DBMind
Xuanhe Zhou, Lianyuan Jin, Ji Sun, Xinyang Zhao, Xiang Yu, Jianhua Feng, Shifu Li, Tianqing Wang, Kun Li, Luyang Liu
Abstract
We demonstrate a self-driving system DBMind, which provides three autonomous capabilities in database, including self-monitoring, self-diagnosis and self-optimization. First, self-monitoring judiciously collects database metrics and detects anomalies (e.g., slow queries and IO contention), which can profile database status while only slightly affecting system performance (<5%). Then, self-diagnosis utilizes an LSTM model to analyze the root causes of the anomalies and automatically detect root causes from a pre-defined failure hierarchy. Next, self-optimization automatically optimizes the database performance using learning-based techniques, including deep reinforcement learning based knob tuning, reinforcement learning based index selection, and encoder-decoder based view selection. We have implemented DBMind in an open source database openGauss and demonstrated real scenarios.