Can you be more specific about numbers? I am not sure that splitting helps so much in the end, in that it has the same effect as executing a smaller number at a time of the large number of tasks that the full cartesian join would generate. The full join is probably intractable no matter what in this case? The OOM is not necessarily directly related. It depends on where it happened, what else you are doing, how much memory you gave, etc.
On Thu, Oct 30, 2014 at 3:29 AM, Ganelin, Ilya <[email protected]> wrote: > Hey all – not writing to necessarily get a fix but more to get an > understanding of what’s going on internally here. > > I wish to take a cross-product of two very large RDDs (using cartesian), the > product of which is well in excess of what can be stored on disk . Clearly > that is intractable, thus my solution is to do things in batches - > essentially I can take the cross product of a small piece of the first data > set with the entirety of the other. To do this, I calculate how many items > can fit into 1 gig of memory. Next, I use RDD.random Split() to partition > the first data set. The issue is that I am trying to partition an RDD of > several million items into several million partitions. This throws the > following error: > > I would like to understand the internals of what’s going on here so that I > can adjust my approach accordingly. Thanks in advance. > > > 14/10/29 22:17:44 ERROR ActorSystemImpl: Uncaught fatal error from thread > [sparkDriver-akka.actor.default-dispatcher-16] shutting down ActorSystem > [sparkDriver] > java.lang.OutOfMemoryError: GC overhead limit exceeded > at com.google.protobuf_spark.ByteString.toByteArray(ByteString.java:213) > at akka.remote.MessageSerializer$.deserialize(MessageSerializer.scala:24) > at > akka.remote.DefaultMessageDispatcher.payload$lzycompute$1(Endpoint.scala:55) > at akka.remote.DefaultMessageDispatcher.payload$1(Endpoint.scala:55) > at akka.remote.DefaultMessageDispatcher.dispatch(Endpoint.scala:73) > at > akka.remote.EndpointReader$$anonfun$receive$2.applyOrElse(Endpoint.scala:764) > at akka.actor.ActorCell.receiveMessage(ActorCell.scala:498) > at akka.actor.ActorCell.invoke(ActorCell.scala:456) > at akka.dispatch.Mailbox.processMailbox(Mailbox.scala:237) > at akka.dispatch.Mailbox.run(Mailbox.scala:219) > at > akka.dispatch.ForkJoinExecutorConfigurator$AkkaForkJoinTask.exec(AbstractDispatcher.scala:386) > at scala.concurrent.forkjoin.ForkJoinTask.doExec(ForkJoinTask.java:260) > at > scala.concurrent.forkjoin.ForkJoinPool$WorkQueue.runTask(ForkJoinPool.java:1339) > at scala.concurrent.forkjoin.ForkJoinPool.runWorker(ForkJoinPool.java:1979) > at > scala.concurrent.forkjoin.ForkJoinWorkerThread.run(ForkJoinWorkerThread.java:107) > Exception in thread "main" java.lang.OutOfMemoryError: GC overhead limit > exceeded > at java.util.Arrays.copyOfRange(Arrays.java:2694) > at java.lang.String.<init>(String.java:203) > at java.lang.String.substring(String.java:1913) > at java.lang.String.subSequence(String.java:1946) > at java.util.regex.Matcher.getSubSequence(Matcher.java:1245) > at java.util.regex.Matcher.group(Matcher.java:490) > at java.util.Formatter$FormatSpecifier.<init>(Formatter.java:2675) > at java.util.Formatter.parse(Formatter.java:2528) > at java.util.Formatter.format(Formatter.java:2469) > at java.util.Formatter.format(Formatter.java:2423) > at java.lang.String.format(String.java:2790) > at scala.collection.immutable.StringLike$class.format(StringLike.scala:266) > at scala.collection.immutable.StringOps.format(StringOps.scala:31) > at org.apache.spark.util.Utils$.getCallSite(Utils.scala:944) > at org.apache.spark.rdd.RDD.<init>(RDD.scala:1227) > at org.apache.spark.rdd.RDD.<init>(RDD.scala:83) > at > org.apache.spark.rdd.PartitionwiseSampledRDD.<init>(PartitionwiseSampledRDD.scala:47) > at org.apache.spark.rdd.RDD$$anonfun$randomSplit$1.apply(RDD.scala:378) > at org.apache.spark.rdd.RDD$$anonfun$randomSplit$1.apply(RDD.scala:377) > at scala.collection.Iterator$$anon$11.next(Iterator.scala:328) > at scala.collection.Iterator$class.foreach(Iterator.scala:727) > at scala.collection.AbstractIterator.foreach(Iterator.scala:1157) > at scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:48) > at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:103) > at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:47) > at scala.collection.TraversableOnce$class.to(TraversableOnce.scala:273) > at scala.collection.AbstractIterator.to(Iterator.scala:1157) > at > scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:265) > at scala.collection.AbstractIterator.toBuffer(Iterator.scala:1157) > at scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:252) > at scala.collection.AbstractIterator.toArray(Iterator.scala:1157) > at org.apache.spark.rdd.RDD.randomSplit(RDD.scala:379) > > > > ________________________________ > > The information contained in this e-mail is confidential and/or proprietary > to Capital One and/or its affiliates. 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