Comparing Optimized Performance Test Results with ChatGPT Insights

At the beginning, the author shares a brief invitation to a product that can generate automated test runs from test cases, design documents, and system architecture descriptions, providing a meeting link and a trial address.

5.5 Optimized Performance Test Results Comparison

After applying optimizations, the author reran concurrent load tests at the system’s performance breakpoint and displayed the results in a series of figures (Figure 5‑4).

5.5.1 Load‑Test Endpoint Data

ChatGPT was prompted to compare two groups of JMeter data. The analysis highlighted:

Group 1: 1,243,226 total samples; average response time 846 ms; throughput 2054.28; error rate 2.84%.

Group 2: 3,743,734 total samples; average response time 828 ms; throughput 265.65; error rate 2.845%.

Conclusion: Group 1 excels in throughput (suitable for high‑concurrency scenarios), while Group 2 has a lower average response time (more stable performance).

5.5.2 System‑Side Data

ChatGPT compared system‑side metrics for the two groups:

CPU usage – Group 2 shows higher user (30.89% vs 28.74%) and system (56.48% vs 53.95%) utilization, indicating higher load handling.

Disk I/O – Group 1 has higher average and peak disk TPS (333 vs 101 TPS average; 8,696 vs 1,292 TPS peak), suggesting stronger disk performance.

Read/Write ratio – Group 1’s ratio is 1.0 (balanced), while Group 2’s is 0.5 (read‑light, potentially lower write efficiency).

5.5.3 CPU Data

Metrics such as user%, system%, wait% and idle% were compared:

Group 2 has higher user% (30.89% vs 28.74%) and system% (56.48% vs 53.95%).

Group 1 shows slightly lower wait% (0.15% vs 0.06%) and higher idle% (17.17% vs 12.58%).

Overall, Group 2 demonstrates higher CPU utilization, which can be advantageous for CPU‑bound workloads.

5.5.4 Disk Data

ChatGPT evaluated disk block size, busy percentage, read/write rates, and transfer rates:

Block size variation is larger in Group 1, indicating occasional high‑load spikes.

Group 2’s maximum busy percentage (94.6%) exceeds Group 1’s (63.1%).

Read/write speeds: Group 1 outperforms Group 2 in both average and maximum values for read and write.

Transfer rate: Group 1 shows higher maximum and average values.

Conclusion: Group 1 delivers superior disk performance.

5.5.5 Memory Data

Memory statistics such as total, free, cached, active, and swap were compared:

Both groups have the same total memory (1920.4 MB).

Group 1’s free memory fluctuates between 90.1 MB and 230.3 MB, while Group 2’s ranges from 83 MB to 167.2 MB.

Cached memory is higher in Group 1 (63‑259 MB) than in Group 2 (35‑66 MB).

Active memory is generally higher in Group 1.

Swap is plentiful in both groups, indicating little swapping activity.

Conclusion: Group 1 shows better memory availability and caching.

5.5.6 Network Data

Network I/O and packet rates were compared:

Group 2 achieves significantly higher average and maximum read/write bandwidth (e.g., average read 11,920.3 KB/s vs 2,426.1 KB/s) and packet rates.

All network metrics favor Group 2.

Conclusion: Group 2 provides superior network performance.

5.5.7 Overall Summary

Based on the metric analysis:

For high‑concurrency request handling, Group 1 is preferable.

For lower response time, stability, and better CPU utilization, Group 2 is advantageous.

Disk‑intensive workloads benefit from Group 1, while CPU‑intensive or network‑sensitive scenarios favor Group 2.

Memory‑heavy applications may lean toward Group 1 due to higher free and cached memory.

Recommendations for E‑Commerce Systems

The author synthesizes the findings for e‑commerce use cases:

If the system must handle massive traffic spikes (e.g., flash sales), choose the configuration represented by Group 1.

If the priority is fast response and overall resource efficiency, adopt the configuration of Group 2.

Finally, the article ends with a reflective quote emphasizing the importance of exploring the “unknown unknowns” in the AI era.