CoLSE: A Lightweight and Robust Hybrid Learned Model for Single-Table Cardinality Estimation using Joint CDF

• URL: http://arxiv.org/abs/2512.12624v1
• Date: Sun, 14 Dec 2025 10:08:20 GMT
• Title: CoLSE: A Lightweight and Robust Hybrid Learned Model for Single-Table Cardinality Estimation using Joint CDF
• Authors: Lankadinee Rathuwadu, Guanli Liu, Christopher Leckie, Renata Borovica-Gajic,
• Abstract summary: Cardinality estimation is a critical component of query optimization.<n>We propose CoLSE, a hybrid learned approach for single-table cardinality estimation.<n> Experimental results show that CoLSE achieves a favorable trade-off among accuracy, training time, latency, and model size, outperforming existing state-of-the-art methods.
• Score: 7.945011337356916
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Cardinality estimation (CE), the task of predicting the result size of queries is a critical component of query optimization. Accurate estimates are essential for generating efficient query execution plans. Recently, machine learning techniques have been applied to CE, broadly categorized into query-driven and data-driven approaches. Data-driven methods learn the joint distribution of data, while query-driven methods construct regression models that map query features to cardinalities. Ideally, a CE technique should strike a balance among three key factors: accuracy, efficiency, and memory footprint. However, existing state-of-the-art models often fail to achieve this balance. To address this, we propose CoLSE, a hybrid learned approach for single-table cardinality estimation. CoLSE directly models the joint probability over queried intervals using a novel algorithm based on copula theory and integrates a lightweight neural network to correct residual estimation errors. Experimental results show that CoLSE achieves a favorable trade-off among accuracy, training time, inference latency, and model size, outperforming existing state-of-the-art methods.

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