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Lero: A Learning-to-Rank Query Optimizer

Summary: Lero: a non‑intrusive learning‑to‑rank layer that uses pairwise classifiers to compare plans instead of regressing costs. Pluggable into existing DBMS (Postgres prototype), it continuously adapts to workloads and cuts execution time vs native optimizer by up to 70% (vs other learned optimizers up to 37%). (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
hbb604c140db832df
Venue
VLDB
Year
2023
Pagerank
8.8257742e-05
Overall Rank
2,210 | 85.15%
DOI
10.14778/3583140.3583160

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{zhu_vldb23,
        title = {{Lero: A Learning-to-Rank Query Optimizer}},
        author = {Zhu, Rong and Chen, Wei and Ding, Bolin and Chen, Xingguang and Pfadler, Andreas and Wu, Ziniu and Zhou, Jingren},
        journal = {PVLDB},
        series = {{VLDB} '23},
        volume = {16},
        number = {6},
        pages = {1466--1479},
        doi = {10.14778/3583140.3583160},
        url = {https://doi.org/10.14778/3583140.3583160},
        year = {2023}
}

Incoming Citations (Sorted by Pagerank)

Showing 41 of 41 citing papers.

Rank Citing Paper Year Venue Pagerank
5,456 Eraser: Eliminating Performance Regression on Learned Query Optimizer 2024 VLDB 6.1239873e-05
5,649 Sample-Efficient Cardinality Estimation Using Geometric Deep Learning 2024 VLDB 6.052326e-05
5,683 How Good are Learned Cost Models, Really? Insights from Query Optimization Tasks 2025 SIGMOD 6.0392183e-05
5,788 Lemo: A Cache-Enhanced Learned Optimizer for Concurrent Queries 2023 SIGMOD 5.9947442e-05
5,871 PilotScope: Steering Databases with Machine Learning Drivers 2024 VLDB 5.9639223e-05
6,308 Is Your Learned Query Optimizer Behaving As You Expect? A Machine Learning Perspective 2024 VLDB 5.8177833e-05
6,586 Can Large Language Models Be Query Optimizer for Relational Databases? 2026 SIGMOD 5.7430662e-05
6,632 Join Order Selection with Deep Reinforcement Learning: Fundamentals, Techniques, and Challenges 2023 VLDB 5.7270153e-05
7,033 Rethinking Learned Cost Models: Why Start from Scratch? 2023 SIGMOD 5.6168499e-05
7,070 E2ETune: End-to-End Knob Tuning via Fine-tuned Generative Language Model 2025 VLDB 5.6080535e-05
7,236 RCRank: Multimodal Ranking of Root Causes of Slow Queries in Cloud Database Systems 2025 VLDB 5.5790509e-05
7,460 T3: Accurate and Fast Performance Prediction for Relational Database Systems With Compiled Decision Trees 2025 SIGMOD 5.5215755e-05
7,977 The Holon Approach for Simultaneously Tuning Multiple Components in a Self-Driving Database Management System with Machine Learning via Synthesized Proto-Actions 2024 VLDB 5.4142519e-05
8,332 Learned Offline Query Planning via Bayesian Optimization 2025 SIGMOD 5.3528188e-05
8,385 NeurDB: On the Design and Implementation of an AI-powered Autonomous Database 2025 CIDR 5.3420959e-05
9,300 GenJoin: Conditional Generative Plan-to-Plan Query Optimizer that Learns from Subplan Hints 2026 SIGMOD 5.1987909e-05
9,546 Athena: An Effective Learning-based Framework for Query Optimizer Performance Improvement 2025 SIGMOD 5.1604755e-05
9,610 LIMAO: A Framework for Lifelong Modular Learned Query Optimization 2025 VLDB 5.1526493e-05
9,670 Low Rank Learning for Offline Query Optimization 2025 SIGMOD 5.1452097e-05
9,781 Optimizing Dataflow Systems for Scalable Interactive Visualization 2024 SIGMOD 5.1300393e-05
9,954 Conformal Prediction for Verifiable Learned Query Optimization 2025 VLDB 5.1038322e-05
9,956 Graph Transformers for Query Plan Representation: Potentials and Challenges 2025 VLDB 5.1038322e-05
10,310 veDB-HTAP: a Highly Integrated, Efficient and Adaptive HTAP System 2025 VLDB 5.0386264e-05
10,336 An Elephant Under The Microscope: Analyzing The Interaction Of Optimizer Components In PostgreSQL 2025 SIGMOD 5.0200193e-05
10,412 Are Learned DBMS Components Robust to Workload Drift?: [Experiments & Analysis] 2026 SIGMOD 4.9793485e-05
10,484 NeurBench: A Benchmark Suite for Learned Database Components with Drift Modeling: [Experiments & Analysis] 2026 SIGMOD 4.9793485e-05
10,596 SEFRQO: A Self-Evolving Fine-Tuned RAG-Based Query Optimizer 2026 SIGMOD 4.9793485e-05
10,633 Divo: Learning a Stable and Effective Query Optimizer with a Diverse Workload 2026 SIGMOD 4.9793485e-05
10,679 Reqo: A Comprehensive Learning-Based Cost Model for Robust and Explainable Query Optimization 2026 SIGMOD 4.9793485e-05
10,693 Practical Parameterized Query Optimization via Efficient Plan Reuse and List-wise Ranking 2026 SIGMOD 4.9793485e-05
10,698 LIO: A lightweight and interpretable query optimizer based on an evolutionary forest 2026 VLDB 4.9793485e-05
10,741 OBELISK: Efficient Offline Query Planning with Bayesian Optimization-Informed Language Model Reasoning 2026 VLDB 4.9793485e-05
10,887 MFTune: An Efficient Multi-fidelity Framework for Spark SQL Configuration Tuning 2026 VLDB 4.9793485e-05
10,919 ScaleSense: Cost-Intelligent Scaling Framework via Learned Resource Estimation in Alibaba AnalyticDB 2026 VLDB 4.9793485e-05
10,924 TATA: An Efficient Framework for Task Transfer in Query Plan Representation 2026 VLDB 4.9793485e-05
10,938 Towards Industrial-Scale Parametric Query Optimization 2026 VLDB 4.9793485e-05
11,238 PlanRGCN: Predicting SPARQL Query Performance 2025 VLDB 4.9793485e-05
11,283 Robust Plan Evaluation based on Approximate Probabilistic Machine Learning 2025 VLDB 4.9793485e-05
11,425 Learned Cost Models for Query Optimization: From Batch to Streaming Systems 2025 VLDB 4.9793485e-05
11,443 LEAP: A Low-cost Spark SQL Query Optimizer using Pairwise Comparison 2025 VLDB 4.9793485e-05
11,453 RankPQO: Learning-to-Rank for Parametric Query Optimization 2025 VLDB 4.9793485e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 29 of 29 cited papers.

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

Rank Cited Paper Year Venue Pagerank
15 How Good Are Query Optimizers, Really? 2016 VLDB 0.00061066921
30 SCOPE: Easy and Efficient Parallel Processing of Massive Data Sets 2008 VLDB 0.00050495102
85 Learned Cardinalities: Estimating Correlated Joins with Deep Learning 2019 CIDR 0.00035864347
145 Neo: A Learned Query Optimizer 2019 VLDB 0.0002908188
151 An Efficient, Cost-Driven Index Selection Tool for Microsoft SQL Server 1997 VLDB 0.00028672526
318 DeepDB: Learn from Data, not from Queries! 2020 VLDB 0.00021167555
362 Bao: Making Learned Query Optimization Practical 2021 SIGMOD 0.00019989474
386 Preventing Bad Plans by Bounding the Impact of Cardinality Estimation Errors 2009 VLDB 0.00019444411
406 Deep Unsupervised Cardinality Estimation 2020 VLDB 0.00019045544
461 An End-to-End Learning-based Cost Estimator 2020 VLDB 0.00017829982
491 Database Tuning Advisor for Microsoft SQL Server 2005 2004 VLDB 0.00017413042
512 NeuroCard: One Cardinality Estimator for All Tables 2021 VLDB 0.00017050173
560 Plan-Structured Deep Neural Network Models for Query Performance Prediction 2019 VLDB 0.00016403151
682 Robust Estimation of Resource Consumption for SQL Queries using Statistical Techniques 2012 VLDB 0.0001481781
692 Selectivity Estimation for Range Predicates using Lightweight Models 2019 VLDB 0.00014741011
982 Cardinality Estimation in DBMS: A Comprehensive Benchmark Evaluation 2022 VLDB 0.00012714044
996 Analyzing Plan Diagrams of Database Query Optimizers 2005 VLDB 0.00012634603
1,060 Lightweight Graphical Models for Selectivity Estimation Without Independence Assumptions 2011 VLDB 0.00012224575
1,064 Are We Ready For Learned Cardinality Estimation? 2021 VLDB 0.00012202282
1,199 Balsa: Learning a Query Optimizer Without Expert Demonstrations 2022 SIGMOD 0.00011563985
1,279 AI Meets AI: Leveraging Query Executions to Improve Index Recommendations 2019 SIGMOD 0.00011226878
1,603 SkinnerDB: Regret-Bounded Query Evaluation via Reinforcement Learning 2019 SIGMOD 0.00010097649
2,004 FLAT: Fast, Lightweight and Accurate Method for Cardinality Estimation 2021 VLDB 9.2065719e-05
2,583 Fauce: Fast and Accurate Deep Ensembles with Uncertainty for Cardinality Estimation 2021 VLDB 8.2589758e-05
2,842 Query Performance Prediction for Concurrent Queries using Graph Embedding 2020 VLDB 7.949193e-05
3,527 Identifying Robust Plans through Plan Diagram Reduction 2008 VLDB 7.2310714e-05
3,978 Efficient Deep Learning Pipelines for Accurate Cost Estimations Over Large Scale Query Workload 2021 SIGMOD 6.8807882e-05
4,855 Efficiently Approximating Query Optimizer Plan Diagrams 2008 VLDB 6.3799739e-05
5,110 Steering Query Optimizers: A Practical Take on Big Data Workloads 2021 SIGMOD 6.269351e-05
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# Overall Rank Paper Year Venue
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