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

Setting up metrics

To set up metrics with Prometheus, we need to understand how the process works. Its key component is that each service that's measured has its own Prometheus client that keeps track of the metrics. The data in the Prometheus server will be available for a Grafana service that will plot the metrics.

The following diagram shows the general architecture:

The Prometheus server pulls information at regular intervals. This method of operation is very lightweight since registering metrics just updates the local memory of the service and scales well. On the other hand, it shows sampled data at certain times and doesn't register each individual event. This has certain implications in terms of storing and representing data and imposes limitations on the resolution of the data, especially for very low rates.

There are lots of available metrics exporters that...