Most applications start as a Monolith: a single large codebase deployed on a single server, connected to a single database. As engineering teams grow to hundreds of developers, monoliths become impossible to manage. This is where Microservices come in.

Step 1 — Monolith vs Microservices

The Monolithic Approach

  • Pros: Easy to develop, easy to test, simple deployments, fast in-memory function calls.
  • Cons: A bug in the billing module can crash the entire app. Any small change requires deploying the whole giant app. Scaling requires copying the whole app.

The Microservices Approach

  • Structure: The app is split into dozens of small, independent services (Users API, Billing API, Search API).
  • Pros: Independent deployments. Teams work autonomously. You can scale only the services that need it (e.g., scale Search, but not Billing). You can mix languages (Node for API, Python for AI).
  • Cons: Drastically increased operational complexity, network latency, distributed transactions, and difficult debugging.

Step 2 — Database per Service

Step 3 — API Gateways

If you have 50 microservices, a mobile app client shouldn't have to know 50 different IP addresses. Instead, you put an API Gateway (like Nginx, Kong, or AWS API Gateway) in front of them.

  1. The client sends a request to api.mycompany.com/billing
  2. The API Gateway receives it, handles Rate Limiting and JWT Authentication.
  3. The API Gateway routes the request to the internal IP of the Billing Service.
  4. The Gateway aggregates the response and sends it back to the client.

Step 4 — Synchronous vs Asynchronous Communication

How do microservices talk to each other?

Communication Patterns

  • Synchronous (HTTP REST / gRPC): Service A calls Service B and waits for a response. Easy, but creates tight coupling. If B is down, A goes down.
  • Asynchronous (Event-Driven): Service A publishes an event 'UserCreated' to Apache Kafka. Service B listens for it and acts. Loose coupling, highly resilient.