> 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?
How does this happen? Is it just inertia that cause people to write large systems in a essentially type free, interpreted scripting language?
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.
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++.
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 makes that language not strictly superior to Python?
put "test abc999 this" into x
add 1 to char 4 to 6 of word 2 of x
put x -- puts "test abc1000 this"
I'd say they are almost strictly superior to Python, but there are some minor factors why you might still choose Python over those. E.g. arbitrary precision integers, or the REPL. Go is a bit tedious and Rust is harder to learn (but productive once you have).
But overall they would all be a better choice than Python. Yes even for startups who need to move fast.
Kotlin owns the mobile development market with 80% Android market share.
Scala was the AI before Python with Hadoop, Spark and friends.
Lisps might be niche, yet they were Python's flexibility, with machine code compilers, since 1958.
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.