DataFaker | Gatling Blog

When simulating real-world load, predictable behavior isn’t realistic. Users don’t send the same data over and over—they log in with different credentials, submit different forms, and trigger different edge cases.

If your load testing scenarios are built on static data, you’re not testing reality. That’s where dynamic test data generation in Gatling comes into play.

Static data = unrealistic testing

Most load tests start with a CSV or JSON file filled with a handful of hardcoded values. It works—for a while. But as your test scales up to simulate hundreds or thousands of users, those data files:

• Get reused too often
• Don’t simulate true user randomness
• Can cause false positives or missed edge cases

Static data becomes a bottleneck. It limits test coverage, masks potential issues, and doesn’t reflect production usage.

Why it matters for your business

Testing with stale, repetitive data can have real impact:

• ❌ You may miss bugs that only appear with specific payloads
  
  
• ❌ Your tests may pass—even though your app can’t handle real-world behavior
  
  
• ❌ You risk production regressions and lost customer trust
  
  

By contrast, realistic test data leads to smarter decisions:

• ✅ More accurate load simulation = fewer surprises in production
  
  
• ✅ Wider test coverage = higher confidence before releases
  
  
• ✅ Less manual work managing test files = more time building what matters
  
  

What’s in it for your team

With Gatling, generating dynamic data means:

• Less maintenance – No more wrangling giant CSVs
  
  
• More flexibility – Craft precise scenarios for specific business cases
  
  
• Faster iteration – Update and tweak your data logic in-code, no re-exports

It’s a major step forward for engineering productivity and test accuracy, and a game-changer when running high-scale simulations with millions of virtual users.

🔗 Want to see how to implement this in Gatling?

Read the full guide →