Enhancing Query Optimization in Cloud-Native Relational Databases: Leveraging Policy Gradient Methods for Intelligent Automation

Abstract

Query optimization is a critical aspect of database management systems (DBMS), directly influencing the performance and efficiency of data retrieval operations. Traditional query optimization techniques, including rule-based and cost-based methods, have been the cornerstone of relational database systems for decades. However, the increasing complexity and scale of modern databases have exposed the limitations of these conventional approaches, prompting the exploration of more adaptive and intelligent methods. This paper investigates the application of Policy Gradient methods, a class of Reinforcement Learning (RL) algorithms, for automated query optimization in relational databases. Unlike traditional methods that rely on static heuristics or exhaustive cost-based searches, Policy Gradient methods learn to optimize queries by interacting with the database environment and receiving feedback in the form of rewards. This dynamic approach allows for continuous improvement and adaptation to the evolving characteristics of the Cloud database. We present a detailed analysis of how query optimization can be framed as a reinforcement learning problem, where the goal is to find the optimal query execution plan by maximizing the expected reward. The paper introduces the specific implementation of Policy Gradient methods, including the REINFORCE algorithm and Actor-Critic methods, and evaluates their effectiveness compared to traditional optimization techniques. Experimental results demonstrate that Policy Gradient methods can achieve significant performance gains, particularly in complex query scenarios.