Why do engineers love to ask fundamental linked list and tree questions in interviews when you rarely code these problems in real-world development?
It’s evolved into a rite of passage. Every engineering candidate, from fresh-faced grads to authors of crucial open source contributions, solves fundamental data structure problems on the spot for interview screenings.
It’s how it’s always been done. But it makes sense. This ritual has sustained itself over the past few decades because it’s a fast, reliable way to spot smart candidates who can think deeply. Plus, it’s better to hire for ability to solve timeless fundamental problems than hire for knowledge based on transient tools.
Hence, each of the top 10 technology companies in the Fortune 500 have asked engineering candidates core computer science concepts, including tree or list-related programming questions:
When front-end developer Stephanie Friend graduated from Cal Poly with an engineering and liberal arts hybrid degree, it had been a while since she sat in a lecture hall to learn about linked lists. It’s a good thing she blew the dust off her old data structure books and practiced challenges online before interviewing at one Silicon Valley startup in May this year:
“I had an interview with 6 different engineers on the same team, and 5 out of 6 interviewers asked me to solve a different linked list problem for a web development position,” Stephanie says.
So, why the need to ask 5 different linked list questions on 5 different occasions for 1 company? Some argue that you can’t be a great programmer unless you have these fundamentals down pat. Others say that CS fundamental knowledge is a good predictor of other useful programming knowledge.
It’s why most programming interview prep books, in even as early as the 2000s, have chapters dedicated solely to basic data structure and algorithm problems (e.g. 1, 2 and 3). Plus, data structure and algorithm questions make up the bulk of upvoted questions on CareerCup, a job prep community.
You might be a little puzzled as to why we’re criticizing these questions, considering tree and linked lists challenges are some of the most popular on our own HackerRank platform. But there’s a big flaw with companies that aren’t preparing candidates sufficiently before an interview and then relying solely on academic CS fundamentals to weed out unqualified candidates. Data structure and algorithm fundamentals are just one part of what makes a great engineer.
Depending on the need, managers should also look at other crucial components, like technical experience, hard-to-acquire knowledge, design and debugging skills to comprehensively assess a candidate.
While fundamentals are crucial, using data structure questions as the be-all end-all filter for great programmers can be detrimental for talented engineers who don’t have CS degrees or who earned their CS degrees years ago. By placing a heavy emphasis on fundamental knowledge—without properly preparing candidates—companies can create a bias toward recent CS graduates. As a solution, interviewers need to empower candidates with preparation material to reduce the number of great programmers who are rejected.
A typical programmer, even at a top tech company, would rarely implement a data structure like a binary tree from scratch. So, many developers might be out of practice with this at their next interview. One of the most famous examples is Max Howell, the author of HomeBrew, a celebrated program management system for Macs. Howell applied for an engineering position at Google and was rejected because, as he claims, he couldn’t “invert a binary tree” during the initial interview.
Google: 90% of our engineers use the software you wrote (Homebrew), but you can’t invert a binary tree on a whiteboard so fuck off.
— Max Howell (@mxcl) June 10, 2015
While we can’t definitively say why Max didn’t get the job (it could have been a number of factors that interviewers don’t reveal), it’s likely that he could have performed better if he had known to brush up on those fundamentals by practicing online. After all, he’s an extremely accomplished and capable engineer. So, there’s a chance he was a classic false negative candidate.
The obsession that top tech companies have with data structure problems can also be unfair to engineers who’ve never sat in a CS class a day in their lives. If you don’t have a CS degree, it can be difficult to gauge how much you need to know to clear the initial bar during interviews.
“While these questions can help select talented developers, they become highly problematic when somebody doesn’t have proper tools to prepare and doesn’t understand what is expected. A candidate might feel they need to read an entire algorithms book, which wastes their time and results in less time actually practicing problems.” says Gayle Laakmann McDowell, tech hiring consultant and author of Cracking the Coding Interview.
Although many great candidates get rejected because they failed to adequately prepare, it’s actually not that hard for smart developers to learn or re-learn the fundamentals. It’s part of why companies are ok with requiring them. Today there are a host of online resources to help you practice data structure questions. Gayle, who’s passionate about teaching programming, once successfully taught a student the required basics in just 2 hours. Another one of Gayle’s students was a self-taught programmer with a degree in music, and eventually learned and practiced enough of the fundamentals to land a job at Facebook. You can learn more about Gayle’s take on the interview process here.
But not everyone’s fortunate enough to have Gayle coach them individually. Self-taught students and experienced programmers are left to fend for themselves, leaving many of them annoyed and confused about the purpose of such fundamentals at dream company interviews, like Google and Apple (complaints evident here, here and here).
The best companies also test for other important factors that make a great engineer. For instance, discussing a technical project that a candidate is proud of can reveal knowledge, passion and ability to communicate well. Again, fundamentals can be very easy to learn if you know how much to prepare.
Since the initial boom in software engineering back in the 1980s, data structure and algorithm questions have been a common way to test candidates. The earliest engineers with growing teams carried CS degrees, and they knew that algorithm classes were a great place that required deep thinking. So, engineers of the 80s created this interview process that resembles algorithm classes. And it works accurately enough. When searching for talent, these questions are fast enough to answer in less than an hour and help interviewers gauge a programmer’s intelligence. It’s certainly not the only way to filter candidates for smartness, but again, it works well enough.
Gayle theorizes that there might be another reason why companies expect knowledge of data structures like linked lists and trees: It’s hard to find enough algorithm questions that don’t involve these.
“Companies test algorithmic problem solving skills because they believe that people who are smart will generally do good work; they’ll find good solutions, write good code, and so on. I suspect companies continue to expect knowledge of data structures like linked lists and trees (which developers rarely directly use) because it’s hard to find enough algorithm problems that don’t cover this knowledge. And, since enough people have CS degrees, and it’s easy enough for those who don’t to learn this material, it creates a pattern where it’s okay to expect that knowledge,” Gayle says.
Companies deem this system effective because successfully answering algorithm questions are a positive indicator of success on the job. As Gayle says, it means they’re smart and they’re likely to do better work.
However, companies that don’t prepare candidates well enough aren’t giving them a chance to perform well at these fundamental questions. Most companies recognize that some good candidates will be rejected through these questions, but they’re okay with the drawback of missing out on good candidates. They figure it’s better to reject a good candidate than hire a bad one. The veteran engineer Joel Spolsky, and author of the Trello software, once penned this common hiring philosophy in detail back in 2004:
“It is much, much better to reject a good candidate than to accept a bad candidate. A bad candidate will cost a lot of money and effort and waste other people’s time fixing all their bugs. Firing someone you hired by mistake can take months and be nightmarishly difficult, especially if they decide to be litigious about it. In some situations it may be completely impossible to fire anyone. Bad employees demoralize the good employees,” Spolsky says.
There might be some validity in the cost per bad hire in Spolsky’s outlook, but rejecting too many good candidates will dramatically increase the cost and time to hire—and ultimately, restrict company growth. All companies should be concerned about this.
In the early 2000s, companies were somewhat more wary about giving candidates preparation material before an interview. It was sometimes considered taboo or even “cheating” because they worried candidates might memorize problems and regurgitate knowledge in the interview.
But that mindset has slowly started to shift as the shortage of talented developers has intensified. In a 2013 survey of over 1,500 senior IT and business executives, more than a third identified availability of talent, employee turnover and labor prices as a business concern.
“Jobs postings will be listed for months without finding a good candidate,” former Zynga software engineer and founder of Appurify, Rahul Jain told TechCrunch.
Given these concerns, it’s actually in a company’s best interest to help candidates with interview prep by giving candidates a chance to practice solving CS fundamental problems. It’s simple: Better prepared candidates lead to fewer false negatives. Plus, most engineers can easily distinguish between someone who’s just memorized answers and someone who can truly solve a hard problem.
The best tech companies realize that it’s actually beneficial to both engineers and companies to give candidates a fair opportunity to put their best foot forward. This is especially true given the obsession with fundamentals that most engineers don’t usually revisit since the good old college days. It’s also a good way to skip the anxiety-ridden phase of the interview and get to other meatier questions that are just as important in assessing candidates, like culture fit and collaborative skills.
Googler Steve Yegge is one engineer who realized candidate preparation is an effective solution to the talent shortage early on. Back in 2008, he “secretly” blogged engineering interview tips for Google candidates in hopes that more of his interviewees would succeed:
Time passes, and interview candidates come and go, and we always wind up saying: ‘Gosh, we sure wish that obviously smart person had prepared a little better for his or her interviews. Is there any way we can help future candidates out with some tips?’
Google doesn’t know I’m publishing these tips. It’s just between you and me, OK? Don’t tell them I prepped you. Just go kick ass on your interviews and we’ll be square….
As late as 2008, people were so against offering candidate prep that Steve even considered publishing his tips under a pseudonym to avoid upsetting people. Ultimately, his desire and need for better prepared candidates outweighed the risk of earning negative sentiments. This is also a huge reason why Gayle also left Google to start her empire of interview prep about seven years ago. As a software engineer at Apple, Google and Microsoft, Gayle interviewed one too many ill-prepared but smart candidates. She wanted to teach and help more engineers become better interviewers; thus, CareerCup was born.
The best tech companies are starting to realize that the more preparation, the better interview-to-hire rate. For instance, today Facebook hires Gayle for a weekly 1.5 hour class for candidates exclusively for interview prep. She walks Facebook candidates through the problems and even offers tips along the way. Facebook found so much success in this recruiting strategy that they doubled the frequency of her class. Today select top-tiered tech companies, like Pinterest, Google, Airbnb, and Twitter, send at least an email that points candidates to resources for better preparation and practice for fundamental CS challenges.
More companies should look at why they’re rejecting great candidates and how they can reduce false negatives to help grow their team more successfully. Empowering smart candidates by setting more realistic expectations for candidates about the interview process is one way to accomplish this.
This decades-old process of testing engineers’ intelligence through fundamental CS questions may be sufficient to identify great programmers.
But this process should come with a mechanism (at the minimum an email with links to resources) to help candidates practice these fundamental challenges for the interview. By helping candidates prepare, companies can more easily identify great developers and reduce the bias against older and nontraditional candidates. They can also focus on other important components that are crucial in evaluating strong engineers. This ultimately reduces hiring costs and fuels company growth—a win for the company and the candidate.
Do you prep your candidates before quizzing them on trees and linked list questions? Let us know in the comments below.