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From "Maciek Kocon (JIRA)" <j...@apache.org>
Subject [jira] [Created] (HIVE-12336) Sort Merge Partition Map Join
Date Wed, 04 Nov 2015 15:30:27 GMT
Maciek Kocon created HIVE-12336:
-----------------------------------

             Summary: Sort Merge Partition Map Join
                 Key: HIVE-12336
                 URL: https://issues.apache.org/jira/browse/HIVE-12336
             Project: Hive
          Issue Type: Improvement
          Components: Logical Optimizer, Physical Optimizer, SQL
    Affects Versions: 0.13.0, 0.14.0, 0.13.1, 1.0.0, 1.1.0
            Reporter: Maciek Kocon


Logically and functionally bucketing and partitioning are quite similar - both provide mechanism
to segregate and separate the table's data based on its content. Thanks to that significant
further optimisations like [partition] PRUNING or [bucket] MAP JOIN are possible.
The difference seems to be imposed by design where the PARTITIONing is open/explicit while
BUCKETing is discrete/implicit.
Partitioning seems to be very common if not a standard feature in all current RDBMS while
BUCKETING seems to be HIVE specific only.
In a way BUCKETING could be also called by "hashing" or simply "IMPLICIT PARTITIONING".

Regardless of the fact that these two are recognised as two separate features available in
Hive there should be nothing to prevent leveraging same existing query/join optimisations
across the two.


①[Sort Merge] PARTITION Map join (no progress yet)
Enable Bucket Map Join or better, the Sort Merge Bucket Map Join equivalent optimisations
when PARTITIONING is used exclusively or in combination with BUCKETING.

For JOIN conditions where partitioning criteria are used respectively:
            ⋮ 
FROM TabA JOIN TabB
   ON TabA.partCol1 = TabB.partCol2
   AND TabA.partCol2 = TabB.partCol2

the optimizer could/should choose to treat it the same way as with bucketed tables: ⋮ 
FROM TabC
  JOIN TabD
     ON TabC.clusteredByCol1 = TabD.clusteredByCol2
   AND TabC.clusteredByCol2 = TabD.clusteredByCol2

and use either Bucket Map Join or better, the Sort Merge Bucket Map Join. The latter would
require capability to create sorted partitions first.

This is based on fact that same way as buckets translate to separate files, the partitions
essentially provide the same mapping.
When data locality is known the optimizer could focus only on joining corresponding partitions
rather than whole data sets.

②BUCKET pruning (taken care by [HIVE-11525|https://issues.apache.org/jira/browse/HIVE-11525])
Enable partition PRUNING equivalent optimisation for queries on BUCKETED tables

Simplest example is for queries like:
"SELECT … FROM x WHERE colA=123123"
to read only the relevant bucket file rather than all file-buckets that belong to a table.



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