Building a High-Performance Graph Storage on Top of Tree-Structured Key-Value Stores - Lin, Heng; Wang, Zhiyong; Qi, Shipeng; Zhu, Xiaowei; Hong, Chuntao; Chen, Wenguang; Luo, Yingwei (Big Data Mining and Analytics, 2024)
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.