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Posted to issues@spark.apache.org by "Li Jin (JIRA)" <ji...@apache.org> on 2018/12/20 15:40:00 UTC
[jira] [Commented] (SPARK-26410) Support per Pandas UDF
configuration
[ https://issues.apache.org/jira/browse/SPARK-26410?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16725948#comment-16725948 ]
Li Jin commented on SPARK-26410:
--------------------------------
I am curious why would user want to configure maxRecordsPerBatch? As far as I remember this is just a performance/memory usage conf. In your example, what's the reason that the user wants 100 rows per batch for one UDF and 120 rows per batch for another?
> 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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