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What is FastAPI? Features and Benefits

What is FastAPI? Features and Benefits

FastAPI, a budding technology with diverse use cases, shines in developing RESTful APIs, backend services, real-time applications, and microservices. Its asynchronous capabilities make it ideal for high-performance applications like IoT solutions and data streaming platforms. FastAPI’s documentation generation simplifies API maintenance. Let’s dive deeper into its standout features.

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Introduction to FastAPI

FastAPI is the latest, high-performance web framework for building APIs with Python 3.7+ based on standard Python-type hints. In simpler terms, think of FastAPI as a tool that helps you build websites and online services (like apps) faster and more efficiently.

To understand FastAPI better, here is a real-life example. Imagine you’re building a toy house. Traditional methods involve manually cutting and painting each piece, which can be time-consuming. FastAPI is like a toy house kit, with pre-cut and painted pieces, allowing you to assemble the house much faster and with greater precision.

Brief History of FastAPI

FastAPI was created by Sebastián Ramírez and was first released in December 2018. Ramírez developed it with the aim of creating an API framework that is easy to use while ensuring the highest performance possible. Over time, it has gained popularity in the developer community due to its speed, ease of use, and extensive documentation.

In traditional frameworks, you might spend a lot of time writing validation logic for incoming data or serializing data for output. With FastAPI, thanks to Python-type hints, you automatically get data validation, serialization, and documentation. For instance, instead of manually parsing and validating an incoming JSON request, you can declare the expected data shape using Pydantic models, and FastAPI handles the rest.

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How to Install FastAPI

If you’re looking to integrate this powerful tool into your development toolkit, here’s a comprehensive guide on installing FastAPI on Windows, Linux, and macOS X.

Windows Installation

Step 1: Install Python

  • Navigate to the official Python website
  • Download the latest version for Windows
  • Run the installer 
  • Ensure you check the box that says “Add Python to PATH” during installation

Step 2: Install FastAPI

  • Open the Command Prompt
  • Enter the following command:

pip install fastapi

Step 3: Install Uvicorn

  • Uvicorn, an ASGI server, is necessary to run FastAPI applications. Install it using the following command:
pip install uvicorn


pip install "uvicorn[standard]"

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Linux Installation

Step 1: Install Python

  • Most Linux distributions come with Python pre-installed. You can check the version using the following command:
python3 --version
  • If not installed, use the package manager for your distribution. For Ubuntu, use the following command:
sudo apt-get update
sudo apt-get install python3

  Step 2: Install FastAPI

  • Use pip to install FastAPI:
pip3 install fastapi

Step 3: Install Uvicorn

  • Install Uvicorn using the following command:
pip3 install uvicorn

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macOS X Installation

Step 1: Install Python

  • macOS comes with Python 2.7 pre-installed, but FastAPI requires Python 3.6+. 
  • Install Homebrew if you haven’t already:
/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
  • Install Python using Homebrew:
brew install python3

Step 2: Install FastAPI

  • Once Python3 is installed, use pip to install FastAPI:
pip3 install fastapi

Step 3: Install Uvicorn

  •  Install Uvicorn with the following command:
pip3 install uvicorn

After installation, you can verify that FastAPI and Uvicorn are correctly installed, regardless of the operating system.

Create a sample FastAPI app. Save the following code in a file named `main.py`:

from typing import Union
from fastapi import FastAPI
app = FastAPI()
def read_root():
    return {"Hello": "World"}
def read_item(item_id: int, q: Union[str, None] = None):
    return {"item_id": item_id, "q": q}

Navigate to the directory containing `main.py` and run:

uvicorn main:app --reload

Open a browser and navigate to If you see the message `{“Hello“:”World“}`, your installation was successful.

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What are FastAPI’s Features and Benefits?

FastAPI has rapidly gained popularity in the developer community. Its rise can be attributed to a suite of distinctive features that set it apart. 

Here are the top FastAPI benefits and features:

  • Type Hints Integration: FastAPI utilizes Python-type hints in function signatures to provide automatic data validation, serialization, and documentation. It not only reduces boilerplate code but also ensures data consistency and safety.
  • High Performance: One of FastAPI’s standout features is its performance. Benchmarks often place it on par with NodeJS and Go, two languages known for their speed. It is especially notable for data-intensive operations and large-scale applications.
  • Automatic Interactive API Documentation: FastAPI comes integrated with tools like Swagger UI and ReDoc, providing automatic interactive API documentation. It aids developers in testing endpoints, understanding request/response models, and offering a professional look for API consumers.
  • Dependency Injection System: FastAPI’s advanced dependency injection system simplifies the management of shared resources, like database sessions or authentication. It promotes cleaner, more maintainable code by handling dependencies in a modular manner.
  • OAuth2 and JWT Integration: Security is paramount in web development. FastAPI provides built-in utilities for OAuth2 password flows and JWT token creation, making authentication and authorization seamless and robust.
  • Asynchronous Support: With native support for asynchronous request handling, FastAPI can handle a large number of simultaneous connections. It is particularly beneficial for I/O bound operations, ensuring applications remain responsive under heavy loads.
  • Extensive Documentation: FastAPI boasts comprehensive documentation that covers everything from basic setup to advanced use cases. This extensive resource accelerates the learning curve and assists developers at all experience levels.

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FastAPI Vs. Django Vs. Flask

When it comes to Python web frameworks, FastAPI, Django, and Flask are among the top contenders. Each has its unique strengths and is tailored to different development needs.

Let’s discover the major differences among FastAPI, Django, and Flask. 

Parameters for FastAPI Vs. Django Vs. FlaskFastAPIDjango Flask
Design PhilosophyA modern framework focused on speed, type safety, and asynchronous capabilitiesA high-level, “batteries-included” framework promoting rapid development and clean designA lightweight micro-framework offering the essentials for web application development
PerformanceKnown for high performance due to its asynchronous natureIt is efficient but can be slightly slower than FastAPI and Flask due to its monolithic nature.Offers commendable performance, especially for lightweight applications
Learning CurveModerate, especially for those familiar with Python-type hintsSteeper, given its comprehensive nature and the “Django way” of doing thingsGentle and straightforward, making it ideal for beginners and small projects
FlexibilityHighly flexible, with automatic interactive API documentationLess flexible due to its structured approach, but offers a lot out-of-the-boxExtremely flexible, allowing developers to choose tools and libraries as per their needs
Built-in FeaturesOffer automatic data validation, serialization, and OAuth2 integrationComes with an ORM, admin interface, authentication, and more, right out of the boxMinimalist, offering only the essentials, but can be extended with a wide range of plugins
Use CaseBest suited for modern web APIs requiring high performance and asynchronous operationsIdeal for larger applications where built-in features like an ORM and admin interface are beneficialPerfect for smaller applications, prototypes, or when starting a project from scratch
Community & EcosystemGrowing rapidly with an active community and increasing third-party packagesMature with a vast ecosystem, numerous plugins, and a large communityMature with a wide range of plugins available and strong community backing
Database IntegrationDoes not include a built-in ORM but can be integrated with any ORM or database toolComes with a built-in ORM that supports various databases and offers a high-level querying systemDoes not have a built-in ORM, but tools like SQLAlchemy can be easily integrated
Asynchronous SupportNative support for asynchronous request handlingLimited support for asynchronous featuresNo native asynchronous support, but it can be achieved using third-party tools

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What are FastAPI Use Cases?

Here are the top seven use cases where FastAPI is a pioneer:

  • High-Performance Web APIs: FastAPI’s asynchronous capabilities make it ideal for building web APIs that require high performance. It automatically handles data validation and serialization, ensuring efficient data processing.
    Example: A stock trading platform requiring real-time data processing can leverage FastAPI for its backend services
  • Data Validation and Serialization: With its integration of Python-type hints, FastAPI offers automatic request and response data validation. It ensures that the data your API receives and sends is of the expected format and type. 
    Example: An e-commerce platform can validate product details, ensuring that all required attributes, such as price and description, are provided.
  • OAuth2 and JWT Authentication: FastAPI provides built-in utilities for OAuth2 password flows and JWT tokens, making it easier to implement secure authentication and authorization mechanisms.
    Example: A cloud storage service can use OAuth2 to ensure secure user access and data sharing.
  • Interactive API Documentation: FastAPI integrates smoothly with tools like Swagger UI and ReDoc, offering automatic interactive API documentation. It aids developers in testing and understanding API endpoints. 
    Example: A startup launching a new API service can provide its developers with interactive documentation, streamlining the development process.
  • WebSockets: For applications requiring real-time communication, FastAPI supports WebSockets, allowing for bidirectional communication between the server and clients. 
    Example: A chat application can use WebSockets to deliver real-time messages.
  • Background Tasks: FastAPI allows for background task execution, ensuring that certain tasks don’t hold up the main application threads. 
    Example: An email notification system can send emails as a background task, ensuring users receive timely notifications without delaying other processes.
  • Dependency Injection: FastAPI’s dependency injection system simplifies managing dependencies, such as database sessions or configuration parameters, making sharing and reusing code easier.
    Example: A multi-module application can share a database session across modules, ensuring consistent and efficient database access.

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FastAPI has emerged as a modern, high-performance web framework for building APIs with Python. Its emphasis on type safety, asynchronous capabilities, and automatic data validation sets it apart from many alternatives. Recent statistics indicate a notable surge in FastAPI’s adoption, with it outshining many traditional frameworks in terms of growth. 

For those fascinated by its capabilities, diving deeper into its asynchronous features and integration with tools like Pydantic is a logical next step. As the digital landscape evolves, frameworks like FastAPI, which prioritize performance and developer ease, will undoubtedly play a key role. To truly kickstart your journey with FastAPI, consider exploring its extensive documentation and real-world application use cases.

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Presenting Sahil Mattoo, a Senior Consultant Analytics & Data Science at Eli Lilly and Company is an accomplished professional with 14 years of experience across data science, analytics, and technical leadership domains, demonstrates a remarkable ability to drive business insights. Sahil holds a Post Graduate Program in Business Analytics and Business Intelligence from Great Lakes Institute of Management.