f62f44f2a2
## What changes were proposed in this pull request? Running PySpark tests with Pandas 0.24.x causes a failure in `test_pandas_udf_grouped_map` test_supported_types: `ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()` This is because a column is an ArrayType and the method `sqlutils ReusedSQLTestCase.assertPandasEqual ` does not properly check this. This PR removes `assertPandasEqual` and replaces it with the built-in `pandas.util.testing.assert_frame_equal` which can properly handle columns of ArrayType and also prints out better diff between the DataFrames when an error occurs. Additionally, imports of pandas and pyarrow were moved to the top of related test files to avoid duplicating the same import many times. ## How was this patch tested? Existing tests Closes #24306 from BryanCutler/python-pandas-assert_frame_equal-SPARK-27387. Authored-by: Bryan Cutler <cutlerb@gmail.com> Signed-off-by: HyukjinKwon <gurwls223@apache.org>
263 lines
7.6 KiB
Python
263 lines
7.6 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 os
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import shutil
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import tempfile
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from contextlib import contextmanager
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from pyspark.sql import SparkSession
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from pyspark.sql.types import ArrayType, DoubleType, UserDefinedType, Row
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from pyspark.testing.utils import ReusedPySparkTestCase
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from pyspark.util import _exception_message
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pandas_requirement_message = None
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try:
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from pyspark.sql.utils import require_minimum_pandas_version
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require_minimum_pandas_version()
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except ImportError as e:
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# If Pandas version requirement is not satisfied, skip related tests.
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pandas_requirement_message = _exception_message(e)
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pyarrow_requirement_message = None
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try:
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from pyspark.sql.utils import require_minimum_pyarrow_version
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require_minimum_pyarrow_version()
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except ImportError as e:
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# If Arrow version requirement is not satisfied, skip related tests.
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pyarrow_requirement_message = _exception_message(e)
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test_not_compiled_message = None
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try:
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from pyspark.sql.utils import require_test_compiled
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require_test_compiled()
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except Exception as e:
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test_not_compiled_message = _exception_message(e)
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have_pandas = pandas_requirement_message is None
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have_pyarrow = pyarrow_requirement_message is None
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test_compiled = test_not_compiled_message is None
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class UTCOffsetTimezone(datetime.tzinfo):
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"""
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Specifies timezone in UTC offset
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"""
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def __init__(self, offset=0):
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self.ZERO = datetime.timedelta(hours=offset)
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def utcoffset(self, dt):
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return self.ZERO
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def dst(self, dt):
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return self.ZERO
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class ExamplePointUDT(UserDefinedType):
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"""
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User-defined type (UDT) for ExamplePoint.
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"""
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@classmethod
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def sqlType(self):
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return ArrayType(DoubleType(), False)
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@classmethod
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def module(cls):
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return 'pyspark.sql.tests'
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@classmethod
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def scalaUDT(cls):
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return 'org.apache.spark.sql.test.ExamplePointUDT'
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def serialize(self, obj):
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return [obj.x, obj.y]
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def deserialize(self, datum):
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return ExamplePoint(datum[0], datum[1])
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class ExamplePoint:
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"""
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An example class to demonstrate UDT in Scala, Java, and Python.
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"""
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__UDT__ = ExamplePointUDT()
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def __init__(self, x, y):
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self.x = x
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self.y = y
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def __repr__(self):
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return "ExamplePoint(%s,%s)" % (self.x, self.y)
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def __str__(self):
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return "(%s,%s)" % (self.x, self.y)
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def __eq__(self, other):
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return isinstance(other, self.__class__) and \
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other.x == self.x and other.y == self.y
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class PythonOnlyUDT(UserDefinedType):
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"""
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User-defined type (UDT) for ExamplePoint.
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"""
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@classmethod
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def sqlType(self):
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return ArrayType(DoubleType(), False)
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@classmethod
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def module(cls):
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return '__main__'
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def serialize(self, obj):
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return [obj.x, obj.y]
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def deserialize(self, datum):
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return PythonOnlyPoint(datum[0], datum[1])
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@staticmethod
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def foo():
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pass
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@property
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def props(self):
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return {}
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class PythonOnlyPoint(ExamplePoint):
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"""
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An example class to demonstrate UDT in only Python
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"""
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__UDT__ = PythonOnlyUDT()
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class MyObject(object):
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def __init__(self, key, value):
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self.key = key
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self.value = value
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class SQLTestUtils(object):
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"""
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This util assumes the instance of this to have 'spark' attribute, having a spark session.
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It is usually used with 'ReusedSQLTestCase' class but can be used if you feel sure the
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the implementation of this class has 'spark' attribute.
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"""
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@contextmanager
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def sql_conf(self, pairs):
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"""
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A convenient context manager to test some configuration specific logic. This sets
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`value` to the configuration `key` and then restores it back when it exits.
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"""
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assert isinstance(pairs, dict), "pairs should be a dictionary."
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assert hasattr(self, "spark"), "it should have 'spark' attribute, having a spark session."
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keys = pairs.keys()
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new_values = pairs.values()
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old_values = [self.spark.conf.get(key, None) for key in keys]
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for key, new_value in zip(keys, new_values):
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self.spark.conf.set(key, new_value)
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try:
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yield
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finally:
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for key, old_value in zip(keys, old_values):
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if old_value is None:
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self.spark.conf.unset(key)
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else:
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self.spark.conf.set(key, old_value)
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@contextmanager
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def database(self, *databases):
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"""
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A convenient context manager to test with some specific databases. This drops the given
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databases if it exists and sets current database to "default" when it exits.
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"""
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assert hasattr(self, "spark"), "it should have 'spark' attribute, having a spark session."
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try:
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yield
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finally:
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for db in databases:
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self.spark.sql("DROP DATABASE IF EXISTS %s CASCADE" % db)
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self.spark.catalog.setCurrentDatabase("default")
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@contextmanager
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def table(self, *tables):
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"""
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A convenient context manager to test with some specific tables. This drops the given tables
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if it exists.
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"""
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assert hasattr(self, "spark"), "it should have 'spark' attribute, having a spark session."
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try:
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yield
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finally:
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for t in tables:
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self.spark.sql("DROP TABLE IF EXISTS %s" % t)
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@contextmanager
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def tempView(self, *views):
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"""
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A convenient context manager to test with some specific views. This drops the given views
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if it exists.
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"""
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assert hasattr(self, "spark"), "it should have 'spark' attribute, having a spark session."
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try:
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yield
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finally:
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for v in views:
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self.spark.catalog.dropTempView(v)
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@contextmanager
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def function(self, *functions):
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"""
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A convenient context manager to test with some specific functions. This drops the given
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functions if it exists.
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"""
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assert hasattr(self, "spark"), "it should have 'spark' attribute, having a spark session."
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try:
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yield
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finally:
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for f in functions:
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self.spark.sql("DROP FUNCTION IF EXISTS %s" % f)
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class ReusedSQLTestCase(ReusedPySparkTestCase, SQLTestUtils):
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@classmethod
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def setUpClass(cls):
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super(ReusedSQLTestCase, cls).setUpClass()
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cls.spark = SparkSession(cls.sc)
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cls.tempdir = tempfile.NamedTemporaryFile(delete=False)
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os.unlink(cls.tempdir.name)
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cls.testData = [Row(key=i, value=str(i)) for i in range(100)]
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cls.df = cls.spark.createDataFrame(cls.testData)
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@classmethod
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def tearDownClass(cls):
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super(ReusedSQLTestCase, cls).tearDownClass()
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cls.spark.stop()
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shutil.rmtree(cls.tempdir.name, ignore_errors=True)
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