I really wish the dev would extract the dependency injection portion of the project and flesh it out a bit. There are a lot of rough edges in there.
This sounds like standard case going with what developers know instead of evaluating tool for job.
A company using 2.7 in 2022 is an indicator that the company as a whole doesn't really prioritize IT, or at least the project the OP worked on. By 2017 or so, it should have been clear that whatever dependencies they were waiting on originally were not going to receive updates to support python3 and alternative arrangements should be made.
I work on a large Django codebase at work, and this is true right up until you stray from the "Django happy path". As soon as you hit something Django doesn't support, you're back to lego-ing a solution together except you now have to do it in a framework with a lot of magic and assumptions to work around.
It's the normal problem with large and all-encompassing frameworks. They abstract around a large surface area, usually in a complex way, to allow things like a uniform API to caches even though the caches themselves support different features. That's great until it doesn't do something you need, and then you end up unwinding that complicated abstraction and it's worse than if you'd just used the native client for the cache.
I guess if you write a lot of custom code into specific hooks that Django offers or use inheritance heavily it can start to hurt. But at the end of the day, it's just python code and you don't have to use abstractions that hurt you.
Could you be more specific? Don't get me wrong, I'm well aware that npm dependency graph mgmt is a PITA, but curious where you an into a wall w/ Node.
As far as going with what you know vs choosing the best tool for the job, that can be a bit of a balancing act. I generally believe that you should go with what the team knows if it is good enough, but you need to be willing to change your mind when it is no longer good enough.
Also I think the node approach is probably still more performant than FastAPI but that's just a hunch.
Hopefully they won't have security issues because someone hijacked the node package that sets the font color to blue or passes the butter or something.
"Python doesn't have native async file I/O." - like almost everybody, as "sane" file async IO on Linux is somehow new (io_uring)
Anyway ..
They claim about an 8x improvement in speed.
>I'll preface this by saying that neither of us has a lot of experience writing Python async code
> I'm actually really interested in spending proper time in becoming more knowledgeable with Python async, but in our context you a) lose precious time that you need to use to ship as an early-stage startup and b) can shoot yourself in the foot very easily in the process.
The best advice for a start-up is to use the tools that you know best. And sometimes that's not the best tool for the job. Let's say you need to build a CLI. It's very likely that Go is the best tool for the job, but if you're a great Python programmer, then just do it in Python.
Here's a clearer case where the author was not very good with Python. Clearly, since they actually used Django instead of FastAPI, which should have been the right tool for the job. And then wrote a blog post about Python being bad, but actually it's about Django. So yeah, they should have started with Node from day one.
Sometimes tools are worth learning!
Given they used TS and performance was a concern I would also question the decision to use Node. Deno or Bun have great TS support and better performance.
Don't get me wrong, I use Bun and I'm happy with it, but it's still young. With Hono/Drizzle/Zod I can always switch back to Node or Deno if necessary.
"drizzle works on the edge"
const results = await query`
SELECT...
FROM...
WHERE x = ${varname}
`;
I'm not sure what additional help you're getting. I'm just not a fan of ORMs as they tend to have hard edges in practice.
Obviously ORMs and query builders won't solve 100% of your queries but they will solve probably +90% with much better DX.
For years I used to be in the SQL-only camp but my productivity has increased substantially since I tried EF for C# and Drizzle for TS.
With an ORM, you can also over-query deeply nested related entities very easy... worse, you can then shove a 100mb+ json payload to the web client to use a fraction of.
Also the overhead of good ORMs is pretty minimal and won't make a difference in the vast majority of cases. If you find a bottleneck you can always use SQL.
What’s going to end up happening is they’ll then create another backend for AI stuff that uses python and then have to deal with multiple backend languages.
They should have just bit the bullet and learned proper async in FastAPI like they mentioned.
I won’t even get started on their love of ORMs.
But who is "we rewrote our stack on week 1 due to hypothetical scaling issues" supposed to impress? Not software professionals. Not savvy investors. Potential junior hires?
All-in, there's no single silver bullet to solving a given issue. Python has a lot of ecosystem around it in terms of integrations that you may or may not need that might be harder with JS. It really just depends.
Glad your migration/switch went relatively smoothly all the same.
Django is great but sometimes it seems it just tries to overdo things and make them harder
Trying to async Django is like trying to do skateboard tricks with a shopping cart. Just don't
Conversely all the node+typescript projects, big and small, have been pretty great the last 10+ years or so. (And the C# .NET ones).
I use python for real data projects, for APIs there are about half a dozen other tech stacks I’d reach for first. I’ll die on this hill these days.
While, `PydanticAI` does the best it can with a limited type system, it just can't match the productivity of typescript.
And I still can't believe what a mess async python is. The worst thing we've encountered was a bug from mixing anyio with asyncio which resulted in our ECS container getting it's CPU pinned to 100% [1]. And constantly running into issue with libraries not handling task cancellation properly.
I get that python has captured the ML ecosystem, but these agent systems are just API calls and parsing json...
edit: ironically I'm the author of a weird third party library trying to second guess the asyncio architecture but mine is good https://awaitlet.sqlalchemy.org/en/latest/ (but I'll likely be retiring it in the coming year due to lack of interest)
FastAPI does have a few benefits over express, auto enforcing json schemas on endpoints is huge, vs the stupidity that is having to define TS types and a second schema that then gets turned into JSON schema that is then attached to an endpoint. That IMHO is the weakest link in the TS backend ecosystem, compiler plugins to convert TS types to runtime types are really needed.
The auto generated docs in FastAPI are also cool, along with the pages that let you test your endpoints. It is funny, Node shops setup a postman subscription for the team and share a bunch of queries, Python gets all that for free.
But man, TS is such a nice language, and Node literally exists to do one thing and one thing only really well: async programming.
Just define all your types as TypeBox schemas and infer the schema from that validator. This way you write it once, it's synced and there's no need for a compiler plugin.
https://github.com/sinclairzx81/typebox?tab=readme-ov-file#u...
In my experience async is something that node.js engineers try to develop/use when they come from node.js, and it's not something that python developers use at all. (with the exception of python engineers that add ASGI support to make the language enticing to node developers.)
Very painfully.
I avoid the async libs where possible. I'm not interested in coloring my entire code-base just for convenience.
To be honest, I never liked the way async is done in python at all.
However, I love Django and Python in general. When I need "async" in a http cycle flow, I use celery and run it in background.
If client side needs to be updated about the state of the background task, the best is to send the data to a websocket channel known to the client side. Either it's Chat response with LLM or importing a huge CSV file.
Simple rule for me is, "don't waste HTTP time, process quick and return quick".
SSE is nice.
but I still hope at some point they will manage to fix the devx with django/python and async
I use a combination or channels and celery for a few projects and it’s works great.
I started ripping them out of a java system even before that.
It was a three day small task?
theres effectts if you need app level control
theres caolan async if you need series and parallel controls
theres rxjs if you need observables
on web frameworks hono seems nice too. if you need performance, theres uwebsockets.js which beats all other web frameworks in http and websocket benchmarks.
for typesafety aside from typescript, theres ark, zod, valibot, etc.
What honest reaction you expect from readers?
I had to look for async versions of most of what I did (e.g. executing external binaries) and use those instead of existing functions or functionality, meaning it was a lot of googling "python subprocess async" or "python http request async".
If there were going to be some kind of Python 4.x in the future, I'd want some sort of inherent, goroutine-esque way of throwing tasks into the ether and then waiting on them if you wanted to. Let people writing code mark functions as "async'able", have Python validate that async'able code isn't calling non-async'able code, and then if you're not in an async runloop then just block on everything instead (as normal).
If I could take code like:
def get_image(image):
return_code = subprocess.check_call(["docker", "pull", image])
if return_code:
raise RuntimeError("idk man it broke")
result = get_image(imagename)
print(result)
def get_image(image):
return_code = subprocess.check_call(["docker", "pull", image])
if return_code:
raise RuntimeError("idk man it broke")
result = async get_image(imagename)
print(result)
Man, what a world that would be.
The whole environment is built for async from the ground up. Thousands and thousands of hours put into creating a runtime and language specifically to make async programming feasible. The runtime handles async IO for you with preemptive scheduling. Ability to look at any runtime state on a production instance. Lovely community. More libraries than you might expect. Excellent language in Elixir.
Give it a shot.
I had to switch my project to .NET in the end because it was too hard to find/form a strong Elixir team. Still love Elixir. Indestructible, simple, and everything is easy once you wrap your head around the functional programming.
It. Just. Works.
Obviously that's not going to give you the benefit of a person who has specifically worked in the ecosystem and knows where the missing stairs are, which does definitely have its own kind of value. But overall, I think a big benefit of working in something like Elixir, Clojure, Rust, etc is that it attracts the kind of senior level people who will jump at the opportunity to work with something different.
One nice side effect of having done this is having a small rolodex of other people who are like that.
So, like, if I had a good use case for Elixir and wanted a pal to hack on that thing with, I know a handful of people who I'd call, none of whom have ever used Elixir before but I know would be excited to learn.
People are reimplementing things that are first class citizens in elixir. Live content update, job runners, queues... Everything is built into the language. Sure you can do it all in typescript, but by then you'll be importing lots of libraries, reimplementing stuff with less reliability and offloading things like queues to third party solutions like pulsar or kafka.
People really should try elixir. I think the initial investment to train your workforce pays itself really quick when you don't have to debug your own schedulers and integrations with third party solutions. Plus it makes it really easy to scale after you have a working solution in elixir.
There are probably less code samples and let’s be honest this is 2025, how well do LLMs generate code for obscure languages where the training data is more sparse?
LOL. Speaking about absolutely horrible ideas ...
As an acceptor of reality, you can begin to accept that as well :).
I've had 3 Elixir jobs and 2 Rust jobs in the last 10 years. All were on real products, not vaporware. I learned a ton, worked with great people, and made real friends doing it.
Luck? Skill? Who knows. It's not impossible to work with the technology of your choice on problems you find interesting if you're a little intentional.
Nothing ever gets better if everybody just does what's already popular.
Normally I do this either through multiprocessing or concurrent.futures, but I figured this was a pretty simple use case for async - a few simple functions, nothing complex, just an inner loop that I wanted to async and then wait for.
Turns out Python has a built in solution for this called a TaskGroup. You create a TaskGroup object, use it as a context manager, and pass it a bunch of async tasks. The TaskGroup context manager exits when all the tasks are complete, so it becomes a great way to spawn a bunch of arbitrary work and then wait for it all to complete.
It was a huge time saver right up until I realized that - surprise! - it wasn't waiting for them to complete in any way shape or form. It was starting the tasks and then immediately exiting the context manager. Despite (as far as I could tell) copying the example code exactly and the context manager doing exactly what I wanted to have happen, I then had to take the list of tasks I'd created and manually await them one by one anyway, then validate their results existed. Otherwise Python was spawning 40 external processes, processing the "results" (which was about three incomplete image downloads), and calling it a day.
I hate writing code in golang and I have to google every single thing I ever do in it, but with golang, goroutines, and a single WaitGroup, I could have had the same thing written in twenty minutes instead of the three hours it took me to write and debug the Python version.
So yeah, technically I got it working eventually but realistically it made concurrency ten times worse and more complicated than any other possible approach in Python or golang could have been. I cannot imagine recommending async Python to anyone after this just on the basis of this one gotcha that I still haven't figured out.
https://medium.com/creativefoundry/i-tried-to-build-an-ai-pr...
But a part of me is reading this and thinking "friend... if PostHog was able to do what they're doing on the stack you're abandoning, do you think that stack is actually going to limit your scalability in any way that matters?" Like, you have the counterexample right there! Other companies are making the "technically worse" choice but making it work.
I love coding and I recognize that human beings are made of narratives, but this feels like 3 days you could have spent on customer needs or feature dev or marketing, and instead you rolled around in the code mud for a bit. It's fine to do that every now and then, and if this was a more radical jump (e.g. a BEAM language like Elixir or Gleam, or hell, even Golang, which has that preemptive scheduler + fast compiles/binary deploys + designed around a type system...) than I'd buy it more. And I'm not in your shoes so it's easy to armchair quarterback. But it smells a bit like getting in your head on technical narratives that are more fun to apply your creativity to, instead of the ones your company really needs.
Personally I don't think there's anything wrong with scratching that itch, especially if its going to make you/your team more comfortable long term. 3 days is probably not make-or-break.
Also, considering the project is an AI framework, do you think the language ChatGPT is built on is a worse choice than the language we use because it's in the browser?
Once you're in the situation of supporting a production system with some of the limitations mentioned, you also owe it to yourself to truly evaluate all available options. A rewrite is rarely the right solution. From an engineering standpoint, assuming you knew the requirements pretty early on, painting yourself into a bad enough corner to scrap the whole thing and pick a new language gives me significant pause for thought.
In all honesty I consider a lot of this blog post to be a real cause for concern -- the tone, the conflating arguments (if your tests were bad before, just revisit them), the premature concern around scaling. It really feels like they may have jumped to an expensive conclusion without adequate research.
In an interview, I would not advance a candidate like this. If I had a report who exhibited this kind of reasoning, I'd be drilling them on fundamentals and double-checking their work through the entire engineering process.
My personal maybe somewhat "stubborn old man" opinion is that no node.js orm is truly production quality, but if I were to consider one I think I would start with it. Be aware it has only one (very talented) maintainer as far as I recall.
Always happy to hear feedback/issues if anyone here would like to try it out. Thanks!
Despite MS, Guido and co throwing their weight, still none of the somewhat promised 5x speedup across the board (more like 1.5x at best), the async story is still a mess (see TFA), the multiple-interpreters/GIL-less is too little, too late, the ecosystem still doesn't settled on a single dependency and venv manager (just make uv standard and be done with it), types are a ham-fisted experience, and so on, and so forth...
I recently wrote about issues debugging this stack[1], but now I feel very comfortable operating async-first.
[1] https://blendingbits.io/p/i-used-claude-code-to-debug-a-nigh...
I always find this line of thought strange. It's as if the entire team hinges their technical decision on a single framework, when in reality it's relatively easy to overcome this level of difficulties. This reminds me of the Uber blunder - the same engineer/team switched Uber's database from MySQL to Postgres and then from Postgres to MySQL a few years later, both times claiming that the replaced DB "does not scale" or "sucks". In reality, though, both systems can work very well, and truth be told, Uber's scale was not large enough for either db to show the difference.
Or if feeling fancy, Erlang, Elixir.
Node.js is such an incredible mess. The ideas are usually ok but the implementation details, the insane dependencies (first time I tried to run a Node.js based project I thought there was something seriously wrong with my machine and that I'd been hacked), the lack of stability, the endless supply chain attacks, maintainers headaches and so on, there is very little to like about Node.js.
C# before Node.js and I can't stand C#. Java Before C#. Yes, it's a language rant, but in the case of Node I am really sorry.
I've been working with Node.js since it came out and it's my go to language for anything backend-related.
The complains about npm are issues that could happen with any other package manager as well. Javascript being so popular is what draws the attention of attackers worldwide and that's why it's newsworthy. i.e. your obscure Rust crate with 3 downloads per year is not "safe", it's just that no one gives af about it.
I would argue that all of these problems that came up, and the fixes that followed, have only made the ecosystem more robust with time :).
>first time I tried to run a Node.js based project I thought there was something seriously wrong with my machine and that I'd been hacked
Hehe, that made me chuckle. As you get more familiar with computers you will understand more and more what's going on. I used to teach older adults who never touched a computer before and many were startled when the cursor moved when they touched the mouse. Your comment kind of reminded of it. First steps are usually like that. It's nice to see all kinds of people getting involved! ^^
>Python async sucks
Python async may make certain types of IO-blocked tasks simpler, but it is not going to scale a web app. Now maybe this isn't a web app, I can't really tell. But this is not going to scale to a cluster of machines.
You need to use a distributed task queue like celery.
goalieca•5h ago
pier25•5h ago