Building a High-Performance Graph Storage on Top of Tree-Structured Key-Value Stores
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 investig...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2024-03-01
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| Series: | Big Data Mining and Analytics |
| Subjects: | |
| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2023.9020015 |
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| _version_ | 1832545238665658368 |
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| author | Heng Lin Zhiyong Wang Shipeng Qi Xiaowei Zhu Chuntao Hong Wenguang Chen Yingwei Luo |
| author_facet | Heng Lin Zhiyong Wang Shipeng Qi Xiaowei Zhu Chuntao Hong Wenguang Chen Yingwei Luo |
| author_sort | Heng Lin |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-6e0bd6dd70d64b49bec16faa3ef27dba |
| institution | Kabale University |
| issn | 2096-0654 |
| language | English |
| publishDate | 2024-03-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj-art-6e0bd6dd70d64b49bec16faa3ef27dba2025-02-03T07:26:27ZengTsinghua University PressBig Data Mining and Analytics2096-06542024-03-017115617010.26599/BDMA.2023.9020015Building a High-Performance Graph Storage on Top of Tree-Structured Key-Value StoresHeng Lin0Zhiyong Wang1Shipeng Qi2Xiaowei Zhu3Chuntao Hong4Wenguang Chen5Yingwei Luo6School of Computer Science, Peking University, Beijing 100871, ChinaAnt Group, Beijing 100020, ChinaAnt Group, Beijing 100020, ChinaAnt Group, Beijing 100020, ChinaAnt Group, Beijing 100020, ChinaAnt Group, Beijing 100020, ChinaSchool of Computer Science, Peking University, Beijing 100871, ChinaGraph 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.https://www.sciopen.com/article/10.26599/BDMA.2023.9020015graph databasehigh-performancegraph storage |
| spellingShingle | Heng Lin Zhiyong Wang Shipeng Qi Xiaowei Zhu Chuntao Hong Wenguang Chen Yingwei Luo Building a High-Performance Graph Storage on Top of Tree-Structured Key-Value Stores Big Data Mining and Analytics graph database high-performance graph storage |
| title | Building a High-Performance Graph Storage on Top of Tree-Structured Key-Value Stores |
| title_full | Building a High-Performance Graph Storage on Top of Tree-Structured Key-Value Stores |
| title_fullStr | Building a High-Performance Graph Storage on Top of Tree-Structured Key-Value Stores |
| title_full_unstemmed | Building a High-Performance Graph Storage on Top of Tree-Structured Key-Value Stores |
| title_short | Building a High-Performance Graph Storage on Top of Tree-Structured Key-Value Stores |
| title_sort | building a high performance graph storage on top of tree structured key value stores |
| topic | graph database high-performance graph storage |
| url | https://www.sciopen.com/article/10.26599/BDMA.2023.9020015 |
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