3.6 创建任务调度器TaskScheduler
TaskScheduler也是SparkContext的重要组成部分，负责任务的提交，并且请求集群管理器对任务调度。TaskScheduler也可以看做任务调度的客户端。创建TaskScheduler的代码如下。
private[spark] var (schedulerBackend, taskScheduler) =
SparkContext.createTaskScheduler(this, master)
createTaskScheduler方法会根据master的配置匹配部署模式，创建TaskSchedulerImpl，并生成不同的SchedulerBackend。本章为了使读者更容易理解Spark的初始化流程，故以local模式为例，其余模式将在第7章详解。master匹配local模式的代码如下。
master match {
case "local" =>
val scheduler = new TaskSchedulerImpl(sc, MAX_LOCAL_TASK_FAILURES, isLocal = true)
val backend = new LocalBackend(scheduler, 1)
scheduler.initialize(backend)
(backend, scheduler)
3.6.1 创建TaskSchedulerImpl
TaskSchedulerImpl的构造过程如下：
1）从SparkConf中读取配置信息，包括每个任务分配的CPU数、调度模式（调度模式有FAIR和FIFO两种，默认为FIFO，可以修改属性spark.scheduler.mode来改变）等。
2）创建TaskResultGetter，它的作用是通过线程池（Executors.newFixedThreadPool创建的，默认4个线程，线程名字以task-result-getter开头，线程工厂默认是Executors.default-ThreadFactory）对Worker上的Executor发送的Task的执行结果进行处理。
TaskSchedulerImpl的实现见代码清单3-29。
代码清单3-29 TaskSchedulerImpl的实现
var dagScheduler: DAGScheduler = null
var backend: SchedulerBackend = null
val mapOutputTracker = SparkEnv.get.mapOutputTracker
var schedulableBuilder: SchedulableBuilder = null
var rootPool: Pool = null
// default scheduler is FIFO
private val schedulingModeConf = conf.get("spark.scheduler.mode", "FIFO")
val schedulingMode: SchedulingMode = try {
SchedulingMode.withName(schedulingModeConf.toUpperCase)
} catch {
case e: java.util.NoSuchElementException =>
throw new SparkException(s"Unrecognized spark.scheduler.mode: $scheduling-ModeConf")
}
// This is a var so that we can reset it for testing purposes.
private[spark] var taskResultGetter = new TaskResultGetter(sc.env, this)
TaskSchedulerImpl的调度模式有FAIR和FIFO两种。任务的最终调度实际都是落实到接口SchedulerBackend的具体实现上的。为方便分析，我们先来看看local模式中SchedulerBackend的实现LocalBackend。LocalBackend依赖于LocalActor与ActorSystem进行消息通信。LocalBackend的实现参见代码清单3-30。
代码清单3-30 LocalBackend的实现
private[spark] class LocalBackend(scheduler: TaskSchedulerImpl, val totalCores: Int)
extends SchedulerBackend with ExecutorBackend {
private val appId = "local-" + System.currentTimeMillis
var localActor: ActorRef = null
override def start() {
localActor = SparkEnv.get.actorSystem.actorOf(
Props(new LocalActor(scheduler, this, totalCores)),
"LocalBackendActor")
}
override def stop() {
localActor ! StopExecutor
}
override def reviveOffers() {
localActor ! ReviveOffers
}
override def defaultParallelism() =
scheduler.conf.getInt("spark.default.parallelism", totalCores)
override def killTask(taskId: Long, executorId: String, interruptThread: Boolean) {
localActor ! KillTask(taskId, interruptThread)
}
override def statusUpdate(taskId: Long, state: TaskState, serializedData: ByteBuffer) {
localActor ! StatusUpdate(taskId, state, serializedData)
}
override def applicationId(): String = appId
}
3.6.2 TaskSchedulerImpl的初始化
创建完TaskSchedulerImpl和LocalBackend后，对TaskSchedulerImpl调用方法initialize进行初始化。以默认的FIFO调度为例，TaskSchedulerImpl的初始化过程如下：
1）使TaskSchedulerImpl持有LocalBackend的引用。
2）创建Pool，Pool中缓存了调度队列、调度算法及TaskSetManager集合等信息。
3）创建FIFOSchedulableBuilder，FIFOSchedulableBuilder用来操作Pool中的调度队列。
initialize方法的实现见代码清单3-31。
代码清单3-31 TaskSchedulerImpl的初始化
def initialize(backend: SchedulerBackend) {
this.backend = backend
rootPool = new Pool("", schedulingMode, 0, 0)
schedulableBuilder = {
schedulingMode match {
case SchedulingMode.FIFO =>
new FIFOSchedulableBuilder(rootPool)
case SchedulingMode.FAIR =>
new FairSchedulableBuilder(rootPool, conf)
}
}
schedulableBuilder.buildPools()
}