自定义二次排序

# ./spark-shell --master spark://192.1368.56.21:7077 --executor-memory 1g --total-executor-cores 2
scala> sc.textFile("hdfs://hadoop21:9000/wc/").flatMap(_.split(" ")).map((_,1)).reduceByKey(_+_)
res0: org.apache.spark.rdd.RDD[(String, Int)] = ShuffledRDD[4] at reduceByKey at <console>:25
scala> sc.textFile("hdfs://hadoop21:9000/wc/").flatMap(_.split(" ")).map((_,1)).reduceByKey(_+_).collect
res1: Array[(String, Int)] = Array((tom,11), (hello,22), (jerry,6), (kitty,1), (hanmeimei,2), (lilei,2))

CustomSort.scala

package day10

import org.apache.spark.rdd.RDD
import org.apache.spark.{SparkConf, SparkContext}

object MySort {
  implicit val girlOrdering = new Ordering[Girl] {
    override def compare(x: Girl, y: Girl): Int = {
      if (x.faceValue != y.faceValue) {
        x.faceValue - y.faceValue
      }else {
//        if (x.age > y.age){
          y.age - x.age
        }
//        else {
//          x.age - y.age
//        }
//      }
    }
  }
}
object CustomSort {

  def main(args: Array[String]): Unit = {
    val conf = new SparkConf().setAppName("CustomSort").setMaster("local[2]")
    val sc = new SparkContext(conf)

    val girlInfo = sc.parallelize(Array(("tingting",80,25),("ningning",90,26),("mimi",90,27)))

//    val res = girlInfo.sortBy(_._2,false)

    // 第一种排序方式
//    import MySort.girlOrdering
//    val res: RDD[(String, Int, Int)] = girlInfo.sortBy(x => Girl(x._2,x._3),false)

    // 第二种排序方式
    val res: RDD[(String, Int, Int)] = girlInfo.sortBy(x => Girl(x._2,x._3),false)

    println(res.collect.toBuffer)



    sc.stop()
  }
}

//第一种排序方式
//case class Girl(faceValue: Int, age: Int){}
//第二种排序方式
case class Girl(faceValue: Int, age: Int) extends Ordered[Girl] {
  override def compare(that: Girl): Int = {
    if (this.faceValue != that.faceValue){
      this.faceValue - that.faceValue
    }else {
      that.age - this.age
    }
  }
}