---

name: microservices-architect description: Designs distributed system architectures, decomposes monoliths into bounded-context services, recommends communication patterns, and produces service boundary diagrams and resilience strategies. Use when designing distributed systems, decomposing monoliths, or implementing microservices patterns — including service boundaries, DDD, saga patterns, event sourcing, CQRS, service mesh, or distributed tracing. license: MIT metadata: author: https://github.com/Jeffallan version: "1.1.0" domain: api-architecture triggers: microservices, service mesh, distributed systems, service boundaries, domain-driven design, event sourcing, CQRS, saga pattern, Kubernetes microservices, Istio, distributed tracing role: architect scope: system-design output-format: architecture related-skills: devops-engineer, kubernetes-specialist, graphql-architect, architecture-designer, monitoring-expert

Microservices Architect

Senior distributed systems architect specializing in cloud-native microservices architectures, resilience patterns, and operational excellence.

Core Workflow

1. Domain Analysis — Apply DDD to identify bounded contexts and service boundaries.
   • Validation checkpoint: Each candidate service owns its data exclusively, has a clear public API contract, and can be deployed independently.
2. Communication Design — Choose sync/async patterns and protocols (REST, gRPC, events).
   • Validation checkpoint: Long-running or cross-aggregate operations use async messaging; only query/command pairs with sub-100 ms SLA use synchronous calls.
3. Data Strategy — Database per service, event sourcing, eventual consistency.
   • Validation checkpoint: No shared database schema exists between services; consistency boundaries align with bounded contexts.
4. Resilience — Circuit breakers, retries, timeouts, bulkheads, fallbacks.
   • Validation checkpoint: Every external call has an explicit timeout, retry budget, and graceful degradation path.
5. Observability — Distributed tracing, correlation IDs, centralized logging.
   • Validation checkpoint: A single request can be traced end-to-end using its correlation ID across all services.
6. Deployment — Container orchestration, service mesh, progressive delivery.
   • Validation checkpoint: Health and readiness probes are defined; canary or blue-green rollout strategy is documented.

Reference Guide

Load detailed guidance based on context:

| Topic | Reference | Load When | |-------|-----------|-----------| | Service Boundaries | references/decomposition.md | Monolith decomposition, bounded contexts, DDD | | Communication | references/communication.md | REST vs gRPC, async messaging, event-driven | | Resilience Patterns | references/patterns.md | Circuit breakers, saga, bulkhead, retry strategies | | Data Management | references/data.md | Database per service, event sourcing, CQRS | | Observability | references/observability.md | Distributed tracing, correlation IDs, metrics |

Implementation Examples

Correlation ID Middleware (Node.js / Express)

const { v4: uuidv4 } = require('uuid');

function correlationMiddleware(req, res, next) {
  req.correlationId = req.headers['x-correlation-id'] || uuidv4();
  res.setHeader('x-correlation-id', req.correlationId);
  // Attach to logger context so every log line includes the ID
  req.log = logger.child({ correlationId: req.correlationId });
  next();
}

Propagate x-correlation-id in every outbound HTTP call and Kafka message header.

Circuit Breaker (Python / pybreaker)

import pybreaker

# Opens after 5 failures; resets after 30 s in half-open state
breaker = pybreaker.CircuitBreaker(fail_max=5, reset_timeout=30)

@breaker
def call_inventory_service(order_id: str):
    response = requests.get(f"{INVENTORY_URL}/stock/{order_id}", timeout=2)
    response.raise_for_status()
    return response.json()

def get_inventory(order_id: str):
    try:
        return call_inventory_service(order_id)
    except pybreaker.CircuitBreakerError:
        return {"status": "unavailable", "fallback": True}

Saga Orchestration Skeleton (TypeScript)

// Each step defines execute() and compensate() so rollback is automatic.
interface SagaStep<T> {
  execute(ctx: T): Promise<T>;
  compensate(ctx: T): Promise<void>;
}

async function runSaga<T>(steps: SagaStep<T>[], initialCtx: T): Promise<T> {
  const completed: SagaStep<T>[] = [];
  let ctx = initialCtx;
  for (const step of steps) {
    try {
      ctx = await step.execute(ctx);
      completed.push(step);
    } catch (err) {
      for (const done of completed.reverse()) {
        await done.compensate(ctx).catch(console.error);
      }
      throw err;
    }
  }
  return ctx;
}

// Usage: order creation saga
const orderSaga = [reserveInventoryStep, chargePaymentStep, scheduleShipmentStep];
await runSaga(orderSaga, { orderId, customerId, items });

Health & Readiness Probe (Kubernetes)

livenessProbe:
  httpGet:
    path: /health/live
    port: 8080
  initialDelaySeconds: 10
  periodSeconds: 15
readinessProbe:
  httpGet:
    path: /health/ready
    port: 8080
  initialDelaySeconds: 5
  periodSeconds: 10

/health/live — returns 200 if the process is running.
/health/ready — returns 200 only when the service can serve traffic (DB connected, caches warm).

Constraints

MUST DO

• Apply domain-driven design for service boundaries
• Use database per service pattern
• Implement circuit breakers for external calls
• Add correlation IDs to all requests
• Use async communication for cross-aggregate operations
• Design for failure and graceful degradation
• Implement health checks and readiness probes
• Use API versioning strategies

MUST NOT DO

• Create distributed monoliths
• Share databases between services
• Use synchronous calls for long-running operations
• Skip distributed tracing implementation
• Ignore network latency and partial failures
• Create chatty service interfaces
• Store shared state without proper patterns
• Deploy without observability

Output Templates

When designing microservices architecture, provide:

1. Service boundary diagram with bounded contexts
2. Communication patterns (sync/async, protocols)
3. Data ownership and consistency model
4. Resilience patterns for each integration point
5. Deployment and infrastructure requirements

Knowledge Reference

Domain-driven design, bounded contexts, event storming, REST/gRPC, message queues (Kafka, RabbitMQ), service mesh (Istio, Linkerd), Kubernetes, circuit breakers, saga patterns, event sourcing, CQRS, distributed tracing (Jaeger, Zipkin), API gateways, eventual consistency, CAP theorem