我试图在scala中编写一个udf函数，并在我的pyspark工作中使用它。我的数据帧架构是

root
|-- vehicle_id: string
|-- driver_id: string
|-- StartDtLocal: timestamp
|-- EndDtLocal: timestamp
|-- trips: array
| |-- element: struct
| | |-- week_start_dt_local: timestamp
| | |-- week_end_dt_local: timestamp
| | |-- start_dt_local: timestamp
| | |-- end_dt_local: timestamp
| | |-- StartDtLocal: timestamp
| | |-- EndDtLocal: timestamp
| | |-- vehicle_id: string
| | |-- duration_sec: float
| | |-- distance_km: float
| | |-- speed_distance_ratio: float
| | |-- speed_duration_ratio: float
| | |-- speed_event_distance_km: float
| | |-- speed_event_duration_sec: float
|-- trip_details: array
| |-- element: struct
| | |-- event_start_dt_local: timestamp
| | |-- force: float
| | |-- speed: float
| | |-- sec_from_start: float
| | |-- sec_from_end: float
| | |-- StartDtLocal: timestamp
| | |-- EndDtLocal: timestamp
| | |-- vehicle_id: string
| | |-- trip_duration_sec: float
我正在尝试编写一个udf函数

def calculateVariables(row: Row):HashMap[String, Float] = {

case class myRow(week_start_dt_local: Timestamp, week_end_dt_local: Timestamp, start_dt_local: Timestamp, end_dt_local :Timestamp, StartDtLocal:Timestamp,EndDtLocal:Timestamp,vehicle_id:String,duration_sec:Int,distance_km:Int,speed_distance_ratio:Float,speed_duration_ratio:Float,speed_event_distance_km:Float,speed_event_duration_sec:Float)

val trips = row.getAs[WrappedArray[myRow]](4)
在这个map函数中我试图将行转换为case类但不能。我得到这个错误。

java.lang.ClassCastException：org.apache.spark.sql.catalyst.expressions.GenericRowWithSchema无法强制转换为VariableCalculation.VariableCalculation $ myRow $ 3