1.spark-submit脚本
我们都知道在yarn集群中提交一个spark作业时，需要调用spark bin目录下的spark-submit脚本
bin/spark-submit \
--class org.apache.spark.examples.SparkPi \
--master yarn \
./examples/jars/spark-examples_2.12-3.0.0.jar \
10
下面为spark-submit脚本内容：
if [ -z "${SPARK_HOME}" ]; then
source "$(dirname "$0")"/find-spark-home
fi# disable randomized hash for string in Python 3.3+
export PYTHONHASHSEED=0exec "${SPARK_HOME}"/bin/spark-class org.apache.spark.deploy.SparkSubmit "$@"
从脚本中我们可以看到最后一行有这样一条命令：/bin/spark-class org.apache.spark.deploy.SparkSubmit
这里的spark-class脚本主要是在寻找java home,所以最后这条命令就变成了：
/bin/java org.apache.spark.deploy.SparkSubmit
这条命令的意思就是启动一个名为SparkSubmit的进程
2.SparkSubmit
2.1 main方法
我们在源码中找到SparkSubmit这个类，这个进程启动后会去执行它的main方法：
可以看到该方法中主要在做两件事：
1.创建了一个sparkSubmit对象
2.调用它的doSubmit(args)方法，这个args就是我们启动job传的相关参数
override def main(args: Array[String]): Unit = {
val submit = new SparkSubmit() {
self =>
override protected def parseArguments(args: Array[String]): SparkSubmitArguments = {
new SparkSubmitArguments(args) {
override protected def logInfo(msg: => String): Unit = self.logInfo(msg)
override protected def logWarning(msg: => String): Unit = self.logWarning(msg)
}
}
override protected def logInfo(msg: => String): Unit = printMessage(msg)
override protected def logWarning(msg: => String): Unit = printMessage(s"Warning: $msg")
override def doSubmit(args: Array[String]): Unit = {
try {
super.doSubmit(args)
} catch {
case e: SparkUserAppException =>
exitFn(e.exitCode)
}
}
}
submit.doSubmit(args)
}
2.2 doSubmit方法
doSubmit方法中主要做了以下几件事：
1. 初始化日志 initializeLogIfNecessary()
2. 解析参数 parseArguments(args)，在这里解析driver-core,driver-memory, executor-core,executor-memory,mainClass等相关参数
3. 运用模式匹配，匹配不同行为调用不同方法，这里我们主要关注submit方法
def doSubmit(args: Array[String]): Unit = { // Initialize logging if it hasn't been done yet. Keep track of whether logging needs to // be reset before the application starts. val uninitLog = initializeLogIfNecessary(true, silent = true) val appArgs = parseArguments(args) if (appArgs.verbose) { logInfo(appArgs.toString) } appArgs.action match { case SparkSubmitAction.SUBMIT => submit(appArgs, uninitLog) case SparkSubmitAction.KILL => kill(appArgs) case SparkSubmitAction.REQUEST_STATUS => requestStatus(appArgs) case SparkSubmitAction.PRINT_VERSION => printVersion() } }
3.submit方法
下面我们进入submit方法进行查看，该方法中主要做了以下几件事：
1. 调用prepareSubmitEnvironment(args)，准备作业的提交环境，方法返回了一个四元组，
里面有sparkConf和childMainClass
2. 调用内部方法doRunMain()方法
3.doRunMain方法中调用runMain方法，使用第一步产生的结果作为参数传递
private def submit(args: SparkSubmitArguments, uninitLog: Boolean): Unit = {
val (childArgs, childClasspath, sparkConf, childMainClass) = prepareSubmitEnvironment(args)
def doRunMain(): Unit = {
if (args.proxyUser != null) {
val proxyUser = UserGroupInformation.createProxyUser(args.proxyUser,
UserGroupInformation.getCurrentUser())
try {
proxyUser.doAs(new PrivilegedExceptionAction[Unit]() {
override def run(): Unit = {
runMain(childArgs, childClasspath, sparkConf, childMainClass, args.verbose)
}
})
} catch {
case e: Exception =>
// Hadoop's AuthorizationException suppresses the exception's stack trace, which
// makes the message printed to the output by the JVM not very helpful. Instead,
// detect exceptions with empty stack traces here, and treat them differently.
if (e.getStackTrace().length == 0) {
error(s"ERROR: ${e.getClass().getName()}: ${e.getMessage()}")
} else {
throw e
}
}
} else {
runMain(childArgs, childClasspath, sparkConf, childMainClass, args.verbose)
}
}
// Let the main class re-initialize the logging system once it starts.
if (uninitLog) {
Logging.uninitialize()
}
// In standalone cluster mode, there are two submission gateways:
// (1) The traditional RPC gateway using o.a.s.deploy.Client as a wrapper
// (2) The new REST-based gateway introduced in Spark 1.3
// The latter is the default behavior as of Spark 1.3, but Spark submit will fail over
// to use the legacy gateway if the master endpoint turns out to be not a REST server.
if (args.isStandaloneCluster && args.useRest) {
try {
logInfo("Running Spark using the REST application submission protocol.")
doRunMain()
} catch {
// Fail over to use the legacy submission gateway
case e: SubmitRestConnectionException =>
logWarning(s"Master endpoint ${args.master} was not a REST server. " +
"Falling back to legacy submission gateway instead.")
args.useRest = false
submit(args, false)
}
// In all other modes, just run the main class as prepared
} else {
doRunMain()
}
}
4.runMain方法
runMain方法中主要做了以下几件事:
1. 给当前线程设置类加载器
2.反射调用用户类中的main方法
3. 判断mainClass是否继承SparkApplication,如果是的话就创建一个实例
4.调用sparkApplication的start方法
private def runMain(
childArgs: Seq[String],
childClasspath: Seq[String],
sparkConf: SparkConf,
childMainClass: String,
verbose: Boolean): Unit = {
if (verbose) {
logInfo(s"Main class:\n$childMainClass")
logInfo(s"Arguments:\n${childArgs.mkString("\n")}")
// sysProps may contain sensitive information, so redact before printing
logInfo(s"Spark config:\n${Utils.redact(sparkConf.getAll.toMap).mkString("\n")}")
logInfo(s"Classpath elements:\n${childClasspath.mkString("\n")}")
logInfo("\n")
}
val loader =
if (sparkConf.get(DRIVER_USER_CLASS_PATH_FIRST)) {
new ChildFirstURLClassLoader(new Array[URL](0),
Thread.currentThread.getContextClassLoader)
} else {
new MutableURLClassLoader(new Array[URL](0),
Thread.currentThread.getContextClassLoader)
}
Thread.currentThread.setContextClassLoader(loader)
for (jar <- childClasspath) {
addJarToClasspath(jar, loader)
}
var mainClass: Class[_] = null
try {
mainClass = Utils.classForName(childMainClass)
} catch {
case e: ClassNotFoundException =>
logWarning(s"Failed to load $childMainClass.", e)
if (childMainClass.contains("thriftserver")) {
logInfo(s"Failed to load main class $childMainClass.")
logInfo("You need to build Spark with -Phive and -Phive-thriftserver.")
}
throw new SparkUserAppException(CLASS_NOT_FOUND_EXIT_STATUS)
case e: NoClassDefFoundError =>
logWarning(s"Failed to load $childMainClass: ${e.getMessage()}")
if (e.getMessage.contains("org/apache/hadoop/hive")) {
logInfo(s"Failed to load hive class.")
logInfo("You need to build Spark with -Phive and -Phive-thriftserver.")
}
throw new SparkUserAppException(CLASS_NOT_FOUND_EXIT_STATUS)
}
val app: SparkApplication = if (classOf[SparkApplication].isAssignableFrom(mainClass)) {
mainClass.newInstance().asInstanceOf[SparkApplication]
} else {
// SPARK-4170
if (classOf[scala.App].isAssignableFrom(mainClass)) {
logWarning("Subclasses of scala.App may not work correctly. Use a main() method instead.")
}
new JavaMainApplication(mainClass)
}
@tailrec
def findCause(t: Throwable): Throwable = t match {
case e: UndeclaredThrowableException =>
if (e.getCause() != null) findCause(e.getCause()) else e
case e: InvocationTargetException =>
if (e.getCause() != null) findCause(e.getCause()) else e
case e: Throwable =>
e
}
try {
app.start(childArgs.toArray, sparkConf)
} catch {
case t: Throwable =>
throw findCause(t)
}
}
反射调用用户类的main方法，main方法调用的时候会首先初始化sparkContext对象，这个对象初始化的时候，内部会初始化三个组件：schedulerBackend, TaskScheduler,DagScheduler，这三个组件在作业转化成DAG,TaskSet，将作业发送到executor中起到重要作用，后面我们会详细讲解这一部分。
3.YarnClusterApplication
因为我们的环境是yarn-cluster环境，YarnClusterApplication继承了SparkApplication，所以上一步启动SparkApplication就会启动YarnClusterApplication，我们接下来看一下它的start方法,该方法中new了一个client对象后并调用它的run方法
private[spark] class YarnClusterApplication extends SparkApplication {
override def start(args: Array[String], conf: SparkConf): Unit = {
// SparkSubmit would use yarn cache to distribute files & jars in yarn mode,
// so remove them from sparkConf here for yarn mode.
conf.remove(JARS)
conf.remove(FILES)
new Client(new ClientArguments(args), conf, null).run()
}
}
4.Client
1.run方法
这个方法中调用了submitApplication方法，返回一个appId,后面是对appId的相关判断和操作，所以下面我们可以看看submitApplication方法做了那些操作
def run(): Unit = {
this.appId = submitApplication()
if (!launcherBackend.isConnected() && fireAndForget) {
val report = getApplicationReport(appId)
val state = report.getYarnApplicationState
logInfo(s"Application report for $appId (state: $state)")
logInfo(formatReportDetails(report))
if (state == YarnApplicationState.FAILED || state == YarnApplicationState.KILLED) {
throw new SparkException(s"Application $appId finished with status: $state")
}
} else {
val YarnAppReport(appState, finalState, diags) = monitorApplication(appId)
if (appState == YarnApplicationState.FAILED || finalState == FinalApplicationStatus.FAILED) {
diags.foreach { err =>
logError(s"Application diagnostics message: $err")
}
throw new SparkException(s"Application $appId finished with failed status")
}
if (appState == YarnApplicationState.KILLED || finalState == FinalApplicationStatus.KILLED) {
throw new SparkException(s"Application $appId is killed")
}
if (finalState == FinalApplicationStatus.UNDEFINED) {
throw new SparkException(s"The final status of application $appId is undefined")
}
}
}
2.submitApplication
该方法中主要做了以下几件事：
1.初始化并启动yarnClient
2.创建一个application
3.验证集群是否有足够的资源启动AM
4.通过yarnClient的submitApplication方法提交作业
def submitApplication(): ApplicationId = {
ResourceRequestHelper.validateResources(sparkConf)
var appId: ApplicationId = null
try {
launcherBackend.connect()
yarnClient.init(hadoopConf)
yarnClient.start()
logInfo("Requesting a new application from cluster with %d NodeManagers"
.format(yarnClient.getYarnClusterMetrics.getNumNodeManagers))
// Get a new application from our RM
val newApp = yarnClient.createApplication()
val newAppResponse = newApp.getNewApplicationResponse()
appId = newAppResponse.getApplicationId()
// The app staging dir based on the STAGING_DIR configuration if configured
// otherwise based on the users home directory.
val appStagingBaseDir = sparkConf.get(STAGING_DIR)
.map { new Path(_, UserGroupInformation.getCurrentUser.getShortUserName) }
.getOrElse(FileSystem.get(hadoopConf).getHomeDirectory())
stagingDirPath = new Path(appStagingBaseDir, getAppStagingDir(appId))
new CallerContext("CLIENT", sparkConf.get(APP_CALLER_CONTEXT),
Option(appId.toString)).setCurrentContext()
// Verify whether the cluster has enough resources for our AM
verifyClusterResources(newAppResponse)
// Set up the appropriate contexts to launch our AM
val containerContext = createContainerLaunchContext(newAppResponse)
val appContext = createApplicationSubmissionContext(newApp, containerContext)
// Finally, submit and monitor the application
logInfo(s"Submitting application $appId to ResourceManager")
yarnClient.submitApplication(appContext)
launcherBackend.setAppId(appId.toString)
reportLauncherState(SparkAppHandle.State.SUBMITTED)
appId
} catch {
case e: Throwable =>
if (stagingDirPath != null) {
cleanupStagingDir()
}
throw e
}
}
3.createContainerLaunchContext方法
该方法中主要做了以下几件事:
1.准备本地的资源 prepareLocalResources()
2.判断是否是集群模式，如果是集群模式启动ApplicationMaster进程
private def createContainerLaunchContext(newAppResponse: GetNewApplicationResponse)
: ContainerLaunchContext = {
logInfo("Setting up container launch context for our AM")
val appId = newAppResponse.getApplicationId
val pySparkArchives =
if (sparkConf.get(IS_PYTHON_APP)) {
findPySparkArchives()
} else {
Nil
}
val launchEnv = setupLaunchEnv(stagingDirPath, pySparkArchives)
val localResources = prepareLocalResources(stagingDirPath, pySparkArchives)
val amContainer = Records.newRecord(classOf[ContainerLaunchContext])
amContainer.setLocalResources(localResources.asJava)
amContainer.setEnvironment(launchEnv.asJava)
val javaOpts = ListBuffer[String]()
// Set the environment variable through a command prefix
// to append to the existing value of the variable
var prefixEnv: Option[String] = None
// Add Xmx for AM memory
javaOpts += "-Xmx" + amMemory + "m"
val tmpDir = new Path(Environment.PWD.$$(), YarnConfiguration.DEFAULT_CONTAINER_TEMP_DIR)
javaOpts += "-Djava.io.tmpdir=" + tmpDir
// TODO: Remove once cpuset version is pushed out.
// The context is, default gc for server class machines ends up using all cores to do gc -
// hence if there are multiple containers in same node, Spark GC affects all other containers'
// performance (which can be that of other Spark containers)
// Instead of using this, rely on cpusets by YARN to enforce "proper" Spark behavior in
// multi-tenant environments. Not sure how default Java GC behaves if it is limited to subset
// of cores on a node.
val useConcurrentAndIncrementalGC = launchEnv.get("SPARK_USE_CONC_INCR_GC").exists(_.toBoolean)
if (useConcurrentAndIncrementalGC) {
// In our expts, using (default) throughput collector has severe perf ramifications in
// multi-tenant machines
javaOpts += "-XX:+UseConcMarkSweepGC"
javaOpts += "-XX:MaxTenuringThreshold=31"
javaOpts += "-XX:SurvivorRatio=8"
javaOpts += "-XX:+CMSIncrementalMode"
javaOpts += "-XX:+CMSIncrementalPacing"
javaOpts += "-XX:CMSIncrementalDutyCycleMin=0"
javaOpts += "-XX:CMSIncrementalDutyCycle=10"
}
// Include driver-specific java options if we are launching a driver
if (isClusterMode) {
sparkConf.get(DRIVER_JAVA_OPTIONS).foreach { opts =>
javaOpts ++= Utils.splitCommandString(opts)
.map(Utils.substituteAppId(_, appId.toString))
.map(YarnSparkHadoopUtil.escapeForShell)
}
val libraryPaths = Seq(sparkConf.get(DRIVER_LIBRARY_PATH),
sys.props.get("spark.driver.libraryPath")).flatten
if (libraryPaths.nonEmpty) {
prefixEnv = Some(createLibraryPathPrefix(libraryPaths.mkString(File.pathSeparator),
sparkConf))
}
if (sparkConf.get(AM_JAVA_OPTIONS).isDefined) {
logWarning(s"${AM_JAVA_OPTIONS.key} will not take effect in cluster mode")
}
} else {
// Validate and include yarn am specific java options in yarn-client mode.
sparkConf.get(AM_JAVA_OPTIONS).foreach { opts =>
if (opts.contains("-Dspark")) {
val msg = s"${AM_JAVA_OPTIONS.key} is not allowed to set Spark options (was '$opts')."
throw new SparkException(msg)
}
if (opts.contains("-Xmx")) {
val msg = s"${AM_JAVA_OPTIONS.key} is not allowed to specify max heap memory settings " +
s"(was '$opts'). Use spark.yarn.am.memory instead."
throw new SparkException(msg)
}
javaOpts ++= Utils.splitCommandString(opts)
.map(Utils.substituteAppId(_, appId.toString))
.map(YarnSparkHadoopUtil.escapeForShell)
}
sparkConf.get(AM_LIBRARY_PATH).foreach { paths =>
prefixEnv = Some(createLibraryPathPrefix(paths, sparkConf))
}
}
// For log4j configuration to reference
javaOpts += ("-Dspark.yarn.app.container.log.dir=" + ApplicationConstants.LOG_DIR_EXPANSION_VAR)
val userClass =
if (isClusterMode) {
Seq("--class", YarnSparkHadoopUtil.escapeForShell(args.userClass))
} else {
Nil
}
val userJar =
if (args.userJar != null) {
Seq("--jar", args.userJar)
} else {
Nil
}
val primaryPyFile =
if (isClusterMode && args.primaryPyFile != null) {
Seq("--primary-py-file", new Path(args.primaryPyFile).getName())
} else {
Nil
}
val primaryRFile =
if (args.primaryRFile != null) {
Seq("--primary-r-file", args.primaryRFile)
} else {
Nil
}
val amClass =
if (isClusterMode) {
Utils.classForName("org.apache.spark.deploy.yarn.ApplicationMaster").getName
} else {
Utils.classForName("org.apache.spark.deploy.yarn.ExecutorLauncher").getName
}
if (args.primaryRFile != null &&
(args.primaryRFile.endsWith(".R") || args.primaryRFile.endsWith(".r"))) {
args.userArgs = ArrayBuffer(args.primaryRFile) ++ args.userArgs
}
val userArgs = args.userArgs.flatMap { arg =>
Seq("--arg", YarnSparkHadoopUtil.escapeForShell(arg))
}
val amArgs =
Seq(amClass) ++ userClass ++ userJar ++ primaryPyFile ++ primaryRFile ++ userArgs ++
Seq("--properties-file",
buildPath(Environment.PWD.$$(), LOCALIZED_CONF_DIR, SPARK_CONF_FILE)) ++
Seq("--dist-cache-conf",
buildPath(Environment.PWD.$$(), LOCALIZED_CONF_DIR, DIST_CACHE_CONF_FILE))
// Command for the ApplicationMaster
val commands = prefixEnv ++
Seq(Environment.JAVA_HOME.$$() + "/bin/java", "-server") ++
javaOpts ++ amArgs ++
Seq(
"1>", ApplicationConstants.LOG_DIR_EXPANSION_VAR + "/stdout",
"2>", ApplicationConstants.LOG_DIR_EXPANSION_VAR + "/stderr")
// TODO: it would be nicer to just make sure there are no null commands here
val printableCommands = commands.map(s => if (s == null) "null" else s).toList
amContainer.setCommands(printableCommands.asJava)
logDebug("===============================================================================")
logDebug("YARN AM launch context:")
logDebug(s" user class: ${Option(args.userClass).getOrElse("N/A")}")
logDebug(" env:")
if (log.isDebugEnabled) {
Utils.redact(sparkConf, launchEnv.toSeq).foreach { case (k, v) =>
logDebug(s" $k -> $v")
}
}
logDebug(" resources:")
localResources.foreach { case (k, v) => logDebug(s" $k -> $v")}
logDebug(" command:")
logDebug(s" ${printableCommands.mkString(" ")}")
logDebug("===============================================================================")
// send the acl settings into YARN to control who has access via YARN interfaces
val securityManager = new SecurityManager(sparkConf)
amContainer.setApplicationACLs(
YarnSparkHadoopUtil.getApplicationAclsForYarn(securityManager).asJava)
setupSecurityToken(amContainer)
amContainer
}
5.ApplicationMaster
1.main方法
ApplicationMaster进程启动后会执行它的main方法：
1.创建ApplicationMasterArguments对象和sparkConf对象
2.创建ApplicationMaster对象
3.在ugi.doAs方法中调用master.run方法
def main(args: Array[String]): Unit = {
SignalUtils.registerLogger(log)
val amArgs = new ApplicationMasterArguments(args)
val sparkConf = new SparkConf()
if (amArgs.propertiesFile != null) {
Utils.getPropertiesFromFile(amArgs.propertiesFile).foreach { case (k, v) =>
sparkConf.set(k, v)
}
}
// Set system properties for each config entry. This covers two use cases:
// - The default configuration stored by the SparkHadoopUtil class
// - The user application creating a new SparkConf in cluster mode
//
// Both cases create a new SparkConf object which reads these configs from system properties.
sparkConf.getAll.foreach { case (k, v) =>
sys.props(k) = v
}
val yarnConf = new YarnConfiguration(SparkHadoopUtil.newConfiguration(sparkConf))
master = new ApplicationMaster(amArgs, sparkConf, yarnConf)
val ugi = sparkConf.get(PRINCIPAL) match {
// We only need to log in with the keytab in cluster mode. In client mode, the driver
// handles the user keytab.
case Some(principal) if master.isClusterMode =>
val originalCreds = UserGroupInformation.getCurrentUser().getCredentials()
SparkHadoopUtil.get.loginUserFromKeytab(principal, sparkConf.get(KEYTAB).orNull)
val newUGI = UserGroupInformation.getCurrentUser()
if (master.appAttemptId == null || master.appAttemptId.getAttemptId > 1) {
// Re-obtain delegation tokens if this is not a first attempt, as they might be outdated
// as of now. Add the fresh tokens on top of the original user's credentials (overwrite).
// Set the context class loader so that the token manager has access to jars
// distributed by the user.
Utils.withContextClassLoader(master.userClassLoader) {
val credentialManager = new HadoopDelegationTokenManager(sparkConf, yarnConf, null)
credentialManager.obtainDelegationTokens(originalCreds)
}
}
// Transfer the original user's tokens to the new user, since it may contain needed tokens
// (such as those user to connect to YARN).
newUGI.addCredentials(originalCreds)
newUGI
case _ =>
SparkHadoopUtil.get.createSparkUser()
}
ugi.doAs(new PrivilegedExceptionAction[Unit]() {
override def run(): Unit = System.exit(master.run())
})
}
2.run方法
这里面主要主要做了系统的参数设置，判断是集群模式的时候调用runDriver方法
final def run(): Int = {
try {
val attemptID = if (isClusterMode) {
// Set the web ui port to be ephemeral for yarn so we don't conflict with
// other spark processes running on the same box
System.setProperty(UI_PORT.key, "0")
// Set the master and deploy mode property to match the requested mode.
System.setProperty("spark.master", "yarn")
System.setProperty(SUBMIT_DEPLOY_MODE.key, "cluster")
// Set this internal configuration if it is running on cluster mode, this
// configuration will be checked in SparkContext to avoid misuse of yarn cluster mode.
System.setProperty("spark.yarn.app.id", appAttemptId.getApplicationId().toString())
Option(appAttemptId.getAttemptId.toString)
} else {
None
}
new CallerContext(
"APPMASTER", sparkConf.get(APP_CALLER_CONTEXT),
Option(appAttemptId.getApplicationId.toString), attemptID).setCurrentContext()
logInfo("ApplicationAttemptId: " + appAttemptId)
// This shutdown hook should run *after* the SparkContext is shut down.
val priority = ShutdownHookManager.SPARK_CONTEXT_SHUTDOWN_PRIORITY - 1
ShutdownHookManager.addShutdownHook(priority) { () =>
val maxAppAttempts = client.getMaxRegAttempts(sparkConf, yarnConf)
val isLastAttempt = appAttemptId.getAttemptId() >= maxAppAttempts
if (!finished) {
// The default state of ApplicationMaster is failed if it is invoked by shut down hook.
// This behavior is different compared to 1.x version.
// If user application is exited ahead of time by calling System.exit(N), here mark
// this application as failed with EXIT_EARLY. For a good shutdown, user shouldn't call
// System.exit(0) to terminate the application.
finish(finalStatus,
ApplicationMaster.EXIT_EARLY,
"Shutdown hook called before final status was reported.")
}
if (!unregistered) {
// we only want to unregister if we don't want the RM to retry
if (finalStatus == FinalApplicationStatus.SUCCEEDED || isLastAttempt) {
unregister(finalStatus, finalMsg)
cleanupStagingDir(new Path(System.getenv("SPARK_YARN_STAGING_DIR")))
}
}
}
if (isClusterMode) {
runDriver()
} else {
runExecutorLauncher()
}
} catch {
case e: Exception =>
// catch everything else if not specifically handled
logError("Uncaught exception: ", e)
finish(FinalApplicationStatus.FAILED,
ApplicationMaster.EXIT_UNCAUGHT_EXCEPTION,
"Uncaught exception: " + StringUtils.stringifyException(e))
} finally {
try {
metricsSystem.foreach { ms =>
ms.report()
ms.stop()
}
} catch {
case e: Exception =>
logWarning("Exception during stopping of the metric system: ", e)
}
}
exitCode
}
3.runDriver方法
该方法中主要做了以下几件事：
1.启动用户程序 ,反射调用main方法，启动driver线程 startUserApplication()
2.初始化sparkContext
3.注册AM registerAM()
4.创建一个分配器 createAllocator()
private def runDriver(): Unit = {
addAmIpFilter(None, System.getenv(ApplicationConstants.APPLICATION_WEB_PROXY_BASE_ENV))
userClassThread = startUserApplication()
// This a bit hacky, but we need to wait until the spark.driver.port property has
// been set by the Thread executing the user class.
logInfo("Waiting for spark context initialization...")
val totalWaitTime = sparkConf.get(AM_MAX_WAIT_TIME)
try {
val sc = ThreadUtils.awaitResult(sparkContextPromise.future,
Duration(totalWaitTime, TimeUnit.MILLISECONDS))
if (sc != null) {
val rpcEnv = sc.env.rpcEnv
val userConf = sc.getConf
val host = userConf.get(DRIVER_HOST_ADDRESS)
val port = userConf.get(DRIVER_PORT)
registerAM(host, port, userConf, sc.ui.map(_.webUrl), appAttemptId)
val driverRef = rpcEnv.setupEndpointRef(
RpcAddress(host, port),
YarnSchedulerBackend.ENDPOINT_NAME)
createAllocator(driverRef, userConf, rpcEnv, appAttemptId, distCacheConf)
} else {
// Sanity check; should never happen in normal operation, since sc should only be null
// if the user app did not create a SparkContext.
throw new IllegalStateException("User did not initialize spark context!")
}
resumeDriver()
userClassThread.join()
} catch {
case e: SparkException if e.getCause().isInstanceOf[TimeoutException] =>
logError(
s"SparkContext did not initialize after waiting for $totalWaitTime ms. " +
"Please check earlier log output for errors. Failing the application.")
finish(FinalApplicationStatus.FAILED,
ApplicationMaster.EXIT_SC_NOT_INITED,
"Timed out waiting for SparkContext.")
} finally {
resumeDriver()
}
}
4.startUserApplication方法
该方法中主要做以下几件事：
1.反射调用用户程序的main方法
2.启动名称为driver的线程
private def startUserApplication(): Thread = {
logInfo("Starting the user application in a separate Thread")
var userArgs = args.userArgs
if (args.primaryPyFile != null && args.primaryPyFile.endsWith(".py")) {
// When running pyspark, the app is run using PythonRunner. The second argument is the list
// of files to add to PYTHONPATH, which Client.scala already handles, so it's empty.
userArgs = Seq(args.primaryPyFile, "") ++ userArgs
}
if (args.primaryRFile != null &&
(args.primaryRFile.endsWith(".R") || args.primaryRFile.endsWith(".r"))) {
// TODO(davies): add R dependencies here
}
val mainMethod = userClassLoader.loadClass(args.userClass)
.getMethod("main", classOf[Array[String]])
val userThread = new Thread {
override def run(): Unit = {
try {
if (!Modifier.isStatic(mainMethod.getModifiers)) {
logError(s"Could not find static main method in object ${args.userClass}")
finish(FinalApplicationStatus.FAILED, ApplicationMaster.EXIT_EXCEPTION_USER_CLASS)
} else {
mainMethod.invoke(null, userArgs.toArray)
finish(FinalApplicationStatus.SUCCEEDED, ApplicationMaster.EXIT_SUCCESS)
logDebug("Done running user class")
}
} catch {
case e: InvocationTargetException =>
e.getCause match {
case _: InterruptedException =>
// Reporter thread can interrupt to stop user class
case SparkUserAppException(exitCode) =>
val msg = s"User application exited with status $exitCode"
logError(msg)
finish(FinalApplicationStatus.FAILED, exitCode, msg)
case cause: Throwable =>
logError("User class threw exception: " + cause, cause)
finish(FinalApplicationStatus.FAILED,
ApplicationMaster.EXIT_EXCEPTION_USER_CLASS,
"User class threw exception: " + StringUtils.stringifyException(cause))
}
sparkContextPromise.tryFailure(e.getCause())
} finally {
// Notify the thread waiting for the SparkContext, in case the application did not
// instantiate one. This will do nothing when the user code instantiates a SparkContext
// (with the correct master), or when the user code throws an exception (due to the
// tryFailure above).
sparkContextPromise.trySuccess(null)
}
}
}
userThread.setContextClassLoader(userClassLoader)
userThread.setName("Driver")
userThread.start()
userThread
}
3.createAllocator方法
该方法里面主要做了以下几件事：
1.调用YarnRMClient.createAllocator方法创建分配器
2.调用YarnAllocator.allocateResources()分配资源
private def createAllocator(
driverRef: RpcEndpointRef,
_sparkConf: SparkConf,
rpcEnv: RpcEnv,
appAttemptId: ApplicationAttemptId,
distCacheConf: SparkConf): Unit = {
// In client mode, the AM may be restarting after delegation tokens have reached their TTL. So
// always contact the driver to get the current set of valid tokens, so that local resources can
// be initialized below.
if (!isClusterMode) {
val tokens = driverRef.askSync[Array[Byte]](RetrieveDelegationTokens)
if (tokens != null) {
SparkHadoopUtil.get.addDelegationTokens(tokens, _sparkConf)
}
}
val appId = appAttemptId.getApplicationId().toString()
val driverUrl = RpcEndpointAddress(driverRef.address.host, driverRef.address.port,
CoarseGrainedSchedulerBackend.ENDPOINT_NAME).toString
val localResources = prepareLocalResources(distCacheConf)
// Before we initialize the allocator, let's log the information about how executors will
// be run up front, to avoid printing this out for every single executor being launched.
// Use placeholders for information that changes such as executor IDs.
logInfo {
val executorMemory = _sparkConf.get(EXECUTOR_MEMORY).toInt
val executorCores = _sparkConf.get(EXECUTOR_CORES)
val dummyRunner = new ExecutorRunnable(None, yarnConf, _sparkConf, driverUrl, "<executorId>",
"<hostname>", executorMemory, executorCores, appId, securityMgr, localResources,
ResourceProfile.DEFAULT_RESOURCE_PROFILE_ID)
dummyRunner.launchContextDebugInfo()
}
allocator = client.createAllocator(
yarnConf,
_sparkConf,
appAttemptId,
driverUrl,
driverRef,
securityMgr,
localResources)
// Initialize the AM endpoint *after* the allocator has been initialized. This ensures
// that when the driver sends an initial executor request (e.g. after an AM restart),
// the allocator is ready to service requests.
rpcEnv.setupEndpoint("YarnAM", new AMEndpoint(rpcEnv, driverRef))
allocator.allocateResources()
val ms = MetricsSystem.createMetricsSystem(MetricsSystemInstances.APPLICATION_MASTER,
sparkConf, securityMgr)
val prefix = _sparkConf.get(YARN_METRICS_NAMESPACE).getOrElse(appId)
ms.registerSource(new ApplicationMasterSource(prefix, allocator))
// do not register static sources in this case as per SPARK-25277
ms.start(false)
metricsSystem = Some(ms)
reporterThread = launchReporterThread()
}
6.YarnAllocator
1.allocateResources方法
获取所有已分配的container，如果container的数量大于0，调用handleAllocatoredContainers方法
def allocateResources(): Unit = synchronized {
updateResourceRequests()
val progressIndicator = 0.1f
// Poll the ResourceManager. This doubles as a heartbeat if there are no pending container
// requests.
val allocateResponse = amClient.allocate(progressIndicator)
val allocatedContainers = allocateResponse.getAllocatedContainers()
allocatorBlacklistTracker.setNumClusterNodes(allocateResponse.getNumClusterNodes)
if (allocatedContainers.size > 0) {
logDebug(("Allocated containers: %d. Current executor count: %d. " +
"Launching executor count: %d. Cluster resources: %s.")
.format(
allocatedContainers.size,
runningExecutors.size,
numExecutorsStarting.get,
allocateResponse.getAvailableResources))
handleAllocatedContainers(allocatedContainers.asScala)
}
val completedContainers = allocateResponse.getCompletedContainersStatuses()
if (completedContainers.size > 0) {
logDebug("Completed %d containers".format(completedContainers.size))
processCompletedContainers(completedContainers.asScala)
logDebug("Finished processing %d completed containers. Current running executor count: %d."
.format(completedContainers.size, runningExecutors.size))
}
}
2.handleAllocatedContainers方法
该方法中会遍历已分配的container,根据机架感知原理，寻找最优的一个container供后续启动executor,最终调用runAllocatedContainers方法
def handleAllocatedContainers(allocatedContainers: Seq[Container]): Unit = {
val containersToUse = new ArrayBuffer[Container](allocatedContainers.size)
// Match incoming requests by host
val remainingAfterHostMatches = new ArrayBuffer[Container]
for (allocatedContainer <- allocatedContainers) {
matchContainerToRequest(allocatedContainer, allocatedContainer.getNodeId.getHost,
containersToUse, remainingAfterHostMatches)
}
// Match remaining by rack. Because YARN's RackResolver swallows thread interrupts
// (see SPARK-27094), which can cause this code to miss interrupts from the AM, use
// a separate thread to perform the operation.
val remainingAfterRackMatches = new ArrayBuffer[Container]
if (remainingAfterHostMatches.nonEmpty) {
var exception: Option[Throwable] = None
val thread = new Thread("spark-rack-resolver") {
override def run(): Unit = {
try {
for (allocatedContainer <- remainingAfterHostMatches) {
val rack = resolver.resolve(allocatedContainer.getNodeId.getHost)
matchContainerToRequest(allocatedContainer, rack, containersToUse,
remainingAfterRackMatches)
}
} catch {
case e: Throwable =>
exception = Some(e)
}
}
}
thread.setDaemon(true)
thread.start()
try {
thread.join()
} catch {
case e: InterruptedException =>
thread.interrupt()
throw e
}
if (exception.isDefined) {
throw exception.get
}
}
// Assign remaining that are neither node-local nor rack-local
val remainingAfterOffRackMatches = new ArrayBuffer[Container]
for (allocatedContainer <- remainingAfterRackMatches) {
matchContainerToRequest(allocatedContainer, ANY_HOST, containersToUse,
remainingAfterOffRackMatches)
}
if (remainingAfterOffRackMatches.nonEmpty) {
logDebug(s"Releasing ${remainingAfterOffRackMatches.size} unneeded containers that were " +
s"allocated to us")
for (container <- remainingAfterOffRackMatches) {
internalReleaseContainer(container)
}
}
runAllocatedContainers(containersToUse)
logInfo("Received %d containers from YARN, launching executors on %d of them."
.format(allocatedContainers.size, containersToUse.size))
}
3.runAllocatedContainers方法
该方法中主要功能是：
1.遍历可用的container，判断container的内存是否足够
2.启动一个executorRunnable线程，调用其run方法
private def runAllocatedContainers(containersToUse: ArrayBuffer[Container]): Unit = {
for (container <- containersToUse) {
executorIdCounter += 1
val executorHostname = container.getNodeId.getHost
val containerId = container.getId
val executorId = executorIdCounter.toString
assert(container.getResource.getMemory >= resource.getMemory)
logInfo(s"Launching container $containerId on host $executorHostname " +
s"for executor with ID $executorId")
def updateInternalState(): Unit = synchronized {
runningExecutors.add(executorId)
numExecutorsStarting.decrementAndGet()
executorIdToContainer(executorId) = container
containerIdToExecutorId(container.getId) = executorId
val containerSet = allocatedHostToContainersMap.getOrElseUpdate(executorHostname,
new HashSet[ContainerId])
containerSet += containerId
allocatedContainerToHostMap.put(containerId, executorHostname)
}
if (runningExecutors.size() < targetNumExecutors) {
numExecutorsStarting.incrementAndGet()
if (launchContainers) {
launcherPool.execute(() => {
try {
new ExecutorRunnable(
Some(container),
conf,
sparkConf,
driverUrl,
executorId,
executorHostname,
executorMemory,
executorCores,
appAttemptId.getApplicationId.toString,
securityMgr,
localResources,
ResourceProfile.DEFAULT_RESOURCE_PROFILE_ID // use until fully supported
).run()
updateInternalState()
} catch {
case e: Throwable =>
numExecutorsStarting.decrementAndGet()
if (NonFatal(e)) {
logError(s"Failed to launch executor $executorId on container $containerId", e)
// Assigned container should be released immediately
// to avoid unnecessary resource occupation.
amClient.releaseAssignedContainer(containerId)
} else {
throw e
}
}
})
} else {
// For test only
updateInternalState()
}
} else {
logInfo(("Skip launching executorRunnable as running executors count: %d " +
"reached target executors count: %d.").format(
runningExecutors.size, targetNumExecutors))
}
}
}
7.ExecutorRunnable
1.run方法
方法主要功能：
1.创建初始化并启动nmClient
2.启动container
def run(): Unit = {
logDebug("Starting Executor Container")
nmClient = NMClient.createNMClient()
nmClient.init(conf)
nmClient.start()
startContainer()
}
2.startContainer方法
该方法主要功能：
1.准备启动container的命令 prepareCommand()
2.调用nmClient的startContainer方法
def startContainer(): java.util.Map[String, ByteBuffer] = {
val ctx = Records.newRecord(classOf[ContainerLaunchContext])
.asInstanceOf[ContainerLaunchContext]
val env = prepareEnvironment().asJava
ctx.setLocalResources(localResources.asJava)
ctx.setEnvironment(env)
val credentials = UserGroupInformation.getCurrentUser().getCredentials()
val dob = new DataOutputBuffer()
credentials.writeTokenStorageToStream(dob)
ctx.setTokens(ByteBuffer.wrap(dob.getData()))
val commands = prepareCommand()
ctx.setCommands(commands.asJava)
ctx.setApplicationACLs(
YarnSparkHadoopUtil.getApplicationAclsForYarn(securityMgr).asJava)
// If external shuffle service is enabled, register with the Yarn shuffle service already
// started on the NodeManager and, if authentication is enabled, provide it with our secret
// key for fetching shuffle files later
if (sparkConf.get(SHUFFLE_SERVICE_ENABLED)) {
val secretString = securityMgr.getSecretKey()
val secretBytes =
if (secretString != null) {
// This conversion must match how the YarnShuffleService decodes our secret
JavaUtils.stringToBytes(secretString)
} else {
// Authentication is not enabled, so just provide dummy metadata
ByteBuffer.allocate(0)
}
ctx.setServiceData(Collections.singletonMap("spark_shuffle", secretBytes))
}
// Send the start request to the ContainerManager
try {
nmClient.startContainer(container.get, ctx)
} catch {
case ex: Exception =>
throw new SparkException(s"Exception while starting container ${container.get.getId}" +
s" on host $hostname", ex)
}
}
3.prepareCommand方法
该方法中主要是准备启动一个JVM虚拟机的命令：
/bin/java YarnCoarseGrainedExecutorBackend
private def prepareCommand(): List[String] = {
// Extra options for the JVM
val javaOpts = ListBuffer[String]()
// Set the JVM memory
val executorMemoryString = executorMemory + "m"
javaOpts += "-Xmx" + executorMemoryString
// Set extra Java options for the executor, if defined
sparkConf.get(EXECUTOR_JAVA_OPTIONS).foreach { opts =>
val subsOpt = Utils.substituteAppNExecIds(opts, appId, executorId)
javaOpts ++= Utils.splitCommandString(subsOpt).map(YarnSparkHadoopUtil.escapeForShell)
}
// Set the library path through a command prefix to append to the existing value of the
// env variable.
val prefixEnv = sparkConf.get(EXECUTOR_LIBRARY_PATH).map { libPath =>
Client.createLibraryPathPrefix(libPath, sparkConf)
}
javaOpts += "-Djava.io.tmpdir=" +
new Path(Environment.PWD.$$(), YarnConfiguration.DEFAULT_CONTAINER_TEMP_DIR)
// Certain configs need to be passed here because they are needed before the Executor
// registers with the Scheduler and transfers the spark configs. Since the Executor backend
// uses RPC to connect to the scheduler, the RPC settings are needed as well as the
// authentication settings.
sparkConf.getAll
.filter { case (k, v) => SparkConf.isExecutorStartupConf(k) }
.foreach { case (k, v) => javaOpts += YarnSparkHadoopUtil.escapeForShell(s"-D$k=$v") }
// 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.executor.extraJavaOptions, so we don't want to mess with it.
// In our expts, using (default) throughput collector has severe perf ramifications in
// multi-tenant 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
javaOpts += "-XX:+UseConcMarkSweepGC"
javaOpts += "-XX:+CMSIncrementalMode"
javaOpts += "-XX:+CMSIncrementalPacing"
javaOpts += "-XX:CMSIncrementalDutyCycleMin=0"
javaOpts += "-XX:CMSIncrementalDutyCycle=10"
}
*/
// For log4j configuration to reference
javaOpts += ("-Dspark.yarn.app.container.log.dir=" + ApplicationConstants.LOG_DIR_EXPANSION_VAR)
val userClassPath = Client.getUserClasspath(sparkConf).flatMap { uri =>
val absPath =
if (new File(uri.getPath()).isAbsolute()) {
Client.getClusterPath(sparkConf, uri.getPath())
} else {
Client.buildPath(Environment.PWD.$(), uri.getPath())
}
Seq("--user-class-path", "file:" + absPath)
}.toSeq
YarnSparkHadoopUtil.addOutOfMemoryErrorArgument(javaOpts)
val commands = prefixEnv ++
Seq(Environment.JAVA_HOME.$$() + "/bin/java", "-server") ++
javaOpts ++
Seq("org.apache.spark.executor.YarnCoarseGrainedExecutorBackend",
"--driver-url", masterAddress,
"--executor-id", executorId,
"--hostname", hostname,
"--cores", executorCores.toString,
"--app-id", appId,
"--resourceProfileId", resourceProfileId.toString) ++
userClassPath ++
Seq(
s"1>${ApplicationConstants.LOG_DIR_EXPANSION_VAR}/stdout",
s"2>${ApplicationConstants.LOG_DIR_EXPANSION_VAR}/stderr")
// TODO: it would be nicer to just make sure there are no null commands here
commands.map(s => if (s == null) "null" else s).toList
}
8.YarnCoarseGrainedExecutorBackend
该类是一个进程，故启动后会运行该类的main方法
1.main方法
该方法主要功能：
1.创建YarnCoarseGrainedExecutorBackend对象，并调用他的parseArguments方法解析参数
2.调用run方法创建executor
def main(args: Array[String]): Unit = {
val createFn: (RpcEnv, CoarseGrainedExecutorBackend.Arguments, SparkEnv, ResourceProfile) =>
CoarseGrainedExecutorBackend = { case (rpcEnv, arguments, env, resourceProfile) =>
new YarnCoarseGrainedExecutorBackend(rpcEnv, arguments.driverUrl, arguments.executorId,
arguments.bindAddress, arguments.hostname, arguments.cores, arguments.userClassPath, env,
arguments.resourcesFileOpt, resourceProfile)
}
val backendArgs = CoarseGrainedExecutorBackend.parseArguments(args,
this.getClass.getCanonicalName.stripSuffix("$"))
CoarseGrainedExecutorBackend.run(backendArgs, createFn)
System.exit(0)
}
2.run方法
该方法主要功能创建executor，executor启动后就会运行相关task,至此作业提交流程完成
private def run(
driverUrl: String,
executorId: String,
hostname: String,
cores: Int,
appId: String,
workerUrl: Option[String],
userClassPath: Seq[URL]) {
Utils.initDaemon(log)
SparkHadoopUtil.get.runAsSparkUser { () =>
// Debug code
Utils.checkHost(hostname)
// Bootstrap to fetch the driver's Spark properties.
val executorConf = new SparkConf
val fetcher = RpcEnv.create(
"driverPropsFetcher",
hostname,
-1,
executorConf,
new SecurityManager(executorConf),
clientMode = true)
val driver = fetcher.setupEndpointRefByURI(driverUrl)
val cfg = driver.askSync[SparkAppConfig](RetrieveSparkAppConfig)
val props = cfg.sparkProperties ++ Seq[(String, String)](("spark.app.id", appId))
fetcher.shutdown()
// Create SparkEnv using properties we fetched from the driver.
val driverConf = new SparkConf()
for ((key, value) <- props) {
// this is required for SSL in standalone mode
if (SparkConf.isExecutorStartupConf(key)) {
driverConf.setIfMissing(key, value)
} else {
driverConf.set(key, value)
}
}
cfg.hadoopDelegationCreds.foreach { tokens =>
SparkHadoopUtil.get.addDelegationTokens(tokens, driverConf)
}
val env = SparkEnv.createExecutorEnv(
driverConf, executorId, hostname, cores, cfg.ioEncryptionKey, isLocal = false)
env.rpcEnv.setupEndpoint("Executor", new CoarseGrainedExecutorBackend(
env.rpcEnv, driverUrl, executorId, hostname, cores, userClassPath, env))
workerUrl.foreach { url =>
env.rpcEnv.setupEndpoint("WorkerWatcher", new WorkerWatcher(env.rpcEnv, url))
}
env.rpcEnv.awaitTermination()
}
}
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