Caching in Backend: From Theory to Production

That’s caching in a nutshell. But since you’re here, let’s dive into the real journey behind it. Let’s look for a simple definition for cache. From this, we understand that cache is a storage where data is stored. Okay, this definition is enough for us to score 4.5/5 in theory exams. But this doesn’t explain: What cache actually does Why we need it How big apps and websites use caching in production-level applications Or how to answer this properly in a technical interview And if you say “cache is temporary storage” in an interview and stop there… the interviewer will smile. Politely. And move to the next candidate.

Here, I’m sharing my understanding of caching and how I use it in production-level applications to handle a huge amount of scale.

Let’s understand caching using real-world examples. Let’s consider Instagram for instance.

Frontend UI (User) → Server (Backend APIs) → Database (All users data, posts, reels are stored here)

Whenever you search for “Doraemon”, the backend server connects to the database and asks it to look for all users whose name is “Doraemon” (or similar). These are request queries. Now, the database responds with the data to the server. This is how a simple and basic architecture looks for any big or small web application.

This is how our newbie project looks 🫠 Super cute. It works perfectly… until real users show up.

But when it goes to production, our database becomes expensive — in terms of money, load, and number of requests. The database is like that hardworking employee who gets 10,000 requests per second and never gets a lunch break.

Imagine if every user search directly hits the database. Poor database. No coffee. No rest.

Years ago, one of my seniors told me: “If every request hits the database, congratulations — you’ve invented a self-DDoS system. And you’re going to shut the company down soon.”

That was the day I realized performance bugs are just slow disasters. At small scale, the pain isn’t visible. The database looks fine serving requests every time. But the same approach does not work when we deal with high traffic. We need someone who can save us. And that’s our bro — caching.

Cache is like keeping snacks on your desk. Instead of going to the kitchen every 5 minutes, you just grab them from your table. It’s easy. It’s fast. Effortless. You’re happy. Your mom is happy because you’re not going to the kitchen again and again for snacks. Right?

Technically, if I add a caching server, this is how the architecture looks now:

Frontend UI (User) → Server (Backend APIs) → Cache Server → Database (All users data, posts, reels are stored here)

When you search for “Doraemon”, the backend server first connects to the cache server instead of the database. Since we just added the caching layer, the data won’t be available initially. So what happens?

If the data is not available in the cache (this is called a cache miss), the backend server fetches the data from the database, stores it in the cache, and responds to the frontend.

Now, the next time you or someone else makes the same request, the backend connects to the cache server. And yes — the data is already there (this is called a cache hit). It sends the response directly to the frontend. This significantly reduces response time.

For example: Earlier, the database might have taken 100ms to respond. Now the cache may take just 10ms for the same request. Why?

🧠 1. Databases are heavy; caches are lightweight. ⚡ 2. Cache systems like Redis live in memory. 💾 3. Disk vs Memory — memory is much faster than disk. 🛣 4. Reduced workload — we avoid repeating complex database queries.

Searching is expensive. When we search through millions of database records, it becomes even more expensive. Caching creates buckets called cache keys. For example, your Instagram profile can be cached using your username as the cache key. That key may store your name, bio, number of followers, following count, posts, profile photo, etc.

Now imagine there are 1 million users on Instagram. That means 1 million cache keys — each representing a user’s basic profile data. This can save the database from handling 1 million repeated profile fetch requests every time users visits to check their profile.

Database = general-purpose system Cache = optimized for fast retrieval

I’d like to share how I started using caching thoroughly. Once, at my company, we had a massive traffic outbreak on a peak day. It was only 500 users — but they generated nearly 30,000 database requests. It was an important day because one of our clients was conducting a large assessment on our platform. Those 500 users belonged to that client.

That day, our database services went down. For users whose pages were loading slowly, frustration increased. They kept clicking and refreshing again and again — which spiked database traffic even more. It became a loop of chaos.

That day, we made a couple of engineering decisions. One of them was adding caching layers before the database. We cached everything possible. That saved us during the next assessment session. And it went smoothly.

But cache is not magic. Caching is temporary storage. That means data gets deleted automatically after a certain period of time. Of course, we can configure how long the data should live — even for years if needed. But imagine this: You change your Instagram profile picture. When you log in again, you still see the old one. Why? Because the cache wasn’t updated. We didn’t invalidate or refresh the cache key.

The solution is simple. Whenever a user updates their profile details, we delete that user’s cache key from the caching server. So the next time the same data is requested:

It gets fetched from the database The updated data is stored again in the cache And everything stays consistent

That’s it. That’s my journey with caching. From not understanding why production was breaking… to realizing that a simple caching layer could save the entire system. The moment your system grows, caching stops being an optimization — it becomes architecture.

Thank you for reading and staying till the end. If every request hits the database, your architecture is still in development mode.