`w.cluster_policies`: Cluster Policies¶

class databricks.sdk.service.compute.ClusterPoliciesAPI¶

You can use cluster policies to control users’ ability to configure clusters based on a set of rules. These rules specify which attributes or attribute values can be used during cluster creation. Cluster policies have ACLs that limit their use to specific users and groups.

With cluster policies, you can: - Auto-install cluster libraries on the next restart by listing them in the policy’s “libraries” field (Public Preview). - Limit users to creating clusters with the prescribed settings. - Simplify the user interface, enabling more users to create clusters, by fixing and hiding some fields. - Manage costs by setting limits on attributes that impact the hourly rate.

Cluster policy permissions limit which policies a user can select in the Policy drop-down when the user creates a cluster: - A user who has unrestricted cluster create permission can select the Unrestricted policy and create fully-configurable clusters. - A user who has both unrestricted cluster create permission and access to cluster policies can select the Unrestricted policy and policies they have access to. - A user that has access to only cluster policies, can select the policies they have access to.

If no policies exist in the workspace, the Policy drop-down doesn’t appear. Only admin users can create, edit, and delete policies. Admin users also have access to all policies.

create(name: str [, definition: Optional[str], description: Optional[str], libraries: Optional[List[Library]], max_clusters_per_user: Optional[int], policy_family_definition_overrides: Optional[str], policy_family_id: Optional[str]]) → CreatePolicyResponse¶

Usage:

import time

from databricks.sdk import WorkspaceClient

w = WorkspaceClient()

created = w.cluster_policies.create(name=f'sdk-{time.time_ns()}',
                                    definition="""{
            "spark_conf.spark.databricks.delta.preview.enabled": {
                "type": "fixed",
                "value": true
            }
        }
""")

# cleanup
w.cluster_policies.delete(policy_id=created.policy_id)

Create a new policy.

Creates a new policy with prescribed settings.

Parameters:

name – str Cluster Policy name requested by the user. This has to be unique. Length must be between 1 and 100 characters.
definition –
str (optional) Policy definition document expressed in [Databricks Cluster Policy Definition Language].

[Databricks Cluster Policy Definition Language]: https://docs.databricks.com/administration-guide/clusters/policy-definition.html
description – str (optional) Additional human-readable description of the cluster policy.
libraries – List[Library] (optional) A list of libraries to be installed on the next cluster restart that uses this policy. The maximum number of libraries is 500.
max_clusters_per_user – int (optional) Max number of clusters per user that can be active using this policy. If not present, there is no max limit.
policy_family_definition_overrides –
str (optional) Policy definition JSON document expressed in [Databricks Policy Definition Language]. The JSON document must be passed as a string and cannot be embedded in the requests.

You can use this to customize the policy definition inherited from the policy family. Policy rules specified here are merged into the inherited policy definition.

[Databricks Policy Definition Language]: https://docs.databricks.com/administration-guide/clusters/policy-definition.html
policy_family_id –
str (optional) ID of the policy family. The cluster policy’s policy definition inherits the policy family’s policy definition.

Cannot be used with definition. Use policy_family_definition_overrides instead to customize the policy definition.

Returns:

CreatePolicyResponse

delete(policy_id: str)¶

Delete a cluster policy.

Delete a policy for a cluster. Clusters governed by this policy can still run, but cannot be edited.

Parameters:: policy_id – str The ID of the policy to delete.

edit(policy_id: str, name: str [, definition: Optional[str], description: Optional[str], libraries: Optional[List[Library]], max_clusters_per_user: Optional[int], policy_family_definition_overrides: Optional[str], policy_family_id: Optional[str]])¶

Usage:

import time

from databricks.sdk import WorkspaceClient

w = WorkspaceClient()

created = w.cluster_policies.create(name=f'sdk-{time.time_ns()}',
                                    definition="""{
            "spark_conf.spark.databricks.delta.preview.enabled": {
                "type": "fixed",
                "value": true
            }
        }
""")

policy = w.cluster_policies.get(policy_id=created.policy_id)

w.cluster_policies.edit(policy_id=policy.policy_id,
                        name=policy.name,
                        definition="""{
            "spark_conf.spark.databricks.delta.preview.enabled": {
                "type": "fixed",
                "value": false
            }
        }
""")

# cleanup
w.cluster_policies.delete(policy_id=created.policy_id)

Update a cluster policy.

Update an existing policy for cluster. This operation may make some clusters governed by the previous policy invalid.

Parameters:

policy_id – str The ID of the policy to update.
name – str Cluster Policy name requested by the user. This has to be unique. Length must be between 1 and 100 characters.
definition –
str (optional) Policy definition document expressed in [Databricks Cluster Policy Definition Language].

[Databricks Cluster Policy Definition Language]: https://docs.databricks.com/administration-guide/clusters/policy-definition.html
description – str (optional) Additional human-readable description of the cluster policy.
libraries – List[Library] (optional) A list of libraries to be installed on the next cluster restart that uses this policy. The maximum number of libraries is 500.
max_clusters_per_user – int (optional) Max number of clusters per user that can be active using this policy. If not present, there is no max limit.
policy_family_definition_overrides –
str (optional) Policy definition JSON document expressed in [Databricks Policy Definition Language]. The JSON document must be passed as a string and cannot be embedded in the requests.

You can use this to customize the policy definition inherited from the policy family. Policy rules specified here are merged into the inherited policy definition.

[Databricks Policy Definition Language]: https://docs.databricks.com/administration-guide/clusters/policy-definition.html
policy_family_id –
str (optional) ID of the policy family. The cluster policy’s policy definition inherits the policy family’s policy definition.

Cannot be used with definition. Use policy_family_definition_overrides instead to customize the policy definition.

get(policy_id: str) → Policy¶

Usage:

import time

from databricks.sdk import WorkspaceClient

w = WorkspaceClient()

created = w.cluster_policies.create(name=f'sdk-{time.time_ns()}',
                                    definition="""{
            "spark_conf.spark.databricks.delta.preview.enabled": {
                "type": "fixed",
                "value": true
            }
        }
""")

policy = w.cluster_policies.get(policy_id=created.policy_id)

# cleanup
w.cluster_policies.delete(policy_id=created.policy_id)

Get a cluster policy.

Get a cluster policy entity. Creation and editing is available to admins only.

Parameters:: policy_id – str Canonical unique identifier for the cluster policy.
Returns:: Policy

get_permission_levels(cluster_policy_id: str) → GetClusterPolicyPermissionLevelsResponse¶

Get cluster policy permission levels.

Gets the permission levels that a user can have on an object.

Parameters:: cluster_policy_id – str The cluster policy for which to get or manage permissions.
Returns:: GetClusterPolicyPermissionLevelsResponse

get_permissions(cluster_policy_id: str) → ClusterPolicyPermissions¶

Get cluster policy permissions.

Gets the permissions of a cluster policy. Cluster policies can inherit permissions from their root object.

Parameters:: cluster_policy_id – str The cluster policy for which to get or manage permissions.
Returns:: ClusterPolicyPermissions

list([, sort_column: Optional[ListSortColumn], sort_order: Optional[ListSortOrder]]) → Iterator[Policy]¶

Usage:

from databricks.sdk import WorkspaceClient
from databricks.sdk.service import compute

w = WorkspaceClient()

all = w.cluster_policies.list(compute.ListClusterPoliciesRequest())

List cluster policies.

Returns a list of policies accessible by the requesting user.

Parameters:

sort_column – ListSortColumn (optional) The cluster policy attribute to sort by. * POLICY_CREATION_TIME - Sort result list by policy creation time. * POLICY_NAME - Sort result list by policy name.
sort_order – ListSortOrder (optional) The order in which the policies get listed. * DESC - Sort result list in descending order. * ASC - Sort result list in ascending order.

Returns:

Iterator over Policy

set_permissions(cluster_policy_id: str [, access_control_list: Optional[List[ClusterPolicyAccessControlRequest]]]) → ClusterPolicyPermissions¶

Set cluster policy permissions.

Sets permissions on a cluster policy. Cluster policies can inherit permissions from their root object.

Parameters:

cluster_policy_id – str The cluster policy for which to get or manage permissions.
access_control_list – List[ClusterPolicyAccessControlRequest] (optional)

Returns:

ClusterPolicyPermissions

update_permissions(cluster_policy_id: str [, access_control_list: Optional[List[ClusterPolicyAccessControlRequest]]]) → ClusterPolicyPermissions¶

Update cluster policy permissions.

Updates the permissions on a cluster policy. Cluster policies can inherit permissions from their root object.

Parameters:

cluster_policy_id – str The cluster policy for which to get or manage permissions.
access_control_list – List[ClusterPolicyAccessControlRequest] (optional)

Returns:

ClusterPolicyPermissions

Navigation

Related Topics

`w.cluster_policies`: Cluster Policies¶

w.cluster_policies: Cluster Policies¶

`w.cluster_policies`: Cluster Policies¶