Merge branch 'master' of github.com:apache/spark into streaming

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

Author: davies

bin/compute-classpath.cmd

@@ -38,7 +38,7 @@ if exist "%FWDIR%conf\spark-env.cmd" call "%FWDIR%conf\spark-env.cmd"
 rem Build up classpath
 set CLASSPATH=%SPARK_CLASSPATH%;%SPARK_SUBMIT_CLASSPATH%
 
-if "x%SPARK_CONF_DIR%"!="x" (
+if not "x%SPARK_CONF_DIR%"=="x" (
   set CLASSPATH=%CLASSPATH%;%SPARK_CONF_DIR%
 ) else (
   set CLASSPATH=%CLASSPATH%;%FWDIR%conf

core/src/main/resources/org/apache/spark/ui/static/webui.css

@@ -51,6 +51,11 @@ table.sortable thead {
   cursor: pointer;
 }
 
+table.sortable td {
+  word-wrap: break-word;
+  max-width: 600px;
+}
+
 .progress {
   margin-bottom: 0px; position: relative
 }

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

@@ -168,8 +168,6 @@ private[spark] class CacheManager(blockManager: BlockManager) extends Logging {
           arr.iterator.asInstanceOf[Iterator[T]]
         case Right(it) =>
           // There is not enough space to cache this partition in memory
-          logWarning(s"Not enough space to cache partition $key in memory! " +
-            s"Free memory is ${blockManager.memoryStore.freeMemory} bytes.")
           val returnValues = it.asInstanceOf[Iterator[T]]
           if (putLevel.useDisk) {
             logWarning(s"Persisting partition $key to disk instead.")

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

@@ -43,9 +43,8 @@ import org.apache.spark.util.{AkkaUtils, Utils}
  * :: DeveloperApi ::
  * Holds all the runtime environment objects for a running Spark instance (either master or worker),
  * including the serializer, Akka actor system, block manager, map output tracker, etc. Currently
- * Spark code finds the SparkEnv through a thread-local variable, so each thread that accesses these
- * objects needs to have the right SparkEnv set. You can get the current environment with
- * SparkEnv.get (e.g. after creating a SparkContext) and set it with SparkEnv.set.
+ * Spark code finds the SparkEnv through a global variable, so all the threads can access the same
+ * SparkEnv. It can be accessed by SparkEnv.get (e.g. after creating a SparkContext).
  *
  * NOTE: This is not intended for external use. This is exposed for Shark and may be made private
  *       in a future release.
@@ -91,9 +90,6 @@ class SparkEnv (
     // actorSystem.awaitTermination()
 
     // Note that blockTransferService is stopped by BlockManager since it is started by it.
-
-    // clear all the references in ThreadLocal object
-    SparkEnv.reset()
   }
 
   private[spark]
@@ -122,36 +118,28 @@ class SparkEnv (
 }
 
 object SparkEnv extends Logging {
-  @volatile private var env = new ThreadLocal[SparkEnv]
-  @volatile private var lastSetSparkEnv : SparkEnv = _
+  @volatile private var env: SparkEnv = _
 
   private[spark] val driverActorSystemName = "sparkDriver"
   private[spark] val executorActorSystemName = "sparkExecutor"
 
   def set(e: SparkEnv) {
-    lastSetSparkEnv = e
-    env.set(e)
-  }
-
-  // clear all the threadlocal references
-  private[spark] def reset(): Unit = {
-    env = new ThreadLocal[SparkEnv]
-    lastSetSparkEnv = null
+    env = e
   }
 
   /**
-   * Returns the ThreadLocal SparkEnv, if non-null. Else returns the SparkEnv
-   * previously set in any thread.
+   * Returns the SparkEnv.
    */
   def get: SparkEnv = {
-    Option(env.get()).getOrElse(lastSetSparkEnv)
+    env
   }
 
   /**
    * Returns the ThreadLocal SparkEnv.
    */
+  @deprecated("Use SparkEnv.get instead", "1.2")
   def getThreadLocal: SparkEnv = {
-    env.get()
+    env
   }
 
   private[spark] def create(

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

@@ -194,7 +194,6 @@ private[spark] class PythonRDD(
 
     override def run(): Unit = Utils.logUncaughtExceptions {
       try {
-        SparkEnv.set(env)
         val stream = new BufferedOutputStream(worker.getOutputStream, bufferSize)
         val dataOut = new DataOutputStream(stream)
         // Partition index
@@ -246,6 +245,11 @@ private[spark] class PythonRDD(
           // will kill the whole executor (see org.apache.spark.executor.Executor).
           _exception = e
           worker.shutdownOutput()
+      } finally {
+        // Release memory used by this thread for shuffles
+        env.shuffleMemoryManager.releaseMemoryForThisThread()
+        // Release memory used by this thread for unrolling blocks
+        env.blockManager.memoryStore.releaseUnrollMemoryForThisThread()
       }
     }
   }

core/src/main/scala/org/apache/spark/executor/Executor.scala

@@ -148,7 +148,6 @@ private[spark] class Executor(
 
     override def run() {
       val startTime = System.currentTimeMillis()
-      SparkEnv.set(env)
       Thread.currentThread.setContextClassLoader(replClassLoader)
       val ser = SparkEnv.get.closureSerializer.newInstance()
       logInfo(s"Running $taskName (TID $taskId)")
@@ -158,7 +157,6 @@ private[spark] class Executor(
       val startGCTime = gcTime
 
       try {
-        SparkEnv.set(env)
         Accumulators.clear()
         val (taskFiles, taskJars, taskBytes) = Task.deserializeWithDependencies(serializedTask)
         updateDependencies(taskFiles, taskJars)

core/src/main/scala/org/apache/spark/rdd/PipedRDD.scala

@@ -131,7 +131,6 @@ private[spark] class PipedRDD[T: ClassTag](
     // Start a thread to feed the process input from our parent's iterator
     new Thread("stdin writer for " + command) {
       override def run() {
-        SparkEnv.set(env)
         val out = new PrintWriter(proc.getOutputStream)
 
         // input the pipe context firstly

core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala

@@ -630,7 +630,6 @@ class DAGScheduler(
   protected def runLocallyWithinThread(job: ActiveJob) {
     var jobResult: JobResult = JobSucceeded
     try {
-      SparkEnv.set(env)
       val rdd = job.finalStage.rdd
       val split = rdd.partitions(job.partitions(0))
       val taskContext =

core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala

@@ -216,8 +216,6 @@ private[spark] class TaskSchedulerImpl(
    * that tasks are balanced across the cluster.
    */
   def resourceOffers(offers: Seq[WorkerOffer]): Seq[Seq[TaskDescription]] = synchronized {
-    SparkEnv.set(sc.env)
-
     // Mark each slave as alive and remember its hostname
     // Also track if new executor is added
     var newExecAvail = false

core/src/main/scala/org/apache/spark/storage/MemoryStore.scala

@@ -132,8 +132,6 @@ private[spark] class MemoryStore(blockManager: BlockManager, maxMemory: Long)
         PutResult(res.size, res.data, droppedBlocks)
       case Right(iteratorValues) =>
         // Not enough space to unroll this block; drop to disk if applicable
-        logWarning(s"Not enough space to store block $blockId in memory! " +
-          s"Free memory is $freeMemory bytes.")
         if (level.useDisk && allowPersistToDisk) {
           logWarning(s"Persisting block $blockId to disk instead.")
           val res = blockManager.diskStore.putIterator(blockId, iteratorValues, level, returnValues)
@@ -265,6 +263,7 @@ private[spark] class MemoryStore(blockManager: BlockManager, maxMemory: Long)
         Left(vector.toArray)
       } else {
         // We ran out of space while unrolling the values for this block
+        logUnrollFailureMessage(blockId, vector.estimateSize())
         Right(vector.iterator ++ values)
       }
 
@@ -424,7 +423,7 @@ private[spark] class MemoryStore(blockManager: BlockManager, maxMemory: Long)
    * Reserve additional memory for unrolling blocks used by this thread.
    * Return whether the request is granted.
    */
-  private[spark] def reserveUnrollMemoryForThisThread(memory: Long): Boolean = {
+  def reserveUnrollMemoryForThisThread(memory: Long): Boolean = {
     accountingLock.synchronized {
       val granted = freeMemory > currentUnrollMemory + memory
       if (granted) {
@@ -439,7 +438,7 @@ private[spark] class MemoryStore(blockManager: BlockManager, maxMemory: Long)
    * Release memory used by this thread for unrolling blocks.
    * If the amount is not specified, remove the current thread's allocation altogether.
    */
-  private[spark] def releaseUnrollMemoryForThisThread(memory: Long = -1L): Unit = {
+  def releaseUnrollMemoryForThisThread(memory: Long = -1L): Unit = {
     val threadId = Thread.currentThread().getId
     accountingLock.synchronized {
       if (memory < 0) {
@@ -457,16 +456,50 @@ private[spark] class MemoryStore(blockManager: BlockManager, maxMemory: Long)
   /**
    * Return the amount of memory currently occupied for unrolling blocks across all threads.
    */
-  private[spark] def currentUnrollMemory: Long = accountingLock.synchronized {
+  def currentUnrollMemory: Long = accountingLock.synchronized {
     unrollMemoryMap.values.sum
   }
 
   /**
    * Return the amount of memory currently occupied for unrolling blocks by this thread.
    */
-  private[spark] def currentUnrollMemoryForThisThread: Long = accountingLock.synchronized {
+  def currentUnrollMemoryForThisThread: Long = accountingLock.synchronized {
     unrollMemoryMap.getOrElse(Thread.currentThread().getId, 0L)
   }
+
+  /**
+   * Return the number of threads currently unrolling blocks.
+   */
+  def numThreadsUnrolling: Int = accountingLock.synchronized { unrollMemoryMap.keys.size }
+
+  /**
+   * Log information about current memory usage.
+   */
+  def logMemoryUsage(): Unit = {
+    val blocksMemory = currentMemory
+    val unrollMemory = currentUnrollMemory
+    val totalMemory = blocksMemory + unrollMemory
+    logInfo(
+      s"Memory use = ${Utils.bytesToString(blocksMemory)} (blocks) + " +
+      s"${Utils.bytesToString(unrollMemory)} (scratch space shared across " +
+      s"$numThreadsUnrolling thread(s)) = ${Utils.bytesToString(totalMemory)}. " +
+      s"Storage limit = ${Utils.bytesToString(maxMemory)}."
+    )
+  }
+
+  /**
+   * Log a warning for failing to unroll a block.
+   *
+   * @param blockId ID of the block we are trying to unroll.
+   * @param finalVectorSize Final size of the vector before unrolling failed.
+   */
+  def logUnrollFailureMessage(blockId: BlockId, finalVectorSize: Long): Unit = {
+    logWarning(
+      s"Not enough space to cache $blockId in memory! " +
+      s"(computed ${Utils.bytesToString(finalVectorSize)} so far)"
+    )
+    logMemoryUsage()
+  }
 }
 
 private[spark] case class ResultWithDroppedBlocks(