When designing multi-tenant applications, it's often desirable to provide users with the ability to define their own custom data fields. However, creating and managing dynamic fields in a relational database can be complex. This article explores various approaches for tackling this challenge in Django.
The django-eav package, now maintained in several thriving forks, implements the Entity-Attribute-Value pattern. By storing dynamic attributes in a few simple Django models, this solution provides:
However, it suffers from potential performance and maintenance issues.
PostgreSQL offers several data types for storing dynamic data, including:
These fields allow for indexed queries and can be used as follows:
class Something(models.Model): data = JSONField(db_index=True) # JSON or JSONB something = Something.objects.create(data={'a': 1, 'b': 2}) Something.objects.filter(data__a=1)
Alternatively, Django MongoDB provides a fully dynamic modeling solution, enabling embedded documents and lists of models.
from djangotoolbox.fields import DictField class Image(models.Model): exif = DictField() image = Image.objects.create(exif=get_exif_data(...))
Django-mutant offers dynamic Foreign Key and many-to-many fields, inspired by Michael Hall's approach. It leverages Django South hooks to dynamically alter database schemas at runtime, allowing for fully dynamic Django apps, models, and fields.
from dynamo import models test = models.DynamicModel.objects.create(name='Test') foo = models.DynamicModelField.objects.create(model=test, name='foo')
It's important to note the potential stability risks and locking considerations when using these dynamic approaches. However, they provide powerful options for managing dynamic data in Django.
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