YAML provides a human-readable way to define structured Argo Workflows as Kubernetes custom resource objects. A workflow file declares metadata, an entrypoint, templates, ordered steps, and artifact dependencies before submission to the cluster. section 9.3.2
A YAML workflow definition can express:
- Workflow identity and generation metadata. section 9.3.2
- Sequential or grouped execution steps through named templates. section 9.3.2
- Inputs and outputs passed between isolated task containers as artifacts. section 9.3.2
Listing 9.2 A sample workflow for Argo Workflows with a series of steps
apiVersion: argoproj.io/v1alpha1
kind: Workflow #1
metadata:
generateName: data-processing-
spec:
entrypoint: argo-steps-workflow-example
templates:
- name: argo-steps-workflow-example
Steps: #2
- - name: check-new-data
template: data-checker #3
- - name: transform-data
template: data-converter
arguments:
artifacts:
- name: data-paths #4
from: "{{steps.check-new-data.outputs.
artifacts.new-data-paths}}" #5
- - name: save-into-db
template: postgres-operator
- - name: notify-data-science-team
template: slack-messenger
- name: data-checker #6
container:
image: docker/data-checker:latest
command: [scan, /datastore/ds/]
outputs:
artifacts:
- name: new-data-paths #7
path: /tmp/data_paths.txt
- name: data-converter
inputs:
artifacts:
- name: data_paths #8
path: /tmp/raw_data/data_paths.txt
container:
image: docker/data-checker:latest
command: [data_converter, /tmp/raw_data/data_paths.txt]
- name: save-into-db
.. .. ..
- name: notify-data-science-team
.. .. ..
Inspect a complete YAML workflow that checks data, transforms it, saves it to a database, and notifies a team. section 9.3.2
