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https://issues.apache.org/jira/browse/SPARK-2931?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14091259#comment-14091259
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Kay Ousterhout commented on SPARK-2931:
---------------------------------------
I tried doing something similar to spark-perf and could not reproduce this
problem; will try running it on multiples machines shortly. In case it's
useful to you, here's what I did:
val randKeys = sc.parallelize(1 to 10, 10).flatMap { x => val r = new
Random(x); (1 to 20).map{ x => r.nextInt(10) }}
val kv = randKeys.map((_, 1))
kv.sortByKey().collect()
> 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
>
>
> 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.
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