aiocache 0.11.0

Installing

• pip install aiocache

• pip install aiocache[redis]

• pip install aiocache[memcached]

• pip install aiocache[redis,memcached]

• pip install aiocache[msgpack]

Usage

Using a cache is as simple as

>>> import asyncio
>>> loop = asyncio.get_event_loop()
>>> from aiocache import Cache
>>> cache = Cache(Cache.MEMORY) # Here you can also use Cache.REDIS and Cache.MEMCACHED, default is Cache.MEMORY
>>> loop.run_until_complete(cache.set('key', 'value'))
True
>>> loop.run_until_complete(cache.get('key'))
'value'

Or as a decorator

import asyncio

from collections import namedtuple

from aiocache import cached, Cache
from aiocache.serializers import PickleSerializer
# With this we can store python objects in backends like Redis!

Result = namedtuple('Result', "content, status")


@cached(
    ttl=10, cache=Cache.REDIS, key="key", serializer=PickleSerializer(), port=6379, namespace="main")
async def cached_call():
    print("Sleeping for three seconds zzzz.....")
    await asyncio.sleep(3)
    return Result("content", 200)


def run():
    loop = asyncio.get_event_loop()
    loop.run_until_complete(cached_call())
    loop.run_until_complete(cached_call())
    loop.run_until_complete(cached_call())
    cache = Cache(Cache.REDIS, endpoint="127.0.0.1", port=6379, namespace="main")
    loop.run_until_complete(cache.delete("key"))

if __name__ == "__main__":
    run()

The recommended approach to instantiate a new cache is using the Cache constructor. However you can also instantiate directly using aiocache.RedisCache, aiocache.SimpleMemoryCache or aiocache.MemcachedCache.

You can also setup cache aliases so its easy to reuse configurations

import asyncio

from aiocache import caches

# You can use either classes or strings for referencing classes
caches.set_config({
    'default': {
        'cache': "aiocache.SimpleMemoryCache",
        'serializer': {
            'class': "aiocache.serializers.StringSerializer"
        }
    },
    'redis_alt': {
        'cache': "aiocache.RedisCache",
        'endpoint': "127.0.0.1",
        'port': 6379,
        'timeout': 1,
        'serializer': {
            'class': "aiocache.serializers.PickleSerializer"
        },
        'plugins': [
            {'class': "aiocache.plugins.HitMissRatioPlugin"},
            {'class': "aiocache.plugins.TimingPlugin"}
        ]
    }
})


async def default_cache():
    cache = caches.get('default')   # This always returns the SAME instance
    await cache.set("key", "value")
    assert await cache.get("key") == "value"


async def alt_cache():
    cache = caches.create('redis_alt')   # This creates a NEW instance on every call
    await cache.set("key", "value")
    assert await cache.get("key") == "value"


def test_alias():
    loop = asyncio.get_event_loop()
    loop.run_until_complete(default_cache())
    loop.run_until_complete(alt_cache())

    loop.run_until_complete(caches.get('redis_alt').delete("key"))


if __name__ == "__main__":
    test_alias()

How does it work

Aiocache provides 3 main entities:

• backends: Allow you specify which backend you want to use for your cache. Currently supporting: SimpleMemoryCache, RedisCache using aioredis and MemCache using aiomcache.

• serializers: Serialize and deserialize the data between your code and the backends. This allows you to save any Python object into your cache. Currently supporting: StringSerializer, PickleSerializer, JsonSerializer, and MsgPackSerializer. But you can also build custom ones.

• plugins: Implement a hooks system that allows to execute extra behavior before and after of each command.

If you are missing an implementation of backend, serializer or plugin you think it could be interesting for the package, do not hesitate to open a new issue.

Those 3 entities combine during some of the cache operations to apply the desired command (backend), data transformation (serializer) and pre/post hooks (plugins). To have a better vision of what happens, here you can check how set function works in aiocache:

Amazing examples

In examples folder you can check different use cases:

• Sanic, Aiohttp and Tornado

• Python object in Redis

• Custom serializer for compressing data

• TimingPlugin and HitMissRatioPlugin demos

• Using marshmallow as a serializer

• Using cached decorator.

• Using multi_cached decorator.

Documentation

• Usage

• Caches

• Serializers

• Plugins

• Configuration

• Decorators

• Testing

• Examples