Master源码
1 package org.apache.spark.deploy.master 2 //伴生类 3 private[deploy] class Master( 4 override val rpcEnv: RpcEnv, 5 address: RpcAddress, 6 webUiPort: Int, 7 val securityMgr: SecurityManager, 8 val conf: SparkConf) 9 extends ThreadSafeRpcEndpoint with Logging with LeaderElectable 10 { 11 ... 12 } 13 //伴生对象 14 private[deploy] object Master extends Logging{ 15 val SYSTEM_NAME = "sparkMaster" 16 val ENDPOINT_NAME = "Master" 17 18 // 启动 Master 的入口函数 19 def main(argStrings: Array[String]) { 20 Utils.initDaemon(log) 21 val conf = new SparkConf 22 // 构建用于参数解析的实例 23 //--host hadoop201 --port 7077 --webui-port 8080 24 val args = new MasterArguments(argStrings, conf) 25 // 启动 RPC 通信环境和 MasterEndPoint(通信终端) 26 //<<1>> 27 val (rpcEnv, _, _): (RpcEnv, Int, Option[Int]) = startRpcEnvAndEndpoint(args.host, args.port, args.webUiPort, conf) 28 rpcEnv.awaitTermination() 29 } 30 ... 31 }
<< 1 >>、启动Mater返回一个三元组
1 /** 2 * Start the Master and return a three tuple of: 3 * 启动 Master, 并返回一个三元组 4 * (1) The Master RpcEnv 5 * (2) The web UI bound port 6 * (3) The REST server bound port, if any 7 */ 8 def startRpcEnvAndEndpoint( 9 host: String, 10 port: Int, 11 webUiPort: Int, 12 conf: SparkConf): (RpcEnv, Int, Option[Int]) = { 13 val securityMgr = new SecurityManager(conf) 14 // 创建 Master 端的 RpcEnv 环境, 并启动 RpcEnv 15 // 参数: sparkMaster hadoop201 7077 conf securityMgr 16 // 返回值 的实际类型是: NettyRpcEnv 17 //<< 1.1 >> 18 val rpcEnv: RpcEnv = RpcEnv.create(SYSTEM_NAME, host, port, conf, securityMgr) 19 // 创建 Master对象, 该对象就是一个 RpcEndpoint, 在 RpcEnv 中注册这个 RpcEndpoint 20 // 返回该 RpcEndpoint 的引用, 使用该引用来接收信息和发送信息 21 //<< 1.2 >> 22 val masterEndpoint: RpcEndpointRef = rpcEnv.setupEndpoint(ENDPOINT_NAME, 23 new Master(rpcEnv, rpcEnv.address, webUiPort, securityMgr, conf)) 24 // 向 Master 的通信终端发法请求，获取 BoundPortsResponse 对象 25 // BoundPortsResponse 是一个样例类包含三个属性: rpcEndpointPort webUIPort restPort 26 val portsResponse: BoundPortsResponse = masterEndpoint.askWithRetry[BoundPortsResponse](BoundPortsRequest) 27 (rpcEnv, portsResponse.webUIPort, portsResponse.restPort) 28 }
<< 1.1 >> RpcEnv的创建
1 def create( 2 name: String, 3 bindAddress: String, 4 advertiseAddress: String, 5 port: Int, 6 conf: SparkConf, 7 securityManager: SecurityManager, 8 clientMode: Boolean): RpcEnv = { 9 // 保存 RpcEnv 的配置信息 10 val config = RpcEnvConfig(conf, name, bindAddress, advertiseAddress, port, securityManager, 11 clientMode) 12 // 创建 NettyRpcEvn 13 //<< 1.1.1 >> 14 new NettyRpcEnvFactory().create(config) 15 }
真正的创建是调用NettyRpcEnvFactory 的 create 方法创建
创建NettyRpcEnv的时候，会创建消息分发器，收件箱和存储远程地址与发件箱的Map
RpcEnv.scala
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<< 1.1.1 >> NettyRpcEnvFactory （ NettyRpcEnv .scala）
1 private[rpc] class NettyRpcEnvFactory extends RpcEnvFactory with Logging { 2 /* 3 创建 NettyRpcEnv, 并且启动为后台程序 4 */ 5 def create(config: RpcEnvConfig): RpcEnv = { 6 val sparkConf: SparkConf = config.conf 7 // Use JavaSerializerInstance in multiple threads is safe. However, if we plan to support 8 // KryoSerializer in future, we have to use ThreadLocal to store SerializerInstance 9 // 用于 Rpc传输对象时的序列化 10 val javaSerializerInstance: JavaSerializerInstance = new JavaSerializer(sparkConf) 11 .newInstance() 12 .asInstanceOf[JavaSerializerInstance] 13 // 实例化 NettyRpcEnv 14 val nettyEnv = new NettyRpcEnv( 15 sparkConf, 16 javaSerializerInstance, 17 config.advertiseAddress, 18 config.securityManager) 19 if (!config.clientMode) { 20 // 定义 NettyRpcEnv 的启动函数 21 val startNettyRpcEnv: Int => (NettyRpcEnv, Int) = { actualPort => 22 nettyEnv.startServer(config.bindAddress, actualPort) 23 (nettyEnv, nettyEnv.address.port) 24 } 25 try { 26 // 启动 NettyRpcEnv 27 Utils.startServiceOnPort(config.port, startNettyRpcEnv, sparkConf, config.name)._1 28 } catch { 29 case NonFatal(e) => 30 nettyEnv.shutdown() 31 throw e 32 } 33 } 34 nettyEnv 35 } 36 }
<< 1.2 >> Master伴生类(Master 端的 RpcEndpoint 启动)
Master是一个RpcEndpoint.
他的生命周期方法是: constructor -> onStart -> receive* -> onStop
onStart 主要代码片段
1 // 创建 WebUI 服务器 2 webUi = new MasterWebUI(this, webUiPort) 3 4 5 // 按照固定的频率去启动线程来检查 Worker 是否超时. 其实就是给自己发信息: CheckForWorkerTimeOut 6 // 默认是每分钟检查一次. 7 checkForWorkerTimeOutTask = forwardMessageThread.scheduleAtFixedRate(new Runnable { 8 override def run(): Unit = Utils.tryLogNonFatalError { 9 // 在 receive 方法中对 CheckForWorkerTimeOut 进行处理 10 //<< 1.2.1 >> 11 self.send(CheckForWorkerTimeOut) 12 } 13 }, 0, WORKER_TIMEOUT_MS, TimeUnit.MILLISECONDS) 14 15 16 private val WORKER_TIMEOUT_MS = conf.getLong("spark.worker.timeout", 60) * 1000
<< 1.2.1 >> 检查并移除超时的worker
1 /** Check for, and remove, any timed-out workers */ 2 private def timeOutDeadWorkers() { 3 // Copy the workers into an array so we don't modify the hashset while iterating through it 4 val currentTime = System.currentTimeMillis() 5 // 把超时的 Worker 从 workers 中移除 6 //过滤出来要移除的worker：（上次心跳时间 小于 当前时间 减去 超时时间 ）即为超时 7 val toRemove = workers.filter(_.lastHeartbeat < currentTime - WORKER_TIMEOUT_MS).toArray 8 for (worker <- toRemove) { 9 // 如果 worker 的状态不是 DEAD 10 if (worker.state != WorkerState.DEAD) { 11 logWarning("Removing %s because we got no heartbeat in %d seconds".format( 12 worker.id, WORKER_TIMEOUT_MS / 1000)) 13 removeWorker(worker) // 14 } else { 15 if (worker.lastHeartbeat < currentTime - ((REAPER_ITERATIONS + 1) * WORKER_TIMEOUT_MS)) { 16 workers -= worker // we've seen this DEAD worker in the UI, etc. for long enough; cull it 17 } 18 } 19 } 20 }