This document is compatible with FastGPT v4.17.0.
What the Data for This Category Looks Like
The core data for this category originates from cloud billing systems, tag data, order systems, and resource monitoring tools. Update frequencies are daily and monthly. Regularly produced deliverables include billing details, cost allocation tables, resource utilization reports, and optimization suggestion sheets. The data structure uses individual resource consumption records as the basic unit. Fields include resource identifier, billing item, billing cycle, cost amount, associated tags, and allocation objects. Cost amounts use the pricing unit corresponding to the business scenario. Tags use key-value pair format for resource ownership verification.
Constraints on Database and Operations Workflows
Multi-source data access requires the database to support storage and mapping of heterogeneous data. It must accommodate both unstructured billing details and structured cost allocation data. The schedule of daily batch data writes and monthly report generation demands that the database connection pool has sufficient concurrent processing capacity to avoid blocking from batch tasks. Tag ownership verification is a core time-consuming step. An association index between tags and resources must be created to reduce full table scan overhead. Data anomalies directly cause billing disputes and budget overruns. Transaction consistency for data writes must be ensured to prevent dirty data from entering business processes.
How to Configure
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MONGODB_URI | mongodb://admin:secure_pass@db-host1:27017,db-host2:27017/cost_data?replicaSet=rs0&maxPoolSize=50 | Adapts to storage of multi-source unstructured billing data, supports multi-node high-availability deployment |
PG_URL | postgresql://cost_user:secure_pass@pg-host:5432/cost_db?sslmode=require&connect_timeout=30 | Used for associated queries of structured cost allocation tables and tag data, ensures data transmission security |
DB_MAX_LINK | 80–120 connections | Adapts to concurrent write requests for daily batch billing data, prevents database connection exhaustion |
REDIS_URL | redis://:redis_secure_pass@redis-host:6379/0?socket_timeout=60 | Caches tag ownership data and optimization suggestion sheets from frequent queries, reduces database query pressure |
Three Common Configuration Errors
- Symptom: Database connection requests return
1130permission denied or10060timeout errors, and billing data cannot be pulled normally. Cause: The public IP of the deployment node was not added to the whitelist configuration of the cloud database, so external access requests were blocked. - Symptom: Syntax errors are returned when executing batch SQL statements, with a prompt about unclosed statement delimiters. Cause: The database connection parameters were not configured to allow batch execution of multiple statements, and only single SQL submissions are supported, causing multi-line query statements to fail to execute.
- Symptom: Knowledge base and agent configurations are empty after backup restoration, and business data cannot be accessed normally. Cause: Only the project file directory was restored, and associated business configuration data in the linked database was not synchronously restored, leading to loss of core information.
How to Confirm Proper Configuration
- Run a database connectivity test script to verify that the addresses configured in
MONGODB_URIandPG_URLcan establish normal connections. - Write a batch write script that simulates daily billing data. Run the script without connection exhaustion or write failure errors to verify that the
DB_MAX_LINKconfiguration is appropriate. - Call the cache query interface to verify the cache hit rate of tag ownership data, confirming that the
REDIS_URLconfiguration is effective. - Run a complete backup and restoration test process to verify that project files and database data can be synchronously restored to the specified node.
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 2026-10-19.