Image Not Found

Design Patterns for Scalable Backend Systems

Design Patterns for Scalable Backend Systems

 

Design patterns help in building scalable, maintainable, and efficient backend systems. While targeting scalability we must remember that scaling is about strategy not just infrastructure. Here are key patterns that can be found in scalable backend architectures:

1. Load Balancer Pattern

  • Use Case: Distributes traffic across multiple servers to prevent overloading.
  • Implementation: Reverse proxy (e.g., Nginx, HAProxy), DNS-based load balancing (AWS Route 53).
  • Example: Load balancer sits between clients and backend servers, routing requests efficiently.

2. Microservices Pattern

  • Use Case: Decomposing a monolithic system into smaller, independent services.
  • Implementation: Each microservice handles a single business function and communicates via APIs (e.g., REST, gRPC, or message queues).
  • Example: An e-commerce platform where payment, user management, and product catalog are separate services.

3. Event-Driven Architecture (Pub/Sub Pattern)

  • Use Case: Asynchronous communication between services to improve decoupling.
  • Implementation: Message brokers like Kafka, RabbitMQ, or AWS SNS/SQS.
  • Example: A user registration service publishes an event, and a separate notification service sends a welcome email.

4. CQRS (Command Query Responsibility Segregation) Pattern

  • Use Case: Separating read and write operations for optimized performance.
  • Implementation: Use different data models or databases for reads and writes.
  • Example: An e-commerce platform where order creation (write-heavy) is separated from order retrieval (read-heavy).

5. API Gateway Pattern

  • Use Case: Unified entry point for clients in a microservices architecture.
  • Implementation: API Gateway (e.g., Kong, AWS API Gateway) routes requests to appropriate microservices and handles authentication, logging, and rate limiting.
  • Example: A mobile app requests data from multiple backend services via a single API gateway.

6. Database Sharding Pattern

  • Use Case: Scaling databases horizontally by partitioning data across multiple database instances.
  • Implementation: Use hashing, range-based, or directory-based partitioning.
  • Example: A large social media platform distributes user data across multiple database servers based on user ID.

7. Cache-aside Pattern

  • Use Case: Improve response times by storing frequently accessed data in a cache.
  • Implementation: Use Redis or Memcached to cache database queries.
  • Example: A blog website caches article content to reduce database hits.

8. Circuit Breaker Pattern

  • Use Case: Prevent cascading failures in a distributed system.
  • Implementation: If a service is failing, the circuit breaker trips and blocks further calls until recovery.
  • Example: Netflix uses circuit breakers to prevent failing services from overloading their system.

9. Rate Limiting Pattern

  • Use Case: Protect APIs from abuse and ensure fair resource allocation.
  • Implementation: Token bucket or leaky bucket algorithms using API gateways or middleware.
  • Example: A SaaS platform limits API requests per user to prevent excessive load.

10. Strangler Fig Pattern

  • Use Case: Gradually refactor a monolithic system into microservices.
  • Implementation: Deploy new microservices alongside existing monolith and route traffic incrementally.
  • Example: Migrating a legacy e-commerce system to a modern cloud-based architecture.

 

#Webfluxy #WebAppDev #WebTechnicalities #LearnWeb #AIAssisted #Programming #SoftwareEngineering

 

ʀᴇᴍᴇᴍʙᴇʀ we ᴅᴇᴠᴇʟᴏᴘ Qᴜᴀʟɪᴛʏ, fast, and reliable websites and ᴀᴘᴘʟɪᴄᴀᴛɪᴏɴꜱ. Reach out to us for your Web and Technical services at:

☎️ +234 813 164 9219 

📧 [email protected]

Or...

🤳 wa.me/2347031382795

Thumb

Ijeoma Onwukwe