Observability Guide: Metrics, Logs & Traces for DevOps

Monitoring tells you when something is wrong. Observability helps you understand why. As CTO of Softechinfra, I've implemented observability across complex distributed systems. For modern applications, observability is essential.

Monitoring vs Observability

Traditional Monitoring

Observability

The Three Pillars

1. Metrics

What they are:
• Numeric measurements over time
• Aggregated data
• Efficient storage
• Great for trends

Key metrics:
• RED: Rate, Errors, Duration
• USE: Utilization, Saturation, Errors
• Golden signals: Latency, traffic, errors, saturation

2. Logs

What they are:
• Discrete events
• Rich context
• Detailed information
• Storage intensive

Best practices:
• Structured logging (JSON)
• Consistent format
• Appropriate levels
• Correlation IDs

3. Traces

What they are:
• Request flow across services
• Distributed context
• Latency breakdown
• Dependency mapping

Components:
• Span: Single operation
• Trace: End-to-end request
• Context: Propagated metadata

Implementation Strategy

Start with Instrumentation

Application level:
• Add tracing libraries
• Structured logging
• Custom metrics
• Error tracking

Infrastructure level:
• Container metrics
• Kubernetes events
• Node telemetry
• Network monitoring

Choose Your Stack

Open Source:
• Prometheus + Grafana (metrics)
• Elasticsearch/Loki (logs)
• Jaeger/Zipkin (traces)
• OpenTelemetry (instrumentation)

Commercial:
• Datadog
• New Relic
• Honeycomb
• Dynatrace

Connect the Dots

The power is in correlation:
• Link traces to logs
• Connect metrics to traces
• Alert on metrics, debug with traces
• Search logs, pivot to context

Best Practices

1. Use Correlation IDs

Every request gets an ID:
• Propagate through services
• Include in all logs
• Attach to traces
• Use in error reports

2. Structured Logging

{
  "timestamp": "2023-05-10T14:30:00Z",
  "level": "error",
  "service": "payment",
  "trace_id": "abc123",
  "message": "Payment failed",
  "error": "timeout",
  "customer_id": "cust_456"
}

3. Meaningful Metrics

4. Alerting Strategy

Common Patterns

Service Level Objectives (SLOs)

Define and measure reliability:
• Availability target
• Latency targets
• Error rate limits
• Error budget tracking

On-Call Practices