A collection of independent computers that appear to its users as one computer
- Andrew Tannenbaum
Life was simple when business applications ran on a single server, connected to a single database sitting on a rack in the basement of a company’s office. Everything was local, contained, and relatively easy to reason about.
But that model doesn’t hold up anymore.
As businesses evolved and data became central to every function — from dashboards to machine learning — the demands on infrastructure grew exponentially. Datasets ballooned in size. It became impractical, even impossible, to store and process everything on a single machine.
Sure, you could buy bigger servers — more CPU, more memory, faster disks. That’s vertical scaling, and it works… until it doesn’t. There’s always a ceiling. Eventually, the cost, power, or physical limits catch up.
So we turned to horizontal scaling — distributing work across many machines. Instead of one enormous box, we used many commodity servers, tied together through software. Each node contributes to a shared task: storing data, running computations, serving traffic. To the outside world, this fabric of machines behaves like one logical system — even though it’s anything but under the hood.
That’s a distributed system.
It’s a shift in mindset as much as it is a change in architecture. Suddenly:
- Failures aren’t the exception — they’re expected.
- Latency isn’t just “slow code” — it could be a network hop.
- Debugging isn’t about one stack trace — it’s about correlating events across nodes, logs, and services.
Distributed systems are about scaling beyond what any one computer can do alone. But they’re also about complexity: coordination, consensus, consistency, failure detection, replication, and a dozen other concerns you rarely think about on a single machine.
And yet, they’re everywhere — powering the apps we use daily, the platforms we build on, and the infrastructure the internet runs on. It’s a big shift — but it’s also beautiful in its own way. We build these messy, resilient, collaborative systems out of unreliable parts… and they work. Mostly.
Summary
- Failure is a first class citizen and the system now needs to be designed around it.
- Solving problems like consistency, availability, and partition tolerance (CAP theorem) becomes crucial.