From be36e52378868accbd0e17b6c99dbcae84cbc666 Mon Sep 17 00:00:00 2001
From: Yicong-Huang <17627829+Yicong-Huang@users.noreply.github.com>
Date: Wed, 24 Jun 2026 02:45:20 +0000
Subject: [PATCH] test: add ASV microbenchmark for
 SQL_GROUPED_AGG_PANDAS_ITER_UDF

---
 python/benchmarks/bench_eval_type.py | 45 ++++++++++++++++++++++++++++
 1 file changed, 45 insertions(+)

diff --git a/python/benchmarks/bench_eval_type.py b/python/benchmarks/bench_eval_type.py
index 9bf005880378..f220b804be0b 100644
--- a/python/benchmarks/bench_eval_type.py
+++ b/python/benchmarks/bench_eval_type.py
@@ -1041,6 +1041,51 @@ class GroupedAggPandasUDFPeakmemBench(_GroupedAggPandasBenchMixin, _PeakmemBench
     pass
 
 
+# -- SQL_GROUPED_AGG_PANDAS_ITER_UDF -------------------------------------------
+# UDF receives an iterator of ``pd.Series`` columns (or tuples of them) per
+# group, returns scalar.
+
+
+class _GroupedAggPandasIterBenchMixin(_GroupedAggPandasBenchMixin):
+    """Provides _write_scenario for SQL_GROUPED_AGG_PANDAS_ITER_UDF.
+
+    Inherits ``_build_scenario`` and ``_write_scenario`` from the Pandas
+    sibling; only the eval type and the UDFs differ. The UDF consumes the
+    per-group batches lazily through an iterator instead of receiving a single
+    concatenated column.
+    """
+
+    def _grouped_agg_pandas_iter_sum(series_iter):
+        """Sum across batches via iterator."""
+        total = 0
+        for col in series_iter:
+            total += col.sum() or 0
+        return total
+
+    def _grouped_agg_pandas_iter_mean_multi(series_iter):
+        """Mean across batches of tuples via iterator."""
+        total = 0.0
+        for col0, col1 in series_iter:
+            total += (col0.mean() or 0) + (col1.mean() or 0)
+        return total
+
+    _eval_type = PythonEvalType.SQL_GROUPED_AGG_PANDAS_ITER_UDF
+    _udfs = {
+        "sum_udf": _grouped_agg_pandas_iter_sum,
+        "mean_multi_udf": _grouped_agg_pandas_iter_mean_multi,
+    }
+    params = [list(_GroupedAggArrowBenchMixin._scenario_configs), list(_udfs)]
+    param_names = ["scenario", "udf"]
+
+
+class GroupedAggPandasIterUDFTimeBench(_GroupedAggPandasIterBenchMixin, _TimeBenchBase):
+    pass
+
+
+class GroupedAggPandasIterUDFPeakmemBench(_GroupedAggPandasIterBenchMixin, _PeakmemBenchBase):
+    pass
+
+
 # -- SQL_GROUPED_MAP_ARROW_UDF ------------------------------------------------
 # UDF receives ``pa.Table``, returns ``pa.Table``.