원문정보
초록
영어
The MapReduce framework has been widely used to process and analyze largescale datasets over large clusters. As an essential problem, join operation among large clusters attracts more and more attention in recent years due to the utilization of MapReduce. Many strategies have been proposed to improve the efficiency of distributed join, among which bloomfilter is a successful one. However, the bloomfilter’s potential has not yet been fully exploited, especially in the MapReduce environment. In this paper, three strategies are presented to build the bloomfilter for the large datasets using MapReduce. Based on these strategies, we design two algorithms for two-way join and one algorithm for multi-way join. The experimental results show that our algorithms can significantly improve the efficiency of current join algorithm. Moreover, cost models of these algorithms are characterized in order to find out the way of improving the performance of two-way and multi-way joins.
목차
1 Introduction
2 MapReduce and Bloom lter
2.1 MapReduce
2.2 Bloom lter
3 Computing Bloom lters using MapReduce
4 Bloomjoins using MapReduce
4.1 Two-way Joins using MapReduce
4.2 Multi-way Joins using MapReduce
5 Cost Model of Bloomjoins using MapReduce
5.1 Cost Model of Computing Bloom lters using MapReduce
5.2 Cost Model of Two-way Bloomjoins
5.3 Cost Model of Multi-way Joins using MapReduce
5.4 Cost Model Validation
6 Related Work
7 Conclusion and Future Work
8 Acknowledgements
References