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From "Amareshwari Sriramadasu (JIRA)" <>
Subject [jira] [Commented] (HIVE-3652) Join optimization for star schema
Date Mon, 05 Nov 2012 09:03:12 GMT


Amareshwari Sriramadasu commented on HIVE-3652:

bq. select /*+ MAPJOIN(b,c) */ from FACT a join DIM1 b on a.k1=b.k1 JOIN DIM2 c on a.k2=c.k2

I modified the above query to be the following  (with a subquery) :

SELECT /*+ MAPJOIN(dim2) */ subq.m1, subq.m2 FROM (SELECT /*+ MAPJOIN(dim1) */ m1, m2, k2
 FROM fact JOIN dim1 ON (fact.k1 = dim1.k1)) subq JOIN dim2 ON (subq.k2 = dim2.k2);

And it is already launching a single map reduce job for both the joins.

> Join optimization for star schema
> ---------------------------------
>                 Key: HIVE-3652
>                 URL:
>             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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