What Data for This Category Looks Like
This category of data is jointly generated by cross-functional teams of manufacturing enterprises including R&D, process, quality, production and procurement. Core documents include ECN/ECR forms, change assessment reports, drawings and BOM version records, process verification reports, switching plans, inventory disposal documents and customer approval documents. Data sources cover PLM, ERP, MES systems, supplier materials, verification test records and customer special requirements. Updates are triggered irregularly with each change. A single data entry includes fields such as change number, version number, effective time, associated materials/process nodes, with no fixed batch update cycle.
What Constraints These Characteristics Impose on the Database and Operations Maintenance Link
Data for this category is updated irregularly with each single change. A single change may associate heterogeneous data from multiple systems such as PLM, ERP and MES, requiring support for queries across multiple data sources. Core documents include structured forms and unstructured drawings, verification reports. The database must support structured field storage and large file indexing capabilities. Change data has strong version correlation, requiring strict guarantee of data version consistency to avoid mixing old and new versions. The data is also associated with production nodes and quality traceability, requiring support for fast traceback by change number and effective time. Operations and maintenance links must ensure high availability to prevent database outages from affecting production processes.
Configuration Settings
The following configurations apply to FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PG_URL | postgresql://user:pass@host:5432/fastgpt_ecn?sslmode=disable | Stores structured change forms, BOM versions and other data, requires compatibility with relational transactions to ensure version consistency |
MONGODB_URI | mongodb://user:pass@host:27017/fastgpt_ecm?authSource=admin | Stores unstructured process verification reports, drawing metadata and other semi-structured/unstructured data |
DB_MAX_LINK | 20–30 connections | A single change for this category associates data from multiple systems, connection pool size must be controlled to avoid resource exhaustion. Value is determined based on actual testing |
REDIS_URL | redis://:pass@host:6379/0 | Caches high-frequency query data such as change effective time, associated node mapping, to reduce database load |
Three Common Misconfigurations
- Phenomenon: After deploying the Docker image, a
connection refusedoraccess deniedmessage appears, and the database cannot be connected. Cause: The access whitelist forPG_URLorMONGODB_URIis not correctly configured within the image, or the container network is not connected to the network where the database resides. - Phenomenon: Chinese content of change documents stored in the database displays garbled characters or question marks. Cause: The correct character set parameter is not specified in the database connection string, or the character set configuration is not synchronized in the container environment variables.
- Phenomenon: A database query timeout error occurs during batch change synchronization, returning a
query timeoutmessage. Cause: TheDB_MAX_LINKvalue is too small, the connection pool is exhausted causing new requests to fail to obtain connections, or a reasonable query timeout threshold is not set.
How to Confirm Proper Configuration
- Run a database connection test script to verify the connectivity of
PG_URL,MONGODB_URIandREDIS_URL, and confirm there are no access permission or network errors. - Import a simulated change data entry, check that the storage formats of structured fields and unstructured files meet expectations, with no garbled characters or missing fields.
- Adjust the
DB_MAX_LINKvalue, verify that connection pool resource allocation is reasonable through simulated concurrent change requests, with no connection exhaustion errors. - View the operations and maintenance monitoring dashboard, confirm that database query latency meets business requirements, and thresholds can be calibrated based on actual business scenarios.
The values given are common starting points and should be measured against the reader's own samples.