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今天就跟大家聊聊有关如何进行CoarseGrainedSchedulerBackend和CoarseGrainedExecutorBackend的分析，可能很多人都不太了解，为了让大家更加了解，小编给大家总结了以下内容，希望大家根据这篇文章可以有所收获。
CoarseGrainedSchedulerBackend是Driver端用到的，CoarseGrainedExecutorBackend是Executor端用到的。他们都是Backend，什么是Backend？Backend其实就是负责端到端通信的，这两个CoarseGrained的Backend是负责Driver和Executor之间的通信的。
什么是Driver呢？
Driver就是我们编写的spark代码，里面的main函数就是Driver跑的代码。
什么是Executor呢？
Executor就是执行spark的Task任务的地方，Backend接收到Driver的LaunchTask消息后，调用Executor类的launchTask方法来执行任务。
Driver会启动CoarseGrainedSchedulerBackend，通过CoarseGrainedSchedulerBackend来向集群申请机器以便启动Executor，会找到一台机器，发送命令让机器启动一个ExecutorRunner，ExecutorRunner里启动CoarseGrainedExecutorBackend向Driver注册，并创建Executor来处理CoarseGrainedExecutorBackend接收到的请求。刚刚说的是Standalone部署下的流程，Yarn下大部分类似，只有向集群申请机器来启动Executor这一步不太一样，这个简单说一下吧。
Yarn环境下，是通过spark-yarn工程里的几个类一级yarn本身的功能来一起完成机器的部署和分区任务的分发。
spark-yarn包含两个文件：client.java和ApplicationMaster.java。
client.java功能是向yarn申请资源来执行ApplicationMaster.java的代码，所以这里主要看下ApplicationMaster.java的代码功能是什么。
ApplicationMaster首先干两件事，启动一个"/bin/mesos-master"和多个"/bin/mesos-slave"，这都是向yarn申请资源然后部署上去执行的，都是yarn的功能部分，"/bin/mesos-master"和"/bin/mesos-slave"是yarn环境里自带的两个bin程序，可以看成是类似Standalone环境下的Master和Worker。
launchContainer方法是启动yarn的container，也就是前面说的在container上启动“/bin/mesos-slave"，mesos-slave会向mesos-master注册的。等需要的slave节点资源全部申请启动完成后，调用startApplication()方法开始执行Driver。
startApplication()方法：
// Start the user's application
private void startApplication() throws IOException {
try {
String sparkClasspath = getSparkClasspath();
String jobJar = new File("job.jar").getAbsolutePath();
String javaArgs = "-Xms" + (masterMem - 128) + "m -Xmx" + (masterMem - 128) + "m";
javaArgs += " -Djava.library.path=" + mesosHome + "/lib/java";
String substitutedArgs = programArgs.replaceAll("\\[MASTER\\]", masterUrl);
if (mainClass.equals("")) {
javaArgs += " -cp " + sparkClasspath + " -jar " + jobJar + " " + substitutedArgs;
} else {
javaArgs += " -cp " + sparkClasspath + ":" + jobJar + " " + mainClass + " " + substitutedArgs;
}
String java = "java";
if (System.getenv("JAVA_HOME") != null) {
java = System.getenv("JAVA_HOME") + "/bin/java";
}
String bashCommand = java + " " + javaArgs +
" 1>" + logDirectory + "/application.stdout" +
" 2>" + logDirectory + "/application.stderr";
LOG.info("Command: " + bashCommand);
String[] command = new String[] {"bash", "-c", bashCommand};
String[] env = new String[] {"SPARK_HOME=" + sparkHome, "MASTER=" + masterUrl,
"SPARK_MEM=" + (slaveMem - 128) + "m"};
application = Runtime.getRuntime().exec(command, env);
new Thread("wait for user application") {
public void run() {
try {
appExitCode = application.waitFor();
appExited = true;
LOG.info("User application exited with code " + appExitCode);
} catch (InterruptedException e) {
e.printStackTrace();
}
}
}.start();
} catch (SparkClasspathException e) {
unregister(false);
System.exit(1);
return;
}
}这就是启动Driver了，masterUrl就是”bin/mesos-master“的地址，设置成了环境变量”MASTER“来用了，yarn下的master的地址格式是”mesos://host:port“，Standalone下是”spark://host:port“。
在SparkContext下会根据master地址格式，做不同的处理，这段代码是这样：
master match {
case "local" =>
checkResourcesPerTask(clusterMode = false, Some(1))
val scheduler = new TaskSchedulerImpl(sc, MAX_LOCAL_TASK_FAILURES, isLocal = true)
val backend = new LocalSchedulerBackend(sc.getConf, scheduler, 1)
scheduler.initialize(backend)
(backend, scheduler)
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")
}
checkResourcesPerTask(clusterMode = false, Some(threadCount))
val scheduler = new TaskSchedulerImpl(sc, MAX_LOCAL_TASK_FAILURES, isLocal = true)
val backend = new LocalSchedulerBackend(sc.getConf, scheduler, threadCount)
scheduler.initialize(backend)
(backend, scheduler)
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
checkResourcesPerTask(clusterMode = false, Some(threadCount))
val scheduler = new TaskSchedulerImpl(sc, maxFailures.toInt, isLocal = true)
val backend = new LocalSchedulerBackend(sc.getConf, scheduler, threadCount)
scheduler.initialize(backend)
(backend, scheduler)
case SPARK_REGEX(sparkUrl) =>
checkResourcesPerTask(clusterMode = true, None)
val scheduler = new TaskSchedulerImpl(sc)
val masterUrls = sparkUrl.split(",").map("spark://" + _)
val backend = new StandaloneSchedulerBackend(scheduler, sc, masterUrls)
scheduler.initialize(backend)
(backend, scheduler)
case LOCAL_CLUSTER_REGEX(numSlaves, coresPerSlave, memoryPerSlave) =>
checkResourcesPerTask(clusterMode = true, Some(coresPerSlave.toInt))
// 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 MiB RAM / worker but requested %d MiB/worker".format(
memoryPerSlaveInt, sc.executorMemory))
}
val scheduler = new TaskSchedulerImpl(sc)
val localCluster = new LocalSparkCluster(
numSlaves.toInt, coresPerSlave.toInt, memoryPerSlaveInt, sc.conf)
val masterUrls = localCluster.start()
val backend = new StandaloneSchedulerBackend(scheduler, sc, masterUrls)
scheduler.initialize(backend)
backend.shutdownCallback = (backend: StandaloneSchedulerBackend) => {
localCluster.stop()
}
(backend, scheduler)
case masterUrl =>
checkResourcesPerTask(clusterMode = true, None)
val cm = getClusterManager(masterUrl) match {
case Some(clusterMgr) => clusterMgr
case None => throw new SparkException("Could not parse Master URL: '" + master + "'")
}
try {
val scheduler = cm.createTaskScheduler(sc, masterUrl)
val backend = cm.createSchedulerBackend(sc, masterUrl, scheduler)
cm.initialize(scheduler, backend)
(backend, scheduler)
} catch {
case se: SparkException => throw se
case NonFatal(e) =>
throw new SparkException("External scheduler cannot be instantiated", e)
}
}
}如果是yarn，会落到最后一个case语句：
case masterUrl =>
checkResourcesPerTask(clusterMode = true, None)
val cm = getClusterManager(masterUrl) match {
case Some(clusterMgr) => clusterMgr
case None => throw new SparkException("Could not parse Master URL: '" + master + "'")
}
try {
val scheduler = cm.createTaskScheduler(sc, masterUrl)
val backend = cm.createSchedulerBackend(sc, masterUrl, scheduler)
cm.initialize(scheduler, backend)
(backend, scheduler)
} catch {
case se: SparkException => throw se
case NonFatal(e) =>
throw new SparkException("External scheduler cannot be instantiated", e)
}这里会用到ClusterManager的类，这又是什么东东呢？spark难就难在这，涉及的概念太多。
private def getClusterManager(url: String): Option[ExternalClusterManager] = {
val loader = Utils.getContextOrSparkClassLoader
val serviceLoaders =
ServiceLoader.load(classOf[ExternalClusterManager], loader).asScala.filter(_.canCreate(url))
if (serviceLoaders.size > 1) {
throw new SparkException(
s"Multiple external cluster managers registered for the url $url: $serviceLoaders")
}
serviceLoaders.headOption
}找到所有的ExternalClusterManager类及子类，看哪个类的canCreate方法对url返回true，我们这里就是找满足"mesos://host:port"的类。
看完上述内容，你们对如何进行CoarseGrainedSchedulerBackend和CoarseGrainedExecutorBackend的分析有进一步的了解吗？如果还想了解更多知识或者相关内容，请关注亿速云行业资讯频道，感谢大家的支持。
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