Yicong-Huang opened a new pull request, #55603:
URL: https://github.com/apache/spark/pull/55603

   ### What changes were proposed in this pull request?
   
   Add ASV micro-benchmarks for `SQL_WINDOW_AGG_PANDAS_UDF` to 
`python/benchmarks/bench_eval_type.py`, mirroring the existing 
`SQL_WINDOW_AGG_ARROW_UDF` benchmarks added in 
[SPARK-56120](https://issues.apache.org/jira/browse/SPARK-56120).
   
   The new mixin `_WindowAggPandasBenchMixin` reuses the same scenario shapes 
and UDF set as the Arrow variant (sum, mean_multi) and writes the worker 
payload with `runner_conf={"window_bound_types": "unbounded"}`.
   
   ### Why are the changes needed?
   
   Part of [SPARK-55724](https://issues.apache.org/jira/browse/SPARK-55724). 
Establishes performance baselines before refactoring this eval type.
   
   ### Does this PR introduce _any_ user-facing change?
   
   No.
   
   ### How was this patch tested?
   
   `./python/asv check --python=same` passes. Local quick run on 
`WindowAggPandasUDFTimeBench` and `WindowAggPandasUDFPeakmemBench`:
   
   ```text
   bench_eval_type.WindowAggPandasUDFTimeBench.time_worker
   ================ ========== ================
   --                           udf
   ---------------- ---------------------------
       scenario      sum_udf    mean_multi_udf
   ================ ========== ================
    few_groups_sm    74.8 ms       75.3 ms
    few_groups_lg    115 ms        125 ms
    many_groups_sm   1.92 s        2.13 s
    many_groups_lg   578 ms        618 ms
      wide_cols      509 ms        539 ms
   ================ ========== ================
   
   bench_eval_type.WindowAggPandasUDFPeakmemBench.peakmem_worker
   ================ ========= ================
   --                          udf
   ---------------- --------------------------
       scenario      sum_udf   mean_multi_udf
   ================ ========= ================
    few_groups_sm     471M         470M
    few_groups_lg     535M         533M
    many_groups_sm    501M         499M
    many_groups_lg    606M         604M
      wide_cols       587M         585M
   ================ ========= ================
   ```
   
   ### Was this patch authored or co-authored using generative AI tooling?
   
   No.


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