CIGraph: Accelerating Graph Queries over Database with Compressed Index

Zhen Lv, Li Wang, Yingfan Liu, Kankan Zhao, Yanguo Peng

Abstract: Graph database management systems (GDBMS) can efficiently store and deal with massive graph data that reflect complex relations among entities. However, most existing works can not adapt well to the query requirements of large-scale complex graph data. In this paper, a prototype CIGraph is presented based on a compressed index, which preserves a high compression ratio and query performance on graph data. Through sufficient experimental evaluations on LDBC-SNB benchmark, CIGraph with compressed index outperforms JanusGraph in aspects of both compression ratio and query performance. Compared with JanusGraph, CIGraph occupies less than a third of the space to store the same graph data, and the acceleration coefficient for graph algorithms is up to 3X on average in CIGraph.