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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.
*/
package org.apache.spark.deploy.yarn
import java.net.URI
import java.nio.ByteBuffer
import java.security.PrivilegedExceptionAction
import scala.collection.JavaConversions._
import scala.collection.mutable.HashMap
import org.apache.hadoop.conf.Configuration
import org.apache.hadoop.fs.Path
import org.apache.hadoop.io.DataOutputBuffer
import org.apache.hadoop.net.NetUtils
import org.apache.hadoop.security.UserGroupInformation
import org.apache.hadoop.yarn.api._
import org.apache.hadoop.yarn.api.ApplicationConstants.Environment
import org.apache.hadoop.yarn.api.records._
import org.apache.hadoop.yarn.api.records.impl.pb.ProtoUtils
import org.apache.hadoop.yarn.api.protocolrecords._
import org.apache.hadoop.yarn.client.api.NMClient
import org.apache.hadoop.yarn.conf.YarnConfiguration
import org.apache.hadoop.yarn.ipc.YarnRPC
import org.apache.hadoop.yarn.util.{Apps, ConverterUtils, Records}
import org.apache.spark.Logging
class WorkerRunnable(
container: Container,
conf: Configuration,
masterAddress: String,
slaveId: String,
hostname: String,
workerMemory: Int,
workerCores: Int)
extends Runnable with Logging {
var rpc: YarnRPC = YarnRPC.create(conf)
var nmClient: NMClient = _
val yarnConf: YarnConfiguration = new YarnConfiguration(conf)
def run = {
logInfo("Starting Worker Container")
nmClient = NMClient.createNMClient()
nmClient.init(yarnConf)
nmClient.start()
startContainer
}
def startContainer = {
logInfo("Setting up ContainerLaunchContext")
val ctx = Records.newRecord(classOf[ContainerLaunchContext])
.asInstanceOf[ContainerLaunchContext]
val localResources = prepareLocalResources
ctx.setLocalResources(localResources)
val env = prepareEnvironment
ctx.setEnvironment(env)
// Extra options for the JVM
var JAVA_OPTS = ""
// Set the JVM memory
val workerMemoryString = workerMemory + "m"
JAVA_OPTS += "-Xms" + workerMemoryString + " -Xmx" + workerMemoryString + " "
if (env.isDefinedAt("SPARK_JAVA_OPTS")) {
JAVA_OPTS += env("SPARK_JAVA_OPTS") + " "
}
JAVA_OPTS += " -Djava.io.tmpdir=" +
new Path(Environment.PWD.$(), YarnConfiguration.DEFAULT_CONTAINER_TEMP_DIR) + " "
// Commenting it out for now - so that people can refer to the properties if required. Remove
// it once cpuset version is pushed out.
// The context is, default gc for server class machines end up using all cores to do gc - hence
// if there are multiple containers in same node, spark gc effects all other containers
// performance (which can also be other spark containers)
// Instead of using this, rely on cpusets by YARN to enforce spark behaves 'properly' in
// multi-tenant environments. Not sure how default java gc behaves if it is limited to subset
// of cores on a node.
/*
else {
// If no java_opts specified, default to using -XX:+CMSIncrementalMode
// It might be possible that other modes/config is being done in SPARK_JAVA_OPTS, so we dont
// want to mess with it.
// In our expts, using (default) throughput collector has severe perf ramnifications in
// multi-tennent machines
// The options are based on
// http://www.oracle.com/technetwork/java/gc-tuning-5-138395.html#0.0.0.%20When%20to%20Use%20the%20Concurrent%20Low%20Pause%20Collector|outline
JAVA_OPTS += " -XX:+UseConcMarkSweepGC "
JAVA_OPTS += " -XX:+CMSIncrementalMode "
JAVA_OPTS += " -XX:+CMSIncrementalPacing "
JAVA_OPTS += " -XX:CMSIncrementalDutyCycleMin=0 "
JAVA_OPTS += " -XX:CMSIncrementalDutyCycle=10 "
}
*/
val credentials = UserGroupInformation.getCurrentUser().getCredentials()
val dob = new DataOutputBuffer()
credentials.writeTokenStorageToStream(dob)
ctx.setTokens(ByteBuffer.wrap(dob.getData()))
var javaCommand = "java"
val javaHome = System.getenv("JAVA_HOME")
if ((javaHome != null && !javaHome.isEmpty()) || env.isDefinedAt("JAVA_HOME")) {
javaCommand = Environment.JAVA_HOME.$() + "/bin/java"
}
val commands = List[String](javaCommand +
" -server " +
// Kill if OOM is raised - leverage yarn's failure handling to cause rescheduling.
// Not killing the task leaves various aspects of the worker and (to some extent) the jvm in
// an inconsistent state.
// TODO: If the OOM is not recoverable by rescheduling it on different node, then do
// 'something' to fail job ... akin to blacklisting trackers in mapred ?
" -XX:OnOutOfMemoryError='kill %p' " +
JAVA_OPTS +
" org.apache.spark.executor.CoarseGrainedExecutorBackend " +
masterAddress + " " +
slaveId + " " +
hostname + " " +
workerCores +
" 1> " + ApplicationConstants.LOG_DIR_EXPANSION_VAR + "/stdout" +
" 2> " + ApplicationConstants.LOG_DIR_EXPANSION_VAR + "/stderr")
logInfo("Setting up worker with commands: " + commands)
ctx.setCommands(commands)
// Send the start request to the ContainerManager
nmClient.startContainer(container, ctx)
}
private def setupDistributedCache(
file: String,
rtype: LocalResourceType,
localResources: HashMap[String, LocalResource],
timestamp: String,
size: String,
vis: String) = {
val uri = new URI(file)
val amJarRsrc = Records.newRecord(classOf[LocalResource]).asInstanceOf[LocalResource]
amJarRsrc.setType(rtype)
amJarRsrc.setVisibility(LocalResourceVisibility.valueOf(vis))
amJarRsrc.setResource(ConverterUtils.getYarnUrlFromURI(uri))
amJarRsrc.setTimestamp(timestamp.toLong)
amJarRsrc.setSize(size.toLong)
localResources(uri.getFragment()) = amJarRsrc
}
def prepareLocalResources: HashMap[String, LocalResource] = {
logInfo("Preparing Local resources")
val localResources = HashMap[String, LocalResource]()
if (System.getenv("SPARK_YARN_CACHE_FILES") != null) {
val timeStamps = System.getenv("SPARK_YARN_CACHE_FILES_TIME_STAMPS").split(',')
val fileSizes = System.getenv("SPARK_YARN_CACHE_FILES_FILE_SIZES").split(',')
val distFiles = System.getenv("SPARK_YARN_CACHE_FILES").split(',')
val visibilities = System.getenv("SPARK_YARN_CACHE_FILES_VISIBILITIES").split(',')
for( i <- 0 to distFiles.length - 1) {
setupDistributedCache(distFiles(i), LocalResourceType.FILE, localResources, timeStamps(i),
fileSizes(i), visibilities(i))
}
}
if (System.getenv("SPARK_YARN_CACHE_ARCHIVES") != null) {
val timeStamps = System.getenv("SPARK_YARN_CACHE_ARCHIVES_TIME_STAMPS").split(',')
val fileSizes = System.getenv("SPARK_YARN_CACHE_ARCHIVES_FILE_SIZES").split(',')
val distArchives = System.getenv("SPARK_YARN_CACHE_ARCHIVES").split(',')
val visibilities = System.getenv("SPARK_YARN_CACHE_ARCHIVES_VISIBILITIES").split(',')
for( i <- 0 to distArchives.length - 1) {
setupDistributedCache(distArchives(i), LocalResourceType.ARCHIVE, localResources,
timeStamps(i), fileSizes(i), visibilities(i))
}
}
logInfo("Prepared Local resources " + localResources)
localResources
}
def prepareEnvironment: HashMap[String, String] = {
val env = new HashMap[String, String]()
Client.populateClasspath(yarnConf, System.getenv("SPARK_YARN_LOG4J_PATH") != null, env)
// Allow users to specify some environment variables
Apps.setEnvFromInputString(env, System.getenv("SPARK_YARN_USER_ENV"))
System.getenv().filterKeys(_.startsWith("SPARK")).foreach { case (k,v) => env(k) = v }
env
}
}
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