A series of studies apply machine learning to assist cost-based query optimizers in DBMS, emphasizing incorporating uncertainty predictions to guide decision-making. While these approaches have demonstrated advancement in some benchmarks, their drawbacks, such as unstable performance, stem from the inherent challenges of using machine learning models to predict the cost of execution plans and the lack of exploration of the intrinsic characteristics of suboptimal plans. In this paper, we introduce an alert system for query optimization, which is built upon cost models to reduce the selection of regressed plans. The key insight is that there are differences in the predictive uncertainty that lead to query optimization and the regression of execution plans. We investigate the causes of these differences in uncertainty and design a discriminator to filter out execution plans with higher risks of regression. The alert system can be integrated with various cost models, enhancing the robustness of query optimizers. In our experiments, the system further reduces execution time by 20% compared to learned optimizers. Meanwhile, the proportion of optimized queries reduced by the alert system is just 15% of the proportion of regressed queries diminished.

Cost estimation is of great importance in query optimization. However, traditional optimizers compute the cost based on heuristics, sacrificing accuracy for efficiency. In recent years, learning-based cost estimation models have achieved high accuracy. However, their poor robustness and inefficiency lead to their failure to meet the needs of practical scenarios. We propose a lightweight and Database-Agnostic Cost Estimation model (DACE) to address the above limitations. To further improve the effectiveness of DACE, we design a tree-structure-based loss adjustment strategy to learn sub-plan information and solve the information redundancy problem. As a pretrained estimator, DACE can efficiently make accurate predictions on unseen databases. For more complex scenarios, we fine-tune DACE with LoRA. The excellent efficiency allows DACE to adapt to challenging scenarios with minimal effort. As a pretrained encoder, DACE can improve the accuracy and robustness of other cost estimation models through knowledge integration and solve the notorious cold start problem. Extensive experiments have shown that DACE's accuracy, efficiency, and robustness are much better than existing methods.