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https://issues.apache.org/jira/browse/SPARK-40307?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Apache Spark reassigned SPARK-40307:
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Assignee: Apache Spark
> Introduce Arrow-optimized Python UDFs
> -------------------------------------
>
> Key: SPARK-40307
> URL: https://issues.apache.org/jira/browse/SPARK-40307
> Project: Spark
> Issue Type: Umbrella
> Components: PySpark
> Affects Versions: 3.4.0
> Reporter: Xinrong Meng
> Assignee: Apache Spark
> Priority: Major
>
> Python user-defined function (UDF) enables users to run arbitrary code
> against PySpark columns. It uses Pickle for (de)serialization and executes
> row by row.
> One major performance bottleneck of Python UDFs is (de)serialization, that
> is, the data interchanging between the worker JVM and the spawned Python
> subprocess which actually executes the UDF. We should seek an alternative to
> handle the (de)serialization: Arrow, which is used in the (de)serialization
> of Pandas UDF already.
> There should be two ways to enable/disable the Arrow optimization for Python
> UDFs:
> - the Spark configuration `spark.sql.execution.pythonUDF.arrow.enabled`,
> disabled by default.
> - the `useArrow` parameter of the `udf` function, None by default.
> The Spark configuration takes effect only when `useArrow` is None. Otherwise,
> `use arrow` decides whether the user-defined function is optimized by Arrow
> or not.
> The reason why we introduce these two ways is to provide convenient,
> per-Spark-session control and finer-grained, per-UDF control of the Arrow
> optimization for Python UDFs.
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