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https://issues.apache.org/jira/browse/HIVE-7292?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14326644#comment-14326644
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Lefty Leverenz edited comment on HIVE-7292 at 2/18/15 10:49 PM:
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Although this issue is still marked Unresolved, the Spark branch has been
merged to trunk and is Resolved for the 1.1.0 release (HIVE-9257 and
HIVE-9352). (Edit: Also HIVE-9448.)
was (Author: leftylev):
Although this issue is still marked Unresolved, the Spark branch has been
merged to trunk and is Resolved for the 1.1.0 release (HIVE-9257 and HIVE-9352).
> Hive on Spark
> -------------
>
> Key: HIVE-7292
> URL: https://issues.apache.org/jira/browse/HIVE-7292
> Project: Hive
> Issue Type: Improvement
> Components: Spark
> Reporter: Xuefu Zhang
> Assignee: Xuefu Zhang
> Labels: Spark-M1, Spark-M2, Spark-M3, Spark-M4, Spark-M5
> Attachments: Hive-on-Spark.pdf
>
>
> Spark as an open-source data analytics cluster computing framework has gained
> significant momentum recently. Many Hive users already have Spark installed
> as their computing backbone. To take advantages of Hive, they still need to
> have either MapReduce or Tez on their cluster. This initiative will provide
> user a new alternative so that those user can consolidate their backend.
> Secondly, providing such an alternative further increases Hive's adoption as
> it exposes Spark users to a viable, feature-rich de facto standard SQL tools
> on Hadoop.
> Finally, allowing Hive to run on Spark also has performance benefits. Hive
> queries, especially those involving multiple reducer stages, will run faster,
> thus improving user experience as Tez does.
> This is an umbrella JIRA which will cover many coming subtask. Design doc
> will be attached here shortly, and will be on the wiki as well. Feedback from
> the community is greatly appreciated!
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