Hybrid Transactional-Analytical Processing (HTAP) has attracted growing attention from both academia and industry. Most HTAP systems adopt a dual-engine architecture, maintaining separate row and column engines to achieve workload isolation: row engines excel at transactional workloads, while column engines are optimized for analytical queries. For such systems, dispatching queries to the appropriate engine with ultra-low latency is highly desirable but remains challenging. Existing approaches often rely on traditional cost estimation, which is often inaccurate and fails to adapt to dynamic workload patterns. Moreover, they generally overlook resource balancing when dispatching workloads. In this paper, we present AQD, an online Adaptive Query Dispatcher framework. AQD operates in two phases: (1) in the offline phase, it trains a LightGBM classifier using self-paced, Taylor-weighted boosting that emphasizes costly mispredictions; (2) in the online phase, it employs a LinTS-Delta bandit to adapt to workload drift via execution feedback, while a Mahalanobis-based regulator ensures balanced CPU and memory utilization across the two engines. We integrate AQD into PolarDB and evaluate it on standard benchmarks as well as real-world datasets. Experimental results show that AQD reduces average query latency by over 90% compared to cost-threshold dispatching and improves HyBench score by 15% over the cost-threshold method and 9% over the current SOTA BRAD.