Fibonacci Calculator - Distributed Application

A full-stack distributed application that calculates Fibonacci numbers using a microservices architecture with Docker containerization and automated CI/CD deployment to Azure Container Instances.

🎯 Basic Functionality

This application allows users to:

1. Submit Fibonacci Index: Users enter a number (index) between 0-40 to calculate the corresponding Fibonacci number
2. Real-time Calculation: The system processes the request asynchronously using a worker pattern
3. View Results: Users can see:
   • All previously submitted indices (stored in PostgreSQL)
   • Calculated Fibonacci values (stored in Redis)
4. Automatic Updates: The UI polls for new results every 2 seconds and refreshes the list of indices every 5 seconds

How It Works

1. User submits an index through the React frontend
2. Server receives the request and:
   • Stores the index in PostgreSQL (permanent storage)
   • Places a placeholder value in Redis ("Nothing yet!")
   • Publishes the index to a Redis channel (insert)
3. Worker process:
   • Subscribes to the Redis insert channel
   • Receives the index and calculates the Fibonacci number
   • Updates Redis with the calculated value
4. Frontend polls Redis and displays the result when ready

🔄 Why Worker Pattern?

1. Separation of Concerns

• Server: Handles HTTP requests, data validation, and user interactions
• Worker: Handles CPU-intensive calculations without blocking the server

2. Scalability

• The server can handle multiple requests simultaneously
• Workers can be scaled independently based on calculation load (using Kubernetes)
• Multiple worker instances can process calculations in parallel

3. Performance

• Non-blocking: Server responds immediately to users without waiting for calculations
• Asynchronous Processing: Calculations happen in the background
• Better Resource Utilization: CPU-intensive tasks don't block I/O operations

4. Resilience

• If a worker crashes, the server continues to accept requests
• Failed calculations can be retried by other workers
• Server remains responsive even during heavy computational loads

💻 Local Development

Prerequisites

• Docker and Docker Compose installed
• Node.js 20+ (for local development without Docker)

Running with Docker Compose

# Start all services
docker-compose -f docker-compose-dev.yml up

# Or in detached mode
docker-compose -f docker-compose-dev.yml up -d

# View logs
docker-compose -f docker-compose-dev.yml logs -f

# Stop all services
docker-compose -f docker-compose-dev.yml down

Accessing the Application

• Frontend: http://localhost:3050
• API: http://localhost:3050/api
• PostgreSQL: localhost:5432
• Redis: localhost:6379

📝 Environment Variables

Server

• REDIS_HOST: Redis server hostname
• REDIS_PORT: Redis server port (default: 6379)
• PGUSER: PostgreSQL username
• PGHOST: PostgreSQL hostname
• PGDATABASE: PostgreSQL database name
• PGPASSWORD: PostgreSQL password
• PGPORT: PostgreSQL port (default: 5432)
• PORT: Server port (default: 5000)

Worker

• REDIS_HOST: Redis server hostname
• REDIS_PORT: Redis server port (default: 6379)

📚 Technology Stack

• Frontend: React 19, TypeScript, Vite, TailwindCSS, TanStack Query
• Backend: Express.js, TypeScript, Node.js
• Worker: TypeScript, Node.js, ioredis
• Database: PostgreSQL 16
• Cache/Message Broker: Redis 7
• Reverse Proxy: Nginx 1.27
• Containerization: Docker

🤝 Contributing

This is a learning project. Feel free to fork and experiment!

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Note: This application is designed for educational purposes to demonstrate microservices architecture, Docker containerization, and cloud deployment patterns.