LocalWordCount中，需要首先创建SparkConf配置Master、AppName等环境参数，如果程序中没有设置，则会读取系统参数。然后，以SparkConf作为参数创建SparkContext，初始化Spark环境。
val sparkConf = new SparkConf().setMaster("local").setAppName("Local Word Count") val sc = new SparkContext(sparkConf)
初始化过程中，根据Console输出的信息可以看出，整个初始化过程做了如下工作：
spark.SecurityManager配置认证，slf4j.Slf4jLogger启动，启动Remoting监听，sparkEnv注册MapOutputTracker和BlockManagerMaster，storage.DiskBlockManager创建目录，storage.MemoryStore分配空间，network.ConnectionManager绑定端口，storage.BlockManagerMaster注册BlockManager，spark.HTTPServer启动，server.AbstractConnector启动相关链接，broadcast.HttpBroadcast启动Broadcast服务,spark.HttpFileServer配置目录，最后启动SparkUI。
15/07/14 13:20:56 INFO spark.SecurityManager: Changing view acls to: Kevin 15/07/14 13:20:56 INFO spark.SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users with view permissions: Set(Kevin) 15/07/14 13:20:58 INFO slf4j.Slf4jLogger: Slf4jLogger started 15/07/14 13:20:58 INFO Remoting: Starting remoting 15/07/14 13:20:58 INFO Remoting: Remoting started; listening on addresses :[akka.tcp://spark@Kevin-ThinkPad:50494] 15/07/14 13:20:58 INFO Remoting: Remoting now listens on addresses: [akka.tcp://spark@Kevin-ThinkPad:50494] 15/07/14 13:20:59 INFO spark.SparkEnv: Registering MapOutputTracker 15/07/14 13:20:59 INFO spark.SparkEnv: Registering BlockManagerMaster 15/07/14 13:20:59 INFO storage.DiskBlockManager: Created local directory at C:UsersKevinAppDataLocalTempspark-local-20150714132059-e5a3 15/07/14 13:20:59 INFO storage.MemoryStore: MemoryStore started with capacity 2.1 GB. 15/07/14 13:20:59 INFO network.ConnectionManager: Bound socket to port 50497 with id = ConnectionManagerId(Kevin-ThinkPad,50497) 15/07/14 13:20:59 INFO storage.BlockManagerMaster: Trying to register BlockManager 15/07/14 13:20:59 INFO storage.BlockManagerInfo: Registering block manager Kevin-ThinkPad:50497 with 2.1 GB RAM 15/07/14 13:20:59 INFO storage.BlockManagerMaster: Registered BlockManager 15/07/14 13:20:59 INFO spark.HttpServer: Starting HTTP Server 15/07/14 13:20:59 INFO server.Server: jetty-8.1.14.v20131031 15/07/14 13:20:59 INFO server.AbstractConnector: Started SocketConnector@0.0.0.0:50498 15/07/14 13:20:59 INFO broadcast.HttpBroadcast: Broadcast server started at http://10.88.121.10:50498 15/07/14 13:20:59 INFO spark.HttpFileServer: HTTP File server directory is C:UsersKevinAppDataLocalTempspark-105cdf2e-8671-4323-af35-1668fd462f55 15/07/14 13:20:59 INFO spark.HttpServer: Starting HTTP Server 15/07/14 13:20:59 INFO server.Server: jetty-8.1.14.v20131031 15/07/14 13:20:59 INFO server.AbstractConnector: Started SocketConnector@0.0.0.0:50499 15/07/14 13:21:00 INFO server.Server: jetty-8.1.14.v20131031 15/07/14 13:21:00 INFO server.AbstractConnector: Started SelectChannelConnector@0.0.0.0:4040 15/07/14 13:21:00 INFO ui.SparkUI: Started SparkUI at http://Kevin-ThinkPad:4040
到此，初始化过程结束。
进入到SparkContext的源码，SparkContext实例化的过程中，类构造函数中执行了几个关键语句：
实例化LiveListenerBus，并启动
private[spark] val listenerBus = new LiveListenerBus ... listenerBus.start()
创建SparkEnv
private[spark] val env = SparkEnv.create( conf, "<driver>", conf.get("spark.driver.host"), conf.get("spark.driver.port").toInt, isDriver = true, isLocal = isLocal, listenerBus =
listenerBus)
SparkEnv.set(env)
SparkEnv包括了众多关键的组件
class SparkEnv ( val executorId: String, val actorSystem: ActorSystem, val serializer: Serializer, val closureSerializer: Serializer, val cacheManager: CacheManager, val mapOutputTracker: MapOutputTracker, val shuffleFetcher: ShuffleFetcher, val broadcastManager: BroadcastManager, val blockManager: BlockManager, val connectionManager: ConnectionManager, val securityManager: SecurityManager, val httpFileServer: HttpFileServer, val sparkFilesDir: String, val metricsSystem: MetricsSystem, val conf: SparkConf) extends Logging
启动SparkUI并启动
private[spark] val ui = new SparkUI(this) ui.bind()
创建TaskScheduler，并以此为参数尝试创建DAGScheduler，之后启动TaskScheduler
// Create and start the scheduler private[spark] var taskScheduler = SparkContext.createTaskScheduler(this, master) @volatile private[spark] var dagScheduler: DAGScheduler = _ try { dagScheduler = new DAGScheduler(this) } catch { case e: Exception => throw new SparkException("DAGScheduler cannot be initialized due to %s".format(e.getMessage)) } // start TaskScheduler after taskScheduler sets DAGScheduler reference in DAGScheduler's // constructor taskScheduler.start()
创建TaskScheduler使用createTaskScheduler通过正则表达式匹配不同的Master类型，创建对应的TaskScheduler和backend
private def createTaskScheduler(sc: SparkContext, master: String): TaskScheduler = { // Regular expression used for local[N] and local[*] master formats val LOCAL_N_REGEX = """local[([0-9*]+)]""".r // Regular expression for local[N, maxRetries], used in tests with failing tasks val LOCAL_N_FAILURES_REGEX = """local[([0-9]+)s*,s*([0-9]+)]""".r // Regular expression for simulating a Spark cluster of [N, cores, memory] locally val LOCAL_CLUSTER_REGEX = """local-cluster[s*([0-9]+)s*,s*([0-9]+)s*,s*([0-9]+)s*]""".r // Regular expression for connecting to Spark deploy clusters val SPARK_REGEX = """spark://(.*)""".r // Regular expression for connection to Mesos cluster by mesos:// or zk:// url val MESOS_REGEX = """(mesos|zk)://.*""".r // Regular expression for connection to Simr cluster val SIMR_REGEX = """simr://(.*)""".r // When running locally, don't try to re-execute tasks on failure. val MAX_LOCAL_TASK_FAILURES = 1 master match { case "local" => val scheduler = new TaskSchedulerImpl(sc, MAX_LOCAL_TASK_FAILURES, isLocal = true) val backend = new LocalBackend(scheduler, 1) scheduler.initialize(backend) scheduler case LOCAL_N_REGEX(threads) => def localCpuCount = Runtime.getRuntime.availableProcessors() // local[*] estimates the number of cores on the machine; local[N] uses exactly N threads. val threadCount = if (threads == "*") localCpuCount else threads.toInt val scheduler = new TaskSchedulerImpl(sc, MAX_LOCAL_TASK_FAILURES, isLocal = true) val backend = new LocalBackend(scheduler, threadCount) scheduler.initialize(backend) scheduler case LOCAL_N_FAILURES_REGEX(threads, maxFailures) => val scheduler = new TaskSchedulerImpl(sc, maxFailures.toInt, isLocal = true) val backend = new LocalBackend(scheduler, threads.toInt) scheduler.initialize(backend) scheduler case SPARK_REGEX(sparkUrl) => val scheduler = new TaskSchedulerImpl(sc) val masterUrls = sparkUrl.split(",").map("spark://" + _) val backend = new SparkDeploySchedulerBackend(scheduler, sc, masterUrls) scheduler.initialize(backend) scheduler case LOCAL_CLUSTER_REGEX(numSlaves, coresPerSlave, memoryPerSlave) => // Check to make sure memory requested <= memoryPerSlave. Otherwise Spark will just hang. val memoryPerSlaveInt = memoryPerSlave.toInt if (sc.executorMemory > memoryPerSlaveInt) { throw new SparkException( "Asked to launch cluster with %d MB RAM / worker but requested %d MB/worker".format( memoryPerSlaveInt, sc.executorMemory)) } val scheduler = new TaskSchedulerImpl(sc) val localCluster = new LocalSparkCluster( numSlaves.toInt, coresPerSlave.toInt, memoryPerSlaveInt) val masterUrls = localCluster.start() val backend = new SparkDeploySchedulerBackend(scheduler, sc, masterUrls) scheduler.initialize(backend) backend.shutdownCallback = (backend: SparkDeploySchedulerBackend) => { localCluster.stop() } scheduler case "yarn-standalone" | "yarn-cluster" => if (master == "yarn-standalone") { logWarning( ""yarn-standalone" is deprecated as of Spark 1.0. Use "yarn-cluster" instead.") } val scheduler = try { val clazz = Class.forName("org.apache.spark.scheduler.cluster.YarnClusterScheduler") val cons = clazz.getConstructor(classOf[SparkContext]) cons.newInstance(sc).asInstanceOf[TaskSchedulerImpl] } catch { // TODO: Enumerate the exact reasons why it can fail // But irrespective of it, it means we cannot proceed ! case e: Exception => { throw new SparkException("YARN mode not available ?", e) } } val backend = new CoarseGrainedSchedulerBackend(scheduler, sc.env.actorSystem) scheduler.initialize(backend) scheduler case "yarn-client" => val scheduler = try { val clazz = Class.forName("org.apache.spark.scheduler.cluster.YarnClientClusterScheduler") val cons = clazz.getConstructor(classOf[SparkContext]) cons.newInstance(sc).asInstanceOf[TaskSchedulerImpl] } catch { case e: Exception => { throw new SparkException("YARN mode not available ?", e) } } val backend = try { val clazz = Class.forName("org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend") val cons = clazz.getConstructor(classOf[TaskSchedulerImpl], classOf[SparkContext]) cons.newInstance(scheduler, sc).asInstanceOf[CoarseGrainedSchedulerBackend] } catch { case e: Exception => { throw new SparkException("YARN mode not available ?", e) } } scheduler.initialize(backend) scheduler case mesosUrl @ MESOS_REGEX(_) => MesosNativeLibrary.load() val scheduler = new TaskSchedulerImpl(sc) val coarseGrained = sc.conf.getBoolean("spark.mesos.coarse", false) val url = mesosUrl.stripPrefix("mesos://") // strip scheme from raw Mesos URLs val backend = if (coarseGrained) { new CoarseMesosSchedulerBackend(scheduler, sc, url) } else { new MesosSchedulerBackend(scheduler, sc, url) } scheduler.initialize(backend) scheduler case SIMR_REGEX(simrUrl) => val scheduler = new TaskSchedulerImpl(sc) val backend = new SimrSchedulerBackend(scheduler, sc, simrUrl) scheduler.initialize(backend) scheduler case _ => throw new SparkException("Could not parse Master URL: '" + master + "'") } }
END