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Date: Mon, 26 Jan 2015 21:38:34 +0000 (UTC)
From: "Xuefu Zhang (JIRA)" <jira@apache.org>
To: issues@spark.apache.org
Message-ID: <JIRA.12729720.1406294065000.176691.1422308314579@Atlassian.JIRA>
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Subject: [jira] [Commented] (SPARK-2688) Need a way to run multiple data
 pipeline concurrently
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    [ https://issues.apache.org/jira/browse/SPARK-2688?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14292448#comment-14292448 ] 

Xuefu Zhang commented on SPARK-2688:
------------------------------------

Yeah. We don't need a syntactic suger, but a transformation that just does one pass of the input RDD. This has performance implications on Hive's multi-insert use cases.

> 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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