# Estimating Cardinalities with Deep Sketches

arXiv: https://arxiv.org/abs/1904.08223 · HTML: https://mlsanity.com/paper/1904.08223/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Apr 2019.

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

- [A Survey on Advancing the DBMS Query Optimizer: Cardinality Estimation, Cost Model, and Plan Enumeration](https://mlsanity.com/paper/2101.01507/) (2021)
- [JoinGym: An Efficient Query Optimization Environment for Reinforcement Learning](https://mlsanity.com/paper/2307.11704/) (2023)
- [Scardina: Scalable Join Cardinality Estimation by Multiple Density Estimators](https://mlsanity.com/paper/2303.18042/) (2023)
- [TiCard: Deployable EXPLAIN-only Residual Learning for Cardinality Estimation](https://mlsanity.com/paper/2512.14358/) (2025)

## Abstract (arXiv)

We introduce Deep Sketches, which are compact models of databases that allow us to estimate the result sizes of SQL queries. Deep Sketches are powered by a new deep learning approach to cardinality estimation that can capture correlations between columns, even across tables. Our demonstration allows users to define such sketches on the TPC-H and IMDb datasets, monitor the training process, and run ad-hoc queries against trained sketches. We also estimate query cardinalities with HyPer and PostgreSQL to visualize the gains over traditional cardinality estimators.