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Facilitating Database Tuning with Hyper-Parameter Optimization: A Comprehensive Experimental Evaluation

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arxiv 2110.12654 v4 pith:QN7ZFSIM submitted 2021-10-25 cs.DB

Xinyi Zhang , Zhuo Chang , Yang Li , Hong Wu , Jian Tan , Feifei Li , Bin Cui This is my paper
classification cs.DB
keywords databasetuningconfigurationevaluationsystemscomprehensivealgorithmalgorithms
verification ladder T0 review T1 audit T2 compute T3 formal
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Recently, using automatic configuration tuning to improve the performance of modern database management systems (DBMSs) has attracted increasing interest from the database community. This is embodied with a number of systems featuring advanced tuning capabilities being developed. However, it remains a challenge to select the best solution for database configuration tuning, considering the large body of algorithm choices. In addition, beyond the applications on database systems, we could find more potential algorithms designed for configuration tuning. To this end, this paper provides a comprehensive evaluation of configuration tuning techniques from a broader perspective, hoping to better benefit the database community. In particular, we summarize three key modules of database configuration tuning systems and conduct extensive ablation studies using various challenging cases. Our evaluation demonstrates that the hyper-parameter optimization algorithms can be borrowed to further enhance the database configuration tuning. Moreover, we identify the best algorithm choices for different modules. Beyond the comprehensive evaluations, we offer an efficient and unified database configuration tuning benchmark via surrogates that reduces the evaluation cost to a minimum, allowing for extensive runs and analysis of new techniques.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. When to Use Which? Benchmarking Optimisers for Configurable Systems under Varying Budgets

    cs.SE 2026-07 conditional novelty 6.0 of 10

    Across 22 configurable systems and budgets from 100 to 10,000 evaluations, FLASH is the most consistently effective optimiser, while GA and IRACE catch up only at large budgets.

  2. AgenticDB: Self-Evolving Reconfiguration Framework for Database Workloads

    cs.DB 2026-06 unverdicted novelty 6.0 of 10

    AgenticDB introduces an agentic AI system that interactively reconfigures MySQL and PostgreSQL workloads using DBMS and OS changes guided by runtime feedback, outperforming baselines by 118.1% on average.

  3. MCTuner: Spatial Decomposition-Enhanced Database Tuning via LLM-Guided Exploration

    cs.DB 2025-09 reject novelty 5.0 of 10

    MCTuner reports up to 19.2% performance gains and roughly 1.4x faster discovery of good configurations by combining LLM knob selection with recursive space decomposition and Bayesian optimization.

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