• TaskScheduler的启动


    《深入理解Spark:核心思想与源码分析》一书前言的内容请看链接《深入理解SPARK:核心思想与源码分析》一书正式出版上市

    《深入理解Spark:核心思想与源码分析》一书第一章的内容请看链接《第1章 环境准备》

    《深入理解Spark:核心思想与源码分析》一书第二章的内容请看链接《第2章 SPARK设计理念与基本架构》

    由于本书的第3章内容较多,所以打算分别开辟四篇随笔分别展现。

    《深入理解Spark:核心思想与源码分析》一书第三章第一部分的内容请看链接《深入理解Spark:核心思想与源码分析》——SparkContext的初始化(伯篇)》

    《深入理解Spark:核心思想与源码分析》一书第三章第二部分的内容请看链接《深入理解Spark:核心思想与源码分析》——SparkContext的初始化(仲篇)》

    本文展现第3章第三部分的内容:

    3.8 TaskScheduler的启动

      3.7节介绍了任务调度器TaskScheduler的创建,要想TaskScheduler发挥作用,必须要启动它,代码如下。

    taskScheduler.start()

    TaskScheduler在启动的时候,实际调用了backend的start方法。

      override def start() {
    
        backend.start()
    
      }

    以LocalBackend为例,启动LocalBackend时向actorSystem注册了LocalActor,见代码清单3-30所示(在《深入理解Spark:核心思想与源码分析》——SparkContext的初始化(中)》一文)。

    3.8.1 创建LocalActor

      创建LocalActor的过程主要是构建本地的Executor,见代码清单3-36。

    代码清单3-36         LocalActor的实现

    private[spark] class LocalActor(scheduler: TaskSchedulerImpl, executorBackend: LocalBackend,
    
      private val totalCores: Int) extends Actor with ActorLogReceive with Logging {
    
      import context.dispatcher   // to use Akka's scheduler.scheduleOnce()
    
      private var freeCores = totalCores
    
      private val localExecutorId = SparkContext.DRIVER_IDENTIFIER
    
      private val localExecutorHostname = "localhost"
    
     
    
      val executor = new Executor(
    
        localExecutorId, localExecutorHostname, scheduler.conf.getAll, totalCores, isLocal = true)
    
     
    
      override def receiveWithLogging = {
    
        case ReviveOffers =>
    
          reviveOffers()
    
     
    
        case StatusUpdate(taskId, state, serializedData) =>
    
          scheduler.statusUpdate(taskId, state, serializedData)
    
          if (TaskState.isFinished(state)) {
    
            freeCores += scheduler.CPUS_PER_TASK
    
            reviveOffers()
    
          }
    
     
    
        case KillTask(taskId, interruptThread) =>
    
          executor.killTask(taskId, interruptThread)
    
     
    
        case StopExecutor =>
    
          executor.stop()
    
      }
    
     
    
    }

    Executor的构建,见代码清单3-37,主要包括以下步骤:

    1) 创建并注册ExecutorSource。ExecutorSource是做什么的呢?笔者将在3.10.2节详细介绍。

    2) 获取SparkEnv。如果是非local模式,Worker上的CoarseGrainedExecutorBackend向Driver上的CoarseGrainedExecutorBackend注册Executor时,则需要新建SparkEnv。可以修改属性spark.executor.port(默认为0,表示随机生成)来配置Executor中的ActorSystem的端口号。

    3) 创建并注册ExecutorActor。ExecutorActor负责接受发送给Executor的消息。

    4) urlClassLoader的创建。为什么需要创建这个ClassLoader?在非local模式中,Driver或者Worker上都会有多个Executor,每个Executor都设置自身的urlClassLoader,用于加载任务上传的jar包中的类,有效对任务的类加载环境进行隔离。

    5) 创建Executor执行TaskRunner任务(TaskRunner将在5.5节介绍)的线程池。此线程池是通过调用Utils.newDaemonCachedThreadPool创建的,具体实现请参阅附录A。

    6) 启动Executor的心跳线程。此线程用于向Driver发送心跳。

    此外,还包括Akka发送消息的帧大小(10485760字节)、结果总大小的字节限制(1073741824字节)、正在运行的task的列表、设置serializer的默认ClassLoader为创建的ClassLoader等。

    代码清单3-37         Executor的构建

      val executorSource = new ExecutorSource(this, executorId)
    
      private val env = {
    
        if (!isLocal) {
    
          val port = conf.getInt("spark.executor.port", 0)
    
          val _env = SparkEnv.createExecutorEnv(
    
            conf, executorId, executorHostname, port, numCores, isLocal, actorSystem)
    
          SparkEnv.set(_env)
    
          _env.metricsSystem.registerSource(executorSource)
    
          _env.blockManager.initialize(conf.getAppId)
    
          _env
    
        } else {
    
          SparkEnv.get
    
        }
    
      }
    
     
    
      private val executorActor = env.actorSystem.actorOf(
    
        Props(new ExecutorActor(executorId)), "ExecutorActor")
    
     
    
      private val urlClassLoader = createClassLoader()
    
      private val replClassLoader = addReplClassLoaderIfNeeded(urlClassLoader)
    
      env.serializer.setDefaultClassLoader(urlClassLoader)
    
     
    
      private val akkaFrameSize = AkkaUtils.maxFrameSizeBytes(conf)
    
      private val maxResultSize = Utils.getMaxResultSize(conf)
    
     
    
      val threadPool = Utils.newDaemonCachedThreadPool("Executor task launch worker")
    
      private val runningTasks = new ConcurrentHashMap[Long, TaskRunner]
    
      startDriverHeartbeater()

    3.8.2 ExecutorSource的创建与注册

      ExecutorSource用于测量系统。通过metricRegistry的register方法注册计量,这些计量信息包括threadpool.activeTasks、threadpool.completeTasks、threadpool.currentPool_size、threadpool.maxPool_size、filesystem.hdfs.write_bytes、filesystem.hdfs.read_ops、filesystem.file.write_bytes、filesystem.hdfs.largeRead_ops、filesystem.hdfs.write_ops等,ExecutorSource的实现见代码清单3-38。Metric接口的具体实现,参考附录D。

    代码清单3-38         ExecutorSource的实现

    private[spark] class ExecutorSource(val executor: Executor, executorId: String) extends Source {
    
      private def fileStats(scheme: String) : Option[FileSystem.Statistics] =
    
        FileSystem.getAllStatistics().filter(s => s.getScheme.equals(scheme)).headOption
    
     
    
      private def registerFileSystemStat[T](
    
            scheme: String, name: String, f: FileSystem.Statistics => T, defaultValue: T) = {
    
        metricRegistry.register(MetricRegistry.name("filesystem", scheme, name), new Gauge[T] {
    
          override def getValue: T = fileStats(scheme).map(f).getOrElse(defaultValue)
    
        })
    
      }
    
      override val metricRegistry = new MetricRegistry()
    
      override val sourceName = "executor"
    
     
    
    metricRegistry.register(MetricRegistry.name("threadpool", "activeTasks"), new Gauge[Int] {
    
        override def getValue: Int = executor.threadPool.getActiveCount()
    
      })
    
     metricRegistry.register(MetricRegistry.name("threadpool", "completeTasks"), new Gauge[Long] {
    
        override def getValue: Long = executor.threadPool.getCompletedTaskCount()
    
      })
    
      metricRegistry.register(MetricRegistry.name("threadpool", "currentPool_size"), new Gauge[Int] {
    
        override def getValue: Int = executor.threadPool.getPoolSize()
    
      })
    
      metricRegistry.register(MetricRegistry.name("threadpool", "maxPool_size"), new Gauge[Int] {
    
        override def getValue: Int = executor.threadPool.getMaximumPoolSize()
    
      })
    
     
    
      // Gauge for file system stats of this executor
    
      for (scheme <- Array("hdfs", "file")) {
    
        registerFileSystemStat(scheme, "read_bytes", _.getBytesRead(), 0L)
    
        registerFileSystemStat(scheme, "write_bytes", _.getBytesWritten(), 0L)
    
        registerFileSystemStat(scheme, "read_ops", _.getReadOps(), 0)
    
        registerFileSystemStat(scheme, "largeRead_ops", _.getLargeReadOps(), 0)
    
        registerFileSystemStat(scheme, "write_ops", _.getWriteOps(), 0)
    
      }
    
    } 

    创建完ExecutorSource后,调用MetricsSystem的registerSource方法将ExecutorSource注册到MetricsSystem。registerSource方法使用MetricRegistry的register方法,将Source注册到MetricRegistry,见代码清单3-39。关于MetricRegistry,具体参阅附录D。

    代码清单3-39         MetricsSystem注册Source的实现

      def registerSource(source: Source) {
    
        sources += source
    
        try {
    
          val regName = buildRegistryName(source)
    
          registry.register(regName, source.metricRegistry)
    
        } catch {
    
          case e: IllegalArgumentException => logInfo("Metrics already registered", e)
    
        }
    
      } 

    3.8.3 ExecutorActor的构建与注册

      ExecutorActor很简单,当接收到SparkUI发来的消息时,将所有线程的栈信息发送回去,代码实现如下。

      override def receiveWithLogging = {
    
        case TriggerThreadDump =>
    
          sender ! Utils.getThreadDump()
    
      }

    3.8.4 Spark自身ClassLoader的创建

      获取要创建的ClassLoader的父加载器currentLoader,然后根据currentJars生成URL数组,spark.files.userClassPathFirst属性指定加载类时是否先从用户的classpath下加载,最后创建ExecutorURLClassLoader或者ChildExecutorURLClassLoader,见代码清单3-40。

    代码清单3-40         Spark自身ClassLoader的创建

      private def createClassLoader(): MutableURLClassLoader = {
    
        val currentLoader = Utils.getContextOrSparkClassLoader
    
     
    
        val urls = currentJars.keySet.map { uri =>
    
          new File(uri.split("/").last).toURI.toURL
    
        }.toArray
    
        val userClassPathFirst = conf.getBoolean("spark.files.userClassPathFirst", false)
    
        userClassPathFirst match {
    
          case true => new ChildExecutorURLClassLoader(urls, currentLoader)
    
          case false => new ExecutorURLClassLoader(urls, currentLoader)
    
        }
    
      } 

    Utils.getContextOrSparkClassLoader的实现见附录A。ExecutorURLClassLoader或者ChildExecutorURLClassLoader实际上都继承了URLClassLoader,见代码清单3-41。 

    代码清单3-41         ChildExecutorURLClassLoader与ExecutorURLClassLoader的实现

    private[spark] class ChildExecutorURLClassLoader(urls: Array[URL], parent: ClassLoader)
    
      extends MutableURLClassLoader {
    
     
    
      private object userClassLoader extends URLClassLoader(urls, null){
    
        override def addURL(url: URL) {
    
          super.addURL(url)
    
        }
    
        override def findClass(name: String): Class[_] = {
    
          super.findClass(name)
    
        }
    
      }
    
     
    
      private val parentClassLoader = new ParentClassLoader(parent)
    
     
    
      override def findClass(name: String): Class[_] = {
    
        try {
    
          userClassLoader.findClass(name)
    
        } catch {
    
          case e: ClassNotFoundException => {
    
            parentClassLoader.loadClass(name)
    
          }
    
        }
    
      }
    
     
    
      def addURL(url: URL) {
    
        userClassLoader.addURL(url)
    
      }
    
     
    
      def getURLs() = {
    
        userClassLoader.getURLs()
    
      }
    
    }
    
     
    
    private[spark] class ExecutorURLClassLoader(urls: Array[URL], parent: ClassLoader)
    
      extends URLClassLoader(urls, parent) with MutableURLClassLoader {
    
     
    
      override def addURL(url: URL) {
    
        super.addURL(url)
    
      }
    
    }

    如果需要REPL交互,还会调用addReplClassLoaderIfNeeded创建replClassLoader,见代码清单3-42。

    代码清单3-42         addReplClassLoaderIfNeeded的实现

      private def addReplClassLoaderIfNeeded(parent: ClassLoader): ClassLoader = {
    
        val classUri = conf.get("spark.repl.class.uri", null)
    
        if (classUri != null) {
    
          logInfo("Using REPL class URI: " + classUri)
    
          val userClassPathFirst: java.lang.Boolean =
    
            conf.getBoolean("spark.files.userClassPathFirst", false)
    
          try {
    
            val klass = Class.forName("org.apache.spark.repl.ExecutorClassLoader")
    
              .asInstanceOf[Class[_ <: ClassLoader]]
    
            val constructor = klass.getConstructor(classOf[SparkConf], classOf[String],
    
              classOf[ClassLoader], classOf[Boolean])
    
            constructor.newInstance(conf, classUri, parent, userClassPathFirst)
    
          } catch {
    
            case _: ClassNotFoundException =>
    
              logError("Could not find org.apache.spark.repl.ExecutorClassLoader on classpath!")
    
              System.exit(1)
    
              null
    
          }
    
        } else {
    
          parent
    
        }
    
      }

    3.8.5 启动Executor的心跳线程

      Executor的心跳由startDriverHeartbeater启动,见代码清单3-43。Executor心跳线程的间隔由属性spark.executor.heartbeatInterval配置,默认是10000毫秒。此外,超时时间是30秒,超时重试次数是3次,重试间隔是3000毫秒,使用actorSystem.actorSelection (url)方法查找到匹配的Actor引用, url是akka.tcp://sparkDriver@ $driverHost:$driverPort/user/HeartbeatReceiver,最终创建一个运行过程中,每次会休眠10000到20000毫秒的线程。此线程从runningTasks获取最新的有关Task的测量信息,将其与executorId、blockManagerId封装为Heartbeat消息,向HeartbeatReceiver发送Heartbeat消息。

    代码清单3-43         启动Executor的心跳线程

      def startDriverHeartbeater() {
    
        val interval = conf.getInt("spark.executor.heartbeatInterval", 10000)
    
        val timeout = AkkaUtils.lookupTimeout(conf)
    
        val retryAttempts = AkkaUtils.numRetries(conf)
    
        val retryIntervalMs = AkkaUtils.retryWaitMs(conf)
    
        val heartbeatReceiverRef = AkkaUtils.makeDriverRef("HeartbeatReceiver", conf,env.actorSystem)
    
        val t = new Thread() {
    
          override def run() {
    
            // Sleep a random interval so the heartbeats don't end up in sync
    
            Thread.sleep(interval + (math.random * interval).asInstanceOf[Int])
    
            while (!isStopped) {
    
              val tasksMetrics = new ArrayBuffer[(Long, TaskMetrics)]()
    
              val curGCTime = gcTime
    
              for (taskRunner <- runningTasks.values()) {
    
                if (!taskRunner.attemptedTask.isEmpty) {
    
                  Option(taskRunner.task).flatMap(_.metrics).foreach { metrics =>
    
                    metrics.updateShuffleReadMetrics
    
                    metrics.jvmGCTime = curGCTime - taskRunner.startGCTime
    
                    if (isLocal) {
    
                      val copiedMetrics = Utils.deserialize[TaskMetrics](Utils.serialize(metrics))
    
                      tasksMetrics += ((taskRunner.taskId, copiedMetrics))
    
                    } else {
    
                      // It will be copied by serialization
    
                      tasksMetrics += ((taskRunner.taskId, metrics))
    
                    }
    
                  }
    
                }
    
              }
    
              val message = Heartbeat(executorId, tasksMetrics.toArray, env.blockManager.blockManagerId)
    
              try {
    
                val response = AkkaUtils.askWithReply[HeartbeatResponse](message, heartbeatReceiverRef,
    
                  retryAttempts, retryIntervalMs, timeout)
    
                if (response.reregisterBlockManager) {
    
                  logWarning("Told to re-register on heartbeat")
    
                  env.blockManager.reregister()
    
                }
    
              } catch {
    
                case NonFatal(t) => logWarning("Issue communicating with driver in heartbeater", t)
    
              }
    
              Thread.sleep(interval)
    
            }
    
          }
    
        }
    
        t.setDaemon(true)
    
        t.setName("Driver Heartbeater")
    
        t.start()
    
      }

    这个心跳线程的作用是什么呢?其作用有两个:

    q  更新正在处理的任务的测量信息;

    q  通知BlockManagerMaster,此Executor上的BlockManager依然活着。

    下面对心跳线程的实现详细分析下,读者可以自行选择是否需要阅读。

      初始化TaskSchedulerImpl后会创建心跳接收器HeartbeatReceiver。HeartbeatReceiver接受所有分配给当前Driver Application的Executor的心跳,并将Task、Task计量信息、心跳等交给TaskSchedulerImpl和DAGScheduler作进一步处理。创建心跳接收器的代码如下。

      private val heartbeatReceiver = env.actorSystem.actorOf(
    
        Props(new HeartbeatReceiver(taskScheduler)), "HeartbeatReceiver")

    HeartbeatReceiver在收到心跳消息后,会调用TaskScheduler的executorHeartbeatReceived方法,代码如下。

      override def receiveWithLogging = {
    
        case Heartbeat(executorId, taskMetrics, blockManagerId) =>
    
          val response = HeartbeatResponse(
    
            !scheduler.executorHeartbeatReceived(executorId, taskMetrics, blockManagerId))
    
          sender ! response
    
      }

    executorHeartbeatReceived的实现代码如下。

        val metricsWithStageIds: Array[(Long, Int, Int, TaskMetrics)] = synchronized {
    
          taskMetrics.flatMap { case (id, metrics) =>
    
            taskIdToTaskSetId.get(id)
    
              .flatMap(activeTaskSets.get)
    
              .map(taskSetMgr => (id, taskSetMgr.stageId, taskSetMgr.taskSet.attempt, metrics))
    
          }
    
        }
    
        dagScheduler.executorHeartbeatReceived(execId, metricsWithStageIds, blockManagerId)

    这段程序通过遍历taskMetrics,依据taskIdToTaskSetId和activeTaskSets找到TaskSetManager。然后将taskId、TaskSetManager.stageId、TaskSetManager .taskSet.attempt、TaskMetrics封装到Array[(Long, Int, Int, TaskMetrics)]的数组metricsWithStageIds中。最后调用了dagScheduler的executorHeartbeatReceived方法,其实现如下。

        listenerBus.post(SparkListenerExecutorMetricsUpdate(execId, taskMetrics))
    
        implicit val timeout = Timeout(600 seconds)
    
     
    
        Await.result(
    
          blockManagerMaster.driverActor ? BlockManagerHeartbeat(blockManagerId),
    
          timeout.duration).asInstanceOf[Boolean]

    dagScheduler将executorId、metricsWithStageIds封装为SparkListenerExecutorMetricsUpdate事件,并post到listenerBus中,此事件用于更新Stage的各种测量数据。最后给BlockManagerMaster持有的BlockManagerMasterActor发送BlockManagerHeartbeat消息。BlockManagerMasterActor在收到消息后会匹配执行heartbeatReceived方法(会在4.3.1节介绍)。heartbeatReceived最终更新BlockManagerMaster对BlockManager最后可见时间(即更新BlockManagerId对应的BlockManagerInfo的_lastSeenMs,见代码清单3-44)。

    代码清单3-44         BlockManagerMasterActor的心跳处理

    private def heartbeatReceived(blockManagerId: BlockManagerId): Boolean = {
    
        if (!blockManagerInfo.contains(blockManagerId)) {
    
          blockManagerId.isDriver && !isLocal
    
        } else {
    
          blockManagerInfo(blockManagerId).updateLastSeenMs()
    
          true
    
        }
    
      }

    local模式下Executor的心跳通信过程,可以用图3-3来表示。

    图3-3       Executor的心跳通信过程

     

    注意:在非local模式中Executor发送心跳的过程是一样的,主要的区别是Executor进程与Driver不在同一个进程,甚至不在同一个节点上。

     

    接下来会初始化块管理器BlockManager,代码如下。

    env.blockManager.initialize(applicationId)

    具体的初始化过程,请参阅第4章。

    未完待续。。。

    后记:自己牺牲了7个月的周末和下班空闲时间,通过研究Spark源码和原理,总结整理的《深入理解Spark:核心思想与源码分析》一书现在已经正式出版上市,目前亚马逊、京东、当当、天猫等网站均有销售,欢迎感兴趣的同学购买。我开始研究源码时的Spark版本是1.2.0,经过7个多月的研究和出版社近4个月的流程,Spark自身的版本迭代也很快,如今最新已经是1.6.0。目前市面上另外2本源码研究的Spark书籍的版本分别是0.9.0版本和1.2.0版本,看来这些书的作者都与我一样,遇到了这种问题。由于研究和出版都需要时间,所以不能及时跟上Spark的脚步,还请大家见谅。但是Spark核心部分的变化相对还是很少的,如果对版本不是过于追求,依然可以选择本书。

    京东(现有满100减30活动):http://item.jd.com/11846120.html 

    当当:http://product.dangdang.com/23838168.html 

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  • 原文地址:https://www.cnblogs.com/jiaan-geng/p/5237230.html
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