Phil Skentelbery
bebd47955f
Implements Phase 1 Foundation with all core services: Core Components: - Configuration management with GONDULF_ environment variables - Database layer with SQLAlchemy and migration system - In-memory code storage with TTL support - Email service with SMTP and TLS support (STARTTLS + implicit TLS) - DNS service with TXT record verification - Structured logging with Python standard logging - FastAPI application with health check endpoint Database Schema: - authorization_codes table for OAuth 2.0 authorization codes - domains table for domain verification - migrations table for tracking schema versions - Simple sequential migration system (001_initial_schema.sql) Configuration: - Environment-based configuration with validation - .env.example template with all GONDULF_ variables - Fail-fast validation on startup - Sensible defaults for optional settings Testing: - 96 comprehensive tests (77 unit, 5 integration) - 94.16% code coverage (exceeds 80% requirement) - All tests passing - Test coverage includes: - Configuration loading and validation - Database migrations and health checks - In-memory storage with expiration - Email service (STARTTLS, implicit TLS, authentication) - DNS service (TXT records, domain verification) - Health check endpoint integration Documentation: - Implementation report with test results - Phase 1 clarifications document - ADRs for key decisions (config, database, email, logging) Technical Details: - Python 3.10+ with type hints - SQLite with configurable database URL - System DNS with public DNS fallback - Port-based TLS detection (465=SSL, 587=STARTTLS) - Lazy configuration loading for testability Exit Criteria Met: ✓ All foundation services implemented ✓ Application starts without errors ✓ Health check endpoint operational ✓ Database migrations working ✓ Test coverage exceeds 80% ✓ All tests passing Ready for Architect review and Phase 2 development. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
54 lines
1.5 KiB
Markdown
54 lines
1.5 KiB
Markdown
# 0007. Logging Strategy for v1.0.0
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Date: 2024-11-20
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## Status
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Accepted
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## Context
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We need structured logging for debugging, security auditing, and operational monitoring. The choice is between JSON-structured logs (machine-parseable), Python's standard logging with structured fields, or simple string logging.
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## Decision
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Use Python's standard logging module with structured string formatting for v1.0.0:
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Format pattern:
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```
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%(asctime)s [%(levelname)s] %(name)s: %(message)s
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```
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Structured information in message strings:
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```python
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logger.info("Domain verification requested", extra={
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"domain": domain,
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"email": email,
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"request_id": request_id
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})
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```
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Log levels:
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- **Development**: `DEBUG` (default when `GONDULF_DEBUG=true`)
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- **Production**: `INFO` (default)
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Configuration:
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```
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GONDULF_LOG_LEVEL=INFO
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GONDULF_DEBUG=false
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```
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Output: stdout/stderr (let deployment environment handle log collection)
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## Consequences
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### Positive
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- Standard Python logging - no additional dependencies
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- Simple to implement and test
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- Human-readable for local development
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- Structured extras can be extracted if needed later
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- Easy to redirect to files or syslog via deployment config
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### Negative
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- Not as machine-parseable as pure JSON logs
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- May need to migrate to structured JSON logging in future versions
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- Extra fields may not be captured by all log handlers
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## Future Consideration
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If operational monitoring requires it, we can migrate to JSON-structured logging in a minor version update without breaking changes. |