Overview of the most important design patterns for microservice architecture. From decomposition to inter-service communication.
What are Microservices Patterns?¶
Microservice architecture breaks down a monolithic application into independent services. Each service has its own data model, its own deployment, and communicates through defined APIs.
Key benefits:
- Independent deployment of individual services
- Technology diversity — each service can use a different stack
- Scalability at the individual service level
- Resilience — failure of one service doesn’t bring down the entire system
Decomposition Patterns¶
The most important decision is how to split the monolith. There are two main approaches:
Decompose by Business Capability — services correspond to business capabilities (orders, payments, inventory).
Decompose by Subdomain — based on DDD, each bounded context = service.
services/
├── order-service/ # Bounded context: Orders
│ ├── src/
│ ├── Dockerfile
│ └── docker-compose.yml
├── payment-service/ # Bounded context: Payments
├── inventory-service/ # Bounded context: Inventory
└── api-gateway/ # Entry point
Communication Patterns¶
Services communicate in two basic ways:
Synchronous (Request/Response): REST API (HTTP/JSON) or gRPC (binary, faster).
Asynchronous (Event-Driven): Message Queue (RabbitMQ) or Event Streaming (Kafka).
// Synchronous call via REST
const order = await fetch('http://order-service/api/orders/123');
// Asynchronous event via Kafka
await kafka.produce('order-events', {
type: 'OrderCreated',
orderId: '123',
items: [{ sku: 'ABC', qty: 2 }]
});
Data Management Patterns¶
Each service should have its own database (Database per Service pattern). This brings independence but complicates queries across services.
- API Composition — query service aggregates data from multiple services
- CQRS — separate models for reading and writing
- Saga Pattern — distributed transactions without 2PC
- Event Sourcing — state as a sequence of events
Summary¶
Microservices patterns aren’t just theory — they are proven solutions to real problems. Start with reasonable granularity, choose the right communication pattern, and solve data consistency from the beginning.
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