Configuration tuning is vital to optimize the performance of a database management system (DBMS). It becomes more tedious and urgent for cloud databases (CDB) due to diverse database instances and query workloads, which make the job of a database administrator (DBA) very difficult. Existing solutions for automatic DBMS configuration tuning have several limitations. Firstly, they adopt a pipelined learning model but cannot optimize the overall performance in an end-to-end manner. Secondly, they rely on large-scale high-quality training samples which are hard to obtain. Thirdly, existing approaches cannot recommend reasonable configurations for a large number of knobs to tune whose potential values live in such high-dimensional continuous space. Lastly, in cloud environments, existing approaches can hardly cope with the changes of hardware configurations and workloads, and have poor adaptability. To address these challenges, we design an end-to-end automatic CDB tuning system, \({\texttt {CDBTune}}^{+}\), using deep reinforcement learning (RL). \({\texttt {CDBTune}}^{+}\) utilizes the deep deterministic policy gradient method to find the optimal configurations in a high-dimensional continuous space. \({\texttt {CDBTune}}^{+}\) adopts a trial-and-error strategy to learn knob settings with a limited number of samples to accomplish the initial training, which alleviates the necessity of collecting a massive amount of high-quality samples. \({\texttt {CDBTune}}^{+}\) adopts the reward-feedback mechanism in RL instead of traditional regression, which enables end-to-end learning and accelerates the convergence speed of our model and improves the efficiency of online tuning. Besides, we propose effective techniques to improve the training and tuning efficiency of \({\texttt {CDBTune}}^{+}\) for practical usage in a cloud environment. We conducted extensive experiments under 7 different workloads on real cloud databases to evaluate \({\texttt {CDBTune}}^{+}\). Experimental results showed that \({\texttt {CDBTune}}^{+}\) adapts well to a new hardware environment or workload, and significantly outperformed the state-of-the-art tuning tools and DBA experts.

配置调优对于优化数据库管理系统 (DBMS) 的性能至关重要。由于数据库实例和查询工作负载的多样性，云数据库（CDB）变得更加繁琐和紧迫，这使得数据库管理员（DBA）的工作变得非常困难。现有的自动 DBMS 配置调整解决方案有几个限制。首先，他们采用流水线学习模型，但无法以端到端的方式优化整体性能。其次，它们依赖于难以获得的大规模高质量训练样本。第三，现有方法无法为大量旋钮推荐合理的配置来调整其潜在值存在于这种高维连续空间中。最后，在云环境中，现有方法难以应对硬件配置和工作负载的变化，适应性较差。为了应对这些挑战，我们设计了一个端到端的自动 CDB 调整系统，\({\texttt {CDBTune}}^{+}\)，使用深度强化学习 (RL)。\({\texttt {CDBTune}}^{+}\)利用深度确定性策略梯度方法在高维连续空间中寻找最优配置。\({\texttt {CDBTune}}^{+}\)采用试错策略，以有限的样本学习旋钮设置来完成初始训练，从而减轻了收集大量高- 质量样品。\({\texttt {CDBTune}}^{+}\)采用强化学习中的奖励反馈机制代替传统的回归，实现了端到端的学习，加快了我们模型的收敛速度，提高了在线调优的效率。此外，我们提出了有效的技术来提高\({\texttt {CDBTune}}^{+}\)在云环境中的实际使用的训练和调整效率。我们在真实云数据库的 7 种不同工作负载下进行了大量实验，以评估\({\texttt {CDBTune}}^{+}\)。实验结果表明，\({\texttt {CDBTune}}^{+}\) 能够很好地适应新的硬件环境或工作负载，并且明显优于最先进的调优工具和 DBA 专家。