Learned Cardinalities: Estimating Correlated Joins with Deep Learning

Summary: Proposes MSCN, a multi-set convolutional network that encodes relational query plans with set semantics to learn cardinalities and capture join-crossing correlations. Combines deep learning with sampling to handle zero-sample cases, yielding much better estimates on real-world data. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID: hac0e2fce2cb9474d
Venue: CIDR
Year: 2019
Pagerank: 0.00035920657
Overall Rank: 84 | 99.44%
DOI: -
PDF: Download (CC BY 3.0)

Incoming Non-self Citations Over Time

Authors

1. Andreas Kipf (Technical University of Munich)
2. Thomas Kipf (University of Amsterdam)
3. Bernhard Radke (Technical University of Munich)
4. Viktor Leis (Technical University of Munich)
5. Peter Boncz (Centrum Wiskunde & Informatica)
6. Alfons Kemper (Technical University of Munich)

BibTeX Citation

@inproceedings{kipf_cidr19,
        address = {Amsterdam, Netherlands},
        series = {{CIDR} '19},
        title = {{Learned Cardinalities: Estimating Correlated Joins with Deep Learning}},
        booktitle = {Proceedings of the {Conference} on {Innovative} {Data} {Systems} {Research}},
        author = {Kipf, Andreas and Kipf, Thomas and Radke, Bernhard and Leis, Viktor and Boncz, Peter and Kemper, Alfons},
        year = {2019}
}

Incoming Citations (Sorted by Pagerank)

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Rank	Citing Paper	Year	Venue	Pagerank
7,257	RCRank: Multimodal Ranking of Root Causes of Slow Queries in Cloud Database Systems	2025	VLDB	5.5579707e-05
7,347	Scalable Multi-Query Execution using Reinforcement Learning	2021	SIGMOD	5.5308106e-05
7,355	Selectivity Functions of Range Queries are Learnable*	2022	SIGMOD	5.529034e-05
7,377	PerfGuard: Deploying ML-for-Systems without Performance Regressions, Almost!	2021	VLDB	5.5238616e-05
7,388	Towards Foundation Database Models	2025	CIDR	5.5211011e-05
7,432	POLAR: Adaptive and Non-invasive Join Order Selection via Plans of Least Resistance	2024	VLDB	5.5133928e-05
7,440	Cardinality Estimation of Approximate Substring Queries using Deep Learning	2022	VLDB	5.5118924e-05
7,446	Learning to be a Statistician: Learned Estimator for Number of Distinct Values	2022	VLDB	5.5113049e-05
7,483	T3: Accurate and Fast Performance Prediction for Relational Database Systems With Compiled Decision Trees	2025	SIGMOD	5.5007125e-05
7,561	Learned Offline Query Planning via Bayesian Optimization	2025	SIGMOD	5.4784915e-05
7,844	Bespoke OLAP: Synthesizing Workload-Specific One-size-fits-one Database Engines	2026	VLDB	5.4255843e-05
7,927	A Practical Approach to Groupjoin and Nested Aggregates	2021	VLDB	5.4082473e-05
7,975	Robust Query Processing: Mission Possible	2020	VLDB	5.3985544e-05
8,188	Efficient Query Re-optimization with Judicious Subquery Selections	2023	SIGMOD	5.3649397e-05
8,274	The Case for NLP-Enhanced Database Tuning: Towards Tuning Tools that “Read the Manual”	2021	VLDB	5.3477381e-05
8,297	Consistent and Flexible Selectivity Estimation for High-Dimensional Data	2021	SIGMOD	5.3438874e-05
8,389	GenJoin: Conditional Generative Plan-to-Plan Query Optimizer that Learns from Subplan Hints	2026	SIGMOD	5.3250456e-05
8,412	PARQO: Penalty-Aware Robust Plan Selection in Query Optimization	2024	VLDB	5.3211467e-05
8,419	The Case for Learned In-Memory Joins	2023	VLDB	5.3198816e-05
8,550	ROME: Robust Query Optimization via Parallel Multi-Plan Execution	2024	SIGMOD	5.3030089e-05
8,558	AirIndex: Versatile Index Tuning Through Data and Storage	2023	SIGMOD	5.3016395e-05
8,604	Conditional Cuckoo Filters	2021	SIGMOD	5.2899825e-05
8,657	LIMAO: A Framework for Lifelong Modular Learned Query Optimization	2025	VLDB	5.2790784e-05
8,685	ShadowAQP: Efficient Approximate Group-by and Join Query via Attribute-oriented Sample Size Allocation and Data Generation	2023	VLDB	5.2730954e-05
8,825	PACE: Poisoning Attacks on Learned Cardinality Estimation	2024	SIGMOD	5.2543246e-05
8,872	SkinnerMT: Parallelizing for Efficiency and Robustness in Adaptive Query Processing on Multicore Platforms	2023	VLDB	5.2459616e-05
8,975	Machine Unlearning in Learned Databases: An Experimental Analysis	2024	SIGMOD	5.2343759e-05
8,978	HAP: An Efficient Hamming Space Index Based on Augmented Pigeonhole Principle	2022	SIGMOD	5.2334381e-05
8,989	Conformal Prediction for Verifiable Learned Query Optimization	2025	VLDB	5.2304457e-05
9,011	Improving DBMS Scheduling Decisions with Accurate Performance Prediction on Concurrent Queries	2025	VLDB	5.2238303e-05
9,031	ByteCard: Enhancing ByteDance’s Data Warehouse with Learned Cardinality Estimation	2024	SIGMOD	5.219814e-05
9,155	Towards Enhancing Database Education: Natural Language Generation Meets Query Execution Plans	2021	SIGMOD	5.204563e-05
9,240	NeuroSketch: Fast and Approximate Evaluation of Range Aggregate Queries with Neural Networks	2023	SIGMOD	5.1867235e-05
9,394	Phoebe: A Learning-based Checkpoint Optimizer	2021	VLDB	5.1676892e-05
9,407	Efficient and Effective Cardinality Estimation for Skyline Family	2023	SIGMOD	5.1672232e-05
9,583	Athena: An Effective Learning-based Framework for Query Optimizer Performance Improvement	2025	SIGMOD	5.140977e-05
9,662	Low Rank Learning for Offline Query Optimization	2025	SIGMOD	5.1283524e-05
9,667	Small Selectivities Matter: Lifting the Burden of Empty Samples	2021	SIGMOD	5.1278365e-05
9,676	CEDA: Learned Cardinality Estimation with Domain Adaptation	2023	VLDB	5.1262369e-05
9,753	A Practical Theory of Generalization in Selectivity Learning	2025	VLDB	5.1159926e-05
9,837	NeuSO: Neural Optimizer for Subgraph Queries	2026	SIGMOD	5.1064323e-05
9,935	Approximate Sketches	2024	SIGMOD	5.0941477e-05
9,952	Cardinality Estimation for Similarity Search on High-Dimensional Data Objects: The Impact of Reference Objects	2025	VLDB	5.0910746e-05
9,995	Graph Transformers for Query Plan Representation: Potentials and Challenges	2025	VLDB	5.0845476e-05
10,014	Selectivity Estimation for Queries Containing Predicates over Set-Valued Attributes	2023	SIGMOD	5.0838565e-05
10,045	Agile-Ant: Self-managing Distributed Cache Management for Cost Optimization of Big Data Applications	2024	VLDB	5.0786422e-05
10,070	Cardinality Estimation of LIKE Predicate Queries using Deep Learning	2025	SIGMOD	5.0733319e-05
10,094	SPACE: Cardinality Estimation for Path Queries Using Cardinality-Aware Sequence-based Learning	2025	SIGMOD	5.068372e-05
10,143	Still Asking: How Good Are Query Optimizers, Really?	2025	VLDB	5.0597449e-05
10,195	Generating Application-Specific Data Layouts for In-memory Databases	2019	VLDB	5.0516135e-05

Outgoing Citations (Sorted by Pagerank)

Showing 14 of 14 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
15	How Good Are Query Optimizers, Really?	2016	VLDB	0.00061463175
40	The Case for Learned Index Structures	2018	SIGMOD	0.00046249672
74	Automatic Database Management System Tuning Through Large-scale Machine Learning	2017	SIGMOD	0.00037164008
103	Selectivity Estimation Without the Attribute Value Independence Assumption	1997	VLDB	0.00033847446
381	Preventing Bad Plans by Bounding the Impact of Cardinality Estimation Errors	2009	VLDB	0.00019497061
676	Robust Estimation of Resource Consumption for SQL Queries using Statistical Techniques	2012	VLDB	0.00014838681
689	Cardinality Estimation Done Right: Index-Based Join Sampling	2017	CIDR	0.00014725904
752	Join Size Estimation Subject to Filter Conditions	2015	VLDB	0.00014234789
1,185	Adaptive Optimization of Very Large Join Queries	2018	SIGMOD	0.00011586181
1,261	Sampling-Based Query Re-Optimization	2016	SIGMOD	0.00011289724
1,541	Improved Selectivity Estimation by Combining Knowledge from Sampling and Synopses	2018	VLDB	0.00010288394
1,681	Two-Level Sampling for Join Size Estimation	2017	SIGMOD	9.8821344e-05
2,174	A Black-Box Approach to Query Cardinality Estimation	2007	CIDR	8.9067128e-05
2,310	Selectivity Estimation in Extensible Databases - A Neural Network Approach	1998	VLDB	8.6493038e-05

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