本文将解析Spark中Driver服务的开启流程，闲言少叙，直接进入源码。
首先Driver服务的开启是在创建Driver的运行时环境的时候完成的，如下所示：
SparkContext中：
// Create the Spark execution environment (cache, map output tracker, etc)
_env = createSparkEnv(_conf, isLocal, listenerBus)
SparkEnv.set(_env)
可以看到执行的是SparkEnv的createDriverEnv：
private[spark] def createSparkEnv(
conf: SparkConf,
isLocal: Boolean,
listenerBus: LiveListenerBus): SparkEnv = {
// 创建Driver的运行时环境，注意这里的numDriverCores是local模式下用来执行计算的cores的个数，如果不是本地模式的话就是0
SparkEnv.createDriverEnv(conf, isLocal, listenerBus, SparkContext.numDriverCores(master))
}
numDriverCores的计算：
/**
* The number of driver cores to use for execution in local mode, 0 otherwise.
*/
private[spark] def numDriverCores(master: String): Int = {
def convertToInt(threads: String): Int = {
if (threads == "*") Runtime.getRuntime.availableProcessors() else threads.toInt
}
master match {
case "local" => 1
case SparkMasterRegex.LOCAL_N_REGEX(threads) => convertToInt(threads)
case SparkMasterRegex.LOCAL_N_FAILURES_REGEX(threads, _) => convertToInt(threads)
case _ => 0 // driver is not used for execution
}
}
在SparkEnv中创建Driver运行时环境的代码：
/**
* Create a SparkEnv for the driver.
*/
private[spark] def createDriverEnv(
conf: SparkConf,
isLocal: Boolean,
listenerBus: LiveListenerBus,
numCores: Int,
mockOutputCommitCoordinator: Option[OutputCommitCoordinator] = None): SparkEnv = {
assert(conf.contains("spark.driver.host"), "spark.driver.host is not set on the driver!")
assert(conf.contains("spark.driver.port"), "spark.driver.port is not set on the driver!")
val hostname = conf.get("spark.driver.host")
val port = conf.get("spark.driver.port").toInt
create(
conf,
SparkContext.DRIVER_IDENTIFIER, // "driver"
hostname,
port,
isDriver = true,
isLocal = isLocal,
numUsableCores = numCores,
listenerBus = listenerBus,
mockOutputCommitCoordinator = mockOutputCommitCoordinator
)
}
我们在前面的文章中大致的浏览过，现在聚焦Driver服务启动相关的部分：
// 这里我们是Driver，所以actorSystemName是"sparkDriver"
// 注意Spark2.x中已经移除了对Akka的依赖，所以在Spark2.x中这里是driverSystemName和executorSystemName
// Create the ActorSystem for Akka and get the port it binds to.
val actorSystemName = if (isDriver) driverActorSystemName else executorActorSystemName
// 创建Driver的运行时环境，注意这里的clientMode等于false
val rpcEnv = RpcEnv.create(actorSystemName, hostname, port, conf, securityManager,
clientMode = !isDriver)
接下来是RpcEnv的create方法：
def create(
name: String,
host: String,
port: Int,
conf: SparkConf,
securityManager: SecurityManager,
clientMode: Boolean = false): RpcEnv = {
// Using Reflection to create the RpcEnv to avoid to depend on Akka directly
// 封装成RpcEnvConfig，这里的name是"sparkDriver"，host是"driver"，clientMode是"false"
val config = RpcEnvConfig(conf, name, host, port, securityManager, clientMode)
// 这里实际上是通过反射的到的是NettyRpcEnvFactory，所以调用的是NettyRpcEnvFactory的create()方法
getRpcEnvFactory(conf).create(config)
}
底层实现是NettyRpcEnvFactory的create方法：
def create(config: RpcEnvConfig): RpcEnv = {
val sparkConf = config.conf
// Use JavaSerializerInstance in multiple threads is safe. However, if we plan to support
// KryoSerializer in future, we have to use ThreadLocal to store SerializerInstance
val javaSerializerInstance =
new JavaSerializer(sparkConf).newInstance().asInstanceOf[JavaSerializerInstance]
// 实例化了NettyRpcEnv，名字为config.host，即driver
val nettyEnv =
new NettyRpcEnv(sparkConf, javaSerializerInstance, config.host, config.securityManager)
// 传进来的clientMode为false，所以这里的判断为true
if (!config.clientMode) {
// 定义了一个函数startNettyRpcEnv
val startNettyRpcEnv: Int => (NettyRpcEnv, Int) = { actualPort =>
nettyEnv.startServer(actualPort)
// 返回NettyRpcEnv及其端口号
(nettyEnv, nettyEnv.address.port)
}
try {
// 开启“sparkDriver”服务，注意此处传进了上面定义的函数，这里的config.name是"sparkDriver"，最后返回了NettyRpcEnv
Utils.startServiceOnPort(config.port, startNettyRpcEnv, sparkConf, config.name)._1
} catch {
case NonFatal(e) =>
nettyEnv.shutdown()
throw e
}
}
// 返回NettyRpcEnv
nettyEnv
}
Utils中的startServiceOnPort方法：
def startServiceOnPort[T](
startPort: Int,
startService: Int => (T, Int),
conf: SparkConf,
serviceName: String = ""): (T, Int) = {
// 我们传进来的startPort为0，所以会生成一个随机的端口号
require(startPort == 0 || (1024 <= startPort && startPort < 65536),
"startPort should be between 1024 and 65535 (inclusive), or 0 for a random free port.")
// " 'sparkDriver'"
val serviceString = if (serviceName.isEmpty) "" else s" '$serviceName'"
// 通过"spark.port.maxRetries"设置，如果没有设置，而设置中包括"spark.testing"，
// 最大重试次数就是100次，否则最大重试次数就是10次
val maxRetries = portMaxRetries(conf)
for (offset <- 0 to maxRetries) {
// 设置端口号
// Do not increment port if startPort is 0, which is treated as a special port
val tryPort = if (startPort == 0) {
startPort
} else {
// If the new port wraps around, do not try a privilege port
((startPort + offset - 1024) % (65536 - 1024)) + 1024
}
try {
// 开启服务，并返回服务和端口号，注意这里的startService是上面传进来的那个函数startNettyRpcEnv
// 所以我们实际上执行的是startNettyRpcEnv(tryPort)，而根据startNettyRpcEnv函数的定义，实际
// 上是调用了nettyEnv.startServer(tryPort)方法
val (service, port) = startService(tryPort)
// 创建成功后打印日志，serviceString就是"sparkDriver"
logInfo(s"Successfully started service$serviceString on port $port.")
// 返回服务和端口号
return (service, port)
} catch {
case e: Exception if isBindCollision(e) =>
if (offset >= maxRetries) {
val exceptionMessage = s"${e.getMessage}: Service$serviceString failed after " +
s"$maxRetries retries! Consider explicitly setting the appropriate port for the " +
s"service$serviceString (for example spark.ui.port for SparkUI) to an available " +
"port or increasing spark.port.maxRetries."
val exception = new BindException(exceptionMessage)
// restore original stack trace
exception.setStackTrace(e.getStackTrace)
throw exception
}
logWarning(s"Service$serviceString could not bind on port $tryPort. " +
s"Attempting port ${tryPort + 1}.")
}
}
// Should never happen
throw new SparkException(s"Failed to start service$serviceString on port $startPort")
}
下面我们就具体看一下NettyRpcEnv中的这个startServer方法，具体的启动方法我们不再追踪了，最后实际上创建了一个TransportServer。
def startServer(port: Int): Unit = {
// 首先实例化bootstraps
val bootstraps: java.util.List[TransportServerBootstrap] =
if (securityManager.isAuthenticationEnabled()) {
java.util.Arrays.asList(new SaslServerBootstrap(transportConf, securityManager))
} else {
java.util.Collections.emptyList()
}
// 实例化server
server = transportContext.createServer(host, port, bootstraps)
// 向dispatcher注册
dispatcher.registerRpcEndpoint(
RpcEndpointVerifier.NAME, new RpcEndpointVerifier(this, dispatcher))
}
再回到SparkEnv中，开启了"sparkDriver"服务后，又创建了Akka的ActorSystem，具体的创建过程就不分析了。
// 开启了sparkDriverActorSystem服务，spark2.x中已经移除了对Akka的依赖
val actorSystem: ActorSystem =
if (rpcEnv.isInstanceOf[AkkaRpcEnv]) {
rpcEnv.asInstanceOf[AkkaRpcEnv].actorSystem
} else {
val actorSystemPort =
if (port == 0 || rpcEnv.address == null) {
port
} else {
rpcEnv.address.port + 1
}
// Create a ActorSystem for legacy codes
AkkaUtils.createActorSystem(
actorSystemName + "ActorSystem",
hostname,
actorSystemPort,
conf,
securityManager
)._1
}
// 最后使用开启的服务的端口替换掉原来的端口
if (isDriver) {
conf.set("spark.driver.port", rpcEnv.address.port.toString)
} else if (rpcEnv.address != null) {
conf.set("spark.executor.port", rpcEnv.address.port.toString)
}
我们使用spark-submit的client模式提交应用程序时，就可以看到关于这部分的日志信息：
17/03/02 09:38:28 INFO Utils: Successfully started service 'sparkDriver' on port 33861.
17/03/02 09:38:29 INFO Slf4jLogger: Slf4jLogger started
17/03/02 09:38:29 INFO Remoting: Starting remoting
17/03/02 09:38:29 INFO Remoting: Remoting started; listening on addresses :[akka.tcp://[email protected]:34803]
17/03/02 09:38:29 INFO Utils: Successfully started service 'sparkDriverActorSystem' on port 34803.
注意：本文基于的是Spark 1.6.3版本的源码，并对Spark 2.x版本的改变进行了相应的说明，这里给出具体的连接供大家参考：
Spark 1.6.3 源码
Spark 2.1.0 源码
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