When designing a test plan for load testing Tableau Server, what is an important factor to consider for ensuring the validity of the test results?
A. Executing the tests only during the server's peak usage hours to assess performance under maximum stress
B. Gradually increasing the load during testing to observe how the server responds to escalating demands
C. Using only synthetic test data to maintain consistency and control over the testing variables
D. Concentrating the tests on the server's newest features to evaluate their impact on performance
B. Gradually increasing the load during testing to observe how the server responds to escalating demands
Explanation:
Why B is Correct?
Incremental load testing helps identify performance thresholds, bottlenecks, and scalability limits without overwhelming the server abruptly.
It mirrors real-world usage patterns (e.g., growing user traffic) and reveals:
Response time degradation (e.g., dashboards slowing at 500+ users).
Resource saturation points (e.g., CPU/memory spikes).
Tableau’s Performance Testing Guide recommends this approach.
Why Other Options Are Less Effective?
A. Testing only during peak hours: Limits understanding of progressive failure modes and risks disrupting live users.
C. Synthetic data only: Real-world data (with its complexities) is critical for accurate performance insights.
D. Focusing only on new features: Neglects end-to-end system performance (e.g., legacy features may bottleneck new ones).
Key Steps for Valid Load Testing:
Baseline testing: Measure performance under normal loads.
Ramp-up: Gradually add users/requests (e.g., +100 users every 5 minutes).
Monitor metrics: CPU, memory, query latency, and error rates.
Repeat with real data: Ensure tests reflect production scenarios
.
Reference:
Load Testing Best Practices (NIST SP 800-146): Emphasizes incremental load increases.
Final Note:
B is the only method that balances realism and safety. Options A/C/D risk skewed results or system crashes. Always simulate realistic growth patterns