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  • Tonic Structural User Guide
  • About Tonic Structural
    • Structural data generation workflow
    • Structural deployment types
    • Structural implementation roles
    • Structural license plans
  • Logging into Structural for the first time
  • Getting started with the Structural free trial
  • Managing your user account
  • Frequently Asked Questions
  • Tutorial videos
  • Creating and managing workspaces
    • Managing workspaces
      • Viewing your list of workspaces
      • Creating, editing, or deleting a workspace
      • Workspace configuration settings
        • Workspace identification and connection type
        • Data connection settings
        • Configuring secrets managers for database connections
        • Data generation settings
        • Enabling and configuring upsert
        • Writing output to Tonic Ephemeral
        • Writing output to a container repository
        • Advanced workspace overrides
      • About the workspace management view
      • About workspace inheritance
      • Assigning tags to a workspace
      • Exporting and importing the workspace configuration
    • Managing access to workspaces
      • Sharing workspace access
      • Transferring ownership of a workspace
    • Viewing workspace jobs and job details
  • Configuring data generation
    • Privacy Hub
    • Database View
      • Viewing and configuring tables
      • Viewing the column list
      • Displaying sample data for a column
      • Configuring an individual column
      • Configuring multiple columns
      • Identifying similar columns
      • Commenting on columns
    • Table View
    • Working with document-based data
      • Performing scans on collections
      • Using Collection View
    • Identifying sensitive data
      • Running the Structural sensitivity scan
      • Manually indicating whether a column is sensitive
      • Built-in sensitivity types that Structural detects
      • Creating and managing custom sensitivity rules
    • Table modes
    • Generator information
      • Generator summary
      • Generator reference
        • Address
        • Algebraic
        • Alphanumeric String Key
        • Array Character Scramble
        • Array JSON Mask
        • Array Regex Mask
        • ASCII Key
        • Business Name
        • Categorical
        • Character Scramble
        • Character Substitution
        • Company Name
        • Conditional
        • Constant
        • Continuous
        • Cross Table Sum
        • CSV Mask
        • Custom Categorical
        • Date Truncation
        • Email
        • Event Timestamps
        • File Name
        • Find and Replace
        • Finnish Personal Identity Code
        • FNR
        • Geo
        • HIPAA Address
        • Hostname
        • HStore Mask
        • HTML Mask
        • Integer Key
        • International Address
        • IP Address
        • JSON Mask
        • MAC Address
        • Mongo ObjectId Key
        • Name
        • Noise Generator
        • Null
        • Numeric String Key
        • Passthrough
        • Phone
        • Random Boolean
        • Random Double
        • Random Hash
        • Random Integer
        • Random Timestamp
        • Random UUID
        • Regex Mask
        • Sequential Integer
        • Shipping Container
        • SIN
        • SSN
        • Struct Mask
        • Timestamp Shift Generator
        • Unique Email
        • URL
        • UUID Key
        • XML Mask
      • Generator characteristics
        • Enabling consistency
        • Linking generators
        • Differential privacy
        • Partitioning a column
        • Data-free generators
        • Supporting uniqueness constraints
        • Format-preserving encryption (FPE)
      • Generator types
        • Composite generators
        • Primary key generators
    • Generator assignment and configuration
      • Reviewing and applying recommended generators
      • Assigning and configuring generators
      • Document View for file connector JSON columns
      • Generator hints and tips
      • Managing generator presets
      • Configuring and using Structural data encryption
      • Custom value processors
    • Subsetting data
      • About subsetting
      • Using table filtering for data warehouses and Spark-based data connectors
      • Viewing the current subsetting configuration
      • Subsetting and foreign keys
      • Configuring subsetting
      • Viewing and managing configuration inheritance
      • Viewing the subset creation steps
      • Viewing previous subsetting data generation runs
      • Generating cohesive subset data from related databases
      • Other subsetting hints and tips
    • Viewing and adding foreign keys
    • Viewing and resolving schema changes
    • Tracking changes to workspaces, generator presets, and sensitivity rules
    • Using the Privacy Report to verify data protection
  • Running data generation
    • Running data generation jobs
      • Types of data generation
      • Data generation process
      • Running data generation manually
      • Scheduling data generation
      • Issues that prevent data generation
    • Managing data generation performance
    • Viewing and downloading container artifacts
    • Post-job scripts
    • Webhooks
  • Installing and Administering Structural
    • Structural architecture
    • Using Structural securely
    • Deploying a self-hosted Structural instance
      • Deployment checklist
      • System requirements
      • Deploying with Docker Compose
      • Deploying on Kubernetes with Helm
      • Enabling the option to write output data to a container repository
        • Setting up a Kubernetes cluster to use to write output data to a container repository
        • Required access to write destination data to a container repository
      • Entering and updating your license key
      • Setting up host integration
      • Working with the application database
      • Setting up a secret
      • Setting a custom certificate
    • Using Structural Cloud
      • Structural Cloud notes
      • Setting up and managing a Structural Cloud pay-as-you-go subscription
      • Structural Cloud onboarding
    • Managing user access to Structural
      • Structural organizations
      • Determining whether users can create accounts
      • Creating a new account in an existing organization
      • Single sign-on (SSO)
        • Structural user authentication with SSO
        • Enabling and configuring SSO on Structural Cloud
        • Synchronizing SSO groups with Structural
        • Viewing the list of SSO groups in Tonic Structural
        • AWS IAM Identity Center
        • Duo
        • GitHub
        • Google
        • Keycloak
        • Microsoft Entra ID (previously Azure Active Directory)
        • Okta
        • OpenID Connect (OIDC)
        • SAML
      • Managing Structural users
      • Managing permissions
        • About permission sets
        • Built-in permission sets
        • Available permissions
        • Viewing the lists of global and workspace permission sets
        • Configuring custom permission sets
        • Selecting default permission sets
        • Configuring access to global permission sets
        • Setting initial access to all global permissions
        • Granting Account Admin access for a Structural Cloud organization
    • Structural monitoring and logging
      • Monitoring Structural services
      • Performing health checks
      • Downloading the usage report
      • Tracking user access and permissions
      • Redacted and diagnostic (unredacted) logs
      • Data that Tonic.ai collects
      • Verifying and enabling telemetry sharing
    • Configuring environment settings
    • Updating Structural
  • Connecting to your data
    • About data connectors
    • Overview for database administrators
    • Data connector summary
    • Amazon DynamoDB
      • System requirements and limitations for DynamoDB
      • Structural differences and limitations with DynamoDB
      • Before you create a DynamoDB workspace
      • Configuring DynamoDB workspace data connections
    • Amazon EMR
      • Structural process overview for Amazon EMR
      • System requirements for Amazon EMR
      • Structural differences and limitations with Amazon EMR
      • Before you create an Amazon EMR workspace
        • Creating IAM roles for Structural and Amazon EMR
        • Creating Athena workgroups
        • Configuration for cross-account setups
      • Configuring Amazon EMR workspace data connections
    • Amazon Redshift
      • Structural process overview for Amazon Redshift
      • Structural differences and limitations with Amazon Redshift
      • Before you create an Amazon Redshift workspace
        • Required AWS instance profile permissions for Amazon Redshift
        • Setting up the AWS Lambda role for Amazon Redshift
        • AWS KMS permissions for Amazon SQS message encryption
        • Amazon Redshift-specific Structural environment settings
        • Source and destination database permissions for Amazon Redshift
      • Configuring Amazon Redshift workspace data connections
    • Databricks
      • Structural process overview for Databricks
      • System requirements for Databricks
      • Structural differences and limitations with Databricks
      • Before you create a Databricks workspace
        • Granting access to storage
        • Setting up your Databricks cluster
        • Configuring the destination database schema creation
      • Configuring Databricks workspace data connections
    • Db2 for LUW
      • System requirements for Db2 for LUW
      • Structural differences and limitations with Db2 for LUW
      • Before you create a Db2 for LUW workspace
      • Configuring Db2 for LUW workspace data connections
    • File connector
      • Overview of the file connector process
      • Supported file and content types
      • Structural differences and limitations with the file connector
      • Before you create a file connector workspace
      • Configuring the file connector storage type and output options
      • Managing file groups in a file connector workspace
      • Downloading generated file connector files
    • Google BigQuery
      • Structural differences and limitations with Google BigQuery
      • Before you create a Google BigQuery workspace
      • Configuring Google BigQuery workspace data connections
      • Resolving schema changes for de-identified views
    • MongoDB
      • System requirements for MongoDB
      • Structural differences and limitations with MongoDB
      • Configuring MongoDB workspace data connections
      • Other MongoDB hints and tips
    • MySQL
      • System requirements for MySQL
      • Before you create a MySQL workspace
      • Configuring MySQL workspace data connections
    • Oracle
      • Known limitations for Oracle schema objects
      • System requirements for Oracle
      • Structural differences and limitations with Oracle
      • Before you create an Oracle workspace
      • Configuring Oracle workspace data connections
      • Troubleshooting Oracle permissions
    • PostgreSQL
      • System requirements for PostgreSQL
      • Before you create a PostgreSQL workspace
      • Configuring PostgreSQL workspace data connections
    • Salesforce
      • System requirements for Salesforce
      • Structural differences and limitations with Salesforce
      • Before you create a Salesforce workspace
      • Configuring Salesforce workspace data connections
    • Snowflake on AWS
      • Structural process overviews for Snowflake on AWS
      • Structural differences and limitations with Snowflake on AWS
      • Before you create a Snowflake on AWS workspace
        • Required AWS instance profile permissions for Snowflake on AWS
        • Other configuration for Lambda processing
        • Source and destination database permissions for Snowflake on AWS
        • Configuring whether Structural creates the Snowflake on AWS destination database schema
      • Configuring Snowflake on AWS workspace data connections
    • Snowflake on Azure
      • Structural process overview for Snowflake on Azure
      • Structural differences and limitations with Snowflake on Azure
      • Before you create a Snowflake on Azure workspace
      • Configuring Snowflake on Azure workspace data connections
    • Spark SDK
      • Structural process overview for the Spark SDK
      • Structural differences and limitations with the Spark SDK
      • Configuring Spark SDK workspace data connections
      • Using Spark to run de-identification of the data
    • SQL Server
      • System requirements for SQL Server
      • Before you create a SQL Server workspace
      • Configuring SQL Server workspace data connections
    • Yugabyte
      • System requirements for Yugabyte
      • Structural differences and limitations with Yugabyte
      • Before you create a Yugabyte workspace
      • Configuring Yugabyte workspace data connections
      • Troubleshooting Yugabyte data generation issues
  • Using the Structural API
    • About the Structural API
    • Getting an API token
    • Getting the workspace ID
    • Using the Structural API to perform tasks
      • Configure environment settings
      • Manage generator presets
        • Retrieving the list of generator presets
        • Structure of a generator preset
        • Creating a custom generator preset
        • Updating an existing generator preset
        • Deleting a generator preset
      • Manage custom sensitivity rules
      • Create a workspace
      • Connect to source and destination data
      • Manage file groups in a file connector workspace
      • Assign table modes and filters to source database tables
      • Set column sensitivity
      • Assign generators to columns
        • Getting the generator IDs and available metadata
        • Updating generator configurations
        • Structure of a generator assignment
        • Generator API reference
          • Address (AddressGenerator)
          • Algebraic (AlgebraicGenerator)
          • Alphanumeric String Key (AlphaNumericPkGenerator)
          • Array Character Scramble (ArrayTextMaskGenerator)
          • Array JSON Mask (ArrayJsonMaskGenerator)
          • Array Regex Mask (ArrayRegexMaskGenerator)
          • ASCII Key (AsciiPkGenerator)
          • Business Name (BusinessNameGenerator)
          • Categorical (CategoricalGenerator)
          • Character Scramble (TextMaskGenerator)
          • Character Substitution (StringMaskGenerator)
          • Company Name (CompanyNameGenerator)
          • Conditional (ConditionalGenerator)
          • Constant (ConstantGenerator)
          • Continuous (GaussianGenerator)
          • Cross Table Sum (CrossTableAggregateGenerator)
          • CSV Mask (CsvMaskGenerator)
          • Custom Categorical (CustomCategoricalGenerator)
          • Date Truncation (DateTruncationGenerator)
          • Email (EmailGenerator)
          • Event Timestamps (EventGenerator)
          • File Name (FileNameGenerator)
          • Find and Replace (FindAndReplaceGenerator)
          • Finnish Personal Identity Code (FinnishPicGenerator)
          • FNR (FnrGenerator)
          • Geo (GeoGenerator)
          • HIPAA Address (HipaaAddressGenerator)
          • Hostname (HostnameGenerator)
          • HStore Mask (HStoreMaskGenerator)
          • HTML Mask (HtmlMaskGenerator)
          • Integer Key (IntegerPkGenerator)
          • International Address (InternationalAddressGenerator)
          • IP Address (IPAddressGenerator)
          • JSON Mask (JsonMaskGenerator)
          • MAC Address (MACAddressGenerator)
          • Mongo ObjectId Key (ObjectIdPkGenerator)
          • Name (NameGenerator)
          • Noise Generator (NoiseGenerator)
          • Null (NullGenerator)
          • Numeric String Key (NumericStringPkGenerator)
          • Passthrough (PassthroughGenerator)
          • Phone (USPhoneNumberGenerator)
          • Random Boolean (RandomBooleanGenerator)
          • Random Double (RandomDoubleGenerator)
          • Random Hash (RandomStringGenerator)
          • Random Integer (RandomIntegerGenerator)
          • Random Timestamp (RandomTimestampGenerator)
          • Random UUID (UUIDGenerator)
          • Regex Mask (RegexMaskGenerator)
          • Sequential Integer (UniqueIntegerGenerator)
          • Shipping Container (ShippingContainerGenerator)
          • SIN (SINGenerator)
          • SSN (SsnGenerator)
          • Struct Mask (StructMaskGenerator)
          • Timestamp Shift (TimestampShiftGenerator)
          • Unique Email (UniqueEmailGenerator)
          • URL (UrlGenerator)
          • UUID Key (UuidPkGenerator)
          • XML Mask (XmlMaskGenerator)
      • Configure subsetting
      • Check for and resolve schema changes
      • Run data generation jobs
      • Schedule data generation jobs
    • Example script: Starting a data generation job
    • Example script: Polling for a job status and creating a Docker package
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On this page
  • Enabling Document View for a JSON column
  • Displaying Document View
  • Selecting the JSON column to configure
  • Selecting the type of view
  • Hybrid view
  • Single view
  • Information in the field list
  • Toggling between source and preview data
  • Filtering Document View fields
  • Filtering single document view by field name or value
  • Filtering hybrid view by field name
  • Filtering hybrid view by field properties
  • Filters panel filters
  • At-risk JSON fields
  • Sensitivity
  • Protection status
  • Recommended generators
  • Field data type
  • Unresolved schema changes
  • Sensitivity type
  • Sensitivity confidence
  • Indicating whether a JSON field is sensitive
  • Assigning a generator to a JSON field

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  1. Configuring data generation
  2. Generator assignment and configuration

Document View for file connector JSON columns

Last updated 16 days ago

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For file connector columns that contain JSON content, you can use the JSON Mask generator to assign generators to individual JSON fields. To identify the fields, you use JSONPath expressions.

Another option is to use Document View, which allows you to view the structure of the JSON content and then assign generators to individual JSON fields.

You can also view this .

Enabling Document View for a JSON column

For a JSON column, the Document View option is available from Privacy Hub, Database View, and Table View.

On the column configuration panel, to enable Document View, toggle Use Document View to the on position. When you enable document parsing:

  • The generator dropdown changes to an Open in Document View button.

  • If this is the first column that you enabled Document View for, then the Document View tab becomes visible.

  • Any existing generator assignment is discarded.

  • On Privacy Hub, in the protection status display, each JSON path is displayed as a separate column. In the Database Tables list, each JSON path is a separate entry.

Structural also runs a scan on the column to detect the JSON structure and identify sensitive fields.

Displaying Document View

On workspace management view, you use Document View to view the JSON structure.

Document View is only available when it is enabled for at least one JSON column.

Selecting the JSON column to configure

From the Column dropdown list, select the JSON column to configure. The dropdown contains the columns that have Document View enabled.

Selecting the type of view

From the View dropdown list, select the view to use for the selected column.

Hybrid view

Hybrid view provides a consolidated view of the schema across all of the rows.

For example, for an array, hybrid view contains a single entry with all of the possible fields.

Single view

Single view shows the structure for one row at a time. You can then page through up to 100 rows. For each row, you see the structure for that row.

For example, for an array, single view shows the actual array entries for each record.

Information in the field list

For each JSON field, Document View always displays:

  • The field name and data type.

  • The assigned generator.

  • An example value. In hybrid view, you can use the magnifying glass icon to display additional example values.

Hybrid view also displays a Field Freq column. Field Freq shows the percentage of rows that contain that permutation of field and type. For example, you might see that a field is Null 33% of the time and contains a numeric value 67% of the time. Or a field value is an Int32 value 3% of the time and an Int64 value 6% of the time. The percentages apply to the first 100 rows.

Toggling between source and preview data

Required workspace permission:

  • Source data: Preview source data

  • Destination data: Preview destination data

The Preview toggle at the top right of Document View allows you to choose whether to display original source data or the transformed data. You can switch back and forth to see exactly how Tonic Structural transforms the data based on the field configuration.

By default, the Preview toggle is in the on position, and the displayed data reflects the assigned generators.

To display the original source data, toggle Preview to the off position.

Filtering Document View fields

In single view, you can filter by either a field name or a field value.

In hybrid view, you can filter by either field name or field properties.

Filtering single document view by field name or value

You can filter single view to only display fields that have specific text in either the field name or the field value.

To filter by value, toggle Search by Value to the on position.

After you select the filter type, in the search field, type text that is in the field name or value. As you type, Structural filters the list to only include fields that contain the filter text.

Filtering hybrid view by field name

To filter hybrid view by field name, in the search field, begin typing text that is in the field name. As you type, Structural filters the list to only include fields with names that include the filter text.

Filtering hybrid view by field properties

From the hybrid document view, you can filter the fields based on field properties.

To display the Filters panel, click Filters.

Searching for a filter

To search for a filter or a filter value, in the search field, start to type the value. The search looks for text in the individual settings.

Adding a filter

To add a filter, depending on the filter type, either check the checkbox or select a filter option. As you add filters, Structural applies them to the field list.

Above the list, Structural displays tags for the selected filters.

Clearing the selected filters

To clear all of the currently selected filters, click Clear All.

Filters panel filters

The Filters panel in hybrid view includes the following options.

At-risk JSON fields

An at-risk JSON field:

  • Is marked as sensitive

  • Is assigned the Passthrough generator.

To only display at-risk JSON fields, on the Filters panel, check At-Risk Field.

When you check At-Risk Field, Structural adds the following filters under Privacy Settings:

  • Sets the sensitivity filter to Sensitive.

  • Sets the protection status filter to Not protected.

Sensitivity

You can filter the JSON fields based on the field sensitivity.

On the Filters panel, under Privacy Settings, the sensitivity filter is by default set to All, which indicates to display both sensitive and non-sensitive JSON fields.

  • To only display sensitive JSON fields, click Sensitive.

  • To only display non-sensitive JSON fields, click Not sensitive.

Note that when you check At-risk Field, Structural automatically selects Sensitive.

Protection status

You can filter the JSON fields based on whether they have any generator other than Passthrough assigned.

On the Filters panel, under Privacy Settings, the field protection filter is by default set to All, which indicates to display both protected and not protected JSON fields.

  • To only display JSON fields that have an assigned generator, click Protected.

  • To only display JSON fields that do not have an assigned generator, click Not protected.

Note that when you check At-Risk Field, Structural automatically selects Not protected.

Recommended generators

When Structural detects that a JSON field is sensitive, it can also determine a recommended generator.

For example, when it detects a name value, it also recommends the Name generator.

You can filter the fields to display the fields that have recommended generators.

On the Filters panel, under Recommended Generators, check the checkbox next to the recommended generator for which to display the fields that have that recommendation.

Field data type

You can filter the fields by the field data type. For example, you might only display columns that contain either numeric or integer values.

To only display fields that have specific data types, on the Filters panel, under Database Data Types, check the checkbox for each data type to include.

The list of data types only includes data types that are present in the currently displayed fields and that are compatible with other applied filters.

To search for a specific data type, in the Filters search field, begin to type the data type.

Unresolved schema changes

When the structure of the JSON changes, you might need to update the configuration to reflect those changes. If you do not resolve the changes, then the data generation might fail.

To only display fields that have unresolved changes to the JSON structure, on the Filters panel, check Unresolved Schema Changes.

Sensitivity type

For detected sensitive fields, the sensitivity type indicates the type of data that was detected. Examples of sensitivity types include First Name, Address, and Email.

To only display fields that contain specific sensitivity types, on the Filters panel, under Sensitivity Type, check the checkbox for each sensitivity type to include.

The list of sensitivity types only includes sensitivity types that are present in the currently displayed fields.

To search for a specific sensitivity type, in the Filters search field, type the sensitivity type.

Sensitivity confidence

When the document scan identifies a value as belonging to a sensitivity type, it also determines how confident it is in that determination.

You can filter the columns based on the confidence level.

To only display columns that have a specific confidence level, on the Filters panel, under Sensitivity confidence, check the checkbox next to each confidence level to include.

Indicating whether a JSON field is sensitive

Required workspace permission: Configure column sensitivity

On the field configuration panel, the sensitivity toggle at the top right indicates whether the field is marked as sensitive.

To mark a field as sensitive, toggle the setting to the Sensitive position.

To mark a field as not sensitive, toggle the setting to the Not Sensitive position.

Assigning a generator to a JSON field

Required workspace permission: Configure column generators

For each node, you assign a generator.

To assign a generator:

  1. Click the generator value for the JSON field.

  2. On the configuration panel, from the Generator Type dropdown list, select the generator. Other than the Conditional and Regex Mask generators, you cannot assign a composite generator to a JSON field.

When you configure a generator in Document View:

  • You can only link to other JSON fields.

  • You can only enable self-consistency.

Configure the generator options. For details about the available configuration options for each generator, go to the e.

video overview of Document View
generator referenc
Column dropdown list on Document View
View dropdown on Document View
Hybrid view of Document view
Single view of Document view
Searching single view by field value
Searching hybrid view by field name
Filters panel on Document View
Filter search for Document View
Document View with applied filters
Field configuration panel on Document View