Optimize API Performance with Cursor-Based Pagination

Your API isn’t slow — your pagination might be. 📉 It worked perfectly… until your data grew. Then every page got slower than the last. Your code didn’t change. But your dataset did. The culprit? Offset pagination. The deeper the page, the more rows your database has to scan and skip. Page 1 → fast Page 1000 → painful Same query shape. Very different cost. The fix isn’t always caching. Sometimes it’s changing the pattern. Switch to cursor-based pagination. No skipping. Just seeking. In Django REST Framework: Use *CursorPagination* instead of *PageNumberPagination*. Performance stays consistent — even at scale. Because most performance issues aren’t complex. They’re patterns that don’t scale. And most developers don’t notice… until production. #BackendDevelopment #Django #Python #WebDevelopment #SoftwareEngineering #APIPerformance #DatabaseOptimization #SystemDesign #ScalableSystems #DjangoRESTFramework

💡 Imagine this... You send ₹1000 to your friend, money is deducted from your account… but your friend never receives it ❌ Scary right? This is where Django’s "transaction.atomic()" comes in 🔥 It ensures: ✅ Either all database operations succeed ❌ Or everything is rolled back When you wrap code inside transaction.atomic(): ✅ All database queries succeed → changes are saved ❌ Any error happens → everything is rolled back No partial updates. No broken data. That’s why I’m learning and using this in my Django projects 🚀 #django #python #backenddevelopment #webdevelopment #learning

How do you handle GET requests in your DRF projects? I have noticed a common point of confusion among Django developers whether to structure response data directly in the view or use a serializer. Here is my take. Always use serializers, even for read only operations. By default, read only fields in a serializer give you a clean declarative way to shape your API responses. Instead of manually reshaping dictionaries inside the view which quickly becomes unmaintainable, serializers act as a contract between your database models and the outside world. They allow you to rename fields conditionally, expose computed properties, nest related objects, and keep your views lean and focused on orchestration rather than transformation. But there is one critical performance caveat. If your serializer pulls data from multiple related objects, make sure you use prefetch related or select related in your queryset before passing it to the serializer. Otherwise you will run into the classic N plus one query problem, one query for the main object plus one query for each related object. That scales terribly. Good serialization is about control over your data shape. Good performance is about intention in your query planning. Do you structure your GET responses in serializers or directly in the view? What is your team's standard? #Django #DRF #APIDesign #Python #WebDevelopment #BackendBestPractices

Day 5 of #50DaysOfDjango ◆URLs & Routing in Django Today I learned how Django connects URLs to views This is the starting point of every user request. Response Flow: URL urls.py → View urls.py acts like a traffic controller Static URL: /about/ Dynamic URL: /user/5/ (passes data) Example: Python path('user/<int:id>/', views.user_profile) Challenge: Create 3 URLs + 1 dynamic route and connect them to views Now I understand how Django handles requests behind the scenes

🔧 Django REST Framework Serializers — the unsung heroes of your API If you've worked with DRF, you know serializers are more than just "JSON converters." They're the gatekeepers of your entire data layer. Here's what makes them genuinely powerful: 1) Validation built-in — field-level and object-level validation in one place. No extra form logic, no scattered checks. 2) Nested relationships — serialize related models automatically, or override with custom logic when you need it. 3) Read/write separation — use read_only / write_only fields cleanly, without duplicating code. 4) ModelSerializer — auto-generates fields from your model. Less boilerplate, faster iteration. 5) SerializerMethodField — add any computed property to your API response without touching the model. One underrated pattern: using serializers for input validation outside of views — in Celery tasks, management commands, anywhere you're ingesting untrusted data. Serializers are where your API contract actually lives. Treat them with the same care you'd give your models. What's your go-to serializer trick? Comment below! #Django #DRF #Python #BackendDevelopment #WebDevelopment #SoftwareEngineering

If you’ve ever built APIs in Django REST Framework, you know the pain of writing multiple views for the same model — list, detail, create, update, delete. Messy, repetitive, and error-prone. That’s where ViewSets come in. With just a few lines of code, you get all CRUD endpoints automatically, clean URL routing, and a scalable request flow. In my latest Medium article, I break down: What ViewSets are and why they matter How to secure your APIs with get_queryset() Performance boosts using prefetch_related Query parameter filtering with filter_backends Custom endpoints with @action (like cancel or recent orders) Common mistakes to avoid (permissions, redundant filtering, lost prefetch) This isn’t just about making APIs that “work” — it’s about building APIs that are secure, efficient, and production-ready. 👉 Read the full guide here: https://lnkd.in/diM6UCiZ #Django #RESTAPI #Python #BackendDevelopment #SoftwareEngineering #APIs

🏗️ Building the Backbone: From Python Classes to Persistent Data "The best way to learn is to build, break, and fix. 🛠️ Over the last few days, I’ve been architecting the backend for my FastAPI TodoApp. It’s been a journey of connecting the dots: Defining the Schema: Using SQLAlchemy to turn Python classes into database tables. Establishing the Link: Setting up the engine and SessionLocal to bridge the gap between my app and SQLite. Overcoming Hurdles: Navigating Windows environment variables and mastering the SQLite3 CLI to verify data integrity. The foundation is now officially set. By calling models.Base.metadata.create_all(engine), my app now automatically generates its own database environment on startup. Next stop: Developing the CRUD API endpoints to bring this data to life! 🚀" #Python #FastAPI #SQLAlchemy #BackendDevelopment #CleanCode #SoftwareEngineering

I was debugging a Django service last week and hit a classic problem memory growing silently across requests, no obvious culprit. The usual suspects (tracemalloc, memory_profiler, objgraph) are great tools. But I wanted something I could drop on any function in 30 seconds and get a readable answer from. Also, honestly I wanted to understand what's happening at the GC and tracemalloc abstraction layer in Python. The best way I know to understand something is to build on top of it. So I built MemGuard over a weekend. What it does: Drop @memguard() on any function and after every call you get: Net memory retained (the actual leak signal) Peak vs net ratio — catches memory churn even when net looks clean Per-type gc object count delta tells you what is accumulating, not just how much Cross-call trend detection if net grows every call, it flags it Allocation hotspots via tracemalloc exact file and line Zero dependencies. Pure stdlib gc, tracemalloc, threading. @memguard() def process_batch(records): That's it. It also works as a context manager if you want to profile a block rather than a function. Biggest thing I learned building this: Python's gc and tracemalloc expose far more than most people use day to day. The object-reference graph alone tells a story that byte counts miss entirely. Repo: https://lnkd.in/gdjkHvfb Would love feedback from anyone who's dealt with Python memory issues in production. #Python #Django #SoftwareEngineering #OpenSource #BackendDevelopment #MemoryManagement

Day 545 of Learning – Choosing the Right Backend Framework 🧠🌐 Explored how to choose the right backend framework based on project requirements rather than just popularity. Different frameworks serve different purposes, and selecting the right one depends on factors like scalability, performance, development speed, and complexity. For large-scale and enterprise applications, frameworks like Django are suitable because they provide built-in features and structure. For lightweight and flexible applications, Flask is a good choice as it gives more control to developers. When performance and speed are critical, especially for APIs and real-time systems, FastAPI or Sanic are better options due to their asynchronous capabilities. Understanding the strengths of each framework helps in building efficient, scalable, and maintainable applications. The key is not choosing the “best” framework, but choosing the right one for the problem. 🚀 #BackendDevelopment #Python #FastAPI #Django #Flask #SoftwareEngineering #APIs #LearningJourney #Day645

One serializer change turned my API from 1.8s to 200ms. ⚡ Everything was working fine. But something felt off. The API was slow… for no obvious reason. The issue wasn’t the database. It wasn’t Django. It was the serializer. What was happening: I was returning way more data than needed. Nested serializers Unnecessary fields Hidden queries All adding up silently. The fixes: 🔹 Use .only() to fetch required fields 🔹 Use .values() for lightweight responses 🔹 Avoid deep nested serializers unless necessary 🔹 Write custom serializers where control matters The realization: APIs don’t slow down suddenly. They get heavier with every extra field you return. The rule: Return only what the client needs. Nothing more. You don’t need all fields. You just never questioned it. What’s one performance mistake you’ve caught in your API? #SoftwareEngineering #BackendDevelopment #Django #Python #APIDesign #Performance #WebDevelopment #RESTAPI #Developers