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.

翻译：现代数据库优化器依赖于基数估计器，其准确性直接影响优化器选择最优执行计划的能力。近期基于数据驱动方法的研究利用概率模型实现了更高的估计精度，但这些方法无法同时保证低推理延迟，且忽视了可扩展性。随着数据维度的增长，优化时间甚至可能超过实际查询执行时间。此外，通过采样或积分过程进行概率模型推理会导致不可预测的估计结果并破坏稳定性，这给查询执行带来不稳定的性能，并使数据库用户难以进行数据库调优。本文提出了一种基于累积分布函数（CDF）的基数估计新方法，该方法无需采样或积分即可计算范围查询的基数，确保准确且可预测的估计结果。通过合并计算实现推理加速，我们能够在保持高精度的同时实现快速且近乎恒定的推理速度，即使维度增加时也是如此，其速度比当前最先进的数据驱动基数估计器快10倍以上。这证明了其优异的维度可扩展性，使其非常适合实际数据库应用。