# Copyright (c) 2013, Mahmoud Hashemi
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are
# met:
#
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
#
# * Redistributions in binary form must reproduce the above
# copyright notice, this list of conditions and the following
# disclaimer in the documentation and/or other materials provided
# with the distribution.
#
# * The names of the contributors may not be used to endorse or
# promote products derived from this software without specific
# prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
# A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
# OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
# DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
# THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
"""``cacheutils`` contains consistent implementations of fundamental
cache types. Currently there are two to choose from:
* :class:`LRI` - Least-recently inserted
* :class:`LRU` - Least-recently used
Both caches are :class:`dict` subtypes, designed to be as
interchangeable as possible, to facilitate experimentation. A key
practice with performance enhancement with caching is ensuring that
the caching strategy is working. If the cache is constantly missing,
it is just adding more overhead and code complexity. The standard
statistics are:
* ``hit_count`` - the number of times the queried key has been in
the cache
* ``miss_count`` - the number of times a key has been absent and/or
fetched by the cache
* ``soft_miss_count`` - the number of times a key has been absent,
but a default has been provided by the caller, as with
:meth:`dict.get` and :meth:`dict.setdefault`. Soft misses are a
subset of misses, so this number is always less than or equal to
``miss_count``.
Additionally, ``cacheutils`` provides :class:`ThresholdCounter`, a
cache-like bounded counter useful for online statistics collection.
Learn more about `caching algorithms on Wikipedia
<https://en.wikipedia.org/wiki/Cache_algorithms#Examples>`_.
"""
# TODO: TimedLRI
# TODO: support 0 max_size?
import heapq
import weakref
import itertools
from operator import attrgetter
try:
from threading import RLock
except Exception:
class RLock:
'Dummy reentrant lock for builds without threads'
def __enter__(self):
pass
def __exit__(self, exctype, excinst, exctb):
pass
try:
from .typeutils import make_sentinel
_MISSING = make_sentinel(var_name='_MISSING')
_KWARG_MARK = make_sentinel(var_name='_KWARG_MARK')
except ImportError:
_MISSING = object()
_KWARG_MARK = object()
PREV, NEXT, KEY, VALUE = range(4) # names for the link fields
DEFAULT_MAX_SIZE = 128
[docs]
class LRI(dict):
"""The ``LRI`` implements the basic *Least Recently Inserted* strategy to
caching. One could also think of this as a ``SizeLimitedDefaultDict``.
*on_miss* is a callable that accepts the missing key (as opposed
to :class:`collections.defaultdict`'s "default_factory", which
accepts no arguments.) Also note that, like the :class:`LRI`,
the ``LRI`` is instrumented with statistics tracking.
>>> cap_cache = LRI(max_size=2)
>>> cap_cache['a'], cap_cache['b'] = 'A', 'B'
>>> from pprint import pprint as pp
>>> pp(dict(cap_cache))
{'a': 'A', 'b': 'B'}
>>> [cap_cache['b'] for i in range(3)][0]
'B'
>>> cap_cache['c'] = 'C'
>>> print(cap_cache.get('a'))
None
>>> cap_cache.hit_count, cap_cache.miss_count, cap_cache.soft_miss_count
(3, 1, 1)
"""
def __init__(self, max_size=DEFAULT_MAX_SIZE, values=None,
on_miss=None):
if max_size <= 0:
raise ValueError('expected max_size > 0, not %r' % max_size)
self.hit_count = self.miss_count = self.soft_miss_count = 0
self.max_size = max_size
self._lock = RLock()
self._init_ll()
if on_miss is not None and not callable(on_miss):
raise TypeError('expected on_miss to be a callable'
' (or None), not %r' % on_miss)
self.on_miss = on_miss
if values:
self.update(values)
# TODO: fromkeys()?
# linked list manipulation methods.
#
# invariants:
# 1) 'anchor' is the sentinel node in the doubly linked list. there is
# always only one, and its KEY and VALUE are both _MISSING.
# 2) the most recently accessed node comes immediately before 'anchor'.
# 3) the least recently accessed node comes immediately after 'anchor'.
def _init_ll(self):
anchor = []
anchor[:] = [anchor, anchor, _MISSING, _MISSING]
# a link lookup table for finding linked list links in O(1)
# time.
self._link_lookup = {}
self._anchor = anchor
def _print_ll(self):
print('***')
for (key, val) in self._get_flattened_ll():
print(key, val)
print('***')
return
def _get_flattened_ll(self):
flattened_list = []
link = self._anchor
while True:
flattened_list.append((link[KEY], link[VALUE]))
link = link[NEXT]
if link is self._anchor:
break
return flattened_list
def _get_link_and_move_to_front_of_ll(self, key):
# find what will become the newest link. this may raise a
# KeyError, which is useful to __getitem__ and __setitem__
newest = self._link_lookup[key]
# splice out what will become the newest link.
newest[PREV][NEXT] = newest[NEXT]
newest[NEXT][PREV] = newest[PREV]
# move what will become the newest link immediately before
# anchor (invariant 2)
anchor = self._anchor
second_newest = anchor[PREV]
second_newest[NEXT] = anchor[PREV] = newest
newest[PREV] = second_newest
newest[NEXT] = anchor
return newest
def _set_key_and_add_to_front_of_ll(self, key, value):
# create a new link and place it immediately before anchor
# (invariant 2).
anchor = self._anchor
second_newest = anchor[PREV]
newest = [second_newest, anchor, key, value]
second_newest[NEXT] = anchor[PREV] = newest
self._link_lookup[key] = newest
def _set_key_and_evict_last_in_ll(self, key, value):
# the link after anchor is the oldest in the linked list
# (invariant 3). the current anchor becomes a link that holds
# the newest key, and the oldest link becomes the new anchor
# (invariant 1). now the newest link comes before anchor
# (invariant 2). no links are moved; only their keys
# and values are changed.
oldanchor = self._anchor
oldanchor[KEY] = key
oldanchor[VALUE] = value
self._anchor = anchor = oldanchor[NEXT]
evicted = anchor[KEY]
anchor[KEY] = anchor[VALUE] = _MISSING
del self._link_lookup[evicted]
self._link_lookup[key] = oldanchor
return evicted
def _remove_from_ll(self, key):
# splice a link out of the list and drop it from our lookup
# table.
link = self._link_lookup.pop(key)
link[PREV][NEXT] = link[NEXT]
link[NEXT][PREV] = link[PREV]
def __setitem__(self, key, value):
with self._lock:
try:
link = self._get_link_and_move_to_front_of_ll(key)
except KeyError:
if len(self) < self.max_size:
self._set_key_and_add_to_front_of_ll(key, value)
else:
evicted = self._set_key_and_evict_last_in_ll(key, value)
super().__delitem__(evicted)
else:
link[VALUE] = value
super().__setitem__(key, value)
return
def __getitem__(self, key):
with self._lock:
try:
link = self._link_lookup[key]
except KeyError:
self.miss_count += 1
if not self.on_miss:
raise
ret = self[key] = self.on_miss(key)
return ret
self.hit_count += 1
return link[VALUE]
[docs]
def get(self, key, default=None):
try:
return self[key]
except KeyError:
self.soft_miss_count += 1
return default
def __delitem__(self, key):
with self._lock:
super().__delitem__(key)
self._remove_from_ll(key)
[docs]
def pop(self, key, default=_MISSING):
# NB: hit/miss counts are bypassed for pop()
with self._lock:
try:
ret = super().pop(key)
except KeyError:
if default is _MISSING:
raise
ret = default
else:
self._remove_from_ll(key)
return ret
[docs]
def popitem(self):
with self._lock:
item = super().popitem()
self._remove_from_ll(item[0])
return item
[docs]
def clear(self):
with self._lock:
super().clear()
self._init_ll()
[docs]
def copy(self):
return self.__class__(max_size=self.max_size, values=self)
[docs]
def setdefault(self, key, default=None):
with self._lock:
try:
return self[key]
except KeyError:
self.soft_miss_count += 1
self[key] = default
return default
[docs]
def update(self, E, **F):
# E and F are throwback names to the dict() __doc__
with self._lock:
if E is self:
return
setitem = self.__setitem__
if callable(getattr(E, 'keys', None)):
for k in E.keys():
setitem(k, E[k])
else:
for k, v in E:
setitem(k, v)
for k in F:
setitem(k, F[k])
return
def __eq__(self, other):
with self._lock:
if self is other:
return True
if len(other) != len(self):
return False
if not isinstance(other, LRI):
return other == self
return super().__eq__(other)
def __ne__(self, other):
return not (self == other)
def __repr__(self):
cn = self.__class__.__name__
val_map = super().__repr__()
return ('%s(max_size=%r, on_miss=%r, values=%s)'
% (cn, self.max_size, self.on_miss, val_map))
[docs]
class LRU(LRI):
"""The ``LRU`` is :class:`dict` subtype implementation of the
*Least-Recently Used* caching strategy.
Args:
max_size (int): Max number of items to cache. Defaults to ``128``.
values (iterable): Initial values for the cache. Defaults to ``None``.
on_miss (callable): a callable which accepts a single argument, the
key not present in the cache, and returns the value to be cached.
>>> cap_cache = LRU(max_size=2)
>>> cap_cache['a'], cap_cache['b'] = 'A', 'B'
>>> from pprint import pprint as pp
>>> pp(dict(cap_cache))
{'a': 'A', 'b': 'B'}
>>> [cap_cache['b'] for i in range(3)][0]
'B'
>>> cap_cache['c'] = 'C'
>>> print(cap_cache.get('a'))
None
This cache is also instrumented with statistics
collection. ``hit_count``, ``miss_count``, and ``soft_miss_count``
are all integer members that can be used to introspect the
performance of the cache. ("Soft" misses are misses that did not
raise :exc:`KeyError`, e.g., ``LRU.get()`` or ``on_miss`` was used to
cache a default.
>>> cap_cache.hit_count, cap_cache.miss_count, cap_cache.soft_miss_count
(3, 1, 1)
Other than the size-limiting caching behavior and statistics,
``LRU`` acts like its parent class, the built-in Python :class:`dict`.
"""
def __getitem__(self, key):
with self._lock:
try:
link = self._get_link_and_move_to_front_of_ll(key)
except KeyError:
self.miss_count += 1
if not self.on_miss:
raise
ret = self[key] = self.on_miss(key)
return ret
self.hit_count += 1
return link[VALUE]
### Cached decorator
# Key-making technique adapted from Python 3.4's functools
class _HashedKey(list):
"""The _HashedKey guarantees that hash() will be called no more than once
per cached function invocation.
"""
__slots__ = 'hash_value'
def __init__(self, key):
self[:] = key
self.hash_value = hash(tuple(key))
def __hash__(self):
return self.hash_value
def __repr__(self):
return f'{self.__class__.__name__}({list.__repr__(self)})'
def make_cache_key(args, kwargs, typed=False,
kwarg_mark=_KWARG_MARK,
fasttypes=frozenset([int, str, frozenset, type(None)])):
"""Make a generic key from a function's positional and keyword
arguments, suitable for use in caches. Arguments within *args* and
*kwargs* must be `hashable`_. If *typed* is ``True``, ``3`` and
``3.0`` will be treated as separate keys.
The key is constructed in a way that is flat as possible rather than
as a nested structure that would take more memory.
If there is only a single argument and its data type is known to cache
its hash value, then that argument is returned without a wrapper. This
saves space and improves lookup speed.
>>> tuple(make_cache_key(('a', 'b'), {'c': ('d')}))
('a', 'b', _KWARG_MARK, ('c', 'd'))
.. _hashable: https://docs.python.org/2/glossary.html#term-hashable
"""
# key = [func_name] if func_name else []
# key.extend(args)
key = list(args)
if kwargs:
sorted_items = sorted(kwargs.items())
key.append(kwarg_mark)
key.extend(sorted_items)
if typed:
key.extend([type(v) for v in args])
if kwargs:
key.extend([type(v) for k, v in sorted_items])
elif len(key) == 1 and type(key[0]) in fasttypes:
return key[0]
return _HashedKey(key)
# for backwards compatibility in case someone was importing it
_make_cache_key = make_cache_key
class CachedFunction:
"""This type is used by :func:`cached`, below. Instances of this
class are used to wrap functions in caching logic.
"""
def __init__(self, func, cache, scoped=True, typed=False, key=None):
self.func = func
if callable(cache):
self.get_cache = cache
elif not (callable(getattr(cache, '__getitem__', None))
and callable(getattr(cache, '__setitem__', None))):
raise TypeError('expected cache to be a dict-like object,'
' or callable returning a dict-like object, not %r'
% cache)
else:
def _get_cache():
return cache
self.get_cache = _get_cache
self.scoped = scoped
self.typed = typed
self.key_func = key or make_cache_key
def __call__(self, *args, **kwargs):
cache = self.get_cache()
key = self.key_func(args, kwargs, typed=self.typed)
try:
ret = cache[key]
except KeyError:
ret = cache[key] = self.func(*args, **kwargs)
return ret
def __repr__(self):
cn = self.__class__.__name__
if self.typed or not self.scoped:
return ("%s(func=%r, scoped=%r, typed=%r)"
% (cn, self.func, self.scoped, self.typed))
return f"{cn}(func={self.func!r})"
class CachedMethod:
"""Similar to :class:`CachedFunction`, this type is used by
:func:`cachedmethod` to wrap methods in caching logic.
"""
def __init__(self, func, cache, scoped=True, typed=False, key=None):
self.func = func
self.__isabstractmethod__ = getattr(func, '__isabstractmethod__', False)
if isinstance(cache, str):
self.get_cache = attrgetter(cache)
elif callable(cache):
self.get_cache = cache
elif not (callable(getattr(cache, '__getitem__', None))
and callable(getattr(cache, '__setitem__', None))):
raise TypeError('expected cache to be an attribute name,'
' dict-like object, or callable returning'
' a dict-like object, not %r' % cache)
else:
def _get_cache(obj):
return cache
self.get_cache = _get_cache
self.scoped = scoped
self.typed = typed
self.key_func = key or make_cache_key
self.bound_to = None
def __get__(self, obj, objtype=None):
if obj is None:
return self
cls = self.__class__
ret = cls(self.func, self.get_cache, typed=self.typed,
scoped=self.scoped, key=self.key_func)
ret.bound_to = obj
return ret
def __call__(self, *args, **kwargs):
obj = args[0] if self.bound_to is None else self.bound_to
cache = self.get_cache(obj)
key_args = (self.bound_to, self.func) + args if self.scoped else args
key = self.key_func(key_args, kwargs, typed=self.typed)
try:
ret = cache[key]
except KeyError:
if self.bound_to is not None:
args = (self.bound_to,) + args
ret = cache[key] = self.func(*args, **kwargs)
return ret
def __repr__(self):
cn = self.__class__.__name__
args = (cn, self.func, self.scoped, self.typed)
if self.bound_to is not None:
args += (self.bound_to,)
return ('<%s func=%r scoped=%r typed=%r bound_to=%r>' % args)
return ("%s(func=%r, scoped=%r, typed=%r)" % args)
[docs]
def cached(cache, scoped=True, typed=False, key=None):
"""Cache any function with the cache object of your choosing. Note
that the function wrapped should take only `hashable`_ arguments.
Args:
cache (Mapping): Any :class:`dict`-like object suitable for
use as a cache. Instances of the :class:`LRU` and
:class:`LRI` are good choices, but a plain :class:`dict`
can work in some cases, as well. This argument can also be
a callable which accepts no arguments and returns a mapping.
scoped (bool): Whether the function itself is part of the
cache key. ``True`` by default, different functions will
not read one another's cache entries, but can evict one
another's results. ``False`` can be useful for certain
shared cache use cases. More advanced behavior can be
produced through the *key* argument.
typed (bool): Whether to factor argument types into the cache
check. Default ``False``, setting to ``True`` causes the
cache keys for ``3`` and ``3.0`` to be considered unequal.
>>> my_cache = LRU()
>>> @cached(my_cache)
... def cached_lower(x):
... return x.lower()
...
>>> cached_lower("CaChInG's FuN AgAiN!")
"caching's fun again!"
>>> len(my_cache)
1
.. _hashable: https://docs.python.org/2/glossary.html#term-hashable
"""
def cached_func_decorator(func):
return CachedFunction(func, cache, scoped=scoped, typed=typed, key=key)
return cached_func_decorator
[docs]
def cachedmethod(cache, scoped=True, typed=False, key=None):
"""Similar to :func:`cached`, ``cachedmethod`` is used to cache
methods based on their arguments, using any :class:`dict`-like
*cache* object.
Args:
cache (str/Mapping/callable): Can be the name of an attribute
on the instance, any Mapping/:class:`dict`-like object, or
a callable which returns a Mapping.
scoped (bool): Whether the method itself and the object it is
bound to are part of the cache keys. ``True`` by default,
different methods will not read one another's cache
results. ``False`` can be useful for certain shared cache
use cases. More advanced behavior can be produced through
the *key* arguments.
typed (bool): Whether to factor argument types into the cache
check. Default ``False``, setting to ``True`` causes the
cache keys for ``3`` and ``3.0`` to be considered unequal.
key (callable): A callable with a signature that matches
:func:`make_cache_key` that returns a tuple of hashable
values to be used as the key in the cache.
>>> class Lowerer(object):
... def __init__(self):
... self.cache = LRI()
...
... @cachedmethod('cache')
... def lower(self, text):
... return text.lower()
...
>>> lowerer = Lowerer()
>>> lowerer.lower('WOW WHO COULD GUESS CACHING COULD BE SO NEAT')
'wow who could guess caching could be so neat'
>>> len(lowerer.cache)
1
"""
def cached_method_decorator(func):
return CachedMethod(func, cache, scoped=scoped, typed=typed, key=key)
return cached_method_decorator
[docs]
class cachedproperty:
"""The ``cachedproperty`` is used similar to :class:`property`, except
that the wrapped method is only called once. This is commonly used
to implement lazy attributes.
After the property has been accessed, the value is stored on the
instance itself, using the same name as the cachedproperty. This
allows the cache to be cleared with :func:`delattr`, or through
manipulating the object's ``__dict__``.
"""
def __init__(self, func):
self.__doc__ = getattr(func, '__doc__')
self.__isabstractmethod__ = getattr(func, '__isabstractmethod__', False)
self.func = func
def __get__(self, obj, objtype=None):
if obj is None:
return self
value = obj.__dict__[self.func.__name__] = self.func(obj)
return value
def __repr__(self):
cn = self.__class__.__name__
return f'<{cn} func={self.func}>'
[docs]
class ThresholdCounter:
"""A **bounded** dict-like Mapping from keys to counts. The
ThresholdCounter automatically compacts after every (1 /
*threshold*) additions, maintaining exact counts for any keys
whose count represents at least a *threshold* ratio of the total
data. In other words, if a particular key is not present in the
ThresholdCounter, its count represents less than *threshold* of
the total data.
>>> tc = ThresholdCounter(threshold=0.1)
>>> tc.add(1)
>>> tc.items()
[(1, 1)]
>>> tc.update([2] * 10)
>>> tc.get(1)
0
>>> tc.add(5)
>>> 5 in tc
True
>>> len(list(tc.elements()))
11
As you can see above, the API is kept similar to
:class:`collections.Counter`. The most notable feature omissions
being that counted items cannot be set directly, uncounted, or
removed, as this would disrupt the math.
Use the ThresholdCounter when you need best-effort long-lived
counts for dynamically-keyed data. Without a bounded datastructure
such as this one, the dynamic keys often represent a memory leak
and can impact application reliability. The ThresholdCounter's
item replacement strategy is fully deterministic and can be
thought of as *Amortized Least Relevant*. The absolute upper bound
of keys it will store is *(2/threshold)*, but realistically
*(1/threshold)* is expected for uniformly random datastreams, and
one or two orders of magnitude better for real-world data.
This algorithm is an implementation of the Lossy Counting
algorithm described in "Approximate Frequency Counts over Data
Streams" by Manku & Motwani. Hat tip to Kurt Rose for discovery
and initial implementation.
"""
# TODO: hit_count/miss_count?
def __init__(self, threshold=0.001):
if not 0 < threshold < 1:
raise ValueError('expected threshold between 0 and 1, not: %r'
% threshold)
self.total = 0
self._count_map = {}
self._threshold = threshold
self._thresh_count = int(1 / threshold)
self._cur_bucket = 1
@property
def threshold(self):
return self._threshold
[docs]
def add(self, key):
"""Increment the count of *key* by 1, automatically adding it if it
does not exist.
Cache compaction is triggered every *1/threshold* additions.
"""
self.total += 1
try:
self._count_map[key][0] += 1
except KeyError:
self._count_map[key] = [1, self._cur_bucket - 1]
if self.total % self._thresh_count == 0:
self._count_map = {k: v for k, v in self._count_map.items()
if sum(v) > self._cur_bucket}
self._cur_bucket += 1
return
[docs]
def elements(self):
"""Return an iterator of all the common elements tracked by the
counter. Yields each key as many times as it has been seen.
"""
repeaters = itertools.starmap(itertools.repeat, self.iteritems())
return itertools.chain.from_iterable(repeaters)
[docs]
def most_common(self, n=None):
"""Get the top *n* keys and counts as tuples. If *n* is omitted,
returns all the pairs.
"""
if not n or n <= 0:
return []
ret = sorted(self.iteritems(), key=lambda x: x[1], reverse=True)
if n is None or n >= len(ret):
return ret
return ret[:n]
[docs]
def get_common_count(self):
"""Get the sum of counts for keys exceeding the configured data
threshold.
"""
return sum([count for count, _ in self._count_map.values()])
[docs]
def get_uncommon_count(self):
"""Get the sum of counts for keys that were culled because the
associated counts represented less than the configured
threshold. The long-tail counts.
"""
return self.total - self.get_common_count()
[docs]
def get_commonality(self):
"""Get a float representation of the effective count accuracy. The
higher the number, the less uniform the keys being added, and
the higher accuracy and efficiency of the ThresholdCounter.
If a stronger measure of data cardinality is required,
consider using hyperloglog.
"""
return float(self.get_common_count()) / self.total
def __getitem__(self, key):
return self._count_map[key][0]
def __len__(self):
return len(self._count_map)
def __contains__(self, key):
return key in self._count_map
def iterkeys(self):
return iter(self._count_map)
def keys(self):
return list(self.iterkeys())
def itervalues(self):
count_map = self._count_map
for k in count_map:
yield count_map[k][0]
def values(self):
return list(self.itervalues())
def iteritems(self):
count_map = self._count_map
for k in count_map:
yield (k, count_map[k][0])
def items(self):
return list(self.iteritems())
[docs]
def get(self, key, default=0):
"Get count for *key*, defaulting to 0."
try:
return self[key]
except KeyError:
return default
[docs]
def update(self, iterable, **kwargs):
"""Like dict.update() but add counts instead of replacing them, used
to add multiple items in one call.
Source can be an iterable of keys to add, or a mapping of keys
to integer counts.
"""
if iterable is not None:
if callable(getattr(iterable, 'iteritems', None)):
for key, count in iterable.iteritems():
for i in range(count):
self.add(key)
else:
for key in iterable:
self.add(key)
if kwargs:
self.update(kwargs)
class MinIDMap:
"""
Assigns arbitrary weakref-able objects the smallest possible unique
integer IDs, such that no two objects have the same ID at the same
time.
Maps arbitrary hashable objects to IDs.
Based on https://gist.github.com/kurtbrose/25b48114de216a5e55df
"""
def __init__(self):
self.mapping = weakref.WeakKeyDictionary()
self.ref_map = {}
self.free = []
def get(self, a):
try:
return self.mapping[a][0] # if object is mapped, return ID
except KeyError:
pass
if self.free: # if there are any free IDs, use the smallest
nxt = heapq.heappop(self.free)
else: # if there are no free numbers, use the next highest ID
nxt = len(self.mapping)
ref = weakref.ref(a, self._clean)
self.mapping[a] = (nxt, ref)
self.ref_map[ref] = nxt
return nxt
def drop(self, a):
freed, ref = self.mapping[a]
del self.mapping[a]
del self.ref_map[ref]
heapq.heappush(self.free, freed)
def _clean(self, ref):
print(self.ref_map[ref])
heapq.heappush(self.free, self.ref_map[ref])
del self.ref_map[ref]
def __contains__(self, a):
return a in self.mapping
def __iter__(self):
return iter(self.mapping)
def __len__(self):
return self.mapping.__len__()
def iteritems(self):
return iter((k, self.mapping[k][0]) for k in iter(self.mapping))
# end cacheutils.py