Hey, welcome back.
Last week, we talked about running tests locally — and why it destroys your results.
Today's mistake is sneakier. The test runs. The numbers look fine. But you're not testing what you think you're testing.
⚠️ The script works. The test is broken.
A few years ago I reviewed a load test for an e-commerce platform. On the surface, everything looked right — ramp-up, think time, user count. Results looked acceptable. Everyone was happy.
Then I looked at the script:
500 virtual users.
All logging in as test_user@example.com.
All searching for "nike shoes".
All adding product ID 1042 to the cart.
I asked: how many unique users in production? Answer: about 100k.
They weren't testing the application. They were testing what happens when one user sends 500 identical requests.
What actually goes wrong
When all users send identical requests, a few things happen — none reflect reality.
The database cache gets artificially warm. The same query runs 500 times. After the first hit, everything comes from cache. In production with real varied data, your database would be working significantly harder. Your test just told you your cache is fast. Congratulations.
Session and token conflicts show up as errors. Most systems won’t allow hundreds of sessions for one user. You see auth errors, 4xx responses — and waste hours debugging non-performance issues.
Real bottlenecks stay invisible. If everyone hits the same page, you'll never discover the real bottlenecks. That's the page that matters on launch day.
The result?
You finish the test, the numbers look okay, and you ship. Then production behaves nothing like your test predicted. Because you tested a single cache-warmed path — not your system.
🛠 The fix is straightforward
Use data files. JMeter, k6, Gatling, Locust — all support CSV/external data.
At minimum, parameterize:
- User credentials — a pool of unique accounts, not one shared login
- Search queries — varied terms that reflect what real users actually search
- Entity IDs — product IDs, order numbers, anything your script references Even 500–1000 unique records are enough to break the cache dependency and get results that mean something.
Parameterization fixes one layer of realism. But it's rarely the only one missing.
In that e-commerce review, fixing the data was the easy part. The harder conversation was about how the load model was designed, why the test environment didn't reflect production, and how to read results without drawing the wrong conclusions.
Those aren't individual mistakes. They're gaps in how the whole testing process was built.
If you want to build that process properly from the start — Performance Testing Fundamentals course covers it end-to-end, from test design to hands-on execution.
That's Part 2. The test ran. The results were meaningless.
Part 3 is coming soon — we'll talk about ignoring think time between requests, and why it turns your load test into something no real user would ever do.
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