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https://issues.apache.org/jira/browse/SPARK-30153?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Apache Spark reassigned SPARK-30153:
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Assignee: Apache Spark
> Extend data exchange options for vectorized UDF functions with vanilla Arrow
> serialization
> ------------------------------------------------------------------------------------------
>
> Key: SPARK-30153
> URL: https://issues.apache.org/jira/browse/SPARK-30153
> Project: Spark
> Issue Type: Improvement
> Components: PySpark
> Affects Versions: 3.1.0
> Reporter: Luca Canali
> Assignee: Apache Spark
> Priority: Minor
> Attachments: Flamegraph_test_pandas_udf_SCALAR.png,
> Flamegraph_test_pandas_udf_SCALAR_ARROW.png
>
>
> Spark has introduced vectorized UDF with pandas_udf and this provides
> considerable speed up by reducing the overhead due to serialization and
> deserialization, where applciable.
> The current implementation of pandas_udf uses Arrow for fast serialization
> and then Pandas Series (or Pandas DF) for processing.
> There are opportunities to improve UDF performance, in certain cases, by
> bypaasing the conversion to and from Pandas and using Arrow Tables, directly
> with the help of specialized libraries able to process Arrow Tables and
> Arrays.
> One such case is for scientific computing of high energy physics data, where
> processing of arrays of data is of key importance.
> A test case using such approach has shown an increase of performance of about
> 3x, compared to the equivalent processing with pandas_udf, for a UDF based on
> plain Arrow serialization using a custom-developed extension of pandas_udf.
> Processing of Arrow data in the test case was done via the "awkward arrays"
> library (https://github.com/scikit-hep/awkward-array).
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