Ultimate Guide to Redis in System Design (Interview Edition)

This blog explains what Redis is and shows key use cases (like caching and rate limiting) so you can confidently use Redis in your system design answers.

Imagine you’re in a system design interview and the interviewer asks, "How would you handle millions of read requests hitting our database every second?"

You know the database alone can’t handle that load efficiently.

That’s when you propose adding an in-memory cache like Redis to offload those reads.

In this blog, we’ll explore how Redis can help you design systems that are scalable, low-latency, and sure to impress in an interview.

What is Redis?

Redis (Remote Dictionary Server) is an open-source, in-memory key–value data store – basically a super-fast NoSQL database that lives in RAM.

Because data is in memory, Redis is extremely fast (often sub-millisecond access, handling hundreds of thousands of operations per second).

Redis isn’t just a plain key-value store either.

It supports a variety of data structures: strings, hashes, lists, sets, sorted sets, etc.

That means you can pick the right tool for each task – for example, a list for a queue or a sorted set for a leaderboard.

Explore the underlying data structures used for Redis.

In system design interviews, bringing up Redis can instantly address common challenges like slow database queries or traffic spikes.

It shows you know how to use the right tool to make a system performant and scalable.

Let’s look at a few ways to use Redis in your designs.

Caching: The Redis Ace Up Your Sleeve

One of Redis’s superpowers is acting as a caching layer.

Instead of hitting the database for every request in a high-traffic service, you can cache popular or frequently accessed data in Redis and serve it from memory.

Imagine your app frequently needs a user’s profile info.

The first time, you fetch it from the database and then store it in Redis.

Next time, the data comes straight from Redis – no database hit, just a quick in-memory lookup.

This drastically cuts down response time and reduces database load.

Many system design scenarios will expect a caching layer for such read-heavy components.

When discussing caching in an interview, be sure to mention cache expiration and invalidation.

You could cache user profile data with a TTL (time-to-live) of 5 minutes so that data refreshes periodically.

Also explain how you’d update or invalidate the cache when the underlying data changes (to avoid serving stale data).

These details show you’re thinking beyond just “add Redis and done” – you understand consistency, too.

A few extra pointers:

• Cache-Aside: On a cache miss, load data from the database and then put it into Redis. (On a hit, return the cached data immediately.) This pattern ensures the cache is populated only when needed.

• Eviction Policy: Since memory is finite, Redis will evict entries (e.g. least-recently-used) when it’s full. Mentioning this shows you’re aware and will design your cache usage accordingly.

• Scaling the Cache: If needed, Redis can be clustered or replicated to handle higher loads. This shows you can scale the caching layer as the system grows.