Section 2 Review
You've finished Designing a Performance Test, the second section of Performance Testing. This page is a dedicated recap — bookmark it as a fast reference, separate from the modules themselves.
Section Summary
✔ Performance Testing Types — load, stress, spike, soak, and volume testing, each shaping load differently and each revealing a distinct kind of problem the others would miss ✔ Performance Test Environment — why a test environment needs to genuinely resemble production (infrastructure, network, dependencies, configuration) or have every gap explicitly documented, and how an unrealistic mock can hide the real bottleneck ✔ Test Data for Performance — volume, shape, and distribution as three separate, necessary properties of realistic test data, extending Database Testing's own small-test-data lesson into deliberate data design
How they build on each other: Module 4 established what to test — the five types and what each reveals. Module 5 established where to test it — an environment that genuinely predicts production. Module 6 closed the section with what data to test it against — realistic in volume, shape, and distribution alike. All three are necessary together: the right test type, run in the wrong environment or against unrealistic data, still produces a result that doesn't transfer to production.
Section 2 Quick Reference
| Question | What to Check |
|---|---|
| Which test type matches this feature's real risk? | Sudden surge → spike; sustained duration → soak; growing data → volume; finding the ceiling → stress |
| Does this environment actually predict production? | Infrastructure, network, third-party dependencies, and configuration all audited, or gaps documented |
| Is this test data realistic? | Volume, shape, and distribution — not just row count alone |
Section 2 Knowledge Check
Five realistic scenarios. For each, decide which module's framework applies, and what you'd check or do. No answers are provided here. Solutions: Section 2 Solutions.
Scenario 1: A feature is expected to see traffic gradually grow over the next year, not sudden surges.
Scenario 2: A load test environment mocks a third-party fraud-check service with an instant response.
Scenario 3: Test data was seeded with 10,000 accounts, each with exactly 20 transactions.
Scenario 4: A team wants to know whether their system can handle a sudden traffic surge from a viral social media mention.
Scenario 5: Two performance test runs, a week apart, produced very different results, and the team isn't sure if performance actually changed.
Continue to Section 3
Executing Performance Tests, starting with Performance Testing Tools — where this section's test design (type, environment, data) becomes an actual, running test.