Scaling Distributed Systems

By Syed Subhan, published Sep 05, 2024, 7 min read.

When your application grows from a single server to a global fleet, the rules change. Latency isn't just a number; it's a geography problem. Consistency becomes a trade-off, not a guarantee.

The CAP Theorem

Consistency, Availability, Partition Tolerance. You can pick two. In a distributed system, network partitions are inevitable (P is mandatory), so the real choice is between C and A.

CP (Consistency)

Bank ledgers. If the network breaks, stop accepting writes to ensure data assumes correctness.

AP (Availability)

Social feeds. Show slightly stale data rather than showing an error page.

Event Sourcing

Instead of storing the current state, store the sequence of events that led to it. This provides a perfect audit log and allows for time-travel debugging.

Database Sharding

Splitting your database horizontally across multiple machines. The key challenge is choosing a good shard key to prevent "hot spots" where one server takes all the load.

Conclusion

Scaling is as much about architecture as it is about discipline. Simple, stateless services are easier to scale than stateful monoliths.