imback82 opened a new pull request #28676: URL: https://github.com/apache/spark/pull/28676
<!-- Thanks for sending a pull request! Here are some tips for you: 1. If this is your first time, please read our contributor guidelines: https://spark.apache.org/contributing.html 2. Ensure you have added or run the appropriate tests for your PR: https://spark.apache.org/developer-tools.html 3. If the PR is unfinished, add '[WIP]' in your PR title, e.g., '[WIP][SPARK-XXXX] Your PR title ...'. 4. Be sure to keep the PR description updated to reflect all changes. 5. Please write your PR title to summarize what this PR proposes. 6. If possible, provide a concise example to reproduce the issue for a faster review. 7. If you want to add a new configuration, please read the guideline first for naming configurations in 'core/src/main/scala/org/apache/spark/internal/config/ConfigEntry.scala'. --> ### What changes were proposed in this pull request? <!-- Please clarify what changes you are proposing. The purpose of this section is to outline the changes and how this PR fixes the issue. If possible, please consider writing useful notes for better and faster reviews in your PR. See the examples below. 1. If you refactor some codes with changing classes, showing the class hierarchy will help reviewers. 2. If you fix some SQL features, you can provide some references of other DBMSes. 3. If there is design documentation, please add the link. 4. If there is a discussion in the mailing list, please add the link. --> Currently, the `BroadcastHashJoinExec`'s `outputPartitioning` only uses the streamed side's `outputPartitioning`. However, since `BroadcastHashJoinExec` is only applied for equi-join, the build side's info (the join keys) can be added to `BroadcastHashJoinExec`'s `outputPartitioning`. For example, ```Scala spark.conf.set("spark.sql.autoBroadcastJoinThreshold", "500") val t1 = (0 until 100).map(i => (i % 5, i % 13)).toDF("i1", "j1") val t2 = (0 until 100).map(i => (i % 5, i % 13)).toDF("i2", "j2") val t3 = (0 until 20).map(i => (i % 7, i % 11)).toDF("i3", "j3") val t4 = (0 until 100).map(i => (i % 5, i % 13)).toDF("i4", "j4") // join1 is a sort merge join. val join1 = t1.join(t2, t1("i1") === t2("i2")) // join2 is a broadcast join where t3 is broadcasted. val join2 = join1.join(t3, join1("i1") === t3("i3")) // Join on the column from the broadcasted side (i3). val join3 = join2.join(t4, join2("i3") === t4("i4")) join3.explain ``` You see that `Exchange hashpartitioning(i2#103, 200)` is introduced because there is no output partitioning info from the build side. ``` == Physical Plan == *(6) SortMergeJoin [i3#29], [i4#40], Inner :- *(4) Sort [i3#29 ASC NULLS FIRST], false, 0 : +- Exchange hashpartitioning(i3#29, 200), true, [id=#55] : +- *(3) BroadcastHashJoin [i1#7], [i3#29], Inner, BuildRight : :- *(3) SortMergeJoin [i1#7], [i2#18], Inner : : :- *(1) Sort [i1#7 ASC NULLS FIRST], false, 0 : : : +- Exchange hashpartitioning(i1#7, 200), true, [id=#28] : : : +- LocalTableScan [i1#7, j1#8] : : +- *(2) Sort [i2#18 ASC NULLS FIRST], false, 0 : : +- Exchange hashpartitioning(i2#18, 200), true, [id=#29] : : +- LocalTableScan [i2#18, j2#19] : +- BroadcastExchange HashedRelationBroadcastMode(List(cast(input[0, int, false] as bigint))), [id=#34] : +- LocalTableScan [i3#29, j3#30] +- *(5) Sort [i4#40 ASC NULLS FIRST], false, 0 +- Exchange hashpartitioning(i4#40, 200), true, [id=#39] +- LocalTableScan [i4#40, j4#41] ``` This PR proposes to introduce output partitioning for the build side for `BroadcastHashJoinExec` if the streamed side has a `HashPartitioning` or a collection of `HashPartitioning`s. ### Why are the changes needed? <!-- Please clarify why the changes are needed. For instance, 1. If you propose a new API, clarify the use case for a new API. 2. If you fix a bug, you can clarify why it is a bug. --> To remove unnecessary shuffle. ### Does this PR introduce _any_ user-facing change? <!-- Note that it means *any* user-facing change including all aspects such as the documentation fix. If yes, please clarify the previous behavior and the change this PR proposes - provide the console output, description and/or an example to show the behavior difference if possible. If possible, please also clarify if this is a user-facing change compared to the released Spark versions or within the unreleased branches such as master. If no, write 'No'. --> Yes, now the shuffle in the above example can be eliminated: ``` == Physical Plan == *(5) SortMergeJoin [i3#108], [i4#119], Inner :- *(3) Sort [i3#108 ASC NULLS FIRST], false, 0 : +- *(3) BroadcastHashJoin [i1#86], [i3#108], Inner, BuildRight : :- *(3) SortMergeJoin [i1#86], [i2#97], Inner : : :- *(1) Sort [i1#86 ASC NULLS FIRST], false, 0 : : : +- Exchange hashpartitioning(i1#86, 200), true, [id=#120] : : : +- LocalTableScan [i1#86, j1#87] : : +- *(2) Sort [i2#97 ASC NULLS FIRST], false, 0 : : +- Exchange hashpartitioning(i2#97, 200), true, [id=#121] : : +- LocalTableScan [i2#97, j2#98] : +- BroadcastExchange HashedRelationBroadcastMode(List(cast(input[0, int, false] as bigint))), [id=#126] : +- LocalTableScan [i3#108, j3#109] +- *(4) Sort [i4#119 ASC NULLS FIRST], false, 0 +- Exchange hashpartitioning(i4#119, 200), true, [id=#130] +- LocalTableScan [i4#119, j4#120] ``` ### How was this patch tested? <!-- If tests were added, say they were added here. Please make sure to add some test cases that check the changes thoroughly including negative and positive cases if possible. If it was tested in a way different from regular unit tests, please clarify how you tested step by step, ideally copy and paste-able, so that other reviewers can test and check, and descendants can verify in the future. If tests were not added, please describe why they were not added and/or why it was difficult to add. --> Added new tests. ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org --------------------------------------------------------------------- To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org