Django Query Optimization: Avoiding Performance Issues

One observability lesson that took me years to fully understand: Slow queries rarely appear suddenly. They usually start small. A feature is added. A queryset grows. A join becomes heavier. Indexes stop being enough. And one day a request that used to take 40ms suddenly takes 900ms. What changed how I build Django systems was starting to observe database behavior continuously, not only when something breaks. A few practices that helped a lot: Tracking query count per request Monitoring slow query logs Measuring endpoint latency over time Profiling ORM-heavy views Making database metrics visible to the team Django’s ORM is powerful, but it can hide the real cost of queries if you don’t measure them. Performance problems don’t usually come from a single bad line of code. They come from systems evolving without visibility. Observability is what lets you see those changes before users feel them. Hashtags #Django #Observability #BackendEngineering #DatabasePerformance #Python #SoftwareEngineering

🎙️ TALK: Advanced ORM kung-fu for on-demand filtering, sorting, and summing 40M financial transactions by Mathias Wedeken Fetching items from the database, grouping them and performing calculations on related data sounds easy in Python, but at scale, it can quickly become a bottleneck. If you're a Django developer working with large and growing datasets, or if you're curious how your app can benefit from the performance of Postgres without writing raw SQL, this talk is for you. 🔗 Grab your ticket: https://lnkd.in/dXN83nct #djangocon #djangoconeu2026

Clean code is good. Efficient queries are better. 🚀 One of the biggest lessons in Django backend development: performance is our responsibility, not the database’s problem. Understanding when to use: ✔️ "select_related()" ✔️ "prefetch_related()" ✔️ Query count monitoring ✔️ Proper DB indexes Small ORM decisions → huge production impact. Avoid N+1 queries early, and your future self will thank you. 💻⚡ #Django #Python #BackendDevelopment #PerformanceOptimization #DjangoORM #SoftwareEngineering

Built an API in Django today. What I expected: “Quick endpoints. Clean responses. Done in 1 hour.” What actually happened: 1 missing comma 2 migrations 5 server restarts 17 “Why is this not working?” moments But when that endpoint finally returns 200 OK… Nothing feels more powerful. 💻🔥 Still building. Still debugging. Still loving Python. #Python #Django #API #BackendDeveloper #DevLife

Switched our core microservices from Node/Express to Django and FastAPI recently as we pivot to an "AI-First" model. Honestly, the difference for complex business logic is stark. With Python, I find I spend less time managing async state and more time actually defining the transformations. Django’s ORM handles migration headaches, and FastAPI’s Pydantic validation keeps my data models clean before hitting Supabase. It just feels tighter when dealing with LLM pipelines. Anyone else seeing better velocity on heavy backend lifting by leaning into Python frameworks now? #BackendDev #FastAPI #Django #Python

💡 One Python Trick That Makes Backend APIs Much Cleaner When building Flask or FastAPI backends, many developers write repetitive validation code. Example: 1. Checking if input exists 2. Checking types 3. Handling missing fields 4. Returning error responses It quickly becomes messy. A cleaner approach is using 𝐏𝐲𝐭𝐡𝐨𝐧 𝐝𝐞𝐜𝐨𝐫𝐚𝐭𝐨𝐫𝐬. Example: from functools import wraps from flask import request, jsonify def validate_json(f):   @wraps(f)   def decorated(*args, **kwargs):     if not request.json:       return jsonify({"error": "JSON body required"}), 400     return f(*args, **kwargs)   return decorated # Usage: @app.route("/create-user", methods=["POST"]) @validate_json def create_user():   data = request.json   # process data Why this helps: ✔ Removes repeated validation code ✔ Keeps API routes clean ✔ Improves maintainability ✔ Makes production code easier to scale Small Python patterns like this make a big difference in backend systems. What’s one Python pattern that improved your backend code? 👇 #Python #BackendDevelopment #Flask #APIDesign #SoftwareEngineering

A common pain point for growing local businesses: The "Slow Dashboard." I recently audited a legacy Python backend where a single report generation was taking 45 seconds. The fix wasn't a faster server or a complex cache. It was fixing a series of N+1 queries and adding a composite index on the database. The Result: 45 seconds down to 0.8 seconds. If your software is slowing down as your business grows, you usually don't need a total rewrite. You need a targeted performance audit. #Python #DatabaseOptimization #Performance #LocalBusinessTech

Just shipped FastReact — a Python library that unifies FastAPI + React into one stack 🐍⚛️ 𝗪𝗵𝗮𝘁 𝗶𝘁 𝘀𝗼𝗹𝘃𝗲𝘀: Running FastAPI + React always meant two servers, manual CORS config, and cryptic JSON errors when something breaks. FastReact eliminates all of that. 𝗛𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁𝘀: ⚡ One CLI command starts everything — fastreact dev main:app --reload 🔴 Python tracebacks render as browser overlays (like React's own error screen) 🔒 Route protection built in — React pages are browser-only, API tools get 405 📡 --call flag shows every live request colored by method and status 🐍 Works with both FastAPI and Flask 𝗜𝗻𝘀𝘁𝗮𝗹𝗹: pip install fastreact 𝗩𝟬.𝟮.𝟬 𝗿𝗼𝗮𝗱𝗺𝗮𝗽 has something groundbreaking for local dev — stay tuned 👀 Would love feedback from anyone building fullstack Python apps! 🔗 PyPI: pypi.org/project/fastreact 🔗 GitHub: https://lnkd.in/gBnGbB6t #Python #FastAPI #React #OpenSource #WebDevelopment #PyPI #Flask #Fullstack #Programming

Most performance problems are not CPU problems. They are database problems. Early in my career, when something was slow, my instinct was to look at the Python code. Later I learned the real bottleneck was almost always: Too many queries Inefficient joins Missing indexes Loading more data than necessary A single endpoint can look perfectly fine in code and still generate hundreds of database queries. A few habits that made a big difference for me: Always checking query counts during development Using select_related and prefetch_related intentionally Avoiding loading full objects when only a few fields are needed Being careful with nested serializers in APIs Django’s ORM is incredibly productive. But performance comes from understanding what SQL is actually being executed behind the scenes. The ORM abstracts the database. It does not eliminate it. Hashtags #Django #Python #BackendEngineering #PerformanceOptimization #DatabasePerformance #SoftwareEngineering

Merdoc.dev now features enhanced runnable code blocks that support # Deno-style imports from libraries via a URL. This means you can execute powerful blocks of code within in your documentation. This could support API working examples, QA assertions, data reporting (e.g., Jupyter Notebooks), or other creative ideas you come up with! Coming next, I'll add support for additional languages beyond JavaScript/TypeScript and Python (which are already supported). And I want to introduce shared context across multiple code blocks in the same document. That means you could define a variable or function at the top of the document and then use it in a code block further down. Or that the results rendered in the first example can be looped through in another block. I'll also add "Run All" so you can execute all of them synchronously with one click. Play around with it yourself at https://merdoc.dev