What Data for This Category Looks Like
Industrial metals investment research data comes from three main sources: public market APIs from the London Metal Exchange (LME) and Shanghai Futures Exchange (SHFE), monthly statistical reports from domestic nonferrous metal industry associations, and daily quotation data from spot traders. Update frequencies differ across sources: futures market data updates every 15 seconds, spot quotations update 2 to 3 times daily, and industry statistical data releases weekly or monthly. Each data entry uses a structured format, with fields including metal_type (variety code), trade_date (transaction date), spot_price (spot price), futures_price (futures settlement price), inventory (inventory quantity), unit (unit), exchange (trading market), and others. Valid units are uniformly yuan/ton, US dollars/ton, or ten thousand tons.
What Constraints Do These Characteristics Bring to Database and Operations
High-frequency real-time market data requires keeping database write latency low. This prevents data backlogs that reduce investment research timeliness. Data sources with varied update frequencies need a hot/cold data tiered storage strategy. This strategy distinguishes storage media for hot and cold data, cutting long-term storage costs. Structured data with multiple fields tied to units needs database field validation rules enabled. These rules unify unit formats, avoiding data errors from mixed units across different sources. Multi-source data access scenarios require configuring data deduplication and conflict verification logic. This logic handles price differences for the same variety across different channels. Data volume grows continuously over time. The database must support online sharding and expansion to avoid single-node performance bottlenecks. It also needs to adapt to incremental backup strategies for high-frequency updates, lowering data loss risks.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
mongodb.watchBatchSize | 100 | Industrial metals real-time market data volume is moderate. A batch size of 100 balances network overhead and write efficiency |
mongodb.maxPoolSize | 50–80 | Single-node concurrent write requests are frequent. This range avoids connection exhaustion |
mongodb.heartbeatFrequencyMS | 2000 milliseconds | Real-time data needs fast detection of primary node switches. This reduces disconnection recovery time |
data.retention.days | 90 days | Real-time data from the past 3 months has the highest usage rate in investment research scenarios. Data beyond this period automatically archives to cold storage |
data.validation.rules | Enable mandatory validation for the unit field, only allowing values including yuan/ton, US dollars/ton, ten thousand tons | Avoid investment research data errors caused by mixed units from different sources |
backup.incremental.interval | 15 minutes | Real-time data updates frequently. Shortening the backup interval reduces data loss risks |
The parameter values provided on this page are common recommended starting points for configuration. Actual values vary based on material forms, data volume and business rules. Specific issues require targeted analysis. Testing on independent samples is recommended before finalizing configuration settings.
Three Common Configuration Mistakes
- Symptom: MongoDB Change Streams throw an
ETIMEDOUTerror after a primary node failover, and cannot automatically reconnect. Cause: TheretryWrites=trueandmaxStalenessSecondsparameters are not configured, and automatic reconnection logic is not enabled. - Symptom: A
UnitValidationFailederror occurs when writing data, and thespot_pricefield is not associated with theunitfield. Cause: Database field validation rules are not enabled, and mandatory binding of corresponding units to price data is not required. - Symptom: Query results return multiple duplicate market data entries for the same variety and date. Cause: Multi-source data deduplication logic is not configured, and duplicate write requests are not verified.
How to Confirm the Configuration Is Correct
- Manually trigger a primary node failover for the MongoDB replica set. Observe whether Change Streams automatically reconnect and resume data writing. Confirm that the reconnection logic takes effect.
- Import a test data entry with an invalid unit. Check whether a field validation error is triggered. Confirm that the validation rules take effect.
- Write multiple duplicate data entries for the same variety and date. Check whether the database automatically deduplicates the data. Confirm that the deduplication logic takes effect.
- View the database monitoring dashboard. Confirm that the connection pool usage does not exceed the preset threshold, and that write latency meets expected standards.
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.