[SPARK-58019][PYTHON] Convert Arrow list columns to Python rows in bulk

python/benchmarks/bench_arrow.py

@@ -114,3 +114,59 @@ def time_long_with_nulls_to_pandas_ext(self, n_rows, method):
 
     def peakmem_long_with_nulls_to_pandas_ext(self, n_rows, method):
         self.run_long_with_nulls_to_pandas_ext(n_rows, method)
+
+
+class ArrowListColumnToRowsBenchmark:
+    """
+    Benchmark for converting Arrow list-typed columns to Python rows, the hot
+    path of Arrow-optimized Python UDF inputs and Spark Connect collect().
+
+    ``baseline`` measures plain ``column.to_pylist()``; ``bulk`` measures
+    ``ArrowTableToRowsConversion._to_pylist`` (see apache/arrow#50326).
+    """
+
+    params = [
+        [100000, 1000000],
+        ["baseline", "bulk"],
+    ]
+    param_names = ["n_rows", "method"]
+
+    def setup(self, n_rows, method):
+        from pyspark.sql.conversion import ArrowTableToRowsConversion
+
+        self.list_of_strings = pa.array(
+            [[f"s{i}", f"t{i}"] for i in range(n_rows)], type=pa.list_(pa.string())
+        )
+        self.nested_ints_with_nulls = pa.array(
+            [[[i, i + 1], None, [i + 2]] if i % 10 != 0 else None for i in range(n_rows)],
+            type=pa.list_(pa.list_(pa.int32())),
+        )
+        self.array_of_structs = pa.array(
+            [
+                [{"i": i, "s": f"a{i}"}, {"i": i + 1, "s": f"b{i}"}] if i % 10 != 0 else None
+                for i in range(n_rows)
+            ],
+            type=pa.list_(pa.struct([("i", pa.int32()), ("s", pa.string())])),
+        )
+        if method == "bulk":
+            self.convert = ArrowTableToRowsConversion._to_pylist
+        else:
+            self.convert = lambda column: column.to_pylist()
+
+    def time_list_of_strings_to_rows(self, n_rows, method):
+        self.convert(self.list_of_strings)
+
+    def time_nested_ints_with_nulls_to_rows(self, n_rows, method):
+        self.convert(self.nested_ints_with_nulls)
+
+    def time_array_of_structs_to_rows(self, n_rows, method):
+        self.convert(self.array_of_structs)
+
+    def peakmem_list_of_strings_to_rows(self, n_rows, method):
+        self.convert(self.list_of_strings)
+
+    def peakmem_nested_ints_with_nulls_to_rows(self, n_rows, method):
+        self.convert(self.nested_ints_with_nulls)
+
+    def peakmem_array_of_structs_to_rows(self, n_rows, method):
+        self.convert(self.array_of_structs)

python/pyspark/sql/conversion.py

@@ -18,6 +18,7 @@
 import array
 import datetime
 import decimal
+import functools
 from typing import TYPE_CHECKING, Any, Callable, List, Optional, Sequence, Union, overload
 
 import pyspark
@@ -980,6 +981,90 @@ class ArrowTableToRowsConversion:
     Conversion from Arrow Table to Rows.
     """
 
+    @staticmethod
+    @functools.cache
+    def _should_manual_bulk() -> bool:
+        """
+        Whether ``_to_pylist`` should convert nested columns manually in bulk.
+
+        Internal helper for ``_to_pylist`` only; do not use externally. Returns True
+        when the installed PyArrow still materializes one Scalar per element in
+        ``to_pylist`` (apache/arrow#50326, fix expected in PyArrow 25.0.1 — adjust the
+        version below if it ships in a different release) and NumPy (used for the
+        offsets and validity buffers) is available.
+
+        This method and the manual bulk paths in ``_to_pylist`` should be removed once
+        the minimum supported PyArrow version contains the fix.
+        """
+        import pyarrow as pa
+        from pyspark.loose_version import LooseVersion
+
+        if LooseVersion(pa.__version__) >= LooseVersion("25.0.1"):
+            # Native to_pylist converts without per-element Scalars.
+            return False
+        try:
+            import numpy  # noqa: F401
+        except ImportError:
+            return False
+        return True
+
+    @staticmethod
+    def _to_pylist(column: Union["pa.Array", "pa.ChunkedArray"]) -> List[Any]:
+        """
+        Equivalent to ``column.to_pylist()``, but converts (nested) list columns in bulk
+        instead of one scalar at a time.
+
+        Internal helper for the worker and ``convert`` call sites; do not use
+        externally.
+
+        ``Array.to_pylist()`` materializes one Scalar per element; for list types each row
+        additionally allocates a C++ scalar, a Python Scalar wrapper and a Python Array
+        wrapper for the row's values before converting elements one by one, which is
+        several times slower than converting the flattened child values in a single pass
+        and slicing the resulting Python list per row (see apache/arrow#50326). The values
+        themselves are still converted by Arrow's own ``to_pylist``, so results are exactly
+        identical: ``None`` stays ``None`` and values inside numeric lists stay Python ints,
+        unlike a pandas round trip which would coerce them to floats/NaN. NumPy is used
+        only for the offsets (non-null integers) and the validity bitmap (booleans), so no
+        value coercion can occur.
+
+        This method should be removed (its call sites reverting to plain
+        ``column.to_pylist()``) once the minimum supported PyArrow version includes the
+        fix for apache/arrow#50326.
+        """
+        import pyarrow as pa
+
+        if not ArrowTableToRowsConversion._should_manual_bulk():
+            return column.to_pylist()
+
+        if isinstance(column, pa.ChunkedArray):
+            result = []
+            for chunk in column.chunks:
+                result.extend(ArrowTableToRowsConversion._to_pylist(chunk))
+            return result
+
+        if (pa.types.is_list(column.type) or pa.types.is_large_list(column.type)) and len(
+            column
+        ) > 0:
+            n = len(column)
+            # List offset buffers never carry a validity bitmap, so this conversion is
+            # always zero-copy; zero_copy_only=True asserts that invariant and would
+            # fail loudly if a future Arrow list variant ever violated it.
+            offsets = column.offsets.to_numpy(zero_copy_only=True).tolist()
+            start = offsets[0]
+            flat = ArrowTableToRowsConversion._to_pylist(
+                column.values.slice(start, offsets[-1] - start)
+            )
+            if column.null_count == 0:
+                return [flat[offsets[i] - start : offsets[i + 1] - start] for i in range(n)]
+            valid = column.is_valid().to_numpy(zero_copy_only=False).tolist()
+            return [
+                flat[offsets[i] - start : offsets[i + 1] - start] if valid[i] else None
+                for i in range(n)
+            ]
+
+        return column.to_pylist()
+
     @staticmethod
     def _need_converter(dataType: DataType) -> bool:
         if isinstance(dataType, NullType):
@@ -1306,7 +1391,11 @@ def convert(
             ]
 
             columnar_data = [
-                [conv(v) for v in column.to_pylist()] if conv is not None else column.to_pylist()
+                (
+                    [conv(v) for v in ArrowTableToRowsConversion._to_pylist(column)]
+                    if conv is not None
+                    else ArrowTableToRowsConversion._to_pylist(column)
+                )
                 for column, conv in zip(table.columns, field_converters)
             ]
 

python/pyspark/sql/tests/test_conversion.py

@@ -16,6 +16,7 @@
 #
 import datetime
 import unittest
+import unittest.mock
 from zoneinfo import ZoneInfo
 
 from pyspark.errors import PySparkRuntimeError, PySparkTypeError, PySparkValueError
@@ -844,6 +845,105 @@ def test_geometry_convert_numpy(self):
         self.assertEqual(len(result), 0)
 
 
+@unittest.skipIf(not have_pyarrow, pyarrow_requirement_message)
+class ArrowColumnToPylistTests(unittest.TestCase):
+    """
+    ArrowTableToRowsConversion._to_pylist must return exactly what
+    column.to_pylist() returns, including exact element types.
+    """
+
+    def setUp(self):
+        # Force the manual bulk paths so they stay covered regardless of the
+        # installed PyArrow version (with a fast native PyArrow the method
+        # short-circuits to column.to_pylist()).
+        self._gate_patcher = unittest.mock.patch.object(
+            ArrowTableToRowsConversion, "_should_manual_bulk", lambda: True
+        )
+        self._gate_patcher.start()
+
+    def tearDown(self):
+        self._gate_patcher.stop()
+
+    def test_native_to_pylist_gate(self):
+        import pyarrow as pa
+
+        column = pa.array([[1, None], None], type=pa.list_(pa.int32()))
+        with unittest.mock.patch.object(
+            ArrowTableToRowsConversion, "_should_manual_bulk", lambda: False
+        ):
+            self.assertEqual(ArrowTableToRowsConversion._to_pylist(column), [[1, None], None])
+
+    def _assert_identical_types(self, actual, expected):
+        self.assertIs(type(actual), type(expected))
+        if isinstance(actual, (list, tuple)):
+            self.assertEqual(len(actual), len(expected))
+            for a, e in zip(actual, expected):
+                self._assert_identical_types(a, e)
+
+    def test_matches_to_pylist(self):
+        import pyarrow as pa
+
+        columns = [
+            pa.array([[1, None, 3], None, [], [4]], type=pa.list_(pa.int32())),
+            pa.array([["a", None], None, [], ["bcd", ""]], type=pa.list_(pa.string())),
+            pa.array([["a", None], None, ["b"]], type=pa.large_list(pa.string())),
+            pa.array([[[1], None, [2, None]], None], type=pa.list_(pa.list_(pa.int32()))),
+            pa.array(
+                [[{"a": 1, "b": "x"}, None], None],
+                type=pa.list_(pa.struct([("a", pa.int32()), ("b", pa.string())])),
+            ),
+            pa.array([[("k1", 1), ("k2", None)], None, []], type=pa.map_(pa.string(), pa.int32())),
+            pa.array([[1.5, None], [float("nan")]], type=pa.list_(pa.float64())),
+            pa.array([1, None, 3], type=pa.int64()),
+            pa.array(["x", None], type=pa.string()),
+            pa.array([], type=pa.list_(pa.int32())),
+            pa.array([None, None], type=pa.list_(pa.string())),
+            pa.array([[1, 2], None], type=pa.list_(pa.int64(), 2)),
+        ]
+        for column in columns:
+            views = [column, column.slice(1), column.slice(0, max(len(column) - 1, 0))]
+            views.append(pa.chunked_array([column, column.slice(1)], type=column.type))
+            for view in views:
+                with self.subTest(type=str(column.type), length=len(view)):
+                    actual = ArrowTableToRowsConversion._to_pylist(view)
+                    expected = view.to_pylist()
+                    # NaN != NaN; compare via repr for the float case
+                    self.assertEqual(repr(actual), repr(expected))
+                    self._assert_identical_types(actual, expected)
+
+    def test_int_list_with_nulls_stays_int(self):
+        # The exact case that makes a pandas round trip unusable: ints must not
+        # become floats/NaN when the list contains nulls.
+        import pyarrow as pa
+
+        result = ArrowTableToRowsConversion._to_pylist(
+            pa.array([[1, None, 3]], type=pa.list_(pa.int32()))
+        )
+        self.assertEqual(result, [[1, None, 3]])
+        self.assertEqual([type(v) for v in result[0]], [int, type(None), int])
+
+    def test_convert_table_with_list_columns(self):
+        import pyarrow as pa
+
+        schema = (
+            StructType()
+            .add("arr", ArrayType(IntegerType()))
+            .add("nested", ArrayType(ArrayType(StringType())))
+        )
+        tbl = pa.table(
+            {
+                "arr": pa.array([[1, None], None, []], type=pa.list_(pa.int32())),
+                "nested": pa.array(
+                    [[["a"], None], [[]], None], type=pa.list_(pa.list_(pa.string()))
+                ),
+            }
+        )
+        actual = ArrowTableToRowsConversion.convert(tbl, schema)
+        self.assertEqual(actual[0], Row(arr=[1, None], nested=[["a"], None]))
+        self.assertEqual(actual[1], Row(arr=None, nested=[[]]))
+        self.assertEqual(actual[2], Row(arr=[], nested=None))
+
+
 if __name__ == "__main__":
     from pyspark.testing import main
 

python/pyspark/worker.py

@@ -1755,9 +1755,9 @@ def func(split_index: int, data: Iterator[pa.RecordBatch]) -> Iterator[pa.Record
                     # then call eval once per input row.
                     pylist = [
                         (
-                            [conv(v) for v in column.to_pylist()]
+                            [conv(v) for v in ArrowTableToRowsConversion._to_pylist(column)]
                             if conv is not None
-                            else column.to_pylist()
+                            else ArrowTableToRowsConversion._to_pylist(column)
                         )
                         for column, conv in zip(batch.columns, converters)
                     ]
@@ -3067,7 +3067,11 @@ def func(split_index: int, data: Iterator[pa.RecordBatch]) -> Iterator[pa.Record
 
                 # --- Input: Arrow -> Python columns ---
                 columns = [
-                    [conv(v) for v in col.to_pylist()] if conv is not None else col.to_pylist()
+                    (
+                        [conv(v) for v in ArrowTableToRowsConversion._to_pylist(col)]
+                        if conv is not None
+                        else ArrowTableToRowsConversion._to_pylist(col)
+                    )
                     for col, conv in zip(input_batch.itercolumns(), arrow_to_py_converters)
                 ]
                 if not columns: