What data for this category looks like
Data for this category mainly comes from ERP orders and inventory, BOMs and process routes, equipment capacity, staff shifts, work-in-progress, and supplier deliveries. Daily documents include sales orders, master production schedules, material requirements plans, weekly and daily scheduling sheets, work orders, material shortage lists, and delivery change notices. Update frequency follows daily, weekly, or triggers when orders or exceptions occur. Data fields include order number, material code, process working hours, equipment number, work-in-progress quantity, and more. Some fields have units: working hours are measured in hours, quantity in pieces.
What constraints these characteristics impose on the database and operations maintenance link
Multi-source heterogeneous data sources require the database to support storage and parsing of both structured and semi-structured data. Fluctuating update rhythms—including scheduled batch sync and real-time exception triggers—require properly configured connection pools to handle concurrent peaks. Daily document volume grows with order counts, so scalable storage capacity must be reserved. Fields with multiple units require unified parsing rules at the database level to avoid data confusion. Additionally, scheduling data is strongly linked to quality traceability. Read-write operation consistency must be ensured to prevent scheduling errors or traceability failures caused by data inconsistency.
Configuration settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MONGODB_URI | mongodb://appuser:securepass@dbhost:27017/prod_plan_trace?authSource=admin | Stores semi-structured scheduling rules, work-in-progress data, and process route information, supports flexible field expansion |
PG_URL | postgresql://appuser:securepass@dbhost:5432/prod_order?sslmode=require | Stores structured data such as sales orders and delivery change notices that require strong consistency, ensures atomicity of data read-write operations |
DB_MAX_LINK | 200–300 | Handles concurrent peaks of daily batch data synchronization and real-time exception-triggered requests, avoids connection pool exhaustion |
REDIS_URL | redis://:redispass@cachehost:6379/0 | Caches frequently accessed equipment capacity and staff shift data, reduces repeated read-write pressure on databases |
| Database sharding rule | Shard by order creation time | Enables fast retrieval of historical scheduling and quality traceability data by time range, improves query efficiency |
Three common mistakes
- Phenomenon: A
connection refusedorauth failederror occurs when connecting to MongoDB after deployment in a Linux environment. Cause: The username, password, and database permissions inMONGODB_URIare not correctly configured, or the system firewall does not open the default MongoDB port 27017. - Phenomenon: Nested structures in BOM fields cannot be parsed normally under MongoDB 7, causing failure to write quality traceability data. Cause: Index configuration is not adjusted for MongoDB 7, or field type definitions incompatible with this version are used.
- Phenomenon: The number of returned results when retrieving quality traceability data does not match expectations. Cause: The
DB_MAX_LINKconfiguration value is too low, causing some concurrent requests to be rejected and data retrieval to be incomplete, or database sharding is not set by order creation time, leading to scanning of too much invalid data during cross-time-range retrieval.
How to confirm configuration is correct
This verification process applies to FastGPT v4.17.0.
- Run the official database connection test script to verify the connection status of
MONGODB_URIandPG_URL, confirm there are no connection timeout or permission errors. - Submit a test order, observe whether data is written to the corresponding database at the configured update frequency, and check whether field units match preset rules.
- Simulate concurrent requests to verify whether the
DB_MAX_LINKconfiguration can support the peak connection count, with no connection pool exhaustion errors. - View the Redis-cached equipment capacity data, confirm that the cache hit rate of frequently accessed data meets business expectations.
The values provided are common starting points and should be measured against the reader's own samples.