Spark修炼之道（高级篇）——Spark源码阅读：第二节 SparkContext的创建

博文推荐：http://blog.csdn.net/anzhsoft/article/details/39268963，由大神张安站写的Spark架构原理，使用Spark版本为1.2，本文以Spark 1.5.0为蓝本，介绍Spark应用程序的执行流程。 本文及后面的源码分析都以下列代码为样板

import org.apache.spark.{SparkConf, SparkContext} object SparkWordCount{ def main(args: Array[String]) { if (args.length == 0) { System.err.println("Usage: SparkWordCount <inputfile> <outputfile>") System.exit(1) } val conf = new SparkConf().setAppName("SparkWordCount") val sc = new SparkContext(conf) val file=sc.textFile("file:///hadoopLearning/spark-1.5.1-bin-hadoop2.4/README.md") val counts=file.flatMap(line=>line.split(" ")) .map(word=>(word,1)) .reduceByKey(_+_) counts.saveAsTextFile("file:///hadoopLearning/spark-1.5.1-bin-hadoop2.4/countReslut.txt") } }

代码中的SparkContext在Spark应用程序的执行过程中起着主导作用，它负责与程序个Spark集群进行交互，包括申请集群资源、创建RDD、accumulators 及广播变量等。SparkContext与集群资源管理器、Worker结节点交互图如下图所示。

官网对图下面几点说明： （1）不同的Spark应用程序对应该不同的Executor，这些Executor在整个应用程序执行期间都存在并且Executor中可以采用多线程的方式执行Task。这样做的好处是，各个Spark应用程序的执行是相互隔离的。除Spark应用程序向外部存储系统写数据进行数据交互这种方式外，各Spark应用程序间无法进行数据共享。 （2）Spark对于其使用的集群资源管理器没有感知能力，只要它能对Executor进行申请并通信即可。这意味着不管使用哪种资源管理器，其执行流程都是不变的。这样Spark可以不同的资源管理器进行交互。 （3）Spark应用程序在整个执行过程中要与Executors进行来回通信。 （4）Driver端负责Spark应用程序任务的调度，因此最好Driver应该靠近Worker节点。

Spark目前支持的集群管理器包括：

Standalone Apache Mesos Hadoop YARN 在提交Spark应用程序时，Spark支持下列几种Master URL

有了前面的知识铺垫后，现在我们来说明一下Spark的创建过程，SparkContext创建部分核心源码如下：

// 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 //根据master及SparkContext对象创建TaskScheduler，返回SchedulerBackend及TaskScheduler val (sched, ts) = SparkContext.createTaskScheduler(this, master) _schedulerBackend = sched _taskScheduler = ts //根据SparkContext对象创建DAGScheduler _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) _env.blockManager.initialize(_applicationId)

跳到createTaskScheduler方法，可以看到如下源码：

/** * Create a task scheduler based on a given master URL. * Return a 2-tuple of the scheduler backend and the task scheduler. */ private def createTaskScheduler( sc: SparkContext, master: String): (SchedulerBackend, TaskScheduler) = { // 正则表达式，用于匹配local[N] 和 local[*] val LOCAL_N_REGEX = """local\[([0-9]+|\*)\]""".r // 正则表达式，用于匹配local[N, maxRetries], maxRetries表示失败后的最大重复次数 val LOCAL_N_FAILURES_REGEX = """local\[([0-9]+|\*)\s*,\s*([0-9]+)\]""".r //正则表达式，用于匹配local-cluster[N, cores, memory]，它是一种伪分布式模式 val LOCAL_CLUSTER_REGEX = """local-cluster\[\s*([0-9]+)\s*,\s*([0-9]+)\s*,\s*([0-9]+)\s*]""".r // 正则表达式用于匹配 Spark Standalone集群运行模式 val SPARK_REGEX = """spark://(.*)""".r // 正则表达式用于匹配 Mesos集群资源管理器运行模式匹配 mesos:// 或 zk:// url val MESOS_REGEX = """(mesos|zk)://.*""".r // 正则表达式和于匹配Spark in MapReduce v1，用于兼容老版本的Hadoop集群 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 { //本地单线程运行 case "local" => //TaskShceduler采用TaskSchedulerImpl //资源调度采用LocalBackend val scheduler = new TaskSchedulerImpl(sc, MAX_LOCAL_TASK_FAILURES, isLocal = true) val backend = new LocalBackend(sc.getConf, scheduler, 1) scheduler.initialize(backend) (backend, scheduler) //匹配本地多线程运行模式,匹配local[N]和Local[*] case LOCAL_N_REGEX(threads) => def localCpuCount: Int = 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 if (threadCount <= 0) { throw new SparkException(s"Asked to run locally with $threadCount threads") } //TaskShceduler采用TaskSchedulerImpl //资源调度采用LocalBackend val scheduler = new TaskSchedulerImpl(sc, MAX_LOCAL_TASK_FAILURES, isLocal = true) val backend = new LocalBackend(sc.getConf, scheduler, threadCount) scheduler.initialize(backend) (backend, scheduler) //匹配local[*, M]和local[N, M] case LOCAL_N_FAILURES_REGEX(threads, maxFailures) => def localCpuCount: Int = 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 //TaskShceduler采用TaskSchedulerImpl //资源调度采用LocalBackend val scheduler = new TaskSchedulerImpl(sc, maxFailures.toInt, isLocal = true) val backend = new LocalBackend(sc.getConf, scheduler, threadCount) scheduler.initialize(backend) (backend, scheduler) //匹配Spark Standalone运行模式 case SPARK_REGEX(sparkUrl) => //TaskShceduler采用TaskSchedulerImpl val scheduler = new TaskSchedulerImpl(sc) val masterUrls = sparkUrl.split(",").map("spark://" + _) //资源调度采用SparkDeploySchedulerBackend val backend = new SparkDeploySchedulerBackend(scheduler, sc, masterUrls) scheduler.initialize(backend) (backend, scheduler) //匹配local-cluster运行模式 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)) } //TaskShceduler采用TaskSchedulerImpl val scheduler = new TaskSchedulerImpl(sc) val localCluster = new LocalSparkCluster( numSlaves.toInt, coresPerSlave.toInt, memoryPerSlaveInt, sc.conf) val masterUrls = localCluster.start() //资源调度采用SparkDeploySchedulerBackend val backend = new SparkDeploySchedulerBackend(scheduler, sc, masterUrls) scheduler.initialize(backend) backend.shutdownCallback = (backend: SparkDeploySchedulerBackend) => { localCluster.stop() } (backend, scheduler) //"yarn-standalone"或"yarn-cluster"运行模式 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 { //TaskShceduler采用YarnClusterScheduler val clazz = Utils.classForName("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 { //资源调度采用YarnClusterSchedulerBackend val clazz = Utils.classForName("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) (backend, scheduler) //yarn-client运行模式 case "yarn-client" => //TaskShceduler采用YarnScheduler，YarnScheduler为TaskSchedulerImpl的子类 org.apache.spark.scheduler.cluster.YarnScheduler val scheduler = try { val clazz = Utils.classForName("org.apache.spark.scheduler.cluster.YarnScheduler") val cons = clazz.getConstructor(classOf[SparkContext]) cons.newInstance(sc).asInstanceOf[TaskSchedulerImpl] } catch { case e: Exception => { throw new SparkException("YARN mode not available ?", e) } } //资源采用org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend val backend = try { val clazz = Utils.classForName("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) (backend, scheduler) //Mesos运行模式 case mesosUrl @ MESOS_REGEX(_) => MesosNativeLibrary.load() //TaskScheduler采用TaskSchedulerImpl 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 //根据coarseGrained选择粗粒度还是细粒度 val backend = if (coarseGrained) { //精粒度资源调度CoarseMesosSchedulerBackend new CoarseMesosSchedulerBackend(scheduler, sc, url, sc.env.securityManager) } else { //细粒度资源调度MesosSchedulerBackend new MesosSchedulerBackend(scheduler, sc, url) } scheduler.initialize(backend) (backend, scheduler) //Spark IN MapReduce V1运行模式 case SIMR_REGEX(simrUrl) => //TaskScheduler采用TaskSchedulerImpl val scheduler = new TaskSchedulerImpl(sc) //资源调度采用SimrSchedulerBackend val backend = new SimrSchedulerBackend(scheduler, sc, simrUrl) scheduler.initialize(backend) (backend, scheduler) case _ => throw new SparkException("Could not parse Master URL: ‘" + master + "‘") } } }

资源调度SchedulerBackend类及相关子类如下图

任务调度器，TaskScheduler类及其子数如下图：

在后续章节中，我们将对具体的内容进行进一步的分析