Developer Guide

This guide provides information for developers looking to extend or contribute to the Reputation Monitor system.

Architecture

System Overview

graph TD
    A[Client] -->|HTTP/HTTPS| B[API Gateway]
    B --> C[Authentication]
    C --> D[Rate Limiting]
    D --> E[Request Router]
    E --> F[Services]
    F --> G[Data Storage]
    F --> H[External APIs]
    G --> I[Analytics Engine]
    H --> I
    I --> J[Notification Service]

Core Components

1. API Layer
2. RESTful API endpoints
3. Authentication & Authorization
4. Request validation

5. Rate limiting

6. Data Collection

7. Social media connectors (Twitter, Reddit, etc.)
8. Web crawlers

9. RSS/Atom feed readers

10. Processing Pipeline

11. Data normalization
12. Sentiment analysis
13. Entity recognition

14. Duplicate detection

15. Storage

16. Relational database (PostgreSQL)
17. Document store (MongoDB)
18. Cache (Redis)

19. Object storage (S3)

20. Analytics

21. Real-time processing
22. Batch processing

23. Machine learning models

24. Frontend

25. Dashboard (React/Vue.js)
26. Real-time updates (WebSockets)
27. Data visualization (D3.js/Chart.js)

Development Setup

Prerequisites

• Python 3.8+
• Node.js 16+
• Docker & Docker Compose
• PostgreSQL 13+
• Redis 6+

Local Development

1. Clone the repository

   git clone https://github.com/yourusername/Online-bad-reputation-.git
   cd Online-bad-reputation-
   

2. Set up Python environment

   python -m venv venv
   source venv/bin/activate  # On Windows: .\venv\Scripts\activate
   pip install -r requirements-dev.txt
   

3. Set up frontend

   cd frontend
   npm install
   npm run dev
   

4. Run services with Docker

   docker-compose up -d postgres redis
   

5. Run database migrations

   alembic upgrade head
   

6. Start the development server

   uvicorn app.main:app --reload
   

Extending Functionality

Adding a New Data Source

1. Create a new module in sources/
2. Implement the BaseSource interface
3. Register the source in sources/__init__.py
4. Add tests in tests/sources/

Creating a New Analyzer

1. Create a new class in analyzers/
2. Implement the BaseAnalyzer interface
3. Register the analyzer in analyzers/__init__.py
4. Add tests in tests/analyzers/

Adding a New Visualization

1. Create a new React component in frontend/src/components/visualizations/
2. Register the component in frontend/src/visualizationRegistry.js
3. Add tests in frontend/src/tests/

Testing

Running Tests

# Run all tests
pytest

# Run specific test file
pytest tests/test_models.py

# Run with coverage
pytest --cov=app tests/

Test Structure

tests/
├── __init__.py
├── conftest.py
├── unit/
│   ├── test_models.py
│   └── test_services.py
├── integration/
│   ├── test_api.py
│   └── test_integrations.py
└── e2e/
    └── test_workflows.py

Code Style

• Follow PEP 8
• Use type hints
• Write docstrings for all public methods
• Keep functions small and focused
• Use meaningful variable names

Documentation

Building Documentation

# Install dependencies
pip install -r docs/requirements.txt

# Build HTML docs
cd docs
make html

Documentation Structure

docs/
├── source/
│   ├── _static/
│   ├── _templates/
│   ├── user/
│   ├── developer/
│   ├── api/
│   └── index.rst
└── build/

Deployment

Production Setup

1. Infrastructure
2. Kubernetes cluster
3. Managed database (RDS/Cloud SQL)
4. Object storage (S3/GCS)

5. CDN

6. Configuration

7. Environment variables
8. Secrets management

9. Feature flags

10. CI/CD

11. Automated testing
12. Container builds
13. Blue/green deployment

Monitoring & Observability

Metrics

• Application metrics (Prometheus)
• Business metrics
• Custom dashboards (Grafana)

Logging

• Structured logging (JSON)
• Log levels
• Log rotation

Tracing

• Distributed tracing (Jaeger/Zipkin)
• Performance metrics
• Error tracking

Security

Best Practices

• Input validation
• Output encoding
• Authentication & authorization
• Rate limiting
• CORS configuration
• Security headers

Security Tools

• Dependency scanning
• Static analysis
• Dynamic analysis
• Secret detection

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Write tests
5. Submit a pull request

Pull Request Checklist

• [ ] Tests pass
• [ ] Documentation updated
• [ ] Code follows style guide
• [ ] No security issues
• [ ] Changelog updated

Support

For support, please:

1. Check the Troubleshooting Guide
2. Search existing issues
3. Open a new issue with details

License

This project is licensed under the MIT License - see the LICENSE file in the repository for details.