We all build horizontally scalable systems, but scaling is not as simple as saying “just add more machines” or “I will configure an autoscaling group”. The challenge comes when we design systems that continue to behave predictably as traffic and load increase.
Here are some common things you will run into and the pointers that will help you when you are building systems that scale horizontally:
- Cache everything that can tolerate stale reads
- Handle noisy neighbors with CPU/memory limits
- Keep services stateless - scaling becomes easy
- Databases do not scale easily - know your limits
- Know your data access patterns before partitioning
- Queue asynchronous work to absorb traffic spikes
- Design for failure - retries, timeouts, fallbacks
- Eliminate single points of failure
- Make operations idempotent - handle retries safely
- Understand consistency tradeoffs early
- Invest in observability - metrics, logs, and tracing
- Avoid distributed transactions where possible
- Rate limit critical services and APIs
- Scale reads and writes differently
- Capacity planning still matters despite autoscaling
By no means is this exhaustive, but these are some of the most common considerations that tend to surface as systems grow.