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A caching library for Python.


  • In-memory caching using dictionary backend

  • Cache manager for easily accessing multiple cache objects

  • Reconfigurable cache settings for runtime setup when using module-level cache objects

  • Maximum cache size enforcement

  • Default cache TTL (time-to-live) as well as custom TTLs per cache entry

  • Bulk set, get, and delete operations

  • Bulk get and delete operations filtered by string, regex, or function

  • Memoization decorators

  • Thread safe

  • Multiple cache implementations:

    • FIFO (First In, First Out)

    • LIFO (Last In, First Out)

    • LRU (Least Recently Used)

    • MRU (Most Recently Used)

    • LFU (Least Frequently Used)

    • RR (Random Replacement)


  • Layered caching (multi-level caching)


  • Python >= 3.7


Install using pip:

pip install cacheout

Let’s start with some basic caching by creating a cache object:

from cacheout import Cache

cache = Cache()

By default the cache object will have a maximum size of 256, default TTL (time-to-live) expiration turned off, TTL timer that uses time.time (meaning TTL is in seconds), and the default for missing keys as None. These values can be set with:

cache = Cache(maxsize=256, ttl=0, timer=time.time, default=None)  # defaults

Set a cache key using cache.set():

cache.set(1, 'foobar')

Get the value of a cache key with cache.get():

assert cache.get(1) == 'foobar'

Get a default value when cache key isn’t set:

assert cache.get(2) is None
assert cache.get(2, default=False) is False
assert 2 not in cache

Provide cache values using a default callable:

assert 2 not in cache
assert cache.get(2, default=lambda key: key) == 2
assert cache.get(2) == 2
assert 2 in cache

Provide a global default:

cache2 = Cache(default=True)
assert cache2.get('missing') is True
assert 'missing' not in cache2

cache3 = Cache(default=lambda key: key)
assert cache3.get('missing') == 'missing'
assert 'missing' in cache3

Set the TTL (time-to-live) expiration per entry (default TTL units are in seconds when Cache.timer is set to the default time.time; otherwise, the units are determined by the custom timer function):

cache.set(3, {'data': {}}, ttl=1)
assert cache.get(3) == {'data': {}}
assert cache.get(3) is None

Memoize a function where cache keys are generated from the called function parameters:

def func(a, b):

Provide a TTL for the memoized function and incorporate argument types into generated cache keys:

@cache.memoize(ttl=5, typed=True)
def func(a, b):

# func(1, 2) has different cache key than func(1.0, 2.0), whereas,
# with "typed=False" (the default), they would have the same key

Access the original memoized function:

def func(a, b):

func.uncached(1, 2)

Get a copy of the entire cache with cache.copy():

assert cache.copy() == {1: 'foobar', 2: ('foo', 'bar', 'baz')}

Delete a cache key with cache.delete():

assert cache.get(1) is None

Clear the entire cache with cache.clear():

assert len(cache) == 0

Perform bulk operations with cache.set_many(), cache.get_many(), and cache.delete_many():

cache.set_many({'a': 1, 'b': 2, 'c': 3})
assert cache.get_many(['a', 'b', 'c']) == {'a': 1, 'b': 2, 'c': 3}
cache.delete_many(['a', 'b', 'c'])
assert cache.count() == 0

Use complex filtering in cache.get_many() and cache.delete_many():

import re
cache.set_many({'a_1': 1, 'a_2': 2, '123': 3, 'b': 4})

cache.get_many('a_*') == {'a_1': 1, 'a_2': 2}
cache.get_many(re.compile(r'\d')) == {'123': 3}
cache.get_many(lambda key: '2' in key) == {'a_2': 2, '123': 3}

assert dict(cache.items()) == {'123': 3, 'b': 4}

Reconfigure the cache object after creation with cache.configure():

cache.configure(maxsize=1000, ttl=5 * 60)

Get keys, values, and items from the cache with cache.keys(), cache.values(), and cache.items():

cache.set_many({'a': 1, 'b': 2, 'c': 3})
assert list(cache.keys()) == ['a', 'b', 'c']
assert list(cache.values()) == [1, 2, 3]
assert list(cache.items()) == [('a', 1), ('b', 2), ('c', 3)]

Iterate over cache keys:

for key in cache:
    print(key, cache.get(key))
    # 'a' 1
    # 'b' 2
    # 'c' 3

Check if key exists with cache.has() and key in cache:

assert cache.has('a')
assert 'a' in cache

Use callbacks to be notified of on-get, on-set, and on-delete events:

def on_get(key, value, exists):

def on_set(key, new_value, old_value):

def on_delete(key, value, cause):

Enable cache statistics:

cache_with_stats = Cache(enable_stats=True)

# Or via configure()

# Or directly via Cache.stats

Get cache statistics:


Manage tracking of statistics:

# Pause tracking (collected stats will not be affected)

# Resume tracking

# Reset stats

# Disable stats (WARNING: This resets stats)

# Disable via configure() (WARNING: This resets stats)

Manage multiple caches using CacheManager:

from cacheout import CacheManager

cacheman = CacheManager({'a': {'maxsize': 100},
                         'b': {'maxsize': 200, 'ttl': 900},
                         'c': {})

cacheman['a'].set('key1', 'value1')
value = cacheman['a'].get('key')

cacheman['b'].set('key2', 'value2')
assert cacheman['b'].maxsize == 200
assert cacheman['b'].ttl == 900

cacheman['c'].set('key3', 'value3')

for name, cache in cacheman:
    assert name in cacheman
    assert len(cache) == 0

For more details, see the full documentation at


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