Our great sponsors
- InfluxDB - Collect and Analyze Billions of Data Points in Real Time
- Onboard AI - Learn any GitHub repo in 59 seconds
- SaaSHub - Software Alternatives and Reviews
-
faster-cpython team has done a lot of work to experiment on it: https://github.com/faster-cpython/ideas/issues/485#issuecomm...
It kind of sounds like migration to register based is a foregone conclusion, but it's not very clear to me.
-
The other thing I don't get is that the whole sub interpreters thing seems to totally break extension modules as well: https://github.com/PyO3/pyo3/issues/2274. In theory parts of sub-interpreters have been around for a while and it just happens that every extension module out there is incompatible with it because no one used it. But if it's going to become the recommended way to do parallelism going forward then they'll have to become compatible with it.
The serialization thing is also a huge issue. Half of the time I want to use multiprocessing I end up finding that the serialization of data is the bottleneck and have to somehow re-architect my code to minimize it.
I would much prefer a world in which asyncio is 2x faster and can benefit from real parallelism across threads. Libraries like anyio already make it super easy to work with async + threads. It would make Python a viable option for workloads where it currently just isn't.
-
InfluxDB
Collect and Analyze Billions of Data Points in Real Time. Manage all types of time series data in a single, purpose-built database. Run at any scale in any environment in the cloud, on-premises, or at the edge.
-
My deep interest is multithreaded code. For a software engineer working on business software, I'm not sure if they should be spending too much time debugging multithreaded bugs because they are operating at the wrong level of abstraction from my perspective for business operations.
I'm looking for an approach to writing concurrent code with parallelism that is elegant and easy to understand and hard to introduce bugs. This requires alternative programming approaches and in my perspective, alternative notations.
One such design uses monotonic state machines which can only move in one direction. I've designed a syntax and written a parser and very toy runtime for the notation.
https://github.com/samsquire/ideas5#56-stateful-circle-progr...
https://github.com/samsquire/ideas4#558-state-machine-formul...
The idea is inspired by LMAX Disruptor and queuing systems.
-
My deep interest is multithreaded code. For a software engineer working on business software, I'm not sure if they should be spending too much time debugging multithreaded bugs because they are operating at the wrong level of abstraction from my perspective for business operations.
I'm looking for an approach to writing concurrent code with parallelism that is elegant and easy to understand and hard to introduce bugs. This requires alternative programming approaches and in my perspective, alternative notations.
One such design uses monotonic state machines which can only move in one direction. I've designed a syntax and written a parser and very toy runtime for the notation.
https://github.com/samsquire/ideas5#56-stateful-circle-progr...
https://github.com/samsquire/ideas4#558-state-machine-formul...
The idea is inspired by LMAX Disruptor and queuing systems.
-
This might be a dumb question, but why would removing the GIL break FFI? Is it just that existing no-GIL implementations/proposals have discarded/ignored it, or is there a fundamental requirement, e.g. C programs unavoidably interact directly with the GIL? I know that the C-API is only stable between minor releases [0] compiled in the same manner [1], so it's not like the ecosystem is dependent upon it never changing.
I cannot seem to find much discussion about this. I have found a no-GIL interpreter that works with numpy, scikit, etc. [2][3] so it doesn't seem to be a hard limit. (That said, it was not stated if that particular no-GIL implementation requires specially built versions of C-API libs or if it's a drop-in replacement.)
[0]: https://docs.python.org/3/c-api/stable.html#c-api-stability
[1]: https://docs.python.org/3/c-api/stable.html#platform-conside...
[2]: https://github.com/colesbury/nogil
[3]: https://discuss.python.org/t/pep-703-making-the-global-inter...
-
-
I'm actually rooting for RustPython to reach a level of maturity that we'd just be able to ship apis and stuff with it.... https://github.com/RustPython/RustPython
-
Onboard AI
Learn any GitHub repo in 59 seconds. Onboard AI learns any GitHub repo in minutes and lets you chat with it to locate functionality, understand different parts, and generate new code. Use it for free at www.getonboard.dev.
-
We get (very) close to cross-environment reproducible builds for Python with https://github.com/pantsbuild/pex (via Pants). For instance, we build Linux x86-64 artifacts that run on AWS Lambda, and can build them natively on ARM macOS.
This is not raw requirements.txt, but isn’t too far off: Pants/PEX can consume one to produce a hash-pinned lock file.
-
GVR called it a fork two weeks ago in his justification for not accepting it [1]. If that surprises you, the backstory is summarized pretty well in the article [2] and FAQ [3].
I'm really not trying to be secretive. We can debate whatever you want, but we have to at least acknowledge CPython's stated position.
[1] https://github.com/python/steering-council/issues/188#issuec...
[2] https://www.artima.com/weblogs/viewpost.jsp?thread=214235
[3] https://docs.python.org/3/faq/library.html#can-t-we-get-rid-...
