Databricks dashboard clone

Unofficial project to allow Databricks SQL dashboard copy from one workspace to another.

Resource clone

Install:

pip install dbsqlclone

Setup for built-in clone:

Create a file named config.json and put your credentials. You need to define the source (where the resources will be copied from) and a list of targets (where the resources will be cloned).

{
  "source": {
    "url": "https://xxxxx.cloud.databricks.com",
    "token": "xxxxxxx", /* your PAT token*/
    "dashboard_tags": ["field_demos"] /* Dashboards having any of these tags matching will be cloned from the SOURCE */
  },
  "delete_target_dashboards": true, /* Erase the dashboards and queries in the targets having the same tags in TARGETS. If false, won't do anything (might endup with duplicates). */
  "targets": [
    {
      "url": "https:/xxxxxxx.cloud.databricks.com",
      "token": "xxxxxxx",
      "endpoint_id": "xxxxxxxxxx4da979", /* Optional, will use the first endpoint available if not set. At least 1 endpoint must exist in the workspace.*/
      "permissions":[ /* Optional, force the permissions to this set of values. In this example we add a CAN_RUN for All Users.*/
        {
          "user_name": "[email protected]",
          "permission_level": "CAN_MANAGE"
        },
        {
          "group_name": "users",
          "permission_level": "CAN_RUN"
        }
      ]
    },
    {
      "url": "https://xxxxxxx.azuredatabricks.net",
      "token": "xxxxxxx"
    }
  ]
}

endpoint_id the ID of the endpoint we'll attach to the queries.

To find your endpoint_id on each target workspace, click in one of your endpoint. The endpoint ID is in the URL: https://xxxx.azuredatabricks.net/sql/endpoints/<endpoint_id>?o=xxxx

Run:

Run the clone_resources.py script to clone all the resources

Dashboard update

If a state file (json.state) exists and the dashboards+queries have already be cloned, the clone operation will try to update the existing dashboards and queries.

Visualizations and widgets are systematically destroyed and re-created to simplify synchronization.

If your state is out of sync, delete the entry matching your target to re-delete all content in the target and re-clone from scratch.

You can delete the state of a single workspace by searching the entry in the json state information.

State file structure

{
  "SOURCE_URL-TARGET_URL": {
    "SOURCE_DASHBOARD_ID": {
      "queries": {
        "SOURCE_QUERY_ID": {
          "new_id": "TARGET_QUERY_ID",
          "visualizations": {
            "SOURCE_VISUALIZATION_ID": "TARGET_VISUALIZATION_ID",...
          }
        },...
      },
      "new_id": "TARGET_DASHBOARD_ID"
    }
  }
}

Custom usage / Working with git

Direct api usage

Get dashboard definition as json:

from dbsqlclone.utils.client import Client
from dbsqlclone.utils import dump_dashboard

source_client = Client("<workspaceUrl>", "<workspaceToken>")
dashboard_id_to_save = "xxx-xxx-xxx-xxx"
dashboard_def = dump_dashboard.get_dashboard_definition_by_id(source_client, dashboard_id_to_save)
print(json.dumps(dashboard_def))

Create the dashboard from the definition. This will just create a new one.

target_client.endpoint_id = "the endpoint=warehouse ID"
#We need to find the datasource from endpoint id
clone_dashboard.set_data_source_id_from_endpoint_id(target_client)
from dbsqlclone.utils import load_dashboard
load_dashboard.clone_dashboard(dashboard_def, target_client, state={}, path=None)

Override an existing dashboard. This will try to update the dashboard queries when the query name match, and delete all queries not matching.

target_client.data_source_id = "the datasource or warehouse ID to use"
dashboard_to_override = "xxx-xxx-xxx-xxx"
load_dashboard.clone_dashboard_without_saved_state(dashboard_def, target_client, dashboard_to_override)

Saving dashboards as json with state file created in current folder:

    source_client = Client("<workspaceUrl>", "<workspaceToken>")
    dashboard_id_to_save = "xxx-xxx-xxx-xxx"
    dump_dashboard.dump_dashboard(source_client, dashboard_id_to_save, "./dashboards/")

Dashboard jsons definition will then be saved under the specified folder ./dashboards/ and can be saved in git as required.

Loading dashboards from json with state file created in current folder:

Once the json is saved, you can load it to build or update the dashboard in any workspace.

dashboard_state contains the link between the source and the cloned dashboard. It is used to update the dashboard clone and avoid having to delete/re-recreate the cloned dashboard everytime.

Loading the dashboard will update the sate definition. If you don't care about it you can pass an empty dict {}

    target_client = Client("<workspaceUrl>", "<workspaceToken>")
    workspace_state = {}
    dashboard_id_to_load = "xxx-xxx-xxx-xxx"
    load_dashboard.load_dashboard(target_client, dashboard_id_to_load, workspace_state, "./dashboards/")