Building a High-Performance Graph Storage on Top of Tree-Structured Key-Value Stores

• Published in: Big Data Mining and Analytics Vol. 7; no. 1; pp. 156 - 170
• Main Authors: Lin, Heng, Wang, Zhiyong, Qi, Shipeng, Zhu, Xiaowei, Hong, Chuntao, Chen, Wenguang, Luo, Yingwei
• Format: Journal Article
• Language: English
• Published: Beijing Tsinghua University Press 01.03.2024
• Subjects: Benchmarks; Computer science; Data base management systems; Data models; Data storage; Design; graph database; graph storage; high-performance; Layouts; Linked Data; Online gambling; Queries; Query languages; Relational data bases; Resource Description Framework-RDF; Risk assessment; Social network analysis; Social networks; Transfer of funds; Workloads
• ISSN: 2096-0654; 2097-406X
• DOI: 10.26599/BDMA.2023.9020015

Graph databases have gained widespread adoption in various industries and have been utilized in a range of applications, including financial risk assessment, commodity recommendation, and data lineage tracking. While the principles and design of these databases have been the subject of some investigation, there remains a lack of comprehensive examination of aspects such as storage layout, query language, and deployment. The present study focuses on the design and implementation of graph storage layout, with a particular emphasis on tree-structured key-value stores. We also examine different design choices in the graph storage layer and present our findings through the development of TuGraph, a highly efficient single-machine graph database that significantly outperforms well-known Graph DataBase Management System (GDBMS). Additionally, TuGraph demonstrates superior performance in the Linked Data Benchmark Council (LDBC) Social Network Benchmark (SNB) interactive benchmark.