Is Django Good For Big Projects

Table of Contents

Introduction

Django is one of my favorite frameworks I’ve used it across the years with great success. I have used Django in both small projects and highly scalable ones that involved bigger teams in the development of it. In this article I’m going to go into the specifics of how well Django does for bigger projects.

Django is designed to be scalable and efficient, and it includes features such as caching and lazy loading that can help to improve the performance of your application. For example, Django’s ORM allows you to interact with your database using high-level Python objects, which makes it easy to query and manipulate data without having to write complex SQL queries. This can help to reduce the load on your database and improve the overall performance of your application.

In addition to its performance and scalability, Django is also known for its user-friendly and intuitive design. It follows the Model-View-Template (MVT) architectural pattern, which separates the business logic of your application from the user interface. This makes it easier to maintain and modify your code, and it also allows you to reuse components and modules across different parts of your application. For example, you can define a common set of templates that can be used across multiple views, which makes it easier to maintain a consistent look and feel across your site.

Django also has a very feature set environment of libraries and helpers. If you pair this with the already rich library that Python has it makes. great combination and toolkit for bigger teams to abstract a lot of the complexity when writing your code. Furthermore the community is large and active, and there are many open-source libraries and packages available that can be used to add additional functionality to your application. There’s plethora of people commenting and answering questions online which will help you streamline some tasks that you may get stuck with.

Cloud Scaling

Django is designed to be scalable, which means that it can handle large amounts of traffic and data without requiring significant resources. In a cloud environment, It can be scaled horizontally by adding additional web server instances to distribute the load across multiple machines. This can help to improve the performance and reliability of your application, and it can also make it easier to handle sudden spikes in traffic.

To scale Django in the cloud, you can use a cloud hosting provider such as Amazon Web Services (AWS), Google Cloud Platform (GCP), or Microsoft Azure. These providers offer a range of services that can be used to deploy and manage Django applications in the cloud, including virtual machines, load balancers, and managed databases.

For example, on AWS you can use Amazon Elastic Compute Cloud (EC2) to launch and manage virtual servers that can run your Django application. You can use Amazon Elastic Load Balancer (ELB) to distribute incoming traffic across multiple EC2 instances, and you can use Amazon Relational Database Service (RDS) to manage and scale your database.

On GCP, you can use Google Compute Engine (GCE) to launch and manage virtual machines that can run your Django application, and you can use Google Cloud Load Balancer (GCLB) to distribute incoming traffic across multiple instances. You can also use Google Cloud SQL to manage and scale your database.

On Azure, you can use Azure Virtual Machines (VMs) to launch and manage virtual servers that can run your Django application, and you can use Azure Load Balancer to distribute incoming traffic across multiple instances. Azure also offers ways for you to scale up your DB backend when connecting it to Django

Multiple Concurrent Web Connections

Django uses a non-blocking, event-driven web server called the Django Async Server (DAS), which is built on top of the asyncio library in Python. This has been optimized to offer concurrency at the very core of the system which lets you basically offload things such as incoming requests to a different lightweight thread. In the same way if you have long executing tasks you can pair it with something like celery which integrates natively in your system to create an easy way to manage these jobs.

The DAS server is able to handle multiple requests simultaneously using a small number of threads or processes, which allows it to efficiently serve a large number of concurrent connections. This is in contrast to traditional web servers, which use a blocking, synchronous model and require a separate thread or process for each incoming request. This can make them less efficient and less able to handle a large number of concurrent connections.

Django also includes a number of other features and tools that can help to improve its performance and scalability. For example, it includes support for caching (we will cover this is more detail below), which allows you to store frequently-used data in memory or on disk so that it can be quickly accessed without having to query the database. It also includes support for lazy loading, which allows you to load related objects on demand instead of all at once, which can help to reduce the amount of memory and CPU resources that are required to serve a request.

I have seen Django scale well and handle a large number of concurrent connections without issues so don’t be afraid to stretch it out when benchmarking or scaling your system load.

Django And Lambda functions

Django is a web framework that is designed to be used for building traditional server-side web applications. It is not natively designed to be used with AWS Lambda functions, which are a serverless compute service that allows you to run code without having to manage any underlying infrastructure.

However, it is possible to use Django with AWS Lambda by using a custom runtime that enables Django to run in a Lambda environment. For example, the Zappa library is a popular open-source tool that allows you to run Django applications on AWS Lambda and API Gateway. It provides a number of features and tools that make it easy to deploy and manage Django applications in a serverless environment, including automatic packaging and deployment, automatic SSL certificate management, and support for running background tasks using AWS Lambda functions.

To use Django with AWS Lambda and Zappa, you will need to create a Django project and application as you would normally. You will then need to install Zappa and use its zappa init command to create a Zappa configuration file. This file will specify the details of your Lambda function and API Gateway, such as the runtime environment and the API endpoint.

Once you have created your Zappa configuration file, you can use the zappa deploy command to deploy your Django application to AWS Lambda and API Gateway. This will create the necessary Lambda function and API Gateway resources, and it will also package and upload your application code to AWS. Once your application is deployed, you can use Zappa to manage and update your application, including running background tasks using AWS Lambda functions.

Overall, while Django is not natively designed to be used with AWS Lambda, it is possible to use it with Lambda by using a custom runtime such as Zappa. This can provide a number of benefits, such as the ability to run your application without having to manage any underlying infrastructure, and the ability to run background tasks using AWS Lambda functions.

This is particularly important because if you are following a micro service approach in your system you will be able to still use Django in your architecture design and provide very powerful micro-architecture components in your system.

Django In Memory Database Backends

The framework offers a way for you to use different pluggable database backends this including both relational (e.g. MySQL, PostgreSQL) and non-relational (e.g. MongoDB, Redis) databases. By default, Django uses the SQLite database, which is a lightweight and easy-to-use database that is built into Python but we can definitely do better than this in a big environment to make it scale.

It is also possible to use an in-memory database with Django. An in-memory database is a database that is stored entirely in the memory of the computer, rather than on the filesystem. This can provide a number of advantages, such as faster access to data and improved performance, as the data can be accessed directly from memory without having to read from and write to disk.

To use an in-memory database with Django, you can use a third-party library such as Django-Memory-DB. This library provides an in-memory database backend for Django that allows you to store your database in memory rather than on the filesystem. It is compatible with the Django ORM and supports most of the same features as other database engines, such as transactions, foreign keys, and database migrations.

To use Django-Memory-DB, you will need to install the library and add it to your Django project’s INSTALLED_APPS setting. You will then need to specify the django_memory_db backend in your project’s DATABASES setting, and you can use the Django ORM and other Django features as you would normally.

One of the projects I worked on in the past basically had me implement my own backend connector to a paid commercial database called SAP HANA. This is an all in memory database which offers great versatility to your system but also speed since it uses memory to handle most of the queries. The power of Django here was that I was able to implement my own database connector and seamlessly integrate it into the Django project without issues. If you are interested into finding more about this ping me below and I’ll be able to offer more details about it.

Django Concurrency

Another way to improve the concurrency and performance of a Django application is to use multiple worker processes. By default, Django runs with a single worker process, which means that it can only handle one request at a time. However, you can use the --workers option when starting the Django development server to specify the number of worker processes to use. This will allow Django to handle multiple requests simultaneously, which can improve its performance and concurrency.

Your ability to extend Django and concurrency is basically using the cloud which we went into a lot of detail earlier in this article.

Django Object Caching

One of the coolest features is the support for caching. It allows you to store frequently-used data in memory or on disk so that it can be quickly accessed without having to query the database. This can help to improve the performance of your application by reducing the load on the database and allowing your application to serve more requests faster.

To use caching in Django, you will need to enable the caching middleware and configure a cache backend. The caching middleware is responsible for storing and retrieving cached data, and the cache backend is responsible for actually storing the data. Django includes a number of built-in cache backends, such as the locmem backend, which stores data in memory, and the db backend, which stores data in the database.

Once you have enabled the caching middleware and configured a cache backend, you can use the cache decorator or the cache_page decorator to specify which views or pages should be cached. For example, you could use the cache_page decorator to cache the entire output of a view for a specified amount of time:

from django.views.decorators.cache import cache_page

@cache_page(99)
def custom_view(request):
    # this page is basically cached for 99 seconds

Alternatively, you can use the cache decorator to cache the output of a specific part of a view, such as a specific query or a specific template fragment:

from django.views.decorators.cache import cache

@cache(99)
def custom_view(request):
    # this code is basically cached for 99 seconds
    people = find_people()
    return render(request, 'people.html', {'people': people})

The caching allows you to easily and efficiently store frequently-used data in memory or on disk, which can help to improve the performance of your application. It provides a range of features and tools that make it easy to specify which data should be cached and for how long, and it integrates seamlessly with the rest of the Django framework.