[SPARK-2538] [PySpark] Hash based disk spilling aggregation

During aggregation in Python worker, if the memory usage is above spark.executor.memory, it will do disk spilling aggregation. 

It will split the aggregation into multiple stage, in each stage, it will partition the aggregated data by hash and dump them into disks. After all the data are aggregated, it will merge all the stages together (partition by partition).
I see, in that case you should add a limit, because I can see it getting into an infinite loop. For example imagine that one hash code has too many items -- you'd keep loading and unloading stuff from disk.