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.

How to manage various data in a unified way is a significant research topic in the field of databases. To address this problem, researchers have proposed multi-model databases to support multiple data models in a uniform platform with a single unified query language. However, since relational databases are predominant in the current market, it is expensive to replace them with others. Besides, due to the theories and technologies of RDBMSs having been enhanced over decades, it is hard to use few years to develop a multi-model database that can be compared with existing RDBMSs in handling security, query optimization, transaction management, etc. In this paper, we reconsider employing relational databases to store and query multi-model data. Unfortunately, the mismatch between the complexity of multi-model data structure and the simplicity of flat relational tables makes this difficult. Against this challenge, we utilize the reinforcement learning (RL) method to learn a relational schema by interacting with an RDBMS. Instead of using the classic Q-learning algorithm, we propose a variant Q-learning algorithm, called Double Q-tables, to reduce the dimension of the original Q-table and improve learning efficiency. Experimental results show that our approach could learn a relational schema outperforming the existing multi-model storage schema in terms of query time and space consumption.

Optimal configuration is vital for a DataBase Management System (DBMS) to achieve high performance. There is no one-size-fits-all configuration that works for different workloads since each workload has varying patterns with different resource requirements. There is a relationship between configuration, workload, and system performance. If a configuration cannot adapt to the dynamic changes of a workload, there could be a significant degradation in the overall performance of DBMS unless a sophisticated administrator is continuously re-configuring the DBMS. In this tutorial, we focus on autonomous workload-aware performance tuning, which is expected to automatically and continuously tune the configuration as the workload changes. We survey three research directions, including 1) workload classification, 2) workload forecasting, and 3) workload-based tuning. While the first two topics address the issue of obtaining accurate workload information, the third one tackles the problem of how to properly use the workload information to optimize performance. We also identify research challenges and open problems, and give real-world examples about leveraging workload information for database tuning in commercial products (e.g., Amazon Redshift). We will demonstrate workload-aware performance tuning in Amazon Redshift in the presentation.