What the data for this category looks like
Data sources include public end-consumer retail monitoring data from textile and apparel industry associations, sales ledgers from brand direct and franchise channels, procurement cost databases from fabric suppliers, and foot traffic and conversion monitoring data from offline retail stores. Full batch synchronization of all SKU data from the prior day is completed each day at midnight. Some real-time market data nodes synchronize fluctuation data once per hour. Each structured document corresponds to data for one brand’s single product category on a single day. Fields include brand identifier, category code, statistical date, per-unit procurement cost, terminal listed price, actual average transaction price, daily revenue, daily net profit, and profit margin. Per-unit procurement cost and average transaction price are denominated in yuan. Revenue and net profit are denominated in ten thousand yuan.
Constraints imposed on database and operations by these characteristics
Multiple heterogeneous data sources require configuring multi-data source adaptation rules to support sales ledgers and monitoring reports in varying formats. Tiered update schedules require designing hot and cold data storage strategies: store hourly real-time market data in memory cache, and archive full historical data to cold storage. Single-record structures for multiple categories and SKUs require creating composite indexes to cover core query scenarios and avoid full table scans. Numeric fields with specified units require configuring unified unit conversion rules to prevent calculation errors caused by unit inconsistencies across data sources. The volume of daily full synchronization tasks requires adjusting batch write parameter thresholds to balance database load and synchronization efficiency.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MONGO_CONNECTION_PORT | 27017 | Matches the default port of most open-source MongoDB deployments, simplifies connection configuration workflows |
DAILY_DATA_SYNC_TIME | 02:00 | Avoids daytime business peak hours, reduces performance impact of data synchronization on real-time queries |
BATCH_INSERT_SIZE | 500 records per batch | Balances write throughput and database load, adapts to the tens of thousands of SKU-level daily data volume for the apparel and home textile category |
INDEX_FIELD_COMBINATION | ["brand ID", "category code", "statistical date"] | Covers core query scenarios by brand, category, and time range, accelerates yield rate data retrieval |
DATA_CACHE_TTL | 3600 seconds | Matches the hourly real-time market update frequency, ensures predictable hot data query latency |
DATA_BACKUP_CRON | 0 3 * * * | Executes backup after daily midnight synchronization is complete, avoids overwriting valid same-day business data |
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 configuration mistakes
- Issue: The database connection node configuration interface does not accept port number input, and the port field is empty after saving. Cause: The advanced configuration switch is not enabled, so the port number input box is hidden by default. Only full connection string configuration is permitted.
- Issue: After installing and starting version v4.14.9, the mongo/data folder is not generated in the same-level working directory. Cause: This version adjusted the default deployment path for the MongoDB data directory. The storage location must be manually specified via the
MONGO_DATA_DIRenvironment variable. - Issue: After importing knowledge base data using mongorestore, executing yield rate queries returns no matching results. Cause: The
--dropparameter was not specified during import to overwrite existing indexes, or the imported document fields do not match the configured index fields, preventing queries from matching the correct records.
How to verify successful configuration
- Run the built-in database connection test tool to verify that the configured port, IP, and other parameters can establish a normal connection, and return a connection successful status.
- Import a single standardized test data set, execute a combined query using brand, category, and date as filters, and confirm that the returned results match the imported data.
- Review the scheduled task log to confirm that the daily midnight full data synchronization task runs normally without error messages.
- Check the database index list to confirm that the composite index
["brand ID", "category code", "statistical date"]has been successfully created.
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.