Building a follow system

Let's build a follow system in your project. This means that your users will be able to follow each other and track what other users share on the platform. The relationship between users is a many-to-many relationship: a user can follow multiple users and they, in turn, can be followed by multiple users.

Creating many-to-many relationships with an intermediate model

In previous chapters, you created many-to-many relationships by adding the ManyToManyField to one of the related models and letting Django create the database table for the relationship. This is suitable for most cases, but sometimes you may need to create an intermediate model for the relationship. Creating an intermediate model is necessary when you want to store additional information about the relationship, for example, the date when the relationship was created, or a field that describes the nature of the relationship.

Let's create an intermediate model to build relationships between...

Building a generic activity stream application

Many social websites display an activity stream to their users so that they can track what other users do on the platform. An activity stream is a list of recent activities performed by a user or a group of users. For example, Facebook's News Feed is an activity stream. Sample actions can be user X bookmarked image Y or user X is now following user Y.

You are going to build an activity stream application so that every user can see the recent interactions of the users they follow. To do so, you will need a model to save the actions performed by users on the website and a simple way to add actions to the feed.

Create a new application named actions inside your project with the following command:

python manage.py startapp actions

Add the new application to INSTALLED_APPS in the settings.py file of your project to activate the application in your project. The new line is highlighted in bold:

INSTALLED_APPS = [
    # ...
    'actions...

Using signals for denormalizing counts

There are some cases when you may want to denormalize your data. Denormalization is making data redundant in such a way that it optimizes read performance. For example, you might be copying related data to an object to avoid expensive read queries to the database when retrieving the related data. You have to be careful about denormalization and only start using it when you really need it. The biggest issue you will find with denormalization is that it's difficult to keep your denormalized data updated.

Let's take a look at an example of how to improve your queries by denormalizing counts. You will denormalize data from your Image model and use Django signals to keep the data updated.

Working with signals

Django comes with a signal dispatcher that allows receiver functions to get notified when certain actions occur. Signals are very useful when you need your code to do something every time something else happens. Signals allow you to decouple...

Using Django Debug Toolbar

At this point, you will already be familiar with Django's debug page. Throughout the previous chapters, you have seen the distinctive yellow and grey Django debug page several times. For example, in Chapter 2, Enhancing Your Blog with Advanced Features, in the Handling pagination errors section, the debug page showed information related to unhandled exceptions when implementing object pagination.

The Django debug page provides useful debug information. However, there is a Django application that includes more detailed debug information and can be really helpful when developing.

Django Debug Toolbar is an external Django application that allows you to see relevant debug information about the current request/response cycle. The information is divided into multiple panels that show different information, including request/response data, Python package versions used, execution time, settings, headers, SQL queries, templates used, cache, signals, and logging...

Counting image views with Redis

Redis is an advanced key/value database that allows you to save different types of data. It also has extremely fast I/O operations. Redis stores everything in memory, but the data can be persisted by dumping the dataset to disk every once in a while, or by adding each command to a log. Redis is very versatile compared to other key/value stores: it provides a set of powerful commands and supports diverse data structures, such as strings, hashes, lists, sets, ordered sets, and even bitmaps or HyperLogLogs.

Although SQL is best suited to schema-defined persistent data storage, Redis offers numerous advantages when dealing with rapidly changing data, volatile storage, or when a quick cache is needed. Let's take a look at how Redis can be used to build new functionality into your project.

You can find more information about Redis on its homepage at https://redis.io/.

Redis provides a Docker image that makes it very easy to deploy a Redis server with a standard...

Additional resources

The following resources provide additional information related to the topics covered in this chapter:

• Source code for this chapter – https://github.com/PacktPublishing/Django-5-by-example/tree/main/Chapter07
• Custom user models – https://docs.djangoproject.com/en/5.0/topics/auth/customizing/#specifying-a-custom-user-model
• The contenttypes framework – https://docs.djangoproject.com/en/5.0/ref/contrib/contenttypes/
• Built-in Django signals – https://docs.djangoproject.com/en/5.0/ref/signals/
• Application configuration classes – https://docs.djangoproject.com/en/5.0/ref/applications/
• Django Debug Toolbar documentation – https://django-debug-toolbar.readthedocs.io/
• Django Debug Toolbar third-party panels – https://django-debug-toolbar.readthedocs.io/en/latest/panels.html#third-party-panels
• Redis in-memory data store – https://redis.io/
• Official Redis Docker image — https://hub.docker.com/_/redis.
• Redis download...

Summary

In this chapter, you built a follow system using many-to-many relationships with an intermediate model. You also created an activity stream using generic relations and you optimized QuerySets to retrieve related objects. This chapter then introduced you to Django signals, and you created a signal receiver function to denormalize related object counts. We covered application configuration classes, which you used to load your signal handlers. You added Django Debug Toolbar to your project. You also learned how to install and configure Redis in your Django project. Finally, you used Redis in your project to store item views, and you built an image ranking with Redis.

In the next chapter, you will learn how to build an online shop. You will create a product catalog and build a shopping cart using sessions. You will learn how to create custom context processors. You will also manage customer orders and send asynchronous notifications using Celery and RabbitMQ.

Additional resources

The following resources provide additional information related to the topics covered in this chapter:

• Source code for this chapter – https://github.com/PacktPublishing/Django-5-by-example/tree/main/Chapter08
• Static files for the project – https://github.com/PacktPublishing/Django-5-by-Example/tree/main/Chapter08/myshop/shop/static
• Django session settings – https://docs.djangoproject.com/en/5.0/ref/settings/#sessions
• Django built-in context processors – https://docs.djangoproject.com/en/5.0/ref/templates/api/#built-in-template-context-processors
• Information about RequestContext – https://docs.djangoproject.com/en/5.0/ref/templates/api/#django.template.RequestContext
• Celery documentation – https://docs.celeryq.dev/en/stable/index.html
• Introduction to Celery – https://docs.celeryq.dev/en/stable/getting-started/introduction.html
• Official RabbitMQ Docker image –...

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