需求：先按颜值降序排列，颜值相同，按照年龄升序排列

元数据

Array(("ningning", 90, 35), ("tingting", 90, 30), ("fangfang", 85, 19))

代码

package scalapackage.testsparkimport org.apache.spark.rdd.RDDimport org.apache.spark.{SparkConf, SparkContext}/**  * Created by Germmy on 2018/5/23.  */object MySort {  implicit val girlOrdering = new Ordering[Girl] {    override def compare(x: Girl, y: Girl): Int = {      if (x.faceVal != y.faceVal) {        x.faceVal - y.faceVal//按照颜值升序      } else {          y.age - x.age      }    }  }}object Test2CiPaixu {  def main(args: Array[String]) {    val sparkConf: SparkConf = new SparkConf().setAppName("Test2CiPaixu").setMaster("local[*]")    val sc: SparkContext = new SparkContext(sparkConf)    val girlInfo = sc.parallelize(Array(("ningning", 90, 35), ("tingting", 90, 30), ("fangfang", 85, 19)))    //    val by: RDD[(String, Int, Int)] = girlInfo.sortBy(_._3, false) //true升序，false降序    //    println(by.collect().toBuffer)     //方法一//    import MySort.girlOrdering//    val by: RDD[(String, Int, Int)] = girlInfo.sortBy(x => Girl(x._1, x._2, x._3), false) //期望结果：ArrayBuffer( (fangfang,85,19)，(ningning,90,35), (tingting,90,30))//    println(by.collect().toBuffer) //期望结果:ArrayBuffer((fangfang,85,19), (tingting,90,30)),(ningning,90,35)         //方法二     val by: RDD[(String, Int, Int)] = girlInfo.sortBy(x => Girl(x._1, x._2, x._3), false) //期望结果：ArrayBuffer( (fangfang,85,19)，(ningning,90,35), (tingting,90,30))    println(by.collect().toBuffer) //期望结果:ArrayBuffer((fangfang,85,19), (tingting,90,30)),(ningning,90,35)    sc.stop()  }}case class Girl(name: String, faceVal: Int, age: Int) extends  Ordered[Girl]{  override def compare(that: Girl): Int = {    if(this.faceVal!=that.faceVal){      this.faceVal-that.faceVal    }else{      that.age-this.age    }  }}

注意：

1、无论是ordering还是ordered,都是x-y或者this-that,正数表示谁权重大，比如颜值，谁权重大表示谁就排在后面；但是年纪，如果用x-y>0，则表示年纪越大谁权重大，这与需求违背，所以应该反过来，用y-x，则为负数了，表示它权重小。

2、sortBy方法，默认为true,表示升序，即权重越大的排在后面。因此如果你想让颜值高的先显示，则用降序