DAG¶
As an alternative to specifying sequences of steps, you can define a workflow as a directed-acyclic graph (DAG) by specifying the dependencies of each task. DAGs can be simpler to maintain for complex workflows and allow for maximum parallelism when running tasks.
In the following workflow, step A runs first, as it has no dependencies.
Once A has finished, steps B and C run in parallel.
Finally, once B and C have completed, step D runs.
apiVersion: argoproj.io/v1alpha1
kind: Workflow
metadata:
generateName: dag-diamond-
spec:
entrypoint: diamond
templates:
- name: echo
inputs:
parameters:
- name: message
container:
image: alpine:3.7
command: [echo, "{{inputs.parameters.message}}"]
- name: diamond
dag:
tasks:
- name: A
template: echo
arguments:
parameters: [{name: message, value: A}]
- name: B
dependencies: [A]
template: echo
arguments:
parameters: [{name: message, value: B}]
- name: C
dependencies: [A]
template: echo
arguments:
parameters: [{name: message, value: C}]
- name: D
dependencies: [B, C]
template: echo
arguments:
parameters: [{name: message, value: D}]
The dependency graph may have multiple roots. The templates called from a DAG or steps template can themselves be DAG or steps templates, allowing complex workflows to be split into manageable pieces.
Enhanced Depends¶
For more complicated, conditional dependencies, you can use the Enhanced Depends feature.
Fail Fast¶
By default, DAGs fail fast: when one task fails, no new tasks will be scheduled. Once all running tasks are completed, the DAG will be marked as failed.
If failFast is set to false for a DAG, all branches will run to completion, regardless of failures in other branches.