diff options
4 files changed, 191 insertions, 24 deletions
diff --git a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala index 3c9a66e504..394228b272 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala @@ -944,7 +944,7 @@ class DAGScheduler( private def submitMissingTasks(stage: Stage, jobId: Int) { logDebug("submitMissingTasks(" + stage + ")") // Get our pending tasks and remember them in our pendingTasks entry - stage.pendingTasks.clear() + stage.pendingPartitions.clear() // First figure out the indexes of partition ids to compute. val (allPartitions: Seq[Int], partitionsToCompute: Seq[Int]) = { @@ -1060,8 +1060,8 @@ class DAGScheduler( if (tasks.size > 0) { logInfo("Submitting " + tasks.size + " missing tasks from " + stage + " (" + stage.rdd + ")") - stage.pendingTasks ++= tasks - logDebug("New pending tasks: " + stage.pendingTasks) + stage.pendingPartitions ++= tasks.map(_.partitionId) + logDebug("New pending partitions: " + stage.pendingPartitions) taskScheduler.submitTasks(new TaskSet( tasks.toArray, stage.id, stage.latestInfo.attemptId, stage.firstJobId, properties)) stage.latestInfo.submissionTime = Some(clock.getTimeMillis()) @@ -1152,7 +1152,7 @@ class DAGScheduler( case Success => listenerBus.post(SparkListenerTaskEnd(stageId, stage.latestInfo.attemptId, taskType, event.reason, event.taskInfo, event.taskMetrics)) - stage.pendingTasks -= task + stage.pendingPartitions -= task.partitionId task match { case rt: ResultTask[_, _] => // Cast to ResultStage here because it's part of the ResultTask @@ -1198,7 +1198,7 @@ class DAGScheduler( shuffleStage.addOutputLoc(smt.partitionId, status) } - if (runningStages.contains(shuffleStage) && shuffleStage.pendingTasks.isEmpty) { + if (runningStages.contains(shuffleStage) && shuffleStage.pendingPartitions.isEmpty) { markStageAsFinished(shuffleStage) logInfo("looking for newly runnable stages") logInfo("running: " + runningStages) @@ -1242,7 +1242,7 @@ class DAGScheduler( case Resubmitted => logInfo("Resubmitted " + task + ", so marking it as still running") - stage.pendingTasks += task + stage.pendingPartitions += task.partitionId case FetchFailed(bmAddress, shuffleId, mapId, reduceId, failureMessage) => val failedStage = stageIdToStage(task.stageId) diff --git a/core/src/main/scala/org/apache/spark/scheduler/Stage.scala b/core/src/main/scala/org/apache/spark/scheduler/Stage.scala index b37eccbd0f..a3829c319c 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/Stage.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/Stage.scala @@ -66,7 +66,7 @@ private[scheduler] abstract class Stage( /** Set of jobs that this stage belongs to. */ val jobIds = new HashSet[Int] - var pendingTasks = new HashSet[Task[_]] + val pendingPartitions = new HashSet[Int] /** The ID to use for the next new attempt for this stage. */ private var nextAttemptId: Int = 0 diff --git a/core/src/main/scala/org/apache/spark/scheduler/TaskSetManager.scala b/core/src/main/scala/org/apache/spark/scheduler/TaskSetManager.scala index 62af9031b9..c02597c436 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/TaskSetManager.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/TaskSetManager.scala @@ -487,8 +487,8 @@ private[spark] class TaskSetManager( // a good proxy to task serialization time. // val timeTaken = clock.getTime() - startTime val taskName = s"task ${info.id} in stage ${taskSet.id}" - logInfo("Starting %s (TID %d, %s, %s, %d bytes)".format( - taskName, taskId, host, taskLocality, serializedTask.limit)) + logInfo(s"Starting $taskName (TID $taskId, $host, partition ${task.partitionId}," + + s"$taskLocality, ${serializedTask.limit} bytes)") sched.dagScheduler.taskStarted(task, info) return Some(new TaskDescription(taskId = taskId, attemptNumber = attemptNum, execId, diff --git a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala index 1c55f90ad9..6b5bcf0574 100644 --- a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala +++ b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala @@ -479,8 +479,8 @@ class DAGSchedulerSuite val reduceRdd = new MyRDD(sc, 2, List(shuffleDep)) submit(reduceRdd, Array(0, 1)) complete(taskSets(0), Seq( - (Success, makeMapStatus("hostA", reduceRdd.partitions.size)), - (Success, makeMapStatus("hostB", reduceRdd.partitions.size)))) + (Success, makeMapStatus("hostA", reduceRdd.partitions.length)), + (Success, makeMapStatus("hostB", reduceRdd.partitions.length)))) // the 2nd ResultTask failed complete(taskSets(1), Seq( (Success, 42), @@ -490,7 +490,7 @@ class DAGSchedulerSuite // ask the scheduler to try it again scheduler.resubmitFailedStages() // have the 2nd attempt pass - complete(taskSets(2), Seq((Success, makeMapStatus("hostA", reduceRdd.partitions.size)))) + complete(taskSets(2), Seq((Success, makeMapStatus("hostA", reduceRdd.partitions.length)))) // we can see both result blocks now assert(mapOutputTracker.getMapSizesByExecutorId(shuffleId, 0).map(_._1.host).toSet === HashSet("hostA", "hostB")) @@ -782,8 +782,8 @@ class DAGSchedulerSuite val reduceRdd = new MyRDD(sc, 2, List(shuffleDep)) submit(reduceRdd, Array(0, 1)) complete(taskSets(0), Seq( - (Success, makeMapStatus("hostA", reduceRdd.partitions.size)), - (Success, makeMapStatus("hostB", reduceRdd.partitions.size)))) + (Success, makeMapStatus("hostA", reduceRdd.partitions.length)), + (Success, makeMapStatus("hostB", reduceRdd.partitions.length)))) // The MapOutputTracker should know about both map output locations. assert(mapOutputTracker.getMapSizesByExecutorId(shuffleId, 0).map(_._1.host).toSet === HashSet("hostA", "hostB")) @@ -1036,6 +1036,173 @@ class DAGSchedulerSuite } /** + * This test runs a three stage job, with a fetch failure in stage 1. but during the retry, we + * have completions from both the first & second attempt of stage 1. So all the map output is + * available before we finish any task set for stage 1. We want to make sure that we don't + * submit stage 2 until the map output for stage 1 is registered + */ + test("don't submit stage until its dependencies map outputs are registered (SPARK-5259)") { + val firstRDD = new MyRDD(sc, 3, Nil) + val firstShuffleDep = new ShuffleDependency(firstRDD, null) + val firstShuffleId = firstShuffleDep.shuffleId + val shuffleMapRdd = new MyRDD(sc, 3, List(firstShuffleDep)) + val shuffleDep = new ShuffleDependency(shuffleMapRdd, null) + val reduceRdd = new MyRDD(sc, 1, List(shuffleDep)) + submit(reduceRdd, Array(0)) + + // things start out smoothly, stage 0 completes with no issues + complete(taskSets(0), Seq( + (Success, makeMapStatus("hostB", shuffleMapRdd.partitions.length)), + (Success, makeMapStatus("hostB", shuffleMapRdd.partitions.length)), + (Success, makeMapStatus("hostA", shuffleMapRdd.partitions.length)) + )) + + // then one executor dies, and a task fails in stage 1 + runEvent(ExecutorLost("exec-hostA")) + runEvent(CompletionEvent( + taskSets(1).tasks(0), + FetchFailed(null, firstShuffleId, 2, 0, "Fetch failed"), + null, + null, + createFakeTaskInfo(), + null)) + + // so we resubmit stage 0, which completes happily + scheduler.resubmitFailedStages() + val stage0Resubmit = taskSets(2) + assert(stage0Resubmit.stageId == 0) + assert(stage0Resubmit.stageAttemptId === 1) + val task = stage0Resubmit.tasks(0) + assert(task.partitionId === 2) + runEvent(CompletionEvent( + task, + Success, + makeMapStatus("hostC", shuffleMapRdd.partitions.length), + null, + createFakeTaskInfo(), + null)) + + // now here is where things get tricky : we will now have a task set representing + // the second attempt for stage 1, but we *also* have some tasks for the first attempt for + // stage 1 still going + val stage1Resubmit = taskSets(3) + assert(stage1Resubmit.stageId == 1) + assert(stage1Resubmit.stageAttemptId === 1) + assert(stage1Resubmit.tasks.length === 3) + + // we'll have some tasks finish from the first attempt, and some finish from the second attempt, + // so that we actually have all stage outputs, though no attempt has completed all its + // tasks + runEvent(CompletionEvent( + taskSets(3).tasks(0), + Success, + makeMapStatus("hostC", reduceRdd.partitions.length), + null, + createFakeTaskInfo(), + null)) + runEvent(CompletionEvent( + taskSets(3).tasks(1), + Success, + makeMapStatus("hostC", reduceRdd.partitions.length), + null, + createFakeTaskInfo(), + null)) + // late task finish from the first attempt + runEvent(CompletionEvent( + taskSets(1).tasks(2), + Success, + makeMapStatus("hostB", reduceRdd.partitions.length), + null, + createFakeTaskInfo(), + null)) + + // What should happen now is that we submit stage 2. However, we might not see an error + // b/c of DAGScheduler's error handling (it tends to swallow errors and just log them). But + // we can check some conditions. + // Note that the really important thing here is not so much that we submit stage 2 *immediately* + // but that we don't end up with some error from these interleaved completions. It would also + // be OK (though sub-optimal) if stage 2 simply waited until the resubmission of stage 1 had + // all its tasks complete + + // check that we have all the map output for stage 0 (it should have been there even before + // the last round of completions from stage 1, but just to double check it hasn't been messed + // up) and also the newly available stage 1 + val stageToReduceIdxs = Seq( + 0 -> (0 until 3), + 1 -> (0 until 1) + ) + for { + (stage, reduceIdxs) <- stageToReduceIdxs + reduceIdx <- reduceIdxs + } { + // this would throw an exception if the map status hadn't been registered + val statuses = mapOutputTracker.getMapSizesByExecutorId(stage, reduceIdx) + // really we should have already thrown an exception rather than fail either of these + // asserts, but just to be extra defensive let's double check the statuses are OK + assert(statuses != null) + assert(statuses.nonEmpty) + } + + // and check that stage 2 has been submitted + assert(taskSets.size == 5) + val stage2TaskSet = taskSets(4) + assert(stage2TaskSet.stageId == 2) + assert(stage2TaskSet.stageAttemptId == 0) + } + + /** + * We lose an executor after completing some shuffle map tasks on it. Those tasks get + * resubmitted, and when they finish the job completes normally + */ + test("register map outputs correctly after ExecutorLost and task Resubmitted") { + val firstRDD = new MyRDD(sc, 3, Nil) + val firstShuffleDep = new ShuffleDependency(firstRDD, null) + val reduceRdd = new MyRDD(sc, 5, List(firstShuffleDep)) + submit(reduceRdd, Array(0)) + + // complete some of the tasks from the first stage, on one host + runEvent(CompletionEvent( + taskSets(0).tasks(0), Success, + makeMapStatus("hostA", reduceRdd.partitions.length), null, createFakeTaskInfo(), null)) + runEvent(CompletionEvent( + taskSets(0).tasks(1), Success, + makeMapStatus("hostA", reduceRdd.partitions.length), null, createFakeTaskInfo(), null)) + + // now that host goes down + runEvent(ExecutorLost("exec-hostA")) + + // so we resubmit those tasks + runEvent(CompletionEvent( + taskSets(0).tasks(0), Resubmitted, null, null, createFakeTaskInfo(), null)) + runEvent(CompletionEvent( + taskSets(0).tasks(1), Resubmitted, null, null, createFakeTaskInfo(), null)) + + // now complete everything on a different host + complete(taskSets(0), Seq( + (Success, makeMapStatus("hostB", reduceRdd.partitions.length)), + (Success, makeMapStatus("hostB", reduceRdd.partitions.length)), + (Success, makeMapStatus("hostB", reduceRdd.partitions.length)) + )) + + // now we should submit stage 1, and the map output from stage 0 should be registered + + // check that we have all the map output for stage 0 + (0 until reduceRdd.partitions.length).foreach { reduceIdx => + val statuses = mapOutputTracker.getMapSizesByExecutorId(0, reduceIdx) + // really we should have already thrown an exception rather than fail either of these + // asserts, but just to be extra defensive let's double check the statuses are OK + assert(statuses != null) + assert(statuses.nonEmpty) + } + + // and check that stage 1 has been submitted + assert(taskSets.size == 2) + val stage1TaskSet = taskSets(1) + assert(stage1TaskSet.stageId == 1) + assert(stage1TaskSet.stageAttemptId == 0) + } + + /** * Makes sure that failures of stage used by multiple jobs are correctly handled. * * This test creates the following dependency graph: @@ -1393,8 +1560,8 @@ class DAGSchedulerSuite // Submit a map stage by itself submitMapStage(shuffleDep) complete(taskSets(0), Seq( - (Success, makeMapStatus("hostA", reduceRdd.partitions.size)), - (Success, makeMapStatus("hostB", reduceRdd.partitions.size)))) + (Success, makeMapStatus("hostA", reduceRdd.partitions.length)), + (Success, makeMapStatus("hostB", reduceRdd.partitions.length)))) assert(results.size === 1) results.clear() assertDataStructuresEmpty() @@ -1407,7 +1574,7 @@ class DAGSchedulerSuite // Ask the scheduler to try it again; TaskSet 2 will rerun the map task that we couldn't fetch // from, then TaskSet 3 will run the reduce stage scheduler.resubmitFailedStages() - complete(taskSets(2), Seq((Success, makeMapStatus("hostA", reduceRdd.partitions.size)))) + complete(taskSets(2), Seq((Success, makeMapStatus("hostA", reduceRdd.partitions.length)))) complete(taskSets(3), Seq((Success, 43))) assert(results === Map(0 -> 42, 1 -> 43)) results.clear() @@ -1452,8 +1619,8 @@ class DAGSchedulerSuite // Complete the first stage assert(taskSets(0).stageId === 0) complete(taskSets(0), Seq( - (Success, makeMapStatus("hostA", rdd1.partitions.size)), - (Success, makeMapStatus("hostB", rdd1.partitions.size)))) + (Success, makeMapStatus("hostA", rdd1.partitions.length)), + (Success, makeMapStatus("hostB", rdd1.partitions.length)))) assert(mapOutputTracker.getMapSizesByExecutorId(dep1.shuffleId, 0).map(_._1).toSet === HashSet(makeBlockManagerId("hostA"), makeBlockManagerId("hostB"))) assert(listener1.results.size === 1) @@ -1461,7 +1628,7 @@ class DAGSchedulerSuite // When attempting the second stage, show a fetch failure assert(taskSets(1).stageId === 1) complete(taskSets(1), Seq( - (Success, makeMapStatus("hostA", rdd2.partitions.size)), + (Success, makeMapStatus("hostA", rdd2.partitions.length)), (FetchFailed(makeBlockManagerId("hostA"), dep1.shuffleId, 0, 0, "ignored"), null))) scheduler.resubmitFailedStages() assert(listener2.results.size === 0) // Second stage listener should not have a result yet @@ -1469,7 +1636,7 @@ class DAGSchedulerSuite // Stage 0 should now be running as task set 2; make its task succeed assert(taskSets(2).stageId === 0) complete(taskSets(2), Seq( - (Success, makeMapStatus("hostC", rdd2.partitions.size)))) + (Success, makeMapStatus("hostC", rdd2.partitions.length)))) assert(mapOutputTracker.getMapSizesByExecutorId(dep1.shuffleId, 0).map(_._1).toSet === HashSet(makeBlockManagerId("hostC"), makeBlockManagerId("hostB"))) assert(listener2.results.size === 0) // Second stage listener should still not have a result @@ -1477,8 +1644,8 @@ class DAGSchedulerSuite // Stage 1 should now be running as task set 3; make its first task succeed assert(taskSets(3).stageId === 1) complete(taskSets(3), Seq( - (Success, makeMapStatus("hostB", rdd2.partitions.size)), - (Success, makeMapStatus("hostD", rdd2.partitions.size)))) + (Success, makeMapStatus("hostB", rdd2.partitions.length)), + (Success, makeMapStatus("hostD", rdd2.partitions.length)))) assert(mapOutputTracker.getMapSizesByExecutorId(dep2.shuffleId, 0).map(_._1).toSet === HashSet(makeBlockManagerId("hostB"), makeBlockManagerId("hostD"))) assert(listener2.results.size === 1) @@ -1494,7 +1661,7 @@ class DAGSchedulerSuite // TaskSet 5 will rerun stage 1's lost task, then TaskSet 6 will rerun stage 2 assert(taskSets(5).stageId === 1) complete(taskSets(5), Seq( - (Success, makeMapStatus("hostE", rdd2.partitions.size)))) + (Success, makeMapStatus("hostE", rdd2.partitions.length)))) complete(taskSets(6), Seq( (Success, 53))) assert(listener3.results === Map(0 -> 52, 1 -> 53)) |