What the data for this category looks like
Data sources for urban gas ledgers include pipeline pressure monitoring terminals, station inspection forms, user payment records, and repair work order systems. Data update frequencies fall into three categories: real-time (pipeline pressure monitoring), daily (station inspections and user payments), and work order-triggered (repair work orders). Individual ledger documents contain fields such as station ID, monitoring time, pressure value, inspector ID, and work order number. Pressure values use kPa as the unit, and inspection duration uses minutes as the unit. Aggregated documents integrate multi-dimensional ledger data by region and monthly timeframe.
What constraints these characteristics impose on deployment and upgrade
The multi-source heterogeneous data feature of urban gas ledgers requires support for connecting multiple database types during deployment, to avoid restricting data access from single sources. The high-frequency updates from real-time pressure monitoring require database connection pool configurations adapted for high-concurrency writes, to prevent connection exhaustion. The requirement for regional and monthly aggregation requires retaining incremental synchronization capabilities for historical data during upgrades, to avoid service interruptions caused by full recalculations. The differentiated field units such as kPa and minutes require configuring unified unit conversion rules during deployment, to prevent unit confusion in aggregated data.
Configuration guidelines
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
DB_MAX_LINK | 100–150 connections | Adapts to high-frequency writes and queries of multi-source gas ledger data, prevents connection pool exhaustion |
MONGODB_URI | Connection string pointing to the gas ledger historical archive database | Stores unstructured work order and inspection documents, supports flexible dimensional queries |
PG_URL | Connection string pointing to the relational database for structured monitoring data | Stores structured ledger data such as pressure and payment records with clear fields, ensures transaction consistency |
REDIS_URL | Local Redis instance or managed Redis service address | Caches frequently queried regional aggregated data, reduces database load |
STORAGE_S3_ENDPOINT | Access endpoint of the enterprise object storage service | Stores PDF reports generated from ledgers and original document backups, meets compliance storage requirements |
FE_DOMAIN | Enterprise public network domain or internal access address | Configures the access entry for the ledger generation interface, ensures normal access for internal and external users |
Three common configuration mistakes
- Phenomenon: When deploying via Docker, the database name suffix is omitted when configuring
PG_URL. After deployment, structured monitoring data cannot be read, and ledger aggregation results are empty. Cause: The full connection string format was not verified, only the host and port information were filled in. - Phenomenon: The port number is incorrectly modified when configuring
STORAGE_S3_ENDPOINT. The system shows a normal connection but cannot generate downloadable report files. Cause: Some storage services allow empty connections with invalid ports, no immediate error is triggered, but actual data writing fails. - Phenomenon: When configuring vector mapping for multi-source ledger data, only a single vector model is selected. This causes ledger data from different fields to fail to generate correct matching vector representations, resulting in abnormal retrieval result counts. Cause: The mapping rules for multiple vector models were not configured during deployment, limited by the interface default restriction that only single model selection is supported.
How to verify successful configuration
- Run the database connection test script to verify that the connection pool configured via
DB_MAX_LINKcan concurrently handle requests from multi-source data, and the connection count does not exceed the configured limit. - Upload a test ledger data entry, trigger an aggregation task, and check that
STORAGE_S3_ENDPOINTcan normally store the generated report files with no upload failure logs. - Access the address configured via
FE_DOMAIN, verify that the interface can normally load ledger configuration and generation functions with no resource loading errors. - View the monitoring metrics for
REDIS_URL, confirm that the cache hit rate for high-frequency aggregated data meets preset requirements.
The values listed here are common starting points, and should be evaluated against the reader's own operational 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.