spark-user mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From ZHANG Wei <wezh...@outlook.com>
Subject Re: [Structured Streaming] NullPointerException in long running query
Date Wed, 29 Apr 2020 10:15:13 GMT
Is there any chance we also print the least recent failure in stage as the
following most recent failure before Driver statcktrace? 

> >>   Caused by: org.apache.spark.SparkException: Job aborted due to stage
> >> failure: Task 10 in stage 1.0 failed 4 times, most recent failure: Lost
> >> task 10.3 in stage 1.0 (TID 81, spark6, executor 1):
> >> java.lang.NullPointerException
> >> Driver stacktrace:

-- 
Cheers,
-z

On Tue, 28 Apr 2020 23:48:17 -0700
"Shixiong(Ryan) Zhu" <shixiong@databricks.com> wrote:

> The stack trace is omitted by JVM when an exception is thrown too
> many times. This usually happens when you have multiple Spark tasks on the
> same executor JVM throwing the same exception. See
> https://stackoverflow.com/a/3010106
> 
> Best Regards,
> Ryan
> 
> 
> On Tue, Apr 28, 2020 at 10:45 PM lec ssmi <shicheng31604@gmail.com> wrote:
> 
> > It should be a problem of my data quality. It's curious why the
> > driver-side exception stack has no specific exception information.
> >
> > Edgardo Szrajber <szrajber@yahoo.com> 于2020年4月28日周二 下午3:32写道:
> >
> >> The exception occured while aborting the stage. It might be interesting
> >> to try to understand the reason for the abortion.
> >> Maybe timeout? How long the query run?
> >> Bentzi
> >>
> >> Sent from Yahoo Mail on Android
> >> <https://go.onelink.me/107872968?pid=InProduct&c=Global_Internal_YGrowth_AndroidEmailSig__AndroidUsers&af_wl=ym&af_sub1=Internal&af_sub2=Global_YGrowth&af_sub3=EmailSignature>
> >>
> >> On Tue, Apr 28, 2020 at 9:25, Jungtaek Lim
> >> <kabhwan.opensource@gmail.com> wrote:
> >> The root cause of exception is occurred in executor side "Lost task 10.3
> >> in stage 1.0 (TID 81, spark6, executor 1)" so you may need to check there.
> >>
> >> On Tue, Apr 28, 2020 at 2:52 PM lec ssmi <shicheng31604@gmail.com> wrote:
> >>
> >> Hi:
> >>   One of my long-running queries occasionally encountered the following
> >> exception:
> >>
> >>
> >>   Caused by: org.apache.spark.SparkException: Job aborted due to stage
> >> failure: Task 10 in stage 1.0 failed 4 times, most recent failure: Lost
> >> task 10.3 in stage 1.0 (TID 81, spark6, executor 1):
> >> java.lang.NullPointerException
> >> Driver stacktrace:
> >> at org.apache.spark.scheduler.DAGScheduler.org
> >> $apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1602)
> >> at
> >> org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1590)
> >> at
> >> org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1589)
> >> at
> >> scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
> >> at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
> >> at
> >> org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1589)
> >> at
> >> org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:831)
> >> at
> >> org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:831)
> >> at scala.Option.foreach(Option.scala:257)
> >> at
> >> org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:831)
> >> at
> >> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1823)
> >> at
> >> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1772)
> >> at
> >> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1761)
> >> at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
> >> at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:642)
> >> at org.apache.spark.SparkContext.runJob(SparkContext.scala:2034)
> >> at org.apache.spark.SparkContext.runJob(SparkContext.scala:2055)
> >> at org.apache.spark.SparkContext.runJob(SparkContext.scala:2074)
> >> at org.apache.spark.SparkContext.runJob(SparkContext.scala:2099)
> >> at
> >> org.apache.spark.rdd.RDD$$anonfun$foreachPartition$1.apply(RDD.scala:929)
> >> at
> >> org.apache.spark.rdd.RDD$$anonfun$foreachPartition$1.apply(RDD.scala:927)
> >> at
> >> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
> >> at
> >> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
> >> at org.apache.spark.rdd.RDD.withScope(RDD.scala:363)
> >> at org.apache.spark.rdd.RDD.foreachPartition(RDD.scala:927)
> >> at
> >> org.apache.spark.sql.execution.streaming.ForeachSink.addBatch(ForeachSink.scala:49)
> >> at
> >> org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$org$apache$spark$sql$execution$streaming$MicroBatchExecution$$runBatch$3$$anonfun$apply$16.apply(MicroBatchExecution.scala:475)
> >> at
> >> org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:77)
> >> at
> >> org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$org$apache$spark$sql$execution$streaming$MicroBatchExecution$$runBatch$3.apply(MicroBatchExecution.scala:473)
> >> at
> >> org.apache.spark.sql.execution.streaming.ProgressReporter$class.reportTimeTaken(ProgressReporter.scala:271)
> >> at
> >> org.apache.spark.sql.execution.streaming.StreamExecution.reportTimeTaken(StreamExecution.scala:58)
> >> at org.apache.spark.sql.execution.streaming.MicroBatchExecution.org
> >> $apache$spark$sql$execution$streaming$MicroBatchExecution$$runBatch(MicroBatchExecution.scala:472)
> >> at
> >> org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$runActivatedStream$1$$anonfun$apply$mcZ$sp$1.apply$mcV$sp(MicroBatchExecution.scala:133)
> >> at
> >> org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$runActivatedStream$1$$anonfun$apply$mcZ$sp$1.apply(MicroBatchExecution.scala:121)
> >> at
> >> org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$runActivatedStream$1$$anonfun$apply$mcZ$sp$1.apply(MicroBatchExecution.scala:121)
> >> at
> >> org.apache.spark.sql.execution.streaming.ProgressReporter$class.reportTimeTaken(ProgressReporter.scala:271)
> >> at
> >> org.apache.spark.sql.execution.streaming.StreamExecution.reportTimeTaken(StreamExecution.scala:58)
> >> at
> >> org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$runActivatedStream$1.apply$mcZ$sp(MicroBatchExecution.scala:121)
> >> at
> >> org.apache.spark.sql.execution.streaming.ProcessingTimeExecutor.execute(TriggerExecutor.scala:56)
> >> at
> >> org.apache.spark.sql.execution.streaming.MicroBatchExecution.runActivatedStream(MicroBatchExecution.scala:117)
> >> at org.apache.spark.sql.execution.streaming.StreamExecution.org
> >> $apache$spark$sql$execution$streaming$StreamExecution$$runStream(StreamExecution.scala:279)
> >> ... 1 more
> >>
> >>
> >>
> >> According to the exception stack, it seems to have nothing to do with the
> >> logic of my code.Is this a spark bug or something? The version of spark is
> >> 2.3.1.
> >>
> >> Best
> >> Lec Ssmi
> >>
> >>

---------------------------------------------------------------------
To unsubscribe e-mail: user-unsubscribe@spark.apache.org


Mime
View raw message