Parameterization with Gatling

Introduction:

Simulating real-world scenarios is critical in performance testing. It’s key for getting accurate and useful results. Gatling is a powerful open-source load testing tool; it does this through parameterization and data-driven testing. Gatling uses feeders. They let Gatling simulate different user inputs and workflows. This makes your tests stronger and more like real user behavior. This blog will explore how to use feeders in Gatling to add parameters to tests. It will also discuss the benefits of data-driven testing with some practical examples. 

What are Feeders?

Feeders in Gatling are data sources that provide input values for your test scripts. They can be CSV files, JSON files, or even in-memory collections. Feeders allow you to insert dynamic data into your scenarios. This ensures that each user can perform unique actions. It closely mimics real-world usage patterns. 

Step-by-Step Guide to Use Feeders:

  1. Create a Feeder Data Source: Prepare a CSV, JSON, or other feeder source with the data you want to use in your tests. For instance, a CSV file (users.csv) containing user credentials looks like this:  
Create a Feeder Data Source for Parameterization with Gatling

2. Load the Feeder in your simulation: In your Gatling script, use the right feeder method to load the feeder file. 

Load the Feeder for Parameterization with Gatling

3. Integrate the Feeder with Your Scenario: Use .feed() method. It injects data from the feeder into your test steps. 

Integrate the Feeder for Parameterization with Gatling

4. Run the Simulation: Execute your Gatling simulation as usual. Gatling will use the data from the feeder to parameterize each user session.  

Benefits of Data-Driven Testing

  • Enhanced Reality: Data-driven testing ensures that your test scenarios better reflect real-world conditions. By varying input data, you can simulate more user behaviors. This will reveal performance issues that static data might be hiding. 
  • Increased Test Coverage: Using different data sets helps you cover more test cases, including edge cases. This coverage can reveal performance bottlenecks and bugs. They could affect different parts of your user base. 
  • Scalability: Data-driven testing allows you to scale your tests. Just expand your feeder data. Then, you can simulate more users or complex user actions. You won’t need to rewrite your test scripts.
  • Maintainability and Reusability: Parameterizing tests through feeders makes your test scripts cleaner and more maintainable. You can reuse the same scripts with different data sets. You can use them for different testing phases, like development, staging, and production. 

Examples  

Example 1: Testing with Different User Credentials

Consider an application where different user roles have different permissions. Using feeders, you can simulate login attempts with various credentials. This ensures that all roles are well-tested.

 Testing with Different User Credentials

Example 2: Load Testing with Various Search Queries

For an e-commerce application, you might want to test how the search handles different terms. 

 Load Testing with Various Search Queries

Example 3: Testing API with Random Payloads

When testing an API, you can use JSON feeders. They provide diverse payloads, to ensure your API can handle different input data. 

Testing API with Random Payloads

Conclusion: 

Parameterization and data-driven testing with Gatling are powerful approaches. They let you simulate real user interactions. This improves the reliability of your performance tests. Using feeders, you can inject dynamic data into your scenarios. This ensures wide test coverage and finds issues early in development. Embrace data-driven testing to make your performance testing more effective and insightful.