Anomaly detection is like finding a puppy in a herd of kittens. It's about spotting unusual behavior in your application that could indicate a problem, such as a sudden spike in error rates or a significant increase in response time. For example, in a web application, anomaly detection could help identify a sudden increase in failed login attempts, which might indicate a new bug in the authentication flow.
Anomaly detection relies on statistical methods, machine learning algorithms, or rule-based systems to analyze and identify irregular patterns in the data.
Explore related concepts
Monitoring
Monitoring is the act of tracking the performance and behavior of a system, application, or service in real-time, allowing for the detection of issues, anomalies, and performance bottlenecks. It involves collecting and analyzing metrics to ensure that everything is running smoothly and to identify potential areas for improvement.
Error Rate
The error rate is the frequency at which errors occur in an application. It is a critical metric for understanding the overall health and performance of an app, as it directly impacts user experience.
Incident Management
Incident Management is the process of identifying, responding to, and resolving incidents that occur within an application. It involves detecting when something goes wrong, alerting the appropriate teams, working to restore normal operations as quickly as possible, and implementing measures to prevent the same issue to occur in the future.