What the data for this category looks like
Thermal coal investment research data sources include industry association public statistics, northern port spot transaction ledgers, railway freight dispatch records, and futures delivery settlement data. Update cycles vary by data type: spot transaction data is updated daily, monthly supply and demand statistics are updated every ten days, and industry analysis reports are released quarterly. A single structured data entry includes fields such as origin, calorific value, total moisture, ash content, transaction price, and release time. The units for these fields are kilocalories per kilogram, mass fraction, mass fraction, yuan per ton, and date, respectively.
What constraints do these characteristics impose on the database and operations link
Multi-source heterogeneous data sources require the database layer to configure unified field mapping rules and data validation logic, to avoid data ingestion failures caused by format differences across different channels. High-frequency updated spot transaction data generates dozens of write requests per second. Adjust database connection pool parameters to adapt to concurrent writes, and prevent connection exhaustion or table lock conflicts. Long-cycle quarterly industry reports have large document sizes. Configure appropriate document sharding thresholds to avoid reduced index efficiency caused by overly long single data entries. Data with different update frequencies must be stored using time partitioning. Cold data archiving reduces operation and maintenance costs and query latency.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
DB_CONNECTION_POOL_SIZE | 16–32 | Thermal coal data has high write concurrency. This parameter controls the number of database connections to adapt to high-frequency write requirements |
PARSE_DOCUMENT_CHUNK_SIZE | 800–1200 characters | Thermal coal industry reports are mostly long texts. This sharding length preserves complete professional terminology and data context |
RECALL_TOP_K | Top 8 entries | Investment research queries need to cover multi-dimensional spot, freight volume, and report data. Retrieving 8 entries balances query efficiency and information completeness |
SIMILARITY_THRESHOLD | 0.65–0.75 | Filter low-relevance historical data to avoid redundant information interfering with investment research conclusions |
DB_WRITE_BATCH_SIZE | 20 entries per batch | Balance write efficiency and database load, and avoid timeouts caused by overly large single batches of writes |
DATA_CLEANUP_CYCLE | 7 days | Clean up expired temporary crawled data, retain core monthly/quarterly reports and spot historical data |
The parameter values provided on this page are all common recommendations used as starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis. It is recommended to test on your own samples before finalizing.
Three Common Misconfigurations
- Phenomenon: A
database query failederror is returned when calling knowledge base question answering, and the interface prompts a query timeout. Cause: TheDB_WRITE_BATCH_SIZEparameter was not adjusted to adapt to the high-frequency data writes of thermal coal, resulting in a backlog of database query queues. This error is unrelated to the model, and database connection configuration should be prioritized for troubleshooting. - Phenomenon: After upgrading FastGPT to the latest version, previously ingested thermal coal data cannot be loaded normally. Cause: No database backup file was exported before the upgrade. The new version adjusted the storage format of structured data, making old data unparseable.
- Phenomenon: The database connection function cannot configure a ClickHouse data source, and an
unsupported database typeerror is reported during query. Cause: The current FastGPT version does not have a built-in ClickHouse driver, and the corresponding dependency package must be installed manually.
How to Verify Successful Configuration
- Initiate a query that includes spot price and origin data, verify that the returned fields and units match the configured mapping rules, to confirm the parameter configuration is effective.
- Batch import 100 simulated thermal coal industry report data entries, observe the ingestion time and database load, to confirm that the
DB_WRITE_BATCH_SIZEandDB_CONNECTION_POOL_SIZEparameters adapt to the current business volume. - View the knowledge base query orchestration logic, confirm that multi-table joint queries have been configured, and can return spot, freight volume, and industry report data simultaneously to meet investment research requirements.
- Perform a database connection test, confirm that connection configurations for different data sources can be established normally, with no connection timeout or rejection errors.
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.