Flask vs FastAPI: Choosing the Right Python Framework

Ever wondered why your backend APIs sometimes feel sluggish even though your code looks clean? One game-changing approach gaining traction for boosting API performance is **Asynchronous Python with asyncio**—and it’s time more developers gave it a serious look. Traditionally, Python’s synchronous code handles one task at a time. This works fine for simple scripts, but APIs that deal with multiple I/O operations—calling databases, external services, or complex file handling—can bottleneck your throughput. The server waits for each task to finish before moving on. Enter asyncio: Python’s native solution for async programming. It lets you write code that can handle multiple operations seemingly at once, without spinning up multiple threads or processes. Instead, it uses event loops and coroutines that pause work where waiting is necessary (like fetching data) and switch to other tasks meanwhile. The result? More efficient resource utilization and snappier responses under load. Why should backend engineers care about asyncio right now? Because modern frameworks like FastAPI and even Django (with recent updates) support asyncio natively. Even better: the community tools and libraries are catching up fast, making integration smooth and practical. Here’s a quick practical tip if you want to play around: - Use async def for your endpoint functions. - Leverage async database drivers (e.g., asyncpg for Postgres). - Await external API calls inside coroutines instead of blocking calls. You might hit a learning curve—async can be different from standard sequential thinking—but once you get the hang of it, your backend can handle far more requests simultaneously without the cost and complexity of threading. By embracing asynchronous Python today, you're not only writing cleaner code but future-proofing your services to handle scale efficiently. Have you experimented with asyncio yet? What challenges or wins did you experience? Let’s chat about the async revolution happening quietly in Python backends. #Python #AsynchronousProgramming #BackendDevelopment #APIPerformance #FastAPI #TechTrends #SoftwareEngineering #DeveloperTips

Requests worked fine for years. Then async happened. Now 100 HTTP calls take 0.19 seconds instead of 10. The gap isn't small. I've watched this shift happen in real-time. HTTPX isn't just another Python library. It's solving problems that Requests simply can't. The numbers tell the story: • HTTPX: 100 concurrent requests in 0.19 seconds • Requests: Same task takes over 10 seconds • Even in sync mode, HTTPX runs nearly twice as fast But speed isn't everything. HTTPX brings features that matter: 🚀 Native async/await support 🔗 HTTP/2 capabilities ⚡ Better connection pooling 🎯 Drop-in compatibility with Requests The Django Rest Framework team built this. They know what modern Python applications need. Requests still works great for simple tasks. But if you're building anything that handles multiple HTTP calls, HTTPX makes sense. One library. Both sync and async. Future-proof. The performance difference in concurrent operations isn't marginal. It's an order of magnitude better. What's holding you back from making the switch? #Python #AsyncProgramming #Python #Async #WebDevelopment 𝗦𝗼𝘂𝗿𝗰𝗲: https://lnkd.in/eV2KkxUR

Requests worked fine for years. Then async happened. Now 100 HTTP calls take 0.19 seconds instead of 10. The gap isn't small. I've watched this shift happen in real-time. HTTPX isn't just another Python library. It's solving problems that Requests simply can't. The numbers tell the story: • HTTPX: 100 concurrent requests in 0.19 seconds • Requests: Same task takes over 10 seconds • Even in sync mode, HTTPX runs nearly twice as fast But speed isn't everything. HTTPX brings features that matter: 🚀 Native async/await support 🔗 HTTP/2 capabilities ⚡ Better connection pooling 🎯 Drop-in compatibility with Requests The Django Rest Framework team built this. They know what modern Python applications need. Requests still works great for simple tasks. But if you're building anything that handles multiple HTTP calls, HTTPX makes sense. One library. Both sync and async. Future-proof. The performance difference in concurrent operations isn't marginal. It's an order of magnitude better. What's holding you back from making the switch? #Python #AsyncProgramming #Python #Async #WebDevelopment 𝗦𝗼𝘂𝗿𝗰𝗲: https://lnkd.in/eV2KkxUR

Building Observability with Custom Metrics — Voting App + Prometheus In modern systems, observability is more than just collecting logs — it’s about truly understanding how your application behaves in real time. To explore this, I built a simple Python Voting App and integrated custom Prometheus metrics to monitor its performance and behavior. #Prometheus #Grafana #Python #custommetrics #Observability #Monitoring Here full GitHub code https://lnkd.in/d_MBGc8t What I Built A small web-based Voting App that allows users to: Cast votes via /vote View results via /result Access the homepage via / Every request and response is measured using Prometheus client instrumentation. 📈 Custom Metrics I Added ✅ Request Count Tracks how many times each endpoint is hit: voting_app_request_count_total{endpoint="/vote", method="POST"} ✅ Response Time Captures latency for each request: voting_app_response_time_seconds_sum voting_app_response_time_seconds_count Why This Matters in Observability Custom metrics help answer critical questions: Which endpoint gets the most traffic? How long does each request take? Is the service slowing down under load? These insights form the "Metrics" pillar of the observability triad: Logs + Metrics + Traces Tech Stack Used Python (Flask) – simple backend for the app Prometheus – metric scraping and storage Grafana – dashboarding and visualization EC2 (Ubuntu) – deployment target GitHub Actions – CI/CD pipeline for auto-deployments 📊 Observability in Action With Prometheus scraping custom metrics and Grafana visualizing them, I can monitor: Request rates Response latency Endpoint-level traffic trends This helps detect performance regressions before they impact users. Key Takeaway Don’t just rely on default system metrics — instrument your own code to measure what truly matters to your business logic. Custom metrics = Real insight

FastAPI: Beyond "Async Python" FastAPI's rise in API development isn't just about async versus non-async. It signifies a significant shift in how Python interacts with the web. The Interface Evolution: WSGI vs ASGI When your browser sends a request, it doesn't directly communicate with your Python code. It speaks HTTP, while your app communicates through Python functions. This necessitates a mediator - WSGI or ASGI, defining the communication between the web server (like Gunicorn or Uvicorn) and your Python app. WSGI - Traditional Approach WSGI (Web Server Gateway Interface) is the longstanding standard (since 2003) utilized by frameworks like Flask and Django. It functions by: - Receiving an HTTP request from the web server - Converting it into a Python-callable format (app(environ, start_response)) - Waiting for your app's response before sending it back as an HTTP response While WSGI is simple and reliable, it operates synchronously, handling one request per worker sequentially, akin to a single phone line scenario. ASGI - The Contemporary Solution ASGI (Asynchronous Server Gateway Interface) modernizes this concept by offering a multi-lane expressway approach. It: - Supports various protocols like HTTP, WebSockets, and background tasks - Enables non-blocking async execution (async def app(scope, receive, send)) - Excels in real-time applications, AI model deployment, and high-concurrency APIs FastAPI leverages ASGI, utilizing servers such as Uvicorn or Hypercorn, tailored for async Python operations. The Significance In today's landscape, systems are not solely serving web pages; they handle diverse tasks like API calls, AI model operations, data streams, and live updates. This necessitates an interface that efficiently communicates across multiple channels, precisely what ASGI provides. FastAPI simplifies this process, offering a user-friendly interface atop this advanced architecture. #FastAPI #Python #ASGI #WSGI #BackendDevelopment #WebArchitecture #SoftwareEngineering #APIs

🚀 Day 41 of #100DaysOfCode — CRUD Operations in FastAPI Today I explored how to perform CRUD (Create, Read, Update, Delete) operations in FastAPI, one of the fastest web frameworks for building APIs with Python. ⚡ 🔹 C - Create → Add a new record using POST method 🔹 R - Read → Fetch data using GET method 🔹 U - Update → Modify existing data using PUT/PATCH 🔹 D - Delete → Remove data using DELETE method 💻 Example Structure: from fastapi import FastAPI app = FastAPI() items = {} @app.post("/items/") def create_item(item_id: int, name: str): items[item_id] = name return {"message": "Item created successfully"} @app.get("/items/{item_id}") def read_item(item_id: int): return {"item": items.get(item_id, "Not Found")} @app.put("/items/{item_id}") def update_item(item_id: int, name: str): items[item_id] = name return {"message": "Item updated"} @app.delete("/items/{item_id}") def delete_item(item_id: int): items.pop(item_id, None) return {"message": "Item deleted"} ✨ Key Takeaways: FastAPI automatically generates interactive docs (Swagger UI). Data validation is super easy with Pydantic models. It’s asynchronous and lightning fast ⚡ 🔥 CRUD operations are the building blocks of any backend system — mastering them is the first step toward building real-world APIs. #Python #FastAPI #BackendDevelopment #100DaysOfCode #LearningJourney #APIDevelopment #CRUDOperations

Over the past week, I wanted to challenge myself to build something end-to-end in Python — a full-stack project that’s simple, fast, and practical. The result is Expense Trackr, a clean and modular Expense Tracking Application powered by FastAPI (backend) and Streamlit (frontend). What I built: - Backend: FastAPI for building high-performance RESTfuls - Frontend: Streamlit for an intuitive & interactive interface - Database: MySQL to store and manage expense data - Testing: Pytest for ensuring reliability and maintanability Highlights: - Real-time expense tracking with CRUD operations - Clear data flow between UI →  API → Database - Modular project structure - Simple to deploy and extend - Learn new Python modules (Pandas, Pydantic) Tech Stack: Python, FastAPI, Streamlit, MySQL, Pytest, Pandas, Pydantic, Uvicorn GitHub Repo: https://lnkd.in/gZPRjRYq #Python #FastAPI #Streamlit #FullStackDeveloper #Pandas The architecture shows a smooth data flow from UI → API → Database

I grew up as a developer to the point of reading my framework's source code (DRF) and seeing it's actually pretty bad. Wow. It's so random and disorganized. E.g. only one field type has NULL_VALUES tuple for interpreting literals as a None value, although allow_null is a feature throughout all the field types. So for some reason only BooleanField can parse 'null' as null. Other fields like IntegerField parse it as a string 'null' 🤨 Why? I've no f/ng idea. Moreover, each derivative from the basic Field implements the interpretation of empty values in its own way although the logic largely stays the same throughout most of the fields. validate_empty_values in IntegerField? The same as in FloatField and the same as in DecimalField. But all are redefined from scratch. Oh and talking about those three - each one of them has MAX_LENGTH class property... Separately defined (yeah there's no NumberField parent). And each one sets it to a 1000. And I'm not even talking about naming conventions and readability. The methods' names do not correspond to what they actually do. And can you understand what's going on from looking at the code of those fields? How are the methods supposed to be used, in what order? No. You have to go look in the code that uses them. Yes, they are sequential steps be design, but you still can call them in any order - it's not enforced. Wtf? Why? Oh and what about the typing? What are the types in/out, can I understand what exactly should be flowing in/out those methods? Eh well. The typing is there yeah. Almost random. Sometimes just Any. Sometimes none. Or 4 different options of return types. Now I understand why it's so hard to work with 😁 It's by design. Also, I just promised myself to type Everything that I do in Python properly. It's so f/ng annoying to go through the code just to understand what type should be here in that value and what its interface is. This one is from my old code too. Actually no, most of it I have done myself and am doing today. Apparently growth feels like more and more disappointment in what you do and the technologies that you do it with. The more you know. But it still feels good. It's like... I get it know! I'm on the level with my framework's source code! Wow. The student outgrew the teacher. All of a sudden. Heh. Feels nice.

𝐄𝐯𝐞𝐫 𝐰𝐨𝐧𝐝𝐞𝐫𝐞𝐝 𝐡𝐨𝐰 𝐭𝐨 𝐭𝐚𝐤𝐞 𝐲𝐨𝐮𝐫 𝐅𝐚𝐬𝐭𝐀𝐏𝐈 𝐬𝐤𝐢𝐥𝐥𝐬 𝐛𝐞𝐲𝐨𝐧𝐝 𝐭𝐡𝐞 𝐛𝐚𝐬𝐢𝐜𝐬 𝐚𝐧𝐝 𝐛𝐮𝐢𝐥𝐝 𝐫𝐞𝐚𝐥-𝐰𝐨𝐫𝐥𝐝, 𝐩𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧-𝐫𝐞𝐚𝐝𝐲 𝐀𝐏𝐈𝐬? In my latest post — “Building Real-World APIs with FastAPI” — I break down: ✅ How to structure scalable API projects ✅ Integrating authentication (JWT, OAuth2) ✅ Database setup with SQLAlchemy ✅ Async endpoints for high performance ✅ Deployment best practices Whether you’re a Python developer starting or scaling your next big project, this guide will help you build robust, high-performance APIs with confidence. 👉 Read here: https://lnkd.in/dbiaWfem #FastAPI #Python #WebDevelopment #API #Backend #SoftwareEngineering #Coding #Developers

Impressive insights shared by Bhavishya Pandit on how Reflex bridges the gap between ML pipelines and frontend development — all within Python. Truly a game changer for developers who want to keep their workflow seamless and production-ready. ⚙️💡