What the data for this category looks like
Data for this category primarily comes from system lists exported from assessment tools, on-site interview records, security device logs, as well as classification rating reports, gap analysis reports, rectification plans, and assessment reports created during project processes. Data is updated on a per-project cycle, with one update per completed cybersecurity level protection assessment project. A single core document includes fields such as control item lists, rectification evidence numbers, and system asset information. There is no unified fixed unit, and values adjust based on project scenarios.
What constraints these characteristics impose on the database and operations link
Since data is updated on a per-project cycle, the database must support isolated storage by project dimension to avoid cross-project data confusion. Core documents contain multiple types of unstructured content, so the system must support importing and indexing documents in different formats. The volume of log data generated during the rectification evidence collection phase fluctuates greatly, so the database connection pool must have elastic adaptation capabilities. Data is not updated frequently during the project cycle, and database read/write pressure is concentrated during the assessment implementation phase. Connection counts must be configured appropriately to avoid resource waste. Additionally, data involves security compliance-related content, so encryption during storage and transmission must be ensured to meet relevant cybersecurity level protection requirements.
How to set configurations
These configurations apply to FastGPT v4.17.0.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
MONGODB_URI | Full connection string including authentication information and project-specific database name, for example mongodb://security_user:pass@host:27017/dengbao_project_001 | Enables data isolation for cybersecurity level protection assessment projects, avoiding cross-project data conflicts |
REDIS_URL | Connection string for a dedicated cache instance, specifying a dedicated database index, for example redis://:cache_pass@host:6379/2 | Caches frequently accessed assessment document metadata to reduce database read/write pressure |
DB_MAX_LINK | 20–30 | Matches read/write peaks during a single project cycle, avoiding excessive connection counts consuming server resources |
PG_URL | PostgreSQL connection string for storing structured assessment data, for example postgresql://report_user:pass@host:5432/security_reports | Used to store structured data such as control item verification results and rectification evidence association information |
| Document import batch size | 50–100 items | Adapts to batch import requirements for unstructured documents collected during rectification evidence gathering |
| Connection timeout threshold | 600 seconds | Prevents connection interruptions during import of large assessment reports |
Three common misconfigurations
- Symptom: After saving database connection configuration, the interface prompts a connection timeout, and the log returns an
ETIMEDOUTerror. Cause:MONGODB_URIis not configured as a project-specific database, and cross-project data conflicts cause the connection to be blocked. - Symptom: An error occurs when saving and publishing after creating a new question classification node, and the error disappears after switching the AI model. Cause:
REDIS_URLis not configured to cache structured data associated with the model, causing node configuration to fail to write to the database normally. - Symptom: After Docker deployment, the MongoDB connection runs normally for several hours then disconnects, and automatic reconnection fails. Cause: No timeout reconnection parameters are set for the database connection pool, and idle connections are forcibly disconnected by the server firewall or load balancer.
How to confirm configurations are correct
- Run a database connection test to verify that the configured connection string can normally read and write tables in the project-specific database.
- Import a test classification rating report document to confirm that document metadata is correctly written to the database and displayed in the interface.
- Simulate simultaneous access to assessment data by multiple users, and observe that database connection counts do not exceed the configured maximum connection count threshold.
- Check the connection status of the cache service to confirm that frequently accessed document metadata can be normally read from the cache.
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.