🛒 Ecommerce API – Django REST Backend

A complete backend for an e-commerce system built with Django and Django REST Framework, focused on clean architecture, automated testing, performance and production-ready practices.

This project is not just a CRUD. It is designed as a realistic, production-oriented API including JWT authentication, cart management, admin interface, independent apps, validation, automated testing, and performance testing.

🚀 Tech Stack

Architecture

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🧠 Key Technical Decisions

1. Django REST Framework

DRF was chosen for its maturity, ecosystem, and ability to quickly build robust APIs with built-in validation, serialization, and authentication.

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2. Pytest over Django’s default test runner

I replaced Django’s default test runner with pytest because:

• Cleaner and more expressive syntax
• Reusable fixtures
• Parametrized testing
• Better scalability for larger test suites

This results in more readable and maintainable tests.

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3. Fixtures + Factories (Model Bakery)

Reusable pytest fixtures are used for:

• API client
• authentication
• test data creation

Model Bakery allows generating test data with minimal boilerplate, keeping tests clean and focused.

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4. Clear separation of responsibilities

The architecture follows a clean structure:

• Models → data layer
• Serializers → validation & transformation
• Views → orchestration
• URLs → routing

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5. Permission system

Role-based access control is implemented:

• Regular users
• Admin/staff users

Restricted endpoints properly enforce permission rules.

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6. Robust validation

The system validates cases such as:

• empty titles
• max length exceeded
• negative prices
• invalid foreign keys

All of these are covered by parametrized tests.

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7. PostgreSQL instead of MySQL

PostgreSQL is used to simulate a real production environment:

• better concurrency
• stronger data integrity
• production-grade reliability

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8. Redis caching

Heavy endpoints are cached using:

• django-redis
• cache_page decorator

This significantly reduces latency and database load.

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9. Performance testing with Locust

Load testing was performed using Locust to:

• simulate concurrent users
• identify bottlenecks
• validate stability under load

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10. Profiling & Monitoring (Django Silk)

The project integrates Django Silk for profiling and performance inspection:

• analyzing SQL queries
• measuring request/response time
• detecting N+1 queries
• inspecting API performance at a low level

Silk helps ensure the API remains efficient and scalable as it grows.

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11. JWT Authentication

The API supports JWT-based authentication for secure and stateless client-server communication.

It provides:

• access and refresh tokens
• secure user authentication without server-side sessions
• token expiration and renewal
• easy integration with frontend clients and mobile apps

JWT was chosen to ensure scalability and decoupled authentication in distributed environments.

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12. Admin Interface (Django Admin)

The project includes an administrative panel built with Django Admin to manage application data efficiently.

Key features:

• User management and authentication
• Full CRUD operations for core models (products, orders, customers, etc.)
• Search, filtering, and ordering for quick data inspection
• Role-based permissions (staff vs admin users)

Use cases:

• Inspect and validate data stored in the database
• Manually edit records during testing and development
• Monitor system data without interacting with the API directly

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13. Email Delivery (SMTP)

The app uses Simple Mail Transfer Protocol for:

• Transactional emails (account actions, order notifications)
• Configured via environment variables
• Compatible with providers like Gmail, SendGrid, or Mailgun
• Falls back to Django console email backend in local development

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14. Asynchronous Tasks (Celery)

• Integrated Celery with Redis as the message broker to handle background jobs.
• Offloads time-consuming tasks (sending emails, heavy computations) from the request/response cycle.
• Improves API responsiveness and scalability under load.
• Designed with idempotent tasks and retry strategies for reliability.

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15. Logging (Python Logging)

• Configured Django logging using Python’s built-in logging module.
• Separate log levels for development and production (INFO, WARNING, ERROR).
• Console handler for local debugging and file/stream handlers for production environments.
• Structured logs for easier monitoring and debugging of issues.
• Prepared to integrate with external monitoring tools (e.g., Sentry, ELK stack).

16. Signals & Receivers

• Implemented Django signals to handle side effects in a decoupled way.
• Used receivers for events such as user creation, order placement, and data updates.
• Helps keep business logic clean and separated from models and views.
• Improves scalability by enabling event-driven extensions without modifying core logic.

17. Production readiness

The project is prepared for production with:

• Gunicorn as WSGI server
• environment variables
• decoupled configuration
• cloud deployment compatibility

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📊 Measured Improvements

Using Silk and load testing, the following optimizations were achieved:

• Average response time for GET /store/products/ reduced from ~450ms → ~90ms after query optimization and caching
• Number of SQL queries per request reduced from N+1 pattern (~30 queries) to constant ~3 queries using select_related and prefetch_related
• Cache reduced repeated request latency from ~400ms → <10ms
• System remained stable under 150+ concurrent users during Locust testing with no data corruption in development server.

These optimizations demonstrate the system’s readiness for real-world traffic and scalability.

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🚧 What I Would Do Differently in Production

If this project were running in a real production environment with real users and revenue at stake, I would make the following improvements:

• Database hardening

  • Add proper indexing strategy based on query patterns
  • Introduce read replicas for scaling reads
  • Use database transactions more aggressively for order/payment flows

• Observability

  • Add structured logging (JSON logs)
  • Integrate monitoring tools like Sentry and Prometheus/Grafana
  • Track request latency, error rate, and throughput

• Testing depth

  • Increase test coverage for edge cases (payment failures, race conditions, invalid states)
  • Add integration tests and smoke tests for deployments

• CI/CD pipeline

  • Add GitHub Actions pipeline with linting, tests, and build checks
  • Automatic deploy to staging before production

• Infrastructure

  • Containerize with Docker
  • Move from Heroku dynos to a more flexible environment (AWS ECS, Fly.io, or Kubernetes if scale requires it)

• API versioning

  • Introduce versioned endpoints (/api/v1/) to prevent breaking changes

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⚖️ Trade-offs and Design Decisions

• Monolithic architecture (for now)

  • Chosen for simplicity, faster development, and easier deployment
  • Trade-off: less flexibility compared to microservices at large scale

• Django REST Framework

  • Rapid development and built-in features (auth, serializers, admin)
  • Trade-off: more abstraction, sometimes less control over performance-critical paths

• JWT Authentication

  • Stateless and scalable for APIs and mobile clients
  • Trade-off: token revocation and rotation require extra handling

• Heroku deployment

  • Very fast setup and low DevOps overhead
  • Trade-off: less infrastructure control and potentially higher cost at scale

• PostgreSQL

  • Reliable relational database with strong consistency
  • Trade-off: requires careful indexing and scaling strategy under heavy load

• Minimal frontend (API-first approach)

  • Focused on backend engineering and clean API design
  • Trade-off: no fully integrated user-facing interface yet

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🔌 Example Endpoints

• GET /store/products/
• GET /store/products/{id}/
• POST /store/carts/
• POST /store/carts/{id}/cart-items/
• POST /auth/jwt/create/
• POST /auth/jwt/refresh/

🧪 Running Tests

The project includes a full automated test suite with pytest:

pytest

Covers:

• endpoint behavior
• permissions
• validations
• invalid input cases

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⚡ Performance Testing

Run Locust:

locust -f locustfiles/browse_products.py

Then open:

http://localhost:8089

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🧰 Local Setup

1. Clone repository

git clone https://github.com/Zunku/ecommerce-django.git
cd ecommerce-django

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2. Create virtual environment

python -m venv .venv
source .venv/bin/activate     # macOS / Linux
.venv\Scripts\activate        # Windows

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3. Install dependencies

pip install .

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4. Run migrations

python manage.py migrate

5. Database settings

# dev.py
DATABASES = {
    'default':{yourdatabase}
}

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6. Start server

python manage.py runserver 8000

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🔐 Environment Variables

The project uses environment variables for secure configuration into production:

• SECRET_KEY
• DATABASE_URL
• DEBUG
• REDIS_URL
• EMAIL_HOST_USER

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☁️ Deployment

The project is ready to be deployed to:

• Render
• Railway
• AWS
• Heroku

Includes configuration for:

• Gunicorn as the WSGI server
• static settings adapted for production
• PostgreSQL database provisioning
• environment-based settings

The application was deployed to Heroku as an initial cloud deployment to validate production readiness.

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🔮 Future Improvements

• Payment integration (Stripe)
• Order history per user
• Inventory tracking with locking
• Rate limiting & abuse protection