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https://issues.apache.org/jira/browse/HIVE-1018?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12901637#action_12901637
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Namit Jain commented on HIVE-1018:
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I think a immediate use case is to convert the following job:
select /*+ MAPJOIN(T2) */ count(1) from T1 join T2 on T1.x=T2.y;
into a single MR job, if T2 is small.
> pushing down group-by before joins
> ----------------------------------
>
> Key: HIVE-1018
> URL: https://issues.apache.org/jira/browse/HIVE-1018
> Project: Hadoop Hive
> Issue Type: Improvement
> Reporter: Ning Zhang
>
> Queries with both Group-by and Joins are very common and they are expensive
> operations. By default Hive evalutes Joins first then group-by. Sometimes it
> is possible to rewrite queries to apply group-by (or map-side partial group
> by) first before join. This will remove a lot of duplicated keys in joins and
> alleviate skewness in join keys for this case. This rewrite should be
> cost-based. Before we have the stats and the CB framework, we can give users
> hints to do the rewrite.
> A particular case is where the join keys are the same as the grouping keys.
> Or the group keys is a superset of the join keys (so that grouping won't
> affect the result of joins).
> Examples:
> -- Q1
> select A.key, B.key
> from A join B on (A.key=B.key)
> group by A.key, B.key;
> --Q2
> select distinct A.key, B.key
> from A join B on (A.key=B.key);
> --Q3, aggregation function is sum, count, min, max, (avg and median cannot be
> handled).
> selec A.key, sum(A.value), count(1), min(value), max(value)
> from A left semi join B on (A.key=B.key)
> group by A.key;
> -- Q4. grouping keys is a superset of join keys
> select distinct A.key, A.value
> from A join B on (A.key=B.key)
> In the case of join keys are not a subset of grouping keys, we can introduce
> a map-side partial grouping operator with the keys of the UNION of the join
> and grouping keys, to remove unnecessary duplications. This should be
> cost-based though.
> Any thoughts and suggestions?
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