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https://issues.apache.org/jira/browse/BEAM-610?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15510576#comment-15510576
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ASF GitHub Bot commented on BEAM-610:
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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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