How FastAPI's async design boosts performance

FastAPI 0.119.0 introduces a critical migration feature: temporary support for both Pydantic v2 and pydantic.v1 models simultaneously within the same application. This enables applications still using Pydantic v1 to gradually migrate to Pydantic v2 without requiring a complete rewrite. (Source: https://lnkd.in/dQNu7HQs) This release officially deprecates Pydantic v1 support, which will be removed in a future FastAPI version soon. The Pydantic team has already stopped supporting Pydantic v1 for Python 3.14+. Developer Takeaway: Use FastAPI 0.119.0's mixed Pydantic support to migrate legacy v1 code to v2 incrementally. Prioritize completing your Pydantic v2 migration before Python 3.14 adoption, as v1 is incompatible with Python 3.14+. Review the Pydantic v2 migration guide to understand breaking changes and new features.(Source: https://lnkd.in/dgPsVCHx) #FastAPI #Python #Pydantic #BackendDev #API #WebDevelopment

\(aiohttp & asyncio\) vs Requests: Comparing Python HTTP Libraries 1. Requests - The Simple Synchronous Library What it is: Synchronous blocking HTTP library Simple, intuitive API Most popular for basic HTTP operations Blocks execution until response is received Simple scripts Sequential API calls Learning/prototyping When performance isn't critical import requests import time # Basic GET request response = requests.get\('https://lnkd.in/gZr2tbvq) print\(response.json\(\)\) # Making multiple requests \(BLOCKING - one at a time\) def fetch\_multiple\_sync\(\): urls = \[ 'https://lnkd.in/geAKeWr3', 'https://lnkd.in/gTPFcRiG', 'https://lnkd.in/gndc7mJB' \] start = time.time\(\) results = \[\] for url in urls: response = requests.get\(url\) results.append\(response.json\(\)\) print\(f"Time taken: \{time.time\(\) - start:.2f\} seconds"\) # Output: \~3 seconds \(1 second per request, sequential\) return results # POST request with headers and data re https://lnkd.in/g9ze5V35

The more I build with FastAPI, the more I realize why companies really like it ✨ Auto-generated Swagger Docs ⚡ Insane Speed thanks to async support 🧩 Pydantic models make validation clean and predictable. 🌟 When you understand HTTP exceptions, error handling becomes intuitive. 💫 Folder structure matters, and moving main.py to /app taught me how imports & module paths actually work. Debugging has taught me more than any theory could : 🎯 Wrong JSON casing → 422 error 🎯 Missing comma → JSON decode error 🎯 Shadowing status function → unexpected AttributeError 🎯 In-memory lists resetting after reload → why databases matter Missing package imports → server loading forever All of these issues became "aha!" moments. #learningjourney #python #FastAPI #VisualisingTech

This week, I revisited how API performance can be improved with just a few small tweaks in backend design and it reminded me how sometimes simple changes make the biggest difference. One thing that stood out was how much database queries impact response time. 🌟 select_related(): it reduces extra queries when fetching related objects. And: 🌟prefetch_related(): it helps when working with many-to-many or reverse relationships. The difference may not look big in small datasets but in real systems, it can reduce dozens of queries per API call. It’s a good reminder that "Writing code is one thing" 👉 Understanding how it executes is where performance comes from If you’ve come across a small optimization recently that made a real difference, I’d love to hear about it. #Python #Django #BackendDevelopment #Performance #LearningJourney #CleanCode

I want to share something really cool that I use every day in FastAPI — a simple and effective way to make API responses much faster While working with FastAPI, I recently explored ways to make JSON serialization faster — especially for endpoints returning large datasets or paginated results. That’s when I discovered ORJSONResponse, a drop-in replacement for FastAPI’s default JSONResponse, powered by orjson — one of the fastest JSON libraries in Python. Here’s why it’s awesome: ✅ Blazing speed — orjson is written in Rust, and it’s significantly faster than Python’s built-in json or ujson. ✅ Smaller payloads — it produces compact, UTF-8 encoded JSON output. ✅ Automatic datetime handling — no more manual .isoformat() conversions. ✅ Better for performance-critical APIs — perfect for high-traffic or data-heavy endpoints. from fastapi.responses import ORJSONResponse from fastapi import FastAPI app = FastAPI(default_response_class=ORJSONResponse) @app.get("/data") async def get_data():   return ORJSONResponse(content={"message": "Success"}) With this small tweak, your FastAPI app automatically returns responses using ORJSON — faster and more efficient 💨. If your API returns large responses or you’re optimizing latency, definitely give orjson a try. #FastAPI #Python #BackendDevelopment #Performance #API #CleanCode #SoftwareEngineering

I see so many people requesting FastAPI to handle async requests in python as if it was the only fast library, yesterday I came across Quart and I was amazed to see a lib that is the code written exactly like in Flask, but handles async and is capable of natively validation of request and response, testing is also really easy and nice. I recommend it, no overhead and can easily migrate from flask sync to an async environment simply by adding the async/await keywords instead of rewriting all the codebase. https://lnkd.in/d2jN-U6f

The library you learn first isn't the one you scale with. httpx picks up where Requests stops-async, HTTP/2, the works. Requests works great for getting started, but httpx is where serious Python projects are heading. The difference becomes obvious once you hit real-world scaling challenges. Here's what makes httpx worth the switch: • Dual mode support: Same API for both sync and async requests. No more juggling different libraries when you need concurrent HTTP calls. • HTTP/2 protocol: Built-in support means faster, more efficient connections without extra setup. • Better connection pooling: Advanced timeout controls and resource management that actually matter under load. • Drop-in compatibility: The API feels familiar if you know Requests. Migration is straightforward. Requests handles simple scripts just fine. But when you're dealing with hundreds of concurrent requests or integrating multiple third-party APIs efficiently, httpx's async support becomes the difference between a system that works and one that performs. The performance gains in high-concurrency scenarios are substantial. Plus, you're future-proofing your HTTP client layer instead of painting yourself into a corner. For new projects that need to scale, httpx is the modern choice. For quick scripts, Requests still gets the job done. #Python #BackendDevelopment #AsyncProgramming

💡 Flask 101: request.args vs request.form — The Real Difference One line of code can save hours of debugging. 👇 ✅ request.args → Data from the URL (GET) username = request.args.get("username") # /login?username=Shubh ✅ request.form → Data from a submitted form (POST) username = request.form.get("username") 🔹 args = comes from the address bar 🔹 form = comes from user input Once you get this, Flask starts to feel effortless. Your frontend and backend finally speak the same language. #Flask #Python #WebDevelopment #CodingTips #BackendDevelopment #LearningJourney

I built SkillBridge to move beyond simple LLM prompts, demonstrating a production-ready Retrieval-Augmented Generation (RAG) architecture. This full-stack application generates hyper-personalized learning roadmaps by vectorizing real job data and performing semantic search using PostgreSQL + pgvector. The Google Gemini API consumes the retrieved context (skill gaps) to output actionable, step-by-step guidance. The application is built for performance: Python FastAPI handles asynchronous backend operations, while Next.js 14 (TypeScript) ensures a robust and scalable frontend. Live demo link: https://lnkd.in/eJtSi77U #RAG #VectorSearch #GeminiAPI #FastAPI #NextJS #FullStack #Python #SoftwareEngineering