A Performance Anomaly Detection and Analysis Framework for DBMS Development
A Performance Anomaly Detection and Analysis Framework for DBMS Development
复制标题
DBMS 开发的性能异常检测和分析框架
DOI：
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发表时间：
2012
影响因子：
8.9
通讯作者：
Arthur H. Lee
中科院分区：
文献类型：
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作者：
Donghun Lee;S. Cha;Arthur H. Lee
检测性能异常并找到其根本原因是一项繁琐的任务，需要大量的手工工作。与大多数软件开发一样，DBMS开发中的功能增强除了bug之外，还经常引入性能问题。为了在引入问题时及时检测问题（通常发生在开发周期的早期阶段），我们在流程的早期采用了性能回归测试。在本文中，我们描述了我们开发的一个框架，该框架在为一个被认为是异常的问题建立了一组条件之后，用于管理性能异常。该框架使用统计过程控制（SPC）图表来检测性能异常和差异分析，以确定其根本原因。通过在框架内自动化任务，我们能够消除检测异常的大部分手工开销，并在大多数情况下将识别根本原因的分析时间减少约90%。基于该框架开发和部署的工具允许我们除了在DBMS开发中进行通常的功能监控之外，还可以对性能进行连续的、自动化的日常监控。
Detecting performance anomalies and finding their root causes are tedious tasks requiring much manual work. Functionality enhancements in DBMS development as in most software development often introduce performance problems in addition to bugs. To detect the problems as soon as they are introduced, which often happens during the early phases of a development cycle, we adopt performance regression testing early in the process. In this paper, we describe a framework that we developed to manage performance anomalies after establishing a set of conditions for a problem to be considered an anomaly. The framework uses Statistical Process Control (SPC) charts to detect performance anomalies and differential profiling to identify their root causes. By automating the tasks within the framework we were able to remove most of the manual overhead in detecting anomalies and reduce the analysis time for identifying the root causes by about 90 percent in most cases. The tools developed and deployed based on the framework allow us continuous, automated daily monitoring of performance in addition to the usual functionality monitoring in our DBMS development.