ResQ: Realistic Performance-Aware Query Generation
Zhengle Wang, Yanfei Zhang, Chunwei Liu

TL;DR
ResQ is a workload synthesis system that generates realistic, executable SQL workloads matching production traces with high fidelity, efficiency, and operator distribution accuracy, addressing privacy and data limitations.
Contribution
ResQ introduces a novel approach combining execution-aware query graphs, Bayesian optimization, and workload reuse to generate high-fidelity SQL workloads efficiently.
Findings
Achieves 96.71% token savings compared to baselines.
Reduces runtime by 86.97%.
Significantly lowers Q-error on CPU time and scanned bytes.
Abstract
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…
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Taxonomy
TopicsAdvanced Database Systems and Queries · Cloud Computing and Resource Management · Data Quality and Management
