standalone-Cluster模式下application提交到执行的流程
- SparkSubmit提交程序
- 通过sparkSubmit命令提交执行SparkSubmit的main函数,
- 在SparkSubmit的main函数中调用createLaunchEnv方法,这个方法用于解析当前用户作业提交命令中包含的集群管理器和Driver部署模式,以及命令参数,对环境进行解析
- 环境解析完成后,在main函数中根据用户提交作业的环境执行launch函数
- 因为standalone-cluster模式,master以spark开头并且deploy-mode为cluster,因此,mainClass是org.apache.spark.deploy.Client 也就是最终的逻辑进入到org.apache.spark.deploy.Client的main函数。Client类用于在Standalone-Cluster模式下启动和停止Driver
- 在org.apache.spark.deploy.Client的main函数中创建ClientActor,这是一个Akka的Actor,定义于org.apache.spark.deploy.Client类中
- ClientActor执行它的preStart方法,主要工作是封装Driver信息,给Master发送RequestSubmitDriver请求,请求参数是DriverDescription
override def preStart() = { masterActor = context.actorSelection(Master.toAkkaUrl(driverArgs.master)) context.system.eventStream.subscribe(self, classOf[RemotingLifecycleEvent]) println(s"Sending ${driverArgs.cmd} command to ${driverArgs.master}") driverArgs.cmd match { case "launch" => ///在ClientActor的preStart方法中,启动Driver // TODO: We could add an env variable here and intercept it in `sc.addJar` that would // truncate filesystem paths similar to what YARN does. For now, we just require // people call `addJar` assuming the jar is in the same directory. val mainClass = "org.apache.spark.deploy.worker.DriverWrapper" val classPathConf = "spark.driver.extraClassPath" val classPathEntries = sys.props.get(classPathConf).toSeq.flatMap { cp => cp.split(java.io.File.pathSeparator) } val libraryPathConf = "spark.driver.extraLibraryPath" val libraryPathEntries = sys.props.get(libraryPathConf).toSeq.flatMap { cp => cp.split(java.io.File.pathSeparator) } val extraJavaOptsConf = "spark.driver.extraJavaOptions" val extraJavaOpts = sys.props.get(extraJavaOptsConf) .map(Utils.splitCommandString).getOrElse(Seq.empty) val sparkJavaOpts = Utils.sparkJavaOpts(conf) val javaOpts = sparkJavaOpts ++ extraJavaOpts //进程启动命令参数:主类是DriverWrapper val command = new Command(mainClass, Seq("{{WORKER_URL}}", driverArgs.mainClass) ++ driverArgs.driverOptions, sys.env, classPathEntries, libraryPathEntries, javaOpts) val driverDescription = new DriverDescription( driverArgs.jarUrl, driverArgs.memory, driverArgs.cores, driverArgs.supervise, command) ///给Master发送提交创建Driver的请求,Driver的各种信息是在Client端完成的 masterActor ! RequestSubmitDriver(driverDescription) case "kill" => val driverId = driverArgs.driverId masterActor ! RequestKillDriver(driverId) } }
- 在Master中处理RequestSubmitDriver请求,是通过createDriver创建DriverInfo对象(此时Driver进程尚未建立)和调用schedule方法。schedule是资源调度的主要方法(schedule the currently available resources among the waiting apps. This method will be called every time a new app joins or resource availability changes.)
- 在Master的schedule中的launchDriver方法中,给Worker发送LauchDriver请求(Master如何确定在哪个Worker上启动Driver?Master使用round-robin方式,依次在Workers上创建多个app的Driver,同时要兼顾Worker上空闲的资源是否满足Driver需要的资源,worker.memoryFree >= driver.desc.mem && worker.coresFree >= driver.desc.cores)
- 在Worker中处理LaunchDriver请求,创建DriverRunner,调用DriverRunner.start方法。同时记录下如下信息:a.将本Worker的可用内存和CPU核数减掉Driver使用的内存数和CPU核数 b.使用drivers集合变量记录下本Worker处理了这个Driver
- 在DriverRunner的start方法中,调用DriverRunner的launchDriver
- 在launchDriver中调用runCommandWithRetry创建Driver进程
下面对创建Driver进程(JVM进程)的代码梳理一下:
1.
private[deploy] def runCommandWithRetry(command: ProcessBuilderLike, initialize: Process => Unit, supervise: Boolean) { // Time to wait between submission retries. var waitSeconds = 1 // A run of this many seconds resets the exponential back-off. val successfulRunDuration = 5 var keepTrying = !killed while (keepTrying) { logInfo("Launch Command: " + command.command.mkString("\"", "\" \"", "\"")) synchronized { if (killed) { return } process = Some(command.start()) ///command的start方法返回Java的Process对象 initialize(process.get) } val processStart = clock.currentTimeMillis() val exitCode = process.get.waitFor() if (clock.currentTimeMillis() - processStart > successfulRunDuration * 1000) { waitSeconds = 1 } if (supervise && exitCode != 0 && !killed) { logInfo(s"Command exited with status $exitCode, re-launching after $waitSeconds s.") sleeper.sleep(waitSeconds) waitSeconds = waitSeconds * 2 // exponential back-off } keepTrying = supervise && exitCode != 0 && !killed finalExitCode = Some(exitCode) } }
2. 传入的command是ProcessBuilderLike类型的对象
private[deploy] object ProcessBuilderLike { def apply(processBuilder: ProcessBuilder) = new ProcessBuilderLike { def start() = processBuilder.start() ///调用Java的ProcessBuilder的start方法 def command = processBuilder.command() } }
3. 传入的commandProcessbuilderLike是基于ProcessBuilder而构建,command = new ProcessBuilderLike(ProcessBuilder)
val builder = CommandUtils.buildProcessBuilder(driverDesc.command, driverDesc.mem, sparkHome.getAbsolutePath, substituteVariables, Seq(localJarFilename)) ///返回ProcessBuilder对象
4.CommandUtils.buildProcessBuilder的代码如下所示:
def buildProcessBuilder( command: Command, memory: Int, sparkHome: String, substituteArguments: String => String, classPaths: Seq[String] = Seq[String](), env: Map[String, String] = sys.env): ProcessBuilder = { val localCommand = buildLocalCommand(command, substituteArguments, classPaths, env) val commandSeq = buildCommandSeq(localCommand, memory, sparkHome) ///构造【${JAVA_HOME}/bin/java options mainClass 参数】 命令 val builder = new ProcessBuilder(commandSeq: _*) val environment = builder.environment() for ((key, value) <- localCommand.environment) { environment.put(key, value) } builder }
5. buildLocalCommand方法
读取Java进程运行所在的机器的环境信息,比如系统变量等
/** * Build a command based on the given one, taking into account the local environment * of where this command is expected to run, substitute any placeholders, and append * any extra class paths. */ private def buildLocalCommand( command: Command, substituteArguments: String => String, classPath: Seq[String] = Seq[String](), env: Map[String, String]): Command = { val libraryPathName = Utils.libraryPathEnvName val libraryPathEntries = command.libraryPathEntries val cmdLibraryPath = command.environment.get(libraryPathName) val newEnvironment = if (libraryPathEntries.nonEmpty && libraryPathName.nonEmpty) { val libraryPaths = libraryPathEntries ++ cmdLibraryPath ++ env.get(libraryPathName) command.environment + ((libraryPathName, libraryPaths.mkString(File.pathSeparator))) } else { command.environment }
6. buildCommandSeq方法
private def buildCommandSeq(command: Command, memory: Int, sparkHome: String): Seq[String] = { val runner = sys.env.get("JAVA_HOME").map(_ + "/bin/java").getOrElse("java") // SPARK-698: do not call the run.cmd script, as process.destroy() // fails to kill a process tree on Windows ///构造【${JAVA_HOME}/bin/java options mainClass 参数】 命令 Seq(runner) ++ buildJavaOpts(command, memory, sparkHome) ++ Seq(command.mainClass) ++ command.arguments }
- Driver进程启动后,即运行我们定义的application的main函数
- 创建SparkContext对象,
1. 创建SparkDeploySchedulerBackend 和 TaskScheduler, private[spark] var (schedulerBackend, taskScheduler) = SparkContext.createTaskScheduler(this, master)
2. 创建 dagScheduler = new DAGScheduler(this)
- 在createTaskScheduler方法中,创建SparkDeploySchedulerBackend对象,SparkDeploySchedulerBackend继承自SparkDeploySchedulerBackend
- 在SparkContext的构造方法中,调用TaskScheduler的start方法,在start方法内部调用SparkDeploySchedulerBackend的start方法
- 在SparkDeploySchedulerBackend的start方法中,构造AppClient对象,并调用AppClient的start方法。AppClient类的含义
* Interface allowing applications to speak with a Spark deploy cluster. Takes a master URL, * an app description, and a listener for cluster events, and calls back the listener when various * events occur.
- 在AppClient中,执行preStart方法以调用 registerWithMaster()方法,将Driver注册给Master,实际上此时给Master发送的是RegisterApplication消息,
- 在Master的RegisterApplication消息处理中,调用Master的schedule方法,在scheduler方法中,Master将为Application分配计算资源,默认情况下,计算资源的分配策略是尽可能的占用少的Worker数目,即一个Worker满足要求,就不会放到放到两个上面
- 在Master的schedule方法中调用launchExecutor方法
- 在Master的launchExecutor方法中,给Worker发送LaunchExecutor消息,同时给Driver发送ExecutorAdded消息,包括在哪个Worker上,分配了多少cores以及多少memory
- 在Workder的LaunchExecutor消息处理器中,创建ExecutorRunner对象,而ExecutorRunner则通过反射的方式创建一个Java进程,这个进程就是启动一个CoarseGrainedExecutorBackend进程
- 调用ExecutorRunner对象的start方法,start方法调用fetchAndRunExecutor方法
- 如下是fetchAndRunExecutor方法的一部分逻辑
val builder = CommandUtils.buildProcessBuilder(appDesc.command, memory, sparkHome.getAbsolutePath, substituteVariables)
问题:appDesc.command是在哪里定义的?这是在SparkDeploySchedulerBackend的start方法定义的
// 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", ///封装到ApplicationDescription中,后面会启动一个进程 args, sc.executorEnvs, classPathEntries, libraryPathEntries, javaOpts) val appUIAddress = sc.ui.map(_.appUIAddress).getOrElse("") val appDesc = new ApplicationDescription(sc.appName, maxCores, sc.executorMemory, command, appUIAddress, sc.eventLogDir) client = new AppClient(sc.env.actorSystem, masters, appDesc, this, conf) client.start()
- CoarseGrainedExecutorBackend是一个Actor,首先运行它的preStart方法,在它的preStart方法中,给Driver发送RegisterExecutor消息
- 此处的Driver是在CoarseGrainedSchedulerBackend中定义的,当它收到RegisterExecutor时,调用CoarseGrainedSchedulerBackend的makeOffers方法
- 在makeOffers中,调用launchTasks方法启动任务
- 在launchTasks中,循环提交所有的Task(这本来是一个TaskSet任务集),每次循环给CoarseGrainedExecutorBackend发送LaunchTask消息
- CoarseGrainedExecutorBackend处理LaunchTask时,调用Executor的launchTask方法
- 在Executor的launchTask方法中,提交给Executor中的线程池执行
def launchTask( context: ExecutorBackend, taskId: Long, taskName: String, serializedTask: ByteBuffer) { val tr = new TaskRunner(context, taskId, taskName, serializedTask) runningTasks.put(taskId, tr) threadPool.execute(tr) }
总结
如下图是网上流传甚广的一幅图片,经常上面的流程分析,可知这幅图是错的,第二步不是RegisterDriver,而是RequestSubmitDriver, 第五步才是RegisterDriver(Driver给Master发送的消息是RegisterApplication)