How can spark infer dataset automatically after join of two datasets?-CodePudding

Is it possible for spark to automatically infer the schema and convert a Dataframe to a Dataset without the programmer having to create a case class for each join?

    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

CodePudding user response：

To stay within the Dataset API, you can use joinWith. This function returns a Dataset of tuples containing both sides of the join:

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

Result:

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

From here you can either continue working with the tuples or you can map the tuples to a third case class.