Book Image

Python Microservices Development

Book Image

Python Microservices Development

Overview of this book

We often deploy our web applications into the cloud, and our code needs to interact with many third-party services. An efficient way to build applications to do this is through microservices architecture. But, in practice, it's hard to get this right due to the complexity of all the pieces interacting with each other. This book will teach you how to overcome these issues and craft applications that are built as small standard units, using all the proven best practices and avoiding the usual traps. It's a practical book: you’ll build everything using Python 3 and its amazing tooling ecosystem. You will understand the principles of TDD and apply them. You will use Flask, Tox, and other tools to build your services using best practices. 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. You will also familiarize yourself with Docker’s role in microservices, and use Docker containers, CoreOS, and Amazon Web Services to deploy your services. This book will take you on a journey, ending with the creation of a complete Python application based on microservices. By the end of the book, you will be well versed with the fundamentals of building, designing, testing, and deploying your Python microservices.
Table of Contents (20 chapters)
Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
Introduction

More splitting


So far, we've split out of our monolith everything related to background tasks, and we've added a few HTTP API views for the new microservices to interact with the main application.

Since the new API allows us to add runs, there's another part we can split out of the monolith--the Training feature.

This feature can run on its own as long as it's able to generate new runs. When a user wants to start a new training plan, the main app can interact with the Training microservice and ask it to generate new runs.

Alternatively, the design could be reversed for better data isolation: the Training microservice publishes an API that returns a list of runs with their specific structure, exactly like the Strava API that returns activities. The main Flask app can then convert them into Runnerly runs. The Training plan can work without any specific knowledge about the Runnerly users: it gets asked to generate a plan given a few params.

But doing this new split should happen for a good reason...