Spark源码分析之-deploy模块

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：

Spark源码分析之-deploy模块

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

    1. RegisterApplication (向master注册application)

  • master to client

    1. RegisteredApplication (作为注册application的reply，回复给client)
    2. ExecutorAdded (通知client worker已经启动了Executor环境，当向worker发送LaunchExecutor后通知client)
    3. 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

    1. LaunchExecutor (发送消息启动Executor环境)
    2. RegisteredWorker (作为worker向master注册的reply)
    3. RegisterWorkerFailed (作为worker向master注册失败的reply)
    4. KillExecutor (发送给worker请求停止executor环境)

  • worker to master

    1. RegisterWorker (向master注册自己)
    2. Heartbeat (定期向master发送心跳信息)
    3. 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：

  1. def start(){
  2. // Just launch an actor; it will call back into the listener.
  3. actor = actorSystem.actorOf(Props(newClientActor))
  4. }
  6. overridedef preStart(){
  7. logInfo("Connecting to master "+ masterUrl)
  8. try{
  9. master = context.actorFor(Master.toAkkaUrl(masterUrl))
  10. masterAddress = master.path.address
  11. master !RegisterApplication(appDescription)//向master注册application
  12. context.system.eventStream.subscribe(self, classOf[RemoteClientLifeCycleEvent])
  13. context.watch(master)// Doesn't work with remote actors, but useful for testing
  14. }catch{
  15. case e:Exception=>
  16. logError("Failed to connect to master", e)
  17. markDisconnected()
  18. context.stop(self)
  19. }
  20. }

Master在收到RegisterApplication请求后会把application加到等待队列中，等待调度：

  1. caseRegisterApplication(description)=>{
  2. logInfo("Registering app "+ description.name)
  3. val app = addApplication(description, sender)
  4. logInfo("Registered app "+ description.name +" with ID "+ app.id)
  5. waitingApps += app
  6. context.watch(sender)// This doesn't work with remote actors but helps for testing
  7. sender !RegisteredApplication(app.id)
  8. schedule()
  9. }

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。

  1. def schedule(){
  2. if(spreadOutApps){
  3. for(app <- waitingApps if app.coresLeft >0){
  4. val usableWorkers = workers.toArray.filter(_.state ==WorkerState.ALIVE)
  5. .filter(canUse(app, _)).sortBy(_.coresFree).reverse
  6. val numUsable = usableWorkers.length
  7. val assigned =newArray[Int](numUsable)// Number of cores to give on each node
  8. var toAssign = math.min(app.coresLeft, usableWorkers.map(_.coresFree).sum)
  9. var pos =0
  10. while(toAssign >0){
  11. if(usableWorkers(pos).coresFree - assigned(pos)>0){
  12. toAssign -=1
  13. assigned(pos)+=1
  14. }
  15. pos =(pos +1)% numUsable
  16. }
  17. // Now that we've decided how many cores to give on each node, let's actually give them
  18. for(pos <-0until numUsable){
  19. if(assigned(pos)>0){
  20. val exec= app.addExecutor(usableWorkers(pos), assigned(pos))
  21. launchExecutor(usableWorkers(pos),exec, app.desc.sparkHome)
  22. app.state =ApplicationState.RUNNING
  23. }
  24. }
  25. }
  26. }else{
  27. // Pack each app into as few nodes as possible until we've assigned all its cores
  28. for(worker <- workers if worker.coresFree >0&& worker.state ==WorkerState.ALIVE){
  29. for(app <- waitingApps if app.coresLeft >0){
  30. if(canUse(app, worker)){
  31. val coresToUse = math.min(worker.coresFree, app.coresLeft)
  32. if(coresToUse >0){
  33. val exec= app.addExecutor(worker, coresToUse)
  34. launchExecutor(worker,exec, app.desc.sparkHome)
  35. app.state =ApplicationState.RUNNING
  36. }
  37. }
  38. }
  39. }
  40. }
  41. }

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：

  1. def launchExecutor(worker:WorkerInfo,exec:ExecutorInfo, sparkHome:String){
  2. worker.addExecutor(exec)
  3. worker.actor !LaunchExecutor(exec.application.id,exec.id,exec.application.desc,exec.cores,exec.memory, sparkHome)
  4. exec.application.driver !ExecutorAdded(exec.id, worker.id, worker.host,exec.cores,exec.memory)
  5. }

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

  1. caseLaunchExecutor(appId, execId, appDesc, cores_, memory_, execSparkHome_)=>
  2. val manager =newExecutorRunner(
  3. appId, execId, appDesc, cores_, memory_,self, workerId, ip,newFile(execSparkHome_), workDir)
  4. executors(appId +"/"+ execId)= manager
  5. manager.start()
  6. coresUsed += cores_
  7. memoryUsed += memory_
  8. 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：

  1. def start(){
  2. workerThread =newThread("ExecutorRunner for "+ fullId){
  3. overridedef run(){ fetchAndRunExecutor()}
  4. }
  5. workerThread.start()
  7. // Shutdown hook that kills actors on shutdown.
  8. ...
  9. }
  11. def fetchAndRunExecutor(){
  12. try{
  13. // Create the executor's working directory
  14. val executorDir =newFile(workDir, appId +"/"+ execId)
  15. if(!executorDir.mkdirs()){
  16. thrownewIOException("Failed to create directory "+ executorDir)
  17. }
  19. // Launch the process
  20. val command = buildCommandSeq()
  21. val builder =newProcessBuilder(command: _*).directory(executorDir)
  22. val env = builder.environment()
  23. for((key, value)<- appDesc.command.environment){
  24. env.put(key, value)
  25. }
  26. env.put("SPARK_MEM", memory.toString +"m")
  27. // In case we are running this from within the Spark Shell, avoid creating a "scala"
  28. // parent process for the executor command
  29. env.put("SPARK_LAUNCH_WITH_SCALA","0")
  30. process = builder.start()
  32. // Redirect its stdout and stderr to files
  33. redirectStream(process.getInputStream,newFile(executorDir,"stdout"))
  34. redirectStream(process.getErrorStream,newFile(executorDir,"stderr"))
  36. // Wait for it to exit; this is actually a bad thing if it happens, because we expect to run
  37. // long-lived processes only. However, in the future, we might restart the executor a few
  38. // times on the same machine.
  39. val exitCode = process.waitFor()
  40. val message ="Command exited with code "+ exitCode
  41. worker !ExecutorStateChanged(appId, execId,ExecutorState.FAILED,Some(message),
  42. Some(exitCode))
  43. }catch{
  44. case interrupted:InterruptedException=>
  45. logInfo("Runner thread for executor "+ fullId +" interrupted")
  47. case e:Exception=>{
  48. logError("Error running executor", e)
  49. if(process !=null){
  50. process.destroy()
  51. }
  52. val message = e.getClass +": "+ e.getMessage
  53. worker !ExecutorStateChanged(appId, execId,ExecutorState.FAILED,Some(message),None)
  54. }
  55. }
  56. }

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：

  1. def removeApplication(app:ApplicationInfo){
  2. if(apps.contains(app)){
  3. logInfo("Removing app "+ app.id)
  4. apps -= app
  5. idToApp -= app.id
  6. actorToApp -= app.driver
  7. addressToWorker -= app.driver.path.address
  8. completedApps += app // Remember it in our history
  9. waitingApps -= app
  10. for(exec<- app.executors.values){
  11. exec.worker.removeExecutor(exec)
  12. exec.worker.actor !KillExecutor(exec.application.id,exec.id)
  13. }
  14. app.markFinished(ApplicationState.FINISHED)// TODO: Mark it as FAILED if it failed
  15. schedule()
  16. }
  17. }

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：

  1. caseKillExecutor(appId, execId)=>
  2. val fullId = appId +"/"+ execId
  3. executors.get(fullId) match {
  4. caseSome(executor)=>
  5. logInfo("Asked to kill executor "+ fullId)
  6. executor.kill()
  7. caseNone=>
  8. logInfo("Asked to kill unknown executor "+ fullId)
  9. }

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：

  1. def kill(){
  2. if(workerThread !=null){
  3. workerThread.interrupt()
  4. workerThread =null
  5. if(process !=null){
  6. logInfo("Killing process!")
  7. process.destroy()
  8. process.waitFor()
  9. }
  10. worker !ExecutorStateChanged(appId, execId,ExecutorState.KILLED,None,None)
  11. Runtime.getRuntime.removeShutdownHook(shutdownHook)
  12. }
  13. }

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。

http://jerryshao.me/architecture/2013/04/30/Spark%E6%BA%90%E7%A0%81%E5%88%86%E6%9E%90%E4%B9%8B-deploy%E6%A8%A1%E5%9D%97/

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