Monitoring and Observability
Monitoring is the process of collecting, analyzing, and using information to track applications and infrastructure in order to guide business decisions. Observability is the measure of how well internal states of a system can be inferred from knowledge of its external outputs.
The Three Pillars
A robust monitoring strategy typically relies on three pillars:
- metrics: Numerical data measured over time (e.g., CPU usage, Request Latency).
- Logs: discrete events that happened (e.g., "Error: Connection failed at 10:00 PM").
- Traces: The path of a request through a distributed system.
Key Deliverables
- Dashboards
- Alerting Rules
- Performance Reports
How AI Can Help: Monitoring
AI transforms raw data into actionable intelligence:
- Anomaly Detection: Tools like Datadog and Dynatrace use AI to automatically detect anomalies without setting manual thresholds.
- Log Analysis: Sumo Logic and Logz.io use ML to cluster millions of log lines into patterns, helping find the "needle in the haystack."
- Alert Fatigue: AI helps group related alerts into a single incident, reducing noise for the on-call engineers.