[jira] [Commented] (SPARK-26410) Support per Pandas UDF configuration

["Bryan Cutler (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-26410?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16751771#comment-16751771 ] 

Bryan Cutler commented on SPARK-26410:
--------------------------------------

This could be useful to have, but it does seem a little strange to bind batch size to a udf. To me, batch size seems more related to the data being used, and merging different batch sizes could complicate the behavior. Still, I can see how someone might want to change batch size at different points in a session.

> Support per Pandas UDF configuration
> ------------------------------------
>
>                 Key: SPARK-26410
>                 URL: https://issues.apache.org/jira/browse/SPARK-26410
>             Project: Spark
>          Issue Type: New Feature
>          Components: PySpark
>    Affects Versions: 3.0.0
>            Reporter: Xiangrui Meng
>            Priority: Major
>
> We use a "maxRecordsPerBatch" conf to control the batch sizes. However, the "right" batch size usually depends on the task itself. It would be nice if user can configure the batch size when they declare the Pandas UDF.
> This is orthogonal to SPARK-23258 (using max buffer size instead of row count).
> Besides API, we should also discuss how to merge Pandas UDFs of different configurations. For example,
> {code}
> df.select(predict1(col("features"), predict2(col("features")))
> {code}
> when predict1 requests 100 rows per batch, while predict2 requests 120 rows per batch.
> cc: [~icexelloss] [~bryanc] [~holdenk] [~hyukjin.kwon] [~ueshin] [~smilegator]



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