How is Anomaly Detection Different from Health Rules?
While both Anomaly Detection and health rules alert you to performance problems in your application, Anomaly Detection provides powerful insights that would be difficult to obtain using health rules.
| Anomaly Detection | Health Rules |
|---|---|
|
Anomaly Detection uses machine learning to discover the normal ranges of key business transaction metrics, base pages, databases, and network request. It alerts you when these metrics deviate significantly from expected values. This enables Anomaly Detection to identify a wider range of problems than a person could capture in Health Rules. |
Health rules are manually created to apply logical conditions that one or more metrics must satisfy. For example, you could monitor the Average Response Time (ART) to check if this metric deviates from the configured baseline. |
| Anomaly Detection requires no configuration except when you want to limit anomaly alerting. |
Splunk AppDynamics provides a default set of health rules and you create additional health rules manually as required, configuring time periods, trends, and schedules. |
| Anomalies are associated with application servers, business transactions, base pages for browser applications, databases, and network request for mobile applications. | Health rules apply to any entity, for example, business transactions, service endpoints. |