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https://issues.apache.org/jira/browse/HIVE-10550?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14541655#comment-14541655
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ASF GitHub Bot commented on HIVE-10550:
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GitHub user ChengXiangLi opened a pull request:
https://github.com/apache/hive/pull/36
HIVE-10550 Dynamic RDD caching optimization for Hive on Spark.
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/ChengXiangLi/hive dynamicrddcaching
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/hive/pull/36.patch
To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:
This closes #36
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commit 8c7668613939cdde44873a8aebe14493acfc6357
Author: chengxiang li <chengxiang...@intel.com>
Date: 2015-05-13T09:37:59Z
HIVE-10550 Dynamic RDD caching optimization for Hive on Spark.
----
> Dynamic RDD caching optimization for HoS.[Spark Branch]
> -------------------------------------------------------
>
> Key: HIVE-10550
> URL: https://issues.apache.org/jira/browse/HIVE-10550
> Project: Hive
> Issue Type: Sub-task
> Components: Spark
> Reporter: Chengxiang Li
> Assignee: Chengxiang Li
> Attachments: HIVE-10550.1.patch
>
>
> A Hive query may try to scan the same table multi times, like self-join,
> self-union, or even share the same subquery, [TPC-DS
> Q39|https://github.com/hortonworks/hive-testbench/blob/hive14/sample-queries-tpcds/query39.sql]
> is an example. As you may know that, Spark support cache RDD data, which
> mean Spark would put the calculated RDD data in memory and get the data from
> memory directly for next time, this avoid the calculation cost of this
> RDD(and all the cost of its dependencies) at the cost of more memory usage.
> Through analyze the query context, we should be able to understand which part
> of query could be shared, so that we can reuse the cached RDD in the
> generated Spark job.
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