Master可以配置两个，Spark原生的standalone模式支持Master主备切换。
Spark Master主备切换可以基于两种机制，一种是基于文件系统的，一种是基于ZooKeeper的，基于文件系统的主备切换机制，需要在Active Master挂掉之后，手动去切换到Standby Master上。基于ZooKeeper的主备切换机制，可以实现自动切换Master。
Master主备切换机制，就是在Active Master挂掉之后，切换到Standby Master会做哪些操作。
org/apache/spark/deploy/master/Master.scala，completeRecovery方法。
/**
* 完成Master的主备切换，就是完成Master的恢复
*/
def completeRecovery() {
// Ensure "only-once" recovery semantics using a short synchronization period.
synchronized {
if (state != RecoveryState.RECOVERING) { return }
state = RecoveryState.COMPLETING_RECOVERY
}
// 将Application和Worker，过滤出来目前状态还是UNKNOWN的
// 然后遍历，分别调用removeWorker和finishApplication方法，对可能已经出故障，
// 或者甚至已经死掉的Application和Worker，进行清理
// 总结一下清理的机制，三点：1、从内存缓存结构中移除；
// 2、从相关的组件的内存缓存中移除；
// 3、从持久化存储中移除。
// Kill off any workers and apps that didn't respond to us.
workers.filter(_.state == WorkerState.UNKNOWN).foreach(removeWorker)
apps.filter(_.state == ApplicationState.UNKNOWN).foreach(finishApplication)
// Reschedule drivers which were not claimed by any workers
drivers.filter(_.worker.isEmpty).foreach { d =>
logWarning(s"Driver ${d.id} was not found after master recovery")
if (d.desc.supervise) {
logWarning(s"Re-launching ${d.id}")
relaunchDriver(d)
} else {
removeDriver(d.id, DriverState.ERROR, None)
logWarning(s"Did not re-launch ${d.id} because it was not supervised")
}
}
state = RecoveryState.ALIVE
schedule()
logInfo("Recovery complete - resuming operations!")
}
Worker的注册，
Driver的注册，
Application的注册，
org/apache/spark/deploy/master/Master.scala，RegisterApplication样例类。
/**
* 处理Application注册的请求
*/
case RegisterApplication(description) => {
// 如果master的状态是standby，也就是当前这个master，是standBy Master，不是Active Master，
// 那么Application来请求注册，什么都不会干
if (state == RecoveryState.STANDBY) {
// ignore, don't send response
} else {
logInfo("Registering app " + description.name)
// 用ApplicationDescription信息，创建ApplicationInfo
val app = createApplication(description, sender)
// 注册Application
// 将ApplicationInfo加入缓存，将Application加入等待调度的队列waitingApps
registerApplication(app)
logInfo("Registered app " + description.name + " with ID " + app.id)
// 使用持久化引擎，将Application进行持久化
persistenceEngine.addApplication(app)
// SparkDeploySchedulerBackend创建的AppClient通过ClientActor线程向Master Actor发送注册请求，
// ClientActor会把自己的引用给带过来，用sender来命名，
// Master就知道RegisterApplication这个消息是谁给我发过来的，
// 反向，向SparkDeploySchedulerBackend的AppClient的ClientActor，发送消息，
// 也就是RegisteredApplication
sender ! RegisteredApplication(app.id, masterUrl)
schedule()
}
}
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
}
applicationMetricsSystem.registerSource(app.appSource)
// 将app的信息加入内存缓存中
apps += app
idToApp(app.id) = app
actorToApp(app.driver) = app
addressToApp(appAddress) = app
// 将app加入等待调度的队列waitingApps
waitingApps += app
}
Driver的状态改变，package org.apache.spark.deploy.master，DriverStateChanged。
case DriverStateChanged(driverId, state, exception) => {
state match {
// 如果Driver的状态是错误、完成、被杀掉、失败
// 那么就移除Driver。
case DriverState.ERROR | DriverState.FINISHED | DriverState.KILLED | DriverState.FAILED =>
removeDriver(driverId, state, exception)
case _ =>
throw new Exception(s"Received unexpected state update for driver $driverId: $state")
}
}
def removeDriver(driverId: String, finalState: DriverState, exception: Option[Exception]) {
// 用scala的find()高阶函数，找到driverId对应的driver
drivers.find(d => d.id == driverId) match {
// 如果找到了，Some，样例类（Option）
case Some(driver) =>
logInfo(s"Removing driver: $driverId")
// 将driver从内存缓存中移除
drivers -= driver
if (completedDrivers.size >= RETAINED_DRIVERS) {
val toRemove = math.max(RETAINED_DRIVERS / 10, 1)
completedDrivers.trimStart(toRemove)
}
// 想completedDrivers中加入driver
completedDrivers += driver
// 使用持久化引擎去除driver的持久化信息
persistenceEngine.removeDriver(driver)
// 设置driver的state、exception
driver.state = finalState
driver.exception = exception
// 将driver所在的worker，移除dirver
driver.worker.foreach(w => w.removeDriver(driver))
// 同样，调用schedule()方法
schedule()
case None =>
logWarning(s"Asked to remove unknown driver: $driverId")
}
}
Executor的状态改变，package org.apache.spark.deploy.master，ExecutorStateChanged。
case ExecutorStateChanged(appId, execId, state, message, exitStatus) => {
// 找到executor对应的app，然后再反过来通过app内部的executors缓存获取executor信息
val execOption = idToApp.get(appId).flatMap(app => app.executors.get(execId))
execOption match {
// 如果有值
case Some(exec) => {
// 设置executor的当前状态
val appInfo = idToApp(appId)
exec.state = state
if (state == ExecutorState.RUNNING) { appInfo.resetRetryCount() }
// 向driver同步发送ExecutorUpdated消息
exec.application.driver ! ExecutorUpdated(execId, state, message, exitStatus)
// 判断，如果executor完成了
if (ExecutorState.isFinished(state)) {
// Remove this executor from the worker and app
logInfo(s"Removing executor ${exec.fullId} because it is $state")
// 从app的缓存中移除executor
appInfo.removeExecutor(exec)
// 从运行executor的worker的缓存中移除executor
exec.worker.removeExecutor(exec)
// 判断，如果executor的退出状态是非正常的
val normalExit = exitStatus == Some(0)
// Only retry certain number of times so we don't go into an infinite loop.
if (!normalExit) {
// 判断application当前的重试次数，是否达到了最大值10，
if (appInfo.incrementRetryCount() < ApplicationState.MAX_NUM_RETRY) {
// 重新进行调度
schedule()
} else {
// 否则，那么就removeApplication操作
// executor反复调度都是失败，那么就认为application也失败了
val execs = appInfo.executors.values
if (!execs.exists(_.state == ExecutorState.RUNNING)) {
logError(s"Application ${appInfo.desc.name} with ID ${appInfo.id} failed " +
s"${appInfo.retryCount} times; removing it")
removeApplication(appInfo, ApplicationState.FAILED)
}
}
}
}
}
case None =>
logWarning(s"Got status update for unknown executor $appId/$execId")
}
}
org/apache/spark/deploy/master/Master.scala，schedule方法。
/**
* Schedule the currently available resources among waiting apps. This method will be called
* every time a new app joins or resource availability changes.
*/
private def schedule() {
// 首先判断，master状态不是ALIVE的话，直接返回，
// 也就是说，standby master是不会进行application等资源的调度的。
if (state != RecoveryState.ALIVE) { return }
// First schedule drivers, they take strict precedence over applications
// Randomization helps balance drivers
// Random.shuffle的原理，就是对传入的集合的元素进行随机的打乱
// 取出workers中的所有之前注册上来的worker，进行过滤，必须是状态为ALIVE的worker
// 对状态为ALIVE的worker，调用Random的shuffle方法，进行随机的打乱
val shuffledAliveWorkers = Random.shuffle(workers.toSeq.filter(_.state == WorkerState.ALIVE))
// 获取shuffledAliveWorkers的大小
val numWorkersAlive = shuffledAliveWorkers.size
var curPos = 0
// 首先调度driver
// 为什么要调度driver，大家想一下，什么情况下，会注册driver，并且会导致driver被调度
// 其实，只有用yarn-cluster模式提交的时候，才会注册driver，因为standalone和yarn-client模式，
// 都会在本地直接启动driver，而不会来注册driver，就更不可能让master来调度driver了。
// driver的调度机制
// 遍历waitingDrivers ArrayBuffer
for (driver <- waitingDrivers.toList) { // iterate over a copy of waitingDrivers
// We assign workers to each waiting driver in a round-robin fashion. For each driver, we
// start from the last worker that was assigned a driver, and continue onwards until we have
// explored all alive workers.
var launched = false
var numWorkersVisited = 0
// numWorkersVisited小于numWorkersAlive
// 就是说只要还有或者的worker没有遍历到，那么就继续进行遍历，
// 而且，当前这个driver还没有被启动，也就是launched为false
while (numWorkersVisited < numWorkersAlive && !launched) {
// 拿到一个活着的worker
val worker = shuffledAliveWorkers(curPos)
// 遍历过的worker加1
numWorkersVisited += 1
// 如果当前这个worker的空闲内存量大于等于driver需要的内存
// 并且worker的空闲cpu数量，大于等于driver需要的cpu数量
if (worker.memoryFree >= driver.desc.mem && worker.coresFree >= driver.desc.cores) {
// 启动driver
launchDriver(worker, driver)
// 并且将driver从waitingDrivers队列中移除，后面就不会调度它了，把launched设为true。
waitingDrivers -= driver
launched = true
}
// 将指针指向下一个worker
curPos = (curPos + 1) % numWorkersAlive
// 这个driver去循环遍历所有活着的worker，只要launched为true，表明当前这个driver已经在某个worker启动。
}
}
// 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.
// Application的调度机制（核心之核心）
// Application的调度算法有两种，一种是spreadOutApps，另一种是非spreadOutApps
// val spreadOutApps = conf.getBoolean("spark.deploy.spreadOut", true)
if (spreadOutApps) {
// Try to spread out each app among all the nodes, until it has all its cores
// 首先遍历waitingApps中的ApplicationInfo，并且过滤出还有需要调度的core的Application
for (app <- waitingApps if app.coresLeft > 0) {
// 从workers中，过滤出状态为ALIVE的，再次过滤出可以被Application使用的Worker，
// 然后，按照剩余cpu数量倒序排序
// canUse，worker.memoryFree >= app.desc.memoryPerSlave && !worker.hasExecutor(app)
val usableWorkers = workers.toArray.filter(_.state == WorkerState.ALIVE)
.filter(canUse(app, _)).sortBy(_.coresFree).reverse
val numUsable = usableWorkers.length
// 创建一个空数组，存储了要分配给每个worker的cpu数量
val assigned = new Array[Int](numUsable) // Number of cores to give on each node
// 获取到底要分配多少cpu，取app剩余要分配的cpu数量和worker总共可用cpu数量的最小值
var toAssign = math.min(app.coresLeft, usableWorkers.map(_.coresFree).sum)
// 通过这种算法，其实会将每个application，要启动的executor，都平均分布到各个worker上去
// 比如有20个cpu core要分配，那么实际会循环两遍worker，每次循环，给每个worker分配1个core
// 最后每个worker分配了2个core
// while条件，只要要分配的cpu，还没分配完，就继续循环
var pos = 0
while (toAssign > 0) {
// 每一个worker，如果空闲的cpu数量，大于已经分配出去的cpu数量
// 也就是说，worker还有可分配的cpu
if (usableWorkers(pos).coresFree - assigned(pos) > 0) {
// 将总共要分配的cpu数量-1，因为这里已经决定在这个worker上分配一个cpu了
toAssign -= 1
// 给这个worker分配的cpu数量，加1
assigned(pos) += 1
}
// 指针移动到下一个worker
pos = (pos + 1) % numUsable
}
// Now that we've decided how many cores to give on each node, let's actually give them
// 给每个worker分配完application要求的cpu core之后
// 遍历worker
for (pos <- 0 until numUsable) {
// 只要判断之前给这个worker分配到了core
if (assigned(pos) > 0) {
// 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分配多个cpu core
// 这里至少是spark1.3.0版本的executor启动的内部机制
// 在spark-submit脚本中，可以指定要多少个executor，每个executor多少个cpu，多少内存
// 基于我们的机制，实际上，最后，executor的实际数量，以及每个executor的cpu，可能与配置是不一样的
// 因为，我们这里是基于总的cpu来分配的，就是说，比如要求3个executor，每个要3个cpu，
// 比如有9个worker，每个有1个cpu，那么其实总共知道，要分配9个core，其实根据这种算法，
// 会给每个worker分配一个core，然后，给每个worker启动一个executor吧，最后，
// 会启动9个executor，每个executor有1个cpu core
val exec = app.addExecutor(usableWorkers(pos), assigned(pos))
// 那么就在worker上启动executor
launchExecutor(usableWorkers(pos), exec)
// 将application的状态设置为RUNNING
app.state = ApplicationState.RUNNING
}
}
}
} else {
// Pack each app into as few nodes as possible until we've assigned all its cores
// 非spreadOutApps调度算法
// 这种算法和spreadOutApps算法正好相反
// 每个application，都尽可能分配到尽量少的worker上去，
// 比如总共有10个worker，每个有10个core，app总共要分配20个core，那么，其实
// 只会分配到两个worker上，每个worker都占满10个core，那么其余的app，就只能分配到下一个worker了。
// 所以，比方说，application，spark-submit里，配置的是要10个executor，每个要2个core，那么总共是20个core，
// 但是在这种算法下，其实只会启动2个executor，每个有10个core。
// 将每一个Application，尽可能少的分配到worker上去
// 首先遍历worker，并且是状态为ALIVE，还有空闲cpu的worker
for (worker <- workers if worker.coresFree > 0 && worker.state == WorkerState.ALIVE) {
// 遍历application，并且是还有需要分配的core的application
for (app <- waitingApps if app.coresLeft > 0) {
// 判断，如果当前这个worker可以被application使用
if (canUse(app, worker)) {
// 取worker剩余cpu数量，与app要分配的cpu数量的最小值
val coresToUse = math.min(worker.coresFree, app.coresLeft)
if (coresToUse > 0) {
// 给app添加一个executor
val exec = app.addExecutor(worker, coresToUse)
// 在worker上启动executor
launchExecutor(worker, exec)
// 将application状态修改为RUNNING
app.state = ApplicationState.RUNNING
}
}
}
}
}
}
launchDriver方法，
def launchDriver(worker: WorkerInfo, driver: DriverInfo) {
logInfo("Launching driver " + driver.id + " on worker " + worker.id)
// 将driver键入worker内部的缓存结构
// 将worker内使用的内存和cpu数量，都加上driver需要的内存和cpu数量
worker.addDriver(driver)
// 同时把worker加入到driver内部的缓存结构中
driver.worker = Some(worker)
// 然后调用worker的actor，给它发送LaunchDriver，让Worker来启动Driver
worker.actor ! LaunchDriver(driver.id, driver.desc)
// 将driver的状态设置为RUNNING
driver.state = DriverState.RUNNING
}
launchExecutor方法，
def launchExecutor(worker: WorkerInfo, exec: ExecutorDesc) {
logInfo("Launching executor " + exec.fullId + " on worker " + worker.id)
// 将executor加入worker内部的缓存
worker.addExecutor(exec)
// 向worker的actor发送LaunchExecutor消息
worker.actor ! LaunchExecutor(masterUrl,
exec.application.id, exec.id, exec.application.desc, exec.cores, exec.memory)
// 向executor对应的application的driver，发送ExecutorAdded消息
exec.application.driver ! ExecutorAdded(
exec.id, worker.id, worker.hostPort, exec.cores, exec.memory)
}
本文首发于steem，感谢阅读，转载请注明。
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