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[SPARK-47068][PYTHON][TESTS] Recover -1 and 0 case for spark.sql.execution.arrow.maxRecordsPerBatch #45132

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[SPARK-47068][PYTHON][TESTS] Recover -1 and 0 case for spark.sql.execution.arrow.maxRecordsPerBatch #45132

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1 change: 1 addition & 0 deletions python/pyspark/sql/pandas/conversion.py
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Original file line number Diff line number Diff line change
Expand Up @@ -630,6 +630,7 @@ def _create_from_pandas_with_arrow(

# Slice the DataFrame to be batched
step = self._jconf.arrowMaxRecordsPerBatch()
step = step if step > 0 else len(pdf)
pdf_slices = (pdf.iloc[start : start + step] for start in range(0, len(pdf), step))

# Create list of Arrow (columns, arrow_type, spark_type) for serializer dump_stream
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10 changes: 10 additions & 0 deletions python/pyspark/sql/tests/test_arrow.py
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Expand Up @@ -1144,6 +1144,16 @@ class MyInheritedTuple(MyTuple):
df = self.spark.createDataFrame([MyInheritedTuple(1, 2, MyInheritedTuple(1, 2, 3))])
self.assertEqual(df.first(), Row(a=1, b=2, c=Row(a=1, b=2, c=3)))

def test_negative_and_zero_batch_size(self):
# SPARK-47068: Negative and zero value should work as unlimited batch size.
with self.sql_conf({"spark.sql.execution.arrow.maxRecordsPerBatch": 0}):
pdf = pd.DataFrame({"a": [123]})
assert_frame_equal(pdf, self.spark.createDataFrame(pdf).toPandas())

with self.sql_conf({"spark.sql.execution.arrow.maxRecordsPerBatch": -1}):
pdf = pd.DataFrame({"a": [123]})
assert_frame_equal(pdf, self.spark.createDataFrame(pdf).toPandas())


@unittest.skipIf(
not have_pandas or not have_pyarrow,
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