论文标题
在eBay上移动公制检测和警报系统
Moving Metric Detection and Alerting System at eBay
论文作者
论文摘要
在eBay，有成千上万个产品健康指标可以监视不同的域名团队。我们建立了一个两阶警报系统，以基于异常检测和警报检索通知用户。在第一阶段，我们开发了一种有效的异常检测算法，称为移动度量检测器（MMD），以确定具有分布不可知标准的指标之间的潜在警报。在第二个警报检索阶段，我们构建了其他逻辑，并使用反馈来选择有效的可操作警报，并具有点数排名模型和业务规则。与其他趋势和季节性分解方法相比，我们的分解器在无监督的情况下检测异常更快，更好。我们的两阶段方法极大地提高了警报精度，并避免了在eBay生产中垃圾邮件的警报。
At eBay, there are thousands of product health metrics for different domain teams to monitor. We built a two-phase alerting system to notify users with actionable alerts based on anomaly detection and alert retrieval. In the first phase, we developed an efficient anomaly detection algorithm, called Moving Metric Detector (MMD), to identify potential alerts among metrics with distribution agnostic criteria. In the second alert retrieval phase, we built additional logic with feedbacks to select valid actionable alerts with point-wise ranking model and business rules. Compared with other trend and seasonality decomposition methods, our decomposer is faster and better to detect anomalies in unsupervised cases. Our two-phase approach dramatically improves alert precision and avoids alert spamming in eBay production.
