spark操作mongodb 遇到的问题

我需要从一个

case class Rating(val uid: Int, val mid: Int, val score: Double, val timestamp: Int)

表中取出用户最近看过的10个电影的数据

难点：需要的不是一个用户是所有用户

样例类（case class）适合用于不可变的数据。它是一种特殊的类，能够被优化以用于模式匹配。

首先创建两个样例类

case class RentlyMovie(val mid: Int, val score: Double, val timestamp: Int)

case class UserRentlyMovie(val uid: Int, recs: Seq[RentlyMovie])

 编写业务逻辑

import spark.implicits._
    val redisData = spark.read
      .option("uri", config("mongo.url"))
      .option("collection", MONGODB_RATING_COLLECTION)
      .format("com.mongodb.spark.sql")
      .load()
      .as[Rating]
      .rdd.groupBy(x => x.uid).map {
      x =>
        UserRentlyMovie(x._1, x._2.toList.map((x => RentlyMovie(x.mid, x.score, x.timestamp))).sortBy(x => x.timestamp)(Ordering.Int.reverse).take(10))
    }
      .toDF().persist()

sortBy()函数介绍：

*.sortBy(x => x.timestamp)(Ordering.Int.reverse)

表示降序排列

*.sortBy(x => x.timestamp)(Ordering.Int)

表示升序排列