A lot of this advice is good or at least interesting. A lot of it is questionable. Python is completely fine for the backend. And using SQLite for your prod database is a bad idea, just use Postgres or similar.
There’s a lot to be said about his approach with go for simplicity. Python needs virtual environments, package managers, dependencies on disk, a wsgi/asgi server to run forked copies of the server, and all of that uses 4x-20x the ram usage of go. Docker usually gets involved around here and before you know it you’re neck deep in helm charts and cursing CNI configs in an EKS cluster.
The go equivalent of just coping one file across to a server a restarting its process has a lot of appeal and clearly works well for him.
Yes. It strikes me as odd how many people will put forward Python with the argument of "simplicity".
It is not. Simple. It may be "easy" but easy != simple (simple is hard, I tend to say).
I'm currently involved in a project that was initially layed out as microservices in rust and some go, to slowly replace a monolyth Django monstrosity of 12+ years tech debt.
But the new hires are pushing back and re-introducing python, eith that argument of simplicity. Sure, python is much easier than a rust equivalent. Esp in early phases. But to me, 25+ years developer/engineer, yet new to python, it's unbelievable complex.
Yes, uv solves some. As does ty and ruff. But, my goodness, what a mess to set up simple ci pipelines, a local development machine (that doesn't break my OS or other software on that machine). Hell, even the dockerfiles are magnitudes more complex than most others I've encountered.
"trivial" falls in the "easy" category. So it may not be hard to do. But what UV makes "easy" is managing something very complex under the hood.
Better example:
FROM python:3.9-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt
COPY . .
CMD ["python", "app.py"]
While "easy" it is nowhere near simple. Aside from the entire complexity of the stack of docker, that `python:3.9-slim` it itself is very complex. It installs over 20 "dev" packages (from bluetooth via tk to xz), it downloads source files, builds a python runtime, (patches that?), installs pip, setuptools, does some (to python people probably familiar?) "wheel" stuff, etc¹. Point being: what you end up with, while easy to get, is very complex.
uv manages a runtime, some virtual environment to hot-swap that with other runtimes, it hooks into a package manager, manages additional tools (linter, typechecker, lsp, etc) and so on. What lies under that is very complex.
¹ I am well aware that node, ruby, php are quite similar.
Python will take you a long way, but its ceiling (both typical and absolute) is far lower than the likes of Go and Rust. For typical implementations, the difference may be a factor of ten. For careful implementations (of both), it can be a lot more than that.
Does the difference matter? You must decide that.
As for your dismissing SQLite: please justify why it’s a bad idea. Because I strongly disagree.
There are a myriad middle states in-between "frupid" (so frugal that it's stupid) and "Instagram scale".
Python requires much more hand-holding that many don't want to do for good reasons (I prefer to work on the product unimpeded and not feeling pride having the knowledge to babysit obsolete stacks carried by university nostalgia).
With Go, Rust, Zig, and a few others -- it's a single binary.
This is a post about keeping your infrastructure simple, so Instagram is not a good ceiling to pick. People do all kinds of hacks to scale Python before they hit Instagram levels
Unless your Cloudflare worker and the DB are scheduled onto the same physical server, they are not local to one another. I don’t know much about D1, but the overwhelming majority of cloud infra makes no such guarantees, nor are they likely to want to architect it in that manner.
Cloudflare's Durable Objects puts your Worker and SQLite DB on the same physical server (and lets you easily spawn millions of these pairs around the world).
D1 is a simplified wrapper around DO, but D1 does not put your DB on the same machine. You need to use DO directly to get local DBs.
I think the point is that your Python webapp will have more problems scaling to let's say 10,000 customers on a 5$ VPS tham Go. Of course you can always get beefier servers, but then that adds up for every project
