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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)

Paper ID
h9ca553690d79ab3c
Venue
SIGMOD
Year
2026
Pagerank
5.7430662e-05
Overall Rank
6,586 | 55.73%
DOI
10.1145/3769771

Incoming Non-self Citations Over Time

Authors

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.

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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.

Rank Cited Paper Year Venue Pagerank
1 Access Path Selection in a Relational Database Management System 1979 SIGMOD 0.0023947656
15 How Good Are Query Optimizers, Really? 2016 VLDB 0.00061066921
145 Neo: A Learned Query Optimizer 2019 VLDB 0.0002908188
272 An Overview of Query Optimization in Relational Systems 1998 PODS 0.00022509573
362 Bao: Making Learned Query Optimization Practical 2021 SIGMOD 0.00019989474
501 Language Models Enable Simple Systems for Generating Structured Views of Heterogeneous Data Lakes 2024 VLDB 0.00017267905
669 CAESURA: Language Models as Multi-Modal Query Planners 2024 CIDR 0.0001495987
1,199 Balsa: Learning a Query Optimizer Without Expert Demonstrations 2022 SIGMOD 0.00011563985
1,515 DSB: A Decision Support Benchmark for Workload-Driven and Traditional Database Systems 2021 VLDB 0.00010417728
1,978 Table-GPT: Table Fine-tuned GPT for Diverse Table Tasks 2024 SIGMOD 9.2730152e-05
2,039 LLM-R^2: A Large Language Model Enhanced Rule-based Rewrite System for Boosting Query Efficiency 2025 VLDB 9.1493268e-05
2,210 Lero: A Learning-to-Rank Query Optimizer 2023 VLDB 8.8257742e-05
2,231 GPTuner: A Manual-Reading Database Tuning System via GPT-Guided Bayesian Optimization 2024 VLDB 8.7982985e-05
2,690 Cost-based or Learning-based? A Hybrid Query Optimizer for Query Plan Selection 2022 VLDB 8.1258173e-05
2,719 TPC-DS, Taking Decision Support Benchmarking to the Next Level 2002 SIGMOD 8.0967665e-05
4,780 Data-Juicer: A One-Stop Data Processing System for Large Language Models 2024 SIGMOD 6.4162513e-05
6,071 Demonstrating GPT-DB: Generating Query-Specific and Customizable Code for SQL Processing with GPT-4 2023 VLDB 5.8962612e-05
6,308 Is Your Learned Query Optimizer Behaving As You Expect? A Machine Learning Perspective 2024 VLDB 5.8177833e-05
8,266 Automated Data Visualization from Natural Language via Large Language Models: An Exploratory Study 2024 SIGMOD 5.3650555e-05
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