[
https://issues.apache.org/jira/browse/SPARK-12591?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15136208#comment-15136208
]
Yuval Itzchakov edited comment on SPARK-12591 at 2/7/16 10:04 AM:
------------------------------------------------------------------
I'm seeing this error as well running Spark 1.6.0 with KryoSerializer.
This error started happening after I reset a spark-worker node while a job is
running. This hasn't happened previously and doesn't occur unless I restart a
worker node.
[~zsxwing] - I'm assuming this patch needs to be applied until 1.6.1, am I
right?
was (Author: yuval.itzchakov):
I'm seeing this error as well running Spark 1.6.0 with KryoSerializer.
This error started happening after I reset a spark-worker node while a job is
running. This hasn't happened previously and doesn't occur unless I restart a
worker node.
[~zsxwing] - Was this supposed to be fixed in 1.6.0? Or is there still a need
to manually patch this until 1.6.1?
> NullPointerException using checkpointed mapWithState with KryoSerializer
> ------------------------------------------------------------------------
>
> Key: SPARK-12591
> URL: https://issues.apache.org/jira/browse/SPARK-12591
> Project: Spark
> Issue Type: Bug
> Components: Streaming
> Affects Versions: 1.6.0
> Environment: MacOSX
> Java(TM) SE Runtime Environment (build 1.8.0_20-ea-b17)
> Reporter: Jan Uyttenhove
> Assignee: Shixiong Zhu
> Fix For: 1.6.1, 2.0.0
>
> Attachments: Screen Shot 2016-01-27 at 10.09.18 AM.png
>
>
> Issue occured after upgrading to the RC4 of Spark (streaming) 1.6.0 to
> (re)test the new mapWithState API, after previously reporting issue
> SPARK-11932 (https://issues.apache.org/jira/browse/SPARK-11932).
> For initial report, see
> http://apache-spark-developers-list.1001551.n3.nabble.com/Spark-streaming-1-6-0-RC4-NullPointerException-using-mapWithState-tt15830.html
> Narrowed it down to an issue unrelated to Kafka directstream, but, after
> observing very unpredictable behavior as a result of changes to the Kafka
> messages format, it seems to be related to KryoSerialization in specific.
> For test case, see my modified version of the StatefulNetworkWordCount
> example: https://gist.github.com/juyttenh/9b4a4103699a7d5f698f
> To reproduce, use RC4 of Spark-1.6.0 and
> - start nc:
> {code}nc -lk 9999{code}
> - execute the supplied test case:
> {code}bin/spark-submit --class
> org.apache.spark.examples.streaming.StatefulNetworkWordCount --master
> local[2] file:///some-assembly-jar localhost 9999{code}
> Error scenario:
> - put some text in the nc console with the job running, and observe correct
> functioning of the word count
> - kill the spark job
> - add some more text in the nc console (with the job not running)
> - restart the spark job and observe the NPE
> (you might need to repeat this a couple of times to trigger the exception)
> Here's the stacktrace:
> {code}
> 15/12/31 11:43:47 ERROR Executor: Exception in task 0.0 in stage 4.0 (TID 5)
> java.lang.NullPointerException
> at
> org.apache.spark.streaming.util.OpenHashMapBasedStateMap.get(StateMap.scala:103)
> at
> org.apache.spark.streaming.util.OpenHashMapBasedStateMap.get(StateMap.scala:111)
> at
> org.apache.spark.streaming.util.OpenHashMapBasedStateMap.get(StateMap.scala:111)
> at
> org.apache.spark.streaming.rdd.MapWithStateRDDRecord$$anonfun$updateRecordWithData$1.apply(MapWithStateRDD.scala:56)
> at
> org.apache.spark.streaming.rdd.MapWithStateRDDRecord$$anonfun$updateRecordWithData$1.apply(MapWithStateRDD.scala:55)
> at scala.collection.Iterator$class.foreach(Iterator.scala:727)
> at
> org.apache.spark.InterruptibleIterator.foreach(InterruptibleIterator.scala:28)
> at
> org.apache.spark.streaming.rdd.MapWithStateRDDRecord$.updateRecordWithData(MapWithStateRDD.scala:55)
> at
> org.apache.spark.streaming.rdd.MapWithStateRDD.compute(MapWithStateRDD.scala:154)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
> at org.apache.spark.CacheManager.getOrCompute(CacheManager.scala:69)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:268)
> at
> org.apache.spark.streaming.rdd.MapWithStateRDD.compute(MapWithStateRDD.scala:148)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
> at org.apache.spark.CacheManager.getOrCompute(CacheManager.scala:69)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:268)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
> at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
> at org.apache.spark.scheduler.Task.run(Task.scala:89)
> at org.apache.spark.executor.Executor$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)
> 15/12/31 11:43:47 INFO TaskSetManager: Starting task 1.0 in stage 4.0 (TID 6,
> localhost, partition 1,NODE_LOCAL, 2239 bytes)
> 15/12/31 11:43:47 INFO Executor: Running task 1.0 in stage 4.0 (TID 6)
> 15/12/31 11:43:47 WARN TaskSetManager: Lost task 0.0 in stage 4.0 (TID 5,
> localhost): java.lang.NullPointerException
> at
> org.apache.spark.streaming.util.OpenHashMapBasedStateMap.get(StateMap.scala:103)
> at
> org.apache.spark.streaming.util.OpenHashMapBasedStateMap.get(StateMap.scala:111)
> at
> org.apache.spark.streaming.util.OpenHashMapBasedStateMap.get(StateMap.scala:111)
> at
> org.apache.spark.streaming.rdd.MapWithStateRDDRecord$$anonfun$updateRecordWithData$1.apply(MapWithStateRDD.scala:56)
> at
> org.apache.spark.streaming.rdd.MapWithStateRDDRecord$$anonfun$updateRecordWithData$1.apply(MapWithStateRDD.scala:55)
> at scala.collection.Iterator$class.foreach(Iterator.scala:727)
> at
> org.apache.spark.InterruptibleIterator.foreach(InterruptibleIterator.scala:28)
> at
> org.apache.spark.streaming.rdd.MapWithStateRDDRecord$.updateRecordWithData(MapWithStateRDD.scala:55)
> at
> org.apache.spark.streaming.rdd.MapWithStateRDD.compute(MapWithStateRDD.scala:154)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
> at org.apache.spark.CacheManager.getOrCompute(CacheManager.scala:69)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:268)
> at
> org.apache.spark.streaming.rdd.MapWithStateRDD.compute(MapWithStateRDD.scala:148)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
> at org.apache.spark.CacheManager.getOrCompute(CacheManager.scala:69)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:268)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
> at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
> at org.apache.spark.scheduler.Task.run(Task.scala:89)
> at org.apache.spark.executor.Executor$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)
> 15/12/31 11:43:47 ERROR TaskSetManager: Task 0 in stage 4.0 failed 1 times;
> aborting job
> 15/12/31 11:43:47 INFO TaskSchedulerImpl: Cancelling stage 4
> 15/12/31 11:43:47 INFO Executor: Executor is trying to kill task 1.0 in stage
> 4.0 (TID 6)
> 15/12/31 11:43:47 INFO TaskSchedulerImpl: Stage 4 was cancelled
> 15/12/31 11:43:47 INFO DAGScheduler: ResultStage 4 (foreachPartition at
> StatefulNetworkCountKryo.scala:72) failed in 0.100 s
> 15/12/31 11:43:47 INFO DAGScheduler: Job 1 failed: foreachPartition at
> StatefulNetworkCountKryo.scala:72, took 0.823996 s
> 15/12/31 11:43:47 INFO JobScheduler: Finished job streaming job 1451558610000
> ms.0 from job set of time 1451558610000 ms
> 15/12/31 11:43:47 INFO JobScheduler: Total delay: 17.994 s for time
> 1451558610000 ms (execution: 0.857 s)
> 15/12/31 11:43:47 INFO JobScheduler: Starting job streaming job 1451558620000
> ms.0 from job set of time 1451558620000 ms
> 15/12/31 11:43:47 ERROR JobScheduler: Error running job streaming job
> 1451558610000 ms.0
> org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in
> stage 4.0 failed 1 times, most recent failure: Lost task 0.0 in stage 4.0
> (TID 5, localhost): java.lang.NullPointerException
> at
> org.apache.spark.streaming.util.OpenHashMapBasedStateMap.get(StateMap.scala:103)
> at
> org.apache.spark.streaming.util.OpenHashMapBasedStateMap.get(StateMap.scala:111)
> at
> org.apache.spark.streaming.util.OpenHashMapBasedStateMap.get(StateMap.scala:111)
> at
> org.apache.spark.streaming.rdd.MapWithStateRDDRecord$$anonfun$updateRecordWithData$1.apply(MapWithStateRDD.scala:56)
> at
> org.apache.spark.streaming.rdd.MapWithStateRDDRecord$$anonfun$updateRecordWithData$1.apply(MapWithStateRDD.scala:55)
> at scala.collection.Iterator$class.foreach(Iterator.scala:727)
> at
> org.apache.spark.InterruptibleIterator.foreach(InterruptibleIterator.scala:28)
> at
> org.apache.spark.streaming.rdd.MapWithStateRDDRecord$.updateRecordWithData(MapWithStateRDD.scala:55)
> at
> org.apache.spark.streaming.rdd.MapWithStateRDD.compute(MapWithStateRDD.scala:154)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
> at org.apache.spark.CacheManager.getOrCompute(CacheManager.scala:69)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:268)
> at
> org.apache.spark.streaming.rdd.MapWithStateRDD.compute(MapWithStateRDD.scala:148)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
> at org.apache.spark.CacheManager.getOrCompute(CacheManager.scala:69)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:268)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
> at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
> at org.apache.spark.scheduler.Task.run(Task.scala:89)
> at org.apache.spark.executor.Executor$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$$failJobAndIndependentStages(DAGScheduler.scala:1431)
> at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1419)
> at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1418)
> at
> scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
> at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
> at
> org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1418)
> at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:799)
> at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:799)
> at scala.Option.foreach(Option.scala:236)
> at
> org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:799)
> at
> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1640)
> at
> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1599)
> at
> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1588)
> at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
> at
> org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:620)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:1832)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:1845)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:1858)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:1929)
> at
> org.apache.spark.rdd.RDD$$anonfun$foreachPartition$1.apply(RDD.scala:920)
> at
> org.apache.spark.rdd.RDD$$anonfun$foreachPartition$1.apply(RDD.scala:918)
> at
> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
> at
> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:111)
> at org.apache.spark.rdd.RDD.withScope(RDD.scala:316)
> at org.apache.spark.rdd.RDD.foreachPartition(RDD.scala:918)
> at
> org.apache.spark.examples.streaming.StatefulNetworkWordCountKryo$$anonfun$createContext$2.apply(StatefulNetworkCountKryo.scala:72)
> at
> org.apache.spark.examples.streaming.StatefulNetworkWordCountKryo$$anonfun$createContext$2.apply(StatefulNetworkCountKryo.scala:71)
> at
> org.apache.spark.streaming.dstream.DStream$$anonfun$foreachRDD$1$$anonfun$apply$mcV$sp$3.apply(DStream.scala:661)
> at
> org.apache.spark.streaming.dstream.DStream$$anonfun$foreachRDD$1$$anonfun$apply$mcV$sp$3.apply(DStream.scala:661)
> at
> org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1$$anonfun$apply$mcV$sp$1.apply$mcV$sp(ForEachDStream.scala:50)
> at
> org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1$$anonfun$apply$mcV$sp$1.apply(ForEachDStream.scala:50)
> at
> org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1$$anonfun$apply$mcV$sp$1.apply(ForEachDStream.scala:50)
> at
> org.apache.spark.streaming.dstream.DStream.createRDDWithLocalProperties(DStream.scala:426)
> at
> org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply$mcV$sp(ForEachDStream.scala:49)
> at
> org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply(ForEachDStream.scala:49)
> at
> org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply(ForEachDStream.scala:49)
> at scala.util.Try$.apply(Try.scala:161)
> at org.apache.spark.streaming.scheduler.Job.run(Job.scala:39)
> at
> org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply$mcV$sp(JobScheduler.scala:224)
> at
> org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply(JobScheduler.scala:224)
> at
> org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply(JobScheduler.scala:224)
> at scala.util.DynamicVariable.withValue(DynamicVariable.scala:57)
> at
> org.apache.spark.streaming.scheduler.JobScheduler$JobHandler.run(JobScheduler.scala:223)
> 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)
> Caused by: java.lang.NullPointerException
> at
> org.apache.spark.streaming.util.OpenHashMapBasedStateMap.get(StateMap.scala:103)
> at
> org.apache.spark.streaming.util.OpenHashMapBasedStateMap.get(StateMap.scala:111)
> at
> org.apache.spark.streaming.util.OpenHashMapBasedStateMap.get(StateMap.scala:111)
> at
> org.apache.spark.streaming.rdd.MapWithStateRDDRecord$$anonfun$updateRecordWithData$1.apply(MapWithStateRDD.scala:56)
> at
> org.apache.spark.streaming.rdd.MapWithStateRDDRecord$$anonfun$updateRecordWithData$1.apply(MapWithStateRDD.scala:55)
> at scala.collection.Iterator$class.foreach(Iterator.scala:727)
> at
> org.apache.spark.InterruptibleIterator.foreach(InterruptibleIterator.scala:28)
> at
> org.apache.spark.streaming.rdd.MapWithStateRDDRecord$.updateRecordWithData(MapWithStateRDD.scala:55)
> at
> org.apache.spark.streaming.rdd.MapWithStateRDD.compute(MapWithStateRDD.scala:154)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
> at org.apache.spark.CacheManager.getOrCompute(CacheManager.scala:69)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:268)
> at
> org.apache.spark.streaming.rdd.MapWithStateRDD.compute(MapWithStateRDD.scala:148)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
> at org.apache.spark.CacheManager.getOrCompute(CacheManager.scala:69)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:268)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
> at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
> at org.apache.spark.scheduler.Task.run(Task.scala:89)
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:213)
> ... 3 more
> {code}
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org
For additional commands, e-mail: issues-h...@spark.apache.org
