CoarseGrainedSchedulerBackend和CoarseGrainedExecutorBackend

CoarseGrainedSchedulerBackend是Driver端用到的，CoarseGrainedExecutorBackend是Executor端用到的。他们都是Backend，什么是Backend？Backend其实就是负责端到端通讯的，这两个CoarseGrained的Backend是负责Driver和Executor之间的通讯的。

什么是Driver呢？Driver就是咱们编写的spark代码，里面的main函数就是Driver跑的代码。

什么是Executor呢？Executor就是执行spark的Task任务的地方，Backend接收到Driver的LaunchTask消息后，调用Executor类的launchTask方法来执行任务。

Driver会启动CoarseGrainedSchedulerBackend，经过CoarseGrainedSchedulerBackend来向集群申请机器以便启动Executor，会找到一台机器，发送命令让机器启动一个ExecutorRunner，ExecutorRunner里启动CoarseGrainedExecutorBackend向Driver注册，并建立Executor来处理CoarseGrainedExecutorBackend接收到的请求。刚刚说的是Standalone部署下的流程，Yarn下大部分相似，只有向集群申请机器来启动Executor这一步不太同样，这个简单说一下吧。

Yarn环境下，是经过spark-yarn工程里的几个类一级yarn自己的功能来一块儿完成机器的部署和分区任务的分发。

spark-yarn包含两个文件：client.java和ApplicationMaster.java。

client.java功能是向yarn申请资源来执行ApplicationMaster.java的代码，因此这里主要看下ApplicationMaster.java的代码功能是什么。

ApplicationMaster首先干两件事，启动一个"/bin/mesos-master"和多个"/bin/mesos-slave"，这都是向yarn申请资源而后部署上去执行的，都是yarn的功能部分，"/bin/mesos-master"和"/bin/mesos-slave"是yarn环境里自带的两个bin程序，能够当作是相似Standalone环境下的Master和Worker。

launchContainer方法是启动yarn的container，也就是前面说的在container上启动“/bin/mesos-slave"，mesos-slave会向mesos-master注册的。等须要的slave节点资源所有申请启动完成后，调用startApplication()方法开始执行Driver。

startApplication()方法：

// Start the user's application
  private void startApplication() throws IOException {
    try {
      String sparkClasspath = getSparkClasspath();
      String jobJar = new File("job.jar").getAbsolutePath();
      String javaArgs = "-Xms" + (masterMem - 128) + "m -Xmx" + (masterMem - 128) + "m";
      javaArgs += " -Djava.library.path=" + mesosHome + "/lib/java";
      String substitutedArgs = programArgs.replaceAll("\\[MASTER\\]", masterUrl);
      if (mainClass.equals("")) {
        javaArgs += " -cp " + sparkClasspath + " -jar " + jobJar + " " + substitutedArgs; 
      } else {
        javaArgs += " -cp " + sparkClasspath + ":" + jobJar + " " + mainClass + " " + substitutedArgs;
      }
      String java = "java";
      if (System.getenv("JAVA_HOME") != null) {
        java = System.getenv("JAVA_HOME") + "/bin/java";
      }
      String bashCommand = java + " " + javaArgs +
          " 1>" + logDirectory + "/application.stdout" +
          " 2>" + logDirectory + "/application.stderr";
      LOG.info("Command: " + bashCommand);
      String[] command = new String[] {"bash", "-c", bashCommand};
      String[] env = new String[] {"SPARK_HOME=" + sparkHome, "MASTER=" + masterUrl, 
          "SPARK_MEM=" + (slaveMem - 128) + "m"};
      application = Runtime.getRuntime().exec(command, env);      
      new Thread("wait for user application") {
        public void run() {
          try {
            appExitCode = application.waitFor();
            appExited = true;
            LOG.info("User application exited with code " + appExitCode);
          } catch (InterruptedException e) {
            e.printStackTrace();
          }
        }
      }.start();
    } catch (SparkClasspathException e) {      
      unregister(false);
      System.exit(1);
      return;
    }
  }

这就是启动Driver了，masterUrl就是”bin/mesos-master“的地址，设置成了环境变量”MASTER“来用了，yarn下的master的地址格式是”mesos://host:port“，Standalone下是”spark://host:port“。

在SparkContext下会根据master地址格式，作不一样的处理，这段代码是这样：

master match {
      case "local" =>
        checkResourcesPerTask(clusterMode = false, Some(1))
        val scheduler = new TaskSchedulerImpl(sc, MAX_LOCAL_TASK_FAILURES, isLocal = true)
        val backend = new LocalSchedulerBackend(sc.getConf, scheduler, 1)
        scheduler.initialize(backend)
        (backend, scheduler)

      case LOCAL_N_REGEX(threads) =>
        def localCpuCount: Int = Runtime.getRuntime.availableProcessors()
        // local[*] estimates the number of cores on the machine; local[N] uses exactly N threads.
        val threadCount = if (threads == "*") localCpuCount else threads.toInt
        if (threadCount <= 0) {
          throw new SparkException(s"Asked to run locally with $threadCount threads")
        }
        checkResourcesPerTask(clusterMode = false, Some(threadCount))
        val scheduler = new TaskSchedulerImpl(sc, MAX_LOCAL_TASK_FAILURES, isLocal = true)
        val backend = new LocalSchedulerBackend(sc.getConf, scheduler, threadCount)
        scheduler.initialize(backend)
        (backend, scheduler)

      case LOCAL_N_FAILURES_REGEX(threads, maxFailures) =>
        def localCpuCount: Int = Runtime.getRuntime.availableProcessors()
        // local[*, M] means the number of cores on the computer with M failures
        // local[N, M] means exactly N threads with M failures
        val threadCount = if (threads == "*") localCpuCount else threads.toInt
        checkResourcesPerTask(clusterMode = false, Some(threadCount))
        val scheduler = new TaskSchedulerImpl(sc, maxFailures.toInt, isLocal = true)
        val backend = new LocalSchedulerBackend(sc.getConf, scheduler, threadCount)
        scheduler.initialize(backend)
        (backend, scheduler)

      case SPARK_REGEX(sparkUrl) =>
        checkResourcesPerTask(clusterMode = true, None)
        val scheduler = new TaskSchedulerImpl(sc)
        val masterUrls = sparkUrl.split(",").map("spark://" + _)
        val backend = new StandaloneSchedulerBackend(scheduler, sc, masterUrls)
        scheduler.initialize(backend)
        (backend, scheduler)

      case LOCAL_CLUSTER_REGEX(numSlaves, coresPerSlave, memoryPerSlave) =>
        checkResourcesPerTask(clusterMode = true, Some(coresPerSlave.toInt))
        // Check to make sure memory requested <= memoryPerSlave. Otherwise Spark will just hang.
        val memoryPerSlaveInt = memoryPerSlave.toInt
        if (sc.executorMemory > memoryPerSlaveInt) {
          throw new SparkException(
            "Asked to launch cluster with %d MiB RAM / worker but requested %d MiB/worker".format(
              memoryPerSlaveInt, sc.executorMemory))
        }

        val scheduler = new TaskSchedulerImpl(sc)
        val localCluster = new LocalSparkCluster(
          numSlaves.toInt, coresPerSlave.toInt, memoryPerSlaveInt, sc.conf)
        val masterUrls = localCluster.start()
        val backend = new StandaloneSchedulerBackend(scheduler, sc, masterUrls)
        scheduler.initialize(backend)
        backend.shutdownCallback = (backend: StandaloneSchedulerBackend) => {
          localCluster.stop()
        }
        (backend, scheduler)

      case masterUrl =>
        checkResourcesPerTask(clusterMode = true, None)
        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)
        }
    }
  }

若是是yarn，会落到最后一个case语句：

case masterUrl =>
        checkResourcesPerTask(clusterMode = true, None)
        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)
        }

这里会用到ClusterManager的类，这又是什么东东呢？spark难就难在这，涉及的概念太多。

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
  }

找到全部的ExternalClusterManager类及子类，看哪一个类的canCreate方法对url返回true，咱们这里就是找知足"mesos://host:port"的类。