Python-Dev February 2013

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103 participants
84 discussions

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, 3 months

Reviving restricted mode?

by Guido van Rossum

I've received some enthusiastic emails from someone who wants to revive restricted mode. He started out with a bunch of patches to the CPython runtime using ctypes, which he attached to an App Engine bug: http://code.google.com/p/googleappengine/issues/detail?id=671 Based on his code (the file secure.py is all you need, included in secure.tar.gz) it seems he believes the only security leaks are __subclasses__, gi_frame and gi_code. (I have since convinced him that if we add "restricted" guards to these attributes, he doesn't need the functions added to sys.) I don't recall the exploits that Samuele once posted that caused the death of rexec.py -- does anyone recall, or have a pointer to the threads? -- --Guido van Rossum (home page: http://www.python.org/~guido/)

5 years, 7 months

cffi in stdlib

by Maciej Fijalkowski

Hello. I would like to discuss on the language summit a potential inclusion of cffi[1] into stdlib. This is a project Armin Rigo has been working for a while, with some input from other developers. It seems that the main reason why people would prefer ctypes over cffi these days is "because it's included in stdlib", which is not generally the reason I would like to hear. Our calls to not use C extensions and to use an FFI instead has seen very limited success with ctypes and quite a lot more since cffi got released. The API is fairly stable right now with minor changes going in and it'll definitely stablize until Python 3.4 release. Notable projects using it: * pypycore - gevent main loop ported to cffi * pgsql2cffi * sdl-cffi bindings * tls-cffi bindings * lxml-cffi port * pyzmq * cairo-cffi * a bunch of others So relatively a lot given that the project is not even a year old (it got 0.1 release in June). As per documentation, the advantages over ctypes: * The goal is to call C code from Python. You should be able to do so without learning a 3rd language: every alternative requires you to learn their own language (Cython, SWIG) or API (ctypes). So we tried to assume that you know Python and C and minimize the extra bits of API that you need to learn. * Keep all the Python-related logic in Python so that you don’t need to write much C code (unlike CPython native C extensions). * Work either at the level of the ABI (Application Binary Interface) or the API (Application Programming Interface). Usually, C libraries have a specified C API but often not an ABI (e.g. they may document a “struct” as having at least these fields, but maybe more). (ctypes works at the ABI level, whereas Cython and native C extensions work at the API level.) * We try to be complete. For now some C99 constructs are not supported, but all C89 should be, including macros (and including macro “abuses”, which you can manually wrap in saner-looking C functions). * We attempt to support both PyPy and CPython, with a reasonable path for other Python implementations like IronPython and Jython. * Note that this project is not about embedding executable C code in Python, unlike Weave. This is about calling existing C libraries from Python. so among other things, making a cffi extension gives you the same level of security as writing C (and unlike ctypes) and brings quite a bit more flexibility (API vs ABI issue) that let's you wrap arbitrary libraries, even those full of macros. Cheers, fijal .. [1]: http://cffi.readthedocs.org/en/release-0.5/

6 years, 3 months

compile python 3.3 with bz2 support

by Isml

hi, everyone: I want to compile python 3.3 with bz2 support on RedHat 5.5 but fail to do that. Here is how I do it: 1、download bzip2 and compile it(make、make -f Makefile_libbz2_so、make install) 2、chang to python 3.3 source directory : ./configure --with-bz2=/usr/local/include 3、make 4、make install after installation complete, I test it： [root@localhost Python-3.3.0]# python3 -c "import bz2" Traceback (most recent call last): File "<string>", line 1, in <module> File "/usr/local/lib/python3.3/bz2.py", line 21, in <module> from _bz2 import BZ2Compressor, BZ2Decompressor ImportError: No module named '_bz2' By the way, RedHat 5.5 has a built-in python 2.4.3. Would it be a problem?

6 years, 5 months

Make extension module initialisation more like Python module initialisation

by Stefan Behnel

Hi, I suspect that this will be put into a proper PEP at some point, but I'd like to bring this up for discussion first. This came out of issues 13429 and 16392. http://bugs.python.org/issue13429 http://bugs.python.org/issue16392 Stefan The problem =========== Python modules and extension modules are not being set up in the same way. For Python modules, the module is created and set up first, then the module code is being executed. For extensions, i.e. shared libraries, the module init function is executed straight away and does both the creation and initialisation. This means that it knows neither the __file__ it is being loaded from nor its package (i.e. its FQMN). This hinders relative imports and resource loading. In Py3, it's also not being added to sys.modules, which means that a (potentially transitive) re-import of the module will really try to reimport it and thus run into an infinite loop when it executes the module init function again. And without the FQMN, it's not trivial to correctly add the module to sys.modules either. We specifically run into this for Cython generated modules, for which it's not uncommon that the module init code has the same level of complexity as that of any 'regular' Python module. Also, the lack of a FQMN and correct file path hinders the compilation of __init__.py modules, i.e. packages, especially when relative imports are being used at module init time. The proposal ============ I propose to split the extension module initialisation into two steps in Python 3.4, in a backwards compatible way. Step 1: The current module init function can be reduced to just creating the module instance and returning it (and potentially doing some simple C level setup). Optionally, after creating the module (and this is the new part), the module init code can register a C callback function that will be called after setting up the module. Step 2: The shared library importer receives the module instance from the module init function, adds __file__, __path__, __package__ and friends to the module dict, and then checks for the callback. If non-NULL, it calls it to continue the module initialisation by user code. The callback ============ The callback is defined as follows:: int (*PyModule_init_callback)(PyObject* the_module, PyModuleInitContext* context) "PyModuleInitContext" is a struct that is meant mostly for making the callback more future proof by allowing additional parameters to be passed in. For now, I can see a use case for the following fields:: struct PyModuleInitContext { char* module_name; char* qualified_module_name; } Both names are encoded in UTF-8. As for the file path, I consider it best to retrieve it from the module's __file__ attribute as a Python string object to reduce filename encoding problems. Note that this struct argument is not strictly required, but given that this proposal would have been much simpler if the module init function had accepted such an argument in the first place, I consider it a good idea not to let this chance pass by again. The registration of the callback uses a new C-API function: int PyModule_SetInitFunction(PyObject* module, PyModule_init_callback callback) The function name uses "Set" instead of "Register" to make it clear that there is only one such function per module. An alternative would be a new module creation function "PyModule_Create3()" that takes the callback as third argument, in addition to what "PyModule_Create2()" accepts. This would require users to explicitly pass in the (second) version argument, which might be considered only a minor issue. Implementation ============== The implementation requires local changes to the extension module importer and a new C-API function. In order to store the callback, it should use a new field in the module object struct. Open questions ============== It is not clear how extensions should be handled that register more than one module in their module init function, e.g. compiled packages. One possibility would be to leave the setup to the user, who would have to know all FQMNs anyway in this case, although not the import file path. Alternatively, the import machinery could use a stack to remember for which modules a callback was registered during the last init function call, set up all of them and then call their callbacks. It's not clear if this meets the intention of the user. Alternatives ============ 1) It would be possible to make extension modules optionally export another symbol, e.g. "PyInit2_modulename", that the shared library loader would call in addition to the required function "PyInit_modulename". This would remove the need for a new API that registers the above callback. The drawback is that it also makes it easier to write broken code because a Python version or implementation that does not support this second symbol would simply not call it, without error. The new C-API function would let the build fail instead if it is not supported. 2) The callback could be made available as a Python function in the module dict, thus also removing the need for an explicit registration API. However, this approach would add overhead to both sides, the importer code and the user provided module init code, as it would require additional dictionary handling and the implementation of a one-time Python function in user code. It would also suffer from the problem that missing support in the runtime would pass silently. 3) The callback could be registered statically in the PyModuleDef struct by adding a new field. This is not trivial to do in a backwards compatible way because the struct would grow longer without explicit initialisation by existing user code. Extending PyModuleDef_HEAD_INIT might be possible but would still break at least binary compatibility. 4) Pass a new context argument into the module init function that contains all information necessary to properly and completely set up the module at creation time. This would provide a much simpler and cleaner solution than the proposed solution. However, it will not be possible before Python 4 as it breaks backwards compatibility with all existing extension modules at both the source and binary level.

6 years, 8 months

PLY in stdlib (was cffi in stdlib)

by David Beazley

> > From: Eli Bendersky <eliben(a)gmail.com> > > I'll be the first one to admit that pycparser is almost certainly not > generally useful enough to be exposed in the stdlib. So just using it as an > implementation detail is absolutely fine. PLY is a more interesting > question, however, since PLY is somewhat more generally useful. That said, > I see all this as implementation details that shouldn't distract us from > the main point of whether cffi should be added. > Regarding the inclusion of PLY or some subcomponent of it in the standard library, it's not an entirely crazy idea in my opinion. LALR(1) parsers have been around for a long time, are generally known to anyone who's used yacc/bison, and would be useful outside the context of cffi or pycparser. PLY has also been around for about 12 years and is what I would call stable. It gets an update about every year or two, but that's about it. PLY is also relatively small--just two files and about 4300 lines of code (much of which could probably be scaled down a bit). The only downside to including PLY might be the fact that there are very few people walking around who've actually had to *implement* an LALR(1) parser generator. Some of the code for that is extremely hairy and mathematical. At this time, I don't think there are any bugs in it, but it's not the sort of thing that one wants to wander into casually. Also, there are some horrible hacks in PLY that I'd really like to get rid of, but am currently stuck with due to backwards compatibility issues. Alex Gaynor has been working on a PLY variant (RPLY) geared at RPython and which has a slightly different programming interface. I'd say if we were to go down this route, he and I should work together to put together some kind of more general "parsing.lalr" package (or similar) that cleans it up and makes it more suitable as a library for building different kinds of parsing tools on top of. Cheers, Dave

6 years, 8 months

PEP 423 : naming conventions and recipes related to packaging

by Benoît Bryon

Hi, Here is an informational PEP proposal: http://hg.python.org/peps/file/52767ab7e140/pep-0423.txt Could you review it for style, consistency and content? Additional notes: * Original discussion posted to distutils-sig(a)python.org * started on May 2012 at http://mail.python.org/pipermail/distutils-sig/2012-May/018551.html * continues in June 2012 at http://mail.python.org/pipermail/distutils-sig/2012-June/018641.html * that's why I set "Discussion-To:<distutils-sig(a)python.org>" header. * Original document was edited as a contrib to cpython documentation: * http://bugs.python.org/issue14899 * file history at https://bitbucket.org/benoitbryon/cpython/history/Doc/packaging/packagename… * but it looked like a PEP, so posted to peps(a)python.org... Regards, Benoit (benoitbb on irc.freenode.net)

6 years, 9 months

mingw32 port

by Matthias Klose

[No, I'm not interested in the port myself] patches for a mingw32 port are floating around on the web and the python bug tracker, although most of them as a set of patches in one issue addressing several things, and which maybe outdated for the trunk. at least for me re-reading a big patch in a new version is more work than having the patches in different issues. So proposing to break down the patches in independent ones, dealing with: - mingw32 support (excluding any cross-build support). tested with a native build with srcdir == builddir. The changes for distutils mingw32 support should be a separate patch. Who could review these? Asked Martin, but didn't get a reply yet. - patches to cross-build for mingw32. - patches to deal with a srcdir != builddir configuration, where the srcdir is read-only (please don't mix with the cross-build support). All work should be done on the tip/trunk. So ok to close issue16526, issue3871, issue3754 and suggest in the reports to start over with more granular changes? Matthias

7 years

Bad python 2.5 build on OSX 10.8 mountain lion

by Guido van Rossum

As discussed here, the python 2.5 binary distributed by Apple on mountain lion is broken. Could someone file an official complaint? This is really bad... --Guido -- --Guido van Rossum (python.org/~guido)

7 years

Python Language Summit at PyCon: Agenda

by Michael Foord

Hello all, PyCon, and the Python Language Summit, is nearly upon us. We have a good number of people confirmed to attend. If you are intending to come to the language summit but haven't let me know please do so. The agenda of topics for discussion so far includes the following: * A report on pypy status - Maciej and Armin * Jython and IronPython status reports - Dino / Frank * Packaging (Doug Hellmann and Monty Taylor at least) * Cleaning up interpreter initialisation (both in hopes of finding areas to rationalise and hence speed things up, as well as making things more embedding friendly). Nick Coghlan * Adding new async capabilities to the standard library (Guido) * cffi and the standard library - Maciej * flufl.enum and the standard library - Barry Warsaw * The argument clinic - Larry Hastings If you have other items you'd like to discuss please let me know and I can add them to the agenda. All the best, Michael Foord -- http://www.voidspace.org.uk/ May you do good and not evil May you find forgiveness for yourself and forgive others May you share freely, never taking more than you give. -- the sqlite blessing http://www.sqlite.org/different.html

7 years

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Python-Dev February 2013