Gatling Load Testing: Scala, CI/CD, and Performance Metrics

Optimize Performance with Gatling Load Tests

Imagine you deploy a new release, and an hour later production collapses under peak load. The team tries to reproduce the scenario manually—no luck. Sound familiar? This is exactly when you need load testing with Gatling. We configure it for your infrastructure: describe stateful scenarios in Scala, integrate into CI/CD, and generate HTML reports with percentiles. With over a decade in the field, we have delivered 50+ projects in e-commerce, fintech, and SaaS. One case: after our Gatling setup, a client reduced bottleneck detection time by 40% and prevented two major incidents, saving at least $15,000 in potential revenue loss.

Why Gatling Over JMeter or k6

JMeter is convenient for rapid prototyping with a GUI, but XML configs for complex scenarios turn into a review nightmare. k6 is excellent for developer-centric tests in JavaScript. Gatling shines in scenarios requiring: complex stateful scenarios with token correlation, high concurrency (tens of thousands of virtual users per machine via Akka/Netty), and built-in HTML reports with percentiles and per-request detail. According to the official Gatling documentation (Gatling Documentation), a single instance can handle up to 100k concurrent users, while JMeter typically caps around 30k. That makes Gatling 2-3 times more efficient in handling concurrent users—benchmarks confirm it. Switching to Gatling cuts bottleneck detection time by 30–50% compared to JMeter scripts.

How Gatling Solves Token Correlation

On an e-commerce project, we tested the flow: search → product page → add to cart → checkout. JMeter struggled with CSRF token correlation between requests. Gatling handled it elegantly with css("#csrf-token").ofType[String].saveAs("csrfToken") and header("X-CSRF-Token", "#{csrfToken}") all within one scenario.

Project Structure and Scenario

Standard Gatling project layout:

gatling-tests/
├── src/test/scala/
│   ├── simulations/
│   │   └── CheckoutSimulation.scala
│   └── config/
│       └── Config.scala
├── src/test/resources/
│   ├── gatling.conf
│   ├── feeders/
│   │   └── users.csv
│   └── bodies/
│       └── checkout-payload.json
└── pom.xml # or build.sbt

A basic scenario with a realistic load profile:

class CheckoutSimulation extends Simulation {
  val httpProtocol = http
    .baseUrl("https://api.example.com")
    .acceptHeader("application/json")
    .contentTypeHeader("application/json")
    .shareConnections

  val userFeeder = csv("feeders/users.csv").circular

  val checkoutScenario = scenario("Checkout Flow")
    .feed(userFeeder)
    .exec(
      http("Login")
        .post("/auth/login")
        .body(StringBody("""{"email":"#{email}","password":"#{password}"}"""))
        .check(jsonPath("$.token").saveAs("authToken"))
        .check(status.is(200))
    )
    .pause(1, 3)
    .exec(
      http("Add to Cart")
        .post("/cart/items")
        .header("Authorization", "Bearer #{authToken}")
        .body(ElFileBody("bodies/checkout-payload.json"))
        .check(status.is(201))
        .check(jsonPath("$.cartId").saveAs("cartId"))
    )
    .pause(2, 5)
    .exec(
      http("Checkout")
        .post("/orders")
        .header("Authorization", "Bearer #{authToken}")
        .body(StringBody("""{"cartId":"#{cartId}","payment":"card"}"""))
        .check(status.in(200, 201))
    )

  setUp(
    checkoutScenario.inject(
      rampUsersPerSec(1).to(50).during(2.minutes),
      constantUsersPerSec(50).during(5.minutes),
      rampUsersPerSec(50).to(1).during(1.minute)
    )
  ).protocols(httpProtocol)
    .assertions(
      global.responseTime.percentile(95).lt(500),
      global.failedRequests.percent.lt(1)
    )
}

The rampUsersPerSec → constantUsersPerSec → rampDown profile is standard for production-like testing. The stepwise "plateau" load allows JVM applications and database caches to warm up, which is closer to real traffic than an instant spike.

How to Set Up Gatling Load Testing

1. Define user flows based on production logs or analytics.
2. Write Scala scenarios using Gatling's DSL.
3. Configure data feeders (CSV, JSON) with realistic test data.
4. Set up CI/CD pipeline with scheduled runs.
5. Run tests and analyze HTML reports.
6. Iterate on bottlenecks and optimize.

Which Metrics Guarantee Production Stability?

Gatling's HTML report is one of the best in the industry. Key metrics: p50/p75/p95/p99 latency per request, throughput (req/s), active users over time. A pattern we see regularly: p50 = 120ms, p99 = 4500ms. That's a "long tail"—slow requests invisible in the average. Typically caused by database connection pool exhaustion, N+1 queries under load, or GC pauses in JVM applications.

If responseTime.percentile(95) grows linearly with load—bottleneck in the application. If it jumps at a certain RPS—hitting a specific resource: CPU, I/O, connection pool.

CI/CD Integration and Launch Infrastructure

For GitLab CI with report publishing:

---
# .gitlab-ci.yml
load-test:
  stage: performance
  image: gatling/gatling:3.10.3
  script:
    - gatling.sh -sf src/test/scala -rf target/gatling -s simulations.CheckoutSimulation
  artifacts:
    paths:
      - target/gatling/
    reports:
      performance:
        - target/gatling/**/simulation.log
  only:
    - schedules # run on schedule, not every commit
---

GitHub Actions configuration is similar: use the gatling/gatling-action action or a custom Docker image. Important: do not run load tests on every commit—it overloads the pipeline and distorts results. Optimal: schedule or before release.

Analysis of Results and Typical Findings

Gatling's report quickly reveals anomalies. For instance, if p95 grows smoothly—scaling issue in the app. If there's a sharp jump at a certain RPS—look for connection pool exhaustion or GC. Our engineers guarantee that after our Gatling setup, you get a clear list of bottlenecks with recommendations. The savings from preventing a single incident can exceed the entire setup cost tenfold.

What's Included in Our Gatling Setup Service

• Development of 1-5 realistic user flow scenarios
• Configuration of data feeders (CSV, JSON)
• Integration with GitLab CI or GitHub Actions
• Custom assertions and pass/fail thresholds
• First test execution and detailed report analysis
• Documentation of findings and recommendations
• 1-hour training session for your team
• 2 weeks of post-setup support

What Our Setup Includes

Our Gatling configuration for your project includes: developing scenarios for real user flows (based on access logs or analytics), configuring data feeders, integrating into the CI/CD pipeline, setting up assertions and alerts on degradation, and a first run with analysis and recommendations. Contact us for an infrastructure audit—get a ready load testing plan. Get a consultation on load testing setup—it helps prevent downtime and save budget.

Cost is calculated individually after auditing your infrastructure and testing requirements.