Python-ideas June 2008

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33 participants
12 discussions

Ideas towards GIL removal
by Greg Ewing 18 Jul '08

18 Jul '08

I've been thinking about some ideas for reducing the amount of refcount adjustment that needs to be done, with a view to making GIL removal easier. 1) Permanent objects In a typical Python program there are many objects that are created at the beginning and exist for the life of the program -- classes, functions, literals, etc. Refcounting these is a waste of effort, since they're never going to go away. So perhaps there could be a way of marking such objects as "permanent" or "immortal". Any refcount operation on a permanent object would be a no-op, so no locking would be needed. This would also have the benefit of eliminating any need to write to the object's memory at all when it's only being read. 2) Objects owned by a thread Python code creates and destroys temporary objects at a high rate -- stack frames, argument tuples, intermediate results, etc. If the code is executed by a thread, those objects are rarely if ever seen outside of that thread. It would be beneficial if refcount operations on such objects could be carried out by the thread that created them without locking. To achieve this, two extra fields could be added to the object header: an "owning thread id" and a "local reference count". (The existing refcount field will be called the "global reference count" in what follows.) An object created by a thread has its owning thread id set to that thread. When adjusting an object's refcount, if the current thread is the object's owning thread, the local refcount is updated without locking. If the object has no owning thread, or belongs to a different thread, the object is locked and the global refcount is updated. The object is considered garbage only when both refcounts drop to zero. Thus, after a decref, both refcounts would need to be checked to see if they are zero. When decrementing the local refcount and it reaches zero, the global refcount can be checked without locking, since a zero will never be written to it until it truly has zero non-local references remaining. I suspect that these two strategies together would eliminate a very large proportion of refcount-related activities requiring locking, perhaps to the point where those remaining are infrequent enough to make GIL removal practical. -- Greg

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Reducing colon uses to increase readability
by Andre Roberge 03 Jul '08

03 Jul '08

Hi everyone, In Python, the humble colon (:) has multiples uses: 1. as a signal to indentation increase, signaling a block of code, such as 1a) for function or class definitions 1b) for while/for/if/elif/else blocks 1c) for try/except/finally blocks In these cases, the majority opinion (to which I subscribe) is that using a colon increases readability. I am NOT suggesting to removing the colon in those instances. However, the colon has also some other uses. 2. in slices [a:b:c] 3. in dict assignments {a:b} 4. in lambda assignments (lambda x: x+1) I would argue that, in these last three examples, there might be better choices. (some of these choices have been inspired by reading http://www.resolversystems.com/documentation/index.php/Differences_Between_…) I don't expect the following suggestions to immediately convince everyone (or anyone!) ... but, at least they will be on record. Slices: --------- I would argue that, the usual slicing notation would be more readable if it were as follows: [a -> b; c] Thus [1:10:2] would become [1 -> 10; 2] [1:10] would become [1 -> 10] The "shorter" combinations would not gain in terms of readability; they would be as follows: [ :10 : 2] would become [10; 2] [ :10] would become [10;] [:: -1] would become [; -1] [:] would become [;] If such a change were to be made, an second slicing notation, *with a different meaning*, could be introduced: [a => b; c] This would be an inclusive range, i.e. [a => b] is equivalent to [a -> b+1] dict assignments ------------------------ Here again, I would argue that using "->" instead of ":" would make the code more readable - at least for beginners. numbers = {'one' -> 1, 'two' -> 2} instead of numbers = {'one': 1, 'two': 2} lambda assignments --------------------------- Once again, same choice. lambda x -> x+1 is, I think, more readable than lambda x: x+1 (but perhaps the last two [dicts and lambda] largely depends on the font choice...) ====== Other considerations: If "->" were to be adopted for dict or lambda assignments, then the "naturalness" of their choice for slices would be reduced. An alternative might be inspired from the mathematical notation [a, ..., b; c] I realize that this is "much" longer than [a: b: c]. Final comment: I have seen other alternatives for simple slices suggested in the past such as [a..b] and [a...b] which would be the equivalent of [a->b] and [a=>b]; however, the extra "." might sometimes be difficult to read, whereas the difference between "->" and "=>" is much easier to see. Cheers, André

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Starred expression in right-hand side
by David Pokorny 26 Jun '08

26 Jun '08

Since PEP 3132 gives us: >>> x = [1,2,3] >>> a, *b = x >>> a 1 >>> b [2, 3] it seems natural that we should be able to do it the other way too: (doesn't actually work) >>> a, b = 1, [2,3] >>> x = [a,*b] >>> x [1, 2, 3] This is essentially itertools.chain, but of course it isn't nearly as much fun: >>> [n for n in itertools.chain([1],[2,3])] [1, 2, 3] Now you might be thinking, "yeah, that's cool, but you don't really need it" but this actually came up in practice: I have a function that has a certain behavior for standard types, but when it sees a type it doesn't recognize, it calls a protocol function (like __iter__ or __next__) and expects to receive an iterable whose 0th element is a string. Now I'm probably not going to use itertools.chain because the only members of itertools I can reliably remember are count() and izip(), so my next alternative (which is perfectly acceptable) is >>> a, b = 1, [2,3] >>> x = [a] + b This isn't too bad, but it is slightly less clear than x = [a,*b] and much less efficient when b is long. It seems so simple...makes me think it came up before and I missed it. David

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(no subject)
by Christopher Cederstrom 25 Jun '08

25 Jun '08

remove chriscederstrom(a)gmail.com

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Small nice addition to rlcompleter
by Roman Susi 25 Jun '08

25 Jun '08

hi! I had that proposal (stated back in 2001 here http://bugs.python.org/issue449227 ), with these main moments to cite myself: <<< I use rlcompleter extensively in interactive Python mode. I think it could be cool if callable objects were added "(" when completed. This way it will be much faster to program, without looking-up __doc__. For example: >>> f.fil<TAB> will give: >>> f.fileno(_ ("_" is to mark cursor position) and: >>> f.so<TAB> will (as before) give: >>> f.softspace _ One more illustration: >>> f = open("myfile", "w") >>> f. f.__class__( f.__repr__( f.next( f.__delattr__( f.__setattr__( f.read( f.__doc__ f.__str__( f.readinto( f.__enter__( f.close( f.readline( f.__exit__( f.closed f.readlines( f.__getattribute__( f.encoding f.seek( f.__hash__( f.fileno( f.softspace f.__init__( f.flush( f.tell( f.__iter__( f.isatty( f.truncate( f.__new__( f.mode f.write( f.__reduce__( f.name f.writelines( f.__reduce_ex__( f.newlines f.xreadlines( >>> f. - nice to remember which attributes are methods and which aren't. >>> There are patches recently made by Manuel Muradás and Facundo Batista for 2.6 (so I assume some people are interested in the patch, not only me), but I have no idea how (and if) it ever gets into Python? 2.6 and 3k. Its a very small feature to make a PEP, but how then? I hope rlcompleter will not get obsoleted before the patch is accepted ;-) Thanks! Regards, Roman

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PyUnit
by J. Pablo Fernández 19 Jun '08

19 Jun '08

Hello, I've made some improvements to PyUnit which adds the concept of skipped test (when, for instance, the setUp fails), and some other stuff. This changes are test-code-wise backward compatible, in the sense that old tests will just work. But since the output of running the tests is different, tools using that output, at any level (reading the text, or running the tests programatically) will fail. I think it would be possible to have a switch somewhere to even then make them backward compatible. Are there any chances of getting this code in Python 3k? I'd need to work on it, polish it, write more tests for it, etc, so I'd like to know if it'd be welcome or not before investing the time. Thank you. -- J. Pablo Fernández <pupeno(a)pupeno.com> (http://pupeno.com) Temporarily using pupeno(a)gmail.com.

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Sort statement
by David Pokorny 16 Jun '08

16 Jun '08

In the spirit of going in the reverse direction of turning print into a function, what does python-ideas think of the sort statement? numlist = [2,5,4] sort numlist sort numlist asc # borrowed from SQL ORDER BY statement sort numlist desc sort by employee.last_name for employee in employee_list # this uses key sorting The main advantage is that it is impossible to make this mistake: x = y.sort() when you really mean x = sorted(y) Cheers, David

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Implement __add__ for set and frozenset
by Brandon Mintern 10 Jun '08

10 Jun '08

I would like to propose that the + operator be a synonym for | (union) on sets. IMHO, the succinct power of being able to say sum(list_of_sets, set()) as opposed to import operator reduce(operator.and_, list_of_sets, set()) far outweighs any problems with having two operators for union. The sum paradigm is certainly more readable as well. I realize that a function named "unionall" could be defined trivially, but with a built-in already serving the purpose in a readable and reasonable way, it seems silly to not use it. I anticipate that someone will ask why such a paradigm should be available for union as opposed to the other set operations. My answer is that several standard algorithms rely on a union over a sequence of sets; one example is the construction of the "follow" set in constructing an LL or SLR parser, and I know I've seen others that I cannot remember off the top of my head. There is even a mathematical symbol (big-U) for it akin to the summation sign (uppercase sigma). Any feedback? Brandon

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Re: [Python-ideas] Implement __add__ for set and frozenset
by Terry Jones 10 Jun '08

10 Jun '08

>>>>> "Arnaud" == Arnaud Delobelle <arnodel(a)googlemail.com> writes: Arnaud> I've written a patch [1] that does that. Following the suggestion Arnaud> of Raymond Hettinger, I've implemented set.intersection by sorting Arnaud> all its sets/frozensets/dicts in increasing order of size first, Arnaud> then iterating over the smallest. Hi Arnaud I don't know if you'll do any benchmarking on this, but I'd suggest: - First find the set with the smallest size (this is O(n) work). - If that size is sufficiently small and the number of sets is sufficiently large (numbers to be determined by testing), don't sort the sets by size - just go for it. - Else do the sorting. The point being that if the smallest set is already quite small, the size of the intersection is already tightly bounded and you're possibly going to do an expensive sort that's really not needed. The O(n) work to find the smallest is tiny compared to just blindly doing O(n lg n) immediately. Most of the juice you get from moving from small to big sets comes from starting with the smallest. A few benchmarks should give an idea of when to sort. BTW, having a quick look at your diff (not the patched source) it looks like you're testing each of the elements of the smallest set against all other hashtables. I haven't thought about it much, but that seems to partly defeat the purpose of sorting. Speed will depend on the inputs, but I'd have guessed that in general you should be testing each member of the smallest for presence in the next set, short-circuiting if empty, then testing each of the survivors against the next set, etc. That's more of a "vertical" approach than the horizontal one you take across all the hashtables (possibly also with a speed benefit due to locality of reference). Also, why not test first against the iterables that are not hashtables? Wouldn't that be faster in the (common?) case of many sets being passed for intersection? Sorry if this is all clueless - it's just my thinking as I looked at your diff. Regards, Terry

2 1

profiling: manual instrumentation with pystones
by Tarek Ziadé 07 Jun '08

07 Jun '08

Hello, To remove bottlenecks I usually instrument some functions in my application inside a dedicated test and set speed goals there until they are met. Then I leave the test to avoid speed regressions, when doable, by translating times in pystones. Unless I missed something in the standard library, I feel like there's a missing tool to do it simply: - the timeit module is nice to try out small code snippets but is not really adapted to manually profile the code of an existing application - the profile module is nice to profile an application as a whole but is not very handy to gather statistics on specific functions in their real execution context What about adding a decorator that fills a statistics mapping in memory (time+stones), like this: >=========== import time import sys import logging from test import pystone benchtime, stones = pystone.pystones() def secs_to_kstones(seconds): return (stones*seconds) / 1000 stats = {} def reset_stats(): global stats stats = {} def log_stats(): template = '%s : %.2f kstones, %.3f secondes' for key, v in stats.items(): logging.debug(template % (key, v['stones'], v['time'])) if sys.platform == 'win32': timer = time.clock else: timer = time.time def profile(name='stats', stats=stats): def _profile(function): def __profile(*args, **kw): start_time = timer() try: return function(*args, **kw) finally: total = timer() - start_time kstones = secs_to_kstones(total) stats[name] = {'time': total, 'stones': kstones} return __profile return _profile >=========== This allows instrumenting the application by decorating some functions, either inside the application, either in a dedicated test: >====== def my_test(): my.app.slow_stuff = profile('seem slow')(my.app.slow_stuff) my.app.other_slow_stuff = profile('seem slow too')(my.app.other_slow_stuff) # should not take more than 40k pystones ! assert stats['seem slow too']['profile'] < 40 # let's log them log_stats() >====== Regards, Tarek -- Tarek Ziadé | Association AfPy | www.afpy.org Blog FR | http://programmation-python.org Blog EN | http://tarekziade.wordpress.com/

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Python-ideas June 2008