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Date: Wed, 13 Feb 2013 06:42:14 +0000 (UTC)
From: "Amareshwari Sriramadasu (JIRA)" <jira@apache.org>
To: hive-dev@hadoop.apache.org
Message-ID: <JIRA.12614474.1351839194468.282564.1360737734137@arcas>
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Subject: [jira] [Commented] (HIVE-3652) Join optimization for star schema
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    [ https://issues.apache.org/jira/browse/HIVE-3652?page=3Dcom.atlassian.=
jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=3D13577=
368#comment-13577368 ]=20

Amareshwari Sriramadasu commented on HIVE-3652:
-----------------------------------------------

bq. Is your size threshold correct =E2=80=93 hive.auto.convert.join.nocondi=
tionaltask.size ?
Yes. The tables are very small. I tested with empty tables as well. I'm see=
ing the same behavior.
               =20
> 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: Vikram Dixit K
>             Fix For: 0.11.0
>
>
> Currently, if we join one fact table with multiple dimension tables, it r=
esults in multiple mapreduce jobs for each join with dimension table, becau=
se join would be on different keys for each dimension.=20
> Usually all the dimension tables will be small and can fit into memory an=
d 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 wi=
th fact table on different keys as well.

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