　TaskScheduler的实例化

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

其调用了org.apache.spark.SparkContext#createTaskScheduler ，源码如下：

 1 /**
 2    * Create a task scheduler based on a given master URL.
 3    * Return a 2-tuple of the scheduler backend and the task scheduler.
 4    */
 5   private def createTaskScheduler(
 6       sc: SparkContext,
 7       master: String,
 8       deployMode: String): (SchedulerBackend, TaskScheduler) = {
 9     import SparkMasterRegex._
10 
11     // When running locally, don't try to re-execute tasks on failure.
12     val MAX_LOCAL_TASK_FAILURES = 1
13 
14     master match {
15       case "local" =>
16         val scheduler = new TaskSchedulerImpl(sc, MAX_LOCAL_TASK_FAILURES, isLocal = true)
17         val backend = new LocalSchedulerBackend(sc.getConf, scheduler, 1)
18         scheduler.initialize(backend)
19         (backend, scheduler)
20 
21       case LOCAL_N_REGEX(threads) =>
22         def localCpuCount: Int = Runtime.getRuntime.availableProcessors()
23         // local[*] estimates the number of cores on the machine; local[N] uses exactly N threads.
24         val threadCount = if (threads == "*") localCpuCount else threads.toInt
25         if (threadCount <= 0) {
26           throw new SparkException(s"Asked to run locally with $threadCount threads")
27         }
28         val scheduler = new TaskSchedulerImpl(sc, MAX_LOCAL_TASK_FAILURES, isLocal = true)
29         val backend = new LocalSchedulerBackend(sc.getConf, scheduler, threadCount)
30         scheduler.initialize(backend)
31         (backend, scheduler)
32 
33       case LOCAL_N_FAILURES_REGEX(threads, maxFailures) =>
34         def localCpuCount: Int = Runtime.getRuntime.availableProcessors()
35         // local[*, M] means the number of cores on the computer with M failures
36         // local[N, M] means exactly N threads with M failures
37         val threadCount = if (threads == "*") localCpuCount else threads.toInt
38         val scheduler = new TaskSchedulerImpl(sc, maxFailures.toInt, isLocal = true)
39         val backend = new LocalSchedulerBackend(sc.getConf, scheduler, threadCount)
40         scheduler.initialize(backend)
41         (backend, scheduler)
42 
43       case SPARK_REGEX(sparkUrl) =>
44         val scheduler = new TaskSchedulerImpl(sc)
45         val masterUrls = sparkUrl.split(",").map("spark://" + _)
46         val backend = new StandaloneSchedulerBackend(scheduler, sc, masterUrls)
47         scheduler.initialize(backend)
48         (backend, scheduler)
49 
50       case LOCAL_CLUSTER_REGEX(numSlaves, coresPerSlave, memoryPerSlave) =>
51         // Check to make sure memory requested <= memoryPerSlave. Otherwise Spark will just hang.
52         val memoryPerSlaveInt = memoryPerSlave.toInt
53         if (sc.executorMemory > memoryPerSlaveInt) {
54           throw new SparkException(
55             "Asked to launch cluster with %d MB RAM / worker but requested %d MB/worker".format(
56               memoryPerSlaveInt, sc.executorMemory))
57         }
58 
59         val scheduler = new TaskSchedulerImpl(sc)
60         val localCluster = new LocalSparkCluster(
61           numSlaves.toInt, coresPerSlave.toInt, memoryPerSlaveInt, sc.conf)
62         val masterUrls = localCluster.start()
63         val backend = new StandaloneSchedulerBackend(scheduler, sc, masterUrls)
64         scheduler.initialize(backend)
65         backend.shutdownCallback = (backend: StandaloneSchedulerBackend) => {
66           localCluster.stop()
67         }
68         (backend, scheduler)
69 
70       case masterUrl =>
71         val cm = getClusterManager(masterUrl) match {
72           case Some(clusterMgr) => clusterMgr
73           case None => throw new SparkException("Could not parse Master URL: '" + master + "'")
74         }
75         try {
76           val scheduler = cm.createTaskScheduler(sc, masterUrl)
77           val backend = cm.createSchedulerBackend(sc, masterUrl, scheduler)
78           cm.initialize(scheduler, backend)
79           (backend, scheduler)
80         } catch {
81           case se: SparkException => throw se
82           case NonFatal(e) =>
83             throw new SparkException("External scheduler cannot be instantiated", e)
84         }
85     }
86   }

　　不同的实现如下：

　　实例化部分剖析完毕，下半部分重点剖析yarn-client mode 下 TaskScheduler 的启动过程

　yarn-client模式TaskScheduler 启动过程

　初始化调度池

　　yarn-client 模式下，TaskScheduler的实现是 org.apache.spark.scheduler.cluster.YarnScheduler, TaskSchedulerBackend的实现是org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend

　　在org.apache.spark.SparkContext#createTaskScheduler 方法中，有如下调用：

 1 case masterUrl =>
 2         val cm = getClusterManager(masterUrl) match {
 3           case Some(clusterMgr) => clusterMgr
 4           case None => throw new SparkException("Could not parse Master URL: '" + master + "'")
 5         }
 6         try {
 7           val scheduler = cm.createTaskScheduler(sc, masterUrl)
 8           val backend = cm.createSchedulerBackend(sc, masterUrl, scheduler)
 9           cm.initialize(scheduler, backend)
10           (backend, scheduler)
11         } catch {
12           case se: SparkException => throw se
13           case NonFatal(e) =>
14             throw new SparkException("External scheduler cannot be instantiated", e)
15         }

　　其中的，cm.initialize(scheduler, backend)中的cm 是org.apache.spark.scheduler.cluster.YarnClusterManager，TaskScheduler的实现是 org.apache.spark.scheduler.cluster.YarnScheduler, TaskSchedulerBackend的实现是org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend。YarnClusterManager 的 initialize 方法实现如下：

1   override def initialize(scheduler: TaskScheduler, backend: SchedulerBackend): Unit = {
2     scheduler.asInstanceOf[TaskSchedulerImpl].initialize(backend)
3   }

其并没有实现 initialize，父类TaskSchedulerImpl 的实现如下：

 1 def initialize(backend: SchedulerBackend) {
 2     this.backend = backend
 3     schedulableBuilder = {
 4       schedulingMode match {
 5         case SchedulingMode.FIFO =>
 6           new FIFOSchedulableBuilder(rootPool)
 7         case SchedulingMode.FAIR =>
 8           new FairSchedulableBuilder(rootPool, conf)
 9         case _ =>
10           throw new IllegalArgumentException(s"Unsupported $SCHEDULER_MODE_PROPERTY: " +
11           s"$schedulingMode")
12       }
13     }
14     schedulableBuilder.buildPools()
15   }

　　可以看出，其重要作用就是设置 TaskScheduler 的 TaskSchedulerBackend 引用。

　　调度模式主要有FIFO和FAIR两种模式。默认是FIFO模式，可以使用spark.scheduler.mode 参数来设定。使用建造者模式来创建 Pool 对象。

　　其中，org.apache.spark.scheduler.FIFOSchedulableBuilder#buildPools是一个空实现，即没有做任何的操作；而 org.apache.spark.scheduler.FairSchedulableBuilder#buildPools会加载相应调度分配策略文件；策略文件可以使用 spark.scheduler.allocation.file 参数来设定，如果没有设定会进一步加载默认的 fairscheduler.xml 文件，如果还没有，则不加载。如果有调度池的配置，则根据配置配置调度pool并将其加入到 root 池中。最后初始化 default 池并将其加入到 root 池中。

在HeartBeatReceiver 中设定 taskscheduler 变量

1 _heartbeatReceiver.ask[Boolean](TaskSchedulerIsSet)

　首先，_heartbeatReceiver 是一个 RpcEndPointRef 对象，其请求最终会被 HeartbeatReceiver（Endpoint）接收并处理。即org.apache.spark.HeartbeatReceiver#receiveAndReply方法：

1 case TaskSchedulerIsSet =>
2       scheduler = sc.taskScheduler
3       context.reply(true)

具体的关于RPC的相关解释，会在后面有专门的文章篇幅介绍。在这里就不做过多解释。 // TODO

　启动TaskScheduler

　 org.apache.spark.SparkContext 的初始化方法有如下代码启动 TaskScheduler：

1 _taskScheduler.start()

　　yarn-client模式下，运行中调用了 org.apache.spark.scheduler.cluster.YarnScheduler 的 start 方法，它沿用了父类 TaskSchedulerImpl 的实现：

 1 override def start() {
 2     // 1. 启动 task scheduler backend
 3     backend.start()
 4     // 2. 设定 speculationScheduler 定时任务
 5     if (!isLocal && conf.getBoolean("spark.speculation", false)) {
 6       logInfo("Starting speculative execution thread")
 7       speculationScheduler.scheduleWithFixedDelay(new Runnable {
 8         override def run(): Unit = Utils.tryOrStopSparkContext(sc) {
 9           checkSpeculatableTasks()
10         }
11       }, SPECULATION_INTERVAL_MS, SPECULATION_INTERVAL_MS, TimeUnit.MILLISECONDS)
12     }
13   }

　　第1步：task scheduler backend 的启动：org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend#start的方法如下：

 1 /**
 2    * Create a Yarn client to submit an application to the ResourceManager.
 3    * This waits until the application is running.
 4    */
 5   override def start() {
 6     // 1. 获取driver 的 host 和 port
 7     val driverHost = conf.get("spark.driver.host")
 8     val driverPort = conf.get("spark.driver.port")
 9     val hostport = driverHost + ":" + driverPort
10     // 2. 设定 driver 的 web UI 地址
11     sc.ui.foreach { ui => conf.set("spark.driver.appUIAddress", ui.webUrl) }
12 
13     val argsArrayBuf = new ArrayBuffer[String]()
14     argsArrayBuf += ("--arg", hostport)
15 
16     logDebug("ClientArguments called with: " + argsArrayBuf.mkString(" "))
17     val args = new ClientArguments(argsArrayBuf.toArray)
18     totalExpectedExecutors = SchedulerBackendUtils.getInitialTargetExecutorNumber(conf)
19     // 3. 启动 deploy client，并切初始化 driverClient 的 Rpc environment，并在该RPC 环境中初始化master 和 driver 的rpc endpoint
20     client = new Client(args, conf)
21     // 4. 将 application id 绑定到 yarn 上
22     bindToYarn(client.submitApplication(), None)
23 
24     // SPARK-8687: Ensure all necessary properties have already been set before
25     // we initialize our driver scheduler backend, which serves these properties
26     // to the executors
27     super.start()
28    // 5. 检查 yarn application的状态，不能为 kill， finished等等
29     waitForApplication()
30    // 6. 监控线程
31     monitorThread = asyncMonitorApplication()
32     monitorThread.start()
33   }

　　重点解释一下第三步，涉及的源码步如下：

 1 object Client {
 2   def main(args: Array[String]) {
 3     // scalastyle:off println
 4     if (!sys.props.contains("SPARK_SUBMIT")) {
 5       println("WARNING: This client is deprecated and will be removed in a future version of Spark")
 6       println("Use ./bin/spark-submit with \"--master spark://host:port\"")
 7     }
 8     // scalastyle:on println
 9     new ClientApp().start(args, new SparkConf())
10   }
11 }
12 
13 private[spark] class ClientApp extends SparkApplication {
14 
15   override def start(args: Array[String], conf: SparkConf): Unit = {
16     val driverArgs = new ClientArguments(args)
17 
18     if (!conf.contains("spark.rpc.askTimeout")) {
19       conf.set("spark.rpc.askTimeout", "10s")
20     }
21     Logger.getRootLogger.setLevel(driverArgs.logLevel)
22 
23     val rpcEnv =
24       RpcEnv.create("driverClient", Utils.localHostName(), 0, conf, new SecurityManager(conf))
25 
26     val masterEndpoints = driverArgs.masters.map(RpcAddress.fromSparkURL).
27       map(rpcEnv.setupEndpointRef(_, Master.ENDPOINT_NAME))
28     rpcEnv.setupEndpoint("client", new ClientEndpoint(rpcEnv, driverArgs, masterEndpoints, conf))
29 
30     rpcEnv.awaitTermination()
31   }
32 
33 }

　　可以看到，在Client 的main方法中，初始化了ClientApp 对象，并调用了其 start 方法，在start 方法中，首先解析了 driver的参数。然后创建了 driver 端的 RPC environment，然后根据解析的 master 的信息，初始化 master 的endpointref，并且建立了 client endpoint 并返回 client endpoint ref。

定时执行推测任务

　　下面继续看 org.apache.spark.scheduler.cluster.YarnScheduler 的 start 方法的第二步方法，首先 spark 推测任务 feature 默认是关闭的，原因如果有很多任务都延迟了，那么它会再启动一个相同的任务，这样可能会消耗掉所有的资源，对集群资源和提交到集群上的任务造成不可控的影响。启动了一个延迟定时器，定时地执行 checkSpeculatableTasks 方法，如下：

 1 // Check for speculatable tasks in all our active jobs.
 2   def checkSpeculatableTasks() {
 3     var shouldRevive = false
 4     synchronized {
 5       shouldRevive = rootPool.checkSpeculatableTasks(MIN_TIME_TO_SPECULATION) // 1. 推测是否应该跑一个新任务
 6     }
 7     if (shouldRevive) {
 8       backend.reviveOffers() // 2. 跑一个新任务
 9     }
10   }

　　其中，第一步推断任务，有两个实现一个是Pool 的实现，一个是TaskSetManager 的实现，Pool 会递归调用子Pool来获取 speculatable tasks。如果需要推测，则运行task scheduler backend 的 reviveOffers方法，大致思路如下，首先获取 executor 上的空闲资源，然后将这些资源分配给推测的 task，供其使用。

　　总结，本篇源码剖析了在Spark Context 启动过程中，以 yarn-client 模式为例，剖析了task scheduler 是如何启动的。

　　其中关于RpcEnv的介绍直接略过了，下一篇会专门讲解Spark 中内置的Rpc 机制的整体架构以及其是如何运行的。

spark 源码分析之四 -- TaskScheduler的创建和启动过程 spark 源码分析之二 -- SparkContext 的初始化过程