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

Splitting the monolith


Let's project into the world where Runnerly, as implemented previously, starts to be used by a lot of people. Features are added, bugs are fixed, and the database is steadily growing.

The first problem that we're facing is the background process that creates reports and calls Strava. Since we're having thousands of users, these tasks take most of the server resources, and users are experiencing slowdowns on the frontend.

It's getting obvious that we need to have them running on separate servers. With the monolithic application using Celery and Redis, it's not an issue. We can dedicate a couple of new servers for the background jobs.

But the biggest concern if we do this is that the Celery worker code needs to import the Flask application code to operate. So the deployment dedicated to the background workers needs to include the whole Flask app. That also means that every time something changes in the app, we'll need to update the Celery workers as well to avoid regression...