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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| Accesso online: | https://arxiv.org/abs/2602.02999 |
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| _version_ | 1866918320796598272 |
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| author | Wang, Zhengle Zhang, Yanfei Liu, Chunwei |
| author_facet | Wang, Zhengle Zhang, Yanfei Liu, Chunwei |
| contents | Database research and development rely heavily on realistic user workloads for benchmarking, instance optimization, migration testing, and database tuning. However, acquiring real-world SQL queries is notoriously challenging due to strict privacy regulations. While cloud database vendors have begun releasing anonymized performance traces to the research community, these traces typi- cally provide only high-level execution statistics without the origi- nal query text or data, which is insufficient for scenarios that require actual execution. Existing tools fail to capture fine-grained perfor- mance patterns or generate runnable workloads that reproduce these public traces with both high fidelity and efficiency. To bridge this gap, we propose ResQ, a fine-grained workload synthesis sys- tem designed to generate executable SQL workloads that faithfully match the per-query execution targets and operator distributions of production traces. ResQ constructs execution-aware query graphs, instantiates them into SQL via Bayesian Optimization-driven pred- icate search, and explicitly models workload repetition through reuse at both exact-query and parameterized-template levels. To ensure practical scalability, ResQ combines search-space bounding with lightweight local cost models to accelerate optimization. Ex- periments on public cloud traces (Snowset, Redset) and a newly released industrial trace (Bendset) demonstrate that ResQ signif- icantly outperforms state-of-the-art baselines, achieving 96.71% token savings and a 86.97% reduction in runtime, while lowering maximum Q-error by 14.8x on CPU time and 997.7x on scanned bytes, and closely matching operator composition. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_02999 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | ResQ: Realistic Performance-Aware Query Generation Wang, Zhengle Zhang, Yanfei Liu, Chunwei Databases Database research and development rely heavily on realistic user workloads for benchmarking, instance optimization, migration testing, and database tuning. However, acquiring real-world SQL queries is notoriously challenging due to strict privacy regulations. While cloud database vendors have begun releasing anonymized performance traces to the research community, these traces typi- cally provide only high-level execution statistics without the origi- nal query text or data, which is insufficient for scenarios that require actual execution. Existing tools fail to capture fine-grained perfor- mance patterns or generate runnable workloads that reproduce these public traces with both high fidelity and efficiency. To bridge this gap, we propose ResQ, a fine-grained workload synthesis sys- tem designed to generate executable SQL workloads that faithfully match the per-query execution targets and operator distributions of production traces. ResQ constructs execution-aware query graphs, instantiates them into SQL via Bayesian Optimization-driven pred- icate search, and explicitly models workload repetition through reuse at both exact-query and parameterized-template levels. To ensure practical scalability, ResQ combines search-space bounding with lightweight local cost models to accelerate optimization. Ex- periments on public cloud traces (Snowset, Redset) and a newly released industrial trace (Bendset) demonstrate that ResQ signif- icantly outperforms state-of-the-art baselines, achieving 96.71% token savings and a 86.97% reduction in runtime, while lowering maximum Q-error by 14.8x on CPU time and 997.7x on scanned bytes, and closely matching operator composition. |
| title | ResQ: Realistic Performance-Aware Query Generation |
| topic | Databases |
| url | https://arxiv.org/abs/2602.02999 |