he didn't know this), so merged in some changes I made during
review. Nothing material apart from changing a new `mask` local
from int to Py_ssize_t. Mostly this is repairing comments that
were made incorrect, and adding new comments. Also a few
minor code rewrites for clarity or helpful succinctness.
using a custom, nearly-identical macro. This probably changes how some of
these functions are compiled, which may result in fractionally slower (or
faster) execution. Considering the nature of traversal, visiting much of the
address space in unpredictable patterns, I'd argue the code readability and
maintainability is well worth it ;P
- The copy module now "copies" function objects (as atomic objects).
- dict.__getitem__ now looks for a __missing__ hook before raising
KeyError.
- Added a new type, defaultdict, to the collections module.
This uses the new __missing__ hook behavior added to dict (see above).
This gives another 30% speedup for operations such as
map(func, d.iteritems()) or list(d.iteritems()) which can both take
advantage of length information when provided.
* Split into three separate types that share everything except the
code for iternext. Saves run time decision making and allows
each iternext function to be specialized.
* Inlined PyDict_Next(). In addition to saving a function call, this
allows a redundant test to be eliminated and further specialization
of the code for the unique needs of each iterator type.
* Created a reusable result tuple for iteritems(). Saves the malloc
time for tuples when the previous result was not kept by client code
(this is the typical use case for iteritems). If the client code
does keep the reference, then a new tuple is created.
Results in a 20% to 30% speedup depending on the size and sparsity
of the dictionary.
* Factored constant structure references out of the inner loops for
PyDict_Next(), dict_keys(), dict_values(), and dict_items().
Gave measurable speedups to each (the improvement varies depending
on the sparseness of the dictionary being measured).
* Added a freelist scheme styled after that for tuples. Saves around
80% of the calls to malloc and free. About 10% of the time, the
previous dictionary was completely empty; in those cases, the
dictionary initialization with memset() can be skipped.
(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.
* Increase dictionary growth rate resulting in more sparse dictionaries,
fewer lookup collisions, increased memory use, and better cache
performance. For dicts with over 50k entries, keep the current
growth rate in case an application is suffering from tight memory
constraints.
* Set the most common case (no resize) to fall-through the test.
the optional proto 2 slot state.
pickle.py, load_build(): CAUTION: Noted that cPickle's
load_build and pickle's load_build really don't do the same
things with the state, and didn't before this patch either.
cPickle never tries to do .update(), and has no backoff if
instance.__dict__ can't be retrieved. There are no tests
that can tell the difference, and part of what cPickle's
load_build() did looked accidental to me, so I don't know
what the true intent is here.
pickletester.py, test_pickle.py: Got rid of the hack for
exempting cPickle from running some of the proto 2 tests.
dictobject.c, PyDict_Next(): documented intended use.
Neal Norwitz
Armin Rigo
Tim Peters
Thomas Wouters
Georg Brandl
Guido van Rossum
Martin v. Löwis
Raymond Hettinger
Nicholas Bastin
Walter Dörwald
Jeremy Hylton