Our study shows that shuffle is a performance bottleneck of mapreduce computing. There are some problems of shuffle:
(1)Shuffle and reduce are tightly-coupled, usually shuffle phase doesn't consume too much memory and CPU, so theoretically, reducetasks's slot can be used for other computing tasks when copying data from maps. This method will enhance cluster utilization. Furthermore, should shuffle be separated from reduce? Then shuffle will not use reduce's slot,we need't distinguish between map slots and reduce slots at all.
(2)For large jobs, shuffle will use too many network connections, Data transmitted by each network connection is very little, which is inefficient. From 0.21.0 one connection can transfer several map outputs, but i think this is not enough. Maybe we can use a per node shuffle client progress(like tasktracker) to shuffle data for all reduce tasks on this node, then we can shuffle more data trough one connection.
(3)Too many concurrent connections will cause shuffle server do massive random IO, which is inefficient. Maybe we can aggregate http request(like delay scheduler), then random IO will be sequential.
(4)How to manage memory used by shuffle efficiently. We use buddy memory allocation, which will waste a considerable amount of memory.
(5)If shuffle separated from reduce, then we must figure out how to do reduce locality?
(6)Can we store map outputs in a Storage system(like hdfs)?
(7)Can shuffle be a general data transfer service, which not only for map/reduce paradigm?