So you‘ve decided to build a new web application. You narrow it down to using either the massively popular Ruby on Rails or versatile Django frameworks to bring your vision to life quickly and efficiently.
But which to choose? Both have stood the test of time and scaled behemoth platforms you likely use daily. Let‘s carefully compare these robust tools to help you make the ideal selection based on the unique needs of your upcoming project!
A Bit of History
First, some background for context…
Ruby on Rails exploded out of nowhere in 2004 – the brainchild of Danish programmer David Heinemeier Hansson building on top of the Ruby language ecosystem created by Yukihiro "Matz" Matsumoto back in 1995. Rails offered convention over configuration simplicity never seen before, letting pioneers like Basecamp and 37Signals ship products lightning fast.
Adrian Holovaty originally conceived Django at the Lawrence Journal-World newspaper in 2003 to manage its newsroom workflow. Django‘s versatility, efficiency and security soon attracted dev teams both small and large – enabling the likes of Instagram, Spotify and Youtube manage insane data demands at scale.
Now clearly no slouches in inspiring widespread adoption, by 2022, Rails and Django combined power over 8% of the top million sites and counting!
Rails usage peaked around 2015 before plateauing as challengers emerged
Yet the playing field tilts surprisingly even still today despite their contrasting designs…
Philosophy & Approach
Rails double downs on simplicity via assumptions and defaults – "convention over configuration" as they call it. All the mundane boilerplate code you need for database models, routes and controllers gets auto-generated so you focus solely on business logic. This lets small teams and startups build functional prototypes at warp speed.
Django favors explicit intent over magic – you manually specify only what‘s absolutely necessary. This takes a bit more upfront effort but pays off as apps grow huge and complex. Instagram saw this flexibility critical in customizing and fine tuning their infrastructure to keep feeds lighting fast.
So think agility vs adaptability.
Languages & Capabilities
Rails is of course built within Ruby – an elegant object oriented language beloved by many devs for readable syntax but not quite as performant as compiled languages. Ruby‘s dynamic nature means your debugging life improves over statically typed alternatives however!
Django leverages Python – arguably even more user friendly coding and far faster execution via optimized byte code. Python‘s vast libraries around machine learning and data science make it a breeze to train models and analyze results right within app logic.
Both language ecosystems interoperate well modern APIs – JSON, REST, GraphQL, Webhooks and all that jazz. Performance depends then on how and where you deploy the framework…
Performance & Scalability
Out of the box, Django edges out Rails in benchmarks:
| Framework | Requests / sec | Latency (ms) | Throughput |
|---|---|---|---|
| Ruby on Rails | 1510 | 120ms | 21 MB/s |
| Django | 1983 | 90ms | 29 MB/s |
Stats above from TechEmpower framework benchmarks
If your web app goes viral, both frameworks provide horizontal scaling capabilities via Treasurer, Sidekiq, Celery and more to distribute load across threads and servers.
Where Django excels is optimizing complex relational data flows. Instagram runs on a heavily customized Django build with advanced caching logic and multimedia pipeline management I‘d need multiple whiteboard sessions to explain!
Security Smarts
Rails bakes in sanity checks against injection attacks, CSRF and other OWASP top 10 threats right from rails new command. But large complex apps benefit from additional rigor like encryption schemes, infrastructure hardening and rigorous testing.
Django too prevents leading web app vulnerabilities by design. Guido actually discourages direct SQL use entirely as a best practice. Higher level abstractions prevent many operational risks and enforce clean architecture as you build out.
Ultimately, frameworks can only get you so far. Ongoing security monitoring, dependencies audits and infrastructure hygiene matter just as much!
Vibrant Communities
As one of the world‘s most popular web frameworks, Rails enjoys overflowing support channels on StackOverflow, Reddit, HackerNews and dedicated Slacks. Chances are any obstacle you hit has likely been covered. Conferences like RailsConf connect thousands of brilliant minds advancing the ecosystem yearly.
Django makes prototyping complex workflows smooth via robust admin screens and intuitive access controls. The latest 4.1 release extends abilities even further with long demanded asynchronous processing and WebSocket integrations. Expect rapid evolution to continue.
The framework caters slightly more so to Python aficionados but helpful guidance for common issues can be found on Django Community channels and their deep documentation archives.
Cloud Hosting & Deployment
Where your apps ultimately run matters just as much for performance!
Rails can be hosted anywhere from bare Linux machines to Docker containers on Kubernetes but really thrives at scale on IaaS providers like AWS and Heroku supporting smooth autoscaling capabilities.
Django apps lend themselves even better to portable containers and orchestrators like Kubernetes allowing replicated processes to handle large traffic surges. Leveraging Django seamlessly as FaaS on AWS Lambda or Netlify Edge Functions gains momentum as well.
Most shared hosts provide one click installs for both frameworks. I‘d recommend at least 2GB RAM Ubuntu/Nginx servers to start. Static asset caching and CDN distribution is a must once user volumes increase!
Sample App Building Flow
Let‘s briefly walk through constructing a basic database-backed web app in Rails and Django…
Rails Way
$ rails new my_app
$ rails generate scaffold Post title:string body:text
$ rails db:migrate
$ rails serverAnd we have CRUD pages for blog posts in just minutes! Rinse and repeat for author model and other entities.
The directory structure and default magic here allows laser focus on only new business functionality. Adding login, API support etc is just as speedy with generator commands. Smooth workflows for rapid iteration.
Django Way
First make and migrate your Post model manually:
# models.py
class Post(models.Model):
title = models.CharField(max_length=100)
body = models.TextField()
$ python manage.py makemigrations
$ python manage.py migrate Then construct views, templates, urls and forms piece by piece to spec out the interface and logic required.
More step-by-step grunt work absolutely. But full control over every aspect from database schema to user authorization and self-documenting code as you go. Rock solid architecture.
To Framework or Not To Framework?
At their core, Rails and Django eliminate grunt work and support sound design practices so you focus on app functionality rather than infrastructure.
Hand rolling own MVC code has compile and optimization benefits but costly downsides:
- Endless security vulnerability scans and fixes
- Operational failures and scaling challenges
- Rebuilding deprecated features
Resist NIH tendencies if on small teams without DevOps firepower!
Have Cake and Eat it Too…
Between polished Rails generators or Django‘s administrator UI and versatility, most standard CRUD+ web applications and MVP prototypes can be built incredibly quickly.
Extend functionality further with RubyGems and Django Packages at your fingertips:
- User authentication
- Email + notifications
- Ready made admin dashboards
- Forms generation
- GraphQL/API integrations
- Visualization libraries
- Async job queues
- Automated testing suites
Why waste precious time reinventing wheels?
Rails vs Django – Making Your Pick
So surveying the landscape, here is my suggested decision making framework:
When to use Ruby on Rails
- Rapid prototyping new product ideas
- Standard database-backed apps and basic CRMs
- Small team without dedicated Ops help
- Startups prioritizing speed over scale
- Apps where Python and ML overkill
When to use Django
- Heavily customized solutions with complex workflows
- Data science apps leveraging Python‘s capabilities
- Speed and scale critical from outset
- Apps integrating AI/ML models and analysis
- Regulated environments requiring stringent controls
As with mosttechnology decisions there is no one choice fits all. I hope mapping key criteria to project demands simplifies selecting the optimum path for your app idea to thrive!
Bottom line – for standard use cases, your amazing talents likely matter far more than any framework advantages 😊.
Let me know if any other questions come up as you evaluate options!
John Doe
Tech Consultant, ACME Solutions
