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

IF 7.7 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Big Data Mining and Analytics Pub Date : 2023-12-25 DOI:10.26599/BDMA.2023.9020015

Heng Lin;Zhiyong Wang;Shipeng Qi;Xiaowei Zhu;Chuntao Hong;Wenguang Chen;Yingwei Luo

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引用次数: 0

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.

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在树状结构键值存储基础上构建高性能图存储

图数据库已在各行各业得到广泛应用，并被用于一系列应用中，包括金融风险评估、商品推荐和数据脉络跟踪。虽然这些数据库的原理和设计已成为一些研究的主题，但仍缺乏对存储布局、查询语言和部署等方面的全面研究。本研究的重点是图存储布局的设计与实现，特别强调树形结构的键值存储。我们还研究了图存储层的不同设计选择，并通过开发高效的单机图数据库TuGraph来展示我们的研究成果，TuGraph的性能明显优于著名的图数据库管理系统（GDBMS）。此外，TuGraph 在关联数据基准委员会（LDBC）的社交网络基准（SNB）交互基准中表现出了卓越的性能。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Big Data Mining and Analytics Computer Science-Computer Science Applications

CiteScore

20.90

自引率

2.20%

发文量

期刊介绍： Big Data Mining and Analytics, a publication by Tsinghua University Press, presents groundbreaking research in the field of big data research and its applications. This comprehensive book delves into the exploration and analysis of vast amounts of data from diverse sources to uncover hidden patterns, correlations, insights, and knowledge. Featuring the latest developments, research issues, and solutions, this book offers valuable insights into the world of big data. It provides a deep understanding of data mining techniques, data analytics, and their practical applications. Big Data Mining and Analytics has gained significant recognition and is indexed and abstracted in esteemed platforms such as ESCI, EI, Scopus, DBLP Computer Science, Google Scholar, INSPEC, CSCD, DOAJ, CNKI, and more. With its wealth of information and its ability to transform the way we perceive and utilize data, this book is a must-read for researchers, professionals, and anyone interested in the field of big data analytics.