Deployment and Upgrade for Biologics Yield Reporting

Market and yield data for the biologics category is sourced primarily from compliant financial data service provider APIs and public market ports of

What data for this category looks like

Market and yield data for the biologics category is sourced primarily from compliant financial data service provider APIs and public market ports of stock exchanges. Data is updated at fixed daily times after market close, with a consistent synchronization cycle. Each individual data document uses a structured format, including fields such as ticker code, ticker name, daily trading price, daily yield, circulating market capitalization, and assigned biologics sub-sector. The yield field uses percentage units, trading price uses yuan units, and circulating market capitalization uses hundred million yuan units.

Constraints imposed by data characteristics on deployment and upgrade

The data characteristics of the biologics category create multiple constraints for deployment and upgrade workflows. Multiple data source integration requirements demand configuration of multiple sets of authentication parameters and interface timeout thresholds during deployment, to prevent single interface failures from impacting overall reporting. Fixed update schedules require alignment of scheduled task trigger times with industry data update windows, to avoid data lag or duplicate pushes. Standardized field requirements demand configuration of field validation rules during deployment, to filter abnormal data with non-compliant units. The periodic rebalancing of biologics sector constituent stocks requires updates to the built-in ticker pool during upgrades, to avoid missing newly added tickers. The large number of sector tickers also demands configuration of reasonable batch processing sharding parameters, to prevent single-batch data processing timeouts.

How to configure settings

Config ItemRecommended ValueRationale
CRON_EXPRESSION0 30 17 * * *Domestic securities markets finalize closing data updates at 17:00 daily. Setting synchronization to trigger at 17:30 ensures access to complete daily market data
MONGO_VERSION_COMPATIBLE6.0 to 7.0FastGPT 4.9.10-fix2 and later versions support MongoDB versions in this range, ensuring production environment stability
BATCH_DATA_SYNC_SIZE50 records per batchThe biologics sector has a large number of tickers. Setting 50 records per batch balances processing efficiency and memory usage, avoiding single batch timeouts
PARSE_DATA_UNIT_CHECKEnabledBiologics data includes multiple fields with units. Enabling validation filters abnormal data with non-compliant units
UPGRADE_VERSION_LOCK4.9.10-fix2Production environments require stable operation. Locking this version avoids functional changes and adaptation costs from major version updates
DATA_SOURCE_API_TIMEOUT10000 millisecondsThird-party compliant data interfaces typically respond within 8 seconds. Setting a 10-second timeout avoids unnecessary waiting

The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis. It is recommended to test on your own samples before finalizing settings.

Three common configuration mistakes

  • Phenomenon: Field mapping errors occur after upgrade, and yield data is missing from broadcast content. Cause: Built-in ticker field mapping rules were not synchronized and updated during the upgrade. Legacy configurations cannot adapt to the new data field structure.
  • Phenomenon: A 429 Too Many Requests error is returned after the scheduled task triggers. Cause: Data source interface timeout and synchronization interval parameters were not adjusted. Frequent interface calls trigger service provider rate limiting.
  • Phenomenon: Local deployment fails to parse biologics industry knowledge base files after upload. Cause: Corresponding file processing parameters were not configured. File size exceeds system default limits or parsing times out, leading to failure.

How to verify successful configuration

  • Manually trigger a data synchronization task, check if the synchronization log includes complete ticker data for the daily biologics sector, and verify that field units comply with preset specifications.
  • View MongoDB connection status logs, confirm that the currently used MongoDB version falls within the compatible range.
  • Check scheduled task trigger records, confirm that synchronization tasks automatically trigger at fixed daily times with no delayed or failed records.
  • Test the version lock configuration, confirm that upgrade operations will not automatically update to unvalidated major versions, and that existing configuration parameters remain stable.

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.