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
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.
(Context: Continuing to prepare for the core dev sprint next week. Since
the sprint is near, *I'd greatly appreciate any quick comments, feedback
and ideas!*)
Following up my collection of past beginning contributor experiences, I've
collected these experiences in a dedicated GitHub repo[1] and written a
(subjective!) summary of main themes that I recognize in the stories, which
I've also included in the repo[2].
A "TL;DR" bullet list of those main themes:
* Slow/no responsiveness
* Long, slow process
* Hard to find where to contribute
* Mentorship helps a lot, but is scarce
* A lot to learn to get started
* It's intimidating
More specifically, something that has come up often is that maintaining
momentum for new contributors is crucial for them to become long-term
contributors. Most often, this comes up in relation to the first two
points: Suggestions or PRs are completely receive no attention at all
("ignored") or stop receiving attention at some point ("lost to the void").
Unfortunately, the probability of this is pretty high for any issue/PR, so
for a new contributor this is almost guaranteed to happen while working on
one of their first few contributions. I've seen this happen many times, and
have found that I have to personally follow promising contributors' work to
ensure that this doesn't happen to them. I've also seen contributors learn
to actively seek out core devs when these situations arise, which is often
a successful tactic, but shouldn't be necessary so often.
Now, this is in large part a result of the fact that us core devs are not a
very large group, made up almost entirely of volunteers working on this in
their spare time. Last I checked, the total amount of paid development time
dedicated to developing Python is less than 3 full-time (i.e. ~100 hours a
week).
The situation being problematic is clear enough that the PSF had concrete
plans to hire paid developers to review issues and PRs. However, those
plans have been put on hold indefinitely, since the PSF's funding has
shrunk dramatically since the COVID-19 outbreak (no PyCon!).
So, what can be done? Besides raising more funds (see a note on this
below), I think we can find ways to reduce how often issues/PRs become
"stalled". Here are some ideas:
1. *Generate reminders for reviewers when an issue or PR becomes "stalled'
due to them.* Personally, I've found that both b.p.o. and GitHub make it
relatively hard to remember to follow up on all of the many issues/PRs
you've taken part in reviewing. It takes considerable attention and
discipline to do so consistently, and reminders like these would have
helped me. Many (many!) times, all it took to get an issue/PR moving
forward (or closed) was a simple "ping?" comment.
2. *Generate reminders for contributors when an issue or PR becomes
"stalled" due to them.* Similar to the above, but I consider these separate.
3. *Advertise something like a "2-for-1" standing offer for reviews.* This
would give contributors an "official", acceptable way to get attention for
their issue/PR, other than "begging" for attention on a mailing list. There
are good ways for new contributors to be of significant help despite being
new to the project, such as checking whether old bugs are still relevant,
searching for duplicate issues, or applying old patches to the current code
and creating a PR. (This would be similar to Martin v. Löwis's 5-for-1
offer in 2012[3], which had little success but lead to some interesting
followup discussion[4]).
4. *Encourage core devs to dedicate some of their time to working through
issues/PRs which are "ignored" or "stalled".* This would require first
generating reliable lists of issues/PRs in such states. This could be in
various forms, such as predefined GitHub/b.p.o. queries, a dedicated
web-page, a periodic message similar to b.p.o.'s "weekly summary" email, or
dedicated tags/labels for issues/PRs. (Perhaps prioritize "stalled" over
"ignored".)
- Tal Einat
[1]: https://github.com/taleinat/python-contribution-feedback
[2]:
https://github.com/taleinat/python-contribution-feedback/blob/master/Takeaw…
[3]:
https://mail.python.org/archives/list/python-dev@python.org/message/7DLUN4Y…
[4]:
https://mail.python.org/archives/list/python-dev@python.org/thread/N4MMHXXO…
Hi folks,
This is a mailing list repost of the Discourse thread at
https://discuss.python.org/t/pep-642-constraint-pattern-syntax-for-structur…
The rendered version of the PEP can be found here:
https://www.python.org/dev/peps/pep-0642/
The full text is also quoted in the Discourse thread.
The remainder of this email is the same introduction that I posted on Discourse.
I’m largely a fan of the Structural Pattern Matching proposal in PEP
634, but there’s one specific piece of the syntax proposal that I
strongly dislike: the idea of basing the distinction between capture
patterns and value patterns purely on whether they use a simple name
or a dotted name.
Thus PEP 642, which retains most of PEP 634 unchanged, but adjusts
value checks to use an explicit prefix syntax (either `?EXPR` for
equality constraints, or `?is EXPR` for identity constraints), rather
than relying on users learning that literals and attribute lookups in
a capture pattern mean a value lookup check, while simple names mean a
capture pattern (unlike both normal expressions, where all three mean
a value lookup, and assignment targets, where both simple and dotted
names bind a new reference).
The PEP itself has a lot of words explaining why I’ve made the design
decisions I have, as well as the immediate and potential future
benefits offered by using an explicit prefix syntax for value
constraints, but the super short form goes like this:
* if you don’t like match statements at all, or wish we were working
on designing a C-style switch statement instead, then PEP 642 isn’t
going to appeal to you any more than PEP 634 does
* if, like me, you don’t like the idea of breaking the existing
property of Python that caching the result of a value lookup
subexpression in a local variable and then using that variable in
place of the original subexpression should “just work”, then PEP 642’s
explicit constraint prefix syntax may be more to your liking
* however, if the idea of the `?` symbol becoming part of Python’s
syntax doesn’t appeal to you, then you may consider any improved
clarity of intent that PEP 642 might offer to not be worth that cost
Cheers,
Nick.
--
Nick Coghlan | ncoghlan(a)gmail.com | Brisbane, Australia
Hi!
The timeline for this year's election will be the same as last year.
* The nomination period will begin Nov 1, 2020 (do not post nominations
until then)
* Nomination period will end Nov 15, 2020
* Voting will begin Dec 1, 2020
* Voting will end Dec 15, 2020
Nominations will be collected via https://discuss.python.org/ (more details
to follow on Nov 1).
New for this year: Ernest W. Durbin III will be running the vote along with
the assistance of Joe Carey, a PSF employee. They will be co-admins going
forward. I have cc'ed them in on this thread as well in case there are any
questions.
Thanks,
Ewa
