Can anybody help me?
Thanks.
Chieh-Yen
On Wed, Apr 16, 2014 at 5:18 PM, Chieh-Yen <r01944...@csie.ntu.edu.tw>wrote:
> Dear all,
>
> I developed a application that the message size of communication
> is greater than 10 MB sometimes.
> For smaller datasets it works fine, but fails for larger datasets.
> Please check the error message following.
>
> I surveyed the situation online and lots of people said
> the problem can be solved by modifying the property of spark.akka.frameSize
> and spark.reducer.maxMbInFlight.
> It may look like:
>
> 134 val conf = new SparkConf()
> 135 .setMaster(master)
> 136 .setAppName("SparkLR")
> 137
> .setSparkHome("/home/user/spark-0.9.0-incubating-bin-hadoop2")
> 138 .setJars(List(jarPath))
> 139 .set("spark.akka.frameSize", "100")
> 140 .set("spark.reducer.maxMbInFlight", "100")
> 141 val sc = new SparkContext(conf)
>
> However, the task still fails with the same error message.
> The communication message is the weight vectors of each sub-problem,
> it may be larger than 10 MB for higher dimensional dataset.
>
> Is there anybody can help me?
> Thanks a lot.
>
> ====
> [error] (run-main) org.apache.spark.SparkException: Job aborted: Exception
> while deserializing and fetching task:*java.lang.OutOfMemoryError: Java
> heap space*
> org.apache.spark.SparkException: Job aborted: Exception while
> deserializing and fetching task: java.lang.OutOfMemoryError: Java heap space
> at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$org$apache$spark$scheduler$DAGScheduler$$abortStage$1.apply(DAGScheduler.scala:1028)
> at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$org$apache$spark$scheduler$DAGScheduler$$abortStage$1.apply(DAGScheduler.scala:1026)
> at
> scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
> at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
> at
> org.apache.spark.scheduler.DAGScheduler.org<http://org.apache.spark.scheduler.dagscheduler.org/>
> $apache$spark$scheduler$DAGScheduler$$abortStage(DAGScheduler.scala:1026)
> at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$processEvent$10.apply(DAGScheduler.scala:619)
> at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$processEvent$10.apply(DAGScheduler.scala:619)
> at scala.Option.foreach(Option.scala:236)
> at
> org.apache.spark.scheduler.DAGScheduler.processEvent(DAGScheduler.scala:619)
> at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$start$1$$anon$2$$anonfun$receive$1.applyOrElse(DAGScheduler.scala:207)
> at akka.actor.ActorCell.receiveMessage(ActorCell.scala:498)
> at akka.actor.ActorCell.invoke(ActorCell.scala:456)
> at akka.dispatch.Mailbox.processMailbox(Mailbox.scala:237)
> at akka.dispatch.Mailbox.run(Mailbox.scala:219)
> at
> akka.dispatch.ForkJoinExecutorConfigurator$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)
> [trace] Stack trace suppressed: run last compile:run for the full output.
> ====
>
> Chieh-Yen
>
