Choosing the Right Backend Framework for Your Project

If you’ve ever built APIs in Django REST Framework, you know the pain of writing multiple views for the same model — list, detail, create, update, delete. Messy, repetitive, and error-prone. That’s where ViewSets come in. With just a few lines of code, you get all CRUD endpoints automatically, clean URL routing, and a scalable request flow. In my latest Medium article, I break down: What ViewSets are and why they matter How to secure your APIs with get_queryset() Performance boosts using prefetch_related Query parameter filtering with filter_backends Custom endpoints with @action (like cancel or recent orders) Common mistakes to avoid (permissions, redundant filtering, lost prefetch) This isn’t just about making APIs that “work” — it’s about building APIs that are secure, efficient, and production-ready. 👉 Read the full guide here: https://lnkd.in/diM6UCiZ #Django #RESTAPI #Python #BackendDevelopment #SoftwareEngineering #APIs

How do you handle GET requests in your DRF projects? I have noticed a common point of confusion among Django developers whether to structure response data directly in the view or use a serializer. Here is my take. Always use serializers, even for read only operations. By default, read only fields in a serializer give you a clean declarative way to shape your API responses. Instead of manually reshaping dictionaries inside the view which quickly becomes unmaintainable, serializers act as a contract between your database models and the outside world. They allow you to rename fields conditionally, expose computed properties, nest related objects, and keep your views lean and focused on orchestration rather than transformation. But there is one critical performance caveat. If your serializer pulls data from multiple related objects, make sure you use prefetch related or select related in your queryset before passing it to the serializer. Otherwise you will run into the classic N plus one query problem, one query for the main object plus one query for each related object. That scales terribly. Good serialization is about control over your data shape. Good performance is about intention in your query planning. Do you structure your GET responses in serializers or directly in the view? What is your team's standard? #Django #DRF #APIDesign #Python #WebDevelopment #BackendBestPractices

🚀 Why Pagination is Important in APIs (A Small Learning) While working with APIs, I realized that returning large amounts of data at once can impact performance and user experience. Here’s what I understood about pagination: 🔹 Instead of sending all records, APIs return data in smaller chunks 🔹 Improves response time and reduces server load 🔹 Makes it easier for frontend to handle and display data 💡 In Django REST Framework, pagination can be easily implemented using built-in classes like PageNumberPagination. ⚠️ One thing I noticed: Without pagination, APIs may work fine initially but can become slow and inefficient as data grows. This made me understand how important it is to design APIs keeping scalability in mind. Still exploring more ways to build efficient and scalable backend systems 🚀 How do you usually handle large data responses in your APIs? #Django #Python #BackendDevelopment #API #WebDevelopment #LearningInPublic

One serializer change turned my API from 1.8s to 200ms. ⚡ Everything was working fine. But something felt off. The API was slow… for no obvious reason. The issue wasn’t the database. It wasn’t Django. It was the serializer. What was happening: I was returning way more data than needed. Nested serializers Unnecessary fields Hidden queries All adding up silently. The fixes: 🔹 Use .only() to fetch required fields 🔹 Use .values() for lightweight responses 🔹 Avoid deep nested serializers unless necessary 🔹 Write custom serializers where control matters The realization: APIs don’t slow down suddenly. They get heavier with every extra field you return. The rule: Return only what the client needs. Nothing more. You don’t need all fields. You just never questioned it. What’s one performance mistake you’ve caught in your API? #SoftwareEngineering #BackendDevelopment #Django #Python #APIDesign #Performance #WebDevelopment #RESTAPI #Developers

⚡ Flask vs FastAPI — Which one should you choose? As a Python backend developer, I’ve worked with both Flask and FastAPI, and here’s a simple breakdown 👇 🔹 Flask ✔️ Lightweight and flexible ✔️ Easy to get started ✔️ Great for small to medium applications 🔹 FastAPI ✔️ High performance (async support) ✔️ Built-in request validation (Pydantic) ✔️ Automatic API documentation (Swagger UI) 👉 My takeaway: - Use Flask when you need simplicity and quick development - Use FastAPI when performance and scalability matter Both are powerful — it’s not about which is better, but which fits your use case. 💬 What do you prefer — Flask or FastAPI? #Python #BackendDevelopment #FastAPI #Flask #SoftwareEngineering #APIs

While working on backend development today, one thing became even clearer — “Writing simple code is easy, but writing scalable code is a skill.” With Python backend, my focus is no longer just on making things work, but on structure, performance, and long-term maintainability. Today’s focus: • Designing clean and structured APIs • Proper request/response handling • Writing modular and reusable code • Optimizing database queries Working with frameworks like FastAPI makes you realize — speed doesn’t just come from the framework, it comes from the developer’s decisions. Backend development continues to teach me this: 👉 “A good system is one that can grow without breaking.” Building consistently, step by step — with a real-world systems mindset 🚀 #Python #BackendDevelopment #FastAPI #APIs #SoftwareEngineering #LearningInPublic

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

💡 Imagine this... You send ₹1000 to your friend, money is deducted from your account… but your friend never receives it ❌ Scary right? This is where Django’s "transaction.atomic()" comes in 🔥 It ensures: ✅ Either all database operations succeed ❌ Or everything is rolled back When you wrap code inside transaction.atomic(): ✅ All database queries succeed → changes are saved ❌ Any error happens → everything is rolled back No partial updates. No broken data. That’s why I’m learning and using this in my Django projects 🚀 #django #python #backenddevelopment #webdevelopment #learning

🚀 From Learning Python to Understanding Backend Systems When I first started learning Python, my focus was mainly on syntax and basic problem-solving. But as I explored more, I realized that backend development is where logic meets real-world application. That’s when I began diving deeper into the Python backend ecosystem. Instead of just learning tools, I started understanding how backend systems actually work—how requests are processed, how data flows between the server and database, and how APIs connect everything together. 🔧 Tools & Technologies I’m Exploring: • Python for core logic • Django for structured and scalable applications • Flask / FastAPI for lightweight API development • Relational Databases for data management • REST APIs for communication between systems • Git & GitHub for version control • JWT for authentication • Basic backend security practices • Deployment fundamentals 💡 What Changed in My Approach: Earlier, I focused on “what to learn.” Now, I focus on “how things work.” This shift helped me: • Understand backend architecture more clearly • Write better and cleaner code • Think like a developer instead of just a learner I’m still at the beginning of this journey, but I’m consistently building, experimenting, and improving every day. The goal is simple — to become a backend developer who not only writes code, but understands the system behind it. Excited for what’s ahead 🚀 #Python #BackendDevelopment #Django #Flask #FastAPI #RESTAPI #LearningJourney #SoftwareDevelopment #snsinstitutions #snsdesignthinkers #designthinking

Your API isn’t slow — your pagination might be. 📉 It worked perfectly… until your data grew. Then every page got slower than the last. Your code didn’t change. But your dataset did. The culprit? Offset pagination. The deeper the page, the more rows your database has to scan and skip. Page 1 → fast Page 1000 → painful Same query shape. Very different cost. The fix isn’t always caching. Sometimes it’s changing the pattern. Switch to cursor-based pagination. No skipping. Just seeking. In Django REST Framework: Use *CursorPagination* instead of *PageNumberPagination*. Performance stays consistent — even at scale. Because most performance issues aren’t complex. They’re patterns that don’t scale. And most developers don’t notice… until production. #BackendDevelopment #Django #Python #WebDevelopment #SoftwareEngineering #APIPerformance #DatabaseOptimization #SystemDesign #ScalableSystems #DjangoRESTFramework