What is Big O Notation?

Big O describes how an algorithm scales. O(1) is constant time (hash lookup). O(log n) is logarithmic (binary search). O(n) is linear (simple loop). O(n log n) is linearithmic (efficient sorting). O(n²) is quadratic (nested loops). O(2^n) is exponential.

Understanding Big O helps choose the right algorithm and data structure. For example, searching a sorted array with binary search O(log n) is vastly faster than linear search O(n) for large datasets.