GCP Cloud Run Job Execution Mode#
Added in version 1.7.
This tutorial will guide you through the steps required to use Cloud Run Job instance as the Execution Mode for your dbt code with Astronomer Cosmos. This guide will walk you through the steps required to build the following architecture:
Prerequisites#
Docker with docker daemon (Docker Desktop on MacOS). Follow the Docker installation guide.
Airflow
Google Cloud SDK (install guide here: gcloud SDK)
Astronomer-cosmos package containing the dbt Cloud Run Job operators
- GCP account with:
A GCP project (setup guide)
- IAM roles:
Basic Role: Owner (control over whole project) or
Predefined Roles: Artifact Registry Administrator, Cloud Run Developer (control over specific services)
- Enabled service APIs:
Artifact Registry API
Cloud Run Admin API
BigQuery API
A service account with BigQuery roles: JobUser and DataEditor
Docker image built with required dbt project and dbt DAG
dbt DAG with Cloud Run Job operators in the Airflow DAGs directory to run in Airflow
Note
Google Cloud Platform provides free tier on many resources, as well as Free Trial with $300 in credit. Learn more here.
More information on how to achieve 2-6 is detailed below.
Step-by-step guide#
Install Airflow and Cosmos
Create a python virtualenv, activate it, upgrade pip to the latest version and install apache airflow & astronomer cosmos:
python3 -m venv venv
source venv/bin/activate
python3 -m pip install --upgrade pip
pip install apache-airflow
pip install "astronomer-cosmos[dbt-bigquery,gcp-cloud-run-job]"
Setup gcloud and environment variables
Set environment variables that will be used to create cloud infrastructure. Replace placeholders with your unique GCP project id and region of the project:
export PROJECT_ID=<<<YOUR_GCP_PROJECT_ID>>>
export REGION=<<<YOUR_GCP_REGION>>>
export REPO_NAME="astronomer-cosmos-dbt"
export IMAGE_NAME="$REGION-docker.pkg.dev/$PROJECT_ID/$REPO_NAME/cosmos-example"
export SERVICE_ACCOUNT_NAME="cloud-run-job-sa"
export DATASET_NAME="astronomer_cosmos_example"
export CLOUD_RUN_JOB_NAME="astronomer-cosmos-example"
Before we do anything in the GCP project, we first need to authorize gcloud to access the Cloud Platform with Google user credentials:
gcloud auth login
You’ll receive a link to sign into Google Cloud SDK using a Google Account.
Next, set default project id using below command:
gcloud config set project $PROJECT_ID
In case BigQuery has never been used before in the project, run below command to enable BigQuery API:
gcloud services enable bigquery.googleapis.com
Setup Artifact Registry
In order to run a container in Cloud Run Job, it needs access to the container image. In our setup, we will use Artifact Registry repository that stores images. To use Artifact Registry, you need to enable the API first:
gcloud services enable artifactregistry.googleapis.com
To set an Artifact Registry repository up, you can use the following bash command:
gcloud artifacts repositories create $REPO_NAME \
--repository-format=docker \
--location=$REGION \
--project $PROJECT_ID
Setup Service Account
In order to use dbt and make transformations in BigQuery, Cloud Run Job needs some BigQuery permissions. One way to achieve that is to set up a separate Service Account with needed permissions:
# create a service account
gcloud iam service-accounts create $SERVICE_ACCOUNT_NAME
# grant JobUser role
gcloud projects add-iam-policy-binding $PROJECT_ID \
--member="serviceAccount:$SERVICE_ACCOUNT_NAME@$PROJECT_ID.iam.gserviceaccount.com" \
--role="roles/bigquery.jobUser"
# grant DataEditor role
gcloud projects add-iam-policy-binding $PROJECT_ID \
--member="serviceAccount:$SERVICE_ACCOUNT_NAME@$PROJECT_ID.iam.gserviceaccount.com" \
--role="roles/bigquery.dataEditor"
Build the dbt Docker image
Now, we are going to download an example dbt project and build a Docker image with it.
Important
You need to ensure Docker is using the right credentials to push images. For Artifact Registry, this can be done by running the following command:
gcloud auth print-access-token | docker login -u oauth2accesstoken --password-stdin https://$REGION-docker.pkg.dev
The token will be valid for 1 hour. After that, you need to create another one, if still needed.
Clone the cosmos-example repo:
git clone https://github.com/astronomer/cosmos-example.git
cd cosmos-example
Open Dockerfile located in gcp_cloud_run_job_example folder and change environments variables GCP_PROJECT_ID and GCP_REGION to your GCP project id and project region.
Build a Docker image using previously modified Dockerfile, which will be used by Cloud Run Job:
docker build -t $IMAGE_NAME -f gcp_cloud_run_job_example/Dockerfile.gcp_cloud_run_job .
Important
Make sure to stay in cosmos-example directory when running docker build command.
After this, the image needs to be pushed to the Artifact Registry:
docker push $IMAGE_NAME
Take a read of the Dockerfile to understand what it does so that you could use it as a reference in your project.
The dags directory containing the dbt project jaffle_shop is added to the image
The bigquery dbt profile file is added to the image
The dbt_project.yml is replaced with bigquery_profile_dbt_project.yml which contains the profile key pointing to postgres_profile as profile creation is not handled at the moment for K8s operators like in local mode.
Create Cloud Run Job instance
When the image is pushed to Artifact Registry, you can finally create Cloud Run Job with the image and previously created service account.
First, enable Cloud Run Admin API using below command:
gcloud services enable run.googleapis.com
Next, set default Cloud Run region to your GCP region:
gcloud config set run/region $REGION
Then, run below command to create Cloud Run Job instance:
gcloud run jobs create $CLOUD_RUN_JOB_NAME \
--image=$IMAGE_NAME \
--task-timeout=180s \
--max-retries=0 \
--cpu=1 \
--memory=512Mi \
--service-account=$SERVICE_ACCOUNT_NAME@$PROJECT_ID.iam.gserviceaccount.com
Setup Airflow Connections
Now, when you have the required Google Cloud infrastructure, you still need to check Airflow configuration to ensure the infrastructure can be used. You’ll need a google_cloud_default connection in order to work on GCP resources.
Check out the airflow-settings.yml file here for an example. If you are using Astro CLI, filling in the right values here will be enough for this to work.
Setup and Trigger the DAG with Airflow
Open jaffle_shop_gcp_cloud_run_job DAG file and update GCP_PROJECT_ID and GCP_LOCATION constants with your GCP project id and project region.
When the DAG is configured, copy the dags directory from cosmos-example repo to your Airflow home:
cp -r dags $AIRFLOW_HOME/
Run Airflow:
airflow standalone
Note
You might need to run airflow standalone with sudo if your Airflow user is not able to access the docker socket URL or pull the images in the Kind cluster.
Log in to Airflow through a web browser http://localhost:8080/, using the user airflow and the password described in the standalone_admin_password.txt file.
Enable and trigger a run of the jaffle_shop_gcp_cloud_run_job DAG. You will be able to see the following successful DAG run.
You can also verify the tables that were created using dbt in BigQuery Studio:
Delete resources
After the successful tests, don’t forget to delete Google Cloud resources to save up costs:
# Delete Cloud Run Job instance
gcloud run jobs delete $CLOUD_RUN_JOB_NAME
# Delete BigQuery main and custom dataset specified in dbt schema.yml with all tables included
bq rm -r -f -d $PROJECT_ID:$DATASET_NAME
bq rm -r -f -d $PROJECT_ID:dbt_dev
# Delete Artifact Registry repository with all images included
gcloud artifacts repositories delete $REPO_NAME \
--location=$REGION