Optimize Django with Generators for Efficient Memory Usage

🚀 Day 17 – Python API Integration Today I explored the power of Django REST Framework and how it simplifies building RESTful APIs in Python. 🔹 Key takeaways: Understood how Django REST Framework extends Django to build APIs efficiently Created a Django project and app structure (countryapi, countries) Built a Model (Country) to represent data Learned how Serializers convert Django models into JSON Used ModelViewSet to handle CRUD operations automatically Configured DefaultRouter to generate API endpoints 🔹 Implemented API endpoints: GET → Retrieve countries POST → Create new country PUT / PATCH → Update data DELETE → Remove data 💡 What stood out: Django REST Framework reduces a lot of boilerplate by providing built-in tools for serialization, routing, and request handling — making API development faster and more structured. 📌 This is a big step forward in building production-ready backend systems. #Python #DataEngineering #Django #DjangoRESTFramework #APIs #BackendDevelopment #LearningJourney #selfLearning

🐍 Python Developer Nuggets — Day 14 N+1 Query Problem — The Silent Performance Killer Why is your API making 101 queries instead of 1? The problem (N+1 Query) - Fetching related data inside a loop - 1 query for main data + N queries for related data - Example: accessing order.user inside a loop What actually happens - 1 query → fetch all orders - N queries → fetch each related user - Total = N + 1 queries Why this is dangerous - Slow APIs - High database load - Increased latency - Poor scalability in production The solution - Use select_related() for ForeignKey / OneToOne - Fetch related data in a single optimized query (JOIN) What changes after fix - Only 1 query is executed - Faster API response - Reduced DB load - Scalable and efficient When does this happen - Accessing related fields inside loops - Example: order.user, post.author Quick rule - If you see a loop + related field access → suspect N+1 Key takeaway - Don’t just write code that works - Write code that scales Small Python tricks, Big Developer Impact! #Python #Django #BackendEngineering #CleanCode #Performance #SoftwareEngineering #DeveloperTips

Most Python developers use Flask, FastAPI, or Django… But many still overlook one fundamental concept: HTTP methods. No matter which framework you choose, everything comes down to how your application handles these requests: • GET – Retrieve data • POST – Create a resource • PUT – Replace an entire resource • PATCH – Update specific fields • DELETE – Remove a resource Here’s where it gets interesting 👇 A lot of developers confuse PUT and PATCH. PUT → Replaces the entire resource PATCH → Updates only what’s necessary Why does this matter? Because choosing the right method leads to: ✔ Cleaner API design ✔ Better performance ✔ Easier maintainability Frameworks may differ in style and complexity, but the foundation remains the same: HTTP. Master these basics once, and switching between Flask, FastAPI, and Django becomes much easier. What’s one concept in backend development that took you time to fully understand? #Python #WebDevelopment #APIDesign #BackendDevelopment #Flask #FastAPI #Django #HTTPMethods

I built a project using Python & Django 🚀 Here’s what it does: 🔹 Handles real-time requests 🔹 Built REST APIs 🔹 Optimized database queries Biggest learning: 👉 Writing code is easy 👉 Writing efficient code is HARD Always optimize. Always improve. #Projects #DjangoDeveloper #PythonProjects #BuildInPublic #Python #Django #BackendDeveloper #TechCareers #hiring #hire #developer #dailypost

🐍 Python Developer Nuggets — Day 17 Retries in APIs Why do APIs fail even when everything looks correct? Because failures happen… and your system doesn’t retry smartly. The problem: Request fails once and stops User sees an error Even though it might have worked on retry The solution: Retry the request Add delay between retries Limit number of retries request = call_api() # retried on failure What changes: First attempt → may fail Retry → succeeds Better user experience Fewer visible failures Don’t retry everything Retry only when: Retry when: Network issues Timeout Temporary failures Don’t retry when: Wrong input Validation errors Golden rule: Retry only when failure is temporary Retry turns small failures into success #Python #Django #BackendEngineering #SystemDesign #CleanCode #Performance #DeveloperTips

🐍 Python Developer Nuggets — Day 16 Idempotency in APIs Why do duplicate orders or notifications happen? Because retries happen… and your API isn’t idempotent. • The problem - Same request processed multiple times - Duplicate orders / double payments • The solution - Use Idempotency-Key - Return same response for repeated requests orders = create_order(request) # handled with idempotency key • What changes - First request → processed - Retry → same response returned - No duplicates • Golden rule - Same request + same key = same result Small Python tricks, Big Developer Impact! 🚀 #Python #Django #BackendEngineering #SystemDesign #CleanCode #Performance #DeveloperTips

Flask vs Django: Key Differences Every Python Developer Should Know #programming #webdesign #rswebsols https://ift.tt/bB6T4co Excited to share our latest dive into Python web frameworks: Flask vs Django. This post breaks down the key differences every developer should know, from project size and complexity to learning curves, speed of development, and ecosystem strength. If you’re choosing the right tool for your next project, this comparison will help you make an informed decision between Django’s batteries-included approach and Flask’s lightweight, flexible design. Read the full analysis and decide which framework aligns with your goals and team needs. Link: https://ift.tt/bB6T4co

🐍📰 13 Project Ideas for Intermediate Python Developers In this article, you'll learn how you can get started on 13 Python project ideas that are just right for intermediate Python developers. #python

Kubernetes Jobs + Python ThreadPoolExecutor + Jeffrey Epstein Files Hej! I am Rina and for today I will be showing how Kubernetes Jobs, Python ThreadPoolExecutor and Jeffrey Epstein Files works. We will give this de@d pedow a little spotlight. In the https://lnkd.in/e7tsiJaU, there are 12 data sets. And each data set consists of many pdf files. I am going to download them randomly using ThreadPoolExecutor with 50 workers and 4 kubernetes pods. Link to the complete video: https://lnkd.in/eyRQirxP Github: https://lnkd.in/eiwPCGEk The Github repo rinavillaruz/jeffrey-epstein-files comes with dev, staging and production Kubernetes, ArgoCD, Jenkins as Code, Python, Skaffold, and Github Actions setup. It's an ongoing project and everything is not perfect. #DevOps #Kubernetes #JeffreyEpsteinFiles #Python

🔍 A difference I recently started noticing: CRUD developer vs Backend developer. CRUD developer: -> Creates models -> Writes basic views -> Makes things "work" Backend developer: -> Thinks about data flow -> Designs API structure -> Handles edge cases & failures Both can build features. But only one builds systems. Made me reflect: Am I just making things work… or actually understanding how they work? Still learning. But now I'm more aware. Which one are you striving to become? #Django #BackendDevelopment #Python #LearningInPublic