Book Image

HashiCorp Packer in Production

By : John Boero
Book Image

HashiCorp Packer in Production

By: John Boero

Overview of this book

Creating machine images can be time-consuming and error-prone when done manually. HashiCorp Packer enables you to automate this process by defining the configuration in a simple, declarative syntax. This configuration is then used to create machine images for multiple environments and cloud providers. The book begins by showing you how to create your first manifest while helping you understand the available components. You’ll then configure the most common built-in builder options for Packer and use runtime provisioners to reconfigure a source image for desired tasks. You’ll also learn how to control logging for troubleshooting errors in complex builds and explore monitoring options for multiple logs at once. As you advance, you’ll build on your initial manifest for a local application that’ll easily migrate to another builder or cloud. The chapters also help you get to grips with basic container image options in different formats while scaling large builds in production. Finally, you’ll develop a life cycle and retention policy for images, automate packer builds, and protect your production environment from nefarious plugins. By the end of this book, you’ll be equipped to smoothen collaboration and reduce the risk of errors by creating machine images consistently and automatically based on your defined configuration.
Table of Contents (18 chapters)
1
Part 1: Packer’s Beginnings
7
Part 2: Managing Large Environments
11
Part 3: Advanced Customized Packer

Scaling Large Builds

In the previous section, we demonstrated strategies for a structured image hierarchy, involving building shared base images and aggregate sub-images that extend the purpose of the common base image. We used a serial build script to build several image trees one at a time. The strategy is to separate these logically so that they can be built in parallel in the quickest possible time. If a minor patch is applied to our gold image, rebuilding it across AWS, Azure, and GCP one at a time will be painfully slow, and it will take a long time to learn of errors at a later stage. When building across multiple environments and complex image trees, development time becomes very important. This will set us up for automation when, in the next chapter, we streamline Packer builds via automation pipelines. In this chapter, we will take the example code from the previous chapter and logically organize it in a way that simplifies parallel builds and storage optimization for a multi...