Technical Interviews: More than a brain teaser?

A request I’ve gotten from a couple of readers (unrelated to the recent charity thing) is to talk about engineering interviews at tech companies. I’ve been at Google, Foursquare, Twitter, and now Dropbox, so I’ve spent some time inside multiple companies that put a lot of effort into recruiting.

Tech interviews get a lot of bad press. Only some of it is deserved.

What you frequently hear in criticism is stuff about “gotcha” questions, or “brain teasers”. Those do happen, and when they do, they deserve condemnation. For example, I have seriously had an interviewer for a software job ask me “Why are manholes round?” That’s stupid. People who like it claim that it’s a test of lateral thinking; I think it’s garbage. But these days, that kind of rubbish seems pretty rare.

Instead, what’s become very common is for interviewers to present a programming problem, and ask the candidate to solve it by writing some code. A lot of people really hate these kinds of interviews, and describe them as just brain-teasers. I disagree, and I’m going to try to explain why.

The underlying problem for tech job interviews is that hiring the right people is really, really hard.

When someone applies for a job as a software engineer with your company, you start off with just their resume. Resumes are not particularly informative. All they give you is a brief, possibly partial history of the candidates work experience. From a resume, can’t tell how much they really contributed to the projects they worked on. You can’t tell how much their work added (or subtracted) from the team they were part of. You can’t tell if they get work done in a reasonable amount of time, or if they’re slower than a snail. You can’t even tell if they can write a simple program at all.

So you start by screening resumes, doing your best to infer as much as you can from them. Next, you often get recommendations. Recommendations can be useful, but let’s be honest: All recommendation letters are positive. You’re not going to ask for a recommendation from someone who isn’t going to say great things about you. So no matter how terrible a candidate is, the recommendations are going to say nice things about them. At best, you can sometimes infer a problem by reading between the lines – but that’s a very subjective process.

So you end up interviewing someone who’s resume looks good on paper, and who got a couple of people to write letters for them. How do you determine whether or not they’re going to be a valuable addition to your team?

You need to do something to decide whether or not to hire a particular person. What can you do?

That’s what the interview is for. It’s a way to try to get more information. Sure, this person has a college degree. Sure, they’ve got N years of experience. But can they program? Can they communicate well with their coworkers? Do they actually know what they’re doing?

A tech interview is generally an attempt to get information about a candidate by watching them work on a problem. The interview isn’t about knowing the right answer. It’s not even about getting the correct solution to the problem. It’s about watching a candidate work.

When I ask a job candidate a technical question, there’s three main things I’m looking for.

  1. What’s their process for solving the problem? On this level, I’m trying to figure out: Do they think about it, or do they jump in and start programming? Do they make sure they understand the problem? Do they clearly state their assumptions?
  2. Can they write a simple program? Here I’m trying to see if they’ve got any ability to write
    code. No one writes great code in an interview setting. But I want to know if they’re
    able to sit down with an unfamiliar problem, and work out a solution in code. I want to see if they start coding immediately, or take time to think through their solution before they start writing.
  3. How well can they communicate ideas about programming? Can they grasp the problem from my description? If not, can they figure out what questions they need to ask to understand it? Once they start solving the problem, how well can they explain what they’re doing? Can they describe the algorithm that they’ve chosen? Can they explain why it works?

To try to clarify this, I’m going to walk through a problem that I used to use in interviews. I haven’t used this question in about 3 years, and as far as I know, no one is using the question anymore. The problem involves something called Gray code. Gray code is an alternative representation of numbers in binary form that’s useful for a range of applications involving things like switching systems.

Here’s a quick run through one of the reasons to use gray code. Imagine a system that uses physical switches. You’ve got an array of 8 switches representing a number. It’s currently presenting the number 7 in standard binary – so the first 5 switches are off, and last 3 are on. You want to increment the number. To do that, you need to change the position of four switches at exactly the same time. The odds of your being able to do that without even a transient state that appeared to be a number other than 7 or 8 are vanishingly small.

Gray code solves that by changing the representation. In Gray code, the representation of every number N+1 is only different from the representation of N by exacly one bit. That’s a nice property which makes it useful, even nowadays when we’re not using physical switches for much of anything anymore.

The easiest way that you get the gray code of numbers is by writing a table. You start off by writing 0 and 1, which are the same in both gray code and standard binary:

Decimal Standard Binary Gray
0 0 0
1 1 1

There’s the one-bit gray codes. To get the two bit, make two copies of the rows in that table.
To the first copy, prepend a 0. To the second copy, reverse the order of the rows, prepend a 1:

Decimal Standard Binary Gray
0 00 00
1 01 01
2 10 11
3 11 10

To get to the three bit gray codes, you repeat the process. Copy the rows, prepend 0s to
the first copy; reverse the order of the second, and prepend 1s.

Decimal Standard Binary Gray
0 000 000
1 001 001
2 010 011
3 011 010
4 100 110
5 101 111
6 110 101
7 111 100

So, the gray code of 6 is 101, and the gray code of 7 is 100.

What I would ask an interview candidate to do is: implement a recursive function that given an integer N, returns a string with the gray code of N.

I can understand how some people look at this question, and say, “Yeah, that’s just a stupid puzzle.” On one level, yeah. It’s obvious an artifical question. In fact, in practice, no one ever uses a recursive algorithm for something like this. Even if you have a problem where gray code is part of a practical solution, there’s a better way of converting numbers to gray code than this silly recursive nonsense.

So I agree that it’s artificial. But interview questions have to be artificial. In a typical interview, you’ve got an hour with a candidate. You’re not going to be able to explain a real problem to them in that amount of time, much less have them solve it!

But it’s artificial in a useful way that allowed me, as an interviewer, to learn about the candidate. I wasn’t trying to see if the candidate was number-puzzle wizard who could instantly see the recursion pattern in a problem like this. Most people have never heard of gray code, and to most people (including me, the first time I saw this problem!), the recursion pattern isn’t obvious. But that’s not the point: there’s a lot more to the interview that just the initial problem statement.

I don’t present the problem, and then sit back and watch silently as they try to solve it. If I did, all I’d be learning is whether or not they’re a number-puzzle wizard. I don’t care about that. So I didn’t just leave them floundering trying to somehow come up with a solution. In the beginning, after describing the problem, I set an initial direction. I usually have them start by extending the table themselves, to make sure they understand the process. Then I take their extended table, and add a new column:

Decimal Standard Binary Gray Rec
0 0000 0000
1 0001 0001
2 0010 0011 “1” + gray(1)
3 0011 0010 “1” + gray(0)
4 0100 0110
5 0101 0111
6 0110 0101
7 0111 0100
8 1000 1100 “1” + gray(7)
9 1001 1101 “1” + gray(6)
10 1010 1111 “1” + gray(5)
11 1011 1110
12 1100 1010
13 1101 1011
14 1110 1001
15 1111 1000 “1” + gray(0)

With that on the board/screen, I’d ask them to try to take what I just gave them, and rewrite it a bit. For example, in row 8, instead of “1” + gray(7), come up with an expression using the numeric value “8” of the row, which will produce 7. They should be able to come up with “15 – 8” – and to see that in every row n , where n \ge 8 and n < 16, the gray code of n is “1” + gray(15 – n).

For most people, that’s enough of a clue to be able to start writing the function. If they can’t get there, it shows me that they’ve got some trouble wrapping their head around this abstraction. I’ve got a few more hints up my sleeve to help, but if without all of the help I can give, they still can’t come up with that, that’s one mark against them.

But even if they can’t come up with the relation at all, it’s not the end of the interview. I have, sometimes, ended up recommending hiring someone who had trouble this far! If they can’t come up with that basic relation, it’s just one piece of information about the candidate. I’d file that away, and move on, by giving them the recurrence relation, and then I would ask them to code it.

There’s one problem that comes up in coding, which is interesting and important. The most naive code for gray is something like:

def gray(n):
  print("gray(%s)" % n)
  if n == 0:
    return "0"
  if n == 1:
    return "1"
  num_digits = math.floor(math.log(n, 2)) + 1
  return "1" + gray(int(2**num_digits - 1 - n))

That’s very close, but not right. If you call gray(10), you get the right answer.
If you call gray(4), you get “110”, which is correct. But if you call gray(8), you’d get “110”, when you should have gotten 1010.

Most candidates make this mistake. And then I ask them to trace it through on 8 as an example. They usually see what the problem is. If they don’t, then that’s another red flag.

If they’re really struggling to put together a recursive function, then I’d ask them to just write a function to convert an integer into standard binary. If they can do that, then I start making suggestions about how to convert that to do gray code.

The big indicator here is whether or not they can write a simple recursive function at all. The systems I was working on at the time made heavy use of recursion – if a candidate couldn’t write recursive code, there was simply no way that they’d be able to do the job. (It was depressing to see just how many qualified-looking candidates came in, but who couldn’t write a simple recursive function.)

Through this whole process, how well they were able to talk about what they were doing was as important as the solution they came up with. If they heard the question, and immediately wrote down perfect, beautiful code, but they couldn’t explain how they got it, or how it worked? They’d get a mediocre rating, which wouldn’t get them a job offer. If they made a lot of mistakes in their code, but they were crystal clear about explaining how they worked out a solution, and how it worked? They’d probably get a better rating than the perfect code candidate.

I hope this shines a bit of light on this kind of interview question. While it’s necessarily artificial, it’s artifical in a way that we hope can help us learn more about the candidate. It’s not a trick question that’s irrelevant to the job, like “Why are manholes round?”: this is an attempt to probe at an area of knowledge that any candidate for a software engineering job should have. It’s not an all or nothing problem: even if you start off with no clue of how to approach it, I’m guiding you through. If you can’t solve it without help, this problem gives me some insight into what it is that you don’t understand, and hopefully whether or not that’s going to be a problem if we hire you.

Is it a great way of doing interviews? Honestly, no. But it’s the best way we know of doing it.

As an interview system, it doesn’t do a good job of identifying the very best people to hire. There’s no correlation between outstanding interview performance and outstanding on-the-job performance. But there’s a strong correlation between poor performance on this kind of interview question and poor performance on the job. Great performers on the job show the same distribution of interview performance as average ones; but poor performers on interviews show a significantly negative-shifted job performance distribution.

We haven’t found a way of interviewing people that does a better job than this. It’s the best we have. Statistically, it works far better at selecting people than “open-ended” interviews that don’t involve any kind of practical programming exercise. So for all of its faults, it’s better that the alternatives.

I’m sure there are pedants out there who are asking “So what’s the correct implementation of gray code?” It’s totally not the point of talking about it, but here’s one sloppy but correct implementation. This isn’t the quality of code I would ever use for anything serious at work, but it’s perfectly adequate for an interview.

import math

def gray(n):
    def required_digits(n):
        """Compute the number of digits required to 
        represent a number in binary
        """
        return int(math.floor(math.log(n, 2))) + 1

    def pad_digits(gray, num_digits):
        if len(gray) < num_digits:
            return "0"*(num_digits - len(gray)) + gray
        return gray

    if n == 0:
        return "0"
    if n == 1:
        return "1"
    num_digits = int(math.floor(math.log(n, 2)) + 1)
    return "1" + pad_digits(gray(int(2**num_digits - 1 - n)), num_digits - 1)

Lychrel Numbers and the 196 Algorithm

The first donor to the Chesapeake Math program asked me to write about the 196 algorithm, a problem also known as the mystery of the Lychrel numbers. To be completely honest, that’s something that people have asked for in the past, but I’ve never written about it, because I didn’t see what I could add. But I made a promise, so… here goes!

Take any number with at least two-digits, N. Reverse the digits of N, giving you M, and then add N+M. That gives you a new number. If you keep repeating this process, most of the time, you’ll get a palindromic number really fast.

For example, let’s start with 16:

  1. 16 reversed is 61.
  2. 16+61=77. 77 is a palindromic number.
  3. So one reverse+add, and we have a palindromic number.

Or 317:

  1. 317 reversed is 713.
  2. 317+713=1030.
  3. 1030 reversed is 301.
  4. 1030 + 301 = 1331, so we have a palindromic number after two steps.

You can play the same game in different number bases. For example, in base 8, we can start with 013 (11 in base-10): in one reverse+add, it becomes 44 (36 in base-10).

For most numbers, you get a palindrome in just a few steps. But for some numbers, in some number bases, you don’t. If you can’t ever get to a palindrome by doing reverse+add starting from a number, then that number is called a Lychrel number.

The process of exploring Lychrel numbers has picked up a lot of devotees, who’ve developed a whole language for talking about it:

chain
A chain is the sequence of numbers produced by reverse+add starting with some number, and possibly converging on a palindromic number.
seed
The seed of a chain is the smallest number that can start that chain. For example, in the example above, we looked at the chain [217, 1030, 1331]. 217 is the seed – even though you could start a chain at 1030, 1030 would never be considered a seed, because it can be the product of a reverse+add on a smaller number.
kin
Two numbers are kin numbers if they’re part of a chain that started at the same seed. If a number is a lychrel number, then (obviously) so are all of its kin.

The question that haunts Lychrel enthusiasts is, will you always eventually get a palindrome? That is, do Lychrel numbers actually exist?

In base-2, we know the answer to that: not all numbers will produce a palindrome; there are base-2 Lychrel numbers. The smallest base-22 Lychrel number is 22 – or 10110 in binary. We can look at its reverse add sequence, and see intuitively why it will never produce a palindrome:

  1. 10110
  2. 100011
  3. 1010100
  4. 1101001
  5. 10110100 (10, 11, 01, 00)
  6. 11100001
  7. 101101000
  8. 110010101
  9. 1011101000 (10, 111, 01, 000)
  10. 110010101
  11. 1101000101
  12. 10111010000
  13. 0b11000101101
  14. 0b101111010000 (10, 1111, 01, 0000)

Starting at step 5, we start seeing a pattern in the sequence, where we have recurring values where that have a pattern of 10, followed by m-1s, followed by 01, followed by m 0s. We’ve got a sequence that’s building larger and larger numbers, in a way that will never converge into a palindrome.

We can find similar sequences in any power-of-two base – base 4, 8, 16, etc. So in power-of-two bases, there are Lychrel numbers. But: are there Lychrel numbers in our familiar base-10?

We think so, but we’re not sure. No one has been able to prove it either way. But we’ve got some numbers, which we call Lychrel candidates, that we think are probably Lychcrel numbers. The smallest one is 196 – which is why this whole discussion is sometimes called the 196 problem, or the 196 algorithm.

People have written programs that follow the Lychrel thread from 196, trying to see if it reaches a palindrome. So far, the record for exploring the 196 Lychrel thread carries it through more than a billion iterations, producing a non-palindromic number with more than 6 million digits.

That’s pretty impressive, given that the longest Lychrel thread for any number smaller than 196 is the thread of 24 steps, starting with 89 (which produces the palindromic number 8,813,200,023,188).

From my perspective, one thing that interests me about this is its nature as a computational problem. As a problem, it’s really easy to implement. For example, here’s a complete implementation in Ratchet, a Scheme-based programming language. (I used ratchet because it features infinite-precision integers, which makes it easier to write.)

(define (reverse-number n)
  (string->number (list->string (reverse (string->list (number->string n))))))

(define (palindromic? n)
  (equal? n (reverse-number n)))

(define (reverse-add n)
  (+ n (reverse-number n)))

(define (find-palindrome seed)
  (define (noisy-find-palindrome n count)
    (if (palindromic? n)
       n
      (begin
        (printf "At iteration ~v, candidate=~v~n" count n)
                (noisy-find-palindrome (reverse-add n) (+ count 1)))))
  (noisy-find-palindrome seed 0))

I literally threw that together in less than five minutes. In that sense, this is a really, really easy problem to solve. But in another sense, it’s a very hard problem: there’s no way to really speed it up.

In modern computing, when we look at a problem that takes a lot of computation to solve, the way that we generally try to approach it is to throw more CPUs at it, and do it in parallel. For most problems that we come across, we can find some reasonable way to divide it into parts that can be run at the same time; then by having a bunch of computers work on those different parts, we can get a solution pretty quickly.

For example, back in the early days of this blog, I did some writing about the Mandelbrot set, and one variant of it that I find particularly interesting, called the Buddhabrot. The Buddhabrot is interesting because it’s a fractal visualization which isn’t naively zoomable. In a typical Mandelbrot set visualization, you can pick a small region of it that you want to see in more detail, and focus your computation on just that part, to get a zoomed in view on that. In the Buddhabrot, due to the chaotic nature of the computation, you can’t. So you just compute the Buddhabrot at a massive size, and then you compress it. When you want to see a region in more detail, you un-compress. To make that work, buddhabrot’s are frequently computed at resolutions like 1 million by 1 million pixels. That translates to enough complex floating point computations to compute several trillion values. That’s a big task. But in modern environments, that’s routine enough that a friend of mine at Google wrote a program, just for kicks, to compute a big buddhabrot image, and ran it on an experimental cluster.

If that kind of computational capability can be exploited just for kicks, why is it that the best effort at exploring the Lychrel thread for 196 only covers 6 million digits?

The answer is that there’s a way of computing the Buddhabrot in parallel. You can throw 10,000 CPUs at it for a couple of days, and get an amazing Buddhabrot image. But for the Lychrel thread, there’s no good way to do it in parallel.

For each additional number in the thread, you need to rearrange and add a couple of million digits. That’s a lot of work, but it’s not crazy. On any halfway decent computer, it won’t take long. To get a sense, I just whipped up a Python program that generated 1,000,000 random pairs of digits, and added them up. It took under 8 seconds – and that’s half-assed code written using a not-particularly fast interpreted language on a not-particularly-speedy laptop. A single iteration of the Lychrel thread computation even for a million-digit candidate doesn’t take that long – it’s on the order of seconds.

The catch is that the process of searching a Lychrel thread is intrinsically serial: you can’t have different CPUs computing the next thread element for different values: you don’t know the next value until you’ve finished the previous one. So even if it took just 1 second to do the reverse+add for million digit numbers, it would takes a long time to actually explore the space. If you want to explore the next million candidates, at 2 seconds per iteration, that will take you around 3 weeks!

Even if you don’t waste time by using a relatively slow interpreter like Python – even if you use carefully hand-optimized code using an efficient algorithm, it would take months at the very least to explore a space of billions of elements of the 196 thread! And to get beyond what anyone has done, you’ll probably need to end up doing years of computation, even with a very fast modern computer – because you can’t parallelize it.

If you’re interested in this kind of thing, I recommend looking at Wade Van Landingham’s p196 site. Wade is the guy who named them Lychrel numbers (based on a rough reversal of his girlfriend (now wife)’s name, Cheryl). He’s got all sorts of data, including tracking some of the longest running efforts to continue the 196 thread.

A couple of edits were made to this, based on error pointed out by commenters.

Support some students!

I’ve been terrible about updating the blog lately. Personal life interferes sometimes, but I’m trying to build up a backlog of posts so that I can get the blog moving again without too much pressure.

In the meantime, I’ve got a request for you folks.

Through this blog, I met a really great SFF writer named Catherine Asaro. Catherine both writes, and also teaches math. She’s running a GoFundMe for her students, who participate in extracurricular math activities – clubs, classes and competitions.

They need money to cover travel expenses for going to math league competitions.

These kids are us: they’re the math geeks of the future. They need help from the math geeks of today. So go, give them a couple of bucks, help them out!

An extra little bit of incentive: the first five people who donate more than $25 can pick a topic, from either math or computer science, and I’ll write a blog post about it. Send me a copy of the thank-you letter you get from GoFundMe to show that you contributed, along with the topic you’d like me to write about.

These thank-you posts for contributing will skip to the top of my priority queue, so you’ll see them soon!