Skip to main content

Django Memory Database

Project description

https://img.shields.io/codeship/a9873030-5c43-0134-5e91-46e8172ca5a4/default.svg https://coveralls.io/repos/bitbucket/hellwig/django-memdb/badge.svg?branch=default https://img.shields.io/pypi/v/django-memdb.svg https://img.shields.io/badge/Donate-PayPal-blue.svg https://img.shields.io/badge/Donate-Patreon-orange.svg

Django Memory Database

What is it?

A library that allows tables to be kept in an in-memory database and optionally can provide persistence between instance restart by spooling the tables to a table that is read upon startup to populate the in-memory tables.

What problem does it solve?

Create in-memory tables with optional data persistence.

How do I install it?

pip install django-memdb

Adding to Django (using integrator)

# At the bottom of your settings.py file.
import django_integrator
django_integrator.add_application('django_memdb')

If you don’t want to use the above, add the application to INSTALLED_APPS and merge the apps settings.py and url.py into the django projects files.

How do I use it?

When defining models, use the class mixin.

For example:

from django.db import models
from django_memdb.mixins import InMemoryDB, PeristentInMemoryDB

class TestModelWithMixin(models.Model, InMemoryDB):
    text = models.TextField()

class TestModelPersistent(models.Model, PeristentInMemoryDB):
    text = models.TextField()

Both mixins work more like a tag, which is used to determine if a table is an in-memory table or not and if the table contents should be stored in the (using the default database) PersistentStorage model.

You can optionally hook into the persistentstorage mechanics by attaching to the signals this app provides. The below example will add compression the data before storing it:

from django_memdb import signals

def compress(arguments):
    "Compress data."
    # pylint: disable=redefined-variable-type
    if arguments['process'] == settings.MEMDB_PROCESS_ENCODE:
        data = arguments['data']
        data = json.dumps(data)
        data = data.encode('utf-8')
        data = zlib.compress(data)
        arguments['data'] = data
    elif arguments['process'] == settings.MEMDB_PROCESS_DECODE:
        data = arguments['data']
        data = zlib.decompress(data)
        data = data.decode('utf-8')
        data = json.loads(data)
        arguments['data'] = data

def callback(sender, **kwargs): # pylint: disable=unused-argument
    "Just insert a hook."
    kwargs['kwargs']['processors'].append(compress)


signals.store_save.connect(callback)
signals.store_load.connect(callback)

Caveat

The in memory database is local to each server instance, thus if you have a setup that uses multiple servers and a single django database instance, you will have synchronisation issues with the in-memory data and hard conflicts when using the persistent storage.

What license is this?

Two-clause BSD

How can I get support?

Please use the repo’s bug tracker to leave behind any questions, feedback, suggestions and comments. I will handle them depending on my time and what looks interesting. If you require guaranteed support please contact me via e-mail so we can discuss appropriate compensation.

Signing Off

Is my work helpful or valuable to you? You can repay me by donating via:

https://paypal.me/MartinHellwig

Donate via PayPal.Me

-or-

https://www.patreon.com/hellwig

Donate via Patreon

Thank you!

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for django-memdb, version 0.0.6
Filename, size & hash File type Python version Upload date
django-memdb-0.0.6.tar.gz (9.3 kB) View hashes Source None

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN SignalFx SignalFx Supporter DigiCert DigiCert EV certificate StatusPage StatusPage Status page