zhengruifeng commented on code in PR #53035:
URL: https://github.com/apache/spark/pull/53035#discussion_r2558225810
##########
python/pyspark/sql/tests/arrow/test_arrow_udf_grouped_agg.py:
##########
@@ -1059,6 +1059,90 @@ def my_grouped_agg_arrow_udf(x):
],
)
+ def test_iterator_grouped_agg_single_column(self):
+ """
+ Test iterator API for grouped aggregation with single column.
+ """
+ import pyarrow as pa
+ from typing import Iterator
+
+ @arrow_udf("double")
+ def arrow_mean_iter(it: Iterator[pa.Array]) -> float:
+ sum_val = 0.0
+ cnt = 0
+ for v in it:
+ assert isinstance(v, pa.Array)
+ sum_val += pa.compute.sum(v).as_py()
+ cnt += len(v)
+ return sum_val / cnt if cnt > 0 else 0.0
+
+ df = self.spark.createDataFrame(
+ [(1, 1.0), (1, 2.0), (2, 3.0), (2, 5.0), (2, 10.0)], ("id", "v")
+ )
+
+ result =
df.groupby("id").agg(arrow_mean_iter(df["v"]).alias("mean")).sort("id")
+ expected =
df.groupby("id").agg(sf.mean(df["v"]).alias("mean")).sort("id").collect()
+
+ self.assertEqual(expected, result.collect())
+
+ @unittest.skipIf(not have_numpy, numpy_requirement_message)
+ def test_iterator_grouped_agg_multiple_columns(self):
+ """
+ Test iterator API for grouped aggregation with multiple columns.
+ """
+ import pyarrow as pa
+ import numpy as np
+ from typing import Iterator, Tuple
+
+ @arrow_udf("double")
+ def arrow_weighted_mean_iter(it: Iterator[Tuple[pa.Array, pa.Array]])
-> float:
+ weighted_sum = 0.0
+ weight = 0.0
+ for v, w in it:
+ assert isinstance(v, pa.Array)
+ assert isinstance(w, pa.Array)
+ weighted_sum += np.dot(v, w)
+ weight += pa.compute.sum(w).as_py()
+ return weighted_sum / weight if weight > 0 else 0.0
+
+ df = self.spark.createDataFrame(
+ [(1, 1.0, 1.0), (1, 2.0, 2.0), (2, 3.0, 1.0), (2, 5.0, 2.0), (2,
10.0, 3.0)],
+ ("id", "v", "w"),
+ )
+
+ result = (
+ df.groupby("id")
+ .agg(arrow_weighted_mean_iter(df["v"], df["w"]).alias("wm"))
+ .sort("id")
+ .collect()
+ )
+
+ # Expected weighted means:
+ # Group 1: (1.0*1.0 + 2.0*2.0) / (1.0 + 2.0) = 5.0 / 3.0
+ # Group 2: (3.0*1.0 + 5.0*2.0 + 10.0*3.0) / (1.0 + 2.0 + 3.0) = 43.0 /
6.0
+ expected = [(1, 5.0 / 3.0), (2, 43.0 / 6.0)]
+
+ self.assertEqual(len(result), len(expected))
+ for r, (exp_id, exp_wm) in zip(result, expected):
+ self.assertEqual(r["id"], exp_id)
+ self.assertAlmostEqual(r["wm"], exp_wm, places=5)
+
+ def test_iterator_grouped_agg_eval_type(self):
+ """
+ Test that the eval type is correctly inferred for iterator grouped agg
UDFs.
+ """
+ import pyarrow as pa
+ from typing import Iterator
+
+ @arrow_udf("double")
+ def arrow_sum_iter(it: Iterator[pa.Array]) -> float:
+ total = 0.0
+ for v in it:
+ total += pa.compute.sum(v).as_py()
+ return total
+
+ self.assertEqual(arrow_sum_iter.evalType,
PythonEvalType.SQL_GROUPED_AGG_ARROW_ITER_UDF)
+
Review Comment:
we need a test to make sure partial consumption works
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