| Name | lru-dict JSON |
| Version |
1.3.0
JSON |
| download |
| home_page | |
| Summary | An Dict like LRU container. |
| upload_time | 2023-11-06 01:40:12 |
| maintainer | |
| docs_url | None |
| author | Amit Dev |
| requires_python | >=3.8 |
| license | MIT |
| keywords |
lru
dict
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
.. image:: https://github.com/amitdev/lru-dict/actions/workflows/tests.yml/badge.svg
:target: https://github.com/amitdev/lru-dict/actions/workflows/tests.yml
.. image:: https://github.com/amitdev/lru-dict/actions/workflows/build-and-deploy.yml/badge.svg
:target: https://github.com/amitdev/lru-dict/actions/workflows/build-and-deploy.yml
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
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 <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
Raw data
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