??????Nullpointer when saving as table with a timestamp column type

[??]

df: [name:String, Place:String, time: time:timestamp]
why not df: [name:String, Place:String,  time:timestamp]



------------------ ???????? ------------------
??????: "Brandon White";<bwwintheho...@gmail.com>;
????????: 2015??7??17??(??????) ????2:18
??????: "user"<user@spark.apache.org>; 

????: Nullpointer when saving as table with a timestamp column type



So I have a very simple dataframe that looks like

df: [name:String, Place:String, time: time:timestamp]


I build this java.sql.Timestamp from a string and it works really well expect 
when I call saveAsTable("tableName") on this df. Without the timestamp, it 
saves fine but with the timestamp, it throws 


java.lang.NullPointerException Driver stacktrace:       at 
org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1230)
        at 
org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1219)
         at 
org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1218)
         at 
scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)  
     at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)   at 
org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1218)  at 
org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:719)
         at 
org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:719)
         at scala.Option.foreach(Option.scala:236)       at 
org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:719)
  at 
org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1419)
   at 
org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1380)
   at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)





Any ideas how I can get around this?