What Data Falls Under This Category
This category covers the full workflow of fiscal expenditure performance evaluation. Data sources include the unified budget management system, project application materials, fund payment data, business system ledgers, and satisfaction surveys. Daily documents include performance target forms, self-evaluation forms, performance evaluation reports, supporting material lists, and rectification reports. Each individual data entry is associated with the full-cycle performance information of a single project. Fields include project identifiers, performance indicator items, fund usage records, and supporting material association information. The update frequency is annual batch updates.
Constraints Imposed by Data Characteristics on Database and Operations
The annual batch update feature requires the database to support efficient batch data import and incremental verification to avoid duplicate data entry or omissions. Multi-source heterogeneous data sources require unified field formats to ensure that ledgers and reports exported from different systems can be properly parsed and imported into the database. Diverse document types include both structured indicator tables and unstructured report attachments. The system must support both structured query and unstructured storage capabilities. Data errors directly affect project scoring and budget allocation. Database operations must focus on monitoring data integrity and consistency to ensure the accuracy of the verification process.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PG_URL | postgresql://user:pass@host:5432/finance_perf | Stores structured associated data such as performance targets, self-evaluation scores, and fund payments, and supports complex associated queries |
MONGODB_URI | mongodb://user:pass@host:27017/perf_docs | Stores unstructured document data such as performance evaluation reports, supporting materials, and rectification reports |
DB_MAX_LINK | 10–15 connections | Matches concurrent connection needs during annual batch data import, and prevents connection exhaustion in high-concurrency scenarios |
REDIS_URL | redis://:pass@host:6379/0 | Caches frequently accessed basic project information and performance indicator libraries, reducing repeated query load on the database |
Three Common Misconfigurations
- Enabling question optimization and result rearrangement causes message return timeouts, with logs showing
ETIMEDOUT. The cause is failure to adjust theDB_MAX_LINKconfiguration value. When pulling multi-source performance data in batches, database connections are exhausted, leading to request blocking. - After local deployment, the database cannot be created, and the interface displays the prompt "Database connection failed". The cause includes incorrect configuration of
PG_URLorMONGODB_URI, unopened access permissions for the corresponding database ports, or insufficient permissions for the database account. - Duplicate entries appear in the generated ledger summary, and concurrent duplicate results occur in tool selection. The cause is failure to add a distributed lock via the cache service configured with
REDIS_URL. Multiple instances writing performance ledgers simultaneously lead to duplicate data writes.
How to Confirm Proper Configuration
- Use the database connectivity testing tool built into FastGPT v4.17.0 to verify the connectivity of
PG_URLandMONGODB_URI. Confirm there are no connection timeout or permission error messages. - Import structured data and unstructured attachments for a single annual performance project. Check that corresponding fields in the database are fully matched, with no format errors or missing fields.
- Simulate 5 concurrent ledger query requests. Confirm through the database monitoring panel that the current number of connections does not exceed the set
DB_MAX_LINKvalue. - Check the Redis cache monitoring to confirm that the cache hit rate for frequently queried basic performance information meets standards. No frequent database penetration occurs.
The values provided are common starting points and should be measured against the samples of the relevant deployments.
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.