This is what has confused me for a long time, I interviewed at Flipkart an Indian ecommerce major (ex?) and their process was horrible. At the second round they asked us to implement a toc tac toe program, others didn't even have compilers, I had Python because it was a Linux machine, was rejected because "code contained bugs and didn't run", the third round was algorithms. I think these companies don't know how to hire that's why they are sticking to algorithms, in real life nobody is going to implement an algo from scratch, if I can learn Go in a week, learn how to write a webapp in 2-3 weeks when I have never written a webapp before why does it matter if I don't know how to implement some random algorithm?

> If in my day-to-day I need to use an AVL tree, I will get a library for it, I won't be reimplementing it from scratch every single time.

Completely agree. How will you know you need an AVL tree, versus, say, a Red-Black tree? What are the performance characteristics of each? Why pick one over the other? When I interview someone, I ask algorithm questions to find out how they reason about the algorithms, not so I can watch them implement one.

OK, I'll bite: this is not something the vast majority of engineers ever need to know.

They are both balanced binary trees with the same big-O complexity. Constant factors are different, but if and when you care about that, they're both binary trees so it should be easy to switch one implementation for another. In practice you're unlikely to care because most of the time you won't be working on performance-critical code. All speculation about performance is hearsay unless you run some benchmarks.

Google search brings up this discussion: http://discuss.fogcreek.com/joelonsoftware/default.asp?cmd=s... There's plenty of interesting stuff there, but it rather reminds me of medieval theologians debating how many angels can dance on the head of a pin.

There's a much, much more important distinction: binary trees (of all kinds) versus sorted arrays. There are many cases where a std::vector will be a lot faster than a tree, due to cache coherency, and use much less memory too.

So, discussing AVL trees and red-black trees in an interview is a waste of time. All it tells you is whether somebody once studied them (and remembers their studies), or possibly just memorized them the night before. Knowing that somebody studied those algorithms (except you don't know, because they may just have crammed it) would be a positive signal but doesn't actually tell you whether their CS course covered useful up-to-date topics, like cache-friendly algorithms and data structures.

> There are many cases where a std::vector will be a lot faster than a tree, due to cache coherency, and use much less memory too.

this is also what is annoying me, the thought of so much ink being devoted to O complexity and so on when with modern processors in the end often "less optimal" algorithms are a lot faster based on how they work and often you end up writing the same thing 3 different ways so you can test and see which one is actually fastest given your language/compiler/toolchain/processor

In a book-level discussion whether a comparison in your algorithms evals to true or false in a predictable or unpredictable pattern doesn't make a difference in its performance, however write that out in code and the branch predictor of your CPU will be a LOT happier (and faster) if you make it so that all the "false" and "true" comparisons happen in streaks as opposed to randomly...

I'm not sure I understand your point -- possibly we're agreeing?

To clarify, it seems to me the interviews tend to focus on things like "what's a good data structure for the social network in a Facebook clone?" but that's only 1% of the actual job, 5% tops.

It's true that those technical design decisions are very important, but they're not decisions that every engineer needs to make on their own on a day-to-day basis. They come up fairly rarely in a typical project and usually multiple people will be involved.

I don't have any easy answers -- I don't know a good way to interview for the other 95%+ of useful skills, like communicating well within a team, testing, debugging, benchmarking, writing documentation, good source control practice, knowledge of standard tools, meta-knowledge of how to learn about standard tools, how to evaluate new tools, system administration, dealing with production panics, etc.

my point was more along the lines that even if you need to figure out cool algorithms as part of your job, interviews as they are now don't seem to be not testing for that, but more for "can you rote learn these specific algorithms".

I think some of this was why some time ago there was a way of interviews with "off the wall" questions, but those seemed to be fairly vilified so I guess that's why they aren't as common now, but personally I would get more of an understanding in how somebody thinks by giving them an unexpected question than by asking them something they could've studied for.

You don't have to memorize 1000 algorithms. Honestly you could have a really good understanding of when and how to use about 10 algorithms and probably do very well on most interviews.

The problem is each company will ask you a different set of algorithms, and you're not sure which they'll ask, so you effectively have to memorize a LOT more than 10 if you want to be prepared for whatever they throw at you (not literally 1000, but maybe 40 or 50, which will take considerable time to prepare for, in addition to all the other preparation or unrelated questions they could end up asking you (architecture, OS, process, design patterns, optimization, advanced language-specific quirks and gotchas, terminology, 3D math and graphics (if games), databases, networking, behavior questions, solving math problems you've never encountered before, questions about your past projects, etc, etc, etc).

When they passed on me after my Google interview, the HR person actually told me "You can try again in 18 months. There are quite a few people who study for it during those 18 months and get it the next time!"

Yeah, I don't want to study for 18 months to get a job at Google, sorry.

The other downside of that is: in 18 months they're going to hassle you for an interview again, whether you want one or not.

I have a friend who was interviewed at Google, rejected, then interviewed again a year later, rejected again, and then a year later a Google recruiter got in touch to see if they'd like another interview. They said it started to feel like a cruel joke.

Oh yeah, recruiters from Google have contacted me three times since then. Actually the most recent recruiter said I could contact her whenever I felt like having another interview.

I might eventually try going through the gauntlet again, but I wasn't quite willing to move to the west coast during that time (new relationship, new home, new puppy, work was going well, I didn't really need more big life changes at that point).

I'm starting to consider a job change but I'm still not sure I want to move to the west coast. I'd probably have to downshift my home quite a bit out there.

You probably won't even get to use all 10 in your 1st two years of work. All the computer science you use in 99% of a typical programming career could be described in a single conference presentation. (Not that one would master all of it that fast, but it would describe what you need to know and know you don't know.)

You might not need to implement it, but you needed to know enough to know what an AVL tree is, why you might (or might not) need it, and to be able to do some basic validations on the library you selected to make sure that it does what you are hiring it to do.

agreed, but let's assume you don't know what an AVL tree is or what the trade-offs are between an AVL, red-black or other type of trees: how long does in this age to find that information if you know what you are doing? I mean, you could allow your interviewee access to a computer and based on the type of googling they do it should be fairly obvious if they know what they are talking about.

In your day-to-day you have wikipedia, stackoverflow, hn and so on: software development hasn't qualitatively changed in the past decades to require multi-day interviews when 15 years ago a single 30-45 min interview was more than enough.

It would actually be interesting to compare the length of, say, a google interview a year after company inception compared to now. I am sure that despite the fact that nowadays the impact of a bad hire would be minuscule compared to back then, the interview process is way, way, way longer and more difficult.

> how long does in this age to find that information if you know what you are doing?

I think we're discussing fluency. If you are hiring someone to edit books written in English, do you want a person who has to look up the meaning of "present participle", or do you want someone who just _knows_ what it means? I am sure that most people can figure out what "present participle" means in a few minutes with a search engine but those aren't the people I want as the editor.