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From "Yin Huai (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (HIVE-4827) Merge a Map-only job to its following MapReduce job with multiple inputs
Date Sun, 21 Jul 2013 03:42:50 GMT

     [ https://issues.apache.org/jira/browse/HIVE-4827?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Yin Huai updated HIVE-4827:
---------------------------

    Description: 
When hive.optimize.mapjoin.mapreduce is on, CommonJoinResolver can attach a Map-only job (MapJoin)
to its following MapReduce job. But this merge only happens when the MapReduce job has a single
input. With Correlation Optimizer (HIVE-2206), it is possible that the MapReduce job can have
multiple inputs (for multiple operation paths). It is desired to improve CommonJoinResolver
to merge a Map-only job to the corresponding Map task of the MapReduce job.

Example:
{code:sql}
set hive.optimize.correlation=true;
set hive.auto.convert.join=true;
set hive.optimize.mapjoin.mapreduce=true;
SELECT tmp1.key, count(*)
FROM (SELECT x1.key1 AS key
      FROM bigTable1 x1 JOIN smallTable1 y1 ON (x1.key1 = y1.key1)
      GROUP BY x1.key1) tmp1
JOIN (SELECT x2.key2 AS key
      FROM bigTable2 x2 JOIN smallTable2 y2 ON (x2.key2 = y2.key2)
      GROUP BY x2.key2) tmp2
ON (tmp1.key = tmp2.key)
GROUP BY tmp1.key;
{\code}
In this query, join operations inside tmp1 and tmp2 will be converted to two MapJoins. With
Correlation Optimizer, aggregations in tmp1, tmp2, and join of tmp1 and tmp2, and the last
aggregation will be executed in the same MapReduce job (Reduce side). Since this MapReduce
job has two inputs, right now, CommonJoinResolver cannot attach two MapJoins to the Map side
of a MapReduce job.

Another example:
{code:sql}
SELECT tmp1.key
FROM (SELECT x1.key2 AS key
      FROM bigTable1 x1 JOIN smallTable1 y1 ON (x1.key1 = y1.key1)
      UNION ALL
      SELECT x2.key2 AS key
      FROM bigTable2 x2 JOIN smallTable2 y2 ON (x2.key1 = y2.key1)) tmp1
{\code}
For this case, we will have three Map-only jobs (two for MapJoins and one for Union). It will
be good to use a single Map-only job to execute this query.

  was:
When hive.optimize.mapjoin.mapreduce is on, CommonJoinResolver can attach a Map-only job (MapJoin)
to its following MapReduce job. But this merge only happens when the MapReduce job has a single
input. With Correlation Optimizer (HIVE-2206), it is possible that the MapReduce job can have
multiple inputs (for multiple operation paths). It is desired to improve CommonJoinResolver
to merge a Map-only job to the corresponding Map task of the MapReduce job.

Example:
{code:sql}
set hive.optimize.correlation=true;
set hive.auto.convert.join=true;
set hive.optimize.mapjoin.mapreduce=true;
SELECT tmp1.key, count(*)
FROM (SELECT x1.key1 AS key
      FROM bigTable1 x1 JOIN smallTable1 y1 ON (x1.key1 = y1.key1)
      GROUP BY x1.key1) tmp1
JOIN (SELECT x2.key2 AS key
      FROM bigTable2 x2 JOIN smallTable2 y2 ON (x2.key1 = y2.key1)
      GROUP BY x2.key2) tmp2
ON (tmp1.key = tmp2.key)
GROUP BY tmp1.key;
{\code}
In this query, join operations inside tmp1 and tmp2 will be converted to two MapJoins. With
Correlation Optimizer, aggregations in tmp1, tmp2, and join of tmp1 and tmp2, and the last
aggregation will be executed in the same MapReduce job (Reduce side). Since this MapReduce
job has two inputs, right now, CommonJoinResolver cannot attach two MapJoins to the Map side
of a MapReduce job.

Another example:
{code:sql}
SELECT tmp1.key
FROM (SELECT x1.key2 AS key
      FROM bigTable1 x1 JOIN smallTable1 y1 ON (x1.key1 = y1.key1)
      UNION ALL
      SELECT x2.key2 AS key
      FROM bigTable2 x2 JOIN smallTable2 y2 ON (x2.key1 = y2.key1)) tmp1
{\code}
For this case, we will have three Map-only jobs (two for MapJoins and one for Union). It will
be good to use a single Map-only job to execute this query.

    
> Merge a Map-only job to its following MapReduce job with multiple inputs
> ------------------------------------------------------------------------
>
>                 Key: HIVE-4827
>                 URL: https://issues.apache.org/jira/browse/HIVE-4827
>             Project: Hive
>          Issue Type: Improvement
>          Components: Query Processor
>    Affects Versions: 0.12.0
>            Reporter: Yin Huai
>            Assignee: Yin Huai
>
> When hive.optimize.mapjoin.mapreduce is on, CommonJoinResolver can attach a Map-only
job (MapJoin) to its following MapReduce job. But this merge only happens when the MapReduce
job has a single input. With Correlation Optimizer (HIVE-2206), it is possible that the MapReduce
job can have multiple inputs (for multiple operation paths). It is desired to improve CommonJoinResolver
to merge a Map-only job to the corresponding Map task of the MapReduce job.
> Example:
> {code:sql}
> set hive.optimize.correlation=true;
> set hive.auto.convert.join=true;
> set hive.optimize.mapjoin.mapreduce=true;
> SELECT tmp1.key, count(*)
> FROM (SELECT x1.key1 AS key
>       FROM bigTable1 x1 JOIN smallTable1 y1 ON (x1.key1 = y1.key1)
>       GROUP BY x1.key1) tmp1
> JOIN (SELECT x2.key2 AS key
>       FROM bigTable2 x2 JOIN smallTable2 y2 ON (x2.key2 = y2.key2)
>       GROUP BY x2.key2) tmp2
> ON (tmp1.key = tmp2.key)
> GROUP BY tmp1.key;
> {\code}
> In this query, join operations inside tmp1 and tmp2 will be converted to two MapJoins.
With Correlation Optimizer, aggregations in tmp1, tmp2, and join of tmp1 and tmp2, and the
last aggregation will be executed in the same MapReduce job (Reduce side). Since this MapReduce
job has two inputs, right now, CommonJoinResolver cannot attach two MapJoins to the Map side
of a MapReduce job.
> Another example:
> {code:sql}
> SELECT tmp1.key
> FROM (SELECT x1.key2 AS key
>       FROM bigTable1 x1 JOIN smallTable1 y1 ON (x1.key1 = y1.key1)
>       UNION ALL
>       SELECT x2.key2 AS key
>       FROM bigTable2 x2 JOIN smallTable2 y2 ON (x2.key1 = y2.key1)) tmp1
> {\code}
> For this case, we will have three Map-only jobs (two for MapJoins and one for Union).
It will be good to use a single Map-only job to execute this query.

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