  Cardinality estimation is a fundamental but long unresolved problem in query
optimization. Recently, multiple papers from different research groups
consistently report that learned models have the potential to replace existing
cardinality estimators. In this paper, we ask a forward-thinking question: Are
we ready to deploy these learned cardinality models in production? Our study
consists of three main parts. Firstly, we focus on the static environment
(i.e., no data updates) and compare five new learned methods with eight
traditional methods on four real-world datasets under a unified workload
setting. The results show that learned models are indeed more accurate than
traditional methods, but they often suffer from high training and inference
costs. Secondly, we explore whether these learned models are ready for dynamic
environments (i.e., frequent data updates). We find that they cannot catch up
with fast data up-dates and return large errors for different reasons. For less
frequent updates, they can perform better but there is no clear winner among
themselves. Thirdly, we take a deeper look into learned models and explore when
they may go wrong. Our results show that the performance of learned methods can
be greatly affected by the changes in correlation, skewness, or domain size.
More importantly, their behaviors are much harder to interpret and often
unpredictable. Based on these findings, we identify two promising research
directions (control the cost of learned models and make learned models
trustworthy) and suggest a number of research opportunities. We hope that our
study can guide researchers and practitioners to work together to eventually
push learned cardinality estimators into real database systems.
