Learned Query Optimizers: Evaluation and Improvement
Artem Mikhaylov, Nina Mazyavkina, Mikhail Salnikov, Ilya Trofimov, Qiang Fu, Evgeny Vladimirovich Burnaev · IEEE Access · 2022
Query Optimization is considered to be one of the most important challenges in database management. Existing built-in query optimizers are very complex and rely on various approximations and hand-picked rules. The rise of deep learning and deep reinforcement learning has aided many scientific and industrial fields, providing an opportunity to develop alearnable query optimizer. In this paper, we analyse and improve the state-of-the-art learned query optimizer, Neo for the JOB benchmark on two database systems: PostgreSQL and Huawei GaussDB.We describe our methods, based on combination of Neo, Tree-Transformers, auxiliary tasks, reward weighting. Combinations of these methods improve latency of the found query execution plans. We also conduct a thorough analysis of the resulting execution plans and devise a set of decision-based rules to indicate the cases when the learned optimizer will outperform the built-in one. We also provide a source code for the proposed methods and experiments. Finally, we provide possible directions for further improvement in this field.