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Posted to dev@mesos.apache.org by "Ajay Viswanathan (JIRA)" <ji...@apache.org> on 2014/06/24 11:06:24 UTC
[jira] [Commented] (MESOS-1535) Pyspark on Mesos scheduler error
[ https://issues.apache.org/jira/browse/MESOS-1535?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14041895#comment-14041895 ]
Ajay Viswanathan commented on MESOS-1535:
-----------------------------------------
I've searched all over for this issue and nothing seems to work. It surely seems to be an issue with mesos, because the code runs as expected with Spark standalone mode. I've tried this on both Spark 1.0.0 and Spark 0.9.1
Could somebody explain the error logs so that it may help me in finding the issue?
> Pyspark on Mesos scheduler error
> --------------------------------
>
> Key: MESOS-1535
> URL: https://issues.apache.org/jira/browse/MESOS-1535
> Project: Mesos
> Issue Type: Bug
> Affects Versions: 0.18.0, 0.18.1
> Environment: Running a Mesos on a cluster of Centos 6.5 machines. 180 GB memory.
> Reporter: Ajay Viswanathan
> Labels: pyspark
>
> This is an error that I get while running fine-grained PySpark on the mesos cluster. This comes after running some 200-1000 tasks generally.
> Pyspark code:
> while True:
> sc.parallelize(range(10)).map(lambda n : n*2).collect()
> Error log:
> org.apache.spark.SparkException: EOF reached before Python server acknowledged
> at org.apache.spark.api.python.PythonAccumulatorParam.addInPlace(PythonR DD.scala:322)
> at org.apache.spark.api.python.PythonAccumulatorParam.addInPlace(PythonR DD.scala:293)
> at org.apache.spark.Accumulable.$plus$plus$eq(Accumulators.scala:70)
> at org.apache.spark.Accumulators$$anonfun$add$2.apply(Accumulators.scala :275)
> at org.apache.spark.Accumulators$$anonfun$add$2.apply(Accumulators.scala :273)
> at scala.collection.TraversableLike$WithFilter$$anonfun$foreach$1.apply( TraversableLike.scala:772)
> at scala.collection.mutable.HashMap$$anonfun$foreach$1.apply(HashMap.sca la:98)
> at scala.collection.mutable.HashMap$$anonfun$foreach$1.apply(HashMap.sca la:98)
> at scala.collection.mutable.HashTable$class.foreachEntry(HashTable.scala :226)
> at scala.collection.mutable.HashMap.foreachEntry(HashMap.scala:39)
> at scala.collection.mutable.HashMap.foreach(HashMap.scala:98)
> at scala.collection.TraversableLike$WithFilter.foreach(TraversableLike.s cala:771)
> at org.apache.spark.Accumulators$.add(Accumulators.scala:273)
> at org.apache.spark.scheduler.DAGScheduler.handleTaskCompletion(DAGSched uler.scala:826)
> at org.apache.spark.scheduler.DAGScheduler.processEvent(DAGScheduler.sca la:601)
> at org.apache.spark.scheduler.DAGScheduler$$anonfun$start$1$$anon$2$$ano nfun$receive$1.applyOrElse(DAGScheduler.scala:190)
> at akka.actor.ActorCell.receiveMessage(ActorCell.scala:498)
> at akka.actor.ActorCell.invoke(ActorCell.scala:456)
> at akka.dispatch.Mailbox.processMailbox(Mailbox.scala:237)
> at akka.dispatch.Mailbox.run(Mailbox.scala:219)
> at akka.dispatch.ForkJoinExecutorConfigurator$AkkaForkJoinTask.exec(Abst ractDispatcher.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:19 79)
> at scala.concurrent.forkjoin.ForkJoinWorkerThread.run(ForkJoinWorkerThre ad.java:107)
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