Developers are continuously on the lookout for technologies that allow them to rapidly and efficiently construct sophisticated APIs and online applications. FastAPI, a relatively new addition to the Python web framework landscape, has quickly garnered traction due to its speed, simplicity, and developer-friendly features. In this article, we will see the introduction of FastAPI and explain why it has become a popular alternative for developing web applications and APIs. To learn more about API please refer to What is API.
What is FastAPI?
FastAPI is a modern web framework that is relatively fast and used for building APIs with Python 3.7+ based on standard Python-type hints. FastAPI also assists us in automatically producing documentation for our web service so that other developers can quickly understand how to use it. This documentation simplifies testing web service to understand what data it requires and what it offers. FastAPI has many features like it offers significant speed for development and also reduces human errors in the code. It is easy to learn and is completely production-ready. FastAPI is fully compatible with well-known standards of APIs (i.e. OpenAPI and JSON schema).
Features of FastAPI
- Automatic Documentation: FastAPI generates interactive API documentation automatically using the OpenAPI standard. You can access this documentation by visiting a specific endpoint in your application, which makes it incredibly easy to understand and test your API without having to write extensive documentation manually.
- Python Type Hints: One of FastAPI’s standout features is its use of Python-type hints. By annotating function parameters and return types with type hints, you not only improve code readability but also enable FastAPI to automatically validate incoming data and generate accurate API documentation. This feature makes your code less error-prone and more self-documenting.
- Data Validation: FastAPI uses Pydantic models for data validation. You can define your data models using Pydantic’s schema and validation capabilities. This ensures incoming data is automatically validated, serialized, and deserialized, reducing the risk of handling invalid data in your application.
- Asynchronous Support: With the rise of asynchronous programming in Python, FastAPI fully embraces asynchronous operations. You can use Python’s async and await keywords to write asynchronous endpoints, making it well-suited for handling I/O-bound tasks and improving the overall responsiveness of your application.
- Dependency Injection: FastAPI supports dependency injection, allowing you to declare dependencies for your endpoints. This helps in keeping your code modular, testable, and maintainable. You can seamlessly inject dependencies like database connections, authentication, and more into your routes.
- Security Features: FastAPI includes various security features out of the box, such as support for OAuth2, JWT (JSON Web Tokens), and automatic validation of request data to prevent common security vulnerabilities like SQL injection and cross-site scripting (XSS) attacks.
Installation and Setup of FastAPI
To get started with FastAPI, you need to install Python, if not then install Python3. Then, you need to install fast API using the following command
pip install fastapi
You also need to install uvicorn
pip install uvicorn
Create a Simple API
Here, we are creating a simple web service that says “Hello” when you visit a specific web address. With FastAPI, you can do this in just a few lines of code, To run this code, you can save it in a Python file, here we are saving the file as main.py.
Python3
from fastapi import FastAPI
app = FastAPI()
@app .get( "/" )
def read_root():
return { "message" : "Hello, FastAPI!" }
|
Now, execute the following command in your terminal:
uvicorn main:app --reload
Once the application is running, open your web browser and navigate to
http://localhost:8000/
You should see a message displayed in your browser or the response if you are using an API testing tool like curl or Postman.
{"message": "Hello, FastAPI!"}
Advantage of FastAPI
Here are simple advantages of using FastAPI:
- Easy to Learn and Use: FastAPI is designed to be straightforward, especially for Python developers. Its simple and intuitive syntax, along with automatic documentation generation, makes it easy to get started and maintain.
- High Performance: FastAPI is built for speed. It’s one of the fastest Python web frameworks available, thanks to its asynchronous support and efficient data handling. This means your web applications can handle a large number of requests without slowing down.
- Automatic Data Validation: With FastAPI, you can use Python type hints to define the data structure you expect for your API requests and responses. FastAPI automatically validates the data, reducing the chances of errors caused by incorrect input.
- Authentication and Authorization: It provides simple ways to handle authentication and authorization, whether using OAuth2, JWT tokens, or custom methods.
- Middleware: We can easily add middleware to your FastAPI application for tasks like logging, authentication, or request/response modification.
Disadvantage of FastAPI
Here are some potential disadvantages of using FastAPI:
- Learning Curve: While FastAPI is designed to be developer-friendly, it may still have a learning curve for those new to asynchronous programming or web frameworks in general. Developers with no prior experience in Python may also need to learn Python first.
- Community and Documentation: Although FastAPI’s community is growing rapidly, it may not have as extensive a support network or documentation as some other frameworks. You may encounter fewer tutorials, guides, and community-contributed packages.
Similar Reads
Best Data Structures and Algorithms Books
Data Structures and Algorithms is one of the most important skills that every Computer Science student must have. There are a number of remarkable publications on DSA in the market, with different difficulty levels, learning approaches and programming languages. In this article we're going to discus
9 min read
GATE Data Science and Artificial Intelligence 2025 - Live Course
It's good to see the addition of GATE Data Science and Artificial Intelligence in the year 2025 which depicts the adaptation of technological advancements. GATE has introduced Data Science and Artificial Intelligence (DA) which will help aspirants to get into a new domain of technology and dive deep
3 min read
Intuit Interview | Set 9 (On-Campus)
Intuit came for on-campus hiring for 2016 batch and internship offerings for 2017 batch in our college last week. Round One (Online round, 90 minutes): The shortlisting was done on the basis of an online round which consisted of 24 questions (one question was for the profile chosen and 20 were a mix
5 min read
Top 75 DSA Questions
In this post, we present a list of the top 75 data structures and algorithms (DSA) coding questions to help you prepare for a thorough revision for interviews at leading tech companies like Meta, Google, Amazon, Apple, Microsoft, etc. This list helps you to cover an extensive variety of DSA Coding q
2 min read
Accolite Interview Experience | Set 4 (On-Campus)
Round 1 (Written) Around 140 students appeared for the offline test. There were 20 MCQs that to be done in (30 minutes), questions were from OS,DBMS,Datastructures. After that coding round was there (paper coding) in which 3 questions were given and we have to attempt any 2 (1 hour) 1. Write your ow
8 min read
Top 50 Problems on Queue Data Structure asked in SDE Interviews
A Queue is defined as a linear data structure that is open at both ends and the operations are performed in First In First Out (FIFO) order. We define a queue to be a list in which all additions to the list are made at one end, and all deletions from the list are made at the other end. The element w
3 min read
QuickSort Based Practice Problems
Problems based on Partitioning AlgorithmSorting an Array of Two Types.Stable Binary SortingStable Binary Sorting with O(1) SpaceThree Way Partitioning Around a RangeThree Way Partitioning Around a ValueSorting an Array of Three TypesProblems based on Quick SortKth Smallest ElementK Smallest Elements
1 min read
Programs to print Interesting Patterns
Program to print the following pattern: Examples : Input : 5 Output: * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * This program is divided into four parts. C/C++ Code // C++ program to print // the given pattern #include<io
15+ min read
Top Interview Questions and Answers on Quick Sort
Quick Sort is a popular sorting algorithm used in computer science. In our article "Top Interview Questions and Answers on Quick Sort", we present a collection of essential coding challenges focused on Quick Sort algorithms. These problems are carefully selected to help you sharpen your problem-solv
7 min read
Guidelines for asymptotic analysis
In this article, the focus is on learning some rules that can help to determine the running time of an algorithm. Asymptotic analysis refers to computing the running time of any operation in mathematical units of computation. In Asymptotic Analysis, the performance of an algorithm in terms of input
4 min read