Big O Notation: The Art of Writing Efficient Algorithms
.gif)
When you’re building software—whether it’s a small utility or a full-scale application— performance plays a critical role. Efficient algorithms are the backbone of scalable systems, and understanding how they behave as the input size grows is essential. This is where Big O Notation comes in. Big O is a mathematical notation that describes the worst-case growth rate of an algorithm in terms of time or space. It doesn’t measure exact speed but rather gives an upper bound on how an algorithm scales. Understanding it helps you compare solutions and make better design decisions. Below is a breakdown of the most common time complexities, explained with simple examples and real-world use cases. O(1) – Constant Time An operation that takes the same amount of time regardless of the input size. Example: Accessing an array element by index: const item = arr[5]; Use Cases: Hash table lookups Boolean checks Retrieving values from dictionaries O(n) – Linear Time Th...
