FastAPI vs Django: Pythonic Design and Performance Comparison

Django vs FastAPI — Which One Is More Pythonic and Faster? (Engineering View) When engineers choose a backend framework, the real questions are simple: • Which framework is more Pythonic? • Which one is faster under load? • Which one follows Python behavior more naturally? Let’s look at it from an engineering perspective. 🧠 1. Pythonic Design FastAPI feels very close to writing normal Python. It relies heavily on modern Python features: Type hints Async/await Clean function-based APIs Example: @app.get("/users/{id}") async def get_user(id: int): return {"id": id} Your Python types become validation, documentation, and API schema automatically. Django, however, follows a framework-centric design with: class-based views Django ORM patterns configuration-heavy structures This makes Django powerful but sometimes less close to plain Python behavior. ✅ Result: FastAPI feels more Pythonic. ⚡ 2. Performance FastAPI is built on ASGI architecture. That means: async I/O high concurrency non-blocking request handling Under high traffic, FastAPI can handle significantly more requests per second. Django traditionally runs on WSGI, which is mostly synchronous. It’s extremely stable but not optimized for high-concurrency APIs by default. ✅ Result: FastAPI is usually faster for API workloads. ⚙️ 3. Engineering Philosophy FastAPI focuses on: • modern API architecture • microservices • async performance • minimal abstraction Django focuses on: • full web applications • rapid development • batteries-included ecosystem • admin panel, ORM, authentication 🎯 Engineering Takeaway If you want: ✔ Clean Python design ✔ High-performance APIs ✔ Modern async architecture ➡ FastAPI is often the better engineering choice. If you want: ✔ A complete web framework ✔ Built-in admin panel ✔ ORM and authentication ready ➡ Django remains one of the most productive frameworks. Both frameworks are excellent. The best choice depends on the system you are designing. #Python #FastAPI #Django #BackendEngineering #SoftwareArchitecture #APIDesign

Day-117 📘 Python Full Stack Journey – Django Models to UI Rendering Today I worked on a complete flow in Django — from creating database models to displaying dynamic data on a webpage. This felt like a true full-stack experience! 🚀 🎯 What I learned today: 🗄️ Model Creation (Database Table) Defined a model in models.py: class Course(models.Model): course_name = models.CharField() course_description = models.TextField() Learned: CharField → for small text data TextField → for larger content Understood that inheriting from models.Model creates a database table 🔄 Migrations & Admin Integration Applied database changes using: py manage.py makemigrations py manage.py migrate Registered model in admin.py: admin.site.register(Course) Managed data through Django Admin (add, edit, delete) 💡 Also learned that missing migrations can cause errors like “no such table” 🌐 Fetching & Displaying Data Retrieved data in views.py: details = { 'data': Course.objects.all() } Passed data to template and displayed using loop: {% for i in data %} <h1>{{i.course_name}}</h1> <p>{{i.course_description}}</p> {% endfor %} 🎨 Styling & Layout Used Flexbox/Grid to design responsive course cards Created a clean UI layout for displaying multiple records dynamically This session helped me understand how Django connects database → backend → frontend, making applications truly dynamic and data-driven. Excited to build more real-world features using Django! 💻✨ #Django #Python #FullStackDevelopment #WebDevelopment #Backend #Frontend #Database #CodingJourney #LearningToCode #Upskilling #ContinuousLearning

I just shipped django-query-doctor — an open-source package that diagnoses slow Django ORM queries and prescribes exact code fixes with file:line references. pip install django-query-doctor Every Django developer knows the ritual. Page loads slow. Open the toolbar. Stare at SQL. Ctrl+F through views. 40 minutes later you find the missing select_related. Ship it. Next sprint, same thing. I got tired of being the detective. So I built the tool that does the detective work for you. What makes it different from debug-toolbar, silk, or nplusone: Those tools tell you WHAT queries ran. django-query-doctor tells you WHERE in your code the problem is, WHY it's slow, and gives you the exact fix — copy, paste, done. → N+1 in blog/views.py:42? Here's the select_related() call to add. → 15 duplicate queries? Here's the cache pattern to eliminate them. → SELECT * on 47 columns? Here's the .only() you need. → DRF serializer triggering hidden queries? Caught and explained. But detection was just the start: → python manage.py fix_queries — auto-patches your code (with dry-run preview) → python manage.py diagnose_project — scans every URL, scores every app → pytest plugin that fails your build if someone introduces an N+1 → CI mode that only flags queries in files YOUR PR touched → Celery, async/ASGI, OpenTelemetry, JSON + HTML reports → One line in settings.py. Zero config. Never crashes your app. 434 tests. 87% coverage. Python 3.10–3.13. Django 4.2–6.0. Built with Claude as my AI development partner — 42 source files, 44 test files, idea to PyPI in weeks instead of months. The architecture and decisions are mine, but I'm not going to pretend this scope was possible solo without AI. GitHub: https://lnkd.in/dPE59U5p PyPI: https://lnkd.in/dwXcJbg7 Try it on your slowest endpoint. Star it, break it, open issues. #Django #Python #OpenSource #DeveloperTools #BackendEngineering #BuildInPublic #AIAssistedDevelopment

FastAPI vs Django When building web applications in Python, two names come up often: FastAPI and Django. Both are powerful. Both are widely used. But they solve different problems in different ways. FastAPI is lightweight, fast, and built for modern API development. It shines when you need: - High performance - Async support - Clean API design - Automatic docs with Swagger/OpenAPI - A great fit for microservices and AI/ML backends Django is a full-featured framework that gives you a lot out of the box. It is ideal when you need: - Authentication and authorization - ORM and database handling - Admin dashboard - Forms, sessions, and security features - A more complete “batteries-included” structure So which one should you choose? If your focus is building APIs, microservices, or machine learning services, FastAPI is an excellent choice. If your focus is building a complete web application with many built-in features, Django may be the better option. In the end, it is not about which one is “better” overall. It is about which one is better for your use case. Both are valuable tools for every Python developer and software engineer to understand. #FastAPI #Django #Python #WebDevelopment #BackendDevelopment #APIDevelopment #SoftwareEngineering #Programming #Tech #MachineLearning #AI #Microservices #RESTAPI #Developer #BackendEngineer #PythonDeveloper #WebFramework #FullStackDevelopment #OpenSource #Coding #SoftwareDevelopment #TechCommunity #BuildInPublic #LearningInPublic #DeveloperTools

bulk_create and bulk_update don't behave like regular Django saves. Most developers find out the hard way or never realise it! The assumption - they're just faster versions of calling .save() in a loop. Same behaviour, better performance. save() on a single instance does several things - 1. runs pre_save and post_save signals 2. calls full_clean() for validation, handles auto-generated fields 3. returns the saved instance with its new primary key bulk_create and bulk_update bypass all of it. No signals. No validation. No per-instance hooks. Django hands a list of objects directly to the database and walks away. bulk_create - the PK problem ~ By default, bulk_create returns instances without primary keys populated(in Python object) - unless update_conflicts or returning_fields is explicitly set. ~ ignore_conflicts=True silently swallows insert failures - no exception, no log, no signal. A uniqueness violation disappears without a trace. bulk_update - what it can't do ~ bulk_update requires an explicit list of fields. Miss a field - it doesn't update. ~ It cannot update fields using expressions - no F(), no computed values. ~ And like bulk_create - no post_save signals fire. Anything listening for model changes never knows. The performance gain is real - 1000 inserts in one query vs 1000 round trips. But the tradeoffs are real too. Takeaway — -> bulk_create / bulk_update - no signals, no validation, no per-instance hooks -> bulk_create → PKs not populated in Python object by default on PostgreSQL, not at all on MySQL -> ignore_conflicts=True → silent failure, uniqueness violations disappear without exception -> bulk_update → explicit fields only, no F() expressions, missed fields silently skip Have you been bitten by missing signals after a bulk operation? How did you handle downstream consistency? #Python #Django #BackendDevelopment #SoftwareEngineering

Moving from Django to NestJS, I ran into something that confused me at first — route conflicts 🤯 Django never had this problem. Turns out, that's not an accident. 🤔 I wrote a short breakdown of how NestJS, Django, Flask, and FastAPI each handle the classic `/users/:id` vs `/users/me` conflict — and what it reveals about their routing design. 🛣️ https://lnkd.in/ddBSzrxh #NestJS #Django #Backend #WebDevelopment #Python #TypeScript

When learning backend development with Django, many developers understand models and queries but struggle with one key concept: How APIs are actually created. In Django, views are where everything comes together. Queries retrieve the data. Serializers convert the data. But views expose the data as APIs. In my latest article, I break down: • What Django views are • Function-based vs class-based views • What API views are • How APIs return JSON responses • Best practices for building clean API endpoints If you're learning backend development or building APIs with Django REST Framework, this will help you understand how requests actually become APIs. Read the article here 👇 https://lnkd.in/dquzq8bZ #Django #Python #BackendDevelopment #APIs #SoftwareEngineering #WebDevelopment

FastAPI vs. Django: Which Framework Should You Choose and When? In Python backend development, a perennial debate exists: FastAPI or Django? Whether you are preparing for an interview or selecting a framework for your next project, this comparison will prove invaluable. Here are 4 major differences between the two: 1. Performance and Speed FastAPI: Built upon Starlette and Pydantic, it ranks among the fastest Python frameworks available. Its asynchronous support (async/await) is superior for handling high-concurrency scenarios. Django: This is a synchronous framework (though it now includes ASGI support). It follows a "Batteries Included" approach, which can make it somewhat heavier. 2. Nature: Micro vs. Monolith FastAPI: A micro-framework. You receive only the essential tools; other functionalities (such as Authentication and Database handling) must be plugged in manually. It is perfect for microservices. Django: A monolith. It comes with a built-in Admin panel, ORM, and Authentication system. It is an excellent choice for full-stack web applications. 3. Data Validation and Documentation FastAPI: It utilizes Pydantic for data validation and automatically generates Swagger UI (OpenAPI) documentation. Developers do not need to write documentation separately. Django: APIs are typically built using the Django REST Framework (DRF), which involves a slightly higher degree of manual configuration. 4. Learning Curve  FastAPI: Being compact and modern, it can be learned quickly—provided you are familiar with modern Python type hints. Django: It possesses a vast ecosystem, so mastering it takes some time; however, once learned, it significantly accelerates the development process. Conclusion: Choose FastAPI if you prioritize high performance, microservices, and rapid API development. Choose Django if you need to build a robust, secure, and feature-rich application quickly. Which one is your favorite? Let us know in the comments! #Python #BackendDevelopment #FastAPI #Django #SoftwareEngineering #WebDevelopment #CodingLife #InterviewPrep

1,076 commits in TypeScript. 635 commits in Python. Same team. Same product. Same year. --- I work on two completely different backends every day. Morning: TypeScript + NestJS — the platform that lets teams at Deel build Slack apps. Afternoon: Python + Django — the legacy backend with 9 running plugins, real users, live data. When I started, switching between them felt like context-switching between two jobs. Now it doesn't. Here's what changed: I stopped thinking in languages. I started thinking in patterns. Dependency injection in NestJS? Same concept as Django's middleware pipeline. Sequelize migrations? Same mental model as Django ORM — just different syntax. NATS consumers? Same pub/sub pattern you'd implement in any event-driven system. The patterns don't care what language you're in. They care about the problem you're solving. Once I understood that — really understood it — switching stacks became as natural as switching between different parts of the same system. Because that's exactly what it is. AI tools helped accelerate this. Not by writing the code — but by letting me ask questions across contexts. "Explain how Django's middleware chain works" when I haven't touched it in two weeks. "Trace this NATS consumer from event to handler" before I start debugging. The pattern is the same in both stacks. AI helps me find it faster. The most dangerous label in engineering: "I'm a [language] developer." It limits what you'll pick up. It limits what you'll contribute to. It limits what you'll learn. Your stack is a tool. The problem is the job. What's a pattern you learned in one stack that completely changed how you think in another? #BackendEngineering #SoftwareEngineering #Python #TypeScript #CareerGrowth

𝗧𝗵𝗲 𝗕𝗲𝗻𝗲𝗳𝗶𝗍𝘀 𝗢𝗳 𝗖𝗹𝗮𝘀𝘀-𝗕𝗮𝘀𝗲𝗱 𝗩𝗶𝗲𝘄𝘀 You can write views as functions or classes in Django. Class-Based Views (CBVs) are a cleaner alternative to function-based views. Here's how you can write a view using both methods: - Function-based view: def post_list(request): posts = Post.objects.all() return render(request, 'core/post_list.html', {'posts': posts}) - Class-based view: class PostListView(ListView): model = Post template_name = 'core/post_list.html' context_object_name = 'posts' Both views do the same thing. The CBV version is shorter because it handles the queryset fetching and context building automatically. Django provides generic views for common patterns. You can use these views to handle tasks like listing, creating, updating, and deleting objects. CBVs need .as_view() when connecting to a URL. You can also use mixins to add functionality to your views. Now that you understand CBVs, Django REST Framework (DRF) becomes easier to understand. DRF is essentially CBVs built for APIs instead of HTML pages. DRF handles tasks like serializing Python objects to JSON, deserializing and validating incoming JSON, and authentication for APIs. You can use serializers to convert model instances to JSON and validate incoming data. API views work like CBVs, and generic API views cut boilerplate to almost nothing. You can use DRF to build a JSON API from the same Django project that serves HTML pages. Source: https://lnkd.in/gswbYync