Spark10- RDD转DataFrame的三种方式

小朋友,你是否有很多问号?2025-09-13 8:51

一、手动直接转换

复制代码

  def rddToDf1(): Unit = {
    val conf = new SparkConf().setMaster("local[*]").setAppName("hello world")
    val spark = SparkSession.builder().config(conf).getOrCreate()
    import spark.implicits._
    val sc = spark.sparkContext
    val rdd: RDD[(String, Int)] = sc.parallelize(Array(("李四", 10), ("zs", 20), ("王无", 21)))
    rdd.toDF("name","age").show
  }

二、使用样例类转换

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  def rddToDf2(): Unit = {
    val conf = new SparkConf().setMaster("local[*]").setAppName("hello world")
    val spark = SparkSession.builder().config(conf).getOrCreate()
    import spark.implicits._
    val sc = spark.sparkContext
    val rdd: RDD[(String, Int)] = sc.parallelize(Array(("李四", 10), ("zs", 20), ("王无", 21)))
    val rdd2: RDD[User] = rdd.map(line => {
      User(line._1, line._2)
    })
    rdd2.toDF().show()
  }

  case class User(name: String, age: Int)

三、通过API转换

复制代码

  def rddToDf3(): Unit = {
     val conf = new SparkConf().setMaster("local[*]").setAppName("hello world")
    val spark = SparkSession.builder().config(conf).getOrCreate()
    val sc = spark.sparkContext
    val rdd: RDD[(String, Int)] = sc.parallelize(Array(("李四", 10), ("zs", 20), ("王无", 21)))
    val rowRdd: RDD[Row] = rdd.map(x => Row(x._1, x._2))
    val types = StructType(Array(StructField("name", StringType), StructField("age", IntegerType)))
    val frame: DataFrame = spark.createDataFrame(rowRdd, types)
    frame.show()

  }