Scaling an API from a few thousand requests to billions is a journey filled with challenges, bottlenecks, and "aha" moments. At ModernSaaS, we recently crossed the 10 billion request mark, and we wanted to share the key engineering principles that made it possible.
1. Design for Observability from Day One
You can't fix what you can't see. We invested heavily in structured logging, distributed tracing, and real-time metrics. Every request that enters our system is tagged with a unique trace ID, allowing us to follow its journey through dozens of microservices.
// Example of our internal request middleware
app.use((req, res, next) => {
const traceId = req.headers['x-trace-id'] || uuid();
req.ctx = { traceId, startTime: Date.now() };
next();
});2. Aggressive Caching Strategies
The fastest request is the one you never have to process. We use a multi-layer caching strategy:
- Edge Caching: Using our CDN to cache static responses close to the user.
- Redis: For frequently accessed data that needs to be shared across service instances.
- In-memory: For high-velocity data within individual services.
3. Embracing Eventual Consistency
In a globally distributed system, strict consistency is often the enemy of performance. By moving non-critical tasks to asynchronous message queues (using RabbitMQ and Kafka), we were able to significantly reduce our p99 latency.
"Performance is a feature. If your API is slow, users will find one that isn't. We treat latency targets with the same importance as bug fixes." — Marcus Johnson, CTO
Lessons Learned
If we had to start over, the biggest thing we'd do differently is automate our load testing earlier. Finding where the system breaks under 10x load is much better than discovering it in production during a traffic spike.
