A systematic review of deep learning applications in database query execution

Modern database management systems (DBMS), primarily designed as general-purpose systems, face the challenging task of efficiently handling data from diverse sources for both analytical services and online transactional processing (OLTP). The volume of data has grown significantly, with distributions ranging from linear to highly skewed, sometimes involving very slow changes or rapid, intensive updates. Recent research in this field has been significantly influenced by advances in machine learning (ML), particularly deep learning (DL), and these developments have led to the application of various ML algorithms to enhance the efficiency of different parts of the query execution engine. While previous research studies were mostly focused on identifying drawbacks to individual components, such as the query optimizer, there is a notable lack of studies examining the applicability and effectiveness of various machine learning approaches across multiple aspects of the query execution engine. This article aims to provide a systematic review of approaches that apply deep learning models at various levels within the query execution engine. We categorize these approaches into three groups based on how such models are applied: improving performance of index structures and consequently data manipulation algorithms, query optimization tasks, and externally controlling query optimizers through parameter tuning. Furthermore, we discuss the key challenges associated with implementing deep learning algorithms in DBMS.