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https://issues.apache.org/jira/browse/SPARK-59755?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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ASF GitHub Bot updated SPARK-59755:
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Labels: pull-request-available (was: )
> Add ASV benchmarks for Arrow incremental aggregate and window UDF eval types
> ----------------------------------------------------------------------------
>
> Key: SPARK-59755
> URL: https://issues.apache.org/jira/browse/SPARK-59755
> Project: Spark
> Issue Type: Sub-task
> Components: PySpark
> Affects Versions: 5.0.0
> Reporter: Yicong Huang
> Priority: Major
> Labels: pull-request-available
>
> Add ASV microbenchmarks to python/benchmarks/bench_eval_type.py for the three
> Arrow-native incremental Aggregator eval types, which have no worker
> microbenchmark today: SQL_GROUPED_AGG_ARROW_INCREMENTAL_PARTIAL_UDF (255),
> SQL_GROUPED_AGG_ARROW_INCREMENTAL_FINAL_UDF (256) and
> SQL_WINDOW_AGG_ARROW_INCREMENTAL_UDF (257). Each gets a _*BenchMixin driving
> worker_main over the mock wire protocol plus Time/Peakmem subclasses,
> following the existing pattern; the UDF payload is a cloudpickled incremental
> Aggregator (zero/reduce/merge/finish + bufferSchema). Scenarios: PARTIAL few
> keys vs many keys hitting the maxRecordsPerBatch cap flush; FINAL many groups
> of struct partial buffers including null buffer rows; WINDOW unbounded,
> running (running-buffer extension) and sliding (refold) frames.
> Benchmark-only, no product change; enables before/after measurement of the
> SPARK-59727 migration.
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