databricks_permissions Resource

This resource allows you to generically manage access control in Databricks workspace. It would guarantee that only _admins_, _authenticated principal_ and those declared within access_control blocks would have specified access. It is not possible to remove management rights from _admins_ group.

Cluster usage

It's possible to separate cluster access control to three different permission levels: CAN_ATTACH_TO, CAN_RESTART and CAN_MANAGE:

resource "databricks_group" "auto" {
  display_name = "Automation"
}

resource "databricks_group" "eng" {
  display_name = "Engineering"
}

resource "databricks_group" "ds" {
  display_name = "Data Science"
}

data "databricks_spark_version" "latest" {}

data "databricks_node_type" "smallest" {
  local_disk = true
}

resource "databricks_cluster" "shared_autoscaling" {
  cluster_name            = "Shared Autoscaling"
  spark_version           = data.databricks_spark_version.latest.id
  node_type_id            = data.databricks_node_type.smallest.id
  autotermination_minutes = 60
  autoscale {
    min_workers = 1
    max_workers = 10
  }
}

resource "databricks_permissions" "cluster_usage" {
  cluster_id = databricks_cluster.shared_autoscaling.id

  access_control {
    group_name       = databricks_group.auto.display_name
    permission_level = "CAN_ATTACH_TO"
  }

  access_control {
    group_name       = databricks_group.eng.display_name
    permission_level = "CAN_RESTART"
  }

  access_control {
    group_name       = databricks_group.ds.display_name
    permission_level = "CAN_MANAGE"
  }
}

Cluster Policy usage

Cluster policies allow creation of clusters, that match given policy. It's possible to assign CAN_USE permission to users and groups:

resource "databricks_group" "ds" {
  display_name = "Data Science"
}

resource "databricks_group" "eng" {
  display_name = "Engineering"
}

resource "databricks_cluster_policy" "something_simple" {
  name = "Some simple policy"
  definition = jsonencode({
    "spark_conf.spark.hadoop.javax.jdo.option.ConnectionURL" : {
      "type" : "forbidden"
    },
    "spark_conf.spark.secondkey" : {
      "type" : "forbidden"
    }
  })
}

resource "databricks_permissions" "policy_usage" {
  cluster_policy_id = databricks_cluster_policy.something_simple.id

  access_control {
    group_name       = databricks_group.ds.display_name
    permission_level = "CAN_USE"
  }

  access_control {
    group_name       = databricks_group.eng.display_name
    permission_level = "CAN_USE"
  }
}

Instance Pool usage

Instance Pools access control allows to assign CAN_ATTACH_TO and CAN_MANAGE permissions to users, service principals, and groups. It's also possible to grant creation of Instance Pools to individual groups and users, service principals.

resource "databricks_group" "auto" {
  display_name = "Automation"
}

resource "databricks_group" "eng" {
  display_name = "Engineering"
}

data "databricks_node_type" "smallest" {
  local_disk = true
}

resource "databricks_instance_pool" "this" {
  instance_pool_name                    = "Reserved Instances"
  idle_instance_autotermination_minutes = 60
  node_type_id                          = data.databricks_node_type.smallest.id
  min_idle_instances                    = 0
  max_capacity                          = 10
}

resource "databricks_permissions" "pool_usage" {
  instance_pool_id = databricks_instance_pool.this.id

  access_control {
    group_name       = databricks_group.auto.display_name
    permission_level = "CAN_ATTACH_TO"
  }

  access_control {
    group_name       = databricks_group.eng.display_name
    permission_level = "CAN_MANAGE"
  }
}

Job usage

There are four assignable permission levels for databricks_job: CAN_VIEW, CAN_MANAGE_RUN, IS_OWNER, and CAN_MANAGE. Admins are granted the CAN_MANAGE permission by default, and they can assign that permission to non-admin users, and service principals.

resource "databricks_group" "auto" {
  display_name = "Automation"
}

resource "databricks_group" "eng" {
  display_name = "Engineering"
}

resource "databricks_service_principal" "aws_principal" {
  display_name = "main"
}

data "databricks_spark_version" "latest" {}

data "databricks_node_type" "smallest" {
  local_disk = true
}

resource "databricks_job" "this" {
  name                = "Featurization"
  max_concurrent_runs = 1

  task {
    task_key = "task1"

    new_cluster {
      num_workers   = 300
      spark_version = data.databricks_spark_version.latest.id
      node_type_id  = data.databricks_node_type.smallest.id
    }

    notebook_task {
      notebook_path = "/Production/MakeFeatures"
    }
  }
}

resource "databricks_permissions" "job_usage" {
  job_id = databricks_job.this.id

  access_control {
    group_name       = "users"
    permission_level = "CAN_VIEW"
  }

  access_control {
    group_name       = databricks_group.auto.display_name
    permission_level = "CAN_MANAGE_RUN"
  }

  access_control {
    group_name       = databricks_group.eng.display_name
    permission_level = "CAN_MANAGE"
  }

  access_control {
    service_principal_name = databricks_service_principal.aws_principal.application_id
    permission_level       = "IS_OWNER"
  }
}

Delta Live Tables usage

There are four assignable permission levels for databricks_pipeline: CAN_VIEW, CAN_RUN, CAN_MANAGE, and IS_OWNER. Admins are granted the CAN_MANAGE permission by default, and they can assign that permission to non-admin users, and service principals.

resource "databricks_group" "eng" {
  display_name = "Engineering"
}

resource "databricks_notebook" "dlt_demo" {
  content_base64 = base64encode(<<-EOT
    import dlt
    json_path = "/databricks-datasets/wikipedia-datasets/data-001/clickstream/raw-uncompressed-json/2015_2_clickstream.json"
    @dlt.table(
       comment="The raw wikipedia clickstream dataset, ingested from /databricks-datasets."
    )
    def clickstream_raw():
        return (spark.read.format("json").load(json_path))
    EOT
  )
  language = "PYTHON"
  path     = "${data.databricks_current_user.me.home}/DLT_Demo"
}

resource "databricks_pipeline" "this" {
  name    = "DLT Demo Pipeline (${data.databricks_current_user.me.alphanumeric})"
  storage = "/test/tf-pipeline"
  configuration = {
    key1 = "value1"
    key2 = "value2"
  }

  library {
    notebook {
      path = databricks_notebook.dlt_demo.id
    }
  }

  continuous = false
  filters {
    include = ["com.databricks.include"]
    exclude = ["com.databricks.exclude"]
  }
}

resource "databricks_permissions" "dlt_usage" {
  pipeline_id = databricks_pipeline.this.id

  access_control {
    group_name       = "users"
    permission_level = "CAN_VIEW"
  }

  access_control {
    group_name       = databricks_group.eng.display_name
    permission_level = "CAN_MANAGE"
  }
}

Notebook usage

Valid permission levels for databricks_notebook are: CAN_READ, CAN_RUN, CAN_EDIT, and CAN_MANAGE.

resource "databricks_group" "auto" {
  display_name = "Automation"
}

resource "databricks_group" "eng" {
  display_name = "Engineering"
}

resource "databricks_notebook" "this" {
  content_base64 = base64encode("# Welcome to your Python notebook")
  path           = "/Production/ETL/Features"
  language       = "PYTHON"
}

resource "databricks_permissions" "notebook_usage" {
  notebook_path = databricks_notebook.this.path

  access_control {
    group_name       = "users"
    permission_level = "CAN_READ"
  }

  access_control {
    group_name       = databricks_group.auto.display_name
    permission_level = "CAN_RUN"
  }

  access_control {
    group_name       = databricks_group.eng.display_name
    permission_level = "CAN_EDIT"
  }
}

Workspace file usage

Valid permission levels for databricks_workspace_file are: CAN_READ, CAN_RUN, CAN_EDIT, and CAN_MANAGE.

resource "databricks_group" "auto" {
  display_name = "Automation"
}

resource "databricks_group" "eng" {
  display_name = "Engineering"
}

resource "databricks_workspace_file" "this" {
  content_base64 = base64encode("print('Hello World')")
  path           = "/Production/ETL/Features.py"
}

resource "databricks_permissions" "workspace_file_usage" {
  workspace_file_path = databricks_workspace_file.this.path

  access_control {
    group_name       = "users"
    permission_level = "CAN_READ"
  }

  access_control {
    group_name       = databricks_group.auto.display_name
    permission_level = "CAN_RUN"
  }

  access_control {
    group_name       = databricks_group.eng.display_name
    permission_level = "CAN_EDIT"
  }
}

Folder usage

Valid permission levels for folders of databricks_directory are: CAN_READ, CAN_RUN, CAN_EDIT, and CAN_MANAGE. Notebooks and experiments in a folder inherit all permissions settings of that folder. For example, a user (or service principal) that has CAN_RUN permission on a folder has CAN_RUN permission on the notebooks in that folder.

resource "databricks_group" "auto" {
  display_name = "Automation"
}

resource "databricks_group" "eng" {
  display_name = "Engineering"
}

resource "databricks_directory" "this" {
  path = "/Production/ETL"
}

resource "databricks_permissions" "folder_usage" {
  directory_path = databricks_directory.this.path
  depends_on     = [databricks_directory.this]

  access_control {
    group_name       = "users"
    permission_level = "CAN_READ"
  }

  access_control {
    group_name       = databricks_group.auto.display_name
    permission_level = "CAN_RUN"
  }

  access_control {
    group_name       = databricks_group.eng.display_name
    permission_level = "CAN_EDIT"
  }
}

Repos usage

Valid permission levels for databricks_repo are: CAN_READ, CAN_RUN, CAN_EDIT, and CAN_MANAGE.

resource "databricks_group" "auto" {
  display_name = "Automation"
}

resource "databricks_group" "eng" {
  display_name = "Engineering"
}

resource "databricks_repo" "this" {
  url = "https://github.com/user/demo.git"
}

resource "databricks_permissions" "repo_usage" {
  repo_id = databricks_repo.this.id

  access_control {
    group_name       = "users"
    permission_level = "CAN_READ"
  }

  access_control {
    group_name       = databricks_group.auto.display_name
    permission_level = "CAN_RUN"
  }

  access_control {
    group_name       = databricks_group.eng.display_name
    permission_level = "CAN_EDIT"
  }
}

MLflow Experiment usage

Valid permission levels for databricks_mlflow_experiment are: CAN_READ, CAN_EDIT, and CAN_MANAGE.

data "databricks_current_user" "me" {}

resource "databricks_mlflow_experiment" "this" {
  name              = "${data.databricks_current_user.me.home}/Sample"
  artifact_location = "dbfs:/tmp/my-experiment"
  description       = "My MLflow experiment description"
}

resource "databricks_group" "auto" {
  display_name = "Automation"
}

resource "databricks_group" "eng" {
  display_name = "Engineering"
}

resource "databricks_permissions" "experiment_usage" {
  experiment_id = databricks_mlflow_experiment.this.id

  access_control {
    group_name       = "users"
    permission_level = "CAN_READ"
  }

  access_control {
    group_name       = databricks_group.auto.display_name
    permission_level = "CAN_MANAGE"
  }

  access_control {
    group_name       = databricks_group.eng.display_name
    permission_level = "CAN_EDIT"
  }
}

MLflow Model usage

Valid permission levels for databricks_mlflow_model are: CAN_READ, CAN_EDIT, CAN_MANAGE_STAGING_VERSIONS, CAN_MANAGE_PRODUCTION_VERSIONS, and CAN_MANAGE. You can also manage permissions for all MLflow models by registered_model_id = "root".

resource "databricks_mlflow_model" "this" {
  name = "SomePredictions"
}

resource "databricks_group" "auto" {
  display_name = "Automation"
}

resource "databricks_group" "eng" {
  display_name = "Engineering"
}

resource "databricks_permissions" "model_usage" {
  registered_model_id = databricks_mlflow_model.this.registered_model_id

  access_control {
    group_name       = "users"
    permission_level = "CAN_READ"
  }

  access_control {
    group_name       = databricks_group.auto.display_name
    permission_level = "CAN_MANAGE_PRODUCTION_VERSIONS"
  }

  access_control {
    group_name       = databricks_group.eng.display_name
    permission_level = "CAN_MANAGE_STAGING_VERSIONS"
  }
}

Model serving usage

Valid permission levels for databricks_model_serving are: CAN_VIEW, CAN_QUERY, and CAN_MANAGE.

resource "databricks_model_serving" "this" {
  name = "tf-test"
  config {
    served_models {
      name                  = "prod_model"
      model_name            = "test"
      model_version         = "1"
      workload_size         = "Small"
      scale_to_zero_enabled = true
    }
  }
}

resource "databricks_group" "auto" {
  display_name = "Automation"
}

resource "databricks_group" "eng" {
  display_name = "Engineering"
}

resource "databricks_permissions" "ml_serving_usage" {
  serving_endpoint_id = databricks_model_serving.this.serving_endpoint_id

  access_control {
    group_name       = "users"
    permission_level = "CAN_VIEW"
  }

  access_control {
    group_name       = databricks_group.auto.display_name
    permission_level = "CAN_MANAGE"
  }

  access_control {
    group_name       = databricks_group.eng.display_name
    permission_level = "CAN_QUERY"
  }
}

Passwords usage

By default on AWS deployments, all admin users can sign in to Databricks using either SSO or their username and password, and all API users can authenticate to the Databricks REST APIs using their username and password. As an admin, you can limit admin users’ and API users’ ability to authenticate with their username and password by configuring CAN_USE permissions using password access control.

resource "databricks_group" "guests" {
  display_name = "Guest Users"
}

resource "databricks_permissions" "password_usage" {
  authorization = "passwords"

  access_control {
    group_name       = databricks_group.guests.display_name
    permission_level = "CAN_USE"
  }
}

Token usage

It is required to have at least 1 personal access token in the workspace before you can manage tokens permissions.

Only possible permission to assign to non-admin group is CAN_USE, where _admins_ CAN_MANAGE all tokens:

resource "databricks_group" "auto" {
  display_name = "Automation"
}

resource "databricks_group" "eng" {
  display_name = "Engineering"
}

resource "databricks_permissions" "token_usage" {
  authorization = "tokens"

  access_control {
    group_name       = databricks_group.auto.display_name
    permission_level = "CAN_USE"
  }

  access_control {
    group_name       = databricks_group.eng.display_name
    permission_level = "CAN_USE"
  }
}

SQL warehouse usage

SQL warehouses have three possible permissions: IS_OWNER, CAN_USE and CAN_MANAGE:

data "databricks_current_user" "me" {}

resource "databricks_group" "auto" {
  display_name = "Automation"
}

resource "databricks_group" "eng" {
  display_name = "Engineering"
}

resource "databricks_sql_endpoint" "this" {
  name             = "Endpoint of ${data.databricks_current_user.me.alphanumeric}"
  cluster_size     = "Small"
  max_num_clusters = 1

  tags {
    custom_tags {
      key   = "City"
      value = "Amsterdam"
    }
  }
}

resource "databricks_permissions" "endpoint_usage" {
  sql_endpoint_id = databricks_sql_endpoint.this.id

  access_control {
    group_name       = databricks_group.auto.display_name
    permission_level = "CAN_USE"
  }

  access_control {
    group_name       = databricks_group.eng.display_name
    permission_level = "CAN_MANAGE"
  }
}

SQL Dashboard usage

SQL dashboards have three possible permissions: CAN_VIEW, CAN_RUN and CAN_MANAGE:

resource "databricks_group" "auto" {
  display_name = "Automation"
}

resource "databricks_group" "eng" {
  display_name = "Engineering"
}

resource "databricks_permissions" "endpoint_usage" {
  sql_dashboard_id = "3244325"

  access_control {
    group_name       = databricks_group.auto.display_name
    permission_level = "CAN_RUN"
  }

  access_control {
    group_name       = databricks_group.eng.display_name
    permission_level = "CAN_MANAGE"
  }
}

SQL Query usage

SQL queries have three possible permissions: CAN_VIEW, CAN_RUN and CAN_MANAGE:

resource "databricks_group" "auto" {
  display_name = "Automation"
}

resource "databricks_group" "eng" {
  display_name = "Engineering"
}

resource "databricks_permissions" "endpoint_usage" {
  sql_query_id = "3244325"

  access_control {
    group_name       = databricks_group.auto.display_name
    permission_level = "CAN_RUN"
  }

  access_control {
    group_name       = databricks_group.eng.display_name
    permission_level = "CAN_MANAGE"
  }
}

SQL Alert usage

SQL alerts have three possible permissions: CAN_VIEW, CAN_RUN and CAN_MANAGE:

resource "databricks_group" "auto" {
  display_name = "Automation"
}

resource "databricks_group" "eng" {
  display_name = "Engineering"
}

resource "databricks_permissions" "endpoint_usage" {
  sql_alert_id = "3244325"

  access_control {
    group_name       = databricks_group.auto.display_name
    permission_level = "CAN_RUN"
  }

  access_control {
    group_name       = databricks_group.eng.display_name
    permission_level = "CAN_MANAGE"
  }
}

Instance Profiles

Instance Profiles are not managed by General Permissions API and therefore databricks_group_instance_profile and databricks_user_instance_profile should be used to allow usage of specific AWS EC2 IAM roles to users or groups.

Secrets

One can control access to databricks_secret through initial_manage_principal argument on databricks_secret_scope or databricks_secret_acl, so that users (or service principals) can READ, WRITE or MANAGE entries within secret scope.

Tables, Views and Databases

General Permissions API does not apply to access control for tables and they have to be managed separately using the databricks_sql_permissions resource, though you're encouraged to use Unity Catalog or migrate to it.

Data Access with Unity Catalog

Initially in Unity Catalog all users have no access to data, which has to be later assigned through databricks_grants resource.

Argument Reference

One type argument and at least one access control block argument are required.

Type Argument

Exactly one of the following arguments is required:

Access Control Argument

One or more access_control blocks are required to actually set the permission levels:

access_control {
  group_name       = databricks_group.datascience.display_name
  permission_level = "CAN_USE"
}

Arguments for the access_control block are:

Exactly one of the below arguments is required:

Attribute Reference

In addition to all arguments above, the following attributes are exported:

Import

The resource permissions can be imported using the object id

terraform import databricks_permissions.this /<object type>/<object id>

Import Example

Configuration file:

resource "databricks_mlflow_model" "model" {
  name        = "example_model"
  description = "MLflow registered model"
}

resource "databricks_permissions" "model_usage" {
  registered_model_id = databricks_mlflow_model.model.registered_model_id

  access_control {
    group_name       = "users"
    permission_level = "CAN_READ"
  }
}

Import command:

terraform import databricks_permissions.model_usage /registered-models/<registered_model_id>