Spark Dataframe suddenly become very slow when I reuse the old cached data iteratively too much time

The problem happened when I try to keep my cached result in a List and try to calculate new DataFrame by all the data from the last list in each iteration. However, Even I use an empty DataFrame and get an empty result each time, the function will suddenly get very slow after about 8~12 round.

Here is my code

testLoop(Nil)
def testLoop(lastDfList:List[DataFrame]){      
  // do some dummy transformation like union and cache the result
  val resultDf = lastDfList.foldLeft(Seq[Data]().toDF){(df, lastDf) => df.union(lastDf)}.cache        

  // always get 0, of course  
  println(resultDf.count)  

  // benchmark action
  benchmark(resultDf.count)    

  testLoop(resultDf::lastDfList)
}

the benchmark result 1~6 round : < 200ms 7 round : 367ms 8 round : 918ms 9 round : 2476ms 10 round : 7833ms 11 round : 24231ms

I don't think GC or Block eviction is the problem in my case since I already use an empty DataFrame, but I don't know what is the cause? Do I misunderstand the meaning of cache or something?

Thanks!

After reading ImDarrenG's solution, I changed my code to be the following:

spark.sparkContext.setCheckpointDir("/tmp")

testLoop(Nil)
def testLoop(lastDfList:List[DataFrame]){      
  // do some dummy transformation like union and cache the result
  val resultDf = lastDfList.foldLeft(Seq[Data]().toDF){(df, lastDf) => df.union(lastDf)}.cache        

  resultDf.checkpoint()  

  // always get 0, of course  
  println(resultDf.count)  

  // benchmark action
  benchmark(resultDf.count)    

  testLoop(resultDf::lastDfList)
}

But it still become very slow after a few iterations.