What data does this category process?
This category processes structured documents including public data resource catalogs, data opening lists, data item descriptions, anonymization rules, interface documents, and update frequency descriptions. Data sources include local public data opening platforms, integrated big data platforms, departmental business databases, and electronic certificate repositories. Update frequency is monthly. Data fields include data item unique identifier, owning department, data format, anonymization requirements, opening permissions, update cycle, and associated interface address. Bulk data volume for single associated interfaces varies widely. Some data can reach GB scale.
What constraints do these characteristics impose on database and operations maintenance?
Monthly update frequency requires regular bulk data synchronization, which places clear requirements on database connection stability and bulk write efficiency. Multi-source heterogeneous data formats and field differences require adaptation to read/write rules of different database types, to avoid failed writes after format conversion. Structured fields contain sensitive permissions and interface addresses. Fine-grained access control and data anonymization storage rules must be configured to prevent sensitive information leaks. The wide variation in data volume requires databases to reserve elastic storage space, while avoiding operational pressure from large-volume data queries. Additionally, public data catalogs must match actual open data. Regular data consistency checks must be performed to prevent discrepancies between catalogs and actual data.
How to configure settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MONGODB_URI | mongodb://fastgpt:securepass@mongodb-host:27017/fastgpt_public_data?authSource=admin | Stores semi-structured documents such as public data catalogs and anonymization rules. Dedicated database instances isolate government data from other business data |
PG_URL | postgresql://fastgpt:securepass@pg-host:5432/fastgpt_struct?sslmode=require | Stores indexed structured data such as structured data items and interface addresses. Enables SSL to ensure transmission security |
DB_MAX_LINK | 20–30 | Adapts to concurrent requirements of monthly bulk synchronization and daily queries, avoiding database connection pool exhaustion |
REDIS_URL | redis://:redispass@redis-host:6379/0 | Caches frequently accessed public data catalog entries, reducing repeated query pressure on databases |
Reference only; verify against the target FastGPT release and deployment documentation.
Three common configuration errors
- Phenomenon: After starting the service, a database connection failure prompt appears, with error messages containing connection refused or invalid credentials. Cause: Connection parameters for
MONGODB_URIorPG_URLare not correctly configured, and dedicated database accounts and permissions for government systems are not used. - Phenomenon: A prompt of column vector does not exist appears when querying structured data. Cause: The vector extension is not installed and enabled in the PostgreSQL instance in advance, or the field structure synchronization initialization script was not executed after extension installation.
- Phenomenon: Timeout errors occur during monthly bulk data synchronization, with error messages showing connection pool exhausted. Cause: The
DB_MAX_LINKsetting value is lower than concurrent connection requirements, causing the database connection pool to fail to support synchronization requests.
How to confirm configuration is complete
- Run the built-in FastGPT database connection test tool to verify that the database instances corresponding to
MONGODB_URIandPG_URLcan perform normal read and write operations. - Check Redis monitoring metrics to confirm that the cache instance corresponding to
REDIS_URLis running normally, and the cache hit rate for frequently accessed public data entries meets business expectations. - Initiate a small-scale single data synchronization test, import simulated public data items, and verify that imported fields are complete and formats comply with database preset rules.
- Simulate multiple concurrent query requests to confirm that the database connection pool does not become exhausted, and query response times meet business requirements.
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 2026-09-28.