Adding info about single key grouping

Oh thanks - I didn't realize this was the reason, and now I understand what Spark does!  I'm glad we are adding this, but can this be reworded though to something like this to be more clear?

To determine which machine to shuffle a pair to, Spark calls a partitioning function on the key of each pair.  Spark spills data to disk when there is more data shuffled onto a single machine than can fit in memory.  However, it flushes out all data for one key at a time - so if a single key has more key-value pairs than can fit in memory, an out of memory exception occurs.  The out of memory exception should be addressed in a later release of Spark, but this scenario should still be avoided - when Spark needs to spill to disk, performance is severely impacted.