解释：
1、master要求worker启动driver和executor
2、worker启动driver的一个基本的原理，worker会启动一个线程DriverRunner，然后DriverRunner会去负责启动driver进程，然后在之后对driver进程进行管理
3、worker启动executor的一个基本的原理，worker会启动一个线程ExecutorRunner，然后ExecutorRunner会去负责启动executor进程，然后在之后对executor进程进行管理
4、driver首先创建driver的工作目录，封装启动driver的命令，用ProcessBuilder启动Driver
5、executor首先创建executor的工作目录，封装启动executor的命令，用ProcessBuilder启动executor，executor找到对应的driver，去反向注册自己，然后就可以启动executor
master启动并管理driver进程源码分析：
第一步：调用schedule方法的launchDriver方法
private def launchDriver(worker: WorkerInfo, driver: DriverInfo) {
logInfo("Launching driver " + driver.id + " on worker " + worker.id)
// 将driver加入到worker内部的缓冲结构中
// 将worker中使用的内存和cpu的数量，都加上driver需要的内存和cpu的数量
worker.addDriver(driver)
// 将worker加入到driver的内存缓冲结构中
driver.worker = Some(worker)
// 调用worker的endpoint，给worker发送注册driver的信息
worker.endpoint.send(LaunchDriver(driver.id, driver.desc))
driver.state = DriverState.RUNNING
}
第二步：调用worker的launchDriver方法
// 启动driver
case LaunchDriver(driverId, driverDesc) => {
logInfo(s"Asked to launch driver $driverId")
val driver = new DriverRunner( //DriverRunner线程
conf,
driverId,
workDir,
sparkHome,
driverDesc.copy(command = Worker.maybeUpdateSSLSettings(driverDesc.command, conf)),
self,
workerUri,
securityMgr)
drivers(driverId) = driver
driver.start() //启动Driver进程
//已使用的cpu和内存
coresUsed += driverDesc.cores
memoryUsed += driverDesc.mem
}
第三步：调用第二步的DriverRunner方法
private[worker] def start() = {
new Thread("DriverRunner for " + driverId) {
override def run() {
var shutdownHook: AnyRef = null
try {
shutdownHook = ShutdownHookManager.addShutdownHook { () =>
logInfo(s"Worker shutting down, killing driver $driverId")
kill()
}
// prepare driver jars and run driver
//在这个类里实现的RunDriver
val exitCode = prepareAndRunDriver()
// set final state depending on if forcibly killed and process exit code
//对prepareAndRunDriver()返回的code做一些逻辑 *** 作
finalState = if (exitCode == 0) {
Some(DriverState.FINISHED)
} else if (killed) {
Some(DriverState.KILLED)
} else {
Some(DriverState.FAILED)
}
} catch {
case e: Exception =>
kill()
finalState = Some(DriverState.ERROR)
finalException = Some(e)
} finally {
if (shutdownHook != null) {
ShutdownHookManager.removeShutdownHook(shutdownHook)
}
}
// notify worker of final driver state, possible exception
//给worker发送Driver状态改变（可以看Spark源码——状态改变 那篇文章）
worker.send(DriverStateChanged(driverId, finalState.get, finalException))
}
}.start()
}
进去prepareAndRunDriver
private[worker] def prepareAndRunDriver(): Int = {
val driverDir = createWorkingDirectory() //创建driver文件目录
val localJarFilename = downloadUserJar(driverDir)//下载用户jar包到上面创建的目录重
def substituteVariables(argument: String): String = argument match {
case "{{WORKER_URL}}" => workerUrl
case "{{USER_JAR}}" => localJarFilename
case other => other
}
// TODO: If we add ability to submit multiple jars they should also be added here
//传入了driver的启动命令、需要的内存大小等信息
val builder = CommandUtils.buildProcessBuilder(driverDesc.command, securityManager,
driverDesc.mem, sparkHome.getAbsolutePath, substituteVariables)
runDriver(builder, driverDir, driverDesc.supervise)
}
进入runDriver看看
private def runDriver(builder: ProcessBuilder, baseDir: File, supervise: Boolean): Int = {
builder.directory(baseDir)
def initialize(process: Process): Unit = {
// Redirect stdout and stderr to files
//这应该是日志，stdout和stderr是运行的输出日志和错误日志
val stdout = new File(baseDir, "stdout")
//重定向到输出流
CommandUtils.redirectStream(process.getInputStream, stdout)
val stderr = new File(baseDir, "stderr")
val formattedCommand = builder.command.asScala.mkString(""", "" "", """)
val header = "Launch Command: %sn%snn".format(formattedCommand, "=" * 40)
Files.append(header, stderr, StandardCharsets.UTF_8)
CommandUtils.redirectStream(process.getErrorStream, stderr)
}
//运行
runCommandWithRetry(ProcessBuilderLike(builder), initialize, supervise)
}
进入runCommandWithRetry看看
private[worker] def runCommandWithRetry(
command: ProcessBuilderLike, initialize: Process => Unit, supervise: Boolean): Int = {
var exitCode = -1
// Time to wait between submission retries.
var waitSeconds = 1
// A run of this many seconds resets the exponential back-off.
val successfulRunDuration = 5 //重试次数
var keepTrying = !killed
while (keepTrying) {
logInfo("Launch Command: " + command.command.mkString(""", "" "", """))
synchronized {
if (killed) { return exitCode }
process = Some(command.start())
initialize(process.get)
}
val processStart = clock.getTimeMillis()
//启动进程，process的waitFor就是启动
exitCode = process.get.waitFor()
// check if attempting another run
keepTrying = supervise && exitCode != 0 && !killed
if (keepTrying) {
if (clock.getTimeMillis() - processStart > successfulRunDuration * 1000) {
waitSeconds = 1
}
logInfo(s"Command exited with status $exitCode, re-launching after $waitSeconds s.")
sleeper.sleep(waitSeconds)
waitSeconds = waitSeconds * 2 // exponential back-off
}
}
exitCode //返回的正是exitCode
}
}
master启动并管理executor进程源码分析：
第一步：调用schedule方法的startExecutorsOnWorkers
private def startExecutorsOnWorkers(): Unit = {
// Right now this is a very simple FIFO scheduler. We keep trying to fit in the first app
// in the queue, then the second app, etc.
// 首先遍历waitingApps中ApplicationInfo，并且还需要判断程序中定义的使用cpu的数量-启动执行application上
// worker上的excutor所使用的的cpu的要大于0
for (app <- waitingApps if app.coresLeft > 0) {
val coresPerExecutor: Option[Int] = app.desc.coresPerExecutor
// Filter out workers that don't have enough resources to launch an executor
// 从workers中，过滤出worker的状态为alive的，按照cpu的数量进行倒序排序
val usableWorkers = workers.toArray.filter(_.state == WorkerState.ALIVE)
.filter(worker => worker.memoryFree >= app.desc.memoryPerExecutorMB &&
worker.coresFree >= coresPerExecutor.getOrElse(1))
.sortBy(_.coresFree).reverse
// 在worker上调度executor
val assignedCores = scheduleExecutorsOnWorkers(app, usableWorkers, spreadOutApps)
// SpreadOut算法，会将每一个application要启动的executor都平均分布到各个worker上去
// 比如说20个cpu core，分配到10个worker上，实际会循环两遍worker，每次循环，
// 给每个worker分配一个 core，最后每个worker分配了2个core
// 总体概括：平均分布
// 非SpreadOut算法，将每一个application，尽可能少的分配到worker上去
// 每个application，都尽可能的分配到尽量少的worker上，比如说10个worker
// 每个worker10个cpu，application要分配20个core，那么其实，只会分配到2个worker上
// 每个worker都占满10个core，其余的app，就只能分配到下一个worker上
// 总体概括：尽可能资源大的分配
// Now that we've decided how many cores to allocate on each worker, let's allocate them
// 给每个worker分配完application要求的cpu core之后，遍历worker，只要判断之前给这个worker分配到了core
// Now that we've decided how many cores to allocate on each worker, let's allocate them
for (pos <- 0 until usableWorkers.length if assignedCores(pos) > 0) {
allocateWorkerResourceToExecutors(
app, assignedCores(pos), coresPerExecutor, usableWorkers(pos))
}
}
}
第二步：调用第一步的allocateWorkerResourceToExecutors方法
private def allocateWorkerResourceToExecutors(
app: ApplicationInfo,
assignedCores: Int,
coresPerExecutor: Option[Int],
worker: WorkerInfo): Unit = {
// If the number of cores per executor is specified, we divide the cores assigned
// to this worker evenly among the executors with no remainder.
// Otherwise, we launch a single executor that grabs all the assignedCores on this worker.
// 首先，在application内部缓存结构中，添加executor
// 并且创建ExecutorDesc对象，其中封装了，给这个executor分配了多少个cpu core
// 基于我们的机制，实际上，最后，executor的实际数量，以及executor所对应的cpu是不一致的
// 我们这里是根据总的机制来分配的，比如说要求启动3个executor，每一个executor3个cpu，9个worker，
// 根据我们的算法来说的话，就是每一个worker启动一个executor，一个executor对应一个cpu core
val numExecutors = coresPerExecutor.map { assignedCores / _ }.getOrElse(1)
val coresToAssign = coresPerExecutor.getOrElse(assignedCores)
for (i <- 1 to numExecutors) {
val exec = app.addExecutor(worker, coresToAssign)
launchExecutor(worker, exec)
app.state = ApplicationState.RUNNING
}
}
第三步：调用第二步的launchExecutor方法
private def launchExecutor(worker: WorkerInfo, exec: ExecutorDesc): Unit = {
logInfo("Launching executor " + exec.fullId + " on worker " + worker.id)
// 将executor加入worker的内存缓存
worker.addExecutor(exec)
// 向worker的endpoint发生LaunchExecutor消息
worker.endpoint.send(LaunchExecutor(masterUrl,
exec.application.id, exec.id, exec.application.desc, exec.cores, exec.memory))
// 向executor对应的application的driver，发生executorAdded消息
exec.application.driver.send(
ExecutorAdded(exec.id, worker.id, worker.hostPort, exec.cores, exec.memory))
}
第四步：调用worker类的LaunchExecutor这个case class
case LaunchExecutor(masterUrl, appId, execId, appDesc, cores_, memory_) =>
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))
// Create the executor's working directory
//创建executor目录
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.
val appLocalDirs = appDirectories.getOrElse(appId,
Utils.getOrCreateLocalRootDirs(conf).map { dir =>
val appDir = Utils.createDirectory(dir, namePrefix = "executor")
Utils.chmod700(appDir)
appDir.getAbsolutePath()
}.toSeq)
appDirectories(appId) = appLocalDirs
//创建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)
//加入本地缓存
executors(appId + "/" + execId) = manager
//启动ExecutorRunner
manager.start()
coresUsed += cores_
memoryUsed += memory_
//发送给master，Executor状态改变
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))
}
}
第五步：调用第四步的ExecutorRunner
看到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")) }
}
进入fetchAndRunExecutor()
private def fetchAndRunExecutor() {
try {
// Launch the process
//封装一个processBuilder
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: $formattedCommand")
//创建目录
builder.directory(executorDir)
//放一些环境变量
builder.environment.put("SPARK_EXECUTOR_DIRS", appLocalDirs.mkString(File.pathSeparator))
// In case we are running this from within the Spark Shell, avoid creating a "scala"
// parent process for the executor command
builder.environment.put("SPARK_LAUNCH_WITH_SCALA", "0")
// Add webUI log urls
val baseUrl =
if (conf.getBoolean("spark.ui.reverseProxy", false)) {
s"/proxy/$workerId/logPage/?appId=$appId&executorId=$execId&logType="
} else {
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")
process = builder.start()
val header = "Spark Executor Command: %sn%snn".format(
formattedCommand, "=" * 40)
// Redirect its stdout and stderr to files
//重定向到创建的目录中
val stdout = new File(executorDir, "stdout")
stdoutAppender = FileAppender(process.getInputStream, stdout, conf)
val stderr = new File(executorDir, "stderr")
Files.write(header, stderr, StandardCharsets.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
//启动进程，waitFor
val exitCode = process.waitFor()
//拿到一个返回状态
state = ExecutorState.EXITED
val message = "Command exited with code " + exitCode
//给worker发送executor状态改变信息
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))
}
}
}
tip：在这种涉及通信的过程，Master和Worker通常会有相同类名的类，涉及不同的处理逻辑，可以对照着看。
欢迎分享，转载请注明来源：内存溢出
微信扫一扫
支付宝扫一扫
评论列表（0条）