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


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


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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@inproceedings {258965,
author = {Yifan Dai and Yien Xu and Aishwarya Ganesan and Ramnatthan Alagappan and Brian Kroth and Andrea Arpaci-Dusseau and Remzi Arpaci-Dusseau},
title = {From {WiscKey} to Bourbon: A Learned Index for {Log-Structured} Merge Trees},
booktitle = {14th USENIX Symposium on Operating Systems Design and Implementation (OSDI 20)},
year = {2020},
isbn = {978-1-939133-19-9},
pages = {155--171},
url = {},
publisher = {USENIX Association},
month = nov

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