Skip to main content

A time constraint LRUCache Implementation

Project description

A time constraint LRUCache Implementation

Installation

pip install timedLruCache

Summary

The timed LRUCache is a dict-like container that is also size limited. It uses the prune method when instantiated with time to remove time expired objects.

Example

Simple LRUCache

from lrucache.lru_cache import LRUCache

lru = LRUCache(maxSize=4)
lru.insert_key_value("a", 99)

lru.insert_key_value("b", 202)
lru["c"] = 203
lru["d"] = 204
lru["e"] = 205

lru.get_value("a")
lru.get("a", None)

print(lru)
print(lru.values())

# LRUCache(timeout=None, size=4, data={'b': 202, 'c': 203, 'd': 204, 'e': 205})
# [202, 203, 204, 205]

# insert into lrucache
lru.insert_key_value("j", 302)
# or
lru["k"] = 403

# retrieve from the lrucache
lru.get_value("z")
# or
lru.get("z", None)

# delete from the lrucache
lru.delete_key("z")
# or
lru.pop("z", None)

Test memoization: Sample use with the decorator

from lrucache.lru_cache import LRUCache
from lrucache.memoize_decorator import memoized_cache

@memoized_cache(cache=LRUCache(maxSize=5))
def get_random(max_value):
    import random
    return random.random() * max_value

print(get_random(1)) # 0.6869437097681024
print(get_random(1)) # 0.6869437097681024
print(get_random(3)) # 1.2792457326076399
print(get_random(4)) # 1.9216226691107239
print(get_random(5)) # 3.442601057826532
print(get_random(5)) # 3.442601057826532
print(get_random(7)) # 0.6831533160972438
print(get_random(8)) # 7.40200570325546
print(get_random(1)) # 0.37636284785825047


print(get_random.misses)
# => 7
print(get_random.hits)
# => 2

# cache info
get_random.cache_info()
# => memoized_cache(hits=2, misses=7, maxsize=5, currsize=5)

# check the cache stored key, value, items pairs
print(get_random.cache.keys())
# => dict_keys([-5205072475343462643, 8575776084210548143, -2238842041537299568, -8811688270097994377, 2613783748954017437])

print(get_random.cache.values())
# => [1.9216226691107239, 3.442601057826532, 0.6831533160972438, 7.40200570325546, 0.37636284785825047]

print(get_random.cache.items())
# => [
#     (-5205072475343462643, 1.9216226691107239), (8575776084210548143, 3.442601057826532),
#     (-2238842041537299568, 0.6831533160972438), (-8811688270097994377, 7.40200570325546),
#     (2613783748954017437, 0.37636284785825047)
# ]


# Simple report on performance
# check report before get_random.reset()
report = f'Hit %: {(float(get_random.hits) / (get_random.hits + get_random.misses))}'
print(report)
# => Hit %: 0.2222222222222222

# reset hits and misses count to zero.
get_random.reset()

Test time constraint

from time import sleep
from lrucache.lru_cache import LRUCache

timed_lru = LRUCache(maxSize=4, timeout=10)

timed_lru["a"] = 202
timed_lru["b"] = 203
timed_lru["c"] = 204
timed_lru["d"] = 205
timed_lru["e"] = 206

# cache size remains 4, after inserting 5 items into cache.
print(timed_lru) # LRUCache(timeout=10, size=4, data={'b': 203, 'c': 204, 'd': 205, 'e': 206})

sleep(60)
# cache should be empty after 60s as it clears its entry after 10s (timeout)
timed_lru["d"] = 203
timed_lru["e"] = 204
timed_lru["f"] = 205
timed_lru["g"] = 206
timed_lru["h"] = 207

# cache now has new entries
print(timed_lru) # LRUCache(timeout=10, size=4, data={'e': 204, 'f': 205, 'g': 206, 'h': 207})

sleep(30)
# cache entry expires after 10s and as a result we have nothing in the cache (data = {}).
print(timed_lru) # LRUCache(timeout=10, size=4, data={})
timed_lru.stop_timer()

Sample Usage with the decorator (maxSize and timeout)

@memoized_cache(cache=LRUCache(maxSize=5, timeout=5))
def get_random(max_value):
    import random
    return random.random() * max_value

get_random(1)
get_random(1)
get_random(3)
get_random(4)
get_random(5)
get_random(5)
get_random(7)
get_random(8)
get_random(1)

get_random.cache_info() # memoized_cache(hits=2, misses=7, maxsize=5, currsize=5)

print(get_random.cache.values())
# => [2.108203625973244, 0.2784180276772963, 3.9932738384806856, 1.2462533799577011, 0.8501249397423805]

print(get_random.cache.items()) # [(7793041093296417556, 2.108203625973244), (-5573334794002472495, 0.2784180276772963), (6169942939433972205, 3.9932738384806856), (-179359314705978364, 1.2462533799577011), (2135404498036021478, 0.8501249397423805)]

print(get_random.cache.keys())
# dict_keys([7793041093296417556, -5573334794002472495, 6169942939433972205, -179359314705978364, 2135404498036021478])

print(get_random.cache.values())
# [2.108203625973244, 0.2784180276772963, 3.9932738384806856, 1.2462533799577011, 0.8501249397423805]

sleep(20)
get_random.cache_info() # memoized_cache(hits=2, misses=7, maxsize=5, currsize=0)

# stop the timer
get_random.cache.stop_timer()

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

timedLruCache-1.0.1.tar.gz (6.3 kB view details)

Uploaded Source

File details

Details for the file timedLruCache-1.0.1.tar.gz.

File metadata

  • Download URL: timedLruCache-1.0.1.tar.gz
  • Upload date:
  • Size: 6.3 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.13.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/41.0.1 requests-toolbelt/0.9.1 tqdm/4.32.1 CPython/3.6.5

File hashes

Hashes for timedLruCache-1.0.1.tar.gz
Algorithm Hash digest
SHA256 64819a807d24dd1b598e77c95db22d4b8ccae694621e6a0a3f1f89cb51e25c37
MD5 c7c6e2dce755fcd42eed30a048e78057
BLAKE2b-256 feca9f8458c74aab252d3fc3da7d3212b3fddc6145f1978632b5c4ab0ae4f5e1

See more details on using hashes here.

Provenance

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page