Build FastAPI without database or ORM

When I first saw our production database hitting 99% utilization, I thought we needed a bigger server. I was wrong. The real problem wasn’t infrastructure. It was one unoptimized query quietly running inside a loop. Development was fast. Production exposed everything. I just published a deep dive on Django Query Optimization — covering N+1 problems, select_related vs prefetch_related, aggregations, indexing strategies, and real-world mistakes that slow apps down. here is my article link https://lnkd.in/gViDwwVd If you’re building APIs in Django, this might save you from a painful production incident. Would love to know: Have you ever faced a performance issue that wasn’t obvious at first? #Django #BackendDevelopment #Python #DatabaseOptimization #WebPerformance

Currently working through a Flask tutorial. At this stage, I’ve been learning and implementing: • Flask routing and request handling (GET vs POST) • User authentication and session management • Extending data models and handling migrations • Server-side forms with validation • Tracking user activity with UTC timestamps • Using Jinja templates and template inheritance It’s been helpful in understanding how backend logic, database state, and templates come together in a real Flask application. #Python #Flask #WebDevelopment #BackendDevelopment #LearningInPublic

I optimized a Django API from 12s → 200ms. Here's the 6-step playbook 👇 1️⃣ Measure first — Django Debug Toolbar showed N+1 queries everywhere 2️⃣ Fix N+1 — select_related() and prefetch_related() = biggest win 3️⃣ Count in SQL — replaced Python loops with annotate() 4️⃣ Paginate — stop returning massive datasets 5️⃣ Add indexes — on fields you actually filter and sort by 6️⃣ Cache last — Redis only after fixing the real problems The lesson? Caching should come LAST, not first. Performance isn't magic. It's: Measure → Fix → Measure again. #Django #Python #BackendDevelopment #APIOptimization #WebDevelopment #DRF #SoftwareEngineering

I built a CLI-based Mini Database Engine using Python standard libraries to understand how databases work internally. Instead of relying on SQL or external frameworks, I implemented the core logic myself, including: Table creation with custom schemas CRUD operations (Create, Read, Update, Delete) Conditional querying and filtering Persistent storage using JSON An interactive command-line interface This project helped me strengthen my understanding of backend fundamentals, data structures, and how real systems manage data under the hood. The short demo video shows the database in action, including inserting records, querying data, updating values, and deleting records. GitHub repository: https://lnkd.in/dHiAartY I am continuing to build and refine projects like this as I deepen my understanding of backend development and DSA. #Database #Python #DSA #learningProject #DataScience #BackendDevelopement #Github

🤔 Flask vs FastAPI: Which Python framework should you choose in 2024? After building APIs with both frameworks, here's my honest breakdown: 🐍 Flask - The Veteran: • Mature ecosystem with tons of resources • Complete flexibility and control • Perfect for complex, custom architectures • Steeper learning curve for beginners ⚡ FastAPI - The Speed Demon: • Built-in async support for high performance • Automatic API documentation with Swagger • Type hints make code more maintainable • Modern Python features out of the box My take: FastAPI wins for new projects requiring speed and modern features. Flask remains king for legacy systems and when you need maximum customization. The real winner? Learning both! Each has its place in different scenarios. What's your experience? Are you team Flask or FastAPI? Share your thoughts below! 👇 #Python #WebDevelopment #API #Flask #FastAPI #Programming #TechTips #SoftwareDevelopment #Coding

One Python backend mistake that quietly hurts performance ❌ Running database queries inside loops for user in users: profile = Profile.objects.get(user=user) Looks harmless… until your user base grows 😬 Congrats, you've just invited the N+1 query problem. ✅ Better approach profiles = Profile.objects.filter(user__in=users) 📈 Why this small change matters: Prevents N+1 queries Drastically cuts down database hits Improves API response time Scales much better in production 💡 Backend performance isn't about writing more code. It's about writing smarter queries. 💬 Have you run into N+1 issues in real-world projects? How did you catch or fix them? #python #django #backenddevelopment #performanceoptimization #database #softwareengineering #webdevelopment https://lnkd.in/dxwAC3FF

It’s “just text”… until you realize it powers almost everything. Usernames. Passwords. Logs. Messages. APIs. Reports. The Python String datatype is simple on the surface — but incredibly powerful when you understand how to Create, Read, Update, and Delete data efficiently. Strong fundamentals = strong developer. 🔥 What’s your favorite string method? 👇 #PythonDeveloper #CodingLife #DataAnalyst #TechLearning #LinkedInTech

𝗗𝗲𝗮𝗿 𝗕𝗮𝗰𝗸𝗲𝗻𝗱 𝗗𝗲𝘃𝘀, When building an application with FastAPI and PostgreSQL, how do you handle database changes that build on a previous migration? Do you prefer rolling back (downgrading) the existing migration before making changes, or do you apply a new migration on top of the existing one? Although, I prefer the latter, what do you think? 😊 #Python #FastAPI #BackendDevelopment #BackendEngineering

📦 Excited to announce pine-script-py v0.1.1 is now live on PyPI! A lightweight Python library that brings Pine Script–like indicators and helpers to Python using only pandas and numpy. 🚀 Get started: pip install pine-script-py What’s included ✅ Pine-style series operations ✅ Core technical indicators (EMA, RSI, ATR, and more) ✅ Lightweight & fast — no heavy dependencies ✅ Easy integration with existing data analysis workflows Why this project? Many traders and data scientists use Pine Script on TradingView but struggle to replicate the same logic in Python. pine-script-py aims to make Pine-style calculations simple, familiar, and efficient in Python environments. 🤝 Looking for contributors & testers! Found a bug? Open an issue. Have a feature idea? Start a discussion. Want to contribute? PRs are welcome. Our goal is to make Pine Script–compatible indicators accessible to Python developers everywhere. 🔗 GitHub: [https://lnkd.in/g9xR2K3b] #Python #OpenSource #FinTech #AlgoTrading #DataScience #TechnicalAnalysis #Pandas

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