Backport: [SPARK-12213][SQL] use multiple partitions for single distinct query #148
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Currently, we could generate different plans for query with single distinct (depends on spark.sql.specializeSingleDistinctAggPlanning), one works better on low cardinality columns, the other
works better for high cardinality column (default one).
This PR change to generate a single plan (three aggregations and two exchanges), which work better in both cases, then we could safely remove the flag `spark.sql.specializeSingleDistinctAggPlanning` (introduced in 1.6).
For a query like `SELECT COUNT(DISTINCT a) FROM table` will be
```
AGG-4 (count distinct)
Shuffle to a single reducer
Partial-AGG-3 (count distinct, no grouping)
Partial-AGG-2 (grouping on a)
Shuffle by a
Partial-AGG-1 (grouping on a)
```
This PR also includes large refactor for aggregation (reduce 500+ lines of code)
cc yhuai nongli marmbrus
Author: Davies Liu <[email protected]>
Closes apache#10228 from davies/single_distinct.
markhamstra
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Feb 1, 2016
Backport: [SPARK-12213][SQL] use multiple partitions for single distinct query
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Original commit message by Davies Liu:
Currently, we could generate different plans for query with single distinct (depends on spark.sql.specializeSingleDistinctAggPlanning), one works better on low cardinality columns, the other
works better for high cardinality column (default one).
This PR change to generate a single plan (three aggregations and two exchanges), which work better in both cases, then we could safely remove the flag
spark.sql.specializeSingleDistinctAggPlanning(introduced in 1.6).For a query like
SELECT COUNT(DISTINCT a) FROM tablewill beThis PR also includes large refactor for aggregation (reduce 500+ lines of code)
cc yhuai nongli marmbrus
Author: Davies Liu [email protected]
Closes apache#10228 from davies/single_distinct.