Backends¶

Cloudfunction¶

Goblet’s default backend is first-generation cloud functions. However, Goblet supports both first- and second-generation cloud functions.

For first-generation:

app = Goblet()
or
app = Goblet(backend="cloudfunction")

For second-generation:

app = Goblet(backend="cloudfunctionv2")

You can use config.json to further customize the function you wish to create. Goblet uses the CloudFunction resource for first-gen cloudfunctions and the Function resource for cloudfunctionv2. You may add any additional fields in config.json under “cloudfunction”
For cloudfunctions v1, python version must be at least python3.8, and for cloudfunctionv2, python version must be at least python3.8. To specify a python version for your cloudfunction, you can set the runtime field in config.json as such:

{"cloudfunction": {"runtime": "python38"}}

To set a custom GCS Bucket to upload the Cloudfunction sourcecode, use the artifact_bucket configuration in the deploy configuration key.

{
    "deploy":{
        "artifact_bucket": "name-of-the-bucket"
    }
}

or the GOBLET_ARTIFACT_BUCKET environment variable.

To tag the .zip file with the code uploaded to the GCS Bucket, use the GOBLET_BUILD_TAGS environment variable with the tag. The Cloudfunction will be created using the file uploaded to this custom location.

GOBLET_BUILD_TAGS=1cac04f3b

In order to use GOBLET_BUILD_TAGS, GOBLET_ARTIFACT_BUCKET must also have a value.

To deploy a Cloudfunction from a tagged .zip file use the artifact_tag configuration in the deploy configuration key

{
    "deploy":{
        "artifact_tag": "1cac04f3b",
    }
}

or use the GOBLET_ARTIFACT_TAG environment variable. When using an artifact_tag to deploy, GOBLET_ARTIFACT_BUCKET must also have a value.

Goblet does not currently support eventarc triggers for cloudfunctions

Cloudrun¶

You can deploy your function to cloudrun updating the backend parameter to main Goblet class.

app = Goblet(backend="cloudrun")

Note

The function name for cloudrun must use only lowercase alphanumeric characters and dashes, cannot begin or end with a dash, and cannot be longer than 63 characters

You can pass in configurations to your cloudrun deployment in the cloudrun section in your config.json.

Supported configurations include:

traffic: assigns a custom amount of traffic to the latest revision and decreases previous revisions’ traffic proportionally. If the service is brand new, the traffic will always default to 100%.

{
    "cloudrun":{
        "traffic": 25
    }
}

Deploying to cloudrun requires either a Dockerfile or Procfile in the directory you are looking to deploy your goblet app. If neither of those files are found, then goblet will create a default Dockerfile that allows the app to be build, deployed, and run correctly. Having a custom Dockerfile if only needed if you would like to customize you container at all.

For revision configurations, pass values into cloudrun_revision section in your config.json. If you’re using a service account, this is where to put it.

{
    "cloudrun_revision":{
        "serviceAccount": "service-account@project.iam.gserviceaccount.com"
    }
}

For Container configurations, pass values into cloudrun_container

Pass in environment variables here. Secrets will also be passed in as environment variables.

"cloudrun_container": {
    "env": [
        {
            "name": "env-variable-name",
            "value": "env-variable-value"
        },
        {
            "name": "env-variable-name",
            "valueSource": {
                "secretKeyRef" : {
                    "secret": "secret-name",
                    "version": "secret-version"
                }
            }
        }
    ]
}

For Cloud Build configurations, pass values into cloudbuild

To install packages from Artifact Registry ensure roles/artifactregistry.reader role has been added to cloudbuild service account and the artifact registry keyring backend install has been enabled within the Dockerfile

RUN pip install keyrings.google-artifactregistry-auth==1.1.1

To set a custom artifact registry where cloudbuild will push new images and from where cloudrun will pull images to deploy, use the artifact_registry configuration in the deploy configuration key.

{
    "deploy":{
        "artifact_registry": "location-docker.pkg.dev/gcp_project/artifact/image"
    }
}

To use an artifact registry from a different project, the service account used in the cloudbuild configuration must have storage permissions in the current project’s bucket and read+write in the project from where artifact_registry belongs to.

This can be done by running:

gcloud projects add-iam-policy-binding project_a \
--member="serviceAccount:service-project_b_id@serverless-robot-prod.iam.gserviceaccount.com" \
--role="roles/artifactregistry.reader"

Here the service account from project_b is granted permissions to read from artifact registry en project_a

To set the tags used to tag the image created in CloudBuild and pushed to artifact registry, use the GOBLET_BUILD_TAGS environment variable with a comma-sepparated list of tags to be created:

GOBLET_BUILD_TAGS=tag1,tag2,tag3

To use a previously built artifact, use the artifact_tag configuration in the deploy configuration key or use the GOBLET_ARTIFACT_TAG environment variable. When using artifact_tag, source code will not be uploaded and cloudbuild will not be called. artifact_tag can be any existing tag or digest in the default registry or the configured artifact_registry.

{
    "deploy":{
        "artifact_tag": "latest",
    }
}

GOBLET_ARTIFACT_TAG=tag1
# or
GOBLET_ARTIFACT_TAG=sha256:1234567890abcdef1234567890abcdef1234567890abcdef1234567890abcdef

To pass Build Arg to docker build during CloudBuild , use the environment variable GOBLET_BUILD_ARGS.

For example, to set the FROM image tag in the Dockerfile

Dockefile

ARG PYTHON_VERSION
FROM python:${PYTHON_VERSION}-slim

# dockerfile instrutions...

Goblet Deploy

$ GOBLET_BUILD_ARGS=PYTHON_VERSION=3.10.8 goblet deploy ...