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

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

@@ -38,7 +38,7 @@ object TaskContext {
    */
   def getPartitionId(): Int = {
     val tc = taskContext.get()
-    if (tc == null) {
+    if (tc eq null) {
       0
     } else {
       tc.partitionId()

core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala

@@ -407,8 +407,8 @@ private[history] class FsHistoryProvider(conf: SparkConf, clock: Clock)
 
   /**
    * Comparison function that defines the sort order for application attempts within the same
-   * application. Order is: running attempts before complete attempts, running attempts sorted
-   * by start time, completed attempts sorted by end time.
+   * application. Order is: attempts are sorted by descending start time.
+   * Most recent attempt state matches with current state of the app.
    *
    * Normally applications should have a single running attempt; but failure to call sc.stop()
    * may cause multiple running attempts to show up.
@@ -418,11 +418,7 @@ private[history] class FsHistoryProvider(conf: SparkConf, clock: Clock)
   private def compareAttemptInfo(
       a1: FsApplicationAttemptInfo,
       a2: FsApplicationAttemptInfo): Boolean = {
-    if (a1.completed == a2.completed) {
-      if (a1.completed) a1.endTime >= a2.endTime else a1.startTime >= a2.startTime
-    } else {
-      !a1.completed
-    }
+    a1.startTime >= a2.startTime
   }
 
   /**

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

@@ -857,7 +857,6 @@ class DAGScheduler(
     // Get our pending tasks and remember them in our pendingTasks entry
     stage.pendingTasks.clear()
 
-
     // First figure out the indexes of partition ids to compute.
     val partitionsToCompute: Seq[Int] = {
       stage match {
@@ -918,7 +917,7 @@ class DAGScheduler(
           partitionsToCompute.map { id =>
             val locs = getPreferredLocs(stage.rdd, id)
             val part = stage.rdd.partitions(id)
-            new ShuffleMapTask(stage.id, taskBinary, part, locs)
+            new ShuffleMapTask(stage.id, stage.latestInfo.attemptId, taskBinary, part, locs)
           }
 
         case stage: ResultStage =>
@@ -927,7 +926,7 @@ class DAGScheduler(
             val p: Int = job.partitions(id)
             val part = stage.rdd.partitions(p)
             val locs = getPreferredLocs(stage.rdd, p)
-            new ResultTask(stage.id, taskBinary, part, locs, id)
+            new ResultTask(stage.id, stage.latestInfo.attemptId, taskBinary, part, locs, id)
           }
       }
     } catch {
@@ -1069,10 +1068,11 @@ class DAGScheduler(
             val execId = status.location.executorId
             logDebug("ShuffleMapTask finished on " + execId)
             if (failedEpoch.contains(execId) && smt.epoch <= failedEpoch(execId)) {
-              logInfo("Ignoring possibly bogus ShuffleMapTask completion from " + execId)
+              logInfo(s"Ignoring possibly bogus $smt completion from executor $execId")
             } else {
               shuffleStage.addOutputLoc(smt.partitionId, status)
             }
+
             if (runningStages.contains(shuffleStage) && shuffleStage.pendingTasks.isEmpty) {
               markStageAsFinished(shuffleStage)
               logInfo("looking for newly runnable stages")
@@ -1132,38 +1132,48 @@ class DAGScheduler(
         val failedStage = stageIdToStage(task.stageId)
         val mapStage = shuffleToMapStage(shuffleId)
 
-        // It is likely that we receive multiple FetchFailed for a single stage (because we have
-        // multiple tasks running concurrently on different executors). In that case, it is possible
-        // the fetch failure has already been handled by the scheduler.
-        if (runningStages.contains(failedStage)) {
-          logInfo(s"Marking $failedStage (${failedStage.name}) as failed " +
-            s"due to a fetch failure from $mapStage (${mapStage.name})")
-          markStageAsFinished(failedStage, Some(failureMessage))
-        }
+        if (failedStage.latestInfo.attemptId != task.stageAttemptId) {
+          logInfo(s"Ignoring fetch failure from $task as it's from $failedStage attempt" +
+            s" ${task.stageAttemptId} and there is a more recent attempt for that stage " +
+            s"(attempt ID ${failedStage.latestInfo.attemptId}) running")
+        } else {
 
-        if (disallowStageRetryForTest) {
-          abortStage(failedStage, "Fetch failure will not retry stage due to testing config")
-        } else if (failedStages.isEmpty) {
-          // Don't schedule an event to resubmit failed stages if failed isn't empty, because
-          // in that case the event will already have been scheduled.
-          // TODO: Cancel running tasks in the stage
-          logInfo(s"Resubmitting $mapStage (${mapStage.name}) and " +
-            s"$failedStage (${failedStage.name}) due to fetch failure")
-          messageScheduler.schedule(new Runnable {
-            override def run(): Unit = eventProcessLoop.post(ResubmitFailedStages)
-          }, DAGScheduler.RESUBMIT_TIMEOUT, TimeUnit.MILLISECONDS)
-        }
-        failedStages += failedStage
-        failedStages += mapStage
-        // Mark the map whose fetch failed as broken in the map stage
-        if (mapId != -1) {
-          mapStage.removeOutputLoc(mapId, bmAddress)
-          mapOutputTracker.unregisterMapOutput(shuffleId, mapId, bmAddress)
-        }
+          // It is likely that we receive multiple FetchFailed for a single stage (because we have
+          // multiple tasks running concurrently on different executors). In that case, it is
+          // possible the fetch failure has already been handled by the scheduler.
+          if (runningStages.contains(failedStage)) {
+            logInfo(s"Marking $failedStage (${failedStage.name}) as failed " +
+              s"due to a fetch failure from $mapStage (${mapStage.name})")
+            markStageAsFinished(failedStage, Some(failureMessage))
+          } else {
+            logDebug(s"Received fetch failure from $task, but its from $failedStage which is no " +
+              s"longer running")
+          }
+
+          if (disallowStageRetryForTest) {
+            abortStage(failedStage, "Fetch failure will not retry stage due to testing config")
+          } else if (failedStages.isEmpty) {
+            // Don't schedule an event to resubmit failed stages if failed isn't empty, because
+            // in that case the event will already have been scheduled.
+            // TODO: Cancel running tasks in the stage
+            logInfo(s"Resubmitting $mapStage (${mapStage.name}) and " +
+              s"$failedStage (${failedStage.name}) due to fetch failure")
+            messageScheduler.schedule(new Runnable {
+              override def run(): Unit = eventProcessLoop.post(ResubmitFailedStages)
+            }, DAGScheduler.RESUBMIT_TIMEOUT, TimeUnit.MILLISECONDS)
+          }
+          failedStages += failedStage
+          failedStages += mapStage
+          // Mark the map whose fetch failed as broken in the map stage
+          if (mapId != -1) {
+            mapStage.removeOutputLoc(mapId, bmAddress)
+            mapOutputTracker.unregisterMapOutput(shuffleId, mapId, bmAddress)
+          }
 
-        // TODO: mark the executor as failed only if there were lots of fetch failures on it
-        if (bmAddress != null) {
-          handleExecutorLost(bmAddress.executorId, fetchFailed = true, Some(task.epoch))
+          // TODO: mark the executor as failed only if there were lots of fetch failures on it
+          if (bmAddress != null) {
+            handleExecutorLost(bmAddress.executorId, fetchFailed = true, Some(task.epoch))
+          }
         }
 
       case commitDenied: TaskCommitDenied =>

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

@@ -41,11 +41,12 @@ import org.apache.spark.rdd.RDD
  */
 private[spark] class ResultTask[T, U](
     stageId: Int,
+    stageAttemptId: Int,
     taskBinary: Broadcast[Array[Byte]],
     partition: Partition,
     @transient locs: Seq[TaskLocation],
     val outputId: Int)
-  extends Task[U](stageId, partition.index) with Serializable {
+  extends Task[U](stageId, stageAttemptId, partition.index) with Serializable {
 
   @transient private[this] val preferredLocs: Seq[TaskLocation] = {
     if (locs == null) Nil else locs.toSet.toSeq

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

@@ -40,14 +40,15 @@ import org.apache.spark.shuffle.ShuffleWriter
  */
 private[spark] class ShuffleMapTask(
     stageId: Int,
+    stageAttemptId: Int,
     taskBinary: Broadcast[Array[Byte]],
     partition: Partition,
     @transient private var locs: Seq[TaskLocation])
-  extends Task[MapStatus](stageId, partition.index) with Logging {
+  extends Task[MapStatus](stageId, stageAttemptId, partition.index) with Logging {
 
   /** A constructor used only in test suites. This does not require passing in an RDD. */
   def this(partitionId: Int) {
-    this(0, null, new Partition { override def index: Int = 0 }, null)
+    this(0, 0, null, new Partition { override def index: Int = 0 }, null)
   }
 
   @transient private val preferredLocs: Seq[TaskLocation] = {

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

@@ -43,7 +43,10 @@ import org.apache.spark.util.Utils
  * @param stageId id of the stage this task belongs to
  * @param partitionId index of the number in the RDD
  */
-private[spark] abstract class Task[T](val stageId: Int, var partitionId: Int) extends Serializable {
+private[spark] abstract class Task[T](
+    val stageId: Int,
+    val stageAttemptId: Int,
+    var partitionId: Int) extends Serializable {
 
   /**
    * The key of the Map is the accumulator id and the value of the Map is the latest accumulator

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

@@ -75,9 +75,9 @@ private[spark] class TaskSchedulerImpl(
 
   // TaskSetManagers are not thread safe, so any access to one should be synchronized
   // on this class.
-  val activeTaskSets = new HashMap[String, TaskSetManager]
+  private val taskSetsByStageIdAndAttempt = new HashMap[Int, HashMap[Int, TaskSetManager]]
 
-  val taskIdToTaskSetId = new HashMap[Long, String]
+  private[scheduler] val taskIdToTaskSetManager = new HashMap[Long, TaskSetManager]
   val taskIdToExecutorId = new HashMap[Long, String]
 
   @volatile private var hasReceivedTask = false
@@ -162,7 +162,17 @@ private[spark] class TaskSchedulerImpl(
     logInfo("Adding task set " + taskSet.id + " with " + tasks.length + " tasks")
     this.synchronized {
       val manager = createTaskSetManager(taskSet, maxTaskFailures)
-      activeTaskSets(taskSet.id) = manager
+      val stage = taskSet.stageId
+      val stageTaskSets =
+        taskSetsByStageIdAndAttempt.getOrElseUpdate(stage, new HashMap[Int, TaskSetManager])
+      stageTaskSets(taskSet.stageAttemptId) = manager
+      val conflictingTaskSet = stageTaskSets.exists { case (_, ts) =>
+        ts.taskSet != taskSet && !ts.isZombie
+      }
+      if (conflictingTaskSet) {
+        throw new IllegalStateException(s"more than one active taskSet for stage $stage:" +
+          s" ${stageTaskSets.toSeq.map{_._2.taskSet.id}.mkString(",")}")
+      }
       schedulableBuilder.addTaskSetManager(manager, manager.taskSet.properties)
 
       if (!isLocal && !hasReceivedTask) {
@@ -192,19 +202,21 @@ private[spark] class TaskSchedulerImpl(
 
   override def cancelTasks(stageId: Int, interruptThread: Boolean): Unit = synchronized {
     logInfo("Cancelling stage " + stageId)
-    activeTaskSets.find(_._2.stageId == stageId).foreach { case (_, tsm) =>
-      // There are two possible cases here:
-      // 1. The task set manager has been created and some tasks have been scheduled.
-      //    In this case, send a kill signal to the executors to kill the task and then abort
-      //    the stage.
-      // 2. The task set manager has been created but no tasks has been scheduled. In this case,
-      //    simply abort the stage.
-      tsm.runningTasksSet.foreach { tid =>
-        val execId = taskIdToExecutorId(tid)
-        backend.killTask(tid, execId, interruptThread)
+    taskSetsByStageIdAndAttempt.get(stageId).foreach { attempts =>
+      attempts.foreach { case (_, tsm) =>
+        // There are two possible cases here:
+        // 1. The task set manager has been created and some tasks have been scheduled.
+        //    In this case, send a kill signal to the executors to kill the task and then abort
+        //    the stage.
+        // 2. The task set manager has been created but no tasks has been scheduled. In this case,
+        //    simply abort the stage.
+        tsm.runningTasksSet.foreach { tid =>
+          val execId = taskIdToExecutorId(tid)
+          backend.killTask(tid, execId, interruptThread)
+        }
+        tsm.abort("Stage %s cancelled".format(stageId))
+        logInfo("Stage %d was cancelled".format(stageId))
       }
-      tsm.abort("Stage %s cancelled".format(stageId))
-      logInfo("Stage %d was cancelled".format(stageId))
     }
   }
 
@@ -214,7 +226,12 @@ private[spark] class TaskSchedulerImpl(
    * cleaned up.
    */
   def taskSetFinished(manager: TaskSetManager): Unit = synchronized {
-    activeTaskSets -= manager.taskSet.id
+    taskSetsByStageIdAndAttempt.get(manager.taskSet.stageId).foreach { taskSetsForStage =>
+      taskSetsForStage -= manager.taskSet.stageAttemptId
+      if (taskSetsForStage.isEmpty) {
+        taskSetsByStageIdAndAttempt -= manager.taskSet.stageId
+      }
+    }
     manager.parent.removeSchedulable(manager)
     logInfo("Removed TaskSet %s, whose tasks have all completed, from pool %s"
       .format(manager.taskSet.id, manager.parent.name))
@@ -235,7 +252,7 @@ private[spark] class TaskSchedulerImpl(
           for (task <- taskSet.resourceOffer(execId, host, maxLocality)) {
             tasks(i) += task
             val tid = task.taskId
-            taskIdToTaskSetId(tid) = taskSet.taskSet.id
+            taskIdToTaskSetManager(tid) = taskSet
             taskIdToExecutorId(tid) = execId
             executorsByHost(host) += execId
             availableCpus(i) -= CPUS_PER_TASK
@@ -319,26 +336,24 @@ private[spark] class TaskSchedulerImpl(
             failedExecutor = Some(execId)
           }
         }
-        taskIdToTaskSetId.get(tid) match {
-          case Some(taskSetId) =>
+        taskIdToTaskSetManager.get(tid) match {
+          case Some(taskSet) =>
             if (TaskState.isFinished(state)) {
-              taskIdToTaskSetId.remove(tid)
+              taskIdToTaskSetManager.remove(tid)
               taskIdToExecutorId.remove(tid)
             }
-            activeTaskSets.get(taskSetId).foreach { taskSet =>
-              if (state == TaskState.FINISHED) {
-                taskSet.removeRunningTask(tid)
-                taskResultGetter.enqueueSuccessfulTask(taskSet, tid, serializedData)
-              } else if (Set(TaskState.FAILED, TaskState.KILLED, TaskState.LOST).contains(state)) {
-                taskSet.removeRunningTask(tid)
-                taskResultGetter.enqueueFailedTask(taskSet, tid, state, serializedData)
-              }
+            if (state == TaskState.FINISHED) {
+              taskSet.removeRunningTask(tid)
+              taskResultGetter.enqueueSuccessfulTask(taskSet, tid, serializedData)
+            } else if (Set(TaskState.FAILED, TaskState.KILLED, TaskState.LOST).contains(state)) {
+              taskSet.removeRunningTask(tid)
+              taskResultGetter.enqueueFailedTask(taskSet, tid, state, serializedData)
             }
           case None =>
             logError(
               ("Ignoring update with state %s for TID %s because its task set is gone (this is " +
-               "likely the result of receiving duplicate task finished status updates)")
-              .format(state, tid))
+                "likely the result of receiving duplicate task finished status updates)")
+                .format(state, tid))
         }
       } catch {
         case e: Exception => logError("Exception in statusUpdate", e)
@@ -363,9 +378,9 @@ private[spark] class TaskSchedulerImpl(
 
     val metricsWithStageIds: Array[(Long, Int, Int, TaskMetrics)] = synchronized {
       taskMetrics.flatMap { case (id, metrics) =>
-        taskIdToTaskSetId.get(id)
-          .flatMap(activeTaskSets.get)
-          .map(taskSetMgr => (id, taskSetMgr.stageId, taskSetMgr.taskSet.attempt, metrics))
+        taskIdToTaskSetManager.get(id).map { taskSetMgr =>
+          (id, taskSetMgr.stageId, taskSetMgr.taskSet.stageAttemptId, metrics)
+        }
       }
     }
     dagScheduler.executorHeartbeatReceived(execId, metricsWithStageIds, blockManagerId)
@@ -397,9 +412,12 @@ private[spark] class TaskSchedulerImpl(
 
   def error(message: String) {
     synchronized {
-      if (activeTaskSets.nonEmpty) {
+      if (taskSetsByStageIdAndAttempt.nonEmpty) {
         // Have each task set throw a SparkException with the error
-        for ((taskSetId, manager) <- activeTaskSets) {
+        for {
+          attempts <- taskSetsByStageIdAndAttempt.values
+          manager <- attempts.values
+        } {
           try {
             manager.abort(message)
           } catch {
@@ -520,6 +538,17 @@ private[spark] class TaskSchedulerImpl(
 
   override def applicationAttemptId(): Option[String] = backend.applicationAttemptId()
 
+  private[scheduler] def taskSetManagerForAttempt(
+      stageId: Int,
+      stageAttemptId: Int): Option[TaskSetManager] = {
+    for {
+      attempts <- taskSetsByStageIdAndAttempt.get(stageId)
+      manager <- attempts.get(stageAttemptId)
+    } yield {
+      manager
+    }
+  }
+
 }
 
 

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

@@ -26,10 +26,10 @@ import java.util.Properties
 private[spark] class TaskSet(
     val tasks: Array[Task[_]],
     val stageId: Int,
-    val attempt: Int,
+    val stageAttemptId: Int,
     val priority: Int,
     val properties: Properties) {
-    val id: String = stageId + "." + attempt
+    val id: String = stageId + "." + stageAttemptId
 
   override def toString: String = "TaskSet " + id
 }

core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala

@@ -191,15 +191,14 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val rpcEnv: Rp
       for (task <- tasks.flatten) {
         val serializedTask = ser.serialize(task)
         if (serializedTask.limit >= akkaFrameSize - AkkaUtils.reservedSizeBytes) {
-          val taskSetId = scheduler.taskIdToTaskSetId(task.taskId)
-          scheduler.activeTaskSets.get(taskSetId).foreach { taskSet =>
+          scheduler.taskIdToTaskSetManager.get(task.taskId).foreach { taskSetMgr =>
             try {
               var msg = "Serialized task %s:%d was %d bytes, which exceeds max allowed: " +
                 "spark.akka.frameSize (%d bytes) - reserved (%d bytes). Consider increasing " +
                 "spark.akka.frameSize or using broadcast variables for large values."
               msg = msg.format(task.taskId, task.index, serializedTask.limit, akkaFrameSize,
                 AkkaUtils.reservedSizeBytes)
-              taskSet.abort(msg)
+              taskSetMgr.abort(msg)
             } catch {
               case e: Exception => logError("Exception in error callback", e)
             }