Moving Metric Detection and Alerting System at eBay
This work addresses alert management for domain teams at eBay, but it is incremental as it builds on existing anomaly detection and retrieval methods.
The paper tackles the problem of monitoring thousands of product health metrics at eBay by building a two-phase alerting system that detects anomalies and retrieves actionable alerts, resulting in dramatically improved alert precision and reduced alert spamming in production.
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.