Spark分组生成 rank_num

Spark实现类似于SQL中的 rank_num()

package com.stnts.monitor

import com.stnts.monitor.util.PercentileApprox._
import org.apache.spark.sql._
import org.apache.spark.sql.expressions.Window
import org.apache.spark.sql.functions._

object AlgorithmTest {

  def main(args: Array[String]): Unit = {
    //System.setProperty("hadoop.home.dir", "D:\\hadoop-common-2.2.0-bin-master")

    val spark = SparkSession
      .builder
      .appName("InterfaceMonitor")
      .master("local[2]")
      .getOrCreate()

    import spark.implicits._


      val ds = Seq(
        ("20181102221610c07vy","10000011","10000032",20.0,1,20.0,0 ,"2019-04-19 22:16:10.0"),
        ("20181102221733dgvcv","10000011","10000032",20.0,1,20.0,0 ,"2019-04-19 22:17:34.0"),
        ("20181102222339oakpn","10000061","10000032",0.2 ,1,0.2 ,5 ,"2019-04-19 22:23:39.0"),
        ("20181102225503nhath","10000061","10000032",20.0,1,20.0,7 ,"2019-04-19 22:55:03.0"),
        ("201811030008236k9yy","10000061","10000032",0.2 ,1,0.2 ,5 ,"2019-04-19 00:08:23.0"),
        ("20181103005135do5zg","10000069","10000015",0.2 ,1,0.2 ,0 ,"2019-04-19 00:51:35.0"),
        ("20181103005148ptr7a","10000069","10000015",0.2 ,1,0.2 ,0 ,"2019-04-19 00:51:48.0"),
        ("20181103005148w9isk","10000069","10000015",0.2 ,1,0.2 ,5 ,"2019-04-19 00:51:48.0"),
        ("20181103005205b8gvm","10000069","10000015",0.2 ,1,0.2 ,0 ,"2019-04-19 00:52:05.0"),
        ("20181103015930m2cz0","10000011","10000063",30.0,1,30.0,0 ,"2019-04-19 01:59:30.0")
      ).toDS()
        .toDF("order_id","play_uid","god_uid","price","num","order_amount","order_status","create_time")

   
     //按照某个字段排序生成 rank_num字段
    ds.withColumn("rank_num",row_number().over(Window.orderBy("price"))).show(10)

    //按照指定字段分组之后，再按照组内某个字段排序生成 rank_num
    ds.withColumn("rank_num",row_number().over(Window.partitionBy("god_uid").orderBy(desc("price")))).show(10)

  }

}