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https://issues.apache.org/jira/browse/SPARK-30586?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17036788#comment-17036788
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Jungtaek Lim edited comment on SPARK-30586 at 2/14/20 8:38 AM:
---------------------------------------------------------------

"onExecutorAdded" doesn't fill up hostPort in LiveExecutor which looks to be 
null in the stack trace. "onBlockManagerAdded" does, so there may be a possible 
gap - this seems to show one of possible case.

If we have event log file for the application encountered the issue, easier to 
check what's happening there.


was (Author: kabhwan):
"onExecutorAdded" doesn't fill up hostPort in LiveExecutor which looks to be 
null in the stack trace. "onBlockManagerAdded" does - this seems to show one of 
possible case.

If we have event log file for the application encountered the issue, easier to 
check what's happening there.

> NPE in LiveRDDDistribution (AppStatusListener)
> ----------------------------------------------
>
>                 Key: SPARK-30586
>                 URL: https://issues.apache.org/jira/browse/SPARK-30586
>             Project: Spark
>          Issue Type: Bug
>          Components: Spark Core
>    Affects Versions: 2.4.4
>         Environment: A Hadoop cluster consisting of Centos 7.4 machines.
>            Reporter: Jan Van den bosch
>            Priority: Major
>
> We've been noticing a great amount of NullPointerExceptions in our 
> long-running Spark job driver logs:
> {noformat}
> 20/01/17 23:40:12 ERROR AsyncEventQueue: Listener AppStatusListener threw an 
> exception
> java.lang.NullPointerException
>         at 
> org.spark_project.guava.base.Preconditions.checkNotNull(Preconditions.java:191)
>         at 
> org.spark_project.guava.collect.MapMakerInternalMap.putIfAbsent(MapMakerInternalMap.java:3507)
>         at 
> org.spark_project.guava.collect.Interners$WeakInterner.intern(Interners.java:85)
>         at 
> org.apache.spark.status.LiveEntityHelpers$.weakIntern(LiveEntity.scala:603)
>         at 
> org.apache.spark.status.LiveRDDDistribution.toApi(LiveEntity.scala:486)
>         at 
> org.apache.spark.status.LiveRDD$$anonfun$2.apply(LiveEntity.scala:548)
>         at 
> org.apache.spark.status.LiveRDD$$anonfun$2.apply(LiveEntity.scala:548)
>         at 
> scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
>         at 
> scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
>         at 
> scala.collection.mutable.HashMap$$anon$2$$anonfun$foreach$3.apply(HashMap.scala:139)
>         at 
> scala.collection.mutable.HashMap$$anon$2$$anonfun$foreach$3.apply(HashMap.scala:139)
>         at 
> scala.collection.mutable.HashTable$class.foreachEntry(HashTable.scala:236)
>         at scala.collection.mutable.HashMap.foreachEntry(HashMap.scala:40)
>         at scala.collection.mutable.HashMap$$anon$2.foreach(HashMap.scala:139)
>         at 
> scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
>         at scala.collection.AbstractTraversable.map(Traversable.scala:104)
>         at org.apache.spark.status.LiveRDD.doUpdate(LiveEntity.scala:548)
>         at org.apache.spark.status.LiveEntity.write(LiveEntity.scala:49)
>         at 
> org.apache.spark.status.AppStatusListener.org$apache$spark$status$AppStatusListener$$update(AppStatusListener.scala:991)
>         at 
> org.apache.spark.status.AppStatusListener.org$apache$spark$status$AppStatusListener$$maybeUpdate(AppStatusListener.scala:997)
>         at 
> org.apache.spark.status.AppStatusListener$$anonfun$onExecutorMetricsUpdate$2.apply(AppStatusListener.scala:764)
>         at 
> org.apache.spark.status.AppStatusListener$$anonfun$onExecutorMetricsUpdate$2.apply(AppStatusListener.scala:764)
>         at 
> scala.collection.mutable.HashMap$$anon$2$$anonfun$foreach$3.apply(HashMap.scala:139)
>         at 
> scala.collection.mutable.HashMap$$anon$2$$anonfun$foreach$3.apply(HashMap.scala:139)
>         at 
> scala.collection.mutable.HashTable$class.foreachEntry(HashTable.scala:236)
>         at scala.collection.mutable.HashMap.foreachEntry(HashMap.scala:40)
>         at scala.collection.mutable.HashMap$$anon$2.foreach(HashMap.scala:139)
>         at 
> org.apache.spark.status.AppStatusListener.org$apache$spark$status$AppStatusListener$$flush(AppStatusListener.scala:788)
>         at 
> org.apache.spark.status.AppStatusListener.onExecutorMetricsUpdate(AppStatusListener.scala:764)
>         at 
> org.apache.spark.scheduler.SparkListenerBus$class.doPostEvent(SparkListenerBus.scala:59)
>         at 
> org.apache.spark.scheduler.AsyncEventQueue.doPostEvent(AsyncEventQueue.scala:37)
>         at 
> org.apache.spark.scheduler.AsyncEventQueue.doPostEvent(AsyncEventQueue.scala:37)
>         at 
> org.apache.spark.util.ListenerBus$class.postToAll(ListenerBus.scala:91)
>         at 
> org.apache.spark.scheduler.AsyncEventQueue.org$apache$spark$scheduler$AsyncEventQueue$$super$postToAll(AsyncEventQueue.scala:92)
>         at 
> org.apache.spark.scheduler.AsyncEventQueue$$anonfun$org$apache$spark$scheduler$AsyncEventQueue$$dispatch$1.apply$mcJ$sp(AsyncEventQueue.scala:92)
>         at 
> org.apache.spark.scheduler.AsyncEventQueue$$anonfun$org$apache$spark$scheduler$AsyncEventQueue$$dispatch$1.apply(AsyncEventQueue.scala:87)
>         at 
> org.apache.spark.scheduler.AsyncEventQueue$$anonfun$org$apache$spark$scheduler$AsyncEventQueue$$dispatch$1.apply(AsyncEventQueue.scala:87)
>         at scala.util.DynamicVariable.withValue(DynamicVariable.scala:58)
>         at 
> org.apache.spark.scheduler.AsyncEventQueue.org$apache$spark$scheduler$AsyncEventQueue$$dispatch(AsyncEventQueue.scala:87)
>         at 
> org.apache.spark.scheduler.AsyncEventQueue$$anon$1$$anonfun$run$1.apply$mcV$sp(AsyncEventQueue.scala:83)
>         at 
> org.apache.spark.util.Utils$.tryOrStopSparkContext(Utils.scala:1302)
>         at 
> org.apache.spark.scheduler.AsyncEventQueue$$anon$1.run(AsyncEventQueue.scala:82)
> {noformat}
> Symptoms of a Spark app that made us investigate the logs in the first place 
> include:
>  * slower execution of submitted jobs
>  * jobs remaining "Active Jobs" in the Spark UI even though they should have 
> completed days ago
>  * these jobs could not be killed from the Spark UI (the page refreshes but 
> the jobs remained there)
>  * stages for these jobs could not be examined in the Spark UI because it 
> returned an error instead.



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