From Wisckey To Bourbon: A Learned Index For Log-Structured Merge Trees

We introduce BOURBON, a log-structured merge (LSM) tree that utilizes machine learning to provide fast lookups. We base the design and implementation of BOURBON on empirically-grounded principles that we derive through careful analysis of LSM design. BOURBON employs greedy piecewise linear regression to learn key distributions, enabling fast lookup with minimal computation, and applies a cost-benefit strategy to decide when learning will be worth- while. Through a series of experiments on both synthetic and real-world datasets, we show that BOURBON improves lookup performance by 1.23x -1.78x as compared to state- of-the-art production LSMs.