Mailman 3 March 2018 - Python-Dev

PEP 448 review
by Guido van Rossum 29 Mar '23

29 Mar '23

I'm back, I've re-read the PEP, and I've re-read the long thread with "(no subject)". I think Georg Brandl nailed it: """ *I like the "sequence and dict flattening" part of the PEP, mostly because itis consistent and should be easy to understand, but the comprehension syntaxenhancements seem to be bad for readability and "comprehending" what the codedoes.The call syntax part is a mixed bag on the one hand it is nice to be consistent with the extended possibilities in literals (flattening), but on the other hand there would be small but annoying inconsistencies anyways (e.g. the duplicate kwarg case above).* """ Greg Ewing followed up explaining that the inconsistency between dict flattening and call syntax is inherent in the pre-existing different rules for dicts vs. keyword args: {'a':1, 'a':2} results in {'a':2}, while f(a=1, a=2) is an error. (This form is a SyntaxError; the dynamic case f(a=1, **{'a': 1}) is a TypeError.) For me, allowing f(*a, *b) and f(**d, **e) and all the other combinations for function calls proposed by the PEP is an easy +1 -- it's a straightforward extension of the existing pattern, and anybody who knows what f(x, *a) does will understand f(x, *a, y, *b). Guessing what f(**d, **e) means shouldn't be hard either. Understanding the edge case for duplicate keys with f(**d, **e) is a little harder, but the error messages are pretty clear, and it is not a new edge case. The sequence and dict flattening syntax proposals are also clean and logical -- we already have *-unpacking on the receiving side, so allowing *x in tuple expressions reads pretty naturally (and the similarity with *a in argument lists certainly helps). From here, having [a, *x, b, *y] is also natural, and then the extension to other displays is natural: {a, *x, b, *y} and {a:1, **d, b:2, **e}. This, too, gets a +1 from me. So that leaves comprehensions. IIRC, during the development of the patch we realized that f(*x for x in xs) is sufficiently ambiguous that we decided to disallow it -- note that f(x for x in xs) is already somewhat of a special case because an argument can only be a "bare" generator expression if it is the only argument. The same reasoning doesn't apply (in that form) to list, set and dict comprehensions -- while f(x for x in xs) is identical in meaning to f((x for x in xs)), [x for x in xs] is NOT the same as [(x for x in xs)] (that's a list of one element, and the element is a generator expression). The basic premise of this part of the proposal is that if you have a few iterables, the new proposal (without comprehensions) lets you create a list or generator expression that iterates over all of them, essentially flattening them: >>> xs = [1, 2, 3] >>> ys = ['abc', 'def'] >>> zs = [99] >>> [*xs, *ys, *zs] [1, 2, 3, 'abc', 'def', 99] >>> But now suppose you have a list of iterables: >>> xss = [[1, 2, 3], ['abc', 'def'], [99]] >>> [*xss[0], *xss[1], *xss[2]] [1, 2, 3, 'abc', 'def', 99] >>> Wouldn't it be nice if you could write the latter using a comprehension? >>> xss = [[1, 2, 3], ['abc', 'def'], [99]] >>> [*xs for xs in xss] [1, 2, 3, 'abc', 'def', 99] >>> This is somewhat seductive, and the following is even nicer: the *xs position may be an expression, e.g.: >>> xss = [[1, 2, 3], ['abc', 'def'], [99]] >>> [*xs[:2] for xs in xss] [1, 2, 'abc', 'def', 99] >>> On the other hand, I had to explore the possibilities here by experimenting in the interpreter, and I discovered some odd edge cases (e.g. you can parenthesize the starred expression, but that seems a syntactic accident). All in all I am personally +0 on the comprehension part of the PEP, and I like that it provides a way to "flatten" a sequence of sequences, but I think very few people in the thread have supported this part. Therefore I would like to ask Neil to update the PEP and the patch to take out the comprehension part, so that the two "easy wins" can make it into Python 3.5 (basically, I am accepting two-thirds of the PEP :-). There is some time yet until alpha 2. I would also like code reviewers (Benjamin?) to start reviewing the patch <http://bugs.python.org/issue2292>, taking into account that the comprehension part needs to be removed. -- --Guido van Rossum (python.org/~guido)

7 17

PEP 1, PEP Purpose and Guidelines
by barry＠zope.com 18 May '21

18 May '21

It has been a while since I posted a copy of PEP 1 to the mailing lists and newsgroups. I've recently done some updating of a few sections, so in the interest of gaining wider community participation in the Python development process, I'm posting the latest revision of PEP 1 here. A version of the PEP is always available on-line at http://www.python.org/peps/pep-0001.html Enjoy, -Barry -------------------- snip snip -------------------- PEP: 1 Title: PEP Purpose and Guidelines Version: $Revision: 1.36 $ Last-Modified: $Date: 2002/07/29 18:34:59 $ Author: Barry A. Warsaw, Jeremy Hylton Status: Active Type: Informational Created: 13-Jun-2000 Post-History: 21-Mar-2001, 29-Jul-2002 What is a PEP? PEP stands for Python Enhancement Proposal. A PEP is a design document providing information to the Python community, or describing a new feature for Python. The PEP should provide a concise technical specification of the feature and a rationale for the feature. We intend PEPs to be the primary mechanisms for proposing new features, for collecting community input on an issue, and for documenting the design decisions that have gone into Python. The PEP author is responsible for building consensus within the community and documenting dissenting opinions. Because the PEPs are maintained as plain text files under CVS control, their revision history is the historical record of the feature proposal[1]. Kinds of PEPs There are two kinds of PEPs. A standards track PEP describes a new feature or implementation for Python. An informational PEP describes a Python design issue, or provides general guidelines or information to the Python community, but does not propose a new feature. Informational PEPs do not necessarily represent a Python community consensus or recommendation, so users and implementors are free to ignore informational PEPs or follow their advice. PEP Work Flow The PEP editor, Barry Warsaw <peps(a)python.org>, assigns numbers for each PEP and changes its status. The PEP process begins with a new idea for Python. It is highly recommended that a single PEP contain a single key proposal or new idea. The more focussed the PEP, the more successfully it tends to be. The PEP editor reserves the right to reject PEP proposals if they appear too unfocussed or too broad. If in doubt, split your PEP into several well-focussed ones. Each PEP must have a champion -- someone who writes the PEP using the style and format described below, shepherds the discussions in the appropriate forums, and attempts to build community consensus around the idea. The PEP champion (a.k.a. Author) should first attempt to ascertain whether the idea is PEP-able. Small enhancements or patches often don't need a PEP and can be injected into the Python development work flow with a patch submission to the SourceForge patch manager[2] or feature request tracker[3]. The PEP champion then emails the PEP editor <peps(a)python.org> with a proposed title and a rough, but fleshed out, draft of the PEP. This draft must be written in PEP style as described below. If the PEP editor approves, he will assign the PEP a number, label it as standards track or informational, give it status 'draft', and create and check-in the initial draft of the PEP. The PEP editor will not unreasonably deny a PEP. Reasons for denying PEP status include duplication of effort, being technically unsound, not providing proper motivation or addressing backwards compatibility, or not in keeping with the Python philosophy. The BDFL (Benevolent Dictator for Life, Guido van Rossum) can be consulted during the approval phase, and is the final arbitrator of the draft's PEP-ability. If a pre-PEP is rejected, the author may elect to take the pre-PEP to the comp.lang.python newsgroup (a.k.a. python-list(a)python.org mailing list) to help flesh it out, gain feedback and consensus from the community at large, and improve the PEP for re-submission. The author of the PEP is then responsible for posting the PEP to the community forums, and marshaling community support for it. As updates are necessary, the PEP author can check in new versions if they have CVS commit permissions, or can email new PEP versions to the PEP editor for committing. Standards track PEPs consists of two parts, a design document and a reference implementation. The PEP should be reviewed and accepted before a reference implementation is begun, unless a reference implementation will aid people in studying the PEP. Standards Track PEPs must include an implementation - in the form of code, patch, or URL to same - before it can be considered Final. PEP authors are responsible for collecting community feedback on a PEP before submitting it for review. A PEP that has not been discussed on python-list(a)python.org and/or python-dev(a)python.org will not be accepted. However, wherever possible, long open-ended discussions on public mailing lists should be avoided. Strategies to keep the discussions efficient include, setting up a separate SIG mailing list for the topic, having the PEP author accept private comments in the early design phases, etc. PEP authors should use their discretion here. Once the authors have completed a PEP, they must inform the PEP editor that it is ready for review. PEPs are reviewed by the BDFL and his chosen consultants, who may accept or reject a PEP or send it back to the author(s) for revision. Once a PEP has been accepted, the reference implementation must be completed. When the reference implementation is complete and accepted by the BDFL, the status will be changed to `Final.' A PEP can also be assigned status `Deferred.' The PEP author or editor can assign the PEP this status when no progress is being made on the PEP. Once a PEP is deferred, the PEP editor can re-assign it to draft status. A PEP can also be `Rejected'. Perhaps after all is said and done it was not a good idea. It is still important to have a record of this fact. PEPs can also be replaced by a different PEP, rendering the original obsolete. This is intended for Informational PEPs, where version 2 of an API can replace version 1. PEP work flow is as follows: Draft -> Accepted -> Final -> Replaced ^ +----> Rejected v Deferred Some informational PEPs may also have a status of `Active' if they are never meant to be completed. E.g. PEP 1. What belongs in a successful PEP? Each PEP should have the following parts: 1. Preamble -- RFC822 style headers containing meta-data about the PEP, including the PEP number, a short descriptive title (limited to a maximum of 44 characters), the names, and optionally the contact info for each author, etc. 2. Abstract -- a short (~200 word) description of the technical issue being addressed. 3. Copyright/public domain -- Each PEP must either be explicitly labelled as placed in the public domain (see this PEP as an example) or licensed under the Open Publication License[4]. 4. Specification -- The technical specification should describe the syntax and semantics of any new language feature. The specification should be detailed enough to allow competing, interoperable implementations for any of the current Python platforms (CPython, JPython, Python .NET). 5. Motivation -- The motivation is critical for PEPs that want to change the Python language. It should clearly explain why the existing language specification is inadequate to address the problem that the PEP solves. PEP submissions without sufficient motivation may be rejected outright. 6. Rationale -- The rationale fleshes out the specification by describing what motivated the design and why particular design decisions were made. It should describe alternate designs that were considered and related work, e.g. how the feature is supported in other languages. The rationale should provide evidence of consensus within the community and discuss important objections or concerns raised during discussion. 7. Backwards Compatibility -- All PEPs that introduce backwards incompatibilities must include a section describing these incompatibilities and their severity. The PEP must explain how the author proposes to deal with these incompatibilities. PEP submissions without a sufficient backwards compatibility treatise may be rejected outright. 8. Reference Implementation -- The reference implementation must be completed before any PEP is given status 'Final,' but it need not be completed before the PEP is accepted. It is better to finish the specification and rationale first and reach consensus on it before writing code. The final implementation must include test code and documentation appropriate for either the Python language reference or the standard library reference. PEP Template PEPs are written in plain ASCII text, and should adhere to a rigid style. There is a Python script that parses this style and converts the plain text PEP to HTML for viewing on the web[5]. PEP 9 contains a boilerplate[7] template you can use to get started writing your PEP. Each PEP must begin with an RFC822 style header preamble. The headers must appear in the following order. Headers marked with `*' are optional and are described below. All other headers are required. PEP: <pep number> Title: <pep title> Version: <cvs version string> Last-Modified: <cvs date string> Author: <list of authors' real names and optionally, email addrs> * Discussions-To: <email address> Status: <Draft | Active | Accepted | Deferred | Final | Replaced> Type: <Informational | Standards Track> * Requires: <pep numbers> Created: <date created on, in dd-mmm-yyyy format> * Python-Version: <version number> Post-History: <dates of postings to python-list and python-dev> * Replaces: <pep number> * Replaced-By: <pep number> The Author: header lists the names and optionally, the email addresses of all the authors/owners of the PEP. The format of the author entry should be address(a)dom.ain (Random J. User) if the email address is included, and just Random J. User if the address is not given. If there are multiple authors, each should be on a separate line following RFC 822 continuation line conventions. Note that personal email addresses in PEPs will be obscured as a defense against spam harvesters. Standards track PEPs must have a Python-Version: header which indicates the version of Python that the feature will be released with. Informational PEPs do not need a Python-Version: header. While a PEP is in private discussions (usually during the initial Draft phase), a Discussions-To: header will indicate the mailing list or URL where the PEP is being discussed. No Discussions-To: header is necessary if the PEP is being discussed privately with the author, or on the python-list or python-dev email mailing lists. Note that email addresses in the Discussions-To: header will not be obscured. Created: records the date that the PEP was assigned a number, while Post-History: is used to record the dates of when new versions of the PEP are posted to python-list and/or python-dev. Both headers should be in dd-mmm-yyyy format, e.g. 14-Aug-2001. PEPs may have a Requires: header, indicating the PEP numbers that this PEP depends on. PEPs may also have a Replaced-By: header indicating that a PEP has been rendered obsolete by a later document; the value is the number of the PEP that replaces the current document. The newer PEP must have a Replaces: header containing the number of the PEP that it rendered obsolete. PEP Formatting Requirements PEP headings must begin in column zero and the initial letter of each word must be capitalized as in book titles. Acronyms should be in all capitals. The body of each section must be indented 4 spaces. Code samples inside body sections should be indented a further 4 spaces, and other indentation can be used as required to make the text readable. You must use two blank lines between the last line of a section's body and the next section heading. You must adhere to the Emacs convention of adding two spaces at the end of every sentence. You should fill your paragraphs to column 70, but under no circumstances should your lines extend past column 79. If your code samples spill over column 79, you should rewrite them. Tab characters must never appear in the document at all. A PEP should include the standard Emacs stanza included by example at the bottom of this PEP. A PEP must contain a Copyright section, and it is strongly recommended to put the PEP in the public domain. When referencing an external web page in the body of a PEP, you should include the title of the page in the text, with a footnote reference to the URL. Do not include the URL in the body text of the PEP. E.g. Refer to the Python Language web site [1] for more details. ... [1] http://www.python.org When referring to another PEP, include the PEP number in the body text, such as "PEP 1". The title may optionally appear. Add a footnote reference that includes the PEP's title and author. It may optionally include the explicit URL on a separate line, but only in the References section. Note that the pep2html.py script will calculate URLs automatically, e.g.: ... Refer to PEP 1 [7] for more information about PEP style ... References [7] PEP 1, PEP Purpose and Guidelines, Warsaw, Hylton http://www.python.org/peps/pep-0001.html If you decide to provide an explicit URL for a PEP, please use this as the URL template: http://www.python.org/peps/pep-xxxx.html PEP numbers in URLs must be padded with zeros from the left, so as to be exactly 4 characters wide, however PEP numbers in text are never padded. Reporting PEP Bugs, or Submitting PEP Updates How you report a bug, or submit a PEP update depends on several factors, such as the maturity of the PEP, the preferences of the PEP author, and the nature of your comments. For the early draft stages of the PEP, it's probably best to send your comments and changes directly to the PEP author. For more mature, or finished PEPs you may want to submit corrections to the SourceForge bug manager[6] or better yet, the SourceForge patch manager[2] so that your changes don't get lost. If the PEP author is a SF developer, assign the bug/patch to him, otherwise assign it to the PEP editor. When in doubt about where to send your changes, please check first with the PEP author and/or PEP editor. PEP authors who are also SF committers, can update the PEPs themselves by using "cvs commit" to commit their changes. Remember to also push the formatted PEP text out to the web by doing the following: % python pep2html.py -i NUM where NUM is the number of the PEP you want to push out. See % python pep2html.py --help for details. Transferring PEP Ownership It occasionally becomes necessary to transfer ownership of PEPs to a new champion. In general, we'd like to retain the original author as a co-author of the transferred PEP, but that's really up to the original author. A good reason to transfer ownership is because the original author no longer has the time or interest in updating it or following through with the PEP process, or has fallen off the face of the 'net (i.e. is unreachable or not responding to email). A bad reason to transfer ownership is because you don't agree with the direction of the PEP. We try to build consensus around a PEP, but if that's not possible, you can always submit a competing PEP. If you are interested assuming ownership of a PEP, send a message asking to take over, addressed to both the original author and the PEP editor <peps(a)python.org>. If the original author doesn't respond to email in a timely manner, the PEP editor will make a unilateral decision (it's not like such decisions can be reversed. :). References and Footnotes [1] This historical record is available by the normal CVS commands for retrieving older revisions. For those without direct access to the CVS tree, you can browse the current and past PEP revisions via the SourceForge web site at http://cvs.sourceforge.net/cgi-bin/cvsweb.cgi/python/nondist/peps/?cvsroot=… [2] http://sourceforge.net/tracker/?group_id=5470&atid=305470 [3] http://sourceforge.net/tracker/?atid=355470&group_id=5470&func=browse [4] http://www.opencontent.org/openpub/ [5] The script referred to here is pep2html.py, which lives in the same directory in the CVS tree as the PEPs themselves. Try "pep2html.py --help" for details. The URL for viewing PEPs on the web is http://www.python.org/peps/ [6] http://sourceforge.net/tracker/?group_id=5470&atid=305470 [7] PEP 9, Sample PEP Template http://www.python.org/peps/pep-0009.html Copyright This document has been placed in the public domain. Local Variables: mode: indented-text indent-tabs-mode: nil sentence-end-double-space: t fill-column: 70 End:

8 14

congrats on 3.5! Alas, windows 7 users are having problems installing it
by Laura Creighton 15 Sep '19

15 Sep '19

webmaster has already heard from 4 people who cannot install it. I sent them to the bug tracker or to python-list but they seem not to have gone either place. Is there some guide I should be sending them to, 'how to debug installation problems'? Laura

5 6

PEP 574 -- Pickle protocol 5 with out-of-band data
by Antoine Pitrou 05 Nov '18

05 Nov '18

Hi, I'd like to submit this PEP for discussion. It is quite specialized and the main target audience of the proposed changes is users and authors of applications/libraries transferring large amounts of data (read: the scientific computing & data science ecosystems). https://www.python.org/dev/peps/pep-0574/ The PEP text is also inlined below. Regards Antoine. PEP: 574 Title: Pickle protocol 5 with out-of-band data Version: $Revision$ Last-Modified: $Date$ Author: Antoine Pitrou <solipsis(a)pitrou.net> Status: Draft Type: Standards Track Content-Type: text/x-rst Created: 23-Mar-2018 Post-History: Resolution: Abstract ======== This PEP proposes to standardize a new pickle protocol version, and accompanying APIs to take full advantage of it: 1. A new pickle protocol version (5) to cover the extra metadata needed for out-of-band data buffers. 2. A new ``PickleBuffer`` type for ``__reduce_ex__`` implementations to return out-of-band data buffers. 3. A new ``buffer_callback`` parameter when pickling, to handle out-of-band data buffers. 4. A new ``buffers`` parameter when unpickling to provide out-of-band data buffers. The PEP guarantees unchanged behaviour for anyone not using the new APIs. Rationale ========= The pickle protocol was originally designed in 1995 for on-disk persistency of arbitrary Python objects. The performance of a 1995-era storage medium probably made it irrelevant to focus on performance metrics such as use of RAM bandwidth when copying temporary data before writing it to disk. Nowadays the pickle protocol sees a growing use in applications where most of the data isn't ever persisted to disk (or, when it is, it uses a portable format instead of Python-specific). Instead, pickle is being used to transmit data and commands from one process to another, either on the same machine or on multiple machines. Those applications will sometimes deal with very large data (such as Numpy arrays or Pandas dataframes) that need to be transferred around. For those applications, pickle is currently wasteful as it imposes spurious memory copies of the data being serialized. As a matter of fact, the standard ``multiprocessing`` module uses pickle for serialization, and therefore also suffers from this problem when sending large data to another process. Third-party Python libraries, such as Dask [#dask]_, PyArrow [#pyarrow]_ and IPyParallel [#ipyparallel]_, have started implementing alternative serialization schemes with the explicit goal of avoiding copies on large data. Implementing a new serialization scheme is difficult and often leads to reduced generality (since many Python objects support pickle but not the new serialization scheme). Falling back on pickle for unsupported types is an option, but then you get back the spurious memory copies you wanted to avoid in the first place. For example, ``dask`` is able to avoid memory copies for Numpy arrays and built-in containers thereof (such as lists or dicts containing Numpy arrays), but if a large Numpy array is an attribute of a user-defined object, ``dask`` will serialize the user-defined object as a pickle stream, leading to memory copies. The common theme of these third-party serialization efforts is to generate a stream of object metadata (which contains pickle-like information about the objects being serialized) and a separate stream of zero-copy buffer objects for the payloads of large objects. Note that, in this scheme, small objects such as ints, etc. can be dumped together with the metadata stream. Refinements can include opportunistic compression of large data depending on its type and layout, like ``dask`` does. This PEP aims to make ``pickle`` usable in a way where large data is handled as a separate stream of zero-copy buffers, letting the application handle those buffers optimally. Example ======= To keep the example simple and avoid requiring knowledge of third-party libraries, we will focus here on a bytearray object (but the issue is conceptually the same with more sophisticated objects such as Numpy arrays). Like most objects, the bytearray object isn't immediately understood by the pickle module and must therefore specify its decomposition scheme. Here is how a bytearray object currently decomposes for pickling:: >>> b.__reduce_ex__(4) (<class 'bytearray'>, (b'abc',), None) This is because the ``bytearray.__reduce_ex__`` implementation reads morally as follows:: class bytearray: def __reduce_ex__(self, protocol): if protocol == 4: return type(self), bytes(self), None # Legacy code for earlier protocols omitted In turn it produces the following pickle code:: >>> pickletools.dis(pickletools.optimize(pickle.dumps(b, protocol=4))) 0: \x80 PROTO 4 2: \x95 FRAME 30 11: \x8c SHORT_BINUNICODE 'builtins' 21: \x8c SHORT_BINUNICODE 'bytearray' 32: \x93 STACK_GLOBAL 33: C SHORT_BINBYTES b'abc' 38: \x85 TUPLE1 39: R REDUCE 40: . STOP (the call to ``pickletools.optimize`` above is only meant to make the pickle stream more readable by removing the MEMOIZE opcodes) We can notice several things about the bytearray's payload (the sequence of bytes ``b'abc'``): * ``bytearray.__reduce_ex__`` produces a first copy by instantiating a new bytes object from the bytearray's data. * ``pickle.dumps`` produces a second copy when inserting the contents of that bytes object into the pickle stream, after the SHORT_BINBYTES opcode. * Furthermore, when deserializing the pickle stream, a temporary bytes object is created when the SHORT_BINBYTES opcode is encountered (inducing a data copy). What we really want is something like the following: * ``bytearray.__reduce_ex__`` produces a *view* of the bytearray's data. * ``pickle.dumps`` doesn't try to copy that data into the pickle stream but instead passes the buffer view to its caller (which can decide on the most efficient handling of that buffer). * When deserializing, ``pickle.loads`` takes the pickle stream and the buffer view separately, and passes the buffer view directly to the bytearray constructor. We see that several conditions are required for the above to work: * ``__reduce__`` or ``__reduce_ex__`` must be able to return *something* that indicates a serializable no-copy buffer view. * The pickle protocol must be able to represent references to such buffer views, instructing the unpickler that it may have to get the actual buffer out of band. * The ``pickle.Pickler`` API must provide its caller with a way to receive such buffer views while serializing. * The ``pickle.Unpickler`` API must similarly allow its caller to provide the buffer views required for deserialization. * For compatibility, the pickle protocol must also be able to contain direct serializations of such buffer views, such that current uses of the ``pickle`` API don't have to be modified if they are not concerned with memory copies. Producer API ============ We are introducing a new type ``pickle.PickleBuffer`` which can be instantiated from any buffer-supporting object, and is specifically meant to be returned from ``__reduce__`` implementations:: class bytearray: def __reduce_ex__(self, protocol): if protocol == 5: return type(self), PickleBuffer(self), None # Legacy code for earlier protocols omitted ``PickleBuffer`` is a simple wrapper that doesn't have all the memoryview semantics and functionality, but is specifically recognized by the ``pickle`` module if protocol 5 or higher is enabled. It is an error to try to serialize a ``PickleBuffer`` with pickle protocol version 4 or earlier. Only the raw *data* of the ``PickleBuffer`` will be considered by the ``pickle`` module. Any type-specific *metadata* (such as shapes or datatype) must be returned separately by the type's ``__reduce__`` implementation, as is already the case. PickleBuffer objects -------------------- The ``PickleBuffer`` class supports a very simple Python API. Its constructor takes a single PEP 3118-compatible object [#pep-3118]_. ``PickleBuffer`` objects themselves support the buffer protocol, so consumers can call ``memoryview(...)`` on them to get additional information about the underlying buffer (such as the original type, shape, etc.). On the C side, a simple API will be provided to create and inspect PickleBuffer objects: ``PyObject *PyPickleBuffer_FromObject(PyObject *obj)`` Create a ``PickleBuffer`` object holding a view over the PEP 3118-compatible *obj*. ``PyPickleBuffer_Check(PyObject *obj)`` Return whether *obj* is a ``PickleBuffer`` instance. ``const Py_buffer *PyPickleBuffer_GetBuffer(PyObject *picklebuf)`` Return a pointer to the internal ``Py_buffer`` owned by the ``PickleBuffer`` instance. ``PickleBuffer`` can wrap any kind of buffer, including non-contiguous buffers. It's up to consumers to decide how best to handle different kinds of buffers (for example, some consumers may find it acceptable to make a contiguous copy of non-contiguous buffers). Consumer API ============ ``pickle.Pickler.__init__`` and ``pickle.dumps`` are augmented with an additional ``buffer_callback`` parameter:: class Pickler: def __init__(self, file, protocol=None, ..., buffer_callback=None): """ If *buffer_callback* is not None, then it is called with a list of out-of-band buffer views when deemed necessary (this could be once every buffer, or only after a certain size is reached, or once at the end, depending on implementation details). The callback should arrange to store or transmit those buffers without changing their order. If *buffer_callback* is None (the default), buffer views are serialized into *file* as part of the pickle stream. It is an error if *buffer_callback* is not None and *protocol* is None or smaller than 5. """ def pickle.dumps(obj, protocol=None, *, ..., buffer_callback=None): """ See above for *buffer_callback*. """ ``pickle.Unpickler.__init__`` and ``pickle.loads`` are augmented with an additional ``buffers`` parameter:: class Unpickler: def __init__(file, *, ..., buffers=None): """ If *buffers* is not None, it should be an iterable of buffer-enabled objects that is consumed each time the pickle stream references an out-of-band buffer view. Such buffers have been given in order to the *buffer_callback* of a Pickler object. If *buffers* is None (the default), then the buffers are taken from the pickle stream, assuming they are serialized there. It is an error for *buffers* to be None if the pickle stream was produced with a non-None *buffer_callback*. """ def pickle.loads(data, *, ..., buffers=None): """ See above for *buffers*. """ Protocol changes ================ Three new opcodes are introduced: * ``BYTEARRAY`` creates a bytearray from the data following it in the pickle stream and pushes it on the stack (just like ``BINBYTES8`` does for bytes objects); * ``NEXT_BUFFER`` fetches a buffer from the ``buffers`` iterable and pushes it on the stack. * ``READONLY_BUFFER`` makes a readonly view of the top of the stack. When pickling encounters a ``PickleBuffer``, there can be four cases: * If a ``buffer_callback`` is given and the ``PickleBuffer`` is writable, the ``PickleBuffer`` is given to the callback and a ``NEXT_BUFFER`` opcode is appended to the pickle stream. * If a ``buffer_callback`` is given and the ``PickleBuffer`` is readonly, the ``PickleBuffer`` is given to the callback and a ``NEXT_BUFFER`` opcode is appended to the pickle stream, followed by a ``READONLY_BUFFER`` opcode. * If no ``buffer_callback`` is given and the ``PickleBuffer`` is writable, it is serialized into the pickle stream as if it were a ``bytearray`` object. * If no ``buffer_callback`` is given and the ``PickleBuffer`` is readonly, it is serialized into the pickle stream as if it were a ``bytes`` object. The distinction between readonly and writable buffers is explained below (see "Mutability"). Caveats ======= Mutability ---------- PEP 3118 buffers [#pep-3118]_ can be readonly or writable. Some objects, such as Numpy arrays, need to be backed by a mutable buffer for full operation. Pickle consumers that use the ``buffer_callback`` and ``buffers`` arguments will have to be careful to recreate mutable buffers. When doing I/O, this implies using buffer-passing API variants such as ``readinto`` (which are also often preferrable for performance). Data sharing ------------ If you pickle and then unpickle an object in the same process, passing out-of-band buffer views, then the unpickled object may be backed by the same buffer as the original pickled object. For example, it might be reasonable to implement reduction of a Numpy array as follows (crucial metadata such as shapes is omitted for simplicity):: class ndarray: def __reduce_ex__(self, protocol): if protocol == 5: return numpy.frombuffer, (PickleBuffer(self), self.dtype) # Legacy code for earlier protocols omitted Then simply passing the PickleBuffer around from ``dumps`` to ``loads`` will produce a new Numpy array sharing the same underlying memory as the original Numpy object (and, incidentally, keeping it alive):: >>> import numpy as np >>> a = np.zeros(10) >>> a[0] 0.0 >>> buffers = [] >>> data = pickle.dumps(a, protocol=5, buffer_callback=buffers.extend) >>> b = pickle.loads(data, buffers=buffers) >>> b[0] = 42 >>> a[0] 42.0 This won't happen with the traditional ``pickle`` API (i.e. without passing ``buffers`` and ``buffer_callback`` parameters), because then the buffer view is serialized inside the pickle stream with a copy. Alternatives ============ The ``pickle`` persistence interface is a way of storing references to designated objects in the pickle stream while handling their actual serialization out of band. For example, one might consider the following for zero-copy serialization of bytearrays:: class MyPickle(pickle.Pickler): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.buffers = [] def persistent_id(self, obj): if type(obj) is not bytearray: return None else: index = len(self.buffers) self.buffers.append(obj) return ('bytearray', index) class MyUnpickle(pickle.Unpickler): def __init__(self, *args, buffers, **kwargs): super().__init__(*args, **kwargs) self.buffers = buffers def persistent_load(self, pid): type_tag, index = pid if type_tag == 'bytearray': return self.buffers[index] else: assert 0 # unexpected type This mechanism has two drawbacks: * Each ``pickle`` consumer must reimplement ``Pickler`` and ``Unpickler`` subclasses, with custom code for each type of interest. Essentially, N pickle consumers end up each implementing custom code for M producers. This is difficult (especially for sophisticated types such as Numpy arrays) and poorly scalable. * Each object encountered by the pickle module (even simple built-in objects such as ints and strings) triggers a call to the user's ``persistent_id()`` method, leading to a possible performance drop compared to nominal. Open questions ============== Should ``buffer_callback`` take a single buffers or a sequence of buffers? * Taking a single buffer would allow returning a boolean indicating whether the given buffer is serialized in-band or out-of-band. * Taking a sequence of buffers is potentially more efficient by reducing function call overhead. Related work ============ Dask.distributed implements a custom zero-copy serialization with fallback to pickle [#dask-serialization]_. PyArrow implements zero-copy component-based serialization for a few selected types [#pyarrow-serialization]_. PEP 554 proposes hosting multiple interpreters in a single process, with provisions for transferring buffers between interpreters as a communication scheme [#pep-554]_. Acknowledgements ================ Thanks to the following people for early feedback: Nick Coghlan, Olivier Grisel, Stefan Krah, MinRK, Matt Rocklin, Eric Snow. References ========== .. [#dask] Dask.distributed -- A lightweight library for distributed computing in Python https://distributed.readthedocs.io/ .. [#dask-serialization] Dask.distributed custom serialization https://distributed.readthedocs.io/en/latest/serialization.html .. [#ipyparallel] IPyParallel -- Using IPython for parallel computing https://ipyparallel.readthedocs.io/ .. [#pyarrow] PyArrow -- A cross-language development platform for in-memory data https://arrow.apache.org/docs/python/ .. [#pyarrow-serialization] PyArrow IPC and component-based serialization https://arrow.apache.org/docs/python/ipc.html#component-based-serialization .. [#pep-3118] PEP 3118 -- Revising the buffer protocol https://www.python.org/dev/peps/pep-3118/ .. [#pep-554] PEP 554 -- Multiple Interpreters in the Stdlib https://www.python.org/dev/peps/pep-0554/ Copyright ========= This document has been placed into the public domain.

11 18

Python startup time
by Victor Stinner 10 Oct '18

10 Oct '18

Hi, On Twitter, Raymond Hettinger wrote: "The decision making process on Python-dev is an anti-pattern, governed by anecdotal data and ambiguity over what problem is solved." https://twitter.com/raymondh/status/887069454693158912 About "anecdotal data", I would like to discuss the Python startup time. == Python 3.7 compared to 2.7 == First of all, on speed.python.org, we have: * Python 2.7: 6.4 ms with site, 3.0 ms without site (-S) * master (3.7): 14.5 ms with site, 8.4 ms without site (-S) Python 3.7 startup time is 2.3x slower with site (default mode), or 2.8x slower without site (-S command line option). (I will skip Python 3.4, 3.5 and 3.6 which are much worse than Python 3.7...) So if an user complained about Python 2.7 startup time: be prepared for a 2x - 3x more angry user when "forced" to upgrade to Python 3! == Mercurial vs Git, Python vs C, startup time == Startup time matters a lot for Mercurial since Mercurial is compared to Git. Git and Mercurial have similar features, but Git is written in C whereas Mercurial is written in Python. Quick benchmark on the speed.python.org server: * hg version: 44.6 ms +- 0.2 ms * git --version: 974 us +- 7 us Mercurial startup time is already 45.8x slower than Git whereas tested Mercurial runs on Python 2.7.12. Now try to sell Python 3 to Mercurial developers, with a startup time 2x - 3x slower... I tested Mecurial 3.7.3 and Git 2.7.4 on Ubuntu 16.04.1 using "python3 -m perf command -- ...". == CPython core developers don't care? no, they do care == Christian Heimes, Naoki INADA, Serhiy Storchaka, Yury Selivanov, me (Victor Stinner) and other core developers made multiple changes last years to reduce the number of imports at startup, optimize impotlib, etc. IHMO all these core developers are well aware of the competition of programming languages, and honesty Python startup time isn't "good". So let's compare it to other programming languages similar to Python. == PHP, Ruby, Perl == I measured the startup time of other programming languages which are similar to Python, still on the speed.python.org server using "python3 -m perf command -- ...": * perl -e ' ': 1.18 ms +- 0.01 ms * php -r ' ': 8.57 ms +- 0.05 ms * ruby -e ' ': 32.8 ms +- 0.1 ms Wow, Perl is quite good! PHP seems as good as Python 2 (but Python 3 is worse). Ruby startup time seems less optimized than other languages. Tested versions: * perl 5, version 22, subversion 1 (v5.22.1) * PHP 7.0.18-0ubuntu0.16.04.1 (cli) ( NTS ) * ruby 2.3.1p112 (2016-04-26) [x86_64-linux-gnu] == Quick Google search == I also searched for "python startup time" and "python slow startup time" on Google and found many articles. Some examples: "Reducing the Python startup time" http://www.draketo.de/book/export/html/498 => "The python startup time always nagged me (17-30ms) and I just searched again for a way to reduce it, when I found this: The Python-Launcher caches GTK imports and forks new processes to reduce the startup time of python GUI programs." https://nelsonslog.wordpress.com/2013/04/08/python-startup-time/ => "Wow, Python startup time is worse than I thought." "How to speed up python starting up and/or reduce file search while loading libraries?" https://stackoverflow.com/questions/15474160/how-to-speed-up-python-startin… => "The first time I log to the system and start one command it takes 6 seconds just to show a few line of help. If I immediately issue the same command again it takes 0.1s. After a couple of minutes it gets back to 6s. (proof of short-lived cache)" "How does one optimise the startup of a Python script/program?" https://www.quora.com/How-does-one-optimise-the-startup-of-a-Python-script-… => "I wrote a Python program that would be used very often (imagine 'cd' or 'ls') for very short runtimes, how would I make it start up as fast as possible?" "Python Interpreter Startup time" https://bytes.com/topic/python/answers/34469-pyhton-interpreter-startup-time "Python is very slow to start on Windows 7" https://stackoverflow.com/questions/29997274/python-is-very-slow-to-start-o… => "Python takes 17 times longer to load on my Windows 7 machine than Ubuntu 14.04 running on a VM" => "returns in 0.614s on Windows and 0.036s on Linux" "How to make a fast command line tool in Python" (old article Python 2.5.2) https://files.bemusement.org/talks/OSDC2008-FastPython/ => "(...) some techniques Bazaar uses to start quickly, such as lazy imports." -- So please continue efforts for make Python startup even faster to beat all other programming languages, and finally convince Mercurial to upgrade ;-) Victor

42 96

Clarifying Cygwin support in CPython
by Erik Bray 31 Jul '18

31 Jul '18

Hi folks, As some people here know I've been working off and on for a while to improve CPython's support of Cygwin. I'm motivated in part by a need to have software working on Python 3.x on Cygwin for the foreseeable future, preferably with minimal graft. (As an incidental side-effect Python's test suite--especially of system-level functionality--serves as an interesting test suite for Cygwin itself too.) This is partly what motivated PEP 539 [1], although that PEP had the advantage of benefiting other POSIX-compatible platforms as well (and in fact was fixing an aspect of CPython that made it unfriendly to supporting other platforms). As far as I can tell, the first commit to Python to add any kind of support for Cygwin was made by Guido (committing a contributed patch) back in 1999 [2]. Since then, bits and pieces have been added for Cygwin's benefit over time, with varying degrees of impact in terms of #ifdefs and the like (for the most part Cygwin does not require *much* in the way of special support, but it does have some differences from a "normal" POSIX-compliant platform, such as the possibility for case-insensitive filesystems and executables that end in .exe). I don't know whether it's ever been "officially supported" but someone with a longer memory of the project can comment on that. I'm not sure if it was discussed at all or not in the context of PEP 11. I have personally put in a fair amount of effort already in either fixing issues on Cygwin (many of these issues also impact MinGW), or more often than not fixing issues in the CPython test suite on Cygwin--these are mostly tests that are broken due to invalid assumptions about the platform (for example, that there is always a "root" user with uid=0; this is not the case on Cygwin). In other cases some tests need to be skipped or worked around due to platform-specific bugs, and Cygwin is hardly the only case of this in the test suite. I also have an experimental AppVeyor configuration for running the tests on Cygwin [3], as well as an experimental buildbot (not available on the internet, but working). These currently rely on a custom branch that includes fixes needed for the test suite to run to completion without crashing or hanging (e.g. https://bugs.python.org/issue31885). It would be nice to add this as an official buildbot, but I'm not sure if it makes sense to do that until it's "green", or at least not crashing. I have several other patches to the tests toward this goal, and am currently down to ~22 tests failing. Before I do any more work on this, however, it would be best to once and for all clarify the support for Cygwin in CPython, as it has never been "officially supported" nor unsupported--this way we can avoid having this discussion every time a patch related to Cygwin comes up. I could provide some arguments for why I believe Cygwin should supported, but before this gets too long I'd just like to float the idea of having the discussion in the first place. It's also not exactly clear to me how to meet the standards in PEP 11 for supporting a platform--in particular it's not clear when a buildbot is considered "stable", or how to achieve that without getting necessary fixes merged into the main branch in the first place. Thanks, Erik [1] https://www.python.org/dev/peps/pep-0539/ [2] https://github.com/python/cpython/commit/717d1fdf2acbef5e6b47d9b4dcf48ef182… [3] https://ci.appveyor.com/project/embray/cpython

3 6

How can we use 48bit pointer safely?
by INADA Naoki 02 Apr '18

02 Apr '18

Hi, As far as I know, most amd64 and arm64 systems use only 48bit address spaces. (except [1]) [1] https://software.intel.com/sites/default/files/managed/2b/80/5-level_paging… It means there are some chance to compact some data structures. I point two examples below. My question is; can we use 48bit pointer safely? It depends on CPU architecture & OS memory map. Maybe, configure option which is available on only (amd64, amd64) * (Linux, Windows, macOS)? # Possible optimizations by 48bit pointer ## PyASCIIObject [snip] unsigned int ready:1; /* Padding to ensure that PyUnicode_DATA() is always aligned to 4 bytes (see issue #19537 on m68k). */ unsigned int :24; } state; wchar_t *wstr; /* wchar_t representation (null-terminated) */ } PyASCIIObject; Currently, state is 8bit + 24bit padding. I think we can pack state and wstr in 64bit. ## PyDictKeyEntry typedef struct { /* Cached hash code of me_key. */ Py_hash_t me_hash; PyObject *me_key; PyObject *me_value; /* This field is only meaningful for combined tables */ } PyDictKeyEntry; There are chance to compact it: Use only 32bit for hash and 48bit*2 for key and value. CompactEntry may be 16byte instead of 24byte. Regards, -- INADA Naoki <songofacandy(a)gmail.com>

3 7

Subtle difference between f-strings and str.format()
by Serhiy Storchaka 31 Mar '18

31 Mar '18

There is a subtle semantic difference between str.format() and "equivalent" f-string. '{}{}'.format(a, b) f'{a}{b}' In the former case b is evaluated before formatting a. This is equivalent to t1 = a t2 = b t3 = format(t1) t4 = format(t2) r = t3 + t4 In the latter case a is formatted before evaluating b. This is equivalent to t1 = a t2 = format(t1) t3 = b t4 = format(t3) r = t2 + t4 In most cases this doesn't matter, but when implement the optimization that transforms the former expression to the the latter one ([1], [2]) we have to make a decision what to do with this difference. 1. Keep the exact semantic of str.format() when optimize it. This means that it should be transformed into AST node different from the AST node used for f-strings. Either introduce a new AST node type, or add a boolean flag to JoinedStr. 2. Change the semantic of f-strings. Make it closer to the semantic of str.format(): evaluate all subexpressions first than format them. This can be implemented in two ways: 2a) Add additional instructions for stack manipulations. This will slow down f-strings. 2b) Introduce a new complex opcode that will replace FORMAT_VALUE and BUILD_STRING. This will speed up f-strings. 3. Transform str.format() into an f-string with changing semantic, and ignore this change. This is not new. The optimizer already changes semantic. Non-optimized "if a and True:" would call bool(a) twice, but optimized code calls it only once. [1] https://bugs.python.org/issue28307 [2] https://bugs.python.org/issue28308

14 37

Summary of Python tracker Issues
by Python tracker 30 Mar '18

30 Mar '18

ACTIVITY SUMMARY (2018-03-23 - 2018-03-30) Python tracker at https://bugs.python.org/ To view or respond to any of the issues listed below, click on the issue. Do NOT respond to this message. Issues counts and deltas: open 6543 (+14) closed 38393 (+46) total 44936 (+60) Open issues with patches: 2548 Issues opened (42) ================== #31793: Allow to specialize smart quotes in documentation translations https://bugs.python.org/issue31793 reopened by mdk #33128: PathFinder is twice on sys.meta_path https://bugs.python.org/issue33128 opened by htgoebel #33129: Add kwarg-only option to dataclass https://bugs.python.org/issue33129 opened by alan_du #33130: functools.reduce signature/docstring discordance https://bugs.python.org/issue33130 opened by vreuter #33131: Upgrade to pip 10 for Python 3.7 https://bugs.python.org/issue33131 opened by ncoghlan #33132: Possible refcount issues in the compiler https://bugs.python.org/issue33132 opened by serhiy.storchaka #33133: Don't return implicit optional types by get_type_hints https://bugs.python.org/issue33133 opened by levkivskyi #33135: Define field prefixes for the various config structs https://bugs.python.org/issue33135 opened by ncoghlan #33136: Harden ssl module against CVE-2018-8970 https://bugs.python.org/issue33136 opened by christian.heimes #33137: line traces may be missed on backward jumps when instrumented https://bugs.python.org/issue33137 opened by xdegaye #33138: Improve standard error for uncopyable types https://bugs.python.org/issue33138 opened by serhiy.storchaka #33139: Bdb doesn't find instruction in linecache after pdb.set_trace( https://bugs.python.org/issue33139 opened by prounce #33140: shutil.chown on Windows https://bugs.python.org/issue33140 opened by eryksun #33144: random._randbelow optimization https://bugs.python.org/issue33144 opened by wolma #33146: contextlib.suppress should capture exception for inspection an https://bugs.python.org/issue33146 opened by jason.coombs #33147: Update references for RFC 3548 to RFC 4648 https://bugs.python.org/issue33147 opened by paulehoffman #33148: RuntimeError('Event loop is closed') after cancelling getaddri https://bugs.python.org/issue33148 opened by vitaly.krug #33150: Signature error for methods of class configparser.Interpolatio https://bugs.python.org/issue33150 opened by acue #33152: Use list comprehension in timeit module instead of loop with a https://bugs.python.org/issue33152 opened by Windson Yang #33153: interpreter crash when multiplying large tuples https://bugs.python.org/issue33153 opened by imz #33154: subprocess.Popen ResourceWarning should have activation-deacti https://bugs.python.org/issue33154 opened by acue #33155: Use super().method instead in Logging https://bugs.python.org/issue33155 opened by madsjensen #33158: Add fileobj property to csv reader and writer objects https://bugs.python.org/issue33158 opened by samwyse #33159: Implement PEP 473 https://bugs.python.org/issue33159 opened by skreft #33161: Refactor of pathlib's _WindowsBehavior.gethomedir https://bugs.python.org/issue33161 opened by onlined #33162: TimedRotatingFileHandler in logging module https://bugs.python.org/issue33162 opened by Nikunj jain #33164: Blake 2 module update https://bugs.python.org/issue33164 opened by David Carlier #33165: Add stacklevel parameter to logging APIs https://bugs.python.org/issue33165 opened by ncoghlan #33166: os.cpu_count() returns wrong number of processors on specific https://bugs.python.org/issue33166 opened by yanirh #33167: RFC Documentation Updates to urllib.parse.rst https://bugs.python.org/issue33167 opened by agnosticdev #33168: distutils build/build_ext and --debug https://bugs.python.org/issue33168 opened by lazka #33169: importlib.invalidate_caches() doesn't clear all caches https://bugs.python.org/issue33169 opened by gvanrossum #33171: multiprocessing won't utilize all of platform resources https://bugs.python.org/issue33171 opened by yanirh #33173: GzipFile's .seekable() returns True even if underlying buffer https://bugs.python.org/issue33173 opened by Walt Askew #33174: error building the _sha3 module with Intel 2018 compilers https://bugs.python.org/issue33174 opened by wscullin #33176: Allow memoryview.cast(readonly=...) https://bugs.python.org/issue33176 opened by pitrou #33178: Add support for BigEndianUnion and LittleEndianUnion in ctypes https://bugs.python.org/issue33178 opened by emezh #33179: Investigate using a context variable for zero-arg super initia https://bugs.python.org/issue33179 opened by ncoghlan #33180: Flag for unusable sys.executable https://bugs.python.org/issue33180 opened by steve.dower #33181: SimpleHTTPRequestHandler shouldn't redirect to directories wit https://bugs.python.org/issue33181 opened by oulenz #33184: Update OpenSSL to 1.1.0h / 1.0.2o https://bugs.python.org/issue33184 opened by ned.deily #33185: Python 3.7.0b3 fails in pydoc where b2 did not. https://bugs.python.org/issue33185 opened by nedbat Most recent 15 issues with no replies (15) ========================================== #33185: Python 3.7.0b3 fails in pydoc where b2 did not. https://bugs.python.org/issue33185 #33184: Update OpenSSL to 1.1.0h / 1.0.2o https://bugs.python.org/issue33184 #33176: Allow memoryview.cast(readonly=...) https://bugs.python.org/issue33176 #33174: error building the _sha3 module with Intel 2018 compilers https://bugs.python.org/issue33174 #33173: GzipFile's .seekable() returns True even if underlying buffer https://bugs.python.org/issue33173 #33171: multiprocessing won't utilize all of platform resources https://bugs.python.org/issue33171 #33168: distutils build/build_ext and --debug https://bugs.python.org/issue33168 #33167: RFC Documentation Updates to urllib.parse.rst https://bugs.python.org/issue33167 #33165: Add stacklevel parameter to logging APIs https://bugs.python.org/issue33165 #33164: Blake 2 module update https://bugs.python.org/issue33164 #33162: TimedRotatingFileHandler in logging module https://bugs.python.org/issue33162 #33161: Refactor of pathlib's _WindowsBehavior.gethomedir https://bugs.python.org/issue33161 #33159: Implement PEP 473 https://bugs.python.org/issue33159 #33155: Use super().method instead in Logging https://bugs.python.org/issue33155 #33150: Signature error for methods of class configparser.Interpolatio https://bugs.python.org/issue33150 Most recent 15 issues waiting for review (15) ============================================= #33173: GzipFile's .seekable() returns True even if underlying buffer https://bugs.python.org/issue33173 #33167: RFC Documentation Updates to urllib.parse.rst https://bugs.python.org/issue33167 #33161: Refactor of pathlib's _WindowsBehavior.gethomedir https://bugs.python.org/issue33161 #33159: Implement PEP 473 https://bugs.python.org/issue33159 #33155: Use super().method instead in Logging https://bugs.python.org/issue33155 #33152: Use list comprehension in timeit module instead of loop with a https://bugs.python.org/issue33152 #33144: random._randbelow optimization https://bugs.python.org/issue33144 #33138: Improve standard error for uncopyable types https://bugs.python.org/issue33138 #33136: Harden ssl module against CVE-2018-8970 https://bugs.python.org/issue33136 #33132: Possible refcount issues in the compiler https://bugs.python.org/issue33132 #33129: Add kwarg-only option to dataclass https://bugs.python.org/issue33129 #33128: PathFinder is twice on sys.meta_path https://bugs.python.org/issue33128 #33124: Lazy execution of module bytecode https://bugs.python.org/issue33124 #33123: Path.unlink should have a missing_ok parameter https://bugs.python.org/issue33123 #33106: Deleting a key in a read-only gdbm results in KeyError, not gd https://bugs.python.org/issue33106 Top 10 most discussed issues (10) ================================= #33166: os.cpu_count() returns wrong number of processors on specific https://bugs.python.org/issue33166 16 msgs #33144: random._randbelow optimization https://bugs.python.org/issue33144 10 msgs #32850: Run gc_collect() before complaining about dangling threads https://bugs.python.org/issue32850 7 msgs #33023: Unable to copy ssl.SSLContext https://bugs.python.org/issue33023 7 msgs #33096: ttk.Treeview.insert() does not allow to insert item with "Fals https://bugs.python.org/issue33096 7 msgs #33131: Upgrade to pip 10 for Python 3.7 https://bugs.python.org/issue33131 6 msgs #32726: macOS installer and framework enhancements and changes for 3.7 https://bugs.python.org/issue32726 5 msgs #33153: interpreter crash when multiplying large tuples https://bugs.python.org/issue33153 5 msgs #33111: Merely importing tkinter breaks parallel code (multiprocessing https://bugs.python.org/issue33111 4 msgs #33128: PathFinder is twice on sys.meta_path https://bugs.python.org/issue33128 4 msgs Issues closed (44) ================== #17994: Change necessary in platform.py to support IronPython https://bugs.python.org/issue17994 closed by csabella #23388: datetime.strftime('%s') does not take timezone into account https://bugs.python.org/issue23388 closed by csabella #25782: CPython hangs on error __context__ set to the error itself https://bugs.python.org/issue25782 closed by gregory.p.smith #27428: Document WindowsRegistryFinder inherits from MetaPathFinder https://bugs.python.org/issue27428 closed by brett.cannon #31455: ElementTree.XMLParser() mishandles exceptions https://bugs.python.org/issue31455 closed by serhiy.storchaka #31550: Inconsistent error message for TypeError with subscripting https://bugs.python.org/issue31550 closed by rhettinger #31639: http.server and SimpleHTTPServer hang after a few requests https://bugs.python.org/issue31639 closed by mdk #32358: json.dump: fp must be a text file object https://bugs.python.org/issue32358 closed by berker.peksag #32517: test_read_pty_output() of test_asyncio hangs on macOS 10.13.2 https://bugs.python.org/issue32517 closed by ned.deily #32563: -Werror=declaration-after-statement expat build failure on Pyt https://bugs.python.org/issue32563 closed by ncoghlan #32844: subprocess may incorrectly redirect a low fd to stderr if anot https://bugs.python.org/issue32844 closed by izbyshev #32873: Pickling of typing types https://bugs.python.org/issue32873 closed by levkivskyi #32932: better error message when __all__ contains non-str objects https://bugs.python.org/issue32932 closed by xiang.zhang #32943: confusing error message for rot13 codec https://bugs.python.org/issue32943 closed by xiang.zhang #33042: New 3.7 startup sequence crashes PyInstaller https://bugs.python.org/issue33042 closed by ncoghlan #33053: Avoid adding an empty directory to sys.path when running a mod https://bugs.python.org/issue33053 closed by ncoghlan #33055: bytes does not implement __bytes__() https://bugs.python.org/issue33055 closed by serhiy.storchaka #33061: NoReturn missing from __all__ in typing.py https://bugs.python.org/issue33061 closed by levkivskyi #33079: subprocess: document the interaction between subprocess.Popen https://bugs.python.org/issue33079 closed by gregory.p.smith #33081: multiprocessing Queue leaks a file descriptor associated with https://bugs.python.org/issue33081 closed by pitrou #33093: Fatal error on SSL transport https://bugs.python.org/issue33093 closed by ned.deily #33114: random.sample() behavior is unexpected/unclear from docs https://bugs.python.org/issue33114 closed by rhettinger #33115: Asyncio loop blocks with a lot of parallel tasks https://bugs.python.org/issue33115 closed by terry.reedy #33119: python sys.argv argument parsing not clear https://bugs.python.org/issue33119 closed by ncoghlan #33120: infinite loop in inspect.unwrap(unittest.mock.call) https://bugs.python.org/issue33120 closed by terry.reedy #33126: Some C buffer protocol APIs not documented https://bugs.python.org/issue33126 closed by pitrou #33127: Python 2.7.14 won't build ssl module with Libressl 2.7.0 https://bugs.python.org/issue33127 closed by christian.heimes #33134: dataclasses: use function dispatch instead of multiple tests f https://bugs.python.org/issue33134 closed by eric.smith #33141: descriptor __set_name__ feature broken for dataclass descripto https://bugs.python.org/issue33141 closed by eric.smith #33142: Fatal Python error: Py_Initialize: Unable to get the locale en https://bugs.python.org/issue33142 closed by ned.deily #33143: encode UTF-16 generates unexpected results https://bugs.python.org/issue33143 closed by serhiy.storchaka #33145: unaligned accesses in siphash24() lead to crashes on sparc https://bugs.python.org/issue33145 closed by serhiy.storchaka #33149: Parser stack overflows https://bugs.python.org/issue33149 closed by ned.deily #33151: importlib.resources breaks on subdirectories https://bugs.python.org/issue33151 closed by barry #33156: Use super().method instead in email classes. https://bugs.python.org/issue33156 closed by r.david.murray #33157: Strings beginning with underscore not removed from lists - fea https://bugs.python.org/issue33157 closed by xiang.zhang #33160: Negative values in positional access inside formatting https://bugs.python.org/issue33160 closed by serhiy.storchaka #33163: Upgrade pip to 9.0.3 and setuptools to v39.0.1 https://bugs.python.org/issue33163 closed by ned.deily #33170: New type based on int() created with typing.NewType is not con https://bugs.python.org/issue33170 closed by avanov #33172: Update built-in version of SQLite3 https://bugs.python.org/issue33172 closed by ned.deily #33175: dataclasses should look up __set_name__ on class, not instance https://bugs.python.org/issue33175 closed by eric.smith #33177: make install hangs on macOS when there is an existing Python a https://bugs.python.org/issue33177 closed by ned.deily #33182: Python 3.7.0b3 fails to build with clang 6.0 https://bugs.python.org/issue33182 closed by ncoghlan #33183: Refactoring: replacing some assertTrue by assertIn https://bugs.python.org/issue33183 closed by serhiy.storchaka

1 0

Re: [Python-Dev] How can we use 48bit pointer safely?
by Ronald Oussoren 30 Mar '18

30 Mar '18

On Mar 30, 2018, at 01:40 PM, Antoine Pitrou <solipsis(a)pitrou.net> wrote: A safer alternative is to use the *lower* bits of pointers. The bottom 3 bits are always available for storing ancillary information, since typically all heap-allocated data will be at least 8-bytes aligned (probably 16-bytes aligned on 64-bit processes). However, you also get less bits :-) The lower bits are more interesting to use. I'm still hoping to find some time to experiment with tagged pointers some day, that could be interesting w.r.t. performance and memory use (at the cost of being ABI incompatible). Ronald

1 0

Python-Dev March 2018