Book Image

Python Microservices Development – 2nd edition - Second Edition

By : Simon Fraser, Tarek Ziadé
Book Image

Python Microservices Development – 2nd edition - Second Edition

By: Simon Fraser, Tarek Ziadé

Overview of this book

The small scope and self-contained nature of microservices make them faster, cleaner, and more scalable than code-heavy monolithic applications. However, building microservices architecture that is efficient as well as lightweight into your applications can be challenging due to the complexity of all the interacting pieces. Python Microservices Development, Second Edition will teach you how to overcome these issues and craft applications that are built as small standard units using proven best practices and avoiding common pitfalls. Through hands-on examples, this book will help you to build efficient microservices using Quart, SQLAlchemy, and other modern Python tools In this updated edition, you will learn how to secure connections between services and how to script Nginx using Lua to build web application firewall features such as rate limiting. Python Microservices Development, Second Edition describes how to use containers and AWS to deploy your services. By the end of the book, you’ll have created a complete Python application based on microservices.
Table of Contents (14 chapters)
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Asynchronous messages

In microservice architecture, asynchronous calls play a fundamental role when a process that is used to be performed in a single application now implicates several microservices. We touched briefly on this in the previous chapter with our change to the Jeeves application, which now communicates with its workers using an asynchronous message queue. To make the best use of these, we will investigate these tools in more depth.

Asynchronous calls can be as simple as a separate thread or process within a microservice app that is receiving some work to be done, and performs it without interfering with the HTTP request/response round trips that are happening at the same time.

But doing everything directly from the same Python process is not very robust. What happens if the process crashes and gets restarted? How do we scale background tasks if they are built like that?

It's much more reliable to send a message that gets picked by another program...