What is Big O notation?
How the work an algorithm does grows as its input grows.
Two pieces of code can give the same answer, and one is still a thousand times slower once the data gets big. Big O is how developers talk about that.
What it is
Big O notation describes how the work an algorithm does grows as its input grows.
How it works
O(1) means constant: the same time no matter how much data, like grabbing an item by its position. O(n) means the work grows with the data, like checking every item in a list. O(n²) means comparing every item with every other item, and that gets slow fast. Ten times the data, a hundred times the work.
Why it matters
It helps you spot the code that will fall over at scale, before your users find it for you.
