原文标题：spark-Core源码精读(1)、Spark Deployment & start-all.sh on Standalone mode
本文为精度Spark-core的源码的第一节，主要内容包括Spark Deployment的简介和Standalone模式下启动集群的详细流程精读。
注：本专题的文章皆使用Spark-1.6.3版本的源码为参考，如果Spark-2.1.0版本有重大改进的地方也会进行说明。
Spark Deployment
Spark 的部署主要有四种方式：local、standalone、yarn、mesos
图片来源：Spark-Essentials-SSW2016-TE1.pdf
其中local和standalone模式主要用于测试学习，实际生产环境下国内一般都是使用yarn，这是历史原因造成的（考虑到集群中同时有Hadoop）；而国外一般都是使用mesos，而且个人认为mesos也是一种趋势，关于yarn和mesos的部分，以后会单独进行分析，下面我们详细解读standalone模式下的集群启动的具体流程。
Standalone mode下集群启动源码精读
我们就从start-all.sh开始，主要代码如下：
# Load the Spark configuration. "${SPARK_HOME}/sbin/spark-config.sh"# Start Master"${SPARK_HOME}/sbin"/start-master.sh $TACHYON_STR# Start Workers"${SPARK_HOME}/sbin"/start-slaves.sh $TACHYON_STR注释说的很明确了，我们继续追踪start-master.sh
CLASS="org.apache.spark.deploy.master.Master"..."${SPARK_HOME}/sbin"/spark-daemon.sh start $CLASS 1 \
--ip $SPARK_MASTER_IP --port $SPARK_MASTER_PORT --webui-port $SPARK_MASTER_WEBUI_PORT \ $ORIGINAL_ARGS...可以看出，是执行了spark-daemon.sh的start方法，即通过动态加载的方式将org.apache.spark.deploy.master.Master作为一个daemon（守护线程）来运行，所以我们直接分析Master的源码：
private[deploy] object Master extends Logging { val SYSTEM_NAME = "sparkMaster"
val ENDPOINT_NAME = "Master"
def main(argStrings: Array[String]) { //注册log
SignalLogger.register(log) //实例化SparkConf，会加载`spark.*`格式的配置信息
val conf = new SparkConf
//使用MasterArguments对传入的参数argStrings和默认加载的conf进行封装，并执行一些初始化操作
val args = new MasterArguments(argStrings, conf) val (rpcEnv, _, _) = startRpcEnvAndEndpoint(args.host, args.port, args.webUiPort, conf)
rpcEnv.awaitTermination()
} /**
* Start the Master and return a three tuple of:
* (1) The Master RpcEnv
* (2) The web UI bound port
* (3) The REST server bound port, if any
*/
def startRpcEnvAndEndpoint(
host: String,
port: Int,
webUiPort: Int,
conf: SparkConf): (RpcEnv, Int, Option[Int]) = { val securityMgr = new SecurityManager(conf) val rpcEnv = RpcEnv.create(SYSTEM_NAME, host, port, conf, securityMgr) val masterEndpoint = rpcEnv.setupEndpoint(ENDPOINT_NAME, new Master(rpcEnv, rpcEnv.address, webUiPort, securityMgr, conf)) val portsResponse = masterEndpoint.askWithRetry[BoundPortsResponse](BoundPortsRequest)
(rpcEnv, portsResponse.webUIPort, portsResponse.restPort)
}
}首先注册log，实例化SparkConf并加载spark.*格式的配置信息，然后使用MasterArguments对传入的参数argStrings和默认加载的conf进行封装，并执行一些初始化操作，主要是加载配置信息，这里不做详细说明，我们接着往下看。
下面才是真正意义上的Start Master，startRpcEnvAndEndpoint函数中首先实例化了SecurityManager（Spark中负责安全的类），然后创建了RpcEnv（Spark的Rpc通信有三个抽象：RpcEnv、RpcEndpoint、RpcEndpointRef，这样做屏蔽了底层的实现，方便用户进行扩展，Spark-1.6.3底层的默认实现方式是Netty，而Spark-2.x已经将Akka的依赖移除），接着实例化Master，实际上就是实例化了一个RpcEndpoint（因为Master实现了ThreadSafeRpcEndpoint接口，而ThreadSafeRpcEndpoint又继承了RpcEndpoint），实例化完成后通过RpcEnv的setupEndpoint向RpcEnv进行注册，注册的时候执行了Master的onStart方法，最后返回了一个RpcEndpointRef（实际上是NettyRpcEndpointRef），通过获得的RpcEndpointRef向Master（Endpoint）发送了一条BoundPortsRequest消息，Master通过receiveAndReply方法接受到该消息（实际上是通过NettyRpcEnv中的Dispatcher进行消息的分配），模式匹配到是BoundPortsRequest类型的消息，然后执行reply方法进行回复，源码如下：
case BoundPortsRequest => {
context.reply(BoundPortsResponse(address.port, webUi.boundPort, restServerBoundPort))
}至此Master启动完成，Rpc部分可以参考另一篇文章：Spark RPC 到底是个什么鬼？，下面贴出Master实例化部分和onStart方法的源码及中文注释：
Master实例化部分：
//默认的情况下，取消的task不会从工作的队列中移除直到延迟时间完成，所以创建一个守护线程来“手动”移除它
private val forwardMessageThread = ThreadUtils.newDaemonSingleThreadScheduledExecutor("master-forward-message-thread") //用于执行重建UI代码的守护线程
private val rebuildUIThread = ThreadUtils.newDaemonSingleThreadExecutor("master-rebuild-ui-thread")
//通过rebuildUIThread获得重建UI的执行上下文
private val rebuildUIContext = ExecutionContext.fromExecutor(rebuildUIThread) //获取hadoop的配置文件
private val hadoopConf = SparkHadoopUtil.get.newConfiguration(conf) //时间格式，用于构建application ID
private def createDateFormat = new SimpleDateFormat("yyyyMMddHHmmss") // For application IDs
//如果Master在60s内没有收到Worker发送的heartbeat信息就认为这个Worker timeout
private val WORKER_TIMEOUT_MS = conf.getLong("spark.worker.timeout", 60) * 1000
//webUI中显示的完成的application的最大个数，超过200个就移除掉(200/10,1)=20个完成的applications
private val RETAINED_APPLICATIONS = conf.getInt("spark.deploy.retainedApplications", 200) //webUI中显示的完成的drivers的最大个数，超过200个就移除掉(200/10,1)=20个完成的drivers
private val RETAINED_DRIVERS = conf.getInt("spark.deploy.retainedDrivers", 200) //如果Master在(REAPER_ITERATIONS + 1) * WORKER_TIMEOUT_MS)秒内仍然没有收到Worker发送的heartbeat信息，就删除这个Worker
private val REAPER_ITERATIONS = conf.getInt("spark.dead.worker.persistence", 15) //recoveryMode：NONE、ZOOKEEPER、FILESYSTEM、CUSTOM，默认是NONE
private val RECOVERY_MODE = conf.get("spark.deploy.recoveryMode", "NONE") //Executor失败的最大重试次数
private val MAX_EXECUTOR_RETRIES = conf.getInt("spark.deploy.maxExecutorRetries", 10) //下面是各种“数据结构”，不再一一说明
val workers = new HashSet[WorkerInfo] val idToApp = new HashMap[String, ApplicationInfo] val waitingApps = new ArrayBuffer[ApplicationInfo] val apps = new HashSet[ApplicationInfo] private val idToWorker = new HashMap[String, WorkerInfo] private val addressToWorker = new HashMap[RpcAddress, WorkerInfo] private val endpointToApp = new HashMap[RpcEndpointRef, ApplicationInfo] private val addressToApp = new HashMap[RpcAddress, ApplicationInfo] private val completedApps = new ArrayBuffer[ApplicationInfo] private var nextAppNumber = 0
// Using ConcurrentHashMap so that master-rebuild-ui-thread can add a UI after asyncRebuildUI
private val appIdToUI = new ConcurrentHashMap[String, SparkUI] private val drivers = new HashSet[DriverInfo] private val completedDrivers = new ArrayBuffer[DriverInfo] // Drivers currently spooled for scheduling
private val waitingDrivers = new ArrayBuffer[DriverInfo] private var nextDriverNumber = 0
Utils.checkHost(address.host, "Expected hostname") //下面是Metrics系统相关的代码
private val masterMetricsSystem = MetricsSystem.createMetricsSystem("master", conf, securityMgr) private val applicationMetricsSystem = MetricsSystem.createMetricsSystem("applications", conf,
securityMgr) private val masterSource = new MasterSource(this) // After onStart, webUi will be set
private var webUi: MasterWebUI = null
private val masterPublicAddress = { val envVar = conf.getenv("SPARK_PUBLIC_DNS") if (envVar != null) envVar else address.host
} private val masterUrl = address.toSparkURL private var masterWebUiUrl: String = _ //当前Master的状态：STANDBY, ALIVE, RECOVERING, COMPLETING_RECOVERY
private var state = RecoveryState.STANDBY
private var persistenceEngine: PersistenceEngine = _ private var leaderElectionAgent: LeaderElectionAgent = _ private var recoveryCompletionTask: ScheduledFuture[_] = _ private var checkForWorkerTimeOutTask: ScheduledFuture[_] = _ // As a temporary workaround before better ways of configuring memory, we allow users to set
// a flag that will perform round-robin scheduling across the nodes (spreading out each app
// among all the nodes) instead of trying to consolidate each app onto a small # of nodes.
// 避免将application的运行限制在固定的几个节点上
private val spreadOutApps = conf.getBoolean("spark.deploy.spreadOut", true) // Default maxCores for applications that don't specify it (i.e. pass Int.MaxValue)
private val defaultCores = conf.getInt("spark.deploy.defaultCores", Int.MaxValue) if (defaultCores < 1) { throw new SparkException("spark.deploy.defaultCores must be positive")
} // Alternative application submission gateway that is stable across Spark versions
// 用来接受application提交的restServer
private val restServerEnabled = conf.getBoolean("spark.master.rest.enabled", true) private var restServer: Option[StandaloneRestServer] = None
private var restServerBoundPort: Option[Int] = NoneonStart方法：
override def onStart(): Unit = { //打日志
logInfo("Starting Spark master at " + masterUrl)
logInfo(s"Running Spark version ${org.apache.spark.SPARK_VERSION}") //实例化standalone模式下的MasterWebUI并绑定到HTTP Server
webUi = new MasterWebUI(this, webUiPort)
webUi.bind() //可以通过这个Url地址看到Master的信息
masterWebUiUrl = "http://" + masterPublicAddress + ":" + webUi.boundPort
//以固定的时间间隔检查并移除time-out的worker
checkForWorkerTimeOutTask = forwardMessageThread.scheduleAtFixedRate(new Runnable { override def run(): Unit = Utils.tryLogNonFatalError {
self.send(CheckForWorkerTimeOut)
}
}, 0, WORKER_TIMEOUT_MS, TimeUnit.MILLISECONDS) //实例化并启动restServer用于接受application的提交
if (restServerEnabled) { val port = conf.getInt("spark.master.rest.port", 6066)
restServer = Some(new StandaloneRestServer(address.host, port, conf, self, masterUrl))
}
restServerBoundPort = restServer.map(_.start()) //启动MetricsSystem
masterMetricsSystem.registerSource(masterSource)
masterMetricsSystem.start()
applicationMetricsSystem.start() // Attach the master and app metrics servlet handler to the web ui after the metrics systems are
// started.
masterMetricsSystem.getServletHandlers.foreach(webUi.attachHandler)
applicationMetricsSystem.getServletHandlers.foreach(webUi.attachHandler) //序列化器
val serializer = new JavaSerializer(conf)
//恢复机制，包括持久化引擎和选举机制
val (persistenceEngine_, leaderElectionAgent_) = RECOVERY_MODE match { case "ZOOKEEPER" =>
logInfo("Persisting recovery state to ZooKeeper") val zkFactory = new ZooKeeperRecoveryModeFactory(conf, serializer)
(zkFactory.createPersistenceEngine(), zkFactory.createLeaderElectionAgent(this)) case "FILESYSTEM" => val fsFactory = new FileSystemRecoveryModeFactory(conf, serializer)
(fsFactory.createPersistenceEngine(), fsFactory.createLeaderElectionAgent(this)) case "CUSTOM" => val clazz = Utils.classForName(conf.get("spark.deploy.recoveryMode.factory")) val factory = clazz.getConstructor(classOf[SparkConf], classOf[Serializer])
.newInstance(conf, serializer)
.asInstanceOf[StandaloneRecoveryModeFactory]
(factory.createPersistenceEngine(), factory.createLeaderElectionAgent(this)) case _ =>
(new BlackHolePersistenceEngine(), new MonarchyLeaderAgent(this))
}
persistenceEngine = persistenceEngine_
leaderElectionAgent = leaderElectionAgent_
}下面介绍Worker的启动
start-slaves.sh:
# Launch the slaves"${SPARK_HOME}/sbin/slaves.sh" cd "${SPARK_HOME}" \; "${SPARK_HOME}/sbin/start-slave.sh" "spark://$SPARK_MASTER_IP:$SPARK_MASTER_PORT"start-slave.sh:
CLASS="org.apache.spark.deploy.worker.Worker"... "${SPARK_HOME}/sbin"/spark-daemon.sh start $CLASS $WORKER_NUM \
--webui-port "$WEBUI_PORT" $PORT_FLAG $PORT_NUM $MASTER "$@"和Master的启动类似，我们直接看Worker文件，仍然从main方法开始：
def main(argStrings: Array[String]) { SignalLogger.register(log) val conf = new SparkConf
val args = new WorkerArguments(argStrings, conf) val rpcEnv = startRpcEnvAndEndpoint(args.host, args.port, args.webUiPort, args.cores,
args.memory, args.masters, args.workDir, conf = conf)
rpcEnv.awaitTermination()
}
def startRpcEnvAndEndpoint(
host: String,
port: Int,
webUiPort: Int,
cores: Int,
memory: Int,
masterUrls: Array[String],
workDir: String,
workerNumber: Option[Int] = None,
conf: SparkConf = new SparkConf): RpcEnv = { // The LocalSparkCluster runs multiple local sparkWorkerX RPC Environments
val systemName = SYSTEM_NAME + workerNumber.map(_.toString).getOrElse("") val securityMgr = new SecurityManager(conf) val rpcEnv = RpcEnv.create(systemName, host, port, conf, securityMgr) val masterAddresses = masterUrls.map(RpcAddress.fromSparkURL(_))
rpcEnv.setupEndpoint(ENDPOINT_NAME, new Worker(rpcEnv, webUiPort, cores, memory,
masterAddresses, systemName, ENDPOINT_NAME, workDir, conf, securityMgr))
rpcEnv
}可以看到前面和Master类似，只不过Worker有可能是多个，所以需要根据workerNumber构造一个systemName，用来创建不同的RpcEnv，然后实例化Worker（即实例化Endpoint），实例化的时候需要传入masterAddresses（注意此处可能有多个Master），以便以后向Master注册，同时由于要向对应的RpcEnv注册，注册的时候同样要执行Worker的onStart方法，我会将Worker实例化和onStart的源码放到后面，这里我们先来看一下Worker向Master注册的代码（onStart方法中调用registerWithMaster）：
private def registerWithMaster() { // onDisconnected may be triggered multiple times, so don't attempt registration
// if there are outstanding registration attempts scheduled.
registrationRetryTimer match { case None =>
registered = false
registerMasterFutures = tryRegisterAllMasters()
connectionAttemptCount = 0
registrationRetryTimer = Some(forwordMessageScheduler.scheduleAtFixedRate( new Runnable { override def run(): Unit = Utils.tryLogNonFatalError { Option(self).foreach(_.send(ReregisterWithMaster))
}
}, INITIAL_REGISTRATION_RETRY_INTERVAL_SECONDS, INITIAL_REGISTRATION_RETRY_INTERVAL_SECONDS, TimeUnit.SECONDS)) case Some(_) =>
logInfo("Not spawning another attempt to register with the master, since there is an" + " attempt scheduled already.")
}
}可以看到内部调用了tryRegisterAllMasters方法：
private def tryRegisterAllMasters(): Array[JFuture[_]] = {
masterRpcAddresses.map { masterAddress =>
registerMasterThreadPool.submit(new Runnable { override def run(): Unit = { try {
logInfo("Connecting to master " + masterAddress + "...") val masterEndpoint =
rpcEnv.setupEndpointRef(Master.SYSTEM_NAME, masterAddress, Master.ENDPOINT_NAME)
registerWithMaster(masterEndpoint)
} catch { case ie: InterruptedException => // Cancelled
case NonFatal(e) => logWarning(s"Failed to connect to master $masterAddress", e)
}
}
})
}
}通过一个名为registerMasterThreadPool的线程池（最大线程数为Worker的个数）来运行run方法中的内容：首先通过setupEndpointRef方法获得其中一个Master的一个引用（RpcEndpointRef），然后执行registerWithMaster(masterEndpoint)方法，刚才得到的Master的引用作为参数传入，下面进入registerWithMaster方法：（注意此处的registerWithMaster方法是有一个RpcEndpointRef作为参数的，和刚开始的那个不一样）
private def registerWithMaster(masterEndpoint: RpcEndpointRef): Unit = {
masterEndpoint.ask[RegisterWorkerResponse](RegisterWorker(
workerId, host, port, self, cores, memory, webUi.boundPort, publicAddress))
.onComplete { // This is a very fast action so we can use "ThreadUtils.sameThread"
case Success(msg) => Utils.tryLogNonFatalError {
handleRegisterResponse(msg)
} case Failure(e) =>
logError(s"Cannot register with master: ${masterEndpoint.address}", e) System.exit(1)
}(ThreadUtils.sameThread)
}内部使用masterEndpoint（Master的RpcEndpointRef）的ask方法向Master发送一条RegisterWorker的消息，并使用onComplete方法接受Master的处理结果，下面我们先来看一下消息到达Master端进行怎样的处理：
override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = { case RegisterWorker(
id, workerHost, workerPort, workerRef, 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) {
context.reply(MasterInStandby)
} else if (idToWorker.contains(id)) {
context.reply(RegisterWorkerFailed("Duplicate worker ID"))
} else { val worker = new WorkerInfo(id, workerHost, workerPort, cores, memory,
workerRef, workerUiPort, publicAddress) if (registerWorker(worker)) {
persistenceEngine.addWorker(worker)
context.reply(RegisteredWorker(self, masterWebUiUrl))
schedule()
} else { val workerAddress = worker.endpoint.address
logWarning("Worker registration failed. Attempted to re-register worker at same " + "address: " + workerAddress)
context.reply(RegisterWorkerFailed("Attempted to re-register worker at same address: "
+ workerAddress))
}
}
}首先receiveAndReply方法匹配到Worker发过来的RegisterWorker消息，然后执行具体的操作：打了一个日志，判断Master现在的状态，如果是STANDBY就reply一个MasterInStandby的消息，如果idToWorker中已经存在该Worker的ID就回复重复的worker ID的失败信息，如果都不是，将获得的Worker信息用WorkerInfo进行封装，然后执行registerWorker(worker)操作注册该Worker，如果成功就向persistenceEngine中添加该Worker并reply给Worker RegisteredWorker(self, masterWebUiUrl)消息并执行schedule方法，如果注册失败就reply RegisterWorkerFailed消息，下面我们具体看一下Master端是如何注册Worker的，即registerWorker(worker)方法：
private def registerWorker(worker: WorkerInfo): Boolean = { // There may be one or more refs to dead workers on this same node (w/ different ID's),
// remove them.
workers.filter { w =>
(w.host == worker.host && w.port == worker.port) && (w.state == WorkerState.DEAD)
}.foreach { w =>
workers -= w
} val workerAddress = worker.endpoint.address if (addressToWorker.contains(workerAddress)) { val oldWorker = addressToWorker(workerAddress) if (oldWorker.state == WorkerState.UNKNOWN) { // A worker registering from UNKNOWN implies that the worker was restarted during recovery.
// The old worker must thus be dead, so we will remove it and accept the new worker.
removeWorker(oldWorker)
} else {
logInfo("Attempted to re-register worker at same address: " + workerAddress) return false
}
}
workers += worker
idToWorker(worker.id) = worker
addressToWorker(workerAddress) = worker true
}首先判断是否有和该Worker的host和port相同且状态为DEAD的Worker，如果有就remove掉，然后获得该Worker的RpcAddress，然后根据RpcAddress判断addressToWorker中是否有相同地址的记录，如果有记录且老的Worker的状态为UNKNOWN就remove掉老的Worker，如果没有记录就打日志并返回false（导致上一步reply：RegisterWorkerFailed）然后分别在workers、idToWorker、addressToWorker中添加该Worker，最后返回true，导致上一步向Worker reply注册成功的消息：context.reply(RegisteredWorker(self, masterWebUiUrl))，并执行schedule()，即向等待的applications分配当前可用的资源（每当新的application加入或者有资源变化时都会调用该方法），这个方法我会用单独的一片文章详细分析，现在我们先来看Worker端是如何进行回复的，回到上面的registerWithMaster方法（有参数的），我们直接看成功后执行的handleRegisterResponse(msg)这个方法：
private def handleRegisterResponse(msg: RegisterWorkerResponse): Unit = synchronized {
msg match { case RegisteredWorker(masterRef, masterWebUiUrl) =>
logInfo("Successfully registered with master " + masterRef.address.toSparkURL)
registered = true
changeMaster(masterRef, masterWebUiUrl)
forwordMessageScheduler.scheduleAtFixedRate(new Runnable { override def run(): Unit = Utils.tryLogNonFatalError {
self.send(SendHeartbeat)
}
}, 0, HEARTBEAT_MILLIS, TimeUnit.MILLISECONDS) if (CLEANUP_ENABLED) {
logInfo( s"Worker cleanup enabled; old application directories will be deleted in: $workDir")
forwordMessageScheduler.scheduleAtFixedRate(new Runnable { override def run(): Unit = Utils.tryLogNonFatalError {
self.send(WorkDirCleanup)
}
}, CLEANUP_INTERVAL_MILLIS, CLEANUP_INTERVAL_MILLIS, TimeUnit.MILLISECONDS)
} case RegisterWorkerFailed(message) => if (!registered) {
logError("Worker registration failed: " + message) System.exit(1)
} case MasterInStandby => // Ignore. Master not yet ready.
}
}依然是模式匹配的方式：
如果接受到的是RegisteredWorker，会执行changeMaster方法，取消最后一次的重试，然后向自己的RpcEnv发送SendHeartBeat消息，使用receive方法接受到该消息后会通过sendToMaster方法向Master发送心跳，最后判断CLEANUP_ENABLED如果开启就向自己的RpcEnv发送WorkDirCleanup消息，接受到消息后将老的application的目录清除
如果接受到的是RegisterWorkerFailed就表明注册失败
changeMaster发送：
private def changeMaster(masterRef: RpcEndpointRef, uiUrl: String) { // activeMasterUrl it's a valid Spark url since we receive it from master.
activeMasterUrl = masterRef.address.toSparkURL
activeMasterWebUiUrl = uiUrl
master = Some(masterRef)
connected = true
// Cancel any outstanding re-registration attempts because we found a new master
cancelLastRegistrationRetry()
}cancelLastRegistrationRetry:
private def cancelLastRegistrationRetry(): Unit = { if (registerMasterFutures != null) {
registerMasterFutures.foreach(_.cancel(true))
registerMasterFutures = null
}
registrationRetryTimer.foreach(_.cancel(true))
registrationRetryTimer = None
}如果Worker注册失败同样会通过registrationRetryTimer进行重试：
registrationRetryTimer = Some(forwordMessageScheduler.scheduleAtFixedRate( new Runnable { override def run(): Unit = Utils.tryLogNonFatalError { Option(self).foreach(_.send(ReregisterWithMaster))
}
}, INITIAL_REGISTRATION_RETRY_INTERVAL_SECONDS, INITIAL_REGISTRATION_RETRY_INTERVAL_SECONDS, TimeUnit.SECONDS))可以看到向自己发送重新注册的消息：ReregisterWithMaster，receive接收到后会执行reregisterWithMaster()方法：
private def reregisterWithMaster(): Unit = { Utils.tryOrExit { //重试次数加1
connectionAttemptCount += 1
if (registered) { //如果已经注册了，就取消重试
cancelLastRegistrationRetry()
} else if (connectionAttemptCount <= TOTAL_REGISTRATION_RETRIES) { //判断是否超过最大重试次数
logInfo(s"Retrying connection to master (attempt # $connectionAttemptCount)") /**
* Re-register with the active master this worker has been communicating with. If there
* is none, then it means this worker is still bootstrapping and hasn't established a
* connection with a master yet, in which case we should re-register with all masters.
*
* It is important to re-register only with the active master during failures. Otherwise,
* if the worker unconditionally attempts to re-register with all masters, the following
* race condition may arise and cause a "duplicate worker" error detailed in SPARK-4592:
*
* (1) Master A fails and Worker attempts to reconnect to all masters
* (2) Master B takes over and notifies Worker
* (3) Worker responds by registering with Master B
* (4) Meanwhile, Worker's previous reconnection attempt reaches Master B,
* causing the same Worker to register with Master B twice
*
* Instead, if we only register with the known active master, we can assume that the
* old master must have died because another master has taken over. Note that this is
* still not safe if the old master recovers within this interval, but this is a much
* less likely scenario.
*/
master match { case Some(masterRef) => // registered == false && master != None means we lost the connection to master, so
// masterRef cannot be used and we need to recreate it again. Note: we must not set
// master to None due to the above comments.
// 这里说的很清楚，如果注册失败了，但是master != None说明我们失去了和master的连接，所以需要重新创建一个masterRef
// 先取消原来阻塞的用来等待消息回复的线程
if (registerMasterFutures != null) {
registerMasterFutures.foreach(_.cancel(true))
}
// 然后创建新的masterRef，然后重新注册
val masterAddress = masterRef.address
registerMasterFutures = Array(registerMasterThreadPool.submit(new Runnable { override def run(): Unit = { try {
logInfo("Connecting to master " + masterAddress + "...") val masterEndpoint =
rpcEnv.setupEndpointRef(Master.SYSTEM_NAME, masterAddress, Master.ENDPOINT_NAME)
registerWithMaster(masterEndpoint)
} catch { case ie: InterruptedException => // Cancelled
case NonFatal(e) => logWarning(s"Failed to connect to master $masterAddress", e)
}
}
})) case None => // 如果没有masterRef，先取消原来阻塞的用来等待消息回复的线程
if (registerMasterFutures != null) {
registerMasterFutures.foreach(_.cancel(true))
}
// 然后执行最初的注册，即tryRegisterAllMasters
// We are retrying the initial registration
registerMasterFutures = tryRegisterAllMasters()
} // We have exceeded the initial registration retry threshold
// All retries from now on should use a higher interval
// 如果超过刚开始设置的重试注册次数，取消之前的重试，开启新的注册，并改变重试次数和时间间隔
// 刚开始的重试默认为6次，时间间隔在5到15秒之间，接下来的10次重试时间间隔在30到90秒之间
if (connectionAttemptCount == INITIAL_REGISTRATION_RETRIES) {
registrationRetryTimer.foreach(_.cancel(true))
registrationRetryTimer = Some(
forwordMessageScheduler.scheduleAtFixedRate(new Runnable { override def run(): Unit = Utils.tryLogNonFatalError {
self.send(ReregisterWithMaster)
}
}, PROLONGED_REGISTRATION_RETRY_INTERVAL_SECONDS, PROLONGED_REGISTRATION_RETRY_INTERVAL_SECONDS, TimeUnit.SECONDS))
}
} else {
logError("All masters are unresponsive! Giving up.") System.exit(1)
}
}
}至此Worker的启动和注册完成，即start-all.sh执行完成。
下面是Worker的初始化部分和onStart方法的源码及注释（重要部分）：
初始化部分：
private val host = rpcEnv.address.host private val port = rpcEnv.address.port Utils.checkHost(host, "Expected hostname")
assert (port > 0) // A scheduled executor used to send messages at the specified time.
private val forwordMessageScheduler = ThreadUtils.newDaemonSingleThreadScheduledExecutor("worker-forward-message-scheduler") // A separated thread to clean up the workDir. Used to provide the implicit parameter of `Future`
// methods.
private val cleanupThreadExecutor = ExecutionContext.fromExecutorService( ThreadUtils.newDaemonSingleThreadExecutor("worker-cleanup-thread")) // For worker and executor IDs
private def createDateFormat = new SimpleDateFormat("yyyyMMddHHmmss") // 发送心跳的时间间隔：timeout的时间 / 4
// Send a heartbeat every (heartbeat timeout) / 4 milliseconds
private val HEARTBEAT_MILLIS = conf.getLong("spark.worker.timeout", 60) * 1000 / 4
// 重试的模型及其次数设置
// Model retries to connect to the master, after Hadoop's model.
// The first six attempts to reconnect are in shorter intervals (between 5 and 15 seconds)
// Afterwards, the next 10 attempts are between 30 and 90 seconds.
// A bit of randomness is introduced so that not all of the workers attempt to reconnect at
// the same time.
private val INITIAL_REGISTRATION_RETRIES = 6
private val TOTAL_REGISTRATION_RETRIES = INITIAL_REGISTRATION_RETRIES + 10
private val FUZZ_MULTIPLIER_INTERVAL_LOWER_BOUND = 0.500
private val REGISTRATION_RETRY_FUZZ_MULTIPLIER = { val randomNumberGenerator = new Random(UUID.randomUUID.getMostSignificantBits)
randomNumberGenerator.nextDouble + FUZZ_MULTIPLIER_INTERVAL_LOWER_BOUND
} private val INITIAL_REGISTRATION_RETRY_INTERVAL_SECONDS = (math.round(10 * REGISTRATION_RETRY_FUZZ_MULTIPLIER)) private val PROLONGED_REGISTRATION_RETRY_INTERVAL_SECONDS = (math.round(60
* REGISTRATION_RETRY_FUZZ_MULTIPLIER)) //CLEANUP相关的设置
private val CLEANUP_ENABLED = conf.getBoolean("spark.worker.cleanup.enabled", false) // How often worker will clean up old app folders
private val CLEANUP_INTERVAL_MILLIS =
conf.getLong("spark.worker.cleanup.interval", 60 * 30) * 1000
// TTL for app folders/data; after TTL expires it will be cleaned up
private val APP_DATA_RETENTION_SECONDS =
conf.getLong("spark.worker.cleanup.appDataTtl", 7 * 24 * 3600) private val testing: Boolean = sys.props.contains("spark.testing") //对master的引用
private var master: Option[RpcEndpointRef] = None
private var activeMasterUrl: String = ""
private[worker] var activeMasterWebUiUrl : String = ""
private val workerUri = rpcEnv.uriOf(systemName, rpcEnv.address, endpointName) private var registered = false
private var connected = false
private val workerId = generateWorkerId() private val sparkHome = if (testing) {
assert(sys.props.contains("spark.test.home"), "spark.test.home is not set!") new File(sys.props("spark.test.home"))
} else { new File(sys.env.get("SPARK_HOME").getOrElse("."))
} var workDir: File = null
val finishedExecutors = new LinkedHashMap[String, ExecutorRunner] val drivers = new HashMap[String, DriverRunner] val executors = new HashMap[String, ExecutorRunner] val finishedDrivers = new LinkedHashMap[String, DriverRunner] val appDirectories = new HashMap[String, Seq[String]] val finishedApps = new HashSet[String] val retainedExecutors = conf.getInt("spark.worker.ui.retainedExecutors", WorkerWebUI.DEFAULT_RETAINED_EXECUTORS) val retainedDrivers = conf.getInt("spark.worker.ui.retainedDrivers", WorkerWebUI.DEFAULT_RETAINED_DRIVERS) // The shuffle service is not actually started unless configured.
private val shuffleService = new ExternalShuffleService(conf, securityMgr) private val publicAddress = { val envVar = conf.getenv("SPARK_PUBLIC_DNS") if (envVar != null) envVar else host
} private var webUi: WorkerWebUI = null
private var connectionAttemptCount = 0
private val metricsSystem = MetricsSystem.createMetricsSystem("worker", conf, securityMgr) private val workerSource = new WorkerSource(this) private var registerMasterFutures: Array[JFuture[_]] = null
private var registrationRetryTimer: Option[JScheduledFuture[_]] = None
// 用来和Master注册使用的线程池，默认线程的最大个数为Worker的个数
// A thread pool for registering with masters. Because registering with a master is a blocking
// action, this thread pool must be able to create "masterRpcAddresses.size" threads at the same
// time so that we can register with all masters.
private val registerMasterThreadPool = ThreadUtils.newDaemonCachedThreadPool( "worker-register-master-threadpool",
masterRpcAddresses.size // Make sure we can register with all masters at the same time
) var coresUsed = 0
var memoryUsed = 0onStart()方法：
override def onStart() {
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) // 创建Work的目录
createWorkDir() // 开启 external shuffle service
shuffleService.startIfEnabled()
webUi = new WorkerWebUI(this, workDir, webUiPort)
webUi.bind() // 向Master注册自己
registerWithMaster() // metrics系统
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)
}本文简单介绍了Spark的几种部署模式，并详细的分析了start-all.sh所执行源码（Master的启动和注册、Worker的启动和向Master的注册）的具体流程，当然Master的schedule方法并没有详细说明，我们会单独用一篇文章进行详细的分析。
本文为原创，欢迎转载，转载请注明出处、作者，谢谢！
作者：sun4lower
链接：https://www.jianshu.com/p/cdff18a21bdd
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