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the design concept of the cardbench benchmark comes from in-depth thinking about database query efficiency and performance optimization issues. it is not only aimed at data distribution modeling technology, but more importantly, it attempts to explore more efficient and accurate cardinality estimation methods from a theoretical and practical perspective in the learning process. cardinality estimation, as the key to optimizing relational database query performance, directly affects query execution time and overall database performance. its accuracy is crucial for selecting an efficient join order, deciding whether to use indexes, and choosing the best join method.
the goal of the cardbench benchmark is to build a standard framework that can effectively evaluate different learning-based cardinality models. the benchmark supports three key settings: instance-based models, zero-shot models, and fine-tuned models. these three settings provide different evaluation methods, providing researchers with a more comprehensive and flexible evaluation method.
the advantages of the cardbench benchmark lie in its powerful testing capabilities and rich evaluation schemes. it contains 9125 single-table queries and 8454 binary join queries, which provides sufficient data samples for different types of database queries, enabling researchers to evaluate and compare models more accurately.
cardbench results show that even training with only a fine-tuned model can significantly improve model performance, which is a great benefit for those who need to develop new models with limited training data.
in summary, the emergence of the cardbench benchmark marks a major breakthrough in the field of learning-based cardinality estimation.cross-border e-commerceit opens up new opportunities, helping companies achieve accurate predictions and optimize database query performance.cross-border e-commerceit paves the way for future development.
