创建或使用现有Session
从Spark 2.0 开始，引入了 SparkSession的概念，创建或使用已有的session 代码如下：
1 val spark = SparkSession2 .builder3 .appName("SparkTC")4 .getOrCreate() 首先，使用了 builder 模式来创建或使用已存在的SparkSession，org.apache.spark.sql.SparkSession.Builder#getOrCreate 代码如下：
1 def getOrCreate(): SparkSession = synchronized { 2 assertOnDriver() // 注意，spark session只能在 driver端创建并访问 3 // Get the session from current thread's active session. 4 // activeThreadSession 是一个InheritableThreadLocal（继承自ThreadLocal）方法。因为数据在 ThreadLocal中存放着，所以不需要加锁 5 var session = activeThreadSession.get() 6 // 如果session不为空，且session对应的sparkContext已经停止了，可以使用现有的session 7 if ((session ne null) && !session.sparkContext.isStopped) { 8 options.foreach { case (k, v) => session.sessionState.conf.setConfString(k, v) } 9 if (options.nonEmpty) {10 logWarning("Using an existing SparkSession; some configuration may not take effect.")11 }12 return session13 }14 15 // 给SparkSession 对象加锁，防止重复初始化 session16 SparkSession.synchronized {17 // If the current thread does not have an active session, get it from the global session.18 // 如果默认session 中有session存在，切其sparkContext 已经停止，也可以使用19 session = defaultSession.get()20 if ((session ne null) && !session.sparkContext.isStopped) {21 options.foreach { case (k, v) => session.sessionState.conf.setConfString(k, v) }22 if (options.nonEmpty) {23 logWarning("Using an existing SparkSession; some configuration may not take effect.")24 }25 return session26 }27 28 // 创建session29 val sparkContext = userSuppliedContext.getOrElse { // 默认userSuppliedContext肯定没有SparkSession对象30 val sparkConf = new SparkConf()31 options.foreach { case (k, v) => sparkConf.set(k, v) }32 33 // set a random app name if not given.34 if (!sparkConf.contains("spark.app.name")) {35 sparkConf.setAppName(java.util.UUID.randomUUID().toString)36 }37 38 SparkContext.getOrCreate(sparkConf)39 // Do not update `SparkConf` for existing `SparkContext`, as it's shared by all sessions.40 }41 42 // Initialize extensions if the user has defined a configurator class.43 val extensionConfOption = sparkContext.conf.get(StaticSQLConf.SPARK_SESSION_EXTENSIONS)44 if (extensionConfOption.isDefined) {45 val extensionConfClassName = extensionConfOption.get46 try {47 val extensionConfClass = Utils.classForName(extensionConfClassName)48 val extensionConf = extensionConfClass.newInstance()49 .asInstanceOf[SparkSessionExtensions => Unit]50 extensionConf(extensions)51 } catch {52 // Ignore the error if we cannot find the class or when the class has the wrong type.53 case e @ (_: ClassCastException |54 _: ClassNotFoundException |55 _: NoClassDefFoundError) =>56 logWarning(s"Cannot use $extensionConfClassName to configure session extensions.", e)57 }58 }59 // 初始化 SparkSession，并把刚初始化的 SparkContext 传递给它60 session = new SparkSession(sparkContext, None, None, extensions)61 options.foreach { case (k, v) => session.initialSessionOptions.put(k, v) }62 // 设置 default session63 setDefaultSession(session)64 // 设置 active session65 setActiveSession(session)66 67 // Register a successfully instantiated context to the singleton. This should be at the68 // end of the class definition so that the singleton is updated only if there is no69 // exception in the construction of the instance.70 // 设置 apark listener ，当application 结束时，default session 重置71 sparkContext.addSparkListener(new SparkListener {72 override def onApplicationEnd(applicationEnd: SparkListenerApplicationEnd): Unit = {73 defaultSession.set(null)74 }75 })76 }77 78 return session79 } org.apache.spark.SparkContext#getOrCreate方法如下：
1 def getOrCreate(config: SparkConf): SparkContext = { 2 // Synchronize to ensure that multiple create requests don't trigger an exception 3 // from assertNoOtherContextIsRunning within setActiveContext 4 // 使用Object 对象锁 5 SPARK_CONTEXT_CONSTRUCTOR_LOCK.synchronized { 6 // activeContext是一个AtomicReference 实例，它的数据set或update都是原子性的 7 if (activeContext.get() == null) { 8 // 一个session 只有一个 SparkContext 上下文对象 9 setActiveContext(new SparkContext(config), allowMultipleContexts = false)10 } else {11 if (config.getAll.nonEmpty) {12 logWarning("Using an existing SparkContext; some configuration may not take effect.")13 }14 }15 activeContext.get()16 }17 } Spark Context 初始化
SparkContext 代表到 spark 集群的连接，它可以用来在spark集群上创建 RDD，accumulator和broadcast 变量。一个JVM 只能有一个活动的 SparkContext 对象，当创建一个新的时候，必须调用stop 方法停止活动的 SparkContext。
当调用了构造方法后，会初始化类的成员变量，然后进入初始化过程。由 try catch 块包围，这个 try catch 块是在执行构造函数时执行的，参照我写的一篇文章：这块孤立的代码块如下：
1 try { 2 // 1. 初始化 configuration 3 _conf = config.clone() 4 _conf.validateSettings() 5 6 if (!_conf.contains("spark.master")) { 7 throw new SparkException("A master URL must be set in your configuration") 8 } 9 if (!_conf.contains("spark.app.name")) { 10 throw new SparkException("An application name must be set in your configuration") 11 } 12 13 // log out spark.app.name in the Spark driver logs 14 logInfo(s"Submitted application: $appName") 15 16 // System property spark.yarn.app.id must be set if user code ran by AM on a YARN cluster 17 if (master == "yarn" && deployMode == "cluster" && !_conf.contains("spark.yarn.app.id")) { 18 throw new SparkException("Detected yarn cluster mode, but isn't running on a cluster. " + 19 "Deployment to YARN is not supported directly by SparkContext. Please use spark-submit.") 20 } 21 22 if (_conf.getBoolean("spark.logConf", false)) { 23 logInfo("Spark configuration:\n" + _conf.toDebugString) 24 } 25 26 // Set Spark driver host and port system properties. This explicitly sets the configuration 27 // instead of relying on the default value of the config constant. 28 _conf.set(DRIVER_HOST_ADDRESS, _conf.get(DRIVER_HOST_ADDRESS)) 29 _conf.setIfMissing("spark.driver.port", "0") 30 31 _conf.set("spark.executor.id", SparkContext.DRIVER_IDENTIFIER) 32 33 _jars = Utils.getUserJars(_conf) 34 _files = _conf.getOption("spark.files").map(_.split(",")).map(_.filter(_.nonEmpty)) 35 .toSeq.flatten 36 // 2. 初始化日志目录并设置压缩类 37 _eventLogDir = 38 if (isEventLogEnabled) { 39 val unresolvedDir = conf.get("spark.eventLog.dir", EventLoggingListener.DEFAULT_LOG_DIR) 40 .stripSuffix("/") 41 Some(Utils.resolveURI(unresolvedDir)) 42 } else { 43 None 44 } 45 46 _eventLogCodec = { 47 val compress = _conf.getBoolean("spark.eventLog.compress", false) 48 if (compress && isEventLogEnabled) { 49 Some(CompressionCodec.getCodecName(_conf)).map(CompressionCodec.getShortName) 50 } else { 51 None 52 } 53 } 54 // 3. LiveListenerBus负责将SparkListenerEvent异步地传递给对应注册的SparkListener. 55 _listenerBus = new LiveListenerBus(_conf) 56 57 // Initialize the app status store and listener before SparkEnv is created so that it gets 58 // all events. 59 // 4. 给 app 提供一个 kv store（in-memory） 60 _statusStore = AppStatusStore.createLiveStore(conf) 61 // 5. 注册 AppStatusListener 到 LiveListenerBus 中 62 listenerBus.addToStatusQueue(_statusStore.listener.get) 63 64 // Create the Spark execution environment (cache, map output tracker, etc) 65 // 6. 创建 driver端的 env 66 // 包含所有的spark 实例运行时对象（master 或 worker），包含了序列化器，RPCEnv，block manager， map out tracker等等。 67 // 当前的spark 通过一个全局的变量代码找到 SparkEnv，所有的线程可以访问同一个SparkEnv， 68 // 创建SparkContext之后，可以通过 SparkEnv.get方法来访问它。 69 _env = createSparkEnv(_conf, isLocal, listenerBus) 70 SparkEnv.set(_env) 71 72 // If running the REPL, register the repl's output dir with the file server. 73 _conf.getOption("spark.repl.class.outputDir").foreach { path => 74 val replUri = _env.rpcEnv.fileServer.addDirectory("/classes", new File(path)) 75 _conf.set("spark.repl.class.uri", replUri) 76 } 77 // 7. 从底层监控 spark job 和 stage 的状态并汇报的 API 78 _statusTracker = new SparkStatusTracker(this, _statusStore) 79 80 // 8. console 进度条 81 _progressBar = 82 if (_conf.get(UI_SHOW_CONSOLE_PROGRESS) && !log.isInfoEnabled) { 83 Some(new ConsoleProgressBar(this)) 84 } else { 85 None 86 } 87 88 // 9. spark ui, 使用jetty 实现 89 _ui = 90 if (conf.getBoolean("spark.ui.enabled", true)) { 91 Some(SparkUI.create(Some(this), _statusStore, _conf, _env.securityManager, appName, "", 92 startTime)) 93 } else { 94 // For tests, do not enable the UI 95 None 96 } 97 // Bind the UI before starting the task scheduler to communicate 98 // the bound port to the cluster manager properly 99 _ui.foreach(_.bind())100 101 // 10. 创建 hadoop configuration102 _hadoopConfiguration = SparkHadoopUtil.get.newConfiguration(_conf)103 104 // 11. Add each JAR given through the constructor105 if (jars != null) {106 jars.foreach(addJar)107 }108 109 if (files != null) {110 files.foreach(addFile)111 }112 // 12. 计算 executor 的内存113 _executorMemory = _conf.getOption("spark.executor.memory")114 .orElse(Option(System.getenv("SPARK_EXECUTOR_MEMORY")))115 .orElse(Option(System.getenv("SPARK_MEM"))116 .map(warnSparkMem))117 .map(Utils.memoryStringToMb)118 .getOrElse(1024)119 120 // Convert java options to env vars as a work around121 // since we can't set env vars directly in sbt.122 for { (envKey, propKey) <- Seq(("SPARK_TESTING", "spark.testing"))123 value <- Option(System.getenv(envKey)).orElse(Option(System.getProperty(propKey)))} {124 executorEnvs(envKey) = value125 }126 Option(System.getenv("SPARK_PREPEND_CLASSES")).foreach { v =>127 executorEnvs("SPARK_PREPEND_CLASSES") = v128 }129 // The Mesos scheduler backend relies on this environment variable to set executor memory.130 // TODO: Set this only in the Mesos scheduler.131 executorEnvs("SPARK_EXECUTOR_MEMORY") = executorMemory + "m"132 executorEnvs ++= _conf.getExecutorEnv133 executorEnvs("SPARK_USER") = sparkUser134 135 // We need to register "HeartbeatReceiver" before "createTaskScheduler" because Executor will136 // retrieve "HeartbeatReceiver" in the constructor. (SPARK-6640)137 // 13. 创建 HeartbeatReceiver endpoint138 _heartbeatReceiver = env.rpcEnv.setupEndpoint(139 HeartbeatReceiver.ENDPOINT_NAME, new HeartbeatReceiver(this))140 141 // Create and start the scheduler142 // 14. 创建 task scheduler 和 scheduler backend143 val (sched, ts) = SparkContext.createTaskScheduler(this, master, deployMode)144 _schedulerBackend = sched145 _taskScheduler = ts146 // 15. 创建DAGScheduler实例147 _dagScheduler = new DAGScheduler(this)148 _heartbeatReceiver.ask[Boolean](TaskSchedulerIsSet)149 150 // start TaskScheduler after taskScheduler sets DAGScheduler reference in DAGScheduler's151 // constructor152 // 16. 启动 task scheduler153 _taskScheduler.start()154 155 // 17. 从task scheduler 获取 application ID156 _applicationId = _taskScheduler.applicationId()157 // 18. 从 task scheduler 获取 application attempt id158 _applicationAttemptId = taskScheduler.applicationAttemptId()159 _conf.set("spark.app.id", _applicationId)160 if (_conf.getBoolean("spark.ui.reverseProxy", false)) {161 System.setProperty("spark.ui.proxyBase", "/proxy/" + _applicationId)162 }163 // 19. 为ui 设置 application id164 _ui.foreach(_.setAppId(_applicationId))165 // 20. 初始化 block manager166 _env.blockManager.initialize(_applicationId)167 168 // The metrics system for Driver need to be set spark.app.id to app ID.169 // So it should start after we get app ID from the task scheduler and set spark.app.id.170 // 21. 启动 metricsSystem171 _env.metricsSystem.start()172 // Attach the driver metrics servlet handler to the web ui after the metrics system is started.173 // 22. 将 metricSystem 的 servlet handler 给 ui 用174 _env.metricsSystem.getServletHandlers.foreach(handler => ui.foreach(_.attachHandler(handler)))175 176 // 23. 初始化 event logger listener177 _eventLogger =178 if (isEventLogEnabled) {179 val logger =180 new EventLoggingListener(_applicationId, _applicationAttemptId, _eventLogDir.get,181 _conf, _hadoopConfiguration)182 logger.start()183 listenerBus.addToEventLogQueue(logger)184 Some(logger)185 } else {186 None187 }188 189 // Optionally scale number of executors dynamically based on workload. Exposed for testing.190 // 24. 如果启用了动态分配 executor， 需要实例化 executorAllocationManager 并启动之191 val dynamicAllocationEnabled = Utils.isDynamicAllocationEnabled(_conf)192 _executorAllocationManager =193 if (dynamicAllocationEnabled) {194 schedulerBackend match {195 case b: ExecutorAllocationClient =>196 Some(new ExecutorAllocationManager(197 schedulerBackend.asInstanceOf[ExecutorAllocationClient], listenerBus, _conf,198 _env.blockManager.master))199 case _ =>200 None201 }202 } else {203 None204 }205 _executorAllocationManager.foreach(_.start())206 207 // 25. 初始化 ContextCleaner，并启动之208 _cleaner =209 if (_conf.getBoolean("spark.cleaner.referenceTracking", true)) {210 Some(new ContextCleaner(this))211 } else {212 None213 }214 _cleaner.foreach(_.start())215 // 26. 建立并启动 listener bus216 setupAndStartListenerBus()217 // 27. task scheduler 已就绪，发送环境已更新请求218 postEnvironmentUpdate()219 // 28. 发送 application start 请求事件220 postApplicationStart()221 222 // Post init223 // 29.等待 直至task scheduler backend 准备好了224 _taskScheduler.postStartHook()225 // 30. 注册 dagScheduler metricsSource226 _env.metricsSystem.registerSource(_dagScheduler.metricsSource)227 // 31. 注册 metric source228 _env.metricsSystem.registerSource(new BlockManagerSource(_env.blockManager))229 //32. 注册 metric source230 _executorAllocationManager.foreach { e =>231 _env.metricsSystem.registerSource(e.executorAllocationManagerSource)232 }233 234 // Make sure the context is stopped if the user forgets about it. This avoids leaving235 // unfinished event logs around after the JVM exits cleanly. It doesn't help if the JVM236 // is killed, though.237 logDebug("Adding shutdown hook") // force eager creation of logger238 // 33. 设置 shutdown hook， 在spark context 关闭时，要做的回调操作239 _shutdownHookRef = ShutdownHookManager.addShutdownHook(240 ShutdownHookManager.SPARK_CONTEXT_SHUTDOWN_PRIORITY) { () =>241 logInfo("Invoking stop() from shutdown hook")242 try {243 stop()244 } catch {245 case e: Throwable =>246 logWarning("Ignoring Exception while stopping SparkContext from shutdown hook", e)247 }248 }249 } catch {250 case NonFatal(e) =>251 logError("Error initializing SparkContext.", e)252 try {253 stop()254 } catch {255 case NonFatal(inner) =>256 logError("Error stopping SparkContext after init error.", inner)257 } finally {258 throw e259 }260 }
从上面可以看出，spark context 的初始化是非常复杂的，涉及的spark 组件很多，包括 异步事务总线系统LiveListenerBus、SparkEnv、SparkUI、DAGScheduler、metrics监测系统、EventLoggingListener、TaskScheduler、ExecutorAllocationManager、ContextCleaner等等。先暂且当作是总述，后面对部分组件会有比较全面的剖析。