aiocache 0.0.3

Asynchronous redis cache

An asynchronous cache implementation with multiple backends for asyncio. Used django-redis-cache and redis-simple-cache as inspiration for the initial structure.

Disclaimer: The code is still in alpha version so new versions may introduce breaking changes. Once version 1.0 is reached, deprecation policy will be introduced.

Current supported backends are:

• SimpleMemoryCache

• RedisCache using aioredis

• MemCache using aiomcache IN PROGRESS

This libraries aims for simplicity over specialization. It provides a common interface for all caches which allows to store any python object. The operations supported by all backends are:

• add

• exists

• get

• set

• multi_get

• multi_set

• delete

Usage

Install the package with pip install aiocache.

cached decorator

The typical use case is to decorate function calls that interact with some external resource. You can do this easily with the cached decorator:

import asyncio

from collections import namedtuple

from aiocache import cached, RedisCache
from aiocache.serializers import PickleSerializer

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


@cached(ttl=10)
async def async_main():
    print("First ASYNC non cached call...")
    await asyncio.sleep(1)
    return Result("content", 200)


if __name__ == "__main__":
    loop = asyncio.get_event_loop()
    print(loop.run_until_complete(async_main()))
    print(loop.run_until_complete(async_main()))
    print(loop.run_until_complete(async_main()))
    print(loop.run_until_complete(async_main()))

The decorator by default will use the SimpleMemoryCache backend and the DefaultSerializer. If you want to use a different backend, you can call it with cached(ttl=10, backend=RedisCache). Also, if you want to use a specific serializer just use cached(ttl=10, serializer=DefaultSerializer())

Sometimes, you will want to use this decorator with specific backend and serializer and explicitly doing that in every decorator doesn’t follow the Do not Repeat Yourself principle. This is why the config_default_cache is provided. This configures a global cache that can be imported from anywhere in your code:

import asyncio
import aiocache

from collections import namedtuple

from aiocache import cached

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

aiocache.config_default_cache()

async def global_cache():
    await aiocache.default_cache.set("key", "value")
    await asyncio.sleep(1)
    return await aiocache.default_cache.get("key")


@cached(ttl=10, namespace="test:")
async def decorator_example():
    print("First ASYNC non cached call...")
    await asyncio.sleep(1)
    return Result("content", 200)


if __name__ == "__main__":
    loop = asyncio.get_event_loop()
    print(loop.run_until_complete(global_cache()))
    print(loop.run_until_complete(decorator_example()))
    print(loop.run_until_complete(decorator_example()))
    print(loop.run_until_complete(decorator_example()))

So, the decorator resolves the cache to use as follows:

1. If a backend is passed, use that one.

2. If there is no backend but a default_cache exists (populated with aiocache.config_default_cache) it will use that one.

3. If any of the previous happened, use the SimpleMemoryCache with DefaultSerializer (if serializer is passed, it will use that one).

Also in some cases, some backends like the RedisCache, may need extra arguments like endpoint or port. You can also pass them in the aiocache.config_default_cache or in the cached decorator.

Backends and serializers

You can instantiate a cache class and interact with it as follows:

import asyncio

from aiocache import RedisCache


async def main():
    cache = RedisCache(endpoint="127.0.0.1", port=6379, namespace="main:")
    await cache.set("key", "value")
    await cache.set("expire_me", "value", ttl=10)  # Key will expire after 10 secs
    print(await cache.get("key"))
    print(await cache.get("expire_me"))
    print(await cache.ttl("expire_me"))


if __name__ == "__main__":
    loop = asyncio.get_event_loop()
    loop.run_until_complete(main())

In some cases, you may want to cache complex objects and depending on the backend, you may need to transform the data before doing that. aiocache provides a couple of serializers you can use:

import asyncio

from collections import namedtuple
from aiocache import RedisCache
from aiocache.serializers import PickleSerializer


MyObject = namedtuple("MyObject", ["x", "y"])


async def main():
    cache = RedisCache(serializer=PickleSerializer(), namespace="default:")
    await cache.set("key", MyObject(x=1, y=2))  # This will serialize to pickle and store in redis with bytes format
    my_object = await cache.get("key")  # This will retrieve the object and deserialize back to MyObject
    print("MyObject x={}, y={}".format(my_object.x, my_object.y))


if __name__ == "__main__":
    loop = asyncio.get_event_loop()
    loop.run_until_complete(main())

For more examples, visit the examples folder of the project.