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https://issues.apache.org/jira/browse/SPARK-57760?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Jungtaek Lim resolved SPARK-57760.
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Fix Version/s: 4.3.0
Resolution: Fixed
Issue resolved by pull request 56786
[https://github.com/apache/spark/pull/56786]
> Optimize StatefulProcessorApiClient._serialize_to_bytes
> -------------------------------------------------------
>
> Key: SPARK-57760
> URL: https://issues.apache.org/jira/browse/SPARK-57760
> Project: Spark
> Issue Type: Improvement
> Components: Structured Streaming
> Affects Versions: 4.2.0
> Reporter: Sagar Mittal
> Assignee: Sagar Mittal
> Priority: Trivial
> Labels: pull-request-available
> Fix For: 4.3.0
>
>
> In an internal benchmark, we have found `_serialize_to_bytes` is a
> performance bottleneck. [Here is a microbenchmark for this
> function|https://gist.github.com/funrollloops/62bf5fae1654910b63a4539e1181db91].
> I identified two optimizations to improve performance:
> 1. Use PickleSerializer() instead of the default CPickleSerializer
> (CloudPickleSerializer). The stateful processor path does not deal with
> code objects so the lighter serializer is sufficient.
> 2. Micro-optimize state value normalization: add a fast-path for Python
> primitives, prefer `map` over generator comprehensions, and hoist the
> numpy import and helper function definition to module level so they are
> executed once rather than per call.
> Together these changes improve transform-with-state throughput on a simple
> rolling-window benchmark by around ~10%.
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