Mailman 3 February 2022 - Python-Dev

PEP 638: Syntactic macros
by Mark Shannon 05 Feb '23

05 Feb '23

Hi everyone, I've submitted my PEP on syntactic macros as PEP 638. https://www.python.org/dev/peps/pep-0638/ All comments and suggestions are welcome. Cheers, Mark

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How about using modern C++ in development of CPython ?
by redradist＠gmail.com 19 Oct '22

19 Oct '22

Hi all, What about of using modern C++ in developing CPython ? With C++ we can get the following benefits: 1) Readability - less code, most code is hidden by abstraction without losing performance 2) Maintainability - no code duplication in favor of using reusable classes 3) RAII - Resource Acquisition Is Initialization, predictable allocation and free resources ...

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PEP 505 (None-aware operators) for Python 3.11
by Doug Swarin 21 Sep '22

21 Sep '22

Hello, I've been following PEP 505 (https://www.python.org/dev/peps/pep-0505/) since it was first proposed for Python 3.8. It's been deferred for some time and I'm interested in seeing it in Python 3.11, but I know there were also a number of objections which resulted in it being deferred (including by one of the original authors, Mr. Dower). I did email both Mr. Dower and Mr. Haase and they graciously gave me their permission to bring it up on this list for discussion and hopefully final pronouncement one way or the other. I personally believe that the PEP will result in a significant reduction in boilerplate code, and it is substantially similar to the same operators now found in a number of other languages, especially C# and JavaScript (https://wikipedia.org/wiki/Null_coalescing_operator and https://wikipedia.org/wiki/Safe_navigation_operator). I believe strong and valid arguments can be made about the use of None being a fundamental flaw in some types of coding (and that adding additional support for it to the language will increase the use of None in this way), but I also believe there are many use cases in programming where it is by far the simplest way to express various semantics, and the fact exists that None is already used extensively in large quantities of code, and further that there is already a great deal of code written to constantly test against None and break out of a statement without throwing an error. I also understand the argument that especially the maybe-dot (?.) and maybe-subscript (?[) operators can decrease readability of code and also believe these are valid arguments against it. While I believe the existence and use of these operators in other languages definitely helps the case that these can be used and understood successfully, I think it is entirely valid to either consider other syntax (though I prefer the chosen syntax of PEP 505), or even to reduce PEP 505 to having only the coalesce operator (??) and the maybe-assign operator (??=). Separately, I have implemented a pure-Python solution for PEP505 (which is definitely rather beta) which might help test the waters for a final implementation in CPython (though the CPython implementation would of course be much more efficient). It can be found at https://pypi.org/project/pep505/ Thanks, Doug

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PEP 563 and Python 3.10.
by Thomas Wouters 22 Jul '22

22 Jul '22

(Starting a new thread so as not to derail any of the ongoing discussions.) Thanks, everyone, for your thoughts on Python 3.10 and the impact of PEP 563 (postponed evaluation of annotations) becoming the default. The Steering Council has considered the issue carefully, along with many of the proposed alternatives and solutions, and we’ve decided that at this point, we simply can’t risk the compatibility breakage of PEP 563. We need to roll back the change that made stringified annotations the default, at least for 3.10. (Pablo is already working on this.) To be clear, we are not reverting PEP 563 itself. The future import will keep working like it did since Python 3.7. We’re delaying making PEP 563 string-based annotations the default until Python 3.11. This will give us time to find a solution that works for everyone (or to find a feasible upgrade path for users who currently rely on evaluated annotations). Some considerations that led us to this decision: - PEP 563’s default change is clearly too disruptive to downstream users and third-party libraries to happen right now. We can’t risk breaking even a small subset of the FastAPI/pydantic users, not to mention other uses of evaluated type annotations that we’re not aware of yet. - PEP 563 provides no warning to users of the feature it’s disabling. Without that, we can’t expect users to be aware of the upcoming breakage. The lack of a warning was by design, and made sense in a world where type annotations were only consumed by static type checkers --- but that’s not actually the situation we’re in. There are clearly existing real-world, run-time uses of type annotations that would be adversely affected by this change. - Originally, PEP 563 was scheduled to take effect in Python 4, and this changed recently (after the discussion in the Language Summit of 2020). It's possible that third-party libraries and users didn’t plan to react in the current time frame as they were not aware of this change in timing. - There isn’t enough time to properly discuss PEP 649 or any of the alternatives before the beta 1 deadline, and we really need to make sure we don’t compound errors here. We need to look for a long term solution, which isn’t possible while still maintaining the release deadlines of Python 3.10. That means we’re also deferring PEP 649 to Python 3.11. In the Steering Council’s unanimous opinion, rolling back the default flip for stringified annotations in Python 3.10 is the least disruptive of all the options. We need to continue discussing the issue and potential solutions, since this merely postpones the problem until 3.11. (For the record, postponing the change further is not off the table, either, for example if the final decision is to treat evaluated annotations as a deprecated feature, with warnings on use.) For what it’s worth, the SC is also considering what we can do to reduce the odds of something like this happening again, but that’s a separate consideration, and a multi-faceted one at that. For the Steering Council, Thomas. -- Thomas Wouters <thomas(a)python.org> Hi! I'm an email virus! Think twice before sending your email to help me spread!

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compiled python3.10 is unable to find _ssl
by Sandeep Gupta 29 May '22

29 May '22

I am having compilation issues again with python3.10 with ssl . The ./configure was invoked with ssl options and ssl modules seems to be build successfully. """ The following modules found by detect_modules() in setup.py, have been built by the Makefile instead, as configured by the Setup files: _abc _hashlib _ssl pwd time """ However, when I do import ssl from python, I get the error: Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/shared/Builds/Python-3.10.0/lib/python3.10/ssl.py", line 98, in <module> import _ssl # if we can't import it, let the error propagate ModuleNotFoundError: No module named '_ssl' I can't find _ssl.so in build or install directory. Thanks -S

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Python multithreading without the GIL
by Sam Gross 25 Apr '22

25 Apr '22

Hi, I've been working on changes to CPython to allow it to run without the global interpreter lock. I'd like to share a working proof-of-concept that can run without the GIL. The proof-of-concept involves substantial changes to CPython internals, but relatively few changes to the C-API. It is compatible with many C extensions: extensions must be rebuilt, but usually require small or no modifications to source code. I've built compatible versions of packages from the scientific Python ecosystem, and they are installable through the bundled "pip". Source code: https://github.com/colesbury/nogil Design overview: https://docs.google.com/document/d/18CXhDb1ygxg-YXNBJNzfzZsDFosB5e6BfnXLlej… My goal with the proof-of-concept is to demonstrate that removing the GIL is feasible and worthwhile, and that the technical ideas of the project could serve as a basis of such an effort. I'd like to start a discussion about these ideas and gauge the community's interest in this approach to removing the GIL. Regards, Sam Gross colesbury(a)gmail.com / sgross(a)fb.com

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Slowly bend the C API towards the limited API to get a stable ABI for everyone
by Victor Stinner 20 Apr '22

20 Apr '22

Hi, There is a reason why I'm bothering C extensions maintainers and Python core developers with my incompatible C API changes since Python 3.8. Let me share my plan with you :-) In 2009 (Python 3.2), Martin v. Löwis did an amazing job with the PEP 384 "Defining a Stable ABI" to provide a "limited C API" and a "stable ABI" for C extensions: build an extension once, use it on multiple Python versions. Some projects like PyQt5 and cryptograpy use it, but it is just a drop in the PyPI ocean (353,084 projects). I'm trying to bend the "default" C API towards this "limited C API" to make it possible tomorrow to build *more* C extensions for the stable ABI. My goal is that the stable ABI would be the default, and only a minority of C extensions would opt-out because they need to access to more functions for best performance. The basic problem is that at the ABI level, C extensions must only call functions, rather than getting and setting directly to structure members. Structures changes frequently in Python (look at changes between Python 3.2 and Python 3.11), and any minor structure change breaks the ABI. The limited C API hides structures and only use function calls to solve this problem. Since 2020, I'm modifying the C API, one function by one, to slowly hide implementations (prepare the API to make strutures opaque). I focused on the following structures: * PyObject and PyVarObject (bpo-39573) * PyTypeObject (bpo-40170) * PyFrameObject (bpo-40421) * PyThreadState (bpo-39947) The majority of C extensions use functions and macros, they don't access directly structure members. There are a few members which are sometimes accessed directly which prevents making these structures opaque. For example, some old C extensions use obj->ob_type rather than Py_TYPE(obj). Fixing the minority of C extensions should benefit to the majority which may become compatible with the stable ABI. I am also converting macros to static inline functions to fix their API: define parameter types, result type and avoid surprising macros side effects ("macro pitfalls"). I wrote the PEP 670 "Convert macros to functions in the Python C API" for these changes. I wrote the upgrade_pythoncapi.py tool in my pythoncapi_project (*) which modify C code to use Py_TYPE(), Py_SIZE() and Py_REFCNT() rather than accessing directly PyObject and PyVarObject members. (*) https://github.com/pythoncapi/pythoncapi_compat In this tool, I also added "Borrow" variant of functions like PyFrame_GetCode() which returns a strong reference, to replace frame->f_code with _PyFrame_GetCodeBorrow(). In Python 3.11, you cannot use the frame->f_code member anymore, since it has been removed! You must call PyFrame_GetCode() (or pythoncapi_compat _PyFrame_GetCodeBorrow() variant). There are also a few macros which can be used as l-values like Py_TYPE(): "Py_TYPE(type1) = type2" must now be written "Py_SET_TYPE(type1, type2)" to avoid setting directly the tp_type type at the ABI level. I proposed the PEP 674 "Disallow using Py_TYPE() and Py_SIZE() macros as l-values" to solve these issues. Currently, many "functions" are still implemented as macros or static inline functions, so C extensions still access structure members at the ABI level for best Python performance. Converting these to regular functions has an impact on performance and I would prefer to first write a PEP giving the rationale for that. Today, it is not possible yet to build numpy for the stable ABI. The gap is just too large for this big C extension. But step by step, the C API becomes closer to the limited API, and more and more code is ready to be built for the stable ABI. Well, these C API changes have other advantages, like preparing Python for further optimizations, ease Python maintenance, clarify the seperation between the limited C API and the default C API, etc. ;-) Victor -- Night gathers, and now my watch begins. It shall not end until my death.

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PEP 678: Enriching Exceptions with Notes
by Zac Hatfield Dodds 12 Apr '22

12 Apr '22

Hi all, I've written PEP 678, proposing the addition of a __note__ attribute which can be used to enrich displayed exceptions. This is particularly useful with ExceptionGroup, or where exception chaining is unsuitable, and ensures that 'orphan' output is not produced if the annotated exception is caught. Link to the PEP: https://www.python.org/dev/peps/pep-0678/ *Please, redirect all discussions to:* https://discuss.python.org/t/pep-678-enriching-exceptions-with-notes/13374 Thanks for your time, and to those who have already made (very helpful!) comments! Best wishes from just-moved-to San Francisco, Zac Hatfield-Dodds

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It's now time to deprecate the stdlib urllib module
by Victor Stinner 31 Mar '22

31 Mar '22

Hi, I propose to deprecate the urllib module in Python 3.11. It would emit a DeprecationWarning which warn users, so users should consider better alternatives like urllib3 or httpx: well known modules, better maintained, more secure, support HTTP/2 (httpx), etc. I don't propose to schedule its removal. Let's discuss the removal in 1 or 2 years. -- urllib has many abstraction to support a wide range of protocols with "handlers": HTTP, HTTPS, FTP, "local file", proxy, HTTP authentication, HTTP Cookie, etc. A simple HTTP request using Basic Authentication requires 10-20 lines of code, whereas it should be a single line. Users (me included) don't like urllib API which was too complicated for common tasks. -- Unhappy users created multiple better alternatives to the stdlib urllib module. In 2008, the "urllib3" module was created to provide an API designed to be as simple as possible for the most common HTTP and HTTPS requests. Example: req = http.request('GET', 'http://httpbin.org/robots.txt'). In 2011, the "requests" module based on urllib3 was created. In 2013, the "aiohttp" module based on asyncio was created. In 2015, new "httpx" module was created: req = httpx.get('https://www.example.org/') Not only httpx has a regular "synchronous" API (blocking function calls), but it also has an asynchronous API! Sadly, while HTTP/3 is being developed, it seems like in this list, httpx is the only HTTP client library supporting HTTP/2 currently :-( For HTTP/2, I also found the "httplib2" module. For HTTP/3, I found the "http3" and "aioquic" modules. -- Let's come back to urllib: * It's API is too complicated * It doesn't support HTTP/2 nor HTTP/3 * It's barely maintained: there are 121 open issues including 3 security issues! The 3 open security issues: * bpo-33661 open 2018; * bpo-36338 open in 2019; * bpo-45795 open in 2021. Usually, it's bad when you refer to an open security issue by its creation year :-( The urllib module has long history of security vulnerabilities. List of *fixed* vulnerabilities: * 2011 (bpo-11662): https://python-security.readthedocs.io/vuln/urllib-redirect.html * 2017 (bpo-30119): https://python-security.readthedocs.io/vuln/urllib-ftp-stream-injection.html * 2017 (bpo-30500): https://python-security.readthedocs.io/vuln/urllib-connects-wrong-host.html * 2019 (bpo-35907): https://python-security.readthedocs.io/vuln/urllib-local-file-scheme.html * 2019 (bpo-38826): https://python-security.readthedocs.io/vuln/urllib-basic-auth-regex.html * 2021 (bpo-42967): https://python-security.readthedocs.io/vuln/urllib-query-string-semicolon-s… * 2021 (bpo-43075): https://python-security.readthedocs.io/vuln/urllib-basic-auth-regex2.html * 2021 (bpo-44022): https://python-security.readthedocs.io/vuln/urllib-100-continue-loop.html urllib is a package made of 4 parts: * urllib.request for opening and reading URLs * urllib.error containing the exceptions raised by urllib.request * urllib.parse for parsing URLs * urllib.robotparser for parsing robots.txt files I propose to deprecate all of them. Maybe the deprecation can be different for each sub-module? Victor -- Night gathers, and now my watch begins. It shall not end until my death.

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A memory map based data persistence and startup speedup approach
by Yichen Yan 20 Mar '22

20 Mar '22

Hi folks, as illustrated in faster-cpython#150 [1], we have implemented a mechanism that supports data persistence of a subset of python date types with mmap, therefore can reduce package import time by caching code object. This could be seen as a more eager pyc format, as they are for the same purpose, but our approach try to avoid [de]serialization. Therefore, we get a speedup in overall python startup by ~15%. Currently, we’ve made it a third-party library and have been working on open-sourcing. Our implementation (whose non-official name is “pycds”) mainly contains two parts: importlib hooks, this implements the mechanism to dump code objects to an archive and a `Finder` that supports loading code object from mapped memory. Dumping and loading (subset of) python types with mmap. In this part, we deal with 1) ASLR by patching `ob_type` fields; 2) hash seed randomization by supporting only basic types who don’t have hash-based layout (i.e. dict is not supported); 3) interned string by re-interning strings while loading mmap archive and so on. After pycds has been installed, complete workflow of our approach includes three parts: Record name of imported packages to heap.lst, `PYCDSMODE=TRACE PYCDSLIST=heap.lst python run.py` Dump memory archive of code objects of imported packages, this step does not involve the python script, `PYCDSMODE=DUMP PYCDSLIST=heap.lst PYCDSARCHIVE=heap.img python` Run other python processes with created archive, `PYCDSMODE=SHARE PYCDSARCHIVE=heap.img python run.py` We could even make use of immortal objects if PEP 683 [2] was accepted, which could give CDS more performance improvements. Currently, any archived object is virtually immortal, we add rc by 1 to who has been copied to the archive to avoid being deallocated. However, without changes to CPython, rc fields of archived objects will still be updated, therefore have extra footprint due to CoW. More background and detailed implementation could be found at [1]. We think it could be an effective way to improve python’s startup performance, and could even do more like sharing large data between python instances. As suggested in python-ideas [3], we posted this here, looking for questions/suggestions to the overall design and workflow, we also welcome code reviews after we get our lawyers happy and can publish the code. Best, Yichen Yan Alibaba Compiler Group [1] “Faster startup -- Share code objects from memory-mapped file”, https://github.com/faster-cpython/ideas/discussions/150 [2] PEP 683: "Immortal Objects, Using a Fixed Refcount" (draft), https://mail.python.org/archives/list/python-dev@python.org/message/TPLEYDC… [3] [Python-ideas] "A memory map based data persistence and startup speedup approach", https://mail.python.org/archives/list/python-ideas@python.org/thread/UKEBNH…

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Python-Dev February 2022