forked from apache/spark
-
Notifications
You must be signed in to change notification settings - Fork 3
First draft of sessionByKey #28
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
Open
tdas
wants to merge
12
commits into
master
Choose a base branch
from
sessionByKey
base: master
Could not load branches
Branch not found: {{ refName }}
Loading
Could not load tags
Nothing to show
Loading
Are you sure you want to change the base?
Some commits from the old base branch may be removed from the timeline,
and old review comments may become outdated.
Open
Conversation
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
tdas
pushed a commit
that referenced
this pull request
Sep 4, 2020
…or its output partitioning ### What changes were proposed in this pull request? Currently, the `BroadcastHashJoinExec`'s `outputPartitioning` only uses the streamed side's `outputPartitioning`. However, if the join type of `BroadcastHashJoinExec` is an inner-like 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=apache#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=apache#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. There is a new internal config `spark.sql.execution.broadcastHashJoin.outputPartitioningExpandLimit`, which can limit the number of partitioning a `HashPartitioning` can expand to. It can be set to "0" to disable this feature. ### Why are the changes needed? To remove unnecessary shuffle. ### Does this PR introduce _any_ user-facing change? 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=apache#120] : : : +- LocalTableScan [i1#86, j1#87] : : +- *(2) Sort [i2#97 ASC NULLS FIRST], false, 0 : : +- Exchange hashpartitioning(i2#97, 200), true, [id=apache#121] : : +- LocalTableScan [i2#97, j2#98] : +- BroadcastExchange HashedRelationBroadcastMode(List(cast(input[0, int, false] as bigint))), [id=apache#126] : +- LocalTableScan [i3#108, j3#109] +- *(4) Sort [i4#119 ASC NULLS FIRST], false, 0 +- Exchange hashpartitioning(i4#119, 200), true, [id=apache#130] +- LocalTableScan [i4#119, j4#120] ``` ### How was this patch tested? Added new tests. Closes apache#28676 from imback82/broadcast_join_output. Authored-by: Terry Kim <yuminkim@gmail.com> Signed-off-by: Wenchen Fan <wenchen@databricks.com>
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
Design doc: https://docs.google.com/document/d/1Y-WnBXQngu7J0OJnppYqJk7HRn_PI0c1xvJuCDYtjVk/edit#
Features currently supported