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Is Your Learned Query Optimizer Behaving As You Expect? A Machine Learning Perspective

Summary: Analyzes ML-centric pitfalls in learned query optimizers—training data generation, arbitrary train/validation splits, and inconsistent benchmarking—and introduces a standardized end-to-end ML evaluation framework for LQOs. Rigorous cross-split experiments show PostgreSQL often outperforms current LQOs, challenging claimed RL-driven gains. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
13586
Venue
VLDB
Year
2024
Pagerank
5.9326197e-05
Overall Rank
6,271 | 56.98%
DOI
10.14778/3654621.3654625

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{lehmann_vldb24,
        title = {{Is Your Learned Query Optimizer Behaving As You Expect? A Machine Learning Perspective}},
        author = {Lehmann, Claude and Sulimov, Pavel and Stockinger, Kurt},
        journal = {PVLDB},
        series = {{VLDB} '24},
        volume = {17},
        number = {7},
        pages = {1565--1577},
        doi = {10.14778/3654621.3654625},
        url = {https://doi.org/10.14778/3654621.3654625},
        year = {2024}
}

Incoming Citations (Sorted by Pagerank)

Showing 12 of 12 citing papers.

Rank Citing Paper Year Venue Pagerank
5,529 Debunking the Myth of Join Ordering: Toward Robust SQL Analytics 2025 SIGMOD 6.18591e-05
6,088 How Good are Learned Cost Models, Really? Insights from Query Optimization Tasks 2025 SIGMOD 5.9813965e-05
7,809 Can Large Language Models Be Query Optimizer for Relational Databases? 2026 SIGMOD 5.5399022e-05
7,846 The Holon Approach for Simultaneously Tuning Multiple Components in a Self-Driving Database Management System with Machine Learning via Synthesized Proto-Actions 2024 VLDB 5.5331459e-05
8,572 T3: Accurate and Fast Performance Prediction for Relational Database Systems With Compiled Decision Trees 2025 SIGMOD 5.4102362e-05
9,599 Understanding Robustness Issues of Updatable Learned Indexes: [Experiments & Analysis] 2026 SIGMOD 5.2492748e-05
9,601 Low Rank Learning for Offline Query Optimization 2025 SIGMOD 5.2487799e-05
10,272 NeurBench: A Benchmark Suite for Learned Database Components with Drift Modeling: [Experiments & Analysis] 2026 SIGMOD 5.093636e-05
10,316 GenJoin: Conditional Generative Plan-to-Plan Query Optimizer that Learns from Subplan Hints 2026 SIGMOD 5.093636e-05
10,506 This is Going to Sound Crazy, But What If We Used Large Language Models to Boost Automatic Database Tuning Algorithms By Leveraging Prior History? We Will Find Better Configurations More Quickly Than Retraining From Scratch! 2026 SIGMOD 5.093636e-05
10,529 Robust Predicate Transfer with Dynamic Execution 2026 VLDB 5.093636e-05
10,559 OBELISK: Efficient Offline Query Planning with Bayesian Optimization-Informed Language Model Reasoning 2026 VLDB 5.093636e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 14 of 14 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
154 Neo: A Learned Query Optimizer 2019 VLDB 0.00028726181
323 DeepDB: Learn from Data, not from Queries! 2020 VLDB 0.00021264788
378 Bao: Making Learned Query Optimization Practical 2021 SIGMOD 0.00019638121
513 NeuroCard: One Cardinality Estimator for All Tables 2021 VLDB 0.00017190574
1,122 Cardinality Estimation in DBMS: A Comprehensive Benchmark Evaluation 2022 VLDB 0.0001209124
1,241 Balsa: Learning a Query Optimizer Without Expert Demonstrations 2022 SIGMOD 0.00011521639
2,420 Lero: A Learning-to-Rank Query Optimizer 2023 VLDB 8.605257e-05
2,762 Cost-based or Learning-based? A Hybrid Query Optimizer for Query Plan Selection 2022 VLDB 8.1539867e-05
3,051 Towards a Hands-Free Query Optimizer through Deep Learning 2019 CIDR 7.8121919e-05
3,516 LOGER: A Learned Optimizer towards Generating Efficient and Robust Query Execution Plans 2023 VLDB 7.3524442e-05
4,434 LEON: A New Framework for ML-Aided Query Optimization 2023 VLDB 6.7079088e-05
6,593 Simple Adaptive Query Processing vs. Learned Query Optimizers: Observations and Analysis 2023 VLDB 5.8297039e-05
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