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Filter user-data based on multiple criteria.

Project description

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Filter user-data based on multiple criteria.

Installation

We are at the cheeseshop:

pip install django_sieve

Usage

To use django_sieve in a project:

  1. Include it in INSTALLED_APPS in your settings file.

  2. Define a sieve model. A sieve model is the control for defining the filtering criteria for user data. For instance, in our example, we have a bookstore in which a user can subscribe to all the books from a particular author or all the books from a particular publisher. In this case, his book list need to only contain books from the publisher and author that he has chosen. In order to do this, we define a sieve model like so:

    class Sieve(models.Model):
            user = models.ForeignKey('auth.User')
            publisher = models.ForeignKey(Publisher)
            author = models.ForeignKey(Author)
    
  3. Declare your project-wide sieve model in settings.py. For instance, if the sieve model is called Sieve and resides in the bookstore app:

    SIEVE_MODEL = 'bookstore.Sieve'
    
  4. Use SieveManager as the ModelManager for all the models in your project that you want to filter based on the criteria defined in the sieve model:

    class Author(models.Model):
            first_name = models.CharField(max_length=30)
            last_name = models.CharField(max_length=40)
            email = models.EmailField()
            objects = SieveManager()
    
  5. Use the sieve method to define your queryset in your views like so:

    class BookView(ListView):
            queryset = Book.objects.sieve(user=request.user)
    

That is all. Site-wide filtering of user-data based on pre-defined criteria without having to write queries for all the views. Please be wary of performance issues though - in our experience, django-sieve works for 90% cases especially when you need to bring everything up for a quick demo. However, as your site matures and the number of users increase, you may want to invest in profiling your views and hand-crafting these queries when and where required.

History

0.1.0 (2013)

  • First release on PyPI.

Project details


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1.0.0

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0.3.0

This version
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0.2.0

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0.1.0

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