  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.
