What is Logging?
Logging is the practice of recording events, errors, and state changes from your application so you can review what happened after the fact. Good logs are your primary debugging tool in production.
In plain English
Logs are like a ship's logbook — a timestamped record of everything that happened during the voyage. When something goes wrong, you don't have to guess; you open the logbook and trace exactly what occurred and when.
How it works
Your application emits log entries — strings or structured JSON objects — describing events as they happen. A log aggregation service (like Datadog, Loki, or Cloudwatch) collects these from all your servers and makes them searchable. Structured logs with consistent fields (user_id, request_id, error_code) are far easier to query than plain text.
Why it matters for AI-built apps
AI-generated code often uses console.log for debugging during development but ships to production without a log strategy. When a bug surfaces for one user in production, unstructured or missing logs make it nearly impossible to reproduce. Structured, centralized logging lets you search for all events related to a specific user, request, or error in seconds.
Best practices
Use log levels (debug, info, warn, error) consistently and filter out debug logs in production to reduce noise and cost. Always include a correlation ID or request ID so you can trace a single request across multiple services. Never log sensitive data like passwords, tokens, or personal information.
Frequently asked questions
How long should I retain logs?
30–90 days covers most debugging needs. Some compliance requirements (HIPAA, PCI) mandate longer retention — check your obligations before setting a retention policy.
Are console.log statements enough in production?
Only if they're shipped to a log aggregator. Raw console output gets lost when containers restart. Use a structured logger like Pino or Winston and stream to a log management platform.
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