sparksql查询完数据保存到一个文件中，并且不出现乱码

实例代码：

object movies2{

  def main(args: Array[String]): Unit = {
    val spark: SparkSession = SparkSession.builder()
      .appName("movies")
      .master("local[*]")
      .getOrCreate()

    val lines: Dataset[String] = spark.read.textFile("file:///C:\\Users\\lenovo\\Desktop\\app-2019-12-12.log")
    import spark.implicits._
//    21022734,妈妈的朋友,2019-05-15 08:58:05,5.2,搜秀影院
    val movies: Dataset[(Int, String, Int, Int, Double, String)] = lines.map(x => {
      val s: Array[String] = x.split(",")
      val userId: Int = s(0).toInt
      val movieName: String = s(1)
      //2019052011
      val day: Int = DateUtils.parseToMin(s(2)).substring(0,6).toInt
      val hours: Int = DateUtils.parseToMin(s(2)).substring(8, 10).toInt
      val score: Double = s(3).toDouble
      val cinema: String = s(4)
      (userId, movieName, day, hours, score, cinema)
    })

    val df: DataFrame = movies.toDF("userId","movieName","day","hours","score","cinema")
    df.createTempView("movies")
//    val r: DataFrame = spark.sql("select * from movies")
//    r.show(30)
    val rs1: DataFrame = spark.sql("select hours,count(*) as count from movies where day = 201912 group by hours order by hours")
    println("某月（12月）内电影观看时间点分布：")
    rs1.show(24)

    val rs2: DataFrame = spark.sql("select cinema,count(*) as Box_office from movies where day = 201912 group by cinema order by Box_office desc")
    println("某月（12月）电影院票房统计")
    rs2.show()
    //将分析后的数据写到一个文件中，调用coalesce方法传入分区数
    rs2.coalesce(1).write.format("csv").save("/fengrui")
    //或者下面这个
    rs2.coalesce(1).write.csv("/fengrui2")
    rs2.coalesce(1).write.json("/fengrui2")

  }

}

我的结果放到hdfs上了，只生成一个文件，不调用coalesce的话会按照并行度来切分文件，会落地好多文件，看起来不方便

，json或者CSV格式数据不会有乱码出现