lru-dict 1.1

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 <http://www.google.com/search?q=python+lru+dict>`_.

Usage
=====

This can be used to build a LRU cache. Usage is almost like a dict.

.. code:: python

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] = '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

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 <https://gist.github.com/amitdev/5773979>`_ against
`pylru <https://pypi.python.org/pypi/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