Building Scalable APIs: Lessons from Production

Introduction

After spending years building and maintaining APIs at scale, I've collected a set of hard-won lessons about what actually matters when your endpoints need to handle real traffic. This post shares the patterns that have consistently proven valuable across different projects and tech stacks.

Pagination Done Right

The most common mistake I see in API design is treating pagination as an afterthought. Offset-based pagination seems simple until you realize that OFFSET 100000 in SQL is essentially scanning 100,000 rows before returning results.

Cursor-based pagination scales infinitely. Each response includes an opaque cursor that encodes the position in the result set. The next request passes this cursor back, and the query uses an indexed column to efficiently seek to the right position. The tradeoff is that you can't jump to arbitrary pages, but in practice, users rarely need that capability.

For truly large datasets, consider keyset pagination with composite keys. If you're sorting by creation time, include the ID as a tiebreaker to ensure stable ordering even when multiple items share the same timestamp.

Caching at Multiple Layers

Effective caching isn't about adding Redis to your architecture—it's about understanding where in the request lifecycle you can store computed results and for how long.

Start with HTTP caching headers. CDN edge caching is essentially free once configured, and it handles the heaviest traffic spikes before requests even reach your servers. Use Cache-Control with appropriate max-age values, and implement ETag headers for conditional requests.

Application-level caching fills the gaps where HTTP caching can't help. Cache expensive database query results in Redis, but be thoughtful about invalidation. Cache stampedes happen when a popular cached item expires and suddenly hundreds of requests simultaneously try to recompute it. Implement cache warming or probabilistic early expiration to smooth over these spikes.

Graceful Degradation

Production systems fail. Networks partition, databases become unavailable, downstream services time out. The question isn't whether your API will encounter failures, but how it behaves when they happen.

Design for partial availability. If your endpoint enriches data from three microservices and one is down, return the data you have rather than failing the entire request. Use feature flags to disable non-critical functionality during incidents.

Circuit breakers prevent cascade failures. When a dependency starts failing, stop sending it traffic and fail fast instead of accumulating timeouts. Combine this with fallback responses—stale cached data is often better than an error message.

Closing Thoughts

Scalability isn't magic. It's the accumulation of many small decisions that trade complexity for resilience. Start simple, measure everything, and optimize the bottlenecks that actually matter.