More Code, Less Load
Today, I learned something that completely flipped my perspective on writing code.
In my Data Structures and Algorithms (DSA) class, the first thing we discussed was this:
“Sometimes, 100 lines of code are better than 2.”
At first, that felt strange. Isn’t writing less code a good thing? We’re taught to optimize, to be concise, to find one-liners and clever shortcuts. But in real-world applications, especially the ones we want to scale, this mindset can backfire.
Let me explain — not just with theory, but with a real example from something I’m building.
More Code, Less Load
Contents
Real-World Example: My Keyword Research Tool
I’m currently building a keyword research tool — something that helps users discover trending or relevant keywords by crawling sources like Google, Reddit, and various forums.
🧠 The Naïve Way (a.k.a. The 2-Line Mindset)
Every time a user enters a keyword, we could do this:
results = crawl(keyword)
return results
Done! Just two lines.
But here’s why this is a bad idea:
- ❌ Every query triggers a full web crawl — slow and expensive.
- ❌ If 10 users search the same keyword, it repeats the same crawl.
- ❌ No learning from past queries.
- ❌ Users get delayed results every single time.
Short and sweet, yes. But also dumb and inefficient.
The Smarter Way (a.k.a. The 100-Line Mindset)
Instead, here’s what I’m doing now — and yes, it takes more code, but also makes the system smarter, faster, and more user-friendly.
✅ Step 1: Cache Inputs
When a user searches a keyword:
- Store it in a PostgreSQL database.
- Record a timestamp and keyword metadata.
This way, we start building memory.
✅ Step 2: Check Before Crawling
When a search request comes in:
- First, look it up in the database.
- If data exists and is recent (e.g. < 14 days), return it.
- If not found or too old → then trigger the crawler.
✅ Step 3: Recrawl Smartly
- Periodically refresh older keywords.
- Prioritize high-traffic or trending terms.
- On launch, pre-crawl popular niches for instant results.
✅ Step 4: Serve Results Fast
Now:
- Users get near-instant results for previously searched terms.
- The system maintains freshness without crawling everything all the time.
- Server load is reduced massively.
More code? Definitely.
But also:
- Smarter architecture.
- Better performance.
- Happier users.
Key Takeaways
- ✨ Less code ≠ better code.
- ✨ Systems that scale need logic, structure, and memory.
- ✨ What looks like “extra effort” often leads to long-term efficiency.
- ✨ Good engineers don’t just write code — they design systems.
Final Thoughts
Learning DSA gave me more than just theory.
It showed me why things like caching, memory usage, and data structures actually matter in real-world projects.
Now, every time I decide whether to go for the “2-line shortcut” or the “100-line system,” I’ll ask myself:
“Do I want to write less code, or do I want to build better tools?”
I’m choosing better tools — even if it takes 100 lines to get there.