Is there any way user can create private dashboard?

[Yassuip]

Hello everyone, I'm new here and just starting to explore. Is it possible in Superset for a user to create a private dashboard that isn't shared with all users?

[dosubot[bot]]

Hey, @Yassuip! 👋 I'm here to help you out. If you have any bugs to report, questions to ask, or want to become a contributor, I'm your bot!

Yes, it is possible in Superset for a user to create a private dashboard. Access to dashboards is managed via owners, who have edit permissions. You can also manage access through dataset permissions or by enabling the DASHBOARD_RBAC feature flag, which allows you to specify which roles can access the dashboard. Additionally, dashboards can be marked as unpublished, making them inaccessible to users who do not own them or have not been granted specific access ^[1].

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[dosubot[bot]]

In Superset, the publish permission for dashboards is managed through user roles and permissions. A user can publish a dashboard if they have the 'can_write' permission for the 'Dashboard' view and are either an admin or the owner of the dashboard ^[1].

If managing individual roles isn't feasible, you might consider customizing the permission model to better fit your needs. Superset allows for customization of permissions through the SupersetSecurityManager class, where you can define custom permissions and roles ^[2]. However, there are no specific configuration options to manage publish permissions without altering roles.

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[Yassuip]

@dosu Where we can configure our database in code to get rid of manually adding it.

[dosubot[bot]]

To configure your database in code and avoid manually adding it in Superset, you can create a superset_config.py file to override the default configurations. In this file, you can set the SQLALCHEMY_DATABASE_URI for your database connection. Here's an example:

# The SQLAlchemy connection string.
SQLALCHEMY_DATABASE_URI = 'your_database_connection_string_here'

Replace 'your_database_connection_string_here' with your actual database connection string, such as:

SQLALCHEMY_DATABASE_URI = 'postgresql://username:password@localhost/mydatabase'

Ensure that your superset_config.py file is in your PYTHONPATH or specify its path using the SUPERSET_CONFIG_PATH environment variable:

export SUPERSET_CONFIG_PATH=/app/superset_config.py

If using Docker, copy your superset_config.py into the container and set the environment variable like this:

COPY --chown=superset superset_config.py /app/
ENV SUPERSET_CONFIG_PATH /app/superset_config.py

This setup will configure Superset to use the specified database without needing to add it manually through the Superset UI ^[1][2][3].

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[Yassuip]

@dosu And how we can create all dataset for a database programmitically?

[dosubot[bot]]

To create all datasets for a database programmatically in Superset, you can use the Superset API. Here's a general approach:

1. Identify the Database ID: First, you need to know the ID of the database for which you want to create datasets. You can retrieve this information using the Superset API's /database/ endpoint.

2. Use the /dataset/ Endpoint: To create a dataset, use the POST method on the /dataset/ endpoint. The request body should be in JSON format and conform to the DatasetPostSchema.

3. Prepare the Request Body: For each table in your database, prepare a JSON request body that includes the database ID and table_name. You can also include other optional fields like schema, sql, and owners.

Example JSON request body for creating a dataset:

{
  "database": 1,  // Replace with your actual database ID
  "table_name": "your_table_name",
  "schema": "public",  // Optional
  "sql": "SELECT * FROM your_table_name",  // Optional
  "owners": [1]  // Optional, replace with actual owner IDs
}

4. Automate the Process: You can automate this process by writing a script that iterates over all tables in your database and sends a POST request for each table to create a dataset.

5. Handle Responses: A successful request will return a 201 status code with the dataset ID and details.

This approach allows you to programmatically create datasets for all tables in a database using the Superset API ^[1].

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