Microservices Architecture

Patterns and best practices for designing and implementing microservices architectures.

Core Patterns

Service Decomposition

• Domain-driven design (bounded contexts)
• Business capability based
• Ownership clarity
• Technology diversity

API Gateway Pattern

Client → API Gateway → Service 1
                    → Service 2
                    → Service 3

• Single entry point
• Authentication/authorization
• Request routing
• Rate limiting

Database per Service

• Each service owns its database
• Data consistency challenges
• Enables technology diversity

Event-Driven Communication

Service A → Event Bus → Service B
           → Service C

• Loose coupling
• Eventual consistency
• Event sourcing

Reliability Patterns

Circuit Breaker

// Fails fast when service is unavailable
const breaker = new CircuitBreaker(async () => {
  return await serviceCall();
}, {
  threshold: 5,        // Fail after 5 errors
  timeout: 60000       // Check after 60s
});

Retry Logic

// Exponential backoff
const maxRetries = 3;
const baseDelay = 1000;
for (let i = 0; i < maxRetries; i++) {
  try {
    return await service.call();
  } catch (e) {
    if (i === maxRetries - 1) throw e;
    await sleep(baseDelay * Math.pow(2, i));
  }
}

Timeout Management

• Set reasonable timeouts
• Cascading timeout calculation
• Fail fast strategy

Consistency Patterns

Two-Phase Commit (2PC)

• Synchronous, strong consistency
• Blocking, reduced availability
• Use sparingly

Saga Pattern

Transaction 1: Service A
  ↓ success
Transaction 2: Service B
  ↓ success
Transaction 3: Service C
  ↓ failure → Compensating transactions

• Long-running transactions
• Eventual consistency
• Compensating actions on failure

Event Sourcing

• Append-only event log
• Reconstible state
• Audit trail built-in

Monitoring & Operations

Distributed Tracing

• Correlate requests across services
• Identify latency bottlenecks
• Tools: Jaeger, Zipkin

Logging

• Structured logging with correlation IDs
• Centralized log aggregation
• Tools: ELK, Splunk

Metrics

• Per-service metrics
• Request latency, throughput
• Resource usage

Service Mesh

Istio/Linkerd

• Manages service-to-service communication
• Traffic management
• Security policies
• Observability

Configuration Management

# Environment-specific config
app:
  database:
    url: ${DB_URL}
    timeout: ${DB_TIMEOUT:-5000}
  cache:
    ttl: ${CACHE_TTL:-3600}
  security:
    jwtSecret: ${JWT_SECRET}

Deployment Strategies

Blue-Green Deployment

• Two identical environments
• Switch traffic instantly
• Quick rollback

Canary Deployment

• Gradual rollout to subset of users
• Monitor metrics
• Expand if successful

Rolling Deployment

• Gradual replacement of old instances
• No downtime
• Longer deploy time

API Design for Microservices

• RESTful or gRPC
• Versioning strategy
• Backward compatibility
• Documentation (OpenAPI)

Challenges & Solutions

Team Organization

• Cross-functional teams per service
• Clear API contracts
• Ownership and accountability
• Communication patterns

References

• Sam Newman – Building Microservices
• Chris Richardson – Microservices Patterns
• Kubernetes in Action
• AWS Microservices Architecture
• Kong API Gateway Guide