sortWith与sortBy

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

/**
  * 先分组， 后排序
  * sorted:
  * sortWith:
  * sortBy:
  */
object GroupTopN {
    val conf = new SparkConf().setMaster("local").setAppName("RDDTest")
    val sc = new SparkContext(conf)
    def main(args: Array[String]): Unit = {
        testSortBy()

        sc.stop()
    }

    def testSorted() = {
        val rdd1 = sc.parallelize(List(("a", 1), ("a", 2), ("a", 3), ("a", 4), ("b", 5), ("b", 6), ("b", 7), ("b", 8)))
        //根据key分组并内部降序
        rdd1.groupByKey().mapValues(f => {
            //分组内部排序的两种方式
            f.toList.sorted.reverse
            //f.toList.sortWith(_>_)
        }).foreach(println)
    }

    def testSortWith() = {

        val rdd3 = sc.parallelize(List(("a", (1, 5)), ("a", (2, 4)), ("a", (3, 6)), ("a", (4, 1)),
            ("b", (5, 2)), ("b", (6, 3)), ("b", (7, 9)), ("b", (8, 8))))
        rdd3.groupByKey().map(t => {
            val its = t._2
            val sortResult = its.toList.sortWith(_._2 > _._2).take(5)
            (t._1, sortResult)
        }).foreach(println)
    }

    def testSortBy() = {
        println("map === sortBy")
        val word = sc.parallelize(List(("a", (1, 5)), ("a", (2, 4)), ("a", (3, 6)), ("a", (4, 1)),
            ("b", (5, 2)), ("b", (6, 3)), ("b", (7, 9)), ("b", (8, 8))))
        val group = word
            .groupByKey()
            .map(t => {
                // reverse 降序
                val topItem = t._2.toArray.sortBy(_._2)(Ordering[Int].reverse)
                for (tp  for (tp