基于人工智能方法的数据库智能诊断

数据库是一种非常重要和基础的计算机系统软件，随着数据库在各行各业的广泛应用，越来越多的人开始关注数据库运行的稳定性.由于各种各样内部或是外部作用的影响，数据库在实际运行的过程中会出现性能异常，而这可能会带来巨大的经济损失.人们大多通过观察监控指标信息来进行数据库异常诊断，但是关于数据库监控指标有数百个，普通的数据库使用者根本无法提取出有价值的信息.一些传统的公司会聘用专业的人员管理数据库，而这种成本会是很多公司难以接受的.因此，如何用较低的成本完成对数据库的自动监控和诊断是一个具有挑战性的问题.现有的OLTP数据库自动异常诊断方法往往存在着监控信息收集成本过高、适用范围小抑或是稳定性较差等问题.在这篇论文里我们提出了一种智能的数据库异常诊断框架AutoMonitor，提供了数据库异常监测、异常指标提取和根因分析这三个模块，这三个模块分别使用了基于LSTM的时间序列异常诊断模型、Kolmogorov-Smirnov检验、和优化的K近邻算法.整个框架分成离线训练和在线诊断这两个阶段.我们将提出的系统部署在PostgreSQL数据库，通过实验表明该框架对于异常诊断具有较高的精确程度，并且不会对系统性能造成太大的影响.

AI-based Database Performance Diagnosis

Database is a kind of important and fundamental computer system software. With the development of database application in all walks of life, a growing number of people begin to concern the stability of the database. Because of the numerous internal of external effect, performance anomaly may emerge when the Database running and it may cause huge economic loss. People usually diagnose database anomaly by analyzing monitoring metrics. However, there are hundreds of metrics in the system and ordinary database users are unable to extract valuable information from them. Some major companies employ DBA to manage the databases but this cost is unacceptable for many other companies. Achieving automatic database monitor and diagnose with low cost is a challenging problem. Current methods have many limitations, including high cost of metrics information collection, narrow range of application or poor stability. In this paper, we propose an anomaly diagnose framework AutoMonitor which deployed on the PostgreSQL database. The framework contains LSTM-based anomaly detection module and modified K Nearest-Neighbor algorithm-based root cause diagnose module. The framework consists of an offline training and an online diagnose stage. Our evaluations on the datasets show that our framework has high diagnose accuracy with minor overload to system performance