What this category of data looks like
This category of data includes quality-related documents and records generated by manufacturing enterprise IPQC, FQC, quality engineering, and laboratories. It covers control plans, patrol inspection records, SPC reports, finished product inspection reports, nonconforming product reviews, release orders, monthly quality reports, and more. Data sources include MES systems, online inspection equipment, laboratory test data, process parameter documents, customer quality standards, and historical defect databases. Data is updated continuously by production batch and shift. Fields include batch identification, inspection item parameters, measured values, qualified thresholds, judgment results, nonconforming product types and disposal records, release approval status, and more. Units vary based on specific inspection items.
What constraints these characteristics impose on the database and operations capability area
Data sources are scattered and update frequencies vary. Database connection capabilities that support multi-source high-frequency writes are required. Structured inspection data and non-standardized documents are stored together. Both transactional consistency of relational databases and flexible storage of non-relational databases must be supported. Batch-level data association requires strong consistency. Transaction commit strategies must be adjusted to ensure data accuracy. The volume of long-term retained historical quality data is large. Hot-cold data tiered storage strategies must be configured to reduce storage costs. Sensitive quality data related to release approval requires fine-grained access permission control. This prevents unauthorized operations.
How to Configure Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MONGODB_URI | mongodb://quality_user:secure_pass@db-host:27017/fastgpt_quality?authSource=admin | Stores unstructured patrol inspection records, SPC report attachments, and other semi-structured data, adapting to massive small file storage needs |
PG_URL | postgresql://quality_admin:db_pass@pg-host:5432/fastgpt_quality_schema | Stores structured inspection items, batches, release approvals, and other strongly associated data, ensuring transaction consistency and associated query performance |
DB_MAX_LINK | 20–30 | Adapts to high-frequency data write scenarios per batch and shift, avoiding write failures caused by exhausted database connections |
REDIS_URL | redis://redis_user:cache_pass@cache-host:6379/0 | Caches frequently queried inspection item standards and historical defect database indexes, reducing query load on underlying databases |
Three Common Mistakes
- Scenario: After deployment, logs prompt database connection failure, and system initialization cannot be completed. Cause: Connection parameters for
MONGODB_URIorPG_URLare not configured correctly, including incorrect usernames, passwords, or database instance addresses. - Scenario: After modifying vector database data via an external MongoDB management tool, the system cannot synchronously update production ledger content. Cause: Modifying underlying storage data directly by bypassing FastGPT's built-in interfaces breaks the consistent binding relationship between the system and the database.
- Scenario: Timeout errors occur when batch importing that day's patrol inspection records, and some data fails to be written to the database. Cause: The
DB_MAX_LINKparameter is not adjusted, and insufficient concurrent connections cannot support the request volume of batch writes.
How to Confirm Configuration is Complete
- Check system initialization logs to confirm there are no error messages about database connection failures or authentication failures.
- Perform a batch import of test data to verify that data is successfully written and can be retrieved and displayed by the system.
- Check database connection pool monitoring indicators to confirm active connection counts do not reach the configured
DB_MAX_LINKlimit. - For FastGPT v4.17.0, use the system's built-in quality ledger query interface to verify that cached inspection item standard data loads normally.
The values provided are common starting points and should be measured against the reader's own samples.