  Automating physical database design has remained a long-term interest in
database research due to substantial performance gains afforded by optimised
structures. Despite significant progress, a majority of today's commercial
solutions are highly manual, requiring offline invocation by database
administrators (DBAs) who are expected to identify and supply representative
training workloads. Even the latest advancements like query stores provide only
limited support for dynamic environments. This status quo is untenable:
identifying representative static workloads is no longer realistic; and
physical design tools remain susceptible to the query optimiser's cost
misestimates. Furthermore, modern application environments such as hybrid
transactional and analytical processing (HTAP) systems render analytical
modelling next to impossible.
  We propose a self-driving approach to online index selection that eschews the
DBA and query optimiser, and instead learns the benefits of viable structures
through strategic exploration and direct performance observation. We view the
problem as one of sequential decision making under uncertainty, specifically
within the bandit learning setting. Multi-armed bandits balance exploration and
exploitation to provably guarantee average performance that converges to
policies that are optimal with perfect hindsight. Our comprehensive empirical
evaluation against a state-of-the-art commercial tuning tool demonstrates up to
75% speed-up on shifting and ad-hoc workloads and up to 28% speed-up on static
workloads in analytical processing environments. In HTAP environments, our
solution provides up to 59% speed-up on shifting and 51% speed-up on static
workloads. Furthermore, our bandit framework outperforms deep reinforcement
learning (RL) in terms of convergence speed and performance volatility
(providing up to 58% speed-up).

Databases;Machine Learning