Design Decisions

This document outlines key architectural and design decisions made in the NSGG Backend project.

Technology Stack

Django & Django REST Framework

  • Decision: Use Django as the primary web framework and DRF for API development
  • Rationale:
  • Robust ORM and admin interface
  • Strong security features out of the box
  • Extensive ecosystem and community support
  • Built-in authentication and permission systems

PostgreSQL

  • Decision: PostgreSQL as the primary database
  • Rationale:
  • ACID compliance
  • Advanced features like JSON field support
  • Excellent performance for complex queries
  • Strong support for concurrent operations

Redis

  • Decision: Redis for caching and message broker
  • Rationale:
  • High performance in-memory data store
  • Support for complex data structures
  • Pub/sub capabilities for real-time features
  • Session management and rate limiting

Architecture Patterns

REST API Design

  • Pattern: Resource-oriented REST API
  • Benefits:
  • Clear and consistent endpoint structure
  • Stateless communication
  • Cacheable responses
  • Standard HTTP methods for operations

Service Layer

  • Pattern: Service layer between views and models
  • Benefits:
  • Separation of business logic from views
  • Reusable business logic
  • Easier unit testing
  • Cleaner view implementations
# Example service layer implementation
class ProductService:
    @staticmethod
    def create_product(data):
        # Business logic for product creation
        return Product.objects.create(**data)

    @staticmethod
    def update_stock(product_id, quantity):
        # Complex stock update logic
        with transaction.atomic():
            product = Product.objects.select_for_update().get(id=product_id)
            product.stock += quantity
            product.save()

Database Design

Data Models

  • Approach: Normalized database design
  • Rationale:
  • Data integrity
  • Minimize redundancy
  • Efficient updates
  • Clear relationships

Migrations

  • Strategy: Incremental migrations with zero downtime
  • Implementation:
  • Multiple deployment steps for breaking changes
  • Backward compatible changes
  • Database constraints added after data validation

Security Decisions

Authentication

  • Implementation: JWT with refresh tokens
  • Benefits:
  • Stateless authentication
  • Reduced database load
  • Support for mobile clients
  • Fine-grained token control

Authorization

  • Implementation: Role-based access control (RBAC)
  • Features:
  • Custom permissions per model
  • Object-level permissions
  • Group-based access control
class ProductPermission(BasePermission):
    def has_permission(self, request, view):
        if view.action in ['list', 'retrieve']:
            return True
        return request.user.has_perm('products.change_product')

Performance Optimizations

Caching Strategy

  • Implementation: Multi-level caching
  • Redis for application cache
  • CDN for static assets
  • Database query optimization

Query Optimization

  • Techniques:
  • Select related for foreign keys
  • Prefetch related for reverse relationships
  • Database indexes on frequently queried fields
# Example of optimized queryset
products = (Product.objects
    .select_related('category')
    .prefetch_related('variants')
    .filter(active=True))

Testing Strategy

Test Types

  1. Unit Tests
  2. Model methods
  3. Service layer functions

  4. Utility functions

  5. Integration Tests

  6. API endpoints
  7. Database interactions

  8. Cache operations

  9. Performance Tests

  10. Load testing critical endpoints
  11. Database query performance
  12. Cache hit rates

Scalability Considerations

Horizontal Scaling

  • Implementation:
  • Stateless application servers
  • Load balancing
  • Distributed caching
  • Database read replicas

Asynchronous Processing

  • Implementation: Celery for background tasks
  • Use Cases:
  • Email notifications
  • Report generation
  • Data processing
  • Scheduled tasks

Monitoring and Logging

Application Monitoring

  • Tools: Sentry for error tracking
  • Metrics:
  • Response times
  • Error rates
  • Resource usage
  • User activity

Logging

  • Implementation: Structured logging
  • Levels:
  • DEBUG: Development information
  • INFO: Normal operations
  • WARNING: Potential issues
  • ERROR: Application errors
  • CRITICAL: System failures

Future Considerations

API Versioning

  • Strategy: URL-based versioning
  • Implementation: 
    urlpatterns = [
    path('api/v1/', include('api.v1.urls')),
    path('api/v2/', include('api.v2.urls')),
]

Microservices

  • Potential Split:
  • Order processing service
  • User management service
  • Product catalog service
  • Notification service

GraphQL Integration

  • Consideration: Adding GraphQL API alongside REST
  • Benefits:
  • Flexible data querying
  • Reduced network overhead
  • Better mobile app support