Cachier
=======

Persistent, stale-free cache / memoization decorators for Python.

.. code-block:: python

  from cachier import cachier
  import datetime
  
  SHELF_LIFE = datetime.timedelta(days=3)
  
  @cachier(stale_after=SHELF_LIFE)
  def foo(arg1, arg2):
    """foo now has a persistent cache, trigerring recalculation for values stored more than 3 days!"""
    return {'arg1': arg1, 'arg2': arg2}


.. role:: python(code)
  :language: python

Dependencies and Setup
----------------------

Cachier uses the following packages:

* pymongo_
* watchdog_

You can install cachier using:

.. code-block:: python

    pip install cachier

Features
----------------------

* A simple interface.
* Defining "shelf life" for cached values.
* Local caching using pickle files.
* Cross-machine caching using MongoDB.
* Thread-safety.

Cachier is not:

* Meant as a transient cache. Python's @lru_cache is better.
* Especially fast. It is meant to replace function calls that take more than... a second, say (overhead is around 1 millisecond).

Future features:
~~~~~~~~~~~~~~~~

* S3 core.
* Multi-core caching.


Use
---

The positional and keyword arguments to the wrapped function must be hashable (i.e. Python's immutable built-in objects, not mutable containers). Also, notice that since objects which are instances of user-defined classes are hashable but all compare unequal (their hash value is their id), equal objects across different sessions will not yield identical keys.

Setting up a Cache
~~~~~~~~~~~~~~~~~~
You can add a deafult, pickle-based, persistent cache to your function - meaning it will last across different Python kernels calling the wrapped function - by decorating it with the ``cachier`` decorator (notice the ``()``!).

.. code-block:: python

  from cachier import cachier
  
  @cachier()
  def foo(arg1, arg2):
    """Your function now has a persistent cache mapped by argument values!"""
    return {'arg1': arg1, 'arg2': arg2}
    
    
Resetting a Cache
~~~~~~~~~~~~~~~~~

The Cachier wrapper adds a ``clear_cache()`` function to each wrapped function. To reset the cache of the wrapped function simply call this method:

.. code-block:: python

  foo.clear_cache()


Setting Shelf Live
~~~~~~~~~~~~~~~~~~
You can set any duration as the shelf life of cached return values of a function by providing a corresponding ``timedelta`` object to the ``stale_after`` parameter:

.. code-block:: python

  import datetime
  
  @cachier(stale_after=datetime.timedelta(weeks=2))
  def bar(arg1, arg2):
    return {'arg1': arg1, 'arg2': arg2}
    
Now when a cached value matching the given arguments is found the time of its calculation is checked; if more than ``stale_after`` time has since passed, the function will be run again for the same arguments and the new value will be cached and returned.

This is usefull for lengthy calculations that depend on a dynamic data source.

Fuzzy Shelf Live
~~~~~~~~~~~~~~~~
Sometimes you may want your function to trigger a calculation when it encounters a stale result, but still not wait on it if it's not that critical. In that case you can set ``next_time`` to ``True`` to have your function trigger a recalculation but return the currently cached stale value:

.. code-block:: python

  @cachier(next_time=True)

Further function calls made while the calculation is being performed will not trigger redundant calculations.


Cachier Cores
-------------

Pickle Core
~~~~~~~~~~~~

The default core for Cachier is pickle based, meaning each function will store its cache is a seperate pickle file in the ``~/.cachier`` directory. Naturally, this kind of cache is both machine-specific and user-specific.

You can slightly optimize pickle-based caching if you know your code will only be used in a single thread environment by setting:

.. code-block:: python

  @cachier(pickle_reload=False)

This will prevent reading the cache file on each cache read, speeding things up a bit, while also nullfying inter-thread functionality (the code is still thread safe, but different threads will have different version of the cache at times, and will sometime make unecessary function calls).


MongoDB Core
~~~~~~~~~~~~
You can set a MongoDB-based cache by assigning ``mongetter`` with a callable that returns a ``pymongo.Collection`` object:

.. code-block:: python

  @cachier(mongetter=False)

This allows you to have a cross-machine, albeit slower, cache.

.. links:
.. _pymongo: https://api.mongodb.com/python/current/
.. _watchdog: https://github.com/gorakhargosh/watchdog