scrapy/scrapy/xlib/lrucache.py

# lrucache.py -- a simple LRU (Least-Recently-Used) cache class

# Copyright 2004 Evan Prodromou <evan@bad.dynu.ca>
# Licensed under the Academic Free License 2.1

# arch-tag: LRU cache main module

"""a simple LRU (Least-Recently-Used) cache module

This module provides very simple LRU (Least-Recently-Used) cache
functionality.

An *in-memory cache* is useful for storing the results of an
'expensive' process (one that takes a lot of time or resources) for
later re-use. Typical examples are accessing data from the filesystem,
a database, or a network location. If you know you'll need to re-read
the data again, it can help to keep it in a cache.

You *can* use a Python dictionary as a cache for some purposes.
However, if the results you're caching are large, or you have a lot of
possible results, this can be impractical memory-wise.

An *LRU cache*, on the other hand, only keeps _some_ of the results in
memory, which keeps you from overusing resources. The cache is bounded
by a maximum size; if you try to add more values to the cache, it will
automatically discard the values that you haven't read or written to
in the longest time. In other words, the least-recently-used items are
discarded. [1]_

.. [1]: 'Discarded' here means 'removed from the cache'.

"""

from __future__ import generators
import time
from heapq import heappush, heappop, heapify

__version__ = "0.2"
__all__ = ['CacheKeyError', 'LRUCache', 'DEFAULT_SIZE']
__docformat__ = 'reStructuredText en'

DEFAULT_SIZE = 16
"""Default size of a new LRUCache object, if no 'size' argument is given."""

class CacheKeyError(KeyError):
    """Error raised when cache requests fail
    
    When a cache record is accessed which no longer exists (or never did),
    this error is raised. To avoid it, you may want to check for the existence
    of a cache record before reading or deleting it."""
    pass

class LRUCache(object):
    """Least-Recently-Used (LRU) cache.
    
    Instances of this class provide a least-recently-used (LRU) cache. They
    emulate a Python mapping type. You can use an LRU cache more or less like
    a Python dictionary, with the exception that objects you put into the
    cache may be discarded before you take them out.
    
    Some example usage::
	
    cache = LRUCache(32) # new cache
    cache['foo'] = get_file_contents('foo') # or whatever
	
    if 'foo' in cache: # if it's still in cache...
	    # use cached version
        contents = cache['foo']
    else:
	    # recalculate
        contents = get_file_contents('foo')
	    # store in cache for next time
        cache['foo'] = contents

    print cache.size # Maximum size
	
    print len(cache) # 0 <= len(cache) <= cache.size
	
    cache.size = 10 # Auto-shrink on size assignment
	
    for i in range(50): # note: larger than cache size
        cache[i] = i
	    
    if 0 not in cache: print 'Zero was discarded.'

    if 42 in cache:
        del cache[42] # Manual deletion

    for j in cache:   # iterate (in LRU order)
        print j, cache[j] # iterator produces keys, not values
    """
    
    class __Node(object):
        """Record of a cached value. Not for public consumption."""
        
        def __init__(self, key, obj, timestamp):
            object.__init__(self)
            self.key = key
            self.obj = obj
            self.atime = timestamp
            self.mtime = self.atime
	    
        def __cmp__(self, other):
            return cmp(self.atime, other.atime)

        def __repr__(self):
            return "<%s %s => %s (%s)>" % \
                   (self.__class__, self.key, self.obj, \
                    time.asctime(time.localtime(self.atime)))

    def __init__(self, size=DEFAULT_SIZE):
        # Check arguments
        if size <= 0:
            raise ValueError, size
        elif type(size) is not type(0):
            raise TypeError, size
        object.__init__(self)	
        self.__heap = []
        self.__dict = {}
        self.size = size
        """Maximum size of the cache.
        If more than 'size' elements are added to the cache,
        the least-recently-used ones will be discarded."""
	
    def __len__(self):
        return len(self.__heap)
    
    def __contains__(self, key):
        return self.__dict.has_key(key)
    
    def __setitem__(self, key, obj):
        if self.__dict.has_key(key):
            node = self.__dict[key]
            node.obj = obj
            node.atime = time.time()
            node.mtime = node.atime
            heapify(self.__heap)
        else:
            # size may have been reset, so we loop
            while len(self.__heap) >= self.size:
                lru = heappop(self.__heap)
                del self.__dict[lru.key]
            node = self.__Node(key, obj, time.time())
            self.__dict[key] = node
            heappush(self.__heap, node)
	
    def __getitem__(self, key):
        if not self.__dict.has_key(key):
            raise CacheKeyError(key)
        else:
            node = self.__dict[key]
            node.atime = time.time()
            heapify(self.__heap)
            return node.obj
	
    def __delitem__(self, key):
        if not self.__dict.has_key(key):
            raise CacheKeyError(key)
        else:
            node = self.__dict[key]
            del self.__dict[key]
            self.__heap.remove(node)
            heapify(self.__heap)
            return node.obj

    def __iter__(self):
        copy = self.__heap[:]
        while len(copy) > 0:
            node = heappop(copy)
            yield node.key
        raise StopIteration

    def __setattr__(self, name, value):
        object.__setattr__(self, name, value)
        # automagically shrink heap on resize
        if name == 'size':
            while len(self.__heap) > value:
                lru = heappop(self.__heap)
                del self.__dict[lru.key]
	    
    def __repr__(self):
        return "<%s (%d elements)>" % (str(self.__class__), len(self.__heap))

    def mtime(self, key):
        """Return the last modification time for the cache record with key.
        May be useful for cache instances where the stored values can get
        'stale', such as caching file or network resource contents."""
        if not self.__dict.has_key(key):
            raise CacheKeyError(key)
        else:
            node = self.__dict[key]
            return node.mtime

if __name__ == "__main__":
    cache = LRUCache(25)
    print cache
    for i in range(50):
        cache[i] = str(i)
    print cache
    if 46 in cache:
        del cache[46]
    print cache
    cache.size = 10
    print cache
    cache[46] = '46'
    print cache
    print len(cache)
    for c in cache:
        print c
    print cache
    print cache.mtime(46)
    for c in cache:
        print c