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Fauce: Fast and Accurate Deep Ensembles with Uncertainty for Cardinality Estimation

Summary: Fauce is a join cardinality estimator learning correlations across columns and tables with deep ensembles. It yields 10x faster inference and 1.3x–6.7x lower error on complex queries, and it is the first DL-based estimator to embed uncertainty. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
hc663871043e78cd6
Venue
VLDB
Year
2021
Pagerank
8.2435322e-05
Overall Rank
2,584 | 82.69%
DOI
10.14778/3476249.3476254
PDF
Download (CC BY-NC-ND 4.0)

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{liu_vldb21,
        title = {{Fauce: Fast and Accurate Deep Ensembles with Uncertainty for Cardinality Estimation}},
        author = {Liu, Jie and Dong, Wenqian and Zhou, Qingqing and Li, Dong},
        journal = {PVLDB},
        series = {{VLDB} '21},
        volume = {14},
        number = {11},
        pages = {1950--1963},
        doi = {10.14778/3476249.3476254},
        url = {https://doi.org/10.14778/3476249.3476254},
        year = {2021}
}

Incoming Citations (Sorted by Pagerank)

Showing 31 of 31 citing papers.

Rank Citing Paper Year Venue Pagerank
2,190 Lero: A Learning-to-Rank Query Optimizer 2023 VLDB 8.8848923e-05
2,348 Learned Cardinality Estimation: An In-depth Study 2022 SIGMOD 8.5910072e-05
2,696 Cost-based or Learning-based? A Hybrid Query Optimizer for Query Plan Selection 2022 VLDB 8.1068354e-05
2,852 FactorJoin: A New Cardinality Estimation Framework for Join Queries 2023 SIGMOD 7.9201312e-05
3,269 Robust Query Driven Cardinality Estimation under Changing Workloads 2023 VLDB 7.463682e-05
4,315 ALECE: An Attention-based Learned Cardinality Estimator for SPJ Queries on Dynamic Workloads 2024 VLDB 6.6545412e-05
4,479 Lightweight and Accurate Cardinality Estimation by Neural Network Gaussian Process 2022 SIGMOD 6.5665703e-05
4,971 Debunking the Myth of Join Ordering: Toward Robust SQL Analytics 2025 SIGMOD 6.3209003e-05
5,043 Fine-Grained Modeling and Optimization for Intelligent Resource Management in Big Data Processing 2022 VLDB 6.28918e-05
5,075 SafeBound: A Practical System for Generating Cardinality Bounds 2023 SIGMOD 6.2721634e-05
5,457 Sample-Efficient Cardinality Estimation Using Geometric Deep Learning 2024 VLDB 6.1058928e-05
6,158 Rethinking Learned Cost Models: Why Start from Scratch? 2023 SIGMOD 5.8518237e-05
6,672 Speeding Up End-to-end Query Execution via Learning-based Progressive Cardinality Estimation 2023 SIGMOD 5.6975871e-05
6,840 ASM: Harmonizing Autoregressive Model, Sampling, and Multi-dimensional Statistics Merging for Cardinality Estimation 2024 SIGMOD 5.6513231e-05
8,026 A Spark Optimizer for Adaptive, Fine-Grained Parameter Tuning 2024 VLDB 5.3884724e-05
8,389 GenJoin: Conditional Generative Plan-to-Plan Query Optimizer that Learns from Subplan Hints 2026 SIGMOD 5.3250456e-05
9,031 ByteCard: Enhancing ByteDance’s Data Warehouse with Learned Cardinality Estimation 2024 SIGMOD 5.219814e-05
9,041 Optimizing the cloud? Don't train models. Build oracles! 2024 CIDR 5.2183053e-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
9,608 A Step Toward Deep Online Aggregation 2023 SIGMOD 5.1376962e-05
9,737 Experimental Analysis of Large-scale Learnable Vector Storage Compression 2024 VLDB 5.1187805e-05
9,771 CAFE: Towards Compact, Adaptive, and Fast Embedding for Large-scale Recommendation Models 2024 SIGMOD 5.115551e-05
9,935 Approximate Sketches 2024 SIGMOD 5.0941477e-05
9,995 Graph Transformers for Query Plan Representation: Potentials and Challenges 2025 VLDB 5.0845476e-05
10,257 PRICE: A Pretrained Model for Cross-Database Cardinality Estimation 2025 VLDB 5.039379e-05
10,370 An Elephant Under The Microscope: Analyzing The Interaction Of Optimizer Components In PostgreSQL 2025 SIGMOD 5.0010514e-05
10,446 Coresets for Robust Query Optimization 2026 PODS 4.9605343e-05
10,751 Reqo: A Comprehensive Learning-Based Cost Model for Robust and Explainable Query Optimization 2026 SIGMOD 4.9605343e-05
10,873 BaCon: Efficient Batch Processing of Counting Queries 2026 VLDB 4.9605343e-05
11,321 ACE: A Cardinality Estimator for Set-Valued Queries 2025 VLDB 4.9605343e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 25 of 25 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
84 Learned Cardinalities: Estimating Correlated Joins with Deep Learning 2019 CIDR 0.00035920657
98 LEO - DB2's LEarning Optimizer 2001 VLDB 0.00034050614
320 DeepDB: Learn from Data, not from Queries! 2020 VLDB 0.00021128745
372 STHoles: A Multidimensional Workload-Aware Histogram 2001 SIGMOD 0.00019768921
405 Deep Unsupervised Cardinality Estimation 2020 VLDB 0.00019020138
508 NeuroCard: One Cardinality Estimator for All Tables 2021 VLDB 0.00017108973
689 Cardinality Estimation Done Right: Index-Based Join Sampling 2017 CIDR 0.00014725904
690 Selectivity Estimation for Range Predicates using Lightweight Models 2019 VLDB 0.00014703258
870 Learning Multi-dimensional Indexes 2020 SIGMOD 0.0001332034
1,061 Lightweight Graphical Models for Selectivity Estimation Without Independence Assumptions 2011 VLDB 0.00012207625
1,064 Are We Ready For Learned Cardinality Estimation? 2021 VLDB 0.00012181459
1,444 Pessimistic Cardinality Estimation: Tighter Upper Bounds for Intermediate Join Cardinalities 2019 SIGMOD 0.00010616242
1,490 Efficient Discovery of Approximate Dependencies 2018 VLDB 0.0001047928
1,496 An Efficient Bitmap Encoding Scheme for Selection Queries 1999 SIGMOD 0.00010460047
1,511 Self-Tuning, GPU-Accelerated Kernel Density Models for Multidimensional Selectivity Estimation 2015 SIGMOD 0.00010408003
1,583 Deep Learning Models for Selectivity Estimation of Multi-Attribute Queries 2020 SIGMOD 0.00010148362
1,740 Flow-Loss: Learning Cardinality Estimates That Matter 2021 VLDB 9.7289523e-05
2,009 FLAT: Fast, Lightweight and Accurate Method for Cardinality Estimation 2021 VLDB 9.1813272e-05
2,040 Database Learning: Toward a Database that Becomes Smarter Every Time 2017 SIGMOD 9.1319608e-05
2,228 Estimating Join Selectivities using Bandwidth-Optimized Kernel Density Models 2017 VLDB 8.7912743e-05
3,212 Efficiently Approximating Selectivity Functions using Low Overhead Regression Models 2020 VLDB 7.5145409e-05
3,282 Estimating Cardinalities with Deep Sketches 2019 SIGMOD 7.4491295e-05
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