Growth spurts are the worst.
Big O Notation describes how the runtime or space requirements of an algorithm grow as the input size increases.
Imagine you're organizing a library. If you have 10 books, it's quick to find one, but if you have 10,000, it might take much longer. Big O tells you roughly how much extra time or space you'll need as the number of books grows, without counting every second.
It helps you compare algorithms to choose the most efficient one for large datasets. You encounter it in coding interviews, system design, and when optimizing code that handles big data.
Many think Big O gives the exact runtime or memory usage, but it only describes the growth rate, not constants or smaller factors. For example, O(n) doesn't mean 'linear time' in absolute seconds, just that time scales linearly with input size.
Big O Notation is a mathematical notation that describes the limiting behavior of a function when the argument tends towards a particular value or infinity. In computer science, it classifies algorithms by how their runtime or space requirements grow relative to input size, ignoring constant factors and lower-order terms.