pycached 0.0.9

multi backend cache

cache supporting multiple backends (memory, redis). Synchronization library based on aiocache.

This library aims for simplicity over specialization. All caches contain the same minimum interface which consists on the following functions:

• add: Only adds key/value if key does not exist.

• get: Retrieve value identified by key.

• set: Sets key/value.

• multi_get: Retrieves multiple key/values.

• multi_set: Sets multiple key/values.

• exists: Returns True if key exists False otherwise.

• increment: Increment the value stored in the given key.

• delete: Deletes key and returns number of deleted items.

• clear: Clears the items stored.

• raw: Executes the specified command using the underlying client.

Contents

• Installing

• Usage

• How does it work

• Amazing examples

• Documentation

Installing

• pip install pycached

• pip install pycached[redis]

• pip install pycached[msgpack]

Usage

Using a cache is as simple as

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

Or as a decorator

import time

from collections import namedtuple

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

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


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


def run():
    cached_call()
    cached_call()
    cached_call()
    cache = Cache(Cache.REDIS, endpoint="127.0.0.1", port=6379, namespace="main")
    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 pycached.RedisCache, pycached.SimpleMemoryCache.

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

from pycached import caches

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


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


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


def test_alias():
    default_cache()
    alt_cache()

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


if __name__ == "__main__":
    test_alias()

How does it work

Pycached provides 3 main entities:

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

• 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 pycached:

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