A read-write balanced key-value store for cloud-edge computing environments

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.