val lines = List("hello world", "hello spark")
    val wordlist = lines.flatMap(line => line.split(" ")).map(word => (word, 1))
    //方法一：先groupBy再map
    wordlist.groupBy(_._1).map {
      case (word, list) => (word, list.size)
    }.foreach(println)

    //方法二：通過aggregate來實現map reduce，效率更高
    val seqop = (result: mutable.HashMap[String, Int], wordcount: (String, Int)) => {
      val addOne = (wordcount._1, result.getOrElse(wordcount._1, 0) + wordcount._2)
      result.+=(addOne)
    }
    val combop = (result1: mutable.HashMap[String, Int], result2: mutable.HashMap[String, Int]) => {
      result1 ++= result2
    }
    val result = wordlist.aggregate(mutable.HashMap[String, Int]())(seqop,combop)
    println(result)
 

從檔案讀取進行word count:

   val lines = Source.fromFile("test.txt").getLines()
    val seqop = (result: mutable.HashMap[String, Int], line: String) => {
      val wordcount = line.replace(",", " ").replace(".", " ").replace("(", " ").replace(")", " ").split(" ").filter(_.trim.length > 0).map(word => (word, 1))
      wordcount.foreach(wc => {
        val addOne = (wc._1, result.getOrElse(wc._1, 0) + wc._2)
        result += addOne
      })
      result
    }
    val combop = (result1: mutable.HashMap[String, Int], result2: mutable.HashMap[String, Int]) => {
      result1 ++= result2
    }

    val test = lines.aggregate(mutable.HashMap[String, Int]())(seqop, combop)
    println(test)