Xiangrui Meng created SPARK-26410:
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Summary: 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
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] [~ueshin] [~smilegator]
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