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[SPARK-33073][PYTHON] Improve error handling on Pandas to Arrow conversion failures #29951

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[SPARK-33073][PYTHON] Improve error handling on Pandas to Arrow conversion failures #29951

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17 changes: 10 additions & 7 deletions python/pyspark/sql/pandas/serializers.py
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Original file line number Diff line number Diff line change
Expand Up @@ -153,13 +153,16 @@ def create_array(s, t):
s = s.astype(s.dtypes.categories.dtype)
try:
array = pa.Array.from_pandas(s, mask=mask, type=t, safe=self._safecheck)
except pa.ArrowException as e:
error_msg = "Exception thrown when converting pandas.Series (%s) to Arrow " + \
"Array (%s). It can be caused by overflows or other unsafe " + \
"conversions warned by Arrow. Arrow safe type check can be " + \
"disabled by using SQL config " + \
"`spark.sql.execution.pandas.convertToArrowArraySafely`."
raise RuntimeError(error_msg % (s.dtype, t), e)
except ValueError as e:
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@BryanCutler BryanCutler Oct 6, 2020

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errors during safe conversion will be ArrowInvalid, which subclasses ValueError

if self._safecheck:
error_msg = "Exception thrown when converting pandas.Series (%s) to " + \
"Arrow Array (%s). It can be caused by overflows or other " + \
"unsafe conversions warned by Arrow. Arrow safe type check " + \
"can be disabled by using SQL config " + \
"`spark.sql.execution.pandas.convertToArrowArraySafely`."
raise ValueError(error_msg % (s.dtype, t)) from e
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@BryanCutler BryanCutler Oct 6, 2020

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Now that we dropped Python 2, this seems more appropriate

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@dongjoon-hyun dongjoon-hyun Oct 7, 2020

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In branch-3.0.

  File "/home/jenkins/workspace/spark-branch-3.0-test-sbt-hadoop-2.7-hive-1.2/python/pyspark/sql/pandas/serializers.py", line 166
    raise ValueError(error_msg % (s.dtype, t)) from e
                                              ^
SyntaxError: invalid syntax

else:
raise e
return array

arrs = []
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9 changes: 5 additions & 4 deletions python/pyspark/sql/tests/test_arrow.py
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Expand Up @@ -264,11 +264,12 @@ def test_createDataFrame_with_schema(self):
def test_createDataFrame_with_incorrect_schema(self):
pdf = self.create_pandas_data_frame()
fields = list(self.schema)
fields[0], fields[1] = fields[1], fields[0] # swap str with int
fields[5], fields[6] = fields[6], fields[5] # swap decimal with date
wrong_schema = StructType(fields)
with QuietTest(self.sc):
with self.assertRaisesRegexp(Exception, "integer.*required"):
self.spark.createDataFrame(pdf, schema=wrong_schema)
with self.sql_conf({"spark.sql.execution.pandas.convertToArrowArraySafely": False}):
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@BryanCutler BryanCutler Oct 6, 2020

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The error here is a TypeError, but in case it gets changed to an ArrowInvalid, we do not want to have the original error chained because the assert does not include it when checking

with QuietTest(self.sc):
with self.assertRaisesRegexp(Exception, "[D|d]ecimal.*got.*date"):
self.spark.createDataFrame(pdf, schema=wrong_schema)

def test_createDataFrame_with_names(self):
pdf = self.create_pandas_data_frame()
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15 changes: 8 additions & 7 deletions python/pyspark/sql/tests/test_pandas_grouped_map.py
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Expand Up @@ -446,15 +446,16 @@ def int_index(pdf):
def column_name_typo(pdf):
return pd.DataFrame({'iid': pdf.id, 'v': pdf.v})

@pandas_udf('id long, v int', PandasUDFType.GROUPED_MAP)
@pandas_udf('id long, v decimal', PandasUDFType.GROUPED_MAP)
def invalid_positional_types(pdf):
return pd.DataFrame([(u'a', 1.2)])
return pd.DataFrame([(1, datetime.date(2020, 10, 5))])

with QuietTest(self.sc):
with self.assertRaisesRegexp(Exception, "KeyError: 'id'"):
grouped_df.apply(column_name_typo).collect()
with self.assertRaisesRegexp(Exception, "an integer is required"):
grouped_df.apply(invalid_positional_types).collect()
with self.sql_conf({"spark.sql.execution.pandas.convertToArrowArraySafely": False}):
with QuietTest(self.sc):
with self.assertRaisesRegexp(Exception, "KeyError: 'id'"):
grouped_df.apply(column_name_typo).collect()
with self.assertRaisesRegexp(Exception, "[D|d]ecimal.*got.*date"):
grouped_df.apply(invalid_positional_types).collect()

def test_positional_assignment_conf(self):
with self.sql_conf({
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