LCKV: Learner-Cleaner Optimized Adaptive Key-Value Separated LSM-Tree Store

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 <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{LC}\text{KV}$</tex>, a key-value separated LSM-tree storage system built using the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\underline{L}earner-\underline{C}leaner$</tex> 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 <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{v}\text{alue logs}$</tex> (vLogs). Cleaner represents the garbage <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{c}\text{ollector}$</tex> (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.