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Posted to reviews@spark.apache.org by "Yicong-Huang (via GitHub)" <gi...@apache.org> on 2026/04/13 03:09:34 UTC
[PR] [SPARK-56424][PYTHON] Add ASV benchmark for SQL_SCALAR_PANDAS_UDF [spark]
Yicong-Huang opened a new pull request, #55316:
URL: https://github.com/apache/spark/pull/55316
### What changes were proposed in this pull request?
Add ASV microbenchmarks for `SQL_SCALAR_PANDAS_UDF` eval type in `bench_eval_type.py`.
The new `_ScalarPandasBenchMixin` follows the same pattern as the existing `_ScalarArrowBenchMixin` but uses Pandas Series operations. It measures the full Arrow-to-Pandas-to-Arrow round-trip that occurs in scalar Pandas UDFs.
**Scenarios** (9): `sm_batch_few_col`, `sm_batch_many_col`, `lg_batch_few_col`, `lg_batch_many_col`, `pure_ints`, `pure_floats`, `pure_strings`, `pure_ts`, `mixed_types`
**UDFs** (3): `identity_udf` (passthrough), `sort_udf` (Series.sort_values), `nullcheck_udf` (Series.notna)
**Benchmark classes** (2): `ScalarPandasUDFTimeBench`, `ScalarPandasUDFPeakmemBench`
### Why are the changes needed?
This is part of the PySpark Serializer & EvalType Refactor effort (SPARK-55724). We need baseline benchmarks for every eval type before refactoring the serialization path, so we can detect performance regressions. `SQL_SCALAR_PANDAS_UDF` is one of the most commonly used eval types and its Arrow-to-Pandas-to-Arrow conversion cost is a key metric for the refactor.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Ran all 54 benchmarks (27 time + 27 peakmem) locally with `python/asv run --python=same --quick`:
```
time_worker results (27/27 passed):
sm_batch_few_col identity_udf: 269ms, sort_udf: 385ms, nullcheck_udf: 300ms
lg_batch_few_col identity_udf: 836ms, sort_udf: 1.06s, nullcheck_udf: 834ms
pure_ints identity_udf: 165ms, sort_udf: 236ms, nullcheck_udf: 170ms
pure_strings identity_udf: 819ms, sort_udf: 1.17s, nullcheck_udf: 818ms
mixed_types identity_udf: 504ms, sort_udf: 551ms, nullcheck_udf: 510ms
(+ 12 more scenarios all passing)
peakmem_worker results (27/27 passed):
Range: 479M - 628M across all scenario/udf combinations
```
### Was this patch authored or co-authored using generative AI tooling?
No.
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Re: [PR] [SPARK-56424][PYTHON][TESTS] Add ASV benchmark for SQL_SCALAR_PANDAS_UDF [spark]
Posted by "zhengruifeng (via GitHub)" <gi...@apache.org>.
zhengruifeng commented on PR #55316:
URL: https://github.com/apache/spark/pull/55316#issuecomment-4248346394
let's reuse the `[TESTS]` flag for benchmark PRs
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Re: [PR] [SPARK-56424][PYTHON][TESTS] Add ASV benchmark for SQL_SCALAR_PANDAS_UDF [spark]
Posted by "zhengruifeng (via GitHub)" <gi...@apache.org>.
zhengruifeng closed pull request #55316: [SPARK-56424][PYTHON][TESTS] Add ASV benchmark for SQL_SCALAR_PANDAS_UDF
URL: https://github.com/apache/spark/pull/55316
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Re: [PR] [SPARK-56424][PYTHON][TESTS] Add ASV benchmark for SQL_SCALAR_PANDAS_UDF [spark]
Posted by "zhengruifeng (via GitHub)" <gi...@apache.org>.
zhengruifeng commented on PR #55316:
URL: https://github.com/apache/spark/pull/55316#issuecomment-4248359254
merged to master
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