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Simple Adaptive Query Processing vs. Learned Query Optimizers: Observations and Analysis

Summary: Compare SOTA RL-based learned optimizers to two simple adaptive methods (on-the-fly NLJ/Hash switching and Lookahead Information Passing) implemented in PostgreSQL. Adaptive methods match or often beat RL, need no training, are interpretable, and handle complex queries RL can't. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
13137
Venue
VLDB
Year
2023
Pagerank
4.8629458e-05
Overall Rank
7,011 | 51.23%
DOI
10.14778/3611479.3611501

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 7 of 7 citing papers.

Rank Citing Paper Year Venue Pagerank
7,008 Is Your Learned Query Optimizer Behaving As You Expect? A Machine Learning Perspective 2024 VLDB 4.8643538e-05
8,718 Parachute: Single-Pass Bi-Directional Information Passing 2025 VLDB 4.4612599e-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
10,241 Robust Predicate Transfer with Dynamic Execution 2026 VLDB 4.1945683e-05
10,271 OBELISK: Efficient Offline Query Planning with Bayesian Optimization-Informed Language Model Reasoning 2026 VLDB 4.1945683e-05
10,627 Robust Plan Evaluation based on Approximate Probabilistic Machine Learning 2025 VLDB 4.1945683e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 26 of 26 cited papers.

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

Rank Cited Paper Year Venue Pagerank
1 Access Path Selection in a Relational Database Management System 1979 SIGMOD 0.0040449103
71 How Good Are Query Optimizers, Really? 2016 VLDB 0.00059038975
115 Eddies: Continuously Adaptive Query Processing 2000 SIGMOD 0.00046221215
204 Learned Cardinalities: Estimating Correlated Joins with Deep Learning 2019 CIDR 0.00034784455
220 Efficient Mid-Query Re-Optimization of Sub-Optimal Query Execution Plans 1998 SIGMOD 0.00033194808
252 Adaptive Selectivity Estimation Using Query Feedback 1994 SIGMOD 0.00030632263
268 R* Optimizer Validation and Performance Evaluation for Local Queries 1986 SIGMOD 0.00029662304
333 Neo: A Learned Query Optimizer 2019 VLDB 0.00027206884
339 Optimization of Dynamic Query Evaluation Plans 1994 SIGMOD 0.00026851113
508 Dynamic Query Evaluation Plans 1989 SIGMOD 0.00021463742
529 Self-tuning Histograms: Building Histograms Without Looking at Data 1999 SIGMOD 0.00020828852
608 DeepDB: Learn from Data, not from Queries! 2020 VLDB 0.00019235898
640 Bao: Making Learned Query Optimization Practical 2021 SIGMOD 0.00018759152
806 An End-to-End Learning-based Cost Estimator 2020 VLDB 0.00016434274
1,043 Adaptive Ordering of Pipelined Stream Filters 2004 SIGMOD 0.00014476247
1,254 Selectivity Estimation for Range Predicates using Lightweight Models 2019 VLDB 0.00013027411
2,121 Balsa: Learning a Query Optimizer Without Expert Demonstrations 2022 SIGMOD 9.5017232e-05
2,156 SkinnerDB: Regret-Bounded Query Evaluation via Reinforcement Learning 2018 VLDB 9.4170209e-05
2,772 Quickstep: A Data Platform Based on the Scaling-Up Approach 2018 VLDB 8.1401661e-05
3,284 Configuration-Parametric Query Optimization for Physical Design Tuning 2008 SIGMOD 7.2790444e-05
3,922 Pushing Data-Induced Predicates Through Joins in Big-Data Clusters 2020 VLDB 6.6291079e-05
4,276 Looking Ahead Makes Query Plans Robust: Making the Initial Case with In-Memory Star Schema Data Warehouse Workloads 2017 VLDB 6.2976602e-05
4,359 Astrid: Accurate Selectivity Estimation for String Predicates using Deep Learning 2021 VLDB 6.2569955e-05
4,943 Lifting the Burden of History from Adaptive Query Processing 2004 VLDB 5.8170713e-05
5,194 Bitvector-aware Query Optimization for Decision Support Queries 2020 SIGMOD 5.6368209e-05
6,192 SQLite: Past, Present, and Future 2022 VLDB 5.1641743e-05
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