如何根据多个字段值的条件对spark Dataframe行执行flatMap？

import org.apache.spark.sql.{Row, SparkSession}
import org.apache.spark.sql.functions._

val spark = SparkSession.builder().appName("Example").getOrCreate()
import spark.implicits._

val df = Seq(
  ("Alice", 25, "New York"),
  ("Bob", 30, "London"),
  ("Charlie", 35, "San Francisco")
).toDF("name", "age", "city")

def processRow(row: Row): Seq[(String, Int, String)] = {
  val name = row.getAs[String]("name")
  val age = row.getAs[Int]("age")
  val city = row.getAs[String]("city")
  
  // 根据多个字段值的条件进行判断和处理
  if (age > 25 && city == "New York") {
    Seq((name, age, city), (name, age + 1, city))
  } else {
    Seq.empty
  }
}

val result = df.flatMap(row => processRow(row))