95aec091e4
### What changes were proposed in this pull request? As part of the Stage level scheduling features, add the Python api's to set resource profiles. This also adds the functionality to properly apply the pyspark memory configuration when specified in the ResourceProfile. The pyspark memory configuration is being passed in the task local properties. This was an easy way to get it to the PythonRunner that needs it. I modeled this off how the barrier task scheduling is passing the addresses. As part of this I added in the JavaRDD api's because those are needed by python. ### Why are the changes needed? python api for this feature ### Does this PR introduce any user-facing change? Yes adds the java and python apis for user to specify a ResourceProfile to use stage level scheduling. ### How was this patch tested? unit tests and manually tested on yarn. Tests also run to verify it errors properly on standalone and local mode where its not yet supported. Closes #28085 from tgravescs/SPARK-29641-pr-base. Lead-authored-by: Thomas Graves <tgraves@nvidia.com> Co-authored-by: Thomas Graves <tgraves@apache.org> Signed-off-by: HyukjinKwon <gurwls223@apache.org>
253 lines
10 KiB
Python
Executable file
253 lines
10 KiB
Python
Executable file
#!/usr/bin/env python
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#
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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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from __future__ import print_function
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import glob
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import os
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import sys
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from setuptools import setup
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from shutil import copyfile, copytree, rmtree
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if sys.version_info < (2, 7):
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print("Python versions prior to 2.7 are not supported for pip installed PySpark.",
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file=sys.stderr)
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sys.exit(-1)
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try:
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exec(open('pyspark/version.py').read())
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except IOError:
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print("Failed to load PySpark version file for packaging. You must be in Spark's python dir.",
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file=sys.stderr)
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sys.exit(-1)
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VERSION = __version__ # noqa
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# A temporary path so we can access above the Python project root and fetch scripts and jars we need
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TEMP_PATH = "deps"
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SPARK_HOME = os.path.abspath("../")
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# Provide guidance about how to use setup.py
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incorrect_invocation_message = """
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If you are installing pyspark from spark source, you must first build Spark and
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run sdist.
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To build Spark with maven you can run:
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./build/mvn -DskipTests clean package
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Building the source dist is done in the Python directory:
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cd python
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python setup.py sdist
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pip install dist/*.tar.gz"""
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# Figure out where the jars are we need to package with PySpark.
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JARS_PATH = glob.glob(os.path.join(SPARK_HOME, "assembly/target/scala-*/jars/"))
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if len(JARS_PATH) == 1:
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JARS_PATH = JARS_PATH[0]
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elif (os.path.isfile("../RELEASE") and len(glob.glob("../jars/spark*core*.jar")) == 1):
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# Release mode puts the jars in a jars directory
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JARS_PATH = os.path.join(SPARK_HOME, "jars")
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elif len(JARS_PATH) > 1:
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print("Assembly jars exist for multiple scalas ({0}), please cleanup assembly/target".format(
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JARS_PATH), file=sys.stderr)
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sys.exit(-1)
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elif len(JARS_PATH) == 0 and not os.path.exists(TEMP_PATH):
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print(incorrect_invocation_message, file=sys.stderr)
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sys.exit(-1)
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EXAMPLES_PATH = os.path.join(SPARK_HOME, "examples/src/main/python")
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SCRIPTS_PATH = os.path.join(SPARK_HOME, "bin")
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USER_SCRIPTS_PATH = os.path.join(SPARK_HOME, "sbin")
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DATA_PATH = os.path.join(SPARK_HOME, "data")
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LICENSES_PATH = os.path.join(SPARK_HOME, "licenses")
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SCRIPTS_TARGET = os.path.join(TEMP_PATH, "bin")
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USER_SCRIPTS_TARGET = os.path.join(TEMP_PATH, "sbin")
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JARS_TARGET = os.path.join(TEMP_PATH, "jars")
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EXAMPLES_TARGET = os.path.join(TEMP_PATH, "examples")
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DATA_TARGET = os.path.join(TEMP_PATH, "data")
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LICENSES_TARGET = os.path.join(TEMP_PATH, "licenses")
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# Check and see if we are under the spark path in which case we need to build the symlink farm.
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# This is important because we only want to build the symlink farm while under Spark otherwise we
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# want to use the symlink farm. And if the symlink farm exists under while under Spark (e.g. a
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# partially built sdist) we should error and have the user sort it out.
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in_spark = (os.path.isfile("../core/src/main/scala/org/apache/spark/SparkContext.scala") or
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(os.path.isfile("../RELEASE") and len(glob.glob("../jars/spark*core*.jar")) == 1))
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def _supports_symlinks():
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"""Check if the system supports symlinks (e.g. *nix) or not."""
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return getattr(os, "symlink", None) is not None
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if (in_spark):
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# Construct links for setup
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try:
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os.mkdir(TEMP_PATH)
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except:
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print("Temp path for symlink to parent already exists {0}".format(TEMP_PATH),
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file=sys.stderr)
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sys.exit(-1)
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# If you are changing the versions here, please also change ./python/pyspark/sql/utils.py
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# For Arrow, you should also check ./pom.xml and ensure there are no breaking changes in the
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# binary format protocol with the Java version, see ARROW_HOME/format/* for specifications.
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_minimum_pandas_version = "0.23.2"
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_minimum_pyarrow_version = "0.15.1"
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try:
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# We copy the shell script to be under pyspark/python/pyspark so that the launcher scripts
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# find it where expected. The rest of the files aren't copied because they are accessed
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# using Python imports instead which will be resolved correctly.
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try:
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os.makedirs("pyspark/python/pyspark")
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except OSError:
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# Don't worry if the directory already exists.
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pass
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copyfile("pyspark/shell.py", "pyspark/python/pyspark/shell.py")
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if (in_spark):
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# Construct the symlink farm - this is necessary since we can't refer to the path above the
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# package root and we need to copy the jars and scripts which are up above the python root.
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if _supports_symlinks():
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os.symlink(JARS_PATH, JARS_TARGET)
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os.symlink(SCRIPTS_PATH, SCRIPTS_TARGET)
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os.symlink(USER_SCRIPTS_PATH, USER_SCRIPTS_TARGET)
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os.symlink(EXAMPLES_PATH, EXAMPLES_TARGET)
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os.symlink(DATA_PATH, DATA_TARGET)
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os.symlink(LICENSES_PATH, LICENSES_TARGET)
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else:
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# For windows fall back to the slower copytree
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copytree(JARS_PATH, JARS_TARGET)
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copytree(SCRIPTS_PATH, SCRIPTS_TARGET)
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copytree(USER_SCRIPTS_PATH, USER_SCRIPTS_TARGET)
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copytree(EXAMPLES_PATH, EXAMPLES_TARGET)
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copytree(DATA_PATH, DATA_TARGET)
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copytree(LICENSES_PATH, LICENSES_TARGET)
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else:
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# If we are not inside of SPARK_HOME verify we have the required symlink farm
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if not os.path.exists(JARS_TARGET):
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print("To build packaging must be in the python directory under the SPARK_HOME.",
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file=sys.stderr)
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if not os.path.isdir(SCRIPTS_TARGET):
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print(incorrect_invocation_message, file=sys.stderr)
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sys.exit(-1)
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# Scripts directive requires a list of each script path and does not take wild cards.
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script_names = os.listdir(SCRIPTS_TARGET)
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scripts = list(map(lambda script: os.path.join(SCRIPTS_TARGET, script), script_names))
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# We add find_spark_home.py to the bin directory we install so that pip installed PySpark
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# will search for SPARK_HOME with Python.
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scripts.append("pyspark/find_spark_home.py")
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with open('README.md') as f:
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long_description = f.read()
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setup(
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name='pyspark',
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version=VERSION,
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description='Apache Spark Python API',
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long_description=long_description,
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long_description_content_type="text/markdown",
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author='Spark Developers',
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author_email='dev@spark.apache.org',
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url='https://github.com/apache/spark/tree/master/python',
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packages=['pyspark',
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'pyspark.mllib',
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'pyspark.mllib.linalg',
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'pyspark.mllib.stat',
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'pyspark.ml',
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'pyspark.ml.linalg',
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'pyspark.ml.param',
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'pyspark.sql',
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'pyspark.sql.avro',
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'pyspark.sql.pandas',
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'pyspark.streaming',
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'pyspark.bin',
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'pyspark.sbin',
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'pyspark.jars',
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'pyspark.python.pyspark',
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'pyspark.python.lib',
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'pyspark.data',
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'pyspark.licenses',
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'pyspark.resource',
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'pyspark.examples.src.main.python'],
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include_package_data=True,
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package_dir={
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'pyspark.jars': 'deps/jars',
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'pyspark.bin': 'deps/bin',
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'pyspark.sbin': 'deps/sbin',
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'pyspark.python.lib': 'lib',
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'pyspark.data': 'deps/data',
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'pyspark.licenses': 'deps/licenses',
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'pyspark.examples.src.main.python': 'deps/examples',
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},
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package_data={
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'pyspark.jars': ['*.jar'],
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'pyspark.bin': ['*'],
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'pyspark.sbin': ['spark-config.sh', 'spark-daemon.sh',
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'start-history-server.sh',
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'stop-history-server.sh', ],
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'pyspark.python.lib': ['*.zip'],
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'pyspark.data': ['*.txt', '*.data'],
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'pyspark.licenses': ['*.txt'],
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'pyspark.examples.src.main.python': ['*.py', '*/*.py']},
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scripts=scripts,
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license='http://www.apache.org/licenses/LICENSE-2.0',
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install_requires=['py4j==0.10.9'],
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extras_require={
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'ml': ['numpy>=1.7'],
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'mllib': ['numpy>=1.7'],
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'sql': [
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'pandas>=%s' % _minimum_pandas_version,
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'pyarrow>=%s' % _minimum_pyarrow_version,
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]
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},
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classifiers=[
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'Development Status :: 5 - Production/Stable',
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'License :: OSI Approved :: Apache Software License',
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'Programming Language :: Python :: 2.7',
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'Programming Language :: Python :: 3',
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'Programming Language :: Python :: 3.4',
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'Programming Language :: Python :: 3.5',
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'Programming Language :: Python :: 3.6',
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'Programming Language :: Python :: 3.7',
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'Programming Language :: Python :: 3.8',
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'Programming Language :: Python :: Implementation :: CPython',
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'Programming Language :: Python :: Implementation :: PyPy']
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)
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finally:
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# We only cleanup the symlink farm if we were in Spark, otherwise we are installing rather than
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# packaging.
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if (in_spark):
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# Depending on cleaning up the symlink farm or copied version
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if _supports_symlinks():
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os.remove(os.path.join(TEMP_PATH, "jars"))
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os.remove(os.path.join(TEMP_PATH, "bin"))
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os.remove(os.path.join(TEMP_PATH, "sbin"))
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os.remove(os.path.join(TEMP_PATH, "examples"))
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os.remove(os.path.join(TEMP_PATH, "data"))
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os.remove(os.path.join(TEMP_PATH, "licenses"))
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else:
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rmtree(os.path.join(TEMP_PATH, "jars"))
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rmtree(os.path.join(TEMP_PATH, "bin"))
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rmtree(os.path.join(TEMP_PATH, "sbin"))
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rmtree(os.path.join(TEMP_PATH, "examples"))
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rmtree(os.path.join(TEMP_PATH, "data"))
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rmtree(os.path.join(TEMP_PATH, "licenses"))
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os.rmdir(TEMP_PATH)
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