[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).
Can you explain why this is here? I still didn't understand why you don't always use self.memory_limit (or say self.memory_limit * 0.9 or something). Who cares if the memory is not 0 after you release it, you should still ramp up all the way before spilling.