Run a CI/CD workflow with a Databricks Asset Bundle and GitHub Actions
This article describes how to run a CI/CD (continuous integration/continuous deployment) workflow in GitHub with GitHub Actions and a Databricks Asset Bundle. See What are Databricks Asset Bundles?
You can use GitHub Actions along with Databricks CLI bundle
commands to automate, customize, and run your CI/CD workflows from within your GitHub repositories.
You can add GitHub Actions YAML files such as the following to your repo’s .github/workflows
directory. The following example GitHub Actions YAML file validates, deploys, and runs the specified job in the bundle within a pre-production target named “qa” as defined within a bundle configuration file. This example GitHub Actions YAML file relies on the following:
A bundle configuration file at the root of the repository, which is explicitly declared through the GitHub Actions YAML file’s setting
working-directory: .
(This setting can be omitted if the bundle configuration file is already at the root of the repository.) This bundle configuration file defines a Databricks workflow namedmy-job
and a target namedqa
. See Databricks Asset Bundle configuration.A GitHub secret named
SP_TOKEN
, representing the Databricks access token for a Databricks service principal that is associated with the Databricks workspace to which this bundle is being deployed and run. See Encrypted secrets.
# This workflow validates, deploys, and runs the specified bundle
# within a pre-production target named "qa".
name: "QA deployment"
# Ensure that only a single job or workflow using the same concurrency group
# runs at a time.
concurrency: 1
# Trigger this workflow whenever a pull request is opened against the repo's
# main branch or an existing pull request's head branch is updated.
on:
pull_request:
types:
- opened
- synchronize
branches:
- main
jobs:
# Used by the "pipeline_update" job to deploy the bundle.
# Bundle validation is automatically performed as part of this deployment.
# If validation fails, this workflow fails.
deploy:
name: "Deploy bundle"
runs-on: ubuntu-latest
steps:
# Check out this repo, so that this workflow can access it.
- uses: actions/checkout@v3
# Download the Databricks CLI.
# See https://github.com/databricks/setup-cli
- uses: databricks/setup-cli@main
# Deploy the bundle to the "qa" target as defined
# in the bundle's settings file.
- run: databricks bundle deploy
working-directory: .
env:
DATABRICKS_TOKEN: ${{ secrets.SP_TOKEN }}
DATABRICKS_BUNDLE_ENV: qa
# Validate, deploy, and then run the bundle.
pipeline_update:
name: "Run pipeline update"
runs-on: ubuntu-latest
# Run the "deploy" job first.
needs:
- deploy
steps:
# Check out this repo, so that this workflow can access it.
- uses: actions/checkout@v3
# Use the downloaded Databricks CLI.
- uses: databricks/setup-cli@main
# Run the Databricks workflow named "my-job" as defined in the
# bundle that was just deployed.
- run: databricks bundle run my-job --refresh-all
working-directory: .
env:
DATABRICKS_TOKEN: ${{ secrets.SP_TOKEN }}
DATABRICKS_BUNDLE_ENV: qa
The following GitHub Actions YAML file can exist in the same repo as the preceding file. This file validates, deploys, and runs the specified bundle within a production target named “prod” as defined within a bundle configuration file. This example GitHub Actions YAML file relies on the following:
A bundle configuration file at the root of the repository, which is explicitly declared through the GitHub Actions YAML file’s setting
working-directory: .
(This setting can be omitted if the bundle configuration file is already at the root of the repository.). This bundle configuration file defines a Databricks workflow namedmy-job
and a target namedprod
. See Databricks Asset Bundle configuration.A GitHub secret named
SP_TOKEN
, representing the Databricks access token for a Databricks service principal that is associated with the Databricks workspace to which this bundle is being deployed and run. See Encrypted secrets.
# This workflow validates, deploys, and runs the specified bundle
# within a production target named "prod".
name: "Production deployment"
# Ensure that only a single job or workflow using the same concurrency group
# runs at a time.
concurrency: 1
# Trigger this workflow whenever a pull request is pushed to the repo's
# main branch.
on:
push:
branches:
- main
jobs:
deploy:
name: "Deploy bundle"
runs-on: ubuntu-latest
steps:
# Check out this repo, so that this workflow can access it.
- uses: actions/checkout@v3
# Download the Databricks CLI.
# See https://github.com/databricks/setup-cli
- uses: databricks/setup-cli@main
# Deploy the bundle to the "prod" target as defined
# in the bundle's settings file.
- run: databricks bundle deploy
working-directory: .
env:
DATABRICKS_TOKEN: ${{ secrets.SP_TOKEN }}
DATABRICKS_BUNDLE_ENV: prod
# Validate, deploy, and then run the bundle.
pipeline_update:
name: "Run pipeline update"
runs-on: ubuntu-latest
# Run the "deploy" job first.
needs:
- deploy
steps:
# Check out this repo, so that this workflow can access it.
- uses: actions/checkout@v3
# Use the downloaded Databricks CLI.
- uses: databricks/setup-cli@main
# Run the Databricks workflow named "my-job" as defined in the
# bundle that was just deployed.
- run: databricks bundle run my-job --refresh-all
working-directory: .
env:
DATABRICKS_TOKEN: ${{ secrets.SP_TOKEN }}
DATABRICKS_BUNDLE_ENV: prod