Turbo-Charging SPJ Query Plans with Learned Physical Join Operator Selections

Summary: TONIC adds learning-based, cardinality-free operator selection to SPJ optimizers, revising join choices along paths via feedback. It introduces QEP-S to capture and reuse optimal operator decisions, delivering up to 2.8x speedups on benchmarks. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 12945
Venue: VLDB
Year: 2022
Pagerank: 5.1764556e-05
Overall Rank: 10,016 | 31.29%
DOI: 10.14778/3551793.3551825

Incoming Non-self Citations Over Time

Authors

1. Axel Hertzschuch (Technical University of Dresden)
2. Claudio Hartmann (Technical University of Dresden)
3. Dirk Habich (Technical University of Dresden)
4. Wolfgang Lehner (Technical University of Dresden)

BibTeX Citation

@article{hertzschuch_vldb22,
        title = {{Turbo-Charging SPJ Query Plans with Learned Physical Join Operator Selections}},
        author = {Hertzschuch, Axel and Hartmann, Claudio and Habich, Dirk and Lehner, Wolfgang},
        journal = {PVLDB},
        series = {{VLDB} '22},
        volume = {15},
        number = {11},
        pages = {2706--2718},
        doi = {10.14778/3551793.3551825},
        url = {https://doi.org/10.14778/3551793.3551825},
        year = {2022}
}

Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
5,277	FASTgres: Making Learned Query Optimizer Hinting Effective	2023	VLDB	6.2859099e-05
6,704	ASM: Harmonizing Autoregressive Model, Sampling, and Multi-dimensional Statistics Merging for Cardinality Estimation	2024	SIGMOD	5.797374e-05
10,108	An Elephant Under The Microscope: Analyzing The Interaction Of Optimizer Components In PostgreSQL	2025	SIGMOD	5.1347137e-05

Outgoing Citations (Sorted by Pagerank)

Showing 25 of 25 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
18	How Good Are Query Optimizers, Really?	2016	VLDB	0.00059284255
53	Eddies: Continuously Adaptive Query Processing	2000	SIGMOD	0.00041071971
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
168	Wavelet-Based Histograms for Selectivity Estimation	1998	SIGMOD	0.00027541029
257	The History of Histograms (abridged)	2003	VLDB	0.00023154793
290	An Overview of Query Optimization in Relational Systems	1998	PODS	0.0002227038
307	Approximate Query Processing Using Wavelets	2000	VLDB	0.00021792475
323	DeepDB: Learn from Data, not from Queries!	2020	VLDB	0.00021264788
365	STHoles: A Multidimensional Workload-Aware Histogram	2001	SIGMOD	0.00020041735
378	Bao: Making Learned Query Optimization Practical	2021	SIGMOD	0.00019638121
513	NeuroCard: One Cardinality Estimator for All Tables	2021	VLDB	0.00017190574
1,278	A Seven-Dimensional Analysis of Hashing Methods and its Implications on Query Processing	2016	VLDB	0.00011362007
1,499	Pessimistic Cardinality Estimation: Tighter Upper Bounds for Intermediate Join Cardinalities	2019	SIGMOD	0.00010564536
1,779	An Adaptive Hash Join Algorithm for Multiuser Environments	1990	VLDB	9.7764427e-05
1,876	Flow-Loss: Learning Cardinality Estimates That Matter	2021	VLDB	9.5717543e-05
2,404	Cardinality Estimation Using Sample Views with Quality Assurance	2007	SIGMOD	8.6225576e-05
2,944	Query Optimizers: Time to Rethink the Contract?	2009	SIGMOD	7.9335187e-05
3,213	Estimating Cardinalities with Deep Sketches	2019	SIGMOD	7.6328677e-05
3,959	Simplicity Done Right for Join Ordering	2021	CIDR	6.9879431e-05
3,988	Adaptive Query Processing in the Looking Glass	2005	CIDR	6.9720123e-05
4,900	COMPASS: Online Sketch-based Query Optimization for In-Memory Databases	2021	SIGMOD	6.4534715e-05
8,183	alpha to omega: The Greek Alphabet of Sampling	2020	CIDR	5.4714466e-05
9,455	Small Selectivities Matter: Lifting the Burden of Empty Samples	2021	SIGMOD	5.2653318e-05

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