管道MapReduce:一种改进的MapReduce并行编程模型

Li Wang, Zhiwei Ni, Yiwen Zhang, Zhangjun Wu, Liyang Tang
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引用次数: 4

摘要

MapReduce是一个并行编程模型,用于处理大型数据集。MapReduce程序可以在大型商用机器上自动并发执行。为了解决数据密集型的信息检索问题,提出了一种改进的MapReduce编程模型——pipelinine -MapReduce。pipelinine -MapReduce允许数据在操作之间通过管道进行传输,扩展了MapReduce的批处理编程模型,并且可以减少完成时间,提高系统利用率。实验结果表明,pipelinine - mapreduce的实现可以很好地扩展,并在商用机器上有效地处理大型数据集。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Notice of Violation of IEEE Publication PrinciplesPipelined-MapReduce: An Improved MapReduce Parallel Programing Model
MapReduce is a parallel programming model, and used to handle large datasets. The MapReduce program can be automatically concurrent executed in large-scale commodity machines. We proposed an improved MapReduce programming model -- Pipelined-MapReduce, to solve the data intensive of information retrieval problems. Pipelined-MapReduce allows data transfer by pipeline between the operations, expanding the batched MapReduce programming model, and can reduce the completion time, and improve the system utilization rate. The experimental results demonstrate that the implemention of Pipelined-MapReduce can scale well and efficiently process large datasets on commodity machines.
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