DevOps for Databricks extension

This extension brings a set of tasks for you to operationalize build, test and deployment of Databricks Jobs and Notebooks.

Pre-requisites

Use Python Version

To run this set of tasks in your build/release pipeline, you first need to explicitly set a Python version. To do so, use this task as a first task for your pipeline.

Supported Hosted Agents

With the new tasks added for supporting Scala Development, the agent support is now defined by task. See each task documentation to check its compatibility with the available Hosted Agents.

It's strongly recommend that you use Hosted Ubuntu 1604 for your pipelines.

Pipeline Tasks

Configure Databricks CLI

This pipeline task installs and configures the Databricks CLI onto the agent. The following steps are performed:

• Installs databricks-cli using pip (that's why using Use Python Version is required);
• Writes a configuration file at ~/.databrickscfg so the CLI will know which Databricks Workspace to connect to.
  • Instead of creating a DEFAULT profile, it creates a profile called AZDO

Supported Agents

• Hosted Ubuntu 1604
• Hosted VS2017

Important: What is done with your Databricks PAT?

Your Databricks Personal Access Token (PAT) is used to grant access to your Databricks Workspace from the Azure DevOps agent which is running your pipeline, either being it Private or Hosted.

Given that the Microsoft Hosted Agents are discarded after one use, your PAT - which was used to create the ~/.databrickscfg - will also be discarded. This means that your PAT will not be used for anything else other than running your own pipeline.

Store your PAT as a variable

It is strongly recommended that you do not pass your Personal Access Token as a plain text to the task. Instead, store it as a Secret Variable and use the variable reference on the task.

Supported Agents

• Hosted Ubuntu 1604
• Hosted VS2017

Deploy Notebooks to Workspace

This Pipeline task recursively deploys Notebooks from given folder to a Databricks Workspace.

Parameters

• Notebooks folder: a folder that contains the notebooks to be deployed. For example:
  • $(System.DefaultWorkingDirectory)/<artifact name>/notebooks
• Workspace folder: the folder to publish the notebooks on the target Workspace. For example:
  • /Shared
  • /Shared/Automation
  • /Users/user@domain.com

Supported Agents

• Hosted Ubuntu 1604
• Hosted VS2017

Execute $(notebookPath)

Executes a notebook given its workspace path. Parameters are:

• Notebook path (at workspace): The path to an existing Notebook in a Workspace.
• Existing Cluster ID: if provided, will use the associated Cluster to run the given Notebook, instead of creating a new Cluster.
• Notebook parameters: if provided, will use the values to override any default parameter values for the notebook. Must be specified in JSON format.

Supported Agents

• Hosted Ubuntu 1604
• Hosted VS2017

Wait for Notebook execution

Makes the Pipeline wait until the Notebook run - invoked by the previous task - finishes.

If the Notebook execution succeeds (status SUCCESS), this task will also succeed.

If the Notebook execution fails (status FAILED), the task (and the Pipeline) will fail.

You can have access to the run URL through the task logs. For example:

2019-06-18T21:22:56.9840342Z The notebook execution suceeded (status SUCCESS)
2019-06-18T21:22:56.9840477Z For details, go to the execution page: https://<region>.azuredatabricks.net/?o=<organization-id>#job/<run-id>/run/1

Start a Databrick Cluster (new!)

This task will start a given Databrick Cluster. It will do nothing if the cluster is already started.

Parameters

• Cluster ID: The ID of the cluster. Can be founds on its URL or on its Tags.

Supported Agents

• Hosted Ubuntu 1604
• Hosted VS2017

Install Scala Tools (new!)

Installs the following tools on the Agent:

• Java SDK
• Scala
• SBT

Supported Agents

• Hosted Ubuntu 1604

Install Spark

Installs Spark libraries on the agent.

Supported Agents

• Hosted Ubuntu 1604

Compiles and Installs JAR using SBT (new!)

This task will:

• Compile a given project using SBT
• Copy the following to your Databricks Cluster:
  • Copy the resulting JAR to the Databricks Cluster
  • Copy a sample data set to the Databricks Cluster
  • Copy a sample dataset file to the Databricks Cluster

Parameters

• Cluster ID: The ID of the cluster you want to install this library.
• Working Directory: The project directory. Where the build.sbt lives.
• JAR package name (overrides build.sbt): The name you want to give to your JAR package name. It will override the value if set on build.sbt.
• Package Version (overrides build.sbt): A version you want to give to your JAR package. It will override the value if set on build.sbt.
• Scala Version (overrides build.sbt): The Scala version you want to specify for this compilation. It will override the value if set on build.sbt.
• Sample dataset path: The path to a dataset file for testing purposes.

Supported Agents

• Hosted Ubuntu 1604

Known issues

Fortunately, no known issues so far. Please feel free to open a new issue on GitHub if you experience any problem.

Release Notes

Please check the Release Notes page on GitHub.

Contributing

To know more about how to contribute to this project, please see CONTRIBUTING page.