Mailman 3 March 2019 - Python-Dev

PEP 448 review
by Guido van Rossum 29 Mar '23

29 Mar '23

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 18 May '21

18 May '21

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

Boundaries between numbers and identifiers
by Serhiy Storchaka 15 Apr '21

15 Apr '21

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

8 10

About "python-porting" mail list
by Facundo Batista 20 Jan '20

20 Jan '20

Hello! This list (which I co-admin, with Georg) is getting less and less traffic as months pass by. See: https://mail.python.org/pipermail/python-porting/ The interwebs has been collecting ton of resources about porting py2 to 3 during these years. Any not-yet-answered question surely can be done in a list with more participants. Can we kill this list? Thanks! Regards, -- . Facundo Blog: http://www.taniquetil.com.ar/plog/ PyAr: http://www.python.org.ar/ Twitter: @facundobatista

4 7

congrats on 3.5! Alas, windows 7 users are having problems installing it
by Laura Creighton 15 Sep '19

15 Sep '19

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

why is not 64-bit installer the default download link for Windows?
by Cosimo Lupo 19 Jun '19

19 Jun '19

If one goes to httWhps://www.python.org/downloads <https://www.python.org/downloads> from a Windows browser, the default download URL is for the 32-bit installer instead of the 64-bit one. I wonder why is this still the case? Shouldn't we encourage new Windows users (who may not even know the distinction between the two architectures) to use the 64-bit version of Python, since most likely they can? If this is not the correct forum for this, please let me know where I can direct my question/feature request, thanks. Cosimo -- Cosimo Lupo

14 22

BDFL-Delegate appointments for several PEPs
by Nick Coghlan 22 May '19

22 May '19

Hi folks, With the revised PEP 1 published, the Steering Council members have been working through the backlog of open PEPs, figuring out which ones are at a stage of maturity where we think it makes sense to appoint a BDFL-Delegate to continue moving the PEP through the review process, and eventually make the final decision on whether or not to accept or reject the change. We'll be announcing those appointments as we go, so I'm happy to report that I will be handling the BDFL-Delegate responsibilities for the following PEPs: * PEP 499: Binding "-m" executed modules under their module name as well as `__main__` * PEP 574: Pickle protocol 5 with out of band data I'm also pleased to report that Petr Viktorin has agreed to take on the responsibility of reviewing the competing proposals to improve the way CPython's C API exposes callables for direct invocation by third party low level code: * PEP 576: Exposing the internal FastCallKeywords convention to 3rd party modules * PEP 580: Revising the callable struct hierarchy internally and in the public C API * PEP 579: Background information for the problems the other two PEPs are attempting to address Regards, Nick. -- Nick Coghlan | ncoghlan(a)gmail.com | Brisbane, Australia

9 61

Actor Model in Python
by Aratz Manterola Lasa 27 Apr '19

27 Apr '19

Hello, I was wondering if there was any Python project aiming to implement the actor model for python concurrency. ¿Does anyone know it? Aratz.

4 4

Invitation to the PyCon typing summit (Thu, May 2nd, 1-5pm, at PyCon in Cleveland)
by Guido van Rossum 24 Apr '19

24 Apr '19

The typing summit is primarily a place for developers of type checkers to collaborate, but we are also inviting (potential) users of type checkers. For example, there are plans to extend the standard Python type system with features intended to support numpy, Pandas, tensorflow and similar libraries, and we will discuss these at the summit. Therefore developers and power-users of such frameworks are especially welcome at the summit. With Ewa's and Dropbox's help I've arranged a room at PyCon. *When: Thursday May 2nd, 1-5 pm (i.e. the day between the Language Summit and the conference proper)* *Where: Room 6 at PyCon in Cleveland* If you're planning to attend, please fill out this form: https://goo.gl/forms/rG9dVTBbgyBgDK8H2 *If you already emailed me about attending, please still fill out the form!* (Also if you already registered in response to my invitation on typing-sig, I've got your registration info.) -- --Guido van Rossum (python.org/~guido)

1 1

PEP 578: Python Runtime Audit Hooks
by Steve Dower 16 Apr '19

16 Apr '19

Hi all Time is short, but I'm hoping to get PEP 578 (formerly PEP 551) into Python 3.8. Here's the current text for review and comment before I submit to the Steering Council. The formatted text is at https://www.python.org/dev/peps/pep-0578/ (update just pushed, so give it an hour or so, but it's fundamentally the same as what's there) No Discourse post, because we don't have a python-dev equivalent there yet, so please reply here for this one. Implementation is at https://github.com/zooba/cpython/tree/pep-578/ and my backport to 3.7 (https://github.com/zooba/cpython/tree/pep-578-3.7/) is already getting some real use (though this will not be added to 3.7, unless people *really* want it, so the backport is just for reference). Cheers, Steve ===== PEP: 578 Title: Python Runtime Audit Hooks Version: $Revision$ Last-Modified: $Date$ Author: Steve Dower <steve.dower(a)python.org> Status: Draft Type: Standards Track Content-Type: text/x-rst Created: 16-Jun-2018 Python-Version: 3.8 Post-History: Abstract ======== This PEP describes additions to the Python API and specific behaviors for the CPython implementation that make actions taken by the Python runtime visible to auditing tools. Visibility into these actions provides opportunities for test frameworks, logging frameworks, and security tools to monitor and optionally limit actions taken by the runtime. This PEP proposes adding two APIs to provide insights into a running Python application: one for arbitrary events, and another specific to the module import system. The APIs are intended to be available in all Python implementations, though the specific messages and values used are unspecified here to allow implementations the freedom to determine how best to provide information to their users. Some examples likely to be used in CPython are provided for explanatory purposes. See PEP 551 for discussion and recommendations on enhancing the security of a Python runtime making use of these auditing APIs. Background ========== Python provides access to a wide range of low-level functionality on many common operating systems. While this is incredibly useful for "write-once, run-anywhere" scripting, it also makes monitoring of software written in Python difficult. Because Python uses native system APIs directly, existing monitoring tools either suffer from limited context or auditing bypass. Limited context occurs when system monitoring can report that an action occurred, but cannot explain the sequence of events leading to it. For example, network monitoring at the OS level may be able to report "listening started on port 5678", but may not be able to provide the process ID, command line, parent process, or the local state in the program at the point that triggered the action. Firewall controls to prevent such an action are similarly limited, typically to process names or some global state such as the current user, and in any case rarely provide a useful log file correlated with other application messages. Auditing bypass can occur when the typical system tool used for an action would ordinarily report its use, but accessing the APIs via Python do not trigger this. For example, invoking "curl" to make HTTP requests may be specifically monitored in an audited system, but Python's "urlretrieve" function is not. Within a long-running Python application, particularly one that processes user-provided information such as a web app, there is a risk of unexpected behavior. This may be due to bugs in the code, or deliberately induced by a malicious user. In both cases, normal application logging may be bypassed resulting in no indication that anything out of the ordinary has occurred. Additionally, and somewhat unique to Python, it is very easy to affect the code that is run in an application by manipulating either the import system's search path or placing files earlier on the path than intended. This is often seen when developers create a script with the same name as the module they intend to use - for example, a ``random.py`` file that attempts to import the standard library ``random`` module. This is not sandboxing, as this proposal does not attempt to prevent malicious behavior (though it enables some new options to do so). See the `Why Not A Sandbox`_ section below for further discussion. Overview of Changes =================== The aim of these changes is to enable both application developers and system administrators to integrate Python into their existing monitoring systems without dictating how those systems look or behave. We propose two API changes to enable this: an Audit Hook and Verified Open Hook. Both are available from Python and native code, allowing applications and frameworks written in pure Python code to take advantage of the extra messages, while also allowing embedders or system administrators to deploy builds of Python where auditing is always enabled. Only CPython is bound to provide the native APIs as described here. Other implementations should provide the pure Python APIs, and may provide native versions as appropriate for their underlying runtimes. Auditing events are likewise considered implementation specific, but are bound by normal feature compatibility guarantees. Audit Hook ---------- In order to observe actions taken by the runtime (on behalf of the caller), an API is required to raise messages from within certain operations. These operations are typically deep within the Python runtime or standard library, such as dynamic code compilation, module imports, DNS resolution, or use of certain modules such as ``ctypes``. The following new C APIs allow embedders and CPython implementors to send and receive audit hook messages:: # Add an auditing hook typedef int (*hook_func)(const char *event, PyObject *args, void *userData); int PySys_AddAuditHook(hook_func hook, void *userData); # Raise an event with all auditing hooks int PySys_Audit(const char *event, PyObject *args); # Internal API used during Py_Finalize() - not publicly accessible void _Py_ClearAuditHooks(void); The new Python APIs for receiving and raising audit hooks are:: # Add an auditing hook sys.addaudithook(hook: Callable[[str, tuple]]) # Raise an event with all auditing hooks sys.audit(str, *args) Hooks are added by calling ``PySys_AddAuditHook()`` from C at any time, including before ``Py_Initialize()``, or by calling ``sys.addaudithook()`` from Python code. Hooks cannot be removed or replaced. When events of interest are occurring, code can either call ``PySys_Audit()`` from C (while the GIL is held) or ``sys.audit()``. The string argument is the name of the event, and the tuple contains arguments. A given event name should have a fixed schema for arguments, which should be considered a public API (for each x.y version release), and thus should only change between feature releases with updated documentation. For maximum compatibility, events using the same name as an event in the reference interpreter CPython should make every attempt to use compatible arguments. Including the name or an abbreviation of the implementation in implementation-specific event names will also help prevent collisions. For example, a ``pypy.jit_invoked`` event is clearly distinguised from an ``ipy.jit_invoked`` event. When an event is audited, each hook is called in the order it was added with the event name and tuple. If any hook returns with an exception set, later hooks are ignored and *in general* the Python runtime should terminate. This is intentional to allow hook implementations to decide how to respond to any particular event. The typical responses will be to log the event, abort the operation with an exception, or to immediately terminate the process with an operating system exit call. When an event is audited but no hooks have been set, the ``audit()`` function should impose minimal overhead. Ideally, each argument is a reference to existing data rather than a value calculated just for the auditing call. As hooks may be Python objects, they need to be freed during ``Py_Finalize()``. To do this, we add an internal API ``_Py_ClearAuditHooks()`` that releases any Python hooks and any memory held. This is an internal function with no public export, and we recommend it raise its own audit event for all current hooks to ensure that unexpected calls are observed. Below in `Suggested Audit Hook Locations`_, we recommend some important operations that should raise audit events. Python implementations should document which operations will raise audit events, along with the event schema. It is intentional that ``sys.addaudithook(print)`` be a trivial way to display all messages. Verified Open Hook ------------------ Most operating systems have a mechanism to distinguish between files that can be executed and those that can not. For example, this may be an execute bit in the permissions field, a verified hash of the file contents to detect potential code tampering, or file system path restrictions. These are an important security mechanism for preventing execution of data or code that is not approved for a given environment. Currently, Python has no way to integrate with these when launching scripts or importing modules. The new public C API for the verified open hook is:: # Set the handler typedef PyObject *(*hook_func)(PyObject *path, void *userData) int PyImport_SetOpenForImportHook(hook_func handler, void *userData) # Open a file using the handler PyObject *PyImport_OpenForImport(const char *path) The new public Python API for the verified open hook is:: # Open a file using the handler importlib.util.open_for_import(path : str) -> io.IOBase The ``importlib.util.open_for_import()`` function is a drop-in replacement for ``open(str(pathlike), 'rb')``. Its default behaviour is to open a file for raw, binary access. To change the behaviour a new handler should be set. Handler functions only accept ``str`` arguments. The C API ``PyImport_OpenForImport`` function assumes UTF-8 encoding. A custom handler may be set by calling ``PyImport_SetOpenForImportHook()`` from C at any time, including before ``Py_Initialize()``. However, if a hook has already been set then the call will fail. When ``open_for_import()`` is called with a hook set, the hook will be passed the path and its return value will be returned directly. The returned object should be an open file-like object that supports reading raw bytes. This is explicitly intended to allow a ``BytesIO`` instance if the open handler has already read the entire file into memory. Note that these hooks can import and call the ``_io.open()`` function on CPython without triggering themselves. They can also use ``_io.BytesIO`` to return a compatible result using an in-memory buffer. If the hook determines that the file should not be loaded, it should raise an exception of its choice, as well as performing any other logging. All import and execution functionality involving code from a file will be changed to use ``open_for_import()`` unconditionally. It is important to note that calls to ``compile()``, ``exec()`` and ``eval()`` do not go through this function - an audit hook that includes the code from these calls is the best opportunity to validate code that is read from the file. Given the current decoupling between import and execution in Python, most imported code will go through both ``open_for_import()`` and the log hook for ``compile``, and so care should be taken to avoid repeating verification steps. There is no Python API provided for changing the open hook. To modify import behavior from Python code, use the existing functionality provided by ``importlib``. API Availability ---------------- While all the functions added here are considered public and stable API, the behavior of the functions is implementation specific. Most descriptions here refer to the CPython implementation, and while other implementations should provide the functions, there is no requirement that they behave the same. For example, ``sys.addaudithook()`` and ``sys.audit()`` should exist but may do nothing. This allows code to make calls to ``sys.audit()`` without having to test for existence, but it should not assume that its call will have any effect. (Including existence tests in security-critical code allows another vector to bypass auditing, so it is preferable that the function always exist.) ``importlib.util.open_for_import(path)`` should at a minimum always return ``_io.open(path, 'rb')``. Code using the function should make no further assumptions about what may occur, and implementations other than CPython are not required to let developers override the behavior of this function with a hook. Suggested Audit Hook Locations ============================== The locations and parameters in calls to ``sys.audit()`` or ``PySys_Audit()`` are to be determined by individual Python implementations. This is to allow maximum freedom for implementations to expose the operations that are most relevant to their platform, and to avoid or ignore potentially expensive or noisy events. Table 1 acts as both suggestions of operations that should trigger audit events on all implementations, and examples of event schemas. Table 2 provides further examples that are not required, but are likely to be available in CPython. Refer to the documentation associated with your version of Python to see which operations provide audit events. .. csv-table:: Table 1: Suggested Audit Hooks :header: "API Function", "Event Name", "Arguments", "Rationale" :widths: 2, 2, 3, 6 ``PySys_AddAuditHook``, ``sys.addaudithook``, "", "Detect when new audit hooks are being added. " ``PyImport_SetOpenForImportHook``, ``setopenforimporthook``, "", " Detects any attempt to set the ``open_for_import`` hook. " "``compile``, ``exec``, ``eval``, ``PyAst_CompileString``, ``PyAST_obj2mod``", ``compile``, "``(code, filename_or_none)``", " Detect dynamic code compilation, where ``code`` could be a string or AST. Note that this will be called for regular imports of source code, including those that were opened with ``open_for_import``. " "``exec``, ``eval``, ``run_mod``", ``exec``, "``(code_object,)``", " Detect dynamic execution of code objects. This only occurs for explicit calls, and is not raised for normal function invocation. " ``import``, ``import``, "``(module, filename, sys.path, sys.meta_path, sys.path_hooks)``", "Detect when modules are imported. This is raised before the module name is resolved to a file. All arguments other than the module name may be ``None`` if they are not used or available. " "``open``", ``open``, "``(path, mode, flags)``", "Detect when a file is about to be opened. *path* and *mode* are the usual parameters to ``open`` if available, while *flags* is provided instead of *mode* in some cases. " ``PyEval_SetProfile``, ``sys.setprofile``, "", "Detect when code is injecting trace functions. Because of the implementation, exceptions raised from the hook will abort the operation, but will not be raised in Python code. Note that ``threading.setprofile`` eventually calls this function, so the event will be audited for each thread. " ``PyEval_SetTrace``, ``sys.settrace``, "", "Detect when code is injecting trace functions. Because of the implementation, exceptions raised from the hook will abort the operation, but will not be raised in Python code. Note that ``threading.settrace`` eventually calls this function, so the event will be audited for each thread. " "``_PyObject_GenericSetAttr``, ``check_set_special_type_attr``, ``object_set_class``, ``func_set_code``, ``func_set_[kw]defaults``"," ``object.__setattr__``","``(object, attr, value)``","Detect monkey patching of types and objects. This event is raised for the ``__class__`` attribute and any attribute on ``type`` objects. " "``_PyObject_GenericSetAttr``",``object.__delattr__``,"``(object, attr)``","Detect deletion of object attributes. This event is raised for any attribute on ``type`` objects. " "``Unpickler.find_class``",``pickle.find_class``,"``(module_name, global_name)``","Detect imports and global name lookup when unpickling. " .. csv-table:: Table 2: Potential CPython Audit Hooks :header: "API Function", "Event Name", "Arguments", "Rationale" :widths: 2, 2, 3, 6 ``_PySys_ClearAuditHooks``, ``sys._clearaudithooks``, "", "Notifies hooks they are being cleaned up, mainly in case the event is triggered unexpectedly. This event cannot be aborted. " ``code_new``, ``code.__new__``, "``(bytecode, filename, name)``", " Detect dynamic creation of code objects. This only occurs for direct instantiation, and is not raised for normal compilation. " ``func_new_impl``, ``function.__new__``, "``(code,)``", "Detect dynamic creation of function objects. This only occurs for direct instantiation, and is not raised for normal compilation. " "``_ctypes.dlopen``, ``_ctypes.LoadLibrary``", ``ctypes.dlopen``, " ``(module_or_path,)``", "Detect when native modules are used. " ``_ctypes._FuncPtr``, ``ctypes.dlsym``, "``(lib_object, name)``", " Collect information about specific symbols retrieved from native modules. " ``_ctypes._CData``, ``ctypes.cdata``, "``(ptr_as_int,)``", "Detect when code is accessing arbitrary memory using ``ctypes``. " "``new_mmap_object``",``mmap.__new__``,"``(fileno, map_size, access, offset)``", "Detects creation of mmap objects. On POSIX, access may have been calculated from the ``prot`` and ``flags`` arguments. " ``sys._getframe``, ``sys._getframe``, "``(frame_object,)``", "Detect when code is accessing frames directly. " ``sys._current_frames``, ``sys._current_frames``, "", "Detect when code is accessing frames directly. " "``socket.bind``, ``socket.connect``, ``socket.connect_ex``, ``socket.getaddrinfo``, ``socket.getnameinfo``, ``socket.sendmsg``, ``socket.sendto``", ``socket.address``, "``(address,)``", "Detect access to network resources. The address is unmodified from the original call. " "``member_get``, ``func_get_code``, ``func_get_[kw]defaults`` ",``object.__getattr__``,"``(object, attr)``","Detect access to restricted attributes. This event is raised for any built-in members that are marked as restricted, and members that may allow bypassing imports. " "``urllib.urlopen``",``urllib.Request``,"``(url, data, headers, method)``", "Detects URL requests. " Performance Impact ================== The important performance impact is the case where events are being raised but there are no hooks attached. This is the unavoidable case - once a developer has added audit hooks they have explicitly chosen to trade performance for functionality. Performance impact with hooks added are not of interest here, since this is opt-in functionality. Analysis using the Python Performance Benchmark Suite [1]_ shows no significant impact, with the vast majority of benchmarks showing between 1.05x faster to 1.05x slower. In our opinion, the performance impact of the set of auditing points described in this PEP is negligible. Rejected Ideas ============== Separate module for audit hooks ------------------------------- The proposal is to add a new module for audit hooks, hypothetically ``audit``. This would separate the API and implementation from the ``sys`` module, and allow naming the C functions ``PyAudit_AddHook`` and ``PyAudit_Audit`` rather than the current variations. Any such module would need to be a built-in module that is guaranteed to always be present. The nature of these hooks is that they must be callable without condition, as any conditional imports or calls provide opportunities to intercept and suppress or modify events. Given it is one of the most core modules, the ``sys`` module is somewhat protected against module shadowing attacks. Replacing ``sys`` with a sufficiently functional module that the application can still run is a much more complicated task than replacing a module with only one function of interest. An attacker that has the ability to shadow the ``sys`` module is already capable of running arbitrary code from files, whereas an ``audit`` module could be replaced with a single line in a ``.pth`` file anywhere on the search path:: import sys; sys.modules['audit'] = type('audit', (object,), {'audit': lambda *a: None, 'addhook': lambda *a: None}) Multiple layers of protection already exist for monkey patching attacks against either ``sys`` or ``audit``, but assignments or insertions to ``sys.modules`` are not audited. This idea is rejected because it makes it trivial to suppress all calls to ``audit``. Flag in sys.flags to indicate "audited" mode -------------------------------------------- The proposal is to add a value in ``sys.flags`` to indicate when Python is running in a "secure" or "audited" mode. This would allow applications to detect when some features are enabled or when hooks have been added and modify their behaviour appropriately. Currently, we are not aware of any legitimate reasons for a program to behave differently in the presence of audit hooks. Both application-level APIs ``sys.audit`` and ``importlib.util.open_for_import`` are always present and functional, regardless of whether the regular ``python`` entry point or some alternative entry point is used. Callers cannot determine whether any hooks have been added (except by performing side-channel analysis), nor do they need to. The calls should be fast enough that callers do not need to avoid them, and the program is responsible for ensuring that any added hooks are fast enough to not affect application performance. The argument that this is "security by obscurity" is valid, but irrelevant. Security by obscurity is only an issue when there are no other protective mechanisms; obscurity as the first step in avoiding attack is strongly recommended (see `this article <https://danielmiessler.com/study/security-by-obscurity/>`_ for discussion). This idea is rejected because there are no appropriate reasons for an application to change its behaviour based on whether these APIs are in use. Why Not A Sandbox ================= Sandboxing CPython has been attempted many times in the past, and each past attempt has failed. Fundamentally, the problem is that certain functionality has to be restricted when executing the sandboxed code, but otherwise needs to be available for normal operation of Python. For example, completely removing the ability to compile strings into bytecode also breaks the ability to import modules from source code, and if it is not completely removed then there are too many ways to get access to that functionality indirectly. There is not yet any feasible way to generically determine whether a given operation is "safe" or not. Further information and references available at [2]_. This proposal does not attempt to restrict functionality, but simply exposes the fact that the functionality is being used. Particularly for intrusion scenarios, detection is significantly more important than early prevention (as early prevention will generally drive attackers to use an alternate, less-detectable, approach). The availability of audit hooks alone does not change the attack surface of Python in any way, but they enable defenders to integrate Python into their environment in ways that are currently not possible. Since audit hooks have the ability to safely prevent an operation occuring, this feature does enable the ability to provide some level of sandboxing. In most cases, however, the intention is to enable logging rather than creating a sandbox. Relationship to PEP 551 ======================= This API was originally presented as part of `PEP 551 <https://www.python.org/dev/peps/pep-0551/>`_ Security Transparency in the Python Runtime. For simpler review purposes, and due to the broader applicability of these APIs beyond security, the API design is now presented separately. PEP 551 is an informational PEP discussing how to integrate Python into a secure or audited environment. References ========== .. [1] Python Performance Benchmark Suite `<https://github.com/python/performance>`_ .. [2] Python Security model - Sandbox `<https://python-security.readthedocs.io/security.html#sandbox>`_ Copyright ========= Copyright (c) 2019 by Microsoft Corporation. This material may be distributed only subject to the terms and conditions set forth in the Open Publication License, v1.0 or later (the latest version is presently available at http://www.opencontent.org/openpub/).

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