What this category of data looks like
Data for this category primarily comes from stress testing platforms, APM monitoring tools, database slow logs, and production traffic baselines. Update frequency is monthly or per major release. Daily produced documents include stress testing plans, capacity assessment reports, stress testing data records, and optimization suggestion sheets. Common data fields include task identifiers, throughput rates, latency, resource usage rates, and more. Units are typically requests/second, milliseconds, and percentage. The size of a single stress testing data record usually ranges from hundreds to thousands of characters.
What constraints these characteristics impose on conversation logs and auditing
Since data sources cover multiple monitoring and business systems, conversation logs must be bound to a unique stress testing task identifier. This ensures complete stress testing chain data can be associated during audits. Data update frequency is low, so log retention periods must match the storage periods of business documents. This prevents gaps between logs and their corresponding stress testing plans or capacity assessment reports. Core indicator fields must be clearly defined. Logs must fully record performance-related content such as throughput rates and latency, and must not include irrelevant business data. Otherwise, bottleneck identification and capacity conclusion review during audits will be impacted. Additionally, single log size limits require configuring reasonable rules to avoid storage and transmission exceptions.
How to configure settings
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId binding rule | Bind to the unique identifier of the stress testing task | Match the task association requirements of multiple data sources, ensure logs are bound to their corresponding stress testing tasks |
responseChatItemId association rule | Associate with single-round stress testing indicator records | Correspond to detailed data for single-round stress testing, facilitate locating abnormal nodes in single-batch stress testing |
Log detail field scope | Include throughput rate, latency, resource usage rate | Cover core monitoring indicators for performance testing, meet indicator analysis needs during audits |
| Log retention period | 30–90 days | Match data update frequency and basic compliance auditing requirements |
| Maximum single log size | 1000 KB | Adapt to the size of single stress testing data records, avoid storage and transmission exceptions |
| Bulk export limit | Calibrate based on actual testing | Avoid triggering system resource bottlenecks during bulk exports, adapt to capacity of different deployment environments |
Three common configuration mistakes
- A
$lookup with 'pipeline' may not specify 'localField'error occurs when viewing logs. The cause is mixing non-stress testing task identifier fields when configuring thechatIdassociation rule, leading to cross-data source association failure. - Log exports stop after retrieving only 50,000 records. The cause is not configuring a reasonable bulk export limit, exceeding the system's default batch processing threshold.
- Core performance indicators are missing from the log
detailfield. The cause is not limiting thedetailfield scope to stress testing-related monitoring data, resulting in irrelevant business logs being included.
How to confirm configuration is complete
- Review the
chatIdfield of a single log entry to confirm it exactly matches the unique identifier of the target stress testing task. - Trigger a small-batch export operation, and check if the number and batch range of exported records match configured requirements.
- Review the
detailfield of logs to confirm it includes core performance testing indicators such as throughput rate and latency. - Check log retention settings in FastGPT v4.17.0 to confirm the period falls within the configured 30–90 day range.
The values provided are common starting points and should be measured against deployment-specific samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-10-19.