Load testing aims to answer a simple question: can the system handle expected traffic within agreed performance targets? To get meaningful answers, you must design realistic scenarios that reflect how users actually use the application.
Designing Realistic Load Test Scenarios
Instead of sending random requests, you model typical user journeys (such as browse, search, checkout) and assign weights that reflect real usage. You also define load profiles—how many virtual users or requests per second—and ramp-up patterns that mirror expected peaks.
Example load scenario:
- Journeys: 60% browse, 30% search, 10% checkout
- Target: 500 concurrent users during peak hour
- Ramp-up: from 0 to 500 users over 15 minutes
- Steady state: hold 500 users for 30 minutes
- Success criteria: < 1% errors, P95 < 800 ms on key APIsWell-designed load tests become a repeatable way to validate performance before major releases or traffic events.
Common Mistakes
Mistake 1 — Ignoring user behaviour in scenarios
This misrepresents reality.
❌ Wrong: Hitting only one API endpoint at full speed with no think time.
✅ Correct: Combine multiple journeys with realistic pacing and data.
Mistake 2 — Testing only at average load
This misses peaks.
❌ Wrong: Validating performance only at normal traffic levels.
✅ Correct: Include scenarios that simulate peak hours or known events.