摘要： 随着社交网络和语义Web 等数据应用的兴起,催生了许多图数据处理产品,包括Neo4j,HyperGraphDB等,然而这些产品在设计时并未充分考虑图应用对数据可用性和可扩展性的更高要求。为此,提出一种基于分布式内存云的图引擎底层建模和存储解决方案。在内存云上搭建分布式键值引擎,进而在键值存储的基础上对图的数据进行建模和读写。在大规模数据集上的实验结果表明,该方案具有较好的图随机访问性能,并能够高效地支持海量规模的图数据应用。
关键词:
图处理,
云计算,
分布式,
数据建模,
存储,
数据结构
Abstract: Graph applications rise with the emerging of social network and semantic Web,and generate many graph data
processing products,including Neo4j,HyperGraphDB,etc. However,current solutions fail to take into consideration graph applications’ higher requirements on data availability and scalability. This paper proposes a modeling and storage solution based on distributed memory cloud. It takes advantage of the prior work to build a key-value system over the memory cloud,then builds data modeling and read-write based on it. Experimental results on large scaled datasets show that this solution has a good figure random access performance,and it can support massive graph applications efficiently.
Key words:
graph processing,
cloud computing,
distributed,
data modeling,
storage,
data structure
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