actor实例之并行编译思想实现单词计数

ActorWordCount.scala

package day05

import scala.actors.{Actor, Future}
import scala.collection.mutable.ListBuffer
import scala.io.Source

/**
  * 用Actor并发编程实现WordCount
  */
object ActorWordCount {

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

    // 存放接收到的每个文件的结果数据
    val replys: ListBuffer[Future[Any]] = new ListBuffer[Future[Any]]

    // 存放有值得Future里的数据
    val res: ListBuffer[Map[String, Int]] = new ListBuffer[Map[String,Int]]

    val files: Array[String] = Array("d:/scala/a.txt","d:/scala/b.txt","d:/scala/c.txt")

    for (file <- files) {

//      val liens: List[String] = Source.fromFile(file).getLines().toList
//      val words: List[String] = lines.flatMap(_.split(" "))
//      val res: Map[String, Int] = words.map((_,1)).groupBy(_._1).mapValues(_.size)

      val task = new Task
      task.start()
      // 接收结果数据，异步发送消息，有返回值
      val reply: Future[Any] = task !! SmTask(file)
      // 把每个文件的数据存储到ListBuffer
      replys += reply
    }

    while (replys.size > 0) {
      // 过滤每个Future对象，如果None类型的，就过滤掉
      val dones: ListBuffer[Future[Any]] = replys.filter(_.isSet)

      for(done <- dones) {
        res += done.apply().asInstanceOf[Map[String,Int]]

        replys -= done
      }
    }
    // 得到全局聚合的数据
    println(res.flatten.groupBy(_._1).mapValues(_.foldLeft(0)(_ + _._2)))

  }
}

class Task extends Actor {
  override def act(): Unit = {
    receive({
      case SmTask(file) => {
        val liens: List[String] = Source.fromFile(file).getLines().toList
        val words: List[String] = liens.flatMap(_.split(" "))
        val res: Map[String, Int] = words.map((_,1)).groupBy(_._1).mapValues(_.size)

        // 异步发送结果数据，没有返回值
        sender ! res
      }
    })
  }
}
case class SmTask(file: String)