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Is the Logging Strategy Sufficient for Troubleshooting?

Type: Structure
Category: Non-functional
Audience: Backend engineers, SREs, platform teams, observability owners

🔍 What This Perspective Covers

Logs are not just for developers—they’re lifelines during failure.

This perspective checks whether your logging strategy provides enough context and structure to support fast, reliable incident diagnosis and postmortem analysis.

Logging Pain Points

  • No correlation ID between API, job, and DB traces
  • User actions are not clearly tied to internal events
  • Logs only show stack traces, not system state
  • High-volume logs drown out important anomalies
  • Sensitive data appears in logs or is overly redacted

⚠️ Failure Patterns

  • “It failed” but no insight into why or what triggered it
  • Can’t trace user impact across distributed components
  • Devs need to SSH into prod to find relevant logs
  • No logs around failure-time due to buffering or crash
  • Logging format inconsistency breaks analysis tools

✅ Smarter Logging Design

  • Use structured logging: JSON or context-rich formats
  • Always include request ID, user ID, operation name
  • Log inputs, outcomes, and durations—not just errors
  • Define log levels clearly: info, warn, error, fatal
  • Secure logs with access control and field redaction

🧠 Principle

If your logs can’t explain failure,
they’re just expensive noise.

❓ FAQ

  • Q: Should everything be logged?
    A: No. Log only what you’d need in a crisis—and ensure it’s understandable.

  • Q: What’s structured logging?
    A: Log data as key-value pairs with traceable metadata, not raw text blobs.