Skip to main content

Understanding Software Architectures

Traditional Monolithic Architecture​

What is a Monolithic Architecture?​

A monolithic architecture is the traditional unified model for software design where all components of an application are:

  • Interconnected and interdependent
  • Part of a single codebase
  • Deployed as a single unit

Characteristics of Monolithic Applications​

  1. Single Codebase

    • All features in one repository
    • Unified development environment
    • Single build process

  2. Shared Database

    • One large database
    • Shared data schema
    • Direct data access

  3. Deployment

    • Single deployment unit
    • All-or-nothing updates
    • Complete application restart

Advantages of Monolithic Architecture​

  1. Simplicity

    • Simple development
    • Easy testing
    • Straightforward deployment

  2. Performance

    • Direct method calls
    • No network overhead
    • Shared memory access

  3. Consistency

    • Single codebase
    • Unified testing
    • Consistent development practices

Challenges of Monolithic Architecture​

  1. Scalability Issues

    • Entire application must scale together
    • Resource inefficiency
    • Limited flexibility

  2. Development Challenges

    • Complex codebase
    • Difficult to maintain
    • Slow development cycles

  3. Technology Lock-in

    • Single technology stack
    • Difficult to change frameworks
    • Limited innovation

Microservices Architecture​

What are Microservices?​

Microservices architecture is an approach where an application is built as a collection of small, independent services that:

  • Are loosely coupled
  • Run in their own process
  • Communicate via APIs

Key Principles​

  1. Single Responsibility

    • Each service does one thing well
    • Independent functionality
    • Clear boundaries

  2. Independence

    • Separate codebases
    • Individual deployments
    • Technology flexibility

  3. Data Ownership

    • Each service owns its data
    • Private databases
    • Data isolation

Benefits of Microservices​

  1. Scalability

    • Independent scaling
    • Resource optimization
    • Better performance management

  2. Technology Flexibility

    • Different tech stacks per service
    • Easy to adopt new technologies
    • Best tool for each job

  3. Development Efficiency

    • Smaller, focused teams
    • Faster development cycles
    • Easier maintenance

  4. Resilience

    • Isolated failures
    • Independent recovery
    • Better fault tolerance

Implementation with Docker​

1. Service Containerization​

# Example User Service
FROM node:18-alpine
WORKDIR /app
COPY package*.json ./
RUN npm install
COPY . .
EXPOSE 3000
CMD ["npm", "start"]

2. Service Orchestration​

# docker-compose.yml
version: '3'
services:
  auth-service:
    build: ./auth
    ports:
      - "3001:3000"
    environment:
      - DB_HOST=auth-db
    depends_on:
      - auth-db

  user-service:
    build: ./users
    ports:
      - "3002:3000"
    environment:
      - DB_HOST=user-db
    depends_on:
      - user-db

  product-service:
    build: ./products
    ports:
      - "3003:3000"
    environment:
      - DB_HOST=product-db
    depends_on:
      - product-db

  # Databases
  auth-db:
    image: mongo:latest
    volumes:
      - auth-data:/data/db

  user-db:
    image: postgres:latest
    volumes:
      - user-data:/var/lib/postgresql/data

  product-db:
    image: mysql:latest
    volumes:
      - product-data:/var/lib/mysql

volumes:
  auth-data:
  user-data:
  product-data:

Transitioning from Monolithic to Microservices​

1. Strangler Fig Pattern​

  • Gradually replace functionality
  • Keep monolith running
  • Incremental migration

2. Domain-Driven Design​

  • Identify bounded contexts
  • Define service boundaries
  • Map dependencies

3. Best Practices​

Architecture Considerations​

  • Service discovery
  • Load balancing
  • API gateway
  • Circuit breakers

Development Practices​

  • CI/CD pipelines
  • Automated testing
  • Monitoring
  • Logging

Challenges and Solutions​

1. Distributed System Complexity​

  • Service discovery
  • Load balancing
  • Network latency

2. Data Management​

  • Data consistency
  • Transaction management
  • Data duplication

3. Operational Complexity​

  • Deployment coordination
  • Monitoring
  • Debugging

Best Practices​

1. Service Design​

  • Keep services small
  • Define clear boundaries
  • Design for failure

2. Communication​

  • Use async communication
  • Implement circuit breakers
  • Design resilient APIs

3. Data Management​

  • Database per service
  • Event sourcing
  • CQRS pattern

Next Steps​

After understanding different architectures:

  1. Evaluate your application needs
  2. Choose appropriate architecture
  3. Plan implementation strategy
  4. Set up development practices

The next section will cover Docker components and their interactions.