System design interviews for beginners

You might be asked to design a URL shortener, a chat app, or a notification service. There is no single correct diagram. The goal is to show that your choices fit the requirements and that you understand what happens when traffic grows or a component fails.

For beginners, the most useful skill is explaining a complete request path before adding advanced infrastructure. You should be able to describe where data lives, how clients read and change it, and which guarantees the system provides. Expected depth varies by role and level.

Separate functional requirements, which describe user actions, from nonfunctional requirements, which describe constraints such as latency, availability, durability, and scale. Ask a few targeted questions, then propose assumptions if the interviewer leaves the decision to you.

For a URL shortener, a useful initial scope is creating a short link and redirecting visitors to its destination. Custom aliases, expiration, and analytics are separate features. Agree on which ones matter before designing them.

Back-of-the-envelope estimates help identify the likely bottleneck. Start with requests per second, data growth, and payload size. Keep units visible and use rounded numbers. An estimate is useful only when it changes a design decision.

Suppose a URL shortener has one million daily users, each making ten redirects per day. That is ten million redirects per day, or about 116 requests per second on average. If you assume a five-times peak for this exercise, plan around 600 requests per second. The peak multiplier is an assumption, not a general rule.

APIs make the design concrete. Describe the main operations, their inputs and outputs, and important error cases. You do not need a complete specification, but you should explain what success means.

For a URL shortener, POST /links can accept a destination URL and return a short code. GET /{code} looks up the destination and returns an HTTP redirect. The data model might contain code, destination_url, owner_id, created_at, and expires_at, with a unique constraint on code.

Choose storage from access patterns and correctness needs. A relational database is a reasonable starting point for this example because it supports indexed lookups and uniqueness constraints. A key-value store can also fit, but you still need to explain unique-code creation, durability, and update behavior.

Begin with a client, an application service, and a database. Walk through a write and a read. Add a load balancer and multiple application instances when you need more serving capacity or resilience to an instance failure. Keep application instances stateless where practical so any healthy instance can handle a request.

For link creation, the service validates the URL, generates a code, and inserts the mapping. If the database rejects a code collision, the service generates another code and retries within a bounded limit. For a redirect, the service looks up the code, checks expiration, and returns the destination in a redirect response.

System design basics are easier to learn as solutions to specific problems. Do not add a cache, queue, or shard just because it appears in a reference architecture. State the bottleneck first, then explain what the new component improves and what complexity it introduces.

A design is incomplete until you explain what happens when a request succeeds in one component but fails in another. For example, a timeout does not prove that a database write failed. Retrying an operation can repeat its effects unless the system recognizes the same logical request.

Choose consistency requirements per operation. A delayed analytics count may be acceptable, while selling the same seat twice is not. Avoid describing the whole system as simply strongly consistent or eventually consistent without naming the data and operation involved.

For the URL shortener, analytics could run asynchronously, but redirects should not depend on the analytics pipeline being healthy. If destinations can change, a cache TTL bounds ordinary cache staleness; strict immediate changes require a stronger read or invalidation design.

For a 45-minute interview, one possible allocation is five minutes for requirements, five for estimates and APIs, ten for the baseline architecture, twenty for deeper discussion, and five for a recap. Adapt to the interviewer rather than treating this as a fixed script.

Say your reasoning out loud: “I will start with one logical database because the current workload does not justify sharding. I will add a cache if repeated reads become the bottleneck, and I will explain how destination updates reach it.” This communicates more than listing products.

Start with problems whose core flow you can explain without advanced distributed algorithms. Build a complete baseline, then add a specific constraint such as a popular link, a duplicate notification, or an application instance failure. This teaches you to connect architecture changes to requirements.

After each attempt, check whether every component has a purpose and whether every important write has clear success and retry semantics. Avoid memorizing diagrams, choosing a database by brand, or claiming that caching and sharding solve every scaling problem.