What the data for this category looks like
Reference only; verify against the target FastGPT release and deployment documentation.
What constraints do these characteristics impose on the database and operations maintenance workflow
Scattered data sources with custom field differences require databases with dynamic storage structures. Updates are not real-time high-frequency, and are mostly batch periodic imports, which impose specific requirements on the connection pool's ability to handle concurrent peak loads. Most fields carry engineering-specific units, so unit information must be stored separately to avoid parsing ambiguity. Single-project ledger data accumulates over cycles, and data volume grows significantly when multiple projects run in parallel, so reasonable data sharding and archiving rules must be planned. State-owned enterprise and public sector projects require compliant audit data retention, so fixed periodic backup processes must be included in operations maintenance.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MONGODB_URI | mongodb://[username]:[password]@host:port/construction_supervision?authSource=admin | Adapts to the custom field structure of construction supervision ledgers, supports dynamic storage of multiple types of supervision data |
PG_URL | postgresql://[username]:[password]@host:port/compliance_db?sslmode=require | Stores audit logs and compliance metadata, meets the audit traceability and permission control requirements of state-owned enterprise and public sector projects |
DB_MAX_LINK | 20–30 connections | Supports batch periodic ledger updates, this range balances connection resource usage and concurrent processing capacity |
REDIS_URL | redis://:[password]@host:port/0 | Caches frequently accessed project ledger metadata, reduces repeated database read and write overhead |
| Database Backup Cycle | Weekly full backup + daily incremental backup | Meets the data compliance retention requirements of state-owned enterprise and public sector projects, balances storage costs and data recovery efficiency |
Three Common Misconfigurations
- Phenomenon: After deployment in a Linux environment, an error
connection timed outorauthentication failedoccurs when connecting to MongoDB. Cause: The Linux firewall has not been configured to allow the MongoDB port, or the username and password inMONGODB_URIdo not match the database instance. - Phenomenon: Engineering unit fields are empty in database query results. Cause: A separate unit storage field was not created in the corresponding database, and directly concatenating numerical values and units caused parsing failure.
- Phenomenon: A
too many open connectionserror occurs when batch importing ledger data. Cause: TheDB_MAX_LINKconfiguration value is too low to support concurrent connection requests from batch tasks.
How to Confirm Proper Configuration
- Execute a database connection test script to verify that the
MONGODB_URIandPG_URLconfigurations can establish normal connections, and the connection status meets preset available thresholds. - Import a test ledger entry, and confirm that the engineering unit field is stored normally and can be parsed correctly.
- Start a batch import task, observe connection pool usage, and confirm that the
DB_MAX_LINKconfiguration supports the concurrent requirements of the task. - Check the update frequency of Redis cached metadata, and confirm that the
REDIS_URLconfiguration can normally cache target data.
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.