You are viewing a plain text version of this content. The canonical link for it is here.
Posted to issues@spark.apache.org by "Zikun (Jira)" <ji...@apache.org> on 2020/09/11 04:59:00 UTC

[jira] [Updated] (SPARK-32096) Improve sorting performance for Spark SQL rank window function​

     [ https://issues.apache.org/jira/browse/SPARK-32096?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Zikun updated SPARK-32096:
--------------------------
    Summary: Improve sorting performance for Spark SQL rank window function​  (was: Support top-N sort for Spark SQL rank window function)

> Improve sorting performance for Spark SQL rank window function​
> ---------------------------------------------------------------
>
>                 Key: SPARK-32096
>                 URL: https://issues.apache.org/jira/browse/SPARK-32096
>             Project: Spark
>          Issue Type: Improvement
>          Components: SQL
>    Affects Versions: 3.0.0
>         Environment: Any environment that supports Spark.
>            Reporter: Zikun
>            Priority: Major
>
> In Spark SQL, there are two types of sort execution, *_SortExec_* and *_TakeOrderedAndProjectExec_* . 
> *_SortExec_* is a general sorting execution and it does not support top-N sort. ​
> *_TakeOrderedAndProjectExec_* is the execution for top-N sort in Spark. 
> Spark SQL rank window function needs to sort the data locally and it relies on the execution plan *_SortExec_* to sort the data in each physical data partition. When the filter of the window rank (e.g. rank <= 100) is specified in a user's query, the filter can actually be pushed down to the SortExec and then we let SortExec operates top-N sort. 
> Right now SortExec does not support top-N sort and we need to extend the capability of SortExec to support top-N sort. 
> Or if SortExec is not considered as the right execution choice, we can create a new execution plan called topNSortExec to do top-N sort in each local partition if a filter on the window rank is specified. 



--
This message was sent by Atlassian Jira
(v8.3.4#803005)

---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org