SparkContext 源码分析
SparkContext 源码分析,带Youtub视频
Youtub 视频分享
Youtub视频（Spark原理分析图解）: https://youtu.be/euIuutjAB4I
Youtub视频(Spark源码分析详解): https://youtu.be/tUH7QnCcwgg
文档说明
原文
Main entry point for Spark functionality.
A SparkContext represents the connection to a Spark cluster,
and can be used to create RDDs, accumulators and broadcast variables on that cluster.
Only one SparkContext may be active per JVM. You must stop() the active SparkContext before creating a new one.
This limitation may eventually be removed; see SPARK-2243 for more details.翻译
).Spark功能主要入口点
).一个SparkContext表示与一个Spark集群的连接
).在Spark集群上，能创建RDDs,累加器，广播变量
).每个JVM仅仅只有一个SparkContext可能是活动的
).在创建一个新的SparkContext之前，你必须停掉活动的SparkContext，这个限制最终可能被 移除，看SPARK-2243 更多详情SparkContext原理图
xmind文件下载
https://github.com/opensourceteams/spark-scala-maven/blob/master/md/images/spark/SparkContext.xmind
配置信息
可配置信息
spark.jars = jar文件路径(可迭代的)
spark.files = 文件路径
spark.eventLog.dir=/tmp/spark-events // 事件日志目录
spark.eventLog.compress=false //事件日志是否压缩
spark.shuffle.manager=sort //指定shuffler manager
// Let the user specify short names for shuffle managers
val shortShuffleMgrNames = Map(
"hash" -> "org.apache.spark.shuffle.hash.HashShuffleManager",
"sort" -> "org.apache.spark.shuffle.sort.SortShuffleManager",
"tungsten-sort" -> "org.apache.spark.shuffle.sort.SortShuffleManager")
val shuffleMgrName = conf.get("spark.shuffle.manager", "sort")
val shuffleMgrClass = shortShuffleMgrNames.getOrElse(shuffleMgrName.toLowerCase, shuffleMgrName)
val shuffleManager = instantiateClass[ShuffleManager](shuffleMgrClass)spark.memory.useLegacyMode=true //指定内存管理器
val useLegacyMemoryManager = conf.getBoolean("spark.memory.useLegacyMode", true) val memoryManager: MemoryManager = if (useLegacyMemoryManager) { new StaticMemoryManager(conf, numUsableCores) } else { UnifiedMemoryManager(conf, numUsableCores) }spark.cores.max=2 设置executor占用cpu内核个数
- spark.ui.showConsoleProgress=true //展示控制台的进度信息
- spark.ui.enabled=true //是否开启SparkUI
- spark.executor.memory= //spark executor 的内存
- SPARK_EXECUTOR_MEMORY= //spark executor 的内存
- SPARK_MEM= //spark executor 的内存
```scala
/**
*查找顺序，找到前面的就不找后面的了(配置文件中设置值单位为 byte)，默认值为1024MB
spark.executor.memory > SPARK_EXECUTOR_MEMORY > SPARK_MEM
*
/
_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)spark.scheduler.mode=FIFO //TaskSchedulerImpl 调度模式,可选(FIFO,FAIR,NONE)
/**
* "FAIR" and "FIFO" determines which policy is used
* to order tasks amongst a Schedulable's sub-queues
* "NONE" is used when the a Schedulable has no sub-queues.
*/
object SchedulingMode extends Enumeration {
type SchedulingMode = Value
val FAIR, FIFO, NONE = Value
}spark.executor.extraJavaOptions= //设置executor启动执行的java参数
spark.executor.extraClassPath= //设置 executor 执行的classpath
spark.executor.extraLibraryPath= //设置 executor LibraryPath
spark.executor.cores= //executor core 个数分配
spark.rpc.lookupTimeout="120s" //设置 RPCTimeout超时时间
spark.network.timeout="120s" //设置 RPCTimeout超时时间
/** Returns the default Spark timeout to use for RPC remote endpoint lookup. */
private[spark] def lookupRpcTimeout(conf: SparkConf): RpcTimeout = {
RpcTimeout(conf, Seq("spark.rpc.lookupTimeout", "spark.network.timeout"), "120s")
}Spark系统设置配置信息
spark.driver.host = Utils.localHostName()
spark.driver.port = 0
spark.executor.id = driver
主要内容
创建作业进度监听器
_jobProgressListener = new JobProgressListener(_conf)
listenerBus.addListener(jobProgressListener)创建SparkEnv
_env = createSparkEnv(_conf, isLocal, listenerBus)
SparkEnv.set(_env)创建DriverEnv
SparkEnv.createDriverEnv(conf, isLocal, listenerBus, SparkContext.numDriverCores(master))指定默认的 spark.rpc = org.apache.spark.rpc.netty.NettyRpcEnvFactory
private def getRpcEnvFactory(conf: SparkConf): RpcEnvFactory = {
val rpcEnvNames = Map(
"akka" -> "org.apache.spark.rpc.akka.AkkaRpcEnvFactory",
"netty" -> "org.apache.spark.rpc.netty.NettyRpcEnvFactory")
val rpcEnvName = conf.get("spark.rpc", "netty")
val rpcEnvFactoryClassName = rpcEnvNames.getOrElse(rpcEnvName.toLowerCase, rpcEnvName)
Utils.classForName(rpcEnvFactoryClassName).newInstance().asInstanceOf[RpcEnvFactory]
}创建NettyRpcEnv并启动，此时启动 'sparkDriver'
def create(config: RpcEnvConfig): RpcEnv = {
val sparkConf = config.conf
// Use JavaSerializerInstance in multiple threads is safe. However, if we plan to support
// KryoSerializer in future, we have to use ThreadLocal to store SerializerInstance
val javaSerializerInstance =
new JavaSerializer(sparkConf).newInstance().asInstanceOf[JavaSerializerInstance]
val nettyEnv =
new NettyRpcEnv(sparkConf, javaSerializerInstance, config.host, config.securityManager)
if (!config.clientMode) {
val startNettyRpcEnv: Int => (NettyRpcEnv, Int) = { actualPort =>
nettyEnv.startServer(actualPort)
(nettyEnv, nettyEnv.address.port)
}
try {
Utils.startServiceOnPort(config.port, startNettyRpcEnv, sparkConf, config.name)._1
} catch {
case NonFatal(e) =>
nettyEnv.shutdown()
throw e
}
}
nettyEnv
}创建ActorSystem并启动，此时启动 'sparkDriverActorSystem'
/**
* Creates an ActorSystem ready for remoting, with various Spark features. Returns both the
* ActorSystem itself and its port (which is hard to get from Akka).
*
* Note: the `name` parameter is important, as even if a client sends a message to right
* host + port, if the system name is incorrect, Akka will drop the message.
*
* If indestructible is set to true, the Actor System will continue running in the event
* of a fatal exception. This is used by [[org.apache.spark.executor.Executor]].
*/
def createActorSystem(
name: String,
host: String,
port: Int,
conf: SparkConf,
securityManager: SecurityManager): (ActorSystem, Int) = {
val startService: Int => (ActorSystem, Int) = { actualPort =>
doCreateActorSystem(name, host, actualPort, conf, securityManager)
}
Utils.startServiceOnPort(port, startService, conf, name)
}指定spark序列化器: org.apache.spark.serializer.JavaSerializer
val serializer = instantiateClassFromConfSerializer logDebug(s"Using serializer: ${serializer.getClass}")
val closureSerializer = instantiateClassFromConf[Serializer](
"spark.closure.serializer", "org.apache.spark.serializer.JavaSerializer")
- 实例化 MapOutputTrackerMaster
```scala
val broadcastManager = new BroadcastManager(isDriver, conf, securityManager)
val mapOutputTracker = if (isDriver) {
new MapOutputTrackerMaster(conf, broadcastManager, isLocal)
} else {
new MapOutputTrackerWorker(conf)
}注册 MapOutputTracker 到 NettyRpcEndpointRef（通信用 和map输出信息的追踪）
def registerRpcEndpoint(name: String, endpoint: RpcEndpoint): NettyRpcEndpointRef = {
val addr = RpcEndpointAddress(nettyEnv.address, name)
val endpointRef = new NettyRpcEndpointRef(nettyEnv.conf, addr, nettyEnv)
synchronized {
if (stopped) {
throw new IllegalStateException("RpcEnv has been stopped")
}
if (endpoints.putIfAbsent(name, new EndpointData(name, endpoint, endpointRef)) != null) {
throw new IllegalArgumentException(s"There is already an RpcEndpoint called $name")
}
val data = endpoints.get(name)
endpointRefs.put(data.endpoint, data.ref)
receivers.offer(data) // for the OnStart message
}
endpointRef
}实例化ShuflleManager
// Let the user specify short names for shuffle managers
val shortShuffleMgrNames = Map(
"hash" -> "org.apache.spark.shuffle.hash.HashShuffleManager",
"sort" -> "org.apache.spark.shuffle.sort.SortShuffleManager",
"tungsten-sort" -> "org.apache.spark.shuffle.sort.SortShuffleManager")
val shuffleMgrName = conf.get("spark.shuffle.manager", "sort")
val shuffleMgrClass = shortShuffleMgrNames.getOrElse(shuffleMgrName.toLowerCase, shuffleMgrName)
val shuffleManager = instantiateClass[ShuffleManager](shuffleMgrClass)实例化内存管理器
val useLegacyMemoryManager = conf.getBoolean("spark.memory.useLegacyMode", true) val memoryManager: MemoryManager = if (useLegacyMemoryManager) { new StaticMemoryManager(conf, numUsableCores) } else { UnifiedMemoryManager(conf, numUsableCores) }
- 注册 BlockManagerMaster 到 NettyRpcEndpointRef（通信用）
```scala
val blockManagerMaster = new BlockManagerMaster(registerOrLookupEndpoint(
BlockManagerMaster.DRIVER_ENDPOINT_NAME,
new BlockManagerMasterEndpoint(rpcEnv, isLocal, conf, listenerBus)),
conf, isDriver)缓存管理器 实例化BlockManager
// NB: blockManager is not valid until initialize() is called later.
val blockManager = new BlockManager(executorId, rpcEnv, blockManagerMaster,
serializer, conf, memoryManager, mapOutputTracker, shuffleManager,
blockTransferService, securityManager, numUsableCores)
val cacheManager = new CacheManager(blockManager)创建测量系统
val metricsSystem = if (isDriver) {
// Don't start metrics system right now for Driver.
// We need to wait for the task scheduler to give us an app ID.
// Then we can start the metrics system.
MetricsSystem.createMetricsSystem("driver", conf, securityManager)
} else {
// We need to set the executor ID before the MetricsSystem is created because sources and
// sinks specified in the metrics configuration file will want to incorporate this executor's
// ID into the metrics they report.
conf.set("spark.executor.id", executorId)
val ms = MetricsSystem.createMetricsSystem("executor", conf, securityManager)
ms.start()
ms
}创建临时目录，如果是分布式模式，这是一个executor的当前工作目录
// Set the sparkFiles directory, used when downloading dependencies. In local mode,
// this is a temporary directory; in distributed mode, this is the executor's current working
// directory.
val sparkFilesDir: String = if (isDriver) {
Utils.createTempDir(Utils.getLocalDir(conf), "userFiles").getAbsolutePath
} else {
"."
}注册 OutputCommitCoordinator 到 NettyRpcEndpointRef（通信用）
val outputCommitCoordinator = mockOutputCommitCoordinator.getOrElse {
new OutputCommitCoordinator(conf, isDriver)
}
val outputCommitCoordinatorRef = registerOrLookupEndpoint("OutputCommitCoordinator",
new OutputCommitCoordinatorEndpoint(rpcEnv, outputCommitCoordinator))
outputCommitCoordinator.coordinatorRef = Some(outputCommitCoordinatorRef)new SparkEnv 并返回
val envInstance = new SparkEnv(
executorId,
rpcEnv,
actorSystem,
serializer,
closureSerializer,
cacheManager,
mapOutputTracker,
shuffleManager,
broadcastManager,
blockTransferService,
blockManager,
securityManager,
sparkFilesDir,
metricsSystem,
memoryManager,
outputCommitCoordinator,
conf)
// Add a reference to tmp dir created by driver, we will delete this tmp dir when stop() is
// called, and we only need to do it for driver. Because driver may run as a service, and if we
// don't delete this tmp dir when sc is stopped, then will create too many tmp dirs.
if (isDriver) {
envInstance.driverTmpDirToDelete = Some(sparkFilesDir)
}
envInstance创建SparkUI
_ui =
if (conf.getBoolean("spark.ui.enabled", true)) {
Some(SparkUI.createLiveUI(this, _conf, listenerBus, _jobProgressListener,
_env.securityManager, appName, startTime = 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())注册心跳接收器
// 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))创建和初使化调度器(TaskScheduler,DAGScheduler)
// Create and start the scheduler
val (sched, ts) = SparkContext.createTaskScheduler(this, master)
_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()org.apache.spark.scheduler.TaskSchedulerImpl 文档说明
/**
).SchedulerBackend 对多种类型的集群调度任务
).LocalBackend 设置 isLocal为true, 也能调度本地任务
).SchedulerBackend.处理常用逻辑，决定跨作业的调度顺序,唤醒和启动推测的任务
).客户端应该先调用 initialize() 和 start(),然后通过 runTasks方法提交任务集
* Schedules tasks for multiple types of clusters by acting through a SchedulerBackend.
* It can also work with a local setup by using a LocalBackend and setting isLocal to true.
* It handles common logic, like determining a scheduling order across jobs, waking up to launch
* speculative tasks, etc.
*
* Clients should first call initialize() and start(), then submit task sets through the
* runTasks method.
*
* THREADING: SchedulerBackends and task-submitting clients can call this class from multiple
* threads, so it needs locks in public API methods to maintain its state. In addition, some
* SchedulerBackends synchronize on themselves when they want to send events here, and then
* acquire a lock on us, so we need to make sure that we don't try to lock the backend while
* we are holding a lock on ourselves.
*/Standalone模式创建TaskSchedulerImpl并初使化中指定 backend为SparkDeploySchedulerBackend
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)
(backend, scheduler) def initialize(backend: SchedulerBackend) {
this.backend = backend
// temporarily set rootPool name to empty
rootPool = new Pool("", schedulingMode, 0, 0)
schedulableBuilder = {
schedulingMode match {
case SchedulingMode.FIFO =>
new FIFOSchedulableBuilder(rootPool)
case SchedulingMode.FAIR =>
new FairSchedulableBuilder(rootPool, conf)
}
}
schedulableBuilder.buildPools()
}任务调度器启动
任务调度器启动_taskScheduler.start()
// start TaskScheduler after taskScheduler sets DAGScheduler reference in DAGScheduler's
// constructor
_taskScheduler.start()调用SparkDeploySchedulerBackend start方法
override def start() {
backend.start()
if (!isLocal && conf.getBoolean("spark.speculation", false)) {
logInfo("Starting speculative execution thread")
speculationScheduler.scheduleAtFixedRate(new Runnable {
override def run(): Unit = Utils.tryOrStopSparkContext(sc) {
checkSpeculatableTasks()
}
}, SPECULATION_INTERVAL_MS, SPECULATION_INTERVAL_MS, TimeUnit.MILLISECONDS)
}
}再调用CoarseGrainedSchedulerBackend 的start方法 registerRpcEndpoint 注册（通信用） [CoarseGrainedScheduler]
实例化ApplicationDescription 包含 command (org.apache.spark.executor.CoarseGrainedExecutorBackend)
启动 AppClient registerRpcEndpoint 注册（通信用）[AppClient]
override def start() {
super.start()
launcherBackend.connect()
// The endpoint for executors to talk to us
val driverUrl = rpcEnv.uriOf(SparkEnv.driverActorSystemName,
RpcAddress(sc.conf.get("spark.driver.host"), sc.conf.get("spark.driver.port").toInt),
CoarseGrainedSchedulerBackend.ENDPOINT_NAME)
val args = Seq(
"--driver-url", driverUrl,
"--executor-id", "{{EXECUTOR_ID}}",
"--hostname", "{{HOSTNAME}}",
"--cores", "{{CORES}}",
"--app-id", "{{APP_ID}}",
"--worker-url", "{{WORKER_URL}}")
val extraJavaOpts = sc.conf.getOption("spark.executor.extraJavaOptions")
.map(Utils.splitCommandString).getOrElse(Seq.empty)
val classPathEntries = sc.conf.getOption("spark.executor.extraClassPath")
.map(_.split(java.io.File.pathSeparator).toSeq).getOrElse(Nil)
val libraryPathEntries = sc.conf.getOption("spark.executor.extraLibraryPath")
.map(_.split(java.io.File.pathSeparator).toSeq).getOrElse(Nil)
// When testing, expose the parent class path to the child. This is processed by
// compute-classpath.{cmd,sh} and makes all needed jars available to child processes
// when the assembly is built with the "*-provided" profiles enabled.
val testingClassPath =
if (sys.props.contains("spark.testing")) {
sys.props("java.class.path").split(java.io.File.pathSeparator).toSeq
} else {
Nil
}
// Start executors with a few necessary configs for registering with the scheduler
val sparkJavaOpts = Utils.sparkJavaOpts(conf, SparkConf.isExecutorStartupConf)
val javaOpts = sparkJavaOpts ++ extraJavaOpts
val command = Command("org.apache.spark.executor.CoarseGrainedExecutorBackend",
args, sc.executorEnvs, classPathEntries ++ testingClassPath, libraryPathEntries, javaOpts)
val appUIAddress = sc.ui.map(_.appUIAddress).getOrElse("")
val coresPerExecutor = conf.getOption("spark.executor.cores").map(_.toInt)
val appDesc = new ApplicationDescription(sc.appName, maxCores, sc.executorMemory,
command, appUIAddress, sc.eventLogDir, sc.eventLogCodec, coresPerExecutor)
client = new AppClient(sc.env.rpcEnv, masters, appDesc, this, conf)
client.start()
launcherBackend.setState(SparkAppHandle.State.SUBMITTED)
waitForRegistration()
launcherBackend.setState(SparkAppHandle.State.RUNNING)
}CoarseGrainedSchedulerBackend 文档
/**
). 一个后端调度器 等待粗粒度 executors 通过 Akka连接他
).在Spark作业期间，这个后端调度接管着每个executor 比起交出executors给别人调度
).无论何时完成任务，都要求调度器启动一个新的executor给每个每个新的任务
).executors 可以以多种方式启动，例如 粗粒度 Mesos模式 Mesos任务
).或Spark standalone 部署模式的 standalone 处理
* A scheduler backend that waits for coarse grained executors to connect to it through Akka.
* This backend holds onto each executor for the duration of the Spark job rather than relinquishing
* executors whenever a task is done and asking the scheduler to launch a new executor for
* each new task. Executors may be launched in a variety of ways, such as Mesos tasks for the
* coarse-grained Mesos mode or standalone processes for Spark's standalone deploy mode
* (spark.deploy.*).
*/
private[spark]
class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val rpcEnv: RpcEnv)
extends ExecutorAllocationClient with SchedulerBackend with Logging
{调用ClientEndpoint 的 onStart方法 异步向所有master注册,向master发送消息： RegisterApplication
override def onStart(): Unit = {
try {
registerWithMaster(1)
} catch {
case e: Exception =>
logWarning("Failed to connect to master", e)
markDisconnected()
stop()
}
} /**
* Register with all masters asynchronously. It will call `registerWithMaster` every
* REGISTRATION_TIMEOUT_SECONDS seconds until exceeding REGISTRATION_RETRIES times.
* Once we connect to a master successfully, all scheduling work and Futures will be cancelled.
*
* nthRetry means this is the nth attempt to register with master.
*/
private def registerWithMaster(nthRetry: Int) {
registerMasterFutures.set(tryRegisterAllMasters())
registrationRetryTimer.set(registrationRetryThread.scheduleAtFixedRate(new Runnable {
override def run(): Unit = {
if (registered.get) {
registerMasterFutures.get.foreach(_.cancel(true))
registerMasterThreadPool.shutdownNow()
} else if (nthRetry >= REGISTRATION_RETRIES) {
markDead("All masters are unresponsive! Giving up.")
} else {
registerMasterFutures.get.foreach(_.cancel(true))
registerWithMaster(nthRetry + 1)
}
}
}, REGISTRATION_TIMEOUT_SECONDS, REGISTRATION_TIMEOUT_SECONDS, TimeUnit.SECONDS))
} /**
* Register with all masters asynchronously and returns an array `Future`s for cancellation.
*/
private def tryRegisterAllMasters(): Array[JFuture[_]] = {
for (masterAddress <- masterRpcAddresses) yield {
registerMasterThreadPool.submit(new Runnable {
override def run(): Unit = try {
if (registered.get) {
return
}
logInfo("Connecting to master " + masterAddress.toSparkURL + "...")
val masterRef =
rpcEnv.setupEndpointRef(Master.SYSTEM_NAME, masterAddress, Master.ENDPOINT_NAME)
masterRef.send(RegisterApplication(appDescription, self))
} catch {
case ie: InterruptedException => // Cancelled
case NonFatal(e) => logWarning(s"Failed to connect to master $masterAddress", e)
}
})
}
}org.apache.spark.rpc.netty.Dispatcher
类文档说明
/**
一个消息分配器，负责将RPC消息路由到适当的端点
* A message dispatcher, responsible for routing RPC messages to the appropriate endpoint(s).
*/
private[netty] class Dispatcher(nettyEnv: NettyRpcEnv) extends Logging {注册RPC端点 (关键通信及OnStart方法的调用)
/**
* new EndpointData(name, endpoint, endpointRef) 的时候会进行
* val inbox = new Inbox(ref, endpoint)的操作
* Inbox 实例化时会进行如下操作，当相于首先增加OnStart消息
// OnStart should be the first message to process
inbox.synchronized {
messages.add(OnStart)
}
*/
def registerRpcEndpoint(name: String, endpoint: RpcEndpoint): NettyRpcEndpointRef = {
val addr = RpcEndpointAddress(nettyEnv.address, name)
val endpointRef = new NettyRpcEndpointRef(nettyEnv.conf, addr, nettyEnv)
synchronized {
if (stopped) {
throw new IllegalStateException("RpcEnv has been stopped")
}
if (endpoints.putIfAbsent(name, new EndpointData(name, endpoint, endpointRef)) != null) {
throw new IllegalArgumentException(s"There is already an RpcEndpoint called $name")
}
val data = endpoints.get(name)
endpointRefs.put(data.endpoint, data.ref)
receivers.offer(data) // for the OnStart message
}
endpointRef
}Inbox
A inbox that stores messages for an [[RpcEndpoint]] and posts messages to it thread-safely.
/**
* Inbox 实例化时会进行如下操作，当相于首先增加OnStart消息给当前的对象
(也就是每个RpcEndpoint 子类进行注册时，首先增加OnStart消息)
* OnStart消息在 process方法中会进行 endpoint实现类的onStart() 方法回调
*/
// OnStart should be the first message to process
inbox.synchronized {
messages.add(OnStart)
} /**
* Process stored messages.
*/
def process(dispatcher: Dispatcher): Unit = {
......
case OnStart =>
endpoint.onStart()
if (!endpoint.isInstanceOf[ThreadSafeRpcEndpoint]) {
inbox.synchronized {
if (!stopped) {
enableConcurrent = true
}
}
}入口代码块 400行
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")
}
// System property spark.yarn.app.id must be set if user code ran by AM on a YARN cluster
// yarn-standalone is deprecated, but still supported
if ((master == "yarn-cluster" || master == "yarn-standalone") &&
!_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
_conf.setIfMissing("spark.driver.host", Utils.localHostName())
_conf.setIfMissing("spark.driver.port", "0")
_conf.set("spark.executor.id", SparkContext.DRIVER_IDENTIFIER)
_jars = _conf.getOption("spark.jars").map(_.split(",")).map(_.filter(_.size != 0)).toSeq.flatten
_files = _conf.getOption("spark.files").map(_.split(",")).map(_.filter(_.size != 0))
.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
}
}
_conf.set("spark.externalBlockStore.folderName", externalBlockStoreFolderName)
if (master == "yarn-client") System.setProperty("SPARK_YARN_MODE", "true")
// "_jobProgressListener" should be set up before creating SparkEnv because when creating
// "SparkEnv", some messages will be posted to "listenerBus" and we should not miss them.
_jobProgressListener = new JobProgressListener(_conf)
listenerBus.addListener(jobProgressListener)
// 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)
}
_metadataCleaner = new MetadataCleaner(MetadataCleanerType.SPARK_CONTEXT, this.cleanup, _conf)
_statusTracker = new SparkStatusTracker(this)
_progressBar =
if (_conf.getBoolean("spark.ui.showConsoleProgress", true) && !log.isInfoEnabled) {
Some(new ConsoleProgressBar(this))
} else {
None
}
_ui =
if (conf.getBoolean("spark.ui.enabled", true)) {
Some(SparkUI.createLiveUI(this, _conf, listenerBus, _jobProgressListener,
_env.securityManager, appName, startTime = 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)
_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)
_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.
metricsSystem.start()
// Attach the driver metrics servlet handler to the web ui after the metrics system is started.
metricsSystem.getServletHandlers.foreach(handler => ui.foreach(_.attachHandler(handler)))
_eventLogger =
if (isEventLogEnabled) {
val logger =
new EventLoggingListener(_applicationId, _applicationAttemptId, _eventLogDir.get,
_conf, _hadoopConfiguration)
logger.start()
listenerBus.addListener(logger)
Some(logger)
} else {
None
}
// Optionally scale number of executors dynamically based on workload. Exposed for testing.
val dynamicAllocationEnabled = Utils.isDynamicAllocationEnabled(_conf)
if (!dynamicAllocationEnabled && _conf.getBoolean("spark.dynamicAllocation.enabled", false)) {
logWarning("Dynamic Allocation and num executors both set, thus dynamic allocation disabled.")
}
_executorAllocationManager =
if (dynamicAllocationEnabled) {
Some(new ExecutorAllocationManager(this, listenerBus, _conf))
} 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.
_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
}
}Last updated
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