Metadata-Version: 2.1
Name: lfudacache
Version: 0.0.2
Summary: Less Frequently Used with Dynamic Aging
Home-page: https://gitlab.com/ergoithz/lfudacache
Author: Felipe A. Hernandez
Author-email: ergoithz@gmail.com
License: MIT
Description: # lfudacache
        
        Python implementation Less Frequently Used with Dynamic Aging (LFUDA),
        implementing an ordered dict-like interface, providing efficient cache with
        reduced cache pollution.
        
        ## Installation
        
        ```sh
        pip install lfudacache
        ```
        
        ## Usage
        
        ```python
        import lfudacache
        
        cache = lfudacache.LFUDACache(1000)  # cache the top 1000 most frequently accessed items
        cache['key1'] = 1
        cache.get('key1')  # 1
        list(cache.items())  # [('key1', 1)]
        cache['key2'] = 2
        list(cache.items())  # [('key1', 1), ('key2', 2)]
        cache.get('key2')  # 2
        list(cache.items())  # [('key2', 2), ('key1', 1)]
        cache.peek('key1')  # 1
        list(cache.items())  # [('key2', 2), ('key1', 1)]
        cache.get('key3')  # None
        list(cache.items())  # [('key2', 2), ('key1', 1)]
        cache['key3'] = 3
        list(cache.items())  # [('key3', 3), ('key2', 2), ('key1', 1)]
        ```
        
        A function memoization decorator is also available. Please note that,
        unhashable arguments will raise `TypeError` just like python's standard
        `functools.lru_cache`.
        
        ```python
        import lfudacache
        
        @lfudacache.memoize(1000)  # cache the top 1000 most frequent call results
        def my_cached_function(param_1, param_2):
            result = my_very_expensive_logic(param_1, param_2)
            return result
        ```
        
        ## Documentation
        
        **LFUDACache** objects implement the `MutableMapping` interface, behaving like any
        other python `dict` (actually, more like an `OrderedDict` as it's ordered).
        but expiring long **unused** items when new ones are inserted.
        
        An item is considered **unused** when cache **ages** above item **hits**.
        
        Cache **ages** when asked for a missing **key** .
        
        Inserting an item when cache is not enough **aged** will result on the item
        not being stored, reducing cache pollution.
        
        **LFUDACache** iteration will start from most to less frequently used items.
        
        ### `LFUDACache`
        
        ```
        Less Frequently Used with Dynamic Aging cache, implementing the entire
        :class:`collections.abc.MutableMapping' interface.
        
        Implementation notes:
            * Most methods are self-optimizing into closures when referenced.
            * Cache behaves as as dict, all operations (except iteration or peek)
              count as cache MISS or HIT, affecting key ordering or insertion
              permeability.
        
        How it works:
            * Every cache hit increases item HIT counter
              (except :method:`LFUDACache.peek`).
            * Every cache miss increases MISSES counter by 1, up to top HITS.
            * When full, a new cache item will only be accepted if MISSES counter
              reaches the less frequently used item HIT counter, which is evicted.
            * When a new item is cached, its HIT counter is set equal to MISSES
              itself.
            * When an existing item is updated, its HIT counter is incremented
              by 1 to at least MISSES + 1.
        ```
        
        #### `LFUDACache.__init__(maxsize)`
        
        ```
        :param maxsize: number of items to keep on cache
        :type maxsize: int
        ```
        
        #### `LFUDACache.peek(key [, default])`
        
        ```
        Get value of key from cache, without updating HIT nor MISSES counters.
        
        If key is not found, and default is not given, KeyError is raised.
        
        :param key: cache item key
        :param default: optional default parameter
        :returns: cache item value
        :raises KeyError: if no default is given and key is not found
        
        Usage
        -----
        value = lfudacache.peek(key, 'default_value')
        ```
        
        ### `memoize(maxsize, fnc=None, key_fnc=make_key)`
        
        ```
        Memoization decorator using Less Frequenty Used with Dynamic Aging cache
        eviction algorithm.
        
        The LFUDACache instance is available on the decorated function, as `cache`
        property.
        
        :param maxsize: maximum cache size
        :type maxsize: int
        :param fnc: optional function to memoize (non-decorating behavior)
        :type fnc: callable or None
        :param key_fnc: optional custom cache key function, receiving argument
                        list and keyword argument dict
        :type key_fnc: callable
        :returns: decorator if fnc is not given, wrapped function otherwise
        :rtype: callable
        ```
        
        ### `make_key(args, kwargs)`
        
        ```
        Hash function for function arguments.
        
        :param args: argument iterable
        :type args: iterable
        :param kwargs: keyword argument dict-like object
        :type kwargs: dict
        :returns: hash of arg and kwargs
        :rtype: int
        ```
        
        ## Rationale
        
        There is a lot of LFU and LRU cache implementations for Python, but not so
        many LFUDA ones, and some of them were a bit too slow due to python attribute
        lookup overhead (thats why python's standard lru_cache implementation is
        purely functional).
        
        This module addresses this performance concerns by making extensive use
        of python closures, while providing an object-oriented interface, via a
        self-optimizing implementation.
        
        ### Why Less Frequently Used with Dynamic Aging (LFUDA)
        
        LFUDA only evicts less used items when they become too old, rejecting new cache
        items until then.
        
        In our implementation, an item become too old when its cache hit count lags
        behind the entire cache misses.
        
        This approach prevents eviction of very used items in favor of potentially less
        used items.
        
        ### Why not Less Recently Used (LRU)
        
        LRU is a very simple approach to caching, evicting older unused items.
        
        This approach is optimal when the most relevant items are accessed frequently,
        but this is not always the case.
        
        The drawback is that very used items will be evicted when a lot of new items
        are added to cache, even if they will never be accesed again, causing cache
        pollution.
        
        This problem is usually minimized increasing the cache size until potentially
        irrelevant cache items become only a tiny fraction of the entire cache but
        this is not always possible or memory efficient.
        
        ### Why not Less Frequenty Used (LFU)
        
        LFU is our base algorithm, which evicts less used items.
        
        This approach has one major drawback when cache is full: a new saved item
        could cause the eviction of an item more frequently used than the new one,
        causing cache pollution.
        
        To minimize this problem, two different item storage tiers can be implemented
        with different eviction policies. But while this method reduces said cache
        pollution issue, it does not fix it.
        
Keywords: cache,memoize
Platform: any
Classifier: Development Status :: 4 - Beta
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Description-Content-Type: text/markdown
