Mailman 3 May 2017 - Python-Dev

PEP 448 review
by Guido van Rossum March 29, 2023

March 29, 2023

I'm back, I've re-read the PEP, and I've re-read the long thread with "(no subject)". I think Georg Brandl nailed it: """ *I like the "sequence and dict flattening" part of the PEP, mostly because itis consistent and should be easy to understand, but the comprehension syntaxenhancements seem to be bad for readability and "comprehending" what the codedoes.The call syntax part is a mixed bag on the one hand it is nice to be consistent with the extended possibilities in literals (flattening), but on the other hand there would be small but annoying inconsistencies anyways (e.g. the duplicate kwarg case above).* """ Greg Ewing followed up explaining that the inconsistency between dict flattening and call syntax is inherent in the pre-existing different rules for dicts vs. keyword args: {'a':1, 'a':2} results in {'a':2}, while f(a=1, a=2) is an error. (This form is a SyntaxError; the dynamic case f(a=1, **{'a': 1}) is a TypeError.) For me, allowing f(*a, *b) and f(**d, **e) and all the other combinations for function calls proposed by the PEP is an easy +1 -- it's a straightforward extension of the existing pattern, and anybody who knows what f(x, *a) does will understand f(x, *a, y, *b). Guessing what f(**d, **e) means shouldn't be hard either. Understanding the edge case for duplicate keys with f(**d, **e) is a little harder, but the error messages are pretty clear, and it is not a new edge case. The sequence and dict flattening syntax proposals are also clean and logical -- we already have *-unpacking on the receiving side, so allowing *x in tuple expressions reads pretty naturally (and the similarity with *a in argument lists certainly helps). From here, having [a, *x, b, *y] is also natural, and then the extension to other displays is natural: {a, *x, b, *y} and {a:1, **d, b:2, **e}. This, too, gets a +1 from me. So that leaves comprehensions. IIRC, during the development of the patch we realized that f(*x for x in xs) is sufficiently ambiguous that we decided to disallow it -- note that f(x for x in xs) is already somewhat of a special case because an argument can only be a "bare" generator expression if it is the only argument. The same reasoning doesn't apply (in that form) to list, set and dict comprehensions -- while f(x for x in xs) is identical in meaning to f((x for x in xs)), [x for x in xs] is NOT the same as [(x for x in xs)] (that's a list of one element, and the element is a generator expression). The basic premise of this part of the proposal is that if you have a few iterables, the new proposal (without comprehensions) lets you create a list or generator expression that iterates over all of them, essentially flattening them: >>> xs = [1, 2, 3] >>> ys = ['abc', 'def'] >>> zs = [99] >>> [*xs, *ys, *zs] [1, 2, 3, 'abc', 'def', 99] >>> But now suppose you have a list of iterables: >>> xss = [[1, 2, 3], ['abc', 'def'], [99]] >>> [*xss[0], *xss[1], *xss[2]] [1, 2, 3, 'abc', 'def', 99] >>> Wouldn't it be nice if you could write the latter using a comprehension? >>> xss = [[1, 2, 3], ['abc', 'def'], [99]] >>> [*xs for xs in xss] [1, 2, 3, 'abc', 'def', 99] >>> This is somewhat seductive, and the following is even nicer: the *xs position may be an expression, e.g.: >>> xss = [[1, 2, 3], ['abc', 'def'], [99]] >>> [*xs[:2] for xs in xss] [1, 2, 'abc', 'def', 99] >>> On the other hand, I had to explore the possibilities here by experimenting in the interpreter, and I discovered some odd edge cases (e.g. you can parenthesize the starred expression, but that seems a syntactic accident). All in all I am personally +0 on the comprehension part of the PEP, and I like that it provides a way to "flatten" a sequence of sequences, but I think very few people in the thread have supported this part. Therefore I would like to ask Neil to update the PEP and the patch to take out the comprehension part, so that the two "easy wins" can make it into Python 3.5 (basically, I am accepting two-thirds of the PEP :-). There is some time yet until alpha 2. I would also like code reviewers (Benjamin?) to start reviewing the patch <http://bugs.python.org/issue2292>, taking into account that the comprehension part needs to be removed. -- --Guido van Rossum (python.org/~guido)

7 17

PEP 1, PEP Purpose and Guidelines
by barry＠zope.com May 18, 2021

May 18, 2021

It has been a while since I posted a copy of PEP 1 to the mailing lists and newsgroups. I've recently done some updating of a few sections, so in the interest of gaining wider community participation in the Python development process, I'm posting the latest revision of PEP 1 here. A version of the PEP is always available on-line at http://www.python.org/peps/pep-0001.html Enjoy, -Barry -------------------- snip snip -------------------- PEP: 1 Title: PEP Purpose and Guidelines Version: $Revision: 1.36 $ Last-Modified: $Date: 2002/07/29 18:34:59 $ Author: Barry A. Warsaw, Jeremy Hylton Status: Active Type: Informational Created: 13-Jun-2000 Post-History: 21-Mar-2001, 29-Jul-2002 What is a PEP? PEP stands for Python Enhancement Proposal. A PEP is a design document providing information to the Python community, or describing a new feature for Python. The PEP should provide a concise technical specification of the feature and a rationale for the feature. We intend PEPs to be the primary mechanisms for proposing new features, for collecting community input on an issue, and for documenting the design decisions that have gone into Python. The PEP author is responsible for building consensus within the community and documenting dissenting opinions. Because the PEPs are maintained as plain text files under CVS control, their revision history is the historical record of the feature proposal[1]. Kinds of PEPs There are two kinds of PEPs. A standards track PEP describes a new feature or implementation for Python. An informational PEP describes a Python design issue, or provides general guidelines or information to the Python community, but does not propose a new feature. Informational PEPs do not necessarily represent a Python community consensus or recommendation, so users and implementors are free to ignore informational PEPs or follow their advice. PEP Work Flow The PEP editor, Barry Warsaw <peps(a)python.org>, assigns numbers for each PEP and changes its status. The PEP process begins with a new idea for Python. It is highly recommended that a single PEP contain a single key proposal or new idea. The more focussed the PEP, the more successfully it tends to be. The PEP editor reserves the right to reject PEP proposals if they appear too unfocussed or too broad. If in doubt, split your PEP into several well-focussed ones. Each PEP must have a champion -- someone who writes the PEP using the style and format described below, shepherds the discussions in the appropriate forums, and attempts to build community consensus around the idea. The PEP champion (a.k.a. Author) should first attempt to ascertain whether the idea is PEP-able. Small enhancements or patches often don't need a PEP and can be injected into the Python development work flow with a patch submission to the SourceForge patch manager[2] or feature request tracker[3]. The PEP champion then emails the PEP editor <peps(a)python.org> with a proposed title and a rough, but fleshed out, draft of the PEP. This draft must be written in PEP style as described below. If the PEP editor approves, he will assign the PEP a number, label it as standards track or informational, give it status 'draft', and create and check-in the initial draft of the PEP. The PEP editor will not unreasonably deny a PEP. Reasons for denying PEP status include duplication of effort, being technically unsound, not providing proper motivation or addressing backwards compatibility, or not in keeping with the Python philosophy. The BDFL (Benevolent Dictator for Life, Guido van Rossum) can be consulted during the approval phase, and is the final arbitrator of the draft's PEP-ability. If a pre-PEP is rejected, the author may elect to take the pre-PEP to the comp.lang.python newsgroup (a.k.a. python-list(a)python.org mailing list) to help flesh it out, gain feedback and consensus from the community at large, and improve the PEP for re-submission. The author of the PEP is then responsible for posting the PEP to the community forums, and marshaling community support for it. As updates are necessary, the PEP author can check in new versions if they have CVS commit permissions, or can email new PEP versions to the PEP editor for committing. Standards track PEPs consists of two parts, a design document and a reference implementation. The PEP should be reviewed and accepted before a reference implementation is begun, unless a reference implementation will aid people in studying the PEP. Standards Track PEPs must include an implementation - in the form of code, patch, or URL to same - before it can be considered Final. PEP authors are responsible for collecting community feedback on a PEP before submitting it for review. A PEP that has not been discussed on python-list(a)python.org and/or python-dev(a)python.org will not be accepted. However, wherever possible, long open-ended discussions on public mailing lists should be avoided. Strategies to keep the discussions efficient include, setting up a separate SIG mailing list for the topic, having the PEP author accept private comments in the early design phases, etc. PEP authors should use their discretion here. Once the authors have completed a PEP, they must inform the PEP editor that it is ready for review. PEPs are reviewed by the BDFL and his chosen consultants, who may accept or reject a PEP or send it back to the author(s) for revision. Once a PEP has been accepted, the reference implementation must be completed. When the reference implementation is complete and accepted by the BDFL, the status will be changed to `Final.' A PEP can also be assigned status `Deferred.' The PEP author or editor can assign the PEP this status when no progress is being made on the PEP. Once a PEP is deferred, the PEP editor can re-assign it to draft status. A PEP can also be `Rejected'. Perhaps after all is said and done it was not a good idea. It is still important to have a record of this fact. PEPs can also be replaced by a different PEP, rendering the original obsolete. This is intended for Informational PEPs, where version 2 of an API can replace version 1. PEP work flow is as follows: Draft -> Accepted -> Final -> Replaced ^ +----> Rejected v Deferred Some informational PEPs may also have a status of `Active' if they are never meant to be completed. E.g. PEP 1. What belongs in a successful PEP? Each PEP should have the following parts: 1. Preamble -- RFC822 style headers containing meta-data about the PEP, including the PEP number, a short descriptive title (limited to a maximum of 44 characters), the names, and optionally the contact info for each author, etc. 2. Abstract -- a short (~200 word) description of the technical issue being addressed. 3. Copyright/public domain -- Each PEP must either be explicitly labelled as placed in the public domain (see this PEP as an example) or licensed under the Open Publication License[4]. 4. Specification -- The technical specification should describe the syntax and semantics of any new language feature. The specification should be detailed enough to allow competing, interoperable implementations for any of the current Python platforms (CPython, JPython, Python .NET). 5. Motivation -- The motivation is critical for PEPs that want to change the Python language. It should clearly explain why the existing language specification is inadequate to address the problem that the PEP solves. PEP submissions without sufficient motivation may be rejected outright. 6. Rationale -- The rationale fleshes out the specification by describing what motivated the design and why particular design decisions were made. It should describe alternate designs that were considered and related work, e.g. how the feature is supported in other languages. The rationale should provide evidence of consensus within the community and discuss important objections or concerns raised during discussion. 7. Backwards Compatibility -- All PEPs that introduce backwards incompatibilities must include a section describing these incompatibilities and their severity. The PEP must explain how the author proposes to deal with these incompatibilities. PEP submissions without a sufficient backwards compatibility treatise may be rejected outright. 8. Reference Implementation -- The reference implementation must be completed before any PEP is given status 'Final,' but it need not be completed before the PEP is accepted. It is better to finish the specification and rationale first and reach consensus on it before writing code. The final implementation must include test code and documentation appropriate for either the Python language reference or the standard library reference. PEP Template PEPs are written in plain ASCII text, and should adhere to a rigid style. There is a Python script that parses this style and converts the plain text PEP to HTML for viewing on the web[5]. PEP 9 contains a boilerplate[7] template you can use to get started writing your PEP. Each PEP must begin with an RFC822 style header preamble. The headers must appear in the following order. Headers marked with `*' are optional and are described below. All other headers are required. PEP: <pep number> Title: <pep title> Version: <cvs version string> Last-Modified: <cvs date string> Author: <list of authors' real names and optionally, email addrs> * Discussions-To: <email address> Status: <Draft | Active | Accepted | Deferred | Final | Replaced> Type: <Informational | Standards Track> * Requires: <pep numbers> Created: <date created on, in dd-mmm-yyyy format> * Python-Version: <version number> Post-History: <dates of postings to python-list and python-dev> * Replaces: <pep number> * Replaced-By: <pep number> The Author: header lists the names and optionally, the email addresses of all the authors/owners of the PEP. The format of the author entry should be address(a)dom.ain (Random J. User) if the email address is included, and just Random J. User if the address is not given. If there are multiple authors, each should be on a separate line following RFC 822 continuation line conventions. Note that personal email addresses in PEPs will be obscured as a defense against spam harvesters. Standards track PEPs must have a Python-Version: header which indicates the version of Python that the feature will be released with. Informational PEPs do not need a Python-Version: header. While a PEP is in private discussions (usually during the initial Draft phase), a Discussions-To: header will indicate the mailing list or URL where the PEP is being discussed. No Discussions-To: header is necessary if the PEP is being discussed privately with the author, or on the python-list or python-dev email mailing lists. Note that email addresses in the Discussions-To: header will not be obscured. Created: records the date that the PEP was assigned a number, while Post-History: is used to record the dates of when new versions of the PEP are posted to python-list and/or python-dev. Both headers should be in dd-mmm-yyyy format, e.g. 14-Aug-2001. PEPs may have a Requires: header, indicating the PEP numbers that this PEP depends on. PEPs may also have a Replaced-By: header indicating that a PEP has been rendered obsolete by a later document; the value is the number of the PEP that replaces the current document. The newer PEP must have a Replaces: header containing the number of the PEP that it rendered obsolete. PEP Formatting Requirements PEP headings must begin in column zero and the initial letter of each word must be capitalized as in book titles. Acronyms should be in all capitals. The body of each section must be indented 4 spaces. Code samples inside body sections should be indented a further 4 spaces, and other indentation can be used as required to make the text readable. You must use two blank lines between the last line of a section's body and the next section heading. You must adhere to the Emacs convention of adding two spaces at the end of every sentence. You should fill your paragraphs to column 70, but under no circumstances should your lines extend past column 79. If your code samples spill over column 79, you should rewrite them. Tab characters must never appear in the document at all. A PEP should include the standard Emacs stanza included by example at the bottom of this PEP. A PEP must contain a Copyright section, and it is strongly recommended to put the PEP in the public domain. When referencing an external web page in the body of a PEP, you should include the title of the page in the text, with a footnote reference to the URL. Do not include the URL in the body text of the PEP. E.g. Refer to the Python Language web site [1] for more details. ... [1] http://www.python.org When referring to another PEP, include the PEP number in the body text, such as "PEP 1". The title may optionally appear. Add a footnote reference that includes the PEP's title and author. It may optionally include the explicit URL on a separate line, but only in the References section. Note that the pep2html.py script will calculate URLs automatically, e.g.: ... Refer to PEP 1 [7] for more information about PEP style ... References [7] PEP 1, PEP Purpose and Guidelines, Warsaw, Hylton http://www.python.org/peps/pep-0001.html If you decide to provide an explicit URL for a PEP, please use this as the URL template: http://www.python.org/peps/pep-xxxx.html PEP numbers in URLs must be padded with zeros from the left, so as to be exactly 4 characters wide, however PEP numbers in text are never padded. Reporting PEP Bugs, or Submitting PEP Updates How you report a bug, or submit a PEP update depends on several factors, such as the maturity of the PEP, the preferences of the PEP author, and the nature of your comments. For the early draft stages of the PEP, it's probably best to send your comments and changes directly to the PEP author. For more mature, or finished PEPs you may want to submit corrections to the SourceForge bug manager[6] or better yet, the SourceForge patch manager[2] so that your changes don't get lost. If the PEP author is a SF developer, assign the bug/patch to him, otherwise assign it to the PEP editor. When in doubt about where to send your changes, please check first with the PEP author and/or PEP editor. PEP authors who are also SF committers, can update the PEPs themselves by using "cvs commit" to commit their changes. Remember to also push the formatted PEP text out to the web by doing the following: % python pep2html.py -i NUM where NUM is the number of the PEP you want to push out. See % python pep2html.py --help for details. Transferring PEP Ownership It occasionally becomes necessary to transfer ownership of PEPs to a new champion. In general, we'd like to retain the original author as a co-author of the transferred PEP, but that's really up to the original author. A good reason to transfer ownership is because the original author no longer has the time or interest in updating it or following through with the PEP process, or has fallen off the face of the 'net (i.e. is unreachable or not responding to email). A bad reason to transfer ownership is because you don't agree with the direction of the PEP. We try to build consensus around a PEP, but if that's not possible, you can always submit a competing PEP. If you are interested assuming ownership of a PEP, send a message asking to take over, addressed to both the original author and the PEP editor <peps(a)python.org>. If the original author doesn't respond to email in a timely manner, the PEP editor will make a unilateral decision (it's not like such decisions can be reversed. :). References and Footnotes [1] This historical record is available by the normal CVS commands for retrieving older revisions. For those without direct access to the CVS tree, you can browse the current and past PEP revisions via the SourceForge web site at http://cvs.sourceforge.net/cgi-bin/cvsweb.cgi/python/nondist/peps/?cvsroot=… [2] http://sourceforge.net/tracker/?group_id=5470&atid=305470 [3] http://sourceforge.net/tracker/?atid=355470&group_id=5470&func=browse [4] http://www.opencontent.org/openpub/ [5] The script referred to here is pep2html.py, which lives in the same directory in the CVS tree as the PEPs themselves. Try "pep2html.py --help" for details. The URL for viewing PEPs on the web is http://www.python.org/peps/ [6] http://sourceforge.net/tracker/?group_id=5470&atid=305470 [7] PEP 9, Sample PEP Template http://www.python.org/peps/pep-0009.html Copyright This document has been placed in the public domain. Local Variables: mode: indented-text indent-tabs-mode: nil sentence-end-double-space: t fill-column: 70 End:

8 14

congrats on 3.5! Alas, windows 7 users are having problems installing it
by Laura Creighton Sept. 15, 2019

Sept. 15, 2019

webmaster has already heard from 4 people who cannot install it. I sent them to the bug tracker or to python-list but they seem not to have gone either place. Is there some guide I should be sending them to, 'how to debug installation problems'? Laura

5 6

Help requested with Python 2.7 performance regression
by Barry Warsaw Aug. 17, 2017

Aug. 17, 2017

Hello all, Over in Ubuntu, we've gotten reports about some performance regressions in Python 2.7 when moving from Trusty (14.04 LTS) to Xenial (16.04 LTS). Trusty's version is based on 2.7.6 while Xenial's version is based on 2.7.12 with bits of .13 cherry picked. We've not been able to identify any change in Python itself (or the Debian/Ubuntu deltas) which could account for this, so the investigation has led to various gcc compiler options and version differences. In particular disabling LTO (link-time optimization) seems to have a positive impact, but doesn't completely regain the loss. Louis (Cc'd here) has done a ton of work to measure and analyze the problem, but we've more or less hit a roadblock, so we're taking the issue public to see if anybody on this mailing list has further ideas. A detailed analysis is available in this Google doc: https://docs.google.com/document/d/1zrV3OIRSo99fd2Ty4YdGk_scmTRDmVauBprKL8e… The document should be public for comments and editing. If you have any thoughts, or other lines of investigation you think are worthwhile pursuing, please add your comments to the document. Cheers, -Barry

11 21

Micro-optimizations by adding special-case bytecodes?
by Ben Hoyt June 28, 2017

June 28, 2017

Hi folks, I was looking at some `dis` output today, and I was wondering if anyone has investigated optimizing Python (slightly) by adding special-case bytecodes for common expressions or statements involving constants? For example, I (and, based on a quick grep of the stdlib, many others) write "x is None" and "x is not None" very often. Or "return True" or "return None" or "return 1" and things like that. These all expand into two bytecodes, which seems pretty non-optimal (LOAD_CONST + COMPARE_OP or LOAD_CONST + RETURN_VALUE). It seems we could get an easy speedup for these common cases by adding a peephole optimization and some new opcodes (maybe COMPARE_IS_SMALL_CONST and RETURN_SMALL_CONST for these cases). I'm not proposing to do this yet, as I'd need to benchmark to see how much of a gain (if any) it would amount to, but I'm just wondering if there's any previous work on this kind of thing. Or, if not, any other thoughts before I try it? Thanks, Ben

9 18

PEP 544: Protocols - second round
by Ivan Levkivskyi June 23, 2017

June 23, 2017

Hi all, After collecting suggestions in the previous discussion on python-dev https://mail.python.org/pipermail/python-dev/2017-March/thread.html#147629 and playing with implementation, here is an updated version of PEP 544. -- Ivan A link for those who don't like reading long e-mails: https://www.python.org/dev/peps/pep-0544/ ========================= PEP: 544 Title: Protocols Version: $Revision$ Last-Modified: $Date$ Author: Ivan Levkivskyi <levkivskyi(a)gmail.com>, Jukka Lehtosalo < jukka.lehtosalo(a)iki.fi>, Łukasz Langa <lukasz(a)langa.pl> Discussions-To: Python-Dev <python-dev(a)python.org> Status: Draft Type: Standards Track Content-Type: text/x-rst Created: 05-Mar-2017 Python-Version: 3.7 Abstract ======== Type hints introduced in PEP 484 can be used to specify type metadata for static type checkers and other third party tools. However, PEP 484 only specifies the semantics of *nominal* subtyping. In this PEP we specify static and runtime semantics of protocol classes that will provide a support for *structural* subtyping (static duck typing). .. _rationale: Rationale and Goals =================== Currently, PEP 484 and the ``typing`` module [typing]_ define abstract base classes for several common Python protocols such as ``Iterable`` and ``Sized``. The problem with them is that a class has to be explicitly marked to support them, which is unpythonic and unlike what one would normally do in idiomatic dynamically typed Python code. For example, this conforms to PEP 484:: from typing import Sized, Iterable, Iterator class Bucket(Sized, Iterable[int]): ... def __len__(self) -> int: ... def __iter__(self) -> Iterator[int]: ... The same problem appears with user-defined ABCs: they must be explicitly subclassed or registered. This is particularly difficult to do with library types as the type objects may be hidden deep in the implementation of the library. Also, extensive use of ABCs might impose additional runtime costs. The intention of this PEP is to solve all these problems by allowing users to write the above code without explicit base classes in the class definition, allowing ``Bucket`` to be implicitly considered a subtype of both ``Sized`` and ``Iterable[int]`` by static type checkers using structural [wiki-structural]_ subtyping:: from typing import Iterator, Iterable class Bucket: ... def __len__(self) -> int: ... def __iter__(self) -> Iterator[int]: ... def collect(items: Iterable[int]) -> int: ... result: int = collect(Bucket()) # Passes type check Note that ABCs in ``typing`` module already provide structural behavior at runtime, ``isinstance(Bucket(), Iterable)`` returns ``True``. The main goal of this proposal is to support such behavior statically. The same functionality will be provided for user-defined protocols, as specified below. The above code with a protocol class matches common Python conventions much better. It is also automatically extensible and works with additional, unrelated classes that happen to implement the required protocol. Nominal vs structural subtyping ------------------------------- Structural subtyping is natural for Python programmers since it matches the runtime semantics of duck typing: an object that has certain properties is treated independently of its actual runtime class. However, as discussed in PEP 483, both nominal and structural subtyping have their strengths and weaknesses. Therefore, in this PEP we *do not propose* to replace the nominal subtyping described by PEP 484 with structural subtyping completely. Instead, protocol classes as specified in this PEP complement normal classes, and users are free to choose where to apply a particular solution. See section on `rejected`_ ideas at the end of this PEP for additional motivation. Non-goals --------- At runtime, protocol classes will be simple ABCs. There is no intent to provide sophisticated runtime instance and class checks against protocol classes. This would be difficult and error-prone and will contradict the logic of PEP 484. As well, following PEP 484 and PEP 526 we state that protocols are **completely optional**: * No runtime semantics will be imposed for variables or parameters annotated with a protocol class. * Any checks will be performed only by third-party type checkers and other tools. * Programmers are free to not use them even if they use type annotations. * There is no intent to make protocols non-optional in the future. Existing Approaches to Structural Subtyping =========================================== Before describing the actual specification, we review and comment on existing approaches related to structural subtyping in Python and other languages: * ``zope.interface`` [zope-interfaces]_ was one of the first widely used approaches to structural subtyping in Python. It is implemented by providing special classes to distinguish interface classes from normal classes, to mark interface attributes, and to explicitly declare implementation. For example:: from zope.interface import Interface, Attribute, implementer class IEmployee(Interface): name = Attribute("Name of employee") def do(work): """Do some work""" @implementer(IEmployee) class Employee: name = 'Anonymous' def do(self, work): return work.start() Zope interfaces support various contracts and constraints for interface classes. For example:: from zope.interface import invariant def required_contact(obj): if not (obj.email or obj.phone): raise Exception("At least one contact info is required") class IPerson(Interface): name = Attribute("Name") email = Attribute("Email Address") phone = Attribute("Phone Number") invariant(required_contact) Even more detailed invariants are supported. However, Zope interfaces rely entirely on runtime validation. Such focus on runtime properties goes beyond the scope of the current proposal, and static support for invariants might be difficult to implement. However, the idea of marking an interface class with a special base class is reasonable and easy to implement both statically and at runtime. * Python abstract base classes [abstract-classes]_ are the standard library tool to provide some functionality similar to structural subtyping. The drawback of this approach is the necessity to either subclass the abstract class or register an implementation explicitly:: from abc import ABC class MyTuple(ABC): pass MyTuple.register(tuple) assert issubclass(tuple, MyTuple) assert isinstance((), MyTuple) As mentioned in the `rationale`_, we want to avoid such necessity, especially in static context. However, in a runtime context, ABCs are good candidates for protocol classes and they are already used extensively in the ``typing`` module. * Abstract classes defined in ``collections.abc`` module [collections-abc]_ are slightly more advanced since they implement a custom ``__subclasshook__()`` method that allows runtime structural checks without explicit registration:: from collections.abc import Iterable class MyIterable: def __iter__(self): return [] assert isinstance(MyIterable(), Iterable) Such behavior seems to be a perfect fit for both runtime and static behavior of protocols. As discussed in `rationale`_, we propose to add static support for such behavior. In addition, to allow users to achieve such runtime behavior for *user-defined* protocols a special ``@runtime`` decorator will be provided, see detailed `discussion`_ below. * TypeScript [typescript]_ provides support for user-defined classes and interfaces. Explicit implementation declaration is not required and structural subtyping is verified statically. For example:: interface LabeledItem { label: string; size?: int; } function printLabel(obj: LabeledItem) { console.log(obj.label); } let myObj = {size: 10, label: "Size 10 Object"}; printLabel(myObj); Note that optional interface members are supported. Also, TypeScript prohibits redundant members in implementations. While the idea of optional members looks interesting, it would complicate this proposal and it is not clear how useful it will be. Therefore it is proposed to postpone this; see `rejected`_ ideas. In general, the idea of static protocol checking without runtime implications looks reasonable, and basically this proposal follows the same line. * Go [golang]_ uses a more radical approach and makes interfaces the primary way to provide type information. Also, assignments are used to explicitly ensure implementation:: type SomeInterface interface { SomeMethod() ([]byte, error) } if _, ok := someval.(SomeInterface); ok { fmt.Printf("value implements some interface") } Both these ideas are questionable in the context of this proposal. See the section on `rejected`_ ideas. .. _specification: Specification ============= Terminology ----------- We propose to use the term *protocols* for types supporting structural subtyping. The reason is that the term *iterator protocol*, for example, is widely understood in the community, and coming up with a new term for this concept in a statically typed context would just create confusion. This has the drawback that the term *protocol* becomes overloaded with two subtly different meanings: the first is the traditional, well-known but slightly fuzzy concept of protocols such as iterator; the second is the more explicitly defined concept of protocols in statically typed code. The distinction is not important most of the time, and in other cases we propose to just add a qualifier such as *protocol classes* when referring to the static type concept. If a class includes a protocol in its MRO, the class is called an *explicit* subclass of the protocol. If a class is a structural subtype of a protocol, it is said to implement the protocol and to be compatible with a protocol. If a class is compatible with a protocol but the protocol is not included in the MRO, the class is an *implicit* subtype of the protocol. (Note that one can explicitly subclass a protocol and still not implement it if a protocol attribute is set to ``None`` in the subclass, see Python [data-model]_ for details.) The attributes (variables and methods) of a protocol that are mandatory for other class in order to be considered a structural subtype are called protocol members. .. _definition: Defining a protocol ------------------- Protocols are defined by including a special new class ``typing.Protocol`` (an instance of ``abc.ABCMeta``) in the base classes list, typically at the end of the list. Here is a simple example:: from typing import Protocol class SupportsClose(Protocol): def close(self) -> None: ... Now if one defines a class ``Resource`` with a ``close()`` method that has a compatible signature, it would implicitly be a subtype of ``SupportsClose``, since the structural subtyping is used for protocol types:: class Resource: ... def close(self) -> None: self.file.close() self.lock.release() Apart from few restrictions explicitly mentioned below, protocol types can be used in every context where a normal types can:: def close_all(things: Iterable[SupportsClose]) -> None: for t in things: t.close() f = open('foo.txt') r = Resource() close_all([f, r]) # OK! close_all([1]) # Error: 'int' has no 'close' method Note that both the user-defined class ``Resource`` and the built-in ``IO`` type (the return type of ``open()``) are considered subtypes of ``SupportsClose``, because they provide a ``close()`` method with a compatible type signature. Protocol members ---------------- All methods defined in the protocol class body are protocol members, both normal and decorated with ``@abstractmethod``. If any parameters of a protocol method are not annotated, then their types are assumed to be ``Any`` (see PEP 484). Bodies of protocol methods are type checked. An abstract method that should not be called via ``super()`` ought to raise ``NotImplementedError``. Example:: from typing import Protocol from abc import abstractmethod class Example(Protocol): def first(self) -> int: # This is a protocol member return 42 @abstractmethod def second(self) -> int: # Method without a default implementation raise NotImplementedError Static methods, class methods, and properties are equally allowed in protocols. To define a protocol variable, one can use PEP 526 variable annotations in the class body. Additional attributes *only* defined in the body of a method by assignment via ``self`` are not allowed. The rationale for this is that the protocol class implementation is often not shared by subtypes, so the interface should not depend on the default implementation. Examples:: from typing import Protocol, List class Template(Protocol): name: str # This is a protocol member value: int = 0 # This one too (with default) def method(self) -> None: self.temp: List[int] = [] # Error in type checker class Concrete: def __init__(self, name: str, value: int) -> None: self.name = name self.value = value var: Template = Concrete('value', 42) # OK To distinguish between protocol class variables and protocol instance variables, the special ``ClassVar`` annotation should be used as specified by PEP 526. By default, protocol variables as defined above are considered readable and writable. To define a read-only protocol variable, one can use an (abstract) property. Explicitly declaring implementation ----------------------------------- To explicitly declare that a certain class implements a given protocol, it can be used as a regular base class. In this case a class could use default implementations of protocol members. ``typing.Sequence`` is a good example of a protocol with useful default methods. Static analysis tools are expected to automatically detect that a class implements a given protocol. So while it's possible to subclass a protocol explicitly, it's *not necessary* to do so for the sake of type-checking. The default implementations cannot be used if the subtype relationship is implicit and only via structural subtyping -- the semantics of inheritance is not changed. Examples:: class PColor(Protocol): @abstractmethod def draw(self) -> str: ... def complex_method(self) -> int: # some complex code here class NiceColor(PColor): def draw(self) -> str: return "deep blue" class BadColor(PColor): def draw(self) -> str: return super().draw() # Error, no default implementation class ImplicitColor: # Note no 'PColor' base here def draw(self) -> str: return "probably gray" def comlex_method(self) -> int: # class needs to implement this nice: NiceColor another: ImplicitColor def represent(c: PColor) -> None: print(c.draw(), c.complex_method()) represent(nice) # OK represent(another) # Also OK Note that there is little difference between explicit and implicit subtypes, the main benefit of explicit subclassing is to get some protocol methods "for free". In addition, type checkers can statically verify that the class actually implements the protocol correctly:: class RGB(Protocol): rgb: Tuple[int, int, int] @abstractmethod def intensity(self) -> int: return 0 class Point(RGB): def __init__(self, red: int, green: int, blue: str) -> None: self.rgb = red, green, blue # Error, 'blue' must be 'int' # Type checker might warn that 'intensity' is not defined A class can explicitly inherit from multiple protocols and also form normal classes. In this case methods are resolved using normal MRO and a type checker verifies that all subtyping are correct. The semantics of ``@abstractmethod`` is not changed, all of them must be implemented by an explicit subclass before it can be instantiated. Merging and extending protocols ------------------------------- The general philosophy is that protocols are mostly like regular ABCs, but a static type checker will handle them specially. Subclassing a protocol class would not turn the subclass into a protocol unless it also has ``typing.Protocol`` as an explicit base class. Without this base, the class is "downgraded" to a regular ABC that cannot be used with structural subtyping. The rationale for this rule is that we don't want to accidentally have some class act as a protocol just because one of its base classes happens to be one. We still slightly prefer nominal subtyping over structural subtyping in the static typing world. A subprotocol can be defined by having *both* one or more protocols as immediate base classes and also having ``typing.Protocol`` as an immediate base class:: from typing import Sized, Protocol class SizedAndClosable(Sized, Protocol): def close(self) -> None: ... Now the protocol ``SizedAndClosable`` is a protocol with two methods, ``__len__`` and ``close``. If one omits ``Protocol`` in the base class list, this would be a regular (non-protocol) class that must implement ``Sized``. Alternatively, one can implement ``SizedAndClosable`` protocol by merging the ``SupportsClose`` protocol from the example in the `definition`_ section with ``typing.Sized``:: from typing import Sized class SupportsClose(Protocol): def close(self) -> None: ... class SizedAndClosable(Sized, SupportsClose, Protocol): pass The two definitions of ``SizedAndClosable`` are equivalent. Subclass relationships between protocols are not meaningful when considering subtyping, since structural compatibility is the criterion, not the MRO. If ``Protocol`` is included in the base class list, all the other base classes must be protocols. A protocol can't extend a regular class, see `rejected`_ ideas for rationale. Note that rules around explicit subclassing are different from regular ABCs, where abstractness is simply defined by having at least one abstract method being unimplemented. Protocol classes must be marked *explicitly*. Generic protocols ----------------- Generic protocols are important. For example, ``SupportsAbs``, ``Iterable`` and ``Iterator`` are generic protocols. They are defined similar to normal non-protocol generic types:: class Iterable(Protocol[T]): @abstractmethod def __iter__(self) -> Iterator[T]: ... ``Protocol[T, S, ...]`` is allowed as a shorthand for ``Protocol, Generic[T, S, ...]``. User-defined generic protocols support explicitly declared variance. Type checkers will warn if the inferred variance is different from the declared variance. Examples:: T = TypeVar('T') T_co = TypeVar('T_co', covariant=True) T_contra = TypeVar('T_contra', contravariant=True) class Box(Protocol[T_co]): def content(self) -> T_co: ... box: Box[float] second_box: Box[int] box = second_box # This is OK due to the covariance of 'Box'. class Sender(Protocol[T_contra]): def send(self, data: T_contra) -> int: ... sender: Sender[float] new_sender: Sender[int] new_sender = sender # OK, 'Sender' is contravariant. class Proto(Protocol[T]): attr: T # this class is invariant, since it has a mutable attribute var: Proto[float] another_var: Proto[int] var = another_var # Error! 'Proto[float]' is incompatible with 'Proto[int]'. Note that unlike nominal classes, de-facto covariant protocols cannot be declared as invariant, since this can break transitivity of subtyping (see `rejected`_ ideas for details). For example:: T = TypeVar('T') class AnotherBox(Protocol[T]): # Error, this protocol is covariant in T, def content(self) -> T: # not invariant. ... Recursive protocols ------------------- Recursive protocols are also supported. Forward references to the protocol class names can be given as strings as specified by PEP 484. Recursive protocols are useful for representing self-referential data structures like trees in an abstract fashion:: class Traversable(Protocol): def leaves(self) -> Iterable['Traversable']: ... Note that for recursive protocols, a class is considered a subtype of the protocol in situations where the decision depends on itself. Continuing the previous example:: class SimpleTree: def leaves(self) -> List['SimpleTree']: ... root: Traversable = SimpleTree() # OK class Tree(Generic[T]): def leaves(self) -> List['Tree[T]']: ... def walk(graph: Traversable) -> None: ... tree: Tree[float] = Tree() walk(tree) # OK, 'Tree[float]' is a subtype of 'Traversable' Using Protocols =============== Subtyping relationships with other types ---------------------------------------- Protocols cannot be instantiated, so there are no values whose runtime type is a protocol. For variables and parameters with protocol types, subtyping relationships are subject to the following rules: * A protocol is never a subtype of a concrete type. * A concrete type ``X`` is a subtype of protocol ``P`` if and only if ``X`` implements all protocol members of ``P`` with compatible types. In other words, subtyping with respect to a protocol is always structural. * A protocol ``P1`` is a subtype of another protocol ``P2`` if ``P1`` defines all protocol members of ``P2`` with compatible types. Generic protocol types follow the same rules of variance as non-protocol types. Protocol types can be used in all contexts where any other types can be used, such as in ``Union``, ``ClassVar``, type variables bounds, etc. Generic protocols follow the rules for generic abstract classes, except for using structural compatibility instead of compatibility defined by inheritance relationships. Unions and intersections of protocols ------------------------------------- ``Union`` of protocol classes behaves the same way as for non-protocol classes. For example:: from typing import Union, Optional, Protocol class Exitable(Protocol): def exit(self) -> int: ... class Quittable(Protocol): def quit(self) -> Optional[int]: ... def finish(task: Union[Exitable, Quittable]) -> int: ... class DefaultJob: ... def quit(self) -> int: return 0 finish(DefaultJob()) # OK One can use multiple inheritance to define an intersection of protocols. Example:: from typing import Sequence, Hashable class HashableFloats(Sequence[float], Hashable, Protocol): pass def cached_func(args: HashableFloats) -> float: ... cached_func((1, 2, 3)) # OK, tuple is both hashable and sequence If this will prove to be a widely used scenario, then a special intersection type construct could be added in future as specified by PEP 483, see `rejected`_ ideas for more details. ``Type[]`` with protocols ------------------------- Variables and parameters annotated with ``Type[Proto]`` accept only concrete (non-protocol) subtypes of ``Proto``. The main reason for this is to allow instantiation of parameters with such type. For example:: class Proto(Protocol): @abstractmethod def meth(self) -> int: ... class Concrete: def meth(self) -> int: return 42 def fun(cls: Type[Proto]) -> int: return cls().meth() # OK fun(Proto) # Error fun(Concrete) # OK The same rule applies to variables:: var: Type[Proto] var = Proto # Error var = Concrete # OK var().meth() # OK Assigning an ABC or a protocol class to a variable is allowed if it is not explicitly typed, and such assignment creates a type alias. For normal (non-abstract) classes, the behavior of ``Type[]`` is not changed. ``NewType()`` and type aliases ------------------------------ Protocols are essentially anonymous. To emphasize this point, static type checkers might refuse protocol classes inside ``NewType()`` to avoid an illusion that a distinct type is provided:: from typing import NewType, Protocol, Iterator class Id(Protocol): code: int secrets: Iterator[bytes] UserId = NewType('UserId', Id) # Error, can't provide distinct type In contrast, type aliases are fully supported, including generic type aliases:: from typing import TypeVar, Reversible, Iterable, Sized T = TypeVar('T') class SizedIterable(Iterable[T], Sized, Protocol): pass CompatReversible = Union[Reversible[T], SizedIterable[T]] .. _discussion: ``@runtime`` decorator and narrowing types by ``isinstance()`` -------------------------------------------------------------- The default semantics is that ``isinstance()`` and ``issubclass()`` fail for protocol types. This is in the spirit of duck typing -- protocols basically would be used to model duck typing statically, not explicitly at runtime. However, it should be possible for protocol types to implement custom instance and class checks when this makes sense, similar to how ``Iterable`` and other ABCs in ``collections.abc`` and ``typing`` already do it, but this is limited to non-generic and unsubscripted generic protocols (``Iterable`` is statically equivalent to ``Iterable[Any]`). The ``typing`` module will define a special ``@runtime`` class decorator that provides the same semantics for class and instance checks as for ``collections.abc`` classes, essentially making them "runtime protocols":: from typing import runtime, Protocol @runtime class Closable(Protocol): def close(self): ... assert isinstance(open('some/file'), Closable) Static type checkers will understand ``isinstance(x, Proto)`` and ``issubclass(C, Proto)`` for protocols defined with this decorator (as they already do for ``Iterable`` etc.). Static type checkers will narrow types after such checks by the type erased ``Proto`` (i.e. with all variables having type ``Any`` and all methods having type ``Callable[..., Any]``). Note that ``isinstance(x, Proto[int])`` etc. will always fail in agreement with PEP 484. Examples:: from typing import Iterable, Iterator, Sequence def process(items: Iterable[int]) -> None: if isinstance(items, Iterator): # 'items' has type 'Iterator[int]' here elif isinstance(items, Sequence[int]): # Error! Can't use 'isinstance()' with subscripted protocols Note that instance checks are not 100% reliable statically, this is why this behavior is opt-in, see section on `rejected`_ ideas for examples. Using Protocols in Python 2.7 - 3.5 =================================== Variable annotation syntax was added in Python 3.6, so that the syntax for defining protocol variables proposed in `specification`_ section can't be used if support for earlier versions is needed. To define these in a manner compatible with older versions of Python one can use properties. Properties can be settable and/or abstract if needed:: class Foo(Protocol): @property def c(self) -> int: return 42 # Default value can be provided for property... @abstractproperty def d(self) -> int: # ... or it can be abstract return 0 Also function type comments can be used as per PEP 484 (for example to provide compatibility with Python 2). The ``typing`` module changes proposed in this PEP will also be backported to earlier versions via the backport currently available on PyPI. Runtime Implementation of Protocol Classes ========================================== Implementation details ---------------------- The runtime implementation could be done in pure Python without any effects on the core interpreter and standard library except in the ``typing`` module, and a minor update to ``collections.abc``: * Define class ``typing.Protocol`` similar to ``typing.Generic``. * Implement metaclass functionality to detect whether a class is a protocol or not. Add a class attribute ``_is_protocol = True`` if that is the case. Verify that a protocol class only has protocol base classes in the MRO (except for object). * Implement ``@runtime`` that allows ``__subclasshook__()`` performing structural instance and subclass checks as in ``collections.abc`` classes. * All structural subtyping checks will be performed by static type checkers, such as ``mypy`` [mypy]_. No additional support for protocol validation will be provided at runtime. * Classes ``Mapping``, ``MutableMapping``, ``Sequence``, and ``MutableSequence`` in ``collections.abc`` module will support structural instance and subclass checks (like e.g. ``collections.abc.Iterable``). Changes in the typing module ---------------------------- The following classes in ``typing`` module will be protocols: * ``Callable`` * ``Awaitable`` * ``Iterable``, ``Iterator`` * ``AsyncIterable``, ``AsyncIterator`` * ``Hashable`` * ``Sized`` * ``Container`` * ``Collection`` * ``Reversible`` * ``Sequence``, ``MutableSequence`` * ``Mapping``, ``MutableMapping`` * ``ContextManager``, ``AsyncContextManager`` * ``SupportsAbs`` (and other ``Supports*`` classes) Most of these classes are small and conceptually simple. It is easy to see what are the methods these protocols implement, and immediately recognize the corresponding runtime protocol counterpart. Practically, few changes will be needed in ``typing`` since some of these classes already behave the necessary way at runtime. Most of these will need to be updated only in the corresponding ``typeshed`` stubs [typeshed]_. All other concrete generic classes such as ``List``, ``Set``, ``IO``, ``Deque``, etc are sufficiently complex that it makes sense to keep them non-protocols (i.e. require code to be explicit about them). Also, it is too easy to leave some methods unimplemented by accident, and explicitly marking the subclass relationship allows type checkers to pinpoint the missing implementations. Introspection ------------- The existing class introspection machinery (``dir``, ``__annotations__`` etc) can be used with protocols. In addition, all introspection tools implemented in the ``typing`` module will support protocols. Since all attributes need to be defined in the class body based on this proposal, protocol classes will have even better perspective for introspection than regular classes where attributes can be defined implicitly -- protocol attributes can't be initialized in ways that are not visible to introspection (using ``setattr()``, assignment via ``self``, etc.). Still, some things like types of attributes will not be visible at runtime in Python 3.5 and earlier, but this looks like a reasonable limitation. There will be only limited support of ``isinstance()`` and ``issubclass()`` as discussed above (these will *always* fail with ``TypeError`` for subscripted generic protocols, since a reliable answer could not be given at runtime in this case). But together with other introspection tools this give a reasonable perspective for runtime type checking tools. .. _rejected: Rejected/Postponed Ideas ======================== The ideas in this section were previously discussed in [several]_ [discussions]_ [elsewhere]_. Make every class a protocol by default -------------------------------------- Some languages such as Go make structural subtyping the only or the primary form of subtyping. We could achieve a similar result by making all classes protocols by default (or even always). However we believe that it is better to require classes to be explicitly marked as protocols, for the following reasons: * Protocols don't have some properties of regular classes. In particular, ``isinstance()``, as defined for normal classes, is based on the nominal hierarchy. In order to make everything a protocol by default, and have ``isinstance()`` work would require changing its semantics, which won't happen. * Protocol classes should generally not have many method implementations, as they describe an interface, not an implementation. Most classes have many method implementations, making them bad protocol classes. * Experience suggests that many classes are not practical as protocols anyway, mainly because their interfaces are too large, complex or implementation-oriented (for example, they may include de facto private attributes and methods without a ``__`` prefix). * Most actually useful protocols in existing Python code seem to be implicit. The ABCs in ``typing`` and ``collections.abc`` are rather an exception, but even they are recent additions to Python and most programmers do not use them yet. * Many built-in functions only accept concrete instances of ``int`` (and subclass instances), and similarly for other built-in classes. Making ``int`` a structural type wouldn't be safe without major changes to the Python runtime, which won't happen. Protocols subclassing normal classes ------------------------------------ The main rationale to prohibit this is to preserve transitivity of subtyping, consider this example:: from typing import Protocol class Base: attr: str class Proto(Base, Protocol): def meth(self) -> int: ... class C: attr: str def meth(self) -> int: return 0 Now, ``C`` is a subtype of ``Proto``, and ``Proto`` is a subtype of ``Base``. But ``C`` cannot be a subtype of ``Base`` (since the latter is not a protocol). This situation would be really weird. In addition, there is an ambiguity about whether attributes of ``Base`` should become protocol members of ``Proto``. Support optional protocol members --------------------------------- We can come up with examples where it would be handy to be able to say that a method or data attribute does not need to be present in a class implementing a protocol, but if it is present, it must conform to a specific signature or type. One could use a ``hasattr()`` check to determine whether they can use the attribute on a particular instance. Languages such as TypeScript have similar features and apparently they are pretty commonly used. The current realistic potential use cases for protocols in Python don't require these. In the interest of simplicity, we propose to not support optional methods or attributes. We can always revisit this later if there is an actual need. Allow only protocol methods and force use of getters and setters ---------------------------------------------------------------- One could argue that protocols typically only define methods, but not variables. However, using getters and setters in cases where only a simple variable is needed would be quite unpythonic. Moreover, the widespread use of properties (that often act as type validators) in large code bases is partially due to previous absence of static type checkers for Python, the problem that PEP 484 and this PEP are aiming to solve. For example:: # without static types class MyClass: @property def my_attr(self): return self._my_attr @my_attr.setter def my_attr(self, value): if not isinstance(value, int): raise ValidationError("An integer expected for my_attr") self._my_attr = value # with static types class MyClass: my_attr: int Support non-protocol members ---------------------------- There was an idea to make some methods "non-protocol" (i.e. not necessary to implement, and inherited in explicit subclassing), but it was rejected, since this complicates things. For example, consider this situation:: class Proto(Protocol): @abstractmethod def first(self) -> int: raise NotImplementedError def second(self) -> int: return self.first() + 1 def fun(arg: Proto) -> None: arg.second() The question is should this be an error? We think most people would expect this to be valid. Therefore, to be on the safe side, we need to require both methods to be implemented in implicit subclasses. In addition, if one looks at definitions in ``collections.abc``, there are very few methods that could be considered "non-protocol". Therefore, it was decided to not introduce "non-protocol" methods. There is only one downside to this: it will require some boilerplate for implicit subtypes of ``Mapping`` and few other "large" protocols. But, this applies to few "built-in" protocols (like ``Mapping`` and ``Sequence``) and people are already subclassing them. Also, such style is discouraged for user-defined protocols. It is recommended to create compact protocols and combine them. Make protocols interoperable with other approaches -------------------------------------------------- The protocols as described here are basically a minimal extension to the existing concept of ABCs. We argue that this is the way they should be understood, instead of as something that *replaces* Zope interfaces, for example. Attempting such interoperabilities will significantly complicate both the concept and the implementation. On the other hand, Zope interfaces are conceptually a superset of protocols defined here, but using an incompatible syntax to define them, because before PEP 526 there was no straightforward way to annotate attributes. In the 3.6+ world, ``zope.interface`` might potentially adopt the ``Protocol`` syntax. In this case, type checkers could be taught to recognize interfaces as protocols and make simple structural checks with respect to them. Use assignments to check explicitly that a class implements a protocol ---------------------------------------------------------------------- In the Go language the explicit checks for implementation are performed via dummy assignments [golang]_. Such a way is also possible with the current proposal. Example:: class A: def __len__(self) -> float: return ... _: Sized = A() # Error: A.__len__ doesn't conform to 'Sized' # (Incompatible return type 'float') This approach moves the check away from the class definition and it almost requires a comment as otherwise the code probably would not make any sense to an average reader -- it looks like dead code. Besides, in the simplest form it requires one to construct an instance of ``A``, which could be problematic if this requires accessing or allocating some resources such as files or sockets. We could work around the latter by using a cast, for example, but then the code would be ugly. Therefore we discourage the use of this pattern. Support ``isinstance()`` checks by default ------------------------------------------ The problem with this is instance checks could be unreliable, except for situations where there is a common signature convention such as ``Iterable``. For example:: class P(Protocol): def common_method_name(self, x: int) -> int: ... class X: <a bunch of methods> def common_method_name(self) -> None: ... # Note different signature def do_stuff(o: Union[P, X]) -> int: if isinstance(o, P): return o.common_method_name(1) # oops, what if it's an X instance? Another potentially problematic case is assignment of attributes *after* instantiation:: class P(Protocol): x: int class C: def initialize(self) -> None: self.x = 0 c = C() isinstance(c1, P) # False c.initialize() isinstance(c, P) # True def f(x: Union[P, int]) -> None: if isinstance(x, P): # static type of x is P here ... else: # type of x is "int" here? print(x + 1) f(C()) # oops We argue that requiring an explicit class decorator would be better, since one can then attach warnings about problems like this in the documentation. The user would be able to evaluate whether the benefits outweigh the potential for confusion for each protocol and explicitly opt in -- but the default behavior would be safer. Finally, it will be easy to make this behavior default if necessary, while it might be problematic to make it opt-in after being default. Provide a special intersection type construct --------------------------------------------- There was an idea to allow ``Proto = All[Proto1, Proto2, ...]`` as a shorthand for:: class Proto(Proto1, Proto2, ..., Protocol): pass However, it is not yet clear how popular/useful it will be and implementing this in type checkers for non-protocol classes could be difficult. Finally, it will be very easy to add this later if needed. Prohibit explicit subclassing of protocols by non-protocols ----------------------------------------------------------- This was rejected for the following reasons: * Backward compatibility: People are already using ABCs, including generic ABCs from ``typing`` module. If we prohibit explicit subclassing of these ABCs, then quite a lot of code will break. * Convenience: There are existing protocol-like ABCs (that will be turned into protocols) that have many useful "mix-in" (non-abstract) methods. For example in the case of ``Sequence`` one only needs to implement ``__getitem__`` and ``__len__`` in an explicit subclass, and one gets ``__iter__``, ``__contains__``, ``__reversed__``, ``index``, and ``count`` for free. * Explicit subclassing makes it explicit that a class implements a particular protocol, making subtyping relationships easier to see. * Type checkers can warn about missing protocol members or members with incompatible types more easily, without having to use hacks like dummy assignments discussed above in this section. * Explicit subclassing makes it possible to force a class to be considered a subtype of a protocol (by using ``# type: ignore`` together with an explicit base class) when it is not strictly compatible, such as when it has an unsafe override. Covariant subtyping of mutable attributes ----------------------------------------- Rejected because covariant subtyping of mutable attributes is not safe. Consider this example:: class P(Protocol): x: float def f(arg: P) -> None: arg.x = 0.42 class C: x: int c = C() f(c) # Would typecheck if covariant subtyping # of mutable attributes were allowed c.x >> 1 # But this fails at runtime It was initially proposed to allow this for practical reasons, but it was subsequently rejected, since this may mask some hard to spot bugs. Overriding inferred variance of protocol classes ------------------------------------------------ It was proposed to allow declaring protocols as invariant if they are actually covariant or contravariant (as it is possible for nominal classes, see PEP 484). However, it was decided not to do this because of several downsides: * Declared protocol invariance breaks transitivity of sub-typing. Consider this situation:: T = TypeVar('T') class P(Protocol[T]): # Declared as invariant def meth(self) -> T: ... class C: def meth(self) -> float: ... class D(C): def meth(self) -> int: ... Now we have that ``D`` is a subtype of ``C``, and ``C`` is a subtype of ``P[float]``. But ``D`` is *not* a subtype of ``P[float]`` since ``D`` implements ``P[int]``, and ``P`` is invariant. There is a possibility to "cure" this by looking for protocol implementations in MROs but this will be too complex in a general case, and this "cure" requires abandoning simple idea of purely structural subtyping for protocols. * Subtyping checks will always require type inference for protocols. In the above example a user may complain: "Why did you infer ``P[int]`` for my ``D``? It implements ``P[float]``!". Normally, inference can be overruled by an explicit annotation, but here this will require explicit subclassing, defeating the purpose of using protocols. * Allowing overriding variance will make impossible more detailed error messages in type checkers citing particular conflicts in member type signatures. * Finally, explicit is better than implicit in this case. Requiring user to declare correct variance will simplify understanding the code and will avoid unexpected errors at the point of use. Support adapters and adaptation ------------------------------- Adaptation was proposed by PEP 246 (rejected) and is supported by ``zope.interface``, see https://docs.zope.org/zope.interface/adapter.html. Adapters is quite an advanced concept, and PEP 484 supports unions and generic aliases that can be used instead of adapters. This can be illustrated with an example of ``Iterable`` protocol, there is another way of supporting iteration by providing ``__getitem__`` and ``__len__``. If a function supports both this way and the now standard ``__iter__`` method, then it could be annotated by a union type:: class OldIterable(Sized, Protocol[T]): def __getitem__(self, item: int) -> T: ... CompatIterable = Union[Iterable[T], OldIterable[T]] class A: def __iter__(self) -> Iterator[str]: ... class B: def __len__(self) -> int: ... def __getitem__(self, item: int) -> str: ... def iterate(it: CompatIterable[str]) -> None: ... iterate(A()) # OK iterate(B()) # OK Since there is a reasonable alternative for such cases with existing tooling, it is therefore proposed not to include adaptation in this PEP. Backwards Compatibility ======================= This PEP is almost fully backwards compatible. Few collection classes such as ``Sequence`` and ``Mapping`` will be turned into runtime protocols, therefore results of ``isinstance()`` checks are going to change in some edge cases. For example, a class that implements the ``Sequence`` protocol but does not explicitly inherit from ``Sequence`` currently returns ``False`` in corresponding instance and class checks. With this PEP implemented, such checks will return ``True``. Implementation ============== A working implementation of this PEP for ``mypy`` type checker is found on GitHub repo at https://github.com/ilevkivskyi/mypy/tree/protocols, corresponding ``typeshed`` stubs for more flavor are found at https://github.com/ilevkivskyi/typeshed/tree/protocols. Installation steps:: git clone --recurse-submodules https://github.com/ilevkivskyi/mypy/ cd mypy && git checkout protocols && cd typeshed git remote add proto https://github.com/ilevkivskyi/typeshed git fetch proto && git checkout proto/protocols cd .. && git add typeshed && sudo python3 -m pip install -U . The runtime implementation of protocols in ``typing`` module is found at https://github.com/ilevkivskyi/typehinting/tree/protocols. The version of ``collections.abc`` with structural behavior for mappings and sequences is found at https://github.com/ilevkivskyi/cpython/tree/protocols. References ========== .. [typing] https://docs.python.org/3/library/typing.html .. [wiki-structural] https://en.wikipedia.org/wiki/Structural_type_system .. [zope-interfaces] https://zopeinterface.readthedocs.io/en/latest/ .. [abstract-classes] https://docs.python.org/3/library/abc.html .. [collections-abc] https://docs.python.org/3/library/collections.abc.html .. [typescript] https://www.typescriptlang.org/docs/handbook/interfaces.html .. [golang] https://golang.org/doc/effective_go.html#interfaces_and_types .. [data-model] https://docs.python.org/3/reference/datamodel.html#special-method-names .. [typeshed] https://github.com/python/typeshed/ .. [mypy] http://github.com/python/mypy/ .. [several] https://mail.python.org/pipermail/python-ideas/2015-September/thread.html#3… .. [discussions] https://github.com/python/typing/issues/11 .. [elsewhere] https://github.com/python/peps/pull/224 Copyright ========= This document has been placed in the public domain.

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Python FTP Injections Allow for Firewall Bypass (oss-security advisory)
by nospam＠curso.re June 21, 2017

June 21, 2017

Hello, I have just noticed that an FTP injection advisory has been made public on the oss-security list. The author says that he an exploit exists but it won't be published until the code is patched You may be already aware, but it would be good to understand what is the position of the core developers about this. The advisory is linked below (with some excerpts in this message): http://blog.blindspotsecurity.com/2017/02/advisory-javapython-ftp-injection… Protocol injection flaws like this have been an area of research of mine for the past few couple of years and as it turns out, this FTP protocol injection allows one to fool a victim's firewall into allowing TCP connections from the Internet to the vulnerable host's system on any "high" port (1024-65535). A nearly identical vulnerability exists in Python's urllib2 and urllib libraries. In the case of Java, this attack can be carried out against desktop users even if those desktop users do not have the Java browser plugin enabled. As of 2017-02-20, the vulnerabilities discussed here have not been patched by the associated vendors, despite advance warning and ample time to do so. [...] Python's built-in URL fetching library (urllib2 in Python 2 and urllib in Python 3) is vulnerable to a nearly identical protocol stream injection, but this injection appears to be limited to attacks via directory names specified in the URL. [...] The Python security team was notified in January 2016. Information provided included an outline of the possibility of FTP/firewall attacks. Despite repeated follow-ups, there has been no apparent action on their part. Best regards, -- Stefano P.S. I am posting from gmane, I hope that this is OK.

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PEP 538 (review round 2): Coercing the legacy C locale to a UTF-8 based locale
by Nick Coghlan June 17, 2017

June 17, 2017

Hi folks, Enough changes have accumulated in PEP 538 since the start of the previous thread that it seems sensible to me to start a new thread specifically covering the current design (which aims to address all the concerns raised in the previous thread). I haven't requoted the PEP in full since it's so long, but will instead refer readers to the web version: https://www.python.org/dev/peps/pep-0538/ I also generated a diff covered the full changes to the PEP text: * https://gist.github.com/ncoghlan/1067805fe673b3735ac854e195747493/revisions (this is the diff covering the last few days of changes Summarising the key technical changes: * to make the runtime behaviour independent of whether or not locale coercion took place, stdin and stderr now always have "surrogateescape" as their error handler in the potential coercion target locales. This means Python will behave the same way regardless of whether the locale gets set externally (e.g. by a parent Python process or a container image definition) or implicitly during CLI startup * for the full locales, the interpreter now sets LC_CTYPE and LANG, *not* LC_ALL. This means LC_ALL is once again a full locale override, and also means that CPython won't inadvertently interfere with other locale categories like LC_MONETARY, LC_NUMERIC, etc * the reference implementation has been refactored so the bulk of the new code lives in the shared library and is exposed to the linker via a couple of underscore prefixed API symbols (_Py_LegacyLocaleDetected() and _Py_CoerceLegacyLocale()). While the current PEP still keeps them private, it would be straightforward to make them public for use in embedding applications if we decided we wanted to do so. * locale coercion and warnings are now enabled by default on all platforms that use the autotools-based build chain - the assumption that some platforms didn't need them turned out to be incorrect In addition to being updated to cover the above changes, the Rationale section of the PEP has also been updated to explain why it doesn't propose setting PYTHONIOENCODING, and to walk through some examples of the problems with GNU readlines compatibility when the current locale isn't set correctly. The essential related changes to the reference implementation can be seen here: * Always set "surrogateescape" for coercion target locales, independently of whether or not coercion occurred: https://github.com/ncoghlan/cpython/commit/188e7807b6d9e49377aacbb287c074e5… * Stop setting LC_ALL: https://github.com/python/peps/commit/2f530ce0d1fd24835ac0c6f984f40db70482a… (There are also some smaller cleanup commits that can be seen by browsing that branch on GitHub) Cheers, Nick. -- Nick Coghlan | ncoghlan(a)gmail.com | Brisbane, Australia

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Issue #21071: change struct.Struct.format type from bytes to str
by Victor Stinner June 16, 2017

June 16, 2017

Hi I would like to change struct.Struct.format type from bytes to str. I don't expect that anyone uses this attribute, and struct.Struct() constructor accepts both bytes and str. http://bugs.python.org/issue21071 It's just to be convenient: more functions accept str than bytes in Python 3. Example: print() (python3 -bb raises an exceptions if you pass bytes to print). Is anything opposed to breaking the backward compatibility? Victor

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RFC: Backport ssl.MemoryBIO and ssl.SSLObject to Python 2.7
by Victor Stinner June 10, 2017

June 10, 2017

Hi, I wrote a PEP based on the previous thread "Backport ssl.MemoryBIO on Python 2.7?". Thanks for Cory Benfield, Alex Gaynor and Nick Coghlan who helped me to write it! HTML version: https://www.python.org/dev/peps/pep-0546/ Inline verison below. Victor PEP: 546 Title: Backport ssl.MemoryBIO and ssl.SSLObject to Python 2.7 Version: $Revision$ Last-Modified: $Date$ Author: Victor Stinner <victor.stinner(a)gmail.com>, Status: Draft Type: Standards Track Content-Type: text/x-rst Created: 30-May-2017 Abstract ======== Backport the ssl.MemoryBIO and ssl.SSLObject classes from Python 3 to Python 2.7 to enhance the overall security of Python 2.7. Rationale ========= While Python 2.7 is getting closer to its end-of-support date (scheduled for 2020), it is still used on production systems and the Python community is still responsible for its security. This PEP will help facilitate the future adoption of :pep:`543` across all supported Python versions, which will improve security for both Python 2 and Python 3 users. This PEP does NOT propose a general exception for backporting new features to Python 2.7 - every new feature proposed for backporting will still need to be justified independently. In particular, it will need to be explained why relying on an independently updated backport on the Python Package Index instead is not an acceptable solution. PEP 543 ------- :pep:`543` defines a new TLS API for Python which would enhance Python security by giving Python applications access to the native TLS implementations on Windows and macOS, instead of using OpenSSL. A side effect is that it gives access to the system trust store and certificates installed locally by system administrators, enabling Python applications to use "company certificates" without having to modify each application and so to correctly validate TLS certificates (instead of having to ignore or bypass TLS certificate validation). For practical reasons, Cory Benfield would like to first implement an I/O-less class similar to ssl.MemoryBIO and ssl.SSLObject for :pep:`543`, and to provide a second class based on the first one to use sockets or file descriptors. This design would help to structure the code to support more backends and simplify testing and auditing, as well as implementation. Later, optimized classes using directly sockets or file descriptors may be added for performance. While :pep:`543` defines an API, the PEP would only make sense if it comes with at least one complete and good implementation. The first implementation would ideally be based on the ``ssl`` module of the Python standard library, as this is shipped to all users by default and can be used as a fallback implementation in the absence of anything more targetted. If this backport is not performed, the only baseline implementation that could be used would be pyOpenSSL. This is problematic, however, because of the interaction with pip, which is shipped with CPython on all supported versions. requests, pip and ensurepip --------------------------- There are plans afoot to look at moving Requests to a more event-loop-y model, and doing so basically mandates a MemoryBIO. In the absence of a Python 2.7 backport, Requests is required to basically use the same solution that Twisted currently does: namely, a mandatory dependency on `pyOpenSSL <https://pypi.python.org/pypi/pyOpenSSL>`_. The `pip <https://pip.pypa.io/>`_ program has to embed all its dependencies for practical reasons: namely, that it cannot rely on any other installation method being present. Since pip depends on requests, it means that it would have to embed a copy of pyOpenSSL. That would imply substantial usability pain to install pip. Currently, pip doesn't support embedding C extensions which must be compiled on each platform and so require a C compiler. Since Python 2.7.9, Python embeds a copy of pip both for default installation and for use in virtual environments via the new ``ensurepip`` module. If pip ends up bundling PyOpenSSL, then CPython will end up bundling PyOpenSSL. Only backporting ``ssl.MemoryBIO`` and ``ssl.SSLObject`` would avoid the need to embed pyOpenSSL, and would fix the bootstrap issue (python -> ensurepip -> pip -> requests -> MemoryBIO). Changes ======= Add ``MemoryBIO`` and ``SSLObject`` classes to the ``ssl`` module of Python 2.7. The code will be backported and adapted from the master branch (Python 3). The backport also significantly reduced the size of the Python 2/Python 3 difference of the ``_ssl`` module, which make maintenance easier. Links ===== * :pep:`543` * `[backport] ssl.MemoryBIO <https://bugs.python.org/issue22559>`_: Implementation of this PEP written by Alex Gaynor (first version written at October 2014) * :pep:`466` Discussions =========== * `[Python-Dev] Backport ssl.MemoryBIO on Python 2.7? <https://mail.python.org/pipermail/python-dev/2017-May/147981.html>`_ (May 2017) Copyright ========= This document has been placed in the public domain.

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