Skip to main content

2 posts tagged with "iac"

View All Tags

Β· 3 min read
Jeffrey Aven

CloudFormation templates in large environments can grow beyond a manageable point. This article provides one approach to breaking up CloudFormation templates into modules which can be imported and used to create a larger template to deploy a complex AWS stack – using Jsonnet.

Jsonnet is a json pre-processing and templating library which includes features including user defined and built-in functions, objects, and inheritance amongst others. If you are not familiar with Jsonnet, here are some good resources to start with:

Advantages

Using Jsonnet you can use imports to break up large stacks into smaller files scoped for each resource. This approach makes CloudFormation template easier to read and write and allows you to apply the DRY (Do Not Repeat Yourself) coding principle (not possible with native CloudFormation templates.

Additionally, although as the template fragments are in Jsonnet format, you can add annotations or comments to your code similar to YAML (not possible with a JSON template alone), although the rendered template is in legal CloudFormation Json format.

Process Overview

The process is summarised here:

CloudFormation and Jsonnet

Code

This example will deploy a stack with a VPC and an S3 bucket with logging. The project directory structure would look like this:

templates/
β”œβ”€ includes/
β”‚ β”œβ”€ vpc.libsonnet
β”‚ β”œβ”€ s3landingbucket.libsonnet
β”‚ β”œβ”€ s3loggingbucket.libsonnet
β”‚ β”œβ”€ tags.libsonnet
β”œβ”€ template.jsonnet

Lets look at all of the constituent files:

template.jsonnet

This is the root document which will be processed by Jsonnet to render a legal CloudFormation JSON template. It will import the other files in the includes directory.

includes/tags.libsonnet

This code module is used to generate re-usable tags for other resources (DRY).

includes/vpc.libsonnet

This code module defines a VPC resource to be created with CloudFormation.

includes/s3loggingbucket.libsonnet

This code module defines an S3 bucket resource to be created in the stack which will be used for logging for other buckets.

includes/s3landingbucket.libsonnet

This code module defines an S3 landing bucket resource to be created in the stack.

Testing

To test the pre-processing, you will need a Jsonnet binary/executable for your environment. You can find Docker images which include this for you, or you could build it yourself.

Once you have a compiled binary, you can run the following to generate a rendered CloudFormation template.

jsonnet template.jsonnet -o template.json

You can validate this template using the AWS CLI as shown here:

aws cloudformation validate-template --template-body file://template.json

Deployment

In a previous article, Simplified AWS Deployments with CloudFormation and GitLab CI, I demonstrated an end-to-end deployment pipeline using GitLab CI. Jsonnet pre-processing can be added to this pipeline as an initial β€˜preprocess’ stage and job. A snippet from the .gitlab-ci.yml file is included here:

Enjoy!

Β· 3 min read
Jeffrey Aven

Managing cloud deployments and IaC pipelines can be challenging. I’ve put together a simple pattern for deploying stacks in AWS using CloudFormation templates using GitLab CI.

This deployment framework enables you to target different environments based upon refs (branches or tags) for instance deploy to a dev environment for a push or merge into develop and deploy to prod on a push or merge into main, otherwise just lint/validate (e.g., for a push to a non-protected feature branch). Templates are uploaded to a designated S3 bucket and staged for use in the pipeline and can be retained as an additional audit trail (in addition to the GitLab project history).

Furthermore, you can review changes (by inspecting change set contents) before deploying, saving you from fat finger deployments 😊.

How it works

The logic is described here:

GitLab CI

The pipleline looks like this in GitLab:

GitLab CI

Prerequisites

You will need to set up GitLab CI variables for AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY and optionally AWS_DEFAULT_REGION. You can do this via Settings -> CI/CD -> Variables in your GitLab project. As AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY are secrets, they should be configured as protected (as they are only required for protected branches) and masked so they are not printed in job logs.

.gitlab-ci.yml code

The GitLab CI code is shown here:

Reviewing change sets (plans) and applying

Once a pipeline is triggered for an existing stack it will run hands off until a change set (plan) is created. You can inspect the plan by clicking on the Plan GitLab CI job where you would see output like this:

Change Set

If you are OK with the changes proposed, you can simply hit the play button on the last stage of the pipeline (Deploy). VoilΓ , stack deployed, enjoy!