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[GitHub] [spark] maryannxue opened a new pull request #28669: [SPARK-31864][SQL] Adjust AQE skew join trigger condition
maryannxue opened a new pull request #28669:
URL: https://github.com/apache/spark/pull/28669
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This PR makes a minor change in deciding whether a partition is skewed by comparing the partition size to the median size of coalesced partitions instead of median size of raw partitions before coalescing.
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This change is line with target size criteria of splitting skew join partitions and can also cope with situations of extra empty partitions caused by over-partitioning. This PR has also improved skew join tests in AQE tests.
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No.
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URL: https://github.com/apache/spark/pull/28669#issuecomment-635735795
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URL: https://github.com/apache/spark/pull/28669#issuecomment-636040748
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URL: https://github.com/apache/spark/pull/28669#issuecomment-636170973
**[Test build #123293 has finished](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/123293/testReport)** for PR 28669 at commit [`10a468e`](https://github.com/apache/spark/commit/10a468ebdf228e8c07d8ac163bd4db15d05acc41).
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[GitHub] [spark] maryannxue commented on pull request #28669: [SPARK-31864][SQL] Adjust AQE skew join trigger condition
Posted by GitBox <gi...@apache.org>.
maryannxue commented on pull request #28669:
URL: https://github.com/apache/spark/pull/28669#issuecomment-635639218
cc @cloud-fan @Ngone51
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[GitHub] [spark] cloud-fan commented on pull request #28669: [SPARK-31864][SQL] Adjust AQE skew join trigger condition
Posted by GitBox <gi...@apache.org>.
cloud-fan commented on pull request #28669:
URL: https://github.com/apache/spark/pull/28669#issuecomment-640574818
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[GitHub] [spark] manuzhang commented on pull request #28669: [SPARK-31864][SQL] Adjust AQE skew join trigger condition
Posted by GitBox <gi...@apache.org>.
manuzhang commented on pull request #28669:
URL: https://github.com/apache/spark/pull/28669#issuecomment-641793942
@JkSelf That will work but the value doesn't make sense to me. In addition, I don't want to tune the configuration for every specific case but come up with a suite of default configurations that work out of box for most cases.
> I think this PR is meaningful when the rule of OptimizeSkewedJoin is behind CoalesceShufflePartitions in the queryStageOptimizerRules of AdaptiveSparkPlanExec
I don't see too much "meaning" here as we split the skewed partition towards the average size of coalesced non-skew partitions or advisory target size whichever is larger. We'll end up with evenly distributed partitions with or without this PR. However, we lose the chance to optimize skew partitions in some cases with this change.
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[GitHub] [spark] cloud-fan closed pull request #28669: [SPARK-31864][SQL] Adjust AQE skew join trigger condition
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cloud-fan closed pull request #28669:
URL: https://github.com/apache/spark/pull/28669
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[GitHub] [spark] maryannxue edited a comment on pull request #28669: [SPARK-31864][SQL] Adjust AQE skew join trigger condition
Posted by GitBox <gi...@apache.org>.
maryannxue edited a comment on pull request #28669:
URL: https://github.com/apache/spark/pull/28669#issuecomment-640703977
I assume there's one only key in that 13G partition. You can always run into this when the number of distinct keys is smaller than the partition number. It is not a good idea to use median whether it's pre-coalescing size or post-coalescing size. Suppose, you have 100 keys, and the partition number is set to 200, even with pre-coalescing size, you won't get the skew handling you want, but with partition number being 201 you can. The skew behavior now depends on the partition number. However, it's probably the best we can do for now given we don't have a more advanced strategy here.
With something like 13G, we should probably always split it.
So think we can:
1) revert this PR, so for extremely clustered keys, skew can work
2) change the `spark.sql.adaptive.skewJoin.skewedPartitionFactor` check condition to ">= 0" instead of ">0" so we can force split large partitions even if there isn't an uneven distribution.
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[GitHub] [spark] cloud-fan commented on pull request #28669: [SPARK-31864][SQL] Adjust AQE skew join trigger condition
Posted by GitBox <gi...@apache.org>.
cloud-fan commented on pull request #28669:
URL: https://github.com/apache/spark/pull/28669#issuecomment-635751861
This makes sense to me. e.g. for partitions [1, 1, 1, ..., 1, 10], which are coalesced to `[9, 10]`, I don't think there are skewed partitions.
@JkSelf what do you think?
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[GitHub] [spark] manuzhang commented on pull request #28669: [SPARK-31864][SQL] Adjust AQE skew join trigger condition
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manuzhang commented on pull request #28669:
URL: https://github.com/apache/spark/pull/28669#issuecomment-640934523
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[GitHub] [spark] JkSelf commented on pull request #28669: [SPARK-31864][SQL] Adjust AQE skew join trigger condition
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JkSelf commented on pull request #28669:
URL: https://github.com/apache/spark/pull/28669#issuecomment-635823877
Good improvement. Except one small comment. LGTM.
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cloud-fan commented on pull request #28669:
URL: https://github.com/apache/spark/pull/28669#issuecomment-636294332
thanks, merging to master/3.0!
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[GitHub] [spark] JkSelf commented on a change in pull request #28669: [SPARK-31864][SQL] Adjust AQE skew join trigger condition
Posted by GitBox <gi...@apache.org>.
JkSelf commented on a change in pull request #28669:
URL: https://github.com/apache/spark/pull/28669#discussion_r432316209
##########
File path: sql/core/src/main/scala/org/apache/spark/sql/execution/adaptive/OptimizeSkewedJoin.scala
##########
@@ -164,15 +164,15 @@ case class OptimizeSkewedJoin(conf: SQLConf) extends Rule[SparkPlan] {
assert(left.partitionsWithSizes.length == right.partitionsWithSizes.length)
val numPartitions = left.partitionsWithSizes.length
// We use the median size of the original shuffle partitions to detect skewed partitions.
Review comment:
We may also need to adjust the comment here.
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[GitHub] [spark] maryannxue commented on pull request #28669: [SPARK-31864][SQL] Adjust AQE skew join trigger condition
Posted by GitBox <gi...@apache.org>.
maryannxue commented on pull request #28669:
URL: https://github.com/apache/spark/pull/28669#issuecomment-640703977
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[GitHub] [spark] JkSelf commented on pull request #28669: [SPARK-31864][SQL] Adjust AQE skew join trigger condition
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JkSelf commented on pull request #28669:
URL: https://github.com/apache/spark/pull/28669#issuecomment-641782521
@manuzhang If you adjust the configuration of `spark.sql.adaptive.skewJoin.skewedPartitionFactor ` between 0 and 1, can the 13GB partition be split? If so, can we set the skewed related configurations to trigger the skew optimization not revert this PR? I think this PR is meaningful when the rule of `OptimizeSkewedJoin` is behind `CoalesceShufflePartitions` in the `queryStageOptimizerRules` of `AdaptiveSparkPlanExec`.
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[GitHub] [spark] manuzhang commented on pull request #28669: [SPARK-31864][SQL] Adjust AQE skew join trigger condition
Posted by GitBox <gi...@apache.org>.
manuzhang commented on pull request #28669:
URL: https://github.com/apache/spark/pull/28669#issuecomment-640235282
@cloud-fan @maryannxue @JkSelf I'm seeing a case where partitions [0,0,0,...,13GB] were coalesced to [13GB] and took 17 min for a SortMergeJoin. With coalescing disabled, partitions would be split into [0,0,0,..., 256MB, 256MB,...,256MB] by OptimizeSkewedJoin and only took 38s. WDYT ?
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