Data Overview
Data for this category originates primarily from log systems, Trace platforms, Metrics systems, and application SDKs. Two update cadences are used: daily synchronization, and updates upon each version release. Daily supporting documentation includes log collection specifications, link tracing instructions, metric dictionaries, and dashboard configuration records. Data fields include timestamps, request IDs, error codes, trace IDs, latency (milliseconds), QPS, memory usage (MB), and more. Data formats and field units vary across different sources. Standardized processing is required to unify these differences.
Constraints for Database and Operations Maintenance
Differences in multi-source data formats require databases to support flexible field mapping and standardized storage. This prevents missing fields or format conflicts. Daily and version-based update cadences require a backup strategy that combines incremental and full backups. This ensures data integrity and recovery efficiency. Versioned management of supporting documentation requires databases to support querying historical configuration records using version numbers. High-frequency log and metric write and query operations require properly configured index and connection pool parameters. This avoids performance bottlenecks.
Configuration Recommendations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MONGODB_URI | mongodb://username:password@host:port/dbname?authSource=admin | Matches the unstructured storage needs of log and Trace data, supports sharded cluster scaling |
PG_URL | postgresql://user:pass@host:5432/metrics_db?sslmode=require | Stores structured data such as metric dictionaries and dashboard configuration records, supports transactional queries |
DB_MAX_LINK | 20–50 | Adapts to the concurrent write volume of log collection, prevents database connection exhaustion |
REDIS_URL | redis://:password@host:6379/0 | Caches frequently accessed metric data and dashboard configurations, reduces database query pressure |
Three Common Configuration Errors
- Phenomenon: Database connection timeout, returns
connection refusederror. Cause: The external request IP of the FastGPT deployment node is not added to the database whitelist, or the access address inMONGODB_URIorPG_URLis not configured correctly. - Phenomenon: Exported operations reports lack business classification fields. Cause: Fields are not collected in accordance with log collection specifications, so corresponding classification metadata is not stored in the database.
- Phenomenon: Full backup takes longer than expected. Cause: Export granularity is not differentiated between business dimensions and knowledge base dimensions. Default full backup configuration is used directly, and incremental backup rules are not configured for business classifications.
How to Verify Successful Configuration
- Run a database connection test script to verify connectivity for
MONGODB_URI,PG_URL, andREDIS_URL. Check for normal connection confirmation responses. - Export an operations report for a test time period. Confirm that it includes complete log collection fields, trace IDs, and metric data.
- View the database connection pool status. Confirm that the current number of connections does not exceed the
DB_MAX_LINKconfiguration value. - In FastGPT v4.17.0, trigger a release note sync for a version update. Check if a configuration record for the new version is added to the database.
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.