Daniel Connelly

Advent of Code 2019: Retrospective

Note: Per-problem commentary is here

This year I participated in Advent of Code for the first time; I previously wrote about why and my goals. Well, yesterday, on Christmas Day, I finished all fifty stars!

After finishing part 1 of Day 25, upon discovering that part 2 was just “have you gotten all the other stars,” I went back and finished part 2 of Day 22 – the only thing I had remaining. After doing that I “activated the warp drive” and got the fiftieth star!

Except for Day 18 and part 2 of Day 22, I finished every problem on the day it came out and blogged about it, which was my initial goal. Although sometimes the blogging took a couple of days :) All the posts are in a single enormous blog post.


Overall I can say that this is my proudest programming-related acheivement and the most fun I’ve ever had programming. I had to relearn a bunch of math and computer science techniques, like 2d and 3d geometry, modular arithmetic, bit twiddling, basic data structures and algorithms, BFS/DFS and pathfinding, dynamic programming, matrix algebra, assembly programming, and cellular automata. I also learned a bit about computer organization in practice, something we only briefly touched in university, by building a virtual machine and disassembling an Intcode program to reverse engineer a problem. The breadth and depth of the topics was truly remarkable. I feel like I used most of my formal education in one single month. This was so refreshing after years of iterative large-system building that is mostly devoid of theoretically-interesting problems. (Not to say it’s not just as important :)

I’ve been skeptical of my formal education for the last few years, but it certainly did help this month: I was able to at least identify the underlying theoretical problem or relevant approach each day within a minute or two, even if I had to read Wikipedia a bit to remember the details. For shortest paths I immediately went to BFS, for the card shuffling I immediately thought “group theory,” and so on.

Problem Solving

I learned a lot about problem solving through countless false starts, wrong answers, and hours of debugging and refactoring. Here’s some of the things that came up repeatedly:

Solve the specific problem and not accidentally a harder one

This bit me on Day 18 (among other things): keeping track of the key visit path at each step was a harder problem than just tracking total distance traveled, since relying on distances alone makes it possible to solve with dynamic programming, which is what made it tractable! I was tracking the paths for debugging, but after dropping this extra state, the actual distance problem was easier to solve.

Don’t generalize early

Generalizing early means introducing early complexity when you’re supposed to be solving a specific problem. Early complexity can mean bugs unrelated to your problem solving! Solve the problem first and refactor later (also often a useful lesson in real engineering).

Use brute force unless you can’t

As far as I can recall, the naive solution worked for every single part 1, and complicated algorithms like A* were never strictly necessary. Using the most straightforward solution and only doing something clever once it’s clear that the program isn’t going to terminate, even with shortcuts and minor tuning, means that there’s less unnecessary complexity (and fewer bugs) in the way of the problem solving. In other words, don’t optimize prematurely!

Optimize/Generalize after the code is correct

Related to avoiding premature generalization and optimization: ensure the algorithm is correctly implemented beforehand! Several times I wrote a bunch of code and then started optimizing it before I’d done sufficient testing to know it was correct. I then spent a bunch of time debugging the optimization before realizing the algorithm was wrong.

Don’t special case in recursion

Recursion is fundamentally about reducing a problem to itself, but slightly smaller. This self-similarity is the heart of it, and trying to add some special casing (beyond base cases) for certain types or nodes or elements or means writing code that’s harder to understand and involves duplicated logic.

Reduce state in recursion to the minimum required

Recursion requires careful reasoning, and the more state you introduce, the harder it is to understand why your code isn’t working. Figure out the minimum amount of data you need to solve the problem and then compute additional metadata/debugging info as necessary – but not in the main algorithm implementation! I’m convinced that one of the reasons that Day 18 was so hard for was that I was carrying around so much state in each recursive step.

Your input data might change the problem complexity

Maybe your specific input doesn’t require solving the general problem outlined in the day’s description! An example of this Day 17, when you could just do the droid path compression by hand instead of trying to write a custom compression algorithm for this use case. Another example is Day 16, when you don’t actually need the full N-repeated input and M matrix multiplications, since there’s a trick based on the specific matrix involved that simplifies the problem drastically. Take time to understand the specific instance of the problem instead of the general theoretical problem .

Write simple concurrency that fits in your head

Most of the concurrency I wrote was very simple: a handful of goroutines that wrap a library function and a handful of coordinating channels. The only time I had trouble was on Day 23, when I used a ton of goroutines with function literals and multiple channels per machine for each step in the network. When I had trouble with deadlock and contention it was hard for me to understand what was going on, because there was simply too much code and the concurrency was too complex to fit in my head. I eventually got an answer, but the code is in serious need of refactoring to abstract out some functions and move more stuff into the main thread with basic for loops and so on.


Having written ~4800 lines of code in Go, I now feel as fluent in Go as I do after seven years of near-uninterrupted C++. The language itself is a refreshing change of pace: it can fit in one person’s head, and the spec is helpful and clear! I really felt my problem solving, and not the language, was the barrier each day – the code just flowed, and I never sat and wondered how to express something.

Apart from the first few days, when it seemed like the input parsing would vary less from day-to-day and so generics might have been nice, I never seriously missed it. The lack of expressiveness that comes with e.g. no generics or sum types or something meant that I just fluently wrote out more explicit code without thinking hard about it. I understand there’s a trade-off here, and I’m not saying this is necessarily the right side of it, but it is nice to generally have one way to do something that is explicit and comprehensible. I’ll be interested to see how they grow the language for Go 2 in a way that keeps it this small. I really think this is a large part of what makes it feel refreshing to me after all this C++ and several periods experimenting with Haskell and Rust, not to mention the occasional Java and Objective-C I also use at work from time to time.

Outside of the language, the standard library is easy to use and comprehensive: the only external dependency I pulled in was tcell, for Day 13.

I also developed simple patterns for sets, stacks, queues, trees and graphs using just slices and maps, which makes it easy to avoid dependencies entirely for data representation. I wrote that up a bit in a Gist.

Channels also made several problems easy, particularly the Intcode problems, although I need to spend some more time developing architectural patterns; Day 23, for example, needs a bunch of refactoring to make it simpler, more comprehensible, and eliminate thrashing and contention. But overall I’m very happy with the way the language approaches concurrency, which mostly avoids the worst bits of the buggy and incomprehensible multithreaded code I see all the time at work.

Anyway, all in all I think Go is a great general purpose language and it was a joy to use it for Advent of Code.


I’m very glad Advent of Code 2019 is over. I got behind at work, I was stuck on the computer while visiting family, and towards the end I was getting a bit burnt out. Still, this is absolutely the most fun I’ve ever had programming, and I’m so happy to have practiced all this material and refreshed my skills, and proud to have finished it all by the last day! Looking forward to participating next year!