The ratio of importance placed on these algorithm design in interviews vs the amount of times they actually come up in real world problems seems skewed IMO.
Electron apps aren't slow, bloated, and awkward because the new junior SWE on the team used a O(n^2) tree-walking algorithm for the app's search feature - they're like that because it's inherent in using a general-purpose web-browser engine for your desktop GUI.
Micro-optimizing application software programs by implementing different algorithms is completely detached from the big engineering choices made at the very start of a project where the application's substrate and platform are chosen - and those decisions are made not with a view towards program computational efficiency, but primarily towards developer-productivity. Thanks to Electron someone who grew-up making websites as a teenager with little to no exposure to the horrendously unproductive and beginner-hostile world of MFC, GTK, and Qt can make an engaging and appropriate cross-platform desktop UI in under a day.
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If we want to see the Electron "problem" fixed, then the best solution is for the Electron team to figure out how to cut down their build of Chromium to remove all of the features unnecessary for trusted desktop applications (no, we don't need process isolation!). I'd love to see a build of Electron+Chromium with all of the JavaScript removed, so that it's a bare-bones HTML+CSS layout and rendering system, and have it wired-up to some OOP application binary (be it Java, .NET, C/C++, etc) which manipulates the DOM - I don't see why that should need more than a few dozen MB RAM and run in a single process.
One is obviously tied to the other, but what I mean is that many slow apps are slow because the people just used the wrong data structure. Sometimes it's as simple and silly as using a list and constantly iterating over it instead of using a dictionary/KV-map. I think the idea with having people know about "algorithms" is to get them in a state of mind where they will automatically pick more appropriate data structures. I really don't remember how to implement RB-trees or AVL-trees, nor do I really know their pros and cons against each other at this moment (I have a very very faint idea), but I know they exist and I certainly have a better idea of when to use a tree versus a list, versus whatever.
Would I fail these interviews? Probably, unless I studied a bit, but do I think that the concepts that they ask about are pointless? No, not at all. I've looked at my fair share of legacy code bases built by subpar developers and the one thing that always pops up is the bad data structures chosen. Once we fix that, usually everything else automatically falls into place.
EDIT: To be clear, what I mean is that in most cases, just picking the right data structure, among the most basic and elementary data structures given to you by the language, is more than enough. Only in rare cases does one then have to go beyond that and carefully engineer a more precise algorithm. The data structures are way more than half the problem, in nearly every application.
For example, a GUI framework typically uses a notion of widget tree that is fundamental to the library design. But the end user UI does not look as an arbitrary tree with deep nesting. It is easy to see that using a tree for this leads to extreme denormalization of data. Normalizing that to a relational form should remove a lot of duplication and code (often hidden) to synchronize that duplicated state. But try that with a popular framework. It is not doable in practice. So one sticks with tree architecture and its inefficiencies.
It's pretty easy to walk away from an algorithms course with the very basic intuitive understanding that "dictionaries trump all other data structures." Certainly that misses out on all of the cases when hash maps are a liability, e.g. when dealing with sequential data, but most questions end up being pro-dictionaries anyway.
Hashmaps, priority-queues/heap-trees, and others are all wildly different approaches of implementing a dictionary - all with their own different Big-O characteristics.
¿Por qué no los dos? If we're going to implement a desktop GUI via a general-purpose web browser, it should at least be as fast as a generic webpage.
As for removing V8 JS engine from blink I guess it is possible. But again, blink is tailored for accessing from JS and the layout and rendering code is huge so one does not save much.
I was into competitive math as a teenager and was somewhat successful, but I actually kind of suck at math.
Similarly, I'm a professional developer but I'm really bad at competitive programming: what usually happens is that I know how to solve the problems but the time limit is too low (for me, at least).
I'd say success in competitions is a good indicator of dedication and perseverance, but not sufficient to spot someone who's good at the job.
Sure if you're applying for a job that really demands algorithmic design skills it should be a great asset but in general the most valuable skills any programmer has is producing simple and robust code that works and others can continue building on. I don't deny that knowing algorithmic design skills well helps a lot but it does seem to feed the egos of the programmers to produce overly complicated solutions.
I'd answer this type of algorithm question in truth by identifying the relevant library wherever possible, not by coding it myself, and I'd strongly expect anyone I was working with to do the same.
Now, I do get that there's a lot of people who don't like spending time at home for interview tasks but when you think about AND it's not skewed to extreme (say, big task 8 working hours worth) then, in terms of time wasted, it's not such a big difference. Interviewer can then see the code quality, can talk about it with candidate, clarify some missing pieces or pitfalls found, etc.
IMHO most important is not if the candidate knows how to solve some hard or even medium problem when I speak to them and they are stressed enough already. What is important is if they're willing to learn, if they know how to search for stuff they may not know and if they can produce performant enough, but excellent to read, code.
Interviewing in eng is broken, but afaict its a “worst solution save all others” kind of scenario.
But let us not begin to deem these intrinsically important.
Some of the most creative and productive coworkers I’ve had struggled with leetcode style interviews. They’re a bad tool for anyone who isnt a new grad, and even then.
The point is not (or shouldn't be) to recite a textbook. The point is you can navigate your way around the textbooks. I've got both The Art of Computer Programming and The Art of Electronics on my shelf. I could find the sections to help sorting a list in seconds. As for the latter, I have no idea why the majority of that book even exists. I can't call myself an electrical engineer, even though all the theory I need is within arm's reach.
I assume you're arguing against the "recite the textbook" approach. I would agree that this is not the way to do things. But equally, "throw the textbooks out" is not the right way either. We need to evaluate a high-level grasp of the literature/theory but don't punish for forgetting minutiae. I might ask a candidate to talk about choice of sorting algorithms. There is, of course, no perfect answer, but what I'll be expecting is general evaluation of algorithms: time/memory tradeoffs, probing for more domain knowledge (e.g. does the data often come in sorted or random), platform constraints etc. I won't even expect a name drop of an actual sorting algorithm as that's not really the point. What they're telling me is they know why Knuth has a whole chapter on sorting. That's the important thing.
I'd much rather hire and work with someone who has the skill to easily assess a situation and use referencing to rebuild knowledge than someone who memorised how to implement tree balancing, so why do we test for the latter rather than the former?
Stop supporting baseless metrics for assessment just because some old person used them before you showed up. We can and should do better.
It could be that by changing interview strategy to look more similar to other professions that profit can be increased even farther, but nobody is risking it.
Not everyone is playing the absurdly doofy “game,” just most.
Local maximum that laziness has us trapped in. Nothing to do with merit.
And I can ask that given the recent issues with data privacy and data abuse by the tech giants, would we be in this place if the interview processes had selected for more holistic engineers, technically able but that refuse to play the game just for the sake of playing the game, that are opinionated and don't conform to something just for the sake of money?
I know that I might be creating a false dichotomy but I would like to think about what kind of pressure this selection process creates, what biases arises from it? How can we make it better?
Because your argument is the most conservative and pro-establishment one: it works so don't touch it and just emulate.
Yes it is.
> Theory can be referenced
How do you know that the person is even able to comprehend theory?
> Interviewing in eng is broken, but afaict its a “worst solution save all others” kind of scenario.
That's your opinion.
> Some of the most creative and productive coworkers I’ve had struggled with leetcode style interviews.
Good for you. But "slumpt_'s most creative and productive coworkers" is not a good metric for hiring.
> They’re a bad tool for anyone who isnt a new grad, and even then.
Again, that's your opinion. I'm not a pro in those interviews, but studying DS and algos opened up and pushed my mind to its limits like nothing else. Your whole thinking process changes when you start working on this, you start thinking about constraints, performance implications, pro and cons of different approaches. It is called Computer SCIENCE for a reason.
The point is neither is demonstrated to bear any relationship to jack shit
Interviewing is and has been broken, even with the changes we’ve made over the years.
If you’re holding onto leetcode challenges that make you think hard as representative of engineering prowess we’re never going to have a reasonable conversation.
Define this first.
All nested loops are harbingers of algorithmic doom, and should be treated as such, and they come up all the time in real code.
And how well that works in practice? How will candidate even know where to look at if he has no idea what he needs to find?
That happens all the time, not just for algorithms. I don't expect candidates to know every possible algorithm (as I surely don't), I expect candidates to identify and learn what's required for the task. A knowledge of the specific algorithm is not of much value. The ability to learn and possibly implement algorithms is.
And that comes for free in people who spent time on Algorithms and Data Structures.
For day jobs, I've done very little computer science relevant work. Instead, it's communication, coordination, code maintenance, infrastructure, verification, managing upwards, ad nauseum.
That includes greenfield development, when I invented entirely new solutions to old problems. Even during the bursts of hardest parts (creatively), the algorithms and such were maybe 5% of the effort.