timecache 0.0.4

==============
timecache
==============

`timecache` is a timeout-based cache implementation for Python 3.x without
any external dependencies. Depending on the selected backend, the cache can be
made persistent to survive a fresh start of a script (currently, json and
pickle files are supported as backend).

Warning: this is an early alpha version so the interface may change in future
versions.


Setup
-----

Install `timecache` using `pip`:

::

pip install -U timecache


Example
-------

A cache with a memory backend can be created as follows:

::

import time
from timecache import Cache, FIVE_SECONDS

# create cache with memory backend
cache = Cache()

# add a value to the cache with a default duration of 1 minute
cache["one"] = 1

# add a value to the cache with a duration of 5 seconds
cache.set("two", 2, FIVE_SECONDS)

print("two in cache: {}".format("two" in cache))

# show cache entry
print(cache)

# wait for 6 s
time.sleep(6)

# show cache entry
print(cache)

print("two in cache: {}".format("two" in cache))


A cache with a json file backend can be created as follows:

::

import time
from timecache import Cache, JsonBackend

# create cache with json backend
cache = Cache(backend=JsonBackend())

if not cache.is_existing:
# add a value to the cache with a default duration of 1 minute
print("adding new value to the cache...")
cache["one"] = 1
print("saving cache to file '{}'".format(cache.filename))
cache.save()

# show cache entry
print(cache)

When calling the script again the cached value will be loaded from the
json file.


Decorators
----------

There are also the three decorators `memorycache`, `jsoncache`,
`picklecache` that allows an easy caching of function return values.


::

import time
from timecache import jsoncache, FIVE_SECONDS


@jsoncache(cache_filename="add.json", ttl=FIVE_SECONDS)
def add(x, y):
return x + y


# new cache entry
print(add(1, 2))

# new cache entry
print(add(1, 3))

# load existing cache entry
print(add(1, 2))

# wait for 6 s
time.sleep(6)

# new cache entry, since old entry has expired
print(add(1, 2))


Debugging
-------

To debug the cache you can simply set the debug level.

::

import logging
logging.basicConfig(level=logging.DEBUG)


Testing
-------

For testing additionally install `nose` and then run the tests:

::

pip install nose
nosetests


Hint
----

If you do not need a time-based cache, consider the `lru_cache` function of
Python's `functools` module
(https://docs.python.org/3/library/functools.html).