timeout is None:
             self._jssc.awaitTermination()
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
index 681ef5997002e..2c44f6cc1d42f 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
@@ -184,8 +184,6 @@ class PythonForeachDStream(
   this.register()
 }
 
-/*
-This does not work. Ignore this for now. -TD
 class PythonTransformedDStream(
     prev: DStream[Array[Byte]],
     transformFunction: PythonRDDFunction
@@ -204,7 +202,6 @@ class PythonTransformedDStream(
   val asJavaDStream  = JavaDStream.fromDStream(this)
   //val asJavaPairDStream : JavaPairDStream[Long, Array[Byte]]  = JavaPairDStream.fromJavaDStream(this)
 }
-*/
 
 /**
  * This is a input stream just for the unitest. This is equivalent to a checkpointable,

From 3166d3146dbc194c098a4f7a8ad32cadffabdd54 Mon Sep 17 00:00:00 2001
From: giwa <ugw.gi.world@gmail.com>
Date: Wed, 20 Aug 2014 16:07:42 -0700
Subject: [PATCH 467/489] clean up

---
 python/pyspark/streaming/context.py           |  9 ++--
 python/pyspark/streaming/dstream.py           | 28 +++++------
 python/pyspark/streaming/jtime.py             |  3 +-
 python/pyspark/streaming/utils.py             | 12 +++--
 .../streaming/api/python/PythonDStream.scala  | 48 +++----------------
 .../api/python/PythonRDDFunction.java         |  4 ++
 .../api/python/PythonTransformedDStream.scala | 42 ----------------
 7 files changed, 37 insertions(+), 109 deletions(-)
 delete mode 100644 streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonTransformedDStream.scala

diff --git a/python/pyspark/streaming/context.py b/python/pyspark/streaming/context.py
index cdd6926bf1c7a..f7e356319ecac 100644
--- a/python/pyspark/streaming/context.py
+++ b/python/pyspark/streaming/context.py
@@ -142,9 +142,9 @@ def stop(self, stopSparkContext=True, stopGraceFully=False):
 
     def _testInputStream(self, test_inputs, numSlices=None):
         """
-        This function is only for test.
-        This implementation is inspired by QueStream implementation.
-        Give list of RDD to generate DStream which contains the RDD.
+        This function is only for unittest.
+        It requires a sequence as input, and returns the i_th element at the i_th batch
+        under manual clock.
         """
         test_rdds = list()
         test_rdd_deserializers = list()
@@ -152,7 +152,8 @@ def _testInputStream(self, test_inputs, numSlices=None):
             test_rdd = self._sc.parallelize(test_input, numSlices)
             test_rdds.append(test_rdd._jrdd)
             test_rdd_deserializers.append(test_rdd._jrdd_deserializer)
-
+        # All deserializer has to be the same.
+        # TODO: add deserializer validation
         jtest_rdds = ListConverter().convert(test_rdds, SparkContext._gateway._gateway_client)
         jinput_stream = self._jvm.PythonTestInputStream(self._jssc, jtest_rdds).asJavaDStream()
 
diff --git a/python/pyspark/streaming/dstream.py b/python/pyspark/streaming/dstream.py
index 0e2641e33032f..0b01a9f02f51f 100644
--- a/python/pyspark/streaming/dstream.py
+++ b/python/pyspark/streaming/dstream.py
@@ -283,23 +283,6 @@ def func(iterator):
             yield list(iterator)
         return self.mapPartitions(func)
 
-    #def transform(self, func): - TD
-    #    from utils import RDDFunction
-    #    wrapped_func = RDDFunction(self.ctx, self._jrdd_deserializer, func)
-    #    jdstream = self.ctx._jvm.PythonTransformedDStream(self._jdstream.dstream(), wrapped_func).toJavaDStream
-    #    return DStream(jdstream, self._ssc, ...)  ## DO NOT KNOW HOW
-
-    def _test_output(self, result):
-        """
-        This function is only for test case.
-        Store data in a DStream to result to verify the result in test case
-        """
-        def get_output(rdd, time):
-            taken = rdd.collect()
-            result.append(taken)
-
-        self.foreachRDD(get_output)
-
     def cache(self):
         """
         Persist this DStream with the default storage level (C{MEMORY_ONLY_SER}).
@@ -404,6 +387,17 @@ def saveAsTextFile(rdd, time):
 
         return self.foreachRDD(saveAsTextFile)
 
+    def _test_output(self, result):
+        """
+        This function is only for test case.
+        Store data in a DStream to result to verify the result in test case
+        """
+        def get_output(rdd, time):
+            collected = rdd.collect()
+            result.append(collected)
+
+        self.foreachRDD(get_output)
+
 
 # TODO: implement updateStateByKey
 # TODO: implement slice
diff --git a/python/pyspark/streaming/jtime.py b/python/pyspark/streaming/jtime.py
index 32ef741051283..f169228e81868 100644
--- a/python/pyspark/streaming/jtime.py
+++ b/python/pyspark/streaming/jtime.py
@@ -19,10 +19,11 @@
 from pyspark.streaming.duration import Duration
 
 """
-The name of this file, time is not good naming for python
+The name of this file, time is not a good naming for python
 because if we do import time when we want to use native python time package, it does
 not import python time package.
 """
+# TODO: add doctest
 
 
 class Time(object):
diff --git a/python/pyspark/streaming/utils.py b/python/pyspark/streaming/utils.py
index 9178577743e0b..5ba179cae7f9c 100644
--- a/python/pyspark/streaming/utils.py
+++ b/python/pyspark/streaming/utils.py
@@ -19,6 +19,9 @@
 
 
 class RDDFunction():
+    """
+    This class is for py4j callback. This
+    """
     def __init__(self, ctx, jrdd_deserializer, func):
         self.ctx = ctx
         self.deserializer = jrdd_deserializer
@@ -38,6 +41,7 @@ class Java:
 
 
 def msDurationToString(ms):
+    #TODO: add doctest
     """
     Returns a human-readable string representing a duration such as "35ms"
     """
@@ -54,8 +58,10 @@ def msDurationToString(ms):
     else:
         return "%.2f h" % (float(ms) / hour)
 
+
 def rddToFileName(prefix, suffix, time):
-    if suffix is not None:
-        return prefix + "-" + str(time) + "." + suffix
-    else:
+    #TODO: add doctest
+    if suffix is None:
         return prefix + "-" + str(time)
+    else:
+        return prefix + "-" + str(time) + "." + suffix
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
index 2c44f6cc1d42f..3c9fff6f4bf5c 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
@@ -18,10 +18,8 @@
 package org.apache.spark.streaming.api.python
 
 import java.io._
-import java.io.{ObjectInputStream, IOException}
-import java.util.{List => JList, ArrayList => JArrayList, Map => JMap, Collections}
+import java.util.{List => JList, ArrayList => JArrayList, Map => JMap}
 
-import scala.collection.mutable.ArrayBuffer
 import scala.reflect.ClassTag
 import scala.collection.JavaConversions._
 
@@ -55,7 +53,9 @@ class PythonDStream[T: ClassTag](
   override def compute(validTime: Time): Option[RDD[Array[Byte]]] = {
     parent.getOrCompute(validTime) match{
       case Some(rdd) =>
-        val pythonRDD = new PythonRDD(rdd, command, envVars, pythonIncludes, preservePartitoning, pythonExec, broadcastVars, accumulator)
+        // create PythonRDD to compute Python functions.
+        val pythonRDD = new PythonRDD(rdd, command, envVars, pythonIncludes,
+          preservePartitoning, pythonExec, broadcastVars, accumulator)
         Some(pythonRDD.asJavaRDD.rdd)
       case None => None
     }
@@ -135,8 +135,8 @@ DStream[Array[Byte]](prev.ssc){
       case Some(rdd)=>Some(rdd)
         val pairwiseRDD = new PairwiseRDD(rdd)
         /*
-         * Since python operation is executed by Scala after StreamingContext.start.
-         * What PythonPairwiseDStream does is equivalent to python code in pySpark.
+         * Since python function is executed by Scala after StreamingContext.start.
+         * What PythonPairwiseDStream does is equivalent to python code in pyspark.
          *
          * with _JavaStackTrace(self.context) as st:
          *    pairRDD = self.ctx._jvm.PairwiseRDD(keyed._jrdd.rdd()).asJavaPairRDD()
@@ -154,23 +154,6 @@ DStream[Array[Byte]](prev.ssc){
 }
 
 
-class PythonTestInputStream3(ssc_ : JavaStreamingContext)
-  extends InputDStream[Any](JavaStreamingContext.toStreamingContext(ssc_)) {
-
-  def start() {}
-
-  def stop() {}
-
-  def compute(validTime: Time): Option[RDD[Any]] = {
-    val index = ((validTime - zeroTime) / slideDuration - 1).toInt
-    val selectedInput = ArrayBuffer(1, 2, 3).toSeq
-    val rdd :RDD[Any] = ssc.sc.makeRDD(selectedInput, 2)
-    Some(rdd)
-  }
-
-  val asJavaDStream = JavaDStream.fromDStream(this)
-}
-
 class PythonForeachDStream(
     prev: DStream[Array[Byte]],
     foreachFunction: PythonRDDFunction
@@ -184,30 +167,11 @@ class PythonForeachDStream(
   this.register()
 }
 
-class PythonTransformedDStream(
-    prev: DStream[Array[Byte]],
-    transformFunction: PythonRDDFunction
-  ) extends DStream[Array[Byte]](prev.ssc) {
-
-  override def dependencies = List(prev)
-
-  override def slideDuration: Duration = prev.slideDuration
-
-  override def compute(validTime: Time): Option[RDD[Array[Byte]]] = {
-    prev.getOrCompute(validTime).map(rdd => {
-      transformFunction.call(rdd.toJavaRDD(), validTime.milliseconds).rdd
-    })
-  }
-
-  val asJavaDStream  = JavaDStream.fromDStream(this)
-  //val asJavaPairDStream : JavaPairDStream[Long, Array[Byte]]  = JavaPairDStream.fromJavaDStream(this)
-}
 
 /**
  * This is a input stream just for the unitest. This is equivalent to a checkpointable,
  * replayable, reliable message queue like Kafka. It requires a sequence as input, and
  * returns the i_th element at the i_th batch under manual clock.
- * This implementation is inspired by QueStream
  */
 
 class PythonTestInputStream(ssc_ : JavaStreamingContext, inputRDDs: JArrayList[JavaRDD[Array[Byte]]])
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonRDDFunction.java b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonRDDFunction.java
index 88f7036c3a05b..b46a644dacb7c 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonRDDFunction.java
+++ b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonRDDFunction.java
@@ -3,6 +3,10 @@
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.streaming.Time;
 
+/*
+ * Interface for py4j callback function.
+ * This function is called by pyspark.streaming.dstream.DStream.foreachRDD .
+ */
 public interface PythonRDDFunction {
   JavaRDD<byte[]> call(JavaRDD<byte[]> rdd, long time);
 }
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonTransformedDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonTransformedDStream.scala
deleted file mode 100644
index bc07e09ec6d03..0000000000000
--- a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonTransformedDStream.scala
+++ /dev/null
@@ -1,42 +0,0 @@
-/*
-
-package org.apache.spark.streaming.api.python
-
-import org.apache.spark.Accumulator
-import org.apache.spark.api.python.PythonRDD
-import org.apache.spark.broadcast.Broadcast
-import org.apache.spark.rdd.RDD
-import org.apache.spark.streaming.api.java.JavaDStream
-import org.apache.spark.streaming.{Time, Duration}
-import org.apache.spark.streaming.dstream.DStream
-
-import scala.reflect.ClassTag
-
-class PythonTransformedDStream[T: ClassTag](
-               parent: DStream[T],
-               command: Array[Byte],
-               envVars: JMap[String, String],
-               pythonIncludes: JList[String],
-               preservePartitoning: Boolean,
-               pythonExec: String,
-               broadcastVars: JList[Broadcast[Array[Byte]]],
-               accumulator: Accumulator[JList[Array[Byte]]]
-               ) extends DStream[Array[Byte]](parent.ssc) {
-
-  override def dependencies = List(parent)
-
-  override def slideDuration: Duration = parent.slideDuration
-
-  //pythonDStream compute
-  override def compute(validTime: Time): Option[RDD[Array[Byte]]] = {
-
-//    val parentRDDs = parents.map(_.getOrCompute(validTime).orNull).toSeq
-//    parents.map(_.getOrCompute(validTime).orNull).to
-//    parent = parents.head.asInstanceOf[RDD]
-//    Some()
-  }
-
-  val asJavaDStream = JavaDStream.fromDStream(this)
-}
-
-*/

From f198d14db09b2896b3450463340f0eecf7f9f802 Mon Sep 17 00:00:00 2001
From: giwa <ugw.gi.world@gmail.com>
Date: Wed, 20 Aug 2014 17:09:34 -0700
Subject: [PATCH 468/489] clean up code

---
 python/pyspark/streaming/context.py | 11 +++--------
 python/pyspark/streaming/dstream.py | 28 ++++++++++++++++------------
 2 files changed, 19 insertions(+), 20 deletions(-)

diff --git a/python/pyspark/streaming/context.py b/python/pyspark/streaming/context.py
index f7e356319ecac..dbb6fdf1694ad 100644
--- a/python/pyspark/streaming/context.py
+++ b/python/pyspark/streaming/context.py
@@ -72,7 +72,7 @@ def __init__(self, master=None, appName=None, sparkHome=None, pyFiles=None,
         # Callback sever is need only by SparkStreming; therefore the callback sever
         # is started in StreamingContext.
         SparkContext._gateway.restart_callback_server()
-        self._clean_up_trigger()
+        self._set_clean_up_trigger()
         self._jvm = self._sc._jvm
         self._jssc = self._initialize_context(self._sc._jsc, duration._jduration)
 
@@ -80,13 +80,11 @@ def __init__(self, master=None, appName=None, sparkHome=None, pyFiles=None,
     def _initialize_context(self, jspark_context, jduration):
         return self._jvm.JavaStreamingContext(jspark_context, jduration)
 
-    def _clean_up_trigger(self):
+    def _set_clean_up_trigger(self):
         """Kill py4j callback server properly using signal lib"""
 
         def clean_up_handler(*args):
             # Make sure stop callback server.
-            # This need improvement how to terminate callback sever properly.
-            SparkContext._gateway._shutdown_callback_server()
             SparkContext._gateway.shutdown()
             sys.exit(0)
 
@@ -132,18 +130,15 @@ def stop(self, stopSparkContext=True, stopGraceFully=False):
         Stop the execution of the streams immediately (does not wait for all received data
         to be processed).
         """
-        
         try:
             self._jssc.stop(stopSparkContext, stopGraceFully)
         finally:
-            # Stop Callback server
-            SparkContext._gateway._shutdown_callback_server()
             SparkContext._gateway.shutdown()
 
     def _testInputStream(self, test_inputs, numSlices=None):
         """
         This function is only for unittest.
-        It requires a sequence as input, and returns the i_th element at the i_th batch
+        It requires a list as input, and returns the i_th element at the i_th batch
         under manual clock.
         """
         test_rdds = list()
diff --git a/python/pyspark/streaming/dstream.py b/python/pyspark/streaming/dstream.py
index 0b01a9f02f51f..22a2751138c41 100644
--- a/python/pyspark/streaming/dstream.py
+++ b/python/pyspark/streaming/dstream.py
@@ -207,7 +207,7 @@ def _defaultReducePartitions(self):
         """
         Returns the default number of partitions to use during reduce tasks (e.g., groupBy).
         If spark.default.parallelism is set, then we'll use the value from SparkContext
-        defaultParallelism, otherwise we'll use the number of partitions in this RDD.
+        defaultParallelism, otherwise we'll use the number of partitions in this RDD
 
         This mirrors the behavior of the Scala Partitioner#defaultPartitioner, intended to reduce
         the likelihood of OOMs. Once PySpark adopts Partitioner-based APIs, this behavior will
@@ -222,7 +222,8 @@ def getNumPartitions(self):
         """
         Return the number of partitions in RDD
         """
-        # TODO: remove hardcoding. RDD has NumPartitions but DStream does not have.
+        # TODO: remove hardcoding. RDD has NumPartitions. How do we get the number of partition
+        # through DStream?
         return 2
 
     def foreachRDD(self, func):
@@ -243,6 +244,10 @@ def pyprint(self):
         operator, so this DStream will be registered as an output stream and there materialized.
         """
         def takeAndPrint(rdd, time):
+            """
+            Closure to take element from RDD and print first 10 elements.
+            This closure is called by py4j callback server.
+            """
             taken = rdd.take(11)
             print "-------------------------------------------"
             print "Time: %s" % (str(time))
@@ -307,17 +312,11 @@ def checkpoint(self, interval):
         Mark this DStream for checkpointing. It will be saved to a file inside the
         checkpoint directory set with L{SparkContext.setCheckpointDir()}
 
-        I am not sure this part in DStream
-        and
-        all references to its parent RDDs will be removed. This function must
-        be called before any job has been executed on this RDD. It is strongly
-        recommended that this RDD is persisted in memory, otherwise saving it
-        on a file will require recomputation.
-
-        interval must be pysprak.streaming.duration
+        @param interval: Time interval after which generated RDD will be checkpointed
+               interval has to be pyspark.streaming.duration.Duration
         """
         self.is_checkpointed = True
-        self._jdstream.checkpoint(interval)
+        self._jdstream.checkpoint(interval._jduration)
         return self
 
     def groupByKey(self, numPartitions=None):
@@ -369,6 +368,10 @@ def saveAsTextFiles(self, prefix, suffix=None):
         Save this DStream as a text file, using string representations of elements.
         """
         def saveAsTextFile(rdd, time):
+            """
+            Closure to save element in RDD in DStream as Pickled data in file.
+            This closure is called by py4j callback server.
+            """
             path = rddToFileName(prefix, suffix, time)
             rdd.saveAsTextFile(path)
 
@@ -410,9 +413,10 @@ def get_output(rdd, time):
 # TODO: implement countByWindow
 # TODO: implement reduceByWindow
 
-# Following operation has dependency to transform
+# transform Operation
 # TODO: implement transform
 # TODO: implement transformWith
+# Following operation has dependency with transform
 # TODO: implement union
 # TODO: implement repertitions
 # TODO: implement cogroup

From b171ec3a94834c8a9efc10510128a2fab2fe954f Mon Sep 17 00:00:00 2001
From: giwa <ugw.gi.world@gmail.com>
Date: Wed, 20 Aug 2014 18:43:08 -0700
Subject: [PATCH 469/489] fixed pep8 violation

---
 python/pyspark/streaming/context.py  |  10 +-
 python/pyspark/streaming/dstream.py  |   9 +-
 python/pyspark/streaming/duration.py |   3 +
 python/pyspark/streaming/tests.py    | 464 +++++++++++++++++++++++++++
 python/pyspark/worker.py             |   2 +-
 python/run-tests                     |   3 +
 6 files changed, 481 insertions(+), 10 deletions(-)
 create mode 100644 python/pyspark/streaming/tests.py

diff --git a/python/pyspark/streaming/context.py b/python/pyspark/streaming/context.py
index dbb6fdf1694ad..12023374333a2 100644
--- a/python/pyspark/streaming/context.py
+++ b/python/pyspark/streaming/context.py
@@ -33,8 +33,8 @@ class StreamingContext(object):
     """
 
     def __init__(self, master=None, appName=None, sparkHome=None, pyFiles=None,
-        environment=None, batchSize=1024, serializer=PickleSerializer(), conf=None,
-        gateway=None, sparkContext=None, duration=None):
+                 environment=None, batchSize=1024, serializer=PickleSerializer(), conf=None,
+                 gateway=None, sparkContext=None, duration=None):
         """
         Create a new StreamingContext. At least the master and app name and duration
         should be set, either through the named parameters here or through C{conf}.
@@ -63,8 +63,8 @@ def __init__(self, master=None, appName=None, sparkHome=None, pyFiles=None,
         if sparkContext is None:
             # Create the Python Sparkcontext
             self._sc = SparkContext(master=master, appName=appName, sparkHome=sparkHome,
-                            pyFiles=pyFiles, environment=environment, batchSize=batchSize,
-                            serializer=serializer, conf=conf, gateway=gateway)
+                                    pyFiles=pyFiles, environment=environment, batchSize=batchSize,
+                                    serializer=serializer, conf=conf, gateway=gateway)
         else:
             self._sc = sparkContext
 
@@ -107,7 +107,7 @@ def awaitTermination(self, timeout=None):
         else:
             self._jssc.awaitTermination(timeout)
 
-    #TODO: add storageLevel
+    # TODO: add storageLevel
     def socketTextStream(self, hostname, port):
         """
         Create an input from TCP source hostname:port. Data is received using
diff --git a/python/pyspark/streaming/dstream.py b/python/pyspark/streaming/dstream.py
index 22a2751138c41..d152485a3a17d 100644
--- a/python/pyspark/streaming/dstream.py
+++ b/python/pyspark/streaming/dstream.py
@@ -81,7 +81,8 @@ def filter(self, f):
         """
         Return a new DStream containing only the elements that satisfy predicate.
         """
-        def func(iterator): return ifilter(f, iterator)
+        def func(iterator):
+            return ifilter(f, iterator)
         return self.mapPartitions(func)
 
     def flatMap(self, f, preservesPartitioning=False):
@@ -136,7 +137,7 @@ def reduceByKey(self, func, numPartitions=None):
         return self.combineByKey(lambda x: x, func, func, numPartitions)
 
     def combineByKey(self, createCombiner, mergeValue, mergeCombiners,
-                      numPartitions = None):
+                     numPartitions=None):
         """
         Count the number of elements for each key, and return the result to the
         master as a dictionary
@@ -159,7 +160,7 @@ def combineLocally(iterator):
         def _mergeCombiners(iterator):
             combiners = {}
             for (k, v) in iterator:
-                if not k in combiners:
+                if k not in combiners:
                     combiners[k] = v
                 else:
                     combiners[k] = mergeCombiners(combiners[k], v)
@@ -194,7 +195,7 @@ def add_shuffle_key(split, iterator):
         keyed._bypass_serializer = True
         with _JavaStackTrace(self.ctx) as st:
             partitioner = self.ctx._jvm.PythonPartitioner(numPartitions,
-                                                      id(partitionFunc))
+                                                          id(partitionFunc))
             jdstream = self.ctx._jvm.PythonPairwiseDStream(keyed._jdstream.dstream(),
                                                            partitioner).asJavaDStream()
         dstream = DStream(jdstream, self._ssc, BatchedSerializer(outputSerializer))
diff --git a/python/pyspark/streaming/duration.py b/python/pyspark/streaming/duration.py
index a7f1036e4b856..85b28b14832b8 100644
--- a/python/pyspark/streaming/duration.py
+++ b/python/pyspark/streaming/duration.py
@@ -333,6 +333,7 @@ def _is_duration(self, instance):
         if not isinstance(instance, Duration):
             raise TypeError("This should be Duration")
 
+
 def Milliseconds(milliseconds):
     """
     Helper function that creates instance of [[pysparkstreaming.duration]] representing
@@ -346,6 +347,7 @@ def Milliseconds(milliseconds):
     """
     return Duration(milliseconds)
 
+
 def Seconds(seconds):
     """
     Helper function that creates instance of [[pysparkstreaming.duration]] representing
@@ -359,6 +361,7 @@ def Seconds(seconds):
     """
     return Duration(seconds * 1000)
 
+
 def Minutes(minutes):
     """
     Helper function that creates instance of [[pysparkstreaming.duration]] representing
diff --git a/python/pyspark/streaming/tests.py b/python/pyspark/streaming/tests.py
new file mode 100644
index 0000000000000..2ed099b1004c3
--- /dev/null
+++ b/python/pyspark/streaming/tests.py
@@ -0,0 +1,464 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""
+Unit tests for PySpark; additional tests are implemented as doctests in
+individual modules.
+
+This file would be merged to tests.py after all functions are ready.
+Since python API for streaming is beta, this file is separated.
+
+Callback server is sometimes unstable sometimes, which cause error in test case.
+But this is very rare case.
+
+"""
+from itertools import chain
+import time
+import operator
+import sys
+
+if sys.version_info[:2] <= (2, 6):
+    import unittest2 as unittest
+else:
+    import unittest
+
+from pyspark.context import SparkContext
+from pyspark.streaming.context import StreamingContext
+from pyspark.streaming.duration import *
+
+
+class PySparkStreamingTestCase(unittest.TestCase):
+    def setUp(self):
+        class_name = self.__class__.__name__
+        self.ssc = StreamingContext(appName=class_name, duration=Seconds(1))
+
+    def tearDown(self):
+        # Do not call pyspark.streaming.context.StreamingContext.stop directly because
+        # we do not wait to shutdown py4j client.
+        self.ssc._jssc.stop()
+        self.ssc._sc.stop()
+        # Why does it long time to terminate StremaingContext and SparkContext?
+        # Should we change the sleep time if this depends on machine spec?
+        time.sleep(1)
+
+    @classmethod
+    def tearDownClass(cls):
+        # Make sure tp shutdown the callback server
+        SparkContext._gateway._shutdown_callback_server()
+
+
+class TestBasicOperationsSuite(PySparkStreamingTestCase):
+    """
+    2 tests for each function for batach deserializer and unbatch deserilizer because
+    the deserializer is not changed dunamically after streaming process starts.
+    Default numInputPartitions is 2.
+    If the number of input element is over 3, that DStream use batach deserializer.
+    If not, that DStream use unbatch deserializer.
+
+    All tests input should have list of lists(3 lists are default). This list represents stream.
+    Every batch interval, the first object of list are chosen to make DStream.
+    e.g The first list in the list is input of the first batch.
+    Please see the BasicTestSuits in Scala which is close to this implementation.
+    """
+    def setUp(self):
+        PySparkStreamingTestCase.setUp(self)
+        self.timeout = 10  # seconds
+        self.numInputPartitions = 2
+
+    def tearDown(self):
+        PySparkStreamingTestCase.tearDown(self)
+
+    @classmethod
+    def tearDownClass(cls):
+        PySparkStreamingTestCase.tearDownClass()
+
+    def test_map_batch(self):
+        """Basic operation test for DStream.map with batch deserializer."""
+        test_input = [range(1, 5), range(5, 9), range(9, 13)]
+
+        def test_func(dstream):
+            return dstream.map(lambda x: str(x))
+        expected_output = map(lambda x: map(lambda y: str(y), x), test_input)
+        output = self._run_stream(test_input, test_func, expected_output)
+        self.assertEqual(expected_output, output)
+
+    def test_map_unbatach(self):
+        """Basic operation test for DStream.map with unbatch deserializer."""
+        test_input = [range(1, 4), range(4, 7), range(7, 10)]
+
+        def test_func(dstream):
+            return dstream.map(lambda x: str(x))
+        expected_output = map(lambda x: map(lambda y: str(y), x), test_input)
+        output = self._run_stream(test_input, test_func, expected_output)
+        self.assertEqual(expected_output, output)
+
+    def test_flatMap_batch(self):
+        """Basic operation test for DStream.faltMap with batch deserializer."""
+        test_input = [range(1, 5), range(5, 9), range(9, 13)]
+
+        def test_func(dstream):
+            return dstream.flatMap(lambda x: (x, x * 2))
+        expected_output = map(lambda x: list(chain.from_iterable((map(lambda y: [y, y * 2], x)))),
+                              test_input)
+        output = self._run_stream(test_input, test_func, expected_output)
+        self.assertEqual(expected_output, output)
+
+    def test_flatMap_unbatch(self):
+        """Basic operation test for DStream.faltMap with unbatch deserializer."""
+        test_input = [range(1, 4), range(4, 7), range(7, 10)]
+
+        def test_func(dstream):
+            return dstream.flatMap(lambda x: (x, x * 2))
+        expected_output = map(lambda x: list(chain.from_iterable((map(lambda y: [y, y * 2], x)))),
+                              test_input)
+        output = self._run_stream(test_input, test_func, expected_output)
+        self.assertEqual(expected_output, output)
+
+    def test_filter_batch(self):
+        """Basic operation test for DStream.filter with batch deserializer."""
+        test_input = [range(1, 5), range(5, 9), range(9, 13)]
+
+        def test_func(dstream):
+            return dstream.filter(lambda x: x % 2 == 0)
+        expected_output = map(lambda x: filter(lambda y: y % 2 == 0, x), test_input)
+        output = self._run_stream(test_input, test_func, expected_output)
+        self.assertEqual(expected_output, output)
+
+    def test_filter_unbatch(self):
+        """Basic operation test for DStream.filter with unbatch deserializer."""
+        test_input = [range(1, 4), range(4, 7), range(7, 10)]
+
+        def test_func(dstream):
+            return dstream.filter(lambda x: x % 2 == 0)
+        expected_output = map(lambda x: filter(lambda y: y % 2 == 0, x), test_input)
+        output = self._run_stream(test_input, test_func, expected_output)
+        self.assertEqual(expected_output, output)
+
+    def test_count_batch(self):
+        """Basic operation test for DStream.count with batch deserializer."""
+        test_input = [range(1, 5), range(1, 10), range(1, 20)]
+
+        def test_func(dstream):
+            return dstream.count()
+        expected_output = map(lambda x: [len(x)], test_input)
+        output = self._run_stream(test_input, test_func, expected_output)
+        self.assertEqual(expected_output, output)
+
+    def test_count_unbatch(self):
+        """Basic operation test for DStream.count with unbatch deserializer."""
+        test_input = [[], [1], range(1, 3), range(1, 4)]
+
+        def test_func(dstream):
+            return dstream.count()
+        expected_output = map(lambda x: [len(x)], test_input)
+        output = self._run_stream(test_input, test_func, expected_output)
+        self.assertEqual(expected_output, output)
+
+    def test_reduce_batch(self):
+        """Basic operation test for DStream.reduce with batch deserializer."""
+        test_input = [range(1, 5), range(5, 9), range(9, 13)]
+
+        def test_func(dstream):
+            return dstream.reduce(operator.add)
+        expected_output = map(lambda x: [reduce(operator.add, x)], test_input)
+        output = self._run_stream(test_input, test_func, expected_output)
+        self.assertEqual(expected_output, output)
+
+    def test_reduce_unbatch(self):
+        """Basic operation test for DStream.reduce with unbatch deserializer."""
+        test_input = [[1], range(1, 3), range(1, 4)]
+
+        def test_func(dstream):
+            return dstream.reduce(operator.add)
+        expected_output = map(lambda x: [reduce(operator.add, x)], test_input)
+        output = self._run_stream(test_input, test_func, expected_output)
+        self.assertEqual(expected_output, output)
+
+    def test_reduceByKey_batch(self):
+        """Basic operation test for DStream.reduceByKey with batch deserializer."""
+        test_input = [[("a", 1), ("a", 1), ("b", 1), ("b", 1)],
+                      [("", 1), ("", 1), ("", 1), ("", 1)],
+                      [(1, 1), (1, 1), (2, 1), (2, 1), (3, 1)]]
+
+        def test_func(dstream):
+            return dstream.reduceByKey(operator.add)
+        expected_output = [[("a", 2), ("b", 2)], [("", 4)], [(1, 2), (2, 2), (3, 1)]]
+        output = self._run_stream(test_input, test_func, expected_output)
+        for result in (output, expected_output):
+            self._sort_result_based_on_key(result)
+        self.assertEqual(expected_output, output)
+
+    def test_reduceByKey_unbatch(self):
+        """Basic operation test for DStream.reduceByKey with unbatch deserializer."""
+        test_input = [[("a", 1), ("a", 1), ("b", 1)], [("", 1), ("", 1)], []]
+
+        def test_func(dstream):
+            return dstream.reduceByKey(operator.add)
+        expected_output = [[("a", 2), ("b", 1)], [("", 2)], []]
+        output = self._run_stream(test_input, test_func, expected_output)
+        for result in (output, expected_output):
+            self._sort_result_based_on_key(result)
+        self.assertEqual(expected_output, output)
+
+    def test_mapValues_batch(self):
+        """Basic operation test for DStream.mapValues with batch deserializer."""
+        test_input = [[("a", 2), ("b", 2), ("c", 1), ("d", 1)],
+                      [("", 4), (1, 1), (2, 2), (3, 3)],
+                      [(1, 1), (2, 1), (3, 1), (4, 1)]]
+
+        def test_func(dstream):
+            return dstream.mapValues(lambda x: x + 10)
+        expected_output = [[("a", 12), ("b", 12), ("c", 11), ("d", 11)],
+                           [("", 14), (1, 11), (2, 12), (3, 13)],
+                           [(1, 11), (2, 11), (3, 11), (4, 11)]]
+        output = self._run_stream(test_input, test_func, expected_output)
+        for result in (output, expected_output):
+            self._sort_result_based_on_key(result)
+        self.assertEqual(expected_output, output)
+
+    def test_mapValues_unbatch(self):
+        """Basic operation test for DStream.mapValues with unbatch deserializer."""
+        test_input = [[("a", 2), ("b", 1)], [("", 2)], [], [(1, 1), (2, 2)]]
+
+        def test_func(dstream):
+            return dstream.mapValues(lambda x: x + 10)
+        expected_output = [[("a", 12), ("b", 11)], [("", 12)], [], [(1, 11), (2, 12)]]
+        output = self._run_stream(test_input, test_func, expected_output)
+        for result in (output, expected_output):
+            self._sort_result_based_on_key(result)
+        self.assertEqual(expected_output, output)
+
+    def test_flatMapValues_batch(self):
+        """Basic operation test for DStream.flatMapValues with batch deserializer."""
+        test_input = [[("a", 2), ("b", 2), ("c", 1), ("d", 1)],
+                      [("", 4), (1, 1), (2, 1), (3, 1)],
+                      [(1, 1), (2, 1), (3, 1), (4, 1)]]
+
+        def test_func(dstream):
+            return dstream.flatMapValues(lambda x: (x, x + 10))
+        expected_output = [[("a", 2), ("a", 12), ("b", 2), ("b", 12),
+                            ("c", 1), ("c", 11), ("d", 1), ("d", 11)],
+                           [("", 4), ("", 14), (1, 1), (1, 11), (2, 1), (2, 11), (3, 1), (3, 11)],
+                           [(1, 1), (1, 11), (2, 1), (2, 11), (3, 1), (3, 11), (4, 1), (4, 11)]]
+        output = self._run_stream(test_input, test_func, expected_output)
+        self.assertEqual(expected_output, output)
+
+    def test_flatMapValues_unbatch(self):
+        """Basic operation test for DStream.flatMapValues with unbatch deserializer."""
+        test_input = [[("a", 2), ("b", 1)], [("", 2)], []]
+
+        def test_func(dstream):
+            return dstream.flatMapValues(lambda x: (x, x + 10))
+        expected_output = [[("a", 2), ("a", 12), ("b", 1), ("b", 11)], [("", 2), ("", 12)], []]
+        output = self._run_stream(test_input, test_func, expected_output)
+        self.assertEqual(expected_output, output)
+
+    def test_glom_batch(self):
+        """Basic operation test for DStream.glom with batch deserializer."""
+        test_input = [range(1, 5), range(5, 9), range(9, 13)]
+        numSlices = 2
+
+        def test_func(dstream):
+            return dstream.glom()
+        expected_output = [[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]]
+        output = self._run_stream(test_input, test_func, expected_output, numSlices)
+        self.assertEqual(expected_output, output)
+
+    def test_glom_unbatach(self):
+        """Basic operation test for DStream.glom with unbatch deserializer."""
+        test_input = [range(1, 4), range(4, 7), range(7, 10)]
+        numSlices = 2
+
+        def test_func(dstream):
+            return dstream.glom()
+        expected_output = [[[1], [2, 3]], [[4], [5, 6]], [[7], [8, 9]]]
+        output = self._run_stream(test_input, test_func, expected_output, numSlices)
+        self.assertEqual(expected_output, output)
+
+    def test_mapPartitions_batch(self):
+        """Basic operation test for DStream.mapPartitions with batch deserializer."""
+        test_input = [range(1, 5), range(5, 9), range(9, 13)]
+        numSlices = 2
+
+        def test_func(dstream):
+            def f(iterator):
+                yield sum(iterator)
+            return dstream.mapPartitions(f)
+        expected_output = [[3, 7], [11, 15], [19, 23]]
+        output = self._run_stream(test_input, test_func, expected_output, numSlices)
+        self.assertEqual(expected_output, output)
+
+    def test_mapPartitions_unbatch(self):
+        """Basic operation test for DStream.mapPartitions with unbatch deserializer."""
+        test_input = [range(1, 4), range(4, 7), range(7, 10)]
+        numSlices = 2
+
+        def test_func(dstream):
+            def f(iterator):
+                yield sum(iterator)
+            return dstream.mapPartitions(f)
+        expected_output = [[1, 5], [4, 11], [7, 17]]
+        output = self._run_stream(test_input, test_func, expected_output, numSlices)
+        self.assertEqual(expected_output, output)
+
+    def test_countByValue_batch(self):
+        """Basic operation test for DStream.countByValue with batch deserializer."""
+        test_input = [range(1, 5) * 2, range(5, 7) + range(5, 9), ["a", "a", "b", ""]]
+
+        def test_func(dstream):
+            return dstream.countByValue()
+        expected_output = [[(1, 2), (2, 2), (3, 2), (4, 2)],
+                           [(5, 2), (6, 2), (7, 1), (8, 1)],
+                           [("a", 2), ("b", 1), ("", 1)]]
+        output = self._run_stream(test_input, test_func, expected_output)
+        for result in (output, expected_output):
+            self._sort_result_based_on_key(result)
+        self.assertEqual(expected_output, output)
+
+    def test_countByValue_unbatch(self):
+        """Basic operation test for DStream.countByValue with unbatch deserializer."""
+        test_input = [range(1, 4), [1, 1, ""], ["a", "a", "b"]]
+
+        def test_func(dstream):
+            return dstream.countByValue()
+        expected_output = [[(1, 1), (2, 1), (3, 1)],
+                           [(1, 2), ("", 1)],
+                           [("a", 2), ("b", 1)]]
+        output = self._run_stream(test_input, test_func, expected_output)
+        for result in (output, expected_output):
+            self._sort_result_based_on_key(result)
+        self.assertEqual(expected_output, output)
+
+    def test_groupByKey_batch(self):
+        """Basic operation test for DStream.groupByKey with batch deserializer."""
+        test_input = [[(1, 1), (2, 1), (3, 1), (4, 1)],
+                      [(1, 1), (1, 1), (1, 1), (2, 1), (2, 1), (3, 1)],
+                      [("a", 1), ("a", 1), ("b", 1), ("", 1), ("", 1), ("", 1)]]
+
+        def test_func(dstream):
+            return dstream.groupByKey()
+        expected_output = [[(1, [1]), (2, [1]), (3, [1]), (4, [1])],
+                           [(1, [1, 1, 1]), (2, [1, 1]), (3, [1])],
+                           [("a", [1, 1]), ("b", [1]), ("", [1, 1, 1])]]
+        scattered_output = self._run_stream(test_input, test_func, expected_output)
+        output = self._convert_iter_value_to_list(scattered_output)
+        for result in (output, expected_output):
+            self._sort_result_based_on_key(result)
+        self.assertEqual(expected_output, output)
+
+    def test_groupByKey_unbatch(self):
+        """Basic operation test for DStream.groupByKey with unbatch deserializer."""
+        test_input = [[(1, 1), (2, 1), (3, 1)],
+                      [(1, 1), (1, 1), ("", 1)],
+                      [("a", 1), ("a", 1), ("b", 1)]]
+
+        def test_func(dstream):
+            return dstream.groupByKey()
+        expected_output = [[(1, [1]), (2, [1]), (3, [1])],
+                           [(1, [1, 1]), ("", [1])],
+                           [("a", [1, 1]), ("b", [1])]]
+        scattered_output = self._run_stream(test_input, test_func, expected_output)
+        output = self._convert_iter_value_to_list(scattered_output)
+        for result in (output, expected_output):
+            self._sort_result_based_on_key(result)
+        self.assertEqual(expected_output, output)
+
+    def test_combineByKey_batch(self):
+        """Basic operation test for DStream.combineByKey with batch deserializer."""
+        test_input = [[(1, 1), (2, 1), (3, 1), (4, 1)],
+                      [(1, 1), (1, 1), (1, 1), (2, 1), (2, 1), (3, 1)],
+                      [("a", 1), ("a", 1), ("b", 1), ("", 1), ("", 1), ("", 1)]]
+
+        def test_func(dstream):
+            def add(a, b):
+                return a + str(b)
+            return dstream.combineByKey(str, add, add)
+        expected_output = [[(1, "1"), (2, "1"), (3, "1"), (4, "1")],
+                           [(1, "111"), (2, "11"), (3, "1")],
+                           [("a", "11"), ("b", "1"), ("", "111")]]
+        output = self._run_stream(test_input, test_func, expected_output)
+        for result in (output, expected_output):
+            self._sort_result_based_on_key(result)
+        self.assertEqual(expected_output, output)
+
+    def test_combineByKey_unbatch(self):
+        """Basic operation test for DStream.combineByKey with unbatch deserializer."""
+        test_input = [[(1, 1), (2, 1), (3, 1)],
+                      [(1, 1), (1, 1), ("", 1)],
+                      [("a", 1),  ("a", 1), ("b", 1)]]
+
+        def test_func(dstream):
+            def add(a, b):
+                return a + str(b)
+            return dstream.combineByKey(str, add, add)
+        expected_output = [[(1, "1"), (2, "1"), (3, "1")],
+                           [(1, "11"), ("", "1")],
+                           [("a", "11"), ("b", "1")]]
+        output = self._run_stream(test_input, test_func, expected_output)
+        for result in (output, expected_output):
+            self._sort_result_based_on_key(result)
+        self.assertEqual(expected_output, output)
+
+    def _convert_iter_value_to_list(self, outputs):
+        """Return key value pair list. Value is converted to iterator to list."""
+        result = list()
+        for output in outputs:
+            result.append(map(lambda (x, y): (x, list(y)), output))
+        return result
+
+    def _sort_result_based_on_key(self, outputs):
+        """Sort the list base onf first value."""
+        for output in outputs:
+            output.sort(key=lambda x: x[0])
+
+    def _run_stream(self, test_input, test_func, expected_output, numSlices=None):
+        """
+        Start stream and return the result.
+        @param test_input: dataset for the test. This should be list of lists.
+        @param test_func: wrapped test_function. This function should return PythonDStream object.
+        @param expected_output: expected output for this testcase.
+        @param numSlices: the number of slices in the rdd in the dstream.
+        """
+        # Generate input stream with user-defined input.
+        numSlices = numSlices or self.numInputPartitions
+        test_input_stream = self.ssc._testInputStream(test_input, numSlices)
+        # Apply test function to stream.
+        test_stream = test_func(test_input_stream)
+        # Add job to get output from stream.
+        result = list()
+        test_stream._test_output(result)
+        self.ssc.start()
+
+        start_time = time.time()
+        # Loop until get the expected the number of the result from the stream.
+        while True:
+            current_time = time.time()
+            # Check time out.
+            if (current_time - start_time) > self.timeout:
+                break
+            # StreamingContext.awaitTermination is not used to wait because
+            # if py4j server is called every 50 milliseconds, it gets an error.
+            time.sleep(0.05)
+            # Check if the output is the same length of expected output.
+            if len(expected_output) == len(result):
+                break
+
+        return result
+
+if __name__ == "__main__":
+    unittest.main()
+    SparkContext._gateway._shutdown_callback_server()
diff --git a/python/pyspark/worker.py b/python/pyspark/worker.py
index 90ea7b453d401..d6c06e2dbef62 100644
--- a/python/pyspark/worker.py
+++ b/python/pyspark/worker.py
@@ -61,7 +61,7 @@ def main(infile, outfile):
         SparkFiles._is_running_on_worker = True
 
         # fetch names of includes (*.zip and *.egg files) and construct PYTHONPATH
-        sys.path.append(spark_files_dir) # *.py files that were added will be copied here
+        sys.path.append(spark_files_dir)  # *.py files that were added will be copied here
         num_python_includes = read_int(infile)
         for _ in range(num_python_includes):
             filename = utf8_deserializer.loads(infile)
diff --git a/python/run-tests b/python/run-tests
index 27f47edaebbad..d1e2a170ed2a5 100755
--- a/python/run-tests
+++ b/python/run-tests
@@ -84,6 +84,9 @@ run_test "pyspark/mllib/stat.py"
 run_test "pyspark/mllib/tests.py"
 run_test "pyspark/mllib/tree.py"
 run_test "pyspark/mllib/util.py"
+if [ -n "$_RUN_STREAMING_TESTS" ]; then
+  run_test "pyspark/streaming/tests.py"
+fi
 
 # Try to test with PyPy
 if [ $(which pypy) ]; then

From f04882c9ad90b0af9cf0e1f8037e84df0c81f74f Mon Sep 17 00:00:00 2001
From: giwa <ugw.gi.world@gmail.com>
Date: Wed, 20 Aug 2014 18:54:22 -0700
Subject: [PATCH 470/489] clen up examples

---
 .../src/main/python/streaming/network_wordcount.py     |  8 +++-----
 examples/src/main/python/streaming/wordcount.py        | 10 +++-------
 2 files changed, 6 insertions(+), 12 deletions(-)

diff --git a/examples/src/main/python/streaming/network_wordcount.py b/examples/src/main/python/streaming/network_wordcount.py
index 9b7af07803b4d..f242f8d29658a 100644
--- a/examples/src/main/python/streaming/network_wordcount.py
+++ b/examples/src/main/python/streaming/network_wordcount.py
@@ -1,7 +1,6 @@
 import sys
 from operator import add
 
-from pyspark.conf import SparkConf
 from pyspark.streaming.context import StreamingContext
 from pyspark.streaming.duration import *
 
@@ -9,15 +8,14 @@
     if len(sys.argv) != 3:
         print >> sys.stderr, "Usage: wordcount <hostname> <port>"
         exit(-1)
-    conf = SparkConf()
-    conf.setAppName("PythonStreamingNetworkWordCount")
-    ssc = StreamingContext(conf=conf, duration=Seconds(1))
+    ssc = StreamingContext(appName="PythonStreamingNetworkWordCount", 
+                           duration=Seconds(1))
 
     lines = ssc.socketTextStream(sys.argv[1], int(sys.argv[2]))
     words = lines.flatMap(lambda line: line.split(" "))
     mapped_words = words.map(lambda word: (word, 1))
     count = mapped_words.reduceByKey(add)
-
     count.pyprint()
+
     ssc.start()
     ssc.awaitTermination()
diff --git a/examples/src/main/python/streaming/wordcount.py b/examples/src/main/python/streaming/wordcount.py
index 2426345711086..e0600bbdb34d2 100644
--- a/examples/src/main/python/streaming/wordcount.py
+++ b/examples/src/main/python/streaming/wordcount.py
@@ -1,7 +1,5 @@
 import sys
-from operator import add
 
-from pyspark.conf import SparkConf
 from pyspark.streaming.context import StreamingContext
 from pyspark.streaming.duration import *
 
@@ -9,16 +7,14 @@
     if len(sys.argv) != 2:
         print >> sys.stderr, "Usage: wordcount <directory>"
         exit(-1)
-    conf = SparkConf()
-    conf.setAppName("PythonStreamingWordCount")
 
-    ssc = StreamingContext(conf=conf, duration=Seconds(1))
+    ssc = StreamingContext(appName="PythonStreamingWordCount", duration=Seconds(1))
 
     lines = ssc.textFileStream(sys.argv[1])
     words = lines.flatMap(lambda line: line.split(" "))
     mapped_words = words.map(lambda x: (x, 1))
-    count = mapped_words.reduceByKey(add)
-    
+    count = mapped_words.reduceByKey(lambda a, b: a+b)
     count.pyprint()
+
     ssc.start()
     ssc.awaitTermination()

From 62dc7a3712e8f8e9983bcb1e8597fe8943acbea4 Mon Sep 17 00:00:00 2001
From: giwa <ugw.gi.world@gmail.com>
Date: Wed, 20 Aug 2014 19:06:03 -0700
Subject: [PATCH 471/489] clean up exmples

---
 .../src/main/python/streaming/network_wordcount.py    |  9 ++++-----
 examples/src/main/python/streaming/wordcount.py       | 11 ++++++-----
 2 files changed, 10 insertions(+), 10 deletions(-)

diff --git a/examples/src/main/python/streaming/network_wordcount.py b/examples/src/main/python/streaming/network_wordcount.py
index f242f8d29658a..cd2a8a73de63b 100644
--- a/examples/src/main/python/streaming/network_wordcount.py
+++ b/examples/src/main/python/streaming/network_wordcount.py
@@ -1,5 +1,4 @@
 import sys
-from operator import add
 
 from pyspark.streaming.context import StreamingContext
 from pyspark.streaming.duration import *
@@ -12,10 +11,10 @@
                            duration=Seconds(1))
 
     lines = ssc.socketTextStream(sys.argv[1], int(sys.argv[2]))
-    words = lines.flatMap(lambda line: line.split(" "))
-    mapped_words = words.map(lambda word: (word, 1))
-    count = mapped_words.reduceByKey(add)
-    count.pyprint()
+    counts = lines.flatMap(lambda line: line.split(" "))\
+                  .map(lambda word: (word, 1))\
+                  .reduceByKey(lambda a,b: a+b)
+    counts.pyprint()
 
     ssc.start()
     ssc.awaitTermination()
diff --git a/examples/src/main/python/streaming/wordcount.py b/examples/src/main/python/streaming/wordcount.py
index e0600bbdb34d2..4c62835ed8025 100644
--- a/examples/src/main/python/streaming/wordcount.py
+++ b/examples/src/main/python/streaming/wordcount.py
@@ -8,13 +8,14 @@
         print >> sys.stderr, "Usage: wordcount <directory>"
         exit(-1)
 
-    ssc = StreamingContext(appName="PythonStreamingWordCount", duration=Seconds(1))
+    ssc = StreamingContext(appName="PythonStreamingWordCount",
+                           duration=Seconds(1))
 
     lines = ssc.textFileStream(sys.argv[1])
-    words = lines.flatMap(lambda line: line.split(" "))
-    mapped_words = words.map(lambda x: (x, 1))
-    count = mapped_words.reduceByKey(lambda a, b: a+b)
-    count.pyprint()
+    counts = lines.flatMap(lambda line: line.split(" "))\
+                  .map(lambda x: (x, 1))\
+                  .reduceByKey(lambda a, b: a+b)
+    counts.pyprint()
 
     ssc.start()
     ssc.awaitTermination()

From 7dc73914439383a29afbe1fa464698f153e875c5 Mon Sep 17 00:00:00 2001
From: giwa <ugw.gi.world@gmail.com>
Date: Wed, 20 Aug 2014 19:21:37 -0700
Subject: [PATCH 472/489] fixed typo

---
 .../src/main/scala/org/apache/spark/deploy/PythonRunner.scala | 1 -
 python/pyspark/java_gateway.py                                | 1 +
 python/pyspark/streaming/dstream.py                           | 4 ++--
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/deploy/PythonRunner.scala b/core/src/main/scala/org/apache/spark/deploy/PythonRunner.scala
index dc68b1fbda8bb..b66c3ba4d5fb0 100644
--- a/core/src/main/scala/org/apache/spark/deploy/PythonRunner.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/PythonRunner.scala
@@ -57,7 +57,6 @@ object PythonRunner {
     val builder = new ProcessBuilder(Seq(pythonExec, "-u", formattedPythonFile) ++ otherArgs)
     val env = builder.environment()
     env.put("PYTHONPATH", pythonPath)
-    env.put("PYSPARK_PYTHON", pythonExec)
     env.put("PYSPARK_GATEWAY_PORT", "" + gatewayServer.getListeningPort)
     builder.redirectErrorStream(true) // Ugly but needed for stdout and stderr to synchronize
     val process = builder.start()
diff --git a/python/pyspark/java_gateway.py b/python/pyspark/java_gateway.py
index f3c6d231ab777..6650a4cd10be4 100644
--- a/python/pyspark/java_gateway.py
+++ b/python/pyspark/java_gateway.py
@@ -118,4 +118,5 @@ def run(self):
     java_import(gateway.jvm, "org.apache.spark.sql.hive.LocalHiveContext")
     java_import(gateway.jvm, "org.apache.spark.sql.hive.TestHiveContext")
     java_import(gateway.jvm, "scala.Tuple2")
+
     return gateway
diff --git a/python/pyspark/streaming/dstream.py b/python/pyspark/streaming/dstream.py
index d152485a3a17d..f75d7c7f1b259 100644
--- a/python/pyspark/streaming/dstream.py
+++ b/python/pyspark/streaming/dstream.py
@@ -414,7 +414,7 @@ def get_output(rdd, time):
 # TODO: implement countByWindow
 # TODO: implement reduceByWindow
 
-# transform Operation
+# Transform Operation
 # TODO: implement transform
 # TODO: implement transformWith
 # Following operation has dependency with transform
@@ -423,7 +423,7 @@ def get_output(rdd, time):
 # TODO: implement cogroup
 # TODO: implement join
 # TODO: implement leftOuterJoin
-# TODO: implemtnt rightOuterJoin
+# TODO: implement rightOuterJoin
 
 
 class PipelinedDStream(DStream):

From 6ae3caa089341521f98af5ac2a7be4126d3fda31 Mon Sep 17 00:00:00 2001
From: giwa <ugw.gi.world@gmail.com>
Date: Wed, 20 Aug 2014 19:28:03 -0700
Subject: [PATCH 473/489] revert pom.xml

---
 python/pyspark/streaming/context.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/python/pyspark/streaming/context.py b/python/pyspark/streaming/context.py
index 12023374333a2..6b6cc653dfa6f 100644
--- a/python/pyspark/streaming/context.py
+++ b/python/pyspark/streaming/context.py
@@ -147,7 +147,7 @@ def _testInputStream(self, test_inputs, numSlices=None):
             test_rdd = self._sc.parallelize(test_input, numSlices)
             test_rdds.append(test_rdd._jrdd)
             test_rdd_deserializers.append(test_rdd._jrdd_deserializer)
-        # All deserializer has to be the same.
+        # All deserializers have to be the same.
         # TODO: add deserializer validation
         jtest_rdds = ListConverter().convert(test_rdds, SparkContext._gateway._gateway_client)
         jinput_stream = self._jvm.PythonTestInputStream(self._jssc, jtest_rdds).asJavaDStream()

From fa4af88be1c68f11a5581706c397ea5ef3cd7f61 Mon Sep 17 00:00:00 2001
From: giwa <ugw.gi.world@gmail.com>
Date: Wed, 20 Aug 2014 19:30:36 -0700
Subject: [PATCH 474/489] remove duplicated import

---
 .../scala/org/apache/spark/streaming/dstream/DStream.scala     | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala
index 39ad591e8896e..e05db236addca 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala
@@ -17,6 +17,7 @@
 
 package org.apache.spark.streaming.dstream
 
+
 import java.io.{IOException, ObjectInputStream, ObjectOutputStream}
 
 import scala.deprecated
@@ -30,7 +31,6 @@ import org.apache.spark.streaming._
 import org.apache.spark.streaming.StreamingContext._
 import org.apache.spark.streaming.scheduler.Job
 import org.apache.spark.util.MetadataCleaner
-import org.apache.spark.streaming.Duration
 
 /**
  * A Discretized Stream (DStream), the basic abstraction in Spark Streaming, is a continuous
@@ -557,7 +557,6 @@ abstract class DStream[T: ClassTag] (
     // because the DStream is reachable from the outer object here, and because 
     // DStreams can't be serialized with closures, we can't proactively check 
     // it for serializability and so we pass the optional false to SparkContext.clean
-
     val cleanedF = context.sparkContext.clean(transformFunc, false)
     val realTransformFunc =  (rdds: Seq[RDD[_]], time: Time) => {
       assert(rdds.length == 1)

From 066ba90666e006c604f027474582460bed48aade Mon Sep 17 00:00:00 2001
From: giwa <ugw.gi.world@gmail.com>
Date: Wed, 20 Aug 2014 19:33:13 -0700
Subject: [PATCH 475/489] revert pom.xml

---
 streaming/pom.xml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/streaming/pom.xml b/streaming/pom.xml
index 483e200ff9f16..4d41efb590233 100644
--- a/streaming/pom.xml
+++ b/streaming/pom.xml
@@ -79,7 +79,7 @@
       </plugin>
 
       <!--
-           This plugin forces the generation of jar containing streaming test classes,
+           This plugin forces the generation of jar containing streaming test classes, 
            so that the tests classes of external modules can use them. The two execution profiles
            are necessary - first one for 'mvn package', second one for 'mvn test-compile'. Ideally,
            'mvn compile' should not compile test classes and therefore should not need this. 

From 8ed93af06232d072f0c401b6255e3c1f3c4df561 Mon Sep 17 00:00:00 2001
From: giwa <ugw.gi.world@gmail.com>
Date: Wed, 20 Aug 2014 19:34:20 -0700
Subject: [PATCH 476/489] fixed explanaiton

---
 streaming/pom.xml                                               | 2 +-
 .../apache/spark/streaming/api/python/PythonRDDFunction.java    | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/streaming/pom.xml b/streaming/pom.xml
index 4d41efb590233..483e200ff9f16 100644
--- a/streaming/pom.xml
+++ b/streaming/pom.xml
@@ -79,7 +79,7 @@
       </plugin>
 
       <!--
-           This plugin forces the generation of jar containing streaming test classes, 
+           This plugin forces the generation of jar containing streaming test classes,
            so that the tests classes of external modules can use them. The two execution profiles
            are necessary - first one for 'mvn package', second one for 'mvn test-compile'. Ideally,
            'mvn compile' should not compile test classes and therefore should not need this. 
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonRDDFunction.java b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonRDDFunction.java
index b46a644dacb7c..84d092ea0048f 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonRDDFunction.java
+++ b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonRDDFunction.java
@@ -5,7 +5,7 @@
 
 /*
  * Interface for py4j callback function.
- * This function is called by pyspark.streaming.dstream.DStream.foreachRDD .
+ * This interface is related to pyspark.streaming.dstream.DStream.foreachRDD .
  */
 public interface PythonRDDFunction {
   JavaRDD<byte[]> call(JavaRDD<byte[]> rdd, long time);

From fbed8da5253e795a555761250faf7da1ce2058cb Mon Sep 17 00:00:00 2001
From: giwa <ugw.gi.world@gmail.com>
Date: Wed, 20 Aug 2014 19:38:38 -0700
Subject: [PATCH 477/489] revert pom.xml

---
 streaming/pom.xml | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/streaming/pom.xml b/streaming/pom.xml
index 483e200ff9f16..12f900c91eb98 100644
--- a/streaming/pom.xml
+++ b/streaming/pom.xml
@@ -77,9 +77,9 @@
         <groupId>org.scalatest</groupId>
         <artifactId>scalatest-maven-plugin</artifactId>
       </plugin>
-
-      <!--
-           This plugin forces the generation of jar containing streaming test classes,
+      
+      <!-- 
+           This plugin forces the generation of jar containing streaming test classes, 
            so that the tests classes of external modules can use them. The two execution profiles
            are necessary - first one for 'mvn package', second one for 'mvn test-compile'. Ideally,
            'mvn compile' should not compile test classes and therefore should not need this. 

From bebb3f3123040a3d48ea46faa9b02a5bf2a837c7 Mon Sep 17 00:00:00 2001
From: giwa <ugw.gi.world@gmail.com>
Date: Wed, 20 Aug 2014 19:44:05 -0700
Subject: [PATCH 478/489] remove the last brank line

---
 .../org/apache/spark/streaming/api/python/PythonDStream.scala   | 2 +-
 .../apache/spark/streaming/api/python/PythonRDDFunction.java    | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
index 3c9fff6f4bf5c..717592dbf8bf9 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
@@ -198,4 +198,4 @@ class PythonTestInputStream(ssc_ : JavaStreamingContext, inputRDDs: JArrayList[J
   }
 
   val asJavaDStream  = JavaDStream.fromDStream(this)
-}
+}
\ No newline at end of file
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonRDDFunction.java b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonRDDFunction.java
index 84d092ea0048f..eacff4b0e6006 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonRDDFunction.java
+++ b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonRDDFunction.java
@@ -9,4 +9,4 @@
  */
 public interface PythonRDDFunction {
   JavaRDD<byte[]> call(JavaRDD<byte[]> rdd, long time);
-}
+}
\ No newline at end of file

From b0f2015c81d754a7a226d4b48f81d388817c61ec Mon Sep 17 00:00:00 2001
From: giwa <ugw.gi.world@gmail.com>
Date: Thu, 21 Aug 2014 12:56:27 -0700
Subject: [PATCH 479/489] added comment in dstream._test_output

---
 python/pyspark/streaming/dstream.py | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/python/pyspark/streaming/dstream.py b/python/pyspark/streaming/dstream.py
index f75d7c7f1b259..b62b9d6e4ecf2 100644
--- a/python/pyspark/streaming/dstream.py
+++ b/python/pyspark/streaming/dstream.py
@@ -397,6 +397,10 @@ def _test_output(self, result):
         Store data in a DStream to result to verify the result in test case
         """
         def get_output(rdd, time):
+            """
+            Closure to get element in RDD in the DStream.
+            This closure is called by py4j callback server.
+            """
             collected = rdd.collect()
             result.append(collected)
 

From f385976aea314c20cd8de34f55162e0d5a7a135e Mon Sep 17 00:00:00 2001
From: giwa <ugw.gi.world@gmail.com>
Date: Thu, 21 Aug 2014 15:15:55 -0700
Subject: [PATCH 480/489] delete inproper comments

---
 python/pyspark/streaming/tests.py | 6 +-----
 1 file changed, 1 insertion(+), 5 deletions(-)

diff --git a/python/pyspark/streaming/tests.py b/python/pyspark/streaming/tests.py
index 2ed099b1004c3..3c9174e64cf77 100644
--- a/python/pyspark/streaming/tests.py
+++ b/python/pyspark/streaming/tests.py
@@ -16,15 +16,11 @@
 #
 
 """
-Unit tests for PySpark; additional tests are implemented as doctests in
+Unit tests for Python SparkStreaming; additional tests are implemented as doctests in
 individual modules.
 
-This file would be merged to tests.py after all functions are ready.
-Since python API for streaming is beta, this file is separated.
-
 Callback server is sometimes unstable sometimes, which cause error in test case.
 But this is very rare case.
-
 """
 from itertools import chain
 import time

From c0a06bc6f293643ffa3a2c6ce7a778d740ff37c7 Mon Sep 17 00:00:00 2001
From: giwa <ugw.gi.world@gmail.com>
Date: Thu, 21 Aug 2014 15:43:57 -0700
Subject: [PATCH 481/489] delete not implemented functions

---
 python/pyspark/streaming/jtime.py | 10 ++++------
 1 file changed, 4 insertions(+), 6 deletions(-)

diff --git a/python/pyspark/streaming/jtime.py b/python/pyspark/streaming/jtime.py
index f169228e81868..eb91ad94457fa 100644
--- a/python/pyspark/streaming/jtime.py
+++ b/python/pyspark/streaming/jtime.py
@@ -126,14 +126,12 @@ def isMultipbleOf(duration):
         Duration._is_duration(duration)
         return self._millis % duration._millis == 0
 
-    def until(time, interval):
-        raise NotImplementedError
-
-    def to(time, interval):
-        raise NotImplementedError
-
     @classmethod
     def _is_time(self, instance):
         """ is instance Time """
         if not isinstance(instance, Time):
             raise TypeError
+
+# TODO: implement until
+# TODO: implement to
+

From 2fdf0def481b8ad83d141370ab6482dadd779c14 Mon Sep 17 00:00:00 2001
From: Matthew Farrellee <matt@redhat.com>
Date: Tue, 26 Aug 2014 17:01:47 -0400
Subject: [PATCH 482/489] Fix scalastyle errors

---
 .../spark/streaming/api/java/JavaStreamingContext.scala      | 3 ++-
 .../apache/spark/streaming/api/python/PythonDStream.scala    | 5 +++--
 2 files changed, 5 insertions(+), 3 deletions(-)

diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala
index 50162e0a1d3b4..662cd8d22c6a5 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala
@@ -549,7 +549,8 @@ class JavaStreamingContext(val ssc: StreamingContext) extends Closeable {
  * JavaStreamingContext object contains a number of utility functions.
  */
 object JavaStreamingContext {
-  implicit def fromStreamingContext(ssc: StreamingContext): JavaStreamingContext = new JavaStreamingContext(ssc)
+  implicit def fromStreamingContext(ssc: StreamingContext):
+    JavaStreamingContext = new JavaStreamingContext(ssc)
 
   implicit def toStreamingContext(jssc: JavaStreamingContext): StreamingContext = jssc.ssc
 
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
index 717592dbf8bf9..15104e464d48e 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
@@ -174,7 +174,8 @@ class PythonForeachDStream(
  * returns the i_th element at the i_th batch under manual clock.
  */
 
-class PythonTestInputStream(ssc_ : JavaStreamingContext, inputRDDs: JArrayList[JavaRDD[Array[Byte]]])
+class PythonTestInputStream(ssc_ : JavaStreamingContext,
+      inputRDDs: JArrayList[JavaRDD[Array[Byte]]])
   extends InputDStream[Array[Byte]](JavaStreamingContext.toStreamingContext(ssc_)) {
 
   def start() {}
@@ -198,4 +199,4 @@ class PythonTestInputStream(ssc_ : JavaStreamingContext, inputRDDs: JArrayList[J
   }
 
   val asJavaDStream  = JavaDStream.fromDStream(this)
-}
\ No newline at end of file
+}

From d542743edfba48e205c54bb9b1b5766ffa282a16 Mon Sep 17 00:00:00 2001
From: giwa <ugw.gi.world@gmail.com>
Date: Sun, 31 Aug 2014 15:56:14 +0900
Subject: [PATCH 483/489] clean up code

---
 .../org/apache/spark/streaming/api/python/PythonDStream.scala | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
index 15104e464d48e..27e74a847a4ad 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
@@ -170,8 +170,8 @@ class PythonForeachDStream(
 
 /**
  * This is a input stream just for the unitest. This is equivalent to a checkpointable,
- * replayable, reliable message queue like Kafka. It requires a sequence as input, and
- * returns the i_th element at the i_th batch under manual clock.
+ * replayable, reliable message queue like Kafka. It requires a JArrayList input of JavaRDD,
+ * and returns the i_th element at the i_th batch under manual clock.
  */
 
 class PythonTestInputStream(ssc_ : JavaStreamingContext,

From d39f10256efeb11b8e887919510e8d2cd39f1926 Mon Sep 17 00:00:00 2001
From: giwa <ugw.gi.world@gmail.com>
Date: Sun, 31 Aug 2014 16:19:38 +0900
Subject: [PATCH 484/489] added StreamingContext.remember

---
 python/pyspark/streaming/context.py | 15 +++++++++++++++
 1 file changed, 15 insertions(+)

diff --git a/python/pyspark/streaming/context.py b/python/pyspark/streaming/context.py
index 6b6cc653dfa6f..a1a9be1eae439 100644
--- a/python/pyspark/streaming/context.py
+++ b/python/pyspark/streaming/context.py
@@ -21,6 +21,7 @@
 from pyspark.serializers import PickleSerializer, BatchedSerializer, UTF8Deserializer
 from pyspark.context import SparkContext
 from pyspark.streaming.dstream import DStream
+from pyspark.streaming.duration import Duration
 
 from py4j.java_collections import ListConverter
 
@@ -107,6 +108,20 @@ def awaitTermination(self, timeout=None):
         else:
             self._jssc.awaitTermination(timeout)
 
+    def remember(self, duration):
+        """
+        Set each DStreams in this context to remember RDDs it generated in the last given duration.
+        DStreams remember RDDs only for a limited duration of time and releases them for garbage
+        collection. This method allows the developer to specify how to long to remember the RDDs (
+        if the developer wishes to query old data outside the DStream computation).
+        @param duration pyspark.streaming.duration.Duration object.
+               Minimum duration that each DStream should remember its RDDs
+        """
+        if not isinstance(duration, Duration):
+            raise TypeError("Input should be pyspark.streaming.duration.Duration object")
+
+        self._jssc.remember(duration._jduration)
+
     # TODO: add storageLevel
     def socketTextStream(self, hostname, port):
         """

From 63c881a188e5d37407470654fb3b3e0a9983ba9c Mon Sep 17 00:00:00 2001
From: giwa <ugw.gi.world@gmail.com>
Date: Sun, 31 Aug 2014 16:29:37 +0900
Subject: [PATCH 485/489] added StreamingContext.sparkContext

---
 python/pyspark/streaming/context.py | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/python/pyspark/streaming/context.py b/python/pyspark/streaming/context.py
index a1a9be1eae439..78828ea4ea3c2 100644
--- a/python/pyspark/streaming/context.py
+++ b/python/pyspark/streaming/context.py
@@ -92,6 +92,10 @@ def clean_up_handler(*args):
         for sig in (SIGINT, SIGTERM):
             signal(sig, clean_up_handler)
 
+    @property
+    def sparkContext(self):
+        return self._sc
+
     def start(self):
         """
         Start the execution of the streams.

From d5f5fcb80ad470fde5d873a6a35e6027a1d1752c Mon Sep 17 00:00:00 2001
From: giwa <ugw.gi.world@gmail.com>
Date: Sun, 31 Aug 2014 16:31:01 +0900
Subject: [PATCH 486/489] added comment for StreamingContext.sparkContext

---
 python/pyspark/streaming/context.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/python/pyspark/streaming/context.py b/python/pyspark/streaming/context.py
index 78828ea4ea3c2..bbb4f6764e266 100644
--- a/python/pyspark/streaming/context.py
+++ b/python/pyspark/streaming/context.py
@@ -94,6 +94,9 @@ def clean_up_handler(*args):
 
     @property
     def sparkContext(self):
+        """
+        Return SparkContext which is associated this StreamingContext
+        """
         return self._sc
 
     def start(self):

From 8ffdbf176efadf973cd32071719fbccc3ddb842f Mon Sep 17 00:00:00 2001
From: giwa <ugw.gi.world@gmail.com>
Date: Sun, 31 Aug 2014 20:48:10 +0900
Subject: [PATCH 487/489] added atexit to handle callback server

---
 python/pyspark/streaming/context.py | 28 +++++++-----
 python/pyspark/streaming/tests.py   | 68 ++++++++++++++++++++++++++++-
 2 files changed, 83 insertions(+), 13 deletions(-)

diff --git a/python/pyspark/streaming/context.py b/python/pyspark/streaming/context.py
index bbb4f6764e266..19e97f38861a6 100644
--- a/python/pyspark/streaming/context.py
+++ b/python/pyspark/streaming/context.py
@@ -17,6 +17,8 @@
 
 import sys
 from signal import signal, SIGTERM, SIGINT
+import atexit
+import time
 
 from pyspark.serializers import PickleSerializer, BatchedSerializer, UTF8Deserializer
 from pyspark.context import SparkContext
@@ -73,7 +75,7 @@ def __init__(self, master=None, appName=None, sparkHome=None, pyFiles=None,
         # Callback sever is need only by SparkStreming; therefore the callback sever
         # is started in StreamingContext.
         SparkContext._gateway.restart_callback_server()
-        self._set_clean_up_trigger()
+        self._set_clean_up_handler()
         self._jvm = self._sc._jvm
         self._jssc = self._initialize_context(self._sc._jsc, duration._jduration)
 
@@ -81,21 +83,22 @@ def __init__(self, master=None, appName=None, sparkHome=None, pyFiles=None,
     def _initialize_context(self, jspark_context, jduration):
         return self._jvm.JavaStreamingContext(jspark_context, jduration)
 
-    def _set_clean_up_trigger(self):
-        """Kill py4j callback server properly using signal lib"""
+    def _set_clean_up_handler(self):
+        """ set clean up hander using atexit """
 
-        def clean_up_handler(*args):
-            # Make sure stop callback server.
+        def clean_up_handler():
             SparkContext._gateway.shutdown()
-            sys.exit(0)
 
+        atexit.register(clean_up_handler)
+        # atext is not called when the program is killed by a signal not handled by
+        # Python.
         for sig in (SIGINT, SIGTERM):
             signal(sig, clean_up_handler)
 
     @property
     def sparkContext(self):
         """
-        Return SparkContext which is associated this StreamingContext
+        Return SparkContext which is associated with this StreamingContext.
         """
         return self._sc
 
@@ -152,11 +155,14 @@ def stop(self, stopSparkContext=True, stopGraceFully=False):
         Stop the execution of the streams immediately (does not wait for all received data
         to be processed).
         """
-        try:
-            self._jssc.stop(stopSparkContext, stopGraceFully)
-        finally:
-            SparkContext._gateway.shutdown()
+        self._jssc.stop(stopSparkContext, stopGraceFully)
+        if stopSparkContext:
+            self._sc.stop()
 
+        # Shutdown only callback server and all py3j client is shutdowned
+        # clean up handler
+        SparkContext._gateway._shutdown_callback_server()
+        
     def _testInputStream(self, test_inputs, numSlices=None):
         """
         This function is only for unittest.
diff --git a/python/pyspark/streaming/tests.py b/python/pyspark/streaming/tests.py
index 3c9174e64cf77..d7f86fc8f5923 100644
--- a/python/pyspark/streaming/tests.py
+++ b/python/pyspark/streaming/tests.py
@@ -33,6 +33,7 @@
     import unittest
 
 from pyspark.context import SparkContext
+from pyspark.conf import SparkConf
 from pyspark.streaming.context import StreamingContext
 from pyspark.streaming.duration import *
 
@@ -47,8 +48,6 @@ def tearDown(self):
         # we do not wait to shutdown py4j client.
         self.ssc._jssc.stop()
         self.ssc._sc.stop()
-        # Why does it long time to terminate StremaingContext and SparkContext?
-        # Should we change the sleep time if this depends on machine spec?
         time.sleep(1)
 
     @classmethod
@@ -455,6 +454,71 @@ def _run_stream(self, test_input, test_func, expected_output, numSlices=None):
 
         return result
 
+
+class TestStreamingContextSuite(unittest.TestCase):
+    """
+    Should we have conf property in  SparkContext?
+    @property
+    def conf(self):
+        return self._conf
+
+    """
+    def setUp(self):
+        self.master = "local[2]"
+        self.appName = self.__class__.__name__
+        self.batachDuration = Milliseconds(500)
+        self.sparkHome = "SomeDir"
+        self.envPair = {"key": "value"}
+
+    def tearDown(self):
+        # Do not call pyspark.streaming.context.StreamingContext.stop directly because
+        # we do not wait to shutdown py4j client.
+        self.ssc._jssc.stop()
+        self.ssc._sc.stop()
+        # Why does it long time to terminate StremaingContext and SparkContext?
+        # Should we change the sleep time if this depends on machine spec?
+        time.sleep(1)
+
+    @classmethod
+    def tearDownClass(cls):
+        # Make sure tp shutdown the callback server
+        SparkContext._gateway._shutdown_callback_server()
+
+
+    def test_from_no_conf_constructor(self):
+        ssc = StreamingContext(master=self.master, appName=self.appName, duration=batachDuration)
+        # Alternative call master: ssc.sparkContext.master
+        # I try to make code close to Scala.
+        self.assertEqual(ssc.sparkContext._conf.get("spark.master"), self.master)
+        self.assertEqual(ssc.sparkContext._conf.get("spark.app.name"), self.appName)
+
+    def test_from_no_conf_plus_spark_home(self):
+        ssc = StreamingContext(master=self.master, appName=self.appName, 
+                               sparkHome=self.sparkHome, duration=batachDuration)
+        self.assertEqual(ssc.sparkContext._conf.get("spark.home"), self.sparkHome)
+
+    def test_from_existing_spark_context(self):
+        sc = SparkContext(master=self.master, appName=self.appName)
+        ssc = StreamingContext(sparkContext=sc)
+
+    def test_existing_spark_context_with_settings(self):
+        conf = SparkConf()
+        conf.set("spark.cleaner.ttl", "10")
+        sc = SparkContext(master=self.master, appName=self.appName, conf=conf)
+        ssc = StreamingContext(context=sc)
+        self.assertEqual(int(ssc.sparkContext._conf.get("spark.cleaner.ttl")), 10)
+
+    def _addInputStream(self, s):
+        test_inputs = map(lambda x: range(1, x), range(5, 101))
+        # make sure numSlice is 2 due to deserializer proglem in pyspark
+        s._testInputStream(test_inputs, 2)
+
+
+
+
+
+
+
 if __name__ == "__main__":
     unittest.main()
     SparkContext._gateway._shutdown_callback_server()

From 4a59e1e8fb7ccaeae99d80380ad1b0f04d7d9b76 Mon Sep 17 00:00:00 2001
From: giwa <ugw.gi.world@gmail.com>
Date: Sun, 31 Aug 2014 21:05:23 +0900
Subject: [PATCH 488/489] WIP:added more test for StreamingContext

---
 python/pyspark/streaming/context.py |  3 +++
 python/pyspark/streaming/tests.py   | 13 +++++++++++--
 2 files changed, 14 insertions(+), 2 deletions(-)

diff --git a/python/pyspark/streaming/context.py b/python/pyspark/streaming/context.py
index 19e97f38861a6..d7a20caac1ee8 100644
--- a/python/pyspark/streaming/context.py
+++ b/python/pyspark/streaming/context.py
@@ -63,6 +63,9 @@ def __init__(self, master=None, appName=None, sparkHome=None, pyFiles=None,
 
         """
 
+        if not isinstance(duration, Duration):
+            raise TypeError("Input should be pyspark.streaming.duration.Duration object")
+
         if sparkContext is None:
             # Create the Python Sparkcontext
             self._sc = SparkContext(master=master, appName=appName, sparkHome=sparkHome,
diff --git a/python/pyspark/streaming/tests.py b/python/pyspark/streaming/tests.py
index d7f86fc8f5923..dab36cc3a9c24 100644
--- a/python/pyspark/streaming/tests.py
+++ b/python/pyspark/streaming/tests.py
@@ -473,6 +473,7 @@ def setUp(self):
     def tearDown(self):
         # Do not call pyspark.streaming.context.StreamingContext.stop directly because
         # we do not wait to shutdown py4j client.
+        # We need change this simply calll streamingConxt.Stop
         self.ssc._jssc.stop()
         self.ssc._sc.stop()
         # Why does it long time to terminate StremaingContext and SparkContext?
@@ -484,7 +485,6 @@ def tearDownClass(cls):
         # Make sure tp shutdown the callback server
         SparkContext._gateway._shutdown_callback_server()
 
-
     def test_from_no_conf_constructor(self):
         ssc = StreamingContext(master=self.master, appName=self.appName, duration=batachDuration)
         # Alternative call master: ssc.sparkContext.master
@@ -513,7 +513,17 @@ def _addInputStream(self, s):
         # make sure numSlice is 2 due to deserializer proglem in pyspark
         s._testInputStream(test_inputs, 2)
 
+    def test_from_no_conf_plus_spark_home_plus_env(self):
+        pass
+
+    def test_from_conf_with_settings(self):
+        pass
+
+    def test_stop_only_streaming_context(self):
+        pass
 
+    def test_await_termination(self):
+        pass
 
 
 
@@ -521,4 +531,3 @@ def _addInputStream(self, s):
 
 if __name__ == "__main__":
     unittest.main()
-    SparkContext._gateway._shutdown_callback_server()

From 2d32a7452382ee2a9962e97af483b7bfa3983f1f Mon Sep 17 00:00:00 2001
From: giwa <ugw.gi.world@gmail.com>
Date: Mon, 1 Sep 2014 10:30:41 +0900
Subject: [PATCH 489/489] added some StreamingContextTestSuite

---
 python/pyspark/streaming/tests.py | 80 ++++++++++++++++++++-----------
 1 file changed, 52 insertions(+), 28 deletions(-)

diff --git a/python/pyspark/streaming/tests.py b/python/pyspark/streaming/tests.py
index dab36cc3a9c24..95cb76a15be07 100644
--- a/python/pyspark/streaming/tests.py
+++ b/python/pyspark/streaming/tests.py
@@ -469,13 +469,18 @@ def setUp(self):
         self.batachDuration = Milliseconds(500)
         self.sparkHome = "SomeDir"
         self.envPair = {"key": "value"}
+        self.ssc = None
+        self.sc = None
 
     def tearDown(self):
         # Do not call pyspark.streaming.context.StreamingContext.stop directly because
         # we do not wait to shutdown py4j client.
         # We need change this simply calll streamingConxt.Stop
-        self.ssc._jssc.stop()
-        self.ssc._sc.stop()
+        #self.ssc._jssc.stop()
+        if self.ssc is not None:
+            self.ssc.stop()
+        if self.sc is not None:
+            self.sc.stop()
         # Why does it long time to terminate StremaingContext and SparkContext?
         # Should we change the sleep time if this depends on machine spec?
         time.sleep(1)
@@ -486,48 +491,67 @@ def tearDownClass(cls):
         SparkContext._gateway._shutdown_callback_server()
 
     def test_from_no_conf_constructor(self):
-        ssc = StreamingContext(master=self.master, appName=self.appName, duration=batachDuration)
+        self.ssc = StreamingContext(master=self.master, appName=self.appName,
+                               duration=self.batachDuration)
         # Alternative call master: ssc.sparkContext.master
         # I try to make code close to Scala.
-        self.assertEqual(ssc.sparkContext._conf.get("spark.master"), self.master)
-        self.assertEqual(ssc.sparkContext._conf.get("spark.app.name"), self.appName)
+        self.assertEqual(self.ssc.sparkContext._conf.get("spark.master"), self.master)
+        self.assertEqual(self.ssc.sparkContext._conf.get("spark.app.name"), self.appName)
 
     def test_from_no_conf_plus_spark_home(self):
-        ssc = StreamingContext(master=self.master, appName=self.appName, 
-                               sparkHome=self.sparkHome, duration=batachDuration)
-        self.assertEqual(ssc.sparkContext._conf.get("spark.home"), self.sparkHome)
+        self.ssc = StreamingContext(master=self.master, appName=self.appName, 
+                               sparkHome=self.sparkHome, duration=self.batachDuration)
+        self.assertEqual(self.ssc.sparkContext._conf.get("spark.home"), self.sparkHome)
+
+    def test_from_no_conf_plus_spark_home_plus_env(self):
+        self.ssc = StreamingContext(master=self.master, appName=self.appName, 
+                               sparkHome=self.sparkHome, environment=self.envPair,
+                               duration=self.batachDuration)
+        self.assertEqual(self.ssc.sparkContext._conf.get("spark.executorEnv.key"), self.envPair["key"])
 
     def test_from_existing_spark_context(self):
-        sc = SparkContext(master=self.master, appName=self.appName)
-        ssc = StreamingContext(sparkContext=sc)
+        self.sc = SparkContext(master=self.master, appName=self.appName)
+        self.ssc = StreamingContext(sparkContext=self.sc, duration=self.batachDuration)
 
     def test_existing_spark_context_with_settings(self):
         conf = SparkConf()
         conf.set("spark.cleaner.ttl", "10")
-        sc = SparkContext(master=self.master, appName=self.appName, conf=conf)
-        ssc = StreamingContext(context=sc)
-        self.assertEqual(int(ssc.sparkContext._conf.get("spark.cleaner.ttl")), 10)
-
-    def _addInputStream(self, s):
-        test_inputs = map(lambda x: range(1, x), range(5, 101))
-        # make sure numSlice is 2 due to deserializer proglem in pyspark
-        s._testInputStream(test_inputs, 2)
-
-    def test_from_no_conf_plus_spark_home_plus_env(self):
-        pass
+        self.sc = SparkContext(master=self.master, appName=self.appName, conf=conf)
+        self.ssc = StreamingContext(sparkContext=self.sc, duration=self.batachDuration)
+        self.assertEqual(int(self.ssc.sparkContext._conf.get("spark.cleaner.ttl")), 10)
 
     def test_from_conf_with_settings(self):
-        pass
+        conf = SparkConf()
+        conf.set("spark.cleaner.ttl", "10")
+        conf.setMaster(self.master)
+        conf.setAppName(self.appName)
+        self.ssc = StreamingContext(conf=conf, duration=self.batachDuration)
+        self.assertEqual(int(self.ssc.sparkContext._conf.get("spark.cleaner.ttl")), 10)
 
     def test_stop_only_streaming_context(self):
-        pass
-
-    def test_await_termination(self):
-        pass
-
-
+        self.sc = SparkContext(master=self.master, appName=self.appName)
+        self.ssc = StreamingContext(sparkContext=self.sc, duration=self.batachDuration)
+        self._addInputStream(self.ssc)
+        self.ssc.start()
+        self.ssc.stop(False)
+        self.assertEqual(len(self.sc.parallelize(range(5), 5).glom().collect()), 5)
 
+    def test_stop_multiple_times(self):
+        self.ssc = StreamingContext(master=self.master, appName=self.appName,
+                               duration=self.batachDuration)
+        self._addInputStream(self.ssc)
+        self.ssc.start()
+        self.ssc.stop()
+        self.ssc.stop()
 
+    def _addInputStream(self, s):
+        # Make sure each length of input is over 3 and 
+        # numSlice is 2 due to deserializer problem in pyspark.streaming
+        test_inputs = map(lambda x: range(1, x), range(5, 101))
+        test_stream = s._testInputStream(test_inputs, 2)
+        # Register fake output operation
+        result = list()
+        test_stream._test_output(result)
 
 if __name__ == "__main__":
     unittest.main()