What data for this category looks like
Data for black home appliance profit margins and daily market reports comes from brand ERP inventory and sales systems, offline retail POS ledgers, and third-party home appliance industry market monitoring datasets. This data is mostly used for home appliance industry market analysis and investment reference in financial scenarios. Full previous day’s data is aggregated every early morning. Each document corresponds to daily market information for one black home appliance model. Document structure uses a standardized key-value pair format, including fields such as sku_code (unique model identifier string), product_name (full model name string), factory_cost (ex-factory procurement cost, unit: yuan), market_avg_price (same-day average market selling price, unit: yuan), daily_sales (same-day shipment volume, unit: units), warehouse_stock (same-day end-of-period inventory, unit: units). No non-standard extended fields are included.
What constraints do these characteristics impose on database and operations workflows
Multi-source data access requires the database to support unified conversion and validation of multiple data formats. This prevents data import failures caused by inconsistent field naming across systems. Daily scheduled full data updates require configured timed synchronization tasks. The update window must not overlap with business peak hours. The single-model daily document structure requires a composite index on sku_code and update_date. This improves response speed for high-frequency queries. Legitimacy requirements for numeric fields require configured automatic validation rules. These rules filter invalid data such as negative values and null values. The non-editable attribute of historical market data requires setting read-only permissions. This prevents accidental overwriting of historical records. As a data source for FastGPT, database connection stability must be maintained. This avoids knowledge base update failures caused by interrupted connections.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
DB_SYNC_CRON | 0 2 * * * | Matches business low-peak period at 2 AM daily to complete previous day’s data synchronization |
INDEX_FIELDS | ["sku_code", "update_date"] | Adapts to high-frequency query scenarios for single model daily market data, improves query efficiency |
DATA_VALIDATION_RULE | {"factory_cost": ">0", "daily_sales": ">=0", "warehouse_stock": ">=0"} | Filters invalid numeric data to ensure legitimacy of imported data |
READ_ONLY_HISTORY | Enabled | Prevents accidental modification of historical market data, ensures accuracy of data traceability |
QUERY_TIMEOUT | 30 seconds | Limits execution duration of single query to avoid excessive resource usage |
BATCH_SYNC_SIZE | 500 records per batch | Balances synchronization efficiency and database load, avoids excessive data volume in single synchronization |
The parameter values provided on this page are common starting points for configuration setup. Actual values are affected by material form, data volume and business rules. Specific issues require targeted analysis. It is recommended to test on your own samples before finalizing settings.
Three common errors
- Symptom: MongoDB connection test fails, database session cannot be established. Cause: The
MONGO_AUTH_SOURCEparameter is not configured correctly, or the entered authentication database name does not match the actually deployed authentication database. - Symptom: Database query node execution stalls with no error returned. Cause: The
QUERY_TIMEOUTparameter is not set, or its value exceeds the maximum wait duration of the database connection pool, leading to exhausted connection resources. - Symptom: Missing fields or format corruption appear after importing code-formatted market data. Cause: The
DATA_VALIDATION_SWITCHis not enabled, and no pre-validation is performed for field types and formats of imported data.
How to confirm the configuration is correct
- Run the database’s built-in connection test tool, verify that authentication parameters match the actual deployed database configuration.
- Manually run a high-frequency query SQL for a single model’s daily data, verify that returned fields match the preset data structure.
- Trigger a manual synchronization task, verify that the number of newly added data entries in the database matches the statistical volume of source data.
- Check database operation logs to confirm there are no timeout or connection failure error records.
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.