Towards a Unified Query Plan Representation
Jinsheng Ba, Manuel Rigger
摘要
In database systems, a query plan is a series of concrete internal steps to execute a query. Multiple testing approaches utilize query plans for finding bugs. However, query plans are represented in a database-specific manner, so implementing these testing approaches requires a non-trivial effort, hindering their adoption. We envision that a unified query plan representation can facilitate the implementation of these approaches. In this paper, we present an exploratory case study to investigate query plan representations in nine widely-used database systems. Our study shows that query plan representations consist of three conceptual components: operations, properties, and formats, which enable us to design a unified query plan representation. Based on it, existing testing methods can be efficiently adopted, finding 17 previously unknown and unique bugs. Additionally, the unified query plan representation can facilitate other applications. Existing visualization tools can support multiple database systems based on the unified query plan representation with moderate implementation effort, and comparing unified query plans across database systems provides actionable insights to improve their performance. We expect that the unified query plan representation will enable the exploration of additional application scenarios.
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引用它的顶会 Paper2
- Understanding and Reusing Test Suites Across Database SystemsSuyang Zhong, Manuel RiggerSIGMOD 2025 · 被引用 4 次
- TATA: An Efficient Framework for Task Transfer in Query Plan RepresentationYue Zhao, Songsong Mo, Gao CongVLDB 2026
它引用的顶会 Paper7
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- Reinforcement Learning with Tree-LSTM for Join Order SelectionXiang Yu, Guoliang Li, Chengliang Chai, Nan TangICDE 2020 · 被引用 168 次
- QueryFormer: A Tree Transformer Model for Query Plan RepresentationYue Zhao, Gao Cong, Jiachen Shi, Chunyan MiaoVLDB 2022 · 被引用 117 次
- Finding bugs in database systems via query partitioningManuel Rigger, Zhendong SuOOPSLA 2020 · 被引用 116 次
- Automatic View Generation with Deep Learning and Reinforcement LearningHaitao Yuan, Guoliang Li, Ling Feng, Ji Sun 等ICDE 2020 · 被引用 66 次
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