FastAPI First Impressions as a Full-Stack Developer

Day 16 of my Python Full Stack journey. ✅ Today's topic: OOP — Object Oriented Programming. The biggest concept in Python so far. And the most important one for Django. Here's what clicked today: Everything in Python is an object. A class is just a blueprint for creating objects. Here's what I typed today: # Class = blueprint class Student: def __init__(self, name, marks): self.name = name self.marks = marks def result(self): if self.marks >= 50: return f"{self.name} — Pass ✅" return f"{self.name} — Fail ❌" # Object = actual instance built from blueprint s1 = Student("Punith", 88) s2 = Student("Rahul", 42) print(s1.result()) # Punith — Pass ✅ print(s2.result()) # Rahul — Fail ❌ Why this matters for Django: → Every Django Model is a class → Every Django View can be a class → Every Django Form is a class If you don't understand OOP — Django will feel like magic. If you do — Django will feel like logic. Today Django finally started making sense. 🤯 What concept made Django finally click for you? #PythonFullStack #Day16 #OOP #BuildingInPublic #100DaysOfCode #Bangalore

Your Django API is not slow because of your database. It's slow because of serialization. A 17-field serializer on 1,000 records = 17,000 Python calls. Just to produce JSON. We hit this in a rental platform in Stockholm. 80ms → 3 seconds at 2,000 users. So we built ClaraX — Rust serialization for Django. One line. No rewrites. No Rust knowledge. Results: → 475ms → 14ms (33x) → 506ms → 10ms (50x) pip install clarax-django python manage.py clarax_doctor https://lnkd.in/dgmZgnK5

Built my first Python API using FastAPI! Coming from a MERN background, I decided to explore Python backend development—and it’s been an eye-opening experience. What I built: A simple REST API with GET & POST endpoints Request validation using Pydantic models Auto-generated API docs (Swagger UI) Key Learnings: How FastAPI handles routing (similar to Express but cleaner) Request body validation without extra libraries Importance of virtual environments (and debugging them the hard way) Running production-ready APIs using Uvicorn One thing that really stood out: FastAPI feels like TypeScript + Express, but with built-in validation and performance advantages. Example: Created a POST /user endpoint that validates incoming data using a schema and returns structured responses. GitHub Repo: https://lnkd.in/gF4FFR2u Would love feedback from the community #Python #FastAPI #BackendDevelopment #LearningInPublic #100DaysOfCode

5 books. 6 database trips. That's your Django app bleeding performance. Most of the time we never notice the N+1 problem — until their app slows down under real data. Here's the fix explained as a story (swipe through) 👇 𝗦𝗹𝗶𝗱𝗲 𝟭 — You have 5 books. Each has an author. Simple. 𝗦𝗹𝗶𝗱𝗲 𝟮 — Without optimization: Django makes 6 separate DB trips. One per book. Painful. 𝗦𝗹𝗶𝗱𝗲 𝟯 — select_related() fixes it with a single JOIN. 1 trip. Everything together. 𝗦𝗹𝗶𝗱𝗲 𝟰 — But JOIN breaks with tags — Book 1 repeats 3 times. Messy. 𝗦𝗹𝗶𝗱𝗲 𝟱 — prefetch_related() makes 2 smart trips. Python glues them in memory. 𝗦𝗹𝗶𝗱𝗲 𝟲 — The rule: ONE thing → select_related. MANY things → prefetch_related. That's it. Two methods. One simple rule. #Django #Python #WebDevelopment #BackendDevelopment #SoftwareEngineering

Not all Python backend frameworks are the same 🤯 If you're new to backend or just curious how apps are built, here’s a simple breakdown: 🔹 Flask → Lightweight & flexible 👉 You build everything yourself 🔹 Django → Full-stack framework 👉 Comes with admin panel, auth, database tools 🔹 FastAPI → Fast & modern 👉 Built for high-performance APIs 💡 Simple way to understand: Flask = Empty kitchen 🍳 Django = Full restaurant 🍽️ FastAPI = Smart automated kitchen ⚡ Each one is powerful — it just depends on your use case 👉 Which one do you prefer or want to learn? #Python #BackendDevelopment #Django #FastAPI #Flask #WebDevelopment #Programming #TechExplained

A thought-provoking piece for crafters: "GitHub - Distributive-Network/PythonMonkey: A Mozilla SpiderMonkey JavaScript engine embedded into the Python VM, using the Python engine to provide the JS host environment." PythonMonkey embeds Mozilla's SpiderMonkey JavaScript engine directly into the Python runtime, letting developers call JavaScript from Python and Python from JavaScript within the same process — no serialization or IPC required. The project shares memory backing stores between languages for strings, typed arrays, and buffers, making cross-language data transfer extremely fast. Python dicts and lists automatically behave as JS objects and arrays (and vice versa), with full method support through proxy wrappers. It ships with a CommonJS module system, an event loop (supporting setTimeout and Promises as Python awaitables), and standard JS globals like console and XMLHttpRequest. The project reached MVP in September 2024, installs via `pip install pythonmonkey`, and Distributive actively maintains it while welcoming external contributions.

Day 544 of Learning – Exploring Python Backend Frameworks 🌐🐍 Explored different Python backend frameworks used to build web applications and APIs, each designed for specific use cases and performance needs. Frameworks like Django provide a full-stack solution with built-in features such as authentication, ORM, and admin panels, making it ideal for large-scale applications. Flask is a lightweight and flexible framework that allows developers to build applications with minimal setup and more control. Modern frameworks like FastAPI and Starlette focus on high performance and asynchronous processing, making them perfect for APIs and real-time applications. Frameworks such as Falcon and Sanic are optimized for speed and are commonly used in high-performance systems. Bottle and CherryPy are simple and beginner-friendly options for smaller applications, while Pyramid offers flexibility by allowing developers to scale from simple to complex applications. Understanding these frameworks helps in choosing the right tool based on project requirements, scalability, and performance needs. Each framework plays an important role in modern backend development and API design. 🚀 #BackendDevelopment #Python #Django #Flask #FastAPI #WebDevelopment #APIs #LearningJourney #Day644

Everyone's talking about Rust. Everyone's shipping in Python. We've been running Go in production for years and I'd make the same call again tomorrow. My honest take after working with different backend technologies: Go isn't the most exciting tool in the stack. But it's the most reliable. 📌 No dependency drama 📌 One binary, deploy anywhere 📌 Goroutines that actually make concurrency click 📌 A language small enough to learn completely - not just the parts your tutorial covered Yes, there are tradeoffs. More boilerplate, no framework ecosystem like Node or Django. We think that's a feature, not a bug. Fast, maintainable backends that just work. That's what we need at Bringe Informationstechnik GmbH. What's your backend stack right now and would you choose it again? 👇

🚀 Build Powerful APIs with Python (Django REST Framework & FastAPI) In this post, I've broken down how to create APIs using two of the most popular Python frameworks: Django REST Framework and FastAPI—in a simple, algorithmic, and visual way. 🔹 What's inside the post? Step-by-step API development flow for both frameworks Clear algorithmic approach (from setup -> models -> endpoints -> testing) Practical code snippets to get started quickly Side-by-side comparison of DRF vs FastAPI Tips on when to use each framework 🔹 Django REST Framework Best for large, database-driven applications where you need a complete ecosystem with authentication, ORM, and scalability. 🔹 FastAPI Perfect for high-performance APIs, microservices, and modern apps with automatic validation and interactive docs. 💡 Key Takeaway: Both frameworks are powerful—choose DRF for full-scale applications and FastAPI for speed and lightweight performance. 🔥 Whether you're preparing for interviews or building real-world projects, mastering these tools is essential for every backend developer. #Python #API #Django #FastAPI #BackendDevelopment #WebDevelopment #SoftwareEngineering 🚀