What the Data for This Category Looks Like
Data related to home goods yield rates comes from three sources: online e-commerce platform transaction API data, offline supermarket POS batch upload files, and public datasets from third-party monitoring institutions in the light manufacturing industry. Update frequencies vary across sources:
- Online channels update daily quotes and sales data.
- Offline supermarkets aggregate cycle shipment data every 3 business days.
- Industry monitoring data updates weekly.
Each data document covers cycle shipment information for a single SKU. Fields include SKU unique identifier, product category name, sales channel type, total shipment volume, unit shipment cost, unit shipment revenue, and cycle sales volume change value. All values use absolute, non-percentage metrics.
Constraints Imposed on Database and Operations
Multi-source heterogeneous data sources require the database to support multiple file parsing formats and API access to avoid format-incompatible write errors. Differentiated update rhythms demand flexible scheduled scheduling rules. Using a unified sync frequency will lead to data update lags or redundant storage. A large number of SKUs and multiple field dimensions require targeted joint indexes for the database. Without these indexes, core retrieval requests will time out. High demand for cross-source data validation means a data consistency check process must be configured during operations. This prevents deviations in shipment data from different channels from impacting the accuracy of yield rate calculations.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MONGODB_READ_PREFERENCE | secondaryPreferred | Adapts to multi-source data concurrent write scenarios, balances read performance and data consistency |
BACKUP_RETENTION_DAYS | 90 days | Covers monthly and quarterly industry data review cycles, prevents loss of critical historical data |
PARSE_DATA_TIMEOUT | 1200 seconds | Reserves sufficient time for format conversion and validation when processing multi-source merged data for a single SKU |
INDEX_FIELD_LIST | ["sku_id", "channel_type", "cycle_date"] | Accelerates joint queries by SKU, channel, and cycle, matches core retrieval requirements for yield rate broadcasts |
DATA_CLEANUP_CRON | 0 2 * * 0 | Runs expired data cleanup every week at 2 AM, retains only 90 days of real-time broadcast data to reduce storage usage |
UPLOAD_FILE_MAX_SIZE | 2000 MB | Adapts to the size of offline supermarket batch POS upload files, prevents large file upload failures |
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 individual analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Symptom: Scheduled backup task fails, with
file size exceeds limiterror in logs. Cause: TheUPLOAD_FILE_MAX_SIZEparameter is not adjusted. Offline supermarket batch POS upload files exceed the default limit, so backup cannot be completed. - Symptom: Knowledge base sync fails to return home goods yield rate data, with
connection refusederror displayed in the interface. Cause: MongoDB internal network access whitelist is not configured correctly. The natively deployed FastGPT instance cannot connect to the Docker-deployed database instance. - Symptom: Yield rate broadcast returns an insufficient number of results, with
no matching documentsprompt displayed in the interface. Cause:sku_idandchannel_typeare not added to the index field list. Joint queries cannot locate cycle data for the target SKU.
How to Confirm Configuration is Successfully Applied
- Run a manual data sync task, check if the task logs include
sync completedto confirm that multi-source data read and write processes are working normally. - Submit a yield rate broadcast request, check if the returned result fields include the preset index fields to confirm that the index configuration is active.
- View the database backup directory, confirm that the latest backup file has been generated and its size matches expectations to confirm that the backup configuration is active.
- View the scheduled task execution logs, confirm that the task configured via
DATA_CLEANUP_CRONruns at the expected time to confirm that the cleanup configuration is active.
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.