This document is based on FastGPT v4.17.0, and explains database operation and maintenance configuration for the production ledger summary and report generation workflow in spraying and coating scenarios.
What does the data for this category look like?
This category of data covers full-process documents and testing data for coating lines including automobiles, home appliances, construction machinery, metal furniture, and other scenarios. Daily documents include coating process cards, paint batch sheets, paint mixing records, film thickness and color difference reports, oven curves, defect repair orders, and VOCs ledgers. Data sources include TDS/SDS documents from paint suppliers, real-time collection data from spray booths and ovens, testing results from colorimeters and film thickness gauges, environmental monitoring data, and MES system data. Update frequency follows per paint batch, per work shift, and continuous real-time collection. Fields include paint batch numbers, film thickness values, oven temperature curve points, VOCs concentrations, defect types for repairs, and others. Units include mg/m³, μm, ℃, and others.
What constraints do these characteristics impose on the "database and operations" workflow?
Multi-source and heterogeneous data sources require the database to support mixed-format storage, including structured batch data, unstructured report files, and time-series curve data. The high-frequency update rhythm (per batch, per shift, continuous collection) requires the database connection pool configuration to adapt to high-concurrency writes and avoid connection exhaustion. Diverse document types and fields require unified data modeling to ensure that fields such as VOCs concentrations and film thickness values from different sources can be associated and summarized. Continuous collected time-series data will generate a large number of write requests, so a reasonable data sharding strategy needs to be configured to ensure query efficiency. At the same time, the compliance requirements of VOCs ledgers mean that data retention and access must be traceable, and the operation and maintenance link must ensure complete data retention and permission control.
How to set the configurations?
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
MONGODB_URI | mongodb://spray_user:SecurePass123@db-host:27017/spray_coating?authSource=admin | Stores unstructured coating reports, process cards, and time-series curve data, requires binding a dedicated business database and authenticated user |
DB_MAX_LINK | 180–250 | Handles high-frequency write requests generated per paint batch and per work shift, avoids database connection exhaustion |
REDIS_URL | redis://redis-host:6379/1?password=SecurePass123 | Caches real-time collected film thickness, color difference data, and common coating process parameters, reduces repeated database queries |
PG_URL | postgresql://spray_user:SecurePass123@pg-host:5432/spray_ledger?sslmode=prefer | Stores structured data such as paint batches, VOCs ledgers, and defect repairs, ensures transaction consistency |
| Calibrate based on actual measurements | Calibrate based on actual measurements | Adapts to data volume and concurrent load of coating lines of different scales |
Three common misconfigurations
- Phenomenon: MongoDB connection error
Authentication failed, or a prompt appears when logging into the database indicating that the user cannot be authenticated. Cause: A dedicated database user was not created for the business scenario, or theauthSourceparameter was not correctly specified inMONGODB_URI. - Phenomenon: A 400 status code is returned when calling database tools with no response body. Cause: The
PG_URLparameter for structured data storage was not correctly configured, or field mapping does not match the actual structure of the coating ledger. - Phenomenon: Excessive database write latency, and summary report generation times out. Cause: A reasonable
DB_MAX_LINKparameter was not set, and the connection pool is insufficient to handle high-frequency continuous collected data writes.
How to verify a successful configuration
- Run the MongoDB connection test command to verify that the user configured in
MONGODB_URIcan normally access the dedicated business database. - Submit a structured ledger data write request to confirm that the
PG_URLconfiguration can complete transactional storage without errors. - Trigger a coating ledger summary task to check whether the parameters cached in Redis are normally called, with no query timeouts.
- Check the database connection pool monitoring to confirm that the current number of connections does not exceed the value set by
DB_MAX_LINK.
The values provided here are common starting points and should be measured against the reader's own samples.