Source code for boltons.cacheutils

# -*- coding: utf-8 -*-
"""``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(object):
        'Dummy reentrant lock for builds without threads'
        def __enter__(self):
            pass

        def __exit__(self, exctype, excinst, exctb):
            pass

try:
    from boltons.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()

try:
    xrange
except NameError:
    # py3
    xrange = range
    unicode, str, bytes, basestring = str, bytes, bytes, (str, bytes)

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(LRI, self).__delitem__(evicted) super(LRI, self).__setitem__(key, value) else: link[VALUE] = value 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(LRI, self).__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(LRI, self).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(LRI, self).popitem() self._remove_from_ll(item[0]) return item
[docs] def clear(self): with self._lock: super(LRI, self).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(LRI, self).__eq__(other) def __ne__(self, other): return not (self == other) def __repr__(self): cn = self.__class__.__name__ val_map = super(LRI, self).__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 '%s(%s)' % (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(object): """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 "%s(func=%r)" % (cn, self.func) class CachedMethod(object): """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, basestring): 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(object): """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 '<%s func=%s>' % (cn, self.func)
[docs]class ThresholdCounter(object): """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 = dict([(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 n <= 0: return [] ret = sorted(self.iteritems(), key=lambda x: x[1][0], 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.itervalues()])
[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 xrange(count): self.add(key) else: for key in iterable: self.add(key) if kwargs: self.update(kwargs)
class MinIDMap(object): """ Assigns arbitrary weakref-able objects the smallest possible unique integer IDs, such that no two objects have the same ID at the same time. 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 self.mapping.itervalues() def __len__(self): return self.mapping.__len__() def iteritems(self): return self.mapping.iteritems() # end cacheutils.py