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FASTgres: Making Learned Query Optimizer Hinting Effective
Summary: Introduces FASTgres, a learning-based context-aware classifier that predicts transparent, direct hint sets to steer PostgreSQL’s cost-based optimizer instead of replacing it. Demonstrates consistent end-to-end speedups up to 3.25× across benchmarks.
(summarized by gpt-5-mini on Feb 09 2026)
- Paper ID
- 13167
- Venue
- VLDB
- Year
- 2023
- Pagerank
- 5.2682075e-05
- Overall Rank
- 5,930 | 58.75%
- DOI
-
10.14778/3611479.3611528
Incoming Non-self Citations Over Time
Incoming Citations (Sorted by Pagerank)
Showing 13 of 13 citing papers.
| Rank |
Citing Paper |
Year |
Venue |
Pagerank |
| 5,832 |
Stage: Query Execution Time Prediction in Amazon Redshift |
2024 |
SIGMOD |
5.3111109e-05 |
| 8,020 |
The Holon Approach for Simultaneously Tuning Multiple Components in a Self-Driving Database Management System with Machine Learning via Synthesized Proto-Actions |
2024 |
VLDB |
4.6040862e-05 |
| 9,485 |
Spatial Query Optimization With Learning |
2024 |
VLDB |
4.3341665e-05 |
| 9,587 |
Low Rank Learning for Offline Query Optimization |
2025 |
SIGMOD |
4.3215645e-05 |
| 9,747 |
Still Asking: How Good Are Query Optimizers, Really? |
2025 |
VLDB |
4.2897489e-05 |
| 9,960 |
An Elephant Under The Microscope: Analyzing The Interaction Of Optimizer Components In PostgreSQL |
2025 |
SIGMOD |
4.2294678e-05 |
| 10,018 |
GenJoin: Conditional Generative Plan-to-Plan Query Optimizer that Learns from Subplan Hints |
2026 |
SIGMOD |
4.1945683e-05 |
| 10,112 |
SEFRQO: A Self-Evolving Fine-Tuned RAG-Based Query Optimizer |
2026 |
SIGMOD |
4.1945683e-05 |
| 10,156 |
Divo: Learning a Stable and Effective Query Optimizer with a Diverse Workload |
2026 |
SIGMOD |
4.1945683e-05 |
| 10,203 |
Reqo: A Comprehensive Learning-Based Cost Model for Robust and Explainable Query Optimization |
2026 |
SIGMOD |
4.1945683e-05 |
| 10,219 |
Practical Parameterized Query Optimization via Efficient Plan Reuse and List-wise Ranking |
2026 |
SIGMOD |
4.1945683e-05 |
| 10,225 |
LIO: A lightweight and interpretable query optimizer based on an evolutionary forest |
2026 |
VLDB |
4.1945683e-05 |
| 10,288 |
TATA: An Efficient Framework for Task Transfer in Query Plan Representation |
2026 |
VLDB |
4.1945683e-05 |
Outgoing Citations (Sorted by Pagerank)
Showing 19 of 19 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank |
Cited Paper |
Year |
Venue |
Pagerank |
| 71 |
How Good Are Query Optimizers, Really? |
2016 |
VLDB |
0.00059038975 |
| 182 |
LEO - DB2's LEarning Optimizer |
2001 |
VLDB |
0.00036962631 |
| 204 |
Learned Cardinalities: Estimating Correlated Joins with Deep Learning |
2019 |
CIDR |
0.00034784455 |
| 333 |
Neo: A Learned Query Optimizer |
2019 |
VLDB |
0.00027206884 |
| 454 |
An Overview of Query Optimization in Relational Systems |
1998 |
PODS |
0.00022734812 |
| 640 |
Bao: Making Learned Query Optimization Practical |
2021 |
SIGMOD |
0.00018759152 |
| 806 |
An End-to-End Learning-based Cost Estimator |
2020 |
VLDB |
0.00016434274 |
| 1,105 |
Cardinality Estimation Done Right: Index-Based Join Sampling |
2017 |
CIDR |
0.00013990395 |
| 1,369 |
Random Sampling over Joins Revisited |
2018 |
SIGMOD |
0.00012339777 |
| 2,121 |
Balsa: Learning a Query Optimizer Without Expert Demonstrations |
2022 |
SIGMOD |
9.5017232e-05 |
| 2,142 |
Pessimistic Cardinality Estimation: Tighter Upper Bounds for Intermediate Join Cardinalities |
2019 |
SIGMOD |
9.4507296e-05 |
| 2,254 |
Two-Level Sampling for Join Size Estimation |
2017 |
SIGMOD |
9.1897043e-05 |
| 3,408 |
Query Optimizers: Time to Rethink the Contract? |
2009 |
SIGMOD |
7.1288167e-05 |
| 3,725 |
Estimating Cardinalities with Deep Sketches |
2019 |
SIGMOD |
6.8170734e-05 |
| 3,727 |
Cost-based or Learning-based? A Hybrid Query Optimizer for Query Plan Selection |
2022 |
VLDB |
6.8141709e-05 |
| 4,523 |
Simplicity Done Right for Join Ordering |
2021 |
CIDR |
6.1135504e-05 |
| 5,880 |
COMPASS: Online Sketch-based Query Optimization for In-Memory Databases |
2021 |
SIGMOD |
5.2898074e-05 |
| 8,350 |
alpha to omega: The Greek Alphabet of Sampling |
2020 |
CIDR |
4.5404832e-05 |
| 9,869 |
Turbo-Charging SPJ Query Plans with Learned Physical Join Operator Selections |
2022 |
VLDB |
4.2675361e-05 |
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