What Data Looks Like for This Category
This category covers end-to-end processes for dyeing and finishing factories, fabric enterprise dyeing, setting, laboratory and quality departments. Daily documents include dyeing formulas, chemical dosing records, dye vat curves, sample confirmation reports, color difference reports, setting process sheets, and sewage ledgers. Data sources include customer color samples, formula libraries, dye and chemical batches, dye vat control equipment, colorimeters, setting machines, and environmental monitoring devices. Data update frequency is per vat, per batch, per shift. Fields include dye dosage, chemical dosing quality, dye vat temperature, holding duration, color difference delta, discharge volume, with corresponding units: kilograms, kilograms, degrees Celsius, minutes, unitless delta, and cubic meters.
What Constraints These Characteristics Impose on Database and Operations Workflows
This category has high-frequency data updates and covers multiple data types from multiple sources, creating multiple constraints for database operations. High-frequency updates per vat, batch, and shift require the database to support high-concurrency writes and low-latency queries to avoid delays in ledger generation. Multiple data types — structured formulas, semi-structured dye vat curves, unstructured color difference reports — require the database to support multi-format storage to adapt to read and write needs for different fields. Batch data has strong correlation, requiring support for cross-document associative queries to ensure the accuracy of ledger summaries. Additionally, data anomalies can lead to entire vat scrap or excessive emissions, so the database must include data verification and backup mechanisms to reduce the risk of data errors.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MONGODB_URI | mongodb://[username]:[password]@[host address]:[port]/fastgpt?authSource=admin | Adapts to distributed storage needs for multi-batch data in dyeing and finishing scenarios, supporting storage of massive structured and semi-structured ledger data |
PG_URL | postgresql://[username]:[password]@[host address]:[port]/fastgpt | Used to store structured data such as formulas and process sheets with high association requirements, ensuring stability for cross-document associative queries |
DB_MAX_LINK | 50–80 connections | Matches high-frequency write requirements per vat, batch, and shift, avoiding write blocking caused by insufficient connection counts, and preventing excessive connections from occupying server resources |
REDIS_URL | redis://:[password]@[host address]:[port]/0 | Used to cache frequently accessed formula libraries and customer color sample data, reducing repeated query pressure on the database and speeding up ledger generation |
Three Common Configuration Mistakes
- Symptom: Workflow stalls after executing a database connection tool call node, with no error message or return result. Cause: Authentication parameters for
MONGODB_URIare not configured correctly, or the database connection count exceeds theDB_MAX_LINKlimit, causing connection timeout and blocking. - Symptom: Unable to connect to domestic relational databases. Cause: FastGPT v4.17.0 only supports databases configured via the
PG_URLparameter using the standard PostgreSQL protocol, and does not include built-in dedicated drivers for domestic databases. Custom plugins are required for adaptation. - Symptom: The mongoose version cannot be queried via npm list inside the container, but the platform can normally call the database. Cause: Dependencies installed inside the FastGPT container are located in a global or project directory non-default path. The mongoose dependency is declared in package.json, and it is loaded via built-in dependency loading during actual runtime.
How to Confirm Configuration Is Successful
- Log in to the database configuration page in the FastGPT backend, test connections for
MONGODB_URIandPG_URL, and check whether a connection success prompt is returned. - Import a simulated dyeing formula data entry, perform a write operation, and confirm that the data can be stored and queried normally.
- Check the logs inside the container to confirm that there are no error messages related to database connection timeouts or exceeded connection counts.
- Generate a simulated production ledger report, and confirm that data can be normally retrieved and summarized from the database.
The values provided are common starting points and should be evaluated against the reader's own samples.