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

可以使用Spark SQL来实现RDD转DataFrame的操作，代码如下：

import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.types.{StructType, StructField, StringType, IntegerType, DoubleType}

val spark = SparkSession.builder().appName("RDD to DataFrame").master("local[*]").getOrCreate()

// 定义ratings.csv的Schema
val ratingsSchema = StructType(Array(
  StructField("userId", IntegerType, true),
  StructField("movieId", IntegerType, true),
  StructField("rating", DoubleType, true),
  StructField("timestamp", StringType, true)
))

// 定义movies.csv的Schema
val moviesSchema = StructType(Array(
  StructField("movieId", IntegerType, true),
  StructField("title", StringType, true),
  StructField("genres", StringType, true)
))

// 读取ratings.csv和movies.csv文件
val ratingsRDD = spark.sparkContext.textFile("path/to/ratings.csv").map(_.split(",")).map(row => (row(0).toInt, row(1).toInt, row(2).toDouble, row(3)))
val moviesRDD = spark.sparkContext.textFile("path/to/movies.csv").map(_.split(",")).map(row => (row(0).toInt, row(1), row(2)))

// 将RDD转为DataFrame
val ratingsDF = spark.createDataFrame(ratingsRDD).toDF("userId", "movieId", "rating", "timestamp")
val moviesDF = spark.createDataFrame(moviesRDD).toDF("movieId", "title", "genres")

// 注册为临时表
ratingsDF.createOrReplaceTempView("ratings")
moviesDF.createOrReplaceTempView("movies")

// 执行SQL查询
val result = spark.sql("SELECT title, AVG(rating) AS avg_rating FROM movies JOIN ratings ON movies.movieId = ratings.movieId GROUP BY title ORDER BY avg_rating DESC LIMIT 10")

result.show()

此处选择的SQL分析是查询每部电影的平均评分，并按照平均评分从高到低排序，返回前10部电影的标题和平均评分。