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https://issues.apache.org/jira/browse/HIVE-3652?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13490521#comment-13490521
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Amareshwari Sriramadasu commented on HIVE-3652:
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bq. select /*+ MAPJOIN(b,c) */ from FACT a join DIM1 b on a.k1=b.k1 JOIN DIM2 c
on a.k2=c.k2
I modified the above query to be the following (with a subquery) :
SELECT /*+ MAPJOIN(dim2) */ subq.m1, subq.m2 FROM (SELECT /*+ MAPJOIN(dim1) */
m1, m2, k2 FROM fact JOIN dim1 ON (fact.k1 = dim1.k1)) subq JOIN dim2 ON
(subq.k2 = dim2.k2);
And it is already launching a single map reduce job for both the joins.
> Join optimization for star schema
> ---------------------------------
>
> Key: HIVE-3652
> URL: https://issues.apache.org/jira/browse/HIVE-3652
> Project: Hive
> Issue Type: Improvement
> Components: Query Processor
> Reporter: Amareshwari Sriramadasu
> Assignee: Amareshwari Sriramadasu
>
> Currently, if we join one fact table with multiple dimension tables, it
> results in multiple mapreduce jobs for each join with dimension table,
> because join would be on different keys for each dimension.
> Usually all the dimension tables will be small and can fit into memory and so
> map-side join can used to join with fact table.
> In this issue I want to look at optimizing such query to generate single
> mapreduce job sothat mapper loads dimension tables into memory and joins with
> fact table on different keys as well.
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