Cost-Based or Learning-Based?

Traditional cost-based optimizers are efficient and stable to generate optimal plans for simple SQL queries, but they may not generate high-quality plans for complicated queries. Thus learning-based optimizers have been proposed recently that can learn high-quality plans based on past experiences. However, learning-based optimizers cannot work well for dynamic workloads that have different distributions with training examples. In this paper, we propose a hybrid optimizer that adopts the advantages and avoids the shortcomings of these two types of optimizers, which first generates high-quality candidate plans from each type of optimizers and then selects the best plan from the candidates. There are two challenges. (1) How to generate high-quality candidates? We propose a hint-based candidate generation method that leverages the learning-based method to generate highly beneficial hints and then uses a cost-based method to supplement the hints to generate complete plans as candidates. (2) How to evaluate different candidate plans and select the best one? We propose an uncertainty-based optimal plan selection model, which predicts the execution time and the uncertainty for each plan. The uncertainty reflects the confidence of the execution time prediction. We select the plan using the uncertainty model. Experiment results on real datasets showed that our method outperformed the state-of-the-art baselines, and reduced the total latency by 25% and the tail latency by 65% compared to PostgreSQL.