d36cce18e2
## What changes were proposed in this pull request? This increases the minimum support version of pyarrow to 0.12.1 and removes workarounds in pyspark to remain compatible with prior versions. This means that users will need to have at least pyarrow 0.12.1 installed and available in the cluster or an `ImportError` will be raised to indicate an upgrade is needed. ## How was this patch tested? Existing tests using: Python 2.7.15, pyarrow 0.12.1, pandas 0.24.2 Python 3.6.7, pyarrow 0.12.1, pandas 0.24.0 Closes #24298 from BryanCutler/arrow-bump-min-pyarrow-SPARK-27276. Authored-by: Bryan Cutler <cutlerb@gmail.com> Signed-off-by: HyukjinKwon <gurwls223@apache.org>
254 lines
9.9 KiB
Python
254 lines
9.9 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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import pandas as pd
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import numpy as np
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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, len(column)))
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# Since 0.11.0, PyArrow supports the feature to raise an error for unsafe 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.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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import pandas as pd
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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] * len(column))
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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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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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