What is wrong with the Python code base that makes this so much harder to implement than seemingly all other code bases? Ruby, PHP, JS. They all seemed to add JITs in significantly less time. A Python JIT has been asked for for like 2 decades at this point.
The Python C api leaks its guts. Too much of the internal representation was made available for extensions and now basically any change would be guaranteed to break backwards compatibility with something.
It's a shame that Python 2->3 transition was so painful, because Python could use a few more clean breaks with the past.
This would be a potential case for a new major version number.
On the other hand, taking backwards compatibility so seriously is a big part of the massive success of Python
>> Python 2->3 transition
> taking backwards compatibility so seriously
Python’s backward compatibility story still isn’t great compared to things like the Go 1.x compatibility promise, and languages with formal specs like JS and C.
The Python devs still make breaking changes, they’ve just learned not to update the major version number when they do so.
Indeed, Python's version format is semver but it's just aesthetics, they remove stuff in most (every?) minor version. Just yesterday I wasted hours trying to figure out a bug before realizing my colleague hadn't read the patch notes.
I would argue that the libraries, and specifically NumPy, are the reason Python is still in the picture today.
It will be interesting to see, moving forward, what languages survive. A 15% perf increase seems nice, until you realize that you get a 10x increase porting to Rust (and the AI does it for you).
Maybe library use/popularity is somewhat related to backwards compatibility.
Disclaimer: I teach Python for a living.
Python it's a language that really good libraries for different domains. like web: django/flask AI numpy pytorch and more. All the ecosystem for scripting and being already installed in most linux distros and on macs. For GUI it has really good bindings for the major frameworks QT,GTK.
> you get a 10x increase porting to Rust (and the AI does it for you)
So, you keep reading/writing Python and push a button to get binary executables through whatever hoops are best today ?
(I haven't seen the "fits your brain" tagline in the recent past ...)
And PyTorch, and Pandas, and, and…
Built and or inspired by NumPy...
Python does not take backwards compatibility very seriously at all. Take a look at all the deprecated APIs.
I would say it's probably worth it to clean up all the junk that Python has accumulated... But it's definitely not very high up the list of languages in terms of backwards compatibility. In fact I'm struggling to think of other languages that are worse. Typescript probably? Certainly Go, C++ and Rust are significantly better.
They don't just deprecate APIs, they remove them completely to make sure you really stop using them.
Python does not take backwards compatibility seriously. 2 to 3 is a big compatibility break. But things like `map(None, seq1, seq2)` also broke; such deliberate compatibility break is motivated by no more than aesthetic purity.
Ooo this makes sense it's like if the Linux had don't break users space AND a whole bunch of other purely internal APIs you also can't refactor.
For what it’s worth Ruby’s JIT took several different implementations, definitely struggled with Rails compatibility and literally used some people’s PhD research. It wasn’t a trivial affair
Some languages are much harder to compile well to machine code. Some big factors (for any languages) are things like: lack of static types and high "type uncertainty", other dynamic language features, established inefficient extension interfaces that have to be maintained, unusual threading models...
That makes sense if you're comparing with Java or C#, but not Ruby, which is way more dynamic than Python.
The more likely reason is that there simply hasn't been that big a push for it. Ruby was dog slow before the JIT and Rails was very popular, so there was a lot of demand and room for improvement. PHP was the primary language used by Facebook for a long time, and they had deep pockets. JS powers the web, so there's a huge incentive for companies like Google to make it faster. Python never really had that same level of investment, at least from a performance standpoint.
To your point, though, the C API has made certain types of optimizations extremely difficult, as the PyPy team has figured out.
> Python never really had that same level of investment, at least from a performance standpoint.
Or lack of incentive?
Alot of big python projects that does machine learning and data processing offloads the heavy data processing from pure python code to libraries like numpy and pandas that take advantage of C api binding to do native execution.
Google, Dropbox, and Microsoft from what I can recall all tried to make Python fast so I don’t buy the “hasn’t seen a huge amount of investment”. For a long time Guido was opposed to any changes and that ossified the ecosystem.
But the main problem was actually that pypy was never adopted as “the JIT” mechanism. That would have made a huge difference a long time ago and made sure they evolved in lock step.
Microsoft is the one the TFA refers to cryptically when it says "the Faster CPython team lost its main sponsor in 2025".
AFAIK it was not driven by anything on the tech side. It was simply unlucky timing, the project getting in the middle of Microsoft's heavy handed push to cut everything. So much so that the people who were hired by MS to work on this found out they were laid off in a middle of a conference where they were giving talks on it.
The simplest JIT just generates the machine code instructions that the interpreter loop would execute anyway. It’s not an extremely difficult thing, but it also doesn’t give you much benefit.
A worthwhile JIT is a fully optimizing compiler, and that is the hard part. Language semantics are much less important - dynamic languages aren’t particularly harder here, but the performance roof is obviously just much lower.
Agree re: different types of JITs producing wildly different results but don't agree about language semantics - even a Java JIT has to give up speed due to certain seemingly minor language and JVM issues. So both matter - no matter how good of a compiler engineer you are, some semantics are just not optimizable. Indeed, the use of a "trace JITs" is a proof of that.
This is orthogonal to the difficulty of actually implementing a JIT compiler.
It’s very much possible to make something less advanced than JVM or CLR, or even V8, that will still outperform an interpreter, even for an extremely dynamic language like Python.
As others have mentioned, the roadblock here is that interpreter internals are too public, so doing it without breaking the C API that extensions use is really hard.
I think that it's just that python people took the problem different, they made working with c and other languages better, and just made bindings for python and offloaded the performant code to these libraries. Ex: numpy
I can't really talk about Ruby. But PHP is much more static and surface of things you have to care about at runtime is like magnitude smaller and there already was opache as a starting point. And speaking of something like JIT in V8 is of the most sophisticated and complicated ever built. There hasn't been near enough man hours and funding to cpython to make it fair comparison
For better or for worse they have been very consistent throughout the years that they don't want want to degrade existing performance. It is why the GIL existed for so long
I thought php hasn't shipped jit yet (as in its behind a disabled by default config)
PHP 8 shipped with JIT on by default unless I'm mistaken.
https://www.php.net/manual/en/opcache.configuration.php says its off by default as of php 8.4 (and prior to that it was technically on but effectively off due to other configs)
PHP and JS had huge tech companies pouring resources into making them fast.
Are you forgetting about PyPy, which has existed for almost 2 decades at this point?
That's a completely separate codebase that purposefully breaks backwards compatibility in specific areas to achieve their goals. That's not the same as having a first-class JIT in CPython, the actual Python implementation that ~everyone uses.
Definitely agree that it’s better to have JIT in the mainline Python, but it’s not like there weren’t options if you needed higher performance before.
Including simply implementing the slow parts in C, such as the high performance machine learning ecosystem that exists in Python.
Money.