Github user pwendell commented on a diff in the pull request:
https://github.com/apache/spark/pull/86#discussion_r10506816
--- Diff: core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala ---
@@ -0,0 +1,188 @@
+/*
+ * 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.
+ */
+
+package org.apache.spark.deploy
+
+import java.io.File
+import java.net.URL
+import java.net.URLClassLoader
+
+import scala.collection.mutable.ArrayBuffer
+import scala.collection.mutable.HashMap
+import scala.collection.mutable.Map
+
+object SparkSubmit {
+ val YARN = 1
+ val STANDALONE = 2
+ val MESOS = 4
+ val LOCAL = 8
+ val ALL_CLUSTER_MGRS = YARN | STANDALONE | MESOS | LOCAL
+
+ var clusterManager: Int = LOCAL
+
+ def main(args: Array[String]) {
+ val appArgs = new SparkSubmitArguments(args)
+ val (childArgs, classpath, sysProps, mainClass) =
createLaunchEnv(appArgs)
+ launch(childArgs, classpath, sysProps, mainClass)
+ }
+
+ /**
+ * @return
+ * a tuple containing the arguments for the child, a list of
classpath
+ * entries for the child, and the main class for the child
+ */
+ def createLaunchEnv(appArgs: SparkSubmitArguments): (ArrayBuffer[String],
+ ArrayBuffer[String], Map[String, String], String) = {
+ if (appArgs.master != null) {
+ if (appArgs.master.startsWith("yarn")) {
+ clusterManager = YARN
+ } else if (appArgs.master.startsWith("spark")) {
+ clusterManager = STANDALONE
+ } else if (appArgs.master.startsWith("mesos")) {
+ clusterManager = MESOS
+ } else if (appArgs.master.startsWith("local")) {
+ clusterManager = LOCAL
+ } else {
+ System.err.println("master must start with yarn, mesos, spark, or
local")
+ System.exit(1)
+ }
+ }
+
+ val deployOnCluster = appArgs.deployMode == "cluster"
+ val childClasspath = new ArrayBuffer[String]()
+ val childArgs = new ArrayBuffer[String]()
+ val sysProps = new HashMap[String, String]()
+ var childMainClass = ""
+
+ if (clusterManager == MESOS && deployOnCluster) {
+ System.err.println("Mesos does not support running the driver on the
cluster")
+ System.exit(1)
+ }
+
+ if (!deployOnCluster) {
+ childMainClass = appArgs.mainClass
+ childClasspath += appArgs.primaryResource
+ } else if (clusterManager == YARN) {
+ childMainClass = "org.apache.spark.deploy.yarn.Client"
+ childArgs += ("--jar", appArgs.primaryResource)
+ childArgs += ("--class", appArgs.mainClass)
+ }
+
+ val options = List[OptionAssigner](
+ new OptionAssigner(appArgs.driverMemory, YARN, true, clOption =
"--master-memory"),
+ new OptionAssigner(appArgs.name, YARN, true, clOption = "--name"),
+ new OptionAssigner(appArgs.queue, YARN, true, clOption = "--queue"),
+ new OptionAssigner(appArgs.queue, YARN, false, sysProp =
"spark.yarn.queue"),
+ new OptionAssigner(appArgs.numExecutors, YARN, true, clOption =
"--num-workers"),
+ new OptionAssigner(appArgs.numExecutors, YARN, false, sysProp =
"spark.worker.instances"),
+ new OptionAssigner(appArgs.executorMemory, YARN, true, clOption =
"--worker-memory"),
+ new OptionAssigner(appArgs.executorMemory, STANDALONE, true,
clOption = "--memory"),
+ new OptionAssigner(appArgs.executorMemory, STANDALONE | MESOS |
YARN, false, sysProp = "spark.executor.memory"),
+ new OptionAssigner(appArgs.executorCores, YARN, true, clOption =
"--worker-cores"),
+ new OptionAssigner(appArgs.executorCores, STANDALONE, true, clOption
= "--cores"),
+ new OptionAssigner(appArgs.executorCores, STANDALONE | MESOS | YARN,
false, sysProp = "spark.cores.max"),
--- End diff --
In mesos and standalone this puts a limit on the _total_ number of cores
not cores per worker. So this is really a distinct thing (maybe
appArgs.totalCores?) and not applicable in YARN mode.
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