Comment by nutjob2
1 day ago
> A lot of important and large codebases were grown and maintained in Python
How does this happen? Is it just inertia that cause people to write large systems in a essentially type free, interpreted scripting language?
1 day ago
> A lot of important and large codebases were grown and maintained in Python
How does this happen? Is it just inertia that cause people to write large systems in a essentially type free, interpreted scripting language?
Small startups end up writing code in whatever gets things working faster, because having too large a codebase with too much load is a champagne problem.
If I told you that we were going to be running a very large payments system, with customers from startups to Amazon, you'd not write it in ruby and put the data in MongoDB, and then using its oplog as a queue... but that's what Stripe looked like. They even hired a compiler team to add type checking to the language, as that made far more sense than porting a giant monorepo to something else.
It's very simple. Large systems start as small systems.
Large systems are often aggregates of small systems, too.
It’s a nice and productive language. Why is that incomprehensible?
Python has types, now even gradual static typing if you want to go further. It's irrelevant whether language is interpreted scripting if it solves your problem.
It’s very natural. Python is fantastic for going from 0 to 1 because it’s easy and forgiving. So lots of projects start with it. Especially anything ML focused. And it’s much harder to change tools once a project is underway.
this is absolutely true, but there's an additional nuance: yes, python is fantastic, yes, it's easy and forgiving, but there are other languages like that too. ...except there really aren't. other than ruby and maybe go, every other popular language sacrifices ease of use for things that simply do not matter for the overwhelming majority of programs. much of python's popularity doesn't come from being easy and forgiving, it's that everything else isn't. for normal programming why would we subject ourselves to anything but python unless we had no choice?
while I'm on the soapbox I'll give java a special mention: a couple years ago I'd have said java was easy even though it's tedious and annoying, but I've become reacquainted with it for a high school program (python wouldn't work for what they're doing and the school's comp sci class already uses java.)
this year we're switching to c++.
Omg, switching to C++ for pupils programming beginners ... "How to turn off the most students from computer programming?" 101. Really can't get much worse than C++ for beginners.
4 replies →
Most large things begin life as small things.
Someone says "let's write a prototype in Python" and someone else says "are you sure we shouldn't use a a better language that is just as productive but isn't going to lock us into abysmal performance down the line?" but everyone else says "nah we don't need to worry about performance yet, and anyway it's just a prototype - we'll write a proper version when we need to"...
10 years later "ok it's too slow; our options are a) spend $10m more on servers, b) spend $5m writing a faster Python runtime before giving up later because nobody uses it, c) spend 2 years rewriting it and probably failing, during which time we can make no new features. a) it is then."
What many startups need to succeed is to be able to pivot/develop/repeat very quickly to find a product+market that makes money. If they don't find that, and most don't, the millions you talk about never come due. They also rarely have enough developers, so developer productivity in the short term is vital to that iteration speed. If that startup turns into Dropbox or Instagram, the millions you mention are round-off error on many billions. Easy business decision, and startups are first and foremost businesses.
Some startups end up in between the two extremes above. I was at one of the Python-based ones that ended up in the middle. At $30M in annual revenue, Python was handling 100M unique monthly visitors on 15 cheap, circa-2010 servers. By the time we hit $1B in annual revenue, we had Spark for both heavy batch computation and streaming computation tasks, and Java for heavy online computational workloads (e.g., online ML inference). There were little bits of Scala, Clojure, Haskell, C++, and Rust here and there (with well over 1K developers, things creep in over the years). 90% of the company's code was still in Python and it worked well. Of course there were pain points, but there always are. At $1B in annual revenue, there was budget for investments to make things better (cleaning up architectural choices that hadn't kept up, adding static types to core things, scaling up tooling around package management and CI, etc.).
But a key to all this... the product that got to $30M (and eventually $1B+) looked nothing like what was pitched to initial investors. It was unlikely that enough things could have been tried to land on the thing that worked without excellent developer productivity early on. Engineering decisions are not only about technical concerns, they are also about the business itself.
What language is “just as productive but isn't going to lock us into abysmal performance down the line”?
What makes that language not strictly superior to Python?
Loose typing makes you really fast at writing code, as long as you can keep all the details in your head. Python is great for smaller stuff. But crossed some threshold, the lack of a mechanism that has your back starts slowing you down.
1 reply →
I don't know a better open source language than Python. Java and C# are both better (platforms) but they come with that obvious corporate catch.
If I made an app in python and in 10 years it grows so successful that it needs a $10m vertical scale or $5m rewrite, I wouldn't even complain.