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Continuous Delivery
Note
Skaffold is used under the hood to power Google Cloud Platform’s Cloud Deploy API. See the docs here for more information on how to use Skaffold + Cloud Deploy for CI/CDSkaffold 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.
Run entire pipeline end-to-end
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.
We recommend 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:
- status-check -
wait for
deployments
to stabilize and succeed only if all deployments are successful skaffold build
- build, tag and push artifacts to a registryskaffold deploy
- deploy built artifacts to a clusterskaffold render
- export the transformed Kubernetes manifestsskaffold apply
- send hydrated Kubernetes manifests to the API server to create resources on the target cluster
Traditional continuous delivery
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
cat build-$STATE.json
looks like:
{"builds":[{"imageName":"gcr.io/k8s-skaffold/skaffold-example","tag":"gcr.io/k8s-skaffold/skaffold-example:v0.41.0-17-g3ad238db@sha256:eeffb639f53368c4039b02a4d337bde44e3acc728b309a84353d4857ee95c369"}]}
We can then use this build result file to deploy with Skaffold:
skaffold deploy -a build-$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
Separation of rendering and deployment
Skaffold allows separating the generation of fully-hydrated Kubernetes manifests from the actual deployment of those manifests, using the skaffold render
and
skaffold apply
commands.
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, such that applying the changes to your cluster can be done as a separate step.
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.
Note: 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 apply
:
- deploy.statusCheckDeadlineSeconds
- deploy.kubeContext
- deploy.logs.prefix
- deploy.kubectl.flags
- deploy.kubectl.defaultNamespace
- deploy.kustomize.flags
- deploy.kustomize.defaultNamespace
Note
skaffold apply
attempts to honor the deployment configuration mentioned above. But when conflicting configuration is detected in a multi-configuration project, skaffold apply
will not work.
skaffold apply
works with any arbitrary Kubernetes YAML, whether it was generated by Skaffold or not, making it an ideal counterpart to skaffold render
.
GitOps-style continuous delivery
You can use this separation of rendering and deploying to enable GitOps CD pipelines.
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.
Example: Hydrate manifests then deploy/apply to cluster
First, use 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