# 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 100   /path/to/examples.jar   1000

def main(args: Array[String]) {
    val appArgs = new SparkSubmitArguments(args)
    val (childArgs, classpath, sysProps, mainClass) = createLaunchEnv(appArgs)
    launch(childArgs, classpath, sysProps, mainClass, appArgs.verbose)
}

//创建一个ActorSystem
val (actorSystem, _) = AkkaUtils.createActorSystem("driverClient",Utils.localHostName(),0,
　　conf, new SecurityManager(conf))
//执行ClientActor的preStart方法和receive方法
actorSystem.actorOf(Props(classOf[ClientActor], driverArgs, conf))
//等待运行结束
actorSystem.awaitTermination()

//定义一个case class用来传递参数
case class Greeting(who: String)
//定义Actor，比较重要的一个方法是receive方法，用来接收信息的
class GreetingActor extends Actor with ActorLogging {
   def receive = {
       case Greeting(who) ? log.info("Hello " + who)
   }
}
//创建一个ActorSystem
val system = ActorSystem("MySystem")
//给ActorSystem设置Actor
val greeter = system.actorOf(Props[GreetingActor], name = "greeter")
//向greeter发送信息，用Greeting来传递
greeter ! Greeting("Charlie Parker")

override def preStart() = {
    // 这里需要把master的地址转换成akka的地址，然后通过这个akka地址获得指定的actor
    // 它的格式是"akka.tcp://%s@%s:%s/user/%s".format(systemName, host, port, actorName)
    masterActor = context.actorSelection(Master.toAkkaUrl(driverArgs.master))
    // 把自身设置成远程生命周期的事件
    context.system.eventStream.subscribe(self, classOf[RemotingLifecycleEvent])

    driverArgs.cmd match {
      case "launch" =>
        // 此处省略100个字
        val mainClass = "org.apache.spark.deploy.worker.DriverWrapper"
        // 此处省略100个字
        // 向master发送提交Driver的请求，把driverDescription传过去，RequestSubmitDriver前面说过了，是个case class
        masterActor ! RequestSubmitDriver(driverDescription)

      case "kill" =>
        val driverId = driverArgs.driverId
        val killFuture = masterActor ! RequestKillDriver(driverId)
    }
}

val (actorSystem, boundPort) = AkkaUtils.createActorSystem(systemName, host, port, conf = conf, 
　　securityManager = securityMgr)
val actor = actorSystem.actorOf(Props(classOf[Master], host, boundPort, webUiPort, securityMgr), actorName)
val timeout = AkkaUtils.askTimeout(conf)
val respFuture = actor.ask(RequestWebUIPort)(timeout)
val resp = Await.result(respFuture, timeout).asInstanceOf[WebUIPortResponse]

    case RequestSubmitDriver(description) => {
        val driver = createDriver(description)
        persistenceEngine.addDriver(driver)
        waitingDrivers += driver
        drivers.add(driver)
        // 调度
        schedule()
         // 告诉client，提交成功了，把driver.id告诉它
        sender ! SubmitDriverResponse(true, Some(driver.id), s"Driver successfully submitted as ${driver.id}")
      }

private def schedule() {
    if (state != RecoveryState.ALIVE) { return }

    // 首先调度Driver程序，从workers里面随机抽一些出来
    val shuffledWorkers = Random.shuffle(workers) 
    for (worker <- shuffledWorkers if worker.state == WorkerState.ALIVE) {
      for (driver <- waitingDrivers) {
        // 判断内存和cpu够不够，够的就执行了哈
        if (worker.memoryFree >= driver.desc.mem && worker.coresFree >= driver.desc.cores) {
          launchDriver(worker, driver)
          waitingDrivers -= driver
        }
      }
    }

    // 这里是按照先进先出的，spreadOutApps是由spark.deploy.spreadOut参数来决定的，默认是true
    if (spreadOutApps) {
      // 遍历一下app
      for (app <- waitingApps if app.coresLeft > 0) {
        val usableWorkers = workers.toArray.filter(_.state == WorkerState.ALIVE)
          .filter(canUse(app, _)).sortBy(_.coresFree).reverse
        val numUsable = usableWorkers.length
        val assigned = new Array[Int](numUsable) // 记录每个节点的核心数
        var toAssign = math.min(app.coresLeft, usableWorkers.map(_.coresFree).sum)
        var pos = 0
        // 遍历直到分配结束
        while (toAssign > 0) {
          // 从0开始遍历可用的work，如果可用的cpu减去已经分配的>0,就可以分配给它
          if (usableWorkers(pos).coresFree - assigned(pos) > 0) {
            toAssign -= 1
            // 这个位置的work的可分配的cpu数+1
            assigned(pos) += 1
          }
          pos = (pos + 1) % numUsable
        }
        // 给刚才标记的worker分配任务
        for (pos <- 0 until numUsable) {
          if (assigned(pos) > 0) {
            val exec = app.addExecutor(usableWorkers(pos), assigned(pos))
            launchExecutor(usableWorkers(pos), exec)
            app.state = ApplicationState.RUNNING
          }
        }
      }
    } else {
      // 这种方式和上面的方式的区别是，这种方式尽可能用少量的节点来完成这个任务
      for (worker <- workers if worker.coresFree > 0 && worker.state == WorkerState.ALIVE) {
        for (app <- waitingApps if app.coresLeft > 0) {
          // 判断条件是worker的内存比app需要的内存多
          if (canUse(app, worker)) {
            val coresToUse = math.min(worker.coresFree, app.coresLeft)
            if (coresToUse > 0) {
              val exec = app.addExecutor(worker, coresToUse)
              launchExecutor(worker, exec)
              app.state = ApplicationState.RUNNING
            }
          }
        }
      }
    }
  }

  def launchDriver(worker: WorkerInfo, driver: DriverInfo) {
    logInfo("Launching driver " + driver.id + " on worker " + worker.id)
    worker.addDriver(driver)
    driver.worker = Some(worker)
    worker.actor ! LaunchDriver(driver.id, driver.desc)
    driver.state = DriverState.RUNNING
  }

  def launchExecutor(worker: WorkerInfo, exec: ExecutorInfo) {
    logInfo("Launching executor " + exec.fullId + " on worker " + worker.id)
    worker.addExecutor(exec)
    worker.actor ! LaunchExecutor(masterUrl,
      exec.application.id, exec.id, exec.application.desc, exec.cores, exec.memory)
    exec.application.driver ! ExecutorAdded(
      exec.id, worker.id, worker.hostPort, exec.cores, exec.memory)
  }

case LaunchDriver(driverId, driverDesc) => {
      logInfo(s"Asked to launch driver $driverId")
      val driver = new DriverRunner(driverId, workDir, sparkHome, driverDesc, self, akkaUrl)
      drivers(driverId) = driver
      driver.start()

      coresUsed += driverDesc.cores
      memoryUsed += driverDesc.mem
}