Dimas Brizuela’s Post

Understanding Django's .get_or_create() Pattern A pattern I see frequently misunderstood in Django codebases: This returns a tuple of (instance, boolean). The comma performs standard Python iterable unpacking—it's not Django-specific syntax. Common mistake: Treating the unpacked variables as a single unit. They're independent references. obj is a fully editable model instance, and created is simply a boolean indicating whether a new record was inserted. Practical application—handling placeholder records: Consider a ProjectMembership model where new clients are created with project=None as a placeholder. This pattern: Finds the existing placeholder if present Creates one if absent Updates the project reference in either case Result: The placeholder is upgraded rather than orphaned. No duplicate records. No cleanup required. Key takeaway: .get_or_create() followed by assignment provides atomic-like "get or upsert" semantics without race conditions that plague separate get-then-create logic. This protection only works if there's a database-level unique constraint on the lookup fields (or Django's unique_together in Meta). Without it, .get_or_create() still has a race condition window. The database constraint is what guarantees atomicity. #Django #Python #BackendEngineering #SoftwareDevelopment

Building APIs shouldn’t feel repetitive. That’s exactly where Django REST Framework (DRF) shines. It abstracts the repetitive parts of backend development—while still giving you control when you need it. You don’t just build APIs faster, you build them cleaner: • Structured serializers • Reusable viewsets • Clear separation of concerns If you’re using Django and not leveraging DRF yet, you’re probably writing more code than you need to. smartData Enterprises Inc. #DjangoRESTFramework #SoftwareEngineering #APIs #Python #smartDataEnterprisesInc

Your Django API is not slow because of your database. It's slow because of serialization. A 17-field serializer on 1,000 records = 17,000 Python calls. Just to produce JSON. We hit this in a rental platform in Stockholm. 80ms → 3 seconds at 2,000 users. So we built ClaraX — Rust serialization for Django. One line. No rewrites. No Rust knowledge. Results: → 475ms → 14ms (33x) → 506ms → 10ms (50x) pip install clarax-django python manage.py clarax_doctor https://lnkd.in/dgmZgnK5

🐍📰 The Ultimate Guide to Django Redirects In this detailed guide, you'll learn everything you need to know about HTTP redirects in Django. All the way from the low-level details of the HTTP protocol to the high-level way of dealing with them. #python

Day 02 of 30 | Django MVT Pattern 🐍 Before writing any code in Django, you need to understand how it thinks. MVT = Model + View + Template. Every request your user makes follows this exact flow: → Browser sends a request → urls.py routes it to the right View → View asks the Model for data → Model queries the database → View sends data to the Template → Template renders HTML and returns it to the browser I made a video explaining each part. My English is A2. The diagram helps. 👀 #Django #Python #30DaysOfDjango #LearningInPublic #Developer #SaaS

Optimizing Django Queries: How to Avoid N+1 Problems One of the quickest ways to slow down your Django backend is the classic N+1 query issue. While working on Inboxit, I had to be deliberate about this especially when dealing with relationships between models. The fix I use most often: prefetch_related() It’s perfect for optimizing reverse relationships (when you have a ForeignKey pointing to your model and you need to access related data). Instead of making one query per object (which explodes with more records), prefetch_related fetches all the related data in just two queries one for the main objects and one for the related ones. This small change keeps response times fast and your API scalable as usage grows. Have you run into N+1 issues in your Django projects? What’s your go-to optimization technique? #Django #DRF #Python #BackendDevelopment #QueryOptimization #TechNigeria #webdev

Understanding Django became much easier once I learned this 🔍 When I first started with Django, everything felt confusing… But one concept changed everything: 👉 Django follows the MVT architecture (Model–View–Template) Here’s how I now see it: ✔ Model → Handles database (data) ✔ View → Contains logic (what to do) ✔ Template → Handles UI (what user sees) Once I understood this flow, building projects became much more structured and easier. Still learning and improving every day 🚀 What was the concept that made Django click for you? 👇 #Django #Python #WebDevelopment #Backend #Learning

🐍📺 In this video course, you'll learn how to use Django Ninja, a FastAPI-inspired tool for turning Django views into REST API endpoints. With Ninja, you can quickly build API endpoints. #python

Most Django developers don’t realize this… The ORM is silently killing their performance. I’ve seen APIs go from: ⚠️ 300 queries → ⚡ 3 queries Just by fixing QuerySet usage. In this carousel, I broke down: - N+1 problem - select_related vs prefetch_related - F expressions - Real production mistakes If you're working with Django, this is a must-know. Full guide here 👇 https://lnkd.in/dVuaXBMq #Django #Python #DjangoORM #WebDevelopment #BackendDevelopment #SoftwareEngineering #DatabaseOptimization #ProgrammingTips #Developers #CodingLife #BuildInPublic

A Python package to clean Django project junk and free space. Over time, my projects kept filling up with things I don’t actually need… __pycache__, .pyc files, logs, staticfiles, even old virtual environments. Cleaning them manually every time was just… annoying 😅 So I made something simple to handle it. 🧹 Django Cleaner It scans your folders, detects Django projects automatically, and removes all the unnecessary stuff safely — without touching your actual code. You just run: django-cleaner ~/Projects and it does the rest. What it handles: • Removes __pycache__ and .pyc files • Cleans logs and staticfiles • Optional removal of venv • Shows how much space you freed I kept it simple on purpose. No setup, no complexity — just something useful that works. Published it on PyPI 📦 🔗 https://lnkd.in/gSvj5U3Y Code on GitHub 👇 🔗 https://lnkd.in/gNur8fN3 This is one of those small things that actually makes working on multiple projects easier. If you use Django, it might help you too. #Python #Django #OpenSource #BuildInPublic #SoftwareDevelopment #BackendDevelopment #DeveloperTools #Programming #Tech #CleanCode #Productivity #CodingLife