Overview
Microservices architecture decomposes applications into small, independent services that communicate through APIs, enabling scalability, flexibility, and maintainability.
Core Principles
Single Responsibility
- Each service has one specific purpose
- Clear boundaries and responsibilities
- Independent deployment and scaling
API-First Design
- Services communicate through well-defined APIs
- Contract-driven development
- API versioning and compatibility
Decentralized Data Management
- Each service manages its own data
- Database per service pattern
- Eventual consistency over transactions
Independent Deployment
- Services can be deployed independently
- No shared codebase or dependencies
- Rolling updates and canary deployments
Service Communication Patterns
Synchronous Communication
- REST APIs: Request-response pattern
- GraphQL: Flexible query interface
- gRPC: High-performance RPC
Asynchronous Communication
- Message Queues: RabbitMQ, Apache Kafka
- Event Streaming: Publish-subscribe pattern
- Event Sourcing: State changes as events
Service Discovery
- Client-side: Eureka, Consul
- Server-side: Load balancers, API gateways
- Service Mesh: Istio, Linkerd
API Design for Microservices
Service Boundaries
- Bounded Context: Domain-driven design
- API Composition: Aggregate data from multiple services
- Backend for Frontend (BFF): Client-specific APIs
Cross-Service Communication
// REST API call between services
const response = await fetch('http://user-service/users/123', {
headers: {
'Authorization': `Bearer ${serviceToken}`,
'X-Request-ID': requestId
}
});API Gateway Pattern
- Single Entry Point: Unified API interface
- Request Routing: Route to appropriate services
- Cross-cutting Concerns: Authentication, logging, rate limiting
Data Management
Database per Service
- Polyglot Persistence: Different databases for different services
- Data Ownership: Each service owns its data
- Data Replication: Event-driven data synchronization
Event-Driven Architecture
// Event publishing
eventBus.publish('user.created', {
userId: '123',
email: 'user@example.com',
timestamp: Date.now()
});
// Event consumption
eventBus.subscribe('user.created', async (event) => {
await emailService.sendWelcomeEmail(event.email);
});Saga Pattern
- Choreography: Services coordinate via events
- Orchestration: Central coordinator manages flow
- Compensation: Rollback on failure
Service Decomposition Strategies
Business Capability
- Group by business functions
- Align with organizational structure
- Independent business value
Subdomain Decomposition
- Core Domain: Main business value
- Supporting Domain: Supporting capabilities
- Generic Domain: Common functionality
Technical Decomposition
- Size-based: Keep services small and focused
- Team-based: Align with team boundaries
- Technology-based: Group by technology stack
Resilience Patterns
Circuit Breaker
class CircuitBreaker {
async call(serviceCall) {
if (this.state === 'OPEN') {
throw new Error('Circuit is open');
}
try {
const result = await serviceCall();
this.recordSuccess();
return result;
} catch (error) {
this.recordFailure();
throw error;
}
}
}Bulkhead Isolation
- Resource Isolation: Prevent cascade failures
- Thread Pools: Separate thread pools per service
- Connection Pools: Limit connections per service
Retry & Timeout
const axiosRetry = require('axios-retry');
axiosRetry(axios, {
retries: 3,
retryDelay: axiosRetry.exponentialDelay,
retryCondition: (error) => {
return axiosRetry.isNetworkOrIdempotentRequestError(error);
}
});Observability
Distributed Tracing
- Trace Context: Request correlation across services
- Jaeger/Zipkin: Trace visualization
- OpenTelemetry: Standardized tracing
Centralized Logging
- Correlation IDs: Track requests across services
- Structured Logging: Consistent log format
- ELK Stack: Elasticsearch, Logstash, Kibana
Health Checks
- Readiness Probes: Service ready to accept traffic
- Liveness Probes: Service healthy and running
- Dependency Checks: External service health
Deployment & Scaling
Container Orchestration
- Kubernetes: Container orchestration platform
- Service Deployment: Rolling updates, scaling
- Configuration Management: ConfigMaps, Secrets
Service Mesh
- Traffic Management: Load balancing, routing
- Security: mTLS, authorization
- Observability: Metrics, tracing, logging
Blue-Green Deployment
- Zero Downtime: Switch between versions
- Instant Rollback: Quick reversion capability
- Testing: Validate new version before switching
Testing Strategies
Unit Testing
- Test individual service components
- Mock external dependencies
- Fast feedback loop
Integration Testing
- Test service-to-service communication
- Contract testing between services
- End-to-end workflow testing
Chaos Engineering
- Failure Injection: Simulate service failures
- Network Latency: Test resilience to delays
- Resource Exhaustion: Test under resource constraints
Common Challenges
Distributed Transactions
- Two-Phase Commit: Complex coordination
- Saga Pattern: Event-driven compensation
- Eventual Consistency: Accept temporary inconsistency
Service Coordination
- Service Discovery: Dynamic service location
- Load Balancing: Distribute requests evenly
- Circuit Breaking: Handle service failures gracefully
Data Consistency
- CAP Theorem: Choose consistency, availability, partition tolerance
- Eventual Consistency: Accept delayed consistency
- CQRS: Separate read and write models
Tools & Frameworks
Development Frameworks
- Spring Boot/Cloud (Java)
- Express.js + Moleculer (Node.js)
- FastAPI + Nameko (Python)
- ASP.NET Core (.NET)
Infrastructure
- Docker: Containerization
- Kubernetes: Orchestration
- Istio: Service mesh
- Kong/Apigee: API gateway
Communication
- RabbitMQ/Kafka: Message queues
- gRPC: High-performance RPC
- GraphQL: Flexible API layer