Adversarial Query Synthesis via Bayesian Optimization
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Abstract (arXiv)
Benchmark workloads are extremely important to the database management research community, especially as more machine learning components are integrated into database systems. Here, we propose a Bayesian optimization technique to automatically search for difficult benchmark queries, significantly reducing the amount of manual effort usually required. In preliminary experiments, we show that our approach can generate queries with more than double the optimization headroom compared to existing benchmarks.
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References · 12
- 2021The Power of Scale for Parameter-Efficient Prompt Tuningcited by 3,769
- 2017The Case for Learned Index Structurescited by 188
- 2023Protein Design with Guided Discrete Diffusioncited by 154
- 2022Design-Bench: Benchmarks for Data-Driven Offline Model-Based Optimizationcited by 96
- 2022Local Latent Space Bayesian Optimization over Structured Inputscited by 78
- 2024XGrammar: Flexible and Efficient Structured Generation Engine for Large Language Modelscited by 73
- 2020Bao: Learning to Steer Query Optimizerscited by 62
- 2020Benchmarking Learned Indexescited by 27
- 2025Learned Offline Query Planning via Bayesian Optimizationcited by 4
- 2025Yannakakis+: Practical Acyclic Query Evaluation with Theoretical Guaranteescited by 2
- 2025Redbench: A Benchmark Reflecting Real Workloadscited by 1
- 2025JOB-Complex: A Challenging Benchmark for Traditional & Learned Query Optimizationcited by 1
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