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From "Rui Li (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (HIVE-13293) Query occurs performance degradation after enabling parallel order by for Hive on sprak
Date Thu, 17 Mar 2016 01:41:33 GMT

    [ https://issues.apache.org/jira/browse/HIVE-13293?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15198571#comment-15198571
] 

Rui Li commented on HIVE-13293:
-------------------------------

My understanding is that to do the sampling, we need to compute the RDD, which can be a big
overhead for complicated queries. Therefore the optimization only works for queries where
order by is dominant. The best use case should be just ordering a big table. For complicated
queries, the re-computation of RDD may eventually hurt the performance.
MR doesn't have this problem because MR launches a separate job to do the ordering, and the
data to be sampled is already on HDFS.

I think one possible solution is that we can break the spark work at parallel order by, i.e.
just as MR, we compute everything to be sorted, and then launch a separate spark job to just
do the ordering. I can do a PoC to see how this works.
[~xuefuz] what do you think?

> Query occurs performance degradation after enabling parallel order by for Hive on sprak
> ---------------------------------------------------------------------------------------
>
>                 Key: HIVE-13293
>                 URL: https://issues.apache.org/jira/browse/HIVE-13293
>             Project: Hive
>          Issue Type: Bug
>          Components: Spark
>    Affects Versions: 2.0.0
>            Reporter: Lifeng Wang
>
> I use TPCx-BB to do some performance test on Hive on Spark engine. And found query 10
has performance degradation when enabling parallel order by.
> It seems that sampling cost much time before running the real query.



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