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Posted to dev@hive.apache.org by "Xin Hao (JIRA)" <ji...@apache.org> on 2015/02/15 08:29:11 UTC
[jira] [Updated] (HIVE-9697) Hive on Spark is not as aggressive as
MR on map join [Spark Branch]
[ https://issues.apache.org/jira/browse/HIVE-9697?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Xin Hao updated HIVE-9697:
--------------------------
Description:
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, 'totalSize' will be used to compare with the threshold 100M ('totalSize' is about 1.5M and smaller than 100M), while for Hive on Spark 'rawDataSize' will be used to compare with the threshold ('rawDataSize' is about 120M and 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.
Summary: Hive on Spark is not as aggressive as MR on map join [Spark Branch] (was: Hive on Spark is not as aggressive as MR on map join)
> 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, 'totalSize' will be used to compare with the threshold 100M ('totalSize' is about 1.5M and smaller than 100M), while for Hive on Spark 'rawDataSize' will be used to compare with the threshold ('rawDataSize' is about 120M and 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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