What This Category's Data Looks Like
Plastics and rubber market and yield data is sourced from domestic commodity futures exchanges and industry spot quotation platforms. Two update schedules apply: Futures products push real-time latest trading data per trading day. Spot products update daily quotes each workday. Each data entry includes fields such as product identifier, delivery month, latest trading price, daily price change, inventory level, and trading volume. Price units are yuan per metric ton, trading volume units are lots, and inventory units are ten thousand metric tons. Data fields must strictly match the source format to prevent ingestion failures caused by missing fields or inconsistent units.
Constraints on Database and Operations
Data sources with different update schedules require differentiated synchronization scheduling rules to avoid concurrent write conflicts. High-precision price and inventory values must use high-precision storage types to prevent precision loss from floating-point arithmetic. Differences in data formats across multiple sources require a pre-cleaning step to unify field names and units. High-frequency written market data places significant load on database connection pools. Reasonable configuration of connection parameters is needed to avoid connection exhaustion. Market data has strict timeliness requirements. Synchronization task stability must be ensured to prevent data update interruptions caused by database failures.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| `MONGOCONNECTIONPOOL_SIZE | 10–15 | High-frequency writes for plastics and rubber market data. A too-small connection pool causes connection wait timeouts, while a too-large pool exceeds MongoDB's default connection limit. |
DATA_SYNC_INTERVAL | 300 seconds (futures), 86400 seconds (spot) | Matches the update schedules of futures (real-time per trading day) and spot (daily per workday) data sources, avoids unnecessary repeated synchronization. |
VECTOR_SEARCH_SCORE_THRESHOLD | 0.75 | Accurately matches products and market data, balances the probability of false matches and missed matches. |
DB_NUMERIC_PRECISION | 12,4 | Stores price and inventory values, retains four decimal places to meet high-precision requirements and prevent precision loss. |
MONGO_CONNECT_TIMEOUT_MS | 5000 milliseconds | Balances tolerance for network fluctuations and task blocking duration, prevents synchronization task failures from short network issues. |
FASTGPT_VERSION | v4.8.10-alpha2 | Adapts to the configuration item naming and parameter validation rules of this version, avoids version compatibility issues. |
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. Each scenario requires targeted analysis. It is recommended to test on local samples before finalizing settings.
Three Common Misconfigurations
- Symptom: The log shows
retry init root user mongo start connect init root user errorafter deployment. Cause: Incorrect MongoDB connection string configuration, including incorrect port or authentication information, leading to initialization failure. - Symptom: Duplicate market data appears in the vector database. Cause: No unique identifier validation rule based on product + delivery month configured, leading to duplicate data ingestion.
- Symptom: Scheduled synchronization tasks return
ETIMEDOUTerrors. Cause: Connection pool size configured too small, unable to support concurrent requests for high-frequency writes.
How to Confirm Correct Configuration
- Execute the official MongoDB connection test command to verify that the configured connection string can establish an authenticated connection successfully.
- Manually trigger a data synchronization task, review the field integrity and unit consistency of ingested data to confirm alignment with the original data source.
- Review database connection pool monitoring metrics to confirm current active connections do not exceed the configured maximum connection pool size.
- Compare the data update time after synchronization with the data source's update schedule to confirm synchronization frequency matches expected requirements.
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.