There’s a common sentiment in our web and mobile development world:
“Do I really need Data Structures and Algorithms (DSA)? Isn’t that just for MAANG interviews?”
My experience says yes.
I want to share a story from one of my projects where a fundamental understanding of DSA saved my day in production.
The Problem: Optimising Our Cash Pool
I was working on an application where we managed invoice payments. We had a specific, real-world optimisation problem:
“Given a fixed pool of cash, how do we pay as many outstanding invoices as possible to maximise the total amount paid, without exceeding the available cash?”
Sounds like a fairly standard business requirement, right?
At first glance, one might start thinking about complex loops, sorting strategies, or even greedy approaches. Many developers might jump straight into writing custom, bespoke logic.
The “Aha!” Moment: Pattern Recognition
But as I looked at the problem, something clicked. I realised this wasn’t a unique, never-before-seen challenge.
This was a classic instance of the Subset Sum Problem, which is a well-known variation of the Knapsack Problem.
Let’s break down why:
- The “Knapsack”: Our fixed cash pool
- The “Items”: Each individual invoice
- The “Weight”https://dev.to/”Value”: The amount of each invoice (since our goal was to maximise the total amount paid, the invoice amount served as both its “weight” in the budget and its “value” towards our goal)
- The Constraint: Invoices had to be paid in full (0/1 choice – either an invoice is paid or it’s not; no partial payments)
The Solution: Leveraging Proven Algorithms
Once I recognised this underlying pattern, the path forward became incredibly clear.
Instead of spending hours or days trying to invent a new, possibly inefficient algorithm, I knew I could leverage well-established solutions for the Subset Sum Problem.
This meant:
- Speed of Development: I wasn’t reinventing the wheel. I could recall or quickly look up the optimal approaches for this class of problem.
- Efficiency and Reliability: I could implement an algorithm that was already proven for its performance characteristics, ensuring our cash allocation was truly optimised.
- Maintainability: Future developers (or even myself later) would recognise the pattern and the algorithmic choice, making the code easier to understand and maintain.
This allowed me to ship a critical feature quickly and with confidence in its efficiency.
Why This Matters for Developers Like Us
This experience solidified a key principle for me: a strong foundation in core computer science principles is an incredible superpower in real-world development.
It’s not about memorising every algorithm, but about:
- Pattern Recognition: Learning to identify the fundamental structure of a problem, even when it’s cloaked in business jargon.
- Leveraging Existing Knowledge: Knowing that many problems have already been solved, and understanding which solutions apply.
- Building Robust Solutions: Choosing algorithms with known performance characteristics rather than creating ad-hoc, potentially inefficient ones.
So, next time you’re wondering if that DSA course or LeetCode challenge is really worth it, remember that it’s training your brain to be a better, more efficient problem-solver in the long run.
What About You?
Have you had a similar “aha!” moment where a seemingly academic concept from DSA (or another CS theory) was the perfect solution to a real-world problem in your projects?
👉 Share your stories in the comments below!
