Cardinality Estimation

Cardinality estimation, the task of accurately predicting the number of results a database query will return without actually executing it, is crucial for optimizing query performance in database systems. Recent research focuses on leveraging machine learning, particularly graph neural networks and transformer-based models, to improve the accuracy and efficiency of cardinality estimation, especially for complex queries involving joins across multiple tables. These advancements address limitations of traditional methods, leading to faster query execution and more efficient database management, with applications ranging from relational databases to knowledge graphs. Ongoing work emphasizes developing robust benchmarks and addressing challenges like transferability across different databases and handling high-cardinality data.