- **IssueOps** - Its like ChatOps but instead of using a chat bot, commands are invoked by commenting on a pull request (PRs are issues under the hood) - Example: commenting `.deploy` on a pull request
- **Branch Deployment** - A branch deploy is a deployment methodology that enables you to deploy a branch (or pull request) to a desired environment before merging to `main` or `master` - More on this below
- **PR** - Short for pull request
### IssueOps 🗨️
The best way to define IssueOps is to compare it to something similar, ChatOps. You may be familiar with the concept ChatOps already but in case you aren't here is a quick definition below:
> ChatOps is the process of interacting with a chat bot to execute commands directly in a chat platform. For example, with ChatOps you might do something like `.ping example.org` to check the status of a website
IssueOps adopts the same mindset but through a different medium. Rather than using a chat service to invoke the commands we use comments on a GitHub Issue or Pull Request. GitHub Actions is the runtime which executes our desired logic
### Branch Deployments 🌲
Branch deployments are a battle tested way of deploying your changes to a given environment for a variety of reasons. Branch deployments allow you to do the following:
- Deploy your changes to production **before** merging
- Deploy changes to a staging, QA, or non-production environment
#### Branch Deployment Core Concepts ⭐
> Note: The `main` branch is considered the base repository branch for all examples below
- The `main` branch is always considered to be a stable and deployable branch
- All changes are deployed to production before they are merged to the `main` branch
- To roll back a branch deployment, you deploy the `main` branch
-`noop` deployments should not make changes but rather report what they "would" have done
#### Why use branch deployments?
> To put the *merge -> deploy* model in the past!
What if your changes are bad and you broke production with the *merge -> deploy* model? Well now you have to revert your PR, get passing CI/builds, and then re-merge your changes to get back to a stable environment. With the **branch deploy** model, this is almost never the case. The `main` branch is considered to be always safe and stable
> This section will go into detail about how this Action works and hopefully inspire you on ways you can leverage it in your own projects
Let's walk through a GitHub Action workflow using this Action line by line:
```yaml
# The name of the workflow, it can be anything you wish
name: "branch deploy demo"
# The workflow to execute on is comments that are newly created
on:
issue_comment:
types: [created]
```
It is important to note that the workflow we want to run IssueOps on is `issue_comment` and `created`. This means we will not run under any other contexts for this workflow. You can edit this as you wish but it does change how this model ultimately works. For example, `issue_comment` workflows **only** use files found on `main` to run. If you do something like `on: pull_request` you could open yourself up to issues as a user could alter a file in a PR and exfil your secrets for example. Only using `issue_comment` is the suggested workflow type
```yaml
# Permissions needed for reacting and adding comments for IssueOps commands
As seen above, we have two steps. One for a noop deploy, and one for a regular deploy. For example, the noop deploy could trigger a `terraform plan` and the regular deploy could be a `terraform apply`. These steps are conditionally gated by two variables:
> Example: You comment `.deploy noop` on a pull request. A noop deployment is detected so this action outputs the `noop` variable to `true`. You also have the correct permissions to execute the IssueOps command so the action also outputs the `continue` variable to `true`. This will allow the "fake noop deploy" step seen above to run and the "fake regular deploy" step will be skipped
| reaction | no | eyes | If set, the specified emoji "reaction" is put on the comment to indicate that the trigger was detected. For example, "rocket" or "eyes" |
| trigger | no | .deploy | The string to look for in comments as an IssueOps trigger. Example: ".deploy" |
| noop_trigger | no | noop | The string to look for in comments as an IssueOps noop trigger. Example: "noop" |
| environment | no | production | The name of the environment to deploy to. Example, "production" |
| stable_branch | no | main | The name of a stable branch to deploy to (rollbacks). Example: "main" |
| prefix_only | no | true | If "false", the trigger can match anywhere in the comment |
## Outputs ⚙️➡️
| Output | Description |
| ------ | ----------- |
| triggered | The string "true" if the trigger was found, otherwise the string "false" |
| noop | The string "true" if the noop trigger was found, otherwise the string "false" - Use this to conditionally control whether your deployment runs as a noop or not |
| continue | The string "true" if the deployment should continue, otherwise empty - Use this to conditionally control if your deployment should proceed or not |
> This is useful to display to the user the status of your deployment. For example, you could display the results of a `terraform apply` in the deployment comment
You can use the GitHub Actions environment to export custom deployment messages from your workflow to be referenced in the post run workflow for the `branch-deploy` Action that comments results back to your PR
Simply set the environment variable `DEPLOY_MESSAGE` to the message you want to be displayed in the post run workflow
To add custom messages to our final deployment message we need to use the GitHub Actions environment. This is so that we can dynamically pass data into the post action workflow that leaves a comment on our PR. The post action workflow will look to see if this environment variable is set (`DEPLOY_MESSAGE`). If the variable is set, it adds to to the PR comment. Otherwise, it will use a simple comment body that doesn't include the custom message.