This project presents a scalable and event-driven microservices architecture designed to support the core functionalities of an e-commerce platform. Each service operates independently with its own database and communicates asynchronously through Apache Kafka. A GraphQL API Gateway provides a unified interface for querying data across the system.
The architecture is optimized for high performance, flexibility, and reliability, with integrated Redis caching, JWT-based security, priority-driven notifications, and comprehensive observability using Prometheus and Grafana. All services are fully containerized to ensure reproducible deployments and simplified management.
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Microservices Architecture
Independent services including User, Product, Order, Notification, and Recommendation, each responsible for a distinct domain and backed by its own MongoDB database. -
GraphQL API Gateway
A unified endpoint providing efficient and flexible data access across all services with support for aggregation and optimized queries. -
Event-Driven Messaging
Services communicate asynchronously using Kafka for improved scalability, system decoupling, and real-time data propagation. -
Caching Layer
Redis is utilized to cache frequently accessed data, reduce query latency, and increase response speed under high traffic. -
Authentication and Authorization
User authentication is secured using JWT tokens. Role-based access control ensures data protection and controlled access. -
Monitoring and Observability
Prometheus collects operational metrics, while Grafana offers customizable dashboards for system analytics and performance tracking. -
Containerized Deployment
All services are encapsulated within Docker containers, orchestrated through Docker Compose, facilitating ease of deployment and environment portability.
Handles user registration, authentication, and profile operations using JWT-secured endpoints and encrypted storage.
- Endpoints for creating, updating, and managing product details.
- Real-time inventory tracking and updates.
- Real-time order placement.
- Inventory reservation system to prevent overselling and ensure consistency.
- Kafka-driven real-time communication between services.
- Dead Letter Queues (DLQs) for error handling and message recovery.
- Enhanced resilience and fault tolerance.
- Consolidated API for querying data across services with flexibility and efficiency.
- Integrated caching of GraphQL responses in Redis for improved query performance.
- Frequently accessed data (e.g., product listings, recommendations) cached in Redis.
- Intelligent cache invalidation for inventory updates and recommendation updates.
- Optimized caching for high-demand scenarios.
- Automated email notifications for transactional, promotional, and recommendation-based messages.
- Priority-based queues:
- Critical Queue: For high-priority notifications like order updates and security alerts.
- Standard Queue: For lower-priority notifications like promotional emails.
- Cron jobs for scheduled or bulk notifications to improve efficiency.
- Event-driven triggers for sending notifications seamlessly.
Generates tailored product recommendations using historical order data and user activity patterns.
Implements Redis caching to minimize computation overhead and deliver instant responses.
- Prometheus for real-time metrics collection and monitoring.
- Grafana dashboards for visualizing system health, performance, and analytics.
- Centralized logging for improved debugging and auditing.
- Fully containerized system using Docker for seamless deployment.
- Orchestration with Docker Compose to run all services together or individually for debugging.
| Component | Technology |
|---|---|
| Core APIs | Node.js (Express.js) |
| API Gateway | GraphQL |
| Event Stream | Apache Kafka |
| Caching | Redis |
| Database | MongoDB |
| Email Delivery | Nodemailer |
| Component | Purpose |
|---|---|
| Docker & Docker Compose | Containerization and orchestration |
| Prometheus | Metrics collection and system monitoring |
| Grafana | Visualization and performance dashboards |
The system is fully containerized and can be deployed locally using Docker Compose.
Execute the following command from the project root:
docker-compose up --build- To run a specific service, navigate to its directory and follow the steps to install dependencies and start it individually.