What this category’s data looks like
Chemical raw material yield rate data primarily comes from domestic bulk commodity spot trading platforms, futures exchange market APIs, and monthly quotation reports from industry associations. Two update frequency categories apply: spot quotations are synchronized after daily market close, futures market data is pushed every 15 minutes, and monthly year-over-year and weekly average price data is updated per calendar week and calendar month. Each single data record includes 7 core fields: raw material common name, CAS registry number, origin identifier, daily settlement price, weekly average price, unit (yuan/ton), and statistical cycle. The data uses a flat structure with no nested levels.
Constraints on database and operations
The mixed update frequency requirements mean the database must support mixed write scheduling. Unified scheduled tasks alone cannot cover full data synchronization. The CAS number serves as the unique identifier field, so a global unique index must be created to avoid duplicate entries, and field format validity must be verified. Quotation formats vary across data sources. Some APIs return numeric values as string types, so type conversion and null value filtering must be completed before writing. Monthly batch statistical data has a large single write volume, so table splitting rules must be configured to split storage by statistical cycle, reducing single-table query pressure. The exclusive unit yuan/ton must be forcibly verified during data import to avoid confusion with data from other categories.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
MONGODB_WRITE_CONCURRENCY | 10–15 concurrent writes | Adapts to the mixed update rhythm of chemical raw material data, balancing write pressure from real-time market data and bulk monthly data |
DATA_VALIDATION_RULES | Validate unit is yuan/ton, CAS number matches standard regular expression | Meets the exclusive field format requirements for chemical raw material data, avoiding confusion with cross-category data |
BATCH_INSERT_SIZE | 200–300 records per batch | Adapts to the single write scale of monthly batch statistical data, preventing single-table write overload |
QUERY_TIMEOUT | 600 seconds | Supports associated query requirements across cycles (daily price and weekly average price), avoiding timeout interruptions |
INDEX_EXPIRY_DAYS | 30 days | Matches the retention cycle of monthly statistical data, regularly archiving expired non-real-time data |
MONITOR_ALERT_THRESHOLD | Alert triggered when write failure rate > 5% | Ensures continuity of real-time market data, timely detecting data source or connection abnormalities |
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, and it is recommended to test against your own samples before finalizing settings.
Three common mistakes
- Symptom: Unable to connect to the local MongoDB instance after deployment, with a
connection refusederror returned in the console. Cause: The correctMONGODB_URIparameter is not configured, or when deploying locally, the MongoDB binding address is not changed to0.0.0.0, allowing only local loopback access. - Symptom: Empty results or missing core fields are returned when querying chemical raw material data. Cause: The
DATA_VALIDATION_RULEScheck is not enabled, invalid data with non-yuan/ton units is not filtered, or a unique index is not created for the CAS number, leading to duplicate entries overwriting existing records. - Symptom: Database table locking or write timeout occurs when processing monthly batch statistical data. Cause: The
MONGODB_WRITE_CONCURRENCYandBATCH_INSERT_SIZEparameters are not adjusted, and default configurations cannot adapt to the scale requirements of batch writes.
How to confirm configurations are correctly set
- Perform a single chemical raw material data write test, verify that records matching the CAS number format and with the unit yuan/ton exist in the database, and adjust the
DATA_VALIDATION_RULESparameter based on test results. - Initiate an associated query across cycles (daily price and weekly average price), verify that the query does not time out, and adjust the
QUERY_TIMEOUTparameter based on actual query duration. - Batch import simulated monthly statistical data, verify that there are no abnormal errors during database writing, and adjust the
BATCH_INSERT_SIZEparameter based on write success rate. - View the system monitoring panel, confirm that the write failure rate does not trigger the preset alert threshold, and adjust the
MONITOR_ALERT_THRESHOLDparameter based on alert trigger frequency.
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.