Python-Dev January 2013

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88 participants
78 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, 6 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, 10 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, 8 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, 11 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)

7 years

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, 3 months

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, 3 months

Cut/Copy/Paste items in IDLE right click context menu

by Andrew Svetlov

Hi. There are issue for subject: http://bugs.python.org/issue1207589 It has patches, implemented well and committed. I've pushed it to versions 2.7, 3.2, 3.3 and 3.4. My thoughts are: the issue is not brand new subject but improvement for current IDLE functionality. Added code cannot break any existing program/library I hope, it's related to humans who use IDLE only. It is desirable feature and probably IDLE users will be ok with new items in context menu even they are still 2.7 users. There are another opinion: it is new feature, not a bug, and the patch should be applied to 3.4 only. If you look on discussion for issue (http://bugs.python.org/issue1207589) you can see we cannot make decision, votes are split. I want to raise the question on this mailing list (as Terry Reedy suggested) to ask for community opinion. Thanks, Andrew Svetlov

7 years, 4 months

Point of building without threads?

by Antoine Pitrou

Hello, I guess a long time ago, threading support in operating systems wasn't very widespread, but these days all our supported platforms have it. Is it still useful for production purposes to configure --without-threads? Do people use this option for something else than curiosity of mind? Regards Antoine.

7 years, 4 months

Proposing "Argument Clinic", a new way of specifying arguments to builtins for CPython

by Larry Hastings

Say there, the Python core development community! Have I got a question for you! *ahem* Which of the following four options do you dislike least? ;-) 1) CPython continues to provide no "function signature" objects (PEP 362) or inspect.getfullargspec() information for any function implemented in C. 2) We add new hand-coded data structures representing the metadata necessary for function signatures for builtins. Which means that, when defining arguments to functions in C, we'd need to repeat ourselves *even more* than we already do. 3) Builtin function arguments are defined using some seriously uncomfortable and impenetrable C preprocessor macros, which produce all the various types of output we need (argument processing code, function signature metadata, possibly the docstrings too). 4) Builtin function arguments are defined in a small DSL; these are expanded to code and data using a custom compile-time preprocessor step. All the core devs I've asked said "given all that, I'd prefer the hairy preprocessor macros". But by the end of the conversation they'd changed their minds to prefer the custom DSL. Maybe I'll make a believer out of you too--read on! I've named this DSL preprocessor "Argument Clinic", or Clinic for short**. Clinic works similarly to Ned Batchelder's brilliant "Cog" tool: http://nedbatchelder.com/code/cog/ You embed the input to Clinic in a comment in your C file, and the output is written out immediately after that comment. The output's overwritten every time the preprocessor is run. In short it looks something like this: /*[clinic] input to the DSL [clinic]*/ ... output from the DSL, overwritten every time ... /*[clinic end:<checksum>]*/ The input to the DSL includes all the metadata about the function that we need for the function signature: * the name of the function, * the return annotation (if any), * each parameter to the function, including * its name, * its type (in C), * its default value, * and a per-parameter docstring; * and the docstring for the function as a whole. The resulting output contains: * the docstring for the function, * declarations for all your parameters, * C code handling all argument processing for you, * and a #define'd methoddef structure for adding the function to the module. I discussed this with Mark "HotPy" Shannon, and he suggested we break our existing C functions into two. We put the argument processing into its own function, generated entirely by Clinic, and have the implementation in a second function called from the first. I like this approach simply because it makes the code cleaner. (Note that this approach should not cause any overhead with a modern compiler, as both functions will be "static".) But it also provides an optimization opportunity for HotPy: it could read the metadata, and when generating the JIT'd code it could skip building the PyObjects and argument tuple (and possibly keyword argument dict), and the subsequent unpacking/decoding, and just call the implementation function directly, giving it a likely-measurable speed boost. And we can go further! If we add a new extension type API allowing you to register both functions, and external modules start using it, sophisticated Python implementations like PyPy might be able to skip building the tuple for extension type function calls--speeding those up too! Another plausible benefit: alternate implementations of Python could read the metadata--or parse the input to Clinic themselves--to ensure their reimplementations of the Python standard library conform to the same API! Clinic can also run general-purpose Python code ("/*[python]"). All output from "print" is redirected into the output section after the Python code. As you've no doubt already guessed, I've made a prototype of Argument Clinic. You can see it--and some sample conversions of builtins using it for argument processing--at this BitBucket repo: https://bitbucket.org/larry/python-clinic I don't claim that it's fabulous, production-ready code. But it's a definite start! To save you a little time, here's a preview of using Clinic for dbm.open(). The stuff at the same indent as a declaration are options; see the "clinic.txt" in the repo above for full documentation. /*[clinic] dbm.open -> mapping basename=dbmopen const char *filename; The filename to open. const char *flags="r"; How to open the file. "r" for reading, "w" for writing, etc. int mode=0666; default=0o666 If creating a new file, the mode bits for the new file (e.g. os.O_RDWR). Returns a database object. [clinic]*/ PyDoc_STRVAR(dbmopen__doc__, "dbm.open(filename[, flags=\'r\'[, mode=0o666]]) -> mapping\n" "\n" " filename\n" " The filename to open.\n" "\n" " flags\n" " How to open the file. \"r\" for reading, \"w\" for writing, etc.\n" "\n" " mode\n" " If creating a new file, the mode bits for the new file\n" " (e.g. os.O_RDWR).\n" "\n" "Returns a database object.\n" "\n"); #define DBMOPEN_METHODDEF \ {"open", (PyCFunction)dbmopen, METH_VARARGS | METH_KEYWORDS, dbmopen__doc__} static PyObject * dbmopen_impl(PyObject *self, const char *filename, const char *flags, int mode); static PyObject * dbmopen(PyObject *self, PyObject *args, PyObject *kwargs) { const char *filename; const char *flags = "r"; int mode = 0666; static char *_keywords[] = {"filename", "flags", "mode", NULL}; if (!PyArg_ParseTupleAndKeywords(args, kwargs, "s|si", _keywords, &filename, &flags, &mode)) return NULL; return dbmopen_impl(self, filename, flags, mode); } static PyObject * dbmopen_impl(PyObject *self, const char *filename, const char *flags, int mode) /*[clinic end:eddc886e542945d959b44b483258bf038acf8872]*/ As of this writing, I also have sample conversions in the following files available for your perusal: Modules/_cursesmodule.c Modules/_dbmmodule.c Modules/posixmodule.c Modules/zlibmodule.c Just search in C files for '[clinic]' and you'll find everything soon enough. As you can see, Clinic has already survived some contact with the enemy. I've already converted some tricky functions--for example, os.stat() and curses.window.addch(). The latter required adding a new positional-only processing mode for functions using a legacy argument processing approach. (See "clinic.txt" for more.) If you can suggest additional tricky functions to support, please do! Big unresolved questions: * How would we convert all the builtins to use Clinic? I fear any solution will involve some work by hand. Even if we can automate big chunks of it, fully automating it would require parsing arbitrary C. This seems like overkill for a one-shot conversion. (Mark Shannon says he has some ideas.) * How do we create the Signature objects? My current favorite idea: Clinic also generates a new, TBD C structure defining all the information necessary for the signature, which is also passed in to the new registration API (you remember, the one that takes both the argument-processing function and the implementation function). This is secreted away in some new part of the C function object. At runtime this is converted on-demand into a Signature object. Default values for arguments are represented in C as strings; the conversion process attempts eval() on the string, and if that works it uses the result, otherwise it simply passes through the string. * Right now Clinic paves over the PyArg_ParseTuple API for you. If we convert CPython to use Clinic everywhere, theoretically we could replace the parsing API with something cleaner and/or faster. Does anyone have good ideas (and time, and energy) here? * There's actually a fifth option, proposed by Brett Cannon. We constrain the format of docstrings for builtin functions to make them machine-readable, then generate the function signature objects from that. But consider: generating *everything* in the signature object may get a bit tricky (e.g. Parameter.POSITIONAL_ONLY), and this might gunk up the docstring. But the biggest unresolved question... is this all actually a terrible idea? //arry/ ** "Is this the right room for an argument?" "I've told you once...!"

7 years, 4 months

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Python-Dev January 2013