REST API Microservices
Large applications often split into many small services. One service handles users, another handles orders, and a third handles payments. REST APIs connect these services. Understanding how REST fits into this style prevents slow, fragile systems.
The Food Court Picture
A traditional restaurant has one kitchen that cooks everything. A food court has many small stalls. Each stall specializes in one cuisine, runs on its own schedule, and serves customers independently. A microservice system resembles the food court. Each service owns one job and talks to the others through clear contracts.
Monolith vs Microservices
Monolith: Microservices: ┌─────────────────────┐ ┌───────┐ ┌────────┐ ┌─────────┐ │ Users + Orders + │ │ Users │ │ Orders │ │ Payments│ │ Payments + Email │ └───┬───┘ └───┬────┘ └────┬────┘ │ (one program, │ │ REST │ REST │ │ one database) │ └──────────┴────────────┘ └─────────────────────┘ each service: own code, own database
Why Teams Choose Microservices
- Teams deploy their own service without waiting for others.
- Busy services scale alone, so an order spike does not force a scale-up of the whole app.
- A bug in one service rarely brings down the rest.
- Each service may use the language and database that suit its job.
The Cost
Calls that once stayed inside one program now cross a network. Networks are slower and less reliable than memory. Teams also manage more deployments, more logs, and more versions. Small projects often run better as a single well-organized application.
Service Boundaries
Each service owns its data. The Orders service never reads the Users database directly. It calls the Users API. This rule keeps services independent.
Client ── POST /orders ──→ Orders Service
│ GET /users/42 ──→ Users Service
│ POST /payments ──→ Payments Service
▼
saves order in its own database
Synchronous and Asynchronous Communication
Synchronous REST
The caller waits for an answer. This style fits reads and actions that need an immediate result, such as checking a user profile.
Asynchronous Messaging
The caller publishes an event to a message broker and moves on. Other services react when they are ready. This style fits actions like sending a confirmation email after an order.
Orders Service ── event: order.created ──→ Message Broker
├──→ Email Service
└──→ Inventory Service
Making Calls Resilient
Timeouts
Every outgoing call needs a time limit. A service that waits forever drags its callers down with it.
Retries with Backoff
Retry idempotent calls a few times, and wait longer between attempts. Add random jitter so many clients do not retry at the same moment.
Circuit Breakers
A circuit breaker watches failures. After too many errors, it stops calls to the sick service for a short period and returns a fallback answer. The breaker tests the service again later and resumes traffic when it recovers.
Closed (normal) ──many failures──→ Open (calls blocked)
▲ │ wait period
└──── success ──── Half-open (test calls) ◄┘
Service-to-Service Security
Internal traffic needs protection as well. Services authenticate each other with tokens, such as OAuth client credentials, or with mutual TLS certificates. Each service checks permissions and never trusts a caller only because it sits inside the network.
Contracts and Versions
Every service publishes an OpenAPI description of its API. Changes must not break existing callers. Add new fields freely, and keep old fields until every consumer has moved on.
Common Mistakes
Chatty Calls
A page that triggers 40 calls between services loads slowly. Combine data with one well-designed endpoint or an aggregation layer.
Shared Databases
Two services reading the same tables lock themselves together. Give each service its own data store.
Splitting Too Early
Start with clear modules inside one application. Split into services when team size or scale demands it.