EMIT：微侵入式数据库配置调优
EMIT: Micro-Invasive Database Configuration Tuning
AI总结：
针对数据库旋钮调优中频繁更新导致性能下降、获取工作负载易引发数据泄露及配置空间庞大难以收敛的问题，提出EMIT微侵入式调优方法，通过克隆库工作负载合成、配置替换策略和知识转移提升收敛效率。
摘要：
数据库旋钮调优的过程一直是一项具有挑战性的任务。最近，数据库旋钮调优方法作为一种有前景的解决方案出现，以缓解这些问题。然而，这些方法仍然面临某些局限性。一方面，在实践中应用旋钮调优算法来优化数据库时，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重放进行优化。前一种方法涉及对数据库配置的不断探索和更新，不可避免地会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，这可能导致数据泄露问题。此外，数据库旋钮的超参数配置空间非常庞大，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实施。为了解决这些问题，我们提出了一种高效且微侵入式的旋钮调优方法。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。并且我们利用配置替换策略来过滤在合成工作负载下表现良好的配置候选，以找到最佳配置。在调优过程中，我们采用知识转移方法来提取通用的高性能空间，以促进优化器的收敛。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
