大数据系列篇-spark-sql使用SQL加DSL方式与RDD-DATAFRAME-DATASET转换

大数据系列篇-spark-sql使用SQL加DSL方式与RDD-DATAFRAME-DATASET转换

package com.test

import org.apache.spark.SparkConf
import org.apache.spark.sql.SparkSession

//测试sql与dsl与转换RDD <-> DATAFRAME <-> DATASET
object SparkSql {

  def main(args: Array[String]): Unit = {

    val sparkConf = new SparkConf().setAppName("练习SparkSql").setMaster("local[*]")
    val spark = SparkSession.builder().config(sparkConf).getOrCreate()
    import spark.implicits._

    //DataFrame
    val df = spark.read.json("data/user.json")
    df.show()

    df.createOrReplaceTempView("user")
    //SQL
    spark.sql("SELECT * FROM user").show()
    //DSL
    df.select("userName").show()
    df.select($"age" + 1).show()
    //或df.select('age + 1).show()

    //DataSet
    val ds = Seq(1, 5, 6).toDS()
    ds.show()

    //RDD<--->DF
    val rdd1 = spark.sparkContext.makeRDD(List((1, "user1", 1), (2, "user1", 2)))
    val df1 = rdd1.toDF("id", "name", "age")
    df1.rdd

    //DF<--->DS
    val ds1 = df1.as[User]
    val df2 = ds1.toDF()

    //RDD<--->DS
    val ds2 = rdd1.map {
      case (id, name, age) => {
        User(id, name, age)
      }
    }.toDS()

    ds2.rdd

    spark.close()

  }

  case class User(id: Long, name: String, age: Int)
}