本来不打算写的了，但是真的是闲来无事，整天看美剧也没啥意思。这一章打算讲一下Spark on yarn的实现，1.0.0里面已经是一个stable的版本了，可是1.0.1也出来了，离1.0.0发布才一个月的时间，更新太快了，节奏跟不上啊，这里仍旧是讲1.0.0的代码，所以各位朋友也不要再问我讲的是哪个版本，目前为止发布的文章都是基于1.0.0的代码。
在第一章《spark-submit提交作业过程》的时候，我们讲过Spark on yarn的在cluster模式下它的main class是org.apache.spark.deploy.yarn.Client。okay，这个就是我们的头号目标。
提交作业
找到main函数，里面调用了run方法，我们直接看run方法。
val appId = runApp()
monitorApplication(appId)
System.exit(0)
运行App，跟踪App，最后退出。我们先看runApp吧。
def runApp(): ApplicationId = {
// 校验参数，内存不能小于384Mb，Executor的数量不能少于1个。
validateArgs()
// 这两个是父类的方法，初始化并且启动Client
init(yarnConf)
start()
// 记录集群的信息(e.g, NodeManagers的数量，队列的信息).
logClusterResourceDetails()
// 准备提交请求到ResourcManager (specifically its ApplicationsManager (ASM)// Get a new client application.
val newApp = super.createApplication()
val newAppResponse = newApp.getNewApplicationResponse()
val appId = newAppResponse.getApplicationId()
// 检查集群的内存是否满足当前的作业需求
verifyClusterResources(newAppResponse)
// 准备资源和环境变量.
//1.获得工作目录的具体地址: /.sparkStaging/appId/
val appStagingDir = getAppStagingDir(appId)
//2.创建工作目录，设置工作目录权限，上传运行时所需要的jar包
val localResources = prepareLocalResources(appStagingDir)
//3.设置运行时需要的环境变量
val launchEnv = setupLaunchEnv(localResources, appStagingDir)
//4.设置运行时JVM参数，设置SPARK_USE_CONC_INCR_GC为true的话，就使用CMS的垃圾回收机制
val amContainer = createContainerLaunchContext(newAppResponse, localResources, launchEnv)
// 设置application submission context.
val appContext = newApp.getApplicationSubmissionContext()
appContext.setApplicationName(args.appName)
appContext.setQueue(args.amQueue)
appContext.setAMContainerSpec(amContainer)
appContext.setApplicationType("SPARK")
// 设置ApplicationMaster的内存，Resource是表示资源的类，目前有CPU和内存两种.
val memoryResource = Records.newRecord(classOf[Resource]).asInstanceOf[Resource]
memoryResource.setMemory(args.amMemory + YarnAllocationHandler.MEMORY_OVERHEAD)
appContext.setResource(memoryResource)
// 提交Application.
submitApp(appContext)
appId
}
monitorApplication就不说了，不停的调用getApplicationReport方法获得最新的Report，然后调用getYarnApplicationState获取当前状态，如果状态为FINISHED、FAILED、KILLED就退出。
说到这里，顺便把跟yarn相关的参数也贴出来一下，大家一看就清楚了。
while (!args.isEmpty) {
args match {
case ("--jar") :: value :: tail =>
userJar = value
args = tail
case ("--class") :: value :: tail =>
userClass = value
args = tail
case ("--args" | "--arg") :: value :: tail =>
if (args(0) == "--args") {
println("--args is deprecated. Use --arg instead.")
}
userArgsBuffer += value
args = tail
case ("--master-class" | "--am-class") :: value :: tail =>
if (args(0) == "--master-class") {
println("--master-class is deprecated. Use --am-class instead.")
}
amClass = value
args = tail
case ("--master-memory" | "--driver-memory") :: MemoryParam(value) :: tail =>
if (args(0) == "--master-memory") {
println("--master-memory is deprecated. Use --driver-memory instead.")
}
amMemory = value
args = tail
case ("--num-workers" | "--num-executors") :: IntParam(value) :: tail =>
if (args(0) == "--num-workers") {
println("--num-workers is deprecated. Use --num-executors instead.")
}
numExecutors = value
args = tail
case ("--worker-memory" | "--executor-memory") :: MemoryParam(value) :: tail =>
if (args(0) == "--worker-memory") {
println("--worker-memory is deprecated. Use --executor-memory instead.")
}
executorMemory = value
args = tail
case ("--worker-cores" | "--executor-cores") :: IntParam(value) :: tail =>
if (args(0) == "--worker-cores") {
println("--worker-cores is deprecated. Use --executor-cores instead.")
}
executorCores = value
args = tail
case ("--queue") :: value :: tail =>
amQueue = value
args = tail
case ("--name") :: value :: tail =>
appName = value
args = tail
case ("--addJars") :: value :: tail =>
addJars = value
args = tail
case ("--files") :: value :: tail =>
files = value
args = tail
case ("--archives") :: value :: tail =>
archives = value
args = tail
case Nil =>
if (userClass == null) {
printUsageAndExit(1)
}
case _ =>
printUsageAndExit(1, args)
}
}
ApplicationMaster
直接看run方法就可以了，main函数就干了那么一件事...
def run() {
// 设置本地目录，默认是先使用yarn的YARN_LOCAL_DIRS目录，再到LOCAL_DIRS
System.setProperty("spark.local.dir", getLocalDirs())
// set the web ui port to be ephemeral for yarn so we don‘t conflict with
// other spark processes running on the same box
System.setProperty("spark.ui.port", "0")
// when running the AM, the Spark master is always "yarn-cluster"
System.setProperty("spark.master", "yarn-cluster")
// 设置优先级为30，和mapreduce的优先级一样。它比HDFS的优先级高，因为它的操作是清理该作业在hdfs上面的Staging目录
ShutdownHookManager.get().addShutdownHook(new AppMasterShutdownHook(this), 30)
appAttemptId = getApplicationAttemptId()
// 通过yarn.resourcemanager.am.max-attempts来设置，默认是2
// 目前发现它只在清理Staging目录的时候用
isLastAMRetry = appAttemptId.getAttemptId() >= maxAppAttempts
amClient = AMRMClient.createAMRMClient()
amClient.init(yarnConf)
amClient.start()
// setup AmIpFilter for the SparkUI - do this before we start the UI
// 方法的介绍说是yarn用来保护ui界面的，我感觉是设置ip代理的
addAmIpFilter()
// 注册ApplicationMaster到内部的列表里
ApplicationMaster.register(this)
// 安全认证相关的东西，默认是不开启的，省得给自己找事
val securityMgr = new SecurityManager(sparkConf)
// 启动driver程序
userThread = startUserClass()
// 等待SparkContext被实例化，主要是等待spark.driver.port property被使用
// 等待结束之后，实例化一个YarnAllocationHandler
waitForSparkContextInitialized()
// Do this after Spark master is up and SparkContext is created so that we can register UI Url.
// 向yarn注册当前的ApplicationMaster, 这个时候isFinished不能为true，是true就说明程序失败了
synchronized {
if (!isFinished) {
registerApplicationMaster()
registered = true
}
}
// 申请Container来启动Executor
allocateExecutors()
// 等待程序运行结束
userThread.join()
System.exit(0)
}
run方法里面主要干了5项工作：
1、初始化工作
2、启动driver程序
3、注册ApplicationMaster
4、分配Executors
5、等待程序运行结束
我们重点看分配Executor方法。
private def allocateExecutors() {
try {
logInfo("Allocating " + args.numExecutors + " executors.")
// 分host、rack、任意机器三种类型向ResourceManager提交ContainerRequest
// 请求的Container数量可能大于需要的数量
yarnAllocator.addResourceRequests(args.numExecutors)
// Exits the loop if the user thread exits.
while (yarnAllocator.getNumExecutorsRunning < args.numExecutors && userThread.isAlive) {
if (yarnAllocator.getNumExecutorsFailed >= maxNumExecutorFailures) {
finishApplicationMaster(FinalApplicationStatus.FAILED, "max number of executor failures reached")
}
// 把请求回来的资源进行分配，并释放掉多余的资源
yarnAllocator.allocateResources()
ApplicationMaster.incrementAllocatorLoop(1)
Thread.sleep(100)
}
} finally {
// In case of exceptions, etc - ensure that count is at least ALLOCATOR_LOOP_WAIT_COUNT,
// so that the loop in ApplicationMaster#sparkContextInitialized() breaks.
ApplicationMaster.incrementAllocatorLoop(ApplicationMaster.ALLOCATOR_LOOP_WAIT_COUNT)
}
logInfo("All executors have launched.")
// 启动一个线程来状态报告
if (userThread.isAlive) {
// Ensure that progress is sent before YarnConfiguration.RM_AM_EXPIRY_INTERVAL_MS elapses.
val timeoutInterval = yarnConf.getInt(YarnConfiguration.RM_AM_EXPIRY_INTERVAL_MS, 120000)
// we want to be reasonably responsive without causing too many requests to RM.
val schedulerInterval = sparkConf.getLong("spark.yarn.scheduler.heartbeat.interval-ms", 5000)
// must be <= timeoutInterval / 2.
val interval = math.min(timeoutInterval / 2, schedulerInterval)
launchReporterThread(interval)
}
}
这里面我们只需要看addResourceRequests和allocateResources方法即可。
先说addResourceRequests方法，代码就不贴了。
Client向ResourceManager提交Container的请求，分三种类型：优先选择机器、同一个rack的机器、任意机器。
优先选择机器是在RDD里面的getPreferredLocations获得的机器位置，如果没有优先选择机器，也就没有同一个rack之说了，可以是任意机器。
下面我们接着看allocateResources方法。
def allocateResources() {
// We have already set the container request. Poll the ResourceManager for a response.
// This doubles as a heartbeat if there are no pending container requests.
// 之前已经提交过Container请求了，现在只需要获取response即可
val progressIndicator = 0.1f
val allocateResponse = amClient.allocate(progressIndicator)
val allocatedContainers = allocateResponse.getAllocatedContainers()
if (allocatedContainers.size > 0) {
var numPendingAllocateNow = numPendingAllocate.addAndGet(-1 * allocatedContainers.size)
if (numPendingAllocateNow < 0) {
numPendingAllocateNow = numPendingAllocate.addAndGet(-1 * numPendingAllocateNow)
}
val hostToContainers = new HashMap[String, ArrayBuffer[Container]]()
for (container <- allocatedContainers) {
// 内存 > Executor所需内存 + 384
if (isResourceConstraintSatisfied(container)) {
// 把container收入名册当中，等待发落
val host = container.getNodeId.getHost
val containersForHost = hostToContainers.getOrElseUpdate(host, new ArrayBuffer[Container]())
containersForHost += container
} else {
// 内存不够，释放掉它
releaseContainer(container)
}
}
// 找到合适的container来使用.
val dataLocalContainers = new HashMap[String, ArrayBuffer[Container]]()
val rackLocalContainers = new HashMap[String, ArrayBuffer[Container]]()
val offRackContainers = new HashMap[String, ArrayBuffer[Container]]()
// 遍历所有的host
for (candidateHost <- hostToContainers.keySet) {
val maxExpectedHostCount = preferredHostToCount.getOrElse(candidateHost, 0)
val requiredHostCount = maxExpectedHostCount - allocatedContainersOnHost(candidateHost)
val remainingContainersOpt = hostToContainers.get(candidateHost)
var remainingContainers = remainingContainersOpt.get
if (requiredHostCount >= remainingContainers.size) {
// 需要的比现有的多，把符合数据本地性的添加到dataLocalContainers映射关系里
dataLocalContainers.put(candidateHost, remainingContainers)
// 没有containner剩下的.
remainingContainers = null
} else if (requiredHostCount > 0) {
// 获得的container比所需要的多，把多余的释放掉
val (dataLocal, remaining) = remainingContainers.splitAt(remainingContainers.size - requiredHostCount)
dataLocalContainers.put(candidateHost, dataLocal)
for (container <- remaining) releaseContainer(container)
remainingContainers = null
}
// 数据所在机器已经分配满任务了，只能在同一个rack里面挑选了
if (remainingContainers != null) {
val rack = YarnAllocationHandler.lookupRack(conf, candidateHost)
if (rack != null) {
val maxExpectedRackCount = preferredRackToCount.getOrElse(rack, 0)
val requiredRackCount = maxExpectedRackCount - allocatedContainersOnRack(rack) -
rackLocalContainers.getOrElse(rack, List()).size
if (requiredRackCount >= remainingContainers.size) {
// Add all remaining containers to to `dataLocalContainers`.
dataLocalContainers.put(rack, remainingContainers)
remainingContainers = null
} else if (requiredRackCount > 0) {
// Container list has more containers that we need for data locality.
val (rackLocal, remaining) = remainingContainers.splitAt(remainingContainers.size - requiredRackCount)
val existingRackLocal = rackLocalContainers.getOrElseUpdate(rack, new ArrayBuffer[Container]())
existingRackLocal ++= rackLocal
remainingContainers = remaining
}
}
}
if (remainingContainers != null) {
// 还是不够，只能放到别的rack的机器上运行了
offRackContainers.put(candidateHost, remainingContainers)
}
}
// 按照数据所在机器、同一个rack、任意机器来排序
val allocatedContainersToProcess = new ArrayBuffer[Container](allocatedContainers.size)
allocatedContainersToProcess ++= TaskSchedulerImpl.prioritizeContainers(dataLocalContainers)
allocatedContainersToProcess ++= TaskSchedulerImpl.prioritizeContainers(rackLocalContainers)
allocatedContainersToProcess ++= TaskSchedulerImpl.prioritizeContainers(offRackContainers)
// 遍历选择了的Container，为每个Container启动一个ExecutorRunnable线程专门负责给它发送命令
for (container <- allocatedContainersToProcess) {
val numExecutorsRunningNow = numExecutorsRunning.incrementAndGet()
val executorHostname = container.getNodeId.getHost
val containerId = container.getId
// 内存需要大于Executor的内存 + 384
val executorMemoryOverhead = (executorMemory + YarnAllocationHandler.MEMORY_OVERHEAD)
if (numExecutorsRunningNow > maxExecutors) {
// 正在运行的比需要的多了，释放掉多余的Container
releaseContainer(container)
numExecutorsRunning.decrementAndGet()
} else {
val executorId = executorIdCounter.incrementAndGet().toString
val driverUrl = "akka.tcp://[email protected]%s:%s/user/%s".format(
sparkConf.get("spark.driver.host"),
sparkConf.get("spark.driver.port"),
CoarseGrainedSchedulerBackend.ACTOR_NAME)
// To be safe, remove the container from `pendingReleaseContainers`.
pendingReleaseContainers.remove(containerId)
// 把container记录到已分配的rack的映射关系当中
val rack = YarnAllocationHandler.lookupRack(conf, executorHostname)
allocatedHostToContainersMap.synchronized {
val containerSet = allocatedHostToContainersMap.getOrElseUpdate(executorHostname,
new HashSet[ContainerId]())
containerSet += containerId
allocatedContainerToHostMap.put(containerId, executorHostname)
if (rack != null) {
allocatedRackCount.put(rack, allocatedRackCount.getOrElse(rack, 0) + 1)
}
}
// 启动一个线程给它进行跟踪服务，给它发送运行Executor的命令
val executorRunnable = new ExecutorRunnable(
container,
conf,
sparkConf,
driverUrl,
executorId,
executorHostname,
executorMemory,
executorCores)
new Thread(executorRunnable).start()
}
}
}
1、把从ResourceManager中获得的Container进行选择，选择顺序是按照前面的介绍的三种类别依次进行，优先选择机器 > 同一个rack的机器 > 任意机器。
2、选择了Container之后，给每一个Container都启动一个ExecutorRunner一对一贴身服务，给它发送运行CoarseGrainedExecutorBackend的命令。
3、ExecutorRunner通过NMClient来向NodeManager发送请求。
总结：
把作业发布到yarn上面去执行这块涉及到的类不多，主要是涉及到Client、ApplicationMaster、YarnAllocationHandler、ExecutorRunner这四个类。
1、Client作为Yarn的客户端，负责向Yarn发送启动ApplicationMaster的命令。
2、ApplicationMaster就像项目经理一样负责整个项目所需要的工作，包括请求资源，分配资源，启动Driver和Executor，Executor启动失败的错误处理。
3、ApplicationMaster的请求、分配资源是通过YarnAllocationHandler来进行的。
4、Container选择的顺序是：优先选择机器 > 同一个rack的机器 > 任意机器。
5、ExecutorRunner只负责向Container发送启动CoarseGrainedExecutorBackend的命令。
6、Executor的错误处理是在ApplicationMaster的launchReporterThread方法里面，它启动的线程除了报告运行状态，还会监控Executor的运行，一旦发现有丢失的Executor就重新请求。
7、在yarn目录下看到的名称里面带有YarnClient的是属于yarn-client模式的类，实现和前面的也差不多。
其它的内容更多是Yarn的客户端api使用，我也不太会，只是看到了能懂个意思，哈哈。
岑玉海
转载请注明出处，谢谢！
Spark源码系列（七）Spark on yarn具体实现
时间： 2024-08-08 02:10:38