EAST: An Interpretable Knob Estimation System for Cloud Database

Database vendors have made significant redesigns and developments to the relational database for providing cloud-hosted and cloud-native database services. Thus, the original knob-tuning experiences of DBAs are no longer applicable to the cloud database era. An interpretable estimation service is urgently needed to provide explicit guidance for database knob tuning. Unfortunately, less attention has been paid to estimating the performance of the knob configuration. To fill this gap, we propose EAST, a knob estimation system to provide interpretable & transferable knob estimation services for cloud databases. Firstly, we design an interpretable knob-embedding-based estimator to achieve the trusted and white-box knob estimation for researchers, practitioners, and even artificial intelligence knob tuners. Secondly, we design a two-stage transfer estimation approach by stacking ensemble learning to utilize historical experiences, improving time efficiency. Thirdly, EAST provides a user-friendly interface to support direct knob estimation and transfer knob estimation services. We have deployed our EAST¹¹https://gitee.com/opengauss/openGauss-DBMind/tree/incubator/dbmind/components/knob_estimator to the DBMind component of OpenGauss and demonstrated the effectiveness of our system under the open-source benchmark TPCC.