Separation of business logic and data access in django

It seems like you are asking about the difference between the data model and the domain model – the latter is where you can find the business logic and entities as perceived by your end user, the former is where you actually store your data.

Furthermore, I've interpreted the 3rd part of your question as: how to notice failure to keep these models separate.

These are two very different concepts and it's always hard to keep them separate. However, there are some common patterns and tools that can be used for this purpose.

About the Domain Model

The first thing you need to recognize is that your domain model is not really about data; it is about actions and questions such as "activate this user", "deactivate this user", "which users are currently activated?", and "what is this user's name?". In classical terms: it's about queries and commands.

Thinking in Commands

Let's start by looking at the commands in your example: "activate this user" and "deactivate this user". The nice thing about commands is that they can easily be expressed by small given-when-then scenario's:

given an inactive user
when the admin activates this user
then the user becomes active
and a confirmation e-mail is sent to the user
and an entry is added to the system log
(etc. etc.)

Such scenario's are useful to see how different parts of your infrastructure can be affected by a single command – in this case your database (some kind of 'active' flag), your mail server, your system log, etc.

Such scenario's also really help you in setting up a Test Driven Development environment.

And finally, thinking in commands really helps you create a task-oriented application. Your users will appreciate this :-)

Expressing Commands

Django provides two easy ways of expressing commands; they are both valid options and it is not unusual to mix the two approaches.

The service layer

The service module has already been described by @Hedde. Here you define a separate module and each command is represented as a function.

services.py

def activate_user(user_id):    user = User.objects.get(pk=user_id)    # set active flag    user.active = True    user.save()    # mail user    send_mail(...)    # etc etc

Using forms

The other way is to use a Django Form for each command. I prefer this approach, because it combines multiple closely related aspects:

• execution of the command (what does it do?)
• validation of the command parameters (can it do this?)
• presentation of the command (how can I do this?)

forms.py

class ActivateUserForm(forms.Form):    user_id = IntegerField(widget = UsernameSelectWidget, verbose_name="Select a user to activate")    # the username select widget is not a standard Django widget, I just made it up    def clean_user_id(self):        user_id = self.cleaned_data['user_id']        if User.objects.get(pk=user_id).active:            raise ValidationError("This user cannot be activated")        # you can also check authorizations etc.         return user_id    def execute(self):        """        This is not a standard method in the forms API; it is intended to replace the         'extract-data-from-form-in-view-and-do-stuff' pattern by a more testable pattern.         """        user_id = self.cleaned_data['user_id']        user = User.objects.get(pk=user_id)        # set active flag        user.active = True        user.save()        # mail user        send_mail(...)        # etc etc

Thinking in Queries

You example did not contain any queries, so I took the liberty of making up a few useful queries. I prefer to use the term "question", but queries is the classical terminology. Interesting queries are: "What is the name of this user?", "Can this user log in?", "Show me a list of deactivated users", and "What is the geographical distribution of deactivated users?"

Before embarking on answering these queries, you should always ask yourself this question, is this:

• a presentational query just for my templates, and/or
• a business logic query tied to executing my commands, and/or
• a reporting query.

Presentational queries are merely made to improve the user interface. The answers to business logic queries directly affect the execution of your commands. Reporting queries are merely for analytical purposes and have looser time constraints. These categories are not mutually exclusive.

The other question is: "do I have complete control over the answers?" For example, when querying the user's name (in this context) we do not have any control over the outcome, because we rely on an external API.

Making Queries

The most basic query in Django is the use of the Manager object:

User.objects.filter(active=True)

Of course, this only works if the data is actually represented in your data model. This is not always the case. In those cases, you can consider the options below.

Custom tags and filters

The first alternative is useful for queries that are merely presentational: custom tags and template filters.

template.html

<h1>Welcome, {{ user|friendly_name }}</h1>

template_tags.py

@register.filterdef friendly_name(user):    return remote_api.get_cached_name(user.id)

Query methods

If your query is not merely presentational, you could add queries to your services.py (if you are using that), or introduce a queries.py module:

queries.py

def inactive_users():    return User.objects.filter(active=False)def users_called_publysher():    for user in User.objects.all():        if remote_api.get_cached_name(user.id) == "publysher":            yield user 

Proxy models

Proxy models are very useful in the context of business logic and reporting. You basically define an enhanced subset of your model. You can override a Manager’s base QuerySet by overriding the Manager.get_queryset() method.

models.py

class InactiveUserManager(models.Manager):    def get_queryset(self):        query_set = super(InactiveUserManager, self).get_queryset()        return query_set.filter(active=False)class InactiveUser(User):    """    >>> for user in InactiveUser.objects.all():    …        assert user.active is False     """    objects = InactiveUserManager()    class Meta:        proxy = True

Query models

For queries that are inherently complex, but are executed quite often, there is the possibility of query models. A query model is a form of denormalization where relevant data for a single query is stored in a separate model. The trick of course is to keep the denormalized model in sync with the primary model. Query models can only be used if changes are entirely under your control.

models.py

class InactiveUserDistribution(models.Model):    country = CharField(max_length=200)    inactive_user_count = IntegerField(default=0)

The first option is to update these models in your commands. This is very useful if these models are only changed by one or two commands.

forms.py

class ActivateUserForm(forms.Form):    # see above       def execute(self):        # see above        query_model = InactiveUserDistribution.objects.get_or_create(country=user.country)        query_model.inactive_user_count -= 1        query_model.save()

A better option would be to use custom signals. These signals are of course emitted by your commands. Signals have the advantage that you can keep multiple query models in sync with your original model. Furthermore, signal processing can be offloaded to background tasks, using Celery or similar frameworks.

signals.py

user_activated = Signal(providing_args = ['user'])user_deactivated = Signal(providing_args = ['user'])

forms.py

class ActivateUserForm(forms.Form):    # see above       def execute(self):        # see above        user_activated.send_robust(sender=self, user=user)

models.py

class InactiveUserDistribution(models.Model):    # see above@receiver(user_activated)def on_user_activated(sender, **kwargs):        user = kwargs['user']        query_model = InactiveUserDistribution.objects.get_or_create(country=user.country)        query_model.inactive_user_count -= 1        query_model.save()    

Keeping it clean

When using this approach, it becomes ridiculously easy to determine if your code stays clean. Just follow these guidelines:

• Does my model contain methods that do more than managing database state? You should extract a command.
• Does my model contain properties that do not map to database fields? You should extract a query.
• Does my model reference infrastructure that is not my database (such as mail)? You should extract a command.

The same goes for views (because views often suffer from the same problem).

• Does my view actively manage database models? You should extract a command.

Some References

Django documentation: proxy models

Django documentation: signals

Architecture: Domain Driven Design

I usually implement a service layer in between views and models. This acts like your project's API and gives you a good helicopter view of what is going on. I inherited this practice from a colleague of mine that uses this layering technique a lot with Java projects (JSF), e.g:

models.py

class Book:   author = models.ForeignKey(User)   title = models.CharField(max_length=125)   class Meta:       app_label = "library"

services.py

from library.models import Bookdef get_books(limit=None, **filters):    """ simple service function for retrieving books can be widely extended """    return Book.objects.filter(**filters)[:limit]  # list[:None] will return the entire list

views.py

from library.services import get_booksclass BookListView(ListView):    """ simple view, e.g. implement a _build and _apply filters function """    queryset = get_books()

Mind you, I usually take models, views and services to module level and separate even further depending on the project's size

First of all, Don't repeat yourself.

Then, please be careful not to overengineer, sometimes it is just a waste of time, and makes someone lose focus on what is important. Review the zen of python from time to time.

Take a look at active projects

• more people = more need to organize properly
• the django repository they have a straightforward structure.
• the pip repository they have a straigtforward directory structure.

• the fabric repository is also a good one to look at.

  • you can place all your models under yourapp/models/logicalgroup.py
• e.g User, Group and related models can go under yourapp/models/users.py
• e.g Poll, Question, Answer ... could go under yourapp/models/polls.py
• load what you need in __all__ inside of yourapp/models/__init__.py

More about MVC

• model is your data
  • this includes your actual data
  • this also includes your session / cookie / cache / fs / index data
• user interacts with controller to manipulate the model
  • this could be an API, or a view that saves/updates your data
  • this can be tuned with request.GET / request.POST ...etc
  • think paging or filtering too.
• the data updates the view
  • the templates take the data and format it accordingly
  • APIs even w/o templates are part of the view; e.g. tastypie or piston
  • this should also account for the middleware.

Take advantage of middleware / templatetags

• If you need some work to be done for each request, middleware is one way to go.
  • e.g. adding timestamps
  • e.g. updating metrics about page hits
  • e.g. populating a cache
• If you have snippets of code that always reoccur for formatting objects, templatetags are good.
  • e.g. active tab / url breadcrumbs

Take advantage of model managers

• creating User can go in a UserManager(models.Manager).
• gory details for instances should go on the models.Model.
• gory details for queryset could go in a models.Manager.
• you might want to create a User one at a time, so you may think that it should live on the model itself, but when creating the object, you probably don't have all the details:

Example:

class UserManager(models.Manager):   def create_user(self, username, ...):      # plain create   def create_superuser(self, username, ...):      # may set is_superuser field.   def activate(self, username):      # may use save() and send_mail()   def activate_in_bulk(self, queryset):      # may use queryset.update() instead of save()      # may use send_mass_mail() instead of send_mail()

Make use of forms where possible

A lot of boilerplate code can be eliminated if you have forms that map to a model. The ModelForm documentation is pretty good. Separating code for forms from model code can be good if you have a lot of customization (or sometimes avoid cyclic import errors for more advanced uses).

Use management commands when possible

• e.g. yourapp/management/commands/createsuperuser.py
• e.g. yourapp/management/commands/activateinbulk.py

if you have business logic, you can separate it out

• django.contrib.auth uses backends, just like db has a backend...etc.
• add a setting for your business logic (e.g. AUTHENTICATION_BACKENDS)
• you could use django.contrib.auth.backends.RemoteUserBackend
• you could use yourapp.backends.remote_api.RemoteUserBackend
• you could use yourapp.backends.memcached.RemoteUserBackend
• delegate the difficult business logic to the backend
• make sure to set the expectation right on the input/output.
• changing business logic is as simple as changing a setting :)

backend example:

class User(db.Models):    def get_present_name(self):         # property became not deterministic in terms of database        # data is taken from another service by api        return remote_api.request_user_name(self.uid) or 'Anonymous' 

could become:

class User(db.Models):   def get_present_name(self):      for backend in get_backends():         try:            return backend.get_present_name(self)         except: # make pylint happy.            pass      return None

more about design patterns

• there's already a good question about design patterns
• a very good video about practical design patterns
• django's backends are obvious use of delegation design pattern.

more about interface boundaries

• Is the code you want to use really part of the models? -> yourapp.models
• Is the code part of business logic? -> yourapp.vendor
• Is the code part of generic tools / libs? -> yourapp.libs
• Is the code part of business logic libs? -> yourapp.libs.vendor or yourapp.vendor.libs
• Here is a good one: can you test your code independently?
  • yes, good :)
  • no, you may have an interface problem
  • when there is clear separation, unittest should be a breeze with the use of mocking
• Is the separation logical?
  • yes, good :)
  • no, you may have trouble testing those logical concepts separately.
• Do you think you will need to refactor when you get 10x more code?
  • yes, no good, no bueno, refactor could be a lot of work
  • no, that's just awesome!

In short, you could have

• yourapp/core/backends.py
• yourapp/core/models/__init__.py
• yourapp/core/models/users.py
• yourapp/core/models/questions.py
• yourapp/core/backends.py
• yourapp/core/forms.py
• yourapp/core/handlers.py
• yourapp/core/management/commands/__init__.py
• yourapp/core/management/commands/closepolls.py
• yourapp/core/management/commands/removeduplicates.py
• yourapp/core/middleware.py
• yourapp/core/signals.py
• yourapp/core/templatetags/__init__.py
• yourapp/core/templatetags/polls_extras.py
• yourapp/core/views/__init__.py
• yourapp/core/views/users.py
• yourapp/core/views/questions.py
• yourapp/core/signals.py
• yourapp/lib/utils.py
• yourapp/lib/textanalysis.py
• yourapp/lib/ratings.py
• yourapp/vendor/backends.py
• yourapp/vendor/morebusinesslogic.py
• yourapp/vendor/handlers.py
• yourapp/vendor/middleware.py
• yourapp/vendor/signals.py
• yourapp/tests/test_polls.py
• yourapp/tests/test_questions.py
• yourapp/tests/test_duplicates.py
• yourapp/tests/test_ratings.py

or anything else that helps you; finding the interfaces you need and the boundaries will help you.