Query Optimization

Query optimization aims to find the most efficient execution plan for database queries, a crucial task impacting application performance and resource consumption. Current research focuses on leveraging machine learning, particularly reinforcement learning and learning-to-rank approaches, to improve upon traditional rule-based and cost-based optimizers, often employing neural networks to model query characteristics and predict optimal plans. These advancements offer significant potential for enhancing database system efficiency, particularly in handling complex queries and diverse workloads, and are leading to more robust and adaptable query processing systems.