# ALECE: An Attention-based Learned Cardinality Estimator for SPJ Queries on Dynamic Workloads (Extended)

arXiv: https://arxiv.org/abs/2310.05349 · HTML: https://mlsanity.com/paper/2310.05349/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Oct 2023.

## Built on by (most cited first, 13 of 13)

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- [Data-Agnostic Cardinality Learning from Imperfect Workloads](https://mlsanity.com/paper/2506.16007/) (2025)
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- [Low Rank Learning for Offline Query Optimization](https://mlsanity.com/paper/2504.06399/) (2025)
- [Sketched Sum-Product Networks for Joins](https://mlsanity.com/paper/2506.14034/) (2025)
- [A Lightweight Learned Cardinality Estimation Model](https://mlsanity.com/paper/2508.09602/) (2025)
- [CoLSE: A Lightweight and Robust Hybrid Learned Model for Single-Table Cardinality Estimation using Joint CDF](https://mlsanity.com/paper/2512.12624/) (2025)
- [Selectivity Estimation for Linear Queries via Online Learning](https://mlsanity.com/paper/2607.02895/) (2026)

## Abstract (arXiv)

For efficient query processing, DBMS query optimizers have for decades relied on delicate cardinality estimation methods. In this work, we propose an Attention-based LEarned Cardinality Estimator (ALECE for short) for SPJ queries. The core idea is to discover the implicit relationships between queries and underlying dynamic data using attention mechanisms in ALECE's two modules that are built on top of carefully designed featurizations for data and queries. In particular, from all attributes in the database, the data-encoder module obtains organic and learnable aggregations which implicitly represent correlations among the attributes, whereas the query-analyzer module builds a bridge between the query featurizations and the data aggregations to predict the query's cardinality. We experimentally evaluate ALECE on multiple dynamic workloads. The results show that ALECE enables PostgreSQL's optimizer to achieve nearly optimal performance, clearly outperforming its built-in cardinality estimator and other alternatives.