我将相同的方法应用于spark scala中的多个数据帧，如何将其并行化？

import org.apache.spark.sql.SparkSession

object ParallelizeDataFrames {
  def main(args: Array[String]): Unit = {
    // 创建SparkSession
    val spark = SparkSession.builder()
      .appName("ParallelizeDataFrames")
      .master("local")
      .getOrCreate()

    // 创建两个数据帧
    val df1 = spark.createDataFrame(Seq(
      (1, "Alice"),
      (2, "Bob"),
      (3, "Charlie")
    )).toDF("id", "name")

    val df2 = spark.createDataFrame(Seq(
      (4, "David"),
      (5, "Eve"),
      (6, "Frank")
    )).toDF("id", "name")

    // 将数据帧转换为RDD
    val rdd1 = df1.rdd
    val rdd2 = df2.rdd

    // 并行化处理RDD
    val resultRDD = spark.sparkContext.parallelize(Seq(rdd1, rdd2)).flatMap(_.collect())

    // 将结果转换为数据帧
    val resultDF = spark.createDataFrame(resultRDD).toDF("id", "name")

    // 显示结果
    resultDF.show()

    // 停止SparkSession
    spark.stop()
  }
}