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