A Python caching library that gives a similar interface to standard Python data structures like Dict and Set but is backed by Redis.

Caches has been used in production for years across many different products, handling millions of requests.

Caches relies on setting the environment variable CACHES_DSN:

redis:https://localhost/0

If you want to cache things using more than one Redis server, you can actually set multiple environment variables and specify the caches.interface.Redis class you want to use:

# redis1 uses the standard Redis interface
export CACHES_DSN_1=redis:https://somedomain.com/0#redis1

# redis2 uses a custom interface child class
export CACHES_DSN_2=custom.interface.Redis:https://someotherdomain.com/0#redis2

After you've set the environment variable, then you just need to import caches in your code:

import caches

Caches will take care of parsing the and DSN urls and creating the associated Redis connections automatically, so after importing, Caches will be ready to use.

All Caches caching classes have a similar interface, their constructor takes a key and data:

c = Cache(['foo', 'bar', 'che'])
print c.key # foo.bar.che

If you would like to init your cache object with a value, use the data **kwarg:

c = Cache('foo', data="boom!")
print c.key # foo
print c # "boom!"

Each Caches base caching class is meant to be extended so you can set some parameters:

• serialize -- boolean -- True if you want all values pickled, False if you don't (ie, you're caching ints or strings or something).

• prefix -- string -- This will be prepended to the key args you pass into the constructor.

• ttl -- integer -- time to live, how many seconds to cache the value. 0 (default) means cache forever.

• connection_name -- string -- if you have more than one caches DSN then you can use this to set the name of the connection you want (the name of the connection is the #connection_name fragment of a DSN url).

class MyIntCache(Cache):
  serialize = False # don't bother to serialize values since we're storing ints
  prefix = "MyIntCache" # every key will have this prefix, change to invalidate all currently cached values
  ttl = 7200 # store each int for 2 hours

This is the traditional caching object, it sets a value into a key:

c = Cache('foo')
c.data = 5 # cache 5
c += 10 # increment 5 by 10, store 15 in the cache

c.clear()
print(c) # None

This caching object acts more or less like a Python dictionary:

c = DictCache('foo')
c['bar'] = 'b'
c['che'] = 'c'
for key, val in c.items():
  print(key, val) # will print "bar b" and then "che c"

This caching object acts more or less like a Python set:

c = SetCache('foo')
c.add('bar')
c.add('che')
print('che' in c) # True

This caching object acts more or less like a Python set but has some changes:

• The add() method takes a tuple (score, elem)
• The pop() method will pop off the lowest score from the set, and pops a tuple (score, elem)
• An rpop() method allows you to pop the highest score from the set.
• Iterating through the set results in tuples of (score, elem), not just elem like in a normal set or the SetCache.

c = SortedSetCache('foo')
c.add((1, 'bar'))
c.add((10, 'che'))
print('che' in c) # True
print(c.pop()) # (1, bar)

Handy for gated access:

c = SentinelCache('foo')

if not c:
    print("sentinel value isn't set so do this")

if not c:
    print("sentinel value is now set so this will never run")

Caches exposes a decorator to make caching the return value of a function easy. This only works for Cache derived caching.

The cached decorator can accept a caching class and also a key function (similar to the python built-in sorted() function key argument), except caches key argument returns a list that can be passed to the constructor of the caching class as *args.

import functools
from caches import Cache

@Cache.cached(key="some_cache_key")
def foo(*args):
    return functools.reduce(lambda x, y: x+y, args)

foo(1, 2) # will compute the value and cache the return value
foo(1, 2) # return value from cache

foo(1, 2, 3) # uh-oh, wrong value, our key was too static

Let's try again, this time with a dynamic key

@Cache.cached(key=lambda *args: args)
def foo(*args):
    return functools.reduce(lambda x, y: x+y, args)

foo(1, 2) # compute and cache, key func returned [1, 2]
foo(1, 2) # grabbed from cache
foo(1, 2, 3) # compute and cache because our key func returned [1, 2, 3]

What about custom caches classes?

class CustomCache(Cache): pass

@CustomCache.cached(key=lambda *args: args)
def foo(*args):
    return functools.reduce(lambda x, y: x+y, args)

Use pip from pypi:

pip install caches

or from source using pip:

pip install -U "git+https://github.com/jaymon/caches#egg=caches"