Django Views: Using commit=False for Database Management

Knowledge bites - Day 46 What is flask in python ? Flask is a lightweight Python web framework used to build web applications and APIs quickly. It follows a minimalistic approach, giving developers full control instead of enforcing strict project structures. Key features : 1. Lightweight and flexible (micro-framework) 2. Built-in development server and debugger 3. Uses Jinja2 templating engine 4. REST API friendly 5. Easy integration with databases and extensions How it works ? 1. Define routes (URLs) using decorators 2. Each route maps to a Python function 3. Function processes request and returns response 4. Server renders output (HTML/JSON) Example use case • Backend for AI apps (e.g., serving a model via API) • Lightweight dashboards • MVPs and quick prototypes Why it’s popular ? • Simple to learn and start • Highly customizable • Large ecosystem of extensions , like Flask SQLAlchemy , Flask Login and more . #Actionpackd #KnowledgeBites

🚀 FastAPI – Hello World Example (Getting Started) FastAPI is a modern Python web framework used to build fast and high-performance APIs. The first step in creating a FastAPI application is to create an application object from the FastAPI class. This object acts as the main interface between the server and the client. 🔹 Step 1: Create FastAPI Application from fastapi import FastAPI app = FastAPI() Here, app is the main application object used by the Uvicorn server to listen for incoming client requests. 🔹 Step 2: Create a Path Operation A path operation links a URL path with a specific HTTP method and function. Example: @app.get("/") async def index(): return {"message": "Hello World"} When a user visits the root URL (/), this function executes and returns a JSON response. FastAPI can also return data types like dictionaries, lists, strings, integers, or Pydantic models. 🔹 Step 3: Run the Server Save the code as main.py and start the FastAPI server using the Uvicorn command: uvicorn main:app --reload The --reload option automatically reloads the server whenever the code changes. 🔹 Step 4: View the Output Open a browser and visit: http://localhost:8000 You will see the JSON response: {"message": "Hello World"} This confirms that your FastAPI application is running successfully. 💡 FastAPI is widely used for building REST APIs, microservices, and machine learning model APIs because of its speed, automatic documentation, and async support. #Python #FastAPI #WebAPI #BackendDevelopment #PythonProgramming #RESTAPI #SoftwareDevelopment #AshokIT

Title: Async Views — Handle High-Volume Flower Orders 🚀 Opening Hook: Imagine a bustling flower shop in spring, bouquets flying off the shelves! 🌸 How do you ensure every order blooms with efficiency? The Problem: Traditional views can wilt under pressure. Let's look at the typical approach: ```python # BAD approach def handle\_order\(request\): flowers = Flower.objects.all\(\) bouquets = Bouquet.objects.filter\(order=request.order.id\) # Processing logic return render\(request, 'order.html', \{'flowers': flowers, 'bouquets': bouquets\}\) ``` The Solution: Let your orders blossom using Django's async views! 🌼 ```python # GOOD approach async def handle\_order\(request\): flowers\_task = Flower.objects.all\(\) bouquets\_task = Bouquet.objects.filter\(order=request.order.id\) flowers, bouquets = await asyncio.gather\(flowers\_task, bouquets\_task\) return render\(request, 'order.html', \{'flowers': flowers, 'bouquets': bouquets\}\) ``` Think of it like arranging multiple bouquets at once, instead of one by one! Did You Know? 💡 Under the hood, Django's async views allow for non-blocking I/O operations, which means your server can handle other requests while waiting for database queries. Why Use It? - ⚡ Performance impact - 🧹 Code quality improvement - 📈 Scalability advantage The Golden Rule: Async views will help your code "rose" to the occasion. Engagement Question: How have async views helped your projects? Or what's your favorite tip for using them? Share below 👇 Hashtags: #Django #Python #WebDevelopment #Backend #Performance #FlowerShop #DjangoORM

🚀 𝗙𝗹𝗮𝘀𝗸 𝘃𝘀 𝗙𝗮𝘀𝘁𝗔𝗣𝗜 If you have worked with Python for backend development, you have probably come across Flask and FastAPI. Both are powerful, but they serve slightly different purposes depending on your use case. 🔹 Flask is a lightweight and flexible micro-framework. It’s been around for years and has a huge community. You get full control over how you structure your application. However, that flexibility comes at a cost — you often need to write more boilerplate code and manage things like validation and async handling manually. 🔹 FastAPI, on the other hand, is relatively newer but built for modern APIs. It leverages async programming and type hints, making it incredibly fast and developer-friendly. ⚡ Why is FastAPI faster? FastAPI is built on Starlette (for async support) and Pydantic (for data validation). It uses asynchronous request handling, which allows it to process multiple requests efficiently without blocking the server. 🐢 Why is Flask slower? Flask is primarily synchronous. While you can use async with Flask, it’s not its core strength. For high-concurrency applications, this can become a bottleneck. 🧠 When to use Flask? 1. Small to medium projects 2. Simple APIs or web apps 3. When you need flexibility and full control ⚡ When to use FastAPI? 1. High-performance APIs 2. Microservices architecture 3. Real-time or async-heavy applications 4. When you want automatic validation and documentation 𝗦𝘂𝗺𝗺𝗮𝗿𝘆 - Flask is like a blank canvas — simple and flexible. FastAPI is like a smart toolkit — optimized and ready for scale. Both are great — the choice depends on your project needs, not just speed. #Python #FastAPI #Flask #BackendDevelopment #WebDevelopment #APIDesign #SoftwareEngineering #Programming #Developers #TechCommunity #CodingLife #LearnToCode #AsyncProgramming

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

Day 59 of #90DaysOfCode Today I built a dynamic blog web application using Flask that fetches content from an external API and renders it using HTML templates. The application displays a list of blog posts on the homepage and allows users to view individual posts through dynamic routing. How the application works • Fetches blog data from an external API • Converts JSON data into structured Python objects • Renders posts dynamically using Jinja templates • Uses dynamic routes to display individual blog posts • Serves styled frontend pages using Flask Key concepts explored • API integration using Requests • Data modeling using Python classes • Template rendering with Jinja2 • Dynamic routing using Flask • Building multi-page web applications This project helped me understand how real-world web applications handle data flow between backend and frontend. GitHub Repository https://lnkd.in/g8cjp9ek #Python #Flask #WebDevelopment #BackendDevelopment #SoftwareEngineering #90DaysOfCode

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

Why Everyone is Talking About FastAPI: A Visual Breakdown If you are building web applications today, speed and developer experience aren't just "nice-to-haves"—they are requirements. Here is a breakdown of why FastAPI has become the go-to framework for modern Python backends: 1. The Core Advantages (The "Top Row") Super Speed: Built on top of Starlette and Uvicorn, it handles requests asynchronously. This means it can manage many connections at once without breaking a sweat. Automatic Docs: This is a game-changer. The moment you write your code, FastAPI generates Swagger UI and ReDoc pages. No more manually updating documentation! Data Validation with Pydantic: It uses Python type hints to ensure that the data coming in (like an email or a price) is exactly what you expect before your code even touches it. Intuitive Coding: It’s "Pythonic." The syntax is clean, making it easier to read, maintain, and scale. 2. The Request Flow (The "Middle Engine") Think of FastAPI as an organized assembly line: Client: Your app or browser sends a request. Uvicorn: The high-speed server that picks up the request. Starlette: The toolkit that routes the request to the right place. FastAPI Logic: This is where the magic happens—handling security (authentication), talking to your Database, and running Background Tasks. Response: The user gets back clean JSON or HTML instantly.

🚀 Day 4 of my 7 Days Django Challenge Today I built CurioLog — Daily Curiosity Journal 🧠✨ It’s a Django-based journaling web app where users can store and organize: ideas questions observations experiments learning notes Instead of making a very basic project, I wanted to build something that feels more practical and meaningful. ✅ Features: User authentication Add / edit / delete entries Categories and tags Search and filters Dashboard analytics Monthly and category charts CSV export Responsive UI 🛠️ Tech Stack: Python, Django, Bootstrap 5, Chart.js, Pandas, SQLite 📚 What I learned: Django authentication CRUD operations Model relationships Search & filtering Dashboard logic Data visualization Exporting data This project gave me a much better understanding of how real-world Django apps can be structured beyond just forms and models. 🔗 GitHub: https://lnkd.in/ggTm7Hyc #Django #Python #WebDevelopment #FullStackDevelopment #BackendDevelopment #StudentDeveloper #Projects #LearningInPublic #GitHub #SoftwareDevelopment

Day 57 of #90DaysOfCode Today I built an interactive web application using Flask where users can guess a randomly generated number directly through the browser. The server generates a random number between 0 and 9 when it starts. Users attempt to guess the number by entering their guess in the URL, and the application responds with feedback indicating whether the guess is too high, too low, or correct. How the application works • Generates a random number using Python • Displays a prompt on the homepage asking the user to guess • Captures the user's guess using dynamic Flask routing • Compares the guess with the generated number • Returns visual feedback with different messages and images Key concepts explored • Building web applications using Flask • Dynamic routing using URL parameters • Handling user input through route variables • Returning HTML responses from Flask routes This project helped reinforce how backend frameworks process requests and return dynamic responses based on application logic. GitHub Repository https://lnkd.in/gUc57VJQ #Python #Flask #BackendDevelopment #WebDevelopment #SoftwareEngineering #90DaysOfCode