Boosting Backend Performance with Async Processing in Task Manager

I just published a compact guide on Medium diving into the 5 Design Patterns every Python architect needs. We're moving past simple scripts and into scalable architecture. What's inside: ✅ Singleton: Managing global state without the mess. ✅ Factory: Decoupling creation from business logic. ✅ Observer: The backbone of event-driven systems. ✅ Protocols: Clean, type-safe Pythonic interfaces. ✅ Strategy: The ultimate cure for "If-Else" spaghetti. If you’re aiming for that Senior title or just want to write more maintainable code, this is for you. #Python #SoftwareArchitecture #Coding #SeniorDeveloper #DesignPatterns #Programming #TechLead

Built a finance backend system using FastAPI Focused on clean architecture, analytics, and role-based access instead of just CRUD slowly getting comfortable with building real backend systems #backend #python #fastapi

Write code that doesn't break in production...! When building end-to-end pipelines, reading data from GitHub or external URLs is common. But relying on a "happy path" is a mistake. For robust development, always implement: Logging: To track the flow and capture specific error details. Exception Handling: To prevent the entire app from crashing and get clear "Unable to load" alerts. It’s a simple habit, but it’s what separates a beginner from a Pro Developer. #Python #MLOps #CleanCode #SoftwareEngineering #DataScience #CodingTips

You write for loops every day. Do you know what actually runs underneath them? Day 03 of 30 -- Generators and Iterators Deep Dive Advanced Python + Real Projects Series Python calls iter() to get the iterator, then next() repeatedly until StopIteration is raised. That is every for loop you have ever written. And yield pauses the function, hands the value out, and resumes from the exact same line next time. Today's topic covers: The lazy vs eager evaluation problem -- why loading 10GB into a list crashes servers The full iterator protocol -- what powers every for loop 3 types -- generator function, expression, async generator Annotated syntax -- basic, yield from, and the send() two-way pattern Real fintech pipeline -- 52GB log file, 4.2MB memory used 5 production mistakes including exhausting a generator twice Generator pipeline architecture -- identical to Unix pipes Key insight: Don't store what you can stream. #Python #PythonProgramming #DataEngineering #BackendDevelopment #LearnPython #100DaysOfCode #PythonDeveloper #SoftwareEngineering #TechContent #BuildInPublic #TechIndia #CleanCode #CodingTips #CodeNewbie #LinkedInCreator #PythonTutorial

Built a VS Code extension that lets you “see” a Python codebase instantly. I was testing it on a small fitness app project and within seconds it mapped: • API routes • Functions & classes • Cross-file dependencies • Execution paths • Impact of code changes Instead of opening 50 files and tracing imports manually. I call it Python Code Graph. It uses AST-based static analysis to understand Python structure and then visualizes the architecture in an interactive graph inside VS Code. A few things it can do: → Detect FastAPI / Flask / Django routes → Find Click / Typer CLI entry points → Detect Celery tasks → Show blast radius before changing code → Highlight critical paths to business logic → Generate AI-powered explanations Built with: Node.js + TypeScript VS Code Extension API Custom heuristic symbol resolution Glassmorphism-inspired UI Still improving it, tested on a smaller codebase but the first working version is live on GitHub. #Python #VSCode #BuildInPublic #DevTools #Programming #SoftwareEngineering

#networking abstractions: In this session we were practicing client-server architecture from different approaches. ⚪ Sockets (Transport Layer) and 🔵Flask (Application Layer). The result, understanding the multiple ways to communicate with a server, leveraging the #OSI model and moreover, creating flask apps for web development using #python. Happy #holyweek

🚀 Built a Scalable Log Processing System using Python Recently, I worked on a backend-focused project where I designed a system to efficiently process large log files using multiprocessing and streaming techniques. 🔹 Key Highlights: Processed large log files without loading everything into memory Implemented batch-based streaming for better memory efficiency Used multiprocessing (Pool) for parallel execution Designed custom chunking logic for workload distribution Applied MapReduce-style aggregation for results Exported structured output in JSON format 🧠 Architecture: File → Batch → Chunk → Parallel Workers → Aggregation → JSON Output 💡 Key Learnings: Difference between multiprocessing and threading Importance of memory-efficient design Task vs process execution model Impact of data structures (append vs extend) 🔗 GitHub Repo: https://lnkd.in/g4Zc2CFf This project helped me understand how real-world backend systems handle large-scale data processing. #Python #BackendDevelopment #SystemDesign #Multiprocessing #Learning #Projects

I’m checking this out. As we need to select the “best” platform for our genAI applications, the permutations become truly daunting. Adrian provides a framework and code to do so - scoring and promoting the highest rated candidates. Fascinating!

The "Shadow" Fix: Python Version Compatibility **Hook:** Building for the "Latest & Greatest" is easy. Building for the "Real World" is where the engineering gets messy. **Body:** While finalizing my Enterprise RAG pipeline, I hit a silent production-breaker: A `TypeError` buried deep in a third-party dependency. The culprit? The `llama-parse` library uses Python 3.10+ type union syntax (`|`), but the production environment was locked to Python 3.9. Result: Immediate crash on boot. Instead of demanding a system-wide upgrade—which isn’t always possible in locked-down enterprise environments—I implemented a **Graceful Fallback Logic**: ✅ **Dynamic Imports**: Wrapped the cloud-parser initialization in a guarded `try-except` block. ✅ **Smart Routing**: If the Python environment is incompatible, the system automatically redirects to a local, high-fidelity `PyMuPDF` parser. ✅ **System Resilience**: The app stays online, the UI remains responsive, and 99% of RAG functionality remains available without a single user noticing a failure. Real Engineering isn't just about using the best tools—it’s about writing code that doesn't break when the environment isn't perfect. #Python #SoftwareEngineering #RAG #AIEngineering #SystemDesign #Resilience

Manual data extraction from websites does not scale. Web scraping automates text collection for competitive analysis, price monitoring, and content aggregation. Eugenijus Denisov, Lead Backend Developer at IPRoyal, covers Python libraries, selectors, best practices, and how to handle dynamic content reliably: https://lnkd.in/dd2mTeBe