spark-instrumented-optimizer/python/pyspark/tests/test_context.py
wuyi cbad616d4c [SPARK-27371][CORE] Support GPU-aware resources scheduling in Standalone
## What changes were proposed in this pull request?

In this PR, we implements a complete process of GPU-aware resources scheduling
in Standalone. The whole process looks like: Worker sets up isolated resources
when it starts up and registers to master along with its resources. And, Master
picks up usable workers according to driver/executor's resource requirements to
launch driver/executor on them. Then, Worker launches the driver/executor after
preparing resources file, which is created under driver/executor's working directory,
with specified resource addresses(told by master). When driver/executor finished,
their resources could be recycled to worker. Finally, if a worker stops, it
should always release its resources firstly.

For the case of Workers and Drivers in **client** mode run on the same host, we introduce
a config option named `spark.resources.coordinate.enable`(default true) to indicate
whether Spark should coordinate resources for user. If `spark.resources.coordinate.enable=false`, user should be responsible for configuring different resources for Workers and Drivers when use resourcesFile or discovery script. If true, Spark would help user to assign different  resources for Workers and Drivers.

The solution for Spark to coordinate resources among Workers and Drivers is:

Generally, use a shared file named *____allocated_resources____.json* to sync allocated
resources info among Workers and Drivers on the same host.

After a Worker or Driver found all resources using the configured resourcesFile and/or
discovery script during launching, it should filter out available resources by excluding resources already allocated in *____allocated_resources____.json* and acquire resources from available resources according to its own requirement. After that, it should write its allocated resources along with its process id (pid) into *____allocated_resources____.json*.  Pid (proposed by tgravescs) here used to check whether the allocated resources are still valid in case of Worker or Driver crashes and doesn't release resources properly. And when a Worker or Driver finished, normally, it would always clean up its own allocated resources in *____allocated_resources____.json*.

Note that we'll always get a file lock before any access to file *____allocated_resources____.json*
and release the lock finally.

Futhermore, we appended resources info in `WorkerSchedulerStateResponse` to work
around master change behaviour in HA mode.

## How was this patch tested?

Added unit tests in WorkerSuite, MasterSuite, SparkContextSuite.

Manually tested with client/cluster mode (e.g. multiple workers) in a single node Standalone.

Closes #25047 from Ngone51/SPARK-27371.

Authored-by: wuyi <ngone_5451@163.com>
Signed-off-by: Thomas Graves <tgraves@apache.org>
2019-08-09 07:49:03 -05:00

303 lines
12 KiB
Python

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import os
import shutil
import stat
import tempfile
import threading
import time
import unittest
from collections import namedtuple
from pyspark import SparkConf, SparkFiles, SparkContext
from pyspark.testing.utils import ReusedPySparkTestCase, PySparkTestCase, QuietTest, SPARK_HOME
class CheckpointTests(ReusedPySparkTestCase):
def setUp(self):
self.checkpointDir = tempfile.NamedTemporaryFile(delete=False)
os.unlink(self.checkpointDir.name)
self.sc.setCheckpointDir(self.checkpointDir.name)
def tearDown(self):
shutil.rmtree(self.checkpointDir.name)
def test_basic_checkpointing(self):
parCollection = self.sc.parallelize([1, 2, 3, 4])
flatMappedRDD = parCollection.flatMap(lambda x: range(1, x + 1))
self.assertFalse(flatMappedRDD.isCheckpointed())
self.assertTrue(flatMappedRDD.getCheckpointFile() is None)
flatMappedRDD.checkpoint()
result = flatMappedRDD.collect()
time.sleep(1) # 1 second
self.assertTrue(flatMappedRDD.isCheckpointed())
self.assertEqual(flatMappedRDD.collect(), result)
self.assertEqual("file:" + self.checkpointDir.name,
os.path.dirname(os.path.dirname(flatMappedRDD.getCheckpointFile())))
def test_checkpoint_and_restore(self):
parCollection = self.sc.parallelize([1, 2, 3, 4])
flatMappedRDD = parCollection.flatMap(lambda x: [x])
self.assertFalse(flatMappedRDD.isCheckpointed())
self.assertTrue(flatMappedRDD.getCheckpointFile() is None)
flatMappedRDD.checkpoint()
flatMappedRDD.count() # forces a checkpoint to be computed
time.sleep(1) # 1 second
self.assertTrue(flatMappedRDD.getCheckpointFile() is not None)
recovered = self.sc._checkpointFile(flatMappedRDD.getCheckpointFile(),
flatMappedRDD._jrdd_deserializer)
self.assertEqual([1, 2, 3, 4], recovered.collect())
class LocalCheckpointTests(ReusedPySparkTestCase):
def test_basic_localcheckpointing(self):
parCollection = self.sc.parallelize([1, 2, 3, 4])
flatMappedRDD = parCollection.flatMap(lambda x: range(1, x + 1))
self.assertFalse(flatMappedRDD.isCheckpointed())
self.assertFalse(flatMappedRDD.isLocallyCheckpointed())
flatMappedRDD.localCheckpoint()
result = flatMappedRDD.collect()
time.sleep(1) # 1 second
self.assertTrue(flatMappedRDD.isCheckpointed())
self.assertTrue(flatMappedRDD.isLocallyCheckpointed())
self.assertEqual(flatMappedRDD.collect(), result)
class AddFileTests(PySparkTestCase):
def test_add_py_file(self):
# To ensure that we're actually testing addPyFile's effects, check that
# this job fails due to `userlibrary` not being on the Python path:
# disable logging in log4j temporarily
def func(x):
from userlibrary import UserClass
return UserClass().hello()
with QuietTest(self.sc):
self.assertRaises(Exception, self.sc.parallelize(range(2)).map(func).first)
# Add the file, so the job should now succeed:
path = os.path.join(SPARK_HOME, "python/test_support/userlibrary.py")
self.sc.addPyFile(path)
res = self.sc.parallelize(range(2)).map(func).first()
self.assertEqual("Hello World!", res)
def test_add_file_locally(self):
path = os.path.join(SPARK_HOME, "python/test_support/hello/hello.txt")
self.sc.addFile(path)
download_path = SparkFiles.get("hello.txt")
self.assertNotEqual(path, download_path)
with open(download_path) as test_file:
self.assertEqual("Hello World!\n", test_file.readline())
def test_add_file_recursively_locally(self):
path = os.path.join(SPARK_HOME, "python/test_support/hello")
self.sc.addFile(path, True)
download_path = SparkFiles.get("hello")
self.assertNotEqual(path, download_path)
with open(download_path + "/hello.txt") as test_file:
self.assertEqual("Hello World!\n", test_file.readline())
with open(download_path + "/sub_hello/sub_hello.txt") as test_file:
self.assertEqual("Sub Hello World!\n", test_file.readline())
def test_add_py_file_locally(self):
# To ensure that we're actually testing addPyFile's effects, check that
# this fails due to `userlibrary` not being on the Python path:
def func():
from userlibrary import UserClass
self.assertRaises(ImportError, func)
path = os.path.join(SPARK_HOME, "python/test_support/userlibrary.py")
self.sc.addPyFile(path)
from userlibrary import UserClass
self.assertEqual("Hello World!", UserClass().hello())
def test_add_egg_file_locally(self):
# To ensure that we're actually testing addPyFile's effects, check that
# this fails due to `userlibrary` not being on the Python path:
def func():
from userlib import UserClass
self.assertRaises(ImportError, func)
path = os.path.join(SPARK_HOME, "python/test_support/userlib-0.1.zip")
self.sc.addPyFile(path)
from userlib import UserClass
self.assertEqual("Hello World from inside a package!", UserClass().hello())
def test_overwrite_system_module(self):
self.sc.addPyFile(os.path.join(SPARK_HOME, "python/test_support/SimpleHTTPServer.py"))
import SimpleHTTPServer
self.assertEqual("My Server", SimpleHTTPServer.__name__)
def func(x):
import SimpleHTTPServer
return SimpleHTTPServer.__name__
self.assertEqual(["My Server"], self.sc.parallelize(range(1)).map(func).collect())
class ContextTests(unittest.TestCase):
def test_failed_sparkcontext_creation(self):
# Regression test for SPARK-1550
self.assertRaises(Exception, lambda: SparkContext("an-invalid-master-name"))
def test_get_or_create(self):
with SparkContext.getOrCreate() as sc:
self.assertTrue(SparkContext.getOrCreate() is sc)
def test_parallelize_eager_cleanup(self):
with SparkContext() as sc:
temp_files = os.listdir(sc._temp_dir)
rdd = sc.parallelize([0, 1, 2])
post_parallalize_temp_files = os.listdir(sc._temp_dir)
self.assertEqual(temp_files, post_parallalize_temp_files)
def test_set_conf(self):
# This is for an internal use case. When there is an existing SparkContext,
# SparkSession's builder needs to set configs into SparkContext's conf.
sc = SparkContext()
sc._conf.set("spark.test.SPARK16224", "SPARK16224")
self.assertEqual(sc._jsc.sc().conf().get("spark.test.SPARK16224"), "SPARK16224")
sc.stop()
def test_stop(self):
sc = SparkContext()
self.assertNotEqual(SparkContext._active_spark_context, None)
sc.stop()
self.assertEqual(SparkContext._active_spark_context, None)
def test_with(self):
with SparkContext() as sc:
self.assertNotEqual(SparkContext._active_spark_context, None)
self.assertEqual(SparkContext._active_spark_context, None)
def test_with_exception(self):
try:
with SparkContext() as sc:
self.assertNotEqual(SparkContext._active_spark_context, None)
raise Exception()
except:
pass
self.assertEqual(SparkContext._active_spark_context, None)
def test_with_stop(self):
with SparkContext() as sc:
self.assertNotEqual(SparkContext._active_spark_context, None)
sc.stop()
self.assertEqual(SparkContext._active_spark_context, None)
def test_progress_api(self):
with SparkContext() as sc:
sc.setJobGroup('test_progress_api', '', True)
rdd = sc.parallelize(range(10)).map(lambda x: time.sleep(100))
def run():
try:
rdd.count()
except Exception:
pass
t = threading.Thread(target=run)
t.daemon = True
t.start()
# wait for scheduler to start
time.sleep(1)
tracker = sc.statusTracker()
jobIds = tracker.getJobIdsForGroup('test_progress_api')
self.assertEqual(1, len(jobIds))
job = tracker.getJobInfo(jobIds[0])
self.assertEqual(1, len(job.stageIds))
stage = tracker.getStageInfo(job.stageIds[0])
self.assertEqual(rdd.getNumPartitions(), stage.numTasks)
sc.cancelAllJobs()
t.join()
# wait for event listener to update the status
time.sleep(1)
job = tracker.getJobInfo(jobIds[0])
self.assertEqual('FAILED', job.status)
self.assertEqual([], tracker.getActiveJobsIds())
self.assertEqual([], tracker.getActiveStageIds())
sc.stop()
def test_startTime(self):
with SparkContext() as sc:
self.assertGreater(sc.startTime, 0)
def test_forbid_insecure_gateway(self):
# Fail immediately if you try to create a SparkContext
# with an insecure gateway
parameters = namedtuple('MockGatewayParameters', 'auth_token')(None)
mock_insecure_gateway = namedtuple('MockJavaGateway', 'gateway_parameters')(parameters)
with self.assertRaises(ValueError) as context:
SparkContext(gateway=mock_insecure_gateway)
self.assertIn("insecure Py4j gateway", str(context.exception))
def test_resources(self):
"""Test the resources are empty by default."""
with SparkContext() as sc:
resources = sc.resources
self.assertEqual(len(resources), 0)
class ContextTestsWithResources(unittest.TestCase):
def setUp(self):
class_name = self.__class__.__name__
self.tempFile = tempfile.NamedTemporaryFile(delete=False)
self.tempFile.write(b'echo {\\"name\\": \\"gpu\\", \\"addresses\\": [\\"0\\"]}')
self.tempFile.close()
os.chmod(self.tempFile.name, stat.S_IRWXU | stat.S_IXGRP | stat.S_IRGRP |
stat.S_IROTH | stat.S_IXOTH)
conf = SparkConf().set("spark.test.home", SPARK_HOME)
conf = conf.set("spark.driver.resource.gpu.amount", "1")
conf = conf.set("spark.driver.resource.gpu.discoveryScript", self.tempFile.name)
self.sc = SparkContext('local-cluster[2,1,1024]', class_name, conf=conf)
def test_resources(self):
"""Test the resources are available."""
resources = self.sc.resources
self.assertEqual(len(resources), 1)
self.assertTrue('gpu' in resources)
self.assertEqual(resources['gpu'].name, 'gpu')
self.assertEqual(resources['gpu'].addresses, ['0'])
def tearDown(self):
os.unlink(self.tempFile.name)
self.sc.stop()
if __name__ == "__main__":
from pyspark.tests.test_context import *
try:
import xmlrunner
testRunner = xmlrunner.XMLTestRunner(output='target/test-reports', verbosity=2)
except ImportError:
testRunner = None
unittest.main(testRunner=testRunner, verbosity=2)