Mailman 3 June 2016 - 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

C99
by Benjamin Peterson Oct. 8, 2016

Oct. 8, 2016

PEP 7 requires CPython to use C code conforming to the venerable C89 standard. Traditionally, we've been stuck with C89 due to poor C support in MSVC. However, MSVC 2013 and 2015 implement the key features of C99. C99 does not offer anything earth-shattering; here are the features I think we'd find most interesting: - Variable declarations can be on any line: removes possibly the most annoying limitation of C89. - Inline functions: We can make Py_DECREF and Py_INCREF inline functions rather than unpleasant macros. - C++-style line comments: Not an killer feature but commonly used. - Booleans In summary, some niceties that would make CPython hacking a little more fun. So, what say you to updating PEP 7 to allow C99 features for Python 3.6 (in so much as GCC and MSVC support them)? Regards, Benjamin

28 65

Python parser performance optimizations
by Artyom Skrobov Sept. 16, 2016

Sept. 16, 2016

Hello, Back in March, I've posted a patch at http://bugs.python.org/issue26526 -- "In parsermodule.c, replace over 2KLOC of hand-crafted validation code, with a DFA". The motivation for this patch was to enable a memory footprint optimization, discussed at http://bugs.python.org/issue26415 My proposed optimization reduces the memory footprint by up to 30% on the standard benchmarks, and by 200% on a degenerate case which sparked the discussion. The run time stays unaffected by this optimization. Python Developer's Guide says: "If you don't get a response within a few days after pinging the issue, then you can try emailing python-dev(a)python.org<mailto:python-dev@python.org> asking for someone to review your patch." So, here I am. IMPORTANT NOTICE: The contents of this email and any attachments are confidential and may also be privileged. If you are not the intended recipient, please notify the sender immediately and do not disclose the contents to any other person, use it for any purpose, or store or copy the information in any medium. Thank you.

6 10

RFC: PEP 509: Add a private version to dict
by Victor Stinner Sept. 7, 2016

Sept. 7, 2016

Hi, I updated my PEP 509 to make the dictionary version globally unique. With *two* use cases of this PEP (Yury's method call patch and my FAT Python project), I think that the PEP is now ready to be accepted. Globally unique identifier is a requirement for Yury's patch optimizing method calls ( https://bugs.python.org/issue26110 ). It allows to check for free if the dictionary was replaced. I also renamed the ma_version field to ma_version_tag. HTML version: https://www.python.org/dev/peps/pep-0509/ Victor PEP: 509 Title: Add a private version to dict Version: $Revision$ Last-Modified: $Date$ Author: Victor Stinner <victor.stinner(a)gmail.com> Status: Draft Type: Standards Track Content-Type: text/x-rst Created: 4-January-2016 Python-Version: 3.6 Abstract ======== Add a new private version to the builtin ``dict`` type, incremented at each dictionary creation and at each dictionary change, to implement fast guards on namespaces. Rationale ========= In Python, the builtin ``dict`` type is used by many instructions. For example, the ``LOAD_GLOBAL`` instruction searchs for a variable in the global namespace, or in the builtins namespace (two dict lookups). Python uses ``dict`` for the builtins namespace, globals namespace, type namespaces, instance namespaces, etc. The local namespace (namespace of a function) is usually optimized to an array, but it can be a dict too. Python is hard to optimize because almost everything is mutable: builtin functions, function code, global variables, local variables, ... can be modified at runtime. Implementing optimizations respecting the Python semantics requires to detect when "something changes": we will call these checks "guards". The speedup of optimizations depends on the speed of guard checks. This PEP proposes to add a version to dictionaries to implement fast guards on namespaces. Dictionary lookups can be skipped if the version does not change which is the common case for most namespaces. Since the version is globally unique, the version is also enough to check if the namespace dictionary was not replaced with a new dictionary. The performance of a guard does not depend on the number of watched dictionary entries, complexity of O(1), if the dictionary version does not change. Example of optimization: copy the value of a global variable to function constants. This optimization requires a guard on the global variable to check if it was modified. If the variable is modified, the variable must be loaded at runtime when the function is called, instead of using the constant. See the `PEP 510 -- Specialized functions with guards <https://www.python.org/dev/peps/pep-0510/>`_ for the concrete usage of guards to specialize functions and for the rationale on Python static optimizers. Guard example ============= Pseudo-code of an fast guard to check if a dictionary entry was modified (created, updated or deleted) using an hypothetical ``dict_get_version(dict)`` function:: UNSET = object() class GuardDictKey: def __init__(self, dict, key): self.dict = dict self.key = key self.value = dict.get(key, UNSET) self.version = dict_get_version(dict) def check(self): """Return True if the dictionary entry did not changed and the dictionary was not replaced.""" # read the version of the dict structure version = dict_get_version(self.dict) if version == self.version: # Fast-path: dictionary lookup avoided return True # lookup in the dictionary value = self.dict.get(self.key, UNSET) if value is self.value: # another key was modified: # cache the new dictionary version self.version = version return True # the key was modified return False Usage of the dict version ========================= Speedup method calls 1.2x ------------------------- Yury Selivanov wrote a `patch to optimize method calls <https://bugs.python.org/issue26110>`_. The patch depends on the `implement per-opcode cache in ceval <https://bugs.python.org/issue26219>`_ patch which requires dictionary versions to invalidate the cache if the globals dictionary or the builtins dictionary has been modified. The cache also requires that the dictionary version is globally unique. It is possible to define a function in a namespace and call it in a different namespace: using ``exec()`` with the *globals* parameter for example. In this case, the globals dictionary was changed and the cache must be invalidated. Specialized functions using guards ---------------------------------- The `PEP 510 -- Specialized functions with guards <https://www.python.org/dev/peps/pep-0510/>`_ proposes an API to support specialized functions with guards. It allows to implement static optimizers for Python without breaking the Python semantics. Example of a static Python optimizer: the `fatoptimizer <http://fatoptimizer.readthedocs.org/>`_ of the `FAT Python <http://faster-cpython.readthedocs.org/fat_python.html>`_ project implements many optimizations which require guards on namespaces. Examples: * Call pure builtins: to replace ``len("abc")`` with ``3``, guards on ``builtins.__dict__['len']`` and ``globals()['len']`` are required * Loop unrolling: to unroll the loop ``for i in range(...): ...``, guards on ``builtins.__dict__['range']`` and ``globals()['range']`` are required Pyjion ------ According of Brett Cannon, one of the two main developers of Pyjion, Pyjion can also benefit from dictionary version to implement optimizations. Pyjion is a JIT compiler for Python based upon CoreCLR (Microsoft .NET Core runtime). Unladen Swallow --------------- Even if dictionary version was not explicitly mentioned, optimizing globals and builtins lookup was part of the Unladen Swallow plan: "Implement one of the several proposed schemes for speeding lookups of globals and builtins." Source: `Unladen Swallow ProjectPlan <https://code.google.com/p/unladen-swallow/wiki/ProjectPlan>`_. Unladen Swallow is a fork of CPython 2.6.1 adding a JIT compiler implemented with LLVM. The project stopped in 2011: `Unladen Swallow Retrospective <http://qinsb.blogspot.com.au/2011/03/unladen-swallow-retrospective.html>`_. Changes ======= Add a ``ma_version_tag`` field to the ``PyDictObject`` structure with the C type ``PY_INT64_T``, 64-bit unsigned integer. Add also a global dictionary version. Each time a dictionary is created, the global version is incremented and the dictionary version is initialized to the global version. The global version is also incremented and copied to the dictionary version at each dictionary change: * ``clear()`` if the dict was non-empty * ``pop(key)`` if the key exists * ``popitem()`` if the dict is non-empty * ``setdefault(key, value)`` if the `key` does not exist * ``__detitem__(key)`` if the key exists * ``__setitem__(key, value)`` if the `key` doesn't exist or if the value is not ``dict[key]`` * ``update(...)`` if new values are different than existing values: values are compared by identity, not by their content; the version can be incremented multiple times The ``PyDictObject`` structure is not part of the stable ABI. The field is called ``ma_version_tag`` rather than ``ma_version`` to suggest to compare it using ``version_tag == old_version_tag`` rather than ``version <= old_version`` which makes the integer overflow much likely. Example using an hypothetical ``dict_get_version(dict)`` function:: >>> d = {} >>> dict_get_version(d) 100 >>> d['key'] = 'value' >>> dict_get_version(d) 101 >>> d['key'] = 'new value' >>> dict_get_version(d) 102 >>> del d['key'] >>> dict_get_version(d) 103 The version is not incremented if an existing key is set to the same value. For efficiency, values are compared by their identity: ``new_value is old_value``, not by their content: ``new_value == old_value``. Example:: >>> d = {} >>> value = object() >>> d['key'] = value >>> dict_get_version(d) 40 >>> d['key'] = value >>> dict_get_version(d) 40 .. note:: CPython uses some singleton like integers in the range [-5; 257], empty tuple, empty strings, Unicode strings of a single character in the range [U+0000; U+00FF], etc. When a key is set twice to the same singleton, the version is not modified. Implementation and Performance ============================== The `issue #26058: PEP 509: Add ma_version_tag to PyDictObject <https://bugs.python.org/issue26058>`_ contains a patch implementing this PEP. On pybench and timeit microbenchmarks, the patch does not seem to add any overhead on dictionary operations. When the version does not change, ``PyDict_GetItem()`` takes 14.8 ns for a dictionary lookup, whereas a guard check only takes 3.8 ns. Moreover, a guard can watch for multiple keys. For example, for an optimization using 10 global variables in a function, 10 dictionary lookups costs 148 ns, whereas the guard still only costs 3.8 ns when the version does not change (39x as fast). The `fat module <http://fatoptimizer.readthedocs.org/en/latest/fat.html>`_ implements such guards: ``fat.GuardDict`` is based on the dictionary version. Integer overflow ================ The implementation uses the C type ``PY_UINT64_T`` to store the version: a 64 bits unsigned integer. The C code uses ``version++``. On integer overflow, the version is wrapped to ``0`` (and then continue to be incremented) according to the C standard. After an integer overflow, a guard can succeed whereas the watched dictionary key was modified. The bug only occurs at a guard check if there are exaclty ``2 ** 64`` dictionary creations or modifications since the previous guard check. If a dictionary is modified every nanosecond, ``2 ** 64`` modifications takes longer than 584 years. Using a 32-bit version, it only takes 4 seconds. That's why a 64-bit unsigned type is also used on 32-bit systems. A dictionary lookup at the C level takes 14.8 ns. A risk of a bug every 584 years is acceptable. Alternatives ============ Expose the version at Python level as a read-only __version__ property ---------------------------------------------------------------------- The first version of the PEP proposed to expose the dictionary version as a read-only ``__version__`` property at Python level, and also to add the property to ``collections.UserDict`` (since this type must mimick the ``dict`` API). There are multiple issues: * To be consistent and avoid bad surprises, the version must be added to all mapping types. Implementing a new mapping type would require extra work for no benefit, since the version is only required on the ``dict`` type in practice. * All Python implementations must implement this new property, it gives more work to other implementations, whereas they may not use the dictionary version at all. * Exposing the dictionary version at Python level can lead the false assumption on performances. Checking ``dict.__version__`` at the Python level is not faster than a dictionary lookup. A dictionary lookup has a cost of 48.7 ns and checking a guard has a cost of 47.5 ns, the difference is only 1.2 ns (3%):: $ ./python -m timeit -s 'd = {str(i):i for i in range(100)}' 'd["33"] == 33' 10000000 loops, best of 3: 0.0487 usec per loop $ ./python -m timeit -s 'd = {str(i):i for i in range(100)}' 'd.__version__ == 100' 10000000 loops, best of 3: 0.0475 usec per loop * The ``__version__`` can be wrapped on integer overflow. It is error prone: using ``dict.__version__ <= guard_version`` is wrong, ``dict.__version__ == guard_version`` must be used instead to reduce the risk of bug on integer overflow (even if the integer overflow is unlikely in practice). Mandatory bikeshedding on the property name: * ``__cache_token__``: name proposed by Nick Coghlan, name coming from `abc.get_cache_token() <https://docs.python.org/3/library/abc.html#abc.get_cache_token>`_. * ``__version__`` * ``__timestamp__`` Add a version to each dict entry -------------------------------- A single version per dictionary requires to keep a strong reference to the value which can keep the value alive longer than expected. If we add also a version per dictionary entry, the guard can only store the entry version to avoid the strong reference to the value (only strong references to the dictionary and to the key are needed). Changes: add a ``me_version`` field to the ``PyDictKeyEntry`` structure, the field has the C type ``PY_INT64_T``. When a key is created or modified, the entry version is set to the dictionary version which is incremented at any change (create, modify, delete). Pseudo-code of an fast guard to check if a dictionary key was modified using hypothetical ``dict_get_version(dict)`` ``dict_get_entry_version(dict)`` functions:: UNSET = object() class GuardDictKey: def __init__(self, dict, key): self.dict = dict self.key = key self.dict_version = dict_get_version(dict) self.entry_version = dict_get_entry_version(dict, key) def check(self): """Return True if the dictionary entry did not changed and the dictionary was not replaced.""" # read the version of the dict structure dict_version = dict_get_version(self.dict) if dict_version == self.version: # Fast-path: dictionary lookup avoided return True # lookup in the dictionary entry_version = get_dict_key_version(dict, key) if entry_version == self.entry_version: # another key was modified: # cache the new dictionary version self.dict_version = dict_version return True # the key was modified return False The main drawback of this option is the impact on the memory footprint. It increases the size of each dictionary entry, so the overhead depends on the number of buckets (dictionary entries, used or unused yet). For example, it increases the size of each dictionary entry by 8 bytes on 64-bit system. In Python, the memory footprint matters and the trend is to reduce it. Examples: * `PEP 393 -- Flexible String Representation <https://www.python.org/dev/peps/pep-0393/>`_ * `PEP 412 -- Key-Sharing Dictionary <https://www.python.org/dev/peps/pep-0412/>`_ Add a new dict subtype ---------------------- Add a new ``verdict`` type, subtype of ``dict``. When guards are needed, use the ``verdict`` for namespaces (module namespace, type namespace, instance namespace, etc.) instead of ``dict``. Leave the ``dict`` type unchanged to not add any overhead (memory footprint) when guards are not needed. Technical issue: a lot of C code in the wild, including CPython core, expecting the exact ``dict`` type. Issues: * ``exec()`` requires a ``dict`` for globals and locals. A lot of code use ``globals={}``. It is not possible to cast the ``dict`` to a ``dict`` subtype because the caller expects the ``globals`` parameter to be modified (``dict`` is mutable). * Functions call directly ``PyDict_xxx()`` functions, instead of calling ``PyObject_xxx()`` if the object is a ``dict`` subtype * ``PyDict_CheckExact()`` check fails on ``dict`` subtype, whereas some functions require the exact ``dict`` type. * ``Python/ceval.c`` does not completely supports dict subtypes for namespaces The ``exec()`` issue is a blocker issue. Other issues: * The garbage collector has a special code to "untrack" ``dict`` instances. If a ``dict`` subtype is used for namespaces, the garbage collector can be unable to break some reference cycles. * Some functions have a fast-path for ``dict`` which would not be taken for ``dict`` subtypes, and so it would make Python a little bit slower. Prior Art ========= Method cache and type version tag --------------------------------- In 2007, Armin Rigo wrote a patch to to implement a cache of methods. It was merged into Python 2.6. The patch adds a "type attribute cache version tag" (``tp_version_tag``) and a "valid version tag" flag to types (the ``PyTypeObject`` structure). The type version tag is not available at the Python level. The version tag has the C type ``unsigned int``. The cache is a global hash table of 4096 entries, shared by all types. The cache is global to "make it fast, have a deterministic and low memory footprint, and be easy to invalidate". Each cache entry has a version tag. A global version tag is used to create the next version tag, it also has the C type ``unsigned int``. By default, a type has its "valid version tag" flag cleared to indicate that the version tag is invalid. When the first method of the type is cached, the version tag and the "valid version tag" flag are set. When a type is modified, the "valid version tag" flag of the type and its subclasses is cleared. Later, when a cache entry of these types is used, the entry is removed because its version tag is outdated. On integer overflow, the whole cache is cleared and the global version tag is reset to ``0``. See `Method cache (issue #1685986) <https://bugs.python.org/issue1685986>`_ and `Armin's method cache optimization updated for Python 2.6 (issue #1700288) <https://bugs.python.org/issue1700288>`_. Globals / builtins cache ------------------------ In 2010, Antoine Pitrou proposed a `Globals / builtins cache (issue #10401) <http://bugs.python.org/issue10401>`_ which adds a private ``ma_version`` field to the ``PyDictObject`` structure (``dict`` type), the field has the C type ``Py_ssize_t``. The patch adds a "global and builtin cache" to functions and frames, and changes ``LOAD_GLOBAL`` and ``STORE_GLOBAL`` instructions to use the cache. The change on the ``PyDictObject`` structure is very similar to this PEP. Cached globals+builtins lookup ------------------------------ In 2006, Andrea Griffini proposed a patch implementing a `Cached globals+builtins lookup optimization <https://bugs.python.org/issue1616125>`_. The patch adds a private ``timestamp`` field to the ``PyDictObject`` structure (``dict`` type), the field has the C type ``size_t``. Thread on python-dev: `About dictionary lookup caching <https://mail.python.org/pipermail/python-dev/2006-December/070348.html>`_. Guard against changing dict during iteration -------------------------------------------- In 2013, Serhiy Storchaka proposed `Guard against changing dict during iteration (issue #19332) <https://bugs.python.org/issue19332>`_ which adds a ``ma_count`` field to the ``PyDictObject`` structure (``dict`` type), the field has the C type ``size_t``. This field is incremented when the dictionary is modified, and so is very similar to the proposed dictionary version. Sadly, the dictionary version proposed in this PEP doesn't help to detect dictionary mutation. The dictionary version changes when values are replaced, whereas modifying dictionary values while iterating on dictionary keys is legit in Python. PySizer ------- `PySizer <http://pysizer.8325.org/>`_: a memory profiler for Python, Google Summer of Code 2005 project by Nick Smallbone. This project has a patch for CPython 2.4 which adds ``key_time`` and ``value_time`` fields to dictionary entries. It uses a global process-wide counter for dictionaries, incremented each time that a dictionary is modified. The times are used to decide when child objects first appeared in their parent objects. Discussion ========== Thread on the mailing lists: * python-dev: `PEP 509: Add a private version to dict <https://mail.python.org/pipermail/python-dev/2016-January/142685.html>`_ (january 2016) * python-ideas: `RFC: PEP: Add dict.__version__ <https://mail.python.org/pipermail/python-ideas/2016-January/037702.html>`_ (january 2016) Copyright ========= This document has been placed in the public domain.

15 37

PEP 467: Minor API improvements to bytes, bytearray, and memoryview
by Ethan Furman July 18, 2016

July 18, 2016

Minor changes: updated version numbers, add punctuation. The current text seems to take into account Guido's last comments. Thoughts before asking for acceptance? PEP: 467 Title: Minor API improvements for binary sequences Version: $Revision$ Last-Modified: $Date$ Author: Nick Coghlan <ncoghlan(a)gmail.com> Status: Draft Type: Standards Track Content-Type: text/x-rst Created: 2014-03-30 Python-Version: 3.5 Post-History: 2014-03-30 2014-08-15 2014-08-16 Abstract ======== During the initial development of the Python 3 language specification, the core ``bytes`` type for arbitrary binary data started as the mutable type that is now referred to as ``bytearray``. Other aspects of operating in the binary domain in Python have also evolved over the course of the Python 3 series. This PEP proposes four small adjustments to the APIs of the ``bytes``, ``bytearray`` and ``memoryview`` types to make it easier to operate entirely in the binary domain: * Deprecate passing single integer values to ``bytes`` and ``bytearray`` * Add ``bytes.zeros`` and ``bytearray.zeros`` alternative constructors * Add ``bytes.byte`` and ``bytearray.byte`` alternative constructors * Add ``bytes.iterbytes``, ``bytearray.iterbytes`` and ``memoryview.iterbytes`` alternative iterators Proposals ========= Deprecation of current "zero-initialised sequence" behaviour ------------------------------------------------------------ Currently, the ``bytes`` and ``bytearray`` constructors accept an integer argument and interpret it as meaning to create a zero-initialised sequence of the given size:: >>> bytes(3) b'\x00\x00\x00' >>> bytearray(3) bytearray(b'\x00\x00\x00') This PEP proposes to deprecate that behaviour in Python 3.6, and remove it entirely in Python 3.7. No other changes are proposed to the existing constructors. Addition of explicit "zero-initialised sequence" constructors ------------------------------------------------------------- To replace the deprecated behaviour, this PEP proposes the addition of an explicit ``zeros`` alternative constructor as a class method on both ``bytes`` and ``bytearray``:: >>> bytes.zeros(3) b'\x00\x00\x00' >>> bytearray.zeros(3) bytearray(b'\x00\x00\x00') It will behave just as the current constructors behave when passed a single integer. The specific choice of ``zeros`` as the alternative constructor name is taken from the corresponding initialisation function in NumPy (although, as these are 1-dimensional sequence types rather than N-dimensional matrices, the constructors take a length as input rather than a shape tuple). Addition of explicit "single byte" constructors ----------------------------------------------- As binary counterparts to the text ``chr`` function, this PEP proposes the addition of an explicit ``byte`` alternative constructor as a class method on both ``bytes`` and ``bytearray``:: >>> bytes.byte(3) b'\x03' >>> bytearray.byte(3) bytearray(b'\x03') These methods will only accept integers in the range 0 to 255 (inclusive):: >>> bytes.byte(512) Traceback (most recent call last): File "<stdin>", line 1, in <module> ValueError: bytes must be in range(0, 256) >>> bytes.byte(1.0) Traceback (most recent call last): File "<stdin>", line 1, in <module> TypeError: 'float' object cannot be interpreted as an integer The documentation of the ``ord`` builtin will be updated to explicitly note that ``bytes.byte`` is the inverse operation for binary data, while ``chr`` is the inverse operation for text data. Behaviourally, ``bytes.byte(x)`` will be equivalent to the current ``bytes([x])`` (and similarly for ``bytearray``). The new spelling is expected to be easier to discover and easier to read (especially when used in conjunction with indexing operations on binary sequence types). As a separate method, the new spelling will also work better with higher order functions like ``map``. Addition of optimised iterator methods that produce ``bytes`` objects --------------------------------------------------------------------- This PEP proposes that ``bytes``, ``bytearray`` and ``memoryview`` gain an optimised ``iterbytes`` method that produces length 1 ``bytes`` objects rather than integers:: for x in data.iterbytes(): # x is a length 1 ``bytes`` object, rather than an integer The method can be used with arbitrary buffer exporting objects by wrapping them in a ``memoryview`` instance first:: for x in memoryview(data).iterbytes(): # x is a length 1 ``bytes`` object, rather than an integer For ``memoryview``, the semantics of ``iterbytes()`` are defined such that:: memview.tobytes() == b''.join(memview.iterbytes()) This allows the raw bytes of the memory view to be iterated over without needing to make a copy, regardless of the defined shape and format. The main advantage this method offers over the ``map(bytes.byte, data)`` approach is that it is guaranteed *not* to fail midstream with a ``ValueError`` or ``TypeError``. By contrast, when using the ``map`` based approach, the type and value of the individual items in the iterable are only checked as they are retrieved and passed through the ``bytes.byte`` constructor. Design discussion ================= Why not rely on sequence repetition to create zero-initialised sequences? ------------------------------------------------------------------------- Zero-initialised sequences can be created via sequence repetition:: >>> b'\x00' * 3 b'\x00\x00\x00' >>> bytearray(b'\x00') * 3 bytearray(b'\x00\x00\x00') However, this was also the case when the ``bytearray`` type was originally designed, and the decision was made to add explicit support for it in the type constructor. The immutable ``bytes`` type then inherited that feature when it was introduced in PEP 3137. This PEP isn't revisiting that original design decision, just changing the spelling as users sometimes find the current behaviour of the binary sequence constructors surprising. In particular, there's a reasonable case to be made that ``bytes(x)`` (where ``x`` is an integer) should behave like the ``bytes.byte(x)`` proposal in this PEP. Providing both behaviours as separate class methods avoids that ambiguity. References ========== .. [1] Initial March 2014 discussion thread on python-ideas (https://mail.python.org/pipermail/python-ideas/2014-March/027295.html) .. [2] Guido's initial feedback in that thread (https://mail.python.org/pipermail/python-ideas/2014-March/027376.html) .. [3] Issue proposing moving zero-initialised sequences to a dedicated API (http://bugs.python.org/issue20895) .. [4] Issue proposing to use calloc() for zero-initialised binary sequences (http://bugs.python.org/issue21644) .. [5] August 2014 discussion thread on python-dev (https://mail.python.org/pipermail/python-ideas/2014-March/027295.html) Copyright ========= This document has been placed in the public domain.

13 34

Request for CPython 3.5.3 release
by Yury Selivanov July 8, 2016

July 8, 2016

Long story short, I've discovered that asyncio is broken in 3.5.2. Specifically, there is a callbacks race in `loop.sock_connect` which can make subsequent `loop.sock_sendall` calls to hang forever. This thing is very tricky and hard to detect and debug; I had to spend a few hours investigating what's going on with a failing unittest in uvloop (asyncio-compatible event loop). I can only imagine how hard it would be to understand what's going on in a larger codebase. For those who is interested, here's a PR for asyncio repo: https://github.com/python/asyncio/pull/366 It explains the bug in detail and there has a proposed patch to fix the problem. Larry and the release team: would it be possible to make an "emergency" 3.5.3 release? Going forward, we need to increase the number of functional tests for asyncio, as most of the current tests use mocks. I'm going to port all functional tests from uvloop to asyncio as a start. Yury

23 67

PEP 487: Simpler customization of class creation
by Martin Teichmann July 4, 2016

July 4, 2016

Hi list, just recently, I posted about the implementation of PEP 487. The discussion quickly diverted to PEP 520, which happened to be strongly related. Hoping to get some comments about the rest of PEP 487, I took out the part that is also in PEP 520. I attached the new version of the PEP. The implementation can be found on the Python issue tracker: http://bugs.python.org/issue27366 So PEP 487 is about simplifying the customization of class creation. Currently, this is done via metaclasses, which are very powerful, but often inflexible, as it is hard to combine two metaclasses. PEP 487 proposes a new metaclass which calls a method on all newly created subclasses. This way, in order to customize the creation of subclasses, one just needs to write a simple method. An absolutely classic example for metaclasses is the need to tell descriptors who they belong to. There are many large frameworks out there, e.g. enthought's traits, IPython's traitlets, Django's forms and many more. Their problem is: they're all fully incompatible. It's really hard to inherit from two classes which have different metaclasses. PEP 487 proposes to have one simple metaclass, which can do all those frameworks need, making them all compatible. As an example, imagine the framework has a generic descriptor called Integer, which describes, well, an integer. Typically you use it like that: class MyClass(FrameworkBaseClass): my_value = Integer() how does my_value know how it's called, how it should put its data into the object's __dict__? Well, this is what the framework's metaclass is for. With PEP 487, a framework doesn't need to declare an own metaclass anymore, but simply uses types.Object of PEP 487 as a base class: class FrameworkBaseClass(types.Object): def __init_subclass__(cls): super().__init_subclass__() for k, v in cls.__dict__.items(): if isinstance(v, FrameworkDescriptorBase): v.__set_owner__(cls, name) and all the framework's descriptors know their name. And if another framework should be used as well: no problem, they just work together easily. Actually, the above example is just that common, that PEP 487 includes it directly: a method __set_owner__ is called for every descriptor. That could make most descriptors in frameworks work out of the box. So now I am hoping for comments! Greetings Martin New version of the PEP follows: PEP: 487 Title: Simpler customisation of class creation Version: $Revision$ Last-Modified: $Date$ Author: Martin Teichmann <lkb.teichmann(a)gmail.com>, Status: Draft Type: Standards Track Content-Type: text/x-rst Created: 27-Feb-2015 Python-Version: 3.6 Post-History: 27-Feb-2015, 5-Feb-2016, 24-Jun-2016 Replaces: 422 Abstract ======== Currently, customising class creation requires the use of a custom metaclass. This custom metaclass then persists for the entire lifecycle of the class, creating the potential for spurious metaclass conflicts. This PEP proposes to instead support a wide range of customisation scenarios through a new ``__init_subclass__`` hook in the class body, a hook to initialize attributes. Those hooks should at first be defined in a metaclass in the standard library, with the option that this metaclass eventually becomes the default ``type`` metaclass. The new mechanism should be easier to understand and use than implementing a custom metaclass, and thus should provide a gentler introduction to the full power Python's metaclass machinery. Background ========== Metaclasses are a powerful tool to customize class creation. They have, however, the problem that there is no automatic way to combine metaclasses. If one wants to use two metaclasses for a class, a new metaclass combining those two needs to be created, typically manually. This need often occurs as a surprise to a user: inheriting from two base classes coming from two different libraries suddenly raises the necessity to manually create a combined metaclass, where typically one is not interested in those details about the libraries at all. This becomes even worse if one library starts to make use of a metaclass which it has not done before. While the library itself continues to work perfectly, suddenly every code combining those classes with classes from another library fails. Proposal ======== While there are many possible ways to use a metaclass, the vast majority of use cases falls into just three categories: some initialization code running after class creation, the initalization of descriptors and keeping the order in which class attributes were defined. Those three use cases can easily be performed by just one metaclass. If this metaclass is put into the standard library, and all libraries that wish to customize class creation use this very metaclass, no combination of metaclasses is necessary anymore. Said metaclass should live in the ``types`` module under the name ``Type``. This should hint the user that in the future, this metaclass may become the default metaclass ``type``. The three use cases are achieved as follows: 1. The metaclass contains an ``__init_subclass__`` hook that initializes all subclasses of a given class, 2. the metaclass calls a ``__set_owner__`` hook on all the attribute (descriptors) defined in the class, and For ease of use, a base class ``types.Object`` is defined, which uses said metaclass and contains an empty stub for the hook described for use case 1. It will eventually become the new replacement for the standard ``object``. As an example, the first use case looks as follows:: >>> class SpamBase(types.Object): ... # this is implicitly a @classmethod ... def __init_subclass__(cls, **kwargs): ... cls.class_args = kwargs ... super().__init_subclass__(cls, **kwargs) >>> class Spam(SpamBase, a=1, b="b"): ... pass >>> Spam.class_args {'a': 1, 'b': 'b'} The base class ``types.Object`` contains an empty ``__init_subclass__`` method which serves as an endpoint for cooperative multiple inheritance. Note that this method has no keyword arguments, meaning that all methods which are more specialized have to process all keyword arguments. This general proposal is not a new idea (it was first suggested for inclusion in the language definition `more than 10 years ago`_, and a similar mechanism has long been supported by `Zope's ExtensionClass`_), but the situation has changed sufficiently in recent years that the idea is worth reconsidering for inclusion. The second part of the proposal adds an ``__set_owner__`` initializer for class attributes, especially if they are descriptors. Descriptors are defined in the body of a class, but they do not know anything about that class, they do not even know the name they are accessed with. They do get to know their owner once ``__get__`` is called, but still they do not know their name. This is unfortunate, for example they cannot put their associated value into their object's ``__dict__`` under their name, since they do not know that name. This problem has been solved many times, and is one of the most important reasons to have a metaclass in a library. While it would be easy to implement such a mechanism using the first part of the proposal, it makes sense to have one solution for this problem for everyone. To give an example of its usage, imagine a descriptor representing weak referenced values:: import weakref class WeakAttribute: def __get__(self, instance, owner): return instance.__dict__[self.name] def __set__(self, instance, value): instance.__dict__[self.name] = weakref.ref(value) # this is the new initializer: def __set_owner__(self, owner, name): self.name = name While this example looks very trivial, it should be noted that until now such an attribute cannot be defined without the use of a metaclass. And given that such a metaclass can make life very hard, this kind of attribute does not exist yet. Key Benefits ============ Easier inheritance of definition time behaviour ----------------------------------------------- Understanding Python's metaclasses requires a deep understanding of the type system and the class construction process. This is legitimately seen as challenging, due to the need to keep multiple moving parts (the code, the metaclass hint, the actual metaclass, the class object, instances of the class object) clearly distinct in your mind. Even when you know the rules, it's still easy to make a mistake if you're not being extremely careful. Understanding the proposed implicit class initialization hook only requires ordinary method inheritance, which isn't quite as daunting a task. The new hook provides a more gradual path towards understanding all of the phases involved in the class definition process. Reduced chance of metaclass conflicts ------------------------------------- One of the big issues that makes library authors reluctant to use metaclasses (even when they would be appropriate) is the risk of metaclass conflicts. These occur whenever two unrelated metaclasses are used by the desired parents of a class definition. This risk also makes it very difficult to *add* a metaclass to a class that has previously been published without one. By contrast, adding an ``__init_subclass__`` method to an existing type poses a similar level of risk to adding an ``__init__`` method: technically, there is a risk of breaking poorly implemented subclasses, but when that occurs, it is recognised as a bug in the subclass rather than the library author breaching backwards compatibility guarantees. A path of introduction into Python ================================== Most of the benefits of this PEP can already be implemented using a simple metaclass. For the ``__init_subclass__`` hook this works all the way down to Python 2.7, while the attribute order needs Python 3.0 to work. Such a class has been `uploaded to PyPI`_. The only drawback of such a metaclass are the mentioned problems with metaclasses and multiple inheritance. Two classes using such a metaclass can only be combined, if they use exactly the same such metaclass. This fact calls for the inclusion of such a class into the standard library, as ``types.Type``, with a ``types.Object`` base class using it. Once all users use this standard library metaclass, classes from different packages can easily be combined. But still such classes cannot be easily combined with other classes using other metaclasses. Authors of metaclasses should bear that in mind and inherit from the standard metaclass if it seems useful for users of the metaclass to add more functionality. Ultimately, if the need for combining with other metaclasses is strong enough, the proposed functionality may be introduced into Python's ``type``. Those arguments strongly hint to the following procedure to include the proposed functionality into Python: 1. The metaclass implementing this proposal is put onto PyPI, so that it can be used and scrutinized. 2. Introduce this class into the Python 3.6 standard library. 3. Consider this as the default behavior for Python 3.7. Steps 2 and 3 would be similar to how the ``set`` datatype was first introduced as ``sets.Set``, and only later made a builtin type (with a slightly different API) based on wider experiences with the ``sets`` module. While the metaclass is still in the standard library and not in the language, it may still clash with other metaclasses. The most prominent metaclass in use is probably ABCMeta. It is also a particularly good example for the need of combining metaclasses. For users who want to define a ABC with subclass initialization, we should support a ``types.ABCMeta`` class, or let ``abc.ABCMeta`` inherit from this PEP's metaclass. As it turns out, most of the behavior of ``abc.ABCMeta`` can be done achieved with our ``types.Type``, except its core behavior, ``__instancecheck__`` and ``__subclasscheck__`` which can be supplied, as per the definition of the Python language, exclusively in a metaclass. Extensions written in C or C++ also often define their own metaclass. It would be very useful if those could also inherit from the metaclass defined here, but this is probably not possible. New Ways of Using Classes ========================= This proposal has many usecases like the following. In the examples, we still inherit from the ``SubclassInit`` base class. This would become unnecessary once this PEP is included in Python directly. Subclass registration --------------------- Especially when writing a plugin system, one likes to register new subclasses of a plugin baseclass. This can be done as follows:: class PluginBase(Object): subclasses = [] def __init_subclass__(cls, **kwargs): super().__init_subclass__(**kwargs) cls.subclasses.append(cls) In this example, ``PluginBase.subclasses`` will contain a plain list of all subclasses in the entire inheritance tree. One should note that this also works nicely as a mixin class. Trait descriptors ----------------- There are many designs of Python descriptors in the wild which, for example, check boundaries of values. Often those "traits" need some support of a metaclass to work. This is how this would look like with this PEP:: class Trait: def __get__(self, instance, owner): return instance.__dict__[self.key] def __set__(self, instance, value): instance.__dict__[self.key] = value def __set_owner__(self, owner, name): self.key = name Rejected Design Options ======================= Calling the hook on the class itself ------------------------------------ Adding an ``__autodecorate__`` hook that would be called on the class itself was the proposed idea of PEP 422. Most examples work the same way or even better if the hook is called on the subclass. In general, it is much easier to explicitly call the hook on the class in which it is defined (to opt-in to such a behavior) than to opt-out, meaning that one does not want the hook to be called on the class it is defined in. This becomes most evident if the class in question is designed as a mixin: it is very unlikely that the code of the mixin is to be executed for the mixin class itself, as it is not supposed to be a complete class on its own. The original proposal also made major changes in the class initialization process, rendering it impossible to back-port the proposal to older Python versions. More importantly, having a pure Python implementation allows us to take two preliminary steps before before we actually change the interpreter, giving us the chance to iron out all possible wrinkles in the API. Other variants of calling the hook ---------------------------------- Other names for the hook were presented, namely ``__decorate__`` or ``__autodecorate__``. This proposal opts for ``__init_subclass__`` as it is very close to the ``__init__`` method, just for the subclass, while it is not very close to decorators, as it does not return the class. Requiring an explicit decorator on ``__init_subclass__`` -------------------------------------------------------- One could require the explicit use of ``@classmethod`` on the ``__init_subclass__`` decorator. It was made implicit since there's no sensible interpretation for leaving it out, and that case would need to be detected anyway in order to give a useful error message. This decision was reinforced after noticing that the user experience of defining ``__prepare__`` and forgetting the ``@classmethod`` method decorator is singularly incomprehensible (particularly since PEP 3115 documents it as an ordinary method, and the current documentation doesn't explicitly say anything one way or the other). Defining arbitrary namespaces ----------------------------- PEP 422 defined a generic way to add arbitrary namespaces for class definitions. This approach is much more flexible than just leaving the definition order in a tuple. The ``__prepare__`` method in a metaclass supports exactly this behavior. But given that effectively the only use cases that could be found out in the wild were the ``OrderedDict`` way of determining the attribute order, it seemed reasonable to only support this special case. The metaclass described in this PEP has been designed to be very simple such that it could be reasonably made the default metaclass. This was especially important when designing the attribute order functionality: This was a highly demanded feature and has been enabled through the ``__prepare__`` method of metaclasses. This method can be abused in very weird ways, making it hard to correctly maintain this feature in CPython. This is why it has been proposed to deprecated this feature, and instead use ``OrderedDict`` as the standard namespace, supporting the most important feature while dropping most of the complexity. But this would have meant that ``OrderedDict`` becomes a language builtin like dict and set, and not just a standard library class. The choice of the ``__attribute_order__`` tuple is a much simpler solution to the problem. A more ``__new__``-like hook ---------------------------- In PEP 422 the hook worked more like the ``__new__`` method than the ``__init__`` method, meaning that it returned a class instead of modifying one. This allows a bit more flexibility, but at the cost of much harder implementation and undesired side effects. Adding a class attribute with the attribute order ------------------------------------------------- This got its own PEP 520. History ======= This used to be a competing proposal to PEP 422 by Nick Coghlan and Daniel Urban. PEP 422 intended to achieve the same goals as this PEP, but with a different way of implementation. In the meantime, PEP 422 has been withdrawn favouring this approach. References ========== .. _published code: http://mail.python.org/pipermail/python-dev/2012-June/119878.html .. _more than 10 years ago: http://mail.python.org/pipermail/python-dev/2001-November/018651.html .. _Zope's ExtensionClass: http://docs.zope.org/zope_secrets/extensionclass.html .. _uploaded to PyPI: https://pypi.python.org/pypi/metaclass 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 coding: utf-8 End:

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AutoNumber Enum
by Ethan Furman June 30, 2016

June 30, 2016

There is a several-month-old request to add aenum's [1] AutoNumberEnum to the stdlib [2]. The requester and two of the three developers of Enum are in favor (the third hasn't chimed in yet). This new addition would enable the following: from Enum import AutoNumberEnum class Color(AutoNumberEnum): # auto-number magic is on Red Green Blue Cyan # magic turns off when non-enum is defined def is_primary(self): # typos in methods, etc, will raise return self in (self.Red, self.Grene, self.Blue) # typos after the initial definition stanza will raise BlueGreen = Blue + Grene There is, of course, the risk of typos during the initial member definition stanza, but since this magic only happens when the user explicitly asks for it (AutoNumberEnum), I think it is acceptable. The `start` parameter is still available, and assigning a number is supported (subsequent numbers will (re)start from the assigned number). Thoughts? Opinions? Flames? -- ~Ethan~ [1] https://pypi.python.org/pypi/aenum [2] http://bugs.python.org/issue26988

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