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https://issues.apache.org/jira/browse/HIVE-3652?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13490470#comment-13490470
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Namit Jain commented on HIVE-3652:
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I was thinking more from the point of the current implementation.
A backup task is per join operation currently.
Thinking more about it, we can have a backup task (which can be a tree of
tasks).
It would be very difficult to fit the following in the current architecture.
There are 10 dimension tables, 9 of them fit into memory and one of them dont.
Perform a map-only join for the first 9, and then a regular backup join for the
last one.
I am not sure, if we want to optimize that.
> 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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