Can Large Language Models Be Query Optimizer for Relational Databases?
Summary: Explores using LLMs as query optimizers by autoregressively generating PostgreSQL execution plans from serialized DB metadata, queries and plans (QInstruct), avoiding explicit plan enumeration. Proposes two-stage fine-tuning (Qit + Qdpo) and shows LLM-QO yields valid, high-quality plans that outperform traditional and learned optimizers on three workloads, suggesting strong generalization and adaptivity. (summarized by gpt-5-mini on Feb 11 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Jie Tan (Chinese University of Hong Kong)
- 2. Kangfei Zhao (Chinese University of Hong Kong)
- 3. Rui Li (Chinese University of Hong Kong)
- 4. Jeffrey Xu Yu (Hong Kong University of Science and Technology)
- 5. Chengzhi Piao (Hong Kong Baptist University)
- 6. Hong Cheng (Chinese University of Hong Kong)
- 7. Helen Meng (Chinese University of Hong Kong)
- 8. Deli Zhao (Alibaba)
- 9. Yu Rong (Alibaba)
BibTeX Citation
@inproceedings{tan_sigmod26,
title = {{Can Large Language Models Be Query Optimizer for Relational Databases?}},
author = {Tan, Jie and Zhao, Kangfei and Li, Rui and Yu, Jeffrey Xu and Piao, Chengzhi and Cheng, Hong and Meng, Helen and Zhao, Deli and Rong, Yu},
series = {{SIGMOD} '26},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3769771},
url = {https://dl.acm.org/doi/10.1145/3769771},
year = {2026}
}
Incoming Citations (Sorted by Pagerank)
Showing 8 of 8 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 4,132 | SQLStorm: Taking Database Benchmarking into the LLM Era | 2025 | VLDB | 6.7885553e-05 |
| 10,415 | Automated Discovery of Test Oracles for Database Management Systems Using LLMs | 2026 | SIGMOD | 4.9793485e-05 |
| 10,596 | SEFRQO: A Self-Evolving Fine-Tuned RAG-Based Query Optimizer | 2026 | SIGMOD | 4.9793485e-05 |
| 10,698 | LIO: A lightweight and interpretable query optimizer based on an evolutionary forest | 2026 | VLDB | 4.9793485e-05 |
| 10,832 | ReSequel: Robust LLM-assisted Query Rewriting and Optimization using Templatization and Sampling | 2026 | VLDB | 4.9793485e-05 |
| 10,883 | QDBO: A Real-time Quantum-augmented Database System Optimizer | 2026 | VLDB | 4.9793485e-05 |
| 10,918 | Evaluating the Practical Effectiveness of LLM-Driven Index Tuning on Microsoft SQL Server | 2026 | VLDB | 4.9793485e-05 |
| 10,941 | Ultron: History-Based Query Optimization at Databricks | 2026 | VLDB | 4.9793485e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 19 of 19 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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