将机器学习应用于参数调优是当前数据库领域的研究热点，具有广阔应用前景。数据库系统的参数调优是指通过修改数据库系统中的参数来获得更好的系统性能表现。传统的数据库系统调优工作由数据库管理员负责，资源和时间开销巨大，无法满足当下调优需求。近年来，自动化参数调优逐渐兴起，众多机器学习算法成功应用于数据库系统参数调优，并取得较好的优化效果。但大部分研究集中于算法层面，少有框架性工作，且这些工作仅面向关系型数据库系统，尚未有成熟的针对分布式数据库系统的调优工具。

本文以CockRoachDB分布式数据库系统作为研究对象，参考面向关系型数据库系统的工作，设计并实现适应分布式数据库系统的自动化参数调优工具。本文的研究工作主要包括三个方面。本文首先分析CockRoachDB分布式数据库系统的参数和指标，明确调优问题和主要调优目标，总结CockRoachDB分布式数据库系统的特点。第二，在分析基础上，本文设计了一种基于机器学习的参数调优方案，包括参数选取、指标选取、参数推荐三个步骤，筛选出重要的参数和指标，建立起参数推荐模型。第三，根据在CockRoachDB分布式系统上的参数优化经验，本文提出针对分布式数据库系统的参数调优工具框架。框架整体采用模块化、可配置的方案，分为交互模块、存储模块和计算模块，各模块提供相应函数接口，保证良好的可迁移性。工具可用于支持更多的数据库系统和机器学习算法。

最后，本文对在CockRoachDB数据库系统上实现的自动化参数调优工具进行实验评价。多个测试集上的实验结果表明，工具在较短的训练时间下可以达到较好的优化效果，能够提升至少25%的数据库系统查询吞吐率，或将查询延迟降低30%以上。

Applying machine learning algorithms to database configuration tuning is a hot topic, and has broad prospects. Configuration tuning in database refers to pursue the better performance of the system by modifying the parameters of the database management system. In the past time, the database administrator takes the responsibility of tuning configuration of database, which causes great resources and time overhead, leading to tuning dissatisfaction. In recent years, automated configuration tuning has gradually emerged, and many machine learning algorithms have been applied parameter tuning in database, achieving high performance. However, most of the research focuses on the tuning algorithms, but pay little attention to the framework, and only works on relational database. There are no mature tools for distributed database systems.

In this paper, based on the distributed database CockRoachDB, an automated configuration tuning tool adapted to the distributed database system is designed and implemented. The research work of this paper mainly includes three aspects. This paper first analyzes the knobs and metrics of CockRoachDB distributed database, clarifies the tuning problem and the main tuning target, and summarizes the features of CockRoachDB database system. Second, based on the analysis, this paper designs a configuration tuning plan based on machine learning algorithms, including knob selection, metric selection, configuration recommendation three steps, then filter out important knobs and metrics, and establish configuration recommendation model. Thirdly, according to the practical experience on CockRoachDB distributed database, this paper puts forward the configuration tuning tool framework for distributed database. Adhering to a modular, configurable design concept, the framework is divided into interaction modules, storage and computation modules. Each module provides function interfaces to ensure good portability. Tools can be used to support more distributed databases and machine learning algorithms.

Finally, the paper evaluates the automated configuration tuning tools implemented on the CockRoachDB distributed database system. The results show that the tool can achieve good optimization results in a short tuning time, improve throughput by at least 25%, or reduce query latency by more than 30%.