Cognitive Query Routing (CQR): Reinforcement learning for adaptive query processing in hybrid databases

Modern database systems increasingly adopt hybrid architectures that integrate multiple specialized engines such as row-store and column-store processing, in-memory and disk-based execution, or transactional and analytical components. While these architectures offer flexibility and performance benefits, they introduce significant challenges in query routing and resource allocation. This paper presents Cognitive Query Routing (CQR), a reinforcement learning-based framework for adaptive query processing in hybrid database environments. CQR leverages deep reinforcement learning to dynamically route queries to optimal execution engines based on workload characteristics, system state, and performance feedback. We synthesize foundational adaptive query processing concepts with recent advances in learned optimization and present a comprehensive framework that addresses the multi-engine routing problem. Our analysis demonstrates how CQR extends classical adaptive processing techniques while incorporating cognitive routing principles to achieve robust, explainable query execution in heterogeneous database architectures.