Add support for aggregate distinct for SUM and AVG.#3
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rf972 wants to merge 20 commits intohuaxingao:pushdownfrom
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Add support for aggregate distinct for SUM and AVG.#3rf972 wants to merge 20 commits intohuaxingao:pushdownfrom
rf972 wants to merge 20 commits intohuaxingao:pushdownfrom
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We found that DISTINCT is not supported for the datasource aggregate pushdown patch. Examples of the use of DISTINCT: SUM(DISTINCT column) or AVG(DISTINCT column). This adds DISTINCT support for both SUM and AVG. Previously spark did not give the datasource any hint that the SUM/AVG was a distinct, and as a result the datasource treated both cases as a regular SUM/AVG. With this PR, spark now passes information on distinct to the datasource, so it can form the appropriate query. We also updated the JDBCV2Suite tests with SUM and AVG distinct cases.
Owner
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@rf972 Thanks for your fix. It looks good to me. I am thinking of refactoring my patch because the arithmetic handling is messy. Is it OK that I merge your changes when I do the refactoring? |
Author
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@huaxingao Sure, this sounds great to merge whenever is convenient. Thanks! |
huaxingao
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Oct 29, 2022
…ly equivalent children in `RewriteDistinctAggregates` ### What changes were proposed in this pull request? In `RewriteDistinctAggregates`, when grouping aggregate expressions by function children, treat children that are semantically equivalent as the same. ### Why are the changes needed? This PR will reduce the number of projections in the Expand operator when there are multiple distinct aggregations with superficially different children. In some cases, it will eliminate the need for an Expand operator. Example: In the following query, the Expand operator creates 3\*n rows (where n is the number of incoming rows) because it has a projection for each of function children `b + 1`, `1 + b` and `c`. ``` create or replace temp view v1 as select * from values (1, 2, 3.0), (1, 3, 4.0), (2, 4, 2.5), (2, 3, 1.0) v1(a, b, c); select a, count(distinct b + 1), avg(distinct 1 + b) filter (where c > 0), sum(c) from v1 group by a; ``` The Expand operator has three projections (each producing a row for each incoming row): ``` [a#87, null, null, 0, null, UnscaledValue(c#89)], <== projection #1 (for regular aggregation) [a#87, (b#88 + 1), null, 1, null, null], <== projection #2 (for distinct aggregation of b + 1) [a#87, null, (1 + b#88), 2, (c#89 > 0.0), null]], <== projection #3 (for distinct aggregation of 1 + b) ``` In reality, the Expand only needs one projection for `1 + b` and `b + 1`, because they are semantically equivalent. With the proposed change, the Expand operator's projections look like this: ``` [a#67, null, 0, null, UnscaledValue(c#69)], <== projection #1 (for regular aggregations) [a#67, (b#68 + 1), 1, (c#69 > 0.0), null]], <== projection #2 (for distinct aggregation on b + 1 and 1 + b) ``` With one less projection, Expand produces 2\*n rows instead of 3\*n rows, but still produces the correct result. In the case where all distinct aggregates have semantically equivalent children, the Expand operator is not needed at all. Benchmark code in the JIRA (SPARK-40382). Before the PR: ``` distinct aggregates: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------------------------------------ all semantically equivalent 14721 14859 195 5.7 175.5 1.0X some semantically equivalent 14569 14572 5 5.8 173.7 1.0X none semantically equivalent 14408 14488 113 5.8 171.8 1.0X ``` After the PR: ``` distinct aggregates: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------------------------------------ all semantically equivalent 3658 3692 49 22.9 43.6 1.0X some semantically equivalent 9124 9214 127 9.2 108.8 0.4X none semantically equivalent 14601 14777 250 5.7 174.1 0.3X ``` ### Does this PR introduce _any_ user-facing change? No. ### How was this patch tested? New unit tests. Closes apache#37825 from bersprockets/rewritedistinct_issue. Authored-by: Bruce Robbins <bersprockets@gmail.com> Signed-off-by: Wenchen Fan <wenchen@databricks.com>
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We're closing this PR because it hasn't been updated in a while. This isn't a judgement on the merit of the PR in any way. It's just a way of keeping the PR queue manageable. |
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We found that DISTINCT is not supported for the datasource aggregate pushdown patch.
Examples of the use of DISTINCT: SUM(DISTINCT column) or AVG(DISTINCT column).
This adds DISTINCT support for both SUM and AVG. Previously spark did not give the datasource any hint that the SUM/AVG was a distinct, and as a result the datasource treated both cases as a regular SUM/AVG. With this PR, spark now passes information on distinct to the datasource, so it can form the appropriate query.
We also updated the JDBCV2Suite tests with SUM and AVG distinct cases.
We presumed that this PR should be directed against the "pushdown" branch, but please let us know if we should be handling this PR differently. Thanks !