Advanced workspace overrides

For self-hosted instances, Structural provides environment settings to configure features that include:

• Consistency across runs and databases

• Data generation performance

The Advanced Workspace Overrides section of the workspace details view allows you to override those environment settings for an individual workspace.

For example, the environment setting TONIC_TABLE_PARALLELISM determines the number of tables that Structural processes simultaneously. You can then override that value within individual workspaces.

The workspace overrides are available on both self-hosted instances and on Structural Cloud.

Configuring the overrides

To display the available override settings, expand Advanced Workspace Overrides.

Advanced Workspace Overrides section for a workspace

Enabling and setting an override

You can use the search field to find a specific setting.

Applying a search filter to the advanced workspace overrides

For information on how to configure the statistics seed, go to Overriding the Structural seed value for a workspace.

For other settings, to enable the override and set the override value:

1. Toggle the setting to the on position.

Enabling and setting an override value

1. Set the value.

Removing an override

To remove the override, toggle the setting to the off position.

Available overrides

Workspace statistics seed for cross-run consistency

For generators where consistency is enabled, a statistics seed enables consistency across data generation runs. The Structural-wide statistics seed value ensures consistency across both data generation runs and workspaces.

You use the Override Statistics Seed setting to override the Structural-wide statistics seed value.

You can either disable consistency across data generations, or provide a seed value for the workspace. The workspace seed value ensures consistency across data generation runs for that workspace, and across other workspaces that have the same seed value.

For details about using seed values to ensure consistency across data generation runs and databases, go to Enabling consistency across runs or multiple databases.

Data generation performance settings

Structural provides environment settings to manage data generation performance. For example, these settings include configuration for parallel processing and client-side compression.

From Advanced Workspace Overrides, you can override some of these data generation performance settings for an individual workspace.

Data encryption and decryption keys

To use Structural data encryption, you must provide encryption and decryption keys.

You use the Override Data Decryption Key and Override Data Encryption Key settings to override the Structural-wide keys that are provided in the environment settings.

Destination database schema creation

Some data connectors allow you to configure whether you provide the schema for the destination database. For more information, go to related information for Databricks, MySQL, Oracle, Snowflake, and SQL Server.

From Advanced Workspace Overrides, you can override the instance-wide configuration.

Overwrite handling for Databricks

Databricks allows you to configure how Structural handles overwrites of existing data.

You use the Override Workspace Default Error on Override and Override Workspace Default Save Mode settings to override the instance-wide configuration.

Enable or disable event-based sensitivity scans

Structural provides an environment setting to determine whether to automatically run a sensitivity scan when you create a workspace, copy a workspace, or change the connection details.

From Advanced Workspace Overrides, you can override the instance-wide configuration.

Enable or disable LLM-based sensitivity detection on Structural Cloud

By default, LLM-based sensitivity detection is enabled on Structural Cloud.

You use the Tonic LLM Enable Enhanced Recommendations setting to determine whether to enable LLM-based sensitivity detection in the workspace.

For information about LLM-based sensitivity detection, go to LLM-based sensitivity detection - Medium confidence.