# AutoCE: An Accurate and Efficient Model Advisor for Learned Cardinality Estimation

arXiv: https://arxiv.org/abs/2409.16027 · HTML: https://mlsanity.com/paper/2409.16027/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Sep 2024.

## Built on by (most cited first, 4 of 4)

- [SAGE: A Framework of Precise Retrieval for RAG](https://mlsanity.com/paper/2503.01713/) (2025)
- [Graph Neural Networks for Databases: A Survey](https://mlsanity.com/paper/2502.12908/) (2025)
- [A Lightweight Learned Cardinality Estimation Model](https://mlsanity.com/paper/2508.09602/) (2025)
- [MICRO: A Lightweight Middleware for Optimizing Cross-store Cross-model Graph-Relation Joins [Technical Report]](https://mlsanity.com/paper/2603.13835/) (2026)

## Abstract (arXiv)

Cardinality estimation (CE) plays a crucial role in many database-related tasks such as query generation, cost estimation, and join ordering. Lately, we have witnessed the emergence of numerous learned CE models. However, no single CE model is invincible when it comes to the datasets with various data distributions. To facilitate data-intensive applications with accurate and efficient cardinality estimation, it is important to have an approach that can judiciously and efficiently select the most suitable CE model for an arbitrary dataset. In this paper, we study a new problem of selecting the best CE models for a variety of datasets. This problem is rather challenging as it is hard to capture the relationship from various datasets to the performance of disparate models. To address this problem, we propose a model advisor, named AutoCE, which can adaptively select the best model for a dataset. The main contribution of AutoCE is the learning-based model selection, where deep metric learning is used to learn a recommendation model and incremental learning is proposed to reduce the labeling overhead and improve the model robustness. We have integrated AutoCE into PostgreSQL and evaluated its impact on query optimization. The results showed that AutoCE achieved the best performance (27% better) and outperformed the baselines concerning accuracy (2.1 times better) and efficacy (4.2 times better).