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Still Asking: How Good Are Query Optimizers, Really?

Summary: Introduces the Join Order Benchmark (JOB) and an empirical methodology to disentangle plan enumeration, cost modeling, and cardinality estimation contributions to optimizer quality. Finds pervasive cardinality-estimation errors dominate bad plans, spurring research on learned estimators, robustness, adaptive execution, and realistic workloads. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
14385
Venue
VLDB
Year
2025
Pagerank
5.1955087e-05
Overall Rank
9,920 | 31.95%
DOI
10.14778/3750601.3760521

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{leis_vldb25,
        title = {{Still Asking: How Good Are Query Optimizers, Really?}},
        author = {Leis, Viktor and Gubichev, Andrey and Mirchev, Atanas and Boncz, Peter and Kemper, Alfons and Neumann, Thomas},
        journal = {PVLDB},
        series = {{VLDB} '25},
        volume = {18},
        number = {12},
        pages = {5531--5536},
        doi = {10.14778/3750601.3760521},
        url = {https://doi.org/10.14778/3750601.3760521},
        year = {2025}
}

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank Citing Paper Year Venue Pagerank
10,135 I Can't Believe It's Not Yannakakis: Pragmatic Bitmap Filters in Microsoft SQL Server 2026 CIDR 5.093636e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 36 of 36 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
100 LEO - DB2's LEarning Optimizer 2001 VLDB 0.00034385207
154 Neo: A Learned Query Optimizer 2019 VLDB 0.00028726181
290 An Overview of Query Optimization in Relational Systems 1998 PODS 0.0002227038
321 Worst-Case Optimal Join Algorithms: Techniques, Results, and Open Problems 2018 PODS 0.00021283186
323 DeepDB: Learn from Data, not from Queries! 2020 VLDB 0.00021264788
378 Bao: Making Learned Query Optimization Practical 2021 SIGMOD 0.00019638121
426 The LDBC Social Network Benchmark: Interactive Workload 2015 SIGMOD 0.00018692185
513 NeuroCard: One Cardinality Estimator for All Tables 2021 VLDB 0.00017190574
809 Optimizing Subgraph Queries by Combining Binary and Worst-Case Optimal Joins 2019 VLDB 0.00013874588
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
1,740 Adopting Worst-Case Optimal Joins in Relational Database Systems 2020 VLDB 9.875587e-05
1,815 SkinnerDB: Regret-Bounded Query Evaluation via Reinforcement Learning 2018 VLDB 9.6894541e-05
1,876 Flow-Loss: Learning Cardinality Estimates That Matter 2021 VLDB 9.5717543e-05
1,936 Consistently Estimating the Selectivity of Conjuncts of Predicates 2005 VLDB 9.4557372e-05
2,543 Learned Cardinality Estimation: An In-depth Study 2022 SIGMOD 8.4445934e-05
3,018 The LDBC Social Network Benchmark: Business Intelligence Workload 2023 VLDB 7.8473755e-05
3,086 A Unified Deep Model of Learning from both Data and Queries for Cardinality Estimation 2021 SIGMOD 7.7708642e-05
3,460 DIAMetrics: Benchmarking Query Engines at Scale 2020 VLDB 7.3939262e-05
3,622 Robust Join Processing with Diamond Hardened Joins 2024 VLDB 7.2465862e-05
4,036 Don’t Hold My Data Hostage – A Case For Client Protocol Redesign 2017 VLDB 6.9436548e-05
4,553 Predicate Transfer: Efficient Pre-Filtering on Multi-Join Queries 2024 CIDR 6.6346951e-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
5,529 Debunking the Myth of Join Ordering: Toward Robust SQL Analytics 2025 SIGMOD 6.18591e-05
5,576 SafeBound: A Practical System for Generating Cardinality Bounds 2023 SIGMOD 6.1663946e-05
5,639 LpBound: Pessimistic Cardinality Estimation using ℓp-Norms of Degree Sequences 2025 SIGMOD 6.1385102e-05
5,744 SQLStorm: Taking Database Benchmarking into the LLM Era 2025 VLDB 6.1019672e-05
5,983 Adaptive and Robust Query Execution for Lakehouses at Scale 2024 VLDB 6.0206841e-05
6,593 Simple Adaptive Query Processing vs. Learned Query Optimizers: Observations and Analysis 2023 VLDB 5.8297039e-05
7,386 Instance-Optimal Acyclic Join Processing Without Regret: Engineering the Yannakakis Algorithm in Column Stores 2025 VLDB 5.6273882e-05
8,494 POLAR: Adaptive and Non-invasive Join Order Selection via Plans of Least Resistance 2024 VLDB 5.4142129e-05
8,721 Accelerate Distributed Joins with Predicate Transfer 2025 SIGMOD 5.3772617e-05
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