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https://issues.apache.org/jira/browse/SPARK-31754?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17169736#comment-17169736
]
fritz commented on SPARK-31754:
-------------------------------
Thanks for your response [~puviarasu]
Yes. Batch processing is not have the NPE issues and working fine.
Totally agreed, the batch is having higher latency. We already having batch
pipeline, so, changing the streaming pipeline to batch is not an options for us.
What we are doing right now is to just re-run the job, and it is working again,
but, the issues is reappear if there is NPE occurred and the job is failed and
get terminated.
I am not sure if this is useful, we are running on Spark 2.4.5 on EMR
> 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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