spark sql 中 java.lang.ClassCastException: org.apache.spark.sql.catalyst.expressions.Gener

最近在做推荐系统的项目，使用了spark 2.0，需要给每个userid 推荐出一个list ，使用了udf函数

udf ()在每行执行的时候 得到

org.apache.spark.SparkException: Failed to execute user defined function($anonfun$calcMaxSimilarity$2$1: (string, array) => array>)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$8$$anon$1.hasNext(WholeStageCodegenExec.scala:377)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:231)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:225)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$25.apply(RDD.scala:826)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$25.apply(RDD.scala:826)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
at org.apache.spark.scheduler.Task.run(Task.scala:99)
at org.apache.spark.executor.Executor$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)
Caused by: java.lang.ClassCastException: org.apache.spark.sql.catalyst.expressions.GenericRowWithSchema cannot be cast to scala.Tuple2
at com.allyes.awise.eng.rec.personal.cf.ItemBasedRec$$anonfun$calcMaxSimilarity$2$1$$anonfun$apply$4$$anonfun$10.apply(ItemBasedRec.scala:153)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
at scala.collection.mutable.WrappedArray.foreach(WrappedArray.scala:35)
at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
at scala.collection.AbstractTraversable.map(Traversable.scala:104)
at com.allyes.awise.eng.rec.personal.cf.ItemBasedRec$$anonfun$calcMaxSimilarity$2$1$$anonfun$apply$4.apply(ItemBasedRec.scala:153)
at com.allyes.awise.eng.rec.personal.cf.ItemBasedRec$$anonfun$calcMaxSimilarity$2$1$$anonfun$apply$4.apply(ItemBasedRec.scala:113)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
at scala.collection.immutable.HashMap$HashMap1.foreach(HashMap.scala:221)
at scala.collection.immutable.HashMap$HashTrieMap.foreach(HashMap.scala:428)
at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
at scala.collection.AbstractTraversable.map(Traversable.scala:104)
at com.allyes.awise.eng.rec.personal.cf.ItemBasedRec$$anonfun$calcMaxSimilarity$2$1.apply(ItemBasedRec.scala:113)
at com.allyes.awise.eng.rec.personal.cf.ItemBasedRec$$anonfun$calcMaxSimilarity$2$1.apply(ItemBasedRec.scala:99)

其中udf 是一个dataFrame，然后itemFactor也是一个dataFrame,因为在dataFrame内部不能嵌套使用df，所以只能 将itemDF.collect到driver端,然后再通过broadCast到executor端, 问题就出在collect到driver端这个过程

原来的写法如下：

val itemFeatures = joinedDf.filter(_ != null).rdd.map((r: Row) =>{
 (r.getString(0),(r.getAs[Seq[Float]](1),r.getAs[Seq[(String,Float)]](2)))
}).collect().toMap

从row对象获取各个列的时候，会根据类型分别取出是String, Seq的，但是之前一直按Seq[Float] 这种去取

r.getAs[Seq[(String,Float)]](2)  

始终报如上错误，能看到这个Seq的值，但就是对这个Seq进行转换就不行，打印也不行, 

后来对亏了同事妹子帮忙查了半天才查到问题： 在row 中的这些列取得时候，要根据类型取，简单的像String，Seq[String]  这种类型就可以直接取出来，但是像

r.getAs[Seq[(String,Float)]](2)

这种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，其实这也是自己认为的，不知道对不对，欢迎各位大神指教

正确写法：

//convert dataframe data to broadcast base data.
//fields: id, features
val itemFeatures = joinedDf.filter(_ != null).rdd.map((r: Row) =>{
  val row: Seq[(String, Float)] = r.getAs[Seq[Row]](2).map(x =>{
    (x.getString(0),x.getFloat(1))
  })
  (r.getString(0),(r.getAs[Seq[Float]](1),row))
}).collect().toMap

具体原因，以后下周再看看，周五啦，赶紧下班喽.....