REST API & Microservices — Complete Guide for Backend Engineers (2025 Edition)

Architecture • Best Practices • Why Microservices • Patterns • Examples

Modern backend systems rely heavily on REST APIs and microservice-based architectures. This guide covers everything a backend engineer needs to know — including architecture diagrams, best practices, advantages, challenges, and real-world examples.

✅ 1. What is a REST API?

A REST API (Representational State Transfer) is a web service architecture that uses:

✅ HTTP methods (GET, POST, PUT, DELETE, PATCH)
✅ Resource-based URLs
✅ Stateless communication
✅ JSON (mostly), sometimes XML

REST API principles:

Client–Server separation
Stateless
Cacheable
Uniform interface
Layered system
Resource-oriented design

✅ 2. REST API Architecture (Text Diagram)


Client (Web / Mobile / Service)
         │
         ▼
   API Gateway / Load Balancer
         │
         ▼
   REST Service / Microservice
         │
         ├── Business Logic Layer
         ├── Data Access Layer
         ├── External Services (Auth, Storage, Payments)
         ▼
       Database

✅ 3. REST API Best Practices (Backend Engineer Level)

✅ 1. Use Nouns, Not Verbs, in URLs

✅ /users/123/orders
❌ /getOrdersForUser

✅ 2. Use Correct HTTP Methods

GET → Read
POST → Create
PUT → Full update
PATCH → Partial update
DELETE → Remove

✅ 3. Use Proper HTTP Status Codes

200 OK
201 Created
400 Bad Request
401 Unauthorized
403 Forbidden
404 Not Found
500 Internal Server Error

✅ 4. Pagination & Filtering


/products?page=1&size=20&sort=price

✅ 5. Versioning


/api/v1/users

✅ 6. Use OpenAPI/Swagger

Auto-generate documentation
API contracts
Client code generation

✅ 7. Secure Your APIs

OAuth2 / JWT
Rate limiting
Input validation
TLS/HTTPS everywhere

✅ 8. Idempotency

Critical for payments and state changes.

✅ PUT is idempotent
✅ DELETE is idempotent
❌ POST is not

✅ 4. What Are Microservices?

Microservices are a software architecture pattern where a system is composed of small, independent services, each responsible for a specific business capability.

Examples:

User service
Order service
Payment service
Inventory service

Each runs independently and communicates via REST, gRPC, events, or message queues.

✅ 5. Why Microservices? (Advantages)

✅ 1. Independent Deployments

You can deploy one service without touching others.

✅ 2. Technology Freedom

Service A → Java
Service B → Go
Service C → Python

✅ 3. Scalability

Scale specific services (e.g., “Order Service”) instead of the entire application.

✅ 4. Fault Isolation

If “Invoice Service” fails, “Order Service” still works.

✅ 5. Faster Development

Small, autonomous teams can work in parallel.

✅ 6. Better System Modularity

Perfect for complex enterprise systems.

✅ 6. Microservices Architecture (Text Diagram)


                   ┌──────────────┐
                   │API Gateway    │
 Client  ─────────►│Rate Limit     │
                   │Routing        │
                   └──────┬───────┘
                          │
      ┌───────────────────┼───────────────────┐
      ▼                   ▼                   ▼
┌───────────┐     ┌───────────┐       ┌────────────┐
│User Svc   │     │Order Svc  │       │Inventory Svc│
└─────┬─────┘     └─────┬─────┘       └───────┬────┘
      │                 │                    │
      ▼                 ▼                    ▼
 Database           Database              Database

Each service owns its own database (important).

✅ 7. Microservices vs Monoliths

Feature	Monolith	Microservices
Deployment	Single deploy	Independent
Scaling	Scale whole app	Scale per service
Complexity	Low initial	High overall
Tech stack	One	Many
Data model	Shared	Private per service
Failure impact	Large	Isolated

✅ 8. Microservices Best Practices

✅ 1. Each service must own its data

Avoid shared databases.

✅ 2. Use an API Gateway

Authentication
Routing
Load balancing
Rate limiting

Examples: Kong, NGINX, Traefik, Envoy

✅ 3. Communication Patterns

REST
gRPC
Async messaging (SQS, Kafka, RabbitMQ, EventBridge)

✅ 4. Observability (very important)

Logs
Metrics
Tracing

Tools: ELK, Prometheus, Grafana, Jaeger, OpenTelemetry

✅ 5. Circuit Breakers

Prevent cascading failures.

Tools: Resilience4j, Hystrix (legacy)

✅ 6. Containerization + Orchestration

Docker
Kubernetes (EKS, GKE, AKS)

✅ 7. CI/CD pipelines

GitHub Actions
GitLab CI
Jenkins
ArgoCD
Spinnaker

✅ 9. Microservices Challenges (And How to Solve Them)

Challenge	Solution
Too many services	Domain-driven design
Complex debugging	Distributed tracing
Network latency	Caching, gRPC
Data consistency	Event-driven architecture
Hard to test	Contract testing
Deployment complexity	Kubernetes + CI/CD

✅ 10. Key Microservices Patterns (Backend Engineers Must Know)

✅ 1. API Gateway Pattern

All client requests go through an API gateway.

✅ 2. Saga Pattern

Transaction management across multiple services.

✅ 3. Database per Service

Each service owns its data.

✅ 4. Event-Driven Architecture

Use events instead of synchronous calls.

✅ 5. Strangler Pattern

Migrating from monolith → microservices gradually.

✅ 11. When Should You NOT Use Microservices?

Microservices are not ideal for:

❌ Small applications
❌ MVPs or early startup prototypes
❌ Teams without strong DevOps
❌ Projects lacking clear domain boundaries
❌ Your system doesn’t need large-scale scalability

Start with a modular monolith, then evolve into microservices.

✅ 12. REST API + Microservices = Modern Backend Architecture

A typical pattern:


Client → API Gateway → Microservices (REST/gRPC) → Databases → Message Queue → Monitoring

This architecture provides:

✅ Scalability
✅ Reliability
✅ Developer velocity
✅ Team autonomy
✅ Cloud-native readiness

✅ 13. FAQ for Interviews

1. Why microservices?

Scalability, fault isolation, team autonomy, independent deployment.

2. When not to use microservices?

Small teams, simple applications, low traffic.

3. How do microservices communicate?

REST, gRPC, events, message queues.

4. How do you handle data consistency?

Event sourcing, outbox pattern, sagas.

5. How do you monitor microservices?

Tracing (Jaeger), logging (ELK), metrics (Prometheus).