16990f9299
## What changes were proposed in this pull request? Upgrade Apache Arrow to version 0.12.0. This includes the Java artifacts and fixes to enable usage with pyarrow 0.12.0 Version 0.12.0 includes the following selected fixes/improvements relevant to Spark users: * Safe cast fails from numpy float64 array with nans to integer, ARROW-4258 * Java, Reduce heap usage for variable width vectors, ARROW-4147 * Binary identity cast not implemented, ARROW-4101 * pyarrow open_stream deprecated, use ipc.open_stream, ARROW-4098 * conversion to date object no longer needed, ARROW-3910 * Error reading IPC file with no record batches, ARROW-3894 * Signed to unsigned integer cast yields incorrect results when type sizes are the same, ARROW-3790 * from_pandas gives incorrect results when converting floating point to bool, ARROW-3428 * Import pyarrow fails if scikit-learn is installed from conda (boost-cpp / libboost issue), ARROW-3048 * Java update to official Flatbuffers version 1.9.0, ARROW-3175 complete list [here](https://issues.apache.org/jira/issues/?jql=project%20%3D%20ARROW%20AND%20status%20in%20(Resolved%2C%20Closed)%20AND%20fixVersion%20%3D%200.12.0) PySpark requires the following fixes to work with PyArrow 0.12.0 * Encrypted pyspark worker fails due to ChunkedStream missing closed property * pyarrow now converts dates as objects by default, which causes error because type is assumed datetime64 * ArrowTests fails due to difference in raised error message * pyarrow.open_stream deprecated * tests fail because groupby adds index column with duplicate name ## How was this patch tested? Ran unit tests with pyarrow versions 0.8.0, 0.10.0, 0.11.1, 0.12.0 Closes #23657 from BryanCutler/arrow-upgrade-012. Authored-by: Bryan Cutler <cutlerb@gmail.com> Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
517 lines
20 KiB
Python
517 lines
20 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 datetime
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import unittest
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from collections import OrderedDict
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from decimal import Decimal
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from distutils.version import LooseVersion
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from pyspark.sql import Row
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from pyspark.sql.functions import array, explode, col, lit, udf, sum, pandas_udf, PandasUDFType
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from pyspark.sql.types import *
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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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@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 GroupedMapPandasUDFTests(ReusedSQLTestCase):
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@property
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def data(self):
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return self.spark.range(10).toDF('id') \
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.withColumn("vs", array([lit(i) for i in range(20, 30)])) \
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.withColumn("v", explode(col('vs'))).drop('vs')
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def test_supported_types(self):
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import pyarrow as pa
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values = [
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1, 2, 3,
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4, 5, 1.1,
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2.2, Decimal(1.123),
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[1, 2, 2], True, 'hello'
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]
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output_fields = [
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('id', IntegerType()), ('byte', ByteType()), ('short', ShortType()),
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('int', IntegerType()), ('long', LongType()), ('float', FloatType()),
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('double', DoubleType()), ('decim', DecimalType(10, 3)),
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('array', ArrayType(IntegerType())), ('bool', BooleanType()), ('str', StringType())
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]
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# TODO: Add BinaryType to variables above once minimum pyarrow version is 0.10.0
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if LooseVersion(pa.__version__) >= LooseVersion("0.10.0"):
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values.append(bytearray([0x01, 0x02]))
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output_fields.append(('bin', BinaryType()))
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output_schema = StructType([StructField(*x) for x in output_fields])
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df = self.spark.createDataFrame([values], schema=output_schema)
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# Different forms of group map pandas UDF, results of these are the same
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udf1 = pandas_udf(
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lambda pdf: pdf.assign(
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byte=pdf.byte * 2,
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short=pdf.short * 2,
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int=pdf.int * 2,
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long=pdf.long * 2,
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float=pdf.float * 2,
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double=pdf.double * 2,
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decim=pdf.decim * 2,
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bool=False if pdf.bool else True,
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str=pdf.str + 'there',
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array=pdf.array,
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),
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output_schema,
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PandasUDFType.GROUPED_MAP
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)
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udf2 = pandas_udf(
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lambda _, pdf: pdf.assign(
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byte=pdf.byte * 2,
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short=pdf.short * 2,
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int=pdf.int * 2,
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long=pdf.long * 2,
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float=pdf.float * 2,
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double=pdf.double * 2,
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decim=pdf.decim * 2,
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bool=False if pdf.bool else True,
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str=pdf.str + 'there',
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array=pdf.array,
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),
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output_schema,
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PandasUDFType.GROUPED_MAP
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)
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udf3 = pandas_udf(
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lambda key, pdf: pdf.assign(
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id=key[0],
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byte=pdf.byte * 2,
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short=pdf.short * 2,
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int=pdf.int * 2,
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long=pdf.long * 2,
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float=pdf.float * 2,
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double=pdf.double * 2,
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decim=pdf.decim * 2,
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bool=False if pdf.bool else True,
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str=pdf.str + 'there',
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array=pdf.array,
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),
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output_schema,
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PandasUDFType.GROUPED_MAP
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)
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result1 = df.groupby('id').apply(udf1).sort('id').toPandas()
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expected1 = df.toPandas().groupby('id').apply(udf1.func).reset_index(drop=True)
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result2 = df.groupby('id').apply(udf2).sort('id').toPandas()
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expected2 = expected1
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result3 = df.groupby('id').apply(udf3).sort('id').toPandas()
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expected3 = expected1
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self.assertPandasEqual(expected1, result1)
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self.assertPandasEqual(expected2, result2)
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self.assertPandasEqual(expected3, result3)
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def test_array_type_correct(self):
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df = self.data.withColumn("arr", array(col("id"))).repartition(1, "id")
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output_schema = StructType(
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[StructField('id', LongType()),
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StructField('v', IntegerType()),
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StructField('arr', ArrayType(LongType()))])
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udf = pandas_udf(
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lambda pdf: pdf,
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output_schema,
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PandasUDFType.GROUPED_MAP
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)
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result = df.groupby('id').apply(udf).sort('id').toPandas()
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expected = df.toPandas().groupby('id').apply(udf.func).reset_index(drop=True)
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self.assertPandasEqual(expected, result)
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def test_register_grouped_map_udf(self):
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foo_udf = pandas_udf(lambda x: x, "id long", PandasUDFType.GROUPED_MAP)
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with QuietTest(self.sc):
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with self.assertRaisesRegexp(
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ValueError,
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'f.*SQL_BATCHED_UDF.*SQL_SCALAR_PANDAS_UDF.*SQL_GROUPED_AGG_PANDAS_UDF.*'):
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self.spark.catalog.registerFunction("foo_udf", foo_udf)
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def test_decorator(self):
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df = self.data
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@pandas_udf(
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'id long, v int, v1 double, v2 long',
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PandasUDFType.GROUPED_MAP
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)
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def foo(pdf):
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return pdf.assign(v1=pdf.v * pdf.id * 1.0, v2=pdf.v + pdf.id)
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result = df.groupby('id').apply(foo).sort('id').toPandas()
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expected = df.toPandas().groupby('id').apply(foo.func).reset_index(drop=True)
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self.assertPandasEqual(expected, result)
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def test_coerce(self):
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df = self.data
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foo = pandas_udf(
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lambda pdf: pdf,
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'id long, v double',
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PandasUDFType.GROUPED_MAP
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)
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result = df.groupby('id').apply(foo).sort('id').toPandas()
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expected = df.toPandas().groupby('id').apply(foo.func).reset_index(drop=True)
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expected = expected.assign(v=expected.v.astype('float64'))
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self.assertPandasEqual(expected, result)
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def test_complex_groupby(self):
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df = self.data
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@pandas_udf(
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'id long, v int, norm double',
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PandasUDFType.GROUPED_MAP
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)
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def normalize(pdf):
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v = pdf.v
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return pdf.assign(norm=(v - v.mean()) / v.std())
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result = df.groupby(col('id') % 2 == 0).apply(normalize).sort('id', 'v').toPandas()
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pdf = df.toPandas()
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expected = pdf.groupby(pdf['id'] % 2 == 0, as_index=False).apply(normalize.func)
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expected = expected.sort_values(['id', 'v']).reset_index(drop=True)
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expected = expected.assign(norm=expected.norm.astype('float64'))
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self.assertPandasEqual(expected, result)
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def test_empty_groupby(self):
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df = self.data
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@pandas_udf(
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'id long, v int, norm double',
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PandasUDFType.GROUPED_MAP
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)
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def normalize(pdf):
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v = pdf.v
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return pdf.assign(norm=(v - v.mean()) / v.std())
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result = df.groupby().apply(normalize).sort('id', 'v').toPandas()
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pdf = df.toPandas()
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expected = normalize.func(pdf)
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expected = expected.sort_values(['id', 'v']).reset_index(drop=True)
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expected = expected.assign(norm=expected.norm.astype('float64'))
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self.assertPandasEqual(expected, result)
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def test_datatype_string(self):
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df = self.data
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foo_udf = pandas_udf(
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lambda pdf: pdf.assign(v1=pdf.v * pdf.id * 1.0, v2=pdf.v + pdf.id),
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'id long, v int, v1 double, v2 long',
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PandasUDFType.GROUPED_MAP
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)
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result = df.groupby('id').apply(foo_udf).sort('id').toPandas()
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expected = df.toPandas().groupby('id').apply(foo_udf.func).reset_index(drop=True)
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self.assertPandasEqual(expected, result)
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def test_wrong_return_type(self):
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with QuietTest(self.sc):
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with self.assertRaisesRegexp(
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NotImplementedError,
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'Invalid returnType.*grouped map Pandas UDF.*MapType'):
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pandas_udf(
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lambda pdf: pdf,
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'id long, v map<int, int>',
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PandasUDFType.GROUPED_MAP)
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def test_wrong_args(self):
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df = self.data
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with QuietTest(self.sc):
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with self.assertRaisesRegexp(ValueError, 'Invalid udf'):
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df.groupby('id').apply(lambda x: x)
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with self.assertRaisesRegexp(ValueError, 'Invalid udf'):
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df.groupby('id').apply(udf(lambda x: x, DoubleType()))
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with self.assertRaisesRegexp(ValueError, 'Invalid udf'):
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df.groupby('id').apply(sum(df.v))
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with self.assertRaisesRegexp(ValueError, 'Invalid udf'):
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df.groupby('id').apply(df.v + 1)
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with self.assertRaisesRegexp(ValueError, 'Invalid function'):
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df.groupby('id').apply(
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pandas_udf(lambda: 1, StructType([StructField("d", DoubleType())])))
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with self.assertRaisesRegexp(ValueError, 'Invalid udf'):
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df.groupby('id').apply(pandas_udf(lambda x, y: x, DoubleType()))
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with self.assertRaisesRegexp(ValueError, 'Invalid udf.*GROUPED_MAP'):
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df.groupby('id').apply(
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pandas_udf(lambda x, y: x, DoubleType(), PandasUDFType.SCALAR))
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def test_unsupported_types(self):
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import pyarrow as pa
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common_err_msg = 'Invalid returnType.*grouped map Pandas UDF.*'
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unsupported_types = [
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StructField('map', MapType(StringType(), IntegerType())),
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StructField('arr_ts', ArrayType(TimestampType())),
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StructField('null', NullType()),
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]
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# TODO: Remove this if-statement once minimum pyarrow version is 0.10.0
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if LooseVersion(pa.__version__) < LooseVersion("0.10.0"):
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unsupported_types.append(StructField('bin', BinaryType()))
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for unsupported_type in unsupported_types:
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schema = StructType([StructField('id', LongType(), True), unsupported_type])
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with QuietTest(self.sc):
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with self.assertRaisesRegexp(NotImplementedError, common_err_msg):
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pandas_udf(lambda x: x, schema, PandasUDFType.GROUPED_MAP)
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# Regression test for SPARK-23314
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def test_timestamp_dst(self):
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# Daylight saving time for Los Angeles for 2015 is Sun, Nov 1 at 2:00 am
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dt = [datetime.datetime(2015, 11, 1, 0, 30),
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datetime.datetime(2015, 11, 1, 1, 30),
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datetime.datetime(2015, 11, 1, 2, 30)]
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df = self.spark.createDataFrame(dt, 'timestamp').toDF('time')
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foo_udf = pandas_udf(lambda pdf: pdf, 'time timestamp', PandasUDFType.GROUPED_MAP)
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result = df.groupby('time').apply(foo_udf).sort('time')
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self.assertPandasEqual(df.toPandas(), result.toPandas())
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def test_udf_with_key(self):
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import numpy as np
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df = self.data
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pdf = df.toPandas()
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def foo1(key, pdf):
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assert type(key) == tuple
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assert type(key[0]) == np.int64
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return pdf.assign(v1=key[0],
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v2=pdf.v * key[0],
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v3=pdf.v * pdf.id,
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v4=pdf.v * pdf.id.mean())
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def foo2(key, pdf):
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assert type(key) == tuple
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assert type(key[0]) == np.int64
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assert type(key[1]) == np.int32
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return pdf.assign(v1=key[0],
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v2=key[1],
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v3=pdf.v * key[0],
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v4=pdf.v + key[1])
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def foo3(key, pdf):
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assert type(key) == tuple
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assert len(key) == 0
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return pdf.assign(v1=pdf.v * pdf.id)
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# v2 is int because numpy.int64 * pd.Series<int32> results in pd.Series<int32>
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# v3 is long because pd.Series<int64> * pd.Series<int32> results in pd.Series<int64>
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udf1 = pandas_udf(
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foo1,
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'id long, v int, v1 long, v2 int, v3 long, v4 double',
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PandasUDFType.GROUPED_MAP)
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udf2 = pandas_udf(
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foo2,
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'id long, v int, v1 long, v2 int, v3 int, v4 int',
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PandasUDFType.GROUPED_MAP)
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udf3 = pandas_udf(
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foo3,
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'id long, v int, v1 long',
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PandasUDFType.GROUPED_MAP)
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# Test groupby column
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result1 = df.groupby('id').apply(udf1).sort('id', 'v').toPandas()
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expected1 = pdf.groupby('id', as_index=False)\
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.apply(lambda x: udf1.func((x.id.iloc[0],), x))\
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.sort_values(['id', 'v']).reset_index(drop=True)
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self.assertPandasEqual(expected1, result1)
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# Test groupby expression
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result2 = df.groupby(df.id % 2).apply(udf1).sort('id', 'v').toPandas()
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expected2 = pdf.groupby(pdf.id % 2, as_index=False)\
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.apply(lambda x: udf1.func((x.id.iloc[0] % 2,), x))\
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.sort_values(['id', 'v']).reset_index(drop=True)
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self.assertPandasEqual(expected2, result2)
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# Test complex groupby
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result3 = df.groupby(df.id, df.v % 2).apply(udf2).sort('id', 'v').toPandas()
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expected3 = pdf.groupby([pdf.id, pdf.v % 2], as_index=False)\
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.apply(lambda x: udf2.func((x.id.iloc[0], (x.v % 2).iloc[0],), x))\
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.sort_values(['id', 'v']).reset_index(drop=True)
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self.assertPandasEqual(expected3, result3)
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# Test empty groupby
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result4 = df.groupby().apply(udf3).sort('id', 'v').toPandas()
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expected4 = udf3.func((), pdf)
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self.assertPandasEqual(expected4, result4)
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def test_column_order(self):
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import pandas as pd
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# Helper function to set column names from a list
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def rename_pdf(pdf, names):
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pdf.rename(columns={old: new for old, new in
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zip(pd_result.columns, names)}, inplace=True)
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df = self.data
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grouped_df = df.groupby('id')
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grouped_pdf = df.toPandas().groupby('id', as_index=False)
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# Function returns a pdf with required column names, but order could be arbitrary using dict
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def change_col_order(pdf):
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# Constructing a DataFrame from a dict should result in the same order,
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# but use from_items to ensure the pdf column order is different than schema
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return pd.DataFrame.from_items([
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('id', pdf.id),
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('u', pdf.v * 2),
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('v', pdf.v)])
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ordered_udf = pandas_udf(
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change_col_order,
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'id long, v int, u int',
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PandasUDFType.GROUPED_MAP
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)
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# The UDF result should assign columns by name from the pdf
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result = grouped_df.apply(ordered_udf).sort('id', 'v')\
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.select('id', 'u', 'v').toPandas()
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pd_result = grouped_pdf.apply(change_col_order)
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expected = pd_result.sort_values(['id', 'v']).reset_index(drop=True)
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self.assertPandasEqual(expected, result)
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# Function returns a pdf with positional columns, indexed by range
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def range_col_order(pdf):
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# Create a DataFrame with positional columns, fix types to long
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return pd.DataFrame(list(zip(pdf.id, pdf.v * 3, pdf.v)), dtype='int64')
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range_udf = pandas_udf(
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range_col_order,
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'id long, u long, v long',
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PandasUDFType.GROUPED_MAP
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)
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# The UDF result uses positional columns from the pdf
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result = grouped_df.apply(range_udf).sort('id', 'v') \
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.select('id', 'u', 'v').toPandas()
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pd_result = grouped_pdf.apply(range_col_order)
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rename_pdf(pd_result, ['id', 'u', 'v'])
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expected = pd_result.sort_values(['id', 'v']).reset_index(drop=True)
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self.assertPandasEqual(expected, result)
|
|
|
|
# Function returns a pdf with columns indexed with integers
|
|
def int_index(pdf):
|
|
return pd.DataFrame(OrderedDict([(0, pdf.id), (1, pdf.v * 4), (2, pdf.v)]))
|
|
|
|
int_index_udf = pandas_udf(
|
|
int_index,
|
|
'id long, u int, v int',
|
|
PandasUDFType.GROUPED_MAP
|
|
)
|
|
|
|
# The UDF result should assign columns by position of integer index
|
|
result = grouped_df.apply(int_index_udf).sort('id', 'v') \
|
|
.select('id', 'u', 'v').toPandas()
|
|
pd_result = grouped_pdf.apply(int_index)
|
|
rename_pdf(pd_result, ['id', 'u', 'v'])
|
|
expected = pd_result.sort_values(['id', 'v']).reset_index(drop=True)
|
|
self.assertPandasEqual(expected, result)
|
|
|
|
@pandas_udf('id long, v int', PandasUDFType.GROUPED_MAP)
|
|
def column_name_typo(pdf):
|
|
return pd.DataFrame({'iid': pdf.id, 'v': pdf.v})
|
|
|
|
@pandas_udf('id long, v int', PandasUDFType.GROUPED_MAP)
|
|
def invalid_positional_types(pdf):
|
|
return pd.DataFrame([(u'a', 1.2)])
|
|
|
|
with QuietTest(self.sc):
|
|
with self.assertRaisesRegexp(Exception, "KeyError: 'id'"):
|
|
grouped_df.apply(column_name_typo).collect()
|
|
import pyarrow as pa
|
|
if LooseVersion(pa.__version__) < LooseVersion("0.11.0"):
|
|
# TODO: see ARROW-1949. Remove when the minimum PyArrow version becomes 0.11.0.
|
|
with self.assertRaisesRegexp(Exception, "No cast implemented"):
|
|
grouped_df.apply(invalid_positional_types).collect()
|
|
else:
|
|
with self.assertRaisesRegexp(Exception, "an integer is required"):
|
|
grouped_df.apply(invalid_positional_types).collect()
|
|
|
|
def test_positional_assignment_conf(self):
|
|
import pandas as pd
|
|
|
|
with self.sql_conf({
|
|
"spark.sql.legacy.execution.pandas.groupedMap.assignColumnsByName": False}):
|
|
|
|
@pandas_udf("a string, b float", PandasUDFType.GROUPED_MAP)
|
|
def foo(_):
|
|
return pd.DataFrame([('hi', 1)], columns=['x', 'y'])
|
|
|
|
df = self.data
|
|
result = df.groupBy('id').apply(foo).select('a', 'b').collect()
|
|
for r in result:
|
|
self.assertEqual(r.a, 'hi')
|
|
self.assertEqual(r.b, 1)
|
|
|
|
def test_self_join_with_pandas(self):
|
|
@pandas_udf('key long, col string', PandasUDFType.GROUPED_MAP)
|
|
def dummy_pandas_udf(df):
|
|
return df[['key', 'col']]
|
|
|
|
df = self.spark.createDataFrame([Row(key=1, col='A'), Row(key=1, col='B'),
|
|
Row(key=2, col='C')])
|
|
df_with_pandas = df.groupBy('key').apply(dummy_pandas_udf)
|
|
|
|
# this was throwing an AnalysisException before SPARK-24208
|
|
res = df_with_pandas.alias('temp0').join(df_with_pandas.alias('temp1'),
|
|
col('temp0.key') == col('temp1.key'))
|
|
self.assertEquals(res.count(), 5)
|
|
|
|
def test_mixed_scalar_udfs_followed_by_grouby_apply(self):
|
|
import pandas as pd
|
|
|
|
df = self.spark.range(0, 10).toDF('v1')
|
|
df = df.withColumn('v2', udf(lambda x: x + 1, 'int')(df['v1'])) \
|
|
.withColumn('v3', pandas_udf(lambda x: x + 2, 'int')(df['v1']))
|
|
|
|
result = df.groupby() \
|
|
.apply(pandas_udf(lambda x: pd.DataFrame([x.sum().sum()]),
|
|
'sum int',
|
|
PandasUDFType.GROUPED_MAP))
|
|
|
|
self.assertEquals(result.collect()[0]['sum'], 165)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
from pyspark.sql.tests.test_pandas_udf_grouped_map import *
|
|
|
|
try:
|
|
import xmlrunner
|
|
testRunner = xmlrunner.XMLTestRunner(output='target/test-reports')
|
|
except ImportError:
|
|
testRunner = None
|
|
unittest.main(testRunner=testRunner, verbosity=2)
|