LCKV: Learner-Cleaner Optimized Adaptive Key-Value Separated LSM-Tree Store
Mingxuan Liu, Jianhua Gu, Tianhai Zhao · 2024
Persistent key-value store based on LSM-trees represents one of the most advanced designs. Recent research shows that key-value separation has become a popular optimization method for LSM-tree. However, this storage architecture still incurs significant overhead when dealing with some query- and update-intensive workloads. In this paper, we propose$\text{LC}\text{KV}$, a key-value separated LSM-tree storage system built using the$\underline{L}earner-\underline{C}leaner$optimization to increase throughput. Learner represents the construction of learned indexes to increase query throughput, responsible for building models for the hot-readcold-written keys stored in the LSM-tree and values stored in the cold-written$\mathrm{v}\text{alue logs}$(vLogs). Cleaner represents the garbage$\mathrm{c}\text{ollector}$(GC) aimedat increasing update throughput, responsible not only for garbage collection but also for maintaining the sorting of the cold-written vLog. Evaluations show that LCKV outperforms other state-of-the-art solutions.