# Glue: Adaptively Merging Single Table Cardinality to Estimate Join Query Size

arXiv: https://arxiv.org/abs/2112.03458 · HTML: https://mlsanity.com/paper/2112.03458/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 43 references that are in the map.
- First posted on arXiv 7 Dec 2021.

## Builds on (most cited first, 11 of 11)

- [Learned Cardinalities: Estimating Correlated Joins with Deep Learning](https://mlsanity.com/paper/1809.00677/) (2018)
- [An End-to-End Learning-based Cost Estimator](https://mlsanity.com/paper/1906.02560/) (2019)
- [Deep Unsupervised Cardinality Estimation](https://mlsanity.com/paper/1905.04278/) (2019)
- [Cardinality Estimation in DBMS: A Comprehensive Benchmark Evaluation](https://mlsanity.com/paper/2109.05877/) (2021)
- [The Randomized Dependence Coefficient](https://mlsanity.com/paper/1304.7717/) (2013)
- [FLAT: Fast, Lightweight and Accurate Method for Cardinality Estimation](https://mlsanity.com/paper/2011.09022/) (2020)
- [A Unified Deep Model of Learning from both Data and Queries for Cardinality Estimation](https://mlsanity.com/paper/2107.12295/) (2021)
- [BayesCard: Revitilizing Bayesian Frameworks for Cardinality Estimation](https://mlsanity.com/paper/2012.14743/) (2020)
- [A Unified Transferable Model for ML-Enhanced DBMS](https://mlsanity.com/paper/2105.02418/) (2021)
- [Multi-Attribute Selectivity Estimation Using Deep Learning](https://mlsanity.com/paper/1903.09999/) (2019)
- [FSPN: A New Class of Probabilistic Graphical Model](https://mlsanity.com/paper/2011.09020/) (2020)

## Built on by (most cited first, 2 of 2)

- [ZeroCard: Cardinality Estimation with Zero Dependence on Target Databases -- No Data, No Query, No Retraining](https://mlsanity.com/paper/2510.07983/) (2025)
- [CoLSE: A Lightweight and Robust Hybrid Learned Model for Single-Table Cardinality Estimation using Joint CDF](https://mlsanity.com/paper/2512.12624/) (2025)

## Abstract (arXiv)

Cardinality estimation (CardEst), a central component of the query optimizer, plays a significant role in generating high-quality query plans in DBMS. The CardEst problem has been extensively studied in the last several decades, using both traditional and ML-enhanced methods. Whereas, the hardest problem in CardEst, i.e., how to estimate the join query size on multiple tables, has not been extensively solved. Current methods either reply on independence assumptions or apply techniques with heavy burden, whose performance is still far from satisfactory. Even worse, existing CardEst methods are often designed to optimize one goal, i.e., inference speed or estimation accuracy, which can not adapt to different occasions. In this paper, we propose a very general framework, called Glue, to tackle with these challenges. Its key idea is to elegantly decouple the correlations across different tables and losslessly merge single table CardEst results to estimate the join query size. Glue supports obtaining the single table-wise CardEst results using any existing CardEst method and can process any complex join schema. Therefore, it easily adapts to different scenarios having different performance requirements, i.e., OLTP with fast estimation time or OLAP with high estimation accuracy. Meanwhile, we show that Glue can be seamlessly integrated into the plan search process and is able to support counting distinct number of values. All these properties exhibit the potential advances of deploying Glue in real-world DBMS.