【Spark Core】任务运行机制和Task源代码浅析1
引言
上一小节《TaskScheduler源代码与任务提交原理浅析2》介绍了Driver側将Stage进行划分。依据Executor闲置情况分发任务，终于通过DriverActor向executorActor发送任务消息。
我们要了解Executor的运行机制首先要了解Executor在Driver側的注冊过程。这篇文章先了解一下Application和Executor的注冊过程。
1. Task类及其相关
1.1 Task类
Spark将由Executor运行的Task分为ShuffleMapTask和ResultTask两种。其源代码存在scheduler package中。
Task是介于DAGScheduler和TaskScheduler中间的接口，在DAGScheduler，须要把DAG中的每一个stage的每一个partitions封装成task。终于把taskset提交给TaskScheduler。
/**
* A unit of execution. We have two kinds of Task's in Spark:
* - [[org.apache.spark.scheduler.ShuffleMapTask]]
* - [[org.apache.spark.scheduler.ResultTask]]
*
* A Spark job consists of one or more stages. The very last stage in a job consists of multiple
* ResultTasks, while earlier stages consist of ShuffleMapTasks. A ResultTask executes the task
* and sends the task output back to the driver application. A ShuffleMapTask executes the task
* and divides the task output to multiple buckets (based on the task's partitioner).
*
* @param stageId id of the stage this task belongs to
* @param partitionId index of the number in the RDD
*/
private[spark] abstract class Task[T](val stageId: Int, var partitionId: Int) extends Serializable Task相应一个stageId和partitionId。
提供runTask()接口、kill()接口等。
提供killed变量、TaskMetrics变量、TaskContext变量等。
除了上述基本接口和变量，Task的伴生对象提供了序列化和反序列化应用依赖的jar包的方法。原因是Task须要保证工作节点具备本次Task须要的其它依赖。注冊到SparkContext下，所以提供了把依赖转成流写入写出的方法。
1.2 ShuffleMapTask
相应于ShuffleMap Stage, 产生的结果作为其它stage的输入。
ShuffleMapTask复写了MapStatus向外读写的方法，由于向外读写的内容包含：stageId，rdd，dep，partitionId，epoch和split(某个partition)。对于当中的stageId，rdd。dep有统一的序列化和反序列化操作并会cache在内存里，再放到ObjectOutput里写出去。序列化操作使用的是Gzip，序列化信息会维护在serializedInfoCache = newHashMap[Int, Array[Byte]]。这部分须要序列化并保存的原因是：stageId，rdd。dep真正代表了本次Shuffle Task的信息，为了减轻master节点负担，把这部分序列化结果cache了起来。
/**
* A ShuffleMapTask divides the elements of an RDD into multiple buckets (based on a partitioner
* specified in the ShuffleDependency).
*
* See [[org.apache.spark.scheduler.Task]] for more information.
*
* @param stageId id of the stage this task belongs to
* @param taskBinary broadcast version of of the RDD and the ShuffleDependency. Once deserialized,
* the type should be (RDD[_], ShuffleDependency[_, _, _]).
* @param partition partition of the RDD this task is associated with
* @param locs preferred task execution locations for locality scheduling
*/
private[spark] class ShuffleMapTask(
stageId: Int,
taskBinary: Broadcast[Array[Byte]],
partition: Partition,
@transient private var locs: Seq[TaskLocation])
extends Task[MapStatus](stageId, partition.index) with Logging {1.3 ResultTask
相应于Result Stage直接产生结果。
/**
* A task that sends back the output to the driver application.
*
* See [[Task]] for more information.
*
* @param stageId id of the stage this task belongs to
* @param taskBinary broadcasted version of the serialized RDD and the function to apply on each
* partition of the given RDD. Once deserialized, the type should be
* (RDD[T], (TaskContext, Iterator[T]) => U).
* @param partition partition of the RDD this task is associated with
* @param locs preferred task execution locations for locality scheduling
* @param outputId index of the task in this job (a job can launch tasks on only a subset of the
* input RDD's partitions).
*/
private[spark] class ResultTask[T, U](
stageId: Int,
taskBinary: Broadcast[Array[Byte]],
partition: Partition,
@transient locs: Seq[TaskLocation],
val outputId: Int)
extends Task[U](stageId, partition.index) with Serializable {1.4 TaskSet
TaskSet是一个数据结构，用于封装一个stage的全部的tasks, 以提交给TaskScheduler。
TaskSet就是能够做pipeline的一组全然同样的task，每一个task的处理逻辑全然同样。不同的是处理数据，每一个task负责处理一个partition。pipeline，能够称为大数据处理的基石。仅仅有数据进行pipeline处理，才干将其放到集群中去运行。对于一个task来说，它从数据源获得逻辑。然后依照拓扑顺序，顺序运行（实际上是调用rdd的compute）。
/**
* A set of tasks submitted together to the low-level TaskScheduler, usually representing
* missing partitions of a particular stage.
*/
private[spark] class TaskSet(
val tasks: Array[Task[_]],
val stageId: Int,
val attempt: Int,
val priority: Int,
val properties: Properties) {
val id: String = stageId + "." + attempt
override def toString: String = "TaskSet " + id
}2. Executor注冊到Driver
Driver发送LaunchTask消息被Executor接收，Executor会使用launchTask对消息进行处理。
只是在这个过程之前。我们要知道，假设Executor没有注冊到Driver，即便接收到LaunchTask指令，也不会做任务处理。所以我们要先搞清楚。Executor是怎样在Driver側进行注冊的。
2.1 Application注冊
Executor的注冊是发生在Application的注冊过程中的，我们以Standalone模式为例：
SparkContext创建schedulerBackend和taskScheduler，schedulerBackend作为TaskScheduler对象的一个成员存在 –> 在TaskScheduler对象调用start函数时，事实上调用了backend.start()函数 –> backend.start()函数中启动了AppClient，AppClient的当中一个參数ApplicationDescription就是封装的运行
CoarseGrainedExecutorBackend的命令–> AppClient内部启动了一个ClientActor。这个ClientActor启动之后，会尝试向Master发送一个指令actor ! RegisterApplication(appDescription)注冊一个Application
以下是SparkDeploySchedulerBackend的start函数中的部分注冊Application的代码：
// Start executors with a few necessary configs for registering with the scheduler
val sparkJavaOpts = Utils.sparkJavaOpts(conf, SparkConf.isExecutorStartupConf)
val javaOpts = sparkJavaOpts ++ extraJavaOpts
val command = Command("org.apache.spark.executor.CoarseGrainedExecutorBackend",
args, sc.executorEnvs, classPathEntries ++ testingClassPath, libraryPathEntries, javaOpts)
val appUIAddress = sc.ui.map(_.appUIAddress).getOrElse("")
val appDesc = new ApplicationDescription(sc.appName, maxCores, sc.executorMemory, command,
appUIAddress, sc.eventLogDir, sc.eventLogCodec)
client = new AppClient(sc.env.actorSystem, masters, appDesc, this, conf)
client.start()AppClient向Master提交Application
AppClient是Application和Master交互的接口。它的包含一个类型为org.apache.spark.deploy.client.AppClient.ClientActor的成员变量actor。它负责了全部的与Master的交互。当中提交Application过程涉及的函数调用为：
ClientActor的preStart() –> 调用registerWithMaster() –> 调用tryRegisterAllMasters –> actor ! RegisterApplication(appDescription) –> Master的receiveWithLogging函数处理RegisterApplication消息。
以下是RegisterApplication(appDescription)消息的相关处理代码（在Master.scala中的receiveWithLogging部分代码）：
case RegisterApplication(description) => {
if (state == RecoveryState.STANDBY) {
// ignore, don't send response
} else {
logInfo("Registering app " + description.name)
val app = createApplication(description, sender)
registerApplication(app)
logInfo("Registered app " + description.name + " with ID " + app.id)
persistenceEngine.addApplication(app)
sender ! RegisteredApplication(app.id, masterUrl)
schedule()//为处于待分配资源的Application分配资源。在每次有新的Application增加或者新的资源增加时都会调用schedule进行调度
}
}
这段代码做了以下几件事：
- createApplication为这个app构建一个描写叙述App数据结构的ApplicationInfo
- 注冊该Application，更新相应的映射关系。增加到等待队列里面
- 用persistenceEngine持久化Application信息，默认是不保存的。另外还有两种方式，保存在文件或者Zookeeper当中
- 通过发送方注冊成功
- 開始作业调度（为处于待分配资源的Application分配资源。在每次有新的Application增加或者新的资源增加时都会调用schedule进行调度）
2.2 Master中的schedule函数
schedule()为处于待分配资源的Application分配资源。在每次有新的Application增加或者新的资源增加时都会调用schedule进行调度。为Application分配资源选择worker（executor），如今有两种策略：
- 尽量的打散。即一个Application尽可能多的分配到不同的节点。
这个能够通过设置spark.deploy.spreadOut来实现。
默认值为true，即尽量的打散。
- 尽量的集中，即一个Application尽量分配到尽可能少的节点。
对于同一个Application，它在一个worker上仅仅能拥有一个executor；当然了。这个executor可能拥有多于1个core。
对于策略1，任务的部署会慢于策略2，可是GC的时间会更快。
schedule函数的源代码，解释在中文凝视中：
/*
* Schedule the currently available resources among waiting apps. This method will be called
* every time a new app joins or resource availability changes.
*/
private def schedule() {
if (state != RecoveryState.ALIVE) { return }
// First schedule drivers, they take strict precedence over applications
// Randomization helps balance drivers
val shuffledAliveWorkers = Random.shuffle(workers.toSeq.filter(_.state == WorkerState.ALIVE))
val numWorkersAlive = shuffledAliveWorkers.size
var curPos = 0
for (driver <- waitingDrivers.toList) { // iterate over a copy of waitingDrivers
// We assign workers to each waiting driver in a round-robin fashion. For each driver, we
// start from the last worker that was assigned a driver, and continue onwards until we have
// explored all alive workers.
var launched = false
var numWorkersVisited = 0
while (numWorkersVisited < numWorkersAlive && !launched) {
val worker = shuffledAliveWorkers(curPos)
numWorkersVisited += 1
if (worker.memoryFree >= driver.desc.mem && worker.coresFree >= driver.desc.cores) {
launchDriver(worker, driver)
waitingDrivers -= driver
launched = true
}
curPos = (curPos + 1) % numWorkersAlive
}
}
// Right now this is a very simple FIFO scheduler. We keep trying to fit in the first app
// in the queue, then the second app, etc.
if (spreadOutApps) {//尽量的打散负载，如有可能。每一个executor分配一个core
// Try to spread out each app among all the nodes, until it has all its cores
for (app <- waitingApps if app.coresLeft > 0) {
// 可用的worker的标准：State是Alive，其上并没有该Application的executor，可用内存满足要求。
// 在可用的worker中，优先选择可用core数多的。
val usableWorkers = workers.toArray.filter(_.state == WorkerState.ALIVE)
.filter(canUse(app, _)).sortBy(_.coresFree).reverse
val numUsable = usableWorkers.length
val assigned = new Array[Int](numUsable) // Number of cores to give on each node
var toAssign = math.min(app.coresLeft, usableWorkers.map(_.coresFree).sum)
var pos = 0
while (toAssign > 0) {
if (usableWorkers(pos).coresFree - assigned(pos) > 0) {
toAssign -= 1
assigned(pos) += 1
}
pos = (pos + 1) % numUsable
}
// Now that we've decided how many cores to give on each node, let's actually give them
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 { //尽可能多的利用worker的core
// Pack each app into as few nodes as possible until we've assigned all its cores
for (worker <- workers if worker.coresFree > 0 && worker.state == WorkerState.ALIVE) {
for (app <- waitingApps if app.coresLeft > 0) {
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
}
}
}
}
}
}
2.3 launchExecutor函数
在选择了worker和确定了worker上得executor须要的CPU core数后。Master会调用 launchExecutor(worker: WorkerInfo, exec: ExecutorInfo)向Worker发送请求，向AppClient发送executor已经增加的消息。
同一时候会更新master保存的worker的信息。包含增加executor，降低可用的CPU core数和memory数。Master不会等到真正在worker上成功启动executor后再更新worker的信息。假设worker启动executor失败。那么它会发送FAILED的消息给Master，Master收到该消息时再次更新worker的信息就可以。
def launchExecutor(worker: WorkerInfo, exec: ExecutorDesc) {
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)
}2.4 Executor的创建
以下的调用关系链是Worker接收到来自Master的LaunchExecutor消息后的调用过程：
LaunchExecutor的消息处理中创建ExecutorRunner –> ExecutorRunner会将在SparkDeploySchedulerBackend中准备好的ApplicationDescription以进程的形式启动起来 –> 启动ApplicationDescription中携带的CoarseGrainedExecutorBackend –> CoarseGrainedExecutorBackend启动后，会首先通过传入的driverUrl这个參数向在CoarseGrainedSchedulerBackend::DriverActor发送RegisterExecutor消息 –> DriverActor会回复RegisteredExecutor –> CoarseGrainedExecutorBackend会创建一个Executor –> Executor创建完成。
CoarseGrainedExecutorBackend启动后。preStart函数运行的相关操作：
override def preStart() {
logInfo("Connecting to driver: " + driverUrl)
driver = context.actorSelection(driverUrl)
driver ! RegisterExecutor(executorId, hostPort, cores, extractLogUrls)
context.system.eventStream.subscribe(self, classOf[RemotingLifecycleEvent])
}CoarseGrainedExecutorBackend接收RegisteredExecutor消息后，创建Executor的操作：
override def receiveWithLogging = {
case RegisteredExecutor =>
logInfo("Successfully registered with driver")
val (hostname, _) = Utils.parseHostPort(hostPort)
executor = new Executor(executorId, hostname, env, userClassPath, isLocal = false)
......參考资料
Spark Core源代码分析: Spark任务模型
Spark技术内幕：Executor分配具体解释 —— 强烈推荐该博文，当中博主结合Spark源代码对Executor的分配解说的很具体
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