Faizkhan Pathan’s Post

Swinging back to backend decisions now that we're moving "AI-First." I often get asked why we lean on Django/FastAPI over Node.js for complex business logic, especially when integrating LLM pipelines. For me, it comes down to clarity under load. Python’s ecosystem—especially with frameworks like FastAPI handling async patterns cleanly—lets me reason about data transformation and complex state management

Async isn’t a nice-to-have when requests start piling up. I’ve been looking at FastAPI for backend work because it makes asynchronous endpoints feel approachable without hiding what’s happening. The combination of Python type hints, automatic request validation, and clear routing keeps the codebase readable, while async/await support helps when the app is doing a lot of I/O (database calls, external APIs). What stands out is how quickly you can go from a small REST endpoint to something that’s structured and testable, without fighting the framework. If you’ve built with FastAPI, where did async genuinely improve performance for you, and where did it add complexity? #Python #FastAPI

I was recently working on a FastAPI CRUD app and realized my Docker image was a "heavyweight" for no reason. Not only was it 1GB+, but it was also running as root which is troublesome in production. To decrease the size and increase the security , I learnt the following concepts:- ✅ Switching to slim: Moving from the full Python image to python:3.9-slim dropped the base size immediately. ✅ Multi-Stage Builds: I used a builder stage to install dependencies and a final stage to run the app. This keeps compilers and cache out of the production image. ✅ Non-Root User: I created an appuser. If the app is ever compromised, the attacker doesn't have root access to the container. ✅ Layer Caching: By copying requirements.txt BEFORE the app code, I ensured that code changes don't trigger a full 5-minute re-install of all packages. The Result ( I tagged it as "slim" for simplicity):- #Docker #FastAPI #Python #DevOps #BackendDevelopment #CloudSecurity #SoftwareEngineering

Many Flask applications work perfectly during development… until they reach production. That’s usually when issues start appearing: – authentication behaving inconsistently – permissions not working as expected – security gaps nobody noticed early I’ve seen this pattern multiple times in real Flask backends. That’s why I’m now helping teams debug, secure, and stabilize their Flask applications — especially around auth, access control, and API security. If you’re dealing with a production issue or want a quick backend/security review, you can find details here: 👉 https://lnkd.in/dV39i2tm Happy to do a short initial review and point you in the right direction. #python #flask

Day 13: From Python-Shell Chaos to API Success: A Debugging Saga Just spent 4 hours debugging what should have been a 10-minute setup. Here's what happened: The Problem: My FastAPI app kept crashing with cryptic errors. Uvicorn said "SyntaxError" but the code looked perfect. Upon Investigation: Virtual environment? Check. Dependencies installed? Check. File structure correct? Check. But still... "unterminated string literal" errors! The Culprit: My app/__init__.py file had PowerShell syntax mixed with Python! Somehow @' | Out-File commands ended up in a .py file. The Big Lesson : Language boundaries matter - PowerShell ≠ Python Cache is sneaky - .pyc files can haunt you Simple problems have simple solutions - sometimes it's just one corrupted file Persistence pays - don't give up when the error messages don't make sense The Fix: bash # The magical fix was simpler than expected rm -rf __pycache__ # Clear Python cache rm app/__init__.py # Delete corrupted file # Create fresh, clean file echo "# Python package init" > app/__init__.py The Result: API running perfectly, analytics dashboard live, and PowerShell utilities working seamlessly! Key Takeaways: Always check file encodings (UTF-8 BOM can break Python) Clear cache when things don't make sense One corrupted file can break everything The solution is often simpler than the problem appears #Debugging #Python #FastAPI #BackendDevelopment #APIDevelopment #Programming #Coding #SoftwareEngineering #TechStruggles #DeveloperLife #LearningFromFailure #CodeQuality #TechSolutions

I spent years thinking 'concurrency' meant 'multithreading'—until a brutal production bug proved me wrong. Here's the critical difference that saved clients (and my sanity) from terrible performance: ❌ **Async != Multithreading** (Even though both aim for concurrency) Think of it this way: * **Async (Asyncio):** One highly efficient chef 🧑🍳 managing *many* tasks simultaneously. While waiting for water to boil, they're chopping veggies. Everything runs on a *single thread*. Perfect for I/O-bound jobs (network requests, database calls). * **Multithreading:** Multiple chefs 🧑🍳🧑🍳🧑🍳 working in parallel. But in Python, due to the GIL, they often end up arguing over the same spice rack! Best for CPU-bound tasks if you can work around the GIL, or for true parallel I/O blocks. This isn't just syntax; it's a fundamental architectural decision. Get it wrong, and your app crawls. Get it right, and your users will thank you. What's one common Python misconception you wish you'd learned sooner? 👇 #Python #AsyncIO #Multithreading #Concurrency #Backend

Many performance issues in Python APIs don’t come from business logic, but from blocking I/O.   Database queries, external API calls or file operations executed synchronously quickly limit throughput under real load. Using async frameworks or async layers correctly allows the backend to handle more concurrent requests without increasing infrastructure. However, mixing async code with blocking libraries cancels most of the benefit and creates hard-to-detect bottlenecks. Performance in Python is less about raw speed and more about how I/O is managed. 🐍 Understanding where the event loop blocks changes everything. #PythonBackend #AsyncIO #APIPerformance #BackendEngineering #ScalableSystems #TechArchitecture #PythonDeveloper

Built a flexible anomaly detection platform that works with any CSV data. You upload your data, choose which columns to analyse, and the system detects outliers using Tukey IQR and Isolation Forest methods.  The frontend is built with Next.js and the backend runs on FastAPI with PostgreSQL. Everything is containerised with Docker for easy setup.  Check out the source code here: https://lnkd.in/e3DPWpjz  #OpenSource #DataScience #Python #NextJS

Stop blocking your Python API with synchronous DB calls. I see this pattern a lot: A developer moves from Flask to FastAPI for "speed," but keeps using the standard synchronous session.query() in their database layer. Result? You have a Ferrari engine (FastAPI) stuck in first gear (Blocking I/O). I just wrote a guide on how to build a fully Async backend using the modern stack: ⚡ FastAPI (The Interface) 🗄️ SQLAlchemy 2.0 (The ORM - using the new 2.0 syntax) 🔄 Aiosqlite/AsyncPG (The Non-blocking Drivers) The shift from query() to await session.execute(select(...)) is small in code, but massive in performance under load. I included the full setup code, dependency injection patterns, and Pydantic V2 schemas in the article. Read the full guide here: [https://lnkd.in/gnT39HMP] #Python #FastAPI #BackendDevelopment #SQLAlchemy #SystemDesign

Ecosystem Highlight: A community-built tool showing how JSON Schema powers real-world workflows across Python and React. This is a community member submission we’re excited to amplify! As part of the Workflow Orchestrator project, they’ve been building Pydantic Forms, an open-source JSON Schema form builder that turns schemas into interactive forms. What makes it special is its integration with Pydantic. You define your data models in Python, expose them as JSON Schema, and the UI automatically renders them as forms. Key features: 🔹 Default inputs for standard field types 🔹 Custom fields 🔹 Automatic validation from JSON Schema rules 🔹 Built on React Hook Form 🔹 Multi-step workflows with history preserved Explore the project: https://lnkd.in/dzrK7_Mf 🔗 https://lnkd.in/dRzfSzfu Npm package: https://lnkd.in/dZXEaKeT They’re already running this in production and continuing to improve documentation and styling options. Please play around it, and if you have any questions, drop them in the comments 👇