6 posts tagged with "Programming"

Introduction: Beyond the Basics​

FizzBuzz has long been a staple of programming interviews. The problem is deceptively simple: print numbers from 1 to n, but replace multiples of 3 with "Fizz", multiples of 5 with "Buzz", and multiples of both with "FizzBuzz". It's not meant to be a challenging algorithmic puzzle; most candidates with basic programming knowledge should solve it without difficulty.

So why does this trivial problem persist in the interview landscape? Because FizzBuzz's true value isn't in filtering out candidates who can't code—it's in opening discussions about complexity, language characteristics, optimisation, and the subtle costs of different operations. The best interviewers don't just ask candidates to solve FizzBuzz; they use it as a starting point for a deeper technical conversation.

A little while ago I set out to find a way to measure the overhead that FQDN filtering places on https web traffic. The results are shared here however I thought I would take the opportunity to discuss the methodology here in a bit more detail.

Introduction​

On a few occasions I have been asked to explain networks to people with no prior experience and it's quite hard to work out where to start; there is so much history and so many concepts from general computer science that have got us to where we are today. I have long believed that to truly understand a concept it's very valuable to be able to organise your understanding in a way that means you can explain it to someone else. My goal here is not just to explain many of the contributions to networking that make the internet work but also organise some of my own understanding and explore areas where I have taken things on faith rather than asking why they are the way they are. The approach for this will be to assume we're starting with nothing and rebuilding the internet from the ground up and solving the problems that were solved to get us where we are today.

Some common search algorithms and how they work. This post summarizes key search algorithms used in computer science, explaining their approaches, strengths, weaknesses, and implementation patterns.

More than just an interview question​

Over my years in networking I've sat on both sides of countless technical interviews. There's a familiar dance that occurs when discussing OSPF: the candidate confidently states "OSPF uses Dijkstra's algorithm for route calculation," and the interviewer will nod approvingly. Yet recently, I had a moment of clarity: in hundreds of these exchanges, I've never once asked a candidate to explain what that actually means, nor have I been asked to explain it myself. This perfunctory mention of Dijkstra has become almost ceremonial in our industry, a shibboleth that we repeat without truly engaging with its significance. Yet understanding this algorithm isn't just academic—it fundamentally shapes how OSPF operates, influences our network designs, and explains why certain design patterns have become best practices. When a link fails in your network and OSPF begins recalculating routes, there's significant computational overhead that many engineers never consider. This processing cost isn't just theoretical—it's the hidden force behind many of our design decisions, from area sizing to adjacency limits. Today, we'll bridge the gap between theory and practice, exploring how this fundamental algorithm shapes the way we deploy and scale OSPF networks, and why it matters for your day-to-day operations.

There used to be a great little website for route summarisation and it did it far more intelligently than Cisco kit does it. It looks like the site has dropped off the internet which is a shame but there is a handy python library called netaddr with has the same capabilities.

I have written a little wrapper for it which will regex the prefixes out of a ‘show ip bgp’ and then list the summary routes. You pass the output of ‘show ip bgp’ as a text file, it’s the only argument the script expects.