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Celery signal processor for Bungiesearch

Project description

A repository that wraps the provided Bungiesearch signal processing class in your own celery tasks.

This Django app allows you to utilize Celery for automatically updating and deleting objects in a Bungiesearch search index.

Compatible with most recent version of Bungiesearch, which is similar to Haystack, but interacts directly with elasticsearch-dsl and therefore allows for more custom ranking functions.


pip install celery-bungiesearch



1. Add 'celery_bungiesearch' to INSTALLED_APPS in settings

    # ..

2. Add CelerySignalProcessor to settings.BUNGIESEARCH['SIGNAL_CLASS'] in settings. This will ensure that any model that’s managed by a BungiesearchManager acquire CelerySignalProcessor as the signal processor.

from celery_bungiesearch import CelerySignalProcessor

   # ..
   'SIGNALS': {
       'SIGNAL_CLASS': 'celery_bungiesearch.signals.CelerySignalProcessor',
       'BUFFER_SIZE' : 100

3. Add celery-bungisearch configuration variables to your settings file. The task below is the default version, but you may include your own custom classes if you desire (note that none of these environment variable are required, and can be entirely excluded from the settings file):

CELERY_BUNGIESEARCH_TASK = 'Your custom index task path'
CELERY_BUNGIESEARCH_CUSTOM_TASK = 'Your custom celery task path'

4. Ensure your Celery instance is running.


You can run tests locally for celery-bungiesearch by simply running the command tox or tox test. You must have an instance of elasticsearch running locally. You can also run tests using continuous integration with Travis CI (build status at the top of this README).


This application borrows liberally from Jannis Leidel’s celery-haystack and from Christopher Rabotin’s Bungiesearch, which itself was the inspiration for this project.


Please submit a pull request or use the Github issue tracker for any bug fixes, bug reports, or feature requests.

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