Book Image

Hands-On Docker for Microservices with Python

By : Jaime Buelta
Book Image

Hands-On Docker for Microservices with Python

By: Jaime Buelta

Overview of this book

Microservices architecture helps create complex systems with multiple, interconnected services that can be maintained by independent teams working in parallel. This book guides you on how to develop these complex systems with the help of containers. You’ll start by learning to design an efficient strategy for migrating a legacy monolithic system to microservices. You’ll build a RESTful microservice with Python and learn how to encapsulate the code for the services into a container using Docker. While developing the services, you’ll understand how to use tools such as GitHub and Travis CI to ensure continuous delivery (CD) and continuous integration (CI). As the systems become complex and grow in size, you’ll be introduced to Kubernetes and explore how to orchestrate a system of containers while managing multiple services. Next, you’ll configure Kubernetes clusters for production-ready environments and secure them for reliable deployments. In the concluding chapters, you’ll learn how to detect and debug critical problems with the help of logs and metrics. Finally, you’ll discover a variety of strategies for working with multiple teams dealing with different microservices for effective collaboration. By the end of this book, you’ll be able to build production-grade microservices as well as orchestrate a complex system of services using containers.
Table of Contents (19 chapters)
Free Chapter
1
Section 1: Introduction to Microservices
3
Section 2: Designing and Operating a Single Service – Creating a Docker Container
7
Section 3:Working with Multiple Services – Operating the System through Kubernetes
13
Section 4: Production-Ready System – Making It Work in Real-Life Environments

Autoscaling the cluster

We've seen before how to change the number of pods for a service, and how to add and remove nodes. This can be automated to describe some rules, allowing the cluster to change its resources elastically.

Keep in mind that autoscaling requires tweaking to adjust to your specific use case. This is a technique to use if the resource utilization changes greatly over time; for example, if there's a daily pattern where some hours present way more activity than others, or if there's a viral element that means the service multiplies the requests by 10 unexpectedly.

If your usage of servers is small and the utilization stays relatively constant, there's probably no need to add autoscaling.

The cluster can be scaled automatically up or down on two different fronts:

  • The number of pods can be set to increase or decrease automatically in a Kubernetes...