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From "Lefty Leverenz (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (HIVE-9697) Hive on Spark is not as aggressive as MR on map join [Spark Branch]
Date Fri, 20 Mar 2015 03:53:40 GMT

    [ https://issues.apache.org/jira/browse/HIVE-9697?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14370666#comment-14370666
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Lefty Leverenz commented on HIVE-9697:
--------------------------------------

bq.  Spark prefers rawDataSize for map-join memory estimation. Thus, hive.stats.collect.rawdatasize
should be set "true", which is the default. If this configuration is set to false, then fileSize
will be used instead for estimation, which may not be as accurate.

Should that information be added to the description of *hive.stats.collect.rawdatasize* in
the wiki, with a link from the Spark section?  And should it be added to the configuration
section in "Hive on Spark: Getting Started"?

* [Configuration Properties -- hive.stats.collect.rawdatasize | https://cwiki.apache.org/confluence/display/Hive/Configuration+Properties#ConfigurationProperties-hive.stats.collect.rawdatasize]
** [Configuration Properties -- Spark | https://cwiki.apache.org/confluence/display/Hive/Configuration+Properties#ConfigurationProperties-Spark]
* [Hive on Spark: Getting Started -- Configuring Hive | https://cwiki.apache.org/confluence/display/Hive/Hive+on+Spark%3A+Getting+Started#HiveonSpark:GettingStarted-ConfiguringHive]

> Hive on Spark is not as aggressive as MR on map join [Spark Branch]
> -------------------------------------------------------------------
>
>                 Key: HIVE-9697
>                 URL: https://issues.apache.org/jira/browse/HIVE-9697
>             Project: Hive
>          Issue Type: Sub-task
>          Components: Spark
>            Reporter: Xin Hao
>
> We have a finding during running some Big-Bench cases:
> when the same small table size threshold is used, Map Join operator will not be generated
in Stage Plans for Hive on Spark, while will be generated for Hive on MR.
> For example, When we run BigBench Q25, the meta info of one input ORC table is as below:
>     totalSize=1748955 (about 1.5M)
>     rawDataSize=123050375 (about 120M)
> If we use the following parameter settings,
>     set hive.auto.convert.join=true;
>     set hive.mapjoin.smalltable.filesize=25000000;
>     set hive.auto.convert.join.noconditionaltask=true;
>     set hive.auto.convert.join.noconditionaltask.size=100000000; (100M)
> Map Join will be enabled for Hive on MR mode, while will not be enabled for Hive on Spark.
> We found that for Hive on MR, the HDFS file size for the table (ContentSummary.getLength(),
should approximate the value of ‘totalSize’) will be used to compare with the threshold
100M (smaller than 100M), while for Hive on Spark 'rawDataSize' will be used to compare with
the threshold 100M (larger than 100M). That's why MapJoin is not enabled for Hive on Spark
for this case. And as a result Hive on Spark will get much lower performance data than Hive
on MR for this case.
> When we set  hive.auto.convert.join.noconditionaltask.size=150000000; (150M), MapJoin
will be enabled for Hive on Spark mode, and Hive on Spark will have similar performance data
with Hive on MR by then.



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