FASTgres: Making Learned Query Optimizer Hinting Effective

Summary: FASTgres uses context-aware learned classification to predict effective PostgreSQL optimizer hint sets, rather than replacing the cost model or optimizer. Across benchmarks and PostgreSQL versions, it delivers transparent, consistent gains—up to 3.25× lower runtime—by steering plan selection. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID: ha98446d9b6583d9c
Venue: VLDB
Year: 2023
Pagerank: 6.2154384e-05
Overall Rank: 5,241 | 64.77%
DOI: 10.14778/3611479.3611528

Incoming Non-self Citations Over Time

Authors

1. Lucas Woltmann (Technical University of Dresden)
2. Jerome Thiessat (Technical University of Dresden)
3. Claudio Hartmann (Technical University of Dresden)
4. Dirk Habich (Technical University of Dresden)
5. Wolfgang Lehner (Technical University of Dresden)

BibTeX Citation

@article{woltmann_vldb23,
        title = {{FASTgres: Making Learned Query Optimizer Hinting Effective}},
        author = {Woltmann, Lucas and Thiessat, Jerome and Hartmann, Claudio and Habich, Dirk and Lehner, Wolfgang},
        journal = {PVLDB},
        series = {{VLDB} '23},
        volume = {16},
        number = {11},
        pages = {3310--3322},
        doi = {10.14778/3611479.3611528},
        url = {https://doi.org/10.14778/3611479.3611528},
        year = {2023}
}

Incoming Citations (Sorted by Pagerank)

Showing 14 of 14 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
5,214	Stage: Query Execution Time Prediction in Amazon Redshift	2024	SIGMOD	6.2248104e-05
7,977	The Holon Approach for Simultaneously Tuning Multiple Components in a Self-Driving Database Management System with Machine Learning via Synthesized Proto-Actions	2024	VLDB	5.4142519e-05
8,879	Spatial Query Optimization With Learning	2024	VLDB	5.2568354e-05
9,300	GenJoin: Conditional Generative Plan-to-Plan Query Optimizer that Learns from Subplan Hints	2026	SIGMOD	5.1987909e-05
9,670	Low Rank Learning for Offline Query Optimization	2025	SIGMOD	5.1452097e-05
10,103	Still Asking: How Good Are Query Optimizers, Really?	2025	VLDB	5.0789354e-05
10,336	An Elephant Under The Microscope: Analyzing The Interaction Of Optimizer Components In PostgreSQL	2025	SIGMOD	5.0200193e-05
10,596	SEFRQO: A Self-Evolving Fine-Tuned RAG-Based Query Optimizer	2026	SIGMOD	4.9793485e-05
10,633	Divo: Learning a Stable and Effective Query Optimizer with a Diverse Workload	2026	SIGMOD	4.9793485e-05
10,679	Reqo: A Comprehensive Learning-Based Cost Model for Robust and Explainable Query Optimization	2026	SIGMOD	4.9793485e-05
10,693	Practical Parameterized Query Optimization via Efficient Plan Reuse and List-wise Ranking	2026	SIGMOD	4.9793485e-05
10,698	LIO: A lightweight and interpretable query optimizer based on an evolutionary forest	2026	VLDB	4.9793485e-05
10,924	TATA: An Efficient Framework for Task Transfer in Query Plan Representation	2026	VLDB	4.9793485e-05
10,941	Ultron: History-Based Query Optimization at Databricks	2026	VLDB	4.9793485e-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
15	How Good Are Query Optimizers, Really?	2016	VLDB	0.00061066921
85	Learned Cardinalities: Estimating Correlated Joins with Deep Learning	2019	CIDR	0.00035864347
98	LEO - DB2's LEarning Optimizer	2001	VLDB	0.00034106982
145	Neo: A Learned Query Optimizer	2019	VLDB	0.0002908188
272	An Overview of Query Optimization in Relational Systems	1998	PODS	0.00022509573
362	Bao: Making Learned Query Optimization Practical	2021	SIGMOD	0.00019989474
461	An End-to-End Learning-based Cost Estimator	2020	VLDB	0.00017829982
688	Cardinality Estimation Done Right: Index-Based Join Sampling	2017	CIDR	0.00014753664
795	Random Sampling over Joins Revisited	2018	SIGMOD	0.00013938779
1,199	Balsa: Learning a Query Optimizer Without Expert Demonstrations	2022	SIGMOD	0.00011563985
1,465	Pessimistic Cardinality Estimation: Tighter Upper Bounds for Intermediate Join Cardinalities	2019	SIGMOD	0.00010576304
1,678	Two-Level Sampling for Join Size Estimation	2017	SIGMOD	9.9088372e-05
2,690	Cost-based or Learning-based? A Hybrid Query Optimizer for Query Plan Selection	2022	VLDB	8.1258173e-05
2,891	Query Optimizers: Time to Rethink the Contract?	2009	SIGMOD	7.9021718e-05
3,271	Estimating Cardinalities with Deep Sketches	2019	SIGMOD	7.4744941e-05
3,982	Simplicity Done Right for Join Ordering	2021	CIDR	6.8750228e-05
5,003	COMPASS: Online Sketch-based Query Optimization for In-Memory Databases	2021	SIGMOD	6.3188773e-05
8,347	alpha to omega: The Greek Alphabet of Sampling	2020	CIDR	5.350539e-05
10,205	Turbo-Charging SPJ Query Plans with Learned Physical Join Operator Selections	2022	VLDB	5.0603873e-05

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