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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 06:01:15 GMT

    [ https://issues.apache.org/jira/browse/HIVE-3652?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13490470#comment-13490470
] 

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

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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