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https://issues.apache.org/jira/browse/SPARK-31754?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17237961#comment-17237961
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fanxin edited comment on SPARK-31754 at 11/24/20, 9:07 AM:
-----------------------------------------------------------
I encountered the same NPE in Spark2.4.4. I have found that the root cause of
the error is that the state store saved a corrupted snapshot files (and delta
files were corrupted too, although corrupted delta file will not result in
NPE). It has already been fixed in Spark3. Issue related to this problem is
https://issues.apache.org/jira/browse/SPARK-29438. By the way, in my case, the
`coalesce` may lead to NPE.
was (Author: fanxiin):
I encountered the same NPE in Spark2.4.4. I have found that the root cause of
the error is that the state store saved a corrupted snapshot files (and delta
files were corrupted too, although corrupted delta file will not result in
NPE). It has already been fixed in Spark3. Issue related to this problem is
https://issues.apache.org/jira/browse/SPARK-29438.
> 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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