Hi everyone,
CPython is slow. We all know that, yet little is done to fix it.
I'd like to change that.
I have a plan to speed up CPython by a factor of five over the next few
years. But it needs funding.
I am aware that there have been several promised speed ups in the past
that have failed. You might wonder why this is different.
Here are three reasons:
1. I already have working code for the first stage.
2. I'm not promising a silver bullet. I recognize that this is a
substantial amount of work and needs funding.
3. I have extensive experience in VM implementation, not to mention a
PhD in the subject.
My ideas for possible funding, as well as the actual plan of
development, can be found here:
https://github.com/markshannon/faster-cpython
I'd love to hear your thoughts on this.
Cheers,
Mark.
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:
In Python 2.5 `0or[]` was accepted by the Python parser. It became an
error in 2.6 because "0o" became recognizing as an incomplete octal
number. `1or[]` still is accepted.
On other hand, `1if 2else 3` is accepted despites the fact that "2e" can
be recognized as an incomplete floating point number. In this case the
tokenizer pushes "e" back and returns "2".
Shouldn't it do the same with "0o"? It is possible to make `0or[]` be
parseable again. Python implementation is able to tokenize this example:
$ echo '0or[]' | ./python -m tokenize
1,0-1,1: NUMBER '0'
1,1-1,3: NAME 'or'
1,3-1,4: OP '['
1,4-1,5: OP ']'
1,5-1,6: NEWLINE '\n'
2,0-2,0: ENDMARKER ''
On other hand, all these examples look weird. There is an assymmetry:
`1or 2` is a valid syntax, but `1 or2` is not. It is hard to recognize
visually the boundary between a number and the following identifier or
keyword, especially if numbers can contain letters ("b", "e", "j", "o",
"x") and underscores, and identifiers can contain digits. On both sides
of the boundary can be letters, digits, and underscores.
I propose to change the Python syntax by adding a requirement that there
should be a whitespace or delimiter between a numeric literal and the
following keyword.
Hi, folks.
Since the previous discussion was suspended without consensus, I wrote
a new PEP for it. (Thank you Victor for reviewing it!)
This PEP looks very similar to PEP 623 "Remove wstr from Unicode",
but for encoder APIs, not for Unicode object APIs.
URL (not available yet): https://www.python.org/dev/peps/pep-0624/
---
PEP: 624
Title: Remove Py_UNICODE encoder APIs
Author: Inada Naoki <songofacandy(a)gmail.com>
Status: Draft
Type: Standards Track
Content-Type: text/x-rst
Created: 06-Jul-2020
Python-Version: 3.11
Abstract
========
This PEP proposes to remove deprecated ``Py_UNICODE`` encoder APIs in
Python 3.11:
* ``PyUnicode_Encode()``
* ``PyUnicode_EncodeASCII()``
* ``PyUnicode_EncodeLatin1()``
* ``PyUnicode_EncodeUTF7()``
* ``PyUnicode_EncodeUTF8()``
* ``PyUnicode_EncodeUTF16()``
* ``PyUnicode_EncodeUTF32()``
* ``PyUnicode_EncodeUnicodeEscape()``
* ``PyUnicode_EncodeRawUnicodeEscape()``
* ``PyUnicode_EncodeCharmap()``
* ``PyUnicode_TranslateCharmap()``
* ``PyUnicode_EncodeDecimal()``
* ``PyUnicode_TransformDecimalToASCII()``
.. note::
`PEP 623 <https://www.python.org/dev/peps/pep-0623/>`_ propose to remove
Unicode object APIs relating to ``Py_UNICODE``. On the other hand, this PEP
is not relating to Unicode object. These PEPs are split because they have
different motivation and need different discussion.
Motivation
==========
In general, reducing the number of APIs that have been deprecated for
a long time and have few users is a good idea for not only it
improves the maintainability of CPython, but it also helps API users
and other Python implementations.
Rationale
=========
Deprecated since Python 3.3
---------------------------
``Py_UNICODE`` and APIs using it are deprecated since Python 3.3.
Inefficient
-----------
All of these APIs are implemented using ``PyUnicode_FromWideChar``.
So these APIs are inefficient when user want to encode Unicode
object.
Not used widely
---------------
When searching from top 4000 PyPI packages [1]_, only pyodbc use
these APIs.
* ``PyUnicode_EncodeUTF8()``
* ``PyUnicode_EncodeUTF16()``
pyodbc uses these APIs to encode Unicode object into bytes object.
So it is easy to fix it. [2]_
Alternative APIs
================
There are alternative APIs to accept ``PyObject *unicode`` instead of
``Py_UNICODE *``. Users can migrate to them.
=========================================
==========================================
Deprecated API Alternative APIs
=========================================
==========================================
``PyUnicode_Encode()`` ``PyUnicode_AsEncodedString()``
``PyUnicode_EncodeASCII()`` ``PyUnicode_AsASCIIString()`` \(1)
``PyUnicode_EncodeLatin1()`` ``PyUnicode_AsLatin1String()`` \(1)
``PyUnicode_EncodeUTF7()`` \(2)
``PyUnicode_EncodeUTF8()`` ``PyUnicode_AsUTF8String()`` \(1)
``PyUnicode_EncodeUTF16()`` ``PyUnicode_AsUTF16String()`` \(3)
``PyUnicode_EncodeUTF32()`` ``PyUnicode_AsUTF32String()`` \(3)
``PyUnicode_EncodeUnicodeEscape()`` ``PyUnicode_AsUnicodeEscapeString()``
``PyUnicode_EncodeRawUnicodeEscape()``
``PyUnicode_AsRawUnicodeEscapeString()``
``PyUnicode_EncodeCharmap()`` ``PyUnicode_AsCharmapString()`` \(1)
``PyUnicode_TranslateCharmap()`` ``PyUnicode_Translate()``
``PyUnicode_EncodeDecimal()`` \(4)
``PyUnicode_TransformDecimalToASCII()`` \(4)
=========================================
==========================================
Notes:
(1)
``const char *errors`` parameter is missing.
(2)
There is no public alternative API. But user can use generic
``PyUnicode_AsEncodedString()`` instead.
(3)
``const char *errors, int byteorder`` parameters are missing.
(4)
There is no direct replacement. But ``Py_UNICODE_TODECIMAL``
can be used instead. CPython uses
``_PyUnicode_TransformDecimalAndSpaceToASCII`` for converting
from Unicode to numbers instead.
Plan
====
Python 3.9
----------
Add ``Py_DEPRECATED(3.3)`` to following APIs. This change is committed
already [3]_. All other APIs have been marked ``Py_DEPRECATED(3.3)``
already.
* ``PyUnicode_EncodeDecimal()``
* ``PyUnicode_TransformDecimalToASCII()``.
Document all APIs as "will be removed in version 3.11".
Python 3.11
-----------
These APIs are removed.
* ``PyUnicode_Encode()``
* ``PyUnicode_EncodeASCII()``
* ``PyUnicode_EncodeLatin1()``
* ``PyUnicode_EncodeUTF7()``
* ``PyUnicode_EncodeUTF8()``
* ``PyUnicode_EncodeUTF16()``
* ``PyUnicode_EncodeUTF32()``
* ``PyUnicode_EncodeUnicodeEscape()``
* ``PyUnicode_EncodeRawUnicodeEscape()``
* ``PyUnicode_EncodeCharmap()``
* ``PyUnicode_TranslateCharmap()``
* ``PyUnicode_EncodeDecimal()``
* ``PyUnicode_TransformDecimalToASCII()``
Alternative ideas
=================
Instead of just removing deprecated APIs, we may be able to use thier
names with different signature.
Make some private APIs public
------------------------------
``PyUnicode_EncodeUTF7()`` doesn't have public alternative APIs.
Some APIs have alternative public APIs. But they are missing
``const char *errors`` or ``int byteorder`` parameters.
We can rename some private APIs and make them public to cover missing
APIs and parameters.
============================= ================================
Rename to Rename from
============================= ================================
``PyUnicode_EncodeASCII()`` ``_PyUnicode_AsASCIIString()``
``PyUnicode_EncodeLatin1()`` ``_PyUnicode_AsLatin1String()``
``PyUnicode_EncodeUTF7()`` ``_PyUnicode_EncodeUTF7()``
``PyUnicode_EncodeUTF8()`` ``_PyUnicode_AsUTF8String()``
``PyUnicode_EncodeUTF16()`` ``_PyUnicode_EncodeUTF16()``
``PyUnicode_EncodeUTF32()`` ``_PyUnicode_EncodeUTF32()``
============================= ================================
Pros:
* We have more consistent API set.
Cons:
* We have more public APIs to maintain.
* Existing public APIs are enough for most use cases, and
``PyUnicode_AsEncodedString()`` can be used in other cases.
Replace ``Py_UNICODE*`` with ``Py_UCS4*``
-----------------------------------------
We can replace ``Py_UNICODE`` (typedef of ``wchar_t``) with
``Py_UCS4``. Since builtin codecs support UCS-4, we don't need to
convert ``Py_UCS4*`` string to Unicode object.
Pros:
* We have more consistent API set.
* User can encode UCS-4 string in C without creating Unicode object.
Cons:
* We have more public APIs to maintain.
* Applications which uses UTF-8 or UTF-32 can not use these APIs
anyway.
* Other Python implementations may not have builtin codec for UCS-4.
* If we change the Unicode internal representation to UTF-8, we need
to keep UCS-4 support only for these APIs.
Replace ``Py_UNICODE*`` with ``wchar_t*``
-----------------------------------------
We can replace ``Py_UNICODE`` to ``wchar_t``.
Pros:
* We have more consistent API set.
* Backward compatible.
Cons:
* We have more public APIs to maintain.
* They are inefficient on platforms ``wchar_t*`` is UTF-16. It is
because built-in codecs supports only UCS-1, UCS-2, and UCS-4
input.
Rejected ideas
==============
Using runtime warning
---------------------
These APIs doesn't release GIL for now. Emitting a warning from
such APIs is not safe. See this example.
.. code-block::
PyObject *u = PyList_GET_ITEM(list, i); // u is borrowed reference.
PyObject *b = PyUnicode_EncodeUTF8(PyUnicode_AS_UNICODE(u),
PyUnicode_GET_SIZE(u), NULL);
// Assumes u is still living reference.
PyObject *t = PyTuple_Pack(2, u, b);
Py_DECREF(b);
return t;
If we emit Python warning from ``PyUnicode_EncodeUTF8()``, warning
filters and other threads may change the ``list`` and ``u`` can be
a dangling reference after ``PyUnicode_EncodeUTF8()`` returned.
Additionally, since we are not changing behavior but removing C APIs,
runtime ``DeprecationWarning`` might not helpful for Python
developers. We should warn to extension developers instead.
Discussions
===========
* `Plan to remove Py_UNICODE APis except PEP 623
<https://mail.python.org/archives/list/python-dev@python.org/thread/S7KW2U6I…>`_
* `bpo-41123: Remove Py_UNICODE APIs except PEP 623:
<https://bugs.python.org/issue41123>`_
References
==========
.. [1] Source package list chosen from top 4000 PyPI packages.
(https://github.com/methane/notes/blob/master/2020/wchar-cache/package_list.…)
.. [2] pyodbc -- Don't use PyUnicode_Encode API #792
(https://github.com/mkleehammer/pyodbc/pull/792)
.. [3] Uncomment Py_DEPRECATED for Py_UNICODE APIs (GH-21318)
(https://github.com/python/cpython/commit/9c3840870814493fed62e140cfa43c2883…)
Copyright
=========
This document has been placed in the public domain.
--
Inada Naoki <songofacandy(a)gmail.com>
Hi all,
Right now, when a debugger is active, the number of local variables can
affect the tracing speed quite a lot.
For instance, having tracing setup in a program such as the one below takes
4.64 seconds to run, yet, changing all the variables to have the same name
-- i.e.: change all assignments to `a = 1` (such that there's only a single
variable in the namespace), it takes 1.47 seconds (in my machine)... the
higher the number of variables, the slower the tracing becomes.
```
import time
t = time.time()
def call():
a = 1
b = 1
c = 1
d = 1
e = 1
f = 1
def noop(frame, event, arg):
return noop
import sys
sys.settrace(noop)
for i in range(1_000_000):
call()
print('%.2fs' % (time.time() - t,))
```
This happens because `PyFrame_FastToLocalsWithError` and
`PyFrame_LocalsToFast` are called inside the `call_trampoline` (
https://github.com/python/cpython/blob/master/Python/sysmodule.c#L946).
So, I'd like to simply remove those calls.
Debuggers can call `PyFrame_LocalsToFast` when needed -- otherwise
mutating non-current frames doesn't work anyways. As a note, pydevd already
has such a call:
https://github.com/fabioz/PyDev.Debugger/blob/0d4d210f01a1c0a8647178b2e665b…
and PyPy also has a counterpart.
As for `PyFrame_FastToLocalsWithError`, I don't really see any reason to
call it at all.
i.e.: something as the code below prints the `a` variable from the `main()`
frame regardless of that and I checked all pydevd tests and nothing seems
to be affected (it seems that accessing f_locals already does this:
https://github.com/python/cpython/blob/cb9879b948a19c9434316f8ab6aba9c4601a…,
so, I don't see much reason to call it at all).
```
def call():
import sys
frame = sys._getframe()
print(frame.f_back.f_locals)
def main():
a = 1
call()
if __name__ == '__main__':
main()
```
Does anyone see any issue with this?
If it's non controversial, is a PEP needed or just an issue to track it
would be enough to remove those 2 lines?
Thanks,
Fabio
Hi,
Pathlib's symlink_to() and link_to() methods have different argument
orders, so:
a.symlink_to(b) # Creates a symlink from A to B
a.link_to(b) # Creates a hard link from B to A
I don't think link_to() was intended to be implemented this way, as the
docs say "Create a hard link pointing to a path named target.". It's also
inconsistent with everything else in pathlib, most obviously symlink_to().
Bug report here: https://bugs.python.org/issue39291
This /really/ irks me. Apparently it's too late to fix link_to(), so I'd
like to suggest we add a new hardlink_to() method that matches the
symlink_to() argument order. link_to() then becomes deprecated/undocumented.
Any thoughts?
Barney
Take as an example a function designed to process a tree of nodes similar to that which might be output by a JSON parser. There are 4 types of node:
- A node representing JSON strings
- A node representing JSON numbers
- A node representing JSON arrays
- A node representing JSON dictionaries
The function transforms a tree of nodes, beginning at the root node, and proceeding recursively through each child node in turn. The result is a Python object, with the following transformation applied to each node type:
- A JSON string `->` Python `str`
- A JSON number `->` Python `float`
- A JSON array `->` Python `list`
- A JSON dictionary `->` Python `dict`
I have implemented this function using 3 different approaches:
- The visitor pattern
- `isinstance` checks against the node type
- Pattern matching
Here is the implementation using the visitor pattern:
```
from typing import List, Tuple
class NodeVisitor:
def visit_string_node(self, node: StringNode):
pass
def visit_number_node(self, node: NumberNode):
pass
def visit_list_node(self, node: ListNode):
pass
def visit_dict_node(self, node: DictNode):
pass
class Node:
def visit(visitor: NodeVisitor):
raise NotImplementedError()
class StringNode(Node):
value: str
def visit(self, visitor: NodeVisitor):
visitor.visit_string_node(self)
class NumberNode(Node):
value: str
def visit(self, visitor: NodeVisitor):
visitor.visit_number_node(self)
class ListNode(Node):
children: List[Node]
def visit(self, visitor: NodeVisitor):
visitor.visit_list_node(self)
class DictNode(Node):
children: List[Tuple[str, Node]]
def visit(self, visitor: NodeVisitor):
visitor.visit_dict_node(self)
class Processor(NodeVisitor):
def process(root_node: Node):
return root_node.visit(self)
def visit_string_node(self, node: StringNode):
return node.value
def visit_number_node(self, node: NumberNode):
return float(node.value)
def visit_list_node(self, node: ListNode):
return [child_node.visit(self) for child_node in node.children]
def visit_dict_node(self, node: DictNode):
return {key: child_node.visit(self) for key, child_node in node.children}
def process(root_node: Node):
processor = Processor()
return processor.process(root_node)
```
Here is the implementation using `isinstance` checks against the node type:
```
from typing import List, Tuple
class Node:
pass
class StringNode(Node):
value: str
class NumberNode(Node):
value: str
class ListNode(Node):
children: List[Node]
class DictNode(Node):
children: List[Tuple[str, Node]]
def process(root_node: Node):
def process_node(node: Node):
if isinstance(node, StringNode):
return node.value
elif isinstance(node, NumberNode):
return float(node.value)
elif isinstance(node, ListNode):
return [process_node(child_node) for child_node in node.children]
elif isinstance(node, DictNode):
return {key: process_node(child_node) for key, child_node in node.children}
else:
raise Exception('Unexpected node')
return process_node(root_node)
```
Finally here is the implementation using pattern matching:
```
from typing import List, Tuple
class Node:
pass
class StringNode(Node):
value: str
class NumberNode(Node):
value: str
class ListNode(Node):
children: List[Node]
class DictNode(Node):
children: List[Tuple[str, Node]]
def process(root_node: Node):
def process_node(node: Node):
match node:
case StringNode(value=str_value):
return str_value
case NumberNode(value=number_value):
return float(number_value)
case ListNode(children=child_nodes):
return [process_node(child_node) for child_node in child_nodes]
case DictNode(children=child_nodes):
return {key: process_node(child_node) for key, child_node in child_nodes}
case _:
raise Exception('Unexpected node')
return process_node(root_node)
```
Here are the lengths of the different implementations:
- Pattern matching `->` 37 lines
- `isinstance` checks `->` 36 lines
- The visitor pattern `->` 69 lines
The visitor pattern implementation is by far the most verbose solution, weighing in at almost twice the length of the alternative implementations due to the large amount of boilerplate that is necessary to achieve double dispatch. The pattern matching and `isinstance` check implementations are very similar in length for this trivial example.
In each implementation, there are 2 operations performed on each node.
- Determine the type of the node
- Destructure the node to extract the desired data
The visitor pattern and `isinstance` check implementations separate these 2 operations, whereas the pattern matching approach combines the operations together. I believe that it is the declarative nature of pattern matching, where the operations of determining the type of the node and destructuring the node are combined into a single clause, which allows pattern matching to express a concise solution to the problem. In this trivial example, the advantage of pattern matching over the alternative of using a sequence of `if`-`elif`-`else` statements is not as obvious as it would be when compared to a more complex example, where a sub-tree of nodes might be matched based on their type and be destructured in a single clause.
I have seen elsewhere an argument that pattern matching should not be accepted into Python as it introduces a pseudo-DSL that is separate from the rest of the language. I agree that pattern matching might be viewed as a pseudo-DSL, but I believe that it is a good thing, if it allows the solution to certain classes of problems to be expressed in a concise manner. People often raise similar objections to operator overloading in other languages, whereas the presence of operator overloading in Python allows mathematical expressions involving custom numeric types such as vectors to be expressed in a natural way. Furthermore, Python has a regular expression module which implements it's own DSL for the purpose of matching string patterns. Regular expressions, in a similar way to pattern matching, allow string patterns to be expressed in a concise and declarative manner.
I really hope that the Steering Council accepts pattern matching into Python. I think that it allows for processing of heterogeneous graphs of objects using recursion in a concise, declarative manner. I would like to thank the authors of the Structural Pattern Matching PEP for their hard work in designing this feature and developing an implementation of it. I believe that it will be a wonderful addition to the language that I am very much looking forward to using.
Hi folks,
I know this is a high-volume list so sorry for being yet another voice
screaming into your mailbox, but I do not know how else to handle this.
A few months ago, I opened a pull request fixing packed bitfields in
ctypes struct/union, which right now are being incorrectly created. The
offsets and size are all wrong in some cases, fields which should be
packed end up with padding between them.
bpo: https://bugs.python.org/issue29753
PR: https://github.com/python/cpython/pull/19850
Since ctypes has no maintainer it has been very hard tracking down
someone who is up to review this. So, if this issue sparks some
interest to you, or you just want to help me out, please have a look :)
Cheers,
Filipe Laíns
Hi everyone,
PEP 634/5/6 presents a possible implementation of pattern matching for
Python.
Much of the discussion around PEP 634, and PEP 622 before it, seems to
imply that PEP 634 is synonymous with pattern matching; that if you
reject PEP 634 then you are rejecting pattern matching.
That simply isn't true.
Can we discuss whether we want pattern matching in Python and
the broader semantics first, before dealing with low level details?
Do we want pattern matching in Python at all?
---------------------------------------------
Pattern matching works really well in statically typed, functional
languages.
The lack of mutability, constrained scope and the ability of the
compiler to distinguish let variables from constants means that pattern
matching code has fewer errors, and can be compiled efficiently.
Pattern matching works less well in dynamically-typed, functional
languages and statically-typed, procedural languages.
Nevertheless, it works well enough for it to be a popular feature in
both erlang and rust.
In dynamically-typed, procedural languages, however, it is not clear (at
least not to me) that it works well enough to be worthwhile.
That is not say that pattern matching could never be of value in Python,
but PEP 635 fails to demonstrate that it can (although it does a better
job than PEP 622).
Should match be an expression, or a statement?
----------------------------------------------
Do we want a fancy switch statement, or a powerful expression?
Expressions have the advantage of not leaking (like comprehensions in
Python 3), but statements are easier to work with.
Can pattern matching make it clear what is assigned?
----------------------------------------------------
Embedding the variables to be assigned into a pattern, makes the pattern
concise, but requires discarding normal Python syntax and inventing a
new sub-language. Could we make patterns fit Python better?
Is it possible to make assignment to variables clear, and unambiguous,
and allow the use of symbolic constants at the same time?
I think it is, but PEP 634 fails to do this.
How should pattern matching be integrated with the object model?
----------------------------------------------------------------
What special method(s) should be added? How and when should they be called?
PEP 634 largely disregards the object model, meaning it has many special
cases, and is inefficient.
The semantics must be well defined.
-----------------------------------
Language extensions PEPs should define the semantics of those
extensions. For example, PEP 343 and PEP 380 both did.
https://www.python.org/dev/peps/pep-0343/#specification-the-with-statementhttps://www.python.org/dev/peps/pep-0380/#formal-semantics
PEP 634 just waves its hands and talks about undefined behavior, which
horrifies me.
In summary,
I would ask anyone who wants pattern matching adding to Python, to not
support PEP 634.
PEP 634 just isn't a good fit for Python, and we deserve something better.
Cheers,
Mark.
On behalf of the PyPA and the pip team, I am pleased to announce that we have just released pip 20.3, a new version of pip. You can install it by running `python -m pip install --upgrade pip`.
This is an important and disruptive release -- we [explained why in a blog post last year](https://pyfound.blogspot.com/2019/12/moss-czi-support-pip.html). We
even made [a video about it](https://www.youtube.com/watch?v=B4GQCBBsuNU).
## Highlights
* **DISRUPTION**: Switch to the new dependency resolver by default. (#9019) Watch out for changes in handling editable
installs, constraints files, and more:
https://pip.pypa.io/en/latest/user_guide/#changes-to-the-pip-dependency-res…
* **DEPRECATION**: Deprecate support for Python 3.5 (to be removed in pip 21.0) (#8181)
* **DEPRECATION**: pip freeze will stop filtering the pip, setuptools, distribute and wheel packages from pip freeze output in a future version. To keep the previous behavior, users should use the new `--exclude` option. (#4256)
* Substantial improvements in new resolver for performance, output and
error messages, avoiding infinite loops, and support for constraints files.
* Support for PEP 600: Future ‘manylinux’ Platform Tags for Portable
Linux Built Distributions. (#9077)
* Documentation improvements: Resolver migration guide, quickstart
guide, and new documentation theme.
* Add support for MacOS Big Sur compatibility tags. (#9138)
The new resolver is now *on by default*. It is significantly stricter
and more consistent when it receives incompatible instructions, and
reduces support for certain kinds of constraints files, so some
workarounds and workflows may break. Please see [our guide on how to
test and migrate, and how to report issues](https://pip.pypa.io/en/latest/user_guide/#changes-to-the-pip-dependency-resolver-in-20-3-2020). You
can use the deprecated (old) resolver, using the flag
`--use-deprecated=legacy-resolver`, until we remove it in the pip 21.0
release in January 2021.
You can find more details (including deprecations and removals) [in the
changelog](https://pip.pypa.io/en/stable/news/).
## User experience
Command-line output for this version of pip, and documentation to help
with errors, is significantly better, because you worked with our
experts to test and improve it. [Contribute to our user experience work: sign up to become a member of the UX Studies group](https://bit.ly/pip-ux-studies) (after you join, we'll notify you about future UX surveys and interviews).
## What to expect in 20.1
We aim to release pip 20.1 in January 2021, per our [usual release cadence](https://pip.pypa.io/en/latest/development/release-process/#release-cadence). You can expect:
* Removal of [Python 2.7](https://pip.pypa.io/en/latest/development/release-process/#python-2-support) and 3.5 support
* Further improvements in the new resolver
* Removal of legacy resolver support
## Thanks
As with all pip releases, a significant amount of the work was
contributed by pip's user community. Huge thanks to all who have
contributed, whether through code, documentation, issue reports and/or
discussion. Your help keeps pip improving, and is hugely appreciated.
Specific thanks go to Mozilla (through its [Mozilla Open Source
Support](https://www.mozilla.org/en-US/moss/) Awards) and to the [Chan
Zuckerberg Initiative](https://chanzuckerberg.com/eoss/) DAF, an
advised fund of Silicon Valley Community Foundation, for their funding
that enabled substantial work on the new resolver.
That funding went to [Simply Secure](https://simplysecure.org/)
(specifically Georgia Bullen, Bernard Tyers, Nicole Harris, Ngọc
Triệu, and Karissa McKelvey), [Changeset
Consulting](https://changeset.nyc/) (Sumana Harihareswara),
[Atos](https://www.atos.net) (Paul F. Moore), [Tzu-ping
Chung](https://uranusjr.com), [Pradyun Gedam](https://pradyunsg.me/),
and Ilan Schnell. Thanks also to Ernest W. Durbin III at the Python
Software Foundation for liaising with the project.
-Sumana Harihareswara, pip project manager
