An Dict like LRU container.
A fixed size dict like container which evicts Least Recently Used (LRU) items once size limit is exceeded. There are many python implementations available which does similar things. This is a fast and efficient C implementation. LRU maximum capacity can be modified at run-time. If you are looking for pure python version, look else where.
This can be used to build a LRU cache. Usage is almost like a dict.
from lru import LRU l = LRU(5) # Create an LRU container that can hold 5 items for i in range(5): l[i] = str(i) print l.items() # Prints items in MRU order # Would print [(4, '4'), (3, '3'), (2, '2'), (1, '1'), (0, '0')] l = '5' # Inserting one more item should evict the old item print l.items() # Would print [(5, '5'), (4, '4'), (3, '3'), (2, '2'), (1, '1')] l # Accessing an item would make it MRU print l.items() # Would print [(3, '3'), (5, '5'), (4, '4'), (2, '2'), (1, '1')] # Now 3 is in front l.keys() # Can get keys alone in MRU order # Would print [3, 5, 4, 2, 1] del l # Delete an item print l.items() # Would print [(3, '3'), (5, '5'), (2, '2'), (1, '1')] print l.get_size() #Would print 5 l.set_size(3) print l.items() # Would print [(3, '3'), (5, '5'), (2, '2')] print l.get_size() # Would print 3 l.get_stats() # Would print (1, 0) l.clear() print l.items() #Would print 
pip install lru-dict
When to use this
Like mentioned above there are many python implementations of an LRU. Use this if you need a faster and memory efficient alternative. It is implemented with a dict and associated linked list to keep track of LRU order. See code for a more detailed explanation. To see an indicative comparison with a pure python module, consider a benchmark against pylru (just chosen at random, it should be similar with other python implementations as well).
$ python bench.py pylru.lrucache Time : 3.31 s, Memory : 453672 Kb $ python bench.py lru.LRU Time : 0.23 s, Memory : 124328 Kb