Skip to content

[SPARK-44876][PYTHON][FOLLOWUP] Fix Arrow-optimized Python UDF to delay wrapping the function with fail_on_stopiteration #42784

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Closed
ueshin wants to merge 1 commit into from
Closed

[SPARK-44876][PYTHON][FOLLOWUP] Fix Arrow-optimized Python UDF to delay wrapping the function with fail_on_stopiteration #42784

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
15 changes: 15 additions & 0 deletions python/pyspark/sql/tests/test_udf.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -1005,6 +1005,21 @@ def test_udf(a, b=0):
with self.subTest(with_b=True, query_no=i):
assertDataFrameEqual(df, [Row(0), Row(101)])

def test_raise_stop_iteration(self):
@udf("int")
def test_udf(a):
if a < 5:
return a
else:
raise StopIteration()

assertDataFrameEqual(
self.spark.range(5).select(test_udf(col("id"))), [Row(i) for i in range(5)]
)

with self.assertRaisesRegex(PythonException, "StopIteration"):
self.spark.range(10).select(test_udf(col("id"))).show()


class UDFTests(BaseUDFTestsMixin, ReusedSQLTestCase):
@classmethod
Expand Down
22 changes: 6 additions & 16 deletions python/pyspark/worker.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -139,6 +139,7 @@ def wrap_arrow_batch_udf(f, return_type):
elif type(return_type) == BinaryType:
result_func = lambda r: bytes(r) if r is not None else r # noqa: E731

@fail_on_stopiteration
def evaluate(*args: pd.Series, **kwargs: pd.Series) -> pd.Series:
keys = list(kwargs.keys())
len_args = len(args)
Expand All @@ -151,18 +152,6 @@ def evaluate(*args: pd.Series, **kwargs: pd.Series) -> pd.Series:
]
)

def verify_result_type(result):
if not hasattr(result, "__len__"):
pd_type = "pandas.DataFrame" if type(return_type) == StructType else "pandas.Series"
raise PySparkTypeError(
error_class="UDF_RETURN_TYPE",
message_parameters={
"expected": pd_type,
"actual": type(result).__name__,
},
)
return result

def verify_result_length(result, length):
if len(result) != length:
raise PySparkRuntimeError(
Expand All @@ -175,9 +164,7 @@ def verify_result_length(result, length):
return result

return lambda *a, **kw: (
verify_result_length(
verify_result_type(evaluate(*a, **kw)), len((list(a) + list(kw.values()))[0])
),
verify_result_length(evaluate(*a, **kw), len((list(a) + list(kw.values()))[0])),
arrow_return_type,
)

Expand Down Expand Up @@ -562,7 +549,10 @@ def read_single_udf(pickleSer, infile, eval_type, runner_conf, udf_index):
else:
chained_func = chain(chained_func, f)

if eval_type == PythonEvalType.SQL_SCALAR_PANDAS_ITER_UDF:
if eval_type in (
PythonEvalType.SQL_SCALAR_PANDAS_ITER_UDF,
PythonEvalType.SQL_ARROW_BATCHED_UDF,
):
func = chained_func
else:
# make sure StopIteration's raised in the user code are not ignored
Expand Down