# System Architecture Overview - v0.1.0

**Version**: 0.1.0
**Date**: 2025-12-22
**Status**: Initial Design

## Introduction

This document describes the high-level architecture for Sneaky Klaus, a self-hosted Secret Santa organization application. The architecture prioritizes simplicity, ease of deployment, and minimal external dependencies while maintaining security and reliability.

## System Architecture

### Deployment Model

Sneaky Klaus is deployed as a **single Docker container** containing all application components:

```mermaid
graph TB
    subgraph "Docker Container"
        direction TB
        Flask[Flask Application]
        Gunicorn[Gunicorn WSGI Server]
        APScheduler[APScheduler Background Jobs]
        SQLite[(SQLite Database)]

        Gunicorn --> Flask
        Flask --> SQLite
        APScheduler --> Flask
        APScheduler --> SQLite
    end

    subgraph "External Services"
        Resend[Resend Email API]
    end

    subgraph "Clients"
        AdminBrowser[Admin Browser]
        ParticipantBrowser[Participant Browser]
    end

    AdminBrowser -->|HTTPS| Gunicorn
    ParticipantBrowser -->|HTTPS| Gunicorn
    Flask -->|HTTPS| Resend

    subgraph "Persistent Storage"
        DBVolume[Database Volume]
        SQLite -.->|Mounted| DBVolume
    end
```

### Component Responsibilities

| Component | Responsibility | Technology |
|-----------|----------------|------------|
| **Gunicorn** | HTTP request handling, worker process management | Gunicorn 21.x |
| **Flask Application** | Request routing, business logic, template rendering | Flask 3.x |
| **SQLite Database** | Data persistence, transactional storage | SQLite 3.40+ |
| **APScheduler** | Background job scheduling (reminders, data purging) | APScheduler 3.10+ |
| **Jinja2** | Server-side HTML template rendering | Jinja2 3.1+ |
| **Resend** | Transactional email delivery | Resend API |

### Application Architecture

The Flask application follows a layered architecture:

```mermaid
graph TB
    subgraph "Presentation Layer"
        Routes[Route Handlers]
        Templates[Jinja2 Templates]
        Forms[WTForms Validation]
    end

    subgraph "Business Logic Layer"
        Services[Service Layer]
        Auth[Authentication Service]
        Matching[Matching Algorithm]
        Notifications[Notification Service]
    end

    subgraph "Data Access Layer"
        Models[SQLAlchemy Models]
        Repositories[Repository Pattern]
    end

    subgraph "Infrastructure"
        Database[(SQLite)]
        EmailProvider[Resend Email]
        Scheduler[APScheduler]
    end

    Routes --> Services
    Routes --> Forms
    Routes --> Templates
    Services --> Models
    Services --> Auth
    Services --> Matching
    Services --> Notifications
    Models --> Repositories
    Repositories --> Database
    Notifications --> EmailProvider
    Scheduler --> Services
```

## Core Components

### Flask Application Structure

```
src/
├── app.py                 # Application factory
├── config.py              # Configuration management
├── models/                # SQLAlchemy models
│   ├── admin.py
│   ├── exchange.py
│   ├── participant.py
│   ├── match.py
│   ├── session.py
│   └── auth_token.py
├── routes/                # Route handlers (blueprints)
│   ├── admin.py
│   ├── participant.py
│   ├── exchange.py
│   └── auth.py
├── services/              # Business logic
│   ├── auth_service.py
│   ├── exchange_service.py
│   ├── matching_service.py
│   ├── notification_service.py
│   └── scheduler_service.py
├── templates/             # Jinja2 templates
│   ├── admin/
│   ├── participant/
│   ├── auth/
│   └── layouts/
├── static/                # Static assets (CSS, minimal JS)
│   ├── css/
│   └── js/
└── utils/                 # Utility functions
    ├── email.py
    ├── security.py
    └── validators.py
```

### Database Layer

**ORM**: SQLAlchemy for database abstraction and model definition

**Migration**: Alembic for schema versioning and migrations

**Configuration**:
- WAL mode enabled for better concurrency
- Foreign keys enabled
- Connection pooling disabled (single file, single process benefit)
- Appropriate timeouts for locked database scenarios

### Background Job Scheduler

**APScheduler Configuration**:
- JobStore: SQLAlchemyJobStore (persists jobs across restarts)
- Executor: ThreadPoolExecutor (4 workers)
- Timezone-aware scheduling

**Scheduled Jobs**:
1. **Reminder Emails**: Cron jobs scheduled per exchange based on configured intervals
2. **Exchange Completion**: Daily check for exchanges past their exchange date
3. **Data Purging**: Daily check for completed exchanges past 30-day retention
4. **Session Cleanup**: Daily purge of expired sessions
5. **Token Cleanup**: Hourly purge of expired auth tokens

### Email Service

**Provider**: Resend API via official Python SDK

**Email Types**:
- Participant registration confirmation
- Magic link authentication
- Match notification (post-matching)
- Reminder emails (configurable schedule)
- Admin notifications (opt-in)
- Password reset

**Template Strategy**:
- HTML templates stored in `templates/emails/`
- Rendered using Jinja2 before sending
- Plain text alternatives for all emails
- Unsubscribe links where appropriate

## Configuration Management

### Environment Variables

| Variable | Purpose | Required | Default |
|----------|---------|----------|---------|
| `SECRET_KEY` | Flask session encryption | Yes | - |
| `DATABASE_URL` | SQLite database file path | No | `sqlite:///data/sneaky-klaus.db` |
| `RESEND_API_KEY` | Resend API authentication | Yes | - |
| `APP_URL` | Base URL for links in emails | Yes | - |
| `ADMIN_EMAIL` | Initial admin email (setup) | Setup only | - |
| `ADMIN_PASSWORD` | Initial admin password (setup) | Setup only | - |
| `LOG_LEVEL` | Logging verbosity | No | `INFO` |
| `TZ` | Container timezone | No | `UTC` |

### Configuration Files

**config.py**: Python-based configuration with environment variable overrides

**Environment-based configs**:
- `DevelopmentConfig`: Debug mode, verbose logging
- `ProductionConfig`: Security headers, minimal logging
- `TestConfig`: In-memory database, mocked email

## Deployment Architecture

### Docker Container

**Base Image**: `python:3.11-slim`

**Exposed Ports**:
- `8000`: HTTP (Gunicorn)

**Volumes**:
- `/app/data`: Database and uploaded files (if any)

**Health Check**:
- Endpoint: `/health`
- Interval: 30 seconds
- Timeout: 5 seconds

**Dockerfile Structure**:
```dockerfile
FROM python:3.11-slim

# Install uv
RUN pip install uv

# Copy application
WORKDIR /app
COPY . /app

# Install dependencies
RUN uv sync --frozen

# Create data directory
RUN mkdir -p /app/data

# Expose port
EXPOSE 8000

# Health check
HEALTHCHECK --interval=30s --timeout=5s --start-period=5s \
  CMD curl -f http://localhost:8000/health || exit 1

# Run application
CMD ["uv", "run", "gunicorn", "-c", "gunicorn.conf.py", "src.app:create_app()"]
```

### Reverse Proxy Configuration

**Recommended**: Deploy behind reverse proxy (Nginx, Traefik, Caddy) for:
- HTTPS termination
- Rate limiting (additional layer beyond app-level)
- Static file caching
- Request buffering

**Example Nginx Config**:
```nginx
server {
    listen 443 ssl http2;
    server_name secretsanta.example.com;

    ssl_certificate /path/to/cert.pem;
    ssl_certificate_key /path/to/key.pem;

    location / {
        proxy_pass http://sneaky-klaus:8000;
        proxy_set_header Host $host;
        proxy_set_header X-Real-IP $remote_addr;
        proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
        proxy_set_header X-Forwarded-Proto $scheme;
    }

    location /static {
        proxy_pass http://sneaky-klaus:8000/static;
        expires 1y;
        add_header Cache-Control "public, immutable";
    }
}
```

## Security Architecture

### Authentication & Authorization

See [ADR-0002: Authentication Strategy](../../decisions/0002-authentication-strategy.md) for detailed authentication design.

**Summary**:
- Admin: Password-based authentication with bcrypt hashing
- Participant: Magic link authentication with time-limited tokens
- Server-side sessions with secure cookies
- Rate limiting on all authentication endpoints

### Security Headers

Flask-Talisman configured with:
- `Content-Security-Policy`: Restrict script sources
- `X-Frame-Options`: Prevent clickjacking
- `X-Content-Type-Options`: Prevent MIME sniffing
- `Strict-Transport-Security`: Enforce HTTPS
- `Referrer-Policy`: Control referrer information

### CSRF Protection

Flask-WTF provides CSRF tokens for all forms:
- Tokens embedded in forms automatically
- Tokens validated on POST/PUT/DELETE requests
- SameSite cookie attribute provides additional protection

### Input Validation

- WTForms for form validation
- SQLAlchemy parameterized queries prevent SQL injection
- Jinja2 auto-escaping prevents XSS
- Email validation for all email inputs

### Secrets Management

- All secrets stored in environment variables
- Never committed to version control
- Docker secrets or .env file for local development
- Secret rotation supported through environment updates

## Data Flow Examples

### Participant Registration Flow

```mermaid
sequenceDiagram
    participant P as Participant Browser
    participant F as Flask App
    participant DB as SQLite Database
    participant E as Resend Email

    P->>F: GET /exchange/{id}/register
    F->>DB: Query exchange details
    DB-->>F: Exchange data
    F-->>P: Registration form

    P->>F: POST /exchange/{id}/register (name, email, gift ideas)
    F->>F: Validate form
    F->>DB: Check email uniqueness in exchange
    F->>DB: Insert participant record
    DB-->>F: Participant created
    F->>F: Generate magic link token
    F->>DB: Store auth token
    F->>E: Send confirmation email with magic link
    E-->>F: Email accepted
    F-->>P: Registration success page
```

### Matching Flow

```mermaid
sequenceDiagram
    participant A as Admin Browser
    participant F as Flask App
    participant M as Matching Service
    participant DB as SQLite Database
    participant E as Resend Email

    A->>F: POST /admin/exchange/{id}/match
    F->>DB: Get all participants
    F->>DB: Get exclusion rules
    F->>M: Execute matching algorithm
    M->>M: Generate valid assignments
    M-->>F: Match assignments
    F->>DB: Store matches (transaction)
    DB-->>F: Matches saved
    F->>E: Send match notification to each participant
    E-->>F: Emails queued
    F->>DB: Update exchange state to "Matched"
    F-->>A: Matching complete
```

### Reminder Email Flow

```mermaid
sequenceDiagram
    participant S as APScheduler
    participant F as Flask App
    participant DB as SQLite Database
    participant E as Resend Email

    S->>F: Trigger reminder job
    F->>DB: Query exchanges needing reminders today
    DB-->>F: Exchange list
    loop For each exchange
        F->>DB: Get opted-in participants
        DB-->>F: Participant list
        loop For each participant
            F->>DB: Get participant's match
            F->>E: Send reminder email
            E-->>F: Email sent
        end
    end
    F->>DB: Log reminder job completion
```

## Performance Considerations

### Expected Load

- **Concurrent Users**: 10-50 typical, 100 maximum
- **Exchanges**: 10-100 per installation
- **Participants per Exchange**: 3-100 typical, 500 maximum
- **Database Size**: <100MB typical, <1GB maximum

### Scaling Strategy

**Vertical Scaling**: Increase container resources (CPU, memory) as needed

**Horizontal Scaling**: Not supported due to SQLite limitation. If horizontal scaling becomes necessary:
1. Migrate database to PostgreSQL
2. Externalize session storage (Redis)
3. Deploy multiple application instances behind load balancer

For the target use case (self-hosted Secret Santa), vertical scaling is sufficient.

### Caching Strategy

**Initial Version**: No caching layer (premature optimization)

**Future Optimization** (if needed):
- Flask-Caching for expensive queries (participant lists, exchange details)
- Redis for session storage (if horizontal scaling needed)
- Reverse proxy caching for static assets

### Database Optimization

- Indexes on frequently queried fields (email, exchange_id, token_hash)
- WAL mode for improved read concurrency
- VACUUM scheduled periodically (after data purges)
- Query optimization through SQLAlchemy query analysis

## Monitoring & Observability

### Logging

**Python logging module** with structured logging:

- **Log Levels**: DEBUG, INFO, WARNING, ERROR, CRITICAL
- **Log Format**: JSON for production, human-readable for development
- **Log Outputs**: stdout (captured by Docker)

**Logged Events**:
- Authentication attempts (success and failure)
- Exchange state transitions
- Matching operations (start, success, failure)
- Email send operations
- Background job execution
- Error exceptions with stack traces

### Metrics (Future)

Potential metrics to track:
- Request count and latency by endpoint
- Authentication success/failure rates
- Email delivery success rates
- Background job execution duration
- Database query performance

**Implementation**: Prometheus metrics endpoint (optional enhancement)

### Health Checks

**`/health` endpoint** returns:
- HTTP 200: Application healthy
- HTTP 503: Application unhealthy (database unreachable, critical failure)

**Checks**:
- Database connectivity
- Email service reachability (optional, cached)
- Scheduler running status

## Disaster Recovery

### Backup Strategy

**Database Backup**:
- SQLite file located at `/app/data/sneaky-klaus.db`
- Backup via volume snapshots or file copy
- Recommended frequency: Daily automatic backups
- Retention: 30 days

**Backup Methods**:
1. Volume snapshots (Docker volume backup)
2. `sqlite3 .backup` command (online backup)
3. File copy (requires application shutdown for consistency)

### Restore Procedure

1. Stop container
2. Replace database file with backup
3. Start container
4. Verify application health

### Data Export

**Future Enhancement**: Admin export functionality
- CSV export of participants and exchanges
- JSON export for full data portability

## Development Workflow

### Local Development

```bash
# Clone repository
git clone https://github.com/user/sneaky-klaus.git
cd sneaky-klaus

# Install dependencies
uv sync

# Set up environment variables
cp .env.example .env
# Edit .env with local values

# Run database migrations
uv run alembic upgrade head

# Run development server
uv run flask run
```

### Testing Strategy

**Test Levels**:
1. **Unit Tests**: Business logic, utilities (pytest)
2. **Integration Tests**: Database operations, email sending (pytest + fixtures)
3. **End-to-End Tests**: Full user flows (Playwright or Selenium)

**Test Coverage Target**: 80%+ for business logic

### CI/CD Pipeline

**Continuous Integration**:
- Run tests on every commit
- Lint code (ruff, mypy)
- Build Docker image
- Security scanning (bandit, safety)

**Continuous Deployment**:
- Tag releases (semantic versioning)
- Push Docker image to registry
- Update deployment documentation

## Future Architectural Considerations

### Potential Enhancements

1. **Multi-tenancy**: Support multiple isolated admin accounts (requires significant schema changes)
2. **PostgreSQL Support**: Optional PostgreSQL backend for larger deployments
3. **Horizontal Scaling**: Redis session storage, multi-instance deployment
4. **API**: REST API for programmatic access or mobile apps
5. **Webhooks**: Notify external systems of events
6. **Internationalization**: Multi-language support

### Migration Paths

If the application needs to scale beyond SQLite:

1. **Database Migration**: Alembic migrations can be adapted for PostgreSQL
2. **Session Storage**: Move to Redis for distributed sessions
3. **Job Queue**: Move to Celery + Redis for distributed background jobs
4. **File Storage**: Move to S3-compatible storage if file uploads are added

These migrations would be disruptive and are not planned for initial versions.

## Conclusion

This architecture prioritizes simplicity and ease of self-hosting while maintaining security, reliability, and maintainability. The single-container deployment model minimizes operational complexity, making Sneaky Klaus accessible to non-technical users who want to self-host their Secret Santa exchanges.

The design is deliberately conservative, avoiding premature optimization and complex infrastructure. Future enhancements can be added incrementally without requiring fundamental architectural changes.

## References

- [ADR-0001: Core Technology Stack](../../decisions/0001-core-technology-stack.md)
- [ADR-0002: Authentication Strategy](../../decisions/0002-authentication-strategy.md)
- [Flask Documentation](https://flask.palletsprojects.com/)
- [SQLAlchemy Documentation](https://docs.sqlalchemy.org/)
- [APScheduler Documentation](https://apscheduler.readthedocs.io/)