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https://issues.apache.org/jira/browse/SPARK-26410?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16752467#comment-16752467
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Xiangrui Meng commented on SPARK-26410:
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There are several possible solutions to this. SPARK-23258 is one. I think it is
more reasonable to limit the buffer size instead of number of records per
batch, because the latter varies per task.
> 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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