Understanding Big-O Notation for Beginners

Measuring Efficiency

Big-O notation tells us how our algorithm scales as the input size grows.

O(1) - Constant Time

Accessing an array element by its index arr[5] takes the same amount of time whether the array has 10 items or 10 million items.

O(N) - Linear Time

Looping through an array to find a specific value. If the array is twice as large, it takes twice as long.

O(N^2) - Quadratic Time

A nested loop. If you have an array of 10 items and compare every item to every other item, that's 100 operations. This scales terribly and should be avoided for large datasets.