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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
0.00020000855
Overall Rank
361 | 97.58%
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 130 citing papers.

Rank Citing Paper Year Venue Pagerank
1,195 Balsa: Learning a Query Optimizer Without Expert Demonstrations 2022 SIGMOD 0.00011574218
1,515 DSB: A Decision Support Benchmark for Workload-Driven and Traditional Database Systems 2021 VLDB 0.00010418766
2,209 Lero: A Learning-to-Rank Query Optimizer 2023 VLDB 8.8360101e-05
2,248 QueryFormer: A Tree Transformer Model for Query Plan Representation 2022 VLDB 8.7567205e-05
2,393 A Learned Query Rewrite System using Monte Carlo Tree Search 2022 VLDB 8.5298464e-05
2,582 Are Updatable Learned Indexes Ready? 2022 VLDB 8.2641447e-05
2,686 Cost-based or Learning-based? A Hybrid Query Optimizer for Query Plan Selection 2022 VLDB 8.1300913e-05
2,879 Zero-Shot Cost Models for Out-of-the-box Learned Cost Prediction 2022 VLDB 7.9126862e-05
3,327 Robust Query Driven Cardinality Estimation under Changing Workloads 2023 VLDB 7.4233639e-05
3,479 LOGER: A Learned Optimizer towards Generating Efficient and Robust Query Execution Plans 2023 VLDB 7.2665349e-05
3,565 Auto-WLM: Machine Learning Enhanced Workload Management in Amazon Redshift 2023 SIGMOD 7.200937e-05
3,588 Make Your Database System Dream of Electric Sheep: Towards Self-Driving Operation 2021 VLDB 7.1841858e-05
3,779 HTAP Databases: What is New and What is Next 2022 SIGMOD 7.0234898e-05
3,900 SQLStorm: Taking Database Benchmarking into the LLM Era 2025 VLDB 6.9320915e-05
3,948 Deploying a Steered Query Optimizer in Production at Microsoft 2022 SIGMOD 6.9051584e-05
4,085 QueryBooster: Improving SQL Performance Using Middleware Services for Human-Centered Query Rewriting 2023 VLDB 6.8129946e-05
4,191 Kepler: Robust Learning for Faster Parametric Query Optimization 2023 SIGMOD 6.7425275e-05
4,240 LEON: A New Framework for ML-Aided Query Optimization 2023 VLDB 6.7064546e-05
4,470 Real-time Workload Pattern Analysis for Large-scale Cloud Databases 2023 VLDB 6.5833414e-05
4,668 Intelligent Scaling in Amazon Redshift 2024 SIGMOD 6.4768105e-05
4,677 AutoSteer: Learned Query Optimization for Any SQL Database 2023 VLDB 6.4721041e-05
4,713 Learned Index Benefits: Machine Learning Based Index Performance Estimation 2022 VLDB 6.4543291e-05
4,890 Can Learned Models Replace Hash Functions? 2023 VLDB 6.3663299e-05
5,042 Leveraging Query Logs and Machine Learning for Parametric Query Optimization 2022 VLDB 6.2995365e-05
5,043 LSched: A Workload-Aware Learned Query Scheduler for Analytical Database Systems 2022 SIGMOD 6.2979214e-05
5,216 Stage: Query Execution Time Prediction in Amazon Redshift 2024 SIGMOD 6.2218868e-05
5,236 FASTgres: Making Learned Query Optimizer Hinting Effective 2023 VLDB 6.2153504e-05
5,316 How Good are Learned Cost Models, Really? Insights from Query Optimization Tasks 2025 SIGMOD 6.1827415e-05
5,429 Efficient Massively Parallel Join Optimization for Large Queries* 2022 SIGMOD 6.1320252e-05
5,438 Eraser: Eliminating Performance Regression on Learned Query Optimizer 2024 VLDB 6.1278045e-05
5,482 A Comparative Study and Component Analysis of Query Plan Representation Techniques in ML4DB Studies 2024 VLDB 6.1123461e-05
5,630 Sample-Efficient Cardinality Estimation Using Geometric Deep Learning 2024 VLDB 6.056758e-05
5,675 HMAB: Self-Driving Hierarchy of Bandits for Integrated Physical Database Design Tuning 2023 VLDB 6.0401656e-05
5,700 PilotScope: Steering Databases with Machine Learning Drivers 2024 VLDB 6.028998e-05
5,715 Detect, Distill and Update: Learned DB Systems Facing Out of Distribution Data 2023 SIGMOD 6.0178585e-05
5,776 Lemo: A Cache-Enhanced Learned Optimizer for Concurrent Queries 2023 SIGMOD 5.9957692e-05
5,850 Modeling Shifting Workloads for Learned Database Systems 2024 SIGMOD 5.9697921e-05
5,911 Quantum-Inspired Digital Annealing for Join Ordering 2024 VLDB 5.9478816e-05
5,973 Towards instance-optimized data systems 2021 VLDB 5.9281867e-05
6,298 Is Your Learned Query Optimizer Behaving As You Expect? A Machine Learning Perspective 2024 VLDB 5.8177684e-05
6,575 Can Large Language Models Be Query Optimizer for Relational Databases? 2026 SIGMOD 5.7428777e-05
6,636 Join Order Selection with Deep Reinforcement Learning: Fundamentals, Techniques, and Challenges 2023 VLDB 5.7243042e-05
6,664 Speeding Up End-to-end Query Execution via Learning-based Progressive Cardinality Estimation 2023 SIGMOD 5.715134e-05
6,714 Simple Adaptive Query Processing vs. Learned Query Optimizers: Observations and Analysis 2023 VLDB 5.6993812e-05
6,796 dbET: Execution Time Distribution-based Plan Selection 2023 SIGMOD 5.6784895e-05
6,824 ASM: Harmonizing Autoregressive Model, Sampling, and Multi-dimensional Statistics Merging for Cardinality Estimation 2024 SIGMOD 5.670071e-05
7,021 Rethinking Learned Cost Models: Why Start from Scratch? 2023 SIGMOD 5.6168049e-05
7,072 E2ETune: End-to-End Knob Tuning via Fine-tuned Generative Language Model 2025 VLDB 5.6053987e-05
7,160 Sibyl: Forecasting Time-Evolving Query Workloads 2024 SIGMOD 5.5945786e-05
7,203 Coresets over Multiple Tables for Feature-rich and Data-efficient Machine Learning 2023 VLDB 5.5845207e-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 0.0023943337
15 How Good Are Query Optimizers, Really? 2016 VLDB 0.00061067652
40 The Case for Learned Index Structures 2018 SIGMOD 0.00046363107
85 Learned Cardinalities: Estimating Correlated Joins with Deep Learning 2019 CIDR 0.00035876108
98 LEO - DB2's LEarning Optimizer 2001 VLDB 0.00034099838
144 Neo: A Learned Query Optimizer 2019 VLDB 0.00029090793
224 Self-Driving Database Management Systems 2017 CIDR 0.00024011047
406 Deep Unsupervised Cardinality Estimation 2020 VLDB 0.00019050182
462 An End-to-End Learning-based Cost Estimator 2020 VLDB 0.00017836105
510 NeuroCard: One Cardinality Estimator for All Tables 2021 VLDB 0.00017059914
555 SageDB: A Learned Database System 2019 CIDR 0.0001650754
560 Plan-Structured Deep Neural Network Models for Query Performance Prediction 2019 VLDB 0.00016408613
682 Robust Estimation of Resource Consumption for SQL Queries using Statistical Techniques 2012 VLDB 0.00014814858
1,155 QuickSel: Quick Selectivity Learning with Mixture Models 2020 SIGMOD 0.00011777046
1,280 AI Meets AI: Leveraging Query Executions to Improve Index Recommendations 2019 SIGMOD 0.00011224914
1,800 SkinnerDB: Regret-Bounded Query Evaluation via Reinforcement Learning 2018 VLDB 9.6082185e-05
1,842 On Predictive Modeling for Optimizing Transaction Execution in Parallel OLTP Systems 2012 VLDB 9.5247433e-05
2,549 SQLShare: Results from a Multi-Year SQL-as-a-Service Experiment 2016 SIGMOD 8.3096391e-05
4,059 Database-Agnostic Workload Management 2019 CIDR 6.8226757e-05
5,090 Releasing Cloud Databases from the Chains of Performance Prediction Models 2017 CIDR 6.278054e-05
Previous Page 1 / 1 Next

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