# A Lightweight Learned Cardinality Estimation Model

arXiv: https://arxiv.org/abs/2508.09602 · HTML: https://mlsanity.com/paper/2508.09602/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 57 references that are in the map.
- First posted on arXiv 13 Aug 2025.

## Builds on (most cited first, 18 of 18)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [SageAttention: Accurate 8-Bit Attention for Plug-and-play Inference Acceleration](https://mlsanity.com/paper/2410.02367/) (2024)
- [Tensor Ring Decomposition](https://mlsanity.com/paper/1606.05535/) (2016)
- [SageAttention2: Efficient Attention with Thorough Outlier Smoothing and Per-thread INT4 Quantization](https://mlsanity.com/paper/2411.10958/) (2024)
- [Learned Cardinalities: Estimating Correlated Joins with Deep Learning](https://mlsanity.com/paper/1809.00677/) (2018)
- [Deep Unsupervised Cardinality Estimation](https://mlsanity.com/paper/1905.04278/) (2019)
- [SageAttention2++: A More Efficient Implementation of SageAttention2](https://mlsanity.com/paper/2505.21136/) (2025)
- [A Unified Deep Model of Learning from both Data and Queries for Cardinality Estimation](https://mlsanity.com/paper/2107.12295/) (2021)
- [FactorJoin: A New Cardinality Estimation Framework for Join Queries](https://mlsanity.com/paper/2212.05526/) (2022)
- [ALECE: An Attention-based Learned Cardinality Estimator for SPJ Queries on Dynamic Workloads (Extended)](https://mlsanity.com/paper/2310.05349/) (2023)
- [QuickSel: Quick Selectivity Learning with Mixture Models](https://mlsanity.com/paper/1812.10568/) (2018)
- [SAGE: A Framework of Precise Retrieval for RAG](https://mlsanity.com/paper/2503.01713/) (2025)
- [Multi-Attribute Selectivity Estimation Using Deep Learning](https://mlsanity.com/paper/1903.09999/) (2019)
- [An Approach Based on Bayesian Networks for Query Selectivity Estimation](https://mlsanity.com/paper/1907.06295/) (2019)
- [FSPN: A New Class of Probabilistic Graphical Model](https://mlsanity.com/paper/2011.09020/) (2020)
- [PACE: Poisoning Attacks on Learned Cardinality Estimation](https://mlsanity.com/paper/2409.15990/) (2024)
- [HTAP Databases: A Survey](https://mlsanity.com/paper/2404.15670/) (2024)
- [AutoCE: An Accurate and Efficient Model Advisor for Learned Cardinality Estimation](https://mlsanity.com/paper/2409.16027/) (2024)

## Abstract (arXiv)

Cardinality estimation is a fundamental task in database management systems, aiming to predict query results accurately without executing the queries. However, existing techniques either achieve low estimation accuracy or incur high inference latency. Simultaneously achieving high speed and accuracy becomes critical for the cardinality estimation problem. In this paper, we propose a novel data-driven approach called CoDe (Covering with Decompositions) to address this problem. CoDe employs the concept of covering design, which divides the table into multiple smaller, overlapping segments. For each segment, CoDe utilizes tensor decomposition to accurately model its data distribution. Moreover, CoDe introduces innovative algorithms to select the best-fitting distributions for each query, combining them to estimate the final result. By employing multiple models to approximate distributions, CoDe excels in effectively modeling discrete distributions and ensuring computational efficiency. Notably, experimental results show that our method represents a significant advancement in cardinality estimation, achieving state-of-the-art levels of both estimation accuracy and inference efficiency. Across various datasets, CoDe achieves absolute accuracy in estimating more than half of the queries.