# Deployment Documentation

Production deployment, operations, and scaling documentation for GitFlow Analytics.

## 🚀 Production Deployment

### [Production Installation](installation.md)
Deploy GitFlow Analytics in production environments:
- Server requirements and system dependencies
- Installation methods for different environments
- Configuration management and secrets handling
- Service setup and process management

### [Security Guide](security.md)
Security best practices and considerations:
- Credential and token security
- Network security and access controls
- Data privacy and compliance requirements
- Audit logging and monitoring

### [Scaling Guide](scaling.md)
Scale GitFlow Analytics for large organizations:
- Performance optimization techniques
- Resource requirements and capacity planning
- Distributed deployment patterns
- Load balancing and high availability

## 📊 Operations & Monitoring

### [Monitoring & Metrics](monitoring.md)
Monitor GitFlow Analytics performance and health:
- Key performance indicators and metrics
- Logging configuration and best practices
- Error monitoring and alerting
- Performance debugging and optimization

## 🎯 Deployment Scenarios

### Single Server Deployment
**Use Case**: Small to medium teams (1-50 developers)
```bash
# Basic server setup
pip install gitflow-analytics
systemctl enable gitflow-analytics
```

**Resources Required**:
- CPU: 2-4 cores
- Memory: 4-8 GB RAM
- Storage: 50-100 GB SSD
- Network: Standard internet connectivity

### Container Deployment  
**Use Case**: Cloud environments and orchestration
```dockerfile
FROM python:3.9-slim
RUN pip install gitflow-analytics
COPY config.yaml /app/
CMD ["gitflow-analytics", "-c", "/app/config.yaml"]
```

**Orchestration Options**:
- Docker Compose for simple setups
- Kubernetes for enterprise deployments
- Container registries for image management

### Enterprise Deployment
**Use Case**: Large organizations (100+ developers, 50+ repositories)

**Architecture Pattern**:
```
Load Balancer → Multiple Analysis Nodes → Shared Cache Layer → Report Storage
```

**Scaling Considerations**:
- Horizontal scaling with worker nodes
- Distributed caching (Redis/Memcached)
- Centralized configuration management
- Automated deployment pipelines

## 🔧 Configuration Management

### Environment-Specific Configurations
```yaml
# production.yaml
github:
  token: "${GITHUB_TOKEN_PROD}"
  api_url: "https://api.github.com"
  
analysis:
  cache_ttl: 3600  # 1 hour cache
  batch_size: 1000 # Optimize for throughput
  
reports:
  output_directory: "/var/lib/gitflow/reports"
  retention_days: 90
```

### Secrets Management
- **Environment Variables**: For development and simple deployments
- **HashiCorp Vault**: For enterprise secret management
- **Kubernetes Secrets**: For containerized deployments
- **AWS/Azure Key Vaults**: For cloud deployments

### Configuration Validation
```bash
# Validate configuration before deployment
gitflow-analytics -c production.yaml --validate-only

# Test with limited scope
gitflow-analytics -c production.yaml --weeks 1 --repositories repo1
```

## 🚨 Operational Considerations

### Resource Planning
- **CPU Usage**: Intensive during analysis phases, idle during caching
- **Memory Usage**: Scales with repository size and commit history
- **Storage Requirements**: Cache and report storage grows over time
- **Network Bandwidth**: GitHub API calls and repository cloning

### Backup and Recovery
- **Configuration Backups**: Version-controlled configuration files
- **Cache Backups**: SQLite database files for performance recovery
- **Report Archives**: Historical reports for compliance and analysis
- **Disaster Recovery**: Documented recovery procedures

### Performance Optimization
- **Caching Strategy**: Maximize cache hit rates for repeated analysis
- **Batch Processing**: Process multiple repositories efficiently
- **Resource Limits**: Set appropriate memory and CPU limits
- **Database Optimization**: Regular cache cleanup and maintenance

## 📈 Monitoring and Alerting

### Key Metrics to Monitor
- **Analysis Success Rate**: Percentage of successful analyses
- **Processing Time**: Time to complete repository analysis
- **Cache Hit Rate**: Efficiency of caching system
- **API Rate Limiting**: GitHub API usage and limits
- **Error Rates**: Failed analyses and error patterns

### Alerting Scenarios
- **Analysis Failures**: When repository analysis fails repeatedly
- **Performance Degradation**: When processing times exceed thresholds
- **API Limits**: When approaching GitHub API rate limits
- **Storage Issues**: When disk space or cache size grows too large
- **Configuration Errors**: When configuration validation fails

### Log Management
```python
# Logging configuration example
logging:
  level: INFO
  format: "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
  handlers:
    - type: file
      filename: "/var/log/gitflow/gitflow-analytics.log"
    - type: syslog
      facility: local0
```

## 🔒 Security Considerations

### Access Control
- **Authentication**: Secure token-based authentication
- **Authorization**: Role-based access to repositories and reports
- **Network Security**: VPN and firewall configurations
- **Audit Logging**: Track access and analysis activities

### Data Protection
- **Encryption at Rest**: Encrypt cache and report files
- **Encryption in Transit**: HTTPS for all API communications
- **Data Retention**: Automated cleanup of old reports and cache
- **Compliance**: GDPR, SOX, and other regulatory requirements

## 🚀 Automation and CI/CD

### Automated Analysis Pipelines
```yaml
# GitHub Actions example
name: Weekly GitFlow Analysis
on:
  schedule:
    - cron: '0 9 * * 1'  # Monday 9 AM
  
jobs:
  analyze:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v2
      - name: Run GitFlow Analytics
        run: |
          pip install gitflow-analytics
          gitflow-analytics -c .github/gitflow-config.yaml
      - name: Upload Reports
        uses: actions/upload-artifact@v2
        with:
          name: gitflow-reports
          path: reports/
```

### Deployment Automation
- **Infrastructure as Code**: Terraform, CloudFormation, or similar
- **Configuration Management**: Ansible, Chef, or Puppet
- **Container Orchestration**: Kubernetes manifests and Helm charts
- **Monitoring Setup**: Automated monitoring and alerting configuration

## 📊 Cost Optimization

### GitHub API Costs
- **Rate Limit Management**: Stay within free tier limits when possible
- **Efficient API Usage**: Batch requests and cache responses
- **Token Management**: Use organization tokens for better limits

### Infrastructure Costs
- **Right-sizing**: Match resources to actual usage patterns
- **Scheduling**: Run analysis during off-peak hours
- **Caching**: Reduce repeated processing through intelligent caching
- **Retention Policies**: Automated cleanup of old data and reports

## 📚 Deployment Checklists

### Pre-Deployment Checklist
- [ ] Server resources meet requirements
- [ ] Dependencies and Python version verified
- [ ] Configuration files validated
- [ ] Secrets and tokens configured securely
- [ ] Network access and firewall rules configured
- [ ] Monitoring and logging setup completed
- [ ] Backup and recovery procedures documented

### Post-Deployment Validation
- [ ] Service starts successfully
- [ ] Configuration validation passes
- [ ] Test analysis completes successfully
- [ ] Reports generate as expected
- [ ] Monitoring and alerts functioning
- [ ] Performance meets expectations
- [ ] Security scan completed

## 🔄 Related Documentation

- **[Architecture](../architecture/)** - System design and scaling patterns
- **[Security Guide](security.md)** - Detailed security implementation
- **[Monitoring Guide](monitoring.md)** - Comprehensive monitoring setup
- **[Reference Documentation](../reference/)** - Configuration specifications

## 🤝 Operations Support

### Troubleshooting Resources
- **[Troubleshooting Guide](../guides/troubleshooting.md)** - Common issues and solutions
- **[Performance Debugging](monitoring.md#performance-debugging)** - Performance analysis tools
- **[Error Reference](../reference/cli-commands.md#error-codes)** - Error codes and meanings

### Community Support  
- **GitHub Issues**: Report deployment and operational issues
- **Documentation Updates**: Contribute operational knowledge
- **Best Practices**: Share successful deployment patterns

Ready for production? Start with [Production Installation](installation.md) and [Security Guide](security.md).