User Guide

Wheezy Caching comes with the following cache implementations:

  • CacheClient
  • MemoryCache
  • NullCache

Wheezy Caching provides integration with:

It introduces the idea of cache dependency that lets you effectively invalidate dependent cache items.


All cache implementations and integrations provide the same contract. That means caches can be swapped without a need to modify the code. However there does exist a challenge: some caches are singletons and correctly provide inter-thread synchronization (thread safe), while others require an instance per thread (not thread safe), for which some sort of pooling is required. This challenge is transparently resolved.

Here is an example how to configure pylibmc - memcached (client written in C):

from wheezy.core.pooling import EagerPool
from wheezy.caching.pylibmc import MemcachedClient
from wheezy.caching.pylibmc import client_factory

# Cache Pool
pool = EagerPool(lambda: client_factory(['/tmp/memcached.sock']), size=10)
# Factory
cache = MemcachedClient(pool)

# Client code

The client code remains unchanged even some cache implementations require pooling to remain thread safe.


CacheClient serves as mediator between a single entry point that implements Cache and one or many namespaces targeted to cache factories.

CacheClient lets us partition application cache by namespaces, effectively hiding details from client code.

CacheClient accepts the following arguments:

  • namespaces - a mapping between namespace and cache factory.
  • default_namespace - namespace to use in case it is not specified in cache operation.

In the example below we partition application cache into three (default, membership and funds):

from wheezy.caching import ClientCache
from wheezy.caching import MemoryCache
from wheezy.caching import NullCache

default_cache = MemoryCache()
membership_cache = MemoryCache()
funds_cache = NullCache()
cache = ClientCache({
    'default': default_cache,
    'membership': membership_cache,
    'funds': funds_cache,
}, default_namespace='default')

Application code is designed to work with a single cache by specifying namespace to use:

cache.add('x1', 1, namespace='default')

At some point of time we might change our partitioning scheme so all namespaces reside in a single cache:

default_cache = MemoryCache()
cachey = ClientCache({
    'default': default_cache,
    'membership': default_cache,
    'funds': default_cache
}, default_namespace='default')

What happened with no changes to application code? These are just configuration settings.


MemoryCache is an effective, high performance in-memory cache implementation. There is no background routine to invalidate expired items in the cache, instead they are checked on each get operation.

In order to effectively manage invalidation of expired items (those that are not actively requested) each item being added to cache is assigned to a time bucket. Each time bucket has a number associated with a point in time. So if incoming store operation relates to time bucket N, all items from that bucket are being checked and expired items removed.

You control a number of buckets during initialization of MemoryCache. Here are attributes that are accepted:

  • buckets - a number of buckets present in cache (defaults to 60).
  • bucket_interval - what is interval in seconds between time buckets (defaults to 15).

Interval set by bucket_interval shows how often items in cache will be checked for expiration. So if it set to 15 means that every 15 seconds cache will choose a bucket related to that point in time and all items in bucket will be checked for expiration. Since there are 60 buckets in the cache that means only 1/60 part of cache items are locked. This lock does not impact items requested by get/get_multi operations. Taking into account this lock happens only once per 15 seconds it cause minor impact on overall cache performance.


NullCache is a cache implementation that actually does not do anything but silently performs cache operations that result in no change to state.

  • get, get_multi operations always report miss.
  • set, add, etc (all store operations) always succeed.


python-memcached is a pure Python memcached client. You can install this package via easy_install:

$ env/bin/easy_install python-memcached

Here is a typical use case:

from wheezy.caching.memcache import MemcachedClient

cache = MemcachedClient(['unix:/tmp/memcached.sock'])

You can specify a key encoding function by passing a key_encode argument that must be a callable that does key encoding. By default string_encode() is applied.

All arguments passed to MemcachedClient() are the same as those passed to the original Client from python-memcache. Note, python-memcached Client implementation is thread local object.


pylibmc is a quick and small memcached client for Python written in C. Since this package is an interface to libmemcached, you need the development version of this library installed so pylibmc can be compiled. If you are using Debian:

apt-get install libmemcached-dev

Now, you can install this package via easy_install:

$ env/bin/easy_install pylibmc

Here is a typical use case:

from wheezy.core.pooling import EagerPool
from wheezy.caching.pylibmc import MemcachedClient
from wheezy.caching.pylibmc import client_factory

pool = EagerPool(lambda: client_factory(['/tmp/memcached.sock']), size=10)
cache = MemcachedClient(pool)

You can specify a key encoding function by passing a key_encode argument that must be a callable that does key encoding. By default string_encode() is applied.

All arguments passed to client_factory() are the same as those passed to the original Client from pylibmc. Default client factory configures pylibmc Client to use binary protocol, tcp_nodelay and ketama algorithm.

Since pylibmc implementation is not thread safe it requires pooling, as we do here. EagerPool holds a number of pylibmc instances.

Key Encoding

Memcached has some restrictions concerning the keys used. Text protocol requires a valid key that contains only ASCII characters except space (0x20), carriage return (0x0d), and line feed (0x0a), since these characters are meaningful in text protocol. Key length is restricted to 250.

  • string_encode() - encodes key with UTF-8 encoding.
  • base64_encode() - encodes key with base64 encoding.
  • hash_encode() - encodes key with given hash function. See list of available hashes in hashlib module from the Python Statndard Library. Additional algorithms may also be available depending upon the OpenSSL library that Python uses on your platform.

There is a general purpose function:

  • encode_keys() - encodes all keys in mapping with key_encode callable. Returns a tuple of: key mapping (encoded key => key) and value mapping (encoded key => value).

You can specify the key encoding function to use, by passing the key_encode argument to memcache and/or pylibmc cache factory.


CacheDependency introduces a wire between cache items so they can be invalidated via a single operation, thus simplifying code necessary to manage dependencies in cache.

CacheDependency is not related to any particular cache implementation.

CacheDependency can be used to invalidate items across different cache partitions (namespaces). Note that delete must be performed for each namespace and/or cache.

Master Key

It is important to avoid key collisions for the master key due to the way in which dependency keys are built. The dependency keys are built by adding a suffix with incremental number to the master key, e.g. if master key is ‘key’ than dependent keys used by CacheDependency will be ‘key1’, ‘key2’, ‘key3’, etc. The master key stores the number of dependent keys thus this number is incremented each time you add something to a dependency.

If a master key is composed as a concatenation with some id it must be suffixed with a delimiter (a symbol that is not part of the id) to avoid key collision. In the example below id is a number so choosing ‘:’ as a delimiter suites our needs:

def master_key_order(id):
    return 'mk:order:' + str(id) + ':'

For order id 100 the master key is ‘mk:order:100:’ and dependent keys take space ‘mk:order:100:1’ for the first item added, ‘mk:order:100:2’ for the second, etc. If we add 2 items to cache dependency the value stored by the master key is 2.


Let’s demostrate this by example. We establish dependency between keys k1, k2 and k3 for 600 seconds. Please note that dependency does not need to be passed between various parts of application. You can create it in one place, than in other, etc. CacheDependency stores it state in cache:

# this is sample from module a.
dependency = CacheDependency('master-key', time=600)
dependency.add_multi(cache, ['k1', 'k2', 'k3'])

# this is sample from module b.
dependency = CacheDependency('master-key', time=600)
dependency.add(cache, 'k4')

Note that module b has no idea about keys used in module a. Instead they share a cache dependency virtually.

Once we need to invalidate items related to cache dependencies, this is what we do:

dependency = CacheDependency('master-key')

delete operation must be repeated for each namespace (it doesn’t manage namespace dependency) and/or cache:

# Using namespaces
dependency = CacheDependency('master-key')
dependency.delete(cache, namespace='membership')
dependency.delete(cache, namespace='funds')

# Using caches
dependency = CacheDependency('master-key')

Cache dependency is an effective way to reduce coupling between modules in terms of cache item invalidation.

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