  Tuning a database system to achieve optimal performance on a given workload
is a long-standing problem in the database community. A number of recent works
have leveraged ML-based approaches to guide the sampling of large parameter
spaces (hundreds of tuning knobs) in search for high performance
configurations. Looking at Microsoft production services operating millions of
databases, sample efficiency emerged as a crucial requirement to use tuners on
diverse workloads. This motivates our investigation in LlamaTune, a tuner
design that leverages domain knowledge to improve the sample efficiency of
existing optimizers. LlamaTune employs an automated dimensionality reduction
technique based on randomized projections, a biased-sampling approach to handle
special values for certain knobs, and knob values bucketization, to reduce the
size of the search space. LlamaTune compares favorably with the
state-of-the-art optimizers across a diverse set of workloads. It identifies
the best performing configurations with up to $11\times$ fewer workload runs,
and reaching up to $21\%$ higher throughput. We also show that benefits from
LlamaTune generalize across both BO-based and RL-based optimizers, as well as
different DBMS versions. While the journey to perform database tuning at
cloud-scale remains long, LlamaTune goes a long way in making automatic DBMS
tuning practical at scale.
