cache: caching for humans

pip install -U anyjson redis rdcache

# For memcache
import pylibmc
from rdcache import Cache
backend = pylibmc.Client(["127.0.0.1"])
cache = Cache(backend)

# For Redis
REDIS_CONFS = {
    "default": {
        "host": "127.0.0.1",
        "port": 6379,
        "password": "",
        "db": 0,
        "socket_timeout": 3600,
        "max_connections": 128,
    },
}
from rdcache.ext import RedisCache, RedisPool
redis = RedisPool(REDIS_CONFS)
cache = RedisCache(redis.get('default'), touch = False)


@cache("mykey-%s")
def some_expensive_method(num):
    sleep(10)
    if not isinstance(num, int):
        if isinstance(num, basestring) and num.isdigit():
            num = int(num)
        else:
            num = 0
    return num

# reads 42 from the cache, the key is mykey-42
some_expensive_method(42)

# re-calculates and writes 42 to the cache
some_expensive_method.refresh(42)

# get the cached value or throw an error
# (unless default= was passed to @cache(...))
some_expensive_method.cached(42)

enabled    If `False`, the backend cache will not be used at all,
           and your functions will be run as-is, even when you call
           `.cached()`.  This is useful for development, when the
           function may be changing rapidly.
           Default: True

default    If given, `.cached()` will return the given value instead
           of raising a KeyError.
           
type       data/json
           string/json/hash/list/set/zset (if backend is redis)
           Default: data
           
time       expire seconds
           Default: -1 (forever)
            
touch      If true, expire time seconds everytime include reading data 

@cache("some_key_%s_%d", type='json', time=3600)
def expensive_method(name, ver=1):
    # ...