WHY DO WE NEED FASTAPI ?
Why Everyone is Switching to FastAPI 🚀 Before FastAPI, almost every Python programmer relied on Flask and Django. While they are great frameworks, developers frequently faced two major bottlenecks: The Concurrency Struggle: Traditional frameworks can struggle to handle thousands of simultaneous connections at once (such as real-time chat apps, AI streaming text, or instant notifications). Data…
FastAPI has become a popular choice for Python developers due to its speed and reliability. Before FastAPI, many Python programmers relied on Flask and Django, but both frameworks had challenges handling large numbers of simultaneous connections and protecting against data input errors.
FastAPI addresses these issues by relying on two key features: Async/Await and Pydantic. Async/Await allows for concurrent processing, enabling the framework to handle thousands of connections simultaneously. For instance, instead of a single waiter in a restaurant taking orders, waiting for food, and serving customers one at a time, Async/Await enables multiple waiters to work simultaneously, significantly speeding up service.
Pydantic acts as a data validation bouncer at the front door of the API. It ensures that data provided by users meets specified criteria, such as an age being a number. If a user submits invalid data, Pydantic blocks it immediately, preventing crashes and ensuring the server remains stable.
FastAPI is particularly beneficial for building AI and ML APIs, real-time data streaming applications, and high-performance microservices. Its unique approach using Async/Await and Pydantic has made it the go-to choice for developers seeking a robust and efficient framework for their projects.
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