Python-Dev October 2017

python-dev@python.org

76 participants
46 discussions

congrats on 3.5! Alas, windows 7 users are having problems installing it

by Laura Creighton

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

10 months

Python startup time

by Victor Stinner

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

1 year, 9 months

iso8601 parsing

by Mike Miller

Hi, Could anyone put this five year-old bug about parsing iso8601 format date-times on the front burner? http://bugs.python.org/issue15873 In the comments there's a lot of hand-wringing about different variations that bogged it down, but right now I only need it to handle the output of datetime.isoformat(): >>> dt.isoformat() '2017-10-20T08:20:08.986166+00:00' Perhaps if we could get that minimum first step in, it could be iterated on and made more lenient in the future. Thank you, -Mike

2 years, 7 months

PEP 559 - built-in noop()

by Barry Warsaw

I couldn’t resist one more PEP from the Core sprint. I won’t reveal where or how this one came to me. -Barry PEP: 559 Title: Built-in noop() Author: Barry Warsaw <barry(a)python.org> Status: Draft Type: Standards Track Content-Type: text/x-rst Created: 2017-09-08 Python-Version: 3.7 Post-History: 2017-09-09 Abstract ======== This PEP proposes adding a new built-in function called ``noop()`` which does nothing but return ``None``. Rationale ========= It is trivial to implement a no-op function in Python. It's so easy in fact that many people do it many times over and over again. It would be useful in many cases to have a common built-in function that does nothing. One use case would be for PEP 553, where you could set the breakpoint environment variable to the following in order to effectively disable it:: $ setenv PYTHONBREAKPOINT=noop Implementation ============== The Python equivalent of the ``noop()`` function is exactly:: def noop(*args, **kws): return None The C built-in implementation is available as a pull request. Rejected alternatives ===================== ``noop()`` returns something ---------------------------- YAGNI. This is rejected because it complicates the semantics. For example, if you always return both ``*args`` and ``**kws``, what do you return when none of those are given? Returning a tuple of ``((), {})`` is kind of ugly, but provides consistency. But you might also want to just return ``None`` since that's also conceptually what the function was passed. Or, what if you pass in exactly one positional argument, e.g. ``noop(7)``. Do you return ``7`` or ``((7,), {})``? And so on. The author claims that you won't ever need the return value of ``noop()`` so it will always return ``None``. Coghlin's Dialogs (edited for formatting): My counterargument to this would be ``map(noop, iterable)``, ``sorted(iterable, key=noop)``, etc. (``filter``, ``max``, and ``min`` all accept callables that accept a single argument, as do many of the itertools operations). Making ``noop()`` a useful default function in those cases just needs the definition to be:: def noop(*args, **kwds): return args[0] if args else None The counterargument to the counterargument is that using ``None`` as the default in all these cases is going to be faster, since it lets the algorithm skip the callback entirely, rather than calling it and having it do nothing useful. Copyright ========= This document has been placed in the public domain. .. Local Variables: mode: indented-text indent-tabs-mode: nil sentence-end-double-space: t fill-column: 70 coding: utf-8 End:

2 years, 7 months

PEP 511 (code transformers) rejected

by Victor Stinner

Hi, I rejected my own PEP 511 "API for code transformers" that I wrote in January 2016: https://github.com/python/peps/commit/9d8fd950014a80324791d7dae3c130b1b64fd… Rejection Notice: """ This PEP was rejected by its author. This PEP was seen as blessing new Python-like programming languages which are close but incompatible with the regular Python language. It was decided to not promote syntaxes incompatible with Python. This PEP was also seen as a nice tool to experiment new Python features, but it is already possible to experiment them without the PEP, only with importlib hooks. If a feature becomes useful, it should be directly part of Python, instead of depending on an third party Python module. Finally, this PEP was driven was the FAT Python optimization project which was abandonned in 2016, since it was not possible to show any significant speedup, but also because of the lack of time to implement the most advanced and complex optimizations. """ Victor

2 years, 8 months

PEP 564: Add new time functions with nanosecond resolution

by Victor Stinner

Hi, While discussions on this PEP are not over on python-ideas, I proposed this PEP directly on python-dev since I consider that my PEP already summarizes current and past proposed alternatives. python-ideas threads: * Add time.time_ns(): system clock with nanosecond resolution * Why not picoseconds? The PEP 564 will be shortly online at: https://www.python.org/dev/peps/pep-0564/ Victor PEP: 564 Title: Add new time functions with nanosecond resolution Version: $Revision$ Last-Modified: $Date$ Author: Victor Stinner <victor.stinner(a)gmail.com> Status: Draft Type: Standards Track Content-Type: text/x-rst Created: 16-October-2017 Python-Version: 3.7 Abstract ======== Add five new functions to the ``time`` module: ``time_ns()``, ``perf_counter_ns()``, ``monotonic_ns()``, ``clock_gettime_ns()`` and ``clock_settime_ns()``. They are similar to the function without the ``_ns`` suffix, but have nanosecond resolution: use a number of nanoseconds as a Python int. The best ``time.time_ns()`` resolution measured in Python is 3 times better then ``time.time()`` resolution on Linux and Windows. Rationale ========= Float type limited to 104 days ------------------------------ The clocks resolution of desktop and latop computers is getting closer to nanosecond resolution. More and more clocks have a frequency in MHz, up to GHz for the CPU TSC clock. The Python ``time.time()`` function returns the current time as a floatting point number which is usually a 64-bit binary floatting number (in the IEEE 754 format). The problem is that the float type starts to lose nanoseconds after 104 days. Conversion from nanoseconds (``int``) to seconds (``float``) and then back to nanoseconds (``int``) to check if conversions lose precision:: # no precision loss >>> x = 2 ** 52 + 1; int(float(x * 1e-9) * 1e9) - x 0 # precision loss! (1 nanosecond) >>> x = 2 ** 53 + 1; int(float(x * 1e-9) * 1e9) - x -1 >>> print(datetime.timedelta(seconds=2 ** 53 / 1e9)) 104 days, 5:59:59.254741 ``time.time()`` returns seconds elapsed since the UNIX epoch: January 1st, 1970. This function loses precision since May 1970 (47 years ago):: >>> import datetime >>> unix_epoch = datetime.datetime(1970, 1, 1) >>> print(unix_epoch + datetime.timedelta(seconds=2**53 / 1e9)) 1970-04-15 05:59:59.254741 Previous rejected PEP --------------------- Five years ago, the PEP 410 proposed a large and complex change in all Python functions returning time to support nanosecond resolution using the ``decimal.Decimal`` type. The PEP was rejected for different reasons: * The idea of adding a new optional parameter to change the result type was rejected. It's an uncommon (and bad?) programming practice in Python. * It was not clear if hardware clocks really had a resolution of 1 nanosecond, especially at the Python level. * The ``decimal.Decimal`` type is uncommon in Python and so requires to adapt code to handle it. CPython enhancements of the last 5 years ---------------------------------------- Since the PEP 410 was rejected: * The ``os.stat_result`` structure got 3 new fields for timestamps as nanoseconds (Python ``int``): ``st_atime_ns``, ``st_ctime_ns`` and ``st_mtime_ns``. * The PEP 418 was accepted, Python 3.3 got 3 new clocks: ``time.monotonic()``, ``time.perf_counter()`` and ``time.process_time()``. * The CPython private "pytime" C API handling time now uses a new ``_PyTime_t`` type: simple 64-bit signed integer (C ``int64_t``). The ``_PyTime_t`` unit is an implementation detail and not part of the API. The unit is currently ``1 nanosecond``. Existing Python APIs using nanoseconds as int --------------------------------------------- The ``os.stat_result`` structure has 3 fields for timestamps as nanoseconds (``int``): ``st_atime_ns``, ``st_ctime_ns`` and ``st_mtime_ns``. The ``ns`` parameter of the ``os.utime()`` function accepts a ``(atime_ns: int, mtime_ns: int)`` tuple: nanoseconds. Changes ======= New functions ------------- This PEP adds five new functions to the ``time`` module: * ``time.clock_gettime_ns(clock_id)`` * ``time.clock_settime_ns(clock_id, time: int)`` * ``time.perf_counter_ns()`` * ``time.monotonic_ns()`` * ``time.time_ns()`` These functions are similar to the version without the ``_ns`` suffix, but use nanoseconds as Python ``int``. For example, ``time.monotonic_ns() == int(time.monotonic() * 1e9)`` if ``monotonic()`` value is small enough to not lose precision. Unchanged functions ------------------- This PEP only proposed to add new functions getting or setting clocks with nanosecond resolution. Clocks are likely to lose precision, especially when their reference is the UNIX epoch. Python has other functions handling time (get time, timeout, etc.), but no nanosecond variant is proposed for them since they are less likely to lose precision. Example of unchanged functions: * ``os`` module: ``sched_rr_get_interval()``, ``times()``, ``wait3()`` and ``wait4()`` * ``resource`` module: ``ru_utime`` and ``ru_stime`` fields of ``getrusage()`` * ``signal`` module: ``getitimer()``, ``setitimer()`` * ``time`` module: ``clock_getres()`` Since the ``time.clock()`` function was deprecated in Python 3.3, no ``time.clock_ns()`` is added. Alternatives and discussion =========================== Sub-nanosecond resolution ------------------------- ``time.time_ns()`` API is not "future-proof": if clocks resolutions increase, new Python functions may be needed. In practive, the resolution of 1 nanosecond is currently enough for all structures used by all operating systems functions. Hardware clock with a resolution better than 1 nanosecond already exists. For example, the frequency of a CPU TSC clock is the CPU base frequency: the resolution is around 0.3 ns for a CPU running at 3 GHz. Users who have access to such hardware and really need sub-nanosecond resolution can easyly extend Python for their needs. Such rare use case don't justify to design the Python standard library to support sub-nanosecond resolution. For the CPython implementation, nanosecond resolution is convenient: the standard and well supported ``int64_t`` type can be used to store time. It supports a time delta between -292 years and 292 years. Using the UNIX epoch as reference, this type supports time since year 1677 to year 2262:: >>> 1970 - 2 ** 63 / (10 ** 9 * 3600 * 24 * 365.25) 1677.728976954687 >>> 1970 + 2 ** 63 / (10 ** 9 * 3600 * 24 * 365.25) 2262.271023045313 Different types --------------- It was proposed to modify ``time.time()`` to use float type with better precision. The PEP 410 proposed to use ``decimal.Decimal``, but it was rejected. Apart ``decimal.Decimal``, no portable ``float`` type with better precision is currently available in Python. Changing the builtin Python ``float`` type is out of the scope of this PEP. Other ideas of new types were proposed to support larger or arbitrary precision: fractions, structures or 2-tuple using integers, fixed-precision floating point number, etc. See also the PEP 410 for a previous long discussion on other types. Adding a new type requires more effort to support it, than reusing ``int``. The standard library, third party code and applications would have to be modified to support it. The Python ``int`` type is well known, well supported, ease to manipulate, and supports all arithmetic operations like: ``dt = t2 - t1``. Moreover, using nanoseconds as integer is not new in Python, it's already used for ``os.stat_result`` and ``os.utime(ns=(atime_ns, mtime_ns))``. .. note:: If the Python ``float`` type becomes larger (ex: decimal128 or float128), the ``time.time()`` precision will increase as well. Different API ------------- The ``time.time(ns=False)`` API was proposed to avoid adding new functions. It's an uncommon (and bad?) programming practice in Python to change the result type depending on a parameter. Different options were proposed to allow the user to choose the time resolution. If each Python module uses a different resolution, it can become difficult to handle different resolutions, instead of just seconds (``time.time()`` returning ``float``) and nanoseconds (``time.time_ns()`` returning ``int``). Moreover, as written above, there is no need for resolution better than 1 nanosecond in practive in the Python standard library. Annex: Clocks Resolution in Python ================================== Script ot measure the smallest difference between two ``time.time()`` and ``time.time_ns()`` reads ignoring differences of zero:: import math import time LOOPS = 10 ** 6 print("time.time_ns(): %s" % time.time_ns()) print("time.time(): %s" % time.time()) min_dt = [abs(time.time_ns() - time.time_ns()) for _ in range(LOOPS)] min_dt = min(filter(bool, min_dt)) print("min time_ns() delta: %s ns" % min_dt) min_dt = [abs(time.time() - time.time()) for _ in range(LOOPS)] min_dt = min(filter(bool, min_dt)) print("min time() delta: %s ns" % math.ceil(min_dt * 1e9)) Results of time(), perf_counter() and monotonic(). Linux (kernel 4.12 on Fedora 26): * time_ns(): **84 ns** * time(): **239 ns** * perf_counter_ns(): 84 ns * perf_counter(): 82 ns * monotonic_ns(): 84 ns * monotonic(): 81 ns Windows 8.1: * time_ns(): **318000 ns** * time(): **894070 ns** * perf_counter_ns(): 100 ns * perf_counter(): 100 ns * monotonic_ns(): 15000000 ns * monotonic(): 15000000 ns The difference on ``time.time()`` is significant: **84 ns (2.8x better) vs 239 ns on Linux and 318 us (2.8x better) vs 894 us on Windows**. The difference (presion loss) will be larger next years since every day adds 864,00,000,000,000 nanoseconds to the system clock. The difference on ``time.perf_counter()`` and ``time.monotonic clock()`` is not visible in this quick script since the script runs less than 1 minute, and the uptime of the computer used to run the script was smaller than 1 week. A significant difference should be seen with an uptime of 104 days or greater. .. note:: Internally, Python starts ``monotonic()`` and ``perf_counter()`` clocks at zero on some platforms which indirectly reduce the precision loss. Copyright ========= This document has been placed in the public domain.

2 years, 8 months

Migrate python-dev to Mailman 3?

by Victor Stinner

Hi, We are using Mailman 3 for the new buildbot-status mailing list and it works well: https://mail.python.org/mm3/archives/list/buildbot-status@python.org/ I prefer to read archives with this UI, it's simpler to follow threads, and it's possible to reply on the web UI! To be honest, we got some issues when the new security-announce mailing list was quickly migrated from Mailman 2 to Mailman 3, but issues were quicky fixed as well. Would it be possible to migrate python-dev to Mailman 3? Do you see any blocker issue? I sent to email to the Python postmaster as well. Victor

2 years, 8 months

PEP 530

by London

can you help me get idol for my computer

2 years, 8 months

The syntax of replacement fields in format strings

by Serhiy Storchaka

According to the specification of format string syntax [1] (I meant str.format(), not f-strings), both argument name and attribute name must be Python identifiers. But the current implementation is more lenient and allow arbitrary sequences of characters while they don't contain '.', '[', ']', '{', '}', ':', '!'. >>> '{#}'.format_map({'#': 42}) '42' >>> import types >>> '{0.#}'.format(types.SimpleNamespace(**{'#': 42})) '42' This can be confusing due to similarity with the format string syntaxes in str.format() and f-strings. >> name = 'abc' >>> f'{name.upper()}' 'ABC' >>> '{name.upper()}'.format(name='abc') Traceback (most recent call last): File "<stdin>", line 1, in <module> AttributeError: 'str' object has no attribute 'upper()' If accept only identifiers, we could produce more specific error message. Is there a bug in the documentation or in the implementation? [1] https://docs.python.org/3/library/string.html#format-string-syntax

2 years, 8 months

The type of the result of the copy() method

by Serhiy Storchaka

The copy() methods of list, dict, bytearray, set, frozenset, WeakValueDictionary, WeakKeyDictionary return an instance of the base type containing the content of the original collection. The copy() methods of deque, defaultdict, OrderedDict, Counter, ChainMap, UserDict, UserList, WeakSet, ElementTree.Element return an instance of the same type as the original collection. The copy() method of mappingproxy returns a copy of the underlying mapping (using its copy() method). os.environ.copy() returns a dict. Shouldn't it be more consistent?

2 years, 8 months

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Python-Dev October 2017