承接上一篇文章，我们继续来分析Executor的启动过程，本文主要分为两部分：
- 向worker发送启动Executor的消息
- 启动完成后向driver发送ExecutorAdded的消息，这里的driver就是ClientEndpoint
private def launchExecutor(worker: WorkerInfo, exec: ExecutorDesc): Unit = {
logInfo("Launching executor " + exec.fullId + " on worker " + worker.id)
worker.addExecutor(exec)
worker.endpoint.send(LaunchExecutor(masterUrl,
exec.application.id, exec.id, exec.application.desc, exec.cores, exec.memory))
exec.application.driver.send(
ExecutorAdded(exec.id, worker.id, worker.hostPort, exec.cores, exec.memory))
}
启动Executor
首先我们分析Worker在接收到LaunchExecutor消息之后所执行的操作：
case LaunchExecutor(masterUrl, appId, execId, appDesc, cores_, memory_) =>
// 首先判断Master是否为Active状态
if (masterUrl != activeMasterUrl) {
logWarning("Invalid Master (" + masterUrl + ") attempted to launch executor.")
} else {
try {
logInfo("Asked to launch executor %s/%d for %s".format(appId, execId, appDesc.name))
// 创建executor的工作目录
// Create the executor's working directory
val executorDir = new File(workDir, appId + "/" + execId)
if (!executorDir.mkdirs()) {
throw new IOException("Failed to create directory " + executorDir)
}
// Create local dirs for the executor. These are passed to the executor via the
// SPARK_EXECUTOR_DIRS environment variable, and deleted by the Worker when the
// application finishes.
根据application创建executor的本地目录，可以通过SPARK_EXECUTOR_DIRS进行配置
val appLocalDirs = appDirectories.get(appId).getOrElse {
Utils.getOrCreateLocalRootDirs(conf).map { dir =>
val appDir = Utils.createDirectory(dir, namePrefix = "executor")
Utils.chmod700(appDir)
appDir.getAbsolutePath()
}.toSeq
}
appDirectories(appId) = appLocalDirs
// 实例化ExecutorRunner
val manager = new ExecutorRunner(
appId,
execId,
appDesc.copy(command = Worker.maybeUpdateSSLSettings(appDesc.command, conf)),
cores_,
memory_,
self,
workerId,
host,
webUi.boundPort,
publicAddress,
sparkHome,
executorDir,
workerUri,
conf,
appLocalDirs, ExecutorState.RUNNING)
// 保存在executors中
executors(appId + "/" + execId) = manager
// 执行ExecutorRunner的start方法
manager.start()
// 修改计算资源的使用情况
coresUsed += cores_
memoryUsed += memory_
// 向Master发送ExecutorStateChanged的消息
sendToMaster(ExecutorStateChanged(appId, execId, manager.state, None, None))
} catch {
case e: Exception => {
logError(s"Failed to launch executor $appId/$execId for ${appDesc.name}.", e)
if (executors.contains(appId + "/" + execId)) {
executors(appId + "/" + execId).kill()
executors -= appId + "/" + execId
}
sendToMaster(ExecutorStateChanged(appId, execId, ExecutorState.FAILED,
Some(e.toString), None))
}
}
}
首先实例化ExecutorRunner，ExecutorRunner就是Standalone模式下用来管理一个executor进程的执行的。然后调用ExecutorRunner的start()方法：
private[worker] def start() {
workerThread = new Thread("ExecutorRunner for " + fullId) {
override def run() { fetchAndRunExecutor() }
}
workerThread.start()
// Shutdown hook that kills actors on shutdown.
shutdownHook = ShutdownHookManager.addShutdownHook { () =>
// It's possible that we arrive here before calling `fetchAndRunExecutor`, then `state` will
// be `ExecutorState.RUNNING`. In this case, we should set `state` to `FAILED`.
if (state == ExecutorState.RUNNING) {
state = ExecutorState.FAILED
}
killProcess(Some("Worker shutting down")) }
}
可以看见内部创建了一条线程用来执行fetchAndRunExecutor方法，当调用线程的start方法时，线程中的run方法运行，即fetchAndRunExecutor()方法开始执行：
private def fetchAndRunExecutor() {
try {
// Launch the process
// 首先构建command
val builder = CommandUtils.buildProcessBuilder(appDesc.command, new SecurityManager(conf),
memory, sparkHome.getAbsolutePath, substituteVariables)
val command = builder.command()
val formattedCommand = command.asScala.mkString("\"", "\" \"", "\"")
logInfo(s"Launch command: $formattedCommand")
// 设置Executor的本地目录并设置一些配置参数
builder.directory(executorDir)
builder.environment.put("SPARK_EXECUTOR_DIRS", appLocalDirs.mkString(File.pathSeparator))
// In case we are running this from within the Spark Shell, avoid creating a "scala"
// parent process for the executor command
builder.environment.put("SPARK_LAUNCH_WITH_SCALA", "0")
// Add webUI log urls
val baseUrl =
s"http://$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")
// 开启一个新的进程运行command
process = builder.start()
val header = "Spark Executor Command: %s\n%s\n\n".format(
formattedCommand, "=" * 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
val exitCode = process.waitFor()
state = ExecutorState.EXITED
val message = "Command exited with code " + exitCode
worker.send(ExecutorStateChanged(appId, execId, state, Some(message), Some(exitCode)))
} catch {
case interrupted: InterruptedException => {
logInfo("Runner thread for executor " + fullId + " interrupted")
state = ExecutorState.KILLED
killProcess(None)
}
case e: Exception => {
logError("Error running executor", e)
state = ExecutorState.FAILED
killProcess(Some(e.toString))
}
}
}
这里最重要的就是process = builder.start()，即开启一个新的线程来运行我们构建的command，也就是说开辟一个新的进程(JVM)来运行"org.apache.spark.executor.CoarseGrainedExecutorBackend"这个类的main方法，还记得这是在哪里设置的吗，没错，就是SparkDeploySchedulerBackend的start()方法中，所以我们现在进入CoarseGrainedExecutorBackend这个类的main方法：
def main(args: Array[String]) {
var driverUrl: String = null
var executorId: String = null
var hostname: String = null
var cores: Int = 0
var appId: String = null
var workerUrl: Option[String] = None
val userClassPath = new mutable.ListBuffer[URL]()
var argv = args.toList
// 这里就是通过我们构建command的时候传入的参数对变量进行初始化操作
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 =>
// scalastyle:off println
System.err.println(s"Unrecognized options: ${tail.mkString(" ")}")
// scalastyle:on println
printUsageAndExit()
}
}
if (driverUrl == null || executorId == null || hostname == null || cores <= 0 ||
appId == null) {
printUsageAndExit()
}
// 如果传入的参数没有问题就执行run方法
run(driverUrl, executorId, hostname, cores, appId, workerUrl, userClassPath)
}
这里要先说明一下，CoarseGrainedExecutorBackend实际上实现的是ExecutorBackend，而ExecutorBackend根据集群的运行模式不同有三种不同的实现，分别是CoarseGrainedExecutorBackend、LocalBackend、MesosExecutorBackend，而这里的CoarseGrainedExecutorBackend就是Standalone模式下的具体实现，而Standalone模式下是通过ExecutorRunner来启动一个进程运行CoarseGrainedExecutorBackend的main方法的。
接下来就是调用run方法：
private def run(
driverUrl: String,
executorId: String,
hostname: String,
cores: Int,
appId: String,
workerUrl: Option[String],
userClassPath: Seq[URL]) {
SignalLogger.register(log)
SparkHadoopUtil.get.runAsSparkUser { () =>
// Debug code
Utils.checkHost(hostname)
// Bootstrap to fetch the driver's Spark properties.
val executorConf = new SparkConf
val port = executorConf.getInt("spark.executor.port", 0)
val fetcher = RpcEnv.create(
"driverPropsFetcher",
hostname,
port,
executorConf,
new SecurityManager(executorConf),
clientMode = true)
val driver = fetcher.setupEndpointRefByURI(driverUrl)
val props = driver.askWithRetry[Seq[(String, String)]](RetrieveSparkProps) ++
Seq[(String, String)](("spark.app.id", appId))
fetcher.shutdown()
// Create SparkEnv using properties we fetched from the driver.
val driverConf = new SparkConf()
for ((key, value) <- props) {
// this is required for SSL in standalone mode
if (SparkConf.isExecutorStartupConf(key)) {
driverConf.setIfMissing(key, value)
} else {
driverConf.set(key, value)
}
}
if (driverConf.contains("spark.yarn.credentials.file")) {
logInfo("Will periodically update credentials from: " +
driverConf.get("spark.yarn.credentials.file"))
SparkHadoopUtil.get.startExecutorDelegationTokenRenewer(driverConf)
}
val env = SparkEnv.createExecutorEnv(
driverConf, executorId, hostname, port, cores, isLocal = false)
// SparkEnv will set spark.executor.port if the rpc env is listening for incoming
// connections (e.g., if it's using akka). Otherwise, the executor is running in
// client mode only, and does not accept incoming connections.
val sparkHostPort = env.conf.getOption("spark.executor.port").map { port =>
hostname + ":" + port
}.orNull
env.rpcEnv.setupEndpoint("Executor", new CoarseGrainedExecutorBackend(
env.rpcEnv, driverUrl, executorId, sparkHostPort, cores, userClassPath, env))
workerUrl.foreach { url =>
env.rpcEnv.setupEndpoint("WorkerWatcher", new WorkerWatcher(env.rpcEnv, url))
}
env.rpcEnv.awaitTermination()
SparkHadoopUtil.get.stopExecutorDelegationTokenRenewer()
}
}
上面的源码主要分为部分：
- 从Driver上获得Spark的一些属性信息
- 使用得到的信息创建ExecutorEnv即Executor的运行时环境
- 然后实例化CoarseGrainedExecutorBackend并向RpcEnv进行注册
- 注册时会调用CoarseGrainedExecutorBackend的onStart方法
WorkerWatcher部分此处我们不做分析，我们看CoarseGrainedExecutorBackend的onStart方法：
override def onStart() {
logInfo("Connecting to driver: " + driverUrl)
rpcEnv.asyncSetupEndpointRefByURI(driverUrl).flatMap { ref =>
// This is a very fast action so we can use "ThreadUtils.sameThread"
driver = Some(ref)
// 向Driver发送RegisterExecutor消息
ref.ask[RegisterExecutorResponse](
RegisterExecutor(executorId, self, hostPort, cores, extractLogUrls))
}(ThreadUtils.sameThread).onComplete {
// This is a very fast action so we can use "ThreadUtils.sameThread"
case Success(msg) => Utils.tryLogNonFatalError {
Option(self).foreach(_.send(msg)) // msg must be RegisterExecutorResponse
}
case Failure(e) => {
logError(s"Cannot register with driver: $driverUrl", e)
System.exit(1)
}
}(ThreadUtils.sameThread)
}
这里我们需要关心的是这个driver到底是谁，即driverUrl到底是什么？
那么我们追踪一下：driverUrl是实例化CoarseGrainedExecutorBackend的时候传入的，而执行实例化时候的这个driverUrl又是通过run方法传入的，而run方法中的driverUrl又是main方法执行的时候传入的，main方法的driverUrl是根据传入的参数获得的，即创建新进程时传入的参数，即执行的command，而command是通过appDesc的command构建的，而appDesc是在SparkDeploySchedulerBackend中的start方法中构建的，如下所示：
// The endpoint for executors to talk to us
val driverUrl = rpcEnv.uriOf(SparkEnv.driverActorSystemName,
RpcAddress(sc.conf.get("spark.driver.host"), sc.conf.get("spark.driver.port").toInt),
CoarseGrainedSchedulerBackend.ENDPOINT_NAME)
val args = Seq(
"--driver-url", driverUrl,
"--executor-id", "{{EXECUTOR_ID}}",
"--hostname", "{{HOSTNAME}}",
"--cores", "{{CORES}}",
"--app-id", "{{APP_ID}}",
"--worker-url", "{{WORKER_URL}}")
这里的CoarseGrainedSchedulerBackend.ENDPOINT_NAME是"CoarseGrainedScheduler"：
private[spark] object CoarseGrainedSchedulerBackend {
val ENDPOINT_NAME = "CoarseGrainedScheduler"
}
而DriverEndpoint注册的时候就是使用的ENDPOINT_NAME
driverEndpoint = rpcEnv.setupEndpoint(ENDPOINT_NAME, createDriverEndpoint(properties))
所以这里的driverUrl指的就是DriverEndpoint，DriverEndpoint在接收到RegisterExecutor消息后执行的操作为：
case RegisterExecutor(executorId, executorRef, hostPort, cores, logUrls) =>
if (executorDataMap.contains(executorId)) {
context.reply(RegisterExecutorFailed("Duplicate executor ID: " + executorId))
} else {
// If the executor's rpc env is not listening for incoming connections, `hostPort`
// will be null, and the client connection should be used to contact the executor.
val executorAddress = if (executorRef.address != null) {
executorRef.address
} else {
context.senderAddress
}
logInfo(s"Registered executor $executorRef ($executorAddress) with ID $executorId")
addressToExecutorId(executorAddress) = executorId
totalCoreCount.addAndGet(cores)
totalRegisteredExecutors.addAndGet(1)
val data = new ExecutorData(executorRef, executorRef.address, executorAddress.host,
cores, cores, logUrls)
// This must be synchronized because variables mutated
// in this block are read when requesting executors
CoarseGrainedSchedulerBackend.this.synchronized {
executorDataMap.put(executorId, data)
if (numPendingExecutors > 0) {
numPendingExecutors -= 1
logDebug(s"Decremented number of pending executors ($numPendingExecutors left)")
}
}
// Note: some tests expect the reply to come after we put the executor in the map
context.reply(RegisteredExecutor(executorAddress.host))
listenerBus.post(
SparkListenerExecutorAdded(System.currentTimeMillis(), executorId, data))
makeOffers()
}
如果一切正常DriverEndpoint会向CoarseGrainedExecutorBackend回复消息RegisteredExecutor，CoarseGrainedExecutorBackend接收到消息后实例化了Executor，具体的实例化过程中比较重要的两个部分就是初始化运行tasks的线程池和向driver发送心跳信息，部分源码如下：
...
// 开启线程池，用来运行提交的tasks
// Start worker thread pool
private val threadPool = ThreadUtils.newDaemonCachedThreadPool("Executor task launch worker")
private val executorSource = new ExecutorSource(threadPool, executorId)
...
// 可以看到是开辟了一个线程来发送心跳
// Executor for the heartbeat task.
private val heartbeater = ThreadUtils.newDaemonSingleThreadScheduledExecutor("driver-heartbeater")
// 使用driver中的HeartbeatReceiver来接收心跳，实际上HeartbeatReceiver是SparkContext实例化的时候创建的
// must be initialized before running startDriverHeartbeat()
private val heartbeatReceiverRef =
RpcUtils.makeDriverRef(HeartbeatReceiver.ENDPOINT_NAME, conf, env.rpcEnv)
/**
* When an executor is unable to send heartbeats to the driver more than `HEARTBEAT_MAX_FAILURES`
* times, it should kill itself. The default value is 60. It means we will retry to send
* heartbeats about 10 minutes because the heartbeat interval is 10s.
*/
// 上面的注释说的很清楚了，最大的失败次数是60次，每隔10s重试一次，也就是说可以重试10分钟
private val HEARTBEAT_MAX_FAILURES = conf.getInt("spark.executor.heartbeat.maxFailures", 60)
/**
* Count the failure times of heartbeat. It should only be acessed in the heartbeat thread. Each
* successful heartbeat will reset it to 0.
*/
private var heartbeatFailures = 0
// 开始发送心跳
startDriverHeartbeater()
具体startDriverHeartbeater()方法的实现这里就不追踪下去了，同时本文上述源码中出现的向Master、Worker、Driver回复消息的部分也不进行说明，大家可以自行阅读，其实原理都是一样的，就跟我们平时工作一样，如果公司来了一个新同事，当他准备完成认为可以工作了，就要向领导汇报，领导接收到汇报之后就会为其分配具体的工作任务。
附上一副图，方便大家理解(注意该图只是画了主要流程，为了便于观看，Rpc通信的部分只是简单的画成了“A发送消息给B”的形式，特此说明)
向driver发消息
下面是向driver发送消息的部分，注意这里的driver指的是ClientEndpoint
exec.application.driver.send(
ExecutorAdded(exec.id, worker.id, worker.hostPort, exec.cores, exec.memory))
}
ClientEndpoint接收到消息后执行的操作：
case ExecutorAdded(id: Int, workerId: String, hostPort: String, cores: Int, memory: Int) =>
val fullId = appId + "/" + id
logInfo("Executor added: %s on %s (%s) with %d cores".format(fullId, workerId, hostPort,
cores))
listener.executorAdded(fullId, workerId, hostPort, cores, memory)
这里主要就是日志相关的工作了，不再阐述。
至此Application的注册和Executor的启动注册大致的流程我们就走完了，接下来就是task的提交和运行的部分了。
本文参照的是Spark 1.6.3版本的源码，同时给出Spark 2.1.0版本的连接：
Spark 1.6.3 源码
Spark 2.1.0 源码
本文为原创，欢迎转载，转载请注明出处、作者，谢谢！