Hacker News Reader

Comment by julvo | Hacker News Reader

For some use cases, Go might be a good alternative to Python. It's performant, yet simple and readable and it has a great ecosystem.

View on HN

I find laying out a go project with dependencies to be miserable though. I write a lot of go and python code and why on God’s green earth did google decide to handle dependencies by having you effectively git clone a library and then have these huge tree deps subdirectories and so on I don’t know. It’s mess. I vastly prefer the approach of either having one canonically designated folder to install dependencies to where each dependency is a top level directory that can be scanned or having them all stored in a project folder relative to root of the workspace similar to node_modules then the current mess.

Drives me nuts. Look at this layout if you don’t know what I’m referencing:

https://golang.org/doc/code.html#remote

Yes I have used dep

And yes I have all kinds of shortcut commands for navigating my Go workspace

But look at this canonical example From the docs and it’s easy to see this is a giant mess that is utterly unnecessary. No other language I’ve used has had such a gross problem with dependency layout. It also leads to gross import strings.

It’s one of my biggest criticisms of Go to be honest.

Thst and it’s reliance on environmental variables that have to be set perfectly in order to actually do anything (thank god for direnv https://direnv.net/)

Isn't what a dependency folder like node_modules pretty much what the vendor folder is to go. Have you tried using a dep mangement tool like glide before?

Thst is one I haven’t tried I tried a few others whose names I forgot because most alt managers seemingly stopped development but this looks active. Thank you!

I now would shift my argument to the fact that Google should really just adopt this as their standard if it works as advertised.

Link for those who haven’t seen it:

https://glide.sh

Personally I found Go almost uniquely UNsuited to Python-style explorative programming. The lack of generics, and the high-ceremony error handling are pretty much 180 degrees from python.

While of course it doesn't have backing from a giant multi-nation, I think Nim (https://nim-lang.org/) is a much better fit for "python, but fast".

Only if you are an experienced programmer. But a lot of great python tools are created by mathematicians, geographers, biologists, students, sys admins, etc.

I agree, but in this case I meant especially command line tools, such as mercurial. Of course, for scripting experiments etc. Python is the better choice, but these applications are normally not too worried about startup time.

Do we have numpy, matplotlib, scipy, pickle etc. for Go?

Do the build processes mentioned in this email make heavy use of numpy, matplotlib, scipy, pickle, etc.?

NumPy is very exciting and all, but it's a subcommunity of the Python community, not 90% of its usage. NumPy users are probably not, in general, trying to spawn twenty five thousand processes in sequence to accomplish some task. The people who are complaining about fractions of a millisecond of startup time are not inverting massive matrices.

People who are importing numpy usually are also not inverting massive matrices. These libraries provide tons of functionality that you would like to use in a short script. For people accustomed to these libraries the code is quick to write in a reliable fashion, easy to read but not quick to run. Everything is great except the startup time (and speed when you have more data).

This group of people is so precise and exclusive that it is irrelevant. "People using NumPy for system scripting who are intensely sensitive to python startup time" is not a large enough group to be trying to argue Python-global policy with.

"People using Python for system scripting who are intensely sensitive to python startup time" is at least large enough to be worth talking about (since speeding up startup time will mostly only help, modulo any possible resources spent to accomplish it), though I'd notice that it hasn't prevented Python from becoming very popular. And plenty of them will find that Go could meet their needs, in a hypothetical universe in which switching languages was free. (That is, I'm not particularly advocating it. It's a last resort for sure.)

(Also this argument is predicated on the false assumption that Go has nothing like those things. They aren't as mature by any means, of course, and I generally consider them a bad idea [2], but they do exist.)

[2]: https://news.ycombinator.com/item?id=16959022

So precise and exclusive that it's irrelevant?

Google, Facebook, Netflix, Uber, Amazon, Microsoft are all using Numpy in their data science pipelines, spinning and taking down dockers for ML-as-a-service. I'm pretty sure they care about startup time of both Go and Python.

Again, read the article for what the topic of conversation is. If you're "spinning up an entire Docker container", a Python startup time is going to disappear into the multiple seconds that already takes. You are not spinning up several hundred docker containers per second, on a sustained basis for hours at a time, on a single piece of hardware, constrained only by Python startup time. That's going to be a vanishing fraction of the problem, even if you are spinning up that many containers that quickly, and the optimization for that is already obvious (don't do that, do more per container).

You are conflating what systems scripting is, which would be what would be managing the docker containers themselves, with what the docker containers would be doing, which would be very likely starting up just one Python instance to "do the thing". I don't imagine there are very many systems scripts out there in the world being started dozens of times per second that use NumPy. Anything that did, again, the obvious optimization would be "don't do that".

Reply on news.ycombinator.com