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.
翻译：最近,利用自动配置调整来改进现代数据库管理系统(DBMS)的性能,引起了数据库界越来越多的兴趣,这体现在正在开发若干具有先进调试能力的系统,然而,考虑到大量的算法选择,选择数据库配置调整的最佳解决办法仍然是一项挑战。此外,除了数据库系统的应用程序外,我们还可以找到更多为配置调整设计的可能的算法。为此,本文件从更广泛的角度对配置调控技术进行了全面评估,希望更好地使数据库界受益。特别是,我们总结了三个数据库配置调控系统的关键模块,并利用各种具有挑战性的案例进行了广泛的调控研究。我们的评估表明,可以借用超参数优化算法来进一步加强数据库配置的调控。此外,我们还确定了不同模块的最佳算法选择。除了全面评估外,我们还提供了一个高效和统一的数据库配置基准,通过代管器将评价费用降低到最低程度,从而能够广泛运行并分析新技术。
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