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#
# 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 os
import shutil
import sys
import tempfile
import unittest
from importlib import reload
import py4j
from pyspark import SparkContext, SQLContext
from pyspark.sql import Row, SparkSession
from pyspark.sql.types import StructType, StringType, StructField
from pyspark.testing.utils import ReusedPySparkTestCase
class HiveContextSQLTests(ReusedPySparkTestCase):
@classmethod
def setUpClass(cls):
ReusedPySparkTestCase.setUpClass()
cls.tempdir = tempfile.NamedTemporaryFile(delete=False)
cls.hive_available = True
cls.spark = None
try:
cls.sc._jvm.org.apache.hadoop.hive.conf.HiveConf()
except py4j.protocol.Py4JError:
cls.tearDownClass()
cls.hive_available = False
except TypeError:
cls.tearDownClass()
cls.hive_available = False
48 ↛ 51line 48 didn't jump to line 51, because the condition on line 48 was never false if cls.hive_available:
cls.spark = SparkSession.builder.enableHiveSupport().getOrCreate()
os.unlink(cls.tempdir.name)
52 ↛ exitline 52 didn't return from function 'setUpClass', because the condition on line 52 was never false if cls.hive_available:
cls.testData = [Row(key=i, value=str(i)) for i in range(100)]
cls.df = cls.sc.parallelize(cls.testData).toDF()
def setUp(self):
57 ↛ 58line 57 didn't jump to line 58, because the condition on line 57 was never true if not self.hive_available:
self.skipTest("Hive is not available.")
@classmethod
def tearDownClass(cls):
ReusedPySparkTestCase.tearDownClass()
shutil.rmtree(cls.tempdir.name, ignore_errors=True)
64 ↛ exitline 64 didn't return from function 'tearDownClass', because the condition on line 64 was never false if cls.spark is not None:
cls.spark.stop()
cls.spark = None
def test_save_and_load_table(self):
df = self.df
tmpPath = tempfile.mkdtemp()
shutil.rmtree(tmpPath)
df.write.saveAsTable("savedJsonTable", "json", "append", path=tmpPath)
actual = self.spark.catalog.createTable("externalJsonTable", tmpPath, "json")
self.assertEqual(sorted(df.collect()),
sorted(self.spark.sql("SELECT * FROM savedJsonTable").collect()))
self.assertEqual(sorted(df.collect()),
sorted(self.spark.sql("SELECT * FROM externalJsonTable").collect()))
self.assertEqual(sorted(df.collect()), sorted(actual.collect()))
self.spark.sql("DROP TABLE externalJsonTable")
df.write.saveAsTable("savedJsonTable", "json", "overwrite", path=tmpPath)
schema = StructType([StructField("value", StringType(), True)])
actual = self.spark.catalog.createTable("externalJsonTable", source="json",
schema=schema, path=tmpPath,
noUse="this options will not be used")
self.assertEqual(sorted(df.collect()),
sorted(self.spark.sql("SELECT * FROM savedJsonTable").collect()))
self.assertEqual(sorted(df.select("value").collect()),
sorted(self.spark.sql("SELECT * FROM externalJsonTable").collect()))
self.assertEqual(sorted(df.select("value").collect()), sorted(actual.collect()))
self.spark.sql("DROP TABLE savedJsonTable")
self.spark.sql("DROP TABLE externalJsonTable")
defaultDataSourceName = self.spark.conf.get("spark.sql.sources.default",
"org.apache.spark.sql.parquet")
self.spark.sql("SET spark.sql.sources.default=org.apache.spark.sql.json")
df.write.saveAsTable("savedJsonTable", path=tmpPath, mode="overwrite")
actual = self.spark.catalog.createTable("externalJsonTable", path=tmpPath)
self.assertEqual(sorted(df.collect()),
sorted(self.spark.sql("SELECT * FROM savedJsonTable").collect()))
self.assertEqual(sorted(df.collect()),
sorted(self.spark.sql("SELECT * FROM externalJsonTable").collect()))
self.assertEqual(sorted(df.collect()), sorted(actual.collect()))
self.spark.sql("DROP TABLE savedJsonTable")
self.spark.sql("DROP TABLE externalJsonTable")
self.spark.sql("SET spark.sql.sources.default=" + defaultDataSourceName)
shutil.rmtree(tmpPath)
def test_limit_and_take(self):
df = self.spark.range(1, 1000, numPartitions=10)
def assert_runs_only_one_job_stage_and_task(job_group_name, f):
tracker = self.sc.statusTracker()
self.sc.setJobGroup(job_group_name, description="")
f()
jobs = tracker.getJobIdsForGroup(job_group_name)
self.assertEqual(1, len(jobs))
stages = tracker.getJobInfo(jobs[0]).stageIds
self.assertEqual(1, len(stages))
self.assertEqual(1, tracker.getStageInfo(stages[0]).numTasks)
# Regression test for SPARK-10731: take should delegate to Scala implementation
assert_runs_only_one_job_stage_and_task("take", lambda: df.take(1))
# Regression test for SPARK-17514: limit(n).collect() should the perform same as take(n)
assert_runs_only_one_job_stage_and_task("collect_limit", lambda: df.limit(1).collect())
def test_unbounded_frames(self):
from pyspark.sql import functions as F
from pyspark.sql import window
df = self.spark.range(0, 3)
def rows_frame_match():
return "ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING" in df.select(
F.count("*").over(window.Window.rowsBetween(-sys.maxsize, sys.maxsize))
).columns[0]
def range_frame_match():
return "RANGE BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING" in df.select(
F.count("*").over(window.Window.rangeBetween(-sys.maxsize, sys.maxsize))
).columns[0]
for new_maxsize in [2 ** 31 - 1, 2 ** 63 - 1, 2 ** 127 - 1]:
old_maxsize = sys.maxsize
sys.maxsize = new_maxsize
try:
# Manually reload window module to use monkey-patched sys.maxsize.
reload(window)
self.assertTrue(rows_frame_match())
self.assertTrue(range_frame_match())
finally:
sys.maxsize = old_maxsize
reload(window)
class SQLContextTests(unittest.TestCase):
def test_get_or_create(self):
sc = None
sql_context = None
try:
sc = SparkContext('local[4]', "SQLContextTests")
sql_context = SQLContext.getOrCreate(sc)
assert(isinstance(sql_context, SQLContext))
finally:
168 ↛ 170line 168 didn't jump to line 170, because the condition on line 168 was never false if sql_context is not None:
sql_context.sparkSession.stop()
170 ↛ exitline 170 didn't return from function 'test_get_or_create', because the condition on line 170 was never false if sc is not None:
sc.stop()
if __name__ == "__main__":
from pyspark.sql.tests.test_context import * # noqa: F401
try:
import xmlrunner # type: ignore[import]
testRunner = xmlrunner.XMLTestRunner(output='target/test-reports', verbosity=2)
except ImportError:
testRunner = None
unittest.main(testRunner=testRunner, verbosity=2)
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