API integrations: how to avoid fragile "point-to-point" systems

Direct API integrations seem simple but can become brittle.

**The problem with point-to-point:**

System A calls System B directly. If B is down, A fails. Timeouts cascade. No audit trail. Hard to debug.

**Better approach: Message queues**

System A publishes events to a queue. System B consumes when ready. Decoupled, resilient, scalable.

**Key patterns:**

**1. Retry with exponential backoff**

Don't give up immediately. Retry with increasing delays. Set maximum attempts.

**2. Circuit breaker**

If a service fails repeatedly, stop calling it. Check health periodically before retrying.

**3. Idempotency**

Same request = same result. Use unique IDs. Prevent duplicate processing.

**4. Monitoring & logging**

Track every integration:
- Success/failure rates
- Response times
- Payload examples
- Error messages

**5. Versioning**

APIs change. Version your endpoints (/v1/, /v2/). Support old versions during transition.

**Tools we use:**

- RabbitMQ, Redis, AWS SQS for queues
- Kong, Tyk for API gateway
- Datadog, New Relic for monitoring
- Postman, Swagger for documentation

**When to use direct vs queues:**

- **Direct:** Real-time required, low volume, both systems reliable
- **Queues:** Background processing, high volume, can tolerate delay

Build integrations that survive failures.

Need integration help? Contact us.