制作測試數據源：

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

spark scala實現代碼：

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()
  }
}

打印結果：

+------+------+---+
|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|
+------+------+---+

Spark：求出分組內的TopN