{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T22:48:26Z","timestamp":1772837306688,"version":"3.50.1"},"reference-count":64,"publisher":"Association for Computing Machinery (ACM)","issue":"12","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2025,8]]},"abstract":"<jats:p>\n            Query rewrite is essential for optimizing SQL queries to improve their execution efficiency without changing their results. Traditionally, this task has been tackled through heuristic and learning-based methods, each with its limitations in terms of inferior quality and low robustness. Recent advancements in LLMs offer a new paradigm by leveraging their superior natural language and code comprehension abilities. Despite their potential, directly applying LLMs like GPT-4 has faced challenges due to problems such as hallucinations, where the model might generate inaccurate or irrelevant results. To address this, we propose\n            <jats:italic toggle=\"yes\">R-Bot<\/jats:italic>\n            , an LLM-based query rewrite system with a systematic approach. We first design a multi-source rewrite evidence preparation pipeline to generate query rewrite evidences for guiding LLMs to avoid hallucinations. We then propose a hybrid structure-semantics retrieval method that combines structural and semantic analysis to retrieve the most relevant rewrite evidences for effectively answering an online query. We next propose a step-by-step LLM rewrite method that iteratively leverages the retrieved evidences to select and arrange rewrite rules with self-reflection. We conduct comprehensive experiments on real-world datasets and widely used benchmarks, and demonstrate the superior performance of our system,\n            <jats:italic toggle=\"yes\">R-Bot<\/jats:italic>\n            , surpassing state-of-the-art query rewrite methods. The\n            <jats:italic toggle=\"yes\">R-Bot<\/jats:italic>\n            system has been deployed at Huawei and with real customers, and the results show that the proposed\n            <jats:italic toggle=\"yes\">R-Bot<\/jats:italic>\n            system achieves lower query latency.\n          <\/jats:p>","DOI":"10.14778\/3750601.3750625","type":"journal-article","created":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T13:38:05Z","timestamp":1758029885000},"page":"5031-5044","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["R-Bot: An LLM-Based Query Rewrite System"],"prefix":"10.14778","volume":"18","author":[{"given":"Zhaoyan","family":"Sun","sequence":"first","affiliation":[{"name":"Tsinghua University"}]},{"given":"Xuanhe","family":"Zhou","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University"}]},{"given":"Guoliang","family":"Li","sequence":"additional","affiliation":[{"name":"Tsinghua University"}]},{"given":"Xiang","family":"Yu","sequence":"additional","affiliation":[{"name":"Huawei Company"}]},{"given":"Jianhua","family":"Feng","sequence":"additional","affiliation":[{"name":"Tsinghua University"}]},{"given":"Yong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Tsinghua University"}]}],"member":"320","published-online":{"date-parts":[[2025,9,16]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"2018. 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