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Workflows on Kubernetes

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What is Argo Workflows?

Argo Workflows is an open source container-native workflow engine for orchestrating parallel jobs on Kubernetes. Argo Workflows is implemented as a Kubernetes CRD (Custom Resource Definition).

  • Define workflows where each step in the workflow is a container.
  • Model multi-step workflows as a sequence of tasks or capture the dependencies between tasks using a directed acyclic graph (DAG).
  • Easily run compute intensive jobs for machine learning or data processing in a fraction of the time using Argo Workflows on Kubernetes.

Use Cases

  • Machine Learning pipelines
  • Data and batch processing
  • Infrastructure automation
  • CI/CD

Why Argo Workflows?

  • Argo Workflows is the most popular workflow execution engine for Kubernetes.
  • Light-weight, scalable, and easier to use.
  • Designed from the ground up for containers without the overhead and limitations of legacy VM and server-based environments.
  • Cloud agnostic and can run on any Kubernetes cluster.

Features

An incomplete list of features Argo Workflows provide:

  • UI to visualize and manage Workflows
  • Artifact support (S3, Artifactory, Alibaba Cloud OSS, Azure Blob Storage, HTTP, Git, GCS, raw)
  • Workflow templating to store commonly used Workflows in the cluster
  • Archiving Workflows after executing for later access
  • Scheduled workflows using cron
  • Server interface with REST API (HTTP and GRPC)
  • DAG or Steps based declaration of workflows
  • Step level input & outputs (artifacts/parameters)
  • Loops
  • Parameterization
  • Conditionals
  • Timeouts (step & workflow level)
  • Retry (step & workflow level)
  • Resubmit (memoized)
  • Suspend & Resume
  • Cancellation
  • K8s resource orchestration
  • Exit Hooks (notifications, cleanup)
  • Garbage collection of completed workflow
  • Scheduling (affinity/tolerations/node selectors)
  • Volumes (ephemeral/existing)
  • Parallelism limits
  • Daemoned steps
  • DinD (docker-in-docker)
  • Script steps
  • Event emission
  • Prometheus metrics
  • Multiple executors
  • Multiple pod and workflow garbage collection strategies
  • Automatically calculated resource usage per step
  • Java/Golang/Python SDKs
  • Pod Disruption Budget support
  • Single-sign on (OAuth2/OIDC)
  • Webhook triggering
  • CLI
  • Out-of-the box and custom Prometheus metrics
  • Windows container support
  • Embedded widgets
  • Multiplex log viewer

Reference

  • https://github.com/argoproj/argo-workflows
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