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Differentially Private Canary Optimization via Thompson Sampling for SQL Performance Fixes

Author

Listed:
  • Chiranjeevi Devi
  • Pradeep Manivannan
  • Radhakrishnan Pachyappan

Abstract

SQL performance tuning often involves deploying multiple query fixes in live systems to identify the most effective solution, a process that can risk data exposure and degrade service quality. This paper proposes a novel framework that integrates differential privacy with Thompson Sampling to optimize "canary deployments" of SQL fixes. Our method ensures that experimental testing across user groups maintains statistical efficiency while preserving user data privacy. By leveraging Bayesian exploration strategies, our approach identifies high-performing query modifications under strict privacy constraints, minimizing performance regression and exposure. Experimental evaluations on benchmark SQL workloads demonstrate that our approach achieves near-optimal performance improvements with provable differential privacy guarantees, offering a robust solution for safely and effectively deploying performance fixes in sensitive environments.

Suggested Citation

  • Chiranjeevi Devi & Pradeep Manivannan & Radhakrishnan Pachyappan, 2024. "Differentially Private Canary Optimization via Thompson Sampling for SQL Performance Fixes," Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023, Open Knowledge, vol. 3(1), pages 532-550.
  • Handle: RePEc:das:njaigs:v:3:y:2024:i:1:p:532-550:id:383
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