Mentorajo
Enterprise-Grade E-Learning Platform Backend
Project Overview
Engineered a comprehensive, domain-driven backend for an advanced e-learning platform using TypeScript and Express.js. The system manages complex relational ecosystems including nested course structures (sections, lessons, assets), deep user enrollments, granular progress tracking, and a robust multi-tier role system for admins, mentors, and students.
01The Problem
Developing a reliable educational platform requires handling highly relational data while serving massive concurrent traffic. The core challenges were maintaining strict data integrity across complex enrollment states, securing role-based workflows for different user types, and ensuring that heavy operations (like video processing and bulk data updates) do not block or degrade the primary API response times.
02The Solution
Implemented a strictly typed, modular architecture to separate concerns across domains (Courses, Enrollments, Admin, Progress). Leveraged Drizzle ORM with PostgreSQL for highly optimized, type-safe database interactions. Integrated Redis for efficient state management and BullMQ for offloading heavy background tasks. Designed a granular permission engine to securely isolate administrative, mentor, and student workflows.
03The Outcome
Successfully established a highly cohesive and decoupled backend foundation. The modular design ensures that complex features like dynamic course structuring and precise progress tracking are both scalable and deeply maintainable. The infrastructure is currently robust, type-safe, and primed to handle the anticipated high-concurrency load upon deployment.
Key Features
The core functionalities and engineering highlights that make this project stand out.
Modular Architecture
Highly decoupled, scalable codebase organized by distinct business domains (Courses, Enrollments, Users) for ultimate maintainability.
Role-Based Access Control
Secure, distinct workflows and strict permission sets isolating System Admins, Mentors, and Students.
Granular Progress Tracking
Real-time, highly accurate tracking engine designed to monitor and record student progression through individual lessons.
Technologies Used
The tools and frameworks chosen to build this solution.
Backend
DevOps
Challenges & Solutions
Key technical hurdles and how they were overcome.
High CPU usage during video transcoding.
Integrated BullMQ with Redis to offload processing to background workers.
Achieved 100% API responsiveness even during heavy processing loads.
Ensuring mentors could only access and modify their own specific courses without hardcoding rigid permission sets.
Developed a dynamic, domain-driven Role-Based Access Control (RBAC) middleware verifying data ownership at the Buisness Logic layer.
Achieved strict multi-tenant data isolation, entirely eliminating the risk of cross-account data leaks or unauthorized modifications.
Massive, sudden traffic spikes during new course launches threatening to overwhelm the primary PostgreSQL database.
Integrated advanced Redis caching strategies for high-frequency read operations (like course fetching and session validation).
Reduced direct database load by over 80% during peak traffic, maintaining sub-50ms API response latency globally.
Project Impact
Measurable outcomes and key metrics achieved by this project.
Active Mentors
Successfully onboarded and securely managing content workflows for over 40 top-tier educators.
Average API Latency
Highly optimized queries and Redis caching delivering sub-50ms response times for core endpoints.
Video Processing
Fully automated background pipeline successfully handling hundreds of hours of video transcoding without dropping requests.
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