2，Spark 任务的提交

我们可以从spark 的官网看到，spark-submit的提交格式如下：
./bin/spark-submit
–class
–master
–deploy-mode
–conf =
… # other options

[application-arguments]
• --class: The entry point for your application (e.g. org.apache.spark.examples.SparkPi) 应用程序的入口
• --master: The master URL for the cluster (e.g. spark://23.195.26.187:7077) master 的URL
• --deploy-mode: Whether to deploy your driver on the worker nodes (cluster) or locally as an external client (client) (default: client) † 集群的部署模式
• --conf: Arbitrary Spark configuration property in key=value format. For values that contain spaces wrap “key=value” in quotes (as shown). Spark的配置文件
• application-jar: Path to a bundled jar including your application and all dependencies. The URL must be globally visible inside of your cluster, for instance, an hdfs:// path or a file:// path that is present on all nodes. 自己编写的jar包的路径
• application-arguments: Arguments passed to the main method of your main class, if any 需要传入的参数
一个具体的实例如下：
#Run on a Spark standalone cluster in cluster deploy mode with supervise
./bin/spark-submit
–class org.apache.spark.examples.SparkPi
–master spark://207.184.161.138:7077
–deploy-mode cluster
–supervise
–executor-memory 20G
–total-executor-cores 100
/path/to/examples.jar
1000
提交任务要使用$SPARK_HOME下bin目录里面的spark-submit脚本，我们来分析一下这个脚本：

//判断SPARK_HOME的目录是否存在
if [ -z "${SPARK_HOME}" ]; then
  source "$(dirname "$0")"/find-spark-home
# disable randomized hash for string in Python 3.3+
export PYTHONHASHSEED=0
//调用bin目录下的spark-class脚本
exec "${SPARK_HOME}"/bin/spark-class org.apache.spark.deploy.SparkSubmit "$@"
我们再次进入spark-class的脚本：
 
//判断SPARK_HOME的目录是否存在
if [ -z "${SPARK_HOME}" ]; then
  source "$(dirname "$0")"/find-spark-home
//加载spark-env.sh 文件
. "${SPARK_HOME}"/bin/load-spark-env.sh
# Find the java binary
//检测java的路径
if [ -n "${JAVA_HOME}" ]; then
  RUNNER="${JAVA_HOME}/bin/java"
  if [ "$(command -v java)" ]; then
    RUNNER="java"
    echo "JAVA_HOME is not set" >&2
    exit 1
# Find Spark jars.
//检测jars是否存在
if [ -d "${SPARK_HOME}/jars" ]; then
  SPARK_JARS_DIR="${SPARK_HOME}/jars"
  SPARK_JARS_DIR="${SPARK_HOME}/assembly/target/scala-$SPARK_SCALA_VERSION/jars"
if [ ! -d "$SPARK_JARS_DIR" ] && [ -z "$SPARK_TESTING$SPARK_SQL_TESTING" ]; then
  echo "Failed to find Spark jars directory ($SPARK_JARS_DIR)." 1>&2
  echo "You need to build Spark with the target \"package\" before running this program." 1>&2
  exit 1
  LAUNCH_CLASSPATH="$SPARK_JARS_DIR/*"
# Add the launcher build dir to the classpath if requested.
if [ -n "$SPARK_PREPEND_CLASSES" ]; then
  LAUNCH_CLASSPATH="${SPARK_HOME}/launcher/target/scala-$SPARK_SCALA_VERSION/classes:$LAUNCH_CLASSPATH"
# For tests
if [[ -n "$SPARK_TESTING" ]]; then
  unset YARN_CONF_DIR
  unset HADOOP_CONF_DIR
# The launcher library will print arguments separated by a NULL character, to allow arguments with
# characters that would be otherwise interpreted by the shell. Read that in a while loop, populating
# an array that will be used to exec the final command.
# The exit code of the launcher is appended to the output, so the parent shell removes it from the
# command array and checks the value to see if the launcher succeeded.
build_command() {
//执行org.apache.spark.launcher.Main的main函数，解析参数
  "$RUNNER" -Xmx128m -cp "$LAUNCH_CLASSPATH" org.apache.spark.launcher.Main "$@"
  printf "%d\0" $?
# Turn off posix mode since it does not allow process substitution
set +o posix
CMD=()
while IFS= read -d '' -r ARG; do
把命令添加到CMD中
  CMD+=("$ARG")
//调用方法创建执行命令
done < <(build_command "$@")
COUNT=${#CMD[@]}
LAST=$((COUNT - 1))
LAUNCHER_EXIT_CODE=${CMD[$LAST]}
# Certain JVM failures result in errors being printed to stdout (instead of stderr), which causes
# the code that parses the output of the launcher to get confused. In those cases, check if the
# exit code is an integer, and if it's not, handle it as a special error case.
if ! [[ $LAUNCHER_EXIT_CODE =~ ^[0-9]+$ ]]; then
  echo "${CMD[@]}" | head -n-1 1>&2
  exit 1
if [ $LAUNCHER_EXIT_CODE != 0 ]; then
  exit $LAUNCHER_EXIT_CODE
CMD=("${CMD[@]:0:$LAST}")
//执行命令
exec "${CMD[@]}"
spark-class的脚本，最主要的就是解析参数，以及创建执行命令，把命令交给spark-class的CMD进行执行。我们进入org.apache.spark.launcher.Main这个类里面，然后看一main函数：
 
public static void main(String[] argsArray) throws Exception {
//检查参数
  checkArgument(argsArray.length > 0, "Not enough arguments: missing class name.");
//创建一个链表存放参数
  List<String> args = new ArrayList<>(Arrays.asList(argsArray));
//我们提交任务时，第一个参数就是要进入org.apache.spark.deploy.SparkSubmit，它也是第一个参数，把这个参数移除，剩下的参数组成args
  String className = args.remove(0);
  boolean printLaunchCommand = !isEmpty(System.getenv("SPARK_PRINT_LAUNCH_COMMAND"));
  Map<String, String> env = new HashMap<>();
  List<String> cmd;
//className
  if (className.equals("org.apache.spark.deploy.SparkSubmit")) {
    try {
//创建一个命令解析器,创建spark-class中exec执行的command
      AbstractCommandBuilder builder = new SparkSubmitCommandBuilder(args);
      cmd = buildCommand(builder, env, printLaunchCommand);
    } catch (IllegalArgumentException e) {
      printLaunchCommand = false;
      System.err.println("Error: " + e.getMessage());
      System.err.println();
      MainClassOptionParser parser = new MainClassOptionParser




    
();
      try {
//解析参数
        parser.parse(args);
      } catch (Exception ignored) {
        // Ignore parsing exceptions.
      List<String> help = new ArrayList<>();
      if (parser.className != null) {
//把CLASS和classname加入到help链表中
        help.add(parser.CLASS);
        help.add(parser.className);
      help.add(parser.USAGE_ERROR);
      AbstractCommandBuilder builder = new SparkSubmitCommandBuilder(help);
      cmd = buildCommand(builder, env, printLaunchCommand);
  } else {
    AbstractCommandBuilder builder = new SparkClassCommandBuilder(className, args);
    cmd = buildCommand(builder, env, printLaunchCommand);
//如果是window环境，那么就创建windows环境下的命令
  if (isWindows()) {
    System.out.println(prepareWindowsCommand(cmd, env));
  } else {
    // In bash, use NULL as the arg separator since it cannot be used in an argument.
    List<String> bashCmd = prepareBashCommand(cmd, env);
    for (String c : bashCmd) {
      System.out.print(c);
      System.out.print('\0');
上面最重要的是完成了一些参数的解析，参数解析正确后会把需要执行的命令加进CMD的数组中，在spark-class的脚本中进行执行，然后进入到org.apache.spark.deploy.SparkSubmit这个类中，看一下main函数：
 
override def main(args: Array[String]): Unit = {
//创建一个SparkSubmit的对象，
  val submit = new SparkSubmit() {
    self =>
    override protected def parseArguments(args: Array[String]): SparkSubmitArguments = {
//创建Spark的提交参数
      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 {
//调用父类的doSubmit方法
        super.doSubmit(args)
      } catch {
        case e: SparkUserAppException =>
          exitFn(e.exitCode)
  submit.doSubmit(args)
再进入到doSubmit的方法中
 
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)
//获取解析的参数
  val appArgs = parseArguments(args)
  if (appArgs.verbose) {
    logInfo(appArgs.toString)
//根据SparkSubmitAction的动作进行模式匹配，我们这里是SUBMIT，所以要进入submit的方法
  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()
根据SparkSubmitAction的动作进行模式匹配，进入submit的方法：
private def submit(args: SparkSubmitArguments, uninitLog: Boolean): Unit = {
//调用prepareSubmitEnvironment方法，根据传入的解析参数，获取以下四个变量
  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.
//如果是StandaloneCluster模式，并且使用REST网关，进入下面这个分支
  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()
上面实际上是首先准备spark的环境，即调用prepareSubmitEnvironment的方法，进入到这个方法里面：
 
private[deploy] def prepareSubmitEnvironment(
    args: SparkSubmitArguments,
    conf: Option[HadoopConfiguration] = None)
    : (Seq[String], Seq[String], SparkConf, String) = {
  // Return values
//定义了以下这四个需要返回的变量
  val childArgs = new ArrayBuffer[String]()
  val childClasspath = new ArrayBuffer[String]()
  val sparkConf = new SparkConf()
  var childMainClass = ""
  // Set the cluster manager
//设置集群的资源管理器
  val clusterManager: Int = args.master match {
    case "yarn" => YARN
    case "yarn-client" | "yarn-cluster" =>
      logWarning(s"Master ${args.master} is deprecated since 2.0." +
        " Please use master \"yarn\" with specified deploy mode instead.")
    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")
prepareSubmitEnvironment这个方法里面，只要做的事情是根据解析的参数，获取集群的部署模式，返回这四个参数：childArgs, childClasspath, sparkConf, childMainClass供后面程序的使用。
 本篇博客是按照Standalone的集群部署模式进行介绍，因此，进入以下代码：
 
if (args.isStandaloneCluster) {
  if (args.useRest) {
//如果使用REST网关，则采用RestSubmissionClientApp的方式提交
    childMainClass = REST_CLUSTER_SUBMIT_CLASS
    childArgs += (args.primaryResource, args.mainClass)
  } else {
//如果不使用REST的网关，则用ClientApp的方式进行提交。
    // 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
上面的代码主要完成，根据是否可以使用REST网关的条件，来匹配不同的提交方式，讨论ClientApp的方式进行提交，这里的childMainClass就是我们自己编写的程序的主函数。回到doRunmain的方法：
 
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")
//创建加载器，用于加载jar包
    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)
//根据指定的路径加载jar包
    for (jar <- childClasspath) {
      addJarToClasspath




    
(jar, loader)
    var mainClass: Class[_] = null
    try {
//获取mainClass
      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.")
//创建一个
      new JavaMainApplication(mainClass)
进入JavaMainApplication的方法中
 
private[deploy] class JavaMainApplication(klass: Class[_]) extends SparkApplication {
  override def start(args: Array[String], conf: SparkConf): Unit = {
//获取main 方法
    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
//通过反射机制调用该方法
    mainMethod.invoke(null, args)
通过反射机制调用用户编写的main 函数。
 假如我们采用的是ClientAPP的方式提交，进入到org.apache.spark.deploy.Client：
 进入main函数
 
object Client {
  def main(args: Array[String]) {
    // scalastyle:off println
    if (!sys.props.contains("SPARK_SUBMIT")) {
      println("WARNING: This client is deprecated and will be removed in a future version of Spark")
      println("Use ./bin/spark-submit with \"--master spark://host:port\"")
    // scalastyle:on println
//创建一个ClientAPP
    new ClientApp().start(args, new SparkConf())
private[spark] class ClientApp extends SparkApplication {
  override def start(args: Array[String], conf: SparkConf): Unit = {
    val driverArgs = new ClientArguments(args)
    if (!conf.contains("spark.rpc.askTimeout")) {
      conf.set("spark.rpc.askTimeout", "10s")
    Logger.getRootLogger.setLevel(driverArgs.logLevel)
//创建rpcEnv的通信环境
    val rpcEnv =
      RpcEnv.create("driverClient", Utils.localHostName(), 0, conf, new SecurityManager(conf))
//创建masterEndpoints和ClientEndpoint
    val masterEndpoints = driverArgs.masters.map(RpcAddress.fromSparkURL).
      map(rpcEnv.setupEndpointRef(_, Master.ENDPOINT_NAME))
    rpcEnv.setupEndpoint("client", new ClientEndpoint(rpcEnv, driverArgs, masterEndpoints, conf))
    rpcEnv.awaitTermination()
}
 在onStart的方法中：
 
override def onStart(): Unit = {
  driverArgs.cmd match {
    case "launch" =>
      // TODO: We could add an env variable here and intercept it in `sc.addJar` that would
      //       truncate filesystem paths similar to what YARN does. For now, we just require
      //       people call `addJar` assuming the jar is in the same directory.
      val mainClass = "org.apache.spark.deploy.worker.DriverWrapper"
      val classPathConf = "spark.driver.extraClassPath"
      val classPathEntries = sys.props.get(classPathConf).toSeq.flatMap { cp =>
        cp.split(java.io.File.pathSeparator)
      val libraryPathConf = "spark.driver.extraLibraryPath"
      val libraryPathEntries = sys.props.get(libraryPathConf).toSeq.flatMap { cp =>
        cp.split(java.io.File.pathSeparator)
      val extraJavaOptsConf = "spark.driver.extraJavaOptions"
      val extraJavaOpts = sys.props.get(extraJavaOptsConf)
        .map(Utils.splitCommandString).getOrElse(Seq.empty)
      val sparkJavaOpts = Utils.sparkJavaOpts(conf)
      val javaOpts = sparkJavaOpts ++ extraJavaOpts
      val command = new Command(mainClass,
        Seq("{{WORKER_URL}}", "{{USER_JAR}}", driverArgs.mainClass) ++ driverArgs.driverOptions,
        sys.env, classPathEntries, libraryPathEntries, javaOpts)
//封装Driver的信息
      val driverDescription = new DriverDescription(
        driverArgs.jarUrl,
        driverArgs.memory,
        driverArgs.cores,
        driverArgs.supervise,
        command)
//向Master发送启动Driver的请求
      asyncSendToMasterAndForwardReply[SubmitDriverResponse](
        RequestSubmitDriver(driverDescription))
    case "kill" =>
      val driverId = driverArgs.driverId
      asyncSendToMasterAndForwardReply[KillDriverResponse](RequestKillDriver(driverId))
在master接收到Client发送来的启动Driver的信息后，整个作业的提交就完成了。接下来，就是Driver的注册。
                    1，简介在上一篇博客中，我们详细介绍了Spark Standalone模式下集群的启动流程。在Spark 集群启动后，我们要想在集群上运行我们自己编写的程序，该如何做呢？本篇博客就主要介绍Spark Submit提交任务的流程。2，Spark 任务的提交我们可以从spark 的官网看到，spark-submit的提交格式如下：./bin/spark-submit –class  –ma...
				
折腾了很久，终于开始学习Spark的源码了，第一篇我打算讲一下Spark作业的提交过程。这个是Spark的App运行图，它通过一个Driver来和集群通信，集群负责作业的分配。今天我要讲的是如何创建这个Driver
Program的过程。我们先看一下用SparkSubmit提交的方法吧，下面是从官方上面摘抄的内容。这个是提交到standalone集群的方式，打开spark-submit这文件，我们会发现它最后是调用了org.apache.spark.deploy.SparkSubmit这个类。我们直接进去看就行了，main函数就几行代码
1、构建spark执行环境（初始化sparkcont）；
2、SparkContext向资源管理器注册并申请Executor资源；
3、资源管理器分配Executor资源，Executor向资源管理器发送心跳汇报状态；
4、Executor向sc注册并申请task；
5、sc向Executor发送task，Executor执行task；
6、运行完成后，sc向资源管理器申请注销资源。
1.2、流程图解（多图）
2.启动脚本调用的是spark-submit，因此我们直接去看spark-submit脚本
# -z是检查后面变量是否为空（空则真） shell可以在双引号之内引用变量，单引号不可
#这一步作用是检查SPARK_HOME变量
				
前几篇博客详细解析了Spark的Job触发机制、Spark的DAGScheduler调度机制、Spark的TaskScheduler调度机制、Spark调度器的终端通信SchedulerBackend和Spark的Executor启动，在对这些源码进行分析之后，头脑中十分混乱，对于各个机制的具体执行过程不是十分了解。网上的各种分析博客也写得不是十分清晰，于是就开始了Spark任务提交流程的分析。本博客的Spark版本为2.12，是以Standalone Cluster部署模式为基础进行分析。
Spark.
由前面的文章Spark基础06-Spark client和cluster提交流程我们已经知道了Spark client和cluster提交模式流程
启动Driver进程，并向集群管理器注册应用程序
集群资源管理器根据任务配置文件分配并启动Executor
Executor启动之后反向到Driver注册，Driver已经获取足够资源可以运行
Driver开始执行main函数，Spark查询为懒执行，当执行到action算子时开始反向推算，根据宽依赖进行stage的划分，随后每一个stage对应一个
				
Spark-submit提交流程代码层面提交Spark-submit提交资源分配
代码层面提交
1.用户传递参数执行spark-submit.sh脚本，查询SPARK_Home是否设置。如果没有设置先执行find-spark-home文件设置，已经设置传递参数    和org.apache.spark.deploy.SparkSubmit类执行spark-class.sh脚本
2.spark-class.sh中检查spark环境变量、java环境变量、spark的jars包路径，加载load-spark-e
				
大数据实验教学系统使用spark-submit工具提交Spark作业对于数据的批处理，通常采用编写程序、打.jar包提交给集群来执行，这需要使用Spark自带的spark-submit工具。
  一般的部署策略是在一个网关机器上提交应用程序，这个机器和Worker机器部署在一个网络中（例如，Standalone模式的集群中的Master节点）。在此部署策略中，client模式更为合适，client模式中的driver直接跟spark-submit进程一起启动，spark-submit进程在此扮演集群中一个c