Reference only; verify against the target FastGPT release and deployment documentation.
What This Category of Data Looks Like
Data for this category comes from enterprise internal control systems, ERP systems, financial sharing systems, audit management systems, and procurement contract project systems. The update frequency is annual. Daily processed documents include internal control self-evaluation reports, audit notices, audit working papers, issue lists, rectification ledgers, and rectification closure certificates. Data fields include issue numbers, rectification responsible entities, rectification deadlines, supporting material association identifiers, closure status, and more. There is no fixed unified format. Adaptation to multi-system heterogeneous data structures is required. Some fields need to be associated with cross-system business voucher information.
What Constraints Do These Characteristics Impose on the Database and Operations Maintenance Link
Multi-source heterogeneous data sources for this category require the database to support multiple system data formats, and enable flexible connection to heterogeneous data sources. The annual batch update feature requires the database to support large-capacity batch writing and have a stable concurrent processing limit, to avoid timeouts for single import tasks. The combination of multiple document types and non-standardized fields requires a flexible metadata management mechanism to adapt to data structures for different business scenarios. The need to retain intermediate sample data and full audit trail traces requires the database to have complete logging and data backup capabilities, to ensure data traceability.
How to Configure the Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
MONGODB_URI | Points to a domestically compatible document database instance, such as mongodb://user:pass@host:27017/fastgpt_audit | Adapts to storage requirements for multi-system heterogeneous unstructured audit documents, and supports flexible field expansion |
PG_URL | Points to a domestically compatible relational database instance, such as postgresql://user:pass@host:5432/fastgpt_audit | Stores standardized rectification ledgers, issue lists and other structured data, and ensures data consistency and transactionality |
DB_MAX_LINK | 100–150 connections | Meets concurrent requirements for annual batch data import and daily audit queries, and prevents database connection pool exhaustion |
REDIS_URL | Points to a domestically compatible caching instance, such as redis://:pass@host:6379/0 | Caches frequently accessed audit issue templates and rectification status data, and reduces database query pressure |
Three Common Misconfigurations
- Phenomenon: Database connection error with
ETIMEDOUTstatus code. Cause: TheDB_MAX_LINKparameter is not configured appropriately. Concurrent requests exceed the database connection limit, leading to connection pool exhaustion. - Phenomenon: Some fields are missing after importing data from the audit system. Cause: Flexible metadata adaptation rules are not configured for multi-system heterogeneous data. Fixed field mapping logic is applied directly.
- Phenomenon: Database connection service cannot be started. Cause: Non-domestically compatible database components are used. Replacement instances that meet scenario requirements are not deployed.
How to Verify Successful Configuration
- Run a batch data import test, verify the database write success rate, and adjust related connection parameters based on actual business data volume.
- View the database connection pool monitoring dashboard, confirm that the number of connections does not reach the configured limit, and optimize parameter values based on daily concurrent request volume.
- Test import and query of multi-system heterogeneous data, confirm that field mapping matches business document structures, and adjust metadata adaptation rules.
- Check the caching instance access logs, confirm that the cache hit rate for high-frequency data meets standards, and adjust caching strategies based on business query frequency.
The values provided here are common starting points, and should be evaluated 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 2026-09-28.