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arXiv · 2005.14213

From WiscKey to Bourbon: A Learned Index for Log-Structured Merge Trees

Yifan Dai·Yien Xu·Aishwarya Ganesan·Ramnatthan Alagappan·Brian Kroth·Andrea C. Arpaci-Dusseau·Remzi H. Arpaci-Dusseau
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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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Yifan Dai, Yien Xu, Aishwarya Ganesan, Ramnatthan Alagappan, Brian Kroth, Andrea C. Arpaci-Dusseau, Remzi H. Arpaci-Dusseau. 2020-05-28. From WiscKey to Bourbon: A Learned Index for Log-Structured Merge Trees. https://arxiv.org/abs/2005.14213

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