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:
Here is some proposed wording. Since it is more of a clarification of what
it takes to garner support -- which is just a new section -- rather than a
complete rewrite I'm including just the diff to make it easier to read the
changes.
*diff -r 49d18bb47ebc pep-0011.txt*
*--- a/pep-0011.txt Wed May 14 11:18:22 2014 -0400*
*+++ b/pep-0011.txt Fri May 16 13:48:30 2014 -0400*
@@ -2,22 +2,21 @@
Title: Removing support for little used platforms
Version: $Revision$
Last-Modified: $Date$
-Author: martin(a)v.loewis.de (Martin von Löwis)
+Author: Martin von Löwis <martin(a)v.loewis.de>,
+ Brett Cannon <brett(a)python.org>
Status: Active
Type: Process
Content-Type: text/x-rst
Created: 07-Jul-2002
Post-History: 18-Aug-2007
+ 16-May-2014
Abstract
--------
-This PEP documents operating systems (platforms) which are not
-supported in Python anymore. For some of these systems,
-supporting code might be still part of Python, but will be removed
-in a future release - unless somebody steps forward as a volunteer
-to maintain this code.
+This PEP documents how an operating system (platform) garners
+support in Python as well as documenting past support.
Rationale
@@ -37,16 +36,53 @@
change to the Python source code will work on all supported
platforms.
-To reduce this risk, this PEP proposes a procedure to remove code
-for platforms with no Python users.
+To reduce this risk, this PEP specifies what is required for a
+platform to be considered supported by Python as well as providing a
+procedure to remove code for platforms with little or no Python
+users.
+Supporting platforms
+--------------------
+
+Gaining official platform support requires two things. First, a core
+developer needs to volunteer to maintain platform-specific code. This
+core developer can either already be a member of the Python
+development team or be given contributor rights on the basis of
+maintaining platform support (it is at the discretion of the Python
+development team to decide if a person is ready to have such rights
+even if it is just for supporting a specific platform).
+
+Second, a stable buildbot must be provided [2]_. This guarantees that
+platform support will not be accidentally broken by a Python core
+developer who does not have personal access to the platform. For a
+buildbot to be considered stable it requires that the machine be
+reliably up and functioning (but it is up to the Python core
+developers to decide whether to promote a buildbot to being
+considered stable).
+
+This policy does not disqualify supporting other platforms
+indirectly. Patches which are not platform-specific but still done to
+add platform support will be considered for inclusion. For example,
+if platform-independent changes were necessary in the configure
+script which was motivated to support a specific platform that would
+be accepted. Patches which add platform-specific code such as the
+name of a specific platform to the configure script will generally
+not be accepted without the platform having official support.
+
+CPU architecture and compiler support are viewed in a similar manner
+as platforms. For example, to consider the ARM architecture supported
+a buildbot running on ARM would be required along with support from
+the Python development team. In general it is not required to have
+a CPU architecture run under every possible platform in order to be
+considered supported.
Unsupporting platforms
----------------------
-If a certain platform that currently has special code in it is
-deemed to be without Python users, a note must be posted in this
-PEP that this platform is no longer actively supported. This
+If a certain platform that currently has special code in Python is
+deemed to be without Python users or lacks proper support from the
+Python development team and/or a buildbot, a note must be posted in
+this PEP that this platform is no longer actively supported. This
note must include:
- the name of the system
@@ -69,8 +105,8 @@
forward and offer maintenance.
-Resupporting platforms
-----------------------
+Re-supporting platforms
+-----------------------
If a user of a platform wants to see this platform supported
again, he may volunteer to maintain the platform support. Such an
@@ -101,7 +137,7 @@
release is made. Developers of extension modules will generally need
to use the same Visual Studio release; they are concerned both with
the availability of the versions they need to use, and with keeping
-the zoo of versions small. The Python source tree will keep
+the zoo of versions small. The Python source tree will keep
unmaintained build files for older Visual Studio releases, for which
patches will be accepted. Such build files will be removed from the
source tree 3 years after the extended support for the compiler has
@@ -223,6 +259,7 @@
----------
.. [1] http://support.microsoft.com/lifecycle/
+.. [2] http://buildbot.python.org/3.x.stable/
Copyright
---------
The current memory layout for dictionaries is
unnecessarily inefficient. It has a sparse table of
24-byte entries containing the hash value, key pointer,
and value pointer.
Instead, the 24-byte entries should be stored in a
dense table referenced by a sparse table of indices.
For example, the dictionary:
d = {'timmy': 'red', 'barry': 'green', 'guido': 'blue'}
is currently stored as:
entries = [['--', '--', '--'],
[-8522787127447073495, 'barry', 'green'],
['--', '--', '--'],
['--', '--', '--'],
['--', '--', '--'],
[-9092791511155847987, 'timmy', 'red'],
['--', '--', '--'],
[-6480567542315338377, 'guido', 'blue']]
Instead, the data should be organized as follows:
indices = [None, 1, None, None, None, 0, None, 2]
entries = [[-9092791511155847987, 'timmy', 'red'],
[-8522787127447073495, 'barry', 'green'],
[-6480567542315338377, 'guido', 'blue']]
Only the data layout needs to change. The hash table
algorithms would stay the same. All of the current
optimizations would be kept, including key-sharing
dicts and custom lookup functions for string-only
dicts. There is no change to the hash functions, the
table search order, or collision statistics.
The memory savings are significant (from 30% to 95%
compression depending on the how full the table is).
Small dicts (size 0, 1, or 2) get the most benefit.
For a sparse table of size t with n entries, the sizes are:
curr_size = 24 * t
new_size = 24 * n + sizeof(index) * t
In the above timmy/barry/guido example, the current
size is 192 bytes (eight 24-byte entries) and the new
size is 80 bytes (three 24-byte entries plus eight
1-byte indices). That gives 58% compression.
Note, the sizeof(index) can be as small as a single
byte for small dicts, two bytes for bigger dicts and
up to sizeof(Py_ssize_t) for huge dict.
In addition to space savings, the new memory layout
makes iteration faster. Currently, keys(), values, and
items() loop over the sparse table, skipping-over free
slots in the hash table. Now, keys/values/items can
loop directly over the dense table, using fewer memory
accesses.
Another benefit is that resizing is faster and
touches fewer pieces of memory. Currently, every
hash/key/value entry is moved or copied during a
resize. In the new layout, only the indices are
updated. For the most part, the hash/key/value entries
never move (except for an occasional swap to fill a
hole left by a deletion).
With the reduced memory footprint, we can also expect
better cache utilization.
For those wanting to experiment with the design,
there is a pure Python proof-of-concept here:
http://code.activestate.com/recipes/578375
YMMV: Keep in mind that the above size statics assume a
build with 64-bit Py_ssize_t and 64-bit pointers. The
space savings percentages are a bit different on other
builds. Also, note that in many applications, the size
of the data dominates the size of the container (i.e.
the weight of a bucket of water is mostly the water,
not the bucket).
Raymond
Hi all,
https://github.com/python/cpython is now live as a semi-official, *read
only* Github mirror of the CPython Mercurial repository. Let me know if you
have any problems/concerns.
I still haven't decided how often to update it (considering either just N
times a day, or maybe use a Hg hook for batching). Suggestions are welcome.
The methodology I used to create it is via hg-fast-export. I also tried to
pack and gc the git repo as much as possible before the initial Github push
- it went down from almost ~2GB to ~200MB (so this is the size of a fresh
clone right now).
Eli
P.S. thanks Jesse for the keys to https://github.com/python
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/)
Hi,
I added a new "stats" page to the bug tracker:
http://bugs.python.org/issue?@template=stats
The page can be reached from the sidebar of the bug tracker: Summaries -> Stats
The data are updated once a week, together with the Summary of Python
tracker issues.
Best Regards,
Ezio Melotti
Hi Python dev folks,
I've written a PEP proposing a specific os.scandir() API for a
directory iterator that returns the stat-like info from the OS, the
main advantage of which is to speed up os.walk() and similar
operations between 4-20x, depending on your OS and file system. Full
details, background info, and context links are in the PEP, which
Victor Stinner has uploaded at the following URL, and I've also copied
inline below.
http://legacy.python.org/dev/peps/pep-0471/
Would love feedback on the PEP, but also of course on the proposal itself.
-Ben
PEP: 471
Title: os.scandir() function -- a better and faster directory iterator
Version: $Revision$
Last-Modified: $Date$
Author: Ben Hoyt <benhoyt(a)gmail.com>
Status: Draft
Type: Standards Track
Content-Type: text/x-rst
Created: 30-May-2014
Python-Version: 3.5
Abstract
========
This PEP proposes including a new directory iteration function,
``os.scandir()``, in the standard library. This new function adds
useful functionality and increases the speed of ``os.walk()`` by 2-10
times (depending on the platform and file system) by significantly
reducing the number of times ``stat()`` needs to be called.
Rationale
=========
Python's built-in ``os.walk()`` is significantly slower than it needs
to be, because -- in addition to calling ``os.listdir()`` on each
directory -- it executes the system call ``os.stat()`` or
``GetFileAttributes()`` on each file to determine whether the entry is
a directory or not.
But the underlying system calls -- ``FindFirstFile`` /
``FindNextFile`` on Windows and ``readdir`` on Linux and OS X --
already tell you whether the files returned are directories or not, so
no further system calls are needed. In short, you can reduce the
number of system calls from approximately 2N to N, where N is the
total number of files and directories in the tree. (And because
directory trees are usually much wider than they are deep, it's often
much better than this.)
In practice, removing all those extra system calls makes ``os.walk()``
about **8-9 times as fast on Windows**, and about **2-3 times as fast
on Linux and Mac OS X**. So we're not talking about micro-
optimizations. See more `benchmarks`_.
.. _`benchmarks`: https://github.com/benhoyt/scandir#benchmarks
Somewhat relatedly, many people (see Python `Issue 11406`_) are also
keen on a version of ``os.listdir()`` that yields filenames as it
iterates instead of returning them as one big list. This improves
memory efficiency for iterating very large directories.
So as well as providing a ``scandir()`` iterator function for calling
directly, Python's existing ``os.walk()`` function could be sped up a
huge amount.
.. _`Issue 11406`: http://bugs.python.org/issue11406
Implementation
==============
The implementation of this proposal was written by Ben Hoyt (initial
version) and Tim Golden (who helped a lot with the C extension
module). It lives on GitHub at `benhoyt/scandir`_.
.. _`benhoyt/scandir`: https://github.com/benhoyt/scandir
Note that this module has been used and tested (see "Use in the wild"
section in this PEP), so it's more than a proof-of-concept. However,
it is marked as beta software and is not extensively battle-tested.
It will need some cleanup and more thorough testing before going into
the standard library, as well as integration into `posixmodule.c`.
Specifics of proposal
=====================
Specifically, this PEP proposes adding a single function to the ``os``
module in the standard library, ``scandir``, that takes a single,
optional string as its argument::
scandir(path='.') -> generator of DirEntry objects
Like ``listdir``, ``scandir`` calls the operating system's directory
iteration system calls to get the names of the files in the ``path``
directory, but it's different from ``listdir`` in two ways:
* Instead of bare filename strings, it returns lightweight
``DirEntry`` objects that hold the filename string and provide
simple methods that allow access to the stat-like data the operating
system returned.
* It returns a generator instead of a list, so that ``scandir`` acts
as a true iterator instead of returning the full list immediately.
``scandir()`` yields a ``DirEntry`` object for each file and directory
in ``path``. Just like ``listdir``, the ``'.'`` and ``'..'``
pseudo-directories are skipped, and the entries are yielded in
system-dependent order. Each ``DirEntry`` object has the following
attributes and methods:
* ``name``: the entry's filename, relative to ``path`` (corresponds to
the return values of ``os.listdir``)
* ``is_dir()``: like ``os.path.isdir()``, but requires no system calls
on most systems (Linux, Windows, OS X)
* ``is_file()``: like ``os.path.isfile()``, but requires no system
calls on most systems (Linux, Windows, OS X)
* ``is_symlink()``: like ``os.path.islink()``, but requires no system
calls on most systems (Linux, Windows, OS X)
* ``lstat()``: like ``os.lstat()``, but requires no system calls on
Windows
The ``DirEntry`` attribute and method names were chosen to be the same
as those in the new ``pathlib`` module for consistency.
Notes on caching
----------------
The ``DirEntry`` objects are relatively dumb -- the ``name`` attribute
is obviously always cached, and the ``is_X`` and ``lstat`` methods
cache their values (immediately on Windows via ``FindNextFile``, and
on first use on Linux / OS X via a ``stat`` call) and never refetch
from the system.
For this reason, ``DirEntry`` objects are intended to be used and
thrown away after iteration, not stored in long-lived data structured
and the methods called again and again.
If a user wants to do that (for example, for watching a file's size
change), they'll need to call the regular ``os.lstat()`` or
``os.path.getsize()`` functions which force a new system call each
time.
Examples
========
Here's a good usage pattern for ``scandir``. This is in fact almost
exactly how the scandir module's faster ``os.walk()`` implementation
uses it::
dirs = []
non_dirs = []
for entry in scandir(path):
if entry.is_dir():
dirs.append(entry)
else:
non_dirs.append(entry)
The above ``os.walk()``-like code will be significantly using scandir
on both Windows and Linux or OS X.
Or, for getting the total size of files in a directory tree -- showing
use of the ``DirEntry.lstat()`` method::
def get_tree_size(path):
"""Return total size of files in path and subdirs."""
size = 0
for entry in scandir(path):
if entry.is_dir():
sub_path = os.path.join(path, entry.name)
size += get_tree_size(sub_path)
else:
size += entry.lstat().st_size
return size
Note that ``get_tree_size()`` will get a huge speed boost on Windows,
because no extra stat call are needed, but on Linux and OS X the size
information is not returned by the directory iteration functions, so
this function won't gain anything there.
Support
=======
The scandir module on GitHub has been forked and used quite a bit (see
"Use in the wild" in this PEP), but there's also been a fair bit of
direct support for a scandir-like function from core developers and
others on the python-dev and python-ideas mailing lists. A sampling:
* **Nick Coghlan**, a core Python developer: "I've had the local Red
Hat release engineering team express their displeasure at having to
stat every file in a network mounted directory tree for info that is
present in the dirent structure, so a definite +1 to os.scandir from
me, so long as it makes that info available."
[`source1 <http://bugs.python.org/issue11406>`_]
* **Tim Golden**, a core Python developer, supports scandir enough to
have spent time refactoring and significantly improving scandir's C
extension module.
[`source2 <https://github.com/tjguk/scandir>`_]
* **Christian Heimes**, a core Python developer: "+1 for something
like yielddir()"
[`source3 <https://mail.python.org/pipermail/python-ideas/2012-November/017772.html>`_]
and "Indeed! I'd like to see the feature in 3.4 so I can remove my
own hack from our code base."
[`source4 <http://bugs.python.org/issue11406>`_]
* **Gregory P. Smith**, a core Python developer: "As 3.4beta1 happens
tonight, this isn't going to make 3.4 so i'm bumping this to 3.5.
I really like the proposed design outlined above."
[`source5 <http://bugs.python.org/issue11406>`_]
* **Guido van Rossum** on the possibility of adding scandir to Python
3.5 (as it was too late for 3.4): "The ship has likewise sailed for
adding scandir() (whether to os or pathlib). By all means experiment
and get it ready for consideration for 3.5, but I don't want to add
it to 3.4."
[`source6 <https://mail.python.org/pipermail/python-dev/2013-November/130583.html>`_]
Support for this PEP itself (meta-support?) was given by Nick Coghlan
on python-dev: "A PEP reviewing all this for 3.5 and proposing a
specific os.scandir API would be a good thing."
[`source7 <https://mail.python.org/pipermail/python-dev/2013-November/130588.html>`_]
Use in the wild
===============
To date, ``scandir`` is definitely useful, but has been clearly marked
"beta", so it's uncertain how much use of it there is in the wild. Ben
Hoyt has had several reports from people using it. For example:
* Chris F: "I am processing some pretty large directories and was half
expecting to have to modify getdents. So thanks for saving me the
effort." [via personal email]
* bschollnick: "I wanted to let you know about this, since I am using
Scandir as a building block for this code. Here's a good example of
scandir making a radical performance improvement over os.listdir."
[`source8 <https://github.com/benhoyt/scandir/issues/19>`_]
* Avram L: "I'm testing our scandir for a project I'm working on.
Seems pretty solid, so first thing, just want to say nice work!"
[via personal email]
Others have `requested a PyPI package`_ for it, which has been
created. See `PyPI package`_.
.. _`requested a PyPI package`: https://github.com/benhoyt/scandir/issues/12
.. _`PyPI package`: https://pypi.python.org/pypi/scandir
GitHub stats don't mean too much, but scandir does have several
watchers, issues, forks, etc. Here's the run-down as of the stats as
of June 5, 2014:
* Watchers: 17
* Stars: 48
* Forks: 15
* Issues: 2 open, 19 closed
**However, the much larger point is this:**, if this PEP is accepted,
``os.walk()`` can easily be reimplemented using ``scandir`` rather
than ``listdir`` and ``stat``, increasing the speed of ``os.walk()``
very significantly. There are thousands of developers, scripts, and
production code that would benefit from this large speedup of
``os.walk()``. For example, on GitHub, there are almost as many uses
of ``os.walk`` (194,000) as there are of ``os.mkdir`` (230,000).
Open issues and optional things
===============================
There are a few open issues or optional additions:
Should scandir be in its own module?
------------------------------------
Should the function be included in the standard library in a new
module, ``scandir.scandir()``, or just as ``os.scandir()`` as
discussed? The preference of this PEP's author (Ben Hoyt) would be
``os.scandir()``, as it's just a single function.
Should there be a way to access the full path?
----------------------------------------------
Should ``DirEntry``'s have a way to get the full path without using
``os.path.join(path, entry.name)``? This is a pretty common pattern,
and it may be useful to add pathlib-like ``str(entry)`` functionality.
This functionality has also been requested in `issue 13`_ on GitHub.
.. _`issue 13`: https://github.com/benhoyt/scandir/issues/13
Should it expose Windows wildcard functionality?
------------------------------------------------
Should ``scandir()`` have a way of exposing the wildcard functionality
in the Windows ``FindFirstFile`` / ``FindNextFile`` functions? The
scandir module on GitHub exposes this as a ``windows_wildcard``
keyword argument, allowing Windows power users the option to pass a
custom wildcard to ``FindFirstFile``, which may avoid the need to use
``fnmatch`` or similar on the resulting names. It is named the
unwieldly ``windows_wildcard`` to remind you you're writing power-
user, Windows-only code if you use it.
This boils down to whether ``scandir`` should be about exposing all of
the system's directory iteration features, or simply providing a fast,
simple, cross-platform directory iteration API.
This PEP's author votes for not including ``windows_wildcard`` in the
standard library version, because even though it could be useful in
rare cases (say the Windows Dropbox client?), it'd be too easy to use
it just because you're a Windows developer, and create code that is
not cross-platform.
Possible improvements
=====================
There are many possible improvements one could make to scandir, but
here is a short list of some this PEP's author has in mind:
* scandir could potentially be further sped up by calling ``readdir``
/ ``FindNextFile`` say 50 times per ``Py_BEGIN_ALLOW_THREADS`` block
so that it stays in the C extension module for longer, and may be
somewhat faster as a result. This approach hasn't been tested, but
was suggested by on Issue 11406 by Antoine Pitrou.
[`source9 <http://bugs.python.org/msg130125>`_]
Previous discussion
===================
* `Original thread Ben Hoyt started on python-ideas`_ about speeding
up ``os.walk()``
* Python `Issue 11406`_, which includes the original proposal for a
scandir-like function
* `Further thread Ben Hoyt started on python-dev`_ that refined the
``scandir()`` API, including Nick Coghlan's suggestion of scandir
yielding ``DirEntry``-like objects
* `Final thread Ben Hoyt started on python-dev`_ to discuss the
interaction between scandir and the new ``pathlib`` module
* `Question on StackOverflow`_ about why ``os.walk()`` is slow and
pointers on how to fix it (this inspired the author of this PEP
early on)
* `BetterWalk`_, this PEP's author's previous attempt at this, on
which the scandir code is based
.. _`Original thread Ben Hoyt started on python-ideas`:
https://mail.python.org/pipermail/python-ideas/2012-November/017770.html
.. _`Further thread Ben Hoyt started on python-dev`:
https://mail.python.org/pipermail/python-dev/2013-May/126119.html
.. _`Final thread Ben Hoyt started on python-dev`:
https://mail.python.org/pipermail/python-dev/2013-November/130572.html
.. _`Question on StackOverflow`:
http://stackoverflow.com/questions/2485719/very-quickly-getting-total-size-…
.. _`BetterWalk`: https://github.com/benhoyt/betterwalk
Copyright
=========
This document has been placed in the public domain.
..
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End:
The buildbot web site seems to have been down for some hours and still
is as of 0915 UTC. I'm not sure who is watching over it but I'll ping
the infrastructure team as well.
--
Ned Deily,
nad(a)acm.org
