FASTune: Towards Fast and Stable Database Tuning System with Reinforcement Learning

Abstract

Configuration tuning is vital to achieving high performance for a database management system (DBMS). Recently, automatic tuning methods using Reinforcement Learning (RL) have been explored to find better configurations compared with database administrators (DBAs) and heuristics. However, existing RL-based methods still have several limitations: (1) Excessive overhead due to reliance on cloned databases; (2) trial-and-error strategy may produce dangerous configurations that lead to database failure; (3) lack the ability to handle dynamic workload. To address the above challenges, a fast and stable RL-based database tuning system, FASTune, is proposed. A virtual environment is proposed to evaluate configurations which is an equivalent yet more efficient scheme than the cloned database. To ensure stability during tuning, FASTune adopts an environment proxy to avoid dangerous configurations. In addition, a Multi-State Soft Actor–Critic (MS-SAC) model is proposed to handle dynamic workloads, which utilizes the soft actor–critic network to tune the database according to workload and database states. The experimental results indicate that, compared with the state-of-the-art methods, FASTune can achieve improvements in performance while maintaining stability in the tuning.