Test Data Queries (G-Queries) Overview
In this lesson, you are going to learn about GenRocket Test Data Queries and GenRocket’s Test Data Queries Management Platform, fondly known as G-Queries.
What is a Test Data Query?
A Test Data Query queries real data from a defined database or CSV file and then blends it with synthetically generated test data when run with a Scenario, Scenario Chain, or Scenario Chain Set.
When should you use Test Data Queries?
- Any time you want to blend real data with synthetically generated test data to meet specific testing goals.
- Test Data Queries can be used on their own to generate test data or added to a Test Data Case.
What is the G-Queries Management Platform?
(Also referred to as the G-Queries Management Dashboard)
- GenRocket’s management platform for test data queries.
What does the G-Queries Management Platform do?
- It allows you to define, organize, and maintain a set of Queries, also referred to as Test Data Query or G-Query Suite.
How to Access the G-Queries Management Platform
Test Data Queries are specific to a Project Version within a given Project.
- Expand the Self Serve Menu Options drop-down menu within the Project Versions Pane.
- Select G-Queries within the drop-down menu.
How to use Test Data Queries?
- Create a Test Data Query.
- Configure the Query Parameters.
- Enter a Database Query or Import the CSV File.
- Add Domains and Attributes.
- Perform a Column Check.
Six Types of Queries
GenRocket Test Data Queries provides six types of query options:
- Query Before – Query columns from one or more database tables as a set of data that is buffered into memory.
- Query Each – Query one row of columns, from one or more database tables, on each row iteration of test data generation.
- CSV List – From a CSV file, pull rows of data having one or more columns, into memory as a list of data.
- CSV Index – From a CSV file, pull rows of data having one or more columns, into memory and retrieve any row via its row index.
- Mongo Before – Query columns from one or more database collections as a set of data that is buffered into memory. This is the same as Query Before but designed specifically for use with Mongo.
- Mongo Each – Query one row of columns, from one or more database collections, on each row iteration of test data generation. This is the same as Query Each but designed specifically for use with Mongo.
What is the Benefit?
- You can easily query real data within a database or CSV file and blend it with synthetically generated data for complex testing challenges!