What data is associated with this use case
The data for this use case primarily comes from the unified budget management system, departmental staffing establishment system, prior-year final account data, project repository, and the Ministry of Finance’s annual budget preparation notice. Daily produced documents include departmental budget drafts, project application forms, performance target forms, personnel expense calculation forms, and budget approval documents.
Data updates follow an annual cycle. Fields mostly cover unit codes, staffing establishment numbers, project expense amounts, performance target parameters, and other related items. Structured and semi-structured data from multiple systems must be linked to ensure the completeness of ledger aggregation.
What constraints do these characteristics impose on database and operations work
Annual update cycles require batch importing structured and semi-structured data from multiple sources. This places clear requirements on the database’s ability to perform batch writes and associated queries.
Field formats vary across multi-system data sources. Standardized cleaning must be completed during database operations to prevent dirty data from entering the ledger process.
High-frequency operations that aggregate budget data from subordinate units require the database to support concurrent queries and have cache adaptation capabilities.
Data errors will lead to budget returns or failed audits. A complete data backup and rollback mechanism must be configured to ensure data consistency and traceability.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PG_URL | postgresql://budget_user:secure_pass@internal-db:5432/budget_main?sslmode=require | Connects to structured ledger data from the unified budget management system; sslmode=require ensures security during cross-system data transmission |
MONGODB_URI | mongodb://project_user:secure_pass@internal-mongo:27017/project_pool?authSource=admin | Stores semi-structured application data from the project repository, adapts to non-fixed-field project information and performance target form content |
DB_MAX_LINK | 20–30 connections | Adapts to batch subordinate unit data aggregation queries during annual budget preparation, prevents task interruptions caused by exhausted connections |
REDIS_URL | redis://cache_user:secure_pass@internal-redis:6379/0 | Caches high-frequency queried subordinate unit budget ledger data, reduces load pressure on the core database |
| Database connection timeout threshold | 600 seconds | Adapts to time-consuming scenarios of multi-system data associated queries, prevents interruptions of batch aggregation tasks mid-process |
Reference only; verify against the target FastGPT release and deployment documentation.
Three Common Mistakes
- The
connect ETIMEDOUTerror occurs. The database is accessible locally but cannot be connected after online deployment. The cause is that the security group or firewall of the online environment has not opened the corresponding database port, or the access address configured inPG_URL/MONGODB_URIdoes not adapt to the intranet domain name. - The number of database query return results does not match expectations. Missing or duplicate ledger entries appear. The cause is that the correct subordinate unit code filtering condition is not added to the SQL query, or duplicate budget data entries are not handled when linking data from multiple systems.
- After calling the DB node, a JSON format result is returned, which cannot be directly used for text splicing in report generation. The cause is that the result formatting rule is not configured. Fields must be parsed via a code node before splicing, or the appropriate result mapping method is not selected.
How to Confirm Proper Configuration
- Test batch import of 100 subordinate unit budget data entries, check that there are no database write errors and that fields match completely.
- View the database connection pool monitoring panel, confirm that the current active connection count does not exceed the
DB_MAX_LINKconfiguration value. - Initiate a simulated annual budget aggregation query, confirm that the returned results include ledger data for all subordinate units and meet format requirements.
- Verify the hit rate of Redis cache, confirm that high-frequency queried subordinate unit budget data can be read quickly.
The configuration values provided here are common starting points. They should be evaluated against the reader’s own specific 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.