引入 import spark.implicits._

一 RDD和DataFrame

1.1 RDD 转换为 DataFrame

实际开发中，一般通过样例类将 RDD 转换为 DataFrame

scala> case class User(name:String, age:Int)
defined class User
scala> sc.makeRDD(List(("zhangsan",30), ("lisi",40))).map(t=>User(t._1,t._2)).toDF.show
+--------+---+
|    name|age|
+--------+---+
|zhangsan| 30|
|   lisi| 40|
+--------+---+

1.2 DataFrame 转换为 RDD

scala> val df = sc.makeRDD(List(("zhangsan",30), ("lisi",40))).map(t=>User(t._1, t._2)).toDF
df: org.apache.spark.sql.DataFrame = [name: string, age: int]
scala> val rdd = df.rdd
rdd: org.apache.spark.rdd.RDD[org.apache.spark.sql.Row] = MapPartitionsRDD[46] 
at rdd at <console>:25

注意：此时得到的 RDD 存储类型为 Row

二 RDD和DataSet

2.1 RDD 转换为 DataSet

scala> case class User(name:String, age:Int)
defined class User
scala> sc.makeRDD(List( ("zhangsan",30), ("lisi",49))).map(t=>User(t._1, t._2)).toDS
res11: org.apache.spark.sql.Dataset[User] = [name: string, age: int]

2.2 DataSet 转换为 RDD

scala> case class User(name:String, age:Int)
defined class User
scala> sc.makeRDD(List(("zhangsan",30), ("lisi",49))).map(t=>User(t._1, t._2)).toDS
res11: org.apache.spark.sql.Dataset[User] = [name: string, age: int]
scala> val rdd = res11.rdd
rdd: org.apache.spark.rdd.RDD[User] = MapPartitionsRDD[51] at rdd at <console>:25
scala> rdd.collect
res12: Array[User] = Array(User(zhangsan,30), User(lisi,49))

注意：DataSet 其实也是对RDD 的封装，所以可以直接获取内部的RDD

三 DataFrame 和 DataSet

3.1 DataFrame 转换为 DataSet

scala> case class User(name:String, age:Int)
defined class User
scala> val df = sc.makeRDD(List(("zhangsan",30),("lisi",49))).toDF("name","age")
df: org.apache.spark.sql.DataFrame = [name: string, age: int]
scala> val ds = df.as[User]
ds: org.apache.spark.sql.Dataset[User] = [name: string, age: int]

3.2 DataSet 转换为 DataFrame

scala> val ds = df.as[User]
ds: org.apache.spark.sql.Dataset[User] = [name: string, age: int]
scala> val df = ds.toDF
df: org.apache.spark.sql.DataFrame = [name: string, age: int]