Logging Best Practices

Apply these logging principles to ensure effective debugging, monitoring, and audit capabilities across applications and services.

Structured Logging

• •Use structured logging formats (JSON) for all log output
• •Include consistent fields across all log entries
• •Make logs machine-parseable while remaining human-readable
• •Use a logging library that supports structured output natively
• •Avoid string concatenation for log messages; use structured fields

Standard Log Fields

Include these fields in every log entry:

• •timestamp: ISO 8601 format with timezone
• •level: Log severity (DEBUG, INFO, WARN, ERROR, FATAL)
• •message: Human-readable description of the event
• •service: Name of the service or application
• •version: Application version or build identifier
• •trace_id: Distributed tracing correlation ID
• •span_id: Current span identifier
• •request_id: Unique identifier for the request

Log Levels

Use appropriate log levels consistently:

• •DEBUG: Detailed diagnostic information for development
• •INFO: Normal operational events and state changes
• •WARN: Unexpected situations that are handled gracefully
• •ERROR: Failures that affect current operation but not the service
• •FATAL: Critical failures requiring immediate attention

Context Propagation

• •Include request context in all log entries within a request lifecycle
• •Propagate trace IDs across service boundaries
• •Add user context (anonymized) for user-initiated actions
• •Include relevant business context for domain events
• •Use MDC (Mapped Diagnostic Context) or equivalent for context management

Security and Privacy

• •Never log sensitive information (passwords, tokens, PII)
• •Mask or redact sensitive data when it must be referenced
• •Implement log access controls appropriate to data sensitivity
• •Consider data retention policies and compliance requirements
• •Audit log access for sensitive systems

Performance Considerations

• •Use asynchronous logging to avoid blocking application threads
• •Implement log sampling for high-volume debug logs in production
• •Buffer logs appropriately to balance latency and throughput
• •Monitor logging infrastructure for bottlenecks
• •Set appropriate log levels per environment

Log Aggregation

• •Centralize logs from all services into a single platform
• •Use consistent formatting across all services
• •Implement log rotation and retention policies
• •Enable full-text search and filtering capabilities
• •Set up log-based alerts for critical patterns

Error Logging

• •Include full error context: message, code, stack trace
• •Log the chain of errors in wrapped/nested exceptions
• •Include relevant request and state information
• •Avoid duplicate error logging across layers
• •Log error recovery actions and outcomes

Best Practices

• •Log at service boundaries (entry and exit points)
• •Include timing information for performance analysis
• •Log configuration changes and deployments
• •Create actionable log messages that aid debugging
• •Review and clean up logging regularly to reduce noise

Log Message Guidelines

• •Write clear, descriptive messages
• •Include relevant identifiers (user ID, order ID, etc.)
• •Avoid generic messages like "Error occurred"
• •Use consistent terminology across the application
• •Include enough context to understand the event without additional lookups

Environment-Specific Configuration

• •Development: DEBUG level, console output, verbose formatting
• •Staging: INFO level, structured JSON, full context
• •Production: INFO/WARN level, structured JSON, sampling for DEBUG