Microservices Architecture
Breaking monoliths into small, independent services - benefits, challenges, and implementation patterns.
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What You'll Learn
Tutorial Contents
1.Architecture Principles
Microservices vs Monolith ``` Choose Monolith when: - Team < 20 engineers - Domain not well-understood yet - Startup/MVP stage - Benefits: simple deployment, easy debugging, low latency
Choose Microservices when: - Teams work on different business domains - Different scaling requirements per service - Polyglot tech stacks needed - Organizational boundaries match service boundaries
Domain-Driven Design (DDD) boundaries: Bounded Context: a domain with its own model and language Ecommerce example: ├─ Catalog Service (products, inventory) ├─ Order Service (cart, checkout, orders) ├─ Payment Service (transactions, refunds) ├─ Shipping Service (fulfillment, tracking) ├─ Notification Service (emails, SMS, push) └─ User Service (auth, profiles, preferences) Each service: separate database, separate deployment, separate team ```
Service Communication Patterns ``` Synchronous (REST/gRPC): Use when: immediate response required Risk: cascading failures if downstream is slow, caller blocks Circuit Breaker Pattern: CLOSED → calls pass through OPEN → calls fail immediately (don't overwhelm failing service) HALF-OPEN → probe with limited calls, recover to CLOSED Implementation (node-circuit-breaker): const breaker = new CircuitBreaker(fetchProduct, { timeout: 3000, // 3s timeout errorThresholdPercentage: 50, // open after 50% failures resetTimeout: 30000, // try again after 30s });
Asynchronous (Message Queue): Use when: eventual consistency acceptable, high volume Broker: Kafka (high throughput) or RabbitMQ (flexible routing) Event types: - Domain events: "order.created", "payment.processed" - Integration events: cross-service communication - Commands: "send-confirmation-email" Guarantee: at-least-once delivery → consumers must be idempotent ```
2.Data Management & Observability
Database Per Service ``` Anti-pattern: Shared database Service A → ┐ Service B → ├─ Same Database ← tight coupling, schema changes break all Service C → ┘
Correct: Database per service Order Service → PostgreSQL (ACID for financial data) Product Service → MongoDB (flexible schema for catalog) Session Service → Redis (fast, TTL-based) Search Service → Elasticsearch (full-text, complex queries)
Handling data consistency across services: Saga Pattern (distributed transactions): Order Saga: 1. Order Service: create order (PENDING) 2. Inventory Service: reserve stock 3. Payment Service: charge customer 4. Order Service: confirm order (CONFIRMED) Compensating transactions on failure: - Payment failed → release inventory reservation → mark order FAILED - All async, eventual consistency ```
Observability Stack ``` Three Pillars: Metrics → Prometheus + Grafana (aggregates, percentiles) Logs → ELK Stack or Loki (structured JSON logs) Traces → Jaeger or Zipkin (request flow across services)
OpenTelemetry (vendor-neutral instrumentation): // Auto-instrument Node.js service import { NodeSDK } from "@opentelemetry/sdk-node"; import { JaegerExporter } from "@opentelemetry/exporter-jaeger";
const sdk = new NodeSDK({ traceExporter: new JaegerExporter({ endpoint: "http://jaeger:14268/api/traces" }), serviceName: "order-service", }); sdk.start();
// Every HTTP request gets a trace ID automatically // Propagated to downstream services via headers // Full request path visible in Jaeger UI
Service health endpoints: GET /health → { status: "ok" } GET /health/live → is the process alive? GET /health/ready → is it ready to accept traffic? ```
Code Example
// Microservices Architecture Example
/**
* Monolithic Approach (tightly coupled)
* All functionality in single application
*/
const monolithApp = {
userManagement: {},
productCatalog: {},
orderProcessing: {},
paymentProcessing: {},
shipping: {},
notification: {}
// Scaling: must scale entire application
// Updates: one bug breaks everything
// Technology: all use same tech stack
};
/**
* Microservices Approach (loosely coupled)
* Independent services, separate databases
*/
const microservicesArchitecture = {
services: {
userService: {
responsibility: 'User accounts, profiles',
endpoint: 'https://users-api.example.com',
database: 'PostgreSQL',
deployment: 'Kubernetes pod'
},
productService: {
responsibility: 'Product catalog, inventory',
endpoint: 'https://products-api.example.com',
database: 'MongoDB',
deployment: 'Kubernetes pod'
},
orderService: {
responsibility: 'Order processing',
endpoint: 'https://orders-api.example.com',
database: 'PostgreSQL',
deployment: 'Kubernetes pod'
},
paymentService: {
responsibility: 'Payment processing',
endpoint: 'https://payments-api.example.com',
database: 'PostgreSQL',
deployment: 'Kubernetes pod'
}
},
communication: {
synchronous: 'REST API calls between services',
asynchronous: 'Message queue (RabbitMQ, Kafka)'
},
deployment: 'Docker + Kubernetes (each service independent)',
scaling: 'Scale individual services based on load'
};
/**
* Service Communication Example
* Order Service calls Product Service
*/
async function createOrder(userId, items) {
// 1. Order Service creates order
const order = {
id: generateId(),
userId: userId,
items: items,
status: 'PENDING_CONFIRMATION'
};
// 2. Validate inventory via Product Service
const inventoryValid = await fetch('https://products-api.example.com/validate', {
method: 'POST',
body: JSON.stringify({ items: items })
});
if (!inventoryValid.ok) {
order.status = 'FAILED_INVENTORY';
return order;
}
// 3. Process payment via Payment Service
const payment = await fetch('https://payments-api.example.com/charge', {
method: 'POST',
body: JSON.stringify({ orderId: order.id, amount: items.total })
});
if (!payment.ok) {
order.status = 'PAYMENT_FAILED';
return order;
}
// 4. Emit event for Shipping Service to consume
await publishEvent('order.created', { orderId: order.id });
order.status = 'CONFIRMED';
return order;
}
/**
* Benefits & Challenges
*/
const comparison = {
benefits: [
'Scale services independently',
'Teams work autonomously',
'Different tech stacks per service',
'Easy to replace/update services',
'Better fault isolation'
],
challenges: [
'Complex distributed system',
'Network latency and failures',
'Data consistency across services',
'Operational complexity',
'Testing is harder'
]
};Pro Tips
- 1Don't start with microservices. Build a well-structured monolith first, understand your domain boundaries, then extract services when you hit real scaling problems.
- 2Each microservice should own its data. Shared databases between services create tight coupling you've just built a distributed monolith.
- 3The hardest part of microservices isn't the technology it's organizational. Conway's Law is real: your architecture will mirror your team structure.
- 4Implement distributed tracing (OpenTelemetry + Jaeger) from day one. Debugging a request that touches 5 services without traces is a nightmare.
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