Datastructures and Algorithms are some of the more interesting aspects of being a software engineer. Especially for those who love problem solving and mathematics. I've come to discover I love DSA because I was able to solve problems with my friends and share new solutions with them as I learn from them too!
I also understand for a lot of new engineers this topic can be a major turn off. When solving problems it's pretty intuitive for everyone to just loop and loop and loop and loop and - you get the point. But it not as intuitive to understand how to optimise your code.
Big O notation - according to Wikipedia - is a mathematical notation that describes the limiting behavior of a function when the argument tends towards a particular value or infinity
For us regular people that means as the length of your input grows the operations your function has to complete also increases (depending on how it's implemented of course).
Let's take a function that has a for loop and just prints each element in an array.
var arr = [1, 2, 3, 4, 5];
function consoleElements(a) {
for(let i = 0; i < a.length; i++){
console.log(a[i]);
}
};So as you can see for each element in arr you will iterate "walk" over each and print it, so we would see 1, 2, 3, 4, 5. Now let us increase arr by 3 elements
var arr = [1, 2, 3, 4, 5, 6, 7, 8];
function consoleElements(a) {
for(let i = 0; i < a.length; i++){
console.log(a[i]);
}
};As you can see we would now have 1, 2, 3, 4, 5, 6, 7. For every element or "n" in our array we have each n times.
Now what about this function?
**[FILL THIS IN]**For most people who are starting out, it isn't obvious what the Big O notation is so I wanted to create a module that will help developers understand the time complexity of their code
TO RUN:
- npm install [FILL THIS IN]
- CD into [FILL THIS IN]
- Write your function to the main.js file
- Save the main.js file
- In the terminal run npm run bigo
- Javascript
- babel-cli
- babel-preset-es2015