在scala中，有ratings.csv和movies.csv，编程实现RDD转DataFrame，选择一个合理的SQL分析

首先，我们可以通过以下代码将ratings.csv和movies.csv文件加载为RDD：

val spark = SparkSession.builder().appName("rating").getOrCreate()
val sc = spark.sparkContext
val ratingsRdd = sc.textFile("ratings.csv")
val moviesRdd = sc.textFile("movies.csv")

然后，我们可以使用case class定义ratings和movies的模式，并将RDD转换为DataFrame：

case class Rating(userId: Int, movieId: Int, rating: Double, timestamp: Long)
case class Movie(movieId: Int, title: String, genres: String)

val ratings = ratingsRdd.map(_.split(",")).map(r => Rating(r(0).toInt, r(1).toInt, r(2).toDouble, r(3).toLong))
val movies = moviesRdd.map(_.split(",")).map(m => Movie(m(0).toInt, m(1), m(2)))

import spark.implicits._
val ratingsDf = ratings.toDF()
val moviesDf = movies.toDF()

接下来，我们可以将ratings和movies表注册为临时表（temp table）并执行SQL查询，例如：

ratingsDf.createOrReplaceTempView("ratings")
moviesDf.createOrReplaceTempView("movies")

val topRatedMovies = spark.sql("SELECT m.title, avg(r.rating) as avg_rating FROM ratings r JOIN movies m ON r.movieId = m.movieId GROUP BY m.title ORDER BY avg_rating DESC LIMIT 10")

topRatedMovies.show()

该查询将返回平均评分最高的前10部电影的标题和平均评分。