Reference only; verify against the target FastGPT release and deployment documentation.
What this type of data looks like
Data for coal chemical industry ledgers and production records comes from three types of systems: distributed control systems (DCS) for production units, testing equipment in coal quality laboratories, and warehouse and safety inspection systems. Data updates follow three rhythms: real-time production parameters update every second, coal quality test data generates batch data per shift, and ledger summary data completes daily compilation. Each single data entry includes a unique device identifier, collection time, physical parameters, testing personnel identifier, and abnormal status flag. Physical parameter fields cover temperature, pressure, ash content, with units degrees Celsius, megapascals, and mass fraction respectively.
Constraints on Database and Operations Maintenance
High-frequency second-level writing of real-time parameters requires database connection pools to support high concurrent connections, avoiding connection exhaustion. Batch submission of batch test data requires adjusting transaction commit thresholds to prevent timeouts caused by overly large single transactions. Large daily ledger summary queries require creating joint indexes on the device identifier and collection time fields to shorten query latency. Mixed storage of multiple data types requires distinguishing hot and cold data: store real-time parameters in time-series storage, and historical ledgers in relational databases. Fast filtering of abnormal status flags requires creating indexes on the corresponding fields to improve screening efficiency for report generation.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
DB_MAX_LINK | 200–300 connections | Adapts to high-concurrency writes of real-time coal chemical industry data, avoids connection pool exhaustion |
PG_URL | postgresql://[account]:[password]@[database address]:5432/coalchemicalreport | Stores structured ledgers and summary report data, supports complex associated queries |
REDIS_URL | redis://[account]:[password]@[cache address]:6379/0 | Caches frequently queried ledger summary results to reduce database load |
MONGODB_URI | mongodb://[account]:[password]@[real-time data address]:27017/coalrealtime?authSource=admin | Stores second-level updated real-time production parameters, supports high-concurrency writes |
Three Common Misconfigurations
- Phenomenon: Database query results return empty when calling the workflow 2-3 times per second. Cause: The
DB_MAX_LINKconfiguration value is too low. The connection pool is exhausted by concurrent requests, and subsequent connection requests cannot be processed. - Phenomenon: Query results obtained from the database node do not match the required ledger field format, requiring additional code for conversion. Cause: The database node’s result formatting switch is not enabled. The default returned raw data structure does not align with the preset ledger field naming rules.
- Phenomenon: Partial records are lost during batch insertion of ledger data. Cause: No reasonable batch commit threshold is set. The single submitted data volume is too large, causing database transaction timeout and partial data to fail to complete writing.
How to Verify Correct Configuration
- Run a single ledger summary task, check the active connection count in the database connection log, and adjust the
DB_MAX_LINKconfiguration value based on log performance. - Call the database node to query historical production parameters, confirm that the returned fields fully match the collection fields of coal chemical industry equipment.
- Initiate a batch data insertion request, check that database transaction commits are normal, with no data loss or error prompts.
- View the monitoring data of the cache service, confirm that frequently summarized ledger results have been correctly cached, reducing repeated database queries.
The values provided are common starting points and should be measured against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.