Accumulative Computation on MapReduce
Yu Liu Kento Emoto Kiminori Matsuzaki Zhenjiang Hu
In this paper we propose a new approach of using the programming pattern accumulate over MapReduce, to handle a large class of problems that cannot be simply divided into independent sub-computations. Using this accumulate pattern, many problems that have computational dependency can be easily expressed, and then the programs will be transformed to MapReduce programs executed on large clusters. Users without much knowledge of MapReduce can also easily write programs in a sequential manner but finally obtain efficient and scalable MapReduce programs.