@article{Lin2024, 
author = {Heng Lin and Zhiyong Wang and Shipeng Qi and Xiaowei Zhu and Chuntao Hong and Wenguang Chen and Yingwei Luo},
title = {Building a High-Performance Graph Storage on Top of Tree-Structured Key-Value Stores},
year = {2024},
journal = {Big Data Mining and Analytics},
volume = {7},
number = {1},
pages = {156-170},
keywords = {high-performance, graph database, graph storage},
url = {https://www.sciopen.com/article/10.26599/BDMA.2023.9020015},
doi = {10.26599/BDMA.2023.9020015},
abstract = {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.}
}