Ask HN: Can engineers from Google or Facebook solve whiteboard questions easily?

I often do, both for fun and to prepare myself to give a question I'd like to use in an interview. The thing about interview questions at Google is there's a very fine line they need to walk: if they're too easy they give no useful information about the candidate, and if they're too hard they can never be solved in 45 minutes, again yielding no useful information. You also need to be able to provide some hints, because the vast majority of candidates need a prod in the right direction now and then.

As someone with access to the entire canon of Google interview questions, I've gone through the 20 or 30 with the highest rating, and none of them are ridiculously hard. The general pattern is either 1. a coding problem, meaning a relatively easy algorithm with plenty of opportunities to screw up the implementation so we can see how well you code and how well you catch your own errors, test your code, etc and 2. an algorithms problem that's really just a dressed-up version of some basic algorithm like BFS or dynamic programming so we can see if you can make the leap from theory to application.

Obviously I'm at an advantage here because I've seen most of the questions in use today, but whenever I encounter a new one I can intuit what's being asked and what it'll take to solve. I'm sure other companies have different sorts of questions that optimize for different things, but for our questions once you do enough of them you can sort of do any of them.

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By the way, in my experience, those seem to be the biggest pitfalls for both job candidates at Google and programmers I meet in the real world. Sure, you crammed algorithms before you came here, but can you apply them in any real-world way? Sure you know your web/mobile app framework back to front, but will you hit a wall when asked to do something novel? Sure you can write a for loop to iterate over a data set, but will you fall on your face when that data set is petabytes in size?

I've become good at Google's questions because they generally test for those two things: nitty-gritty coding skills and an ability to generalize basic algorithms. The best candidates are those who know their algorithms inside and out, have the ability to break down and transform a problem until they can apply something in their toolkit, and have the coding chops to translate their concept into an implementation. All skills which, you may notice, make for a good engineer.

If you've ever wondered why we still do whiteboard interviews, that's why. Except you can use a chromebook now if you prefer, but you get the point.

"these are really hard problems that people have studied for years"

Because of this. So that when you encounter a problem, you know how it can be solved efficiently. If you disregard existing knowledge, you are likely to spend a lot of time cooking up something complex and half-assed that creates an unnecessary maintenance load.

Like music is composed of fundamentals, similarly most problems in computing can be decomposed such that familiar approaches can be applied to them. I think the categorization of Aho's "Foundations of Computer Science" is pretty much to the point on which fundamental formulations are practical to know.

> There is nothing about learning how basic data structures and algorithms work that won't help you in your day to day development.

Having a basic understanding of these things is NOT the same as being able to regurgitate them on a whiteboard, under the stress of the interview process.

> The only reason it seems to even learn them is to pass an interview...

While I agree that the process is broken, a solid understanding of algorithms and data structures is very important for developers at my company. And nobody knows all, or even most of the algos or data structures! But we do have large amounts of data and some pretty complex analytic problems, and a poor design can cost a lot of time and AWS $. Likewise over-engineering something small is a waste of time not just in writing but maintaining it.

I agree that most e-commerce or business web developent probably don't need this level of formal skill.

Whiteboard questions are useful, but not for figuring out trick questions. Instead for figuring out how the candidate approaches problems.

> Sure you can write a for loop to iterate over a data set, but will you fall on your face when that data set is petabytes in size?

I also asked a question in HN yesterday. It looks like there are more questions on data structure and algorithm for big data. However, books I read don't focus much in this area. As I haven't worked on big data before, that would also be a challenge for me.