FastAPI 0.131.0 Boosts JSON Serialization Performance by 2x

Why You Shouldn’t Use UploadFile Inside Pydantic DTOs in FastAPI ? When working with FastAPI, a common mistake developers make is trying to include UploadFile inside a Pydantic model (DTO). Something like this: class UserCreate(BaseModel): name: str profile_image: UploadFile But this doesn’t work the way many expect. ❌ Why? Pydantic models are designed for data validation and serialization (usually JSON). However, UploadFile comes from multipart/form-data, not JSON. That means Pydantic cannot properly validate or parse it. ✅ The Correct Approach Handle files separately using FastAPI’s dependency system: from fastapi import UploadFile, File async def create_user( name: str, profile_image: UploadFile = File(...) ): 📌 Key Idea Use DTOs (Pydantic models) for structured data like: strings numbers booleans nested objects Handle files separately with UploadFile and File(). Understanding this separation keeps your API design clean and predictable. #FastAPI #Python #BackendDevelopment #APIDesign #CleanArchitecture #WebDevelopment

🚀 One thing I like about FastAPI: automatic data validation. While exploring backend development with FastAPI, I learned how request validation works using Pydantic models. Instead of manually checking incoming data from the client, we can define a schema and FastAPI automatically validates the request body. Example model: from pydantic import BaseModel from typing import Optional class Post(BaseModel): name: str roll: str published: bool = True rating: Optional[int] = None What this gives us: • Required fields validation • Default values when data is missing • Optional fields support • Automatically generated API documentation With just a few lines of code, the API becomes structured, validated, and self-documented. Attaching the schema generated in the API docs. Small concept, but a powerful feature when building reliable APIs. #FastAPI #Python #BackendDevelopment #APIs

● Why Async Matters in FastAPI (And When To Use It) While building APIs, I realized something important: Not all endpoints should be written the same way. FastAPI supports both synchronous and asynchronous functions — but using async correctly can significantly improve performance. 🔹 What’s Happening Here? •The request waits for an external API •Instead of blocking the thread, async allows other requests to be processed. •This improves concurrency under high traffic. 🔹 When Should You Use Async? Use async when: •Calling external APIs •Querying databases (with async drivers) •Performing I/O operations 💡 WHY THIS MATTER Efficient backend systems are not just about writing endpoints. They’re about handling multiple users without slowing down. Understanding async is crucial for building scalable APIs. Backend performance is architecture + execution. #BackendDevelopment #FastAPI #Python #APIDesign #ScalableSystems

Day 28 of 150: Scaling Web Scraping with Multi-Page Traversal Moving from single-page extraction to building more robust, scalable crawlers. Today's focus was on Pagination Logic—the ability to programmatically navigate through multiple pages of a website to collect comprehensive datasets. Technical Focus: Pagination Logic: Implementing loops to dynamically construct URLs for subsequent pages (e.g., ?page=2, ?page=3). Session Management: Using requests.Session() to improve performance and maintain consistent headers across multiple network calls. Flow Control: Integrating time.sleep() to implement ethical rate-limiting, ensuring the script respects server load and avoids IP throttling. Data Aggregation: Developing a strategy to accumulate data from multiple iterations into a single, unified dataset before final storage. Transitioning from simple "scrapers" to "crawlers" capable of handling large-scale data collection tasks. 122 days to go. #Python #WebScraping #DataEngineering #SoftwareEngineering #150DaysOfCode #BackendDevelopment

FastAPI vs Flask (Practical Comparison) FastAPI vs Flask — When Should You Choose Which? After working on production APIs using both, here’s my practical take: 🔹 Flask Minimal, flexible micro-framework Great for small to medium apps Large ecosystem More manual setup for validation & docs 🔹 FastAPI Built-in request validation (Pydantic) Automatic OpenAPI/Swagger docs Async support out of the box Higher performance (ASGI-based) In my experience: ✔ Flask works well for lightweight internal tools ✔ FastAPI is better for high-performance, scalable REST APIs ✔ FastAPI reduces boilerplate significantly For backend systems handling high traffic + strict schema validation, FastAPI has been my go-to. What’s your preference for production APIs? #Python #Backend #FastAPI #Flask #RESTAPI #SoftwareEngineering

🌐 Day 15: Understanding HTTP Methods in FastAPI ⚡ Continuing my FastAPI learning journey, today I focused on how APIs handle different operations using standard HTTP methods. Here’s what I explored today: ✅ GET – retrieving data from the server ✅ POST – creating new resources ✅ PUT – updating existing data ✅ DELETE – removing resources ✅ Testing different API operations using FastAPI's interactive documentation (Swagger UI) Working with these methods helped me better understand CRUD operations (Create, Read, Update, Delete), which form the backbone of most backend systems. FastAPI makes implementing these operations clean, structured, and developer-friendly, allowing APIs to be built quickly while maintaining clarity. 💡 Key takeaway: Choosing the correct HTTP method makes APIs more predictable, scalable, and easier for other developers to use. #FastAPI #Python #BackendDevelopment #APIDevelopment #DevJourney #LearningInPublic #SoftwareEngineering

Been deep in a FastAPI backend project and something bugged me — the code I was writing looked different from the official docs. So I dug into it. Turns out FastAPI pushed some massive updates recently; 1. Python 3.9 support? Gone. Pydantic v1? Gone. The entire documentation was rewritten for Python 3.10+ syntax. What used to be: from typing import Optional name: Optional[str] = None Is now just: name: str | None = None Cleaner. No imports needed. 2. .dict() became .model_dump(). 3.  .from_orm() became .model_validate(). 4. JSON responses got 2x faster automatically through Pydantic's Rust serializer. 5. They added native streaming with yield — perfect for AI applications. Spent tonight updating 12 files across my project. Every old pattern eliminated. Lesson: documentation changes. Check the release notes regularly. What you learned 6 months ago might have a newer, better way now. #FastAPI #Python #BackendDevelopment #WebDev

Efficient Data Validation with Pydantic and FastAPI FastAPI uses Pydantic models to validate incoming API data before it reaches the application logic. This ensures the API only processes correctly structured and typed data. Key aspects of the validation process: 1. Define the Data Model - Create a model using Python types to specify required fields, data types, and constraints. 2. Automatic Validation - FastAPI checks incoming JSON against the model. Valid data is parsed into a Pydantic model instance, while invalid data returns a 422 validation error. 3. Type Conversion and Rules - Pydantic can convert compatible types (e.g., "100" → 100) and enforce rules like minimum values or valid date ranges. This approach keeps validation separate from business logic, making APIs more reliable and easier to maintain. #python #fastapi #pydantic #api #backend #softwareengineering #datavalidation #datascience #machinelearning

When I first started building APIs with FastAPI, everything lived in a single file. It worked — but it wasn’t scalable. As I gained more experience, I started structuring my projects using a layered architecture: • Routers → handling endpoints • Services → business logic • Repositories → database interaction • Schemas → request and response validation • Core → configuration and security This separation improved readability, maintainability, and testability. Now, when I build an API, I don’t just think about making it work — I think about how it will grow. Clean structure today prevents technical debt tomorrow. #FastAPI #Python #BackendDevelopment #SoftwareArchitecture #CleanCode #APIDesign

🚀 DSA Challenge – Day 189 Problem: Decode String 🔐📜 Today’s problem is a classic stack-based parsing question that tests your ability to handle nested structures. 🧠 Problem Summary You are given an encoded string s. The encoding rule is: 👉 k[encoded_string] → repeat encoded_string exactly k times Constraints: k is a positive integer The string may contain nested patterns Input is always valid (well-formed brackets, no extra spaces) 🎯 Goal: Return the decoded string. 💡 Key Insight The presence of nested brackets makes recursion possible, but using a stack is more intuitive. 👉 Whenever we encounter ], we know a complete pattern is ready to decode. We then: 1️⃣ Extract the string inside the brackets 2️⃣ Extract the number k before it 3️⃣ Repeat the string k times 4️⃣ Push it back to the stack ⚙️ Approach 1️⃣ Traverse the string character by character. 2️⃣ If the character is not ], push it onto the stack. 3️⃣ When encountering ]: Pop characters until '[' → this gives the substring Pop digits to form the number k Repeat the substring k times Push the result back to the stack 4️⃣ At the end, join everything in the stack. 📈 Complexity Time Complexity: O(n) Space Complexity: O(n) ✨ Why This Problem Is Important This problem highlights a key pattern: 🔥 Stack for parsing nested expressions Similar patterns appear in: ➡️ Valid Parentheses ➡️ Basic Calculator problems ➡️ Expression evaluation ➡️ XML/JSON parsing Understanding how to process nested structures using a stack is crucial for many real-world problems. 🔖 #DSA #100DaysOfCode #Day189 #Stack #Strings #Parsing #LeetCode #Algorithms #ProblemSolving #CodingChallenge #InterviewPrep #Python #SoftwareEngineering #DeveloperJourney #TechCommunity #CodingLife