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https://issues.apache.org/jira/browse/SPARK-28917?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Marcelo Masiero Vanzin resolved SPARK-28917.
--------------------------------------------
Fix Version/s: 3.0.0
2.4.5
Resolution: Fixed
Issue resolved by pull request 25951
[https://github.com/apache/spark/pull/25951]
> Jobs can hang because of race of RDD.dependencies
> -------------------------------------------------
>
> Key: SPARK-28917
> URL: https://issues.apache.org/jira/browse/SPARK-28917
> Project: Spark
> Issue Type: Bug
> Components: Scheduler
> Affects Versions: 2.3.3, 2.4.3
> Reporter: Imran Rashid
> Assignee: Imran Rashid
> Priority: Major
> Fix For: 2.4.5, 3.0.0
>
>
> {{RDD.dependencies}} stores the precomputed cache value, but it is not
> thread-safe. This can lead to a race where the value gets overwritten, but
> the DAGScheduler gets stuck in an inconsistent state. In particular, this
> can happen when there is a race between the DAGScheduler event loop, and
> another thread (eg. a user thread, if there is multi-threaded job submission).
> First, a job is submitted by the user, which then computes the result Stage
> and its parents:
> https://github.com/apache/spark/blob/24655583f1cb5dae2e80bb572604fb4a9761ec07/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala#L983
> Which eventually makes a call to {{rdd.dependencies}}:
> https://github.com/apache/spark/blob/24655583f1cb5dae2e80bb572604fb4a9761ec07/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala#L519
> At the same time, the user could also touch {{rdd.dependencies}} in another
> thread, which could overwrite the stored value because of the race.
> Then the DAGScheduler checks the dependencies *again* later on in the job
> submission, via {{getMissingParentStages}}
> https://github.com/apache/spark/blob/24655583f1cb5dae2e80bb572604fb4a9761ec07/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala#L1025
> Because it will find new dependencies, it will create entirely different
> stages. Now the job has some orphaned stages which will never run.
> One symptoms of this are seeing disjoint sets of stages in the "Parents of
> final stage" and the "Missing parents" messages on job submission (however
> this is not required).
> (*EDIT*: Seeing repeated msgs "Registering RDD X" actually is just fine, it
> is not a symptom of a problem at all. It just means the RDD is the *input*
> to multiple shuffles.)
> {noformat}
> [INFO] 2019-08-15 23:22:31,570 org.apache.spark.SparkContext logInfo -
> Starting job: count at XXX.scala:462
> ...
> [INFO] 2019-08-15 23:22:31,573 org.apache.spark.scheduler.DAGScheduler
> logInfo - Registering RDD 14 (repartition at XXX.scala:421)
> ...
> ...
> [INFO] 2019-08-15 23:22:31,582 org.apache.spark.scheduler.DAGScheduler
> logInfo - Got job 1 (count at XXX.scala:462) with 40 output partitions
> [INFO] 2019-08-15 23:22:31,582 org.apache.spark.scheduler.DAGScheduler
> logInfo - Final stage: ResultStage 5 (count at XXX.scala:462)
> [INFO] 2019-08-15 23:22:31,582 org.apache.spark.scheduler.DAGScheduler
> logInfo - Parents of final stage: List(ShuffleMapStage 4)
> [INFO] 2019-08-15 23:22:31,599 org.apache.spark.scheduler.DAGScheduler
> logInfo - Registering RDD 14 (repartition at XXX.scala:421)
> [INFO] 2019-08-15 23:22:31,599 org.apache.spark.scheduler.DAGScheduler
> logInfo - Missing parents: List(ShuffleMapStage 6)
> {noformat}
> Another symptom is only visible with DEBUG logs turned on for DAGScheduler --
> you will calls to {{submitStage(Stage X)}} multiple times, followed by a
> different set of missing stages. eg. here, we see stage 1 first is missing
> stage 0 as a dependency, and then later on its missing stage 23:
> {noformat}
> 19/09/19 22:28:15 DEBUG scheduler.DAGScheduler: submitStage(ShuffleMapStage 1)
> 19/09/19 22:28:15 DEBUG scheduler.DAGScheduler: missing: List(ShuffleMapStage
> 0)
> ...
> 19/09/19 22:32:01 DEBUG scheduler.DAGScheduler: submitStage(ShuffleMapStage 1)
> 19/09/19 22:32:01 DEBUG scheduler.DAGScheduler: missing: List(ShuffleMapStage
> 23)
> {noformat}
> Note that there is a similar issue w/ {{rdd.partitions}}. In particular for
> some RDDs, {{partitions}} references {{dependencies}} (eg. {{CoGroupedRDD}}).
>
> There is also an issue that {{rdd.storageLevel}} is read and cached in the
> scheduler, but it could be modified simultaneously by the user in another
> thread. But, I can't see a way it could effect the scheduler.
> *WORKAROUND*:
> (a) call {{rdd.dependencies}} while you know that RDD is only getting touched
> by one thread (eg. in the thread that created it, or before you submit
> multiple jobs touching that RDD from other threads). Then that value will get
> cached.
> (b) don't submit jobs from multiple threads.
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