[jira] [Commented] (BEAM-610) Enable spark's checkpointing mechanism for driver-failure recovery in streaming

["ASF GitHub Bot (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/BEAM-610?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15510576#comment-15510576 ] 

ASF GitHub Bot commented on BEAM-610:
-------------------------------------

Github user asfgit closed the pull request at:

    https://github.com/apache/incubator-beam/pull/909


> Enable spark's checkpointing mechanism for driver-failure recovery in streaming
> -------------------------------------------------------------------------------
>
>                 Key: BEAM-610
>                 URL: https://issues.apache.org/jira/browse/BEAM-610
>             Project: Beam
>          Issue Type: Bug
>          Components: runner-spark
>            Reporter: Amit Sela
>            Assignee: Amit Sela
>
> For streaming applications, Spark provides a checkpoint mechanism useful for stateful processing and driver failures. See: https://spark.apache.org/docs/1.6.2/streaming-programming-guide.html#checkpointing
> This requires the "lambdas", or the content of DStream/RDD functions to be Serializable - currently, the runner a lot of the translation work in streaming to the batch translator, which can no longer be the case because it passes along non-serializables.
> This also requires wrapping the creation of the streaming application's graph in a "getOrCreate" manner. See: https://spark.apache.org/docs/1.6.2/streaming-programming-guide.html#how-to-configure-checkpointing
> Another limitation is the need to wrap Accumulators and Broadcast variables in Singletons in order for them to be re-created once stale after recovery.
> This work is a prerequisite to support PerKey workflows, which will be support via Spark's stateful operators such as mapWithState.   



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