Background
在前文Spark源码分析之-scheduler模块中提到了Spark在资源管理和调度上采用了Hadoop YARN的方式：外层的资源管理器和应用内的任务调度器；并且分析了Spark应用内的任务调度模块。本文就Spark的外层资源管理器-deploy模块进行分析，探究Spark是如何协调应用之间的资源调度和管理的。
Spark最初是交由Mesos进行资源管理，为了使得更多的用户，包括没有接触过Mesos的用户使用Spark，Spark的开发者添加了Standalone的部署方式，也就是deploy模块。因此deploy模块只针对不使用Mesos进行资源管理的部署方式。
Deploy模块整体架构
deploy模块主要包含3个子模块：master, worker, client。他们继承于Actor，通过actor实现互相之间的通信。
- Master：master的主要功能是接收worker的注册并管理所有的worker，接收client提交的application，(FIFO)调度等待的application并向worker提交。
- Worker：worker的主要功能是向master注册自己，根据master发送的application配置进程环境，并启动
StandaloneExecutorBackend。 - Client：client的主要功能是向master注册并监控application。当用户创建
SparkContext时会实例化SparkDeploySchedulerBackend，而实例化SparkDeploySchedulerBackend的同时就会启动client，通过向client传递启动参数和application有关信息，client向master发送请求注册application并且在slave node上启动StandaloneExecutorBackend。
下面来看一下deploy模块的类图：
Deploy模块通信消息
Deploy模块并不复杂，代码也不多，主要集中在各个子模块之间的消息传递和处理上，因此在这里列出了各个模块之间传递的主要消息：
- client to master
-
RegisterApplication(向master注册application)
- master to client
-
RegisteredApplication(作为注册application的reply，回复给client) -
ExecutorAdded(通知client worker已经启动了Executor环境，当向worker发送LaunchExecutor后通知client) -
ExecutorUpdated(通知client Executor状态已经发生变化了，包括结束、异常退出等，当worker向master发送ExecutorStateChanged后通知client)
- master to worker
-
LaunchExecutor(发送消息启动Executor环境) -
RegisteredWorker(作为worker向master注册的reply) -
RegisterWorkerFailed(作为worker向master注册失败的reply) -
KillExecutor(发送给worker请求停止executor环境)
- worker to master
-
RegisterWorker(向master注册自己) -
Heartbeat(定期向master发送心跳信息) -
ExecutorStateChanged(向master发送Executor状态改变信息)
Deploy模块代码详解
Deploy模块相比于scheduler模块简单，因此对于deploy模块的代码并不做十分细节的分析，只针对application的提交和结束过程做一定的分析。
Client提交application
Client是由SparkDeploySchedulerBackend创建被启动的，因此client是被嵌入在每一个application中，只为这个applicator所服务，在client启动时首先会先master注册application：
def start() {
// Just launch an actor; it will call back into the listener.
actor = actorSystem.actorOf(Props(new ClientActor))
}
override def preStart() {
logInfo("Connecting to master " + masterUrl)
try {
master = context.actorFor(Master.toAkkaUrl(masterUrl))
masterAddress = master.path.address
master ! RegisterApplication(appDescription) //向master注册application
context.system.eventStream.subscribe(self, classOf[RemoteClientLifeCycleEvent])
context.watch(master) // Doesn't work with remote actors, but useful for testing
} catch {
case e: Exception =>
logError("Failed to connect to master", e)
markDisconnected()
context.stop(self)
}
}
Master在收到RegisterApplication请求后会把application加到等待队列中，等待调度：
case RegisterApplication(description) => {
logInfo("Registering app " + description.name)
val app = addApplication(description, sender)
logInfo("Registered app " + description.name + " with ID " + app.id)
waitingApps += app
context.watch(sender) // This doesn't work with remote actors but helps for testing
sender ! RegisteredApplication(app.id)
schedule()
}
Master会在每次操作后调用schedule()函数，以确保等待的application能够被及时调度。
在前面提到deploy模块是资源管理模块，那么Spark的deploy管理的是什么资源，资源以什么单位进行调度的呢？在当前版本的Spark中，集群的cpu数量是Spark资源管理的一个标准，每个提交的application都会标明自己所需要的资源数(也就是cpu的core数)，Master以FIFO的方式管理所有的application请求，当资源数量满足当前任务执行需求的时候该任务就会被调度，否则就继续等待，当然如果master能给予当前任务部分资源则也会启动该application。schedule()函数实现的就是此功能。
def schedule() {
if (spreadOutApps) {
for (app <- waitingApps if app.coresLeft > 0) {
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.desc.sparkHome)
app.state = ApplicationState.RUNNING
}
}
}
} else {
// 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.desc.sparkHome)
app.state = ApplicationState.RUNNING
}
}
}
}
}
}
当application得到调度后就会调用launchExecutor()向worker发送请求，同时向client汇报状态：
def launchExecutor(worker: WorkerInfo, exec: ExecutorInfo, sparkHome: String) {
worker.addExecutor(exec)
worker.actor ! LaunchExecutor(exec.application.id, exec.id, exec.application.desc, exec.cores, exec.memory, sparkHome)
exec.application.driver ! ExecutorAdded(exec.id, worker.id, worker.host, exec.cores, exec.memory)
}
至此client与master的交互已经转向了master与worker的交互，worker需要配置application启动环境
case LaunchExecutor(appId, execId, appDesc, cores_, memory_, execSparkHome_) =>
val manager = new ExecutorRunner(
appId, execId, appDesc, cores_, memory_, self, workerId, ip, new File(execSparkHome_), workDir)
executors(appId + "/" + execId) = manager
manager.start()
coresUsed += cores_
memoryUsed += memory_
master ! ExecutorStateChanged(appId, execId, ExecutorState.RUNNING, None, None)
Worker在接收到LaunchExecutor消息后创建ExecutorRunner实例，同时汇报master executor环境启动。
ExecutorRunner在启动的过程中会创建线程，配置环境，启动新进程：
def start() {
workerThread = new Thread("ExecutorRunner for " + fullId) {
override def run() { fetchAndRunExecutor() }
}
workerThread.start()
// Shutdown hook that kills actors on shutdown.
...
}
def fetchAndRunExecutor() {
try {
// Create the executor's working directory
val executorDir = new File(workDir, appId + "/" + execId)
if (!executorDir.mkdirs()) {
throw new IOException("Failed to create directory " + executorDir)
}
// Launch the process
val command = buildCommandSeq()
val builder = new ProcessBuilder(command: _*).directory(executorDir)
val env = builder.environment()
for ((key, value) <- appDesc.command.environment) {
env.put(key, value)
}
env.put("SPARK_MEM", memory.toString + "m")
// In case we are running this from within the Spark Shell, avoid creating a "scala"
// parent process for the executor command
env.put("SPARK_LAUNCH_WITH_SCALA", "0")
process = builder.start()
// Redirect its stdout and stderr to files
redirectStream(process.getInputStream, new File(executorDir, "stdout"))
redirectStream(process.getErrorStream, new File(executorDir, "stderr"))
// Wait for it to exit; this is actually a bad thing if it happens, because we expect to run
// long-lived processes only. However, in the future, we might restart the executor a few
// times on the same machine.
val exitCode = process.waitFor()
val message = "Command exited with code " + exitCode
worker ! ExecutorStateChanged(appId, execId, ExecutorState.FAILED, Some(message),
Some(exitCode))
} catch {
case interrupted: InterruptedException =>
logInfo("Runner thread for executor " + fullId + " interrupted")
case e: Exception => {
logError("Error running executor", e)
if (process != null) {
process.destroy()
}
val message = e.getClass + ": " + e.getMessage
worker ! ExecutorStateChanged(appId, execId, ExecutorState.FAILED, Some(message), None)
}
}
}
在ExecutorRunner启动后worker向master汇报ExecutorStateChanged，而master则将消息重新pack成为ExecutorUpdated发送给client。
至此整个application提交过程基本结束，提交的过程并不复杂，主要涉及到的消息的传递。
Application的结束
由于各种原因(包括正常结束，异常返回等)会造成application的结束，我们现在就来看看applicatoin结束的整个流程。
application的结束往往会造成client的结束，而client的结束会被master通过Actor检测到，master检测到后会调用removeApplication()函数进行操作：
def removeApplication(app: ApplicationInfo) {
if (apps.contains(app)) {
logInfo("Removing app " + app.id)
apps -= app
idToApp -= app.id
actorToApp -= app.driver
addressToWorker -= app.driver.path.address
completedApps += app // Remember it in our history
waitingApps -= app
for (exec <- app.executors.values) {
exec.worker.removeExecutor(exec)
exec.worker.actor ! KillExecutor(exec.application.id, exec.id)
}
app.markFinished(ApplicationState.FINISHED) // TODO: Mark it as FAILED if it failed
schedule()
}
}
removeApplicatoin()首先会将application从master自身所管理的数据结构中删除，其次它会通知每一个work，请求其KillExecutor。worker在收到KillExecutor后调用ExecutorRunner的kill()函数：
case KillExecutor(appId, execId) =>
val fullId = appId + "/" + execId
executors.get(fullId) match {
case Some(executor) =>
logInfo("Asked to kill executor " + fullId)
executor.kill()
case None =>
logInfo("Asked to kill unknown executor " + fullId)
}
在ExecutorRunner内部，它会结束监控线程，同时结束监控线程所启动的进程，并且向worker汇报ExecutorStateChanged：
def kill() {
if (workerThread != null) {
workerThread.interrupt()
workerThread = null
if (process != null) {
logInfo("Killing process!")
process.destroy()
process.waitFor()
}
worker ! ExecutorStateChanged(appId, execId, ExecutorState.KILLED, None, None)
Runtime.getRuntime.removeShutdownHook(shutdownHook)
}
}
Application结束的同时清理了master和worker上的关于该application的所有信息，这样关于application结束的整个流程就介绍完了，当然在这里我们对于许多异常处理分支没有细究，但这并不影响我们对主线的把握。
End
至此对于deploy模块的分析暂告一个段落。deploy模块相对来说比较简单，也没有特别复杂的逻辑结构，正如前面所说的deploy模块是为了能让更多的没有部署Mesos的集群的用户能够使用Spark而实现的一种方案。
当然现阶段看来还略微简陋，比如application的调度方式(FIFO)是否会造成小应用长时间等待大应用的结束，是否有更好的调度策略；资源的衡量标准是否可以更多更合理，而不单单是cpu数量，因为现实场景中有的应用是disk intensive，有的是network intensive，这样就算cpu资源有富余，调度新的application也不一定会很有意义。
总的来说作为Mesos的一种简单替代方式，deploy模块对于推广Spark还是有积极意义的。
