A Brief History of Distributed Systems

The Beginning: When One Machine Wasn’t Enough

The origin story isn’t flashy. In the 1960s, machines were rare, brittle, and isolated. You didn’t “connect computers” — you waited your turn. But as scientific ambition outpaced the compute power of individual machines, researchers began to think about networks.

In 1969, ARPANET sent its first message: LOGIN. It crashed at the 'G'. That crash was prophetic. Distributed systems, from day one, have lived in the uncomfortable space between what we want to do and what we can keep from falling over.

By the late 1970s, machines needed to cooperate — which meant they had to reckon with time, trust, and failure. Leslie Lamport, working at SRI, introduced logical clocks: a way to reason about causality without a global clock. This was a philosophical bombshell: in a distributed system, there is no universal “now.”

Lamport would go on to propose Paxos, a protocol for achieving consensus in an unreliable network. It was so opaque that most engineers ignored it — including those at Google, who built Chubby (their own Paxos-based lock service) without fully understanding Paxos. This led to the creation of Raft (2014), a simplification of Paxos designed to be understandable by humans.

The 1980s: Theory with No Audience

The 1980s were quiet — at least in production. But in academia and research labs like DEC and Xerox PARC, foundational work was taking shape:

Two-phase commit, quorum protocols, and failure detectors were being refined.
Jim Gray introduced the ACID model and pioneered recovery mechanisms for fault-tolerant databases at Tandem and DEC.
The Byzantine Generals Problem (1982) asked: how do you reach agreement when participants might be lying?

Much of this work remained theoretical. Hardware was slow. Networks were unreliable. Most companies were still selling workstations, not scaling services. The theory was waiting for the real world to catch up.

The 1990s: The Web Breaks Everything

Then came the Web. With it, a sudden need for scale.

Web servers collapsed under traffic spikes.
Amazon and eBay fought outages with duct tape.
Yahoo built its own data centers from scratch.

There were no off-the-shelf tools. You wrote your own replication daemons and failover scripts. Everything was bespoke.

In 1998, Akamai was founded by Tom Leighton and Daniel Lewin. They pioneered the CDN, reducing latency by distributing content closer to users. Lewin was tragically killed on 9/11, but his work lives on in nearly every edge delivery system today.

The 2000s: Google Breaks the Silence

Google File System (2003) was a seismic event. Its assumptions were radical: hardware will fail constantly. So build software that assumes it.

Replicate everything.
Use heartbeats.
Keep control logic minimal.

Then came MapReduce (2004) and Bigtable (2006) — simple, scalable, brutally effective. Hadoop and HBase emerged as open-source clones. Mid-sized companies everywhere scrambled to stand up “big data” pipelines, even when they didn’t need them.

Amazon, meanwhile, had its own ideas. Werner Vogels pushed for “you build it, you run it” and internal service boundaries. That culminated in AWS (2006) — and changed everything. Infrastructure was no longer hardware. It was code.

The 2010s: Complexity Becomes a Product

Distributed systems became default, and the challenge shifted to management.

Kubernetes, inspired by Google’s internal Borg, brought scheduling, self-healing, and declarative infra mainstream.
etcd, Zookeeper, Consul — all built around consensus.
Observability evolved: from logs to distributed tracing to full-stack telemetry with tools like Jaeger, Zipkin, and OpenTelemetry.

At the data layer:

DynamoDB leaned into eventual consistency.
Google Spanner used atomic clocks and GPS to maintain global consistency.
CockroachDB built a Spanner clone using Raft, RocksDB, and Go.

Meanwhile, Raft (from Diego Ongaro) replaced Paxos in most practical systems — its clarity and educational materials made it the consensus protocol of the decade.

But many systems in this era were fragile. Distributed locks broke silently. Startups spent weeks debugging issues they didn’t need to solve. Hype outpaced understanding.

The 2020s: Observability, Edge, and the Return of Simplicity

As distributed systems became ubiquitous, the complexity became overwhelming.

Observability exploded: Honeycomb, Datadog, Grafana — not just dashboards, but cognitive crutches.
Serverless promised simplicity: functions, not servers. It worked well — until cold starts, debugging nightmares, and vendor lock-in reappeared in new forms.
Edge computing took center stage. Cloudflare, Netlify, Fly.io — compute moved to the user’s doorstep, but global coordination remained hard.

Surprisingly, CRDTs — once an academic curiosity — found real use. Real-time editors like Figma used them to merge state without consensus protocols. It was fast, local-first, and conflict-free.

And quietly, tools got better:

Temporal made workflows reliable.
Litestream brought SQLite into replication-heavy infra.
NATS provided fast, minimal pub-sub with simplicity as a goal.

AI entered the picture too — not as a user of distributed systems, but as an operator. Autoscaling, anomaly detection, even incident triage — increasingly ML-powered.

What Most Histories Miss

Most retellings make distributed systems sound like a linear progression. But the truth is messier:

CRDTs solve problems once handled by locks.
Serverless attempts to hide the same complexity that Kubernetes once abstracted.
Most breakthroughs came from pain, not theory:
GFS because Google’s servers kept failing.
AWS because internal teams were blocked on each other.
Spanner because ad revenue couldn’t tolerate inconsistency.

Distributed systems aren’t about elegant models. They’re about pragmatism under pressure.

2025 and Beyond

Today, distributed systems are everywhere. But we still lack a shared theory of how to reason about them. We’ve built tools that span continents — but our brains struggle to reason across two machines.

That’s the next challenge: mental models, tools, and abstractions that make distribution tractable.

Because in the end, distributed systems aren’t hard because they involve many computers.

They’re hard because they involve many people.