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Abstract:
This document outlines logging standards and best practices, emphasizing the shift from free-form text logs to structured/semantic logging (e.g., JSON). Structured logs are advocated for their machine-readability, which facilitates automated processing, monitoring, alerting, and analysis in complex distributed systems. The document details appropriate use of log levels (DEBUG, INFO, WARNING, ERROR, CRITICAL) and specifies essential information for auditing (when, where, who, what), referencing company policy and OWASP guidelines. It explicitly lists sensitive data types that must not be logged directly (e.g., PII, secrets, source code) and suggests methods like masking or hashing. Enforcement through code reviews and testing is highlighted, introducing "Domain Probes" as a mechanism to tie logging directly to business logic, enabling testable logging guarantees and KPI derivation. Implementation guidance includes Python logger namespacing (logging.getLogger(__name__)) for context and mentions tooling like the structlog library. The overall goal is to achieve Domain-Oriented Observability, where logs provide meaningful business insight beyond just technical diagnostics.
Logging Standards and Best Practices Summary
(No Timestamp) Structured Log Format / Semantic Logging: Use structured formats (like JSON) instead of free-text lines. Logs should be meaningful events with context.
Details: Makes logs machine-parsable, enabling better tooling, monitoring, and analysis. Avoids information overload by aggregating related entries using log contexts.
Key Takeaway: Structured logging is crucial for managing and interpreting logs effectively in complex systems.
(No Timestamp) Appropriate Log Levels: Utilize standard log levels (DEBUG, INFO, WARNING, ERROR, CRITICAL) correctly based on the event's nature.
Details: DEBUG for development, INFO for expected events/success, WARNING for unexpected non-errors, ERROR for recoverable failures, CRITICAL for application-breaking issues. Don't log stack traces as INFO.
Key Takeaway: Proper log levels improve log clarity and filterability.
(No Timestamp) Auditing Requirements: Logs must contain essential information for auditing and monitoring as per policy.
Details: Include timestamp, user ID, relevant entity IDs (data subject, task, service), location accessed (URI, ARN, DB name), program used, action performed, and success/failure status.
Key Takeaway: Logging must fulfill compliance and security auditing needs (When, Where, Who, What).
(No Timestamp) OWASP Logging Guidelines: Follow OWASP recommendations for comprehensive security logging.
Details: Log input/output validation failures, auth/authz events, session issues, errors, system events, startups/shutdowns, use of high-risk functions, and legal opt-ins.
Key Takeaway: Logging should capture security-relevant events thoroughly.
(No Timestamp) What NOT to Log: Avoid logging sensitive data directly.
Details: Exclude source code, session IDs, tokens, PII, passwords, connection strings, keys, payment data, classified info. Use masking, sanitization, hashing, or encryption. Handle file paths and internal network details carefully.
Key Takeaway: Protecting sensitive information in logs is paramount.
(No Timestamp) Enforcement and KPIs: Enforce logging standards through code reviews and tests.
Details: Use Domain Probes combined with structured logging to create testable logging guarantees tied to business logic.
Key Takeaway: Logging quality requires active enforcement and can be integrated into testing, allowing derivation of KPIs.
(No Timestamp) Python Logger Namespacing: Use logging.getLogger(__name__) in Python modules.
Details: Automatically adds the module name to log entries, providing essential context in aggregated log streams without manual effort.
Key Takeaway: Namespacing adds vital context for identifying log origins in distributed systems.
(No Timestamp) Structured Logging Tooling: Leverage libraries to implement structured logging.
Details: Python's standard library offers basic tools; third-party libraries like structlog provide more advanced features.
Key Takeaway: Tools exist to simplify the implementation of structured logging.
(No Timestamp) Logging For Observability: Aim for Domain-Oriented Observability.
Details: Logs should reflect high-level business/domain events, not just low-level technical details, enabling KPI derivation and understanding system behavior from logs.
Key Takeaway: Effective logging provides business insight and supports overall system observability.
(No Timestamp) Domain Probes: Implement Domain Probes as a high-level instrumentation API.
Details: Probes encapsulate low-level logging details, are oriented around domain semantics, and provide natural points for logging within business logic (e.g., class methods).
Key Takeaway: Domain Probes formalize the connection between logging and specific business logic actions, aiding consistency and testability.