SparkContext这是发达国家Spark入学申请，它负责的相互作用和整个集群，它涉及到创建RDD。accumulators and broadcast variables。理解力Spark架构，我们需要从入口开始。下图是图的官方网站。
DriverProgram就是用户提交的程序，这里边定义了SparkContext的实例。
SparkContext定义在core/src/main/scala/org/apache/spark/SparkContext.scala。
Spark默认的构造函数接受org.apache.spark.SparkConf， 通过这个參数我们能够自己定义本次提交的參数，这个參数会覆盖系统的默认配置。
先上一张与SparkContext相关的类图：
以下是SparkContext很重要的数据成员的定义：
// Create and start the scheduler
private[spark] var taskScheduler = SparkContext.createTaskScheduler(this, master)
private val heartbeatReceiver = env.actorSystem.actorOf(
Props(new HeartbeatReceiver(taskScheduler)), "HeartbeatReceiver")
@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()通过createTaskScheduler，我们能够获得不同资源管理类型或者部署类型的调度器。
看一下如今支持的部署方法：
/** Creates a task scheduler based on a given master URL. Extracted for testing. */
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) =>
def localCpuCount = Runtime.getRuntime.availableProcessors()
// local[*, M] means the number of cores on the computer with M failures
// local[N, M] means exactly N threads with M failures
val threadCount = if (threads == "*") localCpuCount else threads.toInt
val scheduler = new TaskSchedulerImpl(sc, maxFailures.toInt, isLocal = true)
val backend = new LocalBackend(scheduler, threadCount)
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 = try {
val clazz =
Class.forName("org.apache.spark.scheduler.cluster.YarnClusterSchedulerBackend")
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 "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 + "'")
}
}
}
基本的逻辑从line 20開始。主要通过传入的Master URL来生成Scheduler 和 Scheduler backend。对于常见的Standalone的部署方式，我们看一下是生成的Scheduler 和 Scheduler backend：
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)
schedulerorg.apache.spark.scheduler.TaskSchedulerImpl通过一个SchedulerBackend管理了全部的cluster的调度；它主要实现了通用的逻辑。对于系统刚启动时，须要理解两个接口，一个是initialize，一个是start。
这个也是在SparkContext初始化时调用的：
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()
}由此可见，初始化主要是SchedulerBackend的初始化。它主要时通过集群的配置来获得调度模式，如今支持的调度模式是FIFO和公平调度，默认的是FIFO。
// default scheduler is FIFO
private val schedulingModeConf = conf.get("spark.scheduler.mode", "FIFO")
val schedulingMode: SchedulingMode = try {
SchedulingMode.withName(schedulingModeConf.toUpperCase)
} catch {
case e: java.util.NoSuchElementException =>
throw new SparkException(s"Unrecognized spark.scheduler.mode: $schedulingModeConf")
}start的实现例如以下：
override def start() {
backend.start()
if (!isLocal && conf.getBoolean("spark.speculation", false)) {
logInfo("Starting speculative execution thread")
import sc.env.actorSystem.dispatcher
sc.env.actorSystem.scheduler.schedule(SPECULATION_INTERVAL milliseconds,
SPECULATION_INTERVAL milliseconds) {
Utils.tryOrExit { checkSpeculatableTasks() }
}
}
}主要是backend的启动。对于非本地模式。而且设置了spark.speculation为true，那么对于指定时间未返回的task将会启动另外的task来运行。事实上对于一般的应用，这个的确可能会降低任务的运行时间，可是也浪费了集群的计算资源。
因此对于离线应用来说，这个设置是不推荐的。
org.apache.spark.scheduler.cluster.SparkDeploySchedulerBackend是Standalone模式的SchedulerBackend。它的定义例如以下：
private[spark] class SparkDeploySchedulerBackend(
scheduler: TaskSchedulerImpl,
sc: SparkContext,
masters: Array[String])
extends CoarseGrainedSchedulerBackend(scheduler, sc.env.actorSystem)
with AppClientListener
with Logging {看一下它的start：
override def start() {
super.start()
// The endpoint for executors to talk to us
val driverUrl = "akka.tcp://%s@%s:%s/user/%s".format(
SparkEnv.driverActorSystemName,
conf.get("spark.driver.host"),
conf.get("spark.driver.port"),
CoarseGrainedSchedulerBackend.ACTOR_NAME)
val args = Seq(driverUrl, "{{EXECUTOR_ID}}", "{{HOSTNAME}}", "{{CORES}}", "{{WORKER_URL}}")
val extraJavaOpts = sc.conf.getOption("spark.executor.extraJavaOptions")
.map(Utils.splitCommandString).getOrElse(Seq.empty)
val classPathEntries = sc.conf.getOption("spark.executor.extraClassPath").toSeq.flatMap { cp =>
cp.split(java.io.File.pathSeparator)
}
val libraryPathEntries =
sc.conf.getOption("spark.executor.extraLibraryPath").toSeq.flatMap { cp =>
cp.split(java.io.File.pathSeparator)
}
// 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, libraryPathEntries, javaOpts)
val appDesc = new ApplicationDescription(sc.appName, maxCores, sc.executorMemory, command,
sc.ui.appUIAddress, sc.eventLogger.map(_.logDir))
client = new AppClient(sc.env.actorSystem, masters, appDesc, this, conf)
client.start()
waitForRegistration()
}接下来，我们将对TaskScheduler。SchedulerBackend和DAG Scheduler进行具体解释。来逐步揭开他们的神奇面纱。
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