Plan File

A data generation plan focuses on data specifications and requirements for one scenario. For example, in one use case, you may want all users to have subscription records. In another case, you may need thirty percent of users to either have canceled subscriptions or never had one. Without changing your schema definition, the data plan file offers a flexible way to create data to fit your needs. A data plan YAML file has four top-level properties:

• name: [required] A string used as an identifier for the plan.
• dialect: A string used to specify the dialect of the plan. Available dialects are: general, MySQL, or Salesforce. The default is general.
• schema: [required] A string used to specify the schema file to be used for the plan.
• data: [required] A list of entity data definitions. Each element in this list matches one entity in the schema.

Sample Plan File to Generate MySQL Records Against the Quickstart Schema

name: "quickstart"
dialect: "mysql"
schema: "quickstart"
data:
  - entity: "users"
    count: 10

Dialect

Dialect specifies the intended platform for the generated data. Different platforms may require data in varying formats or ranges. For instance, the time values generated for MySQL range from ‘-838:59:59’ to ‘838:59:59’, whereas Salesforce uses a 24-hour range. Available dialects are:

• general: Default dialect. No specific platform is targeted. The data generated is based on Java specifications
• mysql: MySQL-specific dialect. The data generated is based on MySQL specifications
• postgres: PostgreSQL-specific dialect. The data generated is based on PostgreSQL specifications
• salesforce: Salesforce-specific dialect. The data generated is based on Salesforce specifications

Entity Data Definition

An entity data definition provides additional specifications for generating data for a specific entity. It includes the following properties:

• entity: [required] The name of the targeted entity.
• dataId: A unique non-negative number (default is 0). It’s used when applying exclusivity to the same entity. For example, if there is a user table with a country column, we can use two entity data definitions—one that generates 100 US users and another that generates 50 UK users.
• count: [required] A positive number that defines how many records are needed.
• properties: A list of property specifications that drill down into the rules applying to individual properties.
• entries: This property allows users to plug in their own data. It’s extremely useful for generating data that fits existing records or for obfuscating sensitive data.

Sample Entity Data Definition

## Generate 5 records for product_category using the provided name,
## applying 70% of desc as null and setting all status to ACTIVE
- entity: "product_category"
      count: 5
      properties:
        - name: "desc"
          values:
            - values:
                - "<?null?>"
              weight: 0.7
        - name: "status"
          defaultValue: "ACTIVE"
      entries:
        properties:
          - "name"
        values:
          - ["Electronics"]
          - ["Computers"]
          - ["Smart Home"]
          - ["Home Garden & Tools"]
          - ["Pet Supplies"]
          - ["Food & Grocery"]