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Table of Contents

Installation / Testing

Requirements

Django 1.1 (or later) and Python 2.5 (or later). This code has been tested on Django 1.1.1 / 1.2 alpha and Python 2.5.4 / 2.6.4 on Linux.

Testing

The repository (or tar file) contains a complete Django project that may be used for tests or experiments.

To run the included test suite, execute:

./manage test polymorphic

The management command pcmd.py in the app pexp (Polymorphic EXPerimenting) can be used for experiments - modify this file (pexp/management/commands/pcmd.py) to your liking, then run:

./manage syncdb      # db is created in /var/tmp/... (settings.py)
./manage pcmd

Installation

In the directory "django_polymorphic", execute sudo python setup.py install.

Alternatively you can simply copy the polymorphic directory (under "django_polymorphic") into your Django project dir.

If you want to use the management command polymorphic_dumpdata, then you need to add polymorphic to your INSTALLED_APPS setting.

In any case, Django's ContentType framework (django.contrib.contenttypes) needs to be listed in INSTALLED_APPS (usually it already is).

Defining Polymorphic Models

To make models polymorphic, use PolymorphicModel instead of Django's models.Model as the superclass of your base model. All models inheriting from your base class will be polymorphic as well:

from polymorphic.models import PolymorphicModel

class ModelA(PolymorphicModel):
    field1 = models.CharField(max_length=10)

class ModelB(ModelA):
    field2 = models.CharField(max_length=10)

class ModelC(ModelB):
    field3 = models.CharField(max_length=10)

Using Polymorphic Models

Most of Django's standard ORM functionality is available and works as expected:

Create some objects

>>> ModelA.objects.create(field1='A1')
>>> ModelB.objects.create(field1='B1', field2='B2')
>>> ModelC.objects.create(field1='C1', field2='C2', field3='C3')

Query results are polymorphic

>>> ModelA.objects.all()
.
[ <ModelA: id 1, field1 (CharField)>,
  <ModelB: id 2, field1 (CharField), field2 (CharField)>,
  <ModelC: id 3, field1 (CharField), field2 (CharField), field3 (CharField)> ]

Filtering for classes (equivalent to python's isinstance() ):

>>> ModelA.objects.instance_of(ModelB)
.
[ <ModelB: id 2, field1 (CharField), field2 (CharField)>,
  <ModelC: id 3, field1 (CharField), field2 (CharField), field3 (CharField)> ]

In general, including or excluding parts of the inheritance tree:

ModelA.objects.instance_of(ModelB [, ModelC ...])
ModelA.objects.not_instance_of(ModelB [, ModelC ...])

Polymorphic filtering (for fields in derived classes)

For example, cherrypicking objects from multiple derived classes anywhere in the inheritance tree, using Q objects (with the syntax: exact model name + three _ + field name):

>>> ModelA.objects.filter(  Q(ModelB___field2 = 'B2') | Q(ModelC___field3 = 'C3')  )
.
[ <ModelB: id 2, field1 (CharField), field2 (CharField)>,
  <ModelC: id 3, field1 (CharField), field2 (CharField), field3 (CharField)> ]

Combining Querysets of different types/models

Querysets may now be regarded as object containers that allow the aggregation of different object types - very similar to python lists (as long as the objects are accessed through the manager of a common base class):

>>> Base.objects.instance_of(ModelX) | Base.objects.instance_of(ModelY)
.
[ <ModelX: id 1, field_x (CharField)>,
  <ModelY: id 2, field_y (CharField)> ]

Using Third Party Models (without modifying them)

Third party models can be used as polymorphic models without restrictions by subclassing them. E.g. using a third party model as the root of a polymorphic inheritance tree:

from thirdparty import ThirdPartyModel

class MyThirdPartyModel(PolymorhpicModel, ThirdPartyModel):
    pass    # or add fields

Or instead integrating the third party model anywhere into an existing polymorphic inheritance tree:

class MyModel(SomePolymorphicModel):
    my_field = models.CharField(max_length=10)

class MyModelWithThirdParty(MyModel, ThirdPartyModel):
    pass    # or add fields

ManyToManyField, ForeignKey, OneToOneField

Relationship fields referring to polymorphic models work as expected: like polymorphic querysets they now always return the referred objects with the same type/class these were created and saved as.

E.g., if in your model you define:

field1 = OneToOneField(ModelA)

then field1 may now also refer to objects of type ModelB or ModelC.

A ManyToManyField example:

# The model holding the relation may be any kind of model, polymorphic or not
class RelatingModel(models.Model):
    many2many = models.ManyToManyField('ModelA')  # ManyToMany relation to a polymorphic model

>>> o=RelatingModel.objects.create()
>>> o.many2many.add(ModelA.objects.get(id=1))
>>> o.many2many.add(ModelB.objects.get(id=2))
>>> o.many2many.add(ModelC.objects.get(id=3))

>>> o.many2many.all()
[ <ModelA: id 1, field1 (CharField)>,
  <ModelB: id 2, field1 (CharField), field2 (CharField)>,
  <ModelC: id 3, field1 (CharField), field2 (CharField), field3 (CharField)> ]

annotate(), aggregate() & extra()

  • annotate() and aggregate() work just as usual, with the addition that the ModelX___field syntax can be used for the keyword arguments (but not for the non-keyword arguments).
  • extra() by default works exactly like the vanilla version, with the resulting queryset not being polymorphic. There is experimental support for polymorphic queries with extra() via the keyword argument polymorphic=True (then only the where and order_by arguments of extra() should be used).

Non-Polymorphic Queries

>>> ModelA.base_objects.all()
.
[ <ModelA: id 1, field1 (CharField)>,
  <ModelA: id 2, field1 (CharField)>,
  <ModelA: id 3, field1 (CharField)> ]

Each polymorphic model has 'base_objects' defined as a normal Django manager. Of course, arbitrary custom managers may be added to the models as well.

manage.py dumpdata

Django's standard dumpdata command requires non-polymorphic behaviour from the querysets it uses and produces incomplete results with polymorphic models. Django_polymorphic includes a slightly modified version, named polymorphic_dumpdata that fixes this. Just use this command instead of Django's (see "installation/testing").

Please note that there are problems using ContentType together with Django's seralisation or fixtures (and all polymorphic models use ContentType). This issue seems to be resolved with Django 1.2 (changeset 11863): http://code.djangoproject.com/ticket/7052

Custom Managers, Querysets & Inheritance

Using a Custom Manager

For creating a custom polymorphic manager class, derive your manager from PolymorphicManager instead of models.Manager. In your model class, explicitly add the default manager first, and then your custom manager:

class MyOrderedManager(PolymorphicManager):
    def get_query_set(self):
        return super(MyOrderedManager,self).get_query_set().order_by('some_field')

class MyModel(PolymorphicModel):
    objects = PolymorphicManager()    # add the default polymorphic manager first
    ordered_objects = MyOrderedManager()    # then add your own manager

The first manager defined ('objects' in the example) is used by Django as automatic manager for several purposes, including accessing related objects. It must not filter objects and it's safest to use the plain PolymorphicManager here.

Manager Inheritance

Polymorphic models inherit/propagate all managers from their base models, as long as these are polymorphic. This means that all managers defined in polymorphic base models work just the same as if they were defined in the new model.

An example (inheriting from MyModel above):

class MyModel2(MyModel):
    pass

# Managers inherited from MyModel:
# the regular 'objects' manager and the custom 'ordered_objects' manager
>>> MyModel2.objects.all()
>>> MyModel2.ordered_objects.all()

Using a Custom Queryset Class

The PolymorphicManager class accepts one initialization argument, which is the queryset class the manager should use. A custom custom queryset class can be defined and used like this:

class MyQuerySet(PolymorphicQuerySet):
    def my_queryset_method(...):
        ...

class MyModel(PolymorphicModel):
    my_objects=PolymorphicManager(MyQuerySet)
    ...

Performance Considerations

The current implementation is pretty simple and does not use any custom SQL - it is purely based on the Django ORM. Right now the query

result_objects = list( ModelA.objects.filter(...) )

performs one SQL query to retrieve ModelA objects and one additional query for each unique derived class occurring in result_objects. The best case for retrieving 100 objects is 1 db query if all are class ModelA. If 50 objects are ModelA and 50 are ModelB, then two queries are executed. If result_objects contains only the base model type (ModelA), the polymorphic models are just as efficient as plain Django models (in terms of executed queries). The pathological worst case is 101 db queries if result_objects contains 100 different object types (with all of them subclasses of ModelA).

Performance ist relative: when Django users create their own polymorphic ad-hoc solution (without a tool like django_polymorphic), this usually results in a variation of

result_objects = [ o.get_real_instance() for o in BaseModel.objects.filter(...) ]

which has really bad performance. Relative to this, the performance of the current django_polymorphic is pretty good. It may well be efficient enough for the majority of use cases.

Chunking: The implementation always requests objects in chunks of size Polymorphic_QuerySet_objects_per_request. This limits the complexity/duration for each query, including the pathological cases.

Possible Optimizations

PolymorphicQuerySet can be optimized to require only one SQL query for the queryset evaluation and retrieval of all objects.

Basically, what ist needed is a possibility to pull in the fields from all relevant sub-models with one SQL query. However, some deeper digging into the Django database layer will be required in order to make this happen.

A viable option might be to get the SQL query from the QuerySet (probably from django.db.models.SQL.compiler.SQLCompiler.as_sql), making sure that all necessary joins are done, and then doing a custom SQL request from there (like in SQLCompiler.execute_sql).

An optimized version could fall back to the current ORM-only implementation for all non-SQL databases.

SQL Complexity

With only one SQL query, one SQL join for each possible subclass would be needed (BaseModel.__subclasses__(), recursively). With two SQL queries, the number of joins could be reduced to the number of actuallly occurring subclasses in the result. A final implementation might want to use one query only if the number of possible subclasses (and therefore joins) is not too large, and two queries otherwise (using the first query to determine the actually occurring subclasses, reducing the number of joins for the second).

A relatively large number of joins may be needed in both cases, which raises concerns regarding the efficiency of these database queries. It is currently unclear however, how many model classes will actually be involved in typical use cases - the total number of classes in the inheritance tree as well as the number of distinct classes in query results. It may well turn out that the increased number of joins is no problem for the DBMS in all realistic use cases. Alternatively, if the SQL query execution time is significantly longer even in common use cases, this may still be acceptable in exchange for the added functionality.

In General

Let's not forget that all of the above is just about optimization. The current implementation already works well - and perhaps well enough for the majority of applications.

Also, it seems that further optimization (down to one DB request) would be restricted to a relatively small area of the code, and be mostly independent from the rest of the module. So it seems this optimization can be done at any later time (like when it's needed).

Unsupported Methods, Restrictions & Caveats

Currently Unsupported Queryset Methods

  • defer() and only(): Full support, including slight polymorphism enhancements, seems to be straighforward (depends on '_get_real_instances').
  • select_related() works just as usual, but it can not (yet) be used to select relations in derived models (like ModelA.objects.select_related('ModelC___fieldxy') )
  • distinct() needs more thought and investigation
  • values() & values_list(): Implementation seems to be mostly straighforward

Restrictions & Caveats

  • Django 1.1 only - the names of polymorphic models must be unique in the whole project, even if they are in two different apps. This results from a restriction in the Django 1.1 "related_name" option (fixed in Django 1.2).
  • Django 1.1 only - when ContentType is used in models, Django's seralisation or fixtures cannot be used. This issue seems to be resolved for Django 1.2 (changeset 11863: Fixed #7052, Added support for natural keys in serialization).
  • Diamond shaped inheritance: There seems to be a general problem with diamond shaped multiple model inheritance with Django models (tested with V1.1). An example is here: http://code.djangoproject.com/ticket/10808. This problem is aggravated when trying to enhance models.Model by subclassing it instead of modifying Django core (as we do here with PolymorphicModel).
  • A reference (ContentType) to the real/leaf model is stored in the base model (the base model directly inheriting from PolymorphicModel). If a model or an app is renamed, then Django's ContentType table needs to be corrected too, if the db content should stay usable after the rename.
  • For all objects that are not instances of the base class, but instances of a subclass, the base class fields are currently transferred twice from the database (an artefact of the current implementation's simplicity).
  • __getattribute__ hack: For base model inheritance back relation fields (like basemodel_ptr), as well as implicit model inheritance forward relation fields, Django internally tries to use our polymorphic manager/queryset in some places, which of course it should not. Currently this is solved with a hacky __getattribute__ in PolymorphicModel, which causes some overhead. A minor patch to Django core would probably get rid of that.

In General

It's important to consider that this code is very new and to some extent still experimental. Please see the docs for current restrictions, caveats, and performance implications.

It does seem to work very well for a number of people, but API changes, code reorganisations or further schema changes are still a possibility. There may also remain larger bugs and problems in the code that have not yet been found.

Links