Many GaussDB customers, particularly small and medium-sized enterprises (SMEs), require high transaction throughput with occasional analytical queries. HTAP systems that deploy both OLTP and OLAP engines on a single server to manage hybrid workloads have become increasingly popular among customers for achieving high cost-efficiency and data freshness. However, co-locating these systems can lead to resource contention, particularly for memory, potentially degrading overall system performance and causing Service-Level Agreements (SLA) violations. To address this issue, we propose T 2 (Twisted Twin), an adaptive memory management approach that dynamically allocates memory between OLTP and OLAP components. This approach ensures OLTP meets SLA while optimizing the efficiency of OLAP query processing. However, this is non-trivial, as memory allocation triggers a cascade of effects, including in-memory column selection and data synchronization, both critical in HTAP systems. To overcome these challenges, we introduce a Bayesian optimization framework tailored for fluctuating workloads that adjusts memory allocation responsively. Experiments conducted on the real-world HTAP system, GaussDB-HTAP, demonstrate the effectiveness and efficiency of T 2 .

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.