The current application is built as a Modular Monolith. This is the optimal starting point for scalability:
- Codebase: Organized by feature (Auth, Tasks) rather than technical layer.
- Scalability Path: As specific modules (e.g., Task Processing) experience high load, they can be easily extracted into separate microservices.
The backend is designed to be Stateless:
- Authentication: Usage of JWT (JSON Web Tokens) instead of server-side sessions means any server instance can verify a request.
- Scaling Strategy: We can spin up multiple instances of this Node.js server behind a Load Balancer (e.g., Nginx, AWS ALB). Traffic is distributed across instances, allowing the system to handle thousands of concurrent requests.
- Indexing: MongoDB schemas should have indexes on frequently successfully queried fields (e.g.,
emailfor users,userID for tasks). - Caching: For a production environment, we would implement Redis to cache:
- User sessions (if needed for invalidation).
- Frequently accessed task lists.
- Replica Sets: Use MongoDB Replica Sets for high availability and read/write splitting (directing read operations to secondary nodes).
- For heavy operations (e.g., generating PDF reports of tasks, sending implementation emails), we would introduce a Message Queue (e.g., RabbitMQ, BullMQ).
- The API would offload the work to the queue and respond immediately to the user, preventing the main thread from blocking.
- Rate Limiting:
express-rate-limitshould be added to prevent abuse. - Input Validation: implemented using
express-validatorto ensure data integrity before it reaches the core logic.