avoids creating an intermediate tuple for iterable arguments other than
lists or tuples.
In other words, a+=b no longer requires extra memory when b is not a
list or tuple. The list and tuple cases are unchanged.
for xrange and list objects).
* list.__reversed__ now checks the length of the sequence object before
calling PyList_GET_ITEM() because the mutable could have changed length.
* all three implementations are now tranparent with respect to length and
maintain the invariant len(it) == len(list(it)) even when the underlying
sequence mutates.
* __builtin__.reversed() now frees the underlying sequence as soon
as the iterator is exhausted.
* the code paths were rearranged so that the most common paths
do not require a jump.
* Replace sprintf message with a constant message string -- this error
message ran on every invocation except straight deletions but it was
only needed when the rhs was not iterable. The message was also
out-of-date and did not reflect that iterable arguments were allowed.
* For inner loops that do not make ref count adjustments, use memmove()
for fast copying and better readability.
* For inner loops that do make ref count adjustments, speed them up by
factoring out the constant structure reference and using vitem[] instead.
* Using addition instead of substraction on array indices allows the
compiler to use a fast addressing mode. Saves about 10%.
* Using PyTuple_GET_ITEM and PyList_SET_ITEM is about 7% faster than
PySequenceFast_GET_ITEM which has to make a list check on every pass.
(Championed by Bob Ippolito.)
The update() method for mappings now accepts all the same argument forms
as the dict() constructor. This includes item lists and/or keyword
arguments.
recent gcc on Linux/x86)
[ 899109 ] 1==float('nan')
by implementing rich comparisons for floats.
Seems to make comparisons involving NaNs somewhat less surprising
when the underlying C compiler actually implements C99 semantics.
utilization, and speed:
* Moved the responsibility for emptying the previous list from list_fill
to list_init.
* Replaced the code in list_extend with the superior code from list_fill.
* Eliminated list_fill.
Results:
* list.extend() no longer creates an intermediate tuple except to handle
the special case of x.extend(x). The saves memory and time.
* list.extend(x) runs
5 to 10% faster when x is a list or tuple
15% faster when x is an iterable not defining __len__
twice as fast when x is an iterable defining __len__
* the code is about 15 lines shorter and no longer duplicates
functionality.
The Py2.3 approach overallocated small lists by up to 8 elements.
The last checkin would limited this to one but slowed down (by 20 to 30%)
the creation of small lists between 3 to 8 elements.
This tune-up balances the two, limiting overallocation to 3 elements
(significantly reducing space consumption from Py2.3) and running faster
than the previous checkin.
The first part of the growth pattern (0, 4, 8, 16) neatly meshes with
allocators that trigger data movement only when crossing a power of two
boundary. Also, then even numbers mesh well with common data alignments.
realloc(). This is achieved by tracking the overallocation size in a new
field and using that information to skip calls to realloc() whenever
possible.
* Simplified and tightened the amount of overallocation. For larger lists,
this overallocates by 1/8th (compared to the previous scheme which ranged
between 1/4th to 1/32nd over-allocation). For smaller lists (n<6), the
maximum overallocation is one byte (formerly it could be upto eight bytes).
This saves memory in applications with large numbers of small lists.
* Eliminated the NRESIZE macro in favor of a new, static list_resize function
that encapsulates the resizing logic. Coverting this back to macro would
give a small (under 1%) speed-up. This was too small to warrant the loss
of readability, maintainability, and de-coupling.
* Some functions using NRESIZE had grown unnecessarily complex in their
efforts to bend to the macro's calling pattern. With the new list_resize
function in place, those other functions could be simplified. That is
being saved for a separate patch.
* The ob_item==NULL check could be eliminated from the new list_resize
function. This would entail finding each piece of code that sets ob_item
to NULL and adding a new line to invalidate the overallocation tracking
field. Rather than impose a new requirement on other pieces of list code,
it was preferred to leave the NULL check in place and retain the benefits
of decoupling, maintainability and information hiding (only PyList_New()
and list_sort() need to know about the new field). This approach also
reduces the odds of breaking an extension module.
(Collaborative effort by Raymond Hettinger, Hye-Shik Chang, Tim Peters,
and Armin Rigo.)
the same object to be collected by the cyclic GC support if they are
only referenced by a cycle. If the weakref being collected was one of
the weakrefs without callbacks, some local variables for the
constructor became invalid and have to be re-computed.
The test caused a segfault under a debug build without the fix applied.
Formerly, length data fetched from sequence objects.
Now, any object that reports its length can benefit from pre-sizing.
On one sample timing, it gave a threefold speedup for list(s) where s
was a set object.
The special-case code that was removed could return a value indicating
success but leave an exception set. test_fileinput failed in a debug
build as a result.
which can be reviewed via
http://coding.derkeiler.com/Archive/Python/comp.lang.python/2003-12/1011.html
Duncan Booth investigated, and discovered that an "optimisation" was
in fact a pessimisation for small numbers of elements in a source list,
compared to not having the optimisation, although with large numbers
of elements in the source list the optimisation was quite beneficial.
He posted his change to comp.lang.python (but not to SF).
Further research has confirmed his assessment that the optimisation only
becomes a net win when the source list has more than 100 elements.
I also found that the optimisation could apply to tuples as well,
but the gains only arrive with source tuples larger than about 320
elements and are nowhere near as significant as the gains with lists,
(~95% gain @ 10000 elements for lists, ~20% gain @ 10000 elements for
tuples) so I haven't proceeded with this.
The code as it was applied the optimisation to list subclasses as
well, and this also appears to be a net loss for all reasonable sized
sources (~80-100% for up to 100 elements, ~20% for more than 500
elements; I tested up to 10000 elements).
Duncan also suggested special casing empty lists, which I've extended
to all empty sequences.
On the basis that list_fill() is only ever called with a list for the
result argument, testing for the source being the destination has
now happens before testing source types.
bit by checking the value of UCHAR_MAX in Include/Python.h. There was a
check in Objects/stringobject.c. Remove that. (Note that we don't define
UCHAR_MAX if it's not defined as the old test did.)
and left shifts. (Thanks to Kalle Svensson for SF patch 849227.)
This addresses most of the remaining semantic changes promised by
PEP 237, except for repr() of a long, which still shows the trailing
'L'. The PEP appears to promise warnings for operations that
changed semantics compared to Python 2.3, but this is not
implemented; we've suffered through enough warnings related to
hex/oct literals and I think it's best to be silent now.
* Add more tests
* Refactor and neaten the code a bit.
* Rename union_update() to update().
* Improve the algorithms (making them a closer to sets.py).
function.
* Add a better test for deepcopying.
* Add tests to show the __init__() function works like it does for list
and tuple. Add related test.
* Have shallow copies of frozensets return self. Add related test.
* Have frozenset(f) return f if f is already a frozenset. Add related test.
* Beefed-up some existing tests.
by the function object or by the method object, the function
object's attribute usually wins. Christian Tismer pointed out that
that this is really a mistake, because this only happens for special
methods (like __reduce__) where the method object's version is
really more appropriate than the function's attribute. So from now
on, all method attributes will have precedence over function
attributes with the same name.
* Improve the hash function to increase the chance that distinct sets will
have distinct xor'd hash totals.
* Use PyDict_Merge where possible (it is faster than an equivalent iter/set
pair).
* Don't rebuild dictionaries where the input already has one.
Also SF patch 843455.
This is a critical bugfix.
I'll backport to 2.3 maint, but not beyond that. The bugs this fixes
have been there since weakrefs were introduced.
* Install the unittests, docs, newsitem, include file, and makefile update.
* Exercise the new functions whereever sets.py was being used.
Includes the docs for libfuncs.tex. Separate docs for the types are
forthcoming.
subtype_dealloc(): This left the dying object exposed to gc, so that
if cyclic gc triggered during the weakref callback, gc tried to delete
the dying object a second time. That's a disaster. subtype_dealloc()
had a (I hope!) unique problem here, as every normal dealloc routine
untracks the object (from gc) before fiddling with weakrefs etc. But
subtype_dealloc has obscure technical reasons for re-registering the
dying object with gc (already explained in a large comment block at
the bottom of the function).
The fix amounts to simply refraining from reregistering the dying object
with gc until after the weakref callback (if any) has been called.
This is a critical bug (hard to predict, and causes seemingly random
memory corruption when it occurs). I'll backport it to 2.3 later.
Raymond Hettinger
Neil Schemenauer
Michael W. Hudson
Jeremy Hylton
Jim Fulton
Skip Montanaro
Walter Dörwald
Fred Drake
Brett Cannon
Armin Rigo
Tim Peters
Hye-Shik Chang
Andrew MacIntyre
Guido van Rossum
Jack Jansen