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https://issues.apache.org/jira/browse/HIVE-3652?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13490513#comment-13490513
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Amareshwari Sriramadasu commented on HIVE-3652:
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bq. "If we have series of MapJoinOperators, and the Operator tree has MapJoin
followed by MapJoin", then we can run all the map joins in single query. Some
of this already solved by HIVE-1246.
Sorry, was too quick here. HIVE-1246 solves case of all join keys being same.
If we have MapJoin followed by MapJoin, can we make the second operator child
of first instead of a sink in between? I'm thinking that should just working
for the cases of joining on different keys. Let me know if I'm wrong.
> 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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