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大数据：Spark Standalone 集群调度（一）从远程调试开始说application创建
远程debug，特别是在集群方式时候，会很方便了解代码的运行方式，这也是码农比较喜欢的方式
虽然scala的语法和java不一样，但是scala是运行在JVM虚拟机上的，也就是scala最后编译成字节码运行在JVM上，那么远程调试方式就是JVM调试方式
在服务器端：
-Xdebug -Xrunjdwp:server=y,transport=dt_socket,address=7001,suspend=y
客户端通过socket就能远程调试代码
1. 调试submit, master, worker代码
1.1 Submit 调试
客户端client 运行Submit，这里就不描述，通常spark的用例都是用spark-submit
提交一个spark任务
其本质就是类似下面命令
/usr/java/jdk1.8.0_111/bin/java -cp /work/spark-2.1.0-bin-hadoop2.7/conf/:/work/spark-2.1.0-bin-hadoop2.7/jars/* -Xdebug -Xrunjdwp:server=y,transport=dt_socket,address=7000,suspend=y -Xmx1g org.apache.spark.deploy.SparkSubmit --master spark://raintungmaster:7077 --class rfcexample --jars /work/spark-2.1.0-bin-hadoop2.7/examples/jars/scopt_2.11-3.3.0.jar,/work/spark-2.1.0-bin-hadoop2.7/examples/jars/spark-examples_2.11-2.1.0.jar /tmp/machinelearning.jar
调用SparkSubmit的类去提交任务，debug的参数直接往上加就是了
1.2 master, worker 的设置调试
export SPARK_WORKER_OPTS="-Xdebug -Xrunjdwp:server=y,transport=dt_socket,address=8000,suspend=n" export SPARK_MASTER_OPTS="-Xdebug -Xrunjdwp:server=y,transport=dt_socket,address=8001,suspend=n"在启动的时候设置环境变量就可以了
2. 调试executor 代码
发现设置woker环境参数，但确一直都无法调试在spark executor 运行的代码，既然executor是在worker上运行的，当然是可以远程debug，但为啥executor不能调试呢？
3. Spark standalone 的集群调度
既然executor不能调试，我们需要把submit, master, worker的调度关系搞清楚
3.1 Submit 提交任务
刚才已经描述过submit实际上初始化了SparkSubmit的类，在SparkSubmit的main方法中调用了runMain方法
try {
mainMethod.invoke(null, childArgs.toArray)
} catch {
case t: Throwable =>
findCause(t) match {
case SparkUserAppException(exitCode) =>
System.exit(exitCode)
case t: Throwable =>
throw t
}
}而核心就是调用了在我们提交的类的main方法，在上面的例子里就是参数
--class rfcexample
调用了rfcexample的main方法
通常我们在写spark的运行的类的方法，会初始化spark的上下文
val sc = new SparkContext(conf)
SparkContext初始化的时候会启动一个task的任务
// 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()在standalone 的模式下最后会调用StandaloneSchedulerBackend.scala 的start方法
val appDesc = new ApplicationDescription(sc.appName, maxCores, sc.executorMemory, command,
appUIAddress, sc.eventLogDir, sc.eventLogCodec, coresPerExecutor, initialExecutorLimit)
client = new StandaloneAppClient(sc.env.rpcEnv, masters, appDesc, this, conf)
client.start()
launcherBackend.setState(SparkAppHandle.State.SUBMITTED)
waitForRegistration()
launcherBackend.setState(SparkAppHandle.State.RUNNING)构建Application的描述符号，启动一个StandaloneAppClient 去connect 的master
3.2 Master 分配任务
Submit 里创建了一个客户端构建了一个application的描述，注册application 到master中，在master的dispatcher分发消息会收到registerapplication的消息
case RegisterApplication(description, driver) =>
// TODO Prevent repeated registrations from some driver
if (state == RecoveryState.STANDBY) {
// ignore, don‘t send response
} else {
logInfo("Registering app " + description.name)
val app = createApplication(description, driver)
registerApplication(app)
logInfo("Registered app " + description.name + " with ID " + app.id)
persistenceEngine.addApplication(app)
driver.send(RegisteredApplication(app.id, self))
schedule()
}创建一个新的application id, 注册这个application，一个application只能绑定一个客户端端口，同一个客户端的ip:port只能注册一个application，在schedule里通过计算application的内存，core的要求，进行对有效的worker分配executor
private def launchExecutor(worker: WorkerInfo, exec: ExecutorDesc): Unit = {
logInfo("Launching executor " + exec.fullId + " on worker " + worker.id)
worker.addExecutor(exec)
worker.endpoint.send(LaunchExecutor(masterUrl,
exec.application.id, exec.id, exec.application.desc, exec.cores, exec.memory))
exec.application.driver.send(
ExecutorAdded(exec.id, worker.id, worker.hostPort, exec.cores, exec.memory))
}在worker的endpoint发送了LaunchExecutor的序列化消息
3.3 Worker 分配任务
在worker.scala中dispatcher收到了LaunchExecutor 消息
case LaunchExecutor(masterUrl, appId, execId, appDesc, cores_, memory_) =>
if (masterUrl != activeMasterUrl) {
logWarning("Invalid Master (" + masterUrl + ") attempted to launch executor.")
} else {
try {
logInfo("Asked to launch executor %s/%d for %s".format(appId, execId, appDesc.name))
// Create the executor‘s working directory
val executorDir = new File(workDir, appId + "/" + execId)
if (!executorDir.mkdirs()) {
throw new IOException("Failed to create directory " + executorDir)
}
// Create local dirs for the executor. These are passed to the executor via the
// SPARK_EXECUTOR_DIRS environment variable, and deleted by the Worker when the
// application finishes.
val appLocalDirs = appDirectories.getOrElse(appId,
Utils.getOrCreateLocalRootDirs(conf).map { dir =>
val appDir = Utils.createDirectory(dir, namePrefix = "executor")
Utils.chmod700(appDir)
appDir.getAbsolutePath()
}.toSeq)
appDirectories(appId) = appLocalDirs
val manager = new ExecutorRunner(
appId,
execId,
appDesc.copy(command = Worker.maybeUpdateSSLSettings(appDesc.command, conf)),
cores_,
memory_,
self,
workerId,
host,
webUi.boundPort,
publicAddress,
sparkHome,
executorDir,
workerUri,
conf,
appLocalDirs, ExecutorState.RUNNING)
executors(appId + "/" + execId) = manager
manager.start()
coresUsed += cores_
memoryUsed += memory_
sendToMaster(ExecutorStateChanged(appId, execId, manager.state, None, None))
} catch {
case e: Exception =>
logError(s"Failed to launch executor $appId/$execId for ${appDesc.name}.", e)
if (executors.contains(appId + "/" + execId)) {
executors(appId + "/" + execId).kill()
executors -= appId + "/" + execId
}
sendToMaster(ExecutorStateChanged(appId, execId, ExecutorState.FAILED,
Some(e.toString), None))
}
}创建了一个工作目录，启动了ExecutorRunner
private[worker] def start() {
workerThread = new Thread("ExecutorRunner for " + fullId) {
override def run() { fetchAndRunExecutor() }
}
workerThread.start()
// Shutdown hook that kills actors on shutdown.
shutdownHook = ShutdownHookManager.addShutdownHook { () =>
// It‘s possible that we arrive here before calling `fetchAndRunExecutor`, then `state` will
// be `ExecutorState.RUNNING`. In this case, we should set `state` to `FAILED`.
if (state == ExecutorState.RUNNING) {
state = ExecutorState.FAILED
}
killProcess(Some("Worker shutting down")) }
}在ExecutorRunner.scala的start的方法里，启动了线程ExecutorRunner for xxx, 运行executor，难道application里的方法就是在这个线程里运行的？
private def fetchAndRunExecutor() {
try {
// Launch the process
val builder = CommandUtils.buildProcessBuilder(appDesc.command, new SecurityManager(conf),
memory, sparkHome.getAbsolutePath, substituteVariables)
val command = builder.command()
val formattedCommand = command.asScala.mkString("\"", "\" \"", "\"")
.....
process = builder.start()
......
val exitCode = process.waitFor()
state = ExecutorState.EXITED
val message = "Command exited with code " + exitCode
worker.send(ExecutorStateChanged(appId, execId, state, Some(message), Some(exitCode)))
} catch {
......
}
}在看fetchAndRunExecutor的方法里，我们看到了builder.start，这是一个ProcessBuilder，也就是当前线程启动了一个子进程运行命令
这就是为什么我们无法通过debug worker的方式去debug executor, 因为这是另一个进程
4. 调试executor进程
我们刚才跟了代码一路，发现在master接受到RegisterApplication消息到发送调度worker的LaunchExecutor消息，并没有对消息进行处理，最后子进程的运行命令是从ApplicationDescription中的command获取到，而我们也知道ApplicationDescription 就是3.1种的Submit创建的，那就回到StandaloneSchedulerBackend.scala 的start方法
val driverUrl = RpcEndpointAddress(
sc.conf.get("spark.driver.host"),
sc.conf.get("spark.driver.port").toInt,
CoarseGrainedSchedulerBackend.ENDPOINT_NAME).toString
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我们看到了executor 的java的参数是在javaOpts里控制的，也就是val extraJavaOpts = sc.conf.getOption("spark.executor.extraJavaOptions")原来是参数spark.executor.extraJavaOptions控制的，反过来去翻spark文档，虽然有点晚| spark.executor.extraJavaOptions (none) | A string of extra JVM options to pass to executors. For instance, GC settings or other logging. Note that it is illegal to set Spark properties or maximum heap size (-Xmx)settings with this option. Spark properties should be set using a SparkConf object or the spark-defaults.conf file used with the spark-submit script. Maximum heap size settings can be set with spark.executor.memory. |
在这个文档里，我们可以通过设置conf 对spark_submit 进行executor 进行JVM参数设置
--conf "spark.executor.extraJavaOptions=-Xdebug -Xrunjdwp:server=y,transport=dt_socket,address=7001,suspend=y"整个运行的submit的进程就是
/usr/java/jdk1.8.0_111/bin/java -cp /work/spark-2.1.0-bin-hadoop2.7/conf/:/work/spark-2.1.0-bin-hadoop2.7/jars/* -Xdebug -Xrunjdwp:server=y,transport=dt_socket,address=7000,suspend=y -Xmx1g org.apache.spark.deploy.SparkSubmit --master spark://raintungmaster:7077 --class rfcexample --jars /work/spark-2.1.0-bin-hadoop2.7/examples/jars/scopt_2.11-3.3.0.jar,/work/spark-2.1.0-bin-hadoop2.7/examples/jars/spark-examples_2.11-2.1.0.jar --conf "spark.executor.extraJavaOptions=-Xdebug -Xrunjdwp:server=y,transport=dt_socket,address=7001,suspend=y" /tmp/machinelearning.jar
如果你的worker 不能起多个executor，毕竟监听端口在一起机器上只能起一个。
大数据：Spark Standalone 集群调度（一）从远程调试开始说application创建
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