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  • Book Overview & Buying Building Python Microservices with FastAPI
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Building Python Microservices with FastAPI

Building Python Microservices with FastAPI

By : Sherwin John C. Tragura
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Building Python Microservices with FastAPI

Building Python Microservices with FastAPI

4 (8)
By: Sherwin John C. Tragura

Overview of this book

FastAPI is an Asynchronous Server Gateway Interface (ASGI)-based framework that can help build modern, manageable, and fast microservices. Because of its asynchronous core platform, this ASGI-based framework provides the best option when it comes to performance, reliability, and scalability over the WSGI-based Django and Flask. When working with Python, Flask, and Django microservices, you’ll be able to put your knowledge to work with this practical guide to building seamlessly manageable and fast microservices. You’ll begin by understanding the background of FastAPI and learning how to install, configure, and use FastAPI to decompose business units. You’ll explore a unique and asynchronous REST API framework that can provide a better option when it comes to building microservices. After that, this book will guide you on how to apply and translate microservices design patterns in building various microservices applications and RESTful APIs using the FastAPI framework. By the end of this microservices book, you’ll be able to understand, build, deploy, test, and experiment with microservices and their components using the FastAPI framework.
Table of Contents (17 chapters)
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Part 1: Application-Related Architectural Concepts for FastAPI microservice development
6
Part 2: Data-Centric and Communication-Focused Microservices Concerns and Issues
11
Part 3: Infrastructure-Related Issues, Numerical and Symbolic Computations, and Testing Microservices

Summary

The use of coroutines is one of the factors that makes the FastAPI microservice application fast, aside from its use of an ASGI-based server. This chapter has proven that using coroutines to implement API services will improve the performance better than utilizing more threads in the thread pool. Since the framework runs on an asyncio platform, we can utilize asyncio utilities to design various design patterns to manage the CPU-bound and I/O-bound services.

This chapter used Celery and Redis for creating and managing asynchronous background tasks for behind-the-scenes transactions such as logging, system monitoring, time-sliced computations, and batch jobs. We learned that RabbitMQ and Apache Kafka provided an integrated solution for building asynchronous and loosely coupled communication between FastAPI components, especially for the message-passing part of these interactions. Most importantly, coroutines were applied to create these asynchronous and non-blocking background...

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