QueryBooster: Improving SQL Performance Using Middleware Services for Human-Centered Query Rewriting
Qiushi Bai, Sadeem Alsudais, Chen Li
摘要
SQL query performance is critical in database applications, and query rewriting is a technique that transforms an original query into an equivalent query with a better performance. In a wide range of database-supported systems, there is a unique problem where both the application and database layer are black boxes, and the developers need to use their knowledge about the data and domain to rewrite queries sent from the application to the database for better performance. Unfortunately, existing solutions do not give the users enough freedom to express their rewriting needs. To address this problem, we propose QueryBooster, a novel middleware-based service architecture for human-centered query rewriting, where users can use its expressive and easy-to-use rule language (called VarSQL) to formulate rewriting rules based on their needs. It also allows users to express rewriting intentions by providing examples of the original query and its rewritten query. QueryBooster automatically generalizes them to rewriting rules and suggests high-quality ones. We conduct a user study to show the benefits of VarSQL to formulate rewriting rules. Our experiments on real and synthetic workloads show the effectiveness of the rule-suggesting framework and the significant advantages of using QueryBooster for human-centered query rewriting to improve the end-to-end query performance.
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引用它的顶会 Paper5
- LLM-R2: A Large Language Model Enhanced Rule-based Rewrite System for Boosting Query EfficiencyZhaodonghui Li, Haitao Yuan, Huiming Wang, Gao Cong 等VLDB 2025 · 被引用 52 次
- Efficient Query Rewrite Rule Discovery via Standardized Enumeration and Learning-to-RankYuan Zhang, Yuxing Chen, Yuekun Yu, Jinbin Huang 等ICDE 2026
- Cracking Query Bottlenecks: Towards Efficiency-Oriented Text-to-SQL GenerationLi Lin, Yunfeng Shen, Lingfeng Bao, Rongxin Wu 等ISSTA 2026
- Dialect-Agnostic SQL Parsing via LLM-Based SegmentationJunwen An, Kabilan Mahathevan, Manuel RiggerSIGMOD 2026
- ReSequel: Robust LLM-assisted Query Rewriting and Optimization using Templatization and SamplingSaeed Fathollahzadeh, Essam Mansour, Matthias BoehmVLDB 2026
它引用的顶会 Paper4
- Bao: Making Learned Query Optimization PracticalRyan Marcus, Parimarjan Negi, Hongzi Mao, Nesime Tatbul 等SIGMOD 2021 · 被引用 242 次
- A Learned Query Rewrite System using Monte Carlo Tree SearchXuanhe Zhou, Guoliang Li, Chengliang Chai, Jianhua FengVLDB 2022 · 被引用 85 次
- WeTune: Automatic Discovery and Verification of Query Rewrite RulesZhaoguo Wang, Zhou Zhou, Yicun Yang, Haoran Ding 等SIGMOD 2022 · 被引用 35 次
- Shedding Light on Opaque Application QueriesKapil Khurana, Jayant R. HaritsaSIGMOD 2021 · 被引用 3 次
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