What This Category's Data Looks Like
Chemical fiber yield and market data comes from three main sources: domestic bulk commodity spot trading platforms, public statistics from chemical fiber industry associations, and ex-factory price disclosures from upstream refining enterprises. Two update schedules are used: full daily market data is aggregated and updated after 16:30 each day. Intra-day abnormal price quotes are synced incrementally every hour. Each data entry uses structured JSON format, with fields including product_code, spec, factory_price, market_price, stock_ton, operating_load and others. Price units are yuan per ton, inventory units are ten thousand tons, and operating load is a decimal value between 0 and 1.
Constraints on Database and Operations Workflows
Multi-source heterogeneous data access requires the database layer to provide unified data cleaning and format conversion. This prevents dirty data from being written to the database due to field differences across data sources. High IO load from daily full syncs requires tasks to run during off-peak business hours, to avoid conflicts with peak business periods. Multi-specification, multi-dimensional query scenarios require database indexes to cover frequently queried fields. Without this, query delays will become excessive. Incremental sync offset management requires reliable storage media. This prevents data duplication or omissions caused by sync interruptions. Additionally, as the number of connected categories increases, data volume continues to grow. Reasonable planning for database storage and expansion strategies is required.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
data_sync_cron | 0 17 * * * | Aligns with the daily post-close update schedule for chemical fiber market data, runs full sync tasks during off-peak business hours |
db_index_fields | ["product_code", "spec", "daily_close_price"] | Covers core dimensions of frequent queries, improves response speed for combined searches |
full_sync_batch_size | 5000 records per batch | Balances disk IO load and sync efficiency, avoids timeouts caused by overly large single-batch sync volumes |
increment_sync_offset_store | redis cluster | Ensures highly available storage for incremental sync offsets, prevents data omissions caused by sync interruptions |
db_connection_timeout | 30 seconds | Adapts to network latency across multi-source data access, avoids invalid connections occupying system resources |
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 individual analysis. It is recommended to test against your own samples before finalizing settings.
Three Common Configuration Mistakes
- Symptom: Database sync logs return the
ECONNREFUSEDstatus code, or the console shows a "database connection failed" prompt. Cause: Database connectionhostandportparameters are not configured correctly, or network firewalls restrict access to database ports. This prevents sync tasks from establishing valid connections. - Symptom: Daily sync tasks time out, and the console shows an
ETIMEDOUTerror. Cause: No reasonablefull_sync_batch_sizevalue is set. The volume of data synced in a single batch exceeds the carrying capacity of the database and network, triggering connection timeouts. - Symptom: Queries for market data by product specification return empty results or an abnormal number of entries. Cause: No index for the corresponding field is configured in
db_index_fields. This causes the database to perform full table scans, and it cannot accurately match query conditions.
How to Confirm Configurations Are Correct
- Run a manually triggered full sync task. Check that the sync logs contain no connection errors or timeout prompts, to confirm database connection configurations are active.
- Run a combined query using
product_codeandspec. Verify that query response speed meets expectations, to confirm index configurations are active. - Check the incremental sync offset storage medium. Confirm that offsets update normally alongside sync tasks, with no duplicated or omitted data entries.
- Compare the total volume of data in the database after daily syncs with public statistics from data sources, to confirm data sync completeness.
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.