From WiscKey to Bourbon: A Learned Index for Log-Structured Merge Trees
University of Wisconsin – Madison（威斯康星大学麦迪逊分校）；Microsoft Gray Systems Lab（微软格雷系统实验室）
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
