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From "Namit Jain (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (HIVE-3652) Join optimization for star schema
Date Mon, 05 Nov 2012 17:22:13 GMT

    [ https://issues.apache.org/jira/browse/HIVE-3652?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13490750#comment-13490750
] 

Namit Jain commented on HIVE-3652:
----------------------------------

Yes, it will not work automatically.

You need to change the auto join conversion logic, where you will create a task tree as a
backup task for multiple mapjoins.
It is do-able, and maybe the cleanest way of fitting it into the current architecture.
                
> 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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