With the right program you can always exhaust any amount of memory :). There is no silver bullet. You have to figure out what is happening in your code that causes a high memory use and address that. I spent all of last week doing this for a simple program of my own. Lessons I learned that may or may not apply to your case:
- If you don't cache (persist) an RDD, it is not stored. This can save memory at the cost of possibly repeating computation. (I read around a TB of files twice, for example, rather than cache them.) - Use combineByKey instead of groupByKey if you can process values one by one. This means they do not need to be all stored. - If you have a lot of keys per partition, set mapSideCombine=false for combineByKey. This avoids creating a large map per partition. - If you have a key with a disproportionate number of values (like the empty string for a missing name), discard it before the computation. - Read https://spark.apache.org/docs/latest/tuning.html for more (and more accurate) information. Good luck. On Wed, Apr 23, 2014 at 1:25 AM, jaeholee <jho...@lbl.gov> wrote: > Ok. I tried setting the partition number to 128 and numbers greater than > 128, > and now I get another error message about "Java heap space". Is it possible > that there is something wrong with the setup of my Spark cluster to begin > with? Or is it still an issue with partitioning my data? Or do I just need > more worker nodes? > > > ERROR TaskSetManager: Task 194.0:14 failed 4 times; aborting job > org.apache.spark.SparkException: Job aborted: Task 194.0:14 failed 4 times > (most recent failure: Exception failure: java.lang.OutOfMemoryError: Java > heap space) > at > > org.apache.spark.scheduler.DAGScheduler$$anonfun$org$apache$spark$scheduler$DAGScheduler$$abortStage$1.apply(DAGScheduler.scala:1020) > at > > org.apache.spark.scheduler.DAGScheduler$$anonfun$org$apache$spark$scheduler$DAGScheduler$$abortStage$1.apply(DAGScheduler.scala:1018) > at > > scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59) > at > scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47) > at > org.apache.spark.scheduler.DAGScheduler.org > $apache$spark$scheduler$DAGScheduler$$abortStage(DAGScheduler.scala:1018) > at > > org.apache.spark.scheduler.DAGScheduler$$anonfun$processEvent$10.apply(DAGScheduler.scala:604) > at > > org.apache.spark.scheduler.DAGScheduler$$anonfun$processEvent$10.apply(DAGScheduler.scala:604) > at scala.Option.foreach(Option.scala:236) > at > > org.apache.spark.scheduler.DAGScheduler.processEvent(DAGScheduler.scala:604) > at > > org.apache.spark.scheduler.DAGScheduler$$anonfun$start$1$$anon$2$$anonfun$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(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) > > > > -- > View this message in context: > http://apache-spark-user-list.1001560.n3.nabble.com/ERROR-TaskSchedulerImpl-Lost-an-executor-tp4566p4623.html > Sent from the Apache Spark User List mailing list archive at Nabble.com. >
