1. because CPython aims to be relatively simple and straightforward by choice
2. because the "huge user base" comes in large parts from the deep and extensive C API, which is absolute hell on a JIT
3. because most of the userbase would not give a shit anyway, it has not exactly migratd en masse to pypy: much of the userbase sees and uses Python as a glue language, Python is an interface to optimised C routines without being a pain in the ass to develop in.
Sacrificing performance for core interpreter developer convenience may have been the right choice when Python was getting started; it's no longer the right choice today. Today it's short-sighted.
> because the "huge user base" comes in large parts from the deep and extensive C API, which is absolute hell on a JIT
We can have both a JIT and a "deep and extensive" (or more importantly, stable) native API, as demonstrated by node.
> because most of the userbase would not give a shit anyway
Actually a lot of the userbase don't use libraries with native extensions, are painfully aware of Python's performance issues, and are intensely interested in addressing them.
Yes, but not that native API. Designing a native API that doesn't create huge problems later is difficult. The JVM, .NET and V8 guys managed it (mostly) but the scripting languages generally didn't. Their API is just literally the entire internals of the interpreter.
Figuring out how to JIT code in the presence of native extensions that expect the implementation to work in exactly the same way it always worked is a research problem. The only people who have got close to solving it are the GraalVM guys. They do it by virtualising the interpreter API and also JIT-compiling the C code! They run LLVM bitcode on the same engine that runs the scripting engine.
It's a painful choice, and having to use numpy for everything that a loop normally could do but is too slow for, or discourage making functions because a function call is so slow, makes an otherwise elegant language less so
Python lets you do _far_ more shenanigans that Javascript does; and a lot of large libraries depend on some of that behavior. Breaking it would probably cause a new 2 -> 3 situation.
https://www.youtube.com/watch?v=qCGofLIzX6g&feature=emb_titl...
Armin makes great points about path dependence of API design and how the CPython API leaks into the Python language spec. But the features being discussed are actually obscure (example: slots) or intended for debugging (example: frame introspection), and most libraries don't have a good reason to use them. We're stuck in a loop: people talk about how Python is special and can't use a JIT because its internals are not JIT-friendly, so we don't have a JIT, so implementers continue to make choices that are not JIT-friendly - not because they want to, but because they have no guidance.
The JIT doesn't have to be amazing on day 1. What it does have to do is show a commitment and a path to performant code, and illuminate situations where optimizations turn off. There's nothing fundamental in Python's design that prevents a JIT from working; a small number of rarely used dynamic features (that most people don't know about and don't know that they can negatively affect performance) should not be used to hold up interpreter design.
Does JavaScript let you do this monstrosity of terrible code?
Python 3.7.2 (tags/v3.7.2:9a3ffc0492, Dec 23 2018, 23:09:28) [MSC v.1916 64 bit (AMD64)] on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> class Dog(object):
... def speak(self):
... print("bark!")
...
>>> class Cat(object):
... def speak(self):
... print("meow!")
...
>>> animal = Dog()
>>> animal.speak()
bark!
>>> animal.__class__ = Cat
>>> animal.speak()
meow!
>>> class Dog {
speak() {
console.log("bark!");
}
}
class Cat {
speak() {
console.log("meow!");
}
}
animal = new Dog();
animal.speak();
Object.setPrototypeOf(animal, Cat.prototype);
animal.speak();
This will produce: bark!
meow!
And why doesn't python's default runtime environment obviously come with a JIT then? I think they should absolutely go for it, given the huge user base of python.
Reasons like "but named functions can dynamically change" are not applicable, since JS has those same properties and can do it. They could start with optimizing the case where you call the same function over and over in a for loop.
An alternative python interpreter is also not the solution, normally what you have is the main standard python interpreter, and that is the one that should be fast, period.