BigO Isn't Performance And That's Where Many Engineers Get Stuck

Why can two O(n) algorithms feel completely different in practice?

If you've ever wondered that, you're not alone.

This is one of the most common and least obvious difficulties engineers face when learning algorithms and complexity analysis. It usually doesn't affect beginners. It appears once you already have real-world experience and your intuition starts to clash with theory.

The mental trap

We're trained to think in terms of what feels expensive:

So when we learn that two sequential loops can still be O(n), or that iterating over an ArrayList and a LinkedList are both O(n), something feels wrong.

If arrays and linked lists allocate memory differently, shouldn't that change BigO?

What BigO does not measure

BigO does **not** measure:

BigO describes how the number of operations grows as input size grows.

In other words, it tells you the shape of the curve, not how high the curve is.

That's why two O(n) algorithms can behave very differently in practice.

Constants, memory access patterns, and hardware effects still matter, BigO simply chooses to ignore them.

Why this matters in real engineering

Understanding this separation helps you make better decisions:

If you only think in BigO, you might ignore cache locality and memory pressure.

If you only think in performance details, you might ship something that collapses at scale.

Good engineers need both!