Spark：求出分组内的TopN

c1 85
c2 77
c3 88
c1 22
c1 66
c3 95
c3 54
c2 91
c2 66
c1 54
c1 65
c2 41
c4 65

import org.apache.spark.SparkConf
import org.apache.spark.sql.SparkSession

object GroupTopN1 {
  System.setProperty("hadoop.home.dir", "D:\\Java_Study\\hadoop-common-2.2.0-bin-master")

  case class Rating(userId: String, rating: Long)

  def main(args: Array[String]) {
    val sparkConf = new SparkConf().setAppName("ALS with ML Pipeline")
    val spark = SparkSession
      .builder()
      .config(sparkConf)
      .master("local")
      .config("spark.sql.warehouse.dir", "/")
      .getOrCreate()

    import spark.implicits._
    import spark.sql

    val lines = spark.read.textFile("C:\\Users\\Administrator\\Desktop\\group.txt")
    val classScores = lines.map(line => Rating(line.split(" ")(0).toString, line.split(" ")(1).toLong))

    classScores.createOrReplaceTempView("tb_test")

    var df = sql(
      s"""|select
          | userId,
          | rating,
          | row_number()over(partition by userId order by rating desc) rn
          |from tb_test
          |having(rn<=3)
          |""".stripMargin)
    df.show()

    spark.stop()
  }
}

+------+------+---+
|userId|rating| rn|
+------+------+---+
|    c1|    85|  1|
|    c1|    66|  2|
|    c1|    65|  3|
|    c4|    65|  1|
|    c3|    95|  1|
|    c3|    88|  2|
|    c3|    54|  3|
|    c2|    91|  1|
|    c2|    77|  2|
|    c2|    66|  3|
+------+------+---+