FastAPI feels like the default choice for new Python APIs more often than Flask now. What stands out to me is the combination of speed, a gentle learning curve, and first-class async support. For backend work, async isn’t just a “nice to have” anymore—handling lots of concurrent requests (and I/O-heavy tasks like database calls) becomes much easier to model when the framework is designed around it. Flask still has a huge ecosystem and is great for simple services, but the industry shift makes sense: FastAPI tends to encourage clearer API structure and modern patterns from the start. When would you still pick Flask over FastAPI today?

Doubling down even further! Did you know the `@transaction` decorator removes almost all database "plumbing" from your Dyne routes? 🛠️ Forget `await req.db` or manual ` await session.commit()` calls. Just decorate your function: it lazily loads the session, commits on success, and automatically rolls back if anything goes wrong. Focus on your logic, not the infrastructure: https://lnkd.in/eqEc4Tuw #Python #SQLAlchemy #Backend #DyneFramework #WebDev

𝐇𝐨𝐰 𝐭𝐨 𝐛𝐮𝐢𝐥𝐝 𝐚 𝐅𝐚𝐬𝐭𝐀𝐏𝐈 𝐚𝐩𝐩 𝐰𝐢𝐭𝐡𝐨𝐮𝐭 𝐚 𝐝𝐚𝐭𝐚𝐛𝐚𝐬𝐞 𝐨𝐫 𝐚𝐧 𝐎𝐑𝐌? 🤔 If you’re a beginner and don’t want to start your journey with a complex setup (database, migrations, ORM…), 𝑑𝑏𝑧𝑒𝑟𝑜 𝑖𝑠 𝑎 𝑔𝑟𝑒𝑎𝑡 𝑠𝑜𝑙𝑢𝑡𝑖𝑜𝑛. With dbzero, you can persist Python objects directly on disk — no SQL, no schema, no boilerplate. Just focus on learning FastAPI, API design, and clean backend logic. I built a simple Todo API to demonstrate how this works in practice 👇 Sometimes, starting simple is the fastest way to move forward 🚀 The link of the project. https://lnkd.in/en2yHrTH #FastAPI #Python #BackendDevelopment #Beginners #APIDesign #LearningByDoing #CleanCode

Many performance issues in Python APIs don’t come from business logic, but from blocking I/O.   Database queries, external API calls or file operations executed synchronously quickly limit throughput under real load. Using async frameworks or async layers correctly allows the backend to handle more concurrent requests without increasing infrastructure. However, mixing async code with blocking libraries cancels most of the benefit and creates hard-to-detect bottlenecks. Performance in Python is less about raw speed and more about how I/O is managed. 🐍 Understanding where the event loop blocks changes everything. #PythonBackend #AsyncIO #APIPerformance #BackendEngineering #ScalableSystems #TechArchitecture #PythonDeveloper

So I recently encountered another shocking and sad accident in my code, which is the race condition introduced by using singleton. I used to think singleton is good for storing things that only exist one copy in the system and can be reused. I saw a few of programs store their Redis connection pool in a singleton class, which looks nice. Then bad thing happens when I use that SDK I wrote in a Flask service with multithread enabled and save user states inside singleton. Then race condition happens, before one session finished, another already came in and overwrote the states. I would say from now I would really be extra meticulous about any designs and patterns I feel good about and then judge them before I put them in any running services. And for the solution? Actually it was quite straightforward, just save the states in each client object, no other tricks. Here is an example from Supervisely, which is an MLops platform which also support running services with their SDK as part of the code. https://lnkd.in/gc9bD8U7 #python #race_condition #singleton

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

🎯 Python & MySQL — Chapter 5: WHERE Clauses & Wildcards Real applications don’t fetch all data — they fetch the right data. In Chapter 5, we dive into filtering records using WHERE conditions and wildcards. 🔧 In this chapter: ✅ Filtering data using WHERE ✅ Using AND / OR conditions ✅ Wildcards with LIKE ✅ Building dynamic queries in Python 🎥 Full lesson is on YouTube — link in the comments 👇 This is a must-know skill for backend developers and analysts. 💬 Comment “FILTER” if you’re following along. #Python #MySQL #SQLWhere #DataFiltering #Backend #YouTube

JSON is the language of the web. If you can parse JSON, you can extract data from almost anywhere. Tools like `jq` in the command line or `json` library in Python are essential. Don't parse HTML if you can find the JSON object embedded in the script tags. It's cleaner data. JSON or XML? (There is only one right answer). #json #webdev #data #coding

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

If you’re using #MicrosoftFabric SQL Database, the #Python notebook is seriously your friend. Row based ingestion straight into SQL Server, and the throughput is… not bad at all. Powered by #DuckDB writing directly to #SQLServer via this excellent extension 👇 https://lnkd.in/gtwAdhmg Simple. Practical. Effective. #duckdb #sqlserver #microsoftfabric #python #dataengineering