FastAPI is a modern and advanced, high-performance web framework that uses Python type hints to develop APIs with Python 3.7+. It is a great option for creating microservices because of its speed, simplicity of usage, and robust capabilities. An introduction to FastAPI will be given in this blogpost, with an emphasis on middleware and microservice development. 

Table of Contents: 

• Abstract 

• Introduction to FastAPI 

• How to install FastAPI 

• Core Features 

• Advanced Features 

• Creating microservices with FastAPI 

• Middleware in FastAPI 

• Unique Features 

• Conclusion 

Abstract  

Using normal Python type hints, FastAPI is a cutting-edge, high-performance online framework for creating APIs using Python 3.7+. FastAPI, which is well-known for its speed and user-friendliness, makes it easier to create scalable and reliable online applications. This page gives a thorough introduction to FastAPI, highlighting its main characteristics and specialties. Some prominent features include the use of path and query parameters for request management, and Pydantic models in request bodies for data validation. 

We investigate advanced features like background processes, dependency injection, and integrated security and authentication systems. FastAPI’s asynchronous nature is emphasized, demonstrating its appropriateness for real-time applications like stock market tickers and live sports scoreboards. This article also discusses FastAPI’s interface with SQL databases, support for WebSockets, and GraphQL. With a focus on high performance, maintainability, and modularity, this article tries to show why FastAPI is a great option for contemporary online development. 

Introduction: What is FastAPI? 

FastAPI is a modern, high-performance, web framework for building APIs with Python. It is based on the standard Python type hints and the powerful Pydantic library for data validation and serialization. 

Features of FastAPI 

• Fast: It is one of the fastest web frameworks available, thanks to its asynchronous nature and the use of Starlette and Uvicorn as the underlying ASGI servers. 

• High Performance: FastAPI is built on Starlette for the web parts and Pydantic for the data parts. It ranks 3^rd in the group of web frameworks after Node.js and Go. 

• Easy: It has a simple and intuitive syntax that makes it easy to write and read code. It also has automatic documentation and interactive tools that help you test and debug your API. 

• Type Hints: Utilizes Python 3.7+ type hints to automatically generate OpenAPI and JSON Schema documentation. 

• Scalable: It supports concurrency and parallelism, allowing you to handle many requests with minimal resources. It also has built-in support for background tasks, websockets, GraphQL, and more. 

• Dependency Injection: Offers a powerful dependency injection system, enabling modular and reusable code. 

• Asynchronous Support: Built-in support for asynchronous programming, allowing developers to write non-blocking code. 

• Secure: It has integrated security features, such as OAuth2, JWT, HTTPS, and CORS. It also helps you prevent common security issues, such as SQL injection, XSS, CSRF, and data leakage. 

• Flexible: It is compatible with many other Python libraries and frameworks, such as SQLAlchemy, Django, Flask, Pytest, and more. It also allows you to customize and extend its functionality with plugins and middleware. 

Because of this, developers searching for a framework that combines speed, ease of use, and sophisticated functionality will find that FastAPI is a great option. It is a desirable alternative for developing APIs that are simple to test and maintain because of its automatic creation of interactive API documentation. The framework’s effective request management and asynchronous capabilities make it a good fit for high-performance applications. 

How to Install and use FastAPI? 

To get started with FastAPI, you need to install both FastAPI and an ASGI server, such as Uvicorn. The installation can be done via pip: 

pip install fastapi uvicorn 

To install FastAPI, you need to have Python 3.7 or higher and pip installed on your system. You can then run the following command in your terminal: 

pip install fastapi[all] 

This will install FastAPI along with all its optional dependencies, such as Uvicorn, Pydantic, Jinja2, and more. You can also install only the required dependencies with: 

pip install fastapi 

To use FastAPI, you need to create a Python file and import the FastAPI class from the fastapi module. Then, you can create an instance of the FastAPI class and use the decorator syntax to define your API endpoints. For example, here is a simple hello world app with FastAPI: 

To run this app, you need to use the Uvicorn server, which is an ASGI server that can handle asynchronous requests. You can run the following command in your terminal, assuming that your file is named main.py: 

This will start the server on 127.0.0.1:8000 and reload the app whenever you make changes to the code. You can also access the automatic documentation and interactive tools that FastAPI provides on 127.0.0.1:8000/docs and 127.0.0.1:8000/redocs. 

The output will look like the following: 

Determining an instance of the FastAPI class and configuring endpoints to support different HTTP methods are the first steps in creating a FastAPI application. One of FastAPI’s advantages is that it makes the process simple, enabling developers to quickly prototype and launch apps. 

Core Features: 

Request Handling:  

By enabling you to define endpoints using simple Python functions, FastAPI encourages the division of responsibility. This results in more understandable and maintainable code by separating the logic involved in processing a request from the underlying routing system. This separation allows developers to concentrate on the application’s business logic rather than the complexities of HTTP methods and routing. As a result, the codebase becomes more organized and modular. 

FastAPI makes request processing easier by using an easy-to-understand syntax for defining endpoints. Because of its simplicity, developers can easily understand and use the API routes without getting mired down in intricate setup steps. The framework’s versatility and adaptability to diverse API needs are attributed to its support for a variety of parameter kinds, such as path parameters, query parameters, and request bodies. 

Path Parameters: 

Path parameters enable you to grab values from the URL and pass them to the endpoint methods. This functionality is especially handy for developing dynamic routes that can handle a wide range of queries. For example, in a normal e-commerce application, you may have routes for retrieving information about a given product based on its ID.  

FastAPI automatically converts these parameters to the correct type specified in the method using Python’s type hints. This automatic type conversion decreases the likelihood of errors and guarantees that the data sent to the destination is in the correct format. Using path parameters, developers may construct flexible and dynamic routes that improve the user experience. 

Query Parameters: 

Query parameters are optional parameters seen in URLs after the ‘?’ character. They can be used to filter, sort, and paginate data in an API. For example, if you have a product listing route, you will want to let customers filter or sort the products by price. This capability can be provided without changing the URL format by using query parameters. FastAPI simplifies the handling of query parameters by allowing you to define them as function arguments with default values. This implies you may provide default behaviors while also giving customers the option to specify settings as needed. Query parameters increase the flexibility and usability of your API calls. 

Request Bodies: 

Pydantic models can be used to define request bodies for more complicated inputs. Pydantic is a package for parsing and data validation that uses Python type annotations. In addition to offering automatic data validation and thorough error messages when the data is erroneous, it makes sure that the incoming request data follows the anticipated format. This functionality is especially helpful for endpoints like user registration and profile updates that call for structured data. 

Declarative definition of the request body schema is achieved through the use of Pydantic models. The data types and validation guidelines for every field must be specified in this regard. As an illustration, you could mandate that a password field fulfills a set of intricate specifications or that an email field holds a legitimate email address. You may easily express intricate hierarchical data structures with Pydantic since it also allows layered models. 

This methodology not only helps readability and maintainability of code but also data security and integrity. As your application grows, it will be simpler to update and maintain because the data validation logic is centralized inside the Pydantic models. Pydantic models also support code reuse and consistency across your application by being reusable across many APIs. 

Advanced Features: 

Dependency Injection: 

FastAPI’s dependency injection system allows you to define reusable dependencies that can be injected into your endpoints. This promotes modularity and clean code architecture, making it easier to manage and test your code. 

Background Tasks: 

FastAPI supports background tasks, which enables you to run tasks in the background while returning a response to the client immediately. This feature is particularly useful for operations that take a long time to complete, such as sending emails, processing large datasets, or performing complex calculations. 

Background tasks in FastAPI are implemented using the BackgroundTasks class. You can add tasks to be run after the response is sent to the client, ensuring that the client does not have to wait for these tasks to be completed. This improves the responsiveness and user experience of your application, as it allows you to handle time-consuming operations asynchronously. 

For example, consider a scenario where you need to send a confirmation email after a user register on your platform. Instead of making the user wait for the email to be sent, you can return a response immediately and send the email in the background. This way, the registration process appears faster and more efficient to the user. 

Security and Authentication: 

FastAPI provides robust tools for handling security and authentication. It supports OAuth2, API key authentication, and more, allowing you to secure your endpoints and manage user authentication seamlessly. 

Creating microservices with FastAPI: 

Microservices Architecture: 

Building software systems as a collection of independently deployable, compact, modular services that each operate in their own process and communicate with one another using lightweight protocols, messaging queues, or HTTP/REST APIs, is known as microservices architecture. This architectural design is in opposition to conventional monolithic architectures, which construct a single application as an indivisible unit. 

Best practices for implementing microservices: 

1. Define Clear Service Boundaries: Identify distinct business capabilities and design services around these capabilities. Clear boundaries help in maintaining the independence of each service. 

2. Use API Gateways: An API gateway can provide a single-entry point for clients, handle request routing, and enforce security policies, simplifying the client interaction with the microservices. 

3. Implement Service Discovery: Use service discovery mechanisms to dynamically locate and connect to service instances, ensuring that services can find each other without hardcoding endpoints. 

4. Embrace Asynchronous Communication: Whenever possible, use asynchronous communication patterns (e.g., message queues, event streams) to decouple services, improve scalability, and fault tolerance. 

5. Automate Testing and Deployment: Develop comprehensive automated tests for unit, integration, and end-to-end scenarios. Automate the deployment pipeline to facilitate continuous delivery. 

Setting up a FastAPI Microservice: 

With a microservices architecture, an application is designed as a group of loosely linked services, each in charge of handling a particular task. Through messaging protocols or HTTP/REST, these services often communicate with one another through APIs. A few advantages of this strategy are enhanced scalability, fault isolation, and autonomous deployment. 

Here is a simple structure for a FastAPI microservice: 

Structuring your project: 

Organizing your FastAPI project involves separating concerns into different modules such as routes, models, and dependencies. This structure enhances maintainability and scalability. 

• Routes: Define API endpoints 

• Models: Define Data models using pydantic 

• Dependencies: Manage shared resources and services 

• Config: Store configuration settings 

Adding Multiple Microservices: 

Creating multiple microservices involves designing and deploying individual, loosely coupled services that work together to form a cohesive application. Here is a theoretical overview of the steps involved: 

1. Define Service Boundaries 

Purpose: Clearly delineate the responsibilities of each microservice based on the application’s business requirements. 

• Example: In an e-commerce platform, you might define services such as User Management, Product Catalog, Orders, and Payments. 

• Outcome: A list of distinct services, each with a different responsibility. 

2. Set Up Individual FastAPI Services 

Purpose: Develop each microservice as a standalone FastAPI application. 

• Components: 

• FastAPI Application: Each service will have its own FastAPI app instance. 

• Dependencies: Manage dependencies independently for each service. 

• Database: Each service typically has its own database schema to avoid tight coupling. 

• Outcome: Independent services that can be developed, deployed, and scaled separately 

3. Organize Your Project Structure 

Purpose: Maintain a clear and organized directory structure for each service. 

4. Implement Inter-Service Communication 

Purpose: Enable services to communicate with each other securely and efficiently. 

• Methods: 

• HTTP/REST APIs: Services expose endpoints that other services can call. 

• Message Queues: Use asynchronous communication for tasks like order processing. 

• Outcome: Reliable communication between services, facilitating data sharing and workflow orchestration. 

5. Deploy Each Microservice 

Purpose: Independently deploy each microservice to the cloud or on-premises infrastructure. 

• Tools: 

• Containers: Dockerize each service for consistent deployment environments. 

• Orchestration: Use Kubernetes or similar tools to manage container deployment. 

• CI/CD Pipelines: Automate the build, test, and deployment processes. 

• Outcome: Scalable and manageable deployment pipelines for each service. 

6. Implement Service Discovery and API Gateway 

Purpose: Facilitate dynamic discovery of services and provide a single-entry point for clients. 

• Service Discovery: Tools like Consul or Eureka enable services to discover each other dynamically. 

• API Gateway: An API gateway (e.g., Kong, API Gateway) handles routing, load balancing, and security. 

• Outcome: Simplified client interactions and enhanced scalability and security. 

7. Monitor and Manage Microservices 

Purpose: Ensure reliability, performance, and health of microservices. 

• Logging: Centralized logging using tools like ELK Stack (Elasticsearch, Logstash, Kibana). 

• Monitoring: Use monitoring tools (e.g., Prometheus, Grafana) to track service metrics and health. 

• Outcome: Proactive detection of issues and informed decision-making for scaling and improvements. 

Middleware in FastAPI: 

As middleware acts as a bridge to intercept and process requests and responses, middleware is an essential part of web applications. It lets developers put in place code that functions anywhere in between the application logic and the client. Middleware is frequently used for logging, authentication, managing errors, and changing data sent in requests or responses. 

One way to conceptualize middleware functions in FastAPI is as a sequence of filters that requests and responses flow through. Before transferring control to the following element in the chain, each middleware component can carry out tasks. Functionality that is modular and reusable can be applied universally throughout your application thanks to this layered approach. 

Implementing middleware in FastAPI: 

FastAPI makes it straightforward to implement middleware using the ‘@app.middleware’ decorator. This decorator is used to define a function that will be executed for every request and response passing through the application. 

Example code:  

Unique Features: 

Integration with SQL Databases 

FastAPI can be easily integrated with SQL databases using libraries like SQLAlchemy. This allows you to manage database operations efficiently within your FastAPI application. 

Websockets 

FastAPI supports WebSockets, enabling real-time communication between the client and the server. This is essential for applications that require instant updates, such as chat applications or live data feeds. 

GraphQL Support 

FastAPI can be extended to support GraphQL using libraries like graphene. GraphQL provides a flexible and efficient way to query and manipulate data, offering an alternative to RESTful APIs. 

Real – time Updates 

Because of its asynchronous features and support for WebSockets, background activities, and streaming answers, FastAPI is a great framework for developing apps that need to be updated in real-time. FastAPI offers the performance and features required to manage real-time communication effectively, whether you are creating a stock market ticker, live sports scoreboard, or any other application that depends on real-time data. 

Conclusion: 

FastAPI offers numerous advantages for building microservices, including high performance, ease of use, and robust support for asynchronous programming. Its built-in features for data validation, dependency injection, and interactive documentation make it a powerful tool for developing and maintaining microservices. 

As the FastAPI community grows, the framework continues to evolve with new features and improvements. Its popularity and adoption are expected to increase, making it a prominent choice for modern web development and microservices architectures. 

Dhairya Bhatt 

AI/ML Team