Python-Dev October 2009

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146 participants
92 discussions

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/)

6 years, 6 months

PEP 3145 (With Contents)

by Eric Pruitt

Alright, I will re-submit with the contents pasted. I never use double backquotes as I think them rather ugly; that is the work of an editor or some automated program in the chain. Plus, it also messed up my line formatting and now I have lines with one word on them... Anyway, the contents of PEP 3145: PEP: 3145 Title: Asynchronous I/O For subprocess.Popen Author: (James) Eric Pruitt, Charles R. McCreary, Josiah Carlson Type: Standards Track Content-Type: text/plain Created: 04-Aug-2009 Python-Version: 3.2 Abstract: In its present form, the subprocess.Popen implementation is prone to dead-locking and blocking of the parent Python script while waiting on data from the child process. Motivation: A search for "python asynchronous subprocess" will turn up numerous accounts of people wanting to execute a child process and communicate with it from time to time reading only the data that is available instead of blocking to wait for the program to produce data [1] [2] [3]. The current behavior of the subprocess module is that when a user sends or receives data via the stdin, stderr and stdout file objects, dead locks are common and documented [4] [5]. While communicate can be used to alleviate some of the buffering issues, it will still cause the parent process to block while attempting to read data when none is available to be read from the child process. Rationale: There is a documented need for asynchronous, non-blocking functionality in subprocess.Popen [6] [7] [2] [3]. Inclusion of the code would improve the utility of the Python standard library that can be used on Unix based and Windows builds of Python. Practically every I/O object in Python has a file-like wrapper of some sort. Sockets already act as such and for strings there is StringIO. Popen can be made to act like a file by simply using the methods attached the the subprocess.Popen.stderr, stdout and stdin file-like objects. But when using the read and write methods of those options, you do not have the benefit of asynchronous I/O. In the proposed solution the wrapper wraps the asynchronous methods to mimic a file object. Reference Implementation: I have been maintaining a Google Code repository that contains all of my changes including tests and documentation [9] as well as blog detailing the problems I have come across in the development process [10]. I have been working on implementing non-blocking asynchronous I/O in the subprocess.Popen module as well as a wrapper class for subprocess.Popen that makes it so that an executed process can take the place of a file by duplicating all of the methods and attributes that file objects have. There are two base functions that have been added to the subprocess.Popen class: Popen.send and Popen._recv, each with two separate implementations, one for Windows and one for Unix based systems. The Windows implementation uses ctypes to access the functions needed to control pipes in the kernel 32 DLL in an asynchronous manner. On Unix based systems, the Python interface for file control serves the same purpose. The different implementations of Popen.send and Popen._recv have identical arguments to make code that uses these functions work across multiple platforms. When calling the Popen._recv function, it requires the pipe name be passed as an argument so there exists the Popen.recv function that passes selects stdout as the pipe for Popen._recv by default. Popen.recv_err selects stderr as the pipe by default. "Popen.recv" and "Popen.recv_err" are much easier to read and understand than "Popen._recv('stdout' ..." and "Popen._recv('stderr' ..." respectively. Since the Popen._recv function does not wait on data to be produced before returning a value, it may return empty bytes. Popen.asyncread handles this issue by returning all data read over a given time interval. The ProcessIOWrapper class uses the asyncread and asyncwrite functions to allow a process to act like a file so that there are no blocking issues that can arise from using the stdout and stdin file objects produced from a subprocess.Popen call. References: [1] [ python-Feature Requests-1191964 ] asynchronous Subprocess http://mail.python.org/pipermail/python-bugs-list/2006-December/ 036524.html [2] Daily Life in an Ivory Basement : /feb-07/problems-with-subprocess http://ivory.idyll.org/blog/feb-07/problems-with-subprocess [3] How can I run an external command asynchronously from Python? - Stack Overflow http://stackoverflow.com/questions/636561/how-can-i-run-an-external- command-asynchronously-from-python [4] 18.1. subprocess - Subprocess management - Python v2.6.2 documentation http://docs.python.org/library/subprocess.html#subprocess.Popen.wait [5] 18.1. subprocess - Subprocess management - Python v2.6.2 documentation http://docs.python.org/library/subprocess.html#subprocess.Popen.kill [6] Issue 1191964: asynchronous Subprocess - Python tracker http://bugs.python.org/issue1191964 [7] Module to allow Asynchronous subprocess use on Windows and Posix platforms - ActiveState Code http://code.activestate.com/recipes/440554/ [8] subprocess.rst - subprocdev - Project Hosting on Google Code http://code.google.com/p/subprocdev/source/browse/doc/subprocess.rst?spec=s… [9] subprocdev - Project Hosting on Google Code http://code.google.com/p/subprocdev [10] Python Subprocess Dev http://subdev.blogspot.com/ Copyright: This P.E.P. is licensed under the Open Publication License; http://www.opencontent.org/openpub/. On Tue, Sep 8, 2009 at 22:56, Benjamin Peterson <benjamin(a)python.org> wrote: > 2009/9/7 Eric Pruitt <eric.pruitt(a)gmail.com>: >> Hello all, >> >> I have been working on adding asynchronous I/O to the Python >> subprocess module as part of my Google Summer of Code project. Now >> that I have finished documenting and pruning the code, I present PEP >> 3145 for its inclusion into the Python core code. Any and all feedback >> on the PEP (http://www.python.org/dev/peps/pep-3145/) is appreciated. > > Hi Eric, > One of the reasons you're not getting many response is that you've not > pasted the contents of the PEP in this message. That makes it really > easy for people to comment on various sections. > > BTW, it seems like you were trying to use reST formatting with the > text PEP layout. Double backquotes only mean something in reST. > > > -- > Regards, > Benjamin >

8 years, 2 months

http://www.pythonlabs.com/logos.html is gone

by Georg Brandl

Which I noticed since it's cited in the BeOpen license we still refer to in LICENSE. Since pythonlabs.com itself is still up, it probably isn't much work to make the logos.html URI work again, but I don't know who maintains that page. cheer, Georg -- Thus spake the Lord: Thou shalt indent with four spaces. No more, no less. Four shall be the number of spaces thou shalt indent, and the number of thy indenting shall be four. Eight shalt thou not indent, nor either indent thou two, excepting that thou then proceed to four. Tabs are right out.

10 years

Problems with hex-conversion functions

by Ender Wiggin

Hello everyone. I see several problems with the two hex-conversion function pairs that Python offers: 1. binascii.hexlify and binascii.unhexlify 2. bytes.fromhex and bytes.hex Problem #1: bytes.hex is not implemented, although it was specified in PEP 358. This means there is no symmetrical function to accompany bytes.fromhex. Problem #2: Both pairs perform the same function, although The Zen Of Python suggests that "There should be one-- and preferably only one --obvious way to do it." I do not understand why PEP 358 specified the bytes function pair although it mentioned the binascii pair... Problem #3: bytes.fromhex may receive spaces in the input string, although binascii.unhexlify may not. I see no good reason for these two functions to have different features. Problem #4: binascii.unhexlify may receive both input types: strings or bytes, whereas bytes.fromhex raises an exception when given a bytes parameter. Again there is no reason for these functions to be different. Problem #5: binascii.hexlify returns a bytes type - although ideally, converting to hex should always return string types and converting from hex should always return bytes. IMO there is no meaning of bytes as an output of hexlify, since the output is a representation of other bytes. This is also the suggested behavior of bytes.hex in PEP 358 Problems #4 and #5 call for a decision about the input and output of the functions being discussed: Option A : Strict input and output unhexlify (and bytes.fromhex) may only receives string and may only return bytes hexlify (and bytes.hex) may only receives bytes and may only return strings Option B : Robust input and strict output unhexlify (and bytes.fromhex) may receive bytes and strings and may only return bytes hexlify (and bytes.hex) may receive bytes or strings and may only return strings Of course we may also consider a third option, which will allow the return type of all functions to be robust (perhaps specified in a keyword argument), but as I wrote in the description of problem #5, I see no sense in that. Note that PEP 3137 describes: "... the more strict definitions of encoding and decoding in Python 3000: encoding always takes a Unicode string and returns a bytes sequence, and decoding always takes a bytes sequence and returns a Unicode string." - suggesting option A. To repeat problems #4 and #5, the current behavior does not match any option: * The return type of binascii.hexlify should be string, and this is not the current behavior. As for the input: * Option A is not the current behavior because binascii.unhexlify may receive both input types. * Option B is not the current behavior because bytes.fromhex does not allow bytes as input. To fix these issues, three changes should be applied: 1. Deprecate bytes.fromhex. This fixes the following problems: #4 (go with option B and remove the function that does not allow bytes input) #2 (the binascii functions will be the only way to "do it") #1 (bytes.hex should not be implemented) 2. In order to keep the functionality that bytes.fromhex has over unhexlify, the latter function should be able to handle spaces in its input (fix #3) 3. binascii.hexlify should return string as its return type (fix #5)

10 years, 5 months

Reworking the GIL

by Antoine Pitrou

Hello there, The last couple of days I've been working on an experimental rewrite of the GIL. Since the work has been turning out rather successful (or, at least, not totally useless and crashing!) I thought I'd announce it here. First I want to stress this is not about removing the GIL. There still is a Global Interpreter Lock which serializes access to most parts of the interpreter. These protected parts haven't changed either, so Python doesn't become really better at extracting computational parallelism out of several cores. Goals ----- The new GIL (which is also the name of the sandbox area I've committed it in, "newgil") addresses the following issues : 1) Switching by opcode counting. Counting opcodes is a very crude way of estimating times, since the time spent executing a single opcode can very wildly. Litterally, an opcode can be as short as a handful of nanoseconds (think something like "... is not None") or as long as a fraction of second, or even longer (think calling a heavy non-GIL releasing C function, such as re.search()). Therefore, releasing the GIL every 100 opcodes, regardless of their length, is a very poor policy. The new GIL does away with this by ditching _Py_Ticker entirely and instead using a fixed interval (by default 5 milliseconds, but settable) after which we ask the main thread to release the GIL and let another thread be scheduled. 2) GIL overhead and efficiency in contended situations. Apparently, some OSes (OS X mainly) have problems with lock performance when the lock is already taken: the system calls are heavy. This is the "Dave Beazley effect", where he took a very trivial loop, therefore made of very short opcodes and therefore releasing the GIL very often (probably 100000 times a second), and runs it in one or two threads on an OS with poor lock performance (OS X). He sees a 50% increase in runtime when using two threads rather than one, in what is admittedly a pathological case. Even on better platforms such as Linux, eliminating the overhead of many GIL acquires and releases (since the new GIL is released on a fixed time basis rather than on an opcode counting basis) yields slightly better performance (read: a smaller performance degradation :-)) when there are several pure Python computation threads running. 3) Thread switching latency. The traditional scheme merely releases the GIL for a couple of CPU cycles, and reacquires it immediately. Unfortunately, this doesn't mean the OS will automatically switch to another, GIL-awaiting thread. In many situations, the same thread will continue running. This, with the opcode counting scheme, is the reason why some people have been complaining about latency problems when an I/O thread competes with a computational thread (the I/O thread wouldn't be scheduled right away when e.g. a packet arrives; or rather, it would be scheduled by the OS, but unscheduled immediately when trying to acquire the GIL, and it would be scheduled again only much later). The new GIL improves on this by combinating two mechanisms: - forced thread switching, which means that when the switching interval is terminated (mentioned in 1) and the GIL is released, we will force any of the threads waiting on the GIL to be scheduled instead of the formerly GIL-holding thread. Which thread exactly is an OS decision, however: the goal here is not to have our own scheduler (this could be discussed but I wanted the design to remain simple :-) After all, man-years of work have been invested in scheduling algorithms by kernel programming teams). - priority requests, which is an option for a thread requesting the GIL to be scheduled as soon as possible, and forcibly (rather than any other threads). This is meant to be used by GIL-releasing methods such as read() on files and sockets. The scheme, again, is very simple: when a priority request is done by a thread, the GIL is released as soon as possible by the thread holding it (including in the eval loop), and then the thread making the priority request is forcibly scheduled (by making all other GIL-awaiting threads wait in the meantime). Implementation -------------- The new GIL is implemented using a couple of mutexes and condition variables. A {mutex, condition} pair is used to protect the GIL itself, which is a mere variable named `gil_locked` (there are a couple of other variables for bookkeeping). Another {mutex, condition} pair is used for forced thread switching (described above). Finally, a separate mutex is used for priority requests (described above). The code is in the sandbox: http://svn.python.org/view/sandbox/trunk/newgil/ The file of interest is Python/ceval_gil.h. Changes in other files are very minimal, except for priority requests which have been added at strategic places (some methods of I/O modules). Also, the code remains rather short, while of course being less trivial than the old one. NB : this is a branch of py3k. There should be no real difficulty porting it back to trunk, provided someone wants to do the job. Platforms --------- I've implemented the new GIL for POSIX and Windows (tested under Linux and Windows XP (running in a VM)). Judging by what I can read in the online MSDN docs, the Windows support should include everything from Windows 2000, and probably recent versions of Windows CE. Other platforms aren't implemented, because I don't have access to the necessary hardware. Besides, I must admit I'm not very motivated in working on niche/obsolete systems. I've e-mailed Andrew MacIntyre in private to ask him if he'd like to do the OS/2 support. Supporting a new platform is not very difficult: it's a matter of writing the 50-or-so lines of necessary platform-specific macros at the beginning of Python/ceval_gil.h. The reason I couldn't use the existing thread support (Python/thread_*.h) is that these abstractions are too poor. Mainly, they don't provide: - events, conditions or an equivalent thereof - the ability to acquire a resource with a timeout Measurements ------------ Before starting this work, I wrote ccbench (*), a little benchmark script ("ccbench" being a shorthand for "concurrency benchmark") which measures two things: - computation throughput with one or several concurrent threads - latency to external events (I use an UDP socket) when there is zero, one, or several background computation threads running (*) http://svn.python.org/view/sandbox/trunk/ccbench/ The benchmark involves several computation workloads with different GIL characteristics. By default there are 3 of them: A- one pure Python workload (computation of a number of digits of pi): that is, something which spends its time in the eval loop B- one mostly C workload where the C implementation doesn't release the GIL (regular expression matching) C- one mostly C workload where the implementation does release the GIL (bz2 compression) In the ccbench directory you will find benchmark results, under Linux, for two different systems I have here. The new GIL shows roughly similar but slightly better throughput results than the old one. And it is much better in the latency tests, especially in workload B (going down from almost a second of average latency with the old GIL, to a couple of milliseconds with the new GIL). This is the combined result of using a time-based scheme (rather than opcode-based) and of forced thread switching (rather than relying on the OS to actually switch threads when we speculatively release the GIL). As a sidenote, I might mention that single-threaded performance is not degraded at all. It is, actually, theoretically a bit better because the old ticker check in the eval loop becomes simpler; however, this goes mostly unnoticed. Now what remains to be done? Having other people test it would be fine. Even better if you have an actual multi-threaded py3k application. But ccbench results for other OSes would be nice too :-) (I get good results under the Windows XP VM but I feel that a VM is not an ideal setup for a concurrency benchmark) Of course, studying and reviewing the code is welcome. As for integrating it into the mainline py3k branch, I guess we have to answer these questions: - is the approach interesting? (we could decide that it's just not worth it, and that a good GIL can only be a dead (removed) GIL) - is the patch good, mature and debugged enough? - how do we deal with the unsupported platforms (POSIX and Windows support should cover most bases, but the fate of OS/2 support depends on Andrew)? Regards Antoine.

11 years, 3 months

Retrieve an arbitrary element from a set without removing it

by Willi Richert

Hi, recently I wrote an algorithm, in which very often I had to get an arbitrary element from a set without removing it. Three possibilities came to mind: 1. x = some_set.pop() some_set.add(x) 2. for x in some_set: break 3. x = iter(some_set).next() Of course, the third should be the fastest. It nevertheless goes through all the iterator creation stuff, which costs some time. I wondered, why the builtin set does not provide a more direct and efficient way for retrieving some element without removing it. Is there any reason for this? I imagine something like x = some_set.get() or x = some_set.pop(False) and am thinking about providing a patch against setobject.c (preferring the .get() solution being a stripped down pop()). Before, I would like to know whether I have overlooked something or whether this can be done in an already existing way. Thanks, wr

11 years, 3 months

Refactoring installation schemes

by Tarek Ziadé

Hello, Since the addition of PEP 370, (per-user site packages), site.py and distutils/command/install.py are *both* providing the various installation directories for Python, depending on the system and the Python version. We have also started to discuss lately in various Mailing Lists the addition of new schemes for IronPython and Jython, meaning that we might add some more in both places. I would like to suggest a simplification by adding a dedicated module to manage these installation schemes in one single place in the stdlib. This new independant module would be used by site.py and distutils and would also make it easier for third party code to work with these schemes. Of course this new module would be rather simple and not add any new import statement to avoid any overhead when Python starts and loads site.py Regards Tarek

11 years, 3 months

nonlocal keyword in 2.x?

by Mike Krell

Is there any possibility of backporting support for the nonlocal keyword into a 2.x release? I see it's not in 2.6, but I don't know if that was an intentional design choice or due to a lack of demand / round tuits. I'm also not sure if this would fall under the scope of the proposed moratorium on new language features (although my first impression was that it could be allowed since it already exists in python 3. One of my motivations for asking is a recent blog post by Fernando Perez of IPython fame that describes an interesting decorator-based idiom inspired by Apple's Grand Central Dispatch which would allow many interesting possibilities for expressing parallelization and other manipulations of execution context for blocks of python code. Unfortunately, using the technique to its fullest extent requires the nonlocal keyword. The blog post is here: https://cirl.berkeley.edu/fperez/py4science/decorators.html Mike

11 years, 4 months

PEP 382: Namespace Packages

by "Martin v. Löwis"

I propose the following PEP for inclusion to Python 3.1. Please comment. Regards, Martin Abstract ======== Namespace packages are a mechanism for splitting a single Python package across multiple directories on disk. In current Python versions, an algorithm to compute the packages __path__ must be formulated. With the enhancement proposed here, the import machinery itself will construct the list of directories that make up the package. Terminology =========== Within this PEP, the term package refers to Python packages as defined by Python's import statement. The term distribution refers to separately installable sets of Python modules as stored in the Python package index, and installed by distutils or setuptools. The term vendor package refers to groups of files installed by an operating system's packaging mechanism (e.g. Debian or Redhat packages install on Linux systems). The term portion refers to a set of files in a single directory (possibly stored in a zip file) that contribute to a namespace package. Namespace packages today ======================== Python currently provides the pkgutil.extend_path to denote a package as a namespace package. The recommended way of using it is to put:: from pkgutil import extend_path __path__ = extend_path(__path__, __name__) int the package's ``__init__.py``. Every distribution needs to provide the same contents in its ``__init__.py``, so that extend_path is invoked independent of which portion of the package gets imported first. As a consequence, the package's ``__init__.py`` cannot practically define any names as it depends on the order of the package fragments on sys.path which portion is imported first. As a special feature, extend_path reads files named ``*.pkg`` which allow to declare additional portions. setuptools provides a similar function pkg_resources.declare_namespace that is used in the form:: import pkg_resources pkg_resources.declare_namespace(__name__) In the portion's __init__.py, no assignment to __path__ is necessary, as declare_namespace modifies the package __path__ through sys.modules. As a special feature, declare_namespace also supports zip files, and registers the package name internally so that future additions to sys.path by setuptools can properly add additional portions to each package. setuptools allows declaring namespace packages in a distribution's setup.py, so that distribution developers don't need to put the magic __path__ modification into __init__.py themselves. Rationale ========= The current imperative approach to namespace packages has lead to multiple slightly-incompatible mechanisms for providing namespace packages. For example, pkgutil supports ``*.pkg`` files; setuptools doesn't. Likewise, setuptools supports inspecting zip files, and supports adding portions to its _namespace_packages variable, whereas pkgutil doesn't. In addition, the current approach causes problems for system vendors. Vendor packages typically must not provide overlapping files, and an attempt to install a vendor package that has a file already on disk will fail or cause unpredictable behavior. As vendors might chose to package distributions such that they will end up all in a single directory for the namespace package, all portions would contribute conflicting __init__.py files. Specification ============= Rather than using an imperative mechanism for importing packages, a declarative approach is proposed here, as an extension to the existing ``*.pkg`` mechanism. The import statement is extended so that it directly considers ``*.pkg`` files during import; a directory is considered a package if it either contains a file named __init__.py, or a file whose name ends with ".pkg". In addition, the format of the ``*.pkg`` file is extended: a line with the single character ``*`` indicates that the entire sys.path will be searched for portions of the namespace package at the time the namespace packages is imported. Importing a package will immediately compute the package's __path__; the ``*.pkg`` files are not considered anymore after the initial import. If a ``*.pkg`` package contains an asterisk, this asterisk is prepended to the package's __path__ to indicate that the package is a namespace package (and that thus further extensions to sys.path might also want to extend __path__). At most one such asterisk gets prepended to the path. extend_path will be extended to recognize namespace packages according to this PEP, and avoid adding directories twice to __path__. No other change to the importing mechanism is made; searching modules (including __init__.py) will continue to stop at the first module encountered. Discussion ========== With the addition of ``*.pkg`` files to the import mechanism, namespace packages can stop filling out the namespace package's __init__.py. As a consequence, extend_path and declare_namespace become obsolete. It is recommended that distributions put a file <distribution>.pkg into their namespace packages, with a single asterisk. This allows vendor packages to install multiple portions of namespace package into a single directory, with no risk of overlapping files. Namespace packages can start providing non-trivial __init__.py implementations; to do so, it is recommended that a single distribution provides a portion with just the namespace package's __init__.py (and potentially other modules that belong to the namespace package proper). The mechanism is mostly compatible with the existing namespace mechanisms. extend_path will be adjusted to this specification; any other mechanism might cause portions to get added twice to __path__. Copyright ========= This document has been placed in the public domain.

11 years, 4 months

language summit topic: issue tracker

by Brett Cannon

Another summit, another potential time to see if people want to change anything about the issue tracker. I would bring up: - Dropping Stage in favor of some keywords (e.g. 'needs unit test', 'needs docs') - Adding a freestyle text box to delineate which, if any, stdlib module is the cause of a bug and tie that into Misc/maintainers.rst; would potentially scale back the Component box -Brett

11 years, 4 months

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Python-Dev October 2009