Useful cache decorators for methods and properties
Project description
Easy caching decorators
This package is intended to simplify caching and invalidation process in python-based (primarily) web applications. It’s possible to cache execution results of functions; instance, class and static methods; properties. Cache keys may be constructed in various different ways and may depend on any number of parameters.
The package supports tag-based cache invalidation and better works with Django, however any other frameworks can be used – see examples below.
Requirements
Library was tested in the following environments:
Python 2.7, 3.5, 3.6
Django 1.8, 1.9, 1.10
Feel free to try it in yours, but it’s not guaranteed it will work. Submit an issue if you think it should.
Installation
pip install easy_cache
Introduction
Different ways to cache something
# classic way
from django.core.cache import cache
def time_consuming_operation(n):
"""Calculate sum of number from 1 to provided n"""
cache_key = 'time_consuming_operation_{}'.format(n)
result = cache.get(cache_key, None)
if result is None:
# not found in cache
result = sum(range(n + 1))
# cache result for one hour
cache.set(cache_key, result, 3600)
return result
def invalidate_cache(n):
cache.delete('time_consuming_operation_{}'.format(n))
Now let’s take a look how easy_cache can help:
# easy way
from easy_cache import ecached
@ecached('time_consuming_operation_{n}', 3600)
def time_consuming_operation(n):
return sum(range(n + 1))
def invalidate_cache(n):
time_consuming_operation.invalidate_cache_by_key(n)
Heart of the package is two decorators with the similar parameters:
ecached
Should be used to decorate any callable and cache returned result.
Parameters:
cache_key – cache key generator, default value is None so the key will be composed automatically based on function name, namespace and passed parameters. Also supports the following parameter types:
string – may contain Python advanced string formatting syntax, later this value will be formatted with dict of parameters provided to decorated function, see examples below.
sequence of strings – each string must be function parameter name.
callable – used to generate cache key, decorated function parameters will be passed to this callable and returned value will be used. Also one additional signature is available: callable(meta), where meta is dict-like object with some additional attributes – see below.
timeout – value will be cached with provided timeout, basically it should be number of seconds, however it depends on cache backend type. Default value is DEFAULT_VALUE – internal constant means that actually no value is provided to cache backend and thus backend should decide what timeout to use. Callable is also supported.
tags – sequence of strings or callable. Should provide or return list of tags added to cached value, so cache may be invalidated later with any tag name. Tag may support advanced string formatting syntax. See cache_key docs and examples for more details.
prefix – this parameter works both: as regular tag and also as cache key prefix, as usual advanced string formatting and callable are supported here.
cache_alias – cache backend alias name, it can also be Django cache backend alias name.
cache_instance – cache backend instance may be provided directly via this parameter.
ecached_property
Should be used to create so-called cached properties, has signature exactly the same as for ecached.
Simple examples
Code examples is the best way to show the power of this package.
Decorators can be simply used with default parameters only
from easy_cache import ecached, create_cache_key
# default parameters
# cache key will be generated automatically:
#
# <__module__>.<__class__>.<function name> + function parameters converted to strings,
#
# so be careful when using complex objects, it's
# better to write custom cache key generator in such cases.
#
# timeout will be default for specified cache backend
# "default" cache backend will be used if you use Django
@ecached()
def time_consuming_operation(*args, **kwargs):
pass
# simple static cache key and cache timeout 100 seconds
@ecached('time_consuming_operation', 100)
def time_consuming_operation():
pass
# cache key with advanced string formatting syntax
@ecached('key:{kwargs[param1]}:{kwargs[param2]}:{args[0]}')
def time_consuming_operation(*args, **kwargs):
pass
# use specific cache alias, see "caches framework" below
from functools import partial
memcached = partial(ecached, cache_alias='memcached')
# equivalent to cache_key='{a}:{b}'
@memcached(['a', 'b'], timeout=600)
def time_consuming_operation(a, b, c='default'):
pass
Using custom cache key generators
# working with parameters provided to cached function
# cache key generator must have the same signature as decorated function
def custom_cache_key(self, a, b, c, d):
return create_cache_key(self.id, a, d)
# working with `meta` object
def custom_cache_key_meta(meta):
return '{}:{}:{}'.format(meta['self'].id, meta['a'], meta['d'])
# or equivalent
from easy_cache import meta_accepted
@meta_accepted
def custom_cache_key_meta(parameter_with_any_name):
meta = parameter_with_any_name
return '{}:{}:{}'.format(meta['self'].id, meta['a'], meta['d'])
class A(object):
id = 1
@ecached(custom_cache_key)
def time_consuming_operation(self, a, b, c=10, d=20):
pass
@ecached(custom_cache_key_meta)
def time_consuming_opeartion(self, a, b, c=10, d=20):
pass
How to cache staticmethod and classmethod correctly
# ecached decorator always comes topmost
class B(object):
# cache only for each different year
@ecached(lambda start_date: 'get_list:{}'.format(start_date.year))
@staticmethod
def get_list_by_date(start_date):
pass
CONST = 'abc'
@ecached('info_cache:{cls.CONST}', 3600, cache_alias='redis_cache')
@classmethod
def get_info(cls):
pass
MetaCallable object description
Meta object has the following parameters:
args – tuple with positional arguments provided to decorated function
kwargs – dictionary with keyword arguments provided to decorated function
returned_value – value returned from decorated function, available only when meta object is handled in tags or prefix generators. You have to check has_returned_value property before using this parameter:
python def generate_cache_key(meta): if meta.has_returned_value: # ... do something with meta.returned_value ...
call_args - dictionary with all positional and keyword arguments provided to decorated function, you may also access them via __getitem__ dict interface, e. g. meta['param1'].
function - decorated callable
scope - object to which decorated callable is attached, None otherwise. Usually it’s an instance or a class.
Prefix usage
Commonly prefix is used to invalidate all cache-keys in one namespace, e. g.:
from functools import partial
class Shop(models.Model):
single_shop_cache = partial(ecached, prefix='shop:{self.id}')
@single_shop_cache('goods_list')
def get_all_goods_list(self):
return [...]
@single_shop_cache('prices_list')
def get_all_prices_list(self):
return [...]
# if you have `shop` object you are able to use the following invalidation
# strategies:
# Invalidate cached list of goods for concrete shop
Shop.get_all_goods_list.invalidate_cache_by_key(shop)
# Invalidate cached list of prices for concrete shop
Shop.get_all_prices_list.invalidate_cache_by_key(shop)
# Invalidate all cached items for concrete shop
Shop.get_all_goods_list.invalidate_cache_by_prefix(shop)
# or
Shop.get_all_prices_list.invalidate_cache_by_prefix(shop)
# or
from easy_cache import invalidate_cache_prefix
invalidate_cache_prefix('shop:{self.id}'.format(self=shop))
Invalidation summary
There are two ways to invalidate cache objects: use ivalidation methods bound to decorated function and separate functions-invalidators.
<decorated>.invalidate_cache_by_key(*args, **kwargs)
<decorated>.invalidate_cache_by_tags(tags=(), *args, **kwargs)
<decorated>.invalidate_cache_by_prefix(*args, **kwargs)
# <decorated> should be used with class instance if it is used in class namespace:
class A:
@ecached()
def method(self):
pass
@ecached_property()
def obj_property(self):
pass
A.method.invalidate_cache_by_key()
# or
A().method.invalidate_cache_by_key()
# only one variant is possible for a property
A.obj_property.invalidate_cache_by_key()
# and
from easy_cache import (
invalidate_cache_key,
invalidate_cache_tags,
invalidate_cache_prefix,
create_cache_key,
)
# Note that `cache_instance` and `cache_alias` may be passed
# to the following invalidators
invalidate_cache_key(cache_key)
invalidate_cache_tags(tags)
invalidate_cache_prefix(prefix)
Here tags can be as string (single tag) or list of tags. Bound methods should be provided with parameters if they are used in cache key/tag/prefix:
@ecached('key:{a}:value:{c}', tags=['tag:{a}'], prefix='pre:{b}', cache_alias='memcached')
def time_consuming_operation(a, b, c=100):
pass
time_consuming_operation.invalidate_cache_by_key(a=1, c=11)
time_consuming_operation.invalidate_cache_by_tags(a=10)
time_consuming_operation.invalidate_cache_by_prefix(b=2)
# or using `create_cache_key` helper
invalidate_cache_key(
create_cache_key('key', 1, 'value', 11), cache_alias='memcached'
)
invalidate_cache_tags(create_cache_key('tag', 10), cache_alias='memcached')
invalidate_cache_prefix('pre:{}'.format(2), cache_alias='memcached')
Internal caches framework
Easy-cache uses build-in Django cache framework by default, so you can choose what cache storage to use on every decorated function, e.g.:
# Django settings
CACHES={
'local_memory': {
'BACKEND': 'django.core.cache.backends.locmem.LocMemCache',
'LOCATION': 'locmem',
'KEY_PREFIX': 'custom_prefix',
},
'memcached': {
'BACKEND': 'django.core.cache.backends.memcached.MemcachedCache',
'LOCATION': '127.0.0.1:11211',
'KEY_PREFIX': 'memcached',
}
}
# then in somewhere code
@ecached(..., cache_alias='memcached')
# or
@ecached(..., cache_alias='local_memory')
# or even
from django.core.cache import caches
another_cache = caches['another_cache']
@ecached(..., cache_instance=another_cache)
However if you don’t use Django, there is cache framework build into easy-cache package, it may be used in the same fashion as Django caches:
# Custom cache instance class must implement AbstractCacheInstance interface:
from easy_cache.abc import AbstractCacheInstance
from easy_cache.core import DEFAULT_TIMEOUT, NOT_FOUND
class CustomCache(AbstractCacheInstance):
def get(self, key, default=NOT_FOUND):
...
def get_many(self, keys):
...
def set(self, key, value, timeout=DEFAULT_TIMEOUT):
...
def set_many(self, data_dict, timeout=DEFAULT_TIMEOUT):
...
def delete(self, key):
...
from easy_cache import caches
custom_cache = CustomCache()
caches['new_cache'] = custom_cache
caches.set_default(CustomCacheDefault())
# and then
@ecached(..., cache_alias='new_cache')
# or
@ecached(..., cache_instance=custom_cache)
# will use `default` alias
@ecached(...)
There is already implemented redis cache instance class, based on redis-py client:
from redis import StrictRedis
from easy_cache.contrib.redis_cache import RedisCacheInstance
from easy_cache import caches
redis_cache = RedisCacheInstance(StrictRedis(host='...', port='...'))
caches.set_default(redis_cache)
# will use `default` alias
@ecached(...)
Dynamic timeout example
You may need to provide cache timeout dynamically depending on function parameters:
def dynamic_timeout(group):
if group == 'admins':
timeout = 10
else:
timeout = 100
return timeout
@ecached('key:{group}', timeout=dynamic_timeout)
def get_users_by_group(group):
...
Development and contribution
Live instances of Redis and Memcached are required for few tests to pass, so it’s recommended to use docker to setup necessary environment:
> docker-machine ip default
[IP] <- your DOCKER_HOST ip address
> docker container create --name=easy_cache-redis -p 6379:6379 redis:latest
> docker container start easy_cache-redis
export EASY_CACHE_REDIS_HOST=[IP]:6379
> docker container create --name=easy_cache-memcached -p 11211:11211 memcached:latest
> docker container start easy_cache-memcached
export EASY_CACHE_MEMCACHED_HOST=[IP]:11211
# to enable debug logs
# export EASY_CACHE_DEBUG="yes"
# install package localy
pip install -e .[tests]
# run tests with pytest or tox
pytest
tox
Performance and overhead
Benchmarking may be executed with tox command and it shows that decorators give about 4% of overhead in worst case and about 1-2% overhead on the average.
If you don’t use tags or prefix you will get one cache request for get and one request for set if result not found in cache, otherwise two consecutive requests will be made: get and get_many to receive actual value from cache and validate its tags (prefix). Then one set_many request will be performed to save a data to cache storage.
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