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https://issues.apache.org/jira/browse/SPARK-37099?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17432910#comment-17432910
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Apache Spark commented on SPARK-37099:
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User 'zhengruifeng' has created a pull request for this issue:
https://github.com/apache/spark/pull/34367
> Impl a rank-based filter to optimize top-k computation
> ------------------------------------------------------
>
> Key: SPARK-37099
> URL: https://issues.apache.org/jira/browse/SPARK-37099
> Project: Spark
> Issue Type: Improvement
> Components: SQL
> Affects Versions: 3.3.0
> Reporter: zhengruifeng
> Priority: Major
> Attachments: skewed_window.png
>
>
> in JD, we found that more than 80% usage of window function follows this
> pattern:
> {code:java}
> select (... row_number() over(partition by ... order by ...) as rn)
> where rn ==[\<=] k{code}
>
> However, existing physical plan is not optimum:
>
> 1, we should select local top-k records within each partitions, and then
> compute the global top-k. this can help reduce the shuffle amount;
>
> 2, skewed-window: some partition is skewed and take a long time to finish
> computation.
>
> A real-world skewed-window case in our system is attached.
>
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