4 ms·

That would be nice to know, but Big-O won't tell you that. It makes no time guarantees, it only provides an idea of an implementation. An O(1) algorithm sounds

That would be nice to know, but Big-O won't tell you that. It makes no time guarantees, it only provides an idea of an implementation. An O(1) algorithm sounds great, until you realize that 1 means 1 hour for that particular algorithm, while a competing algorithm's O(n) might mean n seconds in reality. What's in the parenthesis is not a measure of a unit of time, which is why Big-O fanatics often miss the bigger picture.

tscs37 10y ago
>An O(1) algorithm sounds great, until you realize that 1 means 1 hour for that particular algorithm, while a competing algorithm's O(n) might mean n seconds in reality. Well, I'm not trying to miss the bigger picture, but if you're going to scale that your O(n) algorithm will quickly take longer than a O(1) algorithm. I mean, let's just take a hashtable as example. This hash takes 1 hour to complete while you can search the list in milliseconds. But what if the list has billions and billions of entries and it takes longer than 1 hour? As some others said, Big-O is not something you use to choose one algorithm over the other, it's something you use to choose an algorithm that scales well and has acceptable performance. O(n) vs O(1) is a rather tame comparison, what if your competing algorithm is a O(n!) or worse?