arXiv subjects

Boyang Fang

Publications and source records attributed to Boyang Fang.

2 recordsLinked to original sources

arXiv2025

CUBE: A Cardinality Estimator Based on Neural CDF

Modern database optimizer relies on cardinality estimator, whose accuracy directly affects the optimizer's ability to choose an optimal execution plan. Recent work on data-driven methods has leveraged probabilistic models to achieve higher estimation accuracy, but these approaches cannot guarantee low inference latency at the same time and neglect scalability. As data dimensionality grows, optimization time can even exceed actual query execution time. Furthermore, inference with probabilistic models by sampling or integration procedures unpredictable estimation result and violate stability, which brings unstable performance with query execution and make database tuning hard for database users. In this paper, we propose a novel approach to cardinality estimation based on cumulative distribution function(CDF), which calculates range query cardinality without sampling or integration, ensuring accurate and predictable estimation results. With inference acceleration by merging calculations, we can achieve fast and nearly constant inference speed while maintaining high accuracy, even as dimensionality increases, which is over 10x faster than current state-of-the-art data-driven cardinality estimator. This demonstrates its excellent dimensional scalability, making it well-suited for real-world database applications.

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arXiv2025

Optimize Cardinality Estimation Model Pretraining by Simplifying the Training Datasets

The cardinality estimation is a key aspect of query optimization research, and its performance has significantly improved with the integration of machine learning. To overcome the "cold start" problem or the lack of model transferability in learned cardinality estimators, some pre-training cardinality estimation models have been proposed that use learning across multiple datasets and corresponding workloads. These models typically train on a dataset created by uniformly sampling from many datasets, but this approach may not be optimal. By applying the Group Distributionally Robust Optimization (Group DRO) algorithm to training datasets, we find that some specific training datasets contribute more significantly to model performance than others. Based on this observation, we conduct extensive experiments to delve deeper into pre-training cardinality estimators. Our results show how the performance of these models can be influenced by the datasets and corresponding workloads. Finally, we introduce a simplified training dataset, which has been reduced to a fraction of the size of existing pretraining datasets. Sufficient experimental results demonstrate that the pre-trained cardinality estimator based on this simplified dataset can still achieve comparable performance to existing models in zero-shot setups.

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