Argo Workflows doesn’t just run steps; it orchestrates dependencies between them, forming a Directed Acyclic Graph (DAG).
Let’s watch one in action. Imagine we need to download a file, process it, and then upload the results.
apiVersion: argoproj.io/v1alpha1
kind: Workflow
metadata:
generateName: dag-pipeline-
spec:
entrypoint: dag-template
templates:
- name: dag-template
dag:
tasks:
- name: download
template: download-template
- name: process
template: process-template
dependencies:
- download
- name: upload
template: upload-template
dependencies:
- process
- name: download-template
container:
image: alpine:latest
command: ["sh", "-c"]
args: ["echo 'Downloading data...' > data.txt; echo 'Done downloading.'"]
- name: process-template
container:
image: alpine:latest
command: ["sh", "-c"]
args: ["echo 'Processing data...' >> data.txt; echo 'Done processing.'"]
- name: upload-template
container:
image: alpine:latest
command: ["sh", "-c"]
args: ["echo 'Uploading results...' >> data.txt; echo 'Done uploading.'"]
When you submit this, Argo sees the dag structure. It knows download must finish before process can start, and process must finish before upload can begin. The dependencies field is key here. If download fails, process and upload will never run. If process fails, upload won’t run. This is the core of building robust, sequential workflows.
The magic of DAGs in Argo is how they enable parallelism. If you had two independent processing steps after download, say process-a and process-b, and neither depended on each other, you could define them like this:
- name: process-a
template: process-template-a
dependencies:
- download
- name: process-b
template: process-template-b
dependencies:
- download
- name: upload
template: upload-template
dependencies:
- process-a
- process-b
Argo would then execute process-a and process-b concurrently, as soon as download completes, because their dependencies are met. The upload task would only start after both process-a and process-b have successfully finished. This is where you get significant speedups for tasks that can be broken down into independent sub-problems.
The dag block is where you define the topology. Each item within the tasks list represents a node in your graph. The name is the identifier for that task within the DAG. The template field points to the actual Argo Workflow template (a container, a script, another workflow, etc.) that will be executed for this task. The dependencies field is a list of names of other tasks that must complete successfully before this current task can start.
You can also add arguments to tasks, allowing you to pass outputs from one task as inputs to another. For instance, if download-template produced a file path, you could pass that path to process-template using arguments.parameters.
The most surprising thing to many is how Argo handles retries and failures within a DAG. By default, if a task fails, its dependent tasks are skipped. However, you can configure retry strategies at the task level or even globally for the workflow. If a retried task eventually succeeds, its dependent tasks will then proceed as if it had succeeded on the first attempt. This makes DAGs incredibly resilient to transient errors.
The order in which you define tasks in the tasks list doesn’t dictate execution order; only the dependencies field does. Argo’s scheduler resolves the graph and starts tasks as soon as their prerequisites are met, potentially running many tasks in parallel if the dependency graph allows.
Understanding how to model complex interdependencies with dependencies and leverage parallelism by having multiple tasks ready to run after a common upstream task is the next crucial step in mastering Argo Workflows DAGs.