Hera Blueprint

Blueprint for a Hera project incl. gitops support. You can use this repo as template for your hera project.

HOW TO USE THIS TEMPLATE

1. Create a repo referencing this repo here as a template.
2. Protect the QA environment (Settings menu, Environments --> add a reviewer).
3. Create a qa branch from main.
4. Fix your README.md: Adjust the Heading, intro and delete this section "HOW TO USE THIS TEMPLATE".

Development

In order to allow easy and agile development of your worflows, the best way to do so, is using codespaces. The DevContainer used by codespaces for this repository is preconfigured, allowing to start development without much configuration of an IDE.

Basically you just run run_workflow.sh in your codespace, which does the following:

• First, it generates an ephemaral docker container based on your code (incl. python dependencies) and pushes this to the container registry.
• Afterwards it starts a Workflow which references the new container.
• Finally it outputs the logs of your Workflow steps to the termninal.

TL;DR

1. Open it in GitHub Codespaces
2. (optionally, recommended) Create a branch, do some change in the python code an create a PR
3. Copy the environmental variables from the argo server (--> User menu, Button "COPY TO CLIPBOARD")
4. Paste them into the terminal in your codespace
5. Run ./run_workflow.sh -f example_workflow.py --no-pr
   --> An ephemeral docker image will be created and a workflow will be started using it. The output of the workflow with be written to the terminal.

Prerequisites

• To be able to push a docker image with the run_workflow.sh script, access to this repository's github container registry is required ("package write"). To allow this, set the environmental variables DOCKER_REGISTRY,DOCKER_ORGANIZATION, DOCKER_USER and DOCKER_PASSWORD (e.g. via oranization wide Codespaces secrets)
• As well, the k8s cluster(s) should have read access to used the container registry.

Folder structure

• kubernetes
  Contains the rendered workflow files, deployed via gitops.

• src/main
  Contains the package(s) with the actual code (business logic) and the third-party requirements.
  There is a pipeline which automatically builds a docker image out of it.

• src/workflows
  Contains the workflow code (DAGs or Steps).
  The actual workflows / workflow templates are created by run _workflow.py and render_workflows.py.

Scripts

• render_workflows.py
  Used to render the WorkflowTemplate(s) as yaml, in order to deploy them via gitops.
  (Triggered by github workflow)

• run_workflow.sh
  Script to run a workflow for debugging, it can be started locally or via codespaces. Make sure to set the required environment variables, e.g. via a .env file. Use --help for more info.

Deployment

Prerequisites

For the gitops deployment, the gitops server (flux / argo) needs to poll this repository for changes in the kubernetes folder and on related branches (main branch for deployment to Dev and qa branch for deployment to QA).

Pipeline / Workflow

flowchart TB
    A[Start]-- Push to main -->B[Build Docker Image]
    B-->C[ Patch Workflow-Templates in kubernetes folder on `main`]
    C-->D1[GitOps **Dev Deployment** triggered...]
    C-- Manually approval -->D2[Merge `main` to `qa`]
    D2-->E[GitOps **QA Deployment** triggered...]

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Dev Deployment

The build pipeline automatically builds a docker image and patches the yaml files in kubernetes. Accordingly, via GitOps, the WorkflowTemplates are deployed to the Dev environment.

QA Deployment

The build pipeline' last step automatically merges the changes on main to qa branch. Accordingly, via GitOps, the WorkflowTemplates are deployed to the QA environment.