Reference only; verify against the target FastGPT release and deployment documentation.
What the data for this category looks like
Urban gas ledger data comes from four core business data sources: pipeline network pressure monitoring terminals, station inspection equipment, operation and maintenance work order systems, and user payment systems. Data update rhythms cover four types: real-time (pressure monitoring), hourly (inspection records), triggered by work orders (operation and maintenance records), and daily (user payment ledgers). A single ledger document includes fields such as pipeline network node ID, station number, inspection time, pressure value, leak detection result, operation and maintenance personnel ID, and work order status. The pressure value unit is kPa, the inspection time uses ISO 8601 format, and the work order status is a predefined enumerated value.
What Constraints Do These Characteristics Impose in the Database and Operations Link
Multi-source heterogeneous data update rhythms create mixed read-write pressure. High-frequency real-time data occupies a large amount of database connection resources, so connection pool capacity must be planned in advance. Structured and unstructured data coexist, so split storage is required to optimize query performance, and field indexes must be established to ensure associated query efficiency during ledger aggregation. Inconsistent data formats require a pre-cleaning link to avoid dirty data entering the aggregation process. Long-term retained ledger data needs expansion planning to ensure historical data traceability. Data integrity must be monitored during operations, as abnormal gas-related data may affect subsequent business decisions.
How to Configure the Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MONGODB_URI | mongodb://<user>:<pass>@<host>:27017/gas_ledger?authSource=admin&readPreference=secondaryPreferred | Unstructured gas inspection records are suitable for MongoDB storage. Use secondaryPreferred read to reduce primary database write pressure, and specify the business database to avoid data confusion |
PG_URL | postgresql://<user>:<pass>@<host>:5432/gas_ops?sslmode=require | Structured work order and user ledger data are suitable for relational storage. Enable SSL to ensure transmission security |
DB_MAX_LINK | 20–30 connections | The single-node concurrent write/query peak is approximately 15 times per second. Reserve redundant connections to handle batch aggregation tasks |
REDIS_URL | redis://<host>:6379/0?maxmemory-policy=allkeys-lru | Cache frequently queried pipeline network node basic information. The LRU policy avoids memory overflow |
Three Common Misconfigurations
- Phenomenon: Authentication failure or host resolution failure errors occur after configuring
MONGODB_URI, and the database cannot be connected. Cause: TheauthSourceparameter is not configured correctly, or when deploying locally, the default MongoDB port is not opened, and the bind address is not modified to 0.0.0.0. - Phenomenon: Batch ledger aggregation tasks take too long, and workflow TPS is lower than expected. Cause: The
DB_MAX_LINKvalue is not set reasonably, insufficient connection pool causes task queuing, or no joint index is created for the pipeline network ID and inspection time fields, which affects associated query performance. - Phenomenon: Tool call returns results that do not match the preset logic. In some scenarios, no knowledge base is called and a fixed reply is returned directly, or a specified reply is not triggered when no content is matched in the knowledge base. Cause: Search trigger conditions are not configured correctly, or the association between the knowledge base and tool call is not bound in the workflow, leading to chaotic trigger logic.
How to Confirm the Configuration Is Successful
- Execute database client commands to connect to the business database specified by
MONGODB_URI, and verify that preset pipeline network node ledger records can be queried. - Run a batch aggregation test script, observe database connection usage, and confirm that the number of connections does not reach the upper limit set by
DB_MAX_LINK. - View the table model in the database management module of the FastGPT backend, and confirm that the fields of the structured ledger match the preset structure.
- Trigger a complete ledger generation task, and check whether database connection timeout or write failure errors appear in the system logs.
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.