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1.SparkContext的作用
SparkContext存在于Driver中，是Spark功能的主要入口。代表着与Spark集群的连接，可以在集群上创建RDD，accumulators和广播变量。
2.SparkContext的构造方法
辅助构造方法：
class SparkContext(config: SparkConf) extends Logging {
// ...
def this() = this(new SparkConf())
def this(master: String, appName: String, conf: SparkConf) =
this(SparkContext.updatedConf(conf, master, appName))
def this(
master: String,
appName: String,
sparkHome: String = null,
jars: Seq[String] = Nil,
environment: Map[String, String] = Map()) = {
this(SparkContext.updatedConf(new SparkConf(), master, appName, sparkHome, jars, environment))
}
private[spark] def this(master: String, appName: String) =
this(master, appName, null, Nil, Map())
private[spark] def this(master: String, appName: String, sparkHome: String) =
this(master, appName, sparkHome, Nil, Map())
private[spark] def this(master: String, appName: String, sparkHome: String, jars: Seq[String]) =
this(master, appName, sparkHome, jars, Map())
// ...
}
而其主构造方法主要由一个巨大的try-catch块组成，位于SparkContext.scala的362~586行，它内部包含了很多初始化逻辑。
3.SparkContext的组件初始化
try {
_conf = config.clone()
_conf.validateSettings()
if (!_conf.contains("spark.master")) {
throw new SparkException("A master URL must be set in your configuration")
}
if (!_conf.contains("spark.app.name")) {
throw new SparkException("An application name must be set in your configuration")
}
// log out spark.app.name in the Spark driver logs
logInfo(s"Submitted application: $appName")
// System property spark.yarn.app.id must be set if user code ran by AM on a YARN cluster
if (master == "yarn" && deployMode == "cluster" && !_conf.contains("spark.yarn.app.id")) {
throw new SparkException("Detected yarn cluster mode, but isn't running on a cluster. " +
"Deployment to YARN is not supported directly by SparkContext. Please use spark-submit.")
}
if (_conf.getBoolean("spark.logConf", false)) {
logInfo("Spark configuration:\n" + _conf.toDebugString)
}
// Set Spark driver host and port system properties. This explicitly sets the configuration
// instead of relying on the default value of the config constant.
_conf.set(DRIVER_HOST_ADDRESS, _conf.get(DRIVER_HOST_ADDRESS))
_conf.setIfMissing("spark.driver.port", "0")
_conf.set("spark.executor.id", SparkContext.DRIVER_IDENTIFIER)
_jars = Utils.getUserJars(_conf)
_files = _conf.getOption("spark.files").map(_.split(",")).map(_.filter(_.nonEmpty))
.toSeq.flatten
_eventLogDir =
if (isEventLogEnabled) {
val unresolvedDir = conf.get("spark.eventLog.dir", EventLoggingListener.DEFAULT_LOG_DIR)
.stripSuffix("/")
Some(Utils.resolveURI(unresolvedDir))
} else {
None
}
_eventLogCodec = {
val compress = _conf.getBoolean("spark.eventLog.compress", false)
if (compress && isEventLogEnabled) {
Some(CompressionCodec.getCodecName(_conf)).map(CompressionCodec.getShortName)
} else {
None
}
}
_listenerBus = new LiveListenerBus(_conf)
// Initialize the app status store and listener before SparkEnv is created so that it gets
// all events.
_statusStore = AppStatusStore.createLiveStore(conf)
listenerBus.addToStatusQueue(_statusStore.listener.get)
// Create the Spark execution environment (cache, map output tracker, etc)
_env = createSparkEnv(_conf, isLocal, listenerBus)
SparkEnv.set(_env)
// If running the REPL, register the repl's output dir with the file server.
_conf.getOption("spark.repl.class.outputDir").foreach { path =>
val replUri = _env.rpcEnv.fileServer.addDirectory("/classes", new File(path))
_conf.set("spark.repl.class.uri", replUri)
}
_statusTracker = new SparkStatusTracker(this, _statusStore)
_progressBar =
if (_conf.get(UI_SHOW_CONSOLE_PROGRESS) && !log.isInfoEnabled) {
Some(new ConsoleProgressBar(this))
} else {
None
}
_ui =
if (conf.getBoolean("spark.ui.enabled", true)) {
Some(SparkUI.create(Some(this), _statusStore, _conf, _env.securityManager, appName, "",
startTime))
} else {
// For tests, do not enable the UI
None
}
// Bind the UI before starting the task scheduler to communicate
// the bound port to the cluster manager properly
_ui.foreach(_.bind())
_hadoopConfiguration = SparkHadoopUtil.get.newConfiguration(_conf)
// Add each JAR given through the constructor
if (jars != null) {
jars.foreach(addJar)
}
if (files != null) {
files.foreach(addFile)
}
_executorMemory = _conf.getOption("spark.executor.memory")
.orElse(Option(System.getenv("SPARK_EXECUTOR_MEMORY")))
.orElse(Option(System.getenv("SPARK_MEM"))
.map(warnSparkMem))
.map(Utils.memoryStringToMb)
.getOrElse(1024)
// Convert java options to env vars as a work around
// since we can't set env vars directly in sbt.
for { (envKey, propKey) <- Seq(("SPARK_TESTING", "spark.testing"))
value <- Option(System.getenv(envKey)).orElse(Option(System.getProperty(propKey)))} {
executorEnvs(envKey) = value
}
Option(System.getenv("SPARK_PREPEND_CLASSES")).foreach { v =>
executorEnvs("SPARK_PREPEND_CLASSES") = v
}
// The Mesos scheduler backend relies on this environment variable to set executor memory.
// TODO: Set this only in the Mesos scheduler.
executorEnvs("SPARK_EXECUTOR_MEMORY") = executorMemory + "m"
executorEnvs ++= _conf.getExecutorEnv
executorEnvs("SPARK_USER") = sparkUser
// We need to register "HeartbeatReceiver" before "createTaskScheduler" because Executor will
// retrieve "HeartbeatReceiver" in the constructor. (SPARK-6640)
_heartbeatReceiver = env.rpcEnv.setupEndpoint(
HeartbeatReceiver.ENDPOINT_NAME, new HeartbeatReceiver(this))
// Create and start the scheduler
val (sched, ts) = SparkContext.createTaskScheduler(this, master, deployMode)
_schedulerBackend = sched
_taskScheduler = ts
_dagScheduler = new DAGScheduler(this)
_heartbeatReceiver.ask[Boolean](TaskSchedulerIsSet)
// start TaskScheduler after taskScheduler sets DAGScheduler reference in DAGScheduler's
// constructor
_taskScheduler.start()
_applicationId = _taskScheduler.applicationId()
_applicationAttemptId = taskScheduler.applicationAttemptId()
_conf.set("spark.app.id", _applicationId)
if (_conf.getBoolean("spark.ui.reverseProxy", false)) {
System.setProperty("spark.ui.proxyBase", "/proxy/" + _applicationId)
}
_ui.foreach(_.setAppId(_applicationId))
_env.blockManager.initialize(_applicationId)
// The metrics system for Driver need to be set spark.app.id to app ID.
// So it should start after we get app ID from the task scheduler and set spark.app.id.
_env.metricsSystem.start()
// Attach the driver metrics servlet handler to the web ui after the metrics system is started.
_env.metricsSystem.getServletHandlers.foreach(handler => ui.foreach(_.attachHandler(handler)))
_eventLogger =
if (isEventLogEnabled) {
val logger =
new EventLoggingListener(_applicationId, _applicationAttemptId, _eventLogDir.get,
_conf, _hadoopConfiguration)
logger.start()
listenerBus.addToEventLogQueue(logger)
Some(logger)
} else {
None
}
// Optionally scale number of executors dynamically based on workload. Exposed for testing.
val dynamicAllocationEnabled = Utils.isDynamicAllocationEnabled(_conf)
_executorAllocationManager =
if (dynamicAllocationEnabled) {
schedulerBackend match {
case b: ExecutorAllocationClient =>
Some(new ExecutorAllocationManager(
schedulerBackend.asInstanceOf[ExecutorAllocationClient], listenerBus, _conf,
_env.blockManager.master))
case _ =>
None
}
} else {
None
}
_executorAllocationManager.foreach(_.start())
_cleaner =
if (_conf.getBoolean("spark.cleaner.referenceTracking", true)) {
Some(new ContextCleaner(this))
} else {
None
}
_cleaner.foreach(_.start())
setupAndStartListenerBus()
postEnvironmentUpdate()
postApplicationStart()
// Post init
_taskScheduler.postStartHook()
_env.metricsSystem.registerSource(_dagScheduler.metricsSource)
_env.metricsSystem.registerSource(new BlockManagerSource(_env.blockManager))
_executorAllocationManager.foreach { e =>
_env.metricsSystem.registerSource(e.executorAllocationManagerSource)
}
// Make sure the context is stopped if the user forgets about it. This avoids leaving
// unfinished event logs around after the JVM exits cleanly. It doesn't help if the JVM
// is killed, though.
logDebug("Adding shutdown hook") // force eager creation of logger
_shutdownHookRef = ShutdownHookManager.addShutdownHook(
ShutdownHookManager.SPARK_CONTEXT_SHUTDOWN_PRIORITY) { () =>
logInfo("Invoking stop() from shutdown hook")
stop()
}
} catch {
case NonFatal(e) =>
logError("Error initializing SparkContext.", e)
try {
stop()
} catch {
case NonFatal(inner) =>
logError("Error stopping SparkContext after init error.", inner)
} finally {
throw e
}
}
<1> SparkConf
image.png
SparkConf作为构造函数的参数传进来之后，SparkContext会先将传入的SparkConf克隆一份副本，之后在副本上做校验。
<2> LiveListenerBus
LiveListenerBus是SparkContext中的事件总线。它异步地将事件源产生的事件（SparkListenerEvent）投递给已注册的监听器（SparkListener）。Spark中广泛运用了监听器模式，以适应集群状态下的分布式事件汇报。
除了它之外，Spark中还有多种事件总线，它们都继承自ListenerBus特征。事件总线是Spark底层的重要支撑组件，之后会专门分析。
<3> AppStatusStore
AppStatusStore提供Spark程序运行中各项监控指标的键值对化存储。Web UI中见到的数据指标基本都存储在这里。其初始化代码如下。
image.png
createLiveStore方法：
def createLiveStore(conf: SparkConf): AppStatusStore = {
val store = new ElementTrackingStore(new InMemoryStore(), conf)
val listener = new AppStatusListener(store, conf, true)
new AppStatusStore(store, listener = Some(listener))
}
可见，AppStatusStore底层使用了ElementTrackingStore，它是能够跟踪元素及其数量的键值对存储结构，因此适合用于监控。另外还会产生一个监听器AppStatusListener的实例，并注册到前述LiveListenerBus中，用来收集监控数据。
<4> SparkEnv
SparkEnv是Spark中的执行环境，Driver与Executor的执行都需要SparkEnv提供的各类组件形成的环境作为基础。
image.png
createSparkEnv方法十分复杂，后续继续分析，SparkEnv的初始化依赖于LiveListenerBus，并且在SparkContext初始化时只会创建Driver的执行环境，Executor的执行环境是后话了，在创建Driver执行环境后，会调用SparkEnv伴生对象中的set()方法保存它，这样就可以“一处创建，多处使用”SparkEnv。
<5> SparkStatusTracker
SparkStatusTracker提供报告最近作业执行情况的低级API。它的内部只有6个方法，从AppStatusStore中查询并返回诸如Job/Stage ID、活跃/完成/失败的Task数、Executor内存用量等基础数据。它只能保证非常弱的一致性语义，也就是说它报告的信息会有延迟或缺漏。
<6> ConsoleProgressBar
ConsoleProgressBar按行打印Stage的计算进度。它周期性地从AppStatusStore中查询Stage对应的各状态的Task数，并格式化成字符串输出。它可以通过spark.ui.showConsoleProgress参数控制开关，默认值false。
<7> SparkUI
SparkUI维护监控数据在Spark Web UI界面的展示。它的样子在文章#0的图中已经出现过，因此不再赘述。其初始化代码如下。
_ui =
if (conf.getBoolean("spark.ui.enabled", true)) {
Some(SparkUI.create(Some(this), _statusStore, _conf, _env.securityManager, appName, "",
startTime))
} else {
None
}
_ui.foreach(_.bind())
可以通过spark.ui.enabled参数来控制是否启用Spark UI，默认值true。然后调用SparkUI的父类WebUI的bind()方法，将Spark UI绑定到特定的host:port上，如文章#0中的localhost:4040。
<8> HeartbeatReceiver
HeartbeatReceiver是心跳接收器。Executor需要向Driver定期发送心跳包来表示自己存活。它本质上也是个监听器，继承了SparkListener。其初始化代码如下。
_heartbeatReceiver = env.rpcEnv.setupEndpoint(
HeartbeatReceiver.ENDPOINT_NAME, new HeartbeatReceiver(this))
可见，HeartbeatReceiver通过RpcEnv最终包装成了一个RPC端点的引用，即代码#2.2中的RpcEndpointRef。
Spark集群的节点间必然会涉及大量的网络通信，心跳机制只是其中的一方面而已。因此RPC框架同事件总线一样，是Spark底层不可或缺的组成部分。
<9> SchedulerBackend
SchedulerBackend负责向等待计算的Task分配计算资源，并在Executor上启动Task。它是一个Scala特征，有多种部署模式下的SchedulerBackend实现类。它在SparkContext中是和TaskScheduler一起初始化的，作为一个元组返回。
<10> TaskScheduler
TaskScheduler即任务调度器。它也是一个Scala特征，但只有一种实现，即TaskSchedulerImpl类。它负责提供Task的调度算法，并且会持有SchedulerBackend的实例，通过SchedulerBackend发挥作用。它们两个的初始化代码如下。
val (sched, ts) = SparkContext.createTaskScheduler(this, master, deployMode)
_schedulerBackend = sched
_taskScheduler = ts
<11> DAGScheduler
DAGScheduler即有向无环图（DAG）调度器。DAG用来表示RDD之间的血缘。DAGScheduler负责生成并提交Job，以及按照DAG将RDD和算子划分并提交Stage。每个Stage都包含一组Task，称为TaskSet，它们被传递给TaskScheduler。也就是说DAGScheduler需要先于TaskScheduler进行调度。
DAGScheduler初始化是直接new出来的，但在其构造方法里也会将SparkContext中TaskScheduler的引用传进去。因此要等DAGScheduler创建后，再真正启动TaskScheduler。
_dagScheduler = new DAGScheduler(this)
_heartbeatReceiver.ask[Boolean](TaskSchedulerIsSet)
_taskScheduler.start()
<12> EventLoggingListener
EventLoggingListener是用于事件持久化的监听器。它可以通过spark.eventLog.enabled参数控制开关，默认值false。如果开启，它也会注册到LiveListenerBus里，并将特定的一部分事件写到磁盘。
<13> ExecutorAllocationManager
ExecutorAllocationManager即Executor分配管理器。它可以通过spark.dynamicAllocation.enabled参数控制开关，默认值false。如果开启，并且SchedulerBackend的实现类支持这种机制，Spark就会根据程序运行时的负载动态增减Executor的数量。它的初始化代码如下。
<14>ContextCleaner
ContextCleaner即上下文清理器。它可以通过spark.cleaner.referenceTracking参数控制开关，默认值true。它内部维护着对RDD、Shuffle依赖和广播变量（之后会提到）的弱引用，如果弱引用的对象超出程序的作用域，就异步地将它们清理掉
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