Divo: Learning a Stable and Effective Query Optimizer with a Diverse Workload
Summary: Divo tackles LQO brittleness on highly diverse workloads via template-evolving query generation, a two-phase training pipeline with 100K+ static plan experiences, and a diversity-aware probabilistic loss. The result is a more stable optimizer on PostgreSQL mixed workloads, yielding 1.3x over PG and far outperforming prior LQOs. (summarized by gpt-5-mini on Apr 11 2026)
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Authors
- 1. Tianyi Chen (Peking University)
- 2. Jun Gao (Peking University)
- 3. Yaofeng Tu (ZTE Corporation)
- 4. Yang Lin (ZTE Corporation)
- 5. Mo Xu (ZTE Corporation)
- 6. Yinjun Han (ZTE Corporation)
BibTeX Citation
@inproceedings{chen_sigmod26,
title = {{Divo: Learning a Stable and Effective Query Optimizer with a Diverse Workload}},
author = {Chen, Tianyi and Gao, Jun and Tu, Yaofeng and Lin, Yang and Xu, Mo and Han, Yinjun},
series = {{SIGMOD} '26},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3786641},
url = {https://dl.acm.org/doi/10.1145/3786641},
year = {2026}
}
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