添加了用于加入行的示例，希望它对您有用。 还要找到占位符，您可以在其中添加/修改代码以添加逻辑

import org.apache.spark.sql.functions._

import scala.collection.mutable

object JoinRdds {

  def main(args: Array[String]): Unit = {
    val spark = Constant.getSparkSess

    import spark.implicits._
    var df1 = List((2,5),(3,7)).toDF("x","y")  // 1st Dataframe
    val df2 = List((0,List(5,7)),(1,List(2,4))).toDF("id","coordinates")  // 2nd Dataframe

    df1 = df1.withColumn("id",monotonically_increasing_id())  // Add row num to 1st DF
//    df2.join(df1,df1("id") === df2("id"))    // perform inner join
//      .drop("id")  // drop the id column
//      .show(false)

    val rdd = df2.join(df1,df1("id") === df2("id")).rdd  // here's your RDD you can
    val resultCoordinates : Array[(Int,Int)] = rdd.map(row => { // Iterate the result row by row
      // you can do all sort of operations per row return any type here.
      val coordinates = row.getAs[mutable.WrappedArray[Int]]("coordinates")
      val x = row.getAs[Integer]("x")
      val y = row.getAs[Integer]("y")
      (coordinates(0) - x,coordinates(1) - y )
    }).collect() // the collect call on the output
    resultCoordinates.foreach(r => println(s"(${r._1},${r._2})")) // printing the output result
  }

}