[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).
_first_spill() is only called for the first time, the data already be hold in memory, If we call _partitioned_merge() and _spill(), it will double the memory in worst case.

I had add more docs for these two, does it make sense for you?