论文标题
从威斯基到波旁威斯康：对数结构合并树的学习索引
From WiscKey to Bourbon: A Learned Index for Log-Structured Merge Trees
论文作者
论文摘要
我们介绍了Bourbon，这是一种日志结构合并（LSM）树，它利用机器学习来提供快速查找。我们将波旁威士忌的设计和实施基于经验基本原则，通过对LSM设计的仔细分析得出。波旁威士忌采用贪婪的分段线性回归来学习关键分布，以最少的计算来快速查找，并采用成本效益策略来决定何时学习何时值得。通过一系列关于合成数据集和现实世界数据集的实验，我们表明，与最先进的生产LSM相比，波旁威士忌将查找性能提高了1.23x-1.78倍。
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.
