From WiscKey to Bourbon: A Learned Index for Log-Structured Merge Trees
This addresses performance bottlenecks in database systems that use LSM trees, offering a significant but incremental improvement over existing methods.
The paper tackles the problem of slow lookups in log-structured merge (LSM) trees by introducing BOURBON, a learned index that uses machine learning to learn key distributions, resulting in a 1.23x-1.78x improvement in lookup performance compared to state-of-the-art production LSMs.
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 worthwhile. 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.