Learned Query Optimizer: At the Forefront of AI-Driven Databases

Abstract

Applying ML-based techniques to optimize traditional databases, or AI4DB, has becoming a hot research spot in recent. Learned techniques for query optimizer(QO) is the forefront in AI4DB. QO provides the most suitable experimental plots for utilizing ML techniques and learned QO has exhibited superiority with enough evidence. In this tutorial, we aim at providing a wide and deep review and analysis on learned QO, ranging from algorithm design, real-world applications and system deployment. For algorithm, we would introduce the advances for learning each individual component in QO, as well as the whole QO module. For system, we would analyze the challenges, as well as some attempts, for deploying ML-based QO into actual DBMS. Based on them, we summarize some design principles and point out several future directions. We hope this tutorial could inspire and guide researchers and engineers working on learned QO, as well as other context in AI4DB.