spark-instrumented-optimizer/python/pyspark/sql/tests/test_catalog.py

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[SPARK-26032][PYTHON] Break large sql/tests.py files into smaller files ## What changes were proposed in this pull request? This is the official first attempt to break huge single `tests.py` file - I did it locally before few times and gave up for some reasons. Now, currently it really makes the unittests super hard to read and difficult to check. To me, it even bothers me to to scroll down the big file. It's one single 7000 lines file! This is not only readability issue. Since one big test takes most of tests time, the tests don't run in parallel fully - although it will costs to start and stop the context. We could pick up one example and follow. Given my investigation, the current style looks closer to NumPy structure and looks easier to follow. Please see https://github.com/numpy/numpy/tree/master/numpy. Basically this PR proposes to break down `pyspark/sql/tests.py` into ...: ```bash pyspark ... ├── sql ... │   ├── tests # Includes all tests broken down from 'pyspark/sql/tests.py' │ │  │ # Each matchs to module in 'pyspark/sql'. Additionally, some logical group can │ │  │ # be added. For instance, 'test_arrow.py', 'test_datasources.py' ... │   │   ├── __init__.py │   │   ├── test_appsubmit.py │   │   ├── test_arrow.py │   │   ├── test_catalog.py │   │   ├── test_column.py │   │   ├── test_conf.py │   │   ├── test_context.py │   │   ├── test_dataframe.py │   │   ├── test_datasources.py │   │   ├── test_functions.py │   │   ├── test_group.py │   │   ├── test_pandas_udf.py │   │   ├── test_pandas_udf_grouped_agg.py │   │   ├── test_pandas_udf_grouped_map.py │   │   ├── test_pandas_udf_scalar.py │   │   ├── test_pandas_udf_window.py │   │   ├── test_readwriter.py │   │   ├── test_serde.py │   │   ├── test_session.py │   │   ├── test_streaming.py │   │   ├── test_types.py │   │   ├── test_udf.py │   │   └── test_utils.py ... ├── testing # Includes testing utils that can be used in unittests. │   ├── __init__.py │   └── sqlutils.py ... ``` ## How was this patch tested? Existing tests should cover. `cd python` and `./run-tests-with-coverage`. Manually checked they are actually being ran. Each test (not officially) can be ran via: ``` SPARK_TESTING=1 ./bin/pyspark pyspark.sql.tests.test_pandas_udf_scalar ``` Note that if you're using Mac and Python 3, you might have to `OBJC_DISABLE_INITIALIZE_FORK_SAFETY=YES`. Closes #23021 from HyukjinKwon/SPARK-25344. Authored-by: hyukjinkwon <gurwls223@apache.org> Signed-off-by: hyukjinkwon <gurwls223@apache.org>
2018-11-14 01:51:11 -05:00
#
# 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.
#
from pyspark.sql.types import StructType, StructField, IntegerType
[SPARK-26032][PYTHON] Break large sql/tests.py files into smaller files ## What changes were proposed in this pull request? This is the official first attempt to break huge single `tests.py` file - I did it locally before few times and gave up for some reasons. Now, currently it really makes the unittests super hard to read and difficult to check. To me, it even bothers me to to scroll down the big file. It's one single 7000 lines file! This is not only readability issue. Since one big test takes most of tests time, the tests don't run in parallel fully - although it will costs to start and stop the context. We could pick up one example and follow. Given my investigation, the current style looks closer to NumPy structure and looks easier to follow. Please see https://github.com/numpy/numpy/tree/master/numpy. Basically this PR proposes to break down `pyspark/sql/tests.py` into ...: ```bash pyspark ... ├── sql ... │   ├── tests # Includes all tests broken down from 'pyspark/sql/tests.py' │ │  │ # Each matchs to module in 'pyspark/sql'. Additionally, some logical group can │ │  │ # be added. For instance, 'test_arrow.py', 'test_datasources.py' ... │   │   ├── __init__.py │   │   ├── test_appsubmit.py │   │   ├── test_arrow.py │   │   ├── test_catalog.py │   │   ├── test_column.py │   │   ├── test_conf.py │   │   ├── test_context.py │   │   ├── test_dataframe.py │   │   ├── test_datasources.py │   │   ├── test_functions.py │   │   ├── test_group.py │   │   ├── test_pandas_udf.py │   │   ├── test_pandas_udf_grouped_agg.py │   │   ├── test_pandas_udf_grouped_map.py │   │   ├── test_pandas_udf_scalar.py │   │   ├── test_pandas_udf_window.py │   │   ├── test_readwriter.py │   │   ├── test_serde.py │   │   ├── test_session.py │   │   ├── test_streaming.py │   │   ├── test_types.py │   │   ├── test_udf.py │   │   └── test_utils.py ... ├── testing # Includes testing utils that can be used in unittests. │   ├── __init__.py │   └── sqlutils.py ... ``` ## How was this patch tested? Existing tests should cover. `cd python` and `./run-tests-with-coverage`. Manually checked they are actually being ran. Each test (not officially) can be ran via: ``` SPARK_TESTING=1 ./bin/pyspark pyspark.sql.tests.test_pandas_udf_scalar ``` Note that if you're using Mac and Python 3, you might have to `OBJC_DISABLE_INITIALIZE_FORK_SAFETY=YES`. Closes #23021 from HyukjinKwon/SPARK-25344. Authored-by: hyukjinkwon <gurwls223@apache.org> Signed-off-by: hyukjinkwon <gurwls223@apache.org>
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from pyspark.sql.utils import AnalysisException
from pyspark.testing.sqlutils import ReusedSQLTestCase
class CatalogTests(ReusedSQLTestCase):
def test_current_database(self):
spark = self.spark
with self.database("some_db"):
self.assertEquals(spark.catalog.currentDatabase(), "default")
spark.sql("CREATE DATABASE some_db")
spark.catalog.setCurrentDatabase("some_db")
self.assertEquals(spark.catalog.currentDatabase(), "some_db")
self.assertRaisesRegexp(
AnalysisException,
"does_not_exist",
lambda: spark.catalog.setCurrentDatabase("does_not_exist"))
def test_list_databases(self):
spark = self.spark
with self.database("some_db"):
databases = [db.name for db in spark.catalog.listDatabases()]
self.assertEquals(databases, ["default"])
spark.sql("CREATE DATABASE some_db")
databases = [db.name for db in spark.catalog.listDatabases()]
self.assertEquals(sorted(databases), ["default", "some_db"])
def test_list_tables(self):
from pyspark.sql.catalog import Table
spark = self.spark
with self.database("some_db"):
spark.sql("CREATE DATABASE some_db")
with self.table("tab1", "some_db.tab2", "tab3_via_catalog"):
[SPARK-26032][PYTHON] Break large sql/tests.py files into smaller files ## What changes were proposed in this pull request? This is the official first attempt to break huge single `tests.py` file - I did it locally before few times and gave up for some reasons. Now, currently it really makes the unittests super hard to read and difficult to check. To me, it even bothers me to to scroll down the big file. It's one single 7000 lines file! This is not only readability issue. Since one big test takes most of tests time, the tests don't run in parallel fully - although it will costs to start and stop the context. We could pick up one example and follow. Given my investigation, the current style looks closer to NumPy structure and looks easier to follow. Please see https://github.com/numpy/numpy/tree/master/numpy. Basically this PR proposes to break down `pyspark/sql/tests.py` into ...: ```bash pyspark ... ├── sql ... │   ├── tests # Includes all tests broken down from 'pyspark/sql/tests.py' │ │  │ # Each matchs to module in 'pyspark/sql'. Additionally, some logical group can │ │  │ # be added. For instance, 'test_arrow.py', 'test_datasources.py' ... │   │   ├── __init__.py │   │   ├── test_appsubmit.py │   │   ├── test_arrow.py │   │   ├── test_catalog.py │   │   ├── test_column.py │   │   ├── test_conf.py │   │   ├── test_context.py │   │   ├── test_dataframe.py │   │   ├── test_datasources.py │   │   ├── test_functions.py │   │   ├── test_group.py │   │   ├── test_pandas_udf.py │   │   ├── test_pandas_udf_grouped_agg.py │   │   ├── test_pandas_udf_grouped_map.py │   │   ├── test_pandas_udf_scalar.py │   │   ├── test_pandas_udf_window.py │   │   ├── test_readwriter.py │   │   ├── test_serde.py │   │   ├── test_session.py │   │   ├── test_streaming.py │   │   ├── test_types.py │   │   ├── test_udf.py │   │   └── test_utils.py ... ├── testing # Includes testing utils that can be used in unittests. │   ├── __init__.py │   └── sqlutils.py ... ``` ## How was this patch tested? Existing tests should cover. `cd python` and `./run-tests-with-coverage`. Manually checked they are actually being ran. Each test (not officially) can be ran via: ``` SPARK_TESTING=1 ./bin/pyspark pyspark.sql.tests.test_pandas_udf_scalar ``` Note that if you're using Mac and Python 3, you might have to `OBJC_DISABLE_INITIALIZE_FORK_SAFETY=YES`. Closes #23021 from HyukjinKwon/SPARK-25344. Authored-by: hyukjinkwon <gurwls223@apache.org> Signed-off-by: hyukjinkwon <gurwls223@apache.org>
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with self.tempView("temp_tab"):
self.assertEquals(spark.catalog.listTables(), [])
self.assertEquals(spark.catalog.listTables("some_db"), [])
spark.createDataFrame([(1, 1)]).createOrReplaceTempView("temp_tab")
spark.sql("CREATE TABLE tab1 (name STRING, age INT) USING parquet")
spark.sql("CREATE TABLE some_db.tab2 (name STRING, age INT) USING parquet")
schema = StructType([StructField("a", IntegerType(), True)])
description = "this a table created via Catalog.createTable()"
spark.catalog.createTable(
"tab3_via_catalog", schema=schema, description=description)
[SPARK-26032][PYTHON] Break large sql/tests.py files into smaller files ## What changes were proposed in this pull request? This is the official first attempt to break huge single `tests.py` file - I did it locally before few times and gave up for some reasons. Now, currently it really makes the unittests super hard to read and difficult to check. To me, it even bothers me to to scroll down the big file. It's one single 7000 lines file! This is not only readability issue. Since one big test takes most of tests time, the tests don't run in parallel fully - although it will costs to start and stop the context. We could pick up one example and follow. Given my investigation, the current style looks closer to NumPy structure and looks easier to follow. Please see https://github.com/numpy/numpy/tree/master/numpy. Basically this PR proposes to break down `pyspark/sql/tests.py` into ...: ```bash pyspark ... ├── sql ... │   ├── tests # Includes all tests broken down from 'pyspark/sql/tests.py' │ │  │ # Each matchs to module in 'pyspark/sql'. Additionally, some logical group can │ │  │ # be added. For instance, 'test_arrow.py', 'test_datasources.py' ... │   │   ├── __init__.py │   │   ├── test_appsubmit.py │   │   ├── test_arrow.py │   │   ├── test_catalog.py │   │   ├── test_column.py │   │   ├── test_conf.py │   │   ├── test_context.py │   │   ├── test_dataframe.py │   │   ├── test_datasources.py │   │   ├── test_functions.py │   │   ├── test_group.py │   │   ├── test_pandas_udf.py │   │   ├── test_pandas_udf_grouped_agg.py │   │   ├── test_pandas_udf_grouped_map.py │   │   ├── test_pandas_udf_scalar.py │   │   ├── test_pandas_udf_window.py │   │   ├── test_readwriter.py │   │   ├── test_serde.py │   │   ├── test_session.py │   │   ├── test_streaming.py │   │   ├── test_types.py │   │   ├── test_udf.py │   │   └── test_utils.py ... ├── testing # Includes testing utils that can be used in unittests. │   ├── __init__.py │   └── sqlutils.py ... ``` ## How was this patch tested? Existing tests should cover. `cd python` and `./run-tests-with-coverage`. Manually checked they are actually being ran. Each test (not officially) can be ran via: ``` SPARK_TESTING=1 ./bin/pyspark pyspark.sql.tests.test_pandas_udf_scalar ``` Note that if you're using Mac and Python 3, you might have to `OBJC_DISABLE_INITIALIZE_FORK_SAFETY=YES`. Closes #23021 from HyukjinKwon/SPARK-25344. Authored-by: hyukjinkwon <gurwls223@apache.org> Signed-off-by: hyukjinkwon <gurwls223@apache.org>
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tables = sorted(spark.catalog.listTables(), key=lambda t: t.name)
tablesDefault = \
sorted(spark.catalog.listTables("default"), key=lambda t: t.name)
tablesSomeDb = \
sorted(spark.catalog.listTables("some_db"), key=lambda t: t.name)
self.assertEquals(tables, tablesDefault)
self.assertEquals(len(tables), 3)
[SPARK-26032][PYTHON] Break large sql/tests.py files into smaller files ## What changes were proposed in this pull request? This is the official first attempt to break huge single `tests.py` file - I did it locally before few times and gave up for some reasons. Now, currently it really makes the unittests super hard to read and difficult to check. To me, it even bothers me to to scroll down the big file. It's one single 7000 lines file! This is not only readability issue. Since one big test takes most of tests time, the tests don't run in parallel fully - although it will costs to start and stop the context. We could pick up one example and follow. Given my investigation, the current style looks closer to NumPy structure and looks easier to follow. Please see https://github.com/numpy/numpy/tree/master/numpy. Basically this PR proposes to break down `pyspark/sql/tests.py` into ...: ```bash pyspark ... ├── sql ... │   ├── tests # Includes all tests broken down from 'pyspark/sql/tests.py' │ │  │ # Each matchs to module in 'pyspark/sql'. Additionally, some logical group can │ │  │ # be added. For instance, 'test_arrow.py', 'test_datasources.py' ... │   │   ├── __init__.py │   │   ├── test_appsubmit.py │   │   ├── test_arrow.py │   │   ├── test_catalog.py │   │   ├── test_column.py │   │   ├── test_conf.py │   │   ├── test_context.py │   │   ├── test_dataframe.py │   │   ├── test_datasources.py │   │   ├── test_functions.py │   │   ├── test_group.py │   │   ├── test_pandas_udf.py │   │   ├── test_pandas_udf_grouped_agg.py │   │   ├── test_pandas_udf_grouped_map.py │   │   ├── test_pandas_udf_scalar.py │   │   ├── test_pandas_udf_window.py │   │   ├── test_readwriter.py │   │   ├── test_serde.py │   │   ├── test_session.py │   │   ├── test_streaming.py │   │   ├── test_types.py │   │   ├── test_udf.py │   │   └── test_utils.py ... ├── testing # Includes testing utils that can be used in unittests. │   ├── __init__.py │   └── sqlutils.py ... ``` ## How was this patch tested? Existing tests should cover. `cd python` and `./run-tests-with-coverage`. Manually checked they are actually being ran. Each test (not officially) can be ran via: ``` SPARK_TESTING=1 ./bin/pyspark pyspark.sql.tests.test_pandas_udf_scalar ``` Note that if you're using Mac and Python 3, you might have to `OBJC_DISABLE_INITIALIZE_FORK_SAFETY=YES`. Closes #23021 from HyukjinKwon/SPARK-25344. Authored-by: hyukjinkwon <gurwls223@apache.org> Signed-off-by: hyukjinkwon <gurwls223@apache.org>
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self.assertEquals(len(tablesSomeDb), 2)
self.assertEquals(tables[0], Table(
name="tab1",
database="default",
description=None,
tableType="MANAGED",
isTemporary=False))
self.assertEquals(tables[1], Table(
name="tab3_via_catalog",
database="default",
description=description,
tableType="MANAGED",
isTemporary=False))
self.assertEquals(tables[2], Table(
[SPARK-26032][PYTHON] Break large sql/tests.py files into smaller files ## What changes were proposed in this pull request? This is the official first attempt to break huge single `tests.py` file - I did it locally before few times and gave up for some reasons. Now, currently it really makes the unittests super hard to read and difficult to check. To me, it even bothers me to to scroll down the big file. It's one single 7000 lines file! This is not only readability issue. Since one big test takes most of tests time, the tests don't run in parallel fully - although it will costs to start and stop the context. We could pick up one example and follow. Given my investigation, the current style looks closer to NumPy structure and looks easier to follow. Please see https://github.com/numpy/numpy/tree/master/numpy. Basically this PR proposes to break down `pyspark/sql/tests.py` into ...: ```bash pyspark ... ├── sql ... │   ├── tests # Includes all tests broken down from 'pyspark/sql/tests.py' │ │  │ # Each matchs to module in 'pyspark/sql'. Additionally, some logical group can │ │  │ # be added. For instance, 'test_arrow.py', 'test_datasources.py' ... │   │   ├── __init__.py │   │   ├── test_appsubmit.py │   │   ├── test_arrow.py │   │   ├── test_catalog.py │   │   ├── test_column.py │   │   ├── test_conf.py │   │   ├── test_context.py │   │   ├── test_dataframe.py │   │   ├── test_datasources.py │   │   ├── test_functions.py │   │   ├── test_group.py │   │   ├── test_pandas_udf.py │   │   ├── test_pandas_udf_grouped_agg.py │   │   ├── test_pandas_udf_grouped_map.py │   │   ├── test_pandas_udf_scalar.py │   │   ├── test_pandas_udf_window.py │   │   ├── test_readwriter.py │   │   ├── test_serde.py │   │   ├── test_session.py │   │   ├── test_streaming.py │   │   ├── test_types.py │   │   ├── test_udf.py │   │   └── test_utils.py ... ├── testing # Includes testing utils that can be used in unittests. │   ├── __init__.py │   └── sqlutils.py ... ``` ## How was this patch tested? Existing tests should cover. `cd python` and `./run-tests-with-coverage`. Manually checked they are actually being ran. Each test (not officially) can be ran via: ``` SPARK_TESTING=1 ./bin/pyspark pyspark.sql.tests.test_pandas_udf_scalar ``` Note that if you're using Mac and Python 3, you might have to `OBJC_DISABLE_INITIALIZE_FORK_SAFETY=YES`. Closes #23021 from HyukjinKwon/SPARK-25344. Authored-by: hyukjinkwon <gurwls223@apache.org> Signed-off-by: hyukjinkwon <gurwls223@apache.org>
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name="temp_tab",
database=None,
description=None,
tableType="TEMPORARY",
isTemporary=True))
self.assertEquals(tablesSomeDb[0], Table(
name="tab2",
database="some_db",
description=None,
tableType="MANAGED",
isTemporary=False))
self.assertEquals(tablesSomeDb[1], Table(
name="temp_tab",
database=None,
description=None,
tableType="TEMPORARY",
isTemporary=True))
self.assertRaisesRegexp(
AnalysisException,
"does_not_exist",
lambda: spark.catalog.listTables("does_not_exist"))
def test_list_functions(self):
from pyspark.sql.catalog import Function
spark = self.spark
with self.database("some_db"):
spark.sql("CREATE DATABASE some_db")
functions = dict((f.name, f) for f in spark.catalog.listFunctions())
functionsDefault = dict((f.name, f) for f in spark.catalog.listFunctions("default"))
self.assertTrue(len(functions) > 200)
self.assertTrue("+" in functions)
self.assertTrue("like" in functions)
self.assertTrue("month" in functions)
self.assertTrue("to_date" in functions)
self.assertTrue("to_timestamp" in functions)
self.assertTrue("to_unix_timestamp" in functions)
self.assertTrue("current_database" in functions)
self.assertEquals(functions["+"], Function(
name="+",
description=None,
className="org.apache.spark.sql.catalyst.expressions.Add",
isTemporary=True))
self.assertEquals(functions, functionsDefault)
with self.function("func1", "some_db.func2"):
spark.catalog.registerFunction("temp_func", lambda x: str(x))
spark.sql("CREATE FUNCTION func1 AS 'org.apache.spark.data.bricks'")
spark.sql("CREATE FUNCTION some_db.func2 AS 'org.apache.spark.data.bricks'")
[SPARK-26032][PYTHON] Break large sql/tests.py files into smaller files ## What changes were proposed in this pull request? This is the official first attempt to break huge single `tests.py` file - I did it locally before few times and gave up for some reasons. Now, currently it really makes the unittests super hard to read and difficult to check. To me, it even bothers me to to scroll down the big file. It's one single 7000 lines file! This is not only readability issue. Since one big test takes most of tests time, the tests don't run in parallel fully - although it will costs to start and stop the context. We could pick up one example and follow. Given my investigation, the current style looks closer to NumPy structure and looks easier to follow. Please see https://github.com/numpy/numpy/tree/master/numpy. Basically this PR proposes to break down `pyspark/sql/tests.py` into ...: ```bash pyspark ... ├── sql ... │   ├── tests # Includes all tests broken down from 'pyspark/sql/tests.py' │ │  │ # Each matchs to module in 'pyspark/sql'. Additionally, some logical group can │ │  │ # be added. For instance, 'test_arrow.py', 'test_datasources.py' ... │   │   ├── __init__.py │   │   ├── test_appsubmit.py │   │   ├── test_arrow.py │   │   ├── test_catalog.py │   │   ├── test_column.py │   │   ├── test_conf.py │   │   ├── test_context.py │   │   ├── test_dataframe.py │   │   ├── test_datasources.py │   │   ├── test_functions.py │   │   ├── test_group.py │   │   ├── test_pandas_udf.py │   │   ├── test_pandas_udf_grouped_agg.py │   │   ├── test_pandas_udf_grouped_map.py │   │   ├── test_pandas_udf_scalar.py │   │   ├── test_pandas_udf_window.py │   │   ├── test_readwriter.py │   │   ├── test_serde.py │   │   ├── test_session.py │   │   ├── test_streaming.py │   │   ├── test_types.py │   │   ├── test_udf.py │   │   └── test_utils.py ... ├── testing # Includes testing utils that can be used in unittests. │   ├── __init__.py │   └── sqlutils.py ... ``` ## How was this patch tested? Existing tests should cover. `cd python` and `./run-tests-with-coverage`. Manually checked they are actually being ran. Each test (not officially) can be ran via: ``` SPARK_TESTING=1 ./bin/pyspark pyspark.sql.tests.test_pandas_udf_scalar ``` Note that if you're using Mac and Python 3, you might have to `OBJC_DISABLE_INITIALIZE_FORK_SAFETY=YES`. Closes #23021 from HyukjinKwon/SPARK-25344. Authored-by: hyukjinkwon <gurwls223@apache.org> Signed-off-by: hyukjinkwon <gurwls223@apache.org>
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newFunctions = dict((f.name, f) for f in spark.catalog.listFunctions())
newFunctionsSomeDb = \
dict((f.name, f) for f in spark.catalog.listFunctions("some_db"))
self.assertTrue(set(functions).issubset(set(newFunctions)))
self.assertTrue(set(functions).issubset(set(newFunctionsSomeDb)))
self.assertTrue("temp_func" in newFunctions)
self.assertTrue("func1" in newFunctions)
self.assertTrue("func2" not in newFunctions)
self.assertTrue("temp_func" in newFunctionsSomeDb)
self.assertTrue("func1" not in newFunctionsSomeDb)
self.assertTrue("func2" in newFunctionsSomeDb)
self.assertRaisesRegexp(
AnalysisException,
"does_not_exist",
lambda: spark.catalog.listFunctions("does_not_exist"))
def test_list_columns(self):
from pyspark.sql.catalog import Column
spark = self.spark
with self.database("some_db"):
spark.sql("CREATE DATABASE some_db")
with self.table("tab1", "some_db.tab2"):
spark.sql("CREATE TABLE tab1 (name STRING, age INT) USING parquet")
spark.sql(
"CREATE TABLE some_db.tab2 (nickname STRING, tolerance FLOAT) USING parquet")
columns = sorted(spark.catalog.listColumns("tab1"), key=lambda c: c.name)
columnsDefault = \
sorted(spark.catalog.listColumns("tab1", "default"), key=lambda c: c.name)
self.assertEquals(columns, columnsDefault)
self.assertEquals(len(columns), 2)
self.assertEquals(columns[0], Column(
name="age",
description=None,
dataType="int",
nullable=True,
isPartition=False,
isBucket=False))
self.assertEquals(columns[1], Column(
name="name",
description=None,
dataType="string",
nullable=True,
isPartition=False,
isBucket=False))
columns2 = \
sorted(spark.catalog.listColumns("tab2", "some_db"), key=lambda c: c.name)
self.assertEquals(len(columns2), 2)
self.assertEquals(columns2[0], Column(
name="nickname",
description=None,
dataType="string",
nullable=True,
isPartition=False,
isBucket=False))
self.assertEquals(columns2[1], Column(
name="tolerance",
description=None,
dataType="float",
nullable=True,
isPartition=False,
isBucket=False))
self.assertRaisesRegexp(
AnalysisException,
"tab2",
lambda: spark.catalog.listColumns("tab2"))
self.assertRaisesRegexp(
AnalysisException,
"does_not_exist",
lambda: spark.catalog.listColumns("does_not_exist"))
if __name__ == "__main__":
import unittest
[SPARK-32319][PYSPARK] Disallow the use of unused imports Disallow the use of unused imports: - Unnecessary increases the memory footprint of the application - Removes the imports that are required for the examples in the docstring from the file-scope to the example itself. This keeps the files itself clean, and gives a more complete example as it also includes the imports :) ``` fokkodriesprongFan spark % flake8 python | grep -i "imported but unused" python/pyspark/cloudpickle.py:46:1: F401 'functools.partial' imported but unused python/pyspark/cloudpickle.py:55:1: F401 'traceback' imported but unused python/pyspark/heapq3.py:868:5: F401 '_heapq.*' imported but unused python/pyspark/__init__.py:61:1: F401 'pyspark.version.__version__' imported but unused python/pyspark/__init__.py:62:1: F401 'pyspark._globals._NoValue' imported but unused python/pyspark/__init__.py:115:1: F401 'pyspark.sql.SQLContext' imported but unused python/pyspark/__init__.py:115:1: F401 'pyspark.sql.HiveContext' imported but unused python/pyspark/__init__.py:115:1: F401 'pyspark.sql.Row' imported but unused python/pyspark/rdd.py:21:1: F401 're' imported but unused python/pyspark/rdd.py:29:1: F401 'tempfile.NamedTemporaryFile' imported but unused python/pyspark/mllib/regression.py:26:1: F401 'pyspark.mllib.linalg.SparseVector' imported but unused python/pyspark/mllib/clustering.py:28:1: F401 'pyspark.mllib.linalg.SparseVector' imported but unused python/pyspark/mllib/clustering.py:28:1: F401 'pyspark.mllib.linalg.DenseVector' imported but unused python/pyspark/mllib/classification.py:26:1: F401 'pyspark.mllib.linalg.SparseVector' imported but unused python/pyspark/mllib/feature.py:28:1: F401 'pyspark.mllib.linalg.DenseVector' imported but unused python/pyspark/mllib/feature.py:28:1: F401 'pyspark.mllib.linalg.SparseVector' imported but unused python/pyspark/mllib/feature.py:30:1: F401 'pyspark.mllib.regression.LabeledPoint' imported but unused python/pyspark/mllib/tests/test_linalg.py:18:1: F401 'sys' imported but unused python/pyspark/mllib/tests/test_linalg.py:642:5: F401 'pyspark.mllib.tests.test_linalg.*' imported but unused python/pyspark/mllib/tests/test_feature.py:21:1: F401 'numpy.random' imported but unused python/pyspark/mllib/tests/test_feature.py:21:1: F401 'numpy.exp' imported but unused python/pyspark/mllib/tests/test_feature.py:23:1: F401 'pyspark.mllib.linalg.Vector' imported but unused python/pyspark/mllib/tests/test_feature.py:23:1: F401 'pyspark.mllib.linalg.VectorUDT' imported but unused python/pyspark/mllib/tests/test_feature.py:185:5: F401 'pyspark.mllib.tests.test_feature.*' imported but unused python/pyspark/mllib/tests/test_util.py:97:5: F401 'pyspark.mllib.tests.test_util.*' imported but unused python/pyspark/mllib/tests/test_stat.py:23:1: F401 'pyspark.mllib.linalg.Vector' imported but unused python/pyspark/mllib/tests/test_stat.py:23:1: F401 'pyspark.mllib.linalg.SparseVector' imported but unused python/pyspark/mllib/tests/test_stat.py:23:1: F401 'pyspark.mllib.linalg.DenseVector' imported but unused python/pyspark/mllib/tests/test_stat.py:23:1: F401 'pyspark.mllib.linalg.VectorUDT' imported but unused python/pyspark/mllib/tests/test_stat.py:23:1: F401 'pyspark.mllib.linalg._convert_to_vector' imported but unused python/pyspark/mllib/tests/test_stat.py:23:1: F401 'pyspark.mllib.linalg.DenseMatrix' imported but unused python/pyspark/mllib/tests/test_stat.py:23:1: F401 'pyspark.mllib.linalg.SparseMatrix' imported but unused python/pyspark/mllib/tests/test_stat.py:23:1: F401 'pyspark.mllib.linalg.MatrixUDT' imported but unused python/pyspark/mllib/tests/test_stat.py:181:5: F401 'pyspark.mllib.tests.test_stat.*' imported but unused python/pyspark/mllib/tests/test_streaming_algorithms.py:18:1: F401 'time.time' imported but unused python/pyspark/mllib/tests/test_streaming_algorithms.py:18:1: F401 'time.sleep' imported but unused python/pyspark/mllib/tests/test_streaming_algorithms.py:470:5: F401 'pyspark.mllib.tests.test_streaming_algorithms.*' imported but unused python/pyspark/mllib/tests/test_algorithms.py:295:5: F401 'pyspark.mllib.tests.test_algorithms.*' imported but unused python/pyspark/tests/test_serializers.py:90:13: F401 'xmlrunner' imported but unused python/pyspark/tests/test_rdd.py:21:1: F401 'sys' imported but unused python/pyspark/tests/test_rdd.py:29:1: F401 'pyspark.resource.ResourceProfile' imported but unused python/pyspark/tests/test_rdd.py:885:5: F401 'pyspark.tests.test_rdd.*' imported but unused python/pyspark/tests/test_readwrite.py:19:1: F401 'sys' imported but unused python/pyspark/tests/test_readwrite.py:22:1: F401 'array.array' imported but unused python/pyspark/tests/test_readwrite.py:309:5: F401 'pyspark.tests.test_readwrite.*' imported but unused python/pyspark/tests/test_join.py:62:5: F401 'pyspark.tests.test_join.*' imported but unused python/pyspark/tests/test_taskcontext.py:19:1: F401 'shutil' imported but unused python/pyspark/tests/test_taskcontext.py:325:5: F401 'pyspark.tests.test_taskcontext.*' imported but unused python/pyspark/tests/test_conf.py:36:5: F401 'pyspark.tests.test_conf.*' imported but unused python/pyspark/tests/test_broadcast.py:148:5: F401 'pyspark.tests.test_broadcast.*' imported but unused python/pyspark/tests/test_daemon.py:76:5: F401 'pyspark.tests.test_daemon.*' imported but unused python/pyspark/tests/test_util.py:77:5: F401 'pyspark.tests.test_util.*' imported but unused python/pyspark/tests/test_pin_thread.py:19:1: F401 'random' imported but unused python/pyspark/tests/test_pin_thread.py:149:5: F401 'pyspark.tests.test_pin_thread.*' imported but unused python/pyspark/tests/test_worker.py:19:1: F401 'sys' imported but unused python/pyspark/tests/test_worker.py:26:5: F401 'resource' imported but unused python/pyspark/tests/test_worker.py:203:5: F401 'pyspark.tests.test_worker.*' imported but unused python/pyspark/tests/test_profiler.py:101:5: F401 'pyspark.tests.test_profiler.*' imported but unused python/pyspark/tests/test_shuffle.py:18:1: F401 'sys' imported but unused python/pyspark/tests/test_shuffle.py:171:5: F401 'pyspark.tests.test_shuffle.*' imported but unused python/pyspark/tests/test_rddbarrier.py:43:5: F401 'pyspark.tests.test_rddbarrier.*' imported but unused python/pyspark/tests/test_context.py:129:13: F401 'userlibrary.UserClass' imported but unused python/pyspark/tests/test_context.py:140:13: F401 'userlib.UserClass' imported but unused python/pyspark/tests/test_context.py:310:5: F401 'pyspark.tests.test_context.*' imported but unused python/pyspark/tests/test_appsubmit.py:241:5: F401 'pyspark.tests.test_appsubmit.*' imported but unused python/pyspark/streaming/dstream.py:18:1: F401 'sys' imported but unused python/pyspark/streaming/tests/test_dstream.py:27:1: F401 'pyspark.RDD' imported but unused python/pyspark/streaming/tests/test_dstream.py:647:5: F401 'pyspark.streaming.tests.test_dstream.*' imported but unused python/pyspark/streaming/tests/test_kinesis.py:83:5: F401 'pyspark.streaming.tests.test_kinesis.*' imported but unused python/pyspark/streaming/tests/test_listener.py:152:5: F401 'pyspark.streaming.tests.test_listener.*' imported but unused python/pyspark/streaming/tests/test_context.py:178:5: F401 'pyspark.streaming.tests.test_context.*' imported but unused python/pyspark/testing/utils.py:30:5: F401 'scipy.sparse' imported but unused python/pyspark/testing/utils.py:36:5: F401 'numpy as np' imported but unused python/pyspark/ml/regression.py:25:1: F401 'pyspark.ml.tree._TreeEnsembleParams' imported but unused python/pyspark/ml/regression.py:25:1: F401 'pyspark.ml.tree._HasVarianceImpurity' imported but unused python/pyspark/ml/regression.py:29:1: F401 'pyspark.ml.wrapper.JavaParams' imported but unused python/pyspark/ml/util.py:19:1: F401 'sys' imported but unused python/pyspark/ml/__init__.py:25:1: F401 'pyspark.ml.pipeline' imported but unused python/pyspark/ml/pipeline.py:18:1: F401 'sys' imported but unused python/pyspark/ml/stat.py:22:1: F401 'pyspark.ml.linalg.DenseMatrix' imported but unused python/pyspark/ml/stat.py:22:1: F401 'pyspark.ml.linalg.Vectors' imported but unused python/pyspark/ml/tests/test_training_summary.py:18:1: F401 'sys' imported but unused python/pyspark/ml/tests/test_training_summary.py:364:5: F401 'pyspark.ml.tests.test_training_summary.*' imported but unused python/pyspark/ml/tests/test_linalg.py:381:5: F401 'pyspark.ml.tests.test_linalg.*' imported but unused python/pyspark/ml/tests/test_tuning.py:427:9: F401 'pyspark.sql.functions as F' imported but unused python/pyspark/ml/tests/test_tuning.py:757:5: F401 'pyspark.ml.tests.test_tuning.*' imported but unused python/pyspark/ml/tests/test_wrapper.py:120:5: F401 'pyspark.ml.tests.test_wrapper.*' imported but unused python/pyspark/ml/tests/test_feature.py:19:1: F401 'sys' imported but unused python/pyspark/ml/tests/test_feature.py:304:5: F401 'pyspark.ml.tests.test_feature.*' imported but unused python/pyspark/ml/tests/test_image.py:19:1: F401 'py4j' imported but unused python/pyspark/ml/tests/test_image.py:22:1: F401 'pyspark.testing.mlutils.PySparkTestCase' imported but unused python/pyspark/ml/tests/test_image.py:71:5: F401 'pyspark.ml.tests.test_image.*' imported but unused python/pyspark/ml/tests/test_persistence.py:456:5: F401 'pyspark.ml.tests.test_persistence.*' imported but unused python/pyspark/ml/tests/test_evaluation.py:56:5: F401 'pyspark.ml.tests.test_evaluation.*' imported but unused python/pyspark/ml/tests/test_stat.py:43:5: F401 'pyspark.ml.tests.test_stat.*' imported but unused python/pyspark/ml/tests/test_base.py:70:5: F401 'pyspark.ml.tests.test_base.*' imported but unused python/pyspark/ml/tests/test_param.py:20:1: F401 'sys' imported but unused python/pyspark/ml/tests/test_param.py:375:5: F401 'pyspark.ml.tests.test_param.*' imported but unused python/pyspark/ml/tests/test_pipeline.py:62:5: F401 'pyspark.ml.tests.test_pipeline.*' imported but unused python/pyspark/ml/tests/test_algorithms.py:333:5: F401 'pyspark.ml.tests.test_algorithms.*' imported but unused python/pyspark/ml/param/__init__.py:18:1: F401 'sys' imported but unused python/pyspark/resource/tests/test_resources.py:17:1: F401 'random' imported but unused python/pyspark/resource/tests/test_resources.py:20:1: F401 'pyspark.resource.ResourceProfile' imported but unused python/pyspark/resource/tests/test_resources.py:75:5: F401 'pyspark.resource.tests.test_resources.*' imported but unused python/pyspark/sql/functions.py:32:1: F401 'pyspark.sql.udf.UserDefinedFunction' imported but unused python/pyspark/sql/functions.py:34:1: F401 'pyspark.sql.pandas.functions.pandas_udf' imported but unused python/pyspark/sql/session.py:30:1: F401 'pyspark.sql.types.Row' imported but unused python/pyspark/sql/session.py:30:1: F401 'pyspark.sql.types.StringType' imported but unused python/pyspark/sql/readwriter.py:1084:5: F401 'pyspark.sql.Row' imported but unused python/pyspark/sql/context.py:26:1: F401 'pyspark.sql.types.IntegerType' imported but unused python/pyspark/sql/context.py:26:1: F401 'pyspark.sql.types.Row' imported but unused python/pyspark/sql/context.py:26:1: F401 'pyspark.sql.types.StringType' imported but unused python/pyspark/sql/context.py:27:1: F401 'pyspark.sql.udf.UDFRegistration' imported but unused python/pyspark/sql/streaming.py:1212:5: F401 'pyspark.sql.Row' imported but unused python/pyspark/sql/tests/test_utils.py:55:5: F401 'pyspark.sql.tests.test_utils.*' imported but unused python/pyspark/sql/tests/test_pandas_map.py:18:1: F401 'sys' imported but unused python/pyspark/sql/tests/test_pandas_map.py:22:1: F401 'pyspark.sql.functions.pandas_udf' imported but unused python/pyspark/sql/tests/test_pandas_map.py:22:1: F401 'pyspark.sql.functions.PandasUDFType' imported but unused python/pyspark/sql/tests/test_pandas_map.py:119:5: F401 'pyspark.sql.tests.test_pandas_map.*' imported but unused python/pyspark/sql/tests/test_catalog.py:193:5: F401 'pyspark.sql.tests.test_catalog.*' imported but unused python/pyspark/sql/tests/test_group.py:39:5: F401 'pyspark.sql.tests.test_group.*' imported but unused python/pyspark/sql/tests/test_session.py:361:5: F401 'pyspark.sql.tests.test_session.*' imported but unused python/pyspark/sql/tests/test_conf.py:49:5: F401 'pyspark.sql.tests.test_conf.*' imported but unused python/pyspark/sql/tests/test_pandas_cogrouped_map.py:19:1: F401 'sys' imported but unused python/pyspark/sql/tests/test_pandas_cogrouped_map.py:21:1: F401 'pyspark.sql.functions.sum' imported but unused python/pyspark/sql/tests/test_pandas_cogrouped_map.py:21:1: F401 'pyspark.sql.functions.PandasUDFType' imported but unused python/pyspark/sql/tests/test_pandas_cogrouped_map.py:29:5: F401 'pandas.util.testing.assert_series_equal' imported but unused python/pyspark/sql/tests/test_pandas_cogrouped_map.py:32:5: F401 'pyarrow as pa' imported but unused python/pyspark/sql/tests/test_pandas_cogrouped_map.py:248:5: F401 'pyspark.sql.tests.test_pandas_cogrouped_map.*' imported but unused python/pyspark/sql/tests/test_udf.py:24:1: F401 'py4j' imported but unused python/pyspark/sql/tests/test_pandas_udf_typehints.py:246:5: F401 'pyspark.sql.tests.test_pandas_udf_typehints.*' imported but unused python/pyspark/sql/tests/test_functions.py:19:1: F401 'sys' imported but unused python/pyspark/sql/tests/test_functions.py:362:9: F401 'pyspark.sql.functions.exists' imported but unused python/pyspark/sql/tests/test_functions.py:387:5: F401 'pyspark.sql.tests.test_functions.*' imported but unused python/pyspark/sql/tests/test_pandas_udf_scalar.py:21:1: F401 'sys' imported but unused python/pyspark/sql/tests/test_pandas_udf_scalar.py:45:5: F401 'pyarrow as pa' imported but unused python/pyspark/sql/tests/test_pandas_udf_window.py:355:5: F401 'pyspark.sql.tests.test_pandas_udf_window.*' imported but unused python/pyspark/sql/tests/test_arrow.py:38:5: F401 'pyarrow as pa' imported but unused python/pyspark/sql/tests/test_pandas_grouped_map.py:20:1: F401 'sys' imported but unused python/pyspark/sql/tests/test_pandas_grouped_map.py:38:5: F401 'pyarrow as pa' imported but unused python/pyspark/sql/tests/test_dataframe.py:382:9: F401 'pyspark.sql.DataFrame' imported but unused python/pyspark/sql/avro/functions.py:125:5: F401 'pyspark.sql.Row' imported but unused python/pyspark/sql/pandas/functions.py:19:1: F401 'sys' imported but unused ``` After: ``` fokkodriesprongFan spark % flake8 python | grep -i "imported but unused" fokkodriesprongFan spark % ``` ### What changes were proposed in this pull request? Removing unused imports from the Python files to keep everything nice and tidy. ### Why are the changes needed? Cleaning up of the imports that aren't used, and suppressing the imports that are used as references to other modules, preserving backward compatibility. ### Does this PR introduce _any_ user-facing change? No. ### How was this patch tested? Adding the rule to the existing Flake8 checks. Closes #29121 from Fokko/SPARK-32319. Authored-by: Fokko Driesprong <fokko@apache.org> Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
2020-08-08 11:51:57 -04:00
from pyspark.sql.tests.test_catalog import * # noqa: F401
[SPARK-26032][PYTHON] Break large sql/tests.py files into smaller files ## What changes were proposed in this pull request? This is the official first attempt to break huge single `tests.py` file - I did it locally before few times and gave up for some reasons. Now, currently it really makes the unittests super hard to read and difficult to check. To me, it even bothers me to to scroll down the big file. It's one single 7000 lines file! This is not only readability issue. Since one big test takes most of tests time, the tests don't run in parallel fully - although it will costs to start and stop the context. We could pick up one example and follow. Given my investigation, the current style looks closer to NumPy structure and looks easier to follow. Please see https://github.com/numpy/numpy/tree/master/numpy. Basically this PR proposes to break down `pyspark/sql/tests.py` into ...: ```bash pyspark ... ├── sql ... │   ├── tests # Includes all tests broken down from 'pyspark/sql/tests.py' │ │  │ # Each matchs to module in 'pyspark/sql'. Additionally, some logical group can │ │  │ # be added. For instance, 'test_arrow.py', 'test_datasources.py' ... │   │   ├── __init__.py │   │   ├── test_appsubmit.py │   │   ├── test_arrow.py │   │   ├── test_catalog.py │   │   ├── test_column.py │   │   ├── test_conf.py │   │   ├── test_context.py │   │   ├── test_dataframe.py │   │   ├── test_datasources.py │   │   ├── test_functions.py │   │   ├── test_group.py │   │   ├── test_pandas_udf.py │   │   ├── test_pandas_udf_grouped_agg.py │   │   ├── test_pandas_udf_grouped_map.py │   │   ├── test_pandas_udf_scalar.py │   │   ├── test_pandas_udf_window.py │   │   ├── test_readwriter.py │   │   ├── test_serde.py │   │   ├── test_session.py │   │   ├── test_streaming.py │   │   ├── test_types.py │   │   ├── test_udf.py │   │   └── test_utils.py ... ├── testing # Includes testing utils that can be used in unittests. │   ├── __init__.py │   └── sqlutils.py ... ``` ## How was this patch tested? Existing tests should cover. `cd python` and `./run-tests-with-coverage`. Manually checked they are actually being ran. Each test (not officially) can be ran via: ``` SPARK_TESTING=1 ./bin/pyspark pyspark.sql.tests.test_pandas_udf_scalar ``` Note that if you're using Mac and Python 3, you might have to `OBJC_DISABLE_INITIALIZE_FORK_SAFETY=YES`. Closes #23021 from HyukjinKwon/SPARK-25344. Authored-by: hyukjinkwon <gurwls223@apache.org> Signed-off-by: hyukjinkwon <gurwls223@apache.org>
2018-11-14 01:51:11 -05:00
try:
import xmlrunner # type: ignore[import]
[SPARK-28130][PYTHON] Print pretty messages for skipped tests when xmlrunner is available in PySpark ## What changes were proposed in this pull request? Currently, pretty skipped message added by https://github.com/apache/spark/commit/f7435bec6a9348cfbbe26b13c230c08545d16067 mechanism seems not working when xmlrunner is installed apparently. This PR fixes two things: 1. When `xmlrunner` is installed, seems `xmlrunner` does not respect `vervosity` level in unittests (default is level 1). So the output looks as below ``` Running tests... ---------------------------------------------------------------------- SSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSS ---------------------------------------------------------------------- ``` So it is not caught by our message detection mechanism. 2. If we manually set the `vervocity` level to `xmlrunner`, it prints messages as below: ``` test_mixed_udf (pyspark.sql.tests.test_pandas_udf_scalar.ScalarPandasUDFTests) ... SKIP (0.000s) test_mixed_udf_and_sql (pyspark.sql.tests.test_pandas_udf_scalar.ScalarPandasUDFTests) ... SKIP (0.000s) ... ``` This is different in our Jenkins machine: ``` test_createDataFrame_column_name_encoding (pyspark.sql.tests.test_arrow.ArrowTests) ... skipped 'Pandas >= 0.23.2 must be installed; however, it was not found.' test_createDataFrame_does_not_modify_input (pyspark.sql.tests.test_arrow.ArrowTests) ... skipped 'Pandas >= 0.23.2 must be installed; however, it was not found.' ... ``` Note that last `SKIP` is different. This PR fixes the regular expression to catch `SKIP` case as well. ## How was this patch tested? Manually tested. **Before:** ``` Starting test(python2.7): pyspark.... Finished test(python2.7): pyspark.... (0s) ... Tests passed in 562 seconds ======================================================================== ... ``` **After:** ``` Starting test(python2.7): pyspark.... Finished test(python2.7): pyspark.... (48s) ... 93 tests were skipped ... Tests passed in 560 seconds Skipped tests pyspark.... with python2.7: pyspark...(...) ... SKIP (0.000s) ... ======================================================================== ... ``` Closes #24927 from HyukjinKwon/SPARK-28130. Authored-by: HyukjinKwon <gurwls223@apache.org> Signed-off-by: HyukjinKwon <gurwls223@apache.org>
2019-06-23 20:58:17 -04:00
testRunner = xmlrunner.XMLTestRunner(output='target/test-reports', verbosity=2)
[SPARK-26032][PYTHON] Break large sql/tests.py files into smaller files ## What changes were proposed in this pull request? This is the official first attempt to break huge single `tests.py` file - I did it locally before few times and gave up for some reasons. Now, currently it really makes the unittests super hard to read and difficult to check. To me, it even bothers me to to scroll down the big file. It's one single 7000 lines file! This is not only readability issue. Since one big test takes most of tests time, the tests don't run in parallel fully - although it will costs to start and stop the context. We could pick up one example and follow. Given my investigation, the current style looks closer to NumPy structure and looks easier to follow. Please see https://github.com/numpy/numpy/tree/master/numpy. Basically this PR proposes to break down `pyspark/sql/tests.py` into ...: ```bash pyspark ... ├── sql ... │   ├── tests # Includes all tests broken down from 'pyspark/sql/tests.py' │ │  │ # Each matchs to module in 'pyspark/sql'. Additionally, some logical group can │ │  │ # be added. For instance, 'test_arrow.py', 'test_datasources.py' ... │   │   ├── __init__.py │   │   ├── test_appsubmit.py │   │   ├── test_arrow.py │   │   ├── test_catalog.py │   │   ├── test_column.py │   │   ├── test_conf.py │   │   ├── test_context.py │   │   ├── test_dataframe.py │   │   ├── test_datasources.py │   │   ├── test_functions.py │   │   ├── test_group.py │   │   ├── test_pandas_udf.py │   │   ├── test_pandas_udf_grouped_agg.py │   │   ├── test_pandas_udf_grouped_map.py │   │   ├── test_pandas_udf_scalar.py │   │   ├── test_pandas_udf_window.py │   │   ├── test_readwriter.py │   │   ├── test_serde.py │   │   ├── test_session.py │   │   ├── test_streaming.py │   │   ├── test_types.py │   │   ├── test_udf.py │   │   └── test_utils.py ... ├── testing # Includes testing utils that can be used in unittests. │   ├── __init__.py │   └── sqlutils.py ... ``` ## How was this patch tested? Existing tests should cover. `cd python` and `./run-tests-with-coverage`. Manually checked they are actually being ran. Each test (not officially) can be ran via: ``` SPARK_TESTING=1 ./bin/pyspark pyspark.sql.tests.test_pandas_udf_scalar ``` Note that if you're using Mac and Python 3, you might have to `OBJC_DISABLE_INITIALIZE_FORK_SAFETY=YES`. Closes #23021 from HyukjinKwon/SPARK-25344. Authored-by: hyukjinkwon <gurwls223@apache.org> Signed-off-by: hyukjinkwon <gurwls223@apache.org>
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except ImportError:
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testRunner = None
unittest.main(testRunner=testRunner, verbosity=2)