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

Tuning the benchmark


At this point, we already have a basic workflow of creating a test plan in Apache JMeter and analyzing the preliminary results. From here, there are several parameters we can adjust to achieve our benchmark objectives. In this section, we will iterate on our test plan to identify the limits of our Docker application.

Increasing concurrency

The first parameter that we may want to tune is increase Loop Count of our test plan. Driving our test plan to generate more requests will allow us to see the effects of the load we induced on our application. This increases the precision of our benchmark experiments, because outlier events such as a slow network connection or hardware failure (unless we are testing that specifically!) affect our tests.

After having enough data points for our benchmarks, we may realize that the load being generated is not enough against our Docker application. For example, the current throughput we received from our first analysis may not simulate the...