Power of Big(O) in computer science

To become a better engineer, you shouldn't underestimate the power of big(O)

Big(O) is really helpful to write efficient and scalable code. It is a method to calculate how fast an algorithm will run when we increase input size.

The speed of an algorithm depends on how many operations the computer needs to perform to complete a given task.

In this era, where systems scale based on user load, we still need to consider Big(O) to ensure they do not scale unnecessarily.

Let's understand it based on computer memory. Memory in a computer is like a matrix of blocks, and each block stores 8 bits (1 byte) of information. For example, if you want to store 32 bits of information, you need 4 blocks (i.e., 8*4). This means if you want to access 32 bits of information, the computer needs to perform 4 operations. That's why calculating space complexity is necessary to see how much space aka memory the program is using.