Actor并行化的wordcount怎么实现

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在scala中她能实现很强大的功能,他是基于并发机制的一个事件模型

我们现在学的scala2.10.x版本就是之前的Actor

同步:在主程序上排队执行的任务,只有前一个任务执行完毕后,才能执行下一个任务

异步:指不进入主程序,而进入"任务对列"的任务,只有等主程序任务执行完毕,"任务对列"开始请求主程序,请求任务执行,该任务会进入主程序

java

共享变量 -- 加锁

会出现锁死问题

scala

Actor不共享数据

没有锁的概念

Actor通信之间需要message(通信)

Aactor执行顺序

1.首先调用start()方法启动Actor

2.调用start()方法后act()方法会被执行

3.Actor之间进行发送消息

Actor发送消息的三种方式

! -> 发送异步消息,没有返回值

!? -> 发送同步消息,有返回值,会有线程等待

!! -> 发送异步消息,有返回值,返回值类型Future[Any](用来获取异步操作结果)

Actor并行执行

//注意,这两个actor会并行执行,当其中一个for循环结束后,actor结束

object ActorDemo01 {

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

MyActor1.start()

MyActor2.start()

}

}

object MyActor1 extends Actor{

override def act(): Unit = {

for (i <- 1 to 10){

println(s"actor => $i")

Thread.sleep(2000)

}

}

object MyActor2 extends Actor{

override def act(): Unit = {

for (i <- 1 to 5){

println(s"actor2 => $i")

Thread.sleep(2000)

}

}

}

}

用Actor不断接受消息

执行第一种方式,异步

object ActorDemo02 {

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

val actor: MyActor = new MyActor

actor.start()

//并行执行

actor ! "start" // !->异步

actor ! "stop"

println("发送完成")

}

}

class MyActor extends Actor{

override def act(): Unit = {

while (true){ //死循环

receive { //接收

case "start" => {

println("starting")

Thread.sleep(1000)

println("started")

}

case "stop" => {

println("stopping")

Thread.sleep(1000)

println("stopped")

}

}

}

}

}

第二种方式:利用react来代替receive,也就是说react线程可复用,比receive更高效

object ActorDemo03 {

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

val actor: MyActor3 = new MyActor3

actor.start()

actor ! "start"

actor ! "stop"

println("成功了")

}

}

class MyActor3 extends Actor{

override def act(): Unit = {

loop {

react{

case "start" =>{

println("starting")

Thread.sleep(1000)

println("sarted")

}

case "stop" =>{

println("stoppting")

Thread.sleep(1000)

println("stopped")

}

}

}

}

}

结合样例类练习Actor发送消息

//创建样例类

case class AsyncMsg(id: Int, msg: String)

case class SyncMsg(id: Int, msg: String)

case class ReplyMsg(id: Int, msg: String)

object ActorDemo01 extends Actor {

override def act(): Unit = {

while (true) {

receive {

case "start" => println("starting...")

case AsyncMsg(id, msg) =>

{

println(s"id:$id,msg:$msg")

sender ! ReplyMsg(1,"sucess") //接收到消息后返回响应消息

}

case SyncMsg(id,msg) => {

println(s"id:$id,msg:$msg")

sender ! ReplyMsg(2,"sucess")

}

}

}

}

}

object ActorTest{

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

val actor: Actor = ActorDemo01.start()

// //异步发送消息,没有返回值

// actor ! AsyncMsg(3,"heihei")

// println("异步消息发送完成,没有返回值")

// //同步发送消息,有返回值

// val text: Any = actor !? SyncMsg(4,"OK")

// println(text)

// println("同步消息发送成功")

//异步发送消息,有返回值,返回类型为Future[Any]

val reply: Future[Any] = actor !! SyncMsg(5,"OK is 不存在的")

Thread.sleep(2000)

if (reply.isSet){

val applyMsg: Any = reply.apply()

println(applyMsg)

}else{

println("Nothing")

}

}

}

Actor并行化的wordcount

class Task extends Actor {

override def act(): Unit = {

loop {

react {

case SubmitTask(fileName) => {

val contents = Source.fromFile(new File(fileName)).mkString

val arr = contents.split("\r\n")

val result = arr.flatMap(_.split(" ")).map((_, 1)).groupBy(_._1).mapValues(_.length)

//val result = arr.flatMap(_.split(" ")).map((_, 1)).groupBy(_._1).mapValues(_.foldLeft(0)(_ + _._2))

sender ! ResultTask(result)

}

case StopTask => {

exit()

}

}

}

}

}

object WorkCount {

def main(args: Array[String]) {

val files = Array("c://words.txt", "c://words.log")

val replaySet = new mutable.HashSet[Future[Any]]

val resultList = new mutable.ListBuffer[ResultTask]

for(f <- files) {

val t = new Task

val replay = t.start() !! SubmitTask(f)

replaySet += replay

}

while(replaySet.size > 0){

val toCumpute = replaySet.filter(_.isSet)

for(r <- toCumpute){

val result = r.apply()

resultList += result.asInstanceOf[ResultTask]

replaySet.remove(r)

}

Thread.sleep(100)

}

val finalResult = resultList.map(_.result).flatten.groupBy(_._1).mapValues(x => x.foldLeft(0)(_ + _._2))

println(finalResult)

}

}

case class SubmitTask(fileName: String)

case object StopTask

case class ResultTask(result: Map[String, Int])

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