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Bao: Making Learned Query Optimization Practical

Summary: Bao is a bandit-based learned optimizer atop optimizers, offering per-query hints via Thompson sampling and tree-CNNs. Adapts to workload, data, and schema changes, improving end-to-end and tail latency; cloud tests show cost reductions and stronger performance. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6180
Venue
SIGMOD
Year
2021
Pagerank
0.00019638121
Overall Rank
378 | 97.41%
DOI
10.1145/3448016.3452838

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{marcus_sigmod21,
        title = {{Bao: Making Learned Query Optimization Practical}},
        author = {Marcus, Ryan and Negi, Parimarjan and Mao, Hongzi and Tatbul, Nesime and Alizadeh, Mohammad and Kraska, Tim},
        series = {{SIGMOD} '21},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3448016.3452838},
        url = {https://dl.acm.org/doi/10.1145/3448016.3452838},
        year = {2021}
}

Incoming Citations (Sorted by Pagerank)

Showing 50 of 124 citing papers.

Rank Citing Paper Year Venue Pagerank
1,241 Balsa: Learning a Query Optimizer Without Expert Demonstrations 2022 SIGMOD 0.00011521639
1,832 DSB: A Decision Support Benchmark for Workload-Driven and Traditional Database Systems 2021 VLDB 9.6607418e-05
2,355 QueryFormer: A Tree Transformer Model for Query Plan Representation 2022 VLDB 8.7022189e-05
2,420 Lero: A Learning-to-Rank Query Optimizer 2023 VLDB 8.605257e-05
2,452 A Learned Query Rewrite System using Monte Carlo Tree Search 2022 VLDB 8.5584e-05
2,762 Cost-based or Learning-based? A Hybrid Query Optimizer for Query Plan Selection 2022 VLDB 8.1539867e-05
2,806 Are Updatable Learned Indexes Ready? 2022 VLDB 8.1013097e-05
2,844 Zero-Shot Cost Models for Out-of-the-box Learned Cost Prediction 2022 VLDB 8.0608767e-05
3,338 Robust Query Driven Cardinality Estimation under Changing Workloads 2023 VLDB 7.5068221e-05
3,516 LOGER: A Learned Optimizer towards Generating Efficient and Robust Query Execution Plans 2023 VLDB 7.3524442e-05
3,586 Make Your Database System Dream of Electric Sheep: Towards Self-Driving Operation 2021 VLDB 7.2834069e-05
3,762 HTAP Databases: What is New and What is Next 2022 SIGMOD 7.1449267e-05
3,809 Auto-WLM: Machine Learning Enhanced Workload Management in Amazon Redshift 2023 SIGMOD 7.1074195e-05
3,998 Deploying a Steered Query Optimizer in Production at Microsoft 2022 SIGMOD 6.9676473e-05
4,398 Real-time Workload Pattern Analysis for Large-scale Cloud Databases 2023 VLDB 6.7248611e-05
4,434 LEON: A New Framework for ML-Aided Query Optimization 2023 VLDB 6.7079088e-05
4,470 Kepler: Robust Learning for Faster Parametric Query Optimization 2023 SIGMOD 6.6817353e-05
4,643 Learned Index Benefits: Machine Learning Based Index Performance Estimation 2022 VLDB 6.5907466e-05
4,780 Can Learned Models Replace Hash Functions? 2023 VLDB 6.5118885e-05
4,909 QueryBooster: Improving SQL Performance Using Middleware Services for Human-Centered Query Rewriting 2023 VLDB 6.4486671e-05
4,929 AutoSteer: Learned Query Optimization for Any SQL Database 2023 VLDB 6.4423294e-05
5,107 Stage: Query Execution Time Prediction in Amazon Redshift 2024 SIGMOD 6.3623786e-05
5,148 LSched: A Workload-Aware Learned Query Scheduler for Analytical Database Systems 2022 SIGMOD 6.3465986e-05
5,277 FASTgres: Making Learned Query Optimizer Hinting Effective 2023 VLDB 6.2859099e-05
5,369 Intelligent Scaling in Amazon Redshift 2024 SIGMOD 6.2437078e-05
5,394 Leveraging Query Logs and Machine Learning for Parametric Query Optimization 2022 VLDB 6.2336084e-05
5,399 Efficient Massively Parallel Join Optimization for Large Queries* 2022 SIGMOD 6.2319315e-05
5,558 HMAB: Self-Driving Hierarchy of Bandits for Integrated Physical Database Design Tuning 2023 VLDB 6.1749098e-05
5,573 Eraser: Eliminating Performance Regression on Learned Query Optimizer 2024 VLDB 6.1682747e-05
5,701 Lemo: A Cache-Enhanced Learned Optimizer for Concurrent Queries 2023 SIGMOD 6.1167049e-05
5,712 Sample-Efficient Cardinality Estimation Using Geometric Deep Learning 2024 VLDB 6.1123894e-05
5,744 SQLStorm: Taking Database Benchmarking into the LLM Era 2025 VLDB 6.1019672e-05
5,767 A Comparative Study and Component Analysis of Query Plan Representation Techniques in ML4DB Studies 2024 VLDB 6.0945741e-05
5,974 Towards instance-optimized data systems 2021 VLDB 6.0230488e-05
6,088 How Good are Learned Cost Models, Really? Insights from Query Optimization Tasks 2025 SIGMOD 5.9813965e-05
6,132 Detect, Distill and Update: Learned DB Systems Facing Out of Distribution Data 2023 SIGMOD 5.9660278e-05
6,271 Is Your Learned Query Optimizer Behaving As You Expect? A Machine Learning Perspective 2024 VLDB 5.9326197e-05
6,323 Modeling Shifting Workloads for Learned Database Systems 2024 SIGMOD 5.9141228e-05
6,462 PilotScope: Steering Databases with Machine Learning Drivers 2024 VLDB 5.8717744e-05
6,543 Speeding Up End-to-end Query Execution via Learning-based Progressive Cardinality Estimation 2023 SIGMOD 5.8461929e-05
6,593 Simple Adaptive Query Processing vs. Learned Query Optimizers: Observations and Analysis 2023 VLDB 5.8297039e-05
6,704 ASM: Harmonizing Autoregressive Model, Sampling, and Multi-dimensional Statistics Merging for Cardinality Estimation 2024 SIGMOD 5.797374e-05
6,735 Join Order Selection with Deep Reinforcement Learning: Fundamentals, Techniques, and Challenges 2023 VLDB 5.7878855e-05
6,921 Rethinking Learned Cost Models: Why Start from Scratch? 2023 SIGMOD 5.7388557e-05
6,997 E2ETune: End-to-End Knob Tuning via Fine-tuned Generative Language Model 2025 VLDB 5.7300324e-05
7,076 Refactoring Index Tuning Process with Benefit Estimation 2024 VLDB 5.7098893e-05
7,112 Coresets over Multiple Tables for Feature-rich and Data-efficient Machine Learning 2023 VLDB 5.6990782e-05
7,193 dbET: Execution Time Distribution-based Plan Selection 2023 SIGMOD 5.6770249e-05
7,580 Sibyl: Forecasting Time-Evolving Query Workloads 2024 SIGMOD 5.5925285e-05
7,755 Blueprinting the Cloud: Unifying and Automatically Optimizing Cloud Data Infrastructures with BRAD 2024 VLDB 5.5519655e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 20 of 20 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 0.0024089429
18 How Good Are Query Optimizers, Really? 2016 VLDB 0.00059284255
43 The Case for Learned Index Structures 2018 SIGMOD 0.00046060254
84 Learned Cardinalities: Estimating Correlated Joins with Deep Learning 2019 CIDR 0.00035838391
100 LEO - DB2's LEarning Optimizer 2001 VLDB 0.00034385207
154 Neo: A Learned Query Optimizer 2019 VLDB 0.00028726181
234 Self-Driving Database Management Systems 2017 CIDR 0.00023810722
401 Deep Unsupervised Cardinality Estimation 2020 VLDB 0.00019092557
465 An End-to-End Learning-based Cost Estimator 2020 VLDB 0.0001803934
513 NeuroCard: One Cardinality Estimator for All Tables 2021 VLDB 0.00017190574
563 Plan-Structured Deep Neural Network Models for Query Performance Prediction 2019 VLDB 0.0001650812
568 SageDB: A Learned Database System 2019 CIDR 0.0001641553
682 Robust Estimation of Resource Consumption for SQL Queries using Statistical Techniques 2012 VLDB 0.00015014887
1,170 QuickSel: Quick Selectivity Learning with Mixture Models 2020 SIGMOD 0.00011827259
1,279 AI Meets AI: Leveraging Query Executions to Improve Index Recommendations 2019 SIGMOD 0.00011361878
1,815 SkinnerDB: Regret-Bounded Query Evaluation via Reinforcement Learning 2018 VLDB 9.6894541e-05
1,831 On Predictive Modeling for Optimizing Transaction Execution in Parallel OLTP Systems 2012 VLDB 9.6635729e-05
2,592 SQLShare: Results from a Multi-Year SQL-as-a-Service Experiment 2016 SIGMOD 8.3657526e-05
4,053 Database-Agnostic Workload Management 2019 CIDR 6.9355341e-05
4,987 Releasing Cloud Databases from the Chains of Performance Prediction Models 2017 CIDR 6.4112736e-05
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