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Building a High-Performance Graph Storage on Top of Tree-Structured Key-Value Stores
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作者 Heng Lin Zhiyong Wang +4 位作者 Shipeng Qi Xiaowei Zhu Chuntao Hong Wenguang Chen Yingwei Luo 《Big Data Mining and Analytics》 EI CSCD 2024年第1期156-170,共15页
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 ... 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. 展开更多
关键词 graph database HIGH-PERFORMANCE graph storage
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