This document applies to FastGPT v4.17.0
What this category of data looks like
This category of data is produced by data governance teams, security consultants, and legal compliance teams. Daily documents include data catalogs, classification and grading rules, important data identification tables, and review minutes. Data sources include data asset inventory results, database metadata, business system descriptions, and compliance regulatory documents. Update frequency is quarterly or whenever a business system changes. The data structure primarily uses structured fields, including asset number, sensitivity level, affiliated business domain, identification basis, and more. It covers all data assets except personal information.
What constraints do these characteristics impose on the database and operations link
Since data sources include multi-source database metadata, support must be provided for connecting to different types of database instances to pull data. Because the update frequency is quarterly or when the system changes, periodic synchronization tasks must be configured to ensure data timeliness. Storage of multiple types of documents must adapt to mixed structured and unstructured scenarios. The high time-consuming nature of sensitive field identification and grading rule reviews requires database operations links to have connection number control and cache optimization capabilities. This prevents resource exhaustion due to intensive tasks, which affects business stability.
How to configure settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MONGODB_URI | mongodb://admin:securepass@db-host:27017/asset_db?authSource=admin | Stores unstructured documents such as classification and grading rules and review minutes, and adapts to large text storage needs |
PG_URL | postgresql://admin:securepass@db-host:5432/meta_db?sslmode=require | Stores structured database metadata and asset inventory data, and supports transactional operations and associated queries |
DB_MAX_LINK | 20 connections | Limits the upper limit of database connections, prevents connection exhaustion during concurrent multi-tasks, and alleviates hard disk read and write pressure |
REDIS_URL | redis://:securepass@cache-host:6379/0 | Caches temporarily identified sensitive field results, reducing overhead from repeated database queries |
Three common misconfigurations
- Symptom: High database connection count and abnormal hard disk read/write volume in Docker deployment environments. Cause:
DB_MAX_LINKconnection limit is not configured. Classification and grading tasks frequently pull metadata, leading to connection exhaustion and triggering large-scale hard disk read/write operations. - Symptom: Sensitive field identification task returns empty results. Cause:
MONGODB_URIorPG_URLis not configured correctly. Unable to read database metadata and asset inventory information, causing the identification process to interrupt. - Symptom: Compliance validation does not take effect after updating classification and grading rules. Cause: Scheduled synchronization tasks are not configured. Cached old rules are not refreshed in a timely manner, causing the system to use old configurations for validation.
How to confirm correct configuration
- Run the built-in database connectivity test tool in FastGPT. Verify that the connection status of
MONGODB_URI,PG_URL, andREDIS_URLis normal. - Check the database monitoring panel. Confirm that the current active connection count is lower than the
DB_MAX_LINKconfiguration value, and no connection exhaustion alerts are present. - Upload a test data asset inventory table. Trigger the sensitive field identification task, and confirm that the returned results include non-empty sensitive field tags.
- Modify one classification and grading rule. Wait for the configured synchronization cycle, then initiate a compliance validation request. Confirm that the rule change has taken effect.
The values provided are common starting points and should be measured against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-10-19.