A least recently used (LRU) 2 layer caching mechanism based in part on the Python 2.7 back-port of lru_cache

This was developed by 3Top, Inc. for use with our ranking and recommendation platform, http://www.3top.com.

lru2cache is a decorator that can be used with any user function or method to cache the most recent results in a local cache. It can alse be used with django's cache framework to cache results in a shared cache.

The first layer of caching is stored in a dict within the instance of the function or method. Each instance stores up to maxsize results based on args and kwargs passed to it. The discarding of the LRU cached values is handled by the lru2cache decorator.

The second layer of caching requires a shared cache that behaves the same as Django's cache framework. In this case it is assumed that any LRU mechanism is handled by the shared cache backend.

This arrangement allows an instance that accesses a function multiple times to retrieve the value without the expense of requesting it from a shared cache, while still allowing instances in different threads to access the result from the shared cache.

Arguments to the cached function must be hashable. If available the spooky hash function will be used for generating keys, otherwise it will default back to the slower, hashlib.sha256.

If typed is True, arguments of different types will be cached separately. For example, f(3.0) and f(3) will be treated as distinct calls with distinct results. In the case of methods, the first argument(self) is always typed.

Since the lru2cache decorator does not provide a timeout for its cache although it provides other mechanisms for programatically managing the cache.

As with lru_cache, one can view the cache statistics via a named tuple (l1_hits, l1_misses, l2_hits, l2_misses, l1_maxsize, l1_currsize), with f.cache_info(). These stats are stored within an instance, and therefore are specific to that instance. Cumulative statistics for the shared cache would need to be obtained from the shared cache.

the cache and statistics associated with a function or method can be cleared with:

f.cache_clear()

If you are using a named cache it can easily be cleared with the following:

from django.core.cache import get_cache

lru2cache_cache = get_cache('lru2cache')
lru2cache_cache.clear()

To invalidate the cache for a specific set of arguments, including the instance one can pass the same arguments to invalidate the both L1 and L2 caches:

f.invalidate(*args, **kwargs)

in the case of a method you do need to explicitly pass the instance as in the following:

foo.f.invalidate(foo, a, b)

This is not yet implemented as function but can be accomplished by first calling invalidate and the calling the wrapped function

The un-cached underlying function can always be accessed with f.__wrapped__.

At 3Top We needed a way to improve performance of slow queries, not just those using the Django ORM, but also for queries to other data stores and services. We started off with a simpler centralized caching solution using Memcached, but even those queries, when called frequently, can start to cause delays. Therefore we sought a means of caching at two layers.

Initially we looked at the possibility of using two different mechanisms but we quickly saw the advantage of maintaining the same set of keys for both caches and decided to create our own mechanism.

We used a backport python 3 functools.lru_cache() decorator as a starting point for developing an in instance cache with LRU capabilities. However we needed to ensure the keys would also be unique enough to use with a shared cache. We leverage Django's excellent cache framework for managing the layer 2 cache. This allows the use of any shared cache supported by Django.

As a starting point incorporated most of the tests for lru_cache() with minor changes to make them work with python 2.7 and incorporate the l2_cache stats. We will continue to add tests to validate the additional functionality provided by this decorator.