Gatling Load Testing: Ultimate Crash Course for Beginners

Did you know that effective load testing can be the difference between a seamless user experience and a crashing application? In this crash course, you’ll learn how to build robust Gatling stress testing scripts to ensure your APIs stay performant under pressure.

Getting Started with Gatling

Gatling is an open-source load testing tool written in Scala, offering a high-performance Scala DSL for stress testing HTTP applications and APIs. Compared to other load testing frameworks, Gatling shines with its non-blocking architecture, fast simulation engine, and readable, code-driven scenarios. Whether you need to measure average response times, pinpoint throughput bottlenecks, or verify API stability under heavy load, Gatling provides clear HTML reports and real-time metrics. In this section, we’ll cover how to install Gatling, navigate its core concepts—such as scenarios, injectors, and protocols—and highlight why it’s an ideal choice for Java or Scala developers looking to automate their API performance tests efficiently. By the end, you’ll understand Gatling’s architecture and be ready to write your first stress testing script.

Before You Begin: Prerequisites

Before you dive into writing Gatling scripts, make sure your environment meets the following requirements:

• Java Development Kit (JDK) 8, 11, or 17 properly installed and configured on your PATH.
• Maven (or Gradle) to compile dependencies and run simulations with built-in plugins.
• GitHub CLI and a GitHub account for cloning example repositories.
• An IDE such as IntelliJ IDEA Community Edition for code completion and debugging.

Confirm your installations by running java -version and mvn -version. If you need to install Java, visit the JDK download page. For Maven, follow instructions on the official Apache Maven site. Having these tools in place will streamline cloning the starter project, resolving dependencies, and launching your Gatling load tests without friction.

Choosing the Right Application Under Test

Selecting a stable, representative API is crucial before writing load testing scripts. In this tutorial, we’ll use the Video Game DB API, a simple RESTful service exposing endpoints for HTTP GET, POST, PUT, and DELETE operations. Endpoints include:

• GET /video-game
• GET /video-game/{id}
• POST /video-game
• PUT /video-game/{id}
• DELETE /video-game/{id}

Authentication is required for POST, PUT, and DELETE: supply a JSON body with {"username":"admin","password":"admin"} to /auth, then extract the returned token for Bearer authorization in subsequent calls.

“In this crash course, we’re going to build out an entire Gatling stress testing script from scratch and see how to handle parameterization, environment variables, and dynamic test data.”

Understanding your application’s endpoints and authentication flows upfront prevents surprises when crafting scenarios. Feel free to explore the Swagger UI for Video Game DB, test each operation manually, and note response schemas before automating the load tests.

Setting Up Your Gatling Project

Begin by cloning the starter repository designed for this crash course:

git clone https://github.com/youruser/gatling-video-game-db.git
cd gatling-video-game-db

Open the project in IntelliJ and import the pom.xml. Maven will automatically resolve Gatling dependencies, including the core Gatling engine, Maven plugins, and JSON feeder support. The typical folder structure is:

• src/test/java: Gatling simulation classes
• src/test/resources/bodies: JSON templates for POST/PUT
• src/test/resources/data: feeder files for dynamic data

To verify your setup, run the sample simulation included with Gatling by right-clicking on the Engine class. You should see console output streaming virtual user activity and, upon completion, an HTML report under target/gatling. If that succeeds, you’re ready to replace the demo with your own Video Game DB scripts.

Crafting Your First Basic Gatling Script

Let’s write a minimal Gatling simulation to perform one GET request. In src/test/java, create a package named videogame and add VideoGameBasicSimulation.java:

public class VideoGameBasicSimulation extends Simulation {
  private HttpProtocolBuilder httpProtocol = http
      .baseUrl("https://videogamedb.uk/api")
      .acceptHeader("application/json");

  private ScenarioBuilder scn = scenario("Basic Video Game DB Test")
      .exec(http("Get All Games")
          .get("/video-game"));

  {
    setUp(
      scn.injectOpen(atOnceUsers(1))
    ).protocols(httpProtocol);
  }
}

In this snippet:

• We configure the HTTP protocol with a base URL and default headers.
• We define a scenario named “Basic Video Game DB Test” that executes a single GET request.
• We set up one virtual user (atOnceUsers(1)) to run immediately.

Run this simulation to confirm you receive HTTP 200 responses and see basic timing metrics in the generated HTML report. This “hello world” script establishes the core Gatling structure: protocols, scenarios, and injection profiles.

Advanced Scripting Techniques

Once you have a simple script, it’s time to modularize and parameterize your load test. Break down your HTTP transactions into reusable ChainBuilder methods:

private static ChainBuilder getAllGames() {
  return exec(http("Get All Games")
    .get("/video-game"));
}

private static ChainBuilder authenticate() {
  return exec(http("Authenticate User")
    .post("/auth")
    .body(StringBody("{\"username\":\"admin\",\"password\":\"admin\"}"))
    .check(jsonPath("$.token").saveAs("jwtToken")));
}

private static ChainBuilder createNewGame() {
  return exec(http("Create Game - #{name}")
    .post("/video-game")
    .header("Authorization", "Bearer #{jwtToken}")
    .body(ElFileBody("bodies/new-game-template.json")).asJson());
}

Next, introduce a JSON feeder for dynamic test data. Create src/test/resources/data/games.json with an array of game objects:

[
  { "id": 10, "name": "Tetris", "releaseDate": "1984-06-06", "reviewScore": 90, "category": "Puzzle", "rating": "E" },
  { "id": 20, "name": "Pac-Man", "releaseDate": "1980-05-22", "reviewScore": 85, "category": "Arcade", "rating": "E" }
]

In your simulation class, declare:

private static FeederBuilder<Object> jsonFeeder = jsonFile("data/games.json").random();

Feed data into your scenario:

scn.exec(feed(jsonFeeder))
   .exec(createNewGame())

By combining ChainBuilder methods with JSON feeders and checks (e.g., jsonPath to extract tokens or validate response fields), you create maintainable, scalable Gatling scripts. You can also parameterize the base URL via System.getProperty("baseUrl", "https://videogamedb.uk/api"), making it trivial to switch between development, staging, or production environments without code changes.

Simulating Realistic User Loads

A crucial part of stress testing is simulating realistic traffic patterns. Gatling offers multiple injection profiles:

• atOnceUsers(n): start n users instantly.
• rampUsers(n).during(d): linearly inject n users over d seconds.
• constantUsersPerSec(r).during(d): maintain a constant request rate r per second for d seconds.
• heavisideUsers(n).during(d): smooth ramp that resembles real-world surge patterns.

You can make your simulations configurable by reading runtime parameters:

private static final int userCount = Integer.parseInt(
  System.getProperty("users", "5"));
private static final int rampDuration = Integer.parseInt(
  System.getProperty("ramp", "10"));

{
  before(() -> System.out.printf(
    "Running with %d users over %d seconds%n", userCount, rampDuration));

  setUp(
    scn.injectOpen(
      nothingFor(5),
      rampUsers(userCount).during(rampDuration)
    )
  ).protocols(httpProtocol);
}

Then execute via Maven:

mvn gatling:test \
  -Dgatling.simulationClass=videogame.VideoGameSimulation \
  -Dusers=20 \
  -Dramp=30

This approach lets you tune the load test on-the-fly—ideal for continuous integration pipelines or test lab deployments.

Conclusion and Next Steps

In this guide, you’ve learned how to:

• Set up a Gatling project using Maven and IntelliJ.
• Write a basic simulation script for your API.
• Modularize HTTP transactions into reusable methods.
• Parameterize tests with JSON feeders and environment properties.
• Simulate diverse user load patterns for realistic stress testing.

As you apply these techniques, you’ll be able to verify service-level agreements, detect performance regressions, and optimize your API architecture under authentic load conditions.

• Bold takeaway: Modularize your Gatling scripts with ChainBuilder methods and JSON feeders to maintain flexibility, readability, and ease of parameterized stress testing across environments.

For comprehensive details, consult the official Gatling Documentation and join discussions on the Gatling Community Forum. Happy load testing!