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dagster-pipes-gcp

🚧 Experimental!

Deploy a simple GCP function and orchestrate it with Dagster.

Attribution

A large chunk of this code has been modified from the dagster-pipes AWS Lambda implementation.

Devcontainer

This repository contains a devcontainer. I recommend that you use it.

Setup

Add a service account JSON credentials called 'sa.json' file to '.devcontainer/.secrets'. This service account should have permissions to invoke a cloud function & read logs, but to be honest I've used a service account with very broad permissions for now.

In .devcontainer/.env, change the GOOGLE_PROJECT_ID environment variable to your GCP project id.

Open the project in the devcontainer and run just setup/just s. This will install the dependencies and configure gcloud.

CI/CD

In GitHub secrets, ensure that you have set the GOOGLE_CREDENTIALS secret with the JSON service account credentials of a service account that is allowed to deploy cloud functions.

Also, you should change the 'GCP_PROJECT_ID' environment variable in the following pipelines:

  • '.github/workflows/pipeline-nprod.yaml'
  • '.github/workflows/pipeline-prod.yaml'
  • '.github/workflows/pipeline-setup.yaml'

Go to GitHub and manually trigger the pipeline called 'setup'. This will enable the APIs and create a storage bucket to store the cloud functions code.

CI/CD workflow is as follows:

  • On PR to main: run pre-commit hooks
  • On push to main:
    • Run pre-commit hooks
    • Build function source code
    • Deploy function source code
  • On release:
    • Build function source code
    • Deploy function source code

Executing an asset that orchestrates a cloud function

Go to 'dagster/DAG.py' and change the 'function_url' parameter to the URL of your cloud function.

The cloud function uses the default compute service account because that was easy to set up. You should probably make a custom one.

Then start dagster locally:

just dd

And materialize the asset.