Django Design Patterns and Best Practices

Industry-standard web development techniques and solutions using Python, 2nd Edition

Arun Ravindran

• Publisher: Packt Publishing
• Year: 2018
• ISBN:
  9781788834971
• Edition: 2
• Topic: Computer Science
• Subtopic: Web Development
• Index: Computer Science

Build maintainable websites with elegant Django design patterns and modern best practicesAbout This Book• Explore aspects of Django from Models and Views to testing and deployment• Understand the nuances of web development such as browser attack and data design• Walk through various asynchronous tools such as Celery and ChannelsWho This Book Is ForThis book is for you whether you're new to Django or just want to learn its best practices. You do not have to be an expert in Django or Python. No prior knowledge of patterns is expected for reading this book but it would be helpful.What You Will Learn• Make use of common design patterns to help you write better code• Implement best practices and idioms in this rapidly evolving framework• Deal with legacy code and debugging• Use asynchronous tools such as Celery, Channels, and asyncio• Use patterns while designing API interfaces with the Django REST Framework• Reduce the maintenance burden with well-tested, cleaner code• Host, deploy, and secure your Django projectsIn DetailBuilding secure and maintainable web applications requires comprehensive knowledge. The second edition of this book not only sheds light on Django, but also encapsulates years of experience in the form of design patterns and best practices. Rather than sticking to GoF design patterns, the book looks at higher-level patterns. Using the latest version of Django and Python, you'll learn about Channels and asyncio while building a solid conceptual background. The book compares design choices to help you make everyday decisions faster in a rapidly changing environment.You'll first learn about various architectural patterns, many of which are used to build Django. You'll start with building a fun superhero project by gathering the requirements, creating mockups, and setting up the project. Through project-guided examples, you'll explore the Model, View, templates, workflows, and code reusability techniques. In addition to this, you'll learn practical Python coding techniques in Django that'll enable you to tackle problems related to complex topics such as legacy coding, data modeling, and code reusability.You'll discover API design principles and best practices, and understand the need for asynchronous workflows. During this journey, you'll study popular Python code testing techniques in Django, various web security threats and their countermeasures, and the monitoring and performance of your application.Style and approachThis comprehensive project-driven guide will enhance your skill set by taking you through the well-known design patterns and industry-standard best practices in Django web development.

Models

In this chapter, we will discuss the following topics:

• The importance of models
• Class diagrams
• Model structural patterns
• Model behavioral patterns
• Migrations

I was once consulted by a data analytics start-up in their early stages. Despite data retrieval being limited to a window of recent data, they had performance issues with page load sometimes taking several seconds. After analyzing their architecture, the problem seemed to be in their data model. However, migrating and transforming petabytes of structured live data seemed impossible.

"Show me your flowcharts and conceal your tables, and I shall continue to be mystified. Show me your tables, and I won't usually need your flowcharts; they'll be obvious."

— Fred Brooks, The Mythical Man-month

Traditionally, designing code around well thought-out data is always recommended. But in this age of big data, that advice has become more relevant. If your data model is poorly designed, the volume of data will eventually cause scalability and maintenance issues. I recommend using the following adage on how to balance code and data:

Rule of Representation: Fold knowledge into data so program logic can be stupid and robust.

Think about how you can move the complexity from code to data. It is always harder to understand logic in code compared to data. UNIX has used this philosophy very successfully by giving many simple tools that can be piped to perform any kind of manipulation on textual data.

Finally, data has greater longevity than code. Enterprises might decide to rewrite entire codebases because they don't meet their needs anymore, but the databases are usually maintained and even shared across applications.

Well-designed databases are more of an art than a science. This chapter will give you some fundamental principles such as Normalization and best practices around organizing your data. But before that, let's look at where data models fit in a Django application.

M is bigger than V and C

In Django, models are classes that provide an object-oriented way of dealing with databases. Typically, each class refers to a database table and each attribute refers to a database column. You can make queries to these tables using an automatically generated API.

Models can be the base for many other components. Once you have a model, you can rapidly derive model admins, model forms, and all kinds of generic views. In each case, you would need to write a line of code or two, just so that it does not seem too magical.

Also, models are used in more places than you would expect. This is because Django can be run in several ways. Some of the entry points of Django are as follows:

• The familiar web request-response flow
• Django interactive shell
• Management commands
• Test scripts
• Asynchronous task queues such as Celery

In almost all of these cases, the model modules would get imported (as a part of django.setup()). Hence, it is best to keep your models free from any unnecessary dependencies or to import any other Django components such as views.

In short, designing your models properly is quite important. Now let's get started with the SuperBook model design.

The Brown Bag Lunch:

Author's Note: The progress of the SuperBook project will appear in a box like this. You may skip the box, but you will miss the insights, experiences, and drama of working in a web application project.

Steve's first week with his client, the SuperHero Intelligence and Monitoring (SHIM) for short, was a mixed bag. The office was incredibly futuristic, but getting anything done needed a hundred approvals and sign-offs.

Being the lead Django developer, Steve had finished setting up a mid-sized development server hosting four virtual machines over two days. The next morning, the machine itself had disappeared. A washing machine-sized robot nearby said that it had been taken to the forensic department due to unapproved software installations.

The CTO, Hart, was, however, of great help. He asked the machine to be returned in an hour with all the installations intact. He had also sent pre-approvals for the SuperBook project to avoid any such roadblocks in the future.
Later that afternoon, Steve was having a brown-bag lunch with him. Dressed in a beige blazer and light blue jeans, Hart arrived well in time. Despite being taller than most people and having a clean-shaven head, he seemed cool and approachable. He asked if Steve had checked out the previous attempt to build a superhero database in the sixties.

"Oh yes, the Sentinel project, right?" said Steve. "I did. The database seemed to be designed as an Entity-Attribute-Value model, something that I consider an anti-pattern. Perhaps they had very little idea about the attributes of a superhero those days."

Hart almost winced at the last statement. In a slightly lowered voice, he said, "you are right, I didn't. Besides, they gave me only two days to design the whole thing. I believe there was literally a nuclear bomb ticking somewhere."

Steve's mouth was wide open and his sandwich had frozen at its entrance. Hart smiled. "Certainly not my best work. Once it crossed about a billion entries, it took us days to run any kind of analysis on that damn database. SuperBook would zip through that in mere seconds, right?"

Steve nodded weakly. He had never imagined that there would be around a billion superheroes in the first place.

The model hunt

Here is a first cut at identifying the models in SuperBook. As typical for an early attempt, we have represented only the essential models and their relationships in the form of a simplistic class diagram:

An early attempt at the SuperBook class diagram

Let's forget models for a moment and talk in terms of the objects we are modeling. Each user has a profile. A user can make several comments or several posts. A Like can be related to a single user/post combination.

Drawing a class diagram of your models like this is recommended. Class attributes might be missing at this stage, but you can detail them later. Once the entire project is represented in the diagram, it makes separating the apps easier.

Here are some tips to create this representation:

• Nouns in your write-up typically end up as entities.
• Boxes represent entities, which become models.
• Connector lines are bi-directional and represent one of the three types of relationships in Django: one-to-one, one-to-many (implemented with Foreign Keys), and many-to-many.
• The field denoting the one-to-many relationship is defined in the model on the Entity-relationship model (ER-model). In other words, the n ...