chest 0.2.3

Related Projects

The standard library shelve is an alternative out-of-core dictionary. Chest offers the following benefits over shelve:

1. Chest supports any hashable key (not just strings)

2. Chest supports pluggable serialization and file saving schemes

Alternatively one might consider a traditional key-value store database like Redis.

Shove is another excellent alternative with support for a variety of stores.

How it works

Chest stores data in two locations

1. An in-memory dictionary

2. On the filesystem in a directory owned by the chest

As a user adds contents to the chest the in-memory dictionary fills up. When a chest stores more data in memory than desired (see available_memory= keyword argument) it writes the larger contents of the chest to disk as pickle files (the choice of pickle is configurable). When a user asks for a value chest checks the in-memory store, then checks on-disk and loads the value into memory if necessary, pushing other values to disk.

Chest is a simple project. It was intended to provide a simple interface to assist in the storage and retrieval of numpy arrays. However it’s design and implementation are agnostic to this case and so could be used in a variety of other situations.

With minimal work chest could be extended to serve as a communication point between multiple processes.

Known Failings

Chest was designed to hold a moderate amount of largish numpy arrays. It doesn’t handle the very many small key-value pairs usecase (though could with small effort). In particular chest has the following deficiencies

1. Chest is not multi-process safe. We should institute a file lock at least around the .keys file.

2. Chest does not support mutation of variables on disk.

LICENSE

New BSD. See License

Install

chest is available through conda:

conda install chest

chest is on the Python Package Index (PyPI):

pip install chest

Example

>>> from chest import Chest
>>> c = Chest()

>>> # Acts like a normal dictionary
>>> c['x'] = [1, 2, 3]
>>> c['x']
[1, 2, 3]

>>> # Data persists to local files
>>> c.flush()
>>> import os
>>> os.listdir(c.path)
['.keys', 'x']

>>> # These files hold pickled results
>>> import pickle
>>> pickle.load(open(c.key_to_filename('x')))
[1, 2, 3]

>>> # Though one normally accesses these files with chest itself
>>> c2 = Chest(path=c.path)
>>> c2.keys()
['x']
>>> c2['x']
[1, 2, 3]

>>> # Chest is configurable, so one can use json, etc. instead of pickle
>>> import json
>>> c = Chest(path='my-chest', dump=json.dump, load=json.load)
>>> c['x'] = [1, 2, 3]
>>> c.flush()

>>> json.load(open(c.key_to_filename('x')))
[1, 2, 3]

Dependencies

Chest supports Python 2.6+ and Python 3.2+ with a common codebase.

It currently depends on the heapdict library.

It is a light weight dependency.