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)
Incoming Non-self Citations Over Time
Authors
- 1. Ryan Marcus (Intel; Massachusetts Institute of Technology)
- 2. Parimarjan Negi (Massachusetts Institute of Technology)
- 3. Hongzi Mao (Massachusetts Institute of Technology)
- 4. Nesime Tatbul (Intel; Massachusetts Institute of Technology)
- 5. Mohammad Alizadeh (Massachusetts Institute of Technology)
- 6. Tim Kraska (Massachusetts Institute of Technology)
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 24 of 124 citing papers.
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.
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