The process of database knob tuning has always been a challenging task. Recently, database knob tuning methods has emerged as a promising solution to mitigate these issues. However, these methods still face certain limitations.On one hand, when applying knob tuning algorithms to optimize databases in practice, it either requires frequent updates to the database or necessitates acquiring database workload and optimizing through workload replay. The former approach involves constant exploration and updating of database configurations, inevitably leading to a decline in database performance during optimization. The latter, on the other hand, requires the acquisition of workload data, which could lead to data leakage issues. Moreover, the hyperparameter configuration space for database knobs is vast, making it challenging for optimizers to converge. These factors significantly hinder the practical implementation of database tuning. To address these concerns, we proposes an efficient and micro-invasive knob tuning method. This method relies on workload synthesis on cloned databases to simulate the workload that needs tuning, thus minimizing the intrusion on the database. And we utilizing a configuration replacement strategy to filter configuration candidates that perform well under the synthesized workload to find best configuration. And during the tuning process, we employ a knowledge transfer method to extract a common high-performance space, to boost the convergence of the optimizer.

翻译：数据库参数调优过程一直是一项具有挑战性的任务。近年来，数据库参数调优方法已成为缓解这些问题的一种有前景的解决方案。然而，这些方法仍面临某些局限。一方面，在实际应用参数调优算法优化数据库时，要么需要频繁更新数据库，要么需要获取数据库工作负载并通过工作负载回放进行优化。前一种方法涉及对数据库配置的持续探索与更新，不可避免地导致优化期间数据库性能下降。后一种方法则需要获取工作负载数据，这可能引发数据泄露问题。此外，数据库参数的超参数配置空间庞大，使得优化器难以收敛。这些因素严重阻碍了数据库调优的实际应用。为解决这些问题，我们提出了一种高效且微侵入式的参数调优方法。该方法依赖于在克隆数据库上进行工作负载合成，以模拟需要调优的工作负载，从而最小化对数据库的侵入。我们利用一种配置替换策略来筛选在合成工作负载下表现良好的候选配置，以找到最佳配置。在调优过程中，我们采用一种知识迁移方法来提取一个通用的高性能空间，以加速优化器的收敛。