Summary

Add structured logging capabilities to enable better log analysis, searchability, and integration with log aggregation systems.

Problem Analysis

• Current logging is primarily string-based
• Difficult to parse and analyze logs programmatically
• Limited integration with modern log aggregation tools
• No standardized field extraction

Proposed Solution

Add structured logging support:

• Key-value pair logging API
• JSON output formatter
• Field type safety
• Context propagation (trace IDs, request IDs)

Tasks

Phase 3.1: API Design

• Design structured log entry interface
• Define standard fields (timestamp, level, message, etc.)
• Design context propagation mechanism
• Plan backward compatibility with string logging

Phase 3.2: Core Implementation

• Implement LogEntry class with typed fields
• Add field builder API
• Implement context scope management
• Add trace ID / correlation ID support

Phase 3.3: Formatters

• Implement JSONFormatter
• Implement LogfmtFormatter
• Implement customizable template formatter
• Maintain backward-compatible plain text formatter

Phase 3.4: Integration

• Update all writers to support structured output
• Add filter support for structured fields
• Implement field-based sampling
• Add documentation and examples

Acceptance Criteria

• Structured logging API available
• JSON output format supported
• Context propagation works across async boundaries
• Backward compatible with existing string logging
• Performance impact < 5%
• Documentation with examples

Related

• Kent Beck's Simple Design Principle: "Reveals Intent"
• Enhances log analysis and debugging capabilities