通用的数据库系统为不同的应用需求与数据类型提供统一的处理方式，在取得了巨大成功的同时，也暴露了一定的局限性：由于没有结合具体应用的数据分布与工作负载，系统往往难以保证性能的最优.为了解决这一问题，"学习式数据库系统"成为了目前数据库领域的研究热点，它利用机器学习技术有效捕获负载与数据的特性，从而对数据库系统进行优化.围绕这一方向，近些年工业界与学术界涌现出了大量的研究工作.首先提出了细粒度的分类体系，从数据库架构出发，将现有工作进行了梳理；其次，系统地介绍了学习式数据库各组件的研究动机、基本思路与关键技术；最后，对学习式数据库系统未来的研究方向进行了展望.

Modern database systems provide a general design principle for various data types and application workloads. While gaining great success in the last decades, the principle has a limitation that a database system may not achieve superior performance, if the system cannot be "customized" to the specific data distributions and workload characteristics. To address the problem, learnable database systems have attracted much attention from both industrial and academic communities, with a novel idea of using machine learning to optimize database systems. Along with this direction, extensive efforts have been done very recently to advance the field of learnable database systems. This survey systematically reviews the existing studies from the perspective of database system architecture. A fine-grained taxonomy is provided by categorizing the existing works by their target learnable database components. To help readers better understand each type of learnable components their motivations are presented, demonstrating the insights and introducing the key techniques. Finally, a number of promising future research directions are outlined of learnable database systems.

国家自然科学基金（61632016，U1711261）；中国人民大学科研基金（18XNLG18）