org.apache.spark.sql.catalyst.expressions.GenericRowWithSchema cannot be cast to scala.Tuple2

今天在写spark 提数的时候遇到一个异常，如下

Caused by: java.lang.ClassCastException: org.apache.spark.sql.catalyst.expressions.GenericRowWithSchema cannot be cast to scala.Tuple2  at $anonfun$1$$anonfun$apply$1.apply(<console>:27)  at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)  at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)  at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)  at scala.collection.mutable.WrappedArray.foreach(WrappedArray.scala:35)  at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)  at scala.collection.AbstractTraversable.map(Traversable.scala:104)  at $anonfun$1.apply(<console>:27)  at $anonfun$1.apply(<console>:27)  at scala.collection.Iterator$$anon$11.next(Iterator.scala:409)  at scala.collection.Iterator$class.foreach(Iterator.scala:893)  at scala.collection.AbstractIterator.foreach(Iterator.scala:1336)  at org.apache.spark.rdd.RDD$$anonfun$foreach$1$$anonfun$apply$28.apply(RDD.scala:917)  at org.apache.spark.rdd.RDD$$anonfun$foreach$1$$anonfun$apply$28.apply(RDD.scala:917)  at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1944)  at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1944)  at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)  at org.apache.spark.scheduler.Task.run(Task.scala:99)  at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:282)  at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)  at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)  at java.lang.Thread.run(Thread.java:745)

df的schema如下：

root |-- cityId: long (nullable = true) |-- countryId: long (nullable = true) |-- outline: array (nullable = true) |    |-- element: struct (containsNull = true) |    |    |-- _1: double (nullable = true) |    |    |-- _2: double (nullable = true) |-- provinceId: long (nullable = true) |-- townId: long (nullable = true)

使用如下方法在提取outline 字段时，报错

val outline = df.select("outline").collect().map(row => row.getAs[Seq[(Double,Double)]]("outline"))

解决方法，我已知两种：
第一种，在生成数据时，定义case class对象，不存储tuple，就我个人而言，不适用，数据已经生成完毕，再次生成数据需要2天的时间
第二种方法：

 val lines = json.filter(row => !row.isNullAt(2)).select("outline").rdd.map(r => {      val row:Seq[(Double,Double)] = r.getAs[Seq[Row]](0).map(x =>{(x.getDouble(0),x.getDouble(1))})      row    }).collect()(0)

至于报错的原因，google了一下，我觉得有一种说法可信：
在row 中的这些列取得时候，要根据类型取，简单的像String，Seq[Double] 这种类型就可以直接取出来，但是像 Seq[(Double,Double)] 这种类型直接取得花就会丢失schema信息，虽然值能取到，但是schema信息丢了，在dataFrame中操作的时候就会抛错