Type mismatch with identical types in Spark-shell

I have a workflow script around a spark shell, but I am often annoyed by bizarre type mismatches (probably inherited from scala repl) occurring with identical found and required types. The following example illustrates the problem. Done in paste mode, no problem

scala> :paste
// Entering paste mode (ctrl-D to finish)


import org.apache.spark.rdd.RDD
case class C(S:String)
def f(r:RDD[C]): String = "hello"
val in = sc.parallelize(List(C("hi")))
f(in)

// Exiting paste mode, now interpreting.

import org.apache.spark.rdd.RDD
defined class C
f: (r: org.apache.spark.rdd.RDD[C])String
in: org.apache.spark.rdd.RDD[C] = ParallelCollectionRDD[0] at parallelize at <console>:13
res0: String = hello

      

        
        
        
      

    

but

scala> f(in)
<console>:29: error: type mismatch;
 found   : org.apache.spark.rdd.RDD[C]
 required: org.apache.spark.rdd.RDD[C]
              f(in)
                ^ 

      

        
        
        
      

    

There is a related discussion about scala repl and about spark shells but the mentioned issue seems unrelated (and resolved) to me.

This issue causes serious problems for recording an interactive session with pluggable code in a replica, or it loses much of the benefit of working in repl to begin with. Is there a solution? (And / or is this a known issue?)

edits:

Problems arising from sparking 1.2 and 1.3.0. Spark test 1.3.0 using scala 2.10.4

It seems that, at least in the test, repeating the statement using the class separately from the case class definition will reduce the problem

scala> :paste
// Entering paste mode (ctrl-D to finish)


def f(r:RDD[C]): String = "hello"
val in = sc.parallelize(List(C("hi1")))

// Exiting paste mode, now interpreting.

f: (r: org.apache.spark.rdd.RDD[C])String
in: org.apache.spark.rdd.RDD[C] = ParallelCollectionRDD[1] at parallelize at <console>:26

scala> f(in)
res2: String = hello