[jira] [Commented] (SPARK-37099) Impl a rank-based filter to optimize top-k computation

["Apache Spark (Jira)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-37099?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17432910#comment-17432910 ] 

Apache Spark commented on SPARK-37099:
--------------------------------------

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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