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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