A collection of cache helpers
Project description
Useful cache helpers in one package!
Install
pip install cachelper
Helpers
In memory cache
memoize
Caching function return values in memory.
import cachelper
@cachelper.memoize()
def fibo(n):
if n in (0, 1):
return 1
return fibo(n - 1) + fibo(n - 2)
fibo(10)
Cache with Redis/Memcached
cache decorator
Add cache by decorating a function or method.
from redis import StrictRedis
from werkzeug.contrib.cache import RedisCache as _RedisCache
import cachelper
class RedisCache(_RedisCache, HelperMixin):
'''werkzeug.contrib.cache.RedisCache mixed with HelperMixin'''
def get_many(self, keys):
return super().get_many(*keys)
rds = StrictRedis()
cache = RedisCache(rds)
@cache("key-{user_id}", timeout=300)
def get_name(user_id):
# Fetch user name from database
...
You may use this mixin to create cache class of your own, as long as the following methods are provided:
def get(self, key)
def set(self, key, value, timeout=None)
def delete(self, key)
def get_many(self, keys)
def set_many(self, mapping, timeout=None)
cached function calls
Sometimes we don’t want to cache all calls to a specific function. So the decorator is not suitable, we may cache the call instead the function in this case:
def get_name(user_id):
# Fetch user name from database
...
user_id = 42
key = "key-{user_id}".format(user_id=user_id)
cache.call(lambda: get_name(user_id), key, timeout=300)
cached multiple calls
For most cache backends, it’s much faster to get or set caches in bulk.
def get_name(user_id):
# Fetch user name from database
...
user_ids = [1, 2, 42, 1984]
names = cache.map("key-{user_id}", get_name, user_ids, timeout=300)
Project details
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