Spark 源码解析（三） Saprk的组件三巨头初始化 TaskSchuduler,Backend,DAGScheduler

当我们初始化SparkContext的时候 会创建 很多的组件 这里详解一个 Spark的重要组件 TaskScheduler,Backend，DAGSCheduler

 val (sched, ts) = SparkContext.createTaskScheduler(this, master, deployMode)

进入到SparkContext的createTaskScheduler方法中进行模式匹配

// local模式 略过
// standAlone 模式
case SPARK_REGEX(sparkUrl) =>
// 创建TaskScheduler的实现 TaskSchedulerImpl
 val scheduler = new TaskSchedulerImpl(sc)
 val masterUrls = sparkUrl.split(",").map("spark://" + _)
 val backend = new StandaloneSchedulerBackend(scheduler, sc, masterUrls)
 scheduler.initialize(backend)
 (backend, scheduler)

因为查看TaskScheduler的创建需要 backend 所以我们先看Backend的创建

 // 因为是StandAlone 模式下的 所以实现类是StandaloneSchedulerBackend
 构造方法中依旧没有重要内容

TaskSchedulerImpl 构造方法

  def this(sc: SparkContext) = {
      
        this(sc, sc.conf.get(config.MAX_TASK_FAILURES))
    }
// 构造方法中没有什么内容 我们 通过scheduler.initialize(backend)
// 查看重要的初始化实现 scheduler.initialize(backend) 
def initialize(backend: SchedulerBackend) {
        
        // 把 TaskSchedulerImpl 讲 SchedulerBackend 设置成 TaskScheduler 的成员变量
    	// 维护起来 
        this.backend = backend
        
        schedulableBuilder = {
            schedulingMode match {
                // 配置文件中 默认调度为FIFO
                case SchedulingMode.FIFO => new FIFOSchedulableBuilder(rootPool)
                case SchedulingMode.FAIR => new FairSchedulableBuilder(rootPool, conf)
                case _ => throw new IllegalArgumentException(s"Unsupported $SCHEDULER_MODE_PROPERTY: " + s"$schedulingMode")
            }
        }
   
   
        schedulableBuilder.buildPools()
    }

Yarn 模式

// yarn 模式
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
        //获取到所有实现了ExternalClusterManager接口的实例　再过滤ｕｒｌ不匹配的
        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: $serviceLoaders")
        }
        serviceLoaders.headOption
}
// master 为 yarn 所以 cm就为 YarnClusterManager
// 调用了 
createTaskScheduler
override def createTaskScheduler(sc: SparkContext, masterURL: String): TaskScheduler = {
    sc.deployMode match {
      case "cluster" => new YarnClusterScheduler(sc)
      case "client" => new YarnScheduler(sc)
      case _ => throw new SparkException(s"Unknown deploy mode '${sc.deployMode}' for Yarn")
    }
  }
 
//我们可以看出来 YarnClusterScheduler 是TaskScheduler的一个子类
private[spark] class YarnClusterScheduler(sc: SparkContext) extends YarnScheduler(sc) 
private[spark] class YarnScheduler(sc: SparkContext) extends TaskSchedulerImpl(sc)

// 初始化方法 
  override def initialize(scheduler: TaskScheduler, backend: SchedulerBackend): Unit = {
    scheduler.asInstanceOf[TaskSchedulerImpl].initialize(backend)
  }

创建DAGScheduler

_dagScheduler = new DAGScheduler(this)

def this(sc: SparkContext) = this(sc, sc.taskScheduler)

def this(sc: SparkContext, taskScheduler: TaskScheduler) = {
        this(sc, taskScheduler, sc.listenerBus, sc.env.mapOutputTracker.asInstanceOf[MapOutputTrackerMaster], sc.env.blockManager.master, sc.env)
    }

//最后调用构造方法

private[scheduler] val shuffleIdToMapStage = new HashMap[Int, ShuffleMapStage]
    
    private[scheduler] val jobIdToActiveJob = new HashMap[Int, ActiveJob]
    
    // Stages we need to run whose parents aren't done
    private[scheduler] val waitingStages = new HashSet[Stage]
    
    // Stages we are running right now
    private[scheduler] val runningStages = new HashSet[Stage]
    
    // Stages that must be resubmitted due to fetch failures
    private[scheduler] val failedStages = new HashSet[Stage]
    
    private[scheduler] val activeJobs = new HashSet[ActiveJob]
    
	private val cacheLocs = new HashMap[Int, IndexedSeq[Seq[TaskLocation]]]

	private val executorFailureEpoch = new HashMap[String, Long]
	....... //初始化了一堆需要的数据结构 和组件 不太重要就省略
	
	private[spark] val eventProcessLoop = new DAGSchedulerEventProcessLoop(this)
	// 将DAGScheduler 设置为TaskSchederler的成员变量 和Backend一样
    taskScheduler.setDAGScheduler(this)

我们来看看这个DAGSchedulerEventProcessLoop 1: 重写了父类的onReceive方法 2：父类初始化的时候就会创建并启动一个线程 从队列中获取到事件event 并调用onReceive 3：然后就会调用doonReceive 根据不同的事件类型 来进行不同的处理

private[scheduler] class DAGSchedulerEventProcessLoop(dagScheduler: DAGScheduler) extends EventLoop[DAGSchedulerEvent](
    "dag-scheduler-event-loop") with Logging {
    
    private[this] val timer = dagScheduler.metricsSource.messageProcessingTimer
    
    
    override def onReceive(event: DAGSchedulerEvent): Unit = {
        val timerContext = timer.time()
        try {
            
            doOnReceive(event)
        } finally {
            timerContext.stop()
        }
    }
    
   
    private def doOnReceive(event: DAGSchedulerEvent): Unit = event match {
        
        case JobSubmitted(jobId, rdd, func, partitions, callSite, listener, properties) => dagScheduler
            .handleJobSubmitted(jobId, rdd, func, partitions, callSite, listener, properties)
        
      
        case MapStageSubmitted(jobId, dependency, callSite, listener, properties) => dagScheduler
            .handleMapStageSubmitted(jobId, dependency, callSite, listener, properties)

        case StageCancelled(stageId, reason) => dagScheduler.handleStageCancellation(stageId, reason)
        
      
        case JobCancelled(jobId, reason) => dagScheduler.handleJobCancellation(jobId, reason)
        
     
        case JobGroupCancelled(groupId) => dagScheduler.handleJobGroupCancelled(groupId)
        
        
        case AllJobsCancelled => dagScheduler.doCancelAllJobs()
        
        
        case ExecutorAdded(execId, host) => dagScheduler.handleExecutorAdded(execId, host)
        
       
        case ExecutorLost(execId, reason) => val workerLost = reason match {
            case SlaveLost(_, true) => true
            case _ => false
        }
            dagScheduler.handleExecutorLost(execId, workerLost)
        
        
        case WorkerRemoved(workerId, host, message) => dagScheduler.handleWorkerRemoved(workerId, host, message)
        
        
        case BeginEvent(task, taskInfo) => dagScheduler.handleBeginEvent(task, taskInfo)
        
        
        case SpeculativeTaskSubmitted(task) => dagScheduler.handleSpeculativeTaskSubmitted(task)
        
    
        case GettingResultEvent(taskInfo) => dagScheduler.handleGetTaskResult(taskInfo)
        
        
        case completion: CompletionEvent => dagScheduler.handleTaskCompletion(completion)
        
        
        case TaskSetFailed(taskSet, reason, exception) => dagScheduler.handleTaskSetFailed(taskSet, reason, exception)
        
        
        case ResubmitFailedStages => dagScheduler.resubmitFailedStages()
    }
    
    override def onError(e: Throwable): Unit = {
        logError("DAGSchedulerEventProcessLoop failed; shutting down SparkContext", e)
        try {
            dagScheduler.doCancelAllJobs()
        } catch {
            case t: Throwable => logError("DAGScheduler failed to cancel all jobs.", t)
        }
        dagScheduler.sc.stopInNewThread()
    }
    
    override def onStop(): Unit = {
        // Cancel any active jobs in postStop hook
        dagScheduler.cleanUpAfterSchedulerStop()
    }
}

private[spark] object DAGScheduler {
    // The time, in millis, to wait for fetch failure events to stop coming in after one is detected;
    // this is a simplistic way to avoid resubmitting tasks in the non-fetchable map stage one by one
    // as more failure events come in
    val RESUBMIT_TIMEOUT = 200
    
    // Number of consecutive stage attempts allowed before a stage is aborted
    val DEFAULT_MAX_CONSECUTIVE_STAGE_ATTEMPTS = 4
}

EventLoop

// 我们 开启一个线程 来直接 获取event 队列中的事件 然后 进行处理
// 后面的任务调教代码中 我们会看到向event队列中添加事件
// private val eventQueue: BlockingQueue[E] = new linkedBlockingDeque[E]()
private[spark] val eventThread = new Thread(name) {
        setDaemon(true)
        
        override def run(): Unit = {
            try {
                while (!stopped.get) {
                    
                    
                    // 拿出事件
                    val event = eventQueue.take()
                    try {
                       	
                        onReceive(event)
                    } catch {
                        case NonFatal(e) => try {
                            onError(e)
                        } catch {
                            case NonFatal(e) => logError("Unexpected error in " + name, e)
                        }
                    }
                }
            } catch {
                case ie: InterruptedException => // exit even if eventQueue is not empty
                case NonFatal(e) => logError("Unexpected error in " + name, e)
            }
        }
        
    }

启动TaskScheduler 调用实现类的start方法

override def start() {
    // 这里调用backend的start方法
backend.start()
        
        if (!isLocal && conf.getBoolean("spark.speculation", false)) {
            logInfo("Starting speculative execution thread")
            speculationScheduler.scheduleWithFixedDelay(new Runnable {
                override def run(): Unit = Utils.tryOrStopSparkContext(sc) {
                    // spark的推测执行 
                    // 当stage中的某个Task特别慢的时候 
                    // 就会将其放到另外一个Excutor中启动Task 
                    // 执行得快 就会将其作为结果 并kill掉最先的那个
            
                    checkSpeculatableTasks()
                }
            }, SPECULATION_INTERVAL_MS, SPECULATION_INTERVAL_MS, TimeUnit.MILLISECONDS)
        }
    }

StandAloneBackend 的start方法做的事 1：创建了一个DriverEndPoint通信终端 2：给自己发送一条消息ReviveOffers

​ 当启动TaskScheduler的时候就会调用内部维护的StandaloneBacken的start方法

​ 当方法启动的时候就会先去执父类的start() 方法 然后就会 开启一个定时任务 把

​ 内存中维护的Excotor队列中不可用的删除掉

​ 然后就会拼接java 命令 然后 在命令中写死了的 启动 org.apache.spark.executor.CoarseGrainedExecutorBackend 类

​ 然后调用 这个类 伴生对象的的main 方法 其中再调用run方法

然后执行

env.rpcEnv
    .setupEndpoint("Executor", new CoarseGrainedExecutorBackend(env.rpcEnv, driverUrl, executorId, hostname, cores, userClassPath, env))

​ 来创建了一个 CoarseGrainedExecutorBackend 这个Excutork工作节点的一种实现类

​ 再节点启动的时候就会发送请求了

​

 override def onStart() {
        logInfo("Connecting to driver: " + driverUrl)
        rpcEnv.asyncSetupEndpointRefByURI(driverUrl).flatMap { ref => // This is a very fast action so we can use "ThreadUtils.sameThread"
            driver = Some(ref)
            //TODO: ask 就会到receiveAndReply （ask 问 就会有回答嘛 ）
            // CoarseGrainedSchedulerBackend 的 receiveAndReply RegisterExecutor
            ref.ask[Boolean](RegisterExecutor(executorId, self, hostname, cores, extractLogUrls))
        }(ThreadUtils.sameThread).onComplete {
            // This is a very fast action so we can use "ThreadUtils.sameThread"
            case Success(msg) => // Always receive `true`. Just ignore it
            case Failure(e) => exitExecutor(1, s"Cannot register with driver: $driverUrl", e, notifyDriver = false)
        }(ThreadUtils.sameThread)
    }

然后接受到请求后 就会往一个HashMap中存数据 维护起来

3: 接受到消息 申请资源 加载Task （之后的Task执行源码再详解）

然后我们来看通用父类的start() 方法 CoarseGrainedSchedulerBackend 这里就是我们

方法的调用栈 start()->createDriverEndpointRef()->createDriverEndpoint()->new DriverEndpoint(rpcEnv, properties) 来创建一个Driver终端

在创建Driver的时候就会 创建定时任务 给自己发送 ReviveOffers 消息

然后自己接受到消息后 就会去队列中 过滤掉 死掉的节点

ps:因为这里没task提交 所以也不会去执行Task的加载

然后

// start方法
override def start() {
        val properties = new ArrayBuffer[(String, String)]
        for ((key, value) <- scheduler.sc.conf.getAll) {
            if (key.startsWith("spark.")) {
                properties += ((key, value))
            }
        }
        //创建 维护一个 DriverEndPoint 和主节点 Master进行通信
        driverEndpoint = createDriverEndpointRef(properties)
        }

// 最后就是 new DriverEndpoint(rpcEnv, properties) 然后注册到rpcEnv中去

class DriverEndpoint(override val rpcEnv: RpcEnv, sparkProperties: Seq[(String, String)]) extends ThreadSafeRpcEndpoint with Logging {
        
        // Executors that have been lost, but for which we don't yet know the real exit reason.
        protected val executorsPendingLossReason = new HashSet[String]
        
        protected val addressToExecutorId = new HashMap[RpcAddress, String]

    override def onStart() {
            // Periodically revive offers to allow delay scheduling to work
            val reviveIntervalMs = conf.getTimeAsMs("spark.scheduler.revive.interval", "1s")
            
            
            reviveThread.scheduleAtFixedRate(new Runnable {
                override def run(): Unit = Utils.tryLogNonFatalError {
                    Option(self).foreach(_.send(ReviveOffers))
                }
            }, 0, reviveIntervalMs, TimeUnit.MILLISECONDS)
        }
    override def receive: PartialFunction[Any, Unit] = {
         case StatusUpdate(executorId, taskId, state, data) => scheduler.statusUpdate(taskId, state, data.value)
                if (TaskState.isFinished(state)) {
                    executorDataMap.get(executorId) match {
                        case Some(executorInfo) => executorInfo.freeCores += scheduler.CPUS_PER_TASK
                            makeOffers(executorId)
                        case None => // Ignoring the update since we don't know about the executor.
                            logWarning(s"Ignored task status update ($taskId state $state) " + s"from unknown executor with ID $executorId")
                    }
                }
            
            
            case ReviveOffers => makeOffers()
            
            
            case KillTask(taskId, executorId, interruptThread, reason) => executorDataMap.get(executorId) match {
                case Some(executorInfo) => executorInfo.executorEndpoint.send(KillTask(taskId, executorId, interruptThread, reason))
                case None => // Ignoring the task kill since the executor is not registered.
                    logWarning(s"Attempted to kill task $taskId for unknown executor $executorId.")
            }
            case KillExecutorsOnHost(host) => scheduler.getExecutorsAliveOnHost(host).foreach { exec =>
                killExecutors(exec.toSeq, adjustTargetNumExecutors = false, countFailures = false, force = true)
            }
            case UpdateDelegationTokens(newDelegationTokens) => executorDataMap.values.foreach { ed =>
                ed.executorEndpoint.send(UpdateDelegationTokens(newDelegationTokens))
            }
            case RemoveExecutor(executorId, reason) => // We will remove the executor's state and cannot restore it. However, the connection
                // between the driver and the executor may be still alive so that the executor won't exit
                // automatically, so try to tell the executor to stop itself. See SPARK-13519.
                executorDataMap.get(executorId).foreach(_.executorEndpoint.send(StopExecutor))
                removeExecutor(executorId, reason)
        }
     
   
    
private def makeOffers() {
            // Make sure no executor is killed while some task is launching on it
            val taskDescs = withLock {
                // Filter out executors under killing
                val activeExecutors = executorDataMap.filterKeys(executorIsAlive)
                val workOffers = activeExecutors.map
                { case (id, executorData) => new WorkerOffer(id, executorData.executorHost, executorData.freeCores,
                    Some(executorData.executorAddress.hostPort))
                }.toIndexedSeq
                
                // 申请资源
                scheduler.resourceOffers(workOffers)
            }
            
            
            if (!taskDescs.isEmpty) {
                launchTasks(taskDescs)
            }
        }