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Josh Rosen updated SPARK-2931: ------------------------------ Attachment: scala-sort-by-key.err @ [~kayousterhout]: I can see how that code in {{executorLost()}} could be problematic, but I don't think that's what's triggering this bug since I don't see any "Re-queueing tasks for ..." messages in my log. I've attached a full log from a failing test run. If it helps, I can re-run at DEBUG logging or add extra logging statements. > getAllowedLocalityLevel() throws ArrayIndexOutOfBoundsException > --------------------------------------------------------------- > > Key: SPARK-2931 > URL: https://issues.apache.org/jira/browse/SPARK-2931 > Project: Spark > Issue Type: Bug > Components: Spark Core > Affects Versions: 1.1.0 > Environment: Spark EC2, spark-1.1.0-snapshot1, sort-by-key spark-perf > benchmark > Reporter: Josh Rosen > Priority: Blocker > Fix For: 1.1.0 > > Attachments: scala-sort-by-key.err > > > When running Spark Perf's sort-by-key benchmark on EC2 with v1.1.0-snapshot, > I get the following errors (one per task): > {code} > 14/08/08 18:54:22 INFO scheduler.TaskSetManager: Starting task 39.0 in stage > 0.0 (TID 39, ip-172-31-14-30.us-west-2.compute.internal, PROCESS_LOCAL, 1003 > bytes) > 14/08/08 18:54:22 INFO cluster.SparkDeploySchedulerBackend: Registered > executor: > Actor[akka.tcp://sparkexecu...@ip-172-31-9-213.us-west-2.compute.internal:58901/user/Executor#1436065036] > with ID 0 > 14/08/08 18:54:22 ERROR actor.OneForOneStrategy: 1 > java.lang.ArrayIndexOutOfBoundsException: 1 > at > org.apache.spark.scheduler.TaskSetManager.getAllowedLocalityLevel(TaskSetManager.scala:475) > at > org.apache.spark.scheduler.TaskSetManager.resourceOffer(TaskSetManager.scala:409) > at > org.apache.spark.scheduler.TaskSchedulerImpl$$anonfun$resourceOffers$3$$anonfun$apply$7$$anonfun$apply$2.apply$mcVI$sp(TaskSchedulerImpl.scala:261) > at scala.collection.immutable.Range.foreach$mVc$sp(Range.scala:141) > at > org.apache.spark.scheduler.TaskSchedulerImpl$$anonfun$resourceOffers$3$$anonfun$apply$7.apply(TaskSchedulerImpl.scala:257) > at > org.apache.spark.scheduler.TaskSchedulerImpl$$anonfun$resourceOffers$3$$anonfun$apply$7.apply(TaskSchedulerImpl.scala:254) > at > scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33) > at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:108) > at > org.apache.spark.scheduler.TaskSchedulerImpl$$anonfun$resourceOffers$3.apply(TaskSchedulerImpl.scala:254) > at > org.apache.spark.scheduler.TaskSchedulerImpl$$anonfun$resourceOffers$3.apply(TaskSchedulerImpl.scala:254) > at > scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59) > at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47) > at > org.apache.spark.scheduler.TaskSchedulerImpl.resourceOffers(TaskSchedulerImpl.scala:254) > at > org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend$DriverActor.makeOffers(CoarseGrainedSchedulerBackend.scala:153) > at > org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend$DriverActor$$anonfun$receive$1.applyOrElse(CoarseGrainedSchedulerBackend.scala:103) > at akka.actor.ActorCell.receiveMessage(ActorCell.scala:498) > at akka.actor.ActorCell.invoke(ActorCell.scala:456) > at akka.dispatch.Mailbox.processMailbox(Mailbox.scala:237) > at akka.dispatch.Mailbox.run(Mailbox.scala:219) > at > akka.dispatch.ForkJoinExecutorConfigurator$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} > This causes the job to hang. > I can deterministically reproduce this by re-running the test, either in > isolation or as part of the full performance testing suite. -- This message was sent by Atlassian JIRA (v6.2#6252) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org