[jira] [Resolved] (SPARK-50752) Introduce configs for Python UDF execution

["Hyukjin Kwon (Jira)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-50752?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Hyukjin Kwon resolved SPARK-50752.
----------------------------------
    Fix Version/s: 4.0.0
       Resolution: Fixed

Issue resolved by pull request 49397
[https://github.com/apache/spark/pull/49397]

> Introduce configs for Python UDF execution
> ------------------------------------------
>
>                 Key: SPARK-50752
>                 URL: https://issues.apache.org/jira/browse/SPARK-50752
>             Project: Spark
>          Issue Type: Improvement
>          Components: PySpark, SQL
>    Affects Versions: 4.0.0
>            Reporter: Jungtaek Lim
>            Assignee: Jungtaek Lim
>            Priority: Major
>              Labels: pull-request-available
>             Fix For: 4.0.0
>
>
> Unlike Pandas UDF, Python UDF does not have configurations to tune for performance. It doesn't mean we do not batch the input/output with Python UDF, it means the batch size is hard-coded.
> There are configurations which are available in Pandas UDF and mostly also relevant to Python UDF:
>  * batch size (executor <-> python worker)
>  * buffer size to write to channel



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