spark源码分析之Executor启动与任务提交篇
问题导读：
1.什么是Spark-submit ？2.Executor启动流程是什么？3.Executor怎样进行任务调度？
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Spark-submit
提交一个任务到集群通过的是Spark-submit
通过启动脚本的方式启动它的主类，这里以WordCount为例子
`spark-submit –class cn.itcast.spark.WordCount“
[*]bin/spark-clas -> org.apache.spark.deploy.SparkSubmit 调用这个类的main方法
[*]doRunMain方法中传进来一个自定义spark应用程序的main方法
class cn.itcast.spark.WordCount
[*]通过反射拿到类的实例的引用mainClass = Utils.classForName(childMainClass)
[*]在通过反射调用class cn.itcast.spark.WordCount的main方法
我们来看SparkSubmit的main方法
def main(args: Array): Unit = {
val appArgs = new SparkSubmitArguments(args)
if (appArgs.verbose) {
printStream.println(appArgs)
}
//匹配任务类型
appArgs.action match {
case SparkSubmitAction.SUBMIT => submit(appArgs)
case SparkSubmitAction.KILL => kill(appArgs)
case SparkSubmitAction.REQUEST_STATUS => requestStatus(appArgs)
}
}
这里的类型是submit，调用submit方法
private def submit(args: SparkSubmitArguments): Unit = {
val (childArgs, childClasspath, sysProps, childMainClass) = prepareSubmitEnvironment(args)
def doRunMain(): Unit = {
。。。。。。
try {
proxyUser.doAs(new PrivilegedExceptionAction() {
override def run(): Unit = {
//childMainClass这个你自己定义的App的main所在的全类名
runMain(childArgs, childClasspath, sysProps, childMainClass, args.verbose)
}
})
} catch {
。。。。。。
}
}
。。。。。。。
//掉用上面的doRunMain
doRunMain()
}submit里调用了doRunMain()，然后调用了runMain，来看runMainprivate def runMain(
。。。。。。
try {
//通过反射
mainClass = Class.forName(childMainClass, true, loader)
} catch {
。。。。。。
}
//反射拿到面方法实例
val mainMethod = mainClass.getMethod("main", new Array(0).getClass)
if (!Modifier.isStatic(mainMethod.getModifiers)) {
throw new IllegalStateException("The main method in the given main class must be static")
}
。。。。。。
try {
//调用App的main方法
mainMethod.invoke(null, childArgs.toArray)
} catch {
case t: Throwable =>
throw findCause(t)
}
}
最主要的流程就在这里了，上面的代码注释很清楚，通过反射调用我们写的类的main方法，大体的流程到此
SparkSubmit时序图
Executor启动流程
SparkSubmit通过反射调用了我们程序的main方法后，就开始执行我们的代码 ，一个Spark程序中需要创建SparkContext对象，我们就从这个对象开始
SparkContext的构造方法代码很长，主要关注的地方如下
class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationClient {
。。。。。。
private def createSparkEnv(
conf: SparkConf,
isLocal: Boolean,
listenerBus: LiveListenerBus): SparkEnv = {
//通过SparkEnv来创建createDriverEnv
SparkEnv.createDriverEnv(conf, isLocal, listenerBus)
}
//在这里调用了createSparkEnv，返回一个SparkEnv对象，这个对象里面有很多重要属性，最重要的ActorSystem
private val env = createSparkEnv(conf, isLocal, listenerBus)
SparkEnv.set(env)
//创建taskScheduler
// Create and start the scheduler
private var (schedulerBackend, taskScheduler) =
SparkContext.createTaskScheduler(this, master)
//创建DAGScheduler
dagScheduler = new DAGScheduler(this)
//启动TaksScheduler
taskScheduler.start()
。。。。。
}
Spark的构造方法主要干三件事，创建了一个SparkEnv，taskScheduler，dagScheduler，我们先来看createTaskScheduler里干了什么
//通过给定的URL创建TaskScheduler
private def createTaskScheduler(
.....
//匹配URL选择不同的方式
master match {
。。。。。。
//这个是Spark的Standalone模式
case SPARK_REGEX(sparkUrl) =>
//首先创建TaskScheduler
val scheduler = new TaskSchedulerImpl(sc)
val masterUrls = sparkUrl.split(",").map("spark://" + _)
//很重要
val backend = new SparkDeploySchedulerBackend(scheduler, sc, masterUrls)
//初始化了一个调度器，默认是FIFO
scheduler.initialize(backend)
(backend, scheduler)
。。。。。
}
}
通过master的url来匹配到Standalone模式：然后初始化了SparkDeploySchedulerBackend和TaskSchedulerImpl，这两个对象很重要，是启动任务调度的核心，然后调用了scheduler.initialize(backend)进行初始化
启动TaksScheduler初始化完成，回到我们的SparkContext构造方法后面继续调用了 taskScheduler.start() 启动TaksScheduler
来看start方法
override def start() {
//调用backend的实现的start方法
backend.start()
if (!isLocal && conf.getBoolean("spark.speculation", false)) {
logInfo("Starting speculative execution thread")
import sc.env.actorSystem.dispatcher
sc.env.actorSystem.scheduler.schedule(SPECULATION_INTERVAL milliseconds,
SPECULATION_INTERVAL milliseconds) {
Utils.tryOrExit { checkSpeculatableTasks() }
}
}
}
这里的backend是SparkDeploySchedulerBackend调用了它的start
override def start() {
//CoarseGrainedSchedulerBackend的start方法，在这个方法里面创建了一个DriverActor
super.start()
// The endpoint for executors to talk to us
//下面是为了启动java子进程做准备，准备一下参数
val driverUrl = AkkaUtils.address(
AkkaUtils.protocol(actorSystem),
SparkEnv.driverActorSystemName,
conf.get("spark.driver.host"),
conf.get("spark.driver.port"),
CoarseGrainedSchedulerBackend.ACTOR_NAME)
val args = Seq(
"--driver-url", driverUrl,
"--executor-id", "{{EXECUTOR_ID}}",
"--hostname", "{{HOSTNAME}}",
"--cores", "{{CORES}}",
"--app-id", "{{APP_ID}}",
"--worker-url", "{{WORKER_URL}}")
val extraJavaOpts = sc.conf.getOption("spark.executor.extraJavaOptions")
.map(Utils.splitCommandString).getOrElse(Seq.empty)
val classPathEntries = sc.conf.getOption("spark.executor.extraClassPath")
.map(_.split(java.io.File.pathSeparator).toSeq).getOrElse(Nil)
val libraryPathEntries = sc.conf.getOption("spark.executor.extraLibraryPath")
.map(_.split(java.io.File.pathSeparator).toSeq).getOrElse(Nil)
// When testing, expose the parent class path to the child. This is processed by
// compute-classpath.{cmd,sh} and makes all needed jars available to child processes
// when the assembly is built with the "*-provided" profiles enabled.
val testingClassPath =
if (sys.props.contains("spark.testing")) {
sys.props("java.class.path").split(java.io.File.pathSeparator).toSeq
} else {
Nil
}
// Start executors with a few necessary configs for registering with the scheduler
val sparkJavaOpts = Utils.sparkJavaOpts(conf, SparkConf.isExecutorStartupConf)
val javaOpts = sparkJavaOpts ++ extraJavaOpts
//用command拼接参数，最终会启动org.apache.spark.executor.CoarseGrainedExecutorBackend子进程
val command = Command("org.apache.spark.executor.CoarseGrainedExecutorBackend",
args, sc.executorEnvs, classPathEntries ++ testingClassPath, libraryPathEntries, javaOpts)
val appUIAddress = sc.ui.map(_.appUIAddress).getOrElse("")
//用ApplicationDescription封装了一些重要的参数
val appDesc = new ApplicationDescription(sc.appName, maxCores, sc.executorMemory, command,
appUIAddress, sc.eventLogDir, sc.eventLogCodec)
//在这里面创建ClientActor
client = new AppClient(sc.env.actorSystem, masters, appDesc, this, conf)
//启动ClientActor
client.start()
waitForRegistration()
}
这里是拼装了启动Executor的一些参数，类名+参数 封装成ApplicationDescription。最后传给并创建AppClient并调用它的start方法
AppClient创建时序图
AppClient的start方法
接来下关注start方法
def start() {
// Just launch an actor; it will call back into the listener.
actor = actorSystem.actorOf(Props(new ClientActor))
}在start方法里创建了与Master通信的ClientActor,然后会调用它的preStart方法向Master注册,接下来看它的preStartoverride def preStart() {
context.system.eventStream.subscribe(self, classOf)
try {
//ClientActor向Master注册
registerWithMaster()
} catch {
case e: Exception =>
logWarning("Failed to connect to master", e)
markDisconnected()
context.stop(self)
}
}
最后会调用该方法向所有Master注册
def tryRegisterAllMasters() {
for (masterAkkaUrl <- masterAkkaUrls) {
logInfo("Connecting to master " + masterAkkaUrl + "...")
//t通过actorSelection拿到了Master的引用
val actor = context.actorSelection(masterAkkaUrl)
//向Master发送异步的注册App的消息
actor ! RegisterApplication(appDescription)
}
}
ClientActor发送来的注册App的消息，ApplicationDescription，他包含了需求的资源，要求启动的Executor类名和一些参数
Master的Receiver
case RegisterApplication(description) => {
if (state == RecoveryState.STANDBY) {
// ignore, don't send response
} else {
logInfo("Registering app " + description.name)
//创建Appsender：ClientActor
val app = createApplication(description, sender)
//注册App
registerApplication(app)
logInfo("Registered app " + description.name + " with ID " + app.id)
//持久化App
persistenceEngine.addApplication(app)
//向ClientActor反馈信息，告诉他app注册成功了
sender ! RegisteredApplication(app.id, masterUrl)
//TODO 调度任务
schedule()
}
}
registerApplication（app）
def registerApplication(app: ApplicationInfo): Unit = {
val appAddress = app.driver.path.address
if (addressToApp.contains(appAddress)) {
logInfo("Attempted to re-register application at same address: " + appAddress)
return
}
//把App放到集合里面
applicationMetricsSystem.registerSource(app.appSource)
apps += app
idToApp(app.id) = app
actorToApp(app.driver) = app
addressToApp(appAddress) = app
waitingApps += app
}
Master将接受的信息保存到集合并序列化后发送一个RegisteredApplication消息通知反馈给ClientActor，接着执行schedule()方法,该方法中会遍历workers集合，并执行launchExecutor
def launchExecutor(worker: WorkerInfo, exec: ExecutorDesc) {
logInfo("Launching executor " + exec.fullId + " on worker " + worker.id)
//记录该worker上使用了多少资源
worker.addExecutor(exec)
//Master向Worker发送启动Executor的消息
worker.actor ! LaunchExecutor(masterUrl,
exec.application.id, exec.id, exec.application.desc, exec.cores, exec.memory)
//Master向ClientActor发送消息，告诉ClientActor executor已经启动了
exec.application.driver ! ExecutorAdded(
exec.id, worker.id, worker.hostPort, exec.cores, exec.memory)
}
这里Master向Worker发送启动Executor的消息 worker.actor ! LaunchExecutor(masterUrl, exec.application.id, exec.id, exec.application.desc, exec.cores, exec.memory)
application.desc里包含了Executor类的启动信息
case LaunchExecutor(masterUrl, appId, execId, appDesc, cores_, memory_) =>
。。。。。
appDirectories(appId) = appLocalDirs
//创建一个ExecutorRunner，这个很重要，保存了Executor的执行配置和参数
val manager = new ExecutorRunner(
appId,
execId,
appDesc.copy(command = Worker.maybeUpdateSSLSettings(appDesc.command, conf)),
cores_,
memory_,
self,
workerId,
host,
webUi.boundPort,
publicAddress,
sparkHome,
executorDir,
akkaUrl,
conf,
appLocalDirs, ExecutorState.LOADING)
executors(appId + "/" + execId) = manager
//TODO 开始启动ExecutorRunner
manager.start()
。。。。。。
}
}
}
Worker的Receiver接受到了启动Executor的消息，appDesc对象保存了Command命令、Executor的实现类和参数manager.start()里会创建一个线程
def start() {
//启动一个线程
workerThread = new Thread("ExecutorRunner for " + fullId) {
//用一个子线程来帮助Worker启动Executor子进程
override def run() { fetchAndRunExecutor() }
}
workerThread.start()
// Shutdown hook that kills actors on shutdown.
shutdownHook = new Thread() {
override def run() {
killProcess(Some("Worker shutting down"))
}
}
Runtime.getRuntime.addShutdownHook(shutdownHook)
}
在线程中调用了fetchAndRunExecutor()方法，我们来看该方法
def fetchAndRunExecutor() {
try {
// Launch the process
val builder = CommandUtils.buildProcessBuilder(appDesc.command, memory,
sparkHome.getAbsolutePath, substituteVariables)
//构建命令
val command = builder.command()
logInfo("Launch command: " + command.mkString("\"", "\" \"", "\""))
builder.directory(executorDir)
builder.environment.put("SPARK_LOCAL_DIRS", appLocalDirs.mkString(","))
// In case we are running this from within the Spark Shell, avoid creating a "scala"
// parent process for the executor command
builder.environment.put("SPARK_LAUNCH_WITH_SCALA", "0")
// Add webUI log urls
val baseUrl =
s"http://$publicAddress:$webUiPort/logPage/?appId=$appId&executorId=$execId&logType="
builder.environment.put("SPARK_LOG_URL_STDERR", s"${baseUrl}stderr")
builder.environment.put("SPARK_LOG_URL_STDOUT", s"${baseUrl}stdout")
//启动子进程
process = builder.start()
val header = "Spark Executor Command: %s\n%s\n\n".format(
command.mkString("\"", "\" \"", "\""), "=" * 40)
// Redirect its stdout and stderr to files
val stdout = new File(executorDir, "stdout")
stdoutAppender = FileAppender(process.getInputStream, stdout, conf)
val stderr = new File(executorDir, "stderr")
Files.write(header, stderr, UTF_8)
stderrAppender = FileAppender(process.getErrorStream, stderr, conf)
// Wait for it to exit; executor may exit with code 0 (when driver instructs it to shutdown)
// or with nonzero exit code
//开始执行，等待结束信号
val exitCode = process.waitFor()
。。。。
}
}
这里面进行了类名和参数的拼装，具体拼装过程不用关心，最终builder.start()会以SystemRuntime的方式启动一个子进程，这个是进程的类名是CoarseGrainedExecutorBackend
到此Executor进程就启动起来了
Executor创建时序图
Executor任务调度对象启动
Executor进程后，就首先要执行main方法，main的代码如下
//Executor进程启动的入口
def main(args: Array) {
。。。。
//拼装参数
while (!argv.isEmpty) {
argv match {
case ("--driver-url") :: value :: tail =>
driverUrl = value
argv = tail
case ("--executor-id") :: value :: tail =>
executorId = value
argv = tail
case ("--hostname") :: value :: tail =>
hostname = value
argv = tail
case ("--cores") :: value :: tail =>
cores = value.toInt
argv = tail
case ("--app-id") :: value :: tail =>
appId = value
argv = tail
case ("--worker-url") :: value :: tail =>
// Worker url is used in spark standalone mode to enforce fate-sharing with worker
workerUrl = Some(value)
argv = tail
case ("--user-class-path") :: value :: tail =>
userClassPath += new URL(value)
argv = tail
case Nil =>
case tail =>
System.err.println(s"Unrecognized options: ${tail.mkString(" ")}")
printUsageAndExit()
}
}
if (driverUrl == null || executorId == null || hostname == null || cores <= 0 ||
appId == null) {
printUsageAndExit()
}
//开始执行Executor
run(driverUrl, executorId, hostname, cores, appId, workerUrl, userClassPath)
}
执行了run方法
private def run(
driverUrl: String,
executorId: String,
hostname: String,
cores: Int,
appId: String,
workerUrl: Option,
userClassPath: Seq)
。。。。。
//通过actorSystem创建CoarseGrainedExecutorBackend -> Actor
//CoarseGrainedExecutorBackend -> DriverActor通信
env.actorSystem.actorOf(
Props(classOf,
driverUrl, executorId, sparkHostPort, cores, userClassPath, env),
name = "Executor")
。。。。。。
}
env.actorSystem.awaitTermination()
}
}
run方法中创建了CoarseGrainedExecutorBackend的Actor对象用于准备和DriverActor通信，接着会继续调用preStart生命周期方法
override def preStart() {
logInfo("Connecting to driver: " + driverUrl)
//Executor跟DriverActor建立连接
driver = context.actorSelection(driverUrl)
//Executor向DriverActor发送消息
driver ! RegisterExecutor(executorId, hostPort, cores, extractLogUrls)
context.system.eventStream.subscribe(self, classOf)
}
Executor向DriverActor发送注册的消息
driver ! RegisterExecutor(executorId, hostPort, cores, extractLogUrls)
DriverActor的receiver收到消息后
def receiveWithLogging = {
//Executor发送给DriverActor的注册消息
case RegisterExecutor(executorId, hostPort, cores, logUrls) =>
Utils.checkHostPort(hostPort, "Host port expected " + hostPort)
if (executorDataMap.contains(executorId)) {
sender ! RegisterExecutorFailed("Duplicate executor ID: " + executorId)
} else {
logInfo("Registered executor: " + sender + " with ID " + executorId)
//DriverActor向Executor发送注册成功的消息
sender ! RegisteredExecutor
addressToExecutorId(sender.path.address) = executorId
totalCoreCount.addAndGet(cores)
totalRegisteredExecutors.addAndGet(1)
val (host, _) = Utils.parseHostPort(hostPort)
//将Executor的信息封装起来
val data = new ExecutorData(sender, sender.path.address, host, cores, cores, logUrls)
// This must be synchronized because variables mutated
// in this block are read when requesting executors
CoarseGrainedSchedulerBackend.this.synchronized {
//往集合添加Executor的信息对象
executorDataMap.put(executorId, data)
if (numPendingExecutors > 0) {
numPendingExecutors -=
logDebug(s"Decremented number of pending executors ($numPendingExecutors left)")
}
}
listenerBus.post(
SparkListenerExecutorAdded(System.currentTimeMillis(), executorId, data))
//将来用来执行真正的业务逻辑
makeOffers()
}
DriverActor的receiver里将Executor信息封装到Map中保存起来，并发送反馈消息 sender ! RegisteredExecutor 给CoarseGrainedExecutorBackend
override def receiveWithLogging = {
case RegisteredExecutor =>
logInfo("Successfully registered with driver")
val (hostname, _) = Utils.parseHostPort(hostPort)
executor = new Executor(executorId, hostname, env, userClassPath, isLocal = false)
CoarseGrainedExecutorBackend收到消息后创建一个Executor对象用于准备任务的执行
挺清晰的
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