pyfscache 0.9.12

A file system cache for python.

A simple filesystem cache for python.

Home Page, Documentation, & Repository

• Home Page: http://pyfscache.bravais.net

• Online Documentation: http://pythonhosted.org/pyfscache/

• Code Repository: https://github.com/jcstroud/pyfscache/

Introduction

Pyfscache (python filesystem cache) is a filesystem cache that is easy to use. The principal class is FSCache, instances of which may be used as decorators to create cached functions with very little coding overhead:

import pyfscache
cache_it = pyfscache.FSCache('some/cache/directory',
                             days=13, hours=4, minutes=2.5)
@cache_it
def cached_doit(a, b, c):
  return [a, b, c]

It’s that simple!

Now, every time the function cached_doit is called with a particular set of arguments, the cache cache_it is inspected to see if an identical call has been made before. If it has, then the return value is retrieved from the cache_it cache. If not, the return value is calculated with cached_doit, stored in the cache, and then returned.

Expiration

In the code above, the expiration for cache_it is set to 1,137,750 seconds (13 days, 4 hours, and 2.5 minutes), which means that every item created by cache_it has a lifetime of 1,137,750 seconds, beginning when the item is made (not beginning when cache_it is made). Values specifying lifetime may be provided with the keywords years, months, weeks, days, hours, minutes, and seconds. The lifetime is the total for all keywords.

If these optional keyword arguments are not included, then items added by the FSCache object never expire:

no_expiry_cache = pyfscache.FSCache('some/cache/directory')

Note

Several instances of FSCache objects can use the same cache directory. Each will honor the expirations of the items therein. Thus, it is possible to have a cache mixed with objects of many differening lifetimes, made by many instances of FSCache.

Works Like a Map

Instances of FSCache work like mapping objects, supporting item getting and setting:

>>> cache_it[('some', ['key'])] = {'some': 'value'}
>>> cache_it[('some', ['key'])]
{'some': 'value}

However, deletion with the del statement only works on memory. To erase an item in the cache directory, use expire:

>>> cache_it.get_loaded()
['LIlWpBZL68MBJaXouRjFBL3fzScyxh5q56hqSZ3DBK']
>>> del cache_it[('some', ['key'])]
>>> cache_it.get_loaded()
[]
>>> ('some', ['key']) in cache_it
True
>>> cache_it[('some', ['key'])]
{'some': 'value}
>>> cache_it.expire(('some', ['key']))
>>> ('some', ['key']) in cache_it
False

Decorators

What if you didn’t write the function you want to cache? Although their convenience is manifest in the example above, it is not necessary to use decorators:

import pyfscache
cache = pyfscache.FSCache('some/cache/directory',
                          days=13, hours=4, minutes=2.5)

def uncached_doit(a, b, c):
  return [a, b, c]

cached_doit = cache(uncached_doit)

Versatility

FSCache objects should work on the vast majority of python “callables”, including instance methods and even built-ins:

# a cached built-in
cached_list = cache_it(list)

# a cached instance method
class AClass(object):
  @cahe_it
  def some_cached_instance_method(self, a, r, g, s):
    return (a + r) / (g * s)

Note

The rule of thumb is that if python’s cPickle module can handle the expected arguments to the cached function, then so can pyfscache.