通过toDF方法创建DataFrame

通过toDF的方法创建

• 集合rdd中元素类型是样例类的时候，转成DataFrame之后列名默认是属性名
• 集合rdd中元素类型是元组的时候，转成DataFrame之后列名默认就是_N
• 集合rdd中元素类型是元组/样例类的时候，转成DataFrame(toDF(“ID”,“NAME”,“SEX”,“AGE6”))可以自定义列名

import org.apache.spark.sql.{DataFrame, SparkSession}
import org.junit.Test


case class Person(id:Int,name:String,sex:String,age:Int)
class TestScala {

  val spark = SparkSession
    .builder()
    .appName("test")
    .master("local[4]")
    .getOrCreate()
    import spark.implicits._

  /**
   * 通过toDF的方法创建
   *    集合rdd中元素类型是样例类的时候，转成DataFrame之后列名默认是属性名
   *    集合rdd中元素类型是元组的时候，转成DataFrame之后列名默认就是_N
   */
  @Test
  def createDataFrameByToDF():Unit={
    //TODO 样例类是属性名
    val list = List(Person(1,"zhangsan","man",10),Person(2,"zhang2","woman",66),Person(3,"zhang3","man",70),Person(4,"zhang4","man",22))
    //需要隐士转换
    val df:DataFrame = list.toDF()
    df.show()
    //TODO 元祖是_N
    val list2 = List((1,"zhangsan","man",10),(1,"zhang2","woman",66),(1,"zhang3","man",70),(1,"zhang4","man",22))
    //需要隐士转换
    val df1:DataFrame = list2.toDF()
    df1.show()
    //TODO 自定义属性名
    val list3 = List((1,"zhangsan","man",10),(1,"zhang2","woman",66),(1,"zhang3","man",70),(1,"zhang4","man",22))
    //需要隐士转换
    val df2:DataFrame = list3.toDF("ID","NAME","SEX","AGE6")
    df2.show()
  }
  
  
}

结果

通过读取文件创建DataFrame

json数据

{"age":20,"name":"qiaofeng"}
{"age":19,"name":"xuzhu"}
{"age":18,"name":"duanyu"}

  /**
   * 通过读取文件创建
   */
  @Test
  def createDataFrame():Unit={
    val df = spark.read.json("src/main/resources/user.json")
    df.show()
  }

通过createDataFrame方法创建DF

  @Test
  def createDataFrameByMethod():Unit={
    val fields = Array(StructField("id",IntegerType),StructField("name",StringType),StructField("sex",StringType),StructField("age",IntegerType))
    val schema = StructType(fields)
    val rdd = spark.sparkContext.parallelize(List(Row(1, "zhangsan", "man", 10), Row(2, "zhang2", "woman", 66), Row(3, "zhang3", "man", 70), Row(4, "zhang4", "man", 22)))
    val df = spark.createDataFrame(rdd, schema)
    df.show()
  }