What data for this category looks like
Textile manufacturing yield rate-related data primarily comes from commodity spot trading platforms, domestic weaving industry monitoring institutions, transaction records of downstream apparel manufacturers, and futures exchange contract data. Core market indicators are updated daily. Industry operating rates and order transaction data are updated weekly or monthly. A single data record includes six core fields: data_source, quote_date, category, index_name, index_value, unit. Units for raw material quotation data are yuan/kilogram or yuan/ton. Operating rate data uses operating duration ratio as the statistical unit. Futures contract data uses points as the unit.
What constraints these characteristics impose on database and operations
Multiple heterogeneous data sources require configuration of multiple data source connection tasks. Different update rhythms increase the scheduling complexity of synchronization tasks. Execution cycles for real-time, weekly, and monthly tasks must be differentiated. Diversity of fields and units requires adding standardized processing logic during the data cleaning stage. This avoids yield calculation errors caused by inconsistent units. Long-term historical data storage demands reserving sufficient disk space for the database and configuring appropriate indexing strategies to improve query efficiency. Scheduled yield daily report tasks aggregate multi-cycle data, which can lead to task conflicts or excessive resource usage. Concurrency limits and timeout thresholds must be configured specifically for this scenario.
How to set configurations
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
DATA_SYNC_INTERVAL | 300 seconds – 3600 seconds | Adapts to the different update frequencies of multi-source data for textile manufacturing, balances real-time performance and system resource usage |
MONGO_STORAGE_ENGINE | wiredTiger | Textile manufacturing has large volumes of historical data; the wiredTiger engine supports efficient data compression and index construction |
PARSE_DATA_TIMEOUT | 600 seconds | Multi-source data aggregation and cleaning takes a long time; reserve sufficient timeout to avoid task interruptions |
MAX_CONCURRENT_TASKS | 5 – 8 | Matches the CPU and memory resources of a single node, prevents service lag caused by multiple synchronization tasks running simultaneously |
DATA_CLEANUP_RETENTION | 180 days | Aligns with the standard historical data cycle for textile manufacturing yield rate analysis, balances storage costs and query efficiency |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material forms, data volumes, and business rules. Specific issues require targeted analysis, and it is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Phenomenon: An internal server error with status code 500 is prompted when installing the textile manufacturing market data connection plugin. Cause: The API access key for the corresponding data source is not configured, or the key permissions are insufficient to pull full data.
- Phenomenon: A MongoDB-related error is prompted after FastGPT is updated to a new version, and the service cannot run normally. Cause: Insufficient disk space is reserved for the database storage path, or the
MONGO_STORAGE_ENGINEconfiguration is incompatible with the current operating environment. - Phenomenon: When calling workflow APIs externally, rate limit errors are returned after concurrent requests exceed the threshold. Cause: The
MAX_API_CONCURRENCYparameter is not adjusted according to the data pulling requirements of textile categories, and the default value cannot support synchronous calls of batch market data.
How to confirm proper configuration
- Run a manual data synchronization task, check whether prompts for data source connection failures or field parsing errors appear in the synchronization logs.
- Check the database index status, confirm that corresponding indexes have been created for core fields such as
quote_dateandcategory. - Initiate a single workflow API call, verify the field completeness and unit consistency of the returned data.
- Simulate multiple concurrent requests, observe whether service response timeouts or rate limit prompts occur, and adjust related parameters as needed.
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.