DBMind: A Self-Driving Platform in openGauss

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. PVLDB Reference Format: Xuanhe Zhou, Lianyuan Jin, Ji Sun, Xinyang Zhao, Xiang Yu, Jianhua Feng, Shifu Li, Tianqing Wang, Kun Li, Luyang Liu. DBMind: A Self-Driving Platform in openGauss. PVLDB, 14(12): 2743 2746, 2021. doi:10.14778/3476311.3476334

[1]  Ke Zhou,et al.  An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning , 2019, SIGMOD Conference.

[2]  Guoliang Li,et al.  Automatic View Generation with Deep Learning and Reinforcement Learning , 2020, 2020 IEEE 36th International Conference on Data Engineering (ICDE).

[3]  Guoliang Li,et al.  AI Meets Database: AI4DB and DB4AI , 2021, SIGMOD Conference.

[4]  Liwei Wang,et al.  Deep Reinforcement Learning-Based Approach to Tackle Topic-Aware Influence Maximization , 2020, Data Science and Engineering.

[5]  Xingquan Zhu,et al.  Deep Learning for User Interest and Response Prediction in Online Display Advertising , 2020, Data Science and Engineering.

[6]  Guoliang Li,et al.  XuanYuan: An AI-Native Database , 2019, IEEE Data Eng. Bull..

[7]  Hongzhi Wang,et al.  Mining conditional functional dependency rules on big data , 2020, Big Data Min. Anal..

[8]  Xuanhe Zhou,et al.  openGauss: An Autonomous Database System , 2021, Proc. VLDB Endow..

[9]  Guoliang Li,et al.  Reinforcement Learning with Tree-LSTM for Join Order Selection , 2020, 2020 IEEE 36th International Conference on Data Engineering (ICDE).

[10]  Guoliang Li,et al.  QTune: A Query-Aware Database Tuning System with Deep Reinforcement Learning , 2019, Proc. VLDB Endow..

[11]  Jianhua Feng,et al.  Query performance prediction for concurrent queries using graph embedding , 2020, Proc. VLDB Endow..

[12]  Chengliang Chai,et al.  Database Meets Artificial Intelligence: A Survey , 2020, IEEE Transactions on Knowledge and Data Engineering.

[13]  Guoliang Li,et al.  An End-to-End Learning-based Cost Estimator , 2019, Proc. VLDB Endow..