Django ORM: select_related() vs prefetch_related()

Optimize Queries with only() and defer() When working with Django ORM, it's important to be mindful of the data you retrieve from the database. Fetching all fields when only a few are required can lead to unnecessary overhead and slower performance. ❌ User.objects.all() → Retrieves all fields ✅ User.objects.only('id', 'name') → Retrieves only the specified fields By limiting the data you load, you can significantly improve query efficiency and overall application performance. Key takeaway: Less data fetched = Faster queries ⚡ #Django #Python #WebDevelopment #PerformanceOptimization

One extra line in your Django queryset can turn a 20ms API into a 2s one. ⚡ I ran into this while optimizing an API that was fetching related data inside a loop. Everything worked. But response time kept increasing as data grew. The issue was the classic N+1 query problem. Django gives two powerful tools to solve it. 🔹 select_related Uses SQL JOIN Best for ForeignKey or OneToOne Fetches everything in a single query 🔹 prefetch_related Runs separate queries and combines results in Python Best for ManyToMany or reverse relationships Prevents large JOIN explosions The key difference is not syntax. It is how the data is fetched. Golden rule 👇 Single relationship → select_related Multiple relationship → prefetch_related Getting this wrong does not just slow things down. It can quietly break performance as your data grows. In my experience, fixing N+1 queries is one of the fastest ways to improve API performance in Django. Which one do you end up using more in your projects? #Django #Python #PostgreSQL #BackendDevelopment #DatabaseOptimization #SoftwareEngineering

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

🚀 Optimizing Django Queries for Better Performance! While working on backend development, I explored how inefficient database queries can impact application performance. Here’s what I learnt while working with Django ORM: 🔹 Difference between select_related and prefetch_related 🔹 How to reduce multiple database hits by optimizing queries 🔹 Avoiding N+1 query problems in real-world scenarios 🔹 Using values() and values_list() for efficient data fetching 💡 One thing I found interesting: Even a simple API can become slow if queries are not handled properly. Small optimizations in ORM can significantly improve performance. This helped me understand how backend performance plays a key role in building scalable applications. Excited to apply these learnings in real-world projects 🚀 #Django #Python #BackendDevelopment #PerformanceOptimization #LearningInPublic

Django-style Python ORM. Powered by Rust. We are thrilled to announce the very first release of Ryx. It combines the ergonomic query API you love from Django with the raw, async performance of a compiled Rust core. ✨ Highlights ⚡ Rust Engine: Async `sqlx` core, zero GIL blocking, compiled performance. 🐍 Python API: Familiar `.filter()`, `Q` objects, aggregations, and relationships. 🛠️ Full Stack: Migrations, CLI, Validation, Signals, and 30+ Field types. 🗄️ Multi-Backend: PostgreSQL, MySQL, and SQLite support. ⚡️ Quick Start python import ryx from ryx import Model, CharField, Q class Post(Model): title = CharField(max_length=200) active = BooleanField(default=True) await ryx.setup("postgres://user:pass@localhost/db") Query like Django, run like Rust posts = await Post.objects.filter( Q(active=True) | Q(title__startswith="Draft") ).order_by("-title").limit(10) 📦 Installation ...bash pip install ryx 🔗 Links 📖 [Documentation](https://ryx.alldotpy.com 🤝 [Contributing](https://lnkd.in/ecRyYqy3) 🐛 [Report an Issue](https://lnkd.in/eVVsM5rA) Built from scratch with ❤️. If you find this interesting, please leave a ⭐ — it helps a lot!*

Who knew code written in Python could be faster than Rust? SQLGlot's compiled mode (pip install "sqlglot[c]") now matches or beats Rust-based SQL parsers across most benchmarks. Thanks to the amazing mypyc project, SQLGlot can compile down into efficient C, the only thing we needed to do was add type annotations! The speed comes from compiling the core parsing and expression modules with mypyc the same tool mypy uses internally. No Rust, no C, no FFI... just Python that gets compiled to efficient C extensions. The pure Python version (zero dependencies, works everywhere) is already faster than most alternatives. The compiled version closes the gap with native code entirely. Some highlights from the benchmarks (ratio vs pure Python sqlglot): - sqlglot[c]: 0.17x–0.33x (3–6x faster than Python runtime) - sqloxide (Rust): 0.15x–0.58x - polyglot-sql (Rust): 0.17x–0.44x - sqlparse (Python): 3.6x–19x slower - sqlfluff (Python): 40x–177x slower Full benchmark suite with 16 query types is in the repo. Numbers are on Python 3.14. What I find most interesting is that this disproves the common assumption that you need to rewrite in Rust to get fast performance in Python. A well structured Python codebase compiled with mypyc gets you incredibly far.

Knowledge bites - Day 46 What is flask in python ? Flask is a lightweight Python web framework used to build web applications and APIs quickly. It follows a minimalistic approach, giving developers full control instead of enforcing strict project structures. Key features : 1. Lightweight and flexible (micro-framework) 2. Built-in development server and debugger 3. Uses Jinja2 templating engine 4. REST API friendly 5. Easy integration with databases and extensions How it works ? 1. Define routes (URLs) using decorators 2. Each route maps to a Python function 3. Function processes request and returns response 4. Server renders output (HTML/JSON) Example use case • Backend for AI apps (e.g., serving a model via API) • Lightweight dashboards • MVPs and quick prototypes Why it’s popular ? • Simple to learn and start • Highly customizable • Large ecosystem of extensions , like Flask SQLAlchemy , Flask Login and more . #Actionpackd #KnowledgeBites

Just shipped stup 🚀 A python package that scaffolds production-ready Python projects in seconds. 🛠️ Every time I started a new project, I was doing the same thing: init uv, pin Python, create venv, wire up requirements.txt, set up folders. 10 minutes of setup before writing a single line of actual code. ⏳ stup kills that loop. One command, full scaffold⚡ → `stup django` : Django + DRF + Celery + Redis + PostgreSQL, ready to go → `stup lang-agent` : LangGraph agent with tools, memory, Ollama config → `stup react-fastapi` : Full-stack with Docker Compose and CORS pre-wired → `stup ml` : MLflow + PyTorch + scikit-learn experiment structure → `stup cli` : PyPI-ready package with Typer + Rich, entry_points set All templates build on top of `stup uv` which handles uv init, Python pinning, and venv in one shot. Install once: `pip install stup` source code: https://lnkd.in/duGUCXVH

🚀 Handling Data Validation in Django APIs – A Small Learning While working on backend APIs, I realized that handling invalid data properly is just as important as building the API itself. Here’s what I learnt: 🔹 Validating incoming request data before processing 🔹 Using serializers to enforce data structure and constraints 🔹 Returning meaningful error messages instead of generic responses 🔹 Preventing invalid data from entering the database 💡 One thing I found interesting: Even a small validation miss can lead to inconsistent data and unexpected bugs later. This made me understand how important it is to design APIs carefully, not just for functionality but also for reliability. Still exploring better ways to handle validations and edge cases 🚀 #Django #Python #BackendDevelopment #API #WebDevelopment #LearningInPublic

Python is not good at aggregations, offload it to databases. A common pattern in Django codebases - Fetch all orders. Loop through them in Python. Count items. Calculate totals. Build a summary. It works, but it's one of the most expensive things the ORM can do. A Python loop over 100,000 orders to calculate totals = loading 100,000 model instances into memory, iterating and compute. The database is better at this. It was designed for exactly this. annotate() and aggregate() are how Django handles these computations. 1. aggregate() - one value for the entire queryset aggregate() collapses the entire queryset into a single computed result. One query. One result. No Python loop. 2. annotate() — one value per row annotate() adds a computed column to each row in the queryset. Every order gets its item count attached. Still a single query. Takeaways - → Performance: Database computation is orders of magnitude faster than Python loops at scale → Clarity: aggregate() for summaries, annotate() for per-row enrichment → Memory: Neither loads unnecessary data into Python, computation stays in the database The ORM is not just a way to fetch data. It's a way to compute data at the source, before it ever reaches Python. I’m deep-diving into Django internals and performance. Do follow along and tell your experiences in comments. #Python #Django #DjangoInternals #SoftwareEngineering #BackendDevelopment