Skaffold provides several features and sub-command “building blocks” that make it very useful for integrating with (or creating entirely new) CI/CD pipelines.
The ability to use the same
skaffold.yaml for iterative development and continuous delivery eases handing off an application from a development team to an ops team.
Let’s start with the simplest use case: a single, full deployment of your application.
skaffold run is a single command for a one-off deployment. It runs through every major phase of the Skaffold lifecycle: building your application images, tagging these images (and optionally pushing them to a remote registry), deploying your application to the target cluster, and monitoring the created resources for readiness.
skaffold run for the simplest Continuous Delivery setup, where it is sufficient to have a single step that deploys from version control to a cluster.
For more sophisticated Continuous Delivery pipelines, Skaffold offers building blocks:
- healthcheck -
deploymentsto stabilize and succeed only if all deployments are successful
skaffold build- build, tag and push artifacts to a registry
skaffold deploy- deploy built artifacts to a cluster
skaffold render- export the transformed Kubernetes manifests for GitOps workflows
skaffold apply- send hydrated Kubernetes manifests to the API server to create resources on the target cluster
Waiting for Skaffold deployments using
skaffold deploy optionally performs a
healthcheck for resources of kind
Deployment and waits for them to be stable.
This feature can be very useful in Continuous Delivery pipelines to ensure that the deployed resources are
healthy before proceeding with the next steps in the pipeline.
healthcheckis enabled by default; it can be disabled with the
To determine if a
Deployment resource is up and running, Skaffold relies on
kubectl rollout status to obtain its status.
Waiting for deployments to stabilize - default:deployment/leeroy-app Waiting for rollout to finish: 0 of 1 updated replicas are available... - default:deployment/leeroy-web Waiting for rollout to finish: 0 of 1 updated replicas are available... - default:deployment/leeroy-web is ready. [1/2 deployment(s) still pending] - default:deployment/leeroy-app is ready. Deployments stabilized in 2.168799605s
Configuring status check time for deploy
You can also configure the time for deployments to stabilize with the
statusCheckDeadlineSeconds config field in the
For example, to configure deployments to stabilize within 5 minutes:
deploy: statusCheckDeadlineSeconds: 300 kubectl: manifests: - k8s-*
--status-check flag, for each
skaffold deploy will wait for
the time specified by
from the deployment configuration.
Deployment.spec.progressDeadlineSeconds is not set, Skaffold will either wait for
the time specified in the
statusCheckDeadlineSeconds field of the deployment config stanza in the
default to 10 minutes if this is not specified.
In the case that both
Deployment.spec.progressDeadlineSeconds are set, precedence
is given to
Deployment.spec.progressDeadline only if it is less than
For example, the
Deployment below with
progressDeadlineSeconds set to 5 minutes,
apiVersion: apps/v1 kind: Deployment metadata: name: getting-started spec: progressDeadlineSeconds: 300 template: spec: containers: - name: cannot-run image: gcr.io/k8s-skaffold/getting-started-foo
skaffold.yaml overrides the deadline to make sure deployment stabilizes in a 60 seconds,
apiVersion: skaffold/v1 deploy: statusCheckDeadlineSeconds: 60 kubectl: manifests: - k8s-*
skaffold deploy --status-check
will result in an error after waiting for 1 minute:
Tags used in deployment: Starting deploy... kubectl client version: 1.11+ kubectl version 1.12.0 or greater is recommended for use with Skaffold - deployment.apps/getting-started created Waiting for deployments to stabilize - default:deployment/getting-started Waiting for rollout to finish: 0 of 1 updated replicas are available... - default:deployment/getting-started failed. Error: received Ctrl-C or deployments could not stabilize within 1m: kubectl rollout status command interrupted. FATA 1/1 deployment(s) failed
Traditional continuous delivery:
skaffold build | skaffold deploy
skaffold build will build your project’s artifacts, and push the build images to the specified registry. If your project is already configured to run with Skaffold,
skaffold build can be a very lightweight way of setting up builds for your CI pipeline. Passing the
--file-output flag to Skaffold build will also write out your built artifacts in JSON format to a file on disk, which can then by passed to
skaffold deploy later on. This is a great way of “committing” your artifacts when they have reached a state that you’re comfortable with, especially for projects with multiple artifacts for multiple services.
Example using the current git state as a unique file ID to “commit” build state:
Storing the build result in a commit specific JSON file:
export STATE=$(git rev-list -1 HEAD --abbrev-commit) skaffold build --file-output build-$STATE.json
outputs the tag generation and cache output from Skaffold:
Generating tags... - gcr.io/k8s-skaffold/skaffold-example:v0.41.0-17-g3ad238db Checking cache... - gcr.io/k8s-skaffold/skaffold-example: Found. Tagging
The content of the JSON file
We can then use this build result file to deploy with Skaffold:
skaffold deploy -a build-$STATE.json
and as we’d expect, we see a bit of deploy-related output from Skaffold:
Tags used in deployment: - gcr.io/k8s-skaffold/skaffold-example -> gcr.io/k8s-skaffold/skaffold-example:v0.41.0-17-g3ad238db@sha256:eeffb639f53368c4039b02a4d337bde44e3acc728b309a84353d4857ee95c369 Starting deploy... - pod/getting-started configured
GitOps-style continuous delivery:
skaffold render |
GitOps-based CD pipelines traditionally see fully-hydrated Kubernetes manifests committed to a configuration Git repository (separate from the application source), which triggers a deployment pipeline that applies the changes to resources on the cluster. Skaffold has two built-in commands that enable easy GitOps pipeline workflows -
skaffold render and
skaffold render builds all application images from your artifacts, templates the newly-generated image tags into your Kubernetes manifests (based on your project’s deployment configuration), and then prints out the final hydrated manifests to a file or your terminal. This allows you to capture the full, declarative state of your application in configuration rather than actually applying changes to your cluster, and use this configuration in a GitOps pipeline by committing it to a separate Git repository.
skaffold apply consumes one or more fully-hydrated Kubernetes manifests, and then sends the results directly to the Kubernetes control plane via
kubectl to create resources on the target cluster. After creating the resources on your cluster,
skaffold apply uses Skaffold’s built-in health checking to monitor the created resources for readiness. See resource health checks for more information on how Skaffold’s resource health checking works.
skaffold apply always uses
kubectl to deploy resources to a target cluster, regardless of deployment configuration in the provided skaffold.yaml. Only a small subset of deploy configuration is honored when running
skaffold applyattempts to honor the deployment configuration mentioned above. But when conflicting configuration is detected in a multi-configuration project,
skaffold applywill not work.
skaffold apply works with any arbitrary Kubernetes YAML, whether it was generated by Skaffold or not, making it an ideal counterpart to
Example: Hydrating Kubernetes resources using
skaffold render, then sending them to the cluster using
skaffold render to hydrate the Kubernetes resource file with a newly-built image tag:
$ skaffold render --output render.yaml
# render.yaml apiVersion: v1 kind: Pod metadata: name: getting-started namespace: default spec: containers: - image: gcr.io/k8s-skaffold/skaffold-example:v1.19.0-89-gdbedd2a20-dirty name: getting-started
Then, we can apply this output directly to the cluster:
$ skaffold apply render.yaml Starting deploy... - pod/getting-started created Waiting for deployments to stabilize... Deployments stabilized in 49.277055ms