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An Elephant Under The Microscope: Analyzing The Interaction Of Optimizer Components In PostgreSQL

Summary: Comprehensive experimental study of PostgreSQL's optimizer core: statistics, cardinality estimation, cost model, and plan generation. It reveals cross-component interactions and offers guidance for optimizer research and engineering. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
7070
Venue
SIGMOD
Year
2025
Pagerank
5.1347137e-05
Overall Rank
10,108 | 30.66%
DOI
10.1145/3709659

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{bergmann_sigmod25,
        title = {{An Elephant Under The Microscope: Analyzing The Interaction Of Optimizer Components In PostgreSQL}},
        author = {Bergmann, Rico and Hartmann, Claudio and Habich, Dirk and Lehner, Wolfgang},
        series = {{SIGMOD} '25},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3709659},
        url = {https://dl.acm.org/doi/10.1145/3709659},
        year = {2025}
}

Incoming Citations (Sorted by Pagerank)

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Outgoing Citations (Sorted by Pagerank)

Showing 50 of 53 cited papers.

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

Rank Cited Paper Year Venue Pagerank
1 Access Path Selection in a Relational Database Management System 1979 SIGMOD 0.0024089429
18 How Good Are Query Optimizers, Really? 2016 VLDB 0.00059284255
84 Learned Cardinalities: Estimating Correlated Joins with Deep Learning 2019 CIDR 0.00035838391
154 Neo: A Learned Query Optimizer 2019 VLDB 0.00028726181
323 DeepDB: Learn from Data, not from Queries! 2020 VLDB 0.00021264788
378 Bao: Making Learned Query Optimization Practical 2021 SIGMOD 0.00019638121
388 Preventing Bad Plans by Bounding the Impact of Cardinality Estimation Errors 2009 VLDB 0.00019410042
401 Deep Unsupervised Cardinality Estimation 2020 VLDB 0.00019092557
513 NeuroCard: One Cardinality Estimator for All Tables 2021 VLDB 0.00017190574
694 Cardinality Estimation Done Right: Index-Based Join Sampling 2017 CIDR 0.00014911698
697 Selectivity Estimation for Range Predicates using Lightweight Models 2019 VLDB 0.00014888851
984 Analyzing Plan Diagrams of Database Query Optimizers 2005 VLDB 0.00012825643
1,013 Dynamic Programming Strikes Back 2008 SIGMOD 0.00012652549
1,061 Are We Ready For Learned Cardinality Estimation? 2021 VLDB 0.00012369764
1,122 Cardinality Estimation in DBMS: A Comprehensive Benchmark Evaluation 2022 VLDB 0.0001209124
1,170 QuickSel: Quick Selectivity Learning with Mixture Models 2020 SIGMOD 0.00011827259
1,241 Balsa: Learning a Query Optimizer Without Expert Demonstrations 2022 SIGMOD 0.00011521639
1,499 Pessimistic Cardinality Estimation: Tighter Upper Bounds for Intermediate Join Cardinalities 2019 SIGMOD 0.00010564536
1,573 Deep Learning Models for Selectivity Estimation of Multi-Attribute Queries 2020 SIGMOD 0.00010328171
1,604 The Picasso Database Query Optimizer Visualizer 2010 VLDB 0.00010230973
1,876 Flow-Loss: Learning Cardinality Estimates That Matter 2021 VLDB 9.5717543e-05
1,988 FLAT: Fast, Lightweight and Accurate Method for Cardinality Estimation 2021 VLDB 9.3501502e-05
2,420 Lero: A Learning-to-Rank Query Optimizer 2023 VLDB 8.605257e-05
2,543 Learned Cardinality Estimation: An In-depth Study 2022 SIGMOD 8.4445934e-05
2,620 Fauce: Fast and Accurate Deep Ensembles with Uncertainty for Cardinality Estimation 2021 VLDB 8.3363963e-05
2,723 Learned Cardinality Estimation: A Design Space Exploration and A Comparative Evaluation 2022 VLDB 8.2049453e-05
2,762 Cost-based or Learning-based? A Hybrid Query Optimizer for Query Plan Selection 2022 VLDB 8.1539867e-05
2,844 Zero-Shot Cost Models for Out-of-the-box Learned Cost Prediction 2022 VLDB 8.0608767e-05
2,991 FactorJoin: A New Cardinality Estimation Framework for Join Queries 2023 SIGMOD 7.8880723e-05
3,086 A Unified Deep Model of Learning from both Data and Queries for Cardinality Estimation 2021 SIGMOD 7.7708642e-05
3,338 Robust Query Driven Cardinality Estimation under Changing Workloads 2023 VLDB 7.5068221e-05
3,516 LOGER: A Learned Optimizer towards Generating Efficient and Robust Query Execution Plans 2023 VLDB 7.3524442e-05
3,545 Astrid: Accurate Selectivity Estimation for String Predicates using Deep Learning 2021 VLDB 7.3249967e-05
3,959 Simplicity Done Right for Join Ordering 2021 CIDR 6.9879431e-05
4,434 LEON: A New Framework for ML-Aided Query Optimization 2023 VLDB 6.7079088e-05
4,470 Kepler: Robust Learning for Faster Parametric Query Optimization 2023 SIGMOD 6.6817353e-05
4,900 COMPASS: Online Sketch-based Query Optimization for In-Memory Databases 2021 SIGMOD 6.4534715e-05
4,929 AutoSteer: Learned Query Optimization for Any SQL Database 2023 VLDB 6.4423294e-05
5,010 Analyzing the Impact of Cardinality Estimation on Execution Plans in Microsoft SQL Server 2023 VLDB 6.4023732e-05
5,277 FASTgres: Making Learned Query Optimizer Hinting Effective 2023 VLDB 6.2859099e-05
5,394 Leveraging Query Logs and Machine Learning for Parametric Query Optimization 2022 VLDB 6.2336084e-05
5,576 SafeBound: A Practical System for Generating Cardinality Bounds 2023 SIGMOD 6.1663946e-05
5,701 Lemo: A Cache-Enhanced Learned Optimizer for Concurrent Queries 2023 SIGMOD 6.1167049e-05
6,543 Speeding Up End-to-end Query Execution via Learning-based Progressive Cardinality Estimation 2023 SIGMOD 5.8461929e-05
6,593 Simple Adaptive Query Processing vs. Learned Query Optimizers: Observations and Analysis 2023 VLDB 5.8297039e-05
6,921 Rethinking Learned Cost Models: Why Start from Scratch? 2023 SIGMOD 5.7388557e-05
7,882 Efficiently Computing Join Orders with Heuristic Search 2023 SIGMOD 5.5237338e-05
8,576 A Study of Database Performance Sensitivity to Experiment Settings 2022 VLDB 5.4095894e-05
9,123 BASE: Bridging the Gap between Cost and Latency for Query Optimization 2023 VLDB 5.3193264e-05
9,724 Approximate Sketches 2024 SIGMOD 5.2308295e-05
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