# NeuroCard: One Cardinality Estimator for All Tables

arXiv: https://arxiv.org/abs/2006.08109 · HTML: https://mlsanity.com/paper/2006.08109/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jun 2020.

## Built on by (most cited first, 21 of 21)

- [Cardinality Estimation in DBMS: A Comprehensive Benchmark Evaluation](https://mlsanity.com/paper/2109.05877/) (2021)
- [Lero: A Learning-to-Rank Query Optimizer](https://mlsanity.com/paper/2302.06873/) (2023)
- [A Unified Deep Model of Learning from both Data and Queries for Cardinality Estimation](https://mlsanity.com/paper/2107.12295/) (2021)
- [Balsa: Learning a Query Optimizer Without Expert Demonstrations](https://mlsanity.com/paper/2201.01441/) (2022)
- [Zero-Shot Cost Models for Out-of-the-box Learned Cost Prediction](https://mlsanity.com/paper/2201.00561/) (2022)
- [One Model to Rule them All: Towards Zero-Shot Learning for Databases](https://mlsanity.com/paper/2105.00642/) (2021)
- [PRICE: A Pretrained Model for Cross-Database Cardinality Estimation](https://mlsanity.com/paper/2406.01027/) (2024)
- [Duet: efficient and scalable hybriD neUral rElation undersTanding](https://mlsanity.com/paper/2307.13494/) (2023)
- [A Practical Theory of Generalization in Selectivity Learning](https://mlsanity.com/paper/2409.07014/) (2024)
- [Phoebe: A Learning-based Checkpoint Optimizer](https://mlsanity.com/paper/2110.02313/) (2021)
- [Electra: Conditional Generative Model based Predicate-Aware Query Approximation](https://mlsanity.com/paper/2201.12420/) (2022)
- [Compressing (Multidimensional) Learned Bloom Filters](https://mlsanity.com/paper/2208.03029/) (2022)
- [CardOOD: Robust Query-driven Cardinality Estimation under Out-of-Distribution](https://mlsanity.com/paper/2412.05864/) (2024)
- [DistJoin: A Decoupled Join Cardinality Estimator based on Adaptive Neural Predicate Modulation](https://mlsanity.com/paper/2503.08994/) (2025)
- [Data-Agnostic Cardinality Learning from Imperfect Workloads](https://mlsanity.com/paper/2506.16007/) (2025)
- [Graphical Join: A New Physical Join Algorithm for RDBMSs](https://mlsanity.com/paper/2206.10435/) (2022)
- [Scardina: Scalable Join Cardinality Estimation by Multiple Density Estimators](https://mlsanity.com/paper/2303.18042/) (2023)
- [BitE : Accelerating Learned Query Optimization in a Mixed-Workload Environment](https://mlsanity.com/paper/2306.00845/) (2023)
- [ACE: A Cardinality Estimator for Set-Valued Queries](https://mlsanity.com/paper/2503.14929/) (2025)
- [BQSched: A Non-intrusive Scheduler for Batch Concurrent Queries via Reinforcement Learning](https://mlsanity.com/paper/2504.19142/) (2025)
- [LLM4Hint: Leveraging Large Language Models for Hint Recommendation in Offline Query Optimization](https://mlsanity.com/paper/2507.03384/) (2025)

## Abstract (arXiv)

Query optimizers rely on accurate cardinality estimates to produce good execution plans. Despite decades of research, existing cardinality estimators are inaccurate for complex queries, due to making lossy modeling assumptions and not capturing inter-table correlations. In this work, we show that it is possible to learn the correlations across all tables in a database without any independence assumptions. We present NeuroCard, a join cardinality estimator that builds a single neural density estimator over an entire database. Leveraging join sampling and modern deep autoregressive models, NeuroCard makes no inter-table or inter-column independence assumptions in its probabilistic modeling. NeuroCard achieves orders of magnitude higher accuracy than the best prior methods (a new state-of-the-art result of 8.5× maximum error on JOB-light), scales to dozens of tables, while being compact in space (several MBs) and efficient to construct or update (seconds to minutes).