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Open Access

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

School of Computer Science, Peking University, Beijing 100871, China
Ant Group, Beijing 100020, China
Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China
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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.

Keywords

graph databasehigh-performancegraph storage

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Big Data Mining and Analytics
Volume 7 Issue 1,
March 2024
Pages 156-170
DOI: 10.26599/BDMA.2023.9020015
Cite this article:
Lin H, Wang Z, Qi S, et al. Building a High-Performance Graph Storage on Top of Tree-Structured Key-Value Stores. Big Data Mining and Analytics, 2024, 7(1): 156-170. https://doi.org/10.26599/BDMA.2023.9020015

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Received: 07 January 2023
Revised: 08 June 2023
Accepted: 19 June 2023
Published: 25 December 2023
© The author(s) 2023.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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