如何在连接两个数据集后自动推断数据集？

Question

spark 是否可以自动推断模式并将 Dataframe 转换为数据集，而程序员不必为每个连接创建一个案例 class？

    import spark.implicits._
    case class DfLeftClass(
        id: Long,
        name: String,
        age: Int
                 )
    val dfLeft = Seq(
      (1,"Tim",30),
      (2,"John",15),
      (3,"Pens",20)
    ).toDF("id","name", "age").as[DfLeftClass]

    case class DfRightClass(
                            id: Long,
                            name: String,
                            age: Int
                            hobby: String
                          )
    val dfRight = Seq(
      (1,"Tim",30,"Swimming"),
      (2,"John",15,"Reading"),
      (3,"Pens",20,"Programming")
    ).toDF("id","name", "age", "hobby").as[DfRightClass]

    val joined: DataFrame = dfLeft.join(dfRight) // this results in DataFrame instead of a Dataset

Answer 1

要留在数据集 API 中，您可以使用joinWith 。 这个 function 返回包含连接两侧的元组数据集：

val joined: Dataset[(DfLeftClass, DfRightClass)] = dfLeft.joinWith(dfRight,
                          dfLeft.col("id").eqNullSafe(dfRight.col("id")))

结果：

+-------------+--------------------------+
|_1           |_2                        |
+-------------+--------------------------+
|{1, Tim, 30} |{1, Tim, 30, Swimming}    |
|{2, John, 15}|{2, John, 15, Reading}    |
|{3, Pens, 20}|{3, Pens, 20, Programming}|
+-------------+--------------------------+

从这里您可以继续使用元组，也可以将元组 map 转换为第三种情况 class。