Python-Dev November 2014

python-dev@python.org

75 participants
54 discussions

by Larry Hastings

Argument Clinic "converters" specify how to convert an individual argument to the function you're defining. Although a converter could theoretically represent any sort of conversion, most of the time they directly represent types like "int" or "double" or "str". Because there's such variety in argument parsing, the converters are customizable with parameters. Many of these are common enough that Argument Clinic suggests some standard names. Examples: "zeroes=True" for strings and buffers means "permit internal \0 characters", and "bitwise=True" for unsigned integers means "copy the bits over, even if there's overflow/underflow, and even if the original is negative". A third example is "nullable=True", which means "also accept None for this parameter". This was originally intended for use with strings (compare the "s" and "z" format units for PyArg_ParseTuple), however it looks like we'll have a use for "nullable ints" in the ongoing Argument Clinic conversion work. Several people have said they found the name "nullable" surprising, suggesting I use another name like "allow_none" or "noneable". I, in turn, find their surprise surprising; "nullable" is a term long associated with exactly this concept. It's used in C# and SQL, and the term even has its own Wikipedia page: http://en.wikipedia.org/wiki/Nullable_type Most amusingly, Vala *used* to have an annotation called "(allow-none)", but they've broken it out into two annotations, "(nullable)" and "(optional)". http://blogs.gnome.org/desrt/2014/05/27/allow-none-is-dead-long-live-nullab… Before you say "the term 'nullable' will confuse end users", let me remind you: this is not user-facing. This is a parameter for an Argument Clinic converter, and will only ever be seen by CPython core developers. A group which I hope is not so easily confused. It's my contention that "nullable" is the correct name. But I've been asked to bring up the topic for discussion, to see if a consensus forms around this or around some other name. Let the bike-shedding begin, //arry/

5 years, 5 months

Update to PEP 11 to clarify garnering platform support

by Brett Cannon

Here is some proposed wording. Since it is more of a clarification of what it takes to garner support -- which is just a new section -- rather than a complete rewrite I'm including just the diff to make it easier to read the changes. *diff -r 49d18bb47ebc pep-0011.txt* *--- a/pep-0011.txt Wed May 14 11:18:22 2014 -0400* *+++ b/pep-0011.txt Fri May 16 13:48:30 2014 -0400* @@ -2,22 +2,21 @@ Title: Removing support for little used platforms Version: $Revision$ Last-Modified: $Date$ -Author: martin(a)v.loewis.de (Martin von Löwis) +Author: Martin von Löwis <martin(a)v.loewis.de>, + Brett Cannon <brett(a)python.org> Status: Active Type: Process Content-Type: text/x-rst Created: 07-Jul-2002 Post-History: 18-Aug-2007 + 16-May-2014 Abstract -------- -This PEP documents operating systems (platforms) which are not -supported in Python anymore. For some of these systems, -supporting code might be still part of Python, but will be removed -in a future release - unless somebody steps forward as a volunteer -to maintain this code. +This PEP documents how an operating system (platform) garners +support in Python as well as documenting past support. Rationale @@ -37,16 +36,53 @@ change to the Python source code will work on all supported platforms. -To reduce this risk, this PEP proposes a procedure to remove code -for platforms with no Python users. +To reduce this risk, this PEP specifies what is required for a +platform to be considered supported by Python as well as providing a +procedure to remove code for platforms with little or no Python +users. +Supporting platforms +-------------------- + +Gaining official platform support requires two things. First, a core +developer needs to volunteer to maintain platform-specific code. This +core developer can either already be a member of the Python +development team or be given contributor rights on the basis of +maintaining platform support (it is at the discretion of the Python +development team to decide if a person is ready to have such rights +even if it is just for supporting a specific platform). + +Second, a stable buildbot must be provided [2]_. This guarantees that +platform support will not be accidentally broken by a Python core +developer who does not have personal access to the platform. For a +buildbot to be considered stable it requires that the machine be +reliably up and functioning (but it is up to the Python core +developers to decide whether to promote a buildbot to being +considered stable). + +This policy does not disqualify supporting other platforms +indirectly. Patches which are not platform-specific but still done to +add platform support will be considered for inclusion. For example, +if platform-independent changes were necessary in the configure +script which was motivated to support a specific platform that would +be accepted. Patches which add platform-specific code such as the +name of a specific platform to the configure script will generally +not be accepted without the platform having official support. + +CPU architecture and compiler support are viewed in a similar manner +as platforms. For example, to consider the ARM architecture supported +a buildbot running on ARM would be required along with support from +the Python development team. In general it is not required to have +a CPU architecture run under every possible platform in order to be +considered supported. Unsupporting platforms ---------------------- -If a certain platform that currently has special code in it is -deemed to be without Python users, a note must be posted in this -PEP that this platform is no longer actively supported. This +If a certain platform that currently has special code in Python is +deemed to be without Python users or lacks proper support from the +Python development team and/or a buildbot, a note must be posted in +this PEP that this platform is no longer actively supported. This note must include: - the name of the system @@ -69,8 +105,8 @@ forward and offer maintenance. -Resupporting platforms ----------------------- +Re-supporting platforms +----------------------- If a user of a platform wants to see this platform supported again, he may volunteer to maintain the platform support. Such an @@ -101,7 +137,7 @@ release is made. Developers of extension modules will generally need to use the same Visual Studio release; they are concerned both with the availability of the versions they need to use, and with keeping -the zoo of versions small. The Python source tree will keep +the zoo of versions small. The Python source tree will keep unmaintained build files for older Visual Studio releases, for which patches will be accepted. Such build files will be removed from the source tree 3 years after the extended support for the compiler has @@ -223,6 +259,7 @@ ---------- .. [1] http://support.microsoft.com/lifecycle/ +.. [2] http://buildbot.python.org/3.x.stable/ Copyright ---------

5 years, 7 months

ubuntu buildbot

by Benjamin Peterson

Hi David, I noticed you run the "Builder x86 Ubuntu Shared" buildbot. It seems it's running a very old version of Ubuntu. Is there any chance of getting that updated? Regards, Benjamin

5 years, 7 months

PEP 481 - Migrate Some Supporting Repositories to Git and Github

by Donald Stufft

As promised in the "Move selected documentation repos to PSF BitBucket account?" thread I've written up a PEP for moving selected repositories from hg.python.org to Github. You can see this PEP online at: https://www.python.org/dev/peps/pep-0481/ I've also reproduced the PEP below for inline discussion. ----------------------- Abstract ======== This PEP proposes migrating to Git and Github for certain supporting repositories (such as the repository for Python Enhancement Proposals) in a way that is more accessible to new contributors, and easier to manage for core developers. This is offered as an alternative to PEP 474 which aims to achieve the same overall benefits but while continuing to use the Mercurial DVCS and without relying on a commerical entity. In particular this PEP proposes changes to the following repositories: * https://hg.python.org/devguide/ * https://hg.python.org/devinabox/ * https://hg.python.org/peps/ This PEP does not propose any changes to the core development workflow for CPython itself. Rationale ========= As PEP 474 mentions, there are currently a number of repositories hosted on hg.python.org which are not directly used for the development of CPython but instead are supporting or ancillary repositories. These supporting repositories do not typically have complex workflows or often branches at all other than the primary integration branch. This simplicity makes them very good targets for the "Pull Request" workflow that is commonly found on sites like Github. However where PEP 474 wants to continue to use Mercurial and wishes to use an OSS and self-hosted and therefore restricts itself to only those solutions this PEP expands the scope of that to include migrating to Git and using Github. The existing method of contributing to these repositories generally includes generating a patch and either uploading them to bugs.python.org or emailing them to peps(a)python.org. This process is unfriendly towards non-comitter contributors as well as making the process harder than it needs to be for comitters to accept the patches sent by users. In addition to the benefits in the pull request workflow itself, this style of workflow also enables non techincal contributors, especially those who do not know their way around the DVCS of choice, to contribute using the web based editor. On the committer side the Pull Requests enable them to tell, before merging, whether or not a particular Pull Request will break anything. It also enables them to do a simple "push button" merge which does not require them to check out the changes locally. Another such feature that is useful in particular for docs, is the ability to view a "prose" diff. This Github specific feature enables a committer to view a diff of the rendered output which will hide things like reformatting a paragraph and show you what the actual "meat" of the change actually is. Why Git? -------- Looking at the variety of DVCS which are available today it becomes fairly clear that git has gotten the vast mindshare of people who are currently using it. The Open Hub (Previously Ohloh) statistics [#openhub-stats]_ show that currently 37% of the repositories Open Hub is indexing is using git which is second only to SVN (which has 48%) while Mercurial has just 2% of the indexed repositories (beating only bazaar which has 1%). In additon to the Open Hub statistics a look at the top 100 projects on PyPI (ordered by total download counts) shows us that within the Python space itself there is a majority of projects using git: === ========= ========== ====== === ==== Git Mercurial Subversion Bazaar CVS None === ========= ========== ====== === ==== 62 22 7 4 1 1 === ========= ========== ====== === ==== Chosing a DVCS which has the larger mindshare will make it more likely that any particular person who has experience with DVCS at all will be able to meaningfully use the DVCS that we have chosen without having to learn a new tool. In addition to simply making it more likely that any individual will already know how to use git, the number of projects and people using it means that the resources for learning the tool are likely to be more fully fleshed out and when you run into problems the liklihood that someone else had that problem and posted a question and recieved an answer is also far likelier. Thirdly by using a more popular tool you also increase your options for tooling *around* the DVCS itself. Looking at the various options for hosting repositories it's extremely rare to find a hosting solution (whether OSS or commerical) that supports Mercurial but does not support Git, on the flip side there are a number of tools which support Git but do not support Mercurial. Therefore the popularity of git increases the flexibility of our options going into the future for what toolchain these projects use. Also by moving to the more popular DVCS we increase the likelhood that the knowledge that the person has learned in contributing to these support repositories will transfer to projects outside of the immediate CPython project such as to the larger Python community which is primarily using Git hosted on Github. In previous years there was concern about how well supported git was on Windows in comparison to Mercurial. However git has grown to support Windows as a first class citizen. In addition to that, for Windows users who are not well aquanted with the Windows command line there are GUI options as well. On a techincal level git and Mercurial are fairly similar, however the git branching model is signifcantly better than Mercurial "Named Branches" for non-comitter contributors. Mercurial does have a "Bookmarks" extension however this isn't quite as good as git's branching model. All bookmarks live in the same namespace so it requires individual users to ensure that they namespace the branchnames themselves lest the risk collision. It also is an extension which requires new users to first discover they need an extension at all and then figure out what they need to do in order to enable that extension. Since it is an extension it also means that in general support for them outside of Mercurial core is going to be less than 100% in comparison to git where the feature is built in and core to using git at all. Finally users who are not used to Mercurial are unlikely to discover bookmarks on their own, instead they will likely attempt to use Mercurial's "Named Branches" which, given the fact they live "forever", are not often what a project wants their contributors to use. Why Github? ----------- There are a number of software projects or web services which offer functionality similar to that of Github. These range from commerical web services such as a Bitbucket to self-hosted OSS solutions such as Kallithea or Gitlab. This PEP proposes that we move these repositories to Github. There are two primary reasons for selecting Github: Popularity and Quality/Polish. Github is currently the most popular hosted repository hosting according to Alexa where it currently has a global rank of 121. Much like for Git itself by choosing the most popular tool we gain benefits in increasing the likelhood that a new contributor will have already experienced the toolchain, the quality and availablity of the help, more and better tooling being built around it, and the knowledge transfer to other projects. A look again at the top 100 projects by download counts on PyPI shows the following hosting locations: ====== ========= =========== ========= =========== ========== GitHub BitBucket Google Code Launchpad SourceForge Other/Self ====== ========= =========== ========= =========== ========== 62 18 6 4 3 7 ====== ========= =========== ========= =========== ========== In addition to all of those reasons, Github also has the benefit that while many of the options have similar features when you look at them in a feature matrix the Github version of each of those features tend to work better and be far more polished. This is hard to quantify objectively however it is a fairly common sentiment if you go around and ask people who are using these services often. Finally a reason to choose a web service at all over something that is self-hosted is to be able to more efficiently use volunteer time and donated resources. Every additional service hosted on the PSF infrastruture by the PSF infrastructure team further spreads out the amount of time that the volunteers on that team have to spend and uses some chunk of resources that could potentionally be used for something where there is no free or affordable hosted solution available. One concern that people do have with using a hosted service is that there is a lack of control and that at some point in the future the service may no longer be suitable. It is the opinion of this PEP that Github does not currently and has not in the past engaged in any attempts to lock people into their platform and that if at some point in the future Github is no longer suitable for one reason or another than at that point we can look at migrating away from Github onto a different solution. In other words, we'll cross that bridge if and when we come to it. Example: Scientific Python -------------------------- One of the key ideas behind the move to both git and Github is that a feature of a DVCS, the repository hosting, and the workflow used is the social network and size of the community using said tools. We can see this is true by looking at an example from a sub-community of the Python community: The Scientific Python community. They have already migrated most of the key pieces of the SciPy stack onto Github using the Pull Request based workflow starting with IPython and as more projects moved over it became a natural default for new projects. They claim to have seen a great benefit from this move, where it enables casual contributors to easily move between different projects within their sub-community without having to learn a special, bespoke workflow and a different toolchain for each project. They've found that when people can use their limited time on actually contributing instead of learning the different tools and workflows that not only do they contribute more to one project, that they also expand out and contribute to other projects. This move is also attributed to making it commonplace for members of that community to go so far as publishing their research and educational materials on Github as well. This showcases the real power behind moving to a highly popular toolchain and workflow, as each variance introduces yet another hurdle for new and casual contributors to get past and it makes the time spent learning that workflow less reusable with other projects. Migration ========= Through the use of hg-git [#hg-git]_ we can easily convert a Mercurial repository to a Git repository by simply pushing the Mercurial repository to the Git repository. People who wish to continue to use Mercurual locally can then use hg-git going into the future using the new Github URL, however they will need to re-clone their repositories as using Git as the server seems to trigger a one time change of the changeset ids. As none of the selected repositories have any tags, branches, or bookmarks other than the ``default`` branch the migration will simply map the ``default`` branch in Mercurial to the ``master`` branch in git. In addition since none of the selected projects have any great need of a complex bug tracker, they will also migrate their issue handling to using the GitHub issues. In addition to the migration of the repository hosting itself there are a number of locations for each particular repository which will require updating. The bulk of these will simply be changing commands from the hg equivilant to the git equivilant. In particular this will include: * Updating www.python.org to generate PEPs using a git clone and link to Github. * Updating docs.python.org to pull from Github instead of hg.python.org for the devguide. * Enabling the ability to send an email to python-checkins(a)python.org for each push. * Enabling the ability to send an IRC message to #python-dev on Freenode for each push. * Migrate any issues for these projects to their respective bug tracker on Github. This will restore these repositories to similar functionality as they currently have. In addition to this the migration will also include enabling testing for each pull request using Travis CI [#travisci]_ where possible to ensure that a new PR does not break the ability to render the documentation or PEPs. User Access =========== Moving to Github would involve adding an additional user account that will need to be managed, however it also offers finer grained control, allowing the ability to grant someone access to only one particular repository instead of the coarser grained ACLs available on hg.python.org. References ========== .. [#openhub-stats] `Open Hub Statistics <https://www.openhub.net/repositories/compare>` .. [#hg-git] `hg-git <https://hg-git.github.io/>` .. [#travisci] `Travis CI <https://travis-ci.org/>` --- Donald Stufft PGP: 7C6B 7C5D 5E2B 6356 A926 F04F 6E3C BCE9 3372 DCFA

5 years, 8 months

More compact dictionaries with faster iteration

by Raymond Hettinger

The current memory layout for dictionaries is unnecessarily inefficient. It has a sparse table of 24-byte entries containing the hash value, key pointer, and value pointer. Instead, the 24-byte entries should be stored in a dense table referenced by a sparse table of indices. For example, the dictionary: d = {'timmy': 'red', 'barry': 'green', 'guido': 'blue'} is currently stored as: entries = [['--', '--', '--'], [-8522787127447073495, 'barry', 'green'], ['--', '--', '--'], ['--', '--', '--'], ['--', '--', '--'], [-9092791511155847987, 'timmy', 'red'], ['--', '--', '--'], [-6480567542315338377, 'guido', 'blue']] Instead, the data should be organized as follows: indices = [None, 1, None, None, None, 0, None, 2] entries = [[-9092791511155847987, 'timmy', 'red'], [-8522787127447073495, 'barry', 'green'], [-6480567542315338377, 'guido', 'blue']] Only the data layout needs to change. The hash table algorithms would stay the same. All of the current optimizations would be kept, including key-sharing dicts and custom lookup functions for string-only dicts. There is no change to the hash functions, the table search order, or collision statistics. The memory savings are significant (from 30% to 95% compression depending on the how full the table is). Small dicts (size 0, 1, or 2) get the most benefit. For a sparse table of size t with n entries, the sizes are: curr_size = 24 * t new_size = 24 * n + sizeof(index) * t In the above timmy/barry/guido example, the current size is 192 bytes (eight 24-byte entries) and the new size is 80 bytes (three 24-byte entries plus eight 1-byte indices). That gives 58% compression. Note, the sizeof(index) can be as small as a single byte for small dicts, two bytes for bigger dicts and up to sizeof(Py_ssize_t) for huge dict. In addition to space savings, the new memory layout makes iteration faster. Currently, keys(), values, and items() loop over the sparse table, skipping-over free slots in the hash table. Now, keys/values/items can loop directly over the dense table, using fewer memory accesses. Another benefit is that resizing is faster and touches fewer pieces of memory. Currently, every hash/key/value entry is moved or copied during a resize. In the new layout, only the indices are updated. For the most part, the hash/key/value entries never move (except for an occasional swap to fill a hole left by a deletion). With the reduced memory footprint, we can also expect better cache utilization. For those wanting to experiment with the design, there is a pure Python proof-of-concept here: http://code.activestate.com/recipes/578375 YMMV: Keep in mind that the above size statics assume a build with 64-bit Py_ssize_t and 64-bit pointers. The space savings percentages are a bit different on other builds. Also, note that in many applications, the size of the data dominates the size of the container (i.e. the weight of a bucket of water is mostly the water, not the bucket). Raymond

5 years, 9 months

advice needed: best approach to enabling "metamodules"?

by Nathaniel Smith

Hi all, There was some discussion on python-ideas last month about how to make it easier/more reliable for a module to override attribute access. This is useful for things like autoloading submodules (accessing 'foo.bar' triggers the import of 'bar'), or for deprecating module attributes that aren't functions. (Accessing 'foo.bar' emits a DeprecationWarning, "the bar attribute will be removed soon".) Python has had some basic support for this for a long time -- if a module overwrites its entry in sys.modules[__name__], then the object that's placed there will be returned by 'import'. This allows one to define custom subclasses of module and use them instead of the default, similar to how metaclasses allow one to use custom subclasses of 'type'. In practice though it's very difficult to make this work safely and correctly for a top-level package. The main problem is that when you create a new object to stick into sys.modules, this necessarily means creating a new namespace dict. And now you have a mess, because now you have two dicts: new_module.__dict__ which is the namespace you export, and old_module.__dict__, which is the globals() for the code that's trying to define the module namespace. Keeping these in sync is extremely error-prone -- consider what happens, e.g., when your package __init__.py wants to import submodules which then recursively import the top-level package -- so it's difficult to justify for the kind of large packages that might be worried about deprecating entries in their top-level namespace. So what we'd really like is a way to somehow end up with an object that (a) has the same __dict__ as the original module, but (b) is of our own custom module subclass. If we can do this then metamodules will become safe and easy to write correctly. (There's a little demo of working metamodules here: https://github.com/njsmith/metamodule/ but it uses ctypes hacks that depend on non-stable parts of the CPython ABI, so it's not a long-term solution.) I've now spent some time trying to hack this capability into CPython and I've made a list of the possible options I can think of to fix this. I'm writing to python-dev because none of them are obviously The Right Way so I'd like to get some opinions/ruling/whatever on which approach to follow up on. Option 1: Make it possible to change the type of a module object in-place, so that we can write something like sys.modules[__name__].__class__ = MyModuleSubclass Option 1 downside: The invariants required to make __class__ assignment safe are complicated, and only implemented for heap-allocated type objects. PyModule_Type is not heap-allocated, so making this work would require lots of delicate surgery to typeobject.c. I'd rather not go down that rabbit-hole. ---- Option 2: Make PyModule_Type into a heap type allocated at interpreter startup, so that the above just works. Option 2 downside: PyModule_Type is exposed as a statically-allocated global symbol, so doing this would involve breaking the stable ABI. ---- Option 3: Make it legal to assign to the __dict__ attribute of a module object, so that we can write something like new_module = MyModuleSubclass(...) new_module.__dict__ = sys.modules[__name__].__dict__ sys.modules[__name__].__dict__ = {} # *** sys.modules[__name__] = new_module The line marked *** is necessary because the way modules are designed, they expect to control the lifecycle of their __dict__. When the module object is initialized, it fills in a bunch of stuff in the __dict__. When the module object (not the dict object!) is deallocated, it deletes everything from the __dict__. This latter feature in particular means that having two module objects sharing the same __dict__ is bad news. Option 3 downside: The paragraph above. Also, there's stuff inside the module struct besides just the __dict__, and more stuff has appeared there over time. ---- Option 4: Add a new function sys.swap_module_internals, which takes two module objects and swaps their __dict__ and other attributes. By making the operation a swap instead of an assignment, we avoid the lifecycle pitfalls from Option 3. By making it a builtin, we can make sure it always handles all the module fields that matter, not just __dict__. Usage: new_module = MyModuleSubclass(...) sys.swap_module_internals(new_module, sys.modules[__name__]) sys.modules[__name__] = new_module Option 4 downside: Obviously a hack. ---- Option 3 or 4 both seem workable, it just depends on which way we prefer to hold our nose. Option 4 is slightly more correct in that it works for *all* modules, but OTOH at the moment the only time Option 3 *really* fails is for compiled modules with PEP 3121 metadata, and compiled modules can already use a module subclass via other means (since they instantiate their own module objects). Thoughts? Suggestions on other options I've missed? Should I go ahead and write a patch for one of these? -n -- Nathaniel J. Smith Postdoctoral researcher - Informatics - University of Edinburgh http://vorpus.org

5 years, 10 months

Unicode decode exception

by balaji marisetti

Hi, When I try to iterate through the lines of a file("openssl-1.0.1j/crypto/bn/asm/x86_64-gcc.c"), I get a UnicodeDecodeError (in python 3.4.0 on Ubuntu 14.04). But there is no such error with python 2.7.6. What could be the problem? In [39]: with open("openssl-1.0.1j/crypto/bn/asm/x86_64-gcc.c") as f: for line in f: print (line) --------------------------------------------------------------------------- UnicodeDecodeError Traceback (most recent call last) <ipython-input-39-24a3ae32a691> in <module>() 1 with open("../openssl-1.0.1j/crypto/bn/asm/x86_64-gcc.c") as f: ----> 2 for line in f: 3 print (line) 4 /usr/lib/python3.4/codecs.py in decode(self, input, final) 311 # decode input (taking the buffer into account) 312 data = self.buffer + input --> 313 (result, consumed) = self._buffer_decode(data, self.errors, final) 314 # keep undecoded input until the next call 315 self.buffer = data[consumed:] -- :-)balaji

5 years, 10 months

PEP 479

by Jim J. Jewett

I have a strong suspicion that I'm missing something; I have been persuaded both directions too often to believe I have a grip on the real issue. So I'm putting out some assumptions; please tell me if I'm wrong, and maybe make them more explicit in the PEP. (1) The change will only affect situations where StopIteration is currently raised as an Exception -- i.e., it leaks past the bounds of a loop. (2) This can happen because of an explicit raise StopIteration. This is currently a supported idiom, and that is changing with PEP 479. (2a) Generators in the unwind path will now need to catch and reraise. (3) It can also happen because of an explicit next statement (as opposed the the implicit next of a loop). This is currently supported; after PEP 479, the next statement should be wrapped in a try statement, so that the intent will be explicit. (4) It can happen because of "yield from" yielding from an iterator, rather than a generator? (5) There is no other case where this can happen? (So the generator comprehension case won't matter unless it also includes one of the earlier cases.) -jJ

5 years, 10 months

PEP 479 and asyncio

by Victor Stinner

Hi, I'm trying to follow the discussion about the PEP 479 (Change StopIteration handling inside generators), but it's hard to read all messages. I'm concerned by trollius and asyncio which heavily rely on StopIteration. Trollius currently supports running asyncio coroutines: a trollius coroutine can executes an asyncio coroutine, and and asyncio coroutine can execute a trollius coroutine. I modified the Return class of Trollius to not inherit from StopIteration. All trollius tests pass on Python 3.3 except on one (which makes me happy, the test suite is wide enough to detect bugs ;-)): test_trollius_in_asyncio. This specific test executes an asyncio which executes a trollius coroutine. https://bitbucket.org/enovance/trollius/src/873d21ac0badec36835ed24d13e2aed… The problem is that an asyncio coroutine cannot execute a Trollius coroutine anymore: "yield from coro" raises a Return exception instead of simply "stopping" the generator and return the result (value passed to Return). I don't see how an asyncio coroutine calling "yield from trollius_coroutine" can handle the Return exception if it doesn't inherit from StopIteration. It means that I have to drop this feature in Python 3.5 (or later when the PEP 479 becomes effective)? I'm talking about the current behaviour of Python 3.3, I didn't try the PEP 479 (I don't know if an exception exists). Victor

5 years, 10 months

Please reconsider PEP 479.

by Mark Shannon

Hi, I have serious concerns about this PEP, and would ask you to reconsider it. [ Very short summary: Generators are not the problem. It is the naive use of next() in an iterator that is the problem. (Note that all the examples involve calls to next()). Change next() rather than fiddling with generators. ] I have five main concerns with PEP 479. 1. Is the problem, as stated by the PEP, really the problem at all? 2. The proposed solution does not address the underlying problem. 3. It breaks a fundamental aspect of generators, that they are iterators. 4. This will be a hindrance to porting code from Python 2 to Python 3. 5. The behaviour of next() is not considered, even though it is the real cause of the problem (if there is a problem). 1. The PEP states that "The interaction of generators and StopIteration is currently somewhat surprising, and can conceal obscure bugs." I don't believe that to be the case; if someone knows what StopIteration is and how it is used, then the interaction is entirely as expected. I believe the naive use of next() in an iterator to be the underlying problem. The interaction of generators and next() is just a special case of this. StopIteration is not a normal exception, indicating a problem, rather it exists to signal exhaustion of an iterator. However, next() raises StopIteration for an exhausted iterator, which really is an error. Any iterator code (generator or __next__ method) that calls next() treats the StopIteration as a normal exception and propogates it. The controlling loop then interprets StopIteration as a signal to stop and thus stops. *The problem is the implicit shift from signal to error and back to signal.* 2. The proposed solution does not address this issue at all, but rather legislates against generators raising StopIteration. 3. Generators and the iterator protocol were introduced in Python 2.2, 13 years ago. For all of that time the iterator protocol has been defined by the __iter__(), next()/__next__() methods and the use of StopIteration to terminate iteration. Generators are a way to write iterators without the clunkiness of explicit __iter__() and next()/__next__() methods, but have always obeyed the same protocol as all other iterators. This has allowed code to rewritten from one form to the other whenever desired. Do not forget that despite the addition of the send() and throw() methods and their secondary role as coroutines, generators have primarily always been a clean and elegant way of writing iterators. 4. Porting from Python 2 to Python 3 seems to be hard enough already. 5. I think I've already covered this in the other points, but to reiterate (excuse the pun): Calling next() on an exhausted iterator is, I would suggest, a logical error. However, next() raises StopIteration which is really a signal to the controlling loop. The fault is with next() raising StopIteration. Generators raising StopIteration is not the problem. It also worth noting that calling next() is the only place a StopIteration exception is likely to occur outside of the iterator protocol. An example ---------- Consider a function to return the value from a set with a single member. def value_from_singleton(s): if len(s) < 2: #Intentional error here (should be len(s) == 1) return next(iter(s)) raise ValueError("Not a singleton") Now suppose we pass an empty set to value_from_singleton(s), then we get a StopIteration exception, which is a bit weird, but not too bad. However it is when we use it in a generator (or in the __next__ method of an iterator) that we get a serious problem. Currently the iterator appears to be exhausted early, which is wrong. However, with the proposed change we get RuntimeError("generator raised StopIteration") raised, which is also wrong, just in a different way. Solutions --------- My preferred "solution" is to do nothing except improving the documentation of next(). Explain that it can raise StopIteration which, if allowed to propogate can cause premature exhaustion of an iterator. If something must be done then I would suggest changing the behaviour of next() for an exhausted iterator. Rather than raise StopIteration it should raise ValueError (or IndexError?). Also, it might be worth considering making StopIteration inherit from BaseException, rather than Exception. Cheers, Mark. P.S. 5 days seems a rather short time to respond to a PEP. Could we make it at least a couple of weeks in the future, or better still specify a closing date for comments.

5 years, 10 months

2020

2019

2018

2017

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

2000

1999

Python-Dev November 2014