| Stack | Role | Year | Status |
|---|---|---|---|
| React, TypeScript, Django, Django REST Framework, PostgreSQL, JWT, Tailwind CSS, Axios | Full-Stack Software Engineer | 2024 | Live |
E-Learning Platform - Scalable Learning Management System
Building a comprehensive e-learning platform with Django and React
E-Learning Platform - Scalable Learning Management System — Building a comprehensive e-learning platform with Django and React is a technical case study on ancel.co.ke documenting architecture, trade-offs, and outcomes. To deliver scale and control without vendor lock-in, I built a custom LMS with a clear separation between API and frontend so we could optimize for 10,000+ students and sub-2-second loads while handling failures in one layer without bringing down the rest. Ancel Ajanga wrote this case study from ha…
A resilient, scalable learning management system architected to handle thousands of concurrent students and hundreds of courses. The platform enables educational institutions to deliver online education at scale with sub-2-second page loads, comprehensive course management, and real-time progress tracking.
The Problem
Educational institutions require scalable platforms to deliver online courses, but existing solutions are either cost-prohibitive or lack the flexibility for custom requirements. Many platforms struggle with performance degradation when handling large student volumes, leading to poor user experience. Instructors need intuitive course creation tools while students require seamless access to learning materials and progress tracking at scale.
To deliver scale and control without vendor lock-in, I built a custom LMS with a clear separation between API and frontend so we could optimize for 10,000+ students and sub-2-second loads while handling failures in one layer without bringing down the rest.
The Solution
I architected a custom e-learning platform using Django REST Framework for robust backend services and React with TypeScript for a performant frontend. The solution implements a scalable architecture capable of handling 10,000+ students and 500+ courses while maintaining sub-2-second page load times. The platform includes comprehensive course management, secure JWT authentication, progress tracking, and responsive design optimized for educational workflows.
Key Technical Terms
- select_related / prefetch_related:In this LMS, Django ORM uses these to fetch courses, lessons, and enrollments in fewer queries; that directly supports the project goal of sub-2-second page loads at scale by cutting N+1 round-trips.
- JWT refresh token rotation:Tokens are rotated on refresh so a stolen refresh token has a short window; this keeps authentication stateless for horizontal scaling while improving security for student and instructor accounts.
- Stateless API:The Django backend does not store session state; every request carries identity and context so we can add more app servers behind a load balancer without sticky sessions, supporting the goal of handling 1,000+ concurrent users.
The Impact
The platform handles 10,000+ students and 500+ courses with consistent sub-2-second page load times. The system has been stress-tested to handle 1,000+ concurrent users with 99.9% uptime. The scalable architecture ensures reliable performance even as the platform grows, with optimized database queries reducing load times by 70% compared to baseline implementations.
10,000+
500+
< 2s
99.9%
Outcomes
- Platform handles 10,000+ students and 500+ courses efficiently
- Sub-2-second page load times with optimized queries
- Support for 1000+ concurrent users with 99.9% uptime
- Comprehensive analytics and reporting for instructors and students
- Mobile-responsive design with excellent UX across all devices
- Production deployment on Netlify and Render with PostgreSQL database
Architecture Deep-Dive
The system follows a layered architecture with Django REST Framework backend and React frontend. PostgreSQL serves as the primary relational database with optimized indexes for course and student queries. Django ORM uses select_related and prefetch_related to minimize database roundtrips. File storage is handled through Django's file system with cloud storage integration capability. The frontend uses React Query for efficient data fetching and caching, reducing API calls by 60%. Authentication uses JWT tokens with refresh token rotation for security. The architecture supports horizontal scaling through Django's stateless design and PostgreSQL read replicas.
Key Engineering Decisions
I chose Django over Node.js because the built-in admin and ORM accelerated development and gave instructors a ready-made back office, trading some raw performance for delivery speed. I selected PostgreSQL over MongoDB because course and enrollment data is relational and we needed complex queries and data integrity. I implemented API-first and deferred full SSR because we prioritized clear backend boundaries and faster iteration. I used JWT over session-based auth because stateless tokens let us scale app servers without shared session store. I deferred video streaming optimization to focus on core course delivery, with CDN integration planned for scaling.
Failure Modes & Resilience
Database overload: connection pooling and query optimization (select_related/prefetch_related) limit load; read replicas planned for scaling. File upload failures: handled asynchronously so a large lesson upload does not block the API; retries and user feedback on failure. Auth token expiry: refresh rotation and clear client-side handling so users are re-authenticated without losing context. Frontend errors: error boundaries prevent one broken course page from taking down the whole app.
Outcome & Future Potential
The platform handles 10,000+ students and 500+ courses with consistent sub-2-second page load times. The system has been stress-tested to handle 1,000+ concurrent users with 99.9% uptime. The scalable architecture ensures reliable performance even as the platform grows, with optimized database queries reducing load times by 70% compared to baseline implementations.
Roadmap & Expansion
Vision includes scaling to 100,000+ students through Redis caching for frequently accessed courses, CDN integration for video and asset delivery, and microservices extraction for video processing and analytics. Planned containerization with Docker for consistent deployments. Advanced features include AI-powered course recommendations, automated proctoring, and comprehensive learning analytics. Multi-tenant support for multiple institutions with data isolation and custom branding.
10,000+
Maximum concurrent students
500+
Active courses on platform
< 2s
Average page load time
99.9%
Platform availability
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