What is Observability?
Observability is the ability to understand the internal state of a system by examining its external outputs. Unlike traditional monitoring (which tells you what's wrong), observability helps you understand why something is wrong.
The Three Pillars
1. Metrics
Metrics are numerical measurements collected over time. In Kubernetes, key metrics include:
Infrastructure Metrics:
- Node CPU/memory/disk utilization
- Pod resource usage vs requests vs limits
- Network I/O per pod and node
Application Metrics:
- Request rate (RED method: Rate)
- Error rate (RED method: Errors)
- Response latency (RED method: Duration)
- Business metrics (orders, signups, etc.)
Kubernetes-Specific Metrics:
- Pod restart count
- Deployment replica count vs desired
- HPA scaling events
- PVC capacity utilization
2. Logs
Logs provide detailed event records. Kubernetes logging strategy:
Application Logs:
- Use structured logging (JSON format)
- Include correlation IDs for request tracing
- Log at appropriate levels (DEBUG, INFO, WARN, ERROR)
Kubernetes System Logs:
- API server audit logs
- Kubelet logs
- Controller manager logs
- Scheduler logs
Log Aggregation Stack:
- EFK: Elasticsearch, Fluentd, Kibana
- Loki: Lightweight, Grafana-native
- Cloud-native: CloudWatch, Stackdriver, Azure Monitor
3. Traces
Distributed traces follow requests across services:
- Instrument services with OpenTelemetry
- Collect traces with Jaeger or Zipkin
- Correlate traces with logs using trace IDs
- Identify bottlenecks and slow dependencies
Building an Observability Platform
Step 1: Define What to Observe
Start with SLIs for your most critical services.
Step 2: Instrument Applications
Add metrics endpoints, structured logging, and trace context.
Step 3: Deploy Collection Infrastructure
Set up Prometheus, log aggregation, and trace collection.
Step 4: Build Dashboards
Create dashboards for each team's services.
Step 5: Set Up Alerting
Alert on SLO violations, not raw metrics.
Common Observability Anti-Patterns
- Collecting everything without purpose (data hoarding)
- Dashboard overload (too many charts, no focus)
- Alerting on raw metrics instead of business impact
- Not correlating signals across pillars
How SRExpert Provides Observability
SRExpert unifies metrics, logs, and events across all your Kubernetes clusters in a single platform. Our AI-powered analysis correlates signals across pillars to surface root causes faster — with sub-second latency monitoring and historical analysis.