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. The information transmitted herewith is intended only for use by the individual or entity to which it is addressed. If the reader of this message is not the intended recipient, you are hereby notified that any review, retransmission, dissemination, distribution, copying or other use of, or taking of any action in reliance upon this information is strictly prohibited. If you have received this communication in error, please contact the sender and delete the material from your computer.
