What the data for this category looks like
Metal material processing ledger data comes from three primary sources: production site scanning terminals, ERP production management systems, and online quality inspection equipment. Data updates are tied to production batches. A complete ledger document is generated once processing for a single batch finishes. Each document includes fixed fields: furnace number, batch number, alloy composition parameters (carbon content, manganese content, etc., unit: percentage), rolling temperature, rolling speed (units: degrees Celsius, meters per minute), yield strength, tensile strength (unit: megapascals), quality inspection results, and storage time. The document structure is fixed, with no random unstructured content.
Constraints imposed by these characteristics on the database and operations maintenance link
Multiple data source access requires the database to support batch write requests in different formats, avoiding data format conversion errors. The batch-based update feature concentrates peak concurrent writes and queries around shift handover periods. Sufficient database connection resources must be maintained to prevent connection exhaustion. Fields include multiple types of numerical data with units, so database field precision and types must be matched to avoid data precision loss. Ledgers require long-term traceable retention, so regular backup and disaster recovery strategies must be configured. Indexes must be created for frequently queried fields such as furnace number and batch number to improve retrieval efficiency.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MONGODB_URI | mongodb://metal_user:secure_pass@db-host:27017/fastgpt_metal?authSource=admin | Stores original ledger data and index metadata, requires specifying an authentication source and dedicated business database to ensure data isolation |
PG_URL | postgresql://report_user:secure_pass@db-host:5432/fastgpt_report?sslmode=prefer | Stores summarized report data and user query logs, enables SSL encryption to ensure secure transmission of sensitive business data |
DB_MAX_LINK | 20–30 | The concurrent peak of metal processing ledgers is concentrated during shift handover periods. This connection number range adapts to the concurrent pressure of this scenario, avoiding resource competition |
REDIS_URL | redis://cache_pass@cache-host:6379/0 | Caches high-frequency queried ledger statistical results, reduces repeated database read pressure, and improves report generation speed |
Three Common Misconfigurations
- Phenomenon: Database connection timeout error, with status code 503 returned. Cause: The
DB_MAX_LINKparameter was not adjusted according to the concurrent peak of metal processing ledgers, leading to connection pool resource exhaustion. - Phenomenon: Abnormal number of results or matching deviations when retrieving ledger data. Cause: The functions of database indexes and vector indexes were confused, no database indexes were created for business key fields such as furnace number and batch number, and only relying on vector indexes leads to incorrect matching logic.
- Phenomenon: Frequent cache invalidation leading to report generation delay. Cause: A reasonable cache expiration time was not set according to the update rhythm of ledgers per shift, leading to frequent invalidation of high-frequency caches and increased database read pressure.
How to Confirm Proper Configuration
- Run the built-in database connectivity test tool, verify the connection status of
MONGODB_URI,PG_URL, andREDIS_URL, and check the returned success logs. - Submit a simulated metal processing ledger test data set, confirm that all fields are written to the database normally, with no type errors or null value exceptions.
- Trigger a ledger summary task, check that there are no connection timeout or index missing related errors in the task log, and confirm that the generated report matches the original data.
- After adjusting to the recommended interval of
DB_MAX_LINK, observe that the database connection count during peak hours does not exceed the set threshold, and no connection exhaustion alarms occur.
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.