Challenges for self-driving database systems, which tune their physical design and configuration autonomously, are manifold: Such systems have to anticipate future workloads, find robust configurations efficiently, and incorporate knowledge gained by previous actions into later decisions. We present a component-based framework for self-driving database systems that enables database integration and development of self-managing functionality with low overhead by relying on separation of concerns. By keeping the components of the framework reusable and exchangeable, experiments are simplified, which promotes further research in that area. Moreover, to optimize multiple mutually dependent features, e.g., index selection and compression configurations, we propose a linear programming (LP) based algorithm to derive an efficient tuning order automatically. Afterwards, we demonstrate the applicability and scalability of our approach with reproducible examples.

中文翻译：

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自动驾驶数据库系统：一种概念方法

自动调整其物理设计和配置的自动驾驶数据库系统面临的挑战是多方面的：此类系统必须预测未来的工作负载，有效地找到可靠的配置，并将先前操作获得的知识整合到以后的决策中。我们提出了一个基于组件的自驱动数据库系统框架，该框架通过依赖关注点分离，以低开销实现数据库集成和自管理功能的开发。通过保持框架的组件可重用和可交换，简化了实验，促进了该领域的进一步研究。此外，为了优化多个相互依赖的功能，例如索引选择和压缩配置，我们提出了一种基于线性规划 (LP) 的算法来自动推导出有效的调谐顺序。之后，我们通过可重复的示例证明了我们方法的适用性和可扩展性。