Architecting Microservices

Bash
# 1. Domain discovery
mkdir microservices-project && cd microservices-project
mkdir -p docs/{domain-model,bounded-contexts,services}

# 2. Create initial domain map
cat > docs/domain-model/core-domains.md << EOF
# Core Domains
- User Management (Core)
- Order Processing (Core) 
- Inventory Management (Supporting)
- Notification Service (Generic)
EOF

Progress:

• Domain Discovery & Event Storming
• Bounded Context Identification
• Service Boundary Definition
• Data Architecture Planning
• API Contract Design
• Implementation Strategy
• Deployment Architecture
• Monitoring & Observability Setup
• Verification & Testing
• Performance Optimization

Step 1: Domain Discovery & Event Storming

1. Map business processes as domain events
2. Identify aggregates and entities
3. Document ubiquitous language
4. Verification: Validate with domain experts

Step 2: Bounded Context Identification

Context Map Template:
- Context Name: [UserManagement]
- Core Domain: [Yes/No]
- Upstream/Downstream: [Relationships]
- Integration Pattern: [Shared DB/API/Events]

Step 3: Service Boundary Definition

1. One service per bounded context
2. Define service responsibilities
3. Identify cross-cutting concerns
4. Self-check: Each service should be independently deployable

Step 4: Data Architecture Planning

YAML
# data-strategy.yml
services:
  user-service:
    database: postgresql
    schema: users
    ownership: exclusive
  order-service:
    database: postgresql  
    schema: orders
    ownership: exclusive
shared_data: none # Avoid shared databases

Step 5: API Contract Design

YAML
# user-service-api.yml
openapi: 3.0.0
paths:
  /users:
    post:
      summary: Create user
      requestBody:
        $ref: '#/components/schemas/CreateUserRequest'

Step 6: Implementation Strategy

1. Start with walking skeleton
2. Implement one service completely
3. Add services incrementally
4. Verification: Each service passes health checks independently

Step 7: Deployment Architecture

YAML
# docker-compose.yml
version: '3.8'
services:
  user-service:
    build: ./user-service
    ports: ["8081:8080"]
    environment:
      - DATABASE_URL=postgres://...
  order-service:
    build: ./order-service
    ports: ["8082:8080"]

Step 8: Monitoring & Observability

YAML
# monitoring-stack.yml
- Distributed tracing: Jaeger
- Metrics: Prometheus + Grafana  
- Logs: ELK Stack
- Health checks: Spring Actuator

Step 9: Verification & Testing

Bash
# Service-level tests
./gradlew test

# Contract tests  
./gradlew contractTest

# End-to-end tests
./gradlew e2eTest

# Performance tests
k6 run load-test.js

Step 10: Performance Optimization

1. Profile service interactions
2. Implement caching strategies
3. Optimize database queries
4. Self-check: Response times < 200ms for 95th percentile

Example 1: E-commerce Domain Decomposition Input: Monolithic e-commerce application Output:

• User Service (authentication, profiles)
• Product Catalog Service (inventory, pricing)
• Order Service (cart, checkout, fulfillment)
• Payment Service (transactions, billing)
• Notification Service (emails, SMS)

Example 2: Service API Contract Input: User registration requirement Output:

YAML
POST /api/v1/users
{
  "email": "user@example.com",
  "name": "John Doe"
}
Response: 201 Created
{
  "id": "user-123",
  "email": "user@example.com",
  "created_at": "2024-01-01T00:00:00Z"
}

• Database per Service: Never share databases between services
• API-First Design: Define contracts before implementation
• Eventual Consistency: Embrace async communication via events
• Bulkhead Pattern: Isolate failures between services
• Circuit Breaker: Implement resilience patterns
• Versioning Strategy: Use semantic versioning for APIs
• Idempotency: Ensure operations can be safely retried

• Distributed Monolith: Services too tightly coupled
• Chatty Interfaces: Too many service calls for single operation
• Shared Database: Coupling services through shared data
• Premature Optimization: Building complex architecture before understanding domain
• Missing Monitoring: No visibility into service health and performance
• Ignoring Network Failures: Not handling partial failures gracefully
• Over-Engineering: Creating services that are too granular
• Synchronous Communication: Using REST for everything instead of events