本文主要参考：
a. https://www.cnblogs.com/yy3b2007com/p/10934090.html
0. 说明
a. 关于spark源码会不定期的更新与补充
b. 对于spark源码的历史博文，也会不定期修改、增加、优化
c. spark源码对应的spark版本为2.4.1
1. 引导
该篇主要讲解执行spark-submit.sh脚本时将任务提交给Yarn阶段代码分析。其中spark的代码版本为2.4.1.
(1) spark-submit的入口函数
一般提交一个spark作业的方式采用spark-submit来提交
# Run on a Spark standalone cluster
./bin/spark-submit \
--class org.apache.spark.examples.SparkPi \
--master spark://207.184.161.138:7077 \
--executor-memory 20G \
--total-executor-cores \
/path/to/examples.jar \
这个是提交到standalone集群的方式，其中脚本spark-submit从spark2.4的安装bin目录下找到
#!/usr/bin/env bash
if [ -z "${SPARK_HOME}" ]; then
source "$(dirname "$")"/find-spark-home
fi
# disable randomized hash for string in Python 3.3+
export PYTHONHASHSEED=
exec "${SPARK_HOME}"/bin/spark-class org.apache.spark.deploy.SparkSubmit "$@"
从如上脚本内容上来看，可以发现：
a. spark-submit提交任务时，实际上最终是调用了SparkSubmit类。
b. 调用bin目录下的spark-class脚本，实际上执行的是java进程命令。
从SparkSubmit的伴生类上可以看到入口main函数：
object SparkSubmit extends CommandLineUtils with Logging {
// Cluster managers -------- Spark集群管理的模式
private val YARN =
private val STANDALONE =
private val MESOS =
private val LOCAL =
private val KUBERNETES =
private val ALL_CLUSTER_MGRS = YARN | STANDALONE | MESOS | LOCAL | KUBERNETES
// Deploy modes ---------- 部署模式
private val CLIENT =
private val CLUSTER =
private val ALL_DEPLOY_MODES = CLIENT | CLUSTER
// Special primary resource names that represent shells rather than application jars.
private val SPARK_SHELL = "spark-shell"
private val PYSPARK_SHELL = "pyspark-shell"
private val SPARKR_SHELL = "sparkr-shell"
private val SPARKR_PACKAGE_ARCHIVE = "sparkr.zip"
private val R_PACKAGE_ARCHIVE = "rpkg.zip"
private val CLASS_NOT_FOUND_EXIT_STATUS =
// Following constants are visible for testing.
private[deploy] val YARN_CLUSTER_SUBMIT_CLASS =
"org.apache.spark.deploy.yarn.YarnClusterApplication" // Yarn集群的提交类
private[deploy] val REST_CLUSTER_SUBMIT_CLASS = classOf[RestSubmissionClientApp].getName() // 集群模式提交的其他类名
private[deploy] val STANDALONE_CLUSTER_SUBMIT_CLASS = classOf[ClientApp].getName() // 独立部署模式提交类
private[deploy] val KUBERNETES_CLUSTER_SUBMIT_CLASS =
"org.apache.spark.deploy.k8s.submit.KubernetesClientApplication" // K8s集群提交的类
override def main(args: Array[String]): Unit = {
// 构建SparkSubmit实例
val submit = new SparkSubmit() {
self =>
// 重写SparkSubmit的解析参数方法
override protected def parseArguments(args: Array[String]): SparkSubmitArguments = {
// 构建SparkSubmitArguments对象
new SparkSubmitArguments(args) {
// 重写logInfo和logWarning，调用该类中如下定义的2个方法
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)
}
}
}
// 执行提交代码
submit.doSubmit(args)
}
/**
* Return whether the given primary resource represents a user jar.
*/
private[deploy] def isUserJar(res: String): Boolean = {
!isShell(res) && !isPython(res) && !isInternal(res) && !isR(res)
}
/**
* Return whether the given primary resource represents a shell.
*/
private[deploy] def isShell(res: String): Boolean = {
(res == SPARK_SHELL || res == PYSPARK_SHELL || res == SPARKR_SHELL)
}
/**
* Return whether the given main class represents a sql shell.
*/
private[deploy] def isSqlShell(mainClass: String): Boolean = {
mainClass == "org.apache.spark.sql.hive.thriftserver.SparkSQLCLIDriver"
}
/**
* Return whether the given main class represents a thrift server.
*/
private def isThriftServer(mainClass: String): Boolean = {
mainClass == "org.apache.spark.sql.hive.thriftserver.HiveThriftServer2"
}
/**
* Return whether the given primary resource requires running python.
*/
private[deploy] def isPython(res: String): Boolean = {
res != null && res.endsWith(".py") || res == PYSPARK_SHELL
}
/**
* Return whether the given primary resource requires running R.
*/
private[deploy] def isR(res: String): Boolean = {
res != null && res.endsWith(".R") || res == SPARKR_SHELL
}
private[deploy] def isInternal(res: String): Boolean = {
res == SparkLauncher.NO_RESOURCE
}
}
在SparkSubmit类中doSubmit函数实现十分简单：
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)
// 解析参数信息
val appArgs = parseArguments(args)
if (appArgs.verbose) {
logInfo(appArgs.toString)
}
appArgs.action match {
case SparkSubmitAction.SUBMIT => submit(appArgs, uninitLog) // 应用提交
case SparkSubmitAction.KILL => kill(appArgs) // 应用删除(只适用于standalone和memos集群)
case SparkSubmitAction.REQUEST_STATUS => requestStatus(appArgs) // 查询应用状态(只适用于standalone和memos集群)
case SparkSubmitAction.PRINT_VERSION => printVersion() // 打印应用版本信息
}
}
不难明白这是一个主控函数，根据接受的action类型，调用对应的处理：
a. case SparkSubmitAction.SUBMIT => submit(appArgs, uninitLog)---提交spark任务
b.case SparkSubmitAction.KILL => kill(appArgs)---杀掉spark任务
c. case SparkSubmitAction.REQUEST_STATUS => requestStatus(appArgs)---获取任务状态
d. case SparkSubmitAction.PRINT_VERSION => printVersion()---打印版本信息
我们想明白spark任务提交的具体实现类，需要进入submit函数查看具体的业务：
/**
* 使用提供的参数信息来提交application
* Submit the application using the provided parameters.
*
* 运行包含两步：
* 第一步，我们通过设置适当的类路径，系统属性和应用程序参数来准备启动环境，以便基于集群管理和部署模式运行子主类。
* 第二步，我们使用这个启动环境来调用子主类的主方法。
* This runs in two steps. First, we prepare the launch environment by setting up
* the appropriate classpath, system properties, and application arguments for
* running the child main class based on the cluster manager and the deploy mode.
* Second, we use this launch environment to invoke the main method of the child
* main class.
*/
@tailrec
private def submit(args: SparkSubmitArguments, uninitLog: Boolean): Unit = {
// 通过设置适当的类路径，系统属性和应用程序参数来准备启动环境，以便基于集群管理和部署模式运行子主类。
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()
} //在独立集群模式下，有两个提交网关：
//（1）使用o.a.s.deploy.Client作为包装器的传统RPC网关
//（2）Spark 1.3中引入了新的基于REST的网关
//后者是Spark 1.3的默认行为，但如果主端点不是REST服务器，则Spark Submit将故障转移到使用旧网关。
// 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
// 其他模式，只需直接运行主类
} else {
doRunMain()
}
}
其中：
prepareSubmitEnvironment方法通过设置适当的类路径，系统属性和应用程序参数来准备启动环境，以便基于集群管理和部署模式运行子主类
submit(…)函数最后一行会调用该函数内部自定义函数doRunMain()，该函数会根据应用程序参数（args.proxyUser）做一次判断处理：
a. 如果是代理用户，则使用proxyUser 对runMain()函数包装调用；
b. 如果非代理用户，则直接调用runMain()函数。
(2) 任务运行环境准备
通过设置适当的类路径，系统属性和应用程序参数来准备启动环境，以便基于集群管理和部署模式运行子主类。
val (childArgs, childClasspath, sparkConf, childMainClass) = prepareSubmitEnvironment(args)
/**
* 未提交的应用程序准备环境
* Prepare the environment for submitting an application.
*
* @param args the parsed SparkSubmitArguments used for environment preparation.
* @param conf the Hadoop Configuration, this argument will only be set in unit test.
* 返回一个4元组(childArgs, childClasspath, sparkConf, childMainClass)
* childArgs：子进程的参数
* childClasspath：子级的类路径条目列表
* sparkConf：系统参数map集合
* childMainClass：子级的主类
* @return a 4-tuple:
* (1) the arguments for the child process,
* (2) a list of classpath entries for the child,
* (3) a map of system properties, and
* (4) the main class for the child
*
* Exposed for testing.
*/
private[deploy] def prepareSubmitEnvironment(
args: SparkSubmitArguments,
conf: Option[HadoopConfiguration] = None)
: (Seq[String], Seq[String], SparkConf, String) = {
// Return values
val childArgs = new ArrayBuffer[String]()
val childClasspath = new ArrayBuffer[String]()
val sparkConf = new SparkConf()
var childMainClass = "" // 设置集群管理器，
// 从这个列表中可以得到信息：spark目前支持的集群管理器包含：YARN,STANDLONE,MESOS,KUBERNETES,LOCAL，
// 在spark-submit参数的--master中指定。
// Set the cluster manager
val clusterManager: Int = args.master match {
case "yarn" => YARN
case "yarn-client" | "yarn-cluster" =>
// spark2.0之前可以使用yarn-cleint,yarn-cluster作为--master参数，从spark2.0起，不再支持，这里默认自动转化为yarn，并给出警告信息。
logWarning(s"Master ${args.master} is deprecated since 2.0." +
" Please use master \"yarn\" with specified deploy mode instead.")
YARN
case m if m.startsWith("spark") => STANDALONE
case m if m.startsWith("mesos") => MESOS
case m if m.startsWith("k8s") => KUBERNETES
case m if m.startsWith("local") => LOCAL
case _ =>
error("Master must either be yarn or start with spark, mesos, k8s, or local")
-1
} // 设置部署模式--deploy-mode，默认为client模式。
// Set the deploy mode; default is client mode
var deployMode: Int = args.deployMode match {
case "client" | null => CLIENT
case "cluster" => CLUSTER
case _ =>
error("Deploy mode must be either client or cluster")
-1
} // 由于”yarn-cluster“和”yarn-client“方式已被弃用，因此封装了--master和--deploy-mode。
// 如果只指定了一个--master和--deploy-mode，我们有一些逻辑来推断它们之间的关系；如果它们不一致，我们可以提前退出。
// Because the deprecated way of specifying "yarn-cluster" and "yarn-client" encapsulate both
// the master and deploy mode, we have some logic to infer the master and deploy mode
// from each other if only one is specified, or exit early if they are at odds.
if (clusterManager == YARN) {
(args.master, args.deployMode) match {
case ("yarn-cluster", null) =>
deployMode = CLUSTER
args.master = "yarn"
case ("yarn-cluster", "client") =>
error("Client deploy mode is not compatible with master \"yarn-cluster\"")
case ("yarn-client", "cluster") =>
error("Cluster deploy mode is not compatible with master \"yarn-client\"")
case (_, mode) =>
args.master = "yarn"
} // 如果我们想去使用YARN的话，必须确保它包含在我们构建中。
// Make sure YARN is included in our build if we're trying to use it
if (!Utils.classIsLoadable(YARN_CLUSTER_SUBMIT_CLASS) && !Utils.isTesting) {
error(
"Could not load YARN classes. " +
"This copy of Spark may not have been compiled with YARN support.")
}
} if (clusterManager == KUBERNETES) {
args.master = Utils.checkAndGetK8sMasterUrl(args.master)
// Make sure KUBERNETES is included in our build if we're trying to use it
if (!Utils.classIsLoadable(KUBERNETES_CLUSTER_SUBMIT_CLASS) && !Utils.isTesting) {
error(
"Could not load KUBERNETES classes. " +
"This copy of Spark may not have been compiled with KUBERNETES support.")
}
} // 下边的一些模式是不支持，尽早让它们失败。
// Fail fast, the following modes are not supported or applicable
(clusterManager, deployMode) match {
case (STANDALONE, CLUSTER) if args.isPython =>
error("Cluster deploy mode is currently not supported for python " +
"applications on standalone clusters.")
case (STANDALONE, CLUSTER) if args.isR =>
error("Cluster deploy mode is currently not supported for R " +
"applications on standalone clusters.")
case (LOCAL, CLUSTER) =>
error("Cluster deploy mode is not compatible with master \"local\"")
case (_, CLUSTER) if isShell(args.primaryResource) =>
error("Cluster deploy mode is not applicable to Spark shells.")
case (_, CLUSTER) if isSqlShell(args.mainClass) =>
error("Cluster deploy mode is not applicable to Spark SQL shell.")
case (_, CLUSTER) if isThriftServer(args.mainClass) =>
error("Cluster deploy mode is not applicable to Spark Thrift server.")
case _ =>
} // 如果args.deployMode为null的话，给它赋值更新。稍后它将作为Spark的属性向下传递
// Update args.deployMode if it is null. It will be passed down as a Spark property later.
(args.deployMode, deployMode) match {
case (null, CLIENT) => args.deployMode = "client"
case (null, CLUSTER) => args.deployMode = "cluster"
case _ =>
} // 根据资源管理器和部署模式，进行逻辑判断出几种特殊运行方式。
val isYarnCluster = clusterManager == YARN && deployMode == CLUSTER
val isMesosCluster = clusterManager == MESOS && deployMode == CLUSTER
val isStandAloneCluster = clusterManager == STANDALONE && deployMode == CLUSTER
val isKubernetesCluster = clusterManager == KUBERNETES && deployMode == CLUSTER
val isMesosClient = clusterManager == MESOS && deployMode == CLIENT if (!isMesosCluster && !isStandAloneCluster) {
// Resolve maven dependencies if there are any and add classpath to jars. Add them to py-files
// too for packages that include Python code
val resolvedMavenCoordinates = DependencyUtils.resolveMavenDependencies(
args.packagesExclusions, args.packages, args.repositories, args.ivyRepoPath,
args.ivySettingsPath) if (!StringUtils.isBlank(resolvedMavenCoordinates)) {
args.jars = mergeFileLists(args.jars, resolvedMavenCoordinates)
if (args.isPython || isInternal(args.primaryResource)) {
args.pyFiles = mergeFileLists(args.pyFiles, resolvedMavenCoordinates)
}
} // install any R packages that may have been passed through --jars or --packages.
// Spark Packages may contain R source code inside the jar.
if (args.isR && !StringUtils.isBlank(args.jars)) {
RPackageUtils.checkAndBuildRPackage(args.jars, printStream, args.verbose)
}
} args.sparkProperties.foreach { case (k, v) => sparkConf.set(k, v) }
val hadoopConf = conf.getOrElse(SparkHadoopUtil.newConfiguration(sparkConf))
val targetDir = Utils.createTempDir() // assure a keytab is available from any place in a JVM
if (clusterManager == YARN || clusterManager == LOCAL || isMesosClient) {
if (args.principal != null) {
if (args.keytab != null) {
require(new File(args.keytab).exists(), s"Keytab file: ${args.keytab} does not exist")
// Add keytab and principal configurations in sysProps to make them available
// for later use; e.g. in spark sql, the isolated class loader used to talk
// to HiveMetastore will use these settings. They will be set as Java system
// properties and then loaded by SparkConf
sparkConf.set(KEYTAB, args.keytab)
sparkConf.set(PRINCIPAL, args.principal)
UserGroupInformation.loginUserFromKeytab(args.principal, args.keytab)
}
}
} // Resolve glob path for different resources.
args.jars = Option(args.jars).map(resolveGlobPaths(_, hadoopConf)).orNull
args.files = Option(args.files).map(resolveGlobPaths(_, hadoopConf)).orNull
args.pyFiles = Option(args.pyFiles).map(resolveGlobPaths(_, hadoopConf)).orNull
args.archives = Option(args.archives).map(resolveGlobPaths(_, hadoopConf)).orNull lazy val secMgr = new SecurityManager(sparkConf) // In client mode, download remote files.
var localPrimaryResource: String = null
var localJars: String = null
var localPyFiles: String = null
if (deployMode == CLIENT) {
localPrimaryResource = Option(args.primaryResource).map {
downloadFile(_, targetDir, sparkConf, hadoopConf, secMgr)
}.orNull
localJars = Option(args.jars).map {
downloadFileList(_, targetDir, sparkConf, hadoopConf, secMgr)
}.orNull
localPyFiles = Option(args.pyFiles).map {
downloadFileList(_, targetDir, sparkConf, hadoopConf, secMgr)
}.orNull
} // When running in YARN, for some remote resources with scheme:
// 1. Hadoop FileSystem doesn't support them.
// 2. We explicitly bypass Hadoop FileSystem with "spark.yarn.dist.forceDownloadSchemes".
// We will download them to local disk prior to add to YARN's distributed cache.
// For yarn client mode, since we already download them with above code, so we only need to
// figure out the local path and replace the remote one.
if (clusterManager == YARN) {
val forceDownloadSchemes = sparkConf.get(FORCE_DOWNLOAD_SCHEMES) def shouldDownload(scheme: String): Boolean = {
forceDownloadSchemes.contains("*") || forceDownloadSchemes.contains(scheme) ||
Try { FileSystem.getFileSystemClass(scheme, hadoopConf) }.isFailure
} def downloadResource(resource: String): String = {
val uri = Utils.resolveURI(resource)
uri.getScheme match {
case "local" | "file" => resource
case e if shouldDownload(e) =>
val file = new File(targetDir, new Path(uri).getName)
if (file.exists()) {
file.toURI.toString
} else {
downloadFile(resource, targetDir, sparkConf, hadoopConf, secMgr)
}
case _ => uri.toString
}
} args.primaryResource = Option(args.primaryResource).map { downloadResource }.orNull
args.files = Option(args.files).map { files =>
Utils.stringToSeq(files).map(downloadResource).mkString(",")
}.orNull
args.pyFiles = Option(args.pyFiles).map { pyFiles =>
Utils.stringToSeq(pyFiles).map(downloadResource).mkString(",")
}.orNull
args.jars = Option(args.jars).map { jars =>
Utils.stringToSeq(jars).map(downloadResource).mkString(",")
}.orNull
args.archives = Option(args.archives).map { archives =>
Utils.stringToSeq(archives).map(downloadResource).mkString(",")
}.orNull
} // If we're running a python app, set the main class to our specific python runner
if (args.isPython && deployMode == CLIENT) {
if (args.primaryResource == PYSPARK_SHELL) {
args.mainClass = "org.apache.spark.api.python.PythonGatewayServer"
} else {
// If a python file is provided, add it to the child arguments and list of files to deploy.
// Usage: PythonAppRunner <main python file> <extra python files> [app arguments]
args.mainClass = "org.apache.spark.deploy.PythonRunner"
args.childArgs = ArrayBuffer(localPrimaryResource, localPyFiles) ++ args.childArgs
}
if (clusterManager != YARN) {
// The YARN backend handles python files differently, so don't merge the lists.
args.files = mergeFileLists(args.files, args.pyFiles)
}
} if (localPyFiles != null) {
sparkConf.set("spark.submit.pyFiles", localPyFiles)
} // In YARN mode for an R app, add the SparkR package archive and the R package
// archive containing all of the built R libraries to archives so that they can
// be distributed with the job
if (args.isR && clusterManager == YARN) {
val sparkRPackagePath = RUtils.localSparkRPackagePath
if (sparkRPackagePath.isEmpty) {
error("SPARK_HOME does not exist for R application in YARN mode.")
}
val sparkRPackageFile = new File(sparkRPackagePath.get, SPARKR_PACKAGE_ARCHIVE)
if (!sparkRPackageFile.exists()) {
error(s"$SPARKR_PACKAGE_ARCHIVE does not exist for R application in YARN mode.")
}
val sparkRPackageURI = Utils.resolveURI(sparkRPackageFile.getAbsolutePath).toString // Distribute the SparkR package.
// Assigns a symbol link name "sparkr" to the shipped package.
args.archives = mergeFileLists(args.archives, sparkRPackageURI + "#sparkr") // Distribute the R package archive containing all the built R packages.
if (!RUtils.rPackages.isEmpty) {
val rPackageFile =
RPackageUtils.zipRLibraries(new File(RUtils.rPackages.get), R_PACKAGE_ARCHIVE)
if (!rPackageFile.exists()) {
error("Failed to zip all the built R packages.")
} val rPackageURI = Utils.resolveURI(rPackageFile.getAbsolutePath).toString
// Assigns a symbol link name "rpkg" to the shipped package.
args.archives = mergeFileLists(args.archives, rPackageURI + "#rpkg")
}
} // TODO: Support distributing R packages with standalone cluster
if (args.isR && clusterManager == STANDALONE && !RUtils.rPackages.isEmpty) {
error("Distributing R packages with standalone cluster is not supported.")
} // TODO: Support distributing R packages with mesos cluster
if (args.isR && clusterManager == MESOS && !RUtils.rPackages.isEmpty) {
error("Distributing R packages with mesos cluster is not supported.")
} // If we're running an R app, set the main class to our specific R runner
if (args.isR && deployMode == CLIENT) {
if (args.primaryResource == SPARKR_SHELL) {
args.mainClass = "org.apache.spark.api.r.RBackend"
} else {
// If an R file is provided, add it to the child arguments and list of files to deploy.
// Usage: RRunner <main R file> [app arguments]
args.mainClass = "org.apache.spark.deploy.RRunner"
args.childArgs = ArrayBuffer(localPrimaryResource) ++ args.childArgs
args.files = mergeFileLists(args.files, args.primaryResource)
}
} if (isYarnCluster && args.isR) {
// In yarn-cluster mode for an R app, add primary resource to files
// that can be distributed with the job
args.files = mergeFileLists(args.files, args.primaryResource)
} // Special flag to avoid deprecation warnings at the client
sys.props("SPARK_SUBMIT") = "true" // A list of rules to map each argument to system properties or command-line options in
// each deploy mode; we iterate through these below
val options = List[OptionAssigner]( // All cluster managers
OptionAssigner(args.master, ALL_CLUSTER_MGRS, ALL_DEPLOY_MODES, confKey = "spark.master"),
OptionAssigner(args.deployMode, ALL_CLUSTER_MGRS, ALL_DEPLOY_MODES,
confKey = "spark.submit.deployMode"),
OptionAssigner(args.name, ALL_CLUSTER_MGRS, ALL_DEPLOY_MODES, confKey = "spark.app.name"),
OptionAssigner(args.ivyRepoPath, ALL_CLUSTER_MGRS, CLIENT, confKey = "spark.jars.ivy"),
OptionAssigner(args.driverMemory, ALL_CLUSTER_MGRS, CLIENT,
confKey = "spark.driver.memory"),
OptionAssigner(args.driverExtraClassPath, ALL_CLUSTER_MGRS, ALL_DEPLOY_MODES,
confKey = "spark.driver.extraClassPath"),
OptionAssigner(args.driverExtraJavaOptions, ALL_CLUSTER_MGRS, ALL_DEPLOY_MODES,
confKey = "spark.driver.extraJavaOptions"),
OptionAssigner(args.driverExtraLibraryPath, ALL_CLUSTER_MGRS, ALL_DEPLOY_MODES,
confKey = "spark.driver.extraLibraryPath"), // Propagate attributes for dependency resolution at the driver side
OptionAssigner(args.packages, STANDALONE | MESOS, CLUSTER, confKey = "spark.jars.packages"),
OptionAssigner(args.repositories, STANDALONE | MESOS, CLUSTER,
confKey = "spark.jars.repositories"),
OptionAssigner(args.ivyRepoPath, STANDALONE | MESOS, CLUSTER, confKey = "spark.jars.ivy"),
OptionAssigner(args.packagesExclusions, STANDALONE | MESOS,
CLUSTER, confKey = "spark.jars.excludes"), // Yarn only
OptionAssigner(args.queue, YARN, ALL_DEPLOY_MODES, confKey = "spark.yarn.queue"),
OptionAssigner(args.numExecutors, YARN, ALL_DEPLOY_MODES,
confKey = "spark.executor.instances"),
OptionAssigner(args.pyFiles, YARN, ALL_DEPLOY_MODES, confKey = "spark.yarn.dist.pyFiles"),
OptionAssigner(args.jars, YARN, ALL_DEPLOY_MODES, confKey = "spark.yarn.dist.jars"),
OptionAssigner(args.files, YARN, ALL_DEPLOY_MODES, confKey = "spark.yarn.dist.files"),
OptionAssigner(args.archives, YARN, ALL_DEPLOY_MODES, confKey = "spark.yarn.dist.archives"),
OptionAssigner(args.principal, YARN, ALL_DEPLOY_MODES, confKey = "spark.yarn.principal"),
OptionAssigner(args.keytab, YARN, ALL_DEPLOY_MODES, confKey = "spark.yarn.keytab"), // Other options
OptionAssigner(args.executorCores, STANDALONE | YARN | KUBERNETES, ALL_DEPLOY_MODES,
confKey = "spark.executor.cores"),
OptionAssigner(args.executorMemory, STANDALONE | MESOS | YARN | KUBERNETES, ALL_DEPLOY_MODES,
confKey = "spark.executor.memory"),
OptionAssigner(args.totalExecutorCores, STANDALONE | MESOS | KUBERNETES, ALL_DEPLOY_MODES,
confKey = "spark.cores.max"),
OptionAssigner(args.files, LOCAL | STANDALONE | MESOS | KUBERNETES, ALL_DEPLOY_MODES,
confKey = "spark.files"),
OptionAssigner(args.jars, LOCAL, CLIENT, confKey = "spark.jars"),
OptionAssigner(args.jars, STANDALONE | MESOS | KUBERNETES, ALL_DEPLOY_MODES,
confKey = "spark.jars"),
OptionAssigner(args.driverMemory, STANDALONE | MESOS | YARN | KUBERNETES, CLUSTER,
confKey = "spark.driver.memory"),
OptionAssigner(args.driverCores, STANDALONE | MESOS | YARN | KUBERNETES, CLUSTER,
confKey = "spark.driver.cores"),
OptionAssigner(args.supervise.toString, STANDALONE | MESOS, CLUSTER,
confKey = "spark.driver.supervise"),
OptionAssigner(args.ivyRepoPath, STANDALONE, CLUSTER, confKey = "spark.jars.ivy"), // An internal option used only for spark-shell to add user jars to repl's classloader,
// previously it uses "spark.jars" or "spark.yarn.dist.jars" which now may be pointed to
// remote jars, so adding a new option to only specify local jars for spark-shell internally.
OptionAssigner(localJars, ALL_CLUSTER_MGRS, CLIENT, confKey = "spark.repl.local.jars")
) // In client mode, launch the application main class directly
// In addition, add the main application jar and any added jars (if any) to the classpath
if (deployMode == CLIENT) {
childMainClass = args.mainClass
if (localPrimaryResource != null && isUserJar(localPrimaryResource)) {
childClasspath += localPrimaryResource
}
if (localJars != null) { childClasspath ++= localJars.split(",") }
}
// Add the main application jar and any added jars to classpath in case YARN client
// requires these jars.
// This assumes both primaryResource and user jars are local jars, or already downloaded
// to local by configuring "spark.yarn.dist.forceDownloadSchemes", otherwise it will not be
// added to the classpath of YARN client.
if (isYarnCluster) {
if (isUserJar(args.primaryResource)) {
childClasspath += args.primaryResource
}
if (args.jars != null) { childClasspath ++= args.jars.split(",") }
} if (deployMode == CLIENT) {
if (args.childArgs != null) { childArgs ++= args.childArgs }
} // Map all arguments to command-line options or system properties for our chosen mode
for (opt <- options) {
if (opt.value != null &&
(deployMode & opt.deployMode) != 0 &&
(clusterManager & opt.clusterManager) != 0) {
if (opt.clOption != null) { childArgs += (opt.clOption, opt.value) }
if (opt.confKey != null) { sparkConf.set(opt.confKey, opt.value) }
}
} // In case of shells, spark.ui.showConsoleProgress can be true by default or by user.
if (isShell(args.primaryResource) && !sparkConf.contains(UI_SHOW_CONSOLE_PROGRESS)) {
sparkConf.set(UI_SHOW_CONSOLE_PROGRESS, true)
} // Add the application jar automatically so the user doesn't have to call sc.addJar
// For YARN cluster mode, the jar is already distributed on each node as "app.jar"
// For python and R files, the primary resource is already distributed as a regular file
if (!isYarnCluster && !args.isPython && !args.isR) {
var jars = sparkConf.getOption("spark.jars").map(x => x.split(",").toSeq).getOrElse(Seq.empty)
if (isUserJar(args.primaryResource)) {
jars = jars ++ Seq(args.primaryResource)
}
sparkConf.set("spark.jars", jars.mkString(","))
} // In standalone cluster mode, use the REST client to submit the application (Spark 1.3+).
// All Spark parameters are expected to be passed to the client through system properties.
if (args.isStandaloneCluster) {
if (args.useRest) {
childMainClass = REST_CLUSTER_SUBMIT_CLASS
childArgs += (args.primaryResource, args.mainClass)
} else {
// In legacy standalone cluster mode, use Client as a wrapper around the user class
childMainClass = STANDALONE_CLUSTER_SUBMIT_CLASS
if (args.supervise) { childArgs += "--supervise" }
Option(args.driverMemory).foreach { m => childArgs += ("--memory", m) }
Option(args.driverCores).foreach { c => childArgs += ("--cores", c) }
childArgs += "launch"
childArgs += (args.master, args.primaryResource, args.mainClass)
}
if (args.childArgs != null) {
childArgs ++= args.childArgs
}
} // Let YARN know it's a pyspark app, so it distributes needed libraries.
if (clusterManager == YARN) {
if (args.isPython) {
sparkConf.set("spark.yarn.isPython", "true")
}
} if (clusterManager == MESOS && UserGroupInformation.isSecurityEnabled) {
setRMPrincipal(sparkConf)
} // In yarn-cluster mode, use yarn.Client as a wrapper around the user class
if (isYarnCluster) {
childMainClass = YARN_CLUSTER_SUBMIT_CLASS
if (args.isPython) {
childArgs += ("--primary-py-file", args.primaryResource)
childArgs += ("--class", "org.apache.spark.deploy.PythonRunner")
} else if (args.isR) {
val mainFile = new Path(args.primaryResource).getName
childArgs += ("--primary-r-file", mainFile)
childArgs += ("--class", "org.apache.spark.deploy.RRunner")
} else {
if (args.primaryResource != SparkLauncher.NO_RESOURCE) {
childArgs += ("--jar", args.primaryResource)
}
childArgs += ("--class", args.mainClass)
}
if (args.childArgs != null) {
args.childArgs.foreach { arg => childArgs += ("--arg", arg) }
}
} if (isMesosCluster) {
assert(args.useRest, "Mesos cluster mode is only supported through the REST submission API")
childMainClass = REST_CLUSTER_SUBMIT_CLASS
if (args.isPython) {
// Second argument is main class
childArgs += (args.primaryResource, "")
if (args.pyFiles != null) {
sparkConf.set("spark.submit.pyFiles", args.pyFiles)
}
} else if (args.isR) {
// Second argument is main class
childArgs += (args.primaryResource, "")
} else {
childArgs += (args.primaryResource, args.mainClass)
}
if (args.childArgs != null) {
childArgs ++= args.childArgs
}
} if (isKubernetesCluster) {
childMainClass = KUBERNETES_CLUSTER_SUBMIT_CLASS
if (args.primaryResource != SparkLauncher.NO_RESOURCE) {
if (args.isPython) {
childArgs ++= Array("--primary-py-file", args.primaryResource)
childArgs ++= Array("--main-class", "org.apache.spark.deploy.PythonRunner")
if (args.pyFiles != null) {
childArgs ++= Array("--other-py-files", args.pyFiles)
}
} else if (args.isR) {
childArgs ++= Array("--primary-r-file", args.primaryResource)
childArgs ++= Array("--main-class", "org.apache.spark.deploy.RRunner")
}
else {
childArgs ++= Array("--primary-java-resource", args.primaryResource)
childArgs ++= Array("--main-class", args.mainClass)
}
} else {
childArgs ++= Array("--main-class", args.mainClass)
}
if (args.childArgs != null) {
args.childArgs.foreach { arg =>
childArgs += ("--arg", arg)
}
}
} // Load any properties specified through --conf and the default properties file
for ((k, v) <- args.sparkProperties) {
sparkConf.setIfMissing(k, v)
} // Ignore invalid spark.driver.host in cluster modes.
if (deployMode == CLUSTER) {
sparkConf.remove("spark.driver.host")
} // Resolve paths in certain spark properties
val pathConfigs = Seq(
"spark.jars",
"spark.files",
"spark.yarn.dist.files",
"spark.yarn.dist.archives",
"spark.yarn.dist.jars")
pathConfigs.foreach { config =>
// Replace old URIs with resolved URIs, if they exist
sparkConf.getOption(config).foreach { oldValue =>
sparkConf.set(config, Utils.resolveURIs(oldValue))
}
} // Resolve and format python file paths properly before adding them to the PYTHONPATH.
// The resolving part is redundant in the case of --py-files, but necessary if the user
// explicitly sets `spark.submit.pyFiles` in his/her default properties file.
sparkConf.getOption("spark.submit.pyFiles").foreach { pyFiles =>
val resolvedPyFiles = Utils.resolveURIs(pyFiles)
val formattedPyFiles = if (!isYarnCluster && !isMesosCluster) {
PythonRunner.formatPaths(resolvedPyFiles).mkString(",")
} else {
// Ignoring formatting python path in yarn and mesos cluster mode, these two modes
// support dealing with remote python files, they could distribute and add python files
// locally.
resolvedPyFiles
}
sparkConf.set("spark.submit.pyFiles", formattedPyFiles)
} (childArgs, childClasspath, sparkConf, childMainClass)
}
其中：
a. 当部署模式为client，则子进程的主类为用户通过spark-submit提交的类，即代码中的：childMainClass = args.mainClass
b. 当master为Yarn且部署模式为cluster时，子进程的主类为：org.apache.spark.deploy.yarn.YarnClusterApplication
1) 准备Yarn(Cluster Manager)的执行类：
使用spark-submit启动时，实际上执行的是exec "SPARKHOME"/bin/spark−class org.apache.spark.deploy.SparkSubmit "@"
在SparkSubmit中，prepareSubmitEnvironment方法中会为spark提交做准备，准备好运行环境相关。
private[deploy] def prepareSubmitEnvironment(args: SparkSubmitArguments,conf: Option[HadoopConfiguration] = None): (Seq[String], Seq[String], SparkConf, String)
其中该方法内部代码中，发现当cluster manager为yarn时：
a. 当--deploy-mode:cluster时
会调用YarnClusterApplication进行提交。YarnClusterApplication是org.apache.spark.deploy.yarn.Client中的一个内部类，在YarnClusterApplication中new了一个Client对象，并调用了run方法
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("spark.jars")
conf.remove("spark.files")
new Client(new ClientArguments(args), conf).run()
}
b. 当--deploy-mode:client时
调用application-jar.jar自身main函数，执行的是JavaMainApplication
/*
* 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.lang.reflect.Modifier import org.apache.spark.SparkConf /**
* Entry point for a Spark application. Implementations must provide a no-argument constructor.
*/
private[spark] trait SparkApplication { def start(args: Array[String], conf: SparkConf): Unit } /**
* Implementation of SparkApplication that wraps a standard Java class with a "main" method.
*
* Configuration is propagated to the application via system properties, so running multiple
* of these in the same JVM may lead to undefined behavior due to configuration leaks.
*/
private[deploy] class JavaMainApplication(klass: Class[_]) extends SparkApplication { override def start(args: Array[String], conf: SparkConf): Unit = {
val mainMethod = klass.getMethod("main", new Array[String](0).getClass)
if (!Modifier.isStatic(mainMethod.getModifiers)) {
throw new IllegalStateException("The main method in the given main class must be static")
} val sysProps = conf.getAll.toMap
sysProps.foreach { case (k, v) =>
sys.props(k) = v
} mainMethod.invoke(null, args)
} }
从JavaMainApplication实现可以发现，JavaMainApplication中调用start方法时，只是通过反射执行application-jar.jar的main函数。
(3) 提交到Yarn
1) yarn-cluster运行流程：
当yarn-custer模式中，YarnClusterApplication类中运行的是Client中run方法，Client#run()中实现了任务提交流程：
/**
* Submit an application to the ResourceManager.
* If set spark.yarn.submit.waitAppCompletion to true, it will stay alive
* reporting the application's status until the application has exited for any reason.
* Otherwise, the client process will exit after submission.
* If the application finishes with a failed, killed, or undefined status,
* throw an appropriate SparkException.
*/
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")
}
}
}
在Client类的run()方法中会调用submitApplication()方法，该方法实现：
/**
* Submit an application running our ApplicationMaster to the ResourceManager.
*
* The stable Yarn API provides a convenience method (YarnClient#createApplication) for
* creating applications and setting up the application submission context. This was not
* available in the alpha API.
*/
def submitApplication(): ApplicationId = {
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() 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 (appId != null) {
cleanupStagingDir(appId)
}
throw e
}
}
run()方法则是实现向yarn中的ResourceManager（后文全部简称RM）提交运行任务，并运行我们的ApplicationMaster(后文简称AM)。
稳定的Yarn API提供了一种方便的方法（YarnClient#createApplication），用于创建应用程序和设置应用程序提交上下文。
submitApplication()方法具体操作步骤：
l 初始化并启动YarnClient，后边将使用yarnClient提供的各种API
l 通过调用yarnClient#createApplication()方法，从RM获取一个newApp(application)，该newApp用于运行AM。通过newApp#getNewApplicationResponse()返回newApp需要资源情况(newAppResponse)。
l 通过newAppResponse验证集群是否有足够的资源来运行AM。
l 设置适当的上下文来以启动AM。
l 调用yarnClient#submitApplication(appContext)向yarn提交任务启动的请求，并监控application。
yarn-client运行流程：
- 对于部署方式是Client的情况，SparkSubmit的main函数中通过反射执行应用程序的main方法
- 在应用程序的main方法中，创建SparkContext实例
- 在创建SparkContext的实例过程中，通过如下语句创建Scheduler和Backend实例
private var _schedulerBackend: SchedulerBackend = _
private var _taskScheduler: TaskScheduler = _ private[spark] def schedulerBackend: SchedulerBackend = _schedulerBackend private[spark] def taskScheduler: TaskScheduler = _taskScheduler
private[spark] def taskScheduler_=(ts: TaskScheduler): Unit = {
_taskScheduler = ts
} // 构造函数中初始化赋值
// Create and start the scheduler
val (sched, ts) = SparkContext.createTaskScheduler(this, master, deployMode)
_schedulerBackend = sched
_taskScheduler = ts
https://github.com/apache/spark/blob/master/core/src/main/scala/org/apache/spark/SparkContext.scala
SparkContext初始化过程
在Yarn模式下，SparkContext初始化位置因--deploy-mode不同而不同：
yarn-cluster模式下：client会先申请向RM(Yarn Resource Manager)一个Container，来启动AM（ApplicationMaster）进程，而SparkContext运行在AM（ApplicationMaster）进程中；
yarn-client模式下 ：在提交节点上执行SparkContext初始化，由client类（JavaMainApplication）调用。
/**
* Create a task scheduler based on a given master URL.
* Return a 2-tuple of the scheduler backend and the task scheduler.
*/
private def createTaskScheduler(。。。): (SchedulerBackend, TaskScheduler) = {
。。。
master match {
case "local" =>
。。。
case LOCAL_N_REGEX(threads) =>
。。。
case LOCAL_N_FAILURES_REGEX(threads, maxFailures) =>
。。。。
case SPARK_REGEX(sparkUrl) =>
。。。。
case LOCAL_CLUSTER_REGEX(numSlaves, coresPerSlave, memoryPerSlave) =>
。。。。
case masterUrl =>
val cm = getClusterManager(masterUrl) match {
case Some(clusterMgr) => clusterMgr
case None => throw new SparkException("Could not parse Master URL: '" + master + "'")
}
try {
val scheduler = cm.createTaskScheduler(sc, masterUrl)
val backend = cm.createSchedulerBackend(sc, masterUrl, scheduler)
cm.initialize(scheduler, backend)
(backend, scheduler)
} catch {
case se: SparkException => throw se
case NonFatal(e) =>
throw new SparkException("External scheduler cannot be instantiated", e)
}
}
} private def getClusterManager(url: String): Option[ExternalClusterManager] = {
val loader = Utils.getContextOrSparkClassLoader
val serviceLoaders =
ServiceLoader.load(classOf[ExternalClusterManager], loader).asScala.filter(_.canCreate(url))
if (serviceLoaders.size > 1) {
throw new SparkException(
s"Multiple external cluster managers registered for the url $url: $serviceLoaders")
}
serviceLoaders.headOption
}
1）SparkContext#createTaskScheduler(。。。)
根据不同的资源管理方式cluster manager来创建不同的TaskScheduler，SchedulerBackend。
1.1）SchedulerBackend与cluster manager资源管理器交互取得应用被分配的资源。
1.2）TaskSheduler在不同的job之间调度，同时接收被分配的资源，之后由他来给每一个Task分配资源。
2）SparkContext#createTaskScheduler(。。。)
最后一个match case是对其他资源管理方式（除了local和standelone{spark://}外的mesos，yarn，kubernetes【外部资源管理器】的资源管理方式）的处理。
SparkContext#createTaskScheduler(。。。)#master match#case masterUrl下边调用了getClusterManager(masterUrl)方法，该方法返回对象是实现了ExternalClusterManager接口的YarnClusterManager类对象。
备注：实现了ExternalClusterManager接口的类还包含：
MesosClusterManager （https://github.com/apache/spark/blob/branch-2.4/resource-managers/mesos/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosClusterManager.scala）
KubernetesClusterManager （https://github.com/apache/spark/blob/branch-2.4/resource-managers/kubernetes/core/src/main/scala/org/apache/spark/scheduler/cluster/k8s/KubernetesClusterManager.scala）
ExternalClusterManager接口定义：
private[spark] trait ExternalClusterManager {
def canCreate(masterURL: String): Boolean
def createTaskScheduler(sc: SparkContext, masterURL: String): TaskScheduler
def createSchedulerBackend(sc: SparkContext,
masterURL: String,
scheduler: TaskScheduler): SchedulerBackend
def initialize(scheduler: TaskScheduler, backend: SchedulerBackend): Unit
}
ExternalClusterManager接口提供了4个方法：
-canCreate(masterURL: String):Boolean 检查此群集管理器实例是否可以为某个masterURL创建scheduler组件。
-createTaskScheduler(sc: SparkContext, masterURL: String):TaskScheduler 为给定的SparkContext创建TaskScheduler实例
-createSchedulerBackend(sc: SparkContext,masterURL: String,scheduler: TaskScheduler): SchedulerBackend 为给定的SparkContext和调度程序创建SchedulerBackend 。这是在使用“ExternalClusterManager.createTaskScheduler（）”创建TaskScheduler后调用的。
-initialize(scheduler: TaskScheduler, backend: SchedulerBackend): Unit 初始化TaskScheduler和SchedulerBackend，在创建调度程序组件之后调用。
YarnClusterManager类定义：
private[spark] class YarnClusterManager extends ExternalClusterManager {
override def canCreate(masterURL: String): Boolean = {
masterURL == "yarn"
}
override def createTaskScheduler(sc: SparkContext, masterURL: String): TaskScheduler = {
sc.deployMode match {
case "cluster" => new YarnClusterScheduler(sc)
case "client" => new YarnScheduler(sc)
case _ => throw new SparkException(s"Unknown deploy mode '${sc.deployMode}' for Yarn")
}
}
override def createSchedulerBackend(sc: SparkContext,
masterURL: String,
scheduler: TaskScheduler): SchedulerBackend = {
sc.deployMode match {
case "cluster" =>
new YarnClusterSchedulerBackend(scheduler.asInstanceOf[TaskSchedulerImpl], sc)
case "client" =>
new YarnClientSchedulerBackend(scheduler.asInstanceOf[TaskSchedulerImpl], sc)
case _ =>
throw new SparkException(s"Unknown deploy mode '${sc.deployMode}' for Yarn")
}
}
override def initialize(scheduler: TaskScheduler, backend: SchedulerBackend): Unit = {
scheduler.asInstanceOf[TaskSchedulerImpl].initialize(backend)
}
}
YarnClusterManager#createTaskScheduler(...)：
在该方法中会根据SparkContext对象的deployMode属性来进行分支判断：
client时，返回YarnScheduler(https://github.com/apache/spark/blob/branch-2.4/resource-managers/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnScheduler.scala)实例对象；
cluster时，返回YarnClusterScheduler(https://github.com/apache/spark/blob/branch-2.4/resource-managers/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClusterScheduler.scala)实例对象。
YarnClusterManager#createSchedulerBackend(...)：
在该方法中会根据SparkContext对象的deployMode属性来进行分支判断：
client时，返回YarnClientSchedulerBackend(https://github.com/apache/spark/blob/branch-2.4/resource-managers/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala)实例对象；
cluster时，返回YarnClusterSchedulerBackend(https://github.com/apache/spark/blob/branch-2.4/resource-managers/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClusterSchedulerBackend.scala)实例对象。
Yarn作业运行运行架构原理解析：
1、分析Spark on YARN的Cluster模式，从用户提交作业到作业运行结束整个运行期间的过程分析。
客户端进行操作
- 1、根据yarnConf来初始化yarnClient，并启动yarnClient
- 2、创建客户端Application，并获取Application的ID，进一步判断集群中的资源是否满足executor和ApplicationMaster申请的资源，如果不满足则抛出IllegalArgumentException；
- 3、设置资源、环境变量：其中包括了设置Application的Staging目录、准备本地资源（jar文件、log4j.properties）、设置Application其中的环境变量、创建Container启动的Context等；
- 4、设置Application提交的Context，包括设置应用的名字、队列、AM的申请的Container、标记该作业的类型为Spark；
- 5、申请Memory，并最终通过yarnClient.submitApplication向ResourceManager提交该Application。
当作业提交到YARN上之后，客户端就没事了，甚至在终端关掉那个进程也没事，因为整个作业运行在YARN集群上进行，运行的结果将会保存到HDFS或者日志中。
提交到YARN集群，YARN操作
- 1、运行ApplicationMaster的run方法；
- 2、设置好相关的环境变量。
- 3、创建amClient，并启动；
- 4、在Spark UI启动之前设置Spark UI的AmIpFilter；
- 5、在startUserClass函数专门启动了一个线程（名称为Driver的线程）来启动用户提交的Application，也就是启动了Driver。在Driver中将会初始化SparkContext；
- 6、等待SparkContext初始化完成，最多等待spark.yarn.applicationMaster.waitTries次数（默认为10），如果等待了的次数超过了配置的，程序将会退出；否则用SparkContext初始化yarnAllocator；
怎么知道SparkContext初始化完成？
其实在5步骤中启动Application的过程中会初始化SparkContext，在初始化SparkContext的时候将会创建YarnClusterScheduler，在SparkContext初始化完成的时候，会调用YarnClusterScheduler类中的postStartHook方法，而该方法会通知ApplicationMaster已经初始化好了SparkContext
- 7、当SparkContext、Driver初始化完成的时候，通过amClient向ResourceManager注册ApplicationMaster
- 8、分配并启动Executeors。在启动Executeors之前，先要通过yarnAllocator获取到numExecutors个Container，然后在Container中启动Executeors。如果在启动Executeors的过程中失败的次数达到了maxNumExecutorFailures的次数，maxNumExecutorFailures的计算规则如下：
// Default to numExecutors * 2, with minimum of 3
private val maxNumExecutorFailures =sparkConf.getInt("spark.yarn.max.executor.failures",
sparkConf.getInt("spark.yarn.max.worker.failures", math.max(args.numExecutors *2,3)))
那么这个Application将失败，将Application Status标明为FAILED，并将关闭SparkContext。其实，启动Executeors是通过ExecutorRunnable实现的，而ExecutorRunnable内部是启动CoarseGrainedExecutorBackend的。
- 9、最后，Task将在CoarseGrainedExecutorBackend里面运行，然后运行状况会通过Akka通知CoarseGrainedScheduler，直到作业运行完成。
2、Spark on YARN client 模式作业运行全过程分析
我们知道Spark on yarn有两种模式：yarn-cluster和yarn-client。这两种模式作业虽然都是在yarn上面运行，但是其中的运行方式很不一样，今天我就来谈谈Spark on YARN yarn-client模式作业从提交到运行的过程剖析。
和yarn-cluster模式一样，整个程序也是通过spark-submit脚本提交的。但是yarn-client作业程序的运行不需要通过Client类来封装启动，而是直接通过反射机制调用作业的main函数。下面就来分析：
- 1、通过SparkSubmit类的launch的函数直接调用作业的main函数（通过反射机制实现），如果是集群模式就会调用Client的main函数。
- 2、而应用程序的main函数一定都有个SparkContent，并对其进行初始化；
- 3、在SparkContent初始化中将会依次做如下的事情：设置相关的配置、注册MapOutputTracker、BlockManagerMaster、BlockManager，创建taskScheduler和dagScheduler；其中比较重要的是创建taskScheduler和dagScheduler。在创建taskScheduler的时候会根据我们传进来的master来选择Scheduler和SchedulerBackend。由于我们选择的是yarn-client模式，程序会选择YarnClientClusterScheduler和YarnClientSchedulerBackend，并将YarnClientSchedulerBackend的实例初始化YarnClientClusterScheduler，上面两个实例的获取都是通过反射机制实现的，YarnClientSchedulerBackend类是CoarseGrainedSchedulerBackend类的子类，YarnClientClusterScheduler是TaskSchedulerImpl的子类，仅仅重写了TaskSchedulerImpl中的getRackForHost方法。
- 4、初始化完taskScheduler后，将创建dagScheduler，然后通过taskScheduler.start()启动taskScheduler，而在taskScheduler启动的过程中也会调用SchedulerBackend的start方法。在SchedulerBackend启动的过程中将会初始化一些参数，封装在ClientArguments中，并将封装好的ClientArguments传进Client类中，并client.runApp()方法获取Application ID。
- 5、client.runApp里面的做是和前面客户端进行操作那节类似，不同的是在里面启动是ExecutorLauncher（yarn-cluster模式启动的是ApplicationMaster）。
- 6、在ExecutorLauncher里面会初始化并启动amClient，然后向ApplicationMaster注册该Application。注册完之后将会等待driver的启动，当driver启动完之后，会创建一个MonitorActor对象用于和CoarseGrainedSchedulerBackend进行通信（只有事件AddWebUIFilter他们之间才通信，Task的运行状况不是通过它和CoarseGrainedSchedulerBackend通信的）。然后就是设置addAmIpFilter，当作业完成的时候，ExecutorLauncher将通过amClient设置Application的状态为FinalApplicationStatus.SUCCEEDED。
- 7、分配Executors，这里面的分配逻辑和yarn-cluster里面类似，就不再说了。
- 8、最后，Task将在CoarseGrainedExecutorBackend里面运行，然后运行状况会通过Akka通知CoarseGrainedScheduler，直到作业运行完成。
- 9、在作业运行的时候，YarnClientSchedulerBackend会每隔1秒通过client获取到作业的运行状况，并打印出相应的运行信息，当Application的状态是FINISHED、FAILED和KILLED中的一种，那么程序将退出等待。
- 10、最后有个线程会再次确认Application的状态，当Application的状态是FINISHED、FAILED和KILLED中的一种，程序就运行完成，并停止SparkContext。整个过程就结束了。
YARN-Cluster运行架构原理
在YARN-Cluster模式中，当用户向YARN中提交一个应用程序后，YARN将分两个阶段运行该应用程序：
- 1.第一个阶段是把Spark的Driver作为一个ApplicationMaster在YARN集群中先启动；
- 2.第二个阶段是由ApplicationMaster创建应用程序，然后为它向ResourceManager申请资源，并启动Executor来运行Task，同时监控它的整个运行过程，直到运行完成
说明如下：
- Spark Yarn Client向YARN中提交应用程序，包括ApplicationMaster程序、启动ApplicationMaster的命令、需要在Executor中运行的程序等；
- ResourceManager收到请求后，在集群中选择一个NodeManager，为该应用程序分配第一个Container，要求它在这个Container中启动应用程序的ApplicationMaster，其中ApplicationMaster进行SparkContext等的初始化；
- ApplicationMaster向ResourceManager注册，这样用户可以直接通过ResourceManage查看应用程序的运行状态，然后它将采用轮询的方式通过RPC协议为各个任务申请资源，并监控它们的运行状态直到运行结束；
- 一旦ApplicationMaster申请到资源（也就是Container）后，便与对应的NodeManager通信，要求它在获得的Container中启动CoarseGrainedExecutorBackend，CoarseGrainedExecutorBackend启动后会向ApplicationMaster中的SparkContext注册并申请Task。这一点和Standalone模式一样，只不过SparkContext在Spark Application中初始化时，使用CoarseGrainedSchedulerBackend配合YarnClusterScheduler进行任务的调度，其中YarnClusterScheduler只是对TaskSchedulerImpl的一个简单包装，增加了对Executor的等待逻辑等；
- ApplicationMaster中的SparkContext分配Task给CoarseGrainedExecutorBackend执行，CoarseGrainedExecutorBackend运行Task并向ApplicationMaster汇报运行的状态和进度，以让ApplicationMaster随时掌握各个任务的运行状态，从而可以在任务失败时重新启动任务；
- 应用程序运行完成后，ApplicationMaster向ResourceManager申请注销并关闭自己；
跟踪CoarseGrainedExecutorBackend启动脚本：
YARN-Client运行架构原理
说明如下：
- Spark Yarn Client向YARN的ResourceManager申请启动Application Master。同时在SparkContent初始化中将创建DAGScheduler和TASKScheduler等，由于我们选择的是Yarn-Client模式，程序会选择YarnClientClusterSchedulerYarnScheduler和YarnClientSchedulerBackend；
- ResourceManager收到请求后，在集群中选择一个NodeManager，为该应用程序分配第一个Container，要求它在这个Container中启动应用程序的ApplicationMaster，与YARN-Cluster区别的是在该ApplicationMaster不运行SparkContext，只与SparkContext进行联系进行资源的分派；
- Client中的SparkContext初始化完毕后，与ApplicationMaster建立通讯，向ResourceManager注册，根据任务信息向ResourceManager申请资源（Container）；
- 一旦ApplicationMaster申请到资源（也就是Container）后，便与对应的NodeManager通信，要求它在获得的Container中启动CoarseGrainedExecutorBackend，CoarseGrainedExecutorBackend启动后会向Client中的SparkContext注册并申请Task；
- client中的SparkContext分配Task给CoarseGrainedExecutorBackend执行，CoarseGrainedExecutorBackend运行Task并向Driver汇报运行的状态和进度，以让Client随时掌握各个任务的运行状态，从而可以在任务失败时重新启动任务；
- 应用程序运行完成后，Client的SparkContext向ResourceManager申请注销并关闭自己。
Client模式 vs Cluster模式
- 理解YARN-Client和YARN-Cluster深层次的区别之前先清楚一个概念：Application Master。在YARN中，每个Application实例都有一个ApplicationMaster进程，它是Application启动的第一个容器。它负责和ResourceManager打交道并请求资源，获取资源之后告诉NodeManager为其启动Container。从深层次的含义讲YARN-Cluster和YARN-Client模式的区别其实就是ApplicationMaster进程的区别；
- YARN-Cluster模式下，Driver运行在AM(Application Master)中，它负责向YARN申请资源，并监督作业的运行状况。当用户提交了作业之后，就可以关掉Client，作业会继续在YARN上运行，因而YARN-Cluster模式不适合运行交互类型的作业；
- YARN-Client模式下，Application Master仅仅向YARN请求Executor，Client会和请求的Container通信来调度他们工作，也就是说Client不能离开；