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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
7557
Venue
SIGMOD
Year
2026
Pagerank
5.5399022e-05
Overall Rank
7,809 | 46.43%
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 4 of 4 citing papers.

Rank Citing Paper Year Venue Pagerank
5,744 SQLStorm: Taking Database Benchmarking into the LLM Era 2025 VLDB 6.1019672e-05
10,199 Automated Discovery of Test Oracles for Database Management Systems Using LLMs 2026 SIGMOD 5.093636e-05
10,401 SEFRQO: A Self-Evolving Fine-Tuned RAG-Based Query Optimizer 2026 SIGMOD 5.093636e-05
10,513 LIO: A lightweight and interpretable query optimizer based on an evolutionary forest 2026 VLDB 5.093636e-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.

Rank Cited Paper Year Venue Pagerank
1 Access Path Selection in a Relational Database Management System 1979 SIGMOD 0.0024089429
18 How Good Are Query Optimizers, Really? 2016 VLDB 0.00059284255
154 Neo: A Learned Query Optimizer 2019 VLDB 0.00028726181
290 An Overview of Query Optimization in Relational Systems 1998 PODS 0.0002227038
378 Bao: Making Learned Query Optimization Practical 2021 SIGMOD 0.00019638121
713 Language Models Enable Simple Systems for Generating Structured Views of Heterogeneous Data Lakes 2024 VLDB 0.00014672521
950 CAESURA: Language Models as Multi-Modal Query Planners 2024 CIDR 0.0001302491
1,241 Balsa: Learning a Query Optimizer Without Expert Demonstrations 2022 SIGMOD 0.00011521639
1,832 DSB: A Decision Support Benchmark for Workload-Driven and Traditional Database Systems 2021 VLDB 9.6607418e-05
2,099 Table-GPT: Table Fine-tuned GPT for Diverse Table Tasks 2024 SIGMOD 9.1682353e-05
2,298 GPTuner: A Manual-Reading Database Tuning System via GPT-Guided Bayesian Optimization 2024 VLDB 8.7886538e-05
2,420 Lero: A Learning-to-Rank Query Optimizer 2023 VLDB 8.605257e-05
2,553 LLM-R^2: A Large Language Model Enhanced Rule-based Rewrite System for Boosting Query Efficiency 2025 VLDB 8.4283807e-05
2,726 TPC-DS, Taking Decision Support Benchmarking to the Next Level 2002 SIGMOD 8.203274e-05
2,762 Cost-based or Learning-based? A Hybrid Query Optimizer for Query Plan Selection 2022 VLDB 8.1539867e-05
6,064 Data-Juicer: A One-Stop Data Processing System for Large Language Models 2024 SIGMOD 5.9895838e-05
6,271 Is Your Learned Query Optimizer Behaving As You Expect? A Machine Learning Perspective 2024 VLDB 5.9326197e-05
6,378 Demonstrating GPT-DB: Generating Query-Specific and Customizable Code for SQL Processing with GPT-4 2023 VLDB 5.8929835e-05
8,140 Automated Data Visualization from Natural Language via Large Language Models: An Exploratory Study 2024 SIGMOD 5.4802202e-05
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# Overall Rank Paper Year Venue
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