https://github.com/amitdev/lru-dict
A fast and memory efficient LRU cache for Python
https://github.com/amitdev/lru-dict
python
Last synced: about 1 year ago
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A fast and memory efficient LRU cache for Python
- Host: GitHub
- URL: https://github.com/amitdev/lru-dict
- Owner: amitdev
- License: mit
- Created: 2013-05-27T12:29:37.000Z (about 13 years ago)
- Default Branch: master
- Last Pushed: 2025-04-15T04:17:52.000Z (about 1 year ago)
- Last Synced: 2025-05-15T06:02:11.940Z (about 1 year ago)
- Topics: python
- Language: C
- Homepage:
- Size: 97.7 KB
- Stars: 275
- Watchers: 6
- Forks: 49
- Open Issues: 18
-
Metadata Files:
- Readme: README.rst
- License: LICENSE
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README
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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.
.. code:: python3
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.update(5='0') # Update an item
print l.items()
# Would print [(5, '0'), (3, '3'), (2, '2')]
l.clear()
print l.items()
# Would print []
def evicted(key, value):
print "removing: %s, %s" % (key, value)
l = LRU(1, callback=evicted)
l[1] = '1'
l[2] = '2'
# callback would print removing: 1, 1
l[2] = '3'
# doesn't call the evicted callback
print l.items()
# would print [(2, '3')]
del l[2]
# doesn't call the evicted callback
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