Spark源码分析：Spark运行模式及原理

2019年6月8日 206次阅读 来源: raincoffee

Spark源码分析：Spark运行模式及原理

1.运行模式概述

spark运行模式多种多样，分为以下几种

  • 本地模式
  • 为分布式
  • 集群
    • standalone
    • mesos
    • hadoop yarn

基本框架：

2.相关类介绍

  • taskscheduler／taskschedulerImpl
    private[spark] trait TaskScheduler {

      private val appId = "spark-application-" + System.currentTimeMillis

  def rootPool: Pool

  def schedulingMode: SchedulingMode

  def start(): Unit

  // Invoked after system has successfully initialized (typically in spark context).
  // Yarn uses this to bootstrap allocation of resources based on preferred locations,
  // wait for slave registrations, etc.
  def postStartHook() { }

  // Disconnect from the cluster.
  def stop(): Unit

  // Submit a sequence of tasks to run.
  def submitTasks(taskSet: TaskSet): Unit

  // Cancel a stage.
  def cancelTasks(stageId: Int, interruptThread: Boolean): Unit

  /**
   * Kills a task attempt.
   *
   * @return Whether the task was successfully killed.
   */
  def killTaskAttempt(taskId: Long, interruptThread: Boolean, reason: String): Boolean

  // Set the DAG scheduler for upcalls. This is guaranteed to be set before submitTasks is called.
  def setDAGScheduler(dagScheduler: DAGScheduler): Unit

  // Get the default level of parallelism to use in the cluster, as a hint for sizing jobs.
  def defaultParallelism(): Int

  /**
   * Update metrics for in-progress tasks and let the master know that the BlockManager is still
   * alive. Return true if the driver knows about the given block manager. Otherwise, return false,
   * indicating that the block manager should re-register.
   */
  def executorHeartbeatReceived(
      execId: String,
      accumUpdates: Array[(Long, Seq[AccumulatorV2[_, _]])],
      blockManagerId: BlockManagerId): Boolean

  /**
   * Get an application ID associated with the job.
   *
   * @return An application ID
   */
  def applicationId(): String = appId

  /**
   * Process a lost executor
   */
  def executorLost(executorId: String, reason: ExecutorLossReason): Unit

  /**
   * Get an application's attempt ID associated with the job.
   *
   * @return An application's Attempt ID
   */
  def applicationAttemptId(): Option[String]

}

    taskscheduler主要用于核dagscheduler交互，负责任务的具体调度和运行。
    其核心接口是取消和提交任务sumbittasks和cancletasks

  • schedulerbackend
    主要用于和底层资源调度系统交互（yarn mesos）
    /**
    * A backend interface for scheduling systems that allows plugging in different ones under
    * TaskSchedulerImpl. We assume a Mesos-like model where the application gets resource offers as
    * machines become available and can launch tasks on them.
    */
    private[spark] trait SchedulerBackend {
    private val appId = “spark-application-” + System.currentTimeMillis

      def start(): Unit
  def stop(): Unit
  def reviveOffers(): Unit
  def defaultParallelism(): Int

  /**
   * Requests that an executor kills a running task.
   *
   * @param taskId Id of the task.
   * @param executorId Id of the executor the task is running on.
   * @param interruptThread Whether the executor should interrupt the task thread.
   * @param reason The reason for the task kill.
   */
  def killTask(
      taskId: Long,
      executorId: String,
      interruptThread: Boolean,
      reason: String): Unit =
    throw new UnsupportedOperationException

  def isReady(): Boolean = true

  /**
   * Get an application ID associated with the job.
   *
   * @return An application ID
   */
  def applicationId(): String = appId

  /**
   * Get the attempt ID for this run, if the cluster manager supports multiple
   * attempts. Applications run in client mode will not have attempt IDs.
   *
   * @return The application attempt id, if available.
   */
  def applicationAttemptId(): Option[String] = None

  /**
   * Get the URLs for the driver logs. These URLs are used to display the links in the UI
   * Executors tab for the driver.
   * @return Map containing the log names and their respective URLs
   */
  def getDriverLogUrls: Option[Map[String, String]] = None

}

  • executor

    实际任务的执行都有executor执行，executor对每个人物创建一个taskrunner，交给线程池执行

3.local模式

localbackend 响应scheduler的receiveOffers 请求，根据可用的cpu的核的设定值直接生成cpu资源返回给scheduler，并通过executor在线程池中依次启动核运行scheduler返回的任务列表。

4.yarn

YARN是一个资源管理、任务调度的框架，主要包含三大模块：ResourceManager（RM）、NodeManager（NM）、ApplicationMaster（AM）。

其中，ResourceManager负责所有资源的监控、分配和管理；ApplicationMaster负责每一个具体应用程序的调度和协调；NodeManager负责每一个节点的维护。

对于所有的applications，RM拥有绝对的控制权和对资源的分配权。而每个AM则会和RM协商资源，同时和NodeManager通信来执行和监控task。几个模块之间的关系如图所示

Yarn Cluster 模式

Spark的Yarn Cluster 模式流程如下：

  • 本地用YARN Client 提交App 到 Yarn Resource Manager
  • Yarn Resource Manager 选个 YARN Node Manager，用它来
    创建个ApplicationMaster，SparkContext相当于是这个ApplicationMaster管的APP，生成YarnClusterScheduler与YarnClusterSchedulerBackend选择集群中的容器启动CoarseCrainedExecutorBackend，用来启动spark.executor。
  • ApplicationMaster与CoarseCrainedExecutorBackend会有远程调用。

Spark的Yarn Client 模式流程如下：

  • 本地启动SparkContext，生成YarnClientClusterScheduler 和 YarnClientClusterSchedulerBackend
  • YarnClientClusterSchedulerBackend启动yarn.Client，用它提交App 到 Yarn Resource Manager
  • Yarn Resource Manager 选个 YARN Node Manager，用它来选择集群中的容器启动CoarseCrainedExecutorBackend，用来启动spark.executor
  • YarnClientClusterSchedulerBackend与CoarseCrainedExecutorBackend会有远程调用。

standalone

  1. 启动app，在SparkContxt启动过程中，先初始化DAGScheduler 和 TaskScheduler，并初始化 SparkDeploySchedulerBackend，并在其内部启动DriverEndpoint和ClientEndpoint。

  2. ClientEndpoint想Master注册app，Master收到注册信息后把该app加入到等待运行app列表中，等待由Master分配给该app worker。

  3. app获取到worker后，Master通知Worker的WorkerEndpont创建CoarseGrainedExecutorBackend进程，在该进程中创建执行容器executor

  4. executor创建完毕后发送信息给Master和DriverEndpoint，告知Executor创建完毕，在SparkContext注册，后等待DriverEndpoint发送执行任务的消息。

  5. SparkContext分配TaskSet给CoarseGrainedExecutorBackend，按一定调度策略在executor执行。

  6. CoarseGrainedExecutorBackend在Task处理的过程中，把处理Task的状态发送给DriverEndpoint，Spark根据不同的执行结果来处理。若处理完毕，则继续发送其他TaskSet。

  7. app运行完成后，SparkContext会进行资源回收，销毁Worker的CoarseGrainedExecutorBackend进程，然后注销自己。
    启动master
    private[deploy] object Master extends Logging {
    val SYSTEM_NAME = “sparkMaster”
    val ENDPOINT_NAME = “Master”

      def main(argStrings: Array[String]) {
    Utils.initDaemon(log)
    val conf = new SparkConf
    //解析参数
    val args = new MasterArguments(argStrings, conf)
    val (rpcEnv, _, _) = startRpcEnvAndEndpoint(args.host, args.port, args.webUiPort, conf)
    rpcEnv.awaitTermination()
  }

  /**
   * Start the Master and return a three tuple of:
   *   (1) The Master RpcEnv
   *   (2) The web UI bound port
   *   (3) The REST server bound port, if any
   */
  def startRpcEnvAndEndpoint(
      host: String,
      port: Int,
      webUiPort: Int,
      conf: SparkConf): (RpcEnv, Int, Option[Int]) = {
    val securityMgr = new SecurityManager(conf)
    val rpcEnv = RpcEnv.create(SYSTEM_NAME, host, port, conf, securityMgr)
    val masterEndpoint = rpcEnv.setupEndpoint(ENDPOINT_NAME,
      new Master(rpcEnv, rpcEnv.address, webUiPort, securityMgr, conf))
    val portsResponse = masterEndpoint.askSync[BoundPortsResponse](BoundPortsRequest)
    (rpcEnv, portsResponse.webUIPort, portsResponse.restPort)
  }
}

master解析参数

/**
 * Command-line parser for the master.
 */
private[master] class MasterArguments(args: Array[String], conf: SparkConf) extends Logging {
  var host = Utils.localHostName()
  var port = 7077
  var webUiPort = 8080
  var propertiesFile: String = null

  // Check for settings in environment variables
  if (System.getenv("SPARK_MASTER_IP") != null) {
    logWarning("SPARK_MASTER_IP is deprecated, please use SPARK_MASTER_HOST")
    host = System.getenv("SPARK_MASTER_IP")
  }

  if (System.getenv("SPARK_MASTER_HOST") != null) {
    host = System.getenv("SPARK_MASTER_HOST")
  }
  if (System.getenv("SPARK_MASTER_PORT") != null) {
    port = System.getenv("SPARK_MASTER_PORT").toInt
  }
  if (System.getenv("SPARK_MASTER_WEBUI_PORT") != null) {
    webUiPort = System.getenv("SPARK_MASTER_WEBUI_PORT").toInt
  }

  parse(args.toList)

  // This mutates the SparkConf, so all accesses to it must be made after this line
  propertiesFile = Utils.loadDefaultSparkProperties(conf, propertiesFile)

  if (conf.contains("spark.master.ui.port")) {
    webUiPort = conf.get("spark.master.ui.port").toInt
  }

  @tailrec
  private def parse(args: List[String]): Unit = args match {
    case ("--ip" | "-i") :: value :: tail =>
      Utils.checkHost(value, "ip no longer supported, please use hostname " + value)
      host = value
      parse(tail)

    case ("--host" | "-h") :: value :: tail =>
      Utils.checkHost(value, "Please use hostname " + value)
      host = value
      parse(tail)

    case ("--port" | "-p") :: IntParam(value) :: tail =>
      port = value
      parse(tail)

    case "--webui-port" :: IntParam(value) :: tail =>
      webUiPort = value
      parse(tail)

    case ("--properties-file") :: value :: tail =>
      propertiesFile = value
      parse(tail)

    case ("--help") :: tail =>
      printUsageAndExit(0)

    case Nil => // No-op

    case _ =>
      printUsageAndExit(1)
  }

  /**
   * Print usage and exit JVM with the given exit code.
   */
  private def printUsageAndExit(exitCode: Int) {
    // scalastyle:off println
    System.err.println(
      "Usage: Master [options]\n" +
      "\n" +
      "Options:\n" +
      "  -i HOST, --ip HOST     Hostname to listen on (deprecated, please use --host or -h) \n" +
      "  -h HOST, --host HOST   Hostname to listen on\n" +
      "  -p PORT, --port PORT   Port to listen on (default: 7077)\n" +
      "  --webui-port PORT      Port for web UI (default: 8080)\n" +
      "  --properties-file FILE Path to a custom Spark properties file.\n" +
      "                         Default is conf/spark-defaults.conf.")
    // scalastyle:on println
    System.exit(exitCode)
  }
}

系统环境变量<spark−default.conf中的属性<命令行参数<应用级代码中的参数设置

启动worker

private[deploy] object Worker extends Logging {
  val SYSTEM_NAME = "sparkWorker"
  val ENDPOINT_NAME = "Worker"

  def main(argStrings: Array[String]) {
    Utils.initDaemon(log)
    val conf = new SparkConf
    val args = new WorkerArguments(argStrings, conf)
    val rpcEnv = startRpcEnvAndEndpoint(args.host, args.port, args.webUiPort, args.cores,
      args.memory, args.masters, args.workDir, conf = conf)
    rpcEnv.awaitTermination()
  }

  def startRpcEnvAndEndpoint(
      host: String,
      port: Int,
      webUiPort: Int,
      cores: Int,
      memory: Int,
      masterUrls: Array[String],
      workDir: String,
      workerNumber: Option[Int] = None,
      conf: SparkConf = new SparkConf): RpcEnv = {

    // The LocalSparkCluster runs multiple local sparkWorkerX RPC Environments
    val systemName = SYSTEM_NAME + workerNumber.map(_.toString).getOrElse("")
    val securityMgr = new SecurityManager(conf)
    val rpcEnv = RpcEnv.create(systemName, host, port, conf, securityMgr)
    val masterAddresses = masterUrls.map(RpcAddress.fromSparkURL(_))
    rpcEnv.setupEndpoint(ENDPOINT_NAME, new Worker(rpcEnv, webUiPort, cores, memory,
      masterAddresses, ENDPOINT_NAME, workDir, conf, securityMgr))
    rpcEnv
  }

  def isUseLocalNodeSSLConfig(cmd: Command): Boolean = {
    val pattern = """\-Dspark\.ssl\.useNodeLocalConf\=(.+)""".r
    val result = cmd.javaOpts.collectFirst {
      case pattern(_result) => _result.toBoolean
    }
    result.getOrElse(false)
  }

  def maybeUpdateSSLSettings(cmd: Command, conf: SparkConf): Command = {
    val prefix = "spark.ssl."
    val useNLC = "spark.ssl.useNodeLocalConf"
    if (isUseLocalNodeSSLConfig(cmd)) {
      val newJavaOpts = cmd.javaOpts
          .filter(opt => !opt.startsWith(s"-D$prefix")) ++
          conf.getAll.collect { case (key, value) if key.startsWith(prefix) => s"-D$key=$value" } :+
          s"-D$useNLC=true"
      cmd.copy(javaOpts = newJavaOpts)
    } else {
      cmd
    }
  }
}

剩下的解析和master类似

资源回收

我们在概述中提到了“ app运行完成后，SparkContext会进行资源回收，销毁Worker的CoarseGrainedExecutorBackend进程，然后注销自己。”接下来我们就来讲解下Master和Executor是如何感知到Application的退出的。

调用栈如下：

  • SparkContext.stop
    • DAGScheduler.stop
      • TaskSchedulerImpl.stop
        • CoarseGrainedSchedulerBackend.stop
          • CoarseGrainedSchedulerBackend.stopExecutors
            • CoarseGrainedSchedulerBackend.DriverEndpoint.receiveAndReply – CoarseGrainedExecutorBackend.receive
              – Executor.stop
              • CoarseGrainedSchedulerBackend.DriverEndpoint.receiveAndReply
    原文作者：raincoffee
    原文地址: https://www.jianshu.com/p/4f682cd1031f
    本文转自网络文章，转载此文章仅为分享知识，如有侵权，请联系博主进行删除。
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