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

Chapter 6. Monitoring Your Services

In the previous chapter, we tested services that are interacting with each other in isolation. But when something bad happens in a real deployment, we need to have a global overview of what's going on. For example, when a microservice calls another one which in turn calls a third one, it can be hard to understand which one failed. We need to be able to track down all the interactions that a particular user had with the system that led to a problem.

Python applications can emit logs to help you debug issues, but jumping from one server to another to gather all the information you need to understand the problem can be hard. Thankfully, we can centralize all the logs to monitor a distributed deployment.

Continuously monitoring services are also important to assert the health of the whole system and follow how everything behaves. This involves answering questions such as, Is there a service that's dangerously approaching 100% of RAM usage?, How many requests...