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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
h840803224e5b1dd4
Venue
SIGMOD
Year
2021
Pagerank
0.00020146245
Overall Rank
355 | 97.63%
DOI
10.1145/3448016.3452838
PDF
Download (CC BY 4.0)

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 132 citing papers.

Rank Citing Paper Year Venue Pagerank
1,175 Balsa: Learning a Query Optimizer Without Expert Demonstrations 2022 SIGMOD 0.00011630532
1,513 DSB: A Decision Support Benchmark for Workload-Driven and Traditional Database Systems 2021 VLDB 0.00010394557
2,169 QueryFormer: A Tree Transformer Model for Query Plan Representation 2022 VLDB 8.9151935e-05
2,190 Lero: A Learning-to-Rank Query Optimizer 2023 VLDB 8.8848923e-05
2,389 A Learned Query Rewrite System using Monte Carlo Tree Search 2022 VLDB 8.5228689e-05
2,590 Are Updatable Learned Indexes Ready? 2022 VLDB 8.2368464e-05
2,619 Zero-Shot Cost Models for Out-of-the-box Learned Cost Prediction 2022 VLDB 8.1985665e-05
2,696 Cost-based or Learning-based? A Hybrid Query Optimizer for Query Plan Selection 2022 VLDB 8.1068354e-05
3,269 Robust Query Driven Cardinality Estimation under Changing Workloads 2023 VLDB 7.463682e-05
3,328 Auto-WLM: Machine Learning Enhanced Workload Management in Amazon Redshift 2023 SIGMOD 7.4094448e-05
3,481 LOGER: A Learned Optimizer towards Generating Efficient and Robust Query Execution Plans 2023 VLDB 7.2463722e-05
3,575 Make Your Database System Dream of Electric Sheep: Towards Self-Driving Operation 2021 VLDB 7.1769215e-05
3,783 HTAP Databases: What is New and What is Next 2022 SIGMOD 7.0016354e-05
3,909 SQLStorm: Taking Database Benchmarking into the LLM Era 2025 VLDB 6.9127576e-05
3,951 Deploying a Steered Query Optimizer in Production at Microsoft 2022 SIGMOD 6.8884038e-05
4,079 QueryBooster: Improving SQL Performance Using Middleware Services for Human-Centered Query Rewriting 2023 VLDB 6.8026178e-05
4,202 Kepler: Robust Learning for Faster Parametric Query Optimization 2023 SIGMOD 6.7258607e-05
4,247 LEON: A New Framework for ML-Aided Query Optimization 2023 VLDB 6.6920059e-05
4,343 Intelligent Scaling in Amazon Redshift 2024 SIGMOD 6.6397106e-05
4,489 Real-time Workload Pattern Analysis for Large-scale Cloud Databases 2023 VLDB 6.5619575e-05
4,641 How Good are Learned Cost Models, Really? Insights from Query Optimization Tasks 2025 SIGMOD 6.4739208e-05
4,683 AutoSteer: Learned Query Optimization for Any SQL Database 2023 VLDB 6.4545989e-05
4,726 Learned Index Benefits: Machine Learning Based Index Performance Estimation 2022 VLDB 6.4329878e-05
4,797 LSched: A Workload-Aware Learned Query Scheduler for Analytical Database Systems 2022 SIGMOD 6.3978116e-05
4,833 Stage: Query Execution Time Prediction in Amazon Redshift 2024 SIGMOD 6.3834856e-05
4,915 Can Learned Models Replace Hash Functions? 2023 VLDB 6.3452789e-05
5,062 Leveraging Query Logs and Machine Learning for Parametric Query Optimization 2022 VLDB 6.2791511e-05
5,245 FASTgres: Making Learned Query Optimizer Hinting Effective 2023 VLDB 6.1995935e-05
5,446 Efficient Massively Parallel Join Optimization for Large Queries* 2022 SIGMOD 6.1117489e-05
5,453 Eraser: Eliminating Performance Regression on Learned Query Optimizer 2024 VLDB 6.1077838e-05
5,457 Sample-Efficient Cardinality Estimation Using Geometric Deep Learning 2024 VLDB 6.1058928e-05
5,484 A Comparative Study and Component Analysis of Query Plan Representation Techniques in ML4DB Studies 2024 VLDB 6.0980944e-05
5,687 HMAB: Self-Driving Hierarchy of Bandits for Integrated Physical Database Design Tuning 2023 VLDB 6.0201937e-05
5,713 PilotScope: Steering Databases with Machine Learning Drivers 2024 VLDB 6.0090633e-05
5,728 Detect, Distill and Update: Learned DB Systems Facing Out of Distribution Data 2023 SIGMOD 5.9979958e-05
5,794 Lemo: A Cache-Enhanced Learned Optimizer for Concurrent Queries 2023 SIGMOD 5.9759435e-05
5,854 Modeling Shifting Workloads for Learned Database Systems 2024 SIGMOD 5.9520525e-05
5,926 Quantum-Inspired Digital Annealing for Join Ordering 2024 VLDB 5.9282142e-05
5,982 Towards instance-optimized data systems 2021 VLDB 5.9096991e-05
6,109 Is Your Learned Query Optimizer Behaving As You Expect? A Machine Learning Perspective 2024 VLDB 5.8676956e-05
6,158 Rethinking Learned Cost Models: Why Start from Scratch? 2023 SIGMOD 5.8518237e-05
6,584 Can Large Language Models Be Query Optimizer for Relational Databases? 2026 SIGMOD 5.72395e-05
6,646 Join Order Selection with Deep Reinforcement Learning: Fundamentals, Techniques, and Challenges 2023 VLDB 5.7053761e-05
6,654 Coresets over Multiple Tables for Feature-rich and Data-efficient Machine Learning 2023 VLDB 5.7038474e-05
6,672 Speeding Up End-to-end Query Execution via Learning-based Progressive Cardinality Estimation 2023 SIGMOD 5.6975871e-05
6,725 Simple Adaptive Query Processing vs. Learned Query Optimizers: Observations and Analysis 2023 VLDB 5.6826988e-05
6,812 dbET: Execution Time Distribution-based Plan Selection 2023 SIGMOD 5.6597129e-05
6,840 ASM: Harmonizing Autoregressive Model, Sampling, and Multi-dimensional Statistics Merging for Cardinality Estimation 2024 SIGMOD 5.6513231e-05
7,092 E2ETune: End-to-End Knob Tuning via Fine-tuned Generative Language Model 2025 VLDB 5.5868637e-05
7,162 Sibyl: Forecasting Time-Evolving Query Workloads 2024 SIGMOD 5.5798063e-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 0.0023948467
15 How Good Are Query Optimizers, Really? 2016 VLDB 0.00061463175
40 The Case for Learned Index Structures 2018 SIGMOD 0.00046249672
84 Learned Cardinalities: Estimating Correlated Joins with Deep Learning 2019 CIDR 0.00035920657
98 LEO - DB2's LEarning Optimizer 2001 VLDB 0.00034050614
142 Neo: A Learned Query Optimizer 2019 VLDB 0.00029114937
224 Self-Driving Database Management Systems 2017 CIDR 0.00023973185
405 Deep Unsupervised Cardinality Estimation 2020 VLDB 0.00019020138
453 An End-to-End Learning-based Cost Estimator 2020 VLDB 0.0001801482
508 NeuroCard: One Cardinality Estimator for All Tables 2021 VLDB 0.00017108973
545 Plan-Structured Deep Neural Network Models for Query Performance Prediction 2019 VLDB 0.0001657599
550 SageDB: A Learned Database System 2019 CIDR 0.00016525249
676 Robust Estimation of Resource Consumption for SQL Queries using Statistical Techniques 2012 VLDB 0.00014838681
1,156 QuickSel: Quick Selectivity Learning with Mixture Models 2020 SIGMOD 0.00011759683
1,282 AI Meets AI: Leveraging Query Executions to Improve Index Recommendations 2019 SIGMOD 0.00011197535
1,803 SkinnerDB: Regret-Bounded Query Evaluation via Reinforcement Learning 2018 VLDB 9.5987229e-05
1,840 On Predictive Modeling for Optimizing Transaction Execution in Parallel OLTP Systems 2012 VLDB 9.5171028e-05
2,543 SQLShare: Results from a Multi-Year SQL-as-a-Service Experiment 2016 SIGMOD 8.2993351e-05
4,061 Database-Agnostic Workload Management 2019 CIDR 6.8129772e-05
5,097 Releasing Cloud Databases from the Chains of Performance Prediction Models 2017 CIDR 6.2632543e-05
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