I am not sure what DStream operations you are using, but some operation is internally creating CoalescedRDDs. That is causing the race condition. I might be able help if you can tell me what DStream operations you are using.
TD On Tue, Jun 3, 2014 at 4:54 PM, Michael Chang <m...@tellapart.com> wrote: > Hi Tathagata, > > Thanks for your help! By not using coalesced RDD, do you mean not > repartitioning my Dstream? > > Thanks, > Mike > > > > > On Tue, Jun 3, 2014 at 12:03 PM, Tathagata Das < > tathagata.das1...@gmail.com> wrote: > >> I think I know what is going on! This probably a race condition in the >> DAGScheduler. I have added a JIRA for this. The fix is not trivial though. >> >> https://issues.apache.org/jira/browse/SPARK-2002 >> >> A "not-so-good" workaround for now would be not use coalesced RDD, which >> is avoids the race condition. >> >> TD >> >> >> On Tue, Jun 3, 2014 at 10:09 AM, Michael Chang <m...@tellapart.com> >> wrote: >> >>> I only had the warning level logs, unfortunately. There were no other >>> references of 32855 (except a repeated stack trace, I believe). I'm using >>> Spark 0.9.1 >>> >>> >>> On Mon, Jun 2, 2014 at 5:50 PM, Tathagata Das < >>> tathagata.das1...@gmail.com> wrote: >>> >>>> Do you have the info level logs of the application? Can you grep the >>>> value "32855" to find any references to it? Also what version of the >>>> Spark are you using (so that I can match the stack trace, does not seem to >>>> match with Spark 1.0)? >>>> >>>> TD >>>> >>>> >>>> On Mon, Jun 2, 2014 at 3:27 PM, Michael Chang <m...@tellapart.com> >>>> wrote: >>>> >>>>> Hi all, >>>>> >>>>> Seeing a random exception kill my spark streaming job. Here's a stack >>>>> trace: >>>>> >>>>> java.util.NoSuchElementException: key not found: 32855 >>>>> at scala.collection.MapLike$class.default(MapLike.scala:228) >>>>> at scala.collection.AbstractMap.default(Map.scala:58) >>>>> at scala.collection.mutable.HashMap.apply(HashMap.scala:64) >>>>> at >>>>> org.apache.spark.scheduler.DAGScheduler.getCacheLocs(DAGScheduler.scala:211) >>>>> at >>>>> org.apache.spark.scheduler.DAGScheduler.getPreferredLocs(DAGScheduler.scala:1072) >>>>> at >>>>> org.apache.spark.SparkContext.getPreferredLocs(SparkContext.scala:716) >>>>> at >>>>> org.apache.spark.rdd.PartitionCoalescer.currPrefLocs(CoalescedRDD.scala:172) >>>>> at >>>>> org.apache.spark.rdd.PartitionCoalescer$LocationIterator$$anonfun$4$$anonfun$apply$2.apply(CoalescedRDD.scala:189) >>>>> at >>>>> org.apache.spark.rdd.PartitionCoalescer$LocationIterator$$anonfun$4$$anonfun$apply$2.apply(CoalescedRDD.scala:188) >>>>> at >>>>> scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371) >>>>> at >>>>> scala.collection.Iterator$$anon$12.hasNext(Iterator.scala:351) >>>>> at >>>>> scala.collection.Iterator$$anon$12.hasNext(Iterator.scala:350) >>>>> at >>>>> org.apache.spark.rdd.PartitionCoalescer$LocationIterator.<init>(CoalescedRDD.scala:183) >>>>> at >>>>> org.apache.spark.rdd.PartitionCoalescer.setupGroups(CoalescedRDD.scala:234) >>>>> at >>>>> org.apache.spark.rdd.PartitionCoalescer.run(CoalescedRDD.scala:333) >>>>> at >>>>> org.apache.spark.rdd.CoalescedRDD.getPartitions(CoalescedRDD.scala:81) >>>>> at >>>>> org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:207) >>>>> at >>>>> org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:205) >>>>> at scala.Option.getOrElse(Option.scala:120) >>>>> at org.apache.spark.rdd.RDD.partitions(RDD.scala:205) >>>>> at >>>>> org.apache.spark.rdd.MappedRDD.getPartitions(MappedRDD.scala:28) >>>>> at >>>>> org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:207) >>>>> at >>>>> org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:205) >>>>> at scala.Option.getOrElse(Option.scala:120) >>>>> at org.apache.spark.rdd.RDD.partitions(RDD.scala:205) >>>>> at >>>>> org.apache.spark.rdd.MappedRDD.getPartitions(MappedRDD.scala:28) >>>>> at >>>>> org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:207) >>>>> at >>>>> org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:205) >>>>> at scala.Option.getOrElse(Option.scala:120) >>>>> at org.apache.spark.rdd.RDD.partitions(RDD.scala:205) >>>>> at >>>>> org.apache.spark.rdd.FlatMappedRDD.getPartitions(FlatMappedRDD.scala:30) >>>>> at >>>>> org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:207) >>>>> at >>>>> org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:205) >>>>> at scala.Option.getOrElse(Option.scala:120) >>>>> at org.apache.spark.rdd.RDD.partitions(RDD.scala:205) >>>>> at >>>>> org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:31) >>>>> at >>>>> org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:207) >>>>> at >>>>> org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:205) >>>>> at scala.Option.getOrElse(Option.scala:120) >>>>> at org.apache.spark.rdd.RDD.partitions(RDD.scala:205) >>>>> at org.apache.spark.rdd.RDD.take(RDD.scala:830) >>>>> at >>>>> org.apache.spark.api.java.JavaRDDLike$class.take(JavaRDDLike.scala:337) >>>>> at org.apache.spark.api.java.JavaRDD.take(JavaRDD.scala:27) >>>>> at >>>>> com.tellapart.manifolds.spark.ManifoldsUtil$PersistToKafkaFunction.call(ManifoldsUtil.java:87) >>>>> at >>>>> com.tellapart.manifolds.spark.ManifoldsUtil$PersistToKafkaFunction.call(ManifoldsUtil.java:53) >>>>> at >>>>> org.apache.spark.streaming.api.java.JavaDStreamLike$$anonfun$foreachRDD$1.apply(JavaDStreamLike.scala:270) >>>>> at >>>>> org.apache.spark.streaming.api.java.JavaDStreamLike$$anonfun$foreachRDD$1.apply(JavaDStreamLike.scala:270) >>>>> at >>>>> org.apache.spark.streaming.dstream.DStream$$anonfun$foreachRDD$1.apply(DStream.scala:520) >>>>> at >>>>> org.apache.spark.streaming.dstream.DStream$$anonfun$foreachRDD$1.apply(DStream.scala:520) >>>>> at >>>>> org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply$mcV$sp(ForEachDStream.scala:41) >>>>> at >>>>> org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply(ForEachDStream.scala:40) >>>>> at >>>>> org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply(ForEachDStream.scala:40) >>>>> at scala.util.Try$.apply(Try.scala:161) >>>>> at org.apache.spark.streaming.scheduler.Job.run(Job.scala:32) >>>>> at >>>>> org.apache.spark.streaming.scheduler.JobScheduler$JobHandler.run(JobScheduler.scala:155) >>>>> at >>>>> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145) >>>>> at >>>>> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615) >>>>> at java.lang.Thread.run(Thread.java:744) >>>>> >>>>> It doesn't seem to happen consistently, but I have no idea causes it. >>>>> Has anyone seen this before? The PersistToKafkaFunction here is just >>>>> trying to write the elements in a RDD to a Kafka topic. >>>>> >>>> >>>> >>> >> >
