Also check out this post
http://apache-spark-user-list.1001560.n3.nabble.com/Spark-program-thows-OutOfMemoryError-td4268.html
On Mon, Apr 21, 2014 at 11:49 AM, Akhil Das <ak...@mobipulse.in> wrote:
> Hi Chieh,
>
> You can increase the heap size by exporting the java options (See below,
> will increase the heap size to 10Gb)
>
> export _JAVA_OPTIONS="-Xmx10g"
>
>
>
>
> On Mon, Apr 21, 2014 at 11:43 AM, Chieh-Yen <r01944...@csie.ntu.edu.tw>wrote:
>
>> 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
>>>
>>
>>
>
>
> --
> Thanks
> Best Regards
>
