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: 12757
Venue: VLDB
Year: 2022
Pagerank: 4.2675361e-05
Overall Rank: 9,869 | 31.35%
DOI: 10.14778/3551793.3551825

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
5,930	FASTgres: Making Learned Query Optimizer Hinting Effective	2023	VLDB	5.2682075e-05
7,123	ASM: Harmonizing Autoregressive Model, Sampling, and Multi-dimensional Statistics Merging for Cardinality Estimation	2024	SIGMOD	4.8251036e-05
9,960	An Elephant Under The Microscope: Analyzing The Interaction Of Optimizer Components In PostgreSQL	2025	SIGMOD	4.2294678e-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
71	How Good Are Query Optimizers, Really?	2016	VLDB	0.00059038975
115	Eddies: Continuously Adaptive Query Processing	2000	SIGMOD	0.00046221215
182	LEO - DB2's LEarning Optimizer	2001	VLDB	0.00036962631
204	Learned Cardinalities: Estimating Correlated Joins with Deep Learning	2019	CIDR	0.00034784455
222	Wavelet-Based Histograms for Selectivity Estimation	1998	SIGMOD	0.00032828302
325	The History of Histograms (abridged)	2003	VLDB	0.00027378328
333	Neo: A Learned Query Optimizer	2019	VLDB	0.00027206884
405	Approximate Query Processing Using Wavelets	2000	VLDB	0.00024057494
454	An Overview of Query Optimization in Relational Systems	1998	PODS	0.00022734812
512	STHoles: A Multidimensional Workload-Aware Histogram	2001	SIGMOD	0.00021380733
608	DeepDB: Learn from Data, not from Queries!	2020	VLDB	0.00019235898
640	Bao: Making Learned Query Optimization Practical	2021	SIGMOD	0.00018759152
910	NeuroCard: One Cardinality Estimator for All Tables	2021	VLDB	0.00015423056
1,696	A Seven-Dimensional Analysis of Hashing Methods and its Implications on Query Processing	2016	VLDB	0.00010881034
1,856	An Adaptive Hash Join Algorithm for Multiuser Environments	1990	VLDB	0.00010304993
2,142	Pessimistic Cardinality Estimation: Tighter Upper Bounds for Intermediate Join Cardinalities	2019	SIGMOD	9.4507296e-05
2,783	Flow-Loss: Learning Cardinality Estimates That Matter	2021	VLDB	8.1293383e-05
3,013	Cardinality Estimation Using Sample Views with Quality Assurance	2007	SIGMOD	7.7137441e-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
4,523	Simplicity Done Right for Join Ordering	2021	CIDR	6.1135504e-05
4,617	Adaptive Query Processing in the Looking Glass	2005	CIDR	6.0446738e-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
9,380	Small Selectivities Matter: Lifting the Burden of Empty Samples	2021	SIGMOD	4.3461329e-05

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10,942	Sub-optimal Join Order Identification with L1-error	2024	SIGMOD	4.1945683e-05
1,619	Adaptive Optimization of Very Large Join Queries	2018	SIGMOD	0.00011111678
4,523	Simplicity Done Right for Join Ordering	2021	CIDR	6.1135504e-05
4,397	Estimating Compilation Time of a Query Optimizer	2003	SIGMOD	6.2230918e-05
9,662	Efficient Query Re-optimization with Judicious Subquery Selections	2023	SIGMOD	4.3097631e-05
4,738	Query Simplification: Graceful Degradation for Join-Order Optimization	2009	SIGMOD	5.9600502e-05
7,358	Weighted Distinct Sampling: Cardinality Estimation for SPJ Queries	2021	SIGMOD	4.7529363e-05
3,284	Configuration-Parametric Query Optimization for Physical Design Tuning	2008	SIGMOD	7.2790444e-05
71	How Good Are Query Optimizers, Really?	2016	VLDB	0.00059038975
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