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https://issues.apache.org/jira/browse/SPARK-58132?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Hyukjin Kwon resolved SPARK-58132.
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Fix Version/s: 4.3.0
Resolution: Fixed
Issue resolved by pull request 57263
[https://github.com/apache/spark/pull/57263]
> Reduce memory footprint of grouped-agg UDF batch-slicing tests
> ---------------------------------------------------------------
>
> Key: SPARK-58132
> URL: https://issues.apache.org/jira/browse/SPARK-58132
> Project: Spark
> Issue Type: Test
> Components: PySpark
> Affects Versions: 4.2.1
> Reporter: Gurpreet Singh Multani
> Assignee: Gurpreet Singh Multani
> Priority: Minor
> Labels: pull-request-available
> Fix For: 4.3.0
>
> Original Estimate: 24h
> Remaining Estimate: 24h
>
> The test_arrow_batch_slicing tests in the Arrow and pandas grouped-aggregate
> UDF test suites materialize a 10-million-row DataFrame and slice it into
> batches:
> - python/pyspark/sql/tests/arrow/test_arrow_udf_grouped_agg.py
> - python/pyspark/sql/tests/pandas/test_pandas_udf_grouped_agg.py
> Under constrained driver heap this is unnecessarily memory-hungry and can
> flake. A proportional 10x shrink of the dataset and the maxRecordsPerBatch /
> maxBytesPerBatch configs still produces multiple batches per group, so the
> batch-slicing code path is exercised exactly as before while using far less
> memory:
> Test-only change; no user-facing impact.
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