What Data for This Category Looks Like
Data for this category primarily comes from order and size charts, CAD patterns, raw material and accessory inspection records, workshop hanging system data, needle detector test results, and customer sealed sample information. Daily retained documents include process sheets, pattern drawings, layout plans, cutting orders, first article sealed sample records, process quality sheets, and packaging size matching sheets. Data updates trigger per order, per style, per batch. Due to wide variety of styles and strong seasonality, single-batch data volume fluctuates significantly. Cutting piece and process records are irreversible, so full end-to-end data must be retained.
What Constraints These Characteristics Impose on the Database and Operations Link
The data characteristics of this category impose multiple constraints on the database and operations link. Multi-source heterogeneous business documents and test data require support for storage in different formats to ensure full end-to-end data can be fully imported. The high-frequency update rhythm per order and batch requires the database to support fast batch indexing and archiving to avoid data redundancy. The irreversible nature of cutting piece records requires configuring a write tamper-proof storage strategy to prevent traceability data from being lost or altered. Seasonal data volume peak fluctuations require sufficient operational elasticity to ensure stable query and write performance.
Configuration Settings
The following configurations are adapted for FastGPT v4.17.0:
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MONGODB_URI | mongodb://user:pass@host:27017/fastgpt?authSource=admin | Stores full end-to-end business documents and test data, requires a dedicated database instance |
PG_URL | postgresql://user:pass@host:5432/fastgpt_task?connect_timeout=30 | Stores task scheduling and operation logs, requires support for transactional writes |
DB_MAX_LINK | 20–30 | Adapts to concurrent query demands from multiple workshop workstations, avoids connection exhaustion |
REDIS_URL | redis://:pass@host:6379/0 | Caches batch indexes and process sheet data that are frequently queried, reduces database pressure |
| Database connection timeout | 600 seconds | Adapts to reading and writing large files such as CAD patterns, avoids timeout interruptions |
Three Common Misconfigurations
- Symptom: After deploying FastGPT via Docker for several hours, a MongoDB connection timeout error occurs, with the log showing
connection refused. Cause: A reasonable value forDB_MAX_LINKwas not configured, the connection pool is exhausted and no new connections can be created, and the automatic reconnection policy was not enabled. - Symptom: When creating a new question classification node, saving fails after triggering with the initialized AI model, and normalcy resumes after switching the model. Cause: The database was not correctly configured with transaction support for
PG_URL, causing node configuration writes to fail. Switching the model triggers different storage logic that bypasses this issue. - Symptom: After upgrading from v4.9.13 to v4.10.1, knowledge base content does not display synchronously. Cause: The corresponding version database migration script was not executed. The knowledge base table structures of the old and new versions differ, and data synchronization mapping was not completed.
How to Verify Proper Configuration
- Perform a bulk import test for single-batch cutting orders and process sheets, check database write success rate and query response time to confirm index configurations are effective.
- Run a 24-hour continuous concurrent query test, monitor database connection count and Redis cache hit rate to confirm that
DB_MAX_LINKandREDIS_URLconfigurations adapt to business peaks. - Manually trigger a database backup and recovery operation, verify that the data backup process executes normally and backup files are complete and usable.
- After upgrading the FastGPT version, run the built-in database consistency check script to confirm that table structures and data mappings have no abnormalities.
The values provided are common starting points and should be evaluated against samples specific to the actual deployment.