System Design

Every distributed system talks over a network — DNS, TCP, and HTTP are the bedrock.

Before choosing a solution, you must roughly estimate load, storage, and bandwidth fast.

The first decision when load grows: scale the server up or add more servers?

A load balancer spreads requests across servers and keeps the system up if one dies.

Caching is the fastest performance win — it dramatically cuts database load.

Picking the wrong database can box you in later — understand each type's strengths.

As data or traffic grows, you replicate and partition to spread load and avoid downtime.

In distributed systems you must choose: strong consistency that slows things, or faster eventual consistency.

Decoupling components with queues makes the system resilient and able to absorb spikes.

Splitting into small independent services gives teams speed but adds distributed complexity to manage wisely.

Large systems fail in parts constantly — good design assumes failure and recovers automatically.

In a large distributed system, without logs, metrics, and traces you can't find a problem when it hits.

These patterns solve recurring large-system problems and save you reinventing the wheel.

It all clicks when you design a full real system end-to-end and justify every decision to an interviewer.