André Hangalo’s Post

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

I was debugging a Django service last week and hit a classic problem memory growing silently across requests, no obvious culprit. The usual suspects (tracemalloc, memory_profiler, objgraph) are great tools. But I wanted something I could drop on any function in 30 seconds and get a readable answer from. Also, honestly I wanted to understand what's happening at the GC and tracemalloc abstraction layer in Python. The best way I know to understand something is to build on top of it. So I built MemGuard over a weekend. What it does: Drop @memguard() on any function and after every call you get: Net memory retained (the actual leak signal) Peak vs net ratio — catches memory churn even when net looks clean Per-type gc object count delta tells you what is accumulating, not just how much Cross-call trend detection if net grows every call, it flags it Allocation hotspots via tracemalloc exact file and line Zero dependencies. Pure stdlib gc, tracemalloc, threading. @memguard() def process_batch(records): That's it. It also works as a context manager if you want to profile a block rather than a function. Biggest thing I learned building this: Python's gc and tracemalloc expose far more than most people use day to day. The object-reference graph alone tells a story that byte counts miss entirely. Repo: https://lnkd.in/gdjkHvfb Would love feedback from anyone who's dealt with Python memory issues in production. #Python #Django #SoftwareEngineering #OpenSource #BackendDevelopment #MemoryManagement

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

💡 𝗧𝗶𝗽 𝗼𝗳 𝘁𝗵𝗲 𝗗𝗮𝘆 — 𝗗𝗷𝗮𝗻𝗴𝗼 𝗗𝗶𝗱 𝘆𝗼𝘂 𝗸𝗻𝗼𝘄? Django’s "annotate()" lets you add 𝗰𝗮𝗹𝗰𝘂𝗹𝗮𝘁𝗲𝗱 𝗳𝗶𝗲𝗹𝗱𝘀 𝗱𝗶𝗿𝗲𝗰𝘁𝗹𝘆 𝗶𝗻 𝘆𝗼𝘂𝗿 𝗾𝘂𝗲𝗿𝘆𝘀𝗲𝘁𝘀. Instead of processing data in Python after fetching it, you can compute values at the 𝗱𝗮𝘁𝗮𝗯𝗮𝘀𝗲 𝗹𝗲𝘃𝗲𝗹. 🔧 𝗖𝗼𝗺𝗺𝗼𝗻 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲𝘀: - Counting related objects ("Count") - Calculating averages ("Avg") - Adding computed fields to API responses This reduces data processing in your app and leverages the power of your database. Smarter queries = faster apps. #Django #Python #BackendDevelopment #WebDevelopment #DatabaseOptimization #PerformanceOptimization #SoftwareEngineering #CodingTips #FullstackDeveloper

📌 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

We had 4 coding agents port the same Python service to Go. One of the ports compiled, started, served valid HTTP responses, and passed its test suite. It also never fetched any data. A function that reads an HTTP response body was reading into an empty buffer, so it read zero bytes every time. No linter catches it because the types are correct. The tests pass because they only check that the result is not empty. Both the code and the tests look fine until you actually try to use it. More: https://lnkd.in/dvHWsnB2

 🧪 You can just automate things! `scripts/mark_pr_files_viewed.sh` automates marking all unviewed files in a GitHub pull request as 'Viewed' using the GraphQL API. The script supports batching and multiple authentication methods. The script adds a dependency check for Python 3, removes a redundant and inefficient GraphQL call in the file-fetching loop, and optimises string escaping by using Bash's built-in parameter expansion instead of calling sed in a loop. 🌟 Grab the script for free here: https://lnkd.in/gXFj3JsG #bash #GraphQL #api #script

I built the fastest Python logging framework. 446K ops/sec. 2.7x faster than stdlib. 20% faster than Microsoft's picologging, which is written in C. It's a one-line migration:  import logging → from logxide import logging Same getLogger(). Same format strings. Flask, Django, FastAPI all work. Sentry and OTLP are built in. Zero config. Wrote up the production guide with copy-paste examples. ⬇️ See comment #Python #Rust #OpenSource

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

Been spending my weekends on a Python side project and finally have something worth sharing. Built CareTrack API from scratch, a REST API for managing patient and provider records. It uses FastAPI, PostgreSQL, and JWT authentication, with a full CI/CD pipeline on GitHub Actions and a live deployment on Render. Tech stack: Python · FastAPI · PostgreSQL · SQLAlchemy · Alembic · JWT · GitHub Actions Live API docs: https://lnkd.in/e9beFYgM GitHub: https://lnkd.in/e53nQi9S Feedback always welcome. #Python #SideProject #BackendDevelopment #API