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
-------------------- snip snip --------------------
Title: PEP Purpose and Guidelines
Version: $Revision: 1.36 $
Last-Modified: $Date: 2002/07/29 18:34:59 $
Author: Barry A. Warsaw, Jeremy Hylton
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
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
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 or feature request tracker.
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
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
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
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
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. 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
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.
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.
PEP 9 contains a boilerplate 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
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  for more details.
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  for more information about PEP style
 PEP 1, PEP Purpose and Guidelines, Warsaw, Hylton
If you decide to provide an explicit URL for a PEP, please use
this as the URL template:
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
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 or better yet, the SourceForge patch manager 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
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
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
References and Footnotes
 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
 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
 PEP 9, Sample PEP Template
This document has been placed in the public domain.
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:
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
--Guido van Rossum (home page: http://www.python.org/~guido/)
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:
Title: Asynchronous I/O For subprocess.Popen
Author: (James) Eric Pruitt, Charles R. McCreary, Josiah Carlson
Type: Standards Track
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.
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   . 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  . 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
There is a documented need for asynchronous, non-blocking functionality in
subprocess.Popen    . 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
I have been maintaining a Google Code repository that contains all of my
changes including tests and documentation  as well as blog detailing
the problems I have come across in the development process .
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
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
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.
 [ python-Feature Requests-1191964 ] asynchronous Subprocess
 Daily Life in an Ivory Basement : /feb-07/problems-with-subprocess
 How can I run an external command asynchronously from Python? - Stack
 18.1. subprocess - Subprocess management - Python v2.6.2 documentation
 18.1. subprocess - Subprocess management - Python v2.6.2 documentation
 Issue 1191964: asynchronous Subprocess - Python tracker
 Module to allow Asynchronous subprocess use on Windows and Posix
platforms - ActiveState Code
 subprocess.rst - subprocdev - Project Hosting on Google Code
 subprocdev - Project Hosting on Google Code
 Python Subprocess Dev
This P.E.P. is licensed under the Open Publication License;
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.
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
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.
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?
In reviewing a fix for the metaclass calculation in __build_class__
, 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
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))
Nick Coghlan | ncoghlan(a)gmail.com | Brisbane, Australia
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.
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)
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
- 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:
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
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
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.
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:
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
Alexander Belopolsky proposed to use
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
(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
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 3.3.0a0 (default:52f68c95e025+, Jan 26 2012, 21:54:31)
>>> import time
>>> t1=time.time('decimal'); t2=time.time('decimal'); t2-t1
>>> t1=time.time('float'); t2=time.time('float'); t2-t1
>>> time.clock_gettime(time.CLOCK_MONOTONIC, 'decimal')
>>> time.clock_getres(time.CLOCK_MONOTONIC, 'decimal')
Examples with os.stat:
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
>>> print(s.st_atime - s.st_ctime)
52 days, 1:44:06.191293
>>> os.stat("setup.py", timestamp="timespec").st_ctime
>>> os.stat("setup.py", timestamp="decimal").st_ctime
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
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