How Google rewrote the rules of System Design
Back in 1998, Google had one tiny problem: the entire internet.
Their search engine needed to crawl, index, and serve millions of web pages—a number that would soon balloon to billions, then trillions. Traditional infrastructure wasn’t built for that kind of scale. Legacy systems took weeks to process data. Google needed results in seconds.
So they did what any scrappy team of brilliant engineers would do: they threw out the rulebook and reimagined the stack from scratch.
Fast-forward to today, and Google processes over
These weren’t just optimizations, they were paradigm shifts. And they’ve quietly shaped the infrastructure of every tech company that came after
In today's newsletter, we'll explore Google’s impact on System Design across four key pillars:
Distributed computing innovations like MapReduce and GFS
Breakthrough storage solutions like Bigtable and Spanner
Infrastructure management with Borg and Kubernetes
Consistent hashing and distributed load balancing
By the end, you’ll understand how these technologies reshaped not just Google, but the way the entire industry builds reliable, scalable, and self-healing systems.
Let’s start with the evolution of System Design in light of Google’s game-changing lens.
The evolution of System Design#
Before Google, system architectures were primarily monolithic. Entire applications ran on single machines or clustered databases. Large organizations relied on expensive mainframes and specialized hardware.
At that time, data storage used traditional databases like Oracle and DB2, which were designed for consistency over scalability. Those systems used vertical scaling rather than horizontal scaling.
Limitations of pre-Google architectures #
Systems based on monolithic architecture started showing their age as internet usage increased. This caused several constraints, some of which are mentioned below:
Vertical scaling became prohibitively expensive and reached its physical limits.
A single point of failure compromised the reliability and availability of the system.
Regid schema in relational databases restricted data flexibility and struggled with unstructured data.
Manual intervention was needed for most system operations.
The system was unable to scale to a large number of users and handle concurrent requests.
These constraints made it nearly impossible to build systems that efficiently scale to handle many users or petabytes of data. This set the stage for Google’s game-changing innovations that tackled the limitations of traditional monolithic architecture:
