  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.
