Authors: 

Yifan Dai, Yien Xu, Aishwarya Ganesan, and Ramnatthan Alagappan, University of Wisconsin - Madison; Brian Kroth, Microsoft Gray Systems Lab; Andrea Arpaci-Dusseau and Remzi Arpaci-Dusseau, University of Wisconsin - Madison

Abstract: 

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.

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