Merge branch 'master' into zip

Conflicts:
	python/pyspark/tests.py

Author: davies

.rat-excludes

@@ -25,6 +25,7 @@ log4j-defaults.properties
 bootstrap-tooltip.js
 jquery-1.11.1.min.js
 sorttable.js
+.*avsc
 .*txt
 .*json
 .*data

README.md

@@ -115,6 +115,15 @@ If your project is built with Maven, add this to your POM file's `<dependencies>
     </dependency>
 
 
+## A Note About Thrift JDBC server and CLI for Spark SQL
+
+Spark SQL supports Thrift JDBC server and CLI.
+See sql-programming-guide.md for more information about those features.
+You can use those features by setting `-Phive-thriftserver` when building Spark as follows.
+
+    $ sbt/sbt -Phive-thriftserver assembly
+
+
 ## Configuration
 
 Please refer to the [Configuration guide](http://spark.apache.org/docs/latest/configuration.html)

bin/spark-shell.cmd

@@ -19,4 +19,4 @@ rem
 
 set SPARK_HOME=%~dp0..
 
-cmd /V /E /C %SPARK_HOME%\bin\spark-submit.cmd spark-shell --class org.apache.spark.repl.Main %*
+cmd /V /E /C %SPARK_HOME%\bin\spark-submit.cmd --class org.apache.spark.repl.Main %* spark-shell

bin/spark-sql

@@ -65,30 +65,30 @@ while (($#)); do
   case $1 in
     -d | --define | --database | -f | -h | --hiveconf | --hivevar | -i | -p)
       ensure_arg_number $# 2
-      CLI_ARGS+=($1); shift
-      CLI_ARGS+=($1); shift
+      CLI_ARGS+=("$1"); shift
+      CLI_ARGS+=("$1"); shift
       ;;
 
     -e)
       ensure_arg_number $# 2
-      CLI_ARGS+=($1); shift
-      CLI_ARGS+=(\"$1\"); shift
+      CLI_ARGS+=("$1"); shift
+      CLI_ARGS+=("$1"); shift
       ;;
 
     -s | --silent)
-      CLI_ARGS+=($1); shift
+      CLI_ARGS+=("$1"); shift
       ;;
 
     -v | --verbose)
       # Both SparkSubmit and SparkSQLCLIDriver recognizes -v | --verbose
-      CLI_ARGS+=($1)
-      SUBMISSION_ARGS+=($1); shift
+      CLI_ARGS+=("$1")
+      SUBMISSION_ARGS+=("$1"); shift
       ;;
 
     *)
-      SUBMISSION_ARGS+=($1); shift
+      SUBMISSION_ARGS+=("$1"); shift
       ;;
   esac
 done
 
-eval exec "$FWDIR"/bin/spark-submit --class $CLASS ${SUBMISSION_ARGS[*]} spark-internal ${CLI_ARGS[*]}
+exec "$FWDIR"/bin/spark-submit --class $CLASS "${SUBMISSION_ARGS[@]}" spark-internal "${CLI_ARGS[@]}"

core/src/main/scala/org/apache/spark/ContextCleaner.scala

@@ -66,10 +66,15 @@ private[spark] class ContextCleaner(sc: SparkContext) extends Logging {
 
   /**
    * Whether the cleaning thread will block on cleanup tasks.
-   * This is set to true only for tests.
+   *
+   * Due to SPARK-3015, this is set to true by default. This is intended to be only a temporary
+   * workaround for the issue, which is ultimately caused by the way the BlockManager actors
+   * issue inter-dependent blocking Akka messages to each other at high frequencies. This happens,
+   * for instance, when the driver performs a GC and cleans up all broadcast blocks that are no
+   * longer in scope.
    */
   private val blockOnCleanupTasks = sc.conf.getBoolean(
-    "spark.cleaner.referenceTracking.blocking", false)
+    "spark.cleaner.referenceTracking.blocking", true)
 
   @volatile private var stopped = false
 
@@ -174,9 +179,6 @@ private[spark] class ContextCleaner(sc: SparkContext) extends Logging {
   private def blockManagerMaster = sc.env.blockManager.master
   private def broadcastManager = sc.env.broadcastManager
   private def mapOutputTrackerMaster = sc.env.mapOutputTracker.asInstanceOf[MapOutputTrackerMaster]
-
-  // Used for testing. These methods explicitly blocks until cleanup is completed
-  // to ensure that more reliable testing.
 }
 
 private object ContextCleaner {

core/src/main/scala/org/apache/spark/HeartbeatReceiver.scala

@@ -21,6 +21,7 @@ import akka.actor.Actor
 import org.apache.spark.executor.TaskMetrics
 import org.apache.spark.storage.BlockManagerId
 import org.apache.spark.scheduler.TaskScheduler
+import org.apache.spark.util.ActorLogReceive
 
 /**
  * A heartbeat from executors to the driver. This is a shared message used by several internal
@@ -36,8 +37,10 @@ private[spark] case class HeartbeatResponse(reregisterBlockManager: Boolean)
 /**
  * Lives in the driver to receive heartbeats from executors..
  */
-private[spark] class HeartbeatReceiver(scheduler: TaskScheduler) extends Actor {
-  override def receive = {
+private[spark] class HeartbeatReceiver(scheduler: TaskScheduler)
+  extends Actor with ActorLogReceive with Logging {
+
+  override def receiveWithLogging = {
     case Heartbeat(executorId, taskMetrics, blockManagerId) =>
       val response = HeartbeatResponse(
         !scheduler.executorHeartbeatReceived(executorId, taskMetrics, blockManagerId))

core/src/main/scala/org/apache/spark/InterruptibleIterator.scala

@@ -33,7 +33,7 @@ class InterruptibleIterator[+T](val context: TaskContext, val delegate: Iterator
     // is allowed. The assumption is that Thread.interrupted does not have a memory fence in read
     // (just a volatile field in C), while context.interrupted is a volatile in the JVM, which
     // introduces an expensive read fence.
-    if (context.interrupted) {
+    if (context.isInterrupted) {
       throw new TaskKilledException
     } else {
       delegate.hasNext

core/src/main/scala/org/apache/spark/MapOutputTracker.scala

@@ -38,10 +38,10 @@ private[spark] case object StopMapOutputTracker extends MapOutputTrackerMessage
 
 /** Actor class for MapOutputTrackerMaster */
 private[spark] class MapOutputTrackerMasterActor(tracker: MapOutputTrackerMaster, conf: SparkConf)
-  extends Actor with Logging {
+  extends Actor with ActorLogReceive with Logging {
   val maxAkkaFrameSize = AkkaUtils.maxFrameSizeBytes(conf)
 
-  def receive = {
+  override def receiveWithLogging = {
     case GetMapOutputStatuses(shuffleId: Int) =>
       val hostPort = sender.path.address.hostPort
       logInfo("Asked to send map output locations for shuffle " + shuffleId + " to " + hostPort)

core/src/main/scala/org/apache/spark/SparkEnv.scala

@@ -210,12 +210,22 @@ object SparkEnv extends Logging {
       "MapOutputTracker",
       new MapOutputTrackerMasterActor(mapOutputTracker.asInstanceOf[MapOutputTrackerMaster], conf))
 
+    // Let the user specify short names for shuffle managers
+    val shortShuffleMgrNames = Map(
+      "hash" -> "org.apache.spark.shuffle.hash.HashShuffleManager",
+      "sort" -> "org.apache.spark.shuffle.sort.SortShuffleManager")
+    val shuffleMgrName = conf.get("spark.shuffle.manager", "hash")
+    val shuffleMgrClass = shortShuffleMgrNames.getOrElse(shuffleMgrName.toLowerCase, shuffleMgrName)
+    val shuffleManager = instantiateClass[ShuffleManager](shuffleMgrClass)
+
+    val shuffleMemoryManager = new ShuffleMemoryManager(conf)
+
     val blockManagerMaster = new BlockManagerMaster(registerOrLookup(
       "BlockManagerMaster",
       new BlockManagerMasterActor(isLocal, conf, listenerBus)), conf)
 
     val blockManager = new BlockManager(executorId, actorSystem, blockManagerMaster,
-      serializer, conf, securityManager, mapOutputTracker)
+      serializer, conf, securityManager, mapOutputTracker, shuffleManager)
 
     val connectionManager = blockManager.connectionManager
 
@@ -250,16 +260,6 @@ object SparkEnv extends Logging {
       "."
     }
 
-    // Let the user specify short names for shuffle managers
-    val shortShuffleMgrNames = Map(
-      "hash" -> "org.apache.spark.shuffle.hash.HashShuffleManager",
-      "sort" -> "org.apache.spark.shuffle.sort.SortShuffleManager")
-    val shuffleMgrName = conf.get("spark.shuffle.manager", "hash")
-    val shuffleMgrClass = shortShuffleMgrNames.getOrElse(shuffleMgrName.toLowerCase, shuffleMgrName)
-    val shuffleManager = instantiateClass[ShuffleManager](shuffleMgrClass)
-
-    val shuffleMemoryManager = new ShuffleMemoryManager(conf)
-
     // Warn about deprecated spark.cache.class property
     if (conf.contains("spark.cache.class")) {
       logWarning("The spark.cache.class property is no longer being used! Specify storage " +

core/src/main/scala/org/apache/spark/TaskContext.scala

@@ -21,10 +21,18 @@ import scala.collection.mutable.ArrayBuffer
 
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.executor.TaskMetrics
+import org.apache.spark.util.TaskCompletionListener
+
 
 /**
  * :: DeveloperApi ::
  * Contextual information about a task which can be read or mutated during execution.
+ *
+ * @param stageId stage id
+ * @param partitionId index of the partition
+ * @param attemptId the number of attempts to execute this task
+ * @param runningLocally whether the task is running locally in the driver JVM
+ * @param taskMetrics performance metrics of the task
  */
 @DeveloperApi
 class TaskContext(
@@ -39,27 +47,68 @@ class TaskContext(
   def splitId = partitionId
 
   // List of callback functions to execute when the task completes.
-  @transient private val onCompleteCallbacks = new ArrayBuffer[() => Unit]
+  @transient private val onCompleteCallbacks = new ArrayBuffer[TaskCompletionListener]
 
   // Whether the corresponding task has been killed.
-  @volatile var interrupted: Boolean = false
+  @volatile private var interrupted: Boolean = false
+
+  // Whether the task has completed.
+  @volatile private var completed: Boolean = false
+
+  /** Checks whether the task has completed. */
+  def isCompleted: Boolean = completed
 
-  // Whether the task has completed, before the onCompleteCallbacks are executed.
-  @volatile var completed: Boolean = false
+  /** Checks whether the task has been killed. */
+  def isInterrupted: Boolean = interrupted
+
+  // TODO: Also track whether the task has completed successfully or with exception.
+
+  /**
+   * Add a (Java friendly) listener to be executed on task completion.
+   * This will be called in all situation - success, failure, or cancellation.
+   *
+   * An example use is for HadoopRDD to register a callback to close the input stream.
+   */
+  def addTaskCompletionListener(listener: TaskCompletionListener): this.type = {
+    onCompleteCallbacks += listener
+    this
+  }
+
+  /**
+   * Add a listener in the form of a Scala closure to be executed on task completion.
+   * This will be called in all situation - success, failure, or cancellation.
+   *
+   * An example use is for HadoopRDD to register a callback to close the input stream.
+   */
+  def addTaskCompletionListener(f: TaskContext => Unit): this.type = {
+    onCompleteCallbacks += new TaskCompletionListener {
+      override def onTaskCompletion(context: TaskContext): Unit = f(context)
+    }
+    this
+  }
 
   /**
    * Add a callback function to be executed on task completion. An example use
    * is for HadoopRDD to register a callback to close the input stream.
    * Will be called in any situation - success, failure, or cancellation.
    * @param f Callback function.
    */
+  @deprecated("use addTaskCompletionListener", "1.1.0")
   def addOnCompleteCallback(f: () => Unit) {
-    onCompleteCallbacks += f
+    onCompleteCallbacks += new TaskCompletionListener {
+      override def onTaskCompletion(context: TaskContext): Unit = f()
+    }
   }
 
-  def executeOnCompleteCallbacks() {
+  /** Marks the task as completed and triggers the listeners. */
+  private[spark] def markTaskCompleted(): Unit = {
     completed = true
     // Process complete callbacks in the reverse order of registration
-    onCompleteCallbacks.reverse.foreach { _() }
+    onCompleteCallbacks.reverse.foreach { _.onTaskCompletion(this) }
+  }
+
+  /** Marks the task for interruption, i.e. cancellation. */
+  private[spark] def markInterrupted(): Unit = {
+    interrupted = true
   }
 }

core/src/main/scala/org/apache/spark/api/python/PythonHadoopUtil.scala

@@ -19,6 +19,7 @@ package org.apache.spark.api.python
 
 import org.apache.spark.broadcast.Broadcast
 import org.apache.spark.rdd.RDD
+import org.apache.spark.util.Utils
 import org.apache.spark.{Logging, SerializableWritable, SparkException}
 import org.apache.hadoop.conf.Configuration
 import org.apache.hadoop.io._
@@ -42,7 +43,7 @@ private[python] object Converter extends Logging {
                   defaultConverter: Converter[Any, Any]): Converter[Any, Any] = {
     converterClass.map { cc =>
       Try {
-        val c = Class.forName(cc).newInstance().asInstanceOf[Converter[Any, Any]]
+        val c = Utils.classForName(cc).newInstance().asInstanceOf[Converter[Any, Any]]
         logInfo(s"Loaded converter: $cc")
         c
       } match {