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https://issues.apache.org/jira/browse/BEAM-1444?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17132066#comment-17132066
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Beam JIRA Bot commented on BEAM-1444:
-------------------------------------
This issue is P2 but has been unassigned without any comment for 60 days so it
has been labeled "stale-P2". If this issue is still affecting you, we care!
Please comment and remove the label. Otherwise, in 14 days the issue will be
moved to P3.
Please see https://beam.apache.org/contribute/jira-priorities/ for a detailed
explanation of what these priorities mean.
> Flatten of Bounded and Unbounded repeats the union with the RDD for each
> micro-batch.
> --------------------------------------------------------------------------------------
>
> Key: BEAM-1444
> URL: https://issues.apache.org/jira/browse/BEAM-1444
> Project: Beam
> Issue Type: Bug
> Components: runner-spark
> Reporter: Amit Sela
> Priority: P2
> Labels: stale-P2
>
> Flatten of BOUNDED and UNBOUNDED PCollections in the Spark runner is
> implemented by applying {{SparkContext#union(RDD...)}} inside a
> {{DStream.transform()}} which causes the same RDD to be "unionized" into each
> micro-batch and so multiplying it's content in the resulting stream (x number
> of batches).
> Spark does not seem to provide any out-of-the-box implementations for this.
> One approach I tried was to create a stream from Queue (single RDD stream)
> but this is not an option since this fails checkpointing.
> Another approach would be to create a custom {{InputDStream}} that does this.
> An important note here is that the challenge here is to find a solution that
> holds with checkpointing and recovery from failure.
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