Why Ring?

Caching is a popular concept widely spread on the broad range of computer science. But cache interface is not well-developed yet. Ring is one of the solutions for humans. Its approach is close integration with a programming language.

Common problems of cache

note:Skip this section if you are familiar with cache and decorator patterns for cache in Python world.

The straightforward approach to storage

Traditionally we considered cache as a storage. In that sense, calling useful actual_function() with an argument for the cached result of cached_function() looks like next series of works:

# psuedo code for rough flow
key = create_key()
if storage.has(key):
    result = storage.get(key)
    result = cached_function()
    storage.set(key, result)

What’s the problem? We are interested in cached_function() and actual_function() instead of storage. But the code is full of storage operations.

Decorated cache function

Lots of cache libraries working with immutable functions share the similar solution. Here is a functools.lru_cache() example:

from functools import lru_cache

def cached_function():

result = cached_function()

This code is a lot more readable. Now the last 2 lines of code show what the code does. Note that this code even includes the definition of cached_function() which was not included in the prior code.

If the programming world was built on immutable functions, this is perfect; But actually not. I really love functools.lru_cache() but couldn’t use it for most of the cases.

In the real world, lots of functions are not pure function - but still, need to be cached. Since this also is one of the common problems, there are solutions too. Let’s see Django’s view cache which helps to reuse web page for specific seconds.

from django.views.decorators.cache import cache_page

@cache_page(60 * 15)
def cached_page(request):

It means the view is cached and the cached data is valid for 15 minutes. In this case, actual_function() is inside of the Django. The actual function will generate HTTP response based on the cached_page. It is good enough when cache expiration is not a real-time requirement.

Manual expiration

Unfortunately, websites are often real-time. Suppose it was a list of customer service articles. New articles must be shown up in short time. This is how Django handle it with The per-view cache.

request = Request(...)  # fake request to create key
key = get_cache_key(request)
cache.delete(key)  # expire

get_cache_key and cache are global names from Django framework to control cache. We started from a neat per-view cache decorator - but now it turns into a storage approach which we demonstrated at first section.

You can control them at a consistent level with Ring.

see:Ring controls cache life-cycle with sub-functions section for details.
see:ring.django.cache_page() which exactly solved the problem.

Methods and descriptors support

This kind of convenient decorators commonly works for plain functions. Then how about class components like methods, classmethod and property? Ring supports them in expected convention with a unique form.

see:Methods and descriptors support section for details.

Fixed strategy

Sometimes we need more complicated strategy than normal. Suppose we have very heavy and critical layer. We don’t want to lose cache. It must be updated in every 60 seconds, but without losing the cached version even if it wasn’t updated - or even during it is being updated. With common solutions, we needed to drop the provided feature but to implement a new one.

You can replace semantics of Ring commands and storage behaviors.

see:Ring comes with configurable commands and storage actions section for details.

Hidden backend

You might find another glitch. Their backends are concealed. Memory is ok. There are fewer reasons to uncover data from it. For services, the common cache backends are storages and database. Working high-level APIs are good. But we need to access the storages outside of the original product, or even not a Python project.

Ring has a transparent interface for backends. Moving between high-level ring.ring_base.Ring and low-level storage interfaces are straightforward and smooth.

see:Ring approaches backend transparent way section for details.

Data encoding

How to save non-binary data? Python supports pickle as a standard library to convert python objects to binary. Some of the storage libraries like python-memcached implicitly run pickle to support saving and loading Python objects.

class A(object):
    """Custom object with lots of features and data"""

client = memcache.Client(...)

original_data = A()
client.set(key, original_data)
loaded_data = client.get(key)

assert isinstance(loaded_data, A)  # True
assert original_data == loaded_data  # mostly True

Unfortunately, pickle is not compatible out of the Python world and even some complex Python classes also generate massive pickle data for small information. For example, when you have non-Python code which accesses to the same data, pickle doesn’t fit.

In Ring, data encoder is not a fixed value. Choose a preferred way to encode data by each ring rope.

note:To be fair, you can pass pickler parameter to memcached.Client in python-memcached to change the behavior. In Ring, you can reuse the same memcached.Client to use multiple coders.
see:Ring provides a configurable data-coding layer section for details.

Ring controls cache life-cycle with sub-functions

The basic usage is similar to functools.lru_cache() or Django per-view cache.

import ring

def cached_function():

result = cached_function()

Extra operations are supported as below:

cached_function.update()  # force update
cached_function.delete()  # expire
cached_function.execute()  # this will not generate cache
cached_function.get()  # get value only when cache exists

Ring provides a common auto-cache approach by default but not only that. Extra controllers provide full functions for cache policy and storages.

see:Attributes of Ring object for details.

Function parameters are also supported in an expected manner:

def cached_function(a, b, c):

cached_function(10, 20, 30)  # normal call
cached_function.delete(10, 20, 30)  # delete call

Ring approaches backend transparent way

High-level interface providers like Ring cannot expose full features of the backends. Various storages have their own features by their design. Ring covers common features but does not cover others. ring.func.base.Ring objects serve data extractors instead.

client = memcache.Client(...)

def f(a):

cache_key = f.key(10)  # cache key for 10
assert f.storage.backend is client
encoded_data = f.storage.backend.get(cache_key)  # get from memcache client
actual_data = f.decode(encoded_data)  # decode
see:Attributes of Ring object for details.

Ring provides a configurable data-coding layer

python-memcached supports pickle by default but pylibmc doesn’t. By adding coder='pickle', next code will be cached through pickle even with pylibmc. Of course for other backends too.

client = pylibmc.Client(...)

@ring.memcache(client, coder='pickle')
def f(a):
note:Does it look verbose? functools.partial() is your friend. Try my_cache = functools.partial(ring.memcache, client, coder='pickle').

When you need a special coder for a function, overriding encode/decode for a specific function also is possible. For example, the next code works the same as the above.

client = pylibmc.Client(...)

def f(a):

def f_encode(value):
    return pickle.dumps(value)

def f_decode(data):
    return pickle.loads(data)
see:Save and load rich data for more information about coders.

Methods and descriptors support

Ring supports methods and descriptors including classmethod(), staticmethod() and property() in orthogonal interface. Any custom descriptors written in (weak) common convention also work.

class A(object):

    v = None

    def __ring_key__(self):
        '''convert self value typed 'A' to ring key component'''
        return v

    def method(self):
        '''method support'''

    def cmethod(self):
        '''classmethod support'''

    def smethod(self):
        '''staticmethod support'''

    def property(self):
        '''property support'''

Ring comes with configurable commands and storage actions