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https://issues.apache.org/jira/browse/SPARK-2688?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14291134#comment-14291134
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Xuefu Zhang commented on SPARK-2688:
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I think SPARK-3622 is related to this JIRA but not exactly the same. This JIRA
essentially asks capability of executing a random DAG built of RDDs, while
SPARK-3622 is requesting a custom transformation that can take one input RDD
and generates multiple output RDDs. HIve on Spark projects needs this because
HIve's map-side or reduce-side processing (which is translated to Spark's map
functions) generates multiple outputs in general. On this ground, SPARK-3622 is
more important than SPARK-2688.
It's worth to mention that such a custom transformation can be used in building
a random DAG.
> Need a way to run multiple data pipeline concurrently
> -----------------------------------------------------
>
> Key: SPARK-2688
> URL: https://issues.apache.org/jira/browse/SPARK-2688
> Project: Spark
> Issue Type: New Feature
> Components: Spark Core
> Affects Versions: 1.0.1
> Reporter: Xuefu Zhang
>
> Suppose we want to do the following data processing:
> {code}
> rdd1 -> rdd2 -> rdd3
> | -> rdd4
> | -> rdd5
> \ -> rdd6
> {code}
> where -> represents a transformation. rdd3 to rrdd6 are all derived from an
> intermediate rdd2. We use foreach(fn) with a dummy function to trigger the
> execution. However, rdd.foreach(fn) only trigger pipeline rdd1 -> rdd2 ->
> rdd3. To make things worse, when we call rdd4.foreach(), rdd2 will be
> recomputed. This is very inefficient. Ideally, we should be able to trigger
> the execution the whole graph and reuse rdd2, but there doesn't seem to be a
> way doing so. Tez already realized the importance of this (TEZ-391), so I
> think Spark should provide this too.
> This is required for Hive to support multi-insert queries. HIVE-7292.
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