Query Plan

Query plan optimization aims to find the most efficient execution strategy for database queries, a computationally complex problem with significant performance implications. Current research focuses on leveraging machine learning, particularly graph neural networks and reinforcement learning, to improve the accuracy and speed of plan selection, often by representing query plans as graphs and employing techniques like equality saturation. These advancements are leading to more efficient database systems and improved performance for analytical workloads, impacting both the efficiency of data processing and the development of more sophisticated AutoML approaches for query tuning.