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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
        • 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
    • 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)
          • 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
  • Network IO
  • Disk IO
  • Reducing data loads
  • Configuring parallel processing
  • Settings that are not data connector-specific
  • Data connector-specific settings

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  1. Running data generation

Managing data generation performance

Last updated 1 month ago

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During Tonic Structural data generation, performance bottlenecks typically come from one of the following sources:

  • Network IO - Specifically, the bandwidth capacity of the network that connects Structural to the database instances.

  • Disk IO - The disk IO of the databases.

  • Tonic server and workspace configuration - Structural performs several complex data computations and transformations. Depending on your workspace selections, these tasks can take a long time to perform.

In most cases, slow data generation times are caused by disk IO and network IO.

Network IO

When possible, ensure that Structural has a fast network pipe between Structural and each source and destination database.

It is always advisable to install Structural on or near the hardware that runs your database instances.

Disk IO

This is normally limited by the database hardware.

If you run in a public cloud, you can configure options to access faster disks.

For SQL Server, you can increase your write speeds on your destination database. For details, go to .

Reducing data loads

To reduce the required disk and network IO, you can copy less data from the source to the destination.

In some cases, you don't need the data from every table, or from specific columns within a table. Or you might be happy with the data that is already in the destination, and so you don't need to copy it again from the source.

Here are some tips to reduce the data load:

  • For example, audit or transaction tables might not be needed for typical QA testing.

  • Avoid copying over large columns such as varchar(max), blob, XML, and JSON columns.

    If you do not need the data in a column, then to reduce the required IO, either:

  • For subsequent generation runs from the same source database:

Configuring parallel processing

When you believe that the Structural server is the bottleneck, then to improve performance, you can tune the following settings that control parallel processing.

You apply these settings as environment settings in your tonic_worker container. For more information on configuring environment settings, go to Configuring environment settings.

You can also configure overrides for some of these settings in individual workspaces. For more information on how to configure override values, go to Advanced workspace overrides.

Settings that are not data connector-specific

The following settings are not limited to specific data connectors:

Setting
Description

TONIC_CONSTRAINT_PARALLELISM Default: 8

The number of constraints that a worker can apply in parallel during a job. You can configure this setting from Structural Settings, and override it in individual workspaces.

TONIC_PROCESS_PARALLELISM Default: 1

The number of threads to devote to performing the data transformations.

Certain Structural configurations can introduce CPU bottlenecks. This typically occurs when you configure composite generators such as JSON Mask or XML Mask with a large number of paths.

If your workspace has a very high number of generators, or a large number of JSON Mask, XML Mask, Integer Primary Key, or Alphanumeric Primary Key generators, then you should increase this value to at least 2. You can configure this setting from Structural Settings, and override it in individual workspaces.

TONIC_TABLE_PARALLELISM Default: 1

The number of tables that Structural operates on at the same time.

If your Structural server has enough CPU, and your source and target databases are not fully utilized, then we recommend that you to increase this variable to 2.

Depending on your hardware, you can even increase it higher. You can configure this setting from Structural Settings, and override it in individual workspaces.

TONIC_WRITE_PARALLELISM Default: 2

The number of threads to devote to writing rows to the output database. For Data Pipeline V2 on PostgreSQL, this should be a factor of TONIC_JOBFLOW_MAX_DESTINATION_CONNECTIONS. For example, if TONIC_JOBFLOW_MAX_DESTINATION_CONNECTIONS is 8, then TONIC_WRITE_PARALLELISM should be 1, 2, or 4.

You can configure this setting from Structural Settings, and override it in individual workspaces.

Data connector-specific settings

The following settings apply to specific data connectors:

Setting and default value
Description

TONIC_JOBFLOW_MAX_DESTINATION_CONNECTIONS

Default: 16

Only applies to the Data Pipeline V2 data generation process.

The maximum number of connections to the destination database.

Each action requires at least one connection.

We recommend that you set this value to the number of CPUs on the destination database server.

You can configure this setting from Structural Settings, and override it in individual workspaces.

TONIC_JOBFLOW_MAX_SOURCE_CONNECTIONS

Default: 8

Only applies to the Data Pipeline V2 data generation process.

The maximum number of connections to the source database.

Each action requires at least one connection.

We recommend that you set this value to the number of CPUs on the source database server.

You can configure this setting from Structural Settings, and override it in individual workspaces.

TONIC_READ_RANGES_PARALLELISM Default: 8

Only applies to the Data Pipeline V2 data generation process.

The number of ranges per table to read in parallel.

TONIC_BIGQUERY_READ_PARALLELISM

Default: 2

Google BigQuery only.

The number of read threads per table for Google BigQuery.

TONIC_MYSQL_COPY_TABLE_WRITE_PARALLELISM

Default: 1

MySQL only.

The number of tables that a worker can copy in parallel during a job.

TONIC_ORACLE_DATA_PUMP_PARALLELISM

Default: 0

Oracle only, and only on Oracle Enterprise Edition databases.

The maximum number of processes of active execution for Data Pump to use.

TONIC_PARTITION_PARALLELISM Default: 1

MySQL and SQL Server only.

The number of table partitions per table that are read from concurrently during a job.

TONIC_FILE_GROUP_PARALLELISM Default: 6

File connector only. The number of file groups that Structural operates on concurrently.

TONIC_FILE_GROUP_READ_SCHEMA_PARALLELISM Default: 4

File connector only. Only applies to cloud storage workspaces. The number of file groups that Structural reads the schemas of concurrently. Has the most effect on file types that have serialized schemas, such as Parquet and Avro.

TONIC_FILE_SAMPLE_PARALLELISM Default: 4

File connector only. Within a file group, the number of files that Structural samples concurrently. For very large files, a larger sample value can increase memory usage.

Put large tables that contain unneeded data into . In Truncate mode, Structural does not copy any of the table data to the destination database.

If the column is nullable, apply the .

Apply the .

For large tables that have not changed, use . In Preserve Destination mode, Structural does not copy the table over, but instead uses the existing data in the destination database.

For large tables that have very few changes, use . In Incremental mode, Structural only copies over the changes that occurred since the previous generation.

For subsetting, the number of subsetting steps that a worker processes in parallel during a subsetting job. For more information, go to .

SQL Server
Truncate mode
NULL generator
Constant generator
Preserve Destination mode
Incremental mode
Enabling parallel processing for subsetting