mayadata-io / mayadata-io/d-operators
Define chaos workflows that can sequence experiments in a desired manner
Open
Nobody has claimed this yet.
litmus
liveness
resiliency score
use case
- Dominant language
- Go
- Stars
- 10
- Forks
- 6
- PR merge metrics
- No merged PRs in 30d
Description
Ref: https://github.com/argoproj/argo/blob/master/examples/dag-diamond-steps.yaml
- The workflow creation takes into account the parallel/sequential sub-flows or experiments (target could be one application or multiple, each mapped to the respective sub-flow)
- Ability to launch the liveness controllers described here
- Calculate a resiliency score on the basis of outcome of the workflow
- Expose the resiliency data as a result resource
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start with the referenced Argo DAG example and the linked liveness-controller issue. Work out how parallel and sequential experiments map to applications, how outcomes produce a resiliency score, and how that data is exposed as a result resource. Done means the workflow can sequence the requested experiments and publish the calculated resiliency data.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- go, kubernetes
- Domain
- devops
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100