Spark的Master和Worker集群启动的源码分析

时间：2015-07-11 22:55:30 阅读：489 评论：0 收藏：0 [点我收藏+]

基于spark1.3.1的源码进行分析

spark master启动源码分析

1、在start-master.sh调用master的main方法，main方法调用
def main(argStrings: Array[String]) {
SignalLogger.register(log)
val conf = new SparkConf
val args = new MasterArguments(argStrings, conf)
val (actorSystem, _, _, _) = startSystemAndActor(args.host, args.port, args.webUiPort, conf)//启动系统和actor
actorSystem.awaitTermination()
}
2、调用startSystemAndActor启动系统和创建actor
def startSystemAndActor(
host: String,
port: Int,
webUiPort: Int,
conf: SparkConf): (ActorSystem, Int, Int, Option[Int]) = {
val securityMgr = new SecurityManager(conf)
val (actorSystem, boundPort) = AkkaUtils.createActorSystem(systemName, host, port, conf = conf,
securityManager = securityMgr)
val actor = actorSystem.actorOf(
Props(classOf[Master], host, boundPort, webUiPort, securityMgr, conf), actorName)
val timeout = AkkaUtils.askTimeout(conf)
val portsRequest = actor.ask(BoundPortsRequest)(timeout)
val portsResponse = Await.result(portsRequest, timeout).asInstanceOf[BoundPortsResponse]
(actorSystem, boundPort, portsResponse.webUIPort, portsResponse.restPort)
3、调用AkkaUtils.createActorSystem来创建ActorSystem
def createActorSystem(
name: String,
host: String,
port: Int,
conf: SparkConf,
securityManager: SecurityManager): (ActorSystem, Int) = {
val startService: Int => (ActorSystem, Int) = { actualPort =>
doCreateActorSystem(name, host, actualPort, conf, securityManager)
}
Utils.startServiceOnPort(port, startService, conf, name)
}
4、调用Utils.startServiceOnPort启动一个端口上的服务，创建成功后调用doCreateActorSystem创建ActorSystem
5、ActorSystem创建成功后创建Actor
6、调用Master的主构造函数，执行preStart()

1、start-slaves.sh调用Worker类的main方法
def main(argStrings: Array[String]) {
SignalLogger.register(log)
val conf = new SparkConf
val args = new WorkerArguments(argStrings, conf)
val (actorSystem, _) = startSystemAndActor(args.host, args.port, args.webUiPort, args.cores,
args.memory, args.masters, args.workDir)
actorSystem.awaitTermination()
}
2、调用startSystemAndActor启动系统和创建actor
def startSystemAndActor(
host: String,
port: Int,
webUiPort: Int,
cores: Int,
memory: Int,
masterUrls: Array[String],
workDir: String,
workerNumber: Option[Int] = None,
conf: SparkConf = new SparkConf): (ActorSystem, Int) = {

// The LocalSparkCluster runs multiple local sparkWorkerX actor systems
val systemName = "sparkWorker" + workerNumber.map(_.toString).getOrElse("")
val actorName = "Worker"
val securityMgr = new SecurityManager(conf)
val (actorSystem, boundPort) = AkkaUtils.createActorSystem(systemName, host, port,
conf = conf, securityManager = securityMgr)
val masterAkkaUrls = masterUrls.map(Master.toAkkaUrl(_, AkkaUtils.protocol(actorSystem)))
actorSystem.actorOf(Props(classOf[Worker], host, boundPort, webUiPort, cores, memory,
masterAkkaUrls, systemName, actorName, workDir, conf, securityMgr), name = actorName)
(actorSystem, boundPort)
}
3、调用AkkaUtils的createActorSystem创建ActorSystem
def createActorSystem(
name: String,
host: String,
port: Int,
conf: SparkConf,
securityManager: SecurityManager): (ActorSystem, Int) = {
val startService: Int => (ActorSystem, Int) = { actualPort =>
doCreateActorSystem(name, host, actualPort, conf, securityManager)
}
Utils.startServiceOnPort(port, startService, conf, name)
}
4、创建完ActorSystem后调用Worker的主构造函数，执行preStart方法
override def preStart() {
assert(!registered)
logInfo("Starting Spark worker %s:%d with %d cores, %s RAM".format(
host, port, cores, Utils.megabytesToString(memory)))
logInfo(s"Running Spark version ${org.apache.spark.SPARK_VERSION}")
logInfo("Spark home: " + sparkHome)
createWorkDir()
context.system.eventStream.subscribe(self, classOf[RemotingLifecycleEvent])
shuffleService.startIfEnabled()
webUi = new WorkerWebUI(this, workDir, webUiPort)
webUi.bind()
registerWithMaster()

metricsSystem.registerSource(workerSource)
metricsSystem.start()
// Attach the worker metrics servlet handler to the web ui after the metrics system is started.
metricsSystem.getServletHandlers.foreach(webUi.attachHandler)
}
5、调用registerWithMaster方法向Master注册启动的worker
def registerWithMaster() {
// DisassociatedEvent may be triggered multiple times, so don‘t attempt registration
// if there are outstanding registration attempts scheduled.
registrationRetryTimer match {
case None =>
registered = false
tryRegisterAllMasters()
connectionAttemptCount = 0
registrationRetryTimer = Some {
context.system.scheduler.schedule(INITIAL_REGISTRATION_RETRY_INTERVAL,
INITIAL_REGISTRATION_RETRY_INTERVAL, self, ReregisterWithMaster)
}
case Some(_) =>
logInfo("Not spawning another attempt to register with the master, since there is an" +
" attempt scheduled already.")
}
}
6、调用tryRegisterAllMasters向Master发送注册的Worker消息
private def tryRegisterAllMasters() {
for (masterAkkaUrl <- masterAkkaUrls) {
logInfo("Connecting to master " + masterAkkaUrl + "...")
val actor = context.actorSelection(masterAkkaUrl)
actor ! RegisterWorker(workerId, host, port, cores, memory, webUi.boundPort, publicAddress)
}
}
7、Master的receiveWithLogging接收到消息执行
case RegisterWorker(id, workerHost, workerPort, cores, memory, workerUiPort, publicAddress) =>
{
logInfo("Registering worker %s:%d with %d cores, %s RAM".format(
workerHost, workerPort, cores, Utils.megabytesToString(memory)))
if (state == RecoveryState.STANDBY) {
// ignore, don‘t send response
} else if (idToWorker.contains(id)) {
sender ! RegisterWorkerFailed("Duplicate worker ID")
} else {
val worker = new WorkerInfo(id, workerHost, workerPort, cores, memory,
sender, workerUiPort, publicAddress)
if (registerWorker(worker)) {
persistenceEngine.addWorker(worker)
sender ! RegisteredWorker(masterUrl, masterWebUiUrl)
schedule()
} else {
val workerAddress = worker.actor.path.address
logWarning("Worker registration failed. Attempted to re-register worker at same " +
"address: " + workerAddress)
sender ! RegisterWorkerFailed("Attempted to re-register worker at same address: "
+ workerAddress)
}
}
}
8、失败向worker返回失败消息，成功则返回Master的相关信息
9、返回消息后调用schedule，但是因为没有application，所以这时候不会进行资源的分配

至此整个Spark集群就已经启动完成

标签：spark master worker 源码

原文地址：http://blog.csdn.net/zxl333/article/details/46845181

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