Not a question I'd usually overthink, but the follow-up framing made me realize I was being pretty surface-level.
Start by emphasizing that effective logging is about capturing actionable context, not just recording errors. Then, walk through a structured approach that covers what to log, how to structure logs, and how to use them for debugging. Finally, discuss trade-offs like verbosity vs. performance and how to balance them.
Pro tip: Mention that logs should tell a story: include a unique request ID to trace a request across services, and log at the appropriate level (e.g., ERROR for unexpected failures, WARN for recoverable issues). This shows you understand production debugging realities.
Clarify that the purpose is to quickly diagnose and fix issues, not to log everything. Focus on capturing enough context to reproduce or understand the error.
Log errors with stack traces, input parameters, user/session IDs, and relevant state. Avoid logging sensitive data. Include a unique correlation ID to trace requests across services.
Use structured logging (e.g., JSON) for easy parsing and querying. Assign appropriate log levels (ERROR, WARN, INFO, DEBUG) and ensure consistency across the codebase.
Use a logging framework and centralize logs (e.g., ELK, Splunk, CloudWatch). Ensure logs are searchable and have retention policies. Consider asynchronous logging to avoid performance hits.
Regularly review logs, set up alerts for critical errors, and refine logging based on debugging experiences. Avoid log noise by tuning levels and sampling if needed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.