Comment by pclmulqdq | Hacker News Reader

Comment by pclmulqdq | Hacker News Reader

Parent Full thread

I'm not the person you responded to, but I think the gist of it is: what it is is not defined by how it is used. Python, at its core, is a scripting language, like awk and bash. The other uses don't change that.

Occasionally, a technology breaks out of its intended domain. Python is one of these - it plays host to lots of webservers, and even a filesystem (dropbox). Similarly, HTML is a text markup language, but that doesn't stop people from making video games using it.

The developers of the technology now have to make a philosophical decision about what that technology is. They can decide to re-optimize for the current use cases and risk losing the heart of their technology as tradeoffs get made, or they can decide to keep the old vision. All of the design choices flow down from the vision.

They have decided that Python is a scripting language. Personally, I agree (https://specbranch.com/posts/python-and-asm/). In turn, the people using the language for something other than its intended purpose have choices to make - including whether to abandon the technology.

If instead Python moves toward competing with languages like go, it is going to need to make a lot of different tradeoffs. Ditching the GIL comes with tradeoffs of making Python slower for those who just want to write scripts. Adding more rigorous scoping would make it easier to have confidence in a production server, but harder to tinker. Everything comes with tradeoffs, and decisions on those tradeoffs come from the values of the development team.

Right now, they value scripting.

Python has already become a lot more than a scripting language. To say today that scripting is it's core identity seems naive at best. Yes, it has roots but has object oriented and functional facets which do not exist in awk or bash.

Pandas, numpy, scipy, tensorflow. All of these go way beyond what is possible with a scripting language.

Since when is the runtime performance of a script a serious concern? Why is it a problem if this aspect gets slightly slower if it brings real concurrency support?

Awk is a functional language with a surprising number of features. Bash, not so much. Developers really do care about runtime performance of scripting languages: They often wait for scripts to finish running before doing other work, and if the sum of the time it takes to write and execute a script is too long, they will look for an alternative.

All of the libraries you have cited are from the ML and stats communities, and they are not core language features. From what I understand, ML folks like Python because it is fast to play with and get results. In other words, they like it because it is a scripting language.

Personally, I like that Python has kept the GIL so far because I would never run a 24/7 server in Python and I am happy to use it very frequently for single-threaded scripting tasks.

Edit: I didn't decide that Python was a scripting language. The Python maintainers did. The point is that the identity of a project doesn't flow down from its use cases.

Edit 2: I should have said "the identity of a project doesn't flow down from its users."

>> To say today that scripting is it's core identity seems naive at best. Yes, it has roots but has object oriented and functional facets which do not exist in awk or bash. Pandas, numpy, scipy, tensorflow. All of these go way beyond what is possible with a scripting language.

No, it is not. It's what you can do with a scripting language, which is a fairly straightforward functional categorization. If you are very familiar with it and really want to just ignore the performance issues and don't mind strange workarounds (eg elaborate libraries like Twisted), you probably use python.

> The point is that the identity of a project doesn't flow down from its use cases.

Use cases are what determines the identity of a language. It takes a massive and ongoing marketing campaign to convince people otherwise, with limited success without use-cases. Python is popular because it's a scripting language with a relatively low learning curve (one true way to do things philosophy). That's it. It will improve over time, but it's been slow going...and that's in comparison to the new Java features!

Haskell is a shining example of how the language identity is foisted upon the public until you are convinced enough to try it. It doesn't take long to learn it's a nightmare of incomplete features and overloaded idioms for common tasks, so it isn't used. There aren't good use-cases for a language with haskell's problems, so the developer community and industry-at-large avoids it.

"Personally, I like that Python has kept the GIL so far because I would never run a 24/7 server in Python and I am happy to use it very frequently for single-threaded scripting tasks."

Just as a side-note - my prior gig used Python on both the Server and Data Collection industrial systems. It was very much a 24x7x365 must-never-go-down type of industrial application, and, particularly when we had a lot of data-sources, was very multi-processing. Was not unusual to see 32 processes working together (we used sqlite and kafka as our handoff and output of processes) running on our data collection appliances.

Our core data modelling engine would routinely spin up 500 worker pods to complete the work needed to be done in 1/500th of the time, but we would still see some of the long term historian runs take upwards of a week to complete (many hundreds of machines for multiple years with thousands of tags coming in every 5 seconds is just a lot of data to model).

I say this mostly to demonstrate that people and companies do use python in both large-processing intensive environments as well as industrial-must-never-stop-24x7 mission critical appliances.

I don't ever recall any of our engineers looking at Python as "a scripting language" - it was no different to them than Java, C#, Rust, Go, C++ or any other language.

I know that people do use python for 24/7 "must not fail" applications. I'm just not smart enough to write python that I would trust like that. Python comes with a tremendous number of foot guns and you have to find them because there is no compiler to help, and it can be a real pain to try to understand what is happening in large (>10,000 line) python programs.

It's not that hard, we have compute-intensive servers running 24/7 in production and written entirely in Python on our side, using C++ libraries like PyTorch.

You just have to isolate the complicated parts, define sensible interfaces for them, and make sure they are followed with type hints and a good type checker.

I think the key here is to see using the Python language as an engineering discipline like any other, and just take the classes, read the literature, learn from more Senior Engineers, and projects, before attempting to develop these types of systems on your own.

I don't think anybody expects a recent graduate from computing science, with maybe 4 or 5 classes that used python under their belt (and maybe a co-op or two) to be writing robust code (perhaps in any language).

But, after working with Sr. Engineers who do so, and understanding how to catch your exceptions and respond appropriately, how to fail (and restart) various modules in the face of unexpected issues (memory, disk failures, etc...) - then a python system is just as robust as any other language. I speak from the experience of running them in factories all over the planet and never once (outside of power outages - and even there it was just downtime, not data loss) in 2+ years seeing a single system go down or in any way lose or distort data. And if you want more performance? Make good use of numpy/pandas and throw more cores/processes at the problem.

Just being aware of every exception you can throw (and catching it) and making robust use of type hinting takes you a long way.

Also - and this may be more appropriate to Python than other languages that are a bit more stable, an insane amount of unit and regression testing helps defend quite a bit from underlying libraries like pandas changing the rules from under you. The test code on these project always seemed to outweigh the actual code by 3-5x. "Every line of code is a liability, Every test is a golden asset." was kind of the mantra.

I think that what makes python different from other languages, is that it doesn't enforce guardrails/type checking/etc... As a result, it makes it trivial for anyone who isn't an engineers to start blasting out code that does useful stuff. But, because those guardrails aren't enforced in the language, it's the responsibility of the engineer to add them in to ensure robustness of the developed system.

That's the tradeoff.

Except most of them are bindings written in native languages, being scripted from Python.

But all the “magic” that makes the scientific stack so great is largely due to numpy (and many other Fortran or c driven code) being fantastic. Python is the glue scripting language for organizing and calling procedures. That’s,IMO, it’s original and core purpose.

Now the fact that you can do some fun metaprogramming shenanigans in Python just speaks to how nice it is to write.

If we’re going to leave Python as a scripting language (fine by me), can we get the machine learning community to swap to something better suited?

It strikes me as a bit of a waste of resources to keep stapling engineering effort into the Python ML/data ecosystem when it’s basically a crippled language capable of either: mindlessly driving C binaries, or scripting simple tasks.

What other performance, feature and technique advancements are we leaving on the table because the only “viable” ecosystem is built on a fundamentally crippled language?

From what I can tell, the ML community is moving toward Julia. I don't think anyone predicted that they would end up locked into Python so heavily.

That's not been my experience much at all work or research wise. Tensorflow, pytorch, jax are still very dominant. I've worked at several companies and interviewed at several dozen for ML roles. They have 100% been python/c++ for ml. I'd be impressed if even 2% of ML engineers used Julia.

I feel like Julia will take more of the R people than the Python people, to be honest.

I wanted to use Julia for some experiments but it's so confusing. I would call a function with VSCode and get "potential function call error" or something, with no details. Is it valid or not?

Also, I hate the idea of re-building all the code when the program starts. Python's JIT can at least ignore the performance-critical code that's written in C++.

Reply on news.ycombinator.com