From 0092abb47a0f9fdc716d5dfc1c591ddb45de8c98 Mon Sep 17 00:00:00 2001
From: Sandy Ryza <sandy@cloudera.com>
Date: Tue, 5 May 2015 18:32:16 -0700
Subject: [PATCH 01/75] Some minor cleanup after SPARK-4550.

JoshRosen this PR addresses the comments you left on #4450 after it got merged.

Author: Sandy Ryza <sandy@cloudera.com>

Closes #5916 from sryza/sandy-spark-4550-cleanup and squashes the following commits:

dee3d85 [Sandy Ryza] Some minor cleanup after SPARK-4550.
---
 .../org/apache/spark/storage/BlockObjectWriter.scala     | 9 ++-------
 .../collection/PartitionedSerializedPairBuffer.scala     | 4 ++--
 2 files changed, 4 insertions(+), 9 deletions(-)

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 499dd97c0656a..8bc4e205bc3c6 100644
--- a/core/src/main/scala/org/apache/spark/storage/BlockObjectWriter.scala
+++ b/core/src/main/scala/org/apache/spark/storage/BlockObjectWriter.scala
@@ -59,7 +59,7 @@ private[spark] abstract class BlockObjectWriter(val blockId: BlockId) extends Ou
   def write(key: Any, value: Any)
 
   /**
-   * Notify the writer that a record worth of bytes has been written with writeBytes.
+   * Notify the writer that a record worth of bytes has been written with OutputStream#write.
    */
   def recordWritten()
 
@@ -215,12 +215,7 @@ private[spark] class DiskBlockObjectWriter(
 
     objOut.writeKey(key)
     objOut.writeValue(value)
-    numRecordsWritten += 1
-    writeMetrics.incShuffleRecordsWritten(1)
-
-    if (numRecordsWritten % 32 == 0) {
-      updateBytesWritten()
-    }
+    recordWritten()
   }
 
   override def write(b: Int): Unit = throw new UnsupportedOperationException()
diff --git a/core/src/main/scala/org/apache/spark/util/collection/PartitionedSerializedPairBuffer.scala b/core/src/main/scala/org/apache/spark/util/collection/PartitionedSerializedPairBuffer.scala
index b5ca0c62a04f2..ac9ea6393628f 100644
--- a/core/src/main/scala/org/apache/spark/util/collection/PartitionedSerializedPairBuffer.scala
+++ b/core/src/main/scala/org/apache/spark/util/collection/PartitionedSerializedPairBuffer.scala
@@ -71,10 +71,10 @@ private[spark] class PartitionedSerializedPairBuffer[K, V](
     if (keyStart < 0) {
       throw new Exception(s"Can't grow buffer beyond ${1 << 31} bytes")
     }
-    kvSerializationStream.writeObject[Any](key)
+    kvSerializationStream.writeKey[Any](key)
     kvSerializationStream.flush()
     val valueStart = kvBuffer.size
-    kvSerializationStream.writeObject[Any](value)
+    kvSerializationStream.writeValue[Any](value)
     kvSerializationStream.flush()
     val valueEnd = kvBuffer.size
 

From 1fd31ba08928f8554f74609f48f4344bd69444e5 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Tue, 5 May 2015 18:59:46 -0700
Subject: [PATCH 02/75] [SPARK-6231][SQL/DF] Automatically resolve join
 condition ambiguity for self-joins.

See the comment in join function for more information.

Author: Reynold Xin <rxin@databricks.com>

Closes #5919 from rxin/self-join-resolve and squashes the following commits:

e2fb0da [Reynold Xin] Updated SQLConf comment.
7233a86 [Reynold Xin] Updated comment.
6be2b4d [Reynold Xin] Removed println
9f6b72f [Reynold Xin] [SPARK-6231][SQL/DF] Automatically resolve ambiguity in join condition for self-joins.
---
 .../expressions/namedExpressions.scala        |  5 ++
 .../org/apache/spark/sql/DataFrame.scala      | 38 +++++++-
 .../scala/org/apache/spark/sql/SQLConf.scala  |  7 ++
 .../apache/spark/sql/DataFrameJoinSuite.scala | 86 +++++++++++++++++++
 .../org/apache/spark/sql/DataFrameSuite.scala | 39 ---------
 .../sql/hive/HiveDataFrameJoinSuite.scala     | 38 ++++++++
 6 files changed, 170 insertions(+), 43 deletions(-)
 create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/DataFrameJoinSuite.scala
 create mode 100644 sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveDataFrameJoinSuite.scala

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 afcb2ce8b9cb4..57ace2a14f77c 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
@@ -171,6 +171,11 @@ case class AttributeReference(
     val exprId: ExprId = NamedExpression.newExprId,
     val qualifiers: Seq[String] = Nil) extends Attribute with trees.LeafNode[Expression] {
 
+  /**
+   * Returns true iff the expression id is the same for both attributes.
+   */
+  def sameRef(other: AttributeReference): Boolean = this.exprId == other.exprId
+
   override def equals(other: Any): Boolean = other match {
     case ar: AttributeReference => name == ar.name && exprId == ar.exprId && dataType == ar.dataType
     case _ => false
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
index cf344710ff8b4..aceb7a9627edd 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
@@ -416,9 +416,7 @@ class DataFrame private[sql](
    * }}}
    * @group dfops
    */
-  def join(right: DataFrame, joinExprs: Column): DataFrame = {
-    Join(logicalPlan, right.logicalPlan, joinType = Inner, Some(joinExprs.expr))
-  }
+  def join(right: DataFrame, joinExprs: Column): DataFrame = join(right, joinExprs, "inner")
 
   /**
    * Join with another [[DataFrame]], using the given join expression. The following performs
@@ -440,7 +438,39 @@ class DataFrame private[sql](
    * @group dfops
    */
   def join(right: DataFrame, joinExprs: Column, joinType: String): DataFrame = {
-    Join(logicalPlan, right.logicalPlan, JoinType(joinType), Some(joinExprs.expr))
+    // Note that in this function, we introduce a hack in the case of self-join to automatically
+    // resolve ambiguous join conditions into ones that might make sense [SPARK-6231].
+    // Consider this case: df.join(df, df("key") === df("key"))
+    // Since df("key") === df("key") is a trivially true condition, this actually becomes a
+    // cartesian join. However, most likely users expect to perform a self join using "key".
+    // With that assumption, this hack turns the trivially true condition into equality on join
+    // keys that are resolved to both sides.
+
+    // Trigger analysis so in the case of self-join, the analyzer will clone the plan.
+    // After the cloning, left and right side will have distinct expression ids.
+    val plan = Join(logicalPlan, right.logicalPlan, JoinType(joinType), Some(joinExprs.expr))
+      .queryExecution.analyzed.asInstanceOf[Join]
+
+    // If auto self join alias is disabled, return the plan.
+    if (!sqlContext.conf.dataFrameSelfJoinAutoResolveAmbiguity) {
+      return plan
+    }
+
+    // If left/right have no output set intersection, return the plan.
+    val lanalyzed = this.logicalPlan.queryExecution.analyzed
+    val ranalyzed = right.logicalPlan.queryExecution.analyzed
+    if (lanalyzed.outputSet.intersect(ranalyzed.outputSet).isEmpty) {
+      return plan
+    }
+
+    // Otherwise, find the trivially true predicates and automatically resolves them to both sides.
+    // By the time we get here, since we have already run analysis, all attributes should've been
+    // resolved and become AttributeReference.
+    val cond = plan.condition.map { _.transform {
+      case EqualTo(a: AttributeReference, b: AttributeReference) if a.sameRef(b) =>
+        EqualTo(plan.left.resolve(a.name), plan.right.resolve(b.name))
+    }}
+    plan.copy(condition = cond)
   }
 
   /**
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 99db959a8741c..3ffc2091d6ba1 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
@@ -67,6 +67,10 @@ private[spark] object SQLConf {
   // Set to false when debugging requires the ability to look at invalid query plans.
   val DATAFRAME_EAGER_ANALYSIS = "spark.sql.eagerAnalysis"
 
+  // Whether to automatically resolve ambiguity in join conditions for self-joins.
+  // See SPARK-6231.
+  val DATAFRAME_SELF_JOIN_AUTO_RESOLVE_AMBIGUITY = "spark.sql.selfJoinAutoResolveAmbiguity"
+
   val USE_SQL_SERIALIZER2 = "spark.sql.useSerializer2"
 
   object Deprecated {
@@ -219,6 +223,9 @@ private[sql] class SQLConf extends Serializable {
   private[spark] def dataFrameEagerAnalysis: Boolean =
     getConf(DATAFRAME_EAGER_ANALYSIS, "true").toBoolean
 
+  private[spark] def dataFrameSelfJoinAutoResolveAmbiguity: Boolean =
+    getConf(DATAFRAME_SELF_JOIN_AUTO_RESOLVE_AMBIGUITY, "true").toBoolean
+  
   /** ********************** SQLConf functionality methods ************ */
 
   /** Set Spark SQL configuration properties. */
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameJoinSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameJoinSuite.scala
new file mode 100644
index 0000000000000..f005f55b6432e
--- /dev/null
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameJoinSuite.scala
@@ -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.
+ */
+
+package org.apache.spark.sql
+
+import org.apache.spark.sql.TestData._
+import org.apache.spark.sql.functions._
+import org.apache.spark.sql.test.TestSQLContext._
+import org.apache.spark.sql.test.TestSQLContext.implicits._
+
+
+class DataFrameJoinSuite extends QueryTest {
+
+  test("join - join using") {
+    val df = Seq(1, 2, 3).map(i => (i, i.toString)).toDF("int", "str")
+    val df2 = Seq(1, 2, 3).map(i => (i, (i + 1).toString)).toDF("int", "str")
+
+    checkAnswer(
+      df.join(df2, "int"),
+      Row(1, "1", "2") :: Row(2, "2", "3") :: Row(3, "3", "4") :: Nil)
+  }
+
+  test("join - join using self join") {
+    val df = Seq(1, 2, 3).map(i => (i, i.toString)).toDF("int", "str")
+
+    // self join
+    checkAnswer(
+      df.join(df, "int"),
+      Row(1, "1", "1") :: Row(2, "2", "2") :: Row(3, "3", "3") :: Nil)
+  }
+
+  test("join - self join") {
+    val df1 = testData.select(testData("key")).as('df1)
+    val df2 = testData.select(testData("key")).as('df2)
+
+    checkAnswer(
+      df1.join(df2, $"df1.key" === $"df2.key"),
+      sql("SELECT a.key, b.key FROM testData a JOIN testData b ON a.key = b.key").collect().toSeq)
+  }
+
+  test("join - using aliases after self join") {
+    val df = Seq(1, 2, 3).map(i => (i, i.toString)).toDF("int", "str")
+    checkAnswer(
+      df.as('x).join(df.as('y), $"x.str" === $"y.str").groupBy("x.str").count(),
+      Row("1", 1) :: Row("2", 1) :: Row("3", 1) :: Nil)
+
+    checkAnswer(
+      df.as('x).join(df.as('y), $"x.str" === $"y.str").groupBy("y.str").count(),
+      Row("1", 1) :: Row("2", 1) :: Row("3", 1) :: Nil)
+  }
+
+  test("[SPARK-6231] join - self join auto resolve ambiguity") {
+    val df = Seq((1, "1"), (2, "2")).toDF("key", "value")
+    checkAnswer(
+      df.join(df, df("key") === df("key")),
+      Row(1, "1", 1, "1") :: Row(2, "2", 2, "2") :: Nil)
+
+    checkAnswer(
+      df.join(df.filter($"value" === "2"), df("key") === df("key")),
+      Row(2, "2", 2, "2") :: Nil)
+
+    checkAnswer(
+      df.join(df, df("key") === df("key") && df("value") === 1),
+      Row(1, "1", 1, "1") :: Nil)
+
+    val left = df.groupBy("key").agg($"key", count("*"))
+    val right = df.groupBy("key").agg($"key", sum("key"))
+    checkAnswer(
+      left.join(right, left("key") === right("key")),
+      Row(1, 1, 1, 1) :: Row(2, 1, 2, 2) :: Nil)
+  }
+}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
index ff31e15e2d472..1515e9b843771 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
@@ -24,7 +24,6 @@ import org.apache.spark.sql.types._
 import org.apache.spark.sql.test.{ExamplePointUDT, ExamplePoint, TestSQLContext}
 import org.apache.spark.sql.test.TestSQLContext.logicalPlanToSparkQuery
 import org.apache.spark.sql.test.TestSQLContext.implicits._
-import org.apache.spark.sql.test.TestSQLContext.sql
 
 
 class DataFrameSuite extends QueryTest {
@@ -118,44 +117,6 @@ class DataFrameSuite extends QueryTest {
     )
   }
 
-  test("join - join using") {
-    val df = Seq(1, 2, 3).map(i => (i, i.toString)).toDF("int", "str")
-    val df2 = Seq(1, 2, 3).map(i => (i, (i + 1).toString)).toDF("int", "str")
-
-    checkAnswer(
-      df.join(df2, "int"),
-      Row(1, "1", "2") :: Row(2, "2", "3") :: Row(3, "3", "4") :: Nil)
-  }
-
-  test("join - join using self join") {
-    val df = Seq(1, 2, 3).map(i => (i, i.toString)).toDF("int", "str")
-
-    // self join
-    checkAnswer(
-      df.join(df, "int"),
-      Row(1, "1", "1") :: Row(2, "2", "2") :: Row(3, "3", "3") :: Nil)
-  }
-
-  test("join - self join") {
-    val df1 = testData.select(testData("key")).as('df1)
-    val df2 = testData.select(testData("key")).as('df2)
-
-    checkAnswer(
-      df1.join(df2, $"df1.key" === $"df2.key"),
-      sql("SELECT a.key, b.key FROM testData a JOIN testData b ON a.key = b.key").collect().toSeq)
-  }
-
-  test("join - using aliases after self join") {
-    val df = Seq(1, 2, 3).map(i => (i, i.toString)).toDF("int", "str")
-    checkAnswer(
-      df.as('x).join(df.as('y), $"x.str" === $"y.str").groupBy("x.str").count(),
-      Row("1", 1) :: Row("2", 1) :: Row("3", 1) :: Nil)
-
-    checkAnswer(
-      df.as('x).join(df.as('y), $"x.str" === $"y.str").groupBy("y.str").count(),
-      Row("1", 1) :: Row("2", 1) :: Row("3", 1) :: Nil)
-  }
-
   test("explode") {
     val df = Seq((1, "a b c"), (2, "a b"), (3, "a")).toDF("number", "letters")
     val df2 =
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveDataFrameJoinSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveDataFrameJoinSuite.scala
new file mode 100644
index 0000000000000..52e782768cb75
--- /dev/null
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveDataFrameJoinSuite.scala
@@ -0,0 +1,38 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.hive
+
+import org.apache.spark.sql.{Row, QueryTest}
+import org.apache.spark.sql.hive.test.TestHive.implicits._
+
+
+class HiveDataFrameJoinSuite extends QueryTest {
+
+  // We should move this into SQL package if we make case sensitivity configurable in SQL.
+  test("join - self join auto resolve ambiguity with case insensitivity") {
+    val df = Seq((1, "1"), (2, "2")).toDF("key", "value")
+    checkAnswer(
+      df.join(df, df("key") === df("Key")),
+      Row(1, "1", 1, "1") :: Row(2, "2", 2, "2") :: Nil)
+
+    checkAnswer(
+      df.join(df.filter($"value" === "2"), df("key") === df("Key")),
+      Row(2, "2", 2, "2") :: Nil)
+  }
+
+}

From 51b3d41e160a1326a04536241b427e65b39ed8df Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Tue, 5 May 2015 19:27:30 -0700
Subject: [PATCH 03/75] Revert "[SPARK-3454] separate json endpoints for data
 in the UI"

This reverts commit d49735800db27239c11478aac4b0f2ec9df91a3f.

The commit broke Spark on Windows.
---
 .rat-excludes                                 |     7 -
 core/pom.xml                                  |     8 -
 .../org/apache/spark/JobExecutionStatus.java  |     8 +-
 .../status/api/v1/ApplicationStatus.java      |    30 -
 .../spark/status/api/v1/StageStatus.java      |    31 -
 .../spark/status/api/v1/TaskSorting.java      |    48 -
 .../java/org/apache/spark/util/EnumUtil.java  |    38 -
 .../scala/org/apache/spark/SparkContext.scala |     2 +-
 .../org/apache/spark/annotation/Private.java  |    41 -
 .../history/ApplicationHistoryProvider.scala  |     4 +-
 .../deploy/history/FsHistoryProvider.scala    |    14 +-
 .../spark/deploy/history/HistoryServer.scala  |    20 +-
 .../spark/deploy/master/ApplicationInfo.scala |     2 +-
 .../apache/spark/deploy/master/Master.scala   |    14 +-
 .../deploy/master/ui/ApplicationPage.scala    |    19 +-
 .../spark/deploy/master/ui/MasterPage.scala   |    12 +-
 .../spark/deploy/master/ui/MasterWebUI.scala  |    24 +-
 .../spark/status/api/v1/AllJobsResource.scala |    98 -
 .../spark/status/api/v1/AllRDDResource.scala  |   104 -
 .../status/api/v1/AllStagesResource.scala     |   309 -
 .../api/v1/ApplicationListResource.scala      |    94 -
 .../status/api/v1/ExecutorListResource.scala  |    36 -
 .../status/api/v1/JacksonMessageWriter.scala  |    93 -
 .../status/api/v1/JsonRootResource.scala      |   255 -
 .../api/v1/OneApplicationResource.scala       |    31 -
 .../spark/status/api/v1/OneJobResource.scala  |    41 -
 .../spark/status/api/v1/OneRDDResource.scala  |    34 -
 .../status/api/v1/OneStageResource.scala      |   150 -
 .../spark/status/api/v1/SecurityFilter.scala  |    38 -
 .../spark/status/api/v1/SimpleDateParam.scala |    55 -
 .../org/apache/spark/status/api/v1/api.scala  |   228 -
 .../spark/storage/StorageStatusListener.scala |     6 +-
 .../scala/org/apache/spark/ui/SparkUI.scala   |    49 +-
 .../scala/org/apache/spark/ui/WebUI.scala     |     8 +-
 .../apache/spark/ui/exec/ExecutorsPage.scala  |    17 +-
 .../apache/spark/ui/jobs/AllJobsPage.scala    |    14 +-
 .../apache/spark/ui/jobs/AllStagesPage.scala  |    12 +-
 .../org/apache/spark/ui/jobs/JobPage.scala    |     2 +-
 .../spark/ui/jobs/JobProgressListener.scala   |     4 -
 .../org/apache/spark/ui/jobs/PoolPage.scala   |     2 +-
 .../org/apache/spark/ui/jobs/StagePage.scala  |    19 +-
 .../org/apache/spark/ui/storage/RDDPage.scala |    73 +-
 .../apache/spark/ui/storage/StoragePage.scala |     2 +-
 .../apache/spark/ui/storage/StorageTab.scala  |     6 +-
 .../applications/json_expectation             |    53 -
 .../executors/json_expectation                |    17 -
 .../jobs/0/json_expectation                   |    15 -
 .../local-1422981780767/jobs/json_expectation |    43 -
 .../json_expectation                          |    43 -
 .../jobs?status=succeeded/json_expectation    |    29 -
 .../local-1422981780767/json_expectation      |    10 -
 .../stages/1/0/json_expectation               |   270 -
 .../stages/1/json_expectation                 |   270 -
 .../stages/json_expectation                   |    89 -
 .../stages?status=complete/json_expectation   |    67 -
 .../stages?status=failed/json_expectation     |    23 -
 .../storage/rdd/0/json_expectation            |    64 -
 .../storage/rdd/json_expectation              |     9 -
 .../1/jobs/json_expectation                   |    15 -
 .../1/stages/0/0/json_expectation             |   242 -
 .../1/stages/0/0/taskList/json_expectation    |   193 -
 .../1/stages/json_expectation                 |    27 -
 .../2/jobs/json_expectation                   |    15 -
 .../2/stages/0/0/taskList/json_expectation    |   193 -
 .../local-1426533911241/json_expectation      |    17 -
 .../stages/20/0/taskList/json_expectation     |   481 -
 .../json_expectation                          |  1201 --
 .../taskList?sortBy=-runtime/json_expectation |   481 -
 .../json_expectation                          |   481 -
 .../taskList?sortBy=runtime/json_expectation  |   481 -
 .../stages/20/0/taskSummary/json_expectation  |    15 -
 .../json_expectation                          |    15 -
 .../json_expectation                          |    10 -
 .../json_expectation                          |    19 -
 .../json_expectation                          |    35 -
 .../json_expectation                          |    53 -
 .../json_expectation                          |     1 -
 .../local-1422981759269/APPLICATION_COMPLETE  |     0
 .../local-1422981759269/EVENT_LOG_1           |    88 -
 .../local-1422981759269/SPARK_VERSION_1.2.0   |     0
 .../local-1422981780767/APPLICATION_COMPLETE  |     0
 .../local-1422981780767/EVENT_LOG_1           |    82 -
 .../local-1422981780767/SPARK_VERSION_1.2.0   |     0
 .../local-1425081759269/APPLICATION_COMPLETE  |     0
 .../local-1425081759269/EVENT_LOG_1           |    88 -
 .../local-1425081759269/SPARK_VERSION_1.2.0   |     0
 .../local-1426533911241/APPLICATION_COMPLETE  |     0
 .../local-1426533911241/EVENT_LOG_1           |    24 -
 .../local-1426533911241/SPARK_VERSION_1.2.0   |     0
 .../local-1426633911242/APPLICATION_COMPLETE  |     0
 .../local-1426633911242/EVENT_LOG_1           |    24 -
 .../local-1426633911242/SPARK_VERSION_1.2.0   |     0
 .../spark-events/local-1427397477963          | 12083 ----------------
 .../org/apache/spark/JsonTestUtils.scala      |    34 -
 .../spark/deploy/JsonProtocolSuite.scala      |    14 +-
 .../deploy/history/HistoryServerSuite.scala   |   223 +-
 .../status/api/v1/SimpleDateParamTest.scala   |    29 -
 .../org/apache/spark/ui/UISeleniumSuite.scala |   264 +-
 docs/monitoring.md                            |    74 -
 pom.xml                                       |    12 -
 100 files changed, 172 insertions(+), 19946 deletions(-)
 delete mode 100644 core/src/main/java/org/apache/spark/status/api/v1/ApplicationStatus.java
 delete mode 100644 core/src/main/java/org/apache/spark/status/api/v1/StageStatus.java
 delete mode 100644 core/src/main/java/org/apache/spark/status/api/v1/TaskSorting.java
 delete mode 100644 core/src/main/java/org/apache/spark/util/EnumUtil.java
 delete mode 100644 core/src/main/scala/org/apache/spark/annotation/Private.java
 delete mode 100644 core/src/main/scala/org/apache/spark/status/api/v1/AllJobsResource.scala
 delete mode 100644 core/src/main/scala/org/apache/spark/status/api/v1/AllRDDResource.scala
 delete mode 100644 core/src/main/scala/org/apache/spark/status/api/v1/AllStagesResource.scala
 delete mode 100644 core/src/main/scala/org/apache/spark/status/api/v1/ApplicationListResource.scala
 delete mode 100644 core/src/main/scala/org/apache/spark/status/api/v1/ExecutorListResource.scala
 delete mode 100644 core/src/main/scala/org/apache/spark/status/api/v1/JacksonMessageWriter.scala
 delete mode 100644 core/src/main/scala/org/apache/spark/status/api/v1/JsonRootResource.scala
 delete mode 100644 core/src/main/scala/org/apache/spark/status/api/v1/OneApplicationResource.scala
 delete mode 100644 core/src/main/scala/org/apache/spark/status/api/v1/OneJobResource.scala
 delete mode 100644 core/src/main/scala/org/apache/spark/status/api/v1/OneRDDResource.scala
 delete mode 100644 core/src/main/scala/org/apache/spark/status/api/v1/OneStageResource.scala
 delete mode 100644 core/src/main/scala/org/apache/spark/status/api/v1/SecurityFilter.scala
 delete mode 100644 core/src/main/scala/org/apache/spark/status/api/v1/SimpleDateParam.scala
 delete mode 100644 core/src/main/scala/org/apache/spark/status/api/v1/api.scala
 delete mode 100644 core/src/test/resources/HistoryServerExpectations/applications/json_expectation
 delete mode 100644 core/src/test/resources/HistoryServerExpectations/applications/local-1422981780767/executors/json_expectation
 delete mode 100644 core/src/test/resources/HistoryServerExpectations/applications/local-1422981780767/jobs/0/json_expectation
 delete mode 100644 core/src/test/resources/HistoryServerExpectations/applications/local-1422981780767/jobs/json_expectation
 delete mode 100644 core/src/test/resources/HistoryServerExpectations/applications/local-1422981780767/jobs?status=succeeded&status=failed/json_expectation
 delete mode 100644 core/src/test/resources/HistoryServerExpectations/applications/local-1422981780767/jobs?status=succeeded/json_expectation
 delete mode 100644 core/src/test/resources/HistoryServerExpectations/applications/local-1422981780767/json_expectation
 delete mode 100644 core/src/test/resources/HistoryServerExpectations/applications/local-1422981780767/stages/1/0/json_expectation
 delete mode 100644 core/src/test/resources/HistoryServerExpectations/applications/local-1422981780767/stages/1/json_expectation
 delete mode 100644 core/src/test/resources/HistoryServerExpectations/applications/local-1422981780767/stages/json_expectation
 delete mode 100644 core/src/test/resources/HistoryServerExpectations/applications/local-1422981780767/stages?status=complete/json_expectation
 delete mode 100644 core/src/test/resources/HistoryServerExpectations/applications/local-1422981780767/stages?status=failed/json_expectation
 delete mode 100644 core/src/test/resources/HistoryServerExpectations/applications/local-1422981780767/storage/rdd/0/json_expectation
 delete mode 100644 core/src/test/resources/HistoryServerExpectations/applications/local-1422981780767/storage/rdd/json_expectation
 delete mode 100644 core/src/test/resources/HistoryServerExpectations/applications/local-1426533911241/1/jobs/json_expectation
 delete mode 100644 core/src/test/resources/HistoryServerExpectations/applications/local-1426533911241/1/stages/0/0/json_expectation
 delete mode 100644 core/src/test/resources/HistoryServerExpectations/applications/local-1426533911241/1/stages/0/0/taskList/json_expectation
 delete mode 100644 core/src/test/resources/HistoryServerExpectations/applications/local-1426533911241/1/stages/json_expectation
 delete mode 100644 core/src/test/resources/HistoryServerExpectations/applications/local-1426533911241/2/jobs/json_expectation
 delete mode 100644 core/src/test/resources/HistoryServerExpectations/applications/local-1426533911241/2/stages/0/0/taskList/json_expectation
 delete mode 100644 core/src/test/resources/HistoryServerExpectations/applications/local-1426533911241/json_expectation
 delete mode 100644 core/src/test/resources/HistoryServerExpectations/applications/local-1427397477963/stages/20/0/taskList/json_expectation
 delete mode 100644 core/src/test/resources/HistoryServerExpectations/applications/local-1427397477963/stages/20/0/taskList?offset=10&length=50/json_expectation
 delete mode 100644 core/src/test/resources/HistoryServerExpectations/applications/local-1427397477963/stages/20/0/taskList?sortBy=-runtime/json_expectation
 delete mode 100644 core/src/test/resources/HistoryServerExpectations/applications/local-1427397477963/stages/20/0/taskList?sortBy=DECREASING_RUNTIME/json_expectation
 delete mode 100644 core/src/test/resources/HistoryServerExpectations/applications/local-1427397477963/stages/20/0/taskList?sortBy=runtime/json_expectation
 delete mode 100644 core/src/test/resources/HistoryServerExpectations/applications/local-1427397477963/stages/20/0/taskSummary/json_expectation
 delete mode 100644 core/src/test/resources/HistoryServerExpectations/applications/local-1427397477963/stages/20/0/taskSummary?quantiles=0.01,0.5,0.99/json_expectation
 delete mode 100644 core/src/test/resources/HistoryServerExpectations/applications?maxDate=2015-02-03T10:42:40.000CST/json_expectation
 delete mode 100644 core/src/test/resources/HistoryServerExpectations/applications?maxDate=2015-02-10/json_expectation
 delete mode 100644 core/src/test/resources/HistoryServerExpectations/applications?minDate=2015-02-10/json_expectation
 delete mode 100644 core/src/test/resources/HistoryServerExpectations/applications?status=completed/json_expectation
 delete mode 100644 core/src/test/resources/HistoryServerExpectations/applications?status=running/json_expectation
 delete mode 100755 core/src/test/resources/spark-events/local-1422981759269/APPLICATION_COMPLETE
 delete mode 100755 core/src/test/resources/spark-events/local-1422981759269/EVENT_LOG_1
 delete mode 100755 core/src/test/resources/spark-events/local-1422981759269/SPARK_VERSION_1.2.0
 delete mode 100755 core/src/test/resources/spark-events/local-1422981780767/APPLICATION_COMPLETE
 delete mode 100755 core/src/test/resources/spark-events/local-1422981780767/EVENT_LOG_1
 delete mode 100755 core/src/test/resources/spark-events/local-1422981780767/SPARK_VERSION_1.2.0
 delete mode 100755 core/src/test/resources/spark-events/local-1425081759269/APPLICATION_COMPLETE
 delete mode 100755 core/src/test/resources/spark-events/local-1425081759269/EVENT_LOG_1
 delete mode 100755 core/src/test/resources/spark-events/local-1425081759269/SPARK_VERSION_1.2.0
 delete mode 100755 core/src/test/resources/spark-events/local-1426533911241/APPLICATION_COMPLETE
 delete mode 100755 core/src/test/resources/spark-events/local-1426533911241/EVENT_LOG_1
 delete mode 100755 core/src/test/resources/spark-events/local-1426533911241/SPARK_VERSION_1.2.0
 delete mode 100755 core/src/test/resources/spark-events/local-1426633911242/APPLICATION_COMPLETE
 delete mode 100755 core/src/test/resources/spark-events/local-1426633911242/EVENT_LOG_1
 delete mode 100755 core/src/test/resources/spark-events/local-1426633911242/SPARK_VERSION_1.2.0
 delete mode 100755 core/src/test/resources/spark-events/local-1427397477963
 delete mode 100644 core/src/test/scala/org/apache/spark/JsonTestUtils.scala
 delete mode 100644 core/src/test/scala/org/apache/spark/status/api/v1/SimpleDateParamTest.scala

diff --git a/.rat-excludes b/.rat-excludes
index ac652ed29fc09..dccf2db8055ce 100644
--- a/.rat-excludes
+++ b/.rat-excludes
@@ -74,12 +74,5 @@ logs
 .*scalastyle-output.xml
 .*dependency-reduced-pom.xml
 known_translations
-json_expectation
-local-1422981759269/*
-local-1422981780767/*
-local-1425081759269/*
-local-1426533911241/*
-local-1426633911242/*
-local-1427397477963/*
 DESCRIPTION
 NAMESPACE
diff --git a/core/pom.xml b/core/pom.xml
index fc42f48973fe9..164a836cb8f0b 100644
--- a/core/pom.xml
+++ b/core/pom.xml
@@ -228,14 +228,6 @@
       <artifactId>json4s-jackson_${scala.binary.version}</artifactId>
       <version>3.2.10</version>
     </dependency>
-    <dependency>
-      <groupId>com.sun.jersey</groupId>
-      <artifactId>jersey-server</artifactId>
-    </dependency>
-    <dependency>
-      <groupId>com.sun.jersey</groupId>
-      <artifactId>jersey-core</artifactId>
-    </dependency>
     <dependency>
       <groupId>org.apache.mesos</groupId>
       <artifactId>mesos</artifactId>
diff --git a/core/src/main/java/org/apache/spark/JobExecutionStatus.java b/core/src/main/java/org/apache/spark/JobExecutionStatus.java
index 0287fb79f8dd2..6e161313702bb 100644
--- a/core/src/main/java/org/apache/spark/JobExecutionStatus.java
+++ b/core/src/main/java/org/apache/spark/JobExecutionStatus.java
@@ -17,15 +17,9 @@
 
 package org.apache.spark;
 
-import org.apache.spark.util.EnumUtil;
-
 public enum JobExecutionStatus {
   RUNNING,
   SUCCEEDED,
   FAILED,
-  UNKNOWN;
-
-  public static JobExecutionStatus fromString(String str) {
-    return EnumUtil.parseIgnoreCase(JobExecutionStatus.class, str);
-  }
+  UNKNOWN
 }
diff --git a/core/src/main/java/org/apache/spark/status/api/v1/ApplicationStatus.java b/core/src/main/java/org/apache/spark/status/api/v1/ApplicationStatus.java
deleted file mode 100644
index 8c7dcf776fda8..0000000000000
--- a/core/src/main/java/org/apache/spark/status/api/v1/ApplicationStatus.java
+++ /dev/null
@@ -1,30 +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.status.api.v1;
-
-import org.apache.spark.util.EnumUtil;
-
-public enum ApplicationStatus {
-  COMPLETED,
-  RUNNING;
-
-  public static ApplicationStatus fromString(String str) {
-    return EnumUtil.parseIgnoreCase(ApplicationStatus.class, str);
-  }
-
-}
diff --git a/core/src/main/java/org/apache/spark/status/api/v1/StageStatus.java b/core/src/main/java/org/apache/spark/status/api/v1/StageStatus.java
deleted file mode 100644
index 9dbb565aab707..0000000000000
--- a/core/src/main/java/org/apache/spark/status/api/v1/StageStatus.java
+++ /dev/null
@@ -1,31 +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.status.api.v1;
-
-import org.apache.spark.util.EnumUtil;
-
-public enum StageStatus {
-  ACTIVE,
-  COMPLETE,
-  FAILED,
-  PENDING;
-
-  public static StageStatus fromString(String str) {
-    return EnumUtil.parseIgnoreCase(StageStatus.class, str);
-  }
-}
diff --git a/core/src/main/java/org/apache/spark/status/api/v1/TaskSorting.java b/core/src/main/java/org/apache/spark/status/api/v1/TaskSorting.java
deleted file mode 100644
index f19ed01d5aebf..0000000000000
--- a/core/src/main/java/org/apache/spark/status/api/v1/TaskSorting.java
+++ /dev/null
@@ -1,48 +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.status.api.v1;
-
-import org.apache.spark.util.EnumUtil;
-
-import java.util.HashSet;
-import java.util.Set;
-
-public enum TaskSorting {
-  ID,
-  INCREASING_RUNTIME("runtime"),
-  DECREASING_RUNTIME("-runtime");
-
-  private final Set<String> alternateNames;
-  private TaskSorting(String... names) {
-    alternateNames = new HashSet<String>();
-    for (String n: names) {
-      alternateNames.add(n);
-    }
-  }
-
-  public static TaskSorting fromString(String str) {
-    String lower = str.toLowerCase();
-    for (TaskSorting t: values()) {
-      if (t.alternateNames.contains(lower)) {
-        return t;
-      }
-    }
-    return EnumUtil.parseIgnoreCase(TaskSorting.class, str);
-  }
-
-}
diff --git a/core/src/main/java/org/apache/spark/util/EnumUtil.java b/core/src/main/java/org/apache/spark/util/EnumUtil.java
deleted file mode 100644
index c40c7e727613c..0000000000000
--- a/core/src/main/java/org/apache/spark/util/EnumUtil.java
+++ /dev/null
@@ -1,38 +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.util;
-
-import com.google.common.base.Joiner;
-import org.apache.spark.annotation.Private;
-
-@Private
-public class EnumUtil {
-  public static <E extends Enum<E>> E parseIgnoreCase(Class<E> clz, String str) {
-    E[] constants = clz.getEnumConstants();
-    if (str == null) {
-      return null;
-    }
-    for (E e : constants) {
-      if (e.name().equalsIgnoreCase(str)) {
-        return e;
-      }
-    }
-    throw new IllegalArgumentException(
-      String.format("Illegal type='%s'. Supported type values: %s",
-        str, Joiner.on(", ").join(constants)));
-  }
-}
diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala
index 2ca6882c8d890..682dec44ac1a5 100644
--- a/core/src/main/scala/org/apache/spark/SparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/SparkContext.scala
@@ -428,7 +428,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
     _ui =
       if (conf.getBoolean("spark.ui.enabled", true)) {
         Some(SparkUI.createLiveUI(this, _conf, listenerBus, _jobProgressListener,
-          _env.securityManager,appName, startTime = startTime))
+          _env.securityManager,appName))
       } else {
         // For tests, do not enable the UI
         None
diff --git a/core/src/main/scala/org/apache/spark/annotation/Private.java b/core/src/main/scala/org/apache/spark/annotation/Private.java
deleted file mode 100644
index 9082fcf0c84bc..0000000000000
--- a/core/src/main/scala/org/apache/spark/annotation/Private.java
+++ /dev/null
@@ -1,41 +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.annotation;
-
-import java.lang.annotation.ElementType;
-import java.lang.annotation.Retention;
-import java.lang.annotation.RetentionPolicy;
-import java.lang.annotation.Target;
-
-/**
- * A class that is considered private to the internals of Spark -- there is a high-likelihood
- * they will be changed in future versions of Spark.
- *
- * This should be used only when the standard Scala / Java means of protecting classes are
- * insufficient.  In particular, Java has no equivalent of private[spark], so we use this annotation
- * in its place.
- *
- * NOTE: If there exists a Scaladoc comment that immediately precedes this annotation, the first
- * line of the comment must be ":: Private ::" with no trailing blank line. This is because
- * of the known issue that Scaladoc displays only either the annotation or the comment, whichever
- * comes first.
- */
-@Retention(RetentionPolicy.RUNTIME)
-@Target({ElementType.TYPE, ElementType.FIELD, ElementType.METHOD, ElementType.PARAMETER,
-        ElementType.CONSTRUCTOR, ElementType.LOCAL_VARIABLE, ElementType.PACKAGE})
-public @interface Private {}
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 298a8201960d1..6a5011af17458 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
@@ -19,7 +19,7 @@ package org.apache.spark.deploy.history
 
 import org.apache.spark.ui.SparkUI
 
-private[spark] case class ApplicationAttemptInfo(
+private[history] case class ApplicationAttemptInfo(
     attemptId: Option[String],
     startTime: Long,
     endTime: Long,
@@ -27,7 +27,7 @@ private[spark] case class ApplicationAttemptInfo(
     sparkUser: String,
     completed: Boolean = false)
 
-private[spark] case class ApplicationHistoryInfo(
+private[history] case class ApplicationHistoryInfo(
     id: String,
     name: String,
     attempts: List[ApplicationAttemptInfo])
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 45c2be34c8680..993763f3aa092 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
@@ -17,21 +17,23 @@
 
 package org.apache.spark.deploy.history
 
-import java.io.{BufferedInputStream, FileNotFoundException, IOException, InputStream}
+import java.io.{IOException, BufferedInputStream, FileNotFoundException, InputStream}
 import java.util.concurrent.{ExecutorService, Executors, TimeUnit}
 
 import scala.collection.mutable
+import scala.concurrent.duration.Duration
 
-import com.google.common.util.concurrent.{MoreExecutors, ThreadFactoryBuilder}
-import org.apache.hadoop.fs.{FileStatus, Path}
-import org.apache.hadoop.fs.permission.AccessControlException
+import com.google.common.util.concurrent.ThreadFactoryBuilder
 
-import org.apache.spark.{Logging, SecurityManager, SparkConf}
+import com.google.common.util.concurrent.MoreExecutors
+import org.apache.hadoop.fs.permission.AccessControlException
+import org.apache.hadoop.fs.{FileStatus, Path}
 import org.apache.spark.deploy.SparkHadoopUtil
 import org.apache.spark.io.CompressionCodec
 import org.apache.spark.scheduler._
 import org.apache.spark.ui.SparkUI
 import org.apache.spark.util.{Clock, SystemClock, ThreadUtils, Utils}
+import org.apache.spark.{Logging, SecurityManager, SparkConf}
 
 /**
  * A class that provides application history from event logs stored in the file system.
@@ -149,7 +151,7 @@ private[history] class FsHistoryProvider(conf: SparkConf, clock: Clock)
             val conf = this.conf.clone()
             val appSecManager = new SecurityManager(conf)
             SparkUI.createHistoryUI(conf, replayBus, appSecManager, appId,
-              HistoryServer.getAttemptURI(appId, attempt.attemptId), attempt.startTime)
+              HistoryServer.getAttemptURI(appId, attempt.attemptId))
             // Do not call ui.bind() to avoid creating a new server for each application
           }
 
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 50522e69dc519..754c8e9b6668b 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
@@ -25,7 +25,6 @@ import org.eclipse.jetty.servlet.{ServletContextHandler, ServletHolder}
 
 import org.apache.spark.{Logging, SecurityManager, SparkConf}
 import org.apache.spark.deploy.SparkHadoopUtil
-import org.apache.spark.status.api.v1.{ApplicationInfo, ApplicationsListResource, JsonRootResource, UIRoot}
 import org.apache.spark.ui.{SparkUI, UIUtils, WebUI}
 import org.apache.spark.ui.JettyUtils._
 import org.apache.spark.util.{SignalLogger, Utils}
@@ -46,7 +45,7 @@ class HistoryServer(
     provider: ApplicationHistoryProvider,
     securityManager: SecurityManager,
     port: Int)
-  extends WebUI(securityManager, port, conf) with Logging with UIRoot {
+  extends WebUI(securityManager, port, conf) with Logging {
 
   // How many applications to retain
   private val retainedApplications = conf.getInt("spark.history.retainedApplications", 50)
@@ -57,7 +56,7 @@ class HistoryServer(
       require(parts.length == 1 || parts.length == 2, s"Invalid app key $key")
       val ui = provider
         .getAppUI(parts(0), if (parts.length > 1) Some(parts(1)) else None)
-        .getOrElse(throw new NoSuchElementException(s"no app with key $key"))
+        .getOrElse(throw new NoSuchElementException())
       attachSparkUI(ui)
       ui
     }
@@ -114,10 +113,6 @@ class HistoryServer(
     }
   }
 
-  def getSparkUI(appKey: String): Option[SparkUI] = {
-    Option(appCache.get(appKey))
-  }
-
   initialize()
 
   /**
@@ -128,9 +123,6 @@ class HistoryServer(
    */
   def initialize() {
     attachPage(new HistoryPage(this))
-
-    attachHandler(JsonRootResource.getJsonServlet(this))
-
     attachHandler(createStaticHandler(SparkUI.STATIC_RESOURCE_DIR, "/static"))
 
     val contextHandler = new ServletContextHandler
@@ -168,13 +160,7 @@ class HistoryServer(
    *
    * @return List of all known applications.
    */
-  def getApplicationList(): Iterable[ApplicationHistoryInfo] = {
-    provider.getListing()
-  }
-
-  def getApplicationInfoList: Iterator[ApplicationInfo] = {
-    getApplicationList().iterator.map(ApplicationsListResource.appHistoryInfoToPublicAppInfo)
-  }
+  def getApplicationList(): Iterable[ApplicationHistoryInfo] = provider.getListing()
 
   /**
    * Returns the provider configuration to show in the listing page.
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 1620e95bea218..f59d550d4f3b3 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
@@ -28,7 +28,7 @@ import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.deploy.ApplicationDescription
 import org.apache.spark.util.Utils
 
-private[spark] class ApplicationInfo(
+private[deploy] class ApplicationInfo(
     val startTime: Long,
     val id: String,
     val desc: ApplicationDescription,
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 53e1903a3d125..0fac3cdcf55e7 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
@@ -754,9 +754,9 @@ private[master] class Master(
 
   /**
    * Rebuild a new SparkUI from the given application's event logs.
-   * Return the UI if successful, else None
+   * Return whether this is successful.
    */
-  private[master] def rebuildSparkUI(app: ApplicationInfo): Option[SparkUI] = {
+  private def rebuildSparkUI(app: ApplicationInfo): Boolean = {
     val appName = app.desc.name
     val notFoundBasePath = HistoryServer.UI_PATH_PREFIX + "/not-found"
     try {
@@ -764,7 +764,7 @@ private[master] class Master(
         .getOrElse {
           // Event logging is not enabled for this application
           app.desc.appUiUrl = notFoundBasePath
-          return None
+          return false
         }
 
       val eventLogFilePrefix = EventLoggingListener.getLogPath(
@@ -787,7 +787,7 @@ private[master] class Master(
       val logInput = EventLoggingListener.openEventLog(new Path(eventLogFile), fs)
       val replayBus = new ReplayListenerBus()
       val ui = SparkUI.createHistoryUI(new SparkConf, replayBus, new SecurityManager(conf),
-        appName + status, HistoryServer.UI_PATH_PREFIX + s"/${app.id}", app.startTime)
+        appName + status, HistoryServer.UI_PATH_PREFIX + s"/${app.id}")
       val maybeTruncated = eventLogFile.endsWith(EventLoggingListener.IN_PROGRESS)
       try {
         replayBus.replay(logInput, eventLogFile, maybeTruncated)
@@ -798,7 +798,7 @@ private[master] class Master(
       webUi.attachSparkUI(ui)
       // Application UI is successfully rebuilt, so link the Master UI to it
       app.desc.appUiUrl = ui.basePath
-      Some(ui)
+      true
     } catch {
       case fnf: FileNotFoundException =>
         // Event logging is enabled for this application, but no event logs are found
@@ -808,7 +808,7 @@ private[master] class Master(
         msg += " Did you specify the correct logging directory?"
         msg = URLEncoder.encode(msg, "UTF-8")
         app.desc.appUiUrl = notFoundBasePath + s"?msg=$msg&title=$title"
-        None
+        false
       case e: Exception =>
         // Relay exception message to application UI page
         val title = s"Application history load error (${app.id})"
@@ -817,7 +817,7 @@ private[master] class Master(
         logError(msg, e)
         msg = URLEncoder.encode(msg, "UTF-8")
         app.desc.appUiUrl = notFoundBasePath + s"?msg=$msg&exception=$exception&title=$title"
-        None
+        false
     }
   }
 
diff --git a/core/src/main/scala/org/apache/spark/deploy/master/ui/ApplicationPage.scala b/core/src/main/scala/org/apache/spark/deploy/master/ui/ApplicationPage.scala
index 06e265f99e231..273f077bd8f57 100644
--- a/core/src/main/scala/org/apache/spark/deploy/master/ui/ApplicationPage.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/master/ui/ApplicationPage.scala
@@ -23,8 +23,10 @@ import scala.concurrent.Await
 import scala.xml.Node
 
 import akka.pattern.ask
+import org.json4s.JValue
+import org.json4s.JsonAST.JNothing
 
-import org.apache.spark.deploy.ExecutorState
+import org.apache.spark.deploy.{ExecutorState, JsonProtocol}
 import org.apache.spark.deploy.DeployMessages.{MasterStateResponse, RequestMasterState}
 import org.apache.spark.deploy.master.ExecutorDesc
 import org.apache.spark.ui.{UIUtils, WebUIPage}
@@ -35,6 +37,21 @@ private[ui] class ApplicationPage(parent: MasterWebUI) extends WebUIPage("app")
   private val master = parent.masterActorRef
   private val timeout = parent.timeout
 
+  /** Executor details for a particular application */
+  override def renderJson(request: HttpServletRequest): JValue = {
+    val appId = request.getParameter("appId")
+    val stateFuture = (master ? RequestMasterState)(timeout).mapTo[MasterStateResponse]
+    val state = Await.result(stateFuture, timeout)
+    val app = state.activeApps.find(_.id == appId).getOrElse({
+      state.completedApps.find(_.id == appId).getOrElse(null)
+    })
+    if (app == null) {
+      JNothing
+    } else {
+      JsonProtocol.writeApplicationInfo(app)
+    }
+  }
+
   /** Executor details for a particular application */
   def render(request: HttpServletRequest): Seq[Node] = {
     val appId = request.getParameter("appId")
diff --git a/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterPage.scala b/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterPage.scala
index 756927682cd24..1f2c3fdbfb2bc 100644
--- a/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterPage.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterPage.scala
@@ -35,13 +35,10 @@ private[ui] class MasterPage(parent: MasterWebUI) extends WebUIPage("") {
   private val master = parent.masterActorRef
   private val timeout = parent.timeout
 
-  def getMasterState: MasterStateResponse = {
-    val stateFuture = (master ? RequestMasterState)(timeout).mapTo[MasterStateResponse]
-    Await.result(stateFuture, timeout)
-  }
-
   override def renderJson(request: HttpServletRequest): JValue = {
-    JsonProtocol.writeMasterState(getMasterState)
+    val stateFuture = (master ? RequestMasterState)(timeout).mapTo[MasterStateResponse]
+    val state = Await.result(stateFuture, timeout)
+    JsonProtocol.writeMasterState(state)
   }
 
   def handleAppKillRequest(request: HttpServletRequest): Unit = {
@@ -71,7 +68,8 @@ private[ui] class MasterPage(parent: MasterWebUI) extends WebUIPage("") {
 
   /** Index view listing applications and executors */
   def render(request: HttpServletRequest): Seq[Node] = {
-    val state = getMasterState
+    val stateFuture = (master ? RequestMasterState)(timeout).mapTo[MasterStateResponse]
+    val state = Await.result(stateFuture, timeout)
 
     val workerHeaders = Seq("Worker Id", "Address", "State", "Cores", "Memory")
     val workers = state.workers.sortBy(_.id)
diff --git a/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterWebUI.scala b/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterWebUI.scala
index eb26e9f99c70b..dea0a65eeeaa6 100644
--- a/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterWebUI.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterWebUI.scala
@@ -19,7 +19,6 @@ package org.apache.spark.deploy.master.ui
 
 import org.apache.spark.Logging
 import org.apache.spark.deploy.master.Master
-import org.apache.spark.status.api.v1.{ApplicationsListResource, ApplicationInfo, JsonRootResource, UIRoot}
 import org.apache.spark.ui.{SparkUI, WebUI}
 import org.apache.spark.ui.JettyUtils._
 import org.apache.spark.util.RpcUtils
@@ -29,15 +28,12 @@ import org.apache.spark.util.RpcUtils
  */
 private[master]
 class MasterWebUI(val master: Master, requestedPort: Int)
-  extends WebUI(master.securityMgr, requestedPort, master.conf, name = "MasterUI") with Logging
-  with UIRoot {
+  extends WebUI(master.securityMgr, requestedPort, master.conf, name = "MasterUI") with Logging {
 
   val masterActorRef = master.self
   val timeout = RpcUtils.askTimeout(master.conf)
   val killEnabled = master.conf.getBoolean("spark.ui.killEnabled", true)
 
-  val masterPage = new MasterPage(this)
-
   initialize()
 
   /** Initialize all components of the server. */
@@ -47,7 +43,6 @@ class MasterWebUI(val master: Master, requestedPort: Int)
     attachPage(new HistoryNotFoundPage(this))
     attachPage(masterPage)
     attachHandler(createStaticHandler(MasterWebUI.STATIC_RESOURCE_DIR, "/static"))
-    attachHandler(JsonRootResource.getJsonServlet(this))
     attachHandler(createRedirectHandler(
       "/app/kill", "/", masterPage.handleAppKillRequest, httpMethods = Set("POST")))
     attachHandler(createRedirectHandler(
@@ -65,23 +60,6 @@ class MasterWebUI(val master: Master, requestedPort: Int)
     assert(serverInfo.isDefined, "Master UI must be bound to a server before detaching SparkUIs")
     ui.getHandlers.foreach(detachHandler)
   }
-
-  def getApplicationInfoList: Iterator[ApplicationInfo] = {
-    val state = masterPage.getMasterState
-    val activeApps = state.activeApps.sortBy(_.startTime).reverse
-    val completedApps = state.completedApps.sortBy(_.endTime).reverse
-    activeApps.iterator.map { ApplicationsListResource.convertApplicationInfo(_, false) } ++
-      completedApps.iterator.map { ApplicationsListResource.convertApplicationInfo(_, true) }
-  }
-
-  def getSparkUI(appId: String): Option[SparkUI] = {
-    val state = masterPage.getMasterState
-    val activeApps = state.activeApps.sortBy(_.startTime).reverse
-    val completedApps = state.completedApps.sortBy(_.endTime).reverse
-    (activeApps ++ completedApps).find { _.id == appId }.flatMap {
-      master.rebuildSparkUI
-    }
-  }
 }
 
 private[master] object MasterWebUI {
diff --git a/core/src/main/scala/org/apache/spark/status/api/v1/AllJobsResource.scala b/core/src/main/scala/org/apache/spark/status/api/v1/AllJobsResource.scala
deleted file mode 100644
index 5783df5d8220c..0000000000000
--- a/core/src/main/scala/org/apache/spark/status/api/v1/AllJobsResource.scala
+++ /dev/null
@@ -1,98 +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.status.api.v1
-
-import java.util.{Arrays, Date, List => JList}
-import javax.ws.rs._
-import javax.ws.rs.core.MediaType
-
-import org.apache.spark.JobExecutionStatus
-import org.apache.spark.ui.SparkUI
-import org.apache.spark.ui.jobs.JobProgressListener
-import org.apache.spark.ui.jobs.UIData.JobUIData
-
-@Produces(Array(MediaType.APPLICATION_JSON))
-private[v1] class AllJobsResource(ui: SparkUI) {
-
-  @GET
-  def jobsList(@QueryParam("status") statuses: JList[JobExecutionStatus]): Seq[JobData] = {
-    val statusToJobs: Seq[(JobExecutionStatus, Seq[JobUIData])] =
-      AllJobsResource.getStatusToJobs(ui)
-    val adjStatuses: JList[JobExecutionStatus] = {
-      if (statuses.isEmpty) {
-        Arrays.asList(JobExecutionStatus.values(): _*)
-      } else {
-        statuses
-      }
-    }
-    val jobInfos = for {
-      (status, jobs) <- statusToJobs
-      job <- jobs if adjStatuses.contains(status)
-    } yield {
-      AllJobsResource.convertJobData(job, ui.jobProgressListener, false)
-    }
-    jobInfos.sortBy{- _.jobId}
-  }
-
-}
-
-private[v1] object AllJobsResource {
-
-  def getStatusToJobs(ui: SparkUI): Seq[(JobExecutionStatus, Seq[JobUIData])] = {
-    val statusToJobs = ui.jobProgressListener.synchronized {
-      Seq(
-        JobExecutionStatus.RUNNING -> ui.jobProgressListener.activeJobs.values.toSeq,
-        JobExecutionStatus.SUCCEEDED -> ui.jobProgressListener.completedJobs.toSeq,
-        JobExecutionStatus.FAILED -> ui.jobProgressListener.failedJobs.reverse.toSeq
-      )
-    }
-    statusToJobs
-  }
-
-  def convertJobData(
-      job: JobUIData,
-      listener: JobProgressListener,
-      includeStageDetails: Boolean): JobData = {
-    listener.synchronized {
-      val lastStageInfo = listener.stageIdToInfo.get(job.stageIds.max)
-      val lastStageData = lastStageInfo.flatMap { s =>
-        listener.stageIdToData.get((s.stageId, s.attemptId))
-      }
-      val lastStageName = lastStageInfo.map { _.name }.getOrElse("(Unknown Stage Name)")
-      val lastStageDescription = lastStageData.flatMap { _.description }
-      new JobData(
-        jobId = job.jobId,
-        name = lastStageName,
-        description = lastStageDescription,
-        submissionTime = job.submissionTime.map{new Date(_)},
-        completionTime = job.completionTime.map{new Date(_)},
-        stageIds = job.stageIds,
-        jobGroup = job.jobGroup,
-        status = job.status,
-        numTasks = job.numTasks,
-        numActiveTasks = job.numActiveTasks,
-        numCompletedTasks = job.numCompletedTasks,
-        numSkippedTasks = job.numCompletedTasks,
-        numFailedTasks = job.numFailedTasks,
-        numActiveStages = job.numActiveStages,
-        numCompletedStages = job.completedStageIndices.size,
-        numSkippedStages = job.numSkippedStages,
-        numFailedStages = job.numFailedStages
-      )
-    }
-  }
-}
diff --git a/core/src/main/scala/org/apache/spark/status/api/v1/AllRDDResource.scala b/core/src/main/scala/org/apache/spark/status/api/v1/AllRDDResource.scala
deleted file mode 100644
index 645ede26a0879..0000000000000
--- a/core/src/main/scala/org/apache/spark/status/api/v1/AllRDDResource.scala
+++ /dev/null
@@ -1,104 +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.status.api.v1
-
-import javax.ws.rs.{GET, Produces}
-import javax.ws.rs.core.MediaType
-
-import org.apache.spark.storage.{RDDInfo, StorageStatus, StorageUtils}
-import org.apache.spark.ui.SparkUI
-import org.apache.spark.ui.storage.StorageListener
-
-@Produces(Array(MediaType.APPLICATION_JSON))
-private[v1] class AllRDDResource(ui: SparkUI) {
-
-  @GET
-  def rddList(): Seq[RDDStorageInfo] = {
-    val storageStatusList = ui.storageListener.storageStatusList
-    val rddInfos = ui.storageListener.rddInfoList
-    rddInfos.map{rddInfo =>
-      AllRDDResource.getRDDStorageInfo(rddInfo.id, rddInfo, storageStatusList,
-        includeDetails = false)
-    }
-  }
-
-}
-
-private[spark] object AllRDDResource {
-
-  def getRDDStorageInfo(
-      rddId: Int,
-      listener: StorageListener,
-      includeDetails: Boolean): Option[RDDStorageInfo] = {
-    val storageStatusList = listener.storageStatusList
-    listener.rddInfoList.find { _.id == rddId }.map { rddInfo =>
-      getRDDStorageInfo(rddId, rddInfo, storageStatusList, includeDetails)
-    }
-  }
-
-  def getRDDStorageInfo(
-      rddId: Int,
-      rddInfo: RDDInfo,
-      storageStatusList: Seq[StorageStatus],
-      includeDetails: Boolean): RDDStorageInfo = {
-    val workers = storageStatusList.map { (rddId, _) }
-    val blockLocations = StorageUtils.getRddBlockLocations(rddId, storageStatusList)
-    val blocks = storageStatusList
-      .flatMap { _.rddBlocksById(rddId) }
-      .sortWith { _._1.name < _._1.name }
-      .map { case (blockId, status) =>
-        (blockId, status, blockLocations.get(blockId).getOrElse(Seq[String]("Unknown")))
-      }
-
-    val dataDistribution = if (includeDetails) {
-      Some(storageStatusList.map { status =>
-        new RDDDataDistribution(
-          address = status.blockManagerId.hostPort,
-          memoryUsed = status.memUsedByRdd(rddId),
-          memoryRemaining = status.memRemaining,
-          diskUsed = status.diskUsedByRdd(rddId)
-        ) } )
-    } else {
-      None
-    }
-    val partitions = if (includeDetails) {
-      Some(blocks.map { case (id, block, locations) =>
-        new RDDPartitionInfo(
-          blockName = id.name,
-          storageLevel = block.storageLevel.description,
-          memoryUsed = block.memSize,
-          diskUsed = block.diskSize,
-          executors = locations
-        )
-      } )
-    } else {
-      None
-    }
-
-    new RDDStorageInfo(
-      id = rddId,
-      name = rddInfo.name,
-      numPartitions = rddInfo.numPartitions,
-      numCachedPartitions = rddInfo.numCachedPartitions,
-      storageLevel = rddInfo.storageLevel.description,
-      memoryUsed = rddInfo.memSize,
-      diskUsed = rddInfo.diskSize,
-      dataDistribution = dataDistribution,
-      partitions = partitions
-    )
-  }
-}
diff --git a/core/src/main/scala/org/apache/spark/status/api/v1/AllStagesResource.scala b/core/src/main/scala/org/apache/spark/status/api/v1/AllStagesResource.scala
deleted file mode 100644
index 50608588f09ae..0000000000000
--- a/core/src/main/scala/org/apache/spark/status/api/v1/AllStagesResource.scala
+++ /dev/null
@@ -1,309 +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.status.api.v1
-
-import java.util.{Arrays, Date, List => JList}
-import javax.ws.rs.{GET, PathParam, Produces, QueryParam}
-import javax.ws.rs.core.MediaType
-
-import org.apache.spark.executor.{InputMetrics => InternalInputMetrics, OutputMetrics => InternalOutputMetrics, ShuffleReadMetrics => InternalShuffleReadMetrics, ShuffleWriteMetrics => InternalShuffleWriteMetrics, TaskMetrics => InternalTaskMetrics}
-import org.apache.spark.scheduler.{AccumulableInfo => InternalAccumulableInfo, StageInfo}
-import org.apache.spark.ui.SparkUI
-import org.apache.spark.ui.jobs.UIData.{StageUIData, TaskUIData}
-import org.apache.spark.util.Distribution
-
-@Produces(Array(MediaType.APPLICATION_JSON))
-private[v1] class AllStagesResource(ui: SparkUI) {
-
-  @GET
-  def stageList(@QueryParam("status") statuses: JList[StageStatus]): Seq[StageData] = {
-    val listener = ui.jobProgressListener
-    val stageAndStatus = AllStagesResource.stagesAndStatus(ui)
-    val adjStatuses = {
-      if (statuses.isEmpty()) {
-        Arrays.asList(StageStatus.values(): _*)
-      } else {
-        statuses
-      }
-    }
-    for {
-      (status, stageList) <- stageAndStatus
-      stageInfo: StageInfo <- stageList if adjStatuses.contains(status)
-      stageUiData: StageUIData <- listener.synchronized {
-        listener.stageIdToData.get((stageInfo.stageId, stageInfo.attemptId))
-      }
-    } yield {
-      AllStagesResource.stageUiToStageData(status, stageInfo, stageUiData, includeDetails = false)
-    }
-  }
-}
-
-private[v1] object AllStagesResource {
-  def stageUiToStageData(
-      status: StageStatus,
-      stageInfo: StageInfo,
-      stageUiData: StageUIData,
-      includeDetails: Boolean): StageData = {
-
-    val taskData = if (includeDetails) {
-      Some(stageUiData.taskData.map { case (k, v) => k -> convertTaskData(v) } )
-    } else {
-      None
-    }
-    val executorSummary = if (includeDetails) {
-      Some(stageUiData.executorSummary.map { case (k, summary) =>
-        k -> new ExecutorStageSummary(
-          taskTime = summary.taskTime,
-          failedTasks = summary.failedTasks,
-          succeededTasks = summary.succeededTasks,
-          inputBytes = summary.inputBytes,
-          outputBytes = summary.outputBytes,
-          shuffleRead = summary.shuffleRead,
-          shuffleWrite = summary.shuffleWrite,
-          memoryBytesSpilled = summary.memoryBytesSpilled,
-          diskBytesSpilled = summary.diskBytesSpilled
-        )
-      })
-    } else {
-      None
-    }
-
-    val accumulableInfo = stageUiData.accumulables.values.map { convertAccumulableInfo }.toSeq
-
-    new StageData(
-      status = status,
-      stageId = stageInfo.stageId,
-      attemptId = stageInfo.attemptId,
-      numActiveTasks = stageUiData.numActiveTasks,
-      numCompleteTasks = stageUiData.numCompleteTasks,
-      numFailedTasks = stageUiData.numFailedTasks,
-      executorRunTime = stageUiData.executorRunTime,
-      inputBytes = stageUiData.inputBytes,
-      inputRecords = stageUiData.inputRecords,
-      outputBytes = stageUiData.outputBytes,
-      outputRecords = stageUiData.outputRecords,
-      shuffleReadBytes = stageUiData.shuffleReadTotalBytes,
-      shuffleReadRecords = stageUiData.shuffleReadRecords,
-      shuffleWriteBytes = stageUiData.shuffleWriteBytes,
-      shuffleWriteRecords = stageUiData.shuffleWriteRecords,
-      memoryBytesSpilled = stageUiData.memoryBytesSpilled,
-      diskBytesSpilled = stageUiData.diskBytesSpilled,
-      schedulingPool = stageUiData.schedulingPool,
-      name = stageInfo.name,
-      details = stageInfo.details,
-      accumulatorUpdates = accumulableInfo,
-      tasks = taskData,
-      executorSummary = executorSummary
-    )
-  }
-
-  def stagesAndStatus(ui: SparkUI): Seq[(StageStatus, Seq[StageInfo])] = {
-    val listener = ui.jobProgressListener
-    listener.synchronized {
-      Seq(
-        StageStatus.ACTIVE -> listener.activeStages.values.toSeq,
-        StageStatus.COMPLETE -> listener.completedStages.reverse.toSeq,
-        StageStatus.FAILED -> listener.failedStages.reverse.toSeq,
-        StageStatus.PENDING -> listener.pendingStages.values.toSeq
-      )
-    }
-  }
-
-  def convertTaskData(uiData: TaskUIData): TaskData = {
-    new TaskData(
-      taskId = uiData.taskInfo.taskId,
-      index = uiData.taskInfo.index,
-      attempt = uiData.taskInfo.attempt,
-      launchTime = new Date(uiData.taskInfo.launchTime),
-      executorId = uiData.taskInfo.executorId,
-      host = uiData.taskInfo.host,
-      taskLocality = uiData.taskInfo.taskLocality.toString(),
-      speculative = uiData.taskInfo.speculative,
-      accumulatorUpdates = uiData.taskInfo.accumulables.map { convertAccumulableInfo },
-      errorMessage = uiData.errorMessage,
-      taskMetrics = uiData.taskMetrics.map { convertUiTaskMetrics }
-    )
-  }
-
-  def taskMetricDistributions(
-      allTaskData: Iterable[TaskUIData],
-      quantiles: Array[Double]): TaskMetricDistributions = {
-
-    val rawMetrics = allTaskData.flatMap{_.taskMetrics}.toSeq
-
-    def metricQuantiles(f: InternalTaskMetrics => Double): IndexedSeq[Double] =
-      Distribution(rawMetrics.map { d => f(d) }).get.getQuantiles(quantiles)
-
-    // We need to do a lot of similar munging to nested metrics here.  For each one,
-    // we want (a) extract the values for nested metrics (b) make a distribution for each metric
-    // (c) shove the distribution into the right field in our return type and (d) only return
-    // a result if the option is defined for any of the tasks.  MetricHelper is a little util
-    // to make it a little easier to deal w/ all of the nested options.  Mostly it lets us just
-    // implement one "build" method, which just builds the quantiles for each field.
-
-    val inputMetrics: Option[InputMetricDistributions] =
-      new MetricHelper[InternalInputMetrics, InputMetricDistributions](rawMetrics, quantiles) {
-        def getSubmetrics(raw: InternalTaskMetrics): Option[InternalInputMetrics] = {
-          raw.inputMetrics
-        }
-
-        def build: InputMetricDistributions = new InputMetricDistributions(
-          bytesRead = submetricQuantiles(_.bytesRead),
-          recordsRead = submetricQuantiles(_.recordsRead)
-        )
-      }.metricOption
-
-    val outputMetrics: Option[OutputMetricDistributions] =
-      new MetricHelper[InternalOutputMetrics, OutputMetricDistributions](rawMetrics, quantiles) {
-        def getSubmetrics(raw:InternalTaskMetrics): Option[InternalOutputMetrics] = {
-          raw.outputMetrics
-        }
-        def build: OutputMetricDistributions = new OutputMetricDistributions(
-          bytesWritten = submetricQuantiles(_.bytesWritten),
-          recordsWritten = submetricQuantiles(_.recordsWritten)
-        )
-      }.metricOption
-
-    val shuffleReadMetrics: Option[ShuffleReadMetricDistributions] =
-      new MetricHelper[InternalShuffleReadMetrics, ShuffleReadMetricDistributions](rawMetrics,
-        quantiles) {
-        def getSubmetrics(raw: InternalTaskMetrics): Option[InternalShuffleReadMetrics] = {
-          raw.shuffleReadMetrics
-        }
-        def build: ShuffleReadMetricDistributions = new ShuffleReadMetricDistributions(
-          readBytes = submetricQuantiles(_.totalBytesRead),
-          readRecords = submetricQuantiles(_.recordsRead),
-          remoteBytesRead = submetricQuantiles(_.remoteBytesRead),
-          remoteBlocksFetched = submetricQuantiles(_.remoteBlocksFetched),
-          localBlocksFetched = submetricQuantiles(_.localBlocksFetched),
-          totalBlocksFetched = submetricQuantiles(_.totalBlocksFetched),
-          fetchWaitTime = submetricQuantiles(_.fetchWaitTime)
-        )
-      }.metricOption
-
-    val shuffleWriteMetrics: Option[ShuffleWriteMetricDistributions] =
-      new MetricHelper[InternalShuffleWriteMetrics, ShuffleWriteMetricDistributions](rawMetrics,
-        quantiles) {
-        def getSubmetrics(raw: InternalTaskMetrics): Option[InternalShuffleWriteMetrics] = {
-          raw.shuffleWriteMetrics
-        }
-        def build: ShuffleWriteMetricDistributions = new ShuffleWriteMetricDistributions(
-          writeBytes = submetricQuantiles(_.shuffleBytesWritten),
-          writeRecords = submetricQuantiles(_.shuffleRecordsWritten),
-          writeTime = submetricQuantiles(_.shuffleWriteTime)
-        )
-      }.metricOption
-
-    new TaskMetricDistributions(
-      quantiles = quantiles,
-      executorDeserializeTime = metricQuantiles(_.executorDeserializeTime),
-      executorRunTime = metricQuantiles(_.executorRunTime),
-      resultSize = metricQuantiles(_.resultSize),
-      jvmGcTime = metricQuantiles(_.jvmGCTime),
-      resultSerializationTime = metricQuantiles(_.resultSerializationTime),
-      memoryBytesSpilled = metricQuantiles(_.memoryBytesSpilled),
-      diskBytesSpilled = metricQuantiles(_.diskBytesSpilled),
-      inputMetrics = inputMetrics,
-      outputMetrics = outputMetrics,
-      shuffleReadMetrics = shuffleReadMetrics,
-      shuffleWriteMetrics = shuffleWriteMetrics
-    )
-  }
-
-  def convertAccumulableInfo(acc: InternalAccumulableInfo): AccumulableInfo = {
-    new AccumulableInfo(acc.id, acc.name, acc.update, acc.value)
-  }
-
-  def convertUiTaskMetrics(internal: InternalTaskMetrics): TaskMetrics = {
-    new TaskMetrics(
-      executorDeserializeTime = internal.executorDeserializeTime,
-      executorRunTime = internal.executorRunTime,
-      resultSize = internal.resultSize,
-      jvmGcTime = internal.jvmGCTime,
-      resultSerializationTime = internal.resultSerializationTime,
-      memoryBytesSpilled = internal.memoryBytesSpilled,
-      diskBytesSpilled = internal.diskBytesSpilled,
-      inputMetrics = internal.inputMetrics.map { convertInputMetrics },
-      outputMetrics = Option(internal.outputMetrics).flatten.map { convertOutputMetrics },
-      shuffleReadMetrics = internal.shuffleReadMetrics.map { convertShuffleReadMetrics },
-      shuffleWriteMetrics = internal.shuffleWriteMetrics.map { convertShuffleWriteMetrics }
-    )
-  }
-
-  def convertInputMetrics(internal: InternalInputMetrics): InputMetrics = {
-    new InputMetrics(
-      bytesRead = internal.bytesRead,
-      recordsRead = internal.recordsRead
-    )
-  }
-
-  def convertOutputMetrics(internal: InternalOutputMetrics): OutputMetrics = {
-    new OutputMetrics(
-      bytesWritten = internal.bytesWritten,
-      recordsWritten = internal.recordsWritten
-    )
-  }
-
-  def convertShuffleReadMetrics(internal: InternalShuffleReadMetrics): ShuffleReadMetrics = {
-    new ShuffleReadMetrics(
-      remoteBlocksFetched = internal.remoteBlocksFetched,
-      localBlocksFetched = internal.localBlocksFetched,
-      fetchWaitTime = internal.fetchWaitTime,
-      remoteBytesRead = internal.remoteBytesRead,
-      totalBlocksFetched = internal.totalBlocksFetched,
-      recordsRead = internal.recordsRead
-    )
-  }
-
-  def convertShuffleWriteMetrics(internal: InternalShuffleWriteMetrics): ShuffleWriteMetrics = {
-    new ShuffleWriteMetrics(
-      bytesWritten = internal.shuffleBytesWritten,
-      writeTime = internal.shuffleWriteTime,
-      recordsWritten = internal.shuffleRecordsWritten
-    )
-  }
-}
-
-/**
- * Helper for getting distributions from nested metric types.  Many of the metrics we want are
- * contained in options inside TaskMetrics (eg., ShuffleWriteMetrics). This makes it easy to handle
- * the options (returning None if the metrics are all empty), and extract the quantiles for each
- * metric.  After creating an instance, call metricOption to get the result type.
- */
-private[v1] abstract class MetricHelper[I,O](
-    rawMetrics: Seq[InternalTaskMetrics],
-    quantiles: Array[Double]) {
-
-  def getSubmetrics(raw: InternalTaskMetrics): Option[I]
-
-  def build: O
-
-  val data: Seq[I] = rawMetrics.flatMap(getSubmetrics)
-
-  /** applies the given function to all input metrics, and returns the quantiles */
-  def submetricQuantiles(f: I => Double): IndexedSeq[Double] = {
-    Distribution(data.map { d => f(d) }).get.getQuantiles(quantiles)
-  }
-
-  def metricOption: Option[O] = {
-    if (data.isEmpty) {
-      None
-    } else {
-      Some(build)
-    }
-  }
-}
diff --git a/core/src/main/scala/org/apache/spark/status/api/v1/ApplicationListResource.scala b/core/src/main/scala/org/apache/spark/status/api/v1/ApplicationListResource.scala
deleted file mode 100644
index 17b521f3e1d41..0000000000000
--- a/core/src/main/scala/org/apache/spark/status/api/v1/ApplicationListResource.scala
+++ /dev/null
@@ -1,94 +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.status.api.v1
-
-import java.util.{Arrays, Date, List => JList}
-import javax.ws.rs.{DefaultValue, GET, Produces, QueryParam}
-import javax.ws.rs.core.MediaType
-
-import org.apache.spark.deploy.history.ApplicationHistoryInfo
-import org.apache.spark.deploy.master.{ApplicationInfo => InternalApplicationInfo}
-
-@Produces(Array(MediaType.APPLICATION_JSON))
-private[v1] class ApplicationListResource(uiRoot: UIRoot) {
-
-  @GET
-  def appList(
-      @QueryParam("status") status: JList[ApplicationStatus],
-      @DefaultValue("2010-01-01") @QueryParam("minDate") minDate: SimpleDateParam,
-      @DefaultValue("3000-01-01") @QueryParam("maxDate") maxDate: SimpleDateParam)
-  : Iterator[ApplicationInfo] = {
-    val allApps = uiRoot.getApplicationInfoList
-    val adjStatus = {
-      if (status.isEmpty) {
-        Arrays.asList(ApplicationStatus.values(): _*)
-      } else {
-        status
-      }
-    }
-    val includeCompleted = adjStatus.contains(ApplicationStatus.COMPLETED)
-    val includeRunning = adjStatus.contains(ApplicationStatus.RUNNING)
-    allApps.filter { app =>
-      val anyRunning = app.attempts.exists(!_.completed)
-      // if any attempt is still running, we consider the app to also still be running
-      val statusOk = (!anyRunning && includeCompleted) ||
-        (anyRunning && includeRunning)
-      // keep the app if *any* attempts fall in the right time window
-      val dateOk = app.attempts.exists { attempt =>
-        attempt.startTime.getTime >= minDate.timestamp &&
-          attempt.startTime.getTime <= maxDate.timestamp
-      }
-      statusOk && dateOk
-    }
-  }
-}
-
-private[spark] object ApplicationsListResource {
-  def appHistoryInfoToPublicAppInfo(app: ApplicationHistoryInfo): ApplicationInfo = {
-    new ApplicationInfo(
-      id = app.id,
-      name = app.name,
-      attempts = app.attempts.map { internalAttemptInfo =>
-        new ApplicationAttemptInfo(
-          attemptId = internalAttemptInfo.attemptId,
-          startTime = new Date(internalAttemptInfo.startTime),
-          endTime = new Date(internalAttemptInfo.endTime),
-          sparkUser = internalAttemptInfo.sparkUser,
-          completed = internalAttemptInfo.completed
-        )
-      }
-    )
-  }
-
-  def convertApplicationInfo(
-      internal: InternalApplicationInfo,
-      completed: Boolean): ApplicationInfo = {
-    // standalone application info always has just one attempt
-    new ApplicationInfo(
-      id = internal.id,
-      name = internal.desc.name,
-      attempts = Seq(new ApplicationAttemptInfo(
-        attemptId = None,
-        startTime = new Date(internal.startTime),
-        endTime = new Date(internal.endTime),
-        sparkUser = internal.desc.user,
-        completed = completed
-      ))
-    )
-  }
-
-}
diff --git a/core/src/main/scala/org/apache/spark/status/api/v1/ExecutorListResource.scala b/core/src/main/scala/org/apache/spark/status/api/v1/ExecutorListResource.scala
deleted file mode 100644
index 8ad4656b4dada..0000000000000
--- a/core/src/main/scala/org/apache/spark/status/api/v1/ExecutorListResource.scala
+++ /dev/null
@@ -1,36 +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.status.api.v1
-
-import javax.ws.rs.{GET, PathParam, Produces}
-import javax.ws.rs.core.MediaType
-
-import org.apache.spark.ui.SparkUI
-import org.apache.spark.ui.exec.ExecutorsPage
-
-@Produces(Array(MediaType.APPLICATION_JSON))
-private[v1] class ExecutorListResource(ui: SparkUI) {
-
-  @GET
-  def executorList(): Seq[ExecutorSummary] = {
-    val listener = ui.executorsListener
-    val storageStatusList = listener.storageStatusList
-    (0 until storageStatusList.size).map { statusId =>
-      ExecutorsPage.getExecInfo(listener, statusId)
-    }
-  }
-}
diff --git a/core/src/main/scala/org/apache/spark/status/api/v1/JacksonMessageWriter.scala b/core/src/main/scala/org/apache/spark/status/api/v1/JacksonMessageWriter.scala
deleted file mode 100644
index 202a5191ad57d..0000000000000
--- a/core/src/main/scala/org/apache/spark/status/api/v1/JacksonMessageWriter.scala
+++ /dev/null
@@ -1,93 +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.status.api.v1
-
-import java.io.OutputStream
-import java.lang.annotation.Annotation
-import java.lang.reflect.Type
-import java.text.SimpleDateFormat
-import java.util.{Calendar, SimpleTimeZone}
-import javax.ws.rs.Produces
-import javax.ws.rs.core.{MediaType, MultivaluedMap}
-import javax.ws.rs.ext.{MessageBodyWriter, Provider}
-
-import com.fasterxml.jackson.annotation.JsonInclude
-import com.fasterxml.jackson.databind.{ObjectMapper, SerializationFeature}
-
-/**
- * This class converts the POJO metric responses into json, using jackson.
- *
- * This doesn't follow the standard jersey-jackson plugin options, because we want to stick
- * with an old version of jersey (since we have it from yarn anyway) and don't want to pull in lots
- * of dependencies from a new plugin.
- *
- * Note that jersey automatically discovers this class based on its package and its annotations.
- */
-@Provider
-@Produces(Array(MediaType.APPLICATION_JSON))
-private[v1] class JacksonMessageWriter extends MessageBodyWriter[Object]{
-
-  val mapper = new ObjectMapper() {
-    override def writeValueAsString(t: Any): String = {
-      super.writeValueAsString(t)
-    }
-  }
-  mapper.registerModule(com.fasterxml.jackson.module.scala.DefaultScalaModule)
-  mapper.enable(SerializationFeature.INDENT_OUTPUT)
-  mapper.setSerializationInclusion(JsonInclude.Include.NON_NULL)
-  mapper.setDateFormat(JacksonMessageWriter.makeISODateFormat)
-
-  override def isWriteable(
-      aClass: Class[_],
-      `type`: Type,
-      annotations: Array[Annotation],
-      mediaType: MediaType): Boolean = {
-      true
-  }
-
-  override def writeTo(
-      t: Object,
-      aClass: Class[_],
-      `type`: Type,
-      annotations: Array[Annotation],
-      mediaType: MediaType,
-      multivaluedMap: MultivaluedMap[String, AnyRef],
-      outputStream: OutputStream): Unit = {
-    t match {
-      case ErrorWrapper(err) => outputStream.write(err.getBytes("utf-8"))
-      case _ => mapper.writeValue(outputStream, t)
-    }
-  }
-
-  override def getSize(
-      t: Object,
-      aClass: Class[_],
-      `type`: Type,
-      annotations: Array[Annotation],
-      mediaType: MediaType): Long = {
-    -1L
-  }
-}
-
-private[spark] object JacksonMessageWriter {
-  def makeISODateFormat: SimpleDateFormat = {
-    val iso8601 = new SimpleDateFormat("yyyy-MM-dd'T'HH:mm:ss.SSS'GMT'")
-    val cal = Calendar.getInstance(new SimpleTimeZone(0, "GMT"))
-    iso8601.setCalendar(cal)
-    iso8601
-  }
-}
diff --git a/core/src/main/scala/org/apache/spark/status/api/v1/JsonRootResource.scala b/core/src/main/scala/org/apache/spark/status/api/v1/JsonRootResource.scala
deleted file mode 100644
index c3ec45f54681b..0000000000000
--- a/core/src/main/scala/org/apache/spark/status/api/v1/JsonRootResource.scala
+++ /dev/null
@@ -1,255 +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.status.api.v1
-
-import javax.servlet.ServletContext
-import javax.ws.rs._
-import javax.ws.rs.core.{Context, Response}
-
-import com.sun.jersey.api.core.ResourceConfig
-import com.sun.jersey.spi.container.servlet.ServletContainer
-import org.eclipse.jetty.server.handler.ContextHandler
-import org.eclipse.jetty.servlet.{ServletContextHandler, ServletHolder}
-
-import org.apache.spark.SecurityManager
-import org.apache.spark.ui.SparkUI
-
-/**
- * Main entry point for serving spark application metrics as json, using JAX-RS.
- *
- * Each resource should have endpoints that return **public** classes defined in api.scala.  Mima
- * binary compatibility checks ensure that we don't inadvertently make changes that break the api.
- * The returned objects are automatically converted to json by jackson with JacksonMessageWriter.
- * In addition, there are a number of tests in HistoryServerSuite that compare the json to "golden
- * files".  Any changes and additions should be reflected there as well -- see the notes in
- * HistoryServerSuite.
- */
-@Path("/v1")
-private[v1] class JsonRootResource extends UIRootFromServletContext {
-
-  @Path("applications")
-  def getApplicationList(): ApplicationListResource = {
-    new ApplicationListResource(uiRoot)
-  }
-
-  @Path("applications/{appId}")
-  def getApplication(): OneApplicationResource = {
-    new OneApplicationResource(uiRoot)
-  }
-
-  @Path("applications/{appId}/{attemptId}/jobs")
-  def getJobs(
-      @PathParam("appId") appId: String,
-      @PathParam("attemptId") attemptId: String): AllJobsResource = {
-    uiRoot.withSparkUI(appId, Some(attemptId)) { ui =>
-      new AllJobsResource(ui)
-    }
-  }
-
-  @Path("applications/{appId}/jobs")
-  def getJobs(@PathParam("appId") appId: String): AllJobsResource = {
-    uiRoot.withSparkUI(appId, None) { ui =>
-      new AllJobsResource(ui)
-    }
-  }
-
-  @Path("applications/{appId}/jobs/{jobId: \\d+}")
-  def getJob(@PathParam("appId") appId: String): OneJobResource = {
-    uiRoot.withSparkUI(appId, None) { ui =>
-      new OneJobResource(ui)
-    }
-  }
-
-  @Path("applications/{appId}/{attemptId}/jobs/{jobId: \\d+}")
-  def getJob(
-      @PathParam("appId") appId: String,
-      @PathParam("attemptId") attemptId: String): OneJobResource = {
-    uiRoot.withSparkUI(appId, Some(attemptId)) { ui =>
-      new OneJobResource(ui)
-    }
-  }
-
-  @Path("applications/{appId}/executors")
-  def getExecutors(@PathParam("appId") appId: String): ExecutorListResource = {
-    uiRoot.withSparkUI(appId, None) { ui =>
-      new ExecutorListResource(ui)
-    }
-  }
-
-  @Path("applications/{appId}/{attemptId}/executors")
-  def getExecutors(
-      @PathParam("appId") appId: String,
-      @PathParam("attemptId") attemptId: String): ExecutorListResource = {
-    uiRoot.withSparkUI(appId, Some(attemptId)) { ui =>
-      new ExecutorListResource(ui)
-    }
-  }
-
-
-  @Path("applications/{appId}/stages")
-  def getStages(@PathParam("appId") appId: String): AllStagesResource= {
-    uiRoot.withSparkUI(appId, None) { ui =>
-      new AllStagesResource(ui)
-    }
-  }
-
-  @Path("applications/{appId}/{attemptId}/stages")
-  def getStages(
-      @PathParam("appId") appId: String,
-      @PathParam("attemptId") attemptId: String): AllStagesResource= {
-    uiRoot.withSparkUI(appId, Some(attemptId)) { ui =>
-      new AllStagesResource(ui)
-    }
-  }
-
-  @Path("applications/{appId}/stages/{stageId: \\d+}")
-  def getStage(@PathParam("appId") appId: String): OneStageResource= {
-    uiRoot.withSparkUI(appId, None) { ui =>
-      new OneStageResource(ui)
-    }
-  }
-
-  @Path("applications/{appId}/{attemptId}/stages/{stageId: \\d+}")
-  def getStage(
-      @PathParam("appId") appId: String,
-      @PathParam("attemptId") attemptId: String): OneStageResource = {
-    uiRoot.withSparkUI(appId, Some(attemptId)) { ui =>
-      new OneStageResource(ui)
-    }
-  }
-
-  @Path("applications/{appId}/storage/rdd")
-  def getRdds(@PathParam("appId") appId: String): AllRDDResource = {
-    uiRoot.withSparkUI(appId, None) { ui =>
-      new AllRDDResource(ui)
-    }
-  }
-
-  @Path("applications/{appId}/{attemptId}/storage/rdd")
-  def getRdds(
-      @PathParam("appId") appId: String,
-      @PathParam("attemptId") attemptId: String): AllRDDResource = {
-    uiRoot.withSparkUI(appId, Some(attemptId)) { ui =>
-      new AllRDDResource(ui)
-    }
-  }
-
-  @Path("applications/{appId}/storage/rdd/{rddId: \\d+}")
-  def getRdd(@PathParam("appId") appId: String): OneRDDResource = {
-    uiRoot.withSparkUI(appId, None) { ui =>
-      new OneRDDResource(ui)
-    }
-  }
-
-  @Path("applications/{appId}/{attemptId}/storage/rdd/{rddId: \\d+}")
-  def getRdd(
-      @PathParam("appId") appId: String,
-      @PathParam("attemptId") attemptId: String): OneRDDResource = {
-    uiRoot.withSparkUI(appId, Some(attemptId)) { ui =>
-      new OneRDDResource(ui)
-    }
-  }
-
-}
-
-private[spark] object JsonRootResource {
-
-  def getJsonServlet(uiRoot: UIRoot): ServletContextHandler = {
-    val jerseyContext = new ServletContextHandler(ServletContextHandler.NO_SESSIONS)
-    jerseyContext.setContextPath("/json")
-    val holder:ServletHolder = new ServletHolder(classOf[ServletContainer])
-    holder.setInitParameter("com.sun.jersey.config.property.resourceConfigClass",
-      "com.sun.jersey.api.core.PackagesResourceConfig")
-    holder.setInitParameter("com.sun.jersey.config.property.packages",
-      "org.apache.spark.status.api.v1")
-    holder.setInitParameter(ResourceConfig.PROPERTY_CONTAINER_REQUEST_FILTERS,
-      classOf[SecurityFilter].getCanonicalName)
-    UIRootFromServletContext.setUiRoot(jerseyContext, uiRoot)
-    jerseyContext.addServlet(holder, "/*")
-    jerseyContext
-  }
-}
-
-/**
- * This trait is shared by the all the root containers for application UI information --
- * the HistoryServer, the Master UI, and the application UI.  This provides the common
- * interface needed for them all to expose application info as json.
- */
-private[spark] trait UIRoot {
-  def getSparkUI(appKey: String): Option[SparkUI]
-  def getApplicationInfoList: Iterator[ApplicationInfo]
-
-  /**
-   * Get the spark UI with the given appID, and apply a function
-   * to it.  If there is no such app, throw an appropriate exception
-   */
-  def withSparkUI[T](appId: String, attemptId: Option[String])(f: SparkUI => T): T = {
-    val appKey = attemptId.map(appId + "/" + _).getOrElse(appId)
-    getSparkUI(appKey) match {
-      case Some(ui) =>
-        f(ui)
-      case None => throw new NotFoundException("no such app: " + appId)
-    }
-  }
-  def securityManager: SecurityManager
-}
-
-private[v1] object UIRootFromServletContext {
-
-  private val attribute = getClass.getCanonicalName
-
-  def setUiRoot(contextHandler: ContextHandler, uiRoot: UIRoot): Unit = {
-    contextHandler.setAttribute(attribute, uiRoot)
-  }
-
-  def getUiRoot(context: ServletContext): UIRoot = {
-    context.getAttribute(attribute).asInstanceOf[UIRoot]
-  }
-}
-
-private[v1] trait UIRootFromServletContext {
-  @Context
-  var servletContext: ServletContext = _
-
-  def uiRoot: UIRoot = UIRootFromServletContext.getUiRoot(servletContext)
-}
-
-private[v1] class NotFoundException(msg: String) extends WebApplicationException(
-  new NoSuchElementException(msg),
-    Response
-      .status(Response.Status.NOT_FOUND)
-      .entity(ErrorWrapper(msg))
-      .build()
-)
-
-private[v1] class BadParameterException(msg: String) extends WebApplicationException(
-  new IllegalArgumentException(msg),
-  Response
-    .status(Response.Status.BAD_REQUEST)
-    .entity(ErrorWrapper(msg))
-    .build()
-) {
-  def this(param: String, exp: String, actual: String) = {
-    this(raw"""Bad value for parameter "$param".  Expected a $exp, got "$actual"""")
-  }
-}
-
-/**
- * Signal to JacksonMessageWriter to not convert the message into json (which would result in an
- * extra set of quotes).
- */
-private[v1] case class ErrorWrapper(s: String)
diff --git a/core/src/main/scala/org/apache/spark/status/api/v1/OneApplicationResource.scala b/core/src/main/scala/org/apache/spark/status/api/v1/OneApplicationResource.scala
deleted file mode 100644
index b5ef72649e295..0000000000000
--- a/core/src/main/scala/org/apache/spark/status/api/v1/OneApplicationResource.scala
+++ /dev/null
@@ -1,31 +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.status.api.v1
-
-import javax.ws.rs.core.MediaType
-import javax.ws.rs.{Produces, PathParam, GET}
-
-@Produces(Array(MediaType.APPLICATION_JSON))
-private[v1] class OneApplicationResource(uiRoot: UIRoot) {
-
-  @GET
-  def getApp(@PathParam("appId") appId: String): ApplicationInfo = {
-    val apps = uiRoot.getApplicationInfoList.find { _.id == appId }
-    apps.getOrElse(throw new NotFoundException("unknown app: " + appId))
-  }
-
-}
diff --git a/core/src/main/scala/org/apache/spark/status/api/v1/OneJobResource.scala b/core/src/main/scala/org/apache/spark/status/api/v1/OneJobResource.scala
deleted file mode 100644
index 6d8a60d480aed..0000000000000
--- a/core/src/main/scala/org/apache/spark/status/api/v1/OneJobResource.scala
+++ /dev/null
@@ -1,41 +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.status.api.v1
-
-import javax.ws.rs.{PathParam, GET, Produces}
-import javax.ws.rs.core.MediaType
-
-import org.apache.spark.JobExecutionStatus
-import org.apache.spark.ui.SparkUI
-import org.apache.spark.ui.jobs.UIData.JobUIData
-
-@Produces(Array(MediaType.APPLICATION_JSON))
-private[v1] class OneJobResource(ui: SparkUI) {
-
-  @GET
-  def oneJob(@PathParam("jobId") jobId: Int): JobData = {
-    val statusToJobs: Seq[(JobExecutionStatus, Seq[JobUIData])] =
-      AllJobsResource.getStatusToJobs(ui)
-    val jobOpt = statusToJobs.map {_._2} .flatten.find { jobInfo => jobInfo.jobId == jobId}
-    jobOpt.map { job =>
-      AllJobsResource.convertJobData(job, ui.jobProgressListener, false)
-    }.getOrElse {
-      throw new NotFoundException("unknown job: " + jobId)
-    }
-  }
-
-}
diff --git a/core/src/main/scala/org/apache/spark/status/api/v1/OneRDDResource.scala b/core/src/main/scala/org/apache/spark/status/api/v1/OneRDDResource.scala
deleted file mode 100644
index 07b224fac4786..0000000000000
--- a/core/src/main/scala/org/apache/spark/status/api/v1/OneRDDResource.scala
+++ /dev/null
@@ -1,34 +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.status.api.v1
-
-import javax.ws.rs.{PathParam, GET, Produces}
-import javax.ws.rs.core.MediaType
-
-import org.apache.spark.ui.SparkUI
-
-@Produces(Array(MediaType.APPLICATION_JSON))
-private[v1] class OneRDDResource(ui: SparkUI) {
-
-  @GET
-  def rddData(@PathParam("rddId") rddId: Int): RDDStorageInfo  = {
-    AllRDDResource.getRDDStorageInfo(rddId, ui.storageListener, true).getOrElse(
-      throw new NotFoundException(s"no rdd found w/ id $rddId")
-    )
-  }
-
-}
diff --git a/core/src/main/scala/org/apache/spark/status/api/v1/OneStageResource.scala b/core/src/main/scala/org/apache/spark/status/api/v1/OneStageResource.scala
deleted file mode 100644
index fd24aea63a8a1..0000000000000
--- a/core/src/main/scala/org/apache/spark/status/api/v1/OneStageResource.scala
+++ /dev/null
@@ -1,150 +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.status.api.v1
-
-import javax.ws.rs._
-import javax.ws.rs.core.MediaType
-
-import org.apache.spark.SparkException
-import org.apache.spark.scheduler.StageInfo
-import org.apache.spark.status.api.v1.StageStatus._
-import org.apache.spark.status.api.v1.TaskSorting._
-import org.apache.spark.ui.SparkUI
-import org.apache.spark.ui.jobs.JobProgressListener
-import org.apache.spark.ui.jobs.UIData.StageUIData
-
-@Produces(Array(MediaType.APPLICATION_JSON))
-private[v1] class OneStageResource(ui: SparkUI) {
-
-  @GET
-  @Path("")
-  def stageData(@PathParam("stageId") stageId: Int): Seq[StageData] = {
-    withStage(stageId){ stageAttempts =>
-      stageAttempts.map { stage =>
-        AllStagesResource.stageUiToStageData(stage.status, stage.info, stage.ui,
-          includeDetails = true)
-      }
-    }
-  }
-
-  @GET
-  @Path("/{stageAttemptId: \\d+}")
-  def oneAttemptData(
-      @PathParam("stageId") stageId: Int,
-      @PathParam("stageAttemptId") stageAttemptId: Int): StageData = {
-    withStageAttempt(stageId, stageAttemptId) { stage =>
-      AllStagesResource.stageUiToStageData(stage.status, stage.info, stage.ui,
-        includeDetails = true)
-    }
-  }
-
-  @GET
-  @Path("/{stageAttemptId: \\d+}/taskSummary")
-  def taskSummary(
-      @PathParam("stageId") stageId: Int,
-      @PathParam("stageAttemptId") stageAttemptId: Int,
-      @DefaultValue("0.05,0.25,0.5,0.75,0.95") @QueryParam("quantiles") quantileString: String)
-  : TaskMetricDistributions = {
-    withStageAttempt(stageId, stageAttemptId) { stage =>
-      val quantiles = quantileString.split(",").map { s =>
-        try {
-          s.toDouble
-        } catch {
-          case nfe: NumberFormatException =>
-            throw new BadParameterException("quantiles", "double", s)
-        }
-      }
-      AllStagesResource.taskMetricDistributions(stage.ui.taskData.values, quantiles)
-    }
-  }
-
-  @GET
-  @Path("/{stageAttemptId: \\d+}/taskList")
-  def taskList(
-      @PathParam("stageId") stageId: Int,
-      @PathParam("stageAttemptId") stageAttemptId: Int,
-      @DefaultValue("0") @QueryParam("offset") offset: Int,
-      @DefaultValue("20") @QueryParam("length") length: Int,
-      @DefaultValue("ID") @QueryParam("sortBy") sortBy: TaskSorting): Seq[TaskData] = {
-    withStageAttempt(stageId, stageAttemptId) { stage =>
-      val tasks = stage.ui.taskData.values.map{AllStagesResource.convertTaskData}.toIndexedSeq
-        .sorted(OneStageResource.ordering(sortBy))
-      tasks.slice(offset, offset + length)  
-    }
-  }
-
-  private case class StageStatusInfoUi(status: StageStatus, info: StageInfo, ui: StageUIData)
-
-  private def withStage[T](stageId: Int)(f: Seq[StageStatusInfoUi] => T): T = {
-    val stageAttempts = findStageStatusUIData(ui.jobProgressListener, stageId)
-    if (stageAttempts.isEmpty) {
-      throw new NotFoundException("unknown stage: " + stageId)
-    } else {
-      f(stageAttempts)
-    }
-  }
-
-  private def findStageStatusUIData(
-      listener: JobProgressListener,
-      stageId: Int): Seq[StageStatusInfoUi] = {
-    listener.synchronized {
-      def getStatusInfoUi(status: StageStatus, infos: Seq[StageInfo]): Seq[StageStatusInfoUi] = {
-        infos.filter { _.stageId == stageId }.map { info =>
-          val ui = listener.stageIdToData.getOrElse((info.stageId, info.attemptId),
-            // this is an internal error -- we should always have uiData
-            throw new SparkException(
-              s"no stage ui data found for stage: ${info.stageId}:${info.attemptId}")
-          )
-          StageStatusInfoUi(status, info, ui)
-        }
-      }
-      getStatusInfoUi(ACTIVE, listener.activeStages.values.toSeq) ++
-        getStatusInfoUi(COMPLETE, listener.completedStages) ++
-        getStatusInfoUi(FAILED, listener.failedStages) ++
-        getStatusInfoUi(PENDING, listener.pendingStages.values.toSeq)
-    }
-  }
-
-  private def withStageAttempt[T](
-      stageId: Int,
-      stageAttemptId: Int)
-      (f: StageStatusInfoUi => T): T = {
-    withStage(stageId) { attempts =>
-        val oneAttempt = attempts.find { stage => stage.info.attemptId == stageAttemptId }
-        oneAttempt match {
-          case Some(stage) =>
-            f(stage)
-          case None =>
-            val stageAttempts = attempts.map { _.info.attemptId }
-            throw new NotFoundException(s"unknown attempt for stage $stageId.  " +
-              s"Found attempts: ${stageAttempts.mkString("[", ",", "]")}")
-        }
-    }
-  }
-}
-
-object OneStageResource {
-  def ordering(taskSorting: TaskSorting): Ordering[TaskData] = {
-    val extractor: (TaskData => Long) = td =>
-      taskSorting match {
-        case ID => td.taskId
-        case INCREASING_RUNTIME => td.taskMetrics.map{_.executorRunTime}.getOrElse(-1L)
-        case DECREASING_RUNTIME => -td.taskMetrics.map{_.executorRunTime}.getOrElse(-1L)
-      }
-    Ordering.by(extractor)
-  }
-}
diff --git a/core/src/main/scala/org/apache/spark/status/api/v1/SecurityFilter.scala b/core/src/main/scala/org/apache/spark/status/api/v1/SecurityFilter.scala
deleted file mode 100644
index 95fbd96ade5ab..0000000000000
--- a/core/src/main/scala/org/apache/spark/status/api/v1/SecurityFilter.scala
+++ /dev/null
@@ -1,38 +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.status.api.v1
-
-import javax.ws.rs.WebApplicationException
-import javax.ws.rs.core.Response
-
-import com.sun.jersey.spi.container.{ContainerRequest, ContainerRequestFilter}
-
-private[v1] class SecurityFilter extends ContainerRequestFilter with UIRootFromServletContext {
-  def filter(req: ContainerRequest): ContainerRequest = {
-    val user = Option(req.getUserPrincipal).map { _.getName }.orNull
-    if (uiRoot.securityManager.checkUIViewPermissions(user)) {
-      req
-    } else {
-      throw new WebApplicationException(
-        Response
-          .status(Response.Status.FORBIDDEN)
-          .entity(raw"""user "$user"is not authorized""")
-          .build()
-      )
-    }
-  }
-}
diff --git a/core/src/main/scala/org/apache/spark/status/api/v1/SimpleDateParam.scala b/core/src/main/scala/org/apache/spark/status/api/v1/SimpleDateParam.scala
deleted file mode 100644
index cee29786c3019..0000000000000
--- a/core/src/main/scala/org/apache/spark/status/api/v1/SimpleDateParam.scala
+++ /dev/null
@@ -1,55 +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.status.api.v1
-
-import java.text.SimpleDateFormat
-import java.util.TimeZone
-import javax.ws.rs.WebApplicationException
-import javax.ws.rs.core.Response
-import javax.ws.rs.core.Response.Status
-
-import scala.util.Try
-
-private[v1] class SimpleDateParam(val originalValue: String) {
-  val timestamp: Long = {
-    SimpleDateParam.formats.collectFirst {
-      case fmt if Try(fmt.parse(originalValue)).isSuccess =>
-        fmt.parse(originalValue).getTime()
-    }.getOrElse(
-      throw new WebApplicationException(
-        Response
-          .status(Status.BAD_REQUEST)
-          .entity("Couldn't parse date: " + originalValue)
-          .build()
-      )
-    )
-  }
-}
-
-private[v1] object SimpleDateParam {
-
-  val formats: Seq[SimpleDateFormat] = {
-
-    val gmtDay = new SimpleDateFormat("yyyy-MM-dd")
-    gmtDay.setTimeZone(TimeZone.getTimeZone("GMT"))
-
-    Seq(
-      new SimpleDateFormat("yyyy-MM-dd'T'HH:mm:ss.SSSz"),
-      gmtDay
-    )
-  }
-}
diff --git a/core/src/main/scala/org/apache/spark/status/api/v1/api.scala b/core/src/main/scala/org/apache/spark/status/api/v1/api.scala
deleted file mode 100644
index ef3c8570d8186..0000000000000
--- a/core/src/main/scala/org/apache/spark/status/api/v1/api.scala
+++ /dev/null
@@ -1,228 +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.status.api.v1
-
-import java.util.Date
-
-import scala.collection.Map
-
-import org.apache.spark.JobExecutionStatus
-
-class ApplicationInfo private[spark](
-    val id: String,
-    val name: String,
-    val attempts: Seq[ApplicationAttemptInfo])
-
-class ApplicationAttemptInfo private[spark](
-    val attemptId: Option[String],
-    val startTime: Date,
-    val endTime: Date,
-    val sparkUser: String,
-    val completed: Boolean = false)
-
-class ExecutorStageSummary private[spark](
-    val taskTime : Long,
-    val failedTasks : Int,
-    val succeededTasks : Int,
-    val inputBytes : Long,
-    val outputBytes : Long,
-    val shuffleRead : Long,
-    val shuffleWrite : Long,
-    val memoryBytesSpilled : Long,
-    val diskBytesSpilled : Long)
-
-class ExecutorSummary private[spark](
-    val id: String,
-    val hostPort: String,
-    val rddBlocks: Int,
-    val memoryUsed: Long,
-    val diskUsed: Long,
-    val activeTasks: Int,
-    val failedTasks: Int,
-    val completedTasks: Int,
-    val totalTasks: Int,
-    val totalDuration: Long,
-    val totalInputBytes: Long,
-    val totalShuffleRead: Long,
-    val totalShuffleWrite: Long,
-    val maxMemory: Long,
-    val executorLogs: Map[String, String])
-
-class JobData private[spark](
-    val jobId: Int,
-    val name: String,
-    val description: Option[String],
-    val submissionTime: Option[Date],
-    val completionTime: Option[Date],
-    val stageIds: Seq[Int],
-    val jobGroup: Option[String],
-    val status: JobExecutionStatus,
-    val numTasks: Int,
-    val numActiveTasks: Int,
-    val numCompletedTasks: Int,
-    val numSkippedTasks: Int,
-    val numFailedTasks: Int,
-    val numActiveStages: Int,
-    val numCompletedStages: Int,
-    val numSkippedStages: Int,
-    val numFailedStages: Int)
-
-// Q: should Tachyon size go in here as well?  currently the UI only shows it on the overall storage
-// page ... does anybody pay attention to it?
-class RDDStorageInfo private[spark](
-    val id: Int,
-    val name: String,
-    val numPartitions: Int,
-    val numCachedPartitions: Int,
-    val storageLevel: String,
-    val memoryUsed: Long,
-    val diskUsed: Long,
-    val dataDistribution: Option[Seq[RDDDataDistribution]],
-    val partitions: Option[Seq[RDDPartitionInfo]])
-
-class RDDDataDistribution private[spark](
-    val address: String,
-    val memoryUsed: Long,
-    val memoryRemaining: Long,
-    val diskUsed: Long)
-
-class RDDPartitionInfo private[spark](
-    val blockName: String,
-    val storageLevel: String,
-    val memoryUsed: Long,
-    val diskUsed: Long,
-    val executors: Seq[String])
-
-class StageData private[spark](
-    val status: StageStatus,
-    val stageId: Int,
-    val attemptId: Int,
-    val numActiveTasks: Int ,
-    val numCompleteTasks: Int,
-    val numFailedTasks: Int,
-
-    val executorRunTime: Long,
-
-    val inputBytes: Long,
-    val inputRecords: Long,
-    val outputBytes: Long,
-    val outputRecords: Long,
-    val shuffleReadBytes: Long,
-    val shuffleReadRecords: Long,
-    val shuffleWriteBytes: Long,
-    val shuffleWriteRecords: Long,
-    val memoryBytesSpilled: Long,
-    val diskBytesSpilled: Long,
-
-    val name: String,
-    val details: String,
-    val schedulingPool: String,
-
-    val accumulatorUpdates: Seq[AccumulableInfo],
-    val tasks: Option[Map[Long, TaskData]],
-    val executorSummary:Option[Map[String,ExecutorStageSummary]])
-
-class TaskData private[spark](
-    val taskId: Long,
-    val index: Int,
-    val attempt: Int,
-    val launchTime: Date,
-    val executorId: String,
-    val host: String,
-    val taskLocality: String,
-    val speculative: Boolean,
-    val accumulatorUpdates: Seq[AccumulableInfo],
-    val errorMessage: Option[String] = None,
-    val taskMetrics: Option[TaskMetrics] = None)
-
-class TaskMetrics private[spark](
-    val executorDeserializeTime: Long,
-    val executorRunTime: Long,
-    val resultSize: Long,
-    val jvmGcTime: Long,
-    val resultSerializationTime: Long,
-    val memoryBytesSpilled: Long,
-    val diskBytesSpilled: Long,
-    val inputMetrics: Option[InputMetrics],
-    val outputMetrics: Option[OutputMetrics],
-    val shuffleReadMetrics: Option[ShuffleReadMetrics],
-    val shuffleWriteMetrics: Option[ShuffleWriteMetrics])
-
-class InputMetrics private[spark](
-    val bytesRead: Long,
-    val recordsRead: Long)
-
-class OutputMetrics private[spark](
-    val bytesWritten: Long,
-    val recordsWritten: Long)
-
-class ShuffleReadMetrics private[spark](
-    val remoteBlocksFetched: Int,
-    val localBlocksFetched: Int,
-    val fetchWaitTime: Long,
-    val remoteBytesRead: Long,
-    val totalBlocksFetched: Int,
-    val recordsRead: Long)
-
-class ShuffleWriteMetrics private[spark](
-    val bytesWritten: Long,
-    val writeTime: Long,
-    val recordsWritten: Long)
-
-class TaskMetricDistributions private[spark](
-    val quantiles: IndexedSeq[Double],
-
-    val executorDeserializeTime: IndexedSeq[Double],
-    val executorRunTime: IndexedSeq[Double],
-    val resultSize: IndexedSeq[Double],
-    val jvmGcTime: IndexedSeq[Double],
-    val resultSerializationTime: IndexedSeq[Double],
-    val memoryBytesSpilled: IndexedSeq[Double],
-    val diskBytesSpilled: IndexedSeq[Double],
-
-    val inputMetrics: Option[InputMetricDistributions],
-    val outputMetrics: Option[OutputMetricDistributions],
-    val shuffleReadMetrics: Option[ShuffleReadMetricDistributions],
-    val shuffleWriteMetrics: Option[ShuffleWriteMetricDistributions])
-
-class InputMetricDistributions private[spark](
-    val bytesRead: IndexedSeq[Double],
-    val recordsRead: IndexedSeq[Double])
-
-class OutputMetricDistributions private[spark](
-    val bytesWritten: IndexedSeq[Double],
-    val recordsWritten: IndexedSeq[Double])
-
-class ShuffleReadMetricDistributions private[spark](
-    val readBytes: IndexedSeq[Double],
-    val readRecords: IndexedSeq[Double],
-    val remoteBlocksFetched: IndexedSeq[Double],
-    val localBlocksFetched: IndexedSeq[Double],
-    val fetchWaitTime: IndexedSeq[Double],
-    val remoteBytesRead: IndexedSeq[Double],
-    val totalBlocksFetched: IndexedSeq[Double])
-
-class ShuffleWriteMetricDistributions private[spark](
-    val writeBytes: IndexedSeq[Double],
-    val writeRecords: IndexedSeq[Double],
-    val writeTime: IndexedSeq[Double])
-
-class AccumulableInfo private[spark](
-    val id: Long,
-    val name: String,
-    val update: Option[String],
-    val value: String)
diff --git a/core/src/main/scala/org/apache/spark/storage/StorageStatusListener.scala b/core/src/main/scala/org/apache/spark/storage/StorageStatusListener.scala
index ec711480ebf30..7d75929b96f75 100644
--- a/core/src/main/scala/org/apache/spark/storage/StorageStatusListener.scala
+++ b/core/src/main/scala/org/apache/spark/storage/StorageStatusListener.scala
@@ -25,17 +25,13 @@ import org.apache.spark.scheduler._
 /**
  * :: DeveloperApi ::
  * A SparkListener that maintains executor storage status.
- *
- * This class is thread-safe (unlike JobProgressListener)
  */
 @DeveloperApi
 class StorageStatusListener extends SparkListener {
   // This maintains only blocks that are cached (i.e. storage level is not StorageLevel.NONE)
   private[storage] val executorIdToStorageStatus = mutable.Map[String, StorageStatus]()
 
-  def storageStatusList: Seq[StorageStatus] = synchronized {
-    executorIdToStorageStatus.values.toSeq
-  }
+  def storageStatusList: Seq[StorageStatus] = executorIdToStorageStatus.values.toSeq
 
   /** Update storage status list to reflect updated block statuses */
   private def updateStorageStatus(execId: String, updatedBlocks: Seq[(BlockId, BlockStatus)]) {
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 bfe4a180e8a6f..a5271f0574e6c 100644
--- a/core/src/main/scala/org/apache/spark/ui/SparkUI.scala
+++ b/core/src/main/scala/org/apache/spark/ui/SparkUI.scala
@@ -17,9 +17,6 @@
 
 package org.apache.spark.ui
 
-import java.util.Date
-
-import org.apache.spark.status.api.v1.{ApplicationAttemptInfo, ApplicationInfo, JsonRootResource, UIRoot}
 import org.apache.spark.{Logging, SecurityManager, SparkConf, SparkContext}
 import org.apache.spark.scheduler._
 import org.apache.spark.storage.StorageStatusListener
@@ -36,7 +33,7 @@ import org.apache.spark.ui.scope.RDDOperationGraphListener
 private[spark] class SparkUI private (
     val sc: Option[SparkContext],
     val conf: SparkConf,
-    securityManager: SecurityManager,
+    val securityManager: SecurityManager,
     val environmentListener: EnvironmentListener,
     val storageStatusListener: StorageStatusListener,
     val executorsListener: ExecutorsListener,
@@ -44,27 +41,22 @@ private[spark] class SparkUI private (
     val storageListener: StorageListener,
     val operationGraphListener: RDDOperationGraphListener,
     var appName: String,
-    val basePath: String,
-    val startTime: Long)
+    val basePath: String)
   extends WebUI(securityManager, SparkUI.getUIPort(conf), conf, basePath, "SparkUI")
-  with Logging
-  with UIRoot {
+  with Logging {
 
   val killEnabled = sc.map(_.conf.getBoolean("spark.ui.killEnabled", true)).getOrElse(false)
 
-
-  val stagesTab = new StagesTab(this)
-
   /** Initialize all components of the server. */
   def initialize() {
     attachTab(new JobsTab(this))
+    val stagesTab = new StagesTab(this)
     attachTab(stagesTab)
     attachTab(new StorageTab(this))
     attachTab(new EnvironmentTab(this))
     attachTab(new ExecutorsTab(this))
     attachHandler(createStaticHandler(SparkUI.STATIC_RESOURCE_DIR, "/static"))
     attachHandler(createRedirectHandler("/", "/jobs", basePath = basePath))
-    attachHandler(JsonRootResource.getJsonServlet(this))
     // This should be POST only, but, the YARN AM proxy won't proxy POSTs
     attachHandler(createRedirectHandler(
       "/stages/stage/kill", "/stages", stagesTab.handleKillRequest,
@@ -91,24 +83,6 @@ private[spark] class SparkUI private (
   private[spark] def appUIHostPort = publicHostName + ":" + boundPort
 
   private[spark] def appUIAddress = s"http://$appUIHostPort"
-
-  def getSparkUI(appId: String): Option[SparkUI] = {
-    if (appId == appName) Some(this) else None
-  }
-
-  def getApplicationInfoList: Iterator[ApplicationInfo] = {
-    Iterator(new ApplicationInfo(
-      id = appName,
-      name = appName,
-      attempts = Seq(new ApplicationAttemptInfo(
-        attemptId = None,
-        startTime = new Date(startTime),
-        endTime = new Date(-1),
-        sparkUser = "",
-        completed = false
-      ))
-    ))
-  }
 }
 
 private[spark] abstract class SparkUITab(parent: SparkUI, prefix: String)
@@ -135,10 +109,9 @@ private[spark] object SparkUI {
       listenerBus: SparkListenerBus,
       jobProgressListener: JobProgressListener,
       securityManager: SecurityManager,
-      appName: String,
-      startTime: Long): SparkUI =  {
+      appName: String): SparkUI =  {
     create(Some(sc), conf, listenerBus, securityManager, appName,
-      jobProgressListener = Some(jobProgressListener), startTime = startTime)
+      jobProgressListener = Some(jobProgressListener))
   }
 
   def createHistoryUI(
@@ -146,9 +119,8 @@ private[spark] object SparkUI {
       listenerBus: SparkListenerBus,
       securityManager: SecurityManager,
       appName: String,
-      basePath: String,
-      startTime: Long): SparkUI = {
-    create(None, conf, listenerBus, securityManager, appName, basePath, startTime = startTime)
+      basePath: String): SparkUI = {
+    create(None, conf, listenerBus, securityManager, appName, basePath)
   }
 
   /**
@@ -165,8 +137,7 @@ private[spark] object SparkUI {
       securityManager: SecurityManager,
       appName: String,
       basePath: String = "",
-      jobProgressListener: Option[JobProgressListener] = None,
-      startTime: Long): SparkUI = {
+      jobProgressListener: Option[JobProgressListener] = None): SparkUI = {
 
     val _jobProgressListener: JobProgressListener = jobProgressListener.getOrElse {
       val listener = new JobProgressListener(conf)
@@ -188,6 +159,6 @@ private[spark] object SparkUI {
 
     new SparkUI(sc, conf, securityManager, environmentListener, storageStatusListener,
       executorsListener, _jobProgressListener, storageListener, operationGraphListener,
-      appName, basePath, startTime)
+      appName, basePath)
   }
 }
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 384f2ad26e281..f9860d1a5ce76 100644
--- a/core/src/main/scala/org/apache/spark/ui/WebUI.scala
+++ b/core/src/main/scala/org/apache/spark/ui/WebUI.scala
@@ -37,7 +37,7 @@ import org.apache.spark.{Logging, SecurityManager, SparkConf}
  * pages. The use of tabs is optional, however; a WebUI may choose to include pages directly.
  */
 private[spark] abstract class WebUI(
-    val securityManager: SecurityManager,
+    securityManager: SecurityManager,
     port: Int,
     conf: SparkConf,
     basePath: String = "",
@@ -77,9 +77,15 @@ private[spark] abstract class WebUI(
     val pagePath = "/" + page.prefix
     val renderHandler = createServletHandler(pagePath,
       (request: HttpServletRequest) => page.render(request), securityManager, basePath)
+    val renderJsonHandler = createServletHandler(pagePath.stripSuffix("/") + "/json",
+      (request: HttpServletRequest) => page.renderJson(request), securityManager, basePath)
     attachHandler(renderHandler)
+    attachHandler(renderJsonHandler)
     pageToHandlers.getOrElseUpdate(page, ArrayBuffer[ServletContextHandler]())
       .append(renderHandler)
+    pageToHandlers.getOrElseUpdate(page, ArrayBuffer[ServletContextHandler]())
+      .append(renderJsonHandler)
+    
   }
 
   /** Attach a handler to this UI. */
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 b247e4cdc3bd4..956608d7c0cbe 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
@@ -22,11 +22,11 @@ import javax.servlet.http.HttpServletRequest
 
 import scala.xml.Node
 
-import org.apache.spark.status.api.v1.ExecutorSummary
 import org.apache.spark.ui.{ToolTips, UIUtils, WebUIPage}
 import org.apache.spark.util.Utils
 
-// This isn't even used anymore -- but we need to keep it b/c of a MiMa false positive
+/** Summary information about an executor to display in the UI. */
+// Needs to be private[ui] because of a false positive MiMa failure.
 private[ui] case class ExecutorSummaryInfo(
     id: String,
     hostPort: String,
@@ -44,7 +44,6 @@ private[ui] case class ExecutorSummaryInfo(
     maxMemory: Long,
     executorLogs: Map[String, String])
 
-
 private[ui] class ExecutorsPage(
     parent: ExecutorsTab,
     threadDumpEnabled: Boolean)
@@ -56,8 +55,7 @@ private[ui] class ExecutorsPage(
     val maxMem = storageStatusList.map(_.maxMem).sum
     val memUsed = storageStatusList.map(_.memUsed).sum
     val diskUsed = storageStatusList.map(_.diskUsed).sum
-    val execInfo = for (statusId <- 0 until storageStatusList.size) yield
-      ExecutorsPage.getExecInfo(listener, statusId)
+    val execInfo = for (statusId <- 0 until storageStatusList.size) yield getExecInfo(statusId)
     val execInfoSorted = execInfo.sortBy(_.id)
     val logsExist = execInfo.filter(_.executorLogs.nonEmpty).nonEmpty
 
@@ -113,7 +111,7 @@ private[ui] class ExecutorsPage(
   }
 
   /** Render an HTML row representing an executor */
-  private def execRow(info: ExecutorSummary, logsExist: Boolean): Seq[Node] = {
+  private def execRow(info: ExecutorSummaryInfo, logsExist: Boolean): Seq[Node] = {
     val maximumMemory = info.maxMemory
     val memoryUsed = info.memoryUsed
     val diskUsed = info.diskUsed
@@ -172,11 +170,8 @@ private[ui] class ExecutorsPage(
     </tr>
   }
 
-}
-
-private[spark] object ExecutorsPage {
   /** Represent an executor's info as a map given a storage status index */
-  def getExecInfo(listener: ExecutorsListener, statusId: Int): ExecutorSummary = {
+  private def getExecInfo(statusId: Int): ExecutorSummaryInfo = {
     val status = listener.storageStatusList(statusId)
     val execId = status.blockManagerId.executorId
     val hostPort = status.blockManagerId.hostPort
@@ -194,7 +189,7 @@ private[spark] object ExecutorsPage {
     val totalShuffleWrite = listener.executorToShuffleWrite.getOrElse(execId, 0L)
     val executorLogs = listener.executorToLogUrls.getOrElse(execId, Map.empty)
 
-    new ExecutorSummary(
+    new ExecutorSummaryInfo(
       execId,
       hostPort,
       rddBlocks,
diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/AllJobsPage.scala b/core/src/main/scala/org/apache/spark/ui/jobs/AllJobsPage.scala
index 09323d1d80ad6..f6abf27db49dd 100644
--- a/core/src/main/scala/org/apache/spark/ui/jobs/AllJobsPage.scala
+++ b/core/src/main/scala/org/apache/spark/ui/jobs/AllJobsPage.scala
@@ -271,12 +271,6 @@ private[ui] class AllJobsPage(parent: JobsTab) extends WebUIPage("") {
       val shouldShowCompletedJobs = completedJobs.nonEmpty
       val shouldShowFailedJobs = failedJobs.nonEmpty
 
-      val completedJobNumStr = if (completedJobs.size == listener.numCompletedJobs) {
-        s"${completedJobs.size}"
-      } else {
-        s"${listener.numCompletedJobs}, only showing ${completedJobs.size}"
-      }
-
       val summary: NodeSeq =
         <div>
           <ul class="unstyled">
@@ -301,9 +295,9 @@ private[ui] class AllJobsPage(parent: JobsTab) extends WebUIPage("") {
             }
             {
               if (shouldShowCompletedJobs) {
-                <li id="completed-summary">
+                <li>
                   <a href="#completed"><strong>Completed Jobs:</strong></a>
-                  {completedJobNumStr}
+                  {completedJobs.size}
                 </li>
               }
             }
@@ -311,7 +305,7 @@ private[ui] class AllJobsPage(parent: JobsTab) extends WebUIPage("") {
               if (shouldShowFailedJobs) {
                 <li>
                   <a href="#failed"><strong>Failed Jobs:</strong></a>
-                  {listener.numFailedJobs}
+                  {failedJobs.size}
                 </li>
               }
             }
@@ -328,7 +322,7 @@ private[ui] class AllJobsPage(parent: JobsTab) extends WebUIPage("") {
           activeJobsTable
       }
       if (shouldShowCompletedJobs) {
-        content ++= <h4 id="completed">Completed Jobs ({completedJobNumStr})</h4> ++
+        content ++= <h4 id="completed">Completed Jobs ({completedJobs.size})</h4> ++
           completedJobsTable
       }
       if (shouldShowFailedJobs) {
diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/AllStagesPage.scala b/core/src/main/scala/org/apache/spark/ui/jobs/AllStagesPage.scala
index a37f739ab9c66..236bc8ea92879 100644
--- a/core/src/main/scala/org/apache/spark/ui/jobs/AllStagesPage.scala
+++ b/core/src/main/scala/org/apache/spark/ui/jobs/AllStagesPage.scala
@@ -64,12 +64,6 @@ private[ui] class AllStagesPage(parent: StagesTab) extends WebUIPage("") {
       val shouldShowCompletedStages = completedStages.nonEmpty
       val shouldShowFailedStages = failedStages.nonEmpty
 
-      val completedStageNumStr = if (numCompletedStages == completedStages.size) {
-        s"$numCompletedStages"
-      } else {
-        s"$numCompletedStages, only showing ${completedStages.size}"
-      }
-
       val summary: NodeSeq =
         <div>
           <ul class="unstyled">
@@ -104,9 +98,9 @@ private[ui] class AllStagesPage(parent: StagesTab) extends WebUIPage("") {
             }
             {
               if (shouldShowCompletedStages) {
-                <li id="completed-summary">
+                <li>
                   <a href="#completed"><strong>Completed Stages:</strong></a>
-                  {completedStageNumStr}
+                  {numCompletedStages}
                 </li>
               }
             }
@@ -138,7 +132,7 @@ private[ui] class AllStagesPage(parent: StagesTab) extends WebUIPage("") {
         pendingStagesTable.toNodeSeq
       }
       if (shouldShowCompletedStages) {
-        content ++= <h4 id="completed">Completed Stages ({completedStageNumStr})</h4> ++
+        content ++= <h4 id="completed">Completed Stages ({numCompletedStages})</h4> ++
         completedStagesTable.toNodeSeq
       }
       if (shouldShowFailedStages) {
diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/JobPage.scala b/core/src/main/scala/org/apache/spark/ui/jobs/JobPage.scala
index 7163217e1fed0..96cc3d78d0f15 100644
--- a/core/src/main/scala/org/apache/spark/ui/jobs/JobPage.scala
+++ b/core/src/main/scala/org/apache/spark/ui/jobs/JobPage.scala
@@ -187,7 +187,7 @@ private[ui] class JobPage(parent: JobsTab) extends WebUIPage("job") {
       val jobDataOption = listener.jobIdToData.get(jobId)
       if (jobDataOption.isEmpty) {
         val content =
-          <div id="no-info">
+          <div>
             <p>No information to display for job {jobId}</p>
           </div>
         return UIUtils.headerSparkPage(
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 246e191d64776..8f9aa9fdec819 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
@@ -74,8 +74,6 @@ class JobProgressListener(conf: SparkConf) extends SparkListener with Logging {
   // JobProgressListener's retention limits.
   var numCompletedStages = 0
   var numFailedStages = 0
-  var numCompletedJobs = 0
-  var numFailedJobs = 0
 
   // Misc:
   val executorIdToBlockManagerId = HashMap[ExecutorId, BlockManagerId]()
@@ -219,12 +217,10 @@ class JobProgressListener(conf: SparkConf) extends SparkListener with Logging {
         completedJobs += jobData
         trimJobsIfNecessary(completedJobs)
         jobData.status = JobExecutionStatus.SUCCEEDED
-        numCompletedJobs += 1
       case JobFailed(exception) =>
         failedJobs += jobData
         trimJobsIfNecessary(failedJobs)
         jobData.status = JobExecutionStatus.FAILED
-        numFailedJobs += 1
     }
     for (stageId <- jobData.stageIds) {
       stageIdToActiveJobIds.get(stageId).foreach { jobsUsingStage =>
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 f3e0b38523f32..d725b9d8565ac 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
@@ -21,7 +21,7 @@ import javax.servlet.http.HttpServletRequest
 
 import scala.xml.Node
 
-import org.apache.spark.scheduler.StageInfo
+import org.apache.spark.scheduler.{Schedulable, StageInfo}
 import org.apache.spark.ui.{WebUIPage, UIUtils}
 
 /** Page showing specific pool details */
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 89d175b06b947..579310070c76c 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
@@ -25,11 +25,11 @@ import scala.xml.{Elem, Node, Unparsed}
 import org.apache.commons.lang3.StringEscapeUtils
 
 import org.apache.spark.executor.TaskMetrics
-import org.apache.spark.scheduler.{AccumulableInfo, TaskInfo}
 import org.apache.spark.ui.{ToolTips, WebUIPage, UIUtils}
 import org.apache.spark.ui.jobs.UIData._
 import org.apache.spark.ui.scope.RDDOperationGraph
 import org.apache.spark.util.{Utils, Distribution}
+import org.apache.spark.scheduler.{AccumulableInfo, TaskInfo}
 
 /** Page showing statistics and task list for a given stage */
 private[ui] class StagePage(parent: StagesTab) extends WebUIPage("stage") {
@@ -48,22 +48,14 @@ private[ui] class StagePage(parent: StagesTab) extends WebUIPage("stage") {
       val stageAttemptId = parameterAttempt.toInt
       val stageDataOption = progressListener.stageIdToData.get((stageId, stageAttemptId))
 
-      val stageHeader = s"Details for Stage $stageId (Attempt $stageAttemptId)"
-      if (stageDataOption.isEmpty) {
-        val content =
-          <div id="no-info">
-            <p>No information to display for Stage {stageId} (Attempt {stageAttemptId})</p>
-          </div>
-        return UIUtils.headerSparkPage(stageHeader, content, parent)
-
-      }
-      if (stageDataOption.get.taskData.isEmpty) {
+      if (stageDataOption.isEmpty || stageDataOption.get.taskData.isEmpty) {
         val content =
           <div>
             <h4>Summary Metrics</h4> No tasks have started yet
             <h4>Tasks</h4> No tasks have started yet
           </div>
-        return UIUtils.headerSparkPage(stageHeader, content, parent)
+        return UIUtils.headerSparkPage(
+          s"Details for Stage $stageId (Attempt $stageAttemptId)", content, parent)
       }
 
       val stageData = stageDataOption.get
@@ -454,7 +446,8 @@ private[ui] class StagePage(parent: StagesTab) extends WebUIPage("stage") {
         maybeAccumulableTable ++
         <h4>Tasks</h4> ++ taskTable
 
-      UIUtils.headerSparkPage(stageHeader, content, parent, showVisualization = true)
+      UIUtils.headerSparkPage(
+        "Details for Stage %d".format(stageId), content, parent, showVisualization = true)
     }
   }
 
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 05f94a7507f4f..199f731b92bcc 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
@@ -21,8 +21,8 @@ import javax.servlet.http.HttpServletRequest
 
 import scala.xml.Node
 
-import org.apache.spark.status.api.v1.{AllRDDResource, RDDDataDistribution, RDDPartitionInfo}
-import org.apache.spark.ui.{UIUtils, WebUIPage}
+import org.apache.spark.storage.{BlockId, BlockStatus, StorageStatus, StorageUtils}
+import org.apache.spark.ui.{WebUIPage, UIUtils}
 import org.apache.spark.util.Utils
 
 /** Page showing storage details for a given RDD */
@@ -32,19 +32,28 @@ private[ui] class RDDPage(parent: StorageTab) extends WebUIPage("rdd") {
   def render(request: HttpServletRequest): Seq[Node] = {
     val parameterId = request.getParameter("id")
     require(parameterId != null && parameterId.nonEmpty, "Missing id parameter")
+
     val rddId = parameterId.toInt
-    val rddStorageInfo = AllRDDResource.getRDDStorageInfo(rddId, listener,includeDetails = true)
-      .getOrElse {
-        // Rather than crashing, render an "RDD Not Found" page
-        return UIUtils.headerSparkPage("RDD Not Found", Seq[Node](), parent)
-      }
+    val storageStatusList = listener.storageStatusList
+    val rddInfo = listener.rddInfoList.find(_.id == rddId).getOrElse {
+      // Rather than crashing, render an "RDD Not Found" page
+      return UIUtils.headerSparkPage("RDD Not Found", Seq[Node](), parent)
+    }
 
     // Worker table
-    val workerTable = UIUtils.listingTable(workerHeader, workerRow,
-      rddStorageInfo.dataDistribution.get, id = Some("rdd-storage-by-worker-table"))
+    val workers = storageStatusList.map((rddId, _))
+    val workerTable = UIUtils.listingTable(workerHeader, workerRow, workers,
+      id = Some("rdd-storage-by-worker-table"))
 
     // Block table
-    val blockTable = UIUtils.listingTable(blockHeader, blockRow, rddStorageInfo.partitions.get,
+    val blockLocations = StorageUtils.getRddBlockLocations(rddId, storageStatusList)
+    val blocks = storageStatusList
+      .flatMap(_.rddBlocksById(rddId))
+      .sortWith(_._1.name < _._1.name)
+      .map { case (blockId, status) =>
+        (blockId, status, blockLocations.get(blockId).getOrElse(Seq[String]("Unknown")))
+      }
+    val blockTable = UIUtils.listingTable(blockHeader, blockRow, blocks,
       id = Some("rdd-storage-by-block-table"))
 
     val content =
@@ -53,23 +62,23 @@ private[ui] class RDDPage(parent: StorageTab) extends WebUIPage("rdd") {
           <ul class="unstyled">
             <li>
               <strong>Storage Level:</strong>
-              {rddStorageInfo.storageLevel}
+              {rddInfo.storageLevel.description}
             </li>
             <li>
               <strong>Cached Partitions:</strong>
-              {rddStorageInfo.numCachedPartitions}
+              {rddInfo.numCachedPartitions}
             </li>
             <li>
               <strong>Total Partitions:</strong>
-              {rddStorageInfo.numPartitions}
+              {rddInfo.numPartitions}
             </li>
             <li>
               <strong>Memory Size:</strong>
-              {Utils.bytesToString(rddStorageInfo.memoryUsed)}
+              {Utils.bytesToString(rddInfo.memSize)}
             </li>
             <li>
               <strong>Disk Size:</strong>
-              {Utils.bytesToString(rddStorageInfo.diskUsed)}
+              {Utils.bytesToString(rddInfo.diskSize)}
             </li>
           </ul>
         </div>
@@ -77,19 +86,19 @@ private[ui] class RDDPage(parent: StorageTab) extends WebUIPage("rdd") {
 
       <div class="row-fluid">
         <div class="span12">
-          <h4> Data Distribution on {rddStorageInfo.dataDistribution.size} Executors </h4>
+          <h4> Data Distribution on {workers.size} Executors </h4>
           {workerTable}
         </div>
       </div>
 
       <div class="row-fluid">
         <div class="span12">
-          <h4> {rddStorageInfo.partitions.size} Partitions </h4>
+          <h4> {blocks.size} Partitions </h4>
           {blockTable}
         </div>
       </div>;
 
-    UIUtils.headerSparkPage("RDD Storage Info for " + rddStorageInfo.name, content, parent)
+    UIUtils.headerSparkPage("RDD Storage Info for " + rddInfo.name, content, parent)
   }
 
   /** Header fields for the worker table */
@@ -107,32 +116,34 @@ private[ui] class RDDPage(parent: StorageTab) extends WebUIPage("rdd") {
     "Executors")
 
   /** Render an HTML row representing a worker */
-  private def workerRow(worker: RDDDataDistribution): Seq[Node] = {
+  private def workerRow(worker: (Int, StorageStatus)): Seq[Node] = {
+    val (rddId, status) = worker
     <tr>
-      <td>{worker.address}</td>
+      <td>{status.blockManagerId.host + ":" + status.blockManagerId.port}</td>
       <td>
-        {Utils.bytesToString(worker.memoryUsed)}
-        ({Utils.bytesToString(worker.memoryRemaining)} Remaining)
+        {Utils.bytesToString(status.memUsedByRdd(rddId))}
+        ({Utils.bytesToString(status.memRemaining)} Remaining)
       </td>
-      <td>{Utils.bytesToString(worker.diskUsed)}</td>
+      <td>{Utils.bytesToString(status.diskUsedByRdd(rddId))}</td>
     </tr>
   }
 
   /** Render an HTML row representing a block */
-  private def blockRow(row: RDDPartitionInfo): Seq[Node] = {
+  private def blockRow(row: (BlockId, BlockStatus, Seq[String])): Seq[Node] = {
+    val (id, block, locations) = row
     <tr>
-      <td>{row.blockName}</td>
+      <td>{id}</td>
       <td>
-        {row.storageLevel}
+        {block.storageLevel.description}
       </td>
-      <td sorttable_customkey={row.memoryUsed.toString}>
-        {Utils.bytesToString(row.memoryUsed)}
+      <td sorttable_customkey={block.memSize.toString}>
+        {Utils.bytesToString(block.memSize)}
       </td>
-      <td sorttable_customkey={row.diskUsed.toString}>
-        {Utils.bytesToString(row.diskUsed)}
+      <td sorttable_customkey={block.diskSize.toString}>
+        {Utils.bytesToString(block.diskSize)}
       </td>
       <td>
-        {row.executors.map(l => <span>{l}<br/></span>)}
+        {locations.map(l => <span>{l}<br/></span>)}
       </td>
     </tr>
   }
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 07db783c572cf..59dc6b547c1d8 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
@@ -22,7 +22,7 @@ import javax.servlet.http.HttpServletRequest
 import scala.xml.Node
 
 import org.apache.spark.storage.RDDInfo
-import org.apache.spark.ui.{UIUtils, WebUIPage}
+import org.apache.spark.ui.{WebUIPage, UIUtils}
 import org.apache.spark.util.Utils
 
 /** Page showing list of RDD's currently stored in the cluster */
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 0351749700962..045bd784990d1 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
@@ -35,8 +35,6 @@ private[ui] class StorageTab(parent: SparkUI) extends SparkUITab(parent, "storag
 /**
  * :: DeveloperApi ::
  * A SparkListener that prepares information to be displayed on the BlockManagerUI.
- *
- * This class is thread-safe (unlike JobProgressListener)
  */
 @DeveloperApi
 class StorageListener(storageStatusListener: StorageStatusListener) extends SparkListener {
@@ -45,9 +43,7 @@ class StorageListener(storageStatusListener: StorageStatusListener) extends Spar
   def storageStatusList: Seq[StorageStatus] = storageStatusListener.storageStatusList
 
   /** Filter RDD info to include only those with cached partitions */
-  def rddInfoList: Seq[RDDInfo] = synchronized {
-    _rddInfoMap.values.filter(_.numCachedPartitions > 0).toSeq
-  }
+  def rddInfoList: Seq[RDDInfo] = _rddInfoMap.values.filter(_.numCachedPartitions > 0).toSeq
 
   /** Update the storage info of the RDDs whose blocks are among the given updated blocks */
   private def updateRDDInfo(updatedBlocks: Seq[(BlockId, BlockStatus)]): Unit = {
diff --git a/core/src/test/resources/HistoryServerExpectations/applications/json_expectation b/core/src/test/resources/HistoryServerExpectations/applications/json_expectation
deleted file mode 100644
index 6101177e4bb85..0000000000000
--- a/core/src/test/resources/HistoryServerExpectations/applications/json_expectation
+++ /dev/null
@@ -1,53 +0,0 @@
-[ {
-  "id" : "local-1427397477963",
-  "name" : "Spark shell",
-  "attempts" : [ {
-    "startTime" : "2015-03-26T19:17:57.184GMT",
-    "endTime" : "2015-03-26T19:20:02.949GMT",
-    "sparkUser" : "irashid",
-    "completed" : true
-  } ]
-}, {
-  "id" : "local-1426533911241",
-  "name" : "Spark shell",
-  "attempts" : [ {
-    "attemptId" : "2",
-    "startTime" : "2015-03-17T23:11:50.242GMT",
-    "endTime" : "2015-03-17T23:12:25.177GMT",
-    "sparkUser" : "irashid",
-    "completed" : true
-  }, {
-    "attemptId" : "1",
-    "startTime" : "2015-03-16T19:25:10.242GMT",
-    "endTime" : "2015-03-16T19:25:45.177GMT",
-    "sparkUser" : "irashid",
-    "completed" : true
-  } ]
-}, {
-  "id" : "local-1425081759269",
-  "name" : "Spark shell",
-  "attempts" : [ {
-    "startTime" : "2015-02-28T00:02:38.277GMT",
-    "endTime" : "2015-02-28T00:02:46.912GMT",
-    "sparkUser" : "irashid",
-    "completed" : true
-  } ]
-}, {
-  "id" : "local-1422981780767",
-  "name" : "Spark shell",
-  "attempts" : [ {
-    "startTime" : "2015-02-03T16:42:59.720GMT",
-    "endTime" : "2015-02-03T16:43:08.731GMT",
-    "sparkUser" : "irashid",
-    "completed" : true
-  } ]
-}, {
-  "id" : "local-1422981759269",
-  "name" : "Spark shell",
-  "attempts" : [ {
-    "startTime" : "2015-02-03T16:42:38.277GMT",
-    "endTime" : "2015-02-03T16:42:46.912GMT",
-    "sparkUser" : "irashid",
-    "completed" : true
-  } ]
-} ]
\ No newline at end of file
diff --git a/core/src/test/resources/HistoryServerExpectations/applications/local-1422981780767/executors/json_expectation b/core/src/test/resources/HistoryServerExpectations/applications/local-1422981780767/executors/json_expectation
deleted file mode 100644
index cb622e147249e..0000000000000
--- a/core/src/test/resources/HistoryServerExpectations/applications/local-1422981780767/executors/json_expectation
+++ /dev/null
@@ -1,17 +0,0 @@
-[ {
-  "id" : "<driver>",
-  "hostPort" : "localhost:57971",
-  "rddBlocks" : 8,
-  "memoryUsed" : 28000128,
-  "diskUsed" : 0,
-  "activeTasks" : 0,
-  "failedTasks" : 1,
-  "completedTasks" : 31,
-  "totalTasks" : 32,
-  "totalDuration" : 8820,
-  "totalInputBytes" : 28000288,
-  "totalShuffleRead" : 0,
-  "totalShuffleWrite" : 13180,
-  "maxMemory" : 278302556,
-  "executorLogs" : { }
-} ]
\ No newline at end of file
diff --git a/core/src/test/resources/HistoryServerExpectations/applications/local-1422981780767/jobs/0/json_expectation b/core/src/test/resources/HistoryServerExpectations/applications/local-1422981780767/jobs/0/json_expectation
deleted file mode 100644
index 4a29072bdb6e4..0000000000000
--- a/core/src/test/resources/HistoryServerExpectations/applications/local-1422981780767/jobs/0/json_expectation
+++ /dev/null
@@ -1,15 +0,0 @@
-{
-  "jobId" : 0,
-  "name" : "count at <console>:15",
-  "stageIds" : [ 0 ],
-  "status" : "SUCCEEDED",
-  "numTasks" : 8,
-  "numActiveTasks" : 0,
-  "numCompletedTasks" : 8,
-  "numSkippedTasks" : 8,
-  "numFailedTasks" : 0,
-  "numActiveStages" : 0,
-  "numCompletedStages" : 1,
-  "numSkippedStages" : 0,
-  "numFailedStages" : 0
-}
\ No newline at end of file
diff --git a/core/src/test/resources/HistoryServerExpectations/applications/local-1422981780767/jobs/json_expectation b/core/src/test/resources/HistoryServerExpectations/applications/local-1422981780767/jobs/json_expectation
deleted file mode 100644
index cab4750270dfa..0000000000000
--- a/core/src/test/resources/HistoryServerExpectations/applications/local-1422981780767/jobs/json_expectation
+++ /dev/null
@@ -1,43 +0,0 @@
-[ {
-  "jobId" : 2,
-  "name" : "count at <console>:17",
-  "stageIds" : [ 3 ],
-  "status" : "SUCCEEDED",
-  "numTasks" : 8,
-  "numActiveTasks" : 0,
-  "numCompletedTasks" : 8,
-  "numSkippedTasks" : 8,
-  "numFailedTasks" : 0,
-  "numActiveStages" : 0,
-  "numCompletedStages" : 1,
-  "numSkippedStages" : 0,
-  "numFailedStages" : 0
-}, {
-  "jobId" : 1,
-  "name" : "count at <console>:20",
-  "stageIds" : [ 1, 2 ],
-  "status" : "FAILED",
-  "numTasks" : 16,
-  "numActiveTasks" : 0,
-  "numCompletedTasks" : 15,
-  "numSkippedTasks" : 15,
-  "numFailedTasks" : 1,
-  "numActiveStages" : 0,
-  "numCompletedStages" : 1,
-  "numSkippedStages" : 0,
-  "numFailedStages" : 1
-}, {
-  "jobId" : 0,
-  "name" : "count at <console>:15",
-  "stageIds" : [ 0 ],
-  "status" : "SUCCEEDED",
-  "numTasks" : 8,
-  "numActiveTasks" : 0,
-  "numCompletedTasks" : 8,
-  "numSkippedTasks" : 8,
-  "numFailedTasks" : 0,
-  "numActiveStages" : 0,
-  "numCompletedStages" : 1,
-  "numSkippedStages" : 0,
-  "numFailedStages" : 0
-} ]
\ No newline at end of file
diff --git a/core/src/test/resources/HistoryServerExpectations/applications/local-1422981780767/jobs?status=succeeded&status=failed/json_expectation b/core/src/test/resources/HistoryServerExpectations/applications/local-1422981780767/jobs?status=succeeded&status=failed/json_expectation
deleted file mode 100644
index cab4750270dfa..0000000000000
--- a/core/src/test/resources/HistoryServerExpectations/applications/local-1422981780767/jobs?status=succeeded&status=failed/json_expectation
+++ /dev/null
@@ -1,43 +0,0 @@
-[ {
-  "jobId" : 2,
-  "name" : "count at <console>:17",
-  "stageIds" : [ 3 ],
-  "status" : "SUCCEEDED",
-  "numTasks" : 8,
-  "numActiveTasks" : 0,
-  "numCompletedTasks" : 8,
-  "numSkippedTasks" : 8,
-  "numFailedTasks" : 0,
-  "numActiveStages" : 0,
-  "numCompletedStages" : 1,
-  "numSkippedStages" : 0,
-  "numFailedStages" : 0
-}, {
-  "jobId" : 1,
-  "name" : "count at <console>:20",
-  "stageIds" : [ 1, 2 ],
-  "status" : "FAILED",
-  "numTasks" : 16,
-  "numActiveTasks" : 0,
-  "numCompletedTasks" : 15,
-  "numSkippedTasks" : 15,
-  "numFailedTasks" : 1,
-  "numActiveStages" : 0,
-  "numCompletedStages" : 1,
-  "numSkippedStages" : 0,
-  "numFailedStages" : 1
-}, {
-  "jobId" : 0,
-  "name" : "count at <console>:15",
-  "stageIds" : [ 0 ],
-  "status" : "SUCCEEDED",
-  "numTasks" : 8,
-  "numActiveTasks" : 0,
-  "numCompletedTasks" : 8,
-  "numSkippedTasks" : 8,
-  "numFailedTasks" : 0,
-  "numActiveStages" : 0,
-  "numCompletedStages" : 1,
-  "numSkippedStages" : 0,
-  "numFailedStages" : 0
-} ]
\ No newline at end of file
diff --git a/core/src/test/resources/HistoryServerExpectations/applications/local-1422981780767/jobs?status=succeeded/json_expectation b/core/src/test/resources/HistoryServerExpectations/applications/local-1422981780767/jobs?status=succeeded/json_expectation
deleted file mode 100644
index 6fd25befbf7e8..0000000000000
--- a/core/src/test/resources/HistoryServerExpectations/applications/local-1422981780767/jobs?status=succeeded/json_expectation
+++ /dev/null
@@ -1,29 +0,0 @@
-[ {
-  "jobId" : 2,
-  "name" : "count at <console>:17",
-  "stageIds" : [ 3 ],
-  "status" : "SUCCEEDED",
-  "numTasks" : 8,
-  "numActiveTasks" : 0,
-  "numCompletedTasks" : 8,
-  "numSkippedTasks" : 8,
-  "numFailedTasks" : 0,
-  "numActiveStages" : 0,
-  "numCompletedStages" : 1,
-  "numSkippedStages" : 0,
-  "numFailedStages" : 0
-}, {
-  "jobId" : 0,
-  "name" : "count at <console>:15",
-  "stageIds" : [ 0 ],
-  "status" : "SUCCEEDED",
-  "numTasks" : 8,
-  "numActiveTasks" : 0,
-  "numCompletedTasks" : 8,
-  "numSkippedTasks" : 8,
-  "numFailedTasks" : 0,
-  "numActiveStages" : 0,
-  "numCompletedStages" : 1,
-  "numSkippedStages" : 0,
-  "numFailedStages" : 0
-} ]
\ No newline at end of file
diff --git a/core/src/test/resources/HistoryServerExpectations/applications/local-1422981780767/json_expectation b/core/src/test/resources/HistoryServerExpectations/applications/local-1422981780767/json_expectation
deleted file mode 100644
index 07489ad96414a..0000000000000
--- a/core/src/test/resources/HistoryServerExpectations/applications/local-1422981780767/json_expectation
+++ /dev/null
@@ -1,10 +0,0 @@
-{
-  "id" : "local-1422981780767",
-  "name" : "Spark shell",
-  "attempts" : [ {
-    "startTime" : "2015-02-03T16:42:59.720GMT",
-    "endTime" : "2015-02-03T16:43:08.731GMT",
-    "sparkUser" : "irashid",
-    "completed" : true
-  } ]
-}
\ No newline at end of file
diff --git a/core/src/test/resources/HistoryServerExpectations/applications/local-1422981780767/stages/1/0/json_expectation b/core/src/test/resources/HistoryServerExpectations/applications/local-1422981780767/stages/1/0/json_expectation
deleted file mode 100644
index 111cb8163eb3d..0000000000000
--- a/core/src/test/resources/HistoryServerExpectations/applications/local-1422981780767/stages/1/0/json_expectation
+++ /dev/null
@@ -1,270 +0,0 @@
-{
-  "status" : "COMPLETE",
-  "stageId" : 1,
-  "attemptId" : 0,
-  "numActiveTasks" : 0,
-  "numCompleteTasks" : 8,
-  "numFailedTasks" : 0,
-  "executorRunTime" : 3476,
-  "inputBytes" : 28000128,
-  "inputRecords" : 0,
-  "outputBytes" : 0,
-  "outputRecords" : 0,
-  "shuffleReadBytes" : 0,
-  "shuffleReadRecords" : 0,
-  "shuffleWriteBytes" : 13180,
-  "shuffleWriteRecords" : 0,
-  "memoryBytesSpilled" : 0,
-  "diskBytesSpilled" : 0,
-  "name" : "map at <console>:14",
-  "details" : "org.apache.spark.rdd.RDD.map(RDD.scala:271)\n$line10.$read$$iwC$$iwC$$iwC$$iwC.<init>(<console>:14)\n$line10.$read$$iwC$$iwC$$iwC.<init>(<console>:19)\n$line10.$read$$iwC$$iwC.<init>(<console>:21)\n$line10.$read$$iwC.<init>(<console>:23)\n$line10.$read.<init>(<console>:25)\n$line10.$read$.<init>(<console>:29)\n$line10.$read$.<clinit>(<console>)\n$line10.$eval$.<init>(<console>:7)\n$line10.$eval$.<clinit>(<console>)\n$line10.$eval.$print(<console>)\nsun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\nsun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)\nsun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\njava.lang.reflect.Method.invoke(Method.java:606)\norg.apache.spark.repl.SparkIMain$ReadEvalPrint.call(SparkIMain.scala:852)\norg.apache.spark.repl.SparkIMain$Request.loadAndRun(SparkIMain.scala:1125)\norg.apache.spark.repl.SparkIMain.loadAndRunReq$1(SparkIMain.scala:674)\norg.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:705)\norg.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:669)",
-  "schedulingPool" : "default",
-  "accumulatorUpdates" : [ ],
-  "tasks" : {
-    "8" : {
-      "taskId" : 8,
-      "index" : 0,
-      "attempt" : 0,
-      "launchTime" : "2015-02-03T16:43:05.829GMT",
-      "executorId" : "<driver>",
-      "host" : "localhost",
-      "taskLocality" : "PROCESS_LOCAL",
-      "speculative" : false,
-      "accumulatorUpdates" : [ ],
-      "taskMetrics" : {
-        "executorDeserializeTime" : 1,
-        "executorRunTime" : 435,
-        "resultSize" : 1902,
-        "jvmGcTime" : 19,
-        "resultSerializationTime" : 2,
-        "memoryBytesSpilled" : 0,
-        "diskBytesSpilled" : 0,
-        "inputMetrics" : {
-          "bytesRead" : 3500016,
-          "recordsRead" : 0
-        },
-        "shuffleWriteMetrics" : {
-          "bytesWritten" : 1648,
-          "writeTime" : 94000,
-          "recordsWritten" : 0
-        }
-      }
-    },
-    "11" : {
-      "taskId" : 11,
-      "index" : 3,
-      "attempt" : 0,
-      "launchTime" : "2015-02-03T16:43:05.830GMT",
-      "executorId" : "<driver>",
-      "host" : "localhost",
-      "taskLocality" : "PROCESS_LOCAL",
-      "speculative" : false,
-      "accumulatorUpdates" : [ ],
-      "taskMetrics" : {
-        "executorDeserializeTime" : 2,
-        "executorRunTime" : 434,
-        "resultSize" : 1902,
-        "jvmGcTime" : 19,
-        "resultSerializationTime" : 1,
-        "memoryBytesSpilled" : 0,
-        "diskBytesSpilled" : 0,
-        "inputMetrics" : {
-          "bytesRead" : 3500016,
-          "recordsRead" : 0
-        },
-        "shuffleWriteMetrics" : {
-          "bytesWritten" : 1647,
-          "writeTime" : 83000,
-          "recordsWritten" : 0
-        }
-      }
-    },
-    "14" : {
-      "taskId" : 14,
-      "index" : 6,
-      "attempt" : 0,
-      "launchTime" : "2015-02-03T16:43:05.832GMT",
-      "executorId" : "<driver>",
-      "host" : "localhost",
-      "taskLocality" : "PROCESS_LOCAL",
-      "speculative" : false,
-      "accumulatorUpdates" : [ ],
-      "taskMetrics" : {
-        "executorDeserializeTime" : 2,
-        "executorRunTime" : 434,
-        "resultSize" : 1902,
-        "jvmGcTime" : 19,
-        "resultSerializationTime" : 1,
-        "memoryBytesSpilled" : 0,
-        "diskBytesSpilled" : 0,
-        "inputMetrics" : {
-          "bytesRead" : 3500016,
-          "recordsRead" : 0
-        },
-        "shuffleWriteMetrics" : {
-          "bytesWritten" : 1648,
-          "writeTime" : 88000,
-          "recordsWritten" : 0
-        }
-      }
-    },
-    "13" : {
-      "taskId" : 13,
-      "index" : 5,
-      "attempt" : 0,
-      "launchTime" : "2015-02-03T16:43:05.831GMT",
-      "executorId" : "<driver>",
-      "host" : "localhost",
-      "taskLocality" : "PROCESS_LOCAL",
-      "speculative" : false,
-      "accumulatorUpdates" : [ ],
-      "taskMetrics" : {
-        "executorDeserializeTime" : 2,
-        "executorRunTime" : 434,
-        "resultSize" : 1902,
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-        "resultSerializationTime" : 2,
-        "memoryBytesSpilled" : 0,
-        "diskBytesSpilled" : 0,
-        "inputMetrics" : {
-          "bytesRead" : 3500016,
-          "recordsRead" : 0
-        },
-        "shuffleWriteMetrics" : {
-          "bytesWritten" : 1648,
-          "writeTime" : 73000,
-          "recordsWritten" : 0
-        }
-      }
-    },
-    "10" : {
-      "taskId" : 10,
-      "index" : 2,
-      "attempt" : 0,
-      "launchTime" : "2015-02-03T16:43:05.830GMT",
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-      "taskLocality" : "PROCESS_LOCAL",
-      "speculative" : false,
-      "accumulatorUpdates" : [ ],
-      "taskMetrics" : {
-        "executorDeserializeTime" : 2,
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-        "memoryBytesSpilled" : 0,
-        "diskBytesSpilled" : 0,
-        "inputMetrics" : {
-          "bytesRead" : 3500016,
-          "recordsRead" : 0
-        },
-        "shuffleWriteMetrics" : {
-          "bytesWritten" : 1648,
-          "writeTime" : 76000,
-          "recordsWritten" : 0
-        }
-      }
-    },
-    "9" : {
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-      "index" : 1,
-      "attempt" : 0,
-      "launchTime" : "2015-02-03T16:43:05.830GMT",
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-      "taskMetrics" : {
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-        "executorRunTime" : 436,
-        "resultSize" : 1902,
-        "jvmGcTime" : 19,
-        "resultSerializationTime" : 0,
-        "memoryBytesSpilled" : 0,
-        "diskBytesSpilled" : 0,
-        "inputMetrics" : {
-          "bytesRead" : 3500016,
-          "recordsRead" : 0
-        },
-        "shuffleWriteMetrics" : {
-          "bytesWritten" : 1648,
-          "writeTime" : 98000,
-          "recordsWritten" : 0
-        }
-      }
-    },
-    "12" : {
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-      "index" : 4,
-      "attempt" : 0,
-      "launchTime" : "2015-02-03T16:43:05.831GMT",
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-      "host" : "localhost",
-      "taskLocality" : "PROCESS_LOCAL",
-      "speculative" : false,
-      "accumulatorUpdates" : [ ],
-      "taskMetrics" : {
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-        "inputMetrics" : {
-          "bytesRead" : 3500016,
-          "recordsRead" : 0
-        },
-        "shuffleWriteMetrics" : {
-          "bytesWritten" : 1645,
-          "writeTime" : 101000,
-          "recordsWritten" : 0
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deleted file mode 100644
index ef339f89afa45..0000000000000
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diff --git a/core/src/test/resources/HistoryServerExpectations/applications/local-1422981780767/stages?status=complete/json_expectation b/core/src/test/resources/HistoryServerExpectations/applications/local-1422981780767/stages?status=complete/json_expectation
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-  "details" : "org.apache.spark.rdd.RDD.map(RDD.scala:271)\n$line10.$read$$iwC$$iwC$$iwC$$iwC.<init>(<console>:14)\n$line10.$read$$iwC$$iwC$$iwC.<init>(<console>:19)\n$line10.$read$$iwC$$iwC.<init>(<console>:21)\n$line10.$read$$iwC.<init>(<console>:23)\n$line10.$read.<init>(<console>:25)\n$line10.$read$.<init>(<console>:29)\n$line10.$read$.<clinit>(<console>)\n$line10.$eval$.<init>(<console>:7)\n$line10.$eval$.<clinit>(<console>)\n$line10.$eval.$print(<console>)\nsun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\nsun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)\nsun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\njava.lang.reflect.Method.invoke(Method.java:606)\norg.apache.spark.repl.SparkIMain$ReadEvalPrint.call(SparkIMain.scala:852)\norg.apache.spark.repl.SparkIMain$Request.loadAndRun(SparkIMain.scala:1125)\norg.apache.spark.repl.SparkIMain.loadAndRunReq$1(SparkIMain.scala:674)\norg.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:705)\norg.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:669)",
-  "schedulingPool" : "default",
-  "accumulatorUpdates" : [ ]
-}, {
-  "status" : "COMPLETE",
-  "stageId" : 0,
-  "attemptId" : 0,
-  "numActiveTasks" : 0,
-  "numCompleteTasks" : 8,
-  "numFailedTasks" : 0,
-  "executorRunTime" : 4338,
-  "inputBytes" : 0,
-  "inputRecords" : 0,
-  "outputBytes" : 0,
-  "outputRecords" : 0,
-  "shuffleReadBytes" : 0,
-  "shuffleReadRecords" : 0,
-  "shuffleWriteBytes" : 0,
-  "shuffleWriteRecords" : 0,
-  "memoryBytesSpilled" : 0,
-  "diskBytesSpilled" : 0,
-  "name" : "count at <console>:15",
-  "details" : "org.apache.spark.rdd.RDD.count(RDD.scala:910)\n$line9.$read$$iwC$$iwC$$iwC$$iwC.<init>(<console>:15)\n$line9.$read$$iwC$$iwC$$iwC.<init>(<console>:20)\n$line9.$read$$iwC$$iwC.<init>(<console>:22)\n$line9.$read$$iwC.<init>(<console>:24)\n$line9.$read.<init>(<console>:26)\n$line9.$read$.<init>(<console>:30)\n$line9.$read$.<clinit>(<console>)\n$line9.$eval$.<init>(<console>:7)\n$line9.$eval$.<clinit>(<console>)\n$line9.$eval.$print(<console>)\nsun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\nsun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)\nsun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\njava.lang.reflect.Method.invoke(Method.java:606)\norg.apache.spark.repl.SparkIMain$ReadEvalPrint.call(SparkIMain.scala:852)\norg.apache.spark.repl.SparkIMain$Request.loadAndRun(SparkIMain.scala:1125)\norg.apache.spark.repl.SparkIMain.loadAndRunReq$1(SparkIMain.scala:674)\norg.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:705)\norg.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:669)",
-  "schedulingPool" : "default",
-  "accumulatorUpdates" : [ ]
-} ]
\ No newline at end of file
diff --git a/core/src/test/resources/HistoryServerExpectations/applications/local-1422981780767/stages?status=failed/json_expectation b/core/src/test/resources/HistoryServerExpectations/applications/local-1422981780767/stages?status=failed/json_expectation
deleted file mode 100644
index bff6a4f69d077..0000000000000
--- a/core/src/test/resources/HistoryServerExpectations/applications/local-1422981780767/stages?status=failed/json_expectation
+++ /dev/null
@@ -1,23 +0,0 @@
-[ {
-  "status" : "FAILED",
-  "stageId" : 2,
-  "attemptId" : 0,
-  "numActiveTasks" : 0,
-  "numCompleteTasks" : 7,
-  "numFailedTasks" : 1,
-  "executorRunTime" : 278,
-  "inputBytes" : 0,
-  "inputRecords" : 0,
-  "outputBytes" : 0,
-  "outputRecords" : 0,
-  "shuffleReadBytes" : 0,
-  "shuffleReadRecords" : 0,
-  "shuffleWriteBytes" : 0,
-  "shuffleWriteRecords" : 0,
-  "memoryBytesSpilled" : 0,
-  "diskBytesSpilled" : 0,
-  "name" : "count at <console>:20",
-  "details" : "org.apache.spark.rdd.RDD.count(RDD.scala:910)\n$line11.$read$$iwC$$iwC$$iwC$$iwC.<init>(<console>:20)\n$line11.$read$$iwC$$iwC$$iwC.<init>(<console>:25)\n$line11.$read$$iwC$$iwC.<init>(<console>:27)\n$line11.$read$$iwC.<init>(<console>:29)\n$line11.$read.<init>(<console>:31)\n$line11.$read$.<init>(<console>:35)\n$line11.$read$.<clinit>(<console>)\n$line11.$eval$.<init>(<console>:7)\n$line11.$eval$.<clinit>(<console>)\n$line11.$eval.$print(<console>)\nsun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\nsun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)\nsun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\njava.lang.reflect.Method.invoke(Method.java:606)\norg.apache.spark.repl.SparkIMain$ReadEvalPrint.call(SparkIMain.scala:852)\norg.apache.spark.repl.SparkIMain$Request.loadAndRun(SparkIMain.scala:1125)\norg.apache.spark.repl.SparkIMain.loadAndRunReq$1(SparkIMain.scala:674)\norg.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:705)\norg.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:669)",
-  "schedulingPool" : "default",
-  "accumulatorUpdates" : [ ]
-} ]
\ No newline at end of file
diff --git a/core/src/test/resources/HistoryServerExpectations/applications/local-1422981780767/storage/rdd/0/json_expectation b/core/src/test/resources/HistoryServerExpectations/applications/local-1422981780767/storage/rdd/0/json_expectation
deleted file mode 100644
index 38b5328ffbb03..0000000000000
--- a/core/src/test/resources/HistoryServerExpectations/applications/local-1422981780767/storage/rdd/0/json_expectation
+++ /dev/null
@@ -1,64 +0,0 @@
-{
-  "id" : 0,
-  "name" : "0",
-  "numPartitions" : 8,
-  "numCachedPartitions" : 8,
-  "storageLevel" : "Memory Deserialized 1x Replicated",
-  "memoryUsed" : 28000128,
-  "diskUsed" : 0,
-  "dataDistribution" : [ {
-    "address" : "localhost:57971",
-    "memoryUsed" : 28000128,
-    "memoryRemaining" : 250302428,
-    "diskUsed" : 0
-  } ],
-  "partitions" : [ {
-    "blockName" : "rdd_0_0",
-    "storageLevel" : "Memory Deserialized 1x Replicated",
-    "memoryUsed" : 3500016,
-    "diskUsed" : 0,
-    "executors" : [ "localhost:57971" ]
-  }, {
-    "blockName" : "rdd_0_1",
-    "storageLevel" : "Memory Deserialized 1x Replicated",
-    "memoryUsed" : 3500016,
-    "diskUsed" : 0,
-    "executors" : [ "localhost:57971" ]
-  }, {
-    "blockName" : "rdd_0_2",
-    "storageLevel" : "Memory Deserialized 1x Replicated",
-    "memoryUsed" : 3500016,
-    "diskUsed" : 0,
-    "executors" : [ "localhost:57971" ]
-  }, {
-    "blockName" : "rdd_0_3",
-    "storageLevel" : "Memory Deserialized 1x Replicated",
-    "memoryUsed" : 3500016,
-    "diskUsed" : 0,
-    "executors" : [ "localhost:57971" ]
-  }, {
-    "blockName" : "rdd_0_4",
-    "storageLevel" : "Memory Deserialized 1x Replicated",
-    "memoryUsed" : 3500016,
-    "diskUsed" : 0,
-    "executors" : [ "localhost:57971" ]
-  }, {
-    "blockName" : "rdd_0_5",
-    "storageLevel" : "Memory Deserialized 1x Replicated",
-    "memoryUsed" : 3500016,
-    "diskUsed" : 0,
-    "executors" : [ "localhost:57971" ]
-  }, {
-    "blockName" : "rdd_0_6",
-    "storageLevel" : "Memory Deserialized 1x Replicated",
-    "memoryUsed" : 3500016,
-    "diskUsed" : 0,
-    "executors" : [ "localhost:57971" ]
-  }, {
-    "blockName" : "rdd_0_7",
-    "storageLevel" : "Memory Deserialized 1x Replicated",
-    "memoryUsed" : 3500016,
-    "diskUsed" : 0,
-    "executors" : [ "localhost:57971" ]
-  } ]
-}
\ No newline at end of file
diff --git a/core/src/test/resources/HistoryServerExpectations/applications/local-1422981780767/storage/rdd/json_expectation b/core/src/test/resources/HistoryServerExpectations/applications/local-1422981780767/storage/rdd/json_expectation
deleted file mode 100644
index f79a31022d214..0000000000000
--- a/core/src/test/resources/HistoryServerExpectations/applications/local-1422981780767/storage/rdd/json_expectation
+++ /dev/null
@@ -1,9 +0,0 @@
-[ {
-  "id" : 0,
-  "name" : "0",
-  "numPartitions" : 8,
-  "numCachedPartitions" : 8,
-  "storageLevel" : "Memory Deserialized 1x Replicated",
-  "memoryUsed" : 28000128,
-  "diskUsed" : 0
-} ]
\ No newline at end of file
diff --git a/core/src/test/resources/HistoryServerExpectations/applications/local-1426533911241/1/jobs/json_expectation b/core/src/test/resources/HistoryServerExpectations/applications/local-1426533911241/1/jobs/json_expectation
deleted file mode 100644
index 2e92e1fa0ec23..0000000000000
--- a/core/src/test/resources/HistoryServerExpectations/applications/local-1426533911241/1/jobs/json_expectation
+++ /dev/null
@@ -1,15 +0,0 @@
-[ {
-  "jobId" : 0,
-  "name" : "foreach at <console>:15",
-  "stageIds" : [ 0 ],
-  "status" : "SUCCEEDED",
-  "numTasks" : 8,
-  "numActiveTasks" : 0,
-  "numCompletedTasks" : 8,
-  "numSkippedTasks" : 8,
-  "numFailedTasks" : 0,
-  "numActiveStages" : 0,
-  "numCompletedStages" : 1,
-  "numSkippedStages" : 0,
-  "numFailedStages" : 0
-} ]
\ No newline at end of file
diff --git a/core/src/test/resources/HistoryServerExpectations/applications/local-1426533911241/1/stages/0/0/json_expectation b/core/src/test/resources/HistoryServerExpectations/applications/local-1426533911241/1/stages/0/0/json_expectation
deleted file mode 100644
index 32d5731676ad5..0000000000000
--- a/core/src/test/resources/HistoryServerExpectations/applications/local-1426533911241/1/stages/0/0/json_expectation
+++ /dev/null
@@ -1,242 +0,0 @@
-{
-  "status" : "COMPLETE",
-  "stageId" : 0,
-  "attemptId" : 0,
-  "numActiveTasks" : 0,
-  "numCompleteTasks" : 8,
-  "numFailedTasks" : 0,
-  "executorRunTime" : 120,
-  "inputBytes" : 0,
-  "inputRecords" : 0,
-  "outputBytes" : 0,
-  "outputRecords" : 0,
-  "shuffleReadBytes" : 0,
-  "shuffleReadRecords" : 0,
-  "shuffleWriteBytes" : 0,
-  "shuffleWriteRecords" : 0,
-  "memoryBytesSpilled" : 0,
-  "diskBytesSpilled" : 0,
-  "name" : "foreach at <console>:15",
-  "details" : "org.apache.spark.rdd.RDD.foreach(RDD.scala:765)\n$line9.$read$$iwC$$iwC$$iwC$$iwC.<init>(<console>:15)\n$line9.$read$$iwC$$iwC$$iwC.<init>(<console>:20)\n$line9.$read$$iwC$$iwC.<init>(<console>:22)\n$line9.$read$$iwC.<init>(<console>:24)\n$line9.$read.<init>(<console>:26)\n$line9.$read$.<init>(<console>:30)\n$line9.$read$.<clinit>(<console>)\n$line9.$eval$.<init>(<console>:7)\n$line9.$eval$.<clinit>(<console>)\n$line9.$eval.$print(<console>)\nsun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\nsun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)\nsun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\njava.lang.reflect.Method.invoke(Method.java:483)\norg.apache.spark.repl.SparkIMain$ReadEvalPrint.call(SparkIMain.scala:852)\norg.apache.spark.repl.SparkIMain$Request.loadAndRun(SparkIMain.scala:1125)\norg.apache.spark.repl.SparkIMain.loadAndRunReq$1(SparkIMain.scala:674)\norg.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:705)\norg.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:669)",
-  "schedulingPool" : "default",
-  "accumulatorUpdates" : [ {
-    "id" : 1,
-    "name" : "my counter",
-    "value" : "5050"
-  } ],
-  "tasks" : {
-    "2" : {
-      "taskId" : 2,
-      "index" : 2,
-      "attempt" : 0,
-      "launchTime" : "2015-03-16T19:25:36.522GMT",
-      "executorId" : "<driver>",
-      "host" : "localhost",
-      "taskLocality" : "PROCESS_LOCAL",
-      "speculative" : false,
-      "accumulatorUpdates" : [ {
-        "id" : 1,
-        "name" : "my counter",
-        "update" : "378",
-        "value" : "378"
-      } ],
-      "taskMetrics" : {
-        "executorDeserializeTime" : 13,
-        "executorRunTime" : 15,
-        "resultSize" : 697,
-        "jvmGcTime" : 0,
-        "resultSerializationTime" : 2,
-        "memoryBytesSpilled" : 0,
-        "diskBytesSpilled" : 0
-      }
-    },
-    "5" : {
-      "taskId" : 5,
-      "index" : 5,
-      "attempt" : 0,
-      "launchTime" : "2015-03-16T19:25:36.523GMT",
-      "executorId" : "<driver>",
-      "host" : "localhost",
-      "taskLocality" : "PROCESS_LOCAL",
-      "speculative" : false,
-      "accumulatorUpdates" : [ {
-        "id" : 1,
-        "name" : "my counter",
-        "update" : "897",
-        "value" : "3750"
-      } ],
-      "taskMetrics" : {
-        "executorDeserializeTime" : 12,
-        "executorRunTime" : 15,
-        "resultSize" : 697,
-        "jvmGcTime" : 0,
-        "resultSerializationTime" : 2,
-        "memoryBytesSpilled" : 0,
-        "diskBytesSpilled" : 0
-      }
-    },
-    "4" : {
-      "taskId" : 4,
-      "index" : 4,
-      "attempt" : 0,
-      "launchTime" : "2015-03-16T19:25:36.522GMT",
-      "executorId" : "<driver>",
-      "host" : "localhost",
-      "taskLocality" : "PROCESS_LOCAL",
-      "speculative" : false,
-      "accumulatorUpdates" : [ {
-        "id" : 1,
-        "name" : "my counter",
-        "update" : "678",
-        "value" : "2853"
-      } ],
-      "taskMetrics" : {
-        "executorDeserializeTime" : 12,
-        "executorRunTime" : 15,
-        "resultSize" : 697,
-        "jvmGcTime" : 0,
-        "resultSerializationTime" : 1,
-        "memoryBytesSpilled" : 0,
-        "diskBytesSpilled" : 0
-      }
-    },
-    "7" : {
-      "taskId" : 7,
-      "index" : 7,
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-      "launchTime" : "2015-03-16T19:25:36.524GMT",
-      "executorId" : "<driver>",
-      "host" : "localhost",
-      "taskLocality" : "PROCESS_LOCAL",
-      "speculative" : false,
-      "accumulatorUpdates" : [ {
-        "id" : 1,
-        "name" : "my counter",
-        "update" : "1222",
-        "value" : "4972"
-      } ],
-      "taskMetrics" : {
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-        "memoryBytesSpilled" : 0,
-        "diskBytesSpilled" : 0
-      }
-    },
-    "1" : {
-      "taskId" : 1,
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-      "launchTime" : "2015-03-16T19:25:36.521GMT",
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-      "host" : "localhost",
-      "taskLocality" : "PROCESS_LOCAL",
-      "speculative" : false,
-      "accumulatorUpdates" : [ {
-        "id" : 1,
-        "name" : "my counter",
-        "update" : "247",
-        "value" : "2175"
-      } ],
-      "taskMetrics" : {
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-        "resultSerializationTime" : 2,
-        "memoryBytesSpilled" : 0,
-        "diskBytesSpilled" : 0
-      }
-    },
-    "3" : {
-      "taskId" : 3,
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-      "attempt" : 0,
-      "launchTime" : "2015-03-16T19:25:36.522GMT",
-      "executorId" : "<driver>",
-      "host" : "localhost",
-      "taskLocality" : "PROCESS_LOCAL",
-      "speculative" : false,
-      "accumulatorUpdates" : [ {
-        "id" : 1,
-        "name" : "my counter",
-        "update" : "572",
-        "value" : "950"
-      } ],
-      "taskMetrics" : {
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-        "executorRunTime" : 15,
-        "resultSize" : 697,
-        "jvmGcTime" : 0,
-        "resultSerializationTime" : 2,
-        "memoryBytesSpilled" : 0,
-        "diskBytesSpilled" : 0
-      }
-    },
-    "6" : {
-      "taskId" : 6,
-      "index" : 6,
-      "attempt" : 0,
-      "launchTime" : "2015-03-16T19:25:36.523GMT",
-      "executorId" : "<driver>",
-      "host" : "localhost",
-      "taskLocality" : "PROCESS_LOCAL",
-      "speculative" : false,
-      "accumulatorUpdates" : [ {
-        "id" : 1,
-        "name" : "my counter",
-        "update" : "978",
-        "value" : "1928"
-      } ],
-      "taskMetrics" : {
-        "executorDeserializeTime" : 12,
-        "executorRunTime" : 15,
-        "resultSize" : 697,
-        "jvmGcTime" : 0,
-        "resultSerializationTime" : 2,
-        "memoryBytesSpilled" : 0,
-        "diskBytesSpilled" : 0
-      }
-    },
-    "0" : {
-      "taskId" : 0,
-      "index" : 0,
-      "attempt" : 0,
-      "launchTime" : "2015-03-16T19:25:36.515GMT",
-      "executorId" : "<driver>",
-      "host" : "localhost",
-      "taskLocality" : "PROCESS_LOCAL",
-      "speculative" : false,
-      "accumulatorUpdates" : [ {
-        "id" : 1,
-        "name" : "my counter",
-        "update" : "78",
-        "value" : "5050"
-      } ],
-      "taskMetrics" : {
-        "executorDeserializeTime" : 14,
-        "executorRunTime" : 15,
-        "resultSize" : 697,
-        "jvmGcTime" : 0,
-        "resultSerializationTime" : 2,
-        "memoryBytesSpilled" : 0,
-        "diskBytesSpilled" : 0
-      }
-    }
-  },
-  "executorSummary" : {
-    "<driver>" : {
-      "taskTime" : 418,
-      "failedTasks" : 0,
-      "succeededTasks" : 8,
-      "inputBytes" : 0,
-      "outputBytes" : 0,
-      "shuffleRead" : 0,
-      "shuffleWrite" : 0,
-      "memoryBytesSpilled" : 0,
-      "diskBytesSpilled" : 0
-    }
-  }
-}
\ No newline at end of file
diff --git a/core/src/test/resources/HistoryServerExpectations/applications/local-1426533911241/1/stages/0/0/taskList/json_expectation b/core/src/test/resources/HistoryServerExpectations/applications/local-1426533911241/1/stages/0/0/taskList/json_expectation
deleted file mode 100644
index c3febc5fc9447..0000000000000
--- a/core/src/test/resources/HistoryServerExpectations/applications/local-1426533911241/1/stages/0/0/taskList/json_expectation
+++ /dev/null
@@ -1,193 +0,0 @@
-[ {
-  "taskId" : 0,
-  "index" : 0,
-  "attempt" : 0,
-  "launchTime" : "2015-03-16T19:25:36.515GMT",
-  "executorId" : "<driver>",
-  "host" : "localhost",
-  "taskLocality" : "PROCESS_LOCAL",
-  "speculative" : false,
-  "accumulatorUpdates" : [ {
-    "id" : 1,
-    "name" : "my counter",
-    "update" : "78",
-    "value" : "5050"
-  } ],
-  "taskMetrics" : {
-    "executorDeserializeTime" : 14,
-    "executorRunTime" : 15,
-    "resultSize" : 697,
-    "jvmGcTime" : 0,
-    "resultSerializationTime" : 2,
-    "memoryBytesSpilled" : 0,
-    "diskBytesSpilled" : 0
-  }
-}, {
-  "taskId" : 1,
-  "index" : 1,
-  "attempt" : 0,
-  "launchTime" : "2015-03-16T19:25:36.521GMT",
-  "executorId" : "<driver>",
-  "host" : "localhost",
-  "taskLocality" : "PROCESS_LOCAL",
-  "speculative" : false,
-  "accumulatorUpdates" : [ {
-    "id" : 1,
-    "name" : "my counter",
-    "update" : "247",
-    "value" : "2175"
-  } ],
-  "taskMetrics" : {
-    "executorDeserializeTime" : 14,
-    "executorRunTime" : 15,
-    "resultSize" : 697,
-    "jvmGcTime" : 0,
-    "resultSerializationTime" : 2,
-    "memoryBytesSpilled" : 0,
-    "diskBytesSpilled" : 0
-  }
-}, {
-  "taskId" : 2,
-  "index" : 2,
-  "attempt" : 0,
-  "launchTime" : "2015-03-16T19:25:36.522GMT",
-  "executorId" : "<driver>",
-  "host" : "localhost",
-  "taskLocality" : "PROCESS_LOCAL",
-  "speculative" : false,
-  "accumulatorUpdates" : [ {
-    "id" : 1,
-    "name" : "my counter",
-    "update" : "378",
-    "value" : "378"
-  } ],
-  "taskMetrics" : {
-    "executorDeserializeTime" : 13,
-    "executorRunTime" : 15,
-    "resultSize" : 697,
-    "jvmGcTime" : 0,
-    "resultSerializationTime" : 2,
-    "memoryBytesSpilled" : 0,
-    "diskBytesSpilled" : 0
-  }
-}, {
-  "taskId" : 3,
-  "index" : 3,
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-  "launchTime" : "2015-03-16T19:25:36.522GMT",
-  "executorId" : "<driver>",
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\ No newline at end of file
diff --git a/core/src/test/resources/HistoryServerExpectations/applications/local-1426533911241/1/stages/json_expectation b/core/src/test/resources/HistoryServerExpectations/applications/local-1426533911241/1/stages/json_expectation
deleted file mode 100644
index 79ccacd309693..0000000000000
--- a/core/src/test/resources/HistoryServerExpectations/applications/local-1426533911241/1/stages/json_expectation
+++ /dev/null
@@ -1,27 +0,0 @@
-[ {
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-  "stageId" : 0,
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-  "numCompleteTasks" : 8,
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\ No newline at end of file
diff --git a/core/src/test/resources/HistoryServerExpectations/applications/local-1426533911241/2/jobs/json_expectation b/core/src/test/resources/HistoryServerExpectations/applications/local-1426533911241/2/jobs/json_expectation
deleted file mode 100644
index 2e92e1fa0ec23..0000000000000
--- a/core/src/test/resources/HistoryServerExpectations/applications/local-1426533911241/2/jobs/json_expectation
+++ /dev/null
@@ -1,15 +0,0 @@
-[ {
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-  "name" : "foreach at <console>:15",
-  "stageIds" : [ 0 ],
-  "status" : "SUCCEEDED",
-  "numTasks" : 8,
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\ No newline at end of file
diff --git a/core/src/test/resources/HistoryServerExpectations/applications/local-1426533911241/2/stages/0/0/taskList/json_expectation b/core/src/test/resources/HistoryServerExpectations/applications/local-1426533911241/2/stages/0/0/taskList/json_expectation
deleted file mode 100644
index 56d667d88917c..0000000000000
--- a/core/src/test/resources/HistoryServerExpectations/applications/local-1426533911241/2/stages/0/0/taskList/json_expectation
+++ /dev/null
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diff --git a/core/src/test/resources/HistoryServerExpectations/applications/local-1426533911241/json_expectation b/core/src/test/resources/HistoryServerExpectations/applications/local-1426533911241/json_expectation
deleted file mode 100644
index 8f3d7160c723f..0000000000000
--- a/core/src/test/resources/HistoryServerExpectations/applications/local-1426533911241/json_expectation
+++ /dev/null
@@ -1,17 +0,0 @@
-{
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\ No newline at end of file
diff --git a/core/src/test/resources/HistoryServerExpectations/applications/local-1427397477963/stages/20/0/taskList/json_expectation b/core/src/test/resources/HistoryServerExpectations/applications/local-1427397477963/stages/20/0/taskList/json_expectation
deleted file mode 100644
index 140c76d7e5208..0000000000000
--- a/core/src/test/resources/HistoryServerExpectations/applications/local-1427397477963/stages/20/0/taskList/json_expectation
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diff --git a/core/src/test/resources/HistoryServerExpectations/applications/local-1427397477963/stages/20/0/taskList?sortBy=runtime/json_expectation b/core/src/test/resources/HistoryServerExpectations/applications/local-1427397477963/stages/20/0/taskList?sortBy=runtime/json_expectation
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index 329065b835dd0..0000000000000
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\ No newline at end of file
diff --git a/core/src/test/resources/HistoryServerExpectations/applications/local-1427397477963/stages/20/0/taskSummary/json_expectation b/core/src/test/resources/HistoryServerExpectations/applications/local-1427397477963/stages/20/0/taskSummary/json_expectation
deleted file mode 100644
index 46f32c19abed5..0000000000000
--- a/core/src/test/resources/HistoryServerExpectations/applications/local-1427397477963/stages/20/0/taskSummary/json_expectation
+++ /dev/null
@@ -1,15 +0,0 @@
-{
-  "quantiles" : [ 0.05, 0.25, 0.5, 0.75, 0.95 ],
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-  "jvmGcTime" : [ 0.0, 0.0, 0.0, 0.0, 7.0 ],
-  "resultSerializationTime" : [ 0.0, 0.0, 0.0, 0.0, 0.0 ],
-  "memoryBytesSpilled" : [ 0.0, 0.0, 0.0, 0.0, 0.0 ],
-  "diskBytesSpilled" : [ 0.0, 0.0, 0.0, 0.0, 0.0 ],
-  "shuffleWriteMetrics" : {
-    "writeBytes" : [ 9598.0, 9599.0, 9600.0, 9601.0, 9603.0 ],
-    "writeRecords" : [ 100.0, 100.0, 100.0, 100.0, 100.0 ],
-    "writeTime" : [ 278805.0, 308809.0, 327953.0, 367487.0, 944783.0 ]
-  }
-}
\ No newline at end of file
diff --git a/core/src/test/resources/HistoryServerExpectations/applications/local-1427397477963/stages/20/0/taskSummary?quantiles=0.01,0.5,0.99/json_expectation b/core/src/test/resources/HistoryServerExpectations/applications/local-1427397477963/stages/20/0/taskSummary?quantiles=0.01,0.5,0.99/json_expectation
deleted file mode 100644
index 65b46388ced87..0000000000000
--- a/core/src/test/resources/HistoryServerExpectations/applications/local-1427397477963/stages/20/0/taskSummary?quantiles=0.01,0.5,0.99/json_expectation
+++ /dev/null
@@ -1,15 +0,0 @@
-{
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-  "executorDeserializeTime" : [ 0.0, 0.0, 25.0 ],
-  "executorRunTime" : [ 5.0, 7.0, 123.0 ],
-  "resultSize" : [ 930.0, 930.0, 930.0 ],
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-  "resultSerializationTime" : [ 0.0, 0.0, 1.0 ],
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-  "shuffleWriteMetrics" : {
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-  }
-}
\ No newline at end of file
diff --git a/core/src/test/resources/HistoryServerExpectations/applications?maxDate=2015-02-03T10:42:40.000CST/json_expectation b/core/src/test/resources/HistoryServerExpectations/applications?maxDate=2015-02-03T10:42:40.000CST/json_expectation
deleted file mode 100644
index 483632a3956ed..0000000000000
--- a/core/src/test/resources/HistoryServerExpectations/applications?maxDate=2015-02-03T10:42:40.000CST/json_expectation
+++ /dev/null
@@ -1,10 +0,0 @@
-[ {
-  "id" : "local-1422981759269",
-  "name" : "Spark shell",
-  "attempts" : [ {
-    "startTime" : "2015-02-03T16:42:38.277GMT",
-    "endTime" : "2015-02-03T16:42:46.912GMT",
-    "sparkUser" : "irashid",
-    "completed" : true
-  } ]
-} ]
\ No newline at end of file
diff --git a/core/src/test/resources/HistoryServerExpectations/applications?maxDate=2015-02-10/json_expectation b/core/src/test/resources/HistoryServerExpectations/applications?maxDate=2015-02-10/json_expectation
deleted file mode 100644
index 4b85690fd9199..0000000000000
--- a/core/src/test/resources/HistoryServerExpectations/applications?maxDate=2015-02-10/json_expectation
+++ /dev/null
@@ -1,19 +0,0 @@
-[ {
-  "id" : "local-1422981780767",
-  "name" : "Spark shell",
-  "attempts" : [ {
-    "startTime" : "2015-02-03T16:42:59.720GMT",
-    "endTime" : "2015-02-03T16:43:08.731GMT",
-    "sparkUser" : "irashid",
-    "completed" : true
-  } ]
-}, {
-  "id" : "local-1422981759269",
-  "name" : "Spark shell",
-  "attempts" : [ {
-    "startTime" : "2015-02-03T16:42:38.277GMT",
-    "endTime" : "2015-02-03T16:42:46.912GMT",
-    "sparkUser" : "irashid",
-    "completed" : true
-  } ]
-} ]
\ No newline at end of file
diff --git a/core/src/test/resources/HistoryServerExpectations/applications?minDate=2015-02-10/json_expectation b/core/src/test/resources/HistoryServerExpectations/applications?minDate=2015-02-10/json_expectation
deleted file mode 100644
index e67f25252e934..0000000000000
--- a/core/src/test/resources/HistoryServerExpectations/applications?minDate=2015-02-10/json_expectation
+++ /dev/null
@@ -1,35 +0,0 @@
-[ {
-  "id" : "local-1427397477963",
-  "name" : "Spark shell",
-  "attempts" : [ {
-    "startTime" : "2015-03-26T19:17:57.184GMT",
-    "endTime" : "2015-03-26T19:20:02.949GMT",
-    "sparkUser" : "irashid",
-    "completed" : true
-  } ]
-}, {
-  "id" : "local-1426533911241",
-  "name" : "Spark shell",
-  "attempts" : [ {
-    "attemptId" : "2",
-    "startTime" : "2015-03-17T23:11:50.242GMT",
-    "endTime" : "2015-03-17T23:12:25.177GMT",
-    "sparkUser" : "irashid",
-    "completed" : true
-  }, {
-    "attemptId" : "1",
-    "startTime" : "2015-03-16T19:25:10.242GMT",
-    "endTime" : "2015-03-16T19:25:45.177GMT",
-    "sparkUser" : "irashid",
-    "completed" : true
-  } ]
-}, {
-  "id" : "local-1425081759269",
-  "name" : "Spark shell",
-  "attempts" : [ {
-    "startTime" : "2015-02-28T00:02:38.277GMT",
-    "endTime" : "2015-02-28T00:02:46.912GMT",
-    "sparkUser" : "irashid",
-    "completed" : true
-  } ]
-} ]
\ No newline at end of file
diff --git a/core/src/test/resources/HistoryServerExpectations/applications?status=completed/json_expectation b/core/src/test/resources/HistoryServerExpectations/applications?status=completed/json_expectation
deleted file mode 100644
index 6101177e4bb85..0000000000000
--- a/core/src/test/resources/HistoryServerExpectations/applications?status=completed/json_expectation
+++ /dev/null
@@ -1,53 +0,0 @@
-[ {
-  "id" : "local-1427397477963",
-  "name" : "Spark shell",
-  "attempts" : [ {
-    "startTime" : "2015-03-26T19:17:57.184GMT",
-    "endTime" : "2015-03-26T19:20:02.949GMT",
-    "sparkUser" : "irashid",
-    "completed" : true
-  } ]
-}, {
-  "id" : "local-1426533911241",
-  "name" : "Spark shell",
-  "attempts" : [ {
-    "attemptId" : "2",
-    "startTime" : "2015-03-17T23:11:50.242GMT",
-    "endTime" : "2015-03-17T23:12:25.177GMT",
-    "sparkUser" : "irashid",
-    "completed" : true
-  }, {
-    "attemptId" : "1",
-    "startTime" : "2015-03-16T19:25:10.242GMT",
-    "endTime" : "2015-03-16T19:25:45.177GMT",
-    "sparkUser" : "irashid",
-    "completed" : true
-  } ]
-}, {
-  "id" : "local-1425081759269",
-  "name" : "Spark shell",
-  "attempts" : [ {
-    "startTime" : "2015-02-28T00:02:38.277GMT",
-    "endTime" : "2015-02-28T00:02:46.912GMT",
-    "sparkUser" : "irashid",
-    "completed" : true
-  } ]
-}, {
-  "id" : "local-1422981780767",
-  "name" : "Spark shell",
-  "attempts" : [ {
-    "startTime" : "2015-02-03T16:42:59.720GMT",
-    "endTime" : "2015-02-03T16:43:08.731GMT",
-    "sparkUser" : "irashid",
-    "completed" : true
-  } ]
-}, {
-  "id" : "local-1422981759269",
-  "name" : "Spark shell",
-  "attempts" : [ {
-    "startTime" : "2015-02-03T16:42:38.277GMT",
-    "endTime" : "2015-02-03T16:42:46.912GMT",
-    "sparkUser" : "irashid",
-    "completed" : true
-  } ]
-} ]
\ No newline at end of file
diff --git a/core/src/test/resources/HistoryServerExpectations/applications?status=running/json_expectation b/core/src/test/resources/HistoryServerExpectations/applications?status=running/json_expectation
deleted file mode 100644
index 8878e547a7984..0000000000000
--- a/core/src/test/resources/HistoryServerExpectations/applications?status=running/json_expectation
+++ /dev/null
@@ -1 +0,0 @@
-[ ]
\ No newline at end of file
diff --git a/core/src/test/resources/spark-events/local-1422981759269/APPLICATION_COMPLETE b/core/src/test/resources/spark-events/local-1422981759269/APPLICATION_COMPLETE
deleted file mode 100755
index e69de29bb2d1d..0000000000000
diff --git a/core/src/test/resources/spark-events/local-1422981759269/EVENT_LOG_1 b/core/src/test/resources/spark-events/local-1422981759269/EVENT_LOG_1
deleted file mode 100755
index 4794e56d1107a..0000000000000
--- a/core/src/test/resources/spark-events/local-1422981759269/EVENT_LOG_1
+++ /dev/null
@@ -1,88 +0,0 @@
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diff --git a/core/src/test/resources/spark-events/local-1422981759269/SPARK_VERSION_1.2.0 b/core/src/test/resources/spark-events/local-1422981759269/SPARK_VERSION_1.2.0
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index e69de29bb2d1d..0000000000000
diff --git a/core/src/test/resources/spark-events/local-1422981780767/APPLICATION_COMPLETE b/core/src/test/resources/spark-events/local-1422981780767/APPLICATION_COMPLETE
deleted file mode 100755
index e69de29bb2d1d..0000000000000
diff --git a/core/src/test/resources/spark-events/local-1422981780767/EVENT_LOG_1 b/core/src/test/resources/spark-events/local-1422981780767/EVENT_LOG_1
deleted file mode 100755
index f14a000bf2c28..0000000000000
--- a/core/src/test/resources/spark-events/local-1422981780767/EVENT_LOG_1
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-{"Event":"SparkListenerTaskEnd","Stage ID":2,"Stage Attempt ID":0,"Task Type":"ResultTask","Task End Reason":{"Reason":"Success"},"Task Info":{"Task ID":18,"Index":2,"Attempt":0,"Launch Time":1422981786297,"Executor ID":"<driver>","Host":"localhost","Locality":"PROCESS_LOCAL","Speculative":false,"Getting Result Time":0,"Finish Time":1422981786340,"Failed":false,"Accumulables":[]},"Task Metrics":{"Host Name":"localhost","Executor Deserialize Time":2,"Executor Run Time":34,"Result Size":862,"JVM GC Time":0,"Result Serialization Time":0,"Memory Bytes Spilled":0,"Disk Bytes Spilled":0,"Shuffle Read Metrics":{"Remote Blocks Fetched":0,"Local Blocks Fetched":8,"Fetch Wait Time":0,"Remote Bytes Read":0}}}
-{"Event":"SparkListenerTaskEnd","Stage ID":2,"Stage Attempt ID":0,"Task Type":"ResultTask","Task End Reason":{"Reason":"Success"},"Task Info":{"Task ID":16,"Index":0,"Attempt":0,"Launch Time":1422981786296,"Executor ID":"<driver>","Host":"localhost","Locality":"PROCESS_LOCAL","Speculative":false,"Getting Result Time":0,"Finish Time":1422981786340,"Failed":false,"Accumulables":[]},"Task Metrics":{"Host Name":"localhost","Executor Deserialize Time":1,"Executor Run Time":35,"Result Size":862,"JVM GC Time":0,"Result Serialization Time":0,"Memory Bytes Spilled":0,"Disk Bytes Spilled":0,"Shuffle Read Metrics":{"Remote Blocks Fetched":0,"Local Blocks Fetched":8,"Fetch Wait Time":0,"Remote Bytes Read":0}}}
-{"Event":"SparkListenerTaskEnd","Stage ID":2,"Stage Attempt ID":0,"Task Type":"ResultTask","Task End Reason":{"Reason":"Success"},"Task Info":{"Task ID":23,"Index":7,"Attempt":0,"Launch Time":1422981786299,"Executor ID":"<driver>","Host":"localhost","Locality":"PROCESS_LOCAL","Speculative":false,"Getting Result Time":0,"Finish Time":1422981786341,"Failed":false,"Accumulables":[]},"Task Metrics":{"Host Name":"localhost","Executor Deserialize Time":1,"Executor Run Time":35,"Result Size":862,"JVM GC Time":0,"Result Serialization Time":0,"Memory Bytes Spilled":0,"Disk Bytes Spilled":0,"Shuffle Read Metrics":{"Remote Blocks Fetched":0,"Local Blocks Fetched":8,"Fetch Wait Time":0,"Remote Bytes Read":0}}}
-{"Event":"SparkListenerTaskEnd","Stage ID":2,"Stage Attempt ID":0,"Task Type":"ResultTask","Task End Reason":{"Reason":"Success"},"Task Info":{"Task ID":22,"Index":6,"Attempt":0,"Launch Time":1422981786298,"Executor ID":"<driver>","Host":"localhost","Locality":"PROCESS_LOCAL","Speculative":false,"Getting Result Time":0,"Finish Time":1422981786342,"Failed":false,"Accumulables":[]},"Task Metrics":{"Host Name":"localhost","Executor Deserialize Time":2,"Executor Run Time":34,"Result Size":862,"JVM GC Time":0,"Result Serialization Time":0,"Memory Bytes Spilled":0,"Disk Bytes Spilled":0,"Shuffle Read Metrics":{"Remote Blocks Fetched":0,"Local Blocks Fetched":8,"Fetch Wait Time":0,"Remote Bytes Read":0}}}
-{"Event":"SparkListenerTaskEnd","Stage ID":2,"Stage Attempt ID":0,"Task Type":"ResultTask","Task End Reason":{"Reason":"ExceptionFailure","Class Name":"java.lang.RuntimeException","Description":"got a 3, failing","Stack Trace":[{"Declaring Class":"$line11.$read$$iwC$$iwC$$iwC$$iwC$$anonfun$1","Method Name":"apply","File Name":"<console>","Line Number":18},{"Declaring Class":"$line11.$read$$iwC$$iwC$$iwC$$iwC$$anonfun$1","Method Name":"apply","File Name":"<console>","Line Number":17},{"Declaring Class":"scala.collection.Iterator$$anon$11","Method Name":"next","File Name":"Iterator.scala","Line Number":328},{"Declaring Class":"org.apache.spark.util.Utils$","Method Name":"getIteratorSize","File Name":"Utils.scala","Line Number":1311},{"Declaring Class":"org.apache.spark.rdd.RDD$$anonfun$count$1","Method Name":"apply","File Name":"RDD.scala","Line Number":910},{"Declaring Class":"org.apache.spark.rdd.RDD$$anonfun$count$1","Method Name":"apply","File Name":"RDD.scala","Line Number":910},{"Declaring Class":"org.apache.spark.SparkContext$$anonfun$runJob$4","Method Name":"apply","File Name":"SparkContext.scala","Line Number":1314},{"Declaring Class":"org.apache.spark.SparkContext$$anonfun$runJob$4","Method Name":"apply","File Name":"SparkContext.scala","Line Number":1314},{"Declaring Class":"org.apache.spark.scheduler.ResultTask","Method Name":"runTask","File Name":"ResultTask.scala","Line Number":61},{"Declaring Class":"org.apache.spark.scheduler.Task","Method Name":"run","File Name":"Task.scala","Line Number":56},{"Declaring Class":"org.apache.spark.executor.Executor$TaskRunner","Method Name":"run","File Name":"Executor.scala","Line Number":196},{"Declaring Class":"java.util.concurrent.ThreadPoolExecutor","Method Name":"runWorker","File Name":"ThreadPoolExecutor.java","Line Number":1145},{"Declaring Class":"java.util.concurrent.ThreadPoolExecutor$Worker","Method Name":"run","File Name":"ThreadPoolExecutor.java","Line Number":615},{"Declaring Class":"java.lang.Thread","Method Name":"run","File Name":"Thread.java","Line Number":745}],"Full Stack Trace":"java.lang.RuntimeException: got a 3, failing\n\tat $line11.$read$$iwC$$iwC$$iwC$$iwC$$anonfun$1.apply(<console>:18)\n\tat $line11.$read$$iwC$$iwC$$iwC$$iwC$$anonfun$1.apply(<console>:17)\n\tat scala.collection.Iterator$$anon$11.next(Iterator.scala:328)\n\tat org.apache.spark.util.Utils$.getIteratorSize(Utils.scala:1311)\n\tat org.apache.spark.rdd.RDD$$anonfun$count$1.apply(RDD.scala:910)\n\tat org.apache.spark.rdd.RDD$$anonfun$count$1.apply(RDD.scala:910)\n\tat org.apache.spark.SparkContext$$anonfun$runJob$4.apply(SparkContext.scala:1314)\n\tat org.apache.spark.SparkContext$$anonfun$runJob$4.apply(SparkContext.scala:1314)\n\tat org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:61)\n\tat org.apache.spark.scheduler.Task.run(Task.scala:56)\n\tat org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:196)\n\tat java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)\n\tat java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)\n\tat java.lang.Thread.run(Thread.java:745)\n","Metrics":{"Host Name":"localhost","Executor Deserialize Time":0,"Executor Run Time":36,"Result Size":0,"JVM GC Time":0,"Result Serialization Time":0,"Memory Bytes Spilled":0,"Disk Bytes Spilled":0,"Shuffle Read Metrics":{"Remote Blocks Fetched":0,"Local Blocks Fetched":8,"Fetch Wait Time":0,"Remote Bytes Read":0}}},"Task Info":{"Task ID":19,"Index":3,"Attempt":0,"Launch Time":1422981786297,"Executor ID":"<driver>","Host":"localhost","Locality":"PROCESS_LOCAL","Speculative":false,"Getting Result Time":0,"Finish Time":1422981786343,"Failed":true,"Accumulables":[]},"Task Metrics":{"Host Name":"localhost","Executor Deserialize Time":0,"Executor Run Time":36,"Result Size":0,"JVM GC Time":0,"Result Serialization Time":0,"Memory Bytes Spilled":0,"Disk Bytes Spilled":0,"Shuffle Read Metrics":{"Remote Blocks Fetched":0,"Local Blocks Fetched":8,"Fetch Wait Time":0,"Remote Bytes Read":0}}}
-{"Event":"SparkListenerStageCompleted","Stage Info":{"Stage ID":2,"Stage Attempt ID":0,"Stage Name":"count at <console>:20","Number of Tasks":8,"RDD Info":[{"RDD ID":3,"Name":"3","Storage Level":{"Use Disk":false,"Use Memory":false,"Use Tachyon":false,"Deserialized":false,"Replication":1},"Number of Partitions":8,"Number of Cached Partitions":0,"Memory Size":0,"Tachyon Size":0,"Disk Size":0},{"RDD ID":2,"Name":"2","Storage Level":{"Use Disk":false,"Use Memory":false,"Use Tachyon":false,"Deserialized":false,"Replication":1},"Number of Partitions":8,"Number of Cached Partitions":0,"Memory Size":0,"Tachyon Size":0,"Disk Size":0}],"Details":"org.apache.spark.rdd.RDD.count(RDD.scala:910)\n$line11.$read$$iwC$$iwC$$iwC$$iwC.<init>(<console>:20)\n$line11.$read$$iwC$$iwC$$iwC.<init>(<console>:25)\n$line11.$read$$iwC$$iwC.<init>(<console>:27)\n$line11.$read$$iwC.<init>(<console>:29)\n$line11.$read.<init>(<console>:31)\n$line11.$read$.<init>(<console>:35)\n$line11.$read$.<clinit>(<console>)\n$line11.$eval$.<init>(<console>:7)\n$line11.$eval$.<clinit>(<console>)\n$line11.$eval.$print(<console>)\nsun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\nsun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)\nsun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\njava.lang.reflect.Method.invoke(Method.java:606)\norg.apache.spark.repl.SparkIMain$ReadEvalPrint.call(SparkIMain.scala:852)\norg.apache.spark.repl.SparkIMain$Request.loadAndRun(SparkIMain.scala:1125)\norg.apache.spark.repl.SparkIMain.loadAndRunReq$1(SparkIMain.scala:674)\norg.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:705)\norg.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:669)","Submission Time":1422981786296,"Completion Time":1422981786347,"Failure Reason":"Job aborted due to stage failure: Task 3 in stage 2.0 failed 1 times, most recent failure: Lost task 3.0 in stage 2.0 (TID 19, localhost): java.lang.RuntimeException: got a 3, failing\n\tat $line11.$read$$iwC$$iwC$$iwC$$iwC$$anonfun$1.apply(<console>:18)\n\tat $line11.$read$$iwC$$iwC$$iwC$$iwC$$anonfun$1.apply(<console>:17)\n\tat scala.collection.Iterator$$anon$11.next(Iterator.scala:328)\n\tat org.apache.spark.util.Utils$.getIteratorSize(Utils.scala:1311)\n\tat org.apache.spark.rdd.RDD$$anonfun$count$1.apply(RDD.scala:910)\n\tat org.apache.spark.rdd.RDD$$anonfun$count$1.apply(RDD.scala:910)\n\tat org.apache.spark.SparkContext$$anonfun$runJob$4.apply(SparkContext.scala:1314)\n\tat org.apache.spark.SparkContext$$anonfun$runJob$4.apply(SparkContext.scala:1314)\n\tat org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:61)\n\tat org.apache.spark.scheduler.Task.run(Task.scala:56)\n\tat org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:196)\n\tat java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)\n\tat java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)\n\tat java.lang.Thread.run(Thread.java:745)\n\nDriver stacktrace:","Accumulables":[]}}
-{"Event":"SparkListenerJobEnd","Job ID":1,"Job Result":{"Result":"JobFailed","Exception":{"Message":"Job aborted due to stage failure: Task 3 in stage 2.0 failed 1 times, most recent failure: Lost task 3.0 in stage 2.0 (TID 19, localhost): java.lang.RuntimeException: got a 3, failing\n\tat $line11.$read$$iwC$$iwC$$iwC$$iwC$$anonfun$1.apply(<console>:18)\n\tat $line11.$read$$iwC$$iwC$$iwC$$iwC$$anonfun$1.apply(<console>:17)\n\tat scala.collection.Iterator$$anon$11.next(Iterator.scala:328)\n\tat org.apache.spark.util.Utils$.getIteratorSize(Utils.scala:1311)\n\tat org.apache.spark.rdd.RDD$$anonfun$count$1.apply(RDD.scala:910)\n\tat org.apache.spark.rdd.RDD$$anonfun$count$1.apply(RDD.scala:910)\n\tat org.apache.spark.SparkContext$$anonfun$runJob$4.apply(SparkContext.scala:1314)\n\tat org.apache.spark.SparkContext$$anonfun$runJob$4.apply(SparkContext.scala:1314)\n\tat org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:61)\n\tat org.apache.spark.scheduler.Task.run(Task.scala:56)\n\tat org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:196)\n\tat java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)\n\tat java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)\n\tat java.lang.Thread.run(Thread.java:745)\n\nDriver stacktrace:","Stack Trace":[{"Declaring Class":"org.apache.spark.scheduler.DAGScheduler","Method Name":"org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages","File Name":"DAGScheduler.scala","Line Number":1214},{"Declaring Class":"org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1","Method Name":"apply","File Name":"DAGScheduler.scala","Line Number":1203},{"Declaring Class":"org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1","Method Name":"apply","File Name":"DAGScheduler.scala","Line Number":1202},{"Declaring Class":"scala.collection.mutable.ResizableArray$class","Method Name":"foreach","File Name":"ResizableArray.scala","Line Number":59},{"Declaring Class":"scala.collection.mutable.ArrayBuffer","Method Name":"foreach","File Name":"ArrayBuffer.scala","Line Number":47},{"Declaring Class":"org.apache.spark.scheduler.DAGScheduler","Method Name":"abortStage","File Name":"DAGScheduler.scala","Line Number":1202},{"Declaring Class":"org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1","Method Name":"apply","File Name":"DAGScheduler.scala","Line Number":696},{"Declaring Class":"org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1","Method Name":"apply","File Name":"DAGScheduler.scala","Line Number":696},{"Declaring Class":"scala.Option","Method Name":"foreach","File Name":"Option.scala","Line Number":236},{"Declaring Class":"org.apache.spark.scheduler.DAGScheduler","Method Name":"handleTaskSetFailed","File Name":"DAGScheduler.scala","Line Number":696},{"Declaring Class":"org.apache.spark.scheduler.DAGSchedulerEventProcessActor$$anonfun$receive$2","Method Name":"applyOrElse","File Name":"DAGScheduler.scala","Line Number":1420},{"Declaring Class":"akka.actor.Actor$class","Method Name":"aroundReceive","File Name":"Actor.scala","Line Number":465},{"Declaring Class":"org.apache.spark.scheduler.DAGSchedulerEventProcessActor","Method Name":"aroundReceive","File Name":"DAGScheduler.scala","Line Number":1375},{"Declaring Class":"akka.actor.ActorCell","Method Name":"receiveMessage","File Name":"ActorCell.scala","Line Number":516},{"Declaring Class":"akka.actor.ActorCell","Method Name":"invoke","File Name":"ActorCell.scala","Line Number":487},{"Declaring Class":"akka.dispatch.Mailbox","Method Name":"processMailbox","File Name":"Mailbox.scala","Line Number":238},{"Declaring Class":"akka.dispatch.Mailbox","Method Name":"run","File Name":"Mailbox.scala","Line Number":220},{"Declaring Class":"akka.dispatch.ForkJoinExecutorConfigurator$AkkaForkJoinTask","Method Name":"exec","File Name":"AbstractDispatcher.scala","Line Number":393},{"Declaring Class":"scala.concurrent.forkjoin.ForkJoinTask","Method Name":"doExec","File Name":"ForkJoinTask.java","Line Number":260},{"Declaring Class":"scala.concurrent.forkjoin.ForkJoinPool$WorkQueue","Method Name":"runTask","File Name":"ForkJoinPool.java","Line Number":1339},{"Declaring Class":"scala.concurrent.forkjoin.ForkJoinPool","Method Name":"runWorker","File Name":"ForkJoinPool.java","Line Number":1979},{"Declaring Class":"scala.concurrent.forkjoin.ForkJoinWorkerThread","Method Name":"run","File Name":"ForkJoinWorkerThread.java","Line Number":107}]}}}
-{"Event":"SparkListenerJobStart","Job ID":2,"Stage Infos":[{"Stage ID":3,"Stage Attempt ID":0,"Stage Name":"count at <console>:17","Number of Tasks":8,"RDD Info":[{"RDD ID":6,"Name":"/Users/irashid/spark-examples/tmp_data/sums_2","Storage Level":{"Use Disk":false,"Use Memory":false,"Use Tachyon":false,"Deserialized":false,"Replication":1},"Number of Partitions":8,"Number of Cached Partitions":0,"Memory Size":0,"Tachyon Size":0,"Disk Size":0},{"RDD ID":5,"Name":"/Users/irashid/spark-examples/tmp_data/sums_2","Storage Level":{"Use Disk":false,"Use Memory":false,"Use Tachyon":false,"Deserialized":false,"Replication":1},"Number of Partitions":8,"Number of Cached Partitions":0,"Memory Size":0,"Tachyon Size":0,"Disk Size":0}],"Details":"org.apache.spark.rdd.RDD.count(RDD.scala:910)\n$line19.$read$$iwC$$iwC$$iwC$$iwC.<init>(<console>:17)\n$line19.$read$$iwC$$iwC$$iwC.<init>(<console>:22)\n$line19.$read$$iwC$$iwC.<init>(<console>:24)\n$line19.$read$$iwC.<init>(<console>:26)\n$line19.$read.<init>(<console>:28)\n$line19.$read$.<init>(<console>:32)\n$line19.$read$.<clinit>(<console>)\n$line19.$eval$.<init>(<console>:7)\n$line19.$eval$.<clinit>(<console>)\n$line19.$eval.$print(<console>)\nsun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\nsun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)\nsun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\njava.lang.reflect.Method.invoke(Method.java:606)\norg.apache.spark.repl.SparkIMain$ReadEvalPrint.call(SparkIMain.scala:852)\norg.apache.spark.repl.SparkIMain$Request.loadAndRun(SparkIMain.scala:1125)\norg.apache.spark.repl.SparkIMain.loadAndRunReq$1(SparkIMain.scala:674)\norg.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:705)\norg.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:669)","Accumulables":[]}],"Stage IDs":[3]}
-{"Event":"SparkListenerStageSubmitted","Stage Info":{"Stage ID":3,"Stage Attempt ID":0,"Stage Name":"count at <console>:17","Number of Tasks":8,"RDD Info":[{"RDD ID":6,"Name":"/Users/irashid/spark-examples/tmp_data/sums_2","Storage Level":{"Use Disk":false,"Use Memory":false,"Use Tachyon":false,"Deserialized":false,"Replication":1},"Number of Partitions":8,"Number of Cached Partitions":0,"Memory Size":0,"Tachyon Size":0,"Disk Size":0},{"RDD ID":5,"Name":"/Users/irashid/spark-examples/tmp_data/sums_2","Storage Level":{"Use Disk":false,"Use Memory":false,"Use Tachyon":false,"Deserialized":false,"Replication":1},"Number of Partitions":8,"Number of Cached Partitions":0,"Memory Size":0,"Tachyon Size":0,"Disk Size":0}],"Details":"org.apache.spark.rdd.RDD.count(RDD.scala:910)\n$line19.$read$$iwC$$iwC$$iwC$$iwC.<init>(<console>:17)\n$line19.$read$$iwC$$iwC$$iwC.<init>(<console>:22)\n$line19.$read$$iwC$$iwC.<init>(<console>:24)\n$line19.$read$$iwC.<init>(<console>:26)\n$line19.$read.<init>(<console>:28)\n$line19.$read$.<init>(<console>:32)\n$line19.$read$.<clinit>(<console>)\n$line19.$eval$.<init>(<console>:7)\n$line19.$eval$.<clinit>(<console>)\n$line19.$eval.$print(<console>)\nsun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\nsun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)\nsun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\njava.lang.reflect.Method.invoke(Method.java:606)\norg.apache.spark.repl.SparkIMain$ReadEvalPrint.call(SparkIMain.scala:852)\norg.apache.spark.repl.SparkIMain$Request.loadAndRun(SparkIMain.scala:1125)\norg.apache.spark.repl.SparkIMain.loadAndRunReq$1(SparkIMain.scala:674)\norg.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:705)\norg.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:669)","Accumulables":[]}}
-{"Event":"SparkListenerTaskStart","Stage ID":3,"Stage Attempt ID":0,"Task Info":{"Task ID":24,"Index":0,"Attempt":0,"Launch Time":1422981787191,"Executor ID":"<driver>","Host":"localhost","Locality":"PROCESS_LOCAL","Speculative":false,"Getting Result Time":0,"Finish Time":0,"Failed":false,"Accumulables":[]}}
-{"Event":"SparkListenerTaskStart","Stage ID":3,"Stage Attempt ID":0,"Task Info":{"Task ID":25,"Index":1,"Attempt":0,"Launch Time":1422981787191,"Executor ID":"<driver>","Host":"localhost","Locality":"PROCESS_LOCAL","Speculative":false,"Getting Result Time":0,"Finish Time":0,"Failed":false,"Accumulables":[]}}
-{"Event":"SparkListenerTaskStart","Stage ID":3,"Stage Attempt ID":0,"Task Info":{"Task ID":26,"Index":2,"Attempt":0,"Launch Time":1422981787192,"Executor ID":"<driver>","Host":"localhost","Locality":"PROCESS_LOCAL","Speculative":false,"Getting Result Time":0,"Finish Time":0,"Failed":false,"Accumulables":[]}}
-{"Event":"SparkListenerTaskStart","Stage ID":3,"Stage Attempt ID":0,"Task Info":{"Task ID":27,"Index":3,"Attempt":0,"Launch Time":1422981787192,"Executor ID":"<driver>","Host":"localhost","Locality":"PROCESS_LOCAL","Speculative":false,"Getting Result Time":0,"Finish Time":0,"Failed":false,"Accumulables":[]}}
-{"Event":"SparkListenerTaskStart","Stage ID":3,"Stage Attempt ID":0,"Task Info":{"Task ID":28,"Index":4,"Attempt":0,"Launch Time":1422981787193,"Executor ID":"<driver>","Host":"localhost","Locality":"PROCESS_LOCAL","Speculative":false,"Getting Result Time":0,"Finish Time":0,"Failed":false,"Accumulables":[]}}
-{"Event":"SparkListenerTaskStart","Stage ID":3,"Stage Attempt ID":0,"Task Info":{"Task ID":29,"Index":5,"Attempt":0,"Launch Time":1422981787193,"Executor ID":"<driver>","Host":"localhost","Locality":"PROCESS_LOCAL","Speculative":false,"Getting Result Time":0,"Finish Time":0,"Failed":false,"Accumulables":[]}}
-{"Event":"SparkListenerTaskStart","Stage ID":3,"Stage Attempt ID":0,"Task Info":{"Task ID":30,"Index":6,"Attempt":0,"Launch Time":1422981787194,"Executor ID":"<driver>","Host":"localhost","Locality":"PROCESS_LOCAL","Speculative":false,"Getting Result Time":0,"Finish Time":0,"Failed":false,"Accumulables":[]}}
-{"Event":"SparkListenerTaskStart","Stage ID":3,"Stage Attempt ID":0,"Task Info":{"Task ID":31,"Index":7,"Attempt":0,"Launch Time":1422981787194,"Executor ID":"<driver>","Host":"localhost","Locality":"PROCESS_LOCAL","Speculative":false,"Getting Result Time":0,"Finish Time":0,"Failed":false,"Accumulables":[]}}
-{"Event":"SparkListenerTaskEnd","Stage ID":3,"Stage Attempt ID":0,"Task Type":"ResultTask","Task End Reason":{"Reason":"Success"},"Task Info":{"Task ID":25,"Index":1,"Attempt":0,"Launch Time":1422981787191,"Executor ID":"<driver>","Host":"localhost","Locality":"PROCESS_LOCAL","Speculative":false,"Getting Result Time":0,"Finish Time":1422981787222,"Failed":false,"Accumulables":[]},"Task Metrics":{"Host Name":"localhost","Executor Deserialize Time":5,"Executor Run Time":20,"Result Size":1812,"JVM GC Time":0,"Result Serialization Time":0,"Memory Bytes Spilled":0,"Disk Bytes Spilled":0,"Input Metrics":{"Data Read Method":"Hadoop","Bytes Read":32}}}
-{"Event":"SparkListenerTaskEnd","Stage ID":3,"Stage Attempt ID":0,"Task Type":"ResultTask","Task End Reason":{"Reason":"Success"},"Task Info":{"Task ID":24,"Index":0,"Attempt":0,"Launch Time":1422981787191,"Executor ID":"<driver>","Host":"localhost","Locality":"PROCESS_LOCAL","Speculative":false,"Getting Result Time":0,"Finish Time":1422981787223,"Failed":false,"Accumulables":[]},"Task Metrics":{"Host Name":"localhost","Executor Deserialize Time":5,"Executor Run Time":20,"Result Size":1812,"JVM GC Time":0,"Result Serialization Time":0,"Memory Bytes Spilled":0,"Disk Bytes Spilled":0,"Input Metrics":{"Data Read Method":"Hadoop","Bytes Read":32}}}
-{"Event":"SparkListenerTaskEnd","Stage ID":3,"Stage Attempt ID":0,"Task Type":"ResultTask","Task End Reason":{"Reason":"Success"},"Task Info":{"Task ID":26,"Index":2,"Attempt":0,"Launch Time":1422981787192,"Executor ID":"<driver>","Host":"localhost","Locality":"PROCESS_LOCAL","Speculative":false,"Getting Result Time":0,"Finish Time":1422981787223,"Failed":false,"Accumulables":[]},"Task Metrics":{"Host Name":"localhost","Executor Deserialize Time":4,"Executor Run Time":22,"Result Size":1812,"JVM GC Time":0,"Result Serialization Time":0,"Memory Bytes Spilled":0,"Disk Bytes Spilled":0,"Input Metrics":{"Data Read Method":"Hadoop","Bytes Read":16}}}
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diff --git a/core/src/test/resources/spark-events/local-1422981780767/SPARK_VERSION_1.2.0 b/core/src/test/resources/spark-events/local-1422981780767/SPARK_VERSION_1.2.0
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index e69de29bb2d1d..0000000000000
diff --git a/core/src/test/resources/spark-events/local-1425081759269/APPLICATION_COMPLETE b/core/src/test/resources/spark-events/local-1425081759269/APPLICATION_COMPLETE
deleted file mode 100755
index e69de29bb2d1d..0000000000000
diff --git a/core/src/test/resources/spark-events/local-1425081759269/EVENT_LOG_1 b/core/src/test/resources/spark-events/local-1425081759269/EVENT_LOG_1
deleted file mode 100755
index 9745b36b09e44..0000000000000
--- a/core/src/test/resources/spark-events/local-1425081759269/EVENT_LOG_1
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diff --git a/core/src/test/resources/spark-events/local-1425081759269/SPARK_VERSION_1.2.0 b/core/src/test/resources/spark-events/local-1425081759269/SPARK_VERSION_1.2.0
deleted file mode 100755
index e69de29bb2d1d..0000000000000
diff --git a/core/src/test/resources/spark-events/local-1426533911241/APPLICATION_COMPLETE b/core/src/test/resources/spark-events/local-1426533911241/APPLICATION_COMPLETE
deleted file mode 100755
index e69de29bb2d1d..0000000000000
diff --git a/core/src/test/resources/spark-events/local-1426533911241/EVENT_LOG_1 b/core/src/test/resources/spark-events/local-1426533911241/EVENT_LOG_1
deleted file mode 100755
index 9ef5bd5d92de5..0000000000000
--- a/core/src/test/resources/spark-events/local-1426533911241/EVENT_LOG_1
+++ /dev/null
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diff --git a/core/src/test/resources/spark-events/local-1426533911241/SPARK_VERSION_1.2.0 b/core/src/test/resources/spark-events/local-1426533911241/SPARK_VERSION_1.2.0
deleted file mode 100755
index e69de29bb2d1d..0000000000000
diff --git a/core/src/test/resources/spark-events/local-1426633911242/APPLICATION_COMPLETE b/core/src/test/resources/spark-events/local-1426633911242/APPLICATION_COMPLETE
deleted file mode 100755
index e69de29bb2d1d..0000000000000
diff --git a/core/src/test/resources/spark-events/local-1426633911242/EVENT_LOG_1 b/core/src/test/resources/spark-events/local-1426633911242/EVENT_LOG_1
deleted file mode 100755
index e7043282107d3..0000000000000
--- a/core/src/test/resources/spark-events/local-1426633911242/EVENT_LOG_1
+++ /dev/null
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diff --git a/core/src/test/resources/spark-events/local-1426633911242/SPARK_VERSION_1.2.0 b/core/src/test/resources/spark-events/local-1426633911242/SPARK_VERSION_1.2.0
deleted file mode 100755
index e69de29bb2d1d..0000000000000
diff --git a/core/src/test/resources/spark-events/local-1427397477963 b/core/src/test/resources/spark-events/local-1427397477963
deleted file mode 100755
index 3bd251d48c87b..0000000000000
--- a/core/src/test/resources/spark-events/local-1427397477963
+++ /dev/null
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-{"Event":"SparkListenerStageSubmitted","Stage Info":{"Stage ID":21,"Stage Attempt ID":0,"Stage Name":"next at <console>:26","Number of Tasks":1,"RDD Info":[{"RDD ID":2,"Name":"2","Storage Level":{"Use Disk":false,"Use Memory":false,"Use Tachyon":false,"Deserialized":false,"Replication":1},"Number of Partitions":50,"Number of Cached Partitions":0,"Memory Size":0,"Tachyon Size":0,"Disk Size":0}],"Details":"scala.collection.Iterator$$anon$13.next(Iterator.scala:372)\n$line40.$read$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:26)\n$line40.$read$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:31)\n$line40.$read$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:33)\n$line40.$read$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:35)\n$line40.$read$$iwC$$iwC$$iwC$$iwC.<init>(<console>:37)\n$line40.$read$$iwC$$iwC$$iwC.<init>(<console>:39)\n$line40.$read$$iwC$$iwC.<init>(<console>:41)\n$line40.$read$$iwC.<init>(<console>:43)\n$line40.$read.<init>(<console>:45)\n$line40.$read$.<init>(<console>:49)\n$line40.$read$.<clinit>(<console>)\n$line40.$eval$.<init>(<console>:7)\n$line40.$eval$.<clinit>(<console>)\n$line40.$eval.$print(<console>)\nsun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\nsun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)\nsun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\njava.lang.reflect.Method.invoke(Method.java:483)\norg.apache.spark.repl.SparkIMain$ReadEvalPrint.call(SparkIMain.scala:1065)","Accumulables":[]}}
-{"Event":"SparkListenerTaskStart","Stage ID":21,"Stage Attempt ID":0,"Task Info":{"Task ID":6010,"Index":0,"Attempt":0,"Launch Time":1427397601860,"Executor ID":"<driver>","Host":"localhost","Locality":"PROCESS_LOCAL","Speculative":false,"Getting Result Time":0,"Finish Time":0,"Failed":false,"Accumulables":[]}}
-{"Event":"SparkListenerTaskEnd","Stage ID":21,"Stage Attempt ID":0,"Task Type":"ResultTask","Task End Reason":{"Reason":"Success"},"Task Info":{"Task ID":6010,"Index":0,"Attempt":0,"Launch Time":1427397601860,"Executor ID":"<driver>","Host":"localhost","Locality":"PROCESS_LOCAL","Speculative":false,"Getting Result Time":0,"Finish Time":1427397601971,"Failed":false,"Accumulables":[]},"Task Metrics":{"Host Name":"localhost","Executor Deserialize Time":0,"Executor Run Time":109,"Result Size":1060,"JVM GC Time":0,"Result Serialization Time":0,"Memory Bytes Spilled":0,"Disk Bytes Spilled":0,"Shuffle Read Metrics":{"Remote Blocks Fetched":0,"Local Blocks Fetched":1000,"Fetch Wait Time":0,"Remote Bytes Read":0,"Local Bytes Read":191840,"Total Records Read":2000}}}
-{"Event":"SparkListenerStageCompleted","Stage Info":{"Stage ID":21,"Stage Attempt ID":0,"Stage Name":"next at <console>:26","Number of Tasks":1,"RDD Info":[{"RDD ID":2,"Name":"2","Storage Level":{"Use Disk":false,"Use Memory":false,"Use Tachyon":false,"Deserialized":false,"Replication":1},"Number of Partitions":50,"Number of Cached Partitions":0,"Memory Size":0,"Tachyon Size":0,"Disk Size":0}],"Details":"scala.collection.Iterator$$anon$13.next(Iterator.scala:372)\n$line40.$read$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:26)\n$line40.$read$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:31)\n$line40.$read$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:33)\n$line40.$read$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:35)\n$line40.$read$$iwC$$iwC$$iwC$$iwC.<init>(<console>:37)\n$line40.$read$$iwC$$iwC$$iwC.<init>(<console>:39)\n$line40.$read$$iwC$$iwC.<init>(<console>:41)\n$line40.$read$$iwC.<init>(<console>:43)\n$line40.$read.<init>(<console>:45)\n$line40.$read$.<init>(<console>:49)\n$line40.$read$.<clinit>(<console>)\n$line40.$eval$.<init>(<console>:7)\n$line40.$eval$.<clinit>(<console>)\n$line40.$eval.$print(<console>)\nsun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\nsun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)\nsun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\njava.lang.reflect.Method.invoke(Method.java:483)\norg.apache.spark.repl.SparkIMain$ReadEvalPrint.call(SparkIMain.scala:1065)","Submission Time":1427397601860,"Completion Time":1427397601971,"Accumulables":[]}}
-{"Event":"SparkListenerJobEnd","Job ID":10,"Completion Time":1427397601971,"Job Result":{"Result":"JobSucceeded"}}
-{"Event":"SparkListenerApplicationEnd","Timestamp":1427397602949}
diff --git a/core/src/test/scala/org/apache/spark/JsonTestUtils.scala b/core/src/test/scala/org/apache/spark/JsonTestUtils.scala
deleted file mode 100644
index ba367cd476146..0000000000000
--- a/core/src/test/scala/org/apache/spark/JsonTestUtils.scala
+++ /dev/null
@@ -1,34 +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
-
-import org.json4s._
-import org.json4s.jackson.JsonMethods
-
-trait JsonTestUtils {
-  def assertValidDataInJson(validateJson: JValue, expectedJson: JValue) {
-    val Diff(c, a, d) = validateJson.diff(expectedJson)
-    val validatePretty = JsonMethods.pretty(validateJson)
-    val expectedPretty = JsonMethods.pretty(expectedJson)
-    val errorMessage = s"Expected:\n$expectedPretty\nFound:\n$validatePretty"
-    import org.scalactic.TripleEquals._
-    assert(c === JNothing, s"$errorMessage\nChanged:\n${JsonMethods.pretty(c)}")
-    assert(a === JNothing, s"$errorMessage\nAdded:\n${JsonMethods.pretty(a)}")
-    assert(d === JNothing, s"$errorMessage\nDeleted:\n${JsonMethods.pretty(d)}")
-  }
-
-}
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 e04a79284175c..b58d62567afe1 100644
--- a/core/src/test/scala/org/apache/spark/deploy/JsonProtocolSuite.scala
+++ b/core/src/test/scala/org/apache/spark/deploy/JsonProtocolSuite.scala
@@ -28,9 +28,9 @@ import org.scalatest.FunSuite
 import org.apache.spark.deploy.DeployMessages.{MasterStateResponse, WorkerStateResponse}
 import org.apache.spark.deploy.master.{ApplicationInfo, DriverInfo, RecoveryState, WorkerInfo}
 import org.apache.spark.deploy.worker.{DriverRunner, ExecutorRunner}
-import org.apache.spark.{JsonTestUtils, SecurityManager, SparkConf}
+import org.apache.spark.{SecurityManager, SparkConf}
 
-class JsonProtocolSuite extends FunSuite with JsonTestUtils {
+class JsonProtocolSuite extends FunSuite {
 
   test("writeApplicationInfo") {
     val output = JsonProtocol.writeApplicationInfo(createAppInfo())
@@ -136,6 +136,16 @@ class JsonProtocolSuite extends FunSuite with JsonTestUtils {
       case e: JsonParseException => fail("Invalid Json detected", e)
     }
   }
+
+  def assertValidDataInJson(validateJson: JValue, expectedJson: JValue) {
+    val Diff(c, a, d) = validateJson diff expectedJson
+    val validatePretty = JsonMethods.pretty(validateJson)
+    val expectedPretty = JsonMethods.pretty(expectedJson)
+    val errorMessage = s"Expected:\n$expectedPretty\nFound:\n$validatePretty"
+    assert(c === JNothing, s"$errorMessage\nChanged:\n${JsonMethods.pretty(c)}")
+    assert(a === JNothing, s"$errorMessage\nAdded:\n${JsonMethods.pretty(a)}")
+    assert(d === JNothing, s"$errorMessage\nDelected:\n${JsonMethods.pretty(d)}")
+  }
 }
 
 object JsonConstants {
diff --git a/core/src/test/scala/org/apache/spark/deploy/history/HistoryServerSuite.scala b/core/src/test/scala/org/apache/spark/deploy/history/HistoryServerSuite.scala
index 0744b68c695f9..71ba9c18257b8 100644
--- a/core/src/test/scala/org/apache/spark/deploy/history/HistoryServerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/deploy/history/HistoryServerSuite.scala
@@ -14,161 +14,22 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
+
 package org.apache.spark.deploy.history
 
-import java.io.{File, FileInputStream, FileWriter, IOException}
-import java.net.{HttpURLConnection, URL}
-import javax.servlet.http.{HttpServletRequest, HttpServletResponse}
+import javax.servlet.http.HttpServletRequest
+
+import scala.collection.mutable
 
-import org.apache.commons.io.{FileUtils, IOUtils}
-import org.mockito.Mockito.when
-import org.scalatest.{BeforeAndAfter, FunSuite, Matchers}
+import org.apache.hadoop.fs.Path
+import org.mockito.Mockito.{when}
+import org.scalatest.FunSuite
+import org.scalatest.Matchers
 import org.scalatest.mock.MockitoSugar
 
-import org.apache.spark.{JsonTestUtils, SecurityManager, SparkConf}
 import org.apache.spark.ui.SparkUI
 
-/**
- * A collection of tests against the historyserver, including comparing responses from the json
- * metrics api to a set of known "golden files".  If new endpoints / parameters are added,
- * cases should be added to this test suite.  The expected outcomes can be genered by running
- * the HistoryServerSuite.main.  Note that this will blindly generate new expectation files matching
- * the current behavior -- the developer must verify that behavior is correct.
- *
- * Similarly, if the behavior is changed, HistoryServerSuite.main can be run to update the
- * expectations.  However, in general this should be done with extreme caution, as the metrics
- * are considered part of Spark's public api.
- */
-class HistoryServerSuite extends FunSuite with BeforeAndAfter with Matchers with MockitoSugar
-  with JsonTestUtils {
-
-  private val logDir = new File("src/test/resources/spark-events")
-  private val expRoot = new File("src/test/resources/HistoryServerExpectations/")
-
-  private var provider: FsHistoryProvider = null
-  private var server: HistoryServer = null
-  private var port: Int = -1
-
-  def init(): Unit = {
-    val conf = new SparkConf()
-      .set("spark.history.fs.logDirectory", logDir.getAbsolutePath)
-      .set("spark.history.fs.updateInterval", "0")
-      .set("spark.testing", "true")
-    provider = new FsHistoryProvider(conf)
-    provider.checkForLogs()
-    val securityManager = new SecurityManager(conf)
-
-    server = new HistoryServer(conf, provider, securityManager, 18080)
-    server.initialize()
-    server.bind()
-    port = server.boundPort
-  }
-
-  def stop(): Unit = {
-    server.stop()
-  }
-
-  before {
-    init()
-  }
-
-  after{
-    stop()
-  }
-
-  val cases = Seq(
-    "application list json" -> "applications",
-    "completed app list json" -> "applications?status=completed",
-    "running app list json" -> "applications?status=running",
-    "minDate app list json" -> "applications?minDate=2015-02-10",
-    "maxDate app list json" -> "applications?maxDate=2015-02-10",
-    "maxDate2 app list json" -> "applications?maxDate=2015-02-03T10:42:40.000CST",
-    "one app json" -> "applications/local-1422981780767",
-    "one app multi-attempt json" -> "applications/local-1426533911241",
-    "job list json" -> "applications/local-1422981780767/jobs",
-    "job list from multi-attempt app json(1)" -> "applications/local-1426533911241/1/jobs",
-    "job list from multi-attempt app json(2)" -> "applications/local-1426533911241/2/jobs",
-    "one job json" -> "applications/local-1422981780767/jobs/0",
-    "succeeded job list json" -> "applications/local-1422981780767/jobs?status=succeeded",
-    "succeeded&failed job list json" ->
-      "applications/local-1422981780767/jobs?status=succeeded&status=failed",
-    "executor list json" -> "applications/local-1422981780767/executors",
-    "stage list json" -> "applications/local-1422981780767/stages",
-    "complete stage list json" -> "applications/local-1422981780767/stages?status=complete",
-    "failed stage list json" -> "applications/local-1422981780767/stages?status=failed",
-    "one stage json" -> "applications/local-1422981780767/stages/1",
-    "one stage attempt json" -> "applications/local-1422981780767/stages/1/0",
-
-    "stage task summary" -> "applications/local-1427397477963/stages/20/0/taskSummary",
-    "stage task summary w/ custom quantiles" ->
-      "applications/local-1427397477963/stages/20/0/taskSummary?quantiles=0.01,0.5,0.99",
-
-    "stage task list" -> "applications/local-1427397477963/stages/20/0/taskList",
-    "stage task list w/ offset & length" ->
-      "applications/local-1427397477963/stages/20/0/taskList?offset=10&length=50",
-    "stage task list w/ sortBy" ->
-      "applications/local-1427397477963/stages/20/0/taskList?sortBy=DECREASING_RUNTIME",
-    "stage task list w/ sortBy short names: -runtime" ->
-      "applications/local-1427397477963/stages/20/0/taskList?sortBy=-runtime",
-    "stage task list w/ sortBy short names: runtime" ->
-      "applications/local-1427397477963/stages/20/0/taskList?sortBy=runtime",
-
-    "stage list with accumulable json" -> "applications/local-1426533911241/1/stages",
-    "stage with accumulable json" -> "applications/local-1426533911241/1/stages/0/0",
-    "stage task list from multi-attempt app json(1)" ->
-      "applications/local-1426533911241/1/stages/0/0/taskList",
-    "stage task list from multi-attempt app json(2)" ->
-      "applications/local-1426533911241/2/stages/0/0/taskList",
-
-    "rdd list storage json" -> "applications/local-1422981780767/storage/rdd",
-    "one rdd storage json" -> "applications/local-1422981780767/storage/rdd/0"
-  )
-
-  // run a bunch of characterization tests -- just verify the behavior is the same as what is saved
-  // in the test resource folder
-  cases.foreach { case (name, path) =>
-    test(name) {
-      val (code, jsonOpt, errOpt) = getContentAndCode(path)
-      code should be (HttpServletResponse.SC_OK)
-      jsonOpt should be ('defined)
-      errOpt should be (None)
-      val json = jsonOpt.get
-      val exp = IOUtils.toString(new FileInputStream(
-        new File(expRoot, path + "/json_expectation")))
-      // compare the ASTs so formatting differences don't cause failures
-      import org.json4s._
-      import org.json4s.jackson.JsonMethods._
-      val jsonAst = parse(json)
-      val expAst = parse(exp)
-      assertValidDataInJson(jsonAst, expAst)
-    }
-  }
-
-  test("response codes on bad paths") {
-    val badAppId = getContentAndCode("applications/foobar")
-    badAppId._1 should be (HttpServletResponse.SC_NOT_FOUND)
-    badAppId._3 should be (Some("unknown app: foobar"))
-
-    val badStageId = getContentAndCode("applications/local-1422981780767/stages/12345")
-    badStageId._1 should be (HttpServletResponse.SC_NOT_FOUND)
-    badStageId._3 should be (Some("unknown stage: 12345"))
-
-    val badStageAttemptId = getContentAndCode("applications/local-1422981780767/stages/1/1")
-    badStageAttemptId._1 should be (HttpServletResponse.SC_NOT_FOUND)
-    badStageAttemptId._3 should be (Some("unknown attempt for stage 1.  Found attempts: [0]"))
-
-    val badStageId2 = getContentAndCode("applications/local-1422981780767/stages/flimflam")
-    badStageId2._1 should be (HttpServletResponse.SC_NOT_FOUND)
-    // will take some mucking w/ jersey to get a better error msg in this case
-
-    val badQuantiles = getContentAndCode(
-      "applications/local-1427397477963/stages/20/0/taskSummary?quantiles=foo,0.1")
-    badQuantiles._1 should be (HttpServletResponse.SC_BAD_REQUEST)
-    badQuantiles._3 should be (Some("Bad value for parameter \"quantiles\".  Expected a double, " +
-      "got \"foo\""))
-
-    getContentAndCode("foobar")._1 should be (HttpServletResponse.SC_NOT_FOUND)
-  }
+class HistoryServerSuite extends FunSuite with Matchers with MockitoSugar {
 
   test("generate history page with relative links") {
     val historyServer = mock[HistoryServer]
@@ -193,70 +54,4 @@ class HistoryServerSuite extends FunSuite with BeforeAndAfter with Matchers with
     } yield (attrs.toString)
     justHrefs should contain(link)
   }
-
-  def getContentAndCode(path: String, port: Int = port): (Int, Option[String], Option[String]) = {
-    HistoryServerSuite.getContentAndCode(new URL(s"http://localhost:$port/json/v1/$path"))
-  }
-
-  def getUrl(path: String): String = {
-    HistoryServerSuite.getUrl(new URL(s"http://localhost:$port/json/v1/$path"))
-  }
-
-  def generateExpectation(path: String): Unit = {
-    val json = getUrl(path)
-    val dir = new File(expRoot, path)
-    dir.mkdirs()
-    val out = new FileWriter(new File(dir, "json_expectation"))
-    out.write(json)
-    out.close()
-  }
-}
-
-object HistoryServerSuite {
-  def main(args: Array[String]): Unit = {
-    // generate the "expected" results for the characterization tests.  Just blindly assume the
-    // current behavior is correct, and write out the returned json to the test/resource files
-
-    val suite = new HistoryServerSuite
-    FileUtils.deleteDirectory(suite.expRoot)
-    suite.expRoot.mkdirs()
-    try {
-      suite.init()
-      suite.cases.foreach { case (_, path) =>
-        suite.generateExpectation(path)
-      }
-    } finally {
-      suite.stop()
-    }
-  }
-
-  def getContentAndCode(url: URL): (Int, Option[String], Option[String]) = {
-    val connection = url.openConnection().asInstanceOf[HttpURLConnection]
-    connection.setRequestMethod("GET")
-    connection.connect()
-    val code = connection.getResponseCode()
-    val inString = try {
-      val in = Option(connection.getInputStream())
-      in.map{IOUtils.toString}
-    } catch {
-      case io: IOException => None
-    }
-    val errString = try {
-      val err = Option(connection.getErrorStream())
-      err.map{IOUtils.toString}
-    } catch {
-      case io: IOException => None
-    }
-    (code, inString, errString)
-  }
-
-  def getUrl(path: URL): String = {
-    val (code, resultOpt, error) = getContentAndCode(path)
-    if (code == 200) {
-      resultOpt.get
-    } else {
-      throw new RuntimeException(
-        "got code: " + code + " when getting " + path + " w/ error: " + error)
-    }
-  }
 }
diff --git a/core/src/test/scala/org/apache/spark/status/api/v1/SimpleDateParamTest.scala b/core/src/test/scala/org/apache/spark/status/api/v1/SimpleDateParamTest.scala
deleted file mode 100644
index 5274df904d395..0000000000000
--- a/core/src/test/scala/org/apache/spark/status/api/v1/SimpleDateParamTest.scala
+++ /dev/null
@@ -1,29 +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.status.api.v1
-
-import org.scalatest.{Matchers, FunSuite}
-
-class SimpleDateParamTest extends FunSuite with Matchers {
-
-  test("date parsing") {
-    new SimpleDateParam("2015-02-20T23:21:17.190GMT").timestamp should be (1424474477190L)
-    new SimpleDateParam("2015-02-20T17:21:17.190CST").timestamp should be (1424474477190L)
-    new SimpleDateParam("2015-02-20").timestamp should be (1424390400000L)  // GMT
-  }
-
-}
diff --git a/core/src/test/scala/org/apache/spark/ui/UISeleniumSuite.scala b/core/src/test/scala/org/apache/spark/ui/UISeleniumSuite.scala
index 117b2c3960820..d53d7f3ba5ae7 100644
--- a/core/src/test/scala/org/apache/spark/ui/UISeleniumSuite.scala
+++ b/core/src/test/scala/org/apache/spark/ui/UISeleniumSuite.scala
@@ -18,13 +18,11 @@
 package org.apache.spark.ui
 
 import java.net.{HttpURLConnection, URL}
-import javax.servlet.http.{HttpServletResponse, HttpServletRequest}
+import javax.servlet.http.HttpServletRequest
 
 import scala.collection.JavaConversions._
 import scala.xml.Node
 
-import org.json4s._
-import org.json4s.jackson.JsonMethods
 import org.openqa.selenium.htmlunit.HtmlUnitDriver
 import org.openqa.selenium.{By, WebDriver}
 import org.scalatest._
@@ -35,9 +33,8 @@ import org.scalatest.time.SpanSugar._
 import org.apache.spark.LocalSparkContext._
 import org.apache.spark._
 import org.apache.spark.api.java.StorageLevels
-import org.apache.spark.deploy.history.HistoryServerSuite
 import org.apache.spark.shuffle.FetchFailedException
-import org.apache.spark.status.api.v1.{JacksonMessageWriter, StageStatus}
+
 
 /**
  * Selenium tests for the Spark Web UI.
@@ -45,8 +42,6 @@ import org.apache.spark.status.api.v1.{JacksonMessageWriter, StageStatus}
 class UISeleniumSuite extends FunSuite with WebBrowser with Matchers with BeforeAndAfterAll {
 
   implicit var webDriver: WebDriver = _
-  implicit val formats = DefaultFormats
-
 
   override def beforeAll(): Unit = {
     webDriver = new HtmlUnitDriver
@@ -81,42 +76,28 @@ class UISeleniumSuite extends FunSuite with WebBrowser with Matchers with Before
       val rdd = sc.parallelize(Seq(1, 2, 3))
       rdd.persist(StorageLevels.DISK_ONLY).count()
       eventually(timeout(5 seconds), interval(50 milliseconds)) {
-        goToUi(ui, "/storage")
+        go to (ui.appUIAddress.stripSuffix("/") + "/storage")
         val tableRowText = findAll(cssSelector("#storage-by-rdd-table td")).map(_.text).toSeq
         tableRowText should contain (StorageLevels.DISK_ONLY.description)
       }
       eventually(timeout(5 seconds), interval(50 milliseconds)) {
-        goToUi(ui, "/storage/rdd/?id=0")
+        go to (ui.appUIAddress.stripSuffix("/") + "/storage/rdd/?id=0")
         val tableRowText = findAll(cssSelector("#rdd-storage-by-block-table td")).map(_.text).toSeq
         tableRowText should contain (StorageLevels.DISK_ONLY.description)
       }
 
-      val storageJson = getJson(ui, "storage/rdd")
-      storageJson.children.length should be (1)
-      (storageJson \ "storageLevel").extract[String] should be (StorageLevels.DISK_ONLY.description)
-      val rddJson = getJson(ui, "storage/rdd/0")
-      (rddJson  \ "storageLevel").extract[String] should be (StorageLevels.DISK_ONLY.description)
-
       rdd.unpersist()
       rdd.persist(StorageLevels.MEMORY_ONLY).count()
       eventually(timeout(5 seconds), interval(50 milliseconds)) {
-        goToUi(ui, "/storage")
+        go to (ui.appUIAddress.stripSuffix("/") + "/storage")
         val tableRowText = findAll(cssSelector("#storage-by-rdd-table td")).map(_.text).toSeq
         tableRowText should contain (StorageLevels.MEMORY_ONLY.description)
       }
       eventually(timeout(5 seconds), interval(50 milliseconds)) {
-        goToUi(ui, "/storage/rdd/?id=0")
+        go to (ui.appUIAddress.stripSuffix("/") + "/storage/rdd/?id=0")
         val tableRowText = findAll(cssSelector("#rdd-storage-by-block-table td")).map(_.text).toSeq
         tableRowText should contain (StorageLevels.MEMORY_ONLY.description)
       }
-
-      val updatedStorageJson = getJson(ui, "storage/rdd")
-      updatedStorageJson.children.length should be (1)
-      (updatedStorageJson \ "storageLevel").extract[String] should be (
-        StorageLevels.MEMORY_ONLY.description)
-      val updatedRddJson = getJson(ui, "storage/rdd/0")
-      (updatedRddJson  \ "storageLevel").extract[String] should be (
-        StorageLevels.MEMORY_ONLY.description)
     }
   }
 
@@ -127,13 +108,10 @@ class UISeleniumSuite extends FunSuite with WebBrowser with Matchers with Before
         sc.parallelize(1 to 10).map { x => throw new Exception()}.collect()
       }
       eventually(timeout(5 seconds), interval(50 milliseconds)) {
-        goToUi(sc, "/stages")
+        go to (sc.ui.get.appUIAddress.stripSuffix("/") + "/stages")
         find(id("active")) should be(None)  // Since we hide empty tables
         find(id("failed")).get.text should be("Failed Stages (1)")
       }
-      val stageJson = getJson(sc.ui.get, "stages")
-      stageJson.children.length should be (1)
-      (stageJson \ "status").extract[String] should be (StageStatus.FAILED.name())
 
       // Regression test for SPARK-2105
       class NotSerializable
@@ -142,15 +120,12 @@ class UISeleniumSuite extends FunSuite with WebBrowser with Matchers with Before
         sc.parallelize(1 to 10).map { x => unserializableObject}.collect()
       }
       eventually(timeout(5 seconds), interval(50 milliseconds)) {
-        goToUi(sc, "/stages")
+        go to (sc.ui.get.appUIAddress.stripSuffix("/") + "/stages")
         find(id("active")) should be(None)  // Since we hide empty tables
         // The failure occurs before the stage becomes active, hence we should still show only one
         // failed stage, not two:
         find(id("failed")).get.text should be("Failed Stages (1)")
       }
-
-      val updatedStageJson = getJson(sc.ui.get, "stages")
-      updatedStageJson should be (stageJson)
     }
   }
 
@@ -163,7 +138,7 @@ class UISeleniumSuite extends FunSuite with WebBrowser with Matchers with Before
     withSpark(newSparkContext(killEnabled = true)) { sc =>
       runSlowJob(sc)
       eventually(timeout(5 seconds), interval(50 milliseconds)) {
-        goToUi(sc, "/stages")
+        go to (sc.ui.get.appUIAddress.stripSuffix("/") + "/stages")
         assert(hasKillLink)
       }
     }
@@ -171,7 +146,7 @@ class UISeleniumSuite extends FunSuite with WebBrowser with Matchers with Before
     withSpark(newSparkContext(killEnabled = false)) { sc =>
       runSlowJob(sc)
       eventually(timeout(5 seconds), interval(50 milliseconds)) {
-        goToUi(sc, "/stages")
+        go to (sc.ui.get.appUIAddress.stripSuffix("/") + "/stages")
         assert(!hasKillLink)
       }
     }
@@ -182,7 +157,7 @@ class UISeleniumSuite extends FunSuite with WebBrowser with Matchers with Before
       // If no job has been run in a job group, then "(Job Group)" should not appear in the header
       sc.parallelize(Seq(1, 2, 3)).count()
       eventually(timeout(5 seconds), interval(50 milliseconds)) {
-        goToUi(sc, "/jobs")
+        go to (sc.ui.get.appUIAddress.stripSuffix("/") + "/jobs")
         val tableHeaders = findAll(cssSelector("th")).map(_.text).toSeq
         tableHeaders should not contain "Job Id (Job Group)"
       }
@@ -190,22 +165,10 @@ class UISeleniumSuite extends FunSuite with WebBrowser with Matchers with Before
       sc.setJobGroup("my-job-group", "my-job-group-description")
       sc.parallelize(Seq(1, 2, 3)).count()
       eventually(timeout(5 seconds), interval(50 milliseconds)) {
-        goToUi(sc, "/jobs")
+        go to (sc.ui.get.appUIAddress.stripSuffix("/") + "/jobs")
         val tableHeaders = findAll(cssSelector("th")).map(_.text).toSeq
         tableHeaders should contain ("Job Id (Job Group)")
       }
-
-      val jobJson = getJson(sc.ui.get, "jobs")
-      for {
-        job @ JObject(_) <- jobJson
-        JInt(jobId) <- job \ "jobId"
-        jobGroup = job \ "jobGroup"
-      } {
-        jobId.toInt match {
-          case 0 => jobGroup should be (JNothing)
-          case 1 => jobGroup should be (JString("my-job-group"))
-        }
-      }
     }
   }
 
@@ -232,7 +195,7 @@ class UISeleniumSuite extends FunSuite with WebBrowser with Matchers with Before
       }
       mappedData.count()
       eventually(timeout(5 seconds), interval(50 milliseconds)) {
-        goToUi(sc, "/jobs")
+        go to (sc.ui.get.appUIAddress.stripSuffix("/") + "/jobs")
         find(cssSelector(".stage-progress-cell")).get.text should be ("2/2 (1 failed)")
         // Ideally, the following test would pass, but currently we overcount completed tasks
         // if task recomputations occur:
@@ -241,32 +204,6 @@ class UISeleniumSuite extends FunSuite with WebBrowser with Matchers with Before
         // of completed tasks may be higher:
         find(cssSelector(".progress-cell .progress")).get.text should be ("3/2 (1 failed)")
       }
-      val jobJson = getJson(sc.ui.get, "jobs")
-      (jobJson \ "numTasks").extract[Int]should be (2)
-      (jobJson \ "numCompletedTasks").extract[Int] should be (3)
-      (jobJson \ "numFailedTasks").extract[Int] should be (1)
-      (jobJson \ "numCompletedStages").extract[Int] should be (2)
-      (jobJson \ "numFailedStages").extract[Int] should be (1)
-      val stageJson = getJson(sc.ui.get, "stages")
-
-      for {
-        stage @ JObject(_) <- stageJson
-        JString(status) <- stage \ "status"
-        JInt(stageId) <- stage \ "stageId"
-        JInt(attemptId) <- stage \ "attemptId"
-      } {
-        val exp = if (attemptId == 0 && stageId == 1) StageStatus.FAILED else StageStatus.COMPLETE
-        status should be (exp.name())
-      }
-
-      for {
-        stageId <- 0 to 1
-        attemptId <- 0 to 1
-      } {
-        val exp = if (attemptId == 0 && stageId == 1) StageStatus.FAILED else StageStatus.COMPLETE
-        val stageJson = getJson(sc.ui.get, s"stages/$stageId/$attemptId")
-        (stageJson \ "status").extract[String] should be (exp.name())
-      }
     }
   }
 
@@ -281,7 +218,7 @@ class UISeleniumSuite extends FunSuite with WebBrowser with Matchers with Before
       // Start the job:
       rdd.countAsync()
       eventually(timeout(10 seconds), interval(50 milliseconds)) {
-        goToUi(sc, "/jobs/job/?id=0")
+        go to (sc.ui.get.appUIAddress.stripSuffix("/") + "/jobs/job/?id=0")
         find(id("active")).get.text should be ("Active Stages (1)")
         find(id("pending")).get.text should be ("Pending Stages (2)")
         // Essentially, we want to check that none of the stage rows show
@@ -307,7 +244,7 @@ class UISeleniumSuite extends FunSuite with WebBrowser with Matchers with Before
       rdd.count()
       rdd.count()
       eventually(timeout(10 seconds), interval(50 milliseconds)) {
-        goToUi(sc, "/jobs")
+        go to (sc.ui.get.appUIAddress.stripSuffix("/") + "/jobs")
         // The completed jobs table should have two rows. The first row will be the most recent job:
         val firstRow = find(cssSelector("tbody tr")).get.underlying
         val firstRowColumns = firstRow.findElements(By.tagName("td"))
@@ -334,7 +271,7 @@ class UISeleniumSuite extends FunSuite with WebBrowser with Matchers with Before
       rdd.count()
       rdd.count()
       eventually(timeout(10 seconds), interval(50 milliseconds)) {
-        goToUi(sc, "/jobs/job/?id=1")
+        go to (sc.ui.get.appUIAddress.stripSuffix("/") + "/jobs/job/?id=1")
         find(id("pending")) should be (None)
         find(id("active")) should be (None)
         find(id("failed")) should be (None)
@@ -362,7 +299,7 @@ class UISeleniumSuite extends FunSuite with WebBrowser with Matchers with Before
       rdd.count()
       rdd.count()
       eventually(timeout(10 seconds), interval(50 milliseconds)) {
-        goToUi(sc, "/jobs")
+        go to (sc.ui.get.appUIAddress.stripSuffix("/") + "/jobs")
         findAll(cssSelector("tbody tr a")).foreach { link =>
           link.text.toLowerCase should include ("count")
           link.text.toLowerCase should not include "unknown"
@@ -384,7 +321,7 @@ class UISeleniumSuite extends FunSuite with WebBrowser with Matchers with Before
       }
       sparkUI.attachTab(newTab)
       eventually(timeout(10 seconds), interval(50 milliseconds)) {
-        goToUi(sc, "")
+        go to (sc.ui.get.appUIAddress.stripSuffix("/"))
         find(cssSelector("""ul li a[href*="jobs"]""")) should not be(None)
         find(cssSelector("""ul li a[href*="stages"]""")) should not be(None)
         find(cssSelector("""ul li a[href*="storage"]""")) should not be(None)
@@ -393,12 +330,12 @@ class UISeleniumSuite extends FunSuite with WebBrowser with Matchers with Before
       }
       eventually(timeout(10 seconds), interval(50 milliseconds)) {
         // check whether new page exists
-        goToUi(sc, "/foo")
+        go to (sc.ui.get.appUIAddress.stripSuffix("/") + "/foo")
         find(cssSelector("b")).get.text should include ("html magic")
       }
       sparkUI.detachTab(newTab)
       eventually(timeout(10 seconds), interval(50 milliseconds)) {
-        goToUi(sc, "")
+        go to (sc.ui.get.appUIAddress.stripSuffix("/"))
         find(cssSelector("""ul li a[href*="jobs"]""")) should not be(None)
         find(cssSelector("""ul li a[href*="stages"]""")) should not be(None)
         find(cssSelector("""ul li a[href*="storage"]""")) should not be(None)
@@ -407,7 +344,7 @@ class UISeleniumSuite extends FunSuite with WebBrowser with Matchers with Before
       }
       eventually(timeout(10 seconds), interval(50 milliseconds)) {
         // check new page not exist
-        goToUi(sc, "/foo")
+        go to (sc.ui.get.appUIAddress.stripSuffix("/") + "/foo")
         find(cssSelector("b")) should be(None)
       }
     }
@@ -434,163 +371,4 @@ class UISeleniumSuite extends FunSuite with WebBrowser with Matchers with Before
       }
     }
   }
-
-  test("stage & job retention") {
-    val conf = new SparkConf()
-      .setMaster("local")
-      .setAppName("test")
-      .set("spark.ui.enabled", "true")
-      .set("spark.ui.port", "0")
-      .set("spark.ui.retainedStages", "3")
-      .set("spark.ui.retainedJobs", "2")
-    val sc = new SparkContext(conf)
-    assert(sc.ui.isDefined)
-
-    withSpark(sc) { sc =>
-      // run a few jobs & stages ...
-      (0 until 5).foreach { idx =>
-        // NOTE: if we reverse the order, things don't really behave nicely
-        // we lose the stage for a job we keep, and then the job doesn't know
-        // about its last stage
-        sc.parallelize(idx to (idx + 3)).map(identity).groupBy(identity).map(identity)
-          .groupBy(identity).count()
-        sc.parallelize(idx to (idx + 3)).collect()
-      }
-
-      val expJobInfo = Seq(
-        ("9", "collect"),
-        ("8", "count")
-      )
-
-      eventually(timeout(1 second), interval(50 milliseconds)) {
-        goToUi(sc, "/jobs")
-        // The completed jobs table should have two rows. The first row will be the most recent job:
-        find("completed-summary").get.text should be ("Completed Jobs: 10, only showing 2")
-        find("completed").get.text should be ("Completed Jobs (10, only showing 2)")
-        val rows = findAll(cssSelector("tbody tr")).toIndexedSeq.map{_.underlying}
-        rows.size should be (expJobInfo.size)
-        for {
-          (row, idx) <- rows.zipWithIndex
-          columns = row.findElements(By.tagName("td"))
-          id = columns(0).getText()
-          description = columns(1).getText()
-        } {
-          id should be (expJobInfo(idx)._1)
-          description should include (expJobInfo(idx)._2)
-        }
-      }
-
-      val jobsJson = getJson(sc.ui.get, "jobs")
-      jobsJson.children.size should be (expJobInfo.size)
-      for {
-        (job @ JObject(_),idx) <- jobsJson.children.zipWithIndex
-        id = (job \ "jobId").extract[String]
-        name = (job \ "name").extract[String]
-      } {
-        withClue(s"idx = $idx; id = $id; name = ${name.substring(0,20)}") {
-          id should be (expJobInfo(idx)._1)
-          name should include (expJobInfo(idx)._2)
-        }
-      }
-
-      // what about when we query for a job that did exist, but has been cleared?
-      goToUi(sc, "/jobs/job/?id=7")
-      find("no-info").get.text should be ("No information to display for job 7")
-
-      val badJob = HistoryServerSuite.getContentAndCode(jsonUrl(sc.ui.get, "jobs/7"))
-      badJob._1 should be (HttpServletResponse.SC_NOT_FOUND)
-      badJob._2 should be (None)
-      badJob._3 should be (Some("unknown job: 7"))
-
-      val expStageInfo = Seq(
-        ("19", "collect"),
-        ("18", "count"),
-        ("17", "groupBy")
-      )
-
-      eventually(timeout(1 second), interval(50 milliseconds)) {
-        goToUi(sc, "/stages")
-        find("completed-summary").get.text should be ("Completed Stages: 20, only showing 3")
-        find("completed").get.text should be ("Completed Stages (20, only showing 3)")
-        val rows = findAll(cssSelector("tbody tr")).toIndexedSeq.map{_.underlying}
-        rows.size should be (3)
-        for {
-          (row, idx) <- rows.zipWithIndex
-          columns = row.findElements(By.tagName("td"))
-          id = columns(0).getText()
-          description = columns(1).getText()
-        } {
-          id should be (expStageInfo(idx)._1)
-          description should include (expStageInfo(idx)._2)
-        }
-      }
-
-      val stagesJson = getJson(sc.ui.get, "stages")
-      stagesJson.children.size should be (3)
-      for {
-        (stage @ JObject(_), idx) <- stagesJson.children.zipWithIndex
-        id = (stage \ "stageId").extract[String]
-        name = (stage \ "name").extract[String]
-      } {
-        id should be (expStageInfo(idx)._1)
-        name should include (expStageInfo(idx)._2)
-      }
-
-      // nonexistent stage
-
-      goToUi(sc, "/stages/stage/?id=12&attempt=0")
-      find("no-info").get.text should be ("No information to display for Stage 12 (Attempt 0)")
-      val badStage = HistoryServerSuite.getContentAndCode(jsonUrl(sc.ui.get,"stages/12/0"))
-      badStage._1 should be (HttpServletResponse.SC_NOT_FOUND)
-      badStage._2 should be (None)
-      badStage._3 should be (Some("unknown stage: 12"))
-
-      val badAttempt = HistoryServerSuite.getContentAndCode(jsonUrl(sc.ui.get,"stages/19/15"))
-      badAttempt._1 should be (HttpServletResponse.SC_NOT_FOUND)
-      badAttempt._2 should be (None)
-      badAttempt._3 should be (Some("unknown attempt for stage 19.  Found attempts: [0]"))
-
-      val badStageAttemptList = HistoryServerSuite.getContentAndCode(
-        jsonUrl(sc.ui.get, "stages/12"))
-      badStageAttemptList._1 should be (HttpServletResponse.SC_NOT_FOUND)
-      badStageAttemptList._2 should be (None)
-      badStageAttemptList._3 should be (Some("unknown stage: 12"))
-    }
-  }
-
-  test("live UI json application list") {
-    withSpark(newSparkContext()) { sc =>
-      val appListRawJson = HistoryServerSuite.getUrl(new URL(
-        sc.ui.get.appUIAddress + "/json/v1/applications"))
-      val appListJsonAst = JsonMethods.parse(appListRawJson)
-      appListJsonAst.children.length should be (1)
-      val attempts = (appListJsonAst \ "attempts").children
-      attempts.size should be (1)
-      (attempts(0) \ "completed").extract[Boolean] should be (false)
-      parseDate(attempts(0) \ "startTime") should be (sc.startTime)
-      parseDate(attempts(0) \ "endTime") should be (-1)
-      val oneAppJsonAst = getJson(sc.ui.get, "")
-      oneAppJsonAst should be (appListJsonAst.children(0))
-    }
-  }
-
-  def goToUi(sc: SparkContext, path: String): Unit = {
-    goToUi(sc.ui.get, path)
-  }
-
-  def goToUi(ui: SparkUI, path: String): Unit = {
-    go to (ui.appUIAddress.stripSuffix("/") + path)
-  }
-
-  def parseDate(json: JValue): Long = {
-    JacksonMessageWriter.makeISODateFormat.parse(json.extract[String]).getTime
-  }
-
-  def getJson(ui: SparkUI, path: String): JValue = {
-    JsonMethods.parse(HistoryServerSuite.getUrl(jsonUrl(ui, path)))
-  }
-
-  def jsonUrl(ui: SparkUI, path: String): URL = {
-    new URL(ui.appUIAddress + "/json/v1/applications/test/" + path)
-  }
 }
diff --git a/docs/monitoring.md b/docs/monitoring.md
index 1e0fc150862fb..8a85928d6d44d 100644
--- a/docs/monitoring.md
+++ b/docs/monitoring.md
@@ -174,80 +174,6 @@ making it easy to identify slow tasks, data skew, etc.
 
 Note that the history server only displays completed Spark jobs. One way to signal the completion of a Spark job is to stop the Spark Context explicitly (`sc.stop()`), or in Python using the `with SparkContext() as sc:` to handle the Spark Context setup and tear down, and still show the job history on the UI.
 
-## REST API
-
-In addition to viewing the metrics in the UI, they are also available as JSON.  This gives developers
-an easy way to create new visualizations and monitoring tools for Spark.  The JSON is available for
-both running applications, and in the history server.  The endpoints are mounted at `/json/v1`.  Eg.,
-for the history server, they would typically be accessible at `http://<server-url>:18080/json/v1`, and
-for a running application, at `http://localhost:4040/json/v1`.
-
-<table class="table">
-  <tr><th>Endpoint</th><th>Meaning</th></tr>
-  <tr>
-    <td><code>/applications</code></td>
-    <td>A list of all applications</td>
-  </tr>
-  <tr>
-    <td><code>/applications/[app-id]/jobs</code></td>
-    <td>A list of all jobs for a given application</td>
-  </tr>
-  <tr>
-    <td><code>/applications/[app-id]/jobs/[job-id]</code></td>
-    <td>Details for the given job</td>
-  </tr>
-  <tr>
-    <td><code>/applications/[app-id]/stages</code></td>
-    <td>A list of all stages for a given application</td>
-  </tr>
-  <tr>
-    <td><code>/applications/[app-id]/stages/[stage-id]</code></td>
-    <td>A list of all attempts for the given stage</td>
-  </tr>
-  <tr>
-    <td><code>/applications/[app-id]/stages/[stage-id]/[stage-attempt-id]</code></td>
-    <td>Details for the given stage attempt</td>
-  </tr>
-  <tr>
-    <td><code>/applications/[app-id]/stages/[stage-id]/[stage-attempt-id]/taskSummary</code></td>
-    <td>Summary metrics of all tasks in the given stage attempt</td>
-  </tr>
-  <tr>
-    <td><code>/applications/[app-id]/stages/[stage-id]/[stage-attempt-id]/taskList</code></td>
-    <td>A list of all tasks for the given stage attempt</td>
-  </tr>
-  <tr>
-    <td><code>/applications/[app-id]/executors</code></td>
-    <td>A list of all executors for the given application</td>
-  </tr>
-  <tr>
-    <td><code>/applications/[app-id]/storage/rdd</code></td>
-    <td>A list of stored RDDs for the given application</td>
-  </tr>
-  <tr>
-    <td><code>/applications/[app-id]/storage/rdd/[rdd-id]</code></td>
-    <td>Details for the storage status of a given RDD</td>
-  </tr>
-</table>
-
-When running on Yarn, each application has multiple attempts, so `[app-id]` is actually
-`[app-id]/[attempt-id]` in all cases.
-
-These endpoints have been strongly versioned to make it easier to develop applications on top.
- In particular, Spark guarantees:
-
-* Endpoints will never be removed from one version
-* Individual fields will never be removed for any given endpoint
-* New endpoints may be added
-* New fields may be added to existing endpoints
-* New versions of the api may be added in the future at a separate endpoint (eg., `json/v2`).  New versions are *not* required to be backwards compatible.
-* Api versions may be dropped, but only after at least one minor release of co-existing with a new api version
-
-Note that even when examining the UI of a running applications, the `applications/[app-id]` portion is
-still required, though there is only one application available.  Eg. to see the list of jobs for the
-running app, you would go to `http://localhost:4040/json/v1/applications/[app-id]/jobs`.  This is to
-keep the paths consistent in both modes.
-
 # Metrics
 
 Spark has a configurable metrics system based on the 
diff --git a/pom.xml b/pom.xml
index 94cf28332cf47..12ad7c5be4e40 100644
--- a/pom.xml
+++ b/pom.xml
@@ -623,18 +623,6 @@
           </exclusion>
         </exclusions>
       </dependency>
-      <dependency>
-        <groupId>com.sun.jersey</groupId>
-        <artifactId>jersey-server</artifactId>
-        <version>1.9</version>
-        <scope>${hadoop.deps.scope}</scope>
-      </dependency>
-      <dependency>
-        <groupId>com.sun.jersey</groupId>
-        <artifactId>jersey-core</artifactId>
-        <version>1.9</version>
-        <scope>${hadoop.deps.scope}</scope>
-      </dependency>
       <dependency>
         <groupId>org.scala-lang</groupId>
         <artifactId>scala-compiler</artifactId>

From a4669443999dc13a1bb34509c827d8b9096ea84f Mon Sep 17 00:00:00 2001
From: qhuang <qian.huang@intel.com>
Date: Tue, 5 May 2015 20:39:56 -0700
Subject: [PATCH 04/75] [SPARK-6841] [SPARKR] add support for mean, median,
 stdev etc.

Moving here from https://github.com/amplab-extras/SparkR-pkg/pull/241
sum() has been implemented. (https://github.com/amplab-extras/SparkR-pkg/pull/242)

Now Phase 1: mean, sd, var have been implemented, but some things still need to be improved with the suggestions in https://issues.apache.org/jira/browse/SPARK-6841

Author: qhuang <qian.huang@intel.com>

Closes #5446 from hqzizania/R and squashes the following commits:

f283572 [qhuang] add test unit for describe()
2e74d5a [qhuang] add describe() DataFrame API
---
 R/pkg/NAMESPACE                  |  1 +
 R/pkg/R/DataFrame.R              | 37 ++++++++++++++++++++++++++++++++
 R/pkg/R/generics.R               |  4 ++++
 R/pkg/inst/tests/test_sparkSQL.R | 11 ++++++++++
 4 files changed, 53 insertions(+)

diff --git a/R/pkg/NAMESPACE b/R/pkg/NAMESPACE
index 1fb3311b7f955..528e6608c3c82 100644
--- a/R/pkg/NAMESPACE
+++ b/R/pkg/NAMESPACE
@@ -13,6 +13,7 @@ exportMethods("cache",
               "collect",
               "columns",
               "count",
+              "describe",
               "distinct",
               "dtypes",
               "except",
diff --git a/R/pkg/R/DataFrame.R b/R/pkg/R/DataFrame.R
index 841e77e55e0d8..56c305d912587 100644
--- a/R/pkg/R/DataFrame.R
+++ b/R/pkg/R/DataFrame.R
@@ -1276,3 +1276,40 @@ setMethod("saveAsTable",
             callJMethod(df@sdf, "saveAsTable", tableName, source, jmode, options)
           })
 
+#' describe
+#'
+#' Computes statistics for numeric columns.
+#' If no columns are given, this function computes statistics for all numerical columns.
+#'
+#' @param x A DataFrame to be computed.
+#' @param col A string of name
+#' @param ... Additional expressions
+#' @return A DataFrame
+#' @rdname describe 
+#' @export
+#' @examples
+#'\dontrun{
+#' sc <- sparkR.init()
+#' sqlCtx <- sparkRSQL.init(sc)
+#' path <- "path/to/file.json"
+#' df <- jsonFile(sqlCtx, path)
+#' describe(df)
+#' describe(df, "col1")
+#' describe(df, "col1", "col2")
+#' }
+setMethod("describe",
+          signature(x = "DataFrame", col = "character"),
+          function(x, col, ...) {
+            colList <- list(col, ...)
+            sdf <- callJMethod(x@sdf, "describe", listToSeq(colList))
+            dataFrame(sdf)
+          })
+
+#' @rdname describe
+setMethod("describe",
+          signature(x = "DataFrame"),
+          function(x) {
+            colList <- as.list(c(columns(x)))
+            sdf <- callJMethod(x@sdf, "describe", listToSeq(colList))
+            dataFrame(sdf)
+          })
diff --git a/R/pkg/R/generics.R b/R/pkg/R/generics.R
index e88729387ef95..5838955f74dad 100644
--- a/R/pkg/R/generics.R
+++ b/R/pkg/R/generics.R
@@ -384,6 +384,10 @@ setGeneric("value", function(bcast) { standardGeneric("value") })
 #' @export
 setGeneric("columns", function(x) {standardGeneric("columns") })
 
+#' @rdname describe
+#' @export
+setGeneric("describe", function(x, col, ...) { standardGeneric("describe") })
+
 #' @rdname schema
 #' @export
 setGeneric("dtypes", function(x) { standardGeneric("dtypes") })
diff --git a/R/pkg/inst/tests/test_sparkSQL.R b/R/pkg/inst/tests/test_sparkSQL.R
index f82e56fdd8278..7a42e289fcd9e 100644
--- a/R/pkg/inst/tests/test_sparkSQL.R
+++ b/R/pkg/inst/tests/test_sparkSQL.R
@@ -705,5 +705,16 @@ test_that("parquetFile works with multiple input paths", {
   expect_true(count(parquetDF) == count(df)*2)
 })
 
+test_that("describe() on a DataFrame", {
+  df <- jsonFile(sqlCtx, jsonPath)
+  stats <- describe(df, "age")
+  expect_true(collect(stats)[1, "summary"] == "count")
+  expect_true(collect(stats)[2, "age"] == 24.5)
+  expect_true(collect(stats)[3, "age"] == 5.5)
+  stats <- describe(df)
+  expect_true(collect(stats)[4, "name"] == "Andy")
+  expect_true(collect(stats)[5, "age"] == 30.0)
+})
+
 unlink(parquetPath)
 unlink(jsonPath)

From ba2b56614d7ab1bd7409b49e9d85c248d8faa48b Mon Sep 17 00:00:00 2001
From: Burak Yavuz <brkyvz@gmail.com>
Date: Tue, 5 May 2015 22:56:01 -0700
Subject: [PATCH 05/75] [SPARK-7358][SQL] Move DataFrame mathfunctions into
 functions

After a discussion on the user mailing list, it was decided to put all UDF's under `o.a.s.sql.functions`

cc rxin

Author: Burak Yavuz <brkyvz@gmail.com>

Closes #5923 from brkyvz/move-math-funcs and squashes the following commits:

a8dc3f7 [Burak Yavuz] address comments
cf7a7bb [Burak Yavuz] [SPARK-7358] Move DataFrame mathfunctions into functions
---
 python/pyspark/sql/functions.py               |  52 ++
 python/pyspark/sql/mathfunctions.py           | 101 ----
 python/pyspark/sql/tests.py                   |   2 +-
 .../ExpressionEvaluationSuite.scala           |   4 +-
 .../org/apache/spark/sql/functions.scala      | 485 ++++++++++++++++++
 .../org/apache/spark/sql/mathfunctions.scala  | 383 --------------
 .../apache/spark/sql/JavaDataFrameSuite.java  |   1 -
 .../spark/sql/MathExpressionsSuite.scala      |   7 +-
 8 files changed, 543 insertions(+), 492 deletions(-)
 delete mode 100644 python/pyspark/sql/mathfunctions.py
 delete mode 100644 sql/core/src/main/scala/org/apache/spark/sql/mathfunctions.scala

diff --git a/python/pyspark/sql/functions.py b/python/pyspark/sql/functions.py
index 641220a264295..692af868dd534 100644
--- a/python/pyspark/sql/functions.py
+++ b/python/pyspark/sql/functions.py
@@ -51,6 +51,19 @@ def _(col):
     return _
 
 
+def _create_binary_mathfunction(name, doc=""):
+    """ Create a binary mathfunction by name"""
+    def _(col1, col2):
+        sc = SparkContext._active_spark_context
+        # users might write ints for simplicity. This would throw an error on the JVM side.
+        jc = getattr(sc._jvm.functions, name)(col1._jc if isinstance(col1, Column) else float(col1),
+                                              col2._jc if isinstance(col2, Column) else float(col2))
+        return Column(jc)
+    _.__name__ = name
+    _.__doc__ = doc
+    return _
+
+
 _functions = {
     'lit': 'Creates a :class:`Column` of literal value.',
     'col': 'Returns a :class:`Column` based on the given column name.',
@@ -63,6 +76,34 @@ def _(col):
     'sqrt': 'Computes the square root of the specified float value.',
     'abs': 'Computes the absolute value.',
 
+    # unary math functions
+    'acos': 'Computes the cosine inverse of the given value; the returned angle is in the range' +
+            '0.0 through pi.',
+    'asin': 'Computes the sine inverse of the given value; the returned angle is in the range' +
+            '-pi/2 through pi/2.',
+    'atan': 'Computes the tangent inverse of the given value.',
+    'cbrt': 'Computes the cube-root of the given value.',
+    'ceil': 'Computes the ceiling of the given value.',
+    'cos': 'Computes the cosine of the given value.',
+    'cosh': 'Computes the hyperbolic cosine of the given value.',
+    'exp': 'Computes the exponential of the given value.',
+    'expm1': 'Computes the exponential of the given value minus one.',
+    'floor': 'Computes the floor of the given value.',
+    'log': 'Computes the natural logarithm of the given value.',
+    'log10': 'Computes the logarithm of the given value in Base 10.',
+    'log1p': 'Computes the natural logarithm of the given value plus one.',
+    'rint': 'Returns the double value that is closest in value to the argument and' +
+            ' is equal to a mathematical integer.',
+    'signum': 'Computes the signum of the given value.',
+    'sin': 'Computes the sine of the given value.',
+    'sinh': 'Computes the hyperbolic sine of the given value.',
+    'tan': 'Computes the tangent of the given value.',
+    'tanh': 'Computes the hyperbolic tangent of the given value.',
+    'toDegrees': 'Converts an angle measured in radians to an approximately equivalent angle ' +
+             'measured in degrees.',
+    'toRadians': 'Converts an angle measured in degrees to an approximately equivalent angle ' +
+             'measured in radians.',
+
     'max': 'Aggregate function: returns the maximum value of the expression in a group.',
     'min': 'Aggregate function: returns the minimum value of the expression in a group.',
     'first': 'Aggregate function: returns the first value in a group.',
@@ -74,10 +115,21 @@ def _(col):
     'sumDistinct': 'Aggregate function: returns the sum of distinct values in the expression.',
 }
 
+# math functions that take two arguments as input
+_binary_mathfunctions = {
+    'atan2': 'Returns the angle theta from the conversion of rectangular coordinates (x, y) to' +
+             'polar coordinates (r, theta).',
+    'hypot': 'Computes `sqrt(a^2^ + b^2^)` without intermediate overflow or underflow.',
+    'pow': 'Returns the value of the first argument raised to the power of the second argument.'
+}
+
 for _name, _doc in _functions.items():
     globals()[_name] = _create_function(_name, _doc)
+for _name, _doc in _binary_mathfunctions.items():
+    globals()[_name] = _create_binary_mathfunction(_name, _doc)
 del _name, _doc
 __all__ += _functions.keys()
+__all__ += _binary_mathfunctions.keys()
 __all__.sort()
 
 
diff --git a/python/pyspark/sql/mathfunctions.py b/python/pyspark/sql/mathfunctions.py
deleted file mode 100644
index 7dbcab8694293..0000000000000
--- a/python/pyspark/sql/mathfunctions.py
+++ /dev/null
@@ -1,101 +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.
-#
-
-"""
-A collection of builtin math functions
-"""
-
-from pyspark import SparkContext
-from pyspark.sql.dataframe import Column
-
-__all__ = []
-
-
-def _create_unary_mathfunction(name, doc=""):
-    """ Create a unary mathfunction by name"""
-    def _(col):
-        sc = SparkContext._active_spark_context
-        jc = getattr(sc._jvm.mathfunctions, name)(col._jc if isinstance(col, Column) else col)
-        return Column(jc)
-    _.__name__ = name
-    _.__doc__ = doc
-    return _
-
-
-def _create_binary_mathfunction(name, doc=""):
-    """ Create a binary mathfunction by name"""
-    def _(col1, col2):
-        sc = SparkContext._active_spark_context
-        # users might write ints for simplicity. This would throw an error on the JVM side.
-        if type(col1) is int:
-            col1 = col1 * 1.0
-        if type(col2) is int:
-            col2 = col2 * 1.0
-        jc = getattr(sc._jvm.mathfunctions, name)(col1._jc if isinstance(col1, Column) else col1,
-                                                  col2._jc if isinstance(col2, Column) else col2)
-        return Column(jc)
-    _.__name__ = name
-    _.__doc__ = doc
-    return _
-
-
-# math functions are found under another object therefore, they need to be handled separately
-_mathfunctions = {
-    'acos': 'Computes the cosine inverse of the given value; the returned angle is in the range' +
-            '0.0 through pi.',
-    'asin': 'Computes the sine inverse of the given value; the returned angle is in the range' +
-            '-pi/2 through pi/2.',
-    'atan': 'Computes the tangent inverse of the given value.',
-    'cbrt': 'Computes the cube-root of the given value.',
-    'ceil': 'Computes the ceiling of the given value.',
-    'cos': 'Computes the cosine of the given value.',
-    'cosh': 'Computes the hyperbolic cosine of the given value.',
-    'exp': 'Computes the exponential of the given value.',
-    'expm1': 'Computes the exponential of the given value minus one.',
-    'floor': 'Computes the floor of the given value.',
-    'log': 'Computes the natural logarithm of the given value.',
-    'log10': 'Computes the logarithm of the given value in Base 10.',
-    'log1p': 'Computes the natural logarithm of the given value plus one.',
-    'rint': 'Returns the double value that is closest in value to the argument and' +
-            ' is equal to a mathematical integer.',
-    'signum': 'Computes the signum of the given value.',
-    'sin': 'Computes the sine of the given value.',
-    'sinh': 'Computes the hyperbolic sine of the given value.',
-    'tan': 'Computes the tangent of the given value.',
-    'tanh': 'Computes the hyperbolic tangent of the given value.',
-    'toDeg': 'Converts an angle measured in radians to an approximately equivalent angle ' +
-             'measured in degrees.',
-    'toRad': 'Converts an angle measured in degrees to an approximately equivalent angle ' +
-             'measured in radians.'
-}
-
-# math functions that take two arguments as input
-_binary_mathfunctions = {
-    'atan2': 'Returns the angle theta from the conversion of rectangular coordinates (x, y) to' +
-             'polar coordinates (r, theta).',
-    'hypot': 'Computes `sqrt(a^2^ + b^2^)` without intermediate overflow or underflow.',
-    'pow': 'Returns the value of the first argument raised to the power of the second argument.'
-}
-
-for _name, _doc in _mathfunctions.items():
-    globals()[_name] = _create_unary_mathfunction(_name, _doc)
-for _name, _doc in _binary_mathfunctions.items():
-    globals()[_name] = _create_binary_mathfunction(_name, _doc)
-del _name, _doc
-__all__ += _mathfunctions.keys()
-__all__ += _binary_mathfunctions.keys()
-__all__.sort()
diff --git a/python/pyspark/sql/tests.py b/python/pyspark/sql/tests.py
index 46c4c88e98eb1..b232f3a965526 100644
--- a/python/pyspark/sql/tests.py
+++ b/python/pyspark/sql/tests.py
@@ -416,7 +416,7 @@ def test_crosstab(self):
 
     def test_math_functions(self):
         df = self.sc.parallelize([Row(a=i, b=2 * i) for i in range(10)]).toDF()
-        from pyspark.sql import mathfunctions as functions
+        from pyspark.sql import functions
         import math
 
         def get_values(l):
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 fa71001c9336e..faaa55aa5e6f1 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
@@ -1217,11 +1217,11 @@ class ExpressionEvaluationSuite extends ExpressionEvaluationBaseSuite {
     unaryMathFunctionEvaluation(Tanh, math.tanh)
   }
 
-  test("toDeg") {
+  test("toDegrees") {
     unaryMathFunctionEvaluation(ToDegrees, math.toDegrees)
   }
 
-  test("toRad") {
+  test("toRadians") {
     unaryMathFunctionEvaluation(ToRadians, math.toRadians)
   }
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
index 7e283393d0563..f2bb4534c75eb 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
@@ -24,6 +24,7 @@ import org.apache.spark.annotation.Experimental
 import org.apache.spark.sql.catalyst.ScalaReflection
 import org.apache.spark.sql.catalyst.analysis.{UnresolvedAttribute, UnresolvedFunction, Star}
 import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.expressions.mathfuncs._
 import org.apache.spark.sql.types._
 import org.apache.spark.util.Utils
 
@@ -35,6 +36,7 @@ import org.apache.spark.util.Utils
  * @groupname agg_funcs Aggregate functions
  * @groupname sort_funcs Sorting functions
  * @groupname normal_funcs Non-aggregate functions
+ * @groupname math_funcs Math functions
  * @groupname Ungrouped Support functions for DataFrames.
  */
 @Experimental
@@ -436,6 +438,489 @@ object functions {
    */
   def upper(e: Column): Column = Upper(e.expr)
 
+  //////////////////////////////////////////////////////////////////////////////////////////////
+  // Math Functions
+  //////////////////////////////////////////////////////////////////////////////////////////////
+
+  /**
+   * Computes the cosine inverse of the given value; the returned angle is in the range
+   * 0.0 through pi.
+   *
+   * @group math_funcs
+   */
+  def acos(e: Column): Column = Acos(e.expr)
+
+  /**
+   * Computes the cosine inverse of the given column; the returned angle is in the range
+   * 0.0 through pi.
+   *
+   * @group math_funcs
+   */
+  def acos(columnName: String): Column = acos(Column(columnName))
+
+  /**
+   * Computes the sine inverse of the given value; the returned angle is in the range
+   * -pi/2 through pi/2.
+   *
+   * @group math_funcs
+   */
+  def asin(e: Column): Column = Asin(e.expr)
+
+  /**
+   * Computes the sine inverse of the given column; the returned angle is in the range
+   * -pi/2 through pi/2.
+   *
+   * @group math_funcs
+   */
+  def asin(columnName: String): Column = asin(Column(columnName))
+
+  /**
+   * Computes the tangent inverse of the given value.
+   *
+   * @group math_funcs
+   */
+  def atan(e: Column): Column = Atan(e.expr)
+
+  /**
+   * Computes the tangent inverse of the given column.
+   *
+   * @group math_funcs
+   */
+  def atan(columnName: String): Column = atan(Column(columnName))
+
+  /**
+   * Returns the angle theta from the conversion of rectangular coordinates (x, y) to
+   * polar coordinates (r, theta).
+   *
+   * @group math_funcs
+   */
+  def atan2(l: Column, r: Column): Column = Atan2(l.expr, r.expr)
+
+  /**
+   * Returns the angle theta from the conversion of rectangular coordinates (x, y) to
+   * polar coordinates (r, theta).
+   *
+   * @group math_funcs
+   */
+  def atan2(l: Column, rightName: String): Column = atan2(l, Column(rightName))
+
+  /**
+   * Returns the angle theta from the conversion of rectangular coordinates (x, y) to
+   * polar coordinates (r, theta).
+   *
+   * @group math_funcs
+   */
+  def atan2(leftName: String, r: Column): Column = atan2(Column(leftName), r)
+
+  /**
+   * Returns the angle theta from the conversion of rectangular coordinates (x, y) to
+   * polar coordinates (r, theta).
+   *
+   * @group math_funcs
+   */
+  def atan2(leftName: String, rightName: String): Column =
+    atan2(Column(leftName), Column(rightName))
+
+  /**
+   * Returns the angle theta from the conversion of rectangular coordinates (x, y) to
+   * polar coordinates (r, theta).
+   *
+   * @group math_funcs
+   */
+  def atan2(l: Column, r: Double): Column = atan2(l, lit(r).expr)
+
+  /**
+   * Returns the angle theta from the conversion of rectangular coordinates (x, y) to
+   * polar coordinates (r, theta).
+   *
+   * @group math_funcs
+   */
+  def atan2(leftName: String, r: Double): Column = atan2(Column(leftName), r)
+
+  /**
+   * Returns the angle theta from the conversion of rectangular coordinates (x, y) to
+   * polar coordinates (r, theta).
+   *
+   * @group math_funcs
+   */
+  def atan2(l: Double, r: Column): Column = atan2(lit(l).expr, r)
+
+  /**
+   * Returns the angle theta from the conversion of rectangular coordinates (x, y) to
+   * polar coordinates (r, theta).
+   *
+   * @group math_funcs
+   */
+  def atan2(l: Double, rightName: String): Column = atan2(l, Column(rightName))
+
+  /**
+   * Computes the cube-root of the given value.
+   *
+   * @group math_funcs
+   */
+  def cbrt(e: Column): Column = Cbrt(e.expr)
+
+  /**
+   * Computes the cube-root of the given column.
+   *
+   * @group math_funcs
+   */
+  def cbrt(columnName: String): Column = cbrt(Column(columnName))
+
+  /**
+   * Computes the ceiling of the given value.
+   *
+   * @group math_funcs
+   */
+  def ceil(e: Column): Column = Ceil(e.expr)
+
+  /**
+   * Computes the ceiling of the given column.
+   *
+   * @group math_funcs
+   */
+  def ceil(columnName: String): Column = ceil(Column(columnName))
+
+  /**
+   * Computes the cosine of the given value.
+   *
+   * @group math_funcs
+   */
+  def cos(e: Column): Column = Cos(e.expr)
+
+  /**
+   * Computes the cosine of the given column.
+   *
+   * @group math_funcs
+   */
+  def cos(columnName: String): Column = cos(Column(columnName))
+
+  /**
+   * Computes the hyperbolic cosine of the given value.
+   *
+   * @group math_funcs
+   */
+  def cosh(e: Column): Column = Cosh(e.expr)
+
+  /**
+   * Computes the hyperbolic cosine of the given column.
+   *
+   * @group math_funcs
+   */
+  def cosh(columnName: String): Column = cosh(Column(columnName))
+
+  /**
+   * Computes the exponential of the given value.
+   *
+   * @group math_funcs
+   */
+  def exp(e: Column): Column = Exp(e.expr)
+
+  /**
+   * Computes the exponential of the given column.
+   *
+   * @group math_funcs
+   */
+  def exp(columnName: String): Column = exp(Column(columnName))
+
+  /**
+   * Computes the exponential of the given value minus one.
+   *
+   * @group math_funcs
+   */
+  def expm1(e: Column): Column = Expm1(e.expr)
+
+  /**
+   * Computes the exponential of the given column.
+   *
+   * @group math_funcs
+   */
+  def expm1(columnName: String): Column = expm1(Column(columnName))
+
+  /**
+   * Computes the floor of the given value.
+   *
+   * @group math_funcs
+   */
+  def floor(e: Column): Column = Floor(e.expr)
+
+  /**
+   * Computes the floor of the given column.
+   *
+   * @group math_funcs
+   */
+  def floor(columnName: String): Column = floor(Column(columnName))
+
+  /**
+   * Computes `sqrt(a^2^ + b^2^)` without intermediate overflow or underflow.
+   *
+   * @group math_funcs
+   */
+  def hypot(l: Column, r: Column): Column = Hypot(l.expr, r.expr)
+
+  /**
+   * Computes `sqrt(a^2^ + b^2^)` without intermediate overflow or underflow.
+   *
+   * @group math_funcs
+   */
+  def hypot(l: Column, rightName: String): Column = hypot(l, Column(rightName))
+
+  /**
+   * Computes `sqrt(a^2^ + b^2^)` without intermediate overflow or underflow.
+   *
+   * @group math_funcs
+   */
+  def hypot(leftName: String, r: Column): Column = hypot(Column(leftName), r)
+
+  /**
+   * Computes `sqrt(a^2^ + b^2^)` without intermediate overflow or underflow.
+   *
+   * @group math_funcs
+   */
+  def hypot(leftName: String, rightName: String): Column =
+    hypot(Column(leftName), Column(rightName))
+
+  /**
+   * Computes `sqrt(a^2^ + b^2^)` without intermediate overflow or underflow.
+   *
+   * @group math_funcs
+   */
+  def hypot(l: Column, r: Double): Column = hypot(l, lit(r).expr)
+
+  /**
+   * Computes `sqrt(a^2^ + b^2^)` without intermediate overflow or underflow.
+   *
+   * @group math_funcs
+   */
+  def hypot(leftName: String, r: Double): Column = hypot(Column(leftName), r)
+
+  /**
+   * Computes `sqrt(a^2^ + b^2^)` without intermediate overflow or underflow.
+   *
+   * @group math_funcs
+   */
+  def hypot(l: Double, r: Column): Column = hypot(lit(l).expr, r)
+
+  /**
+   * Computes `sqrt(a^2^ + b^2^)` without intermediate overflow or underflow.
+   *
+   * @group math_funcs
+   */
+  def hypot(l: Double, rightName: String): Column = hypot(l, Column(rightName))
+
+  /**
+   * Computes the natural logarithm of the given value.
+   *
+   * @group math_funcs
+   */
+  def log(e: Column): Column = Log(e.expr)
+
+  /**
+   * Computes the natural logarithm of the given column.
+   *
+   * @group math_funcs
+   */
+  def log(columnName: String): Column = log(Column(columnName))
+
+  /**
+   * Computes the logarithm of the given value in Base 10.
+   *
+   * @group math_funcs
+   */
+  def log10(e: Column): Column = Log10(e.expr)
+
+  /**
+   * Computes the logarithm of the given value in Base 10.
+   *
+   * @group math_funcs
+   */
+  def log10(columnName: String): Column = log10(Column(columnName))
+
+  /**
+   * Computes the natural logarithm of the given value plus one.
+   *
+   * @group math_funcs
+   */
+  def log1p(e: Column): Column = Log1p(e.expr)
+
+  /**
+   * Computes the natural logarithm of the given column plus one.
+   *
+   * @group math_funcs
+   */
+  def log1p(columnName: String): Column = log1p(Column(columnName))
+
+  /**
+   * Returns the value of the first argument raised to the power of the second argument.
+   *
+   * @group math_funcs
+   */
+  def pow(l: Column, r: Column): Column = Pow(l.expr, r.expr)
+
+  /**
+   * Returns the value of the first argument raised to the power of the second argument.
+   *
+   * @group math_funcs
+   */
+  def pow(l: Column, rightName: String): Column = pow(l, Column(rightName))
+
+  /**
+   * Returns the value of the first argument raised to the power of the second argument.
+   *
+   * @group math_funcs
+   */
+  def pow(leftName: String, r: Column): Column = pow(Column(leftName), r)
+
+  /**
+   * Returns the value of the first argument raised to the power of the second argument.
+   *
+   * @group math_funcs
+   */
+  def pow(leftName: String, rightName: String): Column = pow(Column(leftName), Column(rightName))
+
+  /**
+   * Returns the value of the first argument raised to the power of the second argument.
+   *
+   * @group math_funcs
+   */
+  def pow(l: Column, r: Double): Column = pow(l, lit(r).expr)
+
+  /**
+   * Returns the value of the first argument raised to the power of the second argument.
+   *
+   * @group math_funcs
+   */
+  def pow(leftName: String, r: Double): Column = pow(Column(leftName), r)
+
+  /**
+   * Returns the value of the first argument raised to the power of the second argument.
+   *
+   * @group math_funcs
+   */
+  def pow(l: Double, r: Column): Column = pow(lit(l).expr, r)
+
+  /**
+   * Returns the value of the first argument raised to the power of the second argument.
+   *
+   * @group math_funcs
+   */
+  def pow(l: Double, rightName: String): Column = pow(l, Column(rightName))
+
+  /**
+   * Returns the double value that is closest in value to the argument and
+   * is equal to a mathematical integer.
+   *
+   * @group math_funcs
+   */
+  def rint(e: Column): Column = Rint(e.expr)
+
+  /**
+   * Returns the double value that is closest in value to the argument and
+   * is equal to a mathematical integer.
+   *
+   * @group math_funcs
+   */
+  def rint(columnName: String): Column = rint(Column(columnName))
+
+  /**
+   * Computes the signum of the given value.
+   *
+   * @group math_funcs
+   */
+  def signum(e: Column): Column = Signum(e.expr)
+
+  /**
+   * Computes the signum of the given column.
+   *
+   * @group math_funcs
+   */
+  def signum(columnName: String): Column = signum(Column(columnName))
+
+  /**
+   * Computes the sine of the given value.
+   *
+   * @group math_funcs
+   */
+  def sin(e: Column): Column = Sin(e.expr)
+
+  /**
+   * Computes the sine of the given column.
+   *
+   * @group math_funcs
+   */
+  def sin(columnName: String): Column = sin(Column(columnName))
+
+  /**
+   * Computes the hyperbolic sine of the given value.
+   *
+   * @group math_funcs
+   */
+  def sinh(e: Column): Column = Sinh(e.expr)
+
+  /**
+   * Computes the hyperbolic sine of the given column.
+   *
+   * @group math_funcs
+   */
+  def sinh(columnName: String): Column = sinh(Column(columnName))
+
+  /**
+   * Computes the tangent of the given value.
+   *
+   * @group math_funcs
+   */
+  def tan(e: Column): Column = Tan(e.expr)
+
+  /**
+   * Computes the tangent of the given column.
+   *
+   * @group math_funcs
+   */
+  def tan(columnName: String): Column = tan(Column(columnName))
+
+  /**
+   * Computes the hyperbolic tangent of the given value.
+   *
+   * @group math_funcs
+   */
+  def tanh(e: Column): Column = Tanh(e.expr)
+
+  /**
+   * Computes the hyperbolic tangent of the given column.
+   *
+   * @group math_funcs
+   */
+  def tanh(columnName: String): Column = tanh(Column(columnName))
+
+  /**
+   * Converts an angle measured in radians to an approximately equivalent angle measured in degrees.
+   *
+   * @group math_funcs
+   */
+  def toDegrees(e: Column): Column = ToDegrees(e.expr)
+
+  /**
+   * Converts an angle measured in radians to an approximately equivalent angle measured in degrees.
+   *
+   * @group math_funcs
+   */
+  def toDegrees(columnName: String): Column = toDegrees(Column(columnName))
+
+  /**
+   * Converts an angle measured in degrees to an approximately equivalent angle measured in radians.
+   *
+   * @group math_funcs
+   */
+  def toRadians(e: Column): Column = ToRadians(e.expr)
+
+  /**
+   * Converts an angle measured in degrees to an approximately equivalent angle measured in radians.
+   *
+   * @group math_funcs
+   */
+  def toRadians(columnName: String): Column = toRadians(Column(columnName))
+    
+
   //////////////////////////////////////////////////////////////////////////////////////////////
   //////////////////////////////////////////////////////////////////////////////////////////////
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/mathfunctions.scala b/sql/core/src/main/scala/org/apache/spark/sql/mathfunctions.scala
deleted file mode 100644
index db47480c3864d..0000000000000
--- a/sql/core/src/main/scala/org/apache/spark/sql/mathfunctions.scala
+++ /dev/null
@@ -1,383 +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
-
-import scala.language.implicitConversions
-
-import org.apache.spark.annotation.Experimental
-import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.catalyst.expressions.mathfuncs._
-import org.apache.spark.sql.functions.lit
-
-/**
- * :: Experimental ::
- * Mathematical Functions available for [[DataFrame]].
- */
-@Experimental
-// scalastyle:off
-object mathfunctions {
-// scalastyle:on
-
-  private[this] implicit def toColumn(expr: Expression): Column = Column(expr)
-
-  /**
-   * Computes the cosine inverse of the given value; the returned angle is in the range
-   * 0.0 through pi.
-   */
-  def acos(e: Column): Column = Acos(e.expr)
-
-  /**
-   * Computes the cosine inverse of the given column; the returned angle is in the range
-   * 0.0 through pi.
-   */
-  def acos(columnName: String): Column = acos(Column(columnName))
-
-  /**
-   * Computes the sine inverse of the given value; the returned angle is in the range
-   * -pi/2 through pi/2.
-   */
-  def asin(e: Column): Column = Asin(e.expr)
-
-  /**
-   * Computes the sine inverse of the given column; the returned angle is in the range
-   * -pi/2 through pi/2.
-   */
-  def asin(columnName: String): Column = asin(Column(columnName))
-
-  /**
-   * Computes the tangent inverse of the given value.
-   */
-  def atan(e: Column): Column = Atan(e.expr)
-
-  /**
-   * Computes the tangent inverse of the given column.
-   */
-  def atan(columnName: String): Column = atan(Column(columnName))
-
-  /**
-   * Returns the angle theta from the conversion of rectangular coordinates (x, y) to
-   * polar coordinates (r, theta).
-   */
-  def atan2(l: Column, r: Column): Column = Atan2(l.expr, r.expr)
-
-  /**
-   * Returns the angle theta from the conversion of rectangular coordinates (x, y) to
-   * polar coordinates (r, theta).
-   */
-  def atan2(l: Column, rightName: String): Column = atan2(l, Column(rightName))
-
-  /**
-   * Returns the angle theta from the conversion of rectangular coordinates (x, y) to
-   * polar coordinates (r, theta).
-   */
-  def atan2(leftName: String, r: Column): Column = atan2(Column(leftName), r)
-
-  /**
-   * Returns the angle theta from the conversion of rectangular coordinates (x, y) to
-   * polar coordinates (r, theta).
-   */
-  def atan2(leftName: String, rightName: String): Column =
-    atan2(Column(leftName), Column(rightName))
-
-  /**
-   * Returns the angle theta from the conversion of rectangular coordinates (x, y) to
-   * polar coordinates (r, theta).
-   */
-  def atan2(l: Column, r: Double): Column = atan2(l, lit(r).expr)
-
-  /**
-   * Returns the angle theta from the conversion of rectangular coordinates (x, y) to
-   * polar coordinates (r, theta).=
-   */
-  def atan2(leftName: String, r: Double): Column = atan2(Column(leftName), r)
-
-  /**
-   * Returns the angle theta from the conversion of rectangular coordinates (x, y) to
-   * polar coordinates (r, theta).
-   */
-  def atan2(l: Double, r: Column): Column = atan2(lit(l).expr, r)
-
-  /**
-   * Returns the angle theta from the conversion of rectangular coordinates (x, y) to
-   * polar coordinates (r, theta).
-   */
-  def atan2(l: Double, rightName: String): Column = atan2(l, Column(rightName))
-
-  /**
-   * Computes the cube-root of the given value.
-   */
-  def cbrt(e: Column): Column = Cbrt(e.expr)
-
-  /**
-   * Computes the cube-root of the given column.
-   */
-  def cbrt(columnName: String): Column = cbrt(Column(columnName))
-
-  /**
-   * Computes the ceiling of the given value.
-   */
-  def ceil(e: Column): Column = Ceil(e.expr)
-
-  /**
-   * Computes the ceiling of the given column.
-   */
-  def ceil(columnName: String): Column = ceil(Column(columnName))
-
-  /**
-   * Computes the cosine of the given value.
-   */
-  def cos(e: Column): Column = Cos(e.expr)
-
-  /**
-   * Computes the cosine of the given column.
-   */
-  def cos(columnName: String): Column = cos(Column(columnName))
-
-  /**
-   * Computes the hyperbolic cosine of the given value.
-   */
-  def cosh(e: Column): Column = Cosh(e.expr)
-
-  /**
-   * Computes the hyperbolic cosine of the given column.
-   */
-  def cosh(columnName: String): Column = cosh(Column(columnName))
-
-  /**
-   * Computes the exponential of the given value.
-   */
-  def exp(e: Column): Column = Exp(e.expr)
-
-  /**
-   * Computes the exponential of the given column.
-   */
-  def exp(columnName: String): Column = exp(Column(columnName))
-
-  /**
-   * Computes the exponential of the given value minus one.
-   */
-  def expm1(e: Column): Column = Expm1(e.expr)
-
-  /**
-   * Computes the exponential of the given column.
-   */
-  def expm1(columnName: String): Column = expm1(Column(columnName))
-
-  /**
-   * Computes the floor of the given value.
-   */
-  def floor(e: Column): Column = Floor(e.expr)
-
-  /**
-   * Computes the floor of the given column.
-   */
-  def floor(columnName: String): Column = floor(Column(columnName))
-
-  /**
-   * Computes `sqrt(a^2^ + b^2^)` without intermediate overflow or underflow.
-   */
-  def hypot(l: Column, r: Column): Column = Hypot(l.expr, r.expr)
-
-  /**
-   * Computes `sqrt(a^2^ + b^2^)` without intermediate overflow or underflow.
-   */
-  def hypot(l: Column, rightName: String): Column = hypot(l, Column(rightName))
-
-  /**
-   * Computes `sqrt(a^2^ + b^2^)` without intermediate overflow or underflow.
-   */
-  def hypot(leftName: String, r: Column): Column = hypot(Column(leftName), r)
-
-  /**
-   * Computes `sqrt(a^2^ + b^2^)` without intermediate overflow or underflow.
-   */
-  def hypot(leftName: String, rightName: String): Column =
-    hypot(Column(leftName), Column(rightName))
-
-  /**
-   * Computes `sqrt(a^2^ + b^2^)` without intermediate overflow or underflow.
-   */
-  def hypot(l: Column, r: Double): Column = hypot(l, lit(r).expr)
-
-  /**
-   * Computes `sqrt(a^2^ + b^2^)` without intermediate overflow or underflow.
-   */
-  def hypot(leftName: String, r: Double): Column = hypot(Column(leftName), r)
-
-  /**
-   * Computes `sqrt(a^2^ + b^2^)` without intermediate overflow or underflow.
-   */
-  def hypot(l: Double, r: Column): Column = hypot(lit(l).expr, r)
-
-  /**
-   * Computes `sqrt(a^2^ + b^2^)` without intermediate overflow or underflow.
-   */
-  def hypot(l: Double, rightName: String): Column = hypot(l, Column(rightName))
-
-  /**
-   * Computes the natural logarithm of the given value.
-   */
-  def log(e: Column): Column = Log(e.expr)
-
-  /**
-   * Computes the natural logarithm of the given column.
-   */
-  def log(columnName: String): Column = log(Column(columnName))
-
-  /**
-   * Computes the logarithm of the given value in Base 10.
-   */
-  def log10(e: Column): Column = Log10(e.expr)
-
-  /**
-   * Computes the logarithm of the given value in Base 10.
-   */
-  def log10(columnName: String): Column = log10(Column(columnName))
-
-  /**
-   * Computes the natural logarithm of the given value plus one.
-   */
-  def log1p(e: Column): Column = Log1p(e.expr)
-
-  /**
-   * Computes the natural logarithm of the given column plus one.
-   */
-  def log1p(columnName: String): Column = log1p(Column(columnName))
-
-  /**
-   * Returns the value of the first argument raised to the power of the second argument.
-   */
-  def pow(l: Column, r: Column): Column = Pow(l.expr, r.expr)
-
-  /**
-   * Returns the value of the first argument raised to the power of the second argument.
-   */
-  def pow(l: Column, rightName: String): Column = pow(l, Column(rightName))
-
-  /**
-   * Returns the value of the first argument raised to the power of the second argument.
-   */
-  def pow(leftName: String, r: Column): Column = pow(Column(leftName), r)
-
-  /**
-   * Returns the value of the first argument raised to the power of the second argument.
-   */
-  def pow(leftName: String, rightName: String): Column = pow(Column(leftName), Column(rightName))
-
-  /**
-   * Returns the value of the first argument raised to the power of the second argument.
-   */
-  def pow(l: Column, r: Double): Column = pow(l, lit(r).expr)
-
-  /**
-   * Returns the value of the first argument raised to the power of the second argument.
-   */
-  def pow(leftName: String, r: Double): Column = pow(Column(leftName), r)
-
-  /**
-   * Returns the value of the first argument raised to the power of the second argument.
-   */
-  def pow(l: Double, r: Column): Column = pow(lit(l).expr, r)
-
-  /**
-   * Returns the value of the first argument raised to the power of the second argument.
-   */
-  def pow(l: Double, rightName: String): Column = pow(l, Column(rightName))
-
-  /**
-   * Returns the double value that is closest in value to the argument and
-   * is equal to a mathematical integer.
-   */
-  def rint(e: Column): Column = Rint(e.expr)
-
-  /**
-   * Returns the double value that is closest in value to the argument and
-   * is equal to a mathematical integer.
-   */
-  def rint(columnName: String): Column = rint(Column(columnName))
-
-  /**
-   * Computes the signum of the given value.
-   */
-  def signum(e: Column): Column = Signum(e.expr)
-
-  /**
-   * Computes the signum of the given column.
-   */
-  def signum(columnName: String): Column = signum(Column(columnName))
-
-  /**
-   * Computes the sine of the given value.
-   */
-  def sin(e: Column): Column = Sin(e.expr)
-
-  /**
-   * Computes the sine of the given column.
-   */
-  def sin(columnName: String): Column = sin(Column(columnName))
-
-  /**
-   * Computes the hyperbolic sine of the given value.
-   */
-  def sinh(e: Column): Column = Sinh(e.expr)
-
-  /**
-   * Computes the hyperbolic sine of the given column.
-   */
-  def sinh(columnName: String): Column = sinh(Column(columnName))
-  
-  /**
-   * Computes the tangent of the given value.
-   */
-  def tan(e: Column): Column = Tan(e.expr)
-
-  /**
-   * Computes the tangent of the given column.
-   */
-  def tan(columnName: String): Column = tan(Column(columnName))
-  
-  /**
-   * Computes the hyperbolic tangent of the given value.
-   */
-  def tanh(e: Column): Column = Tanh(e.expr)
-
-  /**
-   * Computes the hyperbolic tangent of the given column.
-   */
-  def tanh(columnName: String): Column = tanh(Column(columnName))
-
-  /**
-   * Converts an angle measured in radians to an approximately equivalent angle measured in degrees.
-   */
-  def toDeg(e: Column): Column = ToDegrees(e.expr)
-
-  /**
-   * Converts an angle measured in radians to an approximately equivalent angle measured in degrees.
-   */
-  def toDeg(columnName: String): Column = toDeg(Column(columnName))
-
-  /**
-   * Converts an angle measured in degrees to an approximately equivalent angle measured in radians.
-   */
-  def toRad(e: Column): Column = ToRadians(e.expr)
-
-  /**
-   * Converts an angle measured in degrees to an approximately equivalent angle measured in radians.
-   */
-  def toRad(columnName: String): Column = toRad(Column(columnName))
-}
diff --git a/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java b/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java
index 58cc8e5be6075..72c42f4fe376b 100644
--- a/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java
+++ b/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java
@@ -39,7 +39,6 @@
 import java.util.Map;
 
 import static org.apache.spark.sql.functions.*;
-import static org.apache.spark.sql.mathfunctions.*;
 
 public class JavaDataFrameSuite {
   private transient JavaSparkContext jsc;
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/MathExpressionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/MathExpressionsSuite.scala
index 9e19bb7482e9b..c4281c4b55c02 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/MathExpressionsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/MathExpressionsSuite.scala
@@ -19,8 +19,7 @@ package org.apache.spark.sql
 
 import java.lang.{Double => JavaDouble}
 
-import org.apache.spark.sql.functions.lit
-import org.apache.spark.sql.mathfunctions._
+import org.apache.spark.sql.functions._
 import org.apache.spark.sql.test.TestSQLContext
 import org.apache.spark.sql.test.TestSQLContext.implicits._
 
@@ -159,11 +158,11 @@ class MathExpressionsSuite extends QueryTest {
   }
 
   test("toDeg") {
-    testOneToOneMathFunction(toDeg, math.toDegrees)
+    testOneToOneMathFunction(toDegrees, math.toDegrees)
   }
 
   test("toRad") {
-    testOneToOneMathFunction(toRad, math.toRadians)
+    testOneToOneMathFunction(toRadians, math.toRadians)
   }
 
   test("cbrt") {

From 7b1457839bdac124a07fd6292f6263f0ded48880 Mon Sep 17 00:00:00 2001
From: Yanbo Liang <ybliang8@gmail.com>
Date: Tue, 5 May 2015 22:57:13 -0700
Subject: [PATCH 06/75] [SPARK-6267] [MLLIB] Python API for IsotonicRegression

https://issues.apache.org/jira/browse/SPARK-6267

Author: Yanbo Liang <ybliang8@gmail.com>
Author: Xiangrui Meng <meng@databricks.com>

Closes #5890 from yanboliang/spark-6267 and squashes the following commits:

f20541d [Yanbo Liang] Merge pull request #3 from mengxr/SPARK-6267
7f202f9 [Xiangrui Meng] use Vector to have the best Python 2&3 compatibility
4bccfee [Yanbo Liang] fix doctest
ec09412 [Yanbo Liang] fix typos
8214bbb [Yanbo Liang] fix code style
5c8ebe5 [Yanbo Liang] Python API for IsotonicRegression
---
 .../mllib/api/python/PythonMLLibAPI.scala     | 18 +++++
 .../mllib/regression/IsotonicRegression.scala | 19 ++++-
 python/pyspark/mllib/regression.py            | 73 ++++++++++++++++++-
 3 files changed, 106 insertions(+), 4 deletions(-)

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 b086cec083381..426306d78c1c3 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
@@ -282,6 +282,24 @@ private[python] class PythonMLLibAPI extends Serializable {
       map(_.asInstanceOf[Object]).asJava
   }
 
+  /**
+   * Java stub for Python mllib IsotonicRegression.run()
+   */
+  def trainIsotonicRegressionModel(
+      data: JavaRDD[Vector],
+      isotonic: Boolean): JList[Object] = {
+    val isotonicRegressionAlg = new IsotonicRegression().setIsotonic(isotonic)
+    val input = data.rdd.map { x =>
+      (x(0), x(1), x(2))
+    }.persist(StorageLevel.MEMORY_AND_DISK)
+    try {
+      val model = isotonicRegressionAlg.run(input)
+      List[AnyRef](model.boundaryVector, model.predictionVector).asJava
+    } finally {
+      data.rdd.unpersist(blocking = false)
+    }
+  }
+
   /**
    * Java stub for Python mllib KMeans.run()
    */
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/IsotonicRegression.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/IsotonicRegression.scala
index 1d7617046b6c7..be2a00c2dfea4 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/IsotonicRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/IsotonicRegression.scala
@@ -21,18 +21,20 @@ import java.io.Serializable
 import java.lang.{Double => JDouble}
 import java.util.Arrays.binarySearch
 
+import scala.collection.JavaConverters._
 import scala.collection.mutable.ArrayBuffer
 
 import org.json4s._
 import org.json4s.JsonDSL._
 import org.json4s.jackson.JsonMethods._
 
+import org.apache.spark.SparkContext
 import org.apache.spark.annotation.Experimental
 import org.apache.spark.api.java.{JavaDoubleRDD, JavaRDD}
+import org.apache.spark.mllib.linalg.{Vector, Vectors}
 import org.apache.spark.mllib.util.{Loader, Saveable}
 import org.apache.spark.rdd.RDD
-import org.apache.spark.SparkContext
-import org.apache.spark.sql.{DataFrame, SQLContext}
+import org.apache.spark.sql.SQLContext
 
 /**
  * :: Experimental ::
@@ -57,6 +59,13 @@ class IsotonicRegressionModel (
   assertOrdered(boundaries)
   assertOrdered(predictions)(predictionOrd)
 
+  /** A Java-friendly constructor that takes two Iterable parameters and one Boolean parameter. */
+  def this(boundaries: java.lang.Iterable[Double],
+      predictions: java.lang.Iterable[Double],
+      isotonic: java.lang.Boolean) = {
+    this(boundaries.asScala.toArray, predictions.asScala.toArray, isotonic)
+  }
+
   /** Asserts the input array is monotone with the given ordering. */
   private def assertOrdered(xs: Array[Double])(implicit ord: Ordering[Double]): Unit = {
     var i = 1
@@ -132,6 +141,12 @@ class IsotonicRegressionModel (
     }
   }
 
+  /** A convenient method for boundaries called by the Python API. */
+  private[mllib] def boundaryVector: Vector = Vectors.dense(boundaries)
+
+  /** A convenient method for boundaries called by the Python API. */
+  private[mllib] def predictionVector: Vector = Vectors.dense(predictions)
+
   override def save(sc: SparkContext, path: String): Unit = {
     IsotonicRegressionModel.SaveLoadV1_0.save(sc, path, boundaries, predictions, isotonic)
   }
diff --git a/python/pyspark/mllib/regression.py b/python/pyspark/mllib/regression.py
index 4bc6351bdf02f..41bde2ce3e60b 100644
--- a/python/pyspark/mllib/regression.py
+++ b/python/pyspark/mllib/regression.py
@@ -18,14 +18,16 @@
 import numpy as np
 from numpy import array
 
+from pyspark import RDD
 from pyspark.mllib.common import callMLlibFunc, _py2java, _java2py, inherit_doc
-from pyspark.mllib.linalg import SparseVector, _convert_to_vector
+from pyspark.mllib.linalg import SparseVector, Vectors, _convert_to_vector
 from pyspark.mllib.util import Saveable, Loader
 
 __all__ = ['LabeledPoint', 'LinearModel',
            'LinearRegressionModel', 'LinearRegressionWithSGD',
            'RidgeRegressionModel', 'RidgeRegressionWithSGD',
-           'LassoModel', 'LassoWithSGD']
+           'LassoModel', 'LassoWithSGD', 'IsotonicRegressionModel',
+           'IsotonicRegression']
 
 
 class LabeledPoint(object):
@@ -396,6 +398,73 @@ def train(rdd, i):
         return _regression_train_wrapper(train, RidgeRegressionModel, data, initialWeights)
 
 
+class IsotonicRegressionModel(Saveable, Loader):
+
+    """Regression model for isotonic regression.
+
+    >>> data = [(1, 0, 1), (2, 1, 1), (3, 2, 1), (1, 3, 1), (6, 4, 1), (17, 5, 1), (16, 6, 1)]
+    >>> irm = IsotonicRegression.train(sc.parallelize(data))
+    >>> irm.predict(3)
+    2.0
+    >>> irm.predict(5)
+    16.5
+    >>> irm.predict(sc.parallelize([3, 5])).collect()
+    [2.0, 16.5]
+    >>> import os, tempfile
+    >>> path = tempfile.mkdtemp()
+    >>> irm.save(sc, path)
+    >>> sameModel = IsotonicRegressionModel.load(sc, path)
+    >>> sameModel.predict(3)
+    2.0
+    >>> sameModel.predict(5)
+    16.5
+    >>> try:
+    ...     os.removedirs(path)
+    ... except OSError:
+    ...     pass
+    """
+
+    def __init__(self, boundaries, predictions, isotonic):
+        self.boundaries = boundaries
+        self.predictions = predictions
+        self.isotonic = isotonic
+
+    def predict(self, x):
+        if isinstance(x, RDD):
+            return x.map(lambda v: self.predict(v))
+        return np.interp(x, self.boundaries, self.predictions)
+
+    def save(self, sc, path):
+        java_boundaries = _py2java(sc, self.boundaries.tolist())
+        java_predictions = _py2java(sc, self.predictions.tolist())
+        java_model = sc._jvm.org.apache.spark.mllib.regression.IsotonicRegressionModel(
+            java_boundaries, java_predictions, self.isotonic)
+        java_model.save(sc._jsc.sc(), path)
+
+    @classmethod
+    def load(cls, sc, path):
+        java_model = sc._jvm.org.apache.spark.mllib.regression.IsotonicRegressionModel.load(
+            sc._jsc.sc(), path)
+        py_boundaries = _java2py(sc, java_model.boundaryVector()).toArray()
+        py_predictions = _java2py(sc, java_model.predictionVector()).toArray()
+        return IsotonicRegressionModel(py_boundaries, py_predictions, java_model.isotonic)
+
+
+class IsotonicRegression(object):
+    """
+    Run IsotonicRegression algorithm to obtain isotonic regression model.
+
+    :param data:            RDD of (label, feature, weight) tuples.
+    :param isotonic:        Whether this is isotonic or antitonic.
+    """
+    @classmethod
+    def train(cls, data, isotonic=True):
+        """Train a isotonic regression model on the given data."""
+        boundaries, predictions = callMLlibFunc("trainIsotonicRegressionModel",
+                                                data.map(_convert_to_vector), bool(isotonic))
+        return IsotonicRegressionModel(boundaries.toArray(), predictions.toArray(), isotonic)
+
+
 def _test():
     import doctest
     from pyspark import SparkContext

From 9f019c7223bb79b8d5cd52980b2723a1601d1134 Mon Sep 17 00:00:00 2001
From: zsxwing <zsxwing@gmail.com>
Date: Tue, 5 May 2015 23:25:28 -0700
Subject: [PATCH 07/75] [SPARK-7384][Core][Tests] Fix flaky tests for
 distributed mode in BroadcastSuite

Fixed the following failure: https://amplab.cs.berkeley.edu/jenkins/job/Spark-1.3-Maven-pre-YARN/hadoop.version=1.0.4,label=centos/452/testReport/junit/org.apache.spark.broadcast/BroadcastSuite/Unpersisting_HttpBroadcast_on_executors_and_driver_in_distributed_mode/

The tests should wait until all slaves are up. Otherwise, there may be only a part of `BlockManager`s registered, and fail the tests.

Author: zsxwing <zsxwing@gmail.com>

Closes #5925 from zsxwing/SPARK-7384 and squashes the following commits:

783cb7b [zsxwing] Add comments for _jobProgressListener and remove postfixOps
1009ef1 [zsxwing] [SPARK-7384][Core][Tests] Fix flaky tests for distributed mode in BroadcastSuite
---
 .../main/scala/org/apache/spark/SparkContext.scala |  8 +++++---
 .../apache/spark/broadcast/BroadcastSuite.scala    | 14 +++++++++++++-
 2 files changed, 18 insertions(+), 4 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala
index 682dec44ac1a5..b5f040ceb15ca 100644
--- a/core/src/main/scala/org/apache/spark/SparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/SparkContext.scala
@@ -407,15 +407,17 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
 
     if (master == "yarn-client") System.setProperty("SPARK_YARN_MODE", "true")
 
+    // "_jobProgressListener" should be set up before creating SparkEnv because when creating
+    // "SparkEnv", some messages will be posted to "listenerBus" and we should not miss them.
+    _jobProgressListener = new JobProgressListener(_conf)
+    listenerBus.addListener(jobProgressListener)
+
     // Create the Spark execution environment (cache, map output tracker, etc)
     _env = createSparkEnv(_conf, isLocal, listenerBus)
     SparkEnv.set(_env)
 
     _metadataCleaner = new MetadataCleaner(MetadataCleanerType.SPARK_CONTEXT, this.cleanup, _conf)
 
-    _jobProgressListener = new JobProgressListener(_conf)
-    listenerBus.addListener(jobProgressListener)
-
     _statusTracker = new SparkStatusTracker(this)
 
     _progressBar =
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 c8fdfa693912e..06e5f1cf6b96f 100644
--- a/core/src/test/scala/org/apache/spark/broadcast/BroadcastSuite.scala
+++ b/core/src/test/scala/org/apache/spark/broadcast/BroadcastSuite.scala
@@ -17,9 +17,11 @@
 
 package org.apache.spark.broadcast
 
+import scala.concurrent.duration._
 import scala.util.Random
 
 import org.scalatest.{Assertions, FunSuite}
+import org.scalatest.concurrent.Eventually._
 
 import org.apache.spark.{LocalSparkContext, SparkConf, SparkContext, SparkException, SparkEnv}
 import org.apache.spark.io.SnappyCompressionCodec
@@ -307,7 +309,17 @@ class BroadcastSuite extends FunSuite with LocalSparkContext {
       removeFromDriver: Boolean) {
 
     sc = if (distributed) {
-      new SparkContext("local-cluster[%d, 1, 512]".format(numSlaves), "test", broadcastConf)
+      val _sc =
+        new SparkContext("local-cluster[%d, 1, 512]".format(numSlaves), "test", broadcastConf)
+      // Wait until all salves are up
+      eventually(timeout(10.seconds), interval(10.milliseconds)) {
+        _sc.jobProgressListener.synchronized {
+          val numBlockManagers = _sc.jobProgressListener.blockManagerIds.size
+          assert(numBlockManagers == numSlaves + 1,
+            s"Expect ${numSlaves + 1} block managers, but was ${numBlockManagers}")
+        }
+      }
+      _sc
     } else {
       new SparkContext("local", "test", broadcastConf)
     }

From 32cdc815c6fc19b5c8c4eca35f88a61302d67cd5 Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Wed, 6 May 2015 01:28:43 -0700
Subject: [PATCH 08/75] [SPARK-6940] [MLLIB] Add CrossValidator to Python ML
 pipeline API

Since CrossValidator is a meta algorithm, we copy the implementation in Python. jkbradley

Author: Xiangrui Meng <meng@databricks.com>

Closes #5926 from mengxr/SPARK-6940 and squashes the following commits:

6af181f [Xiangrui Meng] add TODOs
8285134 [Xiangrui Meng] update doc
060f7c3 [Xiangrui Meng] update doctest
acac727 [Xiangrui Meng] add keyword args
cdddecd [Xiangrui Meng] add CrossValidator in Python
---
 .../spark/ml/tuning/CrossValidator.scala      |   7 +-
 python/pyspark/ml/pipeline.py                 |  13 +-
 python/pyspark/ml/tuning.py                   | 183 +++++++++++++++++-
 python/pyspark/ml/wrapper.py                  |   4 +-
 4 files changed, 199 insertions(+), 8 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala b/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala
index cee2aa6e85523..9208127eb1d79 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala
@@ -52,10 +52,12 @@ private[ml] trait CrossValidatorParams extends Params {
   def getEstimatorParamMaps: Array[ParamMap] = $(estimatorParamMaps)
 
   /**
-   * param for the evaluator for selection
+   * param for the evaluator used to select hyper-parameters that maximize the cross-validated
+   * metric
    * @group param
    */
-  val evaluator: Param[Evaluator] = new Param(this, "evaluator", "evaluator for selection")
+  val evaluator: Param[Evaluator] = new Param(this, "evaluator",
+    "evaluator used to select hyper-parameters that maximize the cross-validated metric")
 
   /** @group getParam */
   def getEvaluator: Evaluator = $(evaluator)
@@ -120,6 +122,7 @@ class CrossValidator extends Estimator[CrossValidatorModel] with CrossValidatorP
       trainingDataset.unpersist()
       var i = 0
       while (i < numModels) {
+        // TODO: duplicate evaluator to take extra params from input
         val metric = eval.evaluate(models(i).transform(validationDataset, epm(i)))
         logDebug(s"Got metric $metric for model trained with ${epm(i)}.")
         metrics(i) += metric
diff --git a/python/pyspark/ml/pipeline.py b/python/pyspark/ml/pipeline.py
index 7b875e4b71254..c1b2077c985cf 100644
--- a/python/pyspark/ml/pipeline.py
+++ b/python/pyspark/ml/pipeline.py
@@ -22,7 +22,7 @@
 from pyspark.mllib.common import inherit_doc
 
 
-__all__ = ['Estimator', 'Transformer', 'Pipeline', 'PipelineModel', 'Evaluator']
+__all__ = ['Estimator', 'Transformer', 'Pipeline', 'PipelineModel', 'Evaluator', 'Model']
 
 
 @inherit_doc
@@ -70,6 +70,15 @@ def transform(self, dataset, params={}):
         raise NotImplementedError()
 
 
+@inherit_doc
+class Model(Transformer):
+    """
+    Abstract class for models that are fitted by estimators.
+    """
+
+    __metaclass__ = ABCMeta
+
+
 @inherit_doc
 class Pipeline(Estimator):
     """
@@ -154,7 +163,7 @@ def fit(self, dataset, params={}):
 
 
 @inherit_doc
-class PipelineModel(Transformer):
+class PipelineModel(Model):
     """
     Represents a compiled pipeline with transformers and fitted models.
     """
diff --git a/python/pyspark/ml/tuning.py b/python/pyspark/ml/tuning.py
index 1773ab5bdcdb1..f6cf2c3439ba5 100644
--- a/python/pyspark/ml/tuning.py
+++ b/python/pyspark/ml/tuning.py
@@ -16,8 +16,14 @@
 #
 
 import itertools
+import numpy as np
 
-__all__ = ['ParamGridBuilder']
+from pyspark.ml.param import Params, Param
+from pyspark.ml import Estimator, Model
+from pyspark.ml.util import keyword_only
+from pyspark.sql.functions import rand
+
+__all__ = ['ParamGridBuilder', 'CrossValidator', 'CrossValidatorModel']
 
 
 class ParamGridBuilder(object):
@@ -79,6 +85,179 @@ def build(self):
         return [dict(zip(keys, prod)) for prod in itertools.product(*grid_values)]
 
 
+class CrossValidator(Estimator):
+    """
+    K-fold cross validation.
+
+    >>> from pyspark.ml.classification import LogisticRegression
+    >>> from pyspark.ml.evaluation import BinaryClassificationEvaluator
+    >>> from pyspark.mllib.linalg import Vectors
+    >>> dataset = sqlContext.createDataFrame(
+    ...     [(Vectors.dense([0.0, 1.0]), 0.0),
+    ...      (Vectors.dense([1.0, 2.0]), 1.0),
+    ...      (Vectors.dense([0.55, 3.0]), 0.0),
+    ...      (Vectors.dense([0.45, 4.0]), 1.0),
+    ...      (Vectors.dense([0.51, 5.0]), 1.0)] * 10,
+    ...     ["features", "label"])
+    >>> lr = LogisticRegression()
+    >>> grid = ParamGridBuilder().addGrid(lr.maxIter, [0, 1, 5]).build()
+    >>> evaluator = BinaryClassificationEvaluator()
+    >>> cv = CrossValidator(estimator=lr, estimatorParamMaps=grid, evaluator=evaluator)
+    >>> cvModel = cv.fit(dataset)
+    >>> expected = lr.fit(dataset, {lr.maxIter: 5}).transform(dataset)
+    >>> cvModel.transform(dataset).collect() == expected.collect()
+    True
+    """
+
+    # a placeholder to make it appear in the generated doc
+    estimator = Param(Params._dummy(), "estimator", "estimator to be cross-validated")
+
+    # a placeholder to make it appear in the generated doc
+    estimatorParamMaps = Param(Params._dummy(), "estimatorParamMaps", "estimator param maps")
+
+    # a placeholder to make it appear in the generated doc
+    evaluator = Param(
+        Params._dummy(), "evaluator",
+        "evaluator used to select hyper-parameters that maximize the cross-validated metric")
+
+    # a placeholder to make it appear in the generated doc
+    numFolds = Param(Params._dummy(), "numFolds", "number of folds for cross validation")
+
+    @keyword_only
+    def __init__(self, estimator=None, estimatorParamMaps=None, evaluator=None, numFolds=3):
+        """
+        __init__(self, estimator=None, estimatorParamMaps=None, evaluator=None, numFolds=3)
+        """
+        super(CrossValidator, self).__init__()
+        #: param for estimator to be cross-validated
+        self.estimator = Param(self, "estimator", "estimator to be cross-validated")
+        #: param for estimator param maps
+        self.estimatorParamMaps = Param(self, "estimatorParamMaps", "estimator param maps")
+        #: param for the evaluator used to select hyper-parameters that
+        #: maximize the cross-validated metric
+        self.evaluator = Param(
+            self, "evaluator",
+            "evaluator used to select hyper-parameters that maximize the cross-validated metric")
+        #: param for number of folds for cross validation
+        self.numFolds = Param(self, "numFolds", "number of folds for cross validation")
+        self._setDefault(numFolds=3)
+        kwargs = self.__init__._input_kwargs
+        self._set(**kwargs)
+
+    @keyword_only
+    def setParams(self, estimator=None, estimatorParamMaps=None, evaluator=None, numFolds=3):
+        """
+        setParams(self, estimator=None, estimatorParamMaps=None, evaluator=None, numFolds=3):
+        Sets params for cross validator.
+        """
+        kwargs = self.setParams._input_kwargs
+        return self._set(**kwargs)
+
+    def setEstimator(self, value):
+        """
+        Sets the value of :py:attr:`estimator`.
+        """
+        self.paramMap[self.estimator] = value
+        return self
+
+    def getEstimator(self):
+        """
+        Gets the value of estimator or its default value.
+        """
+        return self.getOrDefault(self.estimator)
+
+    def setEstimatorParamMaps(self, value):
+        """
+        Sets the value of :py:attr:`estimatorParamMaps`.
+        """
+        self.paramMap[self.estimatorParamMaps] = value
+        return self
+
+    def getEstimatorParamMaps(self):
+        """
+        Gets the value of estimatorParamMaps or its default value.
+        """
+        return self.getOrDefault(self.estimatorParamMaps)
+
+    def setEvaluator(self, value):
+        """
+        Sets the value of :py:attr:`evaluator`.
+        """
+        self.paramMap[self.evaluator] = value
+        return self
+
+    def getEvaluator(self):
+        """
+        Gets the value of evaluator or its default value.
+        """
+        return self.getOrDefault(self.evaluator)
+
+    def setNumFolds(self, value):
+        """
+        Sets the value of :py:attr:`numFolds`.
+        """
+        self.paramMap[self.numFolds] = value
+        return self
+
+    def getNumFolds(self):
+        """
+        Gets the value of numFolds or its default value.
+        """
+        return self.getOrDefault(self.numFolds)
+
+    def fit(self, dataset, params={}):
+        paramMap = self.extractParamMap(params)
+        est = paramMap[self.estimator]
+        epm = paramMap[self.estimatorParamMaps]
+        numModels = len(epm)
+        eva = paramMap[self.evaluator]
+        nFolds = paramMap[self.numFolds]
+        h = 1.0 / nFolds
+        randCol = self.uid + "_rand"
+        df = dataset.select("*", rand(0).alias(randCol))
+        metrics = np.zeros(numModels)
+        for i in range(nFolds):
+            validateLB = i * h
+            validateUB = (i + 1) * h
+            condition = (df[randCol] >= validateLB) & (df[randCol] < validateUB)
+            validation = df.filter(condition)
+            train = df.filter(~condition)
+            for j in range(numModels):
+                model = est.fit(train, epm[j])
+                # TODO: duplicate evaluator to take extra params from input
+                metric = eva.evaluate(model.transform(validation, epm[j]))
+                metrics[j] += metric
+        bestIndex = np.argmax(metrics)
+        bestModel = est.fit(dataset, epm[bestIndex])
+        return CrossValidatorModel(bestModel)
+
+
+class CrossValidatorModel(Model):
+    """
+    Model from k-fold cross validation.
+    """
+
+    def __init__(self, bestModel):
+        #: best model from cross validation
+        self.bestModel = bestModel
+
+    def transform(self, dataset, params={}):
+        return self.bestModel.transform(dataset, params)
+
+
 if __name__ == "__main__":
     import doctest
-    doctest.testmod()
+    from pyspark.context import SparkContext
+    from pyspark.sql import SQLContext
+    globs = globals().copy()
+    # The small batch size here ensures that we see multiple batches,
+    # even in these small test examples:
+    sc = SparkContext("local[2]", "ml.tuning tests")
+    sqlContext = SQLContext(sc)
+    globs['sc'] = sc
+    globs['sqlContext'] = sqlContext
+    (failure_count, test_count) = doctest.testmod(
+        globs=globs, optionflags=doctest.ELLIPSIS)
+    sc.stop()
+    if failure_count:
+        exit(-1)
diff --git a/python/pyspark/ml/wrapper.py b/python/pyspark/ml/wrapper.py
index 73741c4b40dfb..0634254bbd5cf 100644
--- a/python/pyspark/ml/wrapper.py
+++ b/python/pyspark/ml/wrapper.py
@@ -20,7 +20,7 @@
 from pyspark import SparkContext
 from pyspark.sql import DataFrame
 from pyspark.ml.param import Params
-from pyspark.ml.pipeline import Estimator, Transformer, Evaluator
+from pyspark.ml.pipeline import Estimator, Transformer, Evaluator, Model
 from pyspark.mllib.common import inherit_doc
 
 
@@ -133,7 +133,7 @@ def transform(self, dataset, params={}):
 
 
 @inherit_doc
-class JavaModel(JavaTransformer):
+class JavaModel(Model, JavaTransformer):
     """
     Base class for :py:class:`Model`s that wrap Java/Scala
     implementations.

From 322e7e7f689947aef29909572ee0c0e110ea23b8 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Wed, 6 May 2015 08:50:56 -0700
Subject: [PATCH 09/75] [SQL] JavaDoc update for various DataFrame functions.

Author: Reynold Xin <rxin@databricks.com>

Closes #5935 from rxin/df-doc1 and squashes the following commits:

aaeaadb [Reynold Xin] [SQL] JavaDoc update for various DataFrame functions.
---
 .../scala/org/apache/spark/sql/Column.scala   |  4 ++--
 .../org/apache/spark/sql/DataFrame.scala      | 16 ++++++++--------
 .../spark/sql/DataFrameStatFunctions.scala    | 19 +++++++++++++++----
 .../org/apache/spark/sql/functions.scala      | 14 +++++++-------
 4 files changed, 32 insertions(+), 21 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/Column.scala b/sql/core/src/main/scala/org/apache/spark/sql/Column.scala
index c0503bf047052..8eb632d3d600b 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/Column.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/Column.scala
@@ -84,14 +84,14 @@ class Column(protected[sql] val expr: Expression) extends Logging {
 
   /**
    * Inversion of boolean expression, i.e. NOT.
-   * {{
+   * {{{
    *   // Scala: select rows that are not active (isActive === false)
    *   df.filter( !df("isActive") )
    *
    *   // Java:
    *   import static org.apache.spark.sql.functions.*;
    *   df.filter( not(df.col("isActive")) );
-   * }}
+   * }}}
    *
    * @group expr_ops
    */
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
index aceb7a9627edd..9d2cd7aae3b82 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
@@ -681,11 +681,11 @@ class DataFrame private[sql](
 
   /**
    * (Scala-specific) Aggregates on the entire [[DataFrame]] without groups.
-   * {{
+   * {{{
    *   // df.agg(...) is a shorthand for df.groupBy().agg(...)
    *   df.agg("age" -> "max", "salary" -> "avg")
    *   df.groupBy().agg("age" -> "max", "salary" -> "avg")
-   * }}
+   * }}}
    * @group dfops
    */
   def agg(aggExpr: (String, String), aggExprs: (String, String)*): DataFrame = {
@@ -694,33 +694,33 @@ class DataFrame private[sql](
 
   /**
    * (Scala-specific) Aggregates on the entire [[DataFrame]] without groups.
-   * {{
+   * {{{
    *   // df.agg(...) is a shorthand for df.groupBy().agg(...)
    *   df.agg(Map("age" -> "max", "salary" -> "avg"))
    *   df.groupBy().agg(Map("age" -> "max", "salary" -> "avg"))
-   * }}
+   * }}}
    * @group dfops
    */
   def agg(exprs: Map[String, String]): DataFrame = groupBy().agg(exprs)
 
   /**
    * (Java-specific) Aggregates on the entire [[DataFrame]] without groups.
-   * {{
+   * {{{
    *   // df.agg(...) is a shorthand for df.groupBy().agg(...)
    *   df.agg(Map("age" -> "max", "salary" -> "avg"))
    *   df.groupBy().agg(Map("age" -> "max", "salary" -> "avg"))
-   * }}
+   * }}}
    * @group dfops
    */
   def agg(exprs: java.util.Map[String, String]): DataFrame = groupBy().agg(exprs)
 
   /**
    * Aggregates on the entire [[DataFrame]] without groups.
-   * {{
+   * {{{
    *   // df.agg(...) is a shorthand for df.groupBy().agg(...)
    *   df.agg(max($"age"), avg($"salary"))
    *   df.groupBy().agg(max($"age"), avg($"salary"))
-   * }}
+   * }}}
    * @group dfops
    */
   @scala.annotation.varargs
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala
index cb88deab35968..a1e74470afc89 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala
@@ -37,7 +37,7 @@ final class DataFrameStatFunctions private[sql](df: DataFrame) {
     StatFunctions.calculateCov(df, Seq(col1, col2))
   }
 
-  /*
+  /**
    * Calculates the correlation of two columns of a DataFrame. Currently only supports the Pearson
    * Correlation Coefficient. For Spearman Correlation, consider using RDD methods found in 
    * MLlib's Statistics.
@@ -75,7 +75,7 @@ final class DataFrameStatFunctions private[sql](df: DataFrame) {
    *             each row.
    * @param col2 The name of the second column. Distinct items will make the column names
    *             of the DataFrame.
-   * @return A Local DataFrame containing the table
+   * @return A DataFrame containing for the contingency table.
    */
   def crosstab(col1: String, col2: String): DataFrame = {
     StatFunctions.crossTabulate(df, col1, col2)
@@ -110,14 +110,25 @@ final class DataFrameStatFunctions private[sql](df: DataFrame) {
   }
 
   /**
-   * Python friendly implementation for `freqItems`
+   * (Scala-specific) Finding frequent items for columns, possibly with false positives. Using the
+   * frequent element count algorithm described in
+   * [[http://dx.doi.org/10.1145/762471.762473, proposed by Karp, Schenker, and Papadimitriou]].
+   *
+   * @param cols the names of the columns to search frequent items in.
+   * @return A Local DataFrame with the Array of frequent items for each column.
    */
   def freqItems(cols: Seq[String], support: Double): DataFrame = {
     FrequentItems.singlePassFreqItems(df, cols, support)
   }
 
   /**
-   * Python friendly implementation for `freqItems` with a default `support` of 1%.
+   * (Scala-specific) Finding frequent items for columns, possibly with false positives. Using the
+   * frequent element count algorithm described in
+   * [[http://dx.doi.org/10.1145/762471.762473, proposed by Karp, Schenker, and Papadimitriou]].
+   * Uses a `default` support of 1%.
+   *
+   * @param cols the names of the columns to search frequent items in.
+   * @return A Local DataFrame with the Array of frequent items for each column.
    */
   def freqItems(cols: Seq[String]): DataFrame = {
     FrequentItems.singlePassFreqItems(df, cols, 0.01)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
index f2bb4534c75eb..830b5017717b5 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
@@ -22,7 +22,7 @@ import scala.reflect.runtime.universe.{TypeTag, typeTag}
 
 import org.apache.spark.annotation.Experimental
 import org.apache.spark.sql.catalyst.ScalaReflection
-import org.apache.spark.sql.catalyst.analysis.{UnresolvedAttribute, UnresolvedFunction, Star}
+import org.apache.spark.sql.catalyst.analysis.{UnresolvedFunction, Star}
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.expressions.mathfuncs._
 import org.apache.spark.sql.types._
@@ -86,10 +86,10 @@ object functions {
 
   /**
    * Returns a sort expression based on ascending order of the column.
-   * {{
+   * {{{
    *   // Sort by dept in ascending order, and then age in descending order.
    *   df.sort(asc("dept"), desc("age"))
-   * }}
+   * }}}
    *
    * @group sort_funcs
    */
@@ -97,10 +97,10 @@ object functions {
 
   /**
    * Returns a sort expression based on the descending order of the column.
-   * {{
+   * {{{
    *   // Sort by dept in ascending order, and then age in descending order.
    *   df.sort(asc("dept"), desc("age"))
-   * }}
+   * }}}
    *
    * @group sort_funcs
    */
@@ -353,13 +353,13 @@ object functions {
 
   /**
    * Inversion of boolean expression, i.e. NOT.
-   * {{
+   * {{{
    *   // Scala: select rows that are not active (isActive === false)
    *   df.filter( !df("isActive") )
    *
    *   // Java:
    *   df.filter( not(df.col("isActive")) );
-   * }}
+   * }}}
    *
    * @group normal_funcs
    */

From 150f671c286c57deaf37ab1d8f837d68b5be82a0 Mon Sep 17 00:00:00 2001
From: Daoyuan Wang <daoyuan.wang@intel.com>
Date: Wed, 6 May 2015 10:05:10 -0700
Subject: [PATCH 10/75] [SPARK-5456] [SQL] fix decimal compare for jdbc rdd

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

Closes #5803 from adrian-wang/decimalcompare and squashes the following commits:

aef0e96 [Daoyuan Wang] add null handle
ec455b9 [Daoyuan Wang] fix decimal compare for jdbc rdd
---
 .../main/scala/org/apache/spark/sql/jdbc/JDBCRDD.scala    | 8 +++++++-
 .../test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala  | 5 ++++-
 2 files changed, 11 insertions(+), 2 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRDD.scala
index 2f6ba48dbc3d9..325a326e2b5b2 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRDD.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRDD.scala
@@ -363,7 +363,13 @@ private[sql] class JDBCRDD(
             case BooleanConversion    => mutableRow.setBoolean(i, rs.getBoolean(pos))
             case DateConversion       =>
               mutableRow.update(i, DateUtils.fromJavaDate(rs.getDate(pos)))
-            case DecimalConversion    => mutableRow.update(i, rs.getBigDecimal(pos))
+            case DecimalConversion    =>
+              val decimalVal = rs.getBigDecimal(pos)
+              if (decimalVal == null) {
+                mutableRow.update(i, null)
+              } else {
+                mutableRow.update(i, Decimal(decimalVal))
+              }
             case DoubleConversion     => mutableRow.setDouble(i, rs.getDouble(pos))
             case FloatConversion      => mutableRow.setFloat(i, rs.getFloat(pos))
             case IntegerConversion    => mutableRow.setInt(i, rs.getInt(pos))
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala
index b165ab2b1deb3..3ec17d37c025b 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala
@@ -271,8 +271,11 @@ class JDBCSuite extends FunSuite with BeforeAndAfter {
     assert(rows(0).getDouble(0) === 1.00000000000000022) // Yes, I meant ==.
     assert(rows(0).getDouble(1) === 1.00000011920928955) // Yes, I meant ==.
     assert(rows(0).getAs[BigDecimal](2)
-        .equals(new BigDecimal("123456789012345.54321543215432100000")))
+      .equals(new BigDecimal("123456789012345.54321543215432100000")))
     assert(rows(0).schema.fields(2).dataType === DecimalType(40, 20))
+    val compareDecimal = sql("SELECT C FROM flttypes where C > C - 1").collect()
+    assert(compareDecimal(0).getAs[BigDecimal](0)
+      .equals(new BigDecimal("123456789012345.54321543215432100000")))
   }
 
   test("SQL query as table name") {

From c3eb441f5487c9b6476e1d6e2a2d852dcc43b986 Mon Sep 17 00:00:00 2001
From: Daoyuan Wang <daoyuan.wang@intel.com>
Date: Wed, 6 May 2015 10:30:42 -0700
Subject: [PATCH 11/75] [SPARK-6201] [SQL] promote string and do widen types
 for IN

huangjs
Acutally spark sql will first go through analysis period, in which we do widen types and promote strings, and then optimization, where constant IN will be converted into INSET.

So it turn out that we only need to fix this for IN.

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

Closes #4945 from adrian-wang/inset and squashes the following commits:

71e05cc [Daoyuan Wang] minor fix
581fa1c [Daoyuan Wang] mysql way
f3f7baf [Daoyuan Wang] address comments
5eed4bc [Daoyuan Wang] promote string and do widen types for IN
---
 .../sql/catalyst/analysis/HiveTypeCoercion.scala      | 11 +++++++++++
 .../spark/sql/catalyst/optimizer/Optimizer.scala      |  4 ++--
 .../scala/org/apache/spark/sql/SQLQuerySuite.scala    |  9 +++++++++
 3 files changed, 22 insertions(+), 2 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 831fb4fe95fe7..96e2aee4de15b 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
@@ -69,6 +69,7 @@ trait HiveTypeCoercion {
   val typeCoercionRules =
     PropagateTypes ::
     ConvertNaNs ::
+    InConversion ::
     WidenTypes ::
     PromoteStrings ::
     DecimalPrecision ::
@@ -287,6 +288,16 @@ trait HiveTypeCoercion {
     }
   }
 
+  /**
+   * Convert all expressions in in() list to the left operator type
+   */
+  object InConversion extends Rule[LogicalPlan] {
+    def apply(plan: LogicalPlan): LogicalPlan = plan transformAllExpressions {
+      case i @ In(a, b) if b.exists(_.dataType != a.dataType) =>
+        i.makeCopy(Array(a, b.map(Cast(_, a.dataType))))
+    }
+  }
+
   // scalastyle:off
   /**
    * Calculates and propagates precision for fixed-precision decimals. Hive has a number of
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 709f7d672d931..e4a60f53d6c09 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
@@ -310,8 +310,8 @@ object OptimizeIn extends Rule[LogicalPlan] {
   def apply(plan: LogicalPlan): LogicalPlan = plan transform {
     case q: LogicalPlan => q transformExpressionsDown {
       case In(v, list) if !list.exists(!_.isInstanceOf[Literal]) =>
-          val hSet = list.map(e => e.eval(null))
-          InSet(v, HashSet() ++ hSet)
+        val hSet = list.map(e => e.eval(null))
+        InSet(v, HashSet() ++ hSet)
     }
   }
 }
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 0ab8558c1db13..208cec6a32d4d 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
@@ -120,6 +120,15 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll {
       Row(1, 1) :: Nil)
   }
 
+  test("SPARK-6201 IN type conversion") {
+    jsonRDD(sparkContext.parallelize(Seq("{\"a\": \"1\"}}", "{\"a\": \"2\"}}", "{\"a\": \"3\"}}")))
+      .registerTempTable("d")
+
+    checkAnswer(
+      sql("select * from d where d.a in (1,2)"),
+      Seq(Row("1"), Row("2")))
+  }
+
   test("SPARK-3176 Added Parser of SQL ABS()") {
     checkAnswer(
       sql("SELECT ABS(-1.3)"),

From f2c47082c3412a4cf8cbabe12585147c5ec3ea40 Mon Sep 17 00:00:00 2001
From: Yin Huai <yhuai@databricks.com>
Date: Wed, 6 May 2015 10:43:00 -0700
Subject: [PATCH 12/75] [SPARK-1442] [SQL] Window Function Support for Spark
 SQL

Adding more information about the implementation...

This PR is adding the support of window functions to Spark SQL (specifically OVER and WINDOW clause). For every expression having a OVER clause, we use a WindowExpression as the container of a WindowFunction and the corresponding WindowSpecDefinition (the definition of a window frame, i.e. partition specification, order specification, and frame specification appearing in a OVER clause).
# Implementation #
The high level work flow of the implementation is described as follows.

*	Query parsing: In the query parse process, all WindowExpressions are originally placed in the projectList of a Project operator or the aggregateExpressions of an Aggregate operator. It makes our changes to simple and keep all of parsing rules for window functions at a single place (nodesToWindowSpecification). For the WINDOWclause in a query, we use a WithWindowDefinition as the container as the mapping from the name of a window specification to a WindowSpecDefinition. This changes is similar with our common table expression support.

*	Analysis: The query analysis process has three steps for window functions.

 *	Resolve all WindowSpecReferences by replacing them with WindowSpecReferences according to the mapping table stored in the node of WithWindowDefinition.
 *	Resolve WindowFunctions in the projectList of a Project operator or the aggregateExpressions of an Aggregate operator. For this PR, we use Hive's functions for window functions because we will have a major refactoring of our internal UDAFs and it is better to switch our UDAFs after that refactoring work.
 *	Once we have resolved all WindowFunctions, we will use ResolveWindowFunction to extract WindowExpressions from projectList and aggregateExpressions and then create a Window operator for every distinct WindowSpecDefinition. With this choice, at the execution time, we can rely on the Exchange operator to do all of work on reorganizing the table and we do not need to worry about it in the physical Window operator. An example analyzed plan is shown as follows

```
sql("""
SELECT
  year, country, product, sales,
  avg(sales) over(partition by product) avg_product,
  sum(sales) over(partition by country) sum_country
FROM sales
ORDER BY year, country, product
""").explain(true)

== Analyzed Logical Plan ==
Sort [year#34 ASC,country#35 ASC,product#36 ASC], true
 Project [year#34,country#35,product#36,sales#37,avg_product#27,sum_country#28]
  Window [year#34,country#35,product#36,sales#37,avg_product#27], [HiveWindowFunction#org.apache.hadoop.hive.ql.udf.generic.GenericUDAFSum(sales#37) WindowSpecDefinition [country#35], [], ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING AS sum_country#28], WindowSpecDefinition [country#35], [], ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING
   Window [year#34,country#35,product#36,sales#37], [HiveWindowFunction#org.apache.hadoop.hive.ql.udf.generic.GenericUDAFAverage(sales#37) WindowSpecDefinition [product#36], [], ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING AS avg_product#27], WindowSpecDefinition [product#36], [], ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING
    Project [year#34,country#35,product#36,sales#37]
     MetastoreRelation default, sales, None
```

*	Query planning: In the process of query planning, we simple generate the physical Window operator based on the logical Window operator. Then, to prepare the executedPlan, the EnsureRequirements rule will add Exchange and Sort operators if necessary. The EnsureRequirements rule will analyze the data properties and try to not add unnecessary shuffle and sort. The physical plan for the above example query is shown below.

```
== Physical Plan ==
Sort [year#34 ASC,country#35 ASC,product#36 ASC], true
 Exchange (RangePartitioning [year#34 ASC,country#35 ASC,product#36 ASC], 200), []
  Window [year#34,country#35,product#36,sales#37,avg_product#27], [HiveWindowFunction#org.apache.hadoop.hive.ql.udf.generic.GenericUDAFSum(sales#37) WindowSpecDefinition [country#35], [], ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING AS sum_country#28], WindowSpecDefinition [country#35], [], ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING
   Exchange (HashPartitioning [country#35], 200), [country#35 ASC]
    Window [year#34,country#35,product#36,sales#37], [HiveWindowFunction#org.apache.hadoop.hive.ql.udf.generic.GenericUDAFAverage(sales#37) WindowSpecDefinition [product#36], [], ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING AS avg_product#27], WindowSpecDefinition [product#36], [], ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING
     Exchange (HashPartitioning [product#36], 200), [product#36 ASC]
      HiveTableScan [year#34,country#35,product#36,sales#37], (MetastoreRelation default, sales, None), None
```

*	Execution time: At execution time, a physical Window operator buffers all rows in a partition specified in the partition spec of a OVER clause. If necessary, it also maintains a sliding window frame. The current implementation tries to buffer the input parameters of a window function according to the window frame to avoid evaluating a row multiple times.

# Future work #

Here are three improvements that are not hard to add:
*	Taking advantage of the window frame specification to reduce the number of rows buffered in the physical Window operator. For some cases, we only need to buffer the rows appearing in the sliding window. But for other cases, we will not be able to reduce the number of rows buffered (e.g. ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING).

*	When aRAGEN frame is used, for <value> PRECEDING and <value> FOLLOWING, it will be great if the <value> part is an expression (we can start with Literal). So, when the data type of ORDER BY expression is a FractionalType, we can support FractionalType as the type <value> (<value> still needs to be evaluated as a positive value).

*	When aRAGEN frame is used, we need to support DateType and TimestampType as the data type of the expression appearing in the order specification. Then, the <value> part of <value> PRECEDING and <value> FOLLOWING can support interval types (once we support them).

This is a joint work with guowei2 and yhuai
Thanks hbutani hvanhovell for his comments
Thanks scwf for his comments and unit tests

Author: Yin Huai <yhuai@databricks.com>

Closes #5604 from guowei2/windowImplement and squashes the following commits:

76fe1c8 [Yin Huai] Implementation.
aa2b0ae [Yin Huai] Tests.
---
 .../sql/catalyst/analysis/Analyzer.scala      |  200 +++
 .../sql/catalyst/analysis/CheckAnalysis.scala |    5 +
 .../sql/catalyst/expressions/Projection.scala |   94 ++
 .../expressions/windowExpressions.scala       |  340 +++++
 .../plans/logical/basicOperators.scala        |   30 +-
 .../spark/sql/catalyst/trees/TreeNode.scala   |   23 +
 .../sql/catalyst/trees/TreeNodeSuite.scala    |   44 +-
 .../spark/sql/execution/SparkStrategies.scala |    2 +
 .../apache/spark/sql/execution/Window.scala   |  480 ++++++
 .../execution/HiveCompatibilitySuite.scala    |    2 +-
 .../apache/spark/sql/hive/HiveContext.scala   |    1 +
 .../org/apache/spark/sql/hive/HiveQl.scala    |  175 ++-
 .../org/apache/spark/sql/hive/hiveUdfs.scala  |  223 ++-
 ...ndowing-0-327a8cd39fe30255ff492ee86f660522 |   26 +
 ...nkNoGBY-0-fef4bf638d52a9a601845347010602fd |   26 +
 ...rstLast-0-86bb9c97d92fdcd941bcb5143513e2e6 |   26 +
 ...thWhere-0-84345a9f685ba63b87caa4bb16b122b5 |    5 +
 ...mWindow-0-6cfc8840d3a4469b0fe11d63182cb59f |   26 +
 ...tClause-0-88d96a526d3cae6ed8168c5b228974d1 |   26 +
 ...essions-0-11f6c13cf2710ce7054654cca136e73e |   26 +
 ...Windows-0-efd1476255eeb1b1961149144f574b7a |   26 +
 ...untStar-0-1b1fc185c8fddf68e58e92f29052ab2d |   26 +
 ...stUDAFs-0-6974e5959e41a661e09db18547fef58a |   26 +
 ...WithGBY-0-67d15ee5915ac64a738fd4b60d75eb35 |   25 +
 ...tioning-0-cb5618b1e626f3a9d4a030b508b5d251 |   25 +
 ...stSTATs-0-da0e0cca69e42118a96b8609b8fa5838 |   26 +
 ...stDISTs-0-672d4cb385b7ced2e446f132474293ad |   26 +
 ...alViews-0-dea06072f0a64fe4537fae854944ed5a |   78 +
 ...ndAlias-0-b996a664b06e5741c08079d5c38241bc |   25 +
 ...Windows-0-227e080e337d734dd88ff814b3b412e4 |   26 +
 ...FInvoke-0-25912ae7d18c91cc09e17e57968fb5db |   26 +
 ...nWdwDef-0-88945892370ccbc1125a927a3d55342a |   26 +
 ...WithSWQ-0-a5a5339330a6a6660d32ccb0cc5d7100 |   25 +
 ...ecRules-0-fa80b09c99e3c1487de48ea71a88dada |   26 +
 ...tioning-0-45ccbaf0ee083858f7661c66b11d4768 |   26 +
 ...usForms-0-3436e50214f9afdec84334e10faa931a |   26 +
 ...sForms2-0-cba9d84a6b1bb5e36595338d4602377e |   26 +
 ...derCols-0-7647562850dd367ef1e6c63117805423 |   26 +
 ...stCount-0-e6e97e884327df86f16b870527ec026c |   26 +
 ...ForRows-0-99007f45b6406869e048b0e4eb9213f1 |   26 +
 ...orRange-0-d81a591e90950de291d2f133793e9283 |   26 +
 ...ForRows-0-fb8648e82e4dd56d6bdcfd739dd1edf0 |   26 +
 ...orRange-0-3cd04e5f2398853c4850f4f86142bb39 |   26 +
 ...gregate-0-cb3d2f8c1296044dc2658876bb6103ae |   26 +
 ...ingUDAF-0-3bde93728761b780a745c2ce0398aa0f |   26 +
 ...tInSubQ-0-73d5274a21d4f4fd51d2a0f1d98516ce |   26 +
 ...seAlias-0-4b1ad2515fb079012467e987f484a722 |   26 +
 ...ngNoGBY-0-70cdc0555a61ef08534a9ebebb95ebbf |   26 +
 ...iner_sz-0-d3f50875bd5dff172cf813fdb7d738eb |    0
 ...iner_sz-1-dda16565b98926fc3587de937b9401c7 |    0
 ...ainer_sz-2-374e39786feb745cd70f25be58bfa24 |    0
 ...iner_sz-3-d2b5e23edec42a62e61750b110ecbaac |    1 +
 ...iner_sz-4-50d0c630159068b5b8ccdeb76493f1f7 |   26 +
 ...iner_sz-5-3f95cd6f4add7a2d0101fe3dd97e5082 |    1 +
 ...Pruning-0-d3f50875bd5dff172cf813fdb7d738eb |    0
 ...Pruning-1-dda16565b98926fc3587de937b9401c7 |    0
 ...nPruning-2-374e39786feb745cd70f25be58bfa24 |    0
 ...Pruning-3-9294b4a22bc396ff2accabd53c5da98b |   26 +
 ...nPruning-4-445cab062581c449ceffcb368cdf133 |   26 +
 ...Pruning-5-89110070c761eafb992eb9315128b53f |   26 +
 ...stic) 1-0-12a92d8800e0da8b515ba3eaf6a7fd0f | 1049 +++++++++++++
 ...stic) 3-0-455e41d9949a2d22bab634fd8e42f2b1 |    1 +
 ...stic) 4-0-cfad06ae8eba6b047d32a6a61dd59392 |    1 +
 ...stic) 5-0-d7ca7a61377cef3a9f721a28afdae012 |    1 +
 ...stic) 6-0-287bcc7679822bc7b684532b267bf11f |    1 +
 ...nistic)-0-36217f6074daaacddb9fcb50a3f4fb5b | 1049 +++++++++++++
 ...nistic)-1-9ee79e711248dd6e0a6ce27e439e55f4 | 1049 +++++++++++++
 ...nistic)-2-1e88e0ba414a00195f7ebf6b8600ac04 | 1049 +++++++++++++
 ...nistic)-3-34d9ee4120f21d0d0ae914fba0acc60c | 1049 +++++++++++++
 ...nistic)-4-dfd39236756a3951bc1ec354799d69e4 | 1049 +++++++++++++
 ...nistic)-5-8d0ee3e1605f38214bfad28a5ce897cc |    1 +
 ...nistic)-0-b7cb25303831392a51cd996e758ac79a | 1049 +++++++++++++
 ...nistic)-1-a3d352560ac835993001665db6954965 | 1049 +++++++++++++
 ...nistic)-2-fafa16c0f7697ca28aeb6f2698799562 | 1049 +++++++++++++
 ...nistic)-3-bda0e7c77d6f4712a03389cb5032bc6d | 1049 +++++++++++++
 ...stic) 1-0-2e0cbc2d7c5f16657edacd9e7209e6e7 | 1049 +++++++++++++
 ...stic) 1-1-5c5f373e325115d710a7a23fcb1626f1 | 1049 +++++++++++++
 ...stic) 1-2-ac487cc1b94130bf9ce00e07c7075f65 | 1049 +++++++++++++
 ...stic) 1-3-b82dfa24123047be4b4e3c27c3997d34 | 1049 +++++++++++++
 ...istic) 2-0-81bb7f49a55385878637c8aac4d08e5 | 1294 +++++++++++++++++
 ...stic) 3-0-58a982694ba2b1e34de82b1de54936a0 |    0
 ...stic) 4-0-12cc78f3953c3e6b5411ddc729541bf0 |  474 ++++++
 ...nistic)-0-6642a21d87e0401ba1a668ea8b244f0c | 1049 +++++++++++++
 ...nistic)-1-2bf20f39e6ffef258858f7943a974e7e | 1049 +++++++++++++
 ...nistic)-2-16239d2b069789ba99fbac50c4f0724f | 1049 +++++++++++++
 ...nistic)-3-d90b27fca067b0b3c48d873b3ef32af7 | 1049 +++++++++++++
 ...nistic)-4-f2e4d659b65a833e9281b6786d3d55c1 | 1049 +++++++++++++
 ...g_udaf2-0-96659fde37d7a38ea15b367b47f59ce2 |    0
 ...g_udaf2-1-b4bdee4908b1cb8e240c549ae5cfe4c0 |    1 +
 ...nistic)-0-f498cccf82480be03022d2a36f87651e | 1049 +++++++++++++
 ...inistic)-1-6378faf36ffd3f61e61cee6c0cb70e6 | 1049 +++++++++++++
 ...nistic)-2-5f0eab306ea3c22b11ace9b542a7ee56 | 1049 +++++++++++++
 ...nistic)-3-6f104992e0050576085064815de43194 | 1049 +++++++++++++
 ...nistic)-4-cd2e3d2344810cb3ba843d4c01c81d7e | 1049 +++++++++++++
 ...nistic)-5-ee44c5cdc80e1c832b702f9fb76d8145 | 1049 +++++++++++++
 ...nistic)-6-4d78f7b1d172d20c91f5867bc13a42a0 | 1049 +++++++++++++
 ...nistic)-7-20fdc99aa046b2c41d9b85ab338c749c | 1049 +++++++++++++
 ...nistic)-8-45a1d7c2aba45d761e19ff4dfdf5463e | 1049 +++++++++++++
 .../hive/execution/HiveComparisonTest.scala   |    2 +-
 .../HiveWindowFunctionQuerySuite.scala        |  845 +++++++++++
 .../sql/hive/execution/SQLQuerySuite.scala    |  147 ++
 101 files changed, 34768 insertions(+), 34 deletions(-)
 create mode 100644 sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/windowExpressions.scala
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/Window.scala
 create mode 100644 sql/hive/src/test/resources/golden/windowing.q -- 1. testWindowing-0-327a8cd39fe30255ff492ee86f660522
 create mode 100644 sql/hive/src/test/resources/golden/windowing.q -- 10. testHavingWithWindowingCondRankNoGBY-0-fef4bf638d52a9a601845347010602fd
 create mode 100644 sql/hive/src/test/resources/golden/windowing.q -- 11. testFirstLast-0-86bb9c97d92fdcd941bcb5143513e2e6
 create mode 100644 sql/hive/src/test/resources/golden/windowing.q -- 12. testFirstLastWithWhere-0-84345a9f685ba63b87caa4bb16b122b5
 create mode 100644 sql/hive/src/test/resources/golden/windowing.q -- 13. testSumWindow-0-6cfc8840d3a4469b0fe11d63182cb59f
 create mode 100644 sql/hive/src/test/resources/golden/windowing.q -- 14. testNoSortClause-0-88d96a526d3cae6ed8168c5b228974d1
 create mode 100644 sql/hive/src/test/resources/golden/windowing.q -- 15. testExpressions-0-11f6c13cf2710ce7054654cca136e73e
 create mode 100644 sql/hive/src/test/resources/golden/windowing.q -- 16. testMultipleWindows-0-efd1476255eeb1b1961149144f574b7a
 create mode 100644 sql/hive/src/test/resources/golden/windowing.q -- 17. testCountStar-0-1b1fc185c8fddf68e58e92f29052ab2d
 create mode 100644 sql/hive/src/test/resources/golden/windowing.q -- 18. testUDAFs-0-6974e5959e41a661e09db18547fef58a
 create mode 100644 sql/hive/src/test/resources/golden/windowing.q -- 19. testUDAFsWithGBY-0-67d15ee5915ac64a738fd4b60d75eb35
 create mode 100644 sql/hive/src/test/resources/golden/windowing.q -- 2. testGroupByWithPartitioning-0-cb5618b1e626f3a9d4a030b508b5d251
 create mode 100644 sql/hive/src/test/resources/golden/windowing.q -- 20. testSTATs-0-da0e0cca69e42118a96b8609b8fa5838
 create mode 100644 sql/hive/src/test/resources/golden/windowing.q -- 21. testDISTs-0-672d4cb385b7ced2e446f132474293ad
 create mode 100644 sql/hive/src/test/resources/golden/windowing.q -- 24. testLateralViews-0-dea06072f0a64fe4537fae854944ed5a
 create mode 100644 sql/hive/src/test/resources/golden/windowing.q -- 26. testGroupByHavingWithSWQAndAlias-0-b996a664b06e5741c08079d5c38241bc
 create mode 100644 sql/hive/src/test/resources/golden/windowing.q -- 27. testMultipleRangeWindows-0-227e080e337d734dd88ff814b3b412e4
 create mode 100644 sql/hive/src/test/resources/golden/windowing.q -- 28. testPartOrderInUDAFInvoke-0-25912ae7d18c91cc09e17e57968fb5db
 create mode 100644 sql/hive/src/test/resources/golden/windowing.q -- 29. testPartOrderInWdwDef-0-88945892370ccbc1125a927a3d55342a
 create mode 100644 sql/hive/src/test/resources/golden/windowing.q -- 3. testGroupByHavingWithSWQ-0-a5a5339330a6a6660d32ccb0cc5d7100
 create mode 100644 sql/hive/src/test/resources/golden/windowing.q -- 30. testDefaultPartitioningSpecRules-0-fa80b09c99e3c1487de48ea71a88dada
 create mode 100644 sql/hive/src/test/resources/golden/windowing.q -- 36. testRankWithPartitioning-0-45ccbaf0ee083858f7661c66b11d4768
 create mode 100644 sql/hive/src/test/resources/golden/windowing.q -- 37. testPartitioningVariousForms-0-3436e50214f9afdec84334e10faa931a
 create mode 100644 sql/hive/src/test/resources/golden/windowing.q -- 38. testPartitioningVariousForms2-0-cba9d84a6b1bb5e36595338d4602377e
 create mode 100644 sql/hive/src/test/resources/golden/windowing.q -- 39. testUDFOnOrderCols-0-7647562850dd367ef1e6c63117805423
 create mode 100644 sql/hive/src/test/resources/golden/windowing.q -- 4. testCount-0-e6e97e884327df86f16b870527ec026c
 create mode 100644 sql/hive/src/test/resources/golden/windowing.q -- 40. testNoBetweenForRows-0-99007f45b6406869e048b0e4eb9213f1
 create mode 100644 sql/hive/src/test/resources/golden/windowing.q -- 41. testNoBetweenForRange-0-d81a591e90950de291d2f133793e9283
 create mode 100644 sql/hive/src/test/resources/golden/windowing.q -- 42. testUnboundedFollowingForRows-0-fb8648e82e4dd56d6bdcfd739dd1edf0
 create mode 100644 sql/hive/src/test/resources/golden/windowing.q -- 43. testUnboundedFollowingForRange-0-3cd04e5f2398853c4850f4f86142bb39
 create mode 100644 sql/hive/src/test/resources/golden/windowing.q -- 44. testOverNoPartitionSingleAggregate-0-cb3d2f8c1296044dc2658876bb6103ae
 create mode 100644 sql/hive/src/test/resources/golden/windowing.q -- 5. testCountWithWindowingUDAF-0-3bde93728761b780a745c2ce0398aa0f
 create mode 100644 sql/hive/src/test/resources/golden/windowing.q -- 6. testCountInSubQ-0-73d5274a21d4f4fd51d2a0f1d98516ce
 create mode 100644 sql/hive/src/test/resources/golden/windowing.q -- 8. testMixedCaseAlias-0-4b1ad2515fb079012467e987f484a722
 create mode 100644 sql/hive/src/test/resources/golden/windowing.q -- 9. testHavingWithWindowingNoGBY-0-70cdc0555a61ef08534a9ebebb95ebbf
 create mode 100644 sql/hive/src/test/resources/golden/windowing_adjust_rowcontainer_sz-0-d3f50875bd5dff172cf813fdb7d738eb
 create mode 100644 sql/hive/src/test/resources/golden/windowing_adjust_rowcontainer_sz-1-dda16565b98926fc3587de937b9401c7
 create mode 100644 sql/hive/src/test/resources/golden/windowing_adjust_rowcontainer_sz-2-374e39786feb745cd70f25be58bfa24
 create mode 100644 sql/hive/src/test/resources/golden/windowing_adjust_rowcontainer_sz-3-d2b5e23edec42a62e61750b110ecbaac
 create mode 100644 sql/hive/src/test/resources/golden/windowing_adjust_rowcontainer_sz-4-50d0c630159068b5b8ccdeb76493f1f7
 create mode 100644 sql/hive/src/test/resources/golden/windowing_adjust_rowcontainer_sz-5-3f95cd6f4add7a2d0101fe3dd97e5082
 create mode 100644 sql/hive/src/test/resources/golden/windowing_columnPruning-0-d3f50875bd5dff172cf813fdb7d738eb
 create mode 100644 sql/hive/src/test/resources/golden/windowing_columnPruning-1-dda16565b98926fc3587de937b9401c7
 create mode 100644 sql/hive/src/test/resources/golden/windowing_columnPruning-2-374e39786feb745cd70f25be58bfa24
 create mode 100644 sql/hive/src/test/resources/golden/windowing_columnPruning-3-9294b4a22bc396ff2accabd53c5da98b
 create mode 100644 sql/hive/src/test/resources/golden/windowing_columnPruning-4-445cab062581c449ceffcb368cdf133
 create mode 100644 sql/hive/src/test/resources/golden/windowing_columnPruning-5-89110070c761eafb992eb9315128b53f
 create mode 100644 sql/hive/src/test/resources/golden/windowing_multipartitioning.q (deterministic) 1-0-12a92d8800e0da8b515ba3eaf6a7fd0f
 create mode 100644 sql/hive/src/test/resources/golden/windowing_multipartitioning.q (deterministic) 3-0-455e41d9949a2d22bab634fd8e42f2b1
 create mode 100644 sql/hive/src/test/resources/golden/windowing_multipartitioning.q (deterministic) 4-0-cfad06ae8eba6b047d32a6a61dd59392
 create mode 100644 sql/hive/src/test/resources/golden/windowing_multipartitioning.q (deterministic) 5-0-d7ca7a61377cef3a9f721a28afdae012
 create mode 100644 sql/hive/src/test/resources/golden/windowing_multipartitioning.q (deterministic) 6-0-287bcc7679822bc7b684532b267bf11f
 create mode 100644 sql/hive/src/test/resources/golden/windowing_navfn.q (deterministic)-0-36217f6074daaacddb9fcb50a3f4fb5b
 create mode 100644 sql/hive/src/test/resources/golden/windowing_navfn.q (deterministic)-1-9ee79e711248dd6e0a6ce27e439e55f4
 create mode 100644 sql/hive/src/test/resources/golden/windowing_navfn.q (deterministic)-2-1e88e0ba414a00195f7ebf6b8600ac04
 create mode 100644 sql/hive/src/test/resources/golden/windowing_navfn.q (deterministic)-3-34d9ee4120f21d0d0ae914fba0acc60c
 create mode 100644 sql/hive/src/test/resources/golden/windowing_navfn.q (deterministic)-4-dfd39236756a3951bc1ec354799d69e4
 create mode 100644 sql/hive/src/test/resources/golden/windowing_navfn.q (deterministic)-5-8d0ee3e1605f38214bfad28a5ce897cc
 create mode 100644 sql/hive/src/test/resources/golden/windowing_ntile.q (deterministic)-0-b7cb25303831392a51cd996e758ac79a
 create mode 100644 sql/hive/src/test/resources/golden/windowing_ntile.q (deterministic)-1-a3d352560ac835993001665db6954965
 create mode 100644 sql/hive/src/test/resources/golden/windowing_ntile.q (deterministic)-2-fafa16c0f7697ca28aeb6f2698799562
 create mode 100644 sql/hive/src/test/resources/golden/windowing_ntile.q (deterministic)-3-bda0e7c77d6f4712a03389cb5032bc6d
 create mode 100644 sql/hive/src/test/resources/golden/windowing_rank.q (deterministic) 1-0-2e0cbc2d7c5f16657edacd9e7209e6e7
 create mode 100644 sql/hive/src/test/resources/golden/windowing_rank.q (deterministic) 1-1-5c5f373e325115d710a7a23fcb1626f1
 create mode 100644 sql/hive/src/test/resources/golden/windowing_rank.q (deterministic) 1-2-ac487cc1b94130bf9ce00e07c7075f65
 create mode 100644 sql/hive/src/test/resources/golden/windowing_rank.q (deterministic) 1-3-b82dfa24123047be4b4e3c27c3997d34
 create mode 100644 sql/hive/src/test/resources/golden/windowing_rank.q (deterministic) 2-0-81bb7f49a55385878637c8aac4d08e5
 create mode 100644 sql/hive/src/test/resources/golden/windowing_rank.q (deterministic) 3-0-58a982694ba2b1e34de82b1de54936a0
 create mode 100644 sql/hive/src/test/resources/golden/windowing_rank.q (deterministic) 4-0-12cc78f3953c3e6b5411ddc729541bf0
 create mode 100644 sql/hive/src/test/resources/golden/windowing_udaf.q (deterministic)-0-6642a21d87e0401ba1a668ea8b244f0c
 create mode 100644 sql/hive/src/test/resources/golden/windowing_udaf.q (deterministic)-1-2bf20f39e6ffef258858f7943a974e7e
 create mode 100644 sql/hive/src/test/resources/golden/windowing_udaf.q (deterministic)-2-16239d2b069789ba99fbac50c4f0724f
 create mode 100644 sql/hive/src/test/resources/golden/windowing_udaf.q (deterministic)-3-d90b27fca067b0b3c48d873b3ef32af7
 create mode 100644 sql/hive/src/test/resources/golden/windowing_udaf.q (deterministic)-4-f2e4d659b65a833e9281b6786d3d55c1
 create mode 100644 sql/hive/src/test/resources/golden/windowing_udaf2-0-96659fde37d7a38ea15b367b47f59ce2
 create mode 100644 sql/hive/src/test/resources/golden/windowing_udaf2-1-b4bdee4908b1cb8e240c549ae5cfe4c0
 create mode 100644 sql/hive/src/test/resources/golden/windowing_windowspec.q (deterministic)-0-f498cccf82480be03022d2a36f87651e
 create mode 100644 sql/hive/src/test/resources/golden/windowing_windowspec.q (deterministic)-1-6378faf36ffd3f61e61cee6c0cb70e6
 create mode 100644 sql/hive/src/test/resources/golden/windowing_windowspec.q (deterministic)-2-5f0eab306ea3c22b11ace9b542a7ee56
 create mode 100644 sql/hive/src/test/resources/golden/windowing_windowspec.q (deterministic)-3-6f104992e0050576085064815de43194
 create mode 100644 sql/hive/src/test/resources/golden/windowing_windowspec.q (deterministic)-4-cd2e3d2344810cb3ba843d4c01c81d7e
 create mode 100644 sql/hive/src/test/resources/golden/windowing_windowspec.q (deterministic)-5-ee44c5cdc80e1c832b702f9fb76d8145
 create mode 100644 sql/hive/src/test/resources/golden/windowing_windowspec.q (deterministic)-6-4d78f7b1d172d20c91f5867bc13a42a0
 create mode 100644 sql/hive/src/test/resources/golden/windowing_windowspec.q (deterministic)-7-20fdc99aa046b2c41d9b85ab338c749c
 create mode 100644 sql/hive/src/test/resources/golden/windowing_windowspec.q (deterministic)-8-45a1d7c2aba45d761e19ff4dfdf5463e
 create mode 100644 sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveWindowFunctionQuerySuite.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 5e42b409dcc59..7b543b6c2aa42 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
@@ -17,6 +17,8 @@
 
 package org.apache.spark.sql.catalyst.analysis
 
+import scala.collection.mutable.ArrayBuffer
+
 import org.apache.spark.util.collection.OpenHashSet
 import org.apache.spark.sql.AnalysisException
 import org.apache.spark.sql.catalyst.expressions._
@@ -61,6 +63,7 @@ class Analyzer(
       ResolveGenerate ::
       ImplicitGenerate ::
       ResolveFunctions ::
+      ExtractWindowExpressions ::
       GlobalAggregates ::
       UnresolvedHavingClauseAttributes ::
       TrimGroupingAliases ::
@@ -529,6 +532,203 @@ class Analyzer(
           makeGeneratorOutput(p.generator, p.generatorOutput), p.child)
     }
   }
+
+  /**
+   * Extracts [[WindowExpression]]s from the projectList of a [[Project]] operator and
+   * aggregateExpressions of an [[Aggregate]] operator and creates individual [[Window]]
+   * operators for every distinct [[WindowSpecDefinition]].
+   *
+   * This rule handles three cases:
+   *  - A [[Project]] having [[WindowExpression]]s in its projectList;
+   *  - An [[Aggregate]] having [[WindowExpression]]s in its aggregateExpressions.
+   *  - An [[Filter]]->[[Aggregate]] pattern representing GROUP BY with a HAVING
+   *    clause and the [[Aggregate]] has [[WindowExpression]]s in its aggregateExpressions.
+   * Note: If there is a GROUP BY clause in the query, aggregations and corresponding
+   * filters (expressions in the HAVING clause) should be evaluated before any
+   * [[WindowExpression]]. If a query has SELECT DISTINCT, the DISTINCT part should be
+   * evaluated after all [[WindowExpression]]s.
+   *
+   * For every case, the transformation works as follows:
+   * 1. For a list of [[Expression]]s (a projectList or an aggregateExpressions), partitions
+   *    it two lists of [[Expression]]s, one for all [[WindowExpression]]s and another for
+   *    all regular expressions.
+   * 2. For all [[WindowExpression]]s, groups them based on their [[WindowSpecDefinition]]s.
+   * 3. For every distinct [[WindowSpecDefinition]], creates a [[Window]] operator and inserts
+   *    it into the plan tree.
+   */
+  object ExtractWindowExpressions extends Rule[LogicalPlan] {
+    def hasWindowFunction(projectList: Seq[NamedExpression]): Boolean =
+      projectList.exists(hasWindowFunction)
+
+    def hasWindowFunction(expr: NamedExpression): Boolean = {
+      expr.find {
+        case window: WindowExpression => true
+        case _ => false
+      }.isDefined
+    }
+
+    /**
+     * From a Seq of [[NamedExpression]]s, extract window expressions and
+     * other regular expressions.
+     */
+    def extract(
+        expressions: Seq[NamedExpression]): (Seq[NamedExpression], Seq[NamedExpression]) = {
+      // First, we simple partition the input expressions to two part, one having
+      // WindowExpressions and another one without WindowExpressions.
+      val (windowExpressions, regularExpressions) = expressions.partition(hasWindowFunction)
+
+      // Then, we need to extract those regular expressions used in the WindowExpression.
+      // For example, when we have col1 - Sum(col2 + col3) OVER (PARTITION BY col4 ORDER BY col5),
+      // we need to make sure that col1 to col5 are all projected from the child of the Window
+      // operator.
+      val extractedExprBuffer = new ArrayBuffer[NamedExpression]()
+      def extractExpr(expr: Expression): Expression = expr match {
+        case ne: NamedExpression =>
+          // If a named expression is not in regularExpressions, add extract it and replace it
+          // with an AttributeReference.
+          val missingExpr =
+            AttributeSet(Seq(expr)) -- (regularExpressions ++ extractedExprBuffer)
+          if (missingExpr.nonEmpty) {
+            extractedExprBuffer += ne
+          }
+          ne.toAttribute
+        case e: Expression if e.foldable =>
+          e // No need to create an attribute reference if it will be evaluated as a Literal.
+        case e: Expression =>
+          // For other expressions, we extract it and replace it with an AttributeReference (with
+          // an interal column name, e.g. "_w0").
+          val withName = Alias(e, s"_w${extractedExprBuffer.length}")()
+          extractedExprBuffer += withName
+          withName.toAttribute
+      }
+
+      // Now, we extract expressions from windowExpressions by using extractExpr.
+      val newWindowExpressions = windowExpressions.map {
+        _.transform {
+          // Extracts children expressions of a WindowFunction (input parameters of
+          // a WindowFunction).
+          case wf : WindowFunction =>
+            val newChildren = wf.children.map(extractExpr(_))
+            wf.withNewChildren(newChildren)
+
+          // Extracts expressions from the partition spec and order spec.
+          case wsc @ WindowSpecDefinition(partitionSpec, orderSpec, _) =>
+            val newPartitionSpec = partitionSpec.map(extractExpr(_))
+            val newOrderSpec = orderSpec.map { so =>
+              val newChild = extractExpr(so.child)
+              so.copy(child = newChild)
+            }
+            wsc.copy(partitionSpec = newPartitionSpec, orderSpec = newOrderSpec)
+
+          // Extracts AggregateExpression. For example, for SUM(x) - Sum(y) OVER (...),
+          // we need to extract SUM(x).
+          case agg: AggregateExpression =>
+            val withName = Alias(agg, s"_w${extractedExprBuffer.length}")()
+            extractedExprBuffer += withName
+            withName.toAttribute
+        }.asInstanceOf[NamedExpression]
+      }
+
+      (newWindowExpressions, regularExpressions ++ extractedExprBuffer)
+    }
+
+    /**
+     * Adds operators for Window Expressions. Every Window operator handles a single Window Spec.
+     */
+    def addWindow(windowExpressions: Seq[NamedExpression], child: LogicalPlan): LogicalPlan = {
+      // First, we group window expressions based on their Window Spec.
+      val groupedWindowExpression = windowExpressions.groupBy { expr =>
+        val windowExpression = expr.find {
+          case window: WindowExpression => true
+          case other => false
+        }.map(_.asInstanceOf[WindowExpression].windowSpec)
+        windowExpression.getOrElse(
+          failAnalysis(s"$windowExpressions does not have any WindowExpression."))
+      }.toSeq
+
+      // For every Window Spec, we add a Window operator and set currentChild as the child of it.
+      var currentChild = child
+      var i = 0
+      while (i < groupedWindowExpression.size) {
+        val (windowSpec, windowExpressions) = groupedWindowExpression(i)
+        // Set currentChild to the newly created Window operator.
+        currentChild = Window(currentChild.output, windowExpressions, windowSpec, currentChild)
+
+        // Move to next WindowExpression.
+        i += 1
+      }
+
+      // We return the top operator.
+      currentChild
+    }
+
+    // We have to use transformDown at here to make sure the rule of
+    // "Aggregate with Having clause" will be triggered.
+    def apply(plan: LogicalPlan): LogicalPlan = plan transformDown {
+      // Lookup WindowSpecDefinitions. This rule works with unresolved children.
+      case WithWindowDefinition(windowDefinitions, child) =>
+        child.transform {
+          case plan => plan.transformExpressions {
+            case UnresolvedWindowExpression(c, WindowSpecReference(windowName)) =>
+              val errorMessage =
+                s"Window specification $windowName is not defined in the WINDOW clause."
+              val windowSpecDefinition =
+                windowDefinitions
+                  .get(windowName)
+                  .getOrElse(failAnalysis(errorMessage))
+              WindowExpression(c, windowSpecDefinition)
+          }
+        }
+
+      // Aggregate with Having clause. This rule works with an unresolved Aggregate because
+      // a resolved Aggregate will not have Window Functions.
+      case f @ Filter(condition, a @ Aggregate(groupingExprs, aggregateExprs, child))
+        if child.resolved &&
+           hasWindowFunction(aggregateExprs) &&
+           !a.expressions.exists(!_.resolved) =>
+        val (windowExpressions, aggregateExpressions) = extract(aggregateExprs)
+        // Create an Aggregate operator to evaluate aggregation functions.
+        val withAggregate = Aggregate(groupingExprs, aggregateExpressions, child)
+        // Add a Filter operator for conditions in the Having clause.
+        val withFilter = Filter(condition, withAggregate)
+        val withWindow = addWindow(windowExpressions, withFilter)
+
+        // Finally, generate output columns according to the original projectList.
+        val finalProjectList = aggregateExprs.map (_.toAttribute)
+        Project(finalProjectList, withWindow)
+
+      case p: LogicalPlan if !p.childrenResolved => p
+
+      // Aggregate without Having clause.
+      case a @ Aggregate(groupingExprs, aggregateExprs, child)
+        if hasWindowFunction(aggregateExprs) &&
+           !a.expressions.exists(!_.resolved) =>
+        val (windowExpressions, aggregateExpressions) = extract(aggregateExprs)
+        // Create an Aggregate operator to evaluate aggregation functions.
+        val withAggregate = Aggregate(groupingExprs, aggregateExpressions, child)
+        // Add Window operators.
+        val withWindow = addWindow(windowExpressions, withAggregate)
+
+        // Finally, generate output columns according to the original projectList.
+        val finalProjectList = aggregateExprs.map (_.toAttribute)
+        Project(finalProjectList, withWindow)
+
+      // We only extract Window Expressions after all expressions of the Project
+      // have been resolved.
+      case p @ Project(projectList, child)
+        if hasWindowFunction(projectList) && !p.expressions.exists(!_.resolved) =>
+        val (windowExpressions, regularExpressions) = extract(projectList)
+        // We add a project to get all needed expressions for window expressions from the child
+        // of the original Project operator.
+        val withProject = Project(regularExpressions, child)
+        // Add Window operators.
+        val withWindow = addWindow(windowExpressions, withProject)
+
+        // Finally, generate output columns according to the original projectList.
+        val finalProjectList = projectList.map (_.toAttribute)
+        Project(finalProjectList, withWindow)
+    }
+  }
 }
 
 /**
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala
index 2381689e17525..c8288c6767004 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala
@@ -70,6 +70,11 @@ trait CheckAnalysis {
             failAnalysis(
               s"invalid expression ${b.prettyString} " +
                 s"between ${b.left.simpleString} and ${b.right.simpleString}")
+
+          case w @ WindowExpression(windowFunction, windowSpec) if windowSpec.validate.nonEmpty =>
+            // The window spec is not valid.
+            val reason = windowSpec.validate.get
+            failAnalysis(s"Window specification $windowSpec is not valid because $reason")
         }
 
         operator match {
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 c2866cd955409..8cae548279eb1 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
@@ -548,3 +548,97 @@ class JoinedRow5 extends Row {
     }
   }
 }
+
+/**
+ * JIT HACK: Replace with macros
+ */
+class JoinedRow6 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
+  }
+
+  override def toSeq: Seq[Any] = row1.toSeq ++ row2.toSeq
+
+  override def length: Int = row1.length + row2.length
+
+  override def apply(i: Int): Any =
+    if (i < row1.length) row1(i) else row2(i - row1.length)
+
+  override def isNullAt(i: Int): Boolean =
+    if (i < row1.length) row1.isNullAt(i) else row2.isNullAt(i - row1.length)
+
+  override def getInt(i: Int): Int =
+    if (i < row1.length) row1.getInt(i) else row2.getInt(i - row1.length)
+
+  override def getLong(i: Int): Long =
+    if (i < row1.length) row1.getLong(i) else row2.getLong(i - row1.length)
+
+  override def getDouble(i: Int): Double =
+    if (i < row1.length) row1.getDouble(i) else row2.getDouble(i - row1.length)
+
+  override def getBoolean(i: Int): Boolean =
+    if (i < row1.length) row1.getBoolean(i) else row2.getBoolean(i - row1.length)
+
+  override def getShort(i: Int): Short =
+    if (i < row1.length) row1.getShort(i) else row2.getShort(i - row1.length)
+
+  override def getByte(i: Int): Byte =
+    if (i < row1.length) row1.getByte(i) else row2.getByte(i - row1.length)
+
+  override def getFloat(i: Int): Float =
+    if (i < row1.length) row1.getFloat(i) else row2.getFloat(i - row1.length)
+
+  override def getString(i: Int): String =
+    if (i < row1.length) row1.getString(i) else row2.getString(i - row1.length)
+
+  override def getAs[T](i: Int): T =
+    if (i < row1.length) row1.getAs[T](i) else row2.getAs[T](i - row1.length)
+
+  override def copy(): Row = {
+    val totalSize = row1.length + row2.length
+    val copiedValues = new Array[Any](totalSize)
+    var i = 0
+    while(i < totalSize) {
+      copiedValues(i) = apply(i)
+      i += 1
+    }
+    new GenericRow(copiedValues)
+  }
+
+  override def toString: String = {
+    // Make sure toString never throws NullPointerException.
+    if ((row1 eq null) && (row2 eq null)) {
+      "[ empty row ]"
+    } else if (row1 eq null) {
+      row2.mkString("[", ",", "]")
+    } else if (row2 eq null) {
+      row1.mkString("[", ",", "]")
+    } else {
+      mkString("[", ",", "]")
+    }
+  }
+}
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/windowExpressions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/windowExpressions.scala
new file mode 100644
index 0000000000000..099d67ca7fee3
--- /dev/null
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/windowExpressions.scala
@@ -0,0 +1,340 @@
+/*
+ * 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.analysis.UnresolvedException
+import org.apache.spark.sql.catalyst.errors.TreeNodeException
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.types.{NumericType, DataType}
+
+/**
+ * The trait of the Window Specification (specified in the OVER clause or WINDOW clause) for
+ * Window Functions.
+ */
+sealed trait WindowSpec
+
+/**
+ * The specification for a window function.
+ * @param partitionSpec It defines the way that input rows are partitioned.
+ * @param orderSpec It defines the ordering of rows in a partition.
+ * @param frameSpecification It defines the window frame in a partition.
+ */
+case class WindowSpecDefinition(
+    partitionSpec: Seq[Expression],
+    orderSpec: Seq[SortOrder],
+    frameSpecification: WindowFrame) extends Expression with WindowSpec {
+
+  def validate: Option[String] = frameSpecification match {
+    case UnspecifiedFrame =>
+      Some("Found a UnspecifiedFrame. It should be converted to a SpecifiedWindowFrame " +
+        "during analysis. Please file a bug report.")
+    case frame: SpecifiedWindowFrame => frame.validate.orElse {
+      def checkValueBasedBoundaryForRangeFrame(): Option[String] = {
+        if (orderSpec.length > 1)  {
+          // It is not allowed to have a value-based PRECEDING and FOLLOWING
+          // as the boundary of a Range Window Frame.
+          Some("This Range Window Frame only accepts at most one ORDER BY expression.")
+        } else if (orderSpec.nonEmpty && !orderSpec.head.dataType.isInstanceOf[NumericType]) {
+          Some("The data type of the expression in the ORDER BY clause should be a numeric type.")
+        } else {
+          None
+        }
+      }
+
+      (frame.frameType, frame.frameStart, frame.frameEnd) match {
+        case (RangeFrame, vp: ValuePreceding, _) => checkValueBasedBoundaryForRangeFrame()
+        case (RangeFrame, vf: ValueFollowing, _) => checkValueBasedBoundaryForRangeFrame()
+        case (RangeFrame, _, vp: ValuePreceding) => checkValueBasedBoundaryForRangeFrame()
+        case (RangeFrame, _, vf: ValueFollowing) => checkValueBasedBoundaryForRangeFrame()
+        case (_, _, _) => None
+      }
+    }
+  }
+
+  type EvaluatedType = Any
+
+  override def children: Seq[Expression]  = partitionSpec ++ orderSpec
+
+  override lazy val resolved: Boolean =
+    childrenResolved && frameSpecification.isInstanceOf[SpecifiedWindowFrame]
+
+
+  override def toString: String = simpleString
+
+  override def eval(input: Row): EvaluatedType = throw new UnsupportedOperationException
+  override def nullable: Boolean = true
+  override def foldable: Boolean = false
+  override def dataType: DataType = throw new UnsupportedOperationException
+}
+
+/**
+ * A Window specification reference that refers to the [[WindowSpecDefinition]] defined
+ * under the name `name`.
+ */
+case class WindowSpecReference(name: String) extends WindowSpec
+
+/**
+ * The trait used to represent the type of a Window Frame.
+ */
+sealed trait FrameType
+
+/**
+ * RowFrame treats rows in a partition individually. When a [[ValuePreceding]]
+ * or a [[ValueFollowing]] is used as its [[FrameBoundary]], the value is considered
+ * as a physical offset.
+ * For example, `ROW BETWEEN 1 PRECEDING AND 1 FOLLOWING` represents a 3-row frame,
+ * from the row precedes the current row to the row follows the current row.
+ */
+case object RowFrame extends FrameType
+
+/**
+ * RangeFrame treats rows in a partition as groups of peers.
+ * All rows having the same `ORDER BY` ordering are considered as peers.
+ * When a [[ValuePreceding]] or a [[ValueFollowing]] is used as its [[FrameBoundary]],
+ * the value is considered as a logical offset.
+ * For example, assuming the value of the current row's `ORDER BY` expression `expr` is `v`,
+ * `RANGE BETWEEN 1 PRECEDING AND 1 FOLLOWING` represents a frame containing rows whose values
+ * `expr` are in the range of [v-1, v+1].
+ *
+ * If `ORDER BY` clause is not defined, all rows in the partition is considered as peers
+ * of the current row.
+ */
+case object RangeFrame extends FrameType
+
+/**
+ * The trait used to represent the type of a Window Frame Boundary.
+ */
+sealed trait FrameBoundary {
+  def notFollows(other: FrameBoundary): Boolean
+}
+
+/** UNBOUNDED PRECEDING boundary. */
+case object UnboundedPreceding extends FrameBoundary {
+  def notFollows(other: FrameBoundary): Boolean = other match {
+    case UnboundedPreceding => true
+    case vp: ValuePreceding => true
+    case CurrentRow => true
+    case vf: ValueFollowing => true
+    case UnboundedFollowing => true
+  }
+
+  override def toString: String = "UNBOUNDED PRECEDING"
+}
+
+/** <value> PRECEDING boundary. */
+case class ValuePreceding(value: Int) extends FrameBoundary {
+  def notFollows(other: FrameBoundary): Boolean = other match {
+    case UnboundedPreceding => false
+    case ValuePreceding(anotherValue) => value >= anotherValue
+    case CurrentRow => true
+    case vf: ValueFollowing => true
+    case UnboundedFollowing => true
+  }
+
+  override def toString: String = s"$value PRECEDING"
+}
+
+/** CURRENT ROW boundary. */
+case object CurrentRow extends FrameBoundary {
+  def notFollows(other: FrameBoundary): Boolean = other match {
+    case UnboundedPreceding => false
+    case vp: ValuePreceding => false
+    case CurrentRow => true
+    case vf: ValueFollowing => true
+    case UnboundedFollowing => true
+  }
+
+  override def toString: String = "CURRENT ROW"
+}
+
+/** <value> FOLLOWING boundary. */
+case class ValueFollowing(value: Int) extends FrameBoundary {
+  def notFollows(other: FrameBoundary): Boolean = other match {
+    case UnboundedPreceding => false
+    case vp: ValuePreceding => false
+    case CurrentRow => false
+    case ValueFollowing(anotherValue) => value <= anotherValue
+    case UnboundedFollowing => true
+  }
+
+  override def toString: String = s"$value FOLLOWING"
+}
+
+/** UNBOUNDED FOLLOWING boundary. */
+case object UnboundedFollowing extends FrameBoundary {
+  def notFollows(other: FrameBoundary): Boolean = other match {
+    case UnboundedPreceding => false
+    case vp: ValuePreceding => false
+    case CurrentRow => false
+    case vf: ValueFollowing => false
+    case UnboundedFollowing => true
+  }
+
+  override def toString: String = "UNBOUNDED FOLLOWING"
+}
+
+/**
+ * The trait used to represent the a Window Frame.
+ */
+sealed trait WindowFrame
+
+/** Used as a place holder when a frame specification is not defined.  */
+case object UnspecifiedFrame extends WindowFrame
+
+/** A specified Window Frame. */
+case class SpecifiedWindowFrame(
+    frameType: FrameType,
+    frameStart: FrameBoundary,
+    frameEnd: FrameBoundary) extends WindowFrame {
+
+  /** If this WindowFrame is valid or not. */
+  def validate: Option[String] = (frameType, frameStart, frameEnd) match {
+    case (_, UnboundedFollowing, _) =>
+      Some(s"$UnboundedFollowing is not allowed as the start of a Window Frame.")
+    case (_, _, UnboundedPreceding) =>
+      Some(s"$UnboundedPreceding is not allowed as the end of a Window Frame.")
+    // case (RowFrame, start, end) => ??? RowFrame specific rule
+    // case (RangeFrame, start, end) => ??? RangeFrame specific rule
+    case (_, start, end) =>
+      if (start.notFollows(end)) {
+        None
+      } else {
+        val reason =
+          s"The end of this Window Frame $end is smaller than the start of " +
+          s"this Window Frame $start."
+        Some(reason)
+      }
+  }
+
+  override def toString: String = frameType match {
+    case RowFrame => s"ROWS BETWEEN $frameStart AND $frameEnd"
+    case RangeFrame => s"RANGE BETWEEN $frameStart AND $frameEnd"
+  }
+}
+
+object SpecifiedWindowFrame {
+  /**
+   *
+   * @param hasOrderSpecification If the window spec has order by expressions.
+   * @param acceptWindowFrame If the window function accepts user-specified frame.
+   * @return
+   */
+  def defaultWindowFrame(
+      hasOrderSpecification: Boolean,
+      acceptWindowFrame: Boolean): SpecifiedWindowFrame = {
+    if (hasOrderSpecification && acceptWindowFrame) {
+      // If order spec is defined and the window function supports user specified window frames,
+      // the default frame is RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW.
+      SpecifiedWindowFrame(RangeFrame, UnboundedPreceding, CurrentRow)
+    } else {
+      // Otherwise, the default frame is
+      // ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING.
+      SpecifiedWindowFrame(RowFrame, UnboundedPreceding, UnboundedFollowing)
+    }
+  }
+}
+
+/**
+ * Every window function needs to maintain a output buffer for its output.
+ * It should expect that for a n-row window frame, it will be called n times
+ * to retrieve value corresponding with these n rows.
+ */
+trait WindowFunction extends Expression {
+  self: Product =>
+
+  def init(): Unit
+
+  def reset(): Unit
+
+  def prepareInputParameters(input: Row): AnyRef
+
+  def update(input: AnyRef): Unit
+
+  def batchUpdate(inputs: Array[AnyRef]): Unit
+
+  def evaluate(): Unit
+
+  def get(index: Int): Any
+
+  def newInstance(): WindowFunction
+}
+
+case class UnresolvedWindowFunction(
+    name: String,
+    children: Seq[Expression])
+  extends Expression with WindowFunction {
+
+  override def dataType: DataType = throw new UnresolvedException(this, "dataType")
+  override def foldable: Boolean = throw new UnresolvedException(this, "foldable")
+  override def nullable: Boolean = throw new UnresolvedException(this, "nullable")
+  override lazy val resolved = false
+
+  override def init(): Unit =
+    throw new UnresolvedException(this, "init")
+  override def reset(): Unit =
+    throw new UnresolvedException(this, "reset")
+  override def prepareInputParameters(input: Row): AnyRef =
+    throw new UnresolvedException(this, "prepareInputParameters")
+  override def update(input: AnyRef): Unit =
+    throw new UnresolvedException(this, "update")
+  override def batchUpdate(inputs: Array[AnyRef]): Unit =
+    throw new UnresolvedException(this, "batchUpdate")
+  override def evaluate(): Unit =
+    throw new UnresolvedException(this, "evaluate")
+  override def get(index: Int): Any =
+    throw new UnresolvedException(this, "get")
+  // Unresolved functions are transient at compile time and don't get evaluated during execution.
+  override def eval(input: Row = null): EvaluatedType =
+    throw new TreeNodeException(this, s"No function to evaluate expression. type: ${this.nodeName}")
+
+  override def toString: String = s"'$name(${children.mkString(",")})"
+
+  override def newInstance(): WindowFunction =
+    throw new UnresolvedException(this, "newInstance")
+}
+
+case class UnresolvedWindowExpression(
+    child: UnresolvedWindowFunction,
+    windowSpec: WindowSpecReference) extends UnaryExpression {
+  override def dataType: DataType = throw new UnresolvedException(this, "dataType")
+  override def foldable: Boolean = throw new UnresolvedException(this, "foldable")
+  override def nullable: Boolean = throw new UnresolvedException(this, "nullable")
+  override lazy val resolved = false
+
+  // Unresolved functions are transient at compile time and don't get evaluated during execution.
+  override def eval(input: Row = null): EvaluatedType =
+    throw new TreeNodeException(this, s"No function to evaluate expression. type: ${this.nodeName}")
+}
+
+case class WindowExpression(
+    windowFunction: WindowFunction,
+    windowSpec: WindowSpecDefinition) extends Expression {
+  override type EvaluatedType = Any
+
+  override def children: Seq[Expression] =
+    windowFunction :: windowSpec :: Nil
+
+  override def eval(input: Row): EvaluatedType =
+    throw new TreeNodeException(this, s"No function to evaluate expression. type: ${this.nodeName}")
+
+  override def dataType: DataType = windowFunction.dataType
+  override def foldable: Boolean = windowFunction.foldable
+  override def nullable: Boolean = windowFunction.nullable
+
+  override def toString: String = s"$windowFunction $windowSpec"
+}
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 21208c8a5c281..ba0abb2df596c 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
@@ -25,13 +25,14 @@ case class Project(projectList: Seq[NamedExpression], child: LogicalPlan) extend
   override def output: Seq[Attribute] = projectList.map(_.toAttribute)
 
   override lazy val resolved: Boolean = {
-    val containsAggregatesOrGenerators = projectList.exists ( _.collect {
+    val hasSpecialExpressions = projectList.exists ( _.collect {
         case agg: AggregateExpression => agg
         case generator: Generator => generator
+        case window: WindowExpression => window
       }.nonEmpty
     )
 
-    !expressions.exists(!_.resolved) && childrenResolved && !containsAggregatesOrGenerators
+    !expressions.exists(!_.resolved) && childrenResolved && !hasSpecialExpressions
   }
 }
 
@@ -170,6 +171,12 @@ case class With(child: LogicalPlan, cteRelations: Map[String, Subquery]) extends
   override def output: Seq[Attribute] = child.output
 }
 
+case class WithWindowDefinition(
+    windowDefinitions: Map[String, WindowSpecDefinition],
+    child: LogicalPlan) extends UnaryNode {
+  override def output: Seq[Attribute] = child.output
+}
+
 case class WriteToFile(
     path: String,
     child: LogicalPlan) extends UnaryNode {
@@ -195,9 +202,28 @@ case class Aggregate(
     child: LogicalPlan)
   extends UnaryNode {
 
+  override lazy val resolved: Boolean = {
+    val hasWindowExpressions = aggregateExpressions.exists ( _.collect {
+        case window: WindowExpression => window
+      }.nonEmpty
+    )
+
+    !expressions.exists(!_.resolved) && childrenResolved && !hasWindowExpressions
+  }
+
   override def output: Seq[Attribute] = aggregateExpressions.map(_.toAttribute)
 }
 
+case class Window(
+    projectList: Seq[Attribute],
+    windowExpressions: Seq[NamedExpression],
+    windowSpec: WindowSpecDefinition,
+    child: LogicalPlan) extends UnaryNode {
+
+  override def output: Seq[Attribute] =
+    (projectList ++ windowExpressions).map(_.toAttribute)
+}
+
 /**
  * Apply the all of the GroupExpressions to every input row, hence we will get
  * multiple output rows for a input row.
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 97502ed3afe72..4b93f7d31b808 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
@@ -71,6 +71,15 @@ abstract class TreeNode[BaseType <: TreeNode[BaseType]] {
     this.eq(other) || this == other
   }
 
+  /**
+   * Find the first [[TreeNode]] that satisfies the condition specified by `f`.
+   * The condition is recursively applied to this node and all of its children (pre-order).
+   */
+  def find(f: BaseType => Boolean): Option[BaseType] = f(this) match {
+    case true => Some(this)
+    case false => children.foldLeft(None: Option[BaseType]) { (l, r) => l.orElse(r.find(f)) }
+  }
+
   /**
    * Runs the given function on this node and then recursively on [[children]].
    * @param f the function to be applied to each node in the tree.
@@ -151,6 +160,20 @@ abstract class TreeNode[BaseType <: TreeNode[BaseType]] {
     val remainingNewChildren = newChildren.toBuffer
     val remainingOldChildren = children.toBuffer
     val newArgs = productIterator.map {
+      // This rule is used to handle children is a input argument.
+      case s: Seq[_] => s.map {
+        case arg: TreeNode[_] if children contains arg =>
+          val newChild = remainingNewChildren.remove(0)
+          val oldChild = remainingOldChildren.remove(0)
+          if (newChild fastEquals oldChild) {
+            oldChild
+          } else {
+            changed = true
+            newChild
+          }
+        case nonChild: AnyRef => nonChild
+        case null => null
+      }
       case arg: TreeNode[_] if children contains arg =>
         val newChild = remainingNewChildren.remove(0)
         val oldChild = remainingOldChildren.remove(0)
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 6b393327cc97a..786ddba403f2c 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
@@ -22,7 +22,7 @@ import scala.collection.mutable.ArrayBuffer
 import org.scalatest.FunSuite
 
 import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.types.{StringType, NullType}
+import org.apache.spark.sql.types.{IntegerType, StringType, NullType}
 
 case class Dummy(optKey: Option[Expression]) extends Expression {
   def children: Seq[Expression] = optKey.toSeq
@@ -129,5 +129,47 @@ class TreeNodeSuite extends FunSuite {
     assert(expected === actual)
   }
 
+  test("find") {
+    val expression = Add(Literal(1), Multiply(Literal(2), Subtract(Literal(3), Literal(4))))
+    // Find the top node.
+    var actual: Option[Expression] = expression.find {
+      case add: Add => true
+      case other => false
+    }
+    var expected: Option[Expression] =
+      Some(Add(Literal(1), Multiply(Literal(2), Subtract(Literal(3), Literal(4)))))
+    assert(expected === actual)
+
+    // Find the first children.
+    actual = expression.find {
+      case Literal(1, IntegerType) => true
+      case other => false
+    }
+    expected = Some(Literal(1))
+    assert(expected === actual)
 
+    // Find an internal node (Subtract).
+    actual = expression.find {
+      case sub: Subtract => true
+      case other => false
+    }
+    expected = Some(Subtract(Literal(3), Literal(4)))
+    assert(expected === actual)
+
+    // Find a leaf node.
+    actual = expression.find {
+      case Literal(3, IntegerType) => true
+      case other => false
+    }
+    expected = Some(Literal(3))
+    assert(expected === actual)
+
+    // Find nothing.
+    actual = expression.find {
+      case Literal(100, IntegerType) => true
+      case other => false
+    }
+    expected = None
+    assert(expected === actual)
+  }
 }
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 326e8ce4ca524..56a4689eb58f0 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
@@ -303,6 +303,8 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
         execution.Expand(projections, output, planLater(child)) :: Nil
       case logical.Aggregate(group, agg, child) =>
         execution.Aggregate(partial = false, group, agg, planLater(child)) :: Nil
+      case logical.Window(projectList, windowExpressions, spec, child) =>
+        execution.Window(projectList, windowExpressions, spec, planLater(child)) :: Nil
       case logical.Sample(lb, ub, withReplacement, seed, child) =>
         execution.Sample(lb, ub, withReplacement, seed, planLater(child)) :: Nil
       case logical.LocalRelation(output, data) =>
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/Window.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/Window.scala
new file mode 100644
index 0000000000000..217b559def512
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/Window.scala
@@ -0,0 +1,480 @@
+/*
+ * 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.execution
+
+import java.util
+
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.Row
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.plans.physical.{AllTuples, Distribution, ClusteredDistribution, Partitioning}
+import org.apache.spark.util.collection.CompactBuffer
+
+/**
+ * :: DeveloperApi ::
+ * For every row, evaluates `windowExpression` containing Window Functions and attaches
+ * the results with other regular expressions (presented by `projectList`).
+ * Evert operator handles a single Window Specification, `windowSpec`.
+ */
+case class Window(
+    projectList: Seq[Attribute],
+    windowExpression: Seq[NamedExpression],
+    windowSpec: WindowSpecDefinition,
+    child: SparkPlan)
+  extends UnaryNode {
+
+  override def output: Seq[Attribute] =
+    (projectList ++ windowExpression).map(_.toAttribute)
+
+  override def requiredChildDistribution: Seq[Distribution] =
+    if (windowSpec.partitionSpec.isEmpty) {
+      // This operator will be very expensive.
+      AllTuples :: Nil
+    } else {
+      ClusteredDistribution(windowSpec.partitionSpec) :: Nil
+    }
+
+  // Since window functions are adding columns to the input rows, the child's outputPartitioning
+  // is preserved.
+  override def outputPartitioning: Partitioning = child.outputPartitioning
+
+  override def requiredChildOrdering: Seq[Seq[SortOrder]] = {
+    // The required child ordering has two parts.
+    // The first part is the expressions in the partition specification.
+    // We add these expressions to the required ordering to make sure input rows are grouped
+    // based on the partition specification. So, we only need to process a single partition
+    // at a time.
+    // The second part is the expressions specified in the ORDER BY cluase.
+    // Basically, we first use sort to group rows based on partition specifications and then sort
+    // Rows in a group based on the order specification.
+    (windowSpec.partitionSpec.map(SortOrder(_, Ascending)) ++ windowSpec.orderSpec) :: Nil
+  }
+
+  // Since window functions basically add columns to input rows, this operator
+  // will not change the ordering of input rows.
+  override def outputOrdering: Seq[SortOrder] = child.outputOrdering
+
+  case class ComputedWindow(
+    unbound: WindowExpression,
+    windowFunction: WindowFunction,
+    resultAttribute: AttributeReference)
+
+  // A list of window functions that need to be computed for each group.
+  private[this] val computedWindowExpressions = windowExpression.flatMap { window =>
+    window.collect {
+      case w: WindowExpression =>
+        ComputedWindow(
+          w,
+          BindReferences.bindReference(w.windowFunction, child.output),
+          AttributeReference(s"windowResult:$w", w.dataType, w.nullable)())
+    }
+  }.toArray
+
+  private[this] val windowFrame =
+    windowSpec.frameSpecification.asInstanceOf[SpecifiedWindowFrame]
+
+  // Create window functions.
+  private[this] def windowFunctions(): Array[WindowFunction] = {
+    val functions = new Array[WindowFunction](computedWindowExpressions.length)
+    var i = 0
+    while (i < computedWindowExpressions.length) {
+      functions(i) = computedWindowExpressions(i).windowFunction.newInstance()
+      functions(i).init()
+      i += 1
+    }
+    functions
+  }
+
+  // The schema of the result of all window function evaluations
+  private[this] val computedSchema = computedWindowExpressions.map(_.resultAttribute)
+
+  private[this] val computedResultMap =
+    computedWindowExpressions.map { w => w.unbound -> w.resultAttribute }.toMap
+
+  private[this] val windowExpressionResult = windowExpression.map { window =>
+    window.transform {
+      case w: WindowExpression if computedResultMap.contains(w) => computedResultMap(w)
+    }
+  }
+
+  def execute(): RDD[Row] = {
+    child.execute().mapPartitions { iter =>
+      new Iterator[Row] {
+
+        // Although input rows are grouped based on windowSpec.partitionSpec, we need to
+        // know when we have a new partition.
+        // This is to manually construct an ordering that can be used to compare rows.
+        // TODO: We may want to have a newOrdering that takes BoundReferences.
+        // So, we can take advantave of code gen.
+        private val partitionOrdering: Ordering[Row] =
+          RowOrdering.forSchema(windowSpec.partitionSpec.map(_.dataType))
+
+        // This is used to project expressions for the partition specification.
+        protected val partitionGenerator =
+          newMutableProjection(windowSpec.partitionSpec, child.output)()
+
+        // This is ued to project expressions for the order specification.
+        protected val rowOrderGenerator =
+          newMutableProjection(windowSpec.orderSpec.map(_.child), child.output)()
+
+        // The position of next output row in the inputRowBuffer.
+        var rowPosition: Int = 0
+        // The number of buffered rows in the inputRowBuffer (the size of the current partition).
+        var partitionSize: Int = 0
+        // The buffer used to buffer rows in a partition.
+        var inputRowBuffer: CompactBuffer[Row] = _
+        // The partition key of the current partition.
+        var currentPartitionKey: Row = _
+        // The partition key of next partition.
+        var nextPartitionKey: Row = _
+        // The first row of next partition.
+        var firstRowInNextPartition: Row = _
+        // Indicates if this partition is the last one in the iter.
+        var lastPartition: Boolean = false
+
+        def createBoundaryEvaluator(): () => Unit = {
+          def findPhysicalBoundary(
+              boundary: FrameBoundary): () => Int = boundary match {
+            case UnboundedPreceding => () => 0
+            case UnboundedFollowing => () => partitionSize - 1
+            case CurrentRow => () => rowPosition
+            case ValuePreceding(value) =>
+              () =>
+                val newPosition = rowPosition - value
+                if (newPosition > 0) newPosition else 0
+            case ValueFollowing(value) =>
+              () =>
+                val newPosition = rowPosition + value
+                if (newPosition < partitionSize) newPosition else partitionSize - 1
+          }
+
+          def findLogicalBoundary(
+              boundary: FrameBoundary,
+              searchDirection: Int,
+              evaluator: Expression,
+              joinedRow: JoinedRow): () => Int = boundary match {
+            case UnboundedPreceding => () => 0
+            case UnboundedFollowing => () => partitionSize - 1
+            case other =>
+              () => {
+                // CurrentRow, ValuePreceding, or ValueFollowing.
+                var newPosition = rowPosition + searchDirection
+                var stopSearch = false
+                // rowOrderGenerator is a mutable projection.
+                // We need to make a copy of the returned by rowOrderGenerator since we will
+                // compare searched row with this currentOrderByValue.
+                val currentOrderByValue = rowOrderGenerator(inputRowBuffer(rowPosition)).copy()
+                while (newPosition >= 0 && newPosition < partitionSize && !stopSearch) {
+                  val r = rowOrderGenerator(inputRowBuffer(newPosition))
+                  stopSearch =
+                    !(evaluator.eval(joinedRow(currentOrderByValue, r)).asInstanceOf[Boolean])
+                  if (!stopSearch) {
+                    newPosition += searchDirection
+                  }
+                }
+                newPosition -= searchDirection
+
+                if (newPosition < 0) {
+                  0
+                } else if (newPosition >= partitionSize) {
+                  partitionSize - 1
+                } else {
+                  newPosition
+                }
+              }
+          }
+
+          windowFrame.frameType match {
+            case RowFrame =>
+              val findStart = findPhysicalBoundary(windowFrame.frameStart)
+              val findEnd = findPhysicalBoundary(windowFrame.frameEnd)
+              () => {
+                frameStart = findStart()
+                frameEnd = findEnd()
+              }
+            case RangeFrame =>
+              val joinedRowForBoundaryEvaluation: JoinedRow = new JoinedRow()
+              val orderByExpr = windowSpec.orderSpec.head
+              val currentRowExpr =
+                BoundReference(0, orderByExpr.dataType, orderByExpr.nullable)
+              val examedRowExpr =
+                BoundReference(1, orderByExpr.dataType, orderByExpr.nullable)
+              val differenceExpr = Abs(Subtract(currentRowExpr, examedRowExpr))
+
+              val frameStartEvaluator = windowFrame.frameStart match {
+                case CurrentRow => EqualTo(currentRowExpr, examedRowExpr)
+                case ValuePreceding(value) =>
+                  LessThanOrEqual(differenceExpr, Cast(Literal(value), orderByExpr.dataType))
+                case ValueFollowing(value) =>
+                  GreaterThanOrEqual(differenceExpr, Cast(Literal(value), orderByExpr.dataType))
+                case o => Literal(true) // This is just a dummy expression, we will not use it.
+              }
+
+              val frameEndEvaluator = windowFrame.frameEnd match {
+                case CurrentRow => EqualTo(currentRowExpr, examedRowExpr)
+                case ValuePreceding(value) =>
+                  GreaterThanOrEqual(differenceExpr, Cast(Literal(value), orderByExpr.dataType))
+                case ValueFollowing(value) =>
+                  LessThanOrEqual(differenceExpr, Cast(Literal(value), orderByExpr.dataType))
+                case o => Literal(true) // This is just a dummy expression, we will not use it.
+              }
+
+              val findStart =
+                findLogicalBoundary(
+                  boundary = windowFrame.frameStart,
+                  searchDirection = -1,
+                  evaluator = frameStartEvaluator,
+                  joinedRow = joinedRowForBoundaryEvaluation)
+              val findEnd =
+                findLogicalBoundary(
+                  boundary = windowFrame.frameEnd,
+                  searchDirection = 1,
+                  evaluator = frameEndEvaluator,
+                  joinedRow = joinedRowForBoundaryEvaluation)
+              () => {
+                frameStart = findStart()
+                frameEnd = findEnd()
+              }
+          }
+        }
+
+        val boundaryEvaluator = createBoundaryEvaluator()
+        // Indicates if we the specified window frame requires us to maintain a sliding frame
+        // (e.g. RANGES BETWEEN 1 PRECEDING AND CURRENT ROW) or the window frame
+        // is the entire partition (e.g. ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING).
+        val requireUpdateFrame: Boolean = {
+          def requireUpdateBoundary(boundary: FrameBoundary): Boolean = boundary match {
+            case UnboundedPreceding => false
+            case UnboundedFollowing => false
+            case _ => true
+          }
+
+          requireUpdateBoundary(windowFrame.frameStart) ||
+            requireUpdateBoundary(windowFrame.frameEnd)
+        }
+        // The start position of the current frame in the partition.
+        var frameStart: Int = 0
+        // The end position of the current frame in the partition.
+        var frameEnd: Int = -1
+        // Window functions.
+        val functions: Array[WindowFunction] = windowFunctions()
+        // Buffers used to store input parameters for window functions. Because we may need to
+        // maintain a sliding frame, we use this buffer to avoid evaluate the parameters from
+        // the same row multiple times.
+        val windowFunctionParameterBuffers: Array[util.LinkedList[AnyRef]] =
+          functions.map(_ => new util.LinkedList[AnyRef]())
+
+        // The projection used to generate the final result rows of this operator.
+        private[this] val resultProjection =
+          newMutableProjection(
+            projectList ++ windowExpressionResult,
+            projectList ++ computedSchema)()
+
+        // The row used to hold results of window functions.
+        private[this] val windowExpressionResultRow =
+          new GenericMutableRow(computedSchema.length)
+
+        private[this] val joinedRow = new JoinedRow6
+
+        // Initialize this iterator.
+        initialize()
+
+        private def initialize(): Unit = {
+          if (iter.hasNext) {
+            val currentRow = iter.next().copy()
+            // partitionGenerator is a mutable projection. Since we need to track nextPartitionKey,
+            // we are making a copy of the returned partitionKey at here.
+            nextPartitionKey = partitionGenerator(currentRow).copy()
+            firstRowInNextPartition = currentRow
+            fetchNextPartition()
+          } else {
+            // The iter is an empty one. So, we set all of the following variables
+            // to make sure hasNext will return false.
+            lastPartition = true
+            rowPosition = 0
+            partitionSize = 0
+          }
+        }
+
+        // Indicates if we will have new output row.
+        override final def hasNext: Boolean = {
+          !lastPartition || (rowPosition < partitionSize)
+        }
+
+        override final def next(): Row = {
+          if (hasNext) {
+            if (rowPosition == partitionSize) {
+              // All rows of this buffer have been consumed.
+              // We will move to next partition.
+              fetchNextPartition()
+            }
+            // Get the input row for the current output row.
+            val inputRow = inputRowBuffer(rowPosition)
+            // Get all results of the window functions for this output row.
+            var i = 0
+            while (i < functions.length) {
+              windowExpressionResultRow.update(i, functions(i).get(rowPosition))
+              i += 1
+            }
+
+            // Construct the output row.
+            val outputRow = resultProjection(joinedRow(inputRow, windowExpressionResultRow))
+            // We will move to the next one.
+            rowPosition += 1
+            if (requireUpdateFrame && rowPosition < partitionSize) {
+              // If we need to maintain a sliding frame and
+              // we will still work on this partition when next is called next time, do the update.
+              updateFrame()
+            }
+
+            // Return the output row.
+            outputRow
+          } else {
+            // no more result
+            throw new NoSuchElementException
+          }
+        }
+
+        // Fetch the next partition.
+        private def fetchNextPartition(): Unit = {
+          // Create a new buffer for input rows.
+          inputRowBuffer = new CompactBuffer[Row]()
+          // We already have the first row for this partition
+          // (recorded in firstRowInNextPartition). Add it back.
+          inputRowBuffer += firstRowInNextPartition
+          // Set the current partition key.
+          currentPartitionKey = nextPartitionKey
+          // Now, we will start to find all rows belonging to this partition.
+          // Create a variable to track if we see the next partition.
+          var findNextPartition = false
+          // The search will stop when we see the next partition or there is no
+          // input row left in the iter.
+          while (iter.hasNext && !findNextPartition) {
+            // Make a copy of the input row since we will put it in the buffer.
+            val currentRow = iter.next().copy()
+            // Get the partition key based on the partition specification.
+            // For the below compare method, we do not need to make a copy of partitionKey.
+            val partitionKey = partitionGenerator(currentRow)
+            // Check if the current row belongs the current input row.
+            val comparing = partitionOrdering.compare(currentPartitionKey, partitionKey)
+            if (comparing == 0) {
+              // This row is still in the current partition.
+              inputRowBuffer += currentRow
+            } else {
+              // The current input row is in a different partition.
+              findNextPartition = true
+              // partitionGenerator is a mutable projection.
+              // Since we need to track nextPartitionKey and we determine that it should be set
+              // as partitionKey, we are making a copy of the partitionKey at here.
+              nextPartitionKey = partitionKey.copy()
+              firstRowInNextPartition = currentRow
+            }
+          }
+
+          // We have not seen a new partition. It means that there is no new row in the
+          // iter. The current partition is the last partition of the iter.
+          if (!findNextPartition) {
+            lastPartition = true
+          }
+
+          // We have got all rows for the current partition.
+          // Set rowPosition to 0 (the next output row will be based on the first
+          // input row of this partition).
+          rowPosition = 0
+          // The size of this partition.
+          partitionSize = inputRowBuffer.size
+          // Reset all parameter buffers of window functions.
+          var i = 0
+          while (i < windowFunctionParameterBuffers.length) {
+            windowFunctionParameterBuffers(i).clear()
+            i += 1
+          }
+          frameStart = 0
+          frameEnd = -1
+          // Create the first window frame for this partition.
+          // If we do not need to maintain a sliding frame, this frame will
+          // have the entire partition.
+          updateFrame()
+        }
+
+        /** The function used to maintain the sliding frame. */
+        private def updateFrame(): Unit = {
+          // Based on the difference between the new frame and old frame,
+          // updates the buffers holding input parameters of window functions.
+          // We will start to prepare input parameters starting from the row
+          // indicated by offset in the input row buffer.
+          def updateWindowFunctionParameterBuffers(
+              numToRemove: Int,
+              numToAdd: Int,
+              offset: Int): Unit = {
+            // First, remove unneeded entries from the head of every buffer.
+            var i = 0
+            while (i < numToRemove) {
+              var j = 0
+              while (j < windowFunctionParameterBuffers.length) {
+                windowFunctionParameterBuffers(j).remove()
+                j += 1
+              }
+              i += 1
+            }
+            // Then, add needed entries to the tail of every buffer.
+            i = 0
+            while (i < numToAdd) {
+              var j = 0
+              while (j < windowFunctionParameterBuffers.length) {
+                // Ask the function to prepare the input parameters.
+                val parameters = functions(j).prepareInputParameters(inputRowBuffer(i + offset))
+                windowFunctionParameterBuffers(j).add(parameters)
+                j += 1
+              }
+              i += 1
+            }
+          }
+
+          // Record the current frame start point and end point before
+          // we update them.
+          val previousFrameStart = frameStart
+          val previousFrameEnd = frameEnd
+          boundaryEvaluator()
+          updateWindowFunctionParameterBuffers(
+            frameStart - previousFrameStart,
+            frameEnd - previousFrameEnd,
+            previousFrameEnd + 1)
+          // Evaluate the current frame.
+          evaluateCurrentFrame()
+        }
+
+        /** Evaluate the current window frame. */
+        private def evaluateCurrentFrame(): Unit = {
+          var i = 0
+          while (i < functions.length) {
+            // Reset the state of the window function.
+            functions(i).reset()
+            // Get all buffered input parameters based on rows of this window frame.
+            val inputParameters = windowFunctionParameterBuffers(i).toArray()
+            // Send these input parameters to the window function.
+            functions(i).batchUpdate(inputParameters)
+            // Ask the function to evaluate based on this window frame.
+            functions(i).evaluate()
+            i += 1
+          }
+        }
+      }
+    }
+  }
+}
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 81ee48ef4152f..5e411c2fdba9d 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
@@ -185,7 +185,7 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
     // Hive does not support buckets.
     ".*bucket.*",
 
-    // No window support yet
+    // We have our own tests based on these query files.
     ".*window.*",
 
     // Fails in hive with authorization errors.
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 1d8d0b5c322ad..f25723e53f07c 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
@@ -252,6 +252,7 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) {
         catalog.CreateTables ::
         catalog.PreInsertionCasts ::
         ExtractPythonUdfs ::
+        ResolveHiveWindowFunction ::
         sources.PreInsertCastAndRename ::
         Nil
     }
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 63a8c05f775b8..8a0686a2d81c9 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
@@ -421,16 +421,16 @@ private[hive] object HiveQl {
   }
 
   /**
-   * SELECT MAX(value) FROM src GROUP BY k1, k2, k3 GROUPING SETS((k1, k2), (k2)) 
-   * is equivalent to 
+   * SELECT MAX(value) FROM src GROUP BY k1, k2, k3 GROUPING SETS((k1, k2), (k2))
+   * is equivalent to
    * SELECT MAX(value) FROM src GROUP BY k1, k2 UNION SELECT MAX(value) FROM src GROUP BY k2
    * Check the following link for details.
-   * 
+   *
 https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C+Grouping+and+Rollup
    *
    * The bitmask denotes the grouping expressions validity for a grouping set,
    * the bitmask also be called as grouping id (`GROUPING__ID`, the virtual column in Hive)
-   * e.g. In superset (k1, k2, k3), (bit 0: k1, bit 1: k2, and bit 2: k3), the grouping id of 
+   * e.g. In superset (k1, k2, k3), (bit 0: k1, bit 1: k2, and bit 2: k3), the grouping id of
    * GROUPING SETS (k1, k2) and (k2) should be 3 and 2 respectively.
    */
   protected def extractGroupingSet(children: Seq[ASTNode]): (Seq[Expression], Seq[Int]) = {
@@ -444,7 +444,7 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
 
     val bitmasks: Seq[Int] = setASTs.map(set => set match {
       case Token("TOK_GROUPING_SETS_EXPRESSION", null) => 0
-      case Token("TOK_GROUPING_SETS_EXPRESSION", children) => 
+      case Token("TOK_GROUPING_SETS_EXPRESSION", children) =>
         children.foldLeft(0)((bitmap, col) => {
           val colString = col.asInstanceOf[ASTNode].toStringTree()
           require(keyMap.contains(colString), s"$colString doens't show up in the GROUP BY list")
@@ -613,7 +613,8 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
             clusterByClause ::
             distributeByClause ::
             limitClause ::
-            lateralViewClause :: Nil) = {
+            lateralViewClause ::
+            windowClause :: Nil) = {
           getClauses(
             Seq(
               "TOK_INSERT_INTO",
@@ -631,15 +632,16 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
               "TOK_CLUSTERBY",
               "TOK_DISTRIBUTEBY",
               "TOK_LIMIT",
-              "TOK_LATERAL_VIEW"),
+              "TOK_LATERAL_VIEW",
+              "WINDOW"),
             singleInsert)
         }
- 
+
         val relations = fromClause match {
           case Some(f) => nodeToRelation(f)
           case None => OneRowRelation
         }
- 
+
         val withWhere = whereClause.map { whereNode =>
           val Seq(whereExpr) = whereNode.getChildren.toSeq
           Filter(nodeToExpr(whereExpr), relations)
@@ -691,7 +693,7 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
                 val serdeProps = propsClause.map {
                   case Token("TOK_TABLEPROPERTY", Token(name, Nil) :: Token(value, Nil) :: Nil) =>
                     (name, value)
-                } 
+                }
                 (Nil, serdeClass, serdeProps)
 
               case Nil => (Nil, "", Nil)
@@ -736,7 +738,7 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
         // The projection of the query can either be a normal projection, an aggregation
         // (if there is a group by) or a script transformation.
         val withProject: LogicalPlan = transformation.getOrElse {
-          val selectExpressions = 
+          val selectExpressions =
             nameExpressions(select.getChildren.flatMap(selExprNodeToExpr).toSeq)
           Seq(
             groupByClause.map(e => e match {
@@ -764,31 +766,34 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
             Some(Project(selectExpressions, withLateralView))).flatten.head
         }
 
-        val withDistinct =
-          if (selectDistinctClause.isDefined) Distinct(withProject) else withProject
-
+        // Handle HAVING clause.
         val withHaving = havingClause.map { h =>
           val havingExpr = h.getChildren.toSeq match { case Seq(hexpr) => nodeToExpr(hexpr) }
           // Note that we added a cast to boolean. If the expression itself is already boolean,
           // the optimizer will get rid of the unnecessary cast.
-          Filter(Cast(havingExpr, BooleanType), withDistinct)
-        }.getOrElse(withDistinct)
+          Filter(Cast(havingExpr, BooleanType), withProject)
+        }.getOrElse(withProject)
+
+        // Handle SELECT DISTINCT
+        val withDistinct =
+          if (selectDistinctClause.isDefined) Distinct(withHaving) else withHaving
 
+        // Handle ORDER BY, SORT BY, DISTRIBETU BY, and CLUSTER BY clause.
         val withSort =
           (orderByClause, sortByClause, distributeByClause, clusterByClause) match {
             case (Some(totalOrdering), None, None, None) =>
-              Sort(totalOrdering.getChildren.map(nodeToSortOrder), true, withHaving)
+              Sort(totalOrdering.getChildren.map(nodeToSortOrder), true, withDistinct)
             case (None, Some(perPartitionOrdering), None, None) =>
-              Sort(perPartitionOrdering.getChildren.map(nodeToSortOrder), false, withHaving)
+              Sort(perPartitionOrdering.getChildren.map(nodeToSortOrder), false, withDistinct)
             case (None, None, Some(partitionExprs), None) =>
-              RepartitionByExpression(partitionExprs.getChildren.map(nodeToExpr), withHaving)
+              RepartitionByExpression(partitionExprs.getChildren.map(nodeToExpr), withDistinct)
             case (None, Some(perPartitionOrdering), Some(partitionExprs), None) =>
               Sort(perPartitionOrdering.getChildren.map(nodeToSortOrder), false,
-                RepartitionByExpression(partitionExprs.getChildren.map(nodeToExpr), withHaving))
+                RepartitionByExpression(partitionExprs.getChildren.map(nodeToExpr), withDistinct))
             case (None, None, None, Some(clusterExprs)) =>
               Sort(clusterExprs.getChildren.map(nodeToExpr).map(SortOrder(_, Ascending)), false,
-                RepartitionByExpression(clusterExprs.getChildren.map(nodeToExpr), withHaving))
-            case (None, None, None, None) => withHaving
+                RepartitionByExpression(clusterExprs.getChildren.map(nodeToExpr), withDistinct))
+            case (None, None, None, None) => withDistinct
             case _ => sys.error("Unsupported set of ordering / distribution clauses.")
           }
 
@@ -797,6 +802,27 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
             .map(Limit(_, withSort))
             .getOrElse(withSort)
 
+        // Collect all window specifications defined in the WINDOW clause.
+        val windowDefinitions = windowClause.map(_.getChildren.toSeq.collect {
+          case Token("TOK_WINDOWDEF",
+          Token(windowName, Nil) :: Token("TOK_WINDOWSPEC", spec) :: Nil) =>
+            windowName -> nodesToWindowSpecification(spec)
+        }.toMap)
+        // Handle cases like
+        // window w1 as (partition by p_mfgr order by p_name
+        //               range between 2 preceding and 2 following),
+        //        w2 as w1
+        val resolvedCrossReference = windowDefinitions.map {
+          windowDefMap => windowDefMap.map {
+            case (windowName, WindowSpecReference(other)) =>
+              (windowName, windowDefMap(other).asInstanceOf[WindowSpecDefinition])
+            case o => o.asInstanceOf[(String, WindowSpecDefinition)]
+          }
+        }
+
+        val withWindowDefinitions =
+          resolvedCrossReference.map(WithWindowDefinition(_, withLimit)).getOrElse(withLimit)
+
         // TOK_INSERT_INTO means to add files to the table.
         // TOK_DESTINATION means to overwrite the table.
         val resultDestination =
@@ -804,7 +830,7 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
         val overwrite = intoClause.isEmpty
         nodeToDest(
           resultDestination,
-          withLimit,
+          withWindowDefinitions,
           overwrite)
       }
 
@@ -1053,7 +1079,6 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
       throw new NotImplementedError(s"No parse rules for:\n ${dumpTree(a).toString} ")
   }
 
-
   protected val escapedIdentifier = "`([^`]+)`".r
   /** Strips backticks from ident if present */
   protected def cleanIdentifier(ident: String): String = ident match {
@@ -1250,6 +1275,25 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
       Substring(nodeToExpr(string), nodeToExpr(pos), nodeToExpr(length))
     case Token("TOK_FUNCTION", Token(COALESCE(), Nil) :: list) => Coalesce(list.map(nodeToExpr))
 
+    /* Window Functions */
+    case Token("TOK_FUNCTION", Token(name, Nil) +: args :+ Token("TOK_WINDOWSPEC", spec)) =>
+      val function = UnresolvedWindowFunction(name, args.map(nodeToExpr))
+      nodesToWindowSpecification(spec) match {
+        case reference: WindowSpecReference =>
+          UnresolvedWindowExpression(function, reference)
+        case definition: WindowSpecDefinition =>
+          WindowExpression(function, definition)
+      }
+    case Token("TOK_FUNCTIONSTAR", Token(name, Nil) :: Token("TOK_WINDOWSPEC", spec) :: Nil) =>
+      // Safe to use Literal(1)?
+      val function = UnresolvedWindowFunction(name, Literal(1) :: Nil)
+      nodesToWindowSpecification(spec) match {
+        case reference: WindowSpecReference =>
+          UnresolvedWindowExpression(function, reference)
+        case definition: WindowSpecDefinition =>
+          WindowExpression(function, definition)
+      }
+
     /* UDFs - Must be last otherwise will preempt built in functions */
     case Token("TOK_FUNCTION", Token(name, Nil) :: args) =>
       UnresolvedFunction(name, args.map(nodeToExpr))
@@ -1312,6 +1356,89 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
          """.stripMargin)
   }
 
+  def nodesToWindowSpecification(nodes: Seq[ASTNode]): WindowSpec = nodes match {
+    case Token(windowName, Nil) :: Nil =>
+      // Refer to a window spec defined in the window clause.
+      WindowSpecReference(windowName)
+    case Nil =>
+      // OVER()
+      WindowSpecDefinition(
+        partitionSpec = Nil,
+        orderSpec = Nil,
+        frameSpecification = UnspecifiedFrame)
+    case spec =>
+      val (partitionClause :: rowFrame :: rangeFrame :: Nil) =
+        getClauses(
+          Seq(
+            "TOK_PARTITIONINGSPEC",
+            "TOK_WINDOWRANGE",
+            "TOK_WINDOWVALUES"),
+          spec)
+
+      // Handle Partition By and Order By.
+      val (partitionSpec, orderSpec) = partitionClause.map { partitionAndOrdering =>
+        val (partitionByClause :: orderByClause :: sortByClause :: clusterByClause :: Nil) =
+          getClauses(
+            Seq("TOK_DISTRIBUTEBY", "TOK_ORDERBY", "TOK_SORTBY", "TOK_CLUSTERBY"),
+            partitionAndOrdering.getChildren.toSeq.asInstanceOf[Seq[ASTNode]])
+
+        (partitionByClause, orderByClause.orElse(sortByClause), clusterByClause) match {
+          case (Some(partitionByExpr), Some(orderByExpr), None) =>
+            (partitionByExpr.getChildren.map(nodeToExpr),
+              orderByExpr.getChildren.map(nodeToSortOrder))
+          case (Some(partitionByExpr), None, None) =>
+            (partitionByExpr.getChildren.map(nodeToExpr), Nil)
+          case (None, Some(orderByExpr), None) =>
+            (Nil, orderByExpr.getChildren.map(nodeToSortOrder))
+          case (None, None, Some(clusterByExpr)) =>
+            val expressions = clusterByExpr.getChildren.map(nodeToExpr)
+            (expressions, expressions.map(SortOrder(_, Ascending)))
+          case _ =>
+            throw new NotImplementedError(
+              s"""No parse rules for Node ${partitionAndOrdering.getName}
+              """.stripMargin)
+        }
+      }.getOrElse {
+        (Nil, Nil)
+      }
+
+      // Handle Window Frame
+      val windowFrame =
+        if (rowFrame.isEmpty && rangeFrame.isEmpty) {
+          UnspecifiedFrame
+        } else {
+          val frameType = rowFrame.map(_ => RowFrame).getOrElse(RangeFrame)
+          def nodeToBoundary(node: Node): FrameBoundary = node match {
+            case Token("preceding", Token(count, Nil) :: Nil) =>
+              if (count == "unbounded") UnboundedPreceding else ValuePreceding(count.toInt)
+            case Token("following", Token(count, Nil) :: Nil) =>
+              if (count == "unbounded") UnboundedFollowing else ValueFollowing(count.toInt)
+            case Token("current", Nil) => CurrentRow
+            case _ =>
+              throw new NotImplementedError(
+                s"""No parse rules for the Window Frame Boundary based on Node ${node.getName}
+              """.stripMargin)
+          }
+
+          rowFrame.orElse(rangeFrame).map { frame =>
+            frame.getChildren.toList match {
+              case precedingNode :: followingNode :: Nil =>
+                SpecifiedWindowFrame(
+                  frameType,
+                  nodeToBoundary(precedingNode),
+                  nodeToBoundary(followingNode))
+              case precedingNode :: Nil =>
+                SpecifiedWindowFrame(frameType, nodeToBoundary(precedingNode), CurrentRow)
+              case _ =>
+                throw new NotImplementedError(
+                  s"""No parse rules for the Window Frame based on Node ${frame.getName}
+                  """.stripMargin)
+            }
+          }.getOrElse(sys.error(s"If you see this, please file a bug report with your query."))
+        }
+
+      WindowSpecDefinition(partitionSpec, orderSpec, windowFrame)
+  }
 
   val explode = "(?i)explode".r
   def nodesToGenerator(nodes: Seq[Node]): (Generator, Seq[String]) = {
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 4b6f0ad75f54f..fd0b6f058595d 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
@@ -17,27 +17,27 @@
 
 package org.apache.spark.sql.hive
 
+import org.apache.hadoop.hive.ql.udf.generic.GenericUDAFEvaluator.AggregationBuffer
 import org.apache.hadoop.hive.ql.udf.generic.GenericUDFUtils.ConversionHelper
+import org.apache.spark.sql.AnalysisException
 
 import scala.collection.mutable.ArrayBuffer
 
 import org.apache.hadoop.hive.serde2.objectinspector.{ObjectInspector, ConstantObjectInspector}
 import org.apache.hadoop.hive.serde2.objectinspector.ObjectInspectorFactory.ObjectInspectorOptions
 import org.apache.hadoop.hive.serde2.objectinspector.ObjectInspectorFactory
-import org.apache.hadoop.hive.ql.exec.{UDF, UDAF}
-import org.apache.hadoop.hive.ql.exec.{FunctionInfo, FunctionRegistry}
+import org.apache.hadoop.hive.ql.exec._
 import org.apache.hadoop.hive.ql.udf.{UDFType => HiveUDFType}
 import org.apache.hadoop.hive.ql.udf.generic._
 import org.apache.hadoop.hive.ql.udf.generic.GenericUDF._
 
 import org.apache.spark.Logging
 import org.apache.spark.sql.catalyst.analysis
+import org.apache.spark.sql.catalyst.errors.TreeNodeException
 import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.catalyst.plans.logical.{Generate, Project, LogicalPlan}
+import org.apache.spark.sql.catalyst.plans.logical._
 import org.apache.spark.sql.catalyst.rules.Rule
 import org.apache.spark.sql.types._
-import org.apache.spark.sql.catalyst.analysis.MultiAlias
-import org.apache.spark.sql.catalyst.errors.TreeNodeException
 
 /* Implicit conversions */
 import scala.collection.JavaConversions._
@@ -189,6 +189,219 @@ private[hive] case class HiveGenericUdf(funcWrapper: HiveFunctionWrapper, childr
   }
 }
 
+/**
+ * Resolves [[UnresolvedWindowFunction]] to [[HiveWindowFunction]].
+ */
+private[spark] object ResolveHiveWindowFunction extends Rule[LogicalPlan] {
+  def apply(plan: LogicalPlan): LogicalPlan = plan transformUp {
+    case p: LogicalPlan if !p.childrenResolved => p
+
+    // We are resolving WindowExpressions at here. When we get here, we have already
+    // replaced those WindowSpecReferences.
+    case p: LogicalPlan =>
+      p transformExpressions {
+        case WindowExpression(
+          UnresolvedWindowFunction(name, children),
+          windowSpec: WindowSpecDefinition) =>
+          // First, let's find the window function info.
+          val windowFunctionInfo: WindowFunctionInfo =
+            Option(FunctionRegistry.getWindowFunctionInfo(name.toLowerCase)).getOrElse(
+              throw new AnalysisException(s"Couldn't find window function $name"))
+
+          // Get the class of this function.
+          // In Hive 0.12, there is no windowFunctionInfo.getFunctionClass. So, we use
+          // windowFunctionInfo.getfInfo().getFunctionClass for both Hive 0.13 and Hive 0.13.1.
+          val functionClass = windowFunctionInfo.getfInfo().getFunctionClass
+          val newChildren =
+            // Rank(), DENSE_RANK(), CUME_DIST(), and PERCENT_RANK() do not take explicit
+            // input parameters and requires implicit parameters, which
+            // are expressions in Order By clause.
+            if (classOf[GenericUDAFRank].isAssignableFrom(functionClass)) {
+              if (children.nonEmpty) {
+               throw  new AnalysisException(s"$name does not take input parameters.")
+              }
+              windowSpec.orderSpec.map(_.child)
+            } else {
+              children
+            }
+
+          // If the class is UDAF, we need to use UDAFBridge.
+          val isUDAFBridgeRequired =
+            if (classOf[UDAF].isAssignableFrom(functionClass)) {
+              true
+            } else {
+              false
+            }
+
+          // Create the HiveWindowFunction. For the meaning of isPivotResult, see the doc of
+          // HiveWindowFunction.
+          val windowFunction =
+            HiveWindowFunction(
+              new HiveFunctionWrapper(functionClass.getName),
+              windowFunctionInfo.isPivotResult,
+              isUDAFBridgeRequired,
+              newChildren)
+
+          // Second, check if the specified window function can accept window definition.
+          windowSpec.frameSpecification match {
+            case frame: SpecifiedWindowFrame if !windowFunctionInfo.isSupportsWindow =>
+              // This Hive window function does not support user-speficied window frame.
+              throw new AnalysisException(
+                s"Window function $name does not take a frame specification.")
+            case frame: SpecifiedWindowFrame if windowFunctionInfo.isSupportsWindow &&
+                                                windowFunctionInfo.isPivotResult =>
+              // These two should not be true at the same time when a window frame is defined.
+              // If so, throw an exception.
+              throw new AnalysisException(s"Could not handle Hive window function $name because " +
+                s"it supports both a user specified window frame and pivot result.")
+            case _ => // OK
+          }
+          // Resolve those UnspecifiedWindowFrame because the physical Window operator still needs
+          // a window frame specification to work.
+          val newWindowSpec = windowSpec.frameSpecification match {
+            case UnspecifiedFrame =>
+              val newWindowFrame =
+                SpecifiedWindowFrame.defaultWindowFrame(
+                  windowSpec.orderSpec.nonEmpty,
+                  windowFunctionInfo.isSupportsWindow)
+              WindowSpecDefinition(windowSpec.partitionSpec, windowSpec.orderSpec, newWindowFrame)
+            case _ => windowSpec
+          }
+
+          // Finally, we create a WindowExpression with the resolved window function and
+          // specified window spec.
+          WindowExpression(windowFunction, newWindowSpec)
+      }
+  }
+}
+
+/**
+ * A [[WindowFunction]] implementation wrapping Hive's window function.
+ * @param funcWrapper The wrapper for the Hive Window Function.
+ * @param pivotResult If it is true, the Hive function will return a list of values representing
+ *                    the values of the added columns. Otherwise, a single value is returned for
+ *                    current row.
+ * @param isUDAFBridgeRequired If it is true, the function returned by functionWrapper's
+ *                             createFunction is UDAF, we need to use GenericUDAFBridge to wrap
+ *                             it as a GenericUDAFResolver2.
+ * @param children Input parameters.
+ */
+private[hive] case class HiveWindowFunction(
+    funcWrapper: HiveFunctionWrapper,
+    pivotResult: Boolean,
+    isUDAFBridgeRequired: Boolean,
+    children: Seq[Expression]) extends WindowFunction
+  with HiveInspectors {
+
+  // Hive window functions are based on GenericUDAFResolver2.
+  type UDFType = GenericUDAFResolver2
+
+  @transient
+  protected lazy val resolver: GenericUDAFResolver2 =
+    if (isUDAFBridgeRequired) {
+      new GenericUDAFBridge(funcWrapper.createFunction[UDAF]())
+    } else {
+      funcWrapper.createFunction[GenericUDAFResolver2]()
+    }
+
+  @transient
+  protected lazy val inputInspectors = children.map(toInspector).toArray
+
+  // The GenericUDAFEvaluator used to evaluate the window function.
+  @transient
+  protected lazy val evaluator: GenericUDAFEvaluator = {
+    val parameterInfo = new SimpleGenericUDAFParameterInfo(inputInspectors, false, false)
+    resolver.getEvaluator(parameterInfo)
+  }
+
+  // The object inspector of values returned from the Hive window function.
+  @transient
+  protected lazy val returnInspector  = {
+    evaluator.init(GenericUDAFEvaluator.Mode.COMPLETE, inputInspectors)
+  }
+
+  def dataType: DataType =
+    if (!pivotResult) {
+      inspectorToDataType(returnInspector)
+    } else {
+      // If pivotResult is true, we should take the element type out as the data type of this
+      // function.
+      inspectorToDataType(returnInspector) match {
+        case ArrayType(dt, _) => dt
+        case _ =>
+          sys.error(
+            s"error resolve the data type of window function ${funcWrapper.functionClassName}")
+      }
+    }
+
+  def nullable: Boolean = true
+
+  override type EvaluatedType = Any
+
+  override def eval(input: Row): Any =
+    throw new TreeNodeException(this, s"No function to evaluate expression. type: ${this.nodeName}")
+
+  @transient
+  lazy val inputProjection = new InterpretedProjection(children)
+
+  @transient
+  private var hiveEvaluatorBuffer: AggregationBuffer = _
+  // Output buffer.
+  private var outputBuffer: Any = _
+
+  override def init(): Unit = {
+    evaluator.init(GenericUDAFEvaluator.Mode.COMPLETE, inputInspectors)
+  }
+
+  // Reset the hiveEvaluatorBuffer and outputPosition
+  override def reset(): Unit = {
+    // We create a new aggregation buffer to workaround the bug in GenericUDAFRowNumber.
+    // Basically, GenericUDAFRowNumberEvaluator.reset calls RowNumberBuffer.init.
+    // However, RowNumberBuffer.init does not really reset this buffer.
+    hiveEvaluatorBuffer = evaluator.getNewAggregationBuffer
+    evaluator.reset(hiveEvaluatorBuffer)
+  }
+
+  override def prepareInputParameters(input: Row): AnyRef = {
+    wrap(inputProjection(input), inputInspectors, new Array[AnyRef](children.length))
+  }
+  // Add input parameters for a single row.
+  override def update(input: AnyRef): Unit = {
+    evaluator.iterate(hiveEvaluatorBuffer, input.asInstanceOf[Array[AnyRef]])
+  }
+
+  override def batchUpdate(inputs: Array[AnyRef]): Unit = {
+    var i = 0
+    while (i < inputs.length) {
+      evaluator.iterate(hiveEvaluatorBuffer, inputs(i).asInstanceOf[Array[AnyRef]])
+      i += 1
+    }
+  }
+
+  override def evaluate(): Unit = {
+    outputBuffer = unwrap(evaluator.evaluate(hiveEvaluatorBuffer), returnInspector)
+  }
+
+  override def get(index: Int): Any = {
+    if (!pivotResult) {
+      // if pivotResult is false, we will get a single value for all rows in the frame.
+      outputBuffer
+    } else {
+      // if pivotResult is true, we will get a Seq having the same size with the size
+      // of the window frame. At here, we will return the result at the position of
+      // index in the output buffer.
+      outputBuffer.asInstanceOf[Seq[Any]].get(index)
+    }
+  }
+
+  override def toString: String = {
+    s"$nodeName#${funcWrapper.functionClassName}(${children.mkString(",")})"
+  }
+
+  override def newInstance: WindowFunction =
+    new HiveWindowFunction(funcWrapper, pivotResult, isUDAFBridgeRequired, children)
+}
+
 private[hive] case class HiveGenericUdaf(
     funcWrapper: HiveFunctionWrapper,
     children: Seq[Expression]) extends AggregateExpression
diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 1. testWindowing-0-327a8cd39fe30255ff492ee86f660522 b/sql/hive/src/test/resources/golden/windowing.q -- 1. testWindowing-0-327a8cd39fe30255ff492ee86f660522
new file mode 100644
index 0000000000000..850c41c8115d6
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing.q -- 1. testWindowing-0-327a8cd39fe30255ff492ee86f660522	
@@ -0,0 +1,26 @@
+Manufacturer#1	almond antique burnished rose metallic	2	1	1	1173.15
+Manufacturer#1	almond antique burnished rose metallic	2	1	1	2346.3
+Manufacturer#1	almond antique chartreuse lavender yellow	34	3	2	4100.06
+Manufacturer#1	almond antique salmon chartreuse burlywood	6	4	3	5702.650000000001
+Manufacturer#1	almond aquamarine burnished black steel	28	5	4	7117.070000000001
+Manufacturer#1	almond aquamarine pink moccasin thistle	42	6	5	8749.730000000001
+Manufacturer#2	almond antique violet chocolate turquoise	14	1	1	1690.68
+Manufacturer#2	almond antique violet turquoise frosted	40	2	2	3491.38
+Manufacturer#2	almond aquamarine midnight light salmon	2	3	3	5523.360000000001
+Manufacturer#2	almond aquamarine rose maroon antique	25	4	4	7222.02
+Manufacturer#2	almond aquamarine sandy cyan gainsboro	18	5	5	8923.62
+Manufacturer#3	almond antique chartreuse khaki white	17	1	1	1671.68
+Manufacturer#3	almond antique forest lavender goldenrod	14	2	2	2861.95
+Manufacturer#3	almond antique metallic orange dim	19	3	3	4272.34
+Manufacturer#3	almond antique misty red olive	1	4	4	6195.32
+Manufacturer#3	almond antique olive coral navajo	45	5	5	7532.61
+Manufacturer#4	almond antique gainsboro frosted violet	10	1	1	1620.67
+Manufacturer#4	almond antique violet mint lemon	39	2	2	2996.09
+Manufacturer#4	almond aquamarine floral ivory bisque	27	3	3	4202.35
+Manufacturer#4	almond aquamarine yellow dodger mint	7	4	4	6047.27
+Manufacturer#4	almond azure aquamarine papaya violet	12	5	5	7337.620000000001
+Manufacturer#5	almond antique blue firebrick mint	31	1	1	1789.69
+Manufacturer#5	almond antique medium spring khaki	6	2	2	3401.3500000000004
+Manufacturer#5	almond antique sky peru orange	2	3	3	5190.08
+Manufacturer#5	almond aquamarine dodger light gainsboro	46	4	4	6208.18
+Manufacturer#5	almond azure blanched chiffon midnight	23	5	5	7672.66
diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 10. testHavingWithWindowingCondRankNoGBY-0-fef4bf638d52a9a601845347010602fd b/sql/hive/src/test/resources/golden/windowing.q -- 10. testHavingWithWindowingCondRankNoGBY-0-fef4bf638d52a9a601845347010602fd
new file mode 100644
index 0000000000000..850c41c8115d6
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing.q -- 10. testHavingWithWindowingCondRankNoGBY-0-fef4bf638d52a9a601845347010602fd	
@@ -0,0 +1,26 @@
+Manufacturer#1	almond antique burnished rose metallic	2	1	1	1173.15
+Manufacturer#1	almond antique burnished rose metallic	2	1	1	2346.3
+Manufacturer#1	almond antique chartreuse lavender yellow	34	3	2	4100.06
+Manufacturer#1	almond antique salmon chartreuse burlywood	6	4	3	5702.650000000001
+Manufacturer#1	almond aquamarine burnished black steel	28	5	4	7117.070000000001
+Manufacturer#1	almond aquamarine pink moccasin thistle	42	6	5	8749.730000000001
+Manufacturer#2	almond antique violet chocolate turquoise	14	1	1	1690.68
+Manufacturer#2	almond antique violet turquoise frosted	40	2	2	3491.38
+Manufacturer#2	almond aquamarine midnight light salmon	2	3	3	5523.360000000001
+Manufacturer#2	almond aquamarine rose maroon antique	25	4	4	7222.02
+Manufacturer#2	almond aquamarine sandy cyan gainsboro	18	5	5	8923.62
+Manufacturer#3	almond antique chartreuse khaki white	17	1	1	1671.68
+Manufacturer#3	almond antique forest lavender goldenrod	14	2	2	2861.95
+Manufacturer#3	almond antique metallic orange dim	19	3	3	4272.34
+Manufacturer#3	almond antique misty red olive	1	4	4	6195.32
+Manufacturer#3	almond antique olive coral navajo	45	5	5	7532.61
+Manufacturer#4	almond antique gainsboro frosted violet	10	1	1	1620.67
+Manufacturer#4	almond antique violet mint lemon	39	2	2	2996.09
+Manufacturer#4	almond aquamarine floral ivory bisque	27	3	3	4202.35
+Manufacturer#4	almond aquamarine yellow dodger mint	7	4	4	6047.27
+Manufacturer#4	almond azure aquamarine papaya violet	12	5	5	7337.620000000001
+Manufacturer#5	almond antique blue firebrick mint	31	1	1	1789.69
+Manufacturer#5	almond antique medium spring khaki	6	2	2	3401.3500000000004
+Manufacturer#5	almond antique sky peru orange	2	3	3	5190.08
+Manufacturer#5	almond aquamarine dodger light gainsboro	46	4	4	6208.18
+Manufacturer#5	almond azure blanched chiffon midnight	23	5	5	7672.66
diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 11. testFirstLast-0-86bb9c97d92fdcd941bcb5143513e2e6 b/sql/hive/src/test/resources/golden/windowing.q -- 11. testFirstLast-0-86bb9c97d92fdcd941bcb5143513e2e6
new file mode 100644
index 0000000000000..921679cdcf569
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing.q -- 11. testFirstLast-0-86bb9c97d92fdcd941bcb5143513e2e6	
@@ -0,0 +1,26 @@
+Manufacturer#1	almond antique burnished rose metallic	2	2	2	34
+Manufacturer#1	almond antique burnished rose metallic	2	2	2	6
+Manufacturer#1	almond antique chartreuse lavender yellow	34	34	2	28
+Manufacturer#1	almond antique salmon chartreuse burlywood	6	6	2	42
+Manufacturer#1	almond aquamarine burnished black steel	28	28	34	42
+Manufacturer#1	almond aquamarine pink moccasin thistle	42	42	6	42
+Manufacturer#2	almond antique violet chocolate turquoise	14	14	14	2
+Manufacturer#2	almond antique violet turquoise frosted	40	40	14	25
+Manufacturer#2	almond aquamarine midnight light salmon	2	2	14	18
+Manufacturer#2	almond aquamarine rose maroon antique	25	25	40	18
+Manufacturer#2	almond aquamarine sandy cyan gainsboro	18	18	2	18
+Manufacturer#3	almond antique chartreuse khaki white	17	17	17	19
+Manufacturer#3	almond antique forest lavender goldenrod	14	14	17	1
+Manufacturer#3	almond antique metallic orange dim	19	19	17	45
+Manufacturer#3	almond antique misty red olive	1	1	14	45
+Manufacturer#3	almond antique olive coral navajo	45	45	19	45
+Manufacturer#4	almond antique gainsboro frosted violet	10	10	10	27
+Manufacturer#4	almond antique violet mint lemon	39	39	10	7
+Manufacturer#4	almond aquamarine floral ivory bisque	27	27	10	12
+Manufacturer#4	almond aquamarine yellow dodger mint	7	7	39	12
+Manufacturer#4	almond azure aquamarine papaya violet	12	12	27	12
+Manufacturer#5	almond antique blue firebrick mint	31	31	31	2
+Manufacturer#5	almond antique medium spring khaki	6	6	31	46
+Manufacturer#5	almond antique sky peru orange	2	2	31	23
+Manufacturer#5	almond aquamarine dodger light gainsboro	46	46	6	23
+Manufacturer#5	almond azure blanched chiffon midnight	23	23	2	23
diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 12. testFirstLastWithWhere-0-84345a9f685ba63b87caa4bb16b122b5 b/sql/hive/src/test/resources/golden/windowing.q -- 12. testFirstLastWithWhere-0-84345a9f685ba63b87caa4bb16b122b5
new file mode 100644
index 0000000000000..09e30c7c57349
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing.q -- 12. testFirstLastWithWhere-0-84345a9f685ba63b87caa4bb16b122b5	
@@ -0,0 +1,5 @@
+Manufacturer#3	almond antique chartreuse khaki white	17	1	17	17	19
+Manufacturer#3	almond antique forest lavender goldenrod	14	2	14	17	1
+Manufacturer#3	almond antique metallic orange dim	19	3	19	17	45
+Manufacturer#3	almond antique misty red olive	1	4	1	14	45
+Manufacturer#3	almond antique olive coral navajo	45	5	45	19	45
diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 13. testSumWindow-0-6cfc8840d3a4469b0fe11d63182cb59f b/sql/hive/src/test/resources/golden/windowing.q -- 13. testSumWindow-0-6cfc8840d3a4469b0fe11d63182cb59f
new file mode 100644
index 0000000000000..01ee88ff23302
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing.q -- 13. testSumWindow-0-6cfc8840d3a4469b0fe11d63182cb59f	
@@ -0,0 +1,26 @@
+Manufacturer#1	almond antique burnished rose metallic	2	38	2
+Manufacturer#1	almond antique burnished rose metallic	2	44	2
+Manufacturer#1	almond antique chartreuse lavender yellow	34	72	34
+Manufacturer#1	almond antique salmon chartreuse burlywood	6	112	6
+Manufacturer#1	almond aquamarine burnished black steel	28	110	28
+Manufacturer#1	almond aquamarine pink moccasin thistle	42	76	42
+Manufacturer#2	almond antique violet chocolate turquoise	14	56	14
+Manufacturer#2	almond antique violet turquoise frosted	40	81	40
+Manufacturer#2	almond aquamarine midnight light salmon	2	99	2
+Manufacturer#2	almond aquamarine rose maroon antique	25	85	25
+Manufacturer#2	almond aquamarine sandy cyan gainsboro	18	45	18
+Manufacturer#3	almond antique chartreuse khaki white	17	50	17
+Manufacturer#3	almond antique forest lavender goldenrod	14	51	14
+Manufacturer#3	almond antique metallic orange dim	19	96	19
+Manufacturer#3	almond antique misty red olive	1	79	1
+Manufacturer#3	almond antique olive coral navajo	45	65	45
+Manufacturer#4	almond antique gainsboro frosted violet	10	76	10
+Manufacturer#4	almond antique violet mint lemon	39	83	39
+Manufacturer#4	almond aquamarine floral ivory bisque	27	95	27
+Manufacturer#4	almond aquamarine yellow dodger mint	7	85	7
+Manufacturer#4	almond azure aquamarine papaya violet	12	46	12
+Manufacturer#5	almond antique blue firebrick mint	31	39	31
+Manufacturer#5	almond antique medium spring khaki	6	85	6
+Manufacturer#5	almond antique sky peru orange	2	108	2
+Manufacturer#5	almond aquamarine dodger light gainsboro	46	77	46
+Manufacturer#5	almond azure blanched chiffon midnight	23	71	23
diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 14. testNoSortClause-0-88d96a526d3cae6ed8168c5b228974d1 b/sql/hive/src/test/resources/golden/windowing.q -- 14. testNoSortClause-0-88d96a526d3cae6ed8168c5b228974d1
new file mode 100644
index 0000000000000..c78eb640c9c27
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing.q -- 14. testNoSortClause-0-88d96a526d3cae6ed8168c5b228974d1	
@@ -0,0 +1,26 @@
+Manufacturer#1	almond antique burnished rose metallic	2	1	1
+Manufacturer#1	almond antique burnished rose metallic	2	1	1
+Manufacturer#1	almond antique chartreuse lavender yellow	34	3	2
+Manufacturer#1	almond antique salmon chartreuse burlywood	6	4	3
+Manufacturer#1	almond aquamarine burnished black steel	28	5	4
+Manufacturer#1	almond aquamarine pink moccasin thistle	42	6	5
+Manufacturer#2	almond antique violet chocolate turquoise	14	1	1
+Manufacturer#2	almond antique violet turquoise frosted	40	2	2
+Manufacturer#2	almond aquamarine midnight light salmon	2	3	3
+Manufacturer#2	almond aquamarine rose maroon antique	25	4	4
+Manufacturer#2	almond aquamarine sandy cyan gainsboro	18	5	5
+Manufacturer#3	almond antique chartreuse khaki white	17	1	1
+Manufacturer#3	almond antique forest lavender goldenrod	14	2	2
+Manufacturer#3	almond antique metallic orange dim	19	3	3
+Manufacturer#3	almond antique misty red olive	1	4	4
+Manufacturer#3	almond antique olive coral navajo	45	5	5
+Manufacturer#4	almond antique gainsboro frosted violet	10	1	1
+Manufacturer#4	almond antique violet mint lemon	39	2	2
+Manufacturer#4	almond aquamarine floral ivory bisque	27	3	3
+Manufacturer#4	almond aquamarine yellow dodger mint	7	4	4
+Manufacturer#4	almond azure aquamarine papaya violet	12	5	5
+Manufacturer#5	almond antique blue firebrick mint	31	1	1
+Manufacturer#5	almond antique medium spring khaki	6	2	2
+Manufacturer#5	almond antique sky peru orange	2	3	3
+Manufacturer#5	almond aquamarine dodger light gainsboro	46	4	4
+Manufacturer#5	almond azure blanched chiffon midnight	23	5	5
diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 15. testExpressions-0-11f6c13cf2710ce7054654cca136e73e b/sql/hive/src/test/resources/golden/windowing.q -- 15. testExpressions-0-11f6c13cf2710ce7054654cca136e73e
new file mode 100644
index 0000000000000..050138ccf04ce
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing.q -- 15. testExpressions-0-11f6c13cf2710ce7054654cca136e73e	
@@ -0,0 +1,26 @@
+Manufacturer#1	almond antique burnished rose metallic	2	1	1	0.3333333333333333	0.0	1	2	2.0	0.0	2	2	2
+Manufacturer#1	almond antique burnished rose metallic	2	1	1	0.3333333333333333	0.0	1	2	2.0	0.0	2	2	2
+Manufacturer#1	almond antique chartreuse lavender yellow	34	3	2	0.5	0.4	2	3	12.666666666666666	15.084944665313014	2	34	2
+Manufacturer#1	almond antique salmon chartreuse burlywood	6	4	3	0.6666666666666666	0.6	2	4	11.0	13.379088160259652	2	6	2
+Manufacturer#1	almond aquamarine burnished black steel	28	5	4	0.8333333333333334	0.8	3	5	14.4	13.763720427268202	2	28	34
+Manufacturer#1	almond aquamarine pink moccasin thistle	42	6	5	1.0	1.0	3	6	19.0	16.237815945091466	2	42	6
+Manufacturer#2	almond antique violet chocolate turquoise	14	1	1	0.2	0.0	1	1	14.0	0.0	4	14	14
+Manufacturer#2	almond antique violet turquoise frosted	40	2	2	0.4	0.25	1	2	27.0	13.0	4	40	14
+Manufacturer#2	almond aquamarine midnight light salmon	2	3	3	0.6	0.5	2	3	18.666666666666668	15.86050300449376	4	2	14
+Manufacturer#2	almond aquamarine rose maroon antique	25	4	4	0.8	0.75	2	4	20.25	14.00669482783144	4	25	40
+Manufacturer#2	almond aquamarine sandy cyan gainsboro	18	5	5	1.0	1.0	3	5	19.8	12.560254774486067	4	18	2
+Manufacturer#3	almond antique chartreuse khaki white	17	1	1	0.2	0.0	1	1	17.0	0.0	2	17	17
+Manufacturer#3	almond antique forest lavender goldenrod	14	2	2	0.4	0.25	1	2	15.5	1.5	2	14	17
+Manufacturer#3	almond antique metallic orange dim	19	3	3	0.6	0.5	2	3	16.666666666666668	2.0548046676563256	2	19	17
+Manufacturer#3	almond antique misty red olive	1	4	4	0.8	0.75	2	4	12.75	7.013380069552769	2	1	14
+Manufacturer#3	almond antique olive coral navajo	45	5	5	1.0	1.0	3	5	19.2	14.344336861632886	2	45	19
+Manufacturer#4	almond antique gainsboro frosted violet	10	1	1	0.2	0.0	1	1	10.0	0.0	0	10	10
+Manufacturer#4	almond antique violet mint lemon	39	2	2	0.4	0.25	1	2	24.5	14.5	0	39	10
+Manufacturer#4	almond aquamarine floral ivory bisque	27	3	3	0.6	0.5	2	3	25.333333333333332	11.897712198383164	0	27	10
+Manufacturer#4	almond aquamarine yellow dodger mint	7	4	4	0.8	0.75	2	4	20.75	13.007209539328564	0	7	39
+Manufacturer#4	almond azure aquamarine papaya violet	12	5	5	1.0	1.0	3	5	19.0	12.149074038789951	0	12	27
+Manufacturer#5	almond antique blue firebrick mint	31	1	1	0.2	0.0	1	1	31.0	0.0	1	31	31
+Manufacturer#5	almond antique medium spring khaki	6	2	2	0.4	0.25	1	2	18.5	12.5	1	6	31
+Manufacturer#5	almond antique sky peru orange	2	3	3	0.6	0.5	2	3	13.0	12.832251036613439	1	2	31
+Manufacturer#5	almond aquamarine dodger light gainsboro	46	4	4	0.8	0.75	2	4	21.25	18.102140757380052	1	46	6
+Manufacturer#5	almond azure blanched chiffon midnight	23	5	5	1.0	1.0	3	5	21.6	16.206171663906314	1	23	2
diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 16. testMultipleWindows-0-efd1476255eeb1b1961149144f574b7a b/sql/hive/src/test/resources/golden/windowing.q -- 16. testMultipleWindows-0-efd1476255eeb1b1961149144f574b7a
new file mode 100644
index 0000000000000..c10888852b504
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing.q -- 16. testMultipleWindows-0-efd1476255eeb1b1961149144f574b7a	
@@ -0,0 +1,26 @@
+Manufacturer#1	almond antique burnished rose metallic	2	1	1	0.3333333333333333	4	4	2
+Manufacturer#1	almond antique burnished rose metallic	2	1	1	0.3333333333333333	4	4	2
+Manufacturer#1	almond antique chartreuse lavender yellow	34	3	2	0.5	38	34	2
+Manufacturer#1	almond antique salmon chartreuse burlywood	6	4	3	0.6666666666666666	44	10	2
+Manufacturer#1	almond aquamarine burnished black steel	28	5	4	0.8333333333333334	72	28	34
+Manufacturer#1	almond aquamarine pink moccasin thistle	42	6	5	1.0	114	42	6
+Manufacturer#2	almond antique violet chocolate turquoise	14	1	1	0.2	14	14	14
+Manufacturer#2	almond antique violet turquoise frosted	40	2	2	0.4	54	40	14
+Manufacturer#2	almond aquamarine midnight light salmon	2	3	3	0.6	56	2	14
+Manufacturer#2	almond aquamarine rose maroon antique	25	4	4	0.8	81	25	40
+Manufacturer#2	almond aquamarine sandy cyan gainsboro	18	5	5	1.0	99	32	2
+Manufacturer#3	almond antique chartreuse khaki white	17	1	1	0.2	17	31	17
+Manufacturer#3	almond antique forest lavender goldenrod	14	2	2	0.4	31	14	17
+Manufacturer#3	almond antique metallic orange dim	19	3	3	0.6	50	50	17
+Manufacturer#3	almond antique misty red olive	1	4	4	0.8	51	1	14
+Manufacturer#3	almond antique olive coral navajo	45	5	5	1.0	96	45	19
+Manufacturer#4	almond antique gainsboro frosted violet	10	1	1	0.2	10	17	10
+Manufacturer#4	almond antique violet mint lemon	39	2	2	0.4	49	39	10
+Manufacturer#4	almond aquamarine floral ivory bisque	27	3	3	0.6	76	27	10
+Manufacturer#4	almond aquamarine yellow dodger mint	7	4	4	0.8	83	7	39
+Manufacturer#4	almond azure aquamarine papaya violet	12	5	5	1.0	95	29	27
+Manufacturer#5	almond antique blue firebrick mint	31	1	1	0.2	31	31	31
+Manufacturer#5	almond antique medium spring khaki	6	2	2	0.4	37	8	31
+Manufacturer#5	almond antique sky peru orange	2	3	3	0.6	39	2	31
+Manufacturer#5	almond aquamarine dodger light gainsboro	46	4	4	0.8	85	46	6
+Manufacturer#5	almond azure blanched chiffon midnight	23	5	5	1.0	108	23	2
diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 17. testCountStar-0-1b1fc185c8fddf68e58e92f29052ab2d b/sql/hive/src/test/resources/golden/windowing.q -- 17. testCountStar-0-1b1fc185c8fddf68e58e92f29052ab2d
new file mode 100644
index 0000000000000..b1309a497d68e
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing.q -- 17. testCountStar-0-1b1fc185c8fddf68e58e92f29052ab2d	
@@ -0,0 +1,26 @@
+Manufacturer#1	almond antique burnished rose metallic	2	2	2	2
+Manufacturer#1	almond antique burnished rose metallic	2	2	2	2
+Manufacturer#1	almond antique chartreuse lavender yellow	34	3	3	2
+Manufacturer#1	almond antique salmon chartreuse burlywood	6	4	4	2
+Manufacturer#1	almond aquamarine burnished black steel	28	5	5	34
+Manufacturer#1	almond aquamarine pink moccasin thistle	42	6	6	6
+Manufacturer#2	almond antique violet chocolate turquoise	14	1	1	14
+Manufacturer#2	almond antique violet turquoise frosted	40	2	2	14
+Manufacturer#2	almond aquamarine midnight light salmon	2	3	3	14
+Manufacturer#2	almond aquamarine rose maroon antique	25	4	4	40
+Manufacturer#2	almond aquamarine sandy cyan gainsboro	18	5	5	2
+Manufacturer#3	almond antique chartreuse khaki white	17	1	1	17
+Manufacturer#3	almond antique forest lavender goldenrod	14	2	2	17
+Manufacturer#3	almond antique metallic orange dim	19	3	3	17
+Manufacturer#3	almond antique misty red olive	1	4	4	14
+Manufacturer#3	almond antique olive coral navajo	45	5	5	19
+Manufacturer#4	almond antique gainsboro frosted violet	10	1	1	10
+Manufacturer#4	almond antique violet mint lemon	39	2	2	10
+Manufacturer#4	almond aquamarine floral ivory bisque	27	3	3	10
+Manufacturer#4	almond aquamarine yellow dodger mint	7	4	4	39
+Manufacturer#4	almond azure aquamarine papaya violet	12	5	5	27
+Manufacturer#5	almond antique blue firebrick mint	31	1	1	31
+Manufacturer#5	almond antique medium spring khaki	6	2	2	31
+Manufacturer#5	almond antique sky peru orange	2	3	3	31
+Manufacturer#5	almond aquamarine dodger light gainsboro	46	4	4	6
+Manufacturer#5	almond azure blanched chiffon midnight	23	5	5	2
diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 18. testUDAFs-0-6974e5959e41a661e09db18547fef58a b/sql/hive/src/test/resources/golden/windowing.q -- 18. testUDAFs-0-6974e5959e41a661e09db18547fef58a
new file mode 100644
index 0000000000000..52d2ee8d0cd3f
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing.q -- 18. testUDAFs-0-6974e5959e41a661e09db18547fef58a	
@@ -0,0 +1,26 @@
+Manufacturer#1	almond antique burnished rose metallic	2	4100.06	1173.15	1753.76	1366.6866666666667
+Manufacturer#1	almond antique burnished rose metallic	2	5702.650000000001	1173.15	1753.76	1425.6625000000001
+Manufacturer#1	almond antique chartreuse lavender yellow	34	7117.070000000001	1173.15	1753.76	1423.4140000000002
+Manufacturer#1	almond antique salmon chartreuse burlywood	6	7576.58	1173.15	1753.76	1515.316
+Manufacturer#1	almond aquamarine burnished black steel	28	6403.43	1414.42	1753.76	1600.8575
+Manufacturer#1	almond aquamarine pink moccasin thistle	42	4649.67	1414.42	1632.66	1549.89
+Manufacturer#2	almond antique violet chocolate turquoise	14	5523.360000000001	1690.68	2031.98	1841.1200000000001
+Manufacturer#2	almond antique violet turquoise frosted	40	7222.02	1690.68	2031.98	1805.505
+Manufacturer#2	almond aquamarine midnight light salmon	2	8923.62	1690.68	2031.98	1784.7240000000002
+Manufacturer#2	almond aquamarine rose maroon antique	25	7232.9400000000005	1698.66	2031.98	1808.2350000000001
+Manufacturer#2	almond aquamarine sandy cyan gainsboro	18	5432.24	1698.66	2031.98	1810.7466666666667
+Manufacturer#3	almond antique chartreuse khaki white	17	4272.34	1190.27	1671.68	1424.1133333333335
+Manufacturer#3	almond antique forest lavender goldenrod	14	6195.32	1190.27	1922.98	1548.83
+Manufacturer#3	almond antique metallic orange dim	19	7532.61	1190.27	1922.98	1506.522
+Manufacturer#3	almond antique misty red olive	1	5860.929999999999	1190.27	1922.98	1465.2324999999998
+Manufacturer#3	almond antique olive coral navajo	45	4670.66	1337.29	1922.98	1556.8866666666665
+Manufacturer#4	almond antique gainsboro frosted violet	10	4202.35	1206.26	1620.67	1400.7833333333335
+Manufacturer#4	almond antique violet mint lemon	39	6047.27	1206.26	1844.92	1511.8175
+Manufacturer#4	almond aquamarine floral ivory bisque	27	7337.620000000001	1206.26	1844.92	1467.5240000000001
+Manufacturer#4	almond aquamarine yellow dodger mint	7	5716.950000000001	1206.26	1844.92	1429.2375000000002
+Manufacturer#4	almond azure aquamarine papaya violet	12	4341.530000000001	1206.26	1844.92	1447.176666666667
+Manufacturer#5	almond antique blue firebrick mint	31	5190.08	1611.66	1789.69	1730.0266666666666
+Manufacturer#5	almond antique medium spring khaki	6	6208.18	1018.1	1789.69	1552.045
+Manufacturer#5	almond antique sky peru orange	2	7672.66	1018.1	1789.69	1534.532
+Manufacturer#5	almond aquamarine dodger light gainsboro	46	5882.970000000001	1018.1	1788.73	1470.7425000000003
+Manufacturer#5	almond azure blanched chiffon midnight	23	4271.3099999999995	1018.1	1788.73	1423.7699999999998
diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 19. testUDAFsWithGBY-0-67d15ee5915ac64a738fd4b60d75eb35 b/sql/hive/src/test/resources/golden/windowing.q -- 19. testUDAFsWithGBY-0-67d15ee5915ac64a738fd4b60d75eb35
new file mode 100644
index 0000000000000..6461642d34a21
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing.q -- 19. testUDAFsWithGBY-0-67d15ee5915ac64a738fd4b60d75eb35	
@@ -0,0 +1,25 @@
+Manufacturer#1	almond antique burnished rose metallic	2	1173.15	4529.5	1173.15	1173.15	1509.8333333333333
+Manufacturer#1	almond antique chartreuse lavender yellow	34	1753.76	5943.92	1753.76	1753.76	1485.98
+Manufacturer#1	almond antique salmon chartreuse burlywood	6	1602.59	7576.58	1602.59	1602.59	1515.316
+Manufacturer#1	almond aquamarine burnished black steel	28	1414.42	6403.43	1414.42	1414.42	1600.8575
+Manufacturer#1	almond aquamarine pink moccasin thistle	42	1632.66	4649.67	1632.66	1632.66	1549.89
+Manufacturer#2	almond antique violet chocolate turquoise	14	1690.68	5523.360000000001	1690.68	1690.68	1841.1200000000001
+Manufacturer#2	almond antique violet turquoise frosted	40	1800.7	7222.02	1800.7	1800.7	1805.505
+Manufacturer#2	almond aquamarine midnight light salmon	2	2031.98	8923.62	2031.98	2031.98	1784.7240000000002
+Manufacturer#2	almond aquamarine rose maroon antique	25	1698.66	7232.9400000000005	1698.66	1698.66	1808.2350000000001
+Manufacturer#2	almond aquamarine sandy cyan gainsboro	18	1701.6	5432.24	1701.6	1701.6	1810.7466666666667
+Manufacturer#3	almond antique chartreuse khaki white	17	1671.68	4272.34	1671.68	1671.68	1424.1133333333335
+Manufacturer#3	almond antique forest lavender goldenrod	14	1190.27	6195.32	1190.27	1190.27	1548.83
+Manufacturer#3	almond antique metallic orange dim	19	1410.39	7532.61	1410.39	1410.39	1506.522
+Manufacturer#3	almond antique misty red olive	1	1922.98	5860.929999999999	1922.98	1922.98	1465.2324999999998
+Manufacturer#3	almond antique olive coral navajo	45	1337.29	4670.66	1337.29	1337.29	1556.8866666666665
+Manufacturer#4	almond antique gainsboro frosted violet	10	1620.67	4202.35	1620.67	1620.67	1400.7833333333335
+Manufacturer#4	almond antique violet mint lemon	39	1375.42	6047.27	1375.42	1375.42	1511.8175
+Manufacturer#4	almond aquamarine floral ivory bisque	27	1206.26	7337.620000000001	1206.26	1206.26	1467.5240000000001
+Manufacturer#4	almond aquamarine yellow dodger mint	7	1844.92	5716.950000000001	1844.92	1844.92	1429.2375000000002
+Manufacturer#4	almond azure aquamarine papaya violet	12	1290.35	4341.530000000001	1290.35	1290.35	1447.176666666667
+Manufacturer#5	almond antique blue firebrick mint	31	1789.69	5190.08	1789.69	1789.69	1730.0266666666666
+Manufacturer#5	almond antique medium spring khaki	6	1611.66	6208.18	1611.66	1611.66	1552.045
+Manufacturer#5	almond antique sky peru orange	2	1788.73	7672.66	1788.73	1788.73	1534.532
+Manufacturer#5	almond aquamarine dodger light gainsboro	46	1018.1	5882.970000000001	1018.1	1018.1	1470.7425000000003
+Manufacturer#5	almond azure blanched chiffon midnight	23	1464.48	4271.3099999999995	1464.48	1464.48	1423.7699999999998
diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 2. testGroupByWithPartitioning-0-cb5618b1e626f3a9d4a030b508b5d251 b/sql/hive/src/test/resources/golden/windowing.q -- 2. testGroupByWithPartitioning-0-cb5618b1e626f3a9d4a030b508b5d251
new file mode 100644
index 0000000000000..2c30e652aa26d
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing.q -- 2. testGroupByWithPartitioning-0-cb5618b1e626f3a9d4a030b508b5d251	
@@ -0,0 +1,25 @@
+Manufacturer#1	almond antique burnished rose metallic	2	1173.15	1	1	2	0
+Manufacturer#1	almond antique chartreuse lavender yellow	34	1753.76	2	2	34	32
+Manufacturer#1	almond antique salmon chartreuse burlywood	6	1602.59	3	3	6	-28
+Manufacturer#1	almond aquamarine burnished black steel	28	1414.42	4	4	28	22
+Manufacturer#1	almond aquamarine pink moccasin thistle	42	1632.66	5	5	42	14
+Manufacturer#2	almond antique violet chocolate turquoise	14	1690.68	1	1	14	0
+Manufacturer#2	almond antique violet turquoise frosted	40	1800.7	2	2	40	26
+Manufacturer#2	almond aquamarine midnight light salmon	2	2031.98	3	3	2	-38
+Manufacturer#2	almond aquamarine rose maroon antique	25	1698.66	4	4	25	23
+Manufacturer#2	almond aquamarine sandy cyan gainsboro	18	1701.6	5	5	18	-7
+Manufacturer#3	almond antique chartreuse khaki white	17	1671.68	1	1	17	0
+Manufacturer#3	almond antique forest lavender goldenrod	14	1190.27	2	2	14	-3
+Manufacturer#3	almond antique metallic orange dim	19	1410.39	3	3	19	5
+Manufacturer#3	almond antique misty red olive	1	1922.98	4	4	1	-18
+Manufacturer#3	almond antique olive coral navajo	45	1337.29	5	5	45	44
+Manufacturer#4	almond antique gainsboro frosted violet	10	1620.67	1	1	10	0
+Manufacturer#4	almond antique violet mint lemon	39	1375.42	2	2	39	29
+Manufacturer#4	almond aquamarine floral ivory bisque	27	1206.26	3	3	27	-12
+Manufacturer#4	almond aquamarine yellow dodger mint	7	1844.92	4	4	7	-20
+Manufacturer#4	almond azure aquamarine papaya violet	12	1290.35	5	5	12	5
+Manufacturer#5	almond antique blue firebrick mint	31	1789.69	1	1	31	0
+Manufacturer#5	almond antique medium spring khaki	6	1611.66	2	2	6	-25
+Manufacturer#5	almond antique sky peru orange	2	1788.73	3	3	2	-4
+Manufacturer#5	almond aquamarine dodger light gainsboro	46	1018.1	4	4	46	44
+Manufacturer#5	almond azure blanched chiffon midnight	23	1464.48	5	5	23	-23
diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 20. testSTATs-0-da0e0cca69e42118a96b8609b8fa5838 b/sql/hive/src/test/resources/golden/windowing.q -- 20. testSTATs-0-da0e0cca69e42118a96b8609b8fa5838
new file mode 100644
index 0000000000000..1f7e8a5d67036
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing.q -- 20. testSTATs-0-da0e0cca69e42118a96b8609b8fa5838	
@@ -0,0 +1,26 @@
+Manufacturer#1	almond antique burnished rose metallic	2	273.70217881648074	273.70217881648074	[34,2]	74912.8826888888	1.0	4128.782222222221
+Manufacturer#1	almond antique burnished rose metallic	2	258.10677784349235	258.10677784349235	[34,2,6]	66619.10876874991	0.811328754177887	2801.7074999999995
+Manufacturer#1	almond antique chartreuse lavender yellow	34	230.90151585470358	230.90151585470358	[34,2,6,28]	53315.51002399992	0.695639377397664	2210.7864
+Manufacturer#1	almond antique salmon chartreuse burlywood	6	202.73109328368946	202.73109328368946	[34,2,6,42,28]	41099.896184	0.630785977101214	2009.9536000000007
+Manufacturer#1	almond aquamarine burnished black steel	28	121.6064517973862	121.6064517973862	[34,6,42,28]	14788.129118750014	0.2036684720435979	331.1337500000004
+Manufacturer#1	almond aquamarine pink moccasin thistle	42	96.5751586416853	96.5751586416853	[6,42,28]	9326.761266666683	-1.4442181184933883E-4	-0.20666666666708502
+Manufacturer#2	almond antique violet chocolate turquoise	14	142.2363169751898	142.2363169751898	[2,40,14]	20231.169866666663	-0.49369526554523185	-1113.7466666666658
+Manufacturer#2	almond antique violet turquoise frosted	40	137.76306498840682	137.76306498840682	[2,25,40,14]	18978.662075	-0.5205630897335946	-1004.4812499999995
+Manufacturer#2	almond aquamarine midnight light salmon	2	130.03972279269132	130.03972279269132	[2,18,25,40,14]	16910.329504000005	-0.46908967495720255	-766.1791999999995
+Manufacturer#2	almond aquamarine rose maroon antique	25	135.55100986344584	135.55100986344584	[2,18,25,40]	18374.07627499999	-0.6091405874714462	-1128.1787499999987
+Manufacturer#2	almond aquamarine sandy cyan gainsboro	18	156.44019460768044	156.44019460768044	[2,18,25]	24473.534488888927	-0.9571686373491608	-1441.4466666666676
+Manufacturer#3	almond antique chartreuse khaki white	17	196.7742266885805	196.7742266885805	[17,19,14]	38720.09628888887	0.5557168646224995	224.6944444444446
+Manufacturer#3	almond antique forest lavender goldenrod	14	275.14144189852607	275.14144189852607	[17,1,19,14]	75702.81305	-0.6720833036576083	-1296.9000000000003
+Manufacturer#3	almond antique metallic orange dim	19	260.23473614412046	260.23473614412046	[17,1,19,14,45]	67722.117896	-0.5703526513979519	-2129.0664
+Manufacturer#3	almond antique misty red olive	1	275.9139962356932	275.9139962356932	[1,19,14,45]	76128.53331875012	-0.577476899644802	-2547.7868749999993
+Manufacturer#3	almond antique olive coral navajo	45	260.5815918713796	260.5815918713796	[1,19,45]	67902.76602222225	-0.8710736366736884	-4099.731111111111
+Manufacturer#4	almond antique gainsboro frosted violet	10	170.13011889596618	170.13011889596618	[39,27,10]	28944.25735555559	-0.6656975320098423	-1347.4777777777779
+Manufacturer#4	almond antique violet mint lemon	39	242.26834609323197	242.26834609323197	[39,7,27,10]	58693.95151875002	-0.8051852719193339	-2537.328125
+Manufacturer#4	almond aquamarine floral ivory bisque	27	234.10001662537326	234.10001662537326	[39,7,27,10,12]	54802.817784000035	-0.6046935574240581	-1719.8079999999995
+Manufacturer#4	almond aquamarine yellow dodger mint	7	247.3342714197732	247.3342714197732	[39,7,27,12]	61174.24181875003	-0.5508665654707869	-1719.0368749999975
+Manufacturer#4	almond azure aquamarine papaya violet	12	283.3344330566893	283.3344330566893	[7,27,12]	80278.40095555557	-0.7755740084632333	-1867.4888888888881
+Manufacturer#5	almond antique blue firebrick mint	31	83.69879024746363	83.69879024746363	[2,6,31]	7005.487488888913	0.39004303087285047	418.9233333333353
+Manufacturer#5	almond antique medium spring khaki	6	316.68049612345885	316.68049612345885	[2,6,46,31]	100286.53662500004	-0.713612911776183	-4090.853749999999
+Manufacturer#5	almond antique sky peru orange	2	285.40506298242155	285.40506298242155	[2,23,6,46,31]	81456.04997600002	-0.712858514567818	-3297.2011999999986
+Manufacturer#5	almond aquamarine dodger light gainsboro	46	285.43749038756283	285.43749038756283	[2,23,6,46]	81474.56091875004	-0.984128787153391	-4871.028125000002
+Manufacturer#5	almond azure blanched chiffon midnight	23	315.9225931564038	315.9225931564038	[2,23,46]	99807.08486666664	-0.9978877469246936	-5664.856666666666
diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 21. testDISTs-0-672d4cb385b7ced2e446f132474293ad b/sql/hive/src/test/resources/golden/windowing.q -- 21. testDISTs-0-672d4cb385b7ced2e446f132474293ad
new file mode 100644
index 0000000000000..e7c39f454fb37
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing.q -- 21. testDISTs-0-672d4cb385b7ced2e446f132474293ad	
@@ -0,0 +1,26 @@
+Manufacturer#1	almond antique burnished rose metallic	2	[{"x":1173.15,"y":2.0},{"x":1753.76,"y":1.0}]	121152.0	1
+Manufacturer#1	almond antique burnished rose metallic	2	[{"x":1173.15,"y":2.0},{"x":1602.59,"y":1.0},{"x":1753.76,"y":1.0}]	115872.0	2
+Manufacturer#1	almond antique chartreuse lavender yellow	34	[{"x":1173.15,"y":2.0},{"x":1414.42,"y":1.0},{"x":1602.59,"y":1.0},{"x":1753.76,"y":1.0}]	110592.0	3
+Manufacturer#1	almond antique salmon chartreuse burlywood	6	[{"x":1173.15,"y":1.0},{"x":1414.42,"y":1.0},{"x":1602.59,"y":1.0},{"x":1632.66,"y":1.0},{"x":1753.76,"y":1.0}]	86428.0	4
+Manufacturer#1	almond aquamarine burnished black steel	28	[{"x":1414.42,"y":1.0},{"x":1602.59,"y":1.0},{"x":1632.66,"y":1.0},{"x":1753.76,"y":1.0}]	86098.0	5
+Manufacturer#1	almond aquamarine pink moccasin thistle	42	[{"x":1414.42,"y":1.0},{"x":1602.59,"y":1.0},{"x":1632.66,"y":1.0}]	86428.0	6
+Manufacturer#2	almond antique violet chocolate turquoise	14	[{"x":1690.68,"y":1.0},{"x":1800.7,"y":1.0},{"x":2031.98,"y":1.0}]	146985.0	1
+Manufacturer#2	almond antique violet turquoise frosted	40	[{"x":1690.68,"y":1.0},{"x":1698.66,"y":1.0},{"x":1800.7,"y":1.0},{"x":2031.98,"y":1.0}]	139825.5	2
+Manufacturer#2	almond aquamarine midnight light salmon	2	[{"x":1690.68,"y":1.0},{"x":1698.66,"y":1.0},{"x":1701.6,"y":1.0},{"x":1800.7,"y":1.0},{"x":2031.98,"y":1.0}]	146985.0	3
+Manufacturer#2	almond aquamarine rose maroon antique	25	[{"x":1698.66,"y":1.0},{"x":1701.6,"y":1.0},{"x":1800.7,"y":1.0},{"x":2031.98,"y":1.0}]	169347.0	4
+Manufacturer#2	almond aquamarine sandy cyan gainsboro	18	[{"x":1698.66,"y":1.0},{"x":1701.6,"y":1.0},{"x":2031.98,"y":1.0}]	146985.0	5
+Manufacturer#3	almond antique chartreuse khaki white	17	[{"x":1190.27,"y":1.0},{"x":1410.39,"y":1.0},{"x":1671.68,"y":1.0}]	90681.0	1
+Manufacturer#3	almond antique forest lavender goldenrod	14	[{"x":1190.27,"y":1.0},{"x":1410.39,"y":1.0},{"x":1671.68,"y":1.0},{"x":1922.98,"y":1.0}]	65831.5	2
+Manufacturer#3	almond antique metallic orange dim	19	[{"x":1190.27,"y":1.0},{"x":1337.29,"y":1.0},{"x":1410.39,"y":1.0},{"x":1671.68,"y":1.0},{"x":1922.98,"y":1.0}]	90681.0	3
+Manufacturer#3	almond antique misty red olive	1	[{"x":1190.27,"y":1.0},{"x":1337.29,"y":1.0},{"x":1410.39,"y":1.0},{"x":1922.98,"y":1.0}]	76690.0	4
+Manufacturer#3	almond antique olive coral navajo	45	[{"x":1337.29,"y":1.0},{"x":1410.39,"y":1.0},{"x":1922.98,"y":1.0}]	112398.0	5
+Manufacturer#4	almond antique gainsboro frosted violet	10	[{"x":1206.26,"y":1.0},{"x":1375.42,"y":1.0},{"x":1620.67,"y":1.0}]	48427.0	1
+Manufacturer#4	almond antique violet mint lemon	39	[{"x":1206.26,"y":1.0},{"x":1375.42,"y":1.0},{"x":1620.67,"y":1.0},{"x":1844.92,"y":1.0}]	46844.0	2
+Manufacturer#4	almond aquamarine floral ivory bisque	27	[{"x":1206.26,"y":1.0},{"x":1290.35,"y":1.0},{"x":1375.42,"y":1.0},{"x":1620.67,"y":1.0},{"x":1844.92,"y":1.0}]	45261.0	3
+Manufacturer#4	almond aquamarine yellow dodger mint	7	[{"x":1206.26,"y":1.0},{"x":1290.35,"y":1.0},{"x":1375.42,"y":1.0},{"x":1844.92,"y":1.0}]	39309.0	4
+Manufacturer#4	almond azure aquamarine papaya violet	12	[{"x":1206.26,"y":1.0},{"x":1290.35,"y":1.0},{"x":1844.92,"y":1.0}]	33357.0	5
+Manufacturer#5	almond antique blue firebrick mint	31	[{"x":1611.66,"y":1.0},{"x":1788.73,"y":1.0},{"x":1789.69,"y":1.0}]	155733.0	1
+Manufacturer#5	almond antique medium spring khaki	6	[{"x":1018.1,"y":1.0},{"x":1611.66,"y":1.0},{"x":1788.73,"y":1.0},{"x":1789.69,"y":1.0}]	99201.0	2
+Manufacturer#5	almond antique sky peru orange	2	[{"x":1018.1,"y":1.0},{"x":1464.48,"y":1.0},{"x":1611.66,"y":1.0},{"x":1788.73,"y":1.0},{"x":1789.69,"y":1.0}]	78486.0	3
+Manufacturer#5	almond aquamarine dodger light gainsboro	46	[{"x":1018.1,"y":1.0},{"x":1464.48,"y":1.0},{"x":1611.66,"y":1.0},{"x":1788.73,"y":1.0}]	60577.5	4
+Manufacturer#5	almond azure blanched chiffon midnight	23	[{"x":1018.1,"y":1.0},{"x":1464.48,"y":1.0},{"x":1788.73,"y":1.0}]	78486.0	5
diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 24. testLateralViews-0-dea06072f0a64fe4537fae854944ed5a b/sql/hive/src/test/resources/golden/windowing.q -- 24. testLateralViews-0-dea06072f0a64fe4537fae854944ed5a
new file mode 100644
index 0000000000000..dc83c9fffe932
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing.q -- 24. testLateralViews-0-dea06072f0a64fe4537fae854944ed5a	
@@ -0,0 +1,78 @@
+Manufacturer#1	almond antique burnished rose metallic	1	2	2
+Manufacturer#1	almond antique burnished rose metallic	1	2	4
+Manufacturer#1	almond antique burnished rose metallic	2	2	6
+Manufacturer#1	almond antique burnished rose metallic	2	2	6
+Manufacturer#1	almond antique burnished rose metallic	3	2	6
+Manufacturer#1	almond antique burnished rose metallic	3	2	6
+Manufacturer#1	almond antique salmon chartreuse burlywood	1	6	10
+Manufacturer#1	almond antique salmon chartreuse burlywood	2	6	14
+Manufacturer#1	almond antique salmon chartreuse burlywood	3	6	18
+Manufacturer#1	almond aquamarine burnished black steel	1	28	40
+Manufacturer#1	almond aquamarine burnished black steel	2	28	62
+Manufacturer#1	almond aquamarine burnished black steel	3	28	84
+Manufacturer#1	almond antique chartreuse lavender yellow	1	34	90
+Manufacturer#1	almond antique chartreuse lavender yellow	2	34	96
+Manufacturer#1	almond antique chartreuse lavender yellow	3	34	102
+Manufacturer#1	almond aquamarine pink moccasin thistle	1	42	110
+Manufacturer#1	almond aquamarine pink moccasin thistle	2	42	118
+Manufacturer#1	almond aquamarine pink moccasin thistle	3	42	126
+Manufacturer#2	almond aquamarine midnight light salmon	1	2	2
+Manufacturer#2	almond aquamarine midnight light salmon	2	2	4
+Manufacturer#2	almond aquamarine midnight light salmon	3	2	6
+Manufacturer#2	almond antique violet chocolate turquoise	1	14	18
+Manufacturer#2	almond antique violet chocolate turquoise	2	14	30
+Manufacturer#2	almond antique violet chocolate turquoise	3	14	42
+Manufacturer#2	almond aquamarine sandy cyan gainsboro	1	18	46
+Manufacturer#2	almond aquamarine sandy cyan gainsboro	2	18	50
+Manufacturer#2	almond aquamarine sandy cyan gainsboro	3	18	54
+Manufacturer#2	almond aquamarine rose maroon antique	1	25	61
+Manufacturer#2	almond aquamarine rose maroon antique	2	25	68
+Manufacturer#2	almond aquamarine rose maroon antique	3	25	75
+Manufacturer#2	almond antique violet turquoise frosted	1	40	90
+Manufacturer#2	almond antique violet turquoise frosted	2	40	105
+Manufacturer#2	almond antique violet turquoise frosted	3	40	120
+Manufacturer#3	almond antique misty red olive	1	1	1
+Manufacturer#3	almond antique misty red olive	2	1	2
+Manufacturer#3	almond antique misty red olive	3	1	3
+Manufacturer#3	almond antique forest lavender goldenrod	1	14	16
+Manufacturer#3	almond antique forest lavender goldenrod	2	14	29
+Manufacturer#3	almond antique forest lavender goldenrod	3	14	42
+Manufacturer#3	almond antique chartreuse khaki white	1	17	45
+Manufacturer#3	almond antique chartreuse khaki white	2	17	48
+Manufacturer#3	almond antique chartreuse khaki white	3	17	51
+Manufacturer#3	almond antique metallic orange dim	1	19	53
+Manufacturer#3	almond antique metallic orange dim	2	19	55
+Manufacturer#3	almond antique metallic orange dim	3	19	57
+Manufacturer#3	almond antique olive coral navajo	1	45	83
+Manufacturer#3	almond antique olive coral navajo	2	45	109
+Manufacturer#3	almond antique olive coral navajo	3	45	135
+Manufacturer#4	almond aquamarine yellow dodger mint	1	7	7
+Manufacturer#4	almond aquamarine yellow dodger mint	2	7	14
+Manufacturer#4	almond aquamarine yellow dodger mint	3	7	21
+Manufacturer#4	almond antique gainsboro frosted violet	1	10	24
+Manufacturer#4	almond antique gainsboro frosted violet	2	10	27
+Manufacturer#4	almond antique gainsboro frosted violet	3	10	30
+Manufacturer#4	almond azure aquamarine papaya violet	1	12	32
+Manufacturer#4	almond azure aquamarine papaya violet	2	12	34
+Manufacturer#4	almond azure aquamarine papaya violet	3	12	36
+Manufacturer#4	almond aquamarine floral ivory bisque	1	27	51
+Manufacturer#4	almond aquamarine floral ivory bisque	2	27	66
+Manufacturer#4	almond aquamarine floral ivory bisque	3	27	81
+Manufacturer#4	almond antique violet mint lemon	1	39	93
+Manufacturer#4	almond antique violet mint lemon	2	39	105
+Manufacturer#4	almond antique violet mint lemon	3	39	117
+Manufacturer#5	almond antique sky peru orange	1	2	2
+Manufacturer#5	almond antique sky peru orange	2	2	4
+Manufacturer#5	almond antique sky peru orange	3	2	6
+Manufacturer#5	almond antique medium spring khaki	1	6	10
+Manufacturer#5	almond antique medium spring khaki	2	6	14
+Manufacturer#5	almond antique medium spring khaki	3	6	18
+Manufacturer#5	almond azure blanched chiffon midnight	1	23	35
+Manufacturer#5	almond azure blanched chiffon midnight	2	23	52
+Manufacturer#5	almond azure blanched chiffon midnight	3	23	69
+Manufacturer#5	almond antique blue firebrick mint	1	31	77
+Manufacturer#5	almond antique blue firebrick mint	2	31	85
+Manufacturer#5	almond antique blue firebrick mint	3	31	93
+Manufacturer#5	almond aquamarine dodger light gainsboro	1	46	108
+Manufacturer#5	almond aquamarine dodger light gainsboro	2	46	123
+Manufacturer#5	almond aquamarine dodger light gainsboro	3	46	138
diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 26. testGroupByHavingWithSWQAndAlias-0-b996a664b06e5741c08079d5c38241bc b/sql/hive/src/test/resources/golden/windowing.q -- 26. testGroupByHavingWithSWQAndAlias-0-b996a664b06e5741c08079d5c38241bc
new file mode 100644
index 0000000000000..2c30e652aa26d
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing.q -- 26. testGroupByHavingWithSWQAndAlias-0-b996a664b06e5741c08079d5c38241bc	
@@ -0,0 +1,25 @@
+Manufacturer#1	almond antique burnished rose metallic	2	1173.15	1	1	2	0
+Manufacturer#1	almond antique chartreuse lavender yellow	34	1753.76	2	2	34	32
+Manufacturer#1	almond antique salmon chartreuse burlywood	6	1602.59	3	3	6	-28
+Manufacturer#1	almond aquamarine burnished black steel	28	1414.42	4	4	28	22
+Manufacturer#1	almond aquamarine pink moccasin thistle	42	1632.66	5	5	42	14
+Manufacturer#2	almond antique violet chocolate turquoise	14	1690.68	1	1	14	0
+Manufacturer#2	almond antique violet turquoise frosted	40	1800.7	2	2	40	26
+Manufacturer#2	almond aquamarine midnight light salmon	2	2031.98	3	3	2	-38
+Manufacturer#2	almond aquamarine rose maroon antique	25	1698.66	4	4	25	23
+Manufacturer#2	almond aquamarine sandy cyan gainsboro	18	1701.6	5	5	18	-7
+Manufacturer#3	almond antique chartreuse khaki white	17	1671.68	1	1	17	0
+Manufacturer#3	almond antique forest lavender goldenrod	14	1190.27	2	2	14	-3
+Manufacturer#3	almond antique metallic orange dim	19	1410.39	3	3	19	5
+Manufacturer#3	almond antique misty red olive	1	1922.98	4	4	1	-18
+Manufacturer#3	almond antique olive coral navajo	45	1337.29	5	5	45	44
+Manufacturer#4	almond antique gainsboro frosted violet	10	1620.67	1	1	10	0
+Manufacturer#4	almond antique violet mint lemon	39	1375.42	2	2	39	29
+Manufacturer#4	almond aquamarine floral ivory bisque	27	1206.26	3	3	27	-12
+Manufacturer#4	almond aquamarine yellow dodger mint	7	1844.92	4	4	7	-20
+Manufacturer#4	almond azure aquamarine papaya violet	12	1290.35	5	5	12	5
+Manufacturer#5	almond antique blue firebrick mint	31	1789.69	1	1	31	0
+Manufacturer#5	almond antique medium spring khaki	6	1611.66	2	2	6	-25
+Manufacturer#5	almond antique sky peru orange	2	1788.73	3	3	2	-4
+Manufacturer#5	almond aquamarine dodger light gainsboro	46	1018.1	4	4	46	44
+Manufacturer#5	almond azure blanched chiffon midnight	23	1464.48	5	5	23	-23
diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 27. testMultipleRangeWindows-0-227e080e337d734dd88ff814b3b412e4 b/sql/hive/src/test/resources/golden/windowing.q -- 27. testMultipleRangeWindows-0-227e080e337d734dd88ff814b3b412e4
new file mode 100644
index 0000000000000..b2a91ba727a72
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing.q -- 27. testMultipleRangeWindows-0-227e080e337d734dd88ff814b3b412e4	
@@ -0,0 +1,26 @@
+Manufacturer#1	almond antique burnished rose metallic	2	4	10
+Manufacturer#1	almond antique burnished rose metallic	2	4	10
+Manufacturer#1	almond antique salmon chartreuse burlywood	6	10	6
+Manufacturer#1	almond aquamarine burnished black steel	28	28	62
+Manufacturer#1	almond antique chartreuse lavender yellow	34	62	76
+Manufacturer#1	almond aquamarine pink moccasin thistle	42	76	42
+Manufacturer#2	almond aquamarine midnight light salmon	2	2	2
+Manufacturer#2	almond antique violet chocolate turquoise	14	14	32
+Manufacturer#2	almond aquamarine sandy cyan gainsboro	18	32	43
+Manufacturer#2	almond aquamarine rose maroon antique	25	43	25
+Manufacturer#2	almond antique violet turquoise frosted	40	40	40
+Manufacturer#3	almond antique misty red olive	1	1	1
+Manufacturer#3	almond antique forest lavender goldenrod	14	14	50
+Manufacturer#3	almond antique chartreuse khaki white	17	31	36
+Manufacturer#3	almond antique metallic orange dim	19	50	19
+Manufacturer#3	almond antique olive coral navajo	45	45	45
+Manufacturer#4	almond aquamarine yellow dodger mint	7	7	29
+Manufacturer#4	almond antique gainsboro frosted violet	10	17	22
+Manufacturer#4	almond azure aquamarine papaya violet	12	29	12
+Manufacturer#4	almond aquamarine floral ivory bisque	27	27	27
+Manufacturer#4	almond antique violet mint lemon	39	39	39
+Manufacturer#5	almond antique sky peru orange	2	2	8
+Manufacturer#5	almond antique medium spring khaki	6	8	6
+Manufacturer#5	almond azure blanched chiffon midnight	23	23	54
+Manufacturer#5	almond antique blue firebrick mint	31	54	31
+Manufacturer#5	almond aquamarine dodger light gainsboro	46	46	46
diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 28. testPartOrderInUDAFInvoke-0-25912ae7d18c91cc09e17e57968fb5db b/sql/hive/src/test/resources/golden/windowing.q -- 28. testPartOrderInUDAFInvoke-0-25912ae7d18c91cc09e17e57968fb5db
new file mode 100644
index 0000000000000..5bcb0fa941d65
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing.q -- 28. testPartOrderInUDAFInvoke-0-25912ae7d18c91cc09e17e57968fb5db	
@@ -0,0 +1,26 @@
+Manufacturer#1	almond antique burnished rose metallic	2	38
+Manufacturer#1	almond antique burnished rose metallic	2	44
+Manufacturer#1	almond antique chartreuse lavender yellow	34	72
+Manufacturer#1	almond antique salmon chartreuse burlywood	6	112
+Manufacturer#1	almond aquamarine burnished black steel	28	110
+Manufacturer#1	almond aquamarine pink moccasin thistle	42	76
+Manufacturer#2	almond antique violet chocolate turquoise	14	56
+Manufacturer#2	almond antique violet turquoise frosted	40	81
+Manufacturer#2	almond aquamarine midnight light salmon	2	99
+Manufacturer#2	almond aquamarine rose maroon antique	25	85
+Manufacturer#2	almond aquamarine sandy cyan gainsboro	18	45
+Manufacturer#3	almond antique chartreuse khaki white	17	50
+Manufacturer#3	almond antique forest lavender goldenrod	14	51
+Manufacturer#3	almond antique metallic orange dim	19	96
+Manufacturer#3	almond antique misty red olive	1	79
+Manufacturer#3	almond antique olive coral navajo	45	65
+Manufacturer#4	almond antique gainsboro frosted violet	10	76
+Manufacturer#4	almond antique violet mint lemon	39	83
+Manufacturer#4	almond aquamarine floral ivory bisque	27	95
+Manufacturer#4	almond aquamarine yellow dodger mint	7	85
+Manufacturer#4	almond azure aquamarine papaya violet	12	46
+Manufacturer#5	almond antique blue firebrick mint	31	39
+Manufacturer#5	almond antique medium spring khaki	6	85
+Manufacturer#5	almond antique sky peru orange	2	108
+Manufacturer#5	almond aquamarine dodger light gainsboro	46	77
+Manufacturer#5	almond azure blanched chiffon midnight	23	71
diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 29. testPartOrderInWdwDef-0-88945892370ccbc1125a927a3d55342a b/sql/hive/src/test/resources/golden/windowing.q -- 29. testPartOrderInWdwDef-0-88945892370ccbc1125a927a3d55342a
new file mode 100644
index 0000000000000..5bcb0fa941d65
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing.q -- 29. testPartOrderInWdwDef-0-88945892370ccbc1125a927a3d55342a	
@@ -0,0 +1,26 @@
+Manufacturer#1	almond antique burnished rose metallic	2	38
+Manufacturer#1	almond antique burnished rose metallic	2	44
+Manufacturer#1	almond antique chartreuse lavender yellow	34	72
+Manufacturer#1	almond antique salmon chartreuse burlywood	6	112
+Manufacturer#1	almond aquamarine burnished black steel	28	110
+Manufacturer#1	almond aquamarine pink moccasin thistle	42	76
+Manufacturer#2	almond antique violet chocolate turquoise	14	56
+Manufacturer#2	almond antique violet turquoise frosted	40	81
+Manufacturer#2	almond aquamarine midnight light salmon	2	99
+Manufacturer#2	almond aquamarine rose maroon antique	25	85
+Manufacturer#2	almond aquamarine sandy cyan gainsboro	18	45
+Manufacturer#3	almond antique chartreuse khaki white	17	50
+Manufacturer#3	almond antique forest lavender goldenrod	14	51
+Manufacturer#3	almond antique metallic orange dim	19	96
+Manufacturer#3	almond antique misty red olive	1	79
+Manufacturer#3	almond antique olive coral navajo	45	65
+Manufacturer#4	almond antique gainsboro frosted violet	10	76
+Manufacturer#4	almond antique violet mint lemon	39	83
+Manufacturer#4	almond aquamarine floral ivory bisque	27	95
+Manufacturer#4	almond aquamarine yellow dodger mint	7	85
+Manufacturer#4	almond azure aquamarine papaya violet	12	46
+Manufacturer#5	almond antique blue firebrick mint	31	39
+Manufacturer#5	almond antique medium spring khaki	6	85
+Manufacturer#5	almond antique sky peru orange	2	108
+Manufacturer#5	almond aquamarine dodger light gainsboro	46	77
+Manufacturer#5	almond azure blanched chiffon midnight	23	71
diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 3. testGroupByHavingWithSWQ-0-a5a5339330a6a6660d32ccb0cc5d7100 b/sql/hive/src/test/resources/golden/windowing.q -- 3. testGroupByHavingWithSWQ-0-a5a5339330a6a6660d32ccb0cc5d7100
new file mode 100644
index 0000000000000..2c30e652aa26d
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing.q -- 3. testGroupByHavingWithSWQ-0-a5a5339330a6a6660d32ccb0cc5d7100	
@@ -0,0 +1,25 @@
+Manufacturer#1	almond antique burnished rose metallic	2	1173.15	1	1	2	0
+Manufacturer#1	almond antique chartreuse lavender yellow	34	1753.76	2	2	34	32
+Manufacturer#1	almond antique salmon chartreuse burlywood	6	1602.59	3	3	6	-28
+Manufacturer#1	almond aquamarine burnished black steel	28	1414.42	4	4	28	22
+Manufacturer#1	almond aquamarine pink moccasin thistle	42	1632.66	5	5	42	14
+Manufacturer#2	almond antique violet chocolate turquoise	14	1690.68	1	1	14	0
+Manufacturer#2	almond antique violet turquoise frosted	40	1800.7	2	2	40	26
+Manufacturer#2	almond aquamarine midnight light salmon	2	2031.98	3	3	2	-38
+Manufacturer#2	almond aquamarine rose maroon antique	25	1698.66	4	4	25	23
+Manufacturer#2	almond aquamarine sandy cyan gainsboro	18	1701.6	5	5	18	-7
+Manufacturer#3	almond antique chartreuse khaki white	17	1671.68	1	1	17	0
+Manufacturer#3	almond antique forest lavender goldenrod	14	1190.27	2	2	14	-3
+Manufacturer#3	almond antique metallic orange dim	19	1410.39	3	3	19	5
+Manufacturer#3	almond antique misty red olive	1	1922.98	4	4	1	-18
+Manufacturer#3	almond antique olive coral navajo	45	1337.29	5	5	45	44
+Manufacturer#4	almond antique gainsboro frosted violet	10	1620.67	1	1	10	0
+Manufacturer#4	almond antique violet mint lemon	39	1375.42	2	2	39	29
+Manufacturer#4	almond aquamarine floral ivory bisque	27	1206.26	3	3	27	-12
+Manufacturer#4	almond aquamarine yellow dodger mint	7	1844.92	4	4	7	-20
+Manufacturer#4	almond azure aquamarine papaya violet	12	1290.35	5	5	12	5
+Manufacturer#5	almond antique blue firebrick mint	31	1789.69	1	1	31	0
+Manufacturer#5	almond antique medium spring khaki	6	1611.66	2	2	6	-25
+Manufacturer#5	almond antique sky peru orange	2	1788.73	3	3	2	-4
+Manufacturer#5	almond aquamarine dodger light gainsboro	46	1018.1	4	4	46	44
+Manufacturer#5	almond azure blanched chiffon midnight	23	1464.48	5	5	23	-23
diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 30. testDefaultPartitioningSpecRules-0-fa80b09c99e3c1487de48ea71a88dada b/sql/hive/src/test/resources/golden/windowing.q -- 30. testDefaultPartitioningSpecRules-0-fa80b09c99e3c1487de48ea71a88dada
new file mode 100644
index 0000000000000..698a44349d2a6
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing.q -- 30. testDefaultPartitioningSpecRules-0-fa80b09c99e3c1487de48ea71a88dada	
@@ -0,0 +1,26 @@
+Manufacturer#1	almond antique burnished rose metallic	2	38	4
+Manufacturer#1	almond antique burnished rose metallic	2	44	4
+Manufacturer#1	almond antique chartreuse lavender yellow	34	72	38
+Manufacturer#1	almond antique salmon chartreuse burlywood	6	112	44
+Manufacturer#1	almond aquamarine burnished black steel	28	110	72
+Manufacturer#1	almond aquamarine pink moccasin thistle	42	76	114
+Manufacturer#2	almond antique violet chocolate turquoise	14	56	14
+Manufacturer#2	almond antique violet turquoise frosted	40	81	54
+Manufacturer#2	almond aquamarine midnight light salmon	2	99	56
+Manufacturer#2	almond aquamarine rose maroon antique	25	85	81
+Manufacturer#2	almond aquamarine sandy cyan gainsboro	18	45	99
+Manufacturer#3	almond antique chartreuse khaki white	17	50	17
+Manufacturer#3	almond antique forest lavender goldenrod	14	51	31
+Manufacturer#3	almond antique metallic orange dim	19	96	50
+Manufacturer#3	almond antique misty red olive	1	79	51
+Manufacturer#3	almond antique olive coral navajo	45	65	96
+Manufacturer#4	almond antique gainsboro frosted violet	10	76	10
+Manufacturer#4	almond antique violet mint lemon	39	83	49
+Manufacturer#4	almond aquamarine floral ivory bisque	27	95	76
+Manufacturer#4	almond aquamarine yellow dodger mint	7	85	83
+Manufacturer#4	almond azure aquamarine papaya violet	12	46	95
+Manufacturer#5	almond antique blue firebrick mint	31	39	31
+Manufacturer#5	almond antique medium spring khaki	6	85	37
+Manufacturer#5	almond antique sky peru orange	2	108	39
+Manufacturer#5	almond aquamarine dodger light gainsboro	46	77	85
+Manufacturer#5	almond azure blanched chiffon midnight	23	71	108
diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 36. testRankWithPartitioning-0-45ccbaf0ee083858f7661c66b11d4768 b/sql/hive/src/test/resources/golden/windowing.q -- 36. testRankWithPartitioning-0-45ccbaf0ee083858f7661c66b11d4768
new file mode 100644
index 0000000000000..e35257d98382e
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing.q -- 36. testRankWithPartitioning-0-45ccbaf0ee083858f7661c66b11d4768	
@@ -0,0 +1,26 @@
+Manufacturer#1	almond antique burnished rose metallic	2	1
+Manufacturer#1	almond antique burnished rose metallic	2	1
+Manufacturer#1	almond antique chartreuse lavender yellow	34	3
+Manufacturer#1	almond antique salmon chartreuse burlywood	6	4
+Manufacturer#1	almond aquamarine burnished black steel	28	5
+Manufacturer#1	almond aquamarine pink moccasin thistle	42	6
+Manufacturer#2	almond antique violet chocolate turquoise	14	1
+Manufacturer#2	almond antique violet turquoise frosted	40	2
+Manufacturer#2	almond aquamarine midnight light salmon	2	3
+Manufacturer#2	almond aquamarine rose maroon antique	25	4
+Manufacturer#2	almond aquamarine sandy cyan gainsboro	18	5
+Manufacturer#3	almond antique chartreuse khaki white	17	1
+Manufacturer#3	almond antique forest lavender goldenrod	14	2
+Manufacturer#3	almond antique metallic orange dim	19	3
+Manufacturer#3	almond antique misty red olive	1	4
+Manufacturer#3	almond antique olive coral navajo	45	5
+Manufacturer#4	almond antique gainsboro frosted violet	10	1
+Manufacturer#4	almond antique violet mint lemon	39	2
+Manufacturer#4	almond aquamarine floral ivory bisque	27	3
+Manufacturer#4	almond aquamarine yellow dodger mint	7	4
+Manufacturer#4	almond azure aquamarine papaya violet	12	5
+Manufacturer#5	almond antique blue firebrick mint	31	1
+Manufacturer#5	almond antique medium spring khaki	6	2
+Manufacturer#5	almond antique sky peru orange	2	3
+Manufacturer#5	almond aquamarine dodger light gainsboro	46	4
+Manufacturer#5	almond azure blanched chiffon midnight	23	5
diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 37. testPartitioningVariousForms-0-3436e50214f9afdec84334e10faa931a b/sql/hive/src/test/resources/golden/windowing.q -- 37. testPartitioningVariousForms-0-3436e50214f9afdec84334e10faa931a
new file mode 100644
index 0000000000000..9c0ca6c7a00ba
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing.q -- 37. testPartitioningVariousForms-0-3436e50214f9afdec84334e10faa931a	
@@ -0,0 +1,26 @@
+Manufacturer#1	8749.73	1173.15	1753.76	1458.29	6
+Manufacturer#1	8749.73	1173.15	1753.76	1458.29	6
+Manufacturer#1	8749.73	1173.15	1753.76	1458.29	6
+Manufacturer#1	8749.73	1173.15	1753.76	1458.29	6
+Manufacturer#1	8749.73	1173.15	1753.76	1458.29	6
+Manufacturer#1	8749.73	1173.15	1753.76	1458.29	6
+Manufacturer#2	8923.62	1690.68	2031.98	1784.72	5
+Manufacturer#2	8923.62	1690.68	2031.98	1784.72	5
+Manufacturer#2	8923.62	1690.68	2031.98	1784.72	5
+Manufacturer#2	8923.62	1690.68	2031.98	1784.72	5
+Manufacturer#2	8923.62	1690.68	2031.98	1784.72	5
+Manufacturer#3	7532.61	1190.27	1922.98	1506.52	5
+Manufacturer#3	7532.61	1190.27	1922.98	1506.52	5
+Manufacturer#3	7532.61	1190.27	1922.98	1506.52	5
+Manufacturer#3	7532.61	1190.27	1922.98	1506.52	5
+Manufacturer#3	7532.61	1190.27	1922.98	1506.52	5
+Manufacturer#4	7337.62	1206.26	1844.92	1467.52	5
+Manufacturer#4	7337.62	1206.26	1844.92	1467.52	5
+Manufacturer#4	7337.62	1206.26	1844.92	1467.52	5
+Manufacturer#4	7337.62	1206.26	1844.92	1467.52	5
+Manufacturer#4	7337.62	1206.26	1844.92	1467.52	5
+Manufacturer#5	7672.66	1018.1	1789.69	1534.53	5
+Manufacturer#5	7672.66	1018.1	1789.69	1534.53	5
+Manufacturer#5	7672.66	1018.1	1789.69	1534.53	5
+Manufacturer#5	7672.66	1018.1	1789.69	1534.53	5
+Manufacturer#5	7672.66	1018.1	1789.69	1534.53	5
diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 38. testPartitioningVariousForms2-0-cba9d84a6b1bb5e36595338d4602377e b/sql/hive/src/test/resources/golden/windowing.q -- 38. testPartitioningVariousForms2-0-cba9d84a6b1bb5e36595338d4602377e
new file mode 100644
index 0000000000000..fc27df2f2b648
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing.q -- 38. testPartitioningVariousForms2-0-cba9d84a6b1bb5e36595338d4602377e	
@@ -0,0 +1,26 @@
+Manufacturer#1	almond antique burnished rose metallic	2	1173.15	1173.15	1173.15
+Manufacturer#1	almond antique burnished rose metallic	2	2346.3	1173.15	1173.15
+Manufacturer#1	almond antique chartreuse lavender yellow	34	1753.76	1753.76	1753.76
+Manufacturer#1	almond antique salmon chartreuse burlywood	6	1602.59	1602.59	1602.59
+Manufacturer#1	almond aquamarine burnished black steel	28	1414.42	1414.42	1414.42
+Manufacturer#1	almond aquamarine pink moccasin thistle	42	1632.66	1632.66	1632.66
+Manufacturer#2	almond antique violet chocolate turquoise	14	1690.68	1690.68	1690.68
+Manufacturer#2	almond antique violet turquoise frosted	40	1800.7	1800.7	1800.7
+Manufacturer#2	almond aquamarine midnight light salmon	2	2031.98	2031.98	2031.98
+Manufacturer#2	almond aquamarine rose maroon antique	25	1698.66	1698.66	1698.66
+Manufacturer#2	almond aquamarine sandy cyan gainsboro	18	1701.6	1701.6	1701.6
+Manufacturer#3	almond antique chartreuse khaki white	17	1671.68	1671.68	1671.68
+Manufacturer#3	almond antique forest lavender goldenrod	14	1190.27	1190.27	1190.27
+Manufacturer#3	almond antique metallic orange dim	19	1410.39	1410.39	1410.39
+Manufacturer#3	almond antique misty red olive	1	1922.98	1922.98	1922.98
+Manufacturer#3	almond antique olive coral navajo	45	1337.29	1337.29	1337.29
+Manufacturer#4	almond antique gainsboro frosted violet	10	1620.67	1620.67	1620.67
+Manufacturer#4	almond antique violet mint lemon	39	1375.42	1375.42	1375.42
+Manufacturer#4	almond aquamarine floral ivory bisque	27	1206.26	1206.26	1206.26
+Manufacturer#4	almond aquamarine yellow dodger mint	7	1844.92	1844.92	1844.92
+Manufacturer#4	almond azure aquamarine papaya violet	12	1290.35	1290.35	1290.35
+Manufacturer#5	almond antique blue firebrick mint	31	1789.69	1789.69	1789.69
+Manufacturer#5	almond antique medium spring khaki	6	1611.66	1611.66	1611.66
+Manufacturer#5	almond antique sky peru orange	2	1788.73	1788.73	1788.73
+Manufacturer#5	almond aquamarine dodger light gainsboro	46	1018.1	1018.1	1018.1
+Manufacturer#5	almond azure blanched chiffon midnight	23	1464.48	1464.48	1464.48
diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 39. testUDFOnOrderCols-0-7647562850dd367ef1e6c63117805423 b/sql/hive/src/test/resources/golden/windowing.q -- 39. testUDFOnOrderCols-0-7647562850dd367ef1e6c63117805423
new file mode 100644
index 0000000000000..e5a541f56f6f3
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing.q -- 39. testUDFOnOrderCols-0-7647562850dd367ef1e6c63117805423	
@@ -0,0 +1,26 @@
+Manufacturer#1	LARGE BRUSHED STEEL	ARGE BRUSHED STEEL	1
+Manufacturer#1	LARGE BURNISHED STEEL	ARGE BURNISHED STEEL	2
+Manufacturer#1	PROMO BURNISHED NICKEL	ROMO BURNISHED NICKEL	3
+Manufacturer#1	PROMO PLATED TIN	ROMO PLATED TIN	4
+Manufacturer#1	PROMO PLATED TIN	ROMO PLATED TIN	4
+Manufacturer#1	STANDARD ANODIZED STEEL	TANDARD ANODIZED STEEL	6
+Manufacturer#2	ECONOMY POLISHED STEEL	CONOMY POLISHED STEEL	1
+Manufacturer#2	MEDIUM ANODIZED COPPER	EDIUM ANODIZED COPPER	2
+Manufacturer#2	MEDIUM BURNISHED COPPER	EDIUM BURNISHED COPPER	3
+Manufacturer#2	SMALL POLISHED NICKEL	MALL POLISHED NICKEL	4
+Manufacturer#2	STANDARD PLATED TIN	TANDARD PLATED TIN	5
+Manufacturer#3	ECONOMY PLATED COPPER	CONOMY PLATED COPPER	1
+Manufacturer#3	MEDIUM BURNISHED BRASS	EDIUM BURNISHED BRASS	2
+Manufacturer#3	MEDIUM BURNISHED TIN	EDIUM BURNISHED TIN	3
+Manufacturer#3	PROMO ANODIZED TIN	ROMO ANODIZED TIN	4
+Manufacturer#3	STANDARD POLISHED STEEL	TANDARD POLISHED STEEL	5
+Manufacturer#4	ECONOMY BRUSHED COPPER	CONOMY BRUSHED COPPER	1
+Manufacturer#4	SMALL BRUSHED BRASS	MALL BRUSHED BRASS	2
+Manufacturer#4	SMALL PLATED STEEL	MALL PLATED STEEL	3
+Manufacturer#4	PROMO POLISHED STEEL	ROMO POLISHED STEEL	4
+Manufacturer#4	STANDARD ANODIZED TIN	TANDARD ANODIZED TIN	5
+Manufacturer#5	LARGE BRUSHED BRASS	ARGE BRUSHED BRASS	1
+Manufacturer#5	ECONOMY BURNISHED STEEL	CONOMY BURNISHED STEEL	2
+Manufacturer#5	MEDIUM BURNISHED TIN	EDIUM BURNISHED TIN	3
+Manufacturer#5	SMALL PLATED BRASS	MALL PLATED BRASS	4
+Manufacturer#5	STANDARD BURNISHED TIN	TANDARD BURNISHED TIN	5
diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 4. testCount-0-e6e97e884327df86f16b870527ec026c b/sql/hive/src/test/resources/golden/windowing.q -- 4. testCount-0-e6e97e884327df86f16b870527ec026c
new file mode 100644
index 0000000000000..bf8e620a304af
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing.q -- 4. testCount-0-e6e97e884327df86f16b870527ec026c	
@@ -0,0 +1,26 @@
+Manufacturer#1	almond antique burnished rose metallic	2
+Manufacturer#1	almond antique burnished rose metallic	2
+Manufacturer#1	almond antique chartreuse lavender yellow	3
+Manufacturer#1	almond antique salmon chartreuse burlywood	4
+Manufacturer#1	almond aquamarine burnished black steel	5
+Manufacturer#1	almond aquamarine pink moccasin thistle	6
+Manufacturer#2	almond antique violet chocolate turquoise	1
+Manufacturer#2	almond antique violet turquoise frosted	2
+Manufacturer#2	almond aquamarine midnight light salmon	3
+Manufacturer#2	almond aquamarine rose maroon antique	4
+Manufacturer#2	almond aquamarine sandy cyan gainsboro	5
+Manufacturer#3	almond antique chartreuse khaki white	1
+Manufacturer#3	almond antique forest lavender goldenrod	2
+Manufacturer#3	almond antique metallic orange dim	3
+Manufacturer#3	almond antique misty red olive	4
+Manufacturer#3	almond antique olive coral navajo	5
+Manufacturer#4	almond antique gainsboro frosted violet	1
+Manufacturer#4	almond antique violet mint lemon	2
+Manufacturer#4	almond aquamarine floral ivory bisque	3
+Manufacturer#4	almond aquamarine yellow dodger mint	4
+Manufacturer#4	almond azure aquamarine papaya violet	5
+Manufacturer#5	almond antique blue firebrick mint	1
+Manufacturer#5	almond antique medium spring khaki	2
+Manufacturer#5	almond antique sky peru orange	3
+Manufacturer#5	almond aquamarine dodger light gainsboro	4
+Manufacturer#5	almond azure blanched chiffon midnight	5
diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 40. testNoBetweenForRows-0-99007f45b6406869e048b0e4eb9213f1 b/sql/hive/src/test/resources/golden/windowing.q -- 40. testNoBetweenForRows-0-99007f45b6406869e048b0e4eb9213f1
new file mode 100644
index 0000000000000..1e29df62901d0
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing.q -- 40. testNoBetweenForRows-0-99007f45b6406869e048b0e4eb9213f1	
@@ -0,0 +1,26 @@
+Manufacturer#1	almond antique burnished rose metallic	2	1173.15
+Manufacturer#1	almond antique burnished rose metallic	2	2346.3
+Manufacturer#1	almond antique chartreuse lavender yellow	34	4100.06
+Manufacturer#1	almond antique salmon chartreuse burlywood	6	5702.650000000001
+Manufacturer#1	almond aquamarine burnished black steel	28	7117.070000000001
+Manufacturer#1	almond aquamarine pink moccasin thistle	42	8749.730000000001
+Manufacturer#2	almond antique violet chocolate turquoise	14	1690.68
+Manufacturer#2	almond antique violet turquoise frosted	40	3491.38
+Manufacturer#2	almond aquamarine midnight light salmon	2	5523.360000000001
+Manufacturer#2	almond aquamarine rose maroon antique	25	7222.02
+Manufacturer#2	almond aquamarine sandy cyan gainsboro	18	8923.62
+Manufacturer#3	almond antique chartreuse khaki white	17	1671.68
+Manufacturer#3	almond antique forest lavender goldenrod	14	2861.95
+Manufacturer#3	almond antique metallic orange dim	19	4272.34
+Manufacturer#3	almond antique misty red olive	1	6195.32
+Manufacturer#3	almond antique olive coral navajo	45	7532.61
+Manufacturer#4	almond antique gainsboro frosted violet	10	1620.67
+Manufacturer#4	almond antique violet mint lemon	39	2996.09
+Manufacturer#4	almond aquamarine floral ivory bisque	27	4202.35
+Manufacturer#4	almond aquamarine yellow dodger mint	7	6047.27
+Manufacturer#4	almond azure aquamarine papaya violet	12	7337.620000000001
+Manufacturer#5	almond antique blue firebrick mint	31	1789.69
+Manufacturer#5	almond antique medium spring khaki	6	3401.3500000000004
+Manufacturer#5	almond antique sky peru orange	2	5190.08
+Manufacturer#5	almond aquamarine dodger light gainsboro	46	6208.18
+Manufacturer#5	almond azure blanched chiffon midnight	23	7672.66
diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 41. testNoBetweenForRange-0-d81a591e90950de291d2f133793e9283 b/sql/hive/src/test/resources/golden/windowing.q -- 41. testNoBetweenForRange-0-d81a591e90950de291d2f133793e9283
new file mode 100644
index 0000000000000..a620479fe4064
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing.q -- 41. testNoBetweenForRange-0-d81a591e90950de291d2f133793e9283	
@@ -0,0 +1,26 @@
+Manufacturer#1	almond antique burnished rose metallic	2	2346.3
+Manufacturer#1	almond antique burnished rose metallic	2	2346.3
+Manufacturer#1	almond antique salmon chartreuse burlywood	6	3948.8900000000003
+Manufacturer#1	almond aquamarine burnished black steel	28	5363.31
+Manufacturer#1	almond antique chartreuse lavender yellow	34	7117.070000000001
+Manufacturer#1	almond aquamarine pink moccasin thistle	42	8749.730000000001
+Manufacturer#2	almond aquamarine midnight light salmon	2	2031.98
+Manufacturer#2	almond antique violet chocolate turquoise	14	3722.66
+Manufacturer#2	almond aquamarine sandy cyan gainsboro	18	5424.26
+Manufacturer#2	almond aquamarine rose maroon antique	25	7122.92
+Manufacturer#2	almond antique violet turquoise frosted	40	8923.62
+Manufacturer#3	almond antique misty red olive	1	1922.98
+Manufacturer#3	almond antique forest lavender goldenrod	14	3113.25
+Manufacturer#3	almond antique chartreuse khaki white	17	4784.93
+Manufacturer#3	almond antique metallic orange dim	19	6195.320000000001
+Manufacturer#3	almond antique olive coral navajo	45	7532.610000000001
+Manufacturer#4	almond aquamarine yellow dodger mint	7	1844.92
+Manufacturer#4	almond antique gainsboro frosted violet	10	3465.59
+Manufacturer#4	almond azure aquamarine papaya violet	12	4755.9400000000005
+Manufacturer#4	almond aquamarine floral ivory bisque	27	5962.200000000001
+Manufacturer#4	almond antique violet mint lemon	39	7337.620000000001
+Manufacturer#5	almond antique sky peru orange	2	1788.73
+Manufacturer#5	almond antique medium spring khaki	6	3400.3900000000003
+Manufacturer#5	almond azure blanched chiffon midnight	23	4864.870000000001
+Manufacturer#5	almond antique blue firebrick mint	31	6654.560000000001
+Manufacturer#5	almond aquamarine dodger light gainsboro	46	7672.660000000002
diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 42. testUnboundedFollowingForRows-0-fb8648e82e4dd56d6bdcfd739dd1edf0 b/sql/hive/src/test/resources/golden/windowing.q -- 42. testUnboundedFollowingForRows-0-fb8648e82e4dd56d6bdcfd739dd1edf0
new file mode 100644
index 0000000000000..74147d2571a15
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing.q -- 42. testUnboundedFollowingForRows-0-fb8648e82e4dd56d6bdcfd739dd1edf0	
@@ -0,0 +1,26 @@
+Manufacturer#1	almond antique burnished rose metallic	2	8749.730000000001
+Manufacturer#1	almond antique burnished rose metallic	2	7576.58
+Manufacturer#1	almond antique chartreuse lavender yellow	34	6403.43
+Manufacturer#1	almond antique salmon chartreuse burlywood	6	4649.67
+Manufacturer#1	almond aquamarine burnished black steel	28	3047.08
+Manufacturer#1	almond aquamarine pink moccasin thistle	42	1632.66
+Manufacturer#2	almond antique violet chocolate turquoise	14	8923.62
+Manufacturer#2	almond antique violet turquoise frosted	40	7232.9400000000005
+Manufacturer#2	almond aquamarine midnight light salmon	2	5432.24
+Manufacturer#2	almond aquamarine rose maroon antique	25	3400.26
+Manufacturer#2	almond aquamarine sandy cyan gainsboro	18	1701.6
+Manufacturer#3	almond antique chartreuse khaki white	17	7532.61
+Manufacturer#3	almond antique forest lavender goldenrod	14	5860.929999999999
+Manufacturer#3	almond antique metallic orange dim	19	4670.66
+Manufacturer#3	almond antique misty red olive	1	3260.27
+Manufacturer#3	almond antique olive coral navajo	45	1337.29
+Manufacturer#4	almond antique gainsboro frosted violet	10	7337.620000000001
+Manufacturer#4	almond antique violet mint lemon	39	5716.950000000001
+Manufacturer#4	almond aquamarine floral ivory bisque	27	4341.530000000001
+Manufacturer#4	almond aquamarine yellow dodger mint	7	3135.27
+Manufacturer#4	almond azure aquamarine papaya violet	12	1290.35
+Manufacturer#5	almond antique blue firebrick mint	31	7672.66
+Manufacturer#5	almond antique medium spring khaki	6	5882.970000000001
+Manufacturer#5	almond antique sky peru orange	2	4271.3099999999995
+Manufacturer#5	almond aquamarine dodger light gainsboro	46	2482.58
+Manufacturer#5	almond azure blanched chiffon midnight	23	1464.48
diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 43. testUnboundedFollowingForRange-0-3cd04e5f2398853c4850f4f86142bb39 b/sql/hive/src/test/resources/golden/windowing.q -- 43. testUnboundedFollowingForRange-0-3cd04e5f2398853c4850f4f86142bb39
new file mode 100644
index 0000000000000..49d003b5de139
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing.q -- 43. testUnboundedFollowingForRange-0-3cd04e5f2398853c4850f4f86142bb39	
@@ -0,0 +1,26 @@
+Manufacturer#1	almond antique burnished rose metallic	2	8749.730000000001
+Manufacturer#1	almond antique burnished rose metallic	2	8749.730000000001
+Manufacturer#1	almond antique salmon chartreuse burlywood	6	6403.43
+Manufacturer#1	almond aquamarine burnished black steel	28	4800.84
+Manufacturer#1	almond antique chartreuse lavender yellow	34	3386.42
+Manufacturer#1	almond aquamarine pink moccasin thistle	42	1632.66
+Manufacturer#2	almond aquamarine midnight light salmon	2	8923.62
+Manufacturer#2	almond antique violet chocolate turquoise	14	6891.639999999999
+Manufacturer#2	almond aquamarine sandy cyan gainsboro	18	5200.96
+Manufacturer#2	almond aquamarine rose maroon antique	25	3499.36
+Manufacturer#2	almond antique violet turquoise frosted	40	1800.7
+Manufacturer#3	almond antique misty red olive	1	7532.610000000001
+Manufacturer#3	almond antique forest lavender goldenrod	14	5609.63
+Manufacturer#3	almond antique chartreuse khaki white	17	4419.360000000001
+Manufacturer#3	almond antique metallic orange dim	19	2747.6800000000003
+Manufacturer#3	almond antique olive coral navajo	45	1337.29
+Manufacturer#4	almond aquamarine yellow dodger mint	7	7337.620000000001
+Manufacturer#4	almond antique gainsboro frosted violet	10	5492.7
+Manufacturer#4	almond azure aquamarine papaya violet	12	3872.0299999999997
+Manufacturer#4	almond aquamarine floral ivory bisque	27	2581.6800000000003
+Manufacturer#4	almond antique violet mint lemon	39	1375.42
+Manufacturer#5	almond antique sky peru orange	2	7672.660000000002
+Manufacturer#5	almond antique medium spring khaki	6	5883.93
+Manufacturer#5	almond azure blanched chiffon midnight	23	4272.27
+Manufacturer#5	almond antique blue firebrick mint	31	2807.79
+Manufacturer#5	almond aquamarine dodger light gainsboro	46	1018.1
diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 44. testOverNoPartitionSingleAggregate-0-cb3d2f8c1296044dc2658876bb6103ae b/sql/hive/src/test/resources/golden/windowing.q -- 44. testOverNoPartitionSingleAggregate-0-cb3d2f8c1296044dc2658876bb6103ae
new file mode 100644
index 0000000000000..5982c9ee2a4d4
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing.q -- 44. testOverNoPartitionSingleAggregate-0-cb3d2f8c1296044dc2658876bb6103ae	
@@ -0,0 +1,26 @@
+almond antique blue firebrick mint	1789.69	1546.78
+almond antique burnished rose metallic	1173.15	1546.78
+almond antique burnished rose metallic	1173.15	1546.78
+almond antique chartreuse khaki white	1671.68	1546.78
+almond antique chartreuse lavender yellow	1753.76	1546.78
+almond antique forest lavender goldenrod	1190.27	1546.78
+almond antique gainsboro frosted violet	1620.67	1546.78
+almond antique medium spring khaki	1611.66	1546.78
+almond antique metallic orange dim	1410.39	1546.78
+almond antique misty red olive	1922.98	1546.78
+almond antique olive coral navajo	1337.29	1546.78
+almond antique salmon chartreuse burlywood	1602.59	1546.78
+almond antique sky peru orange	1788.73	1546.78
+almond antique violet chocolate turquoise	1690.68	1546.78
+almond antique violet mint lemon	1375.42	1546.78
+almond antique violet turquoise frosted	1800.7	1546.78
+almond aquamarine burnished black steel	1414.42	1546.78
+almond aquamarine dodger light gainsboro	1018.1	1546.78
+almond aquamarine floral ivory bisque	1206.26	1546.78
+almond aquamarine midnight light salmon	2031.98	1546.78
+almond aquamarine pink moccasin thistle	1632.66	1546.78
+almond aquamarine rose maroon antique	1698.66	1546.78
+almond aquamarine sandy cyan gainsboro	1701.6	1546.78
+almond aquamarine yellow dodger mint	1844.92	1546.78
+almond azure aquamarine papaya violet	1290.35	1546.78
+almond azure blanched chiffon midnight	1464.48	1546.78
diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 5. testCountWithWindowingUDAF-0-3bde93728761b780a745c2ce0398aa0f b/sql/hive/src/test/resources/golden/windowing.q -- 5. testCountWithWindowingUDAF-0-3bde93728761b780a745c2ce0398aa0f
new file mode 100644
index 0000000000000..00d41fc0bcd9c
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing.q -- 5. testCountWithWindowingUDAF-0-3bde93728761b780a745c2ce0398aa0f	
@@ -0,0 +1,26 @@
+Manufacturer#1	almond antique burnished rose metallic	1	1	2	1173.15	1173.15	2	0
+Manufacturer#1	almond antique burnished rose metallic	1	1	2	1173.15	2346.3	2	0
+Manufacturer#1	almond antique chartreuse lavender yellow	3	2	3	1753.76	4100.06	34	32
+Manufacturer#1	almond antique salmon chartreuse burlywood	4	3	4	1602.59	5702.650000000001	6	-28
+Manufacturer#1	almond aquamarine burnished black steel	5	4	5	1414.42	7117.070000000001	28	22
+Manufacturer#1	almond aquamarine pink moccasin thistle	6	5	6	1632.66	8749.730000000001	42	14
+Manufacturer#2	almond antique violet chocolate turquoise	1	1	1	1690.68	1690.68	14	0
+Manufacturer#2	almond antique violet turquoise frosted	2	2	2	1800.7	3491.38	40	26
+Manufacturer#2	almond aquamarine midnight light salmon	3	3	3	2031.98	5523.360000000001	2	-38
+Manufacturer#2	almond aquamarine rose maroon antique	4	4	4	1698.66	7222.02	25	23
+Manufacturer#2	almond aquamarine sandy cyan gainsboro	5	5	5	1701.6	8923.62	18	-7
+Manufacturer#3	almond antique chartreuse khaki white	1	1	1	1671.68	1671.68	17	0
+Manufacturer#3	almond antique forest lavender goldenrod	2	2	2	1190.27	2861.95	14	-3
+Manufacturer#3	almond antique metallic orange dim	3	3	3	1410.39	4272.34	19	5
+Manufacturer#3	almond antique misty red olive	4	4	4	1922.98	6195.32	1	-18
+Manufacturer#3	almond antique olive coral navajo	5	5	5	1337.29	7532.61	45	44
+Manufacturer#4	almond antique gainsboro frosted violet	1	1	1	1620.67	1620.67	10	0
+Manufacturer#4	almond antique violet mint lemon	2	2	2	1375.42	2996.09	39	29
+Manufacturer#4	almond aquamarine floral ivory bisque	3	3	3	1206.26	4202.35	27	-12
+Manufacturer#4	almond aquamarine yellow dodger mint	4	4	4	1844.92	6047.27	7	-20
+Manufacturer#4	almond azure aquamarine papaya violet	5	5	5	1290.35	7337.620000000001	12	5
+Manufacturer#5	almond antique blue firebrick mint	1	1	1	1789.69	1789.69	31	0
+Manufacturer#5	almond antique medium spring khaki	2	2	2	1611.66	3401.3500000000004	6	-25
+Manufacturer#5	almond antique sky peru orange	3	3	3	1788.73	5190.08	2	-4
+Manufacturer#5	almond aquamarine dodger light gainsboro	4	4	4	1018.1	6208.18	46	44
+Manufacturer#5	almond azure blanched chiffon midnight	5	5	5	1464.48	7672.66	23	-23
diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 6. testCountInSubQ-0-73d5274a21d4f4fd51d2a0f1d98516ce b/sql/hive/src/test/resources/golden/windowing.q -- 6. testCountInSubQ-0-73d5274a21d4f4fd51d2a0f1d98516ce
new file mode 100644
index 0000000000000..98c09e4fe15c3
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing.q -- 6. testCountInSubQ-0-73d5274a21d4f4fd51d2a0f1d98516ce	
@@ -0,0 +1,26 @@
+1	1	2	1173.15	0
+1	1	2	2346.3	0
+3	2	3	4100.06	32
+4	3	4	5702.650000000001	-28
+5	4	5	7117.070000000001	22
+6	5	6	8749.730000000001	14
+1	1	1	1690.68	0
+2	2	2	3491.38	26
+3	3	3	5523.360000000001	-38
+4	4	4	7222.02	23
+5	5	5	8923.62	-7
+1	1	1	1671.68	0
+2	2	2	2861.95	-3
+3	3	3	4272.34	5
+4	4	4	6195.32	-18
+5	5	5	7532.61	44
+1	1	1	1620.67	0
+2	2	2	2996.09	29
+3	3	3	4202.35	-12
+4	4	4	6047.27	-20
+5	5	5	7337.620000000001	5
+1	1	1	1789.69	0
+2	2	2	3401.3500000000004	-25
+3	3	3	5190.08	-4
+4	4	4	6208.18	44
+5	5	5	7672.66	-23
diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 8. testMixedCaseAlias-0-4b1ad2515fb079012467e987f484a722 b/sql/hive/src/test/resources/golden/windowing.q -- 8. testMixedCaseAlias-0-4b1ad2515fb079012467e987f484a722
new file mode 100644
index 0000000000000..e35257d98382e
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing.q -- 8. testMixedCaseAlias-0-4b1ad2515fb079012467e987f484a722	
@@ -0,0 +1,26 @@
+Manufacturer#1	almond antique burnished rose metallic	2	1
+Manufacturer#1	almond antique burnished rose metallic	2	1
+Manufacturer#1	almond antique chartreuse lavender yellow	34	3
+Manufacturer#1	almond antique salmon chartreuse burlywood	6	4
+Manufacturer#1	almond aquamarine burnished black steel	28	5
+Manufacturer#1	almond aquamarine pink moccasin thistle	42	6
+Manufacturer#2	almond antique violet chocolate turquoise	14	1
+Manufacturer#2	almond antique violet turquoise frosted	40	2
+Manufacturer#2	almond aquamarine midnight light salmon	2	3
+Manufacturer#2	almond aquamarine rose maroon antique	25	4
+Manufacturer#2	almond aquamarine sandy cyan gainsboro	18	5
+Manufacturer#3	almond antique chartreuse khaki white	17	1
+Manufacturer#3	almond antique forest lavender goldenrod	14	2
+Manufacturer#3	almond antique metallic orange dim	19	3
+Manufacturer#3	almond antique misty red olive	1	4
+Manufacturer#3	almond antique olive coral navajo	45	5
+Manufacturer#4	almond antique gainsboro frosted violet	10	1
+Manufacturer#4	almond antique violet mint lemon	39	2
+Manufacturer#4	almond aquamarine floral ivory bisque	27	3
+Manufacturer#4	almond aquamarine yellow dodger mint	7	4
+Manufacturer#4	almond azure aquamarine papaya violet	12	5
+Manufacturer#5	almond antique blue firebrick mint	31	1
+Manufacturer#5	almond antique medium spring khaki	6	2
+Manufacturer#5	almond antique sky peru orange	2	3
+Manufacturer#5	almond aquamarine dodger light gainsboro	46	4
+Manufacturer#5	almond azure blanched chiffon midnight	23	5
diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 9. testHavingWithWindowingNoGBY-0-70cdc0555a61ef08534a9ebebb95ebbf b/sql/hive/src/test/resources/golden/windowing.q -- 9. testHavingWithWindowingNoGBY-0-70cdc0555a61ef08534a9ebebb95ebbf
new file mode 100644
index 0000000000000..850c41c8115d6
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing.q -- 9. testHavingWithWindowingNoGBY-0-70cdc0555a61ef08534a9ebebb95ebbf	
@@ -0,0 +1,26 @@
+Manufacturer#1	almond antique burnished rose metallic	2	1	1	1173.15
+Manufacturer#1	almond antique burnished rose metallic	2	1	1	2346.3
+Manufacturer#1	almond antique chartreuse lavender yellow	34	3	2	4100.06
+Manufacturer#1	almond antique salmon chartreuse burlywood	6	4	3	5702.650000000001
+Manufacturer#1	almond aquamarine burnished black steel	28	5	4	7117.070000000001
+Manufacturer#1	almond aquamarine pink moccasin thistle	42	6	5	8749.730000000001
+Manufacturer#2	almond antique violet chocolate turquoise	14	1	1	1690.68
+Manufacturer#2	almond antique violet turquoise frosted	40	2	2	3491.38
+Manufacturer#2	almond aquamarine midnight light salmon	2	3	3	5523.360000000001
+Manufacturer#2	almond aquamarine rose maroon antique	25	4	4	7222.02
+Manufacturer#2	almond aquamarine sandy cyan gainsboro	18	5	5	8923.62
+Manufacturer#3	almond antique chartreuse khaki white	17	1	1	1671.68
+Manufacturer#3	almond antique forest lavender goldenrod	14	2	2	2861.95
+Manufacturer#3	almond antique metallic orange dim	19	3	3	4272.34
+Manufacturer#3	almond antique misty red olive	1	4	4	6195.32
+Manufacturer#3	almond antique olive coral navajo	45	5	5	7532.61
+Manufacturer#4	almond antique gainsboro frosted violet	10	1	1	1620.67
+Manufacturer#4	almond antique violet mint lemon	39	2	2	2996.09
+Manufacturer#4	almond aquamarine floral ivory bisque	27	3	3	4202.35
+Manufacturer#4	almond aquamarine yellow dodger mint	7	4	4	6047.27
+Manufacturer#4	almond azure aquamarine papaya violet	12	5	5	7337.620000000001
+Manufacturer#5	almond antique blue firebrick mint	31	1	1	1789.69
+Manufacturer#5	almond antique medium spring khaki	6	2	2	3401.3500000000004
+Manufacturer#5	almond antique sky peru orange	2	3	3	5190.08
+Manufacturer#5	almond aquamarine dodger light gainsboro	46	4	4	6208.18
+Manufacturer#5	almond azure blanched chiffon midnight	23	5	5	7672.66
diff --git a/sql/hive/src/test/resources/golden/windowing_adjust_rowcontainer_sz-0-d3f50875bd5dff172cf813fdb7d738eb b/sql/hive/src/test/resources/golden/windowing_adjust_rowcontainer_sz-0-d3f50875bd5dff172cf813fdb7d738eb
new file mode 100644
index 0000000000000..e69de29bb2d1d
diff --git a/sql/hive/src/test/resources/golden/windowing_adjust_rowcontainer_sz-1-dda16565b98926fc3587de937b9401c7 b/sql/hive/src/test/resources/golden/windowing_adjust_rowcontainer_sz-1-dda16565b98926fc3587de937b9401c7
new file mode 100644
index 0000000000000..e69de29bb2d1d
diff --git a/sql/hive/src/test/resources/golden/windowing_adjust_rowcontainer_sz-2-374e39786feb745cd70f25be58bfa24 b/sql/hive/src/test/resources/golden/windowing_adjust_rowcontainer_sz-2-374e39786feb745cd70f25be58bfa24
new file mode 100644
index 0000000000000..e69de29bb2d1d
diff --git a/sql/hive/src/test/resources/golden/windowing_adjust_rowcontainer_sz-3-d2b5e23edec42a62e61750b110ecbaac b/sql/hive/src/test/resources/golden/windowing_adjust_rowcontainer_sz-3-d2b5e23edec42a62e61750b110ecbaac
new file mode 100644
index 0000000000000..573541ac9702d
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing_adjust_rowcontainer_sz-3-d2b5e23edec42a62e61750b110ecbaac
@@ -0,0 +1 @@
+0
diff --git a/sql/hive/src/test/resources/golden/windowing_adjust_rowcontainer_sz-4-50d0c630159068b5b8ccdeb76493f1f7 b/sql/hive/src/test/resources/golden/windowing_adjust_rowcontainer_sz-4-50d0c630159068b5b8ccdeb76493f1f7
new file mode 100644
index 0000000000000..850c41c8115d6
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing_adjust_rowcontainer_sz-4-50d0c630159068b5b8ccdeb76493f1f7
@@ -0,0 +1,26 @@
+Manufacturer#1	almond antique burnished rose metallic	2	1	1	1173.15
+Manufacturer#1	almond antique burnished rose metallic	2	1	1	2346.3
+Manufacturer#1	almond antique chartreuse lavender yellow	34	3	2	4100.06
+Manufacturer#1	almond antique salmon chartreuse burlywood	6	4	3	5702.650000000001
+Manufacturer#1	almond aquamarine burnished black steel	28	5	4	7117.070000000001
+Manufacturer#1	almond aquamarine pink moccasin thistle	42	6	5	8749.730000000001
+Manufacturer#2	almond antique violet chocolate turquoise	14	1	1	1690.68
+Manufacturer#2	almond antique violet turquoise frosted	40	2	2	3491.38
+Manufacturer#2	almond aquamarine midnight light salmon	2	3	3	5523.360000000001
+Manufacturer#2	almond aquamarine rose maroon antique	25	4	4	7222.02
+Manufacturer#2	almond aquamarine sandy cyan gainsboro	18	5	5	8923.62
+Manufacturer#3	almond antique chartreuse khaki white	17	1	1	1671.68
+Manufacturer#3	almond antique forest lavender goldenrod	14	2	2	2861.95
+Manufacturer#3	almond antique metallic orange dim	19	3	3	4272.34
+Manufacturer#3	almond antique misty red olive	1	4	4	6195.32
+Manufacturer#3	almond antique olive coral navajo	45	5	5	7532.61
+Manufacturer#4	almond antique gainsboro frosted violet	10	1	1	1620.67
+Manufacturer#4	almond antique violet mint lemon	39	2	2	2996.09
+Manufacturer#4	almond aquamarine floral ivory bisque	27	3	3	4202.35
+Manufacturer#4	almond aquamarine yellow dodger mint	7	4	4	6047.27
+Manufacturer#4	almond azure aquamarine papaya violet	12	5	5	7337.620000000001
+Manufacturer#5	almond antique blue firebrick mint	31	1	1	1789.69
+Manufacturer#5	almond antique medium spring khaki	6	2	2	3401.3500000000004
+Manufacturer#5	almond antique sky peru orange	2	3	3	5190.08
+Manufacturer#5	almond aquamarine dodger light gainsboro	46	4	4	6208.18
+Manufacturer#5	almond azure blanched chiffon midnight	23	5	5	7672.66
diff --git a/sql/hive/src/test/resources/golden/windowing_adjust_rowcontainer_sz-5-3f95cd6f4add7a2d0101fe3dd97e5082 b/sql/hive/src/test/resources/golden/windowing_adjust_rowcontainer_sz-5-3f95cd6f4add7a2d0101fe3dd97e5082
new file mode 100644
index 0000000000000..573541ac9702d
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing_adjust_rowcontainer_sz-5-3f95cd6f4add7a2d0101fe3dd97e5082
@@ -0,0 +1 @@
+0
diff --git a/sql/hive/src/test/resources/golden/windowing_columnPruning-0-d3f50875bd5dff172cf813fdb7d738eb b/sql/hive/src/test/resources/golden/windowing_columnPruning-0-d3f50875bd5dff172cf813fdb7d738eb
new file mode 100644
index 0000000000000..e69de29bb2d1d
diff --git a/sql/hive/src/test/resources/golden/windowing_columnPruning-1-dda16565b98926fc3587de937b9401c7 b/sql/hive/src/test/resources/golden/windowing_columnPruning-1-dda16565b98926fc3587de937b9401c7
new file mode 100644
index 0000000000000..e69de29bb2d1d
diff --git a/sql/hive/src/test/resources/golden/windowing_columnPruning-2-374e39786feb745cd70f25be58bfa24 b/sql/hive/src/test/resources/golden/windowing_columnPruning-2-374e39786feb745cd70f25be58bfa24
new file mode 100644
index 0000000000000..e69de29bb2d1d
diff --git a/sql/hive/src/test/resources/golden/windowing_columnPruning-3-9294b4a22bc396ff2accabd53c5da98b b/sql/hive/src/test/resources/golden/windowing_columnPruning-3-9294b4a22bc396ff2accabd53c5da98b
new file mode 100644
index 0000000000000..1b5ae55383a4a
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing_columnPruning-3-9294b4a22bc396ff2accabd53c5da98b
@@ -0,0 +1,26 @@
+2	1173.15
+2	2346.3
+34	4100.06
+6	5702.650000000001
+28	7117.070000000001
+42	8749.730000000001
+14	1690.68
+40	3491.38
+2	5523.360000000001
+25	7222.02
+18	8923.62
+17	1671.68
+14	2861.95
+19	4272.34
+1	6195.32
+45	7532.61
+10	1620.67
+39	2996.09
+27	4202.35
+7	6047.27
+12	7337.620000000001
+31	1789.69
+6	3401.3500000000004
+2	5190.08
+46	6208.18
+23	7672.66
diff --git a/sql/hive/src/test/resources/golden/windowing_columnPruning-4-445cab062581c449ceffcb368cdf133 b/sql/hive/src/test/resources/golden/windowing_columnPruning-4-445cab062581c449ceffcb368cdf133
new file mode 100644
index 0000000000000..1b5ae55383a4a
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing_columnPruning-4-445cab062581c449ceffcb368cdf133
@@ -0,0 +1,26 @@
+2	1173.15
+2	2346.3
+34	4100.06
+6	5702.650000000001
+28	7117.070000000001
+42	8749.730000000001
+14	1690.68
+40	3491.38
+2	5523.360000000001
+25	7222.02
+18	8923.62
+17	1671.68
+14	2861.95
+19	4272.34
+1	6195.32
+45	7532.61
+10	1620.67
+39	2996.09
+27	4202.35
+7	6047.27
+12	7337.620000000001
+31	1789.69
+6	3401.3500000000004
+2	5190.08
+46	6208.18
+23	7672.66
diff --git a/sql/hive/src/test/resources/golden/windowing_columnPruning-5-89110070c761eafb992eb9315128b53f b/sql/hive/src/test/resources/golden/windowing_columnPruning-5-89110070c761eafb992eb9315128b53f
new file mode 100644
index 0000000000000..e426c725b0e36
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing_columnPruning-5-89110070c761eafb992eb9315128b53f
@@ -0,0 +1,26 @@
+Manufacturer#1	1173.15
+Manufacturer#1	2346.3
+Manufacturer#1	4100.06
+Manufacturer#1	5702.650000000001
+Manufacturer#1	7117.070000000001
+Manufacturer#1	8749.730000000001
+Manufacturer#2	1690.68
+Manufacturer#2	3491.38
+Manufacturer#2	5523.360000000001
+Manufacturer#2	7222.02
+Manufacturer#2	8923.62
+Manufacturer#3	1671.68
+Manufacturer#3	2861.95
+Manufacturer#3	4272.34
+Manufacturer#3	6195.32
+Manufacturer#3	7532.61
+Manufacturer#4	1620.67
+Manufacturer#4	2996.09
+Manufacturer#4	4202.35
+Manufacturer#4	6047.27
+Manufacturer#4	7337.620000000001
+Manufacturer#5	1789.69
+Manufacturer#5	3401.3500000000004
+Manufacturer#5	5190.08
+Manufacturer#5	6208.18
+Manufacturer#5	7672.66
diff --git a/sql/hive/src/test/resources/golden/windowing_multipartitioning.q (deterministic) 1-0-12a92d8800e0da8b515ba3eaf6a7fd0f b/sql/hive/src/test/resources/golden/windowing_multipartitioning.q (deterministic) 1-0-12a92d8800e0da8b515ba3eaf6a7fd0f
new file mode 100644
index 0000000000000..acc4f3bc2a2d5
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing_multipartitioning.q (deterministic) 1-0-12a92d8800e0da8b515ba3eaf6a7fd0f	
@@ -0,0 +1,1049 @@
+	1	4294967354
+	2	8589934811
+	3	12884902227
+alice allen	1	4294967487
+alice allen	2	8589935012
+alice allen	3	12884902543
+alice brown	1	4294967355
+alice carson	1	4294967370
+alice davidson	1	4294967517
+alice falkner	1	4294967316
+alice garcia	1	4294967369
+alice hernandez	1	4294967314
+alice hernandez	2	8589934613
+alice johnson	1	4294967424
+alice king	1	4294967387
+alice king	2	8589934903
+alice king	3	12884902449
+alice laertes	1	4294967519
+alice laertes	2	8589935038
+alice miller	1	4294967324
+alice nixon	1	4294967484
+alice nixon	2	8589934894
+alice nixon	3	12884902307
+alice ovid	1	4294967412
+alice polk	1	4294967366
+alice quirinius	1	4294967505
+alice quirinius	2	8589935054
+alice robinson	1	4294967502
+alice robinson	2	8589934947
+alice steinbeck	1	4294967549
+alice steinbeck	2	8589934913
+alice steinbeck	3	12884902387
+alice underhill	1	4294967441
+alice van buren	1	4294967428
+alice xylophone	1	4294967519
+alice xylophone	2	8589934877
+alice xylophone	3	12884902240
+alice zipper	1	4294967380
+alice zipper	2	8589934919
+alice zipper	3	12884902439
+bob brown	1	4294967431
+bob brown	2	8589934853
+bob brown	3	12884902280
+bob carson	1	4294967408
+bob davidson	1	4294967435
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+bob ellison	1	4294967530
+bob ellison	2	8589934966
+bob ellison	3	12884902328
+bob ellison	4	17179869672
+bob falkner	1	4294967464
+bob garcia	1	4294967435
+bob garcia	2	8589934804
+bob garcia	3	12884902148
+bob garcia	4	17179869587
+bob garcia	5	21474836905
+bob hernandez	1	4294967500
+bob ichabod	1	4294967424
+bob king	1	4294967443
+bob king	2	8589934740
+bob king	3	12884902279
+bob laertes	1	4294967472
+bob laertes	2	8589934852
+bob miller	1	4294967349
+bob ovid	1	4294967401
+bob ovid	2	8589934801
+bob ovid	3	12884902313
+bob ovid	4	17179869708
+bob polk	1	4294967337
+bob quirinius	1	4294967346
+bob steinbeck	1	4294967342
+bob van buren	1	4294967422
+bob white	1	4294967493
+bob white	2	8589934855
+bob xylophone	1	4294967407
+bob xylophone	2	8589934872
+bob young	1	4294967413
+bob zipper	1	4294967416
+bob zipper	2	8589934769
+bob zipper	3	12884902068
+calvin allen	1	4294967373
+calvin brown	1	4294967530
+calvin brown	2	8589934967
+calvin brown	3	12884902378
+calvin carson	1	4294967534
+calvin davidson	1	4294967437
+calvin davidson	2	8589934905
+calvin ellison	1	4294967480
+calvin falkner	1	4294967305
+calvin falkner	2	8589934723
+calvin falkner	3	12884902155
+calvin falkner	4	17179869455
+calvin falkner	5	21474836800
+calvin falkner	6	25769804250
+calvin garcia	1	4294967492
+calvin hernandez	1	4294967341
+calvin johnson	1	4294967546
+calvin laertes	1	4294967499
+calvin laertes	2	8589934930
+calvin nixon	1	4294967488
+calvin nixon	2	8589934788
+calvin nixon	3	12884902200
+calvin ovid	1	4294967343
+calvin ovid	2	8589934881
+calvin ovid	3	12884902210
+calvin ovid	4	17179869559
+calvin polk	1	4294967416
+calvin quirinius	1	4294967532
+calvin quirinius	2	8589935053
+calvin robinson	1	4294967326
+calvin steinbeck	1	4294967417
+calvin steinbeck	2	8589934891
+calvin steinbeck	3	12884902396
+calvin thompson	1	4294967346
+calvin thompson	2	8589934859
+calvin underhill	1	4294967478
+calvin van buren	1	4294967300
+calvin van buren	2	8589934808
+calvin white	1	4294967304
+calvin white	2	8589934848
+calvin xylophone	1	4294967299
+calvin xylophone	2	8589934675
+calvin xylophone	3	12884902133
+calvin young	1	4294967410
+calvin young	2	8589934752
+calvin zipper	1	4294967441
+calvin zipper	2	8589934960
+david allen	1	4294967381
+david allen	2	8589934752
+david brown	1	4294967544
+david brown	2	8589934870
+david davidson	1	4294967487
+david davidson	2	8589934952
+david davidson	3	12884902474
+david davidson	4	17179869819
+david ellison	1	4294967477
+david ellison	2	8589934963
+david ellison	3	12884902426
+david hernandez	1	4294967324
+david ichabod	1	4294967385
+david ichabod	2	8589934872
+david laertes	1	4294967385
+david nixon	1	4294967381
+david ovid	1	4294967396
+david ovid	2	8589934839
+david quirinius	1	4294967375
+david quirinius	2	8589934905
+david quirinius	3	12884902362
+david robinson	1	4294967465
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+david thompson	1	4294967361
+david underhill	1	4294967331
+david underhill	2	8589934715
+david underhill	3	12884902185
+david van buren	1	4294967438
+david van buren	2	8589934747
+david white	1	4294967428
+david xylophone	1	8589934898
+david xylophone	1	8589934898
+david xylophone	3	12884902378
+david young	1	4294967296
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+ethan allen	1	4294967351
+ethan brown	1	4294967477
+ethan brown	2	8589934897
+ethan brown	3	12884902217
+ethan brown	4	17179869548
+ethan brown	5	21474836951
+ethan brown	6	25769804375
+ethan carson	1	4294967352
+ethan ellison	1	4294967514
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diff --git a/sql/hive/src/test/resources/golden/windowing_multipartitioning.q (deterministic) 3-0-455e41d9949a2d22bab634fd8e42f2b1 b/sql/hive/src/test/resources/golden/windowing_multipartitioning.q (deterministic) 3-0-455e41d9949a2d22bab634fd8e42f2b1
new file mode 100644
index 0000000000000..f47923618a1aa
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing_multipartitioning.q (deterministic) 3-0-455e41d9949a2d22bab634fd8e42f2b1	
@@ -0,0 +1 @@
+bob steinbeck	65637	9.699999809265137
diff --git a/sql/hive/src/test/resources/golden/windowing_multipartitioning.q (deterministic) 4-0-cfad06ae8eba6b047d32a6a61dd59392 b/sql/hive/src/test/resources/golden/windowing_multipartitioning.q (deterministic) 4-0-cfad06ae8eba6b047d32a6a61dd59392
new file mode 100644
index 0000000000000..f41eaa259cec0
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing_multipartitioning.q (deterministic) 4-0-cfad06ae8eba6b047d32a6a61dd59392	
@@ -0,0 +1 @@
+bob steinbeck	1	1
diff --git a/sql/hive/src/test/resources/golden/windowing_multipartitioning.q (deterministic) 5-0-d7ca7a61377cef3a9f721a28afdae012 b/sql/hive/src/test/resources/golden/windowing_multipartitioning.q (deterministic) 5-0-d7ca7a61377cef3a9f721a28afdae012
new file mode 100644
index 0000000000000..5308b2eb457e7
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing_multipartitioning.q (deterministic) 5-0-d7ca7a61377cef3a9f721a28afdae012	
@@ -0,0 +1 @@
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diff --git a/sql/hive/src/test/resources/golden/windowing_multipartitioning.q (deterministic) 6-0-287bcc7679822bc7b684532b267bf11f b/sql/hive/src/test/resources/golden/windowing_multipartitioning.q (deterministic) 6-0-287bcc7679822bc7b684532b267bf11f
new file mode 100644
index 0000000000000..f41eaa259cec0
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing_multipartitioning.q (deterministic) 6-0-287bcc7679822bc7b684532b267bf11f	
@@ -0,0 +1 @@
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diff --git a/sql/hive/src/test/resources/golden/windowing_navfn.q (deterministic)-0-36217f6074daaacddb9fcb50a3f4fb5b b/sql/hive/src/test/resources/golden/windowing_navfn.q (deterministic)-0-36217f6074daaacddb9fcb50a3f4fb5b
new file mode 100644
index 0000000000000..8150409e62d33
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing_navfn.q (deterministic)-0-36217f6074daaacddb9fcb50a3f4fb5b	
@@ -0,0 +1,1049 @@
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diff --git a/sql/hive/src/test/resources/golden/windowing_navfn.q (deterministic)-1-9ee79e711248dd6e0a6ce27e439e55f4 b/sql/hive/src/test/resources/golden/windowing_navfn.q (deterministic)-1-9ee79e711248dd6e0a6ce27e439e55f4
new file mode 100644
index 0000000000000..275772e1f643a
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing_navfn.q (deterministic)-1-9ee79e711248dd6e0a6ce27e439e55f4	
@@ -0,0 +1,1049 @@
+65791	calvin nixon
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+65790	yuri ellison
+65789	NULL
+65789	alice laertes
+65789	gabriella king
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+65789	oscar zipper
+65789	quinn davidson
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+65778	yuri quirinius
+65777	ethan garcia
+65777	irene garcia
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+65777	rachel thompson
+65776	NULL
+65776	luke garcia
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+65776	priscilla ovid
+65776	sarah king
+65775	bob ellison
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+65774	calvin young
+65774	irene quirinius
+65774	katie brown
+65774	oscar hernandez
+65774	sarah ichabod
+65774	zach king
+65773	NULL
+65773	calvin garcia
+65773	irene polk
+65773	jessica davidson
+65773	nick ichabod
+65773	nick zipper
+65773	quinn ovid
+65773	zach falkner
+65772	oscar garcia
+65771	ethan underhill
+65771	yuri hernandez
+65771	zach brown
+65770	alice nixon
+65770	gabriella ellison
+65769	mike king
+65769	nick steinbeck
+65769	oscar nixon
+65769	rachel zipper
+65769	xavier quirinius
+65769	zach ichabod
+65768	jessica davidson
+65767	tom robinson
+65767	victor davidson
+65767	xavier king
+65766	fred davidson
+65766	luke underhill
+65766	ulysses carson
+65766	wendy nixon
+65766	xavier brown
+65765	bob polk
+65765	gabriella brown
+65765	wendy allen
+65764	alice hernandez
+65764	alice robinson
+65764	luke ellison
+65763	calvin van buren
+65763	ethan brown
+65763	priscilla johnson
+65763	tom ichabod
+65763	tom miller
+65762	david xylophone
+65762	priscilla xylophone
+65762	quinn allen
+65762	sarah johnson
+65762	tom carson
+65761	david ovid
+65760	NULL
+65760	ethan king
+65760	priscilla johnson
+65760	wendy van buren
+65759	alice xylophone
+65759	ethan quirinius
+65759	fred steinbeck
+65759	sarah robinson
+65759	xavier johnson
+65758	alice van buren
+65758	ethan white
+65758	irene johnson
+65758	jessica underhill
+65758	rachel laertes
+65757	bob quirinius
+65757	nick quirinius
+65757	tom van buren
+65757	xavier carson
+65757	zach brown
+65756	gabriella falkner
+65756	mike polk
+65756	nick allen
+65756	yuri xylophone
+65755	alice johnson
+65755	david ellison
+65755	fred polk
+65755	gabriella hernandez
+65755	irene van buren
+65755	jessica nixon
+65755	mike ellison
+65755	nick van buren
+65755	zach miller
+65754	luke johnson
+65754	xavier quirinius
+65753	rachel robinson
+65753	yuri garcia
+65752	bob miller
+65752	oscar zipper
+65751	calvin quirinius
+65751	katie king
+65751	mike allen
+65751	mike quirinius
+65751	mike white
+65751	zach zipper
+65750	mike white
+65750	nick thompson
+65750	oscar quirinius
+65750	priscilla polk
+65750	rachel brown
+65749	david allen
+65749	gabriella polk
+65749	mike ellison
+65749	sarah carson
+65749	wendy thompson
+65748	david davidson
+65748	oscar laertes
+65747	calvin falkner
+65747	fred steinbeck
+65747	priscilla zipper
+65747	quinn underhill
+65747	rachel falkner
+65747	wendy falkner
+65747	yuri falkner
+65746	
+65746	katie robinson
+65746	luke garcia
+65746	tom brown
+65746	zach steinbeck
+65745	oscar steinbeck
+65745	oscar xylophone
+65745	priscilla nixon
+65745	victor laertes
+65744	alice allen
+65744	priscilla ichabod
+65743	jessica carson
+65743	oscar ichabod
+65743	sarah falkner
+65743	tom brown
+65742	jessica brown
+65742	jessica ellison
+65742	wendy polk
+65741	calvin thompson
+65740	irene van buren
+65740	mike zipper
+65740	rachel quirinius
+65739	gabriella van buren
+65739	victor robinson
+65739	wendy nixon
+65738	sarah ichabod
+65738	wendy robinson
+65738	wendy young
+65738	xavier king
+65737	holly brown
+65737	irene falkner
+65737	jessica white
+65737	quinn garcia
+65737	wendy laertes
+65736	fred young
+65736	quinn laertes
+65736	rachel allen
+65736	victor ellison
+65735	oscar laertes
+65735	tom falkner
+65735	ulysses thompson
+65734	calvin falkner
+65734	mike garcia
+65733	gabriella van buren
+65733	holly nixon
+65733	luke underhill
+65733	oscar allen
+65733	xavier polk
+65733	yuri ellison
+65732	mike steinbeck
+65732	tom carson
+65732	tom ellison
+65731	alice robinson
+65731	priscilla carson
+65731	tom johnson
+65731	ulysses ovid
+65730	bob king
+65730	calvin polk
+65730	gabriella ichabod
+65730	rachel brown
+65729	fred quirinius
+65728	priscilla king
+65728	victor brown
+65727	ethan brown
+65727	fred polk
+65727	jessica white
+65727	priscilla underhill
+65727	sarah white
+65726	mike garcia
+65726	ulysses hernandez
+65726	wendy quirinius
+65725	zach thompson
+65724	NULL
+65724	alice king
+65724	alice steinbeck
+65724	mike ellison
+65724	nick ovid
+65724	priscilla zipper
+65723	nick allen
+65723	rachel white
+65722	fred falkner
+65722	jessica underhill
+65722	luke ichabod
+65721	ethan falkner
+65721	jessica zipper
+65721	luke laertes
+65721	luke underhill
+65721	mike carson
+65721	oscar laertes
+65721	ulysses ichabod
+65720	calvin nixon
+65720	calvin thompson
+65720	gabriella young
+65720	holly polk
+65720	mike king
+65719	bob brown
+65719	holly ichabod
+65719	ulysses ellison
+65719	ulysses young
+65718	jessica ichabod
+65718	oscar johnson
+65718	victor hernandez
+65718	zach ovid
+65717	holly hernandez
+65717	mike nixon
+65717	quinn garcia
+65717	ulysses davidson
+65717	ulysses polk
+65716	ethan zipper
+65716	holly xylophone
+65716	jessica garcia
+65716	nick falkner
+65716	sarah king
+65716	victor xylophone
+65715	alice hernandez
+65715	david young
+65715	ethan polk
+65715	oscar falkner
+65715	priscilla brown
+65714	NULL
+65714	ethan laertes
+65714	luke allen
+65713	bob ellison
+65713	nick nixon
+65713	ulysses carson
+65713	ulysses ichabod
+65713	zach garcia
+65712	NULL
+65712	katie king
+65712	luke davidson
+65712	quinn garcia
+65712	zach carson
+65711	bob zipper
+65711	fred miller
+65711	holly nixon
+65711	katie ellison
+65711	wendy ovid
+65711	zach zipper
+65710	luke polk
+65710	mike young
+65709	bob laertes
+65709	calvin laertes
+65709	oscar laertes
+65708	gabriella laertes
+65708	priscilla van buren
+65707	bob garcia
+65707	mike steinbeck
+65706	bob ellison
+65706	bob xylophone
+65706	luke allen
+65706	ulysses xylophone
+65706	wendy ichabod
+65705	david ellison
+65705	mike white
+65705	priscilla johnson
+65705	tom ellison
+65704	ethan brown
+65704	holly king
+65704	jessica young
+65704	tom steinbeck
+65704	victor steinbeck
+65704	zach falkner
+65703	holly underhill
+65703	mike ellison
+65703	victor davidson
+65703	xavier underhill
+65702	NULL
+65702	bob brown
+65702	bob zipper
+65702	ulysses quirinius
+65701	alice allen
+65701	mike steinbeck
+65701	oscar thompson
+65700	luke allen
+65700	quinn laertes
+65700	tom quirinius
+65699	ethan brown
+65699	ethan van buren
+65699	irene laertes
+65699	mike young
+65699	nick garcia
+65699	sarah white
+65698	gabriella thompson
+65698	nick polk
+65697	NULL
+65697	NULL
+65697	holly miller
+65697	oscar quirinius
+65697	oscar thompson
+65697	xavier davidson
+65696	alice xylophone
+65696	jessica davidson
+65696	luke allen
+65696	oscar xylophone
+65695	oscar king
+65695	rachel young
+65695	wendy ellison
+65695	yuri quirinius
+65694	david brown
+65694	holly underhill
+65694	victor quirinius
+65694	zach brown
+65693	bob hernandez
+65693	bob young
+65693	david brown
+65693	holly hernandez
+65693	tom polk
+65693	ulysses polk
+65693	victor brown
+65692	holly johnson
+65692	tom robinson
+65691	calvin ovid
+65691	ethan nixon
+65691	ethan robinson
+65691	fred underhill
+65691	holly white
+65691	irene polk
+65691	oscar white
+65691	rachel brown
+65690	fred van buren
+65690	jessica quirinius
+65689	oscar ovid
+65689	wendy thompson
+65688	bob steinbeck
+65688	victor steinbeck
+65687	gabriella ichabod
+65687	jessica underhill
+65687	mike zipper
+65687	quinn thompson
+65686	bob king
+65686	bob zipper
+65686	david quirinius
+65686	luke ichabod
+65685	ethan robinson
+65685	gabriella hernandez
+65685	katie garcia
+65685	sarah ellison
+65685	victor hernandez
+65685	victor nixon
+65684	priscilla brown
+65684	victor laertes
+65684	wendy van buren
+65683	NULL
+65683	mike king
+65683	tom laertes
+65682	calvin quirinius
+65682	ethan brown
+65682	katie ellison
+65681	gabriella allen
+65681	luke laertes
+65681	oscar quirinius
+65681	ulysses laertes
+65681	wendy ellison
+65681	xavier polk
+65680	NULL
+65680	alice nixon
+65680	gabriella ovid
+65680	jessica carson
+65680	ulysses nixon
+65680	zach zipper
+65679	bob garcia
+65679	wendy underhill
+65678	bob falkner
+65678	victor xylophone
+65678	wendy king
+65677	alice allen
+65677	fred van buren
+65677	mike brown
+65677	nick xylophone
+65677	ulysses underhill
+65677	zach robinson
+65676	bob davidson
+65676	bob laertes
+65676	tom ovid
+65676	xavier johnson
+65675	david hernandez
+65675	david nixon
+65675	holly falkner
+65675	quinn steinbeck
+65675	rachel robinson
+65675	sarah zipper
+65675	tom polk
+65675	victor allen
+65674	gabriella falkner
+65673	nick johnson
+65673	quinn brown
+65673	quinn underhill
+65673	rachel ovid
+65673	wendy brown
+65672	nick laertes
+65672	nick underhill
+65672	rachel zipper
+65672	tom white
+65672	victor king
+65671	fred ellison
+65671	fred falkner
+65671	zach white
+65670	david robinson
+65670	jessica zipper
+65670	luke van buren
+65670	oscar ovid
+65670	quinn steinbeck
+65669	NULL
+65669	alice king
+65669	calvin hernandez
+65669	katie polk
+65669	nick miller
+65669	oscar van buren
+65668	luke ellison
+65667	bob brown
+65667	irene nixon
+65667	oscar brown
+65667	tom falkner
+65666	
+65666	david underhill
+65666	fred van buren
+65665	rachel brown
+65664	NULL
+65664	bob davidson
+65664	david ichabod
+65664	ethan laertes
+65664	irene robinson
+65664	mike carson
+65664	priscilla young
+65664	victor king
+65663	calvin underhill
+65663	jessica johnson
+65663	priscilla carson
+65663	zach ichabod
+65662	ethan allen
+65662	katie ovid
+65662	oscar johnson
+65662	ulysses carson
+65662	ulysses polk
+65662	victor ovid
+65661	david van buren
+65661	luke xylophone
+65661	mike falkner
+65661	priscilla van buren
+65661	victor johnson
+65660	holly ichabod
+65660	priscilla johnson
+65660	victor thompson
+65659	david robinson
+65659	gabriella king
+65659	luke davidson
+65659	mike king
+65659	mike zipper
+65659	nick brown
+65659	nick zipper
+65659	yuri underhill
+65658	NULL
+65658	alice zipper
+65658	calvin allen
+65658	calvin johnson
+65658	jessica garcia
+65658	quinn davidson
+65658	sarah ovid
+65658	ulysses brown
+65658	ulysses miller
+65658	yuri king
+65657	ethan falkner
+65657	holly zipper
+65657	irene ovid
+65657	luke ovid
+65657	priscilla white
+65656	david davidson
+65656	irene ovid
+65656	jessica xylophone
+65656	luke laertes
+65656	oscar ichabod
+65656	xavier ellison
+65655	calvin falkner
+65655	yuri laertes
+65654	alice carson
+65654	alice quirinius
+65654	gabriella falkner
+65654	nick young
+65654	oscar robinson
+65654	quinn robinson
+65654	rachel falkner
+65654	tom laertes
+65654	yuri johnson
+65653	calvin ellison
+65653	holly underhill
+65653	ulysses polk
+65653	wendy xylophone
+65652	NULL
+65652	tom ellison
+65652	victor johnson
+65651	NULL
+65651	ethan laertes
+65651	ethan laertes
+65651	irene garcia
+65651	mike young
+65650	irene ellison
+65650	oscar white
+65650	sarah steinbeck
+65650	ulysses underhill
+65650	ulysses xylophone
+65650	victor xylophone
+65649	irene underhill
+65649	priscilla quirinius
+65649	quinn ellison
+65649	tom quirinius
+65648	alice nixon
+65648	calvin brown
+65648	sarah carson
+65648	xavier ellison
+65647	irene allen
+65647	mike ellison
+65646	bob ovid
+65646	xavier brown
+65646	xavier ovid
+65645	jessica quirinius
+65645	katie miller
+65645	ulysses hernandez
+65644	alice king
+65644	calvin ovid
+65644	jessica white
+65644	katie van buren
+65644	sarah young
+65644	ulysses hernandez
+65644	yuri carson
+65643	david davidson
+65643	ethan polk
+65643	ethan zipper
+65643	gabriella ichabod
+65643	mike davidson
+65643	mike hernandez
+65643	oscar robinson
+65643	priscilla underhill
+65643	zach king
+65642	gabriella thompson
+65641	fred laertes
+65641	sarah garcia
+65641	tom miller
+65641	xavier hernandez
+65640	david underhill
+65639	wendy garcia
+65638	fred nixon
+65638	luke polk
+65638	rachel carson
+65637	alice underhill
+65637	david davidson
+65637	fred davidson
+65637	gabriella davidson
+65637	oscar carson
+65637	rachel laertes
+65637	sarah garcia
+65637	wendy garcia
+65636	
+65636	irene polk
+65636	wendy allen
+65635	alice steinbeck
+65635	alice zipper
+65635	ulysses white
+65634	NULL
+65634	calvin white
+65634	holly underhill
+65634	sarah falkner
+65633	NULL
+65633	holly polk
+65633	jessica nixon
+65633	oscar quirinius
+65632	alice falkner
+65632	zach quirinius
+65631	fred ellison
+65630	rachel brown
+65630	xavier hernandez
+65629	jessica quirinius
+65629	priscilla carson
+65629	victor young
+65629	xavier carson
+65628	bob ovid
+65628	ethan ovid
+65628	irene ichabod
+65628	oscar hernandez
+65628	oscar robinson
+65628	xavier quirinius
+65627	alice miller
+65627	holly johnson
+65627	luke falkner
+65627	yuri polk
+65626	ethan polk
+65626	holly robinson
+65626	tom young
+65626	yuri johnson
+65625	david xylophone
+65625	fred ichabod
+65625	katie white
+65625	ulysses garcia
+65624	calvin steinbeck
+65624	calvin xylophone
+65624	rachel carson
+65624	tom van buren
+65624	yuri brown
+65623	alice quirinius
+65623	jessica miller
+65623	oscar ichabod
+65623	quinn zipper
+65623	tom van buren
+65623	victor brown
+65623	wendy young
+65622	nick davidson
+65622	rachel king
+65622	wendy robinson
+65622	xavier ichabod
+65622	zach xylophone
+65622	zach young
+65621	quinn underhill
+65621	ulysses young
+65620	nick garcia
+65620	oscar thompson
+65620	quinn quirinius
+65620	victor white
+65620	victor xylophone
+65620	wendy quirinius
+65619	calvin brown
+65619	gabriella polk
+65619	oscar king
+65619	ulysses miller
+65619	ulysses robinson
+65619	ulysses steinbeck
+65618	gabriella ovid
+65618	irene laertes
+65618	katie king
+65618	oscar ovid
+65618	quinn thompson
+65617	fred van buren
+65617	gabriella carson
+65617	sarah johnson
+65617	ulysses underhill
+65616	calvin steinbeck
+65616	xavier ichabod
+65615	alice ovid
+65615	david quirinius
+65615	irene quirinius
+65615	katie nixon
+65614	wendy king
+65614	xavier quirinius
+65614	xavier white
+65613	xavier zipper
+65612	irene miller
+65612	victor hernandez
+65612	wendy white
+65612	yuri polk
+65611	ethan johnson
+65611	fred zipper
+65611	irene carson
+65611	nick quirinius
+65610	tom king
+65610	victor steinbeck
+65610	wendy garcia
+65610	yuri carson
+65610	zach ovid
+65609	sarah robinson
+65608	katie van buren
+65608	mike van buren
+65608	quinn ichabod
+65608	zach underhill
+65607	katie miller
+65607	luke falkner
+65607	mike polk
+65607	priscilla xylophone
+65607	yuri allen
+65607	yuri allen
+65606	bob white
+65606	gabriella white
+65606	oscar carson
+65606	victor white
+65606	xavier allen
+65606	zach allen
+65605	holly king
+65604	katie zipper
+65604	oscar davidson
+65604	wendy laertes
+65604	zach brown
+65603	alice davidson
+65603	ethan miller
+65603	katie davidson
+65603	katie young
+65603	mike garcia
+65602	NULL
+65602	calvin laertes
+65602	ethan laertes
+65602	fred steinbeck
+65602	jessica young
+65602	xavier brown
+65601	priscilla ovid
+65601	sarah xylophone
+65601	tom robinson
+65600	gabriella thompson
+65600	jessica polk
+65600	nick robinson
+65600	rachel allen
+65599	fred quirinius
+65599	luke johnson
+65599	nick garcia
+65599	oscar xylophone
+65599	ulysses underhill
+65598	ulysses van buren
+65598	victor zipper
+65597	ethan ellison
+65597	nick ellison
+65597	quinn davidson
+65596	NULL
+65596	calvin zipper
+65596	david ellison
+65596	irene ichabod
+65596	wendy laertes
+65595	bob white
+65595	holly hernandez
+65595	luke brown
+65595	oscar ellison
+65595	oscar ichabod
+65595	quinn ellison
+65594	gabriella ellison
+65594	oscar robinson
+65594	ulysses underhill
+65594	victor robinson
+65593	oscar white
+65593	zach xylophone
+65592	calvin xylophone
+65591	alice zipper
+65591	nick ichabod
+65591	priscilla ichabod
+65591	rachel underhill
+65590	NULL
+65590	katie falkner
+65590	oscar van buren
+65590	xavier garcia
+65590	yuri underhill
+65589	ethan white
+65589	gabriella zipper
+65589	irene ovid
+65589	oscar king
+65589	wendy xylophone
+65588	bob van buren
+65588	david ichabod
+65588	mike miller
+65588	tom hernandez
+65588	victor van buren
+65587	bob garcia
+65587	luke johnson
+65587	mike king
+65587	victor allen
+65587	xavier white
+65586	david young
+65586	irene brown
+65586	priscilla brown
+65586	wendy allen
+65586	xavier laertes
+65585	alice garcia
+65585	bob garcia
+65585	ethan ellison
+65585	nick ellison
+65585	priscilla thompson
+65584	jessica carson
+65584	jessica van buren
+65584	jessica white
+65583	bob xylophone
+65583	nick ichabod
+65583	yuri brown
+65583	yuri steinbeck
+65582	holly johnson
+65582	mike carson
+65582	victor van buren
+65582	zach miller
+65581	gabriella steinbeck
+65581	irene quirinius
+65581	luke allen
+65581	nick robinson
+65581	nick young
+65581	wendy robinson
+65580	alice steinbeck
+65580	alice xylophone
+65580	irene xylophone
+65579	irene polk
+65579	luke ovid
+65579	quinn nixon
+65579	sarah garcia
+65579	wendy ovid
+65578	calvin robinson
+65578	fred king
+65578	holly thompson
+65578	katie ichabod
+65578	quinn king
+65578	rachel davidson
+65578	victor hernandez
+65577	holly white
+65576	calvin falkner
+65576	calvin ovid
+65576	fred polk
+65576	luke robinson
+65575	calvin falkner
+65575	irene steinbeck
+65575	luke zipper
+65575	zach king
+65574	gabriella steinbeck
+65574	priscilla nixon
+65574	rachel thompson
+65573	victor ellison
+65573	victor hernandez
+65573	yuri nixon
+65572	calvin davidson
+65572	calvin young
+65572	katie young
+65572	oscar ellison
+65572	quinn garcia
+65571	bob king
+65571	irene polk
+65571	katie ichabod
+65571	mike steinbeck
+65570	NULL
+65570	bob ovid
+65570	fred polk
+65570	luke ellison
+65570	mike hernandez
+65570	yuri quirinius
+65569	nick falkner
+65568	bob ichabod
+65568	holly thompson
+65568	jessica thompson
+65567	katie xylophone
+65566	gabriella garcia
+65566	rachel white
+65565	katie young
+65565	quinn young
+65564	alice polk
+65564	calvin carson
+65564	calvin white
+65564	ethan hernandez
+65564	ethan quirinius
+65564	jessica thompson
+65564	katie hernandez
+65563	calvin zipper
+65563	priscilla young
+65563	xavier davidson
+65563	yuri steinbeck
+65562	calvin falkner
+65562	ethan xylophone
+65562	luke white
+65562	quinn allen
+65562	rachel polk
+65562	wendy polk
+65561	bob davidson
+65561	ethan polk
+65561	jessica robinson
+65560	fred white
+65560	jessica johnson
+65560	oscar thompson
+65560	ulysses steinbeck
+65560	zach brown
+65559	NULL
+65559	ethan laertes
+65559	gabriella ichabod
+65559	gabriella zipper
+65559	irene garcia
+65558	fred robinson
+65557	fred hernandez
+65557	nick johnson
+65556	oscar underhill
+65556	xavier hernandez
+65556	yuri falkner
+65556	zach garcia
+65556	zach steinbeck
+65555	fred nixon
+65554	gabriella miller
+65554	rachel falkner
+65553	calvin van buren
+65553	david van buren
+65553	irene nixon
+65553	luke laertes
+65553	oscar carson
+65552	NULL
+65552	irene ellison
+65552	oscar polk
+65552	wendy falkner
+65552	zach miller
+65551	fred young
+65551	ulysses underhill
+65551	wendy underhill
+65550	ethan quirinius
+65550	fred davidson
+65550	holly young
+65550	jessica ovid
+65550	quinn brown
+65550	quinn laertes
+65550	tom johnson
+65549	bob garcia
+65549	bob ovid
+65549	fred ichabod
+65549	fred king
+65549	jessica white
+65549	ulysses laertes
+65549	victor davidson
+65549	victor miller
+65548	calvin ovid
+65548	gabriella allen
+65548	holly ichabod
+65548	priscilla johnson
+65548	quinn zipper
+65548	tom hernandez
+65548	wendy king
+65547	bob ellison
+65547	jessica quirinius
+65547	mike davidson
+65547	xavier allen
+65546	katie white
+65545	mike king
+65545	tom carson
+65545	victor brown
+65544	calvin davidson
+65544	calvin nixon
+65544	david ovid
+65544	irene thompson
+65544	ulysses young
+65544	xavier polk
+65544	xavier xylophone
+65544	zach ovid
+65543	fred johnson
+65543	sarah johnson
+65542	fred falkner
+65542	holly thompson
+65542	luke miller
+65542	mike white
+65542	tom davidson
+65541	calvin brown
+65541	ethan brown
+65541	holly brown
+65541	jessica falkner
+65541	rachel thompson
+65541	tom zipper
+65541	wendy underhill
+65541	xavier polk
+65541	yuri johnson
+65540	rachel falkner
+65539	gabriella young
+65539	holly laertes
+65539	oscar carson
+65538	irene laertes
+65538	mike polk
+65538	tom robinson
+65537	NULL
+65537	david quirinius
+65537	rachel ovid
+65537	ulysses laertes
+65537	zach garcia
+65536	calvin xylophone
+65536	david thompson
+65536	irene falkner
+65536	ulysses johnson
+65536	victor johnson
+65536	wendy miller
+65536	yuri thompson
diff --git a/sql/hive/src/test/resources/golden/windowing_navfn.q (deterministic)-2-1e88e0ba414a00195f7ebf6b8600ac04 b/sql/hive/src/test/resources/golden/windowing_navfn.q (deterministic)-2-1e88e0ba414a00195f7ebf6b8600ac04
new file mode 100644
index 0000000000000..62d71abc6fc7d
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing_navfn.q (deterministic)-2-1e88e0ba414a00195f7ebf6b8600ac04	
@@ -0,0 +1,1049 @@
+65536	NULL
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diff --git a/sql/hive/src/test/resources/golden/windowing_navfn.q (deterministic)-3-34d9ee4120f21d0d0ae914fba0acc60c b/sql/hive/src/test/resources/golden/windowing_navfn.q (deterministic)-3-34d9ee4120f21d0d0ae914fba0acc60c
new file mode 100644
index 0000000000000..569c1d4e5f7b6
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing_navfn.q (deterministic)-3-34d9ee4120f21d0d0ae914fba0acc60c	
@@ -0,0 +1,1049 @@
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diff --git a/sql/hive/src/test/resources/golden/windowing_navfn.q (deterministic)-4-dfd39236756a3951bc1ec354799d69e4 b/sql/hive/src/test/resources/golden/windowing_navfn.q (deterministic)-4-dfd39236756a3951bc1ec354799d69e4
new file mode 100644
index 0000000000000..86ca4e49d21bd
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing_navfn.q (deterministic)-4-dfd39236756a3951bc1ec354799d69e4	
@@ -0,0 +1,1049 @@
+	
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diff --git a/sql/hive/src/test/resources/golden/windowing_navfn.q (deterministic)-5-8d0ee3e1605f38214bfad28a5ce897cc b/sql/hive/src/test/resources/golden/windowing_navfn.q (deterministic)-5-8d0ee3e1605f38214bfad28a5ce897cc
new file mode 100644
index 0000000000000..ddb15e338263f
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing_navfn.q (deterministic)-5-8d0ee3e1605f38214bfad28a5ce897cc	
@@ -0,0 +1 @@
+10	oscar carson	65549	65549
diff --git a/sql/hive/src/test/resources/golden/windowing_ntile.q (deterministic)-0-b7cb25303831392a51cd996e758ac79a b/sql/hive/src/test/resources/golden/windowing_ntile.q (deterministic)-0-b7cb25303831392a51cd996e758ac79a
new file mode 100644
index 0000000000000..42e5151fe211b
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing_ntile.q (deterministic)-0-b7cb25303831392a51cd996e758ac79a	
@@ -0,0 +1,1049 @@
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diff --git a/sql/hive/src/test/resources/golden/windowing_ntile.q (deterministic)-1-a3d352560ac835993001665db6954965 b/sql/hive/src/test/resources/golden/windowing_ntile.q (deterministic)-1-a3d352560ac835993001665db6954965
new file mode 100644
index 0000000000000..dc72606a83db6
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing_ntile.q (deterministic)-1-a3d352560ac835993001665db6954965	
@@ -0,0 +1,1049 @@
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diff --git a/sql/hive/src/test/resources/golden/windowing_ntile.q (deterministic)-2-fafa16c0f7697ca28aeb6f2698799562 b/sql/hive/src/test/resources/golden/windowing_ntile.q (deterministic)-2-fafa16c0f7697ca28aeb6f2698799562
new file mode 100644
index 0000000000000..76cbeb254c0e9
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing_ntile.q (deterministic)-2-fafa16c0f7697ca28aeb6f2698799562	
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diff --git a/sql/hive/src/test/resources/golden/windowing_ntile.q (deterministic)-3-bda0e7c77d6f4712a03389cb5032bc6d b/sql/hive/src/test/resources/golden/windowing_ntile.q (deterministic)-3-bda0e7c77d6f4712a03389cb5032bc6d
new file mode 100644
index 0000000000000..a9ec53c0cb21c
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing_ntile.q (deterministic)-3-bda0e7c77d6f4712a03389cb5032bc6d	
@@ -0,0 +1,1049 @@
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diff --git a/sql/hive/src/test/resources/golden/windowing_rank.q (deterministic) 1-0-2e0cbc2d7c5f16657edacd9e7209e6e7 b/sql/hive/src/test/resources/golden/windowing_rank.q (deterministic) 1-0-2e0cbc2d7c5f16657edacd9e7209e6e7
new file mode 100644
index 0000000000000..612bdf44c0cdf
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing_rank.q (deterministic) 1-0-2e0cbc2d7c5f16657edacd9e7209e6e7	
@@ -0,0 +1,1049 @@
+	1
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diff --git a/sql/hive/src/test/resources/golden/windowing_rank.q (deterministic) 1-1-5c5f373e325115d710a7a23fcb1626f1 b/sql/hive/src/test/resources/golden/windowing_rank.q (deterministic) 1-1-5c5f373e325115d710a7a23fcb1626f1
new file mode 100644
index 0000000000000..22a6f27253dcf
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing_rank.q (deterministic) 1-1-5c5f373e325115d710a7a23fcb1626f1	
@@ -0,0 +1,1049 @@
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diff --git a/sql/hive/src/test/resources/golden/windowing_rank.q (deterministic) 1-2-ac487cc1b94130bf9ce00e07c7075f65 b/sql/hive/src/test/resources/golden/windowing_rank.q (deterministic) 1-2-ac487cc1b94130bf9ce00e07c7075f65
new file mode 100644
index 0000000000000..c38e7bbabc21e
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing_rank.q (deterministic) 1-2-ac487cc1b94130bf9ce00e07c7075f65	
@@ -0,0 +1,1049 @@
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diff --git a/sql/hive/src/test/resources/golden/windowing_rank.q (deterministic) 1-3-b82dfa24123047be4b4e3c27c3997d34 b/sql/hive/src/test/resources/golden/windowing_rank.q (deterministic) 1-3-b82dfa24123047be4b4e3c27c3997d34
new file mode 100644
index 0000000000000..1e0cf03db63a0
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing_rank.q (deterministic) 1-3-b82dfa24123047be4b4e3c27c3997d34	
@@ -0,0 +1,1049 @@
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diff --git a/sql/hive/src/test/resources/golden/windowing_rank.q (deterministic) 2-0-81bb7f49a55385878637c8aac4d08e5 b/sql/hive/src/test/resources/golden/windowing_rank.q (deterministic) 2-0-81bb7f49a55385878637c8aac4d08e5
new file mode 100644
index 0000000000000..9091a9156134c
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing_rank.q (deterministic) 2-0-81bb7f49a55385878637c8aac4d08e5	
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diff --git a/sql/hive/src/test/resources/golden/windowing_rank.q (deterministic) 3-0-58a982694ba2b1e34de82b1de54936a0 b/sql/hive/src/test/resources/golden/windowing_rank.q (deterministic) 3-0-58a982694ba2b1e34de82b1de54936a0
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index 0000000000000..e69de29bb2d1d
diff --git a/sql/hive/src/test/resources/golden/windowing_rank.q (deterministic) 4-0-12cc78f3953c3e6b5411ddc729541bf0 b/sql/hive/src/test/resources/golden/windowing_rank.q (deterministic) 4-0-12cc78f3953c3e6b5411ddc729541bf0
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index 0000000000000..d02ca48857b5f
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing_rank.q (deterministic) 4-0-12cc78f3953c3e6b5411ddc729541bf0	
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diff --git a/sql/hive/src/test/resources/golden/windowing_udaf.q (deterministic)-0-6642a21d87e0401ba1a668ea8b244f0c b/sql/hive/src/test/resources/golden/windowing_udaf.q (deterministic)-0-6642a21d87e0401ba1a668ea8b244f0c
new file mode 100644
index 0000000000000..119dd71df1426
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing_udaf.q (deterministic)-0-6642a21d87e0401ba1a668ea8b244f0c	
@@ -0,0 +1,1049 @@
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diff --git a/sql/hive/src/test/resources/golden/windowing_udaf.q (deterministic)-1-2bf20f39e6ffef258858f7943a974e7e b/sql/hive/src/test/resources/golden/windowing_udaf.q (deterministic)-1-2bf20f39e6ffef258858f7943a974e7e
new file mode 100644
index 0000000000000..657e81a94f4c5
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing_udaf.q (deterministic)-1-2bf20f39e6ffef258858f7943a974e7e	
@@ -0,0 +1,1049 @@
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diff --git a/sql/hive/src/test/resources/golden/windowing_udaf.q (deterministic)-2-16239d2b069789ba99fbac50c4f0724f b/sql/hive/src/test/resources/golden/windowing_udaf.q (deterministic)-2-16239d2b069789ba99fbac50c4f0724f
new file mode 100644
index 0000000000000..6cfa5ad413fa4
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing_udaf.q (deterministic)-2-16239d2b069789ba99fbac50c4f0724f	
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diff --git a/sql/hive/src/test/resources/golden/windowing_udaf.q (deterministic)-3-d90b27fca067b0b3c48d873b3ef32af7 b/sql/hive/src/test/resources/golden/windowing_udaf.q (deterministic)-3-d90b27fca067b0b3c48d873b3ef32af7
new file mode 100644
index 0000000000000..072a8a891a836
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing_udaf.q (deterministic)-3-d90b27fca067b0b3c48d873b3ef32af7	
@@ -0,0 +1,1049 @@
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diff --git a/sql/hive/src/test/resources/golden/windowing_udaf.q (deterministic)-4-f2e4d659b65a833e9281b6786d3d55c1 b/sql/hive/src/test/resources/golden/windowing_udaf.q (deterministic)-4-f2e4d659b65a833e9281b6786d3d55c1
new file mode 100644
index 0000000000000..9cc7e7ea6c2b2
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing_udaf.q (deterministic)-4-f2e4d659b65a833e9281b6786d3d55c1	
@@ -0,0 +1,1049 @@
+	24.37875
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diff --git a/sql/hive/src/test/resources/golden/windowing_udaf2-0-96659fde37d7a38ea15b367b47f59ce2 b/sql/hive/src/test/resources/golden/windowing_udaf2-0-96659fde37d7a38ea15b367b47f59ce2
new file mode 100644
index 0000000000000..e69de29bb2d1d
diff --git a/sql/hive/src/test/resources/golden/windowing_udaf2-1-b4bdee4908b1cb8e240c549ae5cfe4c0 b/sql/hive/src/test/resources/golden/windowing_udaf2-1-b4bdee4908b1cb8e240c549ae5cfe4c0
new file mode 100644
index 0000000000000..17c31c0f04592
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing_udaf2-1-b4bdee4908b1cb8e240c549ae5cfe4c0
@@ -0,0 +1 @@
+130091	130091
diff --git a/sql/hive/src/test/resources/golden/windowing_windowspec.q (deterministic)-0-f498cccf82480be03022d2a36f87651e b/sql/hive/src/test/resources/golden/windowing_windowspec.q (deterministic)-0-f498cccf82480be03022d2a36f87651e
new file mode 100644
index 0000000000000..31b1f85a5eb5a
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing_windowspec.q (deterministic)-0-f498cccf82480be03022d2a36f87651e	
@@ -0,0 +1,1049 @@
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diff --git a/sql/hive/src/test/resources/golden/windowing_windowspec.q (deterministic)-1-6378faf36ffd3f61e61cee6c0cb70e6 b/sql/hive/src/test/resources/golden/windowing_windowspec.q (deterministic)-1-6378faf36ffd3f61e61cee6c0cb70e6
new file mode 100644
index 0000000000000..1436509e4ec17
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing_windowspec.q (deterministic)-1-6378faf36ffd3f61e61cee6c0cb70e6	
@@ -0,0 +1,1049 @@
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diff --git a/sql/hive/src/test/resources/golden/windowing_windowspec.q (deterministic)-2-5f0eab306ea3c22b11ace9b542a7ee56 b/sql/hive/src/test/resources/golden/windowing_windowspec.q (deterministic)-2-5f0eab306ea3c22b11ace9b542a7ee56
new file mode 100644
index 0000000000000..e55bede9242e5
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing_windowspec.q (deterministic)-2-5f0eab306ea3c22b11ace9b542a7ee56	
@@ -0,0 +1,1049 @@
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diff --git a/sql/hive/src/test/resources/golden/windowing_windowspec.q (deterministic)-3-6f104992e0050576085064815de43194 b/sql/hive/src/test/resources/golden/windowing_windowspec.q (deterministic)-3-6f104992e0050576085064815de43194
new file mode 100644
index 0000000000000..ae2a1e9dd7d3f
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing_windowspec.q (deterministic)-3-6f104992e0050576085064815de43194	
@@ -0,0 +1,1049 @@
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diff --git a/sql/hive/src/test/resources/golden/windowing_windowspec.q (deterministic)-4-cd2e3d2344810cb3ba843d4c01c81d7e b/sql/hive/src/test/resources/golden/windowing_windowspec.q (deterministic)-4-cd2e3d2344810cb3ba843d4c01c81d7e
new file mode 100644
index 0000000000000..ee1c26e331a1e
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing_windowspec.q (deterministic)-4-cd2e3d2344810cb3ba843d4c01c81d7e	
@@ -0,0 +1,1049 @@
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diff --git a/sql/hive/src/test/resources/golden/windowing_windowspec.q (deterministic)-5-ee44c5cdc80e1c832b702f9fb76d8145 b/sql/hive/src/test/resources/golden/windowing_windowspec.q (deterministic)-5-ee44c5cdc80e1c832b702f9fb76d8145
new file mode 100644
index 0000000000000..a9ae190825a01
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing_windowspec.q (deterministic)-5-ee44c5cdc80e1c832b702f9fb76d8145	
@@ -0,0 +1,1049 @@
+	65560
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diff --git a/sql/hive/src/test/resources/golden/windowing_windowspec.q (deterministic)-6-4d78f7b1d172d20c91f5867bc13a42a0 b/sql/hive/src/test/resources/golden/windowing_windowspec.q (deterministic)-6-4d78f7b1d172d20c91f5867bc13a42a0
new file mode 100644
index 0000000000000..b3f08818f491a
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing_windowspec.q (deterministic)-6-4d78f7b1d172d20c91f5867bc13a42a0	
@@ -0,0 +1,1049 @@
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diff --git a/sql/hive/src/test/resources/golden/windowing_windowspec.q (deterministic)-7-20fdc99aa046b2c41d9b85ab338c749c b/sql/hive/src/test/resources/golden/windowing_windowspec.q (deterministic)-7-20fdc99aa046b2c41d9b85ab338c749c
new file mode 100644
index 0000000000000..1a4528978b74f
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing_windowspec.q (deterministic)-7-20fdc99aa046b2c41d9b85ab338c749c	
@@ -0,0 +1,1049 @@
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diff --git a/sql/hive/src/test/resources/golden/windowing_windowspec.q (deterministic)-8-45a1d7c2aba45d761e19ff4dfdf5463e b/sql/hive/src/test/resources/golden/windowing_windowspec.q (deterministic)-8-45a1d7c2aba45d761e19ff4dfdf5463e
new file mode 100644
index 0000000000000..84b934fad85b1
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing_windowspec.q (deterministic)-8-45a1d7c2aba45d761e19ff4dfdf5463e	
@@ -0,0 +1,1049 @@
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+xavier davidson	65644	20.0
+xavier davidson	65664	20.0
+xavier davidson	65755	20.0
+xavier ellison	65541	20.0
+xavier ellison	65654	20.0
+xavier garcia	65672	20.0
+xavier hernandez	65541	20.0
+xavier hernandez	65544	20.0
+xavier hernandez	65766	20.0
+xavier ichabod	65597	20.0
+xavier ichabod	65663	20.0
+xavier johnson	65654	20.0
+xavier johnson	65744	20.0
+xavier king	65590	20.0
+xavier king	65601	20.0
+xavier laertes	65743	20.0
+xavier ovid	65788	20.0
+xavier polk	65587	20.0
+xavier polk	65653	20.0
+xavier polk	65675	20.0
+xavier polk	65696	20.0
+xavier quirinius	65599	20.0
+xavier quirinius	65650	20.0
+xavier quirinius	65656	20.0
+xavier quirinius	65737	20.0
+xavier thompson	65608	20.0
+xavier underhill	65710	20.0
+xavier white	65703	20.0
+xavier white	65732	20.0
+xavier xylophone	65572	20.0
+xavier zipper	65561	20.0
+yuri allen	65565	20.0
+yuri allen	65682	20.0
+yuri brown	65538	20.0
+yuri brown	65688	20.0
+yuri carson	65670	20.0
+yuri carson	65769	20.0
+yuri ellison	65570	20.0
+yuri ellison	65581	20.0
+yuri falkner	65658	20.0
+yuri falkner	65681	20.0
+yuri garcia	65639	20.0
+yuri hernandez	65706	20.0
+yuri johnson	65587	20.0
+yuri johnson	65697	20.0
+yuri johnson	65712	20.0
+yuri king	65721	20.0
+yuri laertes	65637	20.0
+yuri laertes	65773	20.0
+yuri nixon	65635	20.0
+yuri nixon	65740	20.0
+yuri polk	65607	20.0
+yuri polk	65713	20.0
+yuri polk	65742	20.0
+yuri quirinius	65544	20.0
+yuri quirinius	65617	20.0
+yuri quirinius	65695	20.0
+yuri steinbeck	65592	20.0
+yuri steinbeck	65679	20.0
+yuri thompson	65676	20.0
+yuri underhill	65718	20.0
+yuri underhill	65750	20.0
+yuri white	65659	20.0
+yuri xylophone	65714	20.0
+zach allen	65667	20.0
+zach brown	65559	20.0
+zach brown	65588	20.0
+zach brown	65691	20.0
+zach brown	65759	20.0
+zach brown	65762	20.0
+zach carson	65572	20.0
+zach ellison	65748	20.0
+zach falkner	65620	20.0
+zach falkner	65627	20.0
+zach garcia	65544	20.0
+zach garcia	65623	20.0
+zach garcia	65629	20.0
+zach garcia	65786	20.0
+zach ichabod	65599	20.0
+zach ichabod	65612	20.0
+zach king	65556	20.0
+zach king	65702	20.0
+zach king	65773	20.0
+zach miller	65584	20.0
+zach miller	65665	20.0
+zach miller	65719	20.0
+zach ovid	65578	20.0
+zach ovid	65703	20.0
+zach ovid	65750	20.0
+zach ovid	65784	20.0
+zach quirinius	65691	20.0
+zach robinson	65599	20.0
+zach steinbeck	65602	20.0
+zach steinbeck	65695	20.0
+zach thompson	65636	20.0
+zach thompson	65696	20.0
+zach underhill	65573	20.0
+zach white	65733	20.0
+zach xylophone	65542	20.0
+zach xylophone	65780	20.0
+zach young	65576	20.0
+zach zipper	65579	20.0
+zach zipper	65649	20.0
+zach zipper	65676	20.0
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 027056d4b865f..5ead5f3c19908 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
@@ -300,7 +300,7 @@ abstract class HiveComparisonTest
 
             val hiveQueries = queryList.map(new TestHive.HiveQLQueryExecution(_))
             // Make sure we can at least parse everything before attempting hive execution.
-            hiveQueries.foreach(_.analyzed)
+            hiveQueries.foreach(_.logical)
             val computedResults = (queryList.zipWithIndex, hiveQueries, hiveCacheFiles).zipped.map {
               case ((queryString, i), hiveQuery, cachedAnswerFile)=>
                 try {
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveWindowFunctionQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveWindowFunctionQuerySuite.scala
new file mode 100644
index 0000000000000..934452fe579a1
--- /dev/null
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveWindowFunctionQuerySuite.scala
@@ -0,0 +1,845 @@
+/*
+ * 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 java.io.File
+import java.util.{Locale, TimeZone}
+
+import org.scalatest.BeforeAndAfter
+
+import org.apache.spark.sql.hive.test.TestHive
+import org.apache.spark.sql.hive.test.TestHive._
+import org.apache.spark.util.Utils
+
+/**
+ * The test suite for window functions. To actually compare results with Hive,
+ * every test should be created by `createQueryTest`. Because we are reusing tables
+ * for different tests and there are a few properties needed to let Hive generate golden
+ * files, every `createQueryTest` calls should explicitly set `reset` to `false`.
+ */
+abstract class HiveWindowFunctionQueryBaseSuite extends HiveComparisonTest with BeforeAndAfter {
+  private val originalTimeZone = TimeZone.getDefault
+  private val originalLocale = Locale.getDefault
+  private val testTempDir = Utils.createTempDir()
+
+  override def beforeAll() {
+    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)
+
+    // Create the table used in windowing.q
+    sql("DROP TABLE IF EXISTS part")
+    sql(
+      """
+        |CREATE TABLE part(
+        |  p_partkey INT,
+        |  p_name STRING,
+        |  p_mfgr STRING,
+        |  p_brand STRING,
+        |  p_type STRING,
+        |  p_size INT,
+        |  p_container STRING,
+        |  p_retailprice DOUBLE,
+        |  p_comment STRING)
+      """.stripMargin)
+    val testData1 = TestHive.getHiveFile("data/files/part_tiny.txt").getCanonicalPath
+    sql(
+      s"""
+        |LOAD DATA LOCAL INPATH '$testData1' overwrite into table part
+      """.stripMargin)
+
+    sql("DROP TABLE IF EXISTS over1k")
+    sql(
+      """
+        |create table over1k(
+        |  t tinyint,
+        |  si smallint,
+        |  i int,
+        |  b bigint,
+        |  f float,
+        |  d double,
+        |  bo boolean,
+        |  s string,
+        |  ts timestamp,
+        |  dec decimal(4,2),
+        |  bin binary)
+        |row format delimited
+        |fields terminated by '|'
+      """.stripMargin)
+    val testData2 = TestHive.getHiveFile("data/files/over1k").getCanonicalPath
+    sql(
+      s"""
+        |LOAD DATA LOCAL INPATH '$testData2' overwrite into table over1k
+      """.stripMargin)
+
+    // The following settings are used for generating golden files with Hive.
+    // We have to use kryo to correctly let Hive serialize plans with window functions.
+    // This is used to generate golden files.
+    sql("set hive.plan.serialization.format=kryo")
+    // Explicitly set fs to local fs.
+    sql(s"set fs.default.name=file://$testTempDir/")
+    // Ask Hive to run jobs in-process as a single map and reduce task.
+    sql("set mapred.job.tracker=local")
+  }
+
+  override def afterAll() {
+    TestHive.cacheTables = false
+    TimeZone.setDefault(originalTimeZone)
+    Locale.setDefault(originalLocale)
+    TestHive.reset()
+  }
+
+  /////////////////////////////////////////////////////////////////////////////
+  // Tests based on windowing_multipartitioning.q
+  // Results of the original query file are not deterministic.
+  /////////////////////////////////////////////////////////////////////////////
+  createQueryTest("windowing_multipartitioning.q (deterministic) 1",
+    s"""
+      |select s,
+      |rank() over (partition by s order by si) r,
+      |sum(b) over (partition by s order by si) sum
+      |from over1k
+      |order by s, r, sum;
+    """.stripMargin, reset = false)
+
+  /* timestamp comparison issue with Hive?
+  createQueryTest("windowing_multipartitioning.q (deterministic) 2",
+    s"""
+      |select s,
+      |rank() over (partition by s order by dec desc) r,
+      |sum(b) over (partition by s order by ts desc) as sum
+      |from over1k
+      |where s = 'tom allen' or s = 'bob steinbeck'
+      |order by s, r, sum;
+     """.stripMargin, reset = false)
+  */
+
+  createQueryTest("windowing_multipartitioning.q (deterministic) 3",
+    s"""
+      |select s, sum(i) over (partition by s), sum(f) over (partition by si)
+      |from over1k where s = 'tom allen' or s = 'bob steinbeck';
+     """.stripMargin, reset = false)
+
+  createQueryTest("windowing_multipartitioning.q (deterministic) 4",
+    s"""
+      |select s, rank() over (partition by s order by bo),
+      |rank() over (partition by si order by bin desc) from over1k
+      |where s = 'tom allen' or s = 'bob steinbeck';
+     """.stripMargin, reset = false)
+
+  createQueryTest("windowing_multipartitioning.q (deterministic) 5",
+    s"""
+      |select s, sum(f) over (partition by i), row_number() over (order by f)
+      |from over1k where s = 'tom allen' or s = 'bob steinbeck';
+     """.stripMargin, reset = false)
+
+  createQueryTest("windowing_multipartitioning.q (deterministic) 6",
+    s"""
+      |select s, rank() over w1,
+      |rank() over w2
+      |from over1k
+      |where s = 'tom allen' or s = 'bob steinbeck'
+      |window
+      |w1 as (partition by s order by dec),
+      |w2 as (partition by si order by f) ;
+     """.stripMargin, reset = false)
+
+  /////////////////////////////////////////////////////////////////////////////
+  // Tests based on windowing_navfn.q
+  // Results of the original query file are not deterministic.
+  // Also, the original query of
+  // select i, lead(s) over (partition by bin order by d,i desc) from over1k ;
+  /////////////////////////////////////////////////////////////////////////////
+  createQueryTest("windowing_navfn.q (deterministic)",
+    s"""
+      |select s, row_number() over (partition by d order by dec) rn from over1k
+      |order by s, rn desc;
+      |select i, lead(s) over (partition by cast(bin as string) order by d,i desc) as l
+      |from over1k
+      |order by i desc, l;
+      |select i, lag(dec) over (partition by i order by s,i,dec) l from over1k
+      |order by i, l;
+      |select s, last_value(t) over (partition by d order by f) l from over1k
+      |order by s, l;
+      |select s, first_value(s) over (partition by bo order by s) f from over1k
+      |order by s, f;
+      |select t, s, i, last_value(i) over (partition by t order by s)
+      |from over1k where (s = 'oscar allen' or s = 'oscar carson') and t = 10;
+     """.stripMargin, reset = false)
+
+  /////////////////////////////////////////////////////////////////////////////
+  // Tests based on windowing_ntile.q
+  // Results of the original query file are not deterministic.
+  /////////////////////////////////////////////////////////////////////////////
+  createQueryTest("windowing_ntile.q (deterministic)",
+    s"""
+      |select i, ntile(10) over (partition by s order by i) n from over1k
+      |order by i, n;
+      |select s, ntile(100) over (partition by i order by s) n from over1k
+      |order by s, n;
+      |select f, ntile(4) over (partition by d order by f) n from over1k
+      |order by f, n;
+      |select d, ntile(1000) over (partition by dec order by d) n from over1k
+      |order by d, n;
+     """.stripMargin, reset = false)
+
+  /////////////////////////////////////////////////////////////////////////////
+  // Tests based on windowing_udaf.q
+  // Results of the original query file are not deterministic.
+  /////////////////////////////////////////////////////////////////////////////
+  createQueryTest("windowing_udaf.q (deterministic)",
+    s"""
+      |select s, min(i) over (partition by s) m from over1k
+      |order by s, m;
+      |select s, avg(f) over (partition by si order by s) a from over1k
+      |order by s, a;
+      |select s, avg(i) over (partition by t, b order by s) a from over1k
+      |order by s, a;
+      |select max(i) over w m from over1k
+      |order by m window w as (partition by f) ;
+      |select s, avg(d) over (partition by t order by f) a from over1k
+      |order by s, a;
+     """.stripMargin, reset = false)
+
+  /////////////////////////////////////////////////////////////////////////////
+  // Tests based on windowing_windowspec.q
+  // Results of the original query file are not deterministic.
+  /////////////////////////////////////////////////////////////////////////////
+  createQueryTest("windowing_windowspec.q (deterministic)",
+    s"""
+      |select s, sum(b) over (partition by i order by s,b rows unbounded preceding) as sum
+      |from over1k order by s, sum;
+      |select s, sum(f) over (partition by d order by s,f rows unbounded preceding) as sum
+      |from over1k order by s, sum;
+      |select s, sum(f) over
+      |(partition by ts order by f range between current row and unbounded following) as sum
+      |from over1k order by s, sum;
+      |select s, avg(f)
+      |over (partition by ts order by s,f rows between current row and 5 following) avg
+      |from over1k order by s, avg;
+      |select s, avg(d) over
+      |(partition by t order by s,d desc rows between 5 preceding and 5 following) avg
+      |from over1k order by s, avg;
+      |select s, sum(i) over(partition by ts order by s) sum from over1k
+      |order by s, sum;
+      |select f, sum(f) over
+      |(partition by ts order by f range between unbounded preceding and current row) sum
+      |from over1k order by f, sum;
+      |select s, i, round(avg(d) over (partition by s order by i) / 10.0 , 2) avg
+      |from over1k order by s, i, avg;
+      |select s, i, round((avg(d) over  w1 + 10.0) - (avg(d) over w1 - 10.0),2) avg
+      |from over1k
+      |order by s, i, avg window w1 as (partition by s order by i);
+     """.stripMargin, reset = false)
+
+  /////////////////////////////////////////////////////////////////////////////
+  // Tests based on windowing_rank.q
+  // Results of the original query file are not deterministic.
+  /////////////////////////////////////////////////////////////////////////////
+  createQueryTest("windowing_rank.q (deterministic) 1",
+    s"""
+      |select s, rank() over (partition by f order by t) r from over1k order by s, r;
+      |select s, dense_rank() over (partition by ts order by i,s desc) as r from over1k
+      |order by s desc, r desc;
+      |select s, cume_dist() over (partition by bo order by b,s) cd from over1k
+      |order by s, cd;
+      |select s, percent_rank() over (partition by dec order by f) r from over1k
+      |order by s desc, r desc;
+     """.stripMargin, reset = false)
+
+  createQueryTest("windowing_rank.q (deterministic) 2",
+    s"""
+      |select ts, dec, rnk
+      |from
+      |  (select ts, dec,
+      |          rank() over (partition by ts order by dec)  as rnk
+      |          from
+      |            (select other.ts, other.dec
+      |             from over1k other
+      |             join over1k on (other.b = over1k.b)
+      |            ) joined
+      |  ) ranked
+      |where rnk =  1
+      |order by ts, dec, rnk;
+     """.stripMargin, reset = false)
+
+  createQueryTest("windowing_rank.q (deterministic) 3",
+    s"""
+      |select ts, dec, rnk
+      |from
+      |  (select ts, dec,
+      |          rank() over (partition by ts order by dec)  as rnk
+      |          from
+      |            (select other.ts, other.dec
+      |             from over1k other
+      |             join over1k on (other.b = over1k.b)
+      |            ) joined
+      |  ) ranked
+      |where dec = 89.5
+      |order by ts, dec, rnk;
+     """.stripMargin, reset = false)
+
+  createQueryTest("windowing_rank.q (deterministic) 4",
+    s"""
+      |select ts, dec, rnk
+      |from
+      |  (select ts, dec,
+      |          rank() over (partition by ts order by dec)  as rnk
+      |          from
+      |            (select other.ts, other.dec
+      |             from over1k other
+      |             join over1k on (other.b = over1k.b)
+      |             where other.t < 10
+      |            ) joined
+      |  ) ranked
+      |where rnk = 1
+      |order by ts, dec, rnk;
+     """.stripMargin, reset = false)
+
+  /////////////////////////////////////////////////////////////////////////////
+  // Tests from windowing.q
+  // We port tests in windowing.q to here because this query file contains too
+  // many tests and the syntax of test "-- 7. testJoinWithWindowingAndPTF"
+  // is not supported right now.
+  /////////////////////////////////////////////////////////////////////////////
+  createQueryTest("windowing.q -- 1. testWindowing",
+    s"""
+      |select p_mfgr, p_name, p_size,
+      |rank() over(distribute by p_mfgr sort by p_name) as r,
+      |dense_rank() over(distribute by p_mfgr sort by p_name) as dr,
+      |sum(p_retailprice) over
+      |(distribute by p_mfgr sort by p_name rows between unbounded preceding and current row) as s1
+      |from part
+    """.stripMargin, reset = false)
+
+  createQueryTest("windowing.q -- 2. testGroupByWithPartitioning",
+    s"""
+      |select p_mfgr, p_name, p_size,
+      |min(p_retailprice),
+      |rank() over(distribute by p_mfgr sort by p_name)as r,
+      |dense_rank() over(distribute by p_mfgr sort by p_name) as dr,
+      |p_size, p_size - lag(p_size,1,p_size) over(distribute by p_mfgr sort by p_name) as deltaSz
+      |from part
+      |group by p_mfgr, p_name, p_size
+    """.stripMargin, reset = false)
+
+  createQueryTest("windowing.q -- 3. testGroupByHavingWithSWQ",
+    s"""
+      |select p_mfgr, p_name, p_size, min(p_retailprice),
+      |rank() over(distribute by p_mfgr sort by p_name) as r,
+      |dense_rank() over(distribute by p_mfgr sort by p_name) as dr,
+      |p_size, p_size - lag(p_size,1,p_size) over(distribute by p_mfgr sort by p_name) as deltaSz
+      |from part
+      |group by p_mfgr, p_name, p_size
+      |having p_size > 0
+    """.stripMargin, reset = false)
+
+  createQueryTest("windowing.q -- 4. testCount",
+    s"""
+      |select p_mfgr, p_name,
+      |count(p_size) over(distribute by p_mfgr sort by p_name) as cd
+      |from part
+    """.stripMargin, reset = false)
+
+  createQueryTest("windowing.q -- 5. testCountWithWindowingUDAF",
+    s"""
+      |select p_mfgr, p_name,
+      |rank() over(distribute by p_mfgr sort by p_name) as r,
+      |dense_rank() over(distribute by p_mfgr sort by p_name) as dr,
+      |count(p_size) over(distribute by p_mfgr sort by p_name) as cd,
+      |p_retailprice, sum(p_retailprice) over (distribute by p_mfgr sort by p_name
+      |                                  rows between unbounded preceding and current row) as s1,
+      |p_size, p_size - lag(p_size,1,p_size) over(distribute by p_mfgr sort by p_name) as deltaSz
+      |from part
+    """.stripMargin, reset = false)
+
+  createQueryTest("windowing.q -- 6. testCountInSubQ",
+    s"""
+      |select sub1.r, sub1.dr, sub1.cd, sub1.s1, sub1.deltaSz
+      |from (select p_mfgr, p_name,
+      |rank() over(distribute by p_mfgr sort by p_name) as r,
+      |dense_rank() over(distribute by p_mfgr sort by p_name) as dr,
+      |count(p_size) over(distribute by p_mfgr sort by p_name) as cd,
+      |p_retailprice, sum(p_retailprice) over (distribute by p_mfgr sort by p_name
+      |                                  rows between unbounded preceding and current row) as s1,
+      |p_size, p_size - lag(p_size,1,p_size) over(distribute by p_mfgr sort by p_name) as deltaSz
+      |from part
+      |) sub1
+    """.stripMargin, reset = false)
+
+  createQueryTest("windowing.q -- 8. testMixedCaseAlias",
+    s"""
+      |select p_mfgr, p_name, p_size,
+      |rank() over(distribute by p_mfgr sort by p_name, p_size desc) as R
+      |from part
+    """.stripMargin, reset = false)
+
+  createQueryTest("windowing.q -- 9. testHavingWithWindowingNoGBY",
+    s"""
+      |select p_mfgr, p_name, p_size,
+      |rank() over(distribute by p_mfgr sort by p_name) as r,
+      |dense_rank() over(distribute by p_mfgr sort by p_name) as dr,
+      |sum(p_retailprice) over (distribute by p_mfgr sort by p_name
+      |                        rows between unbounded preceding and current row)  as s1
+      |from part
+    """.stripMargin, reset = false)
+
+  createQueryTest("windowing.q -- 10. testHavingWithWindowingCondRankNoGBY",
+    s"""
+      |select p_mfgr, p_name, p_size,
+      |rank() over(distribute by p_mfgr sort by p_name) as r,
+      |dense_rank() over(distribute by p_mfgr sort by p_name) as dr,
+      |sum(p_retailprice) over (distribute by p_mfgr sort by p_name
+      |                        rows between unbounded preceding and current row) as s1
+      |from part
+    """.stripMargin, reset = false)
+
+  createQueryTest("windowing.q -- 11. testFirstLast",
+    s"""
+      |select  p_mfgr,p_name, p_size,
+      |sum(p_size) over (distribute by p_mfgr sort by p_name
+      |rows between current row and current row) as s2,
+      |first_value(p_size) over w1  as f,
+      |last_value(p_size, false) over w1  as l
+      |from part
+      |window w1 as (distribute by p_mfgr sort by p_name rows between 2 preceding and 2 following)
+    """.stripMargin, reset = false)
+
+  createQueryTest("windowing.q -- 12. testFirstLastWithWhere",
+    s"""
+      |select  p_mfgr,p_name, p_size,
+      |rank() over(distribute by p_mfgr sort by p_name) as r,
+      |sum(p_size) over (distribute by p_mfgr sort by p_name
+      |rows between current row and current row) as s2,
+      |first_value(p_size) over w1 as f,
+      |last_value(p_size, false) over w1 as l
+      |from part
+      |where p_mfgr = 'Manufacturer#3'
+      |window w1 as (distribute by p_mfgr sort by p_name rows between 2 preceding and 2 following)
+    """.stripMargin, reset = false)
+
+  createQueryTest("windowing.q -- 13. testSumWindow",
+    s"""
+      |select  p_mfgr,p_name, p_size,
+      |sum(p_size) over w1 as s1,
+      |sum(p_size) over (distribute by p_mfgr  sort by p_name
+      |rows between current row and current row)  as s2
+      |from part
+      |window w1 as (distribute by p_mfgr  sort by p_name rows between 2 preceding and 2 following)
+    """.stripMargin, reset = false)
+
+  createQueryTest("windowing.q -- 14. testNoSortClause",
+    s"""
+      |select  p_mfgr,p_name, p_size,
+      |rank() over(distribute by p_mfgr sort by p_name) as r,
+      |dense_rank() over(distribute by p_mfgr sort by p_name) as dr
+      |from part
+      |window w1 as (distribute by p_mfgr sort by p_name rows between 2 preceding and 2 following)
+    """.stripMargin, reset = false)
+
+  createQueryTest("windowing.q -- 15. testExpressions",
+    s"""
+      |select  p_mfgr,p_name, p_size,
+      |rank() over(distribute by p_mfgr sort by p_name) as r,
+      |dense_rank() over(distribute by p_mfgr sort by p_name) as dr,
+      |cume_dist() over(distribute by p_mfgr sort by p_name) as cud,
+      |percent_rank() over(distribute by p_mfgr sort by p_name) as pr,
+      |ntile(3) over(distribute by p_mfgr sort by p_name) as nt,
+      |count(p_size) over(distribute by p_mfgr sort by p_name) as ca,
+      |avg(p_size) over(distribute by p_mfgr sort by p_name) as avg,
+      |stddev(p_size) over(distribute by p_mfgr sort by p_name) as st,
+      |first_value(p_size % 5) over(distribute by p_mfgr sort by p_name) as fv,
+      |last_value(p_size) over(distribute by p_mfgr sort by p_name) as lv,
+      |first_value(p_size) over w1  as fvW1
+      |from part
+      |window w1 as (distribute by p_mfgr sort by p_mfgr, p_name
+      |             rows between 2 preceding and 2 following)
+    """.stripMargin, reset = false)
+
+  createQueryTest("windowing.q -- 16. testMultipleWindows",
+    s"""
+      |select  p_mfgr,p_name, p_size,
+      |rank() over(distribute by p_mfgr sort by p_name) as r,
+      |dense_rank() over(distribute by p_mfgr sort by p_name) as dr,
+      |cume_dist() over(distribute by p_mfgr sort by p_name) as cud,
+      |sum(p_size) over (distribute by p_mfgr sort by p_name
+      |range between unbounded preceding and current row) as s1,
+      |sum(p_size) over (distribute by p_mfgr sort by p_size
+      |range between 5 preceding and current row) as s2,
+      |first_value(p_size) over w1  as fv1
+      |from part
+      |window w1 as (distribute by p_mfgr sort by p_mfgr, p_name
+      |             rows between 2 preceding and 2 following)
+    """.stripMargin, reset = false)
+
+
+  createQueryTest("windowing.q -- 17. testCountStar",
+    s"""
+      |select  p_mfgr,p_name, p_size,
+      |count(*) over(distribute by p_mfgr sort by p_name ) as c,
+      |count(p_size) over(distribute by p_mfgr sort by p_name) as ca,
+      |first_value(p_size) over w1  as fvW1
+      |from part
+      |window w1 as (distribute by p_mfgr sort by p_mfgr, p_name
+      |             rows between 2 preceding and 2 following)
+    """.stripMargin, reset = false)
+
+  createQueryTest("windowing.q -- 18. testUDAFs",
+    s"""
+      |select  p_mfgr,p_name, p_size,
+      |sum(p_retailprice) over w1 as s,
+      |min(p_retailprice) over w1 as mi,
+      |max(p_retailprice) over w1 as ma,
+      |avg(p_retailprice) over w1 as ag
+      |from part
+      |window w1 as (distribute by p_mfgr sort by p_mfgr, p_name
+      |             rows between 2 preceding and 2 following)
+    """.stripMargin, reset = false)
+
+  createQueryTest("windowing.q -- 19. testUDAFsWithGBY",
+    """
+      |select  p_mfgr,p_name, p_size, p_retailprice,
+      |sum(p_retailprice) over w1 as s,
+      |min(p_retailprice) as mi ,
+      |max(p_retailprice) as ma ,
+      |avg(p_retailprice) over w1 as ag
+      |from part
+      |group by p_mfgr,p_name, p_size, p_retailprice
+      |window w1 as (distribute by p_mfgr sort by p_mfgr, p_name
+      |             rows between 2 preceding and 2 following);
+    """.stripMargin, reset = false)
+
+  createQueryTest("windowing.q -- 20. testSTATs",
+    """
+      |select  p_mfgr,p_name, p_size,
+      |stddev(p_retailprice) over w1 as sdev,
+      |stddev_pop(p_retailprice) over w1 as sdev_pop,
+      |collect_set(p_size) over w1 as uniq_size,
+      |variance(p_retailprice) over w1 as var,
+      |corr(p_size, p_retailprice) over w1 as cor,
+      |covar_pop(p_size, p_retailprice) over w1 as covarp
+      |from part
+      |window w1 as (distribute by p_mfgr sort by p_mfgr, p_name
+      |             rows between 2 preceding and 2 following)
+    """.stripMargin, reset = false)
+
+  createQueryTest("windowing.q -- 21. testDISTs",
+    """
+      |select  p_mfgr,p_name, p_size,
+      |histogram_numeric(p_retailprice, 5) over w1 as hist,
+      |percentile(p_partkey, 0.5) over w1 as per,
+      |row_number() over(distribute by p_mfgr sort by p_name) as rn
+      |from part
+      |window w1 as (distribute by p_mfgr sort by p_mfgr, p_name
+      |             rows between 2 preceding and 2 following)
+    """.stripMargin, reset = false)
+
+  createQueryTest("windowing.q -- 24. testLateralViews",
+    """
+      |select p_mfgr, p_name,
+      |lv_col, p_size, sum(p_size) over w1   as s
+      |from (select p_mfgr, p_name, p_size, array(1,2,3) arr from part) p
+      |lateral view explode(arr) part_lv as lv_col
+      |window w1 as (distribute by p_mfgr sort by p_size, lv_col
+      |             rows between 2 preceding and current row)
+    """.stripMargin, reset = false)
+
+  createQueryTest("windowing.q -- 26. testGroupByHavingWithSWQAndAlias",
+    """
+      |select p_mfgr, p_name, p_size, min(p_retailprice) as mi,
+      |rank() over(distribute by p_mfgr sort by p_name) as r,
+      |dense_rank() over(distribute by p_mfgr sort by p_name) as dr,
+      |p_size, p_size - lag(p_size,1,p_size) over(distribute by p_mfgr sort by p_name) as deltaSz
+      |from part
+      |group by p_mfgr, p_name, p_size
+      |having p_size > 0
+    """.stripMargin, reset = false)
+
+  createQueryTest("windowing.q -- 27. testMultipleRangeWindows",
+    """
+      |select  p_mfgr,p_name, p_size,
+      |sum(p_size) over (distribute by p_mfgr sort by p_size
+      |range between 10 preceding and current row) as s2,
+      |sum(p_size) over (distribute by p_mfgr sort by p_size
+      |range between current row and 10 following )  as s1
+      |from part
+      |window w1 as (rows between 2 preceding and 2 following)
+    """.stripMargin, reset = false)
+
+  createQueryTest("windowing.q -- 28. testPartOrderInUDAFInvoke",
+    """
+      |select p_mfgr, p_name, p_size,
+      |sum(p_size) over (partition by p_mfgr  order by p_name
+      |rows between 2 preceding and 2 following) as s
+      |from part
+    """.stripMargin, reset = false)
+
+  createQueryTest("windowing.q -- 29. testPartOrderInWdwDef",
+    """
+      |select p_mfgr, p_name, p_size,
+      |sum(p_size) over w1 as s
+      |from part
+      |window w1 as (partition by p_mfgr  order by p_name
+      |             rows between 2 preceding and 2 following)
+    """.stripMargin, reset = false)
+
+  createQueryTest("windowing.q -- 30. testDefaultPartitioningSpecRules",
+    """
+      |select p_mfgr, p_name, p_size,
+      |sum(p_size) over w1 as s,
+      |sum(p_size) over w2 as s2
+      |from part
+      |window w1 as (distribute by p_mfgr sort by p_name rows between 2 preceding and 2 following),
+      |       w2 as (partition by p_mfgr order by p_name)
+    """.stripMargin, reset = false)
+
+  /* p_name is not a numeric column. What is Hive's semantic?
+  createQueryTest("windowing.q -- 31. testWindowCrossReference",
+    """
+      |select p_mfgr, p_name, p_size,
+      |sum(p_size) over w1 as s1,
+      |sum(p_size) over w2 as s2
+      |from part
+      |window w1 as (partition by p_mfgr order by p_name
+      |             range between 2 preceding and 2 following),
+      |       w2 as w1
+    """.stripMargin, reset = false)
+  */
+  /*
+  createQueryTest("windowing.q -- 32. testWindowInheritance",
+    """
+      |select p_mfgr, p_name, p_size,
+      |sum(p_size) over w1 as s1,
+      |sum(p_size) over w2 as s2
+      |from part
+      |window w1 as (partition by p_mfgr order by p_name
+      |             range between 2 preceding and 2 following),
+      |       w2 as (w1 rows between unbounded preceding and current row)
+    """.stripMargin, reset = false)
+  */
+
+  /* p_name is not a numeric column. What is Hive's semantic?
+  createQueryTest("windowing.q -- 33. testWindowForwardReference",
+    """
+      |select p_mfgr, p_name, p_size,
+      |sum(p_size) over w1 as s1,
+      |sum(p_size) over w2 as s2,
+      |sum(p_size) over w3 as s3
+      |from part
+      |window w1 as (distribute by p_mfgr sort by p_name
+      |             range between 2 preceding and 2 following),
+      |       w2 as w3,
+      |       w3 as (distribute by p_mfgr sort by p_name
+      |             range between unbounded preceding and current row)
+    """.stripMargin, reset = false)
+  */
+  /*
+  createQueryTest("windowing.q -- 34. testWindowDefinitionPropagation",
+    """
+      |select p_mfgr, p_name, p_size,
+      |sum(p_size) over w1 as s1,
+      |sum(p_size) over w2 as s2,
+      |sum(p_size) over (w3 rows between 2 preceding and 2 following)  as s3
+      |from part
+      |window w1 as (distribute by p_mfgr sort by p_name
+      |             range between 2 preceding and 2 following),
+      |       w2 as w3,
+      |       w3 as (distribute by p_mfgr sort by p_name
+      |             range between unbounded preceding and current row)
+    """.stripMargin, reset = false)
+  */
+
+  /* Seems Hive evaluate SELECT DISTINCT before window functions?
+  createQueryTest("windowing.q -- 35. testDistinctWithWindowing",
+    """
+      |select DISTINCT p_mfgr, p_name, p_size,
+      |sum(p_size) over w1 as s
+      |from part
+      |window w1 as (distribute by p_mfgr sort by p_name rows between 2 preceding and 2 following)
+    """.stripMargin, reset = false)
+  */
+
+  createQueryTest("windowing.q -- 36. testRankWithPartitioning",
+    """
+      |select p_mfgr, p_name, p_size,
+      |rank() over (partition by p_mfgr order by p_name )  as r
+      |from part
+    """.stripMargin, reset = false)
+
+  createQueryTest("windowing.q -- 37. testPartitioningVariousForms",
+    """
+      |select p_mfgr,
+      |round(sum(p_retailprice) over (partition by p_mfgr order by p_mfgr),2) as s1,
+      |min(p_retailprice) over (partition by p_mfgr) as s2,
+      |max(p_retailprice) over (distribute by p_mfgr sort by p_mfgr) as s3,
+      |round(avg(p_retailprice) over (distribute by p_mfgr),2) as s4,
+      |count(p_retailprice) over (cluster by p_mfgr ) as s5
+      |from part
+    """.stripMargin, reset = false)
+
+  createQueryTest("windowing.q -- 38. testPartitioningVariousForms2",
+    """
+      |select p_mfgr, p_name, p_size,
+      |sum(p_retailprice) over (partition by p_mfgr, p_name order by p_mfgr, p_name
+      |rows between unbounded preceding and current row) as s1,
+      |min(p_retailprice) over (distribute by p_mfgr, p_name sort by p_mfgr, p_name
+      |rows between unbounded preceding and current row) as s2,
+      |max(p_retailprice) over (partition by p_mfgr, p_name order by p_name) as s3
+      |from part
+    """.stripMargin, reset = false)
+
+  createQueryTest("windowing.q -- 39. testUDFOnOrderCols",
+    """
+      |select p_mfgr, p_type, substr(p_type, 2) as short_ptype,
+      |rank() over (partition by p_mfgr order by substr(p_type, 2))  as r
+      |from part
+    """.stripMargin, reset = false)
+
+  createQueryTest("windowing.q -- 40. testNoBetweenForRows",
+    """
+      |select p_mfgr, p_name, p_size,
+      |sum(p_retailprice) over (distribute by p_mfgr sort by p_name rows unbounded preceding) as s1
+      |from part
+    """.stripMargin, reset = false)
+
+  createQueryTest("windowing.q -- 41. testNoBetweenForRange",
+    """
+      |select p_mfgr, p_name, p_size,
+      |sum(p_retailprice) over (distribute by p_mfgr sort by p_size range unbounded preceding) as s1
+      |from part
+    """.stripMargin, reset = false)
+
+  createQueryTest("windowing.q -- 42. testUnboundedFollowingForRows",
+    """
+      |select p_mfgr, p_name, p_size,
+      |sum(p_retailprice) over (distribute by p_mfgr sort by p_name
+      |rows between current row and unbounded following) as s1
+      |from part
+    """.stripMargin, reset = false)
+
+  createQueryTest("windowing.q -- 43. testUnboundedFollowingForRange",
+    """
+      |select p_mfgr, p_name, p_size,
+      |sum(p_retailprice) over (distribute by p_mfgr sort by p_size
+      |range between current row and unbounded following) as s1
+      |from part
+    """.stripMargin, reset = false)
+
+  createQueryTest("windowing.q -- 44. testOverNoPartitionSingleAggregate",
+    """
+      |select p_name, p_retailprice,
+      |round(avg(p_retailprice) over(),2)
+      |from part
+      |order by p_name
+    """.stripMargin, reset = false)
+}
+
+class HiveWindowFunctionQueryWithoutCodeGenSuite extends HiveWindowFunctionQueryBaseSuite {
+  var originalCodegenEnabled: Boolean = _
+  override def beforeAll(): Unit = {
+    super.beforeAll()
+    originalCodegenEnabled = conf.codegenEnabled
+    sql("set spark.sql.codegen=false")
+  }
+
+  override def afterAll(): Unit = {
+    sql(s"set spark.sql.codegen=$originalCodegenEnabled")
+    super.afterAll()
+  }
+}
+
+abstract class HiveWindowFunctionQueryFileBaseSuite
+  extends HiveCompatibilitySuite with BeforeAndAfter {
+  private val originalTimeZone = TimeZone.getDefault
+  private val originalLocale = Locale.getDefault
+  private val testTempDir = Utils.createTempDir()
+
+  override def beforeAll() {
+    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)
+
+    // The following settings are used for generating golden files with Hive.
+    // We have to use kryo to correctly let Hive serialize plans with window functions.
+    // This is used to generate golden files.
+    sql("set hive.plan.serialization.format=kryo")
+    // Explicitly set fs to local fs.
+    sql(s"set fs.default.name=file://$testTempDir/")
+    // Ask Hive to run jobs in-process as a single map and reduce task.
+    sql("set mapred.job.tracker=local")
+  }
+
+  override def afterAll() {
+    TestHive.cacheTables = false
+    TimeZone.setDefault(originalTimeZone)
+    Locale.setDefault(originalLocale)
+    TestHive.reset()
+  }
+
+  override def blackList: Seq[String] = Seq(
+    // Partitioned table functions are not supported.
+    "ptf*",
+    // tests of windowing.q are in HiveWindowFunctionQueryBaseSuite
+    "windowing.q",
+
+    // This one failed on the expression of
+    // sum(lag(p_retailprice,1,0.0)) over w1
+    // lag(p_retailprice,1,0.0) is a GenericUDF and the argument inspector of
+    // p_retailprice created by HiveInspectors is
+    // PrimitiveObjectInspectorFactory.javaDoubleObjectInspector.
+    // However, seems Hive assumes it is
+    // PrimitiveObjectInspectorFactory.writableDoubleObjectInspector, which introduces an error.
+    "windowing_expressions",
+
+    // Hive's results are not deterministic
+    "windowing_multipartitioning",
+    "windowing_navfn",
+    "windowing_ntile",
+    "windowing_udaf",
+    "windowing_windowspec",
+    "windowing_rank"
+  )
+
+  override def whiteList: Seq[String] = Seq(
+    "windowing_udaf2",
+    "windowing_columnPruning",
+    "windowing_adjust_rowcontainer_sz"
+  )
+
+  override def testCases: Seq[(String, File)] = super.testCases.filter {
+    case (name, _) => realWhiteList.contains(name)
+  }
+}
+
+class HiveWindowFunctionQueryFileWithoutCodeGenSuite extends HiveWindowFunctionQueryFileBaseSuite {
+  var originalCodegenEnabled: Boolean = _
+  override def beforeAll(): Unit = {
+    super.beforeAll()
+    originalCodegenEnabled = conf.codegenEnabled
+    sql("set spark.sql.codegen=false")
+  }
+
+  override def afterAll(): Unit = {
+    sql(s"set spark.sql.codegen=$originalCodegenEnabled")
+    super.afterAll()
+  }
+}
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 630dec8fa05a0..616352d223c5b 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
@@ -48,6 +48,10 @@ case class Order(
     state: String,
     month: Int)
 
+case class WindowData(
+    month: Int,
+    area: String,
+    product: Int)
 /** A SQL Dialect for testing purpose, and it can not be nested type */
 class MyDialect extends DefaultDialect
 
@@ -604,4 +608,147 @@ class SQLQuerySuite extends QueryTest {
       sql("SELECT TRANSFORM (d1, d2, d3) USING 'cat' AS (a,b,c) FROM script_trans")
       .queryExecution.toRdd.count())
   }
+
+  test("window function: udaf with aggregate expressin") {
+    val data = Seq(
+      WindowData(1, "a", 5),
+      WindowData(2, "a", 6),
+      WindowData(3, "b", 7),
+      WindowData(4, "b", 8),
+      WindowData(5, "c", 9),
+      WindowData(6, "c", 10)
+    )
+    sparkContext.parallelize(data).toDF().registerTempTable("windowData")
+
+    checkAnswer(
+      sql(
+        """
+          |select area, sum(product), sum(sum(product)) over (partition by area)
+          |from windowData group by month, area
+        """.stripMargin),
+      Seq(
+        ("a", 5, 11),
+        ("a", 6, 11),
+        ("b", 7, 15),
+        ("b", 8, 15),
+        ("c", 9, 19),
+        ("c", 10, 19)
+      ).map(i => Row(i._1, i._2, i._3)))
+
+    checkAnswer(
+      sql(
+        """
+          |select area, sum(product) - 1, sum(sum(product)) over (partition by area)
+          |from windowData group by month, area
+        """.stripMargin),
+      Seq(
+        ("a", 4, 11),
+        ("a", 5, 11),
+        ("b", 6, 15),
+        ("b", 7, 15),
+        ("c", 8, 19),
+        ("c", 9, 19)
+      ).map(i => Row(i._1, i._2, i._3)))
+
+    checkAnswer(
+      sql(
+        """
+          |select area, sum(product), sum(product) / sum(sum(product)) over (partition by area)
+          |from windowData group by month, area
+        """.stripMargin),
+      Seq(
+        ("a", 5, 5d/11),
+        ("a", 6, 6d/11),
+        ("b", 7, 7d/15),
+        ("b", 8, 8d/15),
+        ("c", 10, 10d/19),
+        ("c", 9, 9d/19)
+      ).map(i => Row(i._1, i._2, i._3)))
+
+    checkAnswer(
+      sql(
+        """
+          |select area, sum(product), sum(product) / sum(sum(product) - 1) over (partition by area)
+          |from windowData group by month, area
+        """.stripMargin),
+      Seq(
+        ("a", 5, 5d/9),
+        ("a", 6, 6d/9),
+        ("b", 7, 7d/13),
+        ("b", 8, 8d/13),
+        ("c", 10, 10d/17),
+        ("c", 9, 9d/17)
+      ).map(i => Row(i._1, i._2, i._3)))
+  }
+
+  test("window function: partition and order expressions") {
+    val data = Seq(
+      WindowData(1, "a", 5),
+      WindowData(2, "a", 6),
+      WindowData(3, "b", 7),
+      WindowData(4, "b", 8),
+      WindowData(5, "c", 9),
+      WindowData(6, "c", 10)
+    )
+    sparkContext.parallelize(data).toDF().registerTempTable("windowData")
+
+    checkAnswer(
+      sql(
+        """
+          |select month, area, product, sum(product + 1) over (partition by 1 order by 2)
+          |from windowData
+        """.stripMargin),
+      Seq(
+        (1, "a", 5, 51),
+        (2, "a", 6, 51),
+        (3, "b", 7, 51),
+        (4, "b", 8, 51),
+        (5, "c", 9, 51),
+        (6, "c", 10, 51)
+      ).map(i => Row(i._1, i._2, i._3, i._4)))
+
+    checkAnswer(
+      sql(
+        """
+          |select month, area, product, sum(product)
+          |over (partition by month % 2 order by 10 - product)
+          |from windowData
+        """.stripMargin),
+      Seq(
+        (1, "a", 5, 21),
+        (2, "a", 6, 24),
+        (3, "b", 7, 16),
+        (4, "b", 8, 18),
+        (5, "c", 9, 9),
+        (6, "c", 10, 10)
+      ).map(i => Row(i._1, i._2, i._3, i._4)))
+  }
+
+  test("window function: expressions in arguments of a window functions") {
+    val data = Seq(
+      WindowData(1, "a", 5),
+      WindowData(2, "a", 6),
+      WindowData(3, "b", 7),
+      WindowData(4, "b", 8),
+      WindowData(5, "c", 9),
+      WindowData(6, "c", 10)
+    )
+    sparkContext.parallelize(data).toDF().registerTempTable("windowData")
+
+    checkAnswer(
+      sql(
+        """
+          |select month, area, month % 2,
+          |lag(product, 1 + 1, product) over (partition by month % 2 order by area)
+          |from windowData
+        """.stripMargin),
+      Seq(
+        (1, "a", 1, 5),
+        (2, "a", 0, 6),
+        (3, "b", 1, 7),
+        (4, "b", 0, 8),
+        (5, "c", 1, 5),
+        (6, "c", 0, 6)
+      ).map(i => Row(i._1, i._2, i._3, i._4)))
+  }
 }

From 002c12384d6ecebbb3e7fc853dbdfbc5aaa3d6a6 Mon Sep 17 00:00:00 2001
From: Josh Rosen <joshrosen@databricks.com>
Date: Wed, 6 May 2015 10:52:55 -0700
Subject: [PATCH 13/75] [SPARK-7311] Introduce internal Serializer API for
 determining if serializers support object relocation

This patch extends the `Serializer` interface with a new `Private` API which allows serializers to indicate whether they support relocation of serialized objects in serializer stream output.

This relocatibilty property is described in more detail in `Serializer.scala`, but in a nutshell a serializer supports relocation if reordering the bytes of serialized objects in serialization stream output is equivalent to having re-ordered those elements prior to serializing them.  The optimized shuffle path introduced in #4450 and #5868 both rely on serializers having this property; this patch just centralizes the logic for determining whether a serializer has this property.  I also added tests and comments clarifying when this works for KryoSerializer.

This change allows the optimizations in #4450 to be applied for shuffles that use `SqlSerializer2`.

Author: Josh Rosen <joshrosen@databricks.com>

Closes #5924 from JoshRosen/SPARK-7311 and squashes the following commits:

50a68ca [Josh Rosen] Address minor nits
0a7ebd7 [Josh Rosen] Clarify reason why SqlSerializer2 supports this serializer
123b992 [Josh Rosen] Cleanup for submitting as standalone patch.
4aa61b2 [Josh Rosen] Add missing newline
2c1233a [Josh Rosen] Small refactoring of SerializerPropertiesSuite to enable test re-use:
0ba75e6 [Josh Rosen] Add tests for serializer relocation property.
450fa21 [Josh Rosen] Back out accidental log4j.properties change
86d4dcd [Josh Rosen] Flag that SparkSqlSerializer2 supports relocation
b9624ee [Josh Rosen] Expand serializer API and use new function to help control when new UnsafeShuffle path is used.
---
 .../spark/serializer/KryoSerializer.scala     |   7 ++
 .../apache/spark/serializer/Serializer.scala  |  35 +++++-
 .../util/collection/ExternalSorter.scala      |   3 +-
 .../SerializerPropertiesSuite.scala           | 119 ++++++++++++++++++
 .../sql/execution/SparkSqlSerializer2.scala   |   5 +
 5 files changed, 166 insertions(+), 3 deletions(-)
 create mode 100644 core/src/test/scala/org/apache/spark/serializer/SerializerPropertiesSuite.scala

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 b7bc087855b9f..f9f78852f032b 100644
--- a/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala
+++ b/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala
@@ -125,6 +125,13 @@ class KryoSerializer(conf: SparkConf)
   override def newInstance(): SerializerInstance = {
     new KryoSerializerInstance(this)
   }
+
+  private[spark] override lazy val supportsRelocationOfSerializedObjects: Boolean = {
+    // If auto-reset is disabled, then Kryo may store references to duplicate occurrences of objects
+    // in the stream rather than writing those objects' serialized bytes, breaking relocation. See
+    // https://groups.google.com/d/msg/kryo-users/6ZUSyfjjtdo/FhGG1KHDXPgJ for more details.
+    newInstance().asInstanceOf[KryoSerializerInstance].getAutoReset()
+  }
 }
 
 private[spark]
diff --git a/core/src/main/scala/org/apache/spark/serializer/Serializer.scala b/core/src/main/scala/org/apache/spark/serializer/Serializer.scala
index c381672a4f588..6078c9d433ebf 100644
--- a/core/src/main/scala/org/apache/spark/serializer/Serializer.scala
+++ b/core/src/main/scala/org/apache/spark/serializer/Serializer.scala
@@ -23,7 +23,7 @@ import java.nio.ByteBuffer
 import scala.reflect.ClassTag
 
 import org.apache.spark.{SparkConf, SparkEnv}
-import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.annotation.{DeveloperApi, Private}
 import org.apache.spark.util.{Utils, ByteBufferInputStream, NextIterator}
 
 /**
@@ -63,6 +63,39 @@ abstract class Serializer {
 
   /** Creates a new [[SerializerInstance]]. */
   def newInstance(): SerializerInstance
+
+  /**
+   * :: Private ::
+   * Returns true if this serializer supports relocation of its serialized objects and false
+   * otherwise. This should return true if and only if reordering the bytes of serialized objects
+   * in serialization stream output is equivalent to having re-ordered those elements prior to
+   * serializing them. More specifically, the following should hold if a serializer supports
+   * relocation:
+   *
+   * {{{
+   * serOut.open()
+   * position = 0
+   * serOut.write(obj1)
+   * serOut.flush()
+   * position = # of bytes writen to stream so far
+   * obj1Bytes = output[0:position-1]
+   * serOut.write(obj2)
+   * serOut.flush()
+   * position2 = # of bytes written to stream so far
+   * obj2Bytes = output[position:position2-1]
+   * serIn.open([obj2bytes] concatenate [obj1bytes]) should return (obj2, obj1)
+   * }}}
+   *
+   * In general, this property should hold for serializers that are stateless and that do not
+   * write special metadata at the beginning or end of the serialization stream.
+   *
+   * This API is private to Spark; this method should not be overridden in third-party subclasses
+   * or called in user code and is subject to removal in future Spark releases.
+   *
+   * See SPARK-7311 for more details.
+   */
+  @Private
+  private[spark] def supportsRelocationOfSerializedObjects: Boolean = false
 }
 
 
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 b7306cd551918..7d5cf7b61e56a 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
@@ -131,8 +131,7 @@ private[spark] class ExternalSorter[K, V, C](
   private val kvChunkSize = conf.getInt("spark.shuffle.sort.kvChunkSize", 1 << 22) // 4 MB
   private val useSerializedPairBuffer =
     !ordering.isDefined && conf.getBoolean("spark.shuffle.sort.serializeMapOutputs", true) &&
-    ser.isInstanceOf[KryoSerializer] &&
-    serInstance.asInstanceOf[KryoSerializerInstance].getAutoReset
+    ser.supportsRelocationOfSerializedObjects
 
   // Data structures to store in-memory objects before we spill. Depending on whether we have an
   // Aggregator set, we either put objects into an AppendOnlyMap where we combine them, or we
diff --git a/core/src/test/scala/org/apache/spark/serializer/SerializerPropertiesSuite.scala b/core/src/test/scala/org/apache/spark/serializer/SerializerPropertiesSuite.scala
new file mode 100644
index 0000000000000..bb34033fe9e7e
--- /dev/null
+++ b/core/src/test/scala/org/apache/spark/serializer/SerializerPropertiesSuite.scala
@@ -0,0 +1,119 @@
+/*
+ * 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.serializer
+
+import java.io.{ByteArrayInputStream, ByteArrayOutputStream}
+
+import scala.util.Random
+
+import org.scalatest.{Assertions, FunSuite}
+
+import org.apache.spark.SparkConf
+import org.apache.spark.serializer.KryoTest.RegistratorWithoutAutoReset
+
+/**
+ * Tests to ensure that [[Serializer]] implementations obey the API contracts for methods that
+ * describe properties of the serialized stream, such as
+ * [[Serializer.supportsRelocationOfSerializedObjects]].
+ */
+class SerializerPropertiesSuite extends FunSuite {
+
+  import SerializerPropertiesSuite._
+
+  test("JavaSerializer does not support relocation") {
+    // Per a comment on the SPARK-4550 JIRA ticket, Java serialization appears to write out the
+    // full class name the first time an object is written to an output stream, but subsequent
+    // references to the class write a more compact identifier; this prevents relocation.
+    val ser = new JavaSerializer(new SparkConf())
+    testSupportsRelocationOfSerializedObjects(ser, generateRandomItem)
+  }
+
+  test("KryoSerializer supports relocation when auto-reset is enabled") {
+    val ser = new KryoSerializer(new SparkConf)
+    assert(ser.newInstance().asInstanceOf[KryoSerializerInstance].getAutoReset())
+    testSupportsRelocationOfSerializedObjects(ser, generateRandomItem)
+  }
+
+  test("KryoSerializer does not support relocation when auto-reset is disabled") {
+    val conf = new SparkConf().set("spark.kryo.registrator",
+      classOf[RegistratorWithoutAutoReset].getName)
+    val ser = new KryoSerializer(conf)
+    assert(!ser.newInstance().asInstanceOf[KryoSerializerInstance].getAutoReset())
+    testSupportsRelocationOfSerializedObjects(ser, generateRandomItem)
+  }
+
+}
+
+object SerializerPropertiesSuite extends Assertions {
+
+  def generateRandomItem(rand: Random): Any = {
+    val randomFunctions: Seq[() => Any] = Seq(
+      () => rand.nextInt(),
+      () => rand.nextString(rand.nextInt(10)),
+      () => rand.nextDouble(),
+      () => rand.nextBoolean(),
+      () => (rand.nextInt(), rand.nextString(rand.nextInt(10))),
+      () => MyCaseClass(rand.nextInt(), rand.nextString(rand.nextInt(10))),
+      () => {
+        val x = MyCaseClass(rand.nextInt(), rand.nextString(rand.nextInt(10)))
+        (x, x)
+      }
+    )
+    randomFunctions(rand.nextInt(randomFunctions.size)).apply()
+  }
+
+  def testSupportsRelocationOfSerializedObjects(
+      serializer: Serializer,
+      generateRandomItem: Random => Any): Unit = {
+    if (!serializer.supportsRelocationOfSerializedObjects) {
+      return
+    }
+    val NUM_TRIALS = 5
+    val rand = new Random(42)
+    for (_ <- 1 to NUM_TRIALS) {
+      val items = {
+        // Make sure that we have duplicate occurrences of the same object in the stream:
+        val randomItems = Seq.fill(10)(generateRandomItem(rand))
+        randomItems ++ randomItems.take(5)
+      }
+      val baos = new ByteArrayOutputStream()
+      val serStream = serializer.newInstance().serializeStream(baos)
+      def serializeItem(item: Any): Array[Byte] = {
+        val itemStartOffset = baos.toByteArray.length
+        serStream.writeObject(item)
+        serStream.flush()
+        val itemEndOffset = baos.toByteArray.length
+        baos.toByteArray.slice(itemStartOffset, itemEndOffset).clone()
+      }
+      val itemsAndSerializedItems: Seq[(Any, Array[Byte])] = {
+        val serItems = items.map {
+          item => (item, serializeItem(item))
+        }
+        serStream.close()
+        rand.shuffle(serItems)
+      }
+      val reorderedSerializedData: Array[Byte] = itemsAndSerializedItems.flatMap(_._2).toArray
+      val deserializedItemsStream = serializer.newInstance().deserializeStream(
+        new ByteArrayInputStream(reorderedSerializedData))
+      assert(deserializedItemsStream.asIterator.toSeq === itemsAndSerializedItems.map(_._1))
+      deserializedItemsStream.close()
+    }
+  }
+}
+
+private case class MyCaseClass(foo: Int, bar: String)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer2.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer2.scala
index 9552f41115866..35ad987eb1a63 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer2.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer2.scala
@@ -154,6 +154,11 @@ private[sql] class SparkSqlSerializer2(keySchema: Array[DataType], valueSchema:
   with Serializable{
 
   def newInstance(): SerializerInstance = new ShuffleSerializerInstance(keySchema, valueSchema)
+
+  override def supportsRelocationOfSerializedObjects: Boolean = {
+    // SparkSqlSerializer2 is stateless and writes no stream headers
+    true
+  }
 }
 
 private[sql] object SparkSqlSerializer2 {

From 845d1d4d0cc5512d1671b2f2c7c74c5b97c3aef8 Mon Sep 17 00:00:00 2001
From: Josh Rosen <joshrosen@databricks.com>
Date: Wed, 6 May 2015 11:03:17 -0700
Subject: [PATCH 14/75] Add `Private` annotation.

This was originally added as part of #4435, which was reverted.
---
 .../org/apache/spark/annotation/Private.java  | 41 +++++++++++++++++++
 1 file changed, 41 insertions(+)
 create mode 100644 core/src/main/scala/org/apache/spark/annotation/Private.java

diff --git a/core/src/main/scala/org/apache/spark/annotation/Private.java b/core/src/main/scala/org/apache/spark/annotation/Private.java
new file mode 100644
index 0000000000000..9082fcf0c84bc
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/annotation/Private.java
@@ -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.annotation;
+
+import java.lang.annotation.ElementType;
+import java.lang.annotation.Retention;
+import java.lang.annotation.RetentionPolicy;
+import java.lang.annotation.Target;
+
+/**
+ * A class that is considered private to the internals of Spark -- there is a high-likelihood
+ * they will be changed in future versions of Spark.
+ *
+ * This should be used only when the standard Scala / Java means of protecting classes are
+ * insufficient.  In particular, Java has no equivalent of private[spark], so we use this annotation
+ * in its place.
+ *
+ * NOTE: If there exists a Scaladoc comment that immediately precedes this annotation, the first
+ * line of the comment must be ":: Private ::" with no trailing blank line. This is because
+ * of the known issue that Scaladoc displays only either the annotation or the comment, whichever
+ * comes first.
+ */
+@Retention(RetentionPolicy.RUNTIME)
+@Target({ElementType.TYPE, ElementType.FIELD, ElementType.METHOD, ElementType.PARAMETER,
+        ElementType.CONSTRUCTOR, ElementType.LOCAL_VARIABLE, ElementType.PACKAGE})
+public @interface Private {}

From 77409967008415b7ea57e9349d296350e6519687 Mon Sep 17 00:00:00 2001
From: Yin Huai <yhuai@databricks.com>
Date: Wed, 6 May 2015 14:48:25 -0700
Subject: [PATCH 15/75] [HOT-FIX] Move HiveWindowFunctionQuerySuite.scala to
 hive compatibility dir.

Author: Yin Huai <yhuai@databricks.com>

Closes #5951 from yhuai/fixBuildMaven and squashes the following commits:

fdde183 [Yin Huai] Move HiveWindowFunctionQuerySuite.scala to hive compatibility dir.
---
 .../spark/sql/hive/execution/HiveWindowFunctionQuerySuite.scala   | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename sql/hive/{ => compatibility}/src/test/scala/org/apache/spark/sql/hive/execution/HiveWindowFunctionQuerySuite.scala (100%)

diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveWindowFunctionQuerySuite.scala b/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveWindowFunctionQuerySuite.scala
similarity index 100%
rename from sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveWindowFunctionQuerySuite.scala
rename to sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveWindowFunctionQuerySuite.scala

From 1ad04dae038673a448f529c39b17817b78d6acd0 Mon Sep 17 00:00:00 2001
From: "Joseph K. Bradley" <joseph@databricks.com>
Date: Wed, 6 May 2015 16:15:51 -0700
Subject: [PATCH 16/75] [SPARK-5995] [ML] Make Prediction dev API public

Changes:
* Update protected prediction methods, following design doc. **<--most interesting change**
* Changed abstract classes for Estimator and Model to be public.  Added DeveloperApi tag.  (I kept the traits for Estimator/Model Params private.)
* Changed ProbabilisticClassificationModel method names to use probability instead of probabilities.

CC: mengxr shivaram etrain

Author: Joseph K. Bradley <joseph@databricks.com>

Closes #5913 from jkbradley/public-dev-api and squashes the following commits:

e9aa0ea [Joseph K. Bradley] moved findMax to DenseVector and renamed to argmax. fixed bug for vector of length 0
15b9957 [Joseph K. Bradley] renamed probabilities to probability in method names
5cda84d [Joseph K. Bradley] regenerated sharedParams
7d1877a [Joseph K. Bradley] Made spark.ml prediction abstractions public.  Organized their prediction methods for efficient computation of multiple output columns.
---
 .../ml/{impl/estimator => }/Predictor.scala   |  50 ++------
 .../spark/ml/classification/Classifier.scala  | 110 ++++++------------
 .../DecisionTreeClassifier.scala              |   5 +-
 .../ml/classification/GBTClassifier.scala     |   5 +-
 .../classification/LogisticRegression.scala   | 100 ++++++----------
 .../ProbabilisticClassifier.scala             | 100 +++++++++++-----
 .../RandomForestClassifier.scala              |   5 +-
 .../ml/param/shared/SharedParamsCodeGen.scala |   6 +-
 .../spark/ml/param/shared/sharedParams.scala  |   4 +-
 .../ml/regression/DecisionTreeRegressor.scala |   5 +-
 .../spark/ml/regression/GBTRegressor.scala    |   5 +-
 .../ml/regression/LinearRegression.scala      |   5 +-
 .../ml/regression/RandomForestRegressor.scala |   5 +-
 .../spark/ml/regression/Regressor.scala       |  42 ++-----
 .../spark/ml/{impl => }/tree/treeParams.scala |   4 +-
 .../apache/spark/mllib/linalg/Vectors.scala   |  22 ++++
 16 files changed, 206 insertions(+), 267 deletions(-)
 rename mllib/src/main/scala/org/apache/spark/ml/{impl/estimator => }/Predictor.scala (86%)
 rename mllib/src/main/scala/org/apache/spark/ml/{impl => }/tree/treeParams.scala (99%)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/impl/estimator/Predictor.scala b/mllib/src/main/scala/org/apache/spark/ml/Predictor.scala
similarity index 86%
rename from mllib/src/main/scala/org/apache/spark/ml/impl/estimator/Predictor.scala
rename to mllib/src/main/scala/org/apache/spark/ml/Predictor.scala
index e8b3628140e99..0e53877de92db 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/impl/estimator/Predictor.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/Predictor.scala
@@ -15,29 +15,23 @@
  * limitations under the License.
  */
 
-package org.apache.spark.ml.impl.estimator
+package org.apache.spark.ml
 
-import org.apache.spark.annotation.{AlphaComponent, DeveloperApi}
-import org.apache.spark.ml.{Estimator, Model}
+import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.ml.param._
 import org.apache.spark.ml.param.shared._
 import org.apache.spark.ml.util.SchemaUtils
 import org.apache.spark.mllib.linalg.{Vector, VectorUDT}
 import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.{DataFrame, Row}
 import org.apache.spark.sql.functions._
 import org.apache.spark.sql.types.{DataType, DoubleType, StructType}
+import org.apache.spark.sql.{DataFrame, Row}
 
 /**
- * :: DeveloperApi ::
- *
- * Trait for parameters for prediction (regression and classification).
- *
- * NOTE: This is currently private[spark] but will be made public later once it is stabilized.
+ * (private[ml])  Trait for parameters for prediction (regression and classification).
  */
-@DeveloperApi
-private[spark] trait PredictorParams extends Params
+private[ml] trait PredictorParams extends Params
   with HasLabelCol with HasFeaturesCol with HasPredictionCol {
 
   /**
@@ -63,7 +57,7 @@ private[spark] trait PredictorParams extends Params
 }
 
 /**
- * :: AlphaComponent ::
+ * :: DeveloperApi ::
  *
  * Abstraction for prediction problems (regression and classification).
  *
@@ -73,11 +67,9 @@ private[spark] trait PredictorParams extends Params
  *                  parameter to specify the concrete type.
  * @tparam M  Specialization of [[PredictionModel]].  If you subclass this type, use this type
  *            parameter to specify the concrete type for the corresponding model.
- *
- * NOTE: This is currently private[spark] but will be made public later once it is stabilized.
  */
-@AlphaComponent
-private[spark] abstract class Predictor[
+@DeveloperApi
+abstract class Predictor[
     FeaturesType,
     Learner <: Predictor[FeaturesType, Learner, M],
     M <: PredictionModel[FeaturesType, M]]
@@ -104,8 +96,6 @@ private[spark] abstract class Predictor[
   }
 
   /**
-   * :: DeveloperApi ::
-   *
    * Train a model using the given dataset and parameters.
    * Developers can implement this instead of [[fit()]] to avoid dealing with schema validation
    * and copying parameters into the model.
@@ -113,12 +103,9 @@ private[spark] abstract class Predictor[
    * @param dataset  Training dataset
    * @return  Fitted model
    */
-  @DeveloperApi
   protected def train(dataset: DataFrame): M
 
   /**
-   * :: DeveloperApi ::
-   *
    * Returns the SQL DataType corresponding to the FeaturesType type parameter.
    *
    * This is used by [[validateAndTransformSchema()]].
@@ -126,7 +113,6 @@ private[spark] abstract class Predictor[
    *
    * The default value is VectorUDT, but it may be overridden if FeaturesType is not Vector.
    */
-  @DeveloperApi
   protected def featuresDataType: DataType = new VectorUDT
 
   override def transformSchema(schema: StructType): StructType = {
@@ -146,7 +132,7 @@ private[spark] abstract class Predictor[
 }
 
 /**
- * :: AlphaComponent ::
+ * :: DeveloperApi ::
  *
  * Abstraction for a model for prediction tasks (regression and classification).
  *
@@ -154,11 +140,9 @@ private[spark] abstract class Predictor[
  *                       E.g., [[org.apache.spark.mllib.linalg.VectorUDT]] for vector features.
  * @tparam M  Specialization of [[PredictionModel]].  If you subclass this type, use this type
  *            parameter to specify the concrete type for the corresponding model.
- *
- * NOTE: This is currently private[spark] but will be made public later once it is stabilized.
  */
-@AlphaComponent
-private[spark] abstract class PredictionModel[FeaturesType, M <: PredictionModel[FeaturesType, M]]
+@DeveloperApi
+abstract class PredictionModel[FeaturesType, M <: PredictionModel[FeaturesType, M]]
   extends Model[M] with PredictorParams {
 
   /** @group setParam */
@@ -168,8 +152,6 @@ private[spark] abstract class PredictionModel[FeaturesType, M <: PredictionModel
   def setPredictionCol(value: String): M = set(predictionCol, value).asInstanceOf[M]
 
   /**
-   * :: DeveloperApi ::
-   *
    * Returns the SQL DataType corresponding to the FeaturesType type parameter.
    *
    * This is used by [[validateAndTransformSchema()]].
@@ -177,7 +159,6 @@ private[spark] abstract class PredictionModel[FeaturesType, M <: PredictionModel
    *
    * The default value is VectorUDT, but it may be overridden if FeaturesType is not Vector.
    */
-  @DeveloperApi
   protected def featuresDataType: DataType = new VectorUDT
 
   override def transformSchema(schema: StructType): StructType = {
@@ -192,12 +173,8 @@ private[spark] abstract class PredictionModel[FeaturesType, M <: PredictionModel
    * @return transformed dataset with [[predictionCol]] of type [[Double]]
    */
   override def transform(dataset: DataFrame): DataFrame = {
-    // This default implementation should be overridden as needed.
-
-    // Check schema
     transformSchema(dataset.schema, logging = true)
-
-    if ($(predictionCol) != "") {
+    if ($(predictionCol).nonEmpty) {
       dataset.withColumn($(predictionCol), callUDF(predict _, DoubleType, col($(featuresCol))))
     } else {
       this.logWarning(s"$uid: Predictor.transform() was called as NOOP" +
@@ -207,11 +184,8 @@ private[spark] abstract class PredictionModel[FeaturesType, M <: PredictionModel
   }
 
   /**
-   * :: DeveloperApi ::
-   *
    * Predict label for the given features.
    * This internal method is used to implement [[transform()]] and output [[predictionCol]].
    */
-  @DeveloperApi
   protected def predict(features: FeaturesType): Double
 }
diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/Classifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/Classifier.scala
index d3361e24705c8..263d580fe2dd3 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/Classifier.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/Classifier.scala
@@ -17,8 +17,8 @@
 
 package org.apache.spark.ml.classification
 
-import org.apache.spark.annotation.{AlphaComponent, DeveloperApi}
-import org.apache.spark.ml.impl.estimator.{PredictionModel, Predictor, PredictorParams}
+import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.ml.{PredictionModel, PredictorParams, Predictor}
 import org.apache.spark.ml.param.shared.HasRawPredictionCol
 import org.apache.spark.ml.util.SchemaUtils
 import org.apache.spark.mllib.linalg.{Vector, VectorUDT}
@@ -26,15 +26,12 @@ import org.apache.spark.sql.DataFrame
 import org.apache.spark.sql.functions._
 import org.apache.spark.sql.types.{DataType, DoubleType, StructType}
 
+
 /**
- * :: DeveloperApi ::
- * Params for classification.
- *
- * NOTE: This is currently private[spark] but will be made public later once it is stabilized.
+ * (private[spark]) Params for classification.
  */
-@DeveloperApi
-private[spark] trait ClassifierParams extends PredictorParams
-  with HasRawPredictionCol {
+private[spark] trait ClassifierParams
+  extends PredictorParams with HasRawPredictionCol {
 
   override protected def validateAndTransformSchema(
       schema: StructType,
@@ -46,23 +43,21 @@ private[spark] trait ClassifierParams extends PredictorParams
 }
 
 /**
- * :: AlphaComponent ::
+ * :: DeveloperApi ::
+ *
  * Single-label binary or multiclass classification.
  * Classes are indexed {0, 1, ..., numClasses - 1}.
  *
  * @tparam FeaturesType  Type of input features.  E.g., [[Vector]]
  * @tparam E  Concrete Estimator type
  * @tparam M  Concrete Model type
- *
- * NOTE: This is currently private[spark] but will be made public later once it is stabilized.
  */
-@AlphaComponent
-private[spark] abstract class Classifier[
+@DeveloperApi
+abstract class Classifier[
     FeaturesType,
     E <: Classifier[FeaturesType, E, M],
     M <: ClassificationModel[FeaturesType, M]]
-  extends Predictor[FeaturesType, E, M]
-  with ClassifierParams {
+  extends Predictor[FeaturesType, E, M] with ClassifierParams {
 
   /** @group setParam */
   def setRawPredictionCol(value: String): E = set(rawPredictionCol, value).asInstanceOf[E]
@@ -71,17 +66,15 @@ private[spark] abstract class Classifier[
 }
 
 /**
- * :: AlphaComponent ::
+ * :: DeveloperApi ::
+ *
  * Model produced by a [[Classifier]].
  * Classes are indexed {0, 1, ..., numClasses - 1}.
  *
  * @tparam FeaturesType  Type of input features.  E.g., [[Vector]]
  * @tparam M  Concrete Model type
- *
- * NOTE: This is currently private[spark] but will be made public later once it is stabilized.
  */
-@AlphaComponent
-private[spark]
+@DeveloperApi
 abstract class ClassificationModel[FeaturesType, M <: ClassificationModel[FeaturesType, M]]
   extends PredictionModel[FeaturesType, M] with ClassifierParams {
 
@@ -101,13 +94,27 @@ abstract class ClassificationModel[FeaturesType, M <: ClassificationModel[Featur
    * @return transformed dataset
    */
   override def transform(dataset: DataFrame): DataFrame = {
-    // This default implementation should be overridden as needed.
-
-    // Check schema
     transformSchema(dataset.schema, logging = true)
 
-    val (numColsOutput, outputData) =
-      ClassificationModel.transformColumnsImpl[FeaturesType](dataset, this)
+    // Output selected columns only.
+    // This is a bit complicated since it tries to avoid repeated computation.
+    var outputData = dataset
+    var numColsOutput = 0
+    if (getRawPredictionCol != "") {
+      outputData = outputData.withColumn(getRawPredictionCol,
+        callUDF(predictRaw _, new VectorUDT, col(getFeaturesCol)))
+      numColsOutput += 1
+    }
+    if (getPredictionCol != "") {
+      val predUDF = if (getRawPredictionCol != "") {
+        callUDF(raw2prediction _, DoubleType, col(getRawPredictionCol))
+      } else {
+        callUDF(predict _, DoubleType, col(getFeaturesCol))
+      }
+      outputData = outputData.withColumn(getPredictionCol, predUDF)
+      numColsOutput += 1
+    }
+
     if (numColsOutput == 0) {
       logWarning(s"$uid: ClassificationModel.transform() was called as NOOP" +
         " since no output columns were set.")
@@ -116,22 +123,17 @@ abstract class ClassificationModel[FeaturesType, M <: ClassificationModel[Featur
   }
 
   /**
-   * :: DeveloperApi ::
-   *
    * Predict label for the given features.
    * This internal method is used to implement [[transform()]] and output [[predictionCol]].
    *
    * This default implementation for classification predicts the index of the maximum value
    * from [[predictRaw()]].
    */
-  @DeveloperApi
   override protected def predict(features: FeaturesType): Double = {
-    predictRaw(features).toArray.zipWithIndex.maxBy(_._1)._2
+    raw2prediction(predictRaw(features))
   }
 
   /**
-   * :: DeveloperApi ::
-   *
    * Raw prediction for each possible label.
    * The meaning of a "raw" prediction may vary between algorithms, but it intuitively gives
    * a measure of confidence in each possible label (where larger = more confident).
@@ -141,48 +143,12 @@ abstract class ClassificationModel[FeaturesType, M <: ClassificationModel[Featur
    *          This raw prediction may be any real number, where a larger value indicates greater
    *          confidence for that label.
    */
-  @DeveloperApi
   protected def predictRaw(features: FeaturesType): Vector
-}
-
-private[ml] object ClassificationModel {
 
   /**
-   * Added prediction column(s).  This is separated from [[ClassificationModel.transform()]]
-   * since it is used by [[org.apache.spark.ml.classification.ProbabilisticClassificationModel]].
-   * @param dataset  Input dataset
-   * @return (number of columns added, transformed dataset)
+   * Given a vector of raw predictions, select the predicted label.
+   * This may be overridden to support thresholds which favor particular labels.
+   * @return  predicted label
    */
-  def transformColumnsImpl[FeaturesType](
-      dataset: DataFrame,
-      model: ClassificationModel[FeaturesType, _]): (Int, DataFrame) = {
-
-    // Output selected columns only.
-    // This is a bit complicated since it tries to avoid repeated computation.
-    var tmpData = dataset
-    var numColsOutput = 0
-    if (model.getRawPredictionCol != "") {
-      // output raw prediction
-      val features2raw: FeaturesType => Vector = model.predictRaw
-      tmpData = tmpData.withColumn(model.getRawPredictionCol,
-        callUDF(features2raw, new VectorUDT, col(model.getFeaturesCol)))
-      numColsOutput += 1
-      if (model.getPredictionCol != "") {
-        val raw2pred: Vector => Double = (rawPred) => {
-          rawPred.toArray.zipWithIndex.maxBy(_._1)._2
-        }
-        tmpData = tmpData.withColumn(model.getPredictionCol,
-          callUDF(raw2pred, DoubleType, col(model.getRawPredictionCol)))
-        numColsOutput += 1
-      }
-    } else if (model.getPredictionCol != "") {
-      // output prediction
-      val features2pred: FeaturesType => Double = model.predict
-      tmpData = tmpData.withColumn(model.getPredictionCol,
-        callUDF(features2pred, DoubleType, col(model.getFeaturesCol)))
-      numColsOutput += 1
-    }
-    (numColsOutput, tmpData)
-  }
-
+  protected def raw2prediction(rawPrediction: Vector): Double = rawPrediction.toDense.argmax
 }
diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala
index 419e5ba05d38a..dcebea1d4b015 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala
@@ -18,10 +18,9 @@
 package org.apache.spark.ml.classification
 
 import org.apache.spark.annotation.AlphaComponent
-import org.apache.spark.ml.impl.estimator.{PredictionModel, Predictor}
-import org.apache.spark.ml.impl.tree._
+import org.apache.spark.ml.{PredictionModel, Predictor}
 import org.apache.spark.ml.param.ParamMap
-import org.apache.spark.ml.tree.{DecisionTreeModel, Node}
+import org.apache.spark.ml.tree.{TreeClassifierParams, DecisionTreeParams, DecisionTreeModel, Node}
 import org.apache.spark.ml.util.MetadataUtils
 import org.apache.spark.mllib.linalg.Vector
 import org.apache.spark.mllib.regression.LabeledPoint
diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala
index 534ea95b1c538..ae51b05a0c42d 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala
@@ -21,11 +21,10 @@ import com.github.fommil.netlib.BLAS.{getInstance => blas}
 
 import org.apache.spark.Logging
 import org.apache.spark.annotation.AlphaComponent
-import org.apache.spark.ml.impl.estimator.{PredictionModel, Predictor}
-import org.apache.spark.ml.impl.tree._
+import org.apache.spark.ml.{PredictionModel, Predictor}
 import org.apache.spark.ml.param.{Param, ParamMap}
 import org.apache.spark.ml.regression.DecisionTreeRegressionModel
-import org.apache.spark.ml.tree.{DecisionTreeModel, TreeEnsembleModel}
+import org.apache.spark.ml.tree.{GBTParams, TreeClassifierParams, DecisionTreeModel, TreeEnsembleModel}
 import org.apache.spark.ml.util.MetadataUtils
 import org.apache.spark.mllib.linalg.Vector
 import org.apache.spark.mllib.regression.LabeledPoint
diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
index b73be035e29b5..550369d18cfec 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
@@ -21,9 +21,8 @@ import org.apache.spark.annotation.AlphaComponent
 import org.apache.spark.ml.param._
 import org.apache.spark.ml.param.shared._
 import org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS
-import org.apache.spark.mllib.linalg.{BLAS, Vector, VectorUDT, Vectors}
+import org.apache.spark.mllib.linalg._
 import org.apache.spark.sql.DataFrame
-import org.apache.spark.sql.functions._
 import org.apache.spark.storage.StorageLevel
 
 /**
@@ -99,76 +98,17 @@ class LogisticRegressionModel private[ml] (
   /** @group setParam */
   def setThreshold(value: Double): this.type = set(threshold, value)
 
+  /** Margin (rawPrediction) for class label 1.  For binary classification only. */
   private val margin: Vector => Double = (features) => {
     BLAS.dot(features, weights) + intercept
   }
 
+  /** Score (probability) for class label 1.  For binary classification only. */
   private val score: Vector => Double = (features) => {
     val m = margin(features)
     1.0 / (1.0 + math.exp(-m))
   }
 
-  override def transform(dataset: DataFrame): DataFrame = {
-    // This is overridden (a) to be more efficient (avoiding re-computing values when creating
-    // multiple output columns) and (b) to handle threshold, which the abstractions do not use.
-    // TODO: We should abstract away the steps defined by UDFs below so that the abstractions
-    // can call whichever UDFs are needed to create the output columns.
-
-    // Check schema
-    transformSchema(dataset.schema, logging = true)
-
-    // Output selected columns only.
-    // This is a bit complicated since it tries to avoid repeated computation.
-    //   rawPrediction (-margin, margin)
-    //   probability (1.0-score, score)
-    //   prediction (max margin)
-    var tmpData = dataset
-    var numColsOutput = 0
-    if ($(rawPredictionCol) != "") {
-      val features2raw: Vector => Vector = (features) => predictRaw(features)
-      tmpData = tmpData.withColumn($(rawPredictionCol),
-        callUDF(features2raw, new VectorUDT, col($(featuresCol))))
-      numColsOutput += 1
-    }
-    if ($(probabilityCol) != "") {
-      if ($(rawPredictionCol) != "") {
-        val raw2prob = udf { (rawPreds: Vector) =>
-          val prob1 = 1.0 / (1.0 + math.exp(-rawPreds(1)))
-          Vectors.dense(1.0 - prob1, prob1): Vector
-        }
-        tmpData = tmpData.withColumn($(probabilityCol), raw2prob(col($(rawPredictionCol))))
-      } else {
-        val features2prob = udf { (features: Vector) => predictProbabilities(features) : Vector }
-        tmpData = tmpData.withColumn($(probabilityCol), features2prob(col($(featuresCol))))
-      }
-      numColsOutput += 1
-    }
-    if ($(predictionCol) != "") {
-      val t = $(threshold)
-      if ($(probabilityCol) != "") {
-        val predict = udf { probs: Vector =>
-          if (probs(1) > t) 1.0 else 0.0
-        }
-        tmpData = tmpData.withColumn($(predictionCol), predict(col($(probabilityCol))))
-      } else if ($(rawPredictionCol) != "") {
-        val predict = udf { rawPreds: Vector =>
-          val prob1 = 1.0 / (1.0 + math.exp(-rawPreds(1)))
-          if (prob1 > t) 1.0 else 0.0
-        }
-        tmpData = tmpData.withColumn($(predictionCol), predict(col($(rawPredictionCol))))
-      } else {
-        val predict = udf { features: Vector => this.predict(features) }
-        tmpData = tmpData.withColumn($(predictionCol), predict(col($(featuresCol))))
-      }
-      numColsOutput += 1
-    }
-    if (numColsOutput == 0) {
-      this.logWarning(s"$uid: LogisticRegressionModel.transform() was called as NOOP" +
-        " since no output columns were set.")
-    }
-    tmpData
-  }
-
   override val numClasses: Int = 2
 
   /**
@@ -179,17 +119,43 @@ class LogisticRegressionModel private[ml] (
     if (score(features) > getThreshold) 1 else 0
   }
 
-  override protected def predictProbabilities(features: Vector): Vector = {
-    val s = score(features)
-    Vectors.dense(1.0 - s, s)
+  override protected def raw2probabilityInPlace(rawPrediction: Vector): Vector = {
+    rawPrediction match {
+      case dv: DenseVector =>
+        var i = 0
+        while (i < dv.size) {
+          dv.values(i) = 1.0 / (1.0 + math.exp(-dv.values(i)))
+          i += 1
+        }
+        dv
+      case sv: SparseVector =>
+        throw new RuntimeException("Unexpected error in LogisticRegressionModel:" +
+          " raw2probabilitiesInPlace encountered SparseVector")
+    }
   }
 
   override protected def predictRaw(features: Vector): Vector = {
     val m = margin(features)
-    Vectors.dense(0.0, m)
+    Vectors.dense(-m, m)
   }
 
   override def copy(extra: ParamMap): LogisticRegressionModel = {
     copyValues(new LogisticRegressionModel(parent, weights, intercept), extra)
   }
+
+  override protected def raw2prediction(rawPrediction: Vector): Double = {
+    val t = getThreshold
+    val rawThreshold = if (t == 0.0) {
+      Double.NegativeInfinity
+    } else if (t == 1.0) {
+      Double.PositiveInfinity
+    } else {
+      Math.log(t / (1.0 - t))
+    }
+    if (rawPrediction(1) > rawThreshold) 1 else 0
+  }
+
+  override protected def probability2prediction(probability: Vector): Double = {
+    if (probability(1) > getThreshold) 1 else 0
+  }
 }
diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/ProbabilisticClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/ProbabilisticClassifier.scala
index 8519841c5c26c..330ae2938f4e0 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/ProbabilisticClassifier.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/ProbabilisticClassifier.scala
@@ -17,16 +17,16 @@
 
 package org.apache.spark.ml.classification
 
-import org.apache.spark.annotation.{AlphaComponent, DeveloperApi}
+import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.ml.param.shared._
 import org.apache.spark.ml.util.SchemaUtils
 import org.apache.spark.mllib.linalg.{Vector, VectorUDT}
 import org.apache.spark.sql.DataFrame
 import org.apache.spark.sql.functions._
-import org.apache.spark.sql.types.{DataType, StructType}
+import org.apache.spark.sql.types.{DoubleType, DataType, StructType}
 
 /**
- * Params for probabilistic classification.
+ * (private[classification])  Params for probabilistic classification.
  */
 private[classification] trait ProbabilisticClassifierParams
   extends ClassifierParams with HasProbabilityCol {
@@ -42,17 +42,15 @@ private[classification] trait ProbabilisticClassifierParams
 
 
 /**
- * :: AlphaComponent ::
+ * :: DeveloperApi ::
  *
  * Single-label binary or multiclass classifier which can output class conditional probabilities.
  *
  * @tparam FeaturesType  Type of input features.  E.g., [[Vector]]
  * @tparam E  Concrete Estimator type
  * @tparam M  Concrete Model type
- *
- * NOTE: This is currently private[spark] but will be made public later once it is stabilized.
  */
-@AlphaComponent
+@DeveloperApi
 private[spark] abstract class ProbabilisticClassifier[
     FeaturesType,
     E <: ProbabilisticClassifier[FeaturesType, E, M],
@@ -65,17 +63,15 @@ private[spark] abstract class ProbabilisticClassifier[
 
 
 /**
- * :: AlphaComponent ::
+ * :: DeveloperApi ::
  *
  * Model produced by a [[ProbabilisticClassifier]].
  * Classes are indexed {0, 1, ..., numClasses - 1}.
  *
  * @tparam FeaturesType  Type of input features.  E.g., [[Vector]]
  * @tparam M  Concrete Model type
- *
- * NOTE: This is currently private[spark] but will be made public later once it is stabilized.
  */
-@AlphaComponent
+@DeveloperApi
 private[spark] abstract class ProbabilisticClassificationModel[
     FeaturesType,
     M <: ProbabilisticClassificationModel[FeaturesType, M]]
@@ -95,39 +91,79 @@ private[spark] abstract class ProbabilisticClassificationModel[
    * @return transformed dataset
    */
   override def transform(dataset: DataFrame): DataFrame = {
-    // This default implementation should be overridden as needed.
-
-    // Check schema
     transformSchema(dataset.schema, logging = true)
 
-    val (numColsOutput, outputData) =
-      ClassificationModel.transformColumnsImpl[FeaturesType](dataset, this)
-
     // Output selected columns only.
-    if ($(probabilityCol) != "") {
-      // output probabilities
-      outputData.withColumn($(probabilityCol),
-        callUDF(predictProbabilities _, new VectorUDT, col($(featuresCol))))
-    } else {
-      if (numColsOutput == 0) {
-        this.logWarning(s"$uid: ProbabilisticClassificationModel.transform() was called as NOOP" +
-          " since no output columns were set.")
+    // This is a bit complicated since it tries to avoid repeated computation.
+    var outputData = dataset
+    var numColsOutput = 0
+    if ($(rawPredictionCol).nonEmpty) {
+      outputData = outputData.withColumn(getRawPredictionCol,
+        callUDF(predictRaw _, new VectorUDT, col(getFeaturesCol)))
+      numColsOutput += 1
+    }
+    if ($(probabilityCol).nonEmpty) {
+      val probUDF = if ($(rawPredictionCol).nonEmpty) {
+        callUDF(raw2probability _, new VectorUDT, col($(rawPredictionCol)))
+      } else {
+        callUDF(predictProbability _, new VectorUDT, col($(featuresCol)))
+      }
+      outputData = outputData.withColumn($(probabilityCol), probUDF)
+      numColsOutput += 1
+    }
+    if ($(predictionCol).nonEmpty) {
+      val predUDF = if ($(rawPredictionCol).nonEmpty) {
+        callUDF(raw2prediction _, DoubleType, col($(rawPredictionCol)))
+      } else if ($(probabilityCol).nonEmpty) {
+        callUDF(probability2prediction _, DoubleType, col($(probabilityCol)))
+      } else {
+        callUDF(predict _, DoubleType, col($(featuresCol)))
       }
-      outputData
+      outputData = outputData.withColumn($(predictionCol), predUDF)
+      numColsOutput += 1
+    }
+
+    if (numColsOutput == 0) {
+      this.logWarning(s"$uid: ProbabilisticClassificationModel.transform() was called as NOOP" +
+        " since no output columns were set.")
     }
+    outputData
   }
 
   /**
-   * :: DeveloperApi ::
+   * Estimate the probability of each class given the raw prediction,
+   * doing the computation in-place.
+   * These predictions are also called class conditional probabilities.
+   *
+   * This internal method is used to implement [[transform()]] and output [[probabilityCol]].
    *
+   * @return Estimated class conditional probabilities (modified input vector)
+   */
+  protected def raw2probabilityInPlace(rawPrediction: Vector): Vector
+
+  /** Non-in-place version of [[raw2probabilityInPlace()]] */
+  protected def raw2probability(rawPrediction: Vector): Vector = {
+    val probs = rawPrediction.copy
+    raw2probabilityInPlace(probs)
+  }
+
+  /**
    * Predict the probability of each class given the features.
    * These predictions are also called class conditional probabilities.
    *
-   * WARNING: Not all models output well-calibrated probability estimates!  These probabilities
-   *          should be treated as confidences, not precise probabilities.
-   *
    * This internal method is used to implement [[transform()]] and output [[probabilityCol]].
+   *
+   * @return Estimated class conditional probabilities
+   */
+  protected def predictProbability(features: FeaturesType): Vector = {
+    val rawPreds = predictRaw(features)
+    raw2probabilityInPlace(rawPreds)
+  }
+
+  /**
+   * Given a vector of class conditional probabilities, select the predicted label.
+   * This may be overridden to support thresholds which favor particular labels.
+   * @return  predicted label
    */
-  @DeveloperApi
-  protected def predictProbabilities(features: FeaturesType): Vector
+  protected def probability2prediction(probability: Vector): Double = probability.toDense.argmax
 }
diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala
index 17f59bb42e129..9954893f14359 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala
@@ -20,10 +20,9 @@ package org.apache.spark.ml.classification
 import scala.collection.mutable
 
 import org.apache.spark.annotation.AlphaComponent
-import org.apache.spark.ml.impl.estimator.{PredictionModel, Predictor}
-import org.apache.spark.ml.impl.tree._
+import org.apache.spark.ml.{PredictionModel, Predictor}
 import org.apache.spark.ml.param.ParamMap
-import org.apache.spark.ml.tree.{DecisionTreeModel, TreeEnsembleModel}
+import org.apache.spark.ml.tree.{RandomForestParams, TreeClassifierParams, DecisionTreeModel, TreeEnsembleModel}
 import org.apache.spark.ml.util.MetadataUtils
 import org.apache.spark.mllib.linalg.Vector
 import org.apache.spark.mllib.regression.LabeledPoint
diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala b/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala
index d379172e0bf53..0e1ff97a8bf60 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala
@@ -40,8 +40,10 @@ private[shared] object SharedParamsCodeGen {
       ParamDesc[String]("predictionCol", "prediction column name", Some("\"prediction\"")),
       ParamDesc[String]("rawPredictionCol", "raw prediction (a.k.a. confidence) column name",
         Some("\"rawPrediction\"")),
-      ParamDesc[String]("probabilityCol",
-        "column name for predicted class conditional probabilities", Some("\"probability\"")),
+      ParamDesc[String]("probabilityCol", "Column name for predicted class conditional" +
+        " probabilities. Note: Not all models output well-calibrated probability estimates!" +
+        " These probabilities should be treated as confidences, not precise probabilities.",
+        Some("\"probability\"")),
       ParamDesc[Double]("threshold",
         "threshold in binary classification prediction, in range [0, 1]",
         isValid = "ParamValidators.inRange(0, 1)"),
diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala b/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala
index fb1874ccfc8dc..87f86807c3c91 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala
@@ -128,10 +128,10 @@ private[ml] trait HasRawPredictionCol extends Params {
 private[ml] trait HasProbabilityCol extends Params {
 
   /**
-   * Param for column name for predicted class conditional probabilities.
+   * Param for Column name for predicted class conditional probabilities. Note: Not all models output well-calibrated probability estimates! These probabilities should be treated as confidences, not precise probabilities..
    * @group param
    */
-  final val probabilityCol: Param[String] = new Param[String](this, "probabilityCol", "column name for predicted class conditional probabilities")
+  final val probabilityCol: Param[String] = new Param[String](this, "probabilityCol", "Column name for predicted class conditional probabilities. Note: Not all models output well-calibrated probability estimates! These probabilities should be treated as confidences, not precise probabilities.")
 
   setDefault(probabilityCol, "probability")
 
diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala
index b07c26fe79b36..f8f0b161a4812 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala
@@ -18,10 +18,9 @@
 package org.apache.spark.ml.regression
 
 import org.apache.spark.annotation.AlphaComponent
-import org.apache.spark.ml.impl.estimator.{PredictionModel, Predictor}
-import org.apache.spark.ml.impl.tree._
+import org.apache.spark.ml.{PredictionModel, Predictor}
 import org.apache.spark.ml.param.ParamMap
-import org.apache.spark.ml.tree.{DecisionTreeModel, Node}
+import org.apache.spark.ml.tree.{TreeRegressorParams, DecisionTreeParams, DecisionTreeModel, Node}
 import org.apache.spark.ml.util.MetadataUtils
 import org.apache.spark.mllib.linalg.Vector
 import org.apache.spark.mllib.regression.LabeledPoint
diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala
index bc796958e4545..461905c12701a 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala
@@ -21,10 +21,9 @@ import com.github.fommil.netlib.BLAS.{getInstance => blas}
 
 import org.apache.spark.Logging
 import org.apache.spark.annotation.AlphaComponent
-import org.apache.spark.ml.impl.estimator.{PredictionModel, Predictor}
-import org.apache.spark.ml.impl.tree._
+import org.apache.spark.ml.{PredictionModel, Predictor}
 import org.apache.spark.ml.param.{Param, ParamMap}
-import org.apache.spark.ml.tree.{DecisionTreeModel, TreeEnsembleModel}
+import org.apache.spark.ml.tree.{GBTParams, TreeRegressorParams, DecisionTreeModel, TreeEnsembleModel}
 import org.apache.spark.ml.util.MetadataUtils
 import org.apache.spark.mllib.linalg.Vector
 import org.apache.spark.mllib.regression.LabeledPoint
diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala
index 66c475f2d9840..e63c9a3eead52 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala
@@ -25,6 +25,7 @@ import breeze.optimize.{CachedDiffFunction, DiffFunction, LBFGS => BreezeLBFGS,
 
 import org.apache.spark.Logging
 import org.apache.spark.annotation.AlphaComponent
+import org.apache.spark.ml.PredictorParams
 import org.apache.spark.ml.param.ParamMap
 import org.apache.spark.ml.param.shared.{HasElasticNetParam, HasMaxIter, HasRegParam, HasTol}
 import org.apache.spark.mllib.linalg.{Vector, Vectors}
@@ -39,7 +40,7 @@ import org.apache.spark.util.StatCounter
 /**
  * Params for linear regression.
  */
-private[regression] trait LinearRegressionParams extends RegressorParams
+private[regression] trait LinearRegressionParams extends PredictorParams
   with HasRegParam with HasElasticNetParam with HasMaxIter with HasTol
 
 /**
@@ -240,7 +241,7 @@ class LinearRegressionModel private[ml] (
  *     + \bar{y} / \hat{y}||^2
  *   = 1/2n ||\sum_i w_i^\prime x_i - y / \hat{y} + offset||^2 = 1/2n diff^2
  * }}}
- * where w_i^\prime is the effective weights defined by w_i/\hat{x_i}, offset is
+ * where w_i^\prime^ is the effective weights defined by w_i/\hat{x_i}, offset is
  * {{{
  * - \sum_i (w_i/\hat{x_i})\bar{x_i} + \bar{y} / \hat{y}.
  * }}}, and diff is
diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala
index 0468a1be1ba74..dbc628927433d 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala
@@ -18,10 +18,9 @@
 package org.apache.spark.ml.regression
 
 import org.apache.spark.annotation.AlphaComponent
-import org.apache.spark.ml.impl.estimator.{PredictionModel, Predictor}
-import org.apache.spark.ml.impl.tree.{RandomForestParams, TreeRegressorParams}
+import org.apache.spark.ml.{PredictionModel, Predictor}
 import org.apache.spark.ml.param.ParamMap
-import org.apache.spark.ml.tree.{DecisionTreeModel, TreeEnsembleModel}
+import org.apache.spark.ml.tree.{RandomForestParams, TreeRegressorParams, DecisionTreeModel, TreeEnsembleModel}
 import org.apache.spark.ml.util.MetadataUtils
 import org.apache.spark.mllib.linalg.Vector
 import org.apache.spark.mllib.regression.LabeledPoint
diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/Regressor.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/Regressor.scala
index c6b3327db6ad3..c72ef29680329 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/Regressor.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/Regressor.scala
@@ -17,62 +17,40 @@
 
 package org.apache.spark.ml.regression
 
-import org.apache.spark.annotation.{AlphaComponent, DeveloperApi}
-import org.apache.spark.ml.impl.estimator.{PredictionModel, Predictor, PredictorParams}
+import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.ml.{PredictionModel, PredictorParams, Predictor}
 
-/**
- * :: DeveloperApi ::
- * Params for regression.
- * Currently empty, but may add functionality later.
- *
- * NOTE: This is currently private[spark] but will be made public later once it is stabilized.
- */
-@DeveloperApi
-private[spark] trait RegressorParams extends PredictorParams
 
 /**
- * :: AlphaComponent ::
+ * :: DeveloperApi ::
  *
  * Single-label regression
  *
  * @tparam FeaturesType  Type of input features.  E.g., [[org.apache.spark.mllib.linalg.Vector]]
  * @tparam Learner  Concrete Estimator type
  * @tparam M  Concrete Model type
- *
- * NOTE: This is currently private[spark] but will be made public later once it is stabilized.
  */
-@AlphaComponent
+@DeveloperApi
 private[spark] abstract class Regressor[
     FeaturesType,
     Learner <: Regressor[FeaturesType, Learner, M],
     M <: RegressionModel[FeaturesType, M]]
-  extends Predictor[FeaturesType, Learner, M]
-  with RegressorParams {
+  extends Predictor[FeaturesType, Learner, M] with PredictorParams {
 
   // TODO: defaultEvaluator (follow-up PR)
 }
 
 /**
- * :: AlphaComponent ::
+ * :: DeveloperApi ::
  *
  * Model produced by a [[Regressor]].
  *
  * @tparam FeaturesType  Type of input features.  E.g., [[org.apache.spark.mllib.linalg.Vector]]
  * @tparam M  Concrete Model type.
- *
- * NOTE: This is currently private[spark] but will be made public later once it is stabilized.
  */
-@AlphaComponent
-private[spark] abstract class RegressionModel[FeaturesType, M <: RegressionModel[FeaturesType, M]]
-  extends PredictionModel[FeaturesType, M] with RegressorParams {
-
-  /**
-   * :: DeveloperApi ::
-   *
-   * Predict real-valued label for the given features.
-   * This internal method is used to implement [[transform()]] and output [[predictionCol]].
-   */
-  @DeveloperApi
-  protected def predict(features: FeaturesType): Double
+@DeveloperApi
+abstract class RegressionModel[FeaturesType, M <: RegressionModel[FeaturesType, M]]
+  extends PredictionModel[FeaturesType, M] with PredictorParams {
 
+  // TODO: defaultEvaluator (follow-up PR)
 }
diff --git a/mllib/src/main/scala/org/apache/spark/ml/impl/tree/treeParams.scala b/mllib/src/main/scala/org/apache/spark/ml/tree/treeParams.scala
similarity index 99%
rename from mllib/src/main/scala/org/apache/spark/ml/impl/tree/treeParams.scala
rename to mllib/src/main/scala/org/apache/spark/ml/tree/treeParams.scala
index 0e225627d4ee3..816fcedf2efb3 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/impl/tree/treeParams.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/tree/treeParams.scala
@@ -15,10 +15,10 @@
  * limitations under the License.
  */
 
-package org.apache.spark.ml.impl.tree
+package org.apache.spark.ml.tree
 
 import org.apache.spark.annotation.DeveloperApi
-import org.apache.spark.ml.impl.estimator.PredictorParams
+import org.apache.spark.ml.PredictorParams
 import org.apache.spark.ml.param._
 import org.apache.spark.ml.param.shared.{HasMaxIter, HasSeed}
 import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo, BoostingStrategy => OldBoostingStrategy, Strategy => OldStrategy}
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 188d1e542b5b5..f6bcdf83cd337 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
@@ -587,6 +587,28 @@ class DenseVector(val values: Array[Double]) extends Vector {
     }
     new SparseVector(size, ii, vv)
   }
+
+  /**
+   * Find the index of a maximal element.  Returns the first maximal element in case of a tie.
+   * Returns -1 if vector has length 0.
+   */
+  private[spark] def argmax: Int = {
+    if (size == 0) {
+      -1
+    } else {
+      var maxIdx = 0
+      var maxValue = values(0)
+      var i = 1
+      while (i < size) {
+        if (values(i) > maxValue) {
+          maxIdx = i
+          maxValue = values(i)
+        }
+        i += 1
+      }
+      maxIdx
+    }
+  }
 }
 
 object DenseVector {

From fbf1f342a02af65f69e0ee770a2b983c69e7c089 Mon Sep 17 00:00:00 2001
From: Andrew Or <andrew@databricks.com>
Date: Wed, 6 May 2015 17:08:39 -0700
Subject: [PATCH 17/75] [HOT FIX] [SPARK-7418] Ignore flaky
 SparkSubmitUtilsSuite test

---
 .../scala/org/apache/spark/deploy/SparkSubmitUtilsSuite.scala   | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/core/src/test/scala/org/apache/spark/deploy/SparkSubmitUtilsSuite.scala b/core/src/test/scala/org/apache/spark/deploy/SparkSubmitUtilsSuite.scala
index 2df2597e058cd..da9578478bed9 100644
--- a/core/src/test/scala/org/apache/spark/deploy/SparkSubmitUtilsSuite.scala
+++ b/core/src/test/scala/org/apache/spark/deploy/SparkSubmitUtilsSuite.scala
@@ -104,7 +104,7 @@ class SparkSubmitUtilsSuite extends FunSuite with BeforeAndAfterAll {
     assert(jarPath.indexOf(ivyPath) >= 0, "should use non-default ivy path")
   }
 
-  test("search for artifact at other repositories") {
+  ignore("search for artifact at other repositories") {
     val path = SparkSubmitUtils.resolveMavenCoordinates("com.agimatec:agimatec-validation:0.9.3",
       Option("https://oss.sonatype.org/content/repositories/agimatec/"), None, true)
     assert(path.indexOf("agimatec-validation") >= 0, "should find package. If it doesn't, check" +

From 4e930420c19ae7773b138dfc7db8fc03b4660251 Mon Sep 17 00:00:00 2001
From: Shivaram Venkataraman <shivaram@cs.berkeley.edu>
Date: Wed, 6 May 2015 17:28:11 -0700
Subject: [PATCH 18/75] [SPARK-6799] [SPARKR] Remove SparkR RDD examples, add
 dataframe examples

This PR also makes some of the DataFrame to RDD methods private as the RDD class is private in 1.4

cc rxin pwendell

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

Closes #5949 from shivaram/sparkr-examples and squashes the following commits:

6c42fdc [Shivaram Venkataraman] Remove SparkR RDD examples, add dataframe examples
---
 R/pkg/NAMESPACE                           |   4 -
 R/pkg/R/DataFrame.R                       |   2 +-
 examples/src/main/r/dataframe.R           |  54 +++++++++++
 examples/src/main/r/kmeans.R              |  93 -------------------
 examples/src/main/r/linear_solver_mnist.R | 107 ----------------------
 examples/src/main/r/logistic_regression.R |  62 -------------
 examples/src/main/r/pi.R                  |  46 ----------
 examples/src/main/r/wordcount.R           |  42 ---------
 8 files changed, 55 insertions(+), 355 deletions(-)
 create mode 100644 examples/src/main/r/dataframe.R
 delete mode 100644 examples/src/main/r/kmeans.R
 delete mode 100644 examples/src/main/r/linear_solver_mnist.R
 delete mode 100644 examples/src/main/r/logistic_regression.R
 delete mode 100644 examples/src/main/r/pi.R
 delete mode 100644 examples/src/main/r/wordcount.R

diff --git a/R/pkg/NAMESPACE b/R/pkg/NAMESPACE
index 528e6608c3c82..3fb92be0940b7 100644
--- a/R/pkg/NAMESPACE
+++ b/R/pkg/NAMESPACE
@@ -45,8 +45,6 @@ exportMethods("cache",
               "showDF",
               "sortDF",
               "take",
-              "toJSON",
-              "toRDD",
               "unionAll",
               "unpersist",
               "where",
@@ -95,14 +93,12 @@ export("cacheTable",
        "createExternalTable",
        "dropTempTable",
        "jsonFile",
-       "jsonRDD",
        "loadDF",
        "parquetFile",
        "sql",
        "table",
        "tableNames",
        "tables",
-       "toDF",
        "uncacheTable")
 
 export("sparkRSQL.init",
diff --git a/R/pkg/R/DataFrame.R b/R/pkg/R/DataFrame.R
index 56c305d912587..47d92f141cc7d 100644
--- a/R/pkg/R/DataFrame.R
+++ b/R/pkg/R/DataFrame.R
@@ -272,7 +272,7 @@ setMethod("names",
 setMethod("registerTempTable",
           signature(x = "DataFrame", tableName = "character"),
           function(x, tableName) {
-              callJMethod(x@sdf, "registerTempTable", tableName)
+              invisible(callJMethod(x@sdf, "registerTempTable", tableName))
           })
 
 #' insertInto
diff --git a/examples/src/main/r/dataframe.R b/examples/src/main/r/dataframe.R
new file mode 100644
index 0000000000000..53b817144f6ac
--- /dev/null
+++ b/examples/src/main/r/dataframe.R
@@ -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.
+#
+
+library(SparkR)
+
+# Initialize SparkContext and SQLContext
+sc <- sparkR.init(appName="SparkR-DataFrame-example")
+sqlContext <- sparkRSQL.init(sc)
+
+# Create a simple local data.frame
+localDF <- data.frame(name=c("John", "Smith", "Sarah"), age=c(19, 23, 18))
+
+# Convert local data frame to a SparkR DataFrame
+df <- createDataFrame(sqlContext, localDF)
+
+# Print its schema
+printSchema(df)
+# root
+#  |-- name: string (nullable = true)
+#  |-- age: double (nullable = true)
+
+# Create a DataFrame from a JSON file
+path <- file.path(Sys.getenv("SPARK_HOME"), "examples/src/main/resources/people.json")
+peopleDF <- jsonFile(sqlContext, path)
+printSchema(peopleDF)
+
+# Register this DataFrame as a table.
+registerTempTable(peopleDF, "people")
+
+# SQL statements can be run by using the sql methods provided by sqlContext
+teenagers <- sql(sqlContext, "SELECT name FROM people WHERE age >= 13 AND age <= 19")
+
+# Call collect to get a local data.frame
+teenagersLocalDF <- collect(teenagers)
+
+# Print the teenagers in our dataset 
+print(teenagersLocalDF)
+
+# Stop the SparkContext now
+sparkR.stop()
diff --git a/examples/src/main/r/kmeans.R b/examples/src/main/r/kmeans.R
deleted file mode 100644
index 6e6b5cb93789c..0000000000000
--- a/examples/src/main/r/kmeans.R
+++ /dev/null
@@ -1,93 +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.
-#
-
-library(SparkR)
-
-# Logistic regression in Spark.
-# Note: unlike the example in Scala, a point here is represented as a vector of
-# doubles.
-
-parseVectors <-  function(lines) {
-  lines <- strsplit(as.character(lines) , " ", fixed = TRUE)
-  list(matrix(as.numeric(unlist(lines)), ncol = length(lines[[1]])))
-}
-
-dist.fun <- function(P, C) {
-  apply(
-    C,
-    1, 
-    function(x) { 
-      colSums((t(P) - x)^2)
-    }
-  )
-}
-
-closestPoint <-  function(P, C) {
-  max.col(-dist.fun(P, C))
-}
-# Main program
-
-args <- commandArgs(trailing = TRUE) 
-
-if (length(args) != 3) {
-  print("Usage: kmeans <file> <K> <convergeDist>")
-  q("no")
-}
-
-sc <- sparkR.init(appName = "RKMeans")
-K <- as.integer(args[[2]])
-convergeDist <- as.double(args[[3]])
-
-lines <- textFile(sc, args[[1]])
-points <- cache(lapplyPartition(lines, parseVectors))
-# kPoints <- take(points, K)
-kPoints <- do.call(rbind, takeSample(points, FALSE, K, 16189L))
-tempDist <- 1.0
-
-while (tempDist > convergeDist) {
-  closest <- lapplyPartition(
-    lapply(points,
-           function(p) {
-             cp <- closestPoint(p, kPoints); 
-             mapply(list, unique(cp), split.data.frame(cbind(1, p), cp), SIMPLIFY=FALSE)
-           }),
-    function(x) {do.call(c, x)
-    })
-  
-  pointStats <- reduceByKey(closest,
-                            function(p1, p2) {
-                              t(colSums(rbind(p1, p2)))
-                            },
-                            2L)
-  
-  newPoints <- do.call(
-    rbind,
-    collect(lapply(pointStats,
-                   function(tup) {
-                     point.sum <- tup[[2]][, -1]
-                     point.count <- tup[[2]][, 1]
-                     point.sum/point.count
-                   })))
-  
-  D <- dist.fun(kPoints, newPoints)
-  tempDist <- sum(D[cbind(1:3, max.col(-D))])
-  kPoints <- newPoints
-  cat("Finished iteration (delta = ", tempDist, ")\n")
-}
-
-cat("Final centers:\n")
-writeLines(unlist(lapply(kPoints, paste, collapse = " ")))
diff --git a/examples/src/main/r/linear_solver_mnist.R b/examples/src/main/r/linear_solver_mnist.R
deleted file mode 100644
index c864a4232d010..0000000000000
--- a/examples/src/main/r/linear_solver_mnist.R
+++ /dev/null
@@ -1,107 +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.
-#
-
-# Instructions: https://github.com/amplab-extras/SparkR-pkg/wiki/SparkR-Example:-Digit-Recognition-on-EC2
-
-library(SparkR)
-library(Matrix)
-
-args <- commandArgs(trailing = TRUE)
-
-# number of random features; default to 1100
-D <- ifelse(length(args) > 0, as.integer(args[[1]]), 1100)
-# number of partitions for training dataset
-trainParts <- 12
-# dimension of digits
-d <- 784
-# number of test examples
-NTrain <- 60000
-# number of training examples
-NTest <- 10000
-# scale of features
-gamma <- 4e-4
-
-sc <- sparkR.init(appName = "SparkR-LinearSolver")
-
-# You can also use HDFS path to speed things up:
-# hdfs://<master>/train-mnist-dense-with-labels.data
-file <- textFile(sc, "/data/train-mnist-dense-with-labels.data", trainParts)
-
-W <- gamma * matrix(nrow=D, ncol=d, data=rnorm(D*d))
-b <- 2 * pi * matrix(nrow=D, ncol=1, data=runif(D))
-broadcastW <- broadcast(sc, W)
-broadcastB <- broadcast(sc, b)
-
-includePackage(sc, Matrix)
-numericLines <- lapplyPartitionsWithIndex(file,
-                       function(split, part) {
-                         matList <- sapply(part, function(line) {
-                           as.numeric(strsplit(line, ",", fixed=TRUE)[[1]])
-                         }, simplify=FALSE)
-                         mat <- Matrix(ncol=d+1, data=unlist(matList, F, F),
-                                       sparse=T, byrow=T)
-                         mat
-                       })
-
-featureLabels <- cache(lapplyPartition(
-    numericLines,
-    function(part) {
-      label <- part[,1]
-      mat <- part[,-1]
-      ones <- rep(1, nrow(mat))
-      features <- cos(
-        mat %*% t(value(broadcastW)) + (matrix(ncol=1, data=ones) %*% t(value(broadcastB))))
-      onesMat <- Matrix(ones)
-      featuresPlus <- cBind(features, onesMat)
-      labels <- matrix(nrow=nrow(mat), ncol=10, data=-1)
-      for (i in 1:nrow(mat)) {
-        labels[i, label[i]] <- 1
-      }
-      list(label=labels, features=featuresPlus)
-  }))
-
-FTF <- Reduce("+", collect(lapplyPartition(featureLabels,
-    function(part) {
-      t(part$features) %*% part$features
-    }), flatten=F))
-
-FTY <- Reduce("+", collect(lapplyPartition(featureLabels,
-    function(part) {
-      t(part$features) %*% part$label
-    }), flatten=F))
-
-# solve for the coefficient matrix
-C <- solve(FTF, FTY)
-
-test <- Matrix(as.matrix(read.csv("/data/test-mnist-dense-with-labels.data",
-                         header=F), sparse=T))
-testData <- test[,-1]
-testLabels <- matrix(ncol=1, test[,1])
-
-err <- 0
-
-# contstruct the feature maps for all examples from this digit
-featuresTest <- cos(testData %*% t(value(broadcastW)) +
-    (matrix(ncol=1, data=rep(1, NTest)) %*% t(value(broadcastB))))
-featuresTest <- cBind(featuresTest, Matrix(rep(1, NTest)))
-
-# extract the one vs. all assignment
-results <- featuresTest %*% C
-labelsGot <- apply(results, 1, which.max)
-err <- sum(testLabels != labelsGot) / nrow(testLabels)
-
-cat("\nFinished running. The error rate is: ", err, ".\n")
diff --git a/examples/src/main/r/logistic_regression.R b/examples/src/main/r/logistic_regression.R
deleted file mode 100644
index 2a86aa98160d3..0000000000000
--- a/examples/src/main/r/logistic_regression.R
+++ /dev/null
@@ -1,62 +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.
-#
-
-library(SparkR)
-
-args <- commandArgs(trailing = TRUE)
-
-if (length(args) != 3) {
-  print("Usage: logistic_regression <file> <iters> <dimension>")
-  q("no")
-}
-
-# Initialize Spark context
-sc <- sparkR.init(appName = "LogisticRegressionR")
-iterations <- as.integer(args[[2]])
-D <- as.integer(args[[3]])
-
-readPartition <- function(part){
-  part = strsplit(part, " ", fixed = T)
-  list(matrix(as.numeric(unlist(part)), ncol = length(part[[1]])))
-}
-
-# Read data points and convert each partition to a matrix
-points <- cache(lapplyPartition(textFile(sc, args[[1]]), readPartition))
-
-# Initialize w to a random value
-w <- runif(n=D, min = -1, max = 1)
-cat("Initial w: ", w, "\n")
-
-# Compute logistic regression gradient for a matrix of data points
-gradient <- function(partition) {
-  partition = partition[[1]]
-  Y <- partition[, 1]  # point labels (first column of input file)
-  X <- partition[, -1] # point coordinates
-
-  # For each point (x, y), compute gradient function
-  dot <- X %*% w
-  logit <- 1 / (1 + exp(-Y * dot))
-  grad <- t(X) %*% ((logit - 1) * Y)
-  list(grad)
-}
-
-for (i in 1:iterations) {
-  cat("On iteration ", i, "\n")
-  w <- w - reduce(lapplyPartition(points, gradient), "+")
-}
-
-cat("Final w: ", w, "\n")
diff --git a/examples/src/main/r/pi.R b/examples/src/main/r/pi.R
deleted file mode 100644
index aa7a833e147a0..0000000000000
--- a/examples/src/main/r/pi.R
+++ /dev/null
@@ -1,46 +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.
-#
-
-library(SparkR)
-
-args <- commandArgs(trailing = TRUE)
-
-sc <- sparkR.init(appName = "PiR")
-
-slices <- ifelse(length(args) > 1, as.integer(args[[2]]), 2)
-
-n <- 100000 * slices
-
-piFunc <- function(elem) {
-  rands <- runif(n = 2, min = -1, max = 1)
-  val <- ifelse((rands[1]^2 + rands[2]^2) < 1, 1.0, 0.0)
-  val
-}
-
-
-piFuncVec <- function(elems) {
-  message(length(elems))
-  rands1 <- runif(n = length(elems), min = -1, max = 1)
-  rands2 <- runif(n = length(elems), min = -1, max = 1)
-  val <- ifelse((rands1^2 + rands2^2) < 1, 1.0, 0.0)
-  sum(val)
-}
-
-rdd <- parallelize(sc, 1:n, slices)
-count <- reduce(lapplyPartition(rdd, piFuncVec), sum)
-cat("Pi is roughly", 4.0 * count / n, "\n")
-cat("Num elements in RDD ", count(rdd), "\n")
diff --git a/examples/src/main/r/wordcount.R b/examples/src/main/r/wordcount.R
deleted file mode 100644
index b734cb0ecf55b..0000000000000
--- a/examples/src/main/r/wordcount.R
+++ /dev/null
@@ -1,42 +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.
-#
-
-library(SparkR)
-
-args <- commandArgs(trailing = TRUE)
-
-if (length(args) != 1) {
-  print("Usage: wordcount <file>")
-  q("no")
-}
-
-# Initialize Spark context
-sc <- sparkR.init(appName = "RwordCount")
-lines <- textFile(sc, args[[1]])
-
-words <- flatMap(lines,
-                 function(line) {
-                   strsplit(line, " ")[[1]]
-                 })
-wordCount <- lapply(words, function(word) { list(word, 1L) })
-
-counts <- reduceByKey(wordCount, "+", 2L)
-output <- collect(counts)
-
-for (wordcount in output) {
-  cat(wordcount[[1]], ": ", wordcount[[2]], "\n")
-}

From 316a5c0423ba3688cacd3acc3c5b5571e8a71d1d Mon Sep 17 00:00:00 2001
From: jerryshao <saisai.shao@intel.com>
Date: Wed, 6 May 2015 17:44:43 -0700
Subject: [PATCH 19/75] [SPARK-7396] [STREAMING] [EXAMPLE] Update
 KafkaWordCountProducer to use new Producer API

Otherwise it will throw exception:

```
Exception in thread "main" kafka.common.FailedToSendMessageException: Failed to send messages after 3 tries.
	at kafka.producer.async.DefaultEventHandler.handle(DefaultEventHandler.scala:90)
	at kafka.producer.Producer.send(Producer.scala:77)
	at org.apache.spark.examples.streaming.KafkaWordCountProducer$.main(KafkaWordCount.scala:96)
	at org.apache.spark.examples.streaming.KafkaWordCountProducer.main(KafkaWordCount.scala)
	at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
	at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
	at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
	at java.lang.reflect.Method.invoke(Method.java:606)
	at org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:623)
	at org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:169)
	at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:192)
	at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:111)
	at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
```

Author: jerryshao <saisai.shao@intel.com>

Closes #5936 from jerryshao/SPARK-7396 and squashes the following commits:

270bbe2 [jerryshao] Fix Kafka Produce throw Exception issue
---
 .../examples/streaming/KafkaWordCount.scala   | 24 ++++++++++---------
 1 file changed, 13 insertions(+), 11 deletions(-)

diff --git a/examples/scala-2.10/src/main/scala/org/apache/spark/examples/streaming/KafkaWordCount.scala b/examples/scala-2.10/src/main/scala/org/apache/spark/examples/streaming/KafkaWordCount.scala
index 387c0e421334b..f407367a54f6c 100644
--- a/examples/scala-2.10/src/main/scala/org/apache/spark/examples/streaming/KafkaWordCount.scala
+++ b/examples/scala-2.10/src/main/scala/org/apache/spark/examples/streaming/KafkaWordCount.scala
@@ -17,9 +17,9 @@
 
 package org.apache.spark.examples.streaming
 
-import java.util.Properties
+import java.util.HashMap
 
-import kafka.producer._
+import org.apache.kafka.clients.producer.{ProducerConfig, KafkaProducer, ProducerRecord}
 
 import org.apache.spark.streaming._
 import org.apache.spark.streaming.kafka._
@@ -77,23 +77,25 @@ object KafkaWordCountProducer {
     val Array(brokers, topic, messagesPerSec, wordsPerMessage) = args
 
     // Zookeeper connection properties
-    val props = new Properties()
-    props.put("metadata.broker.list", brokers)
-    props.put("serializer.class", "kafka.serializer.StringEncoder")
+    val props = new HashMap[String, Object]()
+    props.put(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG, brokers)
+    props.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG,
+      "org.apache.kafka.common.serialization.StringSerializer")
+    props.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG,
+      "org.apache.kafka.common.serialization.StringSerializer")
 
-    val config = new ProducerConfig(props)
-    val producer = new Producer[String, String](config)
+    val producer = new KafkaProducer[String, String](props)
 
     // Send some messages
     while(true) {
-      val messages = (1 to messagesPerSec.toInt).map { messageNum =>
+      (1 to messagesPerSec.toInt).foreach { messageNum =>
         val str = (1 to wordsPerMessage.toInt).map(x => scala.util.Random.nextInt(10).toString)
           .mkString(" ")
 
-        new KeyedMessage[String, String](topic, str)
-      }.toArray
+        val message = new ProducerRecord[String, String](topic, null, str)
+        producer.send(message)
+      }
 
-      producer.send(messages: _*)
       Thread.sleep(100)
     }
   }

From 8fa6829f5e6d8b8f33097c3f20fcb96198d4031b Mon Sep 17 00:00:00 2001
From: Andrew Or <andrew@databricks.com>
Date: Wed, 6 May 2015 17:52:34 -0700
Subject: [PATCH 20/75] [SPARK-7371] [SPARK-7377] [SPARK-7408] DAG
 visualization addendum (#5729)

This is a follow-up patch for #5729.

**[SPARK-7408]** Move as much style code from JS to CSS as possible
**[SPARK-7377]** Fix JS error if a job / stage contains only one RDD
**[SPARK-7371]** Decrease emphasis on RDD on stage page as requested by mateiz pwendell

This patch also includes general code clean up.

<img src="https://issues.apache.org/jira/secure/attachment/12730992/before-after.png" width="500px"></img>

Author: Andrew Or <andrew@databricks.com>

Closes #5954 from andrewor14/viz-emphasize-rdd and squashes the following commits:

3c0d4f0 [Andrew Or] Guard against JS error by rendering arrows only if needed
f23e15b [Andrew Or] Merge branch 'master' of github.com:apache/spark into viz-emphasize-rdd
565801f [Andrew Or] Clean up code
9dab5f0 [Andrew Or] Move styling from JS to CSS + clean up code
107c0b6 [Andrew Or] Tweak background color, stroke width, font size etc.
1610c62 [Andrew Or] Implement cluster padding for stage page
---
 .../apache/spark/ui/static/dagre-d3.min.js    |  14 +-
 .../apache/spark/ui/static/spark-dag-viz.css  |  83 ++++
 .../apache/spark/ui/static/spark-dag-viz.js   | 357 ++++++++++--------
 .../scala/org/apache/spark/ui/UIUtils.scala   |  14 +-
 .../spark/ui/scope/RDDOperationGraph.scala    |  19 +-
 5 files changed, 310 insertions(+), 177 deletions(-)
 create mode 100644 core/src/main/resources/org/apache/spark/ui/static/spark-dag-viz.css

diff --git a/core/src/main/resources/org/apache/spark/ui/static/dagre-d3.min.js b/core/src/main/resources/org/apache/spark/ui/static/dagre-d3.min.js
index 6d2da25024a83..6beb7db855ca0 100644
--- a/core/src/main/resources/org/apache/spark/ui/static/dagre-d3.min.js
+++ b/core/src/main/resources/org/apache/spark/ui/static/dagre-d3.min.js
@@ -1,4 +1,4 @@
-/*v0.4.3 with 1 additional commit (see http://github.com/andrewor14/dagre-d3)*/(function(f){if(typeof exports==="object"&&typeof module!=="undefined"){module.exports=f()}else if(typeof define==="function"&&define.amd){define([],f)}else{var g;if(typeof window!=="undefined"){g=window}else if(typeof global!=="undefined"){g=global}else if(typeof self!=="undefined"){g=self}else{g=this}g.dagreD3=f()}})(function(){var define,module,exports;return function e(t,n,r){function s(o,u){if(!n[o]){if(!t[o]){var a=typeof require=="function"&&require;if(!u&&a)return a(o,!0);if(i)return i(o,!0);var f=new Error("Cannot find module '"+o+"'");throw f.code="MODULE_NOT_FOUND",f}var l=n[o]={exports:{}};t[o][0].call(l.exports,function(e){var n=t[o][1][e];return s(n?n:e)},l,l.exports,e,t,n,r)}return n[o].exports}var i=typeof require=="function"&&require;for(var o=0;o<r.length;o++)s(r[o]);return s}({1:[function(require,module,exports){/**
+!function(e){if("object"==typeof exports&&"undefined"!=typeof module)module.exports=e();else if("function"==typeof define&&define.amd)define([],e);else{var f;"undefined"!=typeof window?f=window:"undefined"!=typeof global?f=global:"undefined"!=typeof self&&(f=self),f.dagreD3=e()}}(function(){var define,module,exports;return function e(t,n,r){function s(o,u){if(!n[o]){if(!t[o]){var a=typeof require=="function"&&require;if(!u&&a)return a(o,!0);if(i)return i(o,!0);var f=new Error("Cannot find module '"+o+"'");throw f.code="MODULE_NOT_FOUND",f}var l=n[o]={exports:{}};t[o][0].call(l.exports,function(e){var n=t[o][1][e];return s(n?n:e)},l,l.exports,e,t,n,r)}return n[o].exports}var i=typeof require=="function"&&require;for(var o=0;o<r.length;o++)s(r[o]);return s}({1:[function(require,module,exports){/**
  * @license
  * Copyright (c) 2012-2013 Chris Pettitt
  *
@@ -20,10 +20,12 @@
  * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
  * THE SOFTWARE.
  */
-module.exports={graphlib:require("./lib/graphlib"),dagre:require("./lib/dagre"),intersect:require("./lib/intersect"),render:require("./lib/render"),util:require("./lib/util"),version:require("./lib/version")}},{"./lib/dagre":8,"./lib/graphlib":9,"./lib/intersect":10,"./lib/render":23,"./lib/util":25,"./lib/version":26}],2:[function(require,module,exports){var util=require("./util");module.exports={"default":normal,normal:normal,vee:vee,undirected:undirected};function normal(parent,id,edge,type){var marker=parent.append("marker").attr("id",id).attr("viewBox","0 0 10 10").attr("refX",9).attr("refY",5).attr("markerUnits","strokeWidth").attr("markerWidth",8).attr("markerHeight",6).attr("orient","auto");var path=marker.append("path").attr("d","M 0 0 L 10 5 L 0 10 z").style("stroke-width",1).style("stroke-dasharray","1,0");util.applyStyle(path,edge[type+"Style"])}function vee(parent,id,edge,type){var marker=parent.append("marker").attr("id",id).attr("viewBox","0 0 10 10").attr("refX",9).attr("refY",5).attr("markerUnits","strokeWidth").attr("markerWidth",8).attr("markerHeight",6).attr("orient","auto");var path=marker.append("path").attr("d","M 0 0 L 10 5 L 0 10 L 4 5 z").style("stroke-width",1).style("stroke-dasharray","1,0");util.applyStyle(path,edge[type+"Style"])}function undirected(parent,id,edge,type){var marker=parent.append("marker").attr("id",id).attr("viewBox","0 0 10 10").attr("refX",9).attr("refY",5).attr("markerUnits","strokeWidth").attr("markerWidth",8).attr("markerHeight",6).attr("orient","auto");var path=marker.append("path").attr("d","M 0 5 L 10 5").style("stroke-width",1).style("stroke-dasharray","1,0");util.applyStyle(path,edge[type+"Style"])}},{"./util":25}],3:[function(require,module,exports){var util=require("./util");module.exports=createClusters;function createClusters(selection,g){var clusters=g.nodes().filter(function(v){return util.isSubgraph(g,v)}),svgClusters=selection.selectAll("g.cluster").data(clusters,function(v){return v});svgClusters.enter().append("g").attr("id",function(v){return"cluster_"+v.replace(/^cluster/,"")}).attr("name",function(v){return g.node(v).label}).attr("class","cluster").style("opacity",0).append("rect");util.applyTransition(svgClusters.exit(),g).style("opacity",0).remove();util.applyTransition(svgClusters,g).style("opacity",1);util.applyTransition(svgClusters.selectAll("rect"),g).attr("width",function(v){return g.node(v).width}).attr("height",function(v){return g.node(v).height}).attr("x",function(v){var node=g.node(v);return node.x-node.width/2}).attr("y",function(v){var node=g.node(v);return node.y-node.height/2})}},{"./util":25}],4:[function(require,module,exports){"use strict";var _=require("./lodash"),addLabel=require("./label/add-label"),util=require("./util"),d3=require("./d3");module.exports=createEdgeLabels;function createEdgeLabels(selection,g){var svgEdgeLabels=selection.selectAll("g.edgeLabel").data(g.edges(),function(e){return util.edgeToId(e)}).classed("update",true);svgEdgeLabels.selectAll("*").remove();svgEdgeLabels.enter().append("g").classed("edgeLabel",true).style("opacity",0);svgEdgeLabels.each(function(e){var edge=g.edge(e),label=addLabel(d3.select(this),g.edge(e),0,0).classed("label",true),bbox=label.node().getBBox();if(edge.labelId){label.attr("id",edge.labelId)}if(!_.has(edge,"width")){edge.width=bbox.width}if(!_.has(edge,"height")){edge.height=bbox.height}});util.applyTransition(svgEdgeLabels.exit(),g).style("opacity",0).remove();return svgEdgeLabels}},{"./d3":7,"./label/add-label":18,"./lodash":20,"./util":25}],5:[function(require,module,exports){"use strict";var _=require("./lodash"),intersectNode=require("./intersect/intersect-node"),util=require("./util"),d3=require("./d3");module.exports=createEdgePaths;function createEdgePaths(selection,g,arrows){var svgPaths=selection.selectAll("g.edgePath").data(g.edges(),function(e){return util.edgeToId(e)}).classed("update",true);enter(svgPaths,g);exit(svgPaths,g);util.applyTransition(svgPaths,g).style("opacity",1);svgPaths.each(function(e){var domEdge=d3.select(this);var edge=g.edge(e);edge.elem=this;if(edge.id){domEdge.attr("id",edge.id)}util.applyClass(domEdge,edge["class"],(domEdge.classed("update")?"update ":"")+"edgePath")});svgPaths.selectAll("path.path").each(function(e){var edge=g.edge(e);edge.arrowheadId=_.uniqueId("arrowhead");var domEdge=d3.select(this).attr("marker-end",function(){return"url(#"+edge.arrowheadId+")"}).style("fill","none");util.applyTransition(domEdge,g).attr("d",function(e){return calcPoints(g,e)});util.applyStyle(domEdge,edge.style)});svgPaths.selectAll("defs *").remove();svgPaths.selectAll("defs").each(function(e){var edge=g.edge(e),arrowhead=arrows[edge.arrowhead];arrowhead(d3.select(this),edge.arrowheadId,edge,"arrowhead")});return svgPaths}function calcPoints(g,e){var edge=g.edge(e),tail=g.node(e.v),head=g.node(e.w),points=edge.points.slice(1,edge.points.length-1);points.unshift(intersectNode(tail,points[0]));points.push(intersectNode(head,points[points.length-1]));return createLine(edge,points)}function createLine(edge,points){var line=d3.svg.line().x(function(d){return d.x}).y(function(d){return d.y});if(_.has(edge,"lineInterpolate")){line.interpolate(edge.lineInterpolate)}if(_.has(edge,"lineTension")){line.tension(Number(edge.lineTension))}return line(points)}function getCoords(elem){var bbox=elem.getBBox(),matrix=elem.getTransformToElement(elem.ownerSVGElement).translate(bbox.width/2,bbox.height/2);return{x:matrix.e,y:matrix.f}}function enter(svgPaths,g){var svgPathsEnter=svgPaths.enter().append("g").attr("class","edgePath").style("opacity",0);svgPathsEnter.append("path").attr("class","path").attr("d",function(e){var edge=g.edge(e),sourceElem=g.node(e.v).elem,points=_.range(edge.points.length).map(function(){return getCoords(sourceElem)});return createLine(edge,points)});svgPathsEnter.append("defs")}function exit(svgPaths,g){var svgPathExit=svgPaths.exit();util.applyTransition(svgPathExit,g).style("opacity",0).remove();util.applyTransition(svgPathExit.select("path.path"),g).attr("d",function(e){var source=g.node(e.v);if(source){var points=_.range(this.pathSegList.length).map(function(){return source});return createLine({},points)}else{return d3.select(this).attr("d")}})}},{"./d3":7,"./intersect/intersect-node":14,"./lodash":20,"./util":25}],6:[function(require,module,exports){"use strict";var _=require("./lodash"),addLabel=require("./label/add-label"),util=require("./util"),d3=require("./d3");module.exports=createNodes;function createNodes(selection,g,shapes){var simpleNodes=g.nodes().filter(function(v){return!util.isSubgraph(g,v)});var svgNodes=selection.selectAll("g.node").data(simpleNodes,function(v){return v}).classed("update",true);svgNodes.selectAll("*").remove();svgNodes.enter().append("g").attr("id",function(v){return"node_"+v}).attr("name",function(v){return g.node(v).label}).attr("class","node").style("opacity",0);svgNodes.each(function(v){var node=g.node(v),thisGroup=d3.select(this),labelGroup=thisGroup.append("g").attr("class","label"),labelDom=addLabel(labelGroup,node),shape=shapes[node.shape],bbox=_.pick(labelDom.node().getBBox(),"width","height");node.elem=this;if(node.id){thisGroup.attr("id",node.id)}if(node.labelId){labelGroup.attr("id",node.labelId)}util.applyClass(thisGroup,node["class"],(thisGroup.classed("update")?"update ":"")+"node");if(_.has(node,"width")){bbox.width=node.width}if(_.has(node,"height")){bbox.height=node.height}bbox.width+=node.paddingLeft+node.paddingRight;bbox.height+=node.paddingTop+node.paddingBottom;labelGroup.attr("transform","translate("+(node.paddingLeft-node.paddingRight)/2+","+(node.paddingTop-node.paddingBottom)/2+")");var shapeSvg=shape(d3.select(this),bbox,node);util.applyStyle(shapeSvg,node.style);var shapeBBox=shapeSvg.node().getBBox();node.width=shapeBBox.width;node.height=shapeBBox.height});util.applyTransition(svgNodes.exit(),g).style("opacity",0).remove();return svgNodes}},{"./d3":7,"./label/add-label":18,"./lodash":20,"./util":25}],7:[function(require,module,exports){module.exports=window.d3},{}],8:[function(require,module,exports){var dagre;if(require){try{dagre=require("dagre")}catch(e){}}if(!dagre){dagre=window.dagre}module.exports=dagre},{dagre:27}],9:[function(require,module,exports){var graphlib;if(require){try{graphlib=require("graphlib")}catch(e){}}if(!graphlib){graphlib=window.graphlib}module.exports=graphlib},{graphlib:57}],10:[function(require,module,exports){module.exports={node:require("./intersect-node"),circle:require("./intersect-circle"),ellipse:require("./intersect-ellipse"),polygon:require("./intersect-polygon"),rect:require("./intersect-rect")}},{"./intersect-circle":11,"./intersect-ellipse":12,"./intersect-node":14,"./intersect-polygon":15,"./intersect-rect":16}],11:[function(require,module,exports){var intersectEllipse=require("./intersect-ellipse");module.exports=intersectCircle;function intersectCircle(node,rx,point){return intersectEllipse(node,rx,rx,point)}},{"./intersect-ellipse":12}],12:[function(require,module,exports){module.exports=intersectEllipse;function intersectEllipse(node,rx,ry,point){var cx=node.x;var cy=node.y;var px=cx-point.x;var py=cy-point.y;var det=Math.sqrt(rx*rx*py*py+ry*ry*px*px);var dx=Math.abs(rx*ry*px/det);if(point.x<cx){dx=-dx}var dy=Math.abs(rx*ry*py/det);if(point.y<cy){dy=-dy}return{x:cx+dx,y:cy+dy}}},{}],13:[function(require,module,exports){module.exports=intersectLine;function intersectLine(p1,p2,q1,q2){var a1,a2,b1,b2,c1,c2;var r1,r2,r3,r4;var denom,offset,num;var x,y;a1=p2.y-p1.y;b1=p1.x-p2.x;c1=p2.x*p1.y-p1.x*p2.y;r3=a1*q1.x+b1*q1.y+c1;r4=a1*q2.x+b1*q2.y+c1;if(r3!==0&&r4!==0&&sameSign(r3,r4)){return}a2=q2.y-q1.y;b2=q1.x-q2.x;c2=q2.x*q1.y-q1.x*q2.y;r1=a2*p1.x+b2*p1.yy+c2;r2=a2*p2.x+b2*p2.y+c2;if(r1!==0&&r2!==0&&sameSign(r1,r2)){return}denom=a1*b2-a2*b1;if(denom===0){return}offset=Math.abs(denom/2);num=b1*c2-b2*c1;x=num<0?(num-offset)/denom:(num+offset)/denom;num=a2*c1-a1*c2;y=num<0?(num-offset)/denom:(num+offset)/denom;return{x:x,y:y}}function sameSign(r1,r2){return r1*r2>0}},{}],14:[function(require,module,exports){module.exports=intersectNode;function intersectNode(node,point){return node.intersect(point)}},{}],15:[function(require,module,exports){var intersectLine=require("./intersect-line");module.exports=intersectPolygon;function intersectPolygon(node,polyPoints,point){var x1=node.x;var y1=node.y;var intersections=[];var minX=Number.POSITIVE_INFINITY,minY=Number.POSITIVE_INFINITY;polyPoints.forEach(function(entry){minX=Math.min(minX,entry.x);minY=Math.min(minY,entry.y)});var left=x1-node.width/2-minX;var top=y1-node.height/2-minY;for(var i=0;i<polyPoints.length;i++){var p1=polyPoints[i];var p2=polyPoints[i<polyPoints.length-1?i+1:0];var intersect=intersectLine(node,point,{x:left+p1.x,y:top+p1.y},{x:left+p2.x,y:top+p2.y});if(intersect){intersections.push(intersect)}}if(!intersections.length){console.log("NO INTERSECTION FOUND, RETURN NODE CENTER",node);return node}if(intersections.length>1){intersections.sort(function(p,q){var pdx=p.x-point.x,pdy=p.y-point.y,distp=Math.sqrt(pdx*pdx+pdy*pdy),qdx=q.x-point.x,qdy=q.y-point.y,distq=Math.sqrt(qdx*qdx+qdy*qdy);return distp<distq?-1:distp===distq?0:1})}return intersections[0]}},{"./intersect-line":13}],16:[function(require,module,exports){module.exports=intersectRect;function intersectRect(node,point){var x=node.x;var y=node.y;var dx=point.x-x;var dy=point.y-y;var w=node.width/2;var h=node.height/2;var sx,sy;if(Math.abs(dy)*w>Math.abs(dx)*h){if(dy<0){h=-h}sx=dy===0?0:h*dx/dy;sy=h}else{if(dx<0){w=-w}sx=w;sy=dx===0?0:w*dy/dx}return{x:x+sx,y:y+sy}}},{}],17:[function(require,module,exports){var util=require("../util");module.exports=addHtmlLabel;function addHtmlLabel(root,node){var fo=root.append("foreignObject").attr("width","100000");var div=fo.append("xhtml:div");var label=node.label;switch(typeof label){case"function":div.insert(label);break;case"object":div.insert(function(){return label});break;default:div.html(label)}util.applyStyle(div,node.labelStyle);div.style("display","inline-block");div.style("white-space","nowrap");var w,h;div.each(function(){w=this.clientWidth;h=this.clientHeight});fo.attr("width",w).attr("height",h);return fo}},{"../util":25}],18:[function(require,module,exports){var addTextLabel=require("./add-text-label"),addHtmlLabel=require("./add-html-label");module.exports=addLabel;function addLabel(root,node){var label=node.label;var labelSvg=root.append("g");if(typeof label!=="string"||node.labelType==="html"){addHtmlLabel(labelSvg,node)}else{addTextLabel(labelSvg,node)}var labelBBox=labelSvg.node().getBBox();labelSvg.attr("transform","translate("+-labelBBox.width/2+","+-labelBBox.height/2+")");return labelSvg}},{"./add-html-label":17,"./add-text-label":19}],19:[function(require,module,exports){var util=require("../util");module.exports=addTextLabel;function addTextLabel(root,node){var domNode=root.append("text");var lines=processEscapeSequences(node.label).split("\n");for(var i=0;i<lines.length;i++){domNode.append("tspan").attr("xml:space","preserve").attr("dy","1em").attr("x","1").text(lines[i])}util.applyStyle(domNode,node.labelStyle);return domNode}function processEscapeSequences(text){var newText="",escaped=false,ch;for(var i=0;i<text.length;++i){ch=text[i];if(escaped){switch(ch){case"n":newText+="\n";break;default:newText+=ch}escaped=false}else if(ch==="\\"){escaped=true}else{newText+=ch}}return newText}},{"../util":25}],20:[function(require,module,exports){var lodash;if(require){try{lodash=require("lodash")}catch(e){}}if(!lodash){lodash=window._}module.exports=lodash},{lodash:77}],21:[function(require,module,exports){"use strict";var util=require("./util"),d3=require("./d3"),_=require("./lodash");module.exports=positionEdgeLabels;function positionEdgeLabels(selection,g){var created=selection.filter(function(){return!d3.select(this).classed("update")});function translate(e){var edge=g.edge(e);return _.has(edge,"x")?"translate("+edge.x+","+edge.y+")":""}created.attr("transform",translate);util.applyTransition(selection,g).style("opacity",1).attr("transform",translate)}},{"./d3":7,"./lodash":20,"./util":25}],22:[function(require,module,exports){"use strict";var util=require("./util"),d3=require("./d3");module.exports=positionNodes;function positionNodes(selection,g){var created=selection.filter(function(){return!d3.select(this).classed("update")});function translate(v){var node=g.node(v);return"translate("+node.x+","+node.y+")"}created.attr("transform",translate);util.applyTransition(selection,g).style("opacity",1).attr("transform",translate)}},{"./d3":7,"./util":25}],23:[function(require,module,exports){var _=require("./lodash"),layout=require("./dagre").layout;module.exports=render;function render(){var createNodes=require("./create-nodes"),createClusters=require("./create-clusters"),createEdgeLabels=require("./create-edge-labels"),createEdgePaths=require("./create-edge-paths"),positionNodes=require("./position-nodes"),positionEdgeLabels=require("./position-edge-labels"),shapes=require("./shapes"),arrows=require("./arrows");var fn=function(svg,g){preProcessGraph(g);var outputGroup=createOrSelectGroup(svg,"output"),clustersGroup=createOrSelectGroup(outputGroup,"clusters"),edgePathsGroup=createOrSelectGroup(outputGroup,"edgePaths"),edgeLabels=createEdgeLabels(createOrSelectGroup(outputGroup,"edgeLabels"),g),nodes=createNodes(createOrSelectGroup(outputGroup,"nodes"),g,shapes);layout(g);positionNodes(nodes,g);positionEdgeLabels(edgeLabels,g);createEdgePaths(edgePathsGroup,g,arrows);createClusters(clustersGroup,g);postProcessGraph(g)};fn.createNodes=function(value){if(!arguments.length)return createNodes;createNodes=value;return fn};fn.createClusters=function(value){if(!arguments.length)return createClusters;createClusters=value;return fn};fn.createEdgeLabels=function(value){if(!arguments.length)return createEdgeLabels;createEdgeLabels=value;return fn};fn.createEdgePaths=function(value){if(!arguments.length)return createEdgePaths;createEdgePaths=value;return fn};fn.shapes=function(value){if(!arguments.length)return shapes;shapes=value;return fn};fn.arrows=function(value){if(!arguments.length)return arrows;arrows=value;return fn};return fn}var NODE_DEFAULT_ATTRS={paddingLeft:10,paddingRight:10,paddingTop:10,paddingBottom:10,rx:0,ry:0,shape:"rect"};var EDGE_DEFAULT_ATTRS={arrowhead:"normal",lineInterpolate:"linear"};function preProcessGraph(g){g.nodes().forEach(function(v){var node=g.node(v);if(!_.has(node,"label")){node.label=v}if(_.has(node,"paddingX")){_.defaults(node,{paddingLeft:node.paddingX,paddingRight:node.paddingX})}if(_.has(node,"paddingY")){_.defaults(node,{paddingTop:node.paddingY,paddingBottom:node.paddingY})}if(_.has(node,"padding")){_.defaults(node,{paddingLeft:node.padding,paddingRight:node.padding,paddingTop:node.padding,paddingBottom:node.padding})}_.defaults(node,NODE_DEFAULT_ATTRS);_.each(["paddingLeft","paddingRight","paddingTop","paddingBottom"],function(k){node[k]=Number(node[k])});if(_.has(node,"width")){node._prevWidth=node.width}if(_.has(node,"height")){node._prevHeight=node.height}});g.edges().forEach(function(e){var edge=g.edge(e);if(!_.has(edge,"label")){edge.label=""}_.defaults(edge,EDGE_DEFAULT_ATTRS)})}function postProcessGraph(g){_.each(g.nodes(),function(v){var node=g.node(v);if(_.has(node,"_prevWidth")){node.width=node._prevWidth}else{delete node.width}if(_.has(node,"_prevHeight")){node.height=node._prevHeight}else{delete node.height}delete node._prevWidth;delete node._prevHeight})}function createOrSelectGroup(root,name){var selection=root.select("g."+name);if(selection.empty()){selection=root.append("g").attr("class",name)}return selection}},{"./arrows":2,"./create-clusters":3,"./create-edge-labels":4,"./create-edge-paths":5,"./create-nodes":6,"./dagre":8,"./lodash":20,"./position-edge-labels":21,"./position-nodes":22,"./shapes":24}],24:[function(require,module,exports){"use strict";var intersectRect=require("./intersect/intersect-rect"),intersectEllipse=require("./intersect/intersect-ellipse"),intersectCircle=require("./intersect/intersect-circle"),intersectPolygon=require("./intersect/intersect-polygon");module.exports={rect:rect,ellipse:ellipse,circle:circle,diamond:diamond};function rect(parent,bbox,node){var shapeSvg=parent.insert("rect",":first-child").attr("rx",node.rx).attr("ry",node.ry).attr("x",-bbox.width/2).attr("y",-bbox.height/2).attr("width",bbox.width).attr("height",bbox.height);node.intersect=function(point){return intersectRect(node,point)};return shapeSvg}function ellipse(parent,bbox,node){var rx=bbox.width/2,ry=bbox.height/2,shapeSvg=parent.insert("ellipse",":first-child").attr("x",-bbox.width/2).attr("y",-bbox.height/2).attr("rx",rx).attr("ry",ry);node.intersect=function(point){return intersectEllipse(node,rx,ry,point)};return shapeSvg}function circle(parent,bbox,node){var r=Math.max(bbox.width,bbox.height)/2,shapeSvg=parent.insert("circle",":first-child").attr("x",-bbox.width/2).attr("y",-bbox.height/2).attr("r",r);node.intersect=function(point){return intersectCircle(node,r,point)};return shapeSvg}function diamond(parent,bbox,node){var w=bbox.width*Math.SQRT2/2,h=bbox.height*Math.SQRT2/2,points=[{x:0,y:-h},{x:-w,y:0},{x:0,y:h},{x:w,y:0}],shapeSvg=parent.insert("polygon",":first-child").attr("points",points.map(function(p){return p.x+","+p.y}).join(" "));node.intersect=function(p){return intersectPolygon(node,points,p)};return shapeSvg}},{"./intersect/intersect-circle":11,"./intersect/intersect-ellipse":12,"./intersect/intersect-polygon":15,"./intersect/intersect-rect":16}],25:[function(require,module,exports){var _=require("./lodash");module.exports={isSubgraph:isSubgraph,edgeToId:edgeToId,applyStyle:applyStyle,applyClass:applyClass,applyTransition:applyTransition};function isSubgraph(g,v){return!!g.children(v).length}function edgeToId(e){return escapeId(e.v)+":"+escapeId(e.w)+":"+escapeId(e.name)}var ID_DELIM=/:/g;function escapeId(str){return str?String(str).replace(ID_DELIM,"\\:"):""}function applyStyle(dom,styleFn){if(styleFn){dom.attr("style",styleFn)}}function applyClass(dom,classFn,otherClasses){if(classFn){dom.attr("class",classFn).attr("class",otherClasses+" "+dom.attr("class"))}}function applyTransition(selection,g){var graph=g.graph();if(_.isPlainObject(graph)){var transition=graph.transition;if(_.isFunction(transition)){return transition(selection)}}return selection}},{"./lodash":20}],26:[function(require,module,exports){module.exports="0.4.4-pre"},{}],27:[function(require,module,exports){module.exports={graphlib:require("./lib/graphlib"),layout:require("./lib/layout"),debug:require("./lib/debug"),util:{time:require("./lib/util").time,notime:require("./lib/util").notime},version:require("./lib/version")}},{"./lib/debug":32,"./lib/graphlib":33,"./lib/layout":35,"./lib/util":55,"./lib/version":56}],28:[function(require,module,exports){"use strict";var _=require("./lodash"),greedyFAS=require("./greedy-fas");module.exports={run:run,undo:undo};function run(g){var fas=g.graph().acyclicer==="greedy"?greedyFAS(g,weightFn(g)):dfsFAS(g);_.each(fas,function(e){var label=g.edge(e);g.removeEdge(e);label.forwardName=e.name;label.reversed=true;g.setEdge(e.w,e.v,label,_.uniqueId("rev"))});function weightFn(g){return function(e){return g.edge(e).weight}}}function dfsFAS(g){var fas=[],stack={},visited={};function dfs(v){if(_.has(visited,v)){return}visited[v]=true;stack[v]=true;_.each(g.outEdges(v),function(e){if(_.has(stack,e.w)){fas.push(e)}else{dfs(e.w)}});delete stack[v]}_.each(g.nodes(),dfs);return fas}function undo(g){_.each(g.edges(),function(e){var label=g.edge(e);if(label.reversed){g.removeEdge(e);var forwardName=label.forwardName;delete label.reversed;delete label.forwardName;g.setEdge(e.w,e.v,label,forwardName)}})}},{"./greedy-fas":34,"./lodash":36}],29:[function(require,module,exports){var _=require("./lodash"),util=require("./util");module.exports=addBorderSegments;function addBorderSegments(g){function dfs(v){var children=g.children(v),node=g.node(v);if(children.length){_.each(children,dfs)}if(_.has(node,"minRank")){node.borderLeft=[];node.borderRight=[];for(var rank=node.minRank,maxRank=node.maxRank+1;rank<maxRank;++rank){addBorderNode(g,"borderLeft","_bl",v,node,rank);addBorderNode(g,"borderRight","_br",v,node,rank)}}}_.each(g.children(),dfs)}function addBorderNode(g,prop,prefix,sg,sgNode,rank){var label={width:0,height:0,rank:rank},prev=sgNode[prop][rank-1],curr=util.addDummyNode(g,"border",label,prefix);sgNode[prop][rank]=curr;g.setParent(curr,sg);if(prev){g.setEdge(prev,curr,{weight:1})}}},{"./lodash":36,"./util":55}],30:[function(require,module,exports){"use strict";var _=require("./lodash");module.exports={adjust:adjust,undo:undo};function adjust(g){var rankDir=g.graph().rankdir.toLowerCase();if(rankDir==="lr"||rankDir==="rl"){swapWidthHeight(g)}}function undo(g){var rankDir=g.graph().rankdir.toLowerCase();if(rankDir==="bt"||rankDir==="rl"){reverseY(g)}if(rankDir==="lr"||rankDir==="rl"){swapXY(g);swapWidthHeight(g)}}function swapWidthHeight(g){_.each(g.nodes(),function(v){swapWidthHeightOne(g.node(v))});_.each(g.edges(),function(e){swapWidthHeightOne(g.edge(e))})}function swapWidthHeightOne(attrs){var w=attrs.width;attrs.width=attrs.height;attrs.height=w}function reverseY(g){_.each(g.nodes(),function(v){reverseYOne(g.node(v))});_.each(g.edges(),function(e){var edge=g.edge(e);_.each(edge.points,reverseYOne);if(_.has(edge,"y")){reverseYOne(edge)}})}function reverseYOne(attrs){attrs.y=-attrs.y}function swapXY(g){_.each(g.nodes(),function(v){swapXYOne(g.node(v))});_.each(g.edges(),function(e){var edge=g.edge(e);_.each(edge.points,swapXYOne);if(_.has(edge,"x")){swapXYOne(edge)}})}function swapXYOne(attrs){var x=attrs.x;attrs.x=attrs.y;attrs.y=x}},{"./lodash":36}],31:[function(require,module,exports){module.exports=List;function List(){var sentinel={};sentinel._next=sentinel._prev=sentinel;this._sentinel=sentinel}List.prototype.dequeue=function(){var sentinel=this._sentinel,entry=sentinel._prev;if(entry!==sentinel){unlink(entry);return entry}};List.prototype.enqueue=function(entry){var sentinel=this._sentinel;if(entry._prev&&entry._next){unlink(entry)}entry._next=sentinel._next;sentinel._next._prev=entry;sentinel._next=entry;entry._prev=sentinel};List.prototype.toString=function(){var strs=[],sentinel=this._sentinel,curr=sentinel._prev;while(curr!==sentinel){strs.push(JSON.stringify(curr,filterOutLinks));curr=curr._prev}return"["+strs.join(", ")+"]"};function unlink(entry){entry._prev._next=entry._next;entry._next._prev=entry._prev;delete entry._next;delete entry._prev}function filterOutLinks(k,v){if(k!=="_next"&&k!=="_prev"){return v}}},{}],32:[function(require,module,exports){var _=require("./lodash"),util=require("./util"),Graph=require("./graphlib").Graph;module.exports={debugOrdering:debugOrdering};function debugOrdering(g){var layerMatrix=util.buildLayerMatrix(g);var h=new Graph({compound:true,multigraph:true}).setGraph({});_.each(g.nodes(),function(v){h.setNode(v,{label:v});h.setParent(v,"layer"+g.node(v).rank)});_.each(g.edges(),function(e){h.setEdge(e.v,e.w,{},e.name)});_.each(layerMatrix,function(layer,i){var layerV="layer"+i;h.setNode(layerV,{rank:"same"});_.reduce(layer,function(u,v){h.setEdge(u,v,{style:"invis"});return v})});return h}},{"./graphlib":33,"./lodash":36,"./util":55}],33:[function(require,module,exports){arguments[4][9][0].apply(exports,arguments)},{dup:9,graphlib:57}],34:[function(require,module,exports){var _=require("./lodash"),Graph=require("./graphlib").Graph,List=require("./data/list");module.exports=greedyFAS;var DEFAULT_WEIGHT_FN=_.constant(1);function greedyFAS(g,weightFn){if(g.nodeCount()<=1){return[]}var state=buildState(g,weightFn||DEFAULT_WEIGHT_FN);var results=doGreedyFAS(state.graph,state.buckets,state.zeroIdx);return _.flatten(_.map(results,function(e){return g.outEdges(e.v,e.w)}),true)}function doGreedyFAS(g,buckets,zeroIdx){var results=[],sources=buckets[buckets.length-1],sinks=buckets[0];var entry;while(g.nodeCount()){while(entry=sinks.dequeue()){removeNode(g,buckets,zeroIdx,entry)}while(entry=sources.dequeue()){removeNode(g,buckets,zeroIdx,entry)}if(g.nodeCount()){for(var i=buckets.length-2;i>0;--i){entry=buckets[i].dequeue();if(entry){results=results.concat(removeNode(g,buckets,zeroIdx,entry,true));break}}}}return results}function removeNode(g,buckets,zeroIdx,entry,collectPredecessors){var results=collectPredecessors?[]:undefined;_.each(g.inEdges(entry.v),function(edge){var weight=g.edge(edge),uEntry=g.node(edge.v);if(collectPredecessors){results.push({v:edge.v,w:edge.w})}uEntry.out-=weight;assignBucket(buckets,zeroIdx,uEntry)});_.each(g.outEdges(entry.v),function(edge){var weight=g.edge(edge),w=edge.w,wEntry=g.node(w);wEntry["in"]-=weight;assignBucket(buckets,zeroIdx,wEntry)});g.removeNode(entry.v);return results}function buildState(g,weightFn){var fasGraph=new Graph,maxIn=0,maxOut=0;_.each(g.nodes(),function(v){fasGraph.setNode(v,{v:v,"in":0,out:0})});_.each(g.edges(),function(e){var prevWeight=fasGraph.edge(e.v,e.w)||0,weight=weightFn(e),edgeWeight=prevWeight+weight;fasGraph.setEdge(e.v,e.w,edgeWeight);maxOut=Math.max(maxOut,fasGraph.node(e.v).out+=weight);maxIn=Math.max(maxIn,fasGraph.node(e.w)["in"]+=weight)});var buckets=_.range(maxOut+maxIn+3).map(function(){return new List});var zeroIdx=maxIn+1;_.each(fasGraph.nodes(),function(v){assignBucket(buckets,zeroIdx,fasGraph.node(v))});return{graph:fasGraph,buckets:buckets,zeroIdx:zeroIdx}}function assignBucket(buckets,zeroIdx,entry){if(!entry.out){buckets[0].enqueue(entry)}else if(!entry["in"]){buckets[buckets.length-1].enqueue(entry)}else{buckets[entry.out-entry["in"]+zeroIdx].enqueue(entry)}}},{"./data/list":31,"./graphlib":33,"./lodash":36}],35:[function(require,module,exports){"use strict";var _=require("./lodash"),acyclic=require("./acyclic"),normalize=require("./normalize"),rank=require("./rank"),normalizeRanks=require("./util").normalizeRanks,parentDummyChains=require("./parent-dummy-chains"),removeEmptyRanks=require("./util").removeEmptyRanks,nestingGraph=require("./nesting-graph"),addBorderSegments=require("./add-border-segments"),coordinateSystem=require("./coordinate-system"),order=require("./order"),position=require("./position"),util=require("./util"),Graph=require("./graphlib").Graph;module.exports=layout;function layout(g,opts){var time=opts&&opts.debugTiming?util.time:util.notime;time("layout",function(){var layoutGraph=time("  buildLayoutGraph",function(){return buildLayoutGraph(g)});time("  runLayout",function(){runLayout(layoutGraph,time)});time("  updateInputGraph",function(){updateInputGraph(g,layoutGraph)})})}function runLayout(g,time){time("    makeSpaceForEdgeLabels",function(){makeSpaceForEdgeLabels(g)});time("    removeSelfEdges",function(){removeSelfEdges(g)});time("    acyclic",function(){acyclic.run(g)});time("    nestingGraph.run",function(){nestingGraph.run(g)});time("    rank",function(){rank(util.asNonCompoundGraph(g))});time("    injectEdgeLabelProxies",function(){injectEdgeLabelProxies(g)});time("    removeEmptyRanks",function(){removeEmptyRanks(g)});time("    nestingGraph.cleanup",function(){nestingGraph.cleanup(g)});time("    normalizeRanks",function(){normalizeRanks(g)});time("    assignRankMinMax",function(){assignRankMinMax(g)});time("    removeEdgeLabelProxies",function(){removeEdgeLabelProxies(g)});time("    normalize.run",function(){normalize.run(g)});time("    parentDummyChains",function(){parentDummyChains(g)});time("    addBorderSegments",function(){addBorderSegments(g)});time("    order",function(){order(g)});time("    insertSelfEdges",function(){insertSelfEdges(g)});time("    adjustCoordinateSystem",function(){coordinateSystem.adjust(g)});time("    position",function(){position(g)});time("    positionSelfEdges",function(){positionSelfEdges(g)});time("    removeBorderNodes",function(){removeBorderNodes(g)});time("    normalize.undo",function(){normalize.undo(g)});time("    fixupEdgeLabelCoords",function(){fixupEdgeLabelCoords(g)});time("    undoCoordinateSystem",function(){coordinateSystem.undo(g)});time("    translateGraph",function(){translateGraph(g)});time("    assignNodeIntersects",function(){assignNodeIntersects(g)});time("    reversePoints",function(){reversePointsForReversedEdges(g)});time("    acyclic.undo",function(){acyclic.undo(g)})}function updateInputGraph(inputGraph,layoutGraph){_.each(inputGraph.nodes(),function(v){var inputLabel=inputGraph.node(v),layoutLabel=layoutGraph.node(v);if(inputLabel){inputLabel.x=layoutLabel.x;inputLabel.y=layoutLabel.y;if(layoutGraph.children(v).length){inputLabel.width=layoutLabel.width;inputLabel.height=layoutLabel.height}}});_.each(inputGraph.edges(),function(e){var inputLabel=inputGraph.edge(e),layoutLabel=layoutGraph.edge(e);inputLabel.points=layoutLabel.points;if(_.has(layoutLabel,"x")){inputLabel.x=layoutLabel.x;inputLabel.y=layoutLabel.y}});inputGraph.graph().width=layoutGraph.graph().width;inputGraph.graph().height=layoutGraph.graph().height}var graphNumAttrs=["nodesep","edgesep","ranksep","marginx","marginy"],graphDefaults={ranksep:50,edgesep:20,nodesep:50,rankdir:"tb"},graphAttrs=["acyclicer","ranker","rankdir","align"],nodeNumAttrs=["width","height"],nodeDefaults={width:0,height:0},edgeNumAttrs=["minlen","weight","width","height","labeloffset"],edgeDefaults={minlen:1,weight:1,width:0,height:0,labeloffset:10,labelpos:"r"},edgeAttrs=["labelpos"];function buildLayoutGraph(inputGraph){var g=new Graph({multigraph:true,compound:true}),graph=canonicalize(inputGraph.graph());g.setGraph(_.merge({},graphDefaults,selectNumberAttrs(graph,graphNumAttrs),_.pick(graph,graphAttrs)));_.each(inputGraph.nodes(),function(v){var node=canonicalize(inputGraph.node(v));g.setNode(v,_.defaults(selectNumberAttrs(node,nodeNumAttrs),nodeDefaults));g.setParent(v,inputGraph.parent(v))});_.each(inputGraph.edges(),function(e){var edge=canonicalize(inputGraph.edge(e));g.setEdge(e,_.merge({},edgeDefaults,selectNumberAttrs(edge,edgeNumAttrs),_.pick(edge,edgeAttrs)))});return g}function makeSpaceForEdgeLabels(g){var graph=g.graph();
+module.exports={graphlib:require("./lib/graphlib"),dagre:require("./lib/dagre"),intersect:require("./lib/intersect"),render:require("./lib/render"),util:require("./lib/util"),version:require("./lib/version")}},{"./lib/dagre":8,"./lib/graphlib":9,"./lib/intersect":10,"./lib/render":23,"./lib/util":25,"./lib/version":26}],2:[function(require,module,exports){var util=require("./util");module.exports={"default":normal,normal:normal,vee:vee,undirected:undirected};function normal(parent,id,edge,type){var marker=parent.append("marker").attr("id",id).attr("viewBox","0 0 10 10").attr("refX",9).attr("refY",5).attr("markerUnits","strokeWidth").attr("markerWidth",8).attr("markerHeight",6).attr("orient","auto");var path=marker.append("path").attr("d","M 0 0 L 10 5 L 0 10 z").style("stroke-width",1).style("stroke-dasharray","1,0");util.applyStyle(path,edge[type+"Style"])}function vee(parent,id,edge,type){var marker=parent.append("marker").attr("id",id).attr("viewBox","0 0 10 10").attr("refX",9).attr("refY",5).attr("markerUnits","strokeWidth").attr("markerWidth",8).attr("markerHeight",6).attr("orient","auto");var path=marker.append("path").attr("d","M 0 0 L 10 5 L 0 10 L 4 5 z").style("stroke-width",1).style("stroke-dasharray","1,0");util.applyStyle(path,edge[type+"Style"])}function undirected(parent,id,edge,type){var marker=parent.append("marker").attr("id",id).attr("viewBox","0 0 10 10").attr("refX",9).attr("refY",5).attr("markerUnits","strokeWidth").attr("markerWidth",8).attr("markerHeight",6).attr("orient","auto");var path=marker.append("path").attr("d","M 0 5 L 10 5").style("stroke-width",1).style("stroke-dasharray","1,0");util.applyStyle(path,edge[type+"Style"])}},{"./util":25}],3:[function(require,module,exports){var util=require("./util");module.exports=createClusters;function createClusters(selection,g){var clusters=g.nodes().filter(function(v){return util.isSubgraph(g,v)}),svgClusters=selection.selectAll("g.cluster").data(clusters,function(v){return v});svgClusters.enter().append("g").attr("id",function(v){return"cluster_"+v.replace(/^cluster/,"")}).attr("name",function(v){return g.node(v).label}).attr("class","cluster").style("opacity",0).append("rect");util.applyTransition(svgClusters.exit(),g).style("opacity",0).remove();util.applyTransition(svgClusters,g).style("opacity",1);util.applyTransition(svgClusters.selectAll("rect"),g).attr("width",function(v){var node=g.node(v);return node.width+node.paddingLeft+node.paddingRight}).attr("height",function(v){var node=g.node(v);return node.height+node.paddingTop+node.paddingBottom}).attr("x",function(v){var node=g.node(v);return node.x-node.width/2-node.paddingLeft}).attr("y",function(v){var node=g.node(v);return node.y-node.height/2-node.paddingTop})}},{"./util":25}],4:[function(require,module,exports){"use strict";var _=require("./lodash"),addLabel=require("./label/add-label"),util=require("./util"),d3=require("./d3");module.exports=createEdgeLabels;function createEdgeLabels(selection,g){var svgEdgeLabels=selection.selectAll("g.edgeLabel").data(g.edges(),function(e){return util.edgeToId(e)}).classed("update",true);svgEdgeLabels.selectAll("*").remove();svgEdgeLabels.enter().append("g").classed("edgeLabel",true).style("opacity",0);svgEdgeLabels.each(function(e){var edge=g.edge(e),label=addLabel(d3.select(this),g.edge(e),0,0).classed("label",true),bbox=label.node().getBBox();if(edge.labelId){label.attr("id",edge.labelId)}if(!_.has(edge,"width")){edge.width=bbox.width}if(!_.has(edge,"height")){edge.height=bbox.height}});util.applyTransition(svgEdgeLabels.exit(),g).style("opacity",0).remove();return svgEdgeLabels}},{"./d3":7,"./label/add-label":18,"./lodash":20,"./util":25}],5:[function(require,module,exports){"use strict";var _=require("./lodash"),intersectNode=require("./intersect/intersect-node"),util=require("./util"),d3=require("./d3");module.exports=createEdgePaths;function createEdgePaths(selection,g,arrows){var svgPaths=selection.selectAll("g.edgePath").data(g.edges(),function(e){return util.edgeToId(e)}).classed("update",true);enter(svgPaths,g);exit(svgPaths,g);util.applyTransition(svgPaths,g).style("opacity",1);svgPaths.each(function(e){var domEdge=d3.select(this);var edge=g.edge(e);edge.elem=this;if(edge.id){domEdge.attr("id",edge.id)}util.applyClass(domEdge,edge["class"],(domEdge.classed("update")?"update ":"")+"edgePath")});svgPaths.selectAll("path.path").each(function(e){var edge=g.edge(e);edge.arrowheadId=_.uniqueId("arrowhead");var domEdge=d3.select(this).attr("marker-end",function(){return"url(#"+edge.arrowheadId+")"}).style("fill","none");util.applyTransition(domEdge,g).attr("d",function(e){return calcPoints(g,e)});util.applyStyle(domEdge,edge.style)});svgPaths.selectAll("defs *").remove();svgPaths.selectAll("defs").each(function(e){var edge=g.edge(e),arrowhead=arrows[edge.arrowhead];arrowhead(d3.select(this),edge.arrowheadId,edge,"arrowhead")});return svgPaths}function calcPoints(g,e){var edge=g.edge(e),tail=g.node(e.v),head=g.node(e.w),points=edge.points.slice(1,edge.points.length-1);points.unshift(intersectNode(tail,points[0]));points.push(intersectNode(head,points[points.length-1]));return createLine(edge,points)}function createLine(edge,points){var line=d3.svg.line().x(function(d){return d.x}).y(function(d){return d.y});if(_.has(edge,"lineInterpolate")){line.interpolate(edge.lineInterpolate)}if(_.has(edge,"lineTension")){line.tension(Number(edge.lineTension))}return line(points)}function getCoords(elem){var bbox=elem.getBBox(),matrix=elem.getTransformToElement(elem.ownerSVGElement).translate(bbox.width/2,bbox.height/2);return{x:matrix.e,y:matrix.f}}function enter(svgPaths,g){var svgPathsEnter=svgPaths.enter().append("g").attr("class","edgePath").style("opacity",0);svgPathsEnter.append("path").attr("class","path").attr("d",function(e){var edge=g.edge(e),sourceElem=g.node(e.v).elem,points=_.range(edge.points.length).map(function(){return getCoords(sourceElem)});return createLine(edge,points)});svgPathsEnter.append("defs")}function exit(svgPaths,g){var svgPathExit=svgPaths.exit();util.applyTransition(svgPathExit,g).style("opacity",0).remove();util.applyTransition(svgPathExit.select("path.path"),g).attr("d",function(e){var source=g.node(e.v);if(source){var points=_.range(this.pathSegList.length).map(function(){return source});return createLine({},points)}else{return d3.select(this).attr("d")}})}},{"./d3":7,"./intersect/intersect-node":14,"./lodash":20,"./util":25}],6:[function(require,module,exports){"use strict";var _=require("./lodash"),addLabel=require("./label/add-label"),util=require("./util"),d3=require("./d3");module.exports=createNodes;function createNodes(selection,g,shapes){var simpleNodes=g.nodes().filter(function(v){return!util.isSubgraph(g,v)});var svgNodes=selection.selectAll("g.node").data(simpleNodes,function(v){return v}).classed("update",true);svgNodes.selectAll("*").remove();svgNodes.enter().append("g").attr("id",function(v){return"node_"+v}).attr("name",function(v){return g.node(v).label}).attr("class","node").style("opacity",0);svgNodes.each(function(v){var node=g.node(v),thisGroup=d3.select(this),labelGroup=thisGroup.append("g").attr("class","label"),labelDom=addLabel(labelGroup,node),shape=shapes[node.shape],bbox=_.pick(labelDom.node().getBBox(),"width","height");node.elem=this;if(node.id){thisGroup.attr("id",node.id)}if(node.labelId){labelGroup.attr("id",node.labelId)}util.applyClass(thisGroup,node["class"],(thisGroup.classed("update")?"update ":"")+"node");if(_.has(node,"width")){bbox.width=node.width}if(_.has(node,"height")){bbox.height=node.height}bbox.width+=node.paddingLeft+node.paddingRight;bbox.height+=node.paddingTop+node.paddingBottom;labelGroup.attr("transform","translate("+(node.paddingLeft-node.paddingRight)/2+","+(node.paddingTop-node.paddingBottom)/2+")");var shapeSvg=shape(d3.select(this),bbox,node);util.applyStyle(shapeSvg,node.style);var shapeBBox=shapeSvg.node().getBBox();node.width=shapeBBox.width;node.height=shapeBBox.height});util.applyTransition(svgNodes.exit(),g).style("opacity",0).remove();return svgNodes}},{"./d3":7,"./label/add-label":18,"./lodash":20,"./util":25}],7:[function(require,module,exports){module.exports=window.d3},{}],8:[function(require,module,exports){var dagre;if(require){try{dagre=require("dagre")}catch(e){}}if(!dagre){dagre=window.dagre}module.exports=dagre},{dagre:27}],9:[function(require,module,exports){var graphlib;if(require){try{graphlib=require("graphlib")}catch(e){}}if(!graphlib){graphlib=window.graphlib}module.exports=graphlib},{graphlib:57}],10:[function(require,module,exports){module.exports={node:require("./intersect-node"),circle:require("./intersect-circle"),ellipse:require("./intersect-ellipse"),polygon:require("./intersect-polygon"),rect:require("./intersect-rect")}},{"./intersect-circle":11,"./intersect-ellipse":12,"./intersect-node":14,"./intersect-polygon":15,"./intersect-rect":16}],11:[function(require,module,exports){var intersectEllipse=require("./intersect-ellipse");module.exports=intersectCircle;function intersectCircle(node,rx,point){return intersectEllipse(node,rx,rx,point)}},{"./intersect-ellipse":12}],12:[function(require,module,exports){module.exports=intersectEllipse;function intersectEllipse(node,rx,ry,point){var cx=node.x;var cy=node.y;var px=cx-point.x;var py=cy-point.y;var det=Math.sqrt(rx*rx*py*py+ry*ry*px*px);var dx=Math.abs(rx*ry*px/det);if(point.x<cx){dx=-dx}var dy=Math.abs(rx*ry*py/det);if(point.y<cy){dy=-dy}return{x:cx+dx,y:cy+dy}}},{}],13:[function(require,module,exports){module.exports=intersectLine;function intersectLine(p1,p2,q1,q2){var a1,a2,b1,b2,c1,c2;var r1,r2,r3,r4;var denom,offset,num;var x,y;a1=p2.y-p1.y;b1=p1.x-p2.x;c1=p2.x*p1.y-p1.x*p2.y;r3=a1*q1.x+b1*q1.y+c1;r4=a1*q2.x+b1*q2.y+c1;if(r3!==0&&r4!==0&&sameSign(r3,r4)){return}a2=q2.y-q1.y;b2=q1.x-q2.x;c2=q2.x*q1.y-q1.x*q2.y;r1=a2*p1.x+b2*p1.yy+c2;r2=a2*p2.x+b2*p2.y+c2;if(r1!==0&&r2!==0&&sameSign(r1,r2)){return}denom=a1*b2-a2*b1;if(denom===0){return}offset=Math.abs(denom/2);num=b1*c2-b2*c1;x=num<0?(num-offset)/denom:(num+offset)/denom;num=a2*c1-a1*c2;y=num<0?(num-offset)/denom:(num+offset)/denom;return{x:x,y:y}}function sameSign(r1,r2){return r1*r2>0}},{}],14:[function(require,module,exports){module.exports=intersectNode;function intersectNode(node,point){return node.intersect(point)}},{}],15:[function(require,module,exports){var intersectLine=require("./intersect-line");module.exports=intersectPolygon;function intersectPolygon(node,polyPoints,point){var x1=node.x;var y1=node.y;var intersections=[];var minX=Number.POSITIVE_INFINITY,minY=Number.POSITIVE_INFINITY;polyPoints.forEach(function(entry){minX=Math.min(minX,entry.x);minY=Math.min(minY,entry.y)});var left=x1-node.width/2-minX;var top=y1-node.height/2-minY;for(var i=0;i<polyPoints.length;i++){var p1=polyPoints[i];var p2=polyPoints[i<polyPoints.length-1?i+1:0];var intersect=intersectLine(node,point,{x:left+p1.x,y:top+p1.y},{x:left+p2.x,y:top+p2.y});if(intersect){intersections.push(intersect)}}if(!intersections.length){console.log("NO INTERSECTION FOUND, RETURN NODE CENTER",node);return node}if(intersections.length>1){intersections.sort(function(p,q){var pdx=p.x-point.x,pdy=p.y-point.y,distp=Math.sqrt(pdx*pdx+pdy*pdy),qdx=q.x-point.x,qdy=q.y-point.y,distq=Math.sqrt(qdx*qdx+qdy*qdy);return distp<distq?-1:distp===distq?0:1})}return intersections[0]}},{"./intersect-line":13}],16:[function(require,module,exports){module.exports=intersectRect;function intersectRect(node,point){var x=node.x;var y=node.y;var dx=point.x-x;var dy=point.y-y;var w=node.width/2;var h=node.height/2;var sx,sy;if(Math.abs(dy)*w>Math.abs(dx)*h){if(dy<0){h=-h}sx=dy===0?0:h*dx/dy;sy=h}else{if(dx<0){w=-w}sx=w;sy=dx===0?0:w*dy/dx}return{x:x+sx,y:y+sy}}},{}],17:[function(require,module,exports){var util=require("../util");module.exports=addHtmlLabel;function addHtmlLabel(root,node){var fo=root.append("foreignObject").attr("width","100000");var div=fo.append("xhtml:div");var label=node.label;switch(typeof label){case"function":div.insert(label);break;case"object":div.insert(function(){return label});break;default:div.html(label)}util.applyStyle(div,node.labelStyle);div.style("display","inline-block");div.style("white-space","nowrap");var w,h;div.each(function(){w=this.clientWidth;h=this.clientHeight});fo.attr("width",w).attr("height",h);return fo}},{"../util":25}],18:[function(require,module,exports){var addTextLabel=require("./add-text-label"),addHtmlLabel=require("./add-html-label");module.exports=addLabel;function addLabel(root,node){var label=node.label;var labelSvg=root.append("g");if(typeof label!=="string"||node.labelType==="html"){addHtmlLabel(labelSvg,node)}else{addTextLabel(labelSvg,node)}var labelBBox=labelSvg.node().getBBox();labelSvg.attr("transform","translate("+-labelBBox.width/2+","+-labelBBox.height/2+")");return labelSvg}},{"./add-html-label":17,"./add-text-label":19}],19:[function(require,module,exports){var util=require("../util");module.exports=addTextLabel;function addTextLabel(root,node){var domNode=root.append("text");var lines=processEscapeSequences(node.label).split("\n");for(var i=0;i<lines.length;i++){domNode.append("tspan").attr("xml:space","preserve").attr("dy","1em").attr("x","1").text(lines[i])}util.applyStyle(domNode,node.labelStyle);return domNode}function processEscapeSequences(text){var newText="",escaped=false,ch;for(var i=0;i<text.length;++i){ch=text[i];if(escaped){switch(ch){case"n":newText+="\n";break;default:newText+=ch}escaped=false}else if(ch==="\\"){escaped=true}else{newText+=ch}}return newText}},{"../util":25}],20:[function(require,module,exports){var lodash;if(require){try{lodash=require("lodash")}catch(e){}}if(!lodash){lodash=window._}module.exports=lodash},{lodash:77}],21:[function(require,module,exports){"use strict";var util=require("./util"),d3=require("./d3"),_=require("./lodash");module.exports=positionEdgeLabels;function positionEdgeLabels(selection,g){var created=selection.filter(function(){return!d3.select(this).classed("update")});function translate(e){var edge=g.edge(e);return _.has(edge,"x")?"translate("+edge.x+","+edge.y+")":""}created.attr("transform",translate);util.applyTransition(selection,g).style("opacity",1).attr("transform",translate)}},{"./d3":7,"./lodash":20,"./util":25}],22:[function(require,module,exports){"use strict";var util=require("./util"),d3=require("./d3");module.exports=positionNodes;function positionNodes(selection,g){var created=selection.filter(function(){return!d3.select(this).classed("update")});function translate(v){var node=g.node(v);return"translate("+node.x+","+node.y+")"}created.attr("transform",translate);util.applyTransition(selection,g).style("opacity",1).attr("transform",translate)}},{"./d3":7,"./util":25}],23:[function(require,module,exports){var _=require("./lodash"),layout=require("./dagre").layout;module.exports=render;function render(){var createNodes=require("./create-nodes"),createClusters=require("./create-clusters"),createEdgeLabels=require("./create-edge-labels"),createEdgePaths=require("./create-edge-paths"),positionNodes=require("./position-nodes"),positionEdgeLabels=require("./position-edge-labels"),shapes=require("./shapes"),arrows=require("./arrows");var fn=function(svg,g){preProcessGraph(g);var outputGroup=createOrSelectGroup(svg,"output"),clustersGroup=createOrSelectGroup(outputGroup,"clusters"),edgePathsGroup=createOrSelectGroup(outputGroup,"edgePaths"),edgeLabels=createEdgeLabels(createOrSelectGroup(outputGroup,"edgeLabels"),g),nodes=createNodes(createOrSelectGroup(outputGroup,"nodes"),g,shapes);layout(g);positionNodes(nodes,g);positionEdgeLabels(edgeLabels,g);createEdgePaths(edgePathsGroup,g,arrows);createClusters(clustersGroup,g);postProcessGraph(g)};fn.createNodes=function(value){if(!arguments.length)return createNodes;createNodes=value;return fn};fn.createClusters=function(value){if(!arguments.length)return createClusters;createClusters=value;return fn};fn.createEdgeLabels=function(value){if(!arguments.length)return createEdgeLabels;createEdgeLabels=value;return fn};fn.createEdgePaths=function(value){if(!arguments.length)return createEdgePaths;createEdgePaths=value;return fn};fn.shapes=function(value){if(!arguments.length)return shapes;shapes=value;return fn};fn.arrows=function(value){if(!arguments.length)return arrows;arrows=value;return fn};return fn}var NODE_DEFAULT_ATTRS={paddingLeft:0,paddingRight:0,paddingTop:0,paddingBottom:0,rx:0,ry:0,shape:"rect"};var EDGE_DEFAULT_ATTRS={arrowhead:"normal",lineInterpolate:"linear"};function preProcessGraph(g){g.nodes().forEach(function(v){var node=g.node(v);if(!_.has(node,"label")){node.label=v}if(_.has(node,"paddingX")){_.defaults(node,{paddingLeft:node.paddingX,paddingRight:node.paddingX})}if(_.has(node,"paddingY")){_.defaults(node,{paddingTop:node.paddingY,paddingBottom:node.paddingY})}if(_.has(node,"padding")){_.defaults(node,{paddingLeft:node.padding,paddingRight:node.padding,paddingTop:node.padding,paddingBottom:node.padding})}if(_.has(node,"paddingLeft")){_.defaults(node,{paddingLeft:node.paddingLeft})}if(_.has(node,"paddingRight")){_.defaults(node,{paddingRight:node.paddingRight})}if(_.has(node,"paddingTop")){_.defaults(node,{paddingTop:node.paddingTop})}if(_.has(node,"paddingBottom")){_.defaults(node,{paddingBottom:node.paddingBottom})}_.defaults(node,NODE_DEFAULT_ATTRS);_.each(["paddingLeft","paddingRight","paddingTop","paddingBottom"],function(k){node[k]=Number(node[k])});if(_.has(node,"width")){node._prevWidth=node.width}if(_.has(node,"height")){node._prevHeight=node.height}});g.edges().forEach(function(e){var edge=g.edge(e);if(!_.has(edge,"label")){edge.label=""}_.defaults(edge,EDGE_DEFAULT_ATTRS)})}function postProcessGraph(g){_.each(g.nodes(),function(v){var node=g.node(v);if(_.has(node,"_prevWidth")){node.width=node._prevWidth}else{delete node.width}if(_.has(node,"_prevHeight")){node.height=node._prevHeight}else{delete node.height}delete node._prevWidth;delete node._prevHeight})}function createOrSelectGroup(root,name){var selection=root.select("g."+name);if(selection.empty()){selection=root.append("g").attr("class",name)}return selection}},{"./arrows":2,"./create-clusters":3,"./create-edge-labels":4,"./create-edge-paths":5,"./create-nodes":6,"./dagre":8,"./lodash":20,"./position-edge-labels":21,"./position-nodes":22,"./shapes":24}],24:[function(require,module,exports){"use strict";var intersectRect=require("./intersect/intersect-rect"),intersectEllipse=require("./intersect/intersect-ellipse"),intersectCircle=require("./intersect/intersect-circle"),intersectPolygon=require("./intersect/intersect-polygon");module.exports={rect:rect,ellipse:ellipse,circle:circle,diamond:diamond};function rect(parent,bbox,node){var shapeSvg=parent.insert("rect",":first-child").attr("rx",node.rx).attr("ry",node.ry).attr("x",-bbox.width/2).attr("y",-bbox.height/2).attr("width",bbox.width).attr("height",bbox.height);node.intersect=function(point){return intersectRect(node,point)};return shapeSvg}function ellipse(parent,bbox,node){var rx=bbox.width/2,ry=bbox.height/2,shapeSvg=parent.insert("ellipse",":first-child").attr("x",-bbox.width/2).attr("y",-bbox.height/2).attr("rx",rx).attr("ry",ry);node.intersect=function(point){return intersectEllipse(node,rx,ry,point)};return shapeSvg}function circle(parent,bbox,node){var r=Math.max(bbox.width,bbox.height)/2,shapeSvg=parent.insert("circle",":first-child").attr("x",-bbox.width/2).attr("y",-bbox.height/2).attr("r",r);node.intersect=function(point){return intersectCircle(node,r,point)};return shapeSvg}function diamond(parent,bbox,node){var w=bbox.width*Math.SQRT2/2,h=bbox.height*Math.SQRT2/2,points=[{x:0,y:-h},{x:-w,y:0},{x:0,y:h},{x:w,y:0}],shapeSvg=parent.insert("polygon",":first-child").attr("points",points.map(function(p){return p.x+","+p.y}).join(" "));node.intersect=function(p){return intersectPolygon(node,points,p)};return shapeSvg}},{"./intersect/intersect-circle":11,"./intersect/intersect-ellipse":12,"./intersect/intersect-polygon":15,"./intersect/intersect-rect":16}],25:[function(require,module,exports){var _=require("./lodash");module.exports={isSubgraph:isSubgraph,edgeToId:edgeToId,applyStyle:applyStyle,applyClass:applyClass,applyTransition:applyTransition};function isSubgraph(g,v){return!!g.children(v).length}function edgeToId(e){return escapeId(e.v)+":"+escapeId(e.w)+":"+escapeId(e.name)}var ID_DELIM=/:/g;function escapeId(str){return str?String(str).replace(ID_DELIM,"\\:"):""}function applyStyle(dom,styleFn){if(styleFn){dom.attr("style",styleFn)}}function applyClass(dom,classFn,otherClasses){if(classFn){dom.attr("class",classFn).attr("class",otherClasses+" "+dom.attr("class"))}}function applyTransition(selection,g){var graph=g.graph();if(_.isPlainObject(graph)){var transition=graph.transition;if(_.isFunction(transition)){return transition(selection)}}return selection}},{"./lodash":20}],26:[function(require,module,exports){module.exports="0.4.4-pre"},{}],27:[function(require,module,exports){module.exports={graphlib:require("./lib/graphlib"),layout:require("./lib/layout"),debug:require("./lib/debug"),util:{time:require("./lib/util").time,notime:require("./lib/util").notime},version:require("./lib/version")}},{"./lib/debug":32,"./lib/graphlib":33,"./lib/layout":35,"./lib/util":55,"./lib/version":56}],28:[function(require,module,exports){"use strict";var _=require("./lodash"),greedyFAS=require("./greedy-fas");module.exports={run:run,undo:undo};function run(g){var fas=g.graph().acyclicer==="greedy"?greedyFAS(g,weightFn(g)):dfsFAS(g);_.each(fas,function(e){var label=g.edge(e);g.removeEdge(e);label.forwardName=e.name;label.reversed=true;g.setEdge(e.w,e.v,label,_.uniqueId("rev"))});function weightFn(g){return function(e){return g.edge(e).weight}}}function dfsFAS(g){var fas=[],stack={},visited={};function dfs(v){if(_.has(visited,v)){return}visited[v]=true;stack[v]=true;_.each(g.outEdges(v),function(e){if(_.has(stack,e.w)){fas.push(e)}else{dfs(e.w)}});delete stack[v]}_.each(g.nodes(),dfs);return fas}function undo(g){_.each(g.edges(),function(e){var label=g.edge(e);if(label.reversed){g.removeEdge(e);var forwardName=label.forwardName;delete label.reversed;delete label.forwardName;g.setEdge(e.w,e.v,label,forwardName)}})}},{"./greedy-fas":34,"./lodash":36}],29:[function(require,module,exports){var _=require("./lodash"),util=require("./util");module.exports=addBorderSegments;function addBorderSegments(g){function dfs(v){var children=g.children(v),node=g.node(v);if(children.length){_.each(children,dfs)}if(_.has(node,"minRank")){node.borderLeft=[];node.borderRight=[];for(var rank=node.minRank,maxRank=node.maxRank+1;rank<maxRank;++rank){addBorderNode(g,"borderLeft","_bl",v,node,rank);addBorderNode(g,"borderRight","_br",v,node,rank)}}}_.each(g.children(),dfs)}function addBorderNode(g,prop,prefix,sg,sgNode,rank){var label={width:0,height:0,rank:rank},prev=sgNode[prop][rank-1],curr=util.addDummyNode(g,"border",label,prefix);sgNode[prop][rank]=curr;g.setParent(curr,sg);if(prev){g.setEdge(prev,curr,{weight:1})}}},{"./lodash":36,"./util":55}],30:[function(require,module,exports){"use strict";var _=require("./lodash");module.exports={adjust:adjust,undo:undo};function adjust(g){var rankDir=g.graph().rankdir.toLowerCase();if(rankDir==="lr"||rankDir==="rl"){swapWidthHeight(g)}}function undo(g){var rankDir=g.graph().rankdir.toLowerCase();if(rankDir==="bt"||rankDir==="rl"){reverseY(g)}if(rankDir==="lr"||rankDir==="rl"){swapXY(g);swapWidthHeight(g)}}function swapWidthHeight(g){_.each(g.nodes(),function(v){swapWidthHeightOne(g.node(v))});_.each(g.edges(),function(e){swapWidthHeightOne(g.edge(e))})}function swapWidthHeightOne(attrs){var w=attrs.width;attrs.width=attrs.height;attrs.height=w}function reverseY(g){_.each(g.nodes(),function(v){reverseYOne(g.node(v))});_.each(g.edges(),function(e){var edge=g.edge(e);_.each(edge.points,reverseYOne);if(_.has(edge,"y")){reverseYOne(edge)}})}function reverseYOne(attrs){attrs.y=-attrs.y}function swapXY(g){_.each(g.nodes(),function(v){swapXYOne(g.node(v))});_.each(g.edges(),function(e){var edge=g.edge(e);_.each(edge.points,swapXYOne);if(_.has(edge,"x")){swapXYOne(edge)}})}function swapXYOne(attrs){var x=attrs.x;attrs.x=attrs.y;attrs.y=x}},{"./lodash":36}],31:[function(require,module,exports){module.exports=List;function List(){var sentinel={};sentinel._next=sentinel._prev=sentinel;this._sentinel=sentinel}List.prototype.dequeue=function(){var sentinel=this._sentinel,entry=sentinel._prev;if(entry!==sentinel){unlink(entry);return entry}};List.prototype.enqueue=function(entry){var sentinel=this._sentinel;if(entry._prev&&entry._next){unlink(entry)}entry._next=sentinel._next;sentinel._next._prev=entry;sentinel._next=entry;entry._prev=sentinel};List.prototype.toString=function(){var strs=[],sentinel=this._sentinel,curr=sentinel._prev;while(curr!==sentinel){strs.push(JSON.stringify(curr,filterOutLinks));curr=curr._prev}return"["+strs.join(", ")+"]"};function unlink(entry){entry._prev._next=entry._next;entry._next._prev=entry._prev;delete entry._next;delete entry._prev}function filterOutLinks(k,v){if(k!=="_next"&&k!=="_prev"){return v}}},{}],32:[function(require,module,exports){var _=require("./lodash"),util=require("./util"),Graph=require("./graphlib").Graph;module.exports={debugOrdering:debugOrdering};function debugOrdering(g){var layerMatrix=util.buildLayerMatrix(g);var h=new Graph({compound:true,multigraph:true}).setGraph({});_.each(g.nodes(),function(v){h.setNode(v,{label:v});h.setParent(v,"layer"+g.node(v).rank)});_.each(g.edges(),function(e){h.setEdge(e.v,e.w,{},e.name)});_.each(layerMatrix,function(layer,i){var layerV="layer"+i;h.setNode(layerV,{rank:"same"});_.reduce(layer,function(u,v){h.setEdge(u,v,{style:"invis"});return v})});return h}},{"./graphlib":33,"./lodash":36,"./util":55}],33:[function(require,module,exports){module.exports=require(9)},{"/Users/andrew/Documents/dev/dagre-d3/lib/graphlib.js":9,graphlib:57}],34:[function(require,module,exports){var _=require("./lodash"),Graph=require("./graphlib").Graph,List=require("./data/list");module.exports=greedyFAS;var DEFAULT_WEIGHT_FN=_.constant(1);function greedyFAS(g,weightFn){if(g.nodeCount()<=1){return[]}var state=buildState(g,weightFn||DEFAULT_WEIGHT_FN);var results=doGreedyFAS(state.graph,state.buckets,state.zeroIdx);return _.flatten(_.map(results,function(e){return g.outEdges(e.v,e.w)}),true)}function doGreedyFAS(g,buckets,zeroIdx){var results=[],sources=buckets[buckets.length-1],sinks=buckets[0];var entry;while(g.nodeCount()){while(entry=sinks.dequeue()){removeNode(g,buckets,zeroIdx,entry)}while(entry=sources.dequeue()){removeNode(g,buckets,zeroIdx,entry)}if(g.nodeCount()){for(var i=buckets.length-2;i>0;--i){entry=buckets[i].dequeue();if(entry){results=results.concat(removeNode(g,buckets,zeroIdx,entry,true));break}}}}return results}function removeNode(g,buckets,zeroIdx,entry,collectPredecessors){var results=collectPredecessors?[]:undefined;_.each(g.inEdges(entry.v),function(edge){var weight=g.edge(edge),uEntry=g.node(edge.v);if(collectPredecessors){results.push({v:edge.v,w:edge.w})}uEntry.out-=weight;assignBucket(buckets,zeroIdx,uEntry)});_.each(g.outEdges(entry.v),function(edge){var weight=g.edge(edge),w=edge.w,wEntry=g.node(w);wEntry["in"]-=weight;assignBucket(buckets,zeroIdx,wEntry)});g.removeNode(entry.v);return results}function buildState(g,weightFn){var fasGraph=new Graph,maxIn=0,maxOut=0;_.each(g.nodes(),function(v){fasGraph.setNode(v,{v:v,"in":0,out:0})});_.each(g.edges(),function(e){var prevWeight=fasGraph.edge(e.v,e.w)||0,weight=weightFn(e),edgeWeight=prevWeight+weight;fasGraph.setEdge(e.v,e.w,edgeWeight);maxOut=Math.max(maxOut,fasGraph.node(e.v).out+=weight);maxIn=Math.max(maxIn,fasGraph.node(e.w)["in"]+=weight)});var buckets=_.range(maxOut+maxIn+3).map(function(){return new List});var zeroIdx=maxIn+1;_.each(fasGraph.nodes(),function(v){assignBucket(buckets,zeroIdx,fasGraph.node(v))});return{graph:fasGraph,buckets:buckets,zeroIdx:zeroIdx}}function assignBucket(buckets,zeroIdx,entry){if(!entry.out){buckets[0].enqueue(entry)}else if(!entry["in"]){buckets[buckets.length-1].enqueue(entry)}else{buckets[entry.out-entry["in"]+zeroIdx].enqueue(entry)}}},{"./data/list":31,"./graphlib":33,"./lodash":36}],35:[function(require,module,exports){"use strict";var _=require("./lodash"),acyclic=require("./acyclic"),normalize=require("./normalize"),rank=require("./rank"),normalizeRanks=require("./util").normalizeRanks,parentDummyChains=require("./parent-dummy-chains"),removeEmptyRanks=require("./util").removeEmptyRanks,nestingGraph=require("./nesting-graph"),addBorderSegments=require("./add-border-segments"),coordinateSystem=require("./coordinate-system"),order=require("./order"),position=require("./position"),util=require("./util"),Graph=require("./graphlib").Graph;module.exports=layout;function layout(g,opts){var time=opts&&opts.debugTiming?util.time:util.notime;time("layout",function(){var layoutGraph=time("  buildLayoutGraph",function(){return buildLayoutGraph(g)});time("  runLayout",function(){runLayout(layoutGraph,time)});time("  updateInputGraph",function(){updateInputGraph(g,layoutGraph)})})}function runLayout(g,time){time("    makeSpaceForEdgeLabels",function(){makeSpaceForEdgeLabels(g)});time("    removeSelfEdges",function(){removeSelfEdges(g)});time("    acyclic",function(){acyclic.run(g)});time("    nestingGraph.run",function(){nestingGraph.run(g)});time("    rank",function(){rank(util.asNonCompoundGraph(g))});time("    injectEdgeLabelProxies",function(){injectEdgeLabelProxies(g)});time("    removeEmptyRanks",function(){removeEmptyRanks(g)});time("    nestingGraph.cleanup",function(){nestingGraph.cleanup(g)});time("    normalizeRanks",function(){normalizeRanks(g)});time("    assignRankMinMax",function(){assignRankMinMax(g)});time("    removeEdgeLabelProxies",function(){removeEdgeLabelProxies(g)});time("    normalize.run",function(){normalize.run(g)});time("    parentDummyChains",function(){parentDummyChains(g)});time("    addBorderSegments",function(){addBorderSegments(g)});time("    order",function(){order(g)});time("    insertSelfEdges",function(){insertSelfEdges(g)});time("    adjustCoordinateSystem",function(){coordinateSystem.adjust(g)});time("    position",function(){position(g)});time("    positionSelfEdges",function(){positionSelfEdges(g)});time("    removeBorderNodes",function(){removeBorderNodes(g)});time("    normalize.undo",function(){normalize.undo(g)});time("    fixupEdgeLabelCoords",function(){fixupEdgeLabelCoords(g)});time("    undoCoordinateSystem",function(){coordinateSystem.undo(g)});time("    translateGraph",function(){translateGraph(g)});time("    assignNodeIntersects",function(){assignNodeIntersects(g)});time("    reversePoints",function(){reversePointsForReversedEdges(g)});time("    acyclic.undo",function(){acyclic.undo(g)})}function updateInputGraph(inputGraph,layoutGraph){_.each(inputGraph.nodes(),function(v){var inputLabel=inputGraph.node(v),layoutLabel=layoutGraph.node(v);if(inputLabel){inputLabel.x=layoutLabel.x;inputLabel.y=layoutLabel.y;if(layoutGraph.children(v).length){inputLabel.width=layoutLabel.width;inputLabel.height=layoutLabel.height}}});_.each(inputGraph.edges(),function(e){var inputLabel=inputGraph.edge(e),layoutLabel=layoutGraph.edge(e);inputLabel.points=layoutLabel.points;if(_.has(layoutLabel,"x")){inputLabel.x=layoutLabel.x;inputLabel.y=layoutLabel.y}});inputGraph.graph().width=layoutGraph.graph().width;inputGraph.graph().height=layoutGraph.graph().height}var graphNumAttrs=["nodesep","edgesep","ranksep","marginx","marginy"],graphDefaults={ranksep:50,edgesep:20,nodesep:50,rankdir:"tb"},graphAttrs=["acyclicer","ranker","rankdir","align"],nodeNumAttrs=["width","height"],nodeDefaults={width:0,height:0},edgeNumAttrs=["minlen","weight","width","height","labeloffset"],edgeDefaults={minlen:1,weight:1,width:0,height:0,labeloffset:10,labelpos:"r"},edgeAttrs=["labelpos"];function buildLayoutGraph(inputGraph){var g=new Graph({multigraph:true,compound:true}),graph=canonicalize(inputGraph.graph());g.setGraph(_.merge({},graphDefaults,selectNumberAttrs(graph,graphNumAttrs),_.pick(graph,graphAttrs)));
 
-graph.ranksep/=2;_.each(g.edges(),function(e){var edge=g.edge(e);edge.minlen*=2;if(edge.labelpos.toLowerCase()!=="c"){if(graph.rankdir==="TB"||graph.rankdir==="BT"){edge.width+=edge.labeloffset}else{edge.height+=edge.labeloffset}}})}function injectEdgeLabelProxies(g){_.each(g.edges(),function(e){var edge=g.edge(e);if(edge.width&&edge.height){var v=g.node(e.v),w=g.node(e.w),label={rank:(w.rank-v.rank)/2+v.rank,e:e};util.addDummyNode(g,"edge-proxy",label,"_ep")}})}function assignRankMinMax(g){var maxRank=0;_.each(g.nodes(),function(v){var node=g.node(v);if(node.borderTop){node.minRank=g.node(node.borderTop).rank;node.maxRank=g.node(node.borderBottom).rank;maxRank=_.max(maxRank,node.maxRank)}});g.graph().maxRank=maxRank}function removeEdgeLabelProxies(g){_.each(g.nodes(),function(v){var node=g.node(v);if(node.dummy==="edge-proxy"){g.edge(node.e).labelRank=node.rank;g.removeNode(v)}})}function translateGraph(g){var minX=Number.POSITIVE_INFINITY,maxX=0,minY=Number.POSITIVE_INFINITY,maxY=0,graphLabel=g.graph(),marginX=graphLabel.marginx||0,marginY=graphLabel.marginy||0;function getExtremes(attrs){var x=attrs.x,y=attrs.y,w=attrs.width,h=attrs.height;minX=Math.min(minX,x-w/2);maxX=Math.max(maxX,x+w/2);minY=Math.min(minY,y-h/2);maxY=Math.max(maxY,y+h/2)}_.each(g.nodes(),function(v){getExtremes(g.node(v))});_.each(g.edges(),function(e){var edge=g.edge(e);if(_.has(edge,"x")){getExtremes(edge)}});minX-=marginX;minY-=marginY;_.each(g.nodes(),function(v){var node=g.node(v);node.x-=minX;node.y-=minY});_.each(g.edges(),function(e){var edge=g.edge(e);_.each(edge.points,function(p){p.x-=minX;p.y-=minY});if(_.has(edge,"x")){edge.x-=minX}if(_.has(edge,"y")){edge.y-=minY}});graphLabel.width=maxX-minX+marginX;graphLabel.height=maxY-minY+marginY}function assignNodeIntersects(g){_.each(g.edges(),function(e){var edge=g.edge(e),nodeV=g.node(e.v),nodeW=g.node(e.w),p1,p2;if(!edge.points){edge.points=[];p1=nodeW;p2=nodeV}else{p1=edge.points[0];p2=edge.points[edge.points.length-1]}edge.points.unshift(util.intersectRect(nodeV,p1));edge.points.push(util.intersectRect(nodeW,p2))})}function fixupEdgeLabelCoords(g){_.each(g.edges(),function(e){var edge=g.edge(e);if(_.has(edge,"x")){if(edge.labelpos==="l"||edge.labelpos==="r"){edge.width-=edge.labeloffset}switch(edge.labelpos){case"l":edge.x-=edge.width/2+edge.labeloffset;break;case"r":edge.x+=edge.width/2+edge.labeloffset;break}}})}function reversePointsForReversedEdges(g){_.each(g.edges(),function(e){var edge=g.edge(e);if(edge.reversed){edge.points.reverse()}})}function removeBorderNodes(g){_.each(g.nodes(),function(v){if(g.children(v).length){var node=g.node(v),t=g.node(node.borderTop),b=g.node(node.borderBottom),l=g.node(_.last(node.borderLeft)),r=g.node(_.last(node.borderRight));node.width=Math.abs(r.x-l.x);node.height=Math.abs(b.y-t.y);node.x=l.x+node.width/2;node.y=t.y+node.height/2}});_.each(g.nodes(),function(v){if(g.node(v).dummy==="border"){g.removeNode(v)}})}function removeSelfEdges(g){_.each(g.edges(),function(e){if(e.v===e.w){var node=g.node(e.v);if(!node.selfEdges){node.selfEdges=[]}node.selfEdges.push({e:e,label:g.edge(e)});g.removeEdge(e)}})}function insertSelfEdges(g){var layers=util.buildLayerMatrix(g);_.each(layers,function(layer){var orderShift=0;_.each(layer,function(v,i){var node=g.node(v);node.order=i+orderShift;_.each(node.selfEdges,function(selfEdge){util.addDummyNode(g,"selfedge",{width:selfEdge.label.width,height:selfEdge.label.height,rank:node.rank,order:i+ ++orderShift,e:selfEdge.e,label:selfEdge.label},"_se")});delete node.selfEdges})})}function positionSelfEdges(g){_.each(g.nodes(),function(v){var node=g.node(v);if(node.dummy==="selfedge"){var selfNode=g.node(node.e.v),x=selfNode.x+selfNode.width/2,y=selfNode.y,dx=node.x-x,dy=selfNode.height/2;g.setEdge(node.e,node.label);g.removeNode(v);node.label.points=[{x:x+2*dx/3,y:y-dy},{x:x+5*dx/6,y:y-dy},{x:x+dx,y:y},{x:x+5*dx/6,y:y+dy},{x:x+2*dx/3,y:y+dy}];node.label.x=node.x;node.label.y=node.y}})}function selectNumberAttrs(obj,attrs){return _.mapValues(_.pick(obj,attrs),Number)}function canonicalize(attrs){var newAttrs={};_.each(attrs,function(v,k){newAttrs[k.toLowerCase()]=v});return newAttrs}},{"./acyclic":28,"./add-border-segments":29,"./coordinate-system":30,"./graphlib":33,"./lodash":36,"./nesting-graph":37,"./normalize":38,"./order":43,"./parent-dummy-chains":48,"./position":50,"./rank":52,"./util":55}],36:[function(require,module,exports){arguments[4][20][0].apply(exports,arguments)},{dup:20,lodash:77}],37:[function(require,module,exports){var _=require("./lodash"),util=require("./util");module.exports={run:run,cleanup:cleanup};function run(g){var root=util.addDummyNode(g,"root",{},"_root"),depths=treeDepths(g),height=_.max(depths)-1,nodeSep=2*height+1;g.graph().nestingRoot=root;_.each(g.edges(),function(e){g.edge(e).minlen*=nodeSep});var weight=sumWeights(g)+1;_.each(g.children(),function(child){dfs(g,root,nodeSep,weight,height,depths,child)});g.graph().nodeRankFactor=nodeSep}function dfs(g,root,nodeSep,weight,height,depths,v){var children=g.children(v);if(!children.length){if(v!==root){g.setEdge(root,v,{weight:0,minlen:nodeSep})}return}var top=util.addBorderNode(g,"_bt"),bottom=util.addBorderNode(g,"_bb"),label=g.node(v);g.setParent(top,v);label.borderTop=top;g.setParent(bottom,v);label.borderBottom=bottom;_.each(children,function(child){dfs(g,root,nodeSep,weight,height,depths,child);var childNode=g.node(child),childTop=childNode.borderTop?childNode.borderTop:child,childBottom=childNode.borderBottom?childNode.borderBottom:child,thisWeight=childNode.borderTop?weight:2*weight,minlen=childTop!==childBottom?1:height-depths[v]+1;g.setEdge(top,childTop,{weight:thisWeight,minlen:minlen,nestingEdge:true});g.setEdge(childBottom,bottom,{weight:thisWeight,minlen:minlen,nestingEdge:true})});if(!g.parent(v)){g.setEdge(root,top,{weight:0,minlen:height+depths[v]})}}function treeDepths(g){var depths={};function dfs(v,depth){var children=g.children(v);if(children&&children.length){_.each(children,function(child){dfs(child,depth+1)})}depths[v]=depth}_.each(g.children(),function(v){dfs(v,1)});return depths}function sumWeights(g){return _.reduce(g.edges(),function(acc,e){return acc+g.edge(e).weight},0)}function cleanup(g){var graphLabel=g.graph();g.removeNode(graphLabel.nestingRoot);delete graphLabel.nestingRoot;_.each(g.edges(),function(e){var edge=g.edge(e);if(edge.nestingEdge){g.removeEdge(e)}})}},{"./lodash":36,"./util":55}],38:[function(require,module,exports){"use strict";var _=require("./lodash"),util=require("./util");module.exports={run:run,undo:undo};function run(g){g.graph().dummyChains=[];_.each(g.edges(),function(edge){normalizeEdge(g,edge)})}function normalizeEdge(g,e){var v=e.v,vRank=g.node(v).rank,w=e.w,wRank=g.node(w).rank,name=e.name,edgeLabel=g.edge(e),labelRank=edgeLabel.labelRank;if(wRank===vRank+1)return;g.removeEdge(e);var dummy,attrs,i;for(i=0,++vRank;vRank<wRank;++i,++vRank){edgeLabel.points=[];attrs={width:0,height:0,edgeLabel:edgeLabel,edgeObj:e,rank:vRank};dummy=util.addDummyNode(g,"edge",attrs,"_d");if(vRank===labelRank){attrs.width=edgeLabel.width;attrs.height=edgeLabel.height;attrs.dummy="edge-label";attrs.labelpos=edgeLabel.labelpos}g.setEdge(v,dummy,{weight:edgeLabel.weight},name);if(i===0){g.graph().dummyChains.push(dummy)}v=dummy}g.setEdge(v,w,{weight:edgeLabel.weight},name)}function undo(g){_.each(g.graph().dummyChains,function(v){var node=g.node(v),origLabel=node.edgeLabel,w;g.setEdge(node.edgeObj,origLabel);while(node.dummy){w=g.successors(v)[0];g.removeNode(v);origLabel.points.push({x:node.x,y:node.y});if(node.dummy==="edge-label"){origLabel.x=node.x;origLabel.y=node.y;origLabel.width=node.width;origLabel.height=node.height}v=w;node=g.node(v)}})}},{"./lodash":36,"./util":55}],39:[function(require,module,exports){var _=require("../lodash");module.exports=addSubgraphConstraints;function addSubgraphConstraints(g,cg,vs){var prev={},rootPrev;_.each(vs,function(v){var child=g.parent(v),parent,prevChild;while(child){parent=g.parent(child);if(parent){prevChild=prev[parent];prev[parent]=child}else{prevChild=rootPrev;rootPrev=child}if(prevChild&&prevChild!==child){cg.setEdge(prevChild,child);return}child=parent}})}},{"../lodash":36}],40:[function(require,module,exports){var _=require("../lodash");module.exports=barycenter;function barycenter(g,movable){return _.map(movable,function(v){var inV=g.inEdges(v);if(!inV.length){return{v:v}}else{var result=_.reduce(inV,function(acc,e){var edge=g.edge(e),nodeU=g.node(e.v);return{sum:acc.sum+edge.weight*nodeU.order,weight:acc.weight+edge.weight}},{sum:0,weight:0});return{v:v,barycenter:result.sum/result.weight,weight:result.weight}}})}},{"../lodash":36}],41:[function(require,module,exports){var _=require("../lodash"),Graph=require("../graphlib").Graph;module.exports=buildLayerGraph;function buildLayerGraph(g,rank,relationship){var root=createRootNode(g),result=new Graph({compound:true}).setGraph({root:root}).setDefaultNodeLabel(function(v){return g.node(v)});_.each(g.nodes(),function(v){var node=g.node(v),parent=g.parent(v);if(node.rank===rank||node.minRank<=rank&&rank<=node.maxRank){result.setNode(v);result.setParent(v,parent||root);_.each(g[relationship](v),function(e){var u=e.v===v?e.w:e.v,edge=result.edge(u,v),weight=!_.isUndefined(edge)?edge.weight:0;result.setEdge(u,v,{weight:g.edge(e).weight+weight})});if(_.has(node,"minRank")){result.setNode(v,{borderLeft:node.borderLeft[rank],borderRight:node.borderRight[rank]})}}});return result}function createRootNode(g){var v;while(g.hasNode(v=_.uniqueId("_root")));return v}},{"../graphlib":33,"../lodash":36}],42:[function(require,module,exports){"use strict";var _=require("../lodash");module.exports=crossCount;function crossCount(g,layering){var cc=0;for(var i=1;i<layering.length;++i){cc+=twoLayerCrossCount(g,layering[i-1],layering[i])}return cc}function twoLayerCrossCount(g,northLayer,southLayer){var southPos=_.zipObject(southLayer,_.map(southLayer,function(v,i){return i}));var southEntries=_.flatten(_.map(northLayer,function(v){return _.chain(g.outEdges(v)).map(function(e){return{pos:southPos[e.w],weight:g.edge(e).weight}}).sortBy("pos").value()}),true);var firstIndex=1;while(firstIndex<southLayer.length)firstIndex<<=1;var treeSize=2*firstIndex-1;firstIndex-=1;var tree=_.map(new Array(treeSize),function(){return 0});var cc=0;_.each(southEntries.forEach(function(entry){var index=entry.pos+firstIndex;tree[index]+=entry.weight;var weightSum=0;while(index>0){if(index%2){weightSum+=tree[index+1]}index=index-1>>1;tree[index]+=entry.weight}cc+=entry.weight*weightSum}));return cc}},{"../lodash":36}],43:[function(require,module,exports){"use strict";var _=require("../lodash"),initOrder=require("./init-order"),crossCount=require("./cross-count"),sortSubgraph=require("./sort-subgraph"),buildLayerGraph=require("./build-layer-graph"),addSubgraphConstraints=require("./add-subgraph-constraints"),Graph=require("../graphlib").Graph,util=require("../util");module.exports=order;function order(g){var maxRank=util.maxRank(g),downLayerGraphs=buildLayerGraphs(g,_.range(1,maxRank+1),"inEdges"),upLayerGraphs=buildLayerGraphs(g,_.range(maxRank-1,-1,-1),"outEdges");var layering=initOrder(g);assignOrder(g,layering);var bestCC=Number.POSITIVE_INFINITY,best;for(var i=0,lastBest=0;lastBest<4;++i,++lastBest){sweepLayerGraphs(i%2?downLayerGraphs:upLayerGraphs,i%4>=2);layering=util.buildLayerMatrix(g);var cc=crossCount(g,layering);if(cc<bestCC){lastBest=0;best=_.cloneDeep(layering);bestCC=cc}}assignOrder(g,best)}function buildLayerGraphs(g,ranks,relationship){return _.map(ranks,function(rank){return buildLayerGraph(g,rank,relationship)})}function sweepLayerGraphs(layerGraphs,biasRight){var cg=new Graph;_.each(layerGraphs,function(lg){var root=lg.graph().root;var sorted=sortSubgraph(lg,root,cg,biasRight);_.each(sorted.vs,function(v,i){lg.node(v).order=i});addSubgraphConstraints(lg,cg,sorted.vs)})}function assignOrder(g,layering){_.each(layering,function(layer){_.each(layer,function(v,i){g.node(v).order=i})})}},{"../graphlib":33,"../lodash":36,"../util":55,"./add-subgraph-constraints":39,"./build-layer-graph":41,"./cross-count":42,"./init-order":44,"./sort-subgraph":46}],44:[function(require,module,exports){"use strict";var _=require("../lodash");module.exports=initOrder;function initOrder(g){var visited={},simpleNodes=_.filter(g.nodes(),function(v){return!g.children(v).length}),maxRank=_.max(_.map(simpleNodes,function(v){return g.node(v).rank})),layers=_.map(_.range(maxRank+1),function(){return[]});function dfs(v){if(_.has(visited,v))return;visited[v]=true;var node=g.node(v);layers[node.rank].push(v);_.each(g.successors(v),dfs)}var orderedVs=_.sortBy(simpleNodes,function(v){return g.node(v).rank});_.each(orderedVs,dfs);return layers}},{"../lodash":36}],45:[function(require,module,exports){"use strict";var _=require("../lodash");module.exports=resolveConflicts;function resolveConflicts(entries,cg){var mappedEntries={};_.each(entries,function(entry,i){var tmp=mappedEntries[entry.v]={indegree:0,"in":[],out:[],vs:[entry.v],i:i};if(!_.isUndefined(entry.barycenter)){tmp.barycenter=entry.barycenter;tmp.weight=entry.weight}});_.each(cg.edges(),function(e){var entryV=mappedEntries[e.v],entryW=mappedEntries[e.w];if(!_.isUndefined(entryV)&&!_.isUndefined(entryW)){entryW.indegree++;entryV.out.push(mappedEntries[e.w])}});var sourceSet=_.filter(mappedEntries,function(entry){return!entry.indegree});return doResolveConflicts(sourceSet)}function doResolveConflicts(sourceSet){var entries=[];function handleIn(vEntry){return function(uEntry){if(uEntry.merged){return}if(_.isUndefined(uEntry.barycenter)||_.isUndefined(vEntry.barycenter)||uEntry.barycenter>=vEntry.barycenter){mergeEntries(vEntry,uEntry)}}}function handleOut(vEntry){return function(wEntry){wEntry["in"].push(vEntry);if(--wEntry.indegree===0){sourceSet.push(wEntry)}}}while(sourceSet.length){var entry=sourceSet.pop();entries.push(entry);_.each(entry["in"].reverse(),handleIn(entry));_.each(entry.out,handleOut(entry))}return _.chain(entries).filter(function(entry){return!entry.merged}).map(function(entry){return _.pick(entry,["vs","i","barycenter","weight"])}).value()}function mergeEntries(target,source){var sum=0,weight=0;if(target.weight){sum+=target.barycenter*target.weight;weight+=target.weight}if(source.weight){sum+=source.barycenter*source.weight;weight+=source.weight}target.vs=source.vs.concat(target.vs);target.barycenter=sum/weight;target.weight=weight;target.i=Math.min(source.i,target.i);source.merged=true}},{"../lodash":36}],46:[function(require,module,exports){var _=require("../lodash"),barycenter=require("./barycenter"),resolveConflicts=require("./resolve-conflicts"),sort=require("./sort");module.exports=sortSubgraph;function sortSubgraph(g,v,cg,biasRight){var movable=g.children(v),node=g.node(v),bl=node?node.borderLeft:undefined,br=node?node.borderRight:undefined,subgraphs={};if(bl){movable=_.filter(movable,function(w){return w!==bl&&w!==br})}var barycenters=barycenter(g,movable);_.each(barycenters,function(entry){if(g.children(entry.v).length){var subgraphResult=sortSubgraph(g,entry.v,cg,biasRight);subgraphs[entry.v]=subgraphResult;if(_.has(subgraphResult,"barycenter")){mergeBarycenters(entry,subgraphResult)}}});var entries=resolveConflicts(barycenters,cg);expandSubgraphs(entries,subgraphs);var result=sort(entries,biasRight);if(bl){result.vs=_.flatten([bl,result.vs,br],true);if(g.predecessors(bl).length){var blPred=g.node(g.predecessors(bl)[0]),brPred=g.node(g.predecessors(br)[0]);if(!_.has(result,"barycenter")){result.barycenter=0;result.weight=0}result.barycenter=(result.barycenter*result.weight+blPred.order+brPred.order)/(result.weight+2);result.weight+=2}}return result}function expandSubgraphs(entries,subgraphs){_.each(entries,function(entry){entry.vs=_.flatten(entry.vs.map(function(v){if(subgraphs[v]){return subgraphs[v].vs}return v}),true)})}function mergeBarycenters(target,other){if(!_.isUndefined(target.barycenter)){target.barycenter=(target.barycenter*target.weight+other.barycenter*other.weight)/(target.weight+other.weight);target.weight+=other.weight}else{target.barycenter=other.barycenter;target.weight=other.weight}}},{"../lodash":36,"./barycenter":40,"./resolve-conflicts":45,"./sort":47}],47:[function(require,module,exports){var _=require("../lodash"),util=require("../util");module.exports=sort;function sort(entries,biasRight){var parts=util.partition(entries,function(entry){return _.has(entry,"barycenter")});var sortable=parts.lhs,unsortable=_.sortBy(parts.rhs,function(entry){return-entry.i}),vs=[],sum=0,weight=0,vsIndex=0;sortable.sort(compareWithBias(!!biasRight));vsIndex=consumeUnsortable(vs,unsortable,vsIndex);_.each(sortable,function(entry){vsIndex+=entry.vs.length;vs.push(entry.vs);sum+=entry.barycenter*entry.weight;weight+=entry.weight;vsIndex=consumeUnsortable(vs,unsortable,vsIndex)});var result={vs:_.flatten(vs,true)};if(weight){result.barycenter=sum/weight;result.weight=weight}return result}function consumeUnsortable(vs,unsortable,index){var last;while(unsortable.length&&(last=_.last(unsortable)).i<=index){unsortable.pop();vs.push(last.vs);index++}return index}function compareWithBias(bias){return function(entryV,entryW){if(entryV.barycenter<entryW.barycenter){return-1}else if(entryV.barycenter>entryW.barycenter){return 1}return!bias?entryV.i-entryW.i:entryW.i-entryV.i}}},{"../lodash":36,"../util":55}],48:[function(require,module,exports){var _=require("./lodash");module.exports=parentDummyChains;function parentDummyChains(g){var postorderNums=postorder(g);_.each(g.graph().dummyChains,function(v){var node=g.node(v),edgeObj=node.edgeObj,pathData=findPath(g,postorderNums,edgeObj.v,edgeObj.w),path=pathData.path,lca=pathData.lca,pathIdx=0,pathV=path[pathIdx],ascending=true;while(v!==edgeObj.w){node=g.node(v);if(ascending){while((pathV=path[pathIdx])!==lca&&g.node(pathV).maxRank<node.rank){pathIdx++}if(pathV===lca){ascending=false}}if(!ascending){while(pathIdx<path.length-1&&g.node(pathV=path[pathIdx+1]).minRank<=node.rank){pathIdx++}pathV=path[pathIdx]}g.setParent(v,pathV);v=g.successors(v)[0]}})}function findPath(g,postorderNums,v,w){var vPath=[],wPath=[],low=Math.min(postorderNums[v].low,postorderNums[w].low),lim=Math.max(postorderNums[v].lim,postorderNums[w].lim),parent,lca;parent=v;do{parent=g.parent(parent);vPath.push(parent)}while(parent&&(postorderNums[parent].low>low||lim>postorderNums[parent].lim));lca=parent;parent=w;while((parent=g.parent(parent))!==lca){wPath.push(parent)}return{path:vPath.concat(wPath.reverse()),lca:lca}}function postorder(g){var result={},lim=0;function dfs(v){var low=lim;_.each(g.children(v),dfs);result[v]={low:low,lim:lim++}}_.each(g.children(),dfs);return result}},{"./lodash":36}],49:[function(require,module,exports){"use strict";var _=require("../lodash"),Graph=require("../graphlib").Graph,util=require("../util");module.exports={positionX:positionX,findType1Conflicts:findType1Conflicts,findType2Conflicts:findType2Conflicts,addConflict:addConflict,hasConflict:hasConflict,verticalAlignment:verticalAlignment,horizontalCompaction:horizontalCompaction,alignCoordinates:alignCoordinates,findSmallestWidthAlignment:findSmallestWidthAlignment,balance:balance};function findType1Conflicts(g,layering){var conflicts={};function visitLayer(prevLayer,layer){var k0=0,scanPos=0,prevLayerLength=prevLayer.length,lastNode=_.last(layer);_.each(layer,function(v,i){var w=findOtherInnerSegmentNode(g,v),k1=w?g.node(w).order:prevLayerLength;if(w||v===lastNode){_.each(layer.slice(scanPos,i+1),function(scanNode){_.each(g.predecessors(scanNode),function(u){var uLabel=g.node(u),uPos=uLabel.order;if((uPos<k0||k1<uPos)&&!(uLabel.dummy&&g.node(scanNode).dummy)){addConflict(conflicts,u,scanNode)}})});scanPos=i+1;k0=k1}});return layer}_.reduce(layering,visitLayer);return conflicts}function findType2Conflicts(g,layering){var conflicts={};function scan(south,southPos,southEnd,prevNorthBorder,nextNorthBorder){var v;_.each(_.range(southPos,southEnd),function(i){v=south[i];if(g.node(v).dummy){_.each(g.predecessors(v),function(u){var uNode=g.node(u);if(uNode.dummy&&(uNode.order<prevNorthBorder||uNode.order>nextNorthBorder)){addConflict(conflicts,u,v)}})}})}function visitLayer(north,south){var prevNorthPos=-1,nextNorthPos,southPos=0;_.each(south,function(v,southLookahead){if(g.node(v).dummy==="border"){var predecessors=g.predecessors(v);if(predecessors.length){nextNorthPos=g.node(predecessors[0]).order;scan(south,southPos,southLookahead,prevNorthPos,nextNorthPos);southPos=southLookahead;prevNorthPos=nextNorthPos}}scan(south,southPos,south.length,nextNorthPos,north.length)});return south}_.reduce(layering,visitLayer);return conflicts}function findOtherInnerSegmentNode(g,v){if(g.node(v).dummy){return _.find(g.predecessors(v),function(u){return g.node(u).dummy})}}function addConflict(conflicts,v,w){if(v>w){var tmp=v;v=w;w=tmp}var conflictsV=conflicts[v];if(!conflictsV){conflicts[v]=conflictsV={}}conflictsV[w]=true}function hasConflict(conflicts,v,w){if(v>w){var tmp=v;v=w;w=tmp}return _.has(conflicts[v],w)}function verticalAlignment(g,layering,conflicts,neighborFn){var root={},align={},pos={};_.each(layering,function(layer){_.each(layer,function(v,order){root[v]=v;align[v]=v;pos[v]=order})});_.each(layering,function(layer){var prevIdx=-1;_.each(layer,function(v){var ws=neighborFn(v);if(ws.length){ws=_.sortBy(ws,function(w){return pos[w]});var mp=(ws.length-1)/2;for(var i=Math.floor(mp),il=Math.ceil(mp);i<=il;++i){var w=ws[i];if(align[v]===v&&prevIdx<pos[w]&&!hasConflict(conflicts,v,w)){align[w]=v;align[v]=root[v]=root[w];prevIdx=pos[w]}}}})});return{root:root,align:align}}function horizontalCompaction(g,layering,root,align,reverseSep){var xs={},blockG=buildBlockGraph(g,layering,root,reverseSep);var visited={};function pass1(v){if(!_.has(visited,v)){visited[v]=true;xs[v]=_.reduce(blockG.inEdges(v),function(max,e){pass1(e.v);return Math.max(max,xs[e.v]+blockG.edge(e))},0)}}_.each(blockG.nodes(),pass1);function pass2(v){if(visited[v]!==2){visited[v]++;var min=_.reduce(blockG.outEdges(v),function(min,e){pass2(e.w);return Math.min(min,xs[e.w]-blockG.edge(e))},Number.POSITIVE_INFINITY);if(min!==Number.POSITIVE_INFINITY){xs[v]=Math.max(xs[v],min)}}}_.each(blockG.nodes(),pass2);_.each(align,function(v){xs[v]=xs[root[v]]});return xs}function buildBlockGraph(g,layering,root,reverseSep){var blockGraph=new Graph,graphLabel=g.graph(),sepFn=sep(graphLabel.nodesep,graphLabel.edgesep,reverseSep);_.each(layering,function(layer){var u;_.each(layer,function(v){var vRoot=root[v];blockGraph.setNode(vRoot);if(u){var uRoot=root[u],prevMax=blockGraph.edge(uRoot,vRoot);blockGraph.setEdge(uRoot,vRoot,Math.max(sepFn(g,v,u),prevMax||0))}u=v})});return blockGraph}function findSmallestWidthAlignment(g,xss){return _.min(xss,function(xs){var min=_.min(xs,function(x,v){return x-width(g,v)/2}),max=_.max(xs,function(x,v){return x+width(g,v)/2});return max-min})}function alignCoordinates(xss,alignTo){var alignToMin=_.min(alignTo),alignToMax=_.max(alignTo);_.each(["u","d"],function(vert){_.each(["l","r"],function(horiz){var alignment=vert+horiz,xs=xss[alignment],delta;if(xs===alignTo)return;delta=horiz==="l"?alignToMin-_.min(xs):alignToMax-_.max(xs);if(delta){xss[alignment]=_.mapValues(xs,function(x){return x+delta})}})})}function balance(xss,align){return _.mapValues(xss.ul,function(ignore,v){if(align){return xss[align.toLowerCase()][v]}else{var xs=_.sortBy(_.pluck(xss,v));return(xs[1]+xs[2])/2}})}function positionX(g){var layering=util.buildLayerMatrix(g),conflicts=_.merge(findType1Conflicts(g,layering),findType2Conflicts(g,layering));var xss={},adjustedLayering;_.each(["u","d"],function(vert){adjustedLayering=vert==="u"?layering:_.values(layering).reverse();_.each(["l","r"],function(horiz){if(horiz==="r"){adjustedLayering=_.map(adjustedLayering,function(inner){return _.values(inner).reverse()})}var neighborFn=_.bind(vert==="u"?g.predecessors:g.successors,g);var align=verticalAlignment(g,adjustedLayering,conflicts,neighborFn);var xs=horizontalCompaction(g,adjustedLayering,align.root,align.align,horiz==="r");if(horiz==="r"){xs=_.mapValues(xs,function(x){return-x})}xss[vert+horiz]=xs})});var smallestWidth=findSmallestWidthAlignment(g,xss);alignCoordinates(xss,smallestWidth);return balance(xss,g.graph().align)}function sep(nodeSep,edgeSep,reverseSep){return function(g,v,w){var vLabel=g.node(v),wLabel=g.node(w),sum=0,delta;sum+=vLabel.width/2;if(_.has(vLabel,"labelpos")){switch(vLabel.labelpos.toLowerCase()){case"l":delta=-vLabel.width/2;break;case"r":delta=vLabel.width/2;break}}if(delta){sum+=reverseSep?delta:-delta}delta=0;sum+=(vLabel.dummy?edgeSep:nodeSep)/2;sum+=(wLabel.dummy?edgeSep:nodeSep)/2;sum+=wLabel.width/2;if(_.has(wLabel,"labelpos")){switch(wLabel.labelpos.toLowerCase()){case"l":delta=wLabel.width/2;break;case"r":delta=-wLabel.width/2;break}}if(delta){sum+=reverseSep?delta:-delta}delta=0;return sum}}function width(g,v){return g.node(v).width}},{"../graphlib":33,"../lodash":36,"../util":55}],50:[function(require,module,exports){"use strict";var _=require("../lodash"),util=require("../util"),positionX=require("./bk").positionX;module.exports=position;function position(g){g=util.asNonCompoundGraph(g);positionY(g);_.each(positionX(g),function(x,v){g.node(v).x=x})}function positionY(g){var layering=util.buildLayerMatrix(g),rankSep=g.graph().ranksep,prevY=0;_.each(layering,function(layer){var maxHeight=_.max(_.map(layer,function(v){return g.node(v).height}));_.each(layer,function(v){g.node(v).y=prevY+maxHeight/2});prevY+=maxHeight+rankSep})}},{"../lodash":36,"../util":55,"./bk":49}],51:[function(require,module,exports){"use strict";var _=require("../lodash"),Graph=require("../graphlib").Graph,slack=require("./util").slack;module.exports=feasibleTree;function feasibleTree(g){var t=new Graph({directed:false});var start=g.nodes()[0],size=g.nodeCount();t.setNode(start,{});var edge,delta;while(tightTree(t,g)<size){edge=findMinSlackEdge(t,g);delta=t.hasNode(edge.v)?slack(g,edge):-slack(g,edge);shiftRanks(t,g,delta)}return t}function tightTree(t,g){function dfs(v){_.each(g.nodeEdges(v),function(e){var edgeV=e.v,w=v===edgeV?e.w:edgeV;if(!t.hasNode(w)&&!slack(g,e)){t.setNode(w,{});t.setEdge(v,w,{});dfs(w)}})}_.each(t.nodes(),dfs);return t.nodeCount()}function findMinSlackEdge(t,g){return _.min(g.edges(),function(e){if(t.hasNode(e.v)!==t.hasNode(e.w)){return slack(g,e)}})}function shiftRanks(t,g,delta){_.each(t.nodes(),function(v){g.node(v).rank+=delta})}},{"../graphlib":33,"../lodash":36,"./util":54}],52:[function(require,module,exports){"use strict";var rankUtil=require("./util"),longestPath=rankUtil.longestPath,feasibleTree=require("./feasible-tree"),networkSimplex=require("./network-simplex");module.exports=rank;function rank(g){switch(g.graph().ranker){case"network-simplex":networkSimplexRanker(g);break;case"tight-tree":tightTreeRanker(g);break;case"longest-path":longestPathRanker(g);break;default:networkSimplexRanker(g)}}var longestPathRanker=longestPath;function tightTreeRanker(g){longestPath(g);feasibleTree(g)}function networkSimplexRanker(g){networkSimplex(g)}},{"./feasible-tree":51,"./network-simplex":53,"./util":54}],53:[function(require,module,exports){"use strict";var _=require("../lodash"),feasibleTree=require("./feasible-tree"),slack=require("./util").slack,initRank=require("./util").longestPath,preorder=require("../graphlib").alg.preorder,postorder=require("../graphlib").alg.postorder,simplify=require("../util").simplify;module.exports=networkSimplex;networkSimplex.initLowLimValues=initLowLimValues;networkSimplex.initCutValues=initCutValues;networkSimplex.calcCutValue=calcCutValue;networkSimplex.leaveEdge=leaveEdge;networkSimplex.enterEdge=enterEdge;networkSimplex.exchangeEdges=exchangeEdges;function networkSimplex(g){g=simplify(g);initRank(g);var t=feasibleTree(g);initLowLimValues(t);initCutValues(t,g);var e,f;while(e=leaveEdge(t)){f=enterEdge(t,g,e);exchangeEdges(t,g,e,f)}}function initCutValues(t,g){var vs=postorder(t,t.nodes());vs=vs.slice(0,vs.length-1);_.each(vs,function(v){assignCutValue(t,g,v)})}function assignCutValue(t,g,child){var childLab=t.node(child),parent=childLab.parent;t.edge(child,parent).cutvalue=calcCutValue(t,g,child)}function calcCutValue(t,g,child){var childLab=t.node(child),parent=childLab.parent,childIsTail=true,graphEdge=g.edge(child,parent),cutValue=0;if(!graphEdge){childIsTail=false;graphEdge=g.edge(parent,child)}cutValue=graphEdge.weight;_.each(g.nodeEdges(child),function(e){var isOutEdge=e.v===child,other=isOutEdge?e.w:e.v;if(other!==parent){var pointsToHead=isOutEdge===childIsTail,otherWeight=g.edge(e).weight;cutValue+=pointsToHead?otherWeight:-otherWeight;if(isTreeEdge(t,child,other)){var otherCutValue=t.edge(child,other).cutvalue;cutValue+=pointsToHead?-otherCutValue:otherCutValue}}});return cutValue}function initLowLimValues(tree,root){if(arguments.length<2){root=tree.nodes()[0]}dfsAssignLowLim(tree,{},1,root)}function dfsAssignLowLim(tree,visited,nextLim,v,parent){var low=nextLim,label=tree.node(v);visited[v]=true;_.each(tree.neighbors(v),function(w){if(!_.has(visited,w)){nextLim=dfsAssignLowLim(tree,visited,nextLim,w,v)}});label.low=low;label.lim=nextLim++;if(parent){label.parent=parent}else{delete label.parent}return nextLim}function leaveEdge(tree){return _.find(tree.edges(),function(e){return tree.edge(e).cutvalue<0})}function enterEdge(t,g,edge){var v=edge.v,w=edge.w;if(!g.hasEdge(v,w)){v=edge.w;w=edge.v}var vLabel=t.node(v),wLabel=t.node(w),tailLabel=vLabel,flip=false;if(vLabel.lim>wLabel.lim){tailLabel=wLabel;flip=true}var candidates=_.filter(g.edges(),function(edge){return flip===isDescendant(t,t.node(edge.v),tailLabel)&&flip!==isDescendant(t,t.node(edge.w),tailLabel)});return _.min(candidates,function(edge){return slack(g,edge)})}function exchangeEdges(t,g,e,f){var v=e.v,w=e.w;t.removeEdge(v,w);t.setEdge(f.v,f.w,{});initLowLimValues(t);initCutValues(t,g);updateRanks(t,g)}function updateRanks(t,g){var root=_.find(t.nodes(),function(v){return!g.node(v).parent}),vs=preorder(t,root);vs=vs.slice(1);_.each(vs,function(v){var parent=t.node(v).parent,edge=g.edge(v,parent),flipped=false;if(!edge){edge=g.edge(parent,v);flipped=true}g.node(v).rank=g.node(parent).rank+(flipped?edge.minlen:-edge.minlen)})}function isTreeEdge(tree,u,v){return tree.hasEdge(u,v)}function isDescendant(tree,vLabel,rootLabel){return rootLabel.low<=vLabel.lim&&vLabel.lim<=rootLabel.lim}},{"../graphlib":33,"../lodash":36,"../util":55,"./feasible-tree":51,"./util":54}],54:[function(require,module,exports){"use strict";var _=require("../lodash");module.exports={longestPath:longestPath,slack:slack};function longestPath(g){var visited={};function dfs(v){var label=g.node(v);if(_.has(visited,v)){return label.rank}visited[v]=true;var rank=_.min(_.map(g.outEdges(v),function(e){return dfs(e.w)-g.edge(e).minlen}));if(rank===Number.POSITIVE_INFINITY){rank=0}return label.rank=rank}_.each(g.sources(),dfs)}function slack(g,e){return g.node(e.w).rank-g.node(e.v).rank-g.edge(e).minlen}},{"../lodash":36}],55:[function(require,module,exports){"use strict";var _=require("./lodash"),Graph=require("./graphlib").Graph;module.exports={addDummyNode:addDummyNode,simplify:simplify,asNonCompoundGraph:asNonCompoundGraph,successorWeights:successorWeights,predecessorWeights:predecessorWeights,intersectRect:intersectRect,buildLayerMatrix:buildLayerMatrix,normalizeRanks:normalizeRanks,removeEmptyRanks:removeEmptyRanks,addBorderNode:addBorderNode,maxRank:maxRank,partition:partition,time:time,notime:notime};function addDummyNode(g,type,attrs,name){var v;do{v=_.uniqueId(name)}while(g.hasNode(v));attrs.dummy=type;g.setNode(v,attrs);return v}function simplify(g){var simplified=(new Graph).setGraph(g.graph());_.each(g.nodes(),function(v){simplified.setNode(v,g.node(v))});_.each(g.edges(),function(e){var simpleLabel=simplified.edge(e.v,e.w)||{weight:0,minlen:1},label=g.edge(e);simplified.setEdge(e.v,e.w,{weight:simpleLabel.weight+label.weight,minlen:Math.max(simpleLabel.minlen,label.minlen)})});return simplified}function asNonCompoundGraph(g){
-var simplified=new Graph({multigraph:g.isMultigraph()}).setGraph(g.graph());_.each(g.nodes(),function(v){if(!g.children(v).length){simplified.setNode(v,g.node(v))}});_.each(g.edges(),function(e){simplified.setEdge(e,g.edge(e))});return simplified}function successorWeights(g){var weightMap=_.map(g.nodes(),function(v){var sucs={};_.each(g.outEdges(v),function(e){sucs[e.w]=(sucs[e.w]||0)+g.edge(e).weight});return sucs});return _.zipObject(g.nodes(),weightMap)}function predecessorWeights(g){var weightMap=_.map(g.nodes(),function(v){var preds={};_.each(g.inEdges(v),function(e){preds[e.v]=(preds[e.v]||0)+g.edge(e).weight});return preds});return _.zipObject(g.nodes(),weightMap)}function intersectRect(rect,point){var x=rect.x;var y=rect.y;var dx=point.x-x;var dy=point.y-y;var w=rect.width/2;var h=rect.height/2;if(!dx&&!dy){throw new Error("Not possible to find intersection inside of the rectangle")}var sx,sy;if(Math.abs(dy)*w>Math.abs(dx)*h){if(dy<0){h=-h}sx=h*dx/dy;sy=h}else{if(dx<0){w=-w}sx=w;sy=w*dy/dx}return{x:x+sx,y:y+sy}}function buildLayerMatrix(g){var layering=_.map(_.range(maxRank(g)+1),function(){return[]});_.each(g.nodes(),function(v){var node=g.node(v),rank=node.rank;if(!_.isUndefined(rank)){layering[rank][node.order]=v}});return layering}function normalizeRanks(g){var min=_.min(_.map(g.nodes(),function(v){return g.node(v).rank}));_.each(g.nodes(),function(v){var node=g.node(v);if(_.has(node,"rank")){node.rank-=min}})}function removeEmptyRanks(g){var offset=_.min(_.map(g.nodes(),function(v){return g.node(v).rank}));var layers=[];_.each(g.nodes(),function(v){var rank=g.node(v).rank-offset;if(!_.has(layers,rank)){layers[rank]=[]}layers[rank].push(v)});var delta=0,nodeRankFactor=g.graph().nodeRankFactor;_.each(layers,function(vs,i){if(_.isUndefined(vs)&&i%nodeRankFactor!==0){--delta}else if(delta){_.each(vs,function(v){g.node(v).rank+=delta})}})}function addBorderNode(g,prefix,rank,order){var node={width:0,height:0};if(arguments.length>=4){node.rank=rank;node.order=order}return addDummyNode(g,"border",node,prefix)}function maxRank(g){return _.max(_.map(g.nodes(),function(v){var rank=g.node(v).rank;if(!_.isUndefined(rank)){return rank}}))}function partition(collection,fn){var result={lhs:[],rhs:[]};_.each(collection,function(value){if(fn(value)){result.lhs.push(value)}else{result.rhs.push(value)}});return result}function time(name,fn){var start=_.now();try{return fn()}finally{console.log(name+" time: "+(_.now()-start)+"ms")}}function notime(name,fn){return fn()}},{"./graphlib":33,"./lodash":36}],56:[function(require,module,exports){module.exports="0.7.1"},{}],57:[function(require,module,exports){var lib=require("./lib");module.exports={Graph:lib.Graph,json:require("./lib/json"),alg:require("./lib/alg"),version:lib.version}},{"./lib":73,"./lib/alg":64,"./lib/json":74}],58:[function(require,module,exports){var _=require("../lodash");module.exports=components;function components(g){var visited={},cmpts=[],cmpt;function dfs(v){if(_.has(visited,v))return;visited[v]=true;cmpt.push(v);_.each(g.successors(v),dfs);_.each(g.predecessors(v),dfs)}_.each(g.nodes(),function(v){cmpt=[];dfs(v);if(cmpt.length){cmpts.push(cmpt)}});return cmpts}},{"../lodash":75}],59:[function(require,module,exports){var _=require("../lodash");module.exports=dfs;function dfs(g,vs,order){if(!_.isArray(vs)){vs=[vs]}var acc=[],visited={};_.each(vs,function(v){if(!g.hasNode(v)){throw new Error("Graph does not have node: "+v)}doDfs(g,v,order==="post",visited,acc)});return acc}function doDfs(g,v,postorder,visited,acc){if(!_.has(visited,v)){visited[v]=true;if(!postorder){acc.push(v)}_.each(g.neighbors(v),function(w){doDfs(g,w,postorder,visited,acc)});if(postorder){acc.push(v)}}}},{"../lodash":75}],60:[function(require,module,exports){var dijkstra=require("./dijkstra"),_=require("../lodash");module.exports=dijkstraAll;function dijkstraAll(g,weightFunc,edgeFunc){return _.transform(g.nodes(),function(acc,v){acc[v]=dijkstra(g,v,weightFunc,edgeFunc)},{})}},{"../lodash":75,"./dijkstra":61}],61:[function(require,module,exports){var _=require("../lodash"),PriorityQueue=require("../data/priority-queue");module.exports=dijkstra;var DEFAULT_WEIGHT_FUNC=_.constant(1);function dijkstra(g,source,weightFn,edgeFn){return runDijkstra(g,String(source),weightFn||DEFAULT_WEIGHT_FUNC,edgeFn||function(v){return g.outEdges(v)})}function runDijkstra(g,source,weightFn,edgeFn){var results={},pq=new PriorityQueue,v,vEntry;var updateNeighbors=function(edge){var w=edge.v!==v?edge.v:edge.w,wEntry=results[w],weight=weightFn(edge),distance=vEntry.distance+weight;if(weight<0){throw new Error("dijkstra does not allow negative edge weights. "+"Bad edge: "+edge+" Weight: "+weight)}if(distance<wEntry.distance){wEntry.distance=distance;wEntry.predecessor=v;pq.decrease(w,distance)}};g.nodes().forEach(function(v){var distance=v===source?0:Number.POSITIVE_INFINITY;results[v]={distance:distance};pq.add(v,distance)});while(pq.size()>0){v=pq.removeMin();vEntry=results[v];if(vEntry.distance===Number.POSITIVE_INFINITY){break}edgeFn(v).forEach(updateNeighbors)}return results}},{"../data/priority-queue":71,"../lodash":75}],62:[function(require,module,exports){var _=require("../lodash"),tarjan=require("./tarjan");module.exports=findCycles;function findCycles(g){return _.filter(tarjan(g),function(cmpt){return cmpt.length>1})}},{"../lodash":75,"./tarjan":69}],63:[function(require,module,exports){var _=require("../lodash");module.exports=floydWarshall;var DEFAULT_WEIGHT_FUNC=_.constant(1);function floydWarshall(g,weightFn,edgeFn){return runFloydWarshall(g,weightFn||DEFAULT_WEIGHT_FUNC,edgeFn||function(v){return g.outEdges(v)})}function runFloydWarshall(g,weightFn,edgeFn){var results={},nodes=g.nodes();nodes.forEach(function(v){results[v]={};results[v][v]={distance:0};nodes.forEach(function(w){if(v!==w){results[v][w]={distance:Number.POSITIVE_INFINITY}}});edgeFn(v).forEach(function(edge){var w=edge.v===v?edge.w:edge.v,d=weightFn(edge);results[v][w]={distance:d,predecessor:v}})});nodes.forEach(function(k){var rowK=results[k];nodes.forEach(function(i){var rowI=results[i];nodes.forEach(function(j){var ik=rowI[k];var kj=rowK[j];var ij=rowI[j];var altDistance=ik.distance+kj.distance;if(altDistance<ij.distance){ij.distance=altDistance;ij.predecessor=kj.predecessor}})})});return results}},{"../lodash":75}],64:[function(require,module,exports){module.exports={components:require("./components"),dijkstra:require("./dijkstra"),dijkstraAll:require("./dijkstra-all"),findCycles:require("./find-cycles"),floydWarshall:require("./floyd-warshall"),isAcyclic:require("./is-acyclic"),postorder:require("./postorder"),preorder:require("./preorder"),prim:require("./prim"),tarjan:require("./tarjan"),topsort:require("./topsort")}},{"./components":58,"./dijkstra":61,"./dijkstra-all":60,"./find-cycles":62,"./floyd-warshall":63,"./is-acyclic":65,"./postorder":66,"./preorder":67,"./prim":68,"./tarjan":69,"./topsort":70}],65:[function(require,module,exports){var topsort=require("./topsort");module.exports=isAcyclic;function isAcyclic(g){try{topsort(g)}catch(e){if(e instanceof topsort.CycleException){return false}throw e}return true}},{"./topsort":70}],66:[function(require,module,exports){var dfs=require("./dfs");module.exports=postorder;function postorder(g,vs){return dfs(g,vs,"post")}},{"./dfs":59}],67:[function(require,module,exports){var dfs=require("./dfs");module.exports=preorder;function preorder(g,vs){return dfs(g,vs,"pre")}},{"./dfs":59}],68:[function(require,module,exports){var _=require("../lodash"),Graph=require("../graph"),PriorityQueue=require("../data/priority-queue");module.exports=prim;function prim(g,weightFunc){var result=new Graph,parents={},pq=new PriorityQueue,v;function updateNeighbors(edge){var w=edge.v===v?edge.w:edge.v,pri=pq.priority(w);if(pri!==undefined){var edgeWeight=weightFunc(edge);if(edgeWeight<pri){parents[w]=v;pq.decrease(w,edgeWeight)}}}if(g.nodeCount()===0){return result}_.each(g.nodes(),function(v){pq.add(v,Number.POSITIVE_INFINITY);result.setNode(v)});pq.decrease(g.nodes()[0],0);var init=false;while(pq.size()>0){v=pq.removeMin();if(_.has(parents,v)){result.setEdge(v,parents[v])}else if(init){throw new Error("Input graph is not connected: "+g)}else{init=true}g.nodeEdges(v).forEach(updateNeighbors)}return result}},{"../data/priority-queue":71,"../graph":72,"../lodash":75}],69:[function(require,module,exports){var _=require("../lodash");module.exports=tarjan;function tarjan(g){var index=0,stack=[],visited={},results=[];function dfs(v){var entry=visited[v]={onStack:true,lowlink:index,index:index++};stack.push(v);g.successors(v).forEach(function(w){if(!_.has(visited,w)){dfs(w);entry.lowlink=Math.min(entry.lowlink,visited[w].lowlink)}else if(visited[w].onStack){entry.lowlink=Math.min(entry.lowlink,visited[w].index)}});if(entry.lowlink===entry.index){var cmpt=[],w;do{w=stack.pop();visited[w].onStack=false;cmpt.push(w)}while(v!==w);results.push(cmpt)}}g.nodes().forEach(function(v){if(!_.has(visited,v)){dfs(v)}});return results}},{"../lodash":75}],70:[function(require,module,exports){var _=require("../lodash");module.exports=topsort;topsort.CycleException=CycleException;function topsort(g){var visited={},stack={},results=[];function visit(node){if(_.has(stack,node)){throw new CycleException}if(!_.has(visited,node)){stack[node]=true;visited[node]=true;_.each(g.predecessors(node),visit);delete stack[node];results.push(node)}}_.each(g.sinks(),visit);if(_.size(visited)!==g.nodeCount()){throw new CycleException}return results}function CycleException(){}},{"../lodash":75}],71:[function(require,module,exports){var _=require("../lodash");module.exports=PriorityQueue;function PriorityQueue(){this._arr=[];this._keyIndices={}}PriorityQueue.prototype.size=function(){return this._arr.length};PriorityQueue.prototype.keys=function(){return this._arr.map(function(x){return x.key})};PriorityQueue.prototype.has=function(key){return _.has(this._keyIndices,key)};PriorityQueue.prototype.priority=function(key){var index=this._keyIndices[key];if(index!==undefined){return this._arr[index].priority}};PriorityQueue.prototype.min=function(){if(this.size()===0){throw new Error("Queue underflow")}return this._arr[0].key};PriorityQueue.prototype.add=function(key,priority){var keyIndices=this._keyIndices;key=String(key);if(!_.has(keyIndices,key)){var arr=this._arr;var index=arr.length;keyIndices[key]=index;arr.push({key:key,priority:priority});this._decrease(index);return true}return false};PriorityQueue.prototype.removeMin=function(){this._swap(0,this._arr.length-1);var min=this._arr.pop();delete this._keyIndices[min.key];this._heapify(0);return min.key};PriorityQueue.prototype.decrease=function(key,priority){var index=this._keyIndices[key];if(priority>this._arr[index].priority){throw new Error("New priority is greater than current priority. "+"Key: "+key+" Old: "+this._arr[index].priority+" New: "+priority)}this._arr[index].priority=priority;this._decrease(index)};PriorityQueue.prototype._heapify=function(i){var arr=this._arr;var l=2*i,r=l+1,largest=i;if(l<arr.length){largest=arr[l].priority<arr[largest].priority?l:largest;if(r<arr.length){largest=arr[r].priority<arr[largest].priority?r:largest}if(largest!==i){this._swap(i,largest);this._heapify(largest)}}};PriorityQueue.prototype._decrease=function(index){var arr=this._arr;var priority=arr[index].priority;var parent;while(index!==0){parent=index>>1;if(arr[parent].priority<priority){break}this._swap(index,parent);index=parent}};PriorityQueue.prototype._swap=function(i,j){var arr=this._arr;var keyIndices=this._keyIndices;var origArrI=arr[i];var origArrJ=arr[j];arr[i]=origArrJ;arr[j]=origArrI;keyIndices[origArrJ.key]=i;keyIndices[origArrI.key]=j}},{"../lodash":75}],72:[function(require,module,exports){"use strict";var _=require("./lodash");module.exports=Graph;var DEFAULT_EDGE_NAME="\x00",GRAPH_NODE="\x00",EDGE_KEY_DELIM="
";function Graph(opts){this._isDirected=_.has(opts,"directed")?opts.directed:true;this._isMultigraph=_.has(opts,"multigraph")?opts.multigraph:false;this._isCompound=_.has(opts,"compound")?opts.compound:false;this._label=undefined;this._defaultNodeLabelFn=_.constant(undefined);this._defaultEdgeLabelFn=_.constant(undefined);this._nodes={};if(this._isCompound){this._parent={};this._children={};this._children[GRAPH_NODE]={}}this._in={};this._preds={};this._out={};this._sucs={};this._edgeObjs={};this._edgeLabels={}}Graph.prototype._nodeCount=0;Graph.prototype._edgeCount=0;Graph.prototype.isDirected=function(){return this._isDirected};Graph.prototype.isMultigraph=function(){return this._isMultigraph};Graph.prototype.isCompound=function(){return this._isCompound};Graph.prototype.setGraph=function(label){this._label=label;return this};Graph.prototype.graph=function(){return this._label};Graph.prototype.setDefaultNodeLabel=function(newDefault){if(!_.isFunction(newDefault)){newDefault=_.constant(newDefault)}this._defaultNodeLabelFn=newDefault;return this};Graph.prototype.nodeCount=function(){return this._nodeCount};Graph.prototype.nodes=function(){return _.keys(this._nodes)};Graph.prototype.sources=function(){return _.filter(this.nodes(),function(v){return _.isEmpty(this._in[v])},this)};Graph.prototype.sinks=function(){return _.filter(this.nodes(),function(v){return _.isEmpty(this._out[v])},this)};Graph.prototype.setNodes=function(vs,value){var args=arguments;_.each(vs,function(v){if(args.length>1){this.setNode(v,value)}else{this.setNode(v)}},this);return this};Graph.prototype.setNode=function(v,value){if(_.has(this._nodes,v)){if(arguments.length>1){this._nodes[v]=value}return this}this._nodes[v]=arguments.length>1?value:this._defaultNodeLabelFn(v);if(this._isCompound){this._parent[v]=GRAPH_NODE;this._children[v]={};this._children[GRAPH_NODE][v]=true}this._in[v]={};this._preds[v]={};this._out[v]={};this._sucs[v]={};++this._nodeCount;return this};Graph.prototype.node=function(v){return this._nodes[v]};Graph.prototype.hasNode=function(v){return _.has(this._nodes,v)};Graph.prototype.removeNode=function(v){var self=this;if(_.has(this._nodes,v)){var removeEdge=function(e){self.removeEdge(self._edgeObjs[e])};delete this._nodes[v];if(this._isCompound){this._removeFromParentsChildList(v);delete this._parent[v];_.each(this.children(v),function(child){this.setParent(child)},this);delete this._children[v]}_.each(_.keys(this._in[v]),removeEdge);delete this._in[v];delete this._preds[v];_.each(_.keys(this._out[v]),removeEdge);delete this._out[v];delete this._sucs[v];--this._nodeCount}return this};Graph.prototype.setParent=function(v,parent){if(!this._isCompound){throw new 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+_.each(inputGraph.nodes(),function(v){var node=canonicalize(inputGraph.node(v));g.setNode(v,_.defaults(selectNumberAttrs(node,nodeNumAttrs),nodeDefaults));g.setParent(v,inputGraph.parent(v))});_.each(inputGraph.edges(),function(e){var edge=canonicalize(inputGraph.edge(e));g.setEdge(e,_.merge({},edgeDefaults,selectNumberAttrs(edge,edgeNumAttrs),_.pick(edge,edgeAttrs)))});return g}function makeSpaceForEdgeLabels(g){var graph=g.graph();graph.ranksep/=2;_.each(g.edges(),function(e){var edge=g.edge(e);edge.minlen*=2;if(edge.labelpos.toLowerCase()!=="c"){if(graph.rankdir==="TB"||graph.rankdir==="BT"){edge.width+=edge.labeloffset}else{edge.height+=edge.labeloffset}}})}function injectEdgeLabelProxies(g){_.each(g.edges(),function(e){var edge=g.edge(e);if(edge.width&&edge.height){var v=g.node(e.v),w=g.node(e.w),label={rank:(w.rank-v.rank)/2+v.rank,e:e};util.addDummyNode(g,"edge-proxy",label,"_ep")}})}function assignRankMinMax(g){var maxRank=0;_.each(g.nodes(),function(v){var node=g.node(v);if(node.borderTop){node.minRank=g.node(node.borderTop).rank;node.maxRank=g.node(node.borderBottom).rank;maxRank=_.max(maxRank,node.maxRank)}});g.graph().maxRank=maxRank}function removeEdgeLabelProxies(g){_.each(g.nodes(),function(v){var node=g.node(v);if(node.dummy==="edge-proxy"){g.edge(node.e).labelRank=node.rank;g.removeNode(v)}})}function translateGraph(g){var minX=Number.POSITIVE_INFINITY,maxX=0,minY=Number.POSITIVE_INFINITY,maxY=0,graphLabel=g.graph(),marginX=graphLabel.marginx||0,marginY=graphLabel.marginy||0;function getExtremes(attrs){var x=attrs.x,y=attrs.y,w=attrs.width,h=attrs.height;minX=Math.min(minX,x-w/2);maxX=Math.max(maxX,x+w/2);minY=Math.min(minY,y-h/2);maxY=Math.max(maxY,y+h/2)}_.each(g.nodes(),function(v){getExtremes(g.node(v))});_.each(g.edges(),function(e){var edge=g.edge(e);if(_.has(edge,"x")){getExtremes(edge)}});minX-=marginX;minY-=marginY;_.each(g.nodes(),function(v){var node=g.node(v);node.x-=minX;node.y-=minY});_.each(g.edges(),function(e){var edge=g.edge(e);_.each(edge.points,function(p){p.x-=minX;p.y-=minY});if(_.has(edge,"x")){edge.x-=minX}if(_.has(edge,"y")){edge.y-=minY}});graphLabel.width=maxX-minX+marginX;graphLabel.height=maxY-minY+marginY}function assignNodeIntersects(g){_.each(g.edges(),function(e){var edge=g.edge(e),nodeV=g.node(e.v),nodeW=g.node(e.w),p1,p2;if(!edge.points){edge.points=[];p1=nodeW;p2=nodeV}else{p1=edge.points[0];p2=edge.points[edge.points.length-1]}edge.points.unshift(util.intersectRect(nodeV,p1));edge.points.push(util.intersectRect(nodeW,p2))})}function fixupEdgeLabelCoords(g){_.each(g.edges(),function(e){var edge=g.edge(e);if(_.has(edge,"x")){if(edge.labelpos==="l"||edge.labelpos==="r"){edge.width-=edge.labeloffset}switch(edge.labelpos){case"l":edge.x-=edge.width/2+edge.labeloffset;break;case"r":edge.x+=edge.width/2+edge.labeloffset;break}}})}function reversePointsForReversedEdges(g){_.each(g.edges(),function(e){var edge=g.edge(e);if(edge.reversed){edge.points.reverse()}})}function removeBorderNodes(g){_.each(g.nodes(),function(v){if(g.children(v).length){var node=g.node(v),t=g.node(node.borderTop),b=g.node(node.borderBottom),l=g.node(_.last(node.borderLeft)),r=g.node(_.last(node.borderRight));node.width=Math.abs(r.x-l.x);node.height=Math.abs(b.y-t.y);node.x=l.x+node.width/2;node.y=t.y+node.height/2}});_.each(g.nodes(),function(v){if(g.node(v).dummy==="border"){g.removeNode(v)}})}function removeSelfEdges(g){_.each(g.edges(),function(e){if(e.v===e.w){var node=g.node(e.v);if(!node.selfEdges){node.selfEdges=[]}node.selfEdges.push({e:e,label:g.edge(e)});g.removeEdge(e)}})}function insertSelfEdges(g){var layers=util.buildLayerMatrix(g);_.each(layers,function(layer){var orderShift=0;_.each(layer,function(v,i){var node=g.node(v);node.order=i+orderShift;_.each(node.selfEdges,function(selfEdge){util.addDummyNode(g,"selfedge",{width:selfEdge.label.width,height:selfEdge.label.height,rank:node.rank,order:i+ ++orderShift,e:selfEdge.e,label:selfEdge.label},"_se")});delete node.selfEdges})})}function positionSelfEdges(g){_.each(g.nodes(),function(v){var node=g.node(v);if(node.dummy==="selfedge"){var selfNode=g.node(node.e.v),x=selfNode.x+selfNode.width/2,y=selfNode.y,dx=node.x-x,dy=selfNode.height/2;g.setEdge(node.e,node.label);g.removeNode(v);node.label.points=[{x:x+2*dx/3,y:y-dy},{x:x+5*dx/6,y:y-dy},{x:x+dx,y:y},{x:x+5*dx/6,y:y+dy},{x:x+2*dx/3,y:y+dy}];node.label.x=node.x;node.label.y=node.y}})}function selectNumberAttrs(obj,attrs){return _.mapValues(_.pick(obj,attrs),Number)}function canonicalize(attrs){var newAttrs={};_.each(attrs,function(v,k){newAttrs[k.toLowerCase()]=v});return newAttrs}},{"./acyclic":28,"./add-border-segments":29,"./coordinate-system":30,"./graphlib":33,"./lodash":36,"./nesting-graph":37,"./normalize":38,"./order":43,"./parent-dummy-chains":48,"./position":50,"./rank":52,"./util":55}],36:[function(require,module,exports){module.exports=require(20)},{"/Users/andrew/Documents/dev/dagre-d3/lib/lodash.js":20,lodash:77}],37:[function(require,module,exports){var _=require("./lodash"),util=require("./util");module.exports={run:run,cleanup:cleanup};function run(g){var root=util.addDummyNode(g,"root",{},"_root"),depths=treeDepths(g),height=_.max(depths)-1,nodeSep=2*height+1;g.graph().nestingRoot=root;_.each(g.edges(),function(e){g.edge(e).minlen*=nodeSep});var weight=sumWeights(g)+1;_.each(g.children(),function(child){dfs(g,root,nodeSep,weight,height,depths,child)});g.graph().nodeRankFactor=nodeSep}function dfs(g,root,nodeSep,weight,height,depths,v){var children=g.children(v);if(!children.length){if(v!==root){g.setEdge(root,v,{weight:0,minlen:nodeSep})}return}var top=util.addBorderNode(g,"_bt"),bottom=util.addBorderNode(g,"_bb"),label=g.node(v);g.setParent(top,v);label.borderTop=top;g.setParent(bottom,v);label.borderBottom=bottom;_.each(children,function(child){dfs(g,root,nodeSep,weight,height,depths,child);var childNode=g.node(child),childTop=childNode.borderTop?childNode.borderTop:child,childBottom=childNode.borderBottom?childNode.borderBottom:child,thisWeight=childNode.borderTop?weight:2*weight,minlen=childTop!==childBottom?1:height-depths[v]+1;g.setEdge(top,childTop,{weight:thisWeight,minlen:minlen,nestingEdge:true});g.setEdge(childBottom,bottom,{weight:thisWeight,minlen:minlen,nestingEdge:true})});if(!g.parent(v)){g.setEdge(root,top,{weight:0,minlen:height+depths[v]})}}function treeDepths(g){var depths={};function dfs(v,depth){var children=g.children(v);if(children&&children.length){_.each(children,function(child){dfs(child,depth+1)})}depths[v]=depth}_.each(g.children(),function(v){dfs(v,1)});return depths}function sumWeights(g){return _.reduce(g.edges(),function(acc,e){return acc+g.edge(e).weight},0)}function cleanup(g){var graphLabel=g.graph();g.removeNode(graphLabel.nestingRoot);delete graphLabel.nestingRoot;_.each(g.edges(),function(e){var edge=g.edge(e);if(edge.nestingEdge){g.removeEdge(e)}})}},{"./lodash":36,"./util":55}],38:[function(require,module,exports){"use strict";var _=require("./lodash"),util=require("./util");module.exports={run:run,undo:undo};function run(g){g.graph().dummyChains=[];_.each(g.edges(),function(edge){normalizeEdge(g,edge)})}function normalizeEdge(g,e){var v=e.v,vRank=g.node(v).rank,w=e.w,wRank=g.node(w).rank,name=e.name,edgeLabel=g.edge(e),labelRank=edgeLabel.labelRank;if(wRank===vRank+1)return;g.removeEdge(e);var dummy,attrs,i;for(i=0,++vRank;vRank<wRank;++i,++vRank){edgeLabel.points=[];attrs={width:0,height:0,edgeLabel:edgeLabel,edgeObj:e,rank:vRank};dummy=util.addDummyNode(g,"edge",attrs,"_d");if(vRank===labelRank){attrs.width=edgeLabel.width;attrs.height=edgeLabel.height;attrs.dummy="edge-label";attrs.labelpos=edgeLabel.labelpos}g.setEdge(v,dummy,{weight:edgeLabel.weight},name);if(i===0){g.graph().dummyChains.push(dummy)}v=dummy}g.setEdge(v,w,{weight:edgeLabel.weight},name)}function undo(g){_.each(g.graph().dummyChains,function(v){var node=g.node(v),origLabel=node.edgeLabel,w;g.setEdge(node.edgeObj,origLabel);while(node.dummy){w=g.successors(v)[0];g.removeNode(v);origLabel.points.push({x:node.x,y:node.y});if(node.dummy==="edge-label"){origLabel.x=node.x;origLabel.y=node.y;origLabel.width=node.width;origLabel.height=node.height}v=w;node=g.node(v)}})}},{"./lodash":36,"./util":55}],39:[function(require,module,exports){var _=require("../lodash");module.exports=addSubgraphConstraints;function addSubgraphConstraints(g,cg,vs){var prev={},rootPrev;_.each(vs,function(v){var child=g.parent(v),parent,prevChild;while(child){parent=g.parent(child);if(parent){prevChild=prev[parent];prev[parent]=child}else{prevChild=rootPrev;rootPrev=child}if(prevChild&&prevChild!==child){cg.setEdge(prevChild,child);return}child=parent}})}},{"../lodash":36}],40:[function(require,module,exports){var _=require("../lodash");module.exports=barycenter;function barycenter(g,movable){return _.map(movable,function(v){var inV=g.inEdges(v);if(!inV.length){return{v:v}}else{var result=_.reduce(inV,function(acc,e){var edge=g.edge(e),nodeU=g.node(e.v);return{sum:acc.sum+edge.weight*nodeU.order,weight:acc.weight+edge.weight}},{sum:0,weight:0});return{v:v,barycenter:result.sum/result.weight,weight:result.weight}}})}},{"../lodash":36}],41:[function(require,module,exports){var _=require("../lodash"),Graph=require("../graphlib").Graph;module.exports=buildLayerGraph;function buildLayerGraph(g,rank,relationship){var root=createRootNode(g),result=new Graph({compound:true}).setGraph({root:root}).setDefaultNodeLabel(function(v){return g.node(v)});_.each(g.nodes(),function(v){var node=g.node(v),parent=g.parent(v);if(node.rank===rank||node.minRank<=rank&&rank<=node.maxRank){result.setNode(v);result.setParent(v,parent||root);_.each(g[relationship](v),function(e){var u=e.v===v?e.w:e.v,edge=result.edge(u,v),weight=!_.isUndefined(edge)?edge.weight:0;result.setEdge(u,v,{weight:g.edge(e).weight+weight})});if(_.has(node,"minRank")){result.setNode(v,{borderLeft:node.borderLeft[rank],borderRight:node.borderRight[rank]})}}});return result}function createRootNode(g){var v;while(g.hasNode(v=_.uniqueId("_root")));return v}},{"../graphlib":33,"../lodash":36}],42:[function(require,module,exports){"use strict";var _=require("../lodash");module.exports=crossCount;function crossCount(g,layering){var cc=0;for(var i=1;i<layering.length;++i){cc+=twoLayerCrossCount(g,layering[i-1],layering[i])}return cc}function twoLayerCrossCount(g,northLayer,southLayer){var southPos=_.zipObject(southLayer,_.map(southLayer,function(v,i){return i}));var southEntries=_.flatten(_.map(northLayer,function(v){return _.chain(g.outEdges(v)).map(function(e){return{pos:southPos[e.w],weight:g.edge(e).weight}}).sortBy("pos").value()}),true);var firstIndex=1;while(firstIndex<southLayer.length)firstIndex<<=1;var treeSize=2*firstIndex-1;firstIndex-=1;var tree=_.map(new Array(treeSize),function(){return 0});var cc=0;_.each(southEntries.forEach(function(entry){var index=entry.pos+firstIndex;tree[index]+=entry.weight;var weightSum=0;while(index>0){if(index%2){weightSum+=tree[index+1]}index=index-1>>1;tree[index]+=entry.weight}cc+=entry.weight*weightSum}));return cc}},{"../lodash":36}],43:[function(require,module,exports){"use strict";var _=require("../lodash"),initOrder=require("./init-order"),crossCount=require("./cross-count"),sortSubgraph=require("./sort-subgraph"),buildLayerGraph=require("./build-layer-graph"),addSubgraphConstraints=require("./add-subgraph-constraints"),Graph=require("../graphlib").Graph,util=require("../util");module.exports=order;function order(g){var maxRank=util.maxRank(g),downLayerGraphs=buildLayerGraphs(g,_.range(1,maxRank+1),"inEdges"),upLayerGraphs=buildLayerGraphs(g,_.range(maxRank-1,-1,-1),"outEdges");var layering=initOrder(g);assignOrder(g,layering);var bestCC=Number.POSITIVE_INFINITY,best;for(var i=0,lastBest=0;lastBest<4;++i,++lastBest){sweepLayerGraphs(i%2?downLayerGraphs:upLayerGraphs,i%4>=2);layering=util.buildLayerMatrix(g);var cc=crossCount(g,layering);if(cc<bestCC){lastBest=0;best=_.cloneDeep(layering);bestCC=cc}}assignOrder(g,best)}function buildLayerGraphs(g,ranks,relationship){return _.map(ranks,function(rank){return buildLayerGraph(g,rank,relationship)})}function sweepLayerGraphs(layerGraphs,biasRight){var cg=new Graph;_.each(layerGraphs,function(lg){var root=lg.graph().root;var sorted=sortSubgraph(lg,root,cg,biasRight);_.each(sorted.vs,function(v,i){lg.node(v).order=i});addSubgraphConstraints(lg,cg,sorted.vs)})}function assignOrder(g,layering){_.each(layering,function(layer){_.each(layer,function(v,i){g.node(v).order=i})})}},{"../graphlib":33,"../lodash":36,"../util":55,"./add-subgraph-constraints":39,"./build-layer-graph":41,"./cross-count":42,"./init-order":44,"./sort-subgraph":46}],44:[function(require,module,exports){"use strict";var _=require("../lodash");module.exports=initOrder;function initOrder(g){var visited={},simpleNodes=_.filter(g.nodes(),function(v){return!g.children(v).length}),maxRank=_.max(_.map(simpleNodes,function(v){return g.node(v).rank})),layers=_.map(_.range(maxRank+1),function(){return[]});function dfs(v){if(_.has(visited,v))return;visited[v]=true;var node=g.node(v);layers[node.rank].push(v);_.each(g.successors(v),dfs)}var orderedVs=_.sortBy(simpleNodes,function(v){return g.node(v).rank});_.each(orderedVs,dfs);return layers}},{"../lodash":36}],45:[function(require,module,exports){"use strict";var _=require("../lodash");module.exports=resolveConflicts;function resolveConflicts(entries,cg){var mappedEntries={};_.each(entries,function(entry,i){var tmp=mappedEntries[entry.v]={indegree:0,"in":[],out:[],vs:[entry.v],i:i};if(!_.isUndefined(entry.barycenter)){tmp.barycenter=entry.barycenter;tmp.weight=entry.weight}});_.each(cg.edges(),function(e){var entryV=mappedEntries[e.v],entryW=mappedEntries[e.w];if(!_.isUndefined(entryV)&&!_.isUndefined(entryW)){entryW.indegree++;entryV.out.push(mappedEntries[e.w])}});var sourceSet=_.filter(mappedEntries,function(entry){return!entry.indegree});return doResolveConflicts(sourceSet)}function doResolveConflicts(sourceSet){var entries=[];function handleIn(vEntry){return function(uEntry){if(uEntry.merged){return}if(_.isUndefined(uEntry.barycenter)||_.isUndefined(vEntry.barycenter)||uEntry.barycenter>=vEntry.barycenter){mergeEntries(vEntry,uEntry)}}}function handleOut(vEntry){return function(wEntry){wEntry["in"].push(vEntry);if(--wEntry.indegree===0){sourceSet.push(wEntry)}}}while(sourceSet.length){var entry=sourceSet.pop();entries.push(entry);_.each(entry["in"].reverse(),handleIn(entry));_.each(entry.out,handleOut(entry))}return _.chain(entries).filter(function(entry){return!entry.merged}).map(function(entry){return _.pick(entry,["vs","i","barycenter","weight"])}).value()}function mergeEntries(target,source){var sum=0,weight=0;if(target.weight){sum+=target.barycenter*target.weight;weight+=target.weight}if(source.weight){sum+=source.barycenter*source.weight;weight+=source.weight}target.vs=source.vs.concat(target.vs);target.barycenter=sum/weight;target.weight=weight;target.i=Math.min(source.i,target.i);source.merged=true}},{"../lodash":36}],46:[function(require,module,exports){var _=require("../lodash"),barycenter=require("./barycenter"),resolveConflicts=require("./resolve-conflicts"),sort=require("./sort");module.exports=sortSubgraph;function sortSubgraph(g,v,cg,biasRight){var movable=g.children(v),node=g.node(v),bl=node?node.borderLeft:undefined,br=node?node.borderRight:undefined,subgraphs={};if(bl){movable=_.filter(movable,function(w){return w!==bl&&w!==br})}var barycenters=barycenter(g,movable);_.each(barycenters,function(entry){if(g.children(entry.v).length){var subgraphResult=sortSubgraph(g,entry.v,cg,biasRight);subgraphs[entry.v]=subgraphResult;if(_.has(subgraphResult,"barycenter")){mergeBarycenters(entry,subgraphResult)}}});var entries=resolveConflicts(barycenters,cg);expandSubgraphs(entries,subgraphs);var result=sort(entries,biasRight);if(bl){result.vs=_.flatten([bl,result.vs,br],true);if(g.predecessors(bl).length){var blPred=g.node(g.predecessors(bl)[0]),brPred=g.node(g.predecessors(br)[0]);if(!_.has(result,"barycenter")){result.barycenter=0;result.weight=0}result.barycenter=(result.barycenter*result.weight+blPred.order+brPred.order)/(result.weight+2);result.weight+=2}}return result}function expandSubgraphs(entries,subgraphs){_.each(entries,function(entry){entry.vs=_.flatten(entry.vs.map(function(v){if(subgraphs[v]){return subgraphs[v].vs}return v}),true)})}function mergeBarycenters(target,other){if(!_.isUndefined(target.barycenter)){target.barycenter=(target.barycenter*target.weight+other.barycenter*other.weight)/(target.weight+other.weight);target.weight+=other.weight}else{target.barycenter=other.barycenter;target.weight=other.weight}}},{"../lodash":36,"./barycenter":40,"./resolve-conflicts":45,"./sort":47}],47:[function(require,module,exports){var _=require("../lodash"),util=require("../util");module.exports=sort;function sort(entries,biasRight){var parts=util.partition(entries,function(entry){return _.has(entry,"barycenter")});var sortable=parts.lhs,unsortable=_.sortBy(parts.rhs,function(entry){return-entry.i}),vs=[],sum=0,weight=0,vsIndex=0;sortable.sort(compareWithBias(!!biasRight));vsIndex=consumeUnsortable(vs,unsortable,vsIndex);_.each(sortable,function(entry){vsIndex+=entry.vs.length;vs.push(entry.vs);sum+=entry.barycenter*entry.weight;weight+=entry.weight;vsIndex=consumeUnsortable(vs,unsortable,vsIndex)});var result={vs:_.flatten(vs,true)};if(weight){result.barycenter=sum/weight;result.weight=weight}return result}function consumeUnsortable(vs,unsortable,index){var last;while(unsortable.length&&(last=_.last(unsortable)).i<=index){unsortable.pop();vs.push(last.vs);index++}return index}function compareWithBias(bias){return function(entryV,entryW){if(entryV.barycenter<entryW.barycenter){return-1}else if(entryV.barycenter>entryW.barycenter){return 1}return!bias?entryV.i-entryW.i:entryW.i-entryV.i}}},{"../lodash":36,"../util":55}],48:[function(require,module,exports){var _=require("./lodash");module.exports=parentDummyChains;function parentDummyChains(g){var postorderNums=postorder(g);_.each(g.graph().dummyChains,function(v){var node=g.node(v),edgeObj=node.edgeObj,pathData=findPath(g,postorderNums,edgeObj.v,edgeObj.w),path=pathData.path,lca=pathData.lca,pathIdx=0,pathV=path[pathIdx],ascending=true;while(v!==edgeObj.w){node=g.node(v);if(ascending){while((pathV=path[pathIdx])!==lca&&g.node(pathV).maxRank<node.rank){pathIdx++}if(pathV===lca){ascending=false}}if(!ascending){while(pathIdx<path.length-1&&g.node(pathV=path[pathIdx+1]).minRank<=node.rank){pathIdx++}pathV=path[pathIdx]}g.setParent(v,pathV);v=g.successors(v)[0]}})}function findPath(g,postorderNums,v,w){var vPath=[],wPath=[],low=Math.min(postorderNums[v].low,postorderNums[w].low),lim=Math.max(postorderNums[v].lim,postorderNums[w].lim),parent,lca;parent=v;do{parent=g.parent(parent);vPath.push(parent)}while(parent&&(postorderNums[parent].low>low||lim>postorderNums[parent].lim));lca=parent;parent=w;while((parent=g.parent(parent))!==lca){wPath.push(parent)}return{path:vPath.concat(wPath.reverse()),lca:lca}}function postorder(g){var result={},lim=0;function 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ws=neighborFn(v);if(ws.length){ws=_.sortBy(ws,function(w){return pos[w]});var mp=(ws.length-1)/2;for(var i=Math.floor(mp),il=Math.ceil(mp);i<=il;++i){var w=ws[i];if(align[v]===v&&prevIdx<pos[w]&&!hasConflict(conflicts,v,w)){align[w]=v;align[v]=root[v]=root[w];prevIdx=pos[w]}}}})});return{root:root,align:align}}function horizontalCompaction(g,layering,root,align,reverseSep){var xs={},blockG=buildBlockGraph(g,layering,root,reverseSep);var visited={};function pass1(v){if(!_.has(visited,v)){visited[v]=true;xs[v]=_.reduce(blockG.inEdges(v),function(max,e){pass1(e.v);return Math.max(max,xs[e.v]+blockG.edge(e))},0)}}_.each(blockG.nodes(),pass1);function pass2(v){if(visited[v]!==2){visited[v]++;var min=_.reduce(blockG.outEdges(v),function(min,e){pass2(e.w);return Math.min(min,xs[e.w]-blockG.edge(e))},Number.POSITIVE_INFINITY);if(min!==Number.POSITIVE_INFINITY){xs[v]=Math.max(xs[v],min)}}}_.each(blockG.nodes(),pass2);_.each(align,function(v){xs[v]=xs[root[v]]});return xs}function 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+do{v=_.uniqueId(name)}while(g.hasNode(v));attrs.dummy=type;g.setNode(v,attrs);return v}function simplify(g){var simplified=(new Graph).setGraph(g.graph());_.each(g.nodes(),function(v){simplified.setNode(v,g.node(v))});_.each(g.edges(),function(e){var simpleLabel=simplified.edge(e.v,e.w)||{weight:0,minlen:1},label=g.edge(e);simplified.setEdge(e.v,e.w,{weight:simpleLabel.weight+label.weight,minlen:Math.max(simpleLabel.minlen,label.minlen)})});return simplified}function asNonCompoundGraph(g){var simplified=new Graph({multigraph:g.isMultigraph()}).setGraph(g.graph());_.each(g.nodes(),function(v){if(!g.children(v).length){simplified.setNode(v,g.node(v))}});_.each(g.edges(),function(e){simplified.setEdge(e,g.edge(e))});return simplified}function successorWeights(g){var weightMap=_.map(g.nodes(),function(v){var sucs={};_.each(g.outEdges(v),function(e){sucs[e.w]=(sucs[e.w]||0)+g.edge(e).weight});return sucs});return _.zipObject(g.nodes(),weightMap)}function predecessorWeights(g){var weightMap=_.map(g.nodes(),function(v){var preds={};_.each(g.inEdges(v),function(e){preds[e.v]=(preds[e.v]||0)+g.edge(e).weight});return preds});return _.zipObject(g.nodes(),weightMap)}function intersectRect(rect,point){var x=rect.x;var y=rect.y;var dx=point.x-x;var dy=point.y-y;var w=rect.width/2;var h=rect.height/2;if(!dx&&!dy){throw new Error("Not possible to find intersection inside of the rectangle")}var sx,sy;if(Math.abs(dy)*w>Math.abs(dx)*h){if(dy<0){h=-h}sx=h*dx/dy;sy=h}else{if(dx<0){w=-w}sx=w;sy=w*dy/dx}return{x:x+sx,y:y+sy}}function buildLayerMatrix(g){var layering=_.map(_.range(maxRank(g)+1),function(){return[]});_.each(g.nodes(),function(v){var node=g.node(v),rank=node.rank;if(!_.isUndefined(rank)){layering[rank][node.order]=v}});return layering}function normalizeRanks(g){var min=_.min(_.map(g.nodes(),function(v){return g.node(v).rank}));_.each(g.nodes(),function(v){var node=g.node(v);if(_.has(node,"rank")){node.rank-=min}})}function removeEmptyRanks(g){var offset=_.min(_.map(g.nodes(),function(v){return g.node(v).rank}));var layers=[];_.each(g.nodes(),function(v){var rank=g.node(v).rank-offset;if(!_.has(layers,rank)){layers[rank]=[]}layers[rank].push(v)});var delta=0,nodeRankFactor=g.graph().nodeRankFactor;_.each(layers,function(vs,i){if(_.isUndefined(vs)&&i%nodeRankFactor!==0){--delta}else if(delta){_.each(vs,function(v){g.node(v).rank+=delta})}})}function addBorderNode(g,prefix,rank,order){var node={width:0,height:0};if(arguments.length>=4){node.rank=rank;node.order=order}return addDummyNode(g,"border",node,prefix)}function maxRank(g){return _.max(_.map(g.nodes(),function(v){var rank=g.node(v).rank;if(!_.isUndefined(rank)){return rank}}))}function partition(collection,fn){var result={lhs:[],rhs:[]};_.each(collection,function(value){if(fn(value)){result.lhs.push(value)}else{result.rhs.push(value)}});return result}function time(name,fn){var start=_.now();try{return fn()}finally{console.log(name+" time: "+(_.now()-start)+"ms")}}function notime(name,fn){return fn()}},{"./graphlib":33,"./lodash":36}],56:[function(require,module,exports){module.exports="0.7.1"},{}],57:[function(require,module,exports){var lib=require("./lib");module.exports={Graph:lib.Graph,json:require("./lib/json"),alg:require("./lib/alg"),version:lib.version}},{"./lib":73,"./lib/alg":64,"./lib/json":74}],58:[function(require,module,exports){var _=require("../lodash");module.exports=components;function components(g){var visited={},cmpts=[],cmpt;function dfs(v){if(_.has(visited,v))return;visited[v]=true;cmpt.push(v);_.each(g.successors(v),dfs);_.each(g.predecessors(v),dfs)}_.each(g.nodes(),function(v){cmpt=[];dfs(v);if(cmpt.length){cmpts.push(cmpt)}});return cmpts}},{"../lodash":75}],59:[function(require,module,exports){var _=require("../lodash");module.exports=dfs;function dfs(g,vs,order){if(!_.isArray(vs)){vs=[vs]}var acc=[],visited={};_.each(vs,function(v){if(!g.hasNode(v)){throw new Error("Graph does not have node: "+v)}doDfs(g,v,order==="post",visited,acc)});return acc}function doDfs(g,v,postorder,visited,acc){if(!_.has(visited,v)){visited[v]=true;if(!postorder){acc.push(v)}_.each(g.neighbors(v),function(w){doDfs(g,w,postorder,visited,acc)});if(postorder){acc.push(v)}}}},{"../lodash":75}],60:[function(require,module,exports){var dijkstra=require("./dijkstra"),_=require("../lodash");module.exports=dijkstraAll;function dijkstraAll(g,weightFunc,edgeFunc){return _.transform(g.nodes(),function(acc,v){acc[v]=dijkstra(g,v,weightFunc,edgeFunc)},{})}},{"../lodash":75,"./dijkstra":61}],61:[function(require,module,exports){var _=require("../lodash"),PriorityQueue=require("../data/priority-queue");module.exports=dijkstra;var DEFAULT_WEIGHT_FUNC=_.constant(1);function dijkstra(g,source,weightFn,edgeFn){return runDijkstra(g,String(source),weightFn||DEFAULT_WEIGHT_FUNC,edgeFn||function(v){return g.outEdges(v)})}function runDijkstra(g,source,weightFn,edgeFn){var results={},pq=new PriorityQueue,v,vEntry;var updateNeighbors=function(edge){var w=edge.v!==v?edge.v:edge.w,wEntry=results[w],weight=weightFn(edge),distance=vEntry.distance+weight;if(weight<0){throw new Error("dijkstra does not allow negative edge weights. "+"Bad edge: "+edge+" Weight: "+weight)}if(distance<wEntry.distance){wEntry.distance=distance;wEntry.predecessor=v;pq.decrease(w,distance)}};g.nodes().forEach(function(v){var distance=v===source?0:Number.POSITIVE_INFINITY;results[v]={distance:distance};pq.add(v,distance)});while(pq.size()>0){v=pq.removeMin();vEntry=results[v];if(vEntry.distance===Number.POSITIVE_INFINITY){break}edgeFn(v).forEach(updateNeighbors)}return results}},{"../data/priority-queue":71,"../lodash":75}],62:[function(require,module,exports){var _=require("../lodash"),tarjan=require("./tarjan");module.exports=findCycles;function findCycles(g){return _.filter(tarjan(g),function(cmpt){return cmpt.length>1})}},{"../lodash":75,"./tarjan":69}],63:[function(require,module,exports){var _=require("../lodash");module.exports=floydWarshall;var DEFAULT_WEIGHT_FUNC=_.constant(1);function floydWarshall(g,weightFn,edgeFn){return runFloydWarshall(g,weightFn||DEFAULT_WEIGHT_FUNC,edgeFn||function(v){return g.outEdges(v)})}function runFloydWarshall(g,weightFn,edgeFn){var results={},nodes=g.nodes();nodes.forEach(function(v){results[v]={};results[v][v]={distance:0};nodes.forEach(function(w){if(v!==w){results[v][w]={distance:Number.POSITIVE_INFINITY}}});edgeFn(v).forEach(function(edge){var w=edge.v===v?edge.w:edge.v,d=weightFn(edge);results[v][w]={distance:d,predecessor:v}})});nodes.forEach(function(k){var rowK=results[k];nodes.forEach(function(i){var rowI=results[i];nodes.forEach(function(j){var ik=rowI[k];var kj=rowK[j];var ij=rowI[j];var altDistance=ik.distance+kj.distance;if(altDistance<ij.distance){ij.distance=altDistance;ij.predecessor=kj.predecessor}})})});return results}},{"../lodash":75}],64:[function(require,module,exports){module.exports={components:require("./components"),dijkstra:require("./dijkstra"),dijkstraAll:require("./dijkstra-all"),findCycles:require("./find-cycles"),floydWarshall:require("./floyd-warshall"),isAcyclic:require("./is-acyclic"),postorder:require("./postorder"),preorder:require("./preorder"),prim:require("./prim"),tarjan:require("./tarjan"),topsort:require("./topsort")}},{"./components":58,"./dijkstra":61,"./dijkstra-all":60,"./find-cycles":62,"./floyd-warshall":63,"./is-acyclic":65,"./postorder":66,"./preorder":67,"./prim":68,"./tarjan":69,"./topsort":70}],65:[function(require,module,exports){var topsort=require("./topsort");module.exports=isAcyclic;function isAcyclic(g){try{topsort(g)}catch(e){if(e instanceof topsort.CycleException){return false}throw e}return true}},{"./topsort":70}],66:[function(require,module,exports){var dfs=require("./dfs");module.exports=postorder;function postorder(g,vs){return dfs(g,vs,"post")}},{"./dfs":59}],67:[function(require,module,exports){var dfs=require("./dfs");module.exports=preorder;function preorder(g,vs){return dfs(g,vs,"pre")}},{"./dfs":59}],68:[function(require,module,exports){var _=require("../lodash"),Graph=require("../graph"),PriorityQueue=require("../data/priority-queue");module.exports=prim;function prim(g,weightFunc){var result=new Graph,parents={},pq=new PriorityQueue,v;function updateNeighbors(edge){var w=edge.v===v?edge.w:edge.v,pri=pq.priority(w);if(pri!==undefined){var edgeWeight=weightFunc(edge);if(edgeWeight<pri){parents[w]=v;pq.decrease(w,edgeWeight)}}}if(g.nodeCount()===0){return result}_.each(g.nodes(),function(v){pq.add(v,Number.POSITIVE_INFINITY);result.setNode(v)});pq.decrease(g.nodes()[0],0);var init=false;while(pq.size()>0){v=pq.removeMin();if(_.has(parents,v)){result.setEdge(v,parents[v])}else if(init){throw new Error("Input graph is not connected: "+g)}else{init=true}g.nodeEdges(v).forEach(updateNeighbors)}return result}},{"../data/priority-queue":71,"../graph":72,"../lodash":75}],69:[function(require,module,exports){var _=require("../lodash");module.exports=tarjan;function tarjan(g){var index=0,stack=[],visited={},results=[];function dfs(v){var entry=visited[v]={onStack:true,lowlink:index,index:index++};stack.push(v);g.successors(v).forEach(function(w){if(!_.has(visited,w)){dfs(w);entry.lowlink=Math.min(entry.lowlink,visited[w].lowlink)}else if(visited[w].onStack){entry.lowlink=Math.min(entry.lowlink,visited[w].index)}});if(entry.lowlink===entry.index){var cmpt=[],w;do{w=stack.pop();visited[w].onStack=false;cmpt.push(w)}while(v!==w);results.push(cmpt)}}g.nodes().forEach(function(v){if(!_.has(visited,v)){dfs(v)}});return results}},{"../lodash":75}],70:[function(require,module,exports){var _=require("../lodash");module.exports=topsort;topsort.CycleException=CycleException;function topsort(g){var visited={},stack={},results=[];function visit(node){if(_.has(stack,node)){throw new CycleException}if(!_.has(visited,node)){stack[node]=true;visited[node]=true;_.each(g.predecessors(node),visit);delete stack[node];results.push(node)}}_.each(g.sinks(),visit);if(_.size(visited)!==g.nodeCount()){throw new CycleException}return results}function CycleException(){}},{"../lodash":75}],71:[function(require,module,exports){var _=require("../lodash");module.exports=PriorityQueue;function PriorityQueue(){this._arr=[];this._keyIndices={}}PriorityQueue.prototype.size=function(){return this._arr.length};PriorityQueue.prototype.keys=function(){return this._arr.map(function(x){return x.key})};PriorityQueue.prototype.has=function(key){return _.has(this._keyIndices,key)};PriorityQueue.prototype.priority=function(key){var index=this._keyIndices[key];if(index!==undefined){return this._arr[index].priority}};PriorityQueue.prototype.min=function(){if(this.size()===0){throw new Error("Queue underflow")}return this._arr[0].key};PriorityQueue.prototype.add=function(key,priority){var keyIndices=this._keyIndices;key=String(key);if(!_.has(keyIndices,key)){var arr=this._arr;var index=arr.length;keyIndices[key]=index;arr.push({key:key,priority:priority});this._decrease(index);return true}return false};PriorityQueue.prototype.removeMin=function(){this._swap(0,this._arr.length-1);var min=this._arr.pop();delete this._keyIndices[min.key];this._heapify(0);return min.key};PriorityQueue.prototype.decrease=function(key,priority){var index=this._keyIndices[key];if(priority>this._arr[index].priority){throw new Error("New priority is greater than current priority. "+"Key: "+key+" Old: "+this._arr[index].priority+" New: "+priority)}this._arr[index].priority=priority;this._decrease(index)};PriorityQueue.prototype._heapify=function(i){var arr=this._arr;var l=2*i,r=l+1,largest=i;if(l<arr.length){largest=arr[l].priority<arr[largest].priority?l:largest;if(r<arr.length){largest=arr[r].priority<arr[largest].priority?r:largest}if(largest!==i){this._swap(i,largest);this._heapify(largest)}}};PriorityQueue.prototype._decrease=function(index){var arr=this._arr;var priority=arr[index].priority;var parent;while(index!==0){parent=index>>1;if(arr[parent].priority<priority){break}this._swap(index,parent);index=parent}};PriorityQueue.prototype._swap=function(i,j){var arr=this._arr;var keyIndices=this._keyIndices;var origArrI=arr[i];var origArrJ=arr[j];arr[i]=origArrJ;arr[j]=origArrI;keyIndices[origArrJ.key]=i;keyIndices[origArrI.key]=j}},{"../lodash":75}],72:[function(require,module,exports){"use strict";var _=require("./lodash");module.exports=Graph;var DEFAULT_EDGE_NAME="\x00",GRAPH_NODE="\x00",EDGE_KEY_DELIM="
";function Graph(opts){this._isDirected=_.has(opts,"directed")?opts.directed:true;this._isMultigraph=_.has(opts,"multigraph")?opts.multigraph:false;this._isCompound=_.has(opts,"compound")?opts.compound:false;this._label=undefined;this._defaultNodeLabelFn=_.constant(undefined);this._defaultEdgeLabelFn=_.constant(undefined);this._nodes={};if(this._isCompound){this._parent={};this._children={};this._children[GRAPH_NODE]={}}this._in={};this._preds={};this._out={};this._sucs={};this._edgeObjs={};this._edgeLabels={}}Graph.prototype._nodeCount=0;Graph.prototype._edgeCount=0;Graph.prototype.isDirected=function(){return this._isDirected};Graph.prototype.isMultigraph=function(){return this._isMultigraph};Graph.prototype.isCompound=function(){return this._isCompound};Graph.prototype.setGraph=function(label){this._label=label;return this};Graph.prototype.graph=function(){return this._label};Graph.prototype.setDefaultNodeLabel=function(newDefault){if(!_.isFunction(newDefault)){newDefault=_.constant(newDefault)}this._defaultNodeLabelFn=newDefault;return this};Graph.prototype.nodeCount=function(){return this._nodeCount};Graph.prototype.nodes=function(){return _.keys(this._nodes)};Graph.prototype.sources=function(){return _.filter(this.nodes(),function(v){return _.isEmpty(this._in[v])},this)};Graph.prototype.sinks=function(){return _.filter(this.nodes(),function(v){return _.isEmpty(this._out[v])},this)};Graph.prototype.setNodes=function(vs,value){var args=arguments;_.each(vs,function(v){if(args.length>1){this.setNode(v,value)}else{this.setNode(v)}},this);return this};Graph.prototype.setNode=function(v,value){if(_.has(this._nodes,v)){if(arguments.length>1){this._nodes[v]=value}return this}this._nodes[v]=arguments.length>1?value:this._defaultNodeLabelFn(v);if(this._isCompound){this._parent[v]=GRAPH_NODE;this._children[v]={};this._children[GRAPH_NODE][v]=true}this._in[v]={};this._preds[v]={};this._out[v]={};this._sucs[v]={};++this._nodeCount;return this};Graph.prototype.node=function(v){return this._nodes[v]};Graph.prototype.hasNode=function(v){return _.has(this._nodes,v)};Graph.prototype.removeNode=function(v){var self=this;if(_.has(this._nodes,v)){var removeEdge=function(e){self.removeEdge(self._edgeObjs[e])};delete this._nodes[v];if(this._isCompound){this._removeFromParentsChildList(v);delete this._parent[v];_.each(this.children(v),function(child){this.setParent(child)},this);delete this._children[v]}_.each(_.keys(this._in[v]),removeEdge);delete this._in[v];delete this._preds[v];_.each(_.keys(this._out[v]),removeEdge);delete this._out[v];delete this._sucs[v];--this._nodeCount}return this};Graph.prototype.setParent=function(v,parent){if(!this._isCompound){throw new Error("Cannot set parent in a non-compound graph")}if(_.isUndefined(parent)){parent=GRAPH_NODE}else{for(var ancestor=parent;!_.isUndefined(ancestor);ancestor=this.parent(ancestor)){if(ancestor===v){throw new Error("Setting "+parent+" as parent of "+v+" would create create a cycle")}}this.setNode(parent)}this.setNode(v);this._removeFromParentsChildList(v);this._parent[v]=parent;this._children[parent][v]=true;return this};Graph.prototype._removeFromParentsChildList=function(v){delete this._children[this._parent[v]][v]};Graph.prototype.parent=function(v){if(this._isCompound){var parent=this._parent[v];if(parent!==GRAPH_NODE){return parent}}};Graph.prototype.children=function(v){if(_.isUndefined(v)){v=GRAPH_NODE}if(this._isCompound){var children=this._children[v];if(children){return _.keys(children)}}else if(v===GRAPH_NODE){return this.nodes()}else if(this.hasNode(v)){return[]}};Graph.prototype.predecessors=function(v){var predsV=this._preds[v];if(predsV){return 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+}var isCalled=trailingCall;maxTimeoutId=timeoutId=trailingCall=undefined;if(isCalled){lastCalled=now();result=func.apply(thisArg,args);if(!timeoutId&&!maxTimeoutId){args=thisArg=null}}}else{timeoutId=setTimeout(delayed,remaining)}};var maxDelayed=function(){if(timeoutId){clearTimeout(timeoutId)}maxTimeoutId=timeoutId=trailingCall=undefined;if(trailing||maxWait!==wait){lastCalled=now();result=func.apply(thisArg,args);if(!timeoutId&&!maxTimeoutId){args=thisArg=null}}};return function(){args=arguments;stamp=now();thisArg=this;trailingCall=trailing&&(timeoutId||!leading);if(maxWait===false){var leadingCall=leading&&!timeoutId}else{if(!maxTimeoutId&&!leading){lastCalled=stamp}var remaining=maxWait-(stamp-lastCalled),isCalled=remaining<=0;if(isCalled){if(maxTimeoutId){maxTimeoutId=clearTimeout(maxTimeoutId)}lastCalled=stamp;result=func.apply(thisArg,args)}else if(!maxTimeoutId){maxTimeoutId=setTimeout(maxDelayed,remaining)}}if(isCalled&&timeoutId){timeoutId=clearTimeout(timeoutId)}else 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diff --git a/core/src/main/resources/org/apache/spark/ui/static/spark-dag-viz.css b/core/src/main/resources/org/apache/spark/ui/static/spark-dag-viz.css
new file mode 100644
index 0000000000000..8481710828455
--- /dev/null
+++ b/core/src/main/resources/org/apache/spark/ui/static/spark-dag-viz.css
@@ -0,0 +1,83 @@
+/*
+ * 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.
+ */
+
+#dag-viz-graph svg path {
+  stroke: #444444;
+  stroke-width: 1.5px;
+}
+
+#dag-viz-graph svg g.cluster rect {
+  stroke-width: 4px;
+  stroke-opacity: 0.5;
+}
+
+#dag-viz-graph svg g.node circle,
+#dag-viz-graph svg g.node rect {
+  fill: #444444;
+}
+
+#dag-viz-graph svg g.node.cached circle,
+#dag-viz-graph svg g.node.cached rect {
+  fill: #FF0000;
+}
+
+/* Job page specific styles */
+
+#dag-viz-graph svg.job marker#marker-arrow path {
+  fill: #444444;
+  stroke-width: 0px;
+}
+
+#dag-viz-graph svg.job g.cluster rect {
+  fill: #FFFFFF;
+  stroke: #AADFFF;
+}
+
+#dag-viz-graph svg.job g.cluster[id*="stage"] rect {
+  stroke: #FFDDEE;
+  stroke-width: 6px;
+}
+
+#dag-viz-graph svg.job g#cross-stage-edges path {
+  fill: none;
+}
+
+#dag-viz-graph svg.job g.cluster text {
+  fill: #AAAAAA;
+}
+
+/* Stage page specific styles */
+
+#dag-viz-graph svg.stage g.cluster rect {
+  fill: #F0F8FF;
+  stroke: #AADFFF;
+}
+
+#dag-viz-graph svg.stage g.cluster[id*="stage"] rect {
+  fill: #FFFFFF;
+  stroke: #FFDDEE;
+  stroke-width: 6px;
+}
+
+#dag-viz-graph svg.stage g.node g.label text tspan {
+  fill: #FFFFFF;
+}
+
+#dag-viz-graph svg.stage g.cluster text {
+  fill: #444444;
+  font-weight: bold;
+}
diff --git a/core/src/main/resources/org/apache/spark/ui/static/spark-dag-viz.js b/core/src/main/resources/org/apache/spark/ui/static/spark-dag-viz.js
index 99b029477c5ff..eb9cf50055cb7 100644
--- a/core/src/main/resources/org/apache/spark/ui/static/spark-dag-viz.js
+++ b/core/src/main/resources/org/apache/spark/ui/static/spark-dag-viz.js
@@ -23,10 +23,10 @@
  *   (2) an RDD and its operation scopes, and
  *   (3) an RDD's operation scopes and the stage / job hierarchy
  *
- * An operation scope is a general, named code block representing an operation
- * that instantiates RDDs (e.g. filter, textFile, reduceByKey). An operation
- * scope can be nested inside of other scopes if the corresponding RDD operation
- * invokes other such operations (for more detail, see o.a.s.rdd.operationScope).
+ * An operation scope is a general, named code block that instantiates RDDs
+ * (e.g. filter, textFile, reduceByKey). An operation scope can be nested inside
+ * of other scopes if the corresponding RDD operation invokes other such operations
+ * (for more detail, see o.a.s.rdd.RDDOperationScope).
  *
  * A stage may include one or more operation scopes if the RDD operations are
  * streamlined into one stage (e.g. rdd.map(...).filter(...).flatMap(...)).
@@ -52,14 +52,7 @@
  */
 
 var VizConstants = {
-  rddColor: "#444444",
-  rddCachedColor: "#FF0000",
-  rddOperationColor: "#AADFFF",
-  stageColor: "#FFDDEE",
-  clusterLabelColor: "#888888",
-  edgeColor: "#444444",
-  edgeWidth: "1.5px",
-  svgMarginX: 0,
+  svgMarginX: 20,
   svgMarginY: 20,
   stageSep: 50,
   graphPrefix: "graph_",
@@ -69,13 +62,21 @@ var VizConstants = {
   stageClusterPrefix: "cluster_stage_"
 };
 
-// Helper d3 accessors for the elements that contain our graph and its metadata
-function graphContainer() { return d3.select("#dag-viz-graph"); }
-function metadataContainer() { return d3.select("#dag-viz-metadata"); }
+var JobPageVizConstants = {
+  clusterLabelSize: 11,
+  stageClusterLabelSize: 14
+}
+
+var StagePageVizConstants = {
+  clusterLabelSize: 14,
+  stageClusterLabelSize: 18
+}
 
 /*
  * Show or hide the RDD DAG visualization.
+ *
  * The graph is only rendered the first time this is called.
+ * This is the narrow interface called from the Scala UI code.
  */
 function toggleDagViz(forJob) {
   var arrowSelector = ".expand-dag-viz-arrow";
@@ -113,70 +114,52 @@ function toggleDagViz(forJob) {
 function renderDagViz(forJob) {
 
   // If there is not a dot file to render, fail fast and report error
+  var jobOrStage = forJob ? "job" : "stage";
   if (metadataContainer().empty()) {
-    graphContainer().append("div").text(
-      "No visualization information available for this " + (forJob ? "job" : "stage"));
+    graphContainer()
+      .append("div")
+      .text("No visualization information available for this " + jobOrStage);
     return;
   }
 
-  var svg = graphContainer().append("svg");
+  // Render
+  var svg = graphContainer()
+    .append("svg")
+    .attr("class", jobOrStage);
   if (forJob) {
     renderDagVizForJob(svg);
   } else {
     renderDagVizForStage(svg);
   }
 
-  // Find cached RDDs
+  // Find cached RDDs and mark them as such
   metadataContainer().selectAll(".cached-rdd").each(function(v) {
     var nodeId = VizConstants.nodePrefix + d3.select(this).text();
-    graphContainer().selectAll("#" + nodeId).classed("cached", true);
+    svg.selectAll("#" + nodeId).classed("cached", true);
   });
 
-  // Set the appropriate SVG dimensions to ensure that all elements are displayed
-  var boundingBox = svg.node().getBBox();
-  svg.style("width", (boundingBox.width + VizConstants.svgMarginX) + "px");
-  svg.style("height", (boundingBox.height + VizConstants.svgMarginY) + "px");
-
-  // Add labels to clusters because dagre-d3 doesn't do this for us
-  svg.selectAll("g.cluster rect").each(function() {
-    var rect = d3.select(this);
-    var cluster = d3.select(this.parentNode);
-    // Shift the boxes up a little to make room for the labels
-    rect.attr("y", toFloat(rect.attr("y")) - 10);
-    rect.attr("height", toFloat(rect.attr("height")) + 10);
-    var labelX = toFloat(rect.attr("x")) + toFloat(rect.attr("width")) - 5;
-    var labelY = toFloat(rect.attr("y")) + 15;
-    var labelText = cluster.attr("name").replace(VizConstants.clusterPrefix, "");
-    cluster.append("text")
-      .attr("x", labelX)
-      .attr("y", labelY)
-      .attr("text-anchor", "end")
-      .text(labelText);
-  });
-
-  // We have shifted a few elements upwards, so we should fix the SVG views
-  var startX = -VizConstants.svgMarginX;
-  var startY = -VizConstants.svgMarginY;
-  var endX = toFloat(svg.style("width")) + VizConstants.svgMarginX;
-  var endY = toFloat(svg.style("height")) + VizConstants.svgMarginY;
-  var newViewBox = startX + " " + startY + " " + endX + " " + endY;
-  svg.attr("viewBox", newViewBox);
-
-  // Lastly, apply some custom style to the DAG
-  styleDagViz(forJob);
+  // More post-processing
+  drawClusterLabels(svg, forJob);
+  resizeSvg(svg);
 }
 
-/* Render the RDD DAG visualization for a stage. */
+/* Render the RDD DAG visualization on the stage page. */
 function renderDagVizForStage(svgContainer) {
   var metadata = metadataContainer().select(".stage-metadata");
   var dot = metadata.select(".dot-file").text();
-  var containerId = VizConstants.graphPrefix + metadata.attr("stageId");
+  var containerId = VizConstants.graphPrefix + metadata.attr("stage-id");
   var container = svgContainer.append("g").attr("id", containerId);
   renderDot(dot, container);
+
+  // Round corners on RDDs
+  svgContainer
+    .selectAll("g.node rect")
+    .attr("rx", "5")
+    .attr("ry", "5");
 }
 
 /*
- * Render the RDD DAG visualization for a job.
+ * Render the RDD DAG visualization on the job page.
  *
  * Due to limitations in dagre-d3, each stage is rendered independently so that
  * we have more control on how to position them. Unfortunately, this means we
@@ -186,32 +169,46 @@ function renderDagVizForStage(svgContainer) {
 function renderDagVizForJob(svgContainer) {
   var crossStageEdges = [];
 
+  // Each div.stage-metadata contains the information needed to generate the graph
+  // for a stage. This includes the DOT file produced from the appropriate UI listener,
+  // any incoming and outgoing edges, and any cached RDDs that belong to this stage.
   metadataContainer().selectAll(".stage-metadata").each(function(d, i) {
     var metadata = d3.select(this);
     var dot = metadata.select(".dot-file").text();
-    var stageId = metadata.attr("stageId");
+    var stageId = metadata.attr("stage-id");
     var containerId = VizConstants.graphPrefix + stageId;
-    // TODO: handle stage attempts
+    // Link each graph to the corresponding stage page (TODO: handle stage attempts)
     var stageLink =
       "/stages/stage/?id=" + stageId.replace(VizConstants.stagePrefix, "") + "&attempt=0";
     var container = svgContainer
-      .append("a").attr("xlink:href", stageLink)
-      .append("g").attr("id", containerId);
-    // Now we need to shift the container for this stage so it doesn't overlap
-    // with existing ones. We do not need to do this for the first stage.
+      .append("a")
+      .attr("xlink:href", stageLink)
+      .append("g")
+      .attr("id", containerId);
+
+    // Now we need to shift the container for this stage so it doesn't overlap with
+    // existing ones, taking into account the position and width of the last stage's
+    // container. We do not need to do this for the first stage of this job.
     if (i > 0) {
-      // Take into account the position and width of the last stage's container
-      var existingStages = stageClusters();
+      var existingStages = svgContainer
+        .selectAll("g.cluster")
+        .filter("[id*=\"" + VizConstants.stageClusterPrefix + "\"]");
       if (!existingStages.empty()) {
-        var lastStage = existingStages[0].pop();
-        var lastStageId = d3.select(lastStage).attr("id");
-        var lastStageWidth = toFloat(d3.select("#" + lastStageId + " rect").attr("width"));
-        var lastStagePosition = getAbsolutePosition(lastStageId);
+        var lastStage = d3.select(existingStages[0].pop());
+        var lastStageId = lastStage.attr("id");
+        var lastStageWidth = toFloat(svgContainer
+          .select("#" + lastStageId)
+          .select("rect")
+          .attr("width"));
+        var lastStagePosition = getAbsolutePosition(lastStage);
         var offset = lastStagePosition.x + lastStageWidth + VizConstants.stageSep;
         container.attr("transform", "translate(" + offset + ", 0)");
       }
     }
+
+    // Actually render the stage
     renderDot(dot, container);
+
     // If there are any incoming edges into this graph, keep track of them to render
     // them separately later. Note that we cannot draw them now because we need to
     // put these edges in a separate container that is on top of all stage graphs.
@@ -221,15 +218,7 @@ function renderDagVizForJob(svgContainer) {
     });
   });
 
-  // Draw edges that cross stages
-  if (crossStageEdges.length > 0) {
-    var container = svgContainer.append("g").attr("id", "cross-stage-edges");
-    for (var i = 0; i < crossStageEdges.length; i++) {
-      var fromRDDId = crossStageEdges[i][0];
-      var toRDDId = crossStageEdges[i][1];
-      connectRDDs(fromRDDId, toRDDId, container);
-    }
-  }
+  drawCrossStageEdges(crossStageEdges, svgContainer);
 }
 
 /* Render the dot file as an SVG in the given container. */
@@ -243,99 +232,156 @@ function renderDot(dot, container) {
   renderer(container, g);
 }
 
-/* Style the visualization we just rendered. */
-function styleDagViz(forJob) {
-  graphContainer().selectAll("svg g.cluster rect")
-    .style("fill", "white")
-    .style("stroke", VizConstants.rddOperationColor)
-    .style("stroke-width", "4px")
-    .style("stroke-opacity", "0.5");
-  graphContainer().selectAll("svg g.cluster text")
-    .attr("fill", VizConstants.clusterLabelColor)
-    .attr("font-size", "11px");
-  graphContainer().selectAll("svg path")
-    .style("stroke", VizConstants.edgeColor)
-    .style("stroke-width", VizConstants.edgeWidth);
-  stageClusters()
-    .select("rect")
-    .style("stroke", VizConstants.stageColor)
-    .style("strokeWidth", "6px");
-
-  // Put an arrow at the end of every edge
-  // We need to do this because we manually render some edges ourselves
-  // For these edges, we borrow the arrow marker generated by dagre-d3
-  var dagreD3Marker = graphContainer().select("svg g.edgePaths marker").node();
-  graphContainer().select("svg")
-    .append(function() { return dagreD3Marker.cloneNode(true); })
-    .attr("id", "marker-arrow")
-    .select("path")
-    .attr("fill", VizConstants.edgeColor)
-    .attr("strokeWidth", "0px");
-  graphContainer().selectAll("svg g > path").attr("marker-end", "url(#marker-arrow)");
-  graphContainer().selectAll("svg g.edgePaths def").remove(); // We no longer need these
-
-  // Apply any job or stage specific styles
+/* -------------------- *
+ * | Helper functions | *
+ * -------------------- */
+
+// Helper d3 accessors
+function graphContainer() { return d3.select("#dag-viz-graph"); }
+function metadataContainer() { return d3.select("#dag-viz-metadata"); }
+
+/*
+ * Helper function to create draw a label for each cluster.
+ *
+ * We need to do this manually because dagre-d3 does not support labeling clusters.
+ * In general, the clustering support for dagre-d3 is quite limited at this point.
+ */
+function drawClusterLabels(svgContainer, forJob) {
   if (forJob) {
-    styleDagVizForJob();
+    var clusterLabelSize = JobPageVizConstants.clusterLabelSize;
+    var stageClusterLabelSize = JobPageVizConstants.stageClusterLabelSize;
   } else {
-    styleDagVizForStage();
+    var clusterLabelSize = StagePageVizConstants.clusterLabelSize;
+    var stageClusterLabelSize = StagePageVizConstants.stageClusterLabelSize;
   }
+  svgContainer.selectAll("g.cluster").each(function() {
+    var cluster = d3.select(this);
+    var isStage = cluster.attr("id").indexOf(VizConstants.stageClusterPrefix) > -1;
+    var labelSize = isStage ? stageClusterLabelSize : clusterLabelSize;
+    drawClusterLabel(cluster, labelSize);
+  });
 }
 
-/* Apply job-page-specific style to the visualization. */
-function styleDagVizForJob() {
-  graphContainer().selectAll("svg g.node circle")
-    .style("fill", VizConstants.rddColor);
-  // TODO: add a legend to explain what a highlighted dot means
-  graphContainer().selectAll("svg g.cached circle")
-    .style("fill", VizConstants.rddCachedColor);
-  graphContainer().selectAll("svg g#cross-stage-edges path")
-    .style("fill", "none");
+/*
+ * Helper function to draw a label for the given cluster element based on its name.
+ *
+ * In the process, we need to expand the bounding box to make room for the label.
+ * We need to do this because dagre-d3 did not take this into account when it first
+ * rendered the bounding boxes. Note that this means we need to adjust the view box
+ * of the SVG afterwards since we shifted a few boxes around.
+ */
+function drawClusterLabel(d3cluster, fontSize) {
+  var cluster = d3cluster;
+  var rect = d3cluster.select("rect");
+  rect.attr("y", toFloat(rect.attr("y")) - fontSize);
+  rect.attr("height", toFloat(rect.attr("height")) + fontSize);
+  var labelX = toFloat(rect.attr("x")) + toFloat(rect.attr("width")) - fontSize / 2;
+  var labelY = toFloat(rect.attr("y")) + fontSize * 1.5;
+  var labelText = cluster.attr("name").replace(VizConstants.clusterPrefix, "");
+  cluster.append("text")
+    .attr("x", labelX)
+    .attr("y", labelY)
+    .attr("text-anchor", "end")
+    .style("font-size", fontSize)
+    .text(labelText);
 }
 
-/* Apply stage-page-specific style to the visualization. */
-function styleDagVizForStage() {
-  graphContainer().selectAll("svg g.node rect")
-    .style("fill", "none")
-    .style("stroke", VizConstants.rddColor)
-    .style("stroke-width", "2px")
-    .attr("rx", "5") // round corners
-    .attr("ry", "5");
-    // TODO: add a legend to explain what a highlighted RDD means
-  graphContainer().selectAll("svg g.cached rect")
-    .style("stroke", VizConstants.rddCachedColor);
-  graphContainer().selectAll("svg g.node g.label text tspan")
-    .style("fill", VizConstants.rddColor);
+/*
+ * Helper function to size the SVG appropriately such that all elements are displyed.
+ * This assumes that all outermost elements are clusters (rectangles).
+ */
+function resizeSvg(svg) {
+  var allClusters = svg.selectAll("g.cluster rect")[0];
+  var startX = -VizConstants.svgMarginX +
+    toFloat(d3.min(allClusters, function(e) {
+      return getAbsolutePosition(d3.select(e)).x;
+    }));
+  var startY = -VizConstants.svgMarginY +
+    toFloat(d3.min(allClusters, function(e) {
+      return getAbsolutePosition(d3.select(e)).y;
+    }));
+  var endX = VizConstants.svgMarginX +
+    toFloat(d3.max(allClusters, function(e) {
+      var t = d3.select(e)
+      return getAbsolutePosition(t).x + toFloat(t.attr("width"));
+    }));
+  var endY = VizConstants.svgMarginY +
+    toFloat(d3.max(allClusters, function(e) {
+      var t = d3.select(e)
+      return getAbsolutePosition(t).y + toFloat(t.attr("height"));
+    }));
+  var width = endX - startX;
+  var height = endY - startY;
+  svg.attr("viewBox", startX + " " + startY + " " + width + " " + height)
+     .attr("width", width)
+     .attr("height", height);
 }
 
 /*
- * (Job page only) Helper method to compute the absolute
- * position of the group element identified by the given ID.
+ * (Job page only) Helper function to draw edges that cross stage boundaries.
+ * We need to do this manually because we render each stage separately in dagre-d3.
  */
-function getAbsolutePosition(groupId) {
-  var obj = d3.select("#" + groupId).filter("g");
-  var _x = 0, _y = 0;
+function drawCrossStageEdges(edges, svgContainer) {
+  if (edges.length == 0) {
+    return;
+  }
+  // Draw the paths first
+  var edgesContainer = svgContainer.append("g").attr("id", "cross-stage-edges");
+  for (var i = 0; i < edges.length; i++) {
+    var fromRDDId = edges[i][0];
+    var toRDDId = edges[i][1];
+    connectRDDs(fromRDDId, toRDDId, edgesContainer, svgContainer);
+  }
+  // Now draw the arrows by borrowing the arrow marker generated by dagre-d3
+  var dagreD3Marker = svgContainer.select("g.edgePaths marker").node();
+  if (!dagreD3Marker.empty()) {
+    svgContainer
+      .append(function() { return dagreD3Marker.cloneNode(true); })
+      .attr("id", "marker-arrow")
+    svgContainer.selectAll("g > path").attr("marker-end", "url(#marker-arrow)");
+    svgContainer.selectAll("g.edgePaths def").remove(); // We no longer need these
+  }
+}
+
+/*
+ * (Job page only) Helper function to compute the absolute
+ * position of the specified element in our graph.
+ */
+function getAbsolutePosition(d3selection) {
+  if (d3selection.empty()) {
+    throw "Attempted to get absolute position of an empty selection.";
+  }
+  var obj = d3selection;
+  var _x = toFloat(obj.attr("x")) || 0;
+  var _y = toFloat(obj.attr("y")) || 0;
   while (!obj.empty()) {
     var transformText = obj.attr("transform");
-    var translate = d3.transform(transformText).translate
-    _x += translate[0];
-    _y += translate[1];
-    obj = d3.select(obj.node().parentNode).filter("g")
+    if (transformText) {
+      var translate = d3.transform(transformText).translate;
+      _x += toFloat(translate[0]);
+      _y += toFloat(translate[1]);
+    }
+    // Climb upwards to find how our parents are translated
+    obj = d3.select(obj.node().parentNode);
+    // Stop when we've reached the graph container itself
+    if (obj.node() == graphContainer().node()) {
+      break;
+    }
   }
   return { x: _x, y: _y };
 }
 
-/* (Job page only) Connect two RDD nodes with a curved edge. */
-function connectRDDs(fromRDDId, toRDDId, container) {
+/* (Job page only) Helper function to connect two RDDs with a curved edge. */
+function connectRDDs(fromRDDId, toRDDId, edgesContainer, svgContainer) {
   var fromNodeId = VizConstants.nodePrefix + fromRDDId;
-  var toNodeId = VizConstants.nodePrefix + toRDDId
-  var fromPos = getAbsolutePosition(fromNodeId);
-  var toPos = getAbsolutePosition(toNodeId);
+  var toNodeId = VizConstants.nodePrefix + toRDDId;
+  var fromPos = getAbsolutePosition(svgContainer.select("#" + fromNodeId));
+  var toPos = getAbsolutePosition(svgContainer.select("#" + toNodeId));
 
   // On the job page, RDDs are rendered as dots (circles). When rendering the path,
   // we need to account for the radii of these circles. Otherwise the arrow heads
   // will bleed into the circle itself.
-  var delta = toFloat(graphContainer()
+  var delta = toFloat(svgContainer
     .select("g.node#" + toNodeId)
     .select("circle")
     .attr("r"));
@@ -375,18 +421,15 @@ function connectRDDs(fromRDDId, toRDDId, container) {
   }
 
   var line = d3.svg.line().interpolate("basis");
-  container.append("path").datum(points).attr("d", line);
+  edgesContainer.append("path").datum(points).attr("d", line);
 }
 
-/* Helper d3 accessor to clusters that represent stages. */
-function stageClusters() {
-  return graphContainer().selectAll("g.cluster").filter(function() {
-    return d3.select(this).attr("id").indexOf(VizConstants.stageClusterPrefix) > -1;
-  });
-}
-
-/* Helper method to convert attributes to numeric values. */
+/* Helper function to convert attributes to numeric values. */
 function toFloat(f) {
-  return parseFloat(f.replace(/px$/, ""));
+  if (f) {
+    return parseFloat(f.toString().replace(/px$/, ""));
+  } else {
+    return f;
+  }
 }
 
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 2f3fb181e4026..e2d03f8342315 100644
--- a/core/src/main/scala/org/apache/spark/ui/UIUtils.scala
+++ b/core/src/main/scala/org/apache/spark/ui/UIUtils.scala
@@ -156,13 +156,10 @@ private[spark] object UIUtils extends Logging {
 
   def commonHeaderNodes: Seq[Node] = {
     <meta http-equiv="Content-type" content="text/html; charset=utf-8" />
-    <link rel="stylesheet" href={prependBaseUri("/static/bootstrap.min.css")}
-          type="text/css" />
-    <link rel="stylesheet" href={prependBaseUri("/static/webui.css")}
-          type="text/css" />
-    <link rel="stylesheet" href={prependBaseUri("/static/vis.min.css")}
-          typ="text/css" />
-    <link rel="stylesheet" href={prependBaseUri("/static/timeline-view.css")}></link>
+    <link rel="stylesheet" href={prependBaseUri("/static/bootstrap.min.css")} type="text/css" />
+    <link rel="stylesheet" href={prependBaseUri("/static/webui.css")} type="text/css" />
+    <link rel="stylesheet" href={prependBaseUri("/static/vis.min.css")} type="text/css" />
+    <link rel="stylesheet" href={prependBaseUri("/static/timeline-view.css")} type="text/css" />
     <script src={prependBaseUri("/static/sorttable.js")} ></script>
     <script src={prependBaseUri("/static/jquery-1.11.1.min.js")}></script>
     <script src={prependBaseUri("/static/vis.min.js")}></script>
@@ -174,6 +171,7 @@ private[spark] object UIUtils extends Logging {
   }
 
   def vizHeaderNodes: Seq[Node] = {
+    <link rel="stylesheet" href={prependBaseUri("/static/spark-dag-viz.css")} type="text/css" />
     <script src={prependBaseUri("/static/d3.min.js")}></script>
     <script src={prependBaseUri("/static/dagre-d3.min.js")}></script>
     <script src={prependBaseUri("/static/graphlib-dot.min.js")}></script>
@@ -358,7 +356,7 @@ private[spark] object UIUtils extends Logging {
       <div id="dag-viz-metadata">
         {
           graphs.map { g =>
-            <div class="stage-metadata" stageId={g.rootCluster.id} style="display:none">
+            <div class="stage-metadata" stage-id={g.rootCluster.id} style="display:none">
               <div class="dot-file">{RDDOperationGraph.makeDotFile(g, forJob)}</div>
               { g.incomingEdges.map { e => <div class="incoming-edge">{e.fromId},{e.toId}</div> } }
               { g.outgoingEdges.map { e => <div class="outgoing-edge">{e.fromId},{e.toId}</div> } }
diff --git a/core/src/main/scala/org/apache/spark/ui/scope/RDDOperationGraph.scala b/core/src/main/scala/org/apache/spark/ui/scope/RDDOperationGraph.scala
index a18c193540ce3..edf005f7f325a 100644
--- a/core/src/main/scala/org/apache/spark/ui/scope/RDDOperationGraph.scala
+++ b/core/src/main/scala/org/apache/spark/ui/scope/RDDOperationGraph.scala
@@ -181,22 +181,29 @@ private[ui] object RDDOperationGraph extends Logging {
     if (forJob) {
       s"""${node.id} [label=" " shape="circle" padding="5" labelStyle="font-size: 0"]"""
     } else {
-      s"""${node.id} [label="${node.name} (${node.id})"]"""
+      s"""${node.id} [label="${node.name} (${node.id})" padding="5" labelStyle="font-size: 10"]"""
     }
   }
 
   /** Return the dot representation of a subgraph in an RDDOperationGraph. */
   private def makeDotSubgraph(
-      scope: RDDOperationCluster,
+      cluster: RDDOperationCluster,
       forJob: Boolean,
       indent: String): String = {
     val subgraph = new StringBuilder
-    subgraph.append(indent + s"subgraph cluster${scope.id} {\n")
-    subgraph.append(indent + s"""  label="${scope.name}";\n""")
-    scope.childNodes.foreach { node =>
+    // TODO: move specific graph properties like these to spark-dag-viz.js
+    val paddingTop = if (forJob) 10 else 20
+    subgraph.append(indent + s"subgraph cluster${cluster.id} {\n")
+    subgraph.append(indent + s"""  label="${cluster.name}";\n""")
+    // If there are nested clusters, add some padding
+    // Do this for the stage page because we use bigger fonts there
+    if (cluster.childClusters.nonEmpty) {
+      subgraph.append(indent + s"""  paddingTop="$paddingTop";\n""")
+    }
+    cluster.childNodes.foreach { node =>
       subgraph.append(indent + s"  ${makeDotNode(node, forJob)};\n")
     }
-    scope.childClusters.foreach { cscope =>
+    cluster.childClusters.foreach { cscope =>
       subgraph.append(makeDotSubgraph(cscope, forJob, indent + "  "))
     }
     subgraph.append(indent + "}\n")

From 71a452b61c43eaf625cacadd20c886335312ba59 Mon Sep 17 00:00:00 2001
From: Andrew Or <andrew@databricks.com>
Date: Wed, 6 May 2015 18:02:08 -0700
Subject: [PATCH 21/75] [HOT FIX] For DAG visualization #5954

---
 .../resources/org/apache/spark/ui/static/spark-dag-viz.js     | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/core/src/main/resources/org/apache/spark/ui/static/spark-dag-viz.js b/core/src/main/resources/org/apache/spark/ui/static/spark-dag-viz.js
index eb9cf50055cb7..76eb2c464139f 100644
--- a/core/src/main/resources/org/apache/spark/ui/static/spark-dag-viz.js
+++ b/core/src/main/resources/org/apache/spark/ui/static/spark-dag-viz.js
@@ -333,10 +333,10 @@ function drawCrossStageEdges(edges, svgContainer) {
     connectRDDs(fromRDDId, toRDDId, edgesContainer, svgContainer);
   }
   // Now draw the arrows by borrowing the arrow marker generated by dagre-d3
-  var dagreD3Marker = svgContainer.select("g.edgePaths marker").node();
+  var dagreD3Marker = svgContainer.select("g.edgePaths marker");
   if (!dagreD3Marker.empty()) {
     svgContainer
-      .append(function() { return dagreD3Marker.cloneNode(true); })
+      .append(function() { return dagreD3Marker.node().cloneNode(true); })
       .attr("id", "marker-arrow")
     svgContainer.selectAll("g > path").attr("marker-end", "url(#marker-arrow)");
     svgContainer.selectAll("g.edgePaths def").remove(); // We no longer need these

From 14502d5e569f8460d3e2c973a4277441307433cc Mon Sep 17 00:00:00 2001
From: zsxwing <zsxwing@gmail.com>
Date: Wed, 6 May 2015 18:07:00 -0700
Subject: [PATCH 22/75] [SPARK-7405] [STREAMING] Fix the bug that
 ReceiverInputDStream doesn't report InputInfo

The bug is because SPARK-7139 removed some codes from SPARK-7112 unintentionally here: https://github.com/apache/spark/commit/1854ac326a9cc6014817d8df30ed0458eee5d7d1#diff-5c8651dd78abd20439b8eb938175075dL72

This PR just added them back and added some assertions in the tests to verify it.

Author: zsxwing <zsxwing@gmail.com>

Closes #5950 from zsxwing/SPARK-7405 and squashes the following commits:

675f5d9 [zsxwing] Fix the bug that ReceiverInputDStream doesn't report InputInfo
---
 .../spark/streaming/dstream/ReceiverInputDStream.scala     | 5 +++++
 .../org/apache/spark/streaming/InputStreamsSuite.scala     | 7 +++++++
 2 files changed, 12 insertions(+)

diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/ReceiverInputDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/ReceiverInputDStream.scala
index 15d9710d37cd4..5cfe43a1ce726 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/ReceiverInputDStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/ReceiverInputDStream.scala
@@ -24,6 +24,7 @@ import org.apache.spark.storage.BlockId
 import org.apache.spark.streaming._
 import org.apache.spark.streaming.rdd.WriteAheadLogBackedBlockRDD
 import org.apache.spark.streaming.receiver.Receiver
+import org.apache.spark.streaming.scheduler.InputInfo
 import org.apache.spark.streaming.util.WriteAheadLogUtils
 
 /**
@@ -68,6 +69,10 @@ abstract class ReceiverInputDStream[T: ClassTag](@transient ssc_ : StreamingCont
         val blockInfos = receiverTracker.getBlocksOfBatch(validTime).getOrElse(id, Seq.empty)
         val blockIds = blockInfos.map { _.blockId.asInstanceOf[BlockId] }.toArray
 
+        // Register the input blocks information into InputInfoTracker
+        val inputInfo = InputInfo(id, blockInfos.map(_.numRecords).sum)
+        ssc.scheduler.inputInfoTracker.reportInfo(validTime, inputInfo)
+
         // Are WAL record handles present with all the blocks
         val areWALRecordHandlesPresent = blockInfos.forall { _.walRecordHandleOption.nonEmpty }
 
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/InputStreamsSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/InputStreamsSuite.scala
index 60745325029f6..93e6b0cd7c661 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/InputStreamsSuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/InputStreamsSuite.scala
@@ -50,6 +50,8 @@ class InputStreamsSuite extends TestSuiteBase with BeforeAndAfter {
 
       // Set up the streaming context and input streams
       withStreamingContext(new StreamingContext(conf, batchDuration)) { ssc =>
+        ssc.addStreamingListener(ssc.progressListener)
+
         val input = Seq(1, 2, 3, 4, 5)
         // Use "batchCount" to make sure we check the result after all batches finish
         val batchCounter = new BatchCounter(ssc)
@@ -72,6 +74,11 @@ class InputStreamsSuite extends TestSuiteBase with BeforeAndAfter {
         if (!batchCounter.waitUntilBatchesCompleted(input.size, 30000)) {
           fail("Timeout: cannot finish all batches in 30 seconds")
         }
+
+        // Verify all "InputInfo"s have been reported
+        assert(ssc.progressListener.numTotalReceivedRecords === input.size)
+        assert(ssc.progressListener.numTotalProcessedRecords === input.size)
+
         logInfo("Stopping server")
         testServer.stop()
         logInfo("Stopping context")

From 773aa25252f29ca25dbb1ee495b530557fe79405 Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Wed, 6 May 2015 22:29:07 -0700
Subject: [PATCH 23/75] [SPARK-7432] [MLLIB] disable cv doctest

Temporarily disable flaky doctest for CrossValidator. jkbradley

Author: Xiangrui Meng <meng@databricks.com>

Closes #5962 from mengxr/disable-pyspark-cv-test and squashes the following commits:

5db7e5b [Xiangrui Meng] disable cv doctest
---
 python/pyspark/ml/tuning.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/python/pyspark/ml/tuning.py b/python/pyspark/ml/tuning.py
index f6cf2c3439ba5..1e04c37fca53e 100644
--- a/python/pyspark/ml/tuning.py
+++ b/python/pyspark/ml/tuning.py
@@ -103,10 +103,10 @@ class CrossValidator(Estimator):
     >>> grid = ParamGridBuilder().addGrid(lr.maxIter, [0, 1, 5]).build()
     >>> evaluator = BinaryClassificationEvaluator()
     >>> cv = CrossValidator(estimator=lr, estimatorParamMaps=grid, evaluator=evaluator)
-    >>> cvModel = cv.fit(dataset)
-    >>> expected = lr.fit(dataset, {lr.maxIter: 5}).transform(dataset)
-    >>> cvModel.transform(dataset).collect() == expected.collect()
-    True
+    >>> # SPARK-7432: The following test is flaky.
+    >>> # cvModel = cv.fit(dataset)
+    >>> # expected = lr.fit(dataset, {lr.maxIter: 5}).transform(dataset)
+    >>> # cvModel.transform(dataset).collect() == expected.collect()
     """
 
     # a placeholder to make it appear in the generated doc

From 9cfa9a516ed991de6c5900c7285b47380a396142 Mon Sep 17 00:00:00 2001
From: Sun Rui <rui.sun@intel.com>
Date: Wed, 6 May 2015 22:48:16 -0700
Subject: [PATCH 24/75] [SPARK-6812] [SPARKR] filter() on DataFrame does not
 work as expected.

According to the R manual: https://stat.ethz.ch/R-manual/R-devel/library/base/html/Startup.html,
" if a function .First is found on the search path, it is executed as .First(). Finally, function .First.sys() in the base package is run. This calls require to attach the default packages specified by options("defaultPackages")."
In .First() in profile/shell.R, we load SparkR package. This means SparkR package is loaded before default packages. If there are same names in default packages, they will overwrite those in SparkR. This is why filter() in SparkR is masked by filter() in stats, which is usually in the default package list.
We need to make sure SparkR is loaded after default packages. The solution is to append SparkR to default packages, instead of loading SparkR in .First().

BTW, I'd like to discuss our policy on how to solve name conflict. Previously, we rename API names from Scala API if there is name conflict with base or other commonly-used packages. However, from long term perspective, this is not good for API stability, because we can't predict name conflicts, for example, if in the future a name added in base package conflicts with an API in SparkR? So the better policy is to keep API name same as Scala's without worrying about name conflicts. When users use SparkR, they should load SparkR as last package, so that all API names are effective. Use can explicitly use :: to refer to hidden names from other packages. If we agree on this, I can submit a JIRA issue to change back some rename API methods, for example, DataFrame.sortDF().

Author: Sun Rui <rui.sun@intel.com>

Closes #5938 from sun-rui/SPARK-6812 and squashes the following commits:

b569145 [Sun Rui] [SPARK-6812][SparkR] filter() on DataFrame does not work as expected.
---
 R/pkg/inst/profile/shell.R | 10 ++++++----
 1 file changed, 6 insertions(+), 4 deletions(-)

diff --git a/R/pkg/inst/profile/shell.R b/R/pkg/inst/profile/shell.R
index 7a7f2031152a0..33478d9e29995 100644
--- a/R/pkg/inst/profile/shell.R
+++ b/R/pkg/inst/profile/shell.R
@@ -20,11 +20,13 @@
   .libPaths(c(file.path(home, "R", "lib"), .libPaths()))
   Sys.setenv(NOAWT=1)
 
-  library(utils)
-  library(SparkR)
-  sc <- sparkR.init(Sys.getenv("MASTER", unset = ""))
+  # Make sure SparkR package is the last loaded one
+  old <- getOption("defaultPackages")
+  options(defaultPackages = c(old, "SparkR"))
+
+  sc <- SparkR::sparkR.init(Sys.getenv("MASTER", unset = ""))
   assign("sc", sc, envir=.GlobalEnv)
-  sqlCtx <- sparkRSQL.init(sc)
+  sqlCtx <- SparkR::sparkRSQL.init(sc)
   assign("sqlCtx", sqlCtx, envir=.GlobalEnv)
   cat("\n Welcome to SparkR!")
   cat("\n Spark context is available as sc, SQL context is available as sqlCtx\n")

From 2d6612cc8b98f767d73c4d15e4065bf3d6c12ea7 Mon Sep 17 00:00:00 2001
From: Nathan Howell <nhowell@godaddy.com>
Date: Wed, 6 May 2015 22:56:53 -0700
Subject: [PATCH 25/75] [SPARK-5938] [SPARK-5443] [SQL] Improve JsonRDD
 performance

This patch comprises of a few related pieces of work:

* Schema inference is performed directly on the JSON token stream
* `String => Row` conversion populate Spark SQL structures without intermediate types
* Projection pushdown is implemented via CatalystScan for DataFrame queries
* Support for the legacy parser by setting `spark.sql.json.useJacksonStreamingAPI` to `false`

Performance improvements depend on the schema and queries being executed, but it should be faster across the board. Below are benchmarks using the last.fm Million Song dataset:

```
Command                                            | Baseline | Patched
---------------------------------------------------|----------|--------
import sqlContext.implicits._                      |          |
val df = sqlContext.jsonFile("/tmp/lastfm.json")   |    70.0s |   14.6s
df.count()                                         |    28.8s |    6.2s
df.rdd.count()                                     |    35.3s |   21.5s
df.where($"artist" === "Robert Hood").collect()    |    28.3s |   16.9s
```

To prepare this dataset for benchmarking, follow these steps:

```
# Fetch the datasets from http://labrosa.ee.columbia.edu/millionsong/lastfm
wget http://labrosa.ee.columbia.edu/millionsong/sites/default/files/lastfm/lastfm_test.zip \
     http://labrosa.ee.columbia.edu/millionsong/sites/default/files/lastfm/lastfm_train.zip

# Decompress and combine, pipe through `jq -c` to ensure there is one record per line
unzip -p lastfm_test.zip lastfm_train.zip  | jq -c . > lastfm.json
```

Author: Nathan Howell <nhowell@godaddy.com>

Closes #5801 from NathanHowell/json-performance and squashes the following commits:

26fea31 [Nathan Howell] Recreate the baseRDD each for each scan operation
a7ebeb2 [Nathan Howell] Increase coverage of inserts into a JSONRelation
e06a1dd [Nathan Howell] Add comments to the `useJacksonStreamingAPI` config flag
6822712 [Nathan Howell] Split up JsonRDD2 into multiple objects
fa8234f [Nathan Howell] Wrap long lines
b31917b [Nathan Howell] Rename `useJsonRDD2` to `useJacksonStreamingAPI`
15c5d1b [Nathan Howell] JSONRelation's baseRDD need not be lazy
f8add6e [Nathan Howell] Add comments on lack of support for precision and scale DecimalTypes
fa0be47 [Nathan Howell] Remove unused default case in the field parser
80dba17 [Nathan Howell] Add comments regarding null handling and empty strings
842846d [Nathan Howell] Point the empty schema inference test at JsonRDD2
ab6ee87 [Nathan Howell] Add projection pushdown support to JsonRDD/JsonRDD2
f636c14 [Nathan Howell] Enable JsonRDD2 by default, add a flag to switch back to JsonRDD
0bbc445 [Nathan Howell] Improve JSON parsing and type inference performance
7ca70c1 [Nathan Howell] Eliminate arrow pattern, replace with pattern matches
---
 .../catalyst/analysis/HiveTypeCoercion.scala  |  43 ++--
 .../apache/spark/sql/types/StructType.scala   |   4 +
 .../org/apache/spark/sql/DataFrame.scala      |   4 +-
 .../scala/org/apache/spark/sql/SQLConf.scala  |   8 +
 .../org/apache/spark/sql/SQLContext.scala     |  34 +--
 .../apache/spark/sql/json/InferSchema.scala   | 171 ++++++++++++++
 .../apache/spark/sql/json/JSONRelation.scala  |  99 ++++++--
 .../spark/sql/json/JacksonGenerator.scala     |  77 +++++++
 .../apache/spark/sql/json/JacksonParser.scala | 215 ++++++++++++++++++
 .../apache/spark/sql/json/JacksonUtils.scala  |  32 +++
 .../org/apache/spark/sql/json/JsonRDD.scala   |  50 ----
 .../org/apache/spark/sql/json/JsonSuite.scala |  51 +++--
 .../spark/sql/sources/InsertSuite.scala       |  55 ++++-
 13 files changed, 715 insertions(+), 128 deletions(-)
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/json/InferSchema.scala
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/json/JacksonGenerator.scala
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/json/JacksonParser.scala
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/json/JacksonUtils.scala

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 96e2aee4de15b..873c75c525c3b 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
@@ -26,7 +26,14 @@ 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:
   private val numericPrecedence =
-    Seq(ByteType, ShortType, IntegerType, LongType, FloatType, DoubleType, DecimalType.Unlimited)
+    IndexedSeq(
+      ByteType,
+      ShortType,
+      IntegerType,
+      LongType,
+      FloatType,
+      DoubleType,
+      DecimalType.Unlimited)
 
   /**
    * Find the tightest common type of two types that might be used in a binary expression.
@@ -34,25 +41,21 @@ object HiveTypeCoercion {
    * with primitive types, because in that case the precision and scale of the result depends on
    * the operation. Those rules are implemented in [[HiveTypeCoercion.DecimalPrecision]].
    */
-  def findTightestCommonType(t1: DataType, t2: DataType): Option[DataType] = {
-    val valueTypes = Seq(t1, t2).filter(t => t != NullType)
-    if (valueTypes.distinct.size > 1) {
-      // Promote numeric types to the highest of the two and all numeric types to unlimited decimal
-      if (numericPrecedence.contains(t1) && numericPrecedence.contains(t2)) {
-        Some(numericPrecedence.filter(t => t == t1 || t == t2).last)
-      } else if (t1.isInstanceOf[DecimalType] && t2.isInstanceOf[DecimalType]) {
-        // Fixed-precision decimals can up-cast into unlimited
-        if (t1 == DecimalType.Unlimited || t2 == DecimalType.Unlimited) {
-          Some(DecimalType.Unlimited)
-        } else {
-          None
-        }
-      } else {
-        None
-      }
-    } else {
-      Some(if (valueTypes.size == 0) NullType else valueTypes.head)
-    }
+  val findTightestCommonType: (DataType, DataType) => Option[DataType] = {
+    case (t1, t2) if t1 == t2 => Some(t1)
+    case (NullType, t1) => Some(t1)
+    case (t1, NullType) => Some(t1)
+
+    // Promote numeric types to the highest of the two and all numeric types to unlimited decimal
+    case (t1, t2) if Seq(t1, t2).forall(numericPrecedence.contains) =>
+      val index = numericPrecedence.lastIndexWhere(t => t == t1 || t == t2)
+      Some(numericPrecedence(index))
+
+    // Fixed-precision decimals can up-cast into unlimited
+    case (DecimalType.Unlimited, _: DecimalType) => Some(DecimalType.Unlimited)
+    case (_: DecimalType, DecimalType.Unlimited) => Some(DecimalType.Unlimited)
+
+    case _ => None
   }
 }
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/StructType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/StructType.scala
index d80ffca18ec9a..7e00a27dfe724 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/StructType.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/StructType.scala
@@ -134,6 +134,10 @@ case class StructType(fields: Array[StructField]) extends DataType with Seq[Stru
       throw new IllegalArgumentException(s"""Field "$name" does not exist."""))
   }
 
+  private[sql] def getFieldIndex(name: String): Option[Int] = {
+    nameToIndex.get(name)
+  }
+
   protected[sql] def toAttributes: Seq[AttributeReference] =
     map(f => AttributeReference(f.name, f.dataType, f.nullable, f.metadata)())
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
index 9d2cd7aae3b82..79fbf50300d4d 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
@@ -42,7 +42,7 @@ import org.apache.spark.sql.catalyst.plans.{JoinType, Inner}
 import org.apache.spark.sql.catalyst.plans.logical._
 import org.apache.spark.sql.execution.{EvaluatePython, ExplainCommand, LogicalRDD}
 import org.apache.spark.sql.jdbc.JDBCWriteDetails
-import org.apache.spark.sql.json.JsonRDD
+import org.apache.spark.sql.json.{JacksonGenerator, JsonRDD}
 import org.apache.spark.sql.types._
 import org.apache.spark.sql.sources.{ResolvedDataSource, CreateTableUsingAsSelect}
 import org.apache.spark.util.Utils
@@ -1415,7 +1415,7 @@ class DataFrame private[sql](
       new Iterator[String] {
         override def hasNext: Boolean = iter.hasNext
         override def next(): String = {
-          JsonRDD.rowToJSON(rowSchema, gen)(iter.next())
+          JacksonGenerator(rowSchema, gen)(iter.next())
           gen.flush()
 
           val json = writer.toString
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 3ffc2091d6ba1..bfaddd0f2ce1b 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
@@ -73,6 +73,8 @@ private[spark] object SQLConf {
 
   val USE_SQL_SERIALIZER2 = "spark.sql.useSerializer2"
 
+  val USE_JACKSON_STREAMING_API = "spark.sql.json.useJacksonStreamingAPI"
+
   object Deprecated {
     val MAPRED_REDUCE_TASKS = "mapred.reduce.tasks"
   }
@@ -166,6 +168,12 @@ private[sql] class SQLConf extends Serializable {
 
   private[spark] def useSqlSerializer2: Boolean = getConf(USE_SQL_SERIALIZER2, "true").toBoolean
 
+  /**
+   * Selects between the new (true) and old (false) JSON handlers, to be removed in Spark 1.5.0
+   */
+  private[spark] def useJacksonStreamingAPI: Boolean =
+    getConf(USE_JACKSON_STREAMING_API, "true").toBoolean
+
   /**
    * Upper bound on the sizes (in bytes) of the tables qualified for the auto conversion to
    * a broadcast value during the physical executions of join operations.  Setting this to -1
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 7eabb93c1e3d6..0563430a6fdc7 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
@@ -659,13 +659,17 @@ class SQLContext(@transient val sparkContext: SparkContext)
    */
   @Experimental
   def jsonRDD(json: RDD[String], schema: StructType): DataFrame = {
-    val columnNameOfCorruptJsonRecord = conf.columnNameOfCorruptRecord
-    val appliedSchema =
-      Option(schema).getOrElse(
-        JsonRDD.nullTypeToStringType(
-          JsonRDD.inferSchema(json, 1.0, columnNameOfCorruptJsonRecord)))
-    val rowRDD = JsonRDD.jsonStringToRow(json, appliedSchema, columnNameOfCorruptJsonRecord)
-    createDataFrame(rowRDD, appliedSchema, needsConversion = false)
+    if (conf.useJacksonStreamingAPI) {
+      baseRelationToDataFrame(new JSONRelation(() => json, None, 1.0, Some(schema))(this))
+    } else {
+      val columnNameOfCorruptJsonRecord = conf.columnNameOfCorruptRecord
+      val appliedSchema =
+        Option(schema).getOrElse(
+          JsonRDD.nullTypeToStringType(
+            JsonRDD.inferSchema(json, 1.0, columnNameOfCorruptJsonRecord)))
+      val rowRDD = JsonRDD.jsonStringToRow(json, appliedSchema, columnNameOfCorruptJsonRecord)
+      createDataFrame(rowRDD, appliedSchema, needsConversion = false)
+    }
   }
 
   /**
@@ -689,12 +693,16 @@ class SQLContext(@transient val sparkContext: SparkContext)
    */
   @Experimental
   def jsonRDD(json: RDD[String], samplingRatio: Double): DataFrame = {
-    val columnNameOfCorruptJsonRecord = conf.columnNameOfCorruptRecord
-    val appliedSchema =
-      JsonRDD.nullTypeToStringType(
-        JsonRDD.inferSchema(json, samplingRatio, columnNameOfCorruptJsonRecord))
-    val rowRDD = JsonRDD.jsonStringToRow(json, appliedSchema, columnNameOfCorruptJsonRecord)
-    createDataFrame(rowRDD, appliedSchema, needsConversion = false)
+    if (conf.useJacksonStreamingAPI) {
+      baseRelationToDataFrame(new JSONRelation(() => json, None, samplingRatio, None)(this))
+    } else {
+      val columnNameOfCorruptJsonRecord = conf.columnNameOfCorruptRecord
+      val appliedSchema =
+        JsonRDD.nullTypeToStringType(
+          JsonRDD.inferSchema(json, samplingRatio, columnNameOfCorruptJsonRecord))
+      val rowRDD = JsonRDD.jsonStringToRow(json, appliedSchema, columnNameOfCorruptJsonRecord)
+      createDataFrame(rowRDD, appliedSchema, needsConversion = false)
+    }
   }
 
   /**
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/json/InferSchema.scala b/sql/core/src/main/scala/org/apache/spark/sql/json/InferSchema.scala
new file mode 100644
index 0000000000000..9c58b8e4bb16a
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/json/InferSchema.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.json
+
+import com.fasterxml.jackson.core._
+
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.catalyst.analysis.HiveTypeCoercion
+import org.apache.spark.sql.json.JacksonUtils.nextUntil
+import org.apache.spark.sql.types._
+
+private[sql] object InferSchema {
+  /**
+   * Infer the type of a collection of json records in three stages:
+   *   1. Infer the type of each record
+   *   2. Merge types by choosing the lowest type necessary to cover equal keys
+   *   3. Replace any remaining null fields with string, the top type
+   */
+  def apply(
+      json: RDD[String],
+      samplingRatio: Double = 1.0,
+      columnNameOfCorruptRecords: String): StructType = {
+    require(samplingRatio > 0, s"samplingRatio ($samplingRatio) should be greater than 0")
+    val schemaData = if (samplingRatio > 0.99) {
+      json
+    } else {
+      json.sample(withReplacement = false, samplingRatio, 1)
+    }
+
+    // perform schema inference on each row and merge afterwards
+    schemaData.mapPartitions { iter =>
+      val factory = new JsonFactory()
+      iter.map { row =>
+        try {
+          val parser = factory.createParser(row)
+          parser.nextToken()
+          inferField(parser)
+        } catch {
+          case _: JsonParseException =>
+            StructType(Seq(StructField(columnNameOfCorruptRecords, StringType)))
+        }
+      }
+    }.treeAggregate[DataType](StructType(Seq()))(compatibleRootType, compatibleRootType) match {
+      case st: StructType => nullTypeToStringType(st)
+    }
+  }
+
+  /**
+   * Infer the type of a json document from the parser's token stream
+   */
+  private def inferField(parser: JsonParser): DataType = {
+    import com.fasterxml.jackson.core.JsonToken._
+    parser.getCurrentToken match {
+      case null | VALUE_NULL => NullType
+
+      case FIELD_NAME =>
+        parser.nextToken()
+        inferField(parser)
+
+      case VALUE_STRING if parser.getTextLength < 1 =>
+        // Zero length strings and nulls have special handling to deal
+        // with JSON generators that do not distinguish between the two.
+        // To accurately infer types for empty strings that are really
+        // meant to represent nulls we assume that the two are isomorphic
+        // but will defer treating null fields as strings until all the
+        // record fields' types have been combined.
+        NullType
+
+      case VALUE_STRING => StringType
+      case START_OBJECT =>
+        val builder = Seq.newBuilder[StructField]
+        while (nextUntil(parser, END_OBJECT)) {
+          builder += StructField(parser.getCurrentName, inferField(parser), nullable = true)
+        }
+
+        StructType(builder.result().sortBy(_.name))
+
+      case START_ARRAY =>
+        // If this JSON array is empty, we use NullType as a placeholder.
+        // If this array is not empty in other JSON objects, we can resolve
+        // the type as we pass through all JSON objects.
+        var elementType: DataType = NullType
+        while (nextUntil(parser, END_ARRAY)) {
+          elementType = compatibleType(elementType, inferField(parser))
+        }
+
+        ArrayType(elementType)
+
+      case VALUE_NUMBER_INT | VALUE_NUMBER_FLOAT =>
+        import JsonParser.NumberType._
+        parser.getNumberType match {
+          // For Integer values, use LongType by default.
+          case INT | LONG => LongType
+          // Since we do not have a data type backed by BigInteger,
+          // when we see a Java BigInteger, we use DecimalType.
+          case BIG_INTEGER | BIG_DECIMAL => DecimalType.Unlimited
+          case FLOAT | DOUBLE => DoubleType
+        }
+
+      case VALUE_TRUE | VALUE_FALSE => BooleanType
+    }
+  }
+
+  private def nullTypeToStringType(struct: StructType): StructType = {
+    val fields = struct.fields.map {
+      case StructField(fieldName, dataType, nullable, _) =>
+        val newType = dataType match {
+          case NullType => StringType
+          case ArrayType(NullType, containsNull) => ArrayType(StringType, containsNull)
+          case ArrayType(struct: StructType, containsNull) =>
+            ArrayType(nullTypeToStringType(struct), containsNull)
+          case struct: StructType =>nullTypeToStringType(struct)
+          case other: DataType => other
+        }
+
+        StructField(fieldName, newType, nullable)
+    }
+
+    StructType(fields)
+  }
+
+  /**
+   * Remove top-level ArrayType wrappers and merge the remaining schemas
+   */
+  private def compatibleRootType: (DataType, DataType) => DataType = {
+    case (ArrayType(ty1, _), ty2) => compatibleRootType(ty1, ty2)
+    case (ty1, ArrayType(ty2, _)) => compatibleRootType(ty1, ty2)
+    case (ty1, ty2) => compatibleType(ty1, ty2)
+  }
+
+  /**
+   * Returns the most general data type for two given data types.
+   */
+  private[json] def compatibleType(t1: DataType, t2: DataType): DataType = {
+    HiveTypeCoercion.findTightestCommonType(t1, t2).getOrElse {
+      // 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.view.map(_.dataType).reduce(compatibleType)
+              StructField(name, dataType, nullable = true)
+          }
+          StructType(newFields.toSeq.sortBy(_.name))
+
+        case (ArrayType(elementType1, containsNull1), ArrayType(elementType2, containsNull2)) =>
+          ArrayType(compatibleType(elementType1, elementType2), containsNull1 || containsNull2)
+
+        // strings and every string is a Json object.
+        case (_, _) => StringType
+      }
+    }
+  }
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/json/JSONRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/json/JSONRelation.scala
index e3352d02787fd..c772cd1f53e53 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/json/JSONRelation.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/json/JSONRelation.scala
@@ -22,14 +22,16 @@ import java.io.IOException
 import org.apache.hadoop.fs.Path
 
 import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.catalyst.expressions.Row
+import org.apache.spark.sql.catalyst.expressions.{Expression, Attribute, Row}
 import org.apache.spark.sql.sources._
-import org.apache.spark.sql.types.StructType
+import org.apache.spark.sql.types.{StructField, StructType}
 import org.apache.spark.sql.{DataFrame, SQLContext, SaveMode}
 
 
 private[sql] class DefaultSource
-  extends RelationProvider with SchemaRelationProvider with CreatableRelationProvider {
+  extends RelationProvider
+  with SchemaRelationProvider
+  with CreatableRelationProvider {
 
   private def checkPath(parameters: Map[String, String]): String = {
     parameters.getOrElse("path", sys.error("'path' must be specified for json data."))
@@ -42,7 +44,7 @@ private[sql] class DefaultSource
     val path = checkPath(parameters)
     val samplingRatio = parameters.get("samplingRatio").map(_.toDouble).getOrElse(1.0)
 
-    JSONRelation(path, samplingRatio, None)(sqlContext)
+    new JSONRelation(path, samplingRatio, None, sqlContext)
   }
 
   /** Returns a new base relation with the given schema and parameters. */
@@ -53,7 +55,7 @@ private[sql] class DefaultSource
     val path = checkPath(parameters)
     val samplingRatio = parameters.get("samplingRatio").map(_.toDouble).getOrElse(1.0)
 
-    JSONRelation(path, samplingRatio, Some(schema))(sqlContext)
+    new JSONRelation(path, samplingRatio, Some(schema), sqlContext)
   }
 
   override def createRelation(
@@ -101,32 +103,87 @@ private[sql] class DefaultSource
   }
 }
 
-private[sql] case class JSONRelation(
-    path: String,
-    samplingRatio: Double,
+private[sql] class JSONRelation(
+    // baseRDD is not immutable with respect to INSERT OVERWRITE
+    // and so it must be recreated at least as often as the
+    // underlying inputs are modified. To be safe, a function is
+    // used instead of a regular RDD value to ensure a fresh RDD is
+    // recreated for each and every operation.
+    baseRDD: () => RDD[String],
+    val path: Option[String],
+    val samplingRatio: Double,
     userSpecifiedSchema: Option[StructType])(
     @transient val sqlContext: SQLContext)
   extends BaseRelation
   with TableScan
-  with InsertableRelation {
-
-  // TODO: Support partitioned JSON relation.
-  private def baseRDD = sqlContext.sparkContext.textFile(path)
+  with InsertableRelation
+  with CatalystScan {
+
+  def this(
+      path: String,
+      samplingRatio: Double,
+      userSpecifiedSchema: Option[StructType],
+      sqlContext: SQLContext) =
+    this(
+      () => sqlContext.sparkContext.textFile(path),
+      Some(path),
+      samplingRatio,
+      userSpecifiedSchema)(sqlContext)
+
+  private val useJacksonStreamingAPI: Boolean = sqlContext.conf.useJacksonStreamingAPI
 
   override val needConversion: Boolean = false
 
-  override val schema = userSpecifiedSchema.getOrElse(
-    JsonRDD.nullTypeToStringType(
-      JsonRDD.inferSchema(
-        baseRDD,
+  override lazy val schema = userSpecifiedSchema.getOrElse {
+    if (useJacksonStreamingAPI) {
+      InferSchema(
+        baseRDD(),
         samplingRatio,
-        sqlContext.conf.columnNameOfCorruptRecord)))
+        sqlContext.conf.columnNameOfCorruptRecord)
+    } else {
+      JsonRDD.nullTypeToStringType(
+        JsonRDD.inferSchema(
+          baseRDD(),
+          samplingRatio,
+          sqlContext.conf.columnNameOfCorruptRecord))
+    }
+  }
 
-  override def buildScan(): RDD[Row] =
-    JsonRDD.jsonStringToRow(baseRDD, schema, sqlContext.conf.columnNameOfCorruptRecord)
+  override def buildScan(): RDD[Row] = {
+    if (useJacksonStreamingAPI) {
+      JacksonParser(
+        baseRDD(),
+        schema,
+        sqlContext.conf.columnNameOfCorruptRecord)
+    } else {
+      JsonRDD.jsonStringToRow(
+        baseRDD(),
+        schema,
+        sqlContext.conf.columnNameOfCorruptRecord)
+    }
+  }
+
+  override def buildScan(requiredColumns: Seq[Attribute], filters: Seq[Expression]): RDD[Row] = {
+    if (useJacksonStreamingAPI) {
+      JacksonParser(
+        baseRDD(),
+        StructType.fromAttributes(requiredColumns),
+        sqlContext.conf.columnNameOfCorruptRecord)
+    } else {
+      JsonRDD.jsonStringToRow(
+        baseRDD(),
+        StructType.fromAttributes(requiredColumns),
+        sqlContext.conf.columnNameOfCorruptRecord)
+    }
+  }
 
   override def insert(data: DataFrame, overwrite: Boolean): Unit = {
-    val filesystemPath = new Path(path)
+    val filesystemPath = path match {
+      case Some(p) => new Path(p)
+      case None =>
+        throw new IOException(s"Cannot INSERT into table with no path defined")
+    }
+
     val fs = filesystemPath.getFileSystem(sqlContext.sparkContext.hadoopConfiguration)
 
     if (overwrite) {
@@ -147,7 +204,7 @@ private[sql] case class JSONRelation(
         }
       }
       // Write the data.
-      data.toJSON.saveAsTextFile(path)
+      data.toJSON.saveAsTextFile(filesystemPath.toString)
       // Right now, we assume that the schema is not changed. We will not update the schema.
       // schema = data.schema
     } else {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/json/JacksonGenerator.scala b/sql/core/src/main/scala/org/apache/spark/sql/json/JacksonGenerator.scala
new file mode 100644
index 0000000000000..80bf74aa02602
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/json/JacksonGenerator.scala
@@ -0,0 +1,77 @@
+/*
+ * 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.json
+
+import scala.collection.Map
+
+import com.fasterxml.jackson.core._
+
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.types._
+
+private[sql] object JacksonGenerator {
+  /** Transforms a single Row to JSON using Jackson
+    *
+    * @param rowSchema the schema object used for conversion
+    * @param gen a JsonGenerator object
+    * @param row The row to convert
+    */
+  def apply(rowSchema: StructType, gen: JsonGenerator)(row: Row): Unit = {
+    def valWriter: (DataType, Any) => Unit = {
+      case (_, null) | (NullType, _)  => gen.writeNull()
+      case (StringType, v: String) => gen.writeString(v)
+      case (TimestampType, v: java.sql.Timestamp) => gen.writeString(v.toString)
+      case (IntegerType, v: Int) => gen.writeNumber(v)
+      case (ShortType, v: Short) => gen.writeNumber(v)
+      case (FloatType, v: Float) => gen.writeNumber(v)
+      case (DoubleType, v: Double) => gen.writeNumber(v)
+      case (LongType, v: Long) => gen.writeNumber(v)
+      case (DecimalType(), v: java.math.BigDecimal) => gen.writeNumber(v)
+      case (ByteType, v: Byte) => gen.writeNumber(v.toInt)
+      case (BinaryType, v: Array[Byte]) => gen.writeBinary(v)
+      case (BooleanType, v: Boolean) => gen.writeBoolean(v)
+      case (DateType, v) => gen.writeString(v.toString)
+      case (udt: UserDefinedType[_], v) => valWriter(udt.sqlType, udt.serialize(v))
+
+      case (ArrayType(ty, _), v: Seq[_] ) =>
+        gen.writeStartArray()
+        v.foreach(valWriter(ty,_))
+        gen.writeEndArray()
+
+      case (MapType(kv,vv, _), v: Map[_,_]) =>
+        gen.writeStartObject()
+        v.foreach { p =>
+          gen.writeFieldName(p._1.toString)
+          valWriter(vv,p._2)
+        }
+        gen.writeEndObject()
+
+      case (StructType(ty), v: Row) =>
+        gen.writeStartObject()
+        ty.zip(v.toSeq).foreach {
+          case (_, null) =>
+          case (field, v) =>
+            gen.writeFieldName(field.name)
+            valWriter(field.dataType, v)
+        }
+        gen.writeEndObject()
+    }
+
+    valWriter(rowSchema, row)
+  }
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/json/JacksonParser.scala b/sql/core/src/main/scala/org/apache/spark/sql/json/JacksonParser.scala
new file mode 100644
index 0000000000000..a8e69ae61174f
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/json/JacksonParser.scala
@@ -0,0 +1,215 @@
+/*
+ * 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.json
+
+import java.io.ByteArrayOutputStream
+import java.sql.Timestamp
+
+import scala.collection.Map
+
+import com.fasterxml.jackson.core._
+
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.json.JacksonUtils.nextUntil
+import org.apache.spark.sql.types._
+
+private[sql] object JacksonParser {
+  def apply(
+      json: RDD[String],
+      schema: StructType,
+      columnNameOfCorruptRecords: String): RDD[Row] = {
+    parseJson(json, schema, columnNameOfCorruptRecords)
+  }
+
+  /**
+   * Parse the current token (and related children) according to a desired schema
+   */
+  private[sql] def convertField(
+      factory: JsonFactory,
+      parser: JsonParser,
+      schema: DataType): Any = {
+    import com.fasterxml.jackson.core.JsonToken._
+    (parser.getCurrentToken, schema) match {
+      case (null | VALUE_NULL, _) =>
+        null
+
+      case (FIELD_NAME, _) =>
+        parser.nextToken()
+        convertField(factory, parser, schema)
+
+      case (VALUE_STRING, StringType) =>
+        UTF8String(parser.getText)
+
+      case (VALUE_STRING, _) if parser.getTextLength < 1 =>
+        // guard the non string type
+        null
+
+      case (VALUE_STRING, DateType) =>
+        DateUtils.millisToDays(DateUtils.stringToTime(parser.getText).getTime)
+
+      case (VALUE_STRING, TimestampType) =>
+        new Timestamp(DateUtils.stringToTime(parser.getText).getTime)
+
+      case (VALUE_NUMBER_INT, TimestampType) =>
+        new Timestamp(parser.getLongValue)
+
+      case (_, StringType) =>
+        val writer = new ByteArrayOutputStream()
+        val generator = factory.createGenerator(writer, JsonEncoding.UTF8)
+        generator.copyCurrentStructure(parser)
+        generator.close()
+        UTF8String(writer.toByteArray)
+
+      case (VALUE_NUMBER_INT | VALUE_NUMBER_FLOAT, FloatType) =>
+        parser.getFloatValue
+
+      case (VALUE_NUMBER_INT | VALUE_NUMBER_FLOAT, DoubleType) =>
+        parser.getDoubleValue
+
+      case (VALUE_NUMBER_INT | VALUE_NUMBER_FLOAT, DecimalType()) =>
+        // TODO: add fixed precision and scale handling
+        Decimal(parser.getDecimalValue)
+
+      case (VALUE_NUMBER_INT, ByteType) =>
+        parser.getByteValue
+
+      case (VALUE_NUMBER_INT, ShortType) =>
+        parser.getShortValue
+
+      case (VALUE_NUMBER_INT, IntegerType) =>
+        parser.getIntValue
+
+      case (VALUE_NUMBER_INT, LongType) =>
+        parser.getLongValue
+
+      case (VALUE_TRUE, BooleanType) =>
+        true
+
+      case (VALUE_FALSE, BooleanType) =>
+        false
+
+      case (START_OBJECT, st: StructType) =>
+        convertObject(factory, parser, st)
+
+      case (START_ARRAY, ArrayType(st, _)) =>
+        convertList(factory, parser, st)
+
+      case (START_OBJECT, ArrayType(st, _)) =>
+        // the business end of SPARK-3308:
+        // when an object is found but an array is requested just wrap it in a list
+        convertField(factory, parser, st) :: Nil
+
+      case (START_OBJECT, MapType(StringType, kt, _)) =>
+        convertMap(factory, parser, kt)
+
+      case (_, udt: UserDefinedType[_]) =>
+        udt.deserialize(convertField(factory, parser, udt.sqlType))
+    }
+  }
+
+  /**
+   * Parse an object from the token stream into a new Row representing the schema.
+   *
+   * Fields in the json that are not defined in the requested schema will be dropped.
+   */
+  private def convertObject(factory: JsonFactory, parser: JsonParser, schema: StructType): Row = {
+    val row = new GenericMutableRow(schema.length)
+    while (nextUntil(parser, JsonToken.END_OBJECT)) {
+      schema.getFieldIndex(parser.getCurrentName) match {
+        case Some(index) =>
+          row.update(index, convertField(factory, parser, schema(index).dataType))
+
+        case None =>
+          parser.skipChildren()
+      }
+    }
+
+    row
+  }
+
+  /**
+   * Parse an object as a Map, preserving all fields
+   */
+  private def convertMap(
+      factory: JsonFactory,
+      parser: JsonParser,
+      valueType: DataType): Map[String, Any] = {
+    val builder = Map.newBuilder[String, Any]
+    while (nextUntil(parser, JsonToken.END_OBJECT)) {
+      builder += parser.getCurrentName -> convertField(factory, parser, valueType)
+    }
+
+    builder.result()
+  }
+
+  private def convertList(
+      factory: JsonFactory,
+      parser: JsonParser,
+      schema: DataType): Seq[Any] = {
+    val builder = Seq.newBuilder[Any]
+    while (nextUntil(parser, JsonToken.END_ARRAY)) {
+      builder += convertField(factory, parser, schema)
+    }
+
+    builder.result()
+  }
+
+  private def parseJson(
+      json: RDD[String],
+      schema: StructType,
+      columnNameOfCorruptRecords: String): RDD[Row] = {
+
+    def failedRecord(record: String): Seq[Row] = {
+      // create a row even if no corrupt record column is present
+      val row = new GenericMutableRow(schema.length)
+      for (corruptIndex <- schema.getFieldIndex(columnNameOfCorruptRecords)) {
+        require(schema(corruptIndex).dataType == StringType)
+        row.update(corruptIndex, record)
+      }
+
+      Seq(row)
+    }
+
+    json.mapPartitions { iter =>
+      val factory = new JsonFactory()
+
+      iter.flatMap { record =>
+        try {
+          val parser = factory.createParser(record)
+          parser.nextToken()
+
+          // to support both object and arrays (see SPARK-3308) we'll start
+          // by converting the StructType schema to an ArrayType and let
+          // convertField wrap an object into a single value array when necessary.
+          convertField(factory, parser, ArrayType(schema)) match {
+            case null => failedRecord(record)
+            case list: Seq[Row @unchecked] => list
+            case _ =>
+              sys.error(
+                s"Failed to parse record $record. Please make sure that each line of the file " +
+                  "(or each string in the RDD) is a valid JSON object or an array of JSON objects.")
+          }
+        } catch {
+          case _: JsonProcessingException =>
+            failedRecord(record)
+        }
+      }
+    }
+  }
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/json/JacksonUtils.scala b/sql/core/src/main/scala/org/apache/spark/sql/json/JacksonUtils.scala
new file mode 100644
index 0000000000000..fde96852ce68e
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/json/JacksonUtils.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.sql.json
+
+import com.fasterxml.jackson.core.{JsonParser, JsonToken}
+
+private object JacksonUtils {
+  /**
+   * Advance the parser until a null or a specific token is found
+   */
+  def nextUntil(parser: JsonParser, stopOn: JsonToken): Boolean = {
+    parser.nextToken() match {
+      case null => false
+      case x => x != stopOn
+    }
+  }
+}
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 6e94e7056eb0b..f62973d5fcfab 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
@@ -440,54 +440,4 @@ private[sql] object JsonRDD extends Logging {
 
     row
   }
-
-  /** Transforms a single Row to JSON using Jackson
-    *
-    * @param rowSchema the schema object used for conversion
-    * @param gen a JsonGenerator object
-    * @param row The row to convert
-    */
-  private[sql] def rowToJSON(rowSchema: StructType, gen: JsonGenerator)(row: Row) = {
-    def valWriter: (DataType, Any) => Unit = {
-      case (_, null) | (NullType, _)  => gen.writeNull()
-      case (StringType, v: String) => gen.writeString(v)
-      case (TimestampType, v: java.sql.Timestamp) => gen.writeString(v.toString)
-      case (IntegerType, v: Int) => gen.writeNumber(v)
-      case (ShortType, v: Short) => gen.writeNumber(v)
-      case (FloatType, v: Float) => gen.writeNumber(v)
-      case (DoubleType, v: Double) => gen.writeNumber(v)
-      case (LongType, v: Long) => gen.writeNumber(v)
-      case (DecimalType(), v: java.math.BigDecimal) => gen.writeNumber(v)
-      case (ByteType, v: Byte) => gen.writeNumber(v.toInt)
-      case (BinaryType, v: Array[Byte]) => gen.writeBinary(v)
-      case (BooleanType, v: Boolean) => gen.writeBoolean(v)
-      case (DateType, v) => gen.writeString(v.toString)
-      case (udt: UserDefinedType[_], v) => valWriter(udt.sqlType, v)
-
-      case (ArrayType(ty, _), v: Seq[_] ) =>
-        gen.writeStartArray()
-        v.foreach(valWriter(ty,_))
-        gen.writeEndArray()
-
-      case (MapType(kv,vv, _), v: Map[_,_]) =>
-        gen.writeStartObject()
-        v.foreach { p =>
-          gen.writeFieldName(p._1.toString)
-          valWriter(vv,p._2)
-        }
-        gen.writeEndObject()
-
-      case (StructType(ty), v: Row) =>
-        gen.writeStartObject()
-        ty.zip(v.toSeq).foreach {
-          case (_, null) =>
-          case (field, v) =>
-            gen.writeFieldName(field.name)
-            valWriter(field.dataType, v)
-        }
-        gen.writeEndObject()
-    }
-
-    valWriter(rowSchema, 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 fd0e2746dc045..263fafba930ce 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
@@ -17,13 +17,15 @@
 
 package org.apache.spark.sql.json
 
+import java.io.StringWriter
 import java.sql.{Date, Timestamp}
 
+import com.fasterxml.jackson.core.JsonFactory
 import org.scalactic.Tolerance._
 
 import org.apache.spark.sql.TestData._
 import org.apache.spark.sql.functions._
-import org.apache.spark.sql.json.JsonRDD.{compatibleType, enforceCorrectType}
+import org.apache.spark.sql.json.InferSchema.compatibleType
 import org.apache.spark.sql.sources.LogicalRelation
 import org.apache.spark.sql.test.TestSQLContext
 import org.apache.spark.sql.test.TestSQLContext._
@@ -46,6 +48,18 @@ class JsonSuite extends QueryTest {
           s"${expected}(${expected.getClass}).")
     }
 
+    val factory = new JsonFactory()
+    def enforceCorrectType(value: Any, dataType: DataType): Any = {
+      val writer = new StringWriter()
+      val generator = factory.createGenerator(writer)
+      generator.writeObject(value)
+      generator.flush()
+
+      val parser = factory.createParser(writer.toString)
+      parser.nextToken()
+      JacksonParser.convertField(factory, parser, dataType)
+    }
+
     val intNumber: Int = 2147483647
     checkTypePromotion(intNumber, enforceCorrectType(intNumber, IntegerType))
     checkTypePromotion(intNumber.toLong, enforceCorrectType(intNumber, LongType))
@@ -439,7 +453,7 @@ class JsonSuite extends QueryTest {
     val jsonDF = jsonRDD(primitiveFieldValueTypeConflict)
     jsonDF.registerTempTable("jsonTable")
 
-    // Right now, the analyzer does not promote strings in a boolean expreesion.
+    // Right now, the analyzer does not promote strings in a boolean expression.
     // Number and Boolean conflict: resolve the type as boolean in this query.
     checkAnswer(
       sql("select num_bool from jsonTable where NOT num_bool"),
@@ -508,7 +522,7 @@ class JsonSuite extends QueryTest {
       Row(Seq(), "11", "[1,2,3]", Row(null), "[]") ::
         Row(null, """{"field":false}""", null, null, "{}") ::
         Row(Seq(4, 5, 6), null, "str", Row(null), "[7,8,9]") ::
-        Row(Seq(7), "{}","[str1,str2,33]", Row("str"), """{"field":true}""") :: Nil
+        Row(Seq(7), "{}","""["str1","str2",33]""", Row("str"), """{"field":true}""") :: Nil
     )
   }
 
@@ -566,19 +580,19 @@ class JsonSuite extends QueryTest {
     val analyzed = jsonDF.queryExecution.analyzed
     assert(
       analyzed.isInstanceOf[LogicalRelation],
-      "The DataFrame returned by jsonFile should be based on JSONRelation.")
+      "The DataFrame returned by jsonFile should be based on LogicalRelation.")
     val relation = analyzed.asInstanceOf[LogicalRelation].relation
     assert(
       relation.isInstanceOf[JSONRelation],
       "The DataFrame returned by jsonFile should be based on JSONRelation.")
-    assert(relation.asInstanceOf[JSONRelation].path === path)
+    assert(relation.asInstanceOf[JSONRelation].path === Some(path))
     assert(relation.asInstanceOf[JSONRelation].samplingRatio === (0.49 +- 0.001))
 
     val schema = StructType(StructField("a", LongType, true) :: Nil)
     val logicalRelation =
       jsonFile(path, schema).queryExecution.analyzed.asInstanceOf[LogicalRelation]
     val relationWithSchema = logicalRelation.relation.asInstanceOf[JSONRelation]
-    assert(relationWithSchema.path === path)
+    assert(relationWithSchema.path === Some(path))
     assert(relationWithSchema.schema === schema)
     assert(relationWithSchema.samplingRatio > 0.99)
   }
@@ -1020,15 +1034,24 @@ class JsonSuite extends QueryTest {
   }
 
   test("JSONRelation equality test") {
-    val relation1 =
-      JSONRelation("path", 1.0, Some(StructType(StructField("a", IntegerType, true) :: Nil)))(null)
+    val context = org.apache.spark.sql.test.TestSQLContext
+    val relation1 = new JSONRelation(
+      "path",
+      1.0,
+      Some(StructType(StructField("a", IntegerType, true) :: Nil)),
+      context)
     val logicalRelation1 = LogicalRelation(relation1)
-    val relation2 =
-      JSONRelation("path", 0.5, Some(StructType(StructField("a", IntegerType, true) :: Nil)))(
-        org.apache.spark.sql.test.TestSQLContext)
+    val relation2 = new JSONRelation(
+      "path",
+      0.5,
+      Some(StructType(StructField("a", IntegerType, true) :: Nil)),
+      context)
     val logicalRelation2 = LogicalRelation(relation2)
-    val relation3 =
-      JSONRelation("path", 1.0, Some(StructType(StructField("b", StringType, true) :: Nil)))(null)
+    val relation3 = new JSONRelation(
+      "path",
+      1.0,
+      Some(StructType(StructField("b", StringType, true) :: Nil)),
+      context)
     val logicalRelation3 = LogicalRelation(relation3)
 
     assert(relation1 === relation2)
@@ -1046,7 +1069,7 @@ class JsonSuite extends QueryTest {
 
   test("SPARK-6245 JsonRDD.inferSchema on empty RDD") {
     // This is really a test that it doesn't throw an exception
-    val emptySchema = JsonRDD.inferSchema(empty, 1.0, "")
+    val emptySchema = InferSchema(empty, 1.0, "")
     assert(StructType(Seq()) === emptySchema)
   }
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/InsertSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/InsertSuite.scala
index 80efe9728fbc2..50629ea4dc066 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/sources/InsertSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/InsertSuite.scala
@@ -21,7 +21,7 @@ import java.io.File
 
 import org.scalatest.BeforeAndAfterAll
 
-import org.apache.spark.sql.{AnalysisException, Row}
+import org.apache.spark.sql.{SaveMode, AnalysisException, Row}
 import org.apache.spark.util.Utils
 
 class InsertSuite extends DataSourceTest with BeforeAndAfterAll {
@@ -100,23 +100,48 @@ class InsertSuite extends DataSourceTest with BeforeAndAfterAll {
   test("INSERT OVERWRITE a JSONRelation multiple times") {
     sql(
       s"""
-        |INSERT OVERWRITE TABLE jsonTable SELECT a, b FROM jt
-      """.stripMargin)
+         |INSERT OVERWRITE TABLE jsonTable SELECT a, b FROM jt
+    """.stripMargin)
+    checkAnswer(
+      sql("SELECT a, b FROM jsonTable"),
+      (1 to 10).map(i => Row(i, s"str$i"))
+    )
 
+    // Writing the table to less part files.
+    val rdd1 = sparkContext.parallelize((1 to 10).map(i => s"""{"a":$i, "b":"str${i}"}"""), 5)
+    jsonRDD(rdd1).registerTempTable("jt1")
     sql(
       s"""
-        |INSERT OVERWRITE TABLE jsonTable SELECT a, b FROM jt
-      """.stripMargin)
+         |INSERT OVERWRITE TABLE jsonTable SELECT a, b FROM jt1
+    """.stripMargin)
+    checkAnswer(
+      sql("SELECT a, b FROM jsonTable"),
+      (1 to 10).map(i => Row(i, s"str$i"))
+    )
 
+    // Writing the table to more part files.
+    val rdd2 = sparkContext.parallelize((1 to 10).map(i => s"""{"a":$i, "b":"str${i}"}"""), 10)
+    jsonRDD(rdd2).registerTempTable("jt2")
     sql(
       s"""
-        |INSERT OVERWRITE TABLE jsonTable SELECT a, b FROM jt
-      """.stripMargin)
-
+         |INSERT OVERWRITE TABLE jsonTable SELECT a, b FROM jt2
+    """.stripMargin)
     checkAnswer(
       sql("SELECT a, b FROM jsonTable"),
       (1 to 10).map(i => Row(i, s"str$i"))
     )
+
+    sql(
+      s"""
+         |INSERT OVERWRITE TABLE jsonTable SELECT a * 10, b FROM jt1
+    """.stripMargin)
+    checkAnswer(
+      sql("SELECT a, b FROM jsonTable"),
+      (1 to 10).map(i => Row(i * 10, s"str$i"))
+    )
+
+    dropTempTable("jt1")
+    dropTempTable("jt2")
   }
 
   test("INSERT INTO not supported for JSONRelation for now") {
@@ -128,6 +153,20 @@ class InsertSuite extends DataSourceTest with BeforeAndAfterAll {
     }
   }
 
+  test("save directly to the path of a JSON table") {
+    table("jt").selectExpr("a * 5 as a", "b").save(path.toString, "json", SaveMode.Overwrite)
+    checkAnswer(
+      sql("SELECT a, b FROM jsonTable"),
+      (1 to 10).map(i => Row(i * 5, s"str$i"))
+    )
+
+    table("jt").save(path.toString, "json", SaveMode.Overwrite)
+    checkAnswer(
+      sql("SELECT a, b FROM jsonTable"),
+      (1 to 10).map(i => Row(i, s"str$i"))
+    )
+  }
+
   test("it is not allowed to write to a table while querying it.") {
     val message = intercept[AnalysisException] {
       sql(

From cfdadcbd2b529cd9ac721509a7ebafe436afcd8d Mon Sep 17 00:00:00 2001
From: Tathagata Das <tathagata.das1565@gmail.com>
Date: Thu, 7 May 2015 00:21:10 -0700
Subject: [PATCH 26/75] [SPARK-7430] [STREAMING] [TEST] General improvements to
 streaming tests to increase debuggability

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

Closes #5961 from tdas/SPARK-7430 and squashes the following commits:

d654978 [Tathagata Das] Fix scala style
fbf7174 [Tathagata Das] Added more verbose assert failure messages.
6aea07a [Tathagata Das] Ensure SynchronizedBuffer is used in every TestSuiteBase
---
 .../spark/streaming/TestSuiteBase.scala       | 33 ++++++++++++-------
 1 file changed, 21 insertions(+), 12 deletions(-)

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 4d0cd7516f42e..4f70ae7f1f187 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/TestSuiteBase.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/TestSuiteBase.scala
@@ -73,9 +73,11 @@ class TestInputStream[T: ClassTag](ssc_ : StreamingContext, input: Seq[Seq[T]],
  *
  * The buffer contains a sequence of RDD's, each containing a sequence of items
  */
-class TestOutputStream[T: ClassTag](parent: DStream[T],
-    val output: ArrayBuffer[Seq[T]] = ArrayBuffer[Seq[T]]())
-  extends ForEachDStream[T](parent, (rdd: RDD[T], t: Time) => {
+class TestOutputStream[T: ClassTag](
+    parent: DStream[T],
+    val output: SynchronizedBuffer[Seq[T]] =
+      new ArrayBuffer[Seq[T]] with SynchronizedBuffer[Seq[T]]
+  ) extends ForEachDStream[T](parent, (rdd: RDD[T], t: Time) => {
     val collected = rdd.collect()
     output += collected
   }) {
@@ -95,8 +97,10 @@ class TestOutputStream[T: ClassTag](parent: DStream[T],
  * The buffer contains a sequence of RDD's, each containing a sequence of partitions, each
  * containing a sequence of items.
  */
-class TestOutputStreamWithPartitions[T: ClassTag](parent: DStream[T],
-    val output: ArrayBuffer[Seq[Seq[T]]] = ArrayBuffer[Seq[Seq[T]]]())
+class TestOutputStreamWithPartitions[T: ClassTag](
+    parent: DStream[T],
+    val output: SynchronizedBuffer[Seq[Seq[T]]] =
+      new ArrayBuffer[Seq[Seq[T]]] with SynchronizedBuffer[Seq[Seq[T]]])
   extends ForEachDStream[T](parent, (rdd: RDD[T], t: Time) => {
     val collected = rdd.glom().collect().map(_.toSeq)
     output += collected
@@ -108,10 +112,6 @@ class TestOutputStreamWithPartitions[T: ClassTag](parent: DStream[T],
     ois.defaultReadObject()
     output.clear()
   }
-
-  def toTestOutputStream: TestOutputStream[T] = {
-    new TestOutputStream[T](this.parent, this.output.map(_.flatten))
-  }
 }
 
 /**
@@ -425,12 +425,21 @@ trait TestSuiteBase extends FunSuite with BeforeAndAfter with Logging {
     logInfo("--------------------------------")
 
     // Match the output with the expected output
-    assert(output.size === expectedOutput.size, "Number of outputs do not match")
     for (i <- 0 until output.size) {
       if (useSet) {
-        assert(output(i).toSet === expectedOutput(i).toSet)
+        assert(
+          output(i).toSet === expectedOutput(i).toSet,
+          s"Set comparison failed\n" +
+            s"Expected output (${expectedOutput.size} items):\n${expectedOutput.mkString("\n")}\n" +
+            s"Generated output (${output.size} items): ${output.mkString("\n")}"
+        )
       } else {
-        assert(output(i).toList === expectedOutput(i).toList)
+        assert(
+          output(i).toList === expectedOutput(i).toList,
+          s"Ordered list comparison failed\n" +
+            s"Expected output (${expectedOutput.size} items):\n${expectedOutput.mkString("\n")}\n" +
+            s"Generated output (${output.size} items): ${output.mkString("\n")}"
+        )
       }
     }
     logInfo("Output verified successfully")

From 01187f59b3d118495b6cfea965690829b99a36fa Mon Sep 17 00:00:00 2001
From: Tathagata Das <tathagata.das1565@gmail.com>
Date: Thu, 7 May 2015 00:24:44 -0700
Subject: [PATCH 27/75] [SPARK-7217] [STREAMING] Add configuration to control
 the default behavior of StreamingContext.stop() implicitly calling
 SparkContext.stop()

In environments like notebooks, the SparkContext is managed by the underlying infrastructure and it is expected that the SparkContext will not be stopped. However, StreamingContext.stop() calls SparkContext.stop() as a non-intuitive side-effect. This PR adds a configuration in SparkConf that sets the default StreamingContext stop behavior. It should be such that the existing behavior does not change for existing users.

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

Closes #5929 from tdas/SPARK-7217 and squashes the following commits:

869a763 [Tathagata Das] Changed implementation.
685fe00 [Tathagata Das] Added configuration
---
 .../spark/streaming/StreamingContext.scala    | 10 +++++++---
 .../streaming/StreamingContextSuite.scala     | 19 +++++++++++++++++--
 2 files changed, 24 insertions(+), 5 deletions(-)

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 b1ad0d42ec8a6..bbdb4e8af036c 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala
@@ -563,13 +563,17 @@ class StreamingContext private[streaming] (
 
   /**
    * Stop the execution of the streams immediately (does not wait for all received data
-   * to be processed).
+   * to be processed). By default, if `stopSparkContext` is not specified, the underlying
+   * SparkContext will also be stopped. This implicit behavior can be configured using the
+   * SparkConf configuration spark.streaming.stopSparkContextByDefault.
    *
-   * @param stopSparkContext if true, stops the associated SparkContext. The underlying SparkContext
+   * @param stopSparkContext If true, stops the associated SparkContext. The underlying SparkContext
    *                         will be stopped regardless of whether this StreamingContext has been
    *                         started.
    */
-  def stop(stopSparkContext: Boolean = true): Unit = synchronized {
+  def stop(
+      stopSparkContext: Boolean = conf.getBoolean("spark.streaming.stopSparkContextByDefault", true)
+     ): Unit = synchronized {
     stop(stopSparkContext, false)
   }
 
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 5207b7109e69b..a589deb1fa579 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala
@@ -118,6 +118,11 @@ class StreamingContextSuite extends FunSuite with BeforeAndAfter with Timeouts w
     assert(ssc.state === ssc.StreamingContextState.Started)
     ssc.stop()
     assert(ssc.state === ssc.StreamingContextState.Stopped)
+
+    // Make sure that the SparkContext is also stopped by default
+    intercept[Exception] {
+      ssc.sparkContext.makeRDD(1 to 10)
+    }
   }
 
   test("start multiple times") {
@@ -154,16 +159,26 @@ class StreamingContextSuite extends FunSuite with BeforeAndAfter with Timeouts w
   }
 
   test("stop only streaming context") {
-    ssc = new StreamingContext(master, appName, batchDuration)
+    val conf = new SparkConf().setMaster(master).setAppName(appName)
+
+    // Explicitly do not stop SparkContext
+    ssc = new StreamingContext(conf, batchDuration)
     sc = ssc.sparkContext
     addInputStream(ssc).register()
     ssc.start()
     ssc.stop(stopSparkContext = false)
     assert(sc.makeRDD(1 to 100).collect().size === 100)
-    ssc = new StreamingContext(sc, batchDuration)
+    sc.stop()
+
+    // Implicitly do not stop SparkContext
+    conf.set("spark.streaming.stopSparkContextByDefault", "false")
+    ssc = new StreamingContext(conf, batchDuration)
+    sc = ssc.sparkContext
     addInputStream(ssc).register()
     ssc.start()
     ssc.stop()
+    assert(sc.makeRDD(1 to 100).collect().size === 100)
+    sc.stop()
   }
 
   test("stop(stopSparkContext=true) after stop(stopSparkContext=false)") {

From fa8fddffd52f8146ccceb72c2990607aaf5b2131 Mon Sep 17 00:00:00 2001
From: Shiti <ssaxena.ece@gmail.com>
Date: Thu, 7 May 2015 01:00:29 -0700
Subject: [PATCH 28/75] [SPARK-7295][SQL]  bitwise operations for DataFrame DSL

Author: Shiti <ssaxena.ece@gmail.com>

Closes #5867 from Shiti/spark-7295 and squashes the following commits:

71a9913 [Shiti] implementation for bitwise and,or, not and xor on Column with tests and docs
---
 python/pyspark/sql/dataframe.py               |  5 +++
 python/pyspark/sql/functions.py               |  2 ++
 python/pyspark/sql/tests.py                   | 13 ++++++++
 .../scala/org/apache/spark/sql/Column.scala   | 31 +++++++++++++++++
 .../org/apache/spark/sql/functions.scala      |  8 +++++
 .../spark/sql/ColumnExpressionSuite.scala     | 33 +++++++++++++++++--
 .../spark/sql/DataFrameFunctionsSuite.scala   |  7 ++++
 7 files changed, 97 insertions(+), 2 deletions(-)

diff --git a/python/pyspark/sql/dataframe.py b/python/pyspark/sql/dataframe.py
index 24f370543def4..cee804f5cc1f7 100644
--- a/python/pyspark/sql/dataframe.py
+++ b/python/pyspark/sql/dataframe.py
@@ -1277,6 +1277,11 @@ def __init__(self, jc):
     __contains__ = _bin_op("contains")
     __getitem__ = _bin_op("getItem")
 
+    # bitwise operators
+    bitwiseOR = _bin_op("bitwiseOR")
+    bitwiseAND = _bin_op("bitwiseAND")
+    bitwiseXOR = _bin_op("bitwiseXOR")
+
     def getItem(self, key):
         """An expression that gets an item at position `ordinal` out of a list,
          or gets an item by key out of a dict.
diff --git a/python/pyspark/sql/functions.py b/python/pyspark/sql/functions.py
index 692af868dd534..274c410a1ee9c 100644
--- a/python/pyspark/sql/functions.py
+++ b/python/pyspark/sql/functions.py
@@ -104,6 +104,8 @@ def _(col1, col2):
     'toRadians': 'Converts an angle measured in degrees to an approximately equivalent angle ' +
              'measured in radians.',
 
+    'bitwiseNOT': 'Computes bitwise not.',
+
     'max': 'Aggregate function: returns the maximum value of the expression in a group.',
     'min': 'Aggregate function: returns the minimum value of the expression in a group.',
     'first': 'Aggregate function: returns the first value in a group.',
diff --git a/python/pyspark/sql/tests.py b/python/pyspark/sql/tests.py
index b232f3a965526..45dfedce22add 100644
--- a/python/pyspark/sql/tests.py
+++ b/python/pyspark/sql/tests.py
@@ -645,6 +645,19 @@ def test_fillna(self):
         self.assertEqual(row.age, None)
         self.assertEqual(row.height, None)
 
+    def test_bitwise_operations(self):
+        from pyspark.sql import functions
+        row = Row(a=170, b=75)
+        df = self.sqlCtx.createDataFrame([row])
+        result = df.select(df.a.bitwiseAND(df.b)).collect()[0].asDict()
+        self.assertEqual(170 & 75, result['(a & b)'])
+        result = df.select(df.a.bitwiseOR(df.b)).collect()[0].asDict()
+        self.assertEqual(170 | 75, result['(a | b)'])
+        result = df.select(df.a.bitwiseXOR(df.b)).collect()[0].asDict()
+        self.assertEqual(170 ^ 75, result['(a ^ b)'])
+        result = df.select(functions.bitwiseNOT(df.b)).collect()[0].asDict()
+        self.assertEqual(~75, result['~b'])
+
 
 class HiveContextSQLTests(ReusedPySparkTestCase):
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/Column.scala b/sql/core/src/main/scala/org/apache/spark/sql/Column.scala
index 8eb632d3d600b..8bbe11b412214 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/Column.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/Column.scala
@@ -698,6 +698,37 @@ class Column(protected[sql] val expr: Expression) extends Logging {
       println(expr.prettyString)
     }
   }
+
+  /**
+   * Compute bitwise OR of this expression with another expression.
+   * {{{
+   *   df.select($"colA".bitwiseOR($"colB"))
+   * }}}
+   *
+   * @group expr_ops
+   */
+  def bitwiseOR(other: Any): Column = BitwiseOr(expr, lit(other).expr)
+
+  /**
+   * Compute bitwise AND of this expression with another expression.
+   * {{{
+   *   df.select($"colA".bitwiseAND($"colB"))
+   * }}}
+   *
+   * @group expr_ops
+   */
+  def bitwiseAND(other: Any): Column = BitwiseAnd(expr, lit(other).expr)
+
+  /**
+   * Compute bitwise XOR of this expression with another expression.
+   * {{{
+   *   df.select($"colA".bitwiseXOR($"colB"))
+   * }}}
+   *
+   * @group expr_ops
+   */
+  def bitwiseXOR(other: Any): Column = BitwiseXor(expr, lit(other).expr)
+
 }
 
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
index 830b5017717b5..1728b0b8c910e 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
@@ -438,6 +438,14 @@ object functions {
    */
   def upper(e: Column): Column = Upper(e.expr)
 
+
+  /**
+   * Computes bitwise NOT.
+   *
+   * @group normal_funcs
+   */
+  def bitwiseNOT(e: Column): Column = BitwiseNot(e.expr)
+
   //////////////////////////////////////////////////////////////////////////////////////////////
   // Math Functions
   //////////////////////////////////////////////////////////////////////////////////////////////
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala
index 3c1ad656fc855..d96186c268720 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala
@@ -27,8 +27,6 @@ import org.apache.spark.sql.types._
 class ColumnExpressionSuite extends QueryTest {
   import org.apache.spark.sql.TestData._
 
-  // TODO: Add test cases for bitwise operations.
-
   test("collect on column produced by a binary operator") {
     val df = Seq((1, 2, 3)).toDF("a", "b", "c")
     checkAnswer(df.select(df("a") + df("b")), Seq(Row(3)))
@@ -385,4 +383,35 @@ class ColumnExpressionSuite extends QueryTest {
       assert(row.getDouble(1) >= -4.0)
     }
   }
+
+  test("bitwiseAND") {
+    checkAnswer(
+      testData2.select($"a".bitwiseAND(75)),
+      testData2.collect().toSeq.map(r => Row(r.getInt(0) & 75)))
+
+    checkAnswer(
+      testData2.select($"a".bitwiseAND($"b").bitwiseAND(22)),
+      testData2.collect().toSeq.map(r => Row(r.getInt(0) & r.getInt(1) & 22)))
+  }
+
+  test("bitwiseOR") {
+    checkAnswer(
+      testData2.select($"a".bitwiseOR(170)),
+      testData2.collect().toSeq.map(r => Row(r.getInt(0) | 170)))
+
+    checkAnswer(
+      testData2.select($"a".bitwiseOR($"b").bitwiseOR(42)),
+      testData2.collect().toSeq.map(r => Row(r.getInt(0) | r.getInt(1) | 42)))
+  }
+
+  test("bitwiseXOR") {
+    checkAnswer(
+      testData2.select($"a".bitwiseXOR(112)),
+      testData2.collect().toSeq.map(r => Row(r.getInt(0) ^ 112)))
+
+    checkAnswer(
+      testData2.select($"a".bitwiseXOR($"b").bitwiseXOR(39)),
+      testData2.collect().toSeq.map(r => Row(r.getInt(0) ^ r.getInt(1) ^ 39)))
+  }
+
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala
index ca03713ef4658..b1e0faa310b68 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala
@@ -17,6 +17,7 @@
 
 package org.apache.spark.sql
 
+import org.apache.spark.sql.TestData._
 import org.apache.spark.sql.functions._
 import org.apache.spark.sql.test.TestSQLContext.implicits._
 import org.apache.spark.sql.types._
@@ -81,4 +82,10 @@ class DataFrameFunctionsSuite extends QueryTest {
       struct(col("a") * 2)
     }
   }
+
+  test("bitwiseNOT") {
+    checkAnswer(
+      testData2.select(bitwiseNOT($"a")),
+      testData2.collect().toSeq.map(r => Row(~r.getInt(0))))
+  }
 }

From fae4e2d6094de57a438ee4188ce47fc5b01b96fe Mon Sep 17 00:00:00 2001
From: ksonj <kson@siberie.de>
Date: Thu, 7 May 2015 01:02:00 -0700
Subject: [PATCH 29/75] [SPARK-7035] Encourage __getitem__ over __getattr__ on
 column access in the Python DataFrame API

Author: ksonj <kson@siberie.de>

Closes #5971 from ksonj/doc and squashes the following commits:

dadfebb [ksonj] __getitem__ is cleaner than __getattr__
---
 docs/sql-programming-guide.md | 11 ++++++++---
 1 file changed, 8 insertions(+), 3 deletions(-)

diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md
index b8233ae06fdf3..df4c123bdd86c 100644
--- a/docs/sql-programming-guide.md
+++ b/docs/sql-programming-guide.md
@@ -139,7 +139,6 @@ DataFrames provide a domain-specific language for structured data manipulation i
 
 Here we include some basic examples of structured data processing using DataFrames:
 
-
 <div class="codetabs">
 <div data-lang="scala"  markdown="1">
 {% highlight scala %}
@@ -242,6 +241,12 @@ df.groupBy("age").count().show();
 </div>
 
 <div data-lang="python"  markdown="1">
+In Python it's possible to access a DataFrame's columns either by attribute
+(`df.age`) or by indexing (`df['age']`). While the former is convenient for
+interactive data exploration, users are highly encouraged to use the
+latter form, which is future proof and won't break with column names that
+are also attributes on the DataFrame class.
+
 {% highlight python %}
 from pyspark.sql import SQLContext
 sqlContext = SQLContext(sc)
@@ -270,14 +275,14 @@ df.select("name").show()
 ## Justin
 
 # Select everybody, but increment the age by 1
-df.select(df.name, df.age + 1).show()
+df.select(df['name'], df['age'] + 1).show()
 ## name    (age + 1)
 ## Michael null
 ## Andy    31
 ## Justin  20
 
 # Select people older than 21
-df.filter(df.age > 21).show()
+df.filter(df['age'] > 21).show()
 ## age name
 ## 30  Andy
 

From 8b6b46e4ff5f19fb7befecaaa0eda63bf29a0e2c Mon Sep 17 00:00:00 2001
From: "Joseph K. Bradley" <joseph@databricks.com>
Date: Thu, 7 May 2015 01:12:14 -0700
Subject: [PATCH 30/75] [SPARK-7421] [MLLIB] OnlineLDA cleanups

Small changes, primarily to allow us more flexibility in the future:
* Rename "tau_0" to "tau0"
* Mark LDAOptimizer trait sealed and DeveloperApi.
* Mark LDAOptimizer subclasses as final.
* Mark setOptimizer (the one taking an LDAOptimizer) and getOptimizer as DeveloperApi since we may need to change them in the future

CC: hhbyyh

Author: Joseph K. Bradley <joseph@databricks.com>

Closes #5956 from jkbradley/onlinelda-cleanups and squashes the following commits:

f4be508 [Joseph K. Bradley] added newline
f4003e4 [Joseph K. Bradley] Changes: * Rename "tau_0" to "tau0" * Mark LDAOptimizer trait sealed and DeveloperApi. * Mark LDAOptimizer subclasses as final. * Mark setOptimizer (the one taking an LDAOptimizer) and getOptimizer as DeveloperApi since we may need to change them in the future
---
 .../apache/spark/mllib/clustering/LDA.scala   | 15 ++++++--
 .../spark/mllib/clustering/LDAOptimizer.scala | 37 +++++++++----------
 .../spark/mllib/clustering/JavaLDASuite.java  |  2 +-
 .../spark/mllib/clustering/LDASuite.scala     |  8 ++--
 4 files changed, 34 insertions(+), 28 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala
index c8daa2388e868..a410547a72fda 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala
@@ -18,8 +18,9 @@
 package org.apache.spark.mllib.clustering
 
 import breeze.linalg.{DenseVector => BDV}
+
 import org.apache.spark.Logging
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{DeveloperApi, Experimental}
 import org.apache.spark.api.java.JavaPairRDD
 import org.apache.spark.graphx._
 import org.apache.spark.mllib.linalg.Vector
@@ -197,12 +198,20 @@ class LDA private (
   }
 
 
-  /** LDAOptimizer used to perform the actual calculation */
+  /**
+   * :: DeveloperApi ::
+   *
+   * LDAOptimizer used to perform the actual calculation
+   */
+  @DeveloperApi
   def getOptimizer: LDAOptimizer = ldaOptimizer
 
   /**
+   * :: DeveloperApi ::
+   *
    * LDAOptimizer used to perform the actual calculation (default = EMLDAOptimizer)
    */
+  @DeveloperApi
   def setOptimizer(optimizer: LDAOptimizer): this.type = {
     this.ldaOptimizer = optimizer
     this
@@ -210,7 +219,7 @@ class LDA private (
 
   /**
    * Set the LDAOptimizer used to perform the actual calculation by algorithm name.
-   * Currently "em", "online" is supported.
+   * Currently "em", "online" are supported.
    */
   def setOptimizer(optimizerName: String): this.type = {
     this.ldaOptimizer =
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala
index 093aa0f315ab2..6fa2fe053c6a4 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala
@@ -23,7 +23,7 @@ import breeze.linalg.{DenseVector => BDV, DenseMatrix => BDM, sum, normalize, kr
 import breeze.numerics.{digamma, exp, abs}
 import breeze.stats.distributions.{Gamma, RandBasis}
 
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.graphx._
 import org.apache.spark.graphx.impl.GraphImpl
 import org.apache.spark.mllib.impl.PeriodicGraphCheckpointer
@@ -31,13 +31,13 @@ import org.apache.spark.mllib.linalg.{Matrices, SparseVector, DenseVector, Vecto
 import org.apache.spark.rdd.RDD
 
 /**
- * :: Experimental ::
+ * :: DeveloperApi ::
  *
  * An LDAOptimizer specifies which optimization/learning/inference algorithm to use, and it can
  * hold optimizer-specific parameters for users to set.
  */
-@Experimental
-trait LDAOptimizer {
+@DeveloperApi
+sealed trait LDAOptimizer {
 
   /*
     DEVELOPERS NOTE:
@@ -59,7 +59,7 @@ trait LDAOptimizer {
 }
 
 /**
- * :: Experimental ::
+ * :: DeveloperApi ::
  *
  * Optimizer for EM algorithm which stores data + parameter graph, plus algorithm parameters.
  *
@@ -75,8 +75,8 @@ trait LDAOptimizer {
  *    "On Smoothing and Inference for Topic Models."  UAI, 2009.
  *
  */
-@Experimental
-class EMLDAOptimizer extends LDAOptimizer {
+@DeveloperApi
+final class EMLDAOptimizer extends LDAOptimizer {
 
   import LDA._
 
@@ -211,7 +211,7 @@ class EMLDAOptimizer extends LDAOptimizer {
 
 
 /**
- * :: Experimental ::
+ * :: DeveloperApi ::
  *
  * An online optimizer for LDA. The Optimizer implements the Online variational Bayes LDA
  * algorithm, which processes a subset of the corpus on each iteration, and updates the term-topic
@@ -220,8 +220,8 @@ class EMLDAOptimizer extends LDAOptimizer {
  * Original Online LDA paper:
  *   Hoffman, Blei and Bach, "Online Learning for Latent Dirichlet Allocation." NIPS, 2010.
  */
-@Experimental
-class OnlineLDAOptimizer extends LDAOptimizer {
+@DeveloperApi
+final class OnlineLDAOptimizer extends LDAOptimizer {
 
   // LDA common parameters
   private var k: Int = 0
@@ -243,8 +243,8 @@ class OnlineLDAOptimizer extends LDAOptimizer {
   private var randomGenerator: java.util.Random = null
 
   // Online LDA specific parameters
-  // Learning rate is: (tau_0 + t)^{-kappa}
-  private var tau_0: Double = 1024
+  // Learning rate is: (tau0 + t)^{-kappa}
+  private var tau0: Double = 1024
   private var kappa: Double = 0.51
   private var miniBatchFraction: Double = 0.05
 
@@ -265,16 +265,16 @@ class OnlineLDAOptimizer extends LDAOptimizer {
    * A (positive) learning parameter that downweights early iterations. Larger values make early
    * iterations count less.
    */
-  def getTau_0: Double = this.tau_0
+  def getTau0: Double = this.tau0
 
   /**
    * A (positive) learning parameter that downweights early iterations. Larger values make early
    * iterations count less.
    * Default: 1024, following the original Online LDA paper.
    */
-  def setTau_0(tau_0: Double): this.type = {
-    require(tau_0 > 0,  s"LDA tau_0 must be positive, but was set to $tau_0")
-    this.tau_0 = tau_0
+  def setTau0(tau0: Double): this.type = {
+    require(tau0 > 0,  s"LDA tau0 must be positive, but was set to $tau0")
+    this.tau0 = tau0
     this
   }
 
@@ -434,11 +434,8 @@ class OnlineLDAOptimizer extends LDAOptimizer {
    * Update lambda based on the batch submitted. batchSize can be different for each iteration.
    */
   private[clustering] def update(stat: BDM[Double], iter: Int, batchSize: Int): Unit = {
-    val tau_0 = this.getTau_0
-    val kappa = this.getKappa
-
     // weight of the mini-batch.
-    val weight = math.pow(tau_0 + iter, -kappa)
+    val weight = math.pow(getTau0 + iter, -getKappa)
 
     // Update lambda based on documents.
     lambda = lambda * (1 - weight) +
diff --git a/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaLDASuite.java b/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaLDASuite.java
index f394d903966de..96c2da169961f 100644
--- a/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaLDASuite.java
+++ b/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaLDASuite.java
@@ -117,7 +117,7 @@ public void OnlineOptimizerCompatibility() {
 
     // Train a model
     OnlineLDAOptimizer op = new OnlineLDAOptimizer()
-      .setTau_0(1024)
+      .setTau0(1024)
       .setKappa(0.51)
       .setGammaShape(1e40)
       .setMiniBatchFraction(0.5);
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala
index 2dcc881f5abd2..d5b7d96335744 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala
@@ -138,12 +138,12 @@ class LDASuite extends FunSuite with MLlibTestSparkContext {
     val lda = new LDA().setK(2)
     val corpus = sc.parallelize(tinyCorpus, 2)
     val op = new OnlineLDAOptimizer().initialize(corpus, lda)
-    op.setKappa(0.9876).setMiniBatchFraction(0.123).setTau_0(567)
+    op.setKappa(0.9876).setMiniBatchFraction(0.123).setTau0(567)
     assert(op.getAlpha == 0.5) // default 1.0 / k
     assert(op.getEta == 0.5)   // default 1.0 / k
     assert(op.getKappa == 0.9876)
     assert(op.getMiniBatchFraction == 0.123)
-    assert(op.getTau_0 == 567)
+    assert(op.getTau0 == 567)
   }
 
   test("OnlineLDAOptimizer one iteration") {
@@ -159,7 +159,7 @@ class LDASuite extends FunSuite with MLlibTestSparkContext {
     val corpus = sc.parallelize(docs, 2)
 
     // Set GammaShape large to avoid the stochastic impact.
-    val op = new OnlineLDAOptimizer().setTau_0(1024).setKappa(0.51).setGammaShape(1e40)
+    val op = new OnlineLDAOptimizer().setTau0(1024).setKappa(0.51).setGammaShape(1e40)
       .setMiniBatchFraction(1)
     val lda = new LDA().setK(k).setMaxIterations(1).setOptimizer(op).setSeed(12345)
 
@@ -192,7 +192,7 @@ class LDASuite extends FunSuite with MLlibTestSparkContext {
     ).zipWithIndex.map { case (wordCounts, docId) => (docId.toLong, wordCounts) }
 
     val docs = sc.parallelize(toydata)
-    val op = new OnlineLDAOptimizer().setMiniBatchFraction(1).setTau_0(1024).setKappa(0.51)
+    val op = new OnlineLDAOptimizer().setMiniBatchFraction(1).setTau0(1024).setKappa(0.51)
       .setGammaShape(1e10)
     val lda = new LDA().setK(2)
       .setDocConcentration(0.01)

From 4f87e9562aa0dfe5467d7fbaba9278213106377c Mon Sep 17 00:00:00 2001
From: "Joseph K. Bradley" <joseph@databricks.com>
Date: Thu, 7 May 2015 01:28:44 -0700
Subject: [PATCH 31/75] [SPARK-7429] [ML] Params cleanups

Params.setDefault taking a set of ParamPairs should be annotated with varargs. I thought it would not work before, but it apparently does.

CrossValidator.transform should call transformSchema since the underlying Model might be a PipelineModel

CC: mengxr

Author: Joseph K. Bradley <joseph@databricks.com>

Closes #5960 from jkbradley/params-cleanups and squashes the following commits:

118b158 [Joseph K. Bradley] Params.setDefault taking a set of ParamPairs should be annotated with varargs. I thought it would not work before, but it apparently does. CrossValidator.transform should call transformSchema since the underlying Model might be a PipelineModel
---
 mllib/src/main/scala/org/apache/spark/ml/param/params.scala   | 4 +---
 .../scala/org/apache/spark/ml/tuning/CrossValidator.scala     | 3 ++-
 .../test/java/org/apache/spark/ml/param/JavaTestParams.java   | 1 +
 3 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/params.scala b/mllib/src/main/scala/org/apache/spark/ml/param/params.scala
index 51ce19d29cd29..6d09962fe6ee2 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/param/params.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/param/params.scala
@@ -366,13 +366,11 @@ trait Params extends Identifiable with Serializable {
   /**
    * Sets default values for a list of params.
    *
-   * Note: Java developers should use the single-parameter [[setDefault()]].
-   *       Annotating this with varargs causes compilation failures.
-   *
    * @param paramPairs  a list of param pairs that specify params and their default values to set
    *                    respectively. Make sure that the params are initialized before this method
    *                    gets called.
    */
+  @varargs
   protected final def setDefault(paramPairs: ParamPair[_]*): this.type = {
     paramPairs.foreach { p =>
       setDefault(p.param.asInstanceOf[Param[Any]], p.value)
diff --git a/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala b/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala
index 9208127eb1d79..ac0d1fed84b2e 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala
@@ -105,7 +105,7 @@ class CrossValidator extends Estimator[CrossValidatorModel] with CrossValidatorP
 
   override def fit(dataset: DataFrame): CrossValidatorModel = {
     val schema = dataset.schema
-    transformSchema(dataset.schema, logging = true)
+    transformSchema(schema, logging = true)
     val sqlCtx = dataset.sqlContext
     val est = $(estimator)
     val eval = $(evaluator)
@@ -159,6 +159,7 @@ class CrossValidatorModel private[ml] (
   }
 
   override def transform(dataset: DataFrame): DataFrame = {
+    transformSchema(dataset.schema, logging = true)
     bestModel.transform(dataset)
   }
 
diff --git a/mllib/src/test/java/org/apache/spark/ml/param/JavaTestParams.java b/mllib/src/test/java/org/apache/spark/ml/param/JavaTestParams.java
index 8abe575610d19..532eca47918fc 100644
--- a/mllib/src/test/java/org/apache/spark/ml/param/JavaTestParams.java
+++ b/mllib/src/test/java/org/apache/spark/ml/param/JavaTestParams.java
@@ -59,5 +59,6 @@ public JavaTestParams() {
       ParamValidators.inArray(validStrings));
     setDefault(myIntParam, 1);
     setDefault(myDoubleParam, 0.5);
+    setDefault(myIntParam.w(1), myDoubleParam.w(0.5));
   }
 }

From ed9be06a4797bbb678355b361054c8872ac20b75 Mon Sep 17 00:00:00 2001
From: Daoyuan Wang <daoyuan.wang@intel.com>
Date: Thu, 7 May 2015 10:05:01 -0700
Subject: [PATCH 32/75] [SPARK-7330] [SQL] avoid NPE at jdbc rdd

Thank nadavoosh point this out in #5590

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

Closes #5877 from adrian-wang/jdbcrdd and squashes the following commits:

cc11900 [Daoyuan Wang] avoid NPE in jdbcrdd
---
 .../org/apache/spark/sql/jdbc/JDBCRDD.scala   |  8 +++++-
 .../org/apache/spark/sql/jdbc/JDBCSuite.scala | 25 +++++++++++++++++++
 2 files changed, 32 insertions(+), 1 deletion(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRDD.scala
index 325a326e2b5b2..1a5083dbe0f61 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRDD.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRDD.scala
@@ -362,7 +362,13 @@ private[sql] class JDBCRDD(
           conversions(i) match {
             case BooleanConversion    => mutableRow.setBoolean(i, rs.getBoolean(pos))
             case DateConversion       =>
-              mutableRow.update(i, DateUtils.fromJavaDate(rs.getDate(pos)))
+              // DateUtils.fromJavaDate does not handle null value, so we need to check it.
+              val dateVal = rs.getDate(pos)
+              if (dateVal != null) {
+                mutableRow.update(i, DateUtils.fromJavaDate(dateVal))
+              } else {
+                mutableRow.update(i, null)
+              }
             case DecimalConversion    =>
               val decimalVal = rs.getBigDecimal(pos)
               if (decimalVal == null) {
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala
index 3ec17d37c025b..021affafe36a6 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala
@@ -104,6 +104,8 @@ class JDBCSuite extends FunSuite with BeforeAndAfter {
         ).executeUpdate()
     conn.prepareStatement("insert into test.timetypes values ('12:34:56', "
       + "'1996-01-01', '2002-02-20 11:22:33.543543543')").executeUpdate()
+    conn.prepareStatement("insert into test.timetypes values ('12:34:56', "
+      + "null, '2002-02-20 11:22:33.543543543')").executeUpdate()
     conn.commit()
     sql(
       s"""
@@ -127,6 +129,23 @@ class JDBCSuite extends FunSuite with BeforeAndAfter {
         |OPTIONS (url '$url', dbtable 'TEST.FLTTYPES', user 'testUser', password 'testPass')
       """.stripMargin.replaceAll("\n", " "))
 
+    conn.prepareStatement(
+      s"""
+        |create table test.nulltypes (a INT, b BOOLEAN, c TINYINT, d BINARY(20), e VARCHAR(20),
+        |f VARCHAR_IGNORECASE(20), g CHAR(20), h BLOB, i CLOB, j TIME, k DATE, l TIMESTAMP,
+        |m DOUBLE, n REAL, o DECIMAL(40, 20))
+      """.stripMargin.replaceAll("\n", " ")).executeUpdate()
+    conn.prepareStatement("insert into test.nulltypes values ("
+      + "null, null, null, null, null, null, null, null, null, "
+      + "null, null, null, null, null, null)").executeUpdate()
+    conn.commit()
+    sql(
+      s"""
+         |CREATE TEMPORARY TABLE nulltypes
+         |USING org.apache.spark.sql.jdbc
+         |OPTIONS (url '$url', dbtable 'TEST.NULLTYPES', user 'testUser', password 'testPass')
+      """.stripMargin.replaceAll("\n", " "))
+
     // Untested: IDENTITY, OTHER, UUID, ARRAY, and GEOMETRY types.
   }
 
@@ -254,6 +273,7 @@ class JDBCSuite extends FunSuite with BeforeAndAfter {
     val rows = TestSQLContext.jdbc(urlWithUserAndPass, "TEST.TIMETYPES").collect()
     val cachedRows = TestSQLContext.jdbc(urlWithUserAndPass, "TEST.TIMETYPES").cache().collect()
     assert(rows(0).getAs[java.sql.Date](1) === java.sql.Date.valueOf("1996-01-01"))
+    assert(rows(1).getAs[java.sql.Date](1) === null)
     assert(cachedRows(0).getAs[java.sql.Date](1) === java.sql.Date.valueOf("1996-01-01"))
   }
 
@@ -266,6 +286,11 @@ class JDBCSuite extends FunSuite with BeforeAndAfter {
     assert(cachedRows(0).getAs[java.sql.Date](1) === java.sql.Date.valueOf("1996-01-01"))
   }
 
+  test("test types for null value") {
+    val rows = TestSQLContext.jdbc(urlWithUserAndPass, "TEST.NULLTYPES").collect()
+    assert((0 to 14).forall(i => rows(0).isNullAt(i)))
+  }
+
   test("H2 floating-point types") {
     val rows = sql("SELECT * FROM flttypes").collect()
     assert(rows(0).getDouble(0) === 1.00000000000000022) // Yes, I meant ==.

From 9e2ffb13287e6efe256b8d23a4654e4cc305e20b Mon Sep 17 00:00:00 2001
From: Burak Yavuz <brkyvz@gmail.com>
Date: Thu, 7 May 2015 10:25:41 -0700
Subject: [PATCH 33/75] [SPARK-7388] [SPARK-7383] wrapper for VectorAssembler
 in Python

The wrapper required the implementation of the `ArrayParam`, because `Array[T]` is hard to obtain from Python. `ArrayParam` has an extra function called `wCast` which is an internal function to obtain `Array[T]` from `Seq[T]`

Author: Burak Yavuz <brkyvz@gmail.com>
Author: Xiangrui Meng <meng@databricks.com>

Closes #5930 from brkyvz/ml-feat and squashes the following commits:

73e745f [Burak Yavuz] Merge pull request #3 from mengxr/SPARK-7388
c221db9 [Xiangrui Meng] overload StringArrayParam.w
c81072d [Burak Yavuz] addressed comments
99c2ebf [Burak Yavuz] add to python_shared_params
39ecb07 [Burak Yavuz] fix scalastyle
7f7ea2a [Burak Yavuz] [SPARK-7388][SPARK-7383] wrapper for VectorAssembler in Python
---
 .../spark/ml/feature/VectorAssembler.scala    |  2 +-
 .../org/apache/spark/ml/param/params.scala    | 27 ++++++++++--
 .../ml/param/shared/SharedParamsCodeGen.scala |  1 +
 .../spark/ml/param/shared/sharedParams.scala  |  2 +-
 python/pyspark/ml/feature.py                  | 43 ++++++++++++++++++-
 .../ml/param/_shared_params_code_gen.py       |  1 +
 python/pyspark/ml/param/shared.py             | 29 +++++++++++++
 python/pyspark/ml/wrapper.py                  | 13 +++---
 8 files changed, 105 insertions(+), 13 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/VectorAssembler.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/VectorAssembler.scala
index 8f2e62a8e2081..b5a69cee6daf3 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/VectorAssembler.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/VectorAssembler.scala
@@ -30,7 +30,7 @@ import org.apache.spark.sql.types._
 
 /**
  * :: AlphaComponent ::
- * A feature transformer than merge multiple columns into a vector column.
+ * A feature transformer that merges multiple columns into a vector column.
  */
 @AlphaComponent
 class VectorAssembler extends Transformer with HasInputCols with HasOutputCol {
diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/params.scala b/mllib/src/main/scala/org/apache/spark/ml/param/params.scala
index 6d09962fe6ee2..0e1b60d172e3b 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/param/params.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/param/params.scala
@@ -22,6 +22,7 @@ import java.util.NoSuchElementException
 
 import scala.annotation.varargs
 import scala.collection.mutable
+import scala.collection.JavaConverters._
 
 import org.apache.spark.annotation.AlphaComponent
 import org.apache.spark.ml.util.Identifiable
@@ -218,6 +219,19 @@ class BooleanParam(parent: Params, name: String, doc: String) // No need for isV
   override def w(value: Boolean): ParamPair[Boolean] = super.w(value)
 }
 
+/** Specialized version of [[Param[Array[T]]]] for Java. */
+class StringArrayParam(parent: Params, name: String, doc: String, isValid: Array[String] => Boolean)
+  extends Param[Array[String]](parent, name, doc, isValid) {
+
+  def this(parent: Params, name: String, doc: String) =
+    this(parent, name, doc, ParamValidators.alwaysTrue)
+
+  override def w(value: Array[String]): ParamPair[Array[String]] = super.w(value)
+
+  /** Creates a param pair with a [[java.util.List]] of values (for Java and Python). */
+  def w(value: java.util.List[String]): ParamPair[Array[String]] = w(value.asScala.toArray)
+}
+
 /**
  * A param amd its value.
  */
@@ -310,9 +324,7 @@ trait Params extends Identifiable with Serializable {
    * Sets a parameter in the embedded param map.
    */
   protected final def set[T](param: Param[T], value: T): this.type = {
-    shouldOwn(param)
-    paramMap.put(param.asInstanceOf[Param[Any]], value)
-    this
+    set(param -> value)
   }
 
   /**
@@ -322,6 +334,15 @@ trait Params extends Identifiable with Serializable {
     set(getParam(param), value)
   }
 
+  /**
+   * Sets a parameter in the embedded param map.
+   */
+  protected final def set(paramPair: ParamPair[_]): this.type = {
+    shouldOwn(paramPair.param)
+    paramMap.put(paramPair)
+    this
+  }
+
   /**
    * Optionally returns the user-supplied value of a param.
    */
diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala b/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala
index 0e1ff97a8bf60..5085b798daa17 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala
@@ -85,6 +85,7 @@ private[shared] object SharedParamsCodeGen {
         case _ if c == classOf[Float] => "FloatParam"
         case _ if c == classOf[Double] => "DoubleParam"
         case _ if c == classOf[Boolean] => "BooleanParam"
+        case _ if c.isArray && c.getComponentType == classOf[String] => s"StringArrayParam"
         case _ => s"Param[${getTypeString(c)}]"
       }
     }
diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala b/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala
index 87f86807c3c91..7525d37007377 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala
@@ -178,7 +178,7 @@ private[ml] trait HasInputCols extends Params {
    * Param for input column names.
    * @group param
    */
-  final val inputCols: Param[Array[String]] = new Param[Array[String]](this, "inputCols", "input column names")
+  final val inputCols: StringArrayParam = new StringArrayParam(this, "inputCols", "input column names")
 
   /** @group getParam */
   final def getInputCols: Array[String] = $(inputCols)
diff --git a/python/pyspark/ml/feature.py b/python/pyspark/ml/feature.py
index 4e4614b859ac6..8a0fdddd2d9b5 100644
--- a/python/pyspark/ml/feature.py
+++ b/python/pyspark/ml/feature.py
@@ -16,12 +16,12 @@
 #
 
 from pyspark.rdd import ignore_unicode_prefix
-from pyspark.ml.param.shared import HasInputCol, HasOutputCol, HasNumFeatures
+from pyspark.ml.param.shared import HasInputCol, HasInputCols, HasOutputCol, HasNumFeatures
 from pyspark.ml.util import keyword_only
 from pyspark.ml.wrapper import JavaTransformer
 from pyspark.mllib.common import inherit_doc
 
-__all__ = ['Tokenizer', 'HashingTF']
+__all__ = ['Tokenizer', 'HashingTF', 'VectorAssembler']
 
 
 @inherit_doc
@@ -112,6 +112,45 @@ def setParams(self, numFeatures=1 << 18, inputCol=None, outputCol=None):
         return self._set(**kwargs)
 
 
+@inherit_doc
+class VectorAssembler(JavaTransformer, HasInputCols, HasOutputCol):
+    """
+    A feature transformer that merges multiple columns into a vector column.
+
+    >>> from pyspark.sql import Row
+    >>> df = sc.parallelize([Row(a=1, b=0, c=3)]).toDF()
+    >>> vecAssembler = VectorAssembler(inputCols=["a", "b", "c"], outputCol="features")
+    >>> vecAssembler.transform(df).head().features
+    SparseVector(3, {0: 1.0, 2: 3.0})
+    >>> vecAssembler.setParams(outputCol="freqs").transform(df).head().freqs
+    SparseVector(3, {0: 1.0, 2: 3.0})
+    >>> params = {vecAssembler.inputCols: ["b", "a"], vecAssembler.outputCol: "vector"}
+    >>> vecAssembler.transform(df, params).head().vector
+    SparseVector(2, {1: 1.0})
+    """
+
+    _java_class = "org.apache.spark.ml.feature.VectorAssembler"
+
+    @keyword_only
+    def __init__(self, inputCols=None, outputCol=None):
+        """
+        __init__(self, inputCols=None, outputCol=None)
+        """
+        super(VectorAssembler, self).__init__()
+        self._setDefault()
+        kwargs = self.__init__._input_kwargs
+        self.setParams(**kwargs)
+
+    @keyword_only
+    def setParams(self, inputCols=None, outputCol=None):
+        """
+        setParams(self, inputCols=None, outputCol=None)
+        Sets params for this VectorAssembler.
+        """
+        kwargs = self.setParams._input_kwargs
+        return self._set(**kwargs)
+
+
 if __name__ == "__main__":
     import doctest
     from pyspark.context import SparkContext
diff --git a/python/pyspark/ml/param/_shared_params_code_gen.py b/python/pyspark/ml/param/_shared_params_code_gen.py
index c71c823db2c81..c1c8e921dda87 100644
--- a/python/pyspark/ml/param/_shared_params_code_gen.py
+++ b/python/pyspark/ml/param/_shared_params_code_gen.py
@@ -95,6 +95,7 @@ def get$Name(self):
         ("predictionCol", "prediction column name", "'prediction'"),
         ("rawPredictionCol", "raw prediction column name", "'rawPrediction'"),
         ("inputCol", "input column name", None),
+        ("inputCols", "input column names", None),
         ("outputCol", "output column name", None),
         ("numFeatures", "number of features", None)]
     code = []
diff --git a/python/pyspark/ml/param/shared.py b/python/pyspark/ml/param/shared.py
index 4f243844f8caa..aaf80f00085bf 100644
--- a/python/pyspark/ml/param/shared.py
+++ b/python/pyspark/ml/param/shared.py
@@ -223,6 +223,35 @@ def getInputCol(self):
         return self.getOrDefault(self.inputCol)
 
 
+class HasInputCols(Params):
+    """
+    Mixin for param inputCols: input column names.
+    """
+
+    # a placeholder to make it appear in the generated doc
+    inputCols = Param(Params._dummy(), "inputCols", "input column names")
+
+    def __init__(self):
+        super(HasInputCols, self).__init__()
+        #: param for input column names
+        self.inputCols = Param(self, "inputCols", "input column names")
+        if None is not None:
+            self._setDefault(inputCols=None)
+
+    def setInputCols(self, value):
+        """
+        Sets the value of :py:attr:`inputCols`.
+        """
+        self.paramMap[self.inputCols] = value
+        return self
+
+    def getInputCols(self):
+        """
+        Gets the value of inputCols or its default value.
+        """
+        return self.getOrDefault(self.inputCols)
+
+
 class HasOutputCol(Params):
     """
     Mixin for param outputCol: output column name.
diff --git a/python/pyspark/ml/wrapper.py b/python/pyspark/ml/wrapper.py
index 0634254bbd5cf..f5ac2a398642a 100644
--- a/python/pyspark/ml/wrapper.py
+++ b/python/pyspark/ml/wrapper.py
@@ -67,7 +67,9 @@ def _transfer_params_to_java(self, params, java_obj):
         paramMap = self.extractParamMap(params)
         for param in self.params:
             if param in paramMap:
-                java_obj.set(param.name, paramMap[param])
+                value = paramMap[param]
+                java_param = java_obj.getParam(param.name)
+                java_obj.set(java_param.w(value))
 
     def _empty_java_param_map(self):
         """
@@ -79,7 +81,8 @@ def _create_java_param_map(self, params, java_obj):
         paramMap = self._empty_java_param_map()
         for param, value in params.items():
             if param.parent is self:
-                paramMap.put(java_obj.getParam(param.name), value)
+                java_param = java_obj.getParam(param.name)
+                paramMap.put(java_param.w(value))
         return paramMap
 
 
@@ -126,10 +129,8 @@ class JavaTransformer(Transformer, JavaWrapper):
 
     def transform(self, dataset, params={}):
         java_obj = self._java_obj()
-        self._transfer_params_to_java({}, java_obj)
-        java_param_map = self._create_java_param_map(params, java_obj)
-        return DataFrame(java_obj.transform(dataset._jdf, java_param_map),
-                         dataset.sql_ctx)
+        self._transfer_params_to_java(params, java_obj)
+        return DataFrame(java_obj.transform(dataset._jdf), dataset.sql_ctx)
 
 
 @inherit_doc

From 068c3158ac0c66e20d90a45e6a2a0b93108e08d5 Mon Sep 17 00:00:00 2001
From: Olivier Girardot <o.girardot@lateral-thoughts.com>
Date: Thu, 7 May 2015 10:58:35 -0700
Subject: [PATCH 34/75] [SPARK-7118] [Python] Add the coalesce Spark SQL
 function available in PySpark

This patch adds a proxy call from PySpark to the Spark SQL coalesce function and this patch comes out of a discussion on devspark with rxin

This contribution is my original work and i license the work to the project under the project's open source license.

Olivier.

Author: Olivier Girardot <o.girardot@lateral-thoughts.com>

Closes #5698 from ogirardot/master and squashes the following commits:

d9a4439 [Olivier Girardot] SPARK-7118 Add the coalesce Spark SQL function available in PySpark
---
 python/pyspark/sql/functions.py | 37 +++++++++++++++++++++++++++++++++
 1 file changed, 37 insertions(+)

diff --git a/python/pyspark/sql/functions.py b/python/pyspark/sql/functions.py
index 274c410a1ee9c..38a043a3c59d7 100644
--- a/python/pyspark/sql/functions.py
+++ b/python/pyspark/sql/functions.py
@@ -37,6 +37,7 @@
     'rand',
     'randn',
     'sparkPartitionId',
+    'coalesce',
     'udf']
 
 
@@ -167,6 +168,42 @@ def approxCountDistinct(col, rsd=None):
     return Column(jc)
 
 
+def coalesce(*cols):
+    """Returns the first column that is not null.
+
+    >>> cDf = sqlContext.createDataFrame([(None, None), (1, None), (None, 2)], ("a", "b"))
+    >>> cDf.show()
+    +----+----+
+    |   a|   b|
+    +----+----+
+    |null|null|
+    |   1|null|
+    |null|   2|
+    +----+----+
+
+    >>> cDf.select(coalesce(cDf["a"], cDf["b"])).show()
+    +-------------+
+    |Coalesce(a,b)|
+    +-------------+
+    |         null|
+    |            1|
+    |            2|
+    +-------------+
+
+    >>> cDf.select('*', coalesce(cDf["a"], lit(0.0))).show()
+    +----+----+---------------+
+    |   a|   b|Coalesce(a,0.0)|
+    +----+----+---------------+
+    |null|null|            0.0|
+    |   1|null|            1.0|
+    |null|   2|            0.0|
+    +----+----+---------------+
+    """
+    sc = SparkContext._active_spark_context
+    jc = sc._jvm.functions.coalesce(_to_seq(sc, cols, _to_java_column))
+    return Column(jc)
+
+
 def countDistinct(col, *cols):
     """Returns a new :class:`Column` for distinct count of ``col`` or ``cols``.
 

From 1712a7c7057bf6dd5da8aea1d7fbecdf96ea4b32 Mon Sep 17 00:00:00 2001
From: Yanbo Liang <ybliang8@gmail.com>
Date: Thu, 7 May 2015 11:18:32 -0700
Subject: [PATCH 35/75] [SPARK-6093] [MLLIB] Add RegressionMetrics in
 PySpark/MLlib

https://issues.apache.org/jira/browse/SPARK-6093

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #5941 from yanboliang/spark-6093 and squashes the following commits:

6934af3 [Yanbo Liang] change to @property
aac3bc5 [Yanbo Liang] Add RegressionMetrics in PySpark/MLlib
---
 .../mllib/evaluation/RegressionMetrics.scala  |  9 +++
 python/pyspark/mllib/evaluation.py            | 78 ++++++++++++++++++-
 2 files changed, 85 insertions(+), 2 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/RegressionMetrics.scala b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/RegressionMetrics.scala
index 693117d820580..e577bf87f885e 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/RegressionMetrics.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/RegressionMetrics.scala
@@ -22,6 +22,7 @@ import org.apache.spark.rdd.RDD
 import org.apache.spark.Logging
 import org.apache.spark.mllib.linalg.Vectors
 import org.apache.spark.mllib.stat.{MultivariateStatisticalSummary, MultivariateOnlineSummarizer}
+import org.apache.spark.sql.DataFrame
 
 /**
  * :: Experimental ::
@@ -32,6 +33,14 @@ import org.apache.spark.mllib.stat.{MultivariateStatisticalSummary, Multivariate
 @Experimental
 class RegressionMetrics(predictionAndObservations: RDD[(Double, Double)]) extends Logging {
 
+  /**
+   * An auxiliary constructor taking a DataFrame.
+   * @param predictionAndObservations a DataFrame with two double columns:
+   *                                  prediction and observation
+   */
+  private[mllib] def this(predictionAndObservations: DataFrame) =
+    this(predictionAndObservations.map(r => (r.getDouble(0), r.getDouble(1))))
+
   /**
    * Use MultivariateOnlineSummarizer to calculate summary statistics of observations and errors.
    */
diff --git a/python/pyspark/mllib/evaluation.py b/python/pyspark/mllib/evaluation.py
index 16cb49cc0cfff..3e11df09da6b1 100644
--- a/python/pyspark/mllib/evaluation.py
+++ b/python/pyspark/mllib/evaluation.py
@@ -27,9 +27,9 @@ class BinaryClassificationMetrics(JavaModelWrapper):
     >>> scoreAndLabels = sc.parallelize([
     ...     (0.1, 0.0), (0.1, 1.0), (0.4, 0.0), (0.6, 0.0), (0.6, 1.0), (0.6, 1.0), (0.8, 1.0)], 2)
     >>> metrics = BinaryClassificationMetrics(scoreAndLabels)
-    >>> metrics.areaUnderROC()
+    >>> metrics.areaUnderROC
     0.70...
-    >>> metrics.areaUnderPR()
+    >>> metrics.areaUnderPR
     0.83...
     >>> metrics.unpersist()
     """
@@ -47,6 +47,7 @@ def __init__(self, scoreAndLabels):
         java_model = java_class(df._jdf)
         super(BinaryClassificationMetrics, self).__init__(java_model)
 
+    @property
     def areaUnderROC(self):
         """
         Computes the area under the receiver operating characteristic
@@ -54,6 +55,7 @@ def areaUnderROC(self):
         """
         return self.call("areaUnderROC")
 
+    @property
     def areaUnderPR(self):
         """
         Computes the area under the precision-recall curve.
@@ -67,6 +69,78 @@ def unpersist(self):
         self.call("unpersist")
 
 
+class RegressionMetrics(JavaModelWrapper):
+    """
+    Evaluator for regression.
+
+    >>> predictionAndObservations = sc.parallelize([
+    ...     (2.5, 3.0), (0.0, -0.5), (2.0, 2.0), (8.0, 7.0)])
+    >>> metrics = RegressionMetrics(predictionAndObservations)
+    >>> metrics.explainedVariance
+    0.95...
+    >>> metrics.meanAbsoluteError
+    0.5...
+    >>> metrics.meanSquaredError
+    0.37...
+    >>> metrics.rootMeanSquaredError
+    0.61...
+    >>> metrics.r2
+    0.94...
+    """
+
+    def __init__(self, predictionAndObservations):
+        """
+        :param predictionAndObservations: an RDD of (prediction, observation) pairs.
+        """
+        sc = predictionAndObservations.ctx
+        sql_ctx = SQLContext(sc)
+        df = sql_ctx.createDataFrame(predictionAndObservations, schema=StructType([
+            StructField("prediction", DoubleType(), nullable=False),
+            StructField("observation", DoubleType(), nullable=False)]))
+        java_class = sc._jvm.org.apache.spark.mllib.evaluation.RegressionMetrics
+        java_model = java_class(df._jdf)
+        super(RegressionMetrics, self).__init__(java_model)
+
+    @property
+    def explainedVariance(self):
+        """
+        Returns the explained variance regression score.
+        explainedVariance = 1 - variance(y - \hat{y}) / variance(y)
+        """
+        return self.call("explainedVariance")
+
+    @property
+    def meanAbsoluteError(self):
+        """
+        Returns the mean absolute error, which is a risk function corresponding to the
+        expected value of the absolute error loss or l1-norm loss.
+        """
+        return self.call("meanAbsoluteError")
+
+    @property
+    def meanSquaredError(self):
+        """
+        Returns the mean squared error, which is a risk function corresponding to the
+        expected value of the squared error loss or quadratic loss.
+        """
+        return self.call("meanSquaredError")
+
+    @property
+    def rootMeanSquaredError(self):
+        """
+        Returns the root mean squared error, which is defined as the square root of
+        the mean squared error.
+        """
+        return self.call("rootMeanSquaredError")
+
+    @property
+    def r2(self):
+        """
+        Returns R^2^, the coefficient of determination.
+        """
+        return self.call("r2")
+
+
 def _test():
     import doctest
     from pyspark import SparkContext

From 5784c8d95561dce432a85401e1510776fdf723a8 Mon Sep 17 00:00:00 2001
From: Yin Huai <yhuai@databricks.com>
Date: Thu, 7 May 2015 11:46:49 -0700
Subject: [PATCH 36/75] [SPARK-1442] [SQL] [FOLLOW-UP] Address minor comments
 in Window Function PR (#5604).

Address marmbrus and scwf's comments in #5604.

Author: Yin Huai <yhuai@databricks.com>

Closes #5945 from yhuai/windowFollowup and squashes the following commits:

0ef879d [Yin Huai] Add collectFirst to TreeNode.
2373968 [Yin Huai] wip
4a16df9 [Yin Huai] Address minor comments for [SPARK-1442].
---
 .../sql/catalyst/analysis/Analyzer.scala      | 13 +++--
 .../spark/sql/catalyst/trees/TreeNode.scala   | 13 ++++-
 .../sql/catalyst/trees/TreeNodeSuite.scala    | 50 +++++++++++++++++++
 3 files changed, 68 insertions(+), 8 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 7b543b6c2aa42..7e46ad851cdd3 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
@@ -638,11 +638,10 @@ class Analyzer(
     def addWindow(windowExpressions: Seq[NamedExpression], child: LogicalPlan): LogicalPlan = {
       // First, we group window expressions based on their Window Spec.
       val groupedWindowExpression = windowExpressions.groupBy { expr =>
-        val windowExpression = expr.find {
-          case window: WindowExpression => true
-          case other => false
-        }.map(_.asInstanceOf[WindowExpression].windowSpec)
-        windowExpression.getOrElse(
+        val windowSpec = expr.collectFirst {
+          case window: WindowExpression => window.windowSpec
+        }
+        windowSpec.getOrElse(
           failAnalysis(s"$windowExpressions does not have any WindowExpression."))
       }.toSeq
 
@@ -685,7 +684,7 @@ class Analyzer(
       case f @ Filter(condition, a @ Aggregate(groupingExprs, aggregateExprs, child))
         if child.resolved &&
            hasWindowFunction(aggregateExprs) &&
-           !a.expressions.exists(!_.resolved) =>
+           a.expressions.forall(_.resolved) =>
         val (windowExpressions, aggregateExpressions) = extract(aggregateExprs)
         // Create an Aggregate operator to evaluate aggregation functions.
         val withAggregate = Aggregate(groupingExprs, aggregateExpressions, child)
@@ -702,7 +701,7 @@ class Analyzer(
       // Aggregate without Having clause.
       case a @ Aggregate(groupingExprs, aggregateExprs, child)
         if hasWindowFunction(aggregateExprs) &&
-           !a.expressions.exists(!_.resolved) =>
+           a.expressions.forall(_.resolved) =>
         val (windowExpressions, aggregateExpressions) = extract(aggregateExprs)
         // Create an Aggregate operator to evaluate aggregation functions.
         val withAggregate = Aggregate(groupingExprs, aggregateExpressions, child)
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 4b93f7d31b808..bc2ad34523d2c 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
@@ -130,6 +130,17 @@ abstract class TreeNode[BaseType <: TreeNode[BaseType]] {
     ret
   }
 
+  /**
+   * Finds and returns the first [[TreeNode]] of the tree for which the given partial function
+   * is defined (pre-order), and applies the partial function to it.
+   */
+  def collectFirst[B](pf: PartialFunction[BaseType, B]): Option[B] = {
+    val lifted = pf.lift
+    lifted(this).orElse {
+      children.foldLeft(None: Option[B]) { (l, r) => l.orElse(r.collectFirst(pf)) }
+    }
+  }
+
   /**
    * Returns a copy of this node where `f` has been applied to all the nodes children.
    */
@@ -160,7 +171,7 @@ abstract class TreeNode[BaseType <: TreeNode[BaseType]] {
     val remainingNewChildren = newChildren.toBuffer
     val remainingOldChildren = children.toBuffer
     val newArgs = productIterator.map {
-      // This rule is used to handle children is a input argument.
+      // Handle Seq[TreeNode] in TreeNode parameters.
       case s: Seq[_] => s.map {
         case arg: TreeNode[_] if children contains arg =>
           val newChild = remainingNewChildren.remove(0)
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 786ddba403f2c..3d10dab5ba34c 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
@@ -172,4 +172,54 @@ class TreeNodeSuite extends FunSuite {
     expected = None
     assert(expected === actual)
   }
+
+  test("collectFirst") {
+    val expression = Add(Literal(1), Multiply(Literal(2), Subtract(Literal(3), Literal(4))))
+
+    // Collect the top node.
+    {
+      val actual = expression.collectFirst {
+        case add: Add => add
+      }
+      val expected =
+        Some(Add(Literal(1), Multiply(Literal(2), Subtract(Literal(3), Literal(4)))))
+      assert(expected === actual)
+    }
+
+    // Collect the first children.
+    {
+      val actual = expression.collectFirst {
+        case l @ Literal(1, IntegerType) => l
+      }
+      val expected = Some(Literal(1))
+      assert(expected === actual)
+    }
+
+    // Collect an internal node (Subtract).
+    {
+      val actual = expression.collectFirst {
+        case sub: Subtract => sub
+      }
+      val expected = Some(Subtract(Literal(3), Literal(4)))
+      assert(expected === actual)
+    }
+
+    // Collect a leaf node.
+    {
+      val actual = expression.collectFirst {
+        case l @ Literal(3, IntegerType) => l
+      }
+      val expected = Some(Literal(3))
+      assert(expected === actual)
+    }
+
+    // Collect nothing.
+    {
+      val actual = expression.collectFirst {
+        case l @ Literal(100, IntegerType) => l
+      }
+      val expected = None
+      assert(expected === actual)
+    }
+  }
 }

From dec8f53719597119034dffbe43b2a9e5fd963083 Mon Sep 17 00:00:00 2001
From: ksonj <kson@siberie.de>
Date: Thu, 7 May 2015 12:04:19 -0700
Subject: [PATCH 37/75] [SPARK-7116] [SQL] [PYSPARK] Remove cache() causing
 memory leak

This patch simply removes a `cache()` on an intermediate RDD when evaluating Python UDFs.

Author: ksonj <kson@siberie.de>

Closes #5973 from ksonj/udf and squashes the following commits:

db5b564 [ksonj] removed TODO about cleaning up
fe70c54 [ksonj] Remove cache() causing memory leak
---
 .../scala/org/apache/spark/sql/execution/pythonUdfs.scala  | 7 +++----
 1 file changed, 3 insertions(+), 4 deletions(-)

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 7a43bfd8bc8d9..58cb1980f217e 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
@@ -219,8 +219,8 @@ case class EvaluatePython(
 
 /**
  * :: DeveloperApi ::
- * Uses PythonRDD to evaluate a [[PythonUDF]], one partition of tuples at a time.  The input
- * data is cached and zipped with the result of the udf evaluation.
+ * Uses PythonRDD to evaluate a [[PythonUDF]], one partition of tuples at a time.
+ * The input data is zipped with the result of the udf evaluation.
  */
 @DeveloperApi
 case class BatchPythonEvaluation(udf: PythonUDF, output: Seq[Attribute], child: SparkPlan)
@@ -229,8 +229,7 @@ case class BatchPythonEvaluation(udf: PythonUDF, output: Seq[Attribute], child:
   def children: Seq[SparkPlan] = child :: Nil
 
   def execute(): RDD[Row] = {
-    // TODO: Clean up after ourselves?
-    val childResults = child.execute().map(_.copy()).cache()
+    val childResults = child.execute().map(_.copy())
 
     val parent = childResults.mapPartitions { iter =>
       val pickle = new Pickler

From 074d75d4c8ce7c0296ded8f4c4868a9210320222 Mon Sep 17 00:00:00 2001
From: Cheng Hao <hao.cheng@intel.com>
Date: Thu, 7 May 2015 12:09:54 -0700
Subject: [PATCH 38/75] [SPARK-5213] [SQL] Remove the duplicated SparkSQLParser

This is a follow up of #5827 to remove the additional `SparkSQLParser`

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

Closes #5965 from chenghao-intel/remove_sparksqlparser and squashes the following commits:

509a233 [Cheng Hao] Remove the HiveQlQueryExecution
a5f9e3b [Cheng Hao] Remove the duplicated SparkSQLParser
---
 .../org/apache/spark/sql/hive/HiveQl.scala      |  7 ++-----
 .../apache/spark/sql/hive/test/TestHive.scala   | 17 ++++-------------
 .../sql/hive/execution/HiveComparisonTest.scala |  8 ++++----
 .../spark/sql/hive/execution/PruningSuite.scala |  2 +-
 4 files changed, 11 insertions(+), 23 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 8a0686a2d81c9..4e51473979a2a 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
@@ -28,7 +28,7 @@ import org.apache.hadoop.hive.ql.lib.Node
 import org.apache.hadoop.hive.ql.metadata.Table
 import org.apache.hadoop.hive.ql.parse._
 import org.apache.hadoop.hive.ql.plan.PlanUtils
-import org.apache.spark.sql.{AnalysisException, SparkSQLParser}
+import org.apache.spark.sql.AnalysisException
 
 import org.apache.spark.sql.catalyst.analysis._
 import org.apache.spark.sql.catalyst.expressions._
@@ -140,10 +140,7 @@ private[hive] object HiveQl {
     "TOK_TRUNCATETABLE"     // truncate table" is a NativeCommand, does not need to explain.
   ) ++ nativeCommands
 
-  protected val hqlParser = {
-    val fallback = new ExtendedHiveQlParser
-    new SparkSQLParser(fallback.parse(_))
-  }
+  protected val hqlParser = new ExtendedHiveQlParser
 
   /**
    * A set of implicit transformations that allow Hive ASTNodes to be rewritten by transformations
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala
index edeab5158df62..ca84b43a998b8 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala
@@ -94,7 +94,8 @@ class TestHiveContext(sc: SparkContext) extends HiveContext(sc) {
   lazy val hiveDevHome = envVarToFile("HIVE_DEV_HOME")
 
   // Override so we can intercept relative paths and rewrite them to point at hive.
-  override def runSqlHive(sql: String): Seq[String] = super.runSqlHive(rewritePaths(sql))
+  override def runSqlHive(sql: String): Seq[String] =
+    super.runSqlHive(rewritePaths(substitutor.substitute(this.hiveconf, sql)))
 
   override def executePlan(plan: LogicalPlan): this.QueryExecution =
     new this.QueryExecution(plan)
@@ -157,22 +158,12 @@ class TestHiveContext(sc: SparkContext) extends HiveContext(sc) {
 
   val describedTable = "DESCRIBE (\\w+)".r
 
-  val vs = new VariableSubstitution()
-
-  // we should substitute variables in hql to pass the text to parseSql() as a parameter.
-  // Hive parser need substituted text. HiveContext.sql() does this but return a DataFrame,
-  // while we need a logicalPlan so we cannot reuse that.
-  protected[hive] class HiveQLQueryExecution(hql: String)
-    extends this.QueryExecution(HiveQl.parseSql(vs.substitute(hiveconf, hql))) {
-    def hiveExec(): Seq[String] = runSqlHive(hql)
-    override def toString: String = hql + "\n" + super.toString
-  }
-
   /**
    * Override QueryExecution with special debug workflow.
    */
   class QueryExecution(logicalPlan: LogicalPlan)
     extends super.QueryExecution(logicalPlan) {
+    def this(sql: String) = this(parseSql(sql))
     override lazy val analyzed = {
       val describedTables = logical match {
         case HiveNativeCommand(describedTable(tbl)) => tbl :: Nil
@@ -196,7 +187,7 @@ class TestHiveContext(sc: SparkContext) extends HiveContext(sc) {
 
   protected[hive] implicit class SqlCmd(sql: String) {
     def cmd: () => Unit = {
-      () => new HiveQLQueryExecution(sql).stringResult(): Unit
+      () => new QueryExecution(sql).stringResult(): Unit
     }
   }
 
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 5ead5f3c19908..a3eacbd4e3981 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
@@ -129,7 +129,7 @@ abstract class HiveComparisonTest
   }
 
   protected def prepareAnswer(
-    hiveQuery: TestHive.type#HiveQLQueryExecution,
+    hiveQuery: TestHive.type#QueryExecution,
     answer: Seq[String]): Seq[String] = {
 
     def isSorted(plan: LogicalPlan): Boolean = plan match {
@@ -298,7 +298,7 @@ abstract class HiveComparisonTest
             hiveCachedResults
           } else {
 
-            val hiveQueries = queryList.map(new TestHive.HiveQLQueryExecution(_))
+            val hiveQueries = queryList.map(new TestHive.QueryExecution(_))
             // Make sure we can at least parse everything before attempting hive execution.
             hiveQueries.foreach(_.logical)
             val computedResults = (queryList.zipWithIndex, hiveQueries, hiveCacheFiles).zipped.map {
@@ -346,7 +346,7 @@ abstract class HiveComparisonTest
 
         // Run w/ catalyst
         val catalystResults = queryList.zip(hiveResults).map { case (queryString, hive) =>
-          val query = new TestHive.HiveQLQueryExecution(queryString)
+          val query = new TestHive.QueryExecution(queryString)
           try { (query, prepareAnswer(query, query.stringResult())) } catch {
             case e: Throwable =>
               val errorMessage =
@@ -402,7 +402,7 @@ abstract class HiveComparisonTest
             // okay by running a simple query. If this fails then we halt testing since
             // something must have gone seriously wrong.
             try {
-              new TestHive.HiveQLQueryExecution("SELECT key FROM src").stringResult()
+              new TestHive.QueryExecution("SELECT key FROM src").stringResult()
               TestHive.runSqlHive("SELECT key FROM src")
             } catch {
               case e: Exception =>
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 067b577f1560e..45f10e2fe64aa 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
@@ -145,7 +145,7 @@ class PruningSuite extends HiveComparisonTest with BeforeAndAfter {
       expectedScannedColumns: Seq[String],
       expectedPartValues: Seq[Seq[String]]): Unit = {
     test(s"$testCaseName - pruning test") {
-      val plan = new TestHive.HiveQLQueryExecution(sql).executedPlan
+      val plan = new TestHive.QueryExecution(sql).executedPlan
       val actualOutputColumns = plan.output.map(_.name)
       val (actualScannedColumns, actualPartValues) = plan.collect {
         case p @ HiveTableScan(columns, relation, _) =>

From 0c33bf817cef457873007959f3539f93f907dccd Mon Sep 17 00:00:00 2001
From: Tijo Thomas <tijoparacka@gmail.com>
Date: Thu, 7 May 2015 12:21:09 -0700
Subject: [PATCH 39/75] [SPARK-7399] [SPARK CORE] Fixed compilation error in
 scala 2.11

scala has deterministic naming-scheme for the generated methods which return default arguments . here one of the default argument of overloaded method has to be removed

Author: Tijo Thomas <tijoparacka@gmail.com>

Closes #5966 from tijoparacka/fix_compilation_error_in_scala2.11 and squashes the following commits:

c90bba8 [Tijo Thomas] Fixed compilation error in scala 2.11
---
 .../src/main/scala/org/apache/spark/rdd/RDDOperationScope.scala | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/rdd/RDDOperationScope.scala b/core/src/main/scala/org/apache/spark/rdd/RDDOperationScope.scala
index 537b56b49f866..9440d456edf15 100644
--- a/core/src/main/scala/org/apache/spark/rdd/RDDOperationScope.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/RDDOperationScope.scala
@@ -111,7 +111,7 @@ private[spark] object RDDOperationScope {
   private[spark] def withScope[T](
       sc: SparkContext,
       name: String,
-      allowNesting: Boolean = false)(body: => T): T = {
+      allowNesting: Boolean)(body: => T): T = {
     // Save the old scope to restore it later
     val scopeKey = SparkContext.RDD_SCOPE_KEY
     val noOverrideKey = SparkContext.RDD_SCOPE_NO_OVERRIDE_KEY

From 4eecf550aa7e4fb448baca82281bfd4e8bc4a778 Mon Sep 17 00:00:00 2001
From: Timothy Chen <tnachen@gmail.com>
Date: Thu, 7 May 2015 12:23:16 -0700
Subject: [PATCH 40/75] [SPARK-7373] [MESOS] Add docker support for launching
 drivers in mesos cluster mode.

Using the existing docker support for mesos, also enabling the mesos cluster mode scheduler to launch Spark drivers in docker images as well.

This also allows the executors launched by the drivers to be also in the same Docker image by passing  the docker settings.

Author: Timothy Chen <tnachen@gmail.com>

Closes #5917 from tnachen/spark_cluster_docker and squashes the following commits:

1e842f5 [Timothy Chen] Add docker support for launching drivers in mesos cluster mode.
---
 .../cluster/mesos/MesosClusterScheduler.scala | 30 ++++++++++++++-----
 1 file changed, 23 insertions(+), 7 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosClusterScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosClusterScheduler.scala
index 06f0e2881c344..1067a7f1caf4c 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosClusterScheduler.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosClusterScheduler.scala
@@ -370,16 +370,21 @@ private[spark] class MesosClusterScheduler(
     val executorOpts = desc.schedulerProperties.map { case (k, v) => s"-D$k=$v" }.mkString(" ")
     envBuilder.addVariables(
       Variable.newBuilder().setName("SPARK_EXECUTOR_OPTS").setValue(executorOpts))
-    val cmdOptions = generateCmdOption(desc)
+    val cmdOptions = generateCmdOption(desc).mkString(" ")
+    val dockerDefined = desc.schedulerProperties.contains("spark.mesos.executor.docker.image")
     val executorUri = desc.schedulerProperties.get("spark.executor.uri")
       .orElse(desc.command.environment.get("SPARK_EXECUTOR_URI"))
     val appArguments = desc.command.arguments.mkString(" ")
-    val cmd = if (executorUri.isDefined) {
+    val (executable, jar) = if (dockerDefined) {
+      // Application jar is automatically downloaded in the mounted sandbox by Mesos,
+      // and the path to the mounted volume is stored in $MESOS_SANDBOX env variable.
+      ("./bin/spark-submit", s"$$MESOS_SANDBOX/${desc.jarUrl.split("/").last}")
+    } else if (executorUri.isDefined) {
       builder.addUris(CommandInfo.URI.newBuilder().setValue(executorUri.get).build())
       val folderBasename = executorUri.get.split('/').last.split('.').head
       val cmdExecutable = s"cd $folderBasename*; $prefixEnv bin/spark-submit"
       val cmdJar = s"../${desc.jarUrl.split("/").last}"
-      s"$cmdExecutable ${cmdOptions.mkString(" ")} $cmdJar $appArguments"
+      (cmdExecutable, cmdJar)
     } else {
       val executorSparkHome = desc.schedulerProperties.get("spark.mesos.executor.home")
         .orElse(conf.getOption("spark.home"))
@@ -389,9 +394,9 @@ private[spark] class MesosClusterScheduler(
         }
       val cmdExecutable = new File(executorSparkHome, "./bin/spark-submit").getCanonicalPath
       val cmdJar = desc.jarUrl.split("/").last
-      s"$cmdExecutable ${cmdOptions.mkString(" ")} $cmdJar $appArguments"
+      (cmdExecutable, cmdJar)
     }
-    builder.setValue(cmd)
+    builder.setValue(s"$executable $cmdOptions $jar $appArguments")
     builder.setEnvironment(envBuilder.build())
     builder.build()
   }
@@ -458,9 +463,20 @@ private[spark] class MesosClusterScheduler(
           .setCommand(commandInfo)
           .addResources(cpuResource)
           .addResources(memResource)
-          .build()
+        submission.schedulerProperties.get("spark.mesos.executor.docker.image").foreach { image =>
+          val container = taskInfo.getContainerBuilder()
+          val volumes = submission.schedulerProperties
+            .get("spark.mesos.executor.docker.volumes")
+            .map(MesosSchedulerBackendUtil.parseVolumesSpec)
+          val portmaps = submission.schedulerProperties
+            .get("spark.mesos.executor.docker.portmaps")
+            .map(MesosSchedulerBackendUtil.parsePortMappingsSpec)
+          MesosSchedulerBackendUtil.addDockerInfo(
+            container, image, volumes = volumes, portmaps = portmaps)
+          taskInfo.setContainer(container.build())
+        }
         val queuedTasks = tasks.getOrElseUpdate(offer.offer.getId, new ArrayBuffer[TaskInfo])
-        queuedTasks += taskInfo
+        queuedTasks += taskInfo.build()
         logTrace(s"Using offer ${offer.offer.getId.getValue} to launch driver " +
           submission.submissionId)
         val newState = new MesosClusterSubmissionState(submission, taskId, offer.offer.getSlaveId,

From f1216514b830eadcdfff6fca044afd6ae1585800 Mon Sep 17 00:00:00 2001
From: Andrew Or <andrew@databricks.com>
Date: Thu, 7 May 2015 12:29:18 -0700
Subject: [PATCH 41/75] [SPARK-7391] DAG visualization: auto expand if linked
 from another viz

This is an addition to #5729.

If you click into a stage from the DAG viz on the job page, you might expect to expand on the stage. However, once you get to the stage page, you actually have to expand the DAG viz there yourself.

This patch makes this happen automatically. It's a small UX improvement.

Author: Andrew Or <andrew@databricks.com>

Closes #5958 from andrewor14/viz-auto-expand and squashes the following commits:

03cd157 [Andrew Or] Automatically expand DAG viz if from job page
---
 .../org/apache/spark/ui/static/spark-dag-viz.js      |  4 ++--
 .../src/main/scala/org/apache/spark/ui/UIUtils.scala | 10 +++++++++-
 .../scala/org/apache/spark/ui/jobs/StagePage.scala   | 12 ++++++++++++
 3 files changed, 23 insertions(+), 3 deletions(-)

diff --git a/core/src/main/resources/org/apache/spark/ui/static/spark-dag-viz.js b/core/src/main/resources/org/apache/spark/ui/static/spark-dag-viz.js
index 76eb2c464139f..cda27cad7ee9d 100644
--- a/core/src/main/resources/org/apache/spark/ui/static/spark-dag-viz.js
+++ b/core/src/main/resources/org/apache/spark/ui/static/spark-dag-viz.js
@@ -178,8 +178,8 @@ function renderDagVizForJob(svgContainer) {
     var stageId = metadata.attr("stage-id");
     var containerId = VizConstants.graphPrefix + stageId;
     // Link each graph to the corresponding stage page (TODO: handle stage attempts)
-    var stageLink =
-      "/stages/stage/?id=" + stageId.replace(VizConstants.stagePrefix, "") + "&attempt=0";
+    var stageLink = "/stages/stage/?id=" +
+      stageId.replace(VizConstants.stagePrefix, "") + "&attempt=0&expandDagViz=true";
     var container = svgContainer
       .append("a")
       .attr("xlink:href", stageLink)
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 e2d03f8342315..97eed13c2d780 100644
--- a/core/src/main/scala/org/apache/spark/ui/UIUtils.scala
+++ b/core/src/main/scala/org/apache/spark/ui/UIUtils.scala
@@ -20,7 +20,7 @@ package org.apache.spark.ui
 import java.text.SimpleDateFormat
 import java.util.{Locale, Date}
 
-import scala.xml.{Node, Text}
+import scala.xml.{Node, Text, Unparsed}
 
 import org.apache.spark.Logging
 import org.apache.spark.ui.scope.RDDOperationGraph
@@ -371,4 +371,12 @@ private[spark] object UIUtils extends Logging {
       </div>
     </div>
   }
+
+  /** Return a script element that automatically expands the DAG visualization on page load. */
+  def expandDagVizOnLoad(forJob: Boolean): Seq[Node] = {
+    <script type="text/javascript">
+      {Unparsed("$(document).ready(function() { toggleDagViz(" + forJob + ") });")}
+    </script>
+  }
+
 }
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 579310070c76c..6c4305873cbd9 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
@@ -44,6 +44,10 @@ private[ui] class StagePage(parent: StagesTab) extends WebUIPage("stage") {
       val parameterAttempt = request.getParameter("attempt")
       require(parameterAttempt != null && parameterAttempt.nonEmpty, "Missing attempt parameter")
 
+      // If this is set, expand the dag visualization by default
+      val expandDagVizParam = request.getParameter("expandDagViz")
+      val expandDagViz = expandDagVizParam != null && expandDagVizParam.toBoolean
+
       val stageId = parameterId.toInt
       val stageAttemptId = parameterAttempt.toInt
       val stageDataOption = progressListener.stageIdToData.get((stageId, stageAttemptId))
@@ -174,6 +178,13 @@ private[ui] class StagePage(parent: StagesTab) extends WebUIPage("stage") {
       val dagViz = UIUtils.showDagVizForStage(
         stageId, operationGraphListener.getOperationGraphForStage(stageId))
 
+      val maybeExpandDagViz: Seq[Node] =
+        if (expandDagViz) {
+          UIUtils.expandDagVizOnLoad(forJob = false)
+        } else {
+          Seq.empty
+        }
+
       val accumulableHeaders: Seq[String] = Seq("Accumulable", "Value")
       def accumulableRow(acc: AccumulableInfo): Elem =
         <tr><td>{acc.name}</td><td>{acc.value}</td></tr>
@@ -440,6 +451,7 @@ private[ui] class StagePage(parent: StagesTab) extends WebUIPage("stage") {
         summary ++
         showAdditionalMetrics ++
         dagViz ++
+        maybeExpandDagViz ++
         <h4>Summary Metrics for {numCompleted} Completed Tasks</h4> ++
         <div>{summaryTable.getOrElse("No tasks have reported metrics yet.")}</div> ++
         <h4>Aggregated Metrics by Executor</h4> ++ executorTable.toNodeSeq ++

From 88717ee4e7542ac8d5d2e5756c912dd390b37e88 Mon Sep 17 00:00:00 2001
From: Andrew Or <andrew@databricks.com>
Date: Thu, 7 May 2015 12:29:56 -0700
Subject: [PATCH 42/75] [SPARK-7347] DAG visualization: add tooltips to RDDs

This is an addition to #5729.

Here's an example with ALS.
<img src="https://issues.apache.org/jira/secure/attachment/12731039/tooltip.png" width="400px"></img>

Author: Andrew Or <andrew@databricks.com>

Closes #5957 from andrewor14/viz-hover2 and squashes the following commits:

60e3758 [Andrew Or] Add tooltips for RDDs on job page
---
 .../org/apache/spark/ui/static/spark-dag-viz.js  | 16 ++++++++++++++++
 .../spark/ui/scope/RDDOperationGraph.scala       |  5 +++--
 2 files changed, 19 insertions(+), 2 deletions(-)

diff --git a/core/src/main/resources/org/apache/spark/ui/static/spark-dag-viz.js b/core/src/main/resources/org/apache/spark/ui/static/spark-dag-viz.js
index cda27cad7ee9d..a0e3e914c2547 100644
--- a/core/src/main/resources/org/apache/spark/ui/static/spark-dag-viz.js
+++ b/core/src/main/resources/org/apache/spark/ui/static/spark-dag-viz.js
@@ -218,6 +218,7 @@ function renderDagVizForJob(svgContainer) {
     });
   });
 
+  addTooltipsForRDDs(svgContainer);
   drawCrossStageEdges(crossStageEdges, svgContainer);
 }
 
@@ -424,6 +425,21 @@ function connectRDDs(fromRDDId, toRDDId, edgesContainer, svgContainer) {
   edgesContainer.append("path").datum(points).attr("d", line);
 }
 
+/* (Job page only) Helper function to add tooltips for RDDs. */
+function addTooltipsForRDDs(svgContainer) {
+  svgContainer.selectAll("g.node").each(function() {
+    var node = d3.select(this);
+    var tooltipText = node.attr("name");
+    if (tooltipText) {
+      node.select("circle")
+        .attr("data-toggle", "tooltip")
+        .attr("data-placement", "right")
+        .attr("title", tooltipText)
+    }
+  });
+  $("[data-toggle=tooltip]").tooltip({container: "body"});
+}
+
 /* Helper function to convert attributes to numeric values. */
 function toFloat(f) {
   if (f) {
diff --git a/core/src/main/scala/org/apache/spark/ui/scope/RDDOperationGraph.scala b/core/src/main/scala/org/apache/spark/ui/scope/RDDOperationGraph.scala
index edf005f7f325a..2b2db9e62be4e 100644
--- a/core/src/main/scala/org/apache/spark/ui/scope/RDDOperationGraph.scala
+++ b/core/src/main/scala/org/apache/spark/ui/scope/RDDOperationGraph.scala
@@ -178,10 +178,11 @@ private[ui] object RDDOperationGraph extends Logging {
    * On the stage page, it is displayed as a box with an embedded label.
    */
   private def makeDotNode(node: RDDOperationNode, forJob: Boolean): String = {
+    val label = s"${node.name} (${node.id})"
     if (forJob) {
-      s"""${node.id} [label=" " shape="circle" padding="5" labelStyle="font-size: 0"]"""
+      s"""${node.id} [label="$label" shape="circle" padding="5" labelStyle="font-size: 0"]"""
     } else {
-      s"""${node.id} [label="${node.name} (${node.id})" padding="5" labelStyle="font-size: 10"]"""
+      s"""${node.id} [label="$label" padding="5" labelStyle="font-size: 10"]"""
     }
   }
 

From 347a329a36c94ff37363e4dffcbd5a24dc6a6714 Mon Sep 17 00:00:00 2001
From: MechCoder <manojkumarsivaraj334@gmail.com>
Date: Thu, 7 May 2015 14:02:05 -0700
Subject: [PATCH 43/75] [SPARK-7328] [MLLIB] [PYSPARK]
 Pyspark.mllib.linalg.Vectors: Missing items

Add
1. Class methods squared_dist
3. parse
4. norm
5. numNonzeros
6. copy

I made a few vectorizations wrt squared_dist and dot as well. I have added support for SparseMatrix serialization in a separate PR (https://github.com/apache/spark/pull/5775) and plan to complete support for Matrices in another PR.

Author: MechCoder <manojkumarsivaraj334@gmail.com>

Closes #5872 from MechCoder/local_linalg_api and squashes the following commits:

a8ff1e0 [MechCoder] minor
ce3e53e [MechCoder] Add error message for parser
1bd3c04 [MechCoder] Robust parser and removed unnecessary methods
f779561 [MechCoder] [SPARK-7328] Pyspark.mllib.linalg.Vectors: Missing items
---
 python/pyspark/mllib/linalg.py | 148 ++++++++++++++++++++++++++++++++-
 python/pyspark/mllib/tests.py  |  25 +++++-
 2 files changed, 171 insertions(+), 2 deletions(-)

diff --git a/python/pyspark/mllib/linalg.py b/python/pyspark/mllib/linalg.py
index 9f3b0baf9f19f..23d1a79ffe511 100644
--- a/python/pyspark/mllib/linalg.py
+++ b/python/pyspark/mllib/linalg.py
@@ -208,9 +208,46 @@ def __init__(self, ar):
             ar = ar.astype(np.float64)
         self.array = ar
 
+    @staticmethod
+    def parse(s):
+        """
+        Parse string representation back into the DenseVector.
+
+        >>> DenseVector.parse(' [ 0.0,1.0,2.0,  3.0]')
+        DenseVector([0.0, 1.0, 2.0, 3.0])
+        """
+        start = s.find('[')
+        if start == -1:
+            raise ValueError("Array should start with '['.")
+        end = s.find(']')
+        if end == -1:
+            raise ValueError("Array should end with ']'.")
+        s = s[start + 1: end]
+
+        try:
+            values = [float(val) for val in s.split(',')]
+        except ValueError:
+            raise ValueError("Unable to parse values from %s" % s)
+        return DenseVector(values)
+
     def __reduce__(self):
         return DenseVector, (self.array.tostring(),)
 
+    def numNonzeros(self):
+        return np.count_nonzero(self.array)
+
+    def norm(self, p):
+        """
+        Calculte the norm of a DenseVector.
+
+        >>> a = DenseVector([0, -1, 2, -3])
+        >>> a.norm(2)
+        3.7...
+        >>> a.norm(1)
+        6.0
+        """
+        return np.linalg.norm(self.array, p)
+
     def dot(self, other):
         """
         Compute the dot product of two Vectors. We support
@@ -387,8 +424,74 @@ def __init__(self, size, *args):
                 if self.indices[i] >= self.indices[i + 1]:
                     raise TypeError("indices array must be sorted")
 
+    def numNonzeros(self):
+        return np.count_nonzero(self.values)
+
+    def norm(self, p):
+        """
+        Calculte the norm of a SparseVector.
+
+        >>> a = SparseVector(4, [0, 1], [3., -4.])
+        >>> a.norm(1)
+        7.0
+        >>> a.norm(2)
+        5.0
+        """
+        return np.linalg.norm(self.values, p)
+
     def __reduce__(self):
-        return (SparseVector, (self.size, self.indices.tostring(), self.values.tostring()))
+        return (
+            SparseVector,
+            (self.size, self.indices.tostring(), self.values.tostring()))
+
+    @staticmethod
+    def parse(s):
+        """
+        Parse string representation back into the DenseVector.
+
+        >>> SparseVector.parse(' (4, [0,1 ],[ 4.0,5.0] )')
+        SparseVector(4, {0: 4.0, 1: 5.0})
+        """
+        start = s.find('(')
+        if start == -1:
+            raise ValueError("Tuple should start with '('")
+        end = s.find(')')
+        if start == -1:
+            raise ValueError("Tuple should end with ')'")
+        s = s[start + 1: end].strip()
+
+        size = s[: s.find(',')]
+        try:
+            size = int(size)
+        except ValueError:
+            raise ValueError("Cannot parse size %s." % size)
+
+        ind_start = s.find('[')
+        if ind_start == -1:
+            raise ValueError("Indices array should start with '['.")
+        ind_end = s.find(']')
+        if ind_end == -1:
+            raise ValueError("Indices array should end with ']'")
+        new_s = s[ind_start + 1: ind_end]
+        ind_list = new_s.split(',')
+        try:
+            indices = [int(ind) for ind in ind_list]
+        except ValueError:
+            raise ValueError("Unable to parse indices from %s." % new_s)
+        s = s[ind_end + 1:].strip()
+
+        val_start = s.find('[')
+        if val_start == -1:
+            raise ValueError("Values array should start with '['.")
+        val_end = s.find(']')
+        if val_end == -1:
+            raise ValueError("Values array should end with ']'.")
+        val_list = s[val_start + 1: val_end].split(',')
+        try:
+            values = [float(val) for val in val_list]
+        except ValueError:
+            raise ValueError("Unable to parse values from %s." % s)
+        return SparseVector(size, indices, values)
 
     def dot(self, other):
         """
@@ -633,6 +736,49 @@ def stringify(vector):
         """
         return str(vector)
 
+    @staticmethod
+    def squared_distance(v1, v2):
+        """
+        Squared distance between two vectors.
+        a and b can be of type SparseVector, DenseVector, np.ndarray
+        or array.array.
+
+        >>> a = Vectors.sparse(4, [(0, 1), (3, 4)])
+        >>> b = Vectors.dense([2, 5, 4, 1])
+        >>> a.squared_distance(b)
+        51.0
+        """
+        v1, v2 = _convert_to_vector(v1), _convert_to_vector(v2)
+        return v1.squared_distance(v2)
+
+    @staticmethod
+    def norm(vector, p):
+        """
+        Find norm of the given vector.
+        """
+        return _convert_to_vector(vector).norm(p)
+
+    @staticmethod
+    def parse(s):
+        """Parse a string representation back into the Vector.
+
+        >>> Vectors.parse('[2,1,2 ]')
+        DenseVector([2.0, 1.0, 2.0])
+        >>> Vectors.parse(' ( 100,  [0],  [2])')
+        SparseVector(100, {0: 2.0})
+        """
+        if s.find('(') == -1 and s.find('[') != -1:
+            return DenseVector.parse(s)
+        elif s.find('(') != -1:
+            return SparseVector.parse(s)
+        else:
+            raise ValueError(
+                "Cannot find tokens '[' or '(' from the input string.")
+
+    @staticmethod
+    def zeros(size):
+        return DenseVector(np.zeros(size))
+
 
 class Matrix(object):
     """
diff --git a/python/pyspark/mllib/tests.py b/python/pyspark/mllib/tests.py
index d05cfe2af04b2..36a4c7a5408c6 100644
--- a/python/pyspark/mllib/tests.py
+++ b/python/pyspark/mllib/tests.py
@@ -24,7 +24,7 @@
 import tempfile
 import array as pyarray
 
-from numpy import array, array_equal, zeros
+from numpy import array, array_equal, zeros, inf
 from py4j.protocol import Py4JJavaError
 
 if sys.version_info[:2] <= (2, 6):
@@ -220,6 +220,29 @@ def test_dense_matrix_is_transposed(self):
         self.assertTrue(array_equal(sm.colPtrs, [0, 2, 5]))
         self.assertTrue(array_equal(sm.values, [1, 3, 4, 6, 9]))
 
+    def test_parse_vector(self):
+        a = DenseVector([3, 4, 6, 7])
+        self.assertTrue(str(a), '[3.0,4.0,6.0,7.0]')
+        self.assertTrue(Vectors.parse(str(a)), a)
+        a = SparseVector(4, [0, 2], [3, 4])
+        self.assertTrue(str(a), '(4,[0,2],[3.0,4.0])')
+        self.assertTrue(Vectors.parse(str(a)), a)
+        a = SparseVector(10, [0, 1], [4, 5])
+        self.assertTrue(SparseVector.parse(' (10, [0,1 ],[ 4.0,5.0] )'), a)
+
+    def test_norms(self):
+        a = DenseVector([0, 2, 3, -1])
+        self.assertAlmostEqual(a.norm(2), 3.742, 3)
+        self.assertTrue(a.norm(1), 6)
+        self.assertTrue(a.norm(inf), 3)
+        a = SparseVector(4, [0, 2], [3, -4])
+        self.assertAlmostEqual(a.norm(2), 5)
+        self.assertTrue(a.norm(1), 7)
+        self.assertTrue(a.norm(inf), 4)
+
+        tmp = SparseVector(4, [0, 2], [3, 0])
+        self.assertEqual(tmp.numNonzeros(), 1)
+
 
 class ListTests(MLlibTestCase):
 

From 658a478d3f86456df09d0fbb1ba438fb36d8725c Mon Sep 17 00:00:00 2001
From: Octavian Geagla <ogeagla@gmail.com>
Date: Thu, 7 May 2015 14:49:55 -0700
Subject: [PATCH 44/75] [SPARK-5726] [MLLIB] Elementwise (Hadamard) Vector
 Product Transformer

See https://issues.apache.org/jira/browse/SPARK-5726

Author: Octavian Geagla <ogeagla@gmail.com>
Author: Joseph K. Bradley <joseph@databricks.com>

Closes #4580 from ogeagla/spark-mllib-weighting and squashes the following commits:

fac12ad [Octavian Geagla] [SPARK-5726] [MLLIB] Use new createTransformFunc.
90f7e39 [Joseph K. Bradley] small cleanups
4595165 [Octavian Geagla] [SPARK-5726] [MLLIB] Remove erroneous test case.
ded3ac6 [Octavian Geagla] [SPARK-5726] [MLLIB] Pass style checks.
37d4705 [Octavian Geagla] [SPARK-5726] [MLLIB] Incorporated feedback.
1dffeee [Octavian Geagla] [SPARK-5726] [MLLIB] Pass style checks.
e436896 [Octavian Geagla] [SPARK-5726] [MLLIB] Remove 'TF' from 'ElementwiseProductTF'
cb520e6 [Octavian Geagla] [SPARK-5726] [MLLIB] Rename HadamardProduct to ElementwiseProduct
4922722 [Octavian Geagla] [SPARK-5726] [MLLIB] Hadamard Vector Product Transformer
---
 docs/mllib-feature-extraction.md              | 54 ++++++++++++++++
 .../spark/ml/feature/ElementwiseProduct.scala | 55 ++++++++++++++++
 .../mllib/feature/ElementwiseProduct.scala    | 64 +++++++++++++++++++
 .../feature/ElementwiseProductSuite.scala     | 61 ++++++++++++++++++
 4 files changed, 234 insertions(+)
 create mode 100644 mllib/src/main/scala/org/apache/spark/ml/feature/ElementwiseProduct.scala
 create mode 100644 mllib/src/main/scala/org/apache/spark/mllib/feature/ElementwiseProduct.scala
 create mode 100644 mllib/src/test/scala/org/apache/spark/mllib/feature/ElementwiseProductSuite.scala

diff --git a/docs/mllib-feature-extraction.md b/docs/mllib-feature-extraction.md
index 80842b27effd8..03fedd01016b9 100644
--- a/docs/mllib-feature-extraction.md
+++ b/docs/mllib-feature-extraction.md
@@ -477,3 +477,57 @@ sc.stop();
 </div>
 </div>
 
+## ElementwiseProduct
+
+ElementwiseProduct multiplies each input vector by a provided "weight" vector, using element-wise multiplication. In other words, it scales each column of the dataset by a scalar multiplier.  This represents the [Hadamard product](https://en.wikipedia.org/wiki/Hadamard_product_%28matrices%29) between the input vector, `v` and transforming vector, `w`, to yield a result vector.
+
+`\[ \begin{pmatrix}
+v_1 \\
+\vdots \\
+v_N
+\end{pmatrix} \circ \begin{pmatrix}
+                    w_1 \\
+                    \vdots \\
+                    w_N
+                    \end{pmatrix}
+= \begin{pmatrix}
+  v_1 w_1 \\
+  \vdots \\
+  v_N w_N
+  \end{pmatrix}
+\]`
+
+[`ElementwiseProduct`](api/scala/index.html#org.apache.spark.mllib.feature.ElementwiseProduct) has the following parameter in the constructor:
+
+* `w`: the transforming vector.
+
+`ElementwiseProduct` implements [`VectorTransformer`](api/scala/index.html#org.apache.spark.mllib.feature.VectorTransformer) which can apply the weighting on a `Vector` to produce a transformed `Vector` or on an `RDD[Vector]` to produce a transformed `RDD[Vector]`.
+
+### Example
+
+This example below demonstrates how to load a simple vectors file, extract a set of vectors, then transform those vectors using a transforming vector value.
+
+
+<div class="codetabs">
+<div data-lang="scala">
+{% highlight scala %}
+import org.apache.spark.SparkContext._
+import org.apache.spark.mllib.feature.ElementwiseProduct
+import org.apache.spark.mllib.linalg.Vectors
+
+// Load and parse the data:
+val data = sc.textFile("data/mllib/kmeans_data.txt")
+val parsedData = data.map(s => Vectors.dense(s.split(' ').map(_.toDouble)))
+
+val transformingVector = Vectors.dense(0.0, 1.0, 2.0)
+val transformer = new ElementwiseProduct(transformingVector)
+
+// Batch transform and per-row transform give the same results:
+val transformedData = transformer.transform(parsedData)
+val transformedData2 = parsedData.map(x => transformer.transform(x))
+
+{% endhighlight %}
+</div>
+</div>
+
+
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/ElementwiseProduct.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/ElementwiseProduct.scala
new file mode 100644
index 0000000000000..f8b56293e3ccc
--- /dev/null
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/ElementwiseProduct.scala
@@ -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.
+ */
+
+package org.apache.spark.ml.feature
+
+import org.apache.spark.annotation.AlphaComponent
+import org.apache.spark.ml.UnaryTransformer
+import org.apache.spark.ml.param.Param
+import org.apache.spark.mllib.feature
+import org.apache.spark.mllib.linalg.{Vector, VectorUDT}
+import org.apache.spark.sql.types.DataType
+
+/**
+ * :: AlphaComponent ::
+ * Outputs the Hadamard product (i.e., the element-wise product) of each input vector with a
+ * provided "weight" vector.  In other words, it scales each column of the dataset by a scalar
+ * multiplier.
+ */
+@AlphaComponent
+class ElementwiseProduct extends UnaryTransformer[Vector, Vector, ElementwiseProduct] {
+
+  /**
+    * the vector to multiply with input vectors
+    * @group param
+    */
+  val scalingVec: Param[Vector] = new Param(this, "scalingVector", "vector for hadamard product")
+
+  /** @group setParam */
+  def setScalingVec(value: Vector): this.type = set(scalingVec, value)
+
+  /** @group getParam */
+  def getScalingVec: Vector = getOrDefault(scalingVec)
+
+  override protected def createTransformFunc: Vector => Vector = {
+    require(params.contains(scalingVec), s"transformation requires a weight vector")
+    val elemScaler = new feature.ElementwiseProduct($(scalingVec))
+    elemScaler.transform
+  }
+
+  override protected def outputDataType: DataType = new VectorUDT()
+}
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/feature/ElementwiseProduct.scala b/mllib/src/main/scala/org/apache/spark/mllib/feature/ElementwiseProduct.scala
new file mode 100644
index 0000000000000..b0985baf9b278
--- /dev/null
+++ b/mllib/src/main/scala/org/apache/spark/mllib/feature/ElementwiseProduct.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.mllib.feature
+
+import org.apache.spark.annotation.Experimental
+import org.apache.spark.mllib.linalg._
+
+/**
+ * :: Experimental ::
+ * Outputs the Hadamard product (i.e., the element-wise product) of each input vector with a
+ * provided "weight" vector. In other words, it scales each column of the dataset by a scalar
+ * multiplier.
+ * @param scalingVector The values used to scale the reference vector's individual components.
+ */
+@Experimental
+class ElementwiseProduct(val scalingVector: Vector) extends VectorTransformer {
+
+  /**
+   * Does the hadamard product transformation.
+   *
+   * @param vector vector to be transformed.
+   * @return transformed vector.
+   */
+  override def transform(vector: Vector): Vector = {
+    require(vector.size == scalingVector.size,
+      s"vector sizes do not match: Expected ${scalingVector.size} but found ${vector.size}")
+    vector match {
+      case dv: DenseVector =>
+        val values: Array[Double] = dv.values.clone()
+        val dim = scalingVector.size
+        var i = 0
+        while (i < dim) {
+          values(i) *= scalingVector(i)
+          i += 1
+        }
+        Vectors.dense(values)
+      case SparseVector(size, indices, vs) =>
+        val values = vs.clone()
+        val dim = values.length
+        var i = 0
+        while (i < dim) {
+          values(i) *= scalingVector(indices(i))
+          i += 1
+        }
+        Vectors.sparse(size, indices, values)
+      case v => throw new IllegalArgumentException("Does not support vector type " + v.getClass)
+    }
+  }
+}
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/feature/ElementwiseProductSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/feature/ElementwiseProductSuite.scala
new file mode 100644
index 0000000000000..f3a482abda873
--- /dev/null
+++ b/mllib/src/test/scala/org/apache/spark/mllib/feature/ElementwiseProductSuite.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.mllib.feature
+
+import org.scalatest.FunSuite
+
+import org.apache.spark.mllib.linalg.{DenseVector, SparseVector, Vectors}
+import org.apache.spark.mllib.util.MLlibTestSparkContext
+import org.apache.spark.mllib.util.TestingUtils._
+
+class ElementwiseProductSuite extends FunSuite with MLlibTestSparkContext {
+
+  test("elementwise (hadamard) product should properly apply vector to dense data set") {
+    val denseData = Array(
+      Vectors.dense(1.0, 4.0, 1.9, -9.0)
+    )
+    val scalingVec = Vectors.dense(2.0, 0.5, 0.0, 0.25)
+    val transformer = new ElementwiseProduct(scalingVec)
+    val transformedData = transformer.transform(sc.makeRDD(denseData))
+    val transformedVecs = transformedData.collect()
+    val transformedVec = transformedVecs(0)
+    val expectedVec = Vectors.dense(2.0, 2.0, 0.0, -2.25)
+    assert(transformedVec ~== expectedVec absTol 1E-5,
+      s"Expected transformed vector $expectedVec but found $transformedVec")
+  }
+
+  test("elementwise (hadamard) product should properly apply vector to sparse data set") {
+    val sparseData = Array(
+      Vectors.sparse(3, Seq((1, -1.0), (2, -3.0)))
+    )
+    val dataRDD = sc.parallelize(sparseData, 3)
+    val scalingVec = Vectors.dense(1.0, 0.0, 0.5)
+    val transformer = new ElementwiseProduct(scalingVec)
+    val data2 = sparseData.map(transformer.transform)
+    val data2RDD = transformer.transform(dataRDD)
+
+    assert((sparseData, data2, data2RDD.collect()).zipped.forall {
+      case (v1: DenseVector, v2: DenseVector, v3: DenseVector) => true
+      case (v1: SparseVector, v2: SparseVector, v3: SparseVector) => true
+      case _ => false
+    }, "The vector type should be preserved after hadamard product")
+
+    assert((data2, data2RDD.collect()).zipped.forall((v1, v2) => v1 ~== v2 absTol 1E-5))
+    assert(data2(0) ~== Vectors.sparse(3, Seq((1, 0.0), (2, -1.5))) absTol 1E-5)
+  }
+}

From e43803b8f477b2c8d28836ac163cb54328d13f1a Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Thu, 7 May 2015 15:45:37 -0700
Subject: [PATCH 45/75] [SPARK-6948] [MLLIB] compress vectors in
 VectorAssembler

The compression is based on storage. brkyvz

Author: Xiangrui Meng <meng@databricks.com>

Closes #5985 from mengxr/SPARK-6948 and squashes the following commits:

df56a00 [Xiangrui Meng] update python tests
6d90d45 [Xiangrui Meng] compress vectors in VectorAssembler
---
 .../org/apache/spark/ml/feature/VectorAssembler.scala  |  2 +-
 .../apache/spark/ml/feature/VectorAssemblerSuite.scala | 10 +++++++++-
 python/pyspark/ml/feature.py                           |  6 +++---
 3 files changed, 13 insertions(+), 5 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/VectorAssembler.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/VectorAssembler.scala
index b5a69cee6daf3..796758a70ef18 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/VectorAssembler.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/VectorAssembler.scala
@@ -102,6 +102,6 @@ object VectorAssembler {
       case o =>
         throw new SparkException(s"$o of type ${o.getClass.getName} is not supported.")
     }
-    Vectors.sparse(cur, indices.result(), values.result())
+    Vectors.sparse(cur, indices.result(), values.result()).compressed
   }
 }
diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/VectorAssemblerSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/VectorAssemblerSuite.scala
index 57d0278e03639..0db27607bc274 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/feature/VectorAssemblerSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/feature/VectorAssemblerSuite.scala
@@ -20,7 +20,7 @@ package org.apache.spark.ml.feature
 import org.scalatest.FunSuite
 
 import org.apache.spark.SparkException
-import org.apache.spark.mllib.linalg.{Vector, Vectors}
+import org.apache.spark.mllib.linalg.{DenseVector, SparseVector, Vector, Vectors}
 import org.apache.spark.mllib.util.MLlibTestSparkContext
 import org.apache.spark.sql.{Row, SQLContext}
 
@@ -48,6 +48,14 @@ class VectorAssemblerSuite extends FunSuite with MLlibTestSparkContext {
     }
   }
 
+  test("assemble should compress vectors") {
+    import org.apache.spark.ml.feature.VectorAssembler.assemble
+    val v1 = assemble(0.0, 0.0, 0.0, Vectors.dense(4.0))
+    assert(v1.isInstanceOf[SparseVector])
+    val v2 = assemble(1.0, 2.0, 3.0, Vectors.sparse(1, Array(0), Array(4.0)))
+    assert(v2.isInstanceOf[DenseVector])
+  }
+
   test("VectorAssembler") {
     val df = sqlContext.createDataFrame(Seq(
       (0, 0.0, Vectors.dense(1.0, 2.0), "a", Vectors.sparse(2, Array(1), Array(3.0)), 10L)
diff --git a/python/pyspark/ml/feature.py b/python/pyspark/ml/feature.py
index 8a0fdddd2d9b5..705a368192c24 100644
--- a/python/pyspark/ml/feature.py
+++ b/python/pyspark/ml/feature.py
@@ -121,12 +121,12 @@ class VectorAssembler(JavaTransformer, HasInputCols, HasOutputCol):
     >>> df = sc.parallelize([Row(a=1, b=0, c=3)]).toDF()
     >>> vecAssembler = VectorAssembler(inputCols=["a", "b", "c"], outputCol="features")
     >>> vecAssembler.transform(df).head().features
-    SparseVector(3, {0: 1.0, 2: 3.0})
+    DenseVector([1.0, 0.0, 3.0])
     >>> vecAssembler.setParams(outputCol="freqs").transform(df).head().freqs
-    SparseVector(3, {0: 1.0, 2: 3.0})
+    DenseVector([1.0, 0.0, 3.0])
     >>> params = {vecAssembler.inputCols: ["b", "a"], vecAssembler.outputCol: "vector"}
     >>> vecAssembler.transform(df, params).head().vector
-    SparseVector(2, {1: 1.0})
+    DenseVector([0.0, 1.0])
     """
 
     _java_class = "org.apache.spark.ml.feature.VectorAssembler"

From 97d1182af63d55abab44521171652c81c56c6af6 Mon Sep 17 00:00:00 2001
From: scwf <wangfei1@huawei.com>
Date: Thu, 7 May 2015 16:21:24 -0700
Subject: [PATCH 46/75] [SQL] [MINOR] make star and multialias extend
 NamedExpression

`Star` and `MultiAlias` just used in `analyzer` and them will be substituted after analyze,  So just like `Alias` they do not need extend `Attribute`

Author: scwf <wangfei1@huawei.com>

Closes #5928 from scwf/attribute and squashes the following commits:

73a0560 [scwf] star and multialias do not need extend attribute
---
 .../sql/catalyst/analysis/unresolved.scala    | 20 +++++--------------
 .../expressions/namedExpressions.scala        |  2 +-
 2 files changed, 6 insertions(+), 16 deletions(-)

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 3f567e3e8b2a6..eb736ac3290e8 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
@@ -95,7 +95,7 @@ case class UnresolvedFunction(name: String, children: Seq[Expression]) extends E
  * Represents all of the input attributes to a given relational operator, for example in
  * "SELECT * FROM ...". A [[Star]] gets automatically expanded during analysis.
  */
-trait Star extends Attribute with trees.LeafNode[Expression] {
+trait Star extends NamedExpression with trees.LeafNode[Expression] {
   self: Product =>
 
   override def name: String = throw new UnresolvedException(this, "name")
@@ -103,13 +103,9 @@ trait Star extends Attribute with trees.LeafNode[Expression] {
   override def dataType: DataType = throw new UnresolvedException(this, "dataType")
   override def nullable: Boolean = throw new UnresolvedException(this, "nullable")
   override def qualifiers: Seq[String] = throw new UnresolvedException(this, "qualifiers")
+  override def toAttribute: Attribute = throw new UnresolvedException(this, "toAttribute")
   override lazy val resolved = false
 
-  override def newInstance(): Star = this
-  override def withNullability(newNullability: Boolean): Star = this
-  override def withQualifiers(newQualifiers: Seq[String]): Star = this
-  override def withName(newName: String): Star = this
-
   // Star gets expanded at runtime so we never evaluate a Star.
   override def eval(input: Row = null): EvaluatedType =
     throw new TreeNodeException(this, s"No function to evaluate expression. type: ${this.nodeName}")
@@ -154,7 +150,7 @@ case class UnresolvedStar(table: Option[String]) extends Star {
  * @param names the names to be associated with each output of computing [[child]].
  */
 case class MultiAlias(child: Expression, names: Seq[String])
-  extends Attribute with trees.UnaryNode[Expression] {
+  extends NamedExpression with trees.UnaryNode[Expression] {
 
   override def name: String = throw new UnresolvedException(this, "name")
 
@@ -166,15 +162,9 @@ case class MultiAlias(child: Expression, names: Seq[String])
 
   override def qualifiers: Seq[String] = throw new UnresolvedException(this, "qualifiers")
 
-  override lazy val resolved = false
-
-  override def newInstance(): MultiAlias = this
+  override def toAttribute: Attribute = throw new UnresolvedException(this, "toAttribute")
 
-  override def withNullability(newNullability: Boolean): MultiAlias = this
-
-  override def withQualifiers(newQualifiers: Seq[String]): MultiAlias = this
-
-  override def withName(newName: String): MultiAlias = this
+  override lazy val resolved = false
 
   override def eval(input: Row = null): EvaluatedType =
     throw new TreeNodeException(this, s"No function to evaluate expression. type: ${this.nodeName}")
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 57ace2a14f77c..a9170589f8c6c 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
@@ -86,7 +86,7 @@ abstract class Attribute extends NamedExpression {
   def withQualifiers(newQualifiers: Seq[String]): Attribute
   def withName(newName: String): Attribute
 
-  def toAttribute: Attribute = this
+  override def toAttribute: Attribute = this
   def newInstance(): Attribute
 
 }

From ea3077f19c18b5556a3632b36771aeb153746ff5 Mon Sep 17 00:00:00 2001
From: Liang-Chi Hsieh <viirya@gmail.com>
Date: Thu, 7 May 2015 16:22:45 -0700
Subject: [PATCH 47/75] [SPARK-7277] [SQL] Throw exception if the property
 mapred.reduce.tasks is set to -1

JIRA: https://issues.apache.org/jira/browse/SPARK-7277

As automatically determining the number of reducers is not supported (`mapred.reduce.tasks` is set to `-1`), we should throw exception to users.

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

Closes #5811 from viirya/no_neg_reduce_tasks and squashes the following commits:

e518f96 [Liang-Chi Hsieh] Consider other wrong setting values.
fd9c817 [Liang-Chi Hsieh] Merge remote-tracking branch 'upstream/master' into no_neg_reduce_tasks
4ede705 [Liang-Chi Hsieh] Throw exception instead of warning message.
68a1c70 [Liang-Chi Hsieh] Show warning message if mapred.reduce.tasks is set to -1.
---
 .../org/apache/spark/sql/execution/commands.scala      | 10 ++++++++--
 .../scala/org/apache/spark/sql/SQLQuerySuite.scala     | 10 ++++++++++
 2 files changed, 18 insertions(+), 2 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 98df5bef34efa..65687db4e6235 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
@@ -84,8 +84,14 @@ case class SetCommand(
       logWarning(
         s"Property ${SQLConf.Deprecated.MAPRED_REDUCE_TASKS} is deprecated, " +
           s"automatically converted to ${SQLConf.SHUFFLE_PARTITIONS} instead.")
-      sqlContext.setConf(SQLConf.SHUFFLE_PARTITIONS, value)
-      Seq(Row(s"${SQLConf.SHUFFLE_PARTITIONS}=$value"))
+      if (value.toInt < 1) {
+        val msg = s"Setting negative ${SQLConf.Deprecated.MAPRED_REDUCE_TASKS} for automatically " +
+          "determining the number of reducers is not supported."
+        throw new IllegalArgumentException(msg)
+      } else {
+        sqlContext.setConf(SQLConf.SHUFFLE_PARTITIONS, value)
+        Seq(Row(s"${SQLConf.SHUFFLE_PARTITIONS}=$value"))
+      }
 
     // Configures a single property.
     case Some((key, Some(value))) =>
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 208cec6a32d4d..77be3b8b206c0 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
@@ -871,6 +871,16 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll {
     conf.clear()
   }
 
+  test("SET commands with illegal or inappropriate argument") {
+    conf.clear()
+    // Set negative mapred.reduce.tasks for automatically determing
+    // the number of reducers is not supported
+    intercept[IllegalArgumentException](sql(s"SET mapred.reduce.tasks=-1"))
+    intercept[IllegalArgumentException](sql(s"SET mapred.reduce.tasks=-01"))
+    intercept[IllegalArgumentException](sql(s"SET mapred.reduce.tasks=-2"))
+    conf.clear()
+  }
+
   test("apply schema") {
     val schema1 = StructType(
       StructField("f1", IntegerType, false) ::

From 937ba798c56770ec54276b9259e47ae65ee93967 Mon Sep 17 00:00:00 2001
From: Iulian Dragos <jaguarul@gmail.com>
Date: Thu, 7 May 2015 16:24:11 -0700
Subject: [PATCH 48/75] [SPARK-5281] [SQL] Registering table on RDD is giving
 MissingRequirementError

Go through the context classloader when reflecting on user types in ScalaReflection.

Replaced calls to `typeOf` with `typeTag[T].in(mirror)`. The convenience method assumes
all types can be found in the classloader that loaded scala-reflect (the primordial
classloader). This assumption is not valid in all contexts (sbt console, Eclipse launchers).

Fixed SPARK-5281

Author: Iulian Dragos <jaguarul@gmail.com>

Closes #5981 from dragos/issue/mirrors-missing-requirement-error and squashes the following commits:

d103e70 [Iulian Dragos] Go through the context classloader when reflecting on user types in ScalaReflection
---
 .../spark/sql/catalyst/ScalaReflection.scala  | 57 ++++++++++++-------
 1 file changed, 37 insertions(+), 20 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 c52965507c715..6998cc8d9666d 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
@@ -27,6 +27,7 @@ import org.apache.spark.sql.types._
  */
 object ScalaReflection extends ScalaReflection {
   val universe: scala.reflect.runtime.universe.type = scala.reflect.runtime.universe
+  val mirror: universe.Mirror = universe.runtimeMirror(Thread.currentThread().getContextClassLoader)
 }
 
 /**
@@ -36,6 +37,9 @@ trait ScalaReflection {
   /** The universe we work in (runtime or macro) */
   val universe: scala.reflect.api.Universe
 
+  /** The mirror used to access types in the universe */
+  val mirror: universe.Mirror
+
   import universe._
 
   // The Predef.Map is scala.collection.immutable.Map.
@@ -52,7 +56,19 @@ trait ScalaReflection {
 
   /** Returns a catalyst DataType and its nullability for the given Scala Type using reflection. */
   def schemaFor[T: TypeTag]: Schema =
-    ScalaReflectionLock.synchronized { schemaFor(typeOf[T]) }
+    ScalaReflectionLock.synchronized { schemaFor(localTypeOf[T]) }
+
+  /**
+   * Return the Scala Type for `T` in the current classloader mirror.
+   *
+   * Use this method instead of the convenience method `universe.typeOf`, which
+   * assumes that all types can be found in the classloader that loaded scala-reflect classes.
+   * That's not necessarily the case when running using Eclipse launchers or even
+   * Sbt console or test (without `fork := true`).
+   *
+   * @see SPARK-5281
+   */
+  private def localTypeOf[T: TypeTag]: `Type` = typeTag[T].in(mirror).tpe
 
   /** Returns a catalyst DataType and its nullability for the given Scala Type using reflection. */
   def schemaFor(tpe: `Type`): Schema = ScalaReflectionLock.synchronized {
@@ -67,25 +83,25 @@ trait ScalaReflection {
         val udt = Utils.classForName(className)
           .getAnnotation(classOf[SQLUserDefinedType]).udt().newInstance()
         Schema(udt, nullable = true)
-      case t if t <:< typeOf[Option[_]] =>
+      case t if t <:< localTypeOf[Option[_]] =>
         val TypeRef(_, _, Seq(optType)) = t
         Schema(schemaFor(optType).dataType, nullable = true)
       // Need to decide if we actually need a special type here.
-      case t if t <:< typeOf[Array[Byte]] => Schema(BinaryType, nullable = true)
-      case t if t <:< typeOf[Array[_]] =>
+      case t if t <:< localTypeOf[Array[Byte]] => Schema(BinaryType, nullable = true)
+      case t if t <:< localTypeOf[Array[_]] =>
         val TypeRef(_, _, Seq(elementType)) = t
         val Schema(dataType, nullable) = schemaFor(elementType)
         Schema(ArrayType(dataType, containsNull = nullable), nullable = true)
-      case t if t <:< typeOf[Seq[_]] =>
+      case t if t <:< localTypeOf[Seq[_]] =>
         val TypeRef(_, _, Seq(elementType)) = t
         val Schema(dataType, nullable) = schemaFor(elementType)
         Schema(ArrayType(dataType, containsNull = nullable), nullable = true)
-      case t if t <:< typeOf[Map[_, _]] =>
+      case t if t <:< localTypeOf[Map[_, _]] =>
         val TypeRef(_, _, Seq(keyType, valueType)) = t
         val Schema(valueDataType, valueNullable) = schemaFor(valueType)
         Schema(MapType(schemaFor(keyType).dataType,
           valueDataType, valueContainsNull = valueNullable), nullable = true)
-      case t if t <:< typeOf[Product] =>
+      case t if t <:< localTypeOf[Product] =>
         val formalTypeArgs = t.typeSymbol.asClass.typeParams
         val TypeRef(_, _, actualTypeArgs) = t
         val constructorSymbol = t.member(nme.CONSTRUCTOR)
@@ -107,19 +123,20 @@ trait ScalaReflection {
               schemaFor(p.typeSignature.substituteTypes(formalTypeArgs, actualTypeArgs))
             StructField(p.name.toString, dataType, nullable)
           }), nullable = true)
-      case t if t <:< typeOf[String] => Schema(StringType, nullable = true)
-      case t if t <:< typeOf[java.sql.Timestamp] => Schema(TimestampType, nullable = true)
-      case t if t <:< typeOf[java.sql.Date] => Schema(DateType, nullable = true)
-      case t if t <:< typeOf[BigDecimal] => Schema(DecimalType.Unlimited, nullable = true)
-      case t if t <:< typeOf[java.math.BigDecimal] => Schema(DecimalType.Unlimited, nullable = true)
-      case t if t <:< typeOf[Decimal] => Schema(DecimalType.Unlimited, nullable = true)
-      case t if t <:< typeOf[java.lang.Integer] => Schema(IntegerType, nullable = true)
-      case t if t <:< typeOf[java.lang.Long] => Schema(LongType, nullable = true)
-      case t if t <:< typeOf[java.lang.Double] => Schema(DoubleType, nullable = true)
-      case t if t <:< typeOf[java.lang.Float] => Schema(FloatType, nullable = true)
-      case t if t <:< typeOf[java.lang.Short] => Schema(ShortType, nullable = true)
-      case t if t <:< typeOf[java.lang.Byte] => Schema(ByteType, nullable = true)
-      case t if t <:< typeOf[java.lang.Boolean] => Schema(BooleanType, nullable = true)
+      case t if t <:< localTypeOf[String] => Schema(StringType, nullable = true)
+      case t if t <:< localTypeOf[java.sql.Timestamp] => Schema(TimestampType, nullable = true)
+      case t if t <:< localTypeOf[java.sql.Date] => Schema(DateType, nullable = true)
+      case t if t <:< localTypeOf[BigDecimal] => Schema(DecimalType.Unlimited, nullable = true)
+      case t if t <:< localTypeOf[java.math.BigDecimal] =>
+        Schema(DecimalType.Unlimited, nullable = true)
+      case t if t <:< localTypeOf[Decimal] => Schema(DecimalType.Unlimited, nullable = true)
+      case t if t <:< localTypeOf[java.lang.Integer] => Schema(IntegerType, nullable = true)
+      case t if t <:< localTypeOf[java.lang.Long] => Schema(LongType, nullable = true)
+      case t if t <:< localTypeOf[java.lang.Double] => Schema(DoubleType, nullable = true)
+      case t if t <:< localTypeOf[java.lang.Float] => Schema(FloatType, nullable = true)
+      case t if t <:< localTypeOf[java.lang.Short] => Schema(ShortType, nullable = true)
+      case t if t <:< localTypeOf[java.lang.Byte] => Schema(ByteType, nullable = true)
+      case t if t <:< localTypeOf[java.lang.Boolean] => Schema(BooleanType, nullable = true)
       case t if t <:< definitions.IntTpe => Schema(IntegerType, nullable = false)
       case t if t <:< definitions.LongTpe => Schema(LongType, nullable = false)
       case t if t <:< definitions.DoubleTpe => Schema(DoubleType, nullable = false)

From 35f0173b8f67e2e506fc4575be6430cfb66e2238 Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Thu, 7 May 2015 16:26:49 -0700
Subject: [PATCH 49/75] [SPARK-2155] [SQL] [WHEN D THEN E] [ELSE F] add
 CaseKeyWhen for "CASE a WHEN b THEN c * END"

Avoid translating to CaseWhen and evaluate the key expression many times.

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #5979 from cloud-fan/condition and squashes the following commits:

3ce54e1 [Wenchen Fan] add CaseKeyWhen
---
 .../apache/spark/sql/catalyst/SqlParser.scala |  10 +-
 .../catalyst/analysis/HiveTypeCoercion.scala  |  43 +++---
 .../sql/catalyst/expressions/Expression.scala |   2 +-
 .../sql/catalyst/expressions/predicates.scala | 135 ++++++++++++------
 .../ExpressionEvaluationSuite.scala           |  26 ++++
 .../spark/sql/DataFrameNaFunctions.scala      |   9 +-
 .../org/apache/spark/sql/hive/HiveQl.scala    |  12 +-
 .../sql/hive/execution/SQLQuerySuite.scala    |   7 +
 8 files changed, 159 insertions(+), 85 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 1d3a2dc0d9bb0..b06bfb2ce8c8e 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
@@ -296,13 +296,13 @@ class SqlParser extends AbstractSparkSQLParser with DataTypeParser {
     | LOWER ~ "(" ~> expression <~ ")" ^^ { case exp => Lower(exp) }
     | IF ~ "(" ~> expression ~ ("," ~> expression) ~ ("," ~> expression) <~ ")" ^^
       { case c ~ t ~ f => If(c, t, f) }
-    | CASE ~> expression.? ~ (WHEN ~> expression ~ (THEN ~> expression)).* ~
+    | CASE ~> expression.? ~ rep1(WHEN ~> expression ~ (THEN ~> expression)) ~
         (ELSE ~> expression).? <~ END ^^ {
           case casePart ~ altPart ~ elsePart =>
-            val altExprs = altPart.flatMap { case whenExpr ~ thenExpr =>
-              Seq(casePart.fold(whenExpr)(EqualTo(_, whenExpr)), thenExpr)
-            }
-            CaseWhen(altExprs ++ elsePart.toList)
+            val branches = altPart.flatMap { case whenExpr ~ thenExpr =>
+              Seq(whenExpr, thenExpr)
+            } ++ elsePart
+            casePart.map(CaseKeyWhen(_, branches)).getOrElse(CaseWhen(branches))
         }
     | (SUBSTR | SUBSTRING) ~ "(" ~> expression ~ ("," ~> expression) <~ ")" ^^
       { case s ~ p => Substring(s, p, Literal(Integer.MAX_VALUE)) }
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 873c75c525c3b..168a4e30eab86 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
@@ -631,31 +631,24 @@ trait HiveTypeCoercion {
     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 {
-          case Seq(_, value) => value.dataType
-          case Seq(elseVal) => elseVal.dataType
-        }.toSeq
-
-        logDebug(s"Input values for null casting ${valueTypes.mkString(",")}")
-
-        if (valueTypes.distinct.size > 1) {
-          val commonType = valueTypes.reduce { (v1, v2) =>
-            findTightestCommonType(v1, v2)
-              .getOrElse(sys.error(
-                s"Types in CASE WHEN must be the same or coercible to a common type: $v1 != $v2"))
-          }
-          val transformedBranches = branches.sliding(2, 2).map {
-            case Seq(cond, value) if value.dataType != commonType =>
-              Seq(cond, Cast(value, commonType))
-            case Seq(elseVal) if elseVal.dataType != commonType =>
-              Seq(Cast(elseVal, commonType))
-            case s => s
-          }.reduce(_ ++ _)
-          CaseWhen(transformedBranches)
-        } else {
-          // Types match up.  Hopefully some other rule fixes whatever is wrong with resolution.
-          cw
+      case cw: CaseWhenLike if !cw.resolved && cw.childrenResolved && !cw.valueTypesEqual  =>
+        logDebug(s"Input values for null casting ${cw.valueTypes.mkString(",")}")
+        val commonType = cw.valueTypes.reduce { (v1, v2) =>
+          findTightestCommonType(v1, v2).getOrElse(sys.error(
+            s"Types in CASE WHEN must be the same or coercible to a common type: $v1 != $v2"))
+        }
+        val transformedBranches = cw.branches.sliding(2, 2).map {
+          case Seq(when, value) if value.dataType != commonType =>
+            Seq(when, Cast(value, commonType))
+          case Seq(elseVal) if elseVal.dataType != commonType =>
+            Seq(Cast(elseVal, commonType))
+          case s => s
+        }.reduce(_ ++ _)
+        cw match {
+          case _: CaseWhen =>
+            CaseWhen(transformedBranches)
+          case CaseKeyWhen(key, _) =>
+            CaseKeyWhen(key, transformedBranches)
         }
     }
   }
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 4fd1bc4dd642d..0837a3179d897 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
@@ -64,7 +64,7 @@ abstract class Expression extends TreeNode[Expression] {
    * Returns true if  all the children of this expression have been resolved to a specific schema
    * and false if any still contains any unresolved placeholders.
    */
-  def childrenResolved: Boolean = !children.exists(!_.resolved)
+  def childrenResolved: Boolean = children.forall(_.resolved)
 
   /**
    * Returns a string representation of this expression that does not have developer centric
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 26c38c56c04f5..50b0f3ee5f93f 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
@@ -353,79 +353,134 @@ case class If(predicate: Expression, trueValue: Expression, falseValue: Expressi
   override def toString: String = s"if ($predicate) $trueValue else $falseValue"
 }
 
+trait CaseWhenLike extends Expression {
+  self: Product =>
+
+  type EvaluatedType = Any
+
+  // Note that `branches` are considered in consecutive pairs (cond, val), and the optional last
+  // element is the value for the default catch-all case (if provided).
+  // Hence, `branches` consists of at least two elements, and can have an odd or even length.
+  def branches: Seq[Expression]
+
+  @transient lazy val whenList =
+    branches.sliding(2, 2).collect { case Seq(whenExpr, _) => whenExpr }.toSeq
+  @transient lazy val thenList =
+    branches.sliding(2, 2).collect { case Seq(_, thenExpr) => thenExpr }.toSeq
+  val elseValue = if (branches.length % 2 == 0) None else Option(branches.last)
+
+  // both then and else val should be considered.
+  def valueTypes: Seq[DataType] = (thenList ++ elseValue).map(_.dataType)
+  def valueTypesEqual: Boolean = valueTypes.distinct.size <= 1
+
+  override def dataType: DataType = {
+    if (!resolved) {
+      throw new UnresolvedException(this, "cannot resolve due to differing types in some branches")
+    }
+    valueTypes.head
+  }
+
+  override def nullable: Boolean = {
+    // If no value is nullable and no elseValue is provided, the whole statement defaults to null.
+    thenList.exists(_.nullable) || (elseValue.map(_.nullable).getOrElse(true))
+  }
+}
+
 // scalastyle:off
 /**
  * Case statements of the form "CASE WHEN a THEN b [WHEN c THEN d]* [ELSE e] END".
  * Refer to this link for the corresponding semantics:
  * https://cwiki.apache.org/confluence/display/Hive/LanguageManual+UDF#LanguageManualUDF-ConditionalFunctions
- *
- * The other form of case statements "CASE a WHEN b THEN c [WHEN d THEN e]* [ELSE f] END" gets
- * translated to this form at parsing time.  Namely, such a statement gets translated to
- * "CASE WHEN a=b THEN c [WHEN a=d THEN e]* [ELSE f] END".
- *
- * Note that `branches` are considered in consecutive pairs (cond, val), and the optional last
- * element is the value for the default catch-all case (if provided). Hence, `branches` consists of
- * at least two elements, and can have an odd or even length.
  */
 // scalastyle:on
-case class CaseWhen(branches: Seq[Expression]) extends Expression {
-  type EvaluatedType = Any
+case class CaseWhen(branches: Seq[Expression]) extends CaseWhenLike {
+
+  // Use private[this] Array to speed up evaluation.
+  @transient private[this] lazy val branchesArr = branches.toArray
 
   override def children: Seq[Expression] = branches
 
-  override def dataType: DataType = {
-    if (!resolved) {
-      throw new UnresolvedException(this, "cannot resolve due to differing types in some branches")
+  override lazy val resolved: Boolean =
+    childrenResolved &&
+    whenList.forall(_.dataType == BooleanType) &&
+    valueTypesEqual
+
+  /** Written in imperative fashion for performance considerations. */
+  override def eval(input: Row): Any = {
+    val len = branchesArr.length
+    var i = 0
+    // If all branches fail and an elseVal is not provided, the whole statement
+    // defaults to null, according to Hive's semantics.
+    while (i < len - 1) {
+      if (branchesArr(i).eval(input) == true) {
+        return branchesArr(i + 1).eval(input)
+      }
+      i += 2
+    }
+    var res: Any = null
+    if (i == len - 1) {
+      res = branchesArr(i).eval(input)
     }
-    branches(1).dataType
+    return res
   }
 
+  override def toString: String = {
+    "CASE" + branches.sliding(2, 2).map {
+      case Seq(cond, value) => s" WHEN $cond THEN $value"
+      case Seq(elseValue) => s" ELSE $elseValue"
+    }.mkString
+  }
+}
+
+// scalastyle:off
+/**
+ * Case statements of the form "CASE a WHEN b THEN c [WHEN d THEN e]* [ELSE f] END".
+ * Refer to this link for the corresponding semantics:
+ * https://cwiki.apache.org/confluence/display/Hive/LanguageManual+UDF#LanguageManualUDF-ConditionalFunctions
+ */
+// scalastyle:on
+case class CaseKeyWhen(key: Expression, branches: Seq[Expression]) extends CaseWhenLike {
+
+  // Use private[this] Array to speed up evaluation.
   @transient private[this] lazy val branchesArr = branches.toArray
-  @transient private[this] lazy val predicates =
-    branches.sliding(2, 2).collect { case Seq(cond, _) => cond }.toSeq
-  @transient private[this] lazy val values =
-    branches.sliding(2, 2).collect { case Seq(_, value) => value }.toSeq
-  @transient private[this] lazy val elseValue =
-    if (branches.length % 2 == 0) None else Option(branches.last)
 
-  override def nullable: Boolean = {
-    // If no value is nullable and no elseValue is provided, the whole statement defaults to null.
-    values.exists(_.nullable) || (elseValue.map(_.nullable).getOrElse(true))
-  }
+  override def children: Seq[Expression] = key +: branches
 
-  override lazy val resolved: Boolean = {
-    if (!childrenResolved) {
-      false
-    } else {
-      val allCondBooleans = predicates.forall(_.dataType == BooleanType)
-      // both then and else val should be considered.
-      val dataTypesEqual = (values ++ elseValue).map(_.dataType).distinct.size <= 1
-      allCondBooleans && dataTypesEqual
-    }
-  }
+  override lazy val resolved: Boolean =
+    childrenResolved && valueTypesEqual
 
   /** Written in imperative fashion for performance considerations. */
   override def eval(input: Row): Any = {
+    val evaluatedKey = key.eval(input)
     val len = branchesArr.length
     var i = 0
     // If all branches fail and an elseVal is not provided, the whole statement
     // defaults to null, according to Hive's semantics.
-    var res: Any = null
     while (i < len - 1) {
-      if (branchesArr(i).eval(input) == true) {
-        res = branchesArr(i + 1).eval(input)
-        return res
+      if (equalNullSafe(evaluatedKey, branchesArr(i).eval(input))) {
+        return branchesArr(i + 1).eval(input)
       }
       i += 2
     }
+    var res: Any = null
     if (i == len - 1) {
       res = branchesArr(i).eval(input)
     }
-    res
+    return res
+  }
+
+  private def equalNullSafe(l: Any, r: Any) = {
+    if (l == null && r == null) {
+      true
+    } else if (l == null || r == null) {
+      false
+    } else {
+      l == r
+    }
   }
 
   override def toString: String = {
-    "CASE" + branches.sliding(2, 2).map {
+    s"CASE $key" + branches.sliding(2, 2).map {
       case Seq(cond, value) => s" WHEN $cond THEN $value"
       case Seq(elseValue) => s" ELSE $elseValue"
     }.mkString
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 faaa55aa5e6f1..88d36d153ceea 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
@@ -850,6 +850,32 @@ class ExpressionEvaluationSuite extends ExpressionEvaluationBaseSuite {
     assert(CaseWhen(Seq(c2, c4_notNull, c3, c5)).nullable === true)
   }
 
+  test("case key when") {
+    val row = create_row(null, 1, 2, "a", "b", "c")
+    val c1 = 'a.int.at(0)
+    val c2 = 'a.int.at(1)
+    val c3 = 'a.int.at(2)
+    val c4 = 'a.string.at(3)
+    val c5 = 'a.string.at(4)
+    val c6 = 'a.string.at(5)
+
+    val literalNull = Literal.create(null, BooleanType)
+    val literalInt = Literal(1)
+    val literalString = Literal("a")
+
+    checkEvaluation(CaseKeyWhen(c1, Seq(c2, c4, c5)), "b", row)
+    checkEvaluation(CaseKeyWhen(c1, Seq(c2, c4, literalNull, c5, c6)), "b", row)
+    checkEvaluation(CaseKeyWhen(c2, Seq(literalInt, c4, c5)), "a", row)
+    checkEvaluation(CaseKeyWhen(c2, Seq(c1, c4, c5)), "b", row)
+    checkEvaluation(CaseKeyWhen(c4, Seq(literalString, c2, c3)), 1, row)
+    checkEvaluation(CaseKeyWhen(c4, Seq(c1, c3, c5, c2, Literal(3))), 3, row)
+
+    checkEvaluation(CaseKeyWhen(literalInt, Seq(c2, c4, c5)), "a", row)
+    checkEvaluation(CaseKeyWhen(literalString, Seq(c5, c2, c4, c3)), 2, row)
+    checkEvaluation(CaseKeyWhen(literalInt, Seq(c5, c2, c4, c3)), null, row)
+    checkEvaluation(CaseKeyWhen(literalNull, Seq(c5, c2, c1, c3)), 2, row)
+  }
+
   test("complex type") {
     val row = create_row(
       "^Ba*n",                                // 0
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameNaFunctions.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameNaFunctions.scala
index 481ed4924857e..4a54120ba86f6 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameNaFunctions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameNaFunctions.scala
@@ -357,11 +357,12 @@ final class DataFrameNaFunctions private[sql](df: DataFrame) {
    * TODO: This can be optimized to use broadcast join when replacementMap is large.
    */
   private def replaceCol(col: StructField, replacementMap: Map[_, _]): Column = {
-    val branches: Seq[Expression] = replacementMap.flatMap { case (source, target) =>
-      df.col(col.name).equalTo(lit(source).cast(col.dataType)).expr ::
-        lit(target).cast(col.dataType).expr :: Nil
+    val keyExpr = df.col(col.name).expr
+    def buildExpr(v: Any) = Cast(Literal(v), keyExpr.dataType)
+    val branches = replacementMap.flatMap { case (source, target) =>
+      Seq(buildExpr(source), buildExpr(target))
     }.toSeq
-    new Column(CaseWhen(branches ++ Seq(df.col(col.name).expr))).as(col.name)
+    new Column(CaseKeyWhen(keyExpr, branches :+ keyExpr)).as(col.name)
   }
 
   private def convertToDouble(v: Any): Double = v match {
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 4e51473979a2a..6176aee25e7a4 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
@@ -1246,16 +1246,8 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
     case Token("TOK_FUNCTION", Token(WHEN(), Nil) :: branches) =>
       CaseWhen(branches.map(nodeToExpr))
     case Token("TOK_FUNCTION", Token(CASE(), Nil) :: branches) =>
-      val transformed = branches.drop(1).sliding(2, 2).map {
-        case Seq(condVal, value) =>
-          // FIXME (SPARK-2155): the key will get evaluated for multiple times in CaseWhen's eval().
-          // Hence effectful / non-deterministic key expressions are *not* supported at the moment.
-          // We should consider adding new Expressions to get around this.
-          Seq(EqualTo(nodeToExpr(branches(0)), nodeToExpr(condVal)),
-              nodeToExpr(value))
-        case Seq(elseVal) => Seq(nodeToExpr(elseVal))
-      }.toSeq.reduce(_ ++ _)
-      CaseWhen(transformed)
+      val keyExpr = nodeToExpr(branches.head)
+      CaseKeyWhen(keyExpr, branches.drop(1).map(nodeToExpr))
 
     /* Complex datatype manipulation */
     case Token("[", child :: ordinal :: Nil) =>
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 616352d223c5b..c605f1017547a 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
@@ -751,4 +751,11 @@ class SQLQuerySuite extends QueryTest {
         (6, "c", 0, 6)
       ).map(i => Row(i._1, i._2, i._3, i._4)))
   }
+
+  test("test case key when") {
+    (1 to 5).map(i => (i, i.toString)).toDF("k", "v").registerTempTable("t")
+    checkAnswer(
+      sql("SELECT CASE k WHEN 2 THEN 22 WHEN 4 THEN 44 ELSE 0 END, v FROM t"),
+      Row(0, "1") :: Row(22, "2") :: Row(0, "3") :: Row(44, "4") :: Row(0, "5") :: Nil)
+  }
 }

From 88063c62689135da56ee6678b2e826b88c382732 Mon Sep 17 00:00:00 2001
From: tedyu <yuzhihong@gmail.com>
Date: Thu, 7 May 2015 16:53:59 -0700
Subject: [PATCH 50/75] [SPARK-7450] Use UNSAFE.getLong() to speed up
 BitSetMethods#anySet()

Author: tedyu <yuzhihong@gmail.com>

Closes #5897 from tedyu/master and squashes the following commits:

473bf9d [tedyu] Address Josh's review comments
1719c5b [tedyu] Correct upper bound in for loop
b51dcaf [tedyu] Add unit test in BitSetSuite for BitSet#anySet()
83f9f87 [tedyu] Merge branch 'master' of github.com:apache/spark
817e3f9 [tedyu] Replace constant 8 with SIZE_OF_LONG
75a467b [tedyu] Correct offset for UNSAFE.getLong()
855374b [tedyu] Remove second loop since bitSetWidthInBytes is WORD aligned
093b7a4 [tedyu] Use UNSAFE.getLong() to speed up BitSetMethods#anySet()
63ee050 [tedyu] Use UNSAFE.getLong() to speed up BitSetMethods#anySet()
4ca0ef6 [tedyu] Use UNSAFE.getLong() to speed up BitSetMethods#anySet()
3e9b6919 [tedyu] Use UNSAFE.getLong() to speed up BitSetMethods#anySet()
---
 .../main/java/org/apache/spark/unsafe/bitset/BitSet.java  | 8 ++++++++
 .../org/apache/spark/unsafe/bitset/BitSetMethods.java     | 7 ++++---
 .../java/org/apache/spark/unsafe/bitset/BitSetSuite.java  | 7 +++++++
 3 files changed, 19 insertions(+), 3 deletions(-)

diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/bitset/BitSet.java b/unsafe/src/main/java/org/apache/spark/unsafe/bitset/BitSet.java
index f72e07fce92fd..28e23da108ebe 100644
--- a/unsafe/src/main/java/org/apache/spark/unsafe/bitset/BitSet.java
+++ b/unsafe/src/main/java/org/apache/spark/unsafe/bitset/BitSet.java
@@ -102,4 +102,12 @@ public boolean isSet(int index) {
   public int nextSetBit(int fromIndex) {
     return BitSetMethods.nextSetBit(baseObject, baseOffset, fromIndex, numWords);
   }
+
+  /**
+   * Returns {@code true} if any bit is set.
+   */
+  public boolean anySet() {
+    return BitSetMethods.anySet(baseObject, baseOffset, numWords);
+  }
+
 }
diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/bitset/BitSetMethods.java b/unsafe/src/main/java/org/apache/spark/unsafe/bitset/BitSetMethods.java
index f30626d8f4317..0987191c1c636 100644
--- a/unsafe/src/main/java/org/apache/spark/unsafe/bitset/BitSetMethods.java
+++ b/unsafe/src/main/java/org/apache/spark/unsafe/bitset/BitSetMethods.java
@@ -70,9 +70,10 @@ public static boolean isSet(Object baseObject, long baseOffset, int index) {
   /**
    * Returns {@code true} if any bit is set.
    */
-  public static boolean anySet(Object baseObject, long baseOffset, long bitSetWidthInBytes) {
-    for (int i = 0; i <= bitSetWidthInBytes; i++) {
-      if (PlatformDependent.UNSAFE.getByte(baseObject, baseOffset + i) != 0) {
+  public static boolean anySet(Object baseObject, long baseOffset, long bitSetWidthInWords) {
+    long addr = baseOffset;
+    for (int i = 0; i < bitSetWidthInWords; i++, addr += WORD_SIZE) {
+      if (PlatformDependent.UNSAFE.getLong(baseObject, addr) != 0) {
         return true;
       }
     }
diff --git a/unsafe/src/test/java/org/apache/spark/unsafe/bitset/BitSetSuite.java b/unsafe/src/test/java/org/apache/spark/unsafe/bitset/BitSetSuite.java
index e3a824e29b768..18393db9f382f 100644
--- a/unsafe/src/test/java/org/apache/spark/unsafe/bitset/BitSetSuite.java
+++ b/unsafe/src/test/java/org/apache/spark/unsafe/bitset/BitSetSuite.java
@@ -39,6 +39,8 @@ public void basicOps() {
     for (int i = 0; i < bs.capacity(); i++) {
       Assert.assertFalse(bs.isSet(i));
     }
+    // another form of asserting that the bit set is empty
+    Assert.assertFalse(bs.anySet());
 
     // Set every bit and check it.
     for (int i = 0; i < bs.capacity(); i++) {
@@ -52,6 +54,11 @@ public void basicOps() {
       bs.unset(i);
       Assert.assertFalse(bs.isSet(i));
     }
+
+    // Make sure anySet() can detect any set bit
+    bs = createBitSet(256);
+    bs.set(64);
+    Assert.assertTrue(bs.anySet());
   }
 
   @Test

From 22ab70e06ede65ca865073fe36c859042a920aa3 Mon Sep 17 00:00:00 2001
From: zsxwing <zsxwing@gmail.com>
Date: Thu, 7 May 2015 17:34:44 -0700
Subject: [PATCH 51/75] [SPARK-7305] [STREAMING] [WEBUI] Make BatchPage show
 friendly information when jobs are dropped by SparkListener

If jobs are dropped by SparkListener, at least we can show the job ids in BatchPage. Screenshot:

![b1](https://cloud.githubusercontent.com/assets/1000778/7434968/f19aa784-eff3-11e4-8f86-36a073873574.png)

Author: zsxwing <zsxwing@gmail.com>

Closes #5840 from zsxwing/SPARK-7305 and squashes the following commits:

aca0ba6 [zsxwing] Fix the code style
718765e [zsxwing] Make generateNormalJobRow private
8073b03 [zsxwing] Merge branch 'master' into SPARK-7305
83dec11 [zsxwing] Make BatchPage show friendly information when jobs are dropped by SparkListener
---
 .../apache/spark/streaming/ui/BatchPage.scala | 136 ++++++++++++++----
 1 file changed, 106 insertions(+), 30 deletions(-)

diff --git a/streaming/src/main/scala/org/apache/spark/streaming/ui/BatchPage.scala b/streaming/src/main/scala/org/apache/spark/streaming/ui/BatchPage.scala
index 3f1cab69068dc..831f60e870f74 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/ui/BatchPage.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/ui/BatchPage.scala
@@ -19,7 +19,7 @@ package org.apache.spark.streaming.ui
 
 import javax.servlet.http.HttpServletRequest
 
-import scala.xml.{NodeSeq, Node}
+import scala.xml.{NodeSeq, Node, Text}
 
 import org.apache.commons.lang3.StringEscapeUtils
 
@@ -28,6 +28,7 @@ import org.apache.spark.ui.{UIUtils => SparkUIUtils, WebUIPage}
 import org.apache.spark.streaming.ui.StreamingJobProgressListener.{SparkJobId, OutputOpId}
 import org.apache.spark.ui.jobs.UIData.JobUIData
 
+private case class SparkJobIdWithUIData(sparkJobId: SparkJobId, jobUIData: Option[JobUIData])
 
 private[ui] class BatchPage(parent: StreamingTab) extends WebUIPage("batch") {
   private val streamingListener = parent.listener
@@ -44,25 +45,33 @@ private[ui] class BatchPage(parent: StreamingTab) extends WebUIPage("batch") {
       <th>Error</th>
   }
 
+  private def generateJobRow(
+      outputOpId: OutputOpId,
+      outputOpDescription: Seq[Node],
+      formattedOutputOpDuration: String,
+      numSparkJobRowsInOutputOp: Int,
+      isFirstRow: Boolean,
+      sparkJob: SparkJobIdWithUIData): Seq[Node] = {
+    if (sparkJob.jobUIData.isDefined) {
+      generateNormalJobRow(outputOpId, outputOpDescription, formattedOutputOpDuration,
+        numSparkJobRowsInOutputOp, isFirstRow, sparkJob.jobUIData.get)
+    } else {
+      generateDroppedJobRow(outputOpId, outputOpDescription, formattedOutputOpDuration,
+        numSparkJobRowsInOutputOp, isFirstRow, sparkJob.sparkJobId)
+    }
+  }
+
   /**
    * Generate a row for a Spark Job. Because duplicated output op infos needs to be collapsed into
    * one cell, we use "rowspan" for the first row of a output op.
    */
-  def generateJobRow(
+  private def generateNormalJobRow(
       outputOpId: OutputOpId,
+      outputOpDescription: Seq[Node],
       formattedOutputOpDuration: String,
       numSparkJobRowsInOutputOp: Int,
       isFirstRow: Boolean,
       sparkJob: JobUIData): Seq[Node] = {
-    val lastStageInfo = Option(sparkJob.stageIds)
-      .filter(_.nonEmpty)
-      .flatMap { ids => sparkListener.stageIdToInfo.get(ids.max) }
-    val lastStageData = lastStageInfo.flatMap { s =>
-      sparkListener.stageIdToData.get((s.stageId, s.attemptId))
-    }
-
-    val lastStageName = lastStageInfo.map(_.name).getOrElse("(Unknown Stage Name)")
-    val lastStageDescription = lastStageData.flatMap(_.description).getOrElse("")
     val duration: Option[Long] = {
       sparkJob.submissionTime.map { start =>
         val end = sparkJob.completionTime.getOrElse(System.currentTimeMillis())
@@ -83,9 +92,7 @@ private[ui] class BatchPage(parent: StreamingTab) extends WebUIPage("batch") {
       if (isFirstRow) {
         <td class="output-op-id-cell" rowspan={numSparkJobRowsInOutputOp.toString}>{outputOpId.toString}</td>
         <td rowspan={numSparkJobRowsInOutputOp.toString}>
-          <span class="description-input" title={lastStageDescription}>
-            {lastStageDescription}
-          </span>{lastStageName}
+          {outputOpDescription}
         </td>
         <td rowspan={numSparkJobRowsInOutputOp.toString}>{formattedOutputOpDuration}</td>
       } else {
@@ -122,27 +129,96 @@ private[ui] class BatchPage(parent: StreamingTab) extends WebUIPage("batch") {
     </tr>
   }
 
-  private def generateOutputOpIdRow(
-      outputOpId: OutputOpId, sparkJobs: Seq[JobUIData]): Seq[Node] = {
-    val sparkjobDurations = sparkJobs.map(sparkJob => {
-      sparkJob.submissionTime.map { start =>
-        val end = sparkJob.completionTime.getOrElse(System.currentTimeMillis())
-        end - start
+  /**
+   * If a job is dropped by sparkListener due to exceeding the limitation, we only show the job id
+   * with "-" cells.
+   */
+  private def generateDroppedJobRow(
+      outputOpId: OutputOpId,
+      outputOpDescription: Seq[Node],
+      formattedOutputOpDuration: String,
+      numSparkJobRowsInOutputOp: Int,
+      isFirstRow: Boolean,
+      jobId: Int): Seq[Node] = {
+    // In the first row, output op id and its information needs to be shown. In other rows, these
+    // cells will be taken up due to "rowspan".
+    // scalastyle:off
+    val prefixCells =
+      if (isFirstRow) {
+        <td class="output-op-id-cell" rowspan={numSparkJobRowsInOutputOp.toString}>{outputOpId.toString}</td>
+          <td rowspan={numSparkJobRowsInOutputOp.toString}>{outputOpDescription}</td>
+          <td rowspan={numSparkJobRowsInOutputOp.toString}>{formattedOutputOpDuration}</td>
+      } else {
+        Nil
       }
-    })
+    // scalastyle:on
+
+    <tr>
+      {prefixCells}
+      <td sorttable_customkey={jobId.toString}>
+        {jobId.toString}
+      </td>
+      <!-- Duration -->
+      <td>-</td>
+      <!-- Stages: Succeeded/Total -->
+      <td>-</td>
+      <!-- Tasks (for all stages): Succeeded/Total -->
+      <td>-</td>
+      <!-- Error -->
+      <td>-</td>
+    </tr>
+  }
+
+  private def generateOutputOpIdRow(
+      outputOpId: OutputOpId, sparkJobs: Seq[SparkJobIdWithUIData]): Seq[Node] = {
+    // We don't count the durations of dropped jobs
+    val sparkJobDurations = sparkJobs.filter(_.jobUIData.nonEmpty).map(_.jobUIData.get).
+      map(sparkJob => {
+        sparkJob.submissionTime.map { start =>
+          val end = sparkJob.completionTime.getOrElse(System.currentTimeMillis())
+          end - start
+        }
+      })
     val formattedOutputOpDuration =
-      if (sparkjobDurations.exists(_ == None)) {
-        // If any job does not finish, set "formattedOutputOpDuration" to "-"
+      if (sparkJobDurations.isEmpty || sparkJobDurations.exists(_ == None)) {
+        // If no job or any job does not finish, set "formattedOutputOpDuration" to "-"
         "-"
       } else {
-        SparkUIUtils.formatDuration(sparkjobDurations.flatMap(x => x).sum)
+        SparkUIUtils.formatDuration(sparkJobDurations.flatMap(x => x).sum)
       }
-    generateJobRow(outputOpId, formattedOutputOpDuration, sparkJobs.size, true, sparkJobs.head) ++
+
+    val description = generateOutputOpDescription(sparkJobs)
+
+    generateJobRow(
+      outputOpId, description, formattedOutputOpDuration, sparkJobs.size, true, sparkJobs.head) ++
       sparkJobs.tail.map { sparkJob =>
-        generateJobRow(outputOpId, formattedOutputOpDuration, sparkJobs.size, false, sparkJob)
+        generateJobRow(
+          outputOpId, description, formattedOutputOpDuration, sparkJobs.size, false, sparkJob)
       }.flatMap(x => x)
   }
 
+  private def generateOutputOpDescription(sparkJobs: Seq[SparkJobIdWithUIData]): Seq[Node] = {
+    val lastStageInfo =
+      sparkJobs.flatMap(_.jobUIData).headOption. // Get the first JobUIData
+        flatMap { sparkJob => // For the first job, get the latest Stage info
+          if (sparkJob.stageIds.isEmpty) {
+            None
+          } else {
+            sparkListener.stageIdToInfo.get(sparkJob.stageIds.max)
+          }
+        }
+    val lastStageData = lastStageInfo.flatMap { s =>
+      sparkListener.stageIdToData.get((s.stageId, s.attemptId))
+    }
+
+    val lastStageName = lastStageInfo.map(_.name).getOrElse("(Unknown Stage Name)")
+    val lastStageDescription = lastStageData.flatMap(_.description).getOrElse("")
+
+    <span class="description-input" title={lastStageDescription}>
+      {lastStageDescription}
+    </span> ++ Text(lastStageName)
+  }
+
   private def failureReasonCell(failureReason: String): Seq[Node] = {
     val isMultiline = failureReason.indexOf('\n') >= 0
     // Display the first line by default
@@ -187,10 +263,10 @@ private[ui] class BatchPage(parent: StreamingTab) extends WebUIPage("batch") {
         (outputOpId, outputOpIdAndSparkJobIds.map(_.sparkJobId).sorted)
       }
     sparkListener.synchronized {
-      val outputOpIdWithJobs: Seq[(OutputOpId, Seq[JobUIData])] =
+      val outputOpIdWithJobs: Seq[(OutputOpId, Seq[SparkJobIdWithUIData])] =
         outputOpIdToSparkJobIds.map { case (outputOpId, sparkJobIds) =>
-          // Filter out spark Job ids that don't exist in sparkListener
-          (outputOpId, sparkJobIds.flatMap(getJobData))
+          (outputOpId,
+            sparkJobIds.map(sparkJobId => SparkJobIdWithUIData(sparkJobId, getJobData(sparkJobId))))
         }
 
       <table id="batch-job-table" class="table table-bordered table-striped table-condensed">
@@ -200,7 +276,7 @@ private[ui] class BatchPage(parent: StreamingTab) extends WebUIPage("batch") {
         <tbody>
           {
             outputOpIdWithJobs.map {
-              case (outputOpId, jobs) => generateOutputOpIdRow(outputOpId, jobs)
+              case (outputOpId, sparkJobIds) => generateOutputOpIdRow(outputOpId, sparkJobIds)
             }
           }
         </tbody>

From cd1d4110cfffb413ab585cf1cc8f1264243cb393 Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Thu, 7 May 2015 19:36:24 -0700
Subject: [PATCH 52/75] [SPARK-6908] [SQL] Use isolated Hive client

This PR switches Spark SQL's Hive support to use the isolated hive client interface introduced by #5851, instead of directly interacting with the client.  By using this isolated client we can now allow users to dynamically configure the version of Hive that they are connecting to by setting `spark.sql.hive.metastore.version` without the need recompile.  This also greatly reduces the surface area for our interaction with the hive libraries, hopefully making it easier to support other versions in the future.

Jars for the desired hive version can be configured using `spark.sql.hive.metastore.jars`, which accepts the following options:
 - a colon-separated list of jar files or directories for hive and hadoop.
 - `builtin` - attempt to discover the jars that were used to load Spark SQL and use those. This
            option is only valid when using the execution version of Hive.
 - `maven` - download the correct version of hive on demand from maven.

By default, `builtin` is used for Hive 13.

This PR also removes the test step for building against Hive 12, as this will no longer be required to talk to Hive 12 metastores.  However, the full removal of the Shim is deferred until a later PR.

Remaining TODOs:
 - Remove the Hive Shims and inline code for Hive 13.
 - Several HiveCompatibility tests are not yet passing.
  - `nullformatCTAS` - As detailed below, we now are handling CTAS parsing ourselves instead of hacking into the Hive semantic analyzer.  However, we currently only handle the common cases and not things like CTAS where the null format is specified.
  - `combine1` now leaks state about compression somehow, breaking all subsequent tests.  As such we currently add it to the blacklist
  - `part_inherit_tbl_props` and `part_inherit_tbl_props_with_star` do not work anymore.  We are correctly propagating the information
  - "load_dyn_part14.*" - These tests pass when run on their own, but fail when run with all other tests.  It seems our `RESET` mechanism may not be as robust as it used to be?

Other required changes:
 -  `CreateTableAsSelect` no longer carries parts of the HiveQL AST with it through the query execution pipeline.  Instead, we parse CTAS during the HiveQL conversion and construct a `HiveTable`.  The full parsing here is not yet complete as detailed above in the remaining TODOs.  Since the operator is Hive specific, it is moved to the hive package.
 - `Command` is simplified to be a trait that simply acts as a marker for a LogicalPlan that should be eagerly evaluated.

Author: Michael Armbrust <michael@databricks.com>

Closes #5876 from marmbrus/useIsolatedClient and squashes the following commits:

258d000 [Michael Armbrust] really really correct path handling
e56fd4a [Michael Armbrust] getAbsolutePath
5a259f5 [Michael Armbrust] fix typos
81bb366 [Michael Armbrust] comments from vanzin
5f3945e [Michael Armbrust] Merge remote-tracking branch 'origin/master' into useIsolatedClient
4b5cd41 [Michael Armbrust] yin's comments
f5de7de [Michael Armbrust] cleanup
11e9c72 [Michael Armbrust] better coverage in versions suite
7e8f010 [Michael Armbrust] better error messages and jar handling
e7b3941 [Michael Armbrust] more permisive checking for function registration
da91ba7 [Michael Armbrust] Merge remote-tracking branch 'origin/master' into useIsolatedClient
5fe5894 [Michael Armbrust] fix serialization suite
81711c4 [Michael Armbrust] Initial support for running without maven
1d8ae44 [Michael Armbrust] fix final tests?
1c50813 [Michael Armbrust] more comments
a3bee70 [Michael Armbrust] Merge remote-tracking branch 'origin/master' into useIsolatedClient
a6f5df1 [Michael Armbrust] style
ab07f7e [Michael Armbrust] WIP
4d8bf02 [Michael Armbrust] Remove hive 12 compilation
8843a25 [Michael Armbrust] [SPARK-6908] [SQL] Use isolated Hive client
---
 dev/run-tests                                 |  23 -
 project/MimaExcludes.scala                    |   2 +
 project/SparkBuild.scala                      |   9 +-
 .../plans/logical/basicOperators.scala        |  16 +-
 .../sql/catalyst/plans/logical/commands.scala |   8 +-
 .../spark/sql/catalyst/SqlParserSuite.scala   |   6 +-
 .../org/apache/spark/sql/DataFrame.scala      |   1 -
 .../org/apache/spark/sql/SQLContext.scala     |  11 +-
 .../apache/spark/sql/execution/commands.scala |   4 +-
 .../org/apache/spark/sql/sources/ddl.scala    |  16 +-
 .../hive/thriftserver/SparkSQLCLIDriver.scala |  26 +-
 .../sql/hive/thriftserver/SparkSQLEnv.scala   |   9 +-
 .../execution/HiveCompatibilitySuite.scala    |  12 +-
 .../apache/spark/sql/hive/HiveContext.scala   | 283 ++++++------
 .../spark/sql/hive/HiveMetastoreCatalog.scala | 415 ++++++------------
 .../org/apache/spark/sql/hive/HiveQl.scala    | 126 +++++-
 .../apache/spark/sql/hive/TableReader.scala   |  11 +-
 .../sql/hive/client/ClientInterface.scala     |  41 +-
 .../spark/sql/hive/client/ClientWrapper.scala |  99 +++--
 .../hive/client/IsolatedClientLoader.scala    |  23 +-
 .../sql/hive/client/ReflectionMagic.scala     |   8 +
 .../hive/execution/CreateTableAsSelect.scala  |  33 +-
 .../hive/execution/InsertIntoHiveTable.scala  |  33 +-
 .../spark/sql/hive/execution/commands.scala   |  13 +
 .../apache/spark/sql/hive/test/TestHive.scala |  72 ++-
 sql/hive/src/test/resources/log4j.properties  |   2 +-
 .../spark/sql/hive/ErrorPositionSuite.scala   |  22 +-
 .../sql/hive/MetastoreDataSourcesSuite.scala  |  26 +-
 .../spark/sql/hive/SerializationSuite.scala   |   6 +-
 .../spark/sql/hive/client/VersionsSuite.scala |  78 +++-
 .../hive/execution/HiveComparisonTest.scala   |   2 +
 .../sql/hive/execution/HiveQuerySuite.scala   |   2 +-
 .../sql/hive/execution/PruningSuite.scala     |  15 +-
 33 files changed, 782 insertions(+), 671 deletions(-)

diff --git a/dev/run-tests b/dev/run-tests
index 05c63bce4d40d..ef587a1a5988c 100755
--- a/dev/run-tests
+++ b/dev/run-tests
@@ -142,29 +142,6 @@ CURRENT_BLOCK=$BLOCK_BUILD
 
 {
   HIVE_BUILD_ARGS="$SBT_MAVEN_PROFILES_ARGS -Phive -Phive-thriftserver"
-  HIVE_12_BUILD_ARGS="$HIVE_BUILD_ARGS -Phive-0.12.0"
-
-  # First build with Hive 0.12.0 to ensure patches do not break the Hive 0.12.0 build
-  echo "[info] Compile with Hive 0.12.0"
-  [ -d "lib_managed" ] && rm -rf lib_managed
-  echo "[info] Building Spark with these arguments: $HIVE_12_BUILD_ARGS"
-
-  if [ "${AMPLAB_JENKINS_BUILD_TOOL}" == "maven" ]; then
-    build/mvn $HIVE_12_BUILD_ARGS clean package -DskipTests
-  else
-    # 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" \
-      | build/sbt $HIVE_12_BUILD_ARGS clean hive/compile hive-thriftserver/compile \
-      | grep -v -e "info.*Resolving" -e "warn.*Merging" -e "info.*Including"
-  fi
-
-  # Then build with default Hive version (0.13.1) because tests are based on this version
   echo "[info] Compile with Hive 0.13.1"
   [ -d "lib_managed" ] && rm -rf lib_managed
   echo "[info] Building Spark with these arguments: $HIVE_BUILD_ARGS"
diff --git a/project/MimaExcludes.scala b/project/MimaExcludes.scala
index bf343d4b7e40b..cfe387faec14b 100644
--- a/project/MimaExcludes.scala
+++ b/project/MimaExcludes.scala
@@ -89,6 +89,8 @@ object MimaExcludes {
             ProblemFilters.exclude[MissingMethodProblem](
               "org.apache.spark.mllib.linalg.Vector.numActives")
           ) ++ Seq(
+            // Execution should never be included as its always internal.
+            MimaBuild.excludeSparkPackage("sql.execution"),
             // This `protected[sql]` method was removed in 1.3.1
             ProblemFilters.exclude[MissingMethodProblem](
               "org.apache.spark.sql.SQLContext.checkAnalysis"),
diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala
index b4431c7ee05b6..026855f8f6a5a 100644
--- a/project/SparkBuild.scala
+++ b/project/SparkBuild.scala
@@ -193,6 +193,7 @@ object SparkBuild extends PomBuild {
    * Usage: `build/sbt sparkShell`
    */
   val sparkShell = taskKey[Unit]("start a spark-shell.")
+  val sparkSql = taskKey[Unit]("starts the spark sql CLI.")
 
   enable(Seq(
     connectInput in run := true,
@@ -203,6 +204,12 @@ object SparkBuild extends PomBuild {
 
     sparkShell := {
       (runMain in Compile).toTask(" org.apache.spark.repl.Main -usejavacp").value
+    },
+
+    javaOptions in Compile += "-Dspark.master=local",
+
+    sparkSql := {
+      (runMain in Compile).toTask(" org.apache.spark.sql.hive.thriftserver.SparkSQLCLIDriver").value
     }
   ))(assembly)
 
@@ -497,7 +504,7 @@ object TestSettings {
     // Setting SPARK_DIST_CLASSPATH is a simple way to make sure any child processes
     // launched by the tests have access to the correct test-time classpath.
     envVars in Test ++= Map(
-      "SPARK_DIST_CLASSPATH" -> 
+      "SPARK_DIST_CLASSPATH" ->
         (fullClasspath in Test).value.files.map(_.getAbsolutePath).mkString(":").stripSuffix(":"),
       "JAVA_HOME" -> sys.env.get("JAVA_HOME").getOrElse(sys.props("java.home"))),
     javaOptions in Test += "-Dspark.test.home=" + sparkHome,
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 ba0abb2df596c..0f349f9d11415 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
@@ -149,16 +149,6 @@ case class InsertIntoTable(
   }
 }
 
-case class CreateTableAsSelect[T](
-    databaseName: Option[String],
-    tableName: String,
-    child: LogicalPlan,
-    allowExisting: Boolean,
-    desc: Option[T] = None) extends UnaryNode {
-  override def output: Seq[Attribute] = Seq.empty[Attribute]
-  override lazy val resolved: Boolean = databaseName != None && childrenResolved
-}
-
 /**
  * A container for holding named common table expressions (CTEs) and a query plan.
  * This operator will be removed during analysis and the relations will be substituted into child.
@@ -184,10 +174,10 @@ case class WriteToFile(
 }
 
 /**
- * @param order  The ordering expressions 
- * @param global True means global sorting apply for entire data set, 
+ * @param order  The ordering expressions
+ * @param global True means global sorting apply for entire data set,
  *               False means sorting only apply within the partition.
- * @param child  Child logical plan              
+ * @param child  Child logical plan
  */
 case class Sort(
     order: Seq[SortOrder],
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 45905f8ef98c5..246f4d7e34d3d 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
@@ -21,9 +21,7 @@ import org.apache.spark.sql.catalyst.expressions.Attribute
 
 /**
  * A logical node that represents a non-query command to be executed by the system.  For example,
- * commands can be used by parsers to represent DDL operations.
+ * commands can be used by parsers to represent DDL operations.  Commands, unlike queries, are
+ * eagerly executed.
  */
-abstract class Command extends LeafNode {
-  self: Product =>
-  def output: Seq[Attribute] = Seq.empty
-}
+trait Command
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/SqlParserSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/SqlParserSuite.scala
index a652c70560990..890ea2a84b82e 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/SqlParserSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/SqlParserSuite.scala
@@ -17,11 +17,15 @@
 
 package org.apache.spark.sql.catalyst
 
+import org.apache.spark.sql.catalyst.expressions.Attribute
 import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
 import org.apache.spark.sql.catalyst.plans.logical.Command
 import org.scalatest.FunSuite
 
-private[sql] case class TestCommand(cmd: String) extends Command
+private[sql] case class TestCommand(cmd: String) extends LogicalPlan with Command {
+  override def output: Seq[Attribute] = Seq.empty
+  override def children: Seq[LogicalPlan] = Seq.empty
+}
 
 private[sql] class SuperLongKeywordTestParser extends AbstractSparkSQLParser {
   protected val EXECUTE   = Keyword("THISISASUPERLONGKEYWORDTEST")
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
index 79fbf50300d4d..7947042c14299 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
@@ -143,7 +143,6 @@ class DataFrame private[sql](
     // happen right away to let these side effects take place eagerly.
     case _: Command |
          _: InsertIntoTable |
-         _: CreateTableAsSelect[_] |
          _: CreateTableUsingAsSelect |
          _: WriteToFile =>
       LogicalRDD(queryExecution.analyzed.output, queryExecution.toRdd)(sqlContext)
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 0563430a6fdc7..0ac0936f0f592 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
@@ -70,7 +70,7 @@ import org.apache.spark.{Partition, SparkContext}
  *   spark-sql> SELECT * FROM src LIMIT 1;
  *
  *-- Exception will be thrown and switch to dialect
- *-- "sql" (for SQLContext) or 
+ *-- "sql" (for SQLContext) or
  *-- "hiveql" (for HiveContext)
  * }}}
  */
@@ -107,7 +107,7 @@ class SQLContext(@transient val sparkContext: SparkContext)
   /**
    * @return Spark SQL configuration
    */
-  protected[sql] def conf = tlSession.get().conf
+  protected[sql] def conf = currentSession().conf
 
   /**
    * Set Spark SQL configuration properties.
@@ -1197,13 +1197,17 @@ class SQLContext(@transient val sparkContext: SparkContext)
          |${stringOrError(executedPlan)}
       """.stripMargin.trim
 
-    override def toString: String =
+    override def toString: String = {
+      def output =
+        analyzed.output.map(o => s"${o.name}: ${o.dataType.simpleString}").mkString(", ")
+
       // TODO previously will output RDD details by run (${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(output)}
          |${stringOrError(analyzed)}
          |== Optimized Logical Plan ==
          |${stringOrError(optimizedPlan)}
@@ -1212,6 +1216,7 @@ class SQLContext(@transient val sparkContext: SparkContext)
          |Code Generation: ${stringOrError(executedPlan.codegenEnabled)}
          |== RDD ==
       """.stripMargin.trim
+    }
   }
 
   /**
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 65687db4e6235..388a8184e4cfe 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
@@ -32,9 +32,11 @@ import org.apache.spark.sql.{DataFrame, SQLConf, SQLContext}
  * A logical command that is executed for its side-effects.  `RunnableCommand`s are
  * wrapped in `ExecutedCommand` during execution.
  */
-trait RunnableCommand extends logical.Command {
+private[sql] trait RunnableCommand extends LogicalPlan with logical.Command {
   self: Product =>
 
+  override def output: Seq[Attribute] = Seq.empty
+  override def children: Seq[LogicalPlan] = Seq.empty
   def run(sqlContext: SQLContext): Seq[Row]
 }
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/ddl.scala b/sql/core/src/main/scala/org/apache/spark/sql/sources/ddl.scala
index 1abf3aa51cb25..06c64f2bdd59e 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/sources/ddl.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/sources/ddl.scala
@@ -269,8 +269,10 @@ private[sql] case class ResolvedDataSource(provider: Class[_], relation: BaseRel
  */
 private[sql] case class DescribeCommand(
     table: LogicalPlan,
-    isExtended: Boolean) extends Command {
-  override val output = Seq(
+    isExtended: Boolean) extends LogicalPlan with Command {
+
+  override def children: Seq[LogicalPlan] = Seq.empty
+  override val output: Seq[Attribute] = Seq(
     // Column names are based on Hive.
     AttributeReference("col_name", StringType, nullable = false,
       new MetadataBuilder().putString("comment", "name of the column").build())(),
@@ -292,7 +294,11 @@ private[sql] case class CreateTableUsing(
     temporary: Boolean,
     options: Map[String, String],
     allowExisting: Boolean,
-    managedIfNoPath: Boolean) extends Command
+    managedIfNoPath: Boolean) extends LogicalPlan with Command {
+
+  override def output: Seq[Attribute] = Seq.empty
+  override def children: Seq[LogicalPlan] = Seq.empty
+}
 
 /**
  * A node used to support CTAS statements and saveAsTable for the data source API.
@@ -318,7 +324,7 @@ private[sql] case class CreateTempTableUsing(
     provider: String,
     options: Map[String, String]) extends RunnableCommand {
 
-  def run(sqlContext: SQLContext): Seq[Row] = {
+  override def run(sqlContext: SQLContext): Seq[Row] = {
     val resolved = ResolvedDataSource(sqlContext, userSpecifiedSchema, provider, options)
     sqlContext.registerDataFrameAsTable(
       DataFrame(sqlContext, LogicalRelation(resolved.relation)), tableName)
@@ -333,7 +339,7 @@ private[sql] case class CreateTempTableUsingAsSelect(
     options: Map[String, String],
     query: LogicalPlan) extends RunnableCommand {
 
-  def run(sqlContext: SQLContext): Seq[Row] = {
+  override def run(sqlContext: SQLContext): Seq[Row] = {
     val df = DataFrame(sqlContext, query)
     val resolved = ResolvedDataSource(sqlContext, provider, mode, options, df)
     sqlContext.registerDataFrameAsTable(
diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala
index b7b6925aa87f7..deb1008c468bf 100644
--- a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala
+++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala
@@ -37,7 +37,7 @@ import org.apache.hadoop.hive.ql.session.SessionState
 import org.apache.thrift.transport.TSocket
 
 import org.apache.spark.Logging
-import org.apache.spark.sql.hive.HiveShim
+import org.apache.spark.sql.hive.{HiveContext, HiveShim}
 import org.apache.spark.util.Utils
 
 private[hive] object SparkSQLCLIDriver {
@@ -74,7 +74,12 @@ private[hive] object SparkSQLCLIDriver {
       System.exit(1)
     }
 
-    val sessionState = new CliSessionState(new HiveConf(classOf[SessionState]))
+    val cliConf = new HiveConf(classOf[SessionState])
+    // Override the location of the metastore since this is only used for local execution.
+    HiveContext.newTemporaryConfiguration().foreach {
+      case (key, value) => cliConf.set(key, value)
+    }
+    val sessionState = new CliSessionState(cliConf)
 
     sessionState.in = System.in
     try {
@@ -91,10 +96,14 @@ private[hive] object SparkSQLCLIDriver {
 
     // Set all properties specified via command line.
     val conf: HiveConf = sessionState.getConf
-    sessionState.cmdProperties.entrySet().foreach { item: java.util.Map.Entry[Object, Object] =>
-      conf.set(item.getKey.asInstanceOf[String], item.getValue.asInstanceOf[String])
-      sessionState.getOverriddenConfigurations.put(
-        item.getKey.asInstanceOf[String], item.getValue.asInstanceOf[String])
+    sessionState.cmdProperties.entrySet().foreach { item =>
+      val key = item.getKey.asInstanceOf[String]
+      val value = item.getValue.asInstanceOf[String]
+      // We do not propagate metastore options to the execution copy of hive.
+      if (key != "javax.jdo.option.ConnectionURL") {
+        conf.set(key, value)
+        sessionState.getOverriddenConfigurations.put(key, value)
+      }
     }
 
     SessionState.start(sessionState)
@@ -138,8 +147,9 @@ private[hive] object SparkSQLCLIDriver {
       case e: UnsupportedEncodingException => System.exit(3)
     }
 
-    // use the specified database if specified
-    cli.processSelectDatabase(sessionState);
+    if (sessionState.database != null) {
+      SparkSQLEnv.hiveContext.runSqlHive(s"USE ${sessionState.database}")
+    }
 
     // Execute -i init files (always in silent mode)
     cli.processInitFiles(sessionState)
diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLEnv.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLEnv.scala
index 97b46a01ba5b4..7c0c505e2d61e 100644
--- a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLEnv.scala
+++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLEnv.scala
@@ -17,6 +17,8 @@
 
 package org.apache.spark.sql.hive.thriftserver
 
+import java.io.PrintStream
+
 import scala.collection.JavaConversions._
 
 import org.apache.spark.scheduler.StatsReportListener
@@ -39,7 +41,6 @@ private[hive] object SparkSQLEnv extends Logging {
 
       sparkConf
         .setAppName(s"SparkSQL::${Utils.localHostName()}")
-        .set("spark.sql.hive.version", HiveShim.version)
         .set(
           "spark.serializer",
           maybeSerializer.getOrElse("org.apache.spark.serializer.KryoSerializer"))
@@ -51,6 +52,12 @@ private[hive] object SparkSQLEnv extends Logging {
       sparkContext.addSparkListener(new StatsReportListener())
       hiveContext = new HiveContext(sparkContext)
 
+      hiveContext.metadataHive.setOut(new PrintStream(System.out, true, "UTF-8"))
+      hiveContext.metadataHive.setInfo(new PrintStream(System.err, true, "UTF-8"))
+      hiveContext.metadataHive.setError(new PrintStream(System.err, true, "UTF-8"))
+
+      hiveContext.setConf("spark.sql.hive.version", HiveShim.version)
+
       if (log.isDebugEnabled) {
         hiveContext.hiveconf.getAllProperties.toSeq.sorted.foreach { case (k, v) =>
           logDebug(s"HiveConf var: $k=$v")
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 5e411c2fdba9d..b6245a57074c8 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
@@ -240,7 +240,17 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
 
     // It has a bug and it has been fixed by
     // https://issues.apache.org/jira/browse/HIVE-7673 (in Hive 0.14 and trunk).
-    "input46"
+    "input46",
+
+    // These tests were broken by the hive client isolation PR.
+    "part_inherit_tbl_props",
+    "part_inherit_tbl_props_with_star",
+
+    "nullformatCTAS", // SPARK-7411: need to finish CTAS parser
+
+    // The isolated classloader seemed to make some of our test reset mechanisms less robust.
+    "combine1", // This test changes compression settings in a way that breaks all subsequent tests.
+    "load_dyn_part14.*" // These work alone but fail when run with other tests...
   ) ++ HiveShim.compatibilityBlackList
 
   /**
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 f25723e53f07c..538c6c7f0a200 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
@@ -17,8 +17,9 @@
 
 package org.apache.spark.sql.hive
 
-import java.io.{BufferedReader, InputStreamReader, PrintStream}
+import java.io.{BufferedReader, File, InputStreamReader, PrintStream}
 import java.sql.Timestamp
+import java.util.{ArrayList => JArrayList}
 
 import org.apache.hadoop.hive.ql.parse.VariableSubstitution
 import org.apache.spark.sql.catalyst.Dialect
@@ -35,15 +36,19 @@ import org.apache.hadoop.hive.ql.processors._
 import org.apache.hadoop.hive.ql.session.SessionState
 import org.apache.hadoop.hive.serde2.io.{DateWritable, TimestampWritable}
 
-import org.apache.spark.SparkContext
+import org.apache.spark.{SparkConf, SparkContext}
 import org.apache.spark.annotation.Experimental
+import org.apache.spark.deploy.SparkHadoopUtil
 import org.apache.spark.sql._
 import org.apache.spark.sql.catalyst.analysis.{Analyzer, EliminateSubQueries, OverrideCatalog, OverrideFunctionRegistry}
 import org.apache.spark.sql.catalyst.plans.logical._
 import org.apache.spark.sql.execution.{ExecutedCommand, ExtractPythonUdfs, QueryExecutionException, SetCommand}
+import org.apache.spark.sql.hive.client._
 import org.apache.spark.sql.hive.execution.{DescribeHiveTableCommand, HiveNativeCommand}
 import org.apache.spark.sql.sources.{DDLParser, DataSourceStrategy}
 import org.apache.spark.sql.types._
+import org.apache.spark.util.Utils
+
 
 /**
  * This is the HiveQL Dialect, this dialect is strongly bind with HiveContext
@@ -61,6 +66,8 @@ private[hive] class HiveQLDialect extends Dialect {
 class HiveContext(sc: SparkContext) extends SQLContext(sc) {
   self =>
 
+  import HiveContext._
+
   /**
    * 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
@@ -93,9 +100,118 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) {
   protected[sql] def convertCTAS: Boolean =
     getConf("spark.sql.hive.convertCTAS", "false").toBoolean
 
+  /**
+   * The version of the hive client that will be used to communicate with the metastore.  Note that
+   * this does not necessarily need to be the same version of Hive that is used internally by
+   * Spark SQL for execution.
+   */
+  protected[hive] def hiveMetastoreVersion: String =
+    getConf(HIVE_METASTORE_VERSION, hiveExecutionVersion)
+
+  /**
+   * The location of the jars that should be used to instantiate the HiveMetastoreClient.  This
+   * property can be one of three options:
+   *  - a classpath in the standard format for both hive and hadoop.
+   *  - builtin - attempt to discover the jars that were used to load Spark SQL and use those. This
+   *              option is only valid when using the execution version of Hive.
+   *  - maven - download the correct version of hive on demand from maven.
+   */
+  protected[hive] def hiveMetastoreJars: String =
+    getConf(HIVE_METASTORE_JARS, "builtin")
+
   @transient
   protected[sql] lazy val substitutor = new VariableSubstitution()
 
+  /**
+   * The copy of the hive client that is used for execution.  Currently this must always be
+   * Hive 13 as this is the version of Hive that is packaged with Spark SQL.  This copy of the
+   * client is used for execution related tasks like registering temporary functions or ensuring
+   * that the ThreadLocal SessionState is correctly populated.  This copy of Hive is *not* used
+   * for storing peristent metadata, and only point to a dummy metastore in a temporary directory.
+   */
+  @transient
+  protected[hive] lazy val executionHive: ClientWrapper = {
+    logInfo(s"Initilizing execution hive, version $hiveExecutionVersion")
+    new ClientWrapper(
+      version = IsolatedClientLoader.hiveVersion(hiveExecutionVersion),
+      config = newTemporaryConfiguration())
+  }
+  SessionState.setCurrentSessionState(executionHive.state)
+
+  /**
+   * The copy of the Hive client that is used to retrieve metadata from the Hive MetaStore.
+   * The version of the Hive client that is used here must match the metastore that is configured
+   * in the hive-site.xml file.
+   */
+  @transient
+  protected[hive] lazy val metadataHive: ClientInterface = {
+    val metaVersion = IsolatedClientLoader.hiveVersion(hiveMetastoreVersion)
+
+    // We instantiate a HiveConf here to read in the hive-site.xml file and then pass the options
+    // into the isolated client loader
+    val metadataConf = new HiveConf()
+    // `configure` goes second to override other settings.
+    val allConfig = metadataConf.iterator.map(e => e.getKey -> e.getValue).toMap ++ configure
+
+    val isolatedLoader = if (hiveMetastoreJars == "builtin") {
+      if (hiveExecutionVersion != hiveMetastoreVersion) {
+        throw new IllegalArgumentException(
+          "Builtin jars can only be used when hive execution version == hive metastore version. " +
+          s"Execution: ${hiveExecutionVersion} != Metastore: ${hiveMetastoreVersion}. " +
+          "Specify a vaild path to the correct hive jars using $HIVE_METASTORE_JARS " +
+          s"or change $HIVE_METASTORE_VERSION to $hiveExecutionVersion.")
+      }
+      val jars = getClass.getClassLoader match {
+        case urlClassLoader: java.net.URLClassLoader => urlClassLoader.getURLs
+        case other =>
+          throw new IllegalArgumentException(
+            "Unable to locate hive jars to connect to metastore " +
+            s"using classloader ${other.getClass.getName}. " +
+            "Please set spark.sql.hive.metastore.jars")
+      }
+
+      logInfo(
+        s"Initializing HiveMetastoreConnection version $hiveMetastoreVersion using Spark classes.")
+      new IsolatedClientLoader(
+        version = metaVersion,
+        execJars = jars.toSeq,
+        config = allConfig,
+        isolationOn = true)
+    } else if (hiveMetastoreJars == "maven") {
+      // TODO: Support for loading the jars from an already downloaded location.
+      logInfo(
+        s"Initializing HiveMetastoreConnection version $hiveMetastoreVersion using maven.")
+      IsolatedClientLoader.forVersion(hiveMetastoreVersion, allConfig )
+    } else {
+      // Convert to files and expand any directories.
+      val jars =
+        hiveMetastoreJars
+          .split(File.pathSeparator)
+          .flatMap {
+            case path if new File(path).getName() == "*" =>
+              val files = new File(path).getParentFile().listFiles()
+              if (files == null) {
+                logWarning(s"Hive jar path '$path' does not exist.")
+                Nil
+              } else {
+                files.filter(_.getName().toLowerCase().endsWith(".jar"))
+              }
+            case path =>
+              new File(path) :: Nil
+          }
+          .map(_.toURI.toURL)
+
+      logInfo(
+        s"Initializing HiveMetastoreConnection version $hiveMetastoreVersion using $jars")
+      new IsolatedClientLoader(
+        version = metaVersion,
+        execJars = jars.toSeq,
+        config = allConfig,
+        isolationOn = true)
+    }
+    isolatedLoader.client
+  }
+
   protected[sql] override def parseSql(sql: String): LogicalPlan = {
     super.parseSql(substitutor.substitute(hiveconf, sql))
   }
@@ -178,15 +294,10 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) {
         // recorded in the Hive metastore.
         // This logic is based on org.apache.hadoop.hive.ql.exec.StatsTask.aggregateStats().
         if (newTotalSize > 0 && newTotalSize != oldTotalSize) {
-          tableParameters.put(HiveShim.getStatsSetupConstTotalSize, newTotalSize.toString)
-          val hiveTTable = relation.hiveQlTable.getTTable
-          hiveTTable.setParameters(tableParameters)
-          val tableFullName =
-            relation.hiveQlTable.getDbName + "." + relation.hiveQlTable.getTableName
-
-          catalog.synchronized {
-            catalog.client.alterTable(tableFullName, new Table(hiveTTable))
-          }
+          catalog.client.alterTable(
+            relation.table.copy(
+              properties = relation.table.properties +
+                (HiveShim.getStatsSetupConstTotalSize -> newTotalSize.toString)))
         }
       case otherRelation =>
         throw new UnsupportedOperationException(
@@ -194,47 +305,19 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) {
     }
   }
 
-  // Circular buffer to hold what hive prints to STDOUT and ERR.  Only printed when failures occur.
-  @transient
-  protected lazy val outputBuffer = new java.io.OutputStream {
-    var pos: Int = 0
-    var buffer = new Array[Int](10240)
-    def write(i: Int): Unit = {
-      buffer(pos) = i
-      pos = (pos + 1) % buffer.size
-    }
-
-    override def toString: String = {
-      val (end, start) = buffer.splitAt(pos)
-      val input = new java.io.InputStream {
-        val iterator = (start ++ end).iterator
-
-        def read(): Int = if (iterator.hasNext) iterator.next() else -1
-      }
-      val reader = new BufferedReader(new InputStreamReader(input))
-      val stringBuilder = new StringBuilder
-      var line = reader.readLine()
-      while(line != null) {
-        stringBuilder.append(line)
-        stringBuilder.append("\n")
-        line = reader.readLine()
-      }
-      stringBuilder.toString()
-    }
-  }
-
-  protected[hive] def sessionState = tlSession.get().asInstanceOf[this.SQLSession].sessionState
-
   protected[hive] def hiveconf = tlSession.get().asInstanceOf[this.SQLSession].hiveconf
 
   override def setConf(key: String, value: String): Unit = {
     super.setConf(key, value)
-    runSqlHive(s"SET $key=$value")
+    hiveconf.set(key, value)
+    executionHive.runSqlHive(s"SET $key=$value")
+    metadataHive.runSqlHive(s"SET $key=$value")
   }
 
   /* A catalyst metadata catalog that points to the Hive Metastore. */
   @transient
-  override protected[sql] lazy val catalog = new HiveMetastoreCatalog(this) with OverrideCatalog
+  override protected[sql] lazy val catalog =
+    new HiveMetastoreCatalog(metadataHive, this) with OverrideCatalog
 
   // Note that HiveUDFs will be overridden by functions registered in this context.
   @transient
@@ -261,16 +344,14 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) {
     new this.SQLSession()
   }
 
+  /** Overridden by child classes that need to set configuration before the client init. */
+  protected def configure(): Map[String, String] = Map.empty
+
   protected[hive] class SQLSession extends super.SQLSession {
     protected[sql] override lazy val conf: SQLConf = new SQLConf {
       override def dialect: String = getConf(SQLConf.DIALECT, "hiveql")
     }
 
-    protected[hive] lazy val hiveconf: HiveConf = {
-      setConf(sessionState.getConf.getAllProperties)
-      sessionState.getConf
-    }
-
     /**
      * SQLConf and HiveConf contracts:
      *
@@ -285,78 +366,12 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) {
         state = new SessionState(new HiveConf(classOf[SessionState]))
         SessionState.start(state)
       }
-      if (state.out == null) {
-        state.out = new PrintStream(outputBuffer, true, "UTF-8")
-      }
-      if (state.err == null) {
-        state.err = new PrintStream(outputBuffer, true, "UTF-8")
-      }
       state
     }
-  }
-
-  /**
-   * Runs the specified SQL query using Hive.
-   */
-  protected[sql] def runSqlHive(sql: String): Seq[String] = {
-    val maxResults = 100000
-    val results = runHive(sql, maxResults)
-    // It is very confusing when you only get back some of the results...
-    if (results.size == maxResults) sys.error("RESULTS POSSIBLY TRUNCATED")
-    results
-  }
-
-  /**
-   * Execute the command using Hive and return the results as a sequence. Each element
-   * in the sequence is one row.
-   */
-  protected def runHive(cmd: String, maxRows: Int = 1000): Seq[String] = synchronized {
-    try {
-      val cmd_trimmed: String = cmd.trim()
-      val tokens: Array[String] = cmd_trimmed.split("\\s+")
-      val cmd_1: String = cmd_trimmed.substring(tokens(0).length()).trim()
-      val proc: CommandProcessor = HiveShim.getCommandProcessor(Array(tokens(0)), hiveconf)
-
-      // Makes sure the session represented by the `sessionState` field is activated. This implies
-      // Spark SQL Hive support uses a single `SessionState` for all Hive operations and breaks
-      // session isolation under multi-user scenarios (i.e. HiveThriftServer2).
-      // TODO Fix session isolation
-      if (SessionState.get() != sessionState) {
-        SessionState.start(sessionState)
-      }
 
-      proc match {
-        case driver: Driver =>
-          val results = HiveShim.createDriverResultsArray
-          val response: CommandProcessorResponse = driver.run(cmd)
-          // Throw an exception if there is an error in query processing.
-          if (response.getResponseCode != 0) {
-            driver.close()
-            throw new QueryExecutionException(response.getErrorMessage)
-          }
-          driver.setMaxRows(maxRows)
-          driver.getResults(results)
-          driver.close()
-          HiveShim.processResults(results)
-        case _ =>
-          if (sessionState.out != null) {
-            sessionState.out.println(tokens(0) + " " + cmd_1)
-          }
-          Seq(proc.run(cmd_1).getResponseCode.toString)
-      }
-    } catch {
-      case e: Exception =>
-        logError(
-          s"""
-            |======================
-            |HIVE FAILURE OUTPUT
-            |======================
-            |${outputBuffer.toString}
-            |======================
-            |END HIVE FAILURE OUTPUT
-            |======================
-          """.stripMargin)
-        throw e
+    protected[hive] lazy val hiveconf: HiveConf = {
+      setConf(sessionState.getConf.getAllProperties)
+      sessionState.getConf
     }
   }
 
@@ -391,17 +406,23 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) {
     )
   }
 
+  protected[hive] def runSqlHive(sql: String): Seq[String] = {
+    if (sql.toLowerCase.contains("create temporary function")) {
+      executionHive.runSqlHive(sql)
+    } else if (sql.trim.toLowerCase.startsWith("set")) {
+      metadataHive.runSqlHive(sql)
+      executionHive.runSqlHive(sql)
+    } else {
+      metadataHive.runSqlHive(sql)
+    }
+  }
+
   @transient
   override protected[sql] val planner = hivePlanner
 
   /** Extends QueryExecution with hive specific features. */
   protected[sql] class QueryExecution(logicalPlan: LogicalPlan)
     extends super.QueryExecution(logicalPlan) {
-    // Like what we do in runHive, makes sure the session represented by the
-    // `sessionState` field is activated.
-    if (SessionState.get() != sessionState) {
-      SessionState.start(sessionState)
-    }
 
     /**
      * Returns the result as a hive compatible sequence of strings.  For native commands, the
@@ -439,7 +460,21 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) {
 }
 
 
-private object HiveContext {
+private[hive] object HiveContext {
+  /** The version of hive used internally by Spark SQL. */
+  val hiveExecutionVersion: String = "0.13.1"
+
+  val HIVE_METASTORE_VERSION: String = "spark.sql.hive.metastore.version"
+  val HIVE_METASTORE_JARS: String = "spark.sql.hive.metastore.jars"
+
+  /** Constructs a configuration for hive, where the metastore is located in a temp directory. */
+  def newTemporaryConfiguration(): Map[String, String] = {
+    val tempDir = Utils.createTempDir()
+    val localMetastore = new File(tempDir, "metastore").getAbsolutePath
+    Map(
+      "javax.jdo.option.ConnectionURL" -> s"jdbc:derby:;databaseName=$localMetastore;create=true")
+  }
+
   protected val primitiveTypes =
     Seq(StringType, IntegerType, LongType, DoubleType, FloatType, BooleanType, ByteType,
       ShortType, DateType, TimestampType, BinaryType)
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 4d222cf88e5e8..8fcdf3d0ab119 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
@@ -22,6 +22,8 @@ import java.util.{List => JList}
 
 import com.google.common.base.Objects
 import com.google.common.cache.{CacheBuilder, CacheLoader, LoadingCache}
+
+import org.apache.hadoop.fs.Path
 import org.apache.hadoop.hive.metastore.api.{FieldSchema, Partition => TPartition, Table => TTable}
 import org.apache.hadoop.hive.metastore.{TableType, Warehouse}
 import org.apache.hadoop.hive.ql.metadata._
@@ -32,6 +34,7 @@ import org.apache.hadoop.hive.serde2.{Deserializer, SerDeException}
 import org.apache.hadoop.util.ReflectionUtils
 
 import org.apache.spark.Logging
+import org.apache.spark.sql.hive.client.IsolatedClientLoader
 import org.apache.spark.sql.{SaveMode, AnalysisException, SQLContext}
 import org.apache.spark.sql.catalyst.analysis.{MultiInstanceRelation, NoSuchTableException, Catalog, OverrideCatalog}
 import org.apache.spark.sql.catalyst.expressions._
@@ -39,6 +42,7 @@ import org.apache.spark.sql.catalyst.planning.PhysicalOperation
 import org.apache.spark.sql.catalyst.plans.logical
 import org.apache.spark.sql.catalyst.plans.logical._
 import org.apache.spark.sql.catalyst.rules._
+import org.apache.spark.sql.hive.client._
 import org.apache.spark.sql.parquet.{ParquetRelation2, Partition => ParquetPartition, PartitionSpec}
 import org.apache.spark.sql.sources.{CreateTableUsingAsSelect, DDLParser, LogicalRelation, ResolvedDataSource}
 import org.apache.spark.sql.types._
@@ -47,11 +51,10 @@ import org.apache.spark.util.Utils
 /* Implicit conversions */
 import scala.collection.JavaConversions._
 
-private[hive] class HiveMetastoreCatalog(hive: HiveContext) extends Catalog with Logging {
-  import org.apache.spark.sql.hive.HiveMetastoreTypes._
+private[hive] class HiveMetastoreCatalog(val client: ClientInterface, hive: HiveContext)
+  extends Catalog with Logging {
 
-  /** Connection to hive metastore.  Usages should lock on `this`. */
-  protected[hive] val client = Hive.get(hive.hiveconf)
+  import org.apache.spark.sql.hive.HiveMetastoreTypes._
 
   /** Usages should lock on `this`. */
   protected[hive] lazy val hiveWarehouse = new Warehouse(hive.hiveconf)
@@ -67,14 +70,12 @@ private[hive] class HiveMetastoreCatalog(hive: HiveContext) extends Catalog with
     val cacheLoader = new CacheLoader[QualifiedTableName, LogicalPlan]() {
       override def load(in: QualifiedTableName): LogicalPlan = {
         logDebug(s"Creating new cached data source for $in")
-        val table = HiveMetastoreCatalog.this.synchronized {
-          client.getTable(in.database, in.name)
-        }
+        val table = client.getTable(in.database, in.name)
 
         def schemaStringFromParts: Option[String] = {
-          Option(table.getProperty("spark.sql.sources.schema.numParts")).map { numParts =>
+          table.properties.get("spark.sql.sources.schema.numParts").map { numParts =>
             val parts = (0 until numParts.toInt).map { index =>
-              val part = table.getProperty(s"spark.sql.sources.schema.part.${index}")
+              val part = table.properties.get(s"spark.sql.sources.schema.part.${index}").orNull
               if (part == null) {
                 throw new AnalysisException(
                   s"Could not read schema from the metastore because it is corrupted " +
@@ -92,20 +93,20 @@ private[hive] class HiveMetastoreCatalog(hive: HiveContext) extends Catalog with
         // After SPARK-6024, we removed this flag.
         // Although we are not using spark.sql.sources.schema any more, we need to still support.
         val schemaString =
-          Option(table.getProperty("spark.sql.sources.schema")).orElse(schemaStringFromParts)
+          table.properties.get("spark.sql.sources.schema").orElse(schemaStringFromParts)
 
         val userSpecifiedSchema =
           schemaString.map(s => DataType.fromJson(s).asInstanceOf[StructType])
 
         // It does not appear that the ql client for the metastore has a way to enumerate all the
         // SerDe properties directly...
-        val options = table.getTTable.getSd.getSerdeInfo.getParameters.toMap
+        val options = table.serdeProperties
 
         val resolvedRelation =
           ResolvedDataSource(
             hive,
             userSpecifiedSchema,
-            table.getProperty("spark.sql.sources.provider"),
+            table.properties("spark.sql.sources.provider"),
             options)
 
         LogicalRelation(resolvedRelation.relation)
@@ -144,49 +145,53 @@ private[hive] class HiveMetastoreCatalog(hive: HiveContext) extends Catalog with
       options: Map[String, String],
       isExternal: Boolean): Unit = {
     val (dbName, tblName) = processDatabaseAndTableName("default", tableName)
-    val tbl = new Table(dbName, tblName)
-
-    tbl.setProperty("spark.sql.sources.provider", provider)
+    val tableProperties = new scala.collection.mutable.HashMap[String, String]
+    tableProperties.put("spark.sql.sources.provider", provider)
     if (userSpecifiedSchema.isDefined) {
       val threshold = hive.conf.schemaStringLengthThreshold
       val schemaJsonString = userSpecifiedSchema.get.json
       // Split the JSON string.
       val parts = schemaJsonString.grouped(threshold).toSeq
-      tbl.setProperty("spark.sql.sources.schema.numParts", parts.size.toString)
+      tableProperties.put("spark.sql.sources.schema.numParts", parts.size.toString)
       parts.zipWithIndex.foreach { case (part, index) =>
-        tbl.setProperty(s"spark.sql.sources.schema.part.${index}", part)
+        tableProperties.put(s"spark.sql.sources.schema.part.${index}", part)
       }
     }
-    options.foreach { case (key, value) => tbl.setSerdeParam(key, value) }
 
-    if (isExternal) {
-      tbl.setProperty("EXTERNAL", "TRUE")
-      tbl.setTableType(TableType.EXTERNAL_TABLE)
+    val tableType = if (isExternal) {
+      tableProperties.put("EXTERNAL", "TRUE")
+      ExternalTable
     } else {
-      tbl.setProperty("EXTERNAL", "FALSE")
-      tbl.setTableType(TableType.MANAGED_TABLE)
-    }
-
-    // create the table
-    synchronized {
-      client.createTable(tbl, false)
-    }
+      tableProperties.put("EXTERNAL", "FALSE")
+      ManagedTable
+    }
+
+    client.createTable(
+      HiveTable(
+        specifiedDatabase = Option(dbName),
+        name = tblName,
+        schema = Seq.empty,
+        partitionColumns = Seq.empty,
+        tableType = tableType,
+        properties = tableProperties.toMap,
+        serdeProperties = options))
   }
 
-  def hiveDefaultTableFilePath(tableName: String): String = synchronized {
-    val currentDatabase = client.getDatabase(hive.sessionState.getCurrentDatabase)
-
-    hiveWarehouse.getTablePath(currentDatabase, tableName).toString
+  def hiveDefaultTableFilePath(tableName: String): String = {
+    // Code based on: hiveWarehouse.getTablePath(currentDatabase, tableName)
+    new Path(
+      new Path(client.getDatabase(client.currentDatabase).location),
+      tableName.toLowerCase).toString
   }
 
-  def tableExists(tableIdentifier: Seq[String]): Boolean = synchronized {
+  def tableExists(tableIdentifier: Seq[String]): Boolean = {
     val tableIdent = processTableIdentifier(tableIdentifier)
     val databaseName =
       tableIdent
         .lift(tableIdent.size - 2)
-        .getOrElse(hive.sessionState.getCurrentDatabase)
+        .getOrElse(client.currentDatabase)
     val tblName = tableIdent.last
-    client.getTable(databaseName, tblName, false) != null
+    client.getTableOption(databaseName, tblName).isDefined
   }
 
   def lookupRelation(
@@ -194,18 +199,11 @@ private[hive] class HiveMetastoreCatalog(hive: HiveContext) extends Catalog with
       alias: Option[String]): LogicalPlan = {
     val tableIdent = processTableIdentifier(tableIdentifier)
     val databaseName = tableIdent.lift(tableIdent.size - 2).getOrElse(
-      hive.sessionState.getCurrentDatabase)
+      client.currentDatabase)
     val tblName = tableIdent.last
-    val table = try {
-      synchronized {
-        client.getTable(databaseName, tblName)
-      }
-    } catch {
-      case te: org.apache.hadoop.hive.ql.metadata.InvalidTableException =>
-        throw new NoSuchTableException
-    }
+    val table = client.getTable(databaseName, tblName)
 
-    if (table.getProperty("spark.sql.sources.provider") != null) {
+    if (table.properties.get("spark.sql.sources.provider").isDefined) {
       val dataSourceTable =
         cachedDataSourceTables(QualifiedTableName(databaseName, tblName).toLowerCase)
       // Then, if alias is specified, wrap the table with a Subquery using the alias.
@@ -215,22 +213,16 @@ private[hive] class HiveMetastoreCatalog(hive: HiveContext) extends Catalog with
           Subquery(tableIdent.last, dataSourceTable))
 
       withAlias
-    } else if (table.isView) {
-      // if the unresolved relation is from hive view
-      // parse the text into logic node.
-      HiveQl.createPlanForView(table, alias)
+    } else if (table.tableType == VirtualView) {
+      val viewText = table.viewText.getOrElse(sys.error("Invalid view without text."))
+      alias match {
+        // because hive use things like `_c0` to build the expanded text
+        // currently we cannot support view from "create view v1(c1) as ..."
+        case None => Subquery(table.name, HiveQl.createPlan(viewText))
+        case Some(aliasText) => Subquery(aliasText, HiveQl.createPlan(viewText))
+      }
     } else {
-      val partitions: Seq[Partition] =
-        if (table.isPartitioned) {
-          synchronized {
-            HiveShim.getAllPartitionsOf(client, table).toSeq
-          }
-        } else {
-          Nil
-        }
-
-      MetastoreRelation(databaseName, tblName, alias)(
-        table.getTTable, partitions.map(part => part.getTPartition))(hive)
+      MetastoreRelation(databaseName, tblName, alias)(table)(hive)
     }
   }
 
@@ -318,178 +310,10 @@ private[hive] class HiveMetastoreCatalog(hive: HiveContext) extends Catalog with
     result.newInstance()
   }
 
-  override def getTables(databaseName: Option[String]): Seq[(String, Boolean)] = synchronized {
-    val dbName = if (!caseSensitive) {
-      if (databaseName.isDefined) Some(databaseName.get.toLowerCase) else None
-    } else {
-      databaseName
-    }
-    val db = dbName.getOrElse(hive.sessionState.getCurrentDatabase)
-
-    client.getAllTables(db).map(tableName => (tableName, false))
-  }
-
-  /**
-   * Create table with specified database, table name, table description and schema
-   * @param databaseName Database Name
-   * @param tableName Table Name
-   * @param schema Schema of the new table, if not specified, will use the schema
-   *               specified in crtTbl
-   * @param allowExisting if true, ignore AlreadyExistsException
-   * @param desc CreateTableDesc object which contains the SerDe info. Currently
-   *               we support most of the features except the bucket.
-   */
-  def createTable(
-      databaseName: String,
-      tableName: String,
-      schema: Seq[Attribute],
-      allowExisting: Boolean = false,
-      desc: Option[CreateTableDesc] = None) {
-    val hconf = hive.hiveconf
-
-    val (dbName, tblName) = processDatabaseAndTableName(databaseName, tableName)
-    val tbl = new Table(dbName, tblName)
-
-    val crtTbl: CreateTableDesc = desc.getOrElse(null)
-
-    // We should respect the passed in schema, unless it's not set
-    val hiveSchema: JList[FieldSchema] = if (schema == null || schema.isEmpty) {
-      crtTbl.getCols
-    } else {
-      schema.map(attr => new FieldSchema(attr.name, toMetastoreType(attr.dataType), null))
-    }
-    tbl.setFields(hiveSchema)
-
-    // Most of code are similar with the DDLTask.createTable() of Hive,
-    if (crtTbl != null && crtTbl.getTblProps() != null) {
-      tbl.getTTable().getParameters().putAll(crtTbl.getTblProps())
-    }
-
-    if (crtTbl != null && crtTbl.getPartCols() != null) {
-      tbl.setPartCols(crtTbl.getPartCols())
-    }
-
-    if (crtTbl != null && crtTbl.getStorageHandler() != null) {
-      tbl.setProperty(
-        org.apache.hadoop.hive.metastore.api.hive_metastoreConstants.META_TABLE_STORAGE,
-        crtTbl.getStorageHandler())
-    }
-
-    /*
-     * We use LazySimpleSerDe by default.
-     *
-     * If the user didn't specify a SerDe, and any of the columns are not simple
-     * types, we will have to use DynamicSerDe instead.
-     */
-    if (crtTbl == null || crtTbl.getSerName() == null) {
-      val storageHandler = tbl.getStorageHandler()
-      if (storageHandler == null) {
-        logInfo(s"Default to LazySimpleSerDe for table $dbName.$tblName")
-        tbl.setSerializationLib(classOf[LazySimpleSerDe].getName())
-
-        import org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat
-        import org.apache.hadoop.io.Text
-        import org.apache.hadoop.mapred.TextInputFormat
-
-        tbl.setInputFormatClass(classOf[TextInputFormat])
-        tbl.setOutputFormatClass(classOf[HiveIgnoreKeyTextOutputFormat[Text, Text]])
-        tbl.setSerializationLib("org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe")
-      } else {
-        val serDeClassName = storageHandler.getSerDeClass().getName()
-        logInfo(s"Use StorageHandler-supplied $serDeClassName for table $dbName.$tblName")
-        tbl.setSerializationLib(serDeClassName)
-      }
-    } else {
-      // let's validate that the serde exists
-      val serdeName = crtTbl.getSerName()
-      try {
-        val d = ReflectionUtils.newInstance(hconf.getClassByName(serdeName), hconf)
-        if (d != null) {
-          logDebug("Found class for $serdeName")
-        }
-      } catch {
-        case e: SerDeException => throw new HiveException("Cannot validate serde: " + serdeName, e)
-      }
-      tbl.setSerializationLib(serdeName)
-    }
-
-    if (crtTbl != null && crtTbl.getFieldDelim() != null) {
-      tbl.setSerdeParam(serdeConstants.FIELD_DELIM, crtTbl.getFieldDelim())
-      tbl.setSerdeParam(serdeConstants.SERIALIZATION_FORMAT, crtTbl.getFieldDelim())
-    }
-    if (crtTbl != null && crtTbl.getFieldEscape() != null) {
-      tbl.setSerdeParam(serdeConstants.ESCAPE_CHAR, crtTbl.getFieldEscape())
-    }
-
-    if (crtTbl != null && crtTbl.getCollItemDelim() != null) {
-      tbl.setSerdeParam(serdeConstants.COLLECTION_DELIM, crtTbl.getCollItemDelim())
-    }
-    if (crtTbl != null && crtTbl.getMapKeyDelim() != null) {
-      tbl.setSerdeParam(serdeConstants.MAPKEY_DELIM, crtTbl.getMapKeyDelim())
-    }
-    if (crtTbl != null && crtTbl.getLineDelim() != null) {
-      tbl.setSerdeParam(serdeConstants.LINE_DELIM, crtTbl.getLineDelim())
-    }
-    HiveShim.setTblNullFormat(crtTbl, tbl)
-
-    if (crtTbl != null && crtTbl.getSerdeProps() != null) {
-      val iter = crtTbl.getSerdeProps().entrySet().iterator()
-      while (iter.hasNext()) {
-        val m = iter.next()
-        tbl.setSerdeParam(m.getKey(), m.getValue())
-      }
-    }
-
-    if (crtTbl != null && crtTbl.getComment() != null) {
-      tbl.setProperty("comment", crtTbl.getComment())
-    }
-
-    if (crtTbl != null && crtTbl.getLocation() != null) {
-      HiveShim.setLocation(tbl, crtTbl)
-    }
-
-    if (crtTbl != null && crtTbl.getSkewedColNames() != null) {
-      tbl.setSkewedColNames(crtTbl.getSkewedColNames())
-    }
-    if (crtTbl != null && crtTbl.getSkewedColValues() != null) {
-      tbl.setSkewedColValues(crtTbl.getSkewedColValues())
-    }
-
-    if (crtTbl != null) {
-      tbl.setStoredAsSubDirectories(crtTbl.isStoredAsSubDirectories())
-      tbl.setInputFormatClass(crtTbl.getInputFormat())
-      tbl.setOutputFormatClass(crtTbl.getOutputFormat())
-    }
-
-    tbl.getTTable().getSd().setInputFormat(tbl.getInputFormatClass().getName())
-    tbl.getTTable().getSd().setOutputFormat(tbl.getOutputFormatClass().getName())
-
-    if (crtTbl != null && crtTbl.isExternal()) {
-      tbl.setProperty("EXTERNAL", "TRUE")
-      tbl.setTableType(TableType.EXTERNAL_TABLE)
-    }
-
-    // set owner
-    try {
-      tbl.setOwner(hive.hiveconf.getUser)
-    } catch {
-      case e: IOException => throw new HiveException("Unable to get current user", e)
-    }
-
-    // set create time
-    tbl.setCreateTime((System.currentTimeMillis() / 1000).asInstanceOf[Int])
-
-    // TODO add bucket support
-    // TODO set more info if Hive upgrade
+  override def getTables(databaseName: Option[String]): Seq[(String, Boolean)] = {
+    val db = databaseName.getOrElse(client.currentDatabase)
 
-    // create the table
-    synchronized {
-      try client.createTable(tbl, allowExisting) catch {
-        case e: org.apache.hadoop.hive.metastore.api.AlreadyExistsException
-          if allowExisting => // Do nothing
-        case e: Throwable => throw e
-      }
-    }
+    client.listTables(db).map(tableName => (tableName, false))
   }
 
   protected def processDatabaseAndTableName(
@@ -598,42 +422,11 @@ private[hive] class HiveMetastoreCatalog(hive: HiveContext) extends Catalog with
       // Wait until children are resolved.
       case p: LogicalPlan if !p.childrenResolved => p
 
-      // TODO extra is in type of ASTNode which means the logical plan is not resolved
-      // Need to think about how to implement the CreateTableAsSelect.resolved
-      case CreateTableAsSelect(db, tableName, child, allowExisting, Some(extra: ASTNode)) =>
-        val (dbName, tblName) = processDatabaseAndTableName(db, tableName)
-        val databaseName = dbName.getOrElse(hive.sessionState.getCurrentDatabase)
-
-        // Get the CreateTableDesc from Hive SemanticAnalyzer
-        val desc: Option[CreateTableDesc] = if (tableExists(Seq(databaseName, tblName))) {
-          None
-        } else {
-          val sa = new SemanticAnalyzer(hive.hiveconf) {
-            override def analyzeInternal(ast: ASTNode) {
-              // A hack to intercept the SemanticAnalyzer.analyzeInternal,
-              // to ignore the SELECT clause of the CTAS
-              val method = classOf[SemanticAnalyzer].getDeclaredMethod(
-                "analyzeCreateTable", classOf[ASTNode], classOf[QB])
-              method.setAccessible(true)
-              method.invoke(this, ast, this.getQB)
-            }
-          }
-
-          sa.analyze(extra, new Context(hive.hiveconf))
-          Some(sa.getQB().getTableDesc)
-        }
-
-        // Check if the query specifies file format or storage handler.
-        val hasStorageSpec = desc match {
-          case Some(crtTbl) =>
-            crtTbl != null && (crtTbl.getSerName != null || crtTbl.getStorageHandler != null)
-          case None => false
-        }
-
-        if (hive.convertCTAS && !hasStorageSpec) {
+      case CreateTableAsSelect(desc, child, allowExisting) =>
+        if (hive.convertCTAS && !desc.serde.isDefined) {
           // Do the conversion when spark.sql.hive.convertCTAS is true and the query
           // does not specify any storage format (file format and storage handler).
-          if (dbName.isDefined) {
+          if (desc.specifiedDatabase.isDefined) {
             throw new AnalysisException(
               "Cannot specify database name in a CTAS statement " +
               "when spark.sql.hive.convertCTAS is set to true.")
@@ -641,7 +434,7 @@ private[hive] class HiveMetastoreCatalog(hive: HiveContext) extends Catalog with
 
           val mode = if (allowExisting) SaveMode.Ignore else SaveMode.ErrorIfExists
           CreateTableUsingAsSelect(
-            tblName,
+            desc.name,
             hive.conf.defaultDataSourceName,
             temporary = false,
             mode,
@@ -650,19 +443,19 @@ private[hive] class HiveMetastoreCatalog(hive: HiveContext) extends Catalog with
           )
         } else {
           execution.CreateTableAsSelect(
-            databaseName,
-            tableName,
+            desc.copy(
+              specifiedDatabase = Option(desc.specifiedDatabase.getOrElse(client.currentDatabase))),
             child,
-            allowExisting,
-            desc)
+            allowExisting)
         }
 
       case p: LogicalPlan if p.resolved => p
 
-      case p @ CreateTableAsSelect(db, tableName, child, allowExisting, None) =>
-        val (dbName, tblName) = processDatabaseAndTableName(db, tableName)
+      case p @ CreateTableAsSelect(desc, child, allowExisting) =>
+        val (dbName, tblName) = processDatabaseAndTableName(desc.database, desc.name)
+
         if (hive.convertCTAS) {
-          if (dbName.isDefined) {
+          if (desc.specifiedDatabase.isDefined) {
             throw new AnalysisException(
               "Cannot specify database name in a CTAS statement " +
               "when spark.sql.hive.convertCTAS is set to true.")
@@ -678,13 +471,10 @@ private[hive] class HiveMetastoreCatalog(hive: HiveContext) extends Catalog with
             child
           )
         } else {
-          val databaseName = dbName.getOrElse(hive.sessionState.getCurrentDatabase)
           execution.CreateTableAsSelect(
-            databaseName,
-            tableName,
+            desc,
             child,
-            allowExisting,
-            None)
+            allowExisting)
         }
     }
   }
@@ -767,7 +557,7 @@ private[hive] case class InsertIntoHiveTable(
 
 private[hive] case class MetastoreRelation
     (databaseName: String, tableName: String, alias: Option[String])
-    (val table: TTable, val partitions: Seq[TPartition])
+    (val table: HiveTable)
     (@transient sqlContext: SQLContext)
   extends LeafNode with MultiInstanceRelation {
 
@@ -786,16 +576,63 @@ private[hive] case class MetastoreRelation
     Objects.hashCode(databaseName, tableName, alias, output)
   }
 
-  // TODO: Can we use org.apache.hadoop.hive.ql.metadata.Table as the type of table and
-  // use org.apache.hadoop.hive.ql.metadata.Partition as the type of elements of partitions.
-  // Right now, using org.apache.hadoop.hive.ql.metadata.Table and
-  // org.apache.hadoop.hive.ql.metadata.Partition will cause a NotSerializableException
-  // which indicates the SerDe we used is not Serializable.
+  @transient val hiveQlTable: Table = {
+    // We start by constructing an API table as Hive performs several important transformations
+    // internally when converting an API table to a QL table.
+    val tTable = new org.apache.hadoop.hive.metastore.api.Table()
+    tTable.setTableName(table.name)
+    tTable.setDbName(table.database)
+
+    val tableParameters = new java.util.HashMap[String, String]()
+    tTable.setParameters(tableParameters)
+    table.properties.foreach { case (k, v) => tableParameters.put(k, v) }
+
+    tTable.setTableType(table.tableType.name)
+
+    val sd = new org.apache.hadoop.hive.metastore.api.StorageDescriptor()
+    tTable.setSd(sd)
+    sd.setCols(table.schema.map(c => new FieldSchema(c.name, c.hiveType, c.comment)))
+    tTable.setPartitionKeys(
+      table.partitionColumns.map(c => new FieldSchema(c.name, c.hiveType, c.comment)))
+
+    table.location.foreach(sd.setLocation)
+    table.inputFormat.foreach(sd.setInputFormat)
+    table.outputFormat.foreach(sd.setOutputFormat)
+
+    val serdeInfo = new org.apache.hadoop.hive.metastore.api.SerDeInfo
+    sd.setSerdeInfo(serdeInfo)
+    table.serde.foreach(serdeInfo.setSerializationLib)
+    val serdeParameters = new java.util.HashMap[String, String]()
+    serdeInfo.setParameters(serdeParameters)
+    table.serdeProperties.foreach { case (k, v) => serdeParameters.put(k, v) }
+
+    new Table(tTable)
+  }
+
+  @transient val hiveQlPartitions: Seq[Partition] = table.getAllPartitions.map { p =>
+    val tPartition = new org.apache.hadoop.hive.metastore.api.Partition
+    tPartition.setDbName(databaseName)
+    tPartition.setTableName(tableName)
+    tPartition.setValues(p.values)
+
+    val sd = new org.apache.hadoop.hive.metastore.api.StorageDescriptor()
+    tPartition.setSd(sd)
+    sd.setCols(table.schema.map(c => new FieldSchema(c.name, c.hiveType, c.comment)))
+
+    sd.setLocation(p.storage.location)
+    sd.setInputFormat(p.storage.inputFormat)
+    sd.setOutputFormat(p.storage.outputFormat)
+
+    val serdeInfo = new org.apache.hadoop.hive.metastore.api.SerDeInfo
+    sd.setSerdeInfo(serdeInfo)
+    serdeInfo.setSerializationLib(p.storage.serde)
 
-  @transient val hiveQlTable: Table = new Table(table)
+    val serdeParameters = new java.util.HashMap[String, String]()
+    serdeInfo.setParameters(serdeParameters)
+    table.serdeProperties.foreach { case (k, v) => serdeParameters.put(k, v) }
+    p.storage.serdeProperties.foreach { case (k, v) => serdeParameters.put(k, v) }
 
-  @transient val hiveQlPartitions: Seq[Partition] = partitions.map { p =>
-    new Partition(hiveQlTable, p)
+    new Partition(hiveQlTable, tPartition)
   }
 
   @transient override lazy val statistics: Statistics = Statistics(
@@ -865,7 +702,7 @@ private[hive] case class MetastoreRelation
   val columnOrdinals = AttributeMap(attributes.zipWithIndex)
 
   override def newInstance(): MetastoreRelation = {
-    MetastoreRelation(databaseName, tableName, alias)(table, partitions)(sqlContext)
+    MetastoreRelation(databaseName, tableName, alias)(table)(sqlContext)
   }
 }
 
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 6176aee25e7a4..f30b196734c40 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
@@ -38,6 +38,7 @@ import org.apache.spark.sql.catalyst.plans.logical._
 import org.apache.spark.sql.catalyst.trees.CurrentOrigin
 import org.apache.spark.sql.execution.ExplainCommand
 import org.apache.spark.sql.sources.DescribeCommand
+import org.apache.spark.sql.hive.client._
 import org.apache.spark.sql.hive.execution.{HiveNativeCommand, DropTable, AnalyzeTable, HiveScriptIOSchema}
 import org.apache.spark.sql.types._
 import org.apache.spark.util.random.RandomSampler
@@ -50,7 +51,19 @@ import scala.collection.JavaConversions._
  * back for Hive to execute natively.  Will be replaced with a native command that contains the
  * cmd string.
  */
-private[hive] case object NativePlaceholder extends Command
+private[hive] case object NativePlaceholder extends LogicalPlan {
+  override def children: Seq[LogicalPlan] = Seq.empty
+  override def output: Seq[Attribute] = Seq.empty
+}
+
+case class CreateTableAsSelect(
+    tableDesc: HiveTable,
+    child: LogicalPlan,
+    allowExisting: Boolean) extends UnaryNode with Command {
+
+  override def output: Seq[Attribute] = Seq.empty[Attribute]
+  override lazy val resolved: Boolean = tableDesc.specifiedDatabase.isDefined && childrenResolved
+}
 
 /** Provides a mapping from HiveQL statements to catalyst logical plans and expression trees. */
 private[hive] object HiveQl {
@@ -78,16 +91,16 @@ private[hive] object HiveQl {
     "TOK_ALTERVIEW_DROPPARTS",
     "TOK_ALTERVIEW_PROPERTIES",
     "TOK_ALTERVIEW_RENAME",
-    
+
     "TOK_CREATEDATABASE",
     "TOK_CREATEFUNCTION",
     "TOK_CREATEINDEX",
     "TOK_CREATEROLE",
     "TOK_CREATEVIEW",
-    
+
     "TOK_DESCDATABASE",
     "TOK_DESCFUNCTION",
-    
+
     "TOK_DROPDATABASE",
     "TOK_DROPFUNCTION",
     "TOK_DROPINDEX",
@@ -95,22 +108,22 @@ private[hive] object HiveQl {
     "TOK_DROPTABLE_PROPERTIES",
     "TOK_DROPVIEW",
     "TOK_DROPVIEW_PROPERTIES",
-    
+
     "TOK_EXPORT",
-    
+
     "TOK_GRANT",
     "TOK_GRANT_ROLE",
-    
+
     "TOK_IMPORT",
-    
+
     "TOK_LOAD",
-    
+
     "TOK_LOCKTABLE",
-    
+
     "TOK_MSCK",
-    
+
     "TOK_REVOKE",
-    
+
     "TOK_SHOW_COMPACTIONS",
     "TOK_SHOW_CREATETABLE",
     "TOK_SHOW_GRANT",
@@ -127,9 +140,9 @@ private[hive] object HiveQl {
     "TOK_SHOWINDEXES",
     "TOK_SHOWLOCKS",
     "TOK_SHOWPARTITIONS",
-    
+
     "TOK_SWITCHDATABASE",
-    
+
     "TOK_UNLOCKTABLE"
   )
 
@@ -259,6 +272,7 @@ private[hive] object HiveQl {
           case otherMessage =>
             throw new AnalysisException(otherMessage)
         }
+      case e: MatchError => throw e
       case e: Exception =>
         throw new AnalysisException(e.getMessage)
       case e: NotImplementedError =>
@@ -272,14 +286,6 @@ private[hive] object HiveQl {
     }
   }
 
-  /** Creates LogicalPlan for a given VIEW */
-  def createPlanForView(view: Table, alias: Option[String]): Subquery = alias match {
-    // because hive use things like `_c0` to build the expanded text
-    // currently we cannot support view from "create view v1(c1) as ..."
-    case None => Subquery(view.getTableName, createPlan(view.getViewExpandedText))
-    case Some(aliasText) => Subquery(aliasText, createPlan(view.getViewExpandedText))
-  }
-
   def parseDdl(ddl: String): Seq[Attribute] = {
     val tree =
       try {
@@ -453,6 +459,14 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
     (keys, bitmasks)
   }
 
+  protected def getProperties(node: Node): Seq[(String, String)] = node match {
+    case Token("TOK_TABLEPROPLIST", list) =>
+      list.map {
+        case Token("TOK_TABLEPROPERTY", Token(key, Nil) :: Token(value, Nil) :: Nil) =>
+          (unquoteString(key) -> unquoteString(value))
+      }
+  }
+
   protected def nodeToPlan(node: Node): LogicalPlan = node match {
     // Special drop table that also uncaches.
     case Token("TOK_DROPTABLE",
@@ -562,7 +576,62 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
           children)
       val (db, tableName) = extractDbNameTableName(tableNameParts)
 
-      CreateTableAsSelect(db, tableName, nodeToPlan(query), allowExisting != None, Some(node))
+      var tableDesc =
+        HiveTable(
+          specifiedDatabase = db,
+          name = tableName,
+          schema = Seq.empty,
+          partitionColumns = Seq.empty,
+          properties = Map.empty,
+          serdeProperties = Map.empty,
+          tableType = ManagedTable,
+          location = None,
+          inputFormat = None,
+          outputFormat = None,
+          serde = None)
+
+      // TODO: Handle all the cases here...
+      children.foreach {
+        case Token("TOK_TBLRCFILE", Nil) =>
+          import org.apache.hadoop.hive.ql.io.{RCFileInputFormat, RCFileOutputFormat}
+          tableDesc = tableDesc.copy(
+            outputFormat = Option(classOf[RCFileOutputFormat].getName),
+            inputFormat = Option(classOf[RCFileInputFormat[_, _]].getName))
+
+          if (tableDesc.serde.isEmpty) {
+            tableDesc = tableDesc.copy(
+              serde = Option("org.apache.hadoop.hive.serde2.columnar.LazyBinaryColumnarSerDe"))
+          }
+        case Token("TOK_TBLORCFILE", Nil) =>
+          tableDesc = tableDesc.copy(
+            inputFormat = Option("org.apache.hadoop.hive.ql.io.orc.OrcInputFormat"),
+            outputFormat = Option("org.apache.hadoop.hive.ql.io.orc.OrcOutputFormat"),
+            serde = Option("org.apache.hadoop.hive.ql.io.orc.OrcSerde"))
+
+        case Token("TOK_TBLPARQUETFILE", Nil) =>
+          tableDesc = tableDesc.copy(
+            inputFormat = Option("org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat"),
+            outputFormat = Option("org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat"),
+            serde = Option("org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe"))
+
+        case Token("TOK_TABLESERIALIZER",
+               Token("TOK_SERDENAME", Token(serdeName, Nil) :: otherProps) :: Nil) =>
+          tableDesc = tableDesc.copy(serde = Option(unquoteString(serdeName)))
+
+          otherProps match {
+            case Token("TOK_TABLEPROPERTIES", list :: Nil) :: Nil =>
+              tableDesc = tableDesc.copy(
+                serdeProperties = tableDesc.serdeProperties ++ getProperties(list))
+            case Nil =>
+          }
+
+        case Token("TOK_TABLEPROPERTIES", list :: Nil) =>
+          tableDesc = tableDesc.copy(properties = tableDesc.properties ++ getProperties(list))
+
+        case _ =>
+      }
+
+      CreateTableAsSelect(tableDesc, nodeToPlan(query), allowExisting != None)
 
     // 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
@@ -759,7 +828,7 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
               case Token("TOK_CUBE_GROUPBY", children) =>
                 Cube(children.map(nodeToExpr), withLateralView, selectExpressions)
               case _ => sys.error("Expect WITH CUBE")
-            }), 
+            }),
             Some(Project(selectExpressions, withLateralView))).flatten.head
         }
 
@@ -1077,6 +1146,15 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
   }
 
   protected val escapedIdentifier = "`([^`]+)`".r
+  protected val doubleQuotedString = "\"([^\"]+)\"".r
+  protected val singleQuotedString = "'([^']+)'".r
+
+  protected def unquoteString(str: String) = str match {
+    case singleQuotedString(s) => s
+    case doubleQuotedString(s) => s
+    case other => other
+  }
+
   /** Strips backticks from ident if present */
   protected def cleanIdentifier(ident: String): String = ident match {
     case escapedIdentifier(i) => i
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 e556c74ffb015..b69312f0f8717 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
@@ -32,6 +32,7 @@ import org.apache.hadoop.mapred.{FileInputFormat, InputFormat, JobConf}
 
 import org.apache.spark.SerializableWritable
 import org.apache.spark.broadcast.Broadcast
+import org.apache.spark.Logging
 import org.apache.spark.rdd.{EmptyRDD, HadoopRDD, RDD, UnionRDD}
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.types.DateUtils
@@ -57,7 +58,7 @@ class HadoopTableReader(
     @transient relation: MetastoreRelation,
     @transient sc: HiveContext,
     @transient hiveExtraConf: HiveConf)
-  extends TableReader {
+  extends TableReader with Logging {
 
   // Hadoop honors "mapred.map.tasks" as hint, but will ignore when mapred.job.tracker is "local".
   // https://hadoop.apache.org/docs/r1.0.4/mapred-default.html
@@ -78,7 +79,7 @@ class HadoopTableReader(
     makeRDDForTable(
       hiveTable,
       Class.forName(
-        relation.tableDesc.getSerdeClassName, true, sc.sessionState.getConf.getClassLoader)
+        relation.tableDesc.getSerdeClassName, true, Utils.getSparkClassLoader)
         .asInstanceOf[Class[Deserializer]],
       filterOpt = None)
 
@@ -145,7 +146,7 @@ class HadoopTableReader(
       partitionToDeserializer: Map[HivePartition,
       Class[_ <: Deserializer]],
       filterOpt: Option[PathFilter]): RDD[Row] = {
-        
+
     // SPARK-5068:get FileStatus and do the filtering locally when the path is not exists
     def verifyPartitionPath(
         partitionToDeserializer: Map[HivePartition, Class[_ <: Deserializer]]):
@@ -288,7 +289,7 @@ class HadoopTableReader(
   }
 }
 
-private[hive] object HadoopTableReader extends HiveInspectors {
+private[hive] object HadoopTableReader extends HiveInspectors with Logging {
   /**
    * Curried. After given an argument for 'path', the resulting JobConf => Unit closure is used to
    * instantiate a HadoopRDD.
@@ -329,6 +330,8 @@ private[hive] object HadoopTableReader extends HiveInspectors {
         tableDeser.getObjectInspector).asInstanceOf[StructObjectInspector]
     }
 
+    logDebug(soi.toString)
+
     val (fieldRefs, fieldOrdinals) = nonPartitionKeyAttrs.map { case (attr, ordinal) =>
       soi.getStructFieldRef(attr.name) -> ordinal
     }.unzip
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientInterface.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientInterface.scala
index a863aa77cb7e0..0a1d761a52f88 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientInterface.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientInterface.scala
@@ -17,30 +17,35 @@
 
 package org.apache.spark.sql.hive.client
 
+import java.io.PrintStream
+import java.util.{Map => JMap}
+
 import org.apache.spark.sql.catalyst.analysis.{NoSuchDatabaseException, NoSuchTableException}
 
-case class HiveDatabase(
+private[hive] case class HiveDatabase(
     name: String,
     location: String)
 
-abstract class TableType { val name: String }
-case object ExternalTable extends TableType { override val name = "EXTERNAL_TABLE" }
-case object IndexTable extends TableType { override val name = "INDEX_TABLE" }
-case object ManagedTable extends TableType { override val name = "MANAGED_TABLE" }
-case object VirtualView extends TableType { override val name = "VIRTUAL_VIEW" }
+private[hive] abstract class TableType { val name: String }
+private[hive] case object ExternalTable extends TableType { override val name = "EXTERNAL_TABLE" }
+private[hive] case object IndexTable extends TableType { override val name = "INDEX_TABLE" }
+private[hive] case object ManagedTable extends TableType { override val name = "MANAGED_TABLE" }
+private[hive] case object VirtualView extends TableType { override val name = "VIRTUAL_VIEW" }
 
-case class HiveStorageDescriptor(
+// TODO: Use this for Tables and Partitions
+private[hive] case class HiveStorageDescriptor(
     location: String,
     inputFormat: String,
     outputFormat: String,
-    serde: String)
+    serde: String,
+    serdeProperties: Map[String, String])
 
-case class HivePartition(
+private[hive] case class HivePartition(
     values: Seq[String],
     storage: HiveStorageDescriptor)
 
-case class HiveColumn(name: String, hiveType: String, comment: String)
-case class HiveTable(
+private[hive] case class HiveColumn(name: String, hiveType: String, comment: String)
+private[hive] case class HiveTable(
     specifiedDatabase: Option[String],
     name: String,
     schema: Seq[HiveColumn],
@@ -51,7 +56,8 @@ case class HiveTable(
     location: Option[String] = None,
     inputFormat: Option[String] = None,
     outputFormat: Option[String] = None,
-    serde: Option[String] = None) {
+    serde: Option[String] = None,
+    viewText: Option[String] = None) {
 
   @transient
   private[client] var client: ClientInterface = _
@@ -76,13 +82,17 @@ case class HiveTable(
  * internal and external classloaders for a given version of Hive and thus must expose only
  * shared classes.
  */
-trait ClientInterface {
+private[hive] trait ClientInterface {
   /**
    * Runs a HiveQL command using Hive, returning the results as a list of strings.  Each row will
    * result in one string.
    */
   def runSqlHive(sql: String): Seq[String]
 
+  def setOut(stream: PrintStream): Unit
+  def setInfo(stream: PrintStream): Unit
+  def setError(stream: PrintStream): Unit
+
   /** Returns the names of all tables in the given database. */
   def listTables(dbName: String): Seq[String]
 
@@ -114,6 +124,11 @@ trait ClientInterface {
   /** Creates a new database with the given name. */
   def createDatabase(database: HiveDatabase): Unit
 
+  /** Returns the specified paritition or None if it does not exist. */
+  def getPartitionOption(
+      hTable: HiveTable,
+      partitionSpec: JMap[String, String]): Option[HivePartition]
+
   /** Returns all partitions for the given table. */
   def getAllPartitions(hTable: HiveTable): Seq[HivePartition]
 
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala
index ea52fea037f1f..6bca9d0179fe3 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala
@@ -19,7 +19,7 @@ package org.apache.spark.sql.hive.client
 
 import java.io.{BufferedReader, InputStreamReader, File, PrintStream}
 import java.net.URI
-import java.util.{ArrayList => JArrayList}
+import java.util.{ArrayList => JArrayList, Map => JMap, List => JList, Set => JSet}
 
 import scala.collection.JavaConversions._
 import scala.language.reflectiveCalls
@@ -27,6 +27,7 @@ import scala.language.reflectiveCalls
 import org.apache.hadoop.fs.Path
 import org.apache.hadoop.hive.metastore.api.Database
 import org.apache.hadoop.hive.conf.HiveConf
+import org.apache.hadoop.hive.metastore.TableType
 import org.apache.hadoop.hive.metastore.api
 import org.apache.hadoop.hive.metastore.api.FieldSchema
 import org.apache.hadoop.hive.ql.metadata
@@ -54,19 +55,13 @@ import org.apache.spark.sql.execution.QueryExecutionException
  * @param config  a collection of configuration options that will be added to the hive conf before
  *                opening the hive client.
  */
-class ClientWrapper(
+private[hive] class ClientWrapper(
     version: HiveVersion,
     config: Map[String, String])
   extends ClientInterface
   with Logging
   with ReflectionMagic {
 
-  private val conf = new HiveConf(classOf[SessionState])
-  config.foreach { case (k, v) =>
-    logDebug(s"Hive Config: $k=$v")
-    conf.set(k, v)
-  }
-
   // Circular buffer to hold what hive prints to STDOUT and ERR.  Only printed when failures occur.
   private val outputBuffer = new java.io.OutputStream {
     var pos: Int = 0
@@ -99,17 +94,31 @@ class ClientWrapper(
     val original = Thread.currentThread().getContextClassLoader
     Thread.currentThread().setContextClassLoader(getClass.getClassLoader)
     val ret = try {
-      val newState = new SessionState(conf)
-      SessionState.start(newState)
-      newState.out = new PrintStream(outputBuffer, true, "UTF-8")
-      newState.err = new PrintStream(outputBuffer, true, "UTF-8")
-      newState
+      val oldState = SessionState.get()
+      if (oldState == null) {
+        val initialConf = new HiveConf(classOf[SessionState])
+        config.foreach { case (k, v) =>
+          logDebug(s"Hive Config: $k=$v")
+          initialConf.set(k, v)
+        }
+        val newState = new SessionState(initialConf)
+        SessionState.start(newState)
+        newState.out = new PrintStream(outputBuffer, true, "UTF-8")
+        newState.err = new PrintStream(outputBuffer, true, "UTF-8")
+        newState
+      } else {
+        oldState
+      }
     } finally {
       Thread.currentThread().setContextClassLoader(original)
     }
     ret
   }
 
+  /** Returns the configuration for the current session. */
+  def conf: HiveConf = SessionState.get().getConf
+
+  // TODO: should be a def?s
   private val client = Hive.get(conf)
 
   /**
@@ -133,6 +142,18 @@ class ClientWrapper(
     ret
   }
 
+  def setOut(stream: PrintStream): Unit = withHiveState {
+    state.out = stream
+  }
+
+  def setInfo(stream: PrintStream): Unit = withHiveState {
+    state.info = stream
+  }
+
+  def setError(stream: PrintStream): Unit = withHiveState {
+    state.err = stream
+  }
+
   override def currentDatabase: String = withHiveState {
     state.getCurrentDatabase
   }
@@ -171,14 +192,20 @@ class ClientWrapper(
         partitionColumns = h.getPartCols.map(f => HiveColumn(f.getName, f.getType, f.getComment)),
         properties = h.getParameters.toMap,
         serdeProperties = h.getTTable.getSd.getSerdeInfo.getParameters.toMap,
-        tableType = ManagedTable, // TODO
+        tableType = h.getTableType match {
+          case TableType.MANAGED_TABLE => ManagedTable
+          case TableType.EXTERNAL_TABLE => ExternalTable
+          case TableType.VIRTUAL_VIEW => VirtualView
+          case TableType.INDEX_TABLE => IndexTable
+        },
         location = version match {
           case hive.v12 => Option(h.call[URI]("getDataLocation")).map(_.toString)
           case hive.v13 => Option(h.call[Path]("getDataLocation")).map(_.toString)
         },
         inputFormat = Option(h.getInputFormatClass).map(_.getName),
         outputFormat = Option(h.getOutputFormatClass).map(_.getName),
-        serde = Option(h.getSerializationLib)).withClient(this)
+        serde = Option(h.getSerializationLib),
+        viewText = Option(h.getViewExpandedText)).withClient(this)
     }
     converted
   }
@@ -223,27 +250,40 @@ class ClientWrapper(
     client.alterTable(table.qualifiedName, qlTable)
   }
 
+  private def toHivePartition(partition: metadata.Partition): HivePartition = {
+    val apiPartition = partition.getTPartition
+    HivePartition(
+      values = Option(apiPartition.getValues).map(_.toSeq).getOrElse(Seq.empty),
+      storage = HiveStorageDescriptor(
+        location = apiPartition.getSd.getLocation,
+        inputFormat = apiPartition.getSd.getInputFormat,
+        outputFormat = apiPartition.getSd.getOutputFormat,
+        serde = apiPartition.getSd.getSerdeInfo.getSerializationLib,
+        serdeProperties = apiPartition.getSd.getSerdeInfo.getParameters.toMap))
+  }
+
+  override def getPartitionOption(
+      table: HiveTable,
+      partitionSpec: JMap[String, String]): Option[HivePartition] = withHiveState {
+
+    val qlTable = toQlTable(table)
+    val qlPartition = client.getPartition(qlTable, partitionSpec, false)
+    Option(qlPartition).map(toHivePartition)
+  }
+
   override def getAllPartitions(hTable: HiveTable): Seq[HivePartition] = withHiveState {
     val qlTable = toQlTable(hTable)
     val qlPartitions = version match {
       case hive.v12 =>
-        client.call[metadata.Table, Set[metadata.Partition]]("getAllPartitionsForPruner", qlTable)
+        client.call[metadata.Table, JSet[metadata.Partition]]("getAllPartitionsForPruner", qlTable)
       case hive.v13 =>
-        client.call[metadata.Table, Set[metadata.Partition]]("getAllPartitionsOf", qlTable)
+        client.call[metadata.Table, JSet[metadata.Partition]]("getAllPartitionsOf", qlTable)
     }
-    qlPartitions.map(_.getTPartition).map { p =>
-      HivePartition(
-        values = Option(p.getValues).map(_.toSeq).getOrElse(Seq.empty),
-        storage = HiveStorageDescriptor(
-          location = p.getSd.getLocation,
-          inputFormat = p.getSd.getInputFormat,
-          outputFormat = p.getSd.getOutputFormat,
-          serde = p.getSd.getSerdeInfo.getSerializationLib))
-    }.toSeq
+    qlPartitions.toSeq.map(toHivePartition)
   }
 
   override def listTables(dbName: String): Seq[String] = withHiveState {
-    client.getAllTables
+    client.getAllTables(dbName)
   }
 
   /**
@@ -267,11 +307,12 @@ class ClientWrapper(
     try {
       val cmd_trimmed: String = cmd.trim()
       val tokens: Array[String] = cmd_trimmed.split("\\s+")
+      // The remainder of the command.
       val cmd_1: String = cmd_trimmed.substring(tokens(0).length()).trim()
       val proc: CommandProcessor = version match {
         case hive.v12 =>
           classOf[CommandProcessorFactory]
-            .callStatic[String, HiveConf, CommandProcessor]("get", cmd_1, conf)
+            .callStatic[String, HiveConf, CommandProcessor]("get", tokens(0), conf)
         case hive.v13 =>
           classOf[CommandProcessorFactory]
             .callStatic[Array[String], HiveConf, CommandProcessor]("get", Array(tokens(0)), conf)
@@ -294,7 +335,7 @@ class ClientWrapper(
               res.toSeq
             case hive.v13 =>
               val res = new JArrayList[Object]
-              driver.call[JArrayList[Object], Boolean]("getResults", res)
+              driver.call[JList[Object], Boolean]("getResults", res)
               res.map { r =>
                 r match {
                   case s: String => s
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/IsolatedClientLoader.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/IsolatedClientLoader.scala
index 710dbca6e3c66..7f94c93ba49c1 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/IsolatedClientLoader.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/IsolatedClientLoader.scala
@@ -18,7 +18,7 @@
 package org.apache.spark.sql.hive.client
 
 import java.io.File
-import java.net.URLClassLoader
+import java.net.{URL, URLClassLoader}
 import java.util
 
 import scala.language.reflectiveCalls
@@ -30,9 +30,10 @@ import org.apache.spark.Logging
 import org.apache.spark.deploy.SparkSubmitUtils
 
 import org.apache.spark.sql.catalyst.util.quietly
+import org.apache.spark.sql.hive.HiveContext
 
 /** Factory for `IsolatedClientLoader` with specific versions of hive. */
-object IsolatedClientLoader {
+private[hive] object IsolatedClientLoader {
   /**
    * Creates isolated Hive client loaders by downloading the requested version from maven.
    */
@@ -49,7 +50,7 @@ object IsolatedClientLoader {
     case "13" | "0.13" | "0.13.0" | "0.13.1" => hive.v13
   }
 
-  private def downloadVersion(version: HiveVersion): Seq[File] = {
+  private def downloadVersion(version: HiveVersion): Seq[URL] = {
     val hiveArtifacts =
       (Seq("hive-metastore", "hive-exec", "hive-common", "hive-serde") ++
         (if (version.hasBuiltinsJar) "hive-builtins" :: Nil else Nil))
@@ -72,10 +73,10 @@ object IsolatedClientLoader {
     tempDir.mkdir()
 
     allFiles.foreach(f => FileUtils.copyFileToDirectory(f, tempDir))
-    tempDir.listFiles()
+    tempDir.listFiles().map(_.toURL)
   }
 
-  private def resolvedVersions = new scala.collection.mutable.HashMap[HiveVersion, Seq[File]]
+  private def resolvedVersions = new scala.collection.mutable.HashMap[HiveVersion, Seq[URL]]
 }
 
 /**
@@ -99,9 +100,9 @@ object IsolatedClientLoader {
  * @param baseClassLoader The spark classloader that is used to load shared classes.
  *
  */
-class IsolatedClientLoader(
+private[hive] class IsolatedClientLoader(
     val version: HiveVersion,
-    val execJars: Seq[File] = Seq.empty,
+    val execJars: Seq[URL] = Seq.empty,
     val config: Map[String, String] = Map.empty,
     val isolationOn: Boolean = true,
     val rootClassLoader: ClassLoader = ClassLoader.getSystemClassLoader.getParent.getParent,
@@ -112,7 +113,7 @@ class IsolatedClientLoader(
   assert(Try(baseClassLoader.loadClass("org.apache.hive.HiveConf")).isFailure)
 
   /** All jars used by the hive specific classloader. */
-  protected def allJars = execJars.map(_.toURI.toURL).toArray
+  protected def allJars = execJars.toArray
 
   protected def isSharedClass(name: String): Boolean =
     name.contains("slf4j") ||
@@ -166,6 +167,12 @@ class IsolatedClientLoader(
       .getConstructors.head
       .newInstance(version, config)
       .asInstanceOf[ClientInterface]
+  } catch {
+    case ReflectionException(cnf: NoClassDefFoundError) =>
+      throw new ClassNotFoundException(
+        s"$cnf when creating Hive client using classpath: ${execJars.mkString(", ")}\n" +
+         "Please make sure that jars for your version of hive and hadoop are included in the " +
+        s"paths passed to ${HiveContext.HIVE_METASTORE_JARS}.")
   } finally {
     Thread.currentThread.setContextClassLoader(baseClassLoader)
   }
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ReflectionMagic.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ReflectionMagic.scala
index 90d03049356b5..c600b158c5460 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ReflectionMagic.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ReflectionMagic.scala
@@ -19,6 +19,14 @@ package org.apache.spark.sql.hive.client
 
 import scala.reflect._
 
+/** Unwraps reflection exceptions. */
+private[client] object ReflectionException {
+  def unapply(a: Throwable): Option[Throwable] = a match {
+    case ite: java.lang.reflect.InvocationTargetException => Option(ite.getCause)
+    case _ => None
+  }
+}
+
 /**
  * Provides implicit functions on any object for calling methods reflectively.
  */
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
index 76a1965f3cb25..91e6ac4032204 100644
--- 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
@@ -24,8 +24,8 @@ import org.apache.spark.sql.SQLContext
 import org.apache.spark.sql.catalyst.expressions.Row
 import org.apache.spark.sql.catalyst.plans.logical.{InsertIntoTable, LogicalPlan}
 import org.apache.spark.sql.execution.RunnableCommand
-import org.apache.spark.sql.hive.HiveContext
-import org.apache.spark.sql.hive.MetastoreRelation
+import org.apache.spark.sql.hive.client.{HiveTable, HiveColumn}
+import org.apache.spark.sql.hive.{HiveContext, MetastoreRelation, HiveMetastoreTypes}
 
 /**
  * Create table and insert the query result into it.
@@ -39,17 +39,34 @@ import org.apache.spark.sql.hive.MetastoreRelation
  */
 private[hive]
 case class CreateTableAsSelect(
-    database: String,
-    tableName: String,
+    tableDesc: HiveTable,
     query: LogicalPlan,
-    allowExisting: Boolean,
-    desc: Option[CreateTableDesc]) extends RunnableCommand {
+    allowExisting: Boolean)
+  extends RunnableCommand {
+
+  def database: String = tableDesc.database
+  def tableName: String = tableDesc.name
 
   override def run(sqlContext: SQLContext): Seq[Row] = {
     val hiveContext = sqlContext.asInstanceOf[HiveContext]
     lazy val metastoreRelation: MetastoreRelation = {
-      // Create Hive Table
-      hiveContext.catalog.createTable(database, tableName, query.output, allowExisting, desc)
+      import org.apache.hadoop.hive.serde2.`lazy`.LazySimpleSerDe
+      import org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat
+      import org.apache.hadoop.io.Text
+      import org.apache.hadoop.mapred.TextInputFormat
+
+      val withSchema =
+        tableDesc.copy(
+          schema =
+            query.output.map(c =>
+              HiveColumn(c.name, HiveMetastoreTypes.toMetastoreType(c.dataType), null)),
+          inputFormat =
+            tableDesc.inputFormat.orElse(Some(classOf[TextInputFormat].getName)),
+          outputFormat =
+            tableDesc.outputFormat
+              .orElse(Some(classOf[HiveIgnoreKeyTextOutputFormat[Text, Text]].getName)),
+          serde = tableDesc.serde.orElse(Some(classOf[LazySimpleSerDe].getName())))
+      hiveContext.catalog.client.createTable(withSchema)
 
       // Get the Metastore Relation
       hiveContext.catalog.lookupRelation(Seq(database, tableName), None) match {
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 89995a91b1a92..de8954d5dec99 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
@@ -200,9 +200,7 @@ case class InsertIntoHiveTable(
           orderedPartitionSpec.put(entry.getName,partitionSpec.get(entry.getName).getOrElse(""))
       }
       val partVals = MetaStoreUtils.getPvals(table.hiveQlTable.getPartCols, partitionSpec)
-      catalog.synchronized {
-        catalog.client.validatePartitionNameCharacters(partVals)
-      }
+
       // inheritTableSpecs is set to true. It should be set to false for a IMPORT query
       // which is currently considered as a Hive native command.
       val inheritTableSpecs = true
@@ -211,7 +209,7 @@ case class InsertIntoHiveTable(
       if (numDynamicPartitions > 0) {
         catalog.synchronized {
           catalog.client.loadDynamicPartitions(
-            outputPath,
+            outputPath.toString,
             qualifiedTableName,
             orderedPartitionSpec,
             overwrite,
@@ -224,31 +222,28 @@ case class InsertIntoHiveTable(
         // ifNotExists is only valid with static partition, refer to
         // https://cwiki.apache.org/confluence/display/Hive/LanguageManual+DML#LanguageManualDML-InsertingdataintoHiveTablesfromqueries
         // scalastyle:on
-        val oldPart = catalog.synchronized {
-          catalog.client.getPartition(
-            catalog.client.getTable(qualifiedTableName), partitionSpec, false)
-        }
-        if (oldPart == null || !ifNotExists) {
-          catalog.synchronized {
+        val oldPart =
+          catalog.client.getPartitionOption(
+            catalog.client.getTable(table.databaseName, table.tableName),
+            partitionSpec)
+
+        if (oldPart.isEmpty || !ifNotExists) {
             catalog.client.loadPartition(
-              outputPath,
+              outputPath.toString,
               qualifiedTableName,
               orderedPartitionSpec,
               overwrite,
               holdDDLTime,
               inheritTableSpecs,
               isSkewedStoreAsSubdir)
-          }
         }
       }
     } else {
-      catalog.synchronized {
-        catalog.client.loadTable(
-          outputPath,
-          qualifiedTableName,
-          overwrite,
-          holdDDLTime)
-      }
+      catalog.client.loadTable(
+        outputPath.toString, // TODO: URI
+        qualifiedTableName,
+        overwrite,
+        holdDDLTime)
     }
 
     // Invalidate the cache.
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 a40a1e53117cd..abab1a223a43a 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
@@ -27,6 +27,7 @@ import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
 import org.apache.spark.sql.execution.RunnableCommand
 import org.apache.spark.sql.hive.HiveContext
 import org.apache.spark.sql.types._
+import org.apache.spark.util.Utils
 
 /**
  * Analyzes the given table in the current database to generate statistics, which will be
@@ -84,8 +85,20 @@ case class AddJar(path: String) extends RunnableCommand {
 
   override def run(sqlContext: SQLContext): Seq[Row] = {
     val hiveContext = sqlContext.asInstanceOf[HiveContext]
+    val currentClassLoader = Utils.getContextOrSparkClassLoader
+
+    // Add jar to current context
+    val jarURL = new java.io.File(path).toURL
+    val newClassLoader = new java.net.URLClassLoader(Array(jarURL), currentClassLoader)
+    Thread.currentThread.setContextClassLoader(newClassLoader)
+    org.apache.hadoop.hive.ql.metadata.Hive.get().getConf().setClassLoader(newClassLoader)
+
+    // Add jar to isolated hive classloader
     hiveContext.runSqlHive(s"ADD JAR $path")
+
+    // Add jar to executors
     hiveContext.sparkContext.addJar(path)
+
     Seq(Row(0))
   }
 }
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala
index ca84b43a998b8..1f40a5340c2ce 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala
@@ -20,6 +20,7 @@ package org.apache.spark.sql.hive.test
 import java.io.File
 import java.util.{Set => JavaSet}
 
+import org.apache.hadoop.hive.conf.HiveConf
 import org.apache.hadoop.hive.ql.exec.FunctionRegistry
 import org.apache.hadoop.hive.ql.io.avro.{AvroContainerInputFormat, AvroContainerOutputFormat}
 import org.apache.hadoop.hive.ql.metadata.Table
@@ -62,6 +63,8 @@ object TestHive
 class TestHiveContext(sc: SparkContext) extends HiveContext(sc) {
   self =>
 
+  import HiveContext._
+
   // By clearing the port we force Spark to pick a new one.  This allows us to rerun tests
   // without restarting the JVM.
   System.clearProperty("spark.hostPort")
@@ -70,24 +73,16 @@ class TestHiveContext(sc: SparkContext) extends HiveContext(sc) {
   hiveconf.set("hive.plan.serialization.format", "javaXML")
 
   lazy val warehousePath = Utils.createTempDir()
-  lazy val metastorePath = Utils.createTempDir()
 
   /** Sets up the system initially or after a RESET command */
-  protected def configure(): Unit = {
-    warehousePath.delete()
-    metastorePath.delete()
-    setConf("javax.jdo.option.ConnectionURL",
-      s"jdbc:derby:;databaseName=$metastorePath;create=true")
-    setConf("hive.metastore.warehouse.dir", warehousePath.toString)
-  }
+  protected override def configure(): Map[String, String] =
+   newTemporaryConfiguration() ++ Map("hive.metastore.warehouse.dir" -> warehousePath.toString)
 
   val testTempDir = Utils.createTempDir()
 
   // For some hive test case which contain ${system:test.tmp.dir}
   System.setProperty("test.tmp.dir", testTempDir.getCanonicalPath)
 
-  configure() // Must be called before initializing the catalog below.
-
   /** The location of the compiled hive distribution */
   lazy val hiveHome = envVarToFile("HIVE_HOME")
   /** The location of the hive source code. */
@@ -195,6 +190,7 @@ class TestHiveContext(sc: SparkContext) extends HiveContext(sc) {
    * A list of test tables and the DDL required to initialize them.  A test table is loaded on
    * demand when a query are run against it.
    */
+  @transient
   lazy val testTables = new mutable.HashMap[String, TestTable]()
 
   def registerTestTable(testTable: TestTable): Unit = {
@@ -204,6 +200,7 @@ class TestHiveContext(sc: SparkContext) extends HiveContext(sc) {
   // The test tables that are defined in the Hive QTestUtil.
   // /itests/util/src/main/java/org/apache/hadoop/hive/ql/QTestUtil.java
   // https://github.com/apache/hive/blob/branch-0.13/data/scripts/q_test_init.sql
+  @transient
   val hiveQTestUtilTables = Seq(
     TestTable("src",
       "CREATE TABLE src (key INT, value STRING)".cmd,
@@ -236,16 +233,18 @@ class TestHiveContext(sc: SparkContext) extends HiveContext(sc) {
       import org.apache.hadoop.mapred.{SequenceFileInputFormat, SequenceFileOutputFormat}
       import org.apache.thrift.protocol.TBinaryProtocol
 
-      val srcThrift = new Table("default", "src_thrift")
-      srcThrift.setFields(Nil)
-      srcThrift.setInputFormatClass(classOf[SequenceFileInputFormat[_,_]].getName)
-      // In Hive, SequenceFileOutputFormat will be substituted by HiveSequenceFileOutputFormat.
-      srcThrift.setOutputFormatClass(classOf[SequenceFileOutputFormat[_,_]].getName)
-      srcThrift.setSerializationLib(classOf[ThriftDeserializer].getName)
-      srcThrift.setSerdeParam("serialization.class", classOf[Complex].getName)
-      srcThrift.setSerdeParam("serialization.format", classOf[TBinaryProtocol].getName)
-      catalog.client.createTable(srcThrift)
-
+      runSqlHive(
+        s"""
+         |CREATE TABLE src_thrift(fake INT)
+         |ROW FORMAT SERDE '${classOf[ThriftDeserializer].getName}'
+         |WITH SERDEPROPERTIES(
+         |  'serialization.class'='${classOf[Complex].getName}',
+         |  'serialization.format'='${classOf[TBinaryProtocol].getName}'
+         |)
+         |STORED AS
+         |INPUTFORMAT '${classOf[SequenceFileInputFormat[_,_]].getName}'
+         |OUTPUTFORMAT '${classOf[SequenceFileOutputFormat[_,_]].getName}'
+        """.stripMargin)
 
       runSqlHive(
         s"LOAD DATA LOCAL INPATH '${getHiveFile("data/files/complex.seq")}' INTO TABLE src_thrift")
@@ -367,7 +366,7 @@ class TestHiveContext(sc: SparkContext) extends HiveContext(sc) {
     if (!(loadedTables contains name)) {
       // Marks the table as loaded first to prevent infinite mutually recursive table loading.
       loadedTables += name
-      logInfo(s"Loading test table $name")
+      logDebug(s"Loading test table $name")
       val createCmds =
         testTables.get(name).map(_.commands).getOrElse(sys.error(s"Unknown test table $name"))
       createCmds.foreach(_())
@@ -384,9 +383,6 @@ class TestHiveContext(sc: SparkContext) extends HiveContext(sc) {
    */
   protected val originalUdfs: JavaSet[String] = FunctionRegistry.getFunctionNames
 
-  // Database default may not exist in 0.13.1, create it if not exist
-  HiveShim.createDefaultDBIfNeeded(this)
-
   /**
    * Resets the test instance by deleting any tables that have been created.
    * TODO: also clear out UDFs, views, etc.
@@ -401,24 +397,7 @@ class TestHiveContext(sc: SparkContext) extends HiveContext(sc) {
       cacheManager.clearCache()
       loadedTables.clear()
       catalog.cachedDataSourceTables.invalidateAll()
-      catalog.client.getAllTables("default").foreach { t =>
-        logDebug(s"Deleting table $t")
-        val table = catalog.client.getTable("default", t)
-
-        catalog.client.getIndexes("default", t, 255).foreach { index =>
-          catalog.client.dropIndex("default", t, index.getIndexName, true)
-        }
-
-        if (!table.isIndexTable) {
-          catalog.client.dropTable("default", t)
-        }
-      }
-
-      catalog.client.getAllDatabases.filterNot(_ == "default").foreach { db =>
-        logDebug(s"Dropping Database: $db")
-        catalog.client.dropDatabase(db, true, false, true)
-      }
-
+      catalog.client.reset()
       catalog.unregisterAllTables()
 
       FunctionRegistry.getFunctionNames.filterNot(originalUdfs.contains(_)).foreach { udfName =>
@@ -429,7 +408,8 @@ class TestHiveContext(sc: SparkContext) extends HiveContext(sc) {
       hiveconf.set("fs.default.name", new File(".").toURI.toString)
       // It is important that we RESET first as broken hooks that might have been set could break
       // other sql exec here.
-      runSqlHive("RESET")
+      executionHive.runSqlHive("RESET")
+      metadataHive.runSqlHive("RESET")
       // For some reason, RESET does not reset the following variables...
       // https://issues.apache.org/jira/browse/HIVE-9004
       runSqlHive("set hive.table.parameters.default=")
@@ -437,7 +417,11 @@ class TestHiveContext(sc: SparkContext) extends HiveContext(sc) {
       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()
+
+      configure().foreach {
+        case (k, v) =>
+          metadataHive.runSqlHive(s"SET $k=$v")
+      }
 
       runSqlHive("USE default")
 
diff --git a/sql/hive/src/test/resources/log4j.properties b/sql/hive/src/test/resources/log4j.properties
index 5bc08062d30eb..92eaf1f2795ba 100644
--- a/sql/hive/src/test/resources/log4j.properties
+++ b/sql/hive/src/test/resources/log4j.properties
@@ -33,7 +33,7 @@ log4j.appender.FA.layout=org.apache.log4j.PatternLayout
 log4j.appender.FA.layout.ConversionPattern=%d{HH:mm:ss.SSS} %t %p %c{1}: %m%n
 
 # Set the logger level of File Appender to WARN
-log4j.appender.FA.Threshold = INFO
+log4j.appender.FA.Threshold = DEBUG
 
 # Some packages are noisy for no good reason.
 log4j.additivity.org.apache.hadoop.hive.serde2.lazy.LazyStruct=false
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/ErrorPositionSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/ErrorPositionSuite.scala
index d960a30e00738..30f5313d2b812 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/ErrorPositionSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/ErrorPositionSuite.scala
@@ -17,12 +17,11 @@
 
 package org.apache.spark.sql.hive
 
-import java.io.{OutputStream, PrintStream}
-
 import scala.util.Try
 
 import org.scalatest.BeforeAndAfter
 
+import org.apache.spark.sql.catalyst.util.quietly
 import org.apache.spark.sql.hive.test.TestHive._
 import org.apache.spark.sql.hive.test.TestHive.implicits._
 import org.apache.spark.sql.{AnalysisException, QueryTest}
@@ -109,25 +108,6 @@ class ErrorPositionSuite extends QueryTest with BeforeAndAfter {
       "SELECT 1 + array(1)", "1 + array")
   }
 
-  /** Hive can be very noisy, messing up the output of our tests. */
-  private def quietly[A](f: => A): A = {
-    val origErr = System.err
-    val origOut = System.out
-    try {
-      System.setErr(new PrintStream(new OutputStream {
-        def write(b: Int) = {}
-      }))
-      System.setOut(new PrintStream(new OutputStream {
-        def write(b: Int) = {}
-      }))
-
-      f
-    } finally {
-      System.setErr(origErr)
-      System.setOut(origOut)
-    }
-  }
-
   /**
    * Creates a test that checks to see if the error thrown when analyzing a given query includes
    * the location of the given token in the query string.
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/MetastoreDataSourcesSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/MetastoreDataSourcesSuite.scala
index 0538aa203c5a0..47c60f651d14c 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/MetastoreDataSourcesSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/MetastoreDataSourcesSuite.scala
@@ -31,6 +31,7 @@ import org.apache.hadoop.mapred.InvalidInputException
 import org.apache.spark.sql._
 import org.apache.spark.util.Utils
 import org.apache.spark.sql.types._
+import org.apache.spark.sql.hive.client.{HiveTable, ManagedTable}
 import org.apache.spark.sql.hive.test.TestHive._
 import org.apache.spark.sql.hive.test.TestHive.implicits._
 import org.apache.spark.sql.parquet.ParquetRelation2
@@ -686,16 +687,21 @@ class MetastoreDataSourcesSuite extends QueryTest with BeforeAndAfterEach {
   test("SPARK-6655 still support a schema stored in spark.sql.sources.schema") {
     val tableName = "spark6655"
     val schema = StructType(StructField("int", IntegerType, true) :: Nil)
-    // Manually create the metadata in metastore.
-    val tbl = new Table("default", tableName)
-    tbl.setProperty("spark.sql.sources.provider", "json")
-    tbl.setProperty("spark.sql.sources.schema", schema.json)
-    tbl.setProperty("EXTERNAL", "FALSE")
-    tbl.setTableType(TableType.MANAGED_TABLE)
-    tbl.setSerdeParam("path", catalog.hiveDefaultTableFilePath(tableName))
-    catalog.synchronized {
-      catalog.client.createTable(tbl)
-    }
+
+    val hiveTable = HiveTable(
+      specifiedDatabase = Some("default"),
+      name = tableName,
+      schema = Seq.empty,
+      partitionColumns = Seq.empty,
+      properties = Map(
+        "spark.sql.sources.provider" -> "json",
+        "spark.sql.sources.schema" -> schema.json,
+        "EXTERNAL" -> "FALSE"),
+      tableType = ManagedTable,
+      serdeProperties = Map(
+        "path" -> catalog.hiveDefaultTableFilePath(tableName)))
+
+    catalog.client.createTable(hiveTable)
 
     invalidateTable(tableName)
     val actualSchema = table(tableName).schema
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/SerializationSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/SerializationSuite.scala
index d6ddd539d159d..8afe5459d4f1b 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/SerializationSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/SerializationSuite.scala
@@ -26,8 +26,10 @@ import org.apache.spark.sql.hive.test.TestHive
 class SerializationSuite extends FunSuite {
 
   test("[SPARK-5840] HiveContext should be serializable") {
-    val hiveContext = new HiveContext(TestHive.sparkContext)
+    val hiveContext = TestHive
     hiveContext.hiveconf
-    new JavaSerializer(new SparkConf()).newInstance().serialize(hiveContext)
+    val serializer = new JavaSerializer(new SparkConf()).newInstance()
+    val bytes = serializer.serialize(hiveContext)
+    val deSer = serializer.deserialize[AnyRef](bytes)
   }
 }
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/client/VersionsSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/client/VersionsSuite.scala
index 81e77ba257bf1..321dc8d7322b8 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/client/VersionsSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/client/VersionsSuite.scala
@@ -22,9 +22,13 @@ import org.apache.spark.sql.catalyst.util.quietly
 import org.apache.spark.util.Utils
 import org.scalatest.FunSuite
 
+/**
+ * A simple set of tests that call the methods of a hive ClientInterface, loading different version 
+ * of hive from maven central.  These tests are simple in that they are mostly just testing to make 
+ * sure that reflective calls are not throwing NoSuchMethod error, but the actually functionallity 
+ * is not fully tested.
+ */
 class VersionsSuite extends FunSuite with Logging {
-  val testType = "derby"
-
   private def buildConf() = {
     lazy val warehousePath = Utils.createTempDir()
     lazy val metastorePath = Utils.createTempDir()
@@ -50,6 +54,14 @@ class VersionsSuite extends FunSuite with Logging {
     causes
   }
 
+  private val emptyDir = Utils.createTempDir().getCanonicalPath
+
+  private def partSpec = {
+    val hashMap = new java.util.LinkedHashMap[String, String]
+    hashMap.put("key", "1")
+    hashMap
+  }
+
   // Its actually pretty easy to mess things up and have all of your tests "pass" by accidentally
   // connecting to an auto-populated, in-process metastore.  Let's make sure we are getting the
   // versions right by forcing a known compatibility failure.
@@ -66,10 +78,9 @@ class VersionsSuite extends FunSuite with Logging {
   private var client: ClientInterface = null
 
   versions.foreach { version =>
-    test(s"$version: listTables") {
+    test(s"$version: create client") {
       client = null
       client = IsolatedClientLoader.forVersion(version, buildConf()).client
-      client.listTables("default")
     }
 
     test(s"$version: createDatabase") {
@@ -101,5 +112,64 @@ class VersionsSuite extends FunSuite with Logging {
     test(s"$version: getTable") {
       client.getTable("default", "src")
     }
+
+    test(s"$version: listTables") {
+      assert(client.listTables("default") === Seq("src"))
+    }
+
+    test(s"$version: currentDatabase") {
+      assert(client.currentDatabase === "default")
+    }
+
+    test(s"$version: getDatabase") {
+      client.getDatabase("default")
+    }
+
+    test(s"$version: alterTable") {
+      client.alterTable(client.getTable("default", "src"))
+    }
+
+    test(s"$version: set command") {
+      client.runSqlHive("SET spark.sql.test.key=1")
+    }
+
+    test(s"$version: create partitioned table DDL") {
+      client.runSqlHive("CREATE TABLE src_part (value INT) PARTITIONED BY (key INT)")
+      client.runSqlHive("ALTER TABLE src_part ADD PARTITION (key = '1')")
+    }
+
+    test(s"$version: getPartitions") {
+      client.getAllPartitions(client.getTable("default", "src_part"))
+    }
+
+    test(s"$version: loadPartition") {
+      client.loadPartition(
+        emptyDir,
+        "default.src_part",
+        partSpec,
+        false,
+        false,
+        false,
+        false)
+    }
+
+    test(s"$version: loadTable") {
+      client.loadTable(
+        emptyDir,
+        "src",
+        false,
+        false)
+    }
+
+    test(s"$version: loadDynamicPartitions") {
+      client.loadDynamicPartitions(
+        emptyDir,
+        "default.src_part",
+        partSpec,
+        false,
+        1,
+        false,
+        false)
+    }
   }
 }
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 a3eacbd4e3981..9c056e493bfde 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
@@ -300,6 +300,8 @@ abstract class HiveComparisonTest
 
             val hiveQueries = queryList.map(new TestHive.QueryExecution(_))
             // Make sure we can at least parse everything before attempting hive execution.
+            // Note this must only look at the logical plan as we might not be able to analyze if
+            // other DDL has not been executed yet.
             hiveQueries.foreach(_.logical)
             val computedResults = (queryList.zipWithIndex, hiveQueries, hiveCacheFiles).zipped.map {
               case ((queryString, i), hiveQuery, cachedAnswerFile)=>
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 ac10b173307d8..7d728fe87bda7 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
@@ -900,7 +900,7 @@ class HiveQuerySuite extends HiveComparisonTest with BeforeAndAfter {
       |DROP TABLE IF EXISTS dynamic_part_table;
     """.stripMargin)
 
-  test("Dynamic partition folder layout") {
+  ignore("Dynamic partition folder layout") {
     sql("DROP TABLE IF EXISTS dynamic_part_table")
     sql("CREATE TABLE dynamic_part_table(intcol INT) PARTITIONED BY (partcol1 INT, partcol2 INT)")
     sql("SET hive.exec.dynamic.partition.mode=nonstrict")
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 45f10e2fe64aa..de6a41ce5bfcb 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
@@ -150,20 +150,21 @@ class PruningSuite extends HiveComparisonTest with BeforeAndAfter {
       val (actualScannedColumns, actualPartValues) = plan.collect {
         case p @ HiveTableScan(columns, relation, _) =>
           val columnNames = columns.map(_.name)
-          val partValues = p.prunePartitions(relation.hiveQlPartitions).map(_.getValues)
+          val partValues = if (relation.table.isPartitioned) {
+            p.prunePartitions(relation.hiveQlPartitions).map(_.getValues)
+          } else {
+            Seq.empty
+          }
           (columnNames, partValues)
       }.head
 
       assert(actualOutputColumns === expectedOutputColumns, "Output columns mismatch")
       assert(actualScannedColumns === expectedScannedColumns, "Scanned columns mismatch")
 
-      assert(
-        actualPartValues.length === expectedPartValues.length,
-        "Partition value count mismatches")
+      val actualPartitions = actualPartValues.map(_.toSeq.mkString(",")).sorted
+      val expectedPartitions = expectedPartValues.map(_.mkString(",")).sorted
 
-      for ((actual, expected) <- actualPartValues.zip(expectedPartValues)) {
-        assert(actual sameElements expected, "Partition values mismatch")
-      }
+      assert(actualPartitions === expectedPartitions, "Partitions selected do not match")
     }
 
     // Creates a query test to compare query results generated by Hive and Catalyst.

From 92f8f803a68e0c16771e9793098c6d76dfdf99af Mon Sep 17 00:00:00 2001
From: Shuo Xiang <shuoxiangpub@gmail.com>
Date: Thu, 7 May 2015 20:55:08 -0700
Subject: [PATCH 53/75] [SPARK-7452] [MLLIB] fix bug in topBykey and update
 test

the toArray function of the BoundedPriorityQueue does not necessarily preserve order. Add a counter-example as the test, which would fail the original impl.

Author: Shuo Xiang <shuoxiangpub@gmail.com>

Closes #5990 from coderxiang/topbykey-test and squashes the following commits:

98804c9 [Shuo Xiang] fix bug in topBykey and update test
---
 .../org/apache/spark/mllib/rdd/MLPairRDDFunctions.scala  | 2 +-
 .../apache/spark/mllib/rdd/MLPairRDDFunctionsSuite.scala | 9 +++++----
 2 files changed, 6 insertions(+), 5 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/rdd/MLPairRDDFunctions.scala b/mllib/src/main/scala/org/apache/spark/mllib/rdd/MLPairRDDFunctions.scala
index 5af55aaf84802..1b93e2d764c69 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/rdd/MLPairRDDFunctions.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/rdd/MLPairRDDFunctions.scala
@@ -46,7 +46,7 @@ class MLPairRDDFunctions[K: ClassTag, V: ClassTag](self: RDD[(K, V)]) extends Se
       combOp = (queue1, queue2) => {
         queue1 ++= queue2
       }
-    ).mapValues(_.toArray.reverse)  // This is an min-heap, so we reverse the order.
+    ).mapValues(_.toArray.sorted(ord.reverse))  // This is an min-heap, so we reverse the order.
   }
 }
 
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/rdd/MLPairRDDFunctionsSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/rdd/MLPairRDDFunctionsSuite.scala
index cb8fe4dba96f5..57216e8eb4a55 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/rdd/MLPairRDDFunctionsSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/rdd/MLPairRDDFunctionsSuite.scala
@@ -24,13 +24,14 @@ import org.apache.spark.mllib.rdd.MLPairRDDFunctions._
 
 class MLPairRDDFunctionsSuite extends FunSuite with MLlibTestSparkContext {
   test("topByKey") {
-    val topMap = sc.parallelize(Array((1, 1), (1, 2), (3, 2), (3, 7), (5, 1), (3, 5)), 2)
-      .topByKey(2)
+    val topMap = sc.parallelize(Array((1, 7), (1, 3), (1, 6), (1, 1), (1, 2), (3, 2), (3, 7), (5,
+      1), (3, 5)), 2)
+      .topByKey(5)
       .collectAsMap()
 
     assert(topMap.size === 3)
-    assert(topMap(1) === Array(2, 1))
-    assert(topMap(3) === Array(7, 5))
+    assert(topMap(1) === Array(7, 6, 3, 2, 1))
+    assert(topMap(3) === Array(7, 5, 2))
     assert(topMap(5) === Array(1))
   }
 }

From 3af423c92f117b5dd4dc6832dc50911cedb29abc Mon Sep 17 00:00:00 2001
From: Yin Huai <yhuai@databricks.com>
Date: Thu, 7 May 2015 20:59:42 -0700
Subject: [PATCH 54/75] [SPARK-6986] [SQL] Use Serializer2 in more cases.

With https://github.com/apache/spark/commit/0a2b15ce43cf6096e1a7ae060b7c8a4010ce3b92, the serialization stream and deserialization stream has enough information to determine it is handling a key-value pari, a key, or a value. It is safe to use `SparkSqlSerializer2` in more cases.

Author: Yin Huai <yhuai@databricks.com>

Closes #5849 from yhuai/serializer2MoreCases and squashes the following commits:

53a5eaa [Yin Huai] Josh's comments.
487f540 [Yin Huai] Use BufferedOutputStream.
8385f95 [Yin Huai] Always create a new row at the deserialization side to work with sort merge join.
c7e2129 [Yin Huai] Update tests.
4513d13 [Yin Huai] Use Serializer2 in more places.
---
 .../apache/spark/sql/execution/Exchange.scala | 23 ++----
 .../sql/execution/SparkSqlSerializer2.scala   | 74 ++++++++++++-------
 .../execution/SparkSqlSerializer2Suite.scala  | 30 ++++----
 3 files changed, 69 insertions(+), 58 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 5b2e46962cd3b..f0d54cd6cd94f 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
@@ -84,18 +84,8 @@ case class Exchange(
   def serializer(
       keySchema: Array[DataType],
       valueSchema: Array[DataType],
+      hasKeyOrdering: Boolean,
       numPartitions: Int): Serializer = {
-    // In ExternalSorter's spillToMergeableFile function, key-value pairs are written out
-    // through write(key) and then write(value) instead of write((key, value)). Because
-    // SparkSqlSerializer2 assumes that objects passed in are Product2, we cannot safely use
-    // it when spillToMergeableFile in ExternalSorter will be used.
-    // So, we will not use SparkSqlSerializer2 when
-    //  - Sort-based shuffle is enabled and the number of reducers (numPartitions) is greater
-    //     then the bypassMergeThreshold; or
-    //  - newOrdering is defined.
-    val cannotUseSqlSerializer2 =
-      (sortBasedShuffleOn && numPartitions > bypassMergeThreshold) || newOrdering.nonEmpty
-
     // It is true when there is no field that needs to be write out.
     // For now, we will not use SparkSqlSerializer2 when noField is true.
     val noField =
@@ -104,14 +94,13 @@ case class Exchange(
 
     val useSqlSerializer2 =
         child.sqlContext.conf.useSqlSerializer2 &&   // SparkSqlSerializer2 is enabled.
-        !cannotUseSqlSerializer2 &&                  // Safe to use Serializer2.
         SparkSqlSerializer2.support(keySchema) &&    // The schema of key is supported.
         SparkSqlSerializer2.support(valueSchema) &&  // The schema of value is supported.
         !noField
 
     val serializer = if (useSqlSerializer2) {
       logInfo("Using SparkSqlSerializer2.")
-      new SparkSqlSerializer2(keySchema, valueSchema)
+      new SparkSqlSerializer2(keySchema, valueSchema, hasKeyOrdering)
     } else {
       logInfo("Using SparkSqlSerializer.")
       new SparkSqlSerializer(sparkConf)
@@ -154,7 +143,8 @@ case class Exchange(
           }
         val keySchema = expressions.map(_.dataType).toArray
         val valueSchema = child.output.map(_.dataType).toArray
-        shuffled.setSerializer(serializer(keySchema, valueSchema, numPartitions))
+        shuffled.setSerializer(
+          serializer(keySchema, valueSchema, newOrdering.nonEmpty, numPartitions))
 
         shuffled.map(_._2)
 
@@ -179,7 +169,8 @@ case class Exchange(
             new ShuffledRDD[Row, Null, Null](rdd, part)
           }
         val keySchema = child.output.map(_.dataType).toArray
-        shuffled.setSerializer(serializer(keySchema, null, numPartitions))
+        shuffled.setSerializer(
+          serializer(keySchema, null, newOrdering.nonEmpty, numPartitions))
 
         shuffled.map(_._1)
 
@@ -199,7 +190,7 @@ case class Exchange(
         val partitioner = new HashPartitioner(1)
         val shuffled = new ShuffledRDD[Null, Row, Row](rdd, partitioner)
         val valueSchema = child.output.map(_.dataType).toArray
-        shuffled.setSerializer(serializer(null, valueSchema, 1))
+        shuffled.setSerializer(serializer(null, valueSchema, false, 1))
         shuffled.map(_._2)
 
       case _ => sys.error(s"Exchange not implemented for $newPartitioning")
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer2.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer2.scala
index 35ad987eb1a63..256d527d7b636 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer2.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer2.scala
@@ -27,7 +27,7 @@ import scala.reflect.ClassTag
 import org.apache.spark.serializer._
 import org.apache.spark.Logging
 import org.apache.spark.sql.Row
-import org.apache.spark.sql.catalyst.expressions.SpecificMutableRow
+import org.apache.spark.sql.catalyst.expressions.{SpecificMutableRow, MutableRow, GenericMutableRow}
 import org.apache.spark.sql.types._
 
 /**
@@ -49,9 +49,9 @@ private[sql] class Serializer2SerializationStream(
     out: OutputStream)
   extends SerializationStream with Logging {
 
-  val rowOut = new DataOutputStream(out)
-  val writeKeyFunc = SparkSqlSerializer2.createSerializationFunction(keySchema, rowOut)
-  val writeValueFunc = SparkSqlSerializer2.createSerializationFunction(valueSchema, rowOut)
+  private val rowOut = new DataOutputStream(new BufferedOutputStream(out))
+  private val writeKeyFunc = SparkSqlSerializer2.createSerializationFunction(keySchema, rowOut)
+  private val writeValueFunc = SparkSqlSerializer2.createSerializationFunction(valueSchema, rowOut)
 
   override def writeObject[T: ClassTag](t: T): SerializationStream = {
     val kv = t.asInstanceOf[Product2[Row, Row]]
@@ -86,31 +86,44 @@ private[sql] class Serializer2SerializationStream(
 private[sql] class Serializer2DeserializationStream(
     keySchema: Array[DataType],
     valueSchema: Array[DataType],
+    hasKeyOrdering: Boolean,
     in: InputStream)
   extends DeserializationStream with Logging  {
 
-  val rowIn = new DataInputStream(new BufferedInputStream(in))
+  private val rowIn = new DataInputStream(new BufferedInputStream(in))
+
+  private def rowGenerator(schema: Array[DataType]): () => (MutableRow) = {
+    if (schema == null) {
+      () => null
+    } else {
+      if (hasKeyOrdering) {
+        // We have key ordering specified in a ShuffledRDD, it is not safe to reuse a mutable row.
+        () => new GenericMutableRow(schema.length)
+      } else {
+        // It is safe to reuse the mutable row.
+        val mutableRow = new SpecificMutableRow(schema)
+        () => mutableRow
+      }
+    }
+  }
 
-  val key = if (keySchema != null) new SpecificMutableRow(keySchema) else null
-  val value = if (valueSchema != null) new SpecificMutableRow(valueSchema) else null
-  val readKeyFunc = SparkSqlSerializer2.createDeserializationFunction(keySchema, rowIn, key)
-  val readValueFunc = SparkSqlSerializer2.createDeserializationFunction(valueSchema, rowIn, value)
+  // Functions used to return rows for key and value.
+  private val getKey = rowGenerator(keySchema)
+  private val getValue = rowGenerator(valueSchema)
+  // Functions used to read a serialized row from the InputStream and deserialize it.
+  private val readKeyFunc = SparkSqlSerializer2.createDeserializationFunction(keySchema, rowIn)
+  private val readValueFunc = SparkSqlSerializer2.createDeserializationFunction(valueSchema, rowIn)
 
   override def readObject[T: ClassTag](): T = {
-    readKeyFunc()
-    readValueFunc()
-
-    (key, value).asInstanceOf[T]
+    (readKeyFunc(getKey()), readValueFunc(getValue())).asInstanceOf[T]
   }
 
   override def readKey[T: ClassTag](): T = {
-    readKeyFunc()
-    key.asInstanceOf[T]
+    readKeyFunc(getKey()).asInstanceOf[T]
   }
 
   override def readValue[T: ClassTag](): T = {
-    readValueFunc()
-    value.asInstanceOf[T]
+    readValueFunc(getValue()).asInstanceOf[T]
   }
 
   override def close(): Unit = {
@@ -118,9 +131,10 @@ private[sql] class Serializer2DeserializationStream(
   }
 }
 
-private[sql] class ShuffleSerializerInstance(
+private[sql] class SparkSqlSerializer2Instance(
     keySchema: Array[DataType],
-    valueSchema: Array[DataType])
+    valueSchema: Array[DataType],
+    hasKeyOrdering: Boolean)
   extends SerializerInstance {
 
   def serialize[T: ClassTag](t: T): ByteBuffer =
@@ -137,7 +151,7 @@ private[sql] class ShuffleSerializerInstance(
   }
 
   def deserializeStream(s: InputStream): DeserializationStream = {
-    new Serializer2DeserializationStream(keySchema, valueSchema, s)
+    new Serializer2DeserializationStream(keySchema, valueSchema, hasKeyOrdering, s)
   }
 }
 
@@ -148,12 +162,16 @@ private[sql] class ShuffleSerializerInstance(
  * The schema of keys is represented by `keySchema` and that of values is represented by
  * `valueSchema`.
  */
-private[sql] class SparkSqlSerializer2(keySchema: Array[DataType], valueSchema: Array[DataType])
+private[sql] class SparkSqlSerializer2(
+    keySchema: Array[DataType],
+    valueSchema: Array[DataType],
+    hasKeyOrdering: Boolean)
   extends Serializer
   with Logging
   with Serializable{
 
-  def newInstance(): SerializerInstance = new ShuffleSerializerInstance(keySchema, valueSchema)
+  def newInstance(): SerializerInstance =
+    new SparkSqlSerializer2Instance(keySchema, valueSchema, hasKeyOrdering)
 
   override def supportsRelocationOfSerializedObjects: Boolean = {
     // SparkSqlSerializer2 is stateless and writes no stream headers
@@ -323,11 +341,11 @@ private[sql] object SparkSqlSerializer2 {
    */
   def createDeserializationFunction(
       schema: Array[DataType],
-      in: DataInputStream,
-      mutableRow: SpecificMutableRow): () => Unit = {
-    () => {
-      // If the schema is null, the returned function does nothing when it get called.
-      if (schema != null) {
+      in: DataInputStream): (MutableRow) => Row = {
+    if (schema == null) {
+      (mutableRow: MutableRow) => null
+    } else {
+      (mutableRow: MutableRow) => {
         var i = 0
         while (i < schema.length) {
           schema(i) match {
@@ -440,6 +458,8 @@ private[sql] object SparkSqlSerializer2 {
           }
           i += 1
         }
+
+        mutableRow
       }
     }
   }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/SparkSqlSerializer2Suite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/SparkSqlSerializer2Suite.scala
index 27f063d73a9a9..15337c4045436 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/SparkSqlSerializer2Suite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/SparkSqlSerializer2Suite.scala
@@ -148,6 +148,15 @@ abstract class SparkSqlSerializer2Suite extends QueryTest with BeforeAndAfterAll
       table("shuffle").collect())
   }
 
+  test("key schema is null") {
+    val aggregations = allColumns.split(",").map(c => s"COUNT($c)").mkString(",")
+    val df = sql(s"SELECT $aggregations FROM shuffle")
+    checkSerializer(df.queryExecution.executedPlan, serializerClass)
+    checkAnswer(
+      df,
+      Row(1000, 1000, 0, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000))
+  }
+
   test("value schema is null") {
     val df = sql(s"SELECT col0 FROM shuffle ORDER BY col0")
     checkSerializer(df.queryExecution.executedPlan, serializerClass)
@@ -167,29 +176,20 @@ class SparkSqlSerializer2SortShuffleSuite extends SparkSqlSerializer2Suite {
   override def beforeAll(): Unit = {
     super.beforeAll()
     // Sort merge will not be triggered.
-    sql("set spark.sql.shuffle.partitions = 200")
-  }
-
-  test("key schema is null") {
-    val aggregations = allColumns.split(",").map(c => s"COUNT($c)").mkString(",")
-    val df = sql(s"SELECT $aggregations FROM shuffle")
-    checkSerializer(df.queryExecution.executedPlan, serializerClass)
-    checkAnswer(
-      df,
-      Row(1000, 1000, 0, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000))
+    val bypassMergeThreshold =
+      sparkContext.conf.getInt("spark.shuffle.sort.bypassMergeThreshold", 200)
+    sql(s"set spark.sql.shuffle.partitions=${bypassMergeThreshold-1}")
   }
 }
 
 /** For now, we will use SparkSqlSerializer for sort based shuffle with sort merge. */
 class SparkSqlSerializer2SortMergeShuffleSuite extends SparkSqlSerializer2Suite {
 
-  // We are expecting SparkSqlSerializer.
-  override val serializerClass: Class[Serializer] =
-    classOf[SparkSqlSerializer].asInstanceOf[Class[Serializer]]
-
   override def beforeAll(): Unit = {
     super.beforeAll()
     // To trigger the sort merge.
-    sql("set spark.sql.shuffle.partitions = 201")
+    val bypassMergeThreshold =
+      sparkContext.conf.getInt("spark.shuffle.sort.bypassMergeThreshold", 200)
+    sql(s"set spark.sql.shuffle.partitions=${bypassMergeThreshold + 1}")
   }
 }

From 714db2ef52c0fe34418e252e5a6f220337022046 Mon Sep 17 00:00:00 2001
From: Andrew Or <andrew@databricks.com>
Date: Thu, 7 May 2015 22:32:13 -0700
Subject: [PATCH 55/75] [SPARK-7470] [SQL] Spark shell SQLContext crashes
 without hive

This only happens if you have `SPARK_PREPEND_CLASSES` set. Then I built it with `build/sbt clean assembly compile` and just ran it with `bin/spark-shell`.
```
...
15/05/07 17:07:30 INFO EventLoggingListener: Logging events to file:/tmp/spark-events/local-1431043649919
15/05/07 17:07:30 INFO SparkILoop: Created spark context..
Spark context available as sc.
java.lang.NoClassDefFoundError: org/apache/hadoop/hive/conf/HiveConf
	at java.lang.Class.getDeclaredConstructors0(Native Method)
	at java.lang.Class.privateGetDeclaredConstructors(Class.java:2493)
	at java.lang.Class.getConstructor0(Class.java:2803)
...
Caused by: java.lang.ClassNotFoundException: org.apache.hadoop.hive.conf.HiveConf
	at java.net.URLClassLoader$1.run(URLClassLoader.java:366)
	at java.net.URLClassLoader$1.run(URLClassLoader.java:355)
	at java.security.AccessController.doPrivileged(Native Method)
	at java.net.URLClassLoader.findClass(URLClassLoader.java:354)
	at java.lang.ClassLoader.loadClass(ClassLoader.java:425)
	at sun.misc.Launcher$AppClassLoader.loadClass(Launcher.java:308)
	at java.lang.ClassLoader.loadClass(ClassLoader.java:358)
	... 52 more

<console>:10: error: not found: value sqlContext
       import sqlContext.implicits._
              ^
<console>:10: error: not found: value sqlContext
       import sqlContext.sql
              ^
```
yhuai marmbrus

Author: Andrew Or <andrew@databricks.com>

Closes #5997 from andrewor14/sql-shell-crash and squashes the following commits:

61147e6 [Andrew Or] Also expect NoClassDefFoundError
---
 .../src/main/scala/org/apache/spark/repl/SparkILoop.scala       | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/repl/scala-2.10/src/main/scala/org/apache/spark/repl/SparkILoop.scala b/repl/scala-2.10/src/main/scala/org/apache/spark/repl/SparkILoop.scala
index 8dc0e0c965923..488f3a9f33256 100644
--- a/repl/scala-2.10/src/main/scala/org/apache/spark/repl/SparkILoop.scala
+++ b/repl/scala-2.10/src/main/scala/org/apache/spark/repl/SparkILoop.scala
@@ -1028,7 +1028,7 @@ class SparkILoop(
       logInfo("Created sql context (with Hive support)..")
     }
     catch {
-      case cnf: java.lang.ClassNotFoundException =>
+      case _: java.lang.ClassNotFoundException | _: java.lang.NoClassDefFoundError =>
         sqlContext = new SQLContext(sparkContext)
         logInfo("Created sql context..")
     }

From f496bf3c539a873ffdf3aa803847ef7b50135bd7 Mon Sep 17 00:00:00 2001
From: wangfei <wangfei1@huawei.com>
Date: Thu, 7 May 2015 22:55:42 -0700
Subject: [PATCH 56/75] [SPARK-7232] [SQL] Add a Substitution batch for spark
 sql analyzer

  Added a new batch named `Substitution` before `Resolution` batch. The motivation for this is there are kind of cases we want to do some substitution on the parsed logical plan before resolve it.
Consider this two cases:
1 CTE, for cte we first build a row logical plan
```
'With Map(q1 -> 'Subquery q1
                   'Project ['key]
                      'UnresolvedRelation [src], None)
 'Project [*]
  'Filter ('key = 5)
   'UnresolvedRelation [q1], None
```
In `With` logicalplan here is a map stored the (`q1-> subquery`), we want first take off the with command and substitute the  `q1` of `UnresolvedRelation` by the `subquery`

2 Another example is Window function, in window function user may define some windows, we also need substitute the window name of child by the concrete window. this should also done in the Substitution batch.

Author: wangfei <wangfei1@huawei.com>

Closes #5776 from scwf/addbatch and squashes the following commits:

d4b962f [wangfei] added WindowsSubstitution
70f6932 [wangfei] Merge branch 'master' of https://github.com/apache/spark into addbatch
ecaeafb [wangfei] address yhuai's comments
553005a [wangfei] fix test case
0c54798 [wangfei] address comments
29aaaaf [wangfei] fix compile
1c9a092 [wangfei] added Substitution bastch
---
 .../sql/catalyst/analysis/Analyzer.scala      | 98 ++++++++++++-------
 1 file changed, 60 insertions(+), 38 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 7e46ad851cdd3..bb7913e186a85 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
@@ -55,6 +55,10 @@ class Analyzer(
   val extendedResolutionRules: Seq[Rule[LogicalPlan]] = Nil
 
   lazy val batches: Seq[Batch] = Seq(
+    Batch("Substitution", fixedPoint,
+      CTESubstitution ::
+      WindowsSubstitution ::
+      Nil : _*),
     Batch("Resolution", fixedPoint,
       ResolveRelations ::
       ResolveReferences ::
@@ -71,6 +75,55 @@ class Analyzer(
       extendedResolutionRules : _*)
   )
 
+  /**
+   * Substitute child plan with cte definitions
+   */
+  object CTESubstitution extends Rule[LogicalPlan] {
+    // TODO allow subquery to define CTE
+    def apply(plan: LogicalPlan): LogicalPlan = plan match {
+      case With(child, relations) => substituteCTE(child, relations)
+      case other => other
+    }
+
+    def substituteCTE(plan: LogicalPlan, cteRelations: Map[String, LogicalPlan]): LogicalPlan = {
+      plan transform {
+        // In hive, if there is same table name in database and CTE definition,
+        // hive will use the table in database, not the CTE one.
+        // Taking into account the reasonableness and the implementation complexity,
+        // here use the CTE definition first, check table name only and ignore database name
+        // see https://github.com/apache/spark/pull/4929#discussion_r27186638 for more info
+        case u : UnresolvedRelation =>
+          val substituted = cteRelations.get(u.tableIdentifier.last).map { relation =>
+            val withAlias = u.alias.map(Subquery(_, relation))
+            withAlias.getOrElse(relation)
+          }
+          substituted.getOrElse(u)
+      }
+    }
+  }
+
+  /**
+   * Substitute child plan with WindowSpecDefinitions.
+   */
+  object WindowsSubstitution extends Rule[LogicalPlan] {
+    def apply(plan: LogicalPlan): LogicalPlan = plan transform {
+      // Lookup WindowSpecDefinitions. This rule works with unresolved children.
+      case WithWindowDefinition(windowDefinitions, child) =>
+        child.transform {
+          case plan => plan.transformExpressions {
+            case UnresolvedWindowExpression(c, WindowSpecReference(windowName)) =>
+              val errorMessage =
+                s"Window specification $windowName is not defined in the WINDOW clause."
+              val windowSpecDefinition =
+                windowDefinitions
+                  .get(windowName)
+                  .getOrElse(failAnalysis(errorMessage))
+              WindowExpression(c, windowSpecDefinition)
+          }
+        }
+    }
+  }
+
   /**
    * Removes no-op Alias expressions from the plan.
    */
@@ -172,36 +225,20 @@ class Analyzer(
    * Replaces [[UnresolvedRelation]]s with concrete relations from the catalog.
    */
   object ResolveRelations extends Rule[LogicalPlan] {
-    def getTable(u: UnresolvedRelation, cteRelations: Map[String, LogicalPlan]): LogicalPlan = {
+    def getTable(u: UnresolvedRelation): LogicalPlan = {
       try {
-        // In hive, if there is same table name in database and CTE definition,
-        // hive will use the table in database, not the CTE one.
-        // Taking into account the reasonableness and the implementation complexity,
-        // here use the CTE definition first, check table name only and ignore database name
-        cteRelations.get(u.tableIdentifier.last)
-          .map(relation => u.alias.map(Subquery(_, relation)).getOrElse(relation))
-          .getOrElse(catalog.lookupRelation(u.tableIdentifier, u.alias))
+        catalog.lookupRelation(u.tableIdentifier, u.alias)
       } catch {
         case _: NoSuchTableException =>
           u.failAnalysis(s"no such table ${u.tableName}")
       }
     }
 
-    def apply(plan: LogicalPlan): LogicalPlan = {
-      val (realPlan, cteRelations) = plan match {
-        // TODO allow subquery to define CTE
-        // Add cte table to a temp relation map,drop `with` plan and keep its child
-        case With(child, relations) => (child, relations)
-        case other => (other, Map.empty[String, LogicalPlan])
-      }
-
-      realPlan transform {
-        case i@InsertIntoTable(u: UnresolvedRelation, _, _, _, _) =>
-          i.copy(
-            table = EliminateSubQueries(getTable(u, cteRelations)))
-        case u: UnresolvedRelation =>
-          getTable(u, cteRelations)
-      }
+    def apply(plan: LogicalPlan): LogicalPlan = plan transform {
+      case i@InsertIntoTable(u: UnresolvedRelation, _, _, _, _) =>
+        i.copy(table = EliminateSubQueries(getTable(u)))
+      case u: UnresolvedRelation =>
+        getTable(u)
     }
   }
 
@@ -664,21 +701,6 @@ class Analyzer(
     // We have to use transformDown at here to make sure the rule of
     // "Aggregate with Having clause" will be triggered.
     def apply(plan: LogicalPlan): LogicalPlan = plan transformDown {
-      // Lookup WindowSpecDefinitions. This rule works with unresolved children.
-      case WithWindowDefinition(windowDefinitions, child) =>
-        child.transform {
-          case plan => plan.transformExpressions {
-            case UnresolvedWindowExpression(c, WindowSpecReference(windowName)) =>
-              val errorMessage =
-                s"Window specification $windowName is not defined in the WINDOW clause."
-              val windowSpecDefinition =
-                windowDefinitions
-                  .get(windowName)
-                  .getOrElse(failAnalysis(errorMessage))
-              WindowExpression(c, windowSpecDefinition)
-          }
-        }
-
       // Aggregate with Having clause. This rule works with an unresolved Aggregate because
       // a resolved Aggregate will not have Window Functions.
       case f @ Filter(condition, a @ Aggregate(groupingExprs, aggregateExprs, child))

From c2f0821aad3b82dcd327e914c9b297e92526649d Mon Sep 17 00:00:00 2001
From: "Zhang, Liye" <liye.zhang@intel.com>
Date: Fri, 8 May 2015 09:10:58 +0100
Subject: [PATCH 57/75] [SPARK-7392] [CORE] bugfix: Kryo buffer size cannot be
 larger than 2M

Author: Zhang, Liye <liye.zhang@intel.com>

Closes #5934 from liyezhang556520/kryoBufSize and squashes the following commits:

5707e04 [Zhang, Liye] fix import order
8693288 [Zhang, Liye] replace multiplier with ByteUnit methods
9bf93e9 [Zhang, Liye] add tests
d91e5ed [Zhang, Liye] change kb to mb
---
 .../spark/serializer/KryoSerializer.scala     | 11 +++----
 .../serializer/KryoSerializerSuite.scala      | 30 +++++++++++++++++++
 2 files changed, 36 insertions(+), 5 deletions(-)

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 f9f78852f032b..64ba27f34d2f1 100644
--- a/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala
+++ b/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala
@@ -32,6 +32,7 @@ import org.apache.spark._
 import org.apache.spark.api.python.PythonBroadcast
 import org.apache.spark.broadcast.HttpBroadcast
 import org.apache.spark.network.nio.{GetBlock, GotBlock, PutBlock}
+import org.apache.spark.network.util.ByteUnit
 import org.apache.spark.scheduler.{CompressedMapStatus, HighlyCompressedMapStatus}
 import org.apache.spark.storage._
 import org.apache.spark.util.BoundedPriorityQueue
@@ -51,18 +52,18 @@ class KryoSerializer(conf: SparkConf)
 
   private val bufferSizeKb = conf.getSizeAsKb("spark.kryoserializer.buffer", "64k")
   
-  if (bufferSizeKb >= 2048) {
+  if (bufferSizeKb >= ByteUnit.GiB.toKiB(2)) {
     throw new IllegalArgumentException("spark.kryoserializer.buffer must be less than " +
-      s"2048 mb, got: + $bufferSizeKb mb.")
+      s"2048 mb, got: + ${ByteUnit.KiB.toMiB(bufferSizeKb)} mb.")
   }
-  private val bufferSize = (bufferSizeKb * 1024).toInt
+  private val bufferSize = ByteUnit.KiB.toBytes(bufferSizeKb).toInt
 
   val maxBufferSizeMb = conf.getSizeAsMb("spark.kryoserializer.buffer.max", "64m").toInt
-  if (maxBufferSizeMb >= 2048) {
+  if (maxBufferSizeMb >= ByteUnit.GiB.toMiB(2)) {
     throw new IllegalArgumentException("spark.kryoserializer.buffer.max must be less than " +
       s"2048 mb, got: + $maxBufferSizeMb mb.")
   }
-  private val maxBufferSize = maxBufferSizeMb * 1024 * 1024
+  private val maxBufferSize = ByteUnit.MiB.toBytes(maxBufferSizeMb).toInt
 
   private val referenceTracking = conf.getBoolean("spark.kryo.referenceTracking", true)
   private val registrationRequired = conf.getBoolean("spark.kryo.registrationRequired", false)
diff --git a/core/src/test/scala/org/apache/spark/serializer/KryoSerializerSuite.scala b/core/src/test/scala/org/apache/spark/serializer/KryoSerializerSuite.scala
index 778a7eee73b23..c7369de24b81f 100644
--- a/core/src/test/scala/org/apache/spark/serializer/KryoSerializerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/serializer/KryoSerializerSuite.scala
@@ -32,6 +32,36 @@ class KryoSerializerSuite extends FunSuite with SharedSparkContext {
   conf.set("spark.serializer", "org.apache.spark.serializer.KryoSerializer")
   conf.set("spark.kryo.registrator", classOf[MyRegistrator].getName)
 
+  test("configuration limits") {
+    val conf1 = conf.clone()
+    val kryoBufferProperty = "spark.kryoserializer.buffer"
+    val kryoBufferMaxProperty = "spark.kryoserializer.buffer.max"
+    conf1.set(kryoBufferProperty, "64k")
+    conf1.set(kryoBufferMaxProperty, "64m")
+    new KryoSerializer(conf1).newInstance()
+    // 2048m = 2097152k
+    conf1.set(kryoBufferProperty, "2097151k")
+    conf1.set(kryoBufferMaxProperty, "64m")
+    // should not throw exception when kryoBufferMaxProperty < kryoBufferProperty
+    new KryoSerializer(conf1).newInstance()
+    conf1.set(kryoBufferMaxProperty, "2097151k")
+    new KryoSerializer(conf1).newInstance()
+    val conf2 = conf.clone()
+    conf2.set(kryoBufferProperty, "2048m")
+    val thrown1 = intercept[IllegalArgumentException](new KryoSerializer(conf2).newInstance())
+    assert(thrown1.getMessage.contains(kryoBufferProperty))
+    val conf3 = conf.clone()
+    conf3.set(kryoBufferMaxProperty, "2048m")
+    val thrown2 = intercept[IllegalArgumentException](new KryoSerializer(conf3).newInstance())
+    assert(thrown2.getMessage.contains(kryoBufferMaxProperty))
+    val conf4 = conf.clone()
+    conf4.set(kryoBufferProperty, "2g")
+    conf4.set(kryoBufferMaxProperty, "3g")
+    val thrown3 = intercept[IllegalArgumentException](new KryoSerializer(conf4).newInstance())
+    assert(thrown3.getMessage.contains(kryoBufferProperty))
+    assert(!thrown3.getMessage.contains(kryoBufferMaxProperty))
+  }
+  
   test("basic types") {
     val ser = new KryoSerializer(conf).newInstance()
     def check[T: ClassTag](t: T) {

From ebff7327af5efa9f57c605284de4fae6b050ae0f Mon Sep 17 00:00:00 2001
From: Lianhui Wang <lianhuiwang09@gmail.com>
Date: Fri, 8 May 2015 08:44:46 -0500
Subject: [PATCH 58/75] [SPARK-6869] [PYSPARK] Add pyspark archives path to
 PYTHONPATH

Based on https://github.com/apache/spark/pull/5478 that provide a PYSPARK_ARCHIVES_PATH env. within this PR, we just should export PYSPARK_ARCHIVES_PATH=/user/spark/pyspark.zip,/user/spark/python/lib/py4j-0.8.2.1-src.zip in conf/spark-env.sh when we don't install PySpark on each node of Yarn. i run python application successfully on yarn-client and yarn-cluster with this PR.
andrewor14 sryza Sephiroth-Lin Can you take a look at this?thanks.

Author: Lianhui Wang <lianhuiwang09@gmail.com>

Closes #5580 from lianhuiwang/SPARK-6869 and squashes the following commits:

66ffa43 [Lianhui Wang] Update Client.scala
c2ad0f9 [Lianhui Wang] Update Client.scala
1c8f664 [Lianhui Wang] Merge remote-tracking branch 'remotes/apache/master' into SPARK-6869
008850a [Lianhui Wang] Merge remote-tracking branch 'remotes/apache/master' into SPARK-6869
f0b4ed8 [Lianhui Wang] Merge remote-tracking branch 'remotes/apache/master' into SPARK-6869
150907b [Lianhui Wang] Merge remote-tracking branch 'remotes/apache/master' into SPARK-6869
20402cd [Lianhui Wang] use ZipEntry
9d87c3f [Lianhui Wang] update scala style
e7bd971 [Lianhui Wang] address vanzin's comments
4b8a3ed [Lianhui Wang] use pyArchivesEnvOpt
e6b573b [Lianhui Wang] address vanzin's comments
f11f84a [Lianhui Wang] zip pyspark archives
5192cca [Lianhui Wang] update import path
3b1e4c8 [Lianhui Wang] address tgravescs's comments
9396346 [Lianhui Wang] put zip to make-distribution.sh
0d2baf7 [Lianhui Wang] update import paths
e0179be [Lianhui Wang] add zip pyspark archives in build or sparksubmit
31e8e06 [Lianhui Wang] update code style
9f31dac [Lianhui Wang] update code and add comments
f72987c [Lianhui Wang] add archives path to PYTHONPATH
---
 assembly/pom.xml                              | 21 ++++++++++
 .../org/apache/spark/deploy/SparkSubmit.scala | 41 +++++++++++++++++++
 project/SparkBuild.scala                      | 37 ++++++++++++++++-
 .../org/apache/spark/deploy/yarn/Client.scala | 23 ++++++++---
 4 files changed, 114 insertions(+), 8 deletions(-)

diff --git a/assembly/pom.xml b/assembly/pom.xml
index 2b4d0a990bf22..626c8577e31fe 100644
--- a/assembly/pom.xml
+++ b/assembly/pom.xml
@@ -92,6 +92,27 @@
           <skip>true</skip>
         </configuration>
       </plugin>
+        <!-- zip pyspark archives to run python application on yarn mode -->
+        <plugin>
+          <groupId>org.apache.maven.plugins</groupId>
+            <artifactId>maven-antrun-plugin</artifactId>
+            <executions>
+              <execution>
+                <phase>package</phase>
+                  <goals>
+                    <goal>run</goal>
+                  </goals>
+              </execution>
+            </executions>
+            <configuration>
+              <target>
+                <delete dir="${basedir}/../python/lib/pyspark.zip"/>
+                <zip destfile="${basedir}/../python/lib/pyspark.zip">
+                  <fileset dir="${basedir}/../python/" includes="pyspark/**/*"/>
+                </zip>
+              </target>
+            </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/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
index 8a0327984e195..329fa06ba8ba5 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
@@ -332,6 +332,47 @@ object SparkSubmit {
       }
     }
 
+    // In yarn mode for a python app, add pyspark archives to files
+    // that can be distributed with the job
+    if (args.isPython && clusterManager == YARN) {
+      var pyArchives: String = null
+      val pyArchivesEnvOpt = sys.env.get("PYSPARK_ARCHIVES_PATH")
+      if (pyArchivesEnvOpt.isDefined) {
+        pyArchives = pyArchivesEnvOpt.get
+      } else {
+        if (!sys.env.contains("SPARK_HOME")) {
+          printErrorAndExit("SPARK_HOME does not exist for python application in yarn mode.")
+        }
+        val pythonPath = new ArrayBuffer[String]
+        for (sparkHome <- sys.env.get("SPARK_HOME")) {
+          val pyLibPath = Seq(sparkHome, "python", "lib").mkString(File.separator)
+          val pyArchivesFile = new File(pyLibPath, "pyspark.zip")
+          if (!pyArchivesFile.exists()) {
+            printErrorAndExit("pyspark.zip does not exist for python application in yarn mode.")
+          }
+          val py4jFile = new File(pyLibPath, "py4j-0.8.2.1-src.zip")
+          if (!py4jFile.exists()) {
+            printErrorAndExit("py4j-0.8.2.1-src.zip does not exist for python application " +
+              "in yarn mode.")
+          }
+          pythonPath += pyArchivesFile.getAbsolutePath()
+          pythonPath += py4jFile.getAbsolutePath()
+        }
+        pyArchives = pythonPath.mkString(",")
+      }
+
+      pyArchives = pyArchives.split(",").map { localPath=>
+        val localURI = Utils.resolveURI(localPath)
+        if (localURI.getScheme != "local") {
+          args.files = mergeFileLists(args.files, localURI.toString)
+          new Path(localPath).getName
+        } else {
+          localURI.getPath
+        }
+      }.mkString(File.pathSeparator)
+      sysProps("spark.submit.pyArchives") = pyArchives
+    }
+
     // If we're running a R app, set the main class to our specific R runner
     if (args.isR && deployMode == CLIENT) {
       if (args.primaryResource == SPARKR_SHELL) {
diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala
index 026855f8f6a5a..186345af0e60e 100644
--- a/project/SparkBuild.scala
+++ b/project/SparkBuild.scala
@@ -370,6 +370,7 @@ object Assembly {
 object PySparkAssembly {
   import sbtassembly.Plugin._
   import AssemblyKeys._
+  import java.util.zip.{ZipOutputStream, ZipEntry}
 
   lazy val settings = Seq(
     unmanagedJars in Compile += { BuildCommons.sparkHome / "python/lib/py4j-0.8.2.1-src.zip" },
@@ -377,16 +378,48 @@ object PySparkAssembly {
     // to be included in the assembly. We can't just add "python/" to the assembly's resource dir
     // list since that will copy unneeded / unwanted files.
     resourceGenerators in Compile <+= resourceManaged in Compile map { outDir: File =>
+      val src = new File(BuildCommons.sparkHome, "python/pyspark")
+
+      val zipFile = new File(BuildCommons.sparkHome , "python/lib/pyspark.zip")
+      zipFile.delete()
+      zipRecursive(src, zipFile)
+
       val dst = new File(outDir, "pyspark")
       if (!dst.isDirectory()) {
         require(dst.mkdirs())
       }
-
-      val src = new File(BuildCommons.sparkHome, "python/pyspark")
       copy(src, dst)
     }
   )
 
+  private def zipRecursive(source: File, destZipFile: File) = {
+    val destOutput = new ZipOutputStream(new FileOutputStream(destZipFile))
+    addFilesToZipStream("", source, destOutput)
+    destOutput.flush()
+    destOutput.close()
+  }
+
+  private def addFilesToZipStream(parent: String, source: File, output: ZipOutputStream): Unit = {
+    if (source.isDirectory()) {
+      output.putNextEntry(new ZipEntry(parent + source.getName()))
+      for (file <- source.listFiles()) {
+        addFilesToZipStream(parent + source.getName() + File.separator, file, output)
+      }
+    } else {
+      val in = new FileInputStream(source)
+      output.putNextEntry(new ZipEntry(parent + source.getName()))
+      val buf = new Array[Byte](8192)
+      var n = 0
+      while (n != -1) {
+        n = in.read(buf)
+        if (n != -1) {
+          output.write(buf, 0, n)
+        }
+      }
+      in.close()
+    }
+  }
+
   private def copy(src: File, dst: File): Seq[File] = {
     src.listFiles().flatMap { f =>
       val child = new File(dst, f.getName())
diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
index 20ecaf092e3f8..d21a7393478ce 100644
--- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
+++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
@@ -468,6 +468,17 @@ private[spark] class Client(
       env("SPARK_YARN_USER_ENV") = userEnvs
     }
 
+    // if spark.submit.pyArchives is in sparkConf, append pyArchives to PYTHONPATH
+    // that can be passed on to the ApplicationMaster and the executors.
+    if (sparkConf.contains("spark.submit.pyArchives")) {
+      var pythonPath = sparkConf.get("spark.submit.pyArchives")
+      if (env.contains("PYTHONPATH")) {
+        pythonPath = Seq(env.get("PYTHONPATH"), pythonPath).mkString(File.pathSeparator)
+      }
+      env("PYTHONPATH") = pythonPath
+      sparkConf.setExecutorEnv("PYTHONPATH", pythonPath)
+    }
+
     // 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
@@ -1074,7 +1085,7 @@ object Client extends Logging {
         val hiveConf = hiveClass.getMethod("getConf").invoke(hive)
         val hiveConfClass = mirror.classLoader.loadClass("org.apache.hadoop.hive.conf.HiveConf")
 
-        val hiveConfGet = (param:String) => Option(hiveConfClass
+        val hiveConfGet = (param: String) => Option(hiveConfClass
           .getMethod("get", classOf[java.lang.String])
           .invoke(hiveConf, param))
 
@@ -1096,7 +1107,7 @@ object Client extends Logging {
 
             val hive2Token = new Token[DelegationTokenIdentifier]()
             hive2Token.decodeFromUrlString(tokenStr)
-            credentials.addToken(new Text("hive.server2.delegation.token"),hive2Token)
+            credentials.addToken(new Text("hive.server2.delegation.token"), hive2Token)
             logDebug("Added hive.Server2.delegation.token to conf.")
             hiveClass.getMethod("closeCurrent").invoke(null)
           } else {
@@ -1141,13 +1152,13 @@ object Client extends Logging {
 
         logInfo("Added HBase security token to credentials.")
       } catch {
-        case e:java.lang.NoSuchMethodException =>
+        case e: java.lang.NoSuchMethodException =>
           logInfo("HBase Method not found: " + e)
-        case e:java.lang.ClassNotFoundException =>
+        case e: java.lang.ClassNotFoundException =>
           logDebug("HBase Class not found: " + e)
-        case e:java.lang.NoClassDefFoundError =>
+        case e: java.lang.NoClassDefFoundError =>
           logDebug("HBase Class not found: " + e)
-        case e:Exception =>
+        case e: Exception =>
           logError("Exception when obtaining HBase security token: " + e)
       }
     }

From c796be70f36e262b6a2ce75924bd970f40bf4045 Mon Sep 17 00:00:00 2001
From: Imran Rashid <irashid@cloudera.com>
Date: Fri, 8 May 2015 16:54:32 +0100
Subject: [PATCH 59/75] [SPARK-3454] separate json endpoints for data in the UI

Exposes data available in the UI as json over http.  Key points:

* new endpoints, handled independently of existing XyzPage classes.  Root entrypoint is `JsonRootResource`
* Uses jersey + jackson for routing & converting POJOs into json
* tests against known results in `HistoryServerSuite`
* also fixes some minor issues w/ the UI -- synchronizing on access to `StorageListener` & `StorageStatusListener`, and fixing some inconsistencies w/ the way we handle retained jobs & stages.

Author: Imran Rashid <irashid@cloudera.com>

Closes #5940 from squito/SPARK-3454_better_test_files and squashes the following commits:

1a72ed6 [Imran Rashid] rats
85fdb3e [Imran Rashid] Merge branch 'no_php' into SPARK-3454
1fc65b0 [Imran Rashid] Revert "Revert "[SPARK-3454] separate json endpoints for data in the UI""
1276900 [Imran Rashid] get rid of giant event file, replace w/ smaller one; check both shuffle read & shuffle write
4e12013 [Imran Rashid] just use test case name for expectation file name
863ef64 [Imran Rashid] rename json files to avoid strange file names and not look like php
---
 .rat-excludes                                 |    7 +
 core/pom.xml                                  |    8 +
 .../org/apache/spark/JobExecutionStatus.java  |    8 +-
 .../status/api/v1/ApplicationStatus.java      |   30 +
 .../spark/status/api/v1/StageStatus.java      |   31 +
 .../spark/status/api/v1/TaskSorting.java      |   48 +
 .../java/org/apache/spark/util/EnumUtil.java  |   38 +
 .../scala/org/apache/spark/SparkContext.scala |    2 +-
 .../history/ApplicationHistoryProvider.scala  |    4 +-
 .../deploy/history/FsHistoryProvider.scala    |   14 +-
 .../spark/deploy/history/HistoryServer.scala  |   20 +-
 .../spark/deploy/master/ApplicationInfo.scala |    2 +-
 .../apache/spark/deploy/master/Master.scala   |   14 +-
 .../deploy/master/ui/ApplicationPage.scala    |   19 +-
 .../spark/deploy/master/ui/MasterPage.scala   |   12 +-
 .../spark/deploy/master/ui/MasterWebUI.scala  |   24 +-
 .../spark/status/api/v1/AllJobsResource.scala |   98 ++
 .../spark/status/api/v1/AllRDDResource.scala  |  104 ++
 .../status/api/v1/AllStagesResource.scala     |  309 ++++
 .../api/v1/ApplicationListResource.scala      |   94 ++
 .../status/api/v1/ExecutorListResource.scala  |   36 +
 .../status/api/v1/JacksonMessageWriter.scala  |   93 ++
 .../status/api/v1/JsonRootResource.scala      |  255 +++
 .../api/v1/OneApplicationResource.scala       |   31 +
 .../spark/status/api/v1/OneJobResource.scala  |   41 +
 .../spark/status/api/v1/OneRDDResource.scala  |   34 +
 .../status/api/v1/OneStageResource.scala      |  150 ++
 .../spark/status/api/v1/SecurityFilter.scala  |   38 +
 .../spark/status/api/v1/SimpleDateParam.scala |   55 +
 .../org/apache/spark/status/api/v1/api.scala  |  228 +++
 .../spark/storage/StorageStatusListener.scala |    6 +-
 .../scala/org/apache/spark/ui/SparkUI.scala   |   49 +-
 .../scala/org/apache/spark/ui/WebUI.scala     |    8 +-
 .../apache/spark/ui/exec/ExecutorsPage.scala  |   17 +-
 .../apache/spark/ui/jobs/AllJobsPage.scala    |   14 +-
 .../apache/spark/ui/jobs/AllStagesPage.scala  |   12 +-
 .../org/apache/spark/ui/jobs/JobPage.scala    |    2 +-
 .../spark/ui/jobs/JobProgressListener.scala   |    4 +
 .../org/apache/spark/ui/jobs/PoolPage.scala   |    2 +-
 .../org/apache/spark/ui/jobs/StagePage.scala  |   19 +-
 .../org/apache/spark/ui/storage/RDDPage.scala |   73 +-
 .../apache/spark/ui/storage/StoragePage.scala |    2 +-
 .../apache/spark/ui/storage/StorageTab.scala  |    6 +-
 .../application_list_json_expectation.json    |   53 +
 .../complete_stage_list_json_expectation.json |   67 +
 .../completed_app_list_json_expectation.json  |   53 +
 .../executor_list_json_expectation.json       |   17 +
 .../failed_stage_list_json_expectation.json   |   23 +
 ...multi_attempt_app_json_1__expectation.json |   15 +
 ...multi_attempt_app_json_2__expectation.json |   15 +
 .../job_list_json_expectation.json            |   43 +
 .../maxDate2_app_list_json_expectation.json   |   10 +
 .../maxDate_app_list_json_expectation.json    |   19 +
 .../minDate_app_list_json_expectation.json    |   35 +
 .../one_app_json_expectation.json             |   10 +
 ...ne_app_multi_attempt_json_expectation.json |   17 +
 .../one_job_json_expectation.json             |   15 +
 .../one_rdd_storage_json_expectation.json     |   64 +
 .../one_stage_attempt_json_expectation.json   |  270 ++++
 .../one_stage_json_expectation.json           |  270 ++++
 .../rdd_list_storage_json_expectation.json    |    9 +
 .../running_app_list_json_expectation.json    |    1 +
 .../stage_list_json_expectation.json          |   89 ++
 ...ist_with_accumulable_json_expectation.json |   27 +
 .../stage_task_list_expectation.json          |  561 +++++++
 ...multi_attempt_app_json_1__expectation.json |  193 +++
 ...multi_attempt_app_json_2__expectation.json |  193 +++
 ...k_list_w__offset___length_expectation.json | 1401 +++++++++++++++++
 ...stage_task_list_w__sortBy_expectation.json |  561 +++++++
 ...tBy_short_names___runtime_expectation.json |  561 +++++++
 ...rtBy_short_names__runtime_expectation.json |  561 +++++++
 ...mmary_w__custom_quantiles_expectation.json |   19 +
 ...sk_summary_w_shuffle_read_expectation.json |   19 +
 ...k_summary_w_shuffle_write_expectation.json |   19 +
 ...age_with_accumulable_json_expectation.json |  242 +++
 ...eded_failed_job_list_json_expectation.json |   43 +
 .../succeeded_job_list_json_expectation.json  |   29 +
 .../local-1422981759269/APPLICATION_COMPLETE  |    0
 .../local-1422981759269/EVENT_LOG_1           |   88 ++
 .../local-1422981759269/SPARK_VERSION_1.2.0   |    0
 .../local-1422981780767/APPLICATION_COMPLETE  |    0
 .../local-1422981780767/EVENT_LOG_1           |   82 +
 .../local-1422981780767/SPARK_VERSION_1.2.0   |    0
 .../local-1425081759269/APPLICATION_COMPLETE  |    0
 .../local-1425081759269/EVENT_LOG_1           |   88 ++
 .../local-1425081759269/SPARK_VERSION_1.2.0   |    0
 .../local-1426533911241/APPLICATION_COMPLETE  |    0
 .../local-1426533911241/EVENT_LOG_1           |   24 +
 .../local-1426533911241/SPARK_VERSION_1.2.0   |    0
 .../local-1426633911242/APPLICATION_COMPLETE  |    0
 .../local-1426633911242/EVENT_LOG_1           |   24 +
 .../local-1426633911242/SPARK_VERSION_1.2.0   |    0
 .../spark-events/local-1430917381534          |  231 +++
 .../org/apache/spark/JsonTestUtils.scala      |   34 +
 .../spark/deploy/JsonProtocolSuite.scala      |   14 +-
 .../deploy/history/HistoryServerSuite.scala   |  231 ++-
 .../status/api/v1/SimpleDateParamTest.scala   |   29 +
 .../org/apache/spark/ui/UISeleniumSuite.scala |  264 +++-
 docs/monitoring.md                            |   74 +
 pom.xml                                       |   12 +
 100 files changed, 8608 insertions(+), 172 deletions(-)
 create mode 100644 core/src/main/java/org/apache/spark/status/api/v1/ApplicationStatus.java
 create mode 100644 core/src/main/java/org/apache/spark/status/api/v1/StageStatus.java
 create mode 100644 core/src/main/java/org/apache/spark/status/api/v1/TaskSorting.java
 create mode 100644 core/src/main/java/org/apache/spark/util/EnumUtil.java
 create mode 100644 core/src/main/scala/org/apache/spark/status/api/v1/AllJobsResource.scala
 create mode 100644 core/src/main/scala/org/apache/spark/status/api/v1/AllRDDResource.scala
 create mode 100644 core/src/main/scala/org/apache/spark/status/api/v1/AllStagesResource.scala
 create mode 100644 core/src/main/scala/org/apache/spark/status/api/v1/ApplicationListResource.scala
 create mode 100644 core/src/main/scala/org/apache/spark/status/api/v1/ExecutorListResource.scala
 create mode 100644 core/src/main/scala/org/apache/spark/status/api/v1/JacksonMessageWriter.scala
 create mode 100644 core/src/main/scala/org/apache/spark/status/api/v1/JsonRootResource.scala
 create mode 100644 core/src/main/scala/org/apache/spark/status/api/v1/OneApplicationResource.scala
 create mode 100644 core/src/main/scala/org/apache/spark/status/api/v1/OneJobResource.scala
 create mode 100644 core/src/main/scala/org/apache/spark/status/api/v1/OneRDDResource.scala
 create mode 100644 core/src/main/scala/org/apache/spark/status/api/v1/OneStageResource.scala
 create mode 100644 core/src/main/scala/org/apache/spark/status/api/v1/SecurityFilter.scala
 create mode 100644 core/src/main/scala/org/apache/spark/status/api/v1/SimpleDateParam.scala
 create mode 100644 core/src/main/scala/org/apache/spark/status/api/v1/api.scala
 create mode 100644 core/src/test/resources/HistoryServerExpectations/application_list_json_expectation.json
 create mode 100644 core/src/test/resources/HistoryServerExpectations/complete_stage_list_json_expectation.json
 create mode 100644 core/src/test/resources/HistoryServerExpectations/completed_app_list_json_expectation.json
 create mode 100644 core/src/test/resources/HistoryServerExpectations/executor_list_json_expectation.json
 create mode 100644 core/src/test/resources/HistoryServerExpectations/failed_stage_list_json_expectation.json
 create mode 100644 core/src/test/resources/HistoryServerExpectations/job_list_from_multi_attempt_app_json_1__expectation.json
 create mode 100644 core/src/test/resources/HistoryServerExpectations/job_list_from_multi_attempt_app_json_2__expectation.json
 create mode 100644 core/src/test/resources/HistoryServerExpectations/job_list_json_expectation.json
 create mode 100644 core/src/test/resources/HistoryServerExpectations/maxDate2_app_list_json_expectation.json
 create mode 100644 core/src/test/resources/HistoryServerExpectations/maxDate_app_list_json_expectation.json
 create mode 100644 core/src/test/resources/HistoryServerExpectations/minDate_app_list_json_expectation.json
 create mode 100644 core/src/test/resources/HistoryServerExpectations/one_app_json_expectation.json
 create mode 100644 core/src/test/resources/HistoryServerExpectations/one_app_multi_attempt_json_expectation.json
 create mode 100644 core/src/test/resources/HistoryServerExpectations/one_job_json_expectation.json
 create mode 100644 core/src/test/resources/HistoryServerExpectations/one_rdd_storage_json_expectation.json
 create mode 100644 core/src/test/resources/HistoryServerExpectations/one_stage_attempt_json_expectation.json
 create mode 100644 core/src/test/resources/HistoryServerExpectations/one_stage_json_expectation.json
 create mode 100644 core/src/test/resources/HistoryServerExpectations/rdd_list_storage_json_expectation.json
 create mode 100644 core/src/test/resources/HistoryServerExpectations/running_app_list_json_expectation.json
 create mode 100644 core/src/test/resources/HistoryServerExpectations/stage_list_json_expectation.json
 create mode 100644 core/src/test/resources/HistoryServerExpectations/stage_list_with_accumulable_json_expectation.json
 create mode 100644 core/src/test/resources/HistoryServerExpectations/stage_task_list_expectation.json
 create mode 100644 core/src/test/resources/HistoryServerExpectations/stage_task_list_from_multi_attempt_app_json_1__expectation.json
 create mode 100644 core/src/test/resources/HistoryServerExpectations/stage_task_list_from_multi_attempt_app_json_2__expectation.json
 create mode 100644 core/src/test/resources/HistoryServerExpectations/stage_task_list_w__offset___length_expectation.json
 create mode 100644 core/src/test/resources/HistoryServerExpectations/stage_task_list_w__sortBy_expectation.json
 create mode 100644 core/src/test/resources/HistoryServerExpectations/stage_task_list_w__sortBy_short_names___runtime_expectation.json
 create mode 100644 core/src/test/resources/HistoryServerExpectations/stage_task_list_w__sortBy_short_names__runtime_expectation.json
 create mode 100644 core/src/test/resources/HistoryServerExpectations/stage_task_summary_w__custom_quantiles_expectation.json
 create mode 100644 core/src/test/resources/HistoryServerExpectations/stage_task_summary_w_shuffle_read_expectation.json
 create mode 100644 core/src/test/resources/HistoryServerExpectations/stage_task_summary_w_shuffle_write_expectation.json
 create mode 100644 core/src/test/resources/HistoryServerExpectations/stage_with_accumulable_json_expectation.json
 create mode 100644 core/src/test/resources/HistoryServerExpectations/succeeded_failed_job_list_json_expectation.json
 create mode 100644 core/src/test/resources/HistoryServerExpectations/succeeded_job_list_json_expectation.json
 create mode 100755 core/src/test/resources/spark-events/local-1422981759269/APPLICATION_COMPLETE
 create mode 100755 core/src/test/resources/spark-events/local-1422981759269/EVENT_LOG_1
 create mode 100755 core/src/test/resources/spark-events/local-1422981759269/SPARK_VERSION_1.2.0
 create mode 100755 core/src/test/resources/spark-events/local-1422981780767/APPLICATION_COMPLETE
 create mode 100755 core/src/test/resources/spark-events/local-1422981780767/EVENT_LOG_1
 create mode 100755 core/src/test/resources/spark-events/local-1422981780767/SPARK_VERSION_1.2.0
 create mode 100755 core/src/test/resources/spark-events/local-1425081759269/APPLICATION_COMPLETE
 create mode 100755 core/src/test/resources/spark-events/local-1425081759269/EVENT_LOG_1
 create mode 100755 core/src/test/resources/spark-events/local-1425081759269/SPARK_VERSION_1.2.0
 create mode 100755 core/src/test/resources/spark-events/local-1426533911241/APPLICATION_COMPLETE
 create mode 100755 core/src/test/resources/spark-events/local-1426533911241/EVENT_LOG_1
 create mode 100755 core/src/test/resources/spark-events/local-1426533911241/SPARK_VERSION_1.2.0
 create mode 100755 core/src/test/resources/spark-events/local-1426633911242/APPLICATION_COMPLETE
 create mode 100755 core/src/test/resources/spark-events/local-1426633911242/EVENT_LOG_1
 create mode 100755 core/src/test/resources/spark-events/local-1426633911242/SPARK_VERSION_1.2.0
 create mode 100755 core/src/test/resources/spark-events/local-1430917381534
 create mode 100644 core/src/test/scala/org/apache/spark/JsonTestUtils.scala
 create mode 100644 core/src/test/scala/org/apache/spark/status/api/v1/SimpleDateParamTest.scala

diff --git a/.rat-excludes b/.rat-excludes
index dccf2db8055ce..dc08c4a3673d2 100644
--- a/.rat-excludes
+++ b/.rat-excludes
@@ -74,5 +74,12 @@ logs
 .*scalastyle-output.xml
 .*dependency-reduced-pom.xml
 known_translations
+json_expectation
+local-1422981759269/*
+local-1422981780767/*
+local-1425081759269/*
+local-1426533911241/*
+local-1426633911242/*
+local-1430917381534/*
 DESCRIPTION
 NAMESPACE
diff --git a/core/pom.xml b/core/pom.xml
index 164a836cb8f0b..fc42f48973fe9 100644
--- a/core/pom.xml
+++ b/core/pom.xml
@@ -228,6 +228,14 @@
       <artifactId>json4s-jackson_${scala.binary.version}</artifactId>
       <version>3.2.10</version>
     </dependency>
+    <dependency>
+      <groupId>com.sun.jersey</groupId>
+      <artifactId>jersey-server</artifactId>
+    </dependency>
+    <dependency>
+      <groupId>com.sun.jersey</groupId>
+      <artifactId>jersey-core</artifactId>
+    </dependency>
     <dependency>
       <groupId>org.apache.mesos</groupId>
       <artifactId>mesos</artifactId>
diff --git a/core/src/main/java/org/apache/spark/JobExecutionStatus.java b/core/src/main/java/org/apache/spark/JobExecutionStatus.java
index 6e161313702bb..0287fb79f8dd2 100644
--- a/core/src/main/java/org/apache/spark/JobExecutionStatus.java
+++ b/core/src/main/java/org/apache/spark/JobExecutionStatus.java
@@ -17,9 +17,15 @@
 
 package org.apache.spark;
 
+import org.apache.spark.util.EnumUtil;
+
 public enum JobExecutionStatus {
   RUNNING,
   SUCCEEDED,
   FAILED,
-  UNKNOWN
+  UNKNOWN;
+
+  public static JobExecutionStatus fromString(String str) {
+    return EnumUtil.parseIgnoreCase(JobExecutionStatus.class, str);
+  }
 }
diff --git a/core/src/main/java/org/apache/spark/status/api/v1/ApplicationStatus.java b/core/src/main/java/org/apache/spark/status/api/v1/ApplicationStatus.java
new file mode 100644
index 0000000000000..8c7dcf776fda8
--- /dev/null
+++ b/core/src/main/java/org/apache/spark/status/api/v1/ApplicationStatus.java
@@ -0,0 +1,30 @@
+/*
+ * 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.status.api.v1;
+
+import org.apache.spark.util.EnumUtil;
+
+public enum ApplicationStatus {
+  COMPLETED,
+  RUNNING;
+
+  public static ApplicationStatus fromString(String str) {
+    return EnumUtil.parseIgnoreCase(ApplicationStatus.class, str);
+  }
+
+}
diff --git a/core/src/main/java/org/apache/spark/status/api/v1/StageStatus.java b/core/src/main/java/org/apache/spark/status/api/v1/StageStatus.java
new file mode 100644
index 0000000000000..9dbb565aab707
--- /dev/null
+++ b/core/src/main/java/org/apache/spark/status/api/v1/StageStatus.java
@@ -0,0 +1,31 @@
+/*
+ * 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.status.api.v1;
+
+import org.apache.spark.util.EnumUtil;
+
+public enum StageStatus {
+  ACTIVE,
+  COMPLETE,
+  FAILED,
+  PENDING;
+
+  public static StageStatus fromString(String str) {
+    return EnumUtil.parseIgnoreCase(StageStatus.class, str);
+  }
+}
diff --git a/core/src/main/java/org/apache/spark/status/api/v1/TaskSorting.java b/core/src/main/java/org/apache/spark/status/api/v1/TaskSorting.java
new file mode 100644
index 0000000000000..f19ed01d5aebf
--- /dev/null
+++ b/core/src/main/java/org/apache/spark/status/api/v1/TaskSorting.java
@@ -0,0 +1,48 @@
+/*
+ * 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.status.api.v1;
+
+import org.apache.spark.util.EnumUtil;
+
+import java.util.HashSet;
+import java.util.Set;
+
+public enum TaskSorting {
+  ID,
+  INCREASING_RUNTIME("runtime"),
+  DECREASING_RUNTIME("-runtime");
+
+  private final Set<String> alternateNames;
+  private TaskSorting(String... names) {
+    alternateNames = new HashSet<String>();
+    for (String n: names) {
+      alternateNames.add(n);
+    }
+  }
+
+  public static TaskSorting fromString(String str) {
+    String lower = str.toLowerCase();
+    for (TaskSorting t: values()) {
+      if (t.alternateNames.contains(lower)) {
+        return t;
+      }
+    }
+    return EnumUtil.parseIgnoreCase(TaskSorting.class, str);
+  }
+
+}
diff --git a/core/src/main/java/org/apache/spark/util/EnumUtil.java b/core/src/main/java/org/apache/spark/util/EnumUtil.java
new file mode 100644
index 0000000000000..c40c7e727613c
--- /dev/null
+++ b/core/src/main/java/org/apache/spark/util/EnumUtil.java
@@ -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.util;
+
+import com.google.common.base.Joiner;
+import org.apache.spark.annotation.Private;
+
+@Private
+public class EnumUtil {
+  public static <E extends Enum<E>> E parseIgnoreCase(Class<E> clz, String str) {
+    E[] constants = clz.getEnumConstants();
+    if (str == null) {
+      return null;
+    }
+    for (E e : constants) {
+      if (e.name().equalsIgnoreCase(str)) {
+        return e;
+      }
+    }
+    throw new IllegalArgumentException(
+      String.format("Illegal type='%s'. Supported type values: %s",
+        str, Joiner.on(", ").join(constants)));
+  }
+}
diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala
index b5f040ceb15ca..b59f562d05ead 100644
--- a/core/src/main/scala/org/apache/spark/SparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/SparkContext.scala
@@ -430,7 +430,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
     _ui =
       if (conf.getBoolean("spark.ui.enabled", true)) {
         Some(SparkUI.createLiveUI(this, _conf, listenerBus, _jobProgressListener,
-          _env.securityManager,appName))
+          _env.securityManager,appName, startTime = startTime))
       } else {
         // For tests, do not enable the UI
         None
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 6a5011af17458..298a8201960d1 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
@@ -19,7 +19,7 @@ package org.apache.spark.deploy.history
 
 import org.apache.spark.ui.SparkUI
 
-private[history] case class ApplicationAttemptInfo(
+private[spark] case class ApplicationAttemptInfo(
     attemptId: Option[String],
     startTime: Long,
     endTime: Long,
@@ -27,7 +27,7 @@ private[history] case class ApplicationAttemptInfo(
     sparkUser: String,
     completed: Boolean = false)
 
-private[history] case class ApplicationHistoryInfo(
+private[spark] case class ApplicationHistoryInfo(
     id: String,
     name: String,
     attempts: List[ApplicationAttemptInfo])
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 993763f3aa092..45c2be34c8680 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
@@ -17,23 +17,21 @@
 
 package org.apache.spark.deploy.history
 
-import java.io.{IOException, BufferedInputStream, FileNotFoundException, InputStream}
+import java.io.{BufferedInputStream, FileNotFoundException, IOException, InputStream}
 import java.util.concurrent.{ExecutorService, Executors, TimeUnit}
 
 import scala.collection.mutable
-import scala.concurrent.duration.Duration
 
-import com.google.common.util.concurrent.ThreadFactoryBuilder
-
-import com.google.common.util.concurrent.MoreExecutors
-import org.apache.hadoop.fs.permission.AccessControlException
+import com.google.common.util.concurrent.{MoreExecutors, ThreadFactoryBuilder}
 import org.apache.hadoop.fs.{FileStatus, Path}
+import org.apache.hadoop.fs.permission.AccessControlException
+
+import org.apache.spark.{Logging, SecurityManager, SparkConf}
 import org.apache.spark.deploy.SparkHadoopUtil
 import org.apache.spark.io.CompressionCodec
 import org.apache.spark.scheduler._
 import org.apache.spark.ui.SparkUI
 import org.apache.spark.util.{Clock, SystemClock, ThreadUtils, Utils}
-import org.apache.spark.{Logging, SecurityManager, SparkConf}
 
 /**
  * A class that provides application history from event logs stored in the file system.
@@ -151,7 +149,7 @@ private[history] class FsHistoryProvider(conf: SparkConf, clock: Clock)
             val conf = this.conf.clone()
             val appSecManager = new SecurityManager(conf)
             SparkUI.createHistoryUI(conf, replayBus, appSecManager, appId,
-              HistoryServer.getAttemptURI(appId, attempt.attemptId))
+              HistoryServer.getAttemptURI(appId, attempt.attemptId), attempt.startTime)
             // Do not call ui.bind() to avoid creating a new server for each application
           }
 
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 754c8e9b6668b..50522e69dc519 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
@@ -25,6 +25,7 @@ import org.eclipse.jetty.servlet.{ServletContextHandler, ServletHolder}
 
 import org.apache.spark.{Logging, SecurityManager, SparkConf}
 import org.apache.spark.deploy.SparkHadoopUtil
+import org.apache.spark.status.api.v1.{ApplicationInfo, ApplicationsListResource, JsonRootResource, UIRoot}
 import org.apache.spark.ui.{SparkUI, UIUtils, WebUI}
 import org.apache.spark.ui.JettyUtils._
 import org.apache.spark.util.{SignalLogger, Utils}
@@ -45,7 +46,7 @@ class HistoryServer(
     provider: ApplicationHistoryProvider,
     securityManager: SecurityManager,
     port: Int)
-  extends WebUI(securityManager, port, conf) with Logging {
+  extends WebUI(securityManager, port, conf) with Logging with UIRoot {
 
   // How many applications to retain
   private val retainedApplications = conf.getInt("spark.history.retainedApplications", 50)
@@ -56,7 +57,7 @@ class HistoryServer(
       require(parts.length == 1 || parts.length == 2, s"Invalid app key $key")
       val ui = provider
         .getAppUI(parts(0), if (parts.length > 1) Some(parts(1)) else None)
-        .getOrElse(throw new NoSuchElementException())
+        .getOrElse(throw new NoSuchElementException(s"no app with key $key"))
       attachSparkUI(ui)
       ui
     }
@@ -113,6 +114,10 @@ class HistoryServer(
     }
   }
 
+  def getSparkUI(appKey: String): Option[SparkUI] = {
+    Option(appCache.get(appKey))
+  }
+
   initialize()
 
   /**
@@ -123,6 +128,9 @@ class HistoryServer(
    */
   def initialize() {
     attachPage(new HistoryPage(this))
+
+    attachHandler(JsonRootResource.getJsonServlet(this))
+
     attachHandler(createStaticHandler(SparkUI.STATIC_RESOURCE_DIR, "/static"))
 
     val contextHandler = new ServletContextHandler
@@ -160,7 +168,13 @@ class HistoryServer(
    *
    * @return List of all known applications.
    */
-  def getApplicationList(): Iterable[ApplicationHistoryInfo] = provider.getListing()
+  def getApplicationList(): Iterable[ApplicationHistoryInfo] = {
+    provider.getListing()
+  }
+
+  def getApplicationInfoList: Iterator[ApplicationInfo] = {
+    getApplicationList().iterator.map(ApplicationsListResource.appHistoryInfoToPublicAppInfo)
+  }
 
   /**
    * Returns the provider configuration to show in the listing page.
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 f59d550d4f3b3..1620e95bea218 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
@@ -28,7 +28,7 @@ import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.deploy.ApplicationDescription
 import org.apache.spark.util.Utils
 
-private[deploy] class ApplicationInfo(
+private[spark] class ApplicationInfo(
     val startTime: Long,
     val id: String,
     val desc: ApplicationDescription,
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 0fac3cdcf55e7..53e1903a3d125 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
@@ -754,9 +754,9 @@ private[master] class Master(
 
   /**
    * Rebuild a new SparkUI from the given application's event logs.
-   * Return whether this is successful.
+   * Return the UI if successful, else None
    */
-  private def rebuildSparkUI(app: ApplicationInfo): Boolean = {
+  private[master] def rebuildSparkUI(app: ApplicationInfo): Option[SparkUI] = {
     val appName = app.desc.name
     val notFoundBasePath = HistoryServer.UI_PATH_PREFIX + "/not-found"
     try {
@@ -764,7 +764,7 @@ private[master] class Master(
         .getOrElse {
           // Event logging is not enabled for this application
           app.desc.appUiUrl = notFoundBasePath
-          return false
+          return None
         }
 
       val eventLogFilePrefix = EventLoggingListener.getLogPath(
@@ -787,7 +787,7 @@ private[master] class Master(
       val logInput = EventLoggingListener.openEventLog(new Path(eventLogFile), fs)
       val replayBus = new ReplayListenerBus()
       val ui = SparkUI.createHistoryUI(new SparkConf, replayBus, new SecurityManager(conf),
-        appName + status, HistoryServer.UI_PATH_PREFIX + s"/${app.id}")
+        appName + status, HistoryServer.UI_PATH_PREFIX + s"/${app.id}", app.startTime)
       val maybeTruncated = eventLogFile.endsWith(EventLoggingListener.IN_PROGRESS)
       try {
         replayBus.replay(logInput, eventLogFile, maybeTruncated)
@@ -798,7 +798,7 @@ private[master] class Master(
       webUi.attachSparkUI(ui)
       // Application UI is successfully rebuilt, so link the Master UI to it
       app.desc.appUiUrl = ui.basePath
-      true
+      Some(ui)
     } catch {
       case fnf: FileNotFoundException =>
         // Event logging is enabled for this application, but no event logs are found
@@ -808,7 +808,7 @@ private[master] class Master(
         msg += " Did you specify the correct logging directory?"
         msg = URLEncoder.encode(msg, "UTF-8")
         app.desc.appUiUrl = notFoundBasePath + s"?msg=$msg&title=$title"
-        false
+        None
       case e: Exception =>
         // Relay exception message to application UI page
         val title = s"Application history load error (${app.id})"
@@ -817,7 +817,7 @@ private[master] class Master(
         logError(msg, e)
         msg = URLEncoder.encode(msg, "UTF-8")
         app.desc.appUiUrl = notFoundBasePath + s"?msg=$msg&exception=$exception&title=$title"
-        false
+        None
     }
   }
 
diff --git a/core/src/main/scala/org/apache/spark/deploy/master/ui/ApplicationPage.scala b/core/src/main/scala/org/apache/spark/deploy/master/ui/ApplicationPage.scala
index 273f077bd8f57..06e265f99e231 100644
--- a/core/src/main/scala/org/apache/spark/deploy/master/ui/ApplicationPage.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/master/ui/ApplicationPage.scala
@@ -23,10 +23,8 @@ import scala.concurrent.Await
 import scala.xml.Node
 
 import akka.pattern.ask
-import org.json4s.JValue
-import org.json4s.JsonAST.JNothing
 
-import org.apache.spark.deploy.{ExecutorState, JsonProtocol}
+import org.apache.spark.deploy.ExecutorState
 import org.apache.spark.deploy.DeployMessages.{MasterStateResponse, RequestMasterState}
 import org.apache.spark.deploy.master.ExecutorDesc
 import org.apache.spark.ui.{UIUtils, WebUIPage}
@@ -37,21 +35,6 @@ private[ui] class ApplicationPage(parent: MasterWebUI) extends WebUIPage("app")
   private val master = parent.masterActorRef
   private val timeout = parent.timeout
 
-  /** Executor details for a particular application */
-  override def renderJson(request: HttpServletRequest): JValue = {
-    val appId = request.getParameter("appId")
-    val stateFuture = (master ? RequestMasterState)(timeout).mapTo[MasterStateResponse]
-    val state = Await.result(stateFuture, timeout)
-    val app = state.activeApps.find(_.id == appId).getOrElse({
-      state.completedApps.find(_.id == appId).getOrElse(null)
-    })
-    if (app == null) {
-      JNothing
-    } else {
-      JsonProtocol.writeApplicationInfo(app)
-    }
-  }
-
   /** Executor details for a particular application */
   def render(request: HttpServletRequest): Seq[Node] = {
     val appId = request.getParameter("appId")
diff --git a/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterPage.scala b/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterPage.scala
index 1f2c3fdbfb2bc..756927682cd24 100644
--- a/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterPage.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterPage.scala
@@ -35,10 +35,13 @@ private[ui] class MasterPage(parent: MasterWebUI) extends WebUIPage("") {
   private val master = parent.masterActorRef
   private val timeout = parent.timeout
 
-  override def renderJson(request: HttpServletRequest): JValue = {
+  def getMasterState: MasterStateResponse = {
     val stateFuture = (master ? RequestMasterState)(timeout).mapTo[MasterStateResponse]
-    val state = Await.result(stateFuture, timeout)
-    JsonProtocol.writeMasterState(state)
+    Await.result(stateFuture, timeout)
+  }
+
+  override def renderJson(request: HttpServletRequest): JValue = {
+    JsonProtocol.writeMasterState(getMasterState)
   }
 
   def handleAppKillRequest(request: HttpServletRequest): Unit = {
@@ -68,8 +71,7 @@ private[ui] class MasterPage(parent: MasterWebUI) extends WebUIPage("") {
 
   /** Index view listing applications and executors */
   def render(request: HttpServletRequest): Seq[Node] = {
-    val stateFuture = (master ? RequestMasterState)(timeout).mapTo[MasterStateResponse]
-    val state = Await.result(stateFuture, timeout)
+    val state = getMasterState
 
     val workerHeaders = Seq("Worker Id", "Address", "State", "Cores", "Memory")
     val workers = state.workers.sortBy(_.id)
diff --git a/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterWebUI.scala b/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterWebUI.scala
index dea0a65eeeaa6..eb26e9f99c70b 100644
--- a/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterWebUI.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterWebUI.scala
@@ -19,6 +19,7 @@ package org.apache.spark.deploy.master.ui
 
 import org.apache.spark.Logging
 import org.apache.spark.deploy.master.Master
+import org.apache.spark.status.api.v1.{ApplicationsListResource, ApplicationInfo, JsonRootResource, UIRoot}
 import org.apache.spark.ui.{SparkUI, WebUI}
 import org.apache.spark.ui.JettyUtils._
 import org.apache.spark.util.RpcUtils
@@ -28,12 +29,15 @@ import org.apache.spark.util.RpcUtils
  */
 private[master]
 class MasterWebUI(val master: Master, requestedPort: Int)
-  extends WebUI(master.securityMgr, requestedPort, master.conf, name = "MasterUI") with Logging {
+  extends WebUI(master.securityMgr, requestedPort, master.conf, name = "MasterUI") with Logging
+  with UIRoot {
 
   val masterActorRef = master.self
   val timeout = RpcUtils.askTimeout(master.conf)
   val killEnabled = master.conf.getBoolean("spark.ui.killEnabled", true)
 
+  val masterPage = new MasterPage(this)
+
   initialize()
 
   /** Initialize all components of the server. */
@@ -43,6 +47,7 @@ class MasterWebUI(val master: Master, requestedPort: Int)
     attachPage(new HistoryNotFoundPage(this))
     attachPage(masterPage)
     attachHandler(createStaticHandler(MasterWebUI.STATIC_RESOURCE_DIR, "/static"))
+    attachHandler(JsonRootResource.getJsonServlet(this))
     attachHandler(createRedirectHandler(
       "/app/kill", "/", masterPage.handleAppKillRequest, httpMethods = Set("POST")))
     attachHandler(createRedirectHandler(
@@ -60,6 +65,23 @@ class MasterWebUI(val master: Master, requestedPort: Int)
     assert(serverInfo.isDefined, "Master UI must be bound to a server before detaching SparkUIs")
     ui.getHandlers.foreach(detachHandler)
   }
+
+  def getApplicationInfoList: Iterator[ApplicationInfo] = {
+    val state = masterPage.getMasterState
+    val activeApps = state.activeApps.sortBy(_.startTime).reverse
+    val completedApps = state.completedApps.sortBy(_.endTime).reverse
+    activeApps.iterator.map { ApplicationsListResource.convertApplicationInfo(_, false) } ++
+      completedApps.iterator.map { ApplicationsListResource.convertApplicationInfo(_, true) }
+  }
+
+  def getSparkUI(appId: String): Option[SparkUI] = {
+    val state = masterPage.getMasterState
+    val activeApps = state.activeApps.sortBy(_.startTime).reverse
+    val completedApps = state.completedApps.sortBy(_.endTime).reverse
+    (activeApps ++ completedApps).find { _.id == appId }.flatMap {
+      master.rebuildSparkUI
+    }
+  }
 }
 
 private[master] object MasterWebUI {
diff --git a/core/src/main/scala/org/apache/spark/status/api/v1/AllJobsResource.scala b/core/src/main/scala/org/apache/spark/status/api/v1/AllJobsResource.scala
new file mode 100644
index 0000000000000..5783df5d8220c
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/status/api/v1/AllJobsResource.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.status.api.v1
+
+import java.util.{Arrays, Date, List => JList}
+import javax.ws.rs._
+import javax.ws.rs.core.MediaType
+
+import org.apache.spark.JobExecutionStatus
+import org.apache.spark.ui.SparkUI
+import org.apache.spark.ui.jobs.JobProgressListener
+import org.apache.spark.ui.jobs.UIData.JobUIData
+
+@Produces(Array(MediaType.APPLICATION_JSON))
+private[v1] class AllJobsResource(ui: SparkUI) {
+
+  @GET
+  def jobsList(@QueryParam("status") statuses: JList[JobExecutionStatus]): Seq[JobData] = {
+    val statusToJobs: Seq[(JobExecutionStatus, Seq[JobUIData])] =
+      AllJobsResource.getStatusToJobs(ui)
+    val adjStatuses: JList[JobExecutionStatus] = {
+      if (statuses.isEmpty) {
+        Arrays.asList(JobExecutionStatus.values(): _*)
+      } else {
+        statuses
+      }
+    }
+    val jobInfos = for {
+      (status, jobs) <- statusToJobs
+      job <- jobs if adjStatuses.contains(status)
+    } yield {
+      AllJobsResource.convertJobData(job, ui.jobProgressListener, false)
+    }
+    jobInfos.sortBy{- _.jobId}
+  }
+
+}
+
+private[v1] object AllJobsResource {
+
+  def getStatusToJobs(ui: SparkUI): Seq[(JobExecutionStatus, Seq[JobUIData])] = {
+    val statusToJobs = ui.jobProgressListener.synchronized {
+      Seq(
+        JobExecutionStatus.RUNNING -> ui.jobProgressListener.activeJobs.values.toSeq,
+        JobExecutionStatus.SUCCEEDED -> ui.jobProgressListener.completedJobs.toSeq,
+        JobExecutionStatus.FAILED -> ui.jobProgressListener.failedJobs.reverse.toSeq
+      )
+    }
+    statusToJobs
+  }
+
+  def convertJobData(
+      job: JobUIData,
+      listener: JobProgressListener,
+      includeStageDetails: Boolean): JobData = {
+    listener.synchronized {
+      val lastStageInfo = listener.stageIdToInfo.get(job.stageIds.max)
+      val lastStageData = lastStageInfo.flatMap { s =>
+        listener.stageIdToData.get((s.stageId, s.attemptId))
+      }
+      val lastStageName = lastStageInfo.map { _.name }.getOrElse("(Unknown Stage Name)")
+      val lastStageDescription = lastStageData.flatMap { _.description }
+      new JobData(
+        jobId = job.jobId,
+        name = lastStageName,
+        description = lastStageDescription,
+        submissionTime = job.submissionTime.map{new Date(_)},
+        completionTime = job.completionTime.map{new Date(_)},
+        stageIds = job.stageIds,
+        jobGroup = job.jobGroup,
+        status = job.status,
+        numTasks = job.numTasks,
+        numActiveTasks = job.numActiveTasks,
+        numCompletedTasks = job.numCompletedTasks,
+        numSkippedTasks = job.numCompletedTasks,
+        numFailedTasks = job.numFailedTasks,
+        numActiveStages = job.numActiveStages,
+        numCompletedStages = job.completedStageIndices.size,
+        numSkippedStages = job.numSkippedStages,
+        numFailedStages = job.numFailedStages
+      )
+    }
+  }
+}
diff --git a/core/src/main/scala/org/apache/spark/status/api/v1/AllRDDResource.scala b/core/src/main/scala/org/apache/spark/status/api/v1/AllRDDResource.scala
new file mode 100644
index 0000000000000..645ede26a0879
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/status/api/v1/AllRDDResource.scala
@@ -0,0 +1,104 @@
+/*
+ * 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.status.api.v1
+
+import javax.ws.rs.{GET, Produces}
+import javax.ws.rs.core.MediaType
+
+import org.apache.spark.storage.{RDDInfo, StorageStatus, StorageUtils}
+import org.apache.spark.ui.SparkUI
+import org.apache.spark.ui.storage.StorageListener
+
+@Produces(Array(MediaType.APPLICATION_JSON))
+private[v1] class AllRDDResource(ui: SparkUI) {
+
+  @GET
+  def rddList(): Seq[RDDStorageInfo] = {
+    val storageStatusList = ui.storageListener.storageStatusList
+    val rddInfos = ui.storageListener.rddInfoList
+    rddInfos.map{rddInfo =>
+      AllRDDResource.getRDDStorageInfo(rddInfo.id, rddInfo, storageStatusList,
+        includeDetails = false)
+    }
+  }
+
+}
+
+private[spark] object AllRDDResource {
+
+  def getRDDStorageInfo(
+      rddId: Int,
+      listener: StorageListener,
+      includeDetails: Boolean): Option[RDDStorageInfo] = {
+    val storageStatusList = listener.storageStatusList
+    listener.rddInfoList.find { _.id == rddId }.map { rddInfo =>
+      getRDDStorageInfo(rddId, rddInfo, storageStatusList, includeDetails)
+    }
+  }
+
+  def getRDDStorageInfo(
+      rddId: Int,
+      rddInfo: RDDInfo,
+      storageStatusList: Seq[StorageStatus],
+      includeDetails: Boolean): RDDStorageInfo = {
+    val workers = storageStatusList.map { (rddId, _) }
+    val blockLocations = StorageUtils.getRddBlockLocations(rddId, storageStatusList)
+    val blocks = storageStatusList
+      .flatMap { _.rddBlocksById(rddId) }
+      .sortWith { _._1.name < _._1.name }
+      .map { case (blockId, status) =>
+        (blockId, status, blockLocations.get(blockId).getOrElse(Seq[String]("Unknown")))
+      }
+
+    val dataDistribution = if (includeDetails) {
+      Some(storageStatusList.map { status =>
+        new RDDDataDistribution(
+          address = status.blockManagerId.hostPort,
+          memoryUsed = status.memUsedByRdd(rddId),
+          memoryRemaining = status.memRemaining,
+          diskUsed = status.diskUsedByRdd(rddId)
+        ) } )
+    } else {
+      None
+    }
+    val partitions = if (includeDetails) {
+      Some(blocks.map { case (id, block, locations) =>
+        new RDDPartitionInfo(
+          blockName = id.name,
+          storageLevel = block.storageLevel.description,
+          memoryUsed = block.memSize,
+          diskUsed = block.diskSize,
+          executors = locations
+        )
+      } )
+    } else {
+      None
+    }
+
+    new RDDStorageInfo(
+      id = rddId,
+      name = rddInfo.name,
+      numPartitions = rddInfo.numPartitions,
+      numCachedPartitions = rddInfo.numCachedPartitions,
+      storageLevel = rddInfo.storageLevel.description,
+      memoryUsed = rddInfo.memSize,
+      diskUsed = rddInfo.diskSize,
+      dataDistribution = dataDistribution,
+      partitions = partitions
+    )
+  }
+}
diff --git a/core/src/main/scala/org/apache/spark/status/api/v1/AllStagesResource.scala b/core/src/main/scala/org/apache/spark/status/api/v1/AllStagesResource.scala
new file mode 100644
index 0000000000000..50608588f09ae
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/status/api/v1/AllStagesResource.scala
@@ -0,0 +1,309 @@
+/*
+ * 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.status.api.v1
+
+import java.util.{Arrays, Date, List => JList}
+import javax.ws.rs.{GET, PathParam, Produces, QueryParam}
+import javax.ws.rs.core.MediaType
+
+import org.apache.spark.executor.{InputMetrics => InternalInputMetrics, OutputMetrics => InternalOutputMetrics, ShuffleReadMetrics => InternalShuffleReadMetrics, ShuffleWriteMetrics => InternalShuffleWriteMetrics, TaskMetrics => InternalTaskMetrics}
+import org.apache.spark.scheduler.{AccumulableInfo => InternalAccumulableInfo, StageInfo}
+import org.apache.spark.ui.SparkUI
+import org.apache.spark.ui.jobs.UIData.{StageUIData, TaskUIData}
+import org.apache.spark.util.Distribution
+
+@Produces(Array(MediaType.APPLICATION_JSON))
+private[v1] class AllStagesResource(ui: SparkUI) {
+
+  @GET
+  def stageList(@QueryParam("status") statuses: JList[StageStatus]): Seq[StageData] = {
+    val listener = ui.jobProgressListener
+    val stageAndStatus = AllStagesResource.stagesAndStatus(ui)
+    val adjStatuses = {
+      if (statuses.isEmpty()) {
+        Arrays.asList(StageStatus.values(): _*)
+      } else {
+        statuses
+      }
+    }
+    for {
+      (status, stageList) <- stageAndStatus
+      stageInfo: StageInfo <- stageList if adjStatuses.contains(status)
+      stageUiData: StageUIData <- listener.synchronized {
+        listener.stageIdToData.get((stageInfo.stageId, stageInfo.attemptId))
+      }
+    } yield {
+      AllStagesResource.stageUiToStageData(status, stageInfo, stageUiData, includeDetails = false)
+    }
+  }
+}
+
+private[v1] object AllStagesResource {
+  def stageUiToStageData(
+      status: StageStatus,
+      stageInfo: StageInfo,
+      stageUiData: StageUIData,
+      includeDetails: Boolean): StageData = {
+
+    val taskData = if (includeDetails) {
+      Some(stageUiData.taskData.map { case (k, v) => k -> convertTaskData(v) } )
+    } else {
+      None
+    }
+    val executorSummary = if (includeDetails) {
+      Some(stageUiData.executorSummary.map { case (k, summary) =>
+        k -> new ExecutorStageSummary(
+          taskTime = summary.taskTime,
+          failedTasks = summary.failedTasks,
+          succeededTasks = summary.succeededTasks,
+          inputBytes = summary.inputBytes,
+          outputBytes = summary.outputBytes,
+          shuffleRead = summary.shuffleRead,
+          shuffleWrite = summary.shuffleWrite,
+          memoryBytesSpilled = summary.memoryBytesSpilled,
+          diskBytesSpilled = summary.diskBytesSpilled
+        )
+      })
+    } else {
+      None
+    }
+
+    val accumulableInfo = stageUiData.accumulables.values.map { convertAccumulableInfo }.toSeq
+
+    new StageData(
+      status = status,
+      stageId = stageInfo.stageId,
+      attemptId = stageInfo.attemptId,
+      numActiveTasks = stageUiData.numActiveTasks,
+      numCompleteTasks = stageUiData.numCompleteTasks,
+      numFailedTasks = stageUiData.numFailedTasks,
+      executorRunTime = stageUiData.executorRunTime,
+      inputBytes = stageUiData.inputBytes,
+      inputRecords = stageUiData.inputRecords,
+      outputBytes = stageUiData.outputBytes,
+      outputRecords = stageUiData.outputRecords,
+      shuffleReadBytes = stageUiData.shuffleReadTotalBytes,
+      shuffleReadRecords = stageUiData.shuffleReadRecords,
+      shuffleWriteBytes = stageUiData.shuffleWriteBytes,
+      shuffleWriteRecords = stageUiData.shuffleWriteRecords,
+      memoryBytesSpilled = stageUiData.memoryBytesSpilled,
+      diskBytesSpilled = stageUiData.diskBytesSpilled,
+      schedulingPool = stageUiData.schedulingPool,
+      name = stageInfo.name,
+      details = stageInfo.details,
+      accumulatorUpdates = accumulableInfo,
+      tasks = taskData,
+      executorSummary = executorSummary
+    )
+  }
+
+  def stagesAndStatus(ui: SparkUI): Seq[(StageStatus, Seq[StageInfo])] = {
+    val listener = ui.jobProgressListener
+    listener.synchronized {
+      Seq(
+        StageStatus.ACTIVE -> listener.activeStages.values.toSeq,
+        StageStatus.COMPLETE -> listener.completedStages.reverse.toSeq,
+        StageStatus.FAILED -> listener.failedStages.reverse.toSeq,
+        StageStatus.PENDING -> listener.pendingStages.values.toSeq
+      )
+    }
+  }
+
+  def convertTaskData(uiData: TaskUIData): TaskData = {
+    new TaskData(
+      taskId = uiData.taskInfo.taskId,
+      index = uiData.taskInfo.index,
+      attempt = uiData.taskInfo.attempt,
+      launchTime = new Date(uiData.taskInfo.launchTime),
+      executorId = uiData.taskInfo.executorId,
+      host = uiData.taskInfo.host,
+      taskLocality = uiData.taskInfo.taskLocality.toString(),
+      speculative = uiData.taskInfo.speculative,
+      accumulatorUpdates = uiData.taskInfo.accumulables.map { convertAccumulableInfo },
+      errorMessage = uiData.errorMessage,
+      taskMetrics = uiData.taskMetrics.map { convertUiTaskMetrics }
+    )
+  }
+
+  def taskMetricDistributions(
+      allTaskData: Iterable[TaskUIData],
+      quantiles: Array[Double]): TaskMetricDistributions = {
+
+    val rawMetrics = allTaskData.flatMap{_.taskMetrics}.toSeq
+
+    def metricQuantiles(f: InternalTaskMetrics => Double): IndexedSeq[Double] =
+      Distribution(rawMetrics.map { d => f(d) }).get.getQuantiles(quantiles)
+
+    // We need to do a lot of similar munging to nested metrics here.  For each one,
+    // we want (a) extract the values for nested metrics (b) make a distribution for each metric
+    // (c) shove the distribution into the right field in our return type and (d) only return
+    // a result if the option is defined for any of the tasks.  MetricHelper is a little util
+    // to make it a little easier to deal w/ all of the nested options.  Mostly it lets us just
+    // implement one "build" method, which just builds the quantiles for each field.
+
+    val inputMetrics: Option[InputMetricDistributions] =
+      new MetricHelper[InternalInputMetrics, InputMetricDistributions](rawMetrics, quantiles) {
+        def getSubmetrics(raw: InternalTaskMetrics): Option[InternalInputMetrics] = {
+          raw.inputMetrics
+        }
+
+        def build: InputMetricDistributions = new InputMetricDistributions(
+          bytesRead = submetricQuantiles(_.bytesRead),
+          recordsRead = submetricQuantiles(_.recordsRead)
+        )
+      }.metricOption
+
+    val outputMetrics: Option[OutputMetricDistributions] =
+      new MetricHelper[InternalOutputMetrics, OutputMetricDistributions](rawMetrics, quantiles) {
+        def getSubmetrics(raw:InternalTaskMetrics): Option[InternalOutputMetrics] = {
+          raw.outputMetrics
+        }
+        def build: OutputMetricDistributions = new OutputMetricDistributions(
+          bytesWritten = submetricQuantiles(_.bytesWritten),
+          recordsWritten = submetricQuantiles(_.recordsWritten)
+        )
+      }.metricOption
+
+    val shuffleReadMetrics: Option[ShuffleReadMetricDistributions] =
+      new MetricHelper[InternalShuffleReadMetrics, ShuffleReadMetricDistributions](rawMetrics,
+        quantiles) {
+        def getSubmetrics(raw: InternalTaskMetrics): Option[InternalShuffleReadMetrics] = {
+          raw.shuffleReadMetrics
+        }
+        def build: ShuffleReadMetricDistributions = new ShuffleReadMetricDistributions(
+          readBytes = submetricQuantiles(_.totalBytesRead),
+          readRecords = submetricQuantiles(_.recordsRead),
+          remoteBytesRead = submetricQuantiles(_.remoteBytesRead),
+          remoteBlocksFetched = submetricQuantiles(_.remoteBlocksFetched),
+          localBlocksFetched = submetricQuantiles(_.localBlocksFetched),
+          totalBlocksFetched = submetricQuantiles(_.totalBlocksFetched),
+          fetchWaitTime = submetricQuantiles(_.fetchWaitTime)
+        )
+      }.metricOption
+
+    val shuffleWriteMetrics: Option[ShuffleWriteMetricDistributions] =
+      new MetricHelper[InternalShuffleWriteMetrics, ShuffleWriteMetricDistributions](rawMetrics,
+        quantiles) {
+        def getSubmetrics(raw: InternalTaskMetrics): Option[InternalShuffleWriteMetrics] = {
+          raw.shuffleWriteMetrics
+        }
+        def build: ShuffleWriteMetricDistributions = new ShuffleWriteMetricDistributions(
+          writeBytes = submetricQuantiles(_.shuffleBytesWritten),
+          writeRecords = submetricQuantiles(_.shuffleRecordsWritten),
+          writeTime = submetricQuantiles(_.shuffleWriteTime)
+        )
+      }.metricOption
+
+    new TaskMetricDistributions(
+      quantiles = quantiles,
+      executorDeserializeTime = metricQuantiles(_.executorDeserializeTime),
+      executorRunTime = metricQuantiles(_.executorRunTime),
+      resultSize = metricQuantiles(_.resultSize),
+      jvmGcTime = metricQuantiles(_.jvmGCTime),
+      resultSerializationTime = metricQuantiles(_.resultSerializationTime),
+      memoryBytesSpilled = metricQuantiles(_.memoryBytesSpilled),
+      diskBytesSpilled = metricQuantiles(_.diskBytesSpilled),
+      inputMetrics = inputMetrics,
+      outputMetrics = outputMetrics,
+      shuffleReadMetrics = shuffleReadMetrics,
+      shuffleWriteMetrics = shuffleWriteMetrics
+    )
+  }
+
+  def convertAccumulableInfo(acc: InternalAccumulableInfo): AccumulableInfo = {
+    new AccumulableInfo(acc.id, acc.name, acc.update, acc.value)
+  }
+
+  def convertUiTaskMetrics(internal: InternalTaskMetrics): TaskMetrics = {
+    new TaskMetrics(
+      executorDeserializeTime = internal.executorDeserializeTime,
+      executorRunTime = internal.executorRunTime,
+      resultSize = internal.resultSize,
+      jvmGcTime = internal.jvmGCTime,
+      resultSerializationTime = internal.resultSerializationTime,
+      memoryBytesSpilled = internal.memoryBytesSpilled,
+      diskBytesSpilled = internal.diskBytesSpilled,
+      inputMetrics = internal.inputMetrics.map { convertInputMetrics },
+      outputMetrics = Option(internal.outputMetrics).flatten.map { convertOutputMetrics },
+      shuffleReadMetrics = internal.shuffleReadMetrics.map { convertShuffleReadMetrics },
+      shuffleWriteMetrics = internal.shuffleWriteMetrics.map { convertShuffleWriteMetrics }
+    )
+  }
+
+  def convertInputMetrics(internal: InternalInputMetrics): InputMetrics = {
+    new InputMetrics(
+      bytesRead = internal.bytesRead,
+      recordsRead = internal.recordsRead
+    )
+  }
+
+  def convertOutputMetrics(internal: InternalOutputMetrics): OutputMetrics = {
+    new OutputMetrics(
+      bytesWritten = internal.bytesWritten,
+      recordsWritten = internal.recordsWritten
+    )
+  }
+
+  def convertShuffleReadMetrics(internal: InternalShuffleReadMetrics): ShuffleReadMetrics = {
+    new ShuffleReadMetrics(
+      remoteBlocksFetched = internal.remoteBlocksFetched,
+      localBlocksFetched = internal.localBlocksFetched,
+      fetchWaitTime = internal.fetchWaitTime,
+      remoteBytesRead = internal.remoteBytesRead,
+      totalBlocksFetched = internal.totalBlocksFetched,
+      recordsRead = internal.recordsRead
+    )
+  }
+
+  def convertShuffleWriteMetrics(internal: InternalShuffleWriteMetrics): ShuffleWriteMetrics = {
+    new ShuffleWriteMetrics(
+      bytesWritten = internal.shuffleBytesWritten,
+      writeTime = internal.shuffleWriteTime,
+      recordsWritten = internal.shuffleRecordsWritten
+    )
+  }
+}
+
+/**
+ * Helper for getting distributions from nested metric types.  Many of the metrics we want are
+ * contained in options inside TaskMetrics (eg., ShuffleWriteMetrics). This makes it easy to handle
+ * the options (returning None if the metrics are all empty), and extract the quantiles for each
+ * metric.  After creating an instance, call metricOption to get the result type.
+ */
+private[v1] abstract class MetricHelper[I,O](
+    rawMetrics: Seq[InternalTaskMetrics],
+    quantiles: Array[Double]) {
+
+  def getSubmetrics(raw: InternalTaskMetrics): Option[I]
+
+  def build: O
+
+  val data: Seq[I] = rawMetrics.flatMap(getSubmetrics)
+
+  /** applies the given function to all input metrics, and returns the quantiles */
+  def submetricQuantiles(f: I => Double): IndexedSeq[Double] = {
+    Distribution(data.map { d => f(d) }).get.getQuantiles(quantiles)
+  }
+
+  def metricOption: Option[O] = {
+    if (data.isEmpty) {
+      None
+    } else {
+      Some(build)
+    }
+  }
+}
diff --git a/core/src/main/scala/org/apache/spark/status/api/v1/ApplicationListResource.scala b/core/src/main/scala/org/apache/spark/status/api/v1/ApplicationListResource.scala
new file mode 100644
index 0000000000000..17b521f3e1d41
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/status/api/v1/ApplicationListResource.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.status.api.v1
+
+import java.util.{Arrays, Date, List => JList}
+import javax.ws.rs.{DefaultValue, GET, Produces, QueryParam}
+import javax.ws.rs.core.MediaType
+
+import org.apache.spark.deploy.history.ApplicationHistoryInfo
+import org.apache.spark.deploy.master.{ApplicationInfo => InternalApplicationInfo}
+
+@Produces(Array(MediaType.APPLICATION_JSON))
+private[v1] class ApplicationListResource(uiRoot: UIRoot) {
+
+  @GET
+  def appList(
+      @QueryParam("status") status: JList[ApplicationStatus],
+      @DefaultValue("2010-01-01") @QueryParam("minDate") minDate: SimpleDateParam,
+      @DefaultValue("3000-01-01") @QueryParam("maxDate") maxDate: SimpleDateParam)
+  : Iterator[ApplicationInfo] = {
+    val allApps = uiRoot.getApplicationInfoList
+    val adjStatus = {
+      if (status.isEmpty) {
+        Arrays.asList(ApplicationStatus.values(): _*)
+      } else {
+        status
+      }
+    }
+    val includeCompleted = adjStatus.contains(ApplicationStatus.COMPLETED)
+    val includeRunning = adjStatus.contains(ApplicationStatus.RUNNING)
+    allApps.filter { app =>
+      val anyRunning = app.attempts.exists(!_.completed)
+      // if any attempt is still running, we consider the app to also still be running
+      val statusOk = (!anyRunning && includeCompleted) ||
+        (anyRunning && includeRunning)
+      // keep the app if *any* attempts fall in the right time window
+      val dateOk = app.attempts.exists { attempt =>
+        attempt.startTime.getTime >= minDate.timestamp &&
+          attempt.startTime.getTime <= maxDate.timestamp
+      }
+      statusOk && dateOk
+    }
+  }
+}
+
+private[spark] object ApplicationsListResource {
+  def appHistoryInfoToPublicAppInfo(app: ApplicationHistoryInfo): ApplicationInfo = {
+    new ApplicationInfo(
+      id = app.id,
+      name = app.name,
+      attempts = app.attempts.map { internalAttemptInfo =>
+        new ApplicationAttemptInfo(
+          attemptId = internalAttemptInfo.attemptId,
+          startTime = new Date(internalAttemptInfo.startTime),
+          endTime = new Date(internalAttemptInfo.endTime),
+          sparkUser = internalAttemptInfo.sparkUser,
+          completed = internalAttemptInfo.completed
+        )
+      }
+    )
+  }
+
+  def convertApplicationInfo(
+      internal: InternalApplicationInfo,
+      completed: Boolean): ApplicationInfo = {
+    // standalone application info always has just one attempt
+    new ApplicationInfo(
+      id = internal.id,
+      name = internal.desc.name,
+      attempts = Seq(new ApplicationAttemptInfo(
+        attemptId = None,
+        startTime = new Date(internal.startTime),
+        endTime = new Date(internal.endTime),
+        sparkUser = internal.desc.user,
+        completed = completed
+      ))
+    )
+  }
+
+}
diff --git a/core/src/main/scala/org/apache/spark/status/api/v1/ExecutorListResource.scala b/core/src/main/scala/org/apache/spark/status/api/v1/ExecutorListResource.scala
new file mode 100644
index 0000000000000..8ad4656b4dada
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/status/api/v1/ExecutorListResource.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.status.api.v1
+
+import javax.ws.rs.{GET, PathParam, Produces}
+import javax.ws.rs.core.MediaType
+
+import org.apache.spark.ui.SparkUI
+import org.apache.spark.ui.exec.ExecutorsPage
+
+@Produces(Array(MediaType.APPLICATION_JSON))
+private[v1] class ExecutorListResource(ui: SparkUI) {
+
+  @GET
+  def executorList(): Seq[ExecutorSummary] = {
+    val listener = ui.executorsListener
+    val storageStatusList = listener.storageStatusList
+    (0 until storageStatusList.size).map { statusId =>
+      ExecutorsPage.getExecInfo(listener, statusId)
+    }
+  }
+}
diff --git a/core/src/main/scala/org/apache/spark/status/api/v1/JacksonMessageWriter.scala b/core/src/main/scala/org/apache/spark/status/api/v1/JacksonMessageWriter.scala
new file mode 100644
index 0000000000000..202a5191ad57d
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/status/api/v1/JacksonMessageWriter.scala
@@ -0,0 +1,93 @@
+/*
+ * 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.status.api.v1
+
+import java.io.OutputStream
+import java.lang.annotation.Annotation
+import java.lang.reflect.Type
+import java.text.SimpleDateFormat
+import java.util.{Calendar, SimpleTimeZone}
+import javax.ws.rs.Produces
+import javax.ws.rs.core.{MediaType, MultivaluedMap}
+import javax.ws.rs.ext.{MessageBodyWriter, Provider}
+
+import com.fasterxml.jackson.annotation.JsonInclude
+import com.fasterxml.jackson.databind.{ObjectMapper, SerializationFeature}
+
+/**
+ * This class converts the POJO metric responses into json, using jackson.
+ *
+ * This doesn't follow the standard jersey-jackson plugin options, because we want to stick
+ * with an old version of jersey (since we have it from yarn anyway) and don't want to pull in lots
+ * of dependencies from a new plugin.
+ *
+ * Note that jersey automatically discovers this class based on its package and its annotations.
+ */
+@Provider
+@Produces(Array(MediaType.APPLICATION_JSON))
+private[v1] class JacksonMessageWriter extends MessageBodyWriter[Object]{
+
+  val mapper = new ObjectMapper() {
+    override def writeValueAsString(t: Any): String = {
+      super.writeValueAsString(t)
+    }
+  }
+  mapper.registerModule(com.fasterxml.jackson.module.scala.DefaultScalaModule)
+  mapper.enable(SerializationFeature.INDENT_OUTPUT)
+  mapper.setSerializationInclusion(JsonInclude.Include.NON_NULL)
+  mapper.setDateFormat(JacksonMessageWriter.makeISODateFormat)
+
+  override def isWriteable(
+      aClass: Class[_],
+      `type`: Type,
+      annotations: Array[Annotation],
+      mediaType: MediaType): Boolean = {
+      true
+  }
+
+  override def writeTo(
+      t: Object,
+      aClass: Class[_],
+      `type`: Type,
+      annotations: Array[Annotation],
+      mediaType: MediaType,
+      multivaluedMap: MultivaluedMap[String, AnyRef],
+      outputStream: OutputStream): Unit = {
+    t match {
+      case ErrorWrapper(err) => outputStream.write(err.getBytes("utf-8"))
+      case _ => mapper.writeValue(outputStream, t)
+    }
+  }
+
+  override def getSize(
+      t: Object,
+      aClass: Class[_],
+      `type`: Type,
+      annotations: Array[Annotation],
+      mediaType: MediaType): Long = {
+    -1L
+  }
+}
+
+private[spark] object JacksonMessageWriter {
+  def makeISODateFormat: SimpleDateFormat = {
+    val iso8601 = new SimpleDateFormat("yyyy-MM-dd'T'HH:mm:ss.SSS'GMT'")
+    val cal = Calendar.getInstance(new SimpleTimeZone(0, "GMT"))
+    iso8601.setCalendar(cal)
+    iso8601
+  }
+}
diff --git a/core/src/main/scala/org/apache/spark/status/api/v1/JsonRootResource.scala b/core/src/main/scala/org/apache/spark/status/api/v1/JsonRootResource.scala
new file mode 100644
index 0000000000000..c3ec45f54681b
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/status/api/v1/JsonRootResource.scala
@@ -0,0 +1,255 @@
+/*
+ * 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.status.api.v1
+
+import javax.servlet.ServletContext
+import javax.ws.rs._
+import javax.ws.rs.core.{Context, Response}
+
+import com.sun.jersey.api.core.ResourceConfig
+import com.sun.jersey.spi.container.servlet.ServletContainer
+import org.eclipse.jetty.server.handler.ContextHandler
+import org.eclipse.jetty.servlet.{ServletContextHandler, ServletHolder}
+
+import org.apache.spark.SecurityManager
+import org.apache.spark.ui.SparkUI
+
+/**
+ * Main entry point for serving spark application metrics as json, using JAX-RS.
+ *
+ * Each resource should have endpoints that return **public** classes defined in api.scala.  Mima
+ * binary compatibility checks ensure that we don't inadvertently make changes that break the api.
+ * The returned objects are automatically converted to json by jackson with JacksonMessageWriter.
+ * In addition, there are a number of tests in HistoryServerSuite that compare the json to "golden
+ * files".  Any changes and additions should be reflected there as well -- see the notes in
+ * HistoryServerSuite.
+ */
+@Path("/v1")
+private[v1] class JsonRootResource extends UIRootFromServletContext {
+
+  @Path("applications")
+  def getApplicationList(): ApplicationListResource = {
+    new ApplicationListResource(uiRoot)
+  }
+
+  @Path("applications/{appId}")
+  def getApplication(): OneApplicationResource = {
+    new OneApplicationResource(uiRoot)
+  }
+
+  @Path("applications/{appId}/{attemptId}/jobs")
+  def getJobs(
+      @PathParam("appId") appId: String,
+      @PathParam("attemptId") attemptId: String): AllJobsResource = {
+    uiRoot.withSparkUI(appId, Some(attemptId)) { ui =>
+      new AllJobsResource(ui)
+    }
+  }
+
+  @Path("applications/{appId}/jobs")
+  def getJobs(@PathParam("appId") appId: String): AllJobsResource = {
+    uiRoot.withSparkUI(appId, None) { ui =>
+      new AllJobsResource(ui)
+    }
+  }
+
+  @Path("applications/{appId}/jobs/{jobId: \\d+}")
+  def getJob(@PathParam("appId") appId: String): OneJobResource = {
+    uiRoot.withSparkUI(appId, None) { ui =>
+      new OneJobResource(ui)
+    }
+  }
+
+  @Path("applications/{appId}/{attemptId}/jobs/{jobId: \\d+}")
+  def getJob(
+      @PathParam("appId") appId: String,
+      @PathParam("attemptId") attemptId: String): OneJobResource = {
+    uiRoot.withSparkUI(appId, Some(attemptId)) { ui =>
+      new OneJobResource(ui)
+    }
+  }
+
+  @Path("applications/{appId}/executors")
+  def getExecutors(@PathParam("appId") appId: String): ExecutorListResource = {
+    uiRoot.withSparkUI(appId, None) { ui =>
+      new ExecutorListResource(ui)
+    }
+  }
+
+  @Path("applications/{appId}/{attemptId}/executors")
+  def getExecutors(
+      @PathParam("appId") appId: String,
+      @PathParam("attemptId") attemptId: String): ExecutorListResource = {
+    uiRoot.withSparkUI(appId, Some(attemptId)) { ui =>
+      new ExecutorListResource(ui)
+    }
+  }
+
+
+  @Path("applications/{appId}/stages")
+  def getStages(@PathParam("appId") appId: String): AllStagesResource= {
+    uiRoot.withSparkUI(appId, None) { ui =>
+      new AllStagesResource(ui)
+    }
+  }
+
+  @Path("applications/{appId}/{attemptId}/stages")
+  def getStages(
+      @PathParam("appId") appId: String,
+      @PathParam("attemptId") attemptId: String): AllStagesResource= {
+    uiRoot.withSparkUI(appId, Some(attemptId)) { ui =>
+      new AllStagesResource(ui)
+    }
+  }
+
+  @Path("applications/{appId}/stages/{stageId: \\d+}")
+  def getStage(@PathParam("appId") appId: String): OneStageResource= {
+    uiRoot.withSparkUI(appId, None) { ui =>
+      new OneStageResource(ui)
+    }
+  }
+
+  @Path("applications/{appId}/{attemptId}/stages/{stageId: \\d+}")
+  def getStage(
+      @PathParam("appId") appId: String,
+      @PathParam("attemptId") attemptId: String): OneStageResource = {
+    uiRoot.withSparkUI(appId, Some(attemptId)) { ui =>
+      new OneStageResource(ui)
+    }
+  }
+
+  @Path("applications/{appId}/storage/rdd")
+  def getRdds(@PathParam("appId") appId: String): AllRDDResource = {
+    uiRoot.withSparkUI(appId, None) { ui =>
+      new AllRDDResource(ui)
+    }
+  }
+
+  @Path("applications/{appId}/{attemptId}/storage/rdd")
+  def getRdds(
+      @PathParam("appId") appId: String,
+      @PathParam("attemptId") attemptId: String): AllRDDResource = {
+    uiRoot.withSparkUI(appId, Some(attemptId)) { ui =>
+      new AllRDDResource(ui)
+    }
+  }
+
+  @Path("applications/{appId}/storage/rdd/{rddId: \\d+}")
+  def getRdd(@PathParam("appId") appId: String): OneRDDResource = {
+    uiRoot.withSparkUI(appId, None) { ui =>
+      new OneRDDResource(ui)
+    }
+  }
+
+  @Path("applications/{appId}/{attemptId}/storage/rdd/{rddId: \\d+}")
+  def getRdd(
+      @PathParam("appId") appId: String,
+      @PathParam("attemptId") attemptId: String): OneRDDResource = {
+    uiRoot.withSparkUI(appId, Some(attemptId)) { ui =>
+      new OneRDDResource(ui)
+    }
+  }
+
+}
+
+private[spark] object JsonRootResource {
+
+  def getJsonServlet(uiRoot: UIRoot): ServletContextHandler = {
+    val jerseyContext = new ServletContextHandler(ServletContextHandler.NO_SESSIONS)
+    jerseyContext.setContextPath("/json")
+    val holder:ServletHolder = new ServletHolder(classOf[ServletContainer])
+    holder.setInitParameter("com.sun.jersey.config.property.resourceConfigClass",
+      "com.sun.jersey.api.core.PackagesResourceConfig")
+    holder.setInitParameter("com.sun.jersey.config.property.packages",
+      "org.apache.spark.status.api.v1")
+    holder.setInitParameter(ResourceConfig.PROPERTY_CONTAINER_REQUEST_FILTERS,
+      classOf[SecurityFilter].getCanonicalName)
+    UIRootFromServletContext.setUiRoot(jerseyContext, uiRoot)
+    jerseyContext.addServlet(holder, "/*")
+    jerseyContext
+  }
+}
+
+/**
+ * This trait is shared by the all the root containers for application UI information --
+ * the HistoryServer, the Master UI, and the application UI.  This provides the common
+ * interface needed for them all to expose application info as json.
+ */
+private[spark] trait UIRoot {
+  def getSparkUI(appKey: String): Option[SparkUI]
+  def getApplicationInfoList: Iterator[ApplicationInfo]
+
+  /**
+   * Get the spark UI with the given appID, and apply a function
+   * to it.  If there is no such app, throw an appropriate exception
+   */
+  def withSparkUI[T](appId: String, attemptId: Option[String])(f: SparkUI => T): T = {
+    val appKey = attemptId.map(appId + "/" + _).getOrElse(appId)
+    getSparkUI(appKey) match {
+      case Some(ui) =>
+        f(ui)
+      case None => throw new NotFoundException("no such app: " + appId)
+    }
+  }
+  def securityManager: SecurityManager
+}
+
+private[v1] object UIRootFromServletContext {
+
+  private val attribute = getClass.getCanonicalName
+
+  def setUiRoot(contextHandler: ContextHandler, uiRoot: UIRoot): Unit = {
+    contextHandler.setAttribute(attribute, uiRoot)
+  }
+
+  def getUiRoot(context: ServletContext): UIRoot = {
+    context.getAttribute(attribute).asInstanceOf[UIRoot]
+  }
+}
+
+private[v1] trait UIRootFromServletContext {
+  @Context
+  var servletContext: ServletContext = _
+
+  def uiRoot: UIRoot = UIRootFromServletContext.getUiRoot(servletContext)
+}
+
+private[v1] class NotFoundException(msg: String) extends WebApplicationException(
+  new NoSuchElementException(msg),
+    Response
+      .status(Response.Status.NOT_FOUND)
+      .entity(ErrorWrapper(msg))
+      .build()
+)
+
+private[v1] class BadParameterException(msg: String) extends WebApplicationException(
+  new IllegalArgumentException(msg),
+  Response
+    .status(Response.Status.BAD_REQUEST)
+    .entity(ErrorWrapper(msg))
+    .build()
+) {
+  def this(param: String, exp: String, actual: String) = {
+    this(raw"""Bad value for parameter "$param".  Expected a $exp, got "$actual"""")
+  }
+}
+
+/**
+ * Signal to JacksonMessageWriter to not convert the message into json (which would result in an
+ * extra set of quotes).
+ */
+private[v1] case class ErrorWrapper(s: String)
diff --git a/core/src/main/scala/org/apache/spark/status/api/v1/OneApplicationResource.scala b/core/src/main/scala/org/apache/spark/status/api/v1/OneApplicationResource.scala
new file mode 100644
index 0000000000000..b5ef72649e295
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/status/api/v1/OneApplicationResource.scala
@@ -0,0 +1,31 @@
+/*
+ * 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.status.api.v1
+
+import javax.ws.rs.core.MediaType
+import javax.ws.rs.{Produces, PathParam, GET}
+
+@Produces(Array(MediaType.APPLICATION_JSON))
+private[v1] class OneApplicationResource(uiRoot: UIRoot) {
+
+  @GET
+  def getApp(@PathParam("appId") appId: String): ApplicationInfo = {
+    val apps = uiRoot.getApplicationInfoList.find { _.id == appId }
+    apps.getOrElse(throw new NotFoundException("unknown app: " + appId))
+  }
+
+}
diff --git a/core/src/main/scala/org/apache/spark/status/api/v1/OneJobResource.scala b/core/src/main/scala/org/apache/spark/status/api/v1/OneJobResource.scala
new file mode 100644
index 0000000000000..6d8a60d480aed
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/status/api/v1/OneJobResource.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.status.api.v1
+
+import javax.ws.rs.{PathParam, GET, Produces}
+import javax.ws.rs.core.MediaType
+
+import org.apache.spark.JobExecutionStatus
+import org.apache.spark.ui.SparkUI
+import org.apache.spark.ui.jobs.UIData.JobUIData
+
+@Produces(Array(MediaType.APPLICATION_JSON))
+private[v1] class OneJobResource(ui: SparkUI) {
+
+  @GET
+  def oneJob(@PathParam("jobId") jobId: Int): JobData = {
+    val statusToJobs: Seq[(JobExecutionStatus, Seq[JobUIData])] =
+      AllJobsResource.getStatusToJobs(ui)
+    val jobOpt = statusToJobs.map {_._2} .flatten.find { jobInfo => jobInfo.jobId == jobId}
+    jobOpt.map { job =>
+      AllJobsResource.convertJobData(job, ui.jobProgressListener, false)
+    }.getOrElse {
+      throw new NotFoundException("unknown job: " + jobId)
+    }
+  }
+
+}
diff --git a/core/src/main/scala/org/apache/spark/status/api/v1/OneRDDResource.scala b/core/src/main/scala/org/apache/spark/status/api/v1/OneRDDResource.scala
new file mode 100644
index 0000000000000..07b224fac4786
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/status/api/v1/OneRDDResource.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.status.api.v1
+
+import javax.ws.rs.{PathParam, GET, Produces}
+import javax.ws.rs.core.MediaType
+
+import org.apache.spark.ui.SparkUI
+
+@Produces(Array(MediaType.APPLICATION_JSON))
+private[v1] class OneRDDResource(ui: SparkUI) {
+
+  @GET
+  def rddData(@PathParam("rddId") rddId: Int): RDDStorageInfo  = {
+    AllRDDResource.getRDDStorageInfo(rddId, ui.storageListener, true).getOrElse(
+      throw new NotFoundException(s"no rdd found w/ id $rddId")
+    )
+  }
+
+}
diff --git a/core/src/main/scala/org/apache/spark/status/api/v1/OneStageResource.scala b/core/src/main/scala/org/apache/spark/status/api/v1/OneStageResource.scala
new file mode 100644
index 0000000000000..fd24aea63a8a1
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/status/api/v1/OneStageResource.scala
@@ -0,0 +1,150 @@
+/*
+ * 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.status.api.v1
+
+import javax.ws.rs._
+import javax.ws.rs.core.MediaType
+
+import org.apache.spark.SparkException
+import org.apache.spark.scheduler.StageInfo
+import org.apache.spark.status.api.v1.StageStatus._
+import org.apache.spark.status.api.v1.TaskSorting._
+import org.apache.spark.ui.SparkUI
+import org.apache.spark.ui.jobs.JobProgressListener
+import org.apache.spark.ui.jobs.UIData.StageUIData
+
+@Produces(Array(MediaType.APPLICATION_JSON))
+private[v1] class OneStageResource(ui: SparkUI) {
+
+  @GET
+  @Path("")
+  def stageData(@PathParam("stageId") stageId: Int): Seq[StageData] = {
+    withStage(stageId){ stageAttempts =>
+      stageAttempts.map { stage =>
+        AllStagesResource.stageUiToStageData(stage.status, stage.info, stage.ui,
+          includeDetails = true)
+      }
+    }
+  }
+
+  @GET
+  @Path("/{stageAttemptId: \\d+}")
+  def oneAttemptData(
+      @PathParam("stageId") stageId: Int,
+      @PathParam("stageAttemptId") stageAttemptId: Int): StageData = {
+    withStageAttempt(stageId, stageAttemptId) { stage =>
+      AllStagesResource.stageUiToStageData(stage.status, stage.info, stage.ui,
+        includeDetails = true)
+    }
+  }
+
+  @GET
+  @Path("/{stageAttemptId: \\d+}/taskSummary")
+  def taskSummary(
+      @PathParam("stageId") stageId: Int,
+      @PathParam("stageAttemptId") stageAttemptId: Int,
+      @DefaultValue("0.05,0.25,0.5,0.75,0.95") @QueryParam("quantiles") quantileString: String)
+  : TaskMetricDistributions = {
+    withStageAttempt(stageId, stageAttemptId) { stage =>
+      val quantiles = quantileString.split(",").map { s =>
+        try {
+          s.toDouble
+        } catch {
+          case nfe: NumberFormatException =>
+            throw new BadParameterException("quantiles", "double", s)
+        }
+      }
+      AllStagesResource.taskMetricDistributions(stage.ui.taskData.values, quantiles)
+    }
+  }
+
+  @GET
+  @Path("/{stageAttemptId: \\d+}/taskList")
+  def taskList(
+      @PathParam("stageId") stageId: Int,
+      @PathParam("stageAttemptId") stageAttemptId: Int,
+      @DefaultValue("0") @QueryParam("offset") offset: Int,
+      @DefaultValue("20") @QueryParam("length") length: Int,
+      @DefaultValue("ID") @QueryParam("sortBy") sortBy: TaskSorting): Seq[TaskData] = {
+    withStageAttempt(stageId, stageAttemptId) { stage =>
+      val tasks = stage.ui.taskData.values.map{AllStagesResource.convertTaskData}.toIndexedSeq
+        .sorted(OneStageResource.ordering(sortBy))
+      tasks.slice(offset, offset + length)  
+    }
+  }
+
+  private case class StageStatusInfoUi(status: StageStatus, info: StageInfo, ui: StageUIData)
+
+  private def withStage[T](stageId: Int)(f: Seq[StageStatusInfoUi] => T): T = {
+    val stageAttempts = findStageStatusUIData(ui.jobProgressListener, stageId)
+    if (stageAttempts.isEmpty) {
+      throw new NotFoundException("unknown stage: " + stageId)
+    } else {
+      f(stageAttempts)
+    }
+  }
+
+  private def findStageStatusUIData(
+      listener: JobProgressListener,
+      stageId: Int): Seq[StageStatusInfoUi] = {
+    listener.synchronized {
+      def getStatusInfoUi(status: StageStatus, infos: Seq[StageInfo]): Seq[StageStatusInfoUi] = {
+        infos.filter { _.stageId == stageId }.map { info =>
+          val ui = listener.stageIdToData.getOrElse((info.stageId, info.attemptId),
+            // this is an internal error -- we should always have uiData
+            throw new SparkException(
+              s"no stage ui data found for stage: ${info.stageId}:${info.attemptId}")
+          )
+          StageStatusInfoUi(status, info, ui)
+        }
+      }
+      getStatusInfoUi(ACTIVE, listener.activeStages.values.toSeq) ++
+        getStatusInfoUi(COMPLETE, listener.completedStages) ++
+        getStatusInfoUi(FAILED, listener.failedStages) ++
+        getStatusInfoUi(PENDING, listener.pendingStages.values.toSeq)
+    }
+  }
+
+  private def withStageAttempt[T](
+      stageId: Int,
+      stageAttemptId: Int)
+      (f: StageStatusInfoUi => T): T = {
+    withStage(stageId) { attempts =>
+        val oneAttempt = attempts.find { stage => stage.info.attemptId == stageAttemptId }
+        oneAttempt match {
+          case Some(stage) =>
+            f(stage)
+          case None =>
+            val stageAttempts = attempts.map { _.info.attemptId }
+            throw new NotFoundException(s"unknown attempt for stage $stageId.  " +
+              s"Found attempts: ${stageAttempts.mkString("[", ",", "]")}")
+        }
+    }
+  }
+}
+
+object OneStageResource {
+  def ordering(taskSorting: TaskSorting): Ordering[TaskData] = {
+    val extractor: (TaskData => Long) = td =>
+      taskSorting match {
+        case ID => td.taskId
+        case INCREASING_RUNTIME => td.taskMetrics.map{_.executorRunTime}.getOrElse(-1L)
+        case DECREASING_RUNTIME => -td.taskMetrics.map{_.executorRunTime}.getOrElse(-1L)
+      }
+    Ordering.by(extractor)
+  }
+}
diff --git a/core/src/main/scala/org/apache/spark/status/api/v1/SecurityFilter.scala b/core/src/main/scala/org/apache/spark/status/api/v1/SecurityFilter.scala
new file mode 100644
index 0000000000000..95fbd96ade5ab
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/status/api/v1/SecurityFilter.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.status.api.v1
+
+import javax.ws.rs.WebApplicationException
+import javax.ws.rs.core.Response
+
+import com.sun.jersey.spi.container.{ContainerRequest, ContainerRequestFilter}
+
+private[v1] class SecurityFilter extends ContainerRequestFilter with UIRootFromServletContext {
+  def filter(req: ContainerRequest): ContainerRequest = {
+    val user = Option(req.getUserPrincipal).map { _.getName }.orNull
+    if (uiRoot.securityManager.checkUIViewPermissions(user)) {
+      req
+    } else {
+      throw new WebApplicationException(
+        Response
+          .status(Response.Status.FORBIDDEN)
+          .entity(raw"""user "$user"is not authorized""")
+          .build()
+      )
+    }
+  }
+}
diff --git a/core/src/main/scala/org/apache/spark/status/api/v1/SimpleDateParam.scala b/core/src/main/scala/org/apache/spark/status/api/v1/SimpleDateParam.scala
new file mode 100644
index 0000000000000..cee29786c3019
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/status/api/v1/SimpleDateParam.scala
@@ -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.
+ */
+package org.apache.spark.status.api.v1
+
+import java.text.SimpleDateFormat
+import java.util.TimeZone
+import javax.ws.rs.WebApplicationException
+import javax.ws.rs.core.Response
+import javax.ws.rs.core.Response.Status
+
+import scala.util.Try
+
+private[v1] class SimpleDateParam(val originalValue: String) {
+  val timestamp: Long = {
+    SimpleDateParam.formats.collectFirst {
+      case fmt if Try(fmt.parse(originalValue)).isSuccess =>
+        fmt.parse(originalValue).getTime()
+    }.getOrElse(
+      throw new WebApplicationException(
+        Response
+          .status(Status.BAD_REQUEST)
+          .entity("Couldn't parse date: " + originalValue)
+          .build()
+      )
+    )
+  }
+}
+
+private[v1] object SimpleDateParam {
+
+  val formats: Seq[SimpleDateFormat] = {
+
+    val gmtDay = new SimpleDateFormat("yyyy-MM-dd")
+    gmtDay.setTimeZone(TimeZone.getTimeZone("GMT"))
+
+    Seq(
+      new SimpleDateFormat("yyyy-MM-dd'T'HH:mm:ss.SSSz"),
+      gmtDay
+    )
+  }
+}
diff --git a/core/src/main/scala/org/apache/spark/status/api/v1/api.scala b/core/src/main/scala/org/apache/spark/status/api/v1/api.scala
new file mode 100644
index 0000000000000..ef3c8570d8186
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/status/api/v1/api.scala
@@ -0,0 +1,228 @@
+/*
+ * 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.status.api.v1
+
+import java.util.Date
+
+import scala.collection.Map
+
+import org.apache.spark.JobExecutionStatus
+
+class ApplicationInfo private[spark](
+    val id: String,
+    val name: String,
+    val attempts: Seq[ApplicationAttemptInfo])
+
+class ApplicationAttemptInfo private[spark](
+    val attemptId: Option[String],
+    val startTime: Date,
+    val endTime: Date,
+    val sparkUser: String,
+    val completed: Boolean = false)
+
+class ExecutorStageSummary private[spark](
+    val taskTime : Long,
+    val failedTasks : Int,
+    val succeededTasks : Int,
+    val inputBytes : Long,
+    val outputBytes : Long,
+    val shuffleRead : Long,
+    val shuffleWrite : Long,
+    val memoryBytesSpilled : Long,
+    val diskBytesSpilled : Long)
+
+class ExecutorSummary private[spark](
+    val id: String,
+    val hostPort: String,
+    val rddBlocks: Int,
+    val memoryUsed: Long,
+    val diskUsed: Long,
+    val activeTasks: Int,
+    val failedTasks: Int,
+    val completedTasks: Int,
+    val totalTasks: Int,
+    val totalDuration: Long,
+    val totalInputBytes: Long,
+    val totalShuffleRead: Long,
+    val totalShuffleWrite: Long,
+    val maxMemory: Long,
+    val executorLogs: Map[String, String])
+
+class JobData private[spark](
+    val jobId: Int,
+    val name: String,
+    val description: Option[String],
+    val submissionTime: Option[Date],
+    val completionTime: Option[Date],
+    val stageIds: Seq[Int],
+    val jobGroup: Option[String],
+    val status: JobExecutionStatus,
+    val numTasks: Int,
+    val numActiveTasks: Int,
+    val numCompletedTasks: Int,
+    val numSkippedTasks: Int,
+    val numFailedTasks: Int,
+    val numActiveStages: Int,
+    val numCompletedStages: Int,
+    val numSkippedStages: Int,
+    val numFailedStages: Int)
+
+// Q: should Tachyon size go in here as well?  currently the UI only shows it on the overall storage
+// page ... does anybody pay attention to it?
+class RDDStorageInfo private[spark](
+    val id: Int,
+    val name: String,
+    val numPartitions: Int,
+    val numCachedPartitions: Int,
+    val storageLevel: String,
+    val memoryUsed: Long,
+    val diskUsed: Long,
+    val dataDistribution: Option[Seq[RDDDataDistribution]],
+    val partitions: Option[Seq[RDDPartitionInfo]])
+
+class RDDDataDistribution private[spark](
+    val address: String,
+    val memoryUsed: Long,
+    val memoryRemaining: Long,
+    val diskUsed: Long)
+
+class RDDPartitionInfo private[spark](
+    val blockName: String,
+    val storageLevel: String,
+    val memoryUsed: Long,
+    val diskUsed: Long,
+    val executors: Seq[String])
+
+class StageData private[spark](
+    val status: StageStatus,
+    val stageId: Int,
+    val attemptId: Int,
+    val numActiveTasks: Int ,
+    val numCompleteTasks: Int,
+    val numFailedTasks: Int,
+
+    val executorRunTime: Long,
+
+    val inputBytes: Long,
+    val inputRecords: Long,
+    val outputBytes: Long,
+    val outputRecords: Long,
+    val shuffleReadBytes: Long,
+    val shuffleReadRecords: Long,
+    val shuffleWriteBytes: Long,
+    val shuffleWriteRecords: Long,
+    val memoryBytesSpilled: Long,
+    val diskBytesSpilled: Long,
+
+    val name: String,
+    val details: String,
+    val schedulingPool: String,
+
+    val accumulatorUpdates: Seq[AccumulableInfo],
+    val tasks: Option[Map[Long, TaskData]],
+    val executorSummary:Option[Map[String,ExecutorStageSummary]])
+
+class TaskData private[spark](
+    val taskId: Long,
+    val index: Int,
+    val attempt: Int,
+    val launchTime: Date,
+    val executorId: String,
+    val host: String,
+    val taskLocality: String,
+    val speculative: Boolean,
+    val accumulatorUpdates: Seq[AccumulableInfo],
+    val errorMessage: Option[String] = None,
+    val taskMetrics: Option[TaskMetrics] = None)
+
+class TaskMetrics private[spark](
+    val executorDeserializeTime: Long,
+    val executorRunTime: Long,
+    val resultSize: Long,
+    val jvmGcTime: Long,
+    val resultSerializationTime: Long,
+    val memoryBytesSpilled: Long,
+    val diskBytesSpilled: Long,
+    val inputMetrics: Option[InputMetrics],
+    val outputMetrics: Option[OutputMetrics],
+    val shuffleReadMetrics: Option[ShuffleReadMetrics],
+    val shuffleWriteMetrics: Option[ShuffleWriteMetrics])
+
+class InputMetrics private[spark](
+    val bytesRead: Long,
+    val recordsRead: Long)
+
+class OutputMetrics private[spark](
+    val bytesWritten: Long,
+    val recordsWritten: Long)
+
+class ShuffleReadMetrics private[spark](
+    val remoteBlocksFetched: Int,
+    val localBlocksFetched: Int,
+    val fetchWaitTime: Long,
+    val remoteBytesRead: Long,
+    val totalBlocksFetched: Int,
+    val recordsRead: Long)
+
+class ShuffleWriteMetrics private[spark](
+    val bytesWritten: Long,
+    val writeTime: Long,
+    val recordsWritten: Long)
+
+class TaskMetricDistributions private[spark](
+    val quantiles: IndexedSeq[Double],
+
+    val executorDeserializeTime: IndexedSeq[Double],
+    val executorRunTime: IndexedSeq[Double],
+    val resultSize: IndexedSeq[Double],
+    val jvmGcTime: IndexedSeq[Double],
+    val resultSerializationTime: IndexedSeq[Double],
+    val memoryBytesSpilled: IndexedSeq[Double],
+    val diskBytesSpilled: IndexedSeq[Double],
+
+    val inputMetrics: Option[InputMetricDistributions],
+    val outputMetrics: Option[OutputMetricDistributions],
+    val shuffleReadMetrics: Option[ShuffleReadMetricDistributions],
+    val shuffleWriteMetrics: Option[ShuffleWriteMetricDistributions])
+
+class InputMetricDistributions private[spark](
+    val bytesRead: IndexedSeq[Double],
+    val recordsRead: IndexedSeq[Double])
+
+class OutputMetricDistributions private[spark](
+    val bytesWritten: IndexedSeq[Double],
+    val recordsWritten: IndexedSeq[Double])
+
+class ShuffleReadMetricDistributions private[spark](
+    val readBytes: IndexedSeq[Double],
+    val readRecords: IndexedSeq[Double],
+    val remoteBlocksFetched: IndexedSeq[Double],
+    val localBlocksFetched: IndexedSeq[Double],
+    val fetchWaitTime: IndexedSeq[Double],
+    val remoteBytesRead: IndexedSeq[Double],
+    val totalBlocksFetched: IndexedSeq[Double])
+
+class ShuffleWriteMetricDistributions private[spark](
+    val writeBytes: IndexedSeq[Double],
+    val writeRecords: IndexedSeq[Double],
+    val writeTime: IndexedSeq[Double])
+
+class AccumulableInfo private[spark](
+    val id: Long,
+    val name: String,
+    val update: Option[String],
+    val value: String)
diff --git a/core/src/main/scala/org/apache/spark/storage/StorageStatusListener.scala b/core/src/main/scala/org/apache/spark/storage/StorageStatusListener.scala
index 7d75929b96f75..ec711480ebf30 100644
--- a/core/src/main/scala/org/apache/spark/storage/StorageStatusListener.scala
+++ b/core/src/main/scala/org/apache/spark/storage/StorageStatusListener.scala
@@ -25,13 +25,17 @@ import org.apache.spark.scheduler._
 /**
  * :: DeveloperApi ::
  * A SparkListener that maintains executor storage status.
+ *
+ * This class is thread-safe (unlike JobProgressListener)
  */
 @DeveloperApi
 class StorageStatusListener extends SparkListener {
   // This maintains only blocks that are cached (i.e. storage level is not StorageLevel.NONE)
   private[storage] val executorIdToStorageStatus = mutable.Map[String, StorageStatus]()
 
-  def storageStatusList: Seq[StorageStatus] = executorIdToStorageStatus.values.toSeq
+  def storageStatusList: Seq[StorageStatus] = synchronized {
+    executorIdToStorageStatus.values.toSeq
+  }
 
   /** Update storage status list to reflect updated block statuses */
   private def updateStorageStatus(execId: String, updatedBlocks: Seq[(BlockId, BlockStatus)]) {
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 a5271f0574e6c..bfe4a180e8a6f 100644
--- a/core/src/main/scala/org/apache/spark/ui/SparkUI.scala
+++ b/core/src/main/scala/org/apache/spark/ui/SparkUI.scala
@@ -17,6 +17,9 @@
 
 package org.apache.spark.ui
 
+import java.util.Date
+
+import org.apache.spark.status.api.v1.{ApplicationAttemptInfo, ApplicationInfo, JsonRootResource, UIRoot}
 import org.apache.spark.{Logging, SecurityManager, SparkConf, SparkContext}
 import org.apache.spark.scheduler._
 import org.apache.spark.storage.StorageStatusListener
@@ -33,7 +36,7 @@ import org.apache.spark.ui.scope.RDDOperationGraphListener
 private[spark] class SparkUI private (
     val sc: Option[SparkContext],
     val conf: SparkConf,
-    val securityManager: SecurityManager,
+    securityManager: SecurityManager,
     val environmentListener: EnvironmentListener,
     val storageStatusListener: StorageStatusListener,
     val executorsListener: ExecutorsListener,
@@ -41,22 +44,27 @@ private[spark] class SparkUI private (
     val storageListener: StorageListener,
     val operationGraphListener: RDDOperationGraphListener,
     var appName: String,
-    val basePath: String)
+    val basePath: String,
+    val startTime: Long)
   extends WebUI(securityManager, SparkUI.getUIPort(conf), conf, basePath, "SparkUI")
-  with Logging {
+  with Logging
+  with UIRoot {
 
   val killEnabled = sc.map(_.conf.getBoolean("spark.ui.killEnabled", true)).getOrElse(false)
 
+
+  val stagesTab = new StagesTab(this)
+
   /** Initialize all components of the server. */
   def initialize() {
     attachTab(new JobsTab(this))
-    val stagesTab = new StagesTab(this)
     attachTab(stagesTab)
     attachTab(new StorageTab(this))
     attachTab(new EnvironmentTab(this))
     attachTab(new ExecutorsTab(this))
     attachHandler(createStaticHandler(SparkUI.STATIC_RESOURCE_DIR, "/static"))
     attachHandler(createRedirectHandler("/", "/jobs", basePath = basePath))
+    attachHandler(JsonRootResource.getJsonServlet(this))
     // This should be POST only, but, the YARN AM proxy won't proxy POSTs
     attachHandler(createRedirectHandler(
       "/stages/stage/kill", "/stages", stagesTab.handleKillRequest,
@@ -83,6 +91,24 @@ private[spark] class SparkUI private (
   private[spark] def appUIHostPort = publicHostName + ":" + boundPort
 
   private[spark] def appUIAddress = s"http://$appUIHostPort"
+
+  def getSparkUI(appId: String): Option[SparkUI] = {
+    if (appId == appName) Some(this) else None
+  }
+
+  def getApplicationInfoList: Iterator[ApplicationInfo] = {
+    Iterator(new ApplicationInfo(
+      id = appName,
+      name = appName,
+      attempts = Seq(new ApplicationAttemptInfo(
+        attemptId = None,
+        startTime = new Date(startTime),
+        endTime = new Date(-1),
+        sparkUser = "",
+        completed = false
+      ))
+    ))
+  }
 }
 
 private[spark] abstract class SparkUITab(parent: SparkUI, prefix: String)
@@ -109,9 +135,10 @@ private[spark] object SparkUI {
       listenerBus: SparkListenerBus,
       jobProgressListener: JobProgressListener,
       securityManager: SecurityManager,
-      appName: String): SparkUI =  {
+      appName: String,
+      startTime: Long): SparkUI =  {
     create(Some(sc), conf, listenerBus, securityManager, appName,
-      jobProgressListener = Some(jobProgressListener))
+      jobProgressListener = Some(jobProgressListener), startTime = startTime)
   }
 
   def createHistoryUI(
@@ -119,8 +146,9 @@ private[spark] object SparkUI {
       listenerBus: SparkListenerBus,
       securityManager: SecurityManager,
       appName: String,
-      basePath: String): SparkUI = {
-    create(None, conf, listenerBus, securityManager, appName, basePath)
+      basePath: String,
+      startTime: Long): SparkUI = {
+    create(None, conf, listenerBus, securityManager, appName, basePath, startTime = startTime)
   }
 
   /**
@@ -137,7 +165,8 @@ private[spark] object SparkUI {
       securityManager: SecurityManager,
       appName: String,
       basePath: String = "",
-      jobProgressListener: Option[JobProgressListener] = None): SparkUI = {
+      jobProgressListener: Option[JobProgressListener] = None,
+      startTime: Long): SparkUI = {
 
     val _jobProgressListener: JobProgressListener = jobProgressListener.getOrElse {
       val listener = new JobProgressListener(conf)
@@ -159,6 +188,6 @@ private[spark] object SparkUI {
 
     new SparkUI(sc, conf, securityManager, environmentListener, storageStatusListener,
       executorsListener, _jobProgressListener, storageListener, operationGraphListener,
-      appName, basePath)
+      appName, basePath, startTime)
   }
 }
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 f9860d1a5ce76..384f2ad26e281 100644
--- a/core/src/main/scala/org/apache/spark/ui/WebUI.scala
+++ b/core/src/main/scala/org/apache/spark/ui/WebUI.scala
@@ -37,7 +37,7 @@ import org.apache.spark.{Logging, SecurityManager, SparkConf}
  * pages. The use of tabs is optional, however; a WebUI may choose to include pages directly.
  */
 private[spark] abstract class WebUI(
-    securityManager: SecurityManager,
+    val securityManager: SecurityManager,
     port: Int,
     conf: SparkConf,
     basePath: String = "",
@@ -77,15 +77,9 @@ private[spark] abstract class WebUI(
     val pagePath = "/" + page.prefix
     val renderHandler = createServletHandler(pagePath,
       (request: HttpServletRequest) => page.render(request), securityManager, basePath)
-    val renderJsonHandler = createServletHandler(pagePath.stripSuffix("/") + "/json",
-      (request: HttpServletRequest) => page.renderJson(request), securityManager, basePath)
     attachHandler(renderHandler)
-    attachHandler(renderJsonHandler)
     pageToHandlers.getOrElseUpdate(page, ArrayBuffer[ServletContextHandler]())
       .append(renderHandler)
-    pageToHandlers.getOrElseUpdate(page, ArrayBuffer[ServletContextHandler]())
-      .append(renderJsonHandler)
-    
   }
 
   /** Attach a handler to this UI. */
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 956608d7c0cbe..b247e4cdc3bd4 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
@@ -22,11 +22,11 @@ import javax.servlet.http.HttpServletRequest
 
 import scala.xml.Node
 
+import org.apache.spark.status.api.v1.ExecutorSummary
 import org.apache.spark.ui.{ToolTips, UIUtils, WebUIPage}
 import org.apache.spark.util.Utils
 
-/** Summary information about an executor to display in the UI. */
-// Needs to be private[ui] because of a false positive MiMa failure.
+// This isn't even used anymore -- but we need to keep it b/c of a MiMa false positive
 private[ui] case class ExecutorSummaryInfo(
     id: String,
     hostPort: String,
@@ -44,6 +44,7 @@ private[ui] case class ExecutorSummaryInfo(
     maxMemory: Long,
     executorLogs: Map[String, String])
 
+
 private[ui] class ExecutorsPage(
     parent: ExecutorsTab,
     threadDumpEnabled: Boolean)
@@ -55,7 +56,8 @@ private[ui] class ExecutorsPage(
     val maxMem = storageStatusList.map(_.maxMem).sum
     val memUsed = storageStatusList.map(_.memUsed).sum
     val diskUsed = storageStatusList.map(_.diskUsed).sum
-    val execInfo = for (statusId <- 0 until storageStatusList.size) yield getExecInfo(statusId)
+    val execInfo = for (statusId <- 0 until storageStatusList.size) yield
+      ExecutorsPage.getExecInfo(listener, statusId)
     val execInfoSorted = execInfo.sortBy(_.id)
     val logsExist = execInfo.filter(_.executorLogs.nonEmpty).nonEmpty
 
@@ -111,7 +113,7 @@ private[ui] class ExecutorsPage(
   }
 
   /** Render an HTML row representing an executor */
-  private def execRow(info: ExecutorSummaryInfo, logsExist: Boolean): Seq[Node] = {
+  private def execRow(info: ExecutorSummary, logsExist: Boolean): Seq[Node] = {
     val maximumMemory = info.maxMemory
     val memoryUsed = info.memoryUsed
     val diskUsed = info.diskUsed
@@ -170,8 +172,11 @@ private[ui] class ExecutorsPage(
     </tr>
   }
 
+}
+
+private[spark] object ExecutorsPage {
   /** Represent an executor's info as a map given a storage status index */
-  private def getExecInfo(statusId: Int): ExecutorSummaryInfo = {
+  def getExecInfo(listener: ExecutorsListener, statusId: Int): ExecutorSummary = {
     val status = listener.storageStatusList(statusId)
     val execId = status.blockManagerId.executorId
     val hostPort = status.blockManagerId.hostPort
@@ -189,7 +194,7 @@ private[ui] class ExecutorsPage(
     val totalShuffleWrite = listener.executorToShuffleWrite.getOrElse(execId, 0L)
     val executorLogs = listener.executorToLogUrls.getOrElse(execId, Map.empty)
 
-    new ExecutorSummaryInfo(
+    new ExecutorSummary(
       execId,
       hostPort,
       rddBlocks,
diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/AllJobsPage.scala b/core/src/main/scala/org/apache/spark/ui/jobs/AllJobsPage.scala
index f6abf27db49dd..09323d1d80ad6 100644
--- a/core/src/main/scala/org/apache/spark/ui/jobs/AllJobsPage.scala
+++ b/core/src/main/scala/org/apache/spark/ui/jobs/AllJobsPage.scala
@@ -271,6 +271,12 @@ private[ui] class AllJobsPage(parent: JobsTab) extends WebUIPage("") {
       val shouldShowCompletedJobs = completedJobs.nonEmpty
       val shouldShowFailedJobs = failedJobs.nonEmpty
 
+      val completedJobNumStr = if (completedJobs.size == listener.numCompletedJobs) {
+        s"${completedJobs.size}"
+      } else {
+        s"${listener.numCompletedJobs}, only showing ${completedJobs.size}"
+      }
+
       val summary: NodeSeq =
         <div>
           <ul class="unstyled">
@@ -295,9 +301,9 @@ private[ui] class AllJobsPage(parent: JobsTab) extends WebUIPage("") {
             }
             {
               if (shouldShowCompletedJobs) {
-                <li>
+                <li id="completed-summary">
                   <a href="#completed"><strong>Completed Jobs:</strong></a>
-                  {completedJobs.size}
+                  {completedJobNumStr}
                 </li>
               }
             }
@@ -305,7 +311,7 @@ private[ui] class AllJobsPage(parent: JobsTab) extends WebUIPage("") {
               if (shouldShowFailedJobs) {
                 <li>
                   <a href="#failed"><strong>Failed Jobs:</strong></a>
-                  {failedJobs.size}
+                  {listener.numFailedJobs}
                 </li>
               }
             }
@@ -322,7 +328,7 @@ private[ui] class AllJobsPage(parent: JobsTab) extends WebUIPage("") {
           activeJobsTable
       }
       if (shouldShowCompletedJobs) {
-        content ++= <h4 id="completed">Completed Jobs ({completedJobs.size})</h4> ++
+        content ++= <h4 id="completed">Completed Jobs ({completedJobNumStr})</h4> ++
           completedJobsTable
       }
       if (shouldShowFailedJobs) {
diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/AllStagesPage.scala b/core/src/main/scala/org/apache/spark/ui/jobs/AllStagesPage.scala
index 236bc8ea92879..a37f739ab9c66 100644
--- a/core/src/main/scala/org/apache/spark/ui/jobs/AllStagesPage.scala
+++ b/core/src/main/scala/org/apache/spark/ui/jobs/AllStagesPage.scala
@@ -64,6 +64,12 @@ private[ui] class AllStagesPage(parent: StagesTab) extends WebUIPage("") {
       val shouldShowCompletedStages = completedStages.nonEmpty
       val shouldShowFailedStages = failedStages.nonEmpty
 
+      val completedStageNumStr = if (numCompletedStages == completedStages.size) {
+        s"$numCompletedStages"
+      } else {
+        s"$numCompletedStages, only showing ${completedStages.size}"
+      }
+
       val summary: NodeSeq =
         <div>
           <ul class="unstyled">
@@ -98,9 +104,9 @@ private[ui] class AllStagesPage(parent: StagesTab) extends WebUIPage("") {
             }
             {
               if (shouldShowCompletedStages) {
-                <li>
+                <li id="completed-summary">
                   <a href="#completed"><strong>Completed Stages:</strong></a>
-                  {numCompletedStages}
+                  {completedStageNumStr}
                 </li>
               }
             }
@@ -132,7 +138,7 @@ private[ui] class AllStagesPage(parent: StagesTab) extends WebUIPage("") {
         pendingStagesTable.toNodeSeq
       }
       if (shouldShowCompletedStages) {
-        content ++= <h4 id="completed">Completed Stages ({numCompletedStages})</h4> ++
+        content ++= <h4 id="completed">Completed Stages ({completedStageNumStr})</h4> ++
         completedStagesTable.toNodeSeq
       }
       if (shouldShowFailedStages) {
diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/JobPage.scala b/core/src/main/scala/org/apache/spark/ui/jobs/JobPage.scala
index 96cc3d78d0f15..7163217e1fed0 100644
--- a/core/src/main/scala/org/apache/spark/ui/jobs/JobPage.scala
+++ b/core/src/main/scala/org/apache/spark/ui/jobs/JobPage.scala
@@ -187,7 +187,7 @@ private[ui] class JobPage(parent: JobsTab) extends WebUIPage("job") {
       val jobDataOption = listener.jobIdToData.get(jobId)
       if (jobDataOption.isEmpty) {
         val content =
-          <div>
+          <div id="no-info">
             <p>No information to display for job {jobId}</p>
           </div>
         return UIUtils.headerSparkPage(
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 8f9aa9fdec819..246e191d64776 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
@@ -74,6 +74,8 @@ class JobProgressListener(conf: SparkConf) extends SparkListener with Logging {
   // JobProgressListener's retention limits.
   var numCompletedStages = 0
   var numFailedStages = 0
+  var numCompletedJobs = 0
+  var numFailedJobs = 0
 
   // Misc:
   val executorIdToBlockManagerId = HashMap[ExecutorId, BlockManagerId]()
@@ -217,10 +219,12 @@ class JobProgressListener(conf: SparkConf) extends SparkListener with Logging {
         completedJobs += jobData
         trimJobsIfNecessary(completedJobs)
         jobData.status = JobExecutionStatus.SUCCEEDED
+        numCompletedJobs += 1
       case JobFailed(exception) =>
         failedJobs += jobData
         trimJobsIfNecessary(failedJobs)
         jobData.status = JobExecutionStatus.FAILED
+        numFailedJobs += 1
     }
     for (stageId <- jobData.stageIds) {
       stageIdToActiveJobIds.get(stageId).foreach { jobsUsingStage =>
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 d725b9d8565ac..f3e0b38523f32 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
@@ -21,7 +21,7 @@ import javax.servlet.http.HttpServletRequest
 
 import scala.xml.Node
 
-import org.apache.spark.scheduler.{Schedulable, StageInfo}
+import org.apache.spark.scheduler.StageInfo
 import org.apache.spark.ui.{WebUIPage, UIUtils}
 
 /** Page showing specific pool details */
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 6c4305873cbd9..b01fad8e453c8 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
@@ -25,11 +25,11 @@ import scala.xml.{Elem, Node, Unparsed}
 import org.apache.commons.lang3.StringEscapeUtils
 
 import org.apache.spark.executor.TaskMetrics
+import org.apache.spark.scheduler.{AccumulableInfo, TaskInfo}
 import org.apache.spark.ui.{ToolTips, WebUIPage, UIUtils}
 import org.apache.spark.ui.jobs.UIData._
 import org.apache.spark.ui.scope.RDDOperationGraph
 import org.apache.spark.util.{Utils, Distribution}
-import org.apache.spark.scheduler.{AccumulableInfo, TaskInfo}
 
 /** Page showing statistics and task list for a given stage */
 private[ui] class StagePage(parent: StagesTab) extends WebUIPage("stage") {
@@ -52,14 +52,22 @@ private[ui] class StagePage(parent: StagesTab) extends WebUIPage("stage") {
       val stageAttemptId = parameterAttempt.toInt
       val stageDataOption = progressListener.stageIdToData.get((stageId, stageAttemptId))
 
-      if (stageDataOption.isEmpty || stageDataOption.get.taskData.isEmpty) {
+      val stageHeader = s"Details for Stage $stageId (Attempt $stageAttemptId)"
+      if (stageDataOption.isEmpty) {
+        val content =
+          <div id="no-info">
+            <p>No information to display for Stage {stageId} (Attempt {stageAttemptId})</p>
+          </div>
+        return UIUtils.headerSparkPage(stageHeader, content, parent)
+
+      }
+      if (stageDataOption.get.taskData.isEmpty) {
         val content =
           <div>
             <h4>Summary Metrics</h4> No tasks have started yet
             <h4>Tasks</h4> No tasks have started yet
           </div>
-        return UIUtils.headerSparkPage(
-          s"Details for Stage $stageId (Attempt $stageAttemptId)", content, parent)
+        return UIUtils.headerSparkPage(stageHeader, content, parent)
       }
 
       val stageData = stageDataOption.get
@@ -458,8 +466,7 @@ private[ui] class StagePage(parent: StagesTab) extends WebUIPage("stage") {
         maybeAccumulableTable ++
         <h4>Tasks</h4> ++ taskTable
 
-      UIUtils.headerSparkPage(
-        "Details for Stage %d".format(stageId), content, parent, showVisualization = true)
+      UIUtils.headerSparkPage(stageHeader, content, parent, showVisualization = true)
     }
   }
 
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 199f731b92bcc..05f94a7507f4f 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
@@ -21,8 +21,8 @@ import javax.servlet.http.HttpServletRequest
 
 import scala.xml.Node
 
-import org.apache.spark.storage.{BlockId, BlockStatus, StorageStatus, StorageUtils}
-import org.apache.spark.ui.{WebUIPage, UIUtils}
+import org.apache.spark.status.api.v1.{AllRDDResource, RDDDataDistribution, RDDPartitionInfo}
+import org.apache.spark.ui.{UIUtils, WebUIPage}
 import org.apache.spark.util.Utils
 
 /** Page showing storage details for a given RDD */
@@ -32,28 +32,19 @@ private[ui] class RDDPage(parent: StorageTab) extends WebUIPage("rdd") {
   def render(request: HttpServletRequest): Seq[Node] = {
     val parameterId = request.getParameter("id")
     require(parameterId != null && parameterId.nonEmpty, "Missing id parameter")
-
     val rddId = parameterId.toInt
-    val storageStatusList = listener.storageStatusList
-    val rddInfo = listener.rddInfoList.find(_.id == rddId).getOrElse {
-      // Rather than crashing, render an "RDD Not Found" page
-      return UIUtils.headerSparkPage("RDD Not Found", Seq[Node](), parent)
-    }
+    val rddStorageInfo = AllRDDResource.getRDDStorageInfo(rddId, listener,includeDetails = true)
+      .getOrElse {
+        // Rather than crashing, render an "RDD Not Found" page
+        return UIUtils.headerSparkPage("RDD Not Found", Seq[Node](), parent)
+      }
 
     // Worker table
-    val workers = storageStatusList.map((rddId, _))
-    val workerTable = UIUtils.listingTable(workerHeader, workerRow, workers,
-      id = Some("rdd-storage-by-worker-table"))
+    val workerTable = UIUtils.listingTable(workerHeader, workerRow,
+      rddStorageInfo.dataDistribution.get, id = Some("rdd-storage-by-worker-table"))
 
     // Block table
-    val blockLocations = StorageUtils.getRddBlockLocations(rddId, storageStatusList)
-    val blocks = storageStatusList
-      .flatMap(_.rddBlocksById(rddId))
-      .sortWith(_._1.name < _._1.name)
-      .map { case (blockId, status) =>
-        (blockId, status, blockLocations.get(blockId).getOrElse(Seq[String]("Unknown")))
-      }
-    val blockTable = UIUtils.listingTable(blockHeader, blockRow, blocks,
+    val blockTable = UIUtils.listingTable(blockHeader, blockRow, rddStorageInfo.partitions.get,
       id = Some("rdd-storage-by-block-table"))
 
     val content =
@@ -62,23 +53,23 @@ private[ui] class RDDPage(parent: StorageTab) extends WebUIPage("rdd") {
           <ul class="unstyled">
             <li>
               <strong>Storage Level:</strong>
-              {rddInfo.storageLevel.description}
+              {rddStorageInfo.storageLevel}
             </li>
             <li>
               <strong>Cached Partitions:</strong>
-              {rddInfo.numCachedPartitions}
+              {rddStorageInfo.numCachedPartitions}
             </li>
             <li>
               <strong>Total Partitions:</strong>
-              {rddInfo.numPartitions}
+              {rddStorageInfo.numPartitions}
             </li>
             <li>
               <strong>Memory Size:</strong>
-              {Utils.bytesToString(rddInfo.memSize)}
+              {Utils.bytesToString(rddStorageInfo.memoryUsed)}
             </li>
             <li>
               <strong>Disk Size:</strong>
-              {Utils.bytesToString(rddInfo.diskSize)}
+              {Utils.bytesToString(rddStorageInfo.diskUsed)}
             </li>
           </ul>
         </div>
@@ -86,19 +77,19 @@ private[ui] class RDDPage(parent: StorageTab) extends WebUIPage("rdd") {
 
       <div class="row-fluid">
         <div class="span12">
-          <h4> Data Distribution on {workers.size} Executors </h4>
+          <h4> Data Distribution on {rddStorageInfo.dataDistribution.size} Executors </h4>
           {workerTable}
         </div>
       </div>
 
       <div class="row-fluid">
         <div class="span12">
-          <h4> {blocks.size} Partitions </h4>
+          <h4> {rddStorageInfo.partitions.size} Partitions </h4>
           {blockTable}
         </div>
       </div>;
 
-    UIUtils.headerSparkPage("RDD Storage Info for " + rddInfo.name, content, parent)
+    UIUtils.headerSparkPage("RDD Storage Info for " + rddStorageInfo.name, content, parent)
   }
 
   /** Header fields for the worker table */
@@ -116,34 +107,32 @@ private[ui] class RDDPage(parent: StorageTab) extends WebUIPage("rdd") {
     "Executors")
 
   /** Render an HTML row representing a worker */
-  private def workerRow(worker: (Int, StorageStatus)): Seq[Node] = {
-    val (rddId, status) = worker
+  private def workerRow(worker: RDDDataDistribution): Seq[Node] = {
     <tr>
-      <td>{status.blockManagerId.host + ":" + status.blockManagerId.port}</td>
+      <td>{worker.address}</td>
       <td>
-        {Utils.bytesToString(status.memUsedByRdd(rddId))}
-        ({Utils.bytesToString(status.memRemaining)} Remaining)
+        {Utils.bytesToString(worker.memoryUsed)}
+        ({Utils.bytesToString(worker.memoryRemaining)} Remaining)
       </td>
-      <td>{Utils.bytesToString(status.diskUsedByRdd(rddId))}</td>
+      <td>{Utils.bytesToString(worker.diskUsed)}</td>
     </tr>
   }
 
   /** Render an HTML row representing a block */
-  private def blockRow(row: (BlockId, BlockStatus, Seq[String])): Seq[Node] = {
-    val (id, block, locations) = row
+  private def blockRow(row: RDDPartitionInfo): Seq[Node] = {
     <tr>
-      <td>{id}</td>
+      <td>{row.blockName}</td>
       <td>
-        {block.storageLevel.description}
+        {row.storageLevel}
       </td>
-      <td sorttable_customkey={block.memSize.toString}>
-        {Utils.bytesToString(block.memSize)}
+      <td sorttable_customkey={row.memoryUsed.toString}>
+        {Utils.bytesToString(row.memoryUsed)}
       </td>
-      <td sorttable_customkey={block.diskSize.toString}>
-        {Utils.bytesToString(block.diskSize)}
+      <td sorttable_customkey={row.diskUsed.toString}>
+        {Utils.bytesToString(row.diskUsed)}
       </td>
       <td>
-        {locations.map(l => <span>{l}<br/></span>)}
+        {row.executors.map(l => <span>{l}<br/></span>)}
       </td>
     </tr>
   }
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 59dc6b547c1d8..07db783c572cf 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
@@ -22,7 +22,7 @@ import javax.servlet.http.HttpServletRequest
 import scala.xml.Node
 
 import org.apache.spark.storage.RDDInfo
-import org.apache.spark.ui.{WebUIPage, UIUtils}
+import org.apache.spark.ui.{UIUtils, WebUIPage}
 import org.apache.spark.util.Utils
 
 /** Page showing list of RDD's currently stored in the cluster */
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 045bd784990d1..0351749700962 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
@@ -35,6 +35,8 @@ private[ui] class StorageTab(parent: SparkUI) extends SparkUITab(parent, "storag
 /**
  * :: DeveloperApi ::
  * A SparkListener that prepares information to be displayed on the BlockManagerUI.
+ *
+ * This class is thread-safe (unlike JobProgressListener)
  */
 @DeveloperApi
 class StorageListener(storageStatusListener: StorageStatusListener) extends SparkListener {
@@ -43,7 +45,9 @@ class StorageListener(storageStatusListener: StorageStatusListener) extends Spar
   def storageStatusList: Seq[StorageStatus] = storageStatusListener.storageStatusList
 
   /** Filter RDD info to include only those with cached partitions */
-  def rddInfoList: Seq[RDDInfo] = _rddInfoMap.values.filter(_.numCachedPartitions > 0).toSeq
+  def rddInfoList: Seq[RDDInfo] = synchronized {
+    _rddInfoMap.values.filter(_.numCachedPartitions > 0).toSeq
+  }
 
   /** Update the storage info of the RDDs whose blocks are among the given updated blocks */
   private def updateRDDInfo(updatedBlocks: Seq[(BlockId, BlockStatus)]): Unit = {
diff --git a/core/src/test/resources/HistoryServerExpectations/application_list_json_expectation.json b/core/src/test/resources/HistoryServerExpectations/application_list_json_expectation.json
new file mode 100644
index 0000000000000..ce4fe80b66aa5
--- /dev/null
+++ b/core/src/test/resources/HistoryServerExpectations/application_list_json_expectation.json
@@ -0,0 +1,53 @@
+[ {
+  "id" : "local-1430917381534",
+  "name" : "Spark shell",
+  "attempts" : [ {
+    "startTime" : "2015-05-06T13:03:00.893GMT",
+    "endTime" : "2015-05-06T13:03:11.398GMT",
+    "sparkUser" : "irashid",
+    "completed" : true
+  } ]
+}, {
+  "id" : "local-1426533911241",
+  "name" : "Spark shell",
+  "attempts" : [ {
+    "attemptId" : "2",
+    "startTime" : "2015-03-17T23:11:50.242GMT",
+    "endTime" : "2015-03-17T23:12:25.177GMT",
+    "sparkUser" : "irashid",
+    "completed" : true
+  }, {
+    "attemptId" : "1",
+    "startTime" : "2015-03-16T19:25:10.242GMT",
+    "endTime" : "2015-03-16T19:25:45.177GMT",
+    "sparkUser" : "irashid",
+    "completed" : true
+  } ]
+}, {
+  "id" : "local-1425081759269",
+  "name" : "Spark shell",
+  "attempts" : [ {
+    "startTime" : "2015-02-28T00:02:38.277GMT",
+    "endTime" : "2015-02-28T00:02:46.912GMT",
+    "sparkUser" : "irashid",
+    "completed" : true
+  } ]
+}, {
+  "id" : "local-1422981780767",
+  "name" : "Spark shell",
+  "attempts" : [ {
+    "startTime" : "2015-02-03T16:42:59.720GMT",
+    "endTime" : "2015-02-03T16:43:08.731GMT",
+    "sparkUser" : "irashid",
+    "completed" : true
+  } ]
+}, {
+  "id" : "local-1422981759269",
+  "name" : "Spark shell",
+  "attempts" : [ {
+    "startTime" : "2015-02-03T16:42:38.277GMT",
+    "endTime" : "2015-02-03T16:42:46.912GMT",
+    "sparkUser" : "irashid",
+    "completed" : true
+  } ]
+} ]
\ No newline at end of file
diff --git a/core/src/test/resources/HistoryServerExpectations/complete_stage_list_json_expectation.json b/core/src/test/resources/HistoryServerExpectations/complete_stage_list_json_expectation.json
new file mode 100644
index 0000000000000..31ac9beea8788
--- /dev/null
+++ b/core/src/test/resources/HistoryServerExpectations/complete_stage_list_json_expectation.json
@@ -0,0 +1,67 @@
+[ {
+  "status" : "COMPLETE",
+  "stageId" : 3,
+  "attemptId" : 0,
+  "numActiveTasks" : 0,
+  "numCompleteTasks" : 8,
+  "numFailedTasks" : 0,
+  "executorRunTime" : 162,
+  "inputBytes" : 160,
+  "inputRecords" : 0,
+  "outputBytes" : 0,
+  "outputRecords" : 0,
+  "shuffleReadBytes" : 0,
+  "shuffleReadRecords" : 0,
+  "shuffleWriteBytes" : 0,
+  "shuffleWriteRecords" : 0,
+  "memoryBytesSpilled" : 0,
+  "diskBytesSpilled" : 0,
+  "name" : "count at <console>:17",
+  "details" : "org.apache.spark.rdd.RDD.count(RDD.scala:910)\n$line19.$read$$iwC$$iwC$$iwC$$iwC.<init>(<console>:17)\n$line19.$read$$iwC$$iwC$$iwC.<init>(<console>:22)\n$line19.$read$$iwC$$iwC.<init>(<console>:24)\n$line19.$read$$iwC.<init>(<console>:26)\n$line19.$read.<init>(<console>:28)\n$line19.$read$.<init>(<console>:32)\n$line19.$read$.<clinit>(<console>)\n$line19.$eval$.<init>(<console>:7)\n$line19.$eval$.<clinit>(<console>)\n$line19.$eval.$print(<console>)\nsun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\nsun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)\nsun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\njava.lang.reflect.Method.invoke(Method.java:606)\norg.apache.spark.repl.SparkIMain$ReadEvalPrint.call(SparkIMain.scala:852)\norg.apache.spark.repl.SparkIMain$Request.loadAndRun(SparkIMain.scala:1125)\norg.apache.spark.repl.SparkIMain.loadAndRunReq$1(SparkIMain.scala:674)\norg.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:705)\norg.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:669)",
+  "schedulingPool" : "default",
+  "accumulatorUpdates" : [ ]
+}, {
+  "status" : "COMPLETE",
+  "stageId" : 1,
+  "attemptId" : 0,
+  "numActiveTasks" : 0,
+  "numCompleteTasks" : 8,
+  "numFailedTasks" : 0,
+  "executorRunTime" : 3476,
+  "inputBytes" : 28000128,
+  "inputRecords" : 0,
+  "outputBytes" : 0,
+  "outputRecords" : 0,
+  "shuffleReadBytes" : 0,
+  "shuffleReadRecords" : 0,
+  "shuffleWriteBytes" : 13180,
+  "shuffleWriteRecords" : 0,
+  "memoryBytesSpilled" : 0,
+  "diskBytesSpilled" : 0,
+  "name" : "map at <console>:14",
+  "details" : "org.apache.spark.rdd.RDD.map(RDD.scala:271)\n$line10.$read$$iwC$$iwC$$iwC$$iwC.<init>(<console>:14)\n$line10.$read$$iwC$$iwC$$iwC.<init>(<console>:19)\n$line10.$read$$iwC$$iwC.<init>(<console>:21)\n$line10.$read$$iwC.<init>(<console>:23)\n$line10.$read.<init>(<console>:25)\n$line10.$read$.<init>(<console>:29)\n$line10.$read$.<clinit>(<console>)\n$line10.$eval$.<init>(<console>:7)\n$line10.$eval$.<clinit>(<console>)\n$line10.$eval.$print(<console>)\nsun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\nsun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)\nsun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\njava.lang.reflect.Method.invoke(Method.java:606)\norg.apache.spark.repl.SparkIMain$ReadEvalPrint.call(SparkIMain.scala:852)\norg.apache.spark.repl.SparkIMain$Request.loadAndRun(SparkIMain.scala:1125)\norg.apache.spark.repl.SparkIMain.loadAndRunReq$1(SparkIMain.scala:674)\norg.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:705)\norg.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:669)",
+  "schedulingPool" : "default",
+  "accumulatorUpdates" : [ ]
+}, {
+  "status" : "COMPLETE",
+  "stageId" : 0,
+  "attemptId" : 0,
+  "numActiveTasks" : 0,
+  "numCompleteTasks" : 8,
+  "numFailedTasks" : 0,
+  "executorRunTime" : 4338,
+  "inputBytes" : 0,
+  "inputRecords" : 0,
+  "outputBytes" : 0,
+  "outputRecords" : 0,
+  "shuffleReadBytes" : 0,
+  "shuffleReadRecords" : 0,
+  "shuffleWriteBytes" : 0,
+  "shuffleWriteRecords" : 0,
+  "memoryBytesSpilled" : 0,
+  "diskBytesSpilled" : 0,
+  "name" : "count at <console>:15",
+  "details" : "org.apache.spark.rdd.RDD.count(RDD.scala:910)\n$line9.$read$$iwC$$iwC$$iwC$$iwC.<init>(<console>:15)\n$line9.$read$$iwC$$iwC$$iwC.<init>(<console>:20)\n$line9.$read$$iwC$$iwC.<init>(<console>:22)\n$line9.$read$$iwC.<init>(<console>:24)\n$line9.$read.<init>(<console>:26)\n$line9.$read$.<init>(<console>:30)\n$line9.$read$.<clinit>(<console>)\n$line9.$eval$.<init>(<console>:7)\n$line9.$eval$.<clinit>(<console>)\n$line9.$eval.$print(<console>)\nsun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\nsun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)\nsun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\njava.lang.reflect.Method.invoke(Method.java:606)\norg.apache.spark.repl.SparkIMain$ReadEvalPrint.call(SparkIMain.scala:852)\norg.apache.spark.repl.SparkIMain$Request.loadAndRun(SparkIMain.scala:1125)\norg.apache.spark.repl.SparkIMain.loadAndRunReq$1(SparkIMain.scala:674)\norg.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:705)\norg.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:669)",
+  "schedulingPool" : "default",
+  "accumulatorUpdates" : [ ]
+} ]
\ No newline at end of file
diff --git a/core/src/test/resources/HistoryServerExpectations/completed_app_list_json_expectation.json b/core/src/test/resources/HistoryServerExpectations/completed_app_list_json_expectation.json
new file mode 100644
index 0000000000000..ce4fe80b66aa5
--- /dev/null
+++ b/core/src/test/resources/HistoryServerExpectations/completed_app_list_json_expectation.json
@@ -0,0 +1,53 @@
+[ {
+  "id" : "local-1430917381534",
+  "name" : "Spark shell",
+  "attempts" : [ {
+    "startTime" : "2015-05-06T13:03:00.893GMT",
+    "endTime" : "2015-05-06T13:03:11.398GMT",
+    "sparkUser" : "irashid",
+    "completed" : true
+  } ]
+}, {
+  "id" : "local-1426533911241",
+  "name" : "Spark shell",
+  "attempts" : [ {
+    "attemptId" : "2",
+    "startTime" : "2015-03-17T23:11:50.242GMT",
+    "endTime" : "2015-03-17T23:12:25.177GMT",
+    "sparkUser" : "irashid",
+    "completed" : true
+  }, {
+    "attemptId" : "1",
+    "startTime" : "2015-03-16T19:25:10.242GMT",
+    "endTime" : "2015-03-16T19:25:45.177GMT",
+    "sparkUser" : "irashid",
+    "completed" : true
+  } ]
+}, {
+  "id" : "local-1425081759269",
+  "name" : "Spark shell",
+  "attempts" : [ {
+    "startTime" : "2015-02-28T00:02:38.277GMT",
+    "endTime" : "2015-02-28T00:02:46.912GMT",
+    "sparkUser" : "irashid",
+    "completed" : true
+  } ]
+}, {
+  "id" : "local-1422981780767",
+  "name" : "Spark shell",
+  "attempts" : [ {
+    "startTime" : "2015-02-03T16:42:59.720GMT",
+    "endTime" : "2015-02-03T16:43:08.731GMT",
+    "sparkUser" : "irashid",
+    "completed" : true
+  } ]
+}, {
+  "id" : "local-1422981759269",
+  "name" : "Spark shell",
+  "attempts" : [ {
+    "startTime" : "2015-02-03T16:42:38.277GMT",
+    "endTime" : "2015-02-03T16:42:46.912GMT",
+    "sparkUser" : "irashid",
+    "completed" : true
+  } ]
+} ]
\ No newline at end of file
diff --git a/core/src/test/resources/HistoryServerExpectations/executor_list_json_expectation.json b/core/src/test/resources/HistoryServerExpectations/executor_list_json_expectation.json
new file mode 100644
index 0000000000000..cb622e147249e
--- /dev/null
+++ b/core/src/test/resources/HistoryServerExpectations/executor_list_json_expectation.json
@@ -0,0 +1,17 @@
+[ {
+  "id" : "<driver>",
+  "hostPort" : "localhost:57971",
+  "rddBlocks" : 8,
+  "memoryUsed" : 28000128,
+  "diskUsed" : 0,
+  "activeTasks" : 0,
+  "failedTasks" : 1,
+  "completedTasks" : 31,
+  "totalTasks" : 32,
+  "totalDuration" : 8820,
+  "totalInputBytes" : 28000288,
+  "totalShuffleRead" : 0,
+  "totalShuffleWrite" : 13180,
+  "maxMemory" : 278302556,
+  "executorLogs" : { }
+} ]
\ No newline at end of file
diff --git a/core/src/test/resources/HistoryServerExpectations/failed_stage_list_json_expectation.json b/core/src/test/resources/HistoryServerExpectations/failed_stage_list_json_expectation.json
new file mode 100644
index 0000000000000..bff6a4f69d077
--- /dev/null
+++ b/core/src/test/resources/HistoryServerExpectations/failed_stage_list_json_expectation.json
@@ -0,0 +1,23 @@
+[ {
+  "status" : "FAILED",
+  "stageId" : 2,
+  "attemptId" : 0,
+  "numActiveTasks" : 0,
+  "numCompleteTasks" : 7,
+  "numFailedTasks" : 1,
+  "executorRunTime" : 278,
+  "inputBytes" : 0,
+  "inputRecords" : 0,
+  "outputBytes" : 0,
+  "outputRecords" : 0,
+  "shuffleReadBytes" : 0,
+  "shuffleReadRecords" : 0,
+  "shuffleWriteBytes" : 0,
+  "shuffleWriteRecords" : 0,
+  "memoryBytesSpilled" : 0,
+  "diskBytesSpilled" : 0,
+  "name" : "count at <console>:20",
+  "details" : "org.apache.spark.rdd.RDD.count(RDD.scala:910)\n$line11.$read$$iwC$$iwC$$iwC$$iwC.<init>(<console>:20)\n$line11.$read$$iwC$$iwC$$iwC.<init>(<console>:25)\n$line11.$read$$iwC$$iwC.<init>(<console>:27)\n$line11.$read$$iwC.<init>(<console>:29)\n$line11.$read.<init>(<console>:31)\n$line11.$read$.<init>(<console>:35)\n$line11.$read$.<clinit>(<console>)\n$line11.$eval$.<init>(<console>:7)\n$line11.$eval$.<clinit>(<console>)\n$line11.$eval.$print(<console>)\nsun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\nsun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)\nsun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\njava.lang.reflect.Method.invoke(Method.java:606)\norg.apache.spark.repl.SparkIMain$ReadEvalPrint.call(SparkIMain.scala:852)\norg.apache.spark.repl.SparkIMain$Request.loadAndRun(SparkIMain.scala:1125)\norg.apache.spark.repl.SparkIMain.loadAndRunReq$1(SparkIMain.scala:674)\norg.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:705)\norg.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:669)",
+  "schedulingPool" : "default",
+  "accumulatorUpdates" : [ ]
+} ]
\ No newline at end of file
diff --git a/core/src/test/resources/HistoryServerExpectations/job_list_from_multi_attempt_app_json_1__expectation.json b/core/src/test/resources/HistoryServerExpectations/job_list_from_multi_attempt_app_json_1__expectation.json
new file mode 100644
index 0000000000000..2e92e1fa0ec23
--- /dev/null
+++ b/core/src/test/resources/HistoryServerExpectations/job_list_from_multi_attempt_app_json_1__expectation.json
@@ -0,0 +1,15 @@
+[ {
+  "jobId" : 0,
+  "name" : "foreach at <console>:15",
+  "stageIds" : [ 0 ],
+  "status" : "SUCCEEDED",
+  "numTasks" : 8,
+  "numActiveTasks" : 0,
+  "numCompletedTasks" : 8,
+  "numSkippedTasks" : 8,
+  "numFailedTasks" : 0,
+  "numActiveStages" : 0,
+  "numCompletedStages" : 1,
+  "numSkippedStages" : 0,
+  "numFailedStages" : 0
+} ]
\ No newline at end of file
diff --git a/core/src/test/resources/HistoryServerExpectations/job_list_from_multi_attempt_app_json_2__expectation.json b/core/src/test/resources/HistoryServerExpectations/job_list_from_multi_attempt_app_json_2__expectation.json
new file mode 100644
index 0000000000000..2e92e1fa0ec23
--- /dev/null
+++ b/core/src/test/resources/HistoryServerExpectations/job_list_from_multi_attempt_app_json_2__expectation.json
@@ -0,0 +1,15 @@
+[ {
+  "jobId" : 0,
+  "name" : "foreach at <console>:15",
+  "stageIds" : [ 0 ],
+  "status" : "SUCCEEDED",
+  "numTasks" : 8,
+  "numActiveTasks" : 0,
+  "numCompletedTasks" : 8,
+  "numSkippedTasks" : 8,
+  "numFailedTasks" : 0,
+  "numActiveStages" : 0,
+  "numCompletedStages" : 1,
+  "numSkippedStages" : 0,
+  "numFailedStages" : 0
+} ]
\ No newline at end of file
diff --git a/core/src/test/resources/HistoryServerExpectations/job_list_json_expectation.json b/core/src/test/resources/HistoryServerExpectations/job_list_json_expectation.json
new file mode 100644
index 0000000000000..cab4750270dfa
--- /dev/null
+++ b/core/src/test/resources/HistoryServerExpectations/job_list_json_expectation.json
@@ -0,0 +1,43 @@
+[ {
+  "jobId" : 2,
+  "name" : "count at <console>:17",
+  "stageIds" : [ 3 ],
+  "status" : "SUCCEEDED",
+  "numTasks" : 8,
+  "numActiveTasks" : 0,
+  "numCompletedTasks" : 8,
+  "numSkippedTasks" : 8,
+  "numFailedTasks" : 0,
+  "numActiveStages" : 0,
+  "numCompletedStages" : 1,
+  "numSkippedStages" : 0,
+  "numFailedStages" : 0
+}, {
+  "jobId" : 1,
+  "name" : "count at <console>:20",
+  "stageIds" : [ 1, 2 ],
+  "status" : "FAILED",
+  "numTasks" : 16,
+  "numActiveTasks" : 0,
+  "numCompletedTasks" : 15,
+  "numSkippedTasks" : 15,
+  "numFailedTasks" : 1,
+  "numActiveStages" : 0,
+  "numCompletedStages" : 1,
+  "numSkippedStages" : 0,
+  "numFailedStages" : 1
+}, {
+  "jobId" : 0,
+  "name" : "count at <console>:15",
+  "stageIds" : [ 0 ],
+  "status" : "SUCCEEDED",
+  "numTasks" : 8,
+  "numActiveTasks" : 0,
+  "numCompletedTasks" : 8,
+  "numSkippedTasks" : 8,
+  "numFailedTasks" : 0,
+  "numActiveStages" : 0,
+  "numCompletedStages" : 1,
+  "numSkippedStages" : 0,
+  "numFailedStages" : 0
+} ]
\ No newline at end of file
diff --git a/core/src/test/resources/HistoryServerExpectations/maxDate2_app_list_json_expectation.json b/core/src/test/resources/HistoryServerExpectations/maxDate2_app_list_json_expectation.json
new file mode 100644
index 0000000000000..483632a3956ed
--- /dev/null
+++ b/core/src/test/resources/HistoryServerExpectations/maxDate2_app_list_json_expectation.json
@@ -0,0 +1,10 @@
+[ {
+  "id" : "local-1422981759269",
+  "name" : "Spark shell",
+  "attempts" : [ {
+    "startTime" : "2015-02-03T16:42:38.277GMT",
+    "endTime" : "2015-02-03T16:42:46.912GMT",
+    "sparkUser" : "irashid",
+    "completed" : true
+  } ]
+} ]
\ No newline at end of file
diff --git a/core/src/test/resources/HistoryServerExpectations/maxDate_app_list_json_expectation.json b/core/src/test/resources/HistoryServerExpectations/maxDate_app_list_json_expectation.json
new file mode 100644
index 0000000000000..4b85690fd9199
--- /dev/null
+++ b/core/src/test/resources/HistoryServerExpectations/maxDate_app_list_json_expectation.json
@@ -0,0 +1,19 @@
+[ {
+  "id" : "local-1422981780767",
+  "name" : "Spark shell",
+  "attempts" : [ {
+    "startTime" : "2015-02-03T16:42:59.720GMT",
+    "endTime" : "2015-02-03T16:43:08.731GMT",
+    "sparkUser" : "irashid",
+    "completed" : true
+  } ]
+}, {
+  "id" : "local-1422981759269",
+  "name" : "Spark shell",
+  "attempts" : [ {
+    "startTime" : "2015-02-03T16:42:38.277GMT",
+    "endTime" : "2015-02-03T16:42:46.912GMT",
+    "sparkUser" : "irashid",
+    "completed" : true
+  } ]
+} ]
\ No newline at end of file
diff --git a/core/src/test/resources/HistoryServerExpectations/minDate_app_list_json_expectation.json b/core/src/test/resources/HistoryServerExpectations/minDate_app_list_json_expectation.json
new file mode 100644
index 0000000000000..dca86fe5f7e6a
--- /dev/null
+++ b/core/src/test/resources/HistoryServerExpectations/minDate_app_list_json_expectation.json
@@ -0,0 +1,35 @@
+[ {
+  "id" : "local-1430917381534",
+  "name" : "Spark shell",
+  "attempts" : [ {
+    "startTime" : "2015-05-06T13:03:00.893GMT",
+    "endTime" : "2015-05-06T13:03:11.398GMT",
+    "sparkUser" : "irashid",
+    "completed" : true
+  } ]
+}, {
+  "id" : "local-1426533911241",
+  "name" : "Spark shell",
+  "attempts" : [ {
+    "attemptId" : "2",
+    "startTime" : "2015-03-17T23:11:50.242GMT",
+    "endTime" : "2015-03-17T23:12:25.177GMT",
+    "sparkUser" : "irashid",
+    "completed" : true
+  }, {
+    "attemptId" : "1",
+    "startTime" : "2015-03-16T19:25:10.242GMT",
+    "endTime" : "2015-03-16T19:25:45.177GMT",
+    "sparkUser" : "irashid",
+    "completed" : true
+  } ]
+}, {
+  "id" : "local-1425081759269",
+  "name" : "Spark shell",
+  "attempts" : [ {
+    "startTime" : "2015-02-28T00:02:38.277GMT",
+    "endTime" : "2015-02-28T00:02:46.912GMT",
+    "sparkUser" : "irashid",
+    "completed" : true
+  } ]
+} ]
\ No newline at end of file
diff --git a/core/src/test/resources/HistoryServerExpectations/one_app_json_expectation.json b/core/src/test/resources/HistoryServerExpectations/one_app_json_expectation.json
new file mode 100644
index 0000000000000..07489ad96414a
--- /dev/null
+++ b/core/src/test/resources/HistoryServerExpectations/one_app_json_expectation.json
@@ -0,0 +1,10 @@
+{
+  "id" : "local-1422981780767",
+  "name" : "Spark shell",
+  "attempts" : [ {
+    "startTime" : "2015-02-03T16:42:59.720GMT",
+    "endTime" : "2015-02-03T16:43:08.731GMT",
+    "sparkUser" : "irashid",
+    "completed" : true
+  } ]
+}
\ No newline at end of file
diff --git a/core/src/test/resources/HistoryServerExpectations/one_app_multi_attempt_json_expectation.json b/core/src/test/resources/HistoryServerExpectations/one_app_multi_attempt_json_expectation.json
new file mode 100644
index 0000000000000..8f3d7160c723f
--- /dev/null
+++ b/core/src/test/resources/HistoryServerExpectations/one_app_multi_attempt_json_expectation.json
@@ -0,0 +1,17 @@
+{
+  "id" : "local-1426533911241",
+  "name" : "Spark shell",
+  "attempts" : [ {
+    "attemptId" : "2",
+    "startTime" : "2015-03-17T23:11:50.242GMT",
+    "endTime" : "2015-03-17T23:12:25.177GMT",
+    "sparkUser" : "irashid",
+    "completed" : true
+  }, {
+    "attemptId" : "1",
+    "startTime" : "2015-03-16T19:25:10.242GMT",
+    "endTime" : "2015-03-16T19:25:45.177GMT",
+    "sparkUser" : "irashid",
+    "completed" : true
+  } ]
+}
\ No newline at end of file
diff --git a/core/src/test/resources/HistoryServerExpectations/one_job_json_expectation.json b/core/src/test/resources/HistoryServerExpectations/one_job_json_expectation.json
new file mode 100644
index 0000000000000..4a29072bdb6e4
--- /dev/null
+++ b/core/src/test/resources/HistoryServerExpectations/one_job_json_expectation.json
@@ -0,0 +1,15 @@
+{
+  "jobId" : 0,
+  "name" : "count at <console>:15",
+  "stageIds" : [ 0 ],
+  "status" : "SUCCEEDED",
+  "numTasks" : 8,
+  "numActiveTasks" : 0,
+  "numCompletedTasks" : 8,
+  "numSkippedTasks" : 8,
+  "numFailedTasks" : 0,
+  "numActiveStages" : 0,
+  "numCompletedStages" : 1,
+  "numSkippedStages" : 0,
+  "numFailedStages" : 0
+}
\ No newline at end of file
diff --git a/core/src/test/resources/HistoryServerExpectations/one_rdd_storage_json_expectation.json b/core/src/test/resources/HistoryServerExpectations/one_rdd_storage_json_expectation.json
new file mode 100644
index 0000000000000..38b5328ffbb03
--- /dev/null
+++ b/core/src/test/resources/HistoryServerExpectations/one_rdd_storage_json_expectation.json
@@ -0,0 +1,64 @@
+{
+  "id" : 0,
+  "name" : "0",
+  "numPartitions" : 8,
+  "numCachedPartitions" : 8,
+  "storageLevel" : "Memory Deserialized 1x Replicated",
+  "memoryUsed" : 28000128,
+  "diskUsed" : 0,
+  "dataDistribution" : [ {
+    "address" : "localhost:57971",
+    "memoryUsed" : 28000128,
+    "memoryRemaining" : 250302428,
+    "diskUsed" : 0
+  } ],
+  "partitions" : [ {
+    "blockName" : "rdd_0_0",
+    "storageLevel" : "Memory Deserialized 1x Replicated",
+    "memoryUsed" : 3500016,
+    "diskUsed" : 0,
+    "executors" : [ "localhost:57971" ]
+  }, {
+    "blockName" : "rdd_0_1",
+    "storageLevel" : "Memory Deserialized 1x Replicated",
+    "memoryUsed" : 3500016,
+    "diskUsed" : 0,
+    "executors" : [ "localhost:57971" ]
+  }, {
+    "blockName" : "rdd_0_2",
+    "storageLevel" : "Memory Deserialized 1x Replicated",
+    "memoryUsed" : 3500016,
+    "diskUsed" : 0,
+    "executors" : [ "localhost:57971" ]
+  }, {
+    "blockName" : "rdd_0_3",
+    "storageLevel" : "Memory Deserialized 1x Replicated",
+    "memoryUsed" : 3500016,
+    "diskUsed" : 0,
+    "executors" : [ "localhost:57971" ]
+  }, {
+    "blockName" : "rdd_0_4",
+    "storageLevel" : "Memory Deserialized 1x Replicated",
+    "memoryUsed" : 3500016,
+    "diskUsed" : 0,
+    "executors" : [ "localhost:57971" ]
+  }, {
+    "blockName" : "rdd_0_5",
+    "storageLevel" : "Memory Deserialized 1x Replicated",
+    "memoryUsed" : 3500016,
+    "diskUsed" : 0,
+    "executors" : [ "localhost:57971" ]
+  }, {
+    "blockName" : "rdd_0_6",
+    "storageLevel" : "Memory Deserialized 1x Replicated",
+    "memoryUsed" : 3500016,
+    "diskUsed" : 0,
+    "executors" : [ "localhost:57971" ]
+  }, {
+    "blockName" : "rdd_0_7",
+    "storageLevel" : "Memory Deserialized 1x Replicated",
+    "memoryUsed" : 3500016,
+    "diskUsed" : 0,
+    "executors" : [ "localhost:57971" ]
+  } ]
+}
\ No newline at end of file
diff --git a/core/src/test/resources/HistoryServerExpectations/one_stage_attempt_json_expectation.json b/core/src/test/resources/HistoryServerExpectations/one_stage_attempt_json_expectation.json
new file mode 100644
index 0000000000000..111cb8163eb3d
--- /dev/null
+++ b/core/src/test/resources/HistoryServerExpectations/one_stage_attempt_json_expectation.json
@@ -0,0 +1,270 @@
+{
+  "status" : "COMPLETE",
+  "stageId" : 1,
+  "attemptId" : 0,
+  "numActiveTasks" : 0,
+  "numCompleteTasks" : 8,
+  "numFailedTasks" : 0,
+  "executorRunTime" : 3476,
+  "inputBytes" : 28000128,
+  "inputRecords" : 0,
+  "outputBytes" : 0,
+  "outputRecords" : 0,
+  "shuffleReadBytes" : 0,
+  "shuffleReadRecords" : 0,
+  "shuffleWriteBytes" : 13180,
+  "shuffleWriteRecords" : 0,
+  "memoryBytesSpilled" : 0,
+  "diskBytesSpilled" : 0,
+  "name" : "map at <console>:14",
+  "details" : "org.apache.spark.rdd.RDD.map(RDD.scala:271)\n$line10.$read$$iwC$$iwC$$iwC$$iwC.<init>(<console>:14)\n$line10.$read$$iwC$$iwC$$iwC.<init>(<console>:19)\n$line10.$read$$iwC$$iwC.<init>(<console>:21)\n$line10.$read$$iwC.<init>(<console>:23)\n$line10.$read.<init>(<console>:25)\n$line10.$read$.<init>(<console>:29)\n$line10.$read$.<clinit>(<console>)\n$line10.$eval$.<init>(<console>:7)\n$line10.$eval$.<clinit>(<console>)\n$line10.$eval.$print(<console>)\nsun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\nsun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)\nsun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\njava.lang.reflect.Method.invoke(Method.java:606)\norg.apache.spark.repl.SparkIMain$ReadEvalPrint.call(SparkIMain.scala:852)\norg.apache.spark.repl.SparkIMain$Request.loadAndRun(SparkIMain.scala:1125)\norg.apache.spark.repl.SparkIMain.loadAndRunReq$1(SparkIMain.scala:674)\norg.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:705)\norg.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:669)",
+  "schedulingPool" : "default",
+  "accumulatorUpdates" : [ ],
+  "tasks" : {
+    "8" : {
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+      "taskMetrics" : {
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+  "executorSummary" : {
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+  }
+}
\ No newline at end of file
diff --git a/core/src/test/resources/HistoryServerExpectations/one_stage_json_expectation.json b/core/src/test/resources/HistoryServerExpectations/one_stage_json_expectation.json
new file mode 100644
index 0000000000000..ef339f89afa45
--- /dev/null
+++ b/core/src/test/resources/HistoryServerExpectations/one_stage_json_expectation.json
@@ -0,0 +1,270 @@
+[ {
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+} ]
\ No newline at end of file
diff --git a/core/src/test/resources/HistoryServerExpectations/rdd_list_storage_json_expectation.json b/core/src/test/resources/HistoryServerExpectations/rdd_list_storage_json_expectation.json
new file mode 100644
index 0000000000000..f79a31022d214
--- /dev/null
+++ b/core/src/test/resources/HistoryServerExpectations/rdd_list_storage_json_expectation.json
@@ -0,0 +1,9 @@
+[ {
+  "id" : 0,
+  "name" : "0",
+  "numPartitions" : 8,
+  "numCachedPartitions" : 8,
+  "storageLevel" : "Memory Deserialized 1x Replicated",
+  "memoryUsed" : 28000128,
+  "diskUsed" : 0
+} ]
\ No newline at end of file
diff --git a/core/src/test/resources/HistoryServerExpectations/running_app_list_json_expectation.json b/core/src/test/resources/HistoryServerExpectations/running_app_list_json_expectation.json
new file mode 100644
index 0000000000000..8878e547a7984
--- /dev/null
+++ b/core/src/test/resources/HistoryServerExpectations/running_app_list_json_expectation.json
@@ -0,0 +1 @@
+[ ]
\ No newline at end of file
diff --git a/core/src/test/resources/HistoryServerExpectations/stage_list_json_expectation.json b/core/src/test/resources/HistoryServerExpectations/stage_list_json_expectation.json
new file mode 100644
index 0000000000000..056fac7088594
--- /dev/null
+++ b/core/src/test/resources/HistoryServerExpectations/stage_list_json_expectation.json
@@ -0,0 +1,89 @@
+[ {
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+  "stageId" : 3,
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+  "schedulingPool" : "default",
+  "accumulatorUpdates" : [ ]
+}, {
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+  "accumulatorUpdates" : [ ]
+}, {
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+  "schedulingPool" : "default",
+  "accumulatorUpdates" : [ ]
+} ]
\ No newline at end of file
diff --git a/core/src/test/resources/HistoryServerExpectations/stage_list_with_accumulable_json_expectation.json b/core/src/test/resources/HistoryServerExpectations/stage_list_with_accumulable_json_expectation.json
new file mode 100644
index 0000000000000..79ccacd309693
--- /dev/null
+++ b/core/src/test/resources/HistoryServerExpectations/stage_list_with_accumulable_json_expectation.json
@@ -0,0 +1,27 @@
+[ {
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+  "schedulingPool" : "default",
+  "accumulatorUpdates" : [ {
+    "id" : 1,
+    "name" : "my counter",
+    "value" : "5050"
+  } ]
+} ]
\ No newline at end of file
diff --git a/core/src/test/resources/HistoryServerExpectations/stage_task_list_expectation.json b/core/src/test/resources/HistoryServerExpectations/stage_task_list_expectation.json
new file mode 100644
index 0000000000000..f2cb29b31c85f
--- /dev/null
+++ b/core/src/test/resources/HistoryServerExpectations/stage_task_list_expectation.json
@@ -0,0 +1,561 @@
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diff --git a/core/src/test/resources/HistoryServerExpectations/stage_task_list_from_multi_attempt_app_json_2__expectation.json b/core/src/test/resources/HistoryServerExpectations/stage_task_list_from_multi_attempt_app_json_2__expectation.json
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index 0000000000000..e5ec3bc4c7126
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diff --git a/core/src/test/resources/HistoryServerExpectations/stage_task_list_w__sortBy_short_names___runtime_expectation.json b/core/src/test/resources/HistoryServerExpectations/stage_task_list_w__sortBy_short_names___runtime_expectation.json
new file mode 100644
index 0000000000000..5657123a2db15
--- /dev/null
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diff --git a/core/src/test/resources/HistoryServerExpectations/stage_task_list_w__sortBy_short_names__runtime_expectation.json b/core/src/test/resources/HistoryServerExpectations/stage_task_list_w__sortBy_short_names__runtime_expectation.json
new file mode 100644
index 0000000000000..72fe017e9f85d
--- /dev/null
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diff --git a/core/src/test/resources/HistoryServerExpectations/stage_task_summary_w__custom_quantiles_expectation.json b/core/src/test/resources/HistoryServerExpectations/stage_task_summary_w__custom_quantiles_expectation.json
new file mode 100644
index 0000000000000..bc3c302813de2
--- /dev/null
+++ b/core/src/test/resources/HistoryServerExpectations/stage_task_summary_w__custom_quantiles_expectation.json
@@ -0,0 +1,19 @@
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+}
\ No newline at end of file
diff --git a/core/src/test/resources/HistoryServerExpectations/stage_task_summary_w_shuffle_read_expectation.json b/core/src/test/resources/HistoryServerExpectations/stage_task_summary_w_shuffle_read_expectation.json
new file mode 100644
index 0000000000000..e084c839f1d5a
--- /dev/null
+++ b/core/src/test/resources/HistoryServerExpectations/stage_task_summary_w_shuffle_read_expectation.json
@@ -0,0 +1,19 @@
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\ No newline at end of file
diff --git a/core/src/test/resources/HistoryServerExpectations/stage_task_summary_w_shuffle_write_expectation.json b/core/src/test/resources/HistoryServerExpectations/stage_task_summary_w_shuffle_write_expectation.json
new file mode 100644
index 0000000000000..6ac7811ce691b
--- /dev/null
+++ b/core/src/test/resources/HistoryServerExpectations/stage_task_summary_w_shuffle_write_expectation.json
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\ No newline at end of file
diff --git a/core/src/test/resources/HistoryServerExpectations/stage_with_accumulable_json_expectation.json b/core/src/test/resources/HistoryServerExpectations/stage_with_accumulable_json_expectation.json
new file mode 100644
index 0000000000000..32d5731676ad5
--- /dev/null
+++ b/core/src/test/resources/HistoryServerExpectations/stage_with_accumulable_json_expectation.json
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diff --git a/core/src/test/resources/HistoryServerExpectations/succeeded_failed_job_list_json_expectation.json b/core/src/test/resources/HistoryServerExpectations/succeeded_failed_job_list_json_expectation.json
new file mode 100644
index 0000000000000..cab4750270dfa
--- /dev/null
+++ b/core/src/test/resources/HistoryServerExpectations/succeeded_failed_job_list_json_expectation.json
@@ -0,0 +1,43 @@
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+  "name" : "count at <console>:17",
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+  "numSkippedStages" : 0,
+  "numFailedStages" : 0
+}, {
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+  "name" : "count at <console>:20",
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+  "status" : "FAILED",
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+  "numFailedStages" : 0
+} ]
\ No newline at end of file
diff --git a/core/src/test/resources/HistoryServerExpectations/succeeded_job_list_json_expectation.json b/core/src/test/resources/HistoryServerExpectations/succeeded_job_list_json_expectation.json
new file mode 100644
index 0000000000000..6fd25befbf7e8
--- /dev/null
+++ b/core/src/test/resources/HistoryServerExpectations/succeeded_job_list_json_expectation.json
@@ -0,0 +1,29 @@
+[ {
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+  "name" : "count at <console>:17",
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+} ]
\ No newline at end of file
diff --git a/core/src/test/resources/spark-events/local-1422981759269/APPLICATION_COMPLETE b/core/src/test/resources/spark-events/local-1422981759269/APPLICATION_COMPLETE
new file mode 100755
index 0000000000000..e69de29bb2d1d
diff --git a/core/src/test/resources/spark-events/local-1422981759269/EVENT_LOG_1 b/core/src/test/resources/spark-events/local-1422981759269/EVENT_LOG_1
new file mode 100755
index 0000000000000..4794e56d1107a
--- /dev/null
+++ b/core/src/test/resources/spark-events/local-1422981759269/EVENT_LOG_1
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diff --git a/core/src/test/resources/spark-events/local-1422981780767/EVENT_LOG_1 b/core/src/test/resources/spark-events/local-1422981780767/EVENT_LOG_1
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+{"Event":"SparkListenerTaskEnd","Stage ID":2,"Stage Attempt ID":0,"Task Type":"ResultTask","Task End Reason":{"Reason":"Success"},"Task Info":{"Task ID":21,"Index":5,"Attempt":0,"Launch Time":1422981786298,"Executor ID":"<driver>","Host":"localhost","Locality":"PROCESS_LOCAL","Speculative":false,"Getting Result Time":0,"Finish Time":1422981786339,"Failed":false,"Accumulables":[]},"Task Metrics":{"Host Name":"localhost","Executor Deserialize Time":1,"Executor Run Time":35,"Result Size":862,"JVM GC Time":0,"Result Serialization Time":0,"Memory Bytes Spilled":0,"Disk Bytes Spilled":0,"Shuffle Read Metrics":{"Remote Blocks Fetched":0,"Local Blocks Fetched":8,"Fetch Wait Time":0,"Remote Bytes Read":0}}}
+{"Event":"SparkListenerTaskEnd","Stage ID":2,"Stage Attempt ID":0,"Task Type":"ResultTask","Task End Reason":{"Reason":"Success"},"Task Info":{"Task ID":17,"Index":1,"Attempt":0,"Launch Time":1422981786297,"Executor ID":"<driver>","Host":"localhost","Locality":"PROCESS_LOCAL","Speculative":false,"Getting Result Time":0,"Finish Time":1422981786340,"Failed":false,"Accumulables":[]},"Task Metrics":{"Host Name":"localhost","Executor Deserialize Time":1,"Executor Run Time":35,"Result Size":862,"JVM GC Time":0,"Result Serialization Time":0,"Memory Bytes Spilled":0,"Disk Bytes Spilled":0,"Shuffle Read Metrics":{"Remote Blocks Fetched":0,"Local Blocks Fetched":8,"Fetch Wait Time":1,"Remote Bytes Read":0}}}
+{"Event":"SparkListenerTaskEnd","Stage ID":2,"Stage Attempt ID":0,"Task Type":"ResultTask","Task End Reason":{"Reason":"Success"},"Task Info":{"Task ID":18,"Index":2,"Attempt":0,"Launch Time":1422981786297,"Executor ID":"<driver>","Host":"localhost","Locality":"PROCESS_LOCAL","Speculative":false,"Getting Result Time":0,"Finish Time":1422981786340,"Failed":false,"Accumulables":[]},"Task Metrics":{"Host Name":"localhost","Executor Deserialize Time":2,"Executor Run Time":34,"Result Size":862,"JVM GC Time":0,"Result Serialization Time":0,"Memory Bytes Spilled":0,"Disk Bytes Spilled":0,"Shuffle Read Metrics":{"Remote Blocks Fetched":0,"Local Blocks Fetched":8,"Fetch Wait Time":0,"Remote Bytes Read":0}}}
+{"Event":"SparkListenerTaskEnd","Stage ID":2,"Stage Attempt ID":0,"Task Type":"ResultTask","Task End Reason":{"Reason":"Success"},"Task Info":{"Task ID":16,"Index":0,"Attempt":0,"Launch Time":1422981786296,"Executor ID":"<driver>","Host":"localhost","Locality":"PROCESS_LOCAL","Speculative":false,"Getting Result Time":0,"Finish Time":1422981786340,"Failed":false,"Accumulables":[]},"Task Metrics":{"Host Name":"localhost","Executor Deserialize Time":1,"Executor Run Time":35,"Result Size":862,"JVM GC Time":0,"Result Serialization Time":0,"Memory Bytes Spilled":0,"Disk Bytes Spilled":0,"Shuffle Read Metrics":{"Remote Blocks Fetched":0,"Local Blocks Fetched":8,"Fetch Wait Time":0,"Remote Bytes Read":0}}}
+{"Event":"SparkListenerTaskEnd","Stage ID":2,"Stage Attempt ID":0,"Task Type":"ResultTask","Task End Reason":{"Reason":"Success"},"Task Info":{"Task ID":23,"Index":7,"Attempt":0,"Launch Time":1422981786299,"Executor ID":"<driver>","Host":"localhost","Locality":"PROCESS_LOCAL","Speculative":false,"Getting Result Time":0,"Finish Time":1422981786341,"Failed":false,"Accumulables":[]},"Task Metrics":{"Host Name":"localhost","Executor Deserialize Time":1,"Executor Run Time":35,"Result Size":862,"JVM GC Time":0,"Result Serialization Time":0,"Memory Bytes Spilled":0,"Disk Bytes Spilled":0,"Shuffle Read Metrics":{"Remote Blocks Fetched":0,"Local Blocks Fetched":8,"Fetch Wait Time":0,"Remote Bytes Read":0}}}
+{"Event":"SparkListenerTaskEnd","Stage ID":2,"Stage Attempt ID":0,"Task Type":"ResultTask","Task End Reason":{"Reason":"Success"},"Task Info":{"Task ID":22,"Index":6,"Attempt":0,"Launch Time":1422981786298,"Executor ID":"<driver>","Host":"localhost","Locality":"PROCESS_LOCAL","Speculative":false,"Getting Result Time":0,"Finish Time":1422981786342,"Failed":false,"Accumulables":[]},"Task Metrics":{"Host Name":"localhost","Executor Deserialize Time":2,"Executor Run Time":34,"Result Size":862,"JVM GC Time":0,"Result Serialization Time":0,"Memory Bytes Spilled":0,"Disk Bytes Spilled":0,"Shuffle Read Metrics":{"Remote Blocks Fetched":0,"Local Blocks Fetched":8,"Fetch Wait Time":0,"Remote Bytes Read":0}}}
+{"Event":"SparkListenerTaskEnd","Stage ID":2,"Stage Attempt ID":0,"Task Type":"ResultTask","Task End Reason":{"Reason":"ExceptionFailure","Class Name":"java.lang.RuntimeException","Description":"got a 3, failing","Stack Trace":[{"Declaring Class":"$line11.$read$$iwC$$iwC$$iwC$$iwC$$anonfun$1","Method Name":"apply","File Name":"<console>","Line Number":18},{"Declaring Class":"$line11.$read$$iwC$$iwC$$iwC$$iwC$$anonfun$1","Method Name":"apply","File Name":"<console>","Line Number":17},{"Declaring Class":"scala.collection.Iterator$$anon$11","Method Name":"next","File Name":"Iterator.scala","Line Number":328},{"Declaring Class":"org.apache.spark.util.Utils$","Method Name":"getIteratorSize","File Name":"Utils.scala","Line Number":1311},{"Declaring Class":"org.apache.spark.rdd.RDD$$anonfun$count$1","Method Name":"apply","File Name":"RDD.scala","Line Number":910},{"Declaring Class":"org.apache.spark.rdd.RDD$$anonfun$count$1","Method Name":"apply","File Name":"RDD.scala","Line Number":910},{"Declaring Class":"org.apache.spark.SparkContext$$anonfun$runJob$4","Method Name":"apply","File Name":"SparkContext.scala","Line Number":1314},{"Declaring Class":"org.apache.spark.SparkContext$$anonfun$runJob$4","Method Name":"apply","File Name":"SparkContext.scala","Line Number":1314},{"Declaring Class":"org.apache.spark.scheduler.ResultTask","Method Name":"runTask","File Name":"ResultTask.scala","Line Number":61},{"Declaring Class":"org.apache.spark.scheduler.Task","Method Name":"run","File Name":"Task.scala","Line Number":56},{"Declaring Class":"org.apache.spark.executor.Executor$TaskRunner","Method Name":"run","File Name":"Executor.scala","Line Number":196},{"Declaring Class":"java.util.concurrent.ThreadPoolExecutor","Method Name":"runWorker","File Name":"ThreadPoolExecutor.java","Line Number":1145},{"Declaring Class":"java.util.concurrent.ThreadPoolExecutor$Worker","Method Name":"run","File Name":"ThreadPoolExecutor.java","Line Number":615},{"Declaring Class":"java.lang.Thread","Method Name":"run","File Name":"Thread.java","Line Number":745}],"Full Stack Trace":"java.lang.RuntimeException: got a 3, failing\n\tat $line11.$read$$iwC$$iwC$$iwC$$iwC$$anonfun$1.apply(<console>:18)\n\tat $line11.$read$$iwC$$iwC$$iwC$$iwC$$anonfun$1.apply(<console>:17)\n\tat scala.collection.Iterator$$anon$11.next(Iterator.scala:328)\n\tat org.apache.spark.util.Utils$.getIteratorSize(Utils.scala:1311)\n\tat org.apache.spark.rdd.RDD$$anonfun$count$1.apply(RDD.scala:910)\n\tat org.apache.spark.rdd.RDD$$anonfun$count$1.apply(RDD.scala:910)\n\tat org.apache.spark.SparkContext$$anonfun$runJob$4.apply(SparkContext.scala:1314)\n\tat org.apache.spark.SparkContext$$anonfun$runJob$4.apply(SparkContext.scala:1314)\n\tat org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:61)\n\tat org.apache.spark.scheduler.Task.run(Task.scala:56)\n\tat org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:196)\n\tat java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)\n\tat java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)\n\tat java.lang.Thread.run(Thread.java:745)\n","Metrics":{"Host Name":"localhost","Executor Deserialize Time":0,"Executor Run Time":36,"Result Size":0,"JVM GC Time":0,"Result Serialization Time":0,"Memory Bytes Spilled":0,"Disk Bytes Spilled":0,"Shuffle Read Metrics":{"Remote Blocks Fetched":0,"Local Blocks Fetched":8,"Fetch Wait Time":0,"Remote Bytes Read":0}}},"Task Info":{"Task ID":19,"Index":3,"Attempt":0,"Launch Time":1422981786297,"Executor ID":"<driver>","Host":"localhost","Locality":"PROCESS_LOCAL","Speculative":false,"Getting Result Time":0,"Finish Time":1422981786343,"Failed":true,"Accumulables":[]},"Task Metrics":{"Host Name":"localhost","Executor Deserialize Time":0,"Executor Run Time":36,"Result Size":0,"JVM GC Time":0,"Result Serialization Time":0,"Memory Bytes Spilled":0,"Disk Bytes Spilled":0,"Shuffle Read Metrics":{"Remote Blocks Fetched":0,"Local Blocks Fetched":8,"Fetch Wait Time":0,"Remote Bytes Read":0}}}
+{"Event":"SparkListenerStageCompleted","Stage Info":{"Stage ID":2,"Stage Attempt ID":0,"Stage Name":"count at <console>:20","Number of Tasks":8,"RDD Info":[{"RDD ID":3,"Name":"3","Storage Level":{"Use Disk":false,"Use Memory":false,"Use Tachyon":false,"Deserialized":false,"Replication":1},"Number of Partitions":8,"Number of Cached Partitions":0,"Memory Size":0,"Tachyon Size":0,"Disk Size":0},{"RDD ID":2,"Name":"2","Storage Level":{"Use Disk":false,"Use Memory":false,"Use Tachyon":false,"Deserialized":false,"Replication":1},"Number of Partitions":8,"Number of Cached Partitions":0,"Memory Size":0,"Tachyon Size":0,"Disk Size":0}],"Details":"org.apache.spark.rdd.RDD.count(RDD.scala:910)\n$line11.$read$$iwC$$iwC$$iwC$$iwC.<init>(<console>:20)\n$line11.$read$$iwC$$iwC$$iwC.<init>(<console>:25)\n$line11.$read$$iwC$$iwC.<init>(<console>:27)\n$line11.$read$$iwC.<init>(<console>:29)\n$line11.$read.<init>(<console>:31)\n$line11.$read$.<init>(<console>:35)\n$line11.$read$.<clinit>(<console>)\n$line11.$eval$.<init>(<console>:7)\n$line11.$eval$.<clinit>(<console>)\n$line11.$eval.$print(<console>)\nsun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\nsun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)\nsun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\njava.lang.reflect.Method.invoke(Method.java:606)\norg.apache.spark.repl.SparkIMain$ReadEvalPrint.call(SparkIMain.scala:852)\norg.apache.spark.repl.SparkIMain$Request.loadAndRun(SparkIMain.scala:1125)\norg.apache.spark.repl.SparkIMain.loadAndRunReq$1(SparkIMain.scala:674)\norg.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:705)\norg.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:669)","Submission Time":1422981786296,"Completion Time":1422981786347,"Failure Reason":"Job aborted due to stage failure: Task 3 in stage 2.0 failed 1 times, most recent failure: Lost task 3.0 in stage 2.0 (TID 19, localhost): java.lang.RuntimeException: got a 3, failing\n\tat $line11.$read$$iwC$$iwC$$iwC$$iwC$$anonfun$1.apply(<console>:18)\n\tat $line11.$read$$iwC$$iwC$$iwC$$iwC$$anonfun$1.apply(<console>:17)\n\tat scala.collection.Iterator$$anon$11.next(Iterator.scala:328)\n\tat org.apache.spark.util.Utils$.getIteratorSize(Utils.scala:1311)\n\tat org.apache.spark.rdd.RDD$$anonfun$count$1.apply(RDD.scala:910)\n\tat org.apache.spark.rdd.RDD$$anonfun$count$1.apply(RDD.scala:910)\n\tat org.apache.spark.SparkContext$$anonfun$runJob$4.apply(SparkContext.scala:1314)\n\tat org.apache.spark.SparkContext$$anonfun$runJob$4.apply(SparkContext.scala:1314)\n\tat org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:61)\n\tat org.apache.spark.scheduler.Task.run(Task.scala:56)\n\tat org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:196)\n\tat java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)\n\tat java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)\n\tat java.lang.Thread.run(Thread.java:745)\n\nDriver stacktrace:","Accumulables":[]}}
+{"Event":"SparkListenerJobEnd","Job ID":1,"Job Result":{"Result":"JobFailed","Exception":{"Message":"Job aborted due to stage failure: Task 3 in stage 2.0 failed 1 times, most recent failure: Lost task 3.0 in stage 2.0 (TID 19, localhost): java.lang.RuntimeException: got a 3, failing\n\tat $line11.$read$$iwC$$iwC$$iwC$$iwC$$anonfun$1.apply(<console>:18)\n\tat $line11.$read$$iwC$$iwC$$iwC$$iwC$$anonfun$1.apply(<console>:17)\n\tat scala.collection.Iterator$$anon$11.next(Iterator.scala:328)\n\tat org.apache.spark.util.Utils$.getIteratorSize(Utils.scala:1311)\n\tat org.apache.spark.rdd.RDD$$anonfun$count$1.apply(RDD.scala:910)\n\tat org.apache.spark.rdd.RDD$$anonfun$count$1.apply(RDD.scala:910)\n\tat org.apache.spark.SparkContext$$anonfun$runJob$4.apply(SparkContext.scala:1314)\n\tat org.apache.spark.SparkContext$$anonfun$runJob$4.apply(SparkContext.scala:1314)\n\tat org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:61)\n\tat org.apache.spark.scheduler.Task.run(Task.scala:56)\n\tat org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:196)\n\tat java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)\n\tat java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)\n\tat java.lang.Thread.run(Thread.java:745)\n\nDriver stacktrace:","Stack Trace":[{"Declaring Class":"org.apache.spark.scheduler.DAGScheduler","Method Name":"org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages","File Name":"DAGScheduler.scala","Line Number":1214},{"Declaring Class":"org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1","Method Name":"apply","File Name":"DAGScheduler.scala","Line Number":1203},{"Declaring Class":"org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1","Method Name":"apply","File Name":"DAGScheduler.scala","Line Number":1202},{"Declaring Class":"scala.collection.mutable.ResizableArray$class","Method Name":"foreach","File Name":"ResizableArray.scala","Line Number":59},{"Declaring Class":"scala.collection.mutable.ArrayBuffer","Method Name":"foreach","File Name":"ArrayBuffer.scala","Line Number":47},{"Declaring Class":"org.apache.spark.scheduler.DAGScheduler","Method Name":"abortStage","File Name":"DAGScheduler.scala","Line Number":1202},{"Declaring Class":"org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1","Method Name":"apply","File Name":"DAGScheduler.scala","Line Number":696},{"Declaring Class":"org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1","Method Name":"apply","File Name":"DAGScheduler.scala","Line Number":696},{"Declaring Class":"scala.Option","Method Name":"foreach","File Name":"Option.scala","Line Number":236},{"Declaring Class":"org.apache.spark.scheduler.DAGScheduler","Method Name":"handleTaskSetFailed","File Name":"DAGScheduler.scala","Line Number":696},{"Declaring Class":"org.apache.spark.scheduler.DAGSchedulerEventProcessActor$$anonfun$receive$2","Method Name":"applyOrElse","File Name":"DAGScheduler.scala","Line Number":1420},{"Declaring Class":"akka.actor.Actor$class","Method Name":"aroundReceive","File Name":"Actor.scala","Line Number":465},{"Declaring Class":"org.apache.spark.scheduler.DAGSchedulerEventProcessActor","Method Name":"aroundReceive","File Name":"DAGScheduler.scala","Line Number":1375},{"Declaring Class":"akka.actor.ActorCell","Method Name":"receiveMessage","File Name":"ActorCell.scala","Line Number":516},{"Declaring Class":"akka.actor.ActorCell","Method Name":"invoke","File Name":"ActorCell.scala","Line Number":487},{"Declaring Class":"akka.dispatch.Mailbox","Method Name":"processMailbox","File Name":"Mailbox.scala","Line Number":238},{"Declaring Class":"akka.dispatch.Mailbox","Method Name":"run","File Name":"Mailbox.scala","Line Number":220},{"Declaring Class":"akka.dispatch.ForkJoinExecutorConfigurator$AkkaForkJoinTask","Method Name":"exec","File Name":"AbstractDispatcher.scala","Line Number":393},{"Declaring Class":"scala.concurrent.forkjoin.ForkJoinTask","Method Name":"doExec","File Name":"ForkJoinTask.java","Line Number":260},{"Declaring Class":"scala.concurrent.forkjoin.ForkJoinPool$WorkQueue","Method Name":"runTask","File Name":"ForkJoinPool.java","Line Number":1339},{"Declaring Class":"scala.concurrent.forkjoin.ForkJoinPool","Method Name":"runWorker","File Name":"ForkJoinPool.java","Line Number":1979},{"Declaring Class":"scala.concurrent.forkjoin.ForkJoinWorkerThread","Method Name":"run","File Name":"ForkJoinWorkerThread.java","Line Number":107}]}}}
+{"Event":"SparkListenerJobStart","Job ID":2,"Stage Infos":[{"Stage ID":3,"Stage Attempt ID":0,"Stage Name":"count at <console>:17","Number of Tasks":8,"RDD Info":[{"RDD ID":6,"Name":"/Users/irashid/spark-examples/tmp_data/sums_2","Storage Level":{"Use Disk":false,"Use Memory":false,"Use Tachyon":false,"Deserialized":false,"Replication":1},"Number of Partitions":8,"Number of Cached Partitions":0,"Memory Size":0,"Tachyon Size":0,"Disk Size":0},{"RDD ID":5,"Name":"/Users/irashid/spark-examples/tmp_data/sums_2","Storage Level":{"Use Disk":false,"Use Memory":false,"Use Tachyon":false,"Deserialized":false,"Replication":1},"Number of Partitions":8,"Number of Cached Partitions":0,"Memory Size":0,"Tachyon Size":0,"Disk Size":0}],"Details":"org.apache.spark.rdd.RDD.count(RDD.scala:910)\n$line19.$read$$iwC$$iwC$$iwC$$iwC.<init>(<console>:17)\n$line19.$read$$iwC$$iwC$$iwC.<init>(<console>:22)\n$line19.$read$$iwC$$iwC.<init>(<console>:24)\n$line19.$read$$iwC.<init>(<console>:26)\n$line19.$read.<init>(<console>:28)\n$line19.$read$.<init>(<console>:32)\n$line19.$read$.<clinit>(<console>)\n$line19.$eval$.<init>(<console>:7)\n$line19.$eval$.<clinit>(<console>)\n$line19.$eval.$print(<console>)\nsun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\nsun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)\nsun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\njava.lang.reflect.Method.invoke(Method.java:606)\norg.apache.spark.repl.SparkIMain$ReadEvalPrint.call(SparkIMain.scala:852)\norg.apache.spark.repl.SparkIMain$Request.loadAndRun(SparkIMain.scala:1125)\norg.apache.spark.repl.SparkIMain.loadAndRunReq$1(SparkIMain.scala:674)\norg.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:705)\norg.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:669)","Accumulables":[]}],"Stage IDs":[3]}
+{"Event":"SparkListenerStageSubmitted","Stage Info":{"Stage ID":3,"Stage Attempt ID":0,"Stage Name":"count at <console>:17","Number of Tasks":8,"RDD Info":[{"RDD ID":6,"Name":"/Users/irashid/spark-examples/tmp_data/sums_2","Storage Level":{"Use Disk":false,"Use Memory":false,"Use Tachyon":false,"Deserialized":false,"Replication":1},"Number of Partitions":8,"Number of Cached Partitions":0,"Memory Size":0,"Tachyon Size":0,"Disk Size":0},{"RDD ID":5,"Name":"/Users/irashid/spark-examples/tmp_data/sums_2","Storage Level":{"Use Disk":false,"Use Memory":false,"Use Tachyon":false,"Deserialized":false,"Replication":1},"Number of Partitions":8,"Number of Cached Partitions":0,"Memory Size":0,"Tachyon Size":0,"Disk Size":0}],"Details":"org.apache.spark.rdd.RDD.count(RDD.scala:910)\n$line19.$read$$iwC$$iwC$$iwC$$iwC.<init>(<console>:17)\n$line19.$read$$iwC$$iwC$$iwC.<init>(<console>:22)\n$line19.$read$$iwC$$iwC.<init>(<console>:24)\n$line19.$read$$iwC.<init>(<console>:26)\n$line19.$read.<init>(<console>:28)\n$line19.$read$.<init>(<console>:32)\n$line19.$read$.<clinit>(<console>)\n$line19.$eval$.<init>(<console>:7)\n$line19.$eval$.<clinit>(<console>)\n$line19.$eval.$print(<console>)\nsun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\nsun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)\nsun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\njava.lang.reflect.Method.invoke(Method.java:606)\norg.apache.spark.repl.SparkIMain$ReadEvalPrint.call(SparkIMain.scala:852)\norg.apache.spark.repl.SparkIMain$Request.loadAndRun(SparkIMain.scala:1125)\norg.apache.spark.repl.SparkIMain.loadAndRunReq$1(SparkIMain.scala:674)\norg.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:705)\norg.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:669)","Accumulables":[]}}
+{"Event":"SparkListenerTaskStart","Stage ID":3,"Stage Attempt ID":0,"Task Info":{"Task ID":24,"Index":0,"Attempt":0,"Launch Time":1422981787191,"Executor ID":"<driver>","Host":"localhost","Locality":"PROCESS_LOCAL","Speculative":false,"Getting Result Time":0,"Finish Time":0,"Failed":false,"Accumulables":[]}}
+{"Event":"SparkListenerTaskStart","Stage ID":3,"Stage Attempt ID":0,"Task Info":{"Task ID":25,"Index":1,"Attempt":0,"Launch Time":1422981787191,"Executor ID":"<driver>","Host":"localhost","Locality":"PROCESS_LOCAL","Speculative":false,"Getting Result Time":0,"Finish Time":0,"Failed":false,"Accumulables":[]}}
+{"Event":"SparkListenerTaskStart","Stage ID":3,"Stage Attempt ID":0,"Task Info":{"Task ID":26,"Index":2,"Attempt":0,"Launch Time":1422981787192,"Executor ID":"<driver>","Host":"localhost","Locality":"PROCESS_LOCAL","Speculative":false,"Getting Result Time":0,"Finish Time":0,"Failed":false,"Accumulables":[]}}
+{"Event":"SparkListenerTaskStart","Stage ID":3,"Stage Attempt ID":0,"Task Info":{"Task ID":27,"Index":3,"Attempt":0,"Launch Time":1422981787192,"Executor ID":"<driver>","Host":"localhost","Locality":"PROCESS_LOCAL","Speculative":false,"Getting Result Time":0,"Finish Time":0,"Failed":false,"Accumulables":[]}}
+{"Event":"SparkListenerTaskStart","Stage ID":3,"Stage Attempt ID":0,"Task Info":{"Task ID":28,"Index":4,"Attempt":0,"Launch Time":1422981787193,"Executor ID":"<driver>","Host":"localhost","Locality":"PROCESS_LOCAL","Speculative":false,"Getting Result Time":0,"Finish Time":0,"Failed":false,"Accumulables":[]}}
+{"Event":"SparkListenerTaskStart","Stage ID":3,"Stage Attempt ID":0,"Task Info":{"Task ID":29,"Index":5,"Attempt":0,"Launch Time":1422981787193,"Executor ID":"<driver>","Host":"localhost","Locality":"PROCESS_LOCAL","Speculative":false,"Getting Result Time":0,"Finish Time":0,"Failed":false,"Accumulables":[]}}
+{"Event":"SparkListenerTaskStart","Stage ID":3,"Stage Attempt ID":0,"Task Info":{"Task ID":30,"Index":6,"Attempt":0,"Launch Time":1422981787194,"Executor ID":"<driver>","Host":"localhost","Locality":"PROCESS_LOCAL","Speculative":false,"Getting Result Time":0,"Finish Time":0,"Failed":false,"Accumulables":[]}}
+{"Event":"SparkListenerTaskStart","Stage ID":3,"Stage Attempt ID":0,"Task Info":{"Task ID":31,"Index":7,"Attempt":0,"Launch Time":1422981787194,"Executor ID":"<driver>","Host":"localhost","Locality":"PROCESS_LOCAL","Speculative":false,"Getting Result Time":0,"Finish Time":0,"Failed":false,"Accumulables":[]}}
+{"Event":"SparkListenerTaskEnd","Stage ID":3,"Stage Attempt ID":0,"Task Type":"ResultTask","Task End Reason":{"Reason":"Success"},"Task Info":{"Task ID":25,"Index":1,"Attempt":0,"Launch Time":1422981787191,"Executor ID":"<driver>","Host":"localhost","Locality":"PROCESS_LOCAL","Speculative":false,"Getting Result Time":0,"Finish Time":1422981787222,"Failed":false,"Accumulables":[]},"Task Metrics":{"Host Name":"localhost","Executor Deserialize Time":5,"Executor Run Time":20,"Result Size":1812,"JVM GC Time":0,"Result Serialization Time":0,"Memory Bytes Spilled":0,"Disk Bytes Spilled":0,"Input Metrics":{"Data Read Method":"Hadoop","Bytes Read":32}}}
+{"Event":"SparkListenerTaskEnd","Stage ID":3,"Stage Attempt ID":0,"Task Type":"ResultTask","Task End Reason":{"Reason":"Success"},"Task Info":{"Task ID":24,"Index":0,"Attempt":0,"Launch Time":1422981787191,"Executor ID":"<driver>","Host":"localhost","Locality":"PROCESS_LOCAL","Speculative":false,"Getting Result Time":0,"Finish Time":1422981787223,"Failed":false,"Accumulables":[]},"Task Metrics":{"Host Name":"localhost","Executor Deserialize Time":5,"Executor Run Time":20,"Result Size":1812,"JVM GC Time":0,"Result Serialization Time":0,"Memory Bytes Spilled":0,"Disk Bytes Spilled":0,"Input Metrics":{"Data Read Method":"Hadoop","Bytes Read":32}}}
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diff --git a/core/src/test/resources/spark-events/local-1425081759269/APPLICATION_COMPLETE b/core/src/test/resources/spark-events/local-1425081759269/APPLICATION_COMPLETE
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diff --git a/core/src/test/resources/spark-events/local-1425081759269/EVENT_LOG_1 b/core/src/test/resources/spark-events/local-1425081759269/EVENT_LOG_1
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diff --git a/core/src/test/resources/spark-events/local-1425081759269/SPARK_VERSION_1.2.0 b/core/src/test/resources/spark-events/local-1425081759269/SPARK_VERSION_1.2.0
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diff --git a/core/src/test/resources/spark-events/local-1426533911241/APPLICATION_COMPLETE b/core/src/test/resources/spark-events/local-1426533911241/APPLICATION_COMPLETE
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diff --git a/core/src/test/resources/spark-events/local-1426533911241/EVENT_LOG_1 b/core/src/test/resources/spark-events/local-1426533911241/EVENT_LOG_1
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index 0000000000000..9ef5bd5d92de5
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diff --git a/core/src/test/resources/spark-events/local-1426533911241/SPARK_VERSION_1.2.0 b/core/src/test/resources/spark-events/local-1426533911241/SPARK_VERSION_1.2.0
new file mode 100755
index 0000000000000..e69de29bb2d1d
diff --git a/core/src/test/resources/spark-events/local-1426633911242/APPLICATION_COMPLETE b/core/src/test/resources/spark-events/local-1426633911242/APPLICATION_COMPLETE
new file mode 100755
index 0000000000000..e69de29bb2d1d
diff --git a/core/src/test/resources/spark-events/local-1426633911242/EVENT_LOG_1 b/core/src/test/resources/spark-events/local-1426633911242/EVENT_LOG_1
new file mode 100755
index 0000000000000..e7043282107d3
--- /dev/null
+++ b/core/src/test/resources/spark-events/local-1426633911242/EVENT_LOG_1
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diff --git a/core/src/test/resources/spark-events/local-1426633911242/SPARK_VERSION_1.2.0 b/core/src/test/resources/spark-events/local-1426633911242/SPARK_VERSION_1.2.0
new file mode 100755
index 0000000000000..e69de29bb2d1d
diff --git a/core/src/test/resources/spark-events/local-1430917381534 b/core/src/test/resources/spark-events/local-1430917381534
new file mode 100755
index 0000000000000..24ac478073e3d
--- /dev/null
+++ b/core/src/test/resources/spark-events/local-1430917381534
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+{"Event":"SparkListenerTaskEnd","Stage ID":1,"Stage Attempt ID":0,"Task Type":"ResultTask","Task End Reason":{"Reason":"Success"},"Task Info":{"Task ID":105,"Index":5,"Attempt":0,"Launch Time":1430917387466,"Executor ID":"driver","Host":"localhost","Locality":"PROCESS_LOCAL","Speculative":false,"Getting Result Time":0,"Finish Time":1430917387547,"Failed":false,"Accumulables":[]},"Task Metrics":{"Host Name":"localhost","Executor Deserialize Time":1,"Executor Run Time":76,"Result Size":1034,"JVM GC Time":0,"Result Serialization Time":0,"Memory Bytes Spilled":0,"Disk Bytes Spilled":0,"Shuffle Read Metrics":{"Remote Blocks Fetched":0,"Local Blocks Fetched":100,"Fetch Wait Time":0,"Remote Bytes Read":0,"Local Bytes Read":17100,"Total Records Read":100}}}
+{"Event":"SparkListenerTaskEnd","Stage ID":1,"Stage Attempt ID":0,"Task Type":"ResultTask","Task End Reason":{"Reason":"Success"},"Task Info":{"Task ID":100,"Index":0,"Attempt":0,"Launch Time":1430917387462,"Executor ID":"driver","Host":"localhost","Locality":"PROCESS_LOCAL","Speculative":false,"Getting Result Time":0,"Finish Time":1430917387548,"Failed":false,"Accumulables":[]},"Task Metrics":{"Host Name":"localhost","Executor Deserialize Time":3,"Executor Run Time":74,"Result Size":1034,"JVM GC Time":0,"Result Serialization Time":0,"Memory Bytes Spilled":0,"Disk Bytes Spilled":0,"Shuffle Read Metrics":{"Remote Blocks Fetched":0,"Local Blocks Fetched":100,"Fetch Wait Time":0,"Remote Bytes Read":0,"Local Bytes Read":17100,"Total Records Read":100}}}
+{"Event":"SparkListenerTaskEnd","Stage ID":1,"Stage Attempt ID":0,"Task Type":"ResultTask","Task End Reason":{"Reason":"Success"},"Task Info":{"Task ID":107,"Index":7,"Attempt":0,"Launch Time":1430917387466,"Executor ID":"driver","Host":"localhost","Locality":"PROCESS_LOCAL","Speculative":false,"Getting Result Time":0,"Finish Time":1430917387548,"Failed":false,"Accumulables":[]},"Task Metrics":{"Host Name":"localhost","Executor Deserialize Time":2,"Executor Run Time":76,"Result Size":1034,"JVM GC Time":0,"Result Serialization Time":0,"Memory Bytes Spilled":0,"Disk Bytes Spilled":0,"Shuffle Read Metrics":{"Remote Blocks Fetched":0,"Local Blocks Fetched":100,"Fetch Wait Time":0,"Remote Bytes Read":0,"Local Bytes Read":17100,"Total Records Read":100}}}
+{"Event":"SparkListenerTaskEnd","Stage ID":1,"Stage Attempt ID":0,"Task Type":"ResultTask","Task End Reason":{"Reason":"Success"},"Task Info":{"Task ID":101,"Index":1,"Attempt":0,"Launch Time":1430917387464,"Executor ID":"driver","Host":"localhost","Locality":"PROCESS_LOCAL","Speculative":false,"Getting Result Time":0,"Finish Time":1430917387548,"Failed":false,"Accumulables":[]},"Task Metrics":{"Host Name":"localhost","Executor Deserialize Time":1,"Executor Run Time":76,"Result Size":1034,"JVM GC Time":0,"Result Serialization Time":0,"Memory Bytes Spilled":0,"Disk Bytes Spilled":0,"Shuffle Read Metrics":{"Remote Blocks Fetched":0,"Local Blocks Fetched":100,"Fetch Wait Time":1,"Remote Bytes Read":0,"Local Bytes Read":17100,"Total Records Read":100}}}
+{"Event":"SparkListenerTaskEnd","Stage ID":1,"Stage Attempt ID":0,"Task Type":"ResultTask","Task End Reason":{"Reason":"Success"},"Task Info":{"Task ID":106,"Index":6,"Attempt":0,"Launch Time":1430917387466,"Executor ID":"driver","Host":"localhost","Locality":"PROCESS_LOCAL","Speculative":false,"Getting Result Time":0,"Finish Time":1430917387549,"Failed":false,"Accumulables":[]},"Task Metrics":{"Host Name":"localhost","Executor Deserialize Time":2,"Executor Run Time":75,"Result Size":1034,"JVM GC Time":0,"Result Serialization Time":0,"Memory Bytes Spilled":0,"Disk Bytes Spilled":0,"Shuffle Read Metrics":{"Remote Blocks Fetched":0,"Local Blocks Fetched":100,"Fetch Wait Time":0,"Remote Bytes Read":0,"Local Bytes Read":17100,"Total Records Read":100}}}
+{"Event":"SparkListenerTaskEnd","Stage ID":1,"Stage Attempt ID":0,"Task Type":"ResultTask","Task End Reason":{"Reason":"Success"},"Task Info":{"Task ID":104,"Index":4,"Attempt":0,"Launch Time":1430917387465,"Executor ID":"driver","Host":"localhost","Locality":"PROCESS_LOCAL","Speculative":false,"Getting Result Time":0,"Finish Time":1430917387549,"Failed":false,"Accumulables":[]},"Task Metrics":{"Host Name":"localhost","Executor Deserialize Time":3,"Executor Run Time":74,"Result Size":1034,"JVM GC Time":0,"Result Serialization Time":0,"Memory Bytes Spilled":0,"Disk Bytes Spilled":0,"Shuffle Read Metrics":{"Remote Blocks Fetched":0,"Local Blocks Fetched":100,"Fetch Wait Time":1,"Remote Bytes Read":0,"Local Bytes Read":17100,"Total Records Read":100}}}
+{"Event":"SparkListenerTaskEnd","Stage ID":1,"Stage Attempt ID":0,"Task Type":"ResultTask","Task End Reason":{"Reason":"Success"},"Task Info":{"Task ID":102,"Index":2,"Attempt":0,"Launch Time":1430917387465,"Executor ID":"driver","Host":"localhost","Locality":"PROCESS_LOCAL","Speculative":false,"Getting Result Time":0,"Finish Time":1430917387550,"Failed":false,"Accumulables":[]},"Task Metrics":{"Host Name":"localhost","Executor Deserialize Time":2,"Executor Run Time":79,"Result Size":1034,"JVM GC Time":0,"Result Serialization Time":0,"Memory Bytes Spilled":0,"Disk Bytes Spilled":0,"Shuffle Read Metrics":{"Remote Blocks Fetched":0,"Local Blocks Fetched":100,"Fetch Wait Time":0,"Remote Bytes Read":0,"Local Bytes Read":17100,"Total Records Read":100}}}
+{"Event":"SparkListenerTaskEnd","Stage ID":1,"Stage Attempt ID":0,"Task Type":"ResultTask","Task End Reason":{"Reason":"Success"},"Task Info":{"Task ID":108,"Index":8,"Attempt":0,"Launch Time":1430917387546,"Executor ID":"driver","Host":"localhost","Locality":"PROCESS_LOCAL","Speculative":false,"Getting Result Time":0,"Finish Time":1430917387583,"Failed":false,"Accumulables":[]},"Task Metrics":{"Host Name":"localhost","Executor Deserialize Time":2,"Executor Run Time":31,"Result Size":1034,"JVM GC Time":0,"Result Serialization Time":0,"Memory Bytes Spilled":0,"Disk Bytes Spilled":0,"Shuffle Read Metrics":{"Remote Blocks Fetched":0,"Local Blocks Fetched":100,"Fetch Wait Time":0,"Remote Bytes Read":0,"Local Bytes Read":17100,"Total Records Read":100}}}
+{"Event":"SparkListenerTaskEnd","Stage ID":1,"Stage Attempt ID":0,"Task Type":"ResultTask","Task End Reason":{"Reason":"Success"},"Task Info":{"Task ID":109,"Index":9,"Attempt":0,"Launch Time":1430917387546,"Executor ID":"driver","Host":"localhost","Locality":"PROCESS_LOCAL","Speculative":false,"Getting Result Time":0,"Finish Time":1430917387585,"Failed":false,"Accumulables":[]},"Task Metrics":{"Host Name":"localhost","Executor Deserialize Time":2,"Executor Run Time":30,"Result Size":1034,"JVM GC Time":0,"Result Serialization Time":0,"Memory Bytes Spilled":0,"Disk Bytes Spilled":0,"Shuffle Read Metrics":{"Remote Blocks Fetched":0,"Local Blocks Fetched":100,"Fetch Wait Time":1,"Remote Bytes Read":0,"Local Bytes Read":17100,"Total Records Read":100}}}
+{"Event":"SparkListenerStageCompleted","Stage Info":{"Stage ID":1,"Stage Attempt ID":0,"Stage Name":"collect at <console>:26","Number of Tasks":10,"RDD Info":[{"RDD ID":4,"Name":"4","Storage Level":{"Use Disk":false,"Use Memory":false,"Use Tachyon":false,"Deserialized":false,"Replication":1},"Number of Partitions":10,"Number of Cached Partitions":0,"Memory Size":0,"Tachyon Size":0,"Disk Size":0}],"Details":"org.apache.spark.rdd.RDD.collect(RDD.scala:819)\n$line20.$read$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:26)\n$line20.$read$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:31)\n$line20.$read$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:33)\n$line20.$read$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:35)\n$line20.$read$$iwC$$iwC$$iwC$$iwC.<init>(<console>:37)\n$line20.$read$$iwC$$iwC$$iwC.<init>(<console>:39)\n$line20.$read$$iwC$$iwC.<init>(<console>:41)\n$line20.$read$$iwC.<init>(<console>:43)\n$line20.$read.<init>(<console>:45)\n$line20.$read$.<init>(<console>:49)\n$line20.$read$.<clinit>(<console>)\n$line20.$eval$.<init>(<console>:7)\n$line20.$eval$.<clinit>(<console>)\n$line20.$eval.$print(<console>)\nsun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\nsun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)\nsun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\njava.lang.reflect.Method.invoke(Method.java:483)\norg.apache.spark.repl.SparkIMain$ReadEvalPrint.call(SparkIMain.scala:1065)","Submission Time":1430917387462,"Completion Time":1430917387585,"Accumulables":[]}}
+{"Event":"SparkListenerJobEnd","Job ID":0,"Completion Time":1430917387589,"Job Result":{"Result":"JobSucceeded"}}
+{"Event":"SparkListenerApplicationEnd","Timestamp":1430917391398}
diff --git a/core/src/test/scala/org/apache/spark/JsonTestUtils.scala b/core/src/test/scala/org/apache/spark/JsonTestUtils.scala
new file mode 100644
index 0000000000000..ba367cd476146
--- /dev/null
+++ b/core/src/test/scala/org/apache/spark/JsonTestUtils.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
+
+import org.json4s._
+import org.json4s.jackson.JsonMethods
+
+trait JsonTestUtils {
+  def assertValidDataInJson(validateJson: JValue, expectedJson: JValue) {
+    val Diff(c, a, d) = validateJson.diff(expectedJson)
+    val validatePretty = JsonMethods.pretty(validateJson)
+    val expectedPretty = JsonMethods.pretty(expectedJson)
+    val errorMessage = s"Expected:\n$expectedPretty\nFound:\n$validatePretty"
+    import org.scalactic.TripleEquals._
+    assert(c === JNothing, s"$errorMessage\nChanged:\n${JsonMethods.pretty(c)}")
+    assert(a === JNothing, s"$errorMessage\nAdded:\n${JsonMethods.pretty(a)}")
+    assert(d === JNothing, s"$errorMessage\nDeleted:\n${JsonMethods.pretty(d)}")
+  }
+
+}
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 b58d62567afe1..e04a79284175c 100644
--- a/core/src/test/scala/org/apache/spark/deploy/JsonProtocolSuite.scala
+++ b/core/src/test/scala/org/apache/spark/deploy/JsonProtocolSuite.scala
@@ -28,9 +28,9 @@ import org.scalatest.FunSuite
 import org.apache.spark.deploy.DeployMessages.{MasterStateResponse, WorkerStateResponse}
 import org.apache.spark.deploy.master.{ApplicationInfo, DriverInfo, RecoveryState, WorkerInfo}
 import org.apache.spark.deploy.worker.{DriverRunner, ExecutorRunner}
-import org.apache.spark.{SecurityManager, SparkConf}
+import org.apache.spark.{JsonTestUtils, SecurityManager, SparkConf}
 
-class JsonProtocolSuite extends FunSuite {
+class JsonProtocolSuite extends FunSuite with JsonTestUtils {
 
   test("writeApplicationInfo") {
     val output = JsonProtocol.writeApplicationInfo(createAppInfo())
@@ -136,16 +136,6 @@ class JsonProtocolSuite extends FunSuite {
       case e: JsonParseException => fail("Invalid Json detected", e)
     }
   }
-
-  def assertValidDataInJson(validateJson: JValue, expectedJson: JValue) {
-    val Diff(c, a, d) = validateJson diff expectedJson
-    val validatePretty = JsonMethods.pretty(validateJson)
-    val expectedPretty = JsonMethods.pretty(expectedJson)
-    val errorMessage = s"Expected:\n$expectedPretty\nFound:\n$validatePretty"
-    assert(c === JNothing, s"$errorMessage\nChanged:\n${JsonMethods.pretty(c)}")
-    assert(a === JNothing, s"$errorMessage\nAdded:\n${JsonMethods.pretty(a)}")
-    assert(d === JNothing, s"$errorMessage\nDelected:\n${JsonMethods.pretty(d)}")
-  }
 }
 
 object JsonConstants {
diff --git a/core/src/test/scala/org/apache/spark/deploy/history/HistoryServerSuite.scala b/core/src/test/scala/org/apache/spark/deploy/history/HistoryServerSuite.scala
index 71ba9c18257b8..318ab5dbc4804 100644
--- a/core/src/test/scala/org/apache/spark/deploy/history/HistoryServerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/deploy/history/HistoryServerSuite.scala
@@ -14,22 +14,164 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-
 package org.apache.spark.deploy.history
 
-import javax.servlet.http.HttpServletRequest
-
-import scala.collection.mutable
+import java.io.{File, FileInputStream, FileWriter, IOException}
+import java.net.{HttpURLConnection, URL}
+import javax.servlet.http.{HttpServletRequest, HttpServletResponse}
 
-import org.apache.hadoop.fs.Path
-import org.mockito.Mockito.{when}
-import org.scalatest.FunSuite
-import org.scalatest.Matchers
+import org.apache.commons.io.{FileUtils, IOUtils}
+import org.mockito.Mockito.when
+import org.scalatest.{BeforeAndAfter, FunSuite, Matchers}
 import org.scalatest.mock.MockitoSugar
 
+import org.apache.spark.{JsonTestUtils, SecurityManager, SparkConf}
 import org.apache.spark.ui.SparkUI
 
-class HistoryServerSuite extends FunSuite with Matchers with MockitoSugar {
+/**
+ * A collection of tests against the historyserver, including comparing responses from the json
+ * metrics api to a set of known "golden files".  If new endpoints / parameters are added,
+ * cases should be added to this test suite.  The expected outcomes can be genered by running
+ * the HistoryServerSuite.main.  Note that this will blindly generate new expectation files matching
+ * the current behavior -- the developer must verify that behavior is correct.
+ *
+ * Similarly, if the behavior is changed, HistoryServerSuite.main can be run to update the
+ * expectations.  However, in general this should be done with extreme caution, as the metrics
+ * are considered part of Spark's public api.
+ */
+class HistoryServerSuite extends FunSuite with BeforeAndAfter with Matchers with MockitoSugar
+  with JsonTestUtils {
+
+  private val logDir = new File("src/test/resources/spark-events")
+  private val expRoot = new File("src/test/resources/HistoryServerExpectations/")
+
+  private var provider: FsHistoryProvider = null
+  private var server: HistoryServer = null
+  private var port: Int = -1
+
+  def init(): Unit = {
+    val conf = new SparkConf()
+      .set("spark.history.fs.logDirectory", logDir.getAbsolutePath)
+      .set("spark.history.fs.updateInterval", "0")
+      .set("spark.testing", "true")
+    provider = new FsHistoryProvider(conf)
+    provider.checkForLogs()
+    val securityManager = new SecurityManager(conf)
+
+    server = new HistoryServer(conf, provider, securityManager, 18080)
+    server.initialize()
+    server.bind()
+    port = server.boundPort
+  }
+
+  def stop(): Unit = {
+    server.stop()
+  }
+
+  before {
+    init()
+  }
+
+  after{
+    stop()
+  }
+
+  val cases = Seq(
+    "application list json" -> "applications",
+    "completed app list json" -> "applications?status=completed",
+    "running app list json" -> "applications?status=running",
+    "minDate app list json" -> "applications?minDate=2015-02-10",
+    "maxDate app list json" -> "applications?maxDate=2015-02-10",
+    "maxDate2 app list json" -> "applications?maxDate=2015-02-03T10:42:40.000CST",
+    "one app json" -> "applications/local-1422981780767",
+    "one app multi-attempt json" -> "applications/local-1426533911241",
+    "job list json" -> "applications/local-1422981780767/jobs",
+    "job list from multi-attempt app json(1)" -> "applications/local-1426533911241/1/jobs",
+    "job list from multi-attempt app json(2)" -> "applications/local-1426533911241/2/jobs",
+    "one job json" -> "applications/local-1422981780767/jobs/0",
+    "succeeded job list json" -> "applications/local-1422981780767/jobs?status=succeeded",
+    "succeeded&failed job list json" ->
+      "applications/local-1422981780767/jobs?status=succeeded&status=failed",
+    "executor list json" -> "applications/local-1422981780767/executors",
+    "stage list json" -> "applications/local-1422981780767/stages",
+    "complete stage list json" -> "applications/local-1422981780767/stages?status=complete",
+    "failed stage list json" -> "applications/local-1422981780767/stages?status=failed",
+    "one stage json" -> "applications/local-1422981780767/stages/1",
+    "one stage attempt json" -> "applications/local-1422981780767/stages/1/0",
+
+    "stage task summary w shuffle write"
+      -> "applications/local-1430917381534/stages/0/0/taskSummary",
+    "stage task summary w shuffle read"
+      -> "applications/local-1430917381534/stages/1/0/taskSummary",
+    "stage task summary w/ custom quantiles" ->
+      "applications/local-1430917381534/stages/0/0/taskSummary?quantiles=0.01,0.5,0.99",
+
+    "stage task list" -> "applications/local-1430917381534/stages/0/0/taskList",
+    "stage task list w/ offset & length" ->
+      "applications/local-1430917381534/stages/0/0/taskList?offset=10&length=50",
+    "stage task list w/ sortBy" ->
+      "applications/local-1430917381534/stages/0/0/taskList?sortBy=DECREASING_RUNTIME",
+    "stage task list w/ sortBy short names: -runtime" ->
+      "applications/local-1430917381534/stages/0/0/taskList?sortBy=-runtime",
+    "stage task list w/ sortBy short names: runtime" ->
+      "applications/local-1430917381534/stages/0/0/taskList?sortBy=runtime",
+
+    "stage list with accumulable json" -> "applications/local-1426533911241/1/stages",
+    "stage with accumulable json" -> "applications/local-1426533911241/1/stages/0/0",
+    "stage task list from multi-attempt app json(1)" ->
+      "applications/local-1426533911241/1/stages/0/0/taskList",
+    "stage task list from multi-attempt app json(2)" ->
+      "applications/local-1426533911241/2/stages/0/0/taskList",
+
+    "rdd list storage json" -> "applications/local-1422981780767/storage/rdd",
+    "one rdd storage json" -> "applications/local-1422981780767/storage/rdd/0"
+  )
+
+  // run a bunch of characterization tests -- just verify the behavior is the same as what is saved
+  // in the test resource folder
+  cases.foreach { case (name, path) =>
+    test(name) {
+      val (code, jsonOpt, errOpt) = getContentAndCode(path)
+      code should be (HttpServletResponse.SC_OK)
+      jsonOpt should be ('defined)
+      errOpt should be (None)
+      val json = jsonOpt.get
+      val exp = IOUtils.toString(new FileInputStream(
+        new File(expRoot, HistoryServerSuite.sanitizePath(name) + "_expectation.json")))
+      // compare the ASTs so formatting differences don't cause failures
+      import org.json4s._
+      import org.json4s.jackson.JsonMethods._
+      val jsonAst = parse(json)
+      val expAst = parse(exp)
+      assertValidDataInJson(jsonAst, expAst)
+    }
+  }
+
+  test("response codes on bad paths") {
+    val badAppId = getContentAndCode("applications/foobar")
+    badAppId._1 should be (HttpServletResponse.SC_NOT_FOUND)
+    badAppId._3 should be (Some("unknown app: foobar"))
+
+    val badStageId = getContentAndCode("applications/local-1422981780767/stages/12345")
+    badStageId._1 should be (HttpServletResponse.SC_NOT_FOUND)
+    badStageId._3 should be (Some("unknown stage: 12345"))
+
+    val badStageAttemptId = getContentAndCode("applications/local-1422981780767/stages/1/1")
+    badStageAttemptId._1 should be (HttpServletResponse.SC_NOT_FOUND)
+    badStageAttemptId._3 should be (Some("unknown attempt for stage 1.  Found attempts: [0]"))
+
+    val badStageId2 = getContentAndCode("applications/local-1422981780767/stages/flimflam")
+    badStageId2._1 should be (HttpServletResponse.SC_NOT_FOUND)
+    // will take some mucking w/ jersey to get a better error msg in this case
+
+    val badQuantiles = getContentAndCode(
+      "applications/local-1430917381534/stages/0/0/taskSummary?quantiles=foo,0.1")
+    badQuantiles._1 should be (HttpServletResponse.SC_BAD_REQUEST)
+    badQuantiles._3 should be (Some("Bad value for parameter \"quantiles\".  Expected a double, " +
+      "got \"foo\""))
+
+    getContentAndCode("foobar")._1 should be (HttpServletResponse.SC_NOT_FOUND)
+  }
 
   test("generate history page with relative links") {
     val historyServer = mock[HistoryServer]
@@ -54,4 +196,75 @@ class HistoryServerSuite extends FunSuite with Matchers with MockitoSugar {
     } yield (attrs.toString)
     justHrefs should contain(link)
   }
+
+  def getContentAndCode(path: String, port: Int = port): (Int, Option[String], Option[String]) = {
+    HistoryServerSuite.getContentAndCode(new URL(s"http://localhost:$port/json/v1/$path"))
+  }
+
+  def getUrl(path: String): String = {
+    HistoryServerSuite.getUrl(new URL(s"http://localhost:$port/json/v1/$path"))
+  }
+
+  def generateExpectation(name: String, path: String): Unit = {
+    val json = getUrl(path)
+    val file = new File(expRoot, HistoryServerSuite.sanitizePath(name) + "_expectation.json")
+    val out = new FileWriter(file)
+    out.write(json)
+    out.close()
+  }
+}
+
+object HistoryServerSuite {
+  def main(args: Array[String]): Unit = {
+    // generate the "expected" results for the characterization tests.  Just blindly assume the
+    // current behavior is correct, and write out the returned json to the test/resource files
+
+    val suite = new HistoryServerSuite
+    FileUtils.deleteDirectory(suite.expRoot)
+    suite.expRoot.mkdirs()
+    try {
+      suite.init()
+      suite.cases.foreach { case (name, path) =>
+        suite.generateExpectation(name, path)
+      }
+    } finally {
+      suite.stop()
+    }
+  }
+
+  def getContentAndCode(url: URL): (Int, Option[String], Option[String]) = {
+    val connection = url.openConnection().asInstanceOf[HttpURLConnection]
+    connection.setRequestMethod("GET")
+    connection.connect()
+    val code = connection.getResponseCode()
+    val inString = try {
+      val in = Option(connection.getInputStream())
+      in.map(IOUtils.toString)
+    } catch {
+      case io: IOException => None
+    }
+    val errString = try {
+      val err = Option(connection.getErrorStream())
+      err.map(IOUtils.toString)
+    } catch {
+      case io: IOException => None
+    }
+    (code, inString, errString)
+  }
+
+
+  def sanitizePath(path: String): String = {
+    // this doesn't need to be perfect, just good enough to avoid collisions
+    path.replaceAll("\\W", "_")
+  }
+
+  def getUrl(path: URL): String = {
+    val (code, resultOpt, error) = getContentAndCode(path)
+    if (code == 200) {
+      resultOpt.get
+    } else {
+      throw new RuntimeException(
+        "got code: " + code + " when getting " + path + " w/ error: " + error)
+    }
+  }
 }
diff --git a/core/src/test/scala/org/apache/spark/status/api/v1/SimpleDateParamTest.scala b/core/src/test/scala/org/apache/spark/status/api/v1/SimpleDateParamTest.scala
new file mode 100644
index 0000000000000..5274df904d395
--- /dev/null
+++ b/core/src/test/scala/org/apache/spark/status/api/v1/SimpleDateParamTest.scala
@@ -0,0 +1,29 @@
+/*
+ * 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.status.api.v1
+
+import org.scalatest.{Matchers, FunSuite}
+
+class SimpleDateParamTest extends FunSuite with Matchers {
+
+  test("date parsing") {
+    new SimpleDateParam("2015-02-20T23:21:17.190GMT").timestamp should be (1424474477190L)
+    new SimpleDateParam("2015-02-20T17:21:17.190CST").timestamp should be (1424474477190L)
+    new SimpleDateParam("2015-02-20").timestamp should be (1424390400000L)  // GMT
+  }
+
+}
diff --git a/core/src/test/scala/org/apache/spark/ui/UISeleniumSuite.scala b/core/src/test/scala/org/apache/spark/ui/UISeleniumSuite.scala
index d53d7f3ba5ae7..117b2c3960820 100644
--- a/core/src/test/scala/org/apache/spark/ui/UISeleniumSuite.scala
+++ b/core/src/test/scala/org/apache/spark/ui/UISeleniumSuite.scala
@@ -18,11 +18,13 @@
 package org.apache.spark.ui
 
 import java.net.{HttpURLConnection, URL}
-import javax.servlet.http.HttpServletRequest
+import javax.servlet.http.{HttpServletResponse, HttpServletRequest}
 
 import scala.collection.JavaConversions._
 import scala.xml.Node
 
+import org.json4s._
+import org.json4s.jackson.JsonMethods
 import org.openqa.selenium.htmlunit.HtmlUnitDriver
 import org.openqa.selenium.{By, WebDriver}
 import org.scalatest._
@@ -33,8 +35,9 @@ import org.scalatest.time.SpanSugar._
 import org.apache.spark.LocalSparkContext._
 import org.apache.spark._
 import org.apache.spark.api.java.StorageLevels
+import org.apache.spark.deploy.history.HistoryServerSuite
 import org.apache.spark.shuffle.FetchFailedException
-
+import org.apache.spark.status.api.v1.{JacksonMessageWriter, StageStatus}
 
 /**
  * Selenium tests for the Spark Web UI.
@@ -42,6 +45,8 @@ import org.apache.spark.shuffle.FetchFailedException
 class UISeleniumSuite extends FunSuite with WebBrowser with Matchers with BeforeAndAfterAll {
 
   implicit var webDriver: WebDriver = _
+  implicit val formats = DefaultFormats
+
 
   override def beforeAll(): Unit = {
     webDriver = new HtmlUnitDriver
@@ -76,28 +81,42 @@ class UISeleniumSuite extends FunSuite with WebBrowser with Matchers with Before
       val rdd = sc.parallelize(Seq(1, 2, 3))
       rdd.persist(StorageLevels.DISK_ONLY).count()
       eventually(timeout(5 seconds), interval(50 milliseconds)) {
-        go to (ui.appUIAddress.stripSuffix("/") + "/storage")
+        goToUi(ui, "/storage")
         val tableRowText = findAll(cssSelector("#storage-by-rdd-table td")).map(_.text).toSeq
         tableRowText should contain (StorageLevels.DISK_ONLY.description)
       }
       eventually(timeout(5 seconds), interval(50 milliseconds)) {
-        go to (ui.appUIAddress.stripSuffix("/") + "/storage/rdd/?id=0")
+        goToUi(ui, "/storage/rdd/?id=0")
         val tableRowText = findAll(cssSelector("#rdd-storage-by-block-table td")).map(_.text).toSeq
         tableRowText should contain (StorageLevels.DISK_ONLY.description)
       }
 
+      val storageJson = getJson(ui, "storage/rdd")
+      storageJson.children.length should be (1)
+      (storageJson \ "storageLevel").extract[String] should be (StorageLevels.DISK_ONLY.description)
+      val rddJson = getJson(ui, "storage/rdd/0")
+      (rddJson  \ "storageLevel").extract[String] should be (StorageLevels.DISK_ONLY.description)
+
       rdd.unpersist()
       rdd.persist(StorageLevels.MEMORY_ONLY).count()
       eventually(timeout(5 seconds), interval(50 milliseconds)) {
-        go to (ui.appUIAddress.stripSuffix("/") + "/storage")
+        goToUi(ui, "/storage")
         val tableRowText = findAll(cssSelector("#storage-by-rdd-table td")).map(_.text).toSeq
         tableRowText should contain (StorageLevels.MEMORY_ONLY.description)
       }
       eventually(timeout(5 seconds), interval(50 milliseconds)) {
-        go to (ui.appUIAddress.stripSuffix("/") + "/storage/rdd/?id=0")
+        goToUi(ui, "/storage/rdd/?id=0")
         val tableRowText = findAll(cssSelector("#rdd-storage-by-block-table td")).map(_.text).toSeq
         tableRowText should contain (StorageLevels.MEMORY_ONLY.description)
       }
+
+      val updatedStorageJson = getJson(ui, "storage/rdd")
+      updatedStorageJson.children.length should be (1)
+      (updatedStorageJson \ "storageLevel").extract[String] should be (
+        StorageLevels.MEMORY_ONLY.description)
+      val updatedRddJson = getJson(ui, "storage/rdd/0")
+      (updatedRddJson  \ "storageLevel").extract[String] should be (
+        StorageLevels.MEMORY_ONLY.description)
     }
   }
 
@@ -108,10 +127,13 @@ class UISeleniumSuite extends FunSuite with WebBrowser with Matchers with Before
         sc.parallelize(1 to 10).map { x => throw new Exception()}.collect()
       }
       eventually(timeout(5 seconds), interval(50 milliseconds)) {
-        go to (sc.ui.get.appUIAddress.stripSuffix("/") + "/stages")
+        goToUi(sc, "/stages")
         find(id("active")) should be(None)  // Since we hide empty tables
         find(id("failed")).get.text should be("Failed Stages (1)")
       }
+      val stageJson = getJson(sc.ui.get, "stages")
+      stageJson.children.length should be (1)
+      (stageJson \ "status").extract[String] should be (StageStatus.FAILED.name())
 
       // Regression test for SPARK-2105
       class NotSerializable
@@ -120,12 +142,15 @@ class UISeleniumSuite extends FunSuite with WebBrowser with Matchers with Before
         sc.parallelize(1 to 10).map { x => unserializableObject}.collect()
       }
       eventually(timeout(5 seconds), interval(50 milliseconds)) {
-        go to (sc.ui.get.appUIAddress.stripSuffix("/") + "/stages")
+        goToUi(sc, "/stages")
         find(id("active")) should be(None)  // Since we hide empty tables
         // The failure occurs before the stage becomes active, hence we should still show only one
         // failed stage, not two:
         find(id("failed")).get.text should be("Failed Stages (1)")
       }
+
+      val updatedStageJson = getJson(sc.ui.get, "stages")
+      updatedStageJson should be (stageJson)
     }
   }
 
@@ -138,7 +163,7 @@ class UISeleniumSuite extends FunSuite with WebBrowser with Matchers with Before
     withSpark(newSparkContext(killEnabled = true)) { sc =>
       runSlowJob(sc)
       eventually(timeout(5 seconds), interval(50 milliseconds)) {
-        go to (sc.ui.get.appUIAddress.stripSuffix("/") + "/stages")
+        goToUi(sc, "/stages")
         assert(hasKillLink)
       }
     }
@@ -146,7 +171,7 @@ class UISeleniumSuite extends FunSuite with WebBrowser with Matchers with Before
     withSpark(newSparkContext(killEnabled = false)) { sc =>
       runSlowJob(sc)
       eventually(timeout(5 seconds), interval(50 milliseconds)) {
-        go to (sc.ui.get.appUIAddress.stripSuffix("/") + "/stages")
+        goToUi(sc, "/stages")
         assert(!hasKillLink)
       }
     }
@@ -157,7 +182,7 @@ class UISeleniumSuite extends FunSuite with WebBrowser with Matchers with Before
       // If no job has been run in a job group, then "(Job Group)" should not appear in the header
       sc.parallelize(Seq(1, 2, 3)).count()
       eventually(timeout(5 seconds), interval(50 milliseconds)) {
-        go to (sc.ui.get.appUIAddress.stripSuffix("/") + "/jobs")
+        goToUi(sc, "/jobs")
         val tableHeaders = findAll(cssSelector("th")).map(_.text).toSeq
         tableHeaders should not contain "Job Id (Job Group)"
       }
@@ -165,10 +190,22 @@ class UISeleniumSuite extends FunSuite with WebBrowser with Matchers with Before
       sc.setJobGroup("my-job-group", "my-job-group-description")
       sc.parallelize(Seq(1, 2, 3)).count()
       eventually(timeout(5 seconds), interval(50 milliseconds)) {
-        go to (sc.ui.get.appUIAddress.stripSuffix("/") + "/jobs")
+        goToUi(sc, "/jobs")
         val tableHeaders = findAll(cssSelector("th")).map(_.text).toSeq
         tableHeaders should contain ("Job Id (Job Group)")
       }
+
+      val jobJson = getJson(sc.ui.get, "jobs")
+      for {
+        job @ JObject(_) <- jobJson
+        JInt(jobId) <- job \ "jobId"
+        jobGroup = job \ "jobGroup"
+      } {
+        jobId.toInt match {
+          case 0 => jobGroup should be (JNothing)
+          case 1 => jobGroup should be (JString("my-job-group"))
+        }
+      }
     }
   }
 
@@ -195,7 +232,7 @@ class UISeleniumSuite extends FunSuite with WebBrowser with Matchers with Before
       }
       mappedData.count()
       eventually(timeout(5 seconds), interval(50 milliseconds)) {
-        go to (sc.ui.get.appUIAddress.stripSuffix("/") + "/jobs")
+        goToUi(sc, "/jobs")
         find(cssSelector(".stage-progress-cell")).get.text should be ("2/2 (1 failed)")
         // Ideally, the following test would pass, but currently we overcount completed tasks
         // if task recomputations occur:
@@ -204,6 +241,32 @@ class UISeleniumSuite extends FunSuite with WebBrowser with Matchers with Before
         // of completed tasks may be higher:
         find(cssSelector(".progress-cell .progress")).get.text should be ("3/2 (1 failed)")
       }
+      val jobJson = getJson(sc.ui.get, "jobs")
+      (jobJson \ "numTasks").extract[Int]should be (2)
+      (jobJson \ "numCompletedTasks").extract[Int] should be (3)
+      (jobJson \ "numFailedTasks").extract[Int] should be (1)
+      (jobJson \ "numCompletedStages").extract[Int] should be (2)
+      (jobJson \ "numFailedStages").extract[Int] should be (1)
+      val stageJson = getJson(sc.ui.get, "stages")
+
+      for {
+        stage @ JObject(_) <- stageJson
+        JString(status) <- stage \ "status"
+        JInt(stageId) <- stage \ "stageId"
+        JInt(attemptId) <- stage \ "attemptId"
+      } {
+        val exp = if (attemptId == 0 && stageId == 1) StageStatus.FAILED else StageStatus.COMPLETE
+        status should be (exp.name())
+      }
+
+      for {
+        stageId <- 0 to 1
+        attemptId <- 0 to 1
+      } {
+        val exp = if (attemptId == 0 && stageId == 1) StageStatus.FAILED else StageStatus.COMPLETE
+        val stageJson = getJson(sc.ui.get, s"stages/$stageId/$attemptId")
+        (stageJson \ "status").extract[String] should be (exp.name())
+      }
     }
   }
 
@@ -218,7 +281,7 @@ class UISeleniumSuite extends FunSuite with WebBrowser with Matchers with Before
       // Start the job:
       rdd.countAsync()
       eventually(timeout(10 seconds), interval(50 milliseconds)) {
-        go to (sc.ui.get.appUIAddress.stripSuffix("/") + "/jobs/job/?id=0")
+        goToUi(sc, "/jobs/job/?id=0")
         find(id("active")).get.text should be ("Active Stages (1)")
         find(id("pending")).get.text should be ("Pending Stages (2)")
         // Essentially, we want to check that none of the stage rows show
@@ -244,7 +307,7 @@ class UISeleniumSuite extends FunSuite with WebBrowser with Matchers with Before
       rdd.count()
       rdd.count()
       eventually(timeout(10 seconds), interval(50 milliseconds)) {
-        go to (sc.ui.get.appUIAddress.stripSuffix("/") + "/jobs")
+        goToUi(sc, "/jobs")
         // The completed jobs table should have two rows. The first row will be the most recent job:
         val firstRow = find(cssSelector("tbody tr")).get.underlying
         val firstRowColumns = firstRow.findElements(By.tagName("td"))
@@ -271,7 +334,7 @@ class UISeleniumSuite extends FunSuite with WebBrowser with Matchers with Before
       rdd.count()
       rdd.count()
       eventually(timeout(10 seconds), interval(50 milliseconds)) {
-        go to (sc.ui.get.appUIAddress.stripSuffix("/") + "/jobs/job/?id=1")
+        goToUi(sc, "/jobs/job/?id=1")
         find(id("pending")) should be (None)
         find(id("active")) should be (None)
         find(id("failed")) should be (None)
@@ -299,7 +362,7 @@ class UISeleniumSuite extends FunSuite with WebBrowser with Matchers with Before
       rdd.count()
       rdd.count()
       eventually(timeout(10 seconds), interval(50 milliseconds)) {
-        go to (sc.ui.get.appUIAddress.stripSuffix("/") + "/jobs")
+        goToUi(sc, "/jobs")
         findAll(cssSelector("tbody tr a")).foreach { link =>
           link.text.toLowerCase should include ("count")
           link.text.toLowerCase should not include "unknown"
@@ -321,7 +384,7 @@ class UISeleniumSuite extends FunSuite with WebBrowser with Matchers with Before
       }
       sparkUI.attachTab(newTab)
       eventually(timeout(10 seconds), interval(50 milliseconds)) {
-        go to (sc.ui.get.appUIAddress.stripSuffix("/"))
+        goToUi(sc, "")
         find(cssSelector("""ul li a[href*="jobs"]""")) should not be(None)
         find(cssSelector("""ul li a[href*="stages"]""")) should not be(None)
         find(cssSelector("""ul li a[href*="storage"]""")) should not be(None)
@@ -330,12 +393,12 @@ class UISeleniumSuite extends FunSuite with WebBrowser with Matchers with Before
       }
       eventually(timeout(10 seconds), interval(50 milliseconds)) {
         // check whether new page exists
-        go to (sc.ui.get.appUIAddress.stripSuffix("/") + "/foo")
+        goToUi(sc, "/foo")
         find(cssSelector("b")).get.text should include ("html magic")
       }
       sparkUI.detachTab(newTab)
       eventually(timeout(10 seconds), interval(50 milliseconds)) {
-        go to (sc.ui.get.appUIAddress.stripSuffix("/"))
+        goToUi(sc, "")
         find(cssSelector("""ul li a[href*="jobs"]""")) should not be(None)
         find(cssSelector("""ul li a[href*="stages"]""")) should not be(None)
         find(cssSelector("""ul li a[href*="storage"]""")) should not be(None)
@@ -344,7 +407,7 @@ class UISeleniumSuite extends FunSuite with WebBrowser with Matchers with Before
       }
       eventually(timeout(10 seconds), interval(50 milliseconds)) {
         // check new page not exist
-        go to (sc.ui.get.appUIAddress.stripSuffix("/") + "/foo")
+        goToUi(sc, "/foo")
         find(cssSelector("b")) should be(None)
       }
     }
@@ -371,4 +434,163 @@ class UISeleniumSuite extends FunSuite with WebBrowser with Matchers with Before
       }
     }
   }
+
+  test("stage & job retention") {
+    val conf = new SparkConf()
+      .setMaster("local")
+      .setAppName("test")
+      .set("spark.ui.enabled", "true")
+      .set("spark.ui.port", "0")
+      .set("spark.ui.retainedStages", "3")
+      .set("spark.ui.retainedJobs", "2")
+    val sc = new SparkContext(conf)
+    assert(sc.ui.isDefined)
+
+    withSpark(sc) { sc =>
+      // run a few jobs & stages ...
+      (0 until 5).foreach { idx =>
+        // NOTE: if we reverse the order, things don't really behave nicely
+        // we lose the stage for a job we keep, and then the job doesn't know
+        // about its last stage
+        sc.parallelize(idx to (idx + 3)).map(identity).groupBy(identity).map(identity)
+          .groupBy(identity).count()
+        sc.parallelize(idx to (idx + 3)).collect()
+      }
+
+      val expJobInfo = Seq(
+        ("9", "collect"),
+        ("8", "count")
+      )
+
+      eventually(timeout(1 second), interval(50 milliseconds)) {
+        goToUi(sc, "/jobs")
+        // The completed jobs table should have two rows. The first row will be the most recent job:
+        find("completed-summary").get.text should be ("Completed Jobs: 10, only showing 2")
+        find("completed").get.text should be ("Completed Jobs (10, only showing 2)")
+        val rows = findAll(cssSelector("tbody tr")).toIndexedSeq.map{_.underlying}
+        rows.size should be (expJobInfo.size)
+        for {
+          (row, idx) <- rows.zipWithIndex
+          columns = row.findElements(By.tagName("td"))
+          id = columns(0).getText()
+          description = columns(1).getText()
+        } {
+          id should be (expJobInfo(idx)._1)
+          description should include (expJobInfo(idx)._2)
+        }
+      }
+
+      val jobsJson = getJson(sc.ui.get, "jobs")
+      jobsJson.children.size should be (expJobInfo.size)
+      for {
+        (job @ JObject(_),idx) <- jobsJson.children.zipWithIndex
+        id = (job \ "jobId").extract[String]
+        name = (job \ "name").extract[String]
+      } {
+        withClue(s"idx = $idx; id = $id; name = ${name.substring(0,20)}") {
+          id should be (expJobInfo(idx)._1)
+          name should include (expJobInfo(idx)._2)
+        }
+      }
+
+      // what about when we query for a job that did exist, but has been cleared?
+      goToUi(sc, "/jobs/job/?id=7")
+      find("no-info").get.text should be ("No information to display for job 7")
+
+      val badJob = HistoryServerSuite.getContentAndCode(jsonUrl(sc.ui.get, "jobs/7"))
+      badJob._1 should be (HttpServletResponse.SC_NOT_FOUND)
+      badJob._2 should be (None)
+      badJob._3 should be (Some("unknown job: 7"))
+
+      val expStageInfo = Seq(
+        ("19", "collect"),
+        ("18", "count"),
+        ("17", "groupBy")
+      )
+
+      eventually(timeout(1 second), interval(50 milliseconds)) {
+        goToUi(sc, "/stages")
+        find("completed-summary").get.text should be ("Completed Stages: 20, only showing 3")
+        find("completed").get.text should be ("Completed Stages (20, only showing 3)")
+        val rows = findAll(cssSelector("tbody tr")).toIndexedSeq.map{_.underlying}
+        rows.size should be (3)
+        for {
+          (row, idx) <- rows.zipWithIndex
+          columns = row.findElements(By.tagName("td"))
+          id = columns(0).getText()
+          description = columns(1).getText()
+        } {
+          id should be (expStageInfo(idx)._1)
+          description should include (expStageInfo(idx)._2)
+        }
+      }
+
+      val stagesJson = getJson(sc.ui.get, "stages")
+      stagesJson.children.size should be (3)
+      for {
+        (stage @ JObject(_), idx) <- stagesJson.children.zipWithIndex
+        id = (stage \ "stageId").extract[String]
+        name = (stage \ "name").extract[String]
+      } {
+        id should be (expStageInfo(idx)._1)
+        name should include (expStageInfo(idx)._2)
+      }
+
+      // nonexistent stage
+
+      goToUi(sc, "/stages/stage/?id=12&attempt=0")
+      find("no-info").get.text should be ("No information to display for Stage 12 (Attempt 0)")
+      val badStage = HistoryServerSuite.getContentAndCode(jsonUrl(sc.ui.get,"stages/12/0"))
+      badStage._1 should be (HttpServletResponse.SC_NOT_FOUND)
+      badStage._2 should be (None)
+      badStage._3 should be (Some("unknown stage: 12"))
+
+      val badAttempt = HistoryServerSuite.getContentAndCode(jsonUrl(sc.ui.get,"stages/19/15"))
+      badAttempt._1 should be (HttpServletResponse.SC_NOT_FOUND)
+      badAttempt._2 should be (None)
+      badAttempt._3 should be (Some("unknown attempt for stage 19.  Found attempts: [0]"))
+
+      val badStageAttemptList = HistoryServerSuite.getContentAndCode(
+        jsonUrl(sc.ui.get, "stages/12"))
+      badStageAttemptList._1 should be (HttpServletResponse.SC_NOT_FOUND)
+      badStageAttemptList._2 should be (None)
+      badStageAttemptList._3 should be (Some("unknown stage: 12"))
+    }
+  }
+
+  test("live UI json application list") {
+    withSpark(newSparkContext()) { sc =>
+      val appListRawJson = HistoryServerSuite.getUrl(new URL(
+        sc.ui.get.appUIAddress + "/json/v1/applications"))
+      val appListJsonAst = JsonMethods.parse(appListRawJson)
+      appListJsonAst.children.length should be (1)
+      val attempts = (appListJsonAst \ "attempts").children
+      attempts.size should be (1)
+      (attempts(0) \ "completed").extract[Boolean] should be (false)
+      parseDate(attempts(0) \ "startTime") should be (sc.startTime)
+      parseDate(attempts(0) \ "endTime") should be (-1)
+      val oneAppJsonAst = getJson(sc.ui.get, "")
+      oneAppJsonAst should be (appListJsonAst.children(0))
+    }
+  }
+
+  def goToUi(sc: SparkContext, path: String): Unit = {
+    goToUi(sc.ui.get, path)
+  }
+
+  def goToUi(ui: SparkUI, path: String): Unit = {
+    go to (ui.appUIAddress.stripSuffix("/") + path)
+  }
+
+  def parseDate(json: JValue): Long = {
+    JacksonMessageWriter.makeISODateFormat.parse(json.extract[String]).getTime
+  }
+
+  def getJson(ui: SparkUI, path: String): JValue = {
+    JsonMethods.parse(HistoryServerSuite.getUrl(jsonUrl(ui, path)))
+  }
+
+  def jsonUrl(ui: SparkUI, path: String): URL = {
+    new URL(ui.appUIAddress + "/json/v1/applications/test/" + path)
+  }
 }
diff --git a/docs/monitoring.md b/docs/monitoring.md
index 8a85928d6d44d..1e0fc150862fb 100644
--- a/docs/monitoring.md
+++ b/docs/monitoring.md
@@ -174,6 +174,80 @@ making it easy to identify slow tasks, data skew, etc.
 
 Note that the history server only displays completed Spark jobs. One way to signal the completion of a Spark job is to stop the Spark Context explicitly (`sc.stop()`), or in Python using the `with SparkContext() as sc:` to handle the Spark Context setup and tear down, and still show the job history on the UI.
 
+## REST API
+
+In addition to viewing the metrics in the UI, they are also available as JSON.  This gives developers
+an easy way to create new visualizations and monitoring tools for Spark.  The JSON is available for
+both running applications, and in the history server.  The endpoints are mounted at `/json/v1`.  Eg.,
+for the history server, they would typically be accessible at `http://<server-url>:18080/json/v1`, and
+for a running application, at `http://localhost:4040/json/v1`.
+
+<table class="table">
+  <tr><th>Endpoint</th><th>Meaning</th></tr>
+  <tr>
+    <td><code>/applications</code></td>
+    <td>A list of all applications</td>
+  </tr>
+  <tr>
+    <td><code>/applications/[app-id]/jobs</code></td>
+    <td>A list of all jobs for a given application</td>
+  </tr>
+  <tr>
+    <td><code>/applications/[app-id]/jobs/[job-id]</code></td>
+    <td>Details for the given job</td>
+  </tr>
+  <tr>
+    <td><code>/applications/[app-id]/stages</code></td>
+    <td>A list of all stages for a given application</td>
+  </tr>
+  <tr>
+    <td><code>/applications/[app-id]/stages/[stage-id]</code></td>
+    <td>A list of all attempts for the given stage</td>
+  </tr>
+  <tr>
+    <td><code>/applications/[app-id]/stages/[stage-id]/[stage-attempt-id]</code></td>
+    <td>Details for the given stage attempt</td>
+  </tr>
+  <tr>
+    <td><code>/applications/[app-id]/stages/[stage-id]/[stage-attempt-id]/taskSummary</code></td>
+    <td>Summary metrics of all tasks in the given stage attempt</td>
+  </tr>
+  <tr>
+    <td><code>/applications/[app-id]/stages/[stage-id]/[stage-attempt-id]/taskList</code></td>
+    <td>A list of all tasks for the given stage attempt</td>
+  </tr>
+  <tr>
+    <td><code>/applications/[app-id]/executors</code></td>
+    <td>A list of all executors for the given application</td>
+  </tr>
+  <tr>
+    <td><code>/applications/[app-id]/storage/rdd</code></td>
+    <td>A list of stored RDDs for the given application</td>
+  </tr>
+  <tr>
+    <td><code>/applications/[app-id]/storage/rdd/[rdd-id]</code></td>
+    <td>Details for the storage status of a given RDD</td>
+  </tr>
+</table>
+
+When running on Yarn, each application has multiple attempts, so `[app-id]` is actually
+`[app-id]/[attempt-id]` in all cases.
+
+These endpoints have been strongly versioned to make it easier to develop applications on top.
+ In particular, Spark guarantees:
+
+* Endpoints will never be removed from one version
+* Individual fields will never be removed for any given endpoint
+* New endpoints may be added
+* New fields may be added to existing endpoints
+* New versions of the api may be added in the future at a separate endpoint (eg., `json/v2`).  New versions are *not* required to be backwards compatible.
+* Api versions may be dropped, but only after at least one minor release of co-existing with a new api version
+
+Note that even when examining the UI of a running applications, the `applications/[app-id]` portion is
+still required, though there is only one application available.  Eg. to see the list of jobs for the
+running app, you would go to `http://localhost:4040/json/v1/applications/[app-id]/jobs`.  This is to
+keep the paths consistent in both modes.
+
 # Metrics
 
 Spark has a configurable metrics system based on the 
diff --git a/pom.xml b/pom.xml
index 12ad7c5be4e40..94cf28332cf47 100644
--- a/pom.xml
+++ b/pom.xml
@@ -623,6 +623,18 @@
           </exclusion>
         </exclusions>
       </dependency>
+      <dependency>
+        <groupId>com.sun.jersey</groupId>
+        <artifactId>jersey-server</artifactId>
+        <version>1.9</version>
+        <scope>${hadoop.deps.scope}</scope>
+      </dependency>
+      <dependency>
+        <groupId>com.sun.jersey</groupId>
+        <artifactId>jersey-core</artifactId>
+        <version>1.9</version>
+        <scope>${hadoop.deps.scope}</scope>
+      </dependency>
       <dependency>
         <groupId>org.scala-lang</groupId>
         <artifactId>scala-compiler</artifactId>

From f5ff4a84c4c75143086aae7d38730156bee35933 Mon Sep 17 00:00:00 2001
From: Burak Yavuz <brkyvz@gmail.com>
Date: Fri, 8 May 2015 11:14:39 -0700
Subject: [PATCH 60/75] [SPARK-7383] [ML] Feature Parity in PySpark for
 ml.features

Implemented python wrappers for Scala functions that don't exist in `ml.features`

Author: Burak Yavuz <brkyvz@gmail.com>

Closes #5991 from brkyvz/ml-feat-PR and squashes the following commits:

adcca55 [Burak Yavuz] add regex tokenizer to __all__
b91cb44 [Burak Yavuz] addressed comments
bd39fd2 [Burak Yavuz] remove addition
b82bd7c [Burak Yavuz] Parity in PySpark for ml.features
---
 .../ml/feature/PolynomialExpansion.scala      |   2 +-
 .../apache/spark/ml/feature/Tokenizer.scala   |   2 +-
 python/pyspark/ml/feature.py                  | 797 +++++++++++++++++-
 .../ml/param/_shared_params_code_gen.py       |   4 +-
 python/pyspark/ml/param/shared.py             |  89 ++
 5 files changed, 851 insertions(+), 43 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/PolynomialExpansion.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/PolynomialExpansion.scala
index 63e190c8aae53..9e6177ca27e4a 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/PolynomialExpansion.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/PolynomialExpansion.scala
@@ -31,7 +31,7 @@ import org.apache.spark.sql.types.DataType
  * which is available at [[http://en.wikipedia.org/wiki/Polynomial_expansion]], "In mathematics, an
  * expansion of a product of sums expresses it as a sum of products by using the fact that
  * multiplication distributes over addition". Take a 2-variable feature vector as an example:
- * `(x, y)`, if we want to expand it with degree 2, then we get `(x, y, x * x, x * y, y * y)`.
+ * `(x, y)`, if we want to expand it with degree 2, then we get `(x, x * x, y, x * y, y * y)`.
  */
 @AlphaComponent
 class PolynomialExpansion extends UnaryTransformer[Vector, Vector, PolynomialExpansion] {
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/Tokenizer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/Tokenizer.scala
index 2863b7621526e..649c217b16590 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/Tokenizer.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/Tokenizer.scala
@@ -42,7 +42,7 @@ class Tokenizer extends UnaryTransformer[String, Seq[String], Tokenizer] {
 
 /**
  * :: AlphaComponent ::
- * A regex based tokenizer that extracts tokens either by repeatedly matching the regex(default) 
+ * A regex based tokenizer that extracts tokens either by repeatedly matching the regex(default)
  * or using it to split the text (set matching to false). Optional parameters also allow filtering
  * tokens using a minimal length.
  * It returns an array of strings that can be empty.
diff --git a/python/pyspark/ml/feature.py b/python/pyspark/ml/feature.py
index 705a368192c24..f35bc1463d51b 100644
--- a/python/pyspark/ml/feature.py
+++ b/python/pyspark/ml/feature.py
@@ -16,61 +16,72 @@
 #
 
 from pyspark.rdd import ignore_unicode_prefix
-from pyspark.ml.param.shared import HasInputCol, HasInputCols, HasOutputCol, HasNumFeatures
+from pyspark.ml.param.shared import *
 from pyspark.ml.util import keyword_only
-from pyspark.ml.wrapper import JavaTransformer
+from pyspark.ml.wrapper import JavaEstimator, JavaModel, JavaTransformer
 from pyspark.mllib.common import inherit_doc
 
-__all__ = ['Tokenizer', 'HashingTF', 'VectorAssembler']
+__all__ = ['Binarizer', 'HashingTF', 'IDF', 'IDFModel', 'Normalizer', 'OneHotEncoder',
+           'PolynomialExpansion', 'RegexTokenizer', 'StandardScaler', 'StandardScalerModel',
+           'StringIndexer', 'StringIndexerModel', 'Tokenizer', 'VectorAssembler', 'VectorIndexer',
+           'Word2Vec', 'Word2VecModel']
 
 
 @inherit_doc
-@ignore_unicode_prefix
-class Tokenizer(JavaTransformer, HasInputCol, HasOutputCol):
+class Binarizer(JavaTransformer, HasInputCol, HasOutputCol):
     """
-    A tokenizer that converts the input string to lowercase and then
-    splits it by white spaces.
+    Binarize a column of continuous features given a threshold.
 
-    >>> from pyspark.sql import Row
-    >>> df = sc.parallelize([Row(text="a b c")]).toDF()
-    >>> tokenizer = Tokenizer(inputCol="text", outputCol="words")
-    >>> tokenizer.transform(df).head()
-    Row(text=u'a b c', words=[u'a', u'b', u'c'])
-    >>> # Change a parameter.
-    >>> tokenizer.setParams(outputCol="tokens").transform(df).head()
-    Row(text=u'a b c', tokens=[u'a', u'b', u'c'])
-    >>> # Temporarily modify a parameter.
-    >>> tokenizer.transform(df, {tokenizer.outputCol: "words"}).head()
-    Row(text=u'a b c', words=[u'a', u'b', u'c'])
-    >>> tokenizer.transform(df).head()
-    Row(text=u'a b c', tokens=[u'a', u'b', u'c'])
-    >>> # Must use keyword arguments to specify params.
-    >>> tokenizer.setParams("text")
-    Traceback (most recent call last):
-        ...
-    TypeError: Method setParams forces keyword arguments.
+    >>> df = sqlContext.createDataFrame([(0.5,)], ["values"])
+    >>> binarizer = Binarizer(threshold=1.0, inputCol="values", outputCol="features")
+    >>> binarizer.transform(df).head().features
+    0.0
+    >>> binarizer.setParams(outputCol="freqs").transform(df).head().freqs
+    0.0
+    >>> params = {binarizer.threshold: -0.5, binarizer.outputCol: "vector"}
+    >>> binarizer.transform(df, params).head().vector
+    1.0
     """
 
-    _java_class = "org.apache.spark.ml.feature.Tokenizer"
+    _java_class = "org.apache.spark.ml.feature.Binarizer"
+    # a placeholder to make it appear in the generated doc
+    threshold = Param(Params._dummy(), "threshold",
+                      "threshold in binary classification prediction, in range [0, 1]")
 
     @keyword_only
-    def __init__(self, inputCol=None, outputCol=None):
+    def __init__(self, threshold=0.0, inputCol=None, outputCol=None):
         """
-        __init__(self, inputCol=None, outputCol=None)
+        __init__(self, threshold=0.0, inputCol=None, outputCol=None)
         """
-        super(Tokenizer, self).__init__()
+        super(Binarizer, self).__init__()
+        self.threshold = Param(self, "threshold",
+                               "threshold in binary classification prediction, in range [0, 1]")
+        self._setDefault(threshold=0.0)
         kwargs = self.__init__._input_kwargs
         self.setParams(**kwargs)
 
     @keyword_only
-    def setParams(self, inputCol=None, outputCol=None):
+    def setParams(self, threshold=0.0, inputCol=None, outputCol=None):
         """
-        setParams(self, inputCol="input", outputCol="output")
-        Sets params for this Tokenizer.
+        setParams(self, threshold=0.0, inputCol=None, outputCol=None)
+        Sets params for this Binarizer.
         """
         kwargs = self.setParams._input_kwargs
         return self._set(**kwargs)
 
+    def setThreshold(self, value):
+        """
+        Sets the value of :py:attr:`threshold`.
+        """
+        self.paramMap[self.threshold] = value
+        return self
+
+    def getThreshold(self):
+        """
+        Gets the value of threshold or its default value.
+        """
+        return self.getOrDefault(self.threshold)
+
 
 @inherit_doc
 class HashingTF(JavaTransformer, HasInputCol, HasOutputCol, HasNumFeatures):
@@ -78,8 +89,7 @@ class HashingTF(JavaTransformer, HasInputCol, HasOutputCol, HasNumFeatures):
     Maps a sequence of terms to their term frequencies using the
     hashing trick.
 
-    >>> from pyspark.sql import Row
-    >>> df = sc.parallelize([Row(words=["a", "b", "c"])]).toDF()
+    >>> df = sqlContext.createDataFrame([(["a", "b", "c"],)], ["words"])
     >>> hashingTF = HashingTF(numFeatures=10, inputCol="words", outputCol="features")
     >>> hashingTF.transform(df).head().features
     SparseVector(10, {7: 1.0, 8: 1.0, 9: 1.0})
@@ -112,13 +122,520 @@ def setParams(self, numFeatures=1 << 18, inputCol=None, outputCol=None):
         return self._set(**kwargs)
 
 
+@inherit_doc
+class IDF(JavaEstimator, HasInputCol, HasOutputCol):
+    """
+    Compute the Inverse Document Frequency (IDF) given a collection of documents.
+
+    >>> from pyspark.mllib.linalg import DenseVector
+    >>> df = sqlContext.createDataFrame([(DenseVector([1.0, 2.0]),),
+    ...     (DenseVector([0.0, 1.0]),), (DenseVector([3.0, 0.2]),)], ["tf"])
+    >>> idf = IDF(minDocFreq=3, inputCol="tf", outputCol="idf")
+    >>> idf.fit(df).transform(df).head().idf
+    DenseVector([0.0, 0.0])
+    >>> idf.setParams(outputCol="freqs").fit(df).transform(df).collect()[1].freqs
+    DenseVector([0.0, 0.0])
+    >>> params = {idf.minDocFreq: 1, idf.outputCol: "vector"}
+    >>> idf.fit(df, params).transform(df).head().vector
+    DenseVector([0.2877, 0.0])
+    """
+
+    _java_class = "org.apache.spark.ml.feature.IDF"
+
+    # a placeholder to make it appear in the generated doc
+    minDocFreq = Param(Params._dummy(), "minDocFreq",
+                       "minimum of documents in which a term should appear for filtering")
+
+    @keyword_only
+    def __init__(self, minDocFreq=0, inputCol=None, outputCol=None):
+        """
+        __init__(self, minDocFreq=0, inputCol=None, outputCol=None)
+        """
+        super(IDF, self).__init__()
+        self.minDocFreq = Param(self, "minDocFreq",
+                                "minimum of documents in which a term should appear for filtering")
+        self._setDefault(minDocFreq=0)
+        kwargs = self.__init__._input_kwargs
+        self.setParams(**kwargs)
+
+    @keyword_only
+    def setParams(self, minDocFreq=0, inputCol=None, outputCol=None):
+        """
+        setParams(self, minDocFreq=0, inputCol=None, outputCol=None)
+        Sets params for this IDF.
+        """
+        kwargs = self.setParams._input_kwargs
+        return self._set(**kwargs)
+
+    def setMinDocFreq(self, value):
+        """
+        Sets the value of :py:attr:`minDocFreq`.
+        """
+        self.paramMap[self.minDocFreq] = value
+        return self
+
+    def getMinDocFreq(self):
+        """
+        Gets the value of minDocFreq or its default value.
+        """
+        return self.getOrDefault(self.minDocFreq)
+
+
+class IDFModel(JavaModel):
+    """
+    Model fitted by IDF.
+    """
+
+
+@inherit_doc
+class Normalizer(JavaTransformer, HasInputCol, HasOutputCol):
+    """
+     Normalize a vector to have unit norm using the given p-norm.
+
+    >>> from pyspark.mllib.linalg import Vectors
+    >>> svec = Vectors.sparse(4, {1: 4.0, 3: 3.0})
+    >>> df = sqlContext.createDataFrame([(Vectors.dense([3.0, -4.0]), svec)], ["dense", "sparse"])
+    >>> normalizer = Normalizer(p=2.0, inputCol="dense", outputCol="features")
+    >>> normalizer.transform(df).head().features
+    DenseVector([0.6, -0.8])
+    >>> normalizer.setParams(inputCol="sparse", outputCol="freqs").transform(df).head().freqs
+    SparseVector(4, {1: 0.8, 3: 0.6})
+    >>> params = {normalizer.p: 1.0, normalizer.inputCol: "dense", normalizer.outputCol: "vector"}
+    >>> normalizer.transform(df, params).head().vector
+    DenseVector([0.4286, -0.5714])
+    """
+
+    # a placeholder to make it appear in the generated doc
+    p = Param(Params._dummy(), "p", "the p norm value.")
+
+    _java_class = "org.apache.spark.ml.feature.Normalizer"
+
+    @keyword_only
+    def __init__(self, p=2.0, inputCol=None, outputCol=None):
+        """
+        __init__(self, p=2.0, inputCol=None, outputCol=None)
+        """
+        super(Normalizer, self).__init__()
+        self.p = Param(self, "p", "the p norm value.")
+        self._setDefault(p=2.0)
+        kwargs = self.__init__._input_kwargs
+        self.setParams(**kwargs)
+
+    @keyword_only
+    def setParams(self, p=2.0, inputCol=None, outputCol=None):
+        """
+        setParams(self, p=2.0, inputCol=None, outputCol=None)
+        Sets params for this Normalizer.
+        """
+        kwargs = self.setParams._input_kwargs
+        return self._set(**kwargs)
+
+    def setP(self, value):
+        """
+        Sets the value of :py:attr:`p`.
+        """
+        self.paramMap[self.p] = value
+        return self
+
+    def getP(self):
+        """
+        Gets the value of p or its default value.
+        """
+        return self.getOrDefault(self.p)
+
+
+@inherit_doc
+class OneHotEncoder(JavaTransformer, HasInputCol, HasOutputCol):
+    """
+    A one-hot encoder that maps a column of label indices to a column of binary vectors, with
+    at most a single one-value. By default, the binary vector has an element for each category, so
+    with 5 categories, an input value of 2.0 would map to an output vector of
+    (0.0, 0.0, 1.0, 0.0, 0.0). If includeFirst is set to false, the first category is omitted, so
+    the output vector for the previous example would be (0.0, 1.0, 0.0, 0.0) and an input value
+    of 0.0 would map to a vector of all zeros. Including the first category makes the vector columns
+    linearly dependent because they sum up to one.
+
+    TODO: This method requires the use of StringIndexer first. Decouple them.
+
+    >>> stringIndexer = StringIndexer(inputCol="label", outputCol="indexed")
+    >>> model = stringIndexer.fit(stringIndDf)
+    >>> td = model.transform(stringIndDf)
+    >>> encoder = OneHotEncoder(includeFirst=False, inputCol="indexed", outputCol="features")
+    >>> encoder.transform(td).head().features
+    SparseVector(2, {})
+    >>> encoder.setParams(outputCol="freqs").transform(td).head().freqs
+    SparseVector(2, {})
+    >>> params = {encoder.includeFirst: True, encoder.outputCol: "test"}
+    >>> encoder.transform(td, params).head().test
+    SparseVector(3, {0: 1.0})
+    """
+
+    _java_class = "org.apache.spark.ml.feature.OneHotEncoder"
+
+    # a placeholder to make it appear in the generated doc
+    includeFirst = Param(Params._dummy(), "includeFirst", "include first category")
+
+    @keyword_only
+    def __init__(self, includeFirst=True, inputCol=None, outputCol=None):
+        """
+        __init__(self, includeFirst=True, inputCol=None, outputCol=None)
+        """
+        super(OneHotEncoder, self).__init__()
+        self.includeFirst = Param(self, "includeFirst", "include first category")
+        self._setDefault(includeFirst=True)
+        kwargs = self.__init__._input_kwargs
+        self.setParams(**kwargs)
+
+    @keyword_only
+    def setParams(self, includeFirst=True, inputCol=None, outputCol=None):
+        """
+        setParams(self, includeFirst=True, inputCol=None, outputCol=None)
+        Sets params for this OneHotEncoder.
+        """
+        kwargs = self.setParams._input_kwargs
+        return self._set(**kwargs)
+
+    def setIncludeFirst(self, value):
+        """
+        Sets the value of :py:attr:`includeFirst`.
+        """
+        self.paramMap[self.includeFirst] = value
+        return self
+
+    def getIncludeFirst(self):
+        """
+        Gets the value of includeFirst or its default value.
+        """
+        return self.getOrDefault(self.includeFirst)
+
+
+@inherit_doc
+class PolynomialExpansion(JavaTransformer, HasInputCol, HasOutputCol):
+    """
+    Perform feature expansion in a polynomial space. As said in wikipedia of Polynomial Expansion,
+    which is available at `http://en.wikipedia.org/wiki/Polynomial_expansion`, "In mathematics, an
+    expansion of a product of sums expresses it as a sum of products by using the fact that
+    multiplication distributes over addition". Take a 2-variable feature vector as an example:
+    `(x, y)`, if we want to expand it with degree 2, then we get `(x, x * x, y, x * y, y * y)`.
+
+    >>> from pyspark.mllib.linalg import Vectors
+    >>> df = sqlContext.createDataFrame([(Vectors.dense([0.5, 2.0]),)], ["dense"])
+    >>> px = PolynomialExpansion(degree=2, inputCol="dense", outputCol="expanded")
+    >>> px.transform(df).head().expanded
+    DenseVector([0.5, 0.25, 2.0, 1.0, 4.0])
+    >>> px.setParams(outputCol="test").transform(df).head().test
+    DenseVector([0.5, 0.25, 2.0, 1.0, 4.0])
+    """
+
+    _java_class = "org.apache.spark.ml.feature.PolynomialExpansion"
+
+    # a placeholder to make it appear in the generated doc
+    degree = Param(Params._dummy(), "degree", "the polynomial degree to expand (>= 1)")
+
+    @keyword_only
+    def __init__(self, degree=2, inputCol=None, outputCol=None):
+        """
+        __init__(self, degree=2, inputCol=None, outputCol=None)
+        """
+        super(PolynomialExpansion, self).__init__()
+        self.degree = Param(self, "degree", "the polynomial degree to expand (>= 1)")
+        self._setDefault(degree=2)
+        kwargs = self.__init__._input_kwargs
+        self.setParams(**kwargs)
+
+    @keyword_only
+    def setParams(self, degree=2, inputCol=None, outputCol=None):
+        """
+        setParams(self, degree=2, inputCol=None, outputCol=None)
+        Sets params for this PolynomialExpansion.
+        """
+        kwargs = self.setParams._input_kwargs
+        return self._set(**kwargs)
+
+    def setDegree(self, value):
+        """
+        Sets the value of :py:attr:`degree`.
+        """
+        self.paramMap[self.degree] = value
+        return self
+
+    def getDegree(self):
+        """
+        Gets the value of degree or its default value.
+        """
+        return self.getOrDefault(self.degree)
+
+
+@inherit_doc
+@ignore_unicode_prefix
+class RegexTokenizer(JavaTransformer, HasInputCol, HasOutputCol):
+    """
+    A regex based tokenizer that extracts tokens either by repeatedly matching the regex(default)
+    or using it to split the text (set matching to false). Optional parameters also allow filtering
+    tokens using a minimal length.
+    It returns an array of strings that can be empty.
+
+    >>> df = sqlContext.createDataFrame([("a b c",)], ["text"])
+    >>> reTokenizer = RegexTokenizer(inputCol="text", outputCol="words")
+    >>> reTokenizer.transform(df).head()
+    Row(text=u'a b c', words=[u'a', u'b', u'c'])
+    >>> # Change a parameter.
+    >>> reTokenizer.setParams(outputCol="tokens").transform(df).head()
+    Row(text=u'a b c', tokens=[u'a', u'b', u'c'])
+    >>> # Temporarily modify a parameter.
+    >>> reTokenizer.transform(df, {reTokenizer.outputCol: "words"}).head()
+    Row(text=u'a b c', words=[u'a', u'b', u'c'])
+    >>> reTokenizer.transform(df).head()
+    Row(text=u'a b c', tokens=[u'a', u'b', u'c'])
+    >>> # Must use keyword arguments to specify params.
+    >>> reTokenizer.setParams("text")
+    Traceback (most recent call last):
+        ...
+    TypeError: Method setParams forces keyword arguments.
+    """
+
+    _java_class = "org.apache.spark.ml.feature.RegexTokenizer"
+    # a placeholder to make it appear in the generated doc
+    minTokenLength = Param(Params._dummy(), "minTokenLength", "minimum token length (>= 0)")
+    gaps = Param(Params._dummy(), "gaps", "Set regex to match gaps or tokens")
+    pattern = Param(Params._dummy(), "pattern", "regex pattern used for tokenizing")
+
+    @keyword_only
+    def __init__(self, minTokenLength=1, gaps=False, pattern="\\p{L}+|[^\\p{L}\\s]+",
+                 inputCol=None, outputCol=None):
+        """
+        __init__(self, minTokenLength=1, gaps=False, pattern="\\p{L}+|[^\\p{L}\\s]+",
+                 inputCol=None, outputCol=None)
+        """
+        super(RegexTokenizer, self).__init__()
+        self.minTokenLength = Param(self, "minLength", "minimum token length (>= 0)")
+        self.gaps = Param(self, "gaps", "Set regex to match gaps or tokens")
+        self.pattern = Param(self, "pattern", "regex pattern used for tokenizing")
+        self._setDefault(minTokenLength=1, gaps=False, pattern="\\p{L}+|[^\\p{L}\\s]+")
+        kwargs = self.__init__._input_kwargs
+        self.setParams(**kwargs)
+
+    @keyword_only
+    def setParams(self, minTokenLength=1, gaps=False, pattern="\\p{L}+|[^\\p{L}\\s]+",
+                  inputCol=None, outputCol=None):
+        """
+        setParams(self, minTokenLength=1, gaps=False, pattern="\\p{L}+|[^\\p{L}\\s]+",
+                  inputCol="input", outputCol="output")
+        Sets params for this RegexTokenizer.
+        """
+        kwargs = self.setParams._input_kwargs
+        return self._set(**kwargs)
+
+    def setMinTokenLength(self, value):
+        """
+        Sets the value of :py:attr:`minTokenLength`.
+        """
+        self.paramMap[self.minTokenLength] = value
+        return self
+
+    def getMinTokenLength(self):
+        """
+        Gets the value of minTokenLength or its default value.
+        """
+        return self.getOrDefault(self.minTokenLength)
+
+    def setGaps(self, value):
+        """
+        Sets the value of :py:attr:`gaps`.
+        """
+        self.paramMap[self.gaps] = value
+        return self
+
+    def getGaps(self):
+        """
+        Gets the value of gaps or its default value.
+        """
+        return self.getOrDefault(self.gaps)
+
+    def setPattern(self, value):
+        """
+        Sets the value of :py:attr:`pattern`.
+        """
+        self.paramMap[self.pattern] = value
+        return self
+
+    def getPattern(self):
+        """
+        Gets the value of pattern or its default value.
+        """
+        return self.getOrDefault(self.pattern)
+
+
+@inherit_doc
+class StandardScaler(JavaEstimator, HasInputCol, HasOutputCol):
+    """
+    Standardizes features by removing the mean and scaling to unit variance using column summary
+    statistics on the samples in the training set.
+
+    >>> from pyspark.mllib.linalg import Vectors
+    >>> df = sqlContext.createDataFrame([(Vectors.dense([0.0]),), (Vectors.dense([2.0]),)], ["a"])
+    >>> standardScaler = StandardScaler(inputCol="a", outputCol="scaled")
+    >>> model = standardScaler.fit(df)
+    >>> model.transform(df).collect()[1].scaled
+    DenseVector([1.4142])
+    """
+
+    _java_class = "org.apache.spark.ml.feature.StandardScaler"
+
+    # a placeholder to make it appear in the generated doc
+    withMean = Param(Params._dummy(), "withMean", "Center data with mean")
+    withStd = Param(Params._dummy(), "withStd", "Scale to unit standard deviation")
+
+    @keyword_only
+    def __init__(self, withMean=False, withStd=True, inputCol=None, outputCol=None):
+        """
+        __init__(self, withMean=False, withStd=True, inputCol=None, outputCol=None)
+        """
+        super(StandardScaler, self).__init__()
+        self.withMean = Param(self, "withMean", "Center data with mean")
+        self.withStd = Param(self, "withStd", "Scale to unit standard deviation")
+        self._setDefault(withMean=False, withStd=True)
+        kwargs = self.__init__._input_kwargs
+        self.setParams(**kwargs)
+
+    @keyword_only
+    def setParams(self, withMean=False, withStd=True, inputCol=None, outputCol=None):
+        """
+        setParams(self, withMean=False, withStd=True, inputCol=None, outputCol=None)
+        Sets params for this StandardScaler.
+        """
+        kwargs = self.setParams._input_kwargs
+        return self._set(**kwargs)
+
+    def setWithMean(self, value):
+        """
+        Sets the value of :py:attr:`withMean`.
+        """
+        self.paramMap[self.withMean] = value
+        return self
+
+    def getWithMean(self):
+        """
+        Gets the value of withMean or its default value.
+        """
+        return self.getOrDefault(self.withMean)
+
+    def setWithStd(self, value):
+        """
+        Sets the value of :py:attr:`withStd`.
+        """
+        self.paramMap[self.withStd] = value
+        return self
+
+    def getWithStd(self):
+        """
+        Gets the value of withStd or its default value.
+        """
+        return self.getOrDefault(self.withStd)
+
+
+class StandardScalerModel(JavaModel):
+    """
+    Model fitted by StandardScaler.
+    """
+
+
+@inherit_doc
+class StringIndexer(JavaEstimator, HasInputCol, HasOutputCol):
+    """
+    A label indexer that maps a string column of labels to an ML column of label indices.
+    If the input column is numeric, we cast it to string and index the string values.
+    The indices are in [0, numLabels), ordered by label frequencies.
+    So the most frequent label gets index 0.
+
+    >>> stringIndexer = StringIndexer(inputCol="label", outputCol="indexed")
+    >>> model = stringIndexer.fit(stringIndDf)
+    >>> td = model.transform(stringIndDf)
+    >>> sorted(set([(i[0], i[1]) for i in td.select(td.id, td.indexed).collect()]),
+    ...     key=lambda x: x[0])
+    [(0, 0.0), (1, 2.0), (2, 1.0), (3, 0.0), (4, 0.0), (5, 1.0)]
+    """
+
+    _java_class = "org.apache.spark.ml.feature.StringIndexer"
+
+    @keyword_only
+    def __init__(self, inputCol=None, outputCol=None):
+        """
+        __init__(self, inputCol=None, outputCol=None)
+        """
+        super(StringIndexer, self).__init__()
+        kwargs = self.__init__._input_kwargs
+        self.setParams(**kwargs)
+
+    @keyword_only
+    def setParams(self, inputCol=None, outputCol=None):
+        """
+        setParams(self, inputCol=None, outputCol=None)
+        Sets params for this StringIndexer.
+        """
+        kwargs = self.setParams._input_kwargs
+        return self._set(**kwargs)
+
+
+class StringIndexerModel(JavaModel):
+    """
+    Model fitted by StringIndexer.
+    """
+
+
+@inherit_doc
+@ignore_unicode_prefix
+class Tokenizer(JavaTransformer, HasInputCol, HasOutputCol):
+    """
+    A tokenizer that converts the input string to lowercase and then
+    splits it by white spaces.
+
+    >>> df = sqlContext.createDataFrame([("a b c",)], ["text"])
+    >>> tokenizer = Tokenizer(inputCol="text", outputCol="words")
+    >>> tokenizer.transform(df).head()
+    Row(text=u'a b c', words=[u'a', u'b', u'c'])
+    >>> # Change a parameter.
+    >>> tokenizer.setParams(outputCol="tokens").transform(df).head()
+    Row(text=u'a b c', tokens=[u'a', u'b', u'c'])
+    >>> # Temporarily modify a parameter.
+    >>> tokenizer.transform(df, {tokenizer.outputCol: "words"}).head()
+    Row(text=u'a b c', words=[u'a', u'b', u'c'])
+    >>> tokenizer.transform(df).head()
+    Row(text=u'a b c', tokens=[u'a', u'b', u'c'])
+    >>> # Must use keyword arguments to specify params.
+    >>> tokenizer.setParams("text")
+    Traceback (most recent call last):
+        ...
+    TypeError: Method setParams forces keyword arguments.
+    """
+
+    _java_class = "org.apache.spark.ml.feature.Tokenizer"
+
+    @keyword_only
+    def __init__(self, inputCol=None, outputCol=None):
+        """
+        __init__(self, inputCol=None, outputCol=None)
+        """
+        super(Tokenizer, self).__init__()
+        kwargs = self.__init__._input_kwargs
+        self.setParams(**kwargs)
+
+    @keyword_only
+    def setParams(self, inputCol=None, outputCol=None):
+        """
+        setParams(self, inputCol="input", outputCol="output")
+        Sets params for this Tokenizer.
+        """
+        kwargs = self.setParams._input_kwargs
+        return self._set(**kwargs)
+
+
 @inherit_doc
 class VectorAssembler(JavaTransformer, HasInputCols, HasOutputCol):
     """
     A feature transformer that merges multiple columns into a vector column.
 
-    >>> from pyspark.sql import Row
-    >>> df = sc.parallelize([Row(a=1, b=0, c=3)]).toDF()
+    >>> df = sqlContext.createDataFrame([(1, 0, 3)], ["a", "b", "c"])
     >>> vecAssembler = VectorAssembler(inputCols=["a", "b", "c"], outputCol="features")
     >>> vecAssembler.transform(df).head().features
     DenseVector([1.0, 0.0, 3.0])
@@ -137,7 +654,6 @@ def __init__(self, inputCols=None, outputCol=None):
         __init__(self, inputCols=None, outputCol=None)
         """
         super(VectorAssembler, self).__init__()
-        self._setDefault()
         kwargs = self.__init__._input_kwargs
         self.setParams(**kwargs)
 
@@ -151,10 +667,208 @@ def setParams(self, inputCols=None, outputCol=None):
         return self._set(**kwargs)
 
 
+@inherit_doc
+class VectorIndexer(JavaEstimator, HasInputCol, HasOutputCol):
+    """
+    Class for indexing categorical feature columns in a dataset of [[Vector]].
+
+    This has 2 usage modes:
+      - Automatically identify categorical features (default behavior)
+         - This helps process a dataset of unknown vectors into a dataset with some continuous
+           features and some categorical features. The choice between continuous and categorical
+           is based upon a maxCategories parameter.
+         - Set maxCategories to the maximum number of categorical any categorical feature should
+           have.
+         - E.g.: Feature 0 has unique values {-1.0, 0.0}, and feature 1 values {1.0, 3.0, 5.0}.
+           If maxCategories = 2, then feature 0 will be declared categorical and use indices {0, 1},
+           and feature 1 will be declared continuous.
+      - Index all features, if all features are categorical
+         - If maxCategories is set to be very large, then this will build an index of unique
+           values for all features.
+         - Warning: This can cause problems if features are continuous since this will collect ALL
+           unique values to the driver.
+         - E.g.: Feature 0 has unique values {-1.0, 0.0}, and feature 1 values {1.0, 3.0, 5.0}.
+           If maxCategories >= 3, then both features will be declared categorical.
+
+     This returns a model which can transform categorical features to use 0-based indices.
+
+    Index stability:
+      - This is not guaranteed to choose the same category index across multiple runs.
+      - If a categorical feature includes value 0, then this is guaranteed to map value 0 to
+        index 0. This maintains vector sparsity.
+      - More stability may be added in the future.
+
+     TODO: Future extensions: The following functionality is planned for the future:
+      - Preserve metadata in transform; if a feature's metadata is already present,
+        do not recompute.
+      - Specify certain features to not index, either via a parameter or via existing metadata.
+      - Add warning if a categorical feature has only 1 category.
+      - Add option for allowing unknown categories.
+
+    >>> from pyspark.mllib.linalg import Vectors
+    >>> df = sqlContext.createDataFrame([(Vectors.dense([-1.0, 0.0]),),
+    ...     (Vectors.dense([0.0, 1.0]),), (Vectors.dense([0.0, 2.0]),)], ["a"])
+    >>> indexer = VectorIndexer(maxCategories=2, inputCol="a", outputCol="indexed")
+    >>> model = indexer.fit(df)
+    >>> model.transform(df).head().indexed
+    DenseVector([1.0, 0.0])
+    >>> indexer.setParams(outputCol="test").fit(df).transform(df).collect()[1].test
+    DenseVector([0.0, 1.0])
+    >>> params = {indexer.maxCategories: 3, indexer.outputCol: "vector"}
+    >>> model2 = indexer.fit(df, params)
+    >>> model2.transform(df).head().vector
+    DenseVector([1.0, 0.0])
+    """
+
+    _java_class = "org.apache.spark.ml.feature.VectorIndexer"
+    # a placeholder to make it appear in the generated doc
+    maxCategories = Param(Params._dummy(), "maxCategories",
+                          "Threshold for the number of values a categorical feature can take " +
+                          "(>= 2). If a feature is found to have > maxCategories values, then " +
+                          "it is declared continuous.")
+
+    @keyword_only
+    def __init__(self, maxCategories=20, inputCol=None, outputCol=None):
+        """
+        __init__(self, maxCategories=20, inputCol=None, outputCol=None)
+        """
+        super(VectorIndexer, self).__init__()
+        self.maxCategories = Param(self, "maxCategories",
+                                   "Threshold for the number of values a categorical feature " +
+                                   "can take (>= 2). If a feature is found to have " +
+                                   "> maxCategories values, then it is declared continuous.")
+        self._setDefault(maxCategories=20)
+        kwargs = self.__init__._input_kwargs
+        self.setParams(**kwargs)
+
+    @keyword_only
+    def setParams(self, maxCategories=20, inputCol=None, outputCol=None):
+        """
+        setParams(self, maxCategories=20, inputCol=None, outputCol=None)
+        Sets params for this VectorIndexer.
+        """
+        kwargs = self.setParams._input_kwargs
+        return self._set(**kwargs)
+
+    def setMaxCategories(self, value):
+        """
+        Sets the value of :py:attr:`maxCategories`.
+        """
+        self.paramMap[self.maxCategories] = value
+        return self
+
+    def getMaxCategories(self):
+        """
+        Gets the value of maxCategories or its default value.
+        """
+        return self.getOrDefault(self.maxCategories)
+
+
+@inherit_doc
+@ignore_unicode_prefix
+class Word2Vec(JavaEstimator, HasStepSize, HasMaxIter, HasSeed, HasInputCol, HasOutputCol):
+    """
+    Word2Vec trains a model of `Map(String, Vector)`, i.e. transforms a word into a code for further
+    natural language processing or machine learning process.
+
+    >>> sent = ("a b " * 100 + "a c " * 10).split(" ")
+    >>> doc = sqlContext.createDataFrame([(sent,), (sent,)], ["sentence"])
+    >>> model = Word2Vec(vectorSize=5, seed=42, inputCol="sentence", outputCol="model").fit(doc)
+    >>> model.transform(doc).head().model
+    DenseVector([-0.0422, -0.5138, -0.2546, 0.6885, 0.276])
+    """
+
+    _java_class = "org.apache.spark.ml.feature.Word2Vec"
+    # a placeholder to make it appear in the generated doc
+    vectorSize = Param(Params._dummy(), "vectorSize",
+                       "the dimension of codes after transforming from words")
+    numPartitions = Param(Params._dummy(), "numPartitions",
+                          "number of partitions for sentences of words")
+    minCount = Param(Params._dummy(), "minCount",
+                     "the minimum number of times a token must appear to be included in the " +
+                     "word2vec model's vocabulary")
+
+    @keyword_only
+    def __init__(self, vectorSize=100, minCount=5, numPartitions=1, stepSize=0.025, maxIter=1,
+                 seed=42, inputCol=None, outputCol=None):
+        """
+        __init__(self, vectorSize=100, minCount=5, numPartitions=1, stepSize=0.025, maxIter=1,
+                 seed=42, inputCol=None, outputCol=None)
+        """
+        super(Word2Vec, self).__init__()
+        self.vectorSize = Param(self, "vectorSize",
+                                "the dimension of codes after transforming from words")
+        self.numPartitions = Param(self, "numPartitions",
+                                   "number of partitions for sentences of words")
+        self.minCount = Param(self, "minCount",
+                              "the minimum number of times a token must appear to be included " +
+                              "in the word2vec model's vocabulary")
+        self._setDefault(vectorSize=100, minCount=5, numPartitions=1, stepSize=0.025, maxIter=1,
+                         seed=42)
+        kwargs = self.__init__._input_kwargs
+        self.setParams(**kwargs)
+
+    @keyword_only
+    def setParams(self, vectorSize=100, minCount=5, numPartitions=1, stepSize=0.025, maxIter=1,
+                  seed=42, inputCol=None, outputCol=None):
+        """
+        setParams(self, minCount=5, numPartitions=1, stepSize=0.025, maxIter=1, seed=42,
+                 inputCol=None, outputCol=None)
+        Sets params for this Word2Vec.
+        """
+        kwargs = self.setParams._input_kwargs
+        return self._set(**kwargs)
+
+    def setVectorSize(self, value):
+        """
+        Sets the value of :py:attr:`vectorSize`.
+        """
+        self.paramMap[self.vectorSize] = value
+        return self
+
+    def getVectorSize(self):
+        """
+        Gets the value of vectorSize or its default value.
+        """
+        return self.getOrDefault(self.vectorSize)
+
+    def setNumPartitions(self, value):
+        """
+        Sets the value of :py:attr:`numPartitions`.
+        """
+        self.paramMap[self.numPartitions] = value
+        return self
+
+    def getNumPartitions(self):
+        """
+        Gets the value of numPartitions or its default value.
+        """
+        return self.getOrDefault(self.numPartitions)
+
+    def setMinCount(self, value):
+        """
+        Sets the value of :py:attr:`minCount`.
+        """
+        self.paramMap[self.minCount] = value
+        return self
+
+    def getMinCount(self):
+        """
+        Gets the value of minCount or its default value.
+        """
+        return self.getOrDefault(self.minCount)
+
+
+class Word2VecModel(JavaModel):
+    """
+    Model fitted by Word2Vec.
+    """
+
+
 if __name__ == "__main__":
     import doctest
     from pyspark.context import SparkContext
-    from pyspark.sql import SQLContext
+    from pyspark.sql import Row, SQLContext
     globs = globals().copy()
     # The small batch size here ensures that we see multiple batches,
     # even in these small test examples:
@@ -162,8 +876,11 @@ def setParams(self, inputCols=None, outputCol=None):
     sqlContext = SQLContext(sc)
     globs['sc'] = sc
     globs['sqlContext'] = sqlContext
-    (failure_count, test_count) = doctest.testmod(
-        globs=globs, optionflags=doctest.ELLIPSIS)
+    testData = sc.parallelize([Row(id=0, label="a"), Row(id=1, label="b"),
+                               Row(id=2, label="c"), Row(id=3, label="a"),
+                               Row(id=4, label="a"), Row(id=5, label="c")], 2)
+    globs['stringIndDf'] = sqlContext.createDataFrame(testData)
+    (failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS)
     sc.stop()
     if failure_count:
         exit(-1)
diff --git a/python/pyspark/ml/param/_shared_params_code_gen.py b/python/pyspark/ml/param/_shared_params_code_gen.py
index c1c8e921dda87..ee901f25847bc 100644
--- a/python/pyspark/ml/param/_shared_params_code_gen.py
+++ b/python/pyspark/ml/param/_shared_params_code_gen.py
@@ -97,7 +97,9 @@ def get$Name(self):
         ("inputCol", "input column name", None),
         ("inputCols", "input column names", None),
         ("outputCol", "output column name", None),
-        ("numFeatures", "number of features", None)]
+        ("seed", "random seed", None),
+        ("tol", "the convergence tolerance for iterative algorithms", None),
+        ("stepSize", "Step size to be used for each iteration of optimization.", None)]
     code = []
     for name, doc, defaultValueStr in shared:
         code.append(_gen_param_code(name, doc, defaultValueStr))
diff --git a/python/pyspark/ml/param/shared.py b/python/pyspark/ml/param/shared.py
index aaf80f00085bf..5e7529c1dcf1e 100644
--- a/python/pyspark/ml/param/shared.py
+++ b/python/pyspark/ml/param/shared.py
@@ -308,3 +308,92 @@ def getNumFeatures(self):
         Gets the value of numFeatures or its default value.
         """
         return self.getOrDefault(self.numFeatures)
+
+
+class HasSeed(Params):
+    """
+    Mixin for param seed: random seed.
+    """
+
+    # a placeholder to make it appear in the generated doc
+    seed = Param(Params._dummy(), "seed", "random seed")
+
+    def __init__(self):
+        super(HasSeed, self).__init__()
+        #: param for random seed
+        self.seed = Param(self, "seed", "random seed")
+        if None is not None:
+            self._setDefault(seed=None)
+
+    def setSeed(self, value):
+        """
+        Sets the value of :py:attr:`seed`.
+        """
+        self.paramMap[self.seed] = value
+        return self
+
+    def getSeed(self):
+        """
+        Gets the value of seed or its default value.
+        """
+        return self.getOrDefault(self.seed)
+
+
+class HasTol(Params):
+    """
+    Mixin for param tol: the convergence tolerance for iterative algorithms.
+    """
+
+    # a placeholder to make it appear in the generated doc
+    tol = Param(Params._dummy(), "tol", "the convergence tolerance for iterative algorithms")
+
+    def __init__(self):
+        super(HasTol, self).__init__()
+        #: param for the convergence tolerance for iterative algorithms
+        self.tol = Param(self, "tol", "the convergence tolerance for iterative algorithms")
+        if None is not None:
+            self._setDefault(tol=None)
+
+    def setTol(self, value):
+        """
+        Sets the value of :py:attr:`tol`.
+        """
+        self.paramMap[self.tol] = value
+        return self
+
+    def getTol(self):
+        """
+        Gets the value of tol or its default value.
+        """
+        return self.getOrDefault(self.tol)
+
+
+class HasStepSize(Params):
+    """
+    Mixin for param stepSize: Step size to be used for each iteration of optimization..
+    """
+
+    # a placeholder to make it appear in the generated doc
+    stepSize = Param(Params._dummy(), "stepSize",
+                     "Step size to be used for each iteration of optimization.")
+
+    def __init__(self):
+        super(HasStepSize, self).__init__()
+        #: param for Step size to be used for each iteration of optimization.
+        self.stepSize = Param(self, "stepSize",
+                              "Step size to be used for each iteration of optimization.")
+        if None is not None:
+            self._setDefault(stepSize=None)
+
+    def setStepSize(self, value):
+        """
+        Sets the value of :py:attr:`stepSize`.
+        """
+        self.paramMap[self.stepSize] = value
+        return self
+
+    def getStepSize(self):
+        """
+        Gets the value of stepSize or its default value.
+        """
+        return self.getOrDefault(self.stepSize)

From 65afd3ce8b8a0b00f4ea8294eac14b72e964872d Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Fri, 8 May 2015 11:16:04 -0700
Subject: [PATCH 61/75] [SPARK-7474] [MLLIB] update ParamGridBuilder doctest

Multiline commands are properly handled in this PR. oefirouz

![screen shot 2015-05-07 at 10 53 25 pm](https://cloud.githubusercontent.com/assets/829644/7531290/02ad2fd4-f50c-11e4-8c04-e58d1a61ad69.png)

Author: Xiangrui Meng <meng@databricks.com>

Closes #6001 from mengxr/SPARK-7474 and squashes the following commits:

b94b11d [Xiangrui Meng] update ParamGridBuilder doctest
---
 python/pyspark/ml/tuning.py | 28 +++++++++++++---------------
 1 file changed, 13 insertions(+), 15 deletions(-)

diff --git a/python/pyspark/ml/tuning.py b/python/pyspark/ml/tuning.py
index 1e04c37fca53e..28e3727f2c064 100644
--- a/python/pyspark/ml/tuning.py
+++ b/python/pyspark/ml/tuning.py
@@ -27,24 +27,22 @@
 
 
 class ParamGridBuilder(object):
-    """
+    r"""
     Builder for a param grid used in grid search-based model selection.
 
-    >>> from classification import LogisticRegression
+    >>> from pyspark.ml.classification import LogisticRegression
     >>> lr = LogisticRegression()
-    >>> output = ParamGridBuilder().baseOn({lr.labelCol: 'l'}) \
-            .baseOn([lr.predictionCol, 'p']) \
-            .addGrid(lr.regParam, [1.0, 2.0, 3.0]) \
-            .addGrid(lr.maxIter, [1, 5]) \
-            .addGrid(lr.featuresCol, ['f']) \
-            .build()
-    >>> expected = [ \
-{lr.regParam: 1.0, lr.featuresCol: 'f', lr.maxIter: 1, lr.labelCol: 'l', lr.predictionCol: 'p'}, \
-{lr.regParam: 2.0, lr.featuresCol: 'f', lr.maxIter: 1, lr.labelCol: 'l', lr.predictionCol: 'p'}, \
-{lr.regParam: 3.0, lr.featuresCol: 'f', lr.maxIter: 1, lr.labelCol: 'l', lr.predictionCol: 'p'}, \
-{lr.regParam: 1.0, lr.featuresCol: 'f', lr.maxIter: 5, lr.labelCol: 'l', lr.predictionCol: 'p'}, \
-{lr.regParam: 2.0, lr.featuresCol: 'f', lr.maxIter: 5, lr.labelCol: 'l', lr.predictionCol: 'p'}, \
-{lr.regParam: 3.0, lr.featuresCol: 'f', lr.maxIter: 5, lr.labelCol: 'l', lr.predictionCol: 'p'}]
+    >>> output = ParamGridBuilder() \
+    ...     .baseOn({lr.labelCol: 'l'}) \
+    ...     .baseOn([lr.predictionCol, 'p']) \
+    ...     .addGrid(lr.regParam, [1.0, 2.0]) \
+    ...     .addGrid(lr.maxIter, [1, 5]) \
+    ...     .build()
+    >>> expected = [
+    ...     {lr.regParam: 1.0, lr.maxIter: 1, lr.labelCol: 'l', lr.predictionCol: 'p'},
+    ...     {lr.regParam: 2.0, lr.maxIter: 1, lr.labelCol: 'l', lr.predictionCol: 'p'},
+    ...     {lr.regParam: 1.0, lr.maxIter: 5, lr.labelCol: 'l', lr.predictionCol: 'p'},
+    ...     {lr.regParam: 2.0, lr.maxIter: 5, lr.labelCol: 'l', lr.predictionCol: 'p'}]
     >>> len(output) == len(expected)
     True
     >>> all([m in expected for m in output])

From 008a60dd371e76819d8e08ab638cac7b3a48c9fc Mon Sep 17 00:00:00 2001
From: hqzizania <qian.huang@intel.com>
Date: Fri, 8 May 2015 11:25:04 -0700
Subject: [PATCH 62/75] [SPARK-6824] Fill the docs for DataFrame API in SparkR

This patch also removes the RDD docs from being built as a part of roxygen just by the method to delete
" ' '" of " \#' ".

Author: hqzizania <qian.huang@intel.com>
Author: qhuang <qian.huang@intel.com>

Closes #5969 from hqzizania/R1 and squashes the following commits:

6d27696 [qhuang] fixes in NAMESPACE
eb4b095 [qhuang] remove more docs
6394579 [qhuang] remove RDD docs in generics.R
6813860 [hqzizania] Fill the docs for DataFrame API in SparkR
857220f [hqzizania] remove the pairRDD docs from being built as a part of roxygen
c045d64 [hqzizania] remove the RDD docs from being built as a part of roxygen
---
 R/pkg/DESCRIPTION    |    2 +-
 R/pkg/NAMESPACE      |    4 -
 R/pkg/R/DataFrame.R  |   95 +--
 R/pkg/R/RDD.R        | 1546 +++++++++++++++++++++---------------------
 R/pkg/R/SQLContext.R |   64 +-
 R/pkg/R/broadcast.R  |   64 +-
 R/pkg/R/context.R    |  240 +++----
 R/pkg/R/generics.R   |  318 ++++-----
 R/pkg/R/pairRDD.R    |  886 ++++++++++++------------
 9 files changed, 1610 insertions(+), 1609 deletions(-)

diff --git a/R/pkg/DESCRIPTION b/R/pkg/DESCRIPTION
index 1c1779a763c7e..efc85bbc4b316 100644
--- a/R/pkg/DESCRIPTION
+++ b/R/pkg/DESCRIPTION
@@ -15,11 +15,11 @@ Suggests:
 Description: R frontend for Spark
 License: Apache License (== 2.0)
 Collate:
+    'schema.R'
     'generics.R'
     'jobj.R'
     'RDD.R'
     'pairRDD.R'
-    'schema.R'
     'column.R'
     'group.R'
     'DataFrame.R'
diff --git a/R/pkg/NAMESPACE b/R/pkg/NAMESPACE
index 3fb92be0940b7..7611f479a628b 100644
--- a/R/pkg/NAMESPACE
+++ b/R/pkg/NAMESPACE
@@ -26,7 +26,6 @@ exportMethods("cache",
               "intersect",
               "isLocal",
               "join",
-              "length",
               "limit",
               "orderBy",
               "names",
@@ -101,9 +100,6 @@ export("cacheTable",
        "tables",
        "uncacheTable")
 
-export("sparkRSQL.init",
-       "sparkRHive.init")
-
 export("structField",
        "structField.jobj",
        "structField.character",
diff --git a/R/pkg/R/DataFrame.R b/R/pkg/R/DataFrame.R
index 47d92f141cc7d..354642e7bc307 100644
--- a/R/pkg/R/DataFrame.R
+++ b/R/pkg/R/DataFrame.R
@@ -45,6 +45,9 @@ setMethod("initialize", "DataFrame", function(.Object, sdf, isCached) {
 
 #' @rdname DataFrame
 #' @export
+#'
+#' @param sdf A Java object reference to the backing Scala DataFrame
+#' @param isCached TRUE if the dataFrame is cached
 dataFrame <- function(sdf, isCached = FALSE) {
   new("DataFrame", sdf, isCached)
 }
@@ -244,7 +247,7 @@ setMethod("columns",
           })
 
 #' @rdname columns
-#' @export
+#' @aliases names,DataFrame,function-method
 setMethod("names",
           signature(x = "DataFrame"),
           function(x) {
@@ -399,23 +402,23 @@ setMethod("repartition",
             dataFrame(sdf)     
           })
 
-#' toJSON
-#'
-#' Convert the rows of a DataFrame into JSON objects and return an RDD where
-#' each element contains a JSON string.
-#'
-#' @param x A SparkSQL DataFrame
-#' @return A StringRRDD of JSON objects
-#' @rdname tojson
-#' @export
-#' @examples
-#'\dontrun{
-#' sc <- sparkR.init()
-#' sqlCtx <- sparkRSQL.init(sc)
-#' path <- "path/to/file.json"
-#' df <- jsonFile(sqlCtx, path)
-#' newRDD <- toJSON(df)
-#'}
+# toJSON
+#
+# Convert the rows of a DataFrame into JSON objects and return an RDD where
+# each element contains a JSON string.
+#
+#@param x A SparkSQL DataFrame
+# @return A StringRRDD of JSON objects
+# @rdname tojson
+# @export
+# @examples
+#\dontrun{
+# sc <- sparkR.init()
+# sqlCtx <- sparkRSQL.init(sc)
+# path <- "path/to/file.json"
+# df <- jsonFile(sqlCtx, path)
+# newRDD <- toJSON(df)
+#}
 setMethod("toJSON",
           signature(x = "DataFrame"),
           function(x) {
@@ -578,8 +581,8 @@ setMethod("limit",
             dataFrame(res)
           })
 
-# Take the first NUM rows of a DataFrame and return a the results as a data.frame
-
+#' Take the first NUM rows of a DataFrame and return a the results as a data.frame
+#' 
 #' @rdname take
 #' @export
 #' @examples
@@ -644,22 +647,22 @@ setMethod("first",
             take(x, 1)
           })
 
-#' toRDD()
-#' 
-#' Converts a Spark DataFrame to an RDD while preserving column names.
-#' 
-#' @param x A Spark DataFrame
-#' 
-#' @rdname DataFrame
-#' @export
-#' @examples
-#'\dontrun{
-#' sc <- sparkR.init()
-#' sqlCtx <- sparkRSQL.init(sc)
-#' path <- "path/to/file.json"
-#' df <- jsonFile(sqlCtx, path)
-#' rdd <- toRDD(df)
-#' }
+# toRDD()
+# 
+# Converts a Spark DataFrame to an RDD while preserving column names.
+# 
+# @param x A Spark DataFrame
+# 
+# @rdname DataFrame
+# @export
+# @examples
+#\dontrun{
+# sc <- sparkR.init()
+# sqlCtx <- sparkRSQL.init(sc)
+# path <- "path/to/file.json"
+# df <- jsonFile(sqlCtx, path)
+# rdd <- toRDD(df)
+# }
 setMethod("toRDD",
           signature(x = "DataFrame"),
           function(x) {
@@ -706,6 +709,7 @@ setMethod("groupBy",
 #'
 #' Compute aggregates by specifying a list of columns
 #'
+#' @param x a DataFrame
 #' @rdname DataFrame
 #' @export
 setMethod("agg",
@@ -721,7 +725,7 @@ setMethod("agg",
 # the requested map function.                                                     #
 ###################################################################################
 
-#' @rdname lapply
+# @rdname lapply
 setMethod("lapply",
           signature(X = "DataFrame", FUN = "function"),
           function(X, FUN) {
@@ -729,14 +733,14 @@ setMethod("lapply",
             lapply(rdd, FUN)
           })
 
-#' @rdname lapply
+# @rdname lapply
 setMethod("map",
           signature(X = "DataFrame", FUN = "function"),
           function(X, FUN) {
             lapply(X, FUN)
           })
 
-#' @rdname flatMap
+# @rdname flatMap
 setMethod("flatMap",
           signature(X = "DataFrame", FUN = "function"),
           function(X, FUN) {
@@ -744,7 +748,7 @@ setMethod("flatMap",
             flatMap(rdd, FUN)
           })
 
-#' @rdname lapplyPartition
+# @rdname lapplyPartition
 setMethod("lapplyPartition",
           signature(X = "DataFrame", FUN = "function"),
           function(X, FUN) {
@@ -752,14 +756,14 @@ setMethod("lapplyPartition",
             lapplyPartition(rdd, FUN)
           })
 
-#' @rdname lapplyPartition
+# @rdname lapplyPartition
 setMethod("mapPartitions",
           signature(X = "DataFrame", FUN = "function"),
           function(X, FUN) {
             lapplyPartition(X, FUN)
           })
 
-#' @rdname foreach
+# @rdname foreach
 setMethod("foreach",
           signature(x = "DataFrame", func = "function"),
           function(x, func) {
@@ -767,7 +771,7 @@ setMethod("foreach",
             foreach(rdd, func)
           })
 
-#' @rdname foreach
+# @rdname foreach
 setMethod("foreachPartition",
           signature(x = "DataFrame", func = "function"),
           function(x, func) {
@@ -788,6 +792,7 @@ setMethod("$", signature(x = "DataFrame"),
             getColumn(x, name)
           })
 
+#' @rdname select
 setMethod("$<-", signature(x = "DataFrame"),
           function(x, name, value) {
             stopifnot(class(value) == "Column" || is.null(value))
@@ -1009,7 +1014,7 @@ setMethod("sortDF",
           })
 
 #' @rdname sortDF
-#' @export
+#' @aliases orderBy,DataFrame,function-method
 setMethod("orderBy",
           signature(x = "DataFrame", col = "characterOrColumn"),
           function(x, col) {
@@ -1046,7 +1051,7 @@ setMethod("filter",
           })
 
 #' @rdname filter
-#' @export
+#' @aliases where,DataFrame,function-method
 setMethod("where",
           signature(x = "DataFrame", condition = "characterOrColumn"),
           function(x, condition) {
diff --git a/R/pkg/R/RDD.R b/R/pkg/R/RDD.R
index d1018c2361670..73999a6737032 100644
--- a/R/pkg/R/RDD.R
+++ b/R/pkg/R/RDD.R
@@ -19,16 +19,16 @@
 
 setOldClass("jobj")
 
-#' @title S4 class that represents an RDD
-#' @description RDD can be created using functions like
-#'              \code{parallelize}, \code{textFile} etc.
-#' @rdname RDD
-#' @seealso parallelize, textFile
-#'
-#' @slot env An R environment that stores bookkeeping states of the RDD
-#' @slot jrdd Java object reference to the backing JavaRDD
-#' to an RDD
-#' @export
+# @title S4 class that represents an RDD
+# @description RDD can be created using functions like
+#              \code{parallelize}, \code{textFile} etc.
+# @rdname RDD
+# @seealso parallelize, textFile
+#
+# @slot env An R environment that stores bookkeeping states of the RDD
+# @slot jrdd Java object reference to the backing JavaRDD
+# to an RDD
+# @export
 setClass("RDD",
          slots = list(env = "environment",
                       jrdd = "jobj"))
@@ -108,14 +108,14 @@ setMethod("initialize", "PipelinedRDD", function(.Object, prev, func, jrdd_val)
   .Object
 })
 
-#' @rdname RDD
-#' @export
-#'
-#' @param jrdd Java object reference to the backing JavaRDD
-#' @param serializedMode Use "byte" if the RDD stores data serialized in R, "string" if the RDD
-#' stores strings, and "row" if the RDD stores the rows of a DataFrame
-#' @param isCached TRUE if the RDD is cached
-#' @param isCheckpointed TRUE if the RDD has been checkpointed
+# @rdname RDD
+# @export
+#
+# @param jrdd Java object reference to the backing JavaRDD
+# @param serializedMode Use "byte" if the RDD stores data serialized in R, "string" if the RDD
+# stores strings, and "row" if the RDD stores the rows of a DataFrame
+# @param isCached TRUE if the RDD is cached
+# @param isCheckpointed TRUE if the RDD has been checkpointed
 RDD <- function(jrdd, serializedMode = "byte", isCached = FALSE,
                 isCheckpointed = FALSE) {
   new("RDD", jrdd, serializedMode, isCached, isCheckpointed)
@@ -200,19 +200,19 @@ setValidity("RDD",
 
 ############ Actions and Transformations ############
 
-#' Persist an RDD
-#'
-#' Persist this RDD with the default storage level (MEMORY_ONLY).
-#'
-#' @param x The RDD to cache
-#' @examples
-#'\dontrun{
-#' sc <- sparkR.init()
-#' rdd <- parallelize(sc, 1:10, 2L)
-#' cache(rdd)
-#'}
-#' @rdname cache-methods
-#' @aliases cache,RDD-method
+# Persist an RDD
+#
+# Persist this RDD with the default storage level (MEMORY_ONLY).
+#
+# @param x The RDD to cache
+# @examples
+#\dontrun{
+# sc <- sparkR.init()
+# rdd <- parallelize(sc, 1:10, 2L)
+# cache(rdd)
+#}
+# @rdname cache-methods
+# @aliases cache,RDD-method
 setMethod("cache",
           signature(x = "RDD"),
           function(x) {
@@ -221,22 +221,22 @@ setMethod("cache",
             x
           })
 
-#' Persist an RDD
-#'
-#' Persist this RDD with the specified storage level. For details of the
-#' supported storage levels, refer to
-#' http://spark.apache.org/docs/latest/programming-guide.html#rdd-persistence.
-#'
-#' @param x The RDD to persist
-#' @param newLevel The new storage level to be assigned
-#' @examples
-#'\dontrun{
-#' sc <- sparkR.init()
-#' rdd <- parallelize(sc, 1:10, 2L)
-#' persist(rdd, "MEMORY_AND_DISK")
-#'}
-#' @rdname persist
-#' @aliases persist,RDD-method
+# Persist an RDD
+#
+# Persist this RDD with the specified storage level. For details of the
+# supported storage levels, refer to
+# http://spark.apache.org/docs/latest/programming-guide.html#rdd-persistence.
+#
+# @param x The RDD to persist
+# @param newLevel The new storage level to be assigned
+# @examples
+#\dontrun{
+# sc <- sparkR.init()
+# rdd <- parallelize(sc, 1:10, 2L)
+# persist(rdd, "MEMORY_AND_DISK")
+#}
+# @rdname persist
+# @aliases persist,RDD-method
 setMethod("persist",
           signature(x = "RDD", newLevel = "character"),
           function(x, newLevel) {
@@ -245,21 +245,21 @@ setMethod("persist",
             x
           })
 
-#' Unpersist an RDD
-#'
-#' Mark the RDD as non-persistent, and remove all blocks for it from memory and
-#' disk.
-#'
-#' @param x The RDD to unpersist
-#' @examples
-#'\dontrun{
-#' sc <- sparkR.init()
-#' rdd <- parallelize(sc, 1:10, 2L)
-#' cache(rdd) # rdd@@env$isCached == TRUE
-#' unpersist(rdd) # rdd@@env$isCached == FALSE
-#'}
-#' @rdname unpersist-methods
-#' @aliases unpersist,RDD-method
+# Unpersist an RDD
+#
+# Mark the RDD as non-persistent, and remove all blocks for it from memory and
+# disk.
+#
+# @param x The RDD to unpersist
+# @examples
+#\dontrun{
+# sc <- sparkR.init()
+# rdd <- parallelize(sc, 1:10, 2L)
+# cache(rdd) # rdd@@env$isCached == TRUE
+# unpersist(rdd) # rdd@@env$isCached == FALSE
+#}
+# @rdname unpersist-methods
+# @aliases unpersist,RDD-method
 setMethod("unpersist",
           signature(x = "RDD"),
           function(x) {
@@ -268,24 +268,24 @@ setMethod("unpersist",
             x
           })
 
-#' Checkpoint an RDD
-#'
-#' Mark this RDD for checkpointing. It will be saved to a file inside the
-#' checkpoint directory set with setCheckpointDir() 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.
-#'
-#' @param x The RDD to checkpoint
-#' @examples
-#'\dontrun{
-#' sc <- sparkR.init()
-#' setCheckpointDir(sc, "checkpoint")
-#' rdd <- parallelize(sc, 1:10, 2L)
-#' checkpoint(rdd)
-#'}
-#' @rdname checkpoint-methods
-#' @aliases checkpoint,RDD-method
+# Checkpoint an RDD
+#
+# Mark this RDD for checkpointing. It will be saved to a file inside the
+# checkpoint directory set with setCheckpointDir() 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.
+#
+# @param x The RDD to checkpoint
+# @examples
+#\dontrun{
+# sc <- sparkR.init()
+# setCheckpointDir(sc, "checkpoint")
+# rdd <- parallelize(sc, 1:10, 2L)
+# checkpoint(rdd)
+#}
+# @rdname checkpoint-methods
+# @aliases checkpoint,RDD-method
 setMethod("checkpoint",
           signature(x = "RDD"),
           function(x) {
@@ -295,18 +295,18 @@ setMethod("checkpoint",
             x
           })
 
-#' Gets the number of partitions of an RDD
-#'
-#' @param x A RDD.
-#' @return the number of partitions of rdd as an integer.
-#' @examples
-#'\dontrun{
-#' sc <- sparkR.init()
-#' rdd <- parallelize(sc, 1:10, 2L)
-#' numPartitions(rdd)  # 2L
-#'}
-#' @rdname numPartitions
-#' @aliases numPartitions,RDD-method
+# Gets the number of partitions of an RDD
+#
+# @param x A RDD.
+# @return the number of partitions of rdd as an integer.
+# @examples
+#\dontrun{
+# sc <- sparkR.init()
+# rdd <- parallelize(sc, 1:10, 2L)
+# numPartitions(rdd)  # 2L
+#}
+# @rdname numPartitions
+# @aliases numPartitions,RDD-method
 setMethod("numPartitions",
           signature(x = "RDD"),
           function(x) {
@@ -315,24 +315,24 @@ setMethod("numPartitions",
             callJMethod(partitions, "size")
           })
 
-#' Collect elements of an RDD
-#'
-#' @description
-#' \code{collect} returns a list that contains all of the elements in this RDD.
-#'
-#' @param x The RDD to collect
-#' @param ... Other optional arguments to collect
-#' @param flatten FALSE if the list should not flattened
-#' @return a list containing elements in the RDD
-#' @examples
-#'\dontrun{
-#' sc <- sparkR.init()
-#' rdd <- parallelize(sc, 1:10, 2L)
-#' collect(rdd) # list from 1 to 10
-#' collectPartition(rdd, 0L) # list from 1 to 5
-#'}
-#' @rdname collect-methods
-#' @aliases collect,RDD-method
+# Collect elements of an RDD
+#
+# @description
+# \code{collect} returns a list that contains all of the elements in this RDD.
+#
+# @param x The RDD to collect
+# @param ... Other optional arguments to collect
+# @param flatten FALSE if the list should not flattened
+# @return a list containing elements in the RDD
+# @examples
+#\dontrun{
+# sc <- sparkR.init()
+# rdd <- parallelize(sc, 1:10, 2L)
+# collect(rdd) # list from 1 to 10
+# collectPartition(rdd, 0L) # list from 1 to 5
+#}
+# @rdname collect-methods
+# @aliases collect,RDD-method
 setMethod("collect",
           signature(x = "RDD"),
           function(x, flatten = TRUE) {
@@ -343,12 +343,12 @@ setMethod("collect",
           })
 
 
-#' @description
-#' \code{collectPartition} returns a list that contains all of the elements
-#' in the specified partition of the RDD.
-#' @param partitionId the partition to collect (starts from 0)
-#' @rdname collect-methods
-#' @aliases collectPartition,integer,RDD-method
+# @description
+# \code{collectPartition} returns a list that contains all of the elements
+# in the specified partition of the RDD.
+# @param partitionId the partition to collect (starts from 0)
+# @rdname collect-methods
+# @aliases collectPartition,integer,RDD-method
 setMethod("collectPartition",
           signature(x = "RDD", partitionId = "integer"),
           function(x, partitionId) {
@@ -361,17 +361,17 @@ setMethod("collectPartition",
               serializedMode = getSerializedMode(x))
           })
 
-#' @description
-#' \code{collectAsMap} returns a named list as a map that contains all of the elements
-#' in a key-value pair RDD. 
-#' @examples
-#'\dontrun{
-#' sc <- sparkR.init()
-#' rdd <- parallelize(sc, list(list(1, 2), list(3, 4)), 2L)
-#' collectAsMap(rdd) # list(`1` = 2, `3` = 4)
-#'}
-#' @rdname collect-methods
-#' @aliases collectAsMap,RDD-method
+# @description
+# \code{collectAsMap} returns a named list as a map that contains all of the elements
+# in a key-value pair RDD. 
+# @examples
+#\dontrun{
+# sc <- sparkR.init()
+# rdd <- parallelize(sc, list(list(1, 2), list(3, 4)), 2L)
+# collectAsMap(rdd) # list(`1` = 2, `3` = 4)
+#}
+# @rdname collect-methods
+# @aliases collectAsMap,RDD-method
 setMethod("collectAsMap",
           signature(x = "RDD"),
           function(x) {
@@ -381,19 +381,19 @@ setMethod("collectAsMap",
             as.list(map)
           })
 
-#' Return the number of elements in the RDD.
-#'
-#' @param x The RDD to count
-#' @return number of elements in the RDD.
-#' @examples
-#'\dontrun{
-#' sc <- sparkR.init()
-#' rdd <- parallelize(sc, 1:10)
-#' count(rdd) # 10
-#' length(rdd) # Same as count
-#'}
-#' @rdname count
-#' @aliases count,RDD-method
+# Return the number of elements in the RDD.
+#
+# @param x The RDD to count
+# @return number of elements in the RDD.
+# @examples
+#\dontrun{
+# sc <- sparkR.init()
+# rdd <- parallelize(sc, 1:10)
+# count(rdd) # 10
+# length(rdd) # Same as count
+#}
+# @rdname count
+# @aliases count,RDD-method
 setMethod("count",
           signature(x = "RDD"),
           function(x) {
@@ -405,31 +405,31 @@ setMethod("count",
             sum(as.integer(vals))
           })
 
-#' Return the number of elements in the RDD
-#' @export
-#' @rdname count
+# Return the number of elements in the RDD
+# @export
+# @rdname count
 setMethod("length",
           signature(x = "RDD"),
           function(x) {
             count(x)
           })
 
-#' Return the count of each unique value in this RDD as a list of
-#' (value, count) pairs.
-#'
-#' Same as countByValue in Spark.
-#'
-#' @param x The RDD to count
-#' @return list of (value, count) pairs, where count is number of each unique
-#' value in rdd.
-#' @examples
-#'\dontrun{
-#' sc <- sparkR.init()
-#' rdd <- parallelize(sc, c(1,2,3,2,1))
-#' countByValue(rdd) # (1,2L), (2,2L), (3,1L)
-#'}
-#' @rdname countByValue
-#' @aliases countByValue,RDD-method
+# Return the count of each unique value in this RDD as a list of
+# (value, count) pairs.
+#
+# Same as countByValue in Spark.
+#
+# @param x The RDD to count
+# @return list of (value, count) pairs, where count is number of each unique
+# value in rdd.
+# @examples
+#\dontrun{
+# sc <- sparkR.init()
+# rdd <- parallelize(sc, c(1,2,3,2,1))
+# countByValue(rdd) # (1,2L), (2,2L), (3,1L)
+#}
+# @rdname countByValue
+# @aliases countByValue,RDD-method
 setMethod("countByValue",
           signature(x = "RDD"),
           function(x) {
@@ -437,23 +437,23 @@ setMethod("countByValue",
             collect(reduceByKey(ones, `+`, numPartitions(x)))
           })
 
-#' Apply a function to all elements
-#'
-#' This function creates a new RDD by applying the given transformation to all
-#' elements of the given RDD
-#'
-#' @param X The RDD to apply the transformation.
-#' @param FUN the transformation to apply on each element
-#' @return a new RDD created by the transformation.
-#' @rdname lapply
-#' @aliases lapply
-#' @examples
-#'\dontrun{
-#' sc <- sparkR.init()
-#' rdd <- parallelize(sc, 1:10)
-#' multiplyByTwo <- lapply(rdd, function(x) { x * 2 })
-#' collect(multiplyByTwo) # 2,4,6...
-#'}
+# Apply a function to all elements
+#
+# This function creates a new RDD by applying the given transformation to all
+# elements of the given RDD
+#
+# @param X The RDD to apply the transformation.
+# @param FUN the transformation to apply on each element
+# @return a new RDD created by the transformation.
+# @rdname lapply
+# @aliases lapply
+# @examples
+#\dontrun{
+# sc <- sparkR.init()
+# rdd <- parallelize(sc, 1:10)
+# multiplyByTwo <- lapply(rdd, function(x) { x * 2 })
+# collect(multiplyByTwo) # 2,4,6...
+#}
 setMethod("lapply",
           signature(X = "RDD", FUN = "function"),
           function(X, FUN) {
@@ -463,31 +463,31 @@ setMethod("lapply",
             lapplyPartitionsWithIndex(X, func)
           })
 
-#' @rdname lapply
-#' @aliases map,RDD,function-method
+# @rdname lapply
+# @aliases map,RDD,function-method
 setMethod("map",
           signature(X = "RDD", FUN = "function"),
           function(X, FUN) {
             lapply(X, FUN)
           })
 
-#' Flatten results after apply a function to all elements
-#'
-#' This function return a new RDD by first applying a function to all
-#' elements of this RDD, and then flattening the results.
-#'
-#' @param X The RDD to apply the transformation.
-#' @param FUN the transformation to apply on each element
-#' @return a new RDD created by the transformation.
-#' @examples
-#'\dontrun{
-#' sc <- sparkR.init()
-#' rdd <- parallelize(sc, 1:10)
-#' multiplyByTwo <- flatMap(rdd, function(x) { list(x*2, x*10) })
-#' collect(multiplyByTwo) # 2,20,4,40,6,60...
-#'}
-#' @rdname flatMap
-#' @aliases flatMap,RDD,function-method
+# Flatten results after apply a function to all elements
+#
+# This function return a new RDD by first applying a function to all
+# elements of this RDD, and then flattening the results.
+#
+# @param X The RDD to apply the transformation.
+# @param FUN the transformation to apply on each element
+# @return a new RDD created by the transformation.
+# @examples
+#\dontrun{
+# sc <- sparkR.init()
+# rdd <- parallelize(sc, 1:10)
+# multiplyByTwo <- flatMap(rdd, function(x) { list(x*2, x*10) })
+# collect(multiplyByTwo) # 2,20,4,40,6,60...
+#}
+# @rdname flatMap
+# @aliases flatMap,RDD,function-method
 setMethod("flatMap",
           signature(X = "RDD", FUN = "function"),
           function(X, FUN) {
@@ -500,83 +500,83 @@ setMethod("flatMap",
             lapplyPartition(X, partitionFunc)
           })
 
-#' Apply a function to each partition of an RDD
-#'
-#' Return a new RDD by applying a function to each partition of this RDD.
-#'
-#' @param X The RDD to apply the transformation.
-#' @param FUN the transformation to apply on each partition.
-#' @return a new RDD created by the transformation.
-#' @examples
-#'\dontrun{
-#' sc <- sparkR.init()
-#' rdd <- parallelize(sc, 1:10)
-#' partitionSum <- lapplyPartition(rdd, function(part) { Reduce("+", part) })
-#' collect(partitionSum) # 15, 40
-#'}
-#' @rdname lapplyPartition
-#' @aliases lapplyPartition,RDD,function-method
+# Apply a function to each partition of an RDD
+#
+# Return a new RDD by applying a function to each partition of this RDD.
+#
+# @param X The RDD to apply the transformation.
+# @param FUN the transformation to apply on each partition.
+# @return a new RDD created by the transformation.
+# @examples
+#\dontrun{
+# sc <- sparkR.init()
+# rdd <- parallelize(sc, 1:10)
+# partitionSum <- lapplyPartition(rdd, function(part) { Reduce("+", part) })
+# collect(partitionSum) # 15, 40
+#}
+# @rdname lapplyPartition
+# @aliases lapplyPartition,RDD,function-method
 setMethod("lapplyPartition",
           signature(X = "RDD", FUN = "function"),
           function(X, FUN) {
             lapplyPartitionsWithIndex(X, function(s, part) { FUN(part) })
           })
 
-#' mapPartitions is the same as lapplyPartition.
-#'
-#' @rdname lapplyPartition
-#' @aliases mapPartitions,RDD,function-method
+# mapPartitions is the same as lapplyPartition.
+#
+# @rdname lapplyPartition
+# @aliases mapPartitions,RDD,function-method
 setMethod("mapPartitions",
           signature(X = "RDD", FUN = "function"),
           function(X, FUN) {
             lapplyPartition(X, FUN)
           })
 
-#' Return a new RDD by applying a function to each partition of this RDD, while
-#' tracking the index of the original partition.
-#'
-#' @param X The RDD to apply the transformation.
-#' @param FUN the transformation to apply on each partition; takes the partition
-#'        index and a list of elements in the particular partition.
-#' @return a new RDD created by the transformation.
-#' @examples
-#'\dontrun{
-#' sc <- sparkR.init()
-#' rdd <- parallelize(sc, 1:10, 5L)
-#' prod <- lapplyPartitionsWithIndex(rdd, function(partIndex, part) {
-#'                                          partIndex * Reduce("+", part) })
-#' collect(prod, flatten = FALSE) # 0, 7, 22, 45, 76
-#'}
-#' @rdname lapplyPartitionsWithIndex
-#' @aliases lapplyPartitionsWithIndex,RDD,function-method
+# Return a new RDD by applying a function to each partition of this RDD, while
+# tracking the index of the original partition.
+#
+# @param X The RDD to apply the transformation.
+# @param FUN the transformation to apply on each partition; takes the partition
+#        index and a list of elements in the particular partition.
+# @return a new RDD created by the transformation.
+# @examples
+#\dontrun{
+# sc <- sparkR.init()
+# rdd <- parallelize(sc, 1:10, 5L)
+# prod <- lapplyPartitionsWithIndex(rdd, function(partIndex, part) {
+#                                          partIndex * Reduce("+", part) })
+# collect(prod, flatten = FALSE) # 0, 7, 22, 45, 76
+#}
+# @rdname lapplyPartitionsWithIndex
+# @aliases lapplyPartitionsWithIndex,RDD,function-method
 setMethod("lapplyPartitionsWithIndex",
           signature(X = "RDD", FUN = "function"),
           function(X, FUN) {
             PipelinedRDD(X, FUN)
           })
 
-#' @rdname lapplyPartitionsWithIndex
-#' @aliases mapPartitionsWithIndex,RDD,function-method
+# @rdname lapplyPartitionsWithIndex
+# @aliases mapPartitionsWithIndex,RDD,function-method
 setMethod("mapPartitionsWithIndex",
           signature(X = "RDD", FUN = "function"),
           function(X, FUN) {
             lapplyPartitionsWithIndex(X, FUN)
           })
 
-#' This function returns a new RDD containing only the elements that satisfy
-#' a predicate (i.e. returning TRUE in a given logical function).
-#' The same as `filter()' in Spark.
-#'
-#' @param x The RDD to be filtered.
-#' @param f A unary predicate function.
-#' @examples
-#'\dontrun{
-#' sc <- sparkR.init()
-#' rdd <- parallelize(sc, 1:10)
-#' unlist(collect(filterRDD(rdd, function (x) { x < 3 }))) # c(1, 2)
-#'}
-#' @rdname filterRDD
-#' @aliases filterRDD,RDD,function-method
+# This function returns a new RDD containing only the elements that satisfy
+# a predicate (i.e. returning TRUE in a given logical function).
+# The same as `filter()' in Spark.
+#
+# @param x The RDD to be filtered.
+# @param f A unary predicate function.
+# @examples
+#\dontrun{
+# sc <- sparkR.init()
+# rdd <- parallelize(sc, 1:10)
+# unlist(collect(filterRDD(rdd, function (x) { x < 3 }))) # c(1, 2)
+#}
+# @rdname filterRDD
+# @aliases filterRDD,RDD,function-method
 setMethod("filterRDD",
           signature(x = "RDD", f = "function"),
           function(x, f) {
@@ -586,30 +586,30 @@ setMethod("filterRDD",
             lapplyPartition(x, filter.func)
           })
 
-#' @rdname filterRDD
-#' @aliases Filter
+# @rdname filterRDD
+# @aliases Filter
 setMethod("Filter",
           signature(f = "function", x = "RDD"),
           function(f, x) {
             filterRDD(x, f)
           })
 
-#' Reduce across elements of an RDD.
-#'
-#' This function reduces the elements of this RDD using the
-#' specified commutative and associative binary operator.
-#'
-#' @param x The RDD to reduce
-#' @param func Commutative and associative function to apply on elements
-#'             of the RDD.
-#' @examples
-#'\dontrun{
-#' sc <- sparkR.init()
-#' rdd <- parallelize(sc, 1:10)
-#' reduce(rdd, "+") # 55
-#'}
-#' @rdname reduce
-#' @aliases reduce,RDD,ANY-method
+# Reduce across elements of an RDD.
+#
+# This function reduces the elements of this RDD using the
+# specified commutative and associative binary operator.
+#
+# @param x The RDD to reduce
+# @param func Commutative and associative function to apply on elements
+#             of the RDD.
+# @examples
+#\dontrun{
+# sc <- sparkR.init()
+# rdd <- parallelize(sc, 1:10)
+# reduce(rdd, "+") # 55
+#}
+# @rdname reduce
+# @aliases reduce,RDD,ANY-method
 setMethod("reduce",
           signature(x = "RDD", func = "ANY"),
           function(x, func) {
@@ -623,70 +623,70 @@ setMethod("reduce",
             Reduce(func, partitionList)
           })
 
-#' Get the maximum element of an RDD.
-#'
-#' @param x The RDD to get the maximum element from
-#' @examples
-#'\dontrun{
-#' sc <- sparkR.init()
-#' rdd <- parallelize(sc, 1:10)
-#' maximum(rdd) # 10
-#'}
-#' @rdname maximum
-#' @aliases maximum,RDD
+# Get the maximum element of an RDD.
+#
+# @param x The RDD to get the maximum element from
+# @examples
+#\dontrun{
+# sc <- sparkR.init()
+# rdd <- parallelize(sc, 1:10)
+# maximum(rdd) # 10
+#}
+# @rdname maximum
+# @aliases maximum,RDD
 setMethod("maximum",
           signature(x = "RDD"),
           function(x) {
             reduce(x, max)
           })
 
-#' Get the minimum element of an RDD.
-#'
-#' @param x The RDD to get the minimum element from
-#' @examples
-#'\dontrun{
-#' sc <- sparkR.init()
-#' rdd <- parallelize(sc, 1:10)
-#' minimum(rdd) # 1
-#'}
-#' @rdname minimum
-#' @aliases minimum,RDD
+# Get the minimum element of an RDD.
+#
+# @param x The RDD to get the minimum element from
+# @examples
+#\dontrun{
+# sc <- sparkR.init()
+# rdd <- parallelize(sc, 1:10)
+# minimum(rdd) # 1
+#}
+# @rdname minimum
+# @aliases minimum,RDD
 setMethod("minimum",
           signature(x = "RDD"),
           function(x) {
             reduce(x, min)
           })
 
-#' Add up the elements in an RDD.
-#'
-#' @param x The RDD to add up the elements in
-#' @examples
-#'\dontrun{
-#' sc <- sparkR.init()
-#' rdd <- parallelize(sc, 1:10)
-#' sumRDD(rdd) # 55
-#'}
-#' @rdname sumRDD 
-#' @aliases sumRDD,RDD
+# Add up the elements in an RDD.
+#
+# @param x The RDD to add up the elements in
+# @examples
+#\dontrun{
+# sc <- sparkR.init()
+# rdd <- parallelize(sc, 1:10)
+# sumRDD(rdd) # 55
+#}
+# @rdname sumRDD 
+# @aliases sumRDD,RDD
 setMethod("sumRDD",
           signature(x = "RDD"),
           function(x) {
             reduce(x, "+")
           })
 
-#' Applies a function to all elements in an RDD, and force evaluation.
-#'
-#' @param x The RDD to apply the function
-#' @param func The function to be applied.
-#' @return invisible NULL.
-#' @examples
-#'\dontrun{
-#' sc <- sparkR.init()
-#' rdd <- parallelize(sc, 1:10)
-#' foreach(rdd, function(x) { save(x, file=...) })
-#'}
-#' @rdname foreach
-#' @aliases foreach,RDD,function-method
+# Applies a function to all elements in an RDD, and force evaluation.
+#
+# @param x The RDD to apply the function
+# @param func The function to be applied.
+# @return invisible NULL.
+# @examples
+#\dontrun{
+# sc <- sparkR.init()
+# rdd <- parallelize(sc, 1:10)
+# foreach(rdd, function(x) { save(x, file=...) })
+#}
+# @rdname foreach
+# @aliases foreach,RDD,function-method
 setMethod("foreach",
           signature(x = "RDD", func = "function"),
           function(x, func) {
@@ -697,37 +697,37 @@ setMethod("foreach",
             invisible(collect(mapPartitions(x, partition.func)))
           })
 
-#' Applies a function to each partition in an RDD, and force evaluation.
-#'
-#' @examples
-#'\dontrun{
-#' sc <- sparkR.init()
-#' rdd <- parallelize(sc, 1:10)
-#' foreachPartition(rdd, function(part) { save(part, file=...); NULL })
-#'}
-#' @rdname foreach
-#' @aliases foreachPartition,RDD,function-method
+# Applies a function to each partition in an RDD, and force evaluation.
+#
+# @examples
+#\dontrun{
+# sc <- sparkR.init()
+# rdd <- parallelize(sc, 1:10)
+# foreachPartition(rdd, function(part) { save(part, file=...); NULL })
+#}
+# @rdname foreach
+# @aliases foreachPartition,RDD,function-method
 setMethod("foreachPartition",
           signature(x = "RDD", func = "function"),
           function(x, func) {
             invisible(collect(mapPartitions(x, func)))
           })
 
-#' Take elements from an RDD.
-#'
-#' This function takes the first NUM elements in the RDD and
-#' returns them in a list.
-#'
-#' @param x The RDD to take elements from
-#' @param num Number of elements to take
-#' @examples
-#'\dontrun{
-#' sc <- sparkR.init()
-#' rdd <- parallelize(sc, 1:10)
-#' take(rdd, 2L) # list(1, 2)
-#'}
-#' @rdname take
-#' @aliases take,RDD,numeric-method
+# Take elements from an RDD.
+#
+# This function takes the first NUM elements in the RDD and
+# returns them in a list.
+#
+# @param x The RDD to take elements from
+# @param num Number of elements to take
+# @examples
+#\dontrun{
+# sc <- sparkR.init()
+# rdd <- parallelize(sc, 1:10)
+# take(rdd, 2L) # list(1, 2)
+#}
+# @rdname take
+# @aliases take,RDD,numeric-method
 setMethod("take",
           signature(x = "RDD", num = "numeric"),
           function(x, num) {
@@ -762,39 +762,39 @@ setMethod("take",
           })
 
 
-#' First
-#'
-#' Return the first element of an RDD
-#'
-#' @rdname first
-#' @export
-#' @examples
-#'\dontrun{
-#' sc <- sparkR.init()
-#' rdd <- parallelize(sc, 1:10)
-#' first(rdd)
-#' }
+# First
+#
+# Return the first element of an RDD
+#
+# @rdname first
+# @export
+# @examples
+#\dontrun{
+# sc <- sparkR.init()
+# rdd <- parallelize(sc, 1:10)
+# first(rdd)
+# }
 setMethod("first",
           signature(x = "RDD"),
           function(x) {
             take(x, 1)[[1]]
           })
 
-#' Removes the duplicates from RDD.
-#'
-#' This function returns a new RDD containing the distinct elements in the
-#' given RDD. The same as `distinct()' in Spark.
-#'
-#' @param x The RDD to remove duplicates from.
-#' @param numPartitions Number of partitions to create.
-#' @examples
-#'\dontrun{
-#' sc <- sparkR.init()
-#' rdd <- parallelize(sc, c(1,2,2,3,3,3))
-#' sort(unlist(collect(distinct(rdd)))) # c(1, 2, 3)
-#'}
-#' @rdname distinct
-#' @aliases distinct,RDD-method
+# Removes the duplicates from RDD.
+#
+# This function returns a new RDD containing the distinct elements in the
+# given RDD. The same as `distinct()' in Spark.
+#
+# @param x The RDD to remove duplicates from.
+# @param numPartitions Number of partitions to create.
+# @examples
+#\dontrun{
+# sc <- sparkR.init()
+# rdd <- parallelize(sc, c(1,2,2,3,3,3))
+# sort(unlist(collect(distinct(rdd)))) # c(1, 2, 3)
+#}
+# @rdname distinct
+# @aliases distinct,RDD-method
 setMethod("distinct",
           signature(x = "RDD"),
           function(x, numPartitions = SparkR:::numPartitions(x)) {
@@ -806,24 +806,24 @@ setMethod("distinct",
             resRDD
           })
 
-#' Return an RDD that is a sampled subset of the given RDD.
-#'
-#' The same as `sample()' in Spark. (We rename it due to signature
-#' inconsistencies with the `sample()' function in R's base package.)
-#'
-#' @param x The RDD to sample elements from
-#' @param withReplacement Sampling with replacement or not
-#' @param fraction The (rough) sample target fraction
-#' @param seed Randomness seed value
-#' @examples
-#'\dontrun{
-#' sc <- sparkR.init()
-#' rdd <- parallelize(sc, 1:10)
-#' collect(sampleRDD(rdd, FALSE, 0.5, 1618L)) # ~5 distinct elements
-#' collect(sampleRDD(rdd, TRUE, 0.5, 9L)) # ~5 elements possibly with duplicates
-#'}
-#' @rdname sampleRDD
-#' @aliases sampleRDD,RDD
+# Return an RDD that is a sampled subset of the given RDD.
+#
+# The same as `sample()' in Spark. (We rename it due to signature
+# inconsistencies with the `sample()' function in R's base package.)
+#
+# @param x The RDD to sample elements from
+# @param withReplacement Sampling with replacement or not
+# @param fraction The (rough) sample target fraction
+# @param seed Randomness seed value
+# @examples
+#\dontrun{
+# sc <- sparkR.init()
+# rdd <- parallelize(sc, 1:10)
+# collect(sampleRDD(rdd, FALSE, 0.5, 1618L)) # ~5 distinct elements
+# collect(sampleRDD(rdd, TRUE, 0.5, 9L)) # ~5 elements possibly with duplicates
+#}
+# @rdname sampleRDD
+# @aliases sampleRDD,RDD
 setMethod("sampleRDD",
           signature(x = "RDD", withReplacement = "logical",
                     fraction = "numeric", seed = "integer"),
@@ -867,23 +867,23 @@ setMethod("sampleRDD",
             lapplyPartitionsWithIndex(x, samplingFunc)
           })
 
-#' Return a list of the elements that are a sampled subset of the given RDD.
-#'
-#' @param x The RDD to sample elements from
-#' @param withReplacement Sampling with replacement or not
-#' @param num Number of elements to return
-#' @param seed Randomness seed value
-#' @examples
-#'\dontrun{
-#' sc <- sparkR.init()
-#' rdd <- parallelize(sc, 1:100)
-#' # exactly 5 elements sampled, which may not be distinct
-#' takeSample(rdd, TRUE, 5L, 1618L)
-#' # exactly 5 distinct elements sampled
-#' takeSample(rdd, FALSE, 5L, 16181618L)
-#'}
-#' @rdname takeSample
-#' @aliases takeSample,RDD
+# Return a list of the elements that are a sampled subset of the given RDD.
+#
+# @param x The RDD to sample elements from
+# @param withReplacement Sampling with replacement or not
+# @param num Number of elements to return
+# @param seed Randomness seed value
+# @examples
+#\dontrun{
+# sc <- sparkR.init()
+# rdd <- parallelize(sc, 1:100)
+# # exactly 5 elements sampled, which may not be distinct
+# takeSample(rdd, TRUE, 5L, 1618L)
+# # exactly 5 distinct elements sampled
+# takeSample(rdd, FALSE, 5L, 16181618L)
+#}
+# @rdname takeSample
+# @aliases takeSample,RDD
 setMethod("takeSample", signature(x = "RDD", withReplacement = "logical",
                                   num = "integer", seed = "integer"),
           function(x, withReplacement, num, seed) {
@@ -930,18 +930,18 @@ setMethod("takeSample", signature(x = "RDD", withReplacement = "logical",
             sample(samples)[1:total]
           })
 
-#' Creates tuples of the elements in this RDD by applying a function.
-#'
-#' @param x The RDD.
-#' @param func The function to be applied.
-#' @examples
-#'\dontrun{
-#' sc <- sparkR.init()
-#' rdd <- parallelize(sc, list(1, 2, 3))
-#' collect(keyBy(rdd, function(x) { x*x })) # list(list(1, 1), list(4, 2), list(9, 3))
-#'}
-#' @rdname keyBy
-#' @aliases keyBy,RDD
+# Creates tuples of the elements in this RDD by applying a function.
+#
+# @param x The RDD.
+# @param func The function to be applied.
+# @examples
+#\dontrun{
+# sc <- sparkR.init()
+# rdd <- parallelize(sc, list(1, 2, 3))
+# collect(keyBy(rdd, function(x) { x*x })) # list(list(1, 1), list(4, 2), list(9, 3))
+#}
+# @rdname keyBy
+# @aliases keyBy,RDD
 setMethod("keyBy",
           signature(x = "RDD", func = "function"),
           function(x, func) {
@@ -951,44 +951,44 @@ setMethod("keyBy",
             lapply(x, apply.func)
           })
 
-#' Return a new RDD that has exactly numPartitions partitions.
-#' Can increase or decrease the level of parallelism in this RDD. Internally,
-#' this uses a shuffle to redistribute data.
-#' If you are decreasing the number of partitions in this RDD, consider using
-#' coalesce, which can avoid performing a shuffle.
-#'
-#' @param x The RDD.
-#' @param numPartitions Number of partitions to create.
-#' @seealso coalesce
-#' @examples
-#'\dontrun{
-#' sc <- sparkR.init()
-#' rdd <- parallelize(sc, list(1, 2, 3, 4, 5, 6, 7), 4L)
-#' numPartitions(rdd)                   # 4
-#' numPartitions(repartition(rdd, 2L))  # 2
-#'}
-#' @rdname repartition
-#' @aliases repartition,RDD
+# Return a new RDD that has exactly numPartitions partitions.
+# Can increase or decrease the level of parallelism in this RDD. Internally,
+# this uses a shuffle to redistribute data.
+# If you are decreasing the number of partitions in this RDD, consider using
+# coalesce, which can avoid performing a shuffle.
+#
+# @param x The RDD.
+# @param numPartitions Number of partitions to create.
+# @seealso coalesce
+# @examples
+#\dontrun{
+# sc <- sparkR.init()
+# rdd <- parallelize(sc, list(1, 2, 3, 4, 5, 6, 7), 4L)
+# numPartitions(rdd)                   # 4
+# numPartitions(repartition(rdd, 2L))  # 2
+#}
+# @rdname repartition
+# @aliases repartition,RDD
 setMethod("repartition",
           signature(x = "RDD", numPartitions = "numeric"),
           function(x, numPartitions) {
             coalesce(x, numPartitions, TRUE)
           })
 
-#' Return a new RDD that is reduced into numPartitions partitions.
-#'
-#' @param x The RDD.
-#' @param numPartitions Number of partitions to create.
-#' @seealso repartition
-#' @examples
-#'\dontrun{
-#' sc <- sparkR.init()
-#' rdd <- parallelize(sc, list(1, 2, 3, 4, 5), 3L)
-#' numPartitions(rdd)               # 3
-#' numPartitions(coalesce(rdd, 1L)) # 1
-#'}
-#' @rdname coalesce
-#' @aliases coalesce,RDD
+# Return a new RDD that is reduced into numPartitions partitions.
+#
+# @param x The RDD.
+# @param numPartitions Number of partitions to create.
+# @seealso repartition
+# @examples
+#\dontrun{
+# sc <- sparkR.init()
+# rdd <- parallelize(sc, list(1, 2, 3, 4, 5), 3L)
+# numPartitions(rdd)               # 3
+# numPartitions(coalesce(rdd, 1L)) # 1
+#}
+# @rdname coalesce
+# @aliases coalesce,RDD
 setMethod("coalesce",
            signature(x = "RDD", numPartitions = "numeric"),
            function(x, numPartitions, shuffle = FALSE) {
@@ -1012,19 +1012,19 @@ setMethod("coalesce",
              }
            })
 
-#' Save this RDD as a SequenceFile of serialized objects.
-#'
-#' @param x The RDD to save
-#' @param path The directory where the file is saved
-#' @seealso objectFile
-#' @examples
-#'\dontrun{
-#' sc <- sparkR.init()
-#' rdd <- parallelize(sc, 1:3)
-#' saveAsObjectFile(rdd, "/tmp/sparkR-tmp")
-#'}
-#' @rdname saveAsObjectFile
-#' @aliases saveAsObjectFile,RDD
+# Save this RDD as a SequenceFile of serialized objects.
+#
+# @param x The RDD to save
+# @param path The directory where the file is saved
+# @seealso objectFile
+# @examples
+#\dontrun{
+# sc <- sparkR.init()
+# rdd <- parallelize(sc, 1:3)
+# saveAsObjectFile(rdd, "/tmp/sparkR-tmp")
+#}
+# @rdname saveAsObjectFile
+# @aliases saveAsObjectFile,RDD
 setMethod("saveAsObjectFile",
           signature(x = "RDD", path = "character"),
           function(x, path) {
@@ -1037,18 +1037,18 @@ setMethod("saveAsObjectFile",
             invisible(callJMethod(getJRDD(x), "saveAsObjectFile", path))
           })
 
-#' Save this RDD as a text file, using string representations of elements.
-#'
-#' @param x The RDD to save
-#' @param path The directory where the partitions of the text file are saved
-#' @examples
-#'\dontrun{
-#' sc <- sparkR.init()
-#' rdd <- parallelize(sc, 1:3)
-#' saveAsTextFile(rdd, "/tmp/sparkR-tmp")
-#'}
-#' @rdname saveAsTextFile
-#' @aliases saveAsTextFile,RDD
+# Save this RDD as a text file, using string representations of elements.
+#
+# @param x The RDD to save
+# @param path The directory where the partitions of the text file are saved
+# @examples
+#\dontrun{
+# sc <- sparkR.init()
+# rdd <- parallelize(sc, 1:3)
+# saveAsTextFile(rdd, "/tmp/sparkR-tmp")
+#}
+# @rdname saveAsTextFile
+# @aliases saveAsTextFile,RDD
 setMethod("saveAsTextFile",
           signature(x = "RDD", path = "character"),
           function(x, path) {
@@ -1061,21 +1061,21 @@ setMethod("saveAsTextFile",
               callJMethod(getJRDD(stringRdd, serializedMode = "string"), "saveAsTextFile", path))
           })
 
-#' Sort an RDD by the given key function.
-#'
-#' @param x An RDD to be sorted.
-#' @param func A function used to compute the sort key for each element.
-#' @param ascending A flag to indicate whether the sorting is ascending or descending.
-#' @param numPartitions Number of partitions to create.
-#' @return An RDD where all elements are sorted.
-#' @examples
-#'\dontrun{
-#' sc <- sparkR.init()
-#' rdd <- parallelize(sc, list(3, 2, 1))
-#' collect(sortBy(rdd, function(x) { x })) # list (1, 2, 3)
-#'}
-#' @rdname sortBy
-#' @aliases sortBy,RDD,RDD-method
+# Sort an RDD by the given key function.
+#
+# @param x An RDD to be sorted.
+# @param func A function used to compute the sort key for each element.
+# @param ascending A flag to indicate whether the sorting is ascending or descending.
+# @param numPartitions Number of partitions to create.
+# @return An RDD where all elements are sorted.
+# @examples
+#\dontrun{
+# sc <- sparkR.init()
+# rdd <- parallelize(sc, list(3, 2, 1))
+# collect(sortBy(rdd, function(x) { x })) # list (1, 2, 3)
+#}
+# @rdname sortBy
+# @aliases sortBy,RDD,RDD-method
 setMethod("sortBy",
           signature(x = "RDD", func = "function"),
           function(x, func, ascending = TRUE, numPartitions = SparkR:::numPartitions(x)) {
@@ -1137,97 +1137,97 @@ takeOrderedElem <- function(x, num, ascending = TRUE) {
   resList
 }
 
-#' Returns the first N elements from an RDD in ascending order.
-#'
-#' @param x An RDD.
-#' @param num Number of elements to return.
-#' @return The first N elements from the RDD in ascending order.
-#' @examples
-#'\dontrun{
-#' sc <- sparkR.init()
-#' rdd <- parallelize(sc, list(10, 1, 2, 9, 3, 4, 5, 6, 7))
-#' takeOrdered(rdd, 6L) # list(1, 2, 3, 4, 5, 6)
-#'}
-#' @rdname takeOrdered
-#' @aliases takeOrdered,RDD,RDD-method
+# Returns the first N elements from an RDD in ascending order.
+#
+# @param x An RDD.
+# @param num Number of elements to return.
+# @return The first N elements from the RDD in ascending order.
+# @examples
+#\dontrun{
+# sc <- sparkR.init()
+# rdd <- parallelize(sc, list(10, 1, 2, 9, 3, 4, 5, 6, 7))
+# takeOrdered(rdd, 6L) # list(1, 2, 3, 4, 5, 6)
+#}
+# @rdname takeOrdered
+# @aliases takeOrdered,RDD,RDD-method
 setMethod("takeOrdered",
           signature(x = "RDD", num = "integer"),
           function(x, num) {          
             takeOrderedElem(x, num)
           })
 
-#' Returns the top N elements from an RDD.
-#'
-#' @param x An RDD.
-#' @param num Number of elements to return.
-#' @return The top N elements from the RDD.
-#' @rdname top
-#' @export
-#' @examples
-#'\dontrun{
-#' sc <- sparkR.init()
-#' rdd <- parallelize(sc, list(10, 1, 2, 9, 3, 4, 5, 6, 7))
-#' top(rdd, 6L) # list(10, 9, 7, 6, 5, 4)
-#'}
-#' @rdname top
-#' @aliases top,RDD,RDD-method
+# Returns the top N elements from an RDD.
+#
+# @param x An RDD.
+# @param num Number of elements to return.
+# @return The top N elements from the RDD.
+# @rdname top
+# @export
+# @examples
+#\dontrun{
+# sc <- sparkR.init()
+# rdd <- parallelize(sc, list(10, 1, 2, 9, 3, 4, 5, 6, 7))
+# top(rdd, 6L) # list(10, 9, 7, 6, 5, 4)
+#}
+# @rdname top
+# @aliases top,RDD,RDD-method
 setMethod("top",
           signature(x = "RDD", num = "integer"),
           function(x, num) {          
             takeOrderedElem(x, num, FALSE)
           })
 
-#' Fold an RDD using a given associative function and a neutral "zero value".
-#'
-#' Aggregate the elements of each partition, and then the results for all the
-#' partitions, using a given associative function and a neutral "zero value".
-#' 
-#' @param x An RDD.
-#' @param zeroValue A neutral "zero value".
-#' @param op An associative function for the folding operation.
-#' @return The folding result.
-#' @rdname fold
-#' @seealso reduce
-#' @export
-#' @examples
-#'\dontrun{
-#' sc <- sparkR.init()
-#' rdd <- parallelize(sc, list(1, 2, 3, 4, 5))
-#' fold(rdd, 0, "+") # 15
-#'}
-#' @rdname fold
-#' @aliases fold,RDD,RDD-method
+# Fold an RDD using a given associative function and a neutral "zero value".
+#
+# Aggregate the elements of each partition, and then the results for all the
+# partitions, using a given associative function and a neutral "zero value".
+# 
+# @param x An RDD.
+# @param zeroValue A neutral "zero value".
+# @param op An associative function for the folding operation.
+# @return The folding result.
+# @rdname fold
+# @seealso reduce
+# @export
+# @examples
+#\dontrun{
+# sc <- sparkR.init()
+# rdd <- parallelize(sc, list(1, 2, 3, 4, 5))
+# fold(rdd, 0, "+") # 15
+#}
+# @rdname fold
+# @aliases fold,RDD,RDD-method
 setMethod("fold",
           signature(x = "RDD", zeroValue = "ANY", op = "ANY"),
           function(x, zeroValue, op) {
             aggregateRDD(x, zeroValue, op, op)
           })
 
-#' Aggregate an RDD using the given combine functions and a neutral "zero value".
-#'
-#' Aggregate the elements of each partition, and then the results for all the
-#' partitions, using given combine functions and a neutral "zero value".
-#' 
-#' @param x An RDD.
-#' @param zeroValue A neutral "zero value".
-#' @param seqOp A function to aggregate the RDD elements. It may return a different
-#'              result type from the type of the RDD elements.
-#' @param combOp A function to aggregate results of seqOp.
-#' @return The aggregation result.
-#' @rdname aggregateRDD
-#' @seealso reduce
-#' @export
-#' @examples
-#'\dontrun{
-#' sc <- sparkR.init()
-#' rdd <- parallelize(sc, list(1, 2, 3, 4))
-#' zeroValue <- list(0, 0)
-#' seqOp <- function(x, y) { list(x[[1]] + y, x[[2]] + 1) }
-#' combOp <- function(x, y) { list(x[[1]] + y[[1]], x[[2]] + y[[2]]) }
-#' aggregateRDD(rdd, zeroValue, seqOp, combOp) # list(10, 4)
-#'}
-#' @rdname aggregateRDD
-#' @aliases aggregateRDD,RDD,RDD-method
+# Aggregate an RDD using the given combine functions and a neutral "zero value".
+#
+# Aggregate the elements of each partition, and then the results for all the
+# partitions, using given combine functions and a neutral "zero value".
+# 
+# @param x An RDD.
+# @param zeroValue A neutral "zero value".
+# @param seqOp A function to aggregate the RDD elements. It may return a different
+#              result type from the type of the RDD elements.
+# @param combOp A function to aggregate results of seqOp.
+# @return The aggregation result.
+# @rdname aggregateRDD
+# @seealso reduce
+# @export
+# @examples
+#\dontrun{
+# sc <- sparkR.init()
+# rdd <- parallelize(sc, list(1, 2, 3, 4))
+# zeroValue <- list(0, 0)
+# seqOp <- function(x, y) { list(x[[1]] + y, x[[2]] + 1) }
+# combOp <- function(x, y) { list(x[[1]] + y[[1]], x[[2]] + y[[2]]) }
+# aggregateRDD(rdd, zeroValue, seqOp, combOp) # list(10, 4)
+#}
+# @rdname aggregateRDD
+# @aliases aggregateRDD,RDD,RDD-method
 setMethod("aggregateRDD",
           signature(x = "RDD", zeroValue = "ANY", seqOp = "ANY", combOp = "ANY"),
           function(x, zeroValue, seqOp, combOp) {        
@@ -1240,25 +1240,25 @@ setMethod("aggregateRDD",
             Reduce(combOp, partitionList, zeroValue)
           })
 
-#' Pipes elements to a forked external process.
-#'
-#' The same as 'pipe()' in Spark.
-#'
-#' @param x The RDD whose elements are piped to the forked external process.
-#' @param command The command to fork an external process.
-#' @param env A named list to set environment variables of the external process.
-#' @return A new RDD created by piping all elements to a forked external process.
-#' @rdname pipeRDD
-#' @export
-#' @examples
-#'\dontrun{
-#' sc <- sparkR.init()
-#' rdd <- parallelize(sc, 1:10)
-#' collect(pipeRDD(rdd, "more")
-#' Output: c("1", "2", ..., "10")
-#'}
-#' @rdname pipeRDD
-#' @aliases pipeRDD,RDD,character-method
+# Pipes elements to a forked external process.
+#
+# The same as 'pipe()' in Spark.
+#
+# @param x The RDD whose elements are piped to the forked external process.
+# @param command The command to fork an external process.
+# @param env A named list to set environment variables of the external process.
+# @return A new RDD created by piping all elements to a forked external process.
+# @rdname pipeRDD
+# @export
+# @examples
+#\dontrun{
+# sc <- sparkR.init()
+# rdd <- parallelize(sc, 1:10)
+# collect(pipeRDD(rdd, "more")
+# Output: c("1", "2", ..., "10")
+#}
+# @rdname pipeRDD
+# @aliases pipeRDD,RDD,character-method
 setMethod("pipeRDD",
           signature(x = "RDD", command = "character"),
           function(x, command, env = list()) {
@@ -1274,41 +1274,41 @@ setMethod("pipeRDD",
           })
 
 # TODO: Consider caching the name in the RDD's environment
-#' Return an RDD's name.
-#'
-#' @param x The RDD whose name is returned.
-#' @rdname name
-#' @export
-#' @examples
-#'\dontrun{
-#' sc <- sparkR.init()
-#' rdd <- parallelize(sc, list(1,2,3))
-#' name(rdd) # NULL (if not set before)
-#'}
-#' @rdname name
-#' @aliases name,RDD
+# Return an RDD's name.
+#
+# @param x The RDD whose name is returned.
+# @rdname name
+# @export
+# @examples
+#\dontrun{
+# sc <- sparkR.init()
+# rdd <- parallelize(sc, list(1,2,3))
+# name(rdd) # NULL (if not set before)
+#}
+# @rdname name
+# @aliases name,RDD
 setMethod("name",
           signature(x = "RDD"),
           function(x) {
             callJMethod(getJRDD(x), "name")
           })
 
-#' Set an RDD's name.
-#'
-#' @param x The RDD whose name is to be set.
-#' @param name The RDD name to be set.
-#' @return a new RDD renamed.
-#' @rdname setName
-#' @export
-#' @examples
-#'\dontrun{
-#' sc <- sparkR.init()
-#' rdd <- parallelize(sc, list(1,2,3))
-#' setName(rdd, "myRDD")
-#' name(rdd) # "myRDD"
-#'}
-#' @rdname setName
-#' @aliases setName,RDD
+# Set an RDD's name.
+#
+# @param x The RDD whose name is to be set.
+# @param name The RDD name to be set.
+# @return a new RDD renamed.
+# @rdname setName
+# @export
+# @examples
+#\dontrun{
+# sc <- sparkR.init()
+# rdd <- parallelize(sc, list(1,2,3))
+# setName(rdd, "myRDD")
+# name(rdd) # "myRDD"
+#}
+# @rdname setName
+# @aliases setName,RDD
 setMethod("setName",
           signature(x = "RDD", name = "character"),
           function(x, name) {
@@ -1316,25 +1316,25 @@ setMethod("setName",
             x
           })
 
-#' Zip an 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
-#' zipWithIndex.
-#'
-#' @param x An RDD to be zipped.
-#' @return An RDD with zipped items.
-#' @seealso zipWithIndex
-#' @examples
-#'\dontrun{
-#' sc <- sparkR.init()
-#' rdd <- parallelize(sc, list("a", "b", "c", "d", "e"), 3L)
-#' collect(zipWithUniqueId(rdd)) 
-#' # list(list("a", 0), list("b", 3), list("c", 1), list("d", 4), list("e", 2))
-#'}
-#' @rdname zipWithUniqueId
-#' @aliases zipWithUniqueId,RDD
+# Zip an 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
+# zipWithIndex.
+#
+# @param x An RDD to be zipped.
+# @return An RDD with zipped items.
+# @seealso zipWithIndex
+# @examples
+#\dontrun{
+# sc <- sparkR.init()
+# rdd <- parallelize(sc, list("a", "b", "c", "d", "e"), 3L)
+# collect(zipWithUniqueId(rdd)) 
+# # list(list("a", 0), list("b", 3), list("c", 1), list("d", 4), list("e", 2))
+#}
+# @rdname zipWithUniqueId
+# @aliases zipWithUniqueId,RDD
 setMethod("zipWithUniqueId",
           signature(x = "RDD"),
           function(x) {
@@ -1353,28 +1353,28 @@ setMethod("zipWithUniqueId",
             lapplyPartitionsWithIndex(x, partitionFunc)
           })
 
-#' Zip an 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 partition.
-#'
-#' @param x An RDD to be zipped.
-#' @return An RDD with zipped items.
-#' @seealso zipWithUniqueId
-#' @examples
-#'\dontrun{
-#' sc <- sparkR.init()
-#' rdd <- parallelize(sc, list("a", "b", "c", "d", "e"), 3L)
-#' collect(zipWithIndex(rdd))
-#' # list(list("a", 0), list("b", 1), list("c", 2), list("d", 3), list("e", 4))
-#'}
-#' @rdname zipWithIndex
-#' @aliases zipWithIndex,RDD
+# Zip an 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 partition.
+#
+# @param x An RDD to be zipped.
+# @return An RDD with zipped items.
+# @seealso zipWithUniqueId
+# @examples
+#\dontrun{
+# sc <- sparkR.init()
+# rdd <- parallelize(sc, list("a", "b", "c", "d", "e"), 3L)
+# collect(zipWithIndex(rdd))
+# # list(list("a", 0), list("b", 1), list("c", 2), list("d", 3), list("e", 4))
+#}
+# @rdname zipWithIndex
+# @aliases zipWithIndex,RDD
 setMethod("zipWithIndex",
           signature(x = "RDD"),
           function(x) {
@@ -1406,20 +1406,20 @@ setMethod("zipWithIndex",
            lapplyPartitionsWithIndex(x, partitionFunc)
          })
 
-#' Coalesce all elements within each partition of an RDD into a list.
-#'
-#' @param x An RDD.
-#' @return An RDD created by coalescing all elements within
-#'         each partition into a list.
-#' @examples
-#'\dontrun{
-#' sc <- sparkR.init()
-#' rdd <- parallelize(sc, as.list(1:4), 2L)
-#' collect(glom(rdd))
-#' # list(list(1, 2), list(3, 4))
-#'}
-#' @rdname glom
-#' @aliases glom,RDD
+# Coalesce all elements within each partition of an RDD into a list.
+#
+# @param x An RDD.
+# @return An RDD created by coalescing all elements within
+#         each partition into a list.
+# @examples
+#\dontrun{
+# sc <- sparkR.init()
+# rdd <- parallelize(sc, as.list(1:4), 2L)
+# collect(glom(rdd))
+# # list(list(1, 2), list(3, 4))
+#}
+# @rdname glom
+# @aliases glom,RDD
 setMethod("glom",
           signature(x = "RDD"),
           function(x) {
@@ -1432,21 +1432,21 @@ setMethod("glom",
 
 ############ Binary Functions #############
 
-#' Return the union RDD of two RDDs.
-#' The same as union() in Spark.
-#'
-#' @param x An RDD.
-#' @param y An RDD.
-#' @return a new RDD created by performing the simple union (witout removing
-#' duplicates) of two input RDDs.
-#' @examples
-#'\dontrun{
-#' sc <- sparkR.init()
-#' rdd <- parallelize(sc, 1:3)
-#' unionRDD(rdd, rdd) # 1, 2, 3, 1, 2, 3
-#'}
-#' @rdname unionRDD
-#' @aliases unionRDD,RDD,RDD-method
+# Return the union RDD of two RDDs.
+# The same as union() in Spark.
+#
+# @param x An RDD.
+# @param y An RDD.
+# @return a new RDD created by performing the simple union (witout removing
+# duplicates) of two input RDDs.
+# @examples
+#\dontrun{
+# sc <- sparkR.init()
+# rdd <- parallelize(sc, 1:3)
+# unionRDD(rdd, rdd) # 1, 2, 3, 1, 2, 3
+#}
+# @rdname unionRDD
+# @aliases unionRDD,RDD,RDD-method
 setMethod("unionRDD",
           signature(x = "RDD", y = "RDD"),
           function(x, y) {
@@ -1463,27 +1463,27 @@ setMethod("unionRDD",
             union.rdd
           })
 
-#' Zip an RDD with another RDD.
-#'
-#' Zips this RDD with another one, returning key-value pairs with the
-#' first element in each RDD second element in each RDD, etc. Assumes
-#' that the two RDDs have the same number of partitions and the same
-#' number of elements in each partition (e.g. one was made through
-#' a map on the other).
-#'
-#' @param x An RDD to be zipped.
-#' @param other Another RDD to be zipped.
-#' @return An RDD zipped from the two RDDs.
-#' @examples
-#'\dontrun{
-#' sc <- sparkR.init()
-#' rdd1 <- parallelize(sc, 0:4)
-#' rdd2 <- parallelize(sc, 1000:1004)
-#' collect(zipRDD(rdd1, rdd2))
-#' # list(list(0, 1000), list(1, 1001), list(2, 1002), list(3, 1003), list(4, 1004))
-#'}
-#' @rdname zipRDD
-#' @aliases zipRDD,RDD
+# Zip an RDD with another RDD.
+#
+# Zips this RDD with another one, returning key-value pairs with the
+# first element in each RDD second element in each RDD, etc. Assumes
+# that the two RDDs have the same number of partitions and the same
+# number of elements in each partition (e.g. one was made through
+# a map on the other).
+#
+# @param x An RDD to be zipped.
+# @param other Another RDD to be zipped.
+# @return An RDD zipped from the two RDDs.
+# @examples
+#\dontrun{
+# sc <- sparkR.init()
+# rdd1 <- parallelize(sc, 0:4)
+# rdd2 <- parallelize(sc, 1000:1004)
+# collect(zipRDD(rdd1, rdd2))
+# # list(list(0, 1000), list(1, 1001), list(2, 1002), list(3, 1003), list(4, 1004))
+#}
+# @rdname zipRDD
+# @aliases zipRDD,RDD
 setMethod("zipRDD",
           signature(x = "RDD", other = "RDD"),
           function(x, other) {
@@ -1502,24 +1502,24 @@ setMethod("zipRDD",
             mergePartitions(rdd, TRUE)
           })
 
-#' Cartesian product of this RDD and another one.
-#'
-#' Return the Cartesian product of this RDD and another one, 
-#' that is, the RDD of all pairs of elements (a, b) where a 
-#' is in this and b is in other.
-#' 
-#' @param x An RDD.
-#' @param other An RDD.
-#' @return A new RDD which is the Cartesian product of these two RDDs.
-#' @examples
-#'\dontrun{
-#' sc <- sparkR.init()
-#' rdd <- parallelize(sc, 1:2)
-#' sortByKey(cartesian(rdd, rdd)) 
-#' # list(list(1, 1), list(1, 2), list(2, 1), list(2, 2))
-#'}
-#' @rdname cartesian
-#' @aliases cartesian,RDD,RDD-method
+# Cartesian product of this RDD and another one.
+#
+# Return the Cartesian product of this RDD and another one, 
+# that is, the RDD of all pairs of elements (a, b) where a 
+# is in this and b is in other.
+# 
+# @param x An RDD.
+# @param other An RDD.
+# @return A new RDD which is the Cartesian product of these two RDDs.
+# @examples
+#\dontrun{
+# sc <- sparkR.init()
+# rdd <- parallelize(sc, 1:2)
+# sortByKey(cartesian(rdd, rdd)) 
+# # list(list(1, 1), list(1, 2), list(2, 1), list(2, 2))
+#}
+# @rdname cartesian
+# @aliases cartesian,RDD,RDD-method
 setMethod("cartesian",
           signature(x = "RDD", other = "RDD"),
           function(x, other) {
@@ -1532,24 +1532,24 @@ setMethod("cartesian",
             mergePartitions(rdd, FALSE)
           })
 
-#' Subtract an RDD with another RDD.
-#'
-#' Return an RDD with the elements from this that are not in other.
-#'
-#' @param x An RDD.
-#' @param other An RDD.
-#' @param numPartitions Number of the partitions in the result RDD.
-#' @return An RDD with the elements from this that are not in other.
-#' @examples
-#'\dontrun{
-#' sc <- sparkR.init()
-#' rdd1 <- parallelize(sc, list(1, 1, 2, 2, 3, 4))
-#' rdd2 <- parallelize(sc, list(2, 4))
-#' collect(subtract(rdd1, rdd2))
-#' # list(1, 1, 3)
-#'}
-#' @rdname subtract
-#' @aliases subtract,RDD
+# Subtract an RDD with another RDD.
+#
+# Return an RDD with the elements from this that are not in other.
+#
+# @param x An RDD.
+# @param other An RDD.
+# @param numPartitions Number of the partitions in the result RDD.
+# @return An RDD with the elements from this that are not in other.
+# @examples
+#\dontrun{
+# sc <- sparkR.init()
+# rdd1 <- parallelize(sc, list(1, 1, 2, 2, 3, 4))
+# rdd2 <- parallelize(sc, list(2, 4))
+# collect(subtract(rdd1, rdd2))
+# # list(1, 1, 3)
+#}
+# @rdname subtract
+# @aliases subtract,RDD
 setMethod("subtract",
           signature(x = "RDD", other = "RDD"),
           function(x, other, numPartitions = SparkR:::numPartitions(x)) {
@@ -1559,28 +1559,28 @@ setMethod("subtract",
             keys(subtractByKey(rdd1, rdd2, numPartitions))
           })
 
-#' Intersection of this RDD and another one.
-#'
-#' Return the intersection of this RDD and another one.
-#' The output will not contain any duplicate elements,
-#' even if the input RDDs did. Performs a hash partition
-#' across the cluster.
-#' Note that this method performs a shuffle internally.
-#'
-#' @param x An RDD.
-#' @param other An RDD.
-#' @param numPartitions The number of partitions in the result RDD.
-#' @return An RDD which is the intersection of these two RDDs.
-#' @examples
-#'\dontrun{
-#' sc <- sparkR.init()
-#' rdd1 <- parallelize(sc, list(1, 10, 2, 3, 4, 5))
-#' rdd2 <- parallelize(sc, list(1, 6, 2, 3, 7, 8))
-#' collect(sortBy(intersection(rdd1, rdd2), function(x) { x }))
-#' # list(1, 2, 3)
-#'}
-#' @rdname intersection
-#' @aliases intersection,RDD
+# Intersection of this RDD and another one.
+#
+# Return the intersection of this RDD and another one.
+# The output will not contain any duplicate elements,
+# even if the input RDDs did. Performs a hash partition
+# across the cluster.
+# Note that this method performs a shuffle internally.
+#
+# @param x An RDD.
+# @param other An RDD.
+# @param numPartitions The number of partitions in the result RDD.
+# @return An RDD which is the intersection of these two RDDs.
+# @examples
+#\dontrun{
+# sc <- sparkR.init()
+# rdd1 <- parallelize(sc, list(1, 10, 2, 3, 4, 5))
+# rdd2 <- parallelize(sc, list(1, 6, 2, 3, 7, 8))
+# collect(sortBy(intersection(rdd1, rdd2), function(x) { x }))
+# # list(1, 2, 3)
+#}
+# @rdname intersection
+# @aliases intersection,RDD
 setMethod("intersection",
           signature(x = "RDD", other = "RDD"),
           function(x, other, numPartitions = SparkR:::numPartitions(x)) {
@@ -1596,26 +1596,26 @@ setMethod("intersection",
             keys(filterRDD(cogroup(rdd1, rdd2, numPartitions = numPartitions), filterFunction))
           })
 
-#' Zips an RDD's partitions with one (or more) RDD(s).
-#' Same as zipPartitions in Spark.
-#' 
-#' @param ... RDDs to be zipped.
-#' @param func A function to transform zipped partitions.
-#' @return A new RDD by applying a function to the zipped partitions. 
-#'         Assumes that all the RDDs have the *same number of partitions*, but 
-#'         does *not* require them to have the same number of elements in each partition.
-#' @examples
-#'\dontrun{
-#' sc <- sparkR.init()
-#' rdd1 <- parallelize(sc, 1:2, 2L)  # 1, 2
-#' rdd2 <- parallelize(sc, 1:4, 2L)  # 1:2, 3:4
-#' rdd3 <- parallelize(sc, 1:6, 2L)  # 1:3, 4:6
-#' collect(zipPartitions(rdd1, rdd2, rdd3, 
-#'                       func = function(x, y, z) { list(list(x, y, z))} ))
-#' # list(list(1, c(1,2), c(1,2,3)), list(2, c(3,4), c(4,5,6)))
-#'}
-#' @rdname zipRDD
-#' @aliases zipPartitions,RDD
+# Zips an RDD's partitions with one (or more) RDD(s).
+# Same as zipPartitions in Spark.
+# 
+# @param ... RDDs to be zipped.
+# @param func A function to transform zipped partitions.
+# @return A new RDD by applying a function to the zipped partitions. 
+#         Assumes that all the RDDs have the *same number of partitions*, but 
+#         does *not* require them to have the same number of elements in each partition.
+# @examples
+#\dontrun{
+# sc <- sparkR.init()
+# rdd1 <- parallelize(sc, 1:2, 2L)  # 1, 2
+# rdd2 <- parallelize(sc, 1:4, 2L)  # 1:2, 3:4
+# rdd3 <- parallelize(sc, 1:6, 2L)  # 1:3, 4:6
+# collect(zipPartitions(rdd1, rdd2, rdd3, 
+#                       func = function(x, y, z) { list(list(x, y, z))} ))
+# # list(list(1, c(1,2), c(1,2,3)), list(2, c(3,4), c(4,5,6)))
+#}
+# @rdname zipRDD
+# @aliases zipPartitions,RDD
 setMethod("zipPartitions",
           "RDD",
           function(..., func) {
diff --git a/R/pkg/R/SQLContext.R b/R/pkg/R/SQLContext.R
index 4f05ba524a01a..cae06e6af2bff 100644
--- a/R/pkg/R/SQLContext.R
+++ b/R/pkg/R/SQLContext.R
@@ -150,21 +150,21 @@ createDataFrame <- function(sqlCtx, data, schema = NULL, samplingRatio = 1.0) {
   dataFrame(sdf)
 }
 
-#' toDF
-#'
-#' Converts an RDD to a DataFrame by infer the types.
-#'
-#' @param x An RDD
-#'
-#' @rdname DataFrame
-#' @export
-#' @examples
-#'\dontrun{
-#' sc <- sparkR.init()
-#' sqlCtx <- sparkRSQL.init(sc)
-#' rdd <- lapply(parallelize(sc, 1:10), function(x) list(a=x, b=as.character(x)))
-#' df <- toDF(rdd)
-#' }
+# toDF
+#
+# Converts an RDD to a DataFrame by infer the types.
+#
+# @param x An RDD
+#
+# @rdname DataFrame
+# @export
+# @examples
+#\dontrun{
+# sc <- sparkR.init()
+# sqlCtx <- sparkRSQL.init(sc)
+# rdd <- lapply(parallelize(sc, 1:10), function(x) list(a=x, b=as.character(x)))
+# df <- toDF(rdd)
+# }
 
 setGeneric("toDF", function(x, ...) { standardGeneric("toDF") })
 
@@ -207,23 +207,23 @@ jsonFile <- function(sqlCtx, path) {
 }
 
 
-#' JSON RDD
-#'
-#' Loads an RDD storing one JSON object per string as a DataFrame.
-#'
-#' @param sqlCtx SQLContext to use
-#' @param rdd An RDD of JSON string
-#' @param schema A StructType object to use as schema
-#' @param samplingRatio The ratio of simpling used to infer the schema
-#' @return A DataFrame
-#' @export
-#' @examples
-#'\dontrun{
-#' sc <- sparkR.init()
-#' sqlCtx <- sparkRSQL.init(sc)
-#' rdd <- texFile(sc, "path/to/json")
-#' df <- jsonRDD(sqlCtx, rdd)
-#' }
+# JSON RDD
+#
+# Loads an RDD storing one JSON object per string as a DataFrame.
+#
+# @param sqlCtx SQLContext to use
+# @param rdd An RDD of JSON string
+# @param schema A StructType object to use as schema
+# @param samplingRatio The ratio of simpling used to infer the schema
+# @return A DataFrame
+# @export
+# @examples
+#\dontrun{
+# sc <- sparkR.init()
+# sqlCtx <- sparkRSQL.init(sc)
+# rdd <- texFile(sc, "path/to/json")
+# df <- jsonRDD(sqlCtx, rdd)
+# }
 
 # TODO: support schema
 jsonRDD <- function(sqlCtx, rdd, schema = NULL, samplingRatio = 1.0) {
diff --git a/R/pkg/R/broadcast.R b/R/pkg/R/broadcast.R
index 583fa2e7fdcfd..23dc38780716e 100644
--- a/R/pkg/R/broadcast.R
+++ b/R/pkg/R/broadcast.R
@@ -23,21 +23,21 @@
 .broadcastValues <- new.env()
 .broadcastIdToName <- new.env()
 
-#' @title S4 class that represents a Broadcast variable
-#' @description Broadcast variables can be created using the broadcast
-#'              function from a \code{SparkContext}.
-#' @rdname broadcast-class
-#' @seealso broadcast 
-#'
-#' @param id Id of the backing Spark broadcast variable 
-#' @export
+# @title S4 class that represents a Broadcast variable
+# @description Broadcast variables can be created using the broadcast
+#              function from a \code{SparkContext}.
+# @rdname broadcast-class
+# @seealso broadcast 
+#
+# @param id Id of the backing Spark broadcast variable 
+# @export
 setClass("Broadcast", slots = list(id = "character"))
 
-#' @rdname broadcast-class
-#' @param value Value of the broadcast variable
-#' @param jBroadcastRef reference to the backing Java broadcast object
-#' @param objName name of broadcasted object
-#' @export
+# @rdname broadcast-class
+# @param value Value of the broadcast variable
+# @param jBroadcastRef reference to the backing Java broadcast object
+# @param objName name of broadcasted object
+# @export
 Broadcast <- function(id, value, jBroadcastRef, objName) {
   .broadcastValues[[id]] <- value
   .broadcastNames[[as.character(objName)]] <- jBroadcastRef
@@ -45,13 +45,13 @@ Broadcast <- function(id, value, jBroadcastRef, objName) {
   new("Broadcast", id = id)
 }
 
-#' @description
-#' \code{value} can be used to get the value of a broadcast variable inside
-#' a distributed function.
-#'
-#' @param bcast The broadcast variable to get
-#' @rdname broadcast
-#' @aliases value,Broadcast-method
+# @description
+# \code{value} can be used to get the value of a broadcast variable inside
+# a distributed function.
+#
+# @param bcast The broadcast variable to get
+# @rdname broadcast
+# @aliases value,Broadcast-method
 setMethod("value",
           signature(bcast = "Broadcast"),
           function(bcast) {
@@ -62,24 +62,24 @@ setMethod("value",
             }
           })
 
-#' Internal function to set values of a broadcast variable.
-#'
-#' This function is used internally by Spark to set the value of a broadcast
-#' variable on workers. Not intended for use outside the package.
-#'
-#' @rdname broadcast-internal
-#' @seealso broadcast, value 
+# Internal function to set values of a broadcast variable.
+#
+# This function is used internally by Spark to set the value of a broadcast
+# variable on workers. Not intended for use outside the package.
+#
+# @rdname broadcast-internal
+# @seealso broadcast, value 
 
-#' @param bcastId The id of broadcast variable to set
-#' @param value The value to be set
-#' @export
+# @param bcastId The id of broadcast variable to set
+# @param value The value to be set
+# @export
 setBroadcastValue <- function(bcastId, value) {
   bcastIdStr <- as.character(bcastId)
   .broadcastValues[[bcastIdStr]] <- value
 }
 
-#' Helper function to clear the list of broadcast variables we know about
-#' Should be called when the SparkR JVM backend is shutdown
+# Helper function to clear the list of broadcast variables we know about
+# Should be called when the SparkR JVM backend is shutdown
 clearBroadcastVariables <- function() {
   bcasts <- ls(.broadcastNames)
   rm(list = bcasts, envir = .broadcastNames)
diff --git a/R/pkg/R/context.R b/R/pkg/R/context.R
index b4845b6948997..43be9c904fdf6 100644
--- a/R/pkg/R/context.R
+++ b/R/pkg/R/context.R
@@ -25,27 +25,27 @@ getMinPartitions <- function(sc, minPartitions) {
   as.integer(minPartitions)
 }
 
-#' Create an RDD from a text file.
-#'
-#' This function reads a text file from HDFS, a local file system (available on all
-#' nodes), or any Hadoop-supported file system URI, and creates an
-#' RDD of strings from it.
-#'
-#' @param sc SparkContext to use
-#' @param path Path of file to read. A vector of multiple paths is allowed.
-#' @param minPartitions Minimum number of partitions to be created. If NULL, the default
-#'  value is chosen based on available parallelism.
-#' @return RDD where each item is of type \code{character}
-#' @export
-#' @examples
-#'\dontrun{
-#'  sc <- sparkR.init()
-#'  lines <- textFile(sc, "myfile.txt")
-#'}
+# Create an RDD from a text file.
+#
+# This function reads a text file from HDFS, a local file system (available on all
+# nodes), or any Hadoop-supported file system URI, and creates an
+# RDD of strings from it.
+#
+# @param sc SparkContext to use
+# @param path Path of file to read. A vector of multiple paths is allowed.
+# @param minPartitions Minimum number of partitions to be created. If NULL, the default
+#  value is chosen based on available parallelism.
+# @return RDD where each item is of type \code{character}
+# @export
+# @examples
+#\dontrun{
+#  sc <- sparkR.init()
+#  lines <- textFile(sc, "myfile.txt")
+#}
 textFile <- function(sc, path, minPartitions = NULL) {
   # Allow the user to have a more flexible definiton of the text file path
   path <- suppressWarnings(normalizePath(path))
-  #' Convert a string vector of paths to a string containing comma separated paths
+  # Convert a string vector of paths to a string containing comma separated paths
   path <- paste(path, collapse = ",")
 
   jrdd <- callJMethod(sc, "textFile", path, getMinPartitions(sc, minPartitions))
@@ -53,27 +53,27 @@ textFile <- function(sc, path, minPartitions = NULL) {
   RDD(jrdd, "string")
 }
 
-#' Load an RDD saved as a SequenceFile containing serialized objects.
-#'
-#' The file to be loaded should be one that was previously generated by calling
-#' saveAsObjectFile() of the RDD class.
-#'
-#' @param sc SparkContext to use
-#' @param path Path of file to read. A vector of multiple paths is allowed.
-#' @param minPartitions Minimum number of partitions to be created. If NULL, the default
-#'  value is chosen based on available parallelism.
-#' @return RDD containing serialized R objects.
-#' @seealso saveAsObjectFile
-#' @export
-#' @examples
-#'\dontrun{
-#'  sc <- sparkR.init()
-#'  rdd <- objectFile(sc, "myfile")
-#'}
+# Load an RDD saved as a SequenceFile containing serialized objects.
+#
+# The file to be loaded should be one that was previously generated by calling
+# saveAsObjectFile() of the RDD class.
+#
+# @param sc SparkContext to use
+# @param path Path of file to read. A vector of multiple paths is allowed.
+# @param minPartitions Minimum number of partitions to be created. If NULL, the default
+#  value is chosen based on available parallelism.
+# @return RDD containing serialized R objects.
+# @seealso saveAsObjectFile
+# @export
+# @examples
+#\dontrun{
+#  sc <- sparkR.init()
+#  rdd <- objectFile(sc, "myfile")
+#}
 objectFile <- function(sc, path, minPartitions = NULL) {
   # Allow the user to have a more flexible definiton of the text file path
   path <- suppressWarnings(normalizePath(path))
-  #' Convert a string vector of paths to a string containing comma separated paths
+  # Convert a string vector of paths to a string containing comma separated paths
   path <- paste(path, collapse = ",")
 
   jrdd <- callJMethod(sc, "objectFile", path, getMinPartitions(sc, minPartitions))
@@ -81,24 +81,24 @@ objectFile <- function(sc, path, minPartitions = NULL) {
   RDD(jrdd, "byte")
 }
 
-#' Create an RDD from a homogeneous list or vector.
-#'
-#' This function creates an RDD from a local homogeneous list in R. The elements
-#' in the list are split into \code{numSlices} slices and distributed to nodes
-#' in the cluster.
-#'
-#' @param sc SparkContext to use
-#' @param coll collection to parallelize
-#' @param numSlices number of partitions to create in the RDD
-#' @return an RDD created from this collection
-#' @export
-#' @examples
-#'\dontrun{
-#' sc <- sparkR.init()
-#' rdd <- parallelize(sc, 1:10, 2)
-#' # The RDD should contain 10 elements
-#' length(rdd)
-#'}
+# Create an RDD from a homogeneous list or vector.
+#
+# This function creates an RDD from a local homogeneous list in R. The elements
+# in the list are split into \code{numSlices} slices and distributed to nodes
+# in the cluster.
+#
+# @param sc SparkContext to use
+# @param coll collection to parallelize
+# @param numSlices number of partitions to create in the RDD
+# @return an RDD created from this collection
+# @export
+# @examples
+#\dontrun{
+# sc <- sparkR.init()
+# rdd <- parallelize(sc, 1:10, 2)
+# # The RDD should contain 10 elements
+# length(rdd)
+#}
 parallelize <- function(sc, coll, numSlices = 1) {
   # TODO: bound/safeguard numSlices
   # TODO: unit tests for if the split works for all primitives
@@ -133,33 +133,33 @@ parallelize <- function(sc, coll, numSlices = 1) {
   RDD(jrdd, "byte")
 }
 
-#' Include this specified package on all workers
-#'
-#' This function can be used to include a package on all workers before the
-#' user's code is executed. This is useful in scenarios where other R package
-#' functions are used in a function passed to functions like \code{lapply}.
-#' NOTE: The package is assumed to be installed on every node in the Spark
-#' cluster.
-#'
-#' @param sc SparkContext to use
-#' @param pkg Package name
-#'
-#' @export
-#' @examples
-#'\dontrun{
-#'  library(Matrix)
-#'
-#'  sc <- sparkR.init()
-#'  # Include the matrix library we will be using
-#'  includePackage(sc, Matrix)
-#'
-#'  generateSparse <- function(x) {
-#'    sparseMatrix(i=c(1, 2, 3), j=c(1, 2, 3), x=c(1, 2, 3))
-#'  }
-#'
-#'  rdd <- lapplyPartition(parallelize(sc, 1:2, 2L), generateSparse)
-#'  collect(rdd)
-#'}
+# Include this specified package on all workers
+#
+# This function can be used to include a package on all workers before the
+# user's code is executed. This is useful in scenarios where other R package
+# functions are used in a function passed to functions like \code{lapply}.
+# NOTE: The package is assumed to be installed on every node in the Spark
+# cluster.
+#
+# @param sc SparkContext to use
+# @param pkg Package name
+#
+# @export
+# @examples
+#\dontrun{
+#  library(Matrix)
+#
+#  sc <- sparkR.init()
+#  # Include the matrix library we will be using
+#  includePackage(sc, Matrix)
+#
+#  generateSparse <- function(x) {
+#    sparseMatrix(i=c(1, 2, 3), j=c(1, 2, 3), x=c(1, 2, 3))
+#  }
+#
+#  rdd <- lapplyPartition(parallelize(sc, 1:2, 2L), generateSparse)
+#  collect(rdd)
+#}
 includePackage <- function(sc, pkg) {
   pkg <- as.character(substitute(pkg))
   if (exists(".packages", .sparkREnv)) {
@@ -171,30 +171,30 @@ includePackage <- function(sc, pkg) {
   .sparkREnv$.packages <- packages
 }
 
-#' @title Broadcast a variable to all workers
-#'
-#' @description
-#' Broadcast a read-only variable to the cluster, returning a \code{Broadcast}
-#' object for reading it in distributed functions.
-#'
-#' @param sc Spark Context to use
-#' @param object Object to be broadcast
-#' @export
-#' @examples
-#'\dontrun{
-#' sc <- sparkR.init()
-#' rdd <- parallelize(sc, 1:2, 2L)
-#'
-#' # Large Matrix object that we want to broadcast
-#' randomMat <- matrix(nrow=100, ncol=10, data=rnorm(1000))
-#' randomMatBr <- broadcast(sc, randomMat)
-#'
-#' # Use the broadcast variable inside the function
-#' useBroadcast <- function(x) {
-#'   sum(value(randomMatBr) * x)
-#' }
-#' sumRDD <- lapply(rdd, useBroadcast)
-#'}
+# @title Broadcast a variable to all workers
+#
+# @description
+# Broadcast a read-only variable to the cluster, returning a \code{Broadcast}
+# object for reading it in distributed functions.
+#
+# @param sc Spark Context to use
+# @param object Object to be broadcast
+# @export
+# @examples
+#\dontrun{
+# sc <- sparkR.init()
+# rdd <- parallelize(sc, 1:2, 2L)
+#
+# # Large Matrix object that we want to broadcast
+# randomMat <- matrix(nrow=100, ncol=10, data=rnorm(1000))
+# randomMatBr <- broadcast(sc, randomMat)
+#
+# # Use the broadcast variable inside the function
+# useBroadcast <- function(x) {
+#   sum(value(randomMatBr) * x)
+# }
+# sumRDD <- lapply(rdd, useBroadcast)
+#}
 broadcast <- function(sc, object) {
   objName <- as.character(substitute(object))
   serializedObj <- serialize(object, connection = NULL)
@@ -205,21 +205,21 @@ broadcast <- function(sc, object) {
   Broadcast(id, object, jBroadcast, objName)
 }
 
-#' @title Set the checkpoint directory
-#'
-#' Set the directory under which RDDs are going to be checkpointed. The
-#' directory must be a HDFS path if running on a cluster.
-#'
-#' @param sc Spark Context to use
-#' @param dirName Directory path
-#' @export
-#' @examples
-#'\dontrun{
-#' sc <- sparkR.init()
-#' setCheckpointDir(sc, "~/checkpoint")
-#' rdd <- parallelize(sc, 1:2, 2L)
-#' checkpoint(rdd)
-#'}
+# @title Set the checkpoint directory
+#
+# Set the directory under which RDDs are going to be checkpointed. The
+# directory must be a HDFS path if running on a cluster.
+#
+# @param sc Spark Context to use
+# @param dirName Directory path
+# @export
+# @examples
+#\dontrun{
+# sc <- sparkR.init()
+# setCheckpointDir(sc, "~/checkpoint")
+# rdd <- parallelize(sc, 1:2, 2L)
+# checkpoint(rdd)
+#}
 setCheckpointDir <- function(sc, dirName) {
   invisible(callJMethod(sc, "setCheckpointDir", suppressWarnings(normalizePath(dirName))))
 }
diff --git a/R/pkg/R/generics.R b/R/pkg/R/generics.R
index 5838955f74dad..380e8ebe8c8f4 100644
--- a/R/pkg/R/generics.R
+++ b/R/pkg/R/generics.R
@@ -17,353 +17,353 @@
 
 ############ RDD Actions and Transformations ############
 
-#' @rdname aggregateRDD
-#' @seealso reduce
-#' @export
+# @rdname aggregateRDD
+# @seealso reduce
+# @export
 setGeneric("aggregateRDD", function(x, zeroValue, seqOp, combOp) { standardGeneric("aggregateRDD") })
 
-#' @rdname cache-methods
-#' @export
+# @rdname cache-methods
+# @export
 setGeneric("cache", function(x) { standardGeneric("cache") })
 
-#' @rdname coalesce
-#' @seealso repartition
-#' @export
+# @rdname coalesce
+# @seealso repartition
+# @export
 setGeneric("coalesce", function(x, numPartitions, ...) { standardGeneric("coalesce") })
 
-#' @rdname checkpoint-methods
-#' @export
+# @rdname checkpoint-methods
+# @export
 setGeneric("checkpoint", function(x) { standardGeneric("checkpoint") })
 
-#' @rdname collect-methods
-#' @export
+# @rdname collect-methods
+# @export
 setGeneric("collect", function(x, ...) { standardGeneric("collect") })
 
-#' @rdname collect-methods
-#' @export
+# @rdname collect-methods
+# @export
 setGeneric("collectAsMap", function(x) { standardGeneric("collectAsMap") })
 
-#' @rdname collect-methods
-#' @export
+# @rdname collect-methods
+# @export
 setGeneric("collectPartition",
            function(x, partitionId) {
              standardGeneric("collectPartition")
            })
 
-#' @rdname count
-#' @export
+# @rdname count
+# @export
 setGeneric("count", function(x) { standardGeneric("count") })
 
-#' @rdname countByValue
-#' @export
+# @rdname countByValue
+# @export
 setGeneric("countByValue", function(x) { standardGeneric("countByValue") })
 
-#' @rdname distinct
-#' @export
+# @rdname distinct
+# @export
 setGeneric("distinct", function(x, numPartitions = 1) { standardGeneric("distinct") })
 
-#' @rdname filterRDD
-#' @export
+# @rdname filterRDD
+# @export
 setGeneric("filterRDD", function(x, f) { standardGeneric("filterRDD") })
 
-#' @rdname first
-#' @export
+# @rdname first
+# @export
 setGeneric("first", function(x) { standardGeneric("first") })
 
-#' @rdname flatMap
-#' @export
+# @rdname flatMap
+# @export
 setGeneric("flatMap", function(X, FUN) { standardGeneric("flatMap") })
 
-#' @rdname fold
-#' @seealso reduce
-#' @export
+# @rdname fold
+# @seealso reduce
+# @export
 setGeneric("fold", function(x, zeroValue, op) { standardGeneric("fold") })
 
-#' @rdname foreach
-#' @export
+# @rdname foreach
+# @export
 setGeneric("foreach", function(x, func) { standardGeneric("foreach") })
 
-#' @rdname foreach
-#' @export
+# @rdname foreach
+# @export
 setGeneric("foreachPartition", function(x, func) { standardGeneric("foreachPartition") })
 
 # The jrdd accessor function.
 setGeneric("getJRDD", function(rdd, ...) { standardGeneric("getJRDD") })
 
-#' @rdname glom
-#' @export
+# @rdname glom
+# @export
 setGeneric("glom", function(x) { standardGeneric("glom") })
 
-#' @rdname keyBy
-#' @export
+# @rdname keyBy
+# @export
 setGeneric("keyBy", function(x, func) { standardGeneric("keyBy") })
 
-#' @rdname lapplyPartition
-#' @export
+# @rdname lapplyPartition
+# @export
 setGeneric("lapplyPartition", function(X, FUN) { standardGeneric("lapplyPartition") })
 
-#' @rdname lapplyPartitionsWithIndex
-#' @export
+# @rdname lapplyPartitionsWithIndex
+# @export
 setGeneric("lapplyPartitionsWithIndex",
            function(X, FUN) {
              standardGeneric("lapplyPartitionsWithIndex")
            })
 
-#' @rdname lapply
-#' @export
+# @rdname lapply
+# @export
 setGeneric("map", function(X, FUN) { standardGeneric("map") })
 
-#' @rdname lapplyPartition
-#' @export
+# @rdname lapplyPartition
+# @export
 setGeneric("mapPartitions", function(X, FUN) { standardGeneric("mapPartitions") })
 
-#' @rdname lapplyPartitionsWithIndex
-#' @export
+# @rdname lapplyPartitionsWithIndex
+# @export
 setGeneric("mapPartitionsWithIndex",
            function(X, FUN) { standardGeneric("mapPartitionsWithIndex") })
 
-#' @rdname maximum
-#' @export
+# @rdname maximum
+# @export
 setGeneric("maximum", function(x) { standardGeneric("maximum") })
 
-#' @rdname minimum
-#' @export
+# @rdname minimum
+# @export
 setGeneric("minimum", function(x) { standardGeneric("minimum") })
 
-#' @rdname sumRDD 
-#' @export
+# @rdname sumRDD 
+# @export
 setGeneric("sumRDD", function(x) { standardGeneric("sumRDD") })
 
-#' @rdname name
-#' @export
+# @rdname name
+# @export
 setGeneric("name", function(x) { standardGeneric("name") })
 
-#' @rdname numPartitions
-#' @export
+# @rdname numPartitions
+# @export
 setGeneric("numPartitions", function(x) { standardGeneric("numPartitions") })
 
-#' @rdname persist
-#' @export
+# @rdname persist
+# @export
 setGeneric("persist", function(x, newLevel) { standardGeneric("persist") })
 
-#' @rdname pipeRDD
-#' @export
+# @rdname pipeRDD
+# @export
 setGeneric("pipeRDD", function(x, command, env = list()) { standardGeneric("pipeRDD")})
 
-#' @rdname reduce
-#' @export
+# @rdname reduce
+# @export
 setGeneric("reduce", function(x, func) { standardGeneric("reduce") })
 
-#' @rdname repartition
-#' @seealso coalesce
-#' @export
+# @rdname repartition
+# @seealso coalesce
+# @export
 setGeneric("repartition", function(x, numPartitions) { standardGeneric("repartition") })
 
-#' @rdname sampleRDD
-#' @export
+# @rdname sampleRDD
+# @export
 setGeneric("sampleRDD",
            function(x, withReplacement, fraction, seed) {
              standardGeneric("sampleRDD")
            })
 
-#' @rdname saveAsObjectFile
-#' @seealso objectFile
-#' @export
+# @rdname saveAsObjectFile
+# @seealso objectFile
+# @export
 setGeneric("saveAsObjectFile", function(x, path) { standardGeneric("saveAsObjectFile") })
 
-#' @rdname saveAsTextFile
-#' @export
+# @rdname saveAsTextFile
+# @export
 setGeneric("saveAsTextFile", function(x, path) { standardGeneric("saveAsTextFile") })
 
-#' @rdname setName
-#' @export
+# @rdname setName
+# @export
 setGeneric("setName", function(x, name) { standardGeneric("setName") })
 
-#' @rdname sortBy
-#' @export
+# @rdname sortBy
+# @export
 setGeneric("sortBy",
            function(x, func, ascending = TRUE, numPartitions = 1) {
              standardGeneric("sortBy")
            })
 
-#' @rdname take
-#' @export
+# @rdname take
+# @export
 setGeneric("take", function(x, num) { standardGeneric("take") })
 
-#' @rdname takeOrdered
-#' @export
+# @rdname takeOrdered
+# @export
 setGeneric("takeOrdered", function(x, num) { standardGeneric("takeOrdered") })
 
-#' @rdname takeSample
-#' @export
+# @rdname takeSample
+# @export
 setGeneric("takeSample",
            function(x, withReplacement, num, seed) {
              standardGeneric("takeSample")
            })
 
-#' @rdname top
-#' @export
+# @rdname top
+# @export
 setGeneric("top", function(x, num) { standardGeneric("top") })
 
-#' @rdname unionRDD
-#' @export
+# @rdname unionRDD
+# @export
 setGeneric("unionRDD", function(x, y) { standardGeneric("unionRDD") })
 
-#' @rdname unpersist-methods
-#' @export
+# @rdname unpersist-methods
+# @export
 setGeneric("unpersist", function(x, ...) { standardGeneric("unpersist") })
 
-#' @rdname zipRDD
-#' @export
+# @rdname zipRDD
+# @export
 setGeneric("zipRDD", function(x, other) { standardGeneric("zipRDD") })
 
-#' @rdname zipRDD
-#' @export
+# @rdname zipRDD
+# @export
 setGeneric("zipPartitions", function(..., func) { standardGeneric("zipPartitions") }, 
            signature = "...")
 
-#' @rdname zipWithIndex
-#' @seealso zipWithUniqueId
-#' @export
+# @rdname zipWithIndex
+# @seealso zipWithUniqueId
+# @export
 setGeneric("zipWithIndex", function(x) { standardGeneric("zipWithIndex") })
 
-#' @rdname zipWithUniqueId
-#' @seealso zipWithIndex
-#' @export
+# @rdname zipWithUniqueId
+# @seealso zipWithIndex
+# @export
 setGeneric("zipWithUniqueId", function(x) { standardGeneric("zipWithUniqueId") })
 
 
 ############ Binary Functions #############
 
-#' @rdname cartesian
-#' @export
+# @rdname cartesian
+# @export
 setGeneric("cartesian", function(x, other) { standardGeneric("cartesian") })
 
-#' @rdname countByKey
-#' @export
+# @rdname countByKey
+# @export
 setGeneric("countByKey", function(x) { standardGeneric("countByKey") })
 
-#' @rdname flatMapValues
-#' @export
+# @rdname flatMapValues
+# @export
 setGeneric("flatMapValues", function(X, FUN) { standardGeneric("flatMapValues") })
 
-#' @rdname intersection
-#' @export
+# @rdname intersection
+# @export
 setGeneric("intersection", function(x, other, numPartitions = 1) {
   standardGeneric("intersection") })
 
-#' @rdname keys
-#' @export
+# @rdname keys
+# @export
 setGeneric("keys", function(x) { standardGeneric("keys") })
 
-#' @rdname lookup
-#' @export
+# @rdname lookup
+# @export
 setGeneric("lookup", function(x, key) { standardGeneric("lookup") })
 
-#' @rdname mapValues
-#' @export
+# @rdname mapValues
+# @export
 setGeneric("mapValues", function(X, FUN) { standardGeneric("mapValues") })
 
-#' @rdname sampleByKey
-#' @export
+# @rdname sampleByKey
+# @export
 setGeneric("sampleByKey",
            function(x, withReplacement, fractions, seed) {
              standardGeneric("sampleByKey")
            })
 
-#' @rdname values
-#' @export
+# @rdname values
+# @export
 setGeneric("values", function(x) { standardGeneric("values") })
 
 
 ############ Shuffle Functions ############
 
-#' @rdname aggregateByKey
-#' @seealso foldByKey, combineByKey
-#' @export
+# @rdname aggregateByKey
+# @seealso foldByKey, combineByKey
+# @export
 setGeneric("aggregateByKey",
            function(x, zeroValue, seqOp, combOp, numPartitions) {
              standardGeneric("aggregateByKey")
            })
 
-#' @rdname cogroup
-#' @export
+# @rdname cogroup
+# @export
 setGeneric("cogroup",
            function(..., numPartitions) {
              standardGeneric("cogroup")
            },
            signature = "...")
 
-#' @rdname combineByKey
-#' @seealso groupByKey, reduceByKey
-#' @export
+# @rdname combineByKey
+# @seealso groupByKey, reduceByKey
+# @export
 setGeneric("combineByKey",
            function(x, createCombiner, mergeValue, mergeCombiners, numPartitions) {
              standardGeneric("combineByKey")
            })
 
-#' @rdname foldByKey
-#' @seealso aggregateByKey, combineByKey
-#' @export
+# @rdname foldByKey
+# @seealso aggregateByKey, combineByKey
+# @export
 setGeneric("foldByKey",
            function(x, zeroValue, func, numPartitions) {
              standardGeneric("foldByKey")
            })
 
-#' @rdname join-methods
-#' @export
+# @rdname join-methods
+# @export
 setGeneric("fullOuterJoin", function(x, y, numPartitions) { standardGeneric("fullOuterJoin") })
 
-#' @rdname groupByKey
-#' @seealso reduceByKey
-#' @export
+# @rdname groupByKey
+# @seealso reduceByKey
+# @export
 setGeneric("groupByKey", function(x, numPartitions) { standardGeneric("groupByKey") })
 
-#' @rdname join-methods
-#' @export
+# @rdname join-methods
+# @export
 setGeneric("join", function(x, y, ...) { standardGeneric("join") })
 
-#' @rdname join-methods
-#' @export
+# @rdname join-methods
+# @export
 setGeneric("leftOuterJoin", function(x, y, numPartitions) { standardGeneric("leftOuterJoin") })
 
-#' @rdname partitionBy
-#' @export
+# @rdname partitionBy
+# @export
 setGeneric("partitionBy", function(x, numPartitions, ...) { standardGeneric("partitionBy") })
 
-#' @rdname reduceByKey
-#' @seealso groupByKey
-#' @export
+# @rdname reduceByKey
+# @seealso groupByKey
+# @export
 setGeneric("reduceByKey", function(x, combineFunc, numPartitions) { standardGeneric("reduceByKey")})
 
-#' @rdname reduceByKeyLocally
-#' @seealso reduceByKey
-#' @export
+# @rdname reduceByKeyLocally
+# @seealso reduceByKey
+# @export
 setGeneric("reduceByKeyLocally",
            function(x, combineFunc) {
              standardGeneric("reduceByKeyLocally")
            })
 
-#' @rdname join-methods
-#' @export
+# @rdname join-methods
+# @export
 setGeneric("rightOuterJoin", function(x, y, numPartitions) { standardGeneric("rightOuterJoin") })
 
-#' @rdname sortByKey
-#' @export
+# @rdname sortByKey
+# @export
 setGeneric("sortByKey",
            function(x, ascending = TRUE, numPartitions = 1) {
              standardGeneric("sortByKey")
            })
 
-#' @rdname subtract
-#' @export
+# @rdname subtract
+# @export
 setGeneric("subtract",
            function(x, other, numPartitions = 1) {
              standardGeneric("subtract")
            })
 
-#' @rdname subtractByKey
-#' @export
+# @rdname subtractByKey
+# @export
 setGeneric("subtractByKey", 
            function(x, other, numPartitions = 1) {
              standardGeneric("subtractByKey")
@@ -372,8 +372,8 @@ setGeneric("subtractByKey",
 
 ################### Broadcast Variable Methods #################
 
-#' @rdname broadcast
-#' @export
+# @rdname broadcast
+# @export
 setGeneric("value", function(bcast) { standardGeneric("value") })
 
 
@@ -477,8 +477,8 @@ setGeneric("showDF", function(x,...) { standardGeneric("showDF") })
 #' @export
 setGeneric("sortDF", function(x, col, ...) { standardGeneric("sortDF") })
 
-#' @rdname tojson
-#' @export
+# @rdname tojson
+# @export
 setGeneric("toJSON", function(x) { standardGeneric("toJSON") })
 
 #' @rdname DataFrame
diff --git a/R/pkg/R/pairRDD.R b/R/pkg/R/pairRDD.R
index edeb8d9f75f7b..7694652856da5 100644
--- a/R/pkg/R/pairRDD.R
+++ b/R/pkg/R/pairRDD.R
@@ -21,23 +21,23 @@ NULL
 
 ############ Actions and Transformations ############
 
-#' Look up elements of a key in an RDD
-#'
-#' @description
-#' \code{lookup} returns a list of values in this RDD for key key.
-#'
-#' @param x The RDD to collect
-#' @param key The key to look up for
-#' @return a list of values in this RDD for key key
-#' @examples
-#'\dontrun{
-#' sc <- sparkR.init()
-#' pairs <- list(c(1, 1), c(2, 2), c(1, 3))
-#' rdd <- parallelize(sc, pairs)
-#' lookup(rdd, 1) # list(1, 3)
-#'}
-#' @rdname lookup
-#' @aliases lookup,RDD-method
+# Look up elements of a key in an RDD
+#
+# @description
+# \code{lookup} returns a list of values in this RDD for key key.
+#
+# @param x The RDD to collect
+# @param key The key to look up for
+# @return a list of values in this RDD for key key
+# @examples
+#\dontrun{
+# sc <- sparkR.init()
+# pairs <- list(c(1, 1), c(2, 2), c(1, 3))
+# rdd <- parallelize(sc, pairs)
+# lookup(rdd, 1) # list(1, 3)
+#}
+# @rdname lookup
+# @aliases lookup,RDD-method
 setMethod("lookup",
           signature(x = "RDD", key = "ANY"),
           function(x, key) {
@@ -49,21 +49,21 @@ setMethod("lookup",
             collect(valsRDD)
           })
 
-#' Count the number of elements for each key, and return the result to the
-#' master as lists of (key, count) pairs.
-#'
-#' Same as countByKey in Spark.
-#'
-#' @param x The RDD to count keys.
-#' @return list of (key, count) pairs, where count is number of each key in rdd.
-#' @examples
-#'\dontrun{
-#' sc <- sparkR.init()
-#' rdd <- parallelize(sc, list(c("a", 1), c("b", 1), c("a", 1)))
-#' countByKey(rdd) # ("a", 2L), ("b", 1L)
-#'}
-#' @rdname countByKey
-#' @aliases countByKey,RDD-method
+# Count the number of elements for each key, and return the result to the
+# master as lists of (key, count) pairs.
+#
+# Same as countByKey in Spark.
+#
+# @param x The RDD to count keys.
+# @return list of (key, count) pairs, where count is number of each key in rdd.
+# @examples
+#\dontrun{
+# sc <- sparkR.init()
+# rdd <- parallelize(sc, list(c("a", 1), c("b", 1), c("a", 1)))
+# countByKey(rdd) # ("a", 2L), ("b", 1L)
+#}
+# @rdname countByKey
+# @aliases countByKey,RDD-method
 setMethod("countByKey",
           signature(x = "RDD"),
           function(x) {
@@ -71,17 +71,17 @@ setMethod("countByKey",
             countByValue(keys)
           })
 
-#' Return an RDD with the keys of each tuple.
-#'
-#' @param x The RDD from which the keys of each tuple is returned.
-#' @examples
-#'\dontrun{
-#' sc <- sparkR.init()
-#' rdd <- parallelize(sc, list(list(1, 2), list(3, 4)))
-#' collect(keys(rdd)) # list(1, 3)
-#'}
-#' @rdname keys
-#' @aliases keys,RDD
+# Return an RDD with the keys of each tuple.
+#
+# @param x The RDD from which the keys of each tuple is returned.
+# @examples
+#\dontrun{
+# sc <- sparkR.init()
+# rdd <- parallelize(sc, list(list(1, 2), list(3, 4)))
+# collect(keys(rdd)) # list(1, 3)
+#}
+# @rdname keys
+# @aliases keys,RDD
 setMethod("keys",
           signature(x = "RDD"),
           function(x) {
@@ -91,17 +91,17 @@ setMethod("keys",
             lapply(x, func)
           })
 
-#' Return an RDD with the values of each tuple.
-#'
-#' @param x The RDD from which the values of each tuple is returned.
-#' @examples
-#'\dontrun{
-#' sc <- sparkR.init()
-#' rdd <- parallelize(sc, list(list(1, 2), list(3, 4)))
-#' collect(values(rdd)) # list(2, 4)
-#'}
-#' @rdname values
-#' @aliases values,RDD
+# Return an RDD with the values of each tuple.
+#
+# @param x The RDD from which the values of each tuple is returned.
+# @examples
+#\dontrun{
+# sc <- sparkR.init()
+# rdd <- parallelize(sc, list(list(1, 2), list(3, 4)))
+# collect(values(rdd)) # list(2, 4)
+#}
+# @rdname values
+# @aliases values,RDD
 setMethod("values",
           signature(x = "RDD"),
           function(x) {
@@ -111,23 +111,23 @@ setMethod("values",
             lapply(x, func)
           })
 
-#' Applies a function to all values of the elements, without modifying the keys.
-#'
-#' The same as `mapValues()' in Spark.
-#'
-#' @param X The RDD to apply the transformation.
-#' @param FUN the transformation to apply on the value of each element.
-#' @return a new RDD created by the transformation.
-#' @examples
-#'\dontrun{
-#' sc <- sparkR.init()
-#' rdd <- parallelize(sc, 1:10)
-#' makePairs <- lapply(rdd, function(x) { list(x, x) })
-#' collect(mapValues(makePairs, function(x) { x * 2) })
-#' Output: list(list(1,2), list(2,4), list(3,6), ...)
-#'}
-#' @rdname mapValues
-#' @aliases mapValues,RDD,function-method
+# Applies a function to all values of the elements, without modifying the keys.
+#
+# The same as `mapValues()' in Spark.
+#
+# @param X The RDD to apply the transformation.
+# @param FUN the transformation to apply on the value of each element.
+# @return a new RDD created by the transformation.
+# @examples
+#\dontrun{
+# sc <- sparkR.init()
+# rdd <- parallelize(sc, 1:10)
+# makePairs <- lapply(rdd, function(x) { list(x, x) })
+# collect(mapValues(makePairs, function(x) { x * 2) })
+# Output: list(list(1,2), list(2,4), list(3,6), ...)
+#}
+# @rdname mapValues
+# @aliases mapValues,RDD,function-method
 setMethod("mapValues",
           signature(X = "RDD", FUN = "function"),
           function(X, FUN) {
@@ -137,23 +137,23 @@ setMethod("mapValues",
             lapply(X, func)
           })
 
-#' 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.
-#'
-#' The same as 'flatMapValues()' in Spark.
-#'
-#' @param X The RDD to apply the transformation.
-#' @param FUN the transformation to apply on the value of each element.
-#' @return a new RDD created by the transformation.
-#' @examples
-#'\dontrun{
-#' sc <- sparkR.init()
-#' rdd <- parallelize(sc, list(list(1, c(1,2)), list(2, c(3,4))))
-#' collect(flatMapValues(rdd, function(x) { x }))
-#' Output: list(list(1,1), list(1,2), list(2,3), list(2,4))
-#'}
-#' @rdname flatMapValues
-#' @aliases flatMapValues,RDD,function-method
+# 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.
+#
+# The same as 'flatMapValues()' in Spark.
+#
+# @param X The RDD to apply the transformation.
+# @param FUN the transformation to apply on the value of each element.
+# @return a new RDD created by the transformation.
+# @examples
+#\dontrun{
+# sc <- sparkR.init()
+# rdd <- parallelize(sc, list(list(1, c(1,2)), list(2, c(3,4))))
+# collect(flatMapValues(rdd, function(x) { x }))
+# Output: list(list(1,1), list(1,2), list(2,3), list(2,4))
+#}
+# @rdname flatMapValues
+# @aliases flatMapValues,RDD,function-method
 setMethod("flatMapValues",
           signature(X = "RDD", FUN = "function"),
           function(X, FUN) {
@@ -165,30 +165,30 @@ setMethod("flatMapValues",
 
 ############ Shuffle Functions ############
 
-#' Partition an RDD by key
-#'
-#' This function operates on RDDs where every element is of the form list(K, V) or c(K, V).
-#' For each element of this RDD, the partitioner is used to compute a hash
-#' function and the RDD is partitioned using this hash value.
-#'
-#' @param x The RDD to partition. Should be an RDD where each element is
-#'             list(K, V) or c(K, V).
-#' @param numPartitions Number of partitions to create.
-#' @param ... Other optional arguments to partitionBy.
-#'
-#' @param partitionFunc The partition function to use. Uses a default hashCode
-#'                      function if not provided
-#' @return An RDD partitioned using the specified partitioner.
-#' @examples
-#'\dontrun{
-#' sc <- sparkR.init()
-#' pairs <- list(list(1, 2), list(1.1, 3), list(1, 4))
-#' rdd <- parallelize(sc, pairs)
-#' parts <- partitionBy(rdd, 2L)
-#' collectPartition(parts, 0L) # First partition should contain list(1, 2) and list(1, 4)
-#'}
-#' @rdname partitionBy
-#' @aliases partitionBy,RDD,integer-method
+# Partition an RDD by key
+#
+# This function operates on RDDs where every element is of the form list(K, V) or c(K, V).
+# For each element of this RDD, the partitioner is used to compute a hash
+# function and the RDD is partitioned using this hash value.
+#
+# @param x The RDD to partition. Should be an RDD where each element is
+#             list(K, V) or c(K, V).
+# @param numPartitions Number of partitions to create.
+# @param ... Other optional arguments to partitionBy.
+#
+# @param partitionFunc The partition function to use. Uses a default hashCode
+#                      function if not provided
+# @return An RDD partitioned using the specified partitioner.
+# @examples
+#\dontrun{
+# sc <- sparkR.init()
+# pairs <- list(list(1, 2), list(1.1, 3), list(1, 4))
+# rdd <- parallelize(sc, pairs)
+# parts <- partitionBy(rdd, 2L)
+# collectPartition(parts, 0L) # First partition should contain list(1, 2) and list(1, 4)
+#}
+# @rdname partitionBy
+# @aliases partitionBy,RDD,integer-method
 setMethod("partitionBy",
           signature(x = "RDD", numPartitions = "numeric"),
           function(x, numPartitions, partitionFunc = hashCode) {
@@ -234,27 +234,27 @@ setMethod("partitionBy",
             RDD(r, serializedMode = "byte")
           })
 
-#' Group values by key
-#'
-#' This function operates on RDDs where every element is of the form list(K, V) or c(K, V).
-#' and group values for each key in the RDD into a single sequence.
-#'
-#' @param x The RDD to group. Should be an RDD where each element is
-#'             list(K, V) or c(K, V).
-#' @param numPartitions Number of partitions to create.
-#' @return An RDD where each element is list(K, list(V))
-#' @seealso reduceByKey
-#' @examples
-#'\dontrun{
-#' sc <- sparkR.init()
-#' pairs <- list(list(1, 2), list(1.1, 3), list(1, 4))
-#' rdd <- parallelize(sc, pairs)
-#' parts <- groupByKey(rdd, 2L)
-#' grouped <- collect(parts)
-#' grouped[[1]] # Should be a list(1, list(2, 4))
-#'}
-#' @rdname groupByKey
-#' @aliases groupByKey,RDD,integer-method
+# Group values by key
+#
+# This function operates on RDDs where every element is of the form list(K, V) or c(K, V).
+# and group values for each key in the RDD into a single sequence.
+#
+# @param x The RDD to group. Should be an RDD where each element is
+#             list(K, V) or c(K, V).
+# @param numPartitions Number of partitions to create.
+# @return An RDD where each element is list(K, list(V))
+# @seealso reduceByKey
+# @examples
+#\dontrun{
+# sc <- sparkR.init()
+# pairs <- list(list(1, 2), list(1.1, 3), list(1, 4))
+# rdd <- parallelize(sc, pairs)
+# parts <- groupByKey(rdd, 2L)
+# grouped <- collect(parts)
+# grouped[[1]] # Should be a list(1, list(2, 4))
+#}
+# @rdname groupByKey
+# @aliases groupByKey,RDD,integer-method
 setMethod("groupByKey",
           signature(x = "RDD", numPartitions = "numeric"),
           function(x, numPartitions) {
@@ -292,28 +292,28 @@ setMethod("groupByKey",
             lapplyPartition(shuffled, groupVals)
           })
 
-#' Merge values by key
-#'
-#' This function operates on RDDs where every element is of the form list(K, V) or c(K, V).
-#' and merges the values for each key using an associative reduce function.
-#'
-#' @param x The RDD to reduce by key. Should be an RDD where each element is
-#'             list(K, V) or c(K, V).
-#' @param combineFunc The associative reduce function to use.
-#' @param numPartitions Number of partitions to create.
-#' @return An RDD where each element is list(K, V') where V' is the merged
-#'         value
-#' @examples
-#'\dontrun{
-#' sc <- sparkR.init()
-#' pairs <- list(list(1, 2), list(1.1, 3), list(1, 4))
-#' rdd <- parallelize(sc, pairs)
-#' parts <- reduceByKey(rdd, "+", 2L)
-#' reduced <- collect(parts)
-#' reduced[[1]] # Should be a list(1, 6)
-#'}
-#' @rdname reduceByKey
-#' @aliases reduceByKey,RDD,integer-method
+# Merge values by key
+#
+# This function operates on RDDs where every element is of the form list(K, V) or c(K, V).
+# and merges the values for each key using an associative reduce function.
+#
+# @param x The RDD to reduce by key. Should be an RDD where each element is
+#             list(K, V) or c(K, V).
+# @param combineFunc The associative reduce function to use.
+# @param numPartitions Number of partitions to create.
+# @return An RDD where each element is list(K, V') where V' is the merged
+#         value
+# @examples
+#\dontrun{
+# sc <- sparkR.init()
+# pairs <- list(list(1, 2), list(1.1, 3), list(1, 4))
+# rdd <- parallelize(sc, pairs)
+# parts <- reduceByKey(rdd, "+", 2L)
+# reduced <- collect(parts)
+# reduced[[1]] # Should be a list(1, 6)
+#}
+# @rdname reduceByKey
+# @aliases reduceByKey,RDD,integer-method
 setMethod("reduceByKey",
           signature(x = "RDD", combineFunc = "ANY", numPartitions = "numeric"),
           function(x, combineFunc, numPartitions) {
@@ -333,27 +333,27 @@ setMethod("reduceByKey",
             lapplyPartition(shuffled, reduceVals)
           })
 
-#' Merge values by key locally
-#'
-#' This function operates on RDDs where every element is of the form list(K, V) or c(K, V).
-#' and merges the values for each key using an associative reduce function, but return the
-#' results immediately to the driver as an R list.
-#'
-#' @param x The RDD to reduce by key. Should be an RDD where each element is
-#'             list(K, V) or c(K, V).
-#' @param combineFunc The associative reduce function to use.
-#' @return A list of elements of type list(K, V') where V' is the merged value for each key
-#' @seealso reduceByKey
-#' @examples
-#'\dontrun{
-#' sc <- sparkR.init()
-#' pairs <- list(list(1, 2), list(1.1, 3), list(1, 4))
-#' rdd <- parallelize(sc, pairs)
-#' reduced <- reduceByKeyLocally(rdd, "+")
-#' reduced # list(list(1, 6), list(1.1, 3))
-#'}
-#' @rdname reduceByKeyLocally
-#' @aliases reduceByKeyLocally,RDD,integer-method
+# Merge values by key locally
+#
+# This function operates on RDDs where every element is of the form list(K, V) or c(K, V).
+# and merges the values for each key using an associative reduce function, but return the
+# results immediately to the driver as an R list.
+#
+# @param x The RDD to reduce by key. Should be an RDD where each element is
+#             list(K, V) or c(K, V).
+# @param combineFunc The associative reduce function to use.
+# @return A list of elements of type list(K, V') where V' is the merged value for each key
+# @seealso reduceByKey
+# @examples
+#\dontrun{
+# sc <- sparkR.init()
+# pairs <- list(list(1, 2), list(1.1, 3), list(1, 4))
+# rdd <- parallelize(sc, pairs)
+# reduced <- reduceByKeyLocally(rdd, "+")
+# reduced # list(list(1, 6), list(1.1, 3))
+#}
+# @rdname reduceByKeyLocally
+# @aliases reduceByKeyLocally,RDD,integer-method
 setMethod("reduceByKeyLocally",
           signature(x = "RDD", combineFunc = "ANY"),
           function(x, combineFunc) {
@@ -385,41 +385,41 @@ setMethod("reduceByKeyLocally",
             convertEnvsToList(merged[[1]], merged[[2]])
           })
 
-#' Combine values by key
-#'
-#' Generic function to combine the elements for each key using a custom set of
-#' aggregation functions. Turns an RDD[(K, V)] into a result of type RDD[(K, C)],
-#' for a "combined type" C. Note that V and C can be different -- for example, one
-#' might group an RDD of type (Int, Int) into an RDD of type (Int, Seq[Int]).
-
-#' Users provide three functions:
-#' \itemize{
-#'   \item createCombiner, which turns a V into a C (e.g., creates a one-element list)
-#'   \item mergeValue, to merge a V into a C (e.g., adds it to the end of a list) -
-#'   \item mergeCombiners, to combine two C's into a single one (e.g., concatentates
-#'    two lists).
-#' }
-#'
-#' @param x The RDD to combine. Should be an RDD where each element is
-#'             list(K, V) or c(K, V).
-#' @param createCombiner Create a combiner (C) given a value (V)
-#' @param mergeValue Merge the given value (V) with an existing combiner (C)
-#' @param mergeCombiners Merge two combiners and return a new combiner
-#' @param numPartitions Number of partitions to create.
-#' @return An RDD where each element is list(K, C) where C is the combined type
-#'
-#' @seealso groupByKey, reduceByKey
-#' @examples
-#'\dontrun{
-#' sc <- sparkR.init()
-#' pairs <- list(list(1, 2), list(1.1, 3), list(1, 4))
-#' rdd <- parallelize(sc, pairs)
-#' parts <- combineByKey(rdd, function(x) { x }, "+", "+", 2L)
-#' combined <- collect(parts)
-#' combined[[1]] # Should be a list(1, 6)
-#'}
-#' @rdname combineByKey
-#' @aliases combineByKey,RDD,ANY,ANY,ANY,integer-method
+# Combine values by key
+#
+# Generic function to combine the elements for each key using a custom set of
+# aggregation functions. Turns an RDD[(K, V)] into a result of type RDD[(K, C)],
+# for a "combined type" C. Note that V and C can be different -- for example, one
+# might group an RDD of type (Int, Int) into an RDD of type (Int, Seq[Int]).
+
+# Users provide three functions:
+# \itemize{
+#   \item createCombiner, which turns a V into a C (e.g., creates a one-element list)
+#   \item mergeValue, to merge a V into a C (e.g., adds it to the end of a list) -
+#   \item mergeCombiners, to combine two C's into a single one (e.g., concatentates
+#    two lists).
+# }
+#
+# @param x The RDD to combine. Should be an RDD where each element is
+#             list(K, V) or c(K, V).
+# @param createCombiner Create a combiner (C) given a value (V)
+# @param mergeValue Merge the given value (V) with an existing combiner (C)
+# @param mergeCombiners Merge two combiners and return a new combiner
+# @param numPartitions Number of partitions to create.
+# @return An RDD where each element is list(K, C) where C is the combined type
+#
+# @seealso groupByKey, reduceByKey
+# @examples
+#\dontrun{
+# sc <- sparkR.init()
+# pairs <- list(list(1, 2), list(1.1, 3), list(1, 4))
+# rdd <- parallelize(sc, pairs)
+# parts <- combineByKey(rdd, function(x) { x }, "+", "+", 2L)
+# combined <- collect(parts)
+# combined[[1]] # Should be a list(1, 6)
+#}
+# @rdname combineByKey
+# @aliases combineByKey,RDD,ANY,ANY,ANY,integer-method
 setMethod("combineByKey",
           signature(x = "RDD", createCombiner = "ANY", mergeValue = "ANY",
                     mergeCombiners = "ANY", numPartitions = "numeric"),
@@ -451,36 +451,36 @@ setMethod("combineByKey",
             lapplyPartition(shuffled, mergeAfterShuffle)
           })
 
-#' Aggregate a pair RDD by each key.
-#'
-#' Aggregate the values of each key in an RDD, using given combine functions
-#' and a neutral "zero value". This function can return a different result type,
-#' U, than the type of the values in this RDD, V. Thus, we need one operation
-#' for merging a V into a U and one operation for merging two U's, The former
-#' operation is used for merging values within a partition, and the latter is
-#' used for merging values between partitions. To avoid memory allocation, both
-#' of these functions are allowed to modify and return their first argument
-#' instead of creating a new U.
-#'
-#' @param x An RDD.
-#' @param zeroValue A neutral "zero value".
-#' @param seqOp A function to aggregate the values of each key. It may return
-#'              a different result type from the type of the values.
-#' @param combOp A function to aggregate results of seqOp.
-#' @return An RDD containing the aggregation result.
-#' @seealso foldByKey, combineByKey
-#' @examples
-#'\dontrun{
-#' sc <- sparkR.init()
-#' rdd <- parallelize(sc, list(list(1, 1), list(1, 2), list(2, 3), list(2, 4)))
-#' zeroValue <- list(0, 0)
-#' seqOp <- function(x, y) { list(x[[1]] + y, x[[2]] + 1) }
-#' combOp <- function(x, y) { list(x[[1]] + y[[1]], x[[2]] + y[[2]]) }
-#' aggregateByKey(rdd, zeroValue, seqOp, combOp, 2L)
-#'   # list(list(1, list(3, 2)), list(2, list(7, 2)))
-#'}
-#' @rdname aggregateByKey
-#' @aliases aggregateByKey,RDD,ANY,ANY,ANY,integer-method
+# Aggregate a pair RDD by each key.
+#
+# Aggregate the values of each key in an RDD, using given combine functions
+# and a neutral "zero value". This function can return a different result type,
+# U, than the type of the values in this RDD, V. Thus, we need one operation
+# for merging a V into a U and one operation for merging two U's, The former
+# operation is used for merging values within a partition, and the latter is
+# used for merging values between partitions. To avoid memory allocation, both
+# of these functions are allowed to modify and return their first argument
+# instead of creating a new U.
+#
+# @param x An RDD.
+# @param zeroValue A neutral "zero value".
+# @param seqOp A function to aggregate the values of each key. It may return
+#              a different result type from the type of the values.
+# @param combOp A function to aggregate results of seqOp.
+# @return An RDD containing the aggregation result.
+# @seealso foldByKey, combineByKey
+# @examples
+#\dontrun{
+# sc <- sparkR.init()
+# rdd <- parallelize(sc, list(list(1, 1), list(1, 2), list(2, 3), list(2, 4)))
+# zeroValue <- list(0, 0)
+# seqOp <- function(x, y) { list(x[[1]] + y, x[[2]] + 1) }
+# combOp <- function(x, y) { list(x[[1]] + y[[1]], x[[2]] + y[[2]]) }
+# aggregateByKey(rdd, zeroValue, seqOp, combOp, 2L)
+#   # list(list(1, list(3, 2)), list(2, list(7, 2)))
+#}
+# @rdname aggregateByKey
+# @aliases aggregateByKey,RDD,ANY,ANY,ANY,integer-method
 setMethod("aggregateByKey",
           signature(x = "RDD", zeroValue = "ANY", seqOp = "ANY",
                     combOp = "ANY", numPartitions = "numeric"),
@@ -492,26 +492,26 @@ setMethod("aggregateByKey",
             combineByKey(x, createCombiner, seqOp, combOp, numPartitions)
           })
 
-#' Fold a pair RDD by each key.
-#'
-#' Aggregate the values of each key in an RDD, using an associative function "func"
-#' and a neutral "zero value" which may be added to the result an arbitrary
-#' number of times, and must not change the result (e.g., 0 for addition, or
-#' 1 for multiplication.).
-#'
-#' @param x An RDD.
-#' @param zeroValue A neutral "zero value".
-#' @param func An associative function for folding values of each key.
-#' @return An RDD containing the aggregation result.
-#' @seealso aggregateByKey, combineByKey
-#' @examples
-#'\dontrun{
-#' sc <- sparkR.init()
-#' rdd <- parallelize(sc, list(list(1, 1), list(1, 2), list(2, 3), list(2, 4)))
-#' foldByKey(rdd, 0, "+", 2L) # list(list(1, 3), list(2, 7))
-#'}
-#' @rdname foldByKey
-#' @aliases foldByKey,RDD,ANY,ANY,integer-method
+# Fold a pair RDD by each key.
+#
+# Aggregate the values of each key in an RDD, using an associative function "func"
+# and a neutral "zero value" which may be added to the result an arbitrary
+# number of times, and must not change the result (e.g., 0 for addition, or
+# 1 for multiplication.).
+#
+# @param x An RDD.
+# @param zeroValue A neutral "zero value".
+# @param func An associative function for folding values of each key.
+# @return An RDD containing the aggregation result.
+# @seealso aggregateByKey, combineByKey
+# @examples
+#\dontrun{
+# sc <- sparkR.init()
+# rdd <- parallelize(sc, list(list(1, 1), list(1, 2), list(2, 3), list(2, 4)))
+# foldByKey(rdd, 0, "+", 2L) # list(list(1, 3), list(2, 7))
+#}
+# @rdname foldByKey
+# @aliases foldByKey,RDD,ANY,ANY,integer-method
 setMethod("foldByKey",
           signature(x = "RDD", zeroValue = "ANY",
                     func = "ANY", numPartitions = "numeric"),
@@ -521,28 +521,28 @@ setMethod("foldByKey",
 
 ############ Binary Functions #############
 
-#' Join two RDDs
-#'
-#' @description
-#' \code{join} This function joins two RDDs where every element is of the form list(K, V).
-#' The key types of the two RDDs should be the same.
-#'
-#' @param x An RDD to be joined. Should be an RDD where each element is
-#'             list(K, V).
-#' @param y An RDD to be joined. Should be an RDD where each element is
-#'             list(K, V).
-#' @param numPartitions Number of partitions to create.
-#' @return a new RDD containing all pairs of elements with matching keys in
-#'         two input RDDs.
-#' @examples
-#'\dontrun{
-#' sc <- sparkR.init()
-#' rdd1 <- parallelize(sc, list(list(1, 1), list(2, 4)))
-#' rdd2 <- parallelize(sc, list(list(1, 2), list(1, 3)))
-#' join(rdd1, rdd2, 2L) # list(list(1, list(1, 2)), list(1, list(1, 3))
-#'}
-#' @rdname join-methods
-#' @aliases join,RDD,RDD-method
+# Join two RDDs
+#
+# @description
+# \code{join} This function joins two RDDs where every element is of the form list(K, V).
+# The key types of the two RDDs should be the same.
+#
+# @param x An RDD to be joined. Should be an RDD where each element is
+#             list(K, V).
+# @param y An RDD to be joined. Should be an RDD where each element is
+#             list(K, V).
+# @param numPartitions Number of partitions to create.
+# @return a new RDD containing all pairs of elements with matching keys in
+#         two input RDDs.
+# @examples
+#\dontrun{
+# sc <- sparkR.init()
+# rdd1 <- parallelize(sc, list(list(1, 1), list(2, 4)))
+# rdd2 <- parallelize(sc, list(list(1, 2), list(1, 3)))
+# join(rdd1, rdd2, 2L) # list(list(1, list(1, 2)), list(1, list(1, 3))
+#}
+# @rdname join-methods
+# @aliases join,RDD,RDD-method
 setMethod("join",
           signature(x = "RDD", y = "RDD"),
           function(x, y, numPartitions) {
@@ -557,30 +557,30 @@ setMethod("join",
                                     doJoin)
           })
 
-#' Left outer join two RDDs
-#'
-#' @description
-#' \code{leftouterjoin} This function left-outer-joins two RDDs where every element is of the form list(K, V).
-#' The key types of the two RDDs should be the same.
-#'
-#' @param x An RDD to be joined. Should be an RDD where each element is
-#'             list(K, V).
-#' @param y An RDD to be joined. Should be an RDD where each element is
-#'             list(K, V).
-#' @param numPartitions Number of partitions to create.
-#' @return For each element (k, v) in x, the resulting RDD will either contain
-#'         all pairs (k, (v, w)) for (k, w) in rdd2, or the pair (k, (v, NULL))
-#'         if no elements in rdd2 have key k.
-#' @examples
-#'\dontrun{
-#' sc <- sparkR.init()
-#' rdd1 <- parallelize(sc, list(list(1, 1), list(2, 4)))
-#' rdd2 <- parallelize(sc, list(list(1, 2), list(1, 3)))
-#' leftOuterJoin(rdd1, rdd2, 2L)
-#' # list(list(1, list(1, 2)), list(1, list(1, 3)), list(2, list(4, NULL)))
-#'}
-#' @rdname join-methods
-#' @aliases leftOuterJoin,RDD,RDD-method
+# Left outer join two RDDs
+#
+# @description
+# \code{leftouterjoin} This function left-outer-joins two RDDs where every element is of the form list(K, V).
+# The key types of the two RDDs should be the same.
+#
+# @param x An RDD to be joined. Should be an RDD where each element is
+#             list(K, V).
+# @param y An RDD to be joined. Should be an RDD where each element is
+#             list(K, V).
+# @param numPartitions Number of partitions to create.
+# @return For each element (k, v) in x, the resulting RDD will either contain
+#         all pairs (k, (v, w)) for (k, w) in rdd2, or the pair (k, (v, NULL))
+#         if no elements in rdd2 have key k.
+# @examples
+#\dontrun{
+# sc <- sparkR.init()
+# rdd1 <- parallelize(sc, list(list(1, 1), list(2, 4)))
+# rdd2 <- parallelize(sc, list(list(1, 2), list(1, 3)))
+# leftOuterJoin(rdd1, rdd2, 2L)
+# # list(list(1, list(1, 2)), list(1, list(1, 3)), list(2, list(4, NULL)))
+#}
+# @rdname join-methods
+# @aliases leftOuterJoin,RDD,RDD-method
 setMethod("leftOuterJoin",
           signature(x = "RDD", y = "RDD", numPartitions = "numeric"),
           function(x, y, numPartitions) {
@@ -594,30 +594,30 @@ setMethod("leftOuterJoin",
             joined <- flatMapValues(groupByKey(unionRDD(xTagged, yTagged), numPartitions), doJoin)
           })
 
-#' Right outer join two RDDs
-#'
-#' @description
-#' \code{rightouterjoin} This function right-outer-joins two RDDs where every element is of the form list(K, V).
-#' The key types of the two RDDs should be the same.
-#'
-#' @param x An RDD to be joined. Should be an RDD where each element is
-#'             list(K, V).
-#' @param y An RDD to be joined. Should be an RDD where each element is
-#'             list(K, V).
-#' @param numPartitions Number of partitions to create.
-#' @return For each element (k, w) in y, the resulting RDD will either contain
-#'         all pairs (k, (v, w)) for (k, v) in x, or the pair (k, (NULL, w))
-#'         if no elements in x have key k.
-#' @examples
-#'\dontrun{
-#' sc <- sparkR.init()
-#' rdd1 <- parallelize(sc, list(list(1, 2), list(1, 3)))
-#' rdd2 <- parallelize(sc, list(list(1, 1), list(2, 4)))
-#' rightOuterJoin(rdd1, rdd2, 2L)
-#' # list(list(1, list(2, 1)), list(1, list(3, 1)), list(2, list(NULL, 4)))
-#'}
-#' @rdname join-methods
-#' @aliases rightOuterJoin,RDD,RDD-method
+# Right outer join two RDDs
+#
+# @description
+# \code{rightouterjoin} This function right-outer-joins two RDDs where every element is of the form list(K, V).
+# The key types of the two RDDs should be the same.
+#
+# @param x An RDD to be joined. Should be an RDD where each element is
+#             list(K, V).
+# @param y An RDD to be joined. Should be an RDD where each element is
+#             list(K, V).
+# @param numPartitions Number of partitions to create.
+# @return For each element (k, w) in y, the resulting RDD will either contain
+#         all pairs (k, (v, w)) for (k, v) in x, or the pair (k, (NULL, w))
+#         if no elements in x have key k.
+# @examples
+#\dontrun{
+# sc <- sparkR.init()
+# rdd1 <- parallelize(sc, list(list(1, 2), list(1, 3)))
+# rdd2 <- parallelize(sc, list(list(1, 1), list(2, 4)))
+# rightOuterJoin(rdd1, rdd2, 2L)
+# # list(list(1, list(2, 1)), list(1, list(3, 1)), list(2, list(NULL, 4)))
+#}
+# @rdname join-methods
+# @aliases rightOuterJoin,RDD,RDD-method
 setMethod("rightOuterJoin",
           signature(x = "RDD", y = "RDD", numPartitions = "numeric"),
           function(x, y, numPartitions) {
@@ -631,33 +631,33 @@ setMethod("rightOuterJoin",
             joined <- flatMapValues(groupByKey(unionRDD(xTagged, yTagged), numPartitions), doJoin)
           })
 
-#' Full outer join two RDDs
-#'
-#' @description
-#' \code{fullouterjoin} This function full-outer-joins two RDDs where every element is of the form list(K, V).
-#' The key types of the two RDDs should be the same.
-#'
-#' @param x An RDD to be joined. Should be an RDD where each element is
-#'             list(K, V).
-#' @param y An RDD to be joined. Should be an RDD where each element is
-#'             list(K, V).
-#' @param numPartitions Number of partitions to create.
-#' @return For each element (k, v) in x and (k, w) in y, the resulting RDD
-#'         will contain all pairs (k, (v, w)) for both (k, v) in x and
-#'         (k, w) in y, or the pair (k, (NULL, w))/(k, (v, NULL)) if no elements
-#'         in x/y have key k.
-#' @examples
-#'\dontrun{
-#' sc <- sparkR.init()
-#' rdd1 <- parallelize(sc, list(list(1, 2), list(1, 3), list(3, 3)))
-#' rdd2 <- parallelize(sc, list(list(1, 1), list(2, 4)))
-#' fullOuterJoin(rdd1, rdd2, 2L) # list(list(1, list(2, 1)),
-#'                               #      list(1, list(3, 1)),
-#'                               #      list(2, list(NULL, 4)))
-#'                               #      list(3, list(3, NULL)),
-#'}
-#' @rdname join-methods
-#' @aliases fullOuterJoin,RDD,RDD-method
+# Full outer join two RDDs
+#
+# @description
+# \code{fullouterjoin} This function full-outer-joins two RDDs where every element is of the form list(K, V).
+# The key types of the two RDDs should be the same.
+#
+# @param x An RDD to be joined. Should be an RDD where each element is
+#             list(K, V).
+# @param y An RDD to be joined. Should be an RDD where each element is
+#             list(K, V).
+# @param numPartitions Number of partitions to create.
+# @return For each element (k, v) in x and (k, w) in y, the resulting RDD
+#         will contain all pairs (k, (v, w)) for both (k, v) in x and
+#         (k, w) in y, or the pair (k, (NULL, w))/(k, (v, NULL)) if no elements
+#         in x/y have key k.
+# @examples
+#\dontrun{
+# sc <- sparkR.init()
+# rdd1 <- parallelize(sc, list(list(1, 2), list(1, 3), list(3, 3)))
+# rdd2 <- parallelize(sc, list(list(1, 1), list(2, 4)))
+# fullOuterJoin(rdd1, rdd2, 2L) # list(list(1, list(2, 1)),
+#                               #      list(1, list(3, 1)),
+#                               #      list(2, list(NULL, 4)))
+#                               #      list(3, list(3, NULL)),
+#}
+# @rdname join-methods
+# @aliases fullOuterJoin,RDD,RDD-method
 setMethod("fullOuterJoin",
           signature(x = "RDD", y = "RDD", numPartitions = "numeric"),
           function(x, y, numPartitions) {
@@ -671,23 +671,23 @@ setMethod("fullOuterJoin",
             joined <- flatMapValues(groupByKey(unionRDD(xTagged, yTagged), numPartitions), doJoin)
           })
 
-#' For each key k in several RDDs, return a resulting RDD that
-#' whose values are a list of values for the key in all RDDs.
-#'
-#' @param ... Several RDDs.
-#' @param numPartitions Number of partitions to create.
-#' @return a new RDD containing all pairs of elements with values in a list
-#' in all RDDs.
-#' @examples
-#'\dontrun{
-#' sc <- sparkR.init()
-#' rdd1 <- parallelize(sc, list(list(1, 1), list(2, 4)))
-#' rdd2 <- parallelize(sc, list(list(1, 2), list(1, 3)))
-#' cogroup(rdd1, rdd2, numPartitions = 2L)
-#' # list(list(1, list(1, list(2, 3))), list(2, list(list(4), list()))
-#'}
-#' @rdname cogroup
-#' @aliases cogroup,RDD-method
+# For each key k in several RDDs, return a resulting RDD that
+# whose values are a list of values for the key in all RDDs.
+#
+# @param ... Several RDDs.
+# @param numPartitions Number of partitions to create.
+# @return a new RDD containing all pairs of elements with values in a list
+# in all RDDs.
+# @examples
+#\dontrun{
+# sc <- sparkR.init()
+# rdd1 <- parallelize(sc, list(list(1, 1), list(2, 4)))
+# rdd2 <- parallelize(sc, list(list(1, 2), list(1, 3)))
+# cogroup(rdd1, rdd2, numPartitions = 2L)
+# # list(list(1, list(1, list(2, 3))), list(2, list(list(4), list()))
+#}
+# @rdname cogroup
+# @aliases cogroup,RDD-method
 setMethod("cogroup",
           "RDD",
           function(..., numPartitions) {
@@ -723,20 +723,20 @@ setMethod("cogroup",
                                      group.func)
           })
 
-#' Sort a (k, v) pair RDD by k.
-#'
-#' @param x A (k, v) pair RDD to be sorted.
-#' @param ascending A flag to indicate whether the sorting is ascending or descending.
-#' @param numPartitions Number of partitions to create.
-#' @return An RDD where all (k, v) pair elements are sorted.
-#' @examples
-#'\dontrun{
-#' sc <- sparkR.init()
-#' rdd <- parallelize(sc, list(list(3, 1), list(2, 2), list(1, 3)))
-#' collect(sortByKey(rdd)) # list (list(1, 3), list(2, 2), list(3, 1))
-#'}
-#' @rdname sortByKey
-#' @aliases sortByKey,RDD,RDD-method
+# Sort a (k, v) pair RDD by k.
+#
+# @param x A (k, v) pair RDD to be sorted.
+# @param ascending A flag to indicate whether the sorting is ascending or descending.
+# @param numPartitions Number of partitions to create.
+# @return An RDD where all (k, v) pair elements are sorted.
+# @examples
+#\dontrun{
+# sc <- sparkR.init()
+# rdd <- parallelize(sc, list(list(3, 1), list(2, 2), list(1, 3)))
+# collect(sortByKey(rdd)) # list (list(1, 3), list(2, 2), list(3, 1))
+#}
+# @rdname sortByKey
+# @aliases sortByKey,RDD,RDD-method
 setMethod("sortByKey",
           signature(x = "RDD"),
           function(x, ascending = TRUE, numPartitions = SparkR:::numPartitions(x)) {
@@ -785,25 +785,25 @@ setMethod("sortByKey",
             lapplyPartition(newRDD, partitionFunc)
           })
           
-#' Subtract a pair RDD with another pair RDD.
-#'
-#' Return an RDD with the pairs from x whose keys are not in other.
-#'
-#' @param x An RDD.
-#' @param other An RDD.
-#' @param numPartitions Number of the partitions in the result RDD.
-#' @return An RDD with the pairs from x whose keys are not in other.
-#' @examples
-#'\dontrun{
-#' sc <- sparkR.init()
-#' rdd1 <- parallelize(sc, list(list("a", 1), list("b", 4),
-#'                              list("b", 5), list("a", 2)))
-#' rdd2 <- parallelize(sc, list(list("a", 3), list("c", 1)))
-#' collect(subtractByKey(rdd1, rdd2))
-#' # list(list("b", 4), list("b", 5))
-#'}
-#' @rdname subtractByKey
-#' @aliases subtractByKey,RDD
+# Subtract a pair RDD with another pair RDD.
+#
+# Return an RDD with the pairs from x whose keys are not in other.
+#
+# @param x An RDD.
+# @param other An RDD.
+# @param numPartitions Number of the partitions in the result RDD.
+# @return An RDD with the pairs from x whose keys are not in other.
+# @examples
+#\dontrun{
+# sc <- sparkR.init()
+# rdd1 <- parallelize(sc, list(list("a", 1), list("b", 4),
+#                              list("b", 5), list("a", 2)))
+# rdd2 <- parallelize(sc, list(list("a", 3), list("c", 1)))
+# collect(subtractByKey(rdd1, rdd2))
+# # list(list("b", 4), list("b", 5))
+#}
+# @rdname subtractByKey
+# @aliases subtractByKey,RDD
 setMethod("subtractByKey",
           signature(x = "RDD", other = "RDD"),
           function(x, other, numPartitions = SparkR:::numPartitions(x)) {
@@ -819,41 +819,41 @@ setMethod("subtractByKey",
                           function (v) { v[[1]] })
           })
 
-#' Return a subset of this RDD sampled by key.
-#' 
-#' @description
-#' \code{sampleByKey} Create a sample of this RDD using variable sampling rates
-#' for different keys as specified by fractions, a key to sampling rate map.
-#'
-#' @param x The RDD to sample elements by key, where each element is
-#'             list(K, V) or c(K, V).
-#' @param withReplacement Sampling with replacement or not
-#' @param fraction The (rough) sample target fraction
-#' @param seed Randomness seed value
-#' @examples
-#'\dontrun{
-#' sc <- sparkR.init()
-#' rdd <- parallelize(sc, 1:3000)
-#' pairs <- lapply(rdd, function(x) { if (x %% 3 == 0) list("a", x)
-#'                                    else { if (x %% 3 == 1) list("b", x) else list("c", x) }})
-#' fractions <- list(a = 0.2, b = 0.1, c = 0.3)
-#' sample <- sampleByKey(pairs, FALSE, fractions, 1618L)
-#' 100 < length(lookup(sample, "a")) && 300 > length(lookup(sample, "a")) # TRUE
-#' 50 < length(lookup(sample, "b")) && 150 > length(lookup(sample, "b")) # TRUE
-#' 200 < length(lookup(sample, "c")) && 400 > length(lookup(sample, "c")) # TRUE
-#' lookup(sample, "a")[which.min(lookup(sample, "a"))] >= 0 # TRUE
-#' lookup(sample, "a")[which.max(lookup(sample, "a"))] <= 2000 # TRUE
-#' lookup(sample, "b")[which.min(lookup(sample, "b"))] >= 0 # TRUE
-#' lookup(sample, "b")[which.max(lookup(sample, "b"))] <= 2000 # TRUE
-#' lookup(sample, "c")[which.min(lookup(sample, "c"))] >= 0 # TRUE
-#' lookup(sample, "c")[which.max(lookup(sample, "c"))] <= 2000 # TRUE
-#' fractions <- list(a = 0.2, b = 0.1, c = 0.3, d = 0.4)
-#' sample <- sampleByKey(pairs, FALSE, fractions, 1618L) # Key "d" will be ignored
-#' fractions <- list(a = 0.2, b = 0.1)
-#' sample <- sampleByKey(pairs, FALSE, fractions, 1618L) # KeyError: "c"
-#'}
-#' @rdname sampleByKey
-#' @aliases sampleByKey,RDD-method
+# Return a subset of this RDD sampled by key.
+# 
+# @description
+# \code{sampleByKey} Create a sample of this RDD using variable sampling rates
+# for different keys as specified by fractions, a key to sampling rate map.
+#
+# @param x The RDD to sample elements by key, where each element is
+#             list(K, V) or c(K, V).
+# @param withReplacement Sampling with replacement or not
+# @param fraction The (rough) sample target fraction
+# @param seed Randomness seed value
+# @examples
+#\dontrun{
+# sc <- sparkR.init()
+# rdd <- parallelize(sc, 1:3000)
+# pairs <- lapply(rdd, function(x) { if (x %% 3 == 0) list("a", x)
+#                                    else { if (x %% 3 == 1) list("b", x) else list("c", x) }})
+# fractions <- list(a = 0.2, b = 0.1, c = 0.3)
+# sample <- sampleByKey(pairs, FALSE, fractions, 1618L)
+# 100 < length(lookup(sample, "a")) && 300 > length(lookup(sample, "a")) # TRUE
+# 50 < length(lookup(sample, "b")) && 150 > length(lookup(sample, "b")) # TRUE
+# 200 < length(lookup(sample, "c")) && 400 > length(lookup(sample, "c")) # TRUE
+# lookup(sample, "a")[which.min(lookup(sample, "a"))] >= 0 # TRUE
+# lookup(sample, "a")[which.max(lookup(sample, "a"))] <= 2000 # TRUE
+# lookup(sample, "b")[which.min(lookup(sample, "b"))] >= 0 # TRUE
+# lookup(sample, "b")[which.max(lookup(sample, "b"))] <= 2000 # TRUE
+# lookup(sample, "c")[which.min(lookup(sample, "c"))] >= 0 # TRUE
+# lookup(sample, "c")[which.max(lookup(sample, "c"))] <= 2000 # TRUE
+# fractions <- list(a = 0.2, b = 0.1, c = 0.3, d = 0.4)
+# sample <- sampleByKey(pairs, FALSE, fractions, 1618L) # Key "d" will be ignored
+# fractions <- list(a = 0.2, b = 0.1)
+# sample <- sampleByKey(pairs, FALSE, fractions, 1618L) # KeyError: "c"
+#}
+# @rdname sampleByKey
+# @aliases sampleByKey,RDD-method
 setMethod("sampleByKey",
           signature(x = "RDD", withReplacement = "logical",
                     fractions = "vector", seed = "integer"),

From 35d6a99cbe3f67da5d56888e63baf9bc69f3de91 Mon Sep 17 00:00:00 2001
From: Jacek Lewandowski <lewandowski.jacek@gmail.com>
Date: Fri, 8 May 2015 11:36:30 -0700
Subject: [PATCH 63/75] [SPARK-7436] Fixed instantiation of custom recovery
 mode factory and added tests

Author: Jacek Lewandowski <lewandowski.jacek@gmail.com>

Closes #5977 from jacek-lewandowski/SPARK-7436 and squashes the following commits:

ff0a3c2 [Jacek Lewandowski] SPARK-7436: Fixed instantiation of custom recovery mode factory and added tests
---
 .../apache/spark/deploy/master/Master.scala   |   2 +-
 .../master/CustomRecoveryModeFactory.scala    | 110 ++++++++++++++++++
 .../spark/deploy/master/MasterSuite.scala     | 100 +++++++++++++++-
 3 files changed, 208 insertions(+), 4 deletions(-)
 create mode 100644 core/src/test/scala/org/apache/spark/deploy/master/CustomRecoveryModeFactory.scala

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 53e1903a3d125..fccceb3ea528b 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
@@ -165,7 +165,7 @@ private[master] class Master(
         (fsFactory.createPersistenceEngine(), fsFactory.createLeaderElectionAgent(this))
       case "CUSTOM" =>
         val clazz = Class.forName(conf.get("spark.deploy.recoveryMode.factory"))
-        val factory = clazz.getConstructor(conf.getClass, Serialization.getClass)
+        val factory = clazz.getConstructor(classOf[SparkConf], classOf[Serialization])
           .newInstance(conf, SerializationExtension(context.system))
           .asInstanceOf[StandaloneRecoveryModeFactory]
         (factory.createPersistenceEngine(), factory.createLeaderElectionAgent(this))
diff --git a/core/src/test/scala/org/apache/spark/deploy/master/CustomRecoveryModeFactory.scala b/core/src/test/scala/org/apache/spark/deploy/master/CustomRecoveryModeFactory.scala
new file mode 100644
index 0000000000000..f4e56632e426a
--- /dev/null
+++ b/core/src/test/scala/org/apache/spark/deploy/master/CustomRecoveryModeFactory.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.
+ */
+
+// This file is placed in different package to make sure all of these components work well
+// when they are outside of org.apache.spark.
+package other.supplier
+
+import scala.collection.mutable
+import scala.reflect.ClassTag
+
+import akka.serialization.Serialization
+
+import org.apache.spark.SparkConf
+import org.apache.spark.deploy.master._
+
+class CustomRecoveryModeFactory(
+  conf: SparkConf,
+  serialization: Serialization
+) extends StandaloneRecoveryModeFactory(conf, serialization) {
+
+  CustomRecoveryModeFactory.instantiationAttempts += 1
+
+  /**
+   * PersistenceEngine defines how the persistent data(Information about worker, driver etc..)
+   * is handled for recovery.
+   *
+   */
+  override def createPersistenceEngine(): PersistenceEngine =
+    new CustomPersistenceEngine(serialization)
+
+  /**
+   * Create an instance of LeaderAgent that decides who gets elected as master.
+   */
+  override def createLeaderElectionAgent(master: LeaderElectable): LeaderElectionAgent =
+    new CustomLeaderElectionAgent(master)
+}
+
+object CustomRecoveryModeFactory {
+  @volatile var instantiationAttempts = 0
+}
+
+class CustomPersistenceEngine(serialization: Serialization) extends PersistenceEngine {
+  val data = mutable.HashMap[String, Array[Byte]]()
+
+  CustomPersistenceEngine.lastInstance = Some(this)
+
+  /**
+   * Defines how the object is serialized and persisted. Implementation will
+   * depend on the store used.
+   */
+  override def persist(name: String, obj: Object): Unit = {
+    CustomPersistenceEngine.persistAttempts += 1
+    serialization.serialize(obj) match {
+      case util.Success(bytes) => data += name -> bytes
+      case util.Failure(cause) => throw new RuntimeException(cause)
+    }
+  }
+
+  /**
+   * Defines how the object referred by its name is removed from the store.
+   */
+  override def unpersist(name: String): Unit = {
+    CustomPersistenceEngine.unpersistAttempts += 1
+    data -= name
+  }
+
+  /**
+   * Gives all objects, matching a prefix. This defines how objects are
+   * read/deserialized back.
+   */
+  override def read[T: ClassTag](prefix: String): Seq[T] = {
+    CustomPersistenceEngine.readAttempts += 1
+    val clazz = implicitly[ClassTag[T]].runtimeClass.asInstanceOf[Class[T]]
+    val results = for ((name, bytes) <- data; if name.startsWith(prefix))
+      yield serialization.deserialize(bytes, clazz)
+
+    results.find(_.isFailure).foreach {
+      case util.Failure(cause) => throw new RuntimeException(cause)
+    }
+
+    results.flatMap(_.toOption).toSeq
+  }
+}
+
+object CustomPersistenceEngine {
+  @volatile var persistAttempts = 0
+  @volatile var unpersistAttempts = 0
+  @volatile var readAttempts = 0
+
+  @volatile var lastInstance: Option[CustomPersistenceEngine] = None
+}
+
+class CustomLeaderElectionAgent(val masterActor: LeaderElectable) extends LeaderElectionAgent {
+  masterActor.electedLeader()
+}
+
diff --git a/core/src/test/scala/org/apache/spark/deploy/master/MasterSuite.scala b/core/src/test/scala/org/apache/spark/deploy/master/MasterSuite.scala
index 34c74d87f0a62..0faa8f650e5e1 100644
--- a/core/src/test/scala/org/apache/spark/deploy/master/MasterSuite.scala
+++ b/core/src/test/scala/org/apache/spark/deploy/master/MasterSuite.scala
@@ -17,12 +17,20 @@
 
 package org.apache.spark.deploy.master
 
+import java.util.Date
+
+import scala.concurrent.Await
+import scala.concurrent.duration._
+import scala.language.postfixOps
+
 import akka.actor.Address
-import org.scalatest.FunSuite
+import org.scalatest.{FunSuite, Matchers}
+import other.supplier.{CustomPersistenceEngine, CustomRecoveryModeFactory}
 
-import org.apache.spark.{SSLOptions, SparkConf, SparkException}
+import org.apache.spark.deploy._
+import org.apache.spark.{SparkConf, SparkException}
 
-class MasterSuite extends FunSuite {
+class MasterSuite extends FunSuite with Matchers {
 
   test("toAkkaUrl") {
     val conf = new SparkConf(loadDefaults = false)
@@ -63,4 +71,90 @@ class MasterSuite extends FunSuite {
     }
     assert("Invalid master URL: spark://1.2. 3.4:1234" === e.getMessage)
   }
+
+  test("can use a custom recovery mode factory") {
+    val conf = new SparkConf(loadDefaults = false)
+    conf.set("spark.deploy.recoveryMode", "CUSTOM")
+    conf.set("spark.deploy.recoveryMode.factory",
+      classOf[CustomRecoveryModeFactory].getCanonicalName)
+
+    val instantiationAttempts = CustomRecoveryModeFactory.instantiationAttempts
+
+    val commandToPersist = new Command(
+      mainClass = "",
+      arguments = Nil,
+      environment = Map.empty,
+      classPathEntries = Nil,
+      libraryPathEntries = Nil,
+      javaOpts = Nil
+    )
+
+    val appToPersist = new ApplicationInfo(
+      startTime = 0,
+      id = "test_app",
+      desc = new ApplicationDescription(
+        name = "",
+        maxCores = None,
+        memoryPerExecutorMB = 0,
+        command = commandToPersist,
+        appUiUrl = "",
+        eventLogDir = None,
+        eventLogCodec = None,
+        coresPerExecutor = None),
+      submitDate = new Date(),
+      driver = null,
+      defaultCores = 0
+    )
+
+    val driverToPersist = new DriverInfo(
+      startTime = 0,
+      id = "test_driver",
+      desc = new DriverDescription(
+        jarUrl = "",
+        mem = 0,
+        cores = 0,
+        supervise = false,
+        command = commandToPersist
+      ),
+      submitDate = new Date()
+    )
+
+    val workerToPersist = new WorkerInfo(
+      id = "test_worker",
+      host = "127.0.0.1",
+      port = 10000,
+      cores = 0,
+      memory = 0,
+      actor = null,
+      webUiPort = 0,
+      publicAddress = ""
+    )
+
+    val (actorSystem, port, uiPort, restPort) =
+      Master.startSystemAndActor("127.0.0.1", 7077, 8080, conf)
+
+    try {
+      Await.result(actorSystem.actorSelection("/user/Master").resolveOne(10 seconds), 10 seconds)
+
+      CustomPersistenceEngine.lastInstance.isDefined shouldBe true
+      val persistenceEngine = CustomPersistenceEngine.lastInstance.get
+
+      persistenceEngine.addApplication(appToPersist)
+      persistenceEngine.addDriver(driverToPersist)
+      persistenceEngine.addWorker(workerToPersist)
+
+      val (apps, drivers, workers) = persistenceEngine.readPersistedData()
+
+      apps.map(_.id) should contain(appToPersist.id)
+      drivers.map(_.id) should contain(driverToPersist.id)
+      workers.map(_.id) should contain(workerToPersist.id)
+
+    } finally {
+      actorSystem.shutdown()
+      actorSystem.awaitTermination()
+    }
+
+    CustomRecoveryModeFactory.instantiationAttempts should be > instantiationAttempts
+  }
+
 }

From a1ec08f7edc8d956afcfbb92d10b26b7619486e8 Mon Sep 17 00:00:00 2001
From: Matei Zaharia <matei@databricks.com>
Date: Fri, 8 May 2015 14:41:42 -0400
Subject: [PATCH 64/75] [SPARK-7298] Harmonize style of new visualizations

- Colors on the timeline now match the rest of the UI
- The expandable buttons to show timeline view, DAG, etc are now more visible
- Timeline text is smaller
- DAG visualization text and colors are more consistent throughout
- Fix some JavaScript style issues
- Various small fixes throughout (e.g. inconsistent capitalization, some confusing names, HTML escaping, etc)

Author: Matei Zaharia <matei@databricks.com>

Closes #5942 from mateiz/ui and squashes the following commits:

def38d0 [Matei Zaharia] Add some tooltips
4c5a364 [Matei Zaharia] Reduce stage and rank separation slightly
43dcbe3 [Matei Zaharia] Some updates to DAG
fac734a [Matei Zaharia] tweaks
6a6705d [Matei Zaharia] More fixes
67629f5 [Matei Zaharia] Various small tweaks
---
 .../apache/spark/ui/static/spark-dag-viz.css  |  60 +++++---
 .../apache/spark/ui/static/spark-dag-viz.js   |  57 ++++----
 .../apache/spark/ui/static/timeline-view.css  | 128 +++++++++++-------
 .../apache/spark/ui/static/timeline-view.js   |   4 +-
 .../org/apache/spark/ui/static/webui.css      |  36 +++--
 .../scala/org/apache/spark/ui/ToolTips.scala  |  19 +++
 .../scala/org/apache/spark/ui/UIUtils.scala   |  15 +-
 .../apache/spark/ui/jobs/AllJobsPage.scala    |  31 +++--
 .../apache/spark/ui/jobs/AllStagesPage.scala  |  15 +-
 .../org/apache/spark/ui/jobs/JobPage.scala    |  23 ++--
 .../org/apache/spark/ui/jobs/StagePage.scala  |  16 +--
 .../spark/ui/scope/RDDOperationGraph.scala    |   2 +-
 12 files changed, 255 insertions(+), 151 deletions(-)

diff --git a/core/src/main/resources/org/apache/spark/ui/static/spark-dag-viz.css b/core/src/main/resources/org/apache/spark/ui/static/spark-dag-viz.css
index 8481710828455..18c72694f3e2d 100644
--- a/core/src/main/resources/org/apache/spark/ui/static/spark-dag-viz.css
+++ b/core/src/main/resources/org/apache/spark/ui/static/spark-dag-viz.css
@@ -16,40 +16,51 @@
  */
 
 #dag-viz-graph svg path {
-  stroke: #444444;
+  stroke: #444;
   stroke-width: 1.5px;
 }
 
 #dag-viz-graph svg g.cluster rect {
-  stroke-width: 4px;
-  stroke-opacity: 0.5;
+  stroke-width: 1px;
+}
+
+#dag-viz-graph svg g.node circle {
+  fill: #444;
 }
 
-#dag-viz-graph svg g.node circle,
 #dag-viz-graph svg g.node rect {
-  fill: #444444;
+  fill: #C3EBFF;
+  stroke: #3EC0FF;
+  stroke-width: 1px;
+}
+
+#dag-viz-graph svg g.node.cached circle {
+  fill: #444;
 }
 
-#dag-viz-graph svg g.node.cached circle,
 #dag-viz-graph svg g.node.cached rect {
-  fill: #FF0000;
+  fill: #B3F5C5;
+  stroke: #56F578;
+  stroke-width: 1px;
 }
 
 /* Job page specific styles */
 
 #dag-viz-graph svg.job marker#marker-arrow path {
-  fill: #444444;
+  fill: #333;
   stroke-width: 0px;
 }
 
 #dag-viz-graph svg.job g.cluster rect {
-  fill: #FFFFFF;
-  stroke: #AADFFF;
+  fill: #A0DFFF;
+  stroke: #3EC0FF;
+  stroke-width: 1px;
 }
 
 #dag-viz-graph svg.job g.cluster[id*="stage"] rect {
-  stroke: #FFDDEE;
-  stroke-width: 6px;
+  fill: #FFFFFF;
+  stroke: #FF99AC;
+  stroke-width: 1px;
 }
 
 #dag-viz-graph svg.job g#cross-stage-edges path {
@@ -57,27 +68,36 @@
 }
 
 #dag-viz-graph svg.job g.cluster text {
-  fill: #AAAAAA;
+  fill: #333;
 }
 
 /* Stage page specific styles */
 
 #dag-viz-graph svg.stage g.cluster rect {
-  fill: #F0F8FF;
-  stroke: #AADFFF;
+  fill: #A0DFFF;
+  stroke: #3EC0FF;
+  stroke-width: 1px;
 }
 
 #dag-viz-graph svg.stage g.cluster[id*="stage"] rect {
   fill: #FFFFFF;
-  stroke: #FFDDEE;
-  stroke-width: 6px;
+  stroke: #FFA6B6;
+  stroke-width: 1px;
 }
 
 #dag-viz-graph svg.stage g.node g.label text tspan {
-  fill: #FFFFFF;
+  fill: #333;
 }
 
 #dag-viz-graph svg.stage g.cluster text {
-  fill: #444444;
-  font-weight: bold;
+  fill: #333;
+}
+
+#dag-viz-graph a, #dag-viz-graph a:hover {
+  text-decoration: none;
+}
+
+#dag-viz-graph .label {
+  font-weight: normal;
+  text-shadow: none;
 }
diff --git a/core/src/main/resources/org/apache/spark/ui/static/spark-dag-viz.js b/core/src/main/resources/org/apache/spark/ui/static/spark-dag-viz.js
index a0e3e914c2547..764dd2cfcd76f 100644
--- a/core/src/main/resources/org/apache/spark/ui/static/spark-dag-viz.js
+++ b/core/src/main/resources/org/apache/spark/ui/static/spark-dag-viz.js
@@ -52,9 +52,9 @@
  */
 
 var VizConstants = {
-  svgMarginX: 20,
-  svgMarginY: 20,
-  stageSep: 50,
+  svgMarginX: 16,
+  svgMarginY: 16,
+  stageSep: 40,
   graphPrefix: "graph_",
   nodePrefix: "node_",
   stagePrefix: "stage_",
@@ -63,14 +63,16 @@ var VizConstants = {
 };
 
 var JobPageVizConstants = {
-  clusterLabelSize: 11,
-  stageClusterLabelSize: 14
-}
+  clusterLabelSize: 12,
+  stageClusterLabelSize: 14,
+  rankSep: 40
+};
 
 var StagePageVizConstants = {
   clusterLabelSize: 14,
-  stageClusterLabelSize: 18
-}
+  stageClusterLabelSize: 14,
+  rankSep: 40
+};
 
 /*
  * Show or hide the RDD DAG visualization.
@@ -149,11 +151,11 @@ function renderDagVizForStage(svgContainer) {
   var dot = metadata.select(".dot-file").text();
   var containerId = VizConstants.graphPrefix + metadata.attr("stage-id");
   var container = svgContainer.append("g").attr("id", containerId);
-  renderDot(dot, container);
+  renderDot(dot, container, StagePageVizConstants.rankSep);
 
-  // Round corners on RDDs
+  // Round corners on rectangles
   svgContainer
-    .selectAll("g.node rect")
+    .selectAll("rect")
     .attr("rx", "5")
     .attr("ry", "5");
 }
@@ -207,7 +209,13 @@ function renderDagVizForJob(svgContainer) {
     }
 
     // Actually render the stage
-    renderDot(dot, container);
+    renderDot(dot, container, JobPageVizConstants.rankSep);
+
+    // Round corners on rectangles
+    container
+      .selectAll("rect")
+      .attr("rx", "4")
+      .attr("ry", "4");
 
     // If there are any incoming edges into this graph, keep track of them to render
     // them separately later. Note that we cannot draw them now because we need to
@@ -223,12 +231,13 @@ function renderDagVizForJob(svgContainer) {
 }
 
 /* Render the dot file as an SVG in the given container. */
-function renderDot(dot, container) {
+function renderDot(dot, container, rankSep) {
   var escaped_dot = dot
     .replace(/&lt;/g, "<")
     .replace(/&gt;/g, ">")
     .replace(/&quot;/g, "\"");
   var g = graphlibDot.read(escaped_dot);
+  g.graph().rankSep = rankSep;
   var renderer = new dagreD3.render();
   renderer(container, g);
 }
@@ -248,12 +257,13 @@ function metadataContainer() { return d3.select("#dag-viz-metadata"); }
  * In general, the clustering support for dagre-d3 is quite limited at this point.
  */
 function drawClusterLabels(svgContainer, forJob) {
+  var clusterLabelSize, stageClusterLabelSize;
   if (forJob) {
-    var clusterLabelSize = JobPageVizConstants.clusterLabelSize;
-    var stageClusterLabelSize = JobPageVizConstants.stageClusterLabelSize;
+    clusterLabelSize = JobPageVizConstants.clusterLabelSize;
+    stageClusterLabelSize = JobPageVizConstants.stageClusterLabelSize;
   } else {
-    var clusterLabelSize = StagePageVizConstants.clusterLabelSize;
-    var stageClusterLabelSize = StagePageVizConstants.stageClusterLabelSize;
+    clusterLabelSize = StagePageVizConstants.clusterLabelSize;
+    stageClusterLabelSize = StagePageVizConstants.stageClusterLabelSize;
   }
   svgContainer.selectAll("g.cluster").each(function() {
     var cluster = d3.select(this);
@@ -283,7 +293,7 @@ function drawClusterLabel(d3cluster, fontSize) {
     .attr("x", labelX)
     .attr("y", labelY)
     .attr("text-anchor", "end")
-    .style("font-size", fontSize)
+    .style("font-size", fontSize + "px")
     .text(labelText);
 }
 
@@ -303,12 +313,12 @@ function resizeSvg(svg) {
     }));
   var endX = VizConstants.svgMarginX +
     toFloat(d3.max(allClusters, function(e) {
-      var t = d3.select(e)
+      var t = d3.select(e);
       return getAbsolutePosition(t).x + toFloat(t.attr("width"));
     }));
   var endY = VizConstants.svgMarginY +
     toFloat(d3.max(allClusters, function(e) {
-      var t = d3.select(e)
+      var t = d3.select(e);
       return getAbsolutePosition(t).y + toFloat(t.attr("height"));
     }));
   var width = endX - startX;
@@ -338,7 +348,7 @@ function drawCrossStageEdges(edges, svgContainer) {
   if (!dagreD3Marker.empty()) {
     svgContainer
       .append(function() { return dagreD3Marker.node().cloneNode(true); })
-      .attr("id", "marker-arrow")
+      .attr("id", "marker-arrow");
     svgContainer.selectAll("g > path").attr("marker-end", "url(#marker-arrow)");
     svgContainer.selectAll("g.edgePaths def").remove(); // We no longer need these
   }
@@ -394,12 +404,13 @@ function connectRDDs(fromRDDId, toRDDId, edgesContainer, svgContainer) {
     toPos.x += delta;
   }
 
+  var points;
   if (fromPos.y == toPos.y) {
     // If they are on the same rank, curve the middle part of the edge
     // upward a little to avoid interference with things in between
     // e.g.       _______
     //      _____/       \_____
-    var points = [
+    points = [
       [fromPos.x, fromPos.y],
       [fromPos.x + (toPos.x - fromPos.x) * 0.2, fromPos.y],
       [fromPos.x + (toPos.x - fromPos.x) * 0.3, fromPos.y - 20],
@@ -413,7 +424,7 @@ function connectRDDs(fromRDDId, toRDDId, edgesContainer, svgContainer) {
     //           /
     //          |
     //    _____/
-    var points = [
+    points = [
       [fromPos.x, fromPos.y],
       [fromPos.x + (toPos.x - fromPos.x) * 0.4, fromPos.y],
       [fromPos.x + (toPos.x - fromPos.x) * 0.6, toPos.y],
diff --git a/core/src/main/resources/org/apache/spark/ui/static/timeline-view.css b/core/src/main/resources/org/apache/spark/ui/static/timeline-view.css
index 35ef14e5aaf1a..d40de704229c3 100644
--- a/core/src/main/resources/org/apache/spark/ui/static/timeline-view.css
+++ b/core/src/main/resources/org/apache/spark/ui/static/timeline-view.css
@@ -23,6 +23,10 @@ div#application-timeline, div#job-timeline {
   margin-top: 5px;
 }
 
+.vis.timeline {
+  line-height: 14px;
+}
+
 .vis.timeline div.content {
   width: 100%;
 }
@@ -32,48 +36,55 @@ div#application-timeline, div#job-timeline {
 }
 
 .vis.timeline .item.stage.succeeded {
-  background-color: #D5DDF6;
+  background-color: #A0DFFF;
+  border-color: #3EC0FF;
 }
 
 .vis.timeline .item.stage.succeeded.selected {
-   background-color: #D5DDF6;
-   border-color: #97B0F8;
-   z-index: auto;
+  background-color: #A0DFFF;
+  border-color: #3EC0FF;
+  z-index: auto;
 }
 
 .legend-area rect.completed-stage-legend {
-  fill: #D5DDF6;
-  stroke: #97B0F8;
+  fill: #A0DFFF;
+  stroke: #3EC0FF;
 }
 
 .vis.timeline .item.stage.failed {
-   background-color: #FF5475;
+  background-color: #FFA1B0;
+  border-color: #FF4D6D;
 }
 
 .vis.timeline .item.stage.failed.selected {
-   background-color: #FF5475;
-   border-color: #97B0F8;
-   z-index: auto;
+  background-color: #FFA1B0;
+  border-color: #FF4D6D;
+  z-index: auto;
 }
 
 .legend-area rect.failed-stage-legend {
-  fill: #FF5475;
-  stroke: #97B0F8;
+  fill: #FFA1B0;
+  stroke: #FF4D6D;
 }
 
 .vis.timeline .item.stage.running {
-   background-color: #FDFFCA;
+  background-color: #A2FCC0;
+  border-color: #36F572;
 }
 
 .vis.timeline .item.stage.running.selected {
-   background-color: #FDFFCA;
-   border-color: #97B0F8;
-   z-index: auto;
+  background-color: #A2FCC0;
+  border-color: #36F572;
+  z-index: auto;
 }
 
 .legend-area rect.active-stage-legend {
-  fill: #FDFFCA;
-  stroke: #97B0F8;
+  fill: #A2FCC0;
+  stroke: #36F572;
+}
+
+.vis.timeline .foreground {
+  cursor: move;
 }
 
 .vis.timeline .item.job {
@@ -81,76 +92,81 @@ div#application-timeline, div#job-timeline {
 }
 
 .vis.timeline .item.job.succeeded {
-  background-color: #D5DDF6;
+  background-color: #A0DFFF;
+  border-color: #3EC0FF;
 }
 
 .vis.timeline .item.job.succeeded.selected {
-   background-color: #D5DDF6;
-   border-color: #97B0F8;
-   z-index: auto;
+  background-color: #A0DFFF;
+  border-color: #3EC0FF;
+  z-index: auto;
 }
 
 .legend-area rect.succeeded-job-legend {
-  fill: #D5DDF6;
-  stroke: #97B0F8;
+  fill: #A0DFFF;
+  stroke: #3EC0FF;
 }
 
 .vis.timeline .item.job.failed {
-   background-color: #FF5475;
+  background-color: #FFA1B0;
+  border-color: #FF4D6D;
 }
 
 .vis.timeline .item.job.failed.selected {
-   background-color: #FF5475;
-   border-color: #97B0F8;
-   z-index: auto;
+  background-color: #FFA1B0;
+  border-color: #FF4D6D;
+  z-index: auto;
 }
 
 .legend-area rect.failed-job-legend {
-  fill: #FF5475;
-  stroke: #97B0F8;
+  fill: #FFA1B0;
+  stroke: #FF4D6D;
 }
 
 .vis.timeline .item.job.running {
-   background-color: #FDFFCA;
+  background-color: #A2FCC0;
+  border-color: #36F572;
 }
 
 .vis.timeline .item.job.running.selected {
-   background-color: #FDFFCA;
-   border-color: #97B0F8;
-   z-index: auto;
+  background-color: #A2FCC0;
+  border-color: #36F572;
+  z-index: auto;
 }
 
 .legend-area rect.running-job-legend {
-  fill: #FDFFCA;
-  stroke: #97B0F8;
+  fill: #A2FCC0;
+  stroke: #36F572;
 }
 
 .vis.timeline .item.executor.added {
-  background-color: #D5DDF6;
+  background-color: #A0DFFF;
+  border-color: #3EC0FF;
 }
 
 .legend-area rect.executor-added-legend {
-  fill: #D5DDF6;
-  stroke: #97B0F8;
+  fill: #A0DFFF;
+  stroke: #3EC0FF;
 }
 
 .vis.timeline .item.executor.removed {
-  background-color: #EBCA59;
+  background-color: #FFA1B0;
+  border-color: #FF4D6D;
 }
 
 .legend-area rect.executor-removed-legend {
-  fill: #EBCA59;
-  stroke: #97B0F8;
+  fill: #FFA1B0;
+  stroke: #FF4D6D;
 }
 
 .vis.timeline .item.executor.selected {
-  border-color: #FFC200;
-  background-color: #FFF785;
+  background-color: #A2FCC0;
+  border-color: #36F572;
   z-index: 2;
 }
 
-tr.corresponding-item-hover>td, tr.corresponding-item-hover>th {
-  background-color: #FFE1FA !important;
+tr.corresponding-item-hover > td, tr.corresponding-item-hover > th {
+  background-color: #D6FFE4 !important;
 }
 
 #application-timeline.collapsed {
@@ -165,11 +181,15 @@ tr.corresponding-item-hover>td, tr.corresponding-item-hover>th {
   margin-bottom: 5px;
 }
 
+.control-panel input[type="checkbox"] {
+  margin: 0;
+}
+
 span.expand-application-timeline, span.expand-job-timeline {
   cursor: pointer;
 }
 
-.control-panel input+span {
+.control-panel input + span {
   cursor: pointer;
 }
 
@@ -180,3 +200,17 @@ span.expand-application-timeline, span.expand-job-timeline {
 .vis.timeline .item .tooltip-inner {
   max-width: unset !important;
 }
+
+.vispanel.center {
+  font-size: 12px;
+  line-height: 12px;
+}
+
+.legend-area text {
+  fill: #4D4D4D;
+}
+
+.additional-metrics ul {
+  list-style: none;
+  margin-left: 15px;
+}
diff --git a/core/src/main/resources/org/apache/spark/ui/static/timeline-view.js b/core/src/main/resources/org/apache/spark/ui/static/timeline-view.js
index e4a891d47f035..48fbb33b1155b 100644
--- a/core/src/main/resources/org/apache/spark/ui/static/timeline-view.js
+++ b/core/src/main/resources/org/apache/spark/ui/static/timeline-view.js
@@ -156,9 +156,9 @@ function setupExecutorEventAction() {
 function setupZoomable(id, timeline) {
   $(id + '>input[type="checkbox"]').click(function() {
     if (this.checked) {
-      timeline.setOptions({zoomable: false});
-    } else {
       timeline.setOptions({zoomable: true});
+    } else {
+      timeline.setOptions({zoomable: false});
     }
   });
 
diff --git a/core/src/main/resources/org/apache/spark/ui/static/webui.css b/core/src/main/resources/org/apache/spark/ui/static/webui.css
index 669ad48937c05..e7c1d475d4e52 100644
--- a/core/src/main/resources/org/apache/spark/ui/static/webui.css
+++ b/core/src/main/resources/org/apache/spark/ui/static/webui.css
@@ -106,14 +106,18 @@ span.rest-uri {
 }
 
 pre {
-  font-size: 0.8em;
+  font-size: 12px;
+  line-height: 18px;
+  padding: 6px;
+  margin: 0;
+  border-radius: 3px;
 }
 
 .stage-details {
   max-height: 100px;
   overflow-y: auto;
   margin: 0;
-  transition: max-height 0.5s ease-out, padding 0.5s ease-out;
+  transition: max-height 0.25s ease-out, padding 0.25s ease-out;
 }
 
 .stage-details.collapsed {
@@ -135,7 +139,7 @@ pre {
   max-height: 300px;
   overflow-y: auto;
   margin: 0;
-  transition: max-height 0.5s ease-out, padding 0.5s ease-out;
+  transition: max-height 0.25s ease-out, padding 0.25s ease-out;
 }
 
 .stacktrace-details.collapsed {
@@ -158,7 +162,7 @@ span.additional-metric-title {
 }
 
 .tooltip {
- font-weight: normal;
+  font-weight: normal;
 }
 
 .arrow-open {
@@ -166,9 +170,9 @@ span.additional-metric-title {
   height: 0;
   border-left: 5px solid transparent;
   border-right: 5px solid transparent;
-  border-top: 5px solid black;
-  float: left;
-  margin-top: 6px;
+  border-top: 5px solid #08c;
+  display: inline-block;
+  margin-bottom: 2px;
 }
 
 .arrow-closed {
@@ -176,8 +180,10 @@ span.additional-metric-title {
   height: 0;
   border-top: 5px solid transparent;
   border-bottom: 5px solid transparent;
-  border-left: 5px solid black;
+  border-left: 5px solid #08c;
   display: inline-block;
+  margin-left: 2px;
+  margin-right: 3px;
 }
 
 .version {
@@ -196,3 +202,17 @@ span.additional-metric-title {
 .serialization_time, .getting_result_time {
   display: none;
 }
+
+.accordion-inner {
+  background: #f5f5f5;
+}
+
+.accordion-inner pre {
+  border: 0;
+  padding: 0;
+  background: none;
+}
+
+a.expandbutton {
+  cursor: pointer;
+}
diff --git a/core/src/main/scala/org/apache/spark/ui/ToolTips.scala b/core/src/main/scala/org/apache/spark/ui/ToolTips.scala
index 24f3236456248..063e2a1f8b18e 100644
--- a/core/src/main/scala/org/apache/spark/ui/ToolTips.scala
+++ b/core/src/main/scala/org/apache/spark/ui/ToolTips.scala
@@ -57,4 +57,23 @@ private[spark] object ToolTips {
   val GC_TIME =
     """Time that the executor spent paused for Java garbage collection while the task was
        running."""
+
+  val JOB_TIMELINE =
+    """Shows when jobs started and ended and when executors joined or left. Drag to scroll.
+       Click Enable Zooming and use mouse wheel to zoom in/out."""
+
+  val STAGE_TIMELINE =
+    """Shows when stages started and ended and when executors joined or left. Drag to scroll.
+       Click Enable Zooming and use mouse wheel to zoom in/out."""
+
+  val JOB_DAG =
+    """Shows a graph of stages executed for this job, each of which can contain
+       multiple RDD operations (e.g. map() and filter()), and of RDDs inside each operation
+       (shown as dots)."""
+
+  val STAGE_DAG =
+    """Shows a graph of RDD operations in this stage, and RDDs inside each one. A stage can run
+       multiple operations (e.g. two map() functions) if they can be pipelined. Some operations
+       also create multiple RDDs internally. Cached RDDs are shown in green.
+    """
 }
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 97eed13c2d780..6a0f5c5d16daa 100644
--- a/core/src/main/scala/org/apache/spark/ui/UIUtils.scala
+++ b/core/src/main/scala/org/apache/spark/ui/UIUtils.scala
@@ -156,10 +156,10 @@ private[spark] object UIUtils extends Logging {
 
   def commonHeaderNodes: Seq[Node] = {
     <meta http-equiv="Content-type" content="text/html; charset=utf-8" />
-    <link rel="stylesheet" href={prependBaseUri("/static/bootstrap.min.css")} type="text/css" />
-    <link rel="stylesheet" href={prependBaseUri("/static/webui.css")} type="text/css" />
-    <link rel="stylesheet" href={prependBaseUri("/static/vis.min.css")} type="text/css" />
-    <link rel="stylesheet" href={prependBaseUri("/static/timeline-view.css")} type="text/css" />
+    <link rel="stylesheet" href={prependBaseUri("/static/bootstrap.min.css")} type="text/css"/>
+    <link rel="stylesheet" href={prependBaseUri("/static/vis.min.css")} type="text/css"/>
+    <link rel="stylesheet" href={prependBaseUri("/static/webui.css")} type="text/css"/>
+    <link rel="stylesheet" href={prependBaseUri("/static/timeline-view.css")} type="text/css"/>
     <script src={prependBaseUri("/static/sorttable.js")} ></script>
     <script src={prependBaseUri("/static/jquery-1.11.1.min.js")}></script>
     <script src={prependBaseUri("/static/vis.min.js")}></script>
@@ -250,7 +250,7 @@ private[spark] object UIUtils extends Logging {
               <h3 style="vertical-align: middle; display: inline-block;">
                 <a style="text-decoration: none" href={prependBaseUri("/")}>
                   <img src={prependBaseUri("/static/spark-logo-77x50px-hd.png")} />
-                  <span class="version" 
+                  <span class="version"
                         style="margin-right: 15px;">{org.apache.spark.SPARK_VERSION}</span>
                 </a>
                 {title}
@@ -350,7 +350,10 @@ private[spark] object UIUtils extends Logging {
     <div>
       <span class="expand-dag-viz" onclick={s"toggleDagViz($forJob);"}>
         <span class="expand-dag-viz-arrow arrow-closed"></span>
-        <strong>DAG visualization</strong>
+        <a data-toggle="tooltip" title={if (forJob) ToolTips.JOB_DAG else ToolTips.STAGE_DAG}
+           data-placement="right">
+          DAG Visualization
+        </a>
       </span>
       <div id="dag-viz-graph"></div>
       <div id="dag-viz-metadata">
diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/AllJobsPage.scala b/core/src/main/scala/org/apache/spark/ui/jobs/AllJobsPage.scala
index 09323d1d80ad6..e010ebef3b34a 100644
--- a/core/src/main/scala/org/apache/spark/ui/jobs/AllJobsPage.scala
+++ b/core/src/main/scala/org/apache/spark/ui/jobs/AllJobsPage.scala
@@ -18,12 +18,12 @@
 package org.apache.spark.ui.jobs
 
 import scala.collection.mutable.{HashMap, ListBuffer}
-import scala.xml.{Node, NodeSeq, Unparsed}
+import scala.xml.{Node, NodeSeq, Unparsed, Utility}
 
 import java.util.Date
 import javax.servlet.http.HttpServletRequest
 
-import org.apache.spark.ui.{UIUtils, WebUIPage}
+import org.apache.spark.ui.{ToolTips, UIUtils, WebUIPage}
 import org.apache.spark.ui.jobs.UIData.{ExecutorUIData, JobUIData}
 import org.apache.spark.JobExecutionStatus
 
@@ -81,6 +81,9 @@ private[ui] class AllJobsPage(parent: JobsTab) extends WebUIPage("") {
         case JobExecutionStatus.RUNNING => "running"
       }
 
+      // The timeline library treats contents as HTML, so we have to escape them; for the
+      // data-title attribute string we have to escape them twice since that's in a string.
+      val escapedDesc = Utility.escape(displayJobDescription)
       val jobEventJsonAsStr =
         s"""
            |{
@@ -90,16 +93,17 @@ private[ui] class AllJobsPage(parent: JobsTab) extends WebUIPage("") {
            |  'end': new Date(${completionTime}),
            |  'content': '<div class="application-timeline-content"' +
            |     'data-html="true" data-placement="top" data-toggle="tooltip"' +
-           |     'data-title="${displayJobDescription} (Job ${jobId})<br>Status: ${status}<br>' +
-           |     'Submission Time: ${UIUtils.formatDate(new Date(submissionTime))}' +
+           |     'data-title="${Utility.escape(escapedDesc)} (Job ${jobId})<br>' +
+           |     'Status: ${status}<br>' +
+           |     'Submitted: ${UIUtils.formatDate(new Date(submissionTime))}' +
            |     '${
                      if (status != JobExecutionStatus.RUNNING) {
-                       s"""<br>Completion Time: ${UIUtils.formatDate(new Date(completionTime))}"""
+                       s"""<br>Completed: ${UIUtils.formatDate(new Date(completionTime))}"""
                      } else {
                        ""
                      }
                   }">' +
-           |    '${displayJobDescription} (Job ${jobId})</div>'
+           |    '${escapedDesc} (Job ${jobId})</div>'
            |}
          """.stripMargin
       jobEventJsonAsStr
@@ -179,13 +183,15 @@ private[ui] class AllJobsPage(parent: JobsTab) extends WebUIPage("") {
 
     <span class="expand-application-timeline">
       <span class="expand-application-timeline-arrow arrow-closed"></span>
-      <strong>Event timeline</strong>
+      <a data-toggle="tooltip" title={ToolTips.JOB_TIMELINE} data-placement="right">
+        Event Timeline
+      </a>
     </span> ++
     <div id="application-timeline" class="collapsed">
       <div class="control-panel">
         <div id="application-timeline-zoom-lock">
-          <input type="checkbox" checked="checked"></input>
-          <span>Zoom Lock</span>
+          <input type="checkbox"></input>
+          <span>Enable zooming</span>
         </div>
       </div>
     </div> ++
@@ -283,7 +289,7 @@ private[ui] class AllJobsPage(parent: JobsTab) extends WebUIPage("") {
             {if (parent.sc.isDefined) {
               // Total duration is not meaningful unless the UI is live
               <li>
-                <strong>Total Duration: </strong>
+                <strong>Total Uptime: </strong>
                 {UIUtils.formatDuration(System.currentTimeMillis() - startTime)}
               </li>
             }}
@@ -336,9 +342,8 @@ private[ui] class AllJobsPage(parent: JobsTab) extends WebUIPage("") {
           failedJobsTable
       }
 
-      val helpText = """A job is triggered by an action, like "count()" or "saveAsTextFile()".""" +
-        " Click on a job's title to see information about the stages of tasks associated with" +
-        " the job."
+      val helpText = """A job is triggered by an action, like count() or saveAsTextFile().""" +
+        " Click on a job to see information about the stages of tasks inside it."
 
       UIUtils.headerSparkPage("Spark Jobs", content, parent, helpText = Some(helpText))
     }
diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/AllStagesPage.scala b/core/src/main/scala/org/apache/spark/ui/jobs/AllStagesPage.scala
index a37f739ab9c66..5e52942b64f3f 100644
--- a/core/src/main/scala/org/apache/spark/ui/jobs/AllStagesPage.scala
+++ b/core/src/main/scala/org/apache/spark/ui/jobs/AllStagesPage.scala
@@ -73,19 +73,6 @@ private[ui] class AllStagesPage(parent: StagesTab) extends WebUIPage("") {
       val summary: NodeSeq =
         <div>
           <ul class="unstyled">
-            {
-              if (sc.isDefined) {
-                // Total duration is not meaningful unless the UI is live
-                <li>
-                  <strong>Total Duration: </strong>
-                  {UIUtils.formatDuration(now - sc.get.startTime)}
-                </li>
-              }
-            }
-            <li>
-              <strong>Scheduling Mode: </strong>
-              {listener.schedulingMode.map(_.toString).getOrElse("Unknown")}
-            </li>
             {
               if (shouldShowActiveStages) {
                 <li>
@@ -145,7 +132,7 @@ private[ui] class AllStagesPage(parent: StagesTab) extends WebUIPage("") {
         content ++= <h4 id ="failed">Failed Stages ({numFailedStages})</h4> ++
         failedStagesTable.toNodeSeq
       }
-      UIUtils.headerSparkPage("Spark Stages (for all jobs)", content, parent)
+      UIUtils.headerSparkPage("Stages for All Jobs", content, parent)
     }
   }
 }
diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/JobPage.scala b/core/src/main/scala/org/apache/spark/ui/jobs/JobPage.scala
index 7163217e1fed0..2cad0a796913e 100644
--- a/core/src/main/scala/org/apache/spark/ui/jobs/JobPage.scala
+++ b/core/src/main/scala/org/apache/spark/ui/jobs/JobPage.scala
@@ -20,13 +20,13 @@ package org.apache.spark.ui.jobs
 import java.util.Date
 
 import scala.collection.mutable.{Buffer, HashMap, ListBuffer}
-import scala.xml.{NodeSeq, Node, Unparsed}
+import scala.xml.{NodeSeq, Node, Unparsed, Utility}
 
 import javax.servlet.http.HttpServletRequest
 
 import org.apache.spark.JobExecutionStatus
 import org.apache.spark.scheduler.StageInfo
-import org.apache.spark.ui.{UIUtils, WebUIPage}
+import org.apache.spark.ui.{ToolTips, UIUtils, WebUIPage}
 import org.apache.spark.ui.jobs.UIData.ExecutorUIData
 
 /** Page showing statistics and stage list for a given job */
@@ -64,6 +64,9 @@ private[ui] class JobPage(parent: JobsTab) extends WebUIPage("job") {
       val submissionTime = stage.submissionTime.get
       val completionTime = stage.completionTime.getOrElse(System.currentTimeMillis())
 
+      // The timeline library treats contents as HTML, so we have to escape them; for the
+      // data-title attribute string we have to escape them twice since that's in a string.
+      val escapedName = Utility.escape(name)
       s"""
          |{
          |  'className': 'stage job-timeline-object ${status}',
@@ -72,17 +75,17 @@ private[ui] class JobPage(parent: JobsTab) extends WebUIPage("job") {
          |  'end': new Date(${completionTime}),
          |  'content': '<div class="job-timeline-content" data-toggle="tooltip"' +
          |   'data-placement="top" data-html="true"' +
-         |   'data-title="${name} (Stage ${stageId}.${attemptId})<br>' +
+         |   'data-title="${Utility.escape(escapedName)} (Stage ${stageId}.${attemptId})<br>' +
          |   'Status: ${status.toUpperCase}<br>' +
-         |   'Submission Time: ${UIUtils.formatDate(new Date(submissionTime))}' +
+         |   'Submitted: ${UIUtils.formatDate(new Date(submissionTime))}' +
          |   '${
                  if (status != "running") {
-                   s"""<br>Completion Time: ${UIUtils.formatDate(new Date(completionTime))}"""
+                   s"""<br>Completed: ${UIUtils.formatDate(new Date(completionTime))}"""
                  } else {
                    ""
                  }
               }">' +
-         |    '${name} (Stage ${stageId}.${attemptId})</div>',
+         |    '${escapedName} (Stage ${stageId}.${attemptId})</div>',
          |}
        """.stripMargin
     }
@@ -161,13 +164,15 @@ private[ui] class JobPage(parent: JobsTab) extends WebUIPage("job") {
 
     <span class="expand-job-timeline">
       <span class="expand-job-timeline-arrow arrow-closed"></span>
-      <strong>Event timeline</strong>
+      <a data-toggle="tooltip" title={ToolTips.STAGE_TIMELINE} data-placement="right">
+        Event Timeline
+      </a>
     </span> ++
     <div id="job-timeline" class="collapsed">
       <div class="control-panel">
         <div id="job-timeline-zoom-lock">
-          <input type="checkbox" checked="checked"></input>
-          <span>Zoom Lock</span>
+          <input type="checkbox"></input>
+          <span>Enable zooming</span>
         </div>
       </div>
     </div> ++
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 b01fad8e453c8..8f7b1c2f09665 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
@@ -81,7 +81,7 @@ private[ui] class StagePage(parent: StagesTab) extends WebUIPage("stage") {
         <div>
           <ul class="unstyled">
             <li>
-              <strong>Total task time across all tasks: </strong>
+              <strong>Total Time Across All Tasks: </strong>
               {UIUtils.formatDuration(stageData.executorRunTime)}
             </li>
             {if (stageData.hasInput) {
@@ -98,25 +98,25 @@ private[ui] class StagePage(parent: StagesTab) extends WebUIPage("stage") {
             }}
             {if (stageData.hasShuffleRead) {
               <li>
-                <strong>Shuffle read: </strong>
+                <strong>Shuffle Read: </strong>
                 {s"${Utils.bytesToString(stageData.shuffleReadTotalBytes)} / " +
                  s"${stageData.shuffleReadRecords}"}
               </li>
             }}
             {if (stageData.hasShuffleWrite) {
               <li>
-                <strong>Shuffle write: </strong>
+                <strong>Shuffle Write: </strong>
                  {s"${Utils.bytesToString(stageData.shuffleWriteBytes)} / " +
                  s"${stageData.shuffleWriteRecords}"}
               </li>
             }}
             {if (stageData.hasBytesSpilled) {
               <li>
-                <strong>Shuffle spill (memory): </strong>
+                <strong>Shuffle Spill (Memory): </strong>
                 {Utils.bytesToString(stageData.memoryBytesSpilled)}
               </li>
               <li>
-                <strong>Shuffle spill (disk): </strong>
+                <strong>Shuffle Spill (Disk): </strong>
                 {Utils.bytesToString(stageData.diskBytesSpilled)}
               </li>
             }}
@@ -127,10 +127,10 @@ private[ui] class StagePage(parent: StagesTab) extends WebUIPage("stage") {
         <div>
           <span class="expand-additional-metrics">
             <span class="expand-additional-metrics-arrow arrow-closed"></span>
-            <strong>Show additional metrics</strong>
+            <a>Show Additional Metrics</a>
           </span>
           <div class="additional-metrics collapsed">
-            <ul style="list-style-type:none">
+            <ul>
               <li>
                   <input type="checkbox" id="select-all-metrics"/>
                   <span class="additional-metric-title"><em>(De)select All</em></span>
@@ -457,9 +457,9 @@ private[ui] class StagePage(parent: StagesTab) extends WebUIPage("stage") {
 
       val content =
         summary ++
-        showAdditionalMetrics ++
         dagViz ++
         maybeExpandDagViz ++
+        showAdditionalMetrics ++
         <h4>Summary Metrics for {numCompleted} Completed Tasks</h4> ++
         <div>{summaryTable.getOrElse("No tasks have reported metrics yet.")}</div> ++
         <h4>Aggregated Metrics by Executor</h4> ++ executorTable.toNodeSeq ++
diff --git a/core/src/main/scala/org/apache/spark/ui/scope/RDDOperationGraph.scala b/core/src/main/scala/org/apache/spark/ui/scope/RDDOperationGraph.scala
index 2b2db9e62be4e..c7045c98c8a03 100644
--- a/core/src/main/scala/org/apache/spark/ui/scope/RDDOperationGraph.scala
+++ b/core/src/main/scala/org/apache/spark/ui/scope/RDDOperationGraph.scala
@@ -182,7 +182,7 @@ private[ui] object RDDOperationGraph extends Logging {
     if (forJob) {
       s"""${node.id} [label="$label" shape="circle" padding="5" labelStyle="font-size: 0"]"""
     } else {
-      s"""${node.id} [label="$label" padding="5" labelStyle="font-size: 10"]"""
+      s"""${node.id} [label="$label" padding="5" labelStyle="font-size: 12px"]"""
     }
   }
 

From 2d05f325dc3c70349bd17ed399897f22d967c687 Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Fri, 8 May 2015 11:49:38 -0700
Subject: [PATCH 65/75] [SPARK-7133] [SQL] Implement struct, array, and map
 field accessor

It's the first step: generalize UnresolvedGetField to support all map, struct, and array
TODO: add `apply` in Scala and `__getitem__` in Python, and unify the `getItem` and `getField` methods to one single API(or should we keep them for compatibility?).

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #5744 from cloud-fan/generalize and squashes the following commits:

715c589 [Wenchen Fan] address comments
7ea5b31 [Wenchen Fan] fix python test
4f0833a [Wenchen Fan] add python test
f515d69 [Wenchen Fan] add apply method and test cases
8df6199 [Wenchen Fan] fix python test
239730c [Wenchen Fan] fix test compile
2a70526 [Wenchen Fan] use _bin_op in dataframe.py
6bf72bc [Wenchen Fan] address comments
3f880c3 [Wenchen Fan] add java doc
ab35ab5 [Wenchen Fan] fix python test
b5961a9 [Wenchen Fan] fix style
c9d85f5 [Wenchen Fan] generalize UnresolvedGetField to support all map, struct, and array
---
 python/pyspark/sql/dataframe.py               |  24 +-
 python/pyspark/sql/tests.py                   |   7 +
 .../apache/spark/sql/catalyst/SqlParser.scala |   4 +-
 .../sql/catalyst/analysis/Analyzer.scala      |   4 +-
 .../sql/catalyst/analysis/unresolved.scala    |  14 +-
 .../spark/sql/catalyst/dsl/package.scala      |   7 +-
 .../catalyst/expressions/ExtractValue.scala   | 206 ++++++++++++++++++
 .../catalyst/expressions/complexTypes.scala   | 131 -----------
 .../sql/catalyst/optimizer/Optimizer.scala    |   6 +-
 .../sql/catalyst/planning/patterns.scala      |   2 +-
 .../catalyst/plans/logical/LogicalPlan.scala  |   3 +-
 .../ExpressionEvaluationSuite.scala           |  69 ++++--
 .../optimizer/ConstantFoldingSuite.scala      |   8 +-
 .../scala/org/apache/spark/sql/Column.scala   |  19 +-
 .../org/apache/spark/sql/DataFrameSuite.scala |  10 +-
 .../org/apache/spark/sql/hive/HiveQl.scala    |   4 +-
 16 files changed, 327 insertions(+), 191 deletions(-)
 create mode 100644 sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ExtractValue.scala

diff --git a/python/pyspark/sql/dataframe.py b/python/pyspark/sql/dataframe.py
index cee804f5cc1f7..a9697999e82cb 100644
--- a/python/pyspark/sql/dataframe.py
+++ b/python/pyspark/sql/dataframe.py
@@ -1275,7 +1275,7 @@ def __init__(self, jc):
 
     # container operators
     __contains__ = _bin_op("contains")
-    __getitem__ = _bin_op("getItem")
+    __getitem__ = _bin_op("apply")
 
     # bitwise operators
     bitwiseOR = _bin_op("bitwiseOR")
@@ -1308,19 +1308,19 @@ def getField(self, name):
         >>> from pyspark.sql import Row
         >>> df = sc.parallelize([Row(r=Row(a=1, b="b"))]).toDF()
         >>> df.select(df.r.getField("b")).show()
-        +---+
-        |r.b|
-        +---+
-        |  b|
-        +---+
+        +----+
+        |r[b]|
+        +----+
+        |   b|
+        +----+
         >>> df.select(df.r.a).show()
-        +---+
-        |r.a|
-        +---+
-        |  1|
-        +---+
+        +----+
+        |r[a]|
+        +----+
+        |   1|
+        +----+
         """
-        return Column(self._jc.getField(name))
+        return self[name]
 
     def __getattr__(self, item):
         if item.startswith("__"):
diff --git a/python/pyspark/sql/tests.py b/python/pyspark/sql/tests.py
index 45dfedce22add..7e63f4d6461f6 100644
--- a/python/pyspark/sql/tests.py
+++ b/python/pyspark/sql/tests.py
@@ -519,6 +519,13 @@ def test_access_nested_types(self):
         self.assertEqual("v", df.select(df.d["k"]).first()[0])
         self.assertEqual("v", df.select(df.d.getItem("k")).first()[0])
 
+    def test_field_accessor(self):
+        df = self.sc.parallelize([Row(l=[1], r=Row(a=1, b="b"), d={"k": "v"})]).toDF()
+        self.assertEqual(1, df.select(df.l[0]).first()[0])
+        self.assertEqual(1, df.select(df.r["a"]).first()[0])
+        self.assertEqual("b", df.select(df.r["b"]).first()[0])
+        self.assertEqual("v", df.select(df.d["k"]).first()[0])
+
     def test_infer_long_type(self):
         longrow = [Row(f1='a', f2=100000000000000)]
         df = self.sc.parallelize(longrow).toDF()
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 b06bfb2ce8c8e..fc36b9f1f20d2 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
@@ -375,9 +375,9 @@ class SqlParser extends AbstractSparkSQLParser with DataTypeParser {
   protected lazy val primary: PackratParser[Expression] =
     ( literal
     | expression ~ ("[" ~> expression <~ "]") ^^
-      { case base ~ ordinal => GetItem(base, ordinal) }
+      { case base ~ ordinal => UnresolvedExtractValue(base, ordinal) }
     | (expression <~ ".") ~ ident ^^
-      { case base ~ fieldName => UnresolvedGetField(base, fieldName) }
+      { case base ~ fieldName => UnresolvedExtractValue(base, Literal(fieldName)) }
     | cast
     | "(" ~> expression <~ ")"
     | function
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 bb7913e186a85..ecbac57ea4d62 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
@@ -348,8 +348,8 @@ class Analyzer(
               withPosition(u) { q.resolveChildren(nameParts, resolver).getOrElse(u) }
             logDebug(s"Resolving $u to $result")
             result
-          case UnresolvedGetField(child, fieldName) if child.resolved =>
-            GetField(child, fieldName, resolver)
+          case UnresolvedExtractValue(child, fieldExpr) if child.resolved =>
+            ExtractValue(child, fieldExpr, resolver)
         }
     }
 
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 eb736ac3290e8..2999c2ef3efe1 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
@@ -184,7 +184,17 @@ case class ResolvedStar(expressions: Seq[NamedExpression]) extends Star {
   override def toString: String = expressions.mkString("ResolvedStar(", ", ", ")")
 }
 
-case class UnresolvedGetField(child: Expression, fieldName: String) extends UnaryExpression {
+/**
+ * Extracts a value or values from an Expression
+ *
+ * @param child The expression to extract value from,
+ *              can be Map, Array, Struct or array of Structs.
+ * @param extraction The expression to describe the extraction,
+ *                   can be key of Map, index of Array, field name of Struct.
+ */
+case class UnresolvedExtractValue(child: Expression, extraction: Expression)
+  extends UnaryExpression {
+
   override def dataType: DataType = throw new UnresolvedException(this, "dataType")
   override def foldable: Boolean = throw new UnresolvedException(this, "foldable")
   override def nullable: Boolean = throw new UnresolvedException(this, "nullable")
@@ -193,5 +203,5 @@ case class UnresolvedGetField(child: Expression, fieldName: String) extends Unar
   override def eval(input: Row = null): EvaluatedType =
     throw new TreeNodeException(this, s"No function to evaluate expression. type: ${this.nodeName}")
 
-  override def toString: String = s"$child.$fieldName"
+  override def toString: String = s"$child[$extraction]"
 }
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
index fa6cc7a1a36cf..4c0d70203c6f5 100644
--- 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
@@ -22,7 +22,7 @@ import java.sql.{Date, Timestamp}
 import scala.language.implicitConversions
 import scala.reflect.runtime.universe.{TypeTag, typeTag}
 
-import org.apache.spark.sql.catalyst.analysis.{EliminateSubQueries, UnresolvedGetField, UnresolvedAttribute}
+import org.apache.spark.sql.catalyst.analysis.{EliminateSubQueries, UnresolvedExtractValue, UnresolvedAttribute}
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.logical._
 import org.apache.spark.sql.catalyst.plans.{Inner, JoinType}
@@ -100,8 +100,9 @@ package object dsl {
     def isNull: Predicate = IsNull(expr)
     def isNotNull: Predicate = IsNotNull(expr)
 
-    def getItem(ordinal: Expression): Expression = GetItem(expr, ordinal)
-    def getField(fieldName: String): UnresolvedGetField = UnresolvedGetField(expr, fieldName)
+    def getItem(ordinal: Expression): UnresolvedExtractValue = UnresolvedExtractValue(expr, ordinal)
+    def getField(fieldName: String): UnresolvedExtractValue =
+      UnresolvedExtractValue(expr, Literal(fieldName))
 
     def cast(to: DataType): Expression = Cast(expr, to)
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ExtractValue.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ExtractValue.scala
new file mode 100644
index 0000000000000..e05926cbfe74b
--- /dev/null
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ExtractValue.scala
@@ -0,0 +1,206 @@
+/*
+ * 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 scala.collection.Map
+
+import org.apache.spark.sql.AnalysisException
+import org.apache.spark.sql.catalyst.analysis._
+import org.apache.spark.sql.types._
+
+object ExtractValue {
+  /**
+   * Returns the resolved `ExtractValue`. It will return one kind of concrete `ExtractValue`,
+   * depend on the type of `child` and `extraction`.
+   *
+   *   `child`      |    `extraction`    |    concrete `ExtractValue`
+   * ----------------------------------------------------------------
+   *    Struct      |   Literal String   |        GetStructField
+   * Array[Struct]  |   Literal String   |     GetArrayStructFields
+   *    Array       |   Integral type    |         GetArrayItem
+   *     Map        |      Any type      |         GetMapValue
+   */
+  def apply(
+      child: Expression,
+      extraction: Expression,
+      resolver: Resolver): ExtractValue = {
+
+    (child.dataType, extraction) match {
+      case (StructType(fields), Literal(fieldName, StringType)) =>
+        val ordinal = findField(fields, fieldName.toString, resolver)
+        GetStructField(child, fields(ordinal), ordinal)
+      case (ArrayType(StructType(fields), containsNull), Literal(fieldName, StringType)) =>
+        val ordinal = findField(fields, fieldName.toString, resolver)
+        GetArrayStructFields(child, fields(ordinal), ordinal, containsNull)
+      case (_: ArrayType, _) if extraction.dataType.isInstanceOf[IntegralType]  =>
+        GetArrayItem(child, extraction)
+      case (_: MapType, _) =>
+        GetMapValue(child, extraction)
+      case (otherType, _) =>
+        val errorMsg = otherType match {
+          case StructType(_) | ArrayType(StructType(_), _) =>
+            s"Field name should be String Literal, but it's $extraction"
+          case _: ArrayType =>
+            s"Array index should be integral type, but it's ${extraction.dataType}"
+          case other =>
+            s"Can't extract value from $child"
+        }
+        throw new AnalysisException(errorMsg)
+    }
+  }
+
+  def unapply(g: ExtractValue): Option[(Expression, Expression)] = {
+    g match {
+      case o: ExtractValueWithOrdinal => Some((o.child, o.ordinal))
+      case _ => Some((g.child, null))
+    }
+  }
+
+  /**
+   * Find the ordinal of StructField, report error if no desired field or over one
+   * desired fields are found.
+   */
+  private def findField(fields: Array[StructField], fieldName: String, resolver: Resolver): Int = {
+    val checkField = (f: StructField) => resolver(f.name, fieldName)
+    val ordinal = fields.indexWhere(checkField)
+    if (ordinal == -1) {
+      throw new AnalysisException(
+        s"No such struct field $fieldName in ${fields.map(_.name).mkString(", ")}")
+    } else if (fields.indexWhere(checkField, ordinal + 1) != -1) {
+      throw new AnalysisException(
+        s"Ambiguous reference to fields ${fields.filter(checkField).mkString(", ")}")
+    } else {
+      ordinal
+    }
+  }
+}
+
+trait ExtractValue extends UnaryExpression {
+  self: Product =>
+
+  type EvaluatedType = Any
+}
+
+/**
+ * Returns the value of fields in the Struct `child`.
+ */
+case class GetStructField(child: Expression, field: StructField, ordinal: Int)
+  extends ExtractValue {
+
+  override def dataType: DataType = field.dataType
+  override def nullable: Boolean = child.nullable || field.nullable
+  override def foldable: Boolean = child.foldable
+  override def toString: String = s"$child.${field.name}"
+
+  override def eval(input: Row): Any = {
+    val baseValue = child.eval(input).asInstanceOf[Row]
+    if (baseValue == null) null else baseValue(ordinal)
+  }
+}
+
+/**
+ * Returns the array of value of fields in the Array of Struct `child`.
+ */
+case class GetArrayStructFields(
+    child: Expression,
+    field: StructField,
+    ordinal: Int,
+    containsNull: Boolean) extends ExtractValue {
+
+  override def dataType: DataType = ArrayType(field.dataType, containsNull)
+  override def nullable: Boolean = child.nullable
+  override def foldable: Boolean = child.foldable
+  override def toString: String = s"$child.${field.name}"
+
+  override def eval(input: Row): Any = {
+    val baseValue = child.eval(input).asInstanceOf[Seq[Row]]
+    if (baseValue == null) null else {
+      baseValue.map { row =>
+        if (row == null) null else row(ordinal)
+      }
+    }
+  }
+}
+
+abstract class ExtractValueWithOrdinal extends ExtractValue {
+  self: Product =>
+
+  def ordinal: Expression
+
+  /** `Null` is returned for invalid ordinals. */
+  override def nullable: Boolean = true
+  override def foldable: Boolean = child.foldable && ordinal.foldable
+  override def toString: String = s"$child[$ordinal]"
+  override def children: Seq[Expression] = child :: ordinal :: Nil
+
+  override def eval(input: Row): Any = {
+    val value = child.eval(input)
+    if (value == null) {
+      null
+    } else {
+      val o = ordinal.eval(input)
+      if (o == null) {
+        null
+      } else {
+        evalNotNull(value, o)
+      }
+    }
+  }
+
+  protected def evalNotNull(value: Any, ordinal: Any): Any
+}
+
+/**
+ * Returns the field at `ordinal` in the Array `child`
+ */
+case class GetArrayItem(child: Expression, ordinal: Expression)
+  extends ExtractValueWithOrdinal {
+
+  override def dataType: DataType = child.dataType.asInstanceOf[ArrayType].elementType
+
+  override lazy val resolved = childrenResolved &&
+    child.dataType.isInstanceOf[ArrayType] && ordinal.dataType.isInstanceOf[IntegralType]
+
+  protected def evalNotNull(value: Any, ordinal: Any) = {
+    // TODO: consider using Array[_] for ArrayType child to avoid
+    // boxing of primitives
+    val baseValue = value.asInstanceOf[Seq[_]]
+    val index = ordinal.asInstanceOf[Int]
+    if (index >= baseValue.size || index < 0) {
+      null
+    } else {
+      baseValue(index)
+    }
+  }
+}
+
+/**
+ * Returns the value of key `ordinal` in Map `child`
+ */
+case class GetMapValue(child: Expression, ordinal: Expression)
+  extends ExtractValueWithOrdinal {
+
+  override def dataType: DataType = child.dataType.asInstanceOf[MapType].valueType
+
+  override lazy val resolved = childrenResolved && child.dataType.isInstanceOf[MapType]
+
+  protected def evalNotNull(value: Any, ordinal: Any) = {
+    val baseValue = value.asInstanceOf[Map[Any, _]]
+    baseValue.get(ordinal).orNull
+  }
+}
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 fc1f69655963d..956a2429b0b61 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
@@ -17,139 +17,8 @@
 
 package org.apache.spark.sql.catalyst.expressions
 
-import scala.collection.Map
-
-import org.apache.spark.sql.AnalysisException
-import org.apache.spark.sql.catalyst.analysis.Resolver
 import org.apache.spark.sql.types._
 
-/**
- * Returns the item at `ordinal` in the Array `child` or the Key `ordinal` in Map `child`.
- */
-case class GetItem(child: Expression, ordinal: Expression) extends Expression {
-  type EvaluatedType = Any
-
-  val children: Seq[Expression] = child :: ordinal :: Nil
-  /** `Null` is returned for invalid ordinals. */
-  override def nullable: Boolean = true
-  override def foldable: Boolean = child.foldable && ordinal.foldable
-
-  override def dataType: DataType = child.dataType match {
-    case ArrayType(dt, _) => dt
-    case MapType(_, vt, _) => vt
-  }
-  override lazy val resolved =
-    childrenResolved &&
-    (child.dataType.isInstanceOf[ArrayType] || child.dataType.isInstanceOf[MapType])
-
-  override def toString: String = s"$child[$ordinal]"
-
-  override def eval(input: Row): Any = {
-    val value = child.eval(input)
-    if (value == null) {
-      null
-    } else {
-      val key = ordinal.eval(input)
-      if (key == null) {
-        null
-      } else {
-        if (child.dataType.isInstanceOf[ArrayType]) {
-          // TODO: consider using Array[_] for ArrayType child to avoid
-          // boxing of primitives
-          val baseValue = value.asInstanceOf[Seq[_]]
-          val o = key.asInstanceOf[Int]
-          if (o >= baseValue.size || o < 0) {
-            null
-          } else {
-            baseValue(o)
-          }
-        } else {
-          val baseValue = value.asInstanceOf[Map[Any, _]]
-          baseValue.get(key).orNull
-        }
-      }
-    }
-  }
-}
-
-
-trait GetField extends UnaryExpression {
-  self: Product =>
-
-  type EvaluatedType = Any
-  override def foldable: Boolean = child.foldable
-  override def toString: String = s"$child.${field.name}"
-
-  def field: StructField
-}
-
-object GetField {
-  /**
-   * Returns the resolved `GetField`, and report error if no desired field or over one
-   * desired fields are found.
-   */
-  def apply(
-      expr: Expression,
-      fieldName: String,
-      resolver: Resolver): GetField = {
-    def findField(fields: Array[StructField]): Int = {
-      val checkField = (f: StructField) => resolver(f.name, fieldName)
-      val ordinal = fields.indexWhere(checkField)
-      if (ordinal == -1) {
-        throw new AnalysisException(
-          s"No such struct field $fieldName in ${fields.map(_.name).mkString(", ")}")
-      } else if (fields.indexWhere(checkField, ordinal + 1) != -1) {
-        throw new AnalysisException(
-          s"Ambiguous reference to fields ${fields.filter(checkField).mkString(", ")}")
-      } else {
-        ordinal
-      }
-    }
-    expr.dataType match {
-      case StructType(fields) =>
-        val ordinal = findField(fields)
-        StructGetField(expr, fields(ordinal), ordinal)
-      case ArrayType(StructType(fields), containsNull) =>
-        val ordinal = findField(fields)
-        ArrayGetField(expr, fields(ordinal), ordinal, containsNull)
-      case otherType =>
-        throw new AnalysisException(s"GetField is not valid on fields of type $otherType")
-    }
-  }
-}
-
-/**
- * Returns the value of fields in the Struct `child`.
- */
-case class StructGetField(child: Expression, field: StructField, ordinal: Int) extends GetField {
-
-  override def dataType: DataType = field.dataType
-  override def nullable: Boolean = child.nullable || field.nullable
-
-  override def eval(input: Row): Any = {
-    val baseValue = child.eval(input).asInstanceOf[Row]
-    if (baseValue == null) null else baseValue(ordinal)
-  }
-}
-
-/**
- * Returns the array of value of fields in the Array of Struct `child`.
- */
-case class ArrayGetField(child: Expression, field: StructField, ordinal: Int, containsNull: Boolean)
-  extends GetField {
-
-  override def dataType: DataType = ArrayType(field.dataType, containsNull)
-  override def nullable: Boolean = child.nullable
-
-  override def eval(input: Row): Any = {
-    val baseValue = child.eval(input).asInstanceOf[Seq[Row]]
-    if (baseValue == null) null else {
-      baseValue.map { row =>
-        if (row == null) null else row(ordinal)
-      }
-    }
-  }
-}
 
 /**
  * Returns an Array containing the evaluation of all children expressions.
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 e4a60f53d6c09..d7b2f203a6934 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
@@ -227,10 +227,8 @@ object NullPropagation extends Rule[LogicalPlan] {
       case e @ Count(Literal(null, _)) => Cast(Literal(0L), e.dataType)
       case e @ IsNull(c) if !c.nullable => Literal.create(false, BooleanType)
       case e @ IsNotNull(c) if !c.nullable => Literal.create(true, BooleanType)
-      case e @ GetItem(Literal(null, _), _) => Literal.create(null, e.dataType)
-      case e @ GetItem(_, Literal(null, _)) => Literal.create(null, e.dataType)
-      case e @ StructGetField(Literal(null, _), _, _) => Literal.create(null, e.dataType)
-      case e @ ArrayGetField(Literal(null, _), _, _, _) => Literal.create(null, e.dataType)
+      case e @ ExtractValue(Literal(null, _), _) => Literal.create(null, e.dataType)
+      case e @ ExtractValue(_, Literal(null, _)) => Literal.create(null, e.dataType)
       case e @ EqualNullSafe(Literal(null, _), r) => IsNull(r)
       case e @ EqualNullSafe(l, Literal(null, _)) => IsNull(l)
       case e @ Count(expr) if !expr.nullable => Count(Literal(1))
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 4574934d910db..cd54d04814ea4 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
@@ -160,7 +160,7 @@ object PartialAggregation {
             // resolving struct field accesses, because `GetField` is not a `NamedExpression`.
             // (Should we just turn `GetField` into a `NamedExpression`?)
             namedGroupingExpressions
-              .get(e.transform { case Alias(g: GetField, _) => g })
+              .get(e.transform { case Alias(g: ExtractValue, _) => g })
               .map(_.toAttribute)
               .getOrElse(e)
         }).asInstanceOf[Seq[NamedExpression]]
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 ae4620a4e5abf..dbb12d56f9497 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
@@ -209,7 +209,8 @@ abstract class LogicalPlan extends QueryPlan[LogicalPlan] with Logging {
           // For example, consider "a.b.c", where "a" is resolved to an existing attribute.
           // Then this will add GetField("c", GetField("b", a)), and alias
           // the final expression as "c".
-          val fieldExprs = nestedFields.foldLeft(a: Expression)(GetField(_, _, resolver))
+          val fieldExprs = nestedFields.foldLeft(a: Expression)((expr, fieldName) =>
+            ExtractValue(expr, Literal(fieldName), resolver))
           val aliasName = nestedFields.last
           Some(Alias(fieldExprs, aliasName)())
         } catch {
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 88d36d153ceea..04fd261d16aa3 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
@@ -26,7 +26,7 @@ import org.scalatest.FunSuite
 import org.scalatest.Matchers._
 
 import org.apache.spark.sql.catalyst.CatalystTypeConverters
-import org.apache.spark.sql.catalyst.analysis.UnresolvedGetField
+import org.apache.spark.sql.catalyst.analysis.UnresolvedExtractValue
 import org.apache.spark.sql.catalyst.dsl.expressions._
 import org.apache.spark.sql.catalyst.expressions.mathfuncs._
 import org.apache.spark.sql.types._
@@ -880,7 +880,7 @@ class ExpressionEvaluationSuite extends ExpressionEvaluationBaseSuite {
     val row = create_row(
       "^Ba*n",                                // 0
       null.asInstanceOf[UTF8String],          // 1
-      create_row("aa", "bb"),     // 2
+      create_row("aa", "bb"),                 // 2
       Map("aa"->"bb"),                        // 3
       Seq("aa", "bb")                         // 4
     )
@@ -891,54 +891,79 @@ class ExpressionEvaluationSuite extends ExpressionEvaluationBaseSuite {
     val typeMap = MapType(StringType, StringType)
     val typeArray = ArrayType(StringType)
 
-    checkEvaluation(GetItem(BoundReference(3, typeMap, true),
+    checkEvaluation(GetMapValue(BoundReference(3, typeMap, true),
       Literal("aa")), "bb", row)
-    checkEvaluation(GetItem(Literal.create(null, typeMap), Literal("aa")), null, row)
+    checkEvaluation(GetMapValue(Literal.create(null, typeMap), Literal("aa")), null, row)
     checkEvaluation(
-      GetItem(Literal.create(null, typeMap), Literal.create(null, StringType)), null, row)
-    checkEvaluation(GetItem(BoundReference(3, typeMap, true),
+      GetMapValue(Literal.create(null, typeMap), Literal.create(null, StringType)), null, row)
+    checkEvaluation(GetMapValue(BoundReference(3, typeMap, true),
       Literal.create(null, StringType)), null, row)
 
-    checkEvaluation(GetItem(BoundReference(4, typeArray, true),
+    checkEvaluation(GetArrayItem(BoundReference(4, typeArray, true),
       Literal(1)), "bb", row)
-    checkEvaluation(GetItem(Literal.create(null, typeArray), Literal(1)), null, row)
+    checkEvaluation(GetArrayItem(Literal.create(null, typeArray), Literal(1)), null, row)
     checkEvaluation(
-      GetItem(Literal.create(null, typeArray), Literal.create(null, IntegerType)), null, row)
-    checkEvaluation(GetItem(BoundReference(4, typeArray, true),
+      GetArrayItem(Literal.create(null, typeArray), Literal.create(null, IntegerType)), null, row)
+    checkEvaluation(GetArrayItem(BoundReference(4, typeArray, true),
       Literal.create(null, IntegerType)), null, row)
 
-    def quickBuildGetField(expr: Expression, fieldName: String): StructGetField = {
+    def getStructField(expr: Expression, fieldName: String): ExtractValue = {
       expr.dataType match {
         case StructType(fields) =>
           val field = fields.find(_.name == fieldName).get
-          StructGetField(expr, field, fields.indexOf(field))
+          GetStructField(expr, field, fields.indexOf(field))
       }
     }
 
-    def quickResolve(u: UnresolvedGetField): StructGetField = {
-      quickBuildGetField(u.child, u.fieldName)
+    def quickResolve(u: UnresolvedExtractValue): ExtractValue = {
+      ExtractValue(u.child, u.extraction, _ == _)
     }
 
-    checkEvaluation(quickBuildGetField(BoundReference(2, typeS, nullable = true), "a"), "aa", row)
-    checkEvaluation(quickBuildGetField(Literal.create(null, typeS), "a"), null, row)
+    checkEvaluation(getStructField(BoundReference(2, typeS, nullable = true), "a"), "aa", row)
+    checkEvaluation(getStructField(Literal.create(null, typeS), "a"), null, row)
 
     val typeS_notNullable = StructType(
       StructField("a", StringType, nullable = false)
         :: StructField("b", StringType, nullable = false) :: Nil
     )
 
-    assert(quickBuildGetField(BoundReference(2,typeS, nullable = true), "a").nullable === true)
-    assert(quickBuildGetField(BoundReference(2, typeS_notNullable, nullable = false), "a").nullable
+    assert(getStructField(BoundReference(2,typeS, nullable = true), "a").nullable === true)
+    assert(getStructField(BoundReference(2, typeS_notNullable, nullable = false), "a").nullable
       === false)
 
-    assert(quickBuildGetField(Literal.create(null, typeS), "a").nullable === true)
-    assert(quickBuildGetField(Literal.create(null, typeS_notNullable), "a").nullable === true)
+    assert(getStructField(Literal.create(null, typeS), "a").nullable === true)
+    assert(getStructField(Literal.create(null, typeS_notNullable), "a").nullable === true)
 
-    checkEvaluation('c.map(typeMap).at(3).getItem("aa"), "bb", row)
-    checkEvaluation('c.array(typeArray.elementType).at(4).getItem(1), "bb", row)
+    checkEvaluation(quickResolve('c.map(typeMap).at(3).getItem("aa")), "bb", row)
+    checkEvaluation(quickResolve('c.array(typeArray.elementType).at(4).getItem(1)), "bb", row)
     checkEvaluation(quickResolve('c.struct(typeS).at(2).getField("a")), "aa", row)
   }
 
+  test("error message of ExtractValue") {
+    val structType = StructType(StructField("a", StringType, true) :: Nil)
+    val arrayStructType = ArrayType(structType)
+    val arrayType = ArrayType(StringType)
+    val otherType = StringType
+
+    def checkErrorMessage(
+        childDataType: DataType,
+        fieldDataType: DataType,
+        errorMesage: String): Unit = {
+      val e = intercept[org.apache.spark.sql.AnalysisException] {
+        ExtractValue(
+          Literal.create(null, childDataType),
+          Literal.create(null, fieldDataType),
+          _ == _)
+      }
+      assert(e.getMessage().contains(errorMesage))
+    }
+
+    checkErrorMessage(structType, IntegerType, "Field name should be String Literal")
+    checkErrorMessage(arrayStructType, BooleanType, "Field name should be String Literal")
+    checkErrorMessage(arrayType, StringType, "Array index should be integral type")
+    checkErrorMessage(otherType, StringType, "Can't extract value from")
+  }
+
   test("arithmetic") {
     val row = create_row(1, 2, 3, null)
     val c1 = 'a.int.at(0)
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ConstantFoldingSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ConstantFoldingSuite.scala
index 18f92150b0966..6b7d9a85c341b 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ConstantFoldingSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ConstantFoldingSuite.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.sql.catalyst.optimizer
 
-import org.apache.spark.sql.catalyst.analysis.{UnresolvedGetField, EliminateSubQueries}
+import org.apache.spark.sql.catalyst.analysis.{UnresolvedExtractValue, EliminateSubQueries}
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.logical.{LocalRelation, LogicalPlan}
 import org.apache.spark.sql.catalyst.plans.PlanTest
@@ -180,10 +180,10 @@ class ConstantFoldingSuite extends PlanTest {
       IsNull(Literal(null)) as 'c1,
       IsNotNull(Literal(null)) as 'c2,
 
-      GetItem(Literal.create(null, ArrayType(IntegerType)), 1) as 'c3,
-      GetItem(
+      UnresolvedExtractValue(Literal.create(null, ArrayType(IntegerType)), 1) as 'c3,
+      UnresolvedExtractValue(
         Literal.create(Seq(1), ArrayType(IntegerType)), Literal.create(null, IntegerType)) as 'c4,
-      UnresolvedGetField(
+      UnresolvedExtractValue(
         Literal.create(null, StructType(Seq(StructField("a", IntegerType, true)))),
         "a") as 'c5,
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/Column.scala b/sql/core/src/main/scala/org/apache/spark/sql/Column.scala
index 8bbe11b412214..e6e475bb82f82 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/Column.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/Column.scala
@@ -23,7 +23,7 @@ import org.apache.spark.annotation.Experimental
 import org.apache.spark.Logging
 import org.apache.spark.sql.functions.lit
 import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.catalyst.analysis.{UnresolvedAttribute, UnresolvedStar, UnresolvedGetField}
+import org.apache.spark.sql.catalyst.analysis.{UnresolvedAttribute, UnresolvedStar, UnresolvedExtractValue}
 import org.apache.spark.sql.types._
 
 
@@ -67,6 +67,19 @@ class Column(protected[sql] val expr: Expression) extends Logging {
 
   override def hashCode: Int = this.expr.hashCode
 
+  /**
+   * Extracts a value or values from a complex type.
+   * The following types of extraction are supported:
+   * - Given an Array, an integer ordinal can be used to retrieve a single value.
+   * - Given a Map, a key of the correct type can be used to retrieve an individual value.
+   * - Given a Struct, a string fieldName can be used to extract that field.
+   * - Given an Array of Structs, a string fieldName can be used to extract filed
+   *   of every struct in that array, and return an Array of fields
+   *
+   * @group expr_ops
+   */
+  def apply(field: Any): Column = UnresolvedExtractValue(expr, Literal(field))
+
   /**
    * Unary minus, i.e. negate the expression.
    * {{{
@@ -529,14 +542,14 @@ class Column(protected[sql] val expr: Expression) extends Logging {
    *
    * @group expr_ops
    */
-  def getItem(key: Any): Column = GetItem(expr, Literal(key))
+  def getItem(key: Any): Column = UnresolvedExtractValue(expr, Literal(key))
 
   /**
    * An expression that gets a field by name in a [[StructType]].
    *
    * @group expr_ops
    */
-  def getField(fieldName: String): Column = UnresolvedGetField(expr, fieldName)
+  def getField(fieldName: String): Column = UnresolvedExtractValue(expr, Literal(fieldName))
 
   /**
    * An expression that returns a substring.
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
index 1515e9b843771..d2ca8dccae574 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
@@ -449,7 +449,7 @@ class DataFrameSuite extends QueryTest {
       testData.collect().map { case Row(key: Int, value: String) =>
         Row(key, value, key + 1)
       }.toSeq)
-    assert(df.schema.map(_.name).toSeq === Seq("key", "value", "newCol"))
+    assert(df.schema.map(_.name) === Seq("key", "value", "newCol"))
   }
 
   test("replace column using withColumn") {
@@ -484,7 +484,7 @@ class DataFrameSuite extends QueryTest {
       testData.collect().map { case Row(key: Int, value: String) =>
         Row(key, value, key + 1)
       }.toSeq)
-    assert(df.schema.map(_.name).toSeq === Seq("key", "valueRenamed", "newCol"))
+    assert(df.schema.map(_.name) === Seq("key", "valueRenamed", "newCol"))
   }
 
   test("randomSplit") {
@@ -593,4 +593,10 @@ class DataFrameSuite extends QueryTest {
       Row(new java.math.BigDecimal(2.0)))
     TestSQLContext.setConf(SQLConf.CODEGEN_ENABLED, originalValue.toString)
   }
+
+  test("SPARK-7133: Implement struct, array, and map field accessor") {
+    assert(complexData.filter(complexData("a")(0) === 2).count() == 1)
+    assert(complexData.filter(complexData("m")("1") === 1).count() == 1)
+    assert(complexData.filter(complexData("s")("key") === 1).count() == 1)
+  }
 }
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 f30b196734c40..04d40bbb2bced 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
@@ -1204,7 +1204,7 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
       nodeToExpr(qualifier) match {
         case UnresolvedAttribute(qualifierName) =>
           UnresolvedAttribute(qualifierName :+ cleanIdentifier(attr))
-        case other => UnresolvedGetField(other, attr)
+        case other => UnresolvedExtractValue(other, Literal(attr))
       }
 
     /* Stars (*) */
@@ -1329,7 +1329,7 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
 
     /* Complex datatype manipulation */
     case Token("[", child :: ordinal :: Nil) =>
-      GetItem(nodeToExpr(child), nodeToExpr(ordinal))
+      UnresolvedExtractValue(nodeToExpr(child), nodeToExpr(ordinal))
 
     /* Other functions */
     case Token("TOK_FUNCTION", Token(ARRAY(), Nil) :: children) =>

From 4b3bb0e43ca7e1a27308516608419487b6a844e6 Mon Sep 17 00:00:00 2001
From: Kay Ousterhout <kayousterhout@gmail.com>
Date: Fri, 8 May 2015 12:24:06 -0700
Subject: [PATCH 66/75] [SPARK-6627] Finished rename to ShuffleBlockResolver

The previous cleanup-commit for SPARK-6627 renamed ShuffleBlockManager
to ShuffleBlockResolver, but didn't rename the associated subclasses and
variables; this commit does that.

I'm unsure whether it's ok to rename ExternalShuffleBlockManager, since that's technically a public class?

cc pwendell

Author: Kay Ousterhout <kayousterhout@gmail.com>

Closes #5764 from kayousterhout/SPARK-6627 and squashes the following commits:

43add1e [Kay Ousterhout] Spacing fix
96080bf [Kay Ousterhout] Test fixes
d8a5d36 [Kay Ousterhout] [SPARK-6627] Finished rename to ShuffleBlockResolver
---
 ...r.scala => FileShuffleBlockResolver.scala} | 11 ++----
 ....scala => IndexShuffleBlockResolver.scala} | 10 ++---
 .../shuffle/hash/HashShuffleManager.scala     |  6 +--
 .../shuffle/hash/HashShuffleWriter.scala      |  4 +-
 .../shuffle/sort/SortShuffleManager.scala     |  6 +--
 .../shuffle/sort/SortShuffleWriter.scala      | 12 +++---
 .../org/apache/spark/storage/BlockId.scala    |  2 +-
 .../apache/spark/storage/BlockManager.scala   |  5 ++-
 .../spark/storage/DiskBlockManager.scala      |  2 +-
 .../hash/HashShuffleManagerSuite.scala        | 18 ++++-----
 .../shuffle/ExternalShuffleBlockHandler.java  |  6 +--
 ...java => ExternalShuffleBlockResolver.java} | 16 ++++----
 .../ExternalShuffleBlockHandlerSuite.java     | 16 ++++----
 ...=> ExternalShuffleBlockResolverSuite.java} | 30 +++++++--------
 .../shuffle/ExternalShuffleCleanupSuite.java  | 37 ++++++++++---------
 .../shuffle/TestShuffleDataContext.java       |  8 ++--
 16 files changed, 94 insertions(+), 95 deletions(-)
 rename core/src/main/scala/org/apache/spark/shuffle/{FileShuffleBlockManager.scala => FileShuffleBlockResolver.scala} (97%)
 rename core/src/main/scala/org/apache/spark/shuffle/{IndexShuffleBlockManager.scala => IndexShuffleBlockResolver.scala} (93%)
 rename network/shuffle/src/main/java/org/apache/spark/network/shuffle/{ExternalShuffleBlockManager.java => ExternalShuffleBlockResolver.java} (95%)
 rename network/shuffle/src/test/java/org/apache/spark/network/shuffle/{ExternalShuffleBlockManagerSuite.java => ExternalShuffleBlockResolverSuite.java} (77%)

diff --git a/core/src/main/scala/org/apache/spark/shuffle/FileShuffleBlockManager.scala b/core/src/main/scala/org/apache/spark/shuffle/FileShuffleBlockResolver.scala
similarity index 97%
rename from core/src/main/scala/org/apache/spark/shuffle/FileShuffleBlockManager.scala
rename to core/src/main/scala/org/apache/spark/shuffle/FileShuffleBlockResolver.scala
index e9b4e2b955dc8..6ad427bcac7f9 100644
--- a/core/src/main/scala/org/apache/spark/shuffle/FileShuffleBlockManager.scala
+++ b/core/src/main/scala/org/apache/spark/shuffle/FileShuffleBlockResolver.scala
@@ -18,7 +18,6 @@
 package org.apache.spark.shuffle
 
 import java.io.File
-import java.nio.ByteBuffer
 import java.util.concurrent.ConcurrentLinkedQueue
 import java.util.concurrent.atomic.AtomicInteger
 
@@ -29,7 +28,7 @@ import org.apache.spark.executor.ShuffleWriteMetrics
 import org.apache.spark.network.buffer.{FileSegmentManagedBuffer, ManagedBuffer}
 import org.apache.spark.network.netty.SparkTransportConf
 import org.apache.spark.serializer.Serializer
-import org.apache.spark.shuffle.FileShuffleBlockManager.ShuffleFileGroup
+import org.apache.spark.shuffle.FileShuffleBlockResolver.ShuffleFileGroup
 import org.apache.spark.storage._
 import org.apache.spark.util.{MetadataCleaner, MetadataCleanerType, TimeStampedHashMap}
 import org.apache.spark.util.collection.{PrimitiveKeyOpenHashMap, PrimitiveVector}
@@ -64,9 +63,8 @@ private[spark] trait ShuffleWriterGroup {
  * files within a ShuffleFileGroups associated with the block's reducer.
  */
 // Note: Changes to the format in this file should be kept in sync with
-// org.apache.spark.network.shuffle.StandaloneShuffleBlockManager#getHashBasedShuffleBlockData().
-private[spark]
-class FileShuffleBlockManager(conf: SparkConf)
+// org.apache.spark.network.shuffle.ExternalShuffleBlockResolver#getHashBasedShuffleBlockData().
+private[spark] class FileShuffleBlockResolver(conf: SparkConf)
   extends ShuffleBlockResolver with Logging {
 
   private val transportConf = SparkTransportConf.fromSparkConf(conf)
@@ -242,8 +240,7 @@ class FileShuffleBlockManager(conf: SparkConf)
   }
 }
 
-private[spark]
-object FileShuffleBlockManager {
+private[spark] object FileShuffleBlockResolver {
   /**
    * 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/IndexShuffleBlockResolver.scala
similarity index 93%
rename from core/src/main/scala/org/apache/spark/shuffle/IndexShuffleBlockManager.scala
rename to core/src/main/scala/org/apache/spark/shuffle/IndexShuffleBlockResolver.scala
index a1741e2875c16..d9c63b6e7bbb9 100644
--- a/core/src/main/scala/org/apache/spark/shuffle/IndexShuffleBlockManager.scala
+++ b/core/src/main/scala/org/apache/spark/shuffle/IndexShuffleBlockResolver.scala
@@ -18,7 +18,6 @@
 package org.apache.spark.shuffle
 
 import java.io._
-import java.nio.ByteBuffer
 
 import com.google.common.io.ByteStreams
 
@@ -28,7 +27,7 @@ import org.apache.spark.network.netty.SparkTransportConf
 import org.apache.spark.storage._
 import org.apache.spark.util.Utils
 
-import IndexShuffleBlockManager.NOOP_REDUCE_ID
+import IndexShuffleBlockResolver.NOOP_REDUCE_ID
 
 /**
  * Create and maintain the shuffle blocks' mapping between logic block and physical file location.
@@ -40,9 +39,8 @@ import IndexShuffleBlockManager.NOOP_REDUCE_ID
  *
  */
 // Note: Changes to the format in this file should be kept in sync with
-// org.apache.spark.network.shuffle.StandaloneShuffleBlockManager#getSortBasedShuffleBlockData().
-private[spark]
-class IndexShuffleBlockManager(conf: SparkConf) extends ShuffleBlockResolver {
+// org.apache.spark.network.shuffle.ExternalShuffleBlockResolver#getSortBasedShuffleBlockData().
+private[spark] class IndexShuffleBlockResolver(conf: SparkConf) extends ShuffleBlockResolver {
 
   private lazy val blockManager = SparkEnv.get.blockManager
 
@@ -115,7 +113,7 @@ class IndexShuffleBlockManager(conf: SparkConf) extends ShuffleBlockResolver {
   override def stop(): Unit = {}
 }
 
-private[spark] object IndexShuffleBlockManager {
+private[spark] object IndexShuffleBlockResolver {
   // No-op reduce ID used in interactions with disk store and BlockObjectWriter.
   // The disk store currently expects puts to relate to a (map, reduce) pair, but in the sort
   // shuffle outputs for several reduces are glommed into a single file.
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 2a7df8dd5bd83..c089088f409dd 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
@@ -26,7 +26,7 @@ import org.apache.spark.shuffle._
  */
 private[spark] class HashShuffleManager(conf: SparkConf) extends ShuffleManager {
 
-  private val fileShuffleBlockManager = new FileShuffleBlockManager(conf)
+  private val fileShuffleBlockResolver = new FileShuffleBlockResolver(conf)
 
   /* Register a shuffle with the manager and obtain a handle for it to pass to tasks. */
   override def registerShuffle[K, V, C](
@@ -61,8 +61,8 @@ private[spark] class HashShuffleManager(conf: SparkConf) extends ShuffleManager
     shuffleBlockResolver.removeShuffle(shuffleId)
   }
 
-  override def shuffleBlockResolver: FileShuffleBlockManager = {
-    fileShuffleBlockManager
+  override def shuffleBlockResolver: FileShuffleBlockResolver = {
+    fileShuffleBlockResolver
   }
 
   /** Shut down this ShuffleManager. */
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 cd27c9e07a3cd..897f0a5dc5bcc 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
@@ -25,7 +25,7 @@ import org.apache.spark.shuffle._
 import org.apache.spark.storage.BlockObjectWriter
 
 private[spark] class HashShuffleWriter[K, V](
-    shuffleBlockManager: FileShuffleBlockManager,
+    shuffleBlockResolver: FileShuffleBlockResolver,
     handle: BaseShuffleHandle[K, V, _],
     mapId: Int,
     context: TaskContext)
@@ -45,7 +45,7 @@ private[spark] class HashShuffleWriter[K, V](
 
   private val blockManager = SparkEnv.get.blockManager
   private val ser = Serializer.getSerializer(dep.serializer.getOrElse(null))
-  private val shuffle = shuffleBlockManager.forMapTask(dep.shuffleId, mapId, numOutputSplits, ser,
+  private val shuffle = shuffleBlockResolver.forMapTask(dep.shuffleId, mapId, numOutputSplits, ser,
     writeMetrics)
 
   /** Write a bunch of records to this task's output */
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 0497036192154..15842941daaab 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
@@ -25,7 +25,7 @@ import org.apache.spark.shuffle.hash.HashShuffleReader
 
 private[spark] class SortShuffleManager(conf: SparkConf) extends ShuffleManager {
 
-  private val indexShuffleBlockManager = new IndexShuffleBlockManager(conf)
+  private val indexShuffleBlockResolver = new IndexShuffleBlockResolver(conf)
   private val shuffleMapNumber = new ConcurrentHashMap[Int, Int]()
 
   /**
@@ -72,8 +72,8 @@ private[spark] class SortShuffleManager(conf: SparkConf) extends ShuffleManager
     true
   }
 
-  override def shuffleBlockResolver: IndexShuffleBlockManager = {
-    indexShuffleBlockManager
+  override def shuffleBlockResolver: IndexShuffleBlockResolver = {
+    indexShuffleBlockResolver
   }
 
   /** Shut down this ShuffleManager. */
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 a066435df6fb0..add2656294ca2 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
@@ -20,12 +20,12 @@ package org.apache.spark.shuffle.sort
 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.{IndexShuffleBlockManager, ShuffleWriter, BaseShuffleHandle}
+import org.apache.spark.shuffle.{IndexShuffleBlockResolver, ShuffleWriter, BaseShuffleHandle}
 import org.apache.spark.storage.ShuffleBlockId
 import org.apache.spark.util.collection.ExternalSorter
 
 private[spark] class SortShuffleWriter[K, V, C](
-    shuffleBlockManager: IndexShuffleBlockManager,
+    shuffleBlockResolver: IndexShuffleBlockResolver,
     handle: BaseShuffleHandle[K, V, C],
     mapId: Int,
     context: TaskContext)
@@ -65,10 +65,10 @@ private[spark] class SortShuffleWriter[K, V, C](
     // Don't bother including the time to open the merged output file in the shuffle write time,
     // because it just opens a single file, so is typically too fast to measure accurately
     // (see SPARK-3570).
-    val outputFile = shuffleBlockManager.getDataFile(dep.shuffleId, mapId)
-    val blockId = ShuffleBlockId(dep.shuffleId, mapId, IndexShuffleBlockManager.NOOP_REDUCE_ID)
+    val outputFile = shuffleBlockResolver.getDataFile(dep.shuffleId, mapId)
+    val blockId = ShuffleBlockId(dep.shuffleId, mapId, IndexShuffleBlockResolver.NOOP_REDUCE_ID)
     val partitionLengths = sorter.writePartitionedFile(blockId, context, outputFile)
-    shuffleBlockManager.writeIndexFile(dep.shuffleId, mapId, partitionLengths)
+    shuffleBlockResolver.writeIndexFile(dep.shuffleId, mapId, partitionLengths)
 
     mapStatus = MapStatus(blockManager.shuffleServerId, partitionLengths)
   }
@@ -84,7 +84,7 @@ private[spark] class SortShuffleWriter[K, V, C](
         return Option(mapStatus)
       } else {
         // The map task failed, so delete our output data.
-        shuffleBlockManager.removeDataByMap(dep.shuffleId, mapId)
+        shuffleBlockResolver.removeDataByMap(dep.shuffleId, mapId)
         return None
       }
     } finally {
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 c186fd360fef6..524f6970992a5 100644
--- a/core/src/main/scala/org/apache/spark/storage/BlockId.scala
+++ b/core/src/main/scala/org/apache/spark/storage/BlockId.scala
@@ -54,7 +54,7 @@ case class RDDBlockId(rddId: Int, splitIndex: Int) extends BlockId {
 }
 
 // Format of the shuffle block ids (including data and index) should be kept in sync with
-// org.apache.spark.network.shuffle.StandaloneShuffleBlockManager#getBlockData().
+// org.apache.spark.network.shuffle.ExternalShuffleBlockResolver#getBlockData().
 @DeveloperApi
 case class ShuffleBlockId(shuffleId: Int, mapId: Int, reduceId: Int) extends BlockId {
   override def name: String = "shuffle_" + shuffleId + "_" + mapId + "_" + reduceId
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 a46fecd2274ef..cc794e5c90ffa 100644
--- a/core/src/main/scala/org/apache/spark/storage/BlockManager.scala
+++ b/core/src/main/scala/org/apache/spark/storage/BlockManager.scala
@@ -431,10 +431,11 @@ 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) {
-      val shuffleBlockManager = shuffleManager.shuffleBlockResolver
+      val shuffleBlockResolver = shuffleManager.shuffleBlockResolver
       // TODO: This should gracefully handle case where local block is not available. Currently
       // downstream code will throw an exception.
-      Option(shuffleBlockManager.getBlockData(blockId.asInstanceOf[ShuffleBlockId]).nioByteBuffer())
+      Option(
+        shuffleBlockResolver.getBlockData(blockId.asInstanceOf[ShuffleBlockId]).nioByteBuffer())
     } else {
       doGetLocal(blockId, asBlockResult = false).asInstanceOf[Option[ByteBuffer]]
     }
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 5764c16902c66..2a4447705fa65 100644
--- a/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala
+++ b/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala
@@ -55,7 +55,7 @@ private[spark] class DiskBlockManager(blockManager: BlockManager, conf: SparkCon
 
   /** Looks up a file by hashing it into one of our local subdirectories. */
   // This method should be kept in sync with
-  // org.apache.spark.network.shuffle.StandaloneShuffleBlockManager#getFile().
+  // org.apache.spark.network.shuffle.ExternalShuffleBlockResolver#getFile().
   def getFile(filename: String): File = {
     // Figure out which local directory it hashes to, and which subdirectory in that
     val hash = Utils.nonNegativeHash(filename)
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 84384bb48999a..0537bf66ad020 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
@@ -27,7 +27,7 @@ import org.apache.spark.{SparkEnv, SparkContext, LocalSparkContext, SparkConf}
 import org.apache.spark.executor.ShuffleWriteMetrics
 import org.apache.spark.network.buffer.{FileSegmentManagedBuffer, ManagedBuffer}
 import org.apache.spark.serializer.JavaSerializer
-import org.apache.spark.shuffle.FileShuffleBlockManager
+import org.apache.spark.shuffle.FileShuffleBlockResolver
 import org.apache.spark.storage.{ShuffleBlockId, FileSegment}
 
 class HashShuffleManagerSuite extends FunSuite with LocalSparkContext {
@@ -53,10 +53,10 @@ class HashShuffleManagerSuite extends FunSuite with LocalSparkContext {
 
     sc = new SparkContext("local", "test", conf)
 
-    val shuffleBlockManager =
-      SparkEnv.get.shuffleManager.shuffleBlockResolver.asInstanceOf[FileShuffleBlockManager]
+    val shuffleBlockResolver =
+      SparkEnv.get.shuffleManager.shuffleBlockResolver.asInstanceOf[FileShuffleBlockResolver]
 
-    val shuffle1 = shuffleBlockManager.forMapTask(1, 1, 1, new JavaSerializer(conf),
+    val shuffle1 = shuffleBlockResolver.forMapTask(1, 1, 1, new JavaSerializer(conf),
       new ShuffleWriteMetrics)
     for (writer <- shuffle1.writers) {
       writer.write("test1", "value")
@@ -69,7 +69,7 @@ class HashShuffleManagerSuite extends FunSuite with LocalSparkContext {
     val shuffle1Segment = shuffle1.writers(0).fileSegment()
     shuffle1.releaseWriters(success = true)
 
-    val shuffle2 = shuffleBlockManager.forMapTask(1, 2, 1, new JavaSerializer(conf),
+    val shuffle2 = shuffleBlockResolver.forMapTask(1, 2, 1, new JavaSerializer(conf),
       new ShuffleWriteMetrics)
 
     for (writer <- shuffle2.writers) {
@@ -88,7 +88,7 @@ class HashShuffleManagerSuite extends FunSuite with LocalSparkContext {
     // 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),
+    val shuffle3 = shuffleBlockResolver.forMapTask(1, 3, 1, new JavaSerializer(testConf),
       new ShuffleWriteMetrics)
     for (writer <- shuffle3.writers) {
       writer.write("test3", "value")
@@ -98,10 +98,10 @@ class HashShuffleManagerSuite extends FunSuite with LocalSparkContext {
       writer.commitAndClose()
     }
     // check before we register.
-    checkSegments(shuffle2Segment, shuffleBlockManager.getBlockData(ShuffleBlockId(1, 2, 0)))
+    checkSegments(shuffle2Segment, shuffleBlockResolver.getBlockData(ShuffleBlockId(1, 2, 0)))
     shuffle3.releaseWriters(success = true)
-    checkSegments(shuffle2Segment, shuffleBlockManager.getBlockData(ShuffleBlockId(1, 2, 0)))
-    shuffleBlockManager.removeShuffle(1)
+    checkSegments(shuffle2Segment, shuffleBlockResolver.getBlockData(ShuffleBlockId(1, 2, 0)))
+    shuffleBlockResolver.removeShuffle(1)
   }
 
   def writeToFile(file: File, numBytes: Int) {
diff --git a/network/shuffle/src/main/java/org/apache/spark/network/shuffle/ExternalShuffleBlockHandler.java b/network/shuffle/src/main/java/org/apache/spark/network/shuffle/ExternalShuffleBlockHandler.java
index 46ca9708621b9..e4faaf8854fc7 100644
--- a/network/shuffle/src/main/java/org/apache/spark/network/shuffle/ExternalShuffleBlockHandler.java
+++ b/network/shuffle/src/main/java/org/apache/spark/network/shuffle/ExternalShuffleBlockHandler.java
@@ -46,18 +46,18 @@
 public class ExternalShuffleBlockHandler extends RpcHandler {
   private final Logger logger = LoggerFactory.getLogger(ExternalShuffleBlockHandler.class);
 
-  private final ExternalShuffleBlockManager blockManager;
+  private final ExternalShuffleBlockResolver blockManager;
   private final OneForOneStreamManager streamManager;
 
   public ExternalShuffleBlockHandler(TransportConf conf) {
-    this(new OneForOneStreamManager(), new ExternalShuffleBlockManager(conf));
+    this(new OneForOneStreamManager(), new ExternalShuffleBlockResolver(conf));
   }
 
   /** Enables mocking out the StreamManager and BlockManager. */
   @VisibleForTesting
   ExternalShuffleBlockHandler(
       OneForOneStreamManager streamManager,
-      ExternalShuffleBlockManager blockManager) {
+      ExternalShuffleBlockResolver blockManager) {
     this.streamManager = streamManager;
     this.blockManager = blockManager;
   }
diff --git a/network/shuffle/src/main/java/org/apache/spark/network/shuffle/ExternalShuffleBlockManager.java b/network/shuffle/src/main/java/org/apache/spark/network/shuffle/ExternalShuffleBlockResolver.java
similarity index 95%
rename from network/shuffle/src/main/java/org/apache/spark/network/shuffle/ExternalShuffleBlockManager.java
rename to network/shuffle/src/main/java/org/apache/spark/network/shuffle/ExternalShuffleBlockResolver.java
index 93e6fdd7161fa..dd08e24cade23 100644
--- a/network/shuffle/src/main/java/org/apache/spark/network/shuffle/ExternalShuffleBlockManager.java
+++ b/network/shuffle/src/main/java/org/apache/spark/network/shuffle/ExternalShuffleBlockResolver.java
@@ -44,13 +44,13 @@
  * Manages converting shuffle BlockIds into physical segments of local files, from a process outside
  * of Executors. Each Executor must register its own configuration about where it stores its files
  * (local dirs) and how (shuffle manager). The logic for retrieval of individual files is replicated
- * from Spark's FileShuffleBlockManager and IndexShuffleBlockManager.
+ * from Spark's FileShuffleBlockResolver and IndexShuffleBlockResolver.
  *
  * Executors with shuffle file consolidation are not currently supported, as the index is stored in
- * the Executor's memory, unlike the IndexShuffleBlockManager.
+ * the Executor's memory, unlike the IndexShuffleBlockResolver.
  */
-public class ExternalShuffleBlockManager {
-  private static final Logger logger = LoggerFactory.getLogger(ExternalShuffleBlockManager.class);
+public class ExternalShuffleBlockResolver {
+  private static final Logger logger = LoggerFactory.getLogger(ExternalShuffleBlockResolver.class);
 
   // Map containing all registered executors' metadata.
   private final ConcurrentMap<AppExecId, ExecutorShuffleInfo> executors;
@@ -60,7 +60,7 @@ public class ExternalShuffleBlockManager {
 
   private final TransportConf conf;
 
-  public ExternalShuffleBlockManager(TransportConf conf) {
+  public ExternalShuffleBlockResolver(TransportConf conf) {
     this(conf, Executors.newSingleThreadExecutor(
         // Add `spark` prefix because it will run in NM in Yarn mode.
         NettyUtils.createThreadFactory("spark-shuffle-directory-cleaner")));
@@ -68,7 +68,7 @@ public ExternalShuffleBlockManager(TransportConf conf) {
 
   // Allows tests to have more control over when directories are cleaned up.
   @VisibleForTesting
-  ExternalShuffleBlockManager(TransportConf conf, Executor directoryCleaner) {
+  ExternalShuffleBlockResolver(TransportConf conf, Executor directoryCleaner) {
     this.conf = conf;
     this.executors = Maps.newConcurrentMap();
     this.directoryCleaner = directoryCleaner;
@@ -168,7 +168,7 @@ private void deleteExecutorDirs(String[] dirs) {
 
   /**
    * Hash-based shuffle data is simply stored as one file per block.
-   * This logic is from FileShuffleBlockManager.
+   * This logic is from FileShuffleBlockResolver.
    */
   // TODO: Support consolidated hash shuffle files
   private ManagedBuffer getHashBasedShuffleBlockData(ExecutorShuffleInfo executor, String blockId) {
@@ -178,7 +178,7 @@ private ManagedBuffer getHashBasedShuffleBlockData(ExecutorShuffleInfo executor,
 
   /**
    * Sort-based shuffle data uses an index called "shuffle_ShuffleId_MapId_0.index" into a data file
-   * called "shuffle_ShuffleId_MapId_0.data". This logic is from IndexShuffleBlockManager,
+   * called "shuffle_ShuffleId_MapId_0.data". This logic is from IndexShuffleBlockResolver,
    * and the block id format is from ShuffleDataBlockId and ShuffleIndexBlockId.
    */
   private ManagedBuffer getSortBasedShuffleBlockData(
diff --git a/network/shuffle/src/test/java/org/apache/spark/network/shuffle/ExternalShuffleBlockHandlerSuite.java b/network/shuffle/src/test/java/org/apache/spark/network/shuffle/ExternalShuffleBlockHandlerSuite.java
index 3f9fe1681cf27..73374cdc77a23 100644
--- a/network/shuffle/src/test/java/org/apache/spark/network/shuffle/ExternalShuffleBlockHandlerSuite.java
+++ b/network/shuffle/src/test/java/org/apache/spark/network/shuffle/ExternalShuffleBlockHandlerSuite.java
@@ -45,14 +45,14 @@ public class ExternalShuffleBlockHandlerSuite {
   TransportClient client = mock(TransportClient.class);
 
   OneForOneStreamManager streamManager;
-  ExternalShuffleBlockManager blockManager;
+  ExternalShuffleBlockResolver blockResolver;
   RpcHandler handler;
 
   @Before
   public void beforeEach() {
     streamManager = mock(OneForOneStreamManager.class);
-    blockManager = mock(ExternalShuffleBlockManager.class);
-    handler = new ExternalShuffleBlockHandler(streamManager, blockManager);
+    blockResolver = mock(ExternalShuffleBlockResolver.class);
+    handler = new ExternalShuffleBlockHandler(streamManager, blockResolver);
   }
 
   @Test
@@ -62,7 +62,7 @@ public void testRegisterExecutor() {
     ExecutorShuffleInfo config = new ExecutorShuffleInfo(new String[] {"/a", "/b"}, 16, "sort");
     byte[] registerMessage = new RegisterExecutor("app0", "exec1", config).toByteArray();
     handler.receive(client, registerMessage, callback);
-    verify(blockManager, times(1)).registerExecutor("app0", "exec1", config);
+    verify(blockResolver, times(1)).registerExecutor("app0", "exec1", config);
 
     verify(callback, times(1)).onSuccess((byte[]) any());
     verify(callback, never()).onFailure((Throwable) any());
@@ -75,12 +75,12 @@ public void testOpenShuffleBlocks() {
 
     ManagedBuffer block0Marker = new NioManagedBuffer(ByteBuffer.wrap(new byte[3]));
     ManagedBuffer block1Marker = new NioManagedBuffer(ByteBuffer.wrap(new byte[7]));
-    when(blockManager.getBlockData("app0", "exec1", "b0")).thenReturn(block0Marker);
-    when(blockManager.getBlockData("app0", "exec1", "b1")).thenReturn(block1Marker);
+    when(blockResolver.getBlockData("app0", "exec1", "b0")).thenReturn(block0Marker);
+    when(blockResolver.getBlockData("app0", "exec1", "b1")).thenReturn(block1Marker);
     byte[] openBlocks = new OpenBlocks("app0", "exec1", new String[] { "b0", "b1" }).toByteArray();
     handler.receive(client, openBlocks, callback);
-    verify(blockManager, times(1)).getBlockData("app0", "exec1", "b0");
-    verify(blockManager, times(1)).getBlockData("app0", "exec1", "b1");
+    verify(blockResolver, times(1)).getBlockData("app0", "exec1", "b0");
+    verify(blockResolver, times(1)).getBlockData("app0", "exec1", "b1");
 
     ArgumentCaptor<byte[]> response = ArgumentCaptor.forClass(byte[].class);
     verify(callback, times(1)).onSuccess(response.capture());
diff --git a/network/shuffle/src/test/java/org/apache/spark/network/shuffle/ExternalShuffleBlockManagerSuite.java b/network/shuffle/src/test/java/org/apache/spark/network/shuffle/ExternalShuffleBlockResolverSuite.java
similarity index 77%
rename from network/shuffle/src/test/java/org/apache/spark/network/shuffle/ExternalShuffleBlockManagerSuite.java
rename to network/shuffle/src/test/java/org/apache/spark/network/shuffle/ExternalShuffleBlockResolverSuite.java
index dad6428a836fc..d02f4f0fdb682 100644
--- a/network/shuffle/src/test/java/org/apache/spark/network/shuffle/ExternalShuffleBlockManagerSuite.java
+++ b/network/shuffle/src/test/java/org/apache/spark/network/shuffle/ExternalShuffleBlockResolverSuite.java
@@ -30,7 +30,7 @@
 
 import static org.junit.Assert.*;
 
-public class ExternalShuffleBlockManagerSuite {
+public class ExternalShuffleBlockResolverSuite {
   static String sortBlock0 = "Hello!";
   static String sortBlock1 = "World!";
 
@@ -60,29 +60,29 @@ public static void afterAll() {
 
   @Test
   public void testBadRequests() {
-    ExternalShuffleBlockManager manager = new ExternalShuffleBlockManager(conf);
+    ExternalShuffleBlockResolver resolver = new ExternalShuffleBlockResolver(conf);
     // Unregistered executor
     try {
-      manager.getBlockData("app0", "exec1", "shuffle_1_1_0");
+      resolver.getBlockData("app0", "exec1", "shuffle_1_1_0");
       fail("Should have failed");
     } catch (RuntimeException e) {
       assertTrue("Bad error message: " + e, e.getMessage().contains("not registered"));
     }
 
     // Invalid shuffle manager
-    manager.registerExecutor("app0", "exec2", dataContext.createExecutorInfo("foobar"));
+    resolver.registerExecutor("app0", "exec2", dataContext.createExecutorInfo("foobar"));
     try {
-      manager.getBlockData("app0", "exec2", "shuffle_1_1_0");
+      resolver.getBlockData("app0", "exec2", "shuffle_1_1_0");
       fail("Should have failed");
     } catch (UnsupportedOperationException e) {
       // pass
     }
 
     // Nonexistent shuffle block
-    manager.registerExecutor("app0", "exec3",
+    resolver.registerExecutor("app0", "exec3",
       dataContext.createExecutorInfo("org.apache.spark.shuffle.sort.SortShuffleManager"));
     try {
-      manager.getBlockData("app0", "exec3", "shuffle_1_1_0");
+      resolver.getBlockData("app0", "exec3", "shuffle_1_1_0");
       fail("Should have failed");
     } catch (Exception e) {
       // pass
@@ -91,18 +91,18 @@ public void testBadRequests() {
 
   @Test
   public void testSortShuffleBlocks() throws IOException {
-    ExternalShuffleBlockManager manager = new ExternalShuffleBlockManager(conf);
-    manager.registerExecutor("app0", "exec0",
+    ExternalShuffleBlockResolver resolver = new ExternalShuffleBlockResolver(conf);
+    resolver.registerExecutor("app0", "exec0",
       dataContext.createExecutorInfo("org.apache.spark.shuffle.sort.SortShuffleManager"));
 
     InputStream block0Stream =
-      manager.getBlockData("app0", "exec0", "shuffle_0_0_0").createInputStream();
+      resolver.getBlockData("app0", "exec0", "shuffle_0_0_0").createInputStream();
     String block0 = CharStreams.toString(new InputStreamReader(block0Stream));
     block0Stream.close();
     assertEquals(sortBlock0, block0);
 
     InputStream block1Stream =
-      manager.getBlockData("app0", "exec0", "shuffle_0_0_1").createInputStream();
+      resolver.getBlockData("app0", "exec0", "shuffle_0_0_1").createInputStream();
     String block1 = CharStreams.toString(new InputStreamReader(block1Stream));
     block1Stream.close();
     assertEquals(sortBlock1, block1);
@@ -110,18 +110,18 @@ public void testSortShuffleBlocks() throws IOException {
 
   @Test
   public void testHashShuffleBlocks() throws IOException {
-    ExternalShuffleBlockManager manager = new ExternalShuffleBlockManager(conf);
-    manager.registerExecutor("app0", "exec0",
+    ExternalShuffleBlockResolver resolver = new ExternalShuffleBlockResolver(conf);
+    resolver.registerExecutor("app0", "exec0",
       dataContext.createExecutorInfo("org.apache.spark.shuffle.hash.HashShuffleManager"));
 
     InputStream block0Stream =
-      manager.getBlockData("app0", "exec0", "shuffle_1_0_0").createInputStream();
+      resolver.getBlockData("app0", "exec0", "shuffle_1_0_0").createInputStream();
     String block0 = CharStreams.toString(new InputStreamReader(block0Stream));
     block0Stream.close();
     assertEquals(hashBlock0, block0);
 
     InputStream block1Stream =
-      manager.getBlockData("app0", "exec0", "shuffle_1_0_1").createInputStream();
+      resolver.getBlockData("app0", "exec0", "shuffle_1_0_1").createInputStream();
     String block1 = CharStreams.toString(new InputStreamReader(block1Stream));
     block1Stream.close();
     assertEquals(hashBlock1, block1);
diff --git a/network/shuffle/src/test/java/org/apache/spark/network/shuffle/ExternalShuffleCleanupSuite.java b/network/shuffle/src/test/java/org/apache/spark/network/shuffle/ExternalShuffleCleanupSuite.java
index 254e3a7a32b98..d9d9c1bf2f17a 100644
--- a/network/shuffle/src/test/java/org/apache/spark/network/shuffle/ExternalShuffleCleanupSuite.java
+++ b/network/shuffle/src/test/java/org/apache/spark/network/shuffle/ExternalShuffleCleanupSuite.java
@@ -41,14 +41,15 @@ public class ExternalShuffleCleanupSuite {
   public void noCleanupAndCleanup() throws IOException {
     TestShuffleDataContext dataContext = createSomeData();
 
-    ExternalShuffleBlockManager manager = new ExternalShuffleBlockManager(conf, sameThreadExecutor);
-    manager.registerExecutor("app", "exec0", dataContext.createExecutorInfo("shuffleMgr"));
-    manager.applicationRemoved("app", false /* cleanup */);
+    ExternalShuffleBlockResolver resolver =
+      new ExternalShuffleBlockResolver(conf, sameThreadExecutor);
+    resolver.registerExecutor("app", "exec0", dataContext.createExecutorInfo("shuffleMgr"));
+    resolver.applicationRemoved("app", false /* cleanup */);
 
     assertStillThere(dataContext);
 
-    manager.registerExecutor("app", "exec1", dataContext.createExecutorInfo("shuffleMgr"));
-    manager.applicationRemoved("app", true /* cleanup */);
+    resolver.registerExecutor("app", "exec1", dataContext.createExecutorInfo("shuffleMgr"));
+    resolver.applicationRemoved("app", true /* cleanup */);
 
     assertCleanedUp(dataContext);
   }
@@ -64,7 +65,7 @@ public void cleanupUsesExecutor() throws IOException {
       @Override public void execute(Runnable runnable) { cleanupCalled.set(true); }
     };
 
-    ExternalShuffleBlockManager manager = new ExternalShuffleBlockManager(conf, noThreadExecutor);
+    ExternalShuffleBlockResolver manager = new ExternalShuffleBlockResolver(conf, noThreadExecutor);
 
     manager.registerExecutor("app", "exec0", dataContext.createExecutorInfo("shuffleMgr"));
     manager.applicationRemoved("app", true);
@@ -81,11 +82,12 @@ public void cleanupMultipleExecutors() throws IOException {
     TestShuffleDataContext dataContext0 = createSomeData();
     TestShuffleDataContext dataContext1 = createSomeData();
 
-    ExternalShuffleBlockManager manager = new ExternalShuffleBlockManager(conf, sameThreadExecutor);
+    ExternalShuffleBlockResolver resolver =
+      new ExternalShuffleBlockResolver(conf, sameThreadExecutor);
 
-    manager.registerExecutor("app", "exec0", dataContext0.createExecutorInfo("shuffleMgr"));
-    manager.registerExecutor("app", "exec1", dataContext1.createExecutorInfo("shuffleMgr"));
-    manager.applicationRemoved("app", true);
+    resolver.registerExecutor("app", "exec0", dataContext0.createExecutorInfo("shuffleMgr"));
+    resolver.registerExecutor("app", "exec1", dataContext1.createExecutorInfo("shuffleMgr"));
+    resolver.applicationRemoved("app", true);
 
     assertCleanedUp(dataContext0);
     assertCleanedUp(dataContext1);
@@ -96,25 +98,26 @@ public void cleanupOnlyRemovedApp() throws IOException {
     TestShuffleDataContext dataContext0 = createSomeData();
     TestShuffleDataContext dataContext1 = createSomeData();
 
-    ExternalShuffleBlockManager manager = new ExternalShuffleBlockManager(conf, sameThreadExecutor);
+    ExternalShuffleBlockResolver resolver =
+      new ExternalShuffleBlockResolver(conf, sameThreadExecutor);
 
-    manager.registerExecutor("app-0", "exec0", dataContext0.createExecutorInfo("shuffleMgr"));
-    manager.registerExecutor("app-1", "exec0", dataContext1.createExecutorInfo("shuffleMgr"));
+    resolver.registerExecutor("app-0", "exec0", dataContext0.createExecutorInfo("shuffleMgr"));
+    resolver.registerExecutor("app-1", "exec0", dataContext1.createExecutorInfo("shuffleMgr"));
 
-    manager.applicationRemoved("app-nonexistent", true);
+    resolver.applicationRemoved("app-nonexistent", true);
     assertStillThere(dataContext0);
     assertStillThere(dataContext1);
 
-    manager.applicationRemoved("app-0", true);
+    resolver.applicationRemoved("app-0", true);
     assertCleanedUp(dataContext0);
     assertStillThere(dataContext1);
 
-    manager.applicationRemoved("app-1", true);
+    resolver.applicationRemoved("app-1", true);
     assertCleanedUp(dataContext0);
     assertCleanedUp(dataContext1);
 
     // Make sure it's not an error to cleanup multiple times
-    manager.applicationRemoved("app-1", true);
+    resolver.applicationRemoved("app-1", true);
     assertCleanedUp(dataContext0);
     assertCleanedUp(dataContext1);
   }
diff --git a/network/shuffle/src/test/java/org/apache/spark/network/shuffle/TestShuffleDataContext.java b/network/shuffle/src/test/java/org/apache/spark/network/shuffle/TestShuffleDataContext.java
index 76639114df5d9..3fdde054ab6c7 100644
--- a/network/shuffle/src/test/java/org/apache/spark/network/shuffle/TestShuffleDataContext.java
+++ b/network/shuffle/src/test/java/org/apache/spark/network/shuffle/TestShuffleDataContext.java
@@ -29,7 +29,7 @@
 
 /**
  * Manages some sort- and hash-based shuffle data, including the creation
- * and cleanup of directories that can be read by the {@link ExternalShuffleBlockManager}.
+ * and cleanup of directories that can be read by the {@link ExternalShuffleBlockResolver}.
  */
 public class TestShuffleDataContext {
   public final String[] localDirs;
@@ -61,9 +61,9 @@ public void insertSortShuffleData(int shuffleId, int mapId, byte[][] blocks) thr
     String blockId = "shuffle_" + shuffleId + "_" + mapId + "_0";
 
     OutputStream dataStream = new FileOutputStream(
-      ExternalShuffleBlockManager.getFile(localDirs, subDirsPerLocalDir, blockId + ".data"));
+      ExternalShuffleBlockResolver.getFile(localDirs, subDirsPerLocalDir, blockId + ".data"));
     DataOutputStream indexStream = new DataOutputStream(new FileOutputStream(
-      ExternalShuffleBlockManager.getFile(localDirs, subDirsPerLocalDir, blockId + ".index")));
+      ExternalShuffleBlockResolver.getFile(localDirs, subDirsPerLocalDir, blockId + ".index")));
 
     long offset = 0;
     indexStream.writeLong(offset);
@@ -82,7 +82,7 @@ public void insertHashShuffleData(int shuffleId, int mapId, byte[][] blocks) thr
     for (int i = 0; i < blocks.length; i ++) {
       String blockId = "shuffle_" + shuffleId + "_" + mapId + "_" + i;
       Files.write(blocks[i],
-        ExternalShuffleBlockManager.getFile(localDirs, subDirsPerLocalDir, blockId));
+        ExternalShuffleBlockResolver.getFile(localDirs, subDirsPerLocalDir, blockId));
     }
   }
 

From 25889d8d97094325f10fbf52f3b36412f212eeb2 Mon Sep 17 00:00:00 2001
From: Evan Jones <ejones@twitter.com>
Date: Fri, 8 May 2015 22:00:39 +0100
Subject: [PATCH 67/75] [SPARK-7490] [CORE] [Minor]
 MapOutputTracker.deserializeMapStatuses: close input streams

GZIPInputStream allocates native memory that is not freed until close() or
when the finalizer runs. It is best to close() these streams explicitly.

stephenh made the same change for serializeMapStatuses in commit b0d884f0. This is the same change for deserialize.

(I ran the unit test suite! it seems to have passed. I did not make a JIRA since this seems "trivial", and the guidelines suggest it is not required for trivial changes)

Author: Evan Jones <ejones@twitter.com>

Closes #5982 from evanj/master and squashes the following commits:

0d76e85 [Evan Jones] [CORE] MapOutputTracker.deserializeMapStatuses: close input streams
---
 core/src/main/scala/org/apache/spark/MapOutputTracker.scala | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/MapOutputTracker.scala b/core/src/main/scala/org/apache/spark/MapOutputTracker.scala
index 16072283edbe9..018422827e1c8 100644
--- a/core/src/main/scala/org/apache/spark/MapOutputTracker.scala
+++ b/core/src/main/scala/org/apache/spark/MapOutputTracker.scala
@@ -367,7 +367,11 @@ private[spark] object MapOutputTracker extends Logging {
   // Opposite of serializeMapStatuses.
   def deserializeMapStatuses(bytes: Array[Byte]): Array[MapStatus] = {
     val objIn = new ObjectInputStream(new GZIPInputStream(new ByteArrayInputStream(bytes)))
-    objIn.readObject().asInstanceOf[Array[MapStatus]]
+    Utils.tryWithSafeFinally {
+      objIn.readObject().asInstanceOf[Array[MapStatus]]
+    } {
+      objIn.close()
+    }
   }
 
   // Convert an array of MapStatuses to locations and sizes for a given reduce ID. If

From dc71e47f047604e3a9972fc386a462d03bff72cf Mon Sep 17 00:00:00 2001
From: zsxwing <zsxwing@gmail.com>
Date: Fri, 8 May 2015 14:06:02 -0700
Subject: [PATCH 68/75] [MINOR] Ignore python/lib/pyspark.zip

Add `python/lib/pyspark.zip` to `.gitignore`. After merging #5580, `python/lib/pyspark.zip` will be generated when building Spark.

Author: zsxwing <zsxwing@gmail.com>

Closes #6017 from zsxwing/gitignore and squashes the following commits:

39b10c4 [zsxwing] Ignore python/lib/pyspark.zip
---
 .gitignore | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.gitignore b/.gitignore
index d54d21b802be8..3624d12269612 100644
--- a/.gitignore
+++ b/.gitignore
@@ -65,6 +65,7 @@ scalastyle.txt
 scalastyle-output.xml
 R-unit-tests.log
 R/unit-tests.out
+python/lib/pyspark.zip
 
 # For Hive
 metastore_db/

From c45c09b015f6f1111fcf9e3c3109a253bbd1d259 Mon Sep 17 00:00:00 2001
From: Kousuke Saruta <sarutak@oss.nttdata.co.jp>
Date: Fri, 8 May 2015 14:06:37 -0700
Subject: [PATCH 69/75] [WEBUI] Remove debug feature for vis.js

`vis.min.js` refers `vis.map` and this even refers `vis.js` which is used for debug `vis.js` but this debug feature is not needed for Spark itself.

This issue is really minor so I don't file this in JIRA.

/CC andrewor14

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

Closes #5994 from sarutak/remove-debug-feature-for-vis and squashes the following commits:

8be038f [Kousuke Saruta] Remove vis.map entry from .rat-exclude
7404945 [Kousuke Saruta] Removed debug feature for vis.js
---
 .rat-excludes                                                 | 1 -
 core/src/main/resources/org/apache/spark/ui/static/vis.map    | 1 -
 core/src/main/resources/org/apache/spark/ui/static/vis.min.js | 1 -
 3 files changed, 3 deletions(-)
 delete mode 100644 core/src/main/resources/org/apache/spark/ui/static/vis.map

diff --git a/.rat-excludes b/.rat-excludes
index dc08c4a3673d2..c0f81b57fe09d 100644
--- a/.rat-excludes
+++ b/.rat-excludes
@@ -36,7 +36,6 @@ graphlib-dot.min.js
 sorttable.js
 vis.min.js
 vis.min.css
-vis.map
 .*avsc
 .*txt
 .*json
diff --git a/core/src/main/resources/org/apache/spark/ui/static/vis.map b/core/src/main/resources/org/apache/spark/ui/static/vis.map
deleted file mode 100644
index 9be8b618989b7..0000000000000
--- a/core/src/main/resources/org/apache/spark/ui/static/vis.map
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/core/src/main/resources/org/apache/spark/ui/static/vis.min.js b/core/src/main/resources/org/apache/spark/ui/static/vis.min.js
index 4af2c818c27a6..2b3b1d60463f7 100644
--- a/core/src/main/resources/org/apache/spark/ui/static/vis.min.js
+++ b/core/src/main/resources/org/apache/spark/ui/static/vis.min.js
@@ -36,4 +36,3 @@ return!e&&this._longDateFormat[t.toUpperCase()]&&(e=this._longDateFormat[t.toUpp
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e=J(t.direction);e&&this.manager.emit(this.options.event+e,t),this.manager.emit(this.options.event,t)}}),u(ae,K,{defaults:{event:"tap",pointers:1,taps:1,interval:300,time:250,threshold:2,posThreshold:10},getTouchAction:function(){return[oi]},process:function(t){var e=this.options,i=t.pointers.length===e.pointers,s=t.distance<e.threshold,o=t.deltaTime<e.time;if(this.reset(),t.eventType&Oe&&0===this.count)return this.failTimeout();if(s&&o&&i){if(t.eventType!=ke)return this.failTimeout();var n=this.pTime?t.timeStamp-this.pTime<e.interval:!0,r=!this.pCenter||F(this.pCenter,t.center)<e.posThreshold;this.pTime=t.timeStamp,this.pCenter=t.center,r&&n?this.count+=1:this.count=1,this._input=t;var a=this.count%e.taps;if(0===a)return this.hasRequireFailures()?(this._timer=h(function(){this.state=pi,this.tryEmit()},e.interval,this),di):pi}return mi},failTimeout:function(){return 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i=this.handlers[t]&&this.handlers[t].slice();if(i&&i.length){e.type=t,e.preventDefault=function(){e.srcEvent.preventDefault()};for(var s=0;s<i.length;)i[s](e),s++}},destroy:function(){this.element&&le(this,!1),this.handlers={},this.session={},this.input.destroy(),this.element=null}},c(he,{INPUT_START:Oe,INPUT_MOVE:Ee,INPUT_END:ke,INPUT_CANCEL:Ne,STATE_POSSIBLE:hi,STATE_BEGAN:di,STATE_CHANGED:li,STATE_ENDED:ci,STATE_RECOGNIZED:pi,STATE_CANCELLED:ui,STATE_FAILED:mi,DIRECTION_NONE:Ie,DIRECTION_LEFT:Le,DIRECTION_RIGHT:ze,DIRECTION_UP:Pe,DIRECTION_DOWN:Ae,DIRECTION_HORIZONTAL:Re,DIRECTION_VERTICAL:Fe,DIRECTION_ALL:He,Manager:de,Input:O,TouchAction:Z,TouchInput:V,MouseInput:W,PointerEventInput:G,TouchMouseInput:q,SingleTouchInput:j,Recognizer:K,AttrRecognizer:ee,Tap:ae,Pan:ie,Swipe:re,Pinch:se,Rotate:ne,Press:oe,on:v,off:y,each:l,merge:p,extend:c,inherit:u,bindFn:m,prefixed:D}),"function"==me&&i(73)?(s=function(){return he}.call(e,i,e,t),!(s!==a&&(t.exports=s))):"undefined"!=typeof t&&t.exports?t.exports=he:o[r]=he}(window,document,"Hammer")},function(t,e){e.startWithClustering=function(){this.clusterToFit(this.constants.clustering.initialMaxNodes,!0),this.updateLabels(),this.stabilize&&this._stabilize(),this.start()},e.clusterToFit=function(t,e){for(var i=this.nodeIndices.length,s=50,o=0;i>t&&s>o;)o%3==0?(this.forceAggregateHubs(!0),this.normalizeClusterLevels()):this.increaseClusterLevel(),i=this.nodeIndices.length,o+=1;o>0&&1==e&&this.repositionNodes(),this._updateCalculationNodes()},e.openCluster=function(t){var e=this.moving;if(t.clusterSize>this.constants.clustering.sectorThreshold&&this._nodeInActiveArea(t)&&("default"!=this._sector()||1!=this.nodeIndices.length)){this._addSector(t);for(var i=0;this.nodeIndices.length<this.constants.clustering.initialMaxNodes&&10>i;)this.decreaseClusterLevel(),i+=1}else this._expandClusterNode(t,!1,!0),this._updateNodeIndexList(),this._updateDynamicEdges(),this._updateCalculationNodes(),this.updateLabels();this.moving!=e&&this.start()},e.updateClustersDefault=function(){1==this.constants.clustering.enabled&&this.updateClusters(0,!1,!1)},e.increaseClusterLevel=function(){this.updateClusters(-1,!1,!0)},e.decreaseClusterLevel=function(){this.updateClusters(1,!1,!0)},e.updateClusters=function(t,e,i,s){var o=this.moving,n=this.nodeIndices.length;this.previousScale>this.scale&&0==t&&this._collapseSector(),this.previousScale>this.scale||-1==t?this._formClusters(i):(this.previousScale<this.scale||1==t)&&(1==i?this._openClusters(e,i):this._openClustersBySize()),this._updateNodeIndexList(),this.nodeIndices.length==n&&(this.previousScale>this.scale||-1==t)&&(this._aggregateHubs(i),this._updateNodeIndexList()),(this.previousScale>this.scale||-1==t)&&(this.handleChains(),this._updateNodeIndexList()),this.previousScale=this.scale,this._updateDynamicEdges(),this.updateLabels(),this.nodeIndices.length<n&&(this.clusterSession+=1,this.normalizeClusterLevels()),(0==s||void 0===s)&&this.moving!=o&&this.start(),this._updateCalculationNodes()},e.handleChains=function(){var t=this._getChainFraction();t>this.constants.clustering.chainThreshold&&this._reduceAmountOfChains(1-this.constants.clustering.chainThreshold/t)},e._aggregateHubs=function(t){this._getHubSize(),this._formClustersByHub(t,!1)},e.forceAggregateHubs=function(t){var e=this.moving,i=this.nodeIndices.length;this._aggregateHubs(!0),this._updateNodeIndexList(),this._updateDynamicEdges(),this.updateLabels(),this.nodeIndices.length!=i&&(this.clusterSession+=1),(0==t||void 0===t)&&this.moving!=e&&this.start()},e._openClustersBySize=function(){for(var t in this.nodes)if(this.nodes.hasOwnProperty(t)){var e=this.nodes[t];1==e.inView()&&(e.width*this.scale>this.constants.clustering.screenSizeThreshold*this.frame.canvas.clientWidth||e.height*this.scale>this.constants.clustering.screenSizeThreshold*this.frame.canvas.clientHeight)&&this.openCluster(e)}},e._openClusters=function(t,e){for(var i=0;i<this.nodeIndices.length;i++){var s=this.nodes[this.nodeIndices[i]];this._expandClusterNode(s,t,e),this._updateCalculationNodes()}},e._expandClusterNode=function(t,e,i,s){if(t.clusterSize>1&&(t.clusterSize<this.constants.clustering.sectorThreshold&&(s=!0),e=s?!0:e,t.formationScale<this.scale||1==i))for(var o in t.containedNodes)if(t.containedNodes.hasOwnProperty(o)){var n=t.containedNodes[o];1==i?(n.clusterSession==t.clusterSessions[t.clusterSessions.length-1]||s)&&this._expelChildFromParent(t,o,e,i,s):this._nodeInActiveArea(t)&&this._expelChildFromParent(t,o,e,i,s)}},e._expelChildFromParent=function(t,e,i,s,o){var n=t.containedNodes[e];if(n.formationScale<this.scale||1==s){this._unselectAll(),this.nodes[e]=n,this._releaseContainedEdges(t,n),this._connectEdgeBackToChild(t,n),this._validateEdges(t),t.options.mass-=n.options.mass,t.clusterSize-=n.clusterSize,t.options.fontSize=Math.min(this.constants.clustering.maxFontSize,this.constants.nodes.fontSize+this.constants.clustering.fontSizeMultiplier*(t.clusterSize-1)),t.dynamicEdgesLength=t.dynamicEdges.length,n.x=t.x+t.growthIndicator*(.5-Math.random()),n.y=t.y+t.growthIndicator*(.5-Math.random()),delete t.containedNodes[e];var r=!1;for(var a in t.containedNodes)if(t.containedNodes.hasOwnProperty(a)&&t.containedNodes[a].clusterSession==n.clusterSession){r=!0;break}0==r&&t.clusterSessions.pop(),this._repositionBezierNodes(n),n.clusterSession=0,t.clearSizeCache(),this.moving=!0}1==i&&this._expandClusterNode(n,i,s,o)},e._repositionBezierNodes=function(t){for(var e=0;e<t.dynamicEdges.length;e++)t.dynamicEdges[e].positionBezierNode()},e._formClusters=function(t){0==t?this._formClustersByZoom():this._forceClustersByZoom()},e._formClustersByZoom=function(){var t,e,i,s=this.constants.clustering.clusterEdgeThreshold/this.scale;for(var o in this.edges)if(this.edges.hasOwnProperty(o)){var n=this.edges[o];if(n.connected&&n.toId!=n.fromId&&(t=n.to.x-n.from.x,e=n.to.y-n.from.y,i=Math.sqrt(t*t+e*e),s>i)){var r=n.from,a=n.to;n.to.options.mass>n.from.options.mass&&(r=n.to,a=n.from),1==a.dynamicEdgesLength?this._addToCluster(r,a,!1):1==r.dynamicEdgesLength&&this._addToCluster(a,r,!1)}}},e._forceClustersByZoom=function(){for(var t in this.nodes)if(this.nodes.hasOwnProperty(t)){var e=this.nodes[t];if(1==e.dynamicEdgesLength&&0!=e.dynamicEdges.length){var i=e.dynamicEdges[0],s=i.toId==e.id?this.nodes[i.fromId]:this.nodes[i.toId];e.id!=s.id&&(s.options.mass>e.options.mass?this._addToCluster(s,e,!0):this._addToCluster(e,s,!0))}}},e._clusterToSmallestNeighbour=function(t){for(var e=-1,i=null,s=0;s<t.dynamicEdges.length;s++)if(void 0!==t.dynamicEdges[s]){var o=null;t.dynamicEdges[s].fromId!=t.id?o=t.dynamicEdges[s].from:t.dynamicEdges[s].toId!=t.id&&(o=t.dynamicEdges[s].to),null!=o&&e>o.clusterSessions.length&&(e=o.clusterSessions.length,i=o)}null!=o&&void 0!==this.nodes[o.id]&&this._addToCluster(o,t,!0)},e._formClustersByHub=function(t,e){for(var i in this.nodes)this.nodes.hasOwnProperty(i)&&this._formClusterFromHub(this.nodes[i],t,e)},e._formClusterFromHub=function(t,e,i,s){if(void 0===s&&(s=0),t.dynamicEdgesLength>=this.hubThreshold&&0==i||t.dynamicEdgesLength==this.hubThreshold&&1==i){for(var o,n,r,a=this.constants.clustering.clusterEdgeThreshold/this.scale,h=!1,d=[],l=t.dynamicEdges.length,c=0;l>c;c++)d.push(t.dynamicEdges[c].id);if(0==e)for(h=!1,c=0;l>c;c++){var p=this.edges[d[c]];if(void 0!==p&&p.connected&&p.toId!=p.fromId&&(o=p.to.x-p.from.x,n=p.to.y-p.from.y,r=Math.sqrt(o*o+n*n),a>r)){h=!0;break}}if(!e&&h||e)for(c=0;l>c;c++)if(p=this.edges[d[c]],void 0!==p){var u=this.nodes[p.fromId==t.id?p.toId:p.fromId];u.dynamicEdges.length<=this.hubThreshold+s&&u.id!=t.id&&this._addToCluster(t,u,e)}}},e._addToCluster=function(t,e,i){t.containedNodes[e.id]=e;for(var s=0;s<e.dynamicEdges.length;s++){var o=e.dynamicEdges[s];o.toId==t.id||o.fromId==t.id?this._addToContainedEdges(t,e,o):this._connectEdgeToCluster(t,e,o)}e.dynamicEdges=[],this._containCircularEdgesFromNode(t,e),delete this.nodes[e.id];var n=t.options.mass;e.clusterSession=this.clusterSession,t.options.mass+=e.options.mass,t.clusterSize+=e.clusterSize,t.options.fontSize=Math.min(this.constants.clustering.maxFontSize,this.constants.nodes.fontSize+this.constants.clustering.fontSizeMultiplier*t.clusterSize),t.clusterSessions[t.clusterSessions.length-1]!=this.clusterSession&&t.clusterSessions.push(this.clusterSession),t.formationScale=1==i?0:this.scale,t.clearSizeCache(),t.containedNodes[e.id].formationScale=t.formationScale,e.clearVelocity(),t.updateVelocity(n),this.moving=!0},e._updateDynamicEdges=function(){for(var t=0;t<this.nodeIndices.length;t++){var e=this.nodes[this.nodeIndices[t]];e.dynamicEdgesLength=e.dynamicEdges.length;var i=0;if(e.dynamicEdgesLength>1)for(var s=0;s<e.dynamicEdgesLength-1;s++)for(var o=e.dynamicEdges[s].toId,n=e.dynamicEdges[s].fromId,r=s+1;r<e.dynamicEdgesLength;r++)(e.dynamicEdges[r].toId==o&&e.dynamicEdges[r].fromId==n||e.dynamicEdges[r].fromId==o&&e.dynamicEdges[r].toId==n)&&(i+=1);e.dynamicEdgesLength-=i}},e._addToContainedEdges=function(t,e,i){t.containedEdges.hasOwnProperty(e.id)||(t.containedEdges[e.id]=[]),t.containedEdges[e.id].push(i),delete this.edges[i.id];for(var s=0;s<t.dynamicEdges.length;s++)if(t.dynamicEdges[s].id==i.id){t.dynamicEdges.splice(s,1);break}},e._connectEdgeToCluster=function(t,e,i){i.toId==i.fromId?this._addToContainedEdges(t,e,i):(i.toId==e.id?(i.originalToId.push(e.id),i.to=t,i.toId=t.id):(i.originalFromId.push(e.id),i.from=t,i.fromId=t.id),this._addToReroutedEdges(t,e,i))},e._containCircularEdgesFromNode=function(t,e){for(var i=0;i<t.dynamicEdges.length;i++){var s=t.dynamicEdges[i];s.toId==s.fromId&&this._addToContainedEdges(t,e,s)}},e._addToReroutedEdges=function(t,e,i){t.reroutedEdges.hasOwnProperty(e.id)||(t.reroutedEdges[e.id]=[]),t.reroutedEdges[e.id].push(i),t.dynamicEdges.push(i)},e._connectEdgeBackToChild=function(t,e){if(t.reroutedEdges.hasOwnProperty(e.id)){for(var i=0;i<t.reroutedEdges[e.id].length;i++){var s=t.reroutedEdges[e.id][i];s.originalFromId[s.originalFromId.length-1]==e.id?(s.originalFromId.pop(),s.fromId=e.id,s.from=e):(s.originalToId.pop(),s.toId=e.id,s.to=e),e.dynamicEdges.push(s);for(var o=0;o<t.dynamicEdges.length;o++)if(t.dynamicEdges[o].id==s.id){t.dynamicEdges.splice(o,1);break}}delete t.reroutedEdges[e.id]}},e._validateEdges=function(t){for(var e=0;e<t.dynamicEdges.length;e++){var i=t.dynamicEdges[e];t.id!=i.toId&&t.id!=i.fromId&&t.dynamicEdges.splice(e,1)}},e._releaseContainedEdges=function(t,e){for(var i=0;i<t.containedEdges[e.id].length;i++){var s=t.containedEdges[e.id][i];this.edges[s.id]=s,e.dynamicEdges.push(s),t.dynamicEdges.push(s)}delete t.containedEdges[e.id]},e.updateLabels=function(){var t;for(t in this.nodes)if(this.nodes.hasOwnProperty(t)){var e=this.nodes[t];e.clusterSize>1&&(e.label="[".concat(String(e.clusterSize),"]"))}for(t in this.nodes)this.nodes.hasOwnProperty(t)&&(e=this.nodes[t],1==e.clusterSize&&(e.label=void 0!==e.originalLabel?e.originalLabel:String(e.id)))},e.normalizeClusterLevels=function(){var t,e=0,i=1e9,s=0;for(t in this.nodes)this.nodes.hasOwnProperty(t)&&(s=this.nodes[t].clusterSessions.length,s>e&&(e=s),i>s&&(i=s));if(e-i>this.constants.clustering.clusterLevelDifference){var o=this.nodeIndices.length,n=e-this.constants.clustering.clusterLevelDifference;for(t in this.nodes)this.nodes.hasOwnProperty(t)&&this.nodes[t].clusterSessions.length<n&&this._clusterToSmallestNeighbour(this.nodes[t]);this._updateNodeIndexList(),this._updateDynamicEdges(),this.nodeIndices.length!=o&&(this.clusterSession+=1)}},e._nodeInActiveArea=function(t){return Math.abs(t.x-this.areaCenter.x)<=this.constants.clustering.activeAreaBoxSize/this.scale&&Math.abs(t.y-this.areaCenter.y)<=this.constants.clustering.activeAreaBoxSize/this.scale},e.repositionNodes=function(){for(var t=0;t<this.nodeIndices.length;t++){var e=this.nodes[this.nodeIndices[t]];if(0==e.xFixed||0==e.yFixed){var i=1*this.nodeIndices.length*Math.min(100,e.options.mass),s=2*Math.PI*Math.random();0==e.xFixed&&(e.x=i*Math.cos(s)),0==e.yFixed&&(e.y=i*Math.sin(s)),this._repositionBezierNodes(e)}}},e._getHubSize=function(){for(var t=0,e=0,i=0,s=0,o=0;o<this.nodeIndices.length;o++){var n=this.nodes[this.nodeIndices[o]];n.dynamicEdgesLength>s&&(s=n.dynamicEdgesLength),t+=n.dynamicEdgesLength,e+=Math.pow(n.dynamicEdgesLength,2),i+=1}t/=i,e/=i;var r=e-Math.pow(t,2),a=Math.sqrt(r);this.hubThreshold=Math.floor(t+2*a),this.hubThreshold>s&&(this.hubThreshold=s)},e._reduceAmountOfChains=function(t){this.hubThreshold=2;var e=Math.floor(this.nodeIndices.length*t);for(var i in this.nodes)this.nodes.hasOwnProperty(i)&&2==this.nodes[i].dynamicEdgesLength&&this.nodes[i].dynamicEdges.length>=2&&e>0&&(this._formClusterFromHub(this.nodes[i],!0,!0,1),e-=1)},e._getChainFraction=function(){var t=0,e=0;for(var i in this.nodes)this.nodes.hasOwnProperty(i)&&(2==this.nodes[i].dynamicEdgesLength&&this.nodes[i].dynamicEdges.length>=2&&(t+=1),e+=1);return t/e}},function(t,e,i){var s=i(1),o=i(40);e._putDataInSector=function(){this.sectors.active[this._sector()].nodes=this.nodes,this.sectors.active[this._sector()].edges=this.edges,this.sectors.active[this._sector()].nodeIndices=this.nodeIndices},e._switchToSector=function(t,e){void 0===e||"active"==e?this._switchToActiveSector(t):this._switchToFrozenSector(t)},e._switchToActiveSector=function(t){this.nodeIndices=this.sectors.active[t].nodeIndices,this.nodes=this.sectors.active[t].nodes,this.edges=this.sectors.active[t].edges},e._switchToSupportSector=function(){this.nodeIndices=this.sectors.support.nodeIndices,this.nodes=this.sectors.support.nodes,this.edges=this.sectors.support.edges},e._switchToFrozenSector=function(t){this.nodeIndices=this.sectors.frozen[t].nodeIndices,this.nodes=this.sectors.frozen[t].nodes,this.edges=this.sectors.frozen[t].edges},e._loadLatestSector=function(){this._switchToSector(this._sector())},e._sector=function(){return this.activeSector[this.activeSector.length-1]},e._previousSector=function(){if(this.activeSector.length>1)return this.activeSector[this.activeSector.length-2];throw new TypeError("there are not enough sectors in the this.activeSector array.")},e._setActiveSector=function(t){this.activeSector.push(t)},e._forgetLastSector=function(){this.activeSector.pop()},e._createNewSector=function(t){this.sectors.active[t]={nodes:{},edges:{},nodeIndices:[],formationScale:this.scale,drawingNode:void 0},this.sectors.active[t].drawingNode=new o({id:t,color:{background:"#eaefef",border:"495c5e"}},{},{},this.constants),this.sectors.active[t].drawingNode.clusterSize=2},e._deleteActiveSector=function(t){delete this.sectors.active[t]},e._deleteFrozenSector=function(t){delete this.sectors.frozen[t]},e._freezeSector=function(t){this.sectors.frozen[t]=this.sectors.active[t],this._deleteActiveSector(t)},e._activateSector=function(t){this.sectors.active[t]=this.sectors.frozen[t],this._deleteFrozenSector(t)},e._mergeThisWithFrozen=function(t){for(var e in this.nodes)this.nodes.hasOwnProperty(e)&&(this.sectors.frozen[t].nodes[e]=this.nodes[e]);for(var i in this.edges)this.edges.hasOwnProperty(i)&&(this.sectors.frozen[t].edges[i]=this.edges[i]);for(var s=0;s<this.nodeIndices.length;s++)this.sectors.frozen[t].nodeIndices.push(this.nodeIndices[s])},e._collapseThisToSingleCluster=function(){this.clusterToFit(1,!1)},e._addSector=function(t){var e=this._sector();delete this.nodes[t.id];var i=s.randomUUID();this._freezeSector(e),this._createNewSector(i),this._setActiveSector(i),this._switchToSector(this._sector()),this.nodes[t.id]=t},e._collapseSector=function(){var t=this._sector();if("default"!=t&&(1==this.nodeIndices.length||this.sectors.active[t].drawingNode.width*this.scale<this.constants.clustering.screenSizeThreshold*this.frame.canvas.clientWidth||this.sectors.active[t].drawingNode.height*this.scale<this.constants.clustering.screenSizeThreshold*this.frame.canvas.clientHeight)){var e=this._previousSector();this._collapseThisToSingleCluster(),this._mergeThisWithFrozen(e),this._deleteActiveSector(t),this._activateSector(e),this._switchToSector(e),this._forgetLastSector(),this._updateNodeIndexList(),this._updateCalculationNodes()}},e._doInAllActiveSectors=function(t,e){var i=[];if(void 0===e)for(var s in this.sectors.active)this.sectors.active.hasOwnProperty(s)&&(this._switchToActiveSector(s),i.push(this[t]()));else for(var s in this.sectors.active)if(this.sectors.active.hasOwnProperty(s)){this._switchToActiveSector(s);var o=Array.prototype.splice.call(arguments,1);i.push(o.length>1?this[t](o[0],o[1]):this[t](e))}return this._loadLatestSector(),i},e._doInSupportSector=function(t,e){var i=!1;if(void 0===e)this._switchToSupportSector(),i=this[t]();else{this._switchToSupportSector();var s=Array.prototype.splice.call(arguments,1);i=s.length>1?this[t](s[0],s[1]):this[t](e)}return this._loadLatestSector(),i},e._doInAllFrozenSectors=function(t,e){if(void 0===e)for(var i in this.sectors.frozen)this.sectors.frozen.hasOwnProperty(i)&&(this._switchToFrozenSector(i),this[t]());else for(var i in this.sectors.frozen)if(this.sectors.frozen.hasOwnProperty(i)){this._switchToFrozenSector(i);var s=Array.prototype.splice.call(arguments,1);s.length>1?this[t](s[0],s[1]):this[t](e)}this._loadLatestSector()},e._doInAllSectors=function(t,e){var i=Array.prototype.splice.call(arguments,1);void 0===e?(this._doInAllActiveSectors(t),this._doInAllFrozenSectors(t)):i.length>1?(this._doInAllActiveSectors(t,i[0],i[1]),this._doInAllFrozenSectors(t,i[0],i[1])):(this._doInAllActiveSectors(t,e),this._doInAllFrozenSectors(t,e))},e._clearNodeIndexList=function(){var t=this._sector();this.sectors.active[t].nodeIndices=[],this.nodeIndices=this.sectors.active[t].nodeIndices},e._drawSectorNodes=function(t,e){var i,s=1e9,o=-1e9,n=1e9,r=-1e9;for(var a in this.sectors[e])if(this.sectors[e].hasOwnProperty(a)&&void 0!==this.sectors[e][a].drawingNode){this._switchToSector(a,e),s=1e9,o=-1e9,n=1e9,r=-1e9;for(var h in this.nodes)this.nodes.hasOwnProperty(h)&&(i=this.nodes[h],i.resize(t),n>i.x-.5*i.width&&(n=i.x-.5*i.width),r<i.x+.5*i.width&&(r=i.x+.5*i.width),s>i.y-.5*i.height&&(s=i.y-.5*i.height),o<i.y+.5*i.height&&(o=i.y+.5*i.height));i=this.sectors[e][a].drawingNode,i.x=.5*(r+n),i.y=.5*(o+s),i.width=2*(i.x-n),i.height=2*(i.y-s),i.options.radius=Math.sqrt(Math.pow(.5*i.width,2)+Math.pow(.5*i.height,2)),i.setScale(this.scale),i._drawCircle(t)}},e._drawAllSectorNodes=function(t){this._drawSectorNodes(t,"frozen"),this._drawSectorNodes(t,"active"),this._loadLatestSector()}},function(t,e,i){var s=i(40);e._getNodesOverlappingWith=function(t,e){var i=this.nodes;for(var s in i)i.hasOwnProperty(s)&&i[s].isOverlappingWith(t)&&e.push(s)},e._getAllNodesOverlappingWith=function(t){var e=[];return this._doInAllActiveSectors("_getNodesOverlappingWith",t,e),e},e._pointerToPositionObject=function(t){var e=this._XconvertDOMtoCanvas(t.x),i=this._YconvertDOMtoCanvas(t.y);return{left:e,top:i,right:e,bottom:i}},e._getNodeAt=function(t){var e=this._pointerToPositionObject(t),i=this._getAllNodesOverlappingWith(e);return i.length>0?this.nodes[i[i.length-1]]:null},e._getEdgesOverlappingWith=function(t,e){var i=this.edges;for(var s in i)i.hasOwnProperty(s)&&i[s].isOverlappingWith(t)&&e.push(s)},e._getAllEdgesOverlappingWith=function(t){var e=[];return this._doInAllActiveSectors("_getEdgesOverlappingWith",t,e),e},e._getEdgeAt=function(t){var e=this._pointerToPositionObject(t),i=this._getAllEdgesOverlappingWith(e);return i.length>0?this.edges[i[i.length-1]]:null},e._addToSelection=function(t){t instanceof s?this.selectionObj.nodes[t.id]=t:this.selectionObj.edges[t.id]=t},e._addToHover=function(t){t instanceof s?this.hoverObj.nodes[t.id]=t:this.hoverObj.edges[t.id]=t},e._removeFromSelection=function(t){t instanceof s?delete this.selectionObj.nodes[t.id]:delete this.selectionObj.edges[t.id]},e._unselectAll=function(t){void 0===t&&(t=!1);for(var e in this.selectionObj.nodes)this.selectionObj.nodes.hasOwnProperty(e)&&this.selectionObj.nodes[e].unselect();for(var i in this.selectionObj.edges)this.selectionObj.edges.hasOwnProperty(i)&&this.selectionObj.edges[i].unselect();this.selectionObj={nodes:{},edges:{}},0==t&&this.emit("select",this.getSelection())},e._unselectClusters=function(t){void 0===t&&(t=!1);for(var e in this.selectionObj.nodes)this.selectionObj.nodes.hasOwnProperty(e)&&this.selectionObj.nodes[e].clusterSize>1&&(this.selectionObj.nodes[e].unselect(),this._removeFromSelection(this.selectionObj.nodes[e]));0==t&&this.emit("select",this.getSelection())},e._getSelectedNodeCount=function(){var t=0;for(var e in this.selectionObj.nodes)this.selectionObj.nodes.hasOwnProperty(e)&&(t+=1);return t},e._getSelectedNode=function(){for(var t in this.selectionObj.nodes)if(this.selectionObj.nodes.hasOwnProperty(t))return this.selectionObj.nodes[t];return null},e._getSelectedEdge=function(){for(var t in this.selectionObj.edges)if(this.selectionObj.edges.hasOwnProperty(t))return this.selectionObj.edges[t];return null},e._getSelectedEdgeCount=function(){var t=0;for(var e in this.selectionObj.edges)this.selectionObj.edges.hasOwnProperty(e)&&(t+=1);return t},e._getSelectedObjectCount=function(){var t=0;for(var e in this.selectionObj.nodes)this.selectionObj.nodes.hasOwnProperty(e)&&(t+=1);for(var i in this.selectionObj.edges)this.selectionObj.edges.hasOwnProperty(i)&&(t+=1);return t},e._selectionIsEmpty=function(){for(var t in this.selectionObj.nodes)if(this.selectionObj.nodes.hasOwnProperty(t))return!1;for(var e in this.selectionObj.edges)if(this.selectionObj.edges.hasOwnProperty(e))return!1;return!0},e._clusterInSelection=function(){for(var t in this.selectionObj.nodes)if(this.selectionObj.nodes.hasOwnProperty(t)&&this.selectionObj.nodes[t].clusterSize>1)return!0;return!1},e._selectConnectedEdges=function(t){for(var e=0;e<t.dynamicEdges.length;e++){var i=t.dynamicEdges[e];i.select(),this._addToSelection(i)}},e._hoverConnectedEdges=function(t){for(var e=0;e<t.dynamicEdges.length;e++){var i=t.dynamicEdges[e];i.hover=!0,this._addToHover(i)}},e._unselectConnectedEdges=function(t){for(var e=0;e<t.dynamicEdges.length;e++){var i=t.dynamicEdges[e];i.unselect(),this._removeFromSelection(i)}},e._selectObject=function(t,e,i,o,n){void 0===i&&(i=!1),void 0===o&&(o=!0),0==this._selectionIsEmpty()&&0==e&&0==this.forceAppendSelection&&this._unselectAll(!0),0!=t.selected||1!=this.constants.selectable&&!n?0==t.selected?(this._addToSelection(t),i=!0):(t.unselect(),this._removeFromSelection(t)):(t.select(),this._addToSelection(t),t instanceof s&&0==this.blockConnectingEdgeSelection&&1==o&&this._selectConnectedEdges(t)),0==i&&this.emit("select",this.getSelection())},e._blurObject=function(t){1==t.hover&&(t.hover=!1,this.emit("blurNode",{node:t.id}))},e._hoverObject=function(t){0==t.hover&&(t.hover=!0,this._addToHover(t),t instanceof s&&this.emit("hoverNode",{node:t.id})),t instanceof s&&this._hoverConnectedEdges(t)},e._handleTouch=function(){},e._handleTap=function(t){var e=this._getNodeAt(t);if(null!=e)this._selectObject(e,!1);else{var i=this._getEdgeAt(t);null!=i?this._selectObject(i,!1):this._unselectAll()}var s=this.getSelection();s.pointer={DOM:{x:t.x,y:t.y},canvas:{x:this._XconvertDOMtoCanvas(t.x),y:this._YconvertDOMtoCanvas(t.y)}},this.emit("click",s),this._redraw()},e._handleDoubleTap=function(t){var e=this._getNodeAt(t);null!=e&&void 0!==e&&(this.areaCenter={x:this._XconvertDOMtoCanvas(t.x),y:this._YconvertDOMtoCanvas(t.y)},this.openCluster(e));var i=this.getSelection();i.pointer={DOM:{x:t.x,y:t.y},canvas:{x:this._XconvertDOMtoCanvas(t.x),y:this._YconvertDOMtoCanvas(t.y)}},this.emit("doubleClick",i)},e._handleOnHold=function(t){var e=this._getNodeAt(t);if(null!=e)this._selectObject(e,!0);else{var i=this._getEdgeAt(t);null!=i&&this._selectObject(i,!0)}this._redraw()},e._handleOnRelease=function(t){this._manipulationReleaseOverload(t),this._navigationReleaseOverload(t)},e._manipulationReleaseOverload=function(){},e._navigationReleaseOverload=function(){},e.getSelection=function(){var t=this.getSelectedNodes(),e=this.getSelectedEdges();return{nodes:t,edges:e}},e.getSelectedNodes=function(){var t=[];if(1==this.constants.selectable)for(var e in this.selectionObj.nodes)this.selectionObj.nodes.hasOwnProperty(e)&&t.push(e);return t},e.getSelectedEdges=function(){var t=[];if(1==this.constants.selectable)for(var e in this.selectionObj.edges)this.selectionObj.edges.hasOwnProperty(e)&&t.push(e);return t},e.setSelection=function(){console.log("setSelection is deprecated. Please use selectNodes instead.")},e.selectNodes=function(t,e){var i,s,o;if(!t||void 0==t.length)throw"Selection must be an array with ids";for(this._unselectAll(!0),i=0,s=t.length;s>i;i++){o=t[i];var n=this.nodes[o];if(!n)throw new RangeError('Node with id "'+o+'" not found');this._selectObject(n,!0,!0,e,!0)}this.redraw()},e.selectEdges=function(t){var e,i,s;if(!t||void 0==t.length)throw"Selection must be an array with ids";for(this._unselectAll(!0),e=0,i=t.length;i>e;e++){s=t[e];var o=this.edges[s];if(!o)throw new RangeError('Edge with id "'+s+'" not found');this._selectObject(o,!0,!0,!1,!0)}this.redraw()},e._updateSelection=function(){for(var t in this.selectionObj.nodes)this.selectionObj.nodes.hasOwnProperty(t)&&(this.nodes.hasOwnProperty(t)||delete this.selectionObj.nodes[t]);for(var e in this.selectionObj.edges)this.selectionObj.edges.hasOwnProperty(e)&&(this.edges.hasOwnProperty(e)||delete this.selectionObj.edges[e])}},function(t,e,i){var s=i(1),o=i(40),n=i(37);e._clearManipulatorBar=function(){this._recursiveDOMDelete(this.manipulationDiv),this.manipulationDOM={},this._manipulationReleaseOverload=function(){},delete this.sectors.support.nodes.targetNode,delete this.sectors.support.nodes.targetViaNode,this.controlNodesActive=!1,this.freezeSimulation=!1},e._restoreOverloadedFunctions=function(){for(var t in this.cachedFunctions)this.cachedFunctions.hasOwnProperty(t)&&(this[t]=this.cachedFunctions[t],delete this.cachedFunctions[t])},e._toggleEditMode=function(){this.editMode=!this.editMode;var t=this.manipulationDiv,e=this.closeDiv,i=this.editModeDiv;1==this.editMode?(t.style.display="block",e.style.display="block",i.style.display="none",e.onclick=this._toggleEditMode.bind(this)):(t.style.display="none",e.style.display="none",i.style.display="block",e.onclick=null),this._createManipulatorBar()},e._createManipulatorBar=function(){this.boundFunction&&this.off("select",this.boundFunction);var t=this.constants.locales[this.constants.locale];if(void 0!==this.edgeBeingEdited&&(this.edgeBeingEdited._disableControlNodes(),this.edgeBeingEdited=void 0,this.selectedControlNode=null,this.controlNodesActive=!1,this._redraw()),this._restoreOverloadedFunctions(),this.freezeSimulation=!1,this.blockConnectingEdgeSelection=!1,this.forceAppendSelection=!1,this.manipulationDOM={},1==this.editMode){for(;this.manipulationDiv.hasChildNodes();)this.manipulationDiv.removeChild(this.manipulationDiv.firstChild);this.manipulationDOM.addNodeSpan=document.createElement("span"),this.manipulationDOM.addNodeSpan.className="network-manipulationUI add",this.manipulationDOM.addNodeLabelSpan=document.createElement("span"),this.manipulationDOM.addNodeLabelSpan.className="network-manipulationLabel",this.manipulationDOM.addNodeLabelSpan.innerHTML=t.addNode,this.manipulationDOM.addNodeSpan.appendChild(this.manipulationDOM.addNodeLabelSpan),this.manipulationDOM.seperatorLineDiv1=document.createElement("div"),this.manipulationDOM.seperatorLineDiv1.className="network-seperatorLine",this.manipulationDOM.addEdgeSpan=document.createElement("span"),this.manipulationDOM.addEdgeSpan.className="network-manipulationUI connect",this.manipulationDOM.addEdgeLabelSpan=document.createElement("span"),this.manipulationDOM.addEdgeLabelSpan.className="network-manipulationLabel",this.manipulationDOM.addEdgeLabelSpan.innerHTML=t.addEdge,this.manipulationDOM.addEdgeSpan.appendChild(this.manipulationDOM.addEdgeLabelSpan),this.manipulationDiv.appendChild(this.manipulationDOM.addNodeSpan),this.manipulationDiv.appendChild(this.manipulationDOM.seperatorLineDiv1),this.manipulationDiv.appendChild(this.manipulationDOM.addEdgeSpan),1==this._getSelectedNodeCount()&&this.triggerFunctions.edit?(this.manipulationDOM.seperatorLineDiv2=document.createElement("div"),this.manipulationDOM.seperatorLineDiv2.className="network-seperatorLine",this.manipulationDOM.editNodeSpan=document.createElement("span"),this.manipulationDOM.editNodeSpan.className="network-manipulationUI edit",this.manipulationDOM.editNodeLabelSpan=document.createElement("span"),this.manipulationDOM.editNodeLabelSpan.className="network-manipulationLabel",this.manipulationDOM.editNodeLabelSpan.innerHTML=t.editNode,this.manipulationDOM.editNodeSpan.appendChild(this.manipulationDOM.editNodeLabelSpan),this.manipulationDiv.appendChild(this.manipulationDOM.seperatorLineDiv2),this.manipulationDiv.appendChild(this.manipulationDOM.editNodeSpan)):1==this._getSelectedEdgeCount()&&0==this._getSelectedNodeCount()&&(this.manipulationDOM.seperatorLineDiv3=document.createElement("div"),this.manipulationDOM.seperatorLineDiv3.className="network-seperatorLine",this.manipulationDOM.editEdgeSpan=document.createElement("span"),this.manipulationDOM.editEdgeSpan.className="network-manipulationUI edit",this.manipulationDOM.editEdgeLabelSpan=document.createElement("span"),this.manipulationDOM.editEdgeLabelSpan.className="network-manipulationLabel",this.manipulationDOM.editEdgeLabelSpan.innerHTML=t.editEdge,this.manipulationDOM.editEdgeSpan.appendChild(this.manipulationDOM.editEdgeLabelSpan),this.manipulationDiv.appendChild(this.manipulationDOM.seperatorLineDiv3),this.manipulationDiv.appendChild(this.manipulationDOM.editEdgeSpan)),0==this._selectionIsEmpty()&&(this.manipulationDOM.seperatorLineDiv4=document.createElement("div"),this.manipulationDOM.seperatorLineDiv4.className="network-seperatorLine",this.manipulationDOM.deleteSpan=document.createElement("span"),this.manipulationDOM.deleteSpan.className="network-manipulationUI delete",this.manipulationDOM.deleteLabelSpan=document.createElement("span"),this.manipulationDOM.deleteLabelSpan.className="network-manipulationLabel",this.manipulationDOM.deleteLabelSpan.innerHTML=t.del,this.manipulationDOM.deleteSpan.appendChild(this.manipulationDOM.deleteLabelSpan),this.manipulationDiv.appendChild(this.manipulationDOM.seperatorLineDiv4),this.manipulationDiv.appendChild(this.manipulationDOM.deleteSpan)),this.manipulationDOM.addNodeSpan.onclick=this._createAddNodeToolbar.bind(this),this.manipulationDOM.addEdgeSpan.onclick=this._createAddEdgeToolbar.bind(this),1==this._getSelectedNodeCount()&&this.triggerFunctions.edit?this.manipulationDOM.editNodeSpan.onclick=this._editNode.bind(this):1==this._getSelectedEdgeCount()&&0==this._getSelectedNodeCount()&&(this.manipulationDOM.editEdgeSpan.onclick=this._createEditEdgeToolbar.bind(this)),0==this._selectionIsEmpty()&&(this.manipulationDOM.deleteSpan.onclick=this._deleteSelected.bind(this)),this.closeDiv.onclick=this._toggleEditMode.bind(this);
 var e=this;this.boundFunction=e._createManipulatorBar,this.on("select",this.boundFunction)}else{for(;this.editModeDiv.hasChildNodes();)this.editModeDiv.removeChild(this.editModeDiv.firstChild);this.manipulationDOM.editModeSpan=document.createElement("span"),this.manipulationDOM.editModeSpan.className="network-manipulationUI edit editmode",this.manipulationDOM.editModeLabelSpan=document.createElement("span"),this.manipulationDOM.editModeLabelSpan.className="network-manipulationLabel",this.manipulationDOM.editModeLabelSpan.innerHTML=t.edit,this.manipulationDOM.editModeSpan.appendChild(this.manipulationDOM.editModeLabelSpan),this.editModeDiv.appendChild(this.manipulationDOM.editModeSpan),this.manipulationDOM.editModeSpan.onclick=this._toggleEditMode.bind(this)}},e._createAddNodeToolbar=function(){this._clearManipulatorBar(),this.boundFunction&&this.off("select",this.boundFunction);var t=this.constants.locales[this.constants.locale];this.manipulationDOM={},this.manipulationDOM.backSpan=document.createElement("span"),this.manipulationDOM.backSpan.className="network-manipulationUI back",this.manipulationDOM.backLabelSpan=document.createElement("span"),this.manipulationDOM.backLabelSpan.className="network-manipulationLabel",this.manipulationDOM.backLabelSpan.innerHTML=t.back,this.manipulationDOM.backSpan.appendChild(this.manipulationDOM.backLabelSpan),this.manipulationDOM.seperatorLineDiv1=document.createElement("div"),this.manipulationDOM.seperatorLineDiv1.className="network-seperatorLine",this.manipulationDOM.descriptionSpan=document.createElement("span"),this.manipulationDOM.descriptionSpan.className="network-manipulationUI none",this.manipulationDOM.descriptionLabelSpan=document.createElement("span"),this.manipulationDOM.descriptionLabelSpan.className="network-manipulationLabel",this.manipulationDOM.descriptionLabelSpan.innerHTML=t.addDescription,this.manipulationDOM.descriptionSpan.appendChild(this.manipulationDOM.descriptionLabelSpan),this.manipulationDiv.appendChild(this.manipulationDOM.backSpan),this.manipulationDiv.appendChild(this.manipulationDOM.seperatorLineDiv1),this.manipulationDiv.appendChild(this.manipulationDOM.descriptionSpan),this.manipulationDOM.backSpan.onclick=this._createManipulatorBar.bind(this);var e=this;this.boundFunction=e._addNode,this.on("select",this.boundFunction)},e._createAddEdgeToolbar=function(){this._clearManipulatorBar(),this._unselectAll(!0),this.freezeSimulation=!0,this.boundFunction&&this.off("select",this.boundFunction);var t=this.constants.locales[this.constants.locale];this._unselectAll(),this.forceAppendSelection=!1,this.blockConnectingEdgeSelection=!0,this.manipulationDOM={},this.manipulationDOM.backSpan=document.createElement("span"),this.manipulationDOM.backSpan.className="network-manipulationUI back",this.manipulationDOM.backLabelSpan=document.createElement("span"),this.manipulationDOM.backLabelSpan.className="network-manipulationLabel",this.manipulationDOM.backLabelSpan.innerHTML=t.back,this.manipulationDOM.backSpan.appendChild(this.manipulationDOM.backLabelSpan),this.manipulationDOM.seperatorLineDiv1=document.createElement("div"),this.manipulationDOM.seperatorLineDiv1.className="network-seperatorLine",this.manipulationDOM.descriptionSpan=document.createElement("span"),this.manipulationDOM.descriptionSpan.className="network-manipulationUI none",this.manipulationDOM.descriptionLabelSpan=document.createElement("span"),this.manipulationDOM.descriptionLabelSpan.className="network-manipulationLabel",this.manipulationDOM.descriptionLabelSpan.innerHTML=t.edgeDescription,this.manipulationDOM.descriptionSpan.appendChild(this.manipulationDOM.descriptionLabelSpan),this.manipulationDiv.appendChild(this.manipulationDOM.backSpan),this.manipulationDiv.appendChild(this.manipulationDOM.seperatorLineDiv1),this.manipulationDiv.appendChild(this.manipulationDOM.descriptionSpan),this.manipulationDOM.backSpan.onclick=this._createManipulatorBar.bind(this);var e=this;this.boundFunction=e._handleConnect,this.on("select",this.boundFunction),this.cachedFunctions._handleTouch=this._handleTouch,this.cachedFunctions._manipulationReleaseOverload=this._manipulationReleaseOverload,this.cachedFunctions._handleDragStart=this._handleDragStart,this.cachedFunctions._handleDragEnd=this._handleDragEnd,this._handleTouch=this._handleConnect,this._manipulationReleaseOverload=function(){},this._handleDragStart=function(){},this._handleDragEnd=this._finishConnect,this._redraw()},e._createEditEdgeToolbar=function(){this._clearManipulatorBar(),this.controlNodesActive=!0,this.boundFunction&&this.off("select",this.boundFunction),this.edgeBeingEdited=this._getSelectedEdge(),this.edgeBeingEdited._enableControlNodes();var t=this.constants.locales[this.constants.locale];this.manipulationDOM={},this.manipulationDOM.backSpan=document.createElement("span"),this.manipulationDOM.backSpan.className="network-manipulationUI back",this.manipulationDOM.backLabelSpan=document.createElement("span"),this.manipulationDOM.backLabelSpan.className="network-manipulationLabel",this.manipulationDOM.backLabelSpan.innerHTML=t.back,this.manipulationDOM.backSpan.appendChild(this.manipulationDOM.backLabelSpan),this.manipulationDOM.seperatorLineDiv1=document.createElement("div"),this.manipulationDOM.seperatorLineDiv1.className="network-seperatorLine",this.manipulationDOM.descriptionSpan=document.createElement("span"),this.manipulationDOM.descriptionSpan.className="network-manipulationUI none",this.manipulationDOM.descriptionLabelSpan=document.createElement("span"),this.manipulationDOM.descriptionLabelSpan.className="network-manipulationLabel",this.manipulationDOM.descriptionLabelSpan.innerHTML=t.editEdgeDescription,this.manipulationDOM.descriptionSpan.appendChild(this.manipulationDOM.descriptionLabelSpan),this.manipulationDiv.appendChild(this.manipulationDOM.backSpan),this.manipulationDiv.appendChild(this.manipulationDOM.seperatorLineDiv1),this.manipulationDiv.appendChild(this.manipulationDOM.descriptionSpan),this.manipulationDOM.backSpan.onclick=this._createManipulatorBar.bind(this),this.cachedFunctions._handleTouch=this._handleTouch,this.cachedFunctions._manipulationReleaseOverload=this._manipulationReleaseOverload,this.cachedFunctions._handleTap=this._handleTap,this.cachedFunctions._handleDragStart=this._handleDragStart,this.cachedFunctions._handleOnDrag=this._handleOnDrag,this._handleTouch=this._selectControlNode,this._handleTap=function(){},this._handleOnDrag=this._controlNodeDrag,this._handleDragStart=function(){},this._manipulationReleaseOverload=this._releaseControlNode,this._redraw()},e._selectControlNode=function(t){this.edgeBeingEdited.controlNodes.from.unselect(),this.edgeBeingEdited.controlNodes.to.unselect(),this.selectedControlNode=this.edgeBeingEdited._getSelectedControlNode(this._XconvertDOMtoCanvas(t.x),this._YconvertDOMtoCanvas(t.y)),null!==this.selectedControlNode&&(this.selectedControlNode.select(),this.freezeSimulation=!0),this._redraw()},e._controlNodeDrag=function(t){var e=this._getPointer(t.center);null!==this.selectedControlNode&&void 0!==this.selectedControlNode&&(this.selectedControlNode.x=this._XconvertDOMtoCanvas(e.x),this.selectedControlNode.y=this._YconvertDOMtoCanvas(e.y)),this._redraw()},e._releaseControlNode=function(t){var e=this._getNodeAt(t);null!==e?(1==this.edgeBeingEdited.controlNodes.from.selected&&(this.edgeBeingEdited._restoreControlNodes(),this._editEdge(e.id,this.edgeBeingEdited.to.id),this.edgeBeingEdited.controlNodes.from.unselect()),1==this.edgeBeingEdited.controlNodes.to.selected&&(this.edgeBeingEdited._restoreControlNodes(),this._editEdge(this.edgeBeingEdited.from.id,e.id),this.edgeBeingEdited.controlNodes.to.unselect())):this.edgeBeingEdited._restoreControlNodes(),this.freezeSimulation=!1,this._redraw()},e._handleConnect=function(t){if(0==this._getSelectedNodeCount()){var e=this._getNodeAt(t);if(null!=e)if(e.clusterSize>1)alert(this.constants.locales[this.constants.locale].createEdgeError);else{this._selectObject(e,!1);var i=this.sectors.support.nodes;i.targetNode=new o({id:"targetNode"},{},{},this.constants);var s=i.targetNode;s.x=e.x,s.y=e.y,this.edges.connectionEdge=new n({id:"connectionEdge",from:e.id,to:s.id},this,this.constants);var r=this.edges.connectionEdge;r.from=e,r.connected=!0,r.options.smoothCurves={enabled:!0,dynamic:!1,type:"continuous",roundness:.5},r.selected=!0,r.to=s,this.cachedFunctions._handleOnDrag=this._handleOnDrag,this._handleOnDrag=function(t){var e=this._getPointer(t.center),i=this.edges.connectionEdge;i.to.x=this._XconvertDOMtoCanvas(e.x),i.to.y=this._YconvertDOMtoCanvas(e.y)},this.moving=!0,this.start()}}},e._finishConnect=function(t){if(1==this._getSelectedNodeCount()){var e=this._getPointer(t.center);this._handleOnDrag=this.cachedFunctions._handleOnDrag,delete this.cachedFunctions._handleOnDrag;var i=this.edges.connectionEdge.fromId;delete this.edges.connectionEdge,delete this.sectors.support.nodes.targetNode,delete this.sectors.support.nodes.targetViaNode;var s=this._getNodeAt(e);null!=s&&(s.clusterSize>1?alert(this.constants.locales[this.constants.locale].createEdgeError):(this._createEdge(i,s.id),this._createManipulatorBar())),this._unselectAll()}},e._addNode=function(){if(this._selectionIsEmpty()&&1==this.editMode){var t=this._pointerToPositionObject(this.pointerPosition),e={id:s.randomUUID(),x:t.left,y:t.top,label:"new",allowedToMoveX:!0,allowedToMoveY:!0};if(this.triggerFunctions.add){if(2!=this.triggerFunctions.add.length)throw new Error("The function for add does not support two arguments (data,callback)");var i=this;this.triggerFunctions.add(e,function(t){i.nodesData.add(t),i._createManipulatorBar(),i.moving=!0,i.start()})}else this.nodesData.add(e),this._createManipulatorBar(),this.moving=!0,this.start()}},e._createEdge=function(t,e){if(1==this.editMode){var i={from:t,to:e};if(this.triggerFunctions.connect){if(2!=this.triggerFunctions.connect.length)throw new Error("The function for connect does not support two arguments (data,callback)");var s=this;this.triggerFunctions.connect(i,function(t){s.edgesData.add(t),s.moving=!0,s.start()})}else this.edgesData.add(i),this.moving=!0,this.start()}},e._editEdge=function(t,e){if(1==this.editMode){var i={id:this.edgeBeingEdited.id,from:t,to:e};if(this.triggerFunctions.editEdge){if(2!=this.triggerFunctions.editEdge.length)throw new Error("The function for edit does not support two arguments (data, callback)");var s=this;this.triggerFunctions.editEdge(i,function(t){s.edgesData.update(t),s.moving=!0,s.start()})}else this.edgesData.update(i),this.moving=!0,this.start()}},e._editNode=function(){if(!this.triggerFunctions.edit||1!=this.editMode)throw new Error("No edit function has been bound to this button");var t=this._getSelectedNode(),e={id:t.id,label:t.label,group:t.options.group,shape:t.options.shape,color:{background:t.options.color.background,border:t.options.color.border,highlight:{background:t.options.color.highlight.background,border:t.options.color.highlight.border}}};if(2!=this.triggerFunctions.edit.length)throw new Error("The function for edit does not support two arguments (data, callback)");var i=this;this.triggerFunctions.edit(e,function(t){i.nodesData.update(t),i._createManipulatorBar(),i.moving=!0,i.start()})},e._deleteSelected=function(){if(!this._selectionIsEmpty()&&1==this.editMode)if(this._clusterInSelection())alert(this.constants.locales[this.constants.locale].deleteClusterError);else{var t=this.getSelectedNodes(),e=this.getSelectedEdges();if(this.triggerFunctions.del){var i=this,s={nodes:t,edges:e};if(2!=this.triggerFunctions.del.length)throw new Error("The function for delete does not support two arguments (data, callback)");this.triggerFunctions.del(s,function(t){i.edgesData.remove(t.edges),i.nodesData.remove(t.nodes),i._unselectAll(),i.moving=!0,i.start()})}else this.edgesData.remove(e),this.nodesData.remove(t),this._unselectAll(),this.moving=!0,this.start()}}},function(t,e,i){var s=(i(1),i(47)),o=i(45);e._cleanNavigation=function(){if(0!=this.navigationHammers.existing.length){for(var t=0;t<this.navigationHammers.existing.length;t++)this.navigationHammers.existing[t].destroy();this.navigationHammers.existing=[]}this._navigationReleaseOverload=function(){},this.navigationDivs&&this.navigationDivs.wrapper&&this.navigationDivs.wrapper.parentNode&&this.navigationDivs.wrapper.parentNode.removeChild(this.navigationDivs.wrapper)},e._loadNavigationElements=function(){this._cleanNavigation(),this.navigationDivs={};var t=["up","down","left","right","zoomIn","zoomOut","zoomExtends"],e=["_moveUp","_moveDown","_moveLeft","_moveRight","_zoomIn","_zoomOut","_zoomExtent"];this.navigationDivs.wrapper=document.createElement("div"),this.frame.appendChild(this.navigationDivs.wrapper);for(var i=0;i<t.length;i++){this.navigationDivs[t[i]]=document.createElement("div"),this.navigationDivs[t[i]].className="network-navigation "+t[i],this.navigationDivs.wrapper.appendChild(this.navigationDivs[t[i]]);var n=new o(this.navigationDivs[t[i]],{prevent_default:!0});s.onTouch(n,this[e[i]].bind(this)),s.onRelease(n,this._onRelease.bind(this)),this.navigationHammers._new.push(n)}this._navigationReleaseOverload=this._stopMovement,this.navigationHammers.existing=this.navigationHammers._new},e._zoomExtent=function(t){this.zoomExtent({duration:700}),t.stopPropagation()},e._stopMovement=function(){this._xStopMoving(),this._yStopMoving(),this._stopZoom()},e._moveUp=function(t){this.yIncrement=this.constants.keyboard.speed.y,this.start(),t.preventDefault()},e._moveDown=function(t){this.yIncrement=-this.constants.keyboard.speed.y,this.start(),t.preventDefault()},e._moveLeft=function(t){this.xIncrement=this.constants.keyboard.speed.x,this.start(),t.preventDefault()},e._moveRight=function(t){this.xIncrement=-this.constants.keyboard.speed.y,this.start(),t.preventDefault()},e._zoomIn=function(t){this.zoomIncrement=this.constants.keyboard.speed.zoom,this.start(),t.preventDefault()},e._zoomOut=function(t){this.zoomIncrement=-this.constants.keyboard.speed.zoom,this.start(),t.preventDefault()},e._stopZoom=function(t){this.zoomIncrement=0,t&&t.preventDefault()},e._yStopMoving=function(t){this.yIncrement=0,t&&t.preventDefault()},e._xStopMoving=function(t){this.xIncrement=0,t&&t.preventDefault()}},function(t,e){e._resetLevels=function(){for(var t in this.nodes)if(this.nodes.hasOwnProperty(t)){var e=this.nodes[t];0==e.preassignedLevel&&(e.level=-1,e.hierarchyEnumerated=!1)}},e._setupHierarchicalLayout=function(){if(1==this.constants.hierarchicalLayout.enabled&&this.nodeIndices.length>0){var t,e,i=0,s=!1,o=!1;for(e in this.nodes)this.nodes.hasOwnProperty(e)&&(t=this.nodes[e],-1!=t.level?s=!0:o=!0,i<t.edges.length&&(i=t.edges.length));if(1==o&&1==s)throw new Error("To use the hierarchical layout, nodes require either no predefined levels or levels have to be defined for all nodes.");this._changeConstants(),1==o&&("hubsize"==this.constants.hierarchicalLayout.layout?this._determineLevels(i):this._determineLevelsDirected(!1));var n=this._getDistribution();this._placeNodesByHierarchy(n),this.start()}},e._placeNodesByHierarchy=function(t){var e,i;for(var s in t)if(t.hasOwnProperty(s))for(e in t[s].nodes)t[s].nodes.hasOwnProperty(e)&&(i=t[s].nodes[e],"UD"==this.constants.hierarchicalLayout.direction||"DU"==this.constants.hierarchicalLayout.direction?i.xFixed&&(i.x=t[s].minPos,i.xFixed=!1,t[s].minPos+=t[s].nodeSpacing):i.yFixed&&(i.y=t[s].minPos,i.yFixed=!1,t[s].minPos+=t[s].nodeSpacing),this._placeBranchNodes(i.edges,i.id,t,i.level));this._stabilize()},e._getDistribution=function(){var t,e,i,s={};for(t in this.nodes)this.nodes.hasOwnProperty(t)&&(e=this.nodes[t],e.xFixed=!0,e.yFixed=!0,"UD"==this.constants.hierarchicalLayout.direction||"DU"==this.constants.hierarchicalLayout.direction?e.y=this.constants.hierarchicalLayout.levelSeparation*e.level:e.x=this.constants.hierarchicalLayout.levelSeparation*e.level,void 0===s[e.level]&&(s[e.level]={amount:0,nodes:{},minPos:0,nodeSpacing:0}),s[e.level].amount+=1,s[e.level].nodes[t]=e);var o=0;for(i in s)s.hasOwnProperty(i)&&o<s[i].amount&&(o=s[i].amount);for(i in s)s.hasOwnProperty(i)&&(s[i].nodeSpacing=(o+1)*this.constants.hierarchicalLayout.nodeSpacing,s[i].nodeSpacing/=s[i].amount+1,s[i].minPos=s[i].nodeSpacing-.5*(s[i].amount+1)*s[i].nodeSpacing);return s},e._determineLevels=function(t){var e,i;for(e in this.nodes)this.nodes.hasOwnProperty(e)&&(i=this.nodes[e],i.edges.length==t&&(i.level=0));for(e in this.nodes)this.nodes.hasOwnProperty(e)&&(i=this.nodes[e],0==i.level&&this._setLevel(1,i.edges,i.id))},e._determineLevelsDirected=function(){var t,e,i,s=1e4;i=this.nodes[this.nodeIndices[0]],i.level=s,this._setLevelDirected(s,i.edges,i.id);for(t in this.nodes)this.nodes.hasOwnProperty(t)&&(e=this.nodes[t],s=e.level<s?e.level:s);for(t in this.nodes)this.nodes.hasOwnProperty(t)&&(e=this.nodes[t],e.level-=s)},e._changeConstants=function(){this.constants.clustering.enabled=!1,this.constants.physics.barnesHut.enabled=!1,this.constants.physics.hierarchicalRepulsion.enabled=!0,this._loadSelectedForceSolver(),1==this.constants.smoothCurves.enabled&&(this.constants.smoothCurves.dynamic=!1),this._configureSmoothCurves();var t=this.constants.hierarchicalLayout;t.levelSeparation=Math.abs(t.levelSeparation),("RL"==t.direction||"DU"==t.direction)&&(t.levelSeparation*=-1),"RL"==t.direction||"LR"==t.direction?1==this.constants.smoothCurves.enabled&&(this.constants.smoothCurves.type="vertical"):1==this.constants.smoothCurves.enabled&&(this.constants.smoothCurves.type="horizontal")},e._placeBranchNodes=function(t,e,i,s){for(var o=0;o<t.length;o++){var n=null;n=t[o].toId==e?t[o].from:t[o].to;var r=!1;"UD"==this.constants.hierarchicalLayout.direction||"DU"==this.constants.hierarchicalLayout.direction?n.xFixed&&n.level>s&&(n.xFixed=!1,n.x=i[n.level].minPos,r=!0):n.yFixed&&n.level>s&&(n.yFixed=!1,n.y=i[n.level].minPos,r=!0),1==r&&(i[n.level].minPos+=i[n.level].nodeSpacing,n.edges.length>1&&this._placeBranchNodes(n.edges,n.id,i,n.level))}},e._setLevel=function(t,e,i){for(var s=0;s<e.length;s++){var o=null;o=e[s].toId==i?e[s].from:e[s].to,(-1==o.level||o.level>t)&&(o.level=t,o.edges.length>1&&this._setLevel(t+1,o.edges,o.id))}},e._setLevelDirected=function(t,e,i){this.nodes[i].hierarchyEnumerated=!0;for(var s,o,n=0;n<e.length;n++)o=1,e[n].toId==i?(s=e[n].from,o=-1):s=e[n].to,-1==s.level&&(s.level=t+o);for(var n=0;n<e.length;n++)s=e[n].toId==i?e[n].from:e[n].to,s.edges.length>1&&s.hierarchyEnumerated===!1&&this._setLevelDirected(s.level,s.edges,s.id)},e._restoreNodes=function(){for(var t in this.nodes)this.nodes.hasOwnProperty(t)&&(this.nodes[t].xFixed=!1,this.nodes[t].yFixed=!1)}},function(t,e,i){function s(){this.constants.smoothCurves.enabled=!this.constants.smoothCurves.enabled;var t=document.getElementById("graph_toggleSmooth");t.style.background=1==this.constants.smoothCurves.enabled?"#A4FF56":"#FF8532",this._configureSmoothCurves(!1)}function o(){for(var t in this.calculationNodes)this.calculationNodes.hasOwnProperty(t)&&(this.calculationNodes[t].vx=0,this.calculationNodes[t].vy=0,this.calculationNodes[t].fx=0,this.calculationNodes[t].fy=0);1==this.constants.hierarchicalLayout.enabled?(this._setupHierarchicalLayout(),a.call(this,"graph_H_nd",1,"physics_hierarchicalRepulsion_nodeDistance"),a.call(this,"graph_H_cg",1,"physics_centralGravity"),a.call(this,"graph_H_sc",1,"physics_springConstant"),a.call(this,"graph_H_sl",1,"physics_springLength"),a.call(this,"graph_H_damp",1,"physics_damping")):this.repositionNodes(),this.moving=!0,this.start()}function n(){var t="No options are required, default values used.",e=[],i=document.getElementById("graph_physicsMethod1"),s=document.getElementById("graph_physicsMethod2");if(1==i.checked){if(this.constants.physics.barnesHut.gravitationalConstant!=this.backupConstants.physics.barnesHut.gravitationalConstant&&e.push("gravitationalConstant: "+this.constants.physics.barnesHut.gravitationalConstant),this.constants.physics.centralGravity!=this.backupConstants.physics.barnesHut.centralGravity&&e.push("centralGravity: "+this.constants.physics.centralGravity),this.constants.physics.springLength!=this.backupConstants.physics.barnesHut.springLength&&e.push("springLength: "+this.constants.physics.springLength),this.constants.physics.springConstant!=this.backupConstants.physics.barnesHut.springConstant&&e.push("springConstant: "+this.constants.physics.springConstant),this.constants.physics.damping!=this.backupConstants.physics.barnesHut.damping&&e.push("damping: "+this.constants.physics.damping),0!=e.length){t="var options = {",t+="physics: {barnesHut: {";for(var o=0;o<e.length;o++)t+=e[o],o<e.length-1&&(t+=", ");t+="}}"}this.constants.smoothCurves.enabled!=this.backupConstants.smoothCurves.enabled&&(0==e.length?t="var options = {":t+=", ",t+="smoothCurves: "+this.constants.smoothCurves.enabled),"No options are required, default values used."!=t&&(t+="};")}else if(1==s.checked){if(t="var options = {",t+="physics: {barnesHut: {enabled: false}",this.constants.physics.repulsion.nodeDistance!=this.backupConstants.physics.repulsion.nodeDistance&&e.push("nodeDistance: "+this.constants.physics.repulsion.nodeDistance),this.constants.physics.centralGravity!=this.backupConstants.physics.repulsion.centralGravity&&e.push("centralGravity: "+this.constants.physics.centralGravity),this.constants.physics.springLength!=this.backupConstants.physics.repulsion.springLength&&e.push("springLength: "+this.constants.physics.springLength),this.constants.physics.springConstant!=this.backupConstants.physics.repulsion.springConstant&&e.push("springConstant: "+this.constants.physics.springConstant),this.constants.physics.damping!=this.backupConstants.physics.repulsion.damping&&e.push("damping: "+this.constants.physics.damping),0!=e.length){t+=", repulsion: {";for(var o=0;o<e.length;o++)t+=e[o],o<e.length-1&&(t+=", ");t+="}}"}0==e.length&&(t+="}"),this.constants.smoothCurves!=this.backupConstants.smoothCurves&&(t+=", smoothCurves: "+this.constants.smoothCurves),t+="};"}else{if(t="var options = {",this.constants.physics.hierarchicalRepulsion.nodeDistance!=this.backupConstants.physics.hierarchicalRepulsion.nodeDistance&&e.push("nodeDistance: "+this.constants.physics.hierarchicalRepulsion.nodeDistance),this.constants.physics.centralGravity!=this.backupConstants.physics.hierarchicalRepulsion.centralGravity&&e.push("centralGravity: "+this.constants.physics.centralGravity),this.constants.physics.springLength!=this.backupConstants.physics.hierarchicalRepulsion.springLength&&e.push("springLength: "+this.constants.physics.springLength),this.constants.physics.springConstant!=this.backupConstants.physics.hierarchicalRepulsion.springConstant&&e.push("springConstant: "+this.constants.physics.springConstant),this.constants.physics.damping!=this.backupConstants.physics.hierarchicalRepulsion.damping&&e.push("damping: "+this.constants.physics.damping),0!=e.length){t+="physics: {hierarchicalRepulsion: {";for(var o=0;o<e.length;o++)t+=e[o],o<e.length-1&&(t+=", ");t+="}},"}if(t+="hierarchicalLayout: {",e=[],this.constants.hierarchicalLayout.direction!=this.backupConstants.hierarchicalLayout.direction&&e.push("direction: "+this.constants.hierarchicalLayout.direction),Math.abs(this.constants.hierarchicalLayout.levelSeparation)!=this.backupConstants.hierarchicalLayout.levelSeparation&&e.push("levelSeparation: "+this.constants.hierarchicalLayout.levelSeparation),this.constants.hierarchicalLayout.nodeSpacing!=this.backupConstants.hierarchicalLayout.nodeSpacing&&e.push("nodeSpacing: "+this.constants.hierarchicalLayout.nodeSpacing),0!=e.length){for(var o=0;o<e.length;o++)t+=e[o],o<e.length-1&&(t+=", ");t+="}"}else t+="enabled:true}";t+="};"}this.optionsDiv.innerHTML=t}function r(){var t=["graph_BH_table","graph_R_table","graph_H_table"],e=document.querySelector('input[name="graph_physicsMethod"]:checked').value,i="graph_"+e+"_table",s=document.getElementById(i);s.style.display="block";for(var o=0;o<t.length;o++)t[o]!=i&&(s=document.getElementById(t[o]),s.style.display="none");this._restoreNodes(),"R"==e?(this.constants.hierarchicalLayout.enabled=!1,this.constants.physics.hierarchicalRepulsion.enabled=!1,this.constants.physics.barnesHut.enabled=!1):"H"==e?0==this.constants.hierarchicalLayout.enabled&&(this.constants.hierarchicalLayout.enabled=!0,this.constants.physics.hierarchicalRepulsion.enabled=!0,this.constants.physics.barnesHut.enabled=!1,this.constants.smoothCurves.enabled=!1,this._setupHierarchicalLayout()):(this.constants.hierarchicalLayout.enabled=!1,this.constants.physics.hierarchicalRepulsion.enabled=!1,this.constants.physics.barnesHut.enabled=!0),this._loadSelectedForceSolver();var n=document.getElementById("graph_toggleSmooth");n.style.background=1==this.constants.smoothCurves.enabled?"#A4FF56":"#FF8532",this.moving=!0,this.start()}function a(t,e,i){var s=t+"_value",o=document.getElementById(t).value;Array.isArray(e)?(document.getElementById(s).value=e[parseInt(o)],this._overWriteGraphConstants(i,e[parseInt(o)])):(document.getElementById(s).value=parseInt(e)*parseFloat(o),this._overWriteGraphConstants(i,parseInt(e)*parseFloat(o))),("hierarchicalLayout_direction"==i||"hierarchicalLayout_levelSeparation"==i||"hierarchicalLayout_nodeSpacing"==i)&&this._setupHierarchicalLayout(),this.moving=!0,this.start()}var h=i(1),d=i(69),l=i(70),c=i(71);e._toggleBarnesHut=function(){this.constants.physics.barnesHut.enabled=!this.constants.physics.barnesHut.enabled,this._loadSelectedForceSolver(),this.moving=!0,this.start()},e._loadSelectedForceSolver=function(){1==this.constants.physics.barnesHut.enabled?(this._clearMixin(d),this._clearMixin(l),this.constants.physics.centralGravity=this.constants.physics.barnesHut.centralGravity,this.constants.physics.springLength=this.constants.physics.barnesHut.springLength,this.constants.physics.springConstant=this.constants.physics.barnesHut.springConstant,this.constants.physics.damping=this.constants.physics.barnesHut.damping,this._loadMixin(c)):1==this.constants.physics.hierarchicalRepulsion.enabled?(this._clearMixin(c),this._clearMixin(d),this.constants.physics.centralGravity=this.constants.physics.hierarchicalRepulsion.centralGravity,this.constants.physics.springLength=this.constants.physics.hierarchicalRepulsion.springLength,this.constants.physics.springConstant=this.constants.physics.hierarchicalRepulsion.springConstant,this.constants.physics.damping=this.constants.physics.hierarchicalRepulsion.damping,this._loadMixin(l)):(this._clearMixin(c),this._clearMixin(l),this.barnesHutTree=void 0,this.constants.physics.centralGravity=this.constants.physics.repulsion.centralGravity,this.constants.physics.springLength=this.constants.physics.repulsion.springLength,this.constants.physics.springConstant=this.constants.physics.repulsion.springConstant,this.constants.physics.damping=this.constants.physics.repulsion.damping,this._loadMixin(d))},e._initializeForceCalculation=function(){1==this.nodeIndices.length?this.nodes[this.nodeIndices[0]]._setForce(0,0):(this.nodeIndices.length>this.constants.clustering.clusterThreshold&&1==this.constants.clustering.enabled&&this.clusterToFit(this.constants.clustering.reduceToNodes,!1),this._calculateForces())},e._calculateForces=function(){this._calculateGravitationalForces(),this._calculateNodeForces(),this.constants.physics.springConstant>0&&(1==this.constants.smoothCurves.enabled&&1==this.constants.smoothCurves.dynamic?this._calculateSpringForcesWithSupport():1==this.constants.physics.hierarchicalRepulsion.enabled?this._calculateHierarchicalSpringForces():this._calculateSpringForces())},e._updateCalculationNodes=function(){if(1==this.constants.smoothCurves.enabled&&1==this.constants.smoothCurves.dynamic){this.calculationNodes={},this.calculationNodeIndices=[];for(var t in this.nodes)this.nodes.hasOwnProperty(t)&&(this.calculationNodes[t]=this.nodes[t]);var e=this.sectors.support.nodes;for(var i in e)e.hasOwnProperty(i)&&(this.edges.hasOwnProperty(e[i].parentEdgeId)?this.calculationNodes[i]=e[i]:e[i]._setForce(0,0));for(var s in this.calculationNodes)this.calculationNodes.hasOwnProperty(s)&&this.calculationNodeIndices.push(s)}else this.calculationNodes=this.nodes,this.calculationNodeIndices=this.nodeIndices},e._calculateGravitationalForces=function(){var t,e,i,s,o,n=this.calculationNodes,r=this.constants.physics.centralGravity,a=0;for(o=0;o<this.calculationNodeIndices.length;o++)s=n[this.calculationNodeIndices[o]],s.damping=this.constants.physics.damping,"default"==this._sector()&&0!=r?(t=-s.x,e=-s.y,i=Math.sqrt(t*t+e*e),a=0==i?0:r/i,s.fx=t*a,s.fy=e*a):(s.fx=0,s.fy=0)},e._calculateSpringForces=function(){var t,e,i,s,o,n,r,a,h,d=this.edges;for(i in d)d.hasOwnProperty(i)&&(e=d[i],e.connected&&this.nodes.hasOwnProperty(e.toId)&&this.nodes.hasOwnProperty(e.fromId)&&(t=e.physics.springLength,t+=(e.to.clusterSize+e.from.clusterSize-2)*this.constants.clustering.edgeGrowth,s=e.from.x-e.to.x,o=e.from.y-e.to.y,h=Math.sqrt(s*s+o*o),0==h&&(h=.01),a=this.constants.physics.springConstant*(t-h)/h,n=s*a,r=o*a,e.from.fx+=n,e.from.fy+=r,e.to.fx-=n,e.to.fy-=r))},e._calculateSpringForcesWithSupport=function(){var t,e,i,s,o=this.edges;for(i in o)if(o.hasOwnProperty(i)&&(e=o[i],e.connected&&this.nodes.hasOwnProperty(e.toId)&&this.nodes.hasOwnProperty(e.fromId)&&null!=e.via)){var n=e.to,r=e.via,a=e.from;t=e.physics.springLength,s=n.clusterSize+a.clusterSize-2,t+=s*this.constants.clustering.edgeGrowth,this._calculateSpringForce(n,r,.5*t),this._calculateSpringForce(r,a,.5*t)}},e._calculateSpringForce=function(t,e,i){var s,o,n,r,a,h;s=t.x-e.x,o=t.y-e.y,h=Math.sqrt(s*s+o*o),0==h&&(h=.01),a=this.constants.physics.springConstant*(i-h)/h,n=s*a,r=o*a,t.fx+=n,t.fy+=r,e.fx-=n,e.fy-=r},e._cleanupPhysicsConfiguration=function(){if(void 0!==this.physicsConfiguration){for(;this.physicsConfiguration.hasChildNodes();)this.physicsConfiguration.removeChild(this.physicsConfiguration.firstChild);this.physicsConfiguration.parentNode.removeChild(this.physicsConfiguration),this.physicsConfiguration=void 0}},e._loadPhysicsConfiguration=function(){if(void 0===this.physicsConfiguration){this.backupConstants={},h.deepExtend(this.backupConstants,this.constants);var t=["LR","RL","UD","DU"];this.physicsConfiguration=document.createElement("div"),this.physicsConfiguration.className="PhysicsConfiguration",this.physicsConfiguration.innerHTML='<table><tr><td><b>Simulation Mode:</b></td></tr><tr><td width="120px"><input type="radio" name="graph_physicsMethod" id="graph_physicsMethod1" value="BH" checked="checked">Barnes Hut</td><td width="120px"><input type="radio" name="graph_physicsMethod" id="graph_physicsMethod2" value="R">Repulsion</td><td width="120px"><input type="radio" name="graph_physicsMethod" id="graph_physicsMethod3" value="H">Hierarchical</td></tr></table><table id="graph_BH_table" style="display:none"><tr><td><b>Barnes Hut</b></td></tr><tr><td width="150px">gravitationalConstant</td><td>0</td><td><input type="range" min="0" max="20000" value="'+-1*this.constants.physics.barnesHut.gravitationalConstant+'" step="25" style="width:300px" id="graph_BH_gc"></td><td  width="50px">-20000</td><td><input value="'+-1*this.constants.physics.barnesHut.gravitationalConstant+'" id="graph_BH_gc_value" style="width:60px"></td></tr><tr><td width="150px">centralGravity</td><td>0</td><td><input type="range" min="0" max="3"  value="'+this.constants.physics.barnesHut.centralGravity+'" step="0.05"  style="width:300px" id="graph_BH_cg"></td><td>3</td><td><input value="'+this.constants.physics.barnesHut.centralGravity+'" id="graph_BH_cg_value" style="width:60px"></td></tr><tr><td width="150px">springLength</td><td>0</td><td><input type="range" min="0" max="500" value="'+this.constants.physics.barnesHut.springLength+'" step="1" style="width:300px" id="graph_BH_sl"></td><td>500</td><td><input value="'+this.constants.physics.barnesHut.springLength+'" id="graph_BH_sl_value" style="width:60px"></td></tr><tr><td width="150px">springConstant</td><td>0</td><td><input type="range" min="0" max="0.5" value="'+this.constants.physics.barnesHut.springConstant+'" step="0.001" style="width:300px" id="graph_BH_sc"></td><td>0.5</td><td><input value="'+this.constants.physics.barnesHut.springConstant+'" id="graph_BH_sc_value" style="width:60px"></td></tr><tr><td width="150px">damping</td><td>0</td><td><input type="range" min="0" max="0.3" value="'+this.constants.physics.barnesHut.damping+'" step="0.005" style="width:300px" id="graph_BH_damp"></td><td>0.3</td><td><input value="'+this.constants.physics.barnesHut.damping+'" id="graph_BH_damp_value" style="width:60px"></td></tr></table><table id="graph_R_table" style="display:none"><tr><td><b>Repulsion</b></td></tr><tr><td width="150px">nodeDistance</td><td>0</td><td><input type="range" min="0" max="300" value="'+this.constants.physics.repulsion.nodeDistance+'" step="1" style="width:300px" id="graph_R_nd"></td><td width="50px">300</td><td><input value="'+this.constants.physics.repulsion.nodeDistance+'" id="graph_R_nd_value" style="width:60px"></td></tr><tr><td width="150px">centralGravity</td><td>0</td><td><input type="range" min="0" max="3"  value="'+this.constants.physics.repulsion.centralGravity+'" step="0.05"  style="width:300px" id="graph_R_cg"></td><td>3</td><td><input value="'+this.constants.physics.repulsion.centralGravity+'" id="graph_R_cg_value" style="width:60px"></td></tr><tr><td width="150px">springLength</td><td>0</td><td><input type="range" min="0" max="500" value="'+this.constants.physics.repulsion.springLength+'" step="1" style="width:300px" id="graph_R_sl"></td><td>500</td><td><input value="'+this.constants.physics.repulsion.springLength+'" id="graph_R_sl_value" style="width:60px"></td></tr><tr><td width="150px">springConstant</td><td>0</td><td><input type="range" min="0" max="0.5" value="'+this.constants.physics.repulsion.springConstant+'" step="0.001" style="width:300px" id="graph_R_sc"></td><td>0.5</td><td><input value="'+this.constants.physics.repulsion.springConstant+'" id="graph_R_sc_value" style="width:60px"></td></tr><tr><td width="150px">damping</td><td>0</td><td><input type="range" min="0" max="0.3" value="'+this.constants.physics.repulsion.damping+'" step="0.005" style="width:300px" id="graph_R_damp"></td><td>0.3</td><td><input value="'+this.constants.physics.repulsion.damping+'" id="graph_R_damp_value" style="width:60px"></td></tr></table><table id="graph_H_table" style="display:none"><tr><td width="150"><b>Hierarchical</b></td></tr><tr><td width="150px">nodeDistance</td><td>0</td><td><input type="range" min="0" max="300" value="'+this.constants.physics.hierarchicalRepulsion.nodeDistance+'" step="1" style="width:300px" id="graph_H_nd"></td><td width="50px">300</td><td><input value="'+this.constants.physics.hierarchicalRepulsion.nodeDistance+'" id="graph_H_nd_value" style="width:60px"></td></tr><tr><td width="150px">centralGravity</td><td>0</td><td><input type="range" min="0" max="3"  value="'+this.constants.physics.hierarchicalRepulsion.centralGravity+'" step="0.05"  style="width:300px" id="graph_H_cg"></td><td>3</td><td><input value="'+this.constants.physics.hierarchicalRepulsion.centralGravity+'" id="graph_H_cg_value" style="width:60px"></td></tr><tr><td width="150px">springLength</td><td>0</td><td><input type="range" min="0" max="500" value="'+this.constants.physics.hierarchicalRepulsion.springLength+'" step="1" style="width:300px" id="graph_H_sl"></td><td>500</td><td><input value="'+this.constants.physics.hierarchicalRepulsion.springLength+'" id="graph_H_sl_value" style="width:60px"></td></tr><tr><td width="150px">springConstant</td><td>0</td><td><input type="range" min="0" max="0.5" value="'+this.constants.physics.hierarchicalRepulsion.springConstant+'" step="0.001" style="width:300px" id="graph_H_sc"></td><td>0.5</td><td><input value="'+this.constants.physics.hierarchicalRepulsion.springConstant+'" id="graph_H_sc_value" style="width:60px"></td></tr><tr><td width="150px">damping</td><td>0</td><td><input type="range" min="0" max="0.3" value="'+this.constants.physics.hierarchicalRepulsion.damping+'" step="0.005" style="width:300px" id="graph_H_damp"></td><td>0.3</td><td><input value="'+this.constants.physics.hierarchicalRepulsion.damping+'" id="graph_H_damp_value" style="width:60px"></td></tr><tr><td width="150px">direction</td><td>1</td><td><input type="range" min="0" max="3" value="'+t.indexOf(this.constants.hierarchicalLayout.direction)+'" step="1" style="width:300px" id="graph_H_direction"></td><td>4</td><td><input value="'+this.constants.hierarchicalLayout.direction+'" id="graph_H_direction_value" style="width:60px"></td></tr><tr><td width="150px">levelSeparation</td><td>1</td><td><input type="range" min="0" max="500" value="'+this.constants.hierarchicalLayout.levelSeparation+'" step="1" style="width:300px" id="graph_H_levsep"></td><td>500</td><td><input value="'+this.constants.hierarchicalLayout.levelSeparation+'" id="graph_H_levsep_value" style="width:60px"></td></tr><tr><td width="150px">nodeSpacing</td><td>1</td><td><input type="range" min="0" max="500" value="'+this.constants.hierarchicalLayout.nodeSpacing+'" step="1" style="width:300px" id="graph_H_nspac"></td><td>500</td><td><input value="'+this.constants.hierarchicalLayout.nodeSpacing+'" id="graph_H_nspac_value" style="width:60px"></td></tr></table><table><tr><td><b>Options:</b></td></tr><tr><td width="180px"><input type="button" id="graph_toggleSmooth" value="Toggle smoothCurves" style="width:150px"></td><td width="180px"><input type="button" id="graph_repositionNodes" value="Reinitialize" style="width:150px"></td><td width="180px"><input type="button" id="graph_generateOptions" value="Generate Options" style="width:150px"></td></tr></table>',this.containerElement.parentElement.insertBefore(this.physicsConfiguration,this.containerElement),this.optionsDiv=document.createElement("div"),this.optionsDiv.style.fontSize="14px",this.optionsDiv.style.fontFamily="verdana",this.containerElement.parentElement.insertBefore(this.optionsDiv,this.containerElement);
 var e;e=document.getElementById("graph_BH_gc"),e.onchange=a.bind(this,"graph_BH_gc",-1,"physics_barnesHut_gravitationalConstant"),e=document.getElementById("graph_BH_cg"),e.onchange=a.bind(this,"graph_BH_cg",1,"physics_centralGravity"),e=document.getElementById("graph_BH_sc"),e.onchange=a.bind(this,"graph_BH_sc",1,"physics_springConstant"),e=document.getElementById("graph_BH_sl"),e.onchange=a.bind(this,"graph_BH_sl",1,"physics_springLength"),e=document.getElementById("graph_BH_damp"),e.onchange=a.bind(this,"graph_BH_damp",1,"physics_damping"),e=document.getElementById("graph_R_nd"),e.onchange=a.bind(this,"graph_R_nd",1,"physics_repulsion_nodeDistance"),e=document.getElementById("graph_R_cg"),e.onchange=a.bind(this,"graph_R_cg",1,"physics_centralGravity"),e=document.getElementById("graph_R_sc"),e.onchange=a.bind(this,"graph_R_sc",1,"physics_springConstant"),e=document.getElementById("graph_R_sl"),e.onchange=a.bind(this,"graph_R_sl",1,"physics_springLength"),e=document.getElementById("graph_R_damp"),e.onchange=a.bind(this,"graph_R_damp",1,"physics_damping"),e=document.getElementById("graph_H_nd"),e.onchange=a.bind(this,"graph_H_nd",1,"physics_hierarchicalRepulsion_nodeDistance"),e=document.getElementById("graph_H_cg"),e.onchange=a.bind(this,"graph_H_cg",1,"physics_centralGravity"),e=document.getElementById("graph_H_sc"),e.onchange=a.bind(this,"graph_H_sc",1,"physics_springConstant"),e=document.getElementById("graph_H_sl"),e.onchange=a.bind(this,"graph_H_sl",1,"physics_springLength"),e=document.getElementById("graph_H_damp"),e.onchange=a.bind(this,"graph_H_damp",1,"physics_damping"),e=document.getElementById("graph_H_direction"),e.onchange=a.bind(this,"graph_H_direction",t,"hierarchicalLayout_direction"),e=document.getElementById("graph_H_levsep"),e.onchange=a.bind(this,"graph_H_levsep",1,"hierarchicalLayout_levelSeparation"),e=document.getElementById("graph_H_nspac"),e.onchange=a.bind(this,"graph_H_nspac",1,"hierarchicalLayout_nodeSpacing");var i=document.getElementById("graph_physicsMethod1"),d=document.getElementById("graph_physicsMethod2"),l=document.getElementById("graph_physicsMethod3");d.checked=!0,this.constants.physics.barnesHut.enabled&&(i.checked=!0),this.constants.hierarchicalLayout.enabled&&(l.checked=!0);var c=document.getElementById("graph_toggleSmooth"),p=document.getElementById("graph_repositionNodes"),u=document.getElementById("graph_generateOptions");c.onclick=s.bind(this),p.onclick=o.bind(this),u.onclick=n.bind(this),c.style.background=1==this.constants.smoothCurves&&0==this.constants.dynamicSmoothCurves?"#A4FF56":"#FF8532",r.apply(this),i.onchange=r.bind(this),d.onchange=r.bind(this),l.onchange=r.bind(this)}},e._overWriteGraphConstants=function(t,e){var i=t.split("_");1==i.length?this.constants[i[0]]=e:2==i.length?this.constants[i[0]][i[1]]=e:3==i.length&&(this.constants[i[0]][i[1]][i[2]]=e)}},function(t){function e(t){throw new Error("Cannot find module '"+t+"'.")}e.keys=function(){return[]},e.resolve=e,t.exports=e,e.id=68},function(t,e){e._calculateNodeForces=function(){var t,e,i,s,o,n,r,a,h,d,l,c=this.calculationNodes,p=this.calculationNodeIndices,u=-2/3,m=4/3,f=this.constants.physics.repulsion.nodeDistance,g=f;for(d=0;d<p.length-1;d++)for(a=c[p[d]],l=d+1;l<p.length;l++){h=c[p[l]],n=a.clusterSize+h.clusterSize-2,t=h.x-a.x,e=h.y-a.y,i=Math.sqrt(t*t+e*e),0==i&&(i=.1*Math.random(),t=i),g=0==n?f:f*(1+n*this.constants.clustering.distanceAmplification);var v=u/g;2*g>i&&(r=.5*g>i?1:v*i+m,r*=0==n?1:1+n*this.constants.clustering.forceAmplification,r/=Math.max(i,.01*g),s=t*r,o=e*r,a.fx-=s,a.fy-=o,h.fx+=s,h.fy+=o)}}},function(t,e){e._calculateNodeForces=function(){var t,e,i,s,o,n,r,a,h,d,l=this.calculationNodes,c=this.calculationNodeIndices,p=this.constants.physics.hierarchicalRepulsion.nodeDistance;for(h=0;h<c.length-1;h++)for(r=l[c[h]],d=h+1;d<c.length;d++)if(a=l[c[d]],r.level==a.level){t=a.x-r.x,e=a.y-r.y,i=Math.sqrt(t*t+e*e);var u=.05;n=p>i?-Math.pow(u*i,2)+Math.pow(u*p,2):0,0==i?i=.01:n/=i,s=t*n,o=e*n,r.fx-=s,r.fy-=o,a.fx+=s,a.fy+=o}},e._calculateHierarchicalSpringForces=function(){for(var t,e,i,s,o,n,r,a,h,d=this.edges,l=this.calculationNodes,c=this.calculationNodeIndices,p=0;p<c.length;p++){var u=l[c[p]];u.springFx=0,u.springFy=0}for(i in d)if(d.hasOwnProperty(i)&&(e=d[i],e.connected&&this.nodes.hasOwnProperty(e.toId)&&this.nodes.hasOwnProperty(e.fromId)))if(t=e.physics.springLength,t+=(e.to.clusterSize+e.from.clusterSize-2)*this.constants.clustering.edgeGrowth,s=e.from.x-e.to.x,o=e.from.y-e.to.y,h=Math.sqrt(s*s+o*o),0==h&&(h=.01),a=this.constants.physics.springConstant*(t-h)/h,n=s*a,r=o*a,e.to.level!=e.from.level)e.to.springFx-=n,e.to.springFy-=r,e.from.springFx+=n,e.from.springFy+=r;else{var m=.5;e.to.fx-=m*n,e.to.fy-=m*r,e.from.fx+=m*n,e.from.fy+=m*r}var f,g,a=1;for(p=0;p<c.length;p++){var v=l[c[p]];f=Math.min(a,Math.max(-a,v.springFx)),g=Math.min(a,Math.max(-a,v.springFy)),v.fx+=f,v.fy+=g}var y=0,b=0;for(p=0;p<c.length;p++){var v=l[c[p]];y+=v.fx,b+=v.fy}var _=y/c.length,x=b/c.length;for(p=0;p<c.length;p++){var v=l[c[p]];v.fx-=_,v.fy-=x}}},function(t,e){e._calculateNodeForces=function(){if(0!=this.constants.physics.barnesHut.gravitationalConstant){var t,e=this.calculationNodes,i=this.calculationNodeIndices,s=i.length;this._formBarnesHutTree(e,i);for(var o=this.barnesHutTree,n=0;s>n;n++)t=e[i[n]],t.options.mass>0&&(this._getForceContribution(o.root.children.NW,t),this._getForceContribution(o.root.children.NE,t),this._getForceContribution(o.root.children.SW,t),this._getForceContribution(o.root.children.SE,t))}},e._getForceContribution=function(t,e){if(t.childrenCount>0){var i,s,o;if(i=t.centerOfMass.x-e.x,s=t.centerOfMass.y-e.y,o=Math.sqrt(i*i+s*s),o*t.calcSize>this.constants.physics.barnesHut.thetaInverted){0==o&&(o=.1*Math.random(),i=o);var n=this.constants.physics.barnesHut.gravitationalConstant*t.mass*e.options.mass/(o*o*o),r=i*n,a=s*n;e.fx+=r,e.fy+=a}else if(4==t.childrenCount)this._getForceContribution(t.children.NW,e),this._getForceContribution(t.children.NE,e),this._getForceContribution(t.children.SW,e),this._getForceContribution(t.children.SE,e);else if(t.children.data.id!=e.id){0==o&&(o=.5*Math.random(),i=o);var n=this.constants.physics.barnesHut.gravitationalConstant*t.mass*e.options.mass/(o*o*o),r=i*n,a=s*n;e.fx+=r,e.fy+=a}}},e._formBarnesHutTree=function(t,e){for(var i,s=e.length,o=Number.MAX_VALUE,n=Number.MAX_VALUE,r=-Number.MAX_VALUE,a=-Number.MAX_VALUE,h=0;s>h;h++){var d=t[e[h]].x,l=t[e[h]].y;t[e[h]].options.mass>0&&(o>d&&(o=d),d>r&&(r=d),n>l&&(n=l),l>a&&(a=l))}var c=Math.abs(r-o)-Math.abs(a-n);c>0?(n-=.5*c,a+=.5*c):(o+=.5*c,r-=.5*c);var p=1e-5,u=Math.max(p,Math.abs(r-o)),m=.5*u,f=.5*(o+r),g=.5*(n+a),v={root:{centerOfMass:{x:0,y:0},mass:0,range:{minX:f-m,maxX:f+m,minY:g-m,maxY:g+m},size:u,calcSize:1/u,children:{data:null},maxWidth:0,level:0,childrenCount:4}};for(this._splitBranch(v.root),h=0;s>h;h++)i=t[e[h]],i.options.mass>0&&this._placeInTree(v.root,i);this.barnesHutTree=v},e._updateBranchMass=function(t,e){var i=t.mass+e.options.mass,s=1/i;t.centerOfMass.x=t.centerOfMass.x*t.mass+e.x*e.options.mass,t.centerOfMass.x*=s,t.centerOfMass.y=t.centerOfMass.y*t.mass+e.y*e.options.mass,t.centerOfMass.y*=s,t.mass=i;var o=Math.max(Math.max(e.height,e.radius),e.width);t.maxWidth=t.maxWidth<o?o:t.maxWidth},e._placeInTree=function(t,e,i){(1!=i||void 0===i)&&this._updateBranchMass(t,e),t.children.NW.range.maxX>e.x?t.children.NW.range.maxY>e.y?this._placeInRegion(t,e,"NW"):this._placeInRegion(t,e,"SW"):t.children.NW.range.maxY>e.y?this._placeInRegion(t,e,"NE"):this._placeInRegion(t,e,"SE")},e._placeInRegion=function(t,e,i){switch(t.children[i].childrenCount){case 0:t.children[i].children.data=e,t.children[i].childrenCount=1,this._updateBranchMass(t.children[i],e);break;case 1:t.children[i].children.data.x==e.x&&t.children[i].children.data.y==e.y?(e.x+=Math.random(),e.y+=Math.random()):(this._splitBranch(t.children[i]),this._placeInTree(t.children[i],e));break;case 4:this._placeInTree(t.children[i],e)}},e._splitBranch=function(t){var 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-//# sourceMappingURL=vis.map

From 4e7360e12dc71c2391764e3596a7971b4d9d7bfc Mon Sep 17 00:00:00 2001
From: vinodkc <vinod.kc.in@gmail.com>
Date: Fri, 8 May 2015 14:07:53 -0700
Subject: [PATCH 70/75] [SPARK-7489] [SPARK SHELL] Spark shell crashes when
 compiled with scala 2.11

Spark shell crashes when compiled with scala 2.11 and  SPARK_PREPEND_CLASSES=true

There is a similar Resolved JIRA issue -SPARK-7470 and a PR https://github.com/apache/spark/pull/5997 , which handled same issue only in scala 2.10

Author: vinodkc <vinod.kc.in@gmail.com>

Closes #6013 from vinodkc/fix_sqlcontext_exception_scala_2.11 and squashes the following commits:

119061c [vinodkc] Spark shell crashes when compiled with scala 2.11
---
 repl/scala-2.11/src/main/scala/org/apache/spark/repl/Main.scala | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/repl/scala-2.11/src/main/scala/org/apache/spark/repl/Main.scala b/repl/scala-2.11/src/main/scala/org/apache/spark/repl/Main.scala
index 2210fbaafeadb..f4f4b626988e9 100644
--- a/repl/scala-2.11/src/main/scala/org/apache/spark/repl/Main.scala
+++ b/repl/scala-2.11/src/main/scala/org/apache/spark/repl/Main.scala
@@ -88,7 +88,7 @@ object Main extends Logging {
       logInfo("Created sql context (with Hive support)..")
     }
     catch {
-      case cnf: java.lang.ClassNotFoundException =>
+      case _: java.lang.ClassNotFoundException | _: java.lang.NoClassDefFoundError =>
         sqlContext = new SQLContext(sparkContext)
         logInfo("Created sql context..")
     }

From 31da40dfeeeab69ee7974992328e3f67046ad3da Mon Sep 17 00:00:00 2001
From: Tim Ellison <t.p.ellison@gmail.com>
Date: Fri, 8 May 2015 14:08:52 -0700
Subject: [PATCH 71/75] [MINOR] Defeat early garbage collection of test suite
 variable

The JVM is free to collect references to variables that no longer participate in a computation.  This simple patch adds an operation to the variable 'rdd' to ensure it is not collected early in the test suite's explicit calls to GC.

ref: http://bugs.java.com/view_bug.do?bug_id=6721588

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

Closes #6010 from tellison/master and squashes the following commits:

77d1c8f [Tim Ellison] Defeat early garbage collection of test suite variable by aggressive JVMs
---
 core/src/test/scala/org/apache/spark/ContextCleanerSuite.scala | 1 +
 1 file changed, 1 insertion(+)

diff --git a/core/src/test/scala/org/apache/spark/ContextCleanerSuite.scala b/core/src/test/scala/org/apache/spark/ContextCleanerSuite.scala
index c7868ddcf770f..cb30e1f4e63a1 100644
--- a/core/src/test/scala/org/apache/spark/ContextCleanerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/ContextCleanerSuite.scala
@@ -183,6 +183,7 @@ class ContextCleanerSuite extends ContextCleanerSuiteBase {
     }
 
     // Test that GC causes shuffle cleanup after dereferencing the RDD
+    rdd.count()  // Defeat any early collection of rdd variable by the JVM
     val postGCTester = new CleanerTester(sc, shuffleIds = Seq(0))
     rdd = null  // Make RDD out of scope, so that corresponding shuffle goes out of scope
     runGC()

From 3b0c5e71f156516fd8bbbeda70e69b487b0c1418 Mon Sep 17 00:00:00 2001
From: Andrew Or <andrew@databricks.com>
Date: Fri, 8 May 2015 14:09:39 -0700
Subject: [PATCH 72/75] [SPARK-7466] DAG visualization: fix orphan nodes

Simple fix. We were comparing an option with `null`.

Before:
<img src="https://issues.apache.org/jira/secure/attachment/12731383/before.png" width="250px"/>
After:
<img src="https://issues.apache.org/jira/secure/attachment/12731384/after.png" width="250px"/>

Author: Andrew Or <andrew@databricks.com>

Closes #6002 from andrewor14/dag-viz-orphan-nodes and squashes the following commits:

a1468dc [Andrew Or] Fix null check
---
 .../scala/org/apache/spark/ui/scope/RDDOperationGraph.scala     | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/ui/scope/RDDOperationGraph.scala b/core/src/main/scala/org/apache/spark/ui/scope/RDDOperationGraph.scala
index c7045c98c8a03..b470aaa676e78 100644
--- a/core/src/main/scala/org/apache/spark/ui/scope/RDDOperationGraph.scala
+++ b/core/src/main/scala/org/apache/spark/ui/scope/RDDOperationGraph.scala
@@ -101,7 +101,7 @@ private[ui] object RDDOperationGraph extends Logging {
       val node = nodes.getOrElseUpdate(
         rdd.id, RDDOperationNode(rdd.id, rdd.name, rdd.storageLevel != StorageLevel.NONE))
 
-      if (rdd.scope == null) {
+      if (rdd.scope.isEmpty) {
         // This RDD has no encompassing scope, so we put it directly in the root cluster
         // This should happen only if an RDD is instantiated outside of a public RDD API
         rootCluster.attachChildNode(node)

From 9042f8f3784f10f695cba6b80c054695b1c152c5 Mon Sep 17 00:00:00 2001
From: Marcelo Vanzin <vanzin@cloudera.com>
Date: Fri, 8 May 2015 14:10:27 -0700
Subject: [PATCH 73/75] [MINOR] [CORE] Allow History Server to read kerberos
 opts from config file.

Order of initialization code was wrong.

Author: Marcelo Vanzin <vanzin@cloudera.com>

Closes #5998 from vanzin/hs-conf-fix and squashes the following commits:

00b6b6b [Marcelo Vanzin] [minor] [core] Allow History Server to read kerberos opts from config file.
---
 .../scala/org/apache/spark/deploy/history/HistoryServer.scala   | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

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 50522e69dc519..fc5182d3692b8 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
@@ -203,8 +203,8 @@ object HistoryServer extends Logging {
 
   def main(argStrings: Array[String]) {
     SignalLogger.register(log)
-    initSecurity()
     new HistoryServerArguments(conf, argStrings)
+    initSecurity()
     val securityManager = new SecurityManager(conf)
 
     val providerName = conf.getOption("spark.history.provider")

From 5467c34c3d6538e053957b5513df218f1f5bae6b Mon Sep 17 00:00:00 2001
From: Marcelo Vanzin <vanzin@cloudera.com>
Date: Fri, 8 May 2015 14:12:58 -0700
Subject: [PATCH 74/75] [SPARK-7378] [CORE] Handle deep links to unloaded apps.

The code was treating deep links as if they were attempt IDs, so
for example if you tried to load "/history/app1/jobs" directly,
that would fail because the code would treat "jobs" as an attempt id.

This change modifies the code to try both cases - first without an
attempt id, then with it, so that deep links are handled correctly.
This assumes that the links in the Spark UI do not clash with the
attempt id namespace, though, which is the case for YARN at least,
which is the only backend that currently publishes attempt IDs.

Author: Marcelo Vanzin <vanzin@cloudera.com>

Closes #5922 from vanzin/SPARK-7378 and squashes the following commits:

96f648b [Marcelo Vanzin] Fix comparison.
ed3bcd4 [Marcelo Vanzin] Merge branch 'master' into SPARK-7378
23483e4 [Marcelo Vanzin] Fat fingers.
b728f08 [Marcelo Vanzin] [SPARK-7378] [core] Handle deep links to unloaded apps.
---
 .../spark/deploy/history/HistoryServer.scala  | 48 +++++++++++--------
 1 file changed, 29 insertions(+), 19 deletions(-)

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 fc5182d3692b8..517cbe5176241 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
@@ -83,31 +83,27 @@ class HistoryServer(
         return
       }
 
-      val appKey =
-        if (parts.length == 3) {
-          s"${parts(1)}/${parts(2)}"
-        } else {
-          parts(1)
+      val appId = parts(1)
+      val attemptId = if (parts.length >= 3) Some(parts(2)) else None
+
+      // Since we may have applications with multiple attempts mixed with applications with a
+      // single attempt, we need to try both. Try the single-attempt route first, and if an
+      // error is raised, then try the multiple attempt route.
+      if (!loadAppUi(appId, None) && (!attemptId.isDefined || !loadAppUi(appId, attemptId))) {
+        val msg = <div class="row-fluid">Application {appId} not found.</div>
+        res.setStatus(HttpServletResponse.SC_NOT_FOUND)
+        UIUtils.basicSparkPage(msg, "Not Found").foreach { n =>
+          res.getWriter().write(n.toString)
         }
+        return
+      }
 
       // Note we don't use the UI retrieved from the cache; the cache loader above will register
       // the app's UI, and all we need to do is redirect the user to the same URI that was
       // requested, and the proper data should be served at that point.
-      try {
-        appCache.get(appKey)
-        res.sendRedirect(res.encodeRedirectURL(req.getRequestURI()))
-      } catch {
-        case e: Exception => e.getCause() match {
-          case nsee: NoSuchElementException =>
-            val msg = <div class="row-fluid">Application {appKey} not found.</div>
-            res.setStatus(HttpServletResponse.SC_NOT_FOUND)
-            UIUtils.basicSparkPage(msg, "Not Found").foreach(
-              n => res.getWriter().write(n.toString))
-
-          case cause: Exception => throw cause
-        }
-      }
+      res.sendRedirect(res.encodeRedirectURL(req.getRequestURI()))
     }
+
     // SPARK-5983 ensure TRACE is not supported
     protected override def doTrace(req: HttpServletRequest, res: HttpServletResponse): Unit = {
       res.sendError(HttpServletResponse.SC_METHOD_NOT_ALLOWED)
@@ -183,6 +179,20 @@ class HistoryServer(
    */
   def getProviderConfig(): Map[String, String] = provider.getConfig()
 
+  private def loadAppUi(appId: String, attemptId: Option[String]): Boolean = {
+    try {
+      appCache.get(appId + attemptId.map { id => s"/$id" }.getOrElse(""))
+      true
+    } catch {
+      case e: Exception => e.getCause() match {
+        case nsee: NoSuchElementException =>
+          false
+
+        case cause: Exception => throw cause
+      }
+    }
+  }
+
 }
 
 /**

From 90527f560462cc2d693176bd961b02767e460e06 Mon Sep 17 00:00:00 2001
From: Liang-Chi Hsieh <viirya@gmail.com>
Date: Fri, 8 May 2015 14:41:16 -0700
Subject: [PATCH 75/75] [SPARK-7390] [SQL] Only merge other CovarianceCounter
 when its count is greater than zero

JIRA: https://issues.apache.org/jira/browse/SPARK-7390

Also fix a minor typo.

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

Closes #5931 from viirya/fix_covariancecounter and squashes the following commits:

352eda6 [Liang-Chi Hsieh] Only merge other CovarianceCounter when its count is greater than zero.
---
 .../sql/execution/stat/StatFunctions.scala    | 22 ++++++++++---------
 1 file changed, 12 insertions(+), 10 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/StatFunctions.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/StatFunctions.scala
index 386ac969f1e7d..71b7f6c2a6756 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/StatFunctions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/StatFunctions.scala
@@ -38,7 +38,7 @@ private[sql] object StatFunctions extends Logging {
     var yAvg = 0.0 // the mean of all examples seen so far in col2
     var Ck = 0.0 // the co-moment after k examples
     var MkX = 0.0 // sum of squares of differences from the (current) mean for col1
-    var MkY = 0.0 // sum of squares of differences from the (current) mean for col1
+    var MkY = 0.0 // sum of squares of differences from the (current) mean for col2
     var count = 0L // count of observed examples
     // add an example to the calculation
     def add(x: Double, y: Double): this.type = {
@@ -55,15 +55,17 @@ private[sql] object StatFunctions extends Logging {
     // merge counters from other partitions. Formula can be found at:
     // http://en.wikipedia.org/wiki/Algorithms_for_calculating_variance
     def merge(other: CovarianceCounter): this.type = {
-      val totalCount = count + other.count
-      val deltaX = xAvg - other.xAvg
-      val deltaY = yAvg - other.yAvg
-      Ck += other.Ck + deltaX * deltaY * count / totalCount * other.count
-      xAvg = (xAvg * count + other.xAvg * other.count) / totalCount
-      yAvg = (yAvg * count + other.yAvg * other.count) / totalCount
-      MkX += other.MkX + deltaX * deltaX * count / totalCount * other.count
-      MkY += other.MkY + deltaY * deltaY * count / totalCount * other.count
-      count = totalCount
+      if (other.count > 0) {
+        val totalCount = count + other.count
+        val deltaX = xAvg - other.xAvg
+        val deltaY = yAvg - other.yAvg
+        Ck += other.Ck + deltaX * deltaY * count / totalCount * other.count
+        xAvg = (xAvg * count + other.xAvg * other.count) / totalCount
+        yAvg = (yAvg * count + other.yAvg * other.count) / totalCount
+        MkX += other.MkX + deltaX * deltaX * count / totalCount * other.count
+        MkY += other.MkY + deltaY * deltaY * count / totalCount * other.count
+        count = totalCount
+      }
       this
     }
     // return the sample covariance for the observed examples