Do you use Big-O complexity evaluation in the 'real world'?

Question

Recently in an interview I was asked several questions related to the Big-O of various algorithms that came up in the course of the technical questions. I don\'t think I di

Answer 1

Although you rarely need to do deep big-o analysis of a piece of code, it's important to know what it means and to be able to quickly evaluate the complexity of the code you're writing and the consequences it might have.

At development time you often feel like it's "good enough". Eh, no-one will ever put more than 100 elements in this array right ? Then, one day, someone will put 1000 elements in the array (trust users on that: if the code allows it, one of them will do it). And that n^2 algorithm that was good enough now is a big performance problem.

It's sometimes usefull the other way around: if you know that you functionaly have to make n^2 operations and the complexity of your algorithm happens to be n^3, there might be something you can do about it to make it n^2. Once it's n^2, you'll have to work on smaller optimizations.

In the contrary, if you just wrote a sorting algorithm and find out it has a linear complexity, you can be sure that there's a problem with it. (Of course, in real life, occasions were you have to write your own sorting algorithm are rare, but I once saw someone in an interview who was plainly satisfied with his one single for loop sorting algorithm).