It would be interesting to see this kind of graph created for other languages like Python, Node.js, etc. to see how they all compare. It would be neat to visualise which language/framework has the worst case of dependency hell
Edit: Spelling
Edit: Spelling
https://crates.io/crates/cargo-graph can be used to produce them easily.
Rust is somewhere between Ruby and Node here: leaning towards small modules (one of my crates is in this graph, and it exports four functions), but not to the same level.
> It would be neat to visualise which language/framework has
> the worst case of dependency hell
Code that is complex, and has low cohesion, is harder to understand, and therefore harder to change. It's the elephant you have to push on every time you want your program to do something new[2].
[1] https://en.wikipedia.org/wiki/Cyclomatic_complexity
[2] https://www.youtube.com/watch?v=rI8tNMsozo0
Edit: Just realized who I responded to... "But, you knew all that."
> Edit
So, what's interesting is, I would often consider many small bits to have a _lower_ cyclomatic complexity number. That is, whenever I've used tools that measure this kind of thing, the solution is always to take the big things and break them up into many, smaller bits. It's possible that this is bias in the tooling, though.
I think some of this comes down to individual preference as well. It's like that joke, would you rather fight one horse-sized duck or 100 duck-sized horses? In this admittedly very stretched metaphor, the former are relatively monolithic codebases, and the latter are relatively modular ones. I know that I used to prefer one hundred-line class to ten ten-line classes, but now, much much prefer the latter. My experience talking to people about this is that people fall somewhere on this line, often in different places, and that makes it harder to understand. The action that I'm taking to reduce complexity can often be perceived as increasing complexity, depending on where the other person falls on that line.
That dependency diagram for Servo actually looked relatively clean. You can clearly see which elements are library or utility code. The visualization would probably be better as a three-dimensional model with weighting.
For OO practice, state of the art is SOLID (with a sprinkling of RAII if you happen to be using C++). SOLID leads you straight down the path of LOLO. Small increments FTW.
One nice example that springs to mind here is that there is a package in some linux C library that defines a 'list', which in turn conflicts with lots of other libraries and applications that also wish to define 'list' but in their own scope.
Dependencies are never free. If you have a bunch of dependencies and not have too much resources it's still hell. Managing dependencies is not hard because of the tooling. It's hard because a dependency upgrade could cause pain, or the current version is buggy but the fix inside the upgrade works, but there will be another bug and so on.
If you really believe what you say, why are you using Go (or Java, or Rust or whatever) at all? Someone else wrote it...
I've worked on very large projects in Ruby, Node.js, and Java. In my experience, Java programs tend to have the most dependencies, especially if you use Spring, but also the best tools for managing dependencies.
People like to rag on Maven, and there is a lot of truth to the criticisms, but I've had a much easier time resolving dependency problems with Maven than with other systems, and I find that it helps me avoid a lot of problems proactively. For example, you can exclude transitive dependencies from imports, if they will conflict with other versions you already have in your project. The result of this is that some of the Java projects I work on have 10x the number of dependencies as some of the Ruby projects, but far fewer dependency problems.
I see nothing wrong with having lots of dependencies, even on small projects, as long as you have the right tooling to manage them and can guarantee reproducible builds.
I use it to see if there is anything I can shave away. (After embracing the fact that we are deploying to a Java EE server builds have been steadily shrinking and I now think the main war is has shrunk by 60% and I have a POC that should shave another 10%)
(For maven/Netbeans users it is trivially simple, just open the pom file and find the dependencies view in the "pr file/ toolbar".)