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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
0.00012181459
Overall Rank
1,064 | 92.87%
DOI
10.14778/3461535.3461552
PDF
Download (CC BY-NC-ND 4.0)

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
10,370 An Elephant Under The Microscope: Analyzing The Interaction Of Optimizer Components In PostgreSQL 2025 SIGMOD 5.0010514e-05
10,676 Understanding and Detecting Query Performance Regression in Practical Index Tuning: [Experiments & Analysis] 2026 SIGMOD 4.9605343e-05
10,699 CorrBound: Cardinality Estimation Accounting for Inter- and Intra-relation Correlations 2026 SIGMOD 4.9605343e-05
10,745 Qualitative Join Discovery in Data Lakes using Examples 2026 SIGMOD 4.9605343e-05
10,764 Practical Parameterized Query Optimization via Efficient Plan Reuse and List-wise Ranking 2026 SIGMOD 4.9605343e-05
10,771 Sample-based Distinct Cardinality Estimation for Multiple Attributes in Multi-Dataset Queries 2026 VLDB 4.9605343e-05
10,784 Robust Predicate Transfer with Dynamic Execution 2026 VLDB 4.9605343e-05
10,988 Evaluating the Practical Effectiveness of LLM-Driven Index Tuning on Microsoft SQL Server 2026 VLDB 4.9605343e-05
11,252 FAAQP: Fast and Accurate Approximate Query Processing based on Bitmap-augmented Sum-Product Network 2025 SIGMOD 4.9605343e-05
11,394 The Accuracy of Cardinality Estimators: Unraveling the Evaluation Result Conundrum 2025 VLDB 4.9605343e-05
11,564 Sub-optimal Join Order Identification with L1-error 2024 SIGMOD 4.9605343e-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 0.00061463175
36 Accurate Estimation Of The Number Of Tuples Satisfying A Condition 1984 SIGMOD 0.00047770864
37 Improved Histograms for Selectivity Estimation of Range Predicates 1996 SIGMOD 0.00047654188
40 The Case for Learned Index Structures 2018 SIGMOD 0.00046249672
79 Practical Selectivity Estimation through Adaptive Sampling 1990 SIGMOD 0.00036394222
84 Learned Cardinalities: Estimating Correlated Joins with Deep Learning 2019 CIDR 0.00035920657
103 Selectivity Estimation Without the Attribute Value Independence Assumption 1997 VLDB 0.00033847446
119 Equi-Depth Histograms For Estimating Selectivity Factors For Multi-Dimensional Queries 1988 SIGMOD 0.00031291457
142 Neo: A Learned Query Optimizer 2019 VLDB 0.00029114937
171 Wavelet-Based Histograms for Selectivity Estimation 1998 SIGMOD 0.00027063233
234 Adaptive Selectivity Estimation Using Query Feedback 1994 SIGMOD 0.00023726296
287 Selectivity Estimation using Probabilistic Models 2001 SIGMOD 0.00022060616
309 An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning 2019 SIGMOD 0.0002143249
320 DeepDB: Learn from Data, not from Queries! 2020 VLDB 0.00021128745
372 STHoles: A Multidimensional Workload-Aware Histogram 2001 SIGMOD 0.00019768921
381 Preventing Bad Plans by Bounding the Impact of Cardinality Estimation Errors 2009 VLDB 0.00019497061
382 Apache Calcite: A Foundational Framework for Optimized Query Processing Over Heterogeneous Data Sources 2018 SIGMOD 0.0001946654
405 Deep Unsupervised Cardinality Estimation 2020 VLDB 0.00019020138
453 An End-to-End Learning-based Cost Estimator 2020 VLDB 0.0001801482
457 Self-tuning Histograms: Building Histograms Without Looking at Data 1999 SIGMOD 0.00017915659
508 NeuroCard: One Cardinality Estimator for All Tables 2021 VLDB 0.00017108973
594 Wander Join: Online Aggregation via Random Walks 2016 SIGMOD 0.00015814493
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
701 Independence is Good: Dependency-Based Histogram Synopses for High-Dimensional Data 2001 SIGMOD 0.0001464136
770 VerdictDB: Universalizing Approximate Query Processing 2018 SIGMOD 0.00014086699
868 Approximating Multi-Dimensional Aggregate Range Queries Over Real Attributes 2000 SIGMOD 0.00013336675
1,061 Lightweight Graphical Models for Selectivity Estimation Without Independence Assumptions 2011 VLDB 0.00012207625
1,156 QuickSel: Quick Selectivity Learning with Mixture Models 2020 SIGMOD 0.00011759683
1,261 Sampling-Based Query Re-Optimization 2016 SIGMOD 0.00011289724
1,281 Orca: A Modular Query Optimizer Architecture for Big Data 2014 SIGMOD 0.00011198917
1,444 Pessimistic Cardinality Estimation: Tighter Upper Bounds for Intermediate Join Cardinalities 2019 SIGMOD 0.00010616242
1,511 Self-Tuning, GPU-Accelerated Kernel Density Models for Multidimensional Selectivity Estimation 2015 SIGMOD 0.00010408003
1,534 Cardinality Estimation: An Experimental Survey 2018 VLDB 0.00010309919
1,541 Improved Selectivity Estimation by Combining Knowledge from Sampling and Synopses 2018 VLDB 0.00010288394
1,583 Deep Learning Models for Selectivity Estimation of Multi-Attribute Queries 2020 SIGMOD 0.00010148362
1,609 SkinnerDB: Regret-Bounded Query Evaluation via Reinforcement Learning 2019 SIGMOD 0.0001007196
1,693 Global Optimization of Histograms 2001 SIGMOD 9.8377504e-05
1,839 DBEst: Revisiting Approximate Query Processing Engines with Machine Learning Models 2019 SIGMOD 9.5187688e-05
1,935 Consistently Estimating the Selectivity of Conjuncts of Predicates 2005 VLDB 9.3407959e-05
2,154 SASH: A Self-Adaptive Histogram Set for Dynamically Changing Workloads 2003 VLDB 8.9382906e-05
2,228 Estimating Join Selectivities using Bandwidth-Optimized Kernel Density Models 2017 VLDB 8.7912743e-05
2,310 Selectivity Estimation in Extensible Databases - A Neural Network Approach 1998 VLDB 8.6493038e-05
2,544 Applying the Golden Rule of Sampling for Query Estimation 2001 SIGMOD 8.2988657e-05
2,929 iBTune: Individualized Buffer Tuning for Large-scale Cloud Databases 2019 VLDB 7.830745e-05
3,009 Multiple Join Size Estimation by Virtual Domains (extended abstract) 1993 PODS 7.7439468e-05
3,212 Efficiently Approximating Selectivity Functions using Low Overhead Regression Models 2020 VLDB 7.5145409e-05
3,435 AQP++: Connecting Approximate Query Processing With Aggregate Precomputation for Interactive Analytics 2018 SIGMOD 7.2845431e-05
3,550 Computation Reuse in Analytics Job Service at Microsoft 2018 SIGMOD 7.1895215e-05
3,715 The Case for a Learned Sorting Algorithm 2020 SIGMOD 7.0549546e-05
Previous Page 1 / 2 Next

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