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https://issues.apache.org/jira/browse/HIVE-3652?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13490446#comment-13490446
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Namit Jain commented on HIVE-3652:
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[~amareshwari], do you think it would be possible to get a cheap implementation
with a single mapper performing
multiple dimension joins one after the other ? I think, we should start with a
few assumptions:
1. The user will not give any map-join hints. Let us derive thin info. from the
statistics, as Mark suggested.
2. This should be a single map-only job (no reducer).
3. As a first cut, it might be useful to optimize the query:
select /*+ MAPJOIN(b,c) */ from FACT a join DIM1 b on a.k1=b.k1 JOIN DIM2 c
on a.k2=c.k2
The above query should not have a back-up task in case the map-join fails, and
it should be run
as a single map-only job. Once that is done, using cost to convert join into
map-join can be explored independently.
> 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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