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Posted to issues@spark.apache.org by "Patrick Wendell (JIRA)" <ji...@apache.org> on 2014/09/02 07:36:20 UTC
[jira] [Commented] (SPARK-3005) Spark with Mesos fine-grained mode
throws UnsupportedOperationException in MesosSchedulerBackend.killTask()
[ https://issues.apache.org/jira/browse/SPARK-3005?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14117909#comment-14117909 ]
Patrick Wendell commented on SPARK-3005:
----------------------------------------
I think it might be safe to just define the semantics of cancelTask to be "best effort" and have the default implementation be empty. From what I can tell the DAGScheduler is already resilient to a task finishing after the stage has been cleaned up (for other reasons I think it needs be resilient to this). So maybe we should just remove this entire ableToCancelStages logic in here and make it simpler. /cc [~kayousterhout] and [~markhamstra] who worked on this code.
Hey [~xuzhongxing] what do you mean that the "tasks themselves already died and exited"? The code here is designed to cancel outstanding tasks that are still running. For instance, I have a job that has 500 tasks running. Then there is a failure of one task multiple time so I need to fail the stage, but there are still many tasks running. I think those tasks need to be killed still. Otherwise you have zombie tasks running. I think in mesos fine-grained mode we just won't be able to support this feature... but we should make it so it doesn't hang.
> Spark with Mesos fine-grained mode throws UnsupportedOperationException in MesosSchedulerBackend.killTask()
> -----------------------------------------------------------------------------------------------------------
>
> Key: SPARK-3005
> URL: https://issues.apache.org/jira/browse/SPARK-3005
> Project: Spark
> Issue Type: Bug
> Components: Spark Core
> Affects Versions: 1.0.2
> Environment: Spark 1.0.2, Mesos 0.18.1, spark-cassandra-connector
> Reporter: Xu Zhongxing
> Attachments: SPARK-3005_1.diff
>
>
> I am using Spark, Mesos, spark-cassandra-connector to do some work on a cassandra cluster.
> During the job running, I killed the Cassandra daemon to simulate some failure cases. This results in task failures.
> If I run the job in Mesos coarse-grained mode, the spark driver program throws an exception and shutdown cleanly.
> But when I run the job in Mesos fine-grained mode, the spark driver program hangs.
> The spark log is:
> {code}
> INFO [spark-akka.actor.default-dispatcher-4] 2014-08-13 15:58:15,794 Logging.scala (line 58) Cancelling stage 1
> INFO [spark-akka.actor.default-dispatcher-4] 2014-08-13 15:58:15,797 Logging.scala (line 79) Could not cancel tasks for stage 1
> java.lang.UnsupportedOperationException
> at org.apache.spark.scheduler.SchedulerBackend$class.killTask(SchedulerBackend.scala:32)
> at org.apache.spark.scheduler.cluster.mesos.MesosSchedulerBackend.killTask(MesosSchedulerBackend.scala:41)
> at org.apache.spark.scheduler.TaskSchedulerImpl$$anonfun$cancelTasks$3$$anonfun$apply$1.apply$mcVJ$sp(TaskSchedulerImpl.scala:185)
> at org.apache.spark.scheduler.TaskSchedulerImpl$$anonfun$cancelTasks$3$$anonfun$apply$1.apply(TaskSchedulerImpl.scala:183)
> at org.apache.spark.scheduler.TaskSchedulerImpl$$anonfun$cancelTasks$3$$anonfun$apply$1.apply(TaskSchedulerImpl.scala:183)
> at scala.collection.mutable.HashSet.foreach(HashSet.scala:79)
> at org.apache.spark.scheduler.TaskSchedulerImpl$$anonfun$cancelTasks$3.apply(TaskSchedulerImpl.scala:183)
> at org.apache.spark.scheduler.TaskSchedulerImpl$$anonfun$cancelTasks$3.apply(TaskSchedulerImpl.scala:176)
> at scala.Option.foreach(Option.scala:236)
> at org.apache.spark.scheduler.TaskSchedulerImpl.cancelTasks(TaskSchedulerImpl.scala:176)
> at org.apache.spark.scheduler.DAGScheduler$$anonfun$org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages$1.apply$mcVI$sp(DAGScheduler.scala:1075)
> at org.apache.spark.scheduler.DAGScheduler$$anonfun$org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages$1.apply(DAGScheduler.scala:1061)
> at org.apache.spark.scheduler.DAGScheduler$$anonfun$org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages$1.apply(DAGScheduler.scala:1061)
> at scala.collection.mutable.HashSet.foreach(HashSet.scala:79)
> at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1061)
> at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1033)
> at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1031)
> at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
> at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
> at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1031)
> at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:635)
> at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:635)
> at scala.Option.foreach(Option.scala:236)
> at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:635)
> at org.apache.spark.scheduler.DAGSchedulerEventProcessActor$$anonfun$receive$2.applyOrElse(DAGScheduler.scala:1234)
> at akka.actor.ActorCell.receiveMessage(ActorCell.scala:498)
> at akka.actor.ActorCell.invoke(ActorCell.scala:456)
> at akka.dispatch.Mailbox.processMailbox(Mailbox.scala:237)
> at akka.dispatch.Mailbox.run(Mailbox.scala:219)
> at akka.dispatch.ForkJoinExecutorConfigurator$AkkaForkJoinTask.exec(AbstractDispatcher.scala:386)
> at scala.concurrent.forkjoin.ForkJoinTask.doExec(ForkJoinTask.java:260)
> at scala.concurrent.forkjoin.ForkJoinPool$WorkQueue.runTask(ForkJoinPool.java:1339)
> at scala.concurrent.forkjoin.ForkJoinPool.runWorker(ForkJoinPool.java:1979)
> at scala.concurrent.forkjoin.ForkJoinWorkerThread.run(ForkJoinWorkerThread.java:107)
> {code}
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