What Data for This Category Includes
Data for this category primarily comes from order design systems, sheet material and wood batch ledgers, CNC cutting and drilling equipment, ERP/MES systems, and moisture content testing equipment. Daily retained documents include order breakdown drawings, cutting lists, edge banding and drilling programs, coating process documents, hardware accessory lists, trial assembly and packaging records. Data update cycles vary by order, batch, or daily. Fields include order identifiers, sheet size parameters, sheet batch numbers, equipment operation logs, coating process parameters, hardware models, and more. Units involved include millimeters, percentages, and other standard units.
Constraints on Database and Operations Maintenance
The custom order-driven production model creates two key traits: high sheet component counts per order, and scattered associated data. This requires databases to support associative storage and fast querying of multi-source data. Multiple data types have distinct update cycles. Real-time equipment logs require low-latency writing. Batch ledgers require batch synchronization. This demands adaptation to mixed read-write load patterns. Data association relies on order identifiers and sheet batch numbers as primary keys. This requires guaranteed uniqueness and integrity for these primary key fields. Sheet size, coating parameters, and other data directly affect final product quality. This necessitates avoiding missing fields or format errors. Operations maintenance links must regularly verify data consistency.
Configuration Settings
All following configurations are compatible with FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MONGODB_URI | mongodb://user:pass@host:27017/furniture_trace?authSource=admin | Supports storage of unstructured data such as order breakdown drawings and equipment logs. Specifies the authentication source to ensure access security |
PG_URL | postgresql://user:pass@host:5432/furniture_erp?sslmode=require | Stores structured associated data such as orders, sheet batches, and hardware lists. SSL mode ensures secure data transmission |
DB_MAX_LINK | 200–300 | Adapts to batch writes and queries from concurrent multiple orders. Prevents connection exhaustion from disrupting production operations |
REDIS_URL | redis://:pass@host:6379/0 | Caches frequently queried sheet size parameters and order association relationships. Reduces overall database load |
Three Common Configuration Errors
- Symptom: The database connection module does not display an Oracle option, preventing completion of database configuration. Cause: The current version only includes official drivers for MongoDB, PostgreSQL, and Redis. It does not integrate Oracle connection dependencies.
- Symptom: Deployment on a Linux environment results in failed MongoDB connections, returning a connection timeout error. Cause: The Linux firewall has not opened the default MongoDB port 27017, or the host address in the configured
MONGODB_URIdoes not match the actual deployment address. - Symptom: Application-related configuration information cannot be queried, with a prompt that the data does not exist. Cause: Application configuration information was not correctly stored in the specified PostgreSQL or MongoDB database. The configuration path does not match the storage repository.
How to Confirm Successful Configuration
- Run a database connection test script. Verify connectivity for
MONGODB_URI,PG_URL, andREDIS_URL. Confirm there are no authentication or address errors. - Import associated data for a test order. Include fields such as sheet size, sheet batch, and hardware list. Verify that the data storage format matches expected standards.
- Simulate write requests from concurrent multiple orders. Observe that database connection counts do not exceed the
DB_MAX_LINKconfiguration threshold. Confirm that load adaptation functions normally. - Check the operations maintenance monitoring panel. Confirm that data update frequencies match business cycles. No delays or data loss occur.
The values provided are common starting points. They should be evaluated against the reader's own samples.