What the data for this category looks like
This category's data comes from cloud audit logs, IAM permission systems, CMDB resource inventories, and security baseline scanning tools. It produces four types of documents on a regular basis: compliance checklists, permission audit reports, resource inventories, and rectification tracking forms. The update cycle is monthly or quarterly. Data fields include resource identifiers, permission configuration items, compliance status, rectification progress, and other relevant items. There are no unified fixed units, and adjustments must be made based on the document type. The most time-consuming steps in the processing workflow are resource inventory checking, permission anomaly identification, and rectification evidence verification. Data anomalies can lead to audit failures, unauthorized access, or contract breaches. This document applies to the cloud resource compliance audit module of FastGPT v4.17.0.
What constraints these characteristics impose on the database and operations maintenance link
Multi-source data access requires databases to support connection protocols for multiple data sources. Hard-coded adaptation logic must be avoided. Single batch data volume fluctuates with resource inventory scale. Reasonable connection pool limits must be configured to avoid database overload. Document fields have no unified standard. Dynamic field mapping must be supported. Fixed table structures are not recommended. Batch processing links require query statement optimization. Full table scans that consume excessive resources must be avoided. Data compliance requirements are high. Complete operation logs must be retained to prevent loss of core audit data.
How to configure settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MONGODB_URI | mongodb://<user>:<pass>@<host>:<port>/fastgpt_cloud_audit?authSource=admin | Persistently stores unstructured cloud resource scan logs and temporary data, supports horizontal scaling for batch inventory |
PG_URL | postgresql://<user>:<pass>@<host>:<port>/fastgpt_audit_report?sslmode=require | Stores structured compliance checklists, permission audit reports, and rectification tracking forms, supports multi-dimensional associated queries |
DB_MAX_LINK | 20–30 connections | Matches concurrent requirements for monthly/quarterly batch data processing, prevents service interruptions caused by exhausted connection pools |
REDIS_URL | redis://<host>:<port>/0?password=<secure_pass> | Caches frequently accessed resource inventory snapshots and permission scan intermediate results, reduces repeated database query load |
| Batch query threshold | 500–1000 entries | Adapts to batch fetch scenarios for resource inventory checking, reduces database load from single queries |
Three common misconfigurations
- Symptom: Login failure after source code deployment. Logs show
connection refusedorauthentication failed. Cause: Connection parameters forMONGODB_URIorPG_URLare not configured correctly. This includes incorrect usernames, passwords, ports, or authentication source settings. - Symptom: Batch resource inventory tasks time out, returning status code 504. Cause:
DB_MAX_LINKor batch query threshold values are not adjusted. Single processing data volume exceeds database carrying capacity. - Symptom: Fields are empty after uploading compliance reports, and audit results cannot be generated. Cause: Dynamic field mapping rules are not configured. Hard-coded fields cannot match the actual field structure of uploaded documents.
How to confirm configuration is complete
- Run a local database connection test to verify that configuration items such as
MONGODB_URIandPG_URLcan establish normal connections. - Run a small-scale resource inventory task to check whether cloud resource data can be fetched normally and temporary cache generated.
- Upload a standard compliance checklist to confirm that the system can recognize document fields and generate corresponding audit entries.
- View system logs to confirm that there are no abnormal errors in database queries, cache read/write operations, and other processes.
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.