Awesome Reviewers

Use structured logging with appropriate context and log levels to maximize the value of log messages. Include relevant metadata with each log message to facilitate filtering and debugging:

1. Use structured logging patterns that allow consistent inclusion of contextual information:

   # Instead of:
   logger.info("Processing item")
      
   # Do this:
   logger.info("Processing item", extra={"project_id": project.id, "item_type": item.type})
      
   # Or with Sentry SDK:
   sentry_sdk.set_context("operation_context", {"project_id": project.id, "item_type": item.type})
   

2. Choose log levels based on actionability:
   • ERROR: For actionable issues requiring immediate attention
   • WARNING: For potential problems that may need attention
   • INFO: For general operational information
   • DEBUG: For detailed troubleshooting information

   Instead of removing logging statements, consider lowering their level to DEBUG for infrequently needed information.

3. When logging exceptions:
   • Include both error type and message
   • Preserve stack traces using logger.exception() or exc_info=True
   • For lower severity issues, use appropriate level with context:

     try:
       # operation
     except ApiError as e:
       # For non-critical errors that don't need immediate action
       logger.warning("API operation failed", exc_info=True, extra={"operation": "fetch_data"})
       # Or with Sentry:
       sentry_sdk.set_context("operation_context", {"operation": "fetch_data"})
       sentry_sdk.capture_exception(error=e, level="warning")
     

4. Avoid duplicate logging. If using a framework that handles logging (like lifecycle), add context to it rather than creating separate log entries:

   # Instead of:
   lifecycle.record_halt(reason)
   logger.info("Operation halted", extra={"reason": reason})
      
   # Do this:
   lifecycle.add_extras({"project_id": project.id})
   lifecycle.record_halt(reason)
   

Following these practices improves troubleshooting efficiency, enables better log filtering, and ensures that logs provide maximum value for debugging production issues.

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