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Posted to issues@spark.apache.org by "fritz (Jira)" <ji...@apache.org> on 2020/08/03 02:27:00 UTC
[jira] [Commented] (SPARK-31754) Spark Structured Streaming:
NullPointerException in Stream Stream join
[ https://issues.apache.org/jira/browse/SPARK-31754?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17169677#comment-17169677 ]
fritz commented on SPARK-31754:
-------------------------------
Hi [~puviarasu] [~kabhwan], recently facing similar issue with the NPE when do stream-stream join. It throwing same exception with the log that [~puviarasu] share above.
The only different with my case is the source from kafka. Other than that is same.
Have checked and ensure the join key is not null.
Any advice? Thanks
> Spark Structured Streaming: NullPointerException in Stream Stream join
> ----------------------------------------------------------------------
>
> Key: SPARK-31754
> URL: https://issues.apache.org/jira/browse/SPARK-31754
> Project: Spark
> Issue Type: Bug
> Components: Structured Streaming
> Affects Versions: 2.4.0
> Environment: Spark Version : 2.4.0
> Hadoop Version : 3.0.0
> Reporter: Puviarasu
> Priority: Major
> Labels: structured-streaming
> Attachments: CodeGen.txt, Excpetion-3.0.0Preview2.txt, Logical-Plan.txt
>
>
> When joining 2 streams with watermarking and windowing we are getting NullPointer Exception after running for few minutes.
> After failure we analyzed the checkpoint offsets/sources and found the files for which the application failed. These files are not having any null values in the join columns.
> We even started the job with the files and the application ran. From this we concluded that the exception is not because of the data from the streams.
> *Code:*
>
> {code:java}
> val optionsMap1 = Map[String, String]("Path" -> "/path/to/source1", "maxFilesPerTrigger" -> "1", "latestFirst" -> "false", "fileNameOnly" ->"false", "checkpointLocation" -> "/path/to/checkpoint1", "rowsPerSecond" -> "1" )
> val optionsMap2 = Map[String, String]("Path" -> "/path/to/source2", "maxFilesPerTrigger" -> "1", "latestFirst" -> "false", "fileNameOnly" ->"false", "checkpointLocation" -> "/path/to/checkpoint2", "rowsPerSecond" -> "1" )
> spark.readStream.format("parquet").options(optionsMap1).load().createTempView("source1")
> spark.readStream.format("parquet").options(optionsMap2).load().createTempView("source2")
> spark.sql("select * from source1 where eventTime1 is not null and col1 is not null").withWatermark("eventTime1", "30 minutes").createTempView("viewNotNull1")
> spark.sql("select * from source2 where eventTime2 is not null and col2 is not null").withWatermark("eventTime2", "30 minutes").createTempView("viewNotNull2")
> spark.sql("select * from viewNotNull1 a join viewNotNull2 b on a.col1 = b.col2 and a.eventTime1 >= b.eventTime2 and a.eventTime1 <= b.eventTime2 + interval 2 hours").createTempView("join")
> val optionsMap3 = Map[String, String]("compression" -> "snappy","path" -> "/path/to/sink", "checkpointLocation" -> "/path/to/checkpoint3")
> spark.sql("select * from join").writeStream.outputMode("append").trigger(Trigger.ProcessingTime("5 seconds")).format("parquet").options(optionsMap3).start()
> {code}
>
> *Exception:*
>
> {code:java}
> Caused by: org.apache.spark.SparkException: Job aborted due to stage failure:
> Aborting TaskSet 4.0 because task 0 (partition 0)
> cannot run anywhere due to node and executor blacklist.
> Most recent failure:
> Lost task 0.2 in stage 4.0 (TID 6, executor 3): java.lang.NullPointerException
> at org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificPredicate.eval(Unknown Source)
> at org.apache.spark.sql.execution.streaming.StreamingSymmetricHashJoinExec$OneSideHashJoiner$$anonfun$26.apply(StreamingSymmetricHashJoinExec.scala:412)
> at org.apache.spark.sql.execution.streaming.StreamingSymmetricHashJoinExec$OneSideHashJoiner$$anonfun$26.apply(StreamingSymmetricHashJoinExec.scala:412)
> at org.apache.spark.sql.execution.streaming.state.SymmetricHashJoinStateManager$$anon$2.findNextValueForIndex(SymmetricHashJoinStateManager.scala:197)
> at org.apache.spark.sql.execution.streaming.state.SymmetricHashJoinStateManager$$anon$2.getNext(SymmetricHashJoinStateManager.scala:221)
> at org.apache.spark.sql.execution.streaming.state.SymmetricHashJoinStateManager$$anon$2.getNext(SymmetricHashJoinStateManager.scala:157)
> at org.apache.spark.util.NextIterator.hasNext(NextIterator.scala:73)
> at scala.collection.Iterator$JoinIterator.hasNext(Iterator.scala:212)
> at org.apache.spark.sql.execution.streaming.StreamingSymmetricHashJoinExec$$anonfun$org$apache$spark$sql$execution$streaming$StreamingSymmetricHashJoinExec$$onOutputCompletion$1$1.apply$mcV$spala:338)
> at org.apache.spark.sql.execution.streaming.StreamingSymmetricHashJoinExec$$anonfun$org$apache$spark$sql$execution$streaming$StreamingSymmetricHashJoinExec$$onOutputCompletion$1$1.apply(Stream)
> at org.apache.spark.sql.execution.streaming.StreamingSymmetricHashJoinExec$$anonfun$org$apache$spark$sql$execution$streaming$StreamingSymmetricHashJoinExec$$onOutputCompletion$1$1.apply(Stream)
> at org.apache.spark.util.Utils$.timeTakenMs(Utils.scala:583)
> at org.apache.spark.sql.execution.streaming.StateStoreWriter$class.timeTakenMs(statefulOperators.scala:108)
> at org.apache.spark.sql.execution.streaming.StreamingSymmetricHashJoinExec.timeTakenMs(StreamingSymmetricHashJoinExec.scala:126)
> at org.apache.spark.sql.execution.streaming.StreamingSymmetricHashJoinExec.org$apache$spark$sql$execution$streaming$StreamingSymmetricHashJoinExec$$onOutputCompletion$1(StreamingSymmetricHashJ
> at org.apache.spark.sql.execution.streaming.StreamingSymmetricHashJoinExec$$anonfun$org$apache$spark$sql$execution$streaming$StreamingSymmetricHashJoinExec$$processPartitions$1.apply$mcV$sp(St:361)
> at org.apache.spark.util.CompletionIterator$$anon$1.completion(CompletionIterator.scala:44)
> at org.apache.spark.util.CompletionIterator.hasNext(CompletionIterator.scala:33)
> at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage3.processNext(Unknown Source)
> at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
> at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$11$$anon$1.hasNext(WholeStageCodegenExec.scala:624)
> at scala.collection.Iterator$$anon$12.hasNext(Iterator.scala:439)
> at org.apache.spark.sql.execution.UnsafeExternalRowSorter.sort(UnsafeExternalRowSorter.java:216)
> at org.apache.spark.sql.execution.SortExec$$anonfun$1.apply(SortExec.scala:108)
> at org.apache.spark.sql.execution.SortExec$$anonfun$1.apply(SortExec.scala:101)
> at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:836)
> at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:836)
> at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
> at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
> at org.apache.spark.scheduler.Task.run(Task.scala:121)
> at org.apache.spark.executor.Executor$TaskRunner$$anonfun$11.apply(Executor.scala:407)
> at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1408)
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:413)
> at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
> at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
> at java.lang.Thread.run(Thread.java:748)
> Blacklisting behavior can be configured via spark.blacklist.*. at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1890)
> at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1878)
> at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1877)
> at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
> at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
> at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1877)
> at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:929)
> at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:929)
> at scala.Option.foreach(Option.scala:257)
> at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:929)
> at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2111)
> at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2060)
> at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2049)
> at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)
> at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:740)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:2081)
> at org.apache.spark.sql.execution.datasources.FileFormatWriter$.write(FileFormatWriter.scala:167)
> ... 19 moreException in thread "main" org.apache.spark.SparkException: Application application_2345 finished with failed status
> at org.apache.spark.deploy.yarn.Client.run(Client.scala:1158)
> at org.apache.spark.deploy.yarn.YarnClusterApplication.start(Client.scala:1606)
> at org.apache.spark.deploy.SparkSubmit.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:851)
> at org.apache.spark.deploy.SparkSubmit.doRunMain$1(SparkSubmit.scala:167)
> at org.apache.spark.deploy.SparkSubmit.submit(SparkSubmit.scala:195)
> at org.apache.spark.deploy.SparkSubmit.doSubmit(SparkSubmit.scala:86)
> at org.apache.spark.deploy.SparkSubmit$$anon$2.doSubmit(SparkSubmit.scala:926)
> at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:935)
> at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
> {code}
>
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