【Scala】利用akka实现Spark启动通信

文章目录

• 思路分析
• 步骤
• 一、创建maven工程，导包
• 二、master进程代码开发
• 三、worker进程代码开发

---

思路分析

1.首先启动master，然后依次启动worker
2.启动worker时，向master发送注册信息（使用case class封装注册信息——workerID,memory,cores）
3.接收注册信息，保存注册的worker信息，返回注册成功的消息
4.worker需要定时向master发送心跳信息，这么做的目的是报活
5.master需要定时进行心跳超时检测，剔除心跳超时的worker

---

步骤

一、创建maven工程，导包

<properties>
    <maven.compiler.source>1.8</maven.compiler.source>
    <maven.compiler.target>1.8</maven.compiler.target>
    <encoding>UTF-8</encoding>
    <scala.version>2.11.8</scala.version>
    <scala.compat.version>2.11</scala.compat.version>
</properties>

<dependencies>
    <dependency>
        <groupId>org.scala-lang</groupId>
        <artifactId>scala-library</artifactId>
        <version>${scala.version}</version>
    </dependency>

    <dependency>
        <groupId>com.typesafe.akka</groupId>
        <artifactId>akka-actor_2.11</artifactId>
        <version>2.3.14</version>
    </dependency>

    <dependency>
        <groupId>com.typesafe.akka</groupId>
        <artifactId>akka-remote_2.11</artifactId>
        <version>2.3.14</version>
    </dependency>

</dependencies>

<build>
    <sourceDirectory>src/main/scala</sourceDirectory>
    <testSourceDirectory>src/test/scala</testSourceDirectory>
    <plugins>
        <!-- 限制jdk的编译版本插件 -->
        <plugin>
            <groupId>org.apache.maven.plugins</groupId>
            <artifactId>maven-compiler-plugin</artifactId>
            <version>3.0</version>
            <configuration>
                <source>1.8</source>
                <target>1.8</target>
                <encoding>UTF-8</encoding>
            </configuration>
        </plugin>


        <plugin>
            <groupId>net.alchim31.maven</groupId>
            <artifactId>scala-maven-plugin</artifactId>
            <version>3.2.2</version>
            <executions>
                <execution>
                    <goals>
                        <goal>compile</goal>
                        <goal>testCompile</goal>
                    </goals>
                    <configuration>
                        <args>
                            <arg>-dependencyfile</arg>
                            <arg>${project.build.directory}/.scala_dependencies</arg>
                        </args>
                    </configuration>
                </execution>
            </executions>
        </plugin>

        <plugin>
            <groupId>org.apache.maven.plugins</groupId>
            <artifactId>maven-shade-plugin</artifactId>
            <version>2.4.3</version>
            <executions>
                <execution>
                    <phase>package</phase>
                    <goals>
                        <goal>shade</goal>
                    </goals>
                    <configuration>
                        <filters>
                            <filter>
                                <artifact>*:*</artifact>
                                <excludes>
                                    <exclude>META-INF/*.SF</exclude>
                                    <exclude>META-INF/*.DSA</exclude>
                                    <exclude>META-INF/*.RSA</exclude>
                                </excludes>
                            </filter>
                        </filters>
                        <transformers>
                            <transformer implementation="org.apache.maven.plugins.shade.resource.AppendingTransformer">
                                <resource>reference.conf</resource>
                            </transformer>
                            <transformer implementation="org.apache.maven.plugins.shade.resource.ManifestResourceTransformer">
                                <mainClass></mainClass>
                            </transformer>
                        </transformers>
                    </configuration>
                </execution>
            </executions>
        </plugin>
    </plugins>
</build>

二、master进程代码开发

import akka.actor.{Actor, ActorRef, ActorSystem, Props}
import com.typesafe.config.{Config, ConfigFactory}

import scala.collection.mutable
import scala.collection.mutable.ArrayBuffer

import scala.concurrent.duration._

//todo 利用akka实现spark通信---master端
class Master extends Actor {

  //定义数据结构map 保存worker注册信息  k:workerId  v:workerInfo
  private val workerInfoMap = new mutable.HashMap[String, WorkerInfo]()

  //定义数据结构保存worker信息，便于后续业务根据worker资源排序 (可选项)
  private val workerInfoArray = new ArrayBuffer[WorkerInfo]()

  override def preStart(): Unit = {
    //在初始化中完成首次心跳超时检查，后续间隔指定的时间检查
    //todo 记得导包
    import context.dispatcher
    context.system.scheduler.schedule(0 millis,12000 millis,self,CheckTimeOut)
  }
  //用receive方法持续不断接收处理akka actor的消息
  override def receive: Receive = {
    //用于接收注册信息
    case RegisterMessage(workerId,memory,cores) => {
      //判断worker是否注册
      if (!workerInfoMap.contains(workerId)){
        //如果未注册
        val workerInfo = new WorkerInfo(workerId, memory, cores)
        workerInfoMap.put(workerId,workerInfo)
        workerInfoArray += workerInfo

        //注册成功 返回注册成功信息
        sender ! RegisterSuccess(s"$workerId,congratulations!You successfully login!")
      }
    }
      //用于接收worker心跳信息
    case HeartBeatMessage(workerId) =>{
      //判断workerId是否注册，如果注册 更新上次心跳时间
        if (workerInfoMap.contains(workerId)){
          //把当前系统的时间更新为worker上次心跳的时间
          val nowTime: Long = System.currentTimeMillis()
          val info: WorkerInfo = workerInfoMap(workerId)
          info.lastHeartBeatTime = nowTime
        }
    }
      //用于心跳超时检查
    case CheckTimeOut =>
      //如何判断心跳超时？
      //当前时间 - 上次心跳时间（10s） > 10   如果没有网络波动应该是这样
      //如果要考虑网络波动，则可以   当前时间 - 上次心跳时间（10s） > 10
      val outTimeWorker: ArrayBuffer[WorkerInfo] = workerInfoArray.filter(x => System.currentTimeMillis() - x.lastHeartBeatTime > 12000)
      //遍历超时worker并剔除
      for (w <- outTimeWorker){
        val workerId = w.workerId
        workerInfoMap.remove(workerId)
        workerInfoArray -= w
        //打印超时的workId
        println(s"$workerId 已超时")
      }
      //打印当前存活的workerId数
      println(s"当前存活的worker个数是 ${workerInfoArray.size}")
      //worker排序
      println(workerInfoArray.sortBy(x => x.memory).reverse)
  }
}

object Master {
  def main(args: Array[String]): Unit = {
    //master host
    val host = args(0)
    //master port
    val port = args(1)

    //创建config需要解析的字符串
    val configStr: String =
      s"""
         |akka.actor.provider = "akka.remote.RemoteActorRefProvider"
         |akka.remote.netty.tcp.hostname = "$host"
         |akka.remote.netty.tcp.port = "$port"
         |""".stripMargin

    //创建ActorSystem需要的config
    val config: Config = ConfigFactory.parseString(configStr)

    //创建ActorSystem
    val masterActorSystem: ActorSystem = ActorSystem.create("masterActorSystem", config)
    //创建masterActor
    val master: ActorRef = masterActorSystem.actorOf(Props(new Master), "masterActor")
  }
}

三、worker进程代码开发

import java.util.UUID

import akka.actor.{Actor, ActorRef, ActorSelection, ActorSystem, Props}
import com.typesafe.config.{Config, ConfigFactory}

import scala.concurrent.duration._

//todo 利用akka实现spark通信---worker端
class Worker(memory: Int,cores: Int) extends Actor {

  var master: ActorSelection = _

  //workerID
  val workerId = UUID.randomUUID().toString

  override def preStart(): Unit = {


    //引用master (协议、masterActorSystem、ip、端口号、actorMaster、actor层级)
    master = context.actorSelection("akka.tcp://masterActorSystem@192.168.0.108:12323/user/masterActor")
    //给master发送注册信息 使用case class封装
    master ! RegisterMessage(workerId,memory,cores)
  }

  override def receive: Receive = {
        //接收master返回的注册成功信息
    case RegisterSuccess(msg) => {
      println(msg)
      //注册成功，立即开始首次心跳，以后间隔指定的时间进行心跳
      //需要四个参数，心跳开始时间、心跳间隔时间、发给谁、发送信息
      //定时的消息只能发送给自己
      //todo 记得导包
      import context.dispatcher
      context.system.scheduler.schedule(0 millis,10000 millis,self,HeartBeat)
    }
      //接收心跳提醒，完成真正的心跳动作
    case HeartBeat => {
      master ! HeartBeatMessage(workerId)
    }
  }
}

object Worker{
  def main(args: Array[String]): Unit = {
    //worker host
    val host = args(0)
    //worker port
    val port = args(1)

    //worker memory
    val memory = args(2).toInt
    //worker cores
    val cores = args(3).toInt

    //创建config要解析的字符串
    val configStr: String =
      s"""
         |akka.actor.provider = "akka.remote.RemoteActorRefProvider"
         |akka.remote.netty.tcp.hostname = "$host"
         |akka.remote.netty.tcp.port = "$port"
         |
         |""".stripMargin

    //创建ActorSystem需要的configuration
    val config: Config = ConfigFactory.parseString(configStr)
    //创建ActorSystem
    val workerActorSystem: ActorSystem = ActorSystem.create("workerActorSystem", config)
    //创建workerActor
    val worker: ActorRef = workerActorSystem.actorOf(Props(new Worker(memory,cores)), "workerActor")
  }
}