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Learned Query Optimizers: Evaluation and Improvement
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AI
DOI：10.1109/ACCESS.2022.3190376.png)
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Abstract
En 中文
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 a learnable 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.
Keywords:
Query processing
Encoding
Costs
Cost function
Reinforcement learning
Distributed databases
Transformers
Database query optimization
join ordering
machine learning
reinforcement learning
Journal
IF:
3.6
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9.8W
Citations:
29.4W