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Flow-Loss: Learning Cardinality Estimates That Matter

Summary: Flow-Loss trains cardinality estimators against optimizer plan costs via a flow-routing formulation over plan graphs, rather than average Q-Error. On the 16K-query CEB benchmark, it yields better runtimes and markedly stronger generalization to unseen templates despite worse estimation accuracy. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
h0ecc8025d49f0dab
Venue
VLDB
Year
2021
Pagerank
9.7545773e-05
Overall Rank
1,734 | 88.35%
DOI
10.14778/3476249.3476259

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{negi_vldb21,
        title = {{Flow-Loss: Learning Cardinality Estimates That Matter}},
        author = {Negi, Parimarjan and Marcus, Ryan and Kipf, Andreas and Mao, Hongzi and Tatbul, Nesime and Kraska, Tim and Alizadeh, Mohammad},
        journal = {PVLDB},
        series = {{VLDB} '21},
        volume = {14},
        number = {11},
        pages = {2019--2032},
        doi = {10.14778/3476249.3476259},
        url = {https://doi.org/10.14778/3476249.3476259},
        year = {2021}
}

Incoming Citations (Sorted by Pagerank)

Showing 10 of 60 citing papers.

Rank Citing Paper Year Venue Pagerank
10,698 LIO: A lightweight and interpretable query optimizer based on an evolutionary forest 2026 VLDB 4.9793485e-05
10,700 Sample-based Distinct Cardinality Estimation for Multiple Attributes in Multi-Dataset Queries 2026 VLDB 4.9793485e-05
10,741 OBELISK: Efficient Offline Query Planning with Bayesian Optimization-Informed Language Model Reasoning 2026 VLDB 4.9793485e-05
10,802 BaCon: Efficient Batch Processing of Counting Queries 2026 VLDB 4.9793485e-05
10,832 ReSequel: Robust LLM-assisted Query Rewriting and Optimization using Templatization and Sampling 2026 VLDB 4.9793485e-05
10,883 QDBO: A Real-time Quantum-augmented Database System Optimizer 2026 VLDB 4.9793485e-05
10,924 TATA: An Efficient Framework for Task Transfer in Query Plan Representation 2026 VLDB 4.9793485e-05
11,283 Robust Plan Evaluation based on Approximate Probabilistic Machine Learning 2025 VLDB 4.9793485e-05
11,443 LEAP: A Low-cost Spark SQL Query Optimizer using Pairwise Comparison 2025 VLDB 4.9793485e-05
11,507 Sub-optimal Join Order Identification with L1-error 2024 SIGMOD 4.9793485e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 28 of 28 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 0.00061066921
85 Learned Cardinalities: Estimating Correlated Joins with Deep Learning 2019 CIDR 0.00035864347
103 Selectivity Estimation Without the Attribute Value Independence Assumption 1997 VLDB 0.00033894985
145 Neo: A Learned Query Optimizer 2019 VLDB 0.0002908188
169 Wavelet-Based Histograms for Selectivity Estimation 1998 SIGMOD 0.00027134723
286 Selectivity Estimation using Probabilistic Models 2001 SIGMOD 0.0002211981
318 DeepDB: Learn from Data, not from Queries! 2020 VLDB 0.00021167555
371 STHoles: A Multidimensional Workload-Aware Histogram 2001 SIGMOD 0.00019829769
386 Preventing Bad Plans by Bounding the Impact of Cardinality Estimation Errors 2009 VLDB 0.00019444411
406 Deep Unsupervised Cardinality Estimation 2020 VLDB 0.00019045544
461 An End-to-End Learning-based Cost Estimator 2020 VLDB 0.00017829982
512 NeuroCard: One Cardinality Estimator for All Tables 2021 VLDB 0.00017050173
596 Wander Join: Online Aggregation via Random Walks 2016 SIGMOD 0.00015785583
688 Cardinality Estimation Done Right: Index-Based Join Sampling 2017 CIDR 0.00014753664
692 Selectivity Estimation for Range Predicates using Lightweight Models 2019 VLDB 0.00014741011
795 Random Sampling over Joins Revisited 2018 SIGMOD 0.00013938779
1,064 Are We Ready For Learned Cardinality Estimation? 2021 VLDB 0.00012202282
1,156 QuickSel: Quick Selectivity Learning with Mixture Models 2020 SIGMOD 0.00011777105
1,433 Towards a Learning Optimizer for Shared Clouds 2019 VLDB 0.00010677711
1,465 Pessimistic Cardinality Estimation: Tighter Upper Bounds for Intermediate Join Cardinalities 2019 SIGMOD 0.00010576304
1,580 Deep Learning Models for Selectivity Estimation of Multi-Attribute Queries 2020 SIGMOD 0.00010180835
1,603 SkinnerDB: Regret-Bounded Query Evaluation via Reinforcement Learning 2019 SIGMOD 0.00010097649
2,216 Estimating Join Selectivities using Bandwidth-Optimized Kernel Density Models 2017 VLDB 8.8177753e-05
2,974 Accurate Summary-based Cardinality Estimation Through the Lens of Cardinality Estimation Graphs 2022 VLDB 7.7938744e-05
3,060 Towards a Hands-Free Query Optimizer through Deep Learning 2019 CIDR 7.6928239e-05
3,210 Efficiently Approximating Selectivity Functions using Low Overhead Regression Models 2020 VLDB 7.5363533e-05
3,271 Estimating Cardinalities with Deep Sketches 2019 SIGMOD 7.4744941e-05
3,982 Simplicity Done Right for Join Ordering 2021 CIDR 6.8750228e-05
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