Django annotate() for database optimization

📌 One thing I underestimated while learning Django: The database. At first, I thought: "Models are just tables." But while building projects, I realized: 👉 Database design decides how clean your backend will be. Bad design = complicated queries + repeated logic + messy relationships Good design = simpler views + cleaner APIs + better performance Now I spend more time thinking about models before writing views. Your code depends on your data structure more than you think. Do you plan your database first or just start coding? #Django #BackendDevelopment #Python #LearningInPublic

Every framework you have ever used is just design patterns written in production code. Day 06 of 30 -- Design Patterns in Python Advanced Python + Real Projects Series Django post_save is the Observer pattern. DRF renderer_classes is the Strategy pattern. logging.getLogger() is the Singleton pattern. @app.route is the Decorator pattern. Most developers use all of these every day without knowing the names. Today's Topic covers: Why patterns exist and the 3-category decision framework 6 patterns every Python backend developer must know Singleton with double-checked locking for thread safety Factory with self-registering decorator pattern Observer event bus with decorator-based subscriptions Strategy using typing.Protocol for structural subtyping Real scenario -- Factory + Strategy + Observer in one order pipeline 6 mistakes including pattern hunting and Observer without error isolation 5 best practices including why Python functions are strategies Key insight: Design patterns are not solutions you add to code. They are names for solutions already in your code. Phase 1 complete -- 6 days of Python internals done. #Python #DesignPatterns #SoftwareEngineering #BackendDevelopment #Django #FastAPI #100DaysOfCode #PythonDeveloper #TechContent #BuildInPublic #TechIndia #CleanCode #PythonProgramming #LinkedInCreator #LearnPython #PythonTutorial

Day 7 of my Python Full Stack journey. ✅ Today's topic: Dictionaries — the most important data structure in Python. A dictionary stores data as key-value pairs. Think of it like a real dictionary — word (key) and its meaning (value). Here's what I typed today: student = { "name": "Punith", "age": 24, "course": "Python Full Stack" } # Access print(student["name"]) # Punith # Add / Update student["city"] = "Bangalore" # Loop through for key, value in student.items(): print(f"{key}: {value}") Why this matters for Django: Every Django model, every API response, every JSON data — all dictionary-like. If you understand dictionaries, Django will make sense. 60 minutes done. Pushed to GitHub. Day 8 tomorrow. One week and 2 days in. Still showing up. 💪 #PythonFullStack #Day7 #Dictionaries #BuildingInPublic #100DaysOfCode #Bangalore

Most Django developers don’t realize this… The ORM is silently killing their performance. I’ve seen APIs go from: ⚠️ 300 queries → ⚡ 3 queries Just by fixing QuerySet usage. In this carousel, I broke down: - N+1 problem - select_related vs prefetch_related - F expressions - Real production mistakes If you're working with Django, this is a must-know. Full guide here 👇 https://lnkd.in/dVuaXBMq #Django #Python #DjangoORM #WebDevelopment #BackendDevelopment #SoftwareEngineering #DatabaseOptimization #ProgrammingTips #Developers #CodingLife #BuildInPublic

Async Programming in Python (asyncio) — Write Faster, Non-Blocking Code Most Python code runs synchronously 👉 One task at a time (slow for I/O-heavy apps) But what if your app could handle multiple tasks simultaneously without waiting? That’s where asyncio comes in. 🧠 What is Async Programming? Async allows your program to: ✔️ Start a task ✔️ Pause it when waiting (API, DB, file) ✔️ Switch to another task 👉 Result: Better performance for I/O operations ⚙️ Basic Example import asyncio async def fetch_data(): print("Fetching...") await asyncio.sleep(2) print("Done!") asyncio.run(fetch_data()) 👉 await = pause here, let other tasks run 🔥 Why It Matters Async is widely used in: ✅ APIs (FastAPI, Django async views) ✅ Web scraping ✅ Real-time apps (chat, notifications) ✅ Microservices ❌ Not useful for CPU-heavy tasks 👉 Best for I/O-bound operations only #Python #AsyncIO #BackendDevelopment #Performance #Django #FastAPI

🚀 Turn any Python CLI script into a modern GUI – with zero extra dependencies. I just open‑sourced PyScript-to-GUI, a tool that instantly wraps your command‑line scripts into a clean, functional graphical interface. ⚡ No more boring terminals. Your users get a real window with dark mode, real‑time output, and interactive input dialogs – without writing a single line of GUI code. ✨ Key features: ✅ Zero external dependencies – uses only tkinter (built into Python) ✅ Smart input() handling – automatically converts prompts into pop‑up dialogs ✅ Live logging – all print() output appears in a scrollable terminal‑style area ✅ Multi‑threaded – the GUI never freezes, even during heavy tasks ✅ Hacker aesthetic – dark grey + lime green theme, ready to impress 🔧 Perfect for: Sharing your scripts with non‑technical colleagues Building quick internal tools with a professional look Teaching Python without scaring beginners with the terminal 🔗 GitHub repo: https://lnkd.in/dDpXCYSk 👨💻 Built by NULL200OK – because every script deserves a beautiful face. #Python #GUI #Tkinter #OpenSource #DeveloperTools #CLItoGUI #PyScriptToGUI #Coding

While digging deeper into environment setup, I noticed an interesting difference between Python and Node.js: Python installs packages globally by default. Unless you create a virtual environment, all dependencies go into a shared global space. Node.js installs packages locally by default. Every project gets its own node_modules directory. At first, this made me think: → Python focuses more on isolation and optimization → Node.js doesn’t care as much But that’s not completely true. Both ecosystems solve the same problem — dependency management — but in different ways: Python → requires you to explicitly create isolation (virtual environments) Node.js → gives you isolation by default (per-project dependencies) Trade-offs: Python → cleaner environments, but extra setup Node.js → easier start, but larger project size and duplication Key insight: It’s not about which is better — it’s about understanding the design decisions behind each ecosystem. This kind of detail matters when you switch between stacks or design scalable systems. #Python #Nodejs #Backend #SoftwareEngineering #LearningInPublic

Django can absolutely scale. But not if you treat it like a tutorial project. Here are 6 patterns we use in production at Horizon Dev that handle real load: → Fat models, thin views → Select_related and prefetch_related everywhere → Custom managers for complex queries → Celery for anything over 500ms → Database routers for read replicas → Cached querysets with smart invalidation Swipe through for details on each ↓ #Django #Python #BackendEngineering

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

Day 5 of learning backend from first principles. Today was about strengthening both application-level and system-level understanding. With Django: • Worked with templates — connecting backend data to what users actually see • Explored error handling — understanding how things break and how to debug them Also started revisiting fundamentals of Operating Systems. Why this matters: Backend isn’t just writing APIs. It sits on top of how the system actually works. Understanding: • How processes run • How memory is managed • How errors propagate …makes you a better developer than just knowing frameworks. Still early, but the goal is clear: Build depth from both sides — systems + applications. #BackendDevelopment #Django #OperatingSystems #ComputerScience #WebDevelopment #LearnInPublic #Python #SoftwareEngineering