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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
h74176804005246b6
Venue
SIGMOD
Year
2025
Pagerank
5.0010514e-05
Overall Rank
10,370 | 30.49%
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 0.0023948467
15 How Good Are Query Optimizers, Really? 2016 VLDB 0.00061463175
84 Learned Cardinalities: Estimating Correlated Joins with Deep Learning 2019 CIDR 0.00035920657
142 Neo: A Learned Query Optimizer 2019 VLDB 0.00029114937
320 DeepDB: Learn from Data, not from Queries! 2020 VLDB 0.00021128745
355 Bao: Making Learned Query Optimization Practical 2021 SIGMOD 0.00020146245
381 Preventing Bad Plans by Bounding the Impact of Cardinality Estimation Errors 2009 VLDB 0.00019497061
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
881 Dynamic Programming Strikes Back 2008 SIGMOD 0.0001324243
983 Cardinality Estimation in DBMS: A Comprehensive Benchmark Evaluation 2022 VLDB 0.00012677179
996 Analyzing Plan Diagrams of Database Query Optimizers 2005 VLDB 0.00012595827
1,064 Are We Ready For Learned Cardinality Estimation? 2021 VLDB 0.00012181459
1,156 QuickSel: Quick Selectivity Learning with Mixture Models 2020 SIGMOD 0.00011759683
1,175 Balsa: Learning a Query Optimizer Without Expert Demonstrations 2022 SIGMOD 0.00011630532
1,444 Pessimistic Cardinality Estimation: Tighter Upper Bounds for Intermediate Join Cardinalities 2019 SIGMOD 0.00010616242
1,583 Deep Learning Models for Selectivity Estimation of Multi-Attribute Queries 2020 SIGMOD 0.00010148362
1,604 The Picasso Database Query Optimizer Visualizer 2010 VLDB 0.00010086711
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
2,190 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
2,523 Learned Cardinality Estimation: A Design Space Exploration and A Comparative Evaluation 2022 VLDB 8.3316032e-05
2,584 Fauce: Fast and Accurate Deep Ensembles with Uncertainty for Cardinality Estimation 2021 VLDB 8.2435322e-05
2,619 Zero-Shot Cost Models for Out-of-the-box Learned Cost Prediction 2022 VLDB 8.1985665e-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
3,060 A Unified Deep Model of Learning from both Data and Queries for Cardinality Estimation 2021 SIGMOD 7.6828463e-05
3,269 Robust Query Driven Cardinality Estimation under Changing Workloads 2023 VLDB 7.463682e-05
3,481 LOGER: A Learned Optimizer towards Generating Efficient and Robust Query Execution Plans 2023 VLDB 7.2463722e-05
3,555 Astrid: Accurate Selectivity Estimation for String Predicates using Deep Learning 2021 VLDB 7.1877332e-05
3,995 Simplicity Done Right for Join Ordering 2021 CIDR 6.8530469e-05
4,202 Kepler: Robust Learning for Faster Parametric Query Optimization 2023 SIGMOD 6.7258607e-05
4,247 LEON: A New Framework for ML-Aided Query Optimization 2023 VLDB 6.6920059e-05
4,683 AutoSteer: Learned Query Optimization for Any SQL Database 2023 VLDB 6.4545989e-05
5,029 COMPASS: Online Sketch-based Query Optimization for In-Memory Databases 2021 SIGMOD 6.2952031e-05
5,044 Analyzing the Impact of Cardinality Estimation on Execution Plans in Microsoft SQL Server 2023 VLDB 6.2888728e-05
5,062 Leveraging Query Logs and Machine Learning for Parametric Query Optimization 2022 VLDB 6.2791511e-05
5,075 SafeBound: A Practical System for Generating Cardinality Bounds 2023 SIGMOD 6.2721634e-05
5,245 FASTgres: Making Learned Query Optimizer Hinting Effective 2023 VLDB 6.1995935e-05
5,794 Lemo: A Cache-Enhanced Learned Optimizer for Concurrent Queries 2023 SIGMOD 5.9759435e-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
6,725 Simple Adaptive Query Processing vs. Learned Query Optimizers: Observations and Analysis 2023 VLDB 5.6826988e-05
8,064 Efficiently Computing Join Orders with Heuristic Search 2023 SIGMOD 5.3813708e-05
8,188 Efficient Query Re-optimization with Judicious Subquery Selections 2023 SIGMOD 5.3649397e-05
8,772 A Study of Database Performance Sensitivity to Experiment Settings 2022 VLDB 5.2683189e-05
9,236 BASE: Bridging the Gap between Cost and Latency for Query Optimization 2023 VLDB 5.1882723e-05
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