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Spark中Client源码分析（一）http://www.jianshu.com/p/339fde3aff5d

DriverClient中的代码比较简单，它只有一个main函数，同时，和AppClient一样，它也有一个ClientEndpoint，只是两者的用途不一样。

1.Client

Client中唯一的main方法如下：
<code>
def main(args: Array[String]) {
if (!sys.props.contains("SPARK_SUBMIT")) {
println("WARNING: This client is deprecated and will be removed in a future version of Spark")
println("Use ./bin/spark-submit with "--master spark://host:port"")
}
val conf = new SparkConf()
val driverArgs = new ClientArguments(args)
if (!driverArgs.logLevel.isGreaterOrEqual(Level.WARN)) {
conf.set("spark.akka.logLifecycleEvents", "true")
}
conf.set("spark.rpc.askTimeout", "10")
conf.set("akka.loglevel", driverArgs.logLevel.toString.replace("WARN", "WARNING"))
Logger.getRootLogger.setLevel(driverArgs.logLevel)
//创建一个driverClient的Rpc环境，并将得到Master和client的远程引用
val rpcEnv =
RpcEnv.create("driverClient", Utils.localHostName(), 0, conf, new SecurityManager(conf))
val masterEndpoints = driverArgs.masters.map(RpcAddress.fromSparkURL).
map(rpcEnv.setupEndpointRef(Master.SYSTEM_NAME, _, Master.ENDPOINT_NAME))
//clientpoint
rpcEnv.setupEndpoint("client", new ClientEndpoint(rpcEnv, driverArgs, masterEndpoints, conf))
//启动rpc环境
rpcEnv.awaitTermination()
}
</code>

2.ClientEndpoint

ClientEndPoint可以看作给Driver传递消息的代理
属性简单，直接略过。
（1）构造函数为ClientEndPoint主构造函数
（2）onstart方法如下，
<code>
override def onStart(): Unit = {
driverArgs.cmd match {
case "launch" =>
//driver包装类，使得Worker和Driver的Rpc环境一样，做到共进退
val mainClass = "org.apache.spark.deploy.worker.DriverWrapper"
//driver类路径
val classPathConf = "spark.driver.extraClassPath"
val classPathEntries = sys.props.get(classPathConf).toSeq.flatMap { cp =>
cp.split(java.io.File.pathSeparator)
}
//driver库路径
val libraryPathConf = "spark.driver.extraLibraryPath"
val libraryPathEntries = sys.props.get(libraryPathConf).toSeq.flatMap { cp =>
cp.split(java.io.File.pathSeparator)
}
//driver Jvm参数
val extraJavaOptsConf = "spark.driver.extraJavaOptions"
val extraJavaOpts = sys.props.get(extraJavaOptsConf)
.map(Utils.splitCommandString).getOrElse(Seq.empty)
//将所有的在SparkConf中设置的属性赋值给java options的序列
val sparkJavaOpts = Utils.sparkJavaOpts(conf)
//所有的javaOpts
val javaOpts = sparkJavaOpts ++ extraJavaOpts
val command = new Command(mainClass,
Seq("{{WORKER_URL}}", "{{USER_JAR}}", driverArgs.mainClass) ++ driverArgs.driverOptions,
sys.env, classPathEntries, libraryPathEntries, javaOpts)
//将以上所有的信息封装在DriverDescription中
val driverDescription = new DriverDescription(
driverArgs.jarUrl,
driverArgs.memory,
driverArgs.cores,
driverArgs.supervise,
command)
//异步请求给master发送Driver的信息
ayncSendToMasterAndForwardReplySubmitDriverResponse
case "kill" =>
val driverId = driverArgs.driverId
ayncSendToMasterAndForwardReplyKillDriverResponse
}
}
</code>
（3）onstop方法简单，略过。
（4）receive方法如下，
<code>
override def receive: PartialFunction[Any, Unit] = {
//收到master的响应回来的Driver信息，因为master是管家，Client是老板
case SubmitDriverResponse(master, success, driverId, message) =>
logInfo(message)
if (success) {
//将当前的activeMasterEndpoint设置为响应消息的master
activeMasterEndpoint = master
//找到driver的信息然后退出JVM
pollAndReportStatus(driverId.get)
} else if (!Utils.responseFromBackup(message)) {
System.exit(-1)
}
case KillDriverResponse(master, driverId, success, message) =>
logInfo(message)
if (success) {
activeMasterEndpoint = master
pollAndReportStatus(driverId),详见下①
} else if (!Utils.responseFromBackup(message)) {
System.exit(-1)
}
}
</code>
①pollAndReportStatus方法如下，用于找到driver的信息然后退出JVM
<code>
def pollAndReportStatus(driverId: String) {
logInfo("... waiting before polling master for driver state")
Thread.sleep(5000)
logInfo("... polling master for driver state")
//master请求得到Driver的信息
val statusResponse =
activeMasterEndpoint.askWithRetryDriverStatusResponse
statusResponse.found match {
case false =>
logError(s"ERROR: Cluster master did not recognize $driverId")
System.exit(-1)
case true =>
logInfo(s"State of $driverId is ${statusResponse.state.get}")
//返回的其实是worker的信息
(statusResponse.workerId, statusResponse.workerHostPort, statusResponse.state) match {
case (Some(id), Some(hostPort), Some(DriverState.RUNNING)) =>
logInfo(s"Driver running on $hostPort ($id)")
case _ =>
}
statusResponse.exception.map { e =>
logError(s"Exception from cluster was: $e")
e.printStackTrace()
System.exit(-1)
}
System.exit(0)
}
}
</code>