Rethinking Learned Cost Models: Why Start from Scratch?

Recent work has applied learning-based approaches to replace the conventional cost model, but these approaches are expensive to train and result in high inference overheads. Furthermore, due to a lack of explainability, models trained for one database may not be easily transferred to another, requiring a complete re-training process. In this paper, we propose a new approach to tuning the conventional formula-based cost model for DBMS. Our approach involves identifying important parameters within the cost model rules and using a fast-learning model to adjust them for each specific hardware and software configuration of the DBMS deployment. We dynamically partition the search space of hardware and software configurations to gradually refine the cost model estimation. To apply our cost model to a new DBMS instance, we start with a rough estimation and progressively refine it with finer granularity. Our experiments with different hardware and software configurations show that our approach enables the conventional cost model to be quickly transferred to any database instance, achieving comparable results to a fine-tuned learning-based model. Overall, our approach provides a practical solution to tuning the conventional cost model for DBMS, with significant benefits in terms of reduced cost and improved performance.