Python-Dev November 2020

python-dev@python.org

92 participants
48 discussions

by Mark Shannon

Hi everyone, CPython is slow. We all know that, yet little is done to fix it. I'd like to change that. I have a plan to speed up CPython by a factor of five over the next few years. But it needs funding. I am aware that there have been several promised speed ups in the past that have failed. You might wonder why this is different. Here are three reasons: 1. I already have working code for the first stage. 2. I'm not promising a silver bullet. I recognize that this is a substantial amount of work and needs funding. 3. I have extensive experience in VM implementation, not to mention a PhD in the subject. My ideas for possible funding, as well as the actual plan of development, can be found here: https://github.com/markshannon/faster-cpython I'd love to hear your thoughts on this. Cheers, Mark.

23 hours, 25 minutes

Boundaries between numbers and identifiers

by Serhiy Storchaka

In Python 2.5 `0or[]` was accepted by the Python parser. It became an error in 2.6 because "0o" became recognizing as an incomplete octal number. `1or[]` still is accepted. On other hand, `1if 2else 3` is accepted despites the fact that "2e" can be recognized as an incomplete floating point number. In this case the tokenizer pushes "e" back and returns "2". Shouldn't it do the same with "0o"? It is possible to make `0or[]` be parseable again. Python implementation is able to tokenize this example: $ echo '0or[]' | ./python -m tokenize 1,0-1,1: NUMBER '0' 1,1-1,3: NAME 'or' 1,3-1,4: OP '[' 1,4-1,5: OP ']' 1,5-1,6: NEWLINE '\n' 2,0-2,0: ENDMARKER '' On other hand, all these examples look weird. There is an assymmetry: `1or 2` is a valid syntax, but `1 or2` is not. It is hard to recognize visually the boundary between a number and the following identifier or keyword, especially if numbers can contain letters ("b", "e", "j", "o", "x") and underscores, and identifiers can contain digits. On both sides of the boundary can be letters, digits, and underscores. I propose to change the Python syntax by adding a requirement that there should be a whitespace or delimiter between a numeric literal and the following keyword.

3 weeks, 2 days

PEP 624: Remove Py_UNICODE encoder APIs

by Inada Naoki

Hi, folks. Since the previous discussion was suspended without consensus, I wrote a new PEP for it. (Thank you Victor for reviewing it!) This PEP looks very similar to PEP 623 "Remove wstr from Unicode", but for encoder APIs, not for Unicode object APIs. URL (not available yet): https://www.python.org/dev/peps/pep-0624/ --- PEP: 624 Title: Remove Py_UNICODE encoder APIs Author: Inada Naoki <songofacandy(a)gmail.com> Status: Draft Type: Standards Track Content-Type: text/x-rst Created: 06-Jul-2020 Python-Version: 3.11 Abstract ======== This PEP proposes to remove deprecated ``Py_UNICODE`` encoder APIs in Python 3.11: * ``PyUnicode_Encode()`` * ``PyUnicode_EncodeASCII()`` * ``PyUnicode_EncodeLatin1()`` * ``PyUnicode_EncodeUTF7()`` * ``PyUnicode_EncodeUTF8()`` * ``PyUnicode_EncodeUTF16()`` * ``PyUnicode_EncodeUTF32()`` * ``PyUnicode_EncodeUnicodeEscape()`` * ``PyUnicode_EncodeRawUnicodeEscape()`` * ``PyUnicode_EncodeCharmap()`` * ``PyUnicode_TranslateCharmap()`` * ``PyUnicode_EncodeDecimal()`` * ``PyUnicode_TransformDecimalToASCII()`` .. note:: `PEP 623 <https://www.python.org/dev/peps/pep-0623/>`_ propose to remove Unicode object APIs relating to ``Py_UNICODE``. On the other hand, this PEP is not relating to Unicode object. These PEPs are split because they have different motivation and need different discussion. Motivation ========== In general, reducing the number of APIs that have been deprecated for a long time and have few users is a good idea for not only it improves the maintainability of CPython, but it also helps API users and other Python implementations. Rationale ========= Deprecated since Python 3.3 --------------------------- ``Py_UNICODE`` and APIs using it are deprecated since Python 3.3. Inefficient ----------- All of these APIs are implemented using ``PyUnicode_FromWideChar``. So these APIs are inefficient when user want to encode Unicode object. Not used widely --------------- When searching from top 4000 PyPI packages [1]_, only pyodbc use these APIs. * ``PyUnicode_EncodeUTF8()`` * ``PyUnicode_EncodeUTF16()`` pyodbc uses these APIs to encode Unicode object into bytes object. So it is easy to fix it. [2]_ Alternative APIs ================ There are alternative APIs to accept ``PyObject *unicode`` instead of ``Py_UNICODE *``. Users can migrate to them. ========================================= ========================================== Deprecated API Alternative APIs ========================================= ========================================== ``PyUnicode_Encode()`` ``PyUnicode_AsEncodedString()`` ``PyUnicode_EncodeASCII()`` ``PyUnicode_AsASCIIString()`` \(1) ``PyUnicode_EncodeLatin1()`` ``PyUnicode_AsLatin1String()`` \(1) ``PyUnicode_EncodeUTF7()`` \(2) ``PyUnicode_EncodeUTF8()`` ``PyUnicode_AsUTF8String()`` \(1) ``PyUnicode_EncodeUTF16()`` ``PyUnicode_AsUTF16String()`` \(3) ``PyUnicode_EncodeUTF32()`` ``PyUnicode_AsUTF32String()`` \(3) ``PyUnicode_EncodeUnicodeEscape()`` ``PyUnicode_AsUnicodeEscapeString()`` ``PyUnicode_EncodeRawUnicodeEscape()`` ``PyUnicode_AsRawUnicodeEscapeString()`` ``PyUnicode_EncodeCharmap()`` ``PyUnicode_AsCharmapString()`` \(1) ``PyUnicode_TranslateCharmap()`` ``PyUnicode_Translate()`` ``PyUnicode_EncodeDecimal()`` \(4) ``PyUnicode_TransformDecimalToASCII()`` \(4) ========================================= ========================================== Notes: (1) ``const char *errors`` parameter is missing. (2) There is no public alternative API. But user can use generic ``PyUnicode_AsEncodedString()`` instead. (3) ``const char *errors, int byteorder`` parameters are missing. (4) There is no direct replacement. But ``Py_UNICODE_TODECIMAL`` can be used instead. CPython uses ``_PyUnicode_TransformDecimalAndSpaceToASCII`` for converting from Unicode to numbers instead. Plan ==== Python 3.9 ---------- Add ``Py_DEPRECATED(3.3)`` to following APIs. This change is committed already [3]_. All other APIs have been marked ``Py_DEPRECATED(3.3)`` already. * ``PyUnicode_EncodeDecimal()`` * ``PyUnicode_TransformDecimalToASCII()``. Document all APIs as "will be removed in version 3.11". Python 3.11 ----------- These APIs are removed. * ``PyUnicode_Encode()`` * ``PyUnicode_EncodeASCII()`` * ``PyUnicode_EncodeLatin1()`` * ``PyUnicode_EncodeUTF7()`` * ``PyUnicode_EncodeUTF8()`` * ``PyUnicode_EncodeUTF16()`` * ``PyUnicode_EncodeUTF32()`` * ``PyUnicode_EncodeUnicodeEscape()`` * ``PyUnicode_EncodeRawUnicodeEscape()`` * ``PyUnicode_EncodeCharmap()`` * ``PyUnicode_TranslateCharmap()`` * ``PyUnicode_EncodeDecimal()`` * ``PyUnicode_TransformDecimalToASCII()`` Alternative ideas ================= Instead of just removing deprecated APIs, we may be able to use thier names with different signature. Make some private APIs public ------------------------------ ``PyUnicode_EncodeUTF7()`` doesn't have public alternative APIs. Some APIs have alternative public APIs. But they are missing ``const char *errors`` or ``int byteorder`` parameters. We can rename some private APIs and make them public to cover missing APIs and parameters. ============================= ================================ Rename to Rename from ============================= ================================ ``PyUnicode_EncodeASCII()`` ``_PyUnicode_AsASCIIString()`` ``PyUnicode_EncodeLatin1()`` ``_PyUnicode_AsLatin1String()`` ``PyUnicode_EncodeUTF7()`` ``_PyUnicode_EncodeUTF7()`` ``PyUnicode_EncodeUTF8()`` ``_PyUnicode_AsUTF8String()`` ``PyUnicode_EncodeUTF16()`` ``_PyUnicode_EncodeUTF16()`` ``PyUnicode_EncodeUTF32()`` ``_PyUnicode_EncodeUTF32()`` ============================= ================================ Pros: * We have more consistent API set. Cons: * We have more public APIs to maintain. * Existing public APIs are enough for most use cases, and ``PyUnicode_AsEncodedString()`` can be used in other cases. Replace ``Py_UNICODE*`` with ``Py_UCS4*`` ----------------------------------------- We can replace ``Py_UNICODE`` (typedef of ``wchar_t``) with ``Py_UCS4``. Since builtin codecs support UCS-4, we don't need to convert ``Py_UCS4*`` string to Unicode object. Pros: * We have more consistent API set. * User can encode UCS-4 string in C without creating Unicode object. Cons: * We have more public APIs to maintain. * Applications which uses UTF-8 or UTF-32 can not use these APIs anyway. * Other Python implementations may not have builtin codec for UCS-4. * If we change the Unicode internal representation to UTF-8, we need to keep UCS-4 support only for these APIs. Replace ``Py_UNICODE*`` with ``wchar_t*`` ----------------------------------------- We can replace ``Py_UNICODE`` to ``wchar_t``. Pros: * We have more consistent API set. * Backward compatible. Cons: * We have more public APIs to maintain. * They are inefficient on platforms ``wchar_t*`` is UTF-16. It is because built-in codecs supports only UCS-1, UCS-2, and UCS-4 input. Rejected ideas ============== Using runtime warning --------------------- These APIs doesn't release GIL for now. Emitting a warning from such APIs is not safe. See this example. .. code-block:: PyObject *u = PyList_GET_ITEM(list, i); // u is borrowed reference. PyObject *b = PyUnicode_EncodeUTF8(PyUnicode_AS_UNICODE(u), PyUnicode_GET_SIZE(u), NULL); // Assumes u is still living reference. PyObject *t = PyTuple_Pack(2, u, b); Py_DECREF(b); return t; If we emit Python warning from ``PyUnicode_EncodeUTF8()``, warning filters and other threads may change the ``list`` and ``u`` can be a dangling reference after ``PyUnicode_EncodeUTF8()`` returned. Additionally, since we are not changing behavior but removing C APIs, runtime ``DeprecationWarning`` might not helpful for Python developers. We should warn to extension developers instead. Discussions =========== * `Plan to remove Py_UNICODE APis except PEP 623 <https://mail.python.org/archives/list/python-dev@python.org/thread/S7KW2U6I…>`_ * `bpo-41123: Remove Py_UNICODE APIs except PEP 623: <https://bugs.python.org/issue41123>`_ References ========== .. [1] Source package list chosen from top 4000 PyPI packages. (https://github.com/methane/notes/blob/master/2020/wchar-cache/package_list.…) .. [2] pyodbc -- Don't use PyUnicode_Encode API #792 (https://github.com/mkleehammer/pyodbc/pull/792) .. [3] Uncomment Py_DEPRECATED for Py_UNICODE APIs (GH-21318) (https://github.com/python/cpython/commit/9c3840870814493fed62e140cfa43c2883…) Copyright ========= This document has been placed in the public domain. -- Inada Naoki <songofacandy(a)gmail.com>

3 months

Improve CPython tracing performance

by Fabio Zadrozny

Hi all, Right now, when a debugger is active, the number of local variables can affect the tracing speed quite a lot. For instance, having tracing setup in a program such as the one below takes 4.64 seconds to run, yet, changing all the variables to have the same name -- i.e.: change all assignments to `a = 1` (such that there's only a single variable in the namespace), it takes 1.47 seconds (in my machine)... the higher the number of variables, the slower the tracing becomes. ``` import time t = time.time() def call(): a = 1 b = 1 c = 1 d = 1 e = 1 f = 1 def noop(frame, event, arg): return noop import sys sys.settrace(noop) for i in range(1_000_000): call() print('%.2fs' % (time.time() - t,)) ``` This happens because `PyFrame_FastToLocalsWithError` and `PyFrame_LocalsToFast` are called inside the `call_trampoline` ( https://github.com/python/cpython/blob/master/Python/sysmodule.c#L946). So, I'd like to simply remove those calls. Debuggers can call `PyFrame_LocalsToFast` when needed -- otherwise mutating non-current frames doesn't work anyways. As a note, pydevd already has such a call: https://github.com/fabioz/PyDev.Debugger/blob/0d4d210f01a1c0a8647178b2e665b… and PyPy also has a counterpart. As for `PyFrame_FastToLocalsWithError`, I don't really see any reason to call it at all. i.e.: something as the code below prints the `a` variable from the `main()` frame regardless of that and I checked all pydevd tests and nothing seems to be affected (it seems that accessing f_locals already does this: https://github.com/python/cpython/blob/cb9879b948a19c9434316f8ab6aba9c4601a…, so, I don't see much reason to call it at all). ``` def call(): import sys frame = sys._getframe() print(frame.f_back.f_locals) def main(): a = 1 call() if __name__ == '__main__': main() ``` Does anyone see any issue with this? If it's non controversial, is a PEP needed or just an issue to track it would be enough to remove those 2 lines? Thanks, Fabio

3 months, 1 week

pathlib.Path: inconsistent symlink_to() and link_to()

by Barney Gale

Hi, Pathlib's symlink_to() and link_to() methods have different argument orders, so: a.symlink_to(b) # Creates a symlink from A to B a.link_to(b) # Creates a hard link from B to A I don't think link_to() was intended to be implemented this way, as the docs say "Create a hard link pointing to a path named target.". It's also inconsistent with everything else in pathlib, most obviously symlink_to(). Bug report here: https://bugs.python.org/issue39291 This /really/ irks me. Apparently it's too late to fix link_to(), so I'd like to suggest we add a new hardlink_to() method that matches the symlink_to() argument order. link_to() then becomes deprecated/undocumented. Any thoughts? Barney

3 months, 2 weeks

Advantages of pattern matching - a simple comparative analysis

by Brian Coleman

Take as an example a function designed to process a tree of nodes similar to that which might be output by a JSON parser. There are 4 types of node: - A node representing JSON strings - A node representing JSON numbers - A node representing JSON arrays - A node representing JSON dictionaries The function transforms a tree of nodes, beginning at the root node, and proceeding recursively through each child node in turn. The result is a Python object, with the following transformation applied to each node type: - A JSON string `->` Python `str` - A JSON number `->` Python `float` - A JSON array `->` Python `list` - A JSON dictionary `->` Python `dict` I have implemented this function using 3 different approaches: - The visitor pattern - `isinstance` checks against the node type - Pattern matching Here is the implementation using the visitor pattern: ``` from typing import List, Tuple class NodeVisitor: def visit_string_node(self, node: StringNode): pass def visit_number_node(self, node: NumberNode): pass def visit_list_node(self, node: ListNode): pass def visit_dict_node(self, node: DictNode): pass class Node: def visit(visitor: NodeVisitor): raise NotImplementedError() class StringNode(Node): value: str def visit(self, visitor: NodeVisitor): visitor.visit_string_node(self) class NumberNode(Node): value: str def visit(self, visitor: NodeVisitor): visitor.visit_number_node(self) class ListNode(Node): children: List[Node] def visit(self, visitor: NodeVisitor): visitor.visit_list_node(self) class DictNode(Node): children: List[Tuple[str, Node]] def visit(self, visitor: NodeVisitor): visitor.visit_dict_node(self) class Processor(NodeVisitor): def process(root_node: Node): return root_node.visit(self) def visit_string_node(self, node: StringNode): return node.value def visit_number_node(self, node: NumberNode): return float(node.value) def visit_list_node(self, node: ListNode): return [child_node.visit(self) for child_node in node.children] def visit_dict_node(self, node: DictNode): return {key: child_node.visit(self) for key, child_node in node.children} def process(root_node: Node): processor = Processor() return processor.process(root_node) ``` Here is the implementation using `isinstance` checks against the node type: ``` from typing import List, Tuple class Node: pass class StringNode(Node): value: str class NumberNode(Node): value: str class ListNode(Node): children: List[Node] class DictNode(Node): children: List[Tuple[str, Node]] def process(root_node: Node): def process_node(node: Node): if isinstance(node, StringNode): return node.value elif isinstance(node, NumberNode): return float(node.value) elif isinstance(node, ListNode): return [process_node(child_node) for child_node in node.children] elif isinstance(node, DictNode): return {key: process_node(child_node) for key, child_node in node.children} else: raise Exception('Unexpected node') return process_node(root_node) ``` Finally here is the implementation using pattern matching: ``` from typing import List, Tuple class Node: pass class StringNode(Node): value: str class NumberNode(Node): value: str class ListNode(Node): children: List[Node] class DictNode(Node): children: List[Tuple[str, Node]] def process(root_node: Node): def process_node(node: Node): match node: case StringNode(value=str_value): return str_value case NumberNode(value=number_value): return float(number_value) case ListNode(children=child_nodes): return [process_node(child_node) for child_node in child_nodes] case DictNode(children=child_nodes): return {key: process_node(child_node) for key, child_node in child_nodes} case _: raise Exception('Unexpected node') return process_node(root_node) ``` Here are the lengths of the different implementations: - Pattern matching `->` 37 lines - `isinstance` checks `->` 36 lines - The visitor pattern `->` 69 lines The visitor pattern implementation is by far the most verbose solution, weighing in at almost twice the length of the alternative implementations due to the large amount of boilerplate that is necessary to achieve double dispatch. The pattern matching and `isinstance` check implementations are very similar in length for this trivial example. In each implementation, there are 2 operations performed on each node. - Determine the type of the node - Destructure the node to extract the desired data The visitor pattern and `isinstance` check implementations separate these 2 operations, whereas the pattern matching approach combines the operations together. I believe that it is the declarative nature of pattern matching, where the operations of determining the type of the node and destructuring the node are combined into a single clause, which allows pattern matching to express a concise solution to the problem. In this trivial example, the advantage of pattern matching over the alternative of using a sequence of `if`-`elif`-`else` statements is not as obvious as it would be when compared to a more complex example, where a sub-tree of nodes might be matched based on their type and be destructured in a single clause. I have seen elsewhere an argument that pattern matching should not be accepted into Python as it introduces a pseudo-DSL that is separate from the rest of the language. I agree that pattern matching might be viewed as a pseudo-DSL, but I believe that it is a good thing, if it allows the solution to certain classes of problems to be expressed in a concise manner. People often raise similar objections to operator overloading in other languages, whereas the presence of operator overloading in Python allows mathematical expressions involving custom numeric types such as vectors to be expressed in a natural way. Furthermore, Python has a regular expression module which implements it's own DSL for the purpose of matching string patterns. Regular expressions, in a similar way to pattern matching, allow string patterns to be expressed in a concise and declarative manner. I really hope that the Steering Council accepts pattern matching into Python. I think that it allows for processing of heterogeneous graphs of objects using recursion in a concise, declarative manner. I would like to thank the authors of the Structural Pattern Matching PEP for their hard work in designing this feature and developing an implementation of it. I believe that it will be a wonderful addition to the language that I am very much looking forward to using.

3 months, 2 weeks

Review patch fixing packed bitfields in ctypes struct/union

by Filipe Laíns

Hi folks, I know this is a high-volume list so sorry for being yet another voice screaming into your mailbox, but I do not know how else to handle this. A few months ago, I opened a pull request fixing packed bitfields in ctypes struct/union, which right now are being incorrectly created. The offsets and size are all wrong in some cases, fields which should be packed end up with padding between them. bpo: https://bugs.python.org/issue29753 PR: https://github.com/python/cpython/pull/19850 Since ctypes has no maintainer it has been very hard tracking down someone who is up to review this. So, if this issue sparks some interest to you, or you just want to help me out, please have a look :) Cheers, Filipe Laíns

4 months

Thoughts on PEP 634 (Structural Pattern Matching)

by Mark Shannon

Hi everyone, PEP 634/5/6 presents a possible implementation of pattern matching for Python. Much of the discussion around PEP 634, and PEP 622 before it, seems to imply that PEP 634 is synonymous with pattern matching; that if you reject PEP 634 then you are rejecting pattern matching. That simply isn't true. Can we discuss whether we want pattern matching in Python and the broader semantics first, before dealing with low level details? Do we want pattern matching in Python at all? --------------------------------------------- Pattern matching works really well in statically typed, functional languages. The lack of mutability, constrained scope and the ability of the compiler to distinguish let variables from constants means that pattern matching code has fewer errors, and can be compiled efficiently. Pattern matching works less well in dynamically-typed, functional languages and statically-typed, procedural languages. Nevertheless, it works well enough for it to be a popular feature in both erlang and rust. In dynamically-typed, procedural languages, however, it is not clear (at least not to me) that it works well enough to be worthwhile. That is not say that pattern matching could never be of value in Python, but PEP 635 fails to demonstrate that it can (although it does a better job than PEP 622). Should match be an expression, or a statement? ---------------------------------------------- Do we want a fancy switch statement, or a powerful expression? Expressions have the advantage of not leaking (like comprehensions in Python 3), but statements are easier to work with. Can pattern matching make it clear what is assigned? ---------------------------------------------------- Embedding the variables to be assigned into a pattern, makes the pattern concise, but requires discarding normal Python syntax and inventing a new sub-language. Could we make patterns fit Python better? Is it possible to make assignment to variables clear, and unambiguous, and allow the use of symbolic constants at the same time? I think it is, but PEP 634 fails to do this. How should pattern matching be integrated with the object model? ---------------------------------------------------------------- What special method(s) should be added? How and when should they be called? PEP 634 largely disregards the object model, meaning it has many special cases, and is inefficient. The semantics must be well defined. ----------------------------------- Language extensions PEPs should define the semantics of those extensions. For example, PEP 343 and PEP 380 both did. https://www.python.org/dev/peps/pep-0343/#specification-the-with-statement https://www.python.org/dev/peps/pep-0380/#formal-semantics PEP 634 just waves its hands and talks about undefined behavior, which horrifies me. In summary, I would ask anyone who wants pattern matching adding to Python, to not support PEP 634. PEP 634 just isn't a good fit for Python, and we deserve something better. Cheers, Mark.

4 months, 1 week

pip 20.3 release (new resolver as default)

by Sumana Harihareswara

On behalf of the PyPA and the pip team, I am pleased to announce that we have just released pip 20.3, a new version of pip. You can install it by running `python -m pip install --upgrade pip`. This is an important and disruptive release -- we [explained why in a blog post last year](https://pyfound.blogspot.com/2019/12/moss-czi-support-pip.html). We even made [a video about it](https://www.youtube.com/watch?v=B4GQCBBsuNU). ## Highlights * **DISRUPTION**: Switch to the new dependency resolver by default. (#9019) Watch out for changes in handling editable installs, constraints files, and more: https://pip.pypa.io/en/latest/user_guide/#changes-to-the-pip-dependency-res… * **DEPRECATION**: Deprecate support for Python 3.5 (to be removed in pip 21.0) (#8181) * **DEPRECATION**: pip freeze will stop filtering the pip, setuptools, distribute and wheel packages from pip freeze output in a future version. To keep the previous behavior, users should use the new `--exclude` option. (#4256) * Substantial improvements in new resolver for performance, output and error messages, avoiding infinite loops, and support for constraints files. * Support for PEP 600: Future ‘manylinux’ Platform Tags for Portable Linux Built Distributions. (#9077) * Documentation improvements: Resolver migration guide, quickstart guide, and new documentation theme. * Add support for MacOS Big Sur compatibility tags. (#9138) The new resolver is now *on by default*. It is significantly stricter and more consistent when it receives incompatible instructions, and reduces support for certain kinds of constraints files, so some workarounds and workflows may break. Please see [our guide on how to test and migrate, and how to report issues](https://pip.pypa.io/en/latest/user_guide/#changes-to-the-pip-dependency-resolver-in-20-3-2020). You can use the deprecated (old) resolver, using the flag `--use-deprecated=legacy-resolver`, until we remove it in the pip 21.0 release in January 2021. You can find more details (including deprecations and removals) [in the changelog](https://pip.pypa.io/en/stable/news/). ## User experience Command-line output for this version of pip, and documentation to help with errors, is significantly better, because you worked with our experts to test and improve it. [Contribute to our user experience work: sign up to become a member of the UX Studies group](https://bit.ly/pip-ux-studies) (after you join, we'll notify you about future UX surveys and interviews). ## What to expect in 20.1 We aim to release pip 20.1 in January 2021, per our [usual release cadence](https://pip.pypa.io/en/latest/development/release-process/#release-cadence). You can expect: * Removal of [Python 2.7](https://pip.pypa.io/en/latest/development/release-process/#python-2-support) and 3.5 support * Further improvements in the new resolver * Removal of legacy resolver support ## Thanks As with all pip releases, a significant amount of the work was contributed by pip's user community. Huge thanks to all who have contributed, whether through code, documentation, issue reports and/or discussion. Your help keeps pip improving, and is hugely appreciated. Specific thanks go to Mozilla (through its [Mozilla Open Source Support](https://www.mozilla.org/en-US/moss/) Awards) and to the [Chan Zuckerberg Initiative](https://chanzuckerberg.com/eoss/) DAF, an advised fund of Silicon Valley Community Foundation, for their funding that enabled substantial work on the new resolver. That funding went to [Simply Secure](https://simplysecure.org/) (specifically Georgia Bullen, Bernard Tyers, Nicole Harris, Ngọc Triệu, and Karissa McKelvey), [Changeset Consulting](https://changeset.nyc/) (Sumana Harihareswara), [Atos](https://www.atos.net) (Paul F. Moore), [Tzu-ping Chung](https://uranusjr.com), [Pradyun Gedam](https://pradyunsg.me/), and Ilan Schnell. Thanks also to Ernest W. Durbin III at the Python Software Foundation for liaising with the project. -Sumana Harihareswara, pip project manager

4 months, 3 weeks

Ideas for improving the contribution experience

by Tal Einat

(Context: Continuing to prepare for the core dev sprint next week. Since the sprint is near, *I'd greatly appreciate any quick comments, feedback and ideas!*) Following up my collection of past beginning contributor experiences, I've collected these experiences in a dedicated GitHub repo[1] and written a (subjective!) summary of main themes that I recognize in the stories, which I've also included in the repo[2]. A "TL;DR" bullet list of those main themes: * Slow/no responsiveness * Long, slow process * Hard to find where to contribute * Mentorship helps a lot, but is scarce * A lot to learn to get started * It's intimidating More specifically, something that has come up often is that maintaining momentum for new contributors is crucial for them to become long-term contributors. Most often, this comes up in relation to the first two points: Suggestions or PRs are completely receive no attention at all ("ignored") or stop receiving attention at some point ("lost to the void"). Unfortunately, the probability of this is pretty high for any issue/PR, so for a new contributor this is almost guaranteed to happen while working on one of their first few contributions. I've seen this happen many times, and have found that I have to personally follow promising contributors' work to ensure that this doesn't happen to them. I've also seen contributors learn to actively seek out core devs when these situations arise, which is often a successful tactic, but shouldn't be necessary so often. Now, this is in large part a result of the fact that us core devs are not a very large group, made up almost entirely of volunteers working on this in their spare time. Last I checked, the total amount of paid development time dedicated to developing Python is less than 3 full-time (i.e. ~100 hours a week). The situation being problematic is clear enough that the PSF had concrete plans to hire paid developers to review issues and PRs. However, those plans have been put on hold indefinitely, since the PSF's funding has shrunk dramatically since the COVID-19 outbreak (no PyCon!). So, what can be done? Besides raising more funds (see a note on this below), I think we can find ways to reduce how often issues/PRs become "stalled". Here are some ideas: 1. *Generate reminders for reviewers when an issue or PR becomes "stalled' due to them.* Personally, I've found that both b.p.o. and GitHub make it relatively hard to remember to follow up on all of the many issues/PRs you've taken part in reviewing. It takes considerable attention and discipline to do so consistently, and reminders like these would have helped me. Many (many!) times, all it took to get an issue/PR moving forward (or closed) was a simple "ping?" comment. 2. *Generate reminders for contributors when an issue or PR becomes "stalled" due to them.* Similar to the above, but I consider these separate. 3. *Advertise something like a "2-for-1" standing offer for reviews.* This would give contributors an "official", acceptable way to get attention for their issue/PR, other than "begging" for attention on a mailing list. There are good ways for new contributors to be of significant help despite being new to the project, such as checking whether old bugs are still relevant, searching for duplicate issues, or applying old patches to the current code and creating a PR. (This would be similar to Martin v. Löwis's 5-for-1 offer in 2012[3], which had little success but lead to some interesting followup discussion[4]). 4. *Encourage core devs to dedicate some of their time to working through issues/PRs which are "ignored" or "stalled".* This would require first generating reliable lists of issues/PRs in such states. This could be in various forms, such as predefined GitHub/b.p.o. queries, a dedicated web-page, a periodic message similar to b.p.o.'s "weekly summary" email, or dedicated tags/labels for issues/PRs. (Perhaps prioritize "stalled" over "ignored".) - Tal Einat [1]: https://github.com/taleinat/python-contribution-feedback [2]: https://github.com/taleinat/python-contribution-feedback/blob/master/Takeaw… [3]: https://mail.python.org/archives/list/python-dev@python.org/message/7DLUN4Y… [4]: https://mail.python.org/archives/list/python-dev@python.org/thread/N4MMHXXO…

5 months

2021

2020

2019

2018

2017

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

2000

1999

Python-Dev November 2020