What this type of data looks like
Iron ore research and investment data covers four dimensions: spot, futures, logistics, supply and demand. Data sources include Dalian Commodity Exchange iron ore main contracts, domestic major port spot quotes, global mine shipping forecasts, steel plant purchase ledgers, and Baltic Dry Index shipping freight rates. Data updates follow three schedules: Futures contracts update in real time during trading days, covering morning and night trading sessions. Port spot quotes update at a fixed time every morning. Mine shipping and steel plant purchase data update weekly and monthly. Each data document includes these fields: variety code, delivery grade, origin, quotation unit, latest price, price change range, trading volume, position volume, release time, data source, and others. Quotation units use a uniform standard: yuan per wet ton. Trading volume is measured in lots, where 1 lot equals 100 tons of iron ore.
What constraints do these characteristics impose on database and operations?
The multi-dimensional and multi-update-frequency characteristics of iron ore data create multiple constraints for database and operations work. Real-time futures data has high-frequency write and query requirements. The database must support low-latency mixed load processing to avoid data loss or query timeouts caused by IO blocking. Data sources with different update frequencies need differentiated synchronization strategies. Real-time data uses a combination of incremental pull and real-time push. Daily and weekly updated data uses scheduled tasks for incremental synchronization. This avoids excessive server resource usage from full data pulls. Field naming varies across multiple data sources. For example, futures exchanges use last_price while ports use spot_price. This requires the database to have field mapping rules. It also needs scheduled verification tasks to ensure consistent field values across data sources. Additionally, metadata such as quotation units must be stored separately. This prevents unit confusion during research and investment analysis.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MONGODB_MAX_POOL_SIZE | 20–30 | Iron ore real-time futures data is written frequently. The connection pool must handle concurrent write requests and avoid connection exhaustion. |
DATA_SYNC_CRON_EXPRESSION | 0 8 * * 1-5,0 21 * * * | Matches synchronization timing before morning and night market openings on Dalian Commodity Exchange trading days. This covers real-time data update windows. |
FIELD_MAPPING_ENABLED | Enabled | Different data sources (futures exchanges, ports) have inconsistent field naming. Unified mapping to research and investment standard fields is required. |
QUERY_TIMEOUT_SECONDS | 15 seconds | Multi-source associated queries for iron ore traverse multiple data tables. Excessive timeout causes workflow blocking. |
INCREMENTAL_SYNC_RETRY_TIMES | 3 times | Addresses network fluctuations during port spot data synchronization. This avoids missing daily data. |
DB_STORAGE_UNIT_FIELD | Enabled | Stores fields such as quotation unit (yuan per wet ton). This prevents unit confusion during research and investment analysis. |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis, and it is recommended to test on your own samples before finalizing.
Three common mistakes
- Symptom: FastGPT service startup prompts
MongoConnectionError, and the database cannot be connected. Cause: The port number parameter in the database connection string is not configured correctly, or the port is blocked by the server firewall, resulting in failed database connection establishment. - Symptom: When calling a workflow to initiate an iron ore research and investment query,
429 Too Many Requestsstatus codes are returned frequently. Cause: The concurrency threshold parameter is not adjusted according to the concurrent query requirements of real-time data. The default concurrency upper limit is too low to support query requests initiated by multiple users simultaneously. - Symptom: After the scheduled synchronization task runs, the daily port spot data is not successfully written to the knowledge base. Cause:
DATA_SYNC_CRON_EXPRESSIONis configured incorrectly and does not match the daily update time of port spot data, so the synchronization task is not triggered as expected.
How to confirm the configuration is properly set
- Log in to the database monitoring panel, check the connection pool usage, confirm that the configured value of
MONGODB_MAX_POOL_SIZEis not continuously exhausted, and that server memory and CPU loads are within reasonable ranges. - Manually trigger an incremental synchronization task, check the synchronization logs, confirm that fields from all data sources have been mapped, and there are no alerts for missing or mismatched fields.
- Initiate multiple concurrent iron ore research and investment query requests, confirm that the workflow does not return
429status codes, and that query responses meet expected latency requirements. - Randomly select one stored iron ore data record, check whether it includes the
unitfield, and confirm that metadata such as the quotation unit has been stored along with the main data.
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.