f92d276653
## What changes were proposed in this pull request? Since 0.11.0, PyArrow supports to raise an error for unsafe cast ([PR](https://github.com/apache/arrow/pull/2504)). We should use it to raise a proper error for pandas udf users when such cast is detected. Added a SQL config `spark.sql.execution.pandas.arrowSafeTypeConversion` to disable Arrow safe type check. ## How was this patch tested? Added test and manually test. Closes #22807 from viirya/SPARK-25811. Authored-by: Liang-Chi Hsieh <viirya@gmail.com> Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
268 lines
11 KiB
Python
268 lines
11 KiB
Python
#
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# Licensed to the Apache Software Foundation (ASF) under one or more
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# contributor license agreements. See the NOTICE file distributed with
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# this work for additional information regarding copyright ownership.
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# The ASF licenses this file to You under the Apache License, Version 2.0
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# (the "License"); you may not use this file except in compliance with
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# the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import unittest
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from pyspark.sql.functions import udf, pandas_udf, PandasUDFType
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from pyspark.sql.types import *
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from pyspark.sql.utils import ParseException
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from pyspark.rdd import PythonEvalType
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from pyspark.testing.sqlutils import ReusedSQLTestCase, have_pandas, have_pyarrow, \
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pandas_requirement_message, pyarrow_requirement_message
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from pyspark.testing.utils import QuietTest
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from py4j.protocol import Py4JJavaError
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@unittest.skipIf(
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not have_pandas or not have_pyarrow,
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pandas_requirement_message or pyarrow_requirement_message)
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class PandasUDFTests(ReusedSQLTestCase):
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def test_pandas_udf_basic(self):
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udf = pandas_udf(lambda x: x, DoubleType())
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self.assertEqual(udf.returnType, DoubleType())
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self.assertEqual(udf.evalType, PythonEvalType.SQL_SCALAR_PANDAS_UDF)
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udf = pandas_udf(lambda x: x, DoubleType(), PandasUDFType.SCALAR)
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self.assertEqual(udf.returnType, DoubleType())
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self.assertEqual(udf.evalType, PythonEvalType.SQL_SCALAR_PANDAS_UDF)
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udf = pandas_udf(lambda x: x, 'double', PandasUDFType.SCALAR)
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self.assertEqual(udf.returnType, DoubleType())
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self.assertEqual(udf.evalType, PythonEvalType.SQL_SCALAR_PANDAS_UDF)
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udf = pandas_udf(lambda x: x, StructType([StructField("v", DoubleType())]),
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PandasUDFType.GROUPED_MAP)
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self.assertEqual(udf.returnType, StructType([StructField("v", DoubleType())]))
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self.assertEqual(udf.evalType, PythonEvalType.SQL_GROUPED_MAP_PANDAS_UDF)
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udf = pandas_udf(lambda x: x, 'v double', PandasUDFType.GROUPED_MAP)
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self.assertEqual(udf.returnType, StructType([StructField("v", DoubleType())]))
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self.assertEqual(udf.evalType, PythonEvalType.SQL_GROUPED_MAP_PANDAS_UDF)
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udf = pandas_udf(lambda x: x, 'v double',
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functionType=PandasUDFType.GROUPED_MAP)
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self.assertEqual(udf.returnType, StructType([StructField("v", DoubleType())]))
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self.assertEqual(udf.evalType, PythonEvalType.SQL_GROUPED_MAP_PANDAS_UDF)
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udf = pandas_udf(lambda x: x, returnType='v double',
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functionType=PandasUDFType.GROUPED_MAP)
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self.assertEqual(udf.returnType, StructType([StructField("v", DoubleType())]))
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self.assertEqual(udf.evalType, PythonEvalType.SQL_GROUPED_MAP_PANDAS_UDF)
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def test_pandas_udf_decorator(self):
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@pandas_udf(DoubleType())
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def foo(x):
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return x
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self.assertEqual(foo.returnType, DoubleType())
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self.assertEqual(foo.evalType, PythonEvalType.SQL_SCALAR_PANDAS_UDF)
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@pandas_udf(returnType=DoubleType())
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def foo(x):
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return x
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self.assertEqual(foo.returnType, DoubleType())
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self.assertEqual(foo.evalType, PythonEvalType.SQL_SCALAR_PANDAS_UDF)
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schema = StructType([StructField("v", DoubleType())])
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@pandas_udf(schema, PandasUDFType.GROUPED_MAP)
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def foo(x):
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return x
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self.assertEqual(foo.returnType, schema)
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self.assertEqual(foo.evalType, PythonEvalType.SQL_GROUPED_MAP_PANDAS_UDF)
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@pandas_udf('v double', PandasUDFType.GROUPED_MAP)
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def foo(x):
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return x
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self.assertEqual(foo.returnType, schema)
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self.assertEqual(foo.evalType, PythonEvalType.SQL_GROUPED_MAP_PANDAS_UDF)
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@pandas_udf(schema, functionType=PandasUDFType.GROUPED_MAP)
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def foo(x):
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return x
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self.assertEqual(foo.returnType, schema)
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self.assertEqual(foo.evalType, PythonEvalType.SQL_GROUPED_MAP_PANDAS_UDF)
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@pandas_udf(returnType='double', functionType=PandasUDFType.SCALAR)
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def foo(x):
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return x
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self.assertEqual(foo.returnType, DoubleType())
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self.assertEqual(foo.evalType, PythonEvalType.SQL_SCALAR_PANDAS_UDF)
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@pandas_udf(returnType=schema, functionType=PandasUDFType.GROUPED_MAP)
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def foo(x):
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return x
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self.assertEqual(foo.returnType, schema)
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self.assertEqual(foo.evalType, PythonEvalType.SQL_GROUPED_MAP_PANDAS_UDF)
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def test_udf_wrong_arg(self):
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with QuietTest(self.sc):
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with self.assertRaises(ParseException):
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@pandas_udf('blah')
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def foo(x):
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return x
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with self.assertRaisesRegexp(ValueError, 'Invalid returnType.*None'):
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@pandas_udf(functionType=PandasUDFType.SCALAR)
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def foo(x):
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return x
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with self.assertRaisesRegexp(ValueError, 'Invalid functionType'):
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@pandas_udf('double', 100)
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def foo(x):
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return x
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with self.assertRaisesRegexp(ValueError, '0-arg pandas_udfs.*not.*supported'):
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pandas_udf(lambda: 1, LongType(), PandasUDFType.SCALAR)
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with self.assertRaisesRegexp(ValueError, '0-arg pandas_udfs.*not.*supported'):
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@pandas_udf(LongType(), PandasUDFType.SCALAR)
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def zero_with_type():
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return 1
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with self.assertRaisesRegexp(TypeError, 'Invalid returnType'):
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@pandas_udf(returnType=PandasUDFType.GROUPED_MAP)
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def foo(df):
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return df
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with self.assertRaisesRegexp(TypeError, 'Invalid returnType'):
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@pandas_udf(returnType='double', functionType=PandasUDFType.GROUPED_MAP)
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def foo(df):
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return df
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with self.assertRaisesRegexp(ValueError, 'Invalid function'):
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@pandas_udf(returnType='k int, v double', functionType=PandasUDFType.GROUPED_MAP)
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def foo(k, v, w):
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return k
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def test_stopiteration_in_udf(self):
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def foo(x):
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raise StopIteration()
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def foofoo(x, y):
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raise StopIteration()
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exc_message = "Caught StopIteration thrown from user's code; failing the task"
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df = self.spark.range(0, 100)
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# plain udf (test for SPARK-23754)
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self.assertRaisesRegexp(
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Py4JJavaError,
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exc_message,
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df.withColumn('v', udf(foo)('id')).collect
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)
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# pandas scalar udf
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self.assertRaisesRegexp(
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Py4JJavaError,
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exc_message,
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df.withColumn(
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'v', pandas_udf(foo, 'double', PandasUDFType.SCALAR)('id')
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).collect
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)
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# pandas grouped map
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self.assertRaisesRegexp(
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Py4JJavaError,
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exc_message,
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df.groupBy('id').apply(
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pandas_udf(foo, df.schema, PandasUDFType.GROUPED_MAP)
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).collect
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)
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self.assertRaisesRegexp(
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Py4JJavaError,
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exc_message,
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df.groupBy('id').apply(
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pandas_udf(foofoo, df.schema, PandasUDFType.GROUPED_MAP)
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).collect
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)
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# pandas grouped agg
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self.assertRaisesRegexp(
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Py4JJavaError,
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exc_message,
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df.groupBy('id').agg(
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pandas_udf(foo, 'double', PandasUDFType.GROUPED_AGG)('id')
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).collect
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)
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def test_pandas_udf_detect_unsafe_type_conversion(self):
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from distutils.version import LooseVersion
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import pandas as pd
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import numpy as np
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import pyarrow as pa
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values = [1.0] * 3
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pdf = pd.DataFrame({'A': values})
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df = self.spark.createDataFrame(pdf).repartition(1)
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@pandas_udf(returnType="int")
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def udf(column):
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return pd.Series(np.linspace(0, 1, 3))
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# Since 0.11.0, PyArrow supports the feature to raise an error for unsafe cast.
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if LooseVersion(pa.__version__) >= LooseVersion("0.11.0"):
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with self.sql_conf({
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"spark.sql.execution.pandas.arrowSafeTypeConversion": True}):
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with self.assertRaisesRegexp(Exception,
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"Exception thrown when converting pandas.Series"):
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df.select(['A']).withColumn('udf', udf('A')).collect()
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# Disabling Arrow safe type check.
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with self.sql_conf({
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"spark.sql.execution.pandas.arrowSafeTypeConversion": False}):
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df.select(['A']).withColumn('udf', udf('A')).collect()
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def test_pandas_udf_arrow_overflow(self):
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from distutils.version import LooseVersion
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import pandas as pd
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import pyarrow as pa
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df = self.spark.range(0, 1)
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@pandas_udf(returnType="byte")
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def udf(column):
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return pd.Series([128])
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# Arrow 0.11.0+ allows enabling or disabling safe type check.
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if LooseVersion(pa.__version__) >= LooseVersion("0.11.0"):
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# When enabling safe type check, Arrow 0.11.0+ disallows overflow cast.
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with self.sql_conf({
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"spark.sql.execution.pandas.arrowSafeTypeConversion": True}):
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with self.assertRaisesRegexp(Exception,
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"Exception thrown when converting pandas.Series"):
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df.withColumn('udf', udf('id')).collect()
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# Disabling safe type check, let Arrow do the cast anyway.
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with self.sql_conf({"spark.sql.execution.pandas.arrowSafeTypeConversion": False}):
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df.withColumn('udf', udf('id')).collect()
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else:
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# SQL config `arrowSafeTypeConversion` no matters for older Arrow.
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# Overflow cast causes an error.
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with self.sql_conf({"spark.sql.execution.pandas.arrowSafeTypeConversion": False}):
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with self.assertRaisesRegexp(Exception,
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"Integer value out of bounds"):
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df.withColumn('udf', udf('id')).collect()
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if __name__ == "__main__":
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from pyspark.sql.tests.test_pandas_udf import *
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try:
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import xmlrunner
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testRunner = xmlrunner.XMLTestRunner(output='target/test-reports')
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except ImportError:
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testRunner = None
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unittest.main(testRunner=testRunner, verbosity=2)
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