52834d761b
(Continued from old repo, prior discussion at https://github.com/apache/incubator-spark/pull/615) This patch cements our deprecation of the SPARK_MEM environment variable by replacing it with three more specialized variables: SPARK_DAEMON_MEMORY, SPARK_EXECUTOR_MEMORY, and SPARK_DRIVER_MEMORY The creation of the latter two variables means that we can safely set driver/job memory without accidentally setting the executor memory. Neither is public. SPARK_EXECUTOR_MEMORY is only used by the Mesos scheduler (and set within SparkContext). The proper way of configuring executor memory is through the "spark.executor.memory" property. SPARK_DRIVER_MEMORY is the new way of specifying the amount of memory run by jobs launched by spark-class, without possibly affecting executor memory. Other memory considerations: - The repl's memory can be set through the "--drivermem" command-line option, which really just sets SPARK_DRIVER_MEMORY. - run-example doesn't use spark-class, so the only way to modify examples' memory is actually an unusual use of SPARK_JAVA_OPTS (which is normally overriden in all cases by spark-class). This patch also fixes a lurking bug where spark-shell misused spark-class (the first argument is supposed to be the main class name, not java options), as well as a bug in the Windows spark-class2.cmd. I have not yet tested this patch on either Windows or Mesos, however. Author: Aaron Davidson <aaron@databricks.com> Closes #99 from aarondav/sparkmem and squashes the following commits: 9df4c68 [Aaron Davidson] SPARK-929: Fully deprecate usage of SPARK_MEM
69 lines
2.7 KiB
Python
69 lines
2.7 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 os
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import sys
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import signal
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import platform
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from subprocess import Popen, PIPE
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from threading import Thread
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from py4j.java_gateway import java_import, JavaGateway, GatewayClient
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SPARK_HOME = os.environ["SPARK_HOME"]
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def launch_gateway():
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# Launch the Py4j gateway using Spark's run command so that we pick up the
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# proper classpath and settings from spark-env.sh
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on_windows = platform.system() == "Windows"
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script = "./bin/spark-class.cmd" if on_windows else "./bin/spark-class"
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command = [os.path.join(SPARK_HOME, script), "py4j.GatewayServer",
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"--die-on-broken-pipe", "0"]
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if not on_windows:
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# Don't send ctrl-c / SIGINT to the Java gateway:
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def preexec_func():
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signal.signal(signal.SIGINT, signal.SIG_IGN)
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proc = Popen(command, stdout=PIPE, stdin=PIPE, preexec_fn=preexec_func)
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else:
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# preexec_fn not supported on Windows
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proc = Popen(command, stdout=PIPE, stdin=PIPE)
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# Determine which ephemeral port the server started on:
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port = int(proc.stdout.readline())
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# Create a thread to echo output from the GatewayServer, which is required
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# for Java log output to show up:
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class EchoOutputThread(Thread):
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def __init__(self, stream):
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Thread.__init__(self)
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self.daemon = True
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self.stream = stream
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def run(self):
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while True:
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line = self.stream.readline()
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sys.stderr.write(line)
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EchoOutputThread(proc.stdout).start()
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# Connect to the gateway
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gateway = JavaGateway(GatewayClient(port=port), auto_convert=False)
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# Import the classes used by PySpark
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java_import(gateway.jvm, "org.apache.spark.SparkConf")
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java_import(gateway.jvm, "org.apache.spark.api.java.*")
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java_import(gateway.jvm, "org.apache.spark.api.python.*")
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java_import(gateway.jvm, "org.apache.spark.mllib.api.python.*")
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java_import(gateway.jvm, "scala.Tuple2")
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return gateway
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