Django vs FastAPI: Performance Comparison

The Most Common Backend Mistake I See in Django APIs It’s not JWT. It’s not caching. It’s not even architecture. It’s database query optimization. I’ve seen APIs built with Django and Django REST Framework that work perfectly in development. Clean code. Proper JWT setup. Everything looks production-ready. But once real data enters the system, things slow down. Why? Because of the silent killer: N+1 queries. Example: You fetch 100 orders. Each order has a user. Without select_related(), Django makes: 1 query for orders 100 extra queries for users That’s 101 queries instead of just 1. And the worst part? There’s no error, no warning. It just becomes slow. What I’ve learned: Always consider how many queries your API makes. Use select_related() for ForeignKey. Use prefetch_related() for ManyToMany. Check the query count before pushing to production. Don’t put heavy database logic inside serializers. Performance issues usually don’t come from complex systems. They arise from small oversights repeated at scale. Good backend development isn’t just about making it work. It’s about making it scale. #Production #Django #DjangoRESTFramework #Python #Backend #BackendDevelopment #DatabaseOptimization #APIPerformance #CleanCode

🧠 Django ORM + DRF: N+1 query problem in APIs While building REST APIs for my job board, restaurant, and task manager projects with Django and Django REST Framework, I faced the classic N+1 query problem. In short: One request from the frontend But the backend hits the database many times because of ForeignKey / ManyToMany relations Simple example: # Bad: N+1 problem   orders = Order.objects.all()   for order in orders:     print(order.customer.name) # each loop hits the DB  The fix is to use: # Good: one query with join   orders = Order.objects.select_related("customer")  I apply the same idea inside my DRF viewsets to optimize API performance: class OrderViewSet(ModelViewSet):     queryset = Order.objects.select_related("customer")     serializer_class = OrderSerializer  These small optimizations make a big difference when the data grows and help keep the API fast and scalable. I’m trying to apply these patterns step by step in my Django & DRF projects and keep learning more about backend performance. 🚀 #Django #DjangoRESTFramework #Python #BackendDevelopment #APIs #Performance

Django Developers: Stop using .count() inside loops (Huge Performance Killer) I recently reviewed a backend codebase where API response time was ~2.8s for a simple dashboard. The culprit? N+1 queries + .count() inside loops Bad Code: for user in users: order_count = Order.objects.filter(user=user).count() Looks clean, right? But this runs 1 query per user If you have 100 users = 101 database queries. Optimized Approach: from django.db.models import Count users = User.objects.annotate(order_count=Count('order')) Now? Only ONE optimized query. Real Impact I saw: • Before: 2.8s API response • After: 320ms response • 88% performance improvement Backend rule I follow: “Never run queries inside loops if ORM can handle it.” 💡 Pro Tip: Use: select_related() → ForeignKey / OneToOne prefetch_related() → ManyToMany / Reverse FK annotate() → Aggregations without extra queries Are you still facing slow Django APIs in production? Comment “PERF” and I’ll share my Django performance checklist. #Python #Django #BackendDevelopment #SoftwareEngineering #APIDesign #SystemDesign

🚀 Leveling Up My Django ORM Game At scale, .filter() and .all() aren’t the hard part. The real difference shows up in how you design your queries. Lately, I’ve been leaning much more on things like annotations, subqueries, and proper prefetching—and the impact is very real: fewer queries, lower memory usage, and much more predictable performance. Instead of pulling data into Python and looping over it, I push the work down to the database. Instead of shipping N+1 queries to production, I’m explicit about how related data is loaded. The result is code that’s not just correct, but actually production-grade. Big takeaway: Django ORM isn’t slow. Bad query patterns are. If you’re building serious Django apps, mastering query design matters just as much as writing clean business logic. #Django #Python #BackendEngineering #SoftwareEngineering #Performance #Scalability #WebDevelopment

🚀 I just published a new Django package! If you have worked on large projects using Django REST, you’ve probably run into this situation: multiple serializers for the same model, each created just to return a different set of fields depending on the view. Over time, this can lead to a lot of duplicated code and serializers that are difficult to maintain. To address this problem, I built a solution that allows you to use a single serializer per model, while letting the view define which fields should be returned in the response. I’ve now packaged this solution and made it available for anyone who wants to solve the same problem. One of the main goals was to make it automatically integrate with Django Virtual Models, allowing it to also handle database query optimization based on the selected fields. I know there are similar packages out there, some with more features, but this one was designed to hadle also database optimization better with Django Virtual Models integration. If you're working with Django REST and dealing with serializer duplication, feel free to check it out. I’d really appreciate any feedback, suggestions, or ideas to improve it! 🔗 Link in the comments. #django #djangorest #python #backend #softwareengineering #opensource #database #optimization

In real-world Django applications, tracking who changed what and when is not optional — it's essential. Instead of building custom audit tables or complex signals, the recommended way is using the third-party package django-simple-history. It automatically tracks: • Object creation • Object updates • Object deletion And stores everything in a separate historical table. 🔧 Setup is surprisingly simple: # models.py from django.db import models from simple_history.models import HistoricalRecords class Order(models.Model): name = models.CharField(max_length=255) amount = models.DecimalField(max_digits=10, decimal_places=2) history = HistoricalRecords() After running migrations, Django automatically creates a historical model like: HistoricalOrder Now you can do: order = Order.objects.get(id=1) order.history.all() # View change history That’s it. No manual audit tables. No complex logic. For admin panels, CRMs, financial systems, or reporting-heavy platforms — this is a game changer. As backend developers, we shouldn’t just build features — we should build traceable systems. Are you using audit logging in your Django projects? #Django #Python #BackendDevelopment #SoftwareEngineering #CleanCode

I recently built AeroGuard, a real-time emergency response and fleet tracking system designed to dispatch units like ambulances and fire trucks instantly using an event-driven architecture. ⚫The Core Features & Workflow: -> Real-Time Tracking: A Python simulation pushes live GPS data to a Django backend. Django logs this in PostgreSQL and immediately publishes it to Redis. Django Channels then broadcasts the coordinates via WebSockets to a Next.js frontend, updating a Leaflet map without refreshing. -> Smart Dispatcher: Clicking the map sends an emergency type and coordinates to the API. Django uses the Haversine formula to locate the closest available unit of that exact type, dispatches it, and updates the live dashboard. -> Offline Fallback (Cache-First): To prevent the command center from going blind during a connection drop, the React frontend constantly caches live WebSocket data into IndexedDB. If the server fails, the map seamlessly renders the fleet's last known locations. ⚫The Tech Stack & Why: ->Django & Django Ninja: Chosen for building robust, type-safe APIs capable of handling the backend spatial math. ->Redis & Django Channels: Standard HTTP polling is too slow for live map tracking. Redis acts as a high-speed message broker to handle high-frequency WebSocket broadcasts. ->Next.js, React & Leaflet: Used to render the interactive map and manage the complex derived state of the live dashboard. ⚫What I Learned: Building this pushed me deep into the asynchronous side of Django. My biggest takeaway was learning how to properly integrate Redis as a message broker and manage persistent WebSocket connections, stepping completely away from the standard HTTP request-response cycle. ⚫Future Scope: The next phase is implementing two-way communication so dispatched vehicles can send status updates back to the dashboard, along with integrating live traffic data for more accurate routing. The deployment will be done in few days as deploying the redis and other things will take time but untill then go through the github repo pls https://lnkd.in/g_CBD5-Q #development #python #nextJS #django

Met a developer who built everything from scratch. No Django. No Flask. No FastAPI. Just raw Python and determination. I thought he was crazy. Then I saw his code. Here's what shocked me: He understood EVERYTHING his application did. No "magic" happening behind the scenes. No dependencies breaking randomly. No framework updates forcing rewrites. The framework developers (me included)? We could build fast. But we didn't really understand how it worked. "Django just handles that." "FastAPI does it automatically." We were building on top of abstractions we'd never looked inside. The conversation that changed my perspective: Me: "But aren't you wasting time reinventing the wheel?" Him: "I'm learning how the wheel works. You're just driving the car." Both approaches have value: Want to ship products fast? Use frameworks. Want to deeply understand systems? Build from scratch (at least once). What I did after meeting him: Spent two weekends building a tiny web server in raw Python. No framework. Just sockets, HTTP parsing, and routing. 150 lines of code. Taught me more about web development than 2 years of using Django. The uncomfortable truth: Frameworks make you productive. But they also hide the fundamentals. You can be a great developer without understanding HTTP. But you'll be a better one if you do. Your challenge: Pick ONE thing your framework does for you. Spend a weekend building a simple version from scratch. Not to replace the framework. To understand what it's doing for you. What framework "magic" do you wish you understood better? #Python #WebDevelopment #LearningToCode #Frameworks

FastAPI vs Django — which performs better in production? From building and deploying projects with both, here’s the honest answer: If we’re talking raw API throughput and concurrency, FastAPI usually wins. If we’re talking full applications and shipping stable systems quickly, Django often wins. FastAPI is async-first (ASGI-based), which makes it strong for: • High-traffic APIs • ML model serving • Microservices • Concurrent workloads Django is batteries-included and gives you: • A powerful ORM • Built-in authentication • An industry-grade admin panel • Strong security defaults But here’s the nuance most comparisons miss: In real production systems, bottlenecks are often • Database queries • Architecture decisions • Caching strategy • System design —not just the framework itself. FastAPI optimizes runtime performance. Django optimizes development speed and stability. Many real-world systems use both — Django for the core application and FastAPI for high-performance services. So which one are you using in production right now? #Python #Django #FastAPI #BackendDevelopment #SoftwareEngineering

You don't control the first 80% of what happens when Django receives a request. Most developers jump straight into writing views and models. Its common to think Django just "routes" a request to their function. But by the time view runs, Django has already done a significant amount of work. Here's what actually happens, in order: 1. Django starts by loading settings and initializing the app registry. 2. Every app in INSTALLED_APPS is registered, models are mapped, and signals are connected. This happens once at startup, not on every request. 3. Then the WSGI/ASGI server hands the raw HTTP request to Django. Django wraps it into an HttpRequest object. 4. Middlewares are executed now, top to bottom through the MIDDLEWARE list. Security checks, session loading, auth, all happen here silently. 5. Then Django hits the URL resolver. It walks through urlpatterns until a pattern matches, then calls the linked view. 6. Your view runs. Returns an HttpResponse. 7. Middleware runs again, this time bottom to top on the way out. Response goes back through WSGI/ASGI to the client. Takeaway: -> Debugging: 80% of "why isn't my view running?" is a middleware or URL issue -> Architecture: Knowing the pipeline tells you exactly where to inject custom logic I’m deep-diving into Django internals and performance. Do follow along and tell your experiences in comments. #Python #PythonInternals #Django #DjangoInternals #SoftwareEngineering #BackendDevelopment