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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
13138
Venue
VLDB
Year
2023
Pagerank
4.8583284e-05
Overall Rank
7,011 | 51.28%
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
7,009 Is Your Learned Query Optimizer Behaving As You Expect? A Machine Learning Perspective 2024 VLDB 4.8597992e-05
8,711 Parachute: Single-Pass Bi-Directional Information Passing 2025 VLDB 4.4582346e-05
9,746 Still Asking: How Good Are Query Optimizers, Really? 2025 VLDB 4.2856385e-05
9,959 An Elephant Under The Microscope: Analyzing The Interaction Of Optimizer Components In PostgreSQL 2025 SIGMOD 4.2254157e-05
10,241 Robust Predicate Transfer with Dynamic Execution 2026 VLDB 4.1905499e-05
10,271 OBELISK: Efficient Offline Query Planning with Bayesian Optimization-Informed Language Model Reasoning 2026 VLDB 4.1905499e-05
10,635 Robust Plan Evaluation based on Approximate Probabilistic Machine Learning 2025 VLDB 4.1905499e-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 0.0040465394
71 How Good Are Query Optimizers, Really? 2016 VLDB 0.00059446482
116 Eddies: Continuously Adaptive Query Processing 2000 SIGMOD 0.00046191288
203 Learned Cardinalities: Estimating Correlated Joins with Deep Learning 2019 CIDR 0.00034868567
221 Efficient Mid-Query Re-Optimization of Sub-Optimal Query Execution Plans 1998 SIGMOD 0.00033182072
250 Adaptive Selectivity Estimation Using Query Feedback 1994 SIGMOD 0.00030640525
269 R* Optimizer Validation and Performance Evaluation for Local Queries 1986 SIGMOD 0.00029665158
329 Neo: A Learned Query Optimizer 2019 VLDB 0.00027301488
342 Optimization of Dynamic Query Evaluation Plans 1994 SIGMOD 0.00026836424
509 Dynamic Query Evaluation Plans 1989 SIGMOD 0.00021463676
527 Self-tuning Histograms: Building Histograms Without Looking at Data 1999 SIGMOD 0.00020862475
606 DeepDB: Learn from Data, not from Queries! 2020 VLDB 0.00019251186
634 Bao: Making Learned Query Optimization Practical 2021 SIGMOD 0.00018844568
804 An End-to-End Learning-based Cost Estimator 2020 VLDB 0.0001643674
1,041 Adaptive Ordering of Pipelined Stream Filters 2004 SIGMOD 0.00014470785
1,239 Selectivity Estimation for Range Predicates using Lightweight Models 2019 VLDB 0.00013091459
2,090 Balsa: Learning a Query Optimizer Without Expert Demonstrations 2022 SIGMOD 9.5668285e-05
2,154 SkinnerDB: Regret-Bounded Query Evaluation via Reinforcement Learning 2018 VLDB 9.4176683e-05
2,777 Quickstep: A Data Platform Based on the Scaling-Up Approach 2018 VLDB 8.1346418e-05
3,276 Configuration-Parametric Query Optimization for Physical Design Tuning 2008 SIGMOD 7.2880761e-05
3,923 Pushing Data-Induced Predicates Through Joins in Big-Data Clusters 2020 VLDB 6.6232068e-05
4,272 Looking Ahead Makes Query Plans Robust: Making the Initial Case with In-Memory Star Schema Data Warehouse Workloads 2017 VLDB 6.2933353e-05
4,352 Astrid: Accurate Selectivity Estimation for String Predicates using Deep Learning 2021 VLDB 6.2542257e-05
4,945 Lifting the Burden of History from Adaptive Query Processing 2004 VLDB 5.8115261e-05
5,195 Bitvector-aware Query Optimization for Decision Support Queries 2020 SIGMOD 5.6314278e-05
6,193 SQLite: Past, Present, and Future 2022 VLDB 5.1593011e-05
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