What the data for this category looks like
Data for footwear yield and market daily reports comes primarily from brand ERP systems, mainstream e-commerce platform sales APIs, and textile raw material spot trading platforms. Two update cadences are used: core business data updates on a T+1 basis, with a full report for the previous day generated each early morning. Associated raw material market data syncs every hour. Each document is structured per SKU. Each record includes SKU number, style name, affiliated category, purchase unit price, listed unit price, actual transaction unit price, daily sales volume, ending inventory quantity, and average daily price of associated raw materials. All units are yuan and pieces, and no percentage-based statistical items are included.
Constraints on deployment and upgrade from these data characteristics
The multi-source, batch-updated data characteristics of the footwear category impose multiple constraints on deployment and upgrade workflows. First, each daily report contains thousands of SKU records. Batch parsing may trigger timeout limits, so concurrency parameters for file reading and parsing must be adjusted. Second, integration with multiple data sources requires multiple sets of authentication and API address configurations. Upgrades must support migration logic for legacy data sources. T+1 batch data updates require scheduled task trigger times to align with brand report generation cycles, to avoid missing data. Real-time synced raw material market data requires incremental pull interval parameters, to prevent interface rate limiting from frequent calls.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 900 seconds | A full daily SKU report contains thousands of records, and the default timeout duration is insufficient for complete parsing |
UPLOAD_FILE_MAX_SIZE | 2000 MB | Full batch reports have large file sizes, this adapts to upload requirements for large-capacity files |
MAX_BATCH_SIZE | 500 records per batch | Splitting batch data into small batches avoids memory overflow and parsing timeouts |
CRON_EXPRESSION | 0 3 0 * * ? | Aligns with the early morning generation cycle of brand T+1 reports, ensuring daily data updates are triggered |
API_RATE_LIMIT | 10 requests per minute | Adapts to rate limiting rules of textile raw material market APIs, meeting hourly sync frequency requirements |
FIELD_MAPPING_RULE | Calibrated based on actual testing | SKU field naming varies across different data sources, so custom matching of corresponding fields is required |
The parameter values provided on this page are common starting points for configuration setup. Actual values are affected by data format, data volume and business rules. Specific issues require individual analysis, and it is recommended to test on your own samples before finalizing settings.
Three common configuration mistakes
- Issue: An
out of memoryerror appears when runningdocker-compose up. Cause: TheUPLOAD_FILE_MAX_SIZEandMAX_BATCH_SIZEparameters are not adjusted, and memory usage exceeds container quotas when parsing large volumes of SKU data in batches. - Issue: The platform does not display connected models after configuring
BASE_URL. Cause: Associated key configurations are not updated synchronously, or the container is not restarted to apply new configurations. - Issue: Only a small amount of data is generated after the scheduled task runs. Cause: The scheduled task trigger time is earlier than the brand report generation time, resulting in empty data source files being pulled.
How to confirm configurations are properly set
- Upload a small sample footwear daily report to the platform, and check if parsed data fields match the preset mapping rules.
- View scheduled task run logs, confirm that trigger times align with brand report generation cycles, and there are no error records for failed data pulls.
- Call the raw material market API for testing, verify that returned data format and fields meet configured mapping requirements.
- Adjust single-batch processing quantity, run a batch parsing test, and confirm there are no memory overflow or timeout errors.
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.