An Empirical Study on Recent Graph Database Systems

  • Ran WangEmail author
  • Zhengyi Yang
  • Wenjie Zhang
  • Xuemin Lin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12274)


Graphs are widely used to model the intricate relationships among objects in a wide range of applications. The advance in graph data has brought significant value to artificial intelligence technologies. Recently, a number of graph database systems have been developed. In this paper, we present a comprehensive overview and empirical investigation on existing property graph database systems such as Neo4j, AgensGraph, TigerGraph and LightGraph (LightGraph has recently renamed to TuGraph.). These systems support declarative graph query languages. Our empirical studies are conducted in a single-machine environment against on the LDBC social network benchmark, consisting of three different large-scale datasets and a set of benchmark queries. This is the first empirical study to compare these graph database systems by evaluating data bulk importing and processing simple and complex queries. Experimental results provide insightful observations of various graph data systems and indicate that AgensGraph works well on SQL based workload and simple update queries, TigerGraph is powerful on complex business intelligence queries, Neo4j is user-friendly and suitable for small queries, and LightGraph is a more balanced product achieving good performance on different queries. The related code, scripts and data of this paper are available online (


Graph database systems Labeled property graph LDBC benchmark 



Xuemin Lin is supported by 2019B1515120048, 2018AAA-0102502 and 2018YFB1003504.


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Ran Wang
    • 1
    Email author
  • Zhengyi Yang
    • 2
  • Wenjie Zhang
    • 2
  • Xuemin Lin
    • 2
  1. 1.East China Normal UniversityShanghaiChina
  2. 2.The University of New South WalesSydneyAustralia

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