Mailman 3 Janeiro de 2012 - Python-Dev

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

18 de Maio de 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

Reviving restricted mode?
by Guido van Rossum 14 de Agosto de 2014

14 de Agosto de 2014

I've received some enthusiastic emails from someone who wants to revive restricted mode. He started out with a bunch of patches to the CPython runtime using ctypes, which he attached to an App Engine bug: http://code.google.com/p/googleappengine/issues/detail?id=671 Based on his code (the file secure.py is all you need, included in secure.tar.gz) it seems he believes the only security leaks are __subclasses__, gi_frame and gi_code. (I have since convinced him that if we add "restricted" guards to these attributes, he doesn't need the functions added to sys.) I don't recall the exploits that Samuele once posted that caused the death of rexec.py -- does anyone recall, or have a pointer to the threads? -- --Guido van Rossum (home page: http://www.python.org/~guido/)

19 35

PEP 3145 (With Contents)
by Eric Pruitt 25 de Dezembro de 2012

25 de Dezembro de 2012

Alright, I will re-submit with the contents pasted. I never use double backquotes as I think them rather ugly; that is the work of an editor or some automated program in the chain. Plus, it also messed up my line formatting and now I have lines with one word on them... Anyway, the contents of PEP 3145: PEP: 3145 Title: Asynchronous I/O For subprocess.Popen Author: (James) Eric Pruitt, Charles R. McCreary, Josiah Carlson Type: Standards Track Content-Type: text/plain Created: 04-Aug-2009 Python-Version: 3.2 Abstract: In its present form, the subprocess.Popen implementation is prone to dead-locking and blocking of the parent Python script while waiting on data from the child process. Motivation: A search for "python asynchronous subprocess" will turn up numerous accounts of people wanting to execute a child process and communicate with it from time to time reading only the data that is available instead of blocking to wait for the program to produce data [1] [2] [3]. The current behavior of the subprocess module is that when a user sends or receives data via the stdin, stderr and stdout file objects, dead locks are common and documented [4] [5]. While communicate can be used to alleviate some of the buffering issues, it will still cause the parent process to block while attempting to read data when none is available to be read from the child process. Rationale: There is a documented need for asynchronous, non-blocking functionality in subprocess.Popen [6] [7] [2] [3]. Inclusion of the code would improve the utility of the Python standard library that can be used on Unix based and Windows builds of Python. Practically every I/O object in Python has a file-like wrapper of some sort. Sockets already act as such and for strings there is StringIO. Popen can be made to act like a file by simply using the methods attached the the subprocess.Popen.stderr, stdout and stdin file-like objects. But when using the read and write methods of those options, you do not have the benefit of asynchronous I/O. In the proposed solution the wrapper wraps the asynchronous methods to mimic a file object. Reference Implementation: I have been maintaining a Google Code repository that contains all of my changes including tests and documentation [9] as well as blog detailing the problems I have come across in the development process [10]. I have been working on implementing non-blocking asynchronous I/O in the subprocess.Popen module as well as a wrapper class for subprocess.Popen that makes it so that an executed process can take the place of a file by duplicating all of the methods and attributes that file objects have. There are two base functions that have been added to the subprocess.Popen class: Popen.send and Popen._recv, each with two separate implementations, one for Windows and one for Unix based systems. The Windows implementation uses ctypes to access the functions needed to control pipes in the kernel 32 DLL in an asynchronous manner. On Unix based systems, the Python interface for file control serves the same purpose. The different implementations of Popen.send and Popen._recv have identical arguments to make code that uses these functions work across multiple platforms. When calling the Popen._recv function, it requires the pipe name be passed as an argument so there exists the Popen.recv function that passes selects stdout as the pipe for Popen._recv by default. Popen.recv_err selects stderr as the pipe by default. "Popen.recv" and "Popen.recv_err" are much easier to read and understand than "Popen._recv('stdout' ..." and "Popen._recv('stderr' ..." respectively. Since the Popen._recv function does not wait on data to be produced before returning a value, it may return empty bytes. Popen.asyncread handles this issue by returning all data read over a given time interval. The ProcessIOWrapper class uses the asyncread and asyncwrite functions to allow a process to act like a file so that there are no blocking issues that can arise from using the stdout and stdin file objects produced from a subprocess.Popen call. References: [1] [ python-Feature Requests-1191964 ] asynchronous Subprocess http://mail.python.org/pipermail/python-bugs-list/2006-December/ 036524.html [2] Daily Life in an Ivory Basement : /feb-07/problems-with-subprocess http://ivory.idyll.org/blog/feb-07/problems-with-subprocess [3] How can I run an external command asynchronously from Python? - Stack Overflow http://stackoverflow.com/questions/636561/how-can-i-run-an-external- command-asynchronously-from-python [4] 18.1. subprocess - Subprocess management - Python v2.6.2 documentation http://docs.python.org/library/subprocess.html#subprocess.Popen.wait [5] 18.1. subprocess - Subprocess management - Python v2.6.2 documentation http://docs.python.org/library/subprocess.html#subprocess.Popen.kill [6] Issue 1191964: asynchronous Subprocess - Python tracker http://bugs.python.org/issue1191964 [7] Module to allow Asynchronous subprocess use on Windows and Posix platforms - ActiveState Code http://code.activestate.com/recipes/440554/ [8] subprocess.rst - subprocdev - Project Hosting on Google Code http://code.google.com/p/subprocdev/source/browse/doc/subprocess.rst?spec=s… [9] subprocdev - Project Hosting on Google Code http://code.google.com/p/subprocdev [10] Python Subprocess Dev http://subdev.blogspot.com/ Copyright: This P.E.P. is licensed under the Open Publication License; http://www.opencontent.org/openpub/. On Tue, Sep 8, 2009 at 22:56, Benjamin Peterson <benjamin(a)python.org> wrote: > 2009/9/7 Eric Pruitt <eric.pruitt(a)gmail.com>: >> Hello all, >> >> I have been working on adding asynchronous I/O to the Python >> subprocess module as part of my Google Summer of Code project. Now >> that I have finished documenting and pruning the code, I present PEP >> 3145 for its inclusion into the Python core code. Any and all feedback >> on the PEP (http://www.python.org/dev/peps/pep-3145/) is appreciated. > > Hi Eric, > One of the reasons you're not getting many response is that you've not > pasted the contents of the PEP in this message. That makes it really > easy for people to comment on various sections. > > BTW, it seems like you were trying to use reST formatting with the > text PEP layout. Double backquotes only mean something in reST. > > > -- > Regards, > Benjamin >

10 26

Testing the tests by modifying the ordering of dict items.
by Mark Shannon 22 de Dezembro de 2012

22 de Dezembro de 2012

Hi, Python code should not depend upon the ordering of items in a dict. Unfortunately it seems that a number of tests in the standard library do just that. Changing PyDict_MINSIZE from 8 to either 4 or 16 causes the following tests to fail: test_dis test_email test_inspect test_nntplib test_packaging test_plistlib test_pprint test_symtable test_trace test_sys also fails, but this is a legitimate failure in sys.getsizeof() Changing the collision resolution function from f(n) = 5n + 1 to f(n) = n + 1 results in the same failures, except for test_packaging and test_symtable which pass. Finally, changing the seed in unicode_hash() from (implicit) 0 to an arbitrary value (12345678) causes the above tests to fail plus: test_json test_set test_ttk_textonly test_urllib test_urlparse I think this is a real issue as the unicode_hash() function is likely to change soon due to http://bugs.python.org/issue13703. Should I: 1. Submit one big bug report? 2. Submit a bug report for each "failing" test separately? 3. Ignore it, since the tests only fail when I start messing about? Cheers, Mark.

4 4

Providing a mechanism for PEP 3115 compliant dynamic class creation
by Nick Coghlan 21 de Abril de 2012

21 de Abril de 2012

In reviewing a fix for the metaclass calculation in __build_class__ [1], I realised that PEP 3115 poses a potential problem for the common practice of using "type(name, bases, ns)" for dynamic class creation. Specifically, if one of the base classes has a metaclass with a significant __prepare__() method, then the current idiom will do the wrong thing (and most likely fail as a result), since "ns" will probably be an ordinary dictionary instead of whatever __prepare__() would have returned. Initially I was going to suggest making __build_class__ part of the language definition rather than a CPython implementation detail, but then I realised that various CPython specific elements in its signature made that a bad idea. Instead, I'm thinking along the lines of an "operator.prepare(metaclass, bases)" function that does the metaclass calculation dance, invoking __prepare__() and returning the result if it exists, otherwise returning an ordinary dict. Under the hood we would refactor this so that operator.prepare and __build_class__ were using a shared implementation of the functionality at the C level - it may even be advisable to expose that implementation via the C API as PyType_PrepareNamespace(). The correct idiom for dynamic type creation in a PEP 3115 world would then be: from operator import prepare cls = type(name, bases, prepare(type, bases)) Thoughts? Cheers, Nick. [1] http://bugs.python.org/issue1294232 -- Nick Coghlan | ncoghlan(a)gmail.com | Brisbane, Australia

3 8

A new dictionary implementation
by Mark Shannon 17 de Fevereiro de 2012

17 de Fevereiro de 2012

Hi, Now that issue 13703 has been largely settled, I want to propose my new dictionary implementation again. It is a little more polished than before. https://bitbucket.org/markshannon/hotpy_new_dict Object-oriented benchmarks use considerably less memory and are sometimes faster (by a small amount). (I've only benchmarked on my old 32bit machine) E.g 2to3 No speed change -28% memory GCbench +10% speed -47% memory Other benchmarks show little or no change in behaviour, mainly minor memory savings. If an application is OO and uses lots of memory the new dict will save a lot of memory and maybe boost performance. Other applications will be largely unaffected. It passes all the tests. (I had to change a couple that relied on dict repr() ordering) Cheers, Mark.

14 35

Counting collisions for the win
by Victor Stinner 16 de Fevereiro de 2012

16 de Fevereiro de 2012

Hi, I'm working on the hash collision issue since 2 or 3 weeks. I evaluated all solutions and I think that I have now a good knowledge of the problem and how it should be solved. The major issue is to have a minor or no impact on applications (don't break backward compatibility). I saw three major solutions: - use a randomized hash - use two hashes, a randomized hash and the actual hash kept for backward compatibility - count collisions on dictionary lookup Using a randomized hash does break a lot of tests (e.g. tests relying on the representation of a dictionary). The patch is huge, too big to backport it directly on stable versions. Using a randomized hash may also break (indirectly) real applications because the application output is also somehow "randomized". For example, in the Django test suite, the HTML output is different at each run. Web browsers may render the web page differently, or crash, or ... I don't think that Django would like to sort attributes of each HTML tag, just because we wanted to fix a vulnerability. Randomized hash has also a major issue: if the attacker is able to compute the secret, (s)he can easily compute collisions and exploit the hash collision vulnerability again. I don't know exactly how complex it is to compute the secret, but our hash function is weak (it is far from being cryptographic, it is really simple to run it backward). If someone writes a fast function to compute the secret, we will go back to the same point. IMO using two hashes has the same disavantages of the randomized hash solution, whereas it is more complex to implement. The last solution is very simple: count collision and raise an exception if it hits a limit. The path is something like 10 lines whereas the randomized hash is more close to 500 lines, add a new file, change Visual Studio project file, etc. First I thaught that it would break more applications than the randomized hash, but I tried on Django: the test suite fails with a limit of 20 collisions, but not with a limit of 50 collisions, whereas the patch uses a limit of 1000 collisions. According to my basic tests, a limit of 35 collisions requires a dictionary with more than 10,000,000 integer keys to raise an error. I am not talking about the attack, but valid data. More details about my tests on the Django test suite: http://bugs.python.org/issue13703#msg151620 -- I propose to solve the hash collision vulnerability by counting collisions because it does fix the vulnerability with a minor or no impact on applications or backward compatibility. I don't see why we should use a different fix for Python 3.3. If counting collisons solves the issue for stable versions, it is also enough for Python 3.3. We now know all issues of the randomized hash solution, and I think that there are more drawbacks than advantages. IMO the randomized hash is overkill to fix the hash collision issue. I just have some requests on Marc Andre Lemburg patch: - the limit should be configurable: a new function in the sys module should be enough. It may be private (or replaced by an environment variable?) in stable versions - the set type should also be patched (I didn't check if it is vulnerable or not using the patch) - the patch has no test! (a class with a fixed hash should be enough to write a test) - the limit must be documented somwhere - the exception type should be different than KeyError Victor

31 76

plugging the hash attack
by Benjamin Peterson 16 de Fevereiro de 2012

16 de Fevereiro de 2012

Hello everyone, In effort to get a fix out before Perl 6 goes mainstream, Barry and I have decided to pronounce on what we want for our stable releases. What we have decided is that 1. Simple hash randomization is the way to go. We think this has the best chance of actually fixing the problem while being fairly straightforward such that we're comfortable putting it in a stable release. 2. It will be off by default in stable releases and enabled by an envar at runtime. This will prevent code breakage from dictionary order changing as well as people depending on the hash stability. -- Regards, Benjamin

10 10

Store timestamps as decimal.Decimal objects
by Victor Stinner 16 de Fevereiro de 2012

16 de Fevereiro de 2012

Hi, In issues #13882 and #11457, I propose to add an argument to functions returning timestamps to choose the timestamp format. Python uses float in most cases whereas float is not enough to store a timestamp with a resolution of 1 nanosecond. I added recently time.clock_gettime() to Python 3.3 which has a resolution of a nanosecond. The (first?) new timestamp format will be decimal.Decimal because it is able to store any timestamp in any resolution without loosing bits. Instead of adding a boolean argument, I would prefer to support more formats. My last patch provides the following formats: - "float": float (used by default) - "decimal": decimal.Decimal - "datetime": datetime.datetime - "timespec": (sec, nsec) tuple # I don't think that we need it, it is just another example The proposed API is: time.time(format="datetime") time.clock_gettime(time.CLOCK_REALTIME, format="decimal") os.stat(path, timestamp="datetime) etc. This API has an issue: importing the datetime or decimal object is implicit, I don't know if it is really an issue. (In my last patch, the import is done too late, but it can be fixed, it is not really a matter.) Alexander Belopolsky proposed to use time.time(format=datetime.datetime) instead. -- The first step would be to add an argument to functions returning timestamps. The second step is to accept these new formats (Decimal?) as input, for datetime.datetime.fromtimestamp() and os.utime() for example. (Using decimal.Decimal, we may remove os.utimens() and use the right function depending on the timestamp resolution.) -- I prefer Decimal over a dummy tuple like (sec, nsec) because you can do arithmetic on it: t2-t1, a+b, t/k, etc. It stores also the resolution of the clock: time.time() and time.clock_gettime() have for example different resolution (sec, ms, us for time.time() and ns for clock_gettime()). The decimal module is still implemented in Python, but there is working implementation in C which is much faster. Store timestamps as Decimal can be a motivation to integrate the C implementation :-) -- Examples with the time module: $ ./python Python 3.3.0a0 (default:52f68c95e025+, Jan 26 2012, 21:54:31) >>> import time >>> time.time() 1327611705.948446 >>> time.time('decimal') Decimal('1327611708.988419') >>> t1=time.time('decimal'); t2=time.time('decimal'); t2-t1 Decimal('0.000550') >>> t1=time.time('float'); t2=time.time('float'); t2-t1 5.9604644775390625e-06 >>> time.clock_gettime(time.CLOCK_MONOTONIC, 'decimal') Decimal('1211833.389740312') >>> time.clock_getres(time.CLOCK_MONOTONIC, 'decimal') Decimal('1E-9') >>> time.clock() 0.12 >>> time.clock('decimal') Decimal('0.120000') Examples with os.stat: $ ./python Python 3.3.0a0 (default:2914ce82bf89+, Jan 30 2012, 23:07:24) >>> import os >>> s=os.stat("setup.py", timestamp="datetime") >>> s.st_mtime - s.st_ctime datetime.timedelta(0) >>> print(s.st_atime - s.st_ctime) 52 days, 1:44:06.191293 >>> os.stat("setup.py", timestamp="timespec").st_ctime (1323458640, 702327236) >>> os.stat("setup.py", timestamp="decimal").st_ctime Decimal('1323458640.702327236') Victor

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Fixing the XML batteries
by Stefan Behnel 8 de Fevereiro de 2012

8 de Fevereiro de 2012

Hi everyone, I think Py3.3 would be a good milestone for cleaning up the stdlib support for XML. Note upfront: you may or may not know me as the maintainer of lxml, the de-facto non-stdlib standard Python XML tool. This (lengthy) post was triggered by the following kind of conversation that I keep having with new XML users in Python (mostly on c.l.py), which hints at some serious flaw in the stdlib. User: I'm trying to do XML stuff XYZ in Python and have problem ABC. Me: What library are you using? Could you show us some code? User: My code looks like this snippet: ... Me: You are using minidom which is known to be hard to use, slow and uses lots of memory. Use the xml.etree.ElementTree package instead, or rather its C implementation cElementTree, also in the stdlib. User (coming back after a while): thanks, that was exactly what [I didn't know] I was looking for. What does this tell us? 1) MiniDOM is what new users find first. It's highly visible because there are still lots of ancient "Python and XML" web pages out there that date back from the time before Python 2.5 (or rather something like 2.2), when it was the only XML tree library in the stdlib. It's also the first hit from the top when you search for "XML" on the stdlib docs page and contains the (to some people) familiar word "DOM", which lets users stop their search and start writing code, not expecting to find a separate alternative in the same stdlib, way further down. And the description as "mini", "simple" and "lightweight" suggests to users that it's going to be easy to use and efficient. 2) MiniDOM is not what users want. It leads to complicated, unpythonic code and lots of problems. It is neither easy to use, nor efficient, nor "lightweight", "simple" or "mini", not in absolute numbers (see http://bugs.python.org/issue11379#msg148584 and following for a recent discussion). It's also badly maintained in the sense that its performance characteristics could likely be improved, but no-one is seriously interested in doing that, because it would not lead to something that actually *is* fast or memory friendly compared to any of the 'real' alternatives that are available right now. 3) ElementTree is what users should use, MiniDOM is not. ElementTree was added to the stdlib in Py2.5 on popular demand, exactly because it is very easy to use, very fast, and very memory friendly. And because users did not want to use MiniDOM any more. Today, ElementTree has a rather straight upgrade path towards lxml.etree if more XML features like validation or XSLT are needed. MiniDOM has nothing like that to offer. It's a dead end. 4) In the stdlib, cElementTree is independent of ElementTree, but totally hidden in the documentation. In conversations like the above, it's unnecessarily complex to explain to users that there is ElementTree (which is documented in the stdlib), but that what they want to use is really cElementTree, which has the same API but does not have a stdlib documentation page that I can send them to. Note that the other Python implementations simply provide cElementTree as an alias for ElementTree. That leaves CPython as the only Python implementation that really has these two separate modules. So, there are many problems here. And I think they make it unnecessarily complicated for users to process XML in Python and that the current situation helps in turning away new users from Python as a language for XML processing. Python does have impressively great tools for working with XML. It's just that the stdlib and its documentation do not reflect or even appreciate that. What should change? a) The stdlib documentation should help users to choose the right tool right from the start. Instead of using the totally misleading wording that it uses now, it should be honest about the performance characteristics of MiniDOM and should actively suggest that those who don't know what to choose (or even *that* they can choose) should not use MiniDOM in the first place. I created a ticket (issue11379) for a minor step in this direction, but given the responses, I'm rather convinced that there's a lot more that can be done and should be done, and that it should be done now, right for the next release. b) cElementTree should finally loose it's "special" status as a separate library and disappear as an accelerator module behind ElementTree. This has been suggested a couple of times already, and AFAIR, there was some opposition because 1) ET was maintained outside of the stdlib and 2) the APIs of both were not identical. However, getting ET 1.3 into Py2.7 and 3.2 was a U-turn. Today, ET is *only* being maintained in the stdlib by Florent Xicluna (who is doing a good job with it), and ET 1.3 has basically made the APIs of both implementations compatible again. So, 3.3 would be the right milestone for fixing the "two libs for one" quirk. Given that this is the third time during the last couple of years that I'm suggesting to finally fix the stdlib and its documentation, I won't provide any further patches before it has finally been accepted that a) this is a problem and b) it should be fixed, thus allowing the patches to actually serve a purpose. If we can agree on that, I'll happily help in making this change happen. Stefan

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