Book Image

Docker High Performance - Second Edition

By : Allan Espinosa, Russ McKendrick
Book Image

Docker High Performance - Second Edition

By: Allan Espinosa, Russ McKendrick

Overview of this book

Docker is an enterprise-grade container platform that allows you to build and deploy your apps. Its portable format lets you run your code right from your desktop workstations to popular cloud computing providers. This comprehensive guide will improve your Docker work?ows and ensure your application's production environment runs smoothly. This book starts with a refresher on setting up and running Docker and details the basic setup for creating a Docker Swarm cluster. You will then learn how to automate this cluster by using the Chef server and cookbooks. After that, you will run the Docker monitoring system with Prometheus and Grafana, and deploy the ELK stack. You will also learn best practices for optimizing Docker images. After deploying containers with the help of Jenkins, you will then move on to a tutorial on using Apache JMeter to analyze your application's performance. You will learn how to use Docker Swarm and NGINX to load-balance your application, and how common debugging tools in Linux can be used to troubleshoot Docker containers. By the end of this book, you will be able to integrate all the optimizations that you have learned and put everything into practice in your applications.
Table of Contents (16 chapters)
Title Page
Copyright and Credits
About Packt
Contributors
Preface
Index

Improving image build time


Docker images are the main artifacts that developers most of their time working on. The simplicity of Docker files and the speed of container technology allows us to enable rapid iteration on the application that we are working on; however, these advantages of using Docker start to diminish once the time it takes to build Docker images starts to grow uncontrollably. In this section, we will discuss some cases of building Docker images that take some time to run. We will then give you a few tips on how to remedy these effects by doing the following:

  • Using registry mirrors
  • Reusing image layers
  • Reducing the build context size
  • Using caching proxies

Using registry mirrors

A big contributor to image build time is the time spent fetching upstream images. Suppose we have a Dockerfile with the following line:

FROM openjdk:jre-stretch

This image will have to download openjdk:jre-stretch to be built. When we move to another Docker host, or if the openjdk:jre-stretch image is updated...