A fast and memory efficient LRU cache for Python
C Python
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README.rst

LRU Dict

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.

Usage

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

print l.peek_first_item(), l.peek_last_item()  #return the MRU key and LRU key
# Would print None None

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')]

print l.peek_first_item(), l.peek_last_item()  #return the MRU key and LRU key
# Would print (4, '4') (0, '0')

l[5] = '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[3]               # 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[4]           # 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
print l.has_key(5)
# Would print True
print 2 in l
# Would print True

l.get_stats()
# Would print (1, 0)

l.clear()
print l.items()
#Would print []

Install

pip install lru-dict

or

easy_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