If you are a programmer, chances are that you have stumbled over the term "Big-O notation". But what does this actually mean?

Big-O notation is used to specify the computational complexity of your program or data structure. This basicly means: How many steps do I need to perform a certain action. "Step" in this context does not mean CPU cycles or lines in a program, but arbitrary steps with a fixed length, no matter how big they are.

Let's start with an example: Check if an element is in an array. In JavaScript you would implement this like so:

function findInArray(array, element) {
    for(let i = 0; i < array.length; i++) {
        if(array[i] === element) {
            return true;
        }
    }
    return false;
}

You can see it quite easily: Everything inside of the loop takes a constant time: Accessing an element in an array with the index and a comparison. Let's define this as 1 "step". But because of the for loop, we do this n times. In Big-O: O(n).

Other standard examples that are described in Big-O notation are sorting algorithms. Take the most simple sorting algorithm as example, bubble sort. Its implementation could be this:

function bubblesort(array) {
    for(let i = 0; i < array.length; i++) {
        for(let j = 0; j < array.length - 1; j++) {
            if(array[j] > array[j + 1]) {
                let tmp = array[j];
                array[j] = array[j + 1];
                array[j + 1] = tmp;
            }
        }
    }
}

For every element in the array it runs through the whole array and swaps if needed. As before, everything in the loop is constant. Then we have a loop doing this n times. But we additionally have a second loop that executes the first loop n times. So we execute n steps n times. In Big-O notation: O(n*n) = O(n²)

But there are also other cases where the answer is not so straight forward. Take this code snippet for example:

function mapAndFilter(x) {
    return x
        .map(n => n * 2)
        .filter(n => n > 0);
}

What is the complexity of this function? Is it

• O(1)?
• O(n)?
• O(n²)?
• Something different?

The correct answer is O(n). Why? While our code in itself is constant (no direct loops), executing other functions will also take some time. In the map case, the browser will run trough all elements and call the function on them (so this would be O(n)), filter is basicly the same as our findInArray, so also O(n). Together our function would have O(n+n) = O(2n), but Big-O notation does not care about constant factors, so the 2 gets erased resulting in O(n).

I think this is a good example that you always have to check not only your own code, but also code you import through a library or through the browser. Every algorithm that operates on a datastructure has a computational complexity. If you know it, you can write your code to have the smallest complexity possible. You can also ask yourself: "What operations do I need to do on my data structure and is there another data structure that has less complexity for those?".

This is the first article in a series about common data structures and how they work internally. Follow me if you are interested in those kinds of articles and also write which data structures you are interested in.