What Data for This Category Looks Like
Data sources for this category include customer surface treatment specifications, bath test results, rectifier operation data, online water quality monitoring records, and hazardous waste management ledgers. Daily documents include bath analysis records, process parameter cards, fixture usage records, film thickness and adhesion test reports, hazardous waste ledgers, and wastewater monitoring records. Data is updated per bath run, per shift, or per fixed monitoring requirements. Fields cover bath parameters, process indicators, environmental monitoring values, and more. Corresponding units include pH, ℃, A/dm², μm, mg/L, and others.
Constraints on Database and Operations Workflows
Data for this category comes from multiple sources and updates frequently. New data is generated per bath run or per shift. This places clear requirements on database write throughput. The structure of different document types varies significantly. Unstructured fixture records and hazardous waste ledgers are suitable for non-relational storage. Structured bath parameters, process parameter cards, and related data are suitable for relational storage. Connections to and read/write operations for both types of databases must be supported. Data involves sensitive information such as process parameters and environmental monitoring values. Storage and access security must be ensured. Full traceability is required for defect analysis, so complete data retention and auditability are core constraints. Frequent bath run updates may lead to rapid data growth. A reasonable archiving strategy must be planned to avoid storage resource overload.
Configuration Recommendations
Configurations below are based on FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MONGODB_URI | mongodb://[username:password@]host1[:port1][,host2[:port2],...]/database?authSource=admin | Adapts to multi-source unstructured data storage, such as fixture records, hazardous waste ledgers, and other documents without fixed schemas |
PG_URL | postgresql://[user[:password]@][netloc][:port][/dbname][?sslmode=require] | Stores structured business data including bath parameters, process parameter cards, and film thickness test data |
DB_MAX_LINK | 50–80 connections | Adapts to scenarios with high-frequency writes per bath run and multiple concurrent queries, avoiding connection exhaustion or resource waste |
REDIS_URL | redis://:password@host:port/0 | Used to cache high-frequency query bath history parameters and process specification data, reducing direct database access pressure |
| Database connection timeout setting | 60 seconds | Adapts to real-time write response requirements for bath data, avoiding long-term workflow blocking |
Three Common Configuration Errors
- Symptom: Workflow runs return an
Access denied for usererror. Cause: Account password permissions inMONGODB_URIorPG_URLare not configured correctly. Corresponding database read, write, and query permissions are not granted. - Symptom: Frequency-limited errors appear during concurrent interface calls, or workflow execution times out. Cause: The
DB_MAX_LINKparameter is not set reasonably. Insufficient connections cause concurrent requests to queue. Redis cache is not configured for high-frequency query data, increasing database load. - Symptom: Result deviations or missing fields appear after multiple rounds of queries. Cause: Structured and unstructured database data is not synchronized according to the data update rhythm. Or the archiving strategy is unreasonable, leading to loss of historical data, which cannot support a complete quality traceability chain.
How to Confirm Proper Configuration
- Log in to the database management interface, verify that the databases corresponding to
MONGODB_URIandPG_URLcan be connected normally, and have read and write permissions. - Simulate multiple bath run data write requests, check that the database connection count does not exceed the
DB_MAX_LINKsetting, and no connection overflow errors occur. - Trigger the quality traceability workflow, verify that associated data such as bath parameters and test reports for the corresponding bath run can be retrieved, with no missing data or timeouts.
- View Redis cache monitoring, confirm that high-frequency query process parameter data has been cached, and direct database query times meet expected levels.
The values provided here are common starting points, and should be evaluated against the reader's own samples.