Mock Data Generation in DBeaver

Note: This feature is available in Enterprise, Ultimate and Team editions only.

Table of contents

• Overview
• Key features
• Generating mock data
• Data generators
  • Boolean
  • Date
  • Numeric
  • String
    • Templates
  • Regex
  • Special data

Overview

The Mock Data generator in DBeaver automates the process of creating test data, making it much faster and more efficient than entering data manually. It is built to support the creation of large amounts of data, making it suitable for projects that require generating thousands of data entities.

Important: Mock Data is intended for generating test data in development environments and should not be used in production. Always ensure you have a backup of your database before initiating data generation.

Key features

• Support across RDBMS: Compatible with all RDBMS supported by DBeaver including DB2, MS SQL Server, MySQL, Oracle, PostgreSQL, SQLite, and others.
• Schema compliance: Automatically generates data that adheres to the database schema, supporting all base data types and constraints (PK, FK, unique, etc.).
• Extensive data generators: Offers more than 20 data generators for various data types with customizable parameters.

Generating mock data

1. To start generating mock data, you can initiate the Mock Data Wizard using one of the following methods, depending on your current context:

   1. Database Navigator: Right-click on a table, then select Tools -> Generate Mock Data.

   2. Data Editor: Access the context menu and choose Generate Mock Data.

   3. Metadata Editor: Right-click on a table and select Tools -> Generate Mock Data.

      

Tip: It is possible to select multiple tables and generate mock data for all of them simultaneously.

2. After launching the Mock Data Wizard:

   1. A configuration window appears where you can set the initial settings:

      Option	Description
      Remove old data	Deletes any existing data before generating new data.
      Row count	Specify the number of rows to generate.
      Batch size	Define the size of batches for data generation.

      

   2. After configuring the initial settings, select a column in the table. Use the dropdown menu to choose the desired generator for each column according to your needs. Refer to the specific parameters section for detailed configuration options for each generator type.

      

   3. In the right column, configure the specific settings for the selected generator. These settings vary depending on the generator type.
      
      

      The following parameters are common across all types of data generators and can be configured to tailor the mock data:

      Parameter	Description
      % of NULLs	Specifies the percentage of NULL values to generate for the column.
      Random Seed	Sets a seed for the data generation process to ensure reproducibility. Leave this field empty for a random seed.

3. Click the Proceed button to start the generation process.

Data generators

Mock data generators provide configurable settings to tailor the generated data according to different requirements and data types.

Boolean

Generator	Description
Random	Generates boolean values randomly.
Sequence	Produces boolean values in a specified sequence.
Constant	Generates boolean values based on specific constants.

Date

Generator	Description
Random	Generates dates randomly within a specified range.
Sequence	Produces dates in a sequence, with options for step and direction.
Constant	Generates dates based on specific constants.

Numeric

Generator	Description
Random	Generates random numbers within a range.
Sequence	Produces numbers sequentially with specified steps and direction.
Constant	Generates numbers based on specific constants.
Price	Generates realistic price values within a specified range.
Coordinate	Produces geographical coordinates.

String

Generator	Description
Text	Generates strings based on a given template.
Random Text	Generates random text.
UUID	Generates unique identifiers.
Regex based	Produces strings that match a specified regular expression pattern.
Email	Generates email addresses with options for gender, surname inclusion, and a numeric suffix.
Name	Produces personal names, configurable by gender and whether to include a surname.

Templates

Directive	Description
Address	Generates US postal addresses.
City	Generates names of major world cities.
Country	Generates country names.
Domain	Generates top internet domain names.
Email	Generates email addresses based on gender and surname inclusion.
Name	Generates personal names based on gender and surname settings.
Random	Generates a random integer between specified limits.
Regex	Generates values matching a specified regex pattern.
Sequence	Generates a sequence of integers starting from a specific value with defined steps.

Regex

Generator	Description
Credit Card	Generates strings that match common credit card number patterns.
Email	Produces email addresses in a random format.
Gender	Generates strings representing gender.
Hex Color	Generates strings that represent hexadecimal color codes.
IP4	Produces strings formatted as IPv4 addresses.
IP6	Generates strings formatted as IPv6 addresses.
Phone Number	Creates strings that resemble phone numbers in various formats.
Postal Code	Generates strings that match postal code formats for different countries.
String Price	Produces strings representing prices with currency symbols.

Special data

Data Type	Description
NULL	Generates NULL values for database columns as specified.
FK (Foreign Key)	Pulls data from referenced tables according to constraints.