大数据2025，Vol.11Issue(3)：49-61,13.DOI:10.11959/j.issn.2096-0271.2025038
面向云边场景的读写均衡键值存储系统
A read-write balanced key-value store for cloud-edge computing environments
摘要
Abstract
The LSM-tree based key-value store has become an ideal choice for cloud and edge data management due to its efficient data storage mechanism.However,the leveled compaction strategy used in LSM-tree suffered from high write amplification,which negatively impacted foreground write performance.Reducing write amplification and further improving write performance without sacrificing read performance become a major challenge in optimizing LSM-tree.To address this issue,we proposed a novel key-value store system,LooseKV,which leveraged the tiered compaction strategy to significantly reduce write amplification.Additionally,LooseKV introduced a skip-list-based memory index,combined with a lightweight index update strategy and integration with iterators in the tiered structure,effectively improving the read performance issues associated with the tiered strategy.Experimental results demonstrated that LooseKV achieved 1.18 to 2.28 times higher random write throughput than LevelDB,1.01 to 1.26 times better random read performance,and slightly lower sequential read performance compared to LevelDB,but comparable to PebblesDB.关键词
键值存储/日志结构合并树/写放大/读性能优化Key words
key-value store/log-structured merge tree/write amplification/read performance optimization分类
计算机与自动化引用本文复制引用
郑宜湉,张余豪,霍志杰,舒继武..面向云边场景的读写均衡键值存储系统[J].大数据,2025,11(3):49-61,13.基金项目
国家自然科学基金青年科学基金项目(No.62402204) （No.62402204）
国家自然科学基金联合基金项目(No.U22B2023) Young Scientists Fund of the National Natural Science Foundation of China(No.62402204),Joint Funds of the National Natural Science Foundation of China(No.U22B2023) （No.U22B2023）
