What System Design Interviews Actually Test (It Is Not What You Think)
Almost every tech company asks some version of the same handful of system design problems -- a rate limiter, a URL shortener, a chat system, a news feed -- and that repetition is the biggest clue to what the interview is actually measuring.
It is not testing whether you have memorized the correct architecture for any one specific system, because there is no single correct architecture -- real systems at real companies solve the same problem in genuinely different ways depending on their actual constraints. What the interview is testing is whether you can reason about scale and tradeoffs out loud, under real constraints, without a memorized answer to fall back on -- which is exactly why the same standard problems keep reappearing across different companies and different interview-prep resources: they are chosen specifically because they force that reasoning regardless of which one comes up.
The approach that transfers across every version of this problem starts before any architecture gets drawn: clarify the actual requirements and scope first, distinguishing what the system must do from what it should explicitly not need to handle, and whether it leans read-heavy or write-heavy, since that single distinction changes most of what follows. Back-of-envelope estimation comes next -- rough numbers for expected users, requests per second, and storage growth -- not because the exact figures matter, but because grounding the design in real scale, rather than an abstract "handle lots of users," is what separates a defensible design from a guess. Only after that does a high-level architecture get sketched, with time spent identifying where it would actually break under real load before drilling into fixing that specific bottleneck.
A handful of building blocks show up across nearly every one of these problems regardless of which specific system is asked about: a rate limiter or load balancer at the edge to protect the system from more traffic than it can handle, consistent hashing to distribute load across nodes without a full reshuffle every time one is added or removed, a key-value store or cache on the hot read path, and a scheme for generating unique IDs safely across distributed writers. Knowing these building blocks well is what actually transfers between "design a chat system" and "design a nearby-friends feature" -- the specific product changes every time; the underlying components mostly do not.
General software-engineering interview practice. The specific problems referenced (rate limiter, URL shortener, chat system, news feed, and similar) are industry-standard interview topics used across many companies and interview-prep resources, not exclusive to any single source.
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