Hyper: Hybrid Physical Design Advisor with Multi-agent Reinforcement Learning

Various physical design (PD) options within a single database have emerged to optimize diverse workloads, including row-based PDs (e.g., index) and column-based PDs (e.g., column-store replica), each with its own acceleration advantages for different workloads. Determining the optimal combination of these two PDs is a labor-intensive and challenging task, yet it could result in significant performance improvements for the system. Recent automated index advisors (AIAs) have concentrated on identifying the most advantageous combination of row-based PDs. However, the extension of these efforts to the present problem has proven challenging due to 1) the larger search space of hybrid PD selections, 2) the inadequate consideration of the complex interactions between heterogeneous PDs, and 3) the inaccurate evaluation made by the what-if optimizer. To address these issues, we propose a Hybrid physical design advisor (Hyper) with multi-agent reinforcement learning. Hyper excels at recommending the optimal combination of PDs under any specific workload, with an overarching emphasis on both efficiency and quality. Comprehensive evaluations on well-established benchmarks show that our approach outperforms state-of-the-art methods.