Easily dump python objects to files, and then load them back.

pip install pickle-warehouse==0.0.13


Pickle Warehouse
Pickle Warehouse makes it easy to save Python
objects to files with meaningful identifiers.

How to use
Pickle Warehouse provides a dictionary-like object
that is associated with a particular directory on
your computer. ::

    from pickle_warehouse import Warehouse
    warehouse = Warehouse('/tmp/a-directory')

The keys correspond to files, and the values get
pickled to the files. ::

    warehouse['filename'] = range(100)

    import pickle
    range(100) == pickle.load(open('/tmp/a-directory/filename', 'rb'))

You can also read and delete things. ::

    # Read
    range(100) == warehouse['filename']

    # Delete

The coolest part is that the key gets interpreted
in a fancy way. Aside from strings and string-like objects,
you can use iterables of strings; all of these indices refer
to the file ``/tmp/a-directory/foo/bar/baz``::


If you pass a relative path to a file, it will be broken up as you'd expect;
that is, strings get split on slashes and backslashes. ::


Note well: Specifying an absolute path won't save things outside the warehouse directory. ::

    warehouse['/foo/bar/baz'] # -> foo, bar, baz
    warehouse['C:\\foo\\bar\\baz'] # -> c, foo, bar, baz
                                   # (lowercase "c")

If you pass a URL, it will also get broken up in a reasonable way. ::

    # /tmp/a-directory/http/thomaslevine.com/!/?foo=bar#baz

    # /tmp/a-directory/thomaslevine.com/!?foo=bar#baz

Dates and datetimes get converted to :code:`YYYY-MM-DD` format. ::

    import datetime

    # /tmp/a-directory/2014-02-26

And you can mix these formats! ::

    # /tmp/a-directory/http/thomaslevine.com/open-data/2014-02-26
    warehouse[('http://thomaslevine.com/open-data', datetime.date(2014,2,26))]

It also has typical dictionary methods like :code:`keys`, :code:`values`, :code:`items`,
and :code:`update`.

When to use
pickle-warehouse is for when you want a persistant store of Python objects.
If you want an in-memory pickle store, look at
_pickleDB: https://pythonhosted.org/pickleDB/.

Pickle Warehouse is strictly better than Mongo, so you should use it anywhere
where you were previously using Mongo. Pickle Warehouse is designed for
write-heavy workloads that need scalability (easy sharding), traditional
database reliability (ACID), flexible schemas, and highly configurable indexing.

Pickle Warehouse is acidic
Here's how it accomplishes that.

    Writes are made to a temporary file that gets renamed.
    I don't get this one, but I'm pretty sure I have it.
    Simultaneous writes are handled quite cleanly. If reads occur during writes, an error gets thrown, and you can try again.
    All data are saved to disk right away.

Mongo replacement feature checklist

* Call fsync twice, just to make sure.
* Schema validation on read and write (configurable), because who knows what you did yesterday or whether you change your mind later?
* PID + random number (+ hash?) for random number generation
* Inode exhaustion