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. >>>> >>> >>> >> >