文章目录

• • 
    
• ​​RDD 与DataFrame转换​​
• ​​RDD 与dataSet 转换​​
• ​​DataFrame 与 DataSet转换​​
  • 
    

RDD 与DataFrame转换

RDD 通过toDF函数转换 DataFrame

val rddData1 = spark.sparkContext.parallelize(Array(("Alice", "18", "Female"), ("Mathew", "20", "Male")))
    val df1 = rddData1.toDF("name", "age", "sex")
    df1.show

DataFrame 通过调用rdd方法转换为RDD

df1.rdd.collect

RDD 与dataSet 转换

RDD 通过toDS函数转换 DataFrame

import org.apache.spark.sql.SparkSession

object TestSQL2 {
  def main(args: Array[String]): Unit = {
    val spark = SparkSession.builder()
      .master("local[*]")
      .appName("test")
      .enableHiveSupport()
      .getOrCreate()
    import spark.implicits._
    val rddData2 = spark.sparkContext.parallelize(Array(("Alice", "18", "Female"), ("Mathew", "20", "Male")))
    val rddData3 = rddData2.map(t => User(t._1, t._2.toInt, t._3))
    val ds1 = rddData3.toDS()
    ds1.show
    spark.stop()
  }
}

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

dataSet 通过调用rdd方法转换为RDD

ds1.rdd.count()

DataFrame 与 DataSet转换

val df2 = spark.createDataFrame(List(
      ("Alice", "Female", "20"),
      ("Tom", "Male", "25"),
      ("Boris", "Male", "18"))).toDF("name", "sex", "age")
    val ds2 = df2.as[Person]
    ds2.show
 case class Person(name: String, age: String, sex: String)

Dataset 通过toDF DataFrame

ds2.toDF().show

由于DataSet数据强数据类型,DataFrame中数据转换DataSet时,对应column中要求个数,类型强一致