A design approach where applications are built as a collection of small, independent services.
Microservices architecture is a design approach where applications are structured as loosely coupled, independently deployable services. Each service focuses on a specific business capability and communicates with others through well-defined APIs.
This contrasts with traditional monolithic architecture where all functionality lives in a single, tightly integrated codebase.
Single Responsibility: Each service does one thing and does it well. Payment service handles payments. Nothing else.
Independent Deployment: Deploy services separately without coordinating with other teams. Ship faster.
Decentralized Data: Each service owns its database. No shared database reduces coupling.
Failure Isolation: Services fail independently. User service down does not crash payment service.
Technology Agnostic: Choose the best tool for each service. No forced standardization.
API Gateway: Front door for all client requests. Routes to appropriate microservices, handles authentication, rate limiting.
Service Registry: Directory of available services. Services register themselves, others discover them dynamically.
Load Balancers: Distribute traffic across multiple instances of the same service.
Message Broker: Enables async communication between services (Kafka, RabbitMQ).
Monitoring & Logging: Centralized observability for distributed services (ELK stack, Prometheus).
Synchronous (REST/gRPC): Service A calls Service B and waits for response. Simple but creates tight coupling.
Asynchronous (Events): Service A publishes event, Service B listens and reacts. Loose coupling, better resilience.
Hybrid: Use both. Synchronous for critical paths (payment processing), async for background tasks (send email).
Database per Service: Each service has its own database. Changes do not affect other services.
Eventual Consistency: Services sync data eventually, not immediately. Trade consistency for availability.
Saga Pattern: Coordinate distributed transactions across services. If one step fails, compensate by undoing previous steps.
E-commerce platform with microservices:
Each deploys independently, scales based on load, uses appropriate technology.
Do not rewrite everything. Gradually extract services from monolith.
This strangler fig pattern reduces risk and allows learning from mistakes.
Service Discovery: Services need to find each other. Use service mesh (Istio) or registry (Consul, Eureka).
Distributed Tracing: Track requests across services. Use Jaeger or Zipkin.
Configuration Management: Centralize config with tools like Consul or AWS Parameter Store.
Testing: Contract testing ensures services communicate correctly. End-to-end tests validate workflows.
Large Organizations: Multiple teams working on different features independently.
Different Scaling Needs: Authentication needs 2 servers, video processing needs 50.
Polyglot Requirements: Some services need Python for ML, others need Go for performance.
Frequent Deployments: Deploy payment fixes without touching recommendation engine.
Small Teams: Managing microservices takes more effort than building features.
Unclear Boundaries: If you do not know how to split services, you will create a distributed monolith - worst of both worlds.
Limited DevOps: Need strong CI/CD, monitoring, orchestration. Without these, microservices become unmanageable.
Most successful companies started with monoliths and migrated to microservices after reaching scale. Instagram, Twitter, Shopify all followed this path.
Microservices architecture is not a trend or best practice. It is a tool that solves specific problems at specific scales. Use it when those problems exist, not before.
The best architecture is the simplest one that meets your requirements. Add complexity only when benefits clearly outweigh costs.
