What this category of data looks like
Data for this category originates from compliant financial market data APIs and official disclosure documents. It is generated in batches each trading day after market close. The data uses a structured format, including fields such as unique asset identifier, full asset name, trading date, daily profit and loss amount, daily trading volume, daily trading turnover, and others. Profit and loss amount is measured in yuan, trading volume in shares, and trading turnover in yuan. Each batch covers complete market information for all assets on the trading day.
What constraints these characteristics impose on conversation logging and auditing workflows
Batch updates require conversation logs to fully record the start and end times of each daily data batch pull. This avoids reprocessing expired or incomplete batches. Multiple structured fields require the auditing workflow to verify the completeness and format compliance of fields such as asset identifiers and profit and loss amounts in each log. This prevents audit failure from missing critical information. The fixed update window requires logs to associate with the trading date dimension. This enables audit workflows to be traced by trading date. High API call frequency in software development scenarios requires logs to record caller identifiers, request parameters, and return results. This supports end-to-end auditing.
How to configure settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
API_LOG_ENABLE | Enabled | Software development scenarios require complete recording of API call details to support end-to-end auditing |
LOG_RETENTION_DAYS | 90 days | Complies with financial industry audit retention compliance requirements |
BATCH_PROCESS_TIMEOUT | 1800 seconds | Batch market data has a large volume, so sufficient processing time must be reserved |
REQUEST_BODY_LOGGING | Enabled | Complete recording of API call parameter information is required to support end-to-end traceability |
LOG_FIELD_VALIDATION | Enabled | Many structured fields require verification of field completeness and format correctness |
ABNORMAL_LOG_ALERT | Enabled | Abnormal situations in the audit process must be detected in a timely manner |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.
Three common configuration mistakes
- Symptom: After deployment and running via docker-compose, logs repeatedly report
slow operation xxxxmserrors, while MongoDB responses are normal. Cause: TheBATCH_PROCESS_TIMEOUTconfiguration was not adjusted. Batch processing of market data times out before completion, and the system marks the operation as slow. - Symptom: Request parameter fields are missing from conversation logs returned by API calls. Cause: The
REQUEST_BODY_LOGGINGconfiguration was not enabled. Request body content is not recorded, making audit traceback of call details impossible. - Symptom: Empty profit and loss amount fields are found for some assets during audits. Cause: The
LOG_FIELD_VALIDATIONconfiguration was not enabled. Field completeness is not verified, allowing logs with missing fields to be recorded normally.
How to confirm configurations are correctly applied
- Initiate an API call, then check if returned logs include request parameters and return results. This confirms relevant logging configurations are active.
- Check the retention duration of the log storage directory. This confirms it matches the preset retention period requirements.
- Simulate a batch data processing task, then check if slow operation alerts are triggered. This confirms the timeout configuration is reasonable.
- Check the status of the abnormal alert switch. This confirms that abnormal log notifications can be received in a timely manner.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.