Django Messaging System with Private Chats and Group Conversations

I used to Just “Make Things Work”… Until I Didn’t Early in my journey, my goal was simple: Build APIs. Make them run. Done. But then I started asking different questions… -> What happens when 1,000 users hit this API at once? -> Why does the system slow down? -> How do real-world systems handle scale? That’s when things changed. I moved from just writing code to actually designing systems. I started working with: - Building REST APIs with proper routing, validation, and async handling using FastAPI, Flask, and Django - Implementing JWT-based authentication (access/refresh tokens, middleware, protected routes) - Containerizing services with Docker and managing PostgreSQL with optimized queries, indexing, and connection pooling - Handling real-world issues like API latency, database bottlenecks, and service reliability And more importantly… ⚡ I began understanding performance, scalability, and real-world challenges. Now, I don’t just build APIs. I think about how they behave under pressure. Still learning. Still improving. But the mindset shift made all the difference. #BackendDevelopment #SystemDesign #FastAPI #Docker #LearningJourney #python #django #redis #postgresql #backend #developer

Just implemented a Celery-based background processing layer for a Django real-time messaging system. This layer handles critical async operations that shouldn’t block user requests: • Syncing unread message counters from Redis → PostgreSQL • Ensuring message state durability beyond in-memory cache • Retry-safe background execution with Celery task retries • Foundation for offline message delivery (push notifications) • Cleanup hooks for stale real-time states like “typing…” indicators The key design idea: Redis handles speed, Celery ensures reliability, and the database remains the source of truth. This separation is what makes real-time systems scalable without losing consistency under load. #Django #Celery #BackendEngineering #SystemDesign #Python #SoftwareEngineering #Scalability

Every time I start a new Django project, I end up rebuilding the same foundation from scratch. Dark/light theme toggle? Yep. Google Analytics wiring? Yep. Security headers, rate limiting, robots.txt, health checks? All of it, again, from memory, at 11pm. So I finally just built the thing once and committed it to GitHub. Introducing my Django boilerplate: a production-ready starter I put together from patterns I've refined building real ML-powered apps. It includes: * A full Django 6 project structure with Postgres, WhiteNoise, and Gunicorn ready to deploy to Heroku. * A complete ML app scaffold (data pipeline, feature engineering, model classes, MLflow tracking, inference pipeline, and a monitoring buffer model) so the first thing you write is actual ML logic, not plumbing * Dark/light theme toggle that doesn't flash on load * CSP middleware, rate limiting, and HSTS wired up correctly out of the box * GitHub Actions CI that lints and runs tests on every push * 27 tests, zero lint errors on day one The idea is simple: next time I start something, I want to be writing the interesting parts in the first hour, not the fourth. If you work in Python and find yourself rebuilding the same boilerplate over and over, feel free to fork it and make it yours. Link in the comments. #Python #Django #MachineLearning #MLOps #SoftwareEngineering #OpenSource

Day 9 - "It works on my machine." Docker fixes that sentence permanently. Here's how — with a real 3-service app, not a toy tutorial. 🚀TechFromZero Series - DockerFromZero This isn't a Hello World. It's a real multi-container application: 📐 Client → FastAPI (Python) → MongoDB (Database) → Redis (Cache) — all orchestrated with Docker Compose 🔗 The full code (with step-by-step commits you can follow): https://lnkd.in/dtnykq35 🧱 What I built (step by step): 1️⃣ Project scaffold — FastAPI app with async endpoints and health check 2️⃣ Dockerfile — FROM, COPY, RUN, EXPOSE, CMD with layer caching explained 3️⃣ .dockerignore — keep secrets and junk out of images 4️⃣ MongoDB connection — async motor driver, Docker DNS (service names, not localhost) 5️⃣ Weather API client — httpx async calls to OpenWeatherMap from inside a container 6️⃣ Full CRUD endpoints — log, list, stats, filter, delete weather data 7️⃣ Docker Compose — 3 services, health checks, depends_on, named volumes, custom network 8️⃣ Redis caching — 5-minute TTL, sub-millisecond cache hits vs 300ms API calls 9️⃣ README — architecture diagram, Docker cheat sheet, step-by-step guide 💡 Every file has detailed comments explaining WHY, not just what. Written for any beginner who wants to learn Docker by reading real code — with full clarity on each step. 👉 If you're a beginner learning Docker, clone it and read the commits one by one. Each commit = one concept. Each file = one lesson. Built from scratch, so nothing is hidden. 🔥 This is Day 9 of a 50-day series. A new technology every day. Follow along! 🌐 See all days: https://lnkd.in/dhDN6Z3F #TechFromZero #Day9 #Docker #DockerCompose #FastAPI #MongoDB #Redis #Python #Containers #DevOps #LearnByDoing #OpenSource #BeginnerGuide #100DaysOfCode #CodingFromScratch

Day 13 - Your server costs $0 when nobody's using it. That's not a bug — that's serverless. 🚀TechFromZero Series - LambdaFromZero This isn't a Hello World. It's a real serverless image processing pipeline: 📐 Client → API Gateway → Lambda handler(event, context) → Pillow → base64 response 🔗 The full code (with step-by-step commits you can follow): https://lnkd.in/dTwhP4Ty 🧱 What I built (step by step): 1️⃣ Project scaffold — Python venv, Pillow, Flask, pytest 2️⃣ Sample image — generated programmatically with Pillow (no downloads) 3️⃣ First Lambda handler — the handler(event, context) contract 4️⃣ Image processor — resize with base64 encoding/decoding 5️⃣ More operations — thumbnail, grayscale, rotate, and blur 6️⃣ Wire handler to processor — event parsing, validation, error responses 7️⃣ Test events — JSON files that simulate real API Gateway requests 8️⃣ Local server — Flask app that does exactly what API Gateway does 9️⃣ S3 trigger handler — auto-process images on upload 🔟 Unit tests — 32 pytest tests for handler and processor 1️⃣1️⃣ SAM template — real AWS deployment config (no account needed to learn) 1️⃣2️⃣ Documentation — README with architecture, quick start, and step guide 💡 Every file has detailed comments explaining WHY, not just what. Written for any beginner who wants to learn AWS Lambda by reading real code — with full clarity on each step. No AWS account needed. Everything runs locally. The Flask server simulates API Gateway, test events simulate S3 triggers, and pytest verifies it all works. 👉 If you're a beginner learning serverless, clone it and read the commits one by one. Each commit = one concept. Each file = one lesson. Built from scratch, so nothing is hidden. 🔥 This is Day 13 of a 50-day series. A new technology every day. Follow along! 🌐 See all days: https://lnkd.in/dhDN6Z3F #TechFromZero #Day13 #AWSLambda #Serverless #Python #Pillow #LearnByDoing #OpenSource #BeginnerGuide #100DaysOfCode #CodingFromScratch #AWS #CloudComputing #FaaS

🚀 Built my first multi-container application using Docker! Over the past few days, I worked through building a simple Flask web app connected to a Redis database — all containerised and orchestrated with Docker Compose. 🔧 What I built: A Flask app with multiple routes: • / → welcome page • /count → increments visit count • /reset → resets the counter Redis used as a key-value store for tracking visits Multi-container setup using Docker Compose 🧠 What I learned: How to containerise a Python application using a Dockerfile Running multiple services with Docker Compose Container-to-container communication using service names (no localhost!) Using environment variables instead of hardcoding configuration Persisting data with Docker volumes 📌 Key takeaway: Understanding how services communicate inside containers (and debugging when they don’t!) was the biggest learning moment. Next step: exploring scaling and load balancing 👀 #Docker #DevOps #Python #Flask #Redis #LearningInPublic CoderCo

I've built a comprehensive Django REST API for project and task management designed to streamline team collaboration. Here's what's under the hood: ✨ Key Features: 1. JWT-based authentication with secure refresh tokens 2. Full CRUD operations for projects and tasks 3. Async task processing with Celery & Redis 4. Email notifications for task updates 5. Complete RESTful API endpoints Tech Stack: Django 5.x | Django REST Framework | PostgreSQL | Celery + Redis 🔗 Check it out: https://lnkd.in/gvBjwbXs #Django #REST_API #Python #Backend #ProjectManagement #OpenSource

What happens between your API returning 202 and the customer receiving a confirmation email? Day 17 of 30 -- Background Tasks with Celery Phase 3 -- Backend and APIs If the answer is "the route handler does it all" -- your architecture has a problem. The correct answer: Celery. Your API queues a task and returns 12ms. A separate worker process picks up the task from Redis, executes it, retries on failure, stores the result, and notifies when done. The API and the worker never share a process. That is the entire point. Today's Topic covers everything: Why background tasks exist and the problem they solve Full 5-component architecture diagram 6 task state machine -- PENDING through FAILURE Annotated syntax -- setup, bind, retries, rate limits, beat schedule chain, group, chord -- sequential, parallel, parallel-with-callback Real order pipeline -- 6 tasks in 1.8s, API responded in 12ms Queue routing -- critical queue for payments, low queue for emails Celery Beat -- daily reports, hourly sync, weekly cleanup in pure Python 5 mistakes including the pickle vulnerability and the double Beat trap Question for you: What was the longest task you ever ran synchronously inside an API handler? Drop it below. #Python #Celery #BackgroundTasks #FastAPI #Redis #100DaysOfCode #BackendDevelopment #TechContent #BuildInPublic #TechIndia #DistributedSystems #LinkedInCreator #LearnPython #OpenToWork #SoftwareEngineering #PythonTutorial

Most Django developers think migrations are simple… until production goes down at midnight. I’ve seen migrations: • Lock massive tables • Break rolling deployments • Corrupt production data So I wrote a complete production guide on Django migrations 👇 In this carousel, I break down: • Zero-downtime migration strategy • Expand-contract pattern • Data migration best practices • Real production mistakes to avoid If you're working with Django in production, this is something you must understand deeply. Full article: https://lnkd.in/dKUjjs-7 If you found this useful, share it with your team 👇 #Django #Python #BackendDevelopment #WebDevelopment #SoftwareEngineering #FullStackDeveloper #Programming #Database #PostgreSQL #DevOps #SystemDesign #Coding #TechLeadership #Developers #LearnToCode

DAY 5 - THE TECH STACK (Tools & Why I Chose Them) Every tool in my stack was a deliberate choice. Here's why - and what I'd tell my past self about each one. No random picks. No "I've always used this." Every decision had a reason. > Python :Primary language for the crawler and extraction logic. Chose it for its rich ecosystem - BeautifulSoup, Playwright, asyncio - nothing else comes close for this use case. Wish I knew: Async Python has footguns. Mixing sync and async code will ruin your day silently. > Playwright (Headless Browser) : Handles JS-rendered pages that raw HTTP requests miss completely. Chose it over Puppeteer for Python-native support and better async handling. Wish I knew: Resource usage adds up fast at scale. Spin up only what you need. > Message Queue (e.g., Redis / RabbitMQ) : Acts as the backbone between crawlers and processors - total decoupling. Chose it because I needed workers to fail independently without cascading crashes. Wish I knew: Queue monitoring is not optional. A silent backlog will sneak up on you. > Database (Structured Storage) : Stores normalized technographic profiles per company, queryable and enriched. Chose a relational model to keep tech profiles structured and joinable. Wish I knew: Schema design early saves migrations later. Do not skip this. > Docker: Containerized each service so workers can be spun up and torn down without friction. Chose it for portability and reproducibility across environments. Wish I knew: Docker networking between containers is its own learning curve. Start simple. Which tool here are you most curious about? Happy to go deeper on any of them 👇 #TechStack #Python #DistributedSystems #WebScraping #BackendDevelopment