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)
Incoming Non-self Citations Over Time
Authors
- 1. Viktor Leis (Technical University of Munich)
- 2. Andrey Gubichev (Databricks)
- 3. Atanas Mirchev (Volkswagen Group)
- 4. Peter Boncz (Centrum Wiskunde & Informatica)
- 5. Alfons Kemper (Technical University of Munich)
- 6. Thomas Neumann (Technical University of Munich)
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 |
|---|---|---|---|---|
| 10,435 | I Can't Believe It's Not Yannakakis: Pragmatic Bitmap Filters in Microsoft SQL Server | 2026 | CIDR | 4.9605343e-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.
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Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 259 | An Overview of Query Optimization in Relational Systems | 1998 | PODS |
| 2 | 4,641 | How Good are Learned Cost Models, Really? Insights from Query Optimization Tasks | 2025 | SIGMOD |
| 3 | 2,888 | Query Optimizers: Time to Rethink the Contract? | 2009 | SIGMOD |
| 4 | 7,619 | On the Calculation of Optimality Ranges for Relational Query Execution Plans | 2018 | SIGMOD |
| 5 | 1,064 | Are We Ready For Learned Cardinality Estimation? | 2021 | VLDB |
| 6 | 568 | Towards a Robust Query Optimizer: A Principled and Practical Approach | 2005 | SIGMOD |
| 7 | 8,188 | Efficient Query Re-optimization with Judicious Subquery Selections | 2023 | SIGMOD |
| 8 | 1,261 | Sampling-Based Query Re-Optimization | 2016 | SIGMOD |
| 9 | 983 | Cardinality Estimation in DBMS: A Comprehensive Benchmark Evaluation | 2022 | VLDB |
| 10 | 15 | How Good Are Query Optimizers, Really? | 2016 | VLDB |