Chest

A dictionary that spills to disk.

Chest acts likes a dictionary but it can write its contents to disk. This is useful in the following two occasions:

1. Chest can hold datasets that are larger than memory
2. Chest persists and so can be saved and loaded for later use

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.