HyukjinKwon commented on a change in pull request #24981: [SPARK-27463][PYTHON] Support Dataframe Cogroup via Pandas UDFs URL: https://github.com/apache/spark/pull/24981#discussion_r326890317
########## File path: python/pyspark/sql/tests/test_pandas_udf_cogrouped_map.py ########## @@ -0,0 +1,280 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import unittest +import sys + +from pyspark.sql.functions import array, explode, col, lit, udf, sum, pandas_udf, PandasUDFType +from pyspark.sql.types import DoubleType, StructType, StructField +from pyspark.testing.sqlutils import ReusedSQLTestCase, have_pandas, have_pyarrow, \ + pandas_requirement_message, pyarrow_requirement_message +from pyspark.testing.utils import QuietTest + +if have_pandas: + import pandas as pd + from pandas.util.testing import assert_frame_equal, assert_series_equal + +if have_pyarrow: + import pyarrow as pa + + +""" +Tests below use pd.DataFrame.assign that will infer mixed types (unicode/str) for column names +from kwargs w/ Python 2, so need to set check_column_type=False and avoid this check +""" +if sys.version < '3': + _check_column_type = False +else: + _check_column_type = True + + [email protected]( + not have_pandas or not have_pyarrow, + pandas_requirement_message or pyarrow_requirement_message) +class CoGroupedMapPandasUDFTests(ReusedSQLTestCase): + + @property + def data1(self): + return self.spark.range(10).toDF('id') \ + .withColumn("ks", array([lit(i) for i in range(20, 30)])) \ + .withColumn("k", explode(col('ks')))\ + .withColumn("v", col('k') * 10)\ + .drop('ks') + + @property + def data2(self): + return self.spark.range(10).toDF('id') \ + .withColumn("ks", array([lit(i) for i in range(20, 30)])) \ + .withColumn("k", explode(col('ks'))) \ + .withColumn("v2", col('k') * 100) \ + .drop('ks') + + def test_simple(self): + self._test_merge(self.data1, self.data2) + + def test_left_group_empty(self): + left = self.data1.where(col("id") % 2 == 0) + self._test_merge(left, self.data2) + + def test_right_group_empty(self): + right = self.data2.where(col("id") % 2 == 0) + self._test_merge(self.data1, right) + + def test_different_schemas(self): + right = self.data2.withColumn('v3', lit('a')) + self._test_merge(self.data1, right, 'id long, k int, v int, v2 int, v3 string') + + def test_complex_group_by(self): + left = pd.DataFrame.from_dict({ + 'id': [1, 2, 3], + 'k': [5, 6, 7], + 'v': [9, 10, 11] + }) + + right = pd.DataFrame.from_dict({ + 'id': [11, 12, 13], + 'k': [5, 6, 7], + 'v2': [90, 100, 110] + }) + + left_gdf = self.spark\ + .createDataFrame(left)\ + .groupby(col('id') % 2 == 0) + + right_gdf = self.spark \ + .createDataFrame(right) \ + .groupby(col('id') % 2 == 0) + + @pandas_udf('k long, v long, v2 long', PandasUDFType.COGROUPED_MAP) + def merge_pandas(l, r): + return pd.merge(l[['k', 'v']], r[['k', 'v2']], on=['k']) + + result = left_gdf \ + .cogroup(right_gdf) \ + .apply(merge_pandas) \ + .sort(['k']) \ + .toPandas() + + expected = pd.DataFrame.from_dict({ + 'k': [5, 6, 7], + 'v': [9, 10, 11], + 'v2': [90, 100, 110] + }) + + assert_frame_equal(expected, result, check_column_type=_check_column_type) + + def test_empty_group_by(self): + left = self.data1 + right = self.data2 + + @pandas_udf('id long, k int, v int, v2 int', PandasUDFType.COGROUPED_MAP) + def merge_pandas(l, r): + return pd.merge(l, r, on=['id', 'k']) + + result = left.groupby().cogroup(right.groupby())\ + .apply(merge_pandas) \ + .sort(['id', 'k']) \ + .toPandas() + + left = left.toPandas() + right = right.toPandas() + + expected = pd \ + .merge(left, right, on=['id', 'k']) \ + .sort_values(by=['id', 'k']) + + assert_frame_equal(expected, result, check_column_type=_check_column_type) + + def test_mixed_scalar_udfs_followed_by_cogrouby_apply(self): + 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().cogroup(df.groupby())\ + .apply(pandas_udf(lambda x, y: pd.DataFrame([(x.sum().sum(), y.sum().sum())]), + 'sum1 int, sum2 int', + PandasUDFType.COGROUPED_MAP)).collect() + + self.assertEquals(result[0]['sum1'], 165) + self.assertEquals(result[0]['sum2'], 165) + + def test_with_key_left(self): + self._test_with_key(self.data1, self.data1, isLeft=True) + + def test_with_key_right(self): + self._test_with_key(self.data1, self.data1, isLeft=False) + + def test_with_key_left_group_empty(self): + left = self.data1.where(col("id") % 2 == 0) + self._test_with_key(left, self.data1, isLeft=True) + + def test_with_key_right_group_empty(self): + right = self.data1.where(col("id") % 2 == 0) + self._test_with_key(self.data1, right, isLeft=False) + + def test_with_key_complex(self): + + @pandas_udf('id long, k int, v int, key boolean', PandasUDFType.COGROUPED_MAP) + def left_assign_key(key, l, _): + return l.assign(key=key[0]) + + result = self.data1 \ + .groupby(col('id') % 2 == 0)\ + .cogroup(self.data2.groupby(col('id') % 2 == 0)) \ + .apply(left_assign_key) \ + .sort(['id', 'k']) \ + .toPandas() + + expected = self.data1.toPandas() + expected = expected.assign(key=expected.id % 2 == 0) + + assert_frame_equal(expected, result, check_column_type=_check_column_type) + + def test_wrong_return_type(self): + with QuietTest(self.sc): + with self.assertRaisesRegexp( + NotImplementedError, + 'Invalid returnType.*cogrouped map Pandas UDF.*MapType'): + pandas_udf( + lambda l, r: l, + 'id long, v map<int, int>', + PandasUDFType.COGROUPED_MAP) + + def test_wrong_args(self): + # Test that we get a sensible exception invalid values passed to apply + left = self.data1 + right = self.data2 + with QuietTest(self.sc): + # Function rather than a udf + with self.assertRaisesRegexp(ValueError, 'Invalid udf'): + left.groupby('id').cogroup(right.groupby('id')).apply(lambda l, r: l) + + # Udf missing return type + with self.assertRaisesRegexp(ValueError, 'Invalid udf'): + left.groupby('id').cogroup(right.groupby('id'))\ + .apply(udf(lambda l, r: l, DoubleType())) + + # Pass in expression rather than udf + with self.assertRaisesRegexp(ValueError, 'Invalid udf'): + left.groupby('id').cogroup(right.groupby('id')).apply(left.v + 1) + + # Zero arg function + with self.assertRaisesRegexp(ValueError, 'Invalid function'): + left.groupby('id').cogroup(right.groupby('id'))\ + .apply(pandas_udf(lambda: 1, StructType([StructField("d", DoubleType())]))) + + # Udf without PandasUDFType + with self.assertRaisesRegexp(ValueError, 'Invalid udf'): + left.groupby('id').cogroup(right.groupby('id'))\ + .apply(pandas_udf(lambda x, y: x, DoubleType())) + + # Udf with incorrect PandasUDFType + with self.assertRaisesRegexp(ValueError, 'Invalid udf.*COGROUPED_MAP'): + left.groupby('id').cogroup(right.groupby('id'))\ + .apply(pandas_udf(lambda x, y: x, DoubleType(), PandasUDFType.SCALAR)) + + @staticmethod + def _test_with_key(left, right, isLeft): + + @pandas_udf('id long, k int, v int, key long', PandasUDFType.COGROUPED_MAP) + def right_assign_key(key, l, r): + return l.assign(key=key[0]) if isLeft else r.assign(key=key[0]) + + result = left \ + .groupby('id') \ + .cogroup(right.groupby('id')) \ + .apply(right_assign_key) \ + .toPandas() + + expected = left.toPandas() if isLeft else right.toPandas() + expected = expected.assign(key=expected.id) + + assert_frame_equal(expected, result, check_column_type=_check_column_type) + + @staticmethod + def _test_merge(left, right, output_schema='id long, k int, v int, v2 int'): + + @pandas_udf(output_schema, PandasUDFType.COGROUPED_MAP) + def merge_pandas(l, r): + return pd.merge(l, r, on=['id', 'k']) + + result = left \ + .groupby('id') \ + .cogroup(right.groupby('id')) \ + .apply(merge_pandas)\ + .sort(['id', 'k']) \ + .toPandas() + + left = left.toPandas() + right = right.toPandas() + + expected = pd \ + .merge(left, right, on=['id', 'k']) \ + .sort_values(by=['id', 'k']) + + assert_frame_equal(expected, result, check_column_type=_check_column_type) + + +if __name__ == "__main__": Review comment: This file should be added into `dev/sparktestsupport/modules.py`; otherwise, we don't actually run the tests ... ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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