# Big O Notation

Understanding Big O has many real world benefits, aside from passing a technical interview. In this post I'll provide a cheat sheet and some real world examples.

When I started writing * The Imposter’s Handbook*, this was the question that was in my head from the start:

*I remember giving myself a few weeks to jump in and figure it out but, fortunately, I found that it was pretty straightforward after putting a few smaller concepts together.*

*what the f*** is Big O and why should I care?**Big O is conceptual*. Many people want to qualify the efficiency of an algorithm based on the number of inputs. A common thought is

*if I have a list with 1 item it can’t be O(n) because there’s only 1 item so it’s O(1)*. This is an understandable approach, but

**Big O is a**, it’s not a benchmarking system. It’s simply using math to describe the efficiency of what you’ve created.

*technical adjective**Big O is worst-case*, always. That means that even if you think you’re looking for is the very first thing in the set, Big O doesn’t care, a loop-based find is still considered O(

*). That’s because Big O is just a descriptive way of thinking about the code you’ve written, not the inputs expected.*

*n*## THERE YOU HAVE IT

I find myself thinking about things in terms of Big O a lot. The cart example, above, happened to me just over a month ago and I needed to make sure that I was flexing the power of Redis as much as possible.

I don’t want to turn this into a Redis commercial, but I will say that it (and systems like it) have a lot to offer when you start thinking about things in terms of * time complexity*, which you should!

**It’s not premature optimization to think about Big O upfront, it’s***and I don’t mean to sound snotty about that! If you can clip an O(*

**programming***) operation down to O(*

*n**) then you should, don’t you think?*

*log n*So, quick review:

- Plucking an item from a list using an index or a key: O(1)
- Looping over a set of
items: O(*n*)*n* - A nested loop over
items: O(*n*)*n^2* - A divide and conquer algorithm: O(
)*log n*