Sparksql的2.x版本dataFrame和dataSet

package sql2

import org.apache.spark.sql.SparkSession

object Spark2Join {
  def main(args: Array[String]): Unit = {
    val spark = SparkSession.builder().appName("joinTest")
      .master("local[*]")
      .getOrCreate()
    import spark.implicits._
    val lines = spark.createDataset(List("1,laozhoa,china", "2,laoduan,usa", "3,laoyang,jp"))

    //对数据进行整理
    val tpDs = lines.map(line => {
      val fields = line.split(",")
      val id = fields(0).toLong
      val name = fields(1)
      val nationCode = fields(2)
      (id, name, nationCode)
    })
    val df1 = tpDs.toDF("id", "name", "nation")

    val nations = spark.createDataset(List("china,中国", "usa,美国"))

    //对数据进行整理
    val ndataset = nations.map(l => {
      val fields = l.split(",")
      val ename = fields(0)
      val cname = fields(1)
      (ename, cname)
    })
    val df2 = ndataset.toDF("ename","cname")

    /*
    第一种基于dataFrame创建视图的方式,通过写sql方式将两者相结合
     */
    df1.createTempView("v_users")
    df2.createTempView("v_nations")

    val rs = spark.sql("select name,cname from v_users left join v_nations on nation = ename")
    rs.show()
    /*
    第二种方式: 基于dataset,默认是innerjoin
     */
    df1.join(df2,$"nation" === $"ename","left_outer").show()

    spark.stop()

  }
}