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@squadcast ・ May 12,2024 ・ 2 min read ・ 601 views ・ Originally posted on www.squadcast.com
Helm dry run, using the helm install --dry-run command, is a valuable technique for validating Helm charts before deployment on a Kubernetes cluster. It helps avoid errors and unexpected behavior by simulating the installation process without modifying the cluster. Helm dry run works alongside other Helm commands like helm template and helm lint to streamline development and ensure charts are well-structured, compatible, and ready for deployment.
This guide explores Helm dry run, a valuable technique for validating Helm charts before deployment on a Kubernetes cluster.
Helm simplifies managing applications within Kubernetes clusters. It packages application configurations as Helm charts, which include deployment manifests and configuration details.
Validating Helm charts before deployment helps avoid introducing errors or unexpected behavior to your cluster. Helm dry run allows you to test a chart without modifying your cluster’s state.
$ helm create mychart
$ helm template mychart mychart
---
# Source: mychart/templates/serviceaccount.yaml
apiVersion: v1
kind: ServiceAccount
metadata:
name: mychart
labels:
helm.sh/chart: mychart-0.1.0
app.kubernetes.io/name: mychart
app.kubernetes.io/instance: mychart
app.kubernetes.io/version: "1.16.0"
app.kubernetes.io/managed-by: Helm
<--- snip --->
$ helm lint mychart
==> Linting mychart
[INFO] Chart.yaml: icon is recommended
1 chart(s) linted, 0 chart(s) failed
$ helm install mychart mychart --dry-run
NAME: mychart
LAST DEPLOYED: Sat Jul 22 09:57:03 2023
NAMESPACE: default
STATUS: pending-install
REVISION: 1
HOOKS:
---
# Source: mychart/templates/tests/test-connection.yaml
apiVersion: v1
kind: Pod
metadata:
<--- snip --->
--debug
flag to see the output even if it contains invalid YAML.Helm dry run is a powerful tool for streamlining Helm chart development and deployment. By incorporating these techniques into your workflow, you can ensure your charts are well-structured, compatible, and ready for deployment on your Kubernetes clusters.
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