Sergey Shelukhin created HIVE-11525:
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Summary: Bucket pruning
Key: HIVE-11525
URL: https://issues.apache.org/jira/browse/HIVE-11525
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
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.
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
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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