Managing Databricks Job Execution with User Permissions

Question

Pulumi · Accepted Answer

Managing job execution in Databricks involves setting up proper user permissions to ensure that only authorized users can create, edit, or execute jobs. In Pulumi, we can perform such tasks by utilizing the `databricks` package. Specifically, you would use resources like `User` and `PermissionAssignment` to manage roles and permissions within your Databricks workspace.

First, we would define a `User` in Databricks that represents the individual who will have permissions to execute jobs. Then, we'd use `PermissionAssignment` to assign the correct permissions to that user. Such a setup ensures that job execution is secured and controlled, adhering to your organization's governance policies.

Here's a Pulumi program in Python that demonstrates how to set up a user and manage their permissions for job execution in Databricks:

```python
import pulumi
import pulumi_databricks as databricks

# Create a new Databricks user. Replace `example_user_name` and `example_display_name`
# with the actual username and display name of the user.
databricks_user = databricks.User("example_user",
    user_name="example_user_name",
    display_name="example_display_name",
    active=True,  # Ensure the user is active
)

# Assign permissions to the user. The permissions must be aligned with their role.
# The following example assumes that the user needs to create and manage clusters.
# You would adjust the permissions according to your requirements.
permissions_assignment = databricks.PermissionAssignment("example_permissions_assignment",
    principal_id=databricks_user.id,  # The ID of the user to assign permissions to.
    permissions=[                      # The permissions that this user will receive.
        "CAN_ATTACH_TO",               # Permission to attach to clusters.
        "CAN_RESTART",                 # Permission to restart clusters.
        # Add additional permissions as necessary for job execution.
    ],
)

# Exporting the user ID and the permissions assignment ID for reference.
pulumi.export("user_id", databricks_user.id)
pulumi.export("permissions_assignment_id", permissions_assignment.id)
```

In the above program:

- We import the necessary Pulumi packages, which include `pulumi` for the Pulumi core library and `pulumi_databricks` for resources related to Databricks.
- We create a `User` resource using `databricks.User`, providing a username and other relevant attributes. This user will represent who can manage job executions in Databricks.
- Next, we create a `PermissionAssignment` using `databricks.PermissionAssignment` to assign the appropriate permissions to the user we have just created, linking it via the `principal_id` parameter. The permissions listed are examples, and they should be configured according to the actual roles and permissions required for job execution in your Databricks environment.

Please ensure that you replace placeholders like `example_user_name` and `example_display_name` with actual values. Additionally, review the permissions to align them with your security requirements.

Finally, we export the user ID and permissions assignment ID, which can be useful for referencing these resources elsewhere in your Pulumi code or in other systems that need to interact with these Databricks configurations.