Spark源码(一)-SparkRPC示例

一、何为SparkRPC

RPC全称为远程过程调用（Remote Procedure Call），它是一种计算机通信协议，允许一个计算机程序调用另一个计算机上的子程序，而无需了解底层网络细节。通过RPC，一个计算机程序可以像调用本地程序一样调用远程程序，使得分布式应用程序的开发更加简单和高效。

二、SparkRPC示意图

三、SparkRPC代码示例

1、基本流程

①启动Master和Worker

②Worker向Master发送**注册信息(封装成一个类:**RegisterWorker)

java 复制代码

case class RegisterWorker(rpcEndpointRef:RpcEndpointRef,workerId:String,workerMemory:Int,workerCores:Int)

③Master收到Worker的注册信息,并将其存放到一个HashMap,其中Key为WorkerId,Value为**WorkerInfo，**其结构如下：

java 复制代码

class WorkerInfo(val workerId:String,var workerMemory:Int,var workerCores:Int){
    var lastHearBeatTime: Long = _
}

其中lastHearBeatTime是该Worker最后一次心跳时间。

④Master中启动一个定时任务(设定为每15s执行一次)， 定时从HashMap 中获取各个Worker信息，并将其中的lastHearBeatTime与当前时间进行比较，如果大于10s,就认为该Worker已经与Master失联，将其从HashMap中剔除

⑤)(与④其实同步进行) Worker同样开启了一个定时任务(设定为每10s执行一次), 定时给Master发送心跳HeartBeat ; Master收到该心跳后,根据WorkerId从HashMap中取出对应的Worker信息，并将其lastHearBeatTime 修改为当前时间，从而不断更新与Master保持通信的Worker的最后心跳时间。

java 复制代码

case class HeartBeat(WorkerId:String)

2、完整代码

（1）Master

java 复制代码

package org.apache.spark.wakedata

import org.apache.spark.SparkConf
import org.apache.spark.SecurityManager
import org.apache.spark.rpc.{RpcCallContext, RpcEnv, ThreadSafeRpcEndpoint}

import java.util.concurrent.{Executors, TimeUnit}
import scala.collection.mutable


class Master(val rpcEnv:RpcEnv) extends ThreadSafeRpcEndpoint{

  val idToWorker = new mutable.HashMap[String, WorkerInfo]()

  override def onStart(): Unit = {

    //启动一个定时器
    val service = Executors.newScheduledThreadPool(1)
    service.scheduleAtFixedRate(new Runnable {
      override def run(): Unit = {
        // 如果Worker最后一次心跳时间距离当前时间 大于10s,就需要移除该Worker
        val deadWorkers = idToWorker.values.filter(w => System.currentTimeMillis() - w.lastHearBeatTime > 10000)
        deadWorkers.foreach(w => {
          idToWorker -= w.workerId
        })
        println(s"当前活跃的Worker数量:${idToWorker.size}")
      }
    },0,15,TimeUnit.SECONDS)

  }

  override def receive: PartialFunction[Any, Unit] = {
//    case "test" => println("接收到了测试消息");
    //      println(s"Master收到了来自Worker的信息workerId:$workerId,workerMemory:$workerMemory,workerCores:$workerCores")
    //给Worker发送异步消息
    //      rpcEndpointRef.send("response")

    // 接收到Worker发送过来的注册消息
    case RegisterWorker(rpcEndpointRef,workerId,workerMemory,workerCores) => {
      //封装Worker传递过来的信息
      val workerInfo = new WorkerInfo(workerId, workerMemory, workerCores)
      idToWorker(workerId) = workerInfo
      //向Worker返回一个注册成功的消息
      rpcEndpointRef.send(RegisteredWorker)}

    //接收到Worker发送过来的心跳信息
    case HeartBeat(workerId) =>{
        val workerInfo = idToWorker(workerId)
        //更新最后一次访问时间
        workerInfo.lastHearBeatTime = System.currentTimeMillis()
      }
}


 //接收同步消息
  override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = {
    case "ask-msg" => {
      println("接收到来自Worker的同步消息")
      //Master响应Worker的请求：给Worker返回消息
      context.reply("reply-msg")
    }

  }
}



object Master {
  def main(args: Array[String]): Unit = {

    val conf = new SparkConf()
   //创建 SecurityManager(安全管理器,对系统资源的访问进行检查和限制)
    val securityMgr  = new SecurityManager(conf)
    //创建rpcEnv,并指定名称、IP地址和端口等
    val rpcEnv = RpcEnv.create("SparkMaster", "localhost", 8888, conf, securityMgr)
    //创建Master RpcEndpoint
    val master = new Master(rpcEnv)

    //将Master的RpcEndpoint传入到setupEndpoint,并指定名称,返回一个RpcEndpoint的引用,
    val masterEndpoint = rpcEnv.setupEndpoint("master", master)

    //通过RpcEndpoint的引用发送消息
//    masterEndpoint.send("test")

    //将程序挂起,等待退出
    rpcEnv.awaitTermination()
  }

}

（2）Worker

java 复制代码

package org.apache.spark.wakedata


import org.apache.spark.SparkConf
import org.apache.spark.SecurityManager
import org.apache.spark.rpc.{RpcAddress, RpcEndpointRef, RpcEnv, ThreadSafeRpcEndpoint}

import java.util.concurrent.{Executors, TimeUnit}
import scala.concurrent.ExecutionContext.Implicits.global




class Worker(val rpcEnv:RpcEnv) extends ThreadSafeRpcEndpoint{
  var masterEndpointRef:RpcEndpointRef = _
  val WORKER_ID =  "worker02"

  override def onStart(): Unit = {
    //向Master发送请求连接(本质上是连接master的endPoint)
    masterEndpointRef = rpcEnv.setupEndpointRef(RpcAddress("localhost", 8888), "master")

    //向Master发送注册Worker的请求(注意这里发送的是同步消息,底层使用的是ask方法.为何是同步发送? 因为必须首先建立好连接,然后才能发送消息)
    //其中self是worker RpcEndpoint的引用
    masterEndpointRef.send(RegisterWorker(self,WORKER_ID,1000,8))
  }

  //接收异步消息
  override def receive: PartialFunction[Any, Unit] = {
//    case "response" => {
//      println("Worker接收到Master返回的消息")
//      //向Master发送同步消息
//      val future = masterEndpointRef.ask[String]("ask-msg")
//      //接收Master返回的消息
//      future.map(res => println(s"Worker接收到Master返回的响应请求消息:$res"))
//    }
    //Worker接收到Master发送过来的异步消息
    case RegisteredWorker => {
      //启动一个定时器
      val service = Executors.newScheduledThreadPool(1)
      service.scheduleAtFixedRate(
        new Runnable {
          override def run(): Unit = {
            masterEndpointRef.send(HeartBeat(WORKER_ID))
          }
        },0,10,TimeUnit.SECONDS)
    }
  }
}

object Worker {
  def main(args: Array[String]): Unit = {
    val conf = new SparkConf()
    val SecurityMgr = new SecurityManager(conf)

    //创建rpcEnv
    val rpcEnv = RpcEnv.create("SparkWorker", "localhost", 9998, conf,SecurityMgr)
    //创建rpcendpoint
    val worker = new Worker(rpcEnv)

    //返回一个RpcEndpoint的引用
    val workerEndpoint = rpcEnv.setupEndpoint("Worker", worker)
    rpcEnv.awaitTermination()
  }

}

（3）RegisterWorker-样例类和伴生对象

此类封装Worker注册信息

java 复制代码

package org.apache.spark.wakedata

import org.apache.spark.rpc.RpcEndpointRef

case class RegisterWorker(rpcEndpointRef:RpcEndpointRef,workerId:String,workerMemory:Int,workerCores:Int)

java 复制代码

package org.apache.spark.wakedata

case object RegisteredWorker

（4）WorkerInfo

java 复制代码

package org.apache.spark.wakedata

class WorkerInfo(val workerId:String,var workerMemory:Int,var workerCores:Int){
    var lastHearBeatTime: Long = _
}
package org.apache.spark.wakedata

class WorkerInfo(val workerId:String,var workerMemory:Int,var workerCores:Int){
    var lastHearBeatTime: Long = _
}
package org.apache.spark.wakedata

class WorkerInfo(val workerId:String,var workerMemory:Int,var workerCores:Int){
    var lastHearBeatTime: Long = _
}

3、关于SparkRPC的一些细节

1、接收异步消息是在receive方法

2、接收同步消息是在receiveAndReply

java 复制代码

 //接收同步消息
  override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = {
    case "ask-msg" => {
      println("接收到来自Worker的同步消息")
      //Master响应Worker的请求：给Worker返回消息
      context.reply("reply-msg")
    }

  }
}

3、发送同步消息使用ask

java 复制代码

//      //向Master发送同步消息
//      val future = masterEndpointRef.ask[String]("ask-msg")