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Argo Workflows

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description Argo Workflows Overview

Argo Workflows is a container-native workflow engine designed specifically for Kubernetes. It allows users to define complex, multi-step workflows as YAML files where each step runs in its own container. Because it is native to Kubernetes, it inherits all the scaling and reliability features of K8s. It is widely used for CI/CD pipelines, data processing, and machine learning training at scale within a Kubernetes cluster.

help Argo Workflows FAQ

What is Argo Workflows primarily used for in Kubernetes?

Argo Workflows is a container-native workflow engine designed to orchestrate parallel jobs and multi-step pipelines on Kubernetes. It allows DevOps teams to define complex CI/CD processes and data processing tasks as a series of isolated containers.

How does Argo Workflows handle auto-restarts and failures?

Argo automatically retries failed steps based on the parameters defined in the YAML configuration, utilizing built-in reliability features inherited from Kubernetes. If a container crashes or fails, the engine can be set to auto-restart the pod until it succeeds or reaches a maximum retry limit.

In what format are Argo Workflows defined?

All workflows in Argo are defined using standard Kubernetes YAML or JSON files. Each step in the workflow is configured to run in its own isolated container, ensuring a clean environment for every task.

Is Argo Workflows related to ArgoCD?

While both are part of the Argo Project under the CNCF (Cloud Native Computing Foundation), they serve entirely different purposes. ArgoCD is used for continuous delivery and application deployment, whereas Argo Workflows is specifically for executing complex job pipelines.

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