基于键值存储的可扩展图形处理系统

Tonglin Li, Chaoqi Ma, Jiabao Li, Xiaobing Zhou, Ke Wang, Dongfang Zhao, Iman Sadooghi, I. Raicu
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引用次数: 23

摘要

随着大数据和社交网络的兴起,对图形处理的需求迅速增加。在传统数据库中,涉及大量顶点和边的图查询操作可能非常慢。最先进的图形处理系统和数据库通常采用主/从架构,这可能会损害他们的贡献如下:可扩展性。本文介绍了一种基于批量同步并行模型的新型图形处理系统的设计与实现。我们的系统建立在ZHT之上,ZHT是一个可扩展的分布式键值存储,它在可伸缩性、性能和持久性方面有利于图形处理。实验结果表明该方法具有良好的可扩展性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
GRAPH/Z: A Key-Value Store Based Scalable Graph Processing System
The emerging applications in big data and social networks issue rapidly increasing demands on graph processing. Graph query operations that involve a large number of vertices and edges can be tremendously slow on traditional databases. The state-of-the-art graph processing systems and databases usually adopt master/slave architecture that potentially impairs their The contributions of this paper are as follows: scalability. This work describes the design and implementation of a new graph processing system based on Bulk Synchronous Parallel model. Our system is built on top of ZHT, a scalable distributed key-value store, which benefits the graph processing in terms of scalability, performance and persistency. The experiment results imply excellent scalability.
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