SerenityMusic
SerenityMusic is a music streaming platform built with a microservices architecture, featuring a NestJS backend, MongoDB database, and an integrated AI-powered recommendation system. The platform provides music streaming capabilities with personalized song recommendations, user management, and playlist functionality. The architecture follows a containerized approach using Docker Compose for orchestration, enabling independent scaling of services and seamless integration between the backend, recommendation system, and frontend.
Overview
SerenityMusic is a music streaming platform built with a microservices architecture, featuring a NestJS backend, MongoDB database, and an integrated AI-powered recommendation system. The platform provides music streaming capabilities with personalized song recommendations, user management, and playlist functionality. The architecture follows a containerized approach using Docker Compose for orchestration, enabling independent scaling of services and seamless integration between the backend, recommendation system, and frontend.
Key Features
Music streaming with audio playback functionality
AI-powered song recommendations using content-based filtering
User management with registration, authentication, and profiles
Playlist creation and management
Listening history tracking
Microservices architecture with independent service scaling
Docker containerization for easy deployment
RESTful API for service communication
Technical Highlights
Built microservices architecture with NestJS backend and Python recommendation service
Implemented AI-powered music recommendations using content-based filtering
Containerized deployment with Docker Compose for easy orchestration
Designed RESTful API for seamless inter-service communication
Integrated MongoDB for scalable data persistence
Created modular architecture enabling independent service scaling
Challenges and Solutions
Microservices Communication
Implemented RESTful APIs for coordinating communication between multiple services
Recommendation System Integration
Created separate recommendation service with API endpoints for seamless ML integration
Container Orchestration
Used Docker Compose for managing multiple containers and their dependencies
Data Consistency
Implemented proper data synchronization and API contracts between services
Technologies
Backend
Recommendation
Frontend
Infrastructure
Database
Project Information
- Status
- Completed
- Year
- 2024
- Architecture
- Microservices Architecture
- Category
- Full-Stack Development