Join Order Selection with Deep Reinforcement Learning: Fundamentals, Techniques, and Challenges

Join Order Selection (JOS) is a fundamental challenge in query optimization, as it significantly affects query performance. However, finding an optimal join order is an NP-hard problem due to the exponentially large search space. Despite the decades-long effort, traditional methods still suffer from limitations. Deep Reinforcement Learning (DRL) approaches have recently gained growing interest and shown superior performance over traditional methods. These DRL-based methods could leverage prior experience through the trial-and-error strategy to automatically explore the optimal join order. This tutorial will focus on recent DRL-based approaches for join order selection by providing a comprehensive overview of the various approaches. We will start by briefly introducing the core concepts of join ordering and the traditional methods for JOS. Next, we will provide some preliminary knowledge about DRL and then delve into DRL-based join order selection approaches by offering detailed information on those methods, analyzing their relationships, and summarizing their weaknesses and strengths. To help the audience gain a deeper understanding of DRL approaches for JOS, we will present two open-source demonstrations and compare their differences. Finally, we will identify research challenges and open problems to provide insights into future research directions. This tutorial will provide valuable guidance for developing more practical DRL approaches for JOS.