  Traditional query optimizers are designed to be fast and stateless: each
query is quickly optimized using approximate statistics, sent off to the
execution engine, and promptly forgotten. Recent work on learned query
optimization have shown that it is possible for a query optimizer to "learn
from its mistakes," correcting erroneous query plans the next time a plan is
produced. But what if query optimizers could avoid mistakes entirely? This
paper presents the idea of learned query superoptimization. A new generation of
query superoptimizers could autonomously experiment to discover optimal plans
using exploration-driven algorithms, iterative Bayesian optimization, and
program synthesis. While such superoptimizers will take significantly longer to
optimize a given query, superoptimizers have the potential to massively
accelerate a large number of important repetitive queries being executed on
data systems today.
