DeepO: A Learned Query Optimizer
Summary: DeepO: a deep-learning–based query optimizer integrated into PostgreSQL, addressing practical DBMS–learning interaction. Web UI enables interactive optimization; preliminary results show DeepO outperforming the baseline PostgreSQL optimizer. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Luming Sun (Renmin University of China)
- 2. Tao Ji (Renmin University of China)
- 3. Cuiping Li (Renmin University of China)
- 4. Hong Chen (Renmin University of China)
BibTeX Citation
@inproceedings{sun_sigmod22,
title = {{DeepO: A Learned Query Optimizer}},
author = {Sun, Luming and Ji, Tao and Li, Cuiping and Chen, Hong},
series = {{SIGMOD} '22},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3514221.3520167},
url = {https://dl.acm.org/doi/10.1145/3514221.3520167},
year = {2022}
}Incoming Citations (Sorted by Pagerank)
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Outgoing Citations (Sorted by Pagerank)
Showing 4 of 4 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 18 | How Good Are Query Optimizers, Really? | 2016 | VLDB | 0.00059284255 |
| 234 | Self-Driving Database Management Systems | 2017 | CIDR | 0.00023810722 |
| 378 | Bao: Making Learned Query Optimization Practical | 2021 | SIGMOD | 0.00019638121 |
| 465 | An End-to-End Learning-based Cost Estimator | 2020 | VLDB | 0.0001803934 |
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