Which release of Spark are you using ? Can you show skeleton of your partitioner and comparator ?
Thanks > On Sep 9, 2015, at 4:45 PM, Ashish Shenoy <ashe...@instartlogic.com> wrote: > > Hi, > > I am trying to sort a RDD pair using repartitionAndSortWithinPartitions() for > my key [which is a custom class, not a java primitive] using a custom > partitioner on that key and a custom comparator. However, it fails > consistently: > > org.apache.spark.SparkException: Job aborted due to stage failure: Task 18 in > stage 1.0 failed 4 times, most recent failure: Lost task 18.3 in stage 1.0 > (TID 202, 172.16.18.25): java.lang.ArrayIndexOutOfBoundsException: -78 > at > org.apache.spark.util.collection.ExternalSorter.spillToPartitionFiles(ExternalSorter.scala:375) > at > org.apache.spark.util.collection.ExternalSorter.insertAll(ExternalSorter.scala:208) > at > org.apache.spark.shuffle.sort.SortShuffleWriter.write(SortShuffleWriter.scala:62) > at > org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:70) > at > org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41) > at org.apache.spark.scheduler.Task.run(Task.scala:70) > at > org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:213) > at > java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142) > at > java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617) > at java.lang.Thread.run(Thread.java:745) > > Driver stacktrace: > at > org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1273) > at > org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1264) > at > org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1263) > at > scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59) > at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47) > at > org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1263) > at > org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:730) > at > org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:730) > at scala.Option.foreach(Option.scala:236) > at > org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:730) > at > org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1457) > at > org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1418) > at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48) > > I also persist the RDD using the "memory and disk" storage level. The stack > trace above comes from spark's code and not my application code. Can you pls > point out what I am doing wrong ? > > Thanks, > Ashish
