What this category’s data looks like
Coal chemical investment research data covers the entire industrial chain, including coal mining, washing and dressing, coking, and downstream olefin processing. Data sources include internal enterprise production reports, public monitoring data from industry associations, commodity trading platform quotes, environmental impact assessment reports, and patent literature. Update frequencies vary widely: production data is updated per shift or daily, industry monitoring data is updated weekly or monthly, and patent and research report data is updated in real time. Document structures include three types: structured capacity, unit consumption, and composition parameters; semi-structured test reports and industry briefings; and unstructured patent texts and site logs. Fields include professional indicators such as ash content, sulfur content, and volatile matter. Most units use coal chemical industry-specific measurement standards, including percentage, ten thousand tons per year, and cubic meters per ton.
Constraints on database and operations
The multi-source nature, varied update frequencies, and specialized field characteristics of coal chemical investment research data impose multiple constraints on the database and operations link. First, structured data with multiple fields requires dedicated indexes for frequently filtered fields such as ash content and sulfur content, to avoid full table scans that slow query speeds. Second, data with different update frequencies needs separate database or table storage. Isolate real-time production data from weekly industry data to reduce the impact of bulk import operations on online query services. Third, unstructured long text documents need to support both relational storage and vector storage capabilities, while meeting structured extraction requirements for specialized fields. Finally, coal chemical data involves industry compliance and enterprise production secrets. The operations link must configure strict data permission isolation and access audit rules.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
DB_CONNECTION_POOL_SIZE | 10–20 | Adapts to the concurrent query scenario of multiple users for coal chemical investment research, avoids connection pool exhaustion leading to queuing, and controls database server load |
VECTOR_STORE_BATCH_SIZE | 50–100 items | Adapts to the batch processing needs of coal chemical long text documents, avoids timeouts caused by overly large single vector storage requests |
PARSE_DOCUMENT_TIMEOUT | 600 seconds | Adapts to the parsing time required for long documents such as coal chemical industry research reports and environmental impact assessment reports, prevents forced interruption of the parsing process |
MONGO_LOG_SOURCE_TAG | Coal Chemical Investment Research Database | Differentiates MongoDB logs from different business databases, facilitating quick location of database issues related to investment research |
DB_CONNECT_TIMEOUT | 30 seconds | Adapts to the dedicated network environment of internal enterprise coal chemical databases, avoids connection failures caused by overly small default timeout values |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Adapts to the upload needs of large-volume documents such as coal chemical large-scale production reports and industry yearbooks |
The parameter values provided on this page are all conventional recommendations used as a starting point 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 settings.
Three Common Errors
- Phenomenon: A
connect ETIMEDOUTerror occurs when using the database connection plugin in a workflow, and local connection works normally. Cause: No network whitelist configured for the dedicated network segment of the internal enterprise coal chemical database, or theDB_CONNECT_TIMEOUTvalue is set too small. - Phenomenon: Database query time for the same workflow increases significantly compared to older versions. Cause: The
DB_CONNECTION_POOL_SIZEhas not been adjusted to the range adapted to the concurrent volume of coal chemical data. The older connection pool configuration does not match the current business scale of coal chemical investment research, leading to prolonged connection queuing wait times. - Phenomenon: MongoDB logs cannot differentiate between data from the investment research database and other business databases. Cause: The
MONGO_LOG_SOURCE_TAGparameter is not configured, or the tag value is not bound to the dedicated identifier of the coal chemical investment research knowledge base, increasing the difficulty of log troubleshooting.
How to Confirm Proper Configuration
- Run a database connection plugin test, query coal-specific parameter fields for coal types, confirm that returned results are normal and time consumption meets expectations.
- View MongoDB logs, filter entries with the
MONGO_LOG_SOURCE_TAGconfiguration label, confirm that logs for the investment research database have been correctly differentiated. - Upload a coal chemical industry research report document, confirm that parsing time does not exceed the threshold set by
PARSE_DOCUMENT_TIMEOUT, and that vector storage is successful. - Simulate multi-user concurrent queries, observe database connection pool usage, confirm that the number of connections does not exceed the upper limit of
DB_CONNECTION_POOL_SIZE.
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.