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Are We Ready For Learned Cardinality Estimation?

Summary: Assess readiness of learned cardinality estimators for production; static workloads yield gains, but training/inference costs are high. Dynamic updates hurt accuracy; sensitivity to correlation, skew, and domain shifts; emphasizes cost control and trustworthiness. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
h5c05fc5e6cf8c2e5
Venue
VLDB
Year
2021
Pagerank
0.00012202282
Overall Rank
1,064 | 92.85%
DOI
10.14778/3461535.3461552

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{wang_vldb21,
        title = {{Are We Ready For Learned Cardinality Estimation?}},
        author = {Wang, Xiaoying and Qu, Changbo and Wu, Weiyuan and Wang, Jiannan and Zhou, Qingqing},
        journal = {PVLDB},
        series = {{VLDB} '21},
        volume = {14},
        number = {9},
        pages = {1640--1654},
        doi = {10.14778/3461535.3461552},
        url = {https://doi.org/10.14778/3461535.3461552},
        year = {2021}
}

Incoming Citations (Sorted by Pagerank)

Showing 11 of 61 citing papers.

Rank Citing Paper Year Venue Pagerank
10,336 An Elephant Under The Microscope: Analyzing The Interaction Of Optimizer Components In PostgreSQL 2025 SIGMOD 5.0200193e-05
10,604 Understanding and Detecting Query Performance Regression in Practical Index Tuning: [Experiments & Analysis] 2026 SIGMOD 4.9793485e-05
10,627 CorrBound: Cardinality Estimation Accounting for Inter- and Intra-relation Correlations 2026 SIGMOD 4.9793485e-05
10,673 Qualitative Join Discovery in Data Lakes using Examples 2026 SIGMOD 4.9793485e-05
10,693 Practical Parameterized Query Optimization via Efficient Plan Reuse and List-wise Ranking 2026 SIGMOD 4.9793485e-05
10,700 Sample-based Distinct Cardinality Estimation for Multiple Attributes in Multi-Dataset Queries 2026 VLDB 4.9793485e-05
10,713 Robust Predicate Transfer with Dynamic Execution 2026 VLDB 4.9793485e-05
10,918 Evaluating the Practical Effectiveness of LLM-Driven Index Tuning on Microsoft SQL Server 2026 VLDB 4.9793485e-05
11,184 FAAQP: Fast and Accurate Approximate Query Processing based on Bitmap-augmented Sum-Product Network 2025 SIGMOD 4.9793485e-05
11,333 The Accuracy of Cardinality Estimators: Unraveling the Evaluation Result Conundrum 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 50 of 52 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
36 Accurate Estimation Of The Number Of Tuples Satisfying A Condition 1984 SIGMOD 0.00047863192
37 Improved Histograms for Selectivity Estimation of Range Predicates 1996 SIGMOD 0.00047731453
40 The Case for Learned Index Structures 2018 SIGMOD 0.00046284649
79 Practical Selectivity Estimation through Adaptive Sampling 1990 SIGMOD 0.00036487763
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
119 Equi-Depth Histograms For Estimating Selectivity Factors For Multi-Dimensional Queries 1988 SIGMOD 0.0003137356
145 Neo: A Learned Query Optimizer 2019 VLDB 0.0002908188
169 Wavelet-Based Histograms for Selectivity Estimation 1998 SIGMOD 0.00027134723
232 Adaptive Selectivity Estimation Using Query Feedback 1994 SIGMOD 0.00023792809
286 Selectivity Estimation using Probabilistic Models 2001 SIGMOD 0.0002211981
314 An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning 2019 SIGMOD 0.00021282642
318 DeepDB: Learn from Data, not from Queries! 2020 VLDB 0.00021167555
371 STHoles: A Multidimensional Workload-Aware Histogram 2001 SIGMOD 0.00019829769
379 Apache Calcite: A Foundational Framework for Optimized Query Processing Over Heterogeneous Data Sources 2018 SIGMOD 0.00019514689
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
454 Self-tuning Histograms: Building Histograms Without Looking at Data 1999 SIGMOD 0.00017962189
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
701 Independence is Good: Dependency-Based Histogram Synopses for High-Dimensional Data 2001 SIGMOD 0.00014680907
784 VerdictDB: Universalizing Approximate Query Processing 2018 SIGMOD 0.00014012614
866 Approximating Multi-Dimensional Aggregate Range Queries Over Real Attributes 2000 SIGMOD 0.00013381261
1,060 Lightweight Graphical Models for Selectivity Estimation Without Independence Assumptions 2011 VLDB 0.00012224575
1,156 QuickSel: Quick Selectivity Learning with Mixture Models 2020 SIGMOD 0.00011777105
1,257 Sampling-Based Query Re-Optimization 2016 SIGMOD 0.00011310561
1,276 Orca: A Modular Query Optimizer Architecture for Big Data 2014 SIGMOD 0.00011239266
1,465 Pessimistic Cardinality Estimation: Tighter Upper Bounds for Intermediate Join Cardinalities 2019 SIGMOD 0.00010576304
1,508 Self-Tuning, GPU-Accelerated Kernel Density Models for Multidimensional Selectivity Estimation 2015 SIGMOD 0.00010440205
1,536 Cardinality Estimation: An Experimental Survey 2018 VLDB 0.00010327422
1,542 Improved Selectivity Estimation by Combining Knowledge from Sampling and Synopses 2018 VLDB 0.00010308631
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
1,687 Global Optimization of Histograms 2001 SIGMOD 9.8655879e-05
1,829 DBEst: Revisiting Approximate Query Processing Engines with Machine Learning Models 2019 SIGMOD 9.5510333e-05
1,929 Consistently Estimating the Selectivity of Conjuncts of Predicates 2005 VLDB 9.3546057e-05
2,140 SASH: A Self-Adaptive Histogram Set for Dynamically Changing Workloads 2003 VLDB 8.9682092e-05
2,216 Estimating Join Selectivities using Bandwidth-Optimized Kernel Density Models 2017 VLDB 8.8177753e-05
2,303 Selectivity Estimation in Extensible Databases - A Neural Network Approach 1998 VLDB 8.6708797e-05
2,538 Applying the Golden Rule of Sampling for Query Estimation 2001 SIGMOD 8.3284106e-05
3,005 Multiple Join Size Estimation by Virtual Domains (extended abstract) 1993 PODS 7.7621043e-05
3,032 iBTune: Individualized Buffer Tuning for Large-scale Cloud Databases 2019 VLDB 7.7351139e-05
3,210 Efficiently Approximating Selectivity Functions using Low Overhead Regression Models 2020 VLDB 7.5363533e-05
3,424 AQP++: Connecting Approximate Query Processing With Aggregate Precomputation for Interactive Analytics 2018 SIGMOD 7.3117029e-05
3,545 Computation Reuse in Analytics Job Service at Microsoft 2018 SIGMOD 7.2134803e-05
3,714 The Case for a Learned Sorting Algorithm 2020 SIGMOD 7.0769061e-05
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