When I started writing *_The Imposter’s Handbook*, this was the question that was in my head from the start: *_what the f*** is Big O and why should I care?* 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.

*_***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 __technical adjective_**, it’s not a benchmarking system. It’s simply using math to describe the efficiency of what you’ve created.

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

## 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** *_*****programming** and I don’t mean to sound snotty about that! If you can clip an O(*_n*) operation down to O(*_log n*) then you should, don’t you think?

So, quick review:

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

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