Research Survey on Intelligent Tuning of Database Knobs Configuration
The database system encompasses numerous configuration knobs that govern various aspects of its operations.These knobs cover a wide spectrum of database functionalities including memory allocation,I/O optimization,backup and recovery processes.The performance of a database system is heavily influenced by how these knobs are tuned,making their optimization a matter of significant importance.As databases and their associated applications continue to grow in scale and complexity,the conventional approach of manually adjusting these configuration knobs by database administrators is proving to be increasingly inadequate.In response to this emerging challenge,the realm of intelligent database knob tuning has surfaced as a promising solution.This innovative approach leverages machine learning techniques to automate the optimization of database knobs.By analyzing workload information,the settings of the database knobs,and performance metrics,intelligent tuning technologies are capable of recommending an optimal set of knobs that enhance database performance.This paper endeavors to conduct a comprehensive review and synthesis of the current methodologies employed in the intelligent tuning of database knobs.The discourse is structured around three primary focal points:the categorization of existing intelligent knob tuning methods,the examination of prevalent tools within the industry,and an exploration of the future challenges and research directions in this domain.The review begins with a systematic categorization of the existing methods of intelligent knob tuning into five distinct groups,each defined by the underlying machine learning algorithms they employ,including bayesian optimization,reinforcement learning,deep learning,searching based and rule based methods.For each category,a detailed exposition is provided,encompassing the principles,techniques,advantages,and limitations inherent to each method.This categorization not only elucidates the current landscape of intelligent knob tuning but also facilitates a deeper understanding of its theoretical and practical aspects.Furthermore,the paper delves into the practical application of intelligent knob tuning within the industry.It presents an overview of the mainstream database knob tuning tools currently in use,coupled with an analytical discussion of the practical challenges and pitfalls encountered in the deployment of these technologies.This segment aims to bridge the gap between theory and practice,shedding light on the real-world implications of intelligent knob tuning.Finally,the paper looks ahead to the future,identifying and discussing potential research directions in the field of intelligent knob tuning for databases.This prospective analysis is aimed at equipping researchers with a clear understanding of the current challenges,thereby inspiring and guiding future research endeavors in this dynamic and evolving field.In summary,this paper aspires to serve as a valuable resource for researchers and practitioners alike,offering a comprehensive overview of the state-of-the-art in intelligent database knob tuning.Through its detailed examination of existing methods,practical applications,and future research directions,it seeks to contribute to the ongoing advancement of research in this important area of database optimization and management.
万方数据
维普
