76791b89f5
Follow up from https://github.com/apache/spark/pull/24981 incorporating some comments from HyukjinKwon. Specifically: - Adding `CoGroupedData` to `pyspark/sql/__init__.py __all__` so that documentation is generated. - Added pydoc, including example, for the use case whereby the user supplies a cogrouping function including a key. - Added the boilerplate for doctests to cogroup.py. Note that cogroup.py only contains the apply() function which has doctests disabled as per the other Pandas Udfs. - Restricted the newly exposed RelationalGroupedDataset constructor parameters to access only by the sql package. - Some minor formatting tweaks. This was tested by running the appropriate unit tests. I'm unsure as to how to check that my change will cause the documentation to be generated correctly, but it someone can describe how I can do this I'd be happy to check. Closes #25939 from d80tb7/SPARK-27463-fixes. Authored-by: Chris Martin <chris@cmartinit.co.uk> Signed-off-by: HyukjinKwon <gurwls223@apache.org>
276 lines
9.8 KiB
Python
276 lines
9.8 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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import sys
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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 DoubleType, StructType, StructField
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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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if have_pandas:
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import pandas as pd
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from pandas.util.testing import assert_frame_equal, assert_series_equal
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if have_pyarrow:
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import pyarrow as pa
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# Tests below use pd.DataFrame.assign that will infer mixed types (unicode/str) for column names
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# From kwargs w/ Python 2, so need to set check_column_type=False and avoid this check
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_check_column_type = sys.version >= '3'
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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 CoGroupedMapPandasUDFTests(ReusedSQLTestCase):
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@property
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def data1(self):
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return self.spark.range(10).toDF('id') \
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.withColumn("ks", array([lit(i) for i in range(20, 30)])) \
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.withColumn("k", explode(col('ks')))\
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.withColumn("v", col('k') * 10)\
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.drop('ks')
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@property
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def data2(self):
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return self.spark.range(10).toDF('id') \
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.withColumn("ks", array([lit(i) for i in range(20, 30)])) \
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.withColumn("k", explode(col('ks'))) \
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.withColumn("v2", col('k') * 100) \
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.drop('ks')
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def test_simple(self):
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self._test_merge(self.data1, self.data2)
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def test_left_group_empty(self):
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left = self.data1.where(col("id") % 2 == 0)
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self._test_merge(left, self.data2)
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def test_right_group_empty(self):
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right = self.data2.where(col("id") % 2 == 0)
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self._test_merge(self.data1, right)
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def test_different_schemas(self):
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right = self.data2.withColumn('v3', lit('a'))
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self._test_merge(self.data1, right, 'id long, k int, v int, v2 int, v3 string')
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def test_complex_group_by(self):
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left = pd.DataFrame.from_dict({
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'id': [1, 2, 3],
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'k': [5, 6, 7],
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'v': [9, 10, 11]
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})
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right = pd.DataFrame.from_dict({
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'id': [11, 12, 13],
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'k': [5, 6, 7],
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'v2': [90, 100, 110]
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})
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left_gdf = self.spark\
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.createDataFrame(left)\
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.groupby(col('id') % 2 == 0)
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right_gdf = self.spark \
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.createDataFrame(right) \
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.groupby(col('id') % 2 == 0)
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@pandas_udf('k long, v long, v2 long', PandasUDFType.COGROUPED_MAP)
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def merge_pandas(l, r):
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return pd.merge(l[['k', 'v']], r[['k', 'v2']], on=['k'])
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result = left_gdf \
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.cogroup(right_gdf) \
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.apply(merge_pandas) \
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.sort(['k']) \
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.toPandas()
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expected = pd.DataFrame.from_dict({
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'k': [5, 6, 7],
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'v': [9, 10, 11],
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'v2': [90, 100, 110]
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})
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assert_frame_equal(expected, result, check_column_type=_check_column_type)
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def test_empty_group_by(self):
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left = self.data1
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right = self.data2
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@pandas_udf('id long, k int, v int, v2 int', PandasUDFType.COGROUPED_MAP)
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def merge_pandas(l, r):
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return pd.merge(l, r, on=['id', 'k'])
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result = left.groupby().cogroup(right.groupby())\
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.apply(merge_pandas) \
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.sort(['id', 'k']) \
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.toPandas()
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left = left.toPandas()
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right = right.toPandas()
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expected = pd \
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.merge(left, right, on=['id', 'k']) \
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.sort_values(by=['id', 'k'])
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assert_frame_equal(expected, result, check_column_type=_check_column_type)
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def test_mixed_scalar_udfs_followed_by_cogrouby_apply(self):
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df = self.spark.range(0, 10).toDF('v1')
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df = df.withColumn('v2', udf(lambda x: x + 1, 'int')(df['v1'])) \
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.withColumn('v3', pandas_udf(lambda x: x + 2, 'int')(df['v1']))
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result = df.groupby().cogroup(df.groupby())\
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.apply(pandas_udf(lambda x, y: pd.DataFrame([(x.sum().sum(), y.sum().sum())]),
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'sum1 int, sum2 int',
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PandasUDFType.COGROUPED_MAP)).collect()
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self.assertEquals(result[0]['sum1'], 165)
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self.assertEquals(result[0]['sum2'], 165)
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def test_with_key_left(self):
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self._test_with_key(self.data1, self.data1, isLeft=True)
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def test_with_key_right(self):
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self._test_with_key(self.data1, self.data1, isLeft=False)
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def test_with_key_left_group_empty(self):
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left = self.data1.where(col("id") % 2 == 0)
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self._test_with_key(left, self.data1, isLeft=True)
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def test_with_key_right_group_empty(self):
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right = self.data1.where(col("id") % 2 == 0)
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self._test_with_key(self.data1, right, isLeft=False)
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def test_with_key_complex(self):
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@pandas_udf('id long, k int, v int, key boolean', PandasUDFType.COGROUPED_MAP)
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def left_assign_key(key, l, _):
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return l.assign(key=key[0])
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result = self.data1 \
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.groupby(col('id') % 2 == 0)\
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.cogroup(self.data2.groupby(col('id') % 2 == 0)) \
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.apply(left_assign_key) \
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.sort(['id', 'k']) \
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.toPandas()
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expected = self.data1.toPandas()
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expected = expected.assign(key=expected.id % 2 == 0)
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assert_frame_equal(expected, result, check_column_type=_check_column_type)
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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.*cogrouped map Pandas UDF.*MapType'):
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pandas_udf(
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lambda l, r: l,
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'id long, v map<int, int>',
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PandasUDFType.COGROUPED_MAP)
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def test_wrong_args(self):
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# Test that we get a sensible exception invalid values passed to apply
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left = self.data1
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right = self.data2
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with QuietTest(self.sc):
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# Function rather than a udf
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with self.assertRaisesRegexp(ValueError, 'Invalid udf'):
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left.groupby('id').cogroup(right.groupby('id')).apply(lambda l, r: l)
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# Udf missing return type
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with self.assertRaisesRegexp(ValueError, 'Invalid udf'):
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left.groupby('id').cogroup(right.groupby('id'))\
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.apply(udf(lambda l, r: l, DoubleType()))
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# Pass in expression rather than udf
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with self.assertRaisesRegexp(ValueError, 'Invalid udf'):
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left.groupby('id').cogroup(right.groupby('id')).apply(left.v + 1)
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# Zero arg function
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with self.assertRaisesRegexp(ValueError, 'Invalid function'):
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left.groupby('id').cogroup(right.groupby('id'))\
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.apply(pandas_udf(lambda: 1, StructType([StructField("d", DoubleType())])))
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# Udf without PandasUDFType
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with self.assertRaisesRegexp(ValueError, 'Invalid udf'):
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left.groupby('id').cogroup(right.groupby('id'))\
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.apply(pandas_udf(lambda x, y: x, DoubleType()))
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# Udf with incorrect PandasUDFType
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with self.assertRaisesRegexp(ValueError, 'Invalid udf.*COGROUPED_MAP'):
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left.groupby('id').cogroup(right.groupby('id'))\
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.apply(pandas_udf(lambda x, y: x, DoubleType(), PandasUDFType.SCALAR))
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@staticmethod
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def _test_with_key(left, right, isLeft):
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@pandas_udf('id long, k int, v int, key long', PandasUDFType.COGROUPED_MAP)
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def right_assign_key(key, l, r):
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return l.assign(key=key[0]) if isLeft else r.assign(key=key[0])
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result = left \
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.groupby('id') \
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.cogroup(right.groupby('id')) \
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.apply(right_assign_key) \
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.toPandas()
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expected = left.toPandas() if isLeft else right.toPandas()
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expected = expected.assign(key=expected.id)
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assert_frame_equal(expected, result, check_column_type=_check_column_type)
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@staticmethod
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def _test_merge(left, right, output_schema='id long, k int, v int, v2 int'):
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@pandas_udf(output_schema, PandasUDFType.COGROUPED_MAP)
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def merge_pandas(l, r):
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return pd.merge(l, r, on=['id', 'k'])
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result = left \
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.groupby('id') \
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.cogroup(right.groupby('id')) \
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.apply(merge_pandas)\
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.sort(['id', 'k']) \
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.toPandas()
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left = left.toPandas()
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right = right.toPandas()
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expected = pd \
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.merge(left, right, on=['id', 'k']) \
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.sort_values(by=['id', 'k'])
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assert_frame_equal(expected, result, check_column_type=_check_column_type)
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if __name__ == "__main__":
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from pyspark.sql.tests.test_pandas_udf_cogrouped_map import *
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try:
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import xmlrunner
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testRunner = xmlrunner.XMLTestRunner(output='target/test-reports', verbosity=2)
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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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