This is an ALPHA stage module

This module is not suitable for production.

There is no guarantee.

This module is only a demo code.

Requirements

• Install the Azure CLI and log in with $ az login
• Install pip with $ sudo apt get install python-pip, and use it to install the Databricks CLI. Note that this may require you to update your path following the pip install, so test the install with $ databricks -h.
• Install Terraform
• Install zip with $ sudo apt install zip on Linux
• Install version 0.2.4 of the Databricks Labs Terraform provider. This can be installed as follows:

curl https://raw.githubusercontent.com/databrickslabs/databricks-terraform/master/godownloader-databricks-provider.sh 0.2.4 | bash -s -- -b $HOME/.terraform.d/plugins

The examples directory contains ready to run usage examples for the module.

Before running, in order for the Databricks provider to complete authentication, you must set several environment variables on your machine as follows:

# ID of the Azure subscription in which the Databricks workspace is located
export ARM_SUBSCRIPTION_ID="your-sub-id"

# ID of the Azure tenant in which the Databricks workspace is located
export ARM_TENANT_ID="your-tenant-id"

# Service Principal or App Registration Client ID for workspace auth
export ARM_CLIENT_ID="your-client-id"

# Service Principal or App Registration Client secret for workspace auth
export ARM_CLIENT_SECRET="your-client-secret"

The module also provides the option to deploy a pre-created local Jupyter notebook to the workspace. This can contain any valid Jupyter notebook content. Use of variable notebook_path triggers this resource deployment, but as in the minimal example it can be foregone entirely, deploying just the clusters to the workspace. For ease of getting started, an example notebook is provided at ./notebooks/notebook.ipynb, and the complete example uploads this by default.

If a notebook is deployed, three APIM APIs are automatically deployed with it, which together use the Databricks Jobs API to invoke and get results from the notebook.

• The first API create_job_api sets up a notebook task job in your Databricks instance.
• The second API invoke_notebook_api runs this job given its ID (1 by default).
• The third API notebook_output_api, given the ID of a run, retrieves any outputs from that notebook run.

All APIs can be used with optional custom parameters immediately after deployment, through the "Test" window of APIM in the Azure Portal.

Providers

Name	Version
databricks	n/a
null	n/a
time	n/a

Inputs

Name	Description	Type	Default	Required
cluster_default_packages	List of uris for any custom Python packages (.whl) to install on clusters by default.	list(string)	[]	no
databricks_workspace	Databricks workspace to deploy resources to.	any	n/a	yes
apim	API Management resource object to deploy notebook invocation API(s) to.	any	n/a	yes
prefix	A naming prefix to be used in the creation of unique names for deployed Databricks resources.	list(string)	[]	no
suffix	A naming suffix to be used in the creation of unique names for deployed Databricks resources.	list(string)	[]	no
whl_upload_script_path	Path to a bash script which downloads the whls in cluster_default_packages, and uploads them to dbfs.	string	""	no
notebook_path	Optional relative path to a local Jupyter notebook to deploy to the workspace.	string	""	no
notebook_name	If deploying, the desired name of the deployed notebook as it will appear in the workspace.	string	"mynotebook"	no

Outputs

Name	Description
high_concurrency_cluster	n/a
standard_cluster	n/a

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.

When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.